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diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_astype.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_astype.py
new file mode 100644
index 0000000000000000000000000000000000000000..311f2b5c9aa598de68543825d1bde9dc814b469e
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_astype.py
@@ -0,0 +1,176 @@
+from datetime import timedelta
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Index,
+    NaT,
+    Timedelta,
+    TimedeltaIndex,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core.arrays import TimedeltaArray
+
+
+class TestTimedeltaIndex:
+    def test_astype_object(self):
+        idx = timedelta_range(start="1 days", periods=4, freq="D", name="idx")
+        expected_list = [
+            Timedelta("1 days"),
+            Timedelta("2 days"),
+            Timedelta("3 days"),
+            Timedelta("4 days"),
+        ]
+        result = idx.astype(object)
+        expected = Index(expected_list, dtype=object, name="idx")
+        tm.assert_index_equal(result, expected)
+        assert idx.tolist() == expected_list
+
+    def test_astype_object_with_nat(self):
+        idx = TimedeltaIndex(
+            [timedelta(days=1), timedelta(days=2), NaT, timedelta(days=4)], name="idx"
+        )
+        expected_list = [
+            Timedelta("1 days"),
+            Timedelta("2 days"),
+            NaT,
+            Timedelta("4 days"),
+        ]
+        result = idx.astype(object)
+        expected = Index(expected_list, dtype=object, name="idx")
+        tm.assert_index_equal(result, expected)
+        assert idx.tolist() == expected_list
+
+    def test_astype(self):
+        # GH 13149, GH 13209
+        idx = TimedeltaIndex([1e14, "NaT", NaT, np.nan], name="idx")
+
+        result = idx.astype(object)
+        expected = Index(
+            [Timedelta("1 days 03:46:40")] + [NaT] * 3, dtype=object, name="idx"
+        )
+        tm.assert_index_equal(result, expected)
+
+        result = idx.astype(np.int64)
+        expected = Index(
+            [100000000000000] + [-9223372036854775808] * 3, dtype=np.int64, name="idx"
+        )
+        tm.assert_index_equal(result, expected)
+
+        result = idx.astype(str)
+        expected = Index([str(x) for x in idx], name="idx", dtype=object)
+        tm.assert_index_equal(result, expected)
+
+        rng = timedelta_range("1 days", periods=10)
+        result = rng.astype("i8")
+        tm.assert_index_equal(result, Index(rng.asi8))
+        tm.assert_numpy_array_equal(rng.asi8, result.values)
+
+    def test_astype_uint(self):
+        arr = timedelta_range("1h", periods=2)
+
+        with pytest.raises(TypeError, match=r"Do obj.astype\('int64'\)"):
+            arr.astype("uint64")
+        with pytest.raises(TypeError, match=r"Do obj.astype\('int64'\)"):
+            arr.astype("uint32")
+
+    def test_astype_timedelta64(self):
+        # GH 13149, GH 13209
+        idx = TimedeltaIndex([1e14, "NaT", NaT, np.nan])
+
+        msg = (
+            r"Cannot convert from timedelta64\[ns\] to timedelta64. "
+            "Supported resolutions are 's', 'ms', 'us', 'ns'"
+        )
+        with pytest.raises(ValueError, match=msg):
+            idx.astype("timedelta64")
+
+        result = idx.astype("timedelta64[ns]")
+        tm.assert_index_equal(result, idx)
+        assert result is not idx
+
+        result = idx.astype("timedelta64[ns]", copy=False)
+        tm.assert_index_equal(result, idx)
+        assert result is idx
+
+    def test_astype_to_td64d_raises(self, index_or_series):
+        # We don't support "D" reso
+        scalar = Timedelta(days=31)
+        td = index_or_series(
+            [scalar, scalar, scalar + timedelta(minutes=5, seconds=3), NaT],
+            dtype="m8[ns]",
+        )
+        msg = (
+            r"Cannot convert from timedelta64\[ns\] to timedelta64\[D\]. "
+            "Supported resolutions are 's', 'ms', 'us', 'ns'"
+        )
+        with pytest.raises(ValueError, match=msg):
+            td.astype("timedelta64[D]")
+
+    def test_astype_ms_to_s(self, index_or_series):
+        scalar = Timedelta(days=31)
+        td = index_or_series(
+            [scalar, scalar, scalar + timedelta(minutes=5, seconds=3), NaT],
+            dtype="m8[ns]",
+        )
+
+        exp_values = np.asarray(td).astype("m8[s]")
+        exp_tda = TimedeltaArray._simple_new(exp_values, dtype=exp_values.dtype)
+        expected = index_or_series(exp_tda)
+        assert expected.dtype == "m8[s]"
+        result = td.astype("timedelta64[s]")
+        tm.assert_equal(result, expected)
+
+    def test_astype_freq_conversion(self):
+        # pre-2.0 td64 astype converted to float64. now for supported units
+        #  (s, ms, us, ns) this converts to the requested dtype.
+        # This matches TDA and Series
+        tdi = timedelta_range("1 Day", periods=30)
+
+        res = tdi.astype("m8[s]")
+        exp_values = np.asarray(tdi).astype("m8[s]")
+        exp_tda = TimedeltaArray._simple_new(
+            exp_values, dtype=exp_values.dtype, freq=tdi.freq
+        )
+        expected = Index(exp_tda)
+        assert expected.dtype == "m8[s]"
+        tm.assert_index_equal(res, expected)
+
+        # check this matches Series and TimedeltaArray
+        res = tdi._data.astype("m8[s]")
+        tm.assert_equal(res, expected._values)
+
+        res = tdi.to_series().astype("m8[s]")
+        tm.assert_equal(res._values, expected._values._with_freq(None))
+
+    @pytest.mark.parametrize("dtype", [float, "datetime64", "datetime64[ns]"])
+    def test_astype_raises(self, dtype):
+        # GH 13149, GH 13209
+        idx = TimedeltaIndex([1e14, "NaT", NaT, np.nan])
+        msg = "Cannot cast TimedeltaIndex to dtype"
+        with pytest.raises(TypeError, match=msg):
+            idx.astype(dtype)
+
+    def test_astype_category(self):
+        obj = timedelta_range("1h", periods=2, freq="h")
+
+        result = obj.astype("category")
+        expected = pd.CategoricalIndex([Timedelta("1h"), Timedelta("2h")])
+        tm.assert_index_equal(result, expected)
+
+        result = obj._data.astype("category")
+        expected = expected.values
+        tm.assert_categorical_equal(result, expected)
+
+    def test_astype_array_fallback(self):
+        obj = timedelta_range("1h", periods=2)
+        result = obj.astype(bool)
+        expected = Index(np.array([True, True]))
+        tm.assert_index_equal(result, expected)
+
+        result = obj._data.astype(bool)
+        expected = np.array([True, True])
+        tm.assert_numpy_array_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_insert.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_insert.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8164102815f61ec61962524db2a2b3dd0ff6d55
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_insert.py
@@ -0,0 +1,145 @@
+from datetime import timedelta
+
+import numpy as np
+import pytest
+
+from pandas._libs import lib
+
+import pandas as pd
+from pandas import (
+    Index,
+    Timedelta,
+    TimedeltaIndex,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+class TestTimedeltaIndexInsert:
+    def test_insert(self):
+        idx = TimedeltaIndex(["4day", "1day", "2day"], name="idx")
+
+        result = idx.insert(2, timedelta(days=5))
+        exp = TimedeltaIndex(["4day", "1day", "5day", "2day"], name="idx")
+        tm.assert_index_equal(result, exp)
+
+        # insertion of non-datetime should coerce to object index
+        result = idx.insert(1, "inserted")
+        expected = Index(
+            [Timedelta("4day"), "inserted", Timedelta("1day"), Timedelta("2day")],
+            name="idx",
+        )
+        assert not isinstance(result, TimedeltaIndex)
+        tm.assert_index_equal(result, expected)
+        assert result.name == expected.name
+
+        idx = timedelta_range("1day 00:00:01", periods=3, freq="s", name="idx")
+
+        # preserve freq
+        expected_0 = TimedeltaIndex(
+            ["1day", "1day 00:00:01", "1day 00:00:02", "1day 00:00:03"],
+            name="idx",
+            freq="s",
+        )
+        expected_3 = TimedeltaIndex(
+            ["1day 00:00:01", "1day 00:00:02", "1day 00:00:03", "1day 00:00:04"],
+            name="idx",
+            freq="s",
+        )
+
+        # reset freq to None
+        expected_1_nofreq = TimedeltaIndex(
+            ["1day 00:00:01", "1day 00:00:01", "1day 00:00:02", "1day 00:00:03"],
+            name="idx",
+            freq=None,
+        )
+        expected_3_nofreq = TimedeltaIndex(
+            ["1day 00:00:01", "1day 00:00:02", "1day 00:00:03", "1day 00:00:05"],
+            name="idx",
+            freq=None,
+        )
+
+        cases = [
+            (0, Timedelta("1day"), expected_0),
+            (-3, Timedelta("1day"), expected_0),
+            (3, Timedelta("1day 00:00:04"), expected_3),
+            (1, Timedelta("1day 00:00:01"), expected_1_nofreq),
+            (3, Timedelta("1day 00:00:05"), expected_3_nofreq),
+        ]
+
+        for n, d, expected in cases:
+            result = idx.insert(n, d)
+            tm.assert_index_equal(result, expected)
+            assert result.name == expected.name
+            assert result.freq == expected.freq
+
+    @pytest.mark.parametrize(
+        "null", [None, np.nan, np.timedelta64("NaT"), pd.NaT, pd.NA]
+    )
+    def test_insert_nat(self, null):
+        # GH 18295 (test missing)
+        idx = timedelta_range("1day", "3day")
+        result = idx.insert(1, null)
+        expected = TimedeltaIndex(["1day", pd.NaT, "2day", "3day"])
+        tm.assert_index_equal(result, expected)
+
+    def test_insert_invalid_na(self):
+        idx = TimedeltaIndex(["4day", "1day", "2day"], name="idx")
+
+        item = np.datetime64("NaT")
+        result = idx.insert(0, item)
+
+        expected = Index([item] + list(idx), dtype=object, name="idx")
+        tm.assert_index_equal(result, expected)
+
+        # Also works if we pass a different dt64nat object
+        item2 = np.datetime64("NaT")
+        result = idx.insert(0, item2)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "item", [0, np.int64(0), np.float64(0), np.array(0), np.datetime64(456, "us")]
+    )
+    def test_insert_mismatched_types_raises(self, item):
+        # GH#33703 dont cast these to td64
+        tdi = TimedeltaIndex(["4day", "1day", "2day"], name="idx")
+
+        result = tdi.insert(1, item)
+
+        expected = Index(
+            [tdi[0], lib.item_from_zerodim(item)] + list(tdi[1:]),
+            dtype=object,
+            name="idx",
+        )
+        tm.assert_index_equal(result, expected)
+
+    def test_insert_castable_str(self):
+        idx = timedelta_range("1day", "3day")
+
+        result = idx.insert(0, "1 Day")
+
+        expected = TimedeltaIndex([idx[0]] + list(idx))
+        tm.assert_index_equal(result, expected)
+
+    def test_insert_non_castable_str(self):
+        idx = timedelta_range("1day", "3day")
+
+        result = idx.insert(0, "foo")
+
+        expected = Index(["foo"] + list(idx), dtype=object)
+        tm.assert_index_equal(result, expected)
+
+    def test_insert_empty(self):
+        # Corner case inserting with length zero doesn't raise IndexError
+        # GH#33573 for freq preservation
+        idx = timedelta_range("1 Day", periods=3)
+        td = idx[0]
+
+        result = idx[:0].insert(0, td)
+        assert result.freq == "D"
+
+        with pytest.raises(IndexError, match="loc must be an integer between"):
+            result = idx[:0].insert(1, td)
+
+        with pytest.raises(IndexError, match="loc must be an integer between"):
+            result = idx[:0].insert(-1, td)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_repeat.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_repeat.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a9b58d1bf322938e9344d0cbacfaa79674fcf0e
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_repeat.py
@@ -0,0 +1,34 @@
+import numpy as np
+
+from pandas import (
+    TimedeltaIndex,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+class TestRepeat:
+    def test_repeat(self):
+        index = timedelta_range("1 days", periods=2, freq="D")
+        exp = TimedeltaIndex(["1 days", "1 days", "2 days", "2 days"])
+        for res in [index.repeat(2), np.repeat(index, 2)]:
+            tm.assert_index_equal(res, exp)
+            assert res.freq is None
+
+        index = TimedeltaIndex(["1 days", "NaT", "3 days"])
+        exp = TimedeltaIndex(
+            [
+                "1 days",
+                "1 days",
+                "1 days",
+                "NaT",
+                "NaT",
+                "NaT",
+                "3 days",
+                "3 days",
+                "3 days",
+            ]
+        )
+        for res in [index.repeat(3), np.repeat(index, 3)]:
+            tm.assert_index_equal(res, exp)
+            assert res.freq is None
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_shift.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_shift.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0986d1496881a2061ac8306d0a064f4393cd4e9
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_shift.py
@@ -0,0 +1,76 @@
+import pytest
+
+from pandas.errors import NullFrequencyError
+
+import pandas as pd
+from pandas import TimedeltaIndex
+import pandas._testing as tm
+
+
+class TestTimedeltaIndexShift:
+    # -------------------------------------------------------------
+    # TimedeltaIndex.shift is used by __add__/__sub__
+
+    def test_tdi_shift_empty(self):
+        # GH#9903
+        idx = TimedeltaIndex([], name="xxx")
+        tm.assert_index_equal(idx.shift(0, freq="h"), idx)
+        tm.assert_index_equal(idx.shift(3, freq="h"), idx)
+
+    def test_tdi_shift_hours(self):
+        # GH#9903
+        idx = TimedeltaIndex(["5 hours", "6 hours", "9 hours"], name="xxx")
+        tm.assert_index_equal(idx.shift(0, freq="h"), idx)
+        exp = TimedeltaIndex(["8 hours", "9 hours", "12 hours"], name="xxx")
+        tm.assert_index_equal(idx.shift(3, freq="h"), exp)
+        exp = TimedeltaIndex(["2 hours", "3 hours", "6 hours"], name="xxx")
+        tm.assert_index_equal(idx.shift(-3, freq="h"), exp)
+
+    def test_tdi_shift_minutes(self):
+        # GH#9903
+        idx = TimedeltaIndex(["5 hours", "6 hours", "9 hours"], name="xxx")
+        tm.assert_index_equal(idx.shift(0, freq="min"), idx)
+        exp = TimedeltaIndex(["05:03:00", "06:03:00", "9:03:00"], name="xxx")
+        tm.assert_index_equal(idx.shift(3, freq="min"), exp)
+        exp = TimedeltaIndex(["04:57:00", "05:57:00", "8:57:00"], name="xxx")
+        tm.assert_index_equal(idx.shift(-3, freq="min"), exp)
+
+    def test_tdi_shift_int(self):
+        # GH#8083
+        tdi = pd.to_timedelta(range(5), unit="d")
+        trange = tdi._with_freq("infer") + pd.offsets.Hour(1)
+        result = trange.shift(1)
+        expected = TimedeltaIndex(
+            [
+                "1 days 01:00:00",
+                "2 days 01:00:00",
+                "3 days 01:00:00",
+                "4 days 01:00:00",
+                "5 days 01:00:00",
+            ],
+            freq="D",
+        )
+        tm.assert_index_equal(result, expected)
+
+    def test_tdi_shift_nonstandard_freq(self):
+        # GH#8083
+        tdi = pd.to_timedelta(range(5), unit="d")
+        trange = tdi._with_freq("infer") + pd.offsets.Hour(1)
+        result = trange.shift(3, freq="2D 1s")
+        expected = TimedeltaIndex(
+            [
+                "6 days 01:00:03",
+                "7 days 01:00:03",
+                "8 days 01:00:03",
+                "9 days 01:00:03",
+                "10 days 01:00:03",
+            ],
+            freq="D",
+        )
+        tm.assert_index_equal(result, expected)
+
+    def test_shift_no_freq(self):
+        # GH#19147
+        tdi = TimedeltaIndex(["1 days 01:00:00", "2 days 01:00:00"], freq=None)
+        with pytest.raises(NullFrequencyError, match="Cannot shift with no freq"):
+            tdi.shift(2)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_ops.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6013baf86edcd566a17cd3127467a7443ac475a
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_ops.py
@@ -0,0 +1,14 @@
+from pandas import (
+    TimedeltaIndex,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+class TestTimedeltaIndexOps:
+    def test_infer_freq(self, freq_sample):
+        # GH#11018
+        idx = timedelta_range("1", freq=freq_sample, periods=10)
+        result = TimedeltaIndex(idx.asi8, freq="infer")
+        tm.assert_index_equal(idx, result)
+        assert result.freq == freq_sample
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_searchsorted.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_searchsorted.py
new file mode 100644
index 0000000000000000000000000000000000000000..710571ef383970097985f44e09ecba77fcf63f74
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_searchsorted.py
@@ -0,0 +1,28 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    TimedeltaIndex,
+    Timestamp,
+)
+import pandas._testing as tm
+
+
+class TestSearchSorted:
+    def test_searchsorted_different_argument_classes(self, listlike_box):
+        idx = TimedeltaIndex(["1 day", "2 days", "3 days"])
+        result = idx.searchsorted(listlike_box(idx))
+        expected = np.arange(len(idx), dtype=result.dtype)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = idx._data.searchsorted(listlike_box(idx))
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "arg", [[1, 2], ["a", "b"], [Timestamp("2020-01-01", tz="Europe/London")] * 2]
+    )
+    def test_searchsorted_invalid_argument_dtype(self, arg):
+        idx = TimedeltaIndex(["1 day", "2 days", "3 days"])
+        msg = "value should be a 'Timedelta', 'NaT', or array of those. Got"
+        with pytest.raises(TypeError, match=msg):
+            idx.searchsorted(arg)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_setops.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_setops.py
new file mode 100644
index 0000000000000000000000000000000000000000..fce10d9176d7438e63a5e46eede1bb96b41bb8bd
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_setops.py
@@ -0,0 +1,254 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Index,
+    TimedeltaIndex,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+from pandas.tseries.offsets import Hour
+
+
+class TestTimedeltaIndex:
+    def test_union(self):
+        i1 = timedelta_range("1day", periods=5)
+        i2 = timedelta_range("3day", periods=5)
+        result = i1.union(i2)
+        expected = timedelta_range("1day", periods=7)
+        tm.assert_index_equal(result, expected)
+
+        i1 = Index(np.arange(0, 20, 2, dtype=np.int64))
+        i2 = timedelta_range(start="1 day", periods=10, freq="D")
+        i1.union(i2)  # Works
+        i2.union(i1)  # Fails with "AttributeError: can't set attribute"
+
+    def test_union_sort_false(self):
+        tdi = timedelta_range("1day", periods=5)
+
+        left = tdi[3:]
+        right = tdi[:3]
+
+        # Check that we are testing the desired code path
+        assert left._can_fast_union(right)
+
+        result = left.union(right)
+        tm.assert_index_equal(result, tdi)
+
+        result = left.union(right, sort=False)
+        expected = TimedeltaIndex(["4 Days", "5 Days", "1 Days", "2 Day", "3 Days"])
+        tm.assert_index_equal(result, expected)
+
+    def test_union_coverage(self):
+        idx = TimedeltaIndex(["3d", "1d", "2d"])
+        ordered = TimedeltaIndex(idx.sort_values(), freq="infer")
+        result = ordered.union(idx)
+        tm.assert_index_equal(result, ordered)
+
+        result = ordered[:0].union(ordered)
+        tm.assert_index_equal(result, ordered)
+        assert result.freq == ordered.freq
+
+    def test_union_bug_1730(self):
+        rng_a = timedelta_range("1 day", periods=4, freq="3h")
+        rng_b = timedelta_range("1 day", periods=4, freq="4h")
+
+        result = rng_a.union(rng_b)
+        exp = TimedeltaIndex(sorted(set(rng_a) | set(rng_b)))
+        tm.assert_index_equal(result, exp)
+
+    def test_union_bug_1745(self):
+        left = TimedeltaIndex(["1 day 15:19:49.695000"])
+        right = TimedeltaIndex(
+            ["2 day 13:04:21.322000", "1 day 15:27:24.873000", "1 day 15:31:05.350000"]
+        )
+
+        result = left.union(right)
+        exp = TimedeltaIndex(sorted(set(left) | set(right)))
+        tm.assert_index_equal(result, exp)
+
+    def test_union_bug_4564(self):
+        left = timedelta_range("1 day", "30d")
+        right = left + pd.offsets.Minute(15)
+
+        result = left.union(right)
+        exp = TimedeltaIndex(sorted(set(left) | set(right)))
+        tm.assert_index_equal(result, exp)
+
+    def test_union_freq_infer(self):
+        # When taking the union of two TimedeltaIndexes, we infer
+        #  a freq even if the arguments don't have freq.  This matches
+        #  DatetimeIndex behavior.
+        tdi = timedelta_range("1 Day", periods=5)
+        left = tdi[[0, 1, 3, 4]]
+        right = tdi[[2, 3, 1]]
+
+        assert left.freq is None
+        assert right.freq is None
+
+        result = left.union(right)
+        tm.assert_index_equal(result, tdi)
+        assert result.freq == "D"
+
+    def test_intersection_bug_1708(self):
+        index_1 = timedelta_range("1 day", periods=4, freq="h")
+        index_2 = index_1 + pd.offsets.Hour(5)
+
+        result = index_1.intersection(index_2)
+        assert len(result) == 0
+
+        index_1 = timedelta_range("1 day", periods=4, freq="h")
+        index_2 = index_1 + pd.offsets.Hour(1)
+
+        result = index_1.intersection(index_2)
+        expected = timedelta_range("1 day 01:00:00", periods=3, freq="h")
+        tm.assert_index_equal(result, expected)
+        assert result.freq == expected.freq
+
+    def test_intersection_equal(self, sort):
+        # GH 24471 Test intersection outcome given the sort keyword
+        # for equal indices intersection should return the original index
+        first = timedelta_range("1 day", periods=4, freq="h")
+        second = timedelta_range("1 day", periods=4, freq="h")
+        intersect = first.intersection(second, sort=sort)
+        if sort is None:
+            tm.assert_index_equal(intersect, second.sort_values())
+        tm.assert_index_equal(intersect, second)
+
+        # Corner cases
+        inter = first.intersection(first, sort=sort)
+        assert inter is first
+
+    @pytest.mark.parametrize("period_1, period_2", [(0, 4), (4, 0)])
+    def test_intersection_zero_length(self, period_1, period_2, sort):
+        # GH 24471 test for non overlap the intersection should be zero length
+        index_1 = timedelta_range("1 day", periods=period_1, freq="h")
+        index_2 = timedelta_range("1 day", periods=period_2, freq="h")
+        expected = timedelta_range("1 day", periods=0, freq="h")
+        result = index_1.intersection(index_2, sort=sort)
+        tm.assert_index_equal(result, expected)
+
+    def test_zero_length_input_index(self, sort):
+        # GH 24966 test for 0-len intersections are copied
+        index_1 = timedelta_range("1 day", periods=0, freq="h")
+        index_2 = timedelta_range("1 day", periods=3, freq="h")
+        result = index_1.intersection(index_2, sort=sort)
+        assert index_1 is not result
+        assert index_2 is not result
+        tm.assert_copy(result, index_1)
+
+    @pytest.mark.parametrize(
+        "rng, expected",
+        # if target has the same name, it is preserved
+        [
+            (
+                timedelta_range("1 day", periods=5, freq="h", name="idx"),
+                timedelta_range("1 day", periods=4, freq="h", name="idx"),
+            ),
+            # if target name is different, it will be reset
+            (
+                timedelta_range("1 day", periods=5, freq="h", name="other"),
+                timedelta_range("1 day", periods=4, freq="h", name=None),
+            ),
+            # if no overlap exists return empty index
+            (
+                timedelta_range("1 day", periods=10, freq="h", name="idx")[5:],
+                TimedeltaIndex([], freq="h", name="idx"),
+            ),
+        ],
+    )
+    def test_intersection(self, rng, expected, sort):
+        # GH 4690 (with tz)
+        base = timedelta_range("1 day", periods=4, freq="h", name="idx")
+        result = base.intersection(rng, sort=sort)
+        if sort is None:
+            expected = expected.sort_values()
+        tm.assert_index_equal(result, expected)
+        assert result.name == expected.name
+        assert result.freq == expected.freq
+
+    @pytest.mark.parametrize(
+        "rng, expected",
+        # part intersection works
+        [
+            (
+                TimedeltaIndex(["5 hour", "2 hour", "4 hour", "9 hour"], name="idx"),
+                TimedeltaIndex(["2 hour", "4 hour"], name="idx"),
+            ),
+            # reordered part intersection
+            (
+                TimedeltaIndex(["2 hour", "5 hour", "5 hour", "1 hour"], name="other"),
+                TimedeltaIndex(["1 hour", "2 hour"], name=None),
+            ),
+            # reversed index
+            (
+                TimedeltaIndex(["1 hour", "2 hour", "4 hour", "3 hour"], name="idx")[
+                    ::-1
+                ],
+                TimedeltaIndex(["1 hour", "2 hour", "4 hour", "3 hour"], name="idx"),
+            ),
+        ],
+    )
+    def test_intersection_non_monotonic(self, rng, expected, sort):
+        # 24471 non-monotonic
+        base = TimedeltaIndex(["1 hour", "2 hour", "4 hour", "3 hour"], name="idx")
+        result = base.intersection(rng, sort=sort)
+        if sort is None:
+            expected = expected.sort_values()
+        tm.assert_index_equal(result, expected)
+        assert result.name == expected.name
+
+        # if reversed order, frequency is still the same
+        if all(base == rng[::-1]) and sort is None:
+            assert isinstance(result.freq, Hour)
+        else:
+            assert result.freq is None
+
+
+class TestTimedeltaIndexDifference:
+    def test_difference_freq(self, sort):
+        # GH14323: Difference of TimedeltaIndex should not preserve frequency
+
+        index = timedelta_range("0 days", "5 days", freq="D")
+
+        other = timedelta_range("1 days", "4 days", freq="D")
+        expected = TimedeltaIndex(["0 days", "5 days"], freq=None)
+        idx_diff = index.difference(other, sort)
+        tm.assert_index_equal(idx_diff, expected)
+        tm.assert_attr_equal("freq", idx_diff, expected)
+
+        # preserve frequency when the difference is a contiguous
+        # subset of the original range
+        other = timedelta_range("2 days", "5 days", freq="D")
+        idx_diff = index.difference(other, sort)
+        expected = TimedeltaIndex(["0 days", "1 days"], freq="D")
+        tm.assert_index_equal(idx_diff, expected)
+        tm.assert_attr_equal("freq", idx_diff, expected)
+
+    def test_difference_sort(self, sort):
+        index = TimedeltaIndex(
+            ["5 days", "3 days", "2 days", "4 days", "1 days", "0 days"]
+        )
+
+        other = timedelta_range("1 days", "4 days", freq="D")
+        idx_diff = index.difference(other, sort)
+
+        expected = TimedeltaIndex(["5 days", "0 days"], freq=None)
+
+        if sort is None:
+            expected = expected.sort_values()
+
+        tm.assert_index_equal(idx_diff, expected)
+        tm.assert_attr_equal("freq", idx_diff, expected)
+
+        other = timedelta_range("2 days", "5 days", freq="D")
+        idx_diff = index.difference(other, sort)
+        expected = TimedeltaIndex(["1 days", "0 days"], freq=None)
+
+        if sort is None:
+            expected = expected.sort_values()
+
+        tm.assert_index_equal(idx_diff, expected)
+        tm.assert_attr_equal("freq", idx_diff, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_timedelta_range.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_timedelta_range.py
new file mode 100644
index 0000000000000000000000000000000000000000..f22bdb7a90516a7162ebdb1cc2d8cbfd9531b9e7
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_timedelta_range.py
@@ -0,0 +1,173 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Timedelta,
+    TimedeltaIndex,
+    timedelta_range,
+    to_timedelta,
+)
+import pandas._testing as tm
+
+from pandas.tseries.offsets import (
+    Day,
+    Second,
+)
+
+
+class TestTimedeltas:
+    def test_timedelta_range_unit(self):
+        # GH#49824
+        tdi = timedelta_range("0 Days", periods=10, freq="100000D", unit="s")
+        exp_arr = (np.arange(10, dtype="i8") * 100_000).view("m8[D]").astype("m8[s]")
+        tm.assert_numpy_array_equal(tdi.to_numpy(), exp_arr)
+
+    def test_timedelta_range(self):
+        expected = to_timedelta(np.arange(5), unit="D")
+        result = timedelta_range("0 days", periods=5, freq="D")
+        tm.assert_index_equal(result, expected)
+
+        expected = to_timedelta(np.arange(11), unit="D")
+        result = timedelta_range("0 days", "10 days", freq="D")
+        tm.assert_index_equal(result, expected)
+
+        expected = to_timedelta(np.arange(5), unit="D") + Second(2) + Day()
+        result = timedelta_range("1 days, 00:00:02", "5 days, 00:00:02", freq="D")
+        tm.assert_index_equal(result, expected)
+
+        expected = to_timedelta([1, 3, 5, 7, 9], unit="D") + Second(2)
+        result = timedelta_range("1 days, 00:00:02", periods=5, freq="2D")
+        tm.assert_index_equal(result, expected)
+
+        expected = to_timedelta(np.arange(50), unit="min") * 30
+        result = timedelta_range("0 days", freq="30min", periods=50)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "depr_unit, unit",
+        [
+            ("H", "hour"),
+            ("T", "minute"),
+            ("t", "minute"),
+            ("S", "second"),
+            ("L", "millisecond"),
+            ("l", "millisecond"),
+            ("U", "microsecond"),
+            ("u", "microsecond"),
+            ("N", "nanosecond"),
+            ("n", "nanosecond"),
+        ],
+    )
+    def test_timedelta_units_H_T_S_L_U_N_deprecated(self, depr_unit, unit):
+        # GH#52536
+        depr_msg = (
+            f"'{depr_unit}' is deprecated and will be removed in a future version."
+        )
+
+        expected = to_timedelta(np.arange(5), unit=unit)
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            result = to_timedelta(np.arange(5), unit=depr_unit)
+            tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "periods, freq", [(3, "2D"), (5, "D"), (6, "19h12min"), (7, "16h"), (9, "12h")]
+    )
+    def test_linspace_behavior(self, periods, freq):
+        # GH 20976
+        result = timedelta_range(start="0 days", end="4 days", periods=periods)
+        expected = timedelta_range(start="0 days", end="4 days", freq=freq)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("msg_freq, freq", [("H", "19H12min"), ("T", "19h12T")])
+    def test_timedelta_range_H_T_deprecated(self, freq, msg_freq):
+        # GH#52536
+        msg = f"'{msg_freq}' is deprecated and will be removed in a future version."
+
+        result = timedelta_range(start="0 days", end="4 days", periods=6)
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            expected = timedelta_range(start="0 days", end="4 days", freq=freq)
+        tm.assert_index_equal(result, expected)
+
+    def test_errors(self):
+        # not enough params
+        msg = (
+            "Of the four parameters: start, end, periods, and freq, "
+            "exactly three must be specified"
+        )
+        with pytest.raises(ValueError, match=msg):
+            timedelta_range(start="0 days")
+
+        with pytest.raises(ValueError, match=msg):
+            timedelta_range(end="5 days")
+
+        with pytest.raises(ValueError, match=msg):
+            timedelta_range(periods=2)
+
+        with pytest.raises(ValueError, match=msg):
+            timedelta_range()
+
+        # too many params
+        with pytest.raises(ValueError, match=msg):
+            timedelta_range(start="0 days", end="5 days", periods=10, freq="h")
+
+    @pytest.mark.parametrize(
+        "start, end, freq, expected_periods",
+        [
+            ("1D", "10D", "2D", (10 - 1) // 2 + 1),
+            ("2D", "30D", "3D", (30 - 2) // 3 + 1),
+            ("2s", "50s", "5s", (50 - 2) // 5 + 1),
+            # tests that worked before GH 33498:
+            ("4D", "16D", "3D", (16 - 4) // 3 + 1),
+            ("8D", "16D", "40s", (16 * 3600 * 24 - 8 * 3600 * 24) // 40 + 1),
+        ],
+    )
+    def test_timedelta_range_freq_divide_end(self, start, end, freq, expected_periods):
+        # GH 33498 only the cases where `(end % freq) == 0` used to fail
+        res = timedelta_range(start=start, end=end, freq=freq)
+        assert Timedelta(start) == res[0]
+        assert Timedelta(end) >= res[-1]
+        assert len(res) == expected_periods
+
+    def test_timedelta_range_infer_freq(self):
+        # https://github.com/pandas-dev/pandas/issues/35897
+        result = timedelta_range("0s", "1s", periods=31)
+        assert result.freq is None
+
+    @pytest.mark.parametrize(
+        "freq_depr, start, end, expected_values, expected_freq",
+        [
+            (
+                "3.5S",
+                "05:03:01",
+                "05:03:10",
+                ["0 days 05:03:01", "0 days 05:03:04.500000", "0 days 05:03:08"],
+                "3500ms",
+            ),
+            (
+                "2.5T",
+                "5 hours",
+                "5 hours 8 minutes",
+                [
+                    "0 days 05:00:00",
+                    "0 days 05:02:30",
+                    "0 days 05:05:00",
+                    "0 days 05:07:30",
+                ],
+                "150s",
+            ),
+        ],
+    )
+    def test_timedelta_range_deprecated_freq(
+        self, freq_depr, start, end, expected_values, expected_freq
+    ):
+        # GH#52536
+        msg = (
+            f"'{freq_depr[-1]}' is deprecated and will be removed in a future version."
+        )
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = timedelta_range(start=start, end=end, freq=freq_depr)
+        expected = TimedeltaIndex(
+            expected_values, dtype="timedelta64[ns]", freq=expected_freq
+        )
+        tm.assert_index_equal(result, expected)
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diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/conftest.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..62b8c59ba8855b2ef4273a33b475b2e16b7b3d1b
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/conftest.py
@@ -0,0 +1,7 @@
+import pytest
+
+
+@pytest.fixture(params=[True, False])
+def sort(request):
+    """Boolean sort keyword for concat and DataFrame.append."""
+    return request.param
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_append.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_append.py
new file mode 100644
index 0000000000000000000000000000000000000000..81ca227fb7afb9768909f3cca7907fdaf74c430a
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_append.py
@@ -0,0 +1,389 @@
+import datetime as dt
+from itertools import combinations
+
+import dateutil
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+    Timestamp,
+    concat,
+    isna,
+)
+import pandas._testing as tm
+
+
+class TestAppend:
+    def test_append(self, sort, float_frame):
+        mixed_frame = float_frame.copy()
+        mixed_frame["foo"] = "bar"
+
+        begin_index = float_frame.index[:5]
+        end_index = float_frame.index[5:]
+
+        begin_frame = float_frame.reindex(begin_index)
+        end_frame = float_frame.reindex(end_index)
+
+        appended = begin_frame._append(end_frame)
+        tm.assert_almost_equal(appended["A"], float_frame["A"])
+
+        del end_frame["A"]
+        partial_appended = begin_frame._append(end_frame, sort=sort)
+        assert "A" in partial_appended
+
+        partial_appended = end_frame._append(begin_frame, sort=sort)
+        assert "A" in partial_appended
+
+        # mixed type handling
+        appended = mixed_frame[:5]._append(mixed_frame[5:])
+        tm.assert_frame_equal(appended, mixed_frame)
+
+        # what to test here
+        mixed_appended = mixed_frame[:5]._append(float_frame[5:], sort=sort)
+        mixed_appended2 = float_frame[:5]._append(mixed_frame[5:], sort=sort)
+
+        # all equal except 'foo' column
+        tm.assert_frame_equal(
+            mixed_appended.reindex(columns=["A", "B", "C", "D"]),
+            mixed_appended2.reindex(columns=["A", "B", "C", "D"]),
+        )
+
+    def test_append_empty(self, float_frame):
+        empty = DataFrame()
+
+        appended = float_frame._append(empty)
+        tm.assert_frame_equal(float_frame, appended)
+        assert appended is not float_frame
+
+        appended = empty._append(float_frame)
+        tm.assert_frame_equal(float_frame, appended)
+        assert appended is not float_frame
+
+    def test_append_overlap_raises(self, float_frame):
+        msg = "Indexes have overlapping values"
+        with pytest.raises(ValueError, match=msg):
+            float_frame._append(float_frame, verify_integrity=True)
+
+    def test_append_new_columns(self):
+        # see gh-6129: new columns
+        df = DataFrame({"a": {"x": 1, "y": 2}, "b": {"x": 3, "y": 4}})
+        row = Series([5, 6, 7], index=["a", "b", "c"], name="z")
+        expected = DataFrame(
+            {
+                "a": {"x": 1, "y": 2, "z": 5},
+                "b": {"x": 3, "y": 4, "z": 6},
+                "c": {"z": 7},
+            }
+        )
+        result = df._append(row)
+        tm.assert_frame_equal(result, expected)
+
+    def test_append_length0_frame(self, sort):
+        df = DataFrame(columns=["A", "B", "C"])
+        df3 = DataFrame(index=[0, 1], columns=["A", "B"])
+        df5 = df._append(df3, sort=sort)
+
+        expected = DataFrame(index=[0, 1], columns=["A", "B", "C"])
+        tm.assert_frame_equal(df5, expected)
+
+    def test_append_records(self):
+        arr1 = np.zeros((2,), dtype=("i4,f4,S10"))
+        arr1[:] = [(1, 2.0, "Hello"), (2, 3.0, "World")]
+
+        arr2 = np.zeros((3,), dtype=("i4,f4,S10"))
+        arr2[:] = [(3, 4.0, "foo"), (5, 6.0, "bar"), (7.0, 8.0, "baz")]
+
+        df1 = DataFrame(arr1)
+        df2 = DataFrame(arr2)
+
+        result = df1._append(df2, ignore_index=True)
+        expected = DataFrame(np.concatenate((arr1, arr2)))
+        tm.assert_frame_equal(result, expected)
+
+    # rewrite sort fixture, since we also want to test default of None
+    def test_append_sorts(self, sort):
+        df1 = DataFrame({"a": [1, 2], "b": [1, 2]}, columns=["b", "a"])
+        df2 = DataFrame({"a": [1, 2], "c": [3, 4]}, index=[2, 3])
+
+        result = df1._append(df2, sort=sort)
+
+        # for None / True
+        expected = DataFrame(
+            {"b": [1, 2, None, None], "a": [1, 2, 1, 2], "c": [None, None, 3, 4]},
+            columns=["a", "b", "c"],
+        )
+        if sort is False:
+            expected = expected[["b", "a", "c"]]
+        tm.assert_frame_equal(result, expected)
+
+    def test_append_different_columns(self, sort):
+        df = DataFrame(
+            {
+                "bools": np.random.default_rng(2).standard_normal(10) > 0,
+                "ints": np.random.default_rng(2).integers(0, 10, 10),
+                "floats": np.random.default_rng(2).standard_normal(10),
+                "strings": ["foo", "bar"] * 5,
+            }
+        )
+
+        a = df[:5].loc[:, ["bools", "ints", "floats"]]
+        b = df[5:].loc[:, ["strings", "ints", "floats"]]
+
+        appended = a._append(b, sort=sort)
+        assert isna(appended["strings"][0:4]).all()
+        assert isna(appended["bools"][5:]).all()
+
+    def test_append_many(self, sort, float_frame):
+        chunks = [
+            float_frame[:5],
+            float_frame[5:10],
+            float_frame[10:15],
+            float_frame[15:],
+        ]
+
+        result = chunks[0]._append(chunks[1:])
+        tm.assert_frame_equal(result, float_frame)
+
+        chunks[-1] = chunks[-1].copy()
+        chunks[-1]["foo"] = "bar"
+        result = chunks[0]._append(chunks[1:], sort=sort)
+        tm.assert_frame_equal(result.loc[:, float_frame.columns], float_frame)
+        assert (result["foo"][15:] == "bar").all()
+        assert result["foo"][:15].isna().all()
+
+    def test_append_preserve_index_name(self):
+        # #980
+        df1 = DataFrame(columns=["A", "B", "C"])
+        df1 = df1.set_index(["A"])
+        df2 = DataFrame(data=[[1, 4, 7], [2, 5, 8], [3, 6, 9]], columns=["A", "B", "C"])
+        df2 = df2.set_index(["A"])
+
+        msg = "The behavior of array concatenation with empty entries is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = df1._append(df2)
+        assert result.index.name == "A"
+
+    indexes_can_append = [
+        pd.RangeIndex(3),
+        Index([4, 5, 6]),
+        Index([4.5, 5.5, 6.5]),
+        Index(list("abc")),
+        pd.CategoricalIndex("A B C".split()),
+        pd.CategoricalIndex("D E F".split(), ordered=True),
+        pd.IntervalIndex.from_breaks([7, 8, 9, 10]),
+        pd.DatetimeIndex(
+            [
+                dt.datetime(2013, 1, 3, 0, 0),
+                dt.datetime(2013, 1, 3, 6, 10),
+                dt.datetime(2013, 1, 3, 7, 12),
+            ]
+        ),
+        pd.MultiIndex.from_arrays(["A B C".split(), "D E F".split()]),
+    ]
+
+    @pytest.mark.parametrize(
+        "index", indexes_can_append, ids=lambda x: type(x).__name__
+    )
+    def test_append_same_columns_type(self, index):
+        # GH18359
+
+        # df wider than ser
+        df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=index)
+        ser_index = index[:2]
+        ser = Series([7, 8], index=ser_index, name=2)
+        result = df._append(ser)
+        expected = DataFrame(
+            [[1, 2, 3.0], [4, 5, 6], [7, 8, np.nan]], index=[0, 1, 2], columns=index
+        )
+        # integer dtype is preserved for columns present in ser.index
+        assert expected.dtypes.iloc[0].kind == "i"
+        assert expected.dtypes.iloc[1].kind == "i"
+
+        tm.assert_frame_equal(result, expected)
+
+        # ser wider than df
+        ser_index = index
+        index = index[:2]
+        df = DataFrame([[1, 2], [4, 5]], columns=index)
+        ser = Series([7, 8, 9], index=ser_index, name=2)
+        result = df._append(ser)
+        expected = DataFrame(
+            [[1, 2, np.nan], [4, 5, np.nan], [7, 8, 9]],
+            index=[0, 1, 2],
+            columns=ser_index,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "df_columns, series_index",
+        combinations(indexes_can_append, r=2),
+        ids=lambda x: type(x).__name__,
+    )
+    def test_append_different_columns_types(self, df_columns, series_index):
+        # GH18359
+        # See also test 'test_append_different_columns_types_raises' below
+        # for errors raised when appending
+
+        df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=df_columns)
+        ser = Series([7, 8, 9], index=series_index, name=2)
+
+        result = df._append(ser)
+        idx_diff = ser.index.difference(df_columns)
+        combined_columns = Index(df_columns.tolist()).append(idx_diff)
+        expected = DataFrame(
+            [
+                [1.0, 2.0, 3.0, np.nan, np.nan, np.nan],
+                [4, 5, 6, np.nan, np.nan, np.nan],
+                [np.nan, np.nan, np.nan, 7, 8, 9],
+            ],
+            index=[0, 1, 2],
+            columns=combined_columns,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_append_dtype_coerce(self, sort):
+        # GH 4993
+        # appending with datetime will incorrectly convert datetime64
+
+        df1 = DataFrame(
+            index=[1, 2],
+            data=[dt.datetime(2013, 1, 1, 0, 0), dt.datetime(2013, 1, 2, 0, 0)],
+            columns=["start_time"],
+        )
+        df2 = DataFrame(
+            index=[4, 5],
+            data=[
+                [dt.datetime(2013, 1, 3, 0, 0), dt.datetime(2013, 1, 3, 6, 10)],
+                [dt.datetime(2013, 1, 4, 0, 0), dt.datetime(2013, 1, 4, 7, 10)],
+            ],
+            columns=["start_time", "end_time"],
+        )
+
+        expected = concat(
+            [
+                Series(
+                    [
+                        pd.NaT,
+                        pd.NaT,
+                        dt.datetime(2013, 1, 3, 6, 10),
+                        dt.datetime(2013, 1, 4, 7, 10),
+                    ],
+                    name="end_time",
+                ),
+                Series(
+                    [
+                        dt.datetime(2013, 1, 1, 0, 0),
+                        dt.datetime(2013, 1, 2, 0, 0),
+                        dt.datetime(2013, 1, 3, 0, 0),
+                        dt.datetime(2013, 1, 4, 0, 0),
+                    ],
+                    name="start_time",
+                ),
+            ],
+            axis=1,
+            sort=sort,
+        )
+        result = df1._append(df2, ignore_index=True, sort=sort)
+        if sort:
+            expected = expected[["end_time", "start_time"]]
+        else:
+            expected = expected[["start_time", "end_time"]]
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_append_missing_column_proper_upcast(self, sort):
+        df1 = DataFrame({"A": np.array([1, 2, 3, 4], dtype="i8")})
+        df2 = DataFrame({"B": np.array([True, False, True, False], dtype=bool)})
+
+        appended = df1._append(df2, ignore_index=True, sort=sort)
+        assert appended["A"].dtype == "f8"
+        assert appended["B"].dtype == "O"
+
+    def test_append_empty_frame_to_series_with_dateutil_tz(self):
+        # GH 23682
+        date = Timestamp("2018-10-24 07:30:00", tz=dateutil.tz.tzutc())
+        ser = Series({"a": 1.0, "b": 2.0, "date": date})
+        df = DataFrame(columns=["c", "d"])
+        result_a = df._append(ser, ignore_index=True)
+        expected = DataFrame(
+            [[np.nan, np.nan, 1.0, 2.0, date]], columns=["c", "d", "a", "b", "date"]
+        )
+        # These columns get cast to object after append
+        expected["c"] = expected["c"].astype(object)
+        expected["d"] = expected["d"].astype(object)
+        tm.assert_frame_equal(result_a, expected)
+
+        expected = DataFrame(
+            [[np.nan, np.nan, 1.0, 2.0, date]] * 2, columns=["c", "d", "a", "b", "date"]
+        )
+        expected["c"] = expected["c"].astype(object)
+        expected["d"] = expected["d"].astype(object)
+        result_b = result_a._append(ser, ignore_index=True)
+        tm.assert_frame_equal(result_b, expected)
+
+        result = df._append([ser, ser], ignore_index=True)
+        tm.assert_frame_equal(result, expected)
+
+    def test_append_empty_tz_frame_with_datetime64ns(self, using_array_manager):
+        # https://github.com/pandas-dev/pandas/issues/35460
+        df = DataFrame(columns=["a"]).astype("datetime64[ns, UTC]")
+
+        # pd.NaT gets inferred as tz-naive, so append result is tz-naive
+        result = df._append({"a": pd.NaT}, ignore_index=True)
+        if using_array_manager:
+            expected = DataFrame({"a": [pd.NaT]}, dtype=object)
+        else:
+            expected = DataFrame({"a": [np.nan]}, dtype=object)
+        tm.assert_frame_equal(result, expected)
+
+        # also test with typed value to append
+        df = DataFrame(columns=["a"]).astype("datetime64[ns, UTC]")
+        other = Series({"a": pd.NaT}, dtype="datetime64[ns]")
+        result = df._append(other, ignore_index=True)
+        tm.assert_frame_equal(result, expected)
+
+        # mismatched tz
+        other = Series({"a": pd.NaT}, dtype="datetime64[ns, US/Pacific]")
+        result = df._append(other, ignore_index=True)
+        expected = DataFrame({"a": [pd.NaT]}).astype(object)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "dtype_str", ["datetime64[ns, UTC]", "datetime64[ns]", "Int64", "int64"]
+    )
+    @pytest.mark.parametrize("val", [1, "NaT"])
+    def test_append_empty_frame_with_timedelta64ns_nat(
+        self, dtype_str, val, using_array_manager
+    ):
+        # https://github.com/pandas-dev/pandas/issues/35460
+        df = DataFrame(columns=["a"]).astype(dtype_str)
+
+        other = DataFrame({"a": [np.timedelta64(val, "ns")]})
+        result = df._append(other, ignore_index=True)
+
+        expected = other.astype(object)
+        if isinstance(val, str) and dtype_str != "int64" and not using_array_manager:
+            # TODO: expected used to be `other.astype(object)` which is a more
+            #  reasonable result.  This was changed when tightening
+            #  assert_frame_equal's treatment of mismatched NAs to match the
+            #  existing behavior.
+            expected = DataFrame({"a": [np.nan]}, dtype=object)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "dtype_str", ["datetime64[ns, UTC]", "datetime64[ns]", "Int64", "int64"]
+    )
+    @pytest.mark.parametrize("val", [1, "NaT"])
+    def test_append_frame_with_timedelta64ns_nat(self, dtype_str, val):
+        # https://github.com/pandas-dev/pandas/issues/35460
+        df = DataFrame({"a": pd.array([1], dtype=dtype_str)})
+
+        other = DataFrame({"a": [np.timedelta64(val, "ns")]})
+        result = df._append(other, ignore_index=True)
+
+        expected = DataFrame({"a": [df.iloc[0, 0], other.iloc[0, 0]]}, dtype=object)
+        tm.assert_frame_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_append_common.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_append_common.py
new file mode 100644
index 0000000000000000000000000000000000000000..31c3ef317622214efe302aae4b87a6bb6592a965
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_append_common.py
@@ -0,0 +1,753 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    Index,
+    Series,
+)
+import pandas._testing as tm
+
+
+@pytest.fixture(
+    params=list(
+        {
+            "bool": [True, False, True],
+            "int64": [1, 2, 3],
+            "float64": [1.1, np.nan, 3.3],
+            "category": Categorical(["X", "Y", "Z"]),
+            "object": ["a", "b", "c"],
+            "datetime64[ns]": [
+                pd.Timestamp("2011-01-01"),
+                pd.Timestamp("2011-01-02"),
+                pd.Timestamp("2011-01-03"),
+            ],
+            "datetime64[ns, US/Eastern]": [
+                pd.Timestamp("2011-01-01", tz="US/Eastern"),
+                pd.Timestamp("2011-01-02", tz="US/Eastern"),
+                pd.Timestamp("2011-01-03", tz="US/Eastern"),
+            ],
+            "timedelta64[ns]": [
+                pd.Timedelta("1 days"),
+                pd.Timedelta("2 days"),
+                pd.Timedelta("3 days"),
+            ],
+            "period[M]": [
+                pd.Period("2011-01", freq="M"),
+                pd.Period("2011-02", freq="M"),
+                pd.Period("2011-03", freq="M"),
+            ],
+        }.items()
+    )
+)
+def item(request):
+    key, data = request.param
+    return key, data
+
+
+@pytest.fixture
+def item2(item):
+    return item
+
+
+class TestConcatAppendCommon:
+    """
+    Test common dtype coercion rules between concat and append.
+    """
+
+    def test_dtypes(self, item, index_or_series, using_infer_string):
+        # to confirm test case covers intended dtypes
+        typ, vals = item
+        obj = index_or_series(vals)
+        if typ == "object" and using_infer_string:
+            typ = "string"
+        if isinstance(obj, Index):
+            assert obj.dtype == typ
+        elif isinstance(obj, Series):
+            if typ.startswith("period"):
+                assert obj.dtype == "Period[M]"
+            else:
+                assert obj.dtype == typ
+
+    def test_concatlike_same_dtypes(self, item):
+        # GH 13660
+        typ1, vals1 = item
+
+        vals2 = vals1
+        vals3 = vals1
+
+        if typ1 == "category":
+            exp_data = Categorical(list(vals1) + list(vals2))
+            exp_data3 = Categorical(list(vals1) + list(vals2) + list(vals3))
+        else:
+            exp_data = vals1 + vals2
+            exp_data3 = vals1 + vals2 + vals3
+
+        # ----- Index ----- #
+
+        # index.append
+        res = Index(vals1).append(Index(vals2))
+        exp = Index(exp_data)
+        tm.assert_index_equal(res, exp)
+
+        # 3 elements
+        res = Index(vals1).append([Index(vals2), Index(vals3)])
+        exp = Index(exp_data3)
+        tm.assert_index_equal(res, exp)
+
+        # index.append name mismatch
+        i1 = Index(vals1, name="x")
+        i2 = Index(vals2, name="y")
+        res = i1.append(i2)
+        exp = Index(exp_data)
+        tm.assert_index_equal(res, exp)
+
+        # index.append name match
+        i1 = Index(vals1, name="x")
+        i2 = Index(vals2, name="x")
+        res = i1.append(i2)
+        exp = Index(exp_data, name="x")
+        tm.assert_index_equal(res, exp)
+
+        # cannot append non-index
+        with pytest.raises(TypeError, match="all inputs must be Index"):
+            Index(vals1).append(vals2)
+
+        with pytest.raises(TypeError, match="all inputs must be Index"):
+            Index(vals1).append([Index(vals2), vals3])
+
+        # ----- Series ----- #
+
+        # series.append
+        res = Series(vals1)._append(Series(vals2), ignore_index=True)
+        exp = Series(exp_data)
+        tm.assert_series_equal(res, exp, check_index_type=True)
+
+        # concat
+        res = pd.concat([Series(vals1), Series(vals2)], ignore_index=True)
+        tm.assert_series_equal(res, exp, check_index_type=True)
+
+        # 3 elements
+        res = Series(vals1)._append([Series(vals2), Series(vals3)], ignore_index=True)
+        exp = Series(exp_data3)
+        tm.assert_series_equal(res, exp)
+
+        res = pd.concat(
+            [Series(vals1), Series(vals2), Series(vals3)],
+            ignore_index=True,
+        )
+        tm.assert_series_equal(res, exp)
+
+        # name mismatch
+        s1 = Series(vals1, name="x")
+        s2 = Series(vals2, name="y")
+        res = s1._append(s2, ignore_index=True)
+        exp = Series(exp_data)
+        tm.assert_series_equal(res, exp, check_index_type=True)
+
+        res = pd.concat([s1, s2], ignore_index=True)
+        tm.assert_series_equal(res, exp, check_index_type=True)
+
+        # name match
+        s1 = Series(vals1, name="x")
+        s2 = Series(vals2, name="x")
+        res = s1._append(s2, ignore_index=True)
+        exp = Series(exp_data, name="x")
+        tm.assert_series_equal(res, exp, check_index_type=True)
+
+        res = pd.concat([s1, s2], ignore_index=True)
+        tm.assert_series_equal(res, exp, check_index_type=True)
+
+        # cannot append non-index
+        msg = (
+            r"cannot concatenate object of type '.+'; "
+            "only Series and DataFrame objs are valid"
+        )
+        with pytest.raises(TypeError, match=msg):
+            Series(vals1)._append(vals2)
+
+        with pytest.raises(TypeError, match=msg):
+            Series(vals1)._append([Series(vals2), vals3])
+
+        with pytest.raises(TypeError, match=msg):
+            pd.concat([Series(vals1), vals2])
+
+        with pytest.raises(TypeError, match=msg):
+            pd.concat([Series(vals1), Series(vals2), vals3])
+
+    def test_concatlike_dtypes_coercion(self, item, item2, request):
+        # GH 13660
+        typ1, vals1 = item
+        typ2, vals2 = item2
+
+        vals3 = vals2
+
+        # basically infer
+        exp_index_dtype = None
+        exp_series_dtype = None
+
+        if typ1 == typ2:
+            pytest.skip("same dtype is tested in test_concatlike_same_dtypes")
+        elif typ1 == "category" or typ2 == "category":
+            pytest.skip("categorical type tested elsewhere")
+
+        # specify expected dtype
+        if typ1 == "bool" and typ2 in ("int64", "float64"):
+            # series coerces to numeric based on numpy rule
+            # index doesn't because bool is object dtype
+            exp_series_dtype = typ2
+            mark = pytest.mark.xfail(reason="GH#39187 casting to object")
+            request.applymarker(mark)
+        elif typ2 == "bool" and typ1 in ("int64", "float64"):
+            exp_series_dtype = typ1
+            mark = pytest.mark.xfail(reason="GH#39187 casting to object")
+            request.applymarker(mark)
+        elif typ1 in {"datetime64[ns, US/Eastern]", "timedelta64[ns]"} or typ2 in {
+            "datetime64[ns, US/Eastern]",
+            "timedelta64[ns]",
+        }:
+            exp_index_dtype = object
+            exp_series_dtype = object
+
+        exp_data = vals1 + vals2
+        exp_data3 = vals1 + vals2 + vals3
+
+        # ----- Index ----- #
+
+        # index.append
+        # GH#39817
+        res = Index(vals1).append(Index(vals2))
+        exp = Index(exp_data, dtype=exp_index_dtype)
+        tm.assert_index_equal(res, exp)
+
+        # 3 elements
+        res = Index(vals1).append([Index(vals2), Index(vals3)])
+        exp = Index(exp_data3, dtype=exp_index_dtype)
+        tm.assert_index_equal(res, exp)
+
+        # ----- Series ----- #
+
+        # series._append
+        # GH#39817
+        res = Series(vals1)._append(Series(vals2), ignore_index=True)
+        exp = Series(exp_data, dtype=exp_series_dtype)
+        tm.assert_series_equal(res, exp, check_index_type=True)
+
+        # concat
+        # GH#39817
+        res = pd.concat([Series(vals1), Series(vals2)], ignore_index=True)
+        tm.assert_series_equal(res, exp, check_index_type=True)
+
+        # 3 elements
+        # GH#39817
+        res = Series(vals1)._append([Series(vals2), Series(vals3)], ignore_index=True)
+        exp = Series(exp_data3, dtype=exp_series_dtype)
+        tm.assert_series_equal(res, exp)
+
+        # GH#39817
+        res = pd.concat(
+            [Series(vals1), Series(vals2), Series(vals3)],
+            ignore_index=True,
+        )
+        tm.assert_series_equal(res, exp)
+
+    def test_concatlike_common_coerce_to_pandas_object(self):
+        # GH 13626
+        # result must be Timestamp/Timedelta, not datetime.datetime/timedelta
+        dti = pd.DatetimeIndex(["2011-01-01", "2011-01-02"])
+        tdi = pd.TimedeltaIndex(["1 days", "2 days"])
+
+        exp = Index(
+            [
+                pd.Timestamp("2011-01-01"),
+                pd.Timestamp("2011-01-02"),
+                pd.Timedelta("1 days"),
+                pd.Timedelta("2 days"),
+            ]
+        )
+
+        res = dti.append(tdi)
+        tm.assert_index_equal(res, exp)
+        assert isinstance(res[0], pd.Timestamp)
+        assert isinstance(res[-1], pd.Timedelta)
+
+        dts = Series(dti)
+        tds = Series(tdi)
+        res = dts._append(tds)
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+        assert isinstance(res.iloc[0], pd.Timestamp)
+        assert isinstance(res.iloc[-1], pd.Timedelta)
+
+        res = pd.concat([dts, tds])
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+        assert isinstance(res.iloc[0], pd.Timestamp)
+        assert isinstance(res.iloc[-1], pd.Timedelta)
+
+    def test_concatlike_datetimetz(self, tz_aware_fixture):
+        tz = tz_aware_fixture
+        # GH 7795
+        dti1 = pd.DatetimeIndex(["2011-01-01", "2011-01-02"], tz=tz)
+        dti2 = pd.DatetimeIndex(["2012-01-01", "2012-01-02"], tz=tz)
+
+        exp = pd.DatetimeIndex(
+            ["2011-01-01", "2011-01-02", "2012-01-01", "2012-01-02"], tz=tz
+        )
+
+        res = dti1.append(dti2)
+        tm.assert_index_equal(res, exp)
+
+        dts1 = Series(dti1)
+        dts2 = Series(dti2)
+        res = dts1._append(dts2)
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        res = pd.concat([dts1, dts2])
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+    @pytest.mark.parametrize("tz", ["UTC", "US/Eastern", "Asia/Tokyo", "EST5EDT"])
+    def test_concatlike_datetimetz_short(self, tz):
+        # GH#7795
+        ix1 = pd.date_range(start="2014-07-15", end="2014-07-17", freq="D", tz=tz)
+        ix2 = pd.DatetimeIndex(["2014-07-11", "2014-07-21"], tz=tz)
+        df1 = DataFrame(0, index=ix1, columns=["A", "B"])
+        df2 = DataFrame(0, index=ix2, columns=["A", "B"])
+
+        exp_idx = pd.DatetimeIndex(
+            ["2014-07-15", "2014-07-16", "2014-07-17", "2014-07-11", "2014-07-21"],
+            tz=tz,
+        ).as_unit("ns")
+        exp = DataFrame(0, index=exp_idx, columns=["A", "B"])
+
+        tm.assert_frame_equal(df1._append(df2), exp)
+        tm.assert_frame_equal(pd.concat([df1, df2]), exp)
+
+    def test_concatlike_datetimetz_to_object(self, tz_aware_fixture):
+        tz = tz_aware_fixture
+        # GH 13660
+
+        # different tz coerces to object
+        dti1 = pd.DatetimeIndex(["2011-01-01", "2011-01-02"], tz=tz)
+        dti2 = pd.DatetimeIndex(["2012-01-01", "2012-01-02"])
+
+        exp = Index(
+            [
+                pd.Timestamp("2011-01-01", tz=tz),
+                pd.Timestamp("2011-01-02", tz=tz),
+                pd.Timestamp("2012-01-01"),
+                pd.Timestamp("2012-01-02"),
+            ],
+            dtype=object,
+        )
+
+        res = dti1.append(dti2)
+        tm.assert_index_equal(res, exp)
+
+        dts1 = Series(dti1)
+        dts2 = Series(dti2)
+        res = dts1._append(dts2)
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        res = pd.concat([dts1, dts2])
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        # different tz
+        dti3 = pd.DatetimeIndex(["2012-01-01", "2012-01-02"], tz="US/Pacific")
+
+        exp = Index(
+            [
+                pd.Timestamp("2011-01-01", tz=tz),
+                pd.Timestamp("2011-01-02", tz=tz),
+                pd.Timestamp("2012-01-01", tz="US/Pacific"),
+                pd.Timestamp("2012-01-02", tz="US/Pacific"),
+            ],
+            dtype=object,
+        )
+
+        res = dti1.append(dti3)
+        tm.assert_index_equal(res, exp)
+
+        dts1 = Series(dti1)
+        dts3 = Series(dti3)
+        res = dts1._append(dts3)
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        res = pd.concat([dts1, dts3])
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+    def test_concatlike_common_period(self):
+        # GH 13660
+        pi1 = pd.PeriodIndex(["2011-01", "2011-02"], freq="M")
+        pi2 = pd.PeriodIndex(["2012-01", "2012-02"], freq="M")
+
+        exp = pd.PeriodIndex(["2011-01", "2011-02", "2012-01", "2012-02"], freq="M")
+
+        res = pi1.append(pi2)
+        tm.assert_index_equal(res, exp)
+
+        ps1 = Series(pi1)
+        ps2 = Series(pi2)
+        res = ps1._append(ps2)
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        res = pd.concat([ps1, ps2])
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+    def test_concatlike_common_period_diff_freq_to_object(self):
+        # GH 13221
+        pi1 = pd.PeriodIndex(["2011-01", "2011-02"], freq="M")
+        pi2 = pd.PeriodIndex(["2012-01-01", "2012-02-01"], freq="D")
+
+        exp = Index(
+            [
+                pd.Period("2011-01", freq="M"),
+                pd.Period("2011-02", freq="M"),
+                pd.Period("2012-01-01", freq="D"),
+                pd.Period("2012-02-01", freq="D"),
+            ],
+            dtype=object,
+        )
+
+        res = pi1.append(pi2)
+        tm.assert_index_equal(res, exp)
+
+        ps1 = Series(pi1)
+        ps2 = Series(pi2)
+        res = ps1._append(ps2)
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        res = pd.concat([ps1, ps2])
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+    def test_concatlike_common_period_mixed_dt_to_object(self):
+        # GH 13221
+        # different datetimelike
+        pi1 = pd.PeriodIndex(["2011-01", "2011-02"], freq="M")
+        tdi = pd.TimedeltaIndex(["1 days", "2 days"])
+        exp = Index(
+            [
+                pd.Period("2011-01", freq="M"),
+                pd.Period("2011-02", freq="M"),
+                pd.Timedelta("1 days"),
+                pd.Timedelta("2 days"),
+            ],
+            dtype=object,
+        )
+
+        res = pi1.append(tdi)
+        tm.assert_index_equal(res, exp)
+
+        ps1 = Series(pi1)
+        tds = Series(tdi)
+        res = ps1._append(tds)
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        res = pd.concat([ps1, tds])
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        # inverse
+        exp = Index(
+            [
+                pd.Timedelta("1 days"),
+                pd.Timedelta("2 days"),
+                pd.Period("2011-01", freq="M"),
+                pd.Period("2011-02", freq="M"),
+            ],
+            dtype=object,
+        )
+
+        res = tdi.append(pi1)
+        tm.assert_index_equal(res, exp)
+
+        ps1 = Series(pi1)
+        tds = Series(tdi)
+        res = tds._append(ps1)
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        res = pd.concat([tds, ps1])
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+    def test_concat_categorical(self):
+        # GH 13524
+
+        # same categories -> category
+        s1 = Series([1, 2, np.nan], dtype="category")
+        s2 = Series([2, 1, 2], dtype="category")
+
+        exp = Series([1, 2, np.nan, 2, 1, 2], dtype="category")
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        # partially different categories => not-category
+        s1 = Series([3, 2], dtype="category")
+        s2 = Series([2, 1], dtype="category")
+
+        exp = Series([3, 2, 2, 1])
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        # completely different categories (same dtype) => not-category
+        s1 = Series([10, 11, np.nan], dtype="category")
+        s2 = Series([np.nan, 1, 3, 2], dtype="category")
+
+        exp = Series([10, 11, np.nan, np.nan, 1, 3, 2], dtype=np.float64)
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+    def test_union_categorical_same_categories_different_order(self):
+        # https://github.com/pandas-dev/pandas/issues/19096
+        a = Series(Categorical(["a", "b", "c"], categories=["a", "b", "c"]))
+        b = Series(Categorical(["a", "b", "c"], categories=["b", "a", "c"]))
+        result = pd.concat([a, b], ignore_index=True)
+        expected = Series(
+            Categorical(["a", "b", "c", "a", "b", "c"], categories=["a", "b", "c"])
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_concat_categorical_coercion(self):
+        # GH 13524
+
+        # category + not-category => not-category
+        s1 = Series([1, 2, np.nan], dtype="category")
+        s2 = Series([2, 1, 2])
+
+        exp = Series([1, 2, np.nan, 2, 1, 2], dtype=np.float64)
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        # result shouldn't be affected by 1st elem dtype
+        exp = Series([2, 1, 2, 1, 2, np.nan], dtype=np.float64)
+        tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
+        tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
+
+        # all values are not in category => not-category
+        s1 = Series([3, 2], dtype="category")
+        s2 = Series([2, 1])
+
+        exp = Series([3, 2, 2, 1])
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        exp = Series([2, 1, 3, 2])
+        tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
+        tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
+
+        # completely different categories => not-category
+        s1 = Series([10, 11, np.nan], dtype="category")
+        s2 = Series([1, 3, 2])
+
+        exp = Series([10, 11, np.nan, 1, 3, 2], dtype=np.float64)
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        exp = Series([1, 3, 2, 10, 11, np.nan], dtype=np.float64)
+        tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
+        tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
+
+        # different dtype => not-category
+        s1 = Series([10, 11, np.nan], dtype="category")
+        s2 = Series(["a", "b", "c"])
+
+        exp = Series([10, 11, np.nan, "a", "b", "c"])
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        exp = Series(["a", "b", "c", 10, 11, np.nan])
+        tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
+        tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
+
+        # if normal series only contains NaN-likes => not-category
+        s1 = Series([10, 11], dtype="category")
+        s2 = Series([np.nan, np.nan, np.nan])
+
+        exp = Series([10, 11, np.nan, np.nan, np.nan])
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        exp = Series([np.nan, np.nan, np.nan, 10, 11])
+        tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
+        tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
+
+    def test_concat_categorical_3elem_coercion(self):
+        # GH 13524
+
+        # mixed dtypes => not-category
+        s1 = Series([1, 2, np.nan], dtype="category")
+        s2 = Series([2, 1, 2], dtype="category")
+        s3 = Series([1, 2, 1, 2, np.nan])
+
+        exp = Series([1, 2, np.nan, 2, 1, 2, 1, 2, 1, 2, np.nan], dtype="float")
+        tm.assert_series_equal(pd.concat([s1, s2, s3], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append([s2, s3], ignore_index=True), exp)
+
+        exp = Series([1, 2, 1, 2, np.nan, 1, 2, np.nan, 2, 1, 2], dtype="float")
+        tm.assert_series_equal(pd.concat([s3, s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s3._append([s1, s2], ignore_index=True), exp)
+
+        # values are all in either category => not-category
+        s1 = Series([4, 5, 6], dtype="category")
+        s2 = Series([1, 2, 3], dtype="category")
+        s3 = Series([1, 3, 4])
+
+        exp = Series([4, 5, 6, 1, 2, 3, 1, 3, 4])
+        tm.assert_series_equal(pd.concat([s1, s2, s3], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append([s2, s3], ignore_index=True), exp)
+
+        exp = Series([1, 3, 4, 4, 5, 6, 1, 2, 3])
+        tm.assert_series_equal(pd.concat([s3, s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s3._append([s1, s2], ignore_index=True), exp)
+
+        # values are all in either category => not-category
+        s1 = Series([4, 5, 6], dtype="category")
+        s2 = Series([1, 2, 3], dtype="category")
+        s3 = Series([10, 11, 12])
+
+        exp = Series([4, 5, 6, 1, 2, 3, 10, 11, 12])
+        tm.assert_series_equal(pd.concat([s1, s2, s3], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append([s2, s3], ignore_index=True), exp)
+
+        exp = Series([10, 11, 12, 4, 5, 6, 1, 2, 3])
+        tm.assert_series_equal(pd.concat([s3, s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s3._append([s1, s2], ignore_index=True), exp)
+
+    def test_concat_categorical_multi_coercion(self):
+        # GH 13524
+
+        s1 = Series([1, 3], dtype="category")
+        s2 = Series([3, 4], dtype="category")
+        s3 = Series([2, 3])
+        s4 = Series([2, 2], dtype="category")
+        s5 = Series([1, np.nan])
+        s6 = Series([1, 3, 2], dtype="category")
+
+        # mixed dtype, values are all in categories => not-category
+        exp = Series([1, 3, 3, 4, 2, 3, 2, 2, 1, np.nan, 1, 3, 2])
+        res = pd.concat([s1, s2, s3, s4, s5, s6], ignore_index=True)
+        tm.assert_series_equal(res, exp)
+        res = s1._append([s2, s3, s4, s5, s6], ignore_index=True)
+        tm.assert_series_equal(res, exp)
+
+        exp = Series([1, 3, 2, 1, np.nan, 2, 2, 2, 3, 3, 4, 1, 3])
+        res = pd.concat([s6, s5, s4, s3, s2, s1], ignore_index=True)
+        tm.assert_series_equal(res, exp)
+        res = s6._append([s5, s4, s3, s2, s1], ignore_index=True)
+        tm.assert_series_equal(res, exp)
+
+    def test_concat_categorical_ordered(self):
+        # GH 13524
+
+        s1 = Series(Categorical([1, 2, np.nan], ordered=True))
+        s2 = Series(Categorical([2, 1, 2], ordered=True))
+
+        exp = Series(Categorical([1, 2, np.nan, 2, 1, 2], ordered=True))
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        exp = Series(Categorical([1, 2, np.nan, 2, 1, 2, 1, 2, np.nan], ordered=True))
+        tm.assert_series_equal(pd.concat([s1, s2, s1], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append([s2, s1], ignore_index=True), exp)
+
+    def test_concat_categorical_coercion_nan(self):
+        # GH 13524
+
+        # some edge cases
+        # category + not-category => not category
+        s1 = Series(np.array([np.nan, np.nan], dtype=np.float64), dtype="category")
+        s2 = Series([np.nan, 1])
+
+        exp = Series([np.nan, np.nan, np.nan, 1])
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        s1 = Series([1, np.nan], dtype="category")
+        s2 = Series([np.nan, np.nan])
+
+        exp = Series([1, np.nan, np.nan, np.nan], dtype="float")
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        # mixed dtype, all nan-likes => not-category
+        s1 = Series([np.nan, np.nan], dtype="category")
+        s2 = Series([np.nan, np.nan])
+
+        exp = Series([np.nan, np.nan, np.nan, np.nan])
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+        tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
+        tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
+
+        # all category nan-likes => category
+        s1 = Series([np.nan, np.nan], dtype="category")
+        s2 = Series([np.nan, np.nan], dtype="category")
+
+        exp = Series([np.nan, np.nan, np.nan, np.nan], dtype="category")
+
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+    def test_concat_categorical_empty(self):
+        # GH 13524
+
+        s1 = Series([], dtype="category")
+        s2 = Series([1, 2], dtype="category")
+
+        msg = "The behavior of array concatenation with empty entries is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), s2)
+            tm.assert_series_equal(s1._append(s2, ignore_index=True), s2)
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), s2)
+            tm.assert_series_equal(s2._append(s1, ignore_index=True), s2)
+
+        s1 = Series([], dtype="category")
+        s2 = Series([], dtype="category")
+
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), s2)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), s2)
+
+        s1 = Series([], dtype="category")
+        s2 = Series([], dtype="object")
+
+        # different dtype => not-category
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), s2)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), s2)
+        tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), s2)
+        tm.assert_series_equal(s2._append(s1, ignore_index=True), s2)
+
+        s1 = Series([], dtype="category")
+        s2 = Series([np.nan, np.nan])
+
+        # empty Series is ignored
+        exp = Series([np.nan, np.nan])
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+            tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
+            tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
+
+    def test_categorical_concat_append(self):
+        cat = Categorical(["a", "b"], categories=["a", "b"])
+        vals = [1, 2]
+        df = DataFrame({"cats": cat, "vals": vals})
+        cat2 = Categorical(["a", "b", "a", "b"], categories=["a", "b"])
+        vals2 = [1, 2, 1, 2]
+        exp = DataFrame({"cats": cat2, "vals": vals2}, index=Index([0, 1, 0, 1]))
+
+        tm.assert_frame_equal(pd.concat([df, df]), exp)
+        tm.assert_frame_equal(df._append(df), exp)
+
+        # GH 13524 can concat different categories
+        cat3 = Categorical(["a", "b"], categories=["a", "b", "c"])
+        vals3 = [1, 2]
+        df_different_categories = DataFrame({"cats": cat3, "vals": vals3})
+
+        res = pd.concat([df, df_different_categories], ignore_index=True)
+        exp = DataFrame({"cats": list("abab"), "vals": [1, 2, 1, 2]})
+        tm.assert_frame_equal(res, exp)
+
+        res = df._append(df_different_categories, ignore_index=True)
+        tm.assert_frame_equal(res, exp)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_categorical.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_categorical.py
new file mode 100644
index 0000000000000000000000000000000000000000..bbaaf0abecfbdff32d5c7526e3c8c37c71e92cc9
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_categorical.py
@@ -0,0 +1,273 @@
+from datetime import datetime
+
+import numpy as np
+
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestCategoricalConcat:
+    def test_categorical_concat(self, sort):
+        # See GH 10177
+        df1 = DataFrame(
+            np.arange(18, dtype="int64").reshape(6, 3), columns=["a", "b", "c"]
+        )
+
+        df2 = DataFrame(np.arange(14, dtype="int64").reshape(7, 2), columns=["a", "c"])
+
+        cat_values = ["one", "one", "two", "one", "two", "two", "one"]
+        df2["h"] = Series(Categorical(cat_values))
+
+        res = pd.concat((df1, df2), axis=0, ignore_index=True, sort=sort)
+        exp = DataFrame(
+            {
+                "a": [0, 3, 6, 9, 12, 15, 0, 2, 4, 6, 8, 10, 12],
+                "b": [
+                    1,
+                    4,
+                    7,
+                    10,
+                    13,
+                    16,
+                    np.nan,
+                    np.nan,
+                    np.nan,
+                    np.nan,
+                    np.nan,
+                    np.nan,
+                    np.nan,
+                ],
+                "c": [2, 5, 8, 11, 14, 17, 1, 3, 5, 7, 9, 11, 13],
+                "h": [None] * 6 + cat_values,
+            }
+        )
+        exp["h"] = exp["h"].astype(df2["h"].dtype)
+        tm.assert_frame_equal(res, exp)
+
+    def test_categorical_concat_dtypes(self, using_infer_string):
+        # GH8143
+        index = ["cat", "obj", "num"]
+        cat = Categorical(["a", "b", "c"])
+        obj = Series(["a", "b", "c"])
+        num = Series([1, 2, 3])
+        df = pd.concat([Series(cat), obj, num], axis=1, keys=index)
+
+        result = df.dtypes == (
+            object if not using_infer_string else "string[pyarrow_numpy]"
+        )
+        expected = Series([False, True, False], index=index)
+        tm.assert_series_equal(result, expected)
+
+        result = df.dtypes == "int64"
+        expected = Series([False, False, True], index=index)
+        tm.assert_series_equal(result, expected)
+
+        result = df.dtypes == "category"
+        expected = Series([True, False, False], index=index)
+        tm.assert_series_equal(result, expected)
+
+    def test_concat_categoricalindex(self):
+        # GH 16111, categories that aren't lexsorted
+        categories = [9, 0, 1, 2, 3]
+
+        a = Series(1, index=pd.CategoricalIndex([9, 0], categories=categories))
+        b = Series(2, index=pd.CategoricalIndex([0, 1], categories=categories))
+        c = Series(3, index=pd.CategoricalIndex([1, 2], categories=categories))
+
+        result = pd.concat([a, b, c], axis=1)
+
+        exp_idx = pd.CategoricalIndex([9, 0, 1, 2], categories=categories)
+        exp = DataFrame(
+            {
+                0: [1, 1, np.nan, np.nan],
+                1: [np.nan, 2, 2, np.nan],
+                2: [np.nan, np.nan, 3, 3],
+            },
+            columns=[0, 1, 2],
+            index=exp_idx,
+        )
+        tm.assert_frame_equal(result, exp)
+
+    def test_categorical_concat_preserve(self):
+        # GH 8641  series concat not preserving category dtype
+        # GH 13524 can concat different categories
+        s = Series(list("abc"), dtype="category")
+        s2 = Series(list("abd"), dtype="category")
+
+        exp = Series(list("abcabd"))
+        res = pd.concat([s, s2], ignore_index=True)
+        tm.assert_series_equal(res, exp)
+
+        exp = Series(list("abcabc"), dtype="category")
+        res = pd.concat([s, s], ignore_index=True)
+        tm.assert_series_equal(res, exp)
+
+        exp = Series(list("abcabc"), index=[0, 1, 2, 0, 1, 2], dtype="category")
+        res = pd.concat([s, s])
+        tm.assert_series_equal(res, exp)
+
+        a = Series(np.arange(6, dtype="int64"))
+        b = Series(list("aabbca"))
+
+        df2 = DataFrame({"A": a, "B": b.astype(CategoricalDtype(list("cab")))})
+        res = pd.concat([df2, df2])
+        exp = DataFrame(
+            {
+                "A": pd.concat([a, a]),
+                "B": pd.concat([b, b]).astype(CategoricalDtype(list("cab"))),
+            }
+        )
+        tm.assert_frame_equal(res, exp)
+
+    def test_categorical_index_preserver(self):
+        a = Series(np.arange(6, dtype="int64"))
+        b = Series(list("aabbca"))
+
+        df2 = DataFrame(
+            {"A": a, "B": b.astype(CategoricalDtype(list("cab")))}
+        ).set_index("B")
+        result = pd.concat([df2, df2])
+        expected = DataFrame(
+            {
+                "A": pd.concat([a, a]),
+                "B": pd.concat([b, b]).astype(CategoricalDtype(list("cab"))),
+            }
+        ).set_index("B")
+        tm.assert_frame_equal(result, expected)
+
+        # wrong categories -> uses concat_compat, which casts to object
+        df3 = DataFrame(
+            {"A": a, "B": Categorical(b, categories=list("abe"))}
+        ).set_index("B")
+        result = pd.concat([df2, df3])
+        expected = pd.concat(
+            [
+                df2.set_axis(df2.index.astype(object), axis=0),
+                df3.set_axis(df3.index.astype(object), axis=0),
+            ]
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_categorical_tz(self):
+        # GH-23816
+        a = Series(pd.date_range("2017-01-01", periods=2, tz="US/Pacific"))
+        b = Series(["a", "b"], dtype="category")
+        result = pd.concat([a, b], ignore_index=True)
+        expected = Series(
+            [
+                pd.Timestamp("2017-01-01", tz="US/Pacific"),
+                pd.Timestamp("2017-01-02", tz="US/Pacific"),
+                "a",
+                "b",
+            ]
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_concat_categorical_datetime(self):
+        # GH-39443
+        df1 = DataFrame(
+            {"x": Series(datetime(2021, 1, 1), index=[0], dtype="category")}
+        )
+        df2 = DataFrame(
+            {"x": Series(datetime(2021, 1, 2), index=[1], dtype="category")}
+        )
+
+        result = pd.concat([df1, df2])
+        expected = DataFrame(
+            {"x": Series([datetime(2021, 1, 1), datetime(2021, 1, 2)])}
+        )
+
+        tm.assert_equal(result, expected)
+
+    def test_concat_categorical_unchanged(self):
+        # GH-12007
+        # test fix for when concat on categorical and float
+        # coerces dtype categorical -> float
+        df = DataFrame(Series(["a", "b", "c"], dtype="category", name="A"))
+        ser = Series([0, 1, 2], index=[0, 1, 3], name="B")
+        result = pd.concat([df, ser], axis=1)
+        expected = DataFrame(
+            {
+                "A": Series(["a", "b", "c", np.nan], dtype="category"),
+                "B": Series([0, 1, np.nan, 2], dtype="float"),
+            }
+        )
+        tm.assert_equal(result, expected)
+
+    def test_categorical_concat_gh7864(self):
+        # GH 7864
+        # make sure ordering is preserved
+        df = DataFrame({"id": [1, 2, 3, 4, 5, 6], "raw_grade": list("abbaae")})
+        df["grade"] = Categorical(df["raw_grade"])
+        df["grade"].cat.set_categories(["e", "a", "b"])
+
+        df1 = df[0:3]
+        df2 = df[3:]
+
+        tm.assert_index_equal(df["grade"].cat.categories, df1["grade"].cat.categories)
+        tm.assert_index_equal(df["grade"].cat.categories, df2["grade"].cat.categories)
+
+        dfx = pd.concat([df1, df2])
+        tm.assert_index_equal(df["grade"].cat.categories, dfx["grade"].cat.categories)
+
+        dfa = df1._append(df2)
+        tm.assert_index_equal(df["grade"].cat.categories, dfa["grade"].cat.categories)
+
+    def test_categorical_index_upcast(self):
+        # GH 17629
+        # test upcasting to object when concatenating on categorical indexes
+        # with non-identical categories
+
+        a = DataFrame({"foo": [1, 2]}, index=Categorical(["foo", "bar"]))
+        b = DataFrame({"foo": [4, 3]}, index=Categorical(["baz", "bar"]))
+
+        res = pd.concat([a, b])
+        exp = DataFrame({"foo": [1, 2, 4, 3]}, index=["foo", "bar", "baz", "bar"])
+
+        tm.assert_equal(res, exp)
+
+        a = Series([1, 2], index=Categorical(["foo", "bar"]))
+        b = Series([4, 3], index=Categorical(["baz", "bar"]))
+
+        res = pd.concat([a, b])
+        exp = Series([1, 2, 4, 3], index=["foo", "bar", "baz", "bar"])
+
+        tm.assert_equal(res, exp)
+
+    def test_categorical_missing_from_one_frame(self):
+        # GH 25412
+        df1 = DataFrame({"f1": [1, 2, 3]})
+        df2 = DataFrame({"f1": [2, 3, 1], "f2": Series([4, 4, 4]).astype("category")})
+        result = pd.concat([df1, df2], sort=True)
+        dtype = CategoricalDtype([4])
+        expected = DataFrame(
+            {
+                "f1": [1, 2, 3, 2, 3, 1],
+                "f2": Categorical.from_codes([-1, -1, -1, 0, 0, 0], dtype=dtype),
+            },
+            index=[0, 1, 2, 0, 1, 2],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_categorical_same_categories_different_order(self):
+        # https://github.com/pandas-dev/pandas/issues/24845
+
+        c1 = pd.CategoricalIndex(["a", "a"], categories=["a", "b"], ordered=False)
+        c2 = pd.CategoricalIndex(["b", "b"], categories=["b", "a"], ordered=False)
+        c3 = pd.CategoricalIndex(
+            ["a", "a", "b", "b"], categories=["a", "b"], ordered=False
+        )
+
+        df1 = DataFrame({"A": [1, 2]}, index=c1)
+        df2 = DataFrame({"A": [3, 4]}, index=c2)
+
+        result = pd.concat((df1, df2))
+        expected = DataFrame({"A": [1, 2, 3, 4]}, index=c3)
+        tm.assert_frame_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_concat.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_concat.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e34d02091e698d2ace7acd74896d6e68c556252
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_concat.py
@@ -0,0 +1,912 @@
+from collections import (
+    abc,
+    deque,
+)
+from collections.abc import Iterator
+from datetime import datetime
+from decimal import Decimal
+
+import numpy as np
+import pytest
+
+from pandas.errors import InvalidIndexError
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    PeriodIndex,
+    Series,
+    concat,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.core.arrays import SparseArray
+from pandas.tests.extension.decimal import to_decimal
+
+
+class TestConcatenate:
+    def test_append_concat(self):
+        # GH#1815
+        d1 = date_range("12/31/1990", "12/31/1999", freq="YE-DEC")
+        d2 = date_range("12/31/2000", "12/31/2009", freq="YE-DEC")
+
+        s1 = Series(np.random.default_rng(2).standard_normal(10), d1)
+        s2 = Series(np.random.default_rng(2).standard_normal(10), d2)
+
+        s1 = s1.to_period()
+        s2 = s2.to_period()
+
+        # drops index
+        result = concat([s1, s2])
+        assert isinstance(result.index, PeriodIndex)
+        assert result.index[0] == s1.index[0]
+
+    def test_concat_copy(self, using_array_manager, using_copy_on_write):
+        df = DataFrame(np.random.default_rng(2).standard_normal((4, 3)))
+        df2 = DataFrame(np.random.default_rng(2).integers(0, 10, size=4).reshape(4, 1))
+        df3 = DataFrame({5: "foo"}, index=range(4))
+
+        # These are actual copies.
+        result = concat([df, df2, df3], axis=1, copy=True)
+
+        if not using_copy_on_write:
+            for arr in result._mgr.arrays:
+                assert not any(
+                    np.shares_memory(arr, y)
+                    for x in [df, df2, df3]
+                    for y in x._mgr.arrays
+                )
+        else:
+            for arr in result._mgr.arrays:
+                assert arr.base is not None
+
+        # These are the same.
+        result = concat([df, df2, df3], axis=1, copy=False)
+
+        for arr in result._mgr.arrays:
+            if arr.dtype.kind == "f":
+                assert arr.base is df._mgr.arrays[0].base
+            elif arr.dtype.kind in ["i", "u"]:
+                assert arr.base is df2._mgr.arrays[0].base
+            elif arr.dtype == object:
+                if using_array_manager:
+                    # we get the same array object, which has no base
+                    assert arr is df3._mgr.arrays[0]
+                else:
+                    assert arr.base is not None
+
+        # Float block was consolidated.
+        df4 = DataFrame(np.random.default_rng(2).standard_normal((4, 1)))
+        result = concat([df, df2, df3, df4], axis=1, copy=False)
+        for arr in result._mgr.arrays:
+            if arr.dtype.kind == "f":
+                if using_array_manager or using_copy_on_write:
+                    # this is a view on some array in either df or df4
+                    assert any(
+                        np.shares_memory(arr, other)
+                        for other in df._mgr.arrays + df4._mgr.arrays
+                    )
+                else:
+                    # the block was consolidated, so we got a copy anyway
+                    assert arr.base is None
+            elif arr.dtype.kind in ["i", "u"]:
+                assert arr.base is df2._mgr.arrays[0].base
+            elif arr.dtype == object:
+                # this is a view on df3
+                assert any(np.shares_memory(arr, other) for other in df3._mgr.arrays)
+
+    def test_concat_with_group_keys(self):
+        # axis=0
+        df = DataFrame(np.random.default_rng(2).standard_normal((3, 4)))
+        df2 = DataFrame(np.random.default_rng(2).standard_normal((4, 4)))
+
+        result = concat([df, df2], keys=[0, 1])
+        exp_index = MultiIndex.from_arrays(
+            [[0, 0, 0, 1, 1, 1, 1], [0, 1, 2, 0, 1, 2, 3]]
+        )
+        expected = DataFrame(np.r_[df.values, df2.values], index=exp_index)
+        tm.assert_frame_equal(result, expected)
+
+        result = concat([df, df], keys=[0, 1])
+        exp_index2 = MultiIndex.from_arrays([[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])
+        expected = DataFrame(np.r_[df.values, df.values], index=exp_index2)
+        tm.assert_frame_equal(result, expected)
+
+        # axis=1
+        df = DataFrame(np.random.default_rng(2).standard_normal((4, 3)))
+        df2 = DataFrame(np.random.default_rng(2).standard_normal((4, 4)))
+
+        result = concat([df, df2], keys=[0, 1], axis=1)
+        expected = DataFrame(np.c_[df.values, df2.values], columns=exp_index)
+        tm.assert_frame_equal(result, expected)
+
+        result = concat([df, df], keys=[0, 1], axis=1)
+        expected = DataFrame(np.c_[df.values, df.values], columns=exp_index2)
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_keys_specific_levels(self):
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 4)))
+        pieces = [df.iloc[:, [0, 1]], df.iloc[:, [2]], df.iloc[:, [3]]]
+        level = ["three", "two", "one", "zero"]
+        result = concat(
+            pieces,
+            axis=1,
+            keys=["one", "two", "three"],
+            levels=[level],
+            names=["group_key"],
+        )
+
+        tm.assert_index_equal(result.columns.levels[0], Index(level, name="group_key"))
+        tm.assert_index_equal(result.columns.levels[1], Index([0, 1, 2, 3]))
+
+        assert result.columns.names == ["group_key", None]
+
+    @pytest.mark.parametrize("mapping", ["mapping", "dict"])
+    def test_concat_mapping(self, mapping, non_dict_mapping_subclass):
+        constructor = dict if mapping == "dict" else non_dict_mapping_subclass
+        frames = constructor(
+            {
+                "foo": DataFrame(np.random.default_rng(2).standard_normal((4, 3))),
+                "bar": DataFrame(np.random.default_rng(2).standard_normal((4, 3))),
+                "baz": DataFrame(np.random.default_rng(2).standard_normal((4, 3))),
+                "qux": DataFrame(np.random.default_rng(2).standard_normal((4, 3))),
+            }
+        )
+
+        sorted_keys = list(frames.keys())
+
+        result = concat(frames)
+        expected = concat([frames[k] for k in sorted_keys], keys=sorted_keys)
+        tm.assert_frame_equal(result, expected)
+
+        result = concat(frames, axis=1)
+        expected = concat([frames[k] for k in sorted_keys], keys=sorted_keys, axis=1)
+        tm.assert_frame_equal(result, expected)
+
+        keys = ["baz", "foo", "bar"]
+        result = concat(frames, keys=keys)
+        expected = concat([frames[k] for k in keys], keys=keys)
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_keys_and_levels(self):
+        df = DataFrame(np.random.default_rng(2).standard_normal((1, 3)))
+        df2 = DataFrame(np.random.default_rng(2).standard_normal((1, 4)))
+
+        levels = [["foo", "baz"], ["one", "two"]]
+        names = ["first", "second"]
+        result = concat(
+            [df, df2, df, df2],
+            keys=[("foo", "one"), ("foo", "two"), ("baz", "one"), ("baz", "two")],
+            levels=levels,
+            names=names,
+        )
+        expected = concat([df, df2, df, df2])
+        exp_index = MultiIndex(
+            levels=levels + [[0]],
+            codes=[[0, 0, 1, 1], [0, 1, 0, 1], [0, 0, 0, 0]],
+            names=names + [None],
+        )
+        expected.index = exp_index
+
+        tm.assert_frame_equal(result, expected)
+
+        # no names
+        result = concat(
+            [df, df2, df, df2],
+            keys=[("foo", "one"), ("foo", "two"), ("baz", "one"), ("baz", "two")],
+            levels=levels,
+        )
+        assert result.index.names == (None,) * 3
+
+        # no levels
+        result = concat(
+            [df, df2, df, df2],
+            keys=[("foo", "one"), ("foo", "two"), ("baz", "one"), ("baz", "two")],
+            names=["first", "second"],
+        )
+        assert result.index.names == ("first", "second", None)
+        tm.assert_index_equal(
+            result.index.levels[0], Index(["baz", "foo"], name="first")
+        )
+
+    def test_concat_keys_levels_no_overlap(self):
+        # GH #1406
+        df = DataFrame(np.random.default_rng(2).standard_normal((1, 3)), index=["a"])
+        df2 = DataFrame(np.random.default_rng(2).standard_normal((1, 4)), index=["b"])
+
+        msg = "Values not found in passed level"
+        with pytest.raises(ValueError, match=msg):
+            concat([df, df], keys=["one", "two"], levels=[["foo", "bar", "baz"]])
+
+        msg = "Key one not in level"
+        with pytest.raises(ValueError, match=msg):
+            concat([df, df2], keys=["one", "two"], levels=[["foo", "bar", "baz"]])
+
+    def test_crossed_dtypes_weird_corner(self):
+        columns = ["A", "B", "C", "D"]
+        df1 = DataFrame(
+            {
+                "A": np.array([1, 2, 3, 4], dtype="f8"),
+                "B": np.array([1, 2, 3, 4], dtype="i8"),
+                "C": np.array([1, 2, 3, 4], dtype="f8"),
+                "D": np.array([1, 2, 3, 4], dtype="i8"),
+            },
+            columns=columns,
+        )
+
+        df2 = DataFrame(
+            {
+                "A": np.array([1, 2, 3, 4], dtype="i8"),
+                "B": np.array([1, 2, 3, 4], dtype="f8"),
+                "C": np.array([1, 2, 3, 4], dtype="i8"),
+                "D": np.array([1, 2, 3, 4], dtype="f8"),
+            },
+            columns=columns,
+        )
+
+        appended = concat([df1, df2], ignore_index=True)
+        expected = DataFrame(
+            np.concatenate([df1.values, df2.values], axis=0), columns=columns
+        )
+        tm.assert_frame_equal(appended, expected)
+
+        df = DataFrame(np.random.default_rng(2).standard_normal((1, 3)), index=["a"])
+        df2 = DataFrame(np.random.default_rng(2).standard_normal((1, 4)), index=["b"])
+        result = concat([df, df2], keys=["one", "two"], names=["first", "second"])
+        assert result.index.names == ("first", "second")
+
+    def test_with_mixed_tuples(self, sort):
+        # 10697
+        # columns have mixed tuples, so handle properly
+        df1 = DataFrame({"A": "foo", ("B", 1): "bar"}, index=range(2))
+        df2 = DataFrame({"B": "foo", ("B", 1): "bar"}, index=range(2))
+
+        # it works
+        concat([df1, df2], sort=sort)
+
+    def test_concat_mixed_objs_columns(self):
+        # Test column-wise concat for mixed series/frames (axis=1)
+        # G2385
+
+        index = date_range("01-Jan-2013", periods=10, freq="h")
+        arr = np.arange(10, dtype="int64")
+        s1 = Series(arr, index=index)
+        s2 = Series(arr, index=index)
+        df = DataFrame(arr.reshape(-1, 1), index=index)
+
+        expected = DataFrame(
+            np.repeat(arr, 2).reshape(-1, 2), index=index, columns=[0, 0]
+        )
+        result = concat([df, df], axis=1)
+        tm.assert_frame_equal(result, expected)
+
+        expected = DataFrame(
+            np.repeat(arr, 2).reshape(-1, 2), index=index, columns=[0, 1]
+        )
+        result = concat([s1, s2], axis=1)
+        tm.assert_frame_equal(result, expected)
+
+        expected = DataFrame(
+            np.repeat(arr, 3).reshape(-1, 3), index=index, columns=[0, 1, 2]
+        )
+        result = concat([s1, s2, s1], axis=1)
+        tm.assert_frame_equal(result, expected)
+
+        expected = DataFrame(
+            np.repeat(arr, 5).reshape(-1, 5), index=index, columns=[0, 0, 1, 2, 3]
+        )
+        result = concat([s1, df, s2, s2, s1], axis=1)
+        tm.assert_frame_equal(result, expected)
+
+        # with names
+        s1.name = "foo"
+        expected = DataFrame(
+            np.repeat(arr, 3).reshape(-1, 3), index=index, columns=["foo", 0, 0]
+        )
+        result = concat([s1, df, s2], axis=1)
+        tm.assert_frame_equal(result, expected)
+
+        s2.name = "bar"
+        expected = DataFrame(
+            np.repeat(arr, 3).reshape(-1, 3), index=index, columns=["foo", 0, "bar"]
+        )
+        result = concat([s1, df, s2], axis=1)
+        tm.assert_frame_equal(result, expected)
+
+        # ignore index
+        expected = DataFrame(
+            np.repeat(arr, 3).reshape(-1, 3), index=index, columns=[0, 1, 2]
+        )
+        result = concat([s1, df, s2], axis=1, ignore_index=True)
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_mixed_objs_index(self):
+        # Test row-wise concat for mixed series/frames with a common name
+        # GH2385, GH15047
+
+        index = date_range("01-Jan-2013", periods=10, freq="h")
+        arr = np.arange(10, dtype="int64")
+        s1 = Series(arr, index=index)
+        s2 = Series(arr, index=index)
+        df = DataFrame(arr.reshape(-1, 1), index=index)
+
+        expected = DataFrame(
+            np.tile(arr, 3).reshape(-1, 1), index=index.tolist() * 3, columns=[0]
+        )
+        result = concat([s1, df, s2])
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_mixed_objs_index_names(self):
+        # Test row-wise concat for mixed series/frames with distinct names
+        # GH2385, GH15047
+
+        index = date_range("01-Jan-2013", periods=10, freq="h")
+        arr = np.arange(10, dtype="int64")
+        s1 = Series(arr, index=index, name="foo")
+        s2 = Series(arr, index=index, name="bar")
+        df = DataFrame(arr.reshape(-1, 1), index=index)
+
+        expected = DataFrame(
+            np.kron(np.where(np.identity(3) == 1, 1, np.nan), arr).T,
+            index=index.tolist() * 3,
+            columns=["foo", 0, "bar"],
+        )
+        result = concat([s1, df, s2])
+        tm.assert_frame_equal(result, expected)
+
+        # Rename all series to 0 when ignore_index=True
+        expected = DataFrame(np.tile(arr, 3).reshape(-1, 1), columns=[0])
+        result = concat([s1, df, s2], ignore_index=True)
+        tm.assert_frame_equal(result, expected)
+
+    def test_dtype_coercion(self):
+        # 12411
+        df = DataFrame({"date": [pd.Timestamp("20130101").tz_localize("UTC"), pd.NaT]})
+
+        result = concat([df.iloc[[0]], df.iloc[[1]]])
+        tm.assert_series_equal(result.dtypes, df.dtypes)
+
+        # 12045
+        df = DataFrame({"date": [datetime(2012, 1, 1), datetime(1012, 1, 2)]})
+        result = concat([df.iloc[[0]], df.iloc[[1]]])
+        tm.assert_series_equal(result.dtypes, df.dtypes)
+
+        # 11594
+        df = DataFrame({"text": ["some words"] + [None] * 9})
+        result = concat([df.iloc[[0]], df.iloc[[1]]])
+        tm.assert_series_equal(result.dtypes, df.dtypes)
+
+    def test_concat_single_with_key(self):
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 4)))
+
+        result = concat([df], keys=["foo"])
+        expected = concat([df, df], keys=["foo", "bar"])
+        tm.assert_frame_equal(result, expected[:10])
+
+    def test_concat_no_items_raises(self):
+        with pytest.raises(ValueError, match="No objects to concatenate"):
+            concat([])
+
+    def test_concat_exclude_none(self):
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 4)))
+
+        pieces = [df[:5], None, None, df[5:]]
+        result = concat(pieces)
+        tm.assert_frame_equal(result, df)
+        with pytest.raises(ValueError, match="All objects passed were None"):
+            concat([None, None])
+
+    def test_concat_keys_with_none(self):
+        # #1649
+        df0 = DataFrame([[10, 20, 30], [10, 20, 30], [10, 20, 30]])
+
+        result = concat({"a": None, "b": df0, "c": df0[:2], "d": df0[:1], "e": df0})
+        expected = concat({"b": df0, "c": df0[:2], "d": df0[:1], "e": df0})
+        tm.assert_frame_equal(result, expected)
+
+        result = concat(
+            [None, df0, df0[:2], df0[:1], df0], keys=["a", "b", "c", "d", "e"]
+        )
+        expected = concat([df0, df0[:2], df0[:1], df0], keys=["b", "c", "d", "e"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_bug_1719(self):
+        ts1 = Series(
+            np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)
+        )
+        ts2 = ts1.copy()[::2]
+
+        # to join with union
+        # these two are of different length!
+        left = concat([ts1, ts2], join="outer", axis=1)
+        right = concat([ts2, ts1], join="outer", axis=1)
+
+        assert len(left) == len(right)
+
+    def test_concat_bug_2972(self):
+        ts0 = Series(np.zeros(5))
+        ts1 = Series(np.ones(5))
+        ts0.name = ts1.name = "same name"
+        result = concat([ts0, ts1], axis=1)
+
+        expected = DataFrame({0: ts0, 1: ts1})
+        expected.columns = ["same name", "same name"]
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_bug_3602(self):
+        # GH 3602, duplicate columns
+        df1 = DataFrame(
+            {
+                "firmNo": [0, 0, 0, 0],
+                "prc": [6, 6, 6, 6],
+                "stringvar": ["rrr", "rrr", "rrr", "rrr"],
+            }
+        )
+        df2 = DataFrame(
+            {"C": [9, 10, 11, 12], "misc": [1, 2, 3, 4], "prc": [6, 6, 6, 6]}
+        )
+        expected = DataFrame(
+            [
+                [0, 6, "rrr", 9, 1, 6],
+                [0, 6, "rrr", 10, 2, 6],
+                [0, 6, "rrr", 11, 3, 6],
+                [0, 6, "rrr", 12, 4, 6],
+            ]
+        )
+        expected.columns = ["firmNo", "prc", "stringvar", "C", "misc", "prc"]
+
+        result = concat([df1, df2], axis=1)
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_iterables(self):
+        # GH8645 check concat works with tuples, list, generators, and weird
+        # stuff like deque and custom iterables
+        df1 = DataFrame([1, 2, 3])
+        df2 = DataFrame([4, 5, 6])
+        expected = DataFrame([1, 2, 3, 4, 5, 6])
+        tm.assert_frame_equal(concat((df1, df2), ignore_index=True), expected)
+        tm.assert_frame_equal(concat([df1, df2], ignore_index=True), expected)
+        tm.assert_frame_equal(
+            concat((df for df in (df1, df2)), ignore_index=True), expected
+        )
+        tm.assert_frame_equal(concat(deque((df1, df2)), ignore_index=True), expected)
+
+        class CustomIterator1:
+            def __len__(self) -> int:
+                return 2
+
+            def __getitem__(self, index):
+                try:
+                    return {0: df1, 1: df2}[index]
+                except KeyError as err:
+                    raise IndexError from err
+
+        tm.assert_frame_equal(concat(CustomIterator1(), ignore_index=True), expected)
+
+        class CustomIterator2(abc.Iterable):
+            def __iter__(self) -> Iterator:
+                yield df1
+                yield df2
+
+        tm.assert_frame_equal(concat(CustomIterator2(), ignore_index=True), expected)
+
+    def test_concat_order(self):
+        # GH 17344, GH#47331
+        dfs = [DataFrame(index=range(3), columns=["a", 1, None])]
+        dfs += [DataFrame(index=range(3), columns=[None, 1, "a"]) for _ in range(100)]
+
+        result = concat(dfs, sort=True).columns
+        expected = Index([1, "a", None])
+        tm.assert_index_equal(result, expected)
+
+    def test_concat_different_extension_dtypes_upcasts(self):
+        a = Series(pd.array([1, 2], dtype="Int64"))
+        b = Series(to_decimal([1, 2]))
+
+        result = concat([a, b], ignore_index=True)
+        expected = Series([1, 2, Decimal(1), Decimal(2)], dtype=object)
+        tm.assert_series_equal(result, expected)
+
+    def test_concat_ordered_dict(self):
+        # GH 21510
+        expected = concat(
+            [Series(range(3)), Series(range(4))], keys=["First", "Another"]
+        )
+        result = concat({"First": Series(range(3)), "Another": Series(range(4))})
+        tm.assert_series_equal(result, expected)
+
+    def test_concat_duplicate_indices_raise(self):
+        # GH 45888: test raise for concat DataFrames with duplicate indices
+        # https://github.com/pandas-dev/pandas/issues/36263
+        df1 = DataFrame(
+            np.random.default_rng(2).standard_normal(5),
+            index=[0, 1, 2, 3, 3],
+            columns=["a"],
+        )
+        df2 = DataFrame(
+            np.random.default_rng(2).standard_normal(5),
+            index=[0, 1, 2, 2, 4],
+            columns=["b"],
+        )
+        msg = "Reindexing only valid with uniquely valued Index objects"
+        with pytest.raises(InvalidIndexError, match=msg):
+            concat([df1, df2], axis=1)
+
+
+def test_concat_no_unnecessary_upcast(float_numpy_dtype, frame_or_series):
+    # GH 13247
+    dims = frame_or_series(dtype=object).ndim
+    dt = float_numpy_dtype
+
+    dfs = [
+        frame_or_series(np.array([1], dtype=dt, ndmin=dims)),
+        frame_or_series(np.array([np.nan], dtype=dt, ndmin=dims)),
+        frame_or_series(np.array([5], dtype=dt, ndmin=dims)),
+    ]
+    x = concat(dfs)
+    assert x.values.dtype == dt
+
+
+@pytest.mark.parametrize("pdt", [Series, DataFrame])
+def test_concat_will_upcast(pdt, any_signed_int_numpy_dtype):
+    dt = any_signed_int_numpy_dtype
+    dims = pdt().ndim
+    dfs = [
+        pdt(np.array([1], dtype=dt, ndmin=dims)),
+        pdt(np.array([np.nan], ndmin=dims)),
+        pdt(np.array([5], dtype=dt, ndmin=dims)),
+    ]
+    x = concat(dfs)
+    assert x.values.dtype == "float64"
+
+
+def test_concat_empty_and_non_empty_frame_regression():
+    # GH 18178 regression test
+    df1 = DataFrame({"foo": [1]})
+    df2 = DataFrame({"foo": []})
+    expected = DataFrame({"foo": [1.0]})
+    result = concat([df1, df2])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_concat_sparse():
+    # GH 23557
+    a = Series(SparseArray([0, 1, 2]))
+    expected = DataFrame(data=[[0, 0], [1, 1], [2, 2]]).astype(
+        pd.SparseDtype(np.int64, 0)
+    )
+    result = concat([a, a], axis=1)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_concat_dense_sparse():
+    # GH 30668
+    dtype = pd.SparseDtype(np.float64, None)
+    a = Series(pd.arrays.SparseArray([1, None]), dtype=dtype)
+    b = Series([1], dtype=float)
+    expected = Series(data=[1, None, 1], index=[0, 1, 0]).astype(dtype)
+    result = concat([a, b], axis=0)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("keys", [["e", "f", "f"], ["f", "e", "f"]])
+def test_duplicate_keys(keys):
+    # GH 33654
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    s1 = Series([7, 8, 9], name="c")
+    s2 = Series([10, 11, 12], name="d")
+    result = concat([df, s1, s2], axis=1, keys=keys)
+    expected_values = [[1, 4, 7, 10], [2, 5, 8, 11], [3, 6, 9, 12]]
+    expected_columns = MultiIndex.from_tuples(
+        [(keys[0], "a"), (keys[0], "b"), (keys[1], "c"), (keys[2], "d")]
+    )
+    expected = DataFrame(expected_values, columns=expected_columns)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_duplicate_keys_same_frame():
+    # GH 43595
+    keys = ["e", "e"]
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    result = concat([df, df], axis=1, keys=keys)
+    expected_values = [[1, 4, 1, 4], [2, 5, 2, 5], [3, 6, 3, 6]]
+    expected_columns = MultiIndex.from_tuples(
+        [(keys[0], "a"), (keys[0], "b"), (keys[1], "a"), (keys[1], "b")]
+    )
+    expected = DataFrame(expected_values, columns=expected_columns)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.filterwarnings(
+    "ignore:Passing a BlockManager|Passing a SingleBlockManager:DeprecationWarning"
+)
+@pytest.mark.parametrize(
+    "obj",
+    [
+        tm.SubclassedDataFrame({"A": np.arange(0, 10)}),
+        tm.SubclassedSeries(np.arange(0, 10), name="A"),
+    ],
+)
+def test_concat_preserves_subclass(obj):
+    # GH28330 -- preserve subclass
+
+    result = concat([obj, obj])
+    assert isinstance(result, type(obj))
+
+
+def test_concat_frame_axis0_extension_dtypes():
+    # preserve extension dtype (through common_dtype mechanism)
+    df1 = DataFrame({"a": pd.array([1, 2, 3], dtype="Int64")})
+    df2 = DataFrame({"a": np.array([4, 5, 6])})
+
+    result = concat([df1, df2], ignore_index=True)
+    expected = DataFrame({"a": [1, 2, 3, 4, 5, 6]}, dtype="Int64")
+    tm.assert_frame_equal(result, expected)
+
+    result = concat([df2, df1], ignore_index=True)
+    expected = DataFrame({"a": [4, 5, 6, 1, 2, 3]}, dtype="Int64")
+    tm.assert_frame_equal(result, expected)
+
+
+def test_concat_preserves_extension_int64_dtype():
+    # GH 24768
+    df_a = DataFrame({"a": [-1]}, dtype="Int64")
+    df_b = DataFrame({"b": [1]}, dtype="Int64")
+    result = concat([df_a, df_b], ignore_index=True)
+    expected = DataFrame({"a": [-1, None], "b": [None, 1]}, dtype="Int64")
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "dtype1,dtype2,expected_dtype",
+    [
+        ("bool", "bool", "bool"),
+        ("boolean", "bool", "boolean"),
+        ("bool", "boolean", "boolean"),
+        ("boolean", "boolean", "boolean"),
+    ],
+)
+def test_concat_bool_types(dtype1, dtype2, expected_dtype):
+    # GH 42800
+    ser1 = Series([True, False], dtype=dtype1)
+    ser2 = Series([False, True], dtype=dtype2)
+    result = concat([ser1, ser2], ignore_index=True)
+    expected = Series([True, False, False, True], dtype=expected_dtype)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    ("keys", "integrity"),
+    [
+        (["red"] * 3, True),
+        (["red"] * 3, False),
+        (["red", "blue", "red"], False),
+        (["red", "blue", "red"], True),
+    ],
+)
+def test_concat_repeated_keys(keys, integrity):
+    # GH: 20816
+    series_list = [Series({"a": 1}), Series({"b": 2}), Series({"c": 3})]
+    result = concat(series_list, keys=keys, verify_integrity=integrity)
+    tuples = list(zip(keys, ["a", "b", "c"]))
+    expected = Series([1, 2, 3], index=MultiIndex.from_tuples(tuples))
+    tm.assert_series_equal(result, expected)
+
+
+def test_concat_null_object_with_dti():
+    # GH#40841
+    dti = pd.DatetimeIndex(
+        ["2021-04-08 21:21:14+00:00"], dtype="datetime64[ns, UTC]", name="Time (UTC)"
+    )
+    right = DataFrame(data={"C": [0.5274]}, index=dti)
+
+    idx = Index([None], dtype="object", name="Maybe Time (UTC)")
+    left = DataFrame(data={"A": [None], "B": [np.nan]}, index=idx)
+
+    result = concat([left, right], axis="columns")
+
+    exp_index = Index([None, dti[0]], dtype=object)
+    expected = DataFrame(
+        {
+            "A": np.array([None, np.nan], dtype=object),
+            "B": [np.nan, np.nan],
+            "C": [np.nan, 0.5274],
+        },
+        index=exp_index,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_concat_multiindex_with_empty_rangeindex():
+    # GH#41234
+    mi = MultiIndex.from_tuples([("B", 1), ("C", 1)])
+    df1 = DataFrame([[1, 2]], columns=mi)
+    df2 = DataFrame(index=[1], columns=pd.RangeIndex(0))
+
+    result = concat([df1, df2])
+    expected = DataFrame([[1, 2], [np.nan, np.nan]], columns=mi)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        Series(data=[1, 2]),
+        DataFrame(
+            data={
+                "col1": [1, 2],
+            }
+        ),
+        DataFrame(dtype=float),
+        Series(dtype=float),
+    ],
+)
+def test_concat_drop_attrs(data):
+    # GH#41828
+    df1 = data.copy()
+    df1.attrs = {1: 1}
+    df2 = data.copy()
+    df2.attrs = {1: 2}
+    df = concat([df1, df2])
+    assert len(df.attrs) == 0
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        Series(data=[1, 2]),
+        DataFrame(
+            data={
+                "col1": [1, 2],
+            }
+        ),
+        DataFrame(dtype=float),
+        Series(dtype=float),
+    ],
+)
+def test_concat_retain_attrs(data):
+    # GH#41828
+    df1 = data.copy()
+    df1.attrs = {1: 1}
+    df2 = data.copy()
+    df2.attrs = {1: 1}
+    df = concat([df1, df2])
+    assert df.attrs[1] == 1
+
+
+@td.skip_array_manager_invalid_test
+@pytest.mark.parametrize("df_dtype", ["float64", "int64", "datetime64[ns]"])
+@pytest.mark.parametrize("empty_dtype", [None, "float64", "object"])
+def test_concat_ignore_empty_object_float(empty_dtype, df_dtype):
+    # https://github.com/pandas-dev/pandas/issues/45637
+    df = DataFrame({"foo": [1, 2], "bar": [1, 2]}, dtype=df_dtype)
+    empty = DataFrame(columns=["foo", "bar"], dtype=empty_dtype)
+
+    msg = "The behavior of DataFrame concatenation with empty or all-NA entries"
+    warn = None
+    if df_dtype == "datetime64[ns]" or (
+        df_dtype == "float64" and empty_dtype != "float64"
+    ):
+        warn = FutureWarning
+    with tm.assert_produces_warning(warn, match=msg):
+        result = concat([empty, df])
+    expected = df
+    if df_dtype == "int64":
+        # TODO what exact behaviour do we want for integer eventually?
+        if empty_dtype == "float64":
+            expected = df.astype("float64")
+        else:
+            expected = df.astype("object")
+    tm.assert_frame_equal(result, expected)
+
+
+@td.skip_array_manager_invalid_test
+@pytest.mark.parametrize("df_dtype", ["float64", "int64", "datetime64[ns]"])
+@pytest.mark.parametrize("empty_dtype", [None, "float64", "object"])
+def test_concat_ignore_all_na_object_float(empty_dtype, df_dtype):
+    df = DataFrame({"foo": [1, 2], "bar": [1, 2]}, dtype=df_dtype)
+    empty = DataFrame({"foo": [np.nan], "bar": [np.nan]}, dtype=empty_dtype)
+
+    if df_dtype == "int64":
+        # TODO what exact behaviour do we want for integer eventually?
+        if empty_dtype == "object":
+            df_dtype = "object"
+        else:
+            df_dtype = "float64"
+
+    msg = "The behavior of DataFrame concatenation with empty or all-NA entries"
+    warn = None
+    if empty_dtype != df_dtype and empty_dtype is not None:
+        warn = FutureWarning
+    elif df_dtype == "datetime64[ns]":
+        warn = FutureWarning
+
+    with tm.assert_produces_warning(warn, match=msg):
+        result = concat([empty, df], ignore_index=True)
+
+    expected = DataFrame({"foo": [np.nan, 1, 2], "bar": [np.nan, 1, 2]}, dtype=df_dtype)
+    tm.assert_frame_equal(result, expected)
+
+
+@td.skip_array_manager_invalid_test
+def test_concat_ignore_empty_from_reindex():
+    # https://github.com/pandas-dev/pandas/pull/43507#issuecomment-920375856
+    df1 = DataFrame({"a": [1], "b": [pd.Timestamp("2012-01-01")]})
+    df2 = DataFrame({"a": [2]})
+
+    aligned = df2.reindex(columns=df1.columns)
+
+    msg = "The behavior of DataFrame concatenation with empty or all-NA entries"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = concat([df1, aligned], ignore_index=True)
+    expected = df1 = DataFrame({"a": [1, 2], "b": [pd.Timestamp("2012-01-01"), pd.NaT]})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_concat_mismatched_keys_length():
+    # GH#43485
+    ser = Series(range(5))
+    sers = [ser + n for n in range(4)]
+    keys = ["A", "B", "C"]
+
+    msg = r"The behavior of pd.concat with len\(keys\) != len\(objs\) is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        concat(sers, keys=keys, axis=1)
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        concat(sers, keys=keys, axis=0)
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        concat((x for x in sers), keys=(y for y in keys), axis=1)
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        concat((x for x in sers), keys=(y for y in keys), axis=0)
+
+
+def test_concat_multiindex_with_category():
+    df1 = DataFrame(
+        {
+            "c1": Series(list("abc"), dtype="category"),
+            "c2": Series(list("eee"), dtype="category"),
+            "i2": Series([1, 2, 3]),
+        }
+    )
+    df1 = df1.set_index(["c1", "c2"])
+    df2 = DataFrame(
+        {
+            "c1": Series(list("abc"), dtype="category"),
+            "c2": Series(list("eee"), dtype="category"),
+            "i2": Series([4, 5, 6]),
+        }
+    )
+    df2 = df2.set_index(["c1", "c2"])
+    result = concat([df1, df2])
+    expected = DataFrame(
+        {
+            "c1": Series(list("abcabc"), dtype="category"),
+            "c2": Series(list("eeeeee"), dtype="category"),
+            "i2": Series([1, 2, 3, 4, 5, 6]),
+        }
+    )
+    expected = expected.set_index(["c1", "c2"])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_concat_ea_upcast():
+    # GH#54848
+    df1 = DataFrame(["a"], dtype="string")
+    df2 = DataFrame([1], dtype="Int64")
+    result = concat([df1, df2])
+    expected = DataFrame(["a", 1], index=[0, 0])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_concat_none_with_timezone_timestamp():
+    # GH#52093
+    df1 = DataFrame([{"A": None}])
+    df2 = DataFrame([{"A": pd.Timestamp("1990-12-20 00:00:00+00:00")}])
+    msg = "The behavior of DataFrame concatenation with empty or all-NA entries"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = concat([df1, df2], ignore_index=True)
+    expected = DataFrame({"A": [None, pd.Timestamp("1990-12-20 00:00:00+00:00")]})
+    tm.assert_frame_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_dataframe.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_dataframe.py
new file mode 100644
index 0000000000000000000000000000000000000000..f288921c25753463030cfe4f765b2709b676c858
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_dataframe.py
@@ -0,0 +1,230 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+    concat,
+)
+import pandas._testing as tm
+
+
+class TestDataFrameConcat:
+    def test_concat_multiple_frames_dtypes(self):
+        # GH#2759
+        df1 = DataFrame(data=np.ones((10, 2)), columns=["foo", "bar"], dtype=np.float64)
+        df2 = DataFrame(data=np.ones((10, 2)), dtype=np.float32)
+        results = concat((df1, df2), axis=1).dtypes
+        expected = Series(
+            [np.dtype("float64")] * 2 + [np.dtype("float32")] * 2,
+            index=["foo", "bar", 0, 1],
+        )
+        tm.assert_series_equal(results, expected)
+
+    def test_concat_tuple_keys(self):
+        # GH#14438
+        df1 = DataFrame(np.ones((2, 2)), columns=list("AB"))
+        df2 = DataFrame(np.ones((3, 2)) * 2, columns=list("AB"))
+        results = concat((df1, df2), keys=[("bee", "bah"), ("bee", "boo")])
+        expected = DataFrame(
+            {
+                "A": {
+                    ("bee", "bah", 0): 1.0,
+                    ("bee", "bah", 1): 1.0,
+                    ("bee", "boo", 0): 2.0,
+                    ("bee", "boo", 1): 2.0,
+                    ("bee", "boo", 2): 2.0,
+                },
+                "B": {
+                    ("bee", "bah", 0): 1.0,
+                    ("bee", "bah", 1): 1.0,
+                    ("bee", "boo", 0): 2.0,
+                    ("bee", "boo", 1): 2.0,
+                    ("bee", "boo", 2): 2.0,
+                },
+            }
+        )
+        tm.assert_frame_equal(results, expected)
+
+    def test_concat_named_keys(self):
+        # GH#14252
+        df = DataFrame({"foo": [1, 2], "bar": [0.1, 0.2]})
+        index = Index(["a", "b"], name="baz")
+        concatted_named_from_keys = concat([df, df], keys=index)
+        expected_named = DataFrame(
+            {"foo": [1, 2, 1, 2], "bar": [0.1, 0.2, 0.1, 0.2]},
+            index=pd.MultiIndex.from_product((["a", "b"], [0, 1]), names=["baz", None]),
+        )
+        tm.assert_frame_equal(concatted_named_from_keys, expected_named)
+
+        index_no_name = Index(["a", "b"], name=None)
+        concatted_named_from_names = concat([df, df], keys=index_no_name, names=["baz"])
+        tm.assert_frame_equal(concatted_named_from_names, expected_named)
+
+        concatted_unnamed = concat([df, df], keys=index_no_name)
+        expected_unnamed = DataFrame(
+            {"foo": [1, 2, 1, 2], "bar": [0.1, 0.2, 0.1, 0.2]},
+            index=pd.MultiIndex.from_product((["a", "b"], [0, 1]), names=[None, None]),
+        )
+        tm.assert_frame_equal(concatted_unnamed, expected_unnamed)
+
+    def test_concat_axis_parameter(self):
+        # GH#14369
+        df1 = DataFrame({"A": [0.1, 0.2]}, index=range(2))
+        df2 = DataFrame({"A": [0.3, 0.4]}, index=range(2))
+
+        # Index/row/0 DataFrame
+        expected_index = DataFrame({"A": [0.1, 0.2, 0.3, 0.4]}, index=[0, 1, 0, 1])
+
+        concatted_index = concat([df1, df2], axis="index")
+        tm.assert_frame_equal(concatted_index, expected_index)
+
+        concatted_row = concat([df1, df2], axis="rows")
+        tm.assert_frame_equal(concatted_row, expected_index)
+
+        concatted_0 = concat([df1, df2], axis=0)
+        tm.assert_frame_equal(concatted_0, expected_index)
+
+        # Columns/1 DataFrame
+        expected_columns = DataFrame(
+            [[0.1, 0.3], [0.2, 0.4]], index=[0, 1], columns=["A", "A"]
+        )
+
+        concatted_columns = concat([df1, df2], axis="columns")
+        tm.assert_frame_equal(concatted_columns, expected_columns)
+
+        concatted_1 = concat([df1, df2], axis=1)
+        tm.assert_frame_equal(concatted_1, expected_columns)
+
+        series1 = Series([0.1, 0.2])
+        series2 = Series([0.3, 0.4])
+
+        # Index/row/0 Series
+        expected_index_series = Series([0.1, 0.2, 0.3, 0.4], index=[0, 1, 0, 1])
+
+        concatted_index_series = concat([series1, series2], axis="index")
+        tm.assert_series_equal(concatted_index_series, expected_index_series)
+
+        concatted_row_series = concat([series1, series2], axis="rows")
+        tm.assert_series_equal(concatted_row_series, expected_index_series)
+
+        concatted_0_series = concat([series1, series2], axis=0)
+        tm.assert_series_equal(concatted_0_series, expected_index_series)
+
+        # Columns/1 Series
+        expected_columns_series = DataFrame(
+            [[0.1, 0.3], [0.2, 0.4]], index=[0, 1], columns=[0, 1]
+        )
+
+        concatted_columns_series = concat([series1, series2], axis="columns")
+        tm.assert_frame_equal(concatted_columns_series, expected_columns_series)
+
+        concatted_1_series = concat([series1, series2], axis=1)
+        tm.assert_frame_equal(concatted_1_series, expected_columns_series)
+
+        # Testing ValueError
+        with pytest.raises(ValueError, match="No axis named"):
+            concat([series1, series2], axis="something")
+
+    def test_concat_numerical_names(self):
+        # GH#15262, GH#12223
+        df = DataFrame(
+            {"col": range(9)},
+            dtype="int32",
+            index=(
+                pd.MultiIndex.from_product(
+                    [["A0", "A1", "A2"], ["B0", "B1", "B2"]], names=[1, 2]
+                )
+            ),
+        )
+        result = concat((df.iloc[:2, :], df.iloc[-2:, :]))
+        expected = DataFrame(
+            {"col": [0, 1, 7, 8]},
+            dtype="int32",
+            index=pd.MultiIndex.from_tuples(
+                [("A0", "B0"), ("A0", "B1"), ("A2", "B1"), ("A2", "B2")], names=[1, 2]
+            ),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_astype_dup_col(self):
+        # GH#23049
+        df = DataFrame([{"a": "b"}])
+        df = concat([df, df], axis=1)
+
+        result = df.astype("category")
+        expected = DataFrame(
+            np.array(["b", "b"]).reshape(1, 2), columns=["a", "a"]
+        ).astype("category")
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_dataframe_keys_bug(self, sort):
+        t1 = DataFrame(
+            {"value": Series([1, 2, 3], index=Index(["a", "b", "c"], name="id"))}
+        )
+        t2 = DataFrame({"value": Series([7, 8], index=Index(["a", "b"], name="id"))})
+
+        # it works
+        result = concat([t1, t2], axis=1, keys=["t1", "t2"], sort=sort)
+        assert list(result.columns) == [("t1", "value"), ("t2", "value")]
+
+    def test_concat_bool_with_int(self):
+        # GH#42092 we may want to change this to return object, but that
+        #  would need a deprecation
+        df1 = DataFrame(Series([True, False, True, True], dtype="bool"))
+        df2 = DataFrame(Series([1, 0, 1], dtype="int64"))
+
+        result = concat([df1, df2])
+        expected = concat([df1.astype("int64"), df2])
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_duplicates_in_index_with_keys(self):
+        # GH#42651
+        index = [1, 1, 3]
+        data = [1, 2, 3]
+
+        df = DataFrame(data=data, index=index)
+        result = concat([df], keys=["A"], names=["ID", "date"])
+        mi = pd.MultiIndex.from_product([["A"], index], names=["ID", "date"])
+        expected = DataFrame(data=data, index=mi)
+        tm.assert_frame_equal(result, expected)
+        tm.assert_index_equal(result.index.levels[1], Index([1, 3], name="date"))
+
+    @pytest.mark.parametrize("ignore_index", [True, False])
+    @pytest.mark.parametrize("order", ["C", "F"])
+    @pytest.mark.parametrize("axis", [0, 1])
+    def test_concat_copies(self, axis, order, ignore_index, using_copy_on_write):
+        # based on asv ConcatDataFrames
+        df = DataFrame(np.zeros((10, 5), dtype=np.float32, order=order))
+
+        res = concat([df] * 5, axis=axis, ignore_index=ignore_index, copy=True)
+
+        if not using_copy_on_write:
+            for arr in res._iter_column_arrays():
+                for arr2 in df._iter_column_arrays():
+                    assert not np.shares_memory(arr, arr2)
+
+    def test_outer_sort_columns(self):
+        # GH#47127
+        df1 = DataFrame({"A": [0], "B": [1], 0: 1})
+        df2 = DataFrame({"A": [100]})
+        result = concat([df1, df2], ignore_index=True, join="outer", sort=True)
+        expected = DataFrame({0: [1.0, np.nan], "A": [0, 100], "B": [1.0, np.nan]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_inner_sort_columns(self):
+        # GH#47127
+        df1 = DataFrame({"A": [0], "B": [1], 0: 1})
+        df2 = DataFrame({"A": [100], 0: 2})
+        result = concat([df1, df2], ignore_index=True, join="inner", sort=True)
+        expected = DataFrame({0: [1, 2], "A": [0, 100]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_sort_columns_one_df(self):
+        # GH#47127
+        df1 = DataFrame({"A": [100], 0: 2})
+        result = concat([df1], ignore_index=True, join="inner", sort=True)
+        expected = DataFrame({0: [2], "A": [100]})
+        tm.assert_frame_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_datetimes.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_datetimes.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c94dc0d51f7e7de9fbd43150b1331282fbabb90
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_datetimes.py
@@ -0,0 +1,606 @@
+import datetime as dt
+from datetime import datetime
+
+import dateutil
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    MultiIndex,
+    Series,
+    Timestamp,
+    concat,
+    date_range,
+    to_timedelta,
+)
+import pandas._testing as tm
+
+
+class TestDatetimeConcat:
+    def test_concat_datetime64_block(self):
+        rng = date_range("1/1/2000", periods=10)
+
+        df = DataFrame({"time": rng})
+
+        result = concat([df, df])
+        assert (result.iloc[:10]["time"] == rng).all()
+        assert (result.iloc[10:]["time"] == rng).all()
+
+    def test_concat_datetime_datetime64_frame(self):
+        # GH#2624
+        rows = []
+        rows.append([datetime(2010, 1, 1), 1])
+        rows.append([datetime(2010, 1, 2), "hi"])
+
+        df2_obj = DataFrame.from_records(rows, columns=["date", "test"])
+
+        ind = date_range(start="2000/1/1", freq="D", periods=10)
+        df1 = DataFrame({"date": ind, "test": range(10)})
+
+        # it works!
+        concat([df1, df2_obj])
+
+    def test_concat_datetime_timezone(self):
+        # GH 18523
+        idx1 = date_range("2011-01-01", periods=3, freq="h", tz="Europe/Paris")
+        idx2 = date_range(start=idx1[0], end=idx1[-1], freq="h")
+        df1 = DataFrame({"a": [1, 2, 3]}, index=idx1)
+        df2 = DataFrame({"b": [1, 2, 3]}, index=idx2)
+        result = concat([df1, df2], axis=1)
+
+        exp_idx = DatetimeIndex(
+            [
+                "2011-01-01 00:00:00+01:00",
+                "2011-01-01 01:00:00+01:00",
+                "2011-01-01 02:00:00+01:00",
+            ],
+            dtype="M8[ns, Europe/Paris]",
+            freq="h",
+        )
+        expected = DataFrame(
+            [[1, 1], [2, 2], [3, 3]], index=exp_idx, columns=["a", "b"]
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+        idx3 = date_range("2011-01-01", periods=3, freq="h", tz="Asia/Tokyo")
+        df3 = DataFrame({"b": [1, 2, 3]}, index=idx3)
+        result = concat([df1, df3], axis=1)
+
+        exp_idx = DatetimeIndex(
+            [
+                "2010-12-31 15:00:00+00:00",
+                "2010-12-31 16:00:00+00:00",
+                "2010-12-31 17:00:00+00:00",
+                "2010-12-31 23:00:00+00:00",
+                "2011-01-01 00:00:00+00:00",
+                "2011-01-01 01:00:00+00:00",
+            ]
+        ).as_unit("ns")
+
+        expected = DataFrame(
+            [
+                [np.nan, 1],
+                [np.nan, 2],
+                [np.nan, 3],
+                [1, np.nan],
+                [2, np.nan],
+                [3, np.nan],
+            ],
+            index=exp_idx,
+            columns=["a", "b"],
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+        # GH 13783: Concat after resample
+        result = concat([df1.resample("h").mean(), df2.resample("h").mean()], sort=True)
+        expected = DataFrame(
+            {"a": [1, 2, 3] + [np.nan] * 3, "b": [np.nan] * 3 + [1, 2, 3]},
+            index=idx1.append(idx1),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_datetimeindex_freq(self):
+        # GH 3232
+        # Monotonic index result
+        dr = date_range("01-Jan-2013", periods=100, freq="50ms", tz="UTC")
+        data = list(range(100))
+        expected = DataFrame(data, index=dr)
+        result = concat([expected[:50], expected[50:]])
+        tm.assert_frame_equal(result, expected)
+
+        # Non-monotonic index result
+        result = concat([expected[50:], expected[:50]])
+        expected = DataFrame(data[50:] + data[:50], index=dr[50:].append(dr[:50]))
+        expected.index._data.freq = None
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_multiindex_datetime_object_index(self):
+        # https://github.com/pandas-dev/pandas/issues/11058
+        idx = Index(
+            [dt.date(2013, 1, 1), dt.date(2014, 1, 1), dt.date(2015, 1, 1)],
+            dtype="object",
+        )
+
+        s = Series(
+            ["a", "b"],
+            index=MultiIndex.from_arrays(
+                [
+                    [1, 2],
+                    idx[:-1],
+                ],
+                names=["first", "second"],
+            ),
+        )
+        s2 = Series(
+            ["a", "b"],
+            index=MultiIndex.from_arrays(
+                [[1, 2], idx[::2]],
+                names=["first", "second"],
+            ),
+        )
+        mi = MultiIndex.from_arrays(
+            [[1, 2, 2], idx],
+            names=["first", "second"],
+        )
+        assert mi.levels[1].dtype == object
+
+        expected = DataFrame(
+            [["a", "a"], ["b", np.nan], [np.nan, "b"]],
+            index=mi,
+        )
+        result = concat([s, s2], axis=1)
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_NaT_series(self):
+        # GH 11693
+        # test for merging NaT series with datetime series.
+        x = Series(
+            date_range("20151124 08:00", "20151124 09:00", freq="1h", tz="US/Eastern")
+        )
+        y = Series(pd.NaT, index=[0, 1], dtype="datetime64[ns, US/Eastern]")
+        expected = Series([x[0], x[1], pd.NaT, pd.NaT])
+
+        result = concat([x, y], ignore_index=True)
+        tm.assert_series_equal(result, expected)
+
+        # all NaT with tz
+        expected = Series(pd.NaT, index=range(4), dtype="datetime64[ns, US/Eastern]")
+        result = concat([y, y], ignore_index=True)
+        tm.assert_series_equal(result, expected)
+
+    def test_concat_NaT_series2(self):
+        # without tz
+        x = Series(date_range("20151124 08:00", "20151124 09:00", freq="1h"))
+        y = Series(date_range("20151124 10:00", "20151124 11:00", freq="1h"))
+        y[:] = pd.NaT
+        expected = Series([x[0], x[1], pd.NaT, pd.NaT])
+        result = concat([x, y], ignore_index=True)
+        tm.assert_series_equal(result, expected)
+
+        # all NaT without tz
+        x[:] = pd.NaT
+        expected = Series(pd.NaT, index=range(4), dtype="datetime64[ns]")
+        result = concat([x, y], ignore_index=True)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("tz", [None, "UTC"])
+    def test_concat_NaT_dataframes(self, tz):
+        # GH 12396
+
+        dti = DatetimeIndex([pd.NaT, pd.NaT], tz=tz)
+        first = DataFrame({0: dti})
+        second = DataFrame(
+            [[Timestamp("2015/01/01", tz=tz)], [Timestamp("2016/01/01", tz=tz)]],
+            index=[2, 3],
+        )
+        expected = DataFrame(
+            [
+                pd.NaT,
+                pd.NaT,
+                Timestamp("2015/01/01", tz=tz),
+                Timestamp("2016/01/01", tz=tz),
+            ]
+        )
+
+        result = concat([first, second], axis=0)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("tz1", [None, "UTC"])
+    @pytest.mark.parametrize("tz2", [None, "UTC"])
+    @pytest.mark.parametrize("item", [pd.NaT, Timestamp("20150101")])
+    def test_concat_NaT_dataframes_all_NaT_axis_0(
+        self, tz1, tz2, item, using_array_manager
+    ):
+        # GH 12396
+
+        # tz-naive
+        first = DataFrame([[pd.NaT], [pd.NaT]]).apply(lambda x: x.dt.tz_localize(tz1))
+        second = DataFrame([item]).apply(lambda x: x.dt.tz_localize(tz2))
+
+        result = concat([first, second], axis=0)
+        expected = DataFrame(Series([pd.NaT, pd.NaT, item], index=[0, 1, 0]))
+        expected = expected.apply(lambda x: x.dt.tz_localize(tz2))
+        if tz1 != tz2:
+            expected = expected.astype(object)
+            if item is pd.NaT and not using_array_manager:
+                # GH#18463
+                # TODO: setting nan here is to keep the test passing as we
+                #  make assert_frame_equal stricter, but is nan really the
+                #  ideal behavior here?
+                if tz1 is not None:
+                    expected.iloc[-1, 0] = np.nan
+                else:
+                    expected.iloc[:-1, 0] = np.nan
+
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("tz1", [None, "UTC"])
+    @pytest.mark.parametrize("tz2", [None, "UTC"])
+    def test_concat_NaT_dataframes_all_NaT_axis_1(self, tz1, tz2):
+        # GH 12396
+
+        first = DataFrame(Series([pd.NaT, pd.NaT]).dt.tz_localize(tz1))
+        second = DataFrame(Series([pd.NaT]).dt.tz_localize(tz2), columns=[1])
+        expected = DataFrame(
+            {
+                0: Series([pd.NaT, pd.NaT]).dt.tz_localize(tz1),
+                1: Series([pd.NaT, pd.NaT]).dt.tz_localize(tz2),
+            }
+        )
+        result = concat([first, second], axis=1)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("tz1", [None, "UTC"])
+    @pytest.mark.parametrize("tz2", [None, "UTC"])
+    def test_concat_NaT_series_dataframe_all_NaT(self, tz1, tz2):
+        # GH 12396
+
+        # tz-naive
+        first = Series([pd.NaT, pd.NaT]).dt.tz_localize(tz1)
+        second = DataFrame(
+            [
+                [Timestamp("2015/01/01", tz=tz2)],
+                [Timestamp("2016/01/01", tz=tz2)],
+            ],
+            index=[2, 3],
+        )
+
+        expected = DataFrame(
+            [
+                pd.NaT,
+                pd.NaT,
+                Timestamp("2015/01/01", tz=tz2),
+                Timestamp("2016/01/01", tz=tz2),
+            ]
+        )
+        if tz1 != tz2:
+            expected = expected.astype(object)
+
+        result = concat([first, second])
+        tm.assert_frame_equal(result, expected)
+
+
+class TestTimezoneConcat:
+    def test_concat_tz_series(self):
+        # gh-11755: tz and no tz
+        x = Series(date_range("20151124 08:00", "20151124 09:00", freq="1h", tz="UTC"))
+        y = Series(date_range("2012-01-01", "2012-01-02"))
+        expected = Series([x[0], x[1], y[0], y[1]], dtype="object")
+        result = concat([x, y], ignore_index=True)
+        tm.assert_series_equal(result, expected)
+
+    def test_concat_tz_series2(self):
+        # gh-11887: concat tz and object
+        x = Series(date_range("20151124 08:00", "20151124 09:00", freq="1h", tz="UTC"))
+        y = Series(["a", "b"])
+        expected = Series([x[0], x[1], y[0], y[1]], dtype="object")
+        result = concat([x, y], ignore_index=True)
+        tm.assert_series_equal(result, expected)
+
+    def test_concat_tz_series3(self, unit, unit2):
+        # see gh-12217 and gh-12306
+        # Concatenating two UTC times
+        first = DataFrame([[datetime(2016, 1, 1)]], dtype=f"M8[{unit}]")
+        first[0] = first[0].dt.tz_localize("UTC")
+
+        second = DataFrame([[datetime(2016, 1, 2)]], dtype=f"M8[{unit2}]")
+        second[0] = second[0].dt.tz_localize("UTC")
+
+        result = concat([first, second])
+        exp_unit = tm.get_finest_unit(unit, unit2)
+        assert result[0].dtype == f"datetime64[{exp_unit}, UTC]"
+
+    def test_concat_tz_series4(self, unit, unit2):
+        # Concatenating two London times
+        first = DataFrame([[datetime(2016, 1, 1)]], dtype=f"M8[{unit}]")
+        first[0] = first[0].dt.tz_localize("Europe/London")
+
+        second = DataFrame([[datetime(2016, 1, 2)]], dtype=f"M8[{unit2}]")
+        second[0] = second[0].dt.tz_localize("Europe/London")
+
+        result = concat([first, second])
+        exp_unit = tm.get_finest_unit(unit, unit2)
+        assert result[0].dtype == f"datetime64[{exp_unit}, Europe/London]"
+
+    def test_concat_tz_series5(self, unit, unit2):
+        # Concatenating 2+1 London times
+        first = DataFrame(
+            [[datetime(2016, 1, 1)], [datetime(2016, 1, 2)]], dtype=f"M8[{unit}]"
+        )
+        first[0] = first[0].dt.tz_localize("Europe/London")
+
+        second = DataFrame([[datetime(2016, 1, 3)]], dtype=f"M8[{unit2}]")
+        second[0] = second[0].dt.tz_localize("Europe/London")
+
+        result = concat([first, second])
+        exp_unit = tm.get_finest_unit(unit, unit2)
+        assert result[0].dtype == f"datetime64[{exp_unit}, Europe/London]"
+
+    def test_concat_tz_series6(self, unit, unit2):
+        # Concatenating 1+2 London times
+        first = DataFrame([[datetime(2016, 1, 1)]], dtype=f"M8[{unit}]")
+        first[0] = first[0].dt.tz_localize("Europe/London")
+
+        second = DataFrame(
+            [[datetime(2016, 1, 2)], [datetime(2016, 1, 3)]], dtype=f"M8[{unit2}]"
+        )
+        second[0] = second[0].dt.tz_localize("Europe/London")
+
+        result = concat([first, second])
+        exp_unit = tm.get_finest_unit(unit, unit2)
+        assert result[0].dtype == f"datetime64[{exp_unit}, Europe/London]"
+
+    def test_concat_tz_series_tzlocal(self):
+        # see gh-13583
+        x = [
+            Timestamp("2011-01-01", tz=dateutil.tz.tzlocal()),
+            Timestamp("2011-02-01", tz=dateutil.tz.tzlocal()),
+        ]
+        y = [
+            Timestamp("2012-01-01", tz=dateutil.tz.tzlocal()),
+            Timestamp("2012-02-01", tz=dateutil.tz.tzlocal()),
+        ]
+
+        result = concat([Series(x), Series(y)], ignore_index=True)
+        tm.assert_series_equal(result, Series(x + y))
+        assert result.dtype == "datetime64[ns, tzlocal()]"
+
+    def test_concat_tz_series_with_datetimelike(self):
+        # see gh-12620: tz and timedelta
+        x = [
+            Timestamp("2011-01-01", tz="US/Eastern"),
+            Timestamp("2011-02-01", tz="US/Eastern"),
+        ]
+        y = [pd.Timedelta("1 day"), pd.Timedelta("2 day")]
+        result = concat([Series(x), Series(y)], ignore_index=True)
+        tm.assert_series_equal(result, Series(x + y, dtype="object"))
+
+        # tz and period
+        y = [pd.Period("2011-03", freq="M"), pd.Period("2011-04", freq="M")]
+        result = concat([Series(x), Series(y)], ignore_index=True)
+        tm.assert_series_equal(result, Series(x + y, dtype="object"))
+
+    def test_concat_tz_frame(self):
+        df2 = DataFrame(
+            {
+                "A": Timestamp("20130102", tz="US/Eastern"),
+                "B": Timestamp("20130603", tz="CET"),
+            },
+            index=range(5),
+        )
+
+        # concat
+        df3 = concat([df2.A.to_frame(), df2.B.to_frame()], axis=1)
+        tm.assert_frame_equal(df2, df3)
+
+    def test_concat_multiple_tzs(self):
+        # GH#12467
+        # combining datetime tz-aware and naive DataFrames
+        ts1 = Timestamp("2015-01-01", tz=None)
+        ts2 = Timestamp("2015-01-01", tz="UTC")
+        ts3 = Timestamp("2015-01-01", tz="EST")
+
+        df1 = DataFrame({"time": [ts1]})
+        df2 = DataFrame({"time": [ts2]})
+        df3 = DataFrame({"time": [ts3]})
+
+        results = concat([df1, df2]).reset_index(drop=True)
+        expected = DataFrame({"time": [ts1, ts2]}, dtype=object)
+        tm.assert_frame_equal(results, expected)
+
+        results = concat([df1, df3]).reset_index(drop=True)
+        expected = DataFrame({"time": [ts1, ts3]}, dtype=object)
+        tm.assert_frame_equal(results, expected)
+
+        results = concat([df2, df3]).reset_index(drop=True)
+        expected = DataFrame({"time": [ts2, ts3]})
+        tm.assert_frame_equal(results, expected)
+
+    def test_concat_multiindex_with_tz(self):
+        # GH 6606
+        df = DataFrame(
+            {
+                "dt": DatetimeIndex(
+                    [
+                        datetime(2014, 1, 1),
+                        datetime(2014, 1, 2),
+                        datetime(2014, 1, 3),
+                    ],
+                    dtype="M8[ns, US/Pacific]",
+                ),
+                "b": ["A", "B", "C"],
+                "c": [1, 2, 3],
+                "d": [4, 5, 6],
+            }
+        )
+        df = df.set_index(["dt", "b"])
+
+        exp_idx1 = DatetimeIndex(
+            ["2014-01-01", "2014-01-02", "2014-01-03"] * 2,
+            dtype="M8[ns, US/Pacific]",
+            name="dt",
+        )
+        exp_idx2 = Index(["A", "B", "C"] * 2, name="b")
+        exp_idx = MultiIndex.from_arrays([exp_idx1, exp_idx2])
+        expected = DataFrame(
+            {"c": [1, 2, 3] * 2, "d": [4, 5, 6] * 2}, index=exp_idx, columns=["c", "d"]
+        )
+
+        result = concat([df, df])
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_tz_not_aligned(self):
+        # GH#22796
+        ts = pd.to_datetime([1, 2]).tz_localize("UTC")
+        a = DataFrame({"A": ts})
+        b = DataFrame({"A": ts, "B": ts})
+        result = concat([a, b], sort=True, ignore_index=True)
+        expected = DataFrame(
+            {"A": list(ts) + list(ts), "B": [pd.NaT, pd.NaT] + list(ts)}
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "t1",
+        [
+            "2015-01-01",
+            pytest.param(
+                pd.NaT,
+                marks=pytest.mark.xfail(
+                    reason="GH23037 incorrect dtype when concatenating"
+                ),
+            ),
+        ],
+    )
+    def test_concat_tz_NaT(self, t1):
+        # GH#22796
+        # Concatenating tz-aware multicolumn DataFrames
+        ts1 = Timestamp(t1, tz="UTC")
+        ts2 = Timestamp("2015-01-01", tz="UTC")
+        ts3 = Timestamp("2015-01-01", tz="UTC")
+
+        df1 = DataFrame([[ts1, ts2]])
+        df2 = DataFrame([[ts3]])
+
+        result = concat([df1, df2])
+        expected = DataFrame([[ts1, ts2], [ts3, pd.NaT]], index=[0, 0])
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_tz_with_empty(self):
+        # GH 9188
+        result = concat(
+            [DataFrame(date_range("2000", periods=1, tz="UTC")), DataFrame()]
+        )
+        expected = DataFrame(date_range("2000", periods=1, tz="UTC"))
+        tm.assert_frame_equal(result, expected)
+
+
+class TestPeriodConcat:
+    def test_concat_period_series(self):
+        x = Series(pd.PeriodIndex(["2015-11-01", "2015-12-01"], freq="D"))
+        y = Series(pd.PeriodIndex(["2015-10-01", "2016-01-01"], freq="D"))
+        expected = Series([x[0], x[1], y[0], y[1]], dtype="Period[D]")
+        result = concat([x, y], ignore_index=True)
+        tm.assert_series_equal(result, expected)
+
+    def test_concat_period_multiple_freq_series(self):
+        x = Series(pd.PeriodIndex(["2015-11-01", "2015-12-01"], freq="D"))
+        y = Series(pd.PeriodIndex(["2015-10-01", "2016-01-01"], freq="M"))
+        expected = Series([x[0], x[1], y[0], y[1]], dtype="object")
+        result = concat([x, y], ignore_index=True)
+        tm.assert_series_equal(result, expected)
+        assert result.dtype == "object"
+
+    def test_concat_period_other_series(self):
+        x = Series(pd.PeriodIndex(["2015-11-01", "2015-12-01"], freq="D"))
+        y = Series(pd.PeriodIndex(["2015-11-01", "2015-12-01"], freq="M"))
+        expected = Series([x[0], x[1], y[0], y[1]], dtype="object")
+        result = concat([x, y], ignore_index=True)
+        tm.assert_series_equal(result, expected)
+        assert result.dtype == "object"
+
+    def test_concat_period_other_series2(self):
+        # non-period
+        x = Series(pd.PeriodIndex(["2015-11-01", "2015-12-01"], freq="D"))
+        y = Series(DatetimeIndex(["2015-11-01", "2015-12-01"]))
+        expected = Series([x[0], x[1], y[0], y[1]], dtype="object")
+        result = concat([x, y], ignore_index=True)
+        tm.assert_series_equal(result, expected)
+        assert result.dtype == "object"
+
+    def test_concat_period_other_series3(self):
+        x = Series(pd.PeriodIndex(["2015-11-01", "2015-12-01"], freq="D"))
+        y = Series(["A", "B"])
+        expected = Series([x[0], x[1], y[0], y[1]], dtype="object")
+        result = concat([x, y], ignore_index=True)
+        tm.assert_series_equal(result, expected)
+        assert result.dtype == "object"
+
+
+def test_concat_timedelta64_block():
+    rng = to_timedelta(np.arange(10), unit="s")
+
+    df = DataFrame({"time": rng})
+
+    result = concat([df, df])
+    tm.assert_frame_equal(result.iloc[:10], df)
+    tm.assert_frame_equal(result.iloc[10:], df)
+
+
+def test_concat_multiindex_datetime_nat():
+    # GH#44900
+    left = DataFrame({"a": 1}, index=MultiIndex.from_tuples([(1, pd.NaT)]))
+    right = DataFrame(
+        {"b": 2}, index=MultiIndex.from_tuples([(1, pd.NaT), (2, pd.NaT)])
+    )
+    result = concat([left, right], axis="columns")
+    expected = DataFrame(
+        {"a": [1.0, np.nan], "b": 2}, MultiIndex.from_tuples([(1, pd.NaT), (2, pd.NaT)])
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_concat_float_datetime64(using_array_manager):
+    # GH#32934
+    df_time = DataFrame({"A": pd.array(["2000"], dtype="datetime64[ns]")})
+    df_float = DataFrame({"A": pd.array([1.0], dtype="float64")})
+
+    expected = DataFrame(
+        {
+            "A": [
+                pd.array(["2000"], dtype="datetime64[ns]")[0],
+                pd.array([1.0], dtype="float64")[0],
+            ]
+        },
+        index=[0, 0],
+    )
+    result = concat([df_time, df_float])
+    tm.assert_frame_equal(result, expected)
+
+    expected = DataFrame({"A": pd.array([], dtype="object")})
+    result = concat([df_time.iloc[:0], df_float.iloc[:0]])
+    tm.assert_frame_equal(result, expected)
+
+    expected = DataFrame({"A": pd.array([1.0], dtype="object")})
+    result = concat([df_time.iloc[:0], df_float])
+    tm.assert_frame_equal(result, expected)
+
+    if not using_array_manager:
+        expected = DataFrame({"A": pd.array(["2000"], dtype="datetime64[ns]")})
+        msg = "The behavior of DataFrame concatenation with empty or all-NA entries"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = concat([df_time, df_float.iloc[:0]])
+        tm.assert_frame_equal(result, expected)
+    else:
+        expected = DataFrame({"A": pd.array(["2000"], dtype="datetime64[ns]")}).astype(
+            {"A": "object"}
+        )
+        result = concat([df_time, df_float.iloc[:0]])
+        tm.assert_frame_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_empty.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_empty.py
new file mode 100644
index 0000000000000000000000000000000000000000..30ef0a934157badba23ce2eb07de691498035254
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_empty.py
@@ -0,0 +1,295 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    RangeIndex,
+    Series,
+    concat,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestEmptyConcat:
+    def test_handle_empty_objects(self, sort, using_infer_string):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 4)), columns=list("abcd")
+        )
+
+        dfcopy = df[:5].copy()
+        dfcopy["foo"] = "bar"
+        empty = df[5:5]
+
+        frames = [dfcopy, empty, empty, df[5:]]
+        concatted = concat(frames, axis=0, sort=sort)
+
+        expected = df.reindex(columns=["a", "b", "c", "d", "foo"])
+        expected["foo"] = expected["foo"].astype(
+            object if not using_infer_string else "string[pyarrow_numpy]"
+        )
+        expected.loc[0:4, "foo"] = "bar"
+
+        tm.assert_frame_equal(concatted, expected)
+
+        # empty as first element with time series
+        # GH3259
+        df = DataFrame(
+            {"A": range(10000)}, index=date_range("20130101", periods=10000, freq="s")
+        )
+        empty = DataFrame()
+        result = concat([df, empty], axis=1)
+        tm.assert_frame_equal(result, df)
+        result = concat([empty, df], axis=1)
+        tm.assert_frame_equal(result, df)
+
+        result = concat([df, empty])
+        tm.assert_frame_equal(result, df)
+        result = concat([empty, df])
+        tm.assert_frame_equal(result, df)
+
+    def test_concat_empty_series(self):
+        # GH 11082
+        s1 = Series([1, 2, 3], name="x")
+        s2 = Series(name="y", dtype="float64")
+        res = concat([s1, s2], axis=1)
+        exp = DataFrame(
+            {"x": [1, 2, 3], "y": [np.nan, np.nan, np.nan]},
+            index=RangeIndex(3),
+        )
+        tm.assert_frame_equal(res, exp)
+
+        s1 = Series([1, 2, 3], name="x")
+        s2 = Series(name="y", dtype="float64")
+        msg = "The behavior of array concatenation with empty entries is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            res = concat([s1, s2], axis=0)
+        # name will be reset
+        exp = Series([1, 2, 3])
+        tm.assert_series_equal(res, exp)
+
+        # empty Series with no name
+        s1 = Series([1, 2, 3], name="x")
+        s2 = Series(name=None, dtype="float64")
+        res = concat([s1, s2], axis=1)
+        exp = DataFrame(
+            {"x": [1, 2, 3], 0: [np.nan, np.nan, np.nan]},
+            columns=["x", 0],
+            index=RangeIndex(3),
+        )
+        tm.assert_frame_equal(res, exp)
+
+    @pytest.mark.parametrize("tz", [None, "UTC"])
+    @pytest.mark.parametrize("values", [[], [1, 2, 3]])
+    def test_concat_empty_series_timelike(self, tz, values):
+        # GH 18447
+
+        first = Series([], dtype="M8[ns]").dt.tz_localize(tz)
+        dtype = None if values else np.float64
+        second = Series(values, dtype=dtype)
+
+        expected = DataFrame(
+            {
+                0: Series([pd.NaT] * len(values), dtype="M8[ns]").dt.tz_localize(tz),
+                1: values,
+            }
+        )
+        result = concat([first, second], axis=1)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "left,right,expected",
+        [
+            # booleans
+            (np.bool_, np.int32, np.object_),  # changed from int32 in 2.0 GH#39817
+            (np.bool_, np.float32, np.object_),
+            # datetime-like
+            ("m8[ns]", np.bool_, np.object_),
+            ("m8[ns]", np.int64, np.object_),
+            ("M8[ns]", np.bool_, np.object_),
+            ("M8[ns]", np.int64, np.object_),
+            # categorical
+            ("category", "category", "category"),
+            ("category", "object", "object"),
+        ],
+    )
+    def test_concat_empty_series_dtypes(self, left, right, expected):
+        # GH#39817, GH#45101
+        result = concat([Series(dtype=left), Series(dtype=right)])
+        assert result.dtype == expected
+
+    @pytest.mark.parametrize(
+        "dtype", ["float64", "int8", "uint8", "bool", "m8[ns]", "M8[ns]"]
+    )
+    def test_concat_empty_series_dtypes_match_roundtrips(self, dtype):
+        dtype = np.dtype(dtype)
+
+        result = concat([Series(dtype=dtype)])
+        assert result.dtype == dtype
+
+        result = concat([Series(dtype=dtype), Series(dtype=dtype)])
+        assert result.dtype == dtype
+
+    @pytest.mark.parametrize("dtype", ["float64", "int8", "uint8", "m8[ns]", "M8[ns]"])
+    @pytest.mark.parametrize(
+        "dtype2",
+        ["float64", "int8", "uint8", "m8[ns]", "M8[ns]"],
+    )
+    def test_concat_empty_series_dtypes_roundtrips(self, dtype, dtype2):
+        # round-tripping with self & like self
+        if dtype == dtype2:
+            pytest.skip("same dtype is not applicable for test")
+
+        def int_result_type(dtype, dtype2):
+            typs = {dtype.kind, dtype2.kind}
+            if not len(typs - {"i", "u", "b"}) and (
+                dtype.kind == "i" or dtype2.kind == "i"
+            ):
+                return "i"
+            elif not len(typs - {"u", "b"}) and (
+                dtype.kind == "u" or dtype2.kind == "u"
+            ):
+                return "u"
+            return None
+
+        def float_result_type(dtype, dtype2):
+            typs = {dtype.kind, dtype2.kind}
+            if not len(typs - {"f", "i", "u"}) and (
+                dtype.kind == "f" or dtype2.kind == "f"
+            ):
+                return "f"
+            return None
+
+        def get_result_type(dtype, dtype2):
+            result = float_result_type(dtype, dtype2)
+            if result is not None:
+                return result
+            result = int_result_type(dtype, dtype2)
+            if result is not None:
+                return result
+            return "O"
+
+        dtype = np.dtype(dtype)
+        dtype2 = np.dtype(dtype2)
+        expected = get_result_type(dtype, dtype2)
+        result = concat([Series(dtype=dtype), Series(dtype=dtype2)]).dtype
+        assert result.kind == expected
+
+    def test_concat_empty_series_dtypes_triple(self):
+        assert (
+            concat(
+                [Series(dtype="M8[ns]"), Series(dtype=np.bool_), Series(dtype=np.int64)]
+            ).dtype
+            == np.object_
+        )
+
+    def test_concat_empty_series_dtype_category_with_array(self):
+        # GH#18515
+        assert (
+            concat(
+                [Series(np.array([]), dtype="category"), Series(dtype="float64")]
+            ).dtype
+            == "float64"
+        )
+
+    def test_concat_empty_series_dtypes_sparse(self):
+        result = concat(
+            [
+                Series(dtype="float64").astype("Sparse"),
+                Series(dtype="float64").astype("Sparse"),
+            ]
+        )
+        assert result.dtype == "Sparse[float64]"
+
+        result = concat(
+            [Series(dtype="float64").astype("Sparse"), Series(dtype="float64")]
+        )
+        expected = pd.SparseDtype(np.float64)
+        assert result.dtype == expected
+
+        result = concat(
+            [Series(dtype="float64").astype("Sparse"), Series(dtype="object")]
+        )
+        expected = pd.SparseDtype("object")
+        assert result.dtype == expected
+
+    def test_concat_empty_df_object_dtype(self):
+        # GH 9149
+        df_1 = DataFrame({"Row": [0, 1, 1], "EmptyCol": np.nan, "NumberCol": [1, 2, 3]})
+        df_2 = DataFrame(columns=df_1.columns)
+        result = concat([df_1, df_2], axis=0)
+        expected = df_1.astype(object)
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_empty_dataframe_dtypes(self):
+        df = DataFrame(columns=list("abc"))
+        df["a"] = df["a"].astype(np.bool_)
+        df["b"] = df["b"].astype(np.int32)
+        df["c"] = df["c"].astype(np.float64)
+
+        result = concat([df, df])
+        assert result["a"].dtype == np.bool_
+        assert result["b"].dtype == np.int32
+        assert result["c"].dtype == np.float64
+
+        result = concat([df, df.astype(np.float64)])
+        assert result["a"].dtype == np.object_
+        assert result["b"].dtype == np.float64
+        assert result["c"].dtype == np.float64
+
+    def test_concat_inner_join_empty(self):
+        # GH 15328
+        df_empty = DataFrame()
+        df_a = DataFrame({"a": [1, 2]}, index=[0, 1], dtype="int64")
+        df_expected = DataFrame({"a": []}, index=RangeIndex(0), dtype="int64")
+
+        result = concat([df_a, df_empty], axis=1, join="inner")
+        tm.assert_frame_equal(result, df_expected)
+
+        result = concat([df_a, df_empty], axis=1, join="outer")
+        tm.assert_frame_equal(result, df_a)
+
+    def test_empty_dtype_coerce(self):
+        # xref to #12411
+        # xref to #12045
+        # xref to #11594
+        # see below
+
+        # 10571
+        df1 = DataFrame(data=[[1, None], [2, None]], columns=["a", "b"])
+        df2 = DataFrame(data=[[3, None], [4, None]], columns=["a", "b"])
+        result = concat([df1, df2])
+        expected = df1.dtypes
+        tm.assert_series_equal(result.dtypes, expected)
+
+    def test_concat_empty_dataframe(self):
+        # 39037
+        df1 = DataFrame(columns=["a", "b"])
+        df2 = DataFrame(columns=["b", "c"])
+        result = concat([df1, df2, df1])
+        expected = DataFrame(columns=["a", "b", "c"])
+        tm.assert_frame_equal(result, expected)
+
+        df3 = DataFrame(columns=["a", "b"])
+        df4 = DataFrame(columns=["b"])
+        result = concat([df3, df4])
+        expected = DataFrame(columns=["a", "b"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_empty_dataframe_different_dtypes(self, using_infer_string):
+        # 39037
+        df1 = DataFrame({"a": [1, 2, 3], "b": ["a", "b", "c"]})
+        df2 = DataFrame({"a": [1, 2, 3]})
+
+        result = concat([df1[:0], df2[:0]])
+        assert result["a"].dtype == np.int64
+        assert result["b"].dtype == np.object_ if not using_infer_string else "string"
+
+    def test_concat_to_empty_ea(self):
+        """48510 `concat` to an empty EA should maintain type EA dtype."""
+        df_empty = DataFrame({"a": pd.array([], dtype=pd.Int64Dtype())})
+        df_new = DataFrame({"a": pd.array([1, 2, 3], dtype=pd.Int64Dtype())})
+        expected = df_new.copy()
+        result = concat([df_empty, df_new])
+        tm.assert_frame_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_index.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_index.py
new file mode 100644
index 0000000000000000000000000000000000000000..52bb9fa0f151bb802d0cb440982230bd99c65b76
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_index.py
@@ -0,0 +1,472 @@
+from copy import deepcopy
+
+import numpy as np
+import pytest
+
+from pandas.errors import PerformanceWarning
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    concat,
+)
+import pandas._testing as tm
+
+
+class TestIndexConcat:
+    def test_concat_ignore_index(self, sort):
+        frame1 = DataFrame(
+            {"test1": ["a", "b", "c"], "test2": [1, 2, 3], "test3": [4.5, 3.2, 1.2]}
+        )
+        frame2 = DataFrame({"test3": [5.2, 2.2, 4.3]})
+        frame1.index = Index(["x", "y", "z"])
+        frame2.index = Index(["x", "y", "q"])
+
+        v1 = concat([frame1, frame2], axis=1, ignore_index=True, sort=sort)
+
+        nan = np.nan
+        expected = DataFrame(
+            [
+                [nan, nan, nan, 4.3],
+                ["a", 1, 4.5, 5.2],
+                ["b", 2, 3.2, 2.2],
+                ["c", 3, 1.2, nan],
+            ],
+            index=Index(["q", "x", "y", "z"]),
+        )
+        if not sort:
+            expected = expected.loc[["x", "y", "z", "q"]]
+
+        tm.assert_frame_equal(v1, expected)
+
+    @pytest.mark.parametrize(
+        "name_in1,name_in2,name_in3,name_out",
+        [
+            ("idx", "idx", "idx", "idx"),
+            ("idx", "idx", None, None),
+            ("idx", None, None, None),
+            ("idx1", "idx2", None, None),
+            ("idx1", "idx1", "idx2", None),
+            ("idx1", "idx2", "idx3", None),
+            (None, None, None, None),
+        ],
+    )
+    def test_concat_same_index_names(self, name_in1, name_in2, name_in3, name_out):
+        # GH13475
+        indices = [
+            Index(["a", "b", "c"], name=name_in1),
+            Index(["b", "c", "d"], name=name_in2),
+            Index(["c", "d", "e"], name=name_in3),
+        ]
+        frames = [
+            DataFrame({c: [0, 1, 2]}, index=i) for i, c in zip(indices, ["x", "y", "z"])
+        ]
+        result = concat(frames, axis=1)
+
+        exp_ind = Index(["a", "b", "c", "d", "e"], name=name_out)
+        expected = DataFrame(
+            {
+                "x": [0, 1, 2, np.nan, np.nan],
+                "y": [np.nan, 0, 1, 2, np.nan],
+                "z": [np.nan, np.nan, 0, 1, 2],
+            },
+            index=exp_ind,
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_rename_index(self):
+        a = DataFrame(
+            np.random.default_rng(2).random((3, 3)),
+            columns=list("ABC"),
+            index=Index(list("abc"), name="index_a"),
+        )
+        b = DataFrame(
+            np.random.default_rng(2).random((3, 3)),
+            columns=list("ABC"),
+            index=Index(list("abc"), name="index_b"),
+        )
+
+        result = concat([a, b], keys=["key0", "key1"], names=["lvl0", "lvl1"])
+
+        exp = concat([a, b], keys=["key0", "key1"], names=["lvl0"])
+        names = list(exp.index.names)
+        names[1] = "lvl1"
+        exp.index.set_names(names, inplace=True)
+
+        tm.assert_frame_equal(result, exp)
+        assert result.index.names == exp.index.names
+
+    def test_concat_copy_index_series(self, axis, using_copy_on_write):
+        # GH 29879
+        ser = Series([1, 2])
+        comb = concat([ser, ser], axis=axis, copy=True)
+        if not using_copy_on_write or axis in [0, "index"]:
+            assert comb.index is not ser.index
+        else:
+            assert comb.index is ser.index
+
+    def test_concat_copy_index_frame(self, axis, using_copy_on_write):
+        # GH 29879
+        df = DataFrame([[1, 2], [3, 4]], columns=["a", "b"])
+        comb = concat([df, df], axis=axis, copy=True)
+        if not using_copy_on_write:
+            assert not comb.index.is_(df.index)
+            assert not comb.columns.is_(df.columns)
+        elif axis in [0, "index"]:
+            assert not comb.index.is_(df.index)
+            assert comb.columns.is_(df.columns)
+        elif axis in [1, "columns"]:
+            assert comb.index.is_(df.index)
+            assert not comb.columns.is_(df.columns)
+
+    def test_default_index(self):
+        # is_series and ignore_index
+        s1 = Series([1, 2, 3], name="x")
+        s2 = Series([4, 5, 6], name="y")
+        res = concat([s1, s2], axis=1, ignore_index=True)
+        assert isinstance(res.columns, pd.RangeIndex)
+        exp = DataFrame([[1, 4], [2, 5], [3, 6]])
+        # use check_index_type=True to check the result have
+        # RangeIndex (default index)
+        tm.assert_frame_equal(res, exp, check_index_type=True, check_column_type=True)
+
+        # is_series and all inputs have no names
+        s1 = Series([1, 2, 3])
+        s2 = Series([4, 5, 6])
+        res = concat([s1, s2], axis=1, ignore_index=False)
+        assert isinstance(res.columns, pd.RangeIndex)
+        exp = DataFrame([[1, 4], [2, 5], [3, 6]])
+        exp.columns = pd.RangeIndex(2)
+        tm.assert_frame_equal(res, exp, check_index_type=True, check_column_type=True)
+
+        # is_dataframe and ignore_index
+        df1 = DataFrame({"A": [1, 2], "B": [5, 6]})
+        df2 = DataFrame({"A": [3, 4], "B": [7, 8]})
+
+        res = concat([df1, df2], axis=0, ignore_index=True)
+        exp = DataFrame([[1, 5], [2, 6], [3, 7], [4, 8]], columns=["A", "B"])
+        tm.assert_frame_equal(res, exp, check_index_type=True, check_column_type=True)
+
+        res = concat([df1, df2], axis=1, ignore_index=True)
+        exp = DataFrame([[1, 5, 3, 7], [2, 6, 4, 8]])
+        tm.assert_frame_equal(res, exp, check_index_type=True, check_column_type=True)
+
+    def test_dups_index(self):
+        # GH 4771
+
+        # single dtypes
+        df = DataFrame(
+            np.random.default_rng(2).integers(0, 10, size=40).reshape(10, 4),
+            columns=["A", "A", "C", "C"],
+        )
+
+        result = concat([df, df], axis=1)
+        tm.assert_frame_equal(result.iloc[:, :4], df)
+        tm.assert_frame_equal(result.iloc[:, 4:], df)
+
+        result = concat([df, df], axis=0)
+        tm.assert_frame_equal(result.iloc[:10], df)
+        tm.assert_frame_equal(result.iloc[10:], df)
+
+        # multi dtypes
+        df = concat(
+            [
+                DataFrame(
+                    np.random.default_rng(2).standard_normal((10, 4)),
+                    columns=["A", "A", "B", "B"],
+                ),
+                DataFrame(
+                    np.random.default_rng(2).integers(0, 10, size=20).reshape(10, 2),
+                    columns=["A", "C"],
+                ),
+            ],
+            axis=1,
+        )
+
+        result = concat([df, df], axis=1)
+        tm.assert_frame_equal(result.iloc[:, :6], df)
+        tm.assert_frame_equal(result.iloc[:, 6:], df)
+
+        result = concat([df, df], axis=0)
+        tm.assert_frame_equal(result.iloc[:10], df)
+        tm.assert_frame_equal(result.iloc[10:], df)
+
+        # append
+        result = df.iloc[0:8, :]._append(df.iloc[8:])
+        tm.assert_frame_equal(result, df)
+
+        result = df.iloc[0:8, :]._append(df.iloc[8:9])._append(df.iloc[9:10])
+        tm.assert_frame_equal(result, df)
+
+        expected = concat([df, df], axis=0)
+        result = df._append(df)
+        tm.assert_frame_equal(result, expected)
+
+
+class TestMultiIndexConcat:
+    def test_concat_multiindex_with_keys(self, multiindex_dataframe_random_data):
+        frame = multiindex_dataframe_random_data
+        index = frame.index
+        result = concat([frame, frame], keys=[0, 1], names=["iteration"])
+
+        assert result.index.names == ("iteration",) + index.names
+        tm.assert_frame_equal(result.loc[0], frame)
+        tm.assert_frame_equal(result.loc[1], frame)
+        assert result.index.nlevels == 3
+
+    def test_concat_multiindex_with_none_in_index_names(self):
+        # GH 15787
+        index = MultiIndex.from_product([[1], range(5)], names=["level1", None])
+        df = DataFrame({"col": range(5)}, index=index, dtype=np.int32)
+
+        result = concat([df, df], keys=[1, 2], names=["level2"])
+        index = MultiIndex.from_product(
+            [[1, 2], [1], range(5)], names=["level2", "level1", None]
+        )
+        expected = DataFrame({"col": list(range(5)) * 2}, index=index, dtype=np.int32)
+        tm.assert_frame_equal(result, expected)
+
+        result = concat([df, df[:2]], keys=[1, 2], names=["level2"])
+        level2 = [1] * 5 + [2] * 2
+        level1 = [1] * 7
+        no_name = list(range(5)) + list(range(2))
+        tuples = list(zip(level2, level1, no_name))
+        index = MultiIndex.from_tuples(tuples, names=["level2", "level1", None])
+        expected = DataFrame({"col": no_name}, index=index, dtype=np.int32)
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_multiindex_rangeindex(self):
+        # GH13542
+        # when multi-index levels are RangeIndex objects
+        # there is a bug in concat with objects of len 1
+
+        df = DataFrame(np.random.default_rng(2).standard_normal((9, 2)))
+        df.index = MultiIndex(
+            levels=[pd.RangeIndex(3), pd.RangeIndex(3)],
+            codes=[np.repeat(np.arange(3), 3), np.tile(np.arange(3), 3)],
+        )
+
+        res = concat([df.iloc[[2, 3, 4], :], df.iloc[[5], :]])
+        exp = df.iloc[[2, 3, 4, 5], :]
+        tm.assert_frame_equal(res, exp)
+
+    def test_concat_multiindex_dfs_with_deepcopy(self):
+        # GH 9967
+        example_multiindex1 = MultiIndex.from_product([["a"], ["b"]])
+        example_dataframe1 = DataFrame([0], index=example_multiindex1)
+
+        example_multiindex2 = MultiIndex.from_product([["a"], ["c"]])
+        example_dataframe2 = DataFrame([1], index=example_multiindex2)
+
+        example_dict = {"s1": example_dataframe1, "s2": example_dataframe2}
+        expected_index = MultiIndex(
+            levels=[["s1", "s2"], ["a"], ["b", "c"]],
+            codes=[[0, 1], [0, 0], [0, 1]],
+            names=["testname", None, None],
+        )
+        expected = DataFrame([[0], [1]], index=expected_index)
+        result_copy = concat(deepcopy(example_dict), names=["testname"])
+        tm.assert_frame_equal(result_copy, expected)
+        result_no_copy = concat(example_dict, names=["testname"])
+        tm.assert_frame_equal(result_no_copy, expected)
+
+    @pytest.mark.parametrize(
+        "mi1_list",
+        [
+            [["a"], range(2)],
+            [["b"], np.arange(2.0, 4.0)],
+            [["c"], ["A", "B"]],
+            [["d"], pd.date_range(start="2017", end="2018", periods=2)],
+        ],
+    )
+    @pytest.mark.parametrize(
+        "mi2_list",
+        [
+            [["a"], range(2)],
+            [["b"], np.arange(2.0, 4.0)],
+            [["c"], ["A", "B"]],
+            [["d"], pd.date_range(start="2017", end="2018", periods=2)],
+        ],
+    )
+    def test_concat_with_various_multiindex_dtypes(
+        self, mi1_list: list, mi2_list: list
+    ):
+        # GitHub #23478
+        mi1 = MultiIndex.from_product(mi1_list)
+        mi2 = MultiIndex.from_product(mi2_list)
+
+        df1 = DataFrame(np.zeros((1, len(mi1))), columns=mi1)
+        df2 = DataFrame(np.zeros((1, len(mi2))), columns=mi2)
+
+        if mi1_list[0] == mi2_list[0]:
+            expected_mi = MultiIndex(
+                levels=[mi1_list[0], list(mi1_list[1])],
+                codes=[[0, 0, 0, 0], [0, 1, 0, 1]],
+            )
+        else:
+            expected_mi = MultiIndex(
+                levels=[
+                    mi1_list[0] + mi2_list[0],
+                    list(mi1_list[1]) + list(mi2_list[1]),
+                ],
+                codes=[[0, 0, 1, 1], [0, 1, 2, 3]],
+            )
+
+        expected_df = DataFrame(np.zeros((1, len(expected_mi))), columns=expected_mi)
+
+        with tm.assert_produces_warning(None):
+            result_df = concat((df1, df2), axis=1)
+
+        tm.assert_frame_equal(expected_df, result_df)
+
+    def test_concat_multiindex_(self):
+        # GitHub #44786
+        df = DataFrame({"col": ["a", "b", "c"]}, index=["1", "2", "2"])
+        df = concat([df], keys=["X"])
+
+        iterables = [["X"], ["1", "2", "2"]]
+        result_index = df.index
+        expected_index = MultiIndex.from_product(iterables)
+
+        tm.assert_index_equal(result_index, expected_index)
+
+        result_df = df
+        expected_df = DataFrame(
+            {"col": ["a", "b", "c"]}, index=MultiIndex.from_product(iterables)
+        )
+        tm.assert_frame_equal(result_df, expected_df)
+
+    def test_concat_with_key_not_unique(self):
+        # GitHub #46519
+        df1 = DataFrame({"name": [1]})
+        df2 = DataFrame({"name": [2]})
+        df3 = DataFrame({"name": [3]})
+        df_a = concat([df1, df2, df3], keys=["x", "y", "x"])
+        # the warning is caused by indexing unsorted multi-index
+        with tm.assert_produces_warning(
+            PerformanceWarning, match="indexing past lexsort depth"
+        ):
+            out_a = df_a.loc[("x", 0), :]
+
+        df_b = DataFrame(
+            {"name": [1, 2, 3]}, index=Index([("x", 0), ("y", 0), ("x", 0)])
+        )
+        with tm.assert_produces_warning(
+            PerformanceWarning, match="indexing past lexsort depth"
+        ):
+            out_b = df_b.loc[("x", 0)]
+
+        tm.assert_frame_equal(out_a, out_b)
+
+        df1 = DataFrame({"name": ["a", "a", "b"]})
+        df2 = DataFrame({"name": ["a", "b"]})
+        df3 = DataFrame({"name": ["c", "d"]})
+        df_a = concat([df1, df2, df3], keys=["x", "y", "x"])
+        with tm.assert_produces_warning(
+            PerformanceWarning, match="indexing past lexsort depth"
+        ):
+            out_a = df_a.loc[("x", 0), :]
+
+        df_b = DataFrame(
+            {
+                "a": ["x", "x", "x", "y", "y", "x", "x"],
+                "b": [0, 1, 2, 0, 1, 0, 1],
+                "name": list("aababcd"),
+            }
+        ).set_index(["a", "b"])
+        df_b.index.names = [None, None]
+        with tm.assert_produces_warning(
+            PerformanceWarning, match="indexing past lexsort depth"
+        ):
+            out_b = df_b.loc[("x", 0), :]
+
+        tm.assert_frame_equal(out_a, out_b)
+
+    def test_concat_with_duplicated_levels(self):
+        # keyword levels should be unique
+        df1 = DataFrame({"A": [1]}, index=["x"])
+        df2 = DataFrame({"A": [1]}, index=["y"])
+        msg = r"Level values not unique: \['x', 'y', 'y'\]"
+        with pytest.raises(ValueError, match=msg):
+            concat([df1, df2], keys=["x", "y"], levels=[["x", "y", "y"]])
+
+    @pytest.mark.parametrize("levels", [[["x", "y"]], [["x", "y", "y"]]])
+    def test_concat_with_levels_with_none_keys(self, levels):
+        df1 = DataFrame({"A": [1]}, index=["x"])
+        df2 = DataFrame({"A": [1]}, index=["y"])
+        msg = "levels supported only when keys is not None"
+        with pytest.raises(ValueError, match=msg):
+            concat([df1, df2], levels=levels)
+
+    def test_concat_range_index_result(self):
+        # GH#47501
+        df1 = DataFrame({"a": [1, 2]})
+        df2 = DataFrame({"b": [1, 2]})
+
+        result = concat([df1, df2], sort=True, axis=1)
+        expected = DataFrame({"a": [1, 2], "b": [1, 2]})
+        tm.assert_frame_equal(result, expected)
+        expected_index = pd.RangeIndex(0, 2)
+        tm.assert_index_equal(result.index, expected_index, exact=True)
+
+    def test_concat_index_keep_dtype(self):
+        # GH#47329
+        df1 = DataFrame([[0, 1, 1]], columns=Index([1, 2, 3], dtype="object"))
+        df2 = DataFrame([[0, 1]], columns=Index([1, 2], dtype="object"))
+        result = concat([df1, df2], ignore_index=True, join="outer", sort=True)
+        expected = DataFrame(
+            [[0, 1, 1.0], [0, 1, np.nan]], columns=Index([1, 2, 3], dtype="object")
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_index_keep_dtype_ea_numeric(self, any_numeric_ea_dtype):
+        # GH#47329
+        df1 = DataFrame(
+            [[0, 1, 1]], columns=Index([1, 2, 3], dtype=any_numeric_ea_dtype)
+        )
+        df2 = DataFrame([[0, 1]], columns=Index([1, 2], dtype=any_numeric_ea_dtype))
+        result = concat([df1, df2], ignore_index=True, join="outer", sort=True)
+        expected = DataFrame(
+            [[0, 1, 1.0], [0, 1, np.nan]],
+            columns=Index([1, 2, 3], dtype=any_numeric_ea_dtype),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", ["Int8", "Int16", "Int32"])
+    def test_concat_index_find_common(self, dtype):
+        # GH#47329
+        df1 = DataFrame([[0, 1, 1]], columns=Index([1, 2, 3], dtype=dtype))
+        df2 = DataFrame([[0, 1]], columns=Index([1, 2], dtype="Int32"))
+        result = concat([df1, df2], ignore_index=True, join="outer", sort=True)
+        expected = DataFrame(
+            [[0, 1, 1.0], [0, 1, np.nan]], columns=Index([1, 2, 3], dtype="Int32")
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_axis_1_sort_false_rangeindex(self, using_infer_string):
+        # GH 46675
+        s1 = Series(["a", "b", "c"])
+        s2 = Series(["a", "b"])
+        s3 = Series(["a", "b", "c", "d"])
+        s4 = Series(
+            [], dtype=object if not using_infer_string else "string[pyarrow_numpy]"
+        )
+        result = concat(
+            [s1, s2, s3, s4], sort=False, join="outer", ignore_index=False, axis=1
+        )
+        expected = DataFrame(
+            [
+                ["a"] * 3 + [np.nan],
+                ["b"] * 3 + [np.nan],
+                ["c", np.nan] * 2,
+                [np.nan] * 2 + ["d"] + [np.nan],
+            ],
+            dtype=object if not using_infer_string else "string[pyarrow_numpy]",
+        )
+        tm.assert_frame_equal(
+            result, expected, check_index_type=True, check_column_type=True
+        )
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_invalid.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_invalid.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1c44fc0debc397659354a3fa26c9ab6d33b8b40
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_invalid.py
@@ -0,0 +1,54 @@
+from io import StringIO
+
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    concat,
+    read_csv,
+)
+import pandas._testing as tm
+
+
+class TestInvalidConcat:
+    @pytest.mark.parametrize("obj", [1, {}, [1, 2], (1, 2)])
+    def test_concat_invalid(self, obj):
+        # trying to concat a ndframe with a non-ndframe
+        df1 = DataFrame(range(2))
+        msg = (
+            f"cannot concatenate object of type '{type(obj)}'; "
+            "only Series and DataFrame objs are valid"
+        )
+        with pytest.raises(TypeError, match=msg):
+            concat([df1, obj])
+
+    def test_concat_invalid_first_argument(self):
+        df1 = DataFrame(range(2))
+        msg = (
+            "first argument must be an iterable of pandas "
+            'objects, you passed an object of type "DataFrame"'
+        )
+        with pytest.raises(TypeError, match=msg):
+            concat(df1)
+
+    def test_concat_generator_obj(self):
+        # generator ok though
+        concat(DataFrame(np.random.default_rng(2).random((5, 5))) for _ in range(3))
+
+    def test_concat_textreader_obj(self):
+        # text reader ok
+        # GH6583
+        data = """index,A,B,C,D
+                  foo,2,3,4,5
+                  bar,7,8,9,10
+                  baz,12,13,14,15
+                  qux,12,13,14,15
+                  foo2,12,13,14,15
+                  bar2,12,13,14,15
+               """
+
+        with read_csv(StringIO(data), chunksize=1) as reader:
+            result = concat(reader, ignore_index=True)
+        expected = read_csv(StringIO(data))
+        tm.assert_frame_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_series.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_series.py
new file mode 100644
index 0000000000000000000000000000000000000000..c12b835cb61e1545d3fc5a61f16f195166edd1fd
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_series.py
@@ -0,0 +1,175 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    MultiIndex,
+    Series,
+    concat,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestSeriesConcat:
+    def test_concat_series(self):
+        ts = Series(
+            np.arange(20, dtype=np.float64),
+            index=date_range("2020-01-01", periods=20),
+            name="foo",
+        )
+        ts.name = "foo"
+
+        pieces = [ts[:5], ts[5:15], ts[15:]]
+
+        result = concat(pieces)
+        tm.assert_series_equal(result, ts)
+        assert result.name == ts.name
+
+        result = concat(pieces, keys=[0, 1, 2])
+        expected = ts.copy()
+
+        ts.index = DatetimeIndex(np.array(ts.index.values, dtype="M8[ns]"))
+
+        exp_codes = [np.repeat([0, 1, 2], [len(x) for x in pieces]), np.arange(len(ts))]
+        exp_index = MultiIndex(levels=[[0, 1, 2], ts.index], codes=exp_codes)
+        expected.index = exp_index
+        tm.assert_series_equal(result, expected)
+
+    def test_concat_empty_and_non_empty_series_regression(self):
+        # GH 18187 regression test
+        s1 = Series([1])
+        s2 = Series([], dtype=object)
+
+        expected = s1
+        msg = "The behavior of array concatenation with empty entries is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = concat([s1, s2])
+        tm.assert_series_equal(result, expected)
+
+    def test_concat_series_axis1(self):
+        ts = Series(
+            np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)
+        )
+
+        pieces = [ts[:-2], ts[2:], ts[2:-2]]
+
+        result = concat(pieces, axis=1)
+        expected = DataFrame(pieces).T
+        tm.assert_frame_equal(result, expected)
+
+        result = concat(pieces, keys=["A", "B", "C"], axis=1)
+        expected = DataFrame(pieces, index=["A", "B", "C"]).T
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_series_axis1_preserves_series_names(self):
+        # preserve series names, #2489
+        s = Series(np.random.default_rng(2).standard_normal(5), name="A")
+        s2 = Series(np.random.default_rng(2).standard_normal(5), name="B")
+
+        result = concat([s, s2], axis=1)
+        expected = DataFrame({"A": s, "B": s2})
+        tm.assert_frame_equal(result, expected)
+
+        s2.name = None
+        result = concat([s, s2], axis=1)
+        tm.assert_index_equal(result.columns, Index(["A", 0], dtype="object"))
+
+    def test_concat_series_axis1_with_reindex(self, sort):
+        # must reindex, #2603
+        s = Series(
+            np.random.default_rng(2).standard_normal(3), index=["c", "a", "b"], name="A"
+        )
+        s2 = Series(
+            np.random.default_rng(2).standard_normal(4),
+            index=["d", "a", "b", "c"],
+            name="B",
+        )
+        result = concat([s, s2], axis=1, sort=sort)
+        expected = DataFrame({"A": s, "B": s2}, index=["c", "a", "b", "d"])
+        if sort:
+            expected = expected.sort_index()
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_series_axis1_names_applied(self):
+        # ensure names argument is not ignored on axis=1, #23490
+        s = Series([1, 2, 3])
+        s2 = Series([4, 5, 6])
+        result = concat([s, s2], axis=1, keys=["a", "b"], names=["A"])
+        expected = DataFrame(
+            [[1, 4], [2, 5], [3, 6]], columns=Index(["a", "b"], name="A")
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = concat([s, s2], axis=1, keys=[("a", 1), ("b", 2)], names=["A", "B"])
+        expected = DataFrame(
+            [[1, 4], [2, 5], [3, 6]],
+            columns=MultiIndex.from_tuples([("a", 1), ("b", 2)], names=["A", "B"]),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_series_axis1_same_names_ignore_index(self):
+        dates = date_range("01-Jan-2013", "01-Jan-2014", freq="MS")[0:-1]
+        s1 = Series(
+            np.random.default_rng(2).standard_normal(len(dates)),
+            index=dates,
+            name="value",
+        )
+        s2 = Series(
+            np.random.default_rng(2).standard_normal(len(dates)),
+            index=dates,
+            name="value",
+        )
+
+        result = concat([s1, s2], axis=1, ignore_index=True)
+        expected = Index(range(2))
+
+        tm.assert_index_equal(result.columns, expected, exact=True)
+
+    @pytest.mark.parametrize(
+        "s1name,s2name", [(np.int64(190), (43, 0)), (190, (43, 0))]
+    )
+    def test_concat_series_name_npscalar_tuple(self, s1name, s2name):
+        # GH21015
+        s1 = Series({"a": 1, "b": 2}, name=s1name)
+        s2 = Series({"c": 5, "d": 6}, name=s2name)
+        result = concat([s1, s2])
+        expected = Series({"a": 1, "b": 2, "c": 5, "d": 6})
+        tm.assert_series_equal(result, expected)
+
+    def test_concat_series_partial_columns_names(self):
+        # GH10698
+        named_series = Series([1, 2], name="foo")
+        unnamed_series1 = Series([1, 2])
+        unnamed_series2 = Series([4, 5])
+
+        result = concat([named_series, unnamed_series1, unnamed_series2], axis=1)
+        expected = DataFrame(
+            {"foo": [1, 2], 0: [1, 2], 1: [4, 5]}, columns=["foo", 0, 1]
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = concat(
+            [named_series, unnamed_series1, unnamed_series2],
+            axis=1,
+            keys=["red", "blue", "yellow"],
+        )
+        expected = DataFrame(
+            {"red": [1, 2], "blue": [1, 2], "yellow": [4, 5]},
+            columns=["red", "blue", "yellow"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = concat(
+            [named_series, unnamed_series1, unnamed_series2], axis=1, ignore_index=True
+        )
+        expected = DataFrame({0: [1, 2], 1: [1, 2], 2: [4, 5]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_series_length_one_reversed(self, frame_or_series):
+        # GH39401
+        obj = frame_or_series([100])
+        result = concat([obj.iloc[::-1]])
+        tm.assert_equal(result, obj)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_sort.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_sort.py
new file mode 100644
index 0000000000000000000000000000000000000000..2724f819588933cc307dd396fdcd024f04c38eaa
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_sort.py
@@ -0,0 +1,118 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import DataFrame
+import pandas._testing as tm
+
+
+class TestConcatSort:
+    def test_concat_sorts_columns(self, sort):
+        # GH-4588
+        df1 = DataFrame({"a": [1, 2], "b": [1, 2]}, columns=["b", "a"])
+        df2 = DataFrame({"a": [3, 4], "c": [5, 6]})
+
+        # for sort=True/None
+        expected = DataFrame(
+            {"a": [1, 2, 3, 4], "b": [1, 2, None, None], "c": [None, None, 5, 6]},
+            columns=["a", "b", "c"],
+        )
+
+        if sort is False:
+            expected = expected[["b", "a", "c"]]
+
+        # default
+        with tm.assert_produces_warning(None):
+            result = pd.concat([df1, df2], ignore_index=True, sort=sort)
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_sorts_index(self, sort):
+        df1 = DataFrame({"a": [1, 2, 3]}, index=["c", "a", "b"])
+        df2 = DataFrame({"b": [1, 2]}, index=["a", "b"])
+
+        # For True/None
+        expected = DataFrame(
+            {"a": [2, 3, 1], "b": [1, 2, None]},
+            index=["a", "b", "c"],
+            columns=["a", "b"],
+        )
+        if sort is False:
+            expected = expected.loc[["c", "a", "b"]]
+
+        # Warn and sort by default
+        with tm.assert_produces_warning(None):
+            result = pd.concat([df1, df2], axis=1, sort=sort)
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_inner_sort(self, sort):
+        # https://github.com/pandas-dev/pandas/pull/20613
+        df1 = DataFrame(
+            {"a": [1, 2], "b": [1, 2], "c": [1, 2]}, columns=["b", "a", "c"]
+        )
+        df2 = DataFrame({"a": [1, 2], "b": [3, 4]}, index=[3, 4])
+
+        with tm.assert_produces_warning(None):
+            # unset sort should *not* warn for inner join
+            # since that never sorted
+            result = pd.concat([df1, df2], sort=sort, join="inner", ignore_index=True)
+
+        expected = DataFrame({"b": [1, 2, 3, 4], "a": [1, 2, 1, 2]}, columns=["b", "a"])
+        if sort is True:
+            expected = expected[["a", "b"]]
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_aligned_sort(self):
+        # GH-4588
+        df = DataFrame({"c": [1, 2], "b": [3, 4], "a": [5, 6]}, columns=["c", "b", "a"])
+        result = pd.concat([df, df], sort=True, ignore_index=True)
+        expected = DataFrame(
+            {"a": [5, 6, 5, 6], "b": [3, 4, 3, 4], "c": [1, 2, 1, 2]},
+            columns=["a", "b", "c"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = pd.concat(
+            [df, df[["c", "b"]]], join="inner", sort=True, ignore_index=True
+        )
+        expected = expected[["b", "c"]]
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_aligned_sort_does_not_raise(self):
+        # GH-4588
+        # We catch TypeErrors from sorting internally and do not re-raise.
+        df = DataFrame({1: [1, 2], "a": [3, 4]}, columns=[1, "a"])
+        expected = DataFrame({1: [1, 2, 1, 2], "a": [3, 4, 3, 4]}, columns=[1, "a"])
+        result = pd.concat([df, df], ignore_index=True, sort=True)
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_frame_with_sort_false(self):
+        # GH 43375
+        result = pd.concat(
+            [DataFrame({i: i}, index=[i]) for i in range(2, 0, -1)], sort=False
+        )
+        expected = DataFrame([[2, np.nan], [np.nan, 1]], index=[2, 1], columns=[2, 1])
+
+        tm.assert_frame_equal(result, expected)
+
+        # GH 37937
+        df1 = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}, index=[1, 2, 3])
+        df2 = DataFrame({"c": [7, 8, 9], "d": [10, 11, 12]}, index=[3, 1, 6])
+        result = pd.concat([df2, df1], axis=1, sort=False)
+        expected = DataFrame(
+            [
+                [7.0, 10.0, 3.0, 6.0],
+                [8.0, 11.0, 1.0, 4.0],
+                [9.0, 12.0, np.nan, np.nan],
+                [np.nan, np.nan, 2.0, 5.0],
+            ],
+            index=[3, 1, 6, 2],
+            columns=["c", "d", "a", "b"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_sort_none_raises(self):
+        # GH#41518
+        df = DataFrame({1: [1, 2], "a": [3, 4]})
+        msg = "The 'sort' keyword only accepts boolean values; None was passed."
+        with pytest.raises(ValueError, match=msg):
+            pd.concat([df, df], sort=None)
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--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_join.py
@@ -0,0 +1,1101 @@
+import re
+
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    Timestamp,
+    bdate_range,
+    concat,
+    merge,
+    option_context,
+)
+import pandas._testing as tm
+
+
+def get_test_data(ngroups=8, n=50):
+    unique_groups = list(range(ngroups))
+    arr = np.asarray(np.tile(unique_groups, n // ngroups))
+
+    if len(arr) < n:
+        arr = np.asarray(list(arr) + unique_groups[: n - len(arr)])
+
+    np.random.default_rng(2).shuffle(arr)
+    return arr
+
+
+class TestJoin:
+    # aggregate multiple columns
+    @pytest.fixture
+    def df(self):
+        df = DataFrame(
+            {
+                "key1": get_test_data(),
+                "key2": get_test_data(),
+                "data1": np.random.default_rng(2).standard_normal(50),
+                "data2": np.random.default_rng(2).standard_normal(50),
+            }
+        )
+
+        # exclude a couple keys for fun
+        df = df[df["key2"] > 1]
+        return df
+
+    @pytest.fixture
+    def df2(self):
+        return DataFrame(
+            {
+                "key1": get_test_data(n=10),
+                "key2": get_test_data(ngroups=4, n=10),
+                "value": np.random.default_rng(2).standard_normal(10),
+            }
+        )
+
+    @pytest.fixture
+    def target_source(self):
+        data = {
+            "A": [0.0, 1.0, 2.0, 3.0, 4.0],
+            "B": [0.0, 1.0, 0.0, 1.0, 0.0],
+            "C": ["foo1", "foo2", "foo3", "foo4", "foo5"],
+            "D": bdate_range("1/1/2009", periods=5),
+        }
+        target = DataFrame(data, index=Index(["a", "b", "c", "d", "e"], dtype=object))
+
+        # Join on string value
+
+        source = DataFrame(
+            {"MergedA": data["A"], "MergedD": data["D"]}, index=data["C"]
+        )
+        return target, source
+
+    def test_left_outer_join(self, df, df2):
+        joined_key2 = merge(df, df2, on="key2")
+        _check_join(df, df2, joined_key2, ["key2"], how="left")
+
+        joined_both = merge(df, df2)
+        _check_join(df, df2, joined_both, ["key1", "key2"], how="left")
+
+    def test_right_outer_join(self, df, df2):
+        joined_key2 = merge(df, df2, on="key2", how="right")
+        _check_join(df, df2, joined_key2, ["key2"], how="right")
+
+        joined_both = merge(df, df2, how="right")
+        _check_join(df, df2, joined_both, ["key1", "key2"], how="right")
+
+    def test_full_outer_join(self, df, df2):
+        joined_key2 = merge(df, df2, on="key2", how="outer")
+        _check_join(df, df2, joined_key2, ["key2"], how="outer")
+
+        joined_both = merge(df, df2, how="outer")
+        _check_join(df, df2, joined_both, ["key1", "key2"], how="outer")
+
+    def test_inner_join(self, df, df2):
+        joined_key2 = merge(df, df2, on="key2", how="inner")
+        _check_join(df, df2, joined_key2, ["key2"], how="inner")
+
+        joined_both = merge(df, df2, how="inner")
+        _check_join(df, df2, joined_both, ["key1", "key2"], how="inner")
+
+    def test_handle_overlap(self, df, df2):
+        joined = merge(df, df2, on="key2", suffixes=(".foo", ".bar"))
+
+        assert "key1.foo" in joined
+        assert "key1.bar" in joined
+
+    def test_handle_overlap_arbitrary_key(self, df, df2):
+        joined = merge(
+            df,
+            df2,
+            left_on="key2",
+            right_on="key1",
+            suffixes=(".foo", ".bar"),
+        )
+        assert "key1.foo" in joined
+        assert "key2.bar" in joined
+
+    @pytest.mark.parametrize(
+        "infer_string", [False, pytest.param(True, marks=td.skip_if_no("pyarrow"))]
+    )
+    def test_join_on(self, target_source, infer_string):
+        target, source = target_source
+
+        merged = target.join(source, on="C")
+        tm.assert_series_equal(merged["MergedA"], target["A"], check_names=False)
+        tm.assert_series_equal(merged["MergedD"], target["D"], check_names=False)
+
+        # join with duplicates (fix regression from DataFrame/Matrix merge)
+        df = DataFrame({"key": ["a", "a", "b", "b", "c"]})
+        df2 = DataFrame({"value": [0, 1, 2]}, index=["a", "b", "c"])
+        joined = df.join(df2, on="key")
+        expected = DataFrame(
+            {"key": ["a", "a", "b", "b", "c"], "value": [0, 0, 1, 1, 2]}
+        )
+        tm.assert_frame_equal(joined, expected)
+
+        # Test when some are missing
+        df_a = DataFrame([[1], [2], [3]], index=["a", "b", "c"], columns=["one"])
+        df_b = DataFrame([["foo"], ["bar"]], index=[1, 2], columns=["two"])
+        df_c = DataFrame([[1], [2]], index=[1, 2], columns=["three"])
+        joined = df_a.join(df_b, on="one")
+        joined = joined.join(df_c, on="one")
+        assert np.isnan(joined["two"]["c"])
+        assert np.isnan(joined["three"]["c"])
+
+        # merge column not p resent
+        with pytest.raises(KeyError, match="^'E'$"):
+            target.join(source, on="E")
+
+        # overlap
+        source_copy = source.copy()
+        msg = (
+            "You are trying to merge on float64 and object|string columns for key "
+            "'A'. If you wish to proceed you should use pd.concat"
+        )
+        with pytest.raises(ValueError, match=msg):
+            target.join(source_copy, on="A")
+
+    def test_join_on_fails_with_different_right_index(self):
+        df = DataFrame(
+            {
+                "a": np.random.default_rng(2).choice(["m", "f"], size=3),
+                "b": np.random.default_rng(2).standard_normal(3),
+            }
+        )
+        df2 = DataFrame(
+            {
+                "a": np.random.default_rng(2).choice(["m", "f"], size=10),
+                "b": np.random.default_rng(2).standard_normal(10),
+            },
+            index=MultiIndex.from_product([range(5), ["A", "B"]]),
+        )
+        msg = r'len\(left_on\) must equal the number of levels in the index of "right"'
+        with pytest.raises(ValueError, match=msg):
+            merge(df, df2, left_on="a", right_index=True)
+
+    def test_join_on_fails_with_different_left_index(self):
+        df = DataFrame(
+            {
+                "a": np.random.default_rng(2).choice(["m", "f"], size=3),
+                "b": np.random.default_rng(2).standard_normal(3),
+            },
+            index=MultiIndex.from_arrays([range(3), list("abc")]),
+        )
+        df2 = DataFrame(
+            {
+                "a": np.random.default_rng(2).choice(["m", "f"], size=10),
+                "b": np.random.default_rng(2).standard_normal(10),
+            }
+        )
+        msg = r'len\(right_on\) must equal the number of levels in the index of "left"'
+        with pytest.raises(ValueError, match=msg):
+            merge(df, df2, right_on="b", left_index=True)
+
+    def test_join_on_fails_with_different_column_counts(self):
+        df = DataFrame(
+            {
+                "a": np.random.default_rng(2).choice(["m", "f"], size=3),
+                "b": np.random.default_rng(2).standard_normal(3),
+            }
+        )
+        df2 = DataFrame(
+            {
+                "a": np.random.default_rng(2).choice(["m", "f"], size=10),
+                "b": np.random.default_rng(2).standard_normal(10),
+            },
+            index=MultiIndex.from_product([range(5), ["A", "B"]]),
+        )
+        msg = r"len\(right_on\) must equal len\(left_on\)"
+        with pytest.raises(ValueError, match=msg):
+            merge(df, df2, right_on="a", left_on=["a", "b"])
+
+    @pytest.mark.parametrize("wrong_type", [2, "str", None, np.array([0, 1])])
+    def test_join_on_fails_with_wrong_object_type(self, wrong_type):
+        # GH12081 - original issue
+
+        # GH21220 - merging of Series and DataFrame is now allowed
+        # Edited test to remove the Series object from test parameters
+
+        df = DataFrame({"a": [1, 1]})
+        msg = (
+            "Can only merge Series or DataFrame objects, "
+            f"a {type(wrong_type)} was passed"
+        )
+        with pytest.raises(TypeError, match=msg):
+            merge(wrong_type, df, left_on="a", right_on="a")
+        with pytest.raises(TypeError, match=msg):
+            merge(df, wrong_type, left_on="a", right_on="a")
+
+    def test_join_on_pass_vector(self, target_source):
+        target, source = target_source
+        expected = target.join(source, on="C")
+        expected = expected.rename(columns={"C": "key_0"})
+        expected = expected[["key_0", "A", "B", "D", "MergedA", "MergedD"]]
+
+        join_col = target.pop("C")
+        result = target.join(source, on=join_col)
+        tm.assert_frame_equal(result, expected)
+
+    def test_join_with_len0(self, target_source):
+        # nothing to merge
+        target, source = target_source
+        merged = target.join(source.reindex([]), on="C")
+        for col in source:
+            assert col in merged
+            assert merged[col].isna().all()
+
+        merged2 = target.join(source.reindex([]), on="C", how="inner")
+        tm.assert_index_equal(merged2.columns, merged.columns)
+        assert len(merged2) == 0
+
+    def test_join_on_inner(self):
+        df = DataFrame({"key": ["a", "a", "d", "b", "b", "c"]})
+        df2 = DataFrame({"value": [0, 1]}, index=["a", "b"])
+
+        joined = df.join(df2, on="key", how="inner")
+
+        expected = df.join(df2, on="key")
+        expected = expected[expected["value"].notna()]
+        tm.assert_series_equal(joined["key"], expected["key"])
+        tm.assert_series_equal(joined["value"], expected["value"], check_dtype=False)
+        tm.assert_index_equal(joined.index, expected.index)
+
+    def test_join_on_singlekey_list(self):
+        df = DataFrame({"key": ["a", "a", "b", "b", "c"]})
+        df2 = DataFrame({"value": [0, 1, 2]}, index=["a", "b", "c"])
+
+        # corner cases
+        joined = df.join(df2, on=["key"])
+        expected = df.join(df2, on="key")
+
+        tm.assert_frame_equal(joined, expected)
+
+    def test_join_on_series(self, target_source):
+        target, source = target_source
+        result = target.join(source["MergedA"], on="C")
+        expected = target.join(source[["MergedA"]], on="C")
+        tm.assert_frame_equal(result, expected)
+
+    def test_join_on_series_buglet(self):
+        # GH #638
+        df = DataFrame({"a": [1, 1]})
+        ds = Series([2], index=[1], name="b")
+        result = df.join(ds, on="a")
+        expected = DataFrame({"a": [1, 1], "b": [2, 2]}, index=df.index)
+        tm.assert_frame_equal(result, expected)
+
+    def test_join_index_mixed(self, join_type):
+        # no overlapping blocks
+        df1 = DataFrame(index=np.arange(10))
+        df1["bool"] = True
+        df1["string"] = "foo"
+
+        df2 = DataFrame(index=np.arange(5, 15))
+        df2["int"] = 1
+        df2["float"] = 1.0
+
+        joined = df1.join(df2, how=join_type)
+        expected = _join_by_hand(df1, df2, how=join_type)
+        tm.assert_frame_equal(joined, expected)
+
+        joined = df2.join(df1, how=join_type)
+        expected = _join_by_hand(df2, df1, how=join_type)
+        tm.assert_frame_equal(joined, expected)
+
+    def test_join_index_mixed_overlap(self):
+        df1 = DataFrame(
+            {"A": 1.0, "B": 2, "C": "foo", "D": True},
+            index=np.arange(10),
+            columns=["A", "B", "C", "D"],
+        )
+        assert df1["B"].dtype == np.int64
+        assert df1["D"].dtype == np.bool_
+
+        df2 = DataFrame(
+            {"A": 1.0, "B": 2, "C": "foo", "D": True},
+            index=np.arange(0, 10, 2),
+            columns=["A", "B", "C", "D"],
+        )
+
+        # overlap
+        joined = df1.join(df2, lsuffix="_one", rsuffix="_two")
+        expected_columns = [
+            "A_one",
+            "B_one",
+            "C_one",
+            "D_one",
+            "A_two",
+            "B_two",
+            "C_two",
+            "D_two",
+        ]
+        df1.columns = expected_columns[:4]
+        df2.columns = expected_columns[4:]
+        expected = _join_by_hand(df1, df2)
+        tm.assert_frame_equal(joined, expected)
+
+    def test_join_empty_bug(self):
+        # generated an exception in 0.4.3
+        x = DataFrame()
+        x.join(DataFrame([3], index=[0], columns=["A"]), how="outer")
+
+    def test_join_unconsolidated(self):
+        # GH #331
+        a = DataFrame(
+            np.random.default_rng(2).standard_normal((30, 2)), columns=["a", "b"]
+        )
+        c = Series(np.random.default_rng(2).standard_normal(30))
+        a["c"] = c
+        d = DataFrame(np.random.default_rng(2).standard_normal((30, 1)), columns=["q"])
+
+        # it works!
+        a.join(d)
+        d.join(a)
+
+    def test_join_multiindex(self):
+        index1 = MultiIndex.from_arrays(
+            [["a", "a", "a", "b", "b", "b"], [1, 2, 3, 1, 2, 3]],
+            names=["first", "second"],
+        )
+
+        index2 = MultiIndex.from_arrays(
+            [["b", "b", "b", "c", "c", "c"], [1, 2, 3, 1, 2, 3]],
+            names=["first", "second"],
+        )
+
+        df1 = DataFrame(
+            data=np.random.default_rng(2).standard_normal(6),
+            index=index1,
+            columns=["var X"],
+        )
+        df2 = DataFrame(
+            data=np.random.default_rng(2).standard_normal(6),
+            index=index2,
+            columns=["var Y"],
+        )
+
+        df1 = df1.sort_index(level=0)
+        df2 = df2.sort_index(level=0)
+
+        joined = df1.join(df2, how="outer")
+        ex_index = Index(index1.values).union(Index(index2.values))
+        expected = df1.reindex(ex_index).join(df2.reindex(ex_index))
+        expected.index.names = index1.names
+        tm.assert_frame_equal(joined, expected)
+        assert joined.index.names == index1.names
+
+        df1 = df1.sort_index(level=1)
+        df2 = df2.sort_index(level=1)
+
+        joined = df1.join(df2, how="outer").sort_index(level=0)
+        ex_index = Index(index1.values).union(Index(index2.values))
+        expected = df1.reindex(ex_index).join(df2.reindex(ex_index))
+        expected.index.names = index1.names
+
+        tm.assert_frame_equal(joined, expected)
+        assert joined.index.names == index1.names
+
+    def test_join_inner_multiindex(self, lexsorted_two_level_string_multiindex):
+        key1 = ["bar", "bar", "bar", "foo", "foo", "baz", "baz", "qux", "qux", "snap"]
+        key2 = [
+            "two",
+            "one",
+            "three",
+            "one",
+            "two",
+            "one",
+            "two",
+            "two",
+            "three",
+            "one",
+        ]
+
+        data = np.random.default_rng(2).standard_normal(len(key1))
+        data = DataFrame({"key1": key1, "key2": key2, "data": data})
+
+        index = lexsorted_two_level_string_multiindex
+        to_join = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 3)),
+            index=index,
+            columns=["j_one", "j_two", "j_three"],
+        )
+
+        joined = data.join(to_join, on=["key1", "key2"], how="inner")
+        expected = merge(
+            data,
+            to_join.reset_index(),
+            left_on=["key1", "key2"],
+            right_on=["first", "second"],
+            how="inner",
+            sort=False,
+        )
+
+        expected2 = merge(
+            to_join,
+            data,
+            right_on=["key1", "key2"],
+            left_index=True,
+            how="inner",
+            sort=False,
+        )
+        tm.assert_frame_equal(joined, expected2.reindex_like(joined))
+
+        expected2 = merge(
+            to_join,
+            data,
+            right_on=["key1", "key2"],
+            left_index=True,
+            how="inner",
+            sort=False,
+        )
+
+        expected = expected.drop(["first", "second"], axis=1)
+        expected.index = joined.index
+
+        assert joined.index.is_monotonic_increasing
+        tm.assert_frame_equal(joined, expected)
+
+        # _assert_same_contents(expected, expected2.loc[:, expected.columns])
+
+    def test_join_hierarchical_mixed_raises(self):
+        # GH 2024
+        # GH 40993: For raising, enforced in 2.0
+        df = DataFrame([(1, 2, 3), (4, 5, 6)], columns=["a", "b", "c"])
+        new_df = df.groupby(["a"]).agg({"b": ["mean", "sum"]})
+        other_df = DataFrame([(1, 2, 3), (7, 10, 6)], columns=["a", "b", "d"])
+        other_df.set_index("a", inplace=True)
+        # GH 9455, 12219
+        with pytest.raises(
+            pd.errors.MergeError, match="Not allowed to merge between different levels"
+        ):
+            merge(new_df, other_df, left_index=True, right_index=True)
+
+    def test_join_float64_float32(self):
+        a = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 2)),
+            columns=["a", "b"],
+            dtype=np.float64,
+        )
+        b = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 1)),
+            columns=["c"],
+            dtype=np.float32,
+        )
+        joined = a.join(b)
+        assert joined.dtypes["a"] == "float64"
+        assert joined.dtypes["b"] == "float64"
+        assert joined.dtypes["c"] == "float32"
+
+        a = np.random.default_rng(2).integers(0, 5, 100).astype("int64")
+        b = np.random.default_rng(2).random(100).astype("float64")
+        c = np.random.default_rng(2).random(100).astype("float32")
+        df = DataFrame({"a": a, "b": b, "c": c})
+        xpdf = DataFrame({"a": a, "b": b, "c": c})
+        s = DataFrame(
+            np.random.default_rng(2).random(5).astype("float32"), columns=["md"]
+        )
+        rs = df.merge(s, left_on="a", right_index=True)
+        assert rs.dtypes["a"] == "int64"
+        assert rs.dtypes["b"] == "float64"
+        assert rs.dtypes["c"] == "float32"
+        assert rs.dtypes["md"] == "float32"
+
+        xp = xpdf.merge(s, left_on="a", right_index=True)
+        tm.assert_frame_equal(rs, xp)
+
+    def test_join_many_non_unique_index(self):
+        df1 = DataFrame({"a": [1, 1], "b": [1, 1], "c": [10, 20]})
+        df2 = DataFrame({"a": [1, 1], "b": [1, 2], "d": [100, 200]})
+        df3 = DataFrame({"a": [1, 1], "b": [1, 2], "e": [1000, 2000]})
+        idf1 = df1.set_index(["a", "b"])
+        idf2 = df2.set_index(["a", "b"])
+        idf3 = df3.set_index(["a", "b"])
+
+        result = idf1.join([idf2, idf3], how="outer")
+
+        df_partially_merged = merge(df1, df2, on=["a", "b"], how="outer")
+        expected = merge(df_partially_merged, df3, on=["a", "b"], how="outer")
+
+        result = result.reset_index()
+        expected = expected[result.columns]
+        expected["a"] = expected.a.astype("int64")
+        expected["b"] = expected.b.astype("int64")
+        tm.assert_frame_equal(result, expected)
+
+        df1 = DataFrame({"a": [1, 1, 1], "b": [1, 1, 1], "c": [10, 20, 30]})
+        df2 = DataFrame({"a": [1, 1, 1], "b": [1, 1, 2], "d": [100, 200, 300]})
+        df3 = DataFrame({"a": [1, 1, 1], "b": [1, 1, 2], "e": [1000, 2000, 3000]})
+        idf1 = df1.set_index(["a", "b"])
+        idf2 = df2.set_index(["a", "b"])
+        idf3 = df3.set_index(["a", "b"])
+        result = idf1.join([idf2, idf3], how="inner")
+
+        df_partially_merged = merge(df1, df2, on=["a", "b"], how="inner")
+        expected = merge(df_partially_merged, df3, on=["a", "b"], how="inner")
+
+        result = result.reset_index()
+
+        tm.assert_frame_equal(result, expected.loc[:, result.columns])
+
+        # GH 11519
+        df = DataFrame(
+            {
+                "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
+                "B": ["one", "one", "two", "three", "two", "two", "one", "three"],
+                "C": np.random.default_rng(2).standard_normal(8),
+                "D": np.random.default_rng(2).standard_normal(8),
+            }
+        )
+        s = Series(
+            np.repeat(np.arange(8), 2), index=np.repeat(np.arange(8), 2), name="TEST"
+        )
+        inner = df.join(s, how="inner")
+        outer = df.join(s, how="outer")
+        left = df.join(s, how="left")
+        right = df.join(s, how="right")
+        tm.assert_frame_equal(inner, outer)
+        tm.assert_frame_equal(inner, left)
+        tm.assert_frame_equal(inner, right)
+
+    @pytest.mark.parametrize(
+        "infer_string", [False, pytest.param(True, marks=td.skip_if_no("pyarrow"))]
+    )
+    def test_join_sort(self, infer_string):
+        with option_context("future.infer_string", infer_string):
+            left = DataFrame(
+                {"key": ["foo", "bar", "baz", "foo"], "value": [1, 2, 3, 4]}
+            )
+            right = DataFrame({"value2": ["a", "b", "c"]}, index=["bar", "baz", "foo"])
+
+            joined = left.join(right, on="key", sort=True)
+            expected = DataFrame(
+                {
+                    "key": ["bar", "baz", "foo", "foo"],
+                    "value": [2, 3, 1, 4],
+                    "value2": ["a", "b", "c", "c"],
+                },
+                index=[1, 2, 0, 3],
+            )
+            tm.assert_frame_equal(joined, expected)
+
+            # smoke test
+            joined = left.join(right, on="key", sort=False)
+            tm.assert_index_equal(joined.index, Index(range(4)), exact=True)
+
+    def test_join_mixed_non_unique_index(self):
+        # GH 12814, unorderable types in py3 with a non-unique index
+        df1 = DataFrame({"a": [1, 2, 3, 4]}, index=[1, 2, 3, "a"])
+        df2 = DataFrame({"b": [5, 6, 7, 8]}, index=[1, 3, 3, 4])
+        result = df1.join(df2)
+        expected = DataFrame(
+            {"a": [1, 2, 3, 3, 4], "b": [5, np.nan, 6, 7, np.nan]},
+            index=[1, 2, 3, 3, "a"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+        df3 = DataFrame({"a": [1, 2, 3, 4]}, index=[1, 2, 2, "a"])
+        df4 = DataFrame({"b": [5, 6, 7, 8]}, index=[1, 2, 3, 4])
+        result = df3.join(df4)
+        expected = DataFrame(
+            {"a": [1, 2, 3, 4], "b": [5, 6, 6, np.nan]}, index=[1, 2, 2, "a"]
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_join_non_unique_period_index(self):
+        # GH #16871
+        index = pd.period_range("2016-01-01", periods=16, freq="M")
+        df = DataFrame(list(range(len(index))), index=index, columns=["pnum"])
+        df2 = concat([df, df])
+        result = df.join(df2, how="inner", rsuffix="_df2")
+        expected = DataFrame(
+            np.tile(np.arange(16, dtype=np.int64).repeat(2).reshape(-1, 1), 2),
+            columns=["pnum", "pnum_df2"],
+            index=df2.sort_index().index,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_mixed_type_join_with_suffix(self):
+        # GH #916
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((20, 6)),
+            columns=["a", "b", "c", "d", "e", "f"],
+        )
+        df.insert(0, "id", 0)
+        df.insert(5, "dt", "foo")
+
+        grouped = df.groupby("id")
+        msg = re.escape("agg function failed [how->mean,dtype->")
+        with pytest.raises(TypeError, match=msg):
+            grouped.mean()
+        mn = grouped.mean(numeric_only=True)
+        cn = grouped.count()
+
+        # it works!
+        mn.join(cn, rsuffix="_right")
+
+    def test_join_many(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 6)), columns=list("abcdef")
+        )
+        df_list = [df[["a", "b"]], df[["c", "d"]], df[["e", "f"]]]
+
+        joined = df_list[0].join(df_list[1:])
+        tm.assert_frame_equal(joined, df)
+
+        df_list = [df[["a", "b"]][:-2], df[["c", "d"]][2:], df[["e", "f"]][1:9]]
+
+        def _check_diff_index(df_list, result, exp_index):
+            reindexed = [x.reindex(exp_index) for x in df_list]
+            expected = reindexed[0].join(reindexed[1:])
+            tm.assert_frame_equal(result, expected)
+
+        # different join types
+        joined = df_list[0].join(df_list[1:], how="outer")
+        _check_diff_index(df_list, joined, df.index)
+
+        joined = df_list[0].join(df_list[1:])
+        _check_diff_index(df_list, joined, df_list[0].index)
+
+        joined = df_list[0].join(df_list[1:], how="inner")
+        _check_diff_index(df_list, joined, df.index[2:8])
+
+        msg = "Joining multiple DataFrames only supported for joining on index"
+        with pytest.raises(ValueError, match=msg):
+            df_list[0].join(df_list[1:], on="a")
+
+    def test_join_many_mixed(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((8, 4)),
+            columns=["A", "B", "C", "D"],
+        )
+        df["key"] = ["foo", "bar"] * 4
+        df1 = df.loc[:, ["A", "B"]]
+        df2 = df.loc[:, ["C", "D"]]
+        df3 = df.loc[:, ["key"]]
+
+        result = df1.join([df2, df3])
+        tm.assert_frame_equal(result, df)
+
+    def test_join_dups(self):
+        # joining dups
+        df = concat(
+            [
+                DataFrame(
+                    np.random.default_rng(2).standard_normal((10, 4)),
+                    columns=["A", "A", "B", "B"],
+                ),
+                DataFrame(
+                    np.random.default_rng(2).integers(0, 10, size=20).reshape(10, 2),
+                    columns=["A", "C"],
+                ),
+            ],
+            axis=1,
+        )
+
+        expected = concat([df, df], axis=1)
+        result = df.join(df, rsuffix="_2")
+        result.columns = expected.columns
+        tm.assert_frame_equal(result, expected)
+
+        # GH 4975, invalid join on dups
+        w = DataFrame(
+            np.random.default_rng(2).standard_normal((4, 2)), columns=["x", "y"]
+        )
+        x = DataFrame(
+            np.random.default_rng(2).standard_normal((4, 2)), columns=["x", "y"]
+        )
+        y = DataFrame(
+            np.random.default_rng(2).standard_normal((4, 2)), columns=["x", "y"]
+        )
+        z = DataFrame(
+            np.random.default_rng(2).standard_normal((4, 2)), columns=["x", "y"]
+        )
+
+        dta = x.merge(y, left_index=True, right_index=True).merge(
+            z, left_index=True, right_index=True, how="outer"
+        )
+        # GH 40991: As of 2.0 causes duplicate columns
+        with pytest.raises(
+            pd.errors.MergeError,
+            match="Passing 'suffixes' which cause duplicate columns",
+        ):
+            dta.merge(w, left_index=True, right_index=True)
+
+    def test_join_multi_to_multi(self, join_type):
+        # GH 20475
+        leftindex = MultiIndex.from_product(
+            [list("abc"), list("xy"), [1, 2]], names=["abc", "xy", "num"]
+        )
+        left = DataFrame({"v1": range(12)}, index=leftindex)
+
+        rightindex = MultiIndex.from_product(
+            [list("abc"), list("xy")], names=["abc", "xy"]
+        )
+        right = DataFrame({"v2": [100 * i for i in range(1, 7)]}, index=rightindex)
+
+        result = left.join(right, on=["abc", "xy"], how=join_type)
+        expected = (
+            left.reset_index()
+            .merge(right.reset_index(), on=["abc", "xy"], how=join_type)
+            .set_index(["abc", "xy", "num"])
+        )
+        tm.assert_frame_equal(expected, result)
+
+        msg = r'len\(left_on\) must equal the number of levels in the index of "right"'
+        with pytest.raises(ValueError, match=msg):
+            left.join(right, on="xy", how=join_type)
+
+        with pytest.raises(ValueError, match=msg):
+            right.join(left, on=["abc", "xy"], how=join_type)
+
+    def test_join_on_tz_aware_datetimeindex(self):
+        # GH 23931, 26335
+        df1 = DataFrame(
+            {
+                "date": pd.date_range(
+                    start="2018-01-01", periods=5, tz="America/Chicago"
+                ),
+                "vals": list("abcde"),
+            }
+        )
+
+        df2 = DataFrame(
+            {
+                "date": pd.date_range(
+                    start="2018-01-03", periods=5, tz="America/Chicago"
+                ),
+                "vals_2": list("tuvwx"),
+            }
+        )
+        result = df1.join(df2.set_index("date"), on="date")
+        expected = df1.copy()
+        expected["vals_2"] = Series([np.nan] * 2 + list("tuv"))
+        tm.assert_frame_equal(result, expected)
+
+    def test_join_datetime_string(self):
+        # GH 5647
+        dfa = DataFrame(
+            [
+                ["2012-08-02", "L", 10],
+                ["2012-08-02", "J", 15],
+                ["2013-04-06", "L", 20],
+                ["2013-04-06", "J", 25],
+            ],
+            columns=["x", "y", "a"],
+        )
+        dfa["x"] = pd.to_datetime(dfa["x"]).astype("M8[ns]")
+        dfb = DataFrame(
+            [["2012-08-02", "J", 1], ["2013-04-06", "L", 2]],
+            columns=["x", "y", "z"],
+            index=[2, 4],
+        )
+        dfb["x"] = pd.to_datetime(dfb["x"]).astype("M8[ns]")
+        result = dfb.join(dfa.set_index(["x", "y"]), on=["x", "y"])
+        expected = DataFrame(
+            [
+                [Timestamp("2012-08-02 00:00:00"), "J", 1, 15],
+                [Timestamp("2013-04-06 00:00:00"), "L", 2, 20],
+            ],
+            index=[2, 4],
+            columns=["x", "y", "z", "a"],
+        )
+        expected["x"] = expected["x"].astype("M8[ns]")
+        tm.assert_frame_equal(result, expected)
+
+    def test_join_with_categorical_index(self):
+        # GH47812
+        ix = ["a", "b"]
+        id1 = pd.CategoricalIndex(ix, categories=ix)
+        id2 = pd.CategoricalIndex(reversed(ix), categories=reversed(ix))
+
+        df1 = DataFrame({"c1": ix}, index=id1)
+        df2 = DataFrame({"c2": reversed(ix)}, index=id2)
+        result = df1.join(df2)
+        expected = DataFrame(
+            {"c1": ["a", "b"], "c2": ["a", "b"]},
+            index=pd.CategoricalIndex(["a", "b"], categories=["a", "b"]),
+        )
+        tm.assert_frame_equal(result, expected)
+
+
+def _check_join(left, right, result, join_col, how="left", lsuffix="_x", rsuffix="_y"):
+    # some smoke tests
+    for c in join_col:
+        assert result[c].notna().all()
+
+    left_grouped = left.groupby(join_col)
+    right_grouped = right.groupby(join_col)
+
+    for group_key, group in result.groupby(join_col):
+        l_joined = _restrict_to_columns(group, left.columns, lsuffix)
+        r_joined = _restrict_to_columns(group, right.columns, rsuffix)
+
+        try:
+            lgroup = left_grouped.get_group(group_key)
+        except KeyError as err:
+            if how in ("left", "inner"):
+                raise AssertionError(
+                    f"key {group_key} should not have been in the join"
+                ) from err
+
+            _assert_all_na(l_joined, left.columns, join_col)
+        else:
+            _assert_same_contents(l_joined, lgroup)
+
+        try:
+            rgroup = right_grouped.get_group(group_key)
+        except KeyError as err:
+            if how in ("right", "inner"):
+                raise AssertionError(
+                    f"key {group_key} should not have been in the join"
+                ) from err
+
+            _assert_all_na(r_joined, right.columns, join_col)
+        else:
+            _assert_same_contents(r_joined, rgroup)
+
+
+def _restrict_to_columns(group, columns, suffix):
+    found = [
+        c for c in group.columns if c in columns or c.replace(suffix, "") in columns
+    ]
+
+    # filter
+    group = group.loc[:, found]
+
+    # get rid of suffixes, if any
+    group = group.rename(columns=lambda x: x.replace(suffix, ""))
+
+    # put in the right order...
+    group = group.loc[:, columns]
+
+    return group
+
+
+def _assert_same_contents(join_chunk, source):
+    NA_SENTINEL = -1234567  # drop_duplicates not so NA-friendly...
+
+    jvalues = join_chunk.fillna(NA_SENTINEL).drop_duplicates().values
+    svalues = source.fillna(NA_SENTINEL).drop_duplicates().values
+
+    rows = {tuple(row) for row in jvalues}
+    assert len(rows) == len(source)
+    assert all(tuple(row) in rows for row in svalues)
+
+
+def _assert_all_na(join_chunk, source_columns, join_col):
+    for c in source_columns:
+        if c in join_col:
+            continue
+        assert join_chunk[c].isna().all()
+
+
+def _join_by_hand(a, b, how="left"):
+    join_index = a.index.join(b.index, how=how)
+
+    a_re = a.reindex(join_index)
+    b_re = b.reindex(join_index)
+
+    result_columns = a.columns.append(b.columns)
+
+    for col, s in b_re.items():
+        a_re[col] = s
+    return a_re.reindex(columns=result_columns)
+
+
+def test_join_inner_multiindex_deterministic_order():
+    # GH: 36910
+    left = DataFrame(
+        data={"e": 5},
+        index=MultiIndex.from_tuples([(1, 2, 4)], names=("a", "b", "d")),
+    )
+    right = DataFrame(
+        data={"f": 6}, index=MultiIndex.from_tuples([(2, 3)], names=("b", "c"))
+    )
+    result = left.join(right, how="inner")
+    expected = DataFrame(
+        {"e": [5], "f": [6]},
+        index=MultiIndex.from_tuples([(1, 2, 4, 3)], names=("a", "b", "d", "c")),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    ("input_col", "output_cols"), [("b", ["a", "b"]), ("a", ["a_x", "a_y"])]
+)
+def test_join_cross(input_col, output_cols):
+    # GH#5401
+    left = DataFrame({"a": [1, 3]})
+    right = DataFrame({input_col: [3, 4]})
+    result = left.join(right, how="cross", lsuffix="_x", rsuffix="_y")
+    expected = DataFrame({output_cols[0]: [1, 1, 3, 3], output_cols[1]: [3, 4, 3, 4]})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_join_multiindex_one_level(join_type):
+    # GH#36909
+    left = DataFrame(
+        data={"c": 3}, index=MultiIndex.from_tuples([(1, 2)], names=("a", "b"))
+    )
+    right = DataFrame(data={"d": 4}, index=MultiIndex.from_tuples([(2,)], names=("b",)))
+    result = left.join(right, how=join_type)
+    if join_type == "right":
+        expected = DataFrame(
+            {"c": [3], "d": [4]},
+            index=MultiIndex.from_tuples([(2, 1)], names=["b", "a"]),
+        )
+    else:
+        expected = DataFrame(
+            {"c": [3], "d": [4]},
+            index=MultiIndex.from_tuples([(1, 2)], names=["a", "b"]),
+        )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "categories, values",
+    [
+        (["Y", "X"], ["Y", "X", "X"]),
+        ([2, 1], [2, 1, 1]),
+        ([2.5, 1.5], [2.5, 1.5, 1.5]),
+        (
+            [Timestamp("2020-12-31"), Timestamp("2019-12-31")],
+            [Timestamp("2020-12-31"), Timestamp("2019-12-31"), Timestamp("2019-12-31")],
+        ),
+    ],
+)
+def test_join_multiindex_not_alphabetical_categorical(categories, values):
+    # GH#38502
+    left = DataFrame(
+        {
+            "first": ["A", "A"],
+            "second": Categorical(categories, categories=categories),
+            "value": [1, 2],
+        }
+    ).set_index(["first", "second"])
+    right = DataFrame(
+        {
+            "first": ["A", "A", "B"],
+            "second": Categorical(values, categories=categories),
+            "value": [3, 4, 5],
+        }
+    ).set_index(["first", "second"])
+    result = left.join(right, lsuffix="_left", rsuffix="_right")
+
+    expected = DataFrame(
+        {
+            "first": ["A", "A"],
+            "second": Categorical(categories, categories=categories),
+            "value_left": [1, 2],
+            "value_right": [3, 4],
+        }
+    ).set_index(["first", "second"])
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "left_empty, how, exp",
+    [
+        (False, "left", "left"),
+        (False, "right", "empty"),
+        (False, "inner", "empty"),
+        (False, "outer", "left"),
+        (False, "cross", "empty"),
+        (True, "left", "empty"),
+        (True, "right", "right"),
+        (True, "inner", "empty"),
+        (True, "outer", "right"),
+        (True, "cross", "empty"),
+    ],
+)
+def test_join_empty(left_empty, how, exp):
+    left = DataFrame({"A": [2, 1], "B": [3, 4]}, dtype="int64").set_index("A")
+    right = DataFrame({"A": [1], "C": [5]}, dtype="int64").set_index("A")
+
+    if left_empty:
+        left = left.head(0)
+    else:
+        right = right.head(0)
+
+    result = left.join(right, how=how)
+
+    if exp == "left":
+        expected = DataFrame({"A": [2, 1], "B": [3, 4], "C": [np.nan, np.nan]})
+        expected = expected.set_index("A")
+    elif exp == "right":
+        expected = DataFrame({"B": [np.nan], "A": [1], "C": [5]})
+        expected = expected.set_index("A")
+    elif exp == "empty":
+        expected = DataFrame(columns=["B", "C"], dtype="int64")
+        if how != "cross":
+            expected = expected.rename_axis("A")
+    if how == "outer":
+        expected = expected.sort_index()
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_join_empty_uncomparable_columns():
+    # GH 57048
+    df1 = DataFrame()
+    df2 = DataFrame(columns=["test"])
+    df3 = DataFrame(columns=["foo", ("bar", "baz")])
+
+    result = df1 + df2
+    expected = DataFrame(columns=["test"])
+    tm.assert_frame_equal(result, expected)
+
+    result = df2 + df3
+    expected = DataFrame(columns=[("bar", "baz"), "foo", "test"])
+    tm.assert_frame_equal(result, expected)
+
+    result = df1 + df3
+    expected = DataFrame(columns=[("bar", "baz"), "foo"])
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "how, values",
+    [
+        ("inner", [0, 1, 2]),
+        ("outer", [0, 1, 2]),
+        ("left", [0, 1, 2]),
+        ("right", [0, 2, 1]),
+    ],
+)
+def test_join_multiindex_categorical_output_index_dtype(how, values):
+    # GH#50906
+    df1 = DataFrame(
+        {
+            "a": Categorical([0, 1, 2]),
+            "b": Categorical([0, 1, 2]),
+            "c": [0, 1, 2],
+        }
+    ).set_index(["a", "b"])
+
+    df2 = DataFrame(
+        {
+            "a": Categorical([0, 2, 1]),
+            "b": Categorical([0, 2, 1]),
+            "d": [0, 2, 1],
+        }
+    ).set_index(["a", "b"])
+
+    expected = DataFrame(
+        {
+            "a": Categorical(values),
+            "b": Categorical(values),
+            "c": values,
+            "d": values,
+        }
+    ).set_index(["a", "b"])
+
+    result = df1.join(df2, how=how)
+    tm.assert_frame_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_merge.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_merge.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed49f3b758cc5cd826a804c8de611681a3e0343c
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_merge.py
@@ -0,0 +1,3020 @@
+from datetime import (
+    date,
+    datetime,
+    timedelta,
+)
+import re
+
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.common import (
+    is_object_dtype,
+    is_string_dtype,
+)
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    CategoricalIndex,
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    IntervalIndex,
+    MultiIndex,
+    PeriodIndex,
+    RangeIndex,
+    Series,
+    TimedeltaIndex,
+)
+import pandas._testing as tm
+from pandas.core.reshape.concat import concat
+from pandas.core.reshape.merge import (
+    MergeError,
+    merge,
+)
+
+
+def get_test_data(ngroups=8, n=50):
+    unique_groups = list(range(ngroups))
+    arr = np.asarray(np.tile(unique_groups, n // ngroups))
+
+    if len(arr) < n:
+        arr = np.asarray(list(arr) + unique_groups[: n - len(arr)])
+
+    np.random.default_rng(2).shuffle(arr)
+    return arr
+
+
+def get_series():
+    return [
+        Series([1], dtype="int64"),
+        Series([1], dtype="Int64"),
+        Series([1.23]),
+        Series(["foo"]),
+        Series([True]),
+        Series([pd.Timestamp("2018-01-01")]),
+        Series([pd.Timestamp("2018-01-01", tz="US/Eastern")]),
+    ]
+
+
+def get_series_na():
+    return [
+        Series([np.nan], dtype="Int64"),
+        Series([np.nan], dtype="float"),
+        Series([np.nan], dtype="object"),
+        Series([pd.NaT]),
+    ]
+
+
+@pytest.fixture(params=get_series(), ids=lambda x: x.dtype.name)
+def series_of_dtype(request):
+    """
+    A parametrized fixture returning a variety of Series of different
+    dtypes
+    """
+    return request.param
+
+
+@pytest.fixture(params=get_series(), ids=lambda x: x.dtype.name)
+def series_of_dtype2(request):
+    """
+    A duplicate of the series_of_dtype fixture, so that it can be used
+    twice by a single function
+    """
+    return request.param
+
+
+@pytest.fixture(params=get_series_na(), ids=lambda x: x.dtype.name)
+def series_of_dtype_all_na(request):
+    """
+    A parametrized fixture returning a variety of Series with all NA
+    values
+    """
+    return request.param
+
+
+@pytest.fixture
+def dfs_for_indicator():
+    df1 = DataFrame({"col1": [0, 1], "col_conflict": [1, 2], "col_left": ["a", "b"]})
+    df2 = DataFrame(
+        {
+            "col1": [1, 2, 3, 4, 5],
+            "col_conflict": [1, 2, 3, 4, 5],
+            "col_right": [2, 2, 2, 2, 2],
+        }
+    )
+    return df1, df2
+
+
+class TestMerge:
+    @pytest.fixture
+    def df(self):
+        df = DataFrame(
+            {
+                "key1": get_test_data(),
+                "key2": get_test_data(),
+                "data1": np.random.default_rng(2).standard_normal(50),
+                "data2": np.random.default_rng(2).standard_normal(50),
+            }
+        )
+
+        # exclude a couple keys for fun
+        df = df[df["key2"] > 1]
+        return df
+
+    @pytest.fixture
+    def df2(self):
+        return DataFrame(
+            {
+                "key1": get_test_data(n=10),
+                "key2": get_test_data(ngroups=4, n=10),
+                "value": np.random.default_rng(2).standard_normal(10),
+            }
+        )
+
+    @pytest.fixture
+    def left(self):
+        return DataFrame(
+            {
+                "key": ["a", "b", "c", "d", "e", "e", "a"],
+                "v1": np.random.default_rng(2).standard_normal(7),
+            }
+        )
+
+    @pytest.fixture
+    def right(self):
+        return DataFrame(
+            {"v2": np.random.default_rng(2).standard_normal(4)},
+            index=["d", "b", "c", "a"],
+        )
+
+    def test_merge_inner_join_empty(self):
+        # GH 15328
+        df_empty = DataFrame()
+        df_a = DataFrame({"a": [1, 2]}, index=[0, 1], dtype="int64")
+        result = merge(df_empty, df_a, left_index=True, right_index=True)
+        expected = DataFrame({"a": []}, dtype="int64")
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_common(self, df, df2):
+        joined = merge(df, df2)
+        exp = merge(df, df2, on=["key1", "key2"])
+        tm.assert_frame_equal(joined, exp)
+
+    def test_merge_non_string_columns(self):
+        # https://github.com/pandas-dev/pandas/issues/17962
+        # Checks that method runs for non string column names
+        left = DataFrame(
+            {0: [1, 0, 1, 0], 1: [0, 1, 0, 0], 2: [0, 0, 2, 0], 3: [1, 0, 0, 3]}
+        )
+
+        right = left.astype(float)
+        expected = left
+        result = merge(left, right)
+        tm.assert_frame_equal(expected, result)
+
+    def test_merge_index_as_on_arg(self, df, df2):
+        # GH14355
+
+        left = df.set_index("key1")
+        right = df2.set_index("key1")
+        result = merge(left, right, on="key1")
+        expected = merge(df, df2, on="key1").set_index("key1")
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_index_singlekey_right_vs_left(self):
+        left = DataFrame(
+            {
+                "key": ["a", "b", "c", "d", "e", "e", "a"],
+                "v1": np.random.default_rng(2).standard_normal(7),
+            }
+        )
+        right = DataFrame(
+            {"v2": np.random.default_rng(2).standard_normal(4)},
+            index=["d", "b", "c", "a"],
+        )
+
+        merged1 = merge(
+            left, right, left_on="key", right_index=True, how="left", sort=False
+        )
+        merged2 = merge(
+            right, left, right_on="key", left_index=True, how="right", sort=False
+        )
+        tm.assert_frame_equal(merged1, merged2.loc[:, merged1.columns])
+
+        merged1 = merge(
+            left, right, left_on="key", right_index=True, how="left", sort=True
+        )
+        merged2 = merge(
+            right, left, right_on="key", left_index=True, how="right", sort=True
+        )
+        tm.assert_frame_equal(merged1, merged2.loc[:, merged1.columns])
+
+    def test_merge_index_singlekey_inner(self):
+        left = DataFrame(
+            {
+                "key": ["a", "b", "c", "d", "e", "e", "a"],
+                "v1": np.random.default_rng(2).standard_normal(7),
+            }
+        )
+        right = DataFrame(
+            {"v2": np.random.default_rng(2).standard_normal(4)},
+            index=["d", "b", "c", "a"],
+        )
+
+        # inner join
+        result = merge(left, right, left_on="key", right_index=True, how="inner")
+        expected = left.join(right, on="key").loc[result.index]
+        tm.assert_frame_equal(result, expected)
+
+        result = merge(right, left, right_on="key", left_index=True, how="inner")
+        expected = left.join(right, on="key").loc[result.index]
+        tm.assert_frame_equal(result, expected.loc[:, result.columns])
+
+    def test_merge_misspecified(self, df, df2, left, right):
+        msg = "Must pass right_on or right_index=True"
+        with pytest.raises(pd.errors.MergeError, match=msg):
+            merge(left, right, left_index=True)
+        msg = "Must pass left_on or left_index=True"
+        with pytest.raises(pd.errors.MergeError, match=msg):
+            merge(left, right, right_index=True)
+
+        msg = (
+            'Can only pass argument "on" OR "left_on" and "right_on", not '
+            "a combination of both"
+        )
+        with pytest.raises(pd.errors.MergeError, match=msg):
+            merge(left, left, left_on="key", on="key")
+
+        msg = r"len\(right_on\) must equal len\(left_on\)"
+        with pytest.raises(ValueError, match=msg):
+            merge(df, df2, left_on=["key1"], right_on=["key1", "key2"])
+
+    def test_index_and_on_parameters_confusion(self, df, df2):
+        msg = "right_index parameter must be of type bool, not <class 'list'>"
+        with pytest.raises(ValueError, match=msg):
+            merge(
+                df,
+                df2,
+                how="left",
+                left_index=False,
+                right_index=["key1", "key2"],
+            )
+        msg = "left_index parameter must be of type bool, not <class 'list'>"
+        with pytest.raises(ValueError, match=msg):
+            merge(
+                df,
+                df2,
+                how="left",
+                left_index=["key1", "key2"],
+                right_index=False,
+            )
+        with pytest.raises(ValueError, match=msg):
+            merge(
+                df,
+                df2,
+                how="left",
+                left_index=["key1", "key2"],
+                right_index=["key1", "key2"],
+            )
+
+    def test_merge_overlap(self, left):
+        merged = merge(left, left, on="key")
+        exp_len = (left["key"].value_counts() ** 2).sum()
+        assert len(merged) == exp_len
+        assert "v1_x" in merged
+        assert "v1_y" in merged
+
+    def test_merge_different_column_key_names(self):
+        left = DataFrame({"lkey": ["foo", "bar", "baz", "foo"], "value": [1, 2, 3, 4]})
+        right = DataFrame({"rkey": ["foo", "bar", "qux", "foo"], "value": [5, 6, 7, 8]})
+
+        merged = left.merge(
+            right, left_on="lkey", right_on="rkey", how="outer", sort=True
+        )
+
+        exp = Series(["bar", "baz", "foo", "foo", "foo", "foo", np.nan], name="lkey")
+        tm.assert_series_equal(merged["lkey"], exp)
+
+        exp = Series(["bar", np.nan, "foo", "foo", "foo", "foo", "qux"], name="rkey")
+        tm.assert_series_equal(merged["rkey"], exp)
+
+        exp = Series([2, 3, 1, 1, 4, 4, np.nan], name="value_x")
+        tm.assert_series_equal(merged["value_x"], exp)
+
+        exp = Series([6, np.nan, 5, 8, 5, 8, 7], name="value_y")
+        tm.assert_series_equal(merged["value_y"], exp)
+
+    def test_merge_copy(self):
+        left = DataFrame({"a": 0, "b": 1}, index=range(10))
+        right = DataFrame({"c": "foo", "d": "bar"}, index=range(10))
+
+        merged = merge(left, right, left_index=True, right_index=True, copy=True)
+
+        merged["a"] = 6
+        assert (left["a"] == 0).all()
+
+        merged["d"] = "peekaboo"
+        assert (right["d"] == "bar").all()
+
+    def test_merge_nocopy(self, using_array_manager, using_infer_string):
+        left = DataFrame({"a": 0, "b": 1}, index=range(10))
+        right = DataFrame({"c": "foo", "d": "bar"}, index=range(10))
+
+        merged = merge(left, right, left_index=True, right_index=True, copy=False)
+
+        assert np.shares_memory(merged["a"]._values, left["a"]._values)
+        if not using_infer_string:
+            assert np.shares_memory(merged["d"]._values, right["d"]._values)
+
+    def test_intelligently_handle_join_key(self):
+        # #733, be a bit more 1337 about not returning unconsolidated DataFrame
+
+        left = DataFrame(
+            {"key": [1, 1, 2, 2, 3], "value": list(range(5))}, columns=["value", "key"]
+        )
+        right = DataFrame({"key": [1, 1, 2, 3, 4, 5], "rvalue": list(range(6))})
+
+        joined = merge(left, right, on="key", how="outer")
+        expected = DataFrame(
+            {
+                "key": [1, 1, 1, 1, 2, 2, 3, 4, 5],
+                "value": np.array([0, 0, 1, 1, 2, 3, 4, np.nan, np.nan]),
+                "rvalue": [0, 1, 0, 1, 2, 2, 3, 4, 5],
+            },
+            columns=["value", "key", "rvalue"],
+        )
+        tm.assert_frame_equal(joined, expected)
+
+    def test_merge_join_key_dtype_cast(self):
+        # #8596
+
+        df1 = DataFrame({"key": [1], "v1": [10]})
+        df2 = DataFrame({"key": [2], "v1": [20]})
+        df = merge(df1, df2, how="outer")
+        assert df["key"].dtype == "int64"
+
+        df1 = DataFrame({"key": [True], "v1": [1]})
+        df2 = DataFrame({"key": [False], "v1": [0]})
+        df = merge(df1, df2, how="outer")
+
+        # GH13169
+        # GH#40073
+        assert df["key"].dtype == "bool"
+
+        df1 = DataFrame({"val": [1]})
+        df2 = DataFrame({"val": [2]})
+        lkey = np.array([1])
+        rkey = np.array([2])
+        df = merge(df1, df2, left_on=lkey, right_on=rkey, how="outer")
+        assert df["key_0"].dtype == np.dtype(int)
+
+    def test_handle_join_key_pass_array(self):
+        left = DataFrame(
+            {"key": [1, 1, 2, 2, 3], "value": np.arange(5)},
+            columns=["value", "key"],
+            dtype="int64",
+        )
+        right = DataFrame({"rvalue": np.arange(6)}, dtype="int64")
+        key = np.array([1, 1, 2, 3, 4, 5], dtype="int64")
+
+        merged = merge(left, right, left_on="key", right_on=key, how="outer")
+        merged2 = merge(right, left, left_on=key, right_on="key", how="outer")
+
+        tm.assert_series_equal(merged["key"], merged2["key"])
+        assert merged["key"].notna().all()
+        assert merged2["key"].notna().all()
+
+        left = DataFrame({"value": np.arange(5)}, columns=["value"])
+        right = DataFrame({"rvalue": np.arange(6)})
+        lkey = np.array([1, 1, 2, 2, 3])
+        rkey = np.array([1, 1, 2, 3, 4, 5])
+
+        merged = merge(left, right, left_on=lkey, right_on=rkey, how="outer")
+        expected = Series([1, 1, 1, 1, 2, 2, 3, 4, 5], dtype=int, name="key_0")
+        tm.assert_series_equal(merged["key_0"], expected)
+
+        left = DataFrame({"value": np.arange(3)})
+        right = DataFrame({"rvalue": np.arange(6)})
+
+        key = np.array([0, 1, 1, 2, 2, 3], dtype=np.int64)
+        merged = merge(left, right, left_index=True, right_on=key, how="outer")
+        tm.assert_series_equal(merged["key_0"], Series(key, name="key_0"))
+
+    def test_no_overlap_more_informative_error(self):
+        dt = datetime.now()
+        df1 = DataFrame({"x": ["a"]}, index=[dt])
+
+        df2 = DataFrame({"y": ["b", "c"]}, index=[dt, dt])
+
+        msg = (
+            "No common columns to perform merge on. "
+            f"Merge options: left_on={None}, right_on={None}, "
+            f"left_index={False}, right_index={False}"
+        )
+
+        with pytest.raises(MergeError, match=msg):
+            merge(df1, df2)
+
+    def test_merge_non_unique_indexes(self):
+        dt = datetime(2012, 5, 1)
+        dt2 = datetime(2012, 5, 2)
+        dt3 = datetime(2012, 5, 3)
+        dt4 = datetime(2012, 5, 4)
+
+        df1 = DataFrame({"x": ["a"]}, index=[dt])
+        df2 = DataFrame({"y": ["b", "c"]}, index=[dt, dt])
+        _check_merge(df1, df2)
+
+        # Not monotonic
+        df1 = DataFrame({"x": ["a", "b", "q"]}, index=[dt2, dt, dt4])
+        df2 = DataFrame(
+            {"y": ["c", "d", "e", "f", "g", "h"]}, index=[dt3, dt3, dt2, dt2, dt, dt]
+        )
+        _check_merge(df1, df2)
+
+        df1 = DataFrame({"x": ["a", "b"]}, index=[dt, dt])
+        df2 = DataFrame({"y": ["c", "d"]}, index=[dt, dt])
+        _check_merge(df1, df2)
+
+    def test_merge_non_unique_index_many_to_many(self):
+        dt = datetime(2012, 5, 1)
+        dt2 = datetime(2012, 5, 2)
+        dt3 = datetime(2012, 5, 3)
+        df1 = DataFrame({"x": ["a", "b", "c", "d"]}, index=[dt2, dt2, dt, dt])
+        df2 = DataFrame(
+            {"y": ["e", "f", "g", " h", "i"]}, index=[dt2, dt2, dt3, dt, dt]
+        )
+        _check_merge(df1, df2)
+
+    def test_left_merge_empty_dataframe(self):
+        left = DataFrame({"key": [1], "value": [2]})
+        right = DataFrame({"key": []})
+
+        result = merge(left, right, on="key", how="left")
+        tm.assert_frame_equal(result, left)
+
+        result = merge(right, left, on="key", how="right")
+        tm.assert_frame_equal(result, left)
+
+    @pytest.mark.parametrize("how", ["inner", "left", "right", "outer"])
+    def test_merge_empty_dataframe(self, index, how):
+        # GH52777
+        left = DataFrame([], index=index[:0])
+        right = left.copy()
+
+        result = left.join(right, how=how)
+        tm.assert_frame_equal(result, left)
+
+    @pytest.mark.parametrize(
+        "kwarg",
+        [
+            {"left_index": True, "right_index": True},
+            {"left_index": True, "right_on": "x"},
+            {"left_on": "a", "right_index": True},
+            {"left_on": "a", "right_on": "x"},
+        ],
+    )
+    def test_merge_left_empty_right_empty(self, join_type, kwarg):
+        # GH 10824
+        left = DataFrame(columns=["a", "b", "c"])
+        right = DataFrame(columns=["x", "y", "z"])
+
+        exp_in = DataFrame(columns=["a", "b", "c", "x", "y", "z"], dtype=object)
+
+        result = merge(left, right, how=join_type, **kwarg)
+        tm.assert_frame_equal(result, exp_in)
+
+    def test_merge_left_empty_right_notempty(self):
+        # GH 10824
+        left = DataFrame(columns=["a", "b", "c"])
+        right = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=["x", "y", "z"])
+
+        exp_out = DataFrame(
+            {
+                "a": np.array([np.nan] * 3, dtype=object),
+                "b": np.array([np.nan] * 3, dtype=object),
+                "c": np.array([np.nan] * 3, dtype=object),
+                "x": [1, 4, 7],
+                "y": [2, 5, 8],
+                "z": [3, 6, 9],
+            },
+            columns=["a", "b", "c", "x", "y", "z"],
+        )
+        exp_in = exp_out[0:0]  # make empty DataFrame keeping dtype
+
+        def check1(exp, kwarg):
+            result = merge(left, right, how="inner", **kwarg)
+            tm.assert_frame_equal(result, exp)
+            result = merge(left, right, how="left", **kwarg)
+            tm.assert_frame_equal(result, exp)
+
+        def check2(exp, kwarg):
+            result = merge(left, right, how="right", **kwarg)
+            tm.assert_frame_equal(result, exp)
+            result = merge(left, right, how="outer", **kwarg)
+            tm.assert_frame_equal(result, exp)
+
+        for kwarg in [
+            {"left_index": True, "right_index": True},
+            {"left_index": True, "right_on": "x"},
+        ]:
+            check1(exp_in, kwarg)
+            check2(exp_out, kwarg)
+
+        kwarg = {"left_on": "a", "right_index": True}
+        check1(exp_in, kwarg)
+        exp_out["a"] = [0, 1, 2]
+        check2(exp_out, kwarg)
+
+        kwarg = {"left_on": "a", "right_on": "x"}
+        check1(exp_in, kwarg)
+        exp_out["a"] = np.array([np.nan] * 3, dtype=object)
+        check2(exp_out, kwarg)
+
+    def test_merge_left_notempty_right_empty(self):
+        # GH 10824
+        left = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=["a", "b", "c"])
+        right = DataFrame(columns=["x", "y", "z"])
+
+        exp_out = DataFrame(
+            {
+                "a": [1, 4, 7],
+                "b": [2, 5, 8],
+                "c": [3, 6, 9],
+                "x": np.array([np.nan] * 3, dtype=object),
+                "y": np.array([np.nan] * 3, dtype=object),
+                "z": np.array([np.nan] * 3, dtype=object),
+            },
+            columns=["a", "b", "c", "x", "y", "z"],
+        )
+        exp_in = exp_out[0:0]  # make empty DataFrame keeping dtype
+        # result will have object dtype
+        exp_in.index = exp_in.index.astype(object)
+
+        def check1(exp, kwarg):
+            result = merge(left, right, how="inner", **kwarg)
+            tm.assert_frame_equal(result, exp)
+            result = merge(left, right, how="right", **kwarg)
+            tm.assert_frame_equal(result, exp)
+
+        def check2(exp, kwarg):
+            result = merge(left, right, how="left", **kwarg)
+            tm.assert_frame_equal(result, exp)
+            result = merge(left, right, how="outer", **kwarg)
+            tm.assert_frame_equal(result, exp)
+
+            # TODO: should the next loop be un-indented? doing so breaks this test
+            for kwarg in [
+                {"left_index": True, "right_index": True},
+                {"left_index": True, "right_on": "x"},
+                {"left_on": "a", "right_index": True},
+                {"left_on": "a", "right_on": "x"},
+            ]:
+                check1(exp_in, kwarg)
+                check2(exp_out, kwarg)
+
+    def test_merge_empty_frame(self, series_of_dtype, series_of_dtype2):
+        # GH 25183
+        df = DataFrame(
+            {"key": series_of_dtype, "value": series_of_dtype2},
+            columns=["key", "value"],
+        )
+        df_empty = df[:0]
+        expected = DataFrame(
+            {
+                "key": Series(dtype=df.dtypes["key"]),
+                "value_x": Series(dtype=df.dtypes["value"]),
+                "value_y": Series(dtype=df.dtypes["value"]),
+            },
+            columns=["key", "value_x", "value_y"],
+        )
+        actual = df_empty.merge(df, on="key")
+        tm.assert_frame_equal(actual, expected)
+
+    def test_merge_all_na_column(self, series_of_dtype, series_of_dtype_all_na):
+        # GH 25183
+        df_left = DataFrame(
+            {"key": series_of_dtype, "value": series_of_dtype_all_na},
+            columns=["key", "value"],
+        )
+        df_right = DataFrame(
+            {"key": series_of_dtype, "value": series_of_dtype_all_na},
+            columns=["key", "value"],
+        )
+        expected = DataFrame(
+            {
+                "key": series_of_dtype,
+                "value_x": series_of_dtype_all_na,
+                "value_y": series_of_dtype_all_na,
+            },
+            columns=["key", "value_x", "value_y"],
+        )
+        actual = df_left.merge(df_right, on="key")
+        tm.assert_frame_equal(actual, expected)
+
+    def test_merge_nosort(self):
+        # GH#2098
+
+        d = {
+            "var1": np.random.default_rng(2).integers(0, 10, size=10),
+            "var2": np.random.default_rng(2).integers(0, 10, size=10),
+            "var3": [
+                datetime(2012, 1, 12),
+                datetime(2011, 2, 4),
+                datetime(2010, 2, 3),
+                datetime(2012, 1, 12),
+                datetime(2011, 2, 4),
+                datetime(2012, 4, 3),
+                datetime(2012, 3, 4),
+                datetime(2008, 5, 1),
+                datetime(2010, 2, 3),
+                datetime(2012, 2, 3),
+            ],
+        }
+        df = DataFrame.from_dict(d)
+        var3 = df.var3.unique()
+        var3 = np.sort(var3)
+        new = DataFrame.from_dict(
+            {"var3": var3, "var8": np.random.default_rng(2).random(7)}
+        )
+
+        result = df.merge(new, on="var3", sort=False)
+        exp = merge(df, new, on="var3", sort=False)
+        tm.assert_frame_equal(result, exp)
+
+        assert (df.var3.unique() == result.var3.unique()).all()
+
+    @pytest.mark.parametrize(
+        ("sort", "values"), [(False, [1, 1, 0, 1, 1]), (True, [0, 1, 1, 1, 1])]
+    )
+    @pytest.mark.parametrize("how", ["left", "right"])
+    def test_merge_same_order_left_right(self, sort, values, how):
+        # GH#35382
+        df = DataFrame({"a": [1, 0, 1]})
+
+        result = df.merge(df, on="a", how=how, sort=sort)
+        expected = DataFrame(values, columns=["a"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_nan_right(self):
+        df1 = DataFrame({"i1": [0, 1], "i2": [0, 1]})
+        df2 = DataFrame({"i1": [0], "i3": [0]})
+        result = df1.join(df2, on="i1", rsuffix="_")
+        expected = (
+            DataFrame(
+                {
+                    "i1": {0: 0.0, 1: 1},
+                    "i2": {0: 0, 1: 1},
+                    "i1_": {0: 0, 1: np.nan},
+                    "i3": {0: 0.0, 1: np.nan},
+                    None: {0: 0, 1: 0},
+                },
+                columns=Index(["i1", "i2", "i1_", "i3", None], dtype=object),
+            )
+            .set_index(None)
+            .reset_index()[["i1", "i2", "i1_", "i3"]]
+        )
+        result.columns = result.columns.astype("object")
+        tm.assert_frame_equal(result, expected, check_dtype=False)
+
+    def test_merge_nan_right2(self):
+        df1 = DataFrame({"i1": [0, 1], "i2": [0.5, 1.5]})
+        df2 = DataFrame({"i1": [0], "i3": [0.7]})
+        result = df1.join(df2, rsuffix="_", on="i1")
+        expected = DataFrame(
+            {
+                "i1": {0: 0, 1: 1},
+                "i1_": {0: 0.0, 1: np.nan},
+                "i2": {0: 0.5, 1: 1.5},
+                "i3": {0: 0.69999999999999996, 1: np.nan},
+            }
+        )[["i1", "i2", "i1_", "i3"]]
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:Passing a BlockManager|Passing a SingleBlockManager:DeprecationWarning"
+    )
+    def test_merge_type(self, df, df2):
+        class NotADataFrame(DataFrame):
+            @property
+            def _constructor(self):
+                return NotADataFrame
+
+        nad = NotADataFrame(df)
+        result = nad.merge(df2, on="key1")
+
+        assert isinstance(result, NotADataFrame)
+
+    def test_join_append_timedeltas(self, using_array_manager):
+        # timedelta64 issues with join/merge
+        # GH 5695
+
+        d = DataFrame.from_dict(
+            {"d": [datetime(2013, 11, 5, 5, 56)], "t": [timedelta(0, 22500)]}
+        )
+        df = DataFrame(columns=list("dt"))
+        msg = "The behavior of DataFrame concatenation with empty or all-NA entries"
+        warn = FutureWarning
+        if using_array_manager:
+            warn = None
+        with tm.assert_produces_warning(warn, match=msg):
+            df = concat([df, d], ignore_index=True)
+            result = concat([df, d], ignore_index=True)
+        expected = DataFrame(
+            {
+                "d": [datetime(2013, 11, 5, 5, 56), datetime(2013, 11, 5, 5, 56)],
+                "t": [timedelta(0, 22500), timedelta(0, 22500)],
+            }
+        )
+        if using_array_manager:
+            # TODO(ArrayManager) decide on exact casting rules in concat
+            expected = expected.astype(object)
+        tm.assert_frame_equal(result, expected)
+
+    def test_join_append_timedeltas2(self):
+        # timedelta64 issues with join/merge
+        # GH 5695
+        td = np.timedelta64(300000000)
+        lhs = DataFrame(Series([td, td], index=["A", "B"]))
+        rhs = DataFrame(Series([td], index=["A"]))
+
+        result = lhs.join(rhs, rsuffix="r", how="left")
+        expected = DataFrame(
+            {
+                "0": Series([td, td], index=list("AB")),
+                "0r": Series([td, pd.NaT], index=list("AB")),
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("unit", ["D", "h", "m", "s", "ms", "us", "ns"])
+    def test_other_datetime_unit(self, unit):
+        # GH 13389
+        df1 = DataFrame({"entity_id": [101, 102]})
+        ser = Series([None, None], index=[101, 102], name="days")
+
+        dtype = f"datetime64[{unit}]"
+
+        if unit in ["D", "h", "m"]:
+            # not supported so we cast to the nearest supported unit, seconds
+            exp_dtype = "datetime64[s]"
+        else:
+            exp_dtype = dtype
+        df2 = ser.astype(exp_dtype).to_frame("days")
+        assert df2["days"].dtype == exp_dtype
+
+        result = df1.merge(df2, left_on="entity_id", right_index=True)
+
+        days = np.array(["nat", "nat"], dtype=exp_dtype)
+        days = pd.core.arrays.DatetimeArray._simple_new(days, dtype=days.dtype)
+        exp = DataFrame(
+            {
+                "entity_id": [101, 102],
+                "days": days,
+            },
+            columns=["entity_id", "days"],
+        )
+        assert exp["days"].dtype == exp_dtype
+        tm.assert_frame_equal(result, exp)
+
+    @pytest.mark.parametrize("unit", ["D", "h", "m", "s", "ms", "us", "ns"])
+    def test_other_timedelta_unit(self, unit):
+        # GH 13389
+        df1 = DataFrame({"entity_id": [101, 102]})
+        ser = Series([None, None], index=[101, 102], name="days")
+
+        dtype = f"m8[{unit}]"
+        if unit in ["D", "h", "m"]:
+            # We cannot astype, instead do nearest supported unit, i.e. "s"
+            msg = "Supported resolutions are 's', 'ms', 'us', 'ns'"
+            with pytest.raises(ValueError, match=msg):
+                ser.astype(dtype)
+
+            df2 = ser.astype("m8[s]").to_frame("days")
+        else:
+            df2 = ser.astype(dtype).to_frame("days")
+            assert df2["days"].dtype == dtype
+
+        result = df1.merge(df2, left_on="entity_id", right_index=True)
+
+        exp = DataFrame(
+            {"entity_id": [101, 102], "days": np.array(["nat", "nat"], dtype=dtype)},
+            columns=["entity_id", "days"],
+        )
+        tm.assert_frame_equal(result, exp)
+
+    def test_overlapping_columns_error_message(self):
+        df = DataFrame({"key": [1, 2, 3], "v1": [4, 5, 6], "v2": [7, 8, 9]})
+        df2 = DataFrame({"key": [1, 2, 3], "v1": [4, 5, 6], "v2": [7, 8, 9]})
+
+        df.columns = ["key", "foo", "foo"]
+        df2.columns = ["key", "bar", "bar"]
+        expected = DataFrame(
+            {
+                "key": [1, 2, 3],
+                "v1": [4, 5, 6],
+                "v2": [7, 8, 9],
+                "v3": [4, 5, 6],
+                "v4": [7, 8, 9],
+            }
+        )
+        expected.columns = ["key", "foo", "foo", "bar", "bar"]
+        tm.assert_frame_equal(merge(df, df2), expected)
+
+        # #2649, #10639
+        df2.columns = ["key1", "foo", "foo"]
+        msg = r"Data columns not unique: Index\(\['foo'\], dtype='object|string'\)"
+        with pytest.raises(MergeError, match=msg):
+            merge(df, df2)
+
+    def test_merge_on_datetime64tz(self):
+        # GH11405
+        left = DataFrame(
+            {
+                "key": pd.date_range("20151010", periods=2, tz="US/Eastern"),
+                "value": [1, 2],
+            }
+        )
+        right = DataFrame(
+            {
+                "key": pd.date_range("20151011", periods=3, tz="US/Eastern"),
+                "value": [1, 2, 3],
+            }
+        )
+
+        expected = DataFrame(
+            {
+                "key": pd.date_range("20151010", periods=4, tz="US/Eastern"),
+                "value_x": [1, 2, np.nan, np.nan],
+                "value_y": [np.nan, 1, 2, 3],
+            }
+        )
+        result = merge(left, right, on="key", how="outer")
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_datetime64tz_values(self):
+        left = DataFrame(
+            {
+                "key": [1, 2],
+                "value": pd.date_range("20151010", periods=2, tz="US/Eastern"),
+            }
+        )
+        right = DataFrame(
+            {
+                "key": [2, 3],
+                "value": pd.date_range("20151011", periods=2, tz="US/Eastern"),
+            }
+        )
+        expected = DataFrame(
+            {
+                "key": [1, 2, 3],
+                "value_x": list(pd.date_range("20151010", periods=2, tz="US/Eastern"))
+                + [pd.NaT],
+                "value_y": [pd.NaT]
+                + list(pd.date_range("20151011", periods=2, tz="US/Eastern")),
+            }
+        )
+        result = merge(left, right, on="key", how="outer")
+        tm.assert_frame_equal(result, expected)
+        assert result["value_x"].dtype == "datetime64[ns, US/Eastern]"
+        assert result["value_y"].dtype == "datetime64[ns, US/Eastern]"
+
+    def test_merge_on_datetime64tz_empty(self):
+        # https://github.com/pandas-dev/pandas/issues/25014
+        dtz = pd.DatetimeTZDtype(tz="UTC")
+        right = DataFrame(
+            {
+                "date": DatetimeIndex(["2018"], dtype=dtz),
+                "value": [4.0],
+                "date2": DatetimeIndex(["2019"], dtype=dtz),
+            },
+            columns=["date", "value", "date2"],
+        )
+        left = right[:0]
+        result = left.merge(right, on="date")
+        expected = DataFrame(
+            {
+                "date": Series(dtype=dtz),
+                "value_x": Series(dtype=float),
+                "date2_x": Series(dtype=dtz),
+                "value_y": Series(dtype=float),
+                "date2_y": Series(dtype=dtz),
+            },
+            columns=["date", "value_x", "date2_x", "value_y", "date2_y"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_datetime64tz_with_dst_transition(self):
+        # GH 18885
+        df1 = DataFrame(
+            pd.date_range("2017-10-29 01:00", periods=4, freq="h", tz="Europe/Madrid"),
+            columns=["date"],
+        )
+        df1["value"] = 1
+        df2 = DataFrame(
+            {
+                "date": pd.to_datetime(
+                    [
+                        "2017-10-29 03:00:00",
+                        "2017-10-29 04:00:00",
+                        "2017-10-29 05:00:00",
+                    ]
+                ),
+                "value": 2,
+            }
+        )
+        df2["date"] = df2["date"].dt.tz_localize("UTC").dt.tz_convert("Europe/Madrid")
+        result = merge(df1, df2, how="outer", on="date")
+        expected = DataFrame(
+            {
+                "date": pd.date_range(
+                    "2017-10-29 01:00", periods=7, freq="h", tz="Europe/Madrid"
+                ),
+                "value_x": [1] * 4 + [np.nan] * 3,
+                "value_y": [np.nan] * 4 + [2] * 3,
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_non_unique_period_index(self):
+        # GH #16871
+        index = pd.period_range("2016-01-01", periods=16, freq="M")
+        df = DataFrame(list(range(len(index))), index=index, columns=["pnum"])
+        df2 = concat([df, df])
+        result = df.merge(df2, left_index=True, right_index=True, how="inner")
+        expected = DataFrame(
+            np.tile(np.arange(16, dtype=np.int64).repeat(2).reshape(-1, 1), 2),
+            columns=["pnum_x", "pnum_y"],
+            index=df2.sort_index().index,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_on_periods(self):
+        left = DataFrame(
+            {"key": pd.period_range("20151010", periods=2, freq="D"), "value": [1, 2]}
+        )
+        right = DataFrame(
+            {
+                "key": pd.period_range("20151011", periods=3, freq="D"),
+                "value": [1, 2, 3],
+            }
+        )
+
+        expected = DataFrame(
+            {
+                "key": pd.period_range("20151010", periods=4, freq="D"),
+                "value_x": [1, 2, np.nan, np.nan],
+                "value_y": [np.nan, 1, 2, 3],
+            }
+        )
+        result = merge(left, right, on="key", how="outer")
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_period_values(self):
+        left = DataFrame(
+            {"key": [1, 2], "value": pd.period_range("20151010", periods=2, freq="D")}
+        )
+        right = DataFrame(
+            {"key": [2, 3], "value": pd.period_range("20151011", periods=2, freq="D")}
+        )
+
+        exp_x = pd.period_range("20151010", periods=2, freq="D")
+        exp_y = pd.period_range("20151011", periods=2, freq="D")
+        expected = DataFrame(
+            {
+                "key": [1, 2, 3],
+                "value_x": list(exp_x) + [pd.NaT],
+                "value_y": [pd.NaT] + list(exp_y),
+            }
+        )
+        result = merge(left, right, on="key", how="outer")
+        tm.assert_frame_equal(result, expected)
+        assert result["value_x"].dtype == "Period[D]"
+        assert result["value_y"].dtype == "Period[D]"
+
+    def test_indicator(self, dfs_for_indicator):
+        # PR #10054. xref #7412 and closes #8790.
+        df1, df2 = dfs_for_indicator
+        df1_copy = df1.copy()
+
+        df2_copy = df2.copy()
+
+        df_result = DataFrame(
+            {
+                "col1": [0, 1, 2, 3, 4, 5],
+                "col_conflict_x": [1, 2, np.nan, np.nan, np.nan, np.nan],
+                "col_left": ["a", "b", np.nan, np.nan, np.nan, np.nan],
+                "col_conflict_y": [np.nan, 1, 2, 3, 4, 5],
+                "col_right": [np.nan, 2, 2, 2, 2, 2],
+            }
+        )
+        df_result["_merge"] = Categorical(
+            [
+                "left_only",
+                "both",
+                "right_only",
+                "right_only",
+                "right_only",
+                "right_only",
+            ],
+            categories=["left_only", "right_only", "both"],
+        )
+
+        df_result = df_result[
+            [
+                "col1",
+                "col_conflict_x",
+                "col_left",
+                "col_conflict_y",
+                "col_right",
+                "_merge",
+            ]
+        ]
+
+        test = merge(df1, df2, on="col1", how="outer", indicator=True)
+        tm.assert_frame_equal(test, df_result)
+        test = df1.merge(df2, on="col1", how="outer", indicator=True)
+        tm.assert_frame_equal(test, df_result)
+
+        # No side effects
+        tm.assert_frame_equal(df1, df1_copy)
+        tm.assert_frame_equal(df2, df2_copy)
+
+        # Check with custom name
+        df_result_custom_name = df_result
+        df_result_custom_name = df_result_custom_name.rename(
+            columns={"_merge": "custom_name"}
+        )
+
+        test_custom_name = merge(
+            df1, df2, on="col1", how="outer", indicator="custom_name"
+        )
+        tm.assert_frame_equal(test_custom_name, df_result_custom_name)
+        test_custom_name = df1.merge(
+            df2, on="col1", how="outer", indicator="custom_name"
+        )
+        tm.assert_frame_equal(test_custom_name, df_result_custom_name)
+
+    def test_merge_indicator_arg_validation(self, dfs_for_indicator):
+        # Check only accepts strings and booleans
+        df1, df2 = dfs_for_indicator
+
+        msg = "indicator option can only accept boolean or string arguments"
+        with pytest.raises(ValueError, match=msg):
+            merge(df1, df2, on="col1", how="outer", indicator=5)
+        with pytest.raises(ValueError, match=msg):
+            df1.merge(df2, on="col1", how="outer", indicator=5)
+
+    def test_merge_indicator_result_integrity(self, dfs_for_indicator):
+        # Check result integrity
+        df1, df2 = dfs_for_indicator
+
+        test2 = merge(df1, df2, on="col1", how="left", indicator=True)
+        assert (test2._merge != "right_only").all()
+        test2 = df1.merge(df2, on="col1", how="left", indicator=True)
+        assert (test2._merge != "right_only").all()
+
+        test3 = merge(df1, df2, on="col1", how="right", indicator=True)
+        assert (test3._merge != "left_only").all()
+        test3 = df1.merge(df2, on="col1", how="right", indicator=True)
+        assert (test3._merge != "left_only").all()
+
+        test4 = merge(df1, df2, on="col1", how="inner", indicator=True)
+        assert (test4._merge == "both").all()
+        test4 = df1.merge(df2, on="col1", how="inner", indicator=True)
+        assert (test4._merge == "both").all()
+
+    def test_merge_indicator_invalid(self, dfs_for_indicator):
+        # Check if working name in df
+        df1, _ = dfs_for_indicator
+
+        for i in ["_right_indicator", "_left_indicator", "_merge"]:
+            df_badcolumn = DataFrame({"col1": [1, 2], i: [2, 2]})
+
+            msg = (
+                "Cannot use `indicator=True` option when data contains a "
+                f"column named {i}|"
+                "Cannot use name of an existing column for indicator column"
+            )
+            with pytest.raises(ValueError, match=msg):
+                merge(df1, df_badcolumn, on="col1", how="outer", indicator=True)
+            with pytest.raises(ValueError, match=msg):
+                df1.merge(df_badcolumn, on="col1", how="outer", indicator=True)
+
+        # Check for name conflict with custom name
+        df_badcolumn = DataFrame({"col1": [1, 2], "custom_column_name": [2, 2]})
+
+        msg = "Cannot use name of an existing column for indicator column"
+        with pytest.raises(ValueError, match=msg):
+            merge(
+                df1,
+                df_badcolumn,
+                on="col1",
+                how="outer",
+                indicator="custom_column_name",
+            )
+        with pytest.raises(ValueError, match=msg):
+            df1.merge(
+                df_badcolumn, on="col1", how="outer", indicator="custom_column_name"
+            )
+
+    def test_merge_indicator_multiple_columns(self):
+        # Merge on multiple columns
+        df3 = DataFrame({"col1": [0, 1], "col2": ["a", "b"]})
+
+        df4 = DataFrame({"col1": [1, 1, 3], "col2": ["b", "x", "y"]})
+
+        hand_coded_result = DataFrame(
+            {"col1": [0, 1, 1, 3], "col2": ["a", "b", "x", "y"]}
+        )
+        hand_coded_result["_merge"] = Categorical(
+            ["left_only", "both", "right_only", "right_only"],
+            categories=["left_only", "right_only", "both"],
+        )
+
+        test5 = merge(df3, df4, on=["col1", "col2"], how="outer", indicator=True)
+        tm.assert_frame_equal(test5, hand_coded_result)
+        test5 = df3.merge(df4, on=["col1", "col2"], how="outer", indicator=True)
+        tm.assert_frame_equal(test5, hand_coded_result)
+
+    def test_validation(self):
+        left = DataFrame(
+            {"a": ["a", "b", "c", "d"], "b": ["cat", "dog", "weasel", "horse"]},
+            index=range(4),
+        )
+
+        right = DataFrame(
+            {
+                "a": ["a", "b", "c", "d", "e"],
+                "c": ["meow", "bark", "um... weasel noise?", "nay", "chirp"],
+            },
+            index=range(5),
+        )
+
+        # Make sure no side effects.
+        left_copy = left.copy()
+        right_copy = right.copy()
+
+        result = merge(left, right, left_index=True, right_index=True, validate="1:1")
+        tm.assert_frame_equal(left, left_copy)
+        tm.assert_frame_equal(right, right_copy)
+
+        # make sure merge still correct
+        expected = DataFrame(
+            {
+                "a_x": ["a", "b", "c", "d"],
+                "b": ["cat", "dog", "weasel", "horse"],
+                "a_y": ["a", "b", "c", "d"],
+                "c": ["meow", "bark", "um... weasel noise?", "nay"],
+            },
+            index=range(4),
+            columns=["a_x", "b", "a_y", "c"],
+        )
+
+        result = merge(
+            left, right, left_index=True, right_index=True, validate="one_to_one"
+        )
+        tm.assert_frame_equal(result, expected)
+
+        expected_2 = DataFrame(
+            {
+                "a": ["a", "b", "c", "d"],
+                "b": ["cat", "dog", "weasel", "horse"],
+                "c": ["meow", "bark", "um... weasel noise?", "nay"],
+            },
+            index=range(4),
+        )
+
+        result = merge(left, right, on="a", validate="1:1")
+        tm.assert_frame_equal(left, left_copy)
+        tm.assert_frame_equal(right, right_copy)
+        tm.assert_frame_equal(result, expected_2)
+
+        result = merge(left, right, on="a", validate="one_to_one")
+        tm.assert_frame_equal(result, expected_2)
+
+        # One index, one column
+        expected_3 = DataFrame(
+            {
+                "b": ["cat", "dog", "weasel", "horse"],
+                "a": ["a", "b", "c", "d"],
+                "c": ["meow", "bark", "um... weasel noise?", "nay"],
+            },
+            columns=["b", "a", "c"],
+            index=range(4),
+        )
+
+        left_index_reset = left.set_index("a")
+        result = merge(
+            left_index_reset,
+            right,
+            left_index=True,
+            right_on="a",
+            validate="one_to_one",
+        )
+        tm.assert_frame_equal(result, expected_3)
+
+        # Dups on right
+        right_w_dups = concat([right, DataFrame({"a": ["e"], "c": ["moo"]}, index=[4])])
+        merge(
+            left,
+            right_w_dups,
+            left_index=True,
+            right_index=True,
+            validate="one_to_many",
+        )
+
+        msg = "Merge keys are not unique in right dataset; not a one-to-one merge"
+        with pytest.raises(MergeError, match=msg):
+            merge(
+                left,
+                right_w_dups,
+                left_index=True,
+                right_index=True,
+                validate="one_to_one",
+            )
+
+        with pytest.raises(MergeError, match=msg):
+            merge(left, right_w_dups, on="a", validate="one_to_one")
+
+        # Dups on left
+        left_w_dups = concat(
+            [left, DataFrame({"a": ["a"], "c": ["cow"]}, index=[3])], sort=True
+        )
+        merge(
+            left_w_dups,
+            right,
+            left_index=True,
+            right_index=True,
+            validate="many_to_one",
+        )
+
+        msg = "Merge keys are not unique in left dataset; not a one-to-one merge"
+        with pytest.raises(MergeError, match=msg):
+            merge(
+                left_w_dups,
+                right,
+                left_index=True,
+                right_index=True,
+                validate="one_to_one",
+            )
+
+        with pytest.raises(MergeError, match=msg):
+            merge(left_w_dups, right, on="a", validate="one_to_one")
+
+        # Dups on both
+        merge(left_w_dups, right_w_dups, on="a", validate="many_to_many")
+
+        msg = "Merge keys are not unique in right dataset; not a many-to-one merge"
+        with pytest.raises(MergeError, match=msg):
+            merge(
+                left_w_dups,
+                right_w_dups,
+                left_index=True,
+                right_index=True,
+                validate="many_to_one",
+            )
+
+        msg = "Merge keys are not unique in left dataset; not a one-to-many merge"
+        with pytest.raises(MergeError, match=msg):
+            merge(left_w_dups, right_w_dups, on="a", validate="one_to_many")
+
+        # Check invalid arguments
+        msg = (
+            '"jibberish" is not a valid argument. '
+            "Valid arguments are:\n"
+            '- "1:1"\n'
+            '- "1:m"\n'
+            '- "m:1"\n'
+            '- "m:m"\n'
+            '- "one_to_one"\n'
+            '- "one_to_many"\n'
+            '- "many_to_one"\n'
+            '- "many_to_many"'
+        )
+        with pytest.raises(ValueError, match=msg):
+            merge(left, right, on="a", validate="jibberish")
+
+        # Two column merge, dups in both, but jointly no dups.
+        left = DataFrame(
+            {
+                "a": ["a", "a", "b", "b"],
+                "b": [0, 1, 0, 1],
+                "c": ["cat", "dog", "weasel", "horse"],
+            },
+            index=range(4),
+        )
+
+        right = DataFrame(
+            {
+                "a": ["a", "a", "b"],
+                "b": [0, 1, 0],
+                "d": ["meow", "bark", "um... weasel noise?"],
+            },
+            index=range(3),
+        )
+
+        expected_multi = DataFrame(
+            {
+                "a": ["a", "a", "b"],
+                "b": [0, 1, 0],
+                "c": ["cat", "dog", "weasel"],
+                "d": ["meow", "bark", "um... weasel noise?"],
+            },
+            index=range(3),
+        )
+
+        msg = (
+            "Merge keys are not unique in either left or right dataset; "
+            "not a one-to-one merge"
+        )
+        with pytest.raises(MergeError, match=msg):
+            merge(left, right, on="a", validate="1:1")
+
+        result = merge(left, right, on=["a", "b"], validate="1:1")
+        tm.assert_frame_equal(result, expected_multi)
+
+    def test_merge_two_empty_df_no_division_error(self):
+        # GH17776, PR #17846
+        a = DataFrame({"a": [], "b": [], "c": []})
+        with np.errstate(divide="raise"):
+            merge(a, a, on=("a", "b"))
+
+    @pytest.mark.parametrize("how", ["right", "outer"])
+    @pytest.mark.parametrize(
+        "index,expected_index",
+        [
+            (
+                CategoricalIndex([1, 2, 4]),
+                CategoricalIndex([1, 2, 4, None, None, None]),
+            ),
+            (
+                DatetimeIndex(
+                    ["2001-01-01", "2002-02-02", "2003-03-03"], dtype="M8[ns]"
+                ),
+                DatetimeIndex(
+                    ["2001-01-01", "2002-02-02", "2003-03-03", pd.NaT, pd.NaT, pd.NaT],
+                    dtype="M8[ns]",
+                ),
+            ),
+            *[
+                (
+                    Index([1, 2, 3], dtype=dtyp),
+                    Index([1, 2, 3, None, None, None], dtype=np.float64),
+                )
+                for dtyp in tm.ALL_REAL_NUMPY_DTYPES
+            ],
+            (
+                IntervalIndex.from_tuples([(1, 2), (2, 3), (3, 4)]),
+                IntervalIndex.from_tuples(
+                    [(1, 2), (2, 3), (3, 4), np.nan, np.nan, np.nan]
+                ),
+            ),
+            (
+                PeriodIndex(["2001-01-01", "2001-01-02", "2001-01-03"], freq="D"),
+                PeriodIndex(
+                    ["2001-01-01", "2001-01-02", "2001-01-03", pd.NaT, pd.NaT, pd.NaT],
+                    freq="D",
+                ),
+            ),
+            (
+                TimedeltaIndex(["1d", "2d", "3d"]),
+                TimedeltaIndex(["1d", "2d", "3d", pd.NaT, pd.NaT, pd.NaT]),
+            ),
+        ],
+    )
+    def test_merge_on_index_with_more_values(self, how, index, expected_index):
+        # GH 24212
+        # pd.merge gets [0, 1, 2, -1, -1, -1] as left_indexer, ensure that
+        # -1 is interpreted as a missing value instead of the last element
+        df1 = DataFrame({"a": [0, 1, 2], "key": [0, 1, 2]}, index=index)
+        df2 = DataFrame({"b": [0, 1, 2, 3, 4, 5]})
+        result = df1.merge(df2, left_on="key", right_index=True, how=how)
+        expected = DataFrame(
+            [
+                [0, 0, 0],
+                [1, 1, 1],
+                [2, 2, 2],
+                [np.nan, 3, 3],
+                [np.nan, 4, 4],
+                [np.nan, 5, 5],
+            ],
+            columns=["a", "key", "b"],
+        )
+        expected.set_index(expected_index, inplace=True)
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_right_index_right(self):
+        # Note: the expected output here is probably incorrect.
+        # See https://github.com/pandas-dev/pandas/issues/17257 for more.
+        # We include this as a regression test for GH-24897.
+        left = DataFrame({"a": [1, 2, 3], "key": [0, 1, 1]})
+        right = DataFrame({"b": [1, 2, 3]})
+
+        expected = DataFrame(
+            {"a": [1, 2, 3, None], "key": [0, 1, 1, 2], "b": [1, 2, 2, 3]},
+            columns=["a", "key", "b"],
+            index=[0, 1, 2, np.nan],
+        )
+        result = left.merge(right, left_on="key", right_index=True, how="right")
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("how", ["left", "right"])
+    def test_merge_preserves_row_order(self, how):
+        # GH 27453
+        left_df = DataFrame({"animal": ["dog", "pig"], "max_speed": [40, 11]})
+        right_df = DataFrame({"animal": ["quetzal", "pig"], "max_speed": [80, 11]})
+        result = left_df.merge(right_df, on=["animal", "max_speed"], how=how)
+        if how == "right":
+            expected = DataFrame({"animal": ["quetzal", "pig"], "max_speed": [80, 11]})
+        else:
+            expected = DataFrame({"animal": ["dog", "pig"], "max_speed": [40, 11]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_take_missing_values_from_index_of_other_dtype(self):
+        # GH 24212
+        left = DataFrame(
+            {
+                "a": [1, 2, 3],
+                "key": Categorical(["a", "a", "b"], categories=list("abc")),
+            }
+        )
+        right = DataFrame({"b": [1, 2, 3]}, index=CategoricalIndex(["a", "b", "c"]))
+        result = left.merge(right, left_on="key", right_index=True, how="right")
+        expected = DataFrame(
+            {
+                "a": [1, 2, 3, None],
+                "key": Categorical(["a", "a", "b", "c"]),
+                "b": [1, 1, 2, 3],
+            },
+            index=[0, 1, 2, np.nan],
+        )
+        expected = expected.reindex(columns=["a", "key", "b"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_readonly(self):
+        # https://github.com/pandas-dev/pandas/issues/27943
+        data1 = DataFrame(
+            np.arange(20).reshape((4, 5)) + 1, columns=["a", "b", "c", "d", "e"]
+        )
+        data2 = DataFrame(
+            np.arange(20).reshape((5, 4)) + 1, columns=["a", "b", "x", "y"]
+        )
+
+        # make each underlying block array / column array read-only
+        for arr in data1._mgr.arrays:
+            arr.flags.writeable = False
+
+        data1.merge(data2)  # no error
+
+
+def _check_merge(x, y):
+    for how in ["inner", "left", "outer"]:
+        for sort in [True, False]:
+            result = x.join(y, how=how, sort=sort)
+
+            expected = merge(x.reset_index(), y.reset_index(), how=how, sort=sort)
+            expected = expected.set_index("index")
+
+            # TODO check_names on merge?
+            tm.assert_frame_equal(result, expected, check_names=False)
+
+
+class TestMergeDtypes:
+    @pytest.mark.parametrize(
+        "right_vals", [["foo", "bar"], Series(["foo", "bar"]).astype("category")]
+    )
+    def test_different(self, right_vals):
+        left = DataFrame(
+            {
+                "A": ["foo", "bar"],
+                "B": Series(["foo", "bar"]).astype("category"),
+                "C": [1, 2],
+                "D": [1.0, 2.0],
+                "E": Series([1, 2], dtype="uint64"),
+                "F": Series([1, 2], dtype="int32"),
+            }
+        )
+        right = DataFrame({"A": right_vals})
+
+        # GH 9780
+        # We allow merging on object and categorical cols and cast
+        # categorical cols to object
+        result = merge(left, right, on="A")
+        assert is_object_dtype(result.A.dtype) or is_string_dtype(result.A.dtype)
+
+    @pytest.mark.parametrize(
+        "d1", [np.int64, np.int32, np.intc, np.int16, np.int8, np.uint8]
+    )
+    @pytest.mark.parametrize("d2", [np.int64, np.float64, np.float32, np.float16])
+    def test_join_multi_dtypes(self, d1, d2):
+        dtype1 = np.dtype(d1)
+        dtype2 = np.dtype(d2)
+
+        left = DataFrame(
+            {
+                "k1": np.array([0, 1, 2] * 8, dtype=dtype1),
+                "k2": ["foo", "bar"] * 12,
+                "v": np.array(np.arange(24), dtype=np.int64),
+            }
+        )
+
+        index = MultiIndex.from_tuples([(2, "bar"), (1, "foo")])
+        right = DataFrame({"v2": np.array([5, 7], dtype=dtype2)}, index=index)
+
+        result = left.join(right, on=["k1", "k2"])
+
+        expected = left.copy()
+
+        if dtype2.kind == "i":
+            dtype2 = np.dtype("float64")
+        expected["v2"] = np.array(np.nan, dtype=dtype2)
+        expected.loc[(expected.k1 == 2) & (expected.k2 == "bar"), "v2"] = 5
+        expected.loc[(expected.k1 == 1) & (expected.k2 == "foo"), "v2"] = 7
+
+        tm.assert_frame_equal(result, expected)
+
+        result = left.join(right, on=["k1", "k2"], sort=True)
+        expected.sort_values(["k1", "k2"], kind="mergesort", inplace=True)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "int_vals, float_vals, exp_vals",
+        [
+            ([1, 2, 3], [1.0, 2.0, 3.0], {"X": [1, 2, 3], "Y": [1.0, 2.0, 3.0]}),
+            ([1, 2, 3], [1.0, 3.0], {"X": [1, 3], "Y": [1.0, 3.0]}),
+            ([1, 2], [1.0, 2.0, 3.0], {"X": [1, 2], "Y": [1.0, 2.0]}),
+        ],
+    )
+    def test_merge_on_ints_floats(self, int_vals, float_vals, exp_vals):
+        # GH 16572
+        # Check that float column is not cast to object if
+        # merging on float and int columns
+        A = DataFrame({"X": int_vals})
+        B = DataFrame({"Y": float_vals})
+        expected = DataFrame(exp_vals)
+
+        result = A.merge(B, left_on="X", right_on="Y")
+        tm.assert_frame_equal(result, expected)
+
+        result = B.merge(A, left_on="Y", right_on="X")
+        tm.assert_frame_equal(result, expected[["Y", "X"]])
+
+    def test_merge_key_dtype_cast(self):
+        # GH 17044
+        df1 = DataFrame({"key": [1.0, 2.0], "v1": [10, 20]}, columns=["key", "v1"])
+        df2 = DataFrame({"key": [2], "v2": [200]}, columns=["key", "v2"])
+        result = df1.merge(df2, on="key", how="left")
+        expected = DataFrame(
+            {"key": [1.0, 2.0], "v1": [10, 20], "v2": [np.nan, 200.0]},
+            columns=["key", "v1", "v2"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_on_ints_floats_warning(self):
+        # GH 16572
+        # merge will produce a warning when merging on int and
+        # float columns where the float values are not exactly
+        # equal to their int representation
+        A = DataFrame({"X": [1, 2, 3]})
+        B = DataFrame({"Y": [1.1, 2.5, 3.0]})
+        expected = DataFrame({"X": [3], "Y": [3.0]})
+
+        with tm.assert_produces_warning(UserWarning):
+            result = A.merge(B, left_on="X", right_on="Y")
+            tm.assert_frame_equal(result, expected)
+
+        with tm.assert_produces_warning(UserWarning):
+            result = B.merge(A, left_on="Y", right_on="X")
+            tm.assert_frame_equal(result, expected[["Y", "X"]])
+
+        # test no warning if float has NaNs
+        B = DataFrame({"Y": [np.nan, np.nan, 3.0]})
+
+        with tm.assert_produces_warning(None):
+            result = B.merge(A, left_on="Y", right_on="X")
+            tm.assert_frame_equal(result, expected[["Y", "X"]])
+
+    def test_merge_incompat_infer_boolean_object(self):
+        # GH21119: bool + object bool merge OK
+        df1 = DataFrame({"key": Series([True, False], dtype=object)})
+        df2 = DataFrame({"key": [True, False]})
+
+        expected = DataFrame({"key": [True, False]}, dtype=object)
+        result = merge(df1, df2, on="key")
+        tm.assert_frame_equal(result, expected)
+        result = merge(df2, df1, on="key")
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_incompat_infer_boolean_object_with_missing(self):
+        # GH21119: bool + object bool merge OK
+        # with missing value
+        df1 = DataFrame({"key": Series([True, False, np.nan], dtype=object)})
+        df2 = DataFrame({"key": [True, False]})
+
+        expected = DataFrame({"key": [True, False]}, dtype=object)
+        result = merge(df1, df2, on="key")
+        tm.assert_frame_equal(result, expected)
+        result = merge(df2, df1, on="key")
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "df1_vals, df2_vals",
+        [
+            # merge on category coerces to object
+            ([0, 1, 2], Series(["a", "b", "a"]).astype("category")),
+            ([0.0, 1.0, 2.0], Series(["a", "b", "a"]).astype("category")),
+            # no not infer
+            ([0, 1], Series([False, True], dtype=object)),
+            ([0, 1], Series([False, True], dtype=bool)),
+        ],
+    )
+    def test_merge_incompat_dtypes_are_ok(self, df1_vals, df2_vals):
+        # these are explicitly allowed incompat merges, that pass thru
+        # the result type is dependent on if the values on the rhs are
+        # inferred, otherwise these will be coerced to object
+
+        df1 = DataFrame({"A": df1_vals})
+        df2 = DataFrame({"A": df2_vals})
+
+        result = merge(df1, df2, on=["A"])
+        assert is_object_dtype(result.A.dtype)
+        result = merge(df2, df1, on=["A"])
+        assert is_object_dtype(result.A.dtype) or is_string_dtype(result.A.dtype)
+
+    @pytest.mark.parametrize(
+        "df1_vals, df2_vals",
+        [
+            # do not infer to numeric
+            (Series([1, 2], dtype="uint64"), ["a", "b", "c"]),
+            (Series([1, 2], dtype="int32"), ["a", "b", "c"]),
+            ([0, 1, 2], ["0", "1", "2"]),
+            ([0.0, 1.0, 2.0], ["0", "1", "2"]),
+            ([0, 1, 2], ["0", "1", "2"]),
+            (
+                pd.date_range("1/1/2011", periods=2, freq="D"),
+                ["2011-01-01", "2011-01-02"],
+            ),
+            (pd.date_range("1/1/2011", periods=2, freq="D"), [0, 1]),
+            (pd.date_range("1/1/2011", periods=2, freq="D"), [0.0, 1.0]),
+            (
+                pd.date_range("20130101", periods=3),
+                pd.date_range("20130101", periods=3, tz="US/Eastern"),
+            ),
+        ],
+    )
+    def test_merge_incompat_dtypes_error(self, df1_vals, df2_vals):
+        # GH 9780, GH 15800
+        # Raise a ValueError when a user tries to merge on
+        # dtypes that are incompatible (e.g., obj and int/float)
+
+        df1 = DataFrame({"A": df1_vals})
+        df2 = DataFrame({"A": df2_vals})
+
+        msg = (
+            f"You are trying to merge on {df1['A'].dtype} and {df2['A'].dtype} "
+            "columns for key 'A'. If you wish to proceed you should use pd.concat"
+        )
+        msg = re.escape(msg)
+        with pytest.raises(ValueError, match=msg):
+            merge(df1, df2, on=["A"])
+
+        # Check that error still raised when swapping order of dataframes
+        msg = (
+            f"You are trying to merge on {df2['A'].dtype} and {df1['A'].dtype} "
+            "columns for key 'A'. If you wish to proceed you should use pd.concat"
+        )
+        msg = re.escape(msg)
+        with pytest.raises(ValueError, match=msg):
+            merge(df2, df1, on=["A"])
+
+        # Check that error still raised when merging on multiple columns
+        # The error message should mention the first incompatible column
+        if len(df1_vals) == len(df2_vals):
+            # Column A in df1 and df2 is of compatible (the same) dtype
+            # Columns B and C in df1 and df2 are of incompatible dtypes
+            df3 = DataFrame({"A": df2_vals, "B": df1_vals, "C": df1_vals})
+            df4 = DataFrame({"A": df2_vals, "B": df2_vals, "C": df2_vals})
+
+            # Check that error raised correctly when merging all columns A, B, and C
+            # The error message should mention key 'B'
+            msg = (
+                f"You are trying to merge on {df3['B'].dtype} and {df4['B'].dtype} "
+                "columns for key 'B'. If you wish to proceed you should use pd.concat"
+            )
+            msg = re.escape(msg)
+            with pytest.raises(ValueError, match=msg):
+                merge(df3, df4)
+
+            # Check that error raised correctly when merging columns A and C
+            # The error message should mention key 'C'
+            msg = (
+                f"You are trying to merge on {df3['C'].dtype} and {df4['C'].dtype} "
+                "columns for key 'C'. If you wish to proceed you should use pd.concat"
+            )
+            msg = re.escape(msg)
+            with pytest.raises(ValueError, match=msg):
+                merge(df3, df4, on=["A", "C"])
+
+    @pytest.mark.parametrize(
+        "expected_data, how",
+        [
+            ([1, 2], "outer"),
+            ([], "inner"),
+            ([2], "right"),
+            ([1], "left"),
+        ],
+    )
+    def test_merge_EA_dtype(self, any_numeric_ea_dtype, how, expected_data):
+        # GH#40073
+        d1 = DataFrame([(1,)], columns=["id"], dtype=any_numeric_ea_dtype)
+        d2 = DataFrame([(2,)], columns=["id"], dtype=any_numeric_ea_dtype)
+        result = merge(d1, d2, how=how)
+        exp_index = RangeIndex(len(expected_data))
+        expected = DataFrame(
+            expected_data, index=exp_index, columns=["id"], dtype=any_numeric_ea_dtype
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "expected_data, how",
+        [
+            (["a", "b"], "outer"),
+            ([], "inner"),
+            (["b"], "right"),
+            (["a"], "left"),
+        ],
+    )
+    def test_merge_string_dtype(self, how, expected_data, any_string_dtype):
+        # GH#40073
+        d1 = DataFrame([("a",)], columns=["id"], dtype=any_string_dtype)
+        d2 = DataFrame([("b",)], columns=["id"], dtype=any_string_dtype)
+        result = merge(d1, d2, how=how)
+        exp_idx = RangeIndex(len(expected_data))
+        expected = DataFrame(
+            expected_data, index=exp_idx, columns=["id"], dtype=any_string_dtype
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "how, expected_data",
+        [
+            ("inner", [[True, 1, 4], [False, 5, 3]]),
+            ("outer", [[False, 5, 3], [True, 1, 4]]),
+            ("left", [[True, 1, 4], [False, 5, 3]]),
+            ("right", [[False, 5, 3], [True, 1, 4]]),
+        ],
+    )
+    def test_merge_bool_dtype(self, how, expected_data):
+        # GH#40073
+        df1 = DataFrame({"A": [True, False], "B": [1, 5]})
+        df2 = DataFrame({"A": [False, True], "C": [3, 4]})
+        result = merge(df1, df2, how=how)
+        expected = DataFrame(expected_data, columns=["A", "B", "C"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_ea_with_string(self, join_type, string_dtype):
+        # GH 43734 Avoid the use of `assign` with multi-index
+        df1 = DataFrame(
+            data={
+                ("lvl0", "lvl1-a"): ["1", "2", "3", "4", None],
+                ("lvl0", "lvl1-b"): ["4", "5", "6", "7", "8"],
+            },
+            dtype=pd.StringDtype(),
+        )
+        df1_copy = df1.copy()
+        df2 = DataFrame(
+            data={
+                ("lvl0", "lvl1-a"): ["1", "2", "3", pd.NA, "5"],
+                ("lvl0", "lvl1-c"): ["7", "8", "9", pd.NA, "11"],
+            },
+            dtype=string_dtype,
+        )
+        df2_copy = df2.copy()
+        merged = merge(left=df1, right=df2, on=[("lvl0", "lvl1-a")], how=join_type)
+
+        # No change in df1 and df2
+        tm.assert_frame_equal(df1, df1_copy)
+        tm.assert_frame_equal(df2, df2_copy)
+
+        # Check the expected types for the merged data frame
+        expected = Series(
+            [np.dtype("O"), pd.StringDtype(), np.dtype("O")],
+            index=MultiIndex.from_tuples(
+                [("lvl0", "lvl1-a"), ("lvl0", "lvl1-b"), ("lvl0", "lvl1-c")]
+            ),
+        )
+        tm.assert_series_equal(merged.dtypes, expected)
+
+    @pytest.mark.parametrize(
+        "left_empty, how, exp",
+        [
+            (False, "left", "left"),
+            (False, "right", "empty"),
+            (False, "inner", "empty"),
+            (False, "outer", "left"),
+            (False, "cross", "empty_cross"),
+            (True, "left", "empty"),
+            (True, "right", "right"),
+            (True, "inner", "empty"),
+            (True, "outer", "right"),
+            (True, "cross", "empty_cross"),
+        ],
+    )
+    def test_merge_empty(self, left_empty, how, exp):
+        left = DataFrame({"A": [2, 1], "B": [3, 4]})
+        right = DataFrame({"A": [1], "C": [5]}, dtype="int64")
+
+        if left_empty:
+            left = left.head(0)
+        else:
+            right = right.head(0)
+
+        result = left.merge(right, how=how)
+
+        if exp == "left":
+            expected = DataFrame({"A": [2, 1], "B": [3, 4], "C": [np.nan, np.nan]})
+        elif exp == "right":
+            expected = DataFrame({"A": [1], "B": [np.nan], "C": [5]})
+        elif exp == "empty":
+            expected = DataFrame(columns=["A", "B", "C"], dtype="int64")
+        elif exp == "empty_cross":
+            expected = DataFrame(columns=["A_x", "B", "A_y", "C"], dtype="int64")
+
+        if how == "outer":
+            expected = expected.sort_values("A", ignore_index=True)
+
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.fixture
+def left():
+    return DataFrame(
+        {
+            "X": Series(
+                np.random.default_rng(2).choice(["foo", "bar"], size=(10,))
+            ).astype(CategoricalDtype(["foo", "bar"])),
+            "Y": np.random.default_rng(2).choice(["one", "two", "three"], size=(10,)),
+        }
+    )
+
+
+@pytest.fixture
+def right():
+    return DataFrame(
+        {
+            "X": Series(["foo", "bar"]).astype(CategoricalDtype(["foo", "bar"])),
+            "Z": [1, 2],
+        }
+    )
+
+
+class TestMergeCategorical:
+    def test_identical(self, left, using_infer_string):
+        # merging on the same, should preserve dtypes
+        merged = merge(left, left, on="X")
+        result = merged.dtypes.sort_index()
+        dtype = np.dtype("O") if not using_infer_string else "string"
+        expected = Series(
+            [CategoricalDtype(categories=["foo", "bar"]), dtype, dtype],
+            index=["X", "Y_x", "Y_y"],
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_basic(self, left, right, using_infer_string):
+        # we have matching Categorical dtypes in X
+        # so should preserve the merged column
+        merged = merge(left, right, on="X")
+        result = merged.dtypes.sort_index()
+        dtype = np.dtype("O") if not using_infer_string else "string"
+        expected = Series(
+            [
+                CategoricalDtype(categories=["foo", "bar"]),
+                dtype,
+                np.dtype("int64"),
+            ],
+            index=["X", "Y", "Z"],
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_merge_categorical(self):
+        # GH 9426
+
+        right = DataFrame(
+            {
+                "c": {0: "a", 1: "b", 2: "c", 3: "d", 4: "e"},
+                "d": {0: "null", 1: "null", 2: "null", 3: "null", 4: "null"},
+            }
+        )
+        left = DataFrame(
+            {
+                "a": {0: "f", 1: "f", 2: "f", 3: "f", 4: "f"},
+                "b": {0: "g", 1: "g", 2: "g", 3: "g", 4: "g"},
+            }
+        )
+        df = merge(left, right, how="left", left_on="b", right_on="c")
+
+        # object-object
+        expected = df.copy()
+
+        # object-cat
+        # note that we propagate the category
+        # because we don't have any matching rows
+        cright = right.copy()
+        cright["d"] = cright["d"].astype("category")
+        result = merge(left, cright, how="left", left_on="b", right_on="c")
+        expected["d"] = expected["d"].astype(CategoricalDtype(["null"]))
+        tm.assert_frame_equal(result, expected)
+
+        # cat-object
+        cleft = left.copy()
+        cleft["b"] = cleft["b"].astype("category")
+        result = merge(cleft, cright, how="left", left_on="b", right_on="c")
+        tm.assert_frame_equal(result, expected)
+
+        # cat-cat
+        cright = right.copy()
+        cright["d"] = cright["d"].astype("category")
+        cleft = left.copy()
+        cleft["b"] = cleft["b"].astype("category")
+        result = merge(cleft, cright, how="left", left_on="b", right_on="c")
+        tm.assert_frame_equal(result, expected)
+
+    def tests_merge_categorical_unordered_equal(self):
+        # GH-19551
+        df1 = DataFrame(
+            {
+                "Foo": Categorical(["A", "B", "C"], categories=["A", "B", "C"]),
+                "Left": ["A0", "B0", "C0"],
+            }
+        )
+
+        df2 = DataFrame(
+            {
+                "Foo": Categorical(["C", "B", "A"], categories=["C", "B", "A"]),
+                "Right": ["C1", "B1", "A1"],
+            }
+        )
+        result = merge(df1, df2, on=["Foo"])
+        expected = DataFrame(
+            {
+                "Foo": Categorical(["A", "B", "C"]),
+                "Left": ["A0", "B0", "C0"],
+                "Right": ["A1", "B1", "C1"],
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("ordered", [True, False])
+    def test_multiindex_merge_with_unordered_categoricalindex(self, ordered):
+        # GH 36973
+        pcat = CategoricalDtype(categories=["P2", "P1"], ordered=ordered)
+        df1 = DataFrame(
+            {
+                "id": ["C", "C", "D"],
+                "p": Categorical(["P2", "P1", "P2"], dtype=pcat),
+                "a": [0, 1, 2],
+            }
+        ).set_index(["id", "p"])
+        df2 = DataFrame(
+            {
+                "id": ["A", "C", "C"],
+                "p": Categorical(["P2", "P2", "P1"], dtype=pcat),
+                "d1": [10, 11, 12],
+            }
+        ).set_index(["id", "p"])
+        result = merge(df1, df2, how="left", left_index=True, right_index=True)
+        expected = DataFrame(
+            {
+                "id": ["C", "C", "D"],
+                "p": Categorical(["P2", "P1", "P2"], dtype=pcat),
+                "a": [0, 1, 2],
+                "d1": [11.0, 12.0, np.nan],
+            }
+        ).set_index(["id", "p"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_other_columns(self, left, right, using_infer_string):
+        # non-merge columns should preserve if possible
+        right = right.assign(Z=right.Z.astype("category"))
+
+        merged = merge(left, right, on="X")
+        result = merged.dtypes.sort_index()
+        dtype = np.dtype("O") if not using_infer_string else "string"
+        expected = Series(
+            [
+                CategoricalDtype(categories=["foo", "bar"]),
+                dtype,
+                CategoricalDtype(categories=[1, 2]),
+            ],
+            index=["X", "Y", "Z"],
+        )
+        tm.assert_series_equal(result, expected)
+
+        # categories are preserved
+        assert left.X.values._categories_match_up_to_permutation(merged.X.values)
+        assert right.Z.values._categories_match_up_to_permutation(merged.Z.values)
+
+    @pytest.mark.parametrize(
+        "change",
+        [
+            lambda x: x,
+            lambda x: x.astype(CategoricalDtype(["foo", "bar", "bah"])),
+            lambda x: x.astype(CategoricalDtype(ordered=True)),
+        ],
+    )
+    def test_dtype_on_merged_different(
+        self, change, join_type, left, right, using_infer_string
+    ):
+        # our merging columns, X now has 2 different dtypes
+        # so we must be object as a result
+
+        X = change(right.X.astype("object"))
+        right = right.assign(X=X)
+        assert isinstance(left.X.values.dtype, CategoricalDtype)
+        # assert not left.X.values._categories_match_up_to_permutation(right.X.values)
+
+        merged = merge(left, right, on="X", how=join_type)
+
+        result = merged.dtypes.sort_index()
+        dtype = np.dtype("O") if not using_infer_string else "string"
+        expected = Series([dtype, dtype, np.dtype("int64")], index=["X", "Y", "Z"])
+        tm.assert_series_equal(result, expected)
+
+    def test_self_join_multiple_categories(self):
+        # GH 16767
+        # non-duplicates should work with multiple categories
+        m = 5
+        df = DataFrame(
+            {
+                "a": ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"] * m,
+                "b": ["t", "w", "x", "y", "z"] * 2 * m,
+                "c": [
+                    letter
+                    for each in ["m", "n", "u", "p", "o"]
+                    for letter in [each] * 2 * m
+                ],
+                "d": [
+                    letter
+                    for each in [
+                        "aa",
+                        "bb",
+                        "cc",
+                        "dd",
+                        "ee",
+                        "ff",
+                        "gg",
+                        "hh",
+                        "ii",
+                        "jj",
+                    ]
+                    for letter in [each] * m
+                ],
+            }
+        )
+
+        # change them all to categorical variables
+        df = df.apply(lambda x: x.astype("category"))
+
+        # self-join should equal ourselves
+        result = merge(df, df, on=list(df.columns))
+
+        tm.assert_frame_equal(result, df)
+
+    def test_dtype_on_categorical_dates(self):
+        # GH 16900
+        # dates should not be coerced to ints
+
+        df = DataFrame(
+            [[date(2001, 1, 1), 1.1], [date(2001, 1, 2), 1.3]], columns=["date", "num2"]
+        )
+        df["date"] = df["date"].astype("category")
+
+        df2 = DataFrame(
+            [[date(2001, 1, 1), 1.3], [date(2001, 1, 3), 1.4]], columns=["date", "num4"]
+        )
+        df2["date"] = df2["date"].astype("category")
+
+        expected_outer = DataFrame(
+            [
+                [pd.Timestamp("2001-01-01").date(), 1.1, 1.3],
+                [pd.Timestamp("2001-01-02").date(), 1.3, np.nan],
+                [pd.Timestamp("2001-01-03").date(), np.nan, 1.4],
+            ],
+            columns=["date", "num2", "num4"],
+        )
+        result_outer = merge(df, df2, how="outer", on=["date"])
+        tm.assert_frame_equal(result_outer, expected_outer)
+
+        expected_inner = DataFrame(
+            [[pd.Timestamp("2001-01-01").date(), 1.1, 1.3]],
+            columns=["date", "num2", "num4"],
+        )
+        result_inner = merge(df, df2, how="inner", on=["date"])
+        tm.assert_frame_equal(result_inner, expected_inner)
+
+    @pytest.mark.parametrize("ordered", [True, False])
+    @pytest.mark.parametrize(
+        "category_column,categories,expected_categories",
+        [
+            ([False, True, True, False], [True, False], [True, False]),
+            ([2, 1, 1, 2], [1, 2], [1, 2]),
+            (["False", "True", "True", "False"], ["True", "False"], ["True", "False"]),
+        ],
+    )
+    def test_merging_with_bool_or_int_cateorical_column(
+        self, category_column, categories, expected_categories, ordered
+    ):
+        # GH 17187
+        # merging with a boolean/int categorical column
+        df1 = DataFrame({"id": [1, 2, 3, 4], "cat": category_column})
+        df1["cat"] = df1["cat"].astype(CategoricalDtype(categories, ordered=ordered))
+        df2 = DataFrame({"id": [2, 4], "num": [1, 9]})
+        result = df1.merge(df2)
+        expected = DataFrame({"id": [2, 4], "cat": expected_categories, "num": [1, 9]})
+        expected["cat"] = expected["cat"].astype(
+            CategoricalDtype(categories, ordered=ordered)
+        )
+        tm.assert_frame_equal(expected, result)
+
+    def test_merge_on_int_array(self):
+        # GH 23020
+        df = DataFrame({"A": Series([1, 2, np.nan], dtype="Int64"), "B": 1})
+        result = merge(df, df, on="A")
+        expected = DataFrame(
+            {"A": Series([1, 2, np.nan], dtype="Int64"), "B_x": 1, "B_y": 1}
+        )
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.fixture
+def left_df():
+    return DataFrame({"a": [20, 10, 0]}, index=[2, 1, 0])
+
+
+@pytest.fixture
+def right_df():
+    return DataFrame({"b": [300, 100, 200]}, index=[3, 1, 2])
+
+
+class TestMergeOnIndexes:
+    @pytest.mark.parametrize(
+        "how, sort, expected",
+        [
+            ("inner", False, DataFrame({"a": [20, 10], "b": [200, 100]}, index=[2, 1])),
+            ("inner", True, DataFrame({"a": [10, 20], "b": [100, 200]}, index=[1, 2])),
+            (
+                "left",
+                False,
+                DataFrame({"a": [20, 10, 0], "b": [200, 100, np.nan]}, index=[2, 1, 0]),
+            ),
+            (
+                "left",
+                True,
+                DataFrame({"a": [0, 10, 20], "b": [np.nan, 100, 200]}, index=[0, 1, 2]),
+            ),
+            (
+                "right",
+                False,
+                DataFrame(
+                    {"a": [np.nan, 10, 20], "b": [300, 100, 200]}, index=[3, 1, 2]
+                ),
+            ),
+            (
+                "right",
+                True,
+                DataFrame(
+                    {"a": [10, 20, np.nan], "b": [100, 200, 300]}, index=[1, 2, 3]
+                ),
+            ),
+            (
+                "outer",
+                False,
+                DataFrame(
+                    {"a": [0, 10, 20, np.nan], "b": [np.nan, 100, 200, 300]},
+                    index=[0, 1, 2, 3],
+                ),
+            ),
+            (
+                "outer",
+                True,
+                DataFrame(
+                    {"a": [0, 10, 20, np.nan], "b": [np.nan, 100, 200, 300]},
+                    index=[0, 1, 2, 3],
+                ),
+            ),
+        ],
+    )
+    def test_merge_on_indexes(self, left_df, right_df, how, sort, expected):
+        result = merge(
+            left_df, right_df, left_index=True, right_index=True, how=how, sort=sort
+        )
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "index",
+    [Index([1, 2], dtype=dtyp, name="index_col") for dtyp in tm.ALL_REAL_NUMPY_DTYPES]
+    + [
+        CategoricalIndex(["A", "B"], categories=["A", "B"], name="index_col"),
+        RangeIndex(start=0, stop=2, name="index_col"),
+        DatetimeIndex(["2018-01-01", "2018-01-02"], name="index_col"),
+    ],
+    ids=lambda x: f"{type(x).__name__}[{x.dtype}]",
+)
+def test_merge_index_types(index):
+    # gh-20777
+    # assert key access is consistent across index types
+    left = DataFrame({"left_data": [1, 2]}, index=index)
+    right = DataFrame({"right_data": [1.0, 2.0]}, index=index)
+
+    result = left.merge(right, on=["index_col"])
+
+    expected = DataFrame({"left_data": [1, 2], "right_data": [1.0, 2.0]}, index=index)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "on,left_on,right_on,left_index,right_index,nm",
+    [
+        (["outer", "inner"], None, None, False, False, "B"),
+        (None, None, None, True, True, "B"),
+        (None, ["outer", "inner"], None, False, True, "B"),
+        (None, None, ["outer", "inner"], True, False, "B"),
+        (["outer", "inner"], None, None, False, False, None),
+        (None, None, None, True, True, None),
+        (None, ["outer", "inner"], None, False, True, None),
+        (None, None, ["outer", "inner"], True, False, None),
+    ],
+)
+def test_merge_series(on, left_on, right_on, left_index, right_index, nm):
+    # GH 21220
+    a = DataFrame(
+        {"A": [1, 2, 3, 4]},
+        index=MultiIndex.from_product([["a", "b"], [0, 1]], names=["outer", "inner"]),
+    )
+    b = Series(
+        [1, 2, 3, 4],
+        index=MultiIndex.from_product([["a", "b"], [1, 2]], names=["outer", "inner"]),
+        name=nm,
+    )
+    expected = DataFrame(
+        {"A": [2, 4], "B": [1, 3]},
+        index=MultiIndex.from_product([["a", "b"], [1]], names=["outer", "inner"]),
+    )
+    if nm is not None:
+        result = merge(
+            a,
+            b,
+            on=on,
+            left_on=left_on,
+            right_on=right_on,
+            left_index=left_index,
+            right_index=right_index,
+        )
+        tm.assert_frame_equal(result, expected)
+    else:
+        msg = "Cannot merge a Series without a name"
+        with pytest.raises(ValueError, match=msg):
+            result = merge(
+                a,
+                b,
+                on=on,
+                left_on=left_on,
+                right_on=right_on,
+                left_index=left_index,
+                right_index=right_index,
+            )
+
+
+def test_merge_series_multilevel():
+    # GH#47946
+    # GH 40993: For raising, enforced in 2.0
+    a = DataFrame(
+        {"A": [1, 2, 3, 4]},
+        index=MultiIndex.from_product([["a", "b"], [0, 1]], names=["outer", "inner"]),
+    )
+    b = Series(
+        [1, 2, 3, 4],
+        index=MultiIndex.from_product([["a", "b"], [1, 2]], names=["outer", "inner"]),
+        name=("B", "C"),
+    )
+    with pytest.raises(
+        MergeError, match="Not allowed to merge between different levels"
+    ):
+        merge(a, b, on=["outer", "inner"])
+
+
+@pytest.mark.parametrize(
+    "col1, col2, kwargs, expected_cols",
+    [
+        (0, 0, {"suffixes": ("", "_dup")}, ["0", "0_dup"]),
+        (0, 0, {"suffixes": (None, "_dup")}, [0, "0_dup"]),
+        (0, 0, {"suffixes": ("_x", "_y")}, ["0_x", "0_y"]),
+        (0, 0, {"suffixes": ["_x", "_y"]}, ["0_x", "0_y"]),
+        ("a", 0, {"suffixes": (None, "_y")}, ["a", 0]),
+        (0.0, 0.0, {"suffixes": ("_x", None)}, ["0.0_x", 0.0]),
+        ("b", "b", {"suffixes": (None, "_y")}, ["b", "b_y"]),
+        ("a", "a", {"suffixes": ("_x", None)}, ["a_x", "a"]),
+        ("a", "b", {"suffixes": ("_x", None)}, ["a", "b"]),
+        ("a", "a", {"suffixes": (None, "_x")}, ["a", "a_x"]),
+        (0, 0, {"suffixes": ("_a", None)}, ["0_a", 0]),
+        ("a", "a", {}, ["a_x", "a_y"]),
+        (0, 0, {}, ["0_x", "0_y"]),
+    ],
+)
+def test_merge_suffix(col1, col2, kwargs, expected_cols):
+    # issue: 24782
+    a = DataFrame({col1: [1, 2, 3]})
+    b = DataFrame({col2: [4, 5, 6]})
+
+    expected = DataFrame([[1, 4], [2, 5], [3, 6]], columns=expected_cols)
+
+    result = a.merge(b, left_index=True, right_index=True, **kwargs)
+    tm.assert_frame_equal(result, expected)
+
+    result = merge(a, b, left_index=True, right_index=True, **kwargs)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "how,expected",
+    [
+        (
+            "right",
+            DataFrame(
+                {"A": [100, 200, 300], "B1": [60, 70, np.nan], "B2": [600, 700, 800]}
+            ),
+        ),
+        (
+            "outer",
+            DataFrame(
+                {
+                    "A": [1, 100, 200, 300],
+                    "B1": [80, 60, 70, np.nan],
+                    "B2": [np.nan, 600, 700, 800],
+                }
+            ),
+        ),
+    ],
+)
+def test_merge_duplicate_suffix(how, expected):
+    left_df = DataFrame({"A": [100, 200, 1], "B": [60, 70, 80]})
+    right_df = DataFrame({"A": [100, 200, 300], "B": [600, 700, 800]})
+    result = merge(left_df, right_df, on="A", how=how, suffixes=("_x", "_x"))
+    expected.columns = ["A", "B_x", "B_x"]
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "col1, col2, suffixes",
+    [("a", "a", (None, None)), ("a", "a", ("", None)), (0, 0, (None, ""))],
+)
+def test_merge_suffix_error(col1, col2, suffixes):
+    # issue: 24782
+    a = DataFrame({col1: [1, 2, 3]})
+    b = DataFrame({col2: [3, 4, 5]})
+
+    # TODO: might reconsider current raise behaviour, see issue 24782
+    msg = "columns overlap but no suffix specified"
+    with pytest.raises(ValueError, match=msg):
+        merge(a, b, left_index=True, right_index=True, suffixes=suffixes)
+
+
+@pytest.mark.parametrize("suffixes", [{"left", "right"}, {"left": 0, "right": 0}])
+def test_merge_suffix_raises(suffixes):
+    a = DataFrame({"a": [1, 2, 3]})
+    b = DataFrame({"b": [3, 4, 5]})
+
+    with pytest.raises(TypeError, match="Passing 'suffixes' as a"):
+        merge(a, b, left_index=True, right_index=True, suffixes=suffixes)
+
+
+@pytest.mark.parametrize(
+    "col1, col2, suffixes, msg",
+    [
+        ("a", "a", ("a", "b", "c"), r"too many values to unpack \(expected 2\)"),
+        ("a", "a", tuple("a"), r"not enough values to unpack \(expected 2, got 1\)"),
+    ],
+)
+def test_merge_suffix_length_error(col1, col2, suffixes, msg):
+    a = DataFrame({col1: [1, 2, 3]})
+    b = DataFrame({col2: [3, 4, 5]})
+
+    with pytest.raises(ValueError, match=msg):
+        merge(a, b, left_index=True, right_index=True, suffixes=suffixes)
+
+
+@pytest.mark.parametrize("cat_dtype", ["one", "two"])
+@pytest.mark.parametrize("reverse", [True, False])
+def test_merge_equal_cat_dtypes(cat_dtype, reverse):
+    # see gh-22501
+    cat_dtypes = {
+        "one": CategoricalDtype(categories=["a", "b", "c"], ordered=False),
+        "two": CategoricalDtype(categories=["a", "b", "c"], ordered=False),
+    }
+
+    df1 = DataFrame(
+        {"foo": Series(["a", "b", "c"]).astype(cat_dtypes["one"]), "left": [1, 2, 3]}
+    ).set_index("foo")
+
+    data_foo = ["a", "b", "c"]
+    data_right = [1, 2, 3]
+
+    if reverse:
+        data_foo.reverse()
+        data_right.reverse()
+
+    df2 = DataFrame(
+        {"foo": Series(data_foo).astype(cat_dtypes[cat_dtype]), "right": data_right}
+    ).set_index("foo")
+
+    result = df1.merge(df2, left_index=True, right_index=True)
+
+    expected = DataFrame(
+        {
+            "left": [1, 2, 3],
+            "right": [1, 2, 3],
+            "foo": Series(["a", "b", "c"]).astype(cat_dtypes["one"]),
+        }
+    ).set_index("foo")
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_equal_cat_dtypes2():
+    # see gh-22501
+    cat_dtype = CategoricalDtype(categories=["a", "b", "c"], ordered=False)
+
+    # Test Data
+    df1 = DataFrame(
+        {"foo": Series(["a", "b"]).astype(cat_dtype), "left": [1, 2]}
+    ).set_index("foo")
+
+    df2 = DataFrame(
+        {"foo": Series(["a", "b", "c"]).astype(cat_dtype), "right": [3, 2, 1]}
+    ).set_index("foo")
+
+    result = df1.merge(df2, left_index=True, right_index=True)
+
+    expected = DataFrame(
+        {"left": [1, 2], "right": [3, 2], "foo": Series(["a", "b"]).astype(cat_dtype)}
+    ).set_index("foo")
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_on_cat_and_ext_array():
+    # GH 28668
+    right = DataFrame(
+        {"a": Series([pd.Interval(0, 1), pd.Interval(1, 2)], dtype="interval")}
+    )
+    left = right.copy()
+    left["a"] = left["a"].astype("category")
+
+    result = merge(left, right, how="inner", on="a")
+    expected = right.copy()
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_multiindex_columns():
+    # Issue #28518
+    # Verify that merging two dataframes give the expected labels
+    # The original cause of this issue come from a bug lexsort_depth and is tested in
+    # test_lexsort_depth
+
+    letters = ["a", "b", "c", "d"]
+    numbers = ["1", "2", "3"]
+    index = MultiIndex.from_product((letters, numbers), names=["outer", "inner"])
+
+    frame_x = DataFrame(columns=index)
+    frame_x["id"] = ""
+    frame_y = DataFrame(columns=index)
+    frame_y["id"] = ""
+
+    l_suf = "_x"
+    r_suf = "_y"
+    result = frame_x.merge(frame_y, on="id", suffixes=((l_suf, r_suf)))
+
+    # Constructing the expected results
+    tuples = [(letter + l_suf, num) for letter in letters for num in numbers]
+    tuples += [("id", "")]
+    tuples += [(letter + r_suf, num) for letter in letters for num in numbers]
+
+    expected_index = MultiIndex.from_tuples(tuples, names=["outer", "inner"])
+    expected = DataFrame(columns=expected_index)
+
+    tm.assert_frame_equal(result, expected, check_dtype=False)
+
+
+def test_merge_datetime_upcast_dtype():
+    # https://github.com/pandas-dev/pandas/issues/31208
+    df1 = DataFrame({"x": ["a", "b", "c"], "y": ["1", "2", "4"]})
+    df2 = DataFrame(
+        {"y": ["1", "2", "3"], "z": pd.to_datetime(["2000", "2001", "2002"])}
+    )
+    result = merge(df1, df2, how="left", on="y")
+    expected = DataFrame(
+        {
+            "x": ["a", "b", "c"],
+            "y": ["1", "2", "4"],
+            "z": pd.to_datetime(["2000", "2001", "NaT"]),
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("n_categories", [5, 128])
+def test_categorical_non_unique_monotonic(n_categories):
+    # GH 28189
+    # With n_categories as 5, we test the int8 case is hit in libjoin,
+    # with n_categories as 128 we test the int16 case.
+    left_index = CategoricalIndex([0] + list(range(n_categories)))
+    df1 = DataFrame(range(n_categories + 1), columns=["value"], index=left_index)
+    df2 = DataFrame(
+        [[6]],
+        columns=["value"],
+        index=CategoricalIndex([0], categories=list(range(n_categories))),
+    )
+
+    result = merge(df1, df2, how="left", left_index=True, right_index=True)
+    expected = DataFrame(
+        [[i, 6.0] if i < 2 else [i, np.nan] for i in range(n_categories + 1)],
+        columns=["value_x", "value_y"],
+        index=left_index,
+    )
+    tm.assert_frame_equal(expected, result)
+
+
+def test_merge_join_categorical_multiindex():
+    # From issue 16627
+    a = {
+        "Cat1": Categorical(["a", "b", "a", "c", "a", "b"], ["a", "b", "c"]),
+        "Int1": [0, 1, 0, 1, 0, 0],
+    }
+    a = DataFrame(a)
+
+    b = {
+        "Cat": Categorical(["a", "b", "c", "a", "b", "c"], ["a", "b", "c"]),
+        "Int": [0, 0, 0, 1, 1, 1],
+        "Factor": [1.1, 1.2, 1.3, 1.4, 1.5, 1.6],
+    }
+    b = DataFrame(b).set_index(["Cat", "Int"])["Factor"]
+
+    expected = merge(
+        a,
+        b.reset_index(),
+        left_on=["Cat1", "Int1"],
+        right_on=["Cat", "Int"],
+        how="left",
+    )
+    expected = expected.drop(["Cat", "Int"], axis=1)
+    result = a.join(b, on=["Cat1", "Int1"])
+    tm.assert_frame_equal(expected, result)
+
+    # Same test, but with ordered categorical
+    a = {
+        "Cat1": Categorical(
+            ["a", "b", "a", "c", "a", "b"], ["b", "a", "c"], ordered=True
+        ),
+        "Int1": [0, 1, 0, 1, 0, 0],
+    }
+    a = DataFrame(a)
+
+    b = {
+        "Cat": Categorical(
+            ["a", "b", "c", "a", "b", "c"], ["b", "a", "c"], ordered=True
+        ),
+        "Int": [0, 0, 0, 1, 1, 1],
+        "Factor": [1.1, 1.2, 1.3, 1.4, 1.5, 1.6],
+    }
+    b = DataFrame(b).set_index(["Cat", "Int"])["Factor"]
+
+    expected = merge(
+        a,
+        b.reset_index(),
+        left_on=["Cat1", "Int1"],
+        right_on=["Cat", "Int"],
+        how="left",
+    )
+    expected = expected.drop(["Cat", "Int"], axis=1)
+    result = a.join(b, on=["Cat1", "Int1"])
+    tm.assert_frame_equal(expected, result)
+
+
+@pytest.mark.parametrize("func", ["merge", "merge_asof"])
+@pytest.mark.parametrize(
+    ("kwargs", "err_msg"),
+    [
+        ({"left_on": "a", "left_index": True}, ["left_on", "left_index"]),
+        ({"right_on": "a", "right_index": True}, ["right_on", "right_index"]),
+    ],
+)
+def test_merge_join_cols_error_reporting_duplicates(func, kwargs, err_msg):
+    # GH: 16228
+    left = DataFrame({"a": [1, 2], "b": [3, 4]})
+    right = DataFrame({"a": [1, 1], "c": [5, 6]})
+    msg = rf'Can only pass argument "{err_msg[0]}" OR "{err_msg[1]}" not both\.'
+    with pytest.raises(MergeError, match=msg):
+        getattr(pd, func)(left, right, **kwargs)
+
+
+@pytest.mark.parametrize("func", ["merge", "merge_asof"])
+@pytest.mark.parametrize(
+    ("kwargs", "err_msg"),
+    [
+        ({"left_on": "a"}, ["right_on", "right_index"]),
+        ({"right_on": "a"}, ["left_on", "left_index"]),
+    ],
+)
+def test_merge_join_cols_error_reporting_missing(func, kwargs, err_msg):
+    # GH: 16228
+    left = DataFrame({"a": [1, 2], "b": [3, 4]})
+    right = DataFrame({"a": [1, 1], "c": [5, 6]})
+    msg = rf'Must pass "{err_msg[0]}" OR "{err_msg[1]}"\.'
+    with pytest.raises(MergeError, match=msg):
+        getattr(pd, func)(left, right, **kwargs)
+
+
+@pytest.mark.parametrize("func", ["merge", "merge_asof"])
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {"right_index": True},
+        {"left_index": True},
+    ],
+)
+def test_merge_join_cols_error_reporting_on_and_index(func, kwargs):
+    # GH: 16228
+    left = DataFrame({"a": [1, 2], "b": [3, 4]})
+    right = DataFrame({"a": [1, 1], "c": [5, 6]})
+    msg = (
+        r'Can only pass argument "on" OR "left_index" '
+        r'and "right_index", not a combination of both\.'
+    )
+    with pytest.raises(MergeError, match=msg):
+        getattr(pd, func)(left, right, on="a", **kwargs)
+
+
+def test_merge_right_left_index():
+    # GH#38616
+    left = DataFrame({"x": [1, 1], "z": ["foo", "foo"]})
+    right = DataFrame({"x": [1, 1], "z": ["foo", "foo"]})
+    result = merge(left, right, how="right", left_index=True, right_on="x")
+    expected = DataFrame(
+        {
+            "x": [1, 1],
+            "x_x": [1, 1],
+            "z_x": ["foo", "foo"],
+            "x_y": [1, 1],
+            "z_y": ["foo", "foo"],
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_result_empty_index_and_on():
+    # GH#33814
+    df1 = DataFrame({"a": [1], "b": [2]}).set_index(["a", "b"])
+    df2 = DataFrame({"b": [1]}).set_index(["b"])
+    expected = DataFrame({"a": [], "b": []}, dtype=np.int64).set_index(["a", "b"])
+    result = merge(df1, df2, left_on=["b"], right_index=True)
+    tm.assert_frame_equal(result, expected)
+
+    result = merge(df2, df1, left_index=True, right_on=["b"])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_suffixes_produce_dup_columns_raises():
+    # GH#22818; Enforced in 2.0
+    left = DataFrame({"a": [1, 2, 3], "b": 1, "b_x": 2})
+    right = DataFrame({"a": [1, 2, 3], "b": 2})
+
+    with pytest.raises(MergeError, match="Passing 'suffixes' which cause duplicate"):
+        merge(left, right, on="a")
+
+    with pytest.raises(MergeError, match="Passing 'suffixes' which cause duplicate"):
+        merge(right, left, on="a", suffixes=("_y", "_x"))
+
+
+def test_merge_duplicate_columns_with_suffix_no_warning():
+    # GH#22818
+    # Do not raise warning when duplicates are caused by duplicates in origin
+    left = DataFrame([[1, 1, 1], [2, 2, 2]], columns=["a", "b", "b"])
+    right = DataFrame({"a": [1, 3], "b": 2})
+    result = merge(left, right, on="a")
+    expected = DataFrame([[1, 1, 1, 2]], columns=["a", "b_x", "b_x", "b_y"])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_duplicate_columns_with_suffix_causing_another_duplicate_raises():
+    # GH#22818, Enforced in 2.0
+    # This should raise warning because suffixes cause another collision
+    left = DataFrame([[1, 1, 1, 1], [2, 2, 2, 2]], columns=["a", "b", "b", "b_x"])
+    right = DataFrame({"a": [1, 3], "b": 2})
+    with pytest.raises(MergeError, match="Passing 'suffixes' which cause duplicate"):
+        merge(left, right, on="a")
+
+
+def test_merge_string_float_column_result():
+    # GH 13353
+    df1 = DataFrame([[1, 2], [3, 4]], columns=Index(["a", 114.0]))
+    df2 = DataFrame([[9, 10], [11, 12]], columns=["x", "y"])
+    result = merge(df2, df1, how="inner", left_index=True, right_index=True)
+    expected = DataFrame(
+        [[9, 10, 1, 2], [11, 12, 3, 4]], columns=Index(["x", "y", "a", 114.0])
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_mergeerror_on_left_index_mismatched_dtypes():
+    # GH 22449
+    df_1 = DataFrame(data=["X"], columns=["C"], index=[22])
+    df_2 = DataFrame(data=["X"], columns=["C"], index=[999])
+    with pytest.raises(MergeError, match="Can only pass argument"):
+        merge(df_1, df_2, on=["C"], left_index=True)
+
+
+def test_merge_on_left_categoricalindex():
+    # GH#48464 don't raise when left_on is a CategoricalIndex
+    ci = CategoricalIndex(range(3))
+
+    right = DataFrame({"A": ci, "B": range(3)})
+    left = DataFrame({"C": range(3, 6)})
+
+    res = merge(left, right, left_on=ci, right_on="A")
+    expected = merge(left, right, left_on=ci._data, right_on="A")
+    tm.assert_frame_equal(res, expected)
+
+
+@pytest.mark.parametrize("dtype", [None, "Int64"])
+def test_merge_outer_with_NaN(dtype):
+    # GH#43550
+    left = DataFrame({"key": [1, 2], "col1": [1, 2]}, dtype=dtype)
+    right = DataFrame({"key": [np.nan, np.nan], "col2": [3, 4]}, dtype=dtype)
+    result = merge(left, right, on="key", how="outer")
+    expected = DataFrame(
+        {
+            "key": [1, 2, np.nan, np.nan],
+            "col1": [1, 2, np.nan, np.nan],
+            "col2": [np.nan, np.nan, 3, 4],
+        },
+        dtype=dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # switch left and right
+    result = merge(right, left, on="key", how="outer")
+    expected = DataFrame(
+        {
+            "key": [1, 2, np.nan, np.nan],
+            "col2": [np.nan, np.nan, 3, 4],
+            "col1": [1, 2, np.nan, np.nan],
+        },
+        dtype=dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_different_index_names():
+    # GH#45094
+    left = DataFrame({"a": [1]}, index=Index([1], name="c"))
+    right = DataFrame({"a": [1]}, index=Index([1], name="d"))
+    result = merge(left, right, left_on="c", right_on="d")
+    expected = DataFrame({"a_x": [1], "a_y": 1})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_ea(any_numeric_ea_dtype, join_type):
+    # GH#44240
+    left = DataFrame({"a": [1, 2, 3], "b": 1}, dtype=any_numeric_ea_dtype)
+    right = DataFrame({"a": [1, 2, 3], "c": 2}, dtype=any_numeric_ea_dtype)
+    result = left.merge(right, how=join_type)
+    expected = DataFrame({"a": [1, 2, 3], "b": 1, "c": 2}, dtype=any_numeric_ea_dtype)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_ea_and_non_ea(any_numeric_ea_dtype, join_type):
+    # GH#44240
+    left = DataFrame({"a": [1, 2, 3], "b": 1}, dtype=any_numeric_ea_dtype)
+    right = DataFrame({"a": [1, 2, 3], "c": 2}, dtype=any_numeric_ea_dtype.lower())
+    result = left.merge(right, how=join_type)
+    expected = DataFrame(
+        {
+            "a": Series([1, 2, 3], dtype=any_numeric_ea_dtype),
+            "b": Series([1, 1, 1], dtype=any_numeric_ea_dtype),
+            "c": Series([2, 2, 2], dtype=any_numeric_ea_dtype.lower()),
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["int64", "int64[pyarrow]"])
+def test_merge_arrow_and_numpy_dtypes(dtype):
+    # GH#52406
+    pytest.importorskip("pyarrow")
+    df = DataFrame({"a": [1, 2]}, dtype=dtype)
+    df2 = DataFrame({"a": [1, 2]}, dtype="int64[pyarrow]")
+    result = df.merge(df2)
+    expected = df.copy()
+    tm.assert_frame_equal(result, expected)
+
+    result = df2.merge(df)
+    expected = df2.copy()
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("how", ["inner", "left", "outer", "right"])
+@pytest.mark.parametrize("tz", [None, "America/Chicago"])
+def test_merge_datetime_different_resolution(tz, how):
+    # https://github.com/pandas-dev/pandas/issues/53200
+    vals = [
+        pd.Timestamp(2023, 5, 12, tz=tz),
+        pd.Timestamp(2023, 5, 13, tz=tz),
+        pd.Timestamp(2023, 5, 14, tz=tz),
+    ]
+    df1 = DataFrame({"t": vals[:2], "a": [1.0, 2.0]})
+    df1["t"] = df1["t"].dt.as_unit("ns")
+    df2 = DataFrame({"t": vals[1:], "b": [1.0, 2.0]})
+    df2["t"] = df2["t"].dt.as_unit("s")
+
+    expected = DataFrame({"t": vals, "a": [1.0, 2.0, np.nan], "b": [np.nan, 1.0, 2.0]})
+    expected["t"] = expected["t"].dt.as_unit("ns")
+    if how == "inner":
+        expected = expected.iloc[[1]].reset_index(drop=True)
+    elif how == "left":
+        expected = expected.iloc[[0, 1]]
+    elif how == "right":
+        expected = expected.iloc[[1, 2]].reset_index(drop=True)
+
+    result = df1.merge(df2, on="t", how=how)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_multiindex_single_level():
+    # GH52331
+    df = DataFrame({"col": ["A", "B"]})
+    df2 = DataFrame(
+        data={"b": [100]},
+        index=MultiIndex.from_tuples([("A",), ("C",)], names=["col"]),
+    )
+    expected = DataFrame({"col": ["A", "B"], "b": [100, np.nan]})
+
+    result = df.merge(df2, left_on=["col"], right_index=True, how="left")
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("how", ["left", "right", "inner", "outer"])
+@pytest.mark.parametrize("sort", [True, False])
+@pytest.mark.parametrize("on_index", [True, False])
+@pytest.mark.parametrize("left_unique", [True, False])
+@pytest.mark.parametrize("left_monotonic", [True, False])
+@pytest.mark.parametrize("right_unique", [True, False])
+@pytest.mark.parametrize("right_monotonic", [True, False])
+def test_merge_combinations(
+    how, sort, on_index, left_unique, left_monotonic, right_unique, right_monotonic
+):
+    # GH 54611
+    left = [2, 3]
+    if left_unique:
+        left.append(4 if left_monotonic else 1)
+    else:
+        left.append(3 if left_monotonic else 2)
+
+    right = [2, 3]
+    if right_unique:
+        right.append(4 if right_monotonic else 1)
+    else:
+        right.append(3 if right_monotonic else 2)
+
+    left = DataFrame({"key": left})
+    right = DataFrame({"key": right})
+
+    if on_index:
+        left = left.set_index("key")
+        right = right.set_index("key")
+        on_kwargs = {"left_index": True, "right_index": True}
+    else:
+        on_kwargs = {"on": "key"}
+
+    result = merge(left, right, how=how, sort=sort, **on_kwargs)
+
+    if on_index:
+        left = left.reset_index()
+        right = right.reset_index()
+
+    if how in ["left", "right", "inner"]:
+        if how in ["left", "inner"]:
+            expected, other, other_unique = left, right, right_unique
+        else:
+            expected, other, other_unique = right, left, left_unique
+        if how == "inner":
+            keep_values = set(left["key"].values).intersection(right["key"].values)
+            keep_mask = expected["key"].isin(keep_values)
+            expected = expected[keep_mask]
+        if sort:
+            expected = expected.sort_values("key")
+        if not other_unique:
+            other_value_counts = other["key"].value_counts()
+            repeats = other_value_counts.reindex(expected["key"].values, fill_value=1)
+            repeats = repeats.astype(np.intp)
+            expected = expected["key"].repeat(repeats.values)
+            expected = expected.to_frame()
+    elif how == "outer":
+        left_counts = left["key"].value_counts()
+        right_counts = right["key"].value_counts()
+        expected_counts = left_counts.mul(right_counts, fill_value=1)
+        expected_counts = expected_counts.astype(np.intp)
+        expected = expected_counts.index.values.repeat(expected_counts.values)
+        expected = DataFrame({"key": expected})
+        expected = expected.sort_values("key")
+
+    if on_index:
+        expected = expected.set_index("key")
+    else:
+        expected = expected.reset_index(drop=True)
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_ea_int_and_float_numpy():
+    # GH#46178
+    df1 = DataFrame([1.0, np.nan], dtype=pd.Int64Dtype())
+    df2 = DataFrame([1.5])
+    expected = DataFrame(columns=[0], dtype="Int64")
+
+    with tm.assert_produces_warning(UserWarning, match="You are merging"):
+        result = df1.merge(df2)
+    tm.assert_frame_equal(result, expected)
+
+    with tm.assert_produces_warning(UserWarning, match="You are merging"):
+        result = df2.merge(df1)
+    tm.assert_frame_equal(result, expected.astype("float64"))
+
+    df2 = DataFrame([1.0])
+    expected = DataFrame([1], columns=[0], dtype="Int64")
+    result = df1.merge(df2)
+    tm.assert_frame_equal(result, expected)
+
+    result = df2.merge(df1)
+    tm.assert_frame_equal(result, expected.astype("float64"))
+
+
+def test_merge_arrow_string_index(any_string_dtype):
+    # GH#54894
+    pytest.importorskip("pyarrow")
+    left = DataFrame({"a": ["a", "b"]}, dtype=any_string_dtype)
+    right = DataFrame({"b": 1}, index=Index(["a", "c"], dtype=any_string_dtype))
+    result = left.merge(right, left_on="a", right_index=True, how="left")
+    expected = DataFrame(
+        {"a": Series(["a", "b"], dtype=any_string_dtype), "b": [1, np.nan]}
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("left_empty", [True, False])
+@pytest.mark.parametrize("right_empty", [True, False])
+def test_merge_empty_frames_column_order(left_empty, right_empty):
+    # GH 51929
+    df1 = DataFrame(1, index=[0], columns=["A", "B"])
+    df2 = DataFrame(1, index=[0], columns=["A", "C", "D"])
+
+    if left_empty:
+        df1 = df1.iloc[:0]
+    if right_empty:
+        df2 = df2.iloc[:0]
+
+    result = merge(df1, df2, on=["A"], how="outer")
+    expected = DataFrame(1, index=[0], columns=["A", "B", "C", "D"])
+    if left_empty and right_empty:
+        expected = expected.iloc[:0]
+    elif left_empty:
+        expected["B"] = np.nan
+    elif right_empty:
+        expected[["C", "D"]] = np.nan
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("how", ["left", "right", "inner", "outer"])
+def test_merge_datetime_and_timedelta(how):
+    left = DataFrame({"key": Series([1, None], dtype="datetime64[ns]")})
+    right = DataFrame({"key": Series([1], dtype="timedelta64[ns]")})
+
+    msg = (
+        f"You are trying to merge on {left['key'].dtype} and {right['key'].dtype} "
+        "columns for key 'key'. If you wish to proceed you should use pd.concat"
+    )
+    with pytest.raises(ValueError, match=re.escape(msg)):
+        left.merge(right, on="key", how=how)
+
+    msg = (
+        f"You are trying to merge on {right['key'].dtype} and {left['key'].dtype} "
+        "columns for key 'key'. If you wish to proceed you should use pd.concat"
+    )
+    with pytest.raises(ValueError, match=re.escape(msg)):
+        right.merge(left, on="key", how=how)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_merge_asof.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_merge_asof.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2e22ea73fd86ef1795298d99404bfdb04be81f2
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_merge_asof.py
@@ -0,0 +1,3657 @@
+import datetime
+
+import numpy as np
+import pytest
+import pytz
+
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    Index,
+    Timedelta,
+    merge_asof,
+    option_context,
+    to_datetime,
+)
+import pandas._testing as tm
+from pandas.core.reshape.merge import MergeError
+
+
+@pytest.fixture(params=["s", "ms", "us", "ns"])
+def unit(request):
+    """
+    Resolution for datetimelike dtypes.
+    """
+    return request.param
+
+
+class TestAsOfMerge:
+    def prep_data(self, df, dedupe=False):
+        if dedupe:
+            df = df.drop_duplicates(["time", "ticker"], keep="last").reset_index(
+                drop=True
+            )
+        df.time = to_datetime(df.time)
+        return df
+
+    @pytest.fixture
+    def trades(self):
+        df = pd.DataFrame(
+            [
+                ["20160525 13:30:00.023", "MSFT", "51.9500", "75", "NASDAQ"],
+                ["20160525 13:30:00.038", "MSFT", "51.9500", "155", "NASDAQ"],
+                ["20160525 13:30:00.048", "GOOG", "720.7700", "100", "NASDAQ"],
+                ["20160525 13:30:00.048", "GOOG", "720.9200", "100", "NASDAQ"],
+                ["20160525 13:30:00.048", "GOOG", "720.9300", "200", "NASDAQ"],
+                ["20160525 13:30:00.048", "GOOG", "720.9300", "300", "NASDAQ"],
+                ["20160525 13:30:00.048", "GOOG", "720.9300", "600", "NASDAQ"],
+                ["20160525 13:30:00.048", "GOOG", "720.9300", "44", "NASDAQ"],
+                ["20160525 13:30:00.074", "AAPL", "98.6700", "478343", "NASDAQ"],
+                ["20160525 13:30:00.075", "AAPL", "98.6700", "478343", "NASDAQ"],
+                ["20160525 13:30:00.075", "AAPL", "98.6600", "6", "NASDAQ"],
+                ["20160525 13:30:00.075", "AAPL", "98.6500", "30", "NASDAQ"],
+                ["20160525 13:30:00.075", "AAPL", "98.6500", "75", "NASDAQ"],
+                ["20160525 13:30:00.075", "AAPL", "98.6500", "20", "NASDAQ"],
+                ["20160525 13:30:00.075", "AAPL", "98.6500", "35", "NASDAQ"],
+                ["20160525 13:30:00.075", "AAPL", "98.6500", "10", "NASDAQ"],
+                ["20160525 13:30:00.075", "AAPL", "98.5500", "6", "ARCA"],
+                ["20160525 13:30:00.075", "AAPL", "98.5500", "6", "ARCA"],
+                ["20160525 13:30:00.076", "AAPL", "98.5600", "1000", "ARCA"],
+                ["20160525 13:30:00.076", "AAPL", "98.5600", "200", "ARCA"],
+                ["20160525 13:30:00.076", "AAPL", "98.5600", "300", "ARCA"],
+                ["20160525 13:30:00.076", "AAPL", "98.5600", "400", "ARCA"],
+                ["20160525 13:30:00.076", "AAPL", "98.5600", "600", "ARCA"],
+                ["20160525 13:30:00.076", "AAPL", "98.5600", "200", "ARCA"],
+                ["20160525 13:30:00.078", "MSFT", "51.9500", "783", "NASDAQ"],
+                ["20160525 13:30:00.078", "MSFT", "51.9500", "100", "NASDAQ"],
+                ["20160525 13:30:00.078", "MSFT", "51.9500", "100", "NASDAQ"],
+            ],
+            columns="time,ticker,price,quantity,marketCenter".split(","),
+        )
+        df["price"] = df["price"].astype("float64")
+        df["quantity"] = df["quantity"].astype("int64")
+        return self.prep_data(df)
+
+    @pytest.fixture
+    def quotes(self):
+        df = pd.DataFrame(
+            [
+                ["20160525 13:30:00.023", "GOOG", "720.50", "720.93"],
+                ["20160525 13:30:00.023", "MSFT", "51.95", "51.95"],
+                ["20160525 13:30:00.041", "MSFT", "51.95", "51.95"],
+                ["20160525 13:30:00.048", "GOOG", "720.50", "720.93"],
+                ["20160525 13:30:00.048", "GOOG", "720.50", "720.93"],
+                ["20160525 13:30:00.048", "GOOG", "720.50", "720.93"],
+                ["20160525 13:30:00.048", "GOOG", "720.50", "720.93"],
+                ["20160525 13:30:00.072", "GOOG", "720.50", "720.88"],
+                ["20160525 13:30:00.075", "AAPL", "98.55", "98.56"],
+                ["20160525 13:30:00.076", "AAPL", "98.55", "98.56"],
+                ["20160525 13:30:00.076", "AAPL", "98.55", "98.56"],
+                ["20160525 13:30:00.076", "AAPL", "98.55", "98.56"],
+                ["20160525 13:30:00.078", "MSFT", "51.95", "51.95"],
+                ["20160525 13:30:00.078", "MSFT", "51.95", "51.95"],
+                ["20160525 13:30:00.078", "MSFT", "51.95", "51.95"],
+                ["20160525 13:30:00.078", "MSFT", "51.92", "51.95"],
+            ],
+            columns="time,ticker,bid,ask".split(","),
+        )
+        df["bid"] = df["bid"].astype("float64")
+        df["ask"] = df["ask"].astype("float64")
+        return self.prep_data(df, dedupe=True)
+
+    @pytest.fixture
+    def asof(self):
+        df = pd.DataFrame(
+            [
+                [
+                    "20160525 13:30:00.023",
+                    "MSFT",
+                    "51.95",
+                    "75",
+                    "NASDAQ",
+                    "51.95",
+                    "51.95",
+                ],
+                [
+                    "20160525 13:30:00.038",
+                    "MSFT",
+                    "51.95",
+                    "155",
+                    "NASDAQ",
+                    "51.95",
+                    "51.95",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.77",
+                    "100",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.92",
+                    "100",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.93",
+                    "200",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.93",
+                    "300",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.93",
+                    "600",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.93",
+                    "44",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.074",
+                    "AAPL",
+                    "98.67",
+                    "478343",
+                    "NASDAQ",
+                    np.nan,
+                    np.nan,
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.67",
+                    "478343",
+                    "NASDAQ",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.66",
+                    "6",
+                    "NASDAQ",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "30",
+                    "NASDAQ",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "75",
+                    "NASDAQ",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "20",
+                    "NASDAQ",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "35",
+                    "NASDAQ",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "10",
+                    "NASDAQ",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.55",
+                    "6",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.55",
+                    "6",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "1000",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "200",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "300",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "400",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "600",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "200",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.078",
+                    "MSFT",
+                    "51.95",
+                    "783",
+                    "NASDAQ",
+                    "51.92",
+                    "51.95",
+                ],
+                [
+                    "20160525 13:30:00.078",
+                    "MSFT",
+                    "51.95",
+                    "100",
+                    "NASDAQ",
+                    "51.92",
+                    "51.95",
+                ],
+                [
+                    "20160525 13:30:00.078",
+                    "MSFT",
+                    "51.95",
+                    "100",
+                    "NASDAQ",
+                    "51.92",
+                    "51.95",
+                ],
+            ],
+            columns="time,ticker,price,quantity,marketCenter,bid,ask".split(","),
+        )
+        df["price"] = df["price"].astype("float64")
+        df["quantity"] = df["quantity"].astype("int64")
+        df["bid"] = df["bid"].astype("float64")
+        df["ask"] = df["ask"].astype("float64")
+        return self.prep_data(df)
+
+    @pytest.fixture
+    def tolerance(self):
+        df = pd.DataFrame(
+            [
+                [
+                    "20160525 13:30:00.023",
+                    "MSFT",
+                    "51.95",
+                    "75",
+                    "NASDAQ",
+                    "51.95",
+                    "51.95",
+                ],
+                [
+                    "20160525 13:30:00.038",
+                    "MSFT",
+                    "51.95",
+                    "155",
+                    "NASDAQ",
+                    "51.95",
+                    "51.95",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.77",
+                    "100",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.92",
+                    "100",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.93",
+                    "200",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.93",
+                    "300",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.93",
+                    "600",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.93",
+                    "44",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.074",
+                    "AAPL",
+                    "98.67",
+                    "478343",
+                    "NASDAQ",
+                    np.nan,
+                    np.nan,
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.67",
+                    "478343",
+                    "NASDAQ",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.66",
+                    "6",
+                    "NASDAQ",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "30",
+                    "NASDAQ",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "75",
+                    "NASDAQ",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "20",
+                    "NASDAQ",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "35",
+                    "NASDAQ",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "10",
+                    "NASDAQ",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.55",
+                    "6",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.55",
+                    "6",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "1000",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "200",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "300",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "400",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "600",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "200",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.078",
+                    "MSFT",
+                    "51.95",
+                    "783",
+                    "NASDAQ",
+                    "51.92",
+                    "51.95",
+                ],
+                [
+                    "20160525 13:30:00.078",
+                    "MSFT",
+                    "51.95",
+                    "100",
+                    "NASDAQ",
+                    "51.92",
+                    "51.95",
+                ],
+                [
+                    "20160525 13:30:00.078",
+                    "MSFT",
+                    "51.95",
+                    "100",
+                    "NASDAQ",
+                    "51.92",
+                    "51.95",
+                ],
+            ],
+            columns="time,ticker,price,quantity,marketCenter,bid,ask".split(","),
+        )
+        df["price"] = df["price"].astype("float64")
+        df["quantity"] = df["quantity"].astype("int64")
+        df["bid"] = df["bid"].astype("float64")
+        df["ask"] = df["ask"].astype("float64")
+        return self.prep_data(df)
+
+    @pytest.fixture
+    def allow_exact_matches(self, datapath):
+        df = pd.DataFrame(
+            [
+                [
+                    "20160525 13:30:00.023",
+                    "MSFT",
+                    "51.95",
+                    "75",
+                    "NASDAQ",
+                    np.nan,
+                    np.nan,
+                ],
+                [
+                    "20160525 13:30:00.038",
+                    "MSFT",
+                    "51.95",
+                    "155",
+                    "NASDAQ",
+                    "51.95",
+                    "51.95",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.77",
+                    "100",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.92",
+                    "100",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.93",
+                    "200",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.93",
+                    "300",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.93",
+                    "600",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.93",
+                    "44",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.074",
+                    "AAPL",
+                    "98.67",
+                    "478343",
+                    "NASDAQ",
+                    np.nan,
+                    np.nan,
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.67",
+                    "478343",
+                    "NASDAQ",
+                    np.nan,
+                    np.nan,
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.66",
+                    "6",
+                    "NASDAQ",
+                    np.nan,
+                    np.nan,
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "30",
+                    "NASDAQ",
+                    np.nan,
+                    np.nan,
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "75",
+                    "NASDAQ",
+                    np.nan,
+                    np.nan,
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "20",
+                    "NASDAQ",
+                    np.nan,
+                    np.nan,
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "35",
+                    "NASDAQ",
+                    np.nan,
+                    np.nan,
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "10",
+                    "NASDAQ",
+                    np.nan,
+                    np.nan,
+                ],
+                ["20160525 13:30:00.075", "AAPL", "98.55", "6", "ARCA", np.nan, np.nan],
+                ["20160525 13:30:00.075", "AAPL", "98.55", "6", "ARCA", np.nan, np.nan],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "1000",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "200",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "300",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "400",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "600",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "200",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.078",
+                    "MSFT",
+                    "51.95",
+                    "783",
+                    "NASDAQ",
+                    "51.95",
+                    "51.95",
+                ],
+                [
+                    "20160525 13:30:00.078",
+                    "MSFT",
+                    "51.95",
+                    "100",
+                    "NASDAQ",
+                    "51.95",
+                    "51.95",
+                ],
+                [
+                    "20160525 13:30:00.078",
+                    "MSFT",
+                    "51.95",
+                    "100",
+                    "NASDAQ",
+                    "51.95",
+                    "51.95",
+                ],
+            ],
+            columns="time,ticker,price,quantity,marketCenter,bid,ask".split(","),
+        )
+        df["price"] = df["price"].astype("float64")
+        df["quantity"] = df["quantity"].astype("int64")
+        df["bid"] = df["bid"].astype("float64")
+        df["ask"] = df["ask"].astype("float64")
+        return self.prep_data(df)
+
+    @pytest.fixture
+    def allow_exact_matches_and_tolerance(self):
+        df = pd.DataFrame(
+            [
+                [
+                    "20160525 13:30:00.023",
+                    "MSFT",
+                    "51.95",
+                    "75",
+                    "NASDAQ",
+                    np.nan,
+                    np.nan,
+                ],
+                [
+                    "20160525 13:30:00.038",
+                    "MSFT",
+                    "51.95",
+                    "155",
+                    "NASDAQ",
+                    "51.95",
+                    "51.95",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.77",
+                    "100",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.92",
+                    "100",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.93",
+                    "200",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.93",
+                    "300",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.93",
+                    "600",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.93",
+                    "44",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.074",
+                    "AAPL",
+                    "98.67",
+                    "478343",
+                    "NASDAQ",
+                    np.nan,
+                    np.nan,
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.67",
+                    "478343",
+                    "NASDAQ",
+                    np.nan,
+                    np.nan,
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.66",
+                    "6",
+                    "NASDAQ",
+                    np.nan,
+                    np.nan,
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "30",
+                    "NASDAQ",
+                    np.nan,
+                    np.nan,
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "75",
+                    "NASDAQ",
+                    np.nan,
+                    np.nan,
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "20",
+                    "NASDAQ",
+                    np.nan,
+                    np.nan,
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "35",
+                    "NASDAQ",
+                    np.nan,
+                    np.nan,
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "10",
+                    "NASDAQ",
+                    np.nan,
+                    np.nan,
+                ],
+                ["20160525 13:30:00.075", "AAPL", "98.55", "6", "ARCA", np.nan, np.nan],
+                ["20160525 13:30:00.075", "AAPL", "98.55", "6", "ARCA", np.nan, np.nan],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "1000",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "200",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "300",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "400",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "600",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "200",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.078",
+                    "MSFT",
+                    "51.95",
+                    "783",
+                    "NASDAQ",
+                    "51.95",
+                    "51.95",
+                ],
+                [
+                    "20160525 13:30:00.078",
+                    "MSFT",
+                    "51.95",
+                    "100",
+                    "NASDAQ",
+                    "51.95",
+                    "51.95",
+                ],
+                [
+                    "20160525 13:30:00.078",
+                    "MSFT",
+                    "51.95",
+                    "100",
+                    "NASDAQ",
+                    "51.95",
+                    "51.95",
+                ],
+            ],
+            columns="time,ticker,price,quantity,marketCenter,bid,ask".split(","),
+        )
+        df["price"] = df["price"].astype("float64")
+        df["quantity"] = df["quantity"].astype("int64")
+        df["bid"] = df["bid"].astype("float64")
+        df["ask"] = df["ask"].astype("float64")
+        return self.prep_data(df)
+
+    def test_examples1(self):
+        """doc-string examples"""
+        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
+        right = pd.DataFrame({"a": [1, 2, 3, 6, 7], "right_val": [1, 2, 3, 6, 7]})
+
+        expected = pd.DataFrame(
+            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [1, 3, 7]}
+        )
+
+        result = merge_asof(left, right, on="a")
+        tm.assert_frame_equal(result, expected)
+
+    def test_examples2(self, unit):
+        """doc-string examples"""
+        if unit == "s":
+            pytest.skip(
+                "This test is invalid for unit='s' because that would "
+                "round the trades['time']]"
+            )
+        trades = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    [
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.038",
+                        "20160525 13:30:00.048",
+                        "20160525 13:30:00.048",
+                        "20160525 13:30:00.048",
+                    ]
+                ).astype(f"M8[{unit}]"),
+                "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
+                "price": [51.95, 51.95, 720.77, 720.92, 98.00],
+                "quantity": [75, 155, 100, 100, 100],
+            },
+            columns=["time", "ticker", "price", "quantity"],
+        )
+
+        quotes = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    [
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.030",
+                        "20160525 13:30:00.041",
+                        "20160525 13:30:00.048",
+                        "20160525 13:30:00.049",
+                        "20160525 13:30:00.072",
+                        "20160525 13:30:00.075",
+                    ]
+                ).astype(f"M8[{unit}]"),
+                "ticker": [
+                    "GOOG",
+                    "MSFT",
+                    "MSFT",
+                    "MSFT",
+                    "GOOG",
+                    "AAPL",
+                    "GOOG",
+                    "MSFT",
+                ],
+                "bid": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99, 720.50, 52.01],
+                "ask": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01, 720.88, 52.03],
+            },
+            columns=["time", "ticker", "bid", "ask"],
+        )
+
+        merge_asof(trades, quotes, on="time", by="ticker")
+
+        merge_asof(trades, quotes, on="time", by="ticker", tolerance=Timedelta("2ms"))
+
+        expected = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    [
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.038",
+                        "20160525 13:30:00.048",
+                        "20160525 13:30:00.048",
+                        "20160525 13:30:00.048",
+                    ]
+                ).astype(f"M8[{unit}]"),
+                "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
+                "price": [51.95, 51.95, 720.77, 720.92, 98.00],
+                "quantity": [75, 155, 100, 100, 100],
+                "bid": [np.nan, 51.97, np.nan, np.nan, np.nan],
+                "ask": [np.nan, 51.98, np.nan, np.nan, np.nan],
+            },
+            columns=["time", "ticker", "price", "quantity", "bid", "ask"],
+        )
+
+        result = merge_asof(
+            trades,
+            quotes,
+            on="time",
+            by="ticker",
+            tolerance=Timedelta("10ms"),
+            allow_exact_matches=False,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_examples3(self):
+        """doc-string examples"""
+        # GH14887
+
+        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
+        right = pd.DataFrame({"a": [1, 2, 3, 6, 7], "right_val": [1, 2, 3, 6, 7]})
+
+        expected = pd.DataFrame(
+            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [1, 6, np.nan]}
+        )
+
+        result = merge_asof(left, right, on="a", direction="forward")
+        tm.assert_frame_equal(result, expected)
+
+    def test_examples4(self):
+        """doc-string examples"""
+        # GH14887
+
+        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
+        right = pd.DataFrame({"a": [1, 2, 3, 6, 7], "right_val": [1, 2, 3, 6, 7]})
+
+        expected = pd.DataFrame(
+            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [1, 6, 7]}
+        )
+
+        result = merge_asof(left, right, on="a", direction="nearest")
+        tm.assert_frame_equal(result, expected)
+
+    def test_basic(self, trades, asof, quotes):
+        expected = asof
+
+        result = merge_asof(trades, quotes, on="time", by="ticker")
+        tm.assert_frame_equal(result, expected)
+
+    def test_basic_categorical(self, trades, asof, quotes):
+        expected = asof
+        trades.ticker = trades.ticker.astype("category")
+        quotes.ticker = quotes.ticker.astype("category")
+        expected.ticker = expected.ticker.astype("category")
+
+        result = merge_asof(trades, quotes, on="time", by="ticker")
+        tm.assert_frame_equal(result, expected)
+
+    def test_basic_left_index(self, trades, asof, quotes):
+        # GH14253
+        expected = asof
+        trades = trades.set_index("time")
+
+        result = merge_asof(
+            trades, quotes, left_index=True, right_on="time", by="ticker"
+        )
+        # left-only index uses right"s index, oddly
+        expected.index = result.index
+        # time column appears after left"s columns
+        expected = expected[result.columns]
+        tm.assert_frame_equal(result, expected)
+
+    def test_basic_right_index(self, trades, asof, quotes):
+        expected = asof
+        quotes = quotes.set_index("time")
+
+        result = merge_asof(
+            trades, quotes, left_on="time", right_index=True, by="ticker"
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_basic_left_index_right_index(self, trades, asof, quotes):
+        expected = asof.set_index("time")
+        trades = trades.set_index("time")
+        quotes = quotes.set_index("time")
+
+        result = merge_asof(
+            trades, quotes, left_index=True, right_index=True, by="ticker"
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_multi_index_left(self, trades, quotes):
+        # MultiIndex is prohibited
+        trades = trades.set_index(["time", "price"])
+        quotes = quotes.set_index("time")
+        with pytest.raises(MergeError, match="left can only have one index"):
+            merge_asof(trades, quotes, left_index=True, right_index=True)
+
+    def test_multi_index_right(self, trades, quotes):
+        # MultiIndex is prohibited
+        trades = trades.set_index("time")
+        quotes = quotes.set_index(["time", "bid"])
+        with pytest.raises(MergeError, match="right can only have one index"):
+            merge_asof(trades, quotes, left_index=True, right_index=True)
+
+    def test_on_and_index_left_on(self, trades, quotes):
+        # "on" parameter and index together is prohibited
+        trades = trades.set_index("time")
+        quotes = quotes.set_index("time")
+        msg = 'Can only pass argument "left_on" OR "left_index" not both.'
+        with pytest.raises(MergeError, match=msg):
+            merge_asof(
+                trades, quotes, left_on="price", left_index=True, right_index=True
+            )
+
+    def test_on_and_index_right_on(self, trades, quotes):
+        trades = trades.set_index("time")
+        quotes = quotes.set_index("time")
+        msg = 'Can only pass argument "right_on" OR "right_index" not both.'
+        with pytest.raises(MergeError, match=msg):
+            merge_asof(
+                trades, quotes, right_on="bid", left_index=True, right_index=True
+            )
+
+    def test_basic_left_by_right_by(self, trades, asof, quotes):
+        # GH14253
+        expected = asof
+
+        result = merge_asof(
+            trades, quotes, on="time", left_by="ticker", right_by="ticker"
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_missing_right_by(self, trades, asof, quotes):
+        expected = asof
+
+        q = quotes[quotes.ticker != "MSFT"]
+        result = merge_asof(trades, q, on="time", by="ticker")
+        expected.loc[expected.ticker == "MSFT", ["bid", "ask"]] = np.nan
+        tm.assert_frame_equal(result, expected)
+
+    def test_multiby(self):
+        # GH13936
+        trades = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    [
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.046",
+                        "20160525 13:30:00.048",
+                        "20160525 13:30:00.050",
+                    ]
+                ),
+                "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
+                "exch": ["ARCA", "NSDQ", "NSDQ", "BATS", "NSDQ"],
+                "price": [51.95, 51.95, 720.77, 720.92, 98.00],
+                "quantity": [75, 155, 100, 100, 100],
+            },
+            columns=["time", "ticker", "exch", "price", "quantity"],
+        )
+
+        quotes = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    [
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.030",
+                        "20160525 13:30:00.041",
+                        "20160525 13:30:00.045",
+                        "20160525 13:30:00.049",
+                    ]
+                ),
+                "ticker": ["GOOG", "MSFT", "MSFT", "MSFT", "GOOG", "AAPL"],
+                "exch": ["BATS", "NSDQ", "ARCA", "ARCA", "NSDQ", "ARCA"],
+                "bid": [720.51, 51.95, 51.97, 51.99, 720.50, 97.99],
+                "ask": [720.92, 51.96, 51.98, 52.00, 720.93, 98.01],
+            },
+            columns=["time", "ticker", "exch", "bid", "ask"],
+        )
+
+        expected = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    [
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.046",
+                        "20160525 13:30:00.048",
+                        "20160525 13:30:00.050",
+                    ]
+                ),
+                "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
+                "exch": ["ARCA", "NSDQ", "NSDQ", "BATS", "NSDQ"],
+                "price": [51.95, 51.95, 720.77, 720.92, 98.00],
+                "quantity": [75, 155, 100, 100, 100],
+                "bid": [np.nan, 51.95, 720.50, 720.51, np.nan],
+                "ask": [np.nan, 51.96, 720.93, 720.92, np.nan],
+            },
+            columns=["time", "ticker", "exch", "price", "quantity", "bid", "ask"],
+        )
+
+        result = merge_asof(trades, quotes, on="time", by=["ticker", "exch"])
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", ["object", "string"])
+    def test_multiby_heterogeneous_types(self, dtype):
+        # GH13936
+        trades = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    [
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.046",
+                        "20160525 13:30:00.048",
+                        "20160525 13:30:00.050",
+                    ]
+                ),
+                "ticker": [0, 0, 1, 1, 2],
+                "exch": ["ARCA", "NSDQ", "NSDQ", "BATS", "NSDQ"],
+                "price": [51.95, 51.95, 720.77, 720.92, 98.00],
+                "quantity": [75, 155, 100, 100, 100],
+            },
+            columns=["time", "ticker", "exch", "price", "quantity"],
+        )
+        trades = trades.astype({"ticker": dtype, "exch": dtype})
+
+        quotes = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    [
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.030",
+                        "20160525 13:30:00.041",
+                        "20160525 13:30:00.045",
+                        "20160525 13:30:00.049",
+                    ]
+                ),
+                "ticker": [1, 0, 0, 0, 1, 2],
+                "exch": ["BATS", "NSDQ", "ARCA", "ARCA", "NSDQ", "ARCA"],
+                "bid": [720.51, 51.95, 51.97, 51.99, 720.50, 97.99],
+                "ask": [720.92, 51.96, 51.98, 52.00, 720.93, 98.01],
+            },
+            columns=["time", "ticker", "exch", "bid", "ask"],
+        )
+        quotes = quotes.astype({"ticker": dtype, "exch": dtype})
+
+        expected = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    [
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.046",
+                        "20160525 13:30:00.048",
+                        "20160525 13:30:00.050",
+                    ]
+                ),
+                "ticker": [0, 0, 1, 1, 2],
+                "exch": ["ARCA", "NSDQ", "NSDQ", "BATS", "NSDQ"],
+                "price": [51.95, 51.95, 720.77, 720.92, 98.00],
+                "quantity": [75, 155, 100, 100, 100],
+                "bid": [np.nan, 51.95, 720.50, 720.51, np.nan],
+                "ask": [np.nan, 51.96, 720.93, 720.92, np.nan],
+            },
+            columns=["time", "ticker", "exch", "price", "quantity", "bid", "ask"],
+        )
+        expected = expected.astype({"ticker": dtype, "exch": dtype})
+
+        result = merge_asof(trades, quotes, on="time", by=["ticker", "exch"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_mismatched_index_dtype(self):
+        # similar to test_multiby_indexed, but we change the dtype on left.index
+        left = pd.DataFrame(
+            [
+                [to_datetime("20160602"), 1, "a"],
+                [to_datetime("20160602"), 2, "a"],
+                [to_datetime("20160603"), 1, "b"],
+                [to_datetime("20160603"), 2, "b"],
+            ],
+            columns=["time", "k1", "k2"],
+        ).set_index("time")
+        # different dtype for the index
+        left.index = left.index - pd.Timestamp(0)
+
+        right = pd.DataFrame(
+            [
+                [to_datetime("20160502"), 1, "a", 1.0],
+                [to_datetime("20160502"), 2, "a", 2.0],
+                [to_datetime("20160503"), 1, "b", 3.0],
+                [to_datetime("20160503"), 2, "b", 4.0],
+            ],
+            columns=["time", "k1", "k2", "value"],
+        ).set_index("time")
+
+        msg = "incompatible merge keys"
+        with pytest.raises(MergeError, match=msg):
+            merge_asof(left, right, left_index=True, right_index=True, by=["k1", "k2"])
+
+    def test_multiby_indexed(self):
+        # GH15676
+        left = pd.DataFrame(
+            [
+                [to_datetime("20160602"), 1, "a"],
+                [to_datetime("20160602"), 2, "a"],
+                [to_datetime("20160603"), 1, "b"],
+                [to_datetime("20160603"), 2, "b"],
+            ],
+            columns=["time", "k1", "k2"],
+        ).set_index("time")
+
+        right = pd.DataFrame(
+            [
+                [to_datetime("20160502"), 1, "a", 1.0],
+                [to_datetime("20160502"), 2, "a", 2.0],
+                [to_datetime("20160503"), 1, "b", 3.0],
+                [to_datetime("20160503"), 2, "b", 4.0],
+            ],
+            columns=["time", "k1", "k2", "value"],
+        ).set_index("time")
+
+        expected = pd.DataFrame(
+            [
+                [to_datetime("20160602"), 1, "a", 1.0],
+                [to_datetime("20160602"), 2, "a", 2.0],
+                [to_datetime("20160603"), 1, "b", 3.0],
+                [to_datetime("20160603"), 2, "b", 4.0],
+            ],
+            columns=["time", "k1", "k2", "value"],
+        ).set_index("time")
+
+        result = merge_asof(
+            left, right, left_index=True, right_index=True, by=["k1", "k2"]
+        )
+
+        tm.assert_frame_equal(expected, result)
+
+        with pytest.raises(
+            MergeError, match="left_by and right_by must be the same length"
+        ):
+            merge_asof(
+                left,
+                right,
+                left_index=True,
+                right_index=True,
+                left_by=["k1", "k2"],
+                right_by=["k1"],
+            )
+
+    def test_basic2(self, datapath):
+        expected = pd.DataFrame(
+            [
+                [
+                    "20160525 13:30:00.023",
+                    "MSFT",
+                    "51.95",
+                    "75",
+                    "NASDAQ",
+                    "51.95",
+                    "51.95",
+                ],
+                [
+                    "20160525 13:30:00.038",
+                    "MSFT",
+                    "51.95",
+                    "155",
+                    "NASDAQ",
+                    "51.95",
+                    "51.95",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.77",
+                    "100",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.92",
+                    "100",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.93",
+                    "200",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.93",
+                    "300",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.93",
+                    "600",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.048",
+                    "GOOG",
+                    "720.93",
+                    "44",
+                    "NASDAQ",
+                    "720.5",
+                    "720.93",
+                ],
+                [
+                    "20160525 13:30:00.074",
+                    "AAPL",
+                    "98.67",
+                    "478343",
+                    "NASDAQ",
+                    np.nan,
+                    np.nan,
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.67",
+                    "478343",
+                    "NASDAQ",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.66",
+                    "6",
+                    "NASDAQ",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "30",
+                    "NASDAQ",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "75",
+                    "NASDAQ",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "20",
+                    "NASDAQ",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "35",
+                    "NASDAQ",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.65",
+                    "10",
+                    "NASDAQ",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.55",
+                    "6",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.075",
+                    "AAPL",
+                    "98.55",
+                    "6",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "1000",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "200",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "300",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "400",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "600",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.076",
+                    "AAPL",
+                    "98.56",
+                    "200",
+                    "ARCA",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.078",
+                    "MSFT",
+                    "51.95",
+                    "783",
+                    "NASDAQ",
+                    "51.92",
+                    "51.95",
+                ],
+                [
+                    "20160525 13:30:00.078",
+                    "MSFT",
+                    "51.95",
+                    "100",
+                    "NASDAQ",
+                    "51.92",
+                    "51.95",
+                ],
+                [
+                    "20160525 13:30:00.078",
+                    "MSFT",
+                    "51.95",
+                    "100",
+                    "NASDAQ",
+                    "51.92",
+                    "51.95",
+                ],
+                [
+                    "20160525 13:30:00.084",
+                    "AAPL",
+                    "98.64",
+                    "40",
+                    "NASDAQ",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.084",
+                    "AAPL",
+                    "98.55",
+                    "149",
+                    "EDGX",
+                    "98.55",
+                    "98.56",
+                ],
+                [
+                    "20160525 13:30:00.086",
+                    "AAPL",
+                    "98.56",
+                    "500",
+                    "ARCA",
+                    "98.55",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.104",
+                    "AAPL",
+                    "98.63",
+                    "647",
+                    "EDGX",
+                    "98.62",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.104",
+                    "AAPL",
+                    "98.63",
+                    "300",
+                    "EDGX",
+                    "98.62",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.104",
+                    "AAPL",
+                    "98.63",
+                    "50",
+                    "NASDAQ",
+                    "98.62",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.104",
+                    "AAPL",
+                    "98.63",
+                    "50",
+                    "NASDAQ",
+                    "98.62",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.104",
+                    "AAPL",
+                    "98.63",
+                    "70",
+                    "NASDAQ",
+                    "98.62",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.104",
+                    "AAPL",
+                    "98.63",
+                    "70",
+                    "NASDAQ",
+                    "98.62",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.104",
+                    "AAPL",
+                    "98.63",
+                    "1",
+                    "NASDAQ",
+                    "98.62",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.104",
+                    "AAPL",
+                    "98.63",
+                    "62",
+                    "NASDAQ",
+                    "98.62",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.104",
+                    "AAPL",
+                    "98.63",
+                    "10",
+                    "NASDAQ",
+                    "98.62",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.104",
+                    "AAPL",
+                    "98.63",
+                    "100",
+                    "ARCA",
+                    "98.62",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.105",
+                    "AAPL",
+                    "98.63",
+                    "100",
+                    "ARCA",
+                    "98.62",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.105",
+                    "AAPL",
+                    "98.63",
+                    "700",
+                    "ARCA",
+                    "98.62",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.106",
+                    "AAPL",
+                    "98.63",
+                    "61",
+                    "EDGX",
+                    "98.62",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.107",
+                    "AAPL",
+                    "98.63",
+                    "100",
+                    "ARCA",
+                    "98.62",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.107",
+                    "AAPL",
+                    "98.63",
+                    "53",
+                    "ARCA",
+                    "98.62",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.108",
+                    "AAPL",
+                    "98.63",
+                    "100",
+                    "ARCA",
+                    "98.62",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.108",
+                    "AAPL",
+                    "98.63",
+                    "839",
+                    "ARCA",
+                    "98.62",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.115",
+                    "AAPL",
+                    "98.63",
+                    "5",
+                    "EDGX",
+                    "98.62",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.118",
+                    "AAPL",
+                    "98.63",
+                    "295",
+                    "EDGX",
+                    "98.62",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.118",
+                    "AAPL",
+                    "98.63",
+                    "5",
+                    "EDGX",
+                    "98.62",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.128",
+                    "AAPL",
+                    "98.63",
+                    "100",
+                    "NASDAQ",
+                    "98.62",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.128",
+                    "AAPL",
+                    "98.63",
+                    "100",
+                    "NASDAQ",
+                    "98.62",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.128",
+                    "MSFT",
+                    "51.92",
+                    "100",
+                    "ARCA",
+                    "51.92",
+                    "51.95",
+                ],
+                [
+                    "20160525 13:30:00.129",
+                    "AAPL",
+                    "98.62",
+                    "100",
+                    "NASDAQ",
+                    "98.61",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.129",
+                    "AAPL",
+                    "98.62",
+                    "10",
+                    "NASDAQ",
+                    "98.61",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.129",
+                    "AAPL",
+                    "98.62",
+                    "59",
+                    "NASDAQ",
+                    "98.61",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.129",
+                    "AAPL",
+                    "98.62",
+                    "31",
+                    "NASDAQ",
+                    "98.61",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.129",
+                    "AAPL",
+                    "98.62",
+                    "69",
+                    "NASDAQ",
+                    "98.61",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.129",
+                    "AAPL",
+                    "98.62",
+                    "12",
+                    "NASDAQ",
+                    "98.61",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.129",
+                    "AAPL",
+                    "98.62",
+                    "12",
+                    "EDGX",
+                    "98.61",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.129",
+                    "AAPL",
+                    "98.62",
+                    "100",
+                    "ARCA",
+                    "98.61",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.129",
+                    "AAPL",
+                    "98.62",
+                    "100",
+                    "ARCA",
+                    "98.61",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.130",
+                    "MSFT",
+                    "51.95",
+                    "317",
+                    "ARCA",
+                    "51.93",
+                    "51.95",
+                ],
+                [
+                    "20160525 13:30:00.130",
+                    "MSFT",
+                    "51.95",
+                    "283",
+                    "ARCA",
+                    "51.93",
+                    "51.95",
+                ],
+                [
+                    "20160525 13:30:00.135",
+                    "MSFT",
+                    "51.93",
+                    "100",
+                    "EDGX",
+                    "51.92",
+                    "51.95",
+                ],
+                [
+                    "20160525 13:30:00.135",
+                    "AAPL",
+                    "98.62",
+                    "100",
+                    "ARCA",
+                    "98.61",
+                    "98.62",
+                ],
+                [
+                    "20160525 13:30:00.144",
+                    "AAPL",
+                    "98.62",
+                    "12",
+                    "NASDAQ",
+                    "98.61",
+                    "98.62",
+                ],
+                [
+                    "20160525 13:30:00.144",
+                    "AAPL",
+                    "98.62",
+                    "88",
+                    "NASDAQ",
+                    "98.61",
+                    "98.62",
+                ],
+                [
+                    "20160525 13:30:00.144",
+                    "AAPL",
+                    "98.62",
+                    "162",
+                    "NASDAQ",
+                    "98.61",
+                    "98.62",
+                ],
+                [
+                    "20160525 13:30:00.144",
+                    "AAPL",
+                    "98.61",
+                    "100",
+                    "BATS",
+                    "98.61",
+                    "98.62",
+                ],
+                [
+                    "20160525 13:30:00.144",
+                    "AAPL",
+                    "98.62",
+                    "61",
+                    "ARCA",
+                    "98.61",
+                    "98.62",
+                ],
+                [
+                    "20160525 13:30:00.144",
+                    "AAPL",
+                    "98.62",
+                    "25",
+                    "ARCA",
+                    "98.61",
+                    "98.62",
+                ],
+                [
+                    "20160525 13:30:00.144",
+                    "AAPL",
+                    "98.62",
+                    "14",
+                    "ARCA",
+                    "98.61",
+                    "98.62",
+                ],
+                [
+                    "20160525 13:30:00.145",
+                    "AAPL",
+                    "98.62",
+                    "12",
+                    "ARCA",
+                    "98.6",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.145",
+                    "AAPL",
+                    "98.62",
+                    "100",
+                    "ARCA",
+                    "98.6",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.145",
+                    "AAPL",
+                    "98.63",
+                    "100",
+                    "NASDAQ",
+                    "98.6",
+                    "98.63",
+                ],
+                [
+                    "20160525 13:30:00.145",
+                    "AAPL",
+                    "98.63",
+                    "100",
+                    "NASDAQ",
+                    "98.6",
+                    "98.63",
+                ],
+            ],
+            columns="time,ticker,price,quantity,marketCenter,bid,ask".split(","),
+        )
+        expected["price"] = expected["price"].astype("float64")
+        expected["quantity"] = expected["quantity"].astype("int64")
+        expected["bid"] = expected["bid"].astype("float64")
+        expected["ask"] = expected["ask"].astype("float64")
+        expected = self.prep_data(expected)
+
+        trades = pd.DataFrame(
+            [
+                ["20160525 13:30:00.023", "MSFT", "51.9500", "75", "NASDAQ"],
+                ["20160525 13:30:00.038", "MSFT", "51.9500", "155", "NASDAQ"],
+                ["20160525 13:30:00.048", "GOOG", "720.7700", "100", "NASDAQ"],
+                ["20160525 13:30:00.048", "GOOG", "720.9200", "100", "NASDAQ"],
+                ["20160525 13:30:00.048", "GOOG", "720.9300", "200", "NASDAQ"],
+                ["20160525 13:30:00.048", "GOOG", "720.9300", "300", "NASDAQ"],
+                ["20160525 13:30:00.048", "GOOG", "720.9300", "600", "NASDAQ"],
+                ["20160525 13:30:00.048", "GOOG", "720.9300", "44", "NASDAQ"],
+                ["20160525 13:30:00.074", "AAPL", "98.6700", "478343", "NASDAQ"],
+                ["20160525 13:30:00.075", "AAPL", "98.6700", "478343", "NASDAQ"],
+                ["20160525 13:30:00.075", "AAPL", "98.6600", "6", "NASDAQ"],
+                ["20160525 13:30:00.075", "AAPL", "98.6500", "30", "NASDAQ"],
+                ["20160525 13:30:00.075", "AAPL", "98.6500", "75", "NASDAQ"],
+                ["20160525 13:30:00.075", "AAPL", "98.6500", "20", "NASDAQ"],
+                ["20160525 13:30:00.075", "AAPL", "98.6500", "35", "NASDAQ"],
+                ["20160525 13:30:00.075", "AAPL", "98.6500", "10", "NASDAQ"],
+                ["20160525 13:30:00.075", "AAPL", "98.5500", "6", "ARCA"],
+                ["20160525 13:30:00.075", "AAPL", "98.5500", "6", "ARCA"],
+                ["20160525 13:30:00.076", "AAPL", "98.5600", "1000", "ARCA"],
+                ["20160525 13:30:00.076", "AAPL", "98.5600", "200", "ARCA"],
+                ["20160525 13:30:00.076", "AAPL", "98.5600", "300", "ARCA"],
+                ["20160525 13:30:00.076", "AAPL", "98.5600", "400", "ARCA"],
+                ["20160525 13:30:00.076", "AAPL", "98.5600", "600", "ARCA"],
+                ["20160525 13:30:00.076", "AAPL", "98.5600", "200", "ARCA"],
+                ["20160525 13:30:00.078", "MSFT", "51.9500", "783", "NASDAQ"],
+                ["20160525 13:30:00.078", "MSFT", "51.9500", "100", "NASDAQ"],
+                ["20160525 13:30:00.078", "MSFT", "51.9500", "100", "NASDAQ"],
+                ["20160525 13:30:00.084", "AAPL", "98.6400", "40", "NASDAQ"],
+                ["20160525 13:30:00.084", "AAPL", "98.5500", "149", "EDGX"],
+                ["20160525 13:30:00.086", "AAPL", "98.5600", "500", "ARCA"],
+                ["20160525 13:30:00.104", "AAPL", "98.6300", "647", "EDGX"],
+                ["20160525 13:30:00.104", "AAPL", "98.6300", "300", "EDGX"],
+                ["20160525 13:30:00.104", "AAPL", "98.6300", "50", "NASDAQ"],
+                ["20160525 13:30:00.104", "AAPL", "98.6300", "50", "NASDAQ"],
+                ["20160525 13:30:00.104", "AAPL", "98.6300", "70", "NASDAQ"],
+                ["20160525 13:30:00.104", "AAPL", "98.6300", "70", "NASDAQ"],
+                ["20160525 13:30:00.104", "AAPL", "98.6300", "1", "NASDAQ"],
+                ["20160525 13:30:00.104", "AAPL", "98.6300", "62", "NASDAQ"],
+                ["20160525 13:30:00.104", "AAPL", "98.6300", "10", "NASDAQ"],
+                ["20160525 13:30:00.104", "AAPL", "98.6300", "100", "ARCA"],
+                ["20160525 13:30:00.105", "AAPL", "98.6300", "100", "ARCA"],
+                ["20160525 13:30:00.105", "AAPL", "98.6300", "700", "ARCA"],
+                ["20160525 13:30:00.106", "AAPL", "98.6300", "61", "EDGX"],
+                ["20160525 13:30:00.107", "AAPL", "98.6300", "100", "ARCA"],
+                ["20160525 13:30:00.107", "AAPL", "98.6300", "53", "ARCA"],
+                ["20160525 13:30:00.108", "AAPL", "98.6300", "100", "ARCA"],
+                ["20160525 13:30:00.108", "AAPL", "98.6300", "839", "ARCA"],
+                ["20160525 13:30:00.115", "AAPL", "98.6300", "5", "EDGX"],
+                ["20160525 13:30:00.118", "AAPL", "98.6300", "295", "EDGX"],
+                ["20160525 13:30:00.118", "AAPL", "98.6300", "5", "EDGX"],
+                ["20160525 13:30:00.128", "AAPL", "98.6300", "100", "NASDAQ"],
+                ["20160525 13:30:00.128", "AAPL", "98.6300", "100", "NASDAQ"],
+                ["20160525 13:30:00.128", "MSFT", "51.9200", "100", "ARCA"],
+                ["20160525 13:30:00.129", "AAPL", "98.6200", "100", "NASDAQ"],
+                ["20160525 13:30:00.129", "AAPL", "98.6200", "10", "NASDAQ"],
+                ["20160525 13:30:00.129", "AAPL", "98.6200", "59", "NASDAQ"],
+                ["20160525 13:30:00.129", "AAPL", "98.6200", "31", "NASDAQ"],
+                ["20160525 13:30:00.129", "AAPL", "98.6200", "69", "NASDAQ"],
+                ["20160525 13:30:00.129", "AAPL", "98.6200", "12", "NASDAQ"],
+                ["20160525 13:30:00.129", "AAPL", "98.6200", "12", "EDGX"],
+                ["20160525 13:30:00.129", "AAPL", "98.6200", "100", "ARCA"],
+                ["20160525 13:30:00.129", "AAPL", "98.6200", "100", "ARCA"],
+                ["20160525 13:30:00.130", "MSFT", "51.9500", "317", "ARCA"],
+                ["20160525 13:30:00.130", "MSFT", "51.9500", "283", "ARCA"],
+                ["20160525 13:30:00.135", "MSFT", "51.9300", "100", "EDGX"],
+                ["20160525 13:30:00.135", "AAPL", "98.6200", "100", "ARCA"],
+                ["20160525 13:30:00.144", "AAPL", "98.6200", "12", "NASDAQ"],
+                ["20160525 13:30:00.144", "AAPL", "98.6200", "88", "NASDAQ"],
+                ["20160525 13:30:00.144", "AAPL", "98.6200", "162", "NASDAQ"],
+                ["20160525 13:30:00.144", "AAPL", "98.6100", "100", "BATS"],
+                ["20160525 13:30:00.144", "AAPL", "98.6200", "61", "ARCA"],
+                ["20160525 13:30:00.144", "AAPL", "98.6200", "25", "ARCA"],
+                ["20160525 13:30:00.144", "AAPL", "98.6200", "14", "ARCA"],
+                ["20160525 13:30:00.145", "AAPL", "98.6200", "12", "ARCA"],
+                ["20160525 13:30:00.145", "AAPL", "98.6200", "100", "ARCA"],
+                ["20160525 13:30:00.145", "AAPL", "98.6300", "100", "NASDAQ"],
+                ["20160525 13:30:00.145", "AAPL", "98.6300", "100", "NASDAQ"],
+            ],
+            columns="time,ticker,price,quantity,marketCenter".split(","),
+        )
+        trades["price"] = trades["price"].astype("float64")
+        trades["quantity"] = trades["quantity"].astype("int64")
+        trades = self.prep_data(trades)
+
+        quotes = pd.DataFrame(
+            [
+                ["20160525 13:30:00.023", "GOOG", "720.50", "720.93"],
+                ["20160525 13:30:00.023", "MSFT", "51.95", "51.95"],
+                ["20160525 13:30:00.041", "MSFT", "51.95", "51.95"],
+                ["20160525 13:30:00.048", "GOOG", "720.50", "720.93"],
+                ["20160525 13:30:00.048", "GOOG", "720.50", "720.93"],
+                ["20160525 13:30:00.048", "GOOG", "720.50", "720.93"],
+                ["20160525 13:30:00.048", "GOOG", "720.50", "720.93"],
+                ["20160525 13:30:00.072", "GOOG", "720.50", "720.88"],
+                ["20160525 13:30:00.075", "AAPL", "98.55", "98.56"],
+                ["20160525 13:30:00.076", "AAPL", "98.55", "98.56"],
+                ["20160525 13:30:00.076", "AAPL", "98.55", "98.56"],
+                ["20160525 13:30:00.076", "AAPL", "98.55", "98.56"],
+                ["20160525 13:30:00.078", "MSFT", "51.95", "51.95"],
+                ["20160525 13:30:00.078", "MSFT", "51.95", "51.95"],
+                ["20160525 13:30:00.078", "MSFT", "51.95", "51.95"],
+                ["20160525 13:30:00.078", "MSFT", "51.92", "51.95"],
+                ["20160525 13:30:00.079", "MSFT", "51.92", "51.95"],
+                ["20160525 13:30:00.080", "AAPL", "98.55", "98.56"],
+                ["20160525 13:30:00.084", "AAPL", "98.55", "98.56"],
+                ["20160525 13:30:00.086", "AAPL", "98.55", "98.63"],
+                ["20160525 13:30:00.088", "AAPL", "98.65", "98.63"],
+                ["20160525 13:30:00.089", "AAPL", "98.63", "98.63"],
+                ["20160525 13:30:00.104", "AAPL", "98.63", "98.63"],
+                ["20160525 13:30:00.104", "AAPL", "98.63", "98.63"],
+                ["20160525 13:30:00.104", "AAPL", "98.63", "98.63"],
+                ["20160525 13:30:00.104", "AAPL", "98.63", "98.63"],
+                ["20160525 13:30:00.104", "AAPL", "98.62", "98.63"],
+                ["20160525 13:30:00.105", "AAPL", "98.62", "98.63"],
+                ["20160525 13:30:00.107", "AAPL", "98.62", "98.63"],
+                ["20160525 13:30:00.115", "AAPL", "98.62", "98.63"],
+                ["20160525 13:30:00.115", "AAPL", "98.62", "98.63"],
+                ["20160525 13:30:00.118", "AAPL", "98.62", "98.63"],
+                ["20160525 13:30:00.128", "AAPL", "98.62", "98.63"],
+                ["20160525 13:30:00.128", "AAPL", "98.62", "98.63"],
+                ["20160525 13:30:00.129", "AAPL", "98.62", "98.63"],
+                ["20160525 13:30:00.129", "AAPL", "98.61", "98.63"],
+                ["20160525 13:30:00.129", "AAPL", "98.62", "98.63"],
+                ["20160525 13:30:00.129", "AAPL", "98.62", "98.63"],
+                ["20160525 13:30:00.129", "AAPL", "98.61", "98.63"],
+                ["20160525 13:30:00.130", "MSFT", "51.93", "51.95"],
+                ["20160525 13:30:00.130", "MSFT", "51.93", "51.95"],
+                ["20160525 13:30:00.130", "AAPL", "98.61", "98.63"],
+                ["20160525 13:30:00.131", "AAPL", "98.61", "98.62"],
+                ["20160525 13:30:00.131", "AAPL", "98.61", "98.62"],
+                ["20160525 13:30:00.135", "MSFT", "51.92", "51.95"],
+                ["20160525 13:30:00.135", "AAPL", "98.61", "98.62"],
+                ["20160525 13:30:00.136", "AAPL", "98.61", "98.62"],
+                ["20160525 13:30:00.136", "AAPL", "98.61", "98.62"],
+                ["20160525 13:30:00.144", "AAPL", "98.61", "98.62"],
+                ["20160525 13:30:00.144", "AAPL", "98.61", "98.62"],
+                ["20160525 13:30:00.145", "AAPL", "98.61", "98.62"],
+                ["20160525 13:30:00.145", "AAPL", "98.61", "98.63"],
+                ["20160525 13:30:00.145", "AAPL", "98.61", "98.63"],
+                ["20160525 13:30:00.145", "AAPL", "98.60", "98.63"],
+                ["20160525 13:30:00.145", "AAPL", "98.61", "98.63"],
+                ["20160525 13:30:00.145", "AAPL", "98.60", "98.63"],
+            ],
+            columns="time,ticker,bid,ask".split(","),
+        )
+        quotes["bid"] = quotes["bid"].astype("float64")
+        quotes["ask"] = quotes["ask"].astype("float64")
+        quotes = self.prep_data(quotes, dedupe=True)
+
+        result = merge_asof(trades, quotes, on="time", by="ticker")
+        tm.assert_frame_equal(result, expected)
+
+    def test_basic_no_by(self, trades, asof, quotes):
+        f = (
+            lambda x: x[x.ticker == "MSFT"]
+            .drop("ticker", axis=1)
+            .reset_index(drop=True)
+        )
+
+        # just use a single ticker
+        expected = f(asof)
+        trades = f(trades)
+        quotes = f(quotes)
+
+        result = merge_asof(trades, quotes, on="time")
+        tm.assert_frame_equal(result, expected)
+
+    def test_valid_join_keys(self, trades, quotes):
+        msg = r"incompatible merge keys \[1\] .* must be the same type"
+
+        with pytest.raises(MergeError, match=msg):
+            merge_asof(trades, quotes, left_on="time", right_on="bid", by="ticker")
+
+        with pytest.raises(MergeError, match="can only asof on a key for left"):
+            merge_asof(trades, quotes, on=["time", "ticker"], by="ticker")
+
+        with pytest.raises(MergeError, match="can only asof on a key for left"):
+            merge_asof(trades, quotes, by="ticker")
+
+    def test_with_duplicates(self, datapath, trades, quotes, asof):
+        q = (
+            pd.concat([quotes, quotes])
+            .sort_values(["time", "ticker"])
+            .reset_index(drop=True)
+        )
+        result = merge_asof(trades, q, on="time", by="ticker")
+        expected = self.prep_data(asof)
+        tm.assert_frame_equal(result, expected)
+
+    def test_with_duplicates_no_on(self):
+        df1 = pd.DataFrame({"key": [1, 1, 3], "left_val": [1, 2, 3]})
+        df2 = pd.DataFrame({"key": [1, 2, 2], "right_val": [1, 2, 3]})
+        result = merge_asof(df1, df2, on="key")
+        expected = pd.DataFrame(
+            {"key": [1, 1, 3], "left_val": [1, 2, 3], "right_val": [1, 1, 3]}
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_valid_allow_exact_matches(self, trades, quotes):
+        msg = "allow_exact_matches must be boolean, passed foo"
+
+        with pytest.raises(MergeError, match=msg):
+            merge_asof(
+                trades, quotes, on="time", by="ticker", allow_exact_matches="foo"
+            )
+
+    def test_valid_tolerance(self, trades, quotes):
+        # dti
+        merge_asof(trades, quotes, on="time", by="ticker", tolerance=Timedelta("1s"))
+
+        # integer
+        merge_asof(
+            trades.reset_index(),
+            quotes.reset_index(),
+            on="index",
+            by="ticker",
+            tolerance=1,
+        )
+
+        msg = r"incompatible tolerance .*, must be compat with type .*"
+
+        # incompat
+        with pytest.raises(MergeError, match=msg):
+            merge_asof(trades, quotes, on="time", by="ticker", tolerance=1)
+
+        # invalid
+        with pytest.raises(MergeError, match=msg):
+            merge_asof(
+                trades.reset_index(),
+                quotes.reset_index(),
+                on="index",
+                by="ticker",
+                tolerance=1.0,
+            )
+
+        msg = "tolerance must be positive"
+
+        # invalid negative
+        with pytest.raises(MergeError, match=msg):
+            merge_asof(
+                trades, quotes, on="time", by="ticker", tolerance=-Timedelta("1s")
+            )
+
+        with pytest.raises(MergeError, match=msg):
+            merge_asof(
+                trades.reset_index(),
+                quotes.reset_index(),
+                on="index",
+                by="ticker",
+                tolerance=-1,
+            )
+
+    def test_non_sorted(self, trades, quotes):
+        trades = trades.sort_values("time", ascending=False)
+        quotes = quotes.sort_values("time", ascending=False)
+
+        # we require that we are already sorted on time & quotes
+        assert not trades.time.is_monotonic_increasing
+        assert not quotes.time.is_monotonic_increasing
+        with pytest.raises(ValueError, match="left keys must be sorted"):
+            merge_asof(trades, quotes, on="time", by="ticker")
+
+        trades = trades.sort_values("time")
+        assert trades.time.is_monotonic_increasing
+        assert not quotes.time.is_monotonic_increasing
+        with pytest.raises(ValueError, match="right keys must be sorted"):
+            merge_asof(trades, quotes, on="time", by="ticker")
+
+        quotes = quotes.sort_values("time")
+        assert trades.time.is_monotonic_increasing
+        assert quotes.time.is_monotonic_increasing
+
+        # ok, though has dupes
+        merge_asof(trades, quotes, on="time", by="ticker")
+
+    @pytest.mark.parametrize(
+        "tolerance_ts",
+        [Timedelta("1day"), datetime.timedelta(days=1)],
+        ids=["Timedelta", "datetime.timedelta"],
+    )
+    def test_tolerance(self, tolerance_ts, trades, quotes, tolerance):
+        result = merge_asof(
+            trades, quotes, on="time", by="ticker", tolerance=tolerance_ts
+        )
+        expected = tolerance
+        tm.assert_frame_equal(result, expected)
+
+    def test_tolerance_forward(self):
+        # GH14887
+
+        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
+        right = pd.DataFrame({"a": [1, 2, 3, 7, 11], "right_val": [1, 2, 3, 7, 11]})
+
+        expected = pd.DataFrame(
+            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [1, np.nan, 11]}
+        )
+
+        result = merge_asof(left, right, on="a", direction="forward", tolerance=1)
+        tm.assert_frame_equal(result, expected)
+
+    def test_tolerance_nearest(self):
+        # GH14887
+
+        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
+        right = pd.DataFrame({"a": [1, 2, 3, 7, 11], "right_val": [1, 2, 3, 7, 11]})
+
+        expected = pd.DataFrame(
+            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [1, np.nan, 11]}
+        )
+
+        result = merge_asof(left, right, on="a", direction="nearest", tolerance=1)
+        tm.assert_frame_equal(result, expected)
+
+    def test_tolerance_tz(self, unit):
+        # GH 14844
+        left = pd.DataFrame(
+            {
+                "date": pd.date_range(
+                    start=to_datetime("2016-01-02"),
+                    freq="D",
+                    periods=5,
+                    tz=pytz.timezone("UTC"),
+                    unit=unit,
+                ),
+                "value1": np.arange(5),
+            }
+        )
+        right = pd.DataFrame(
+            {
+                "date": pd.date_range(
+                    start=to_datetime("2016-01-01"),
+                    freq="D",
+                    periods=5,
+                    tz=pytz.timezone("UTC"),
+                    unit=unit,
+                ),
+                "value2": list("ABCDE"),
+            }
+        )
+        result = merge_asof(left, right, on="date", tolerance=Timedelta("1 day"))
+
+        expected = pd.DataFrame(
+            {
+                "date": pd.date_range(
+                    start=to_datetime("2016-01-02"),
+                    freq="D",
+                    periods=5,
+                    tz=pytz.timezone("UTC"),
+                    unit=unit,
+                ),
+                "value1": np.arange(5),
+                "value2": list("BCDEE"),
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_tolerance_float(self):
+        # GH22981
+        left = pd.DataFrame({"a": [1.1, 3.5, 10.9], "left_val": ["a", "b", "c"]})
+        right = pd.DataFrame(
+            {"a": [1.0, 2.5, 3.3, 7.5, 11.5], "right_val": [1.0, 2.5, 3.3, 7.5, 11.5]}
+        )
+
+        expected = pd.DataFrame(
+            {
+                "a": [1.1, 3.5, 10.9],
+                "left_val": ["a", "b", "c"],
+                "right_val": [1, 3.3, np.nan],
+            }
+        )
+
+        result = merge_asof(left, right, on="a", direction="nearest", tolerance=0.5)
+        tm.assert_frame_equal(result, expected)
+
+    def test_index_tolerance(self, trades, quotes, tolerance):
+        # GH 15135
+        expected = tolerance.set_index("time")
+        trades = trades.set_index("time")
+        quotes = quotes.set_index("time")
+
+        result = merge_asof(
+            trades,
+            quotes,
+            left_index=True,
+            right_index=True,
+            by="ticker",
+            tolerance=Timedelta("1day"),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_allow_exact_matches(self, trades, quotes, allow_exact_matches):
+        result = merge_asof(
+            trades, quotes, on="time", by="ticker", allow_exact_matches=False
+        )
+        expected = allow_exact_matches
+        tm.assert_frame_equal(result, expected)
+
+    def test_allow_exact_matches_forward(self):
+        # GH14887
+
+        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
+        right = pd.DataFrame({"a": [1, 2, 3, 7, 11], "right_val": [1, 2, 3, 7, 11]})
+
+        expected = pd.DataFrame(
+            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [2, 7, 11]}
+        )
+
+        result = merge_asof(
+            left, right, on="a", direction="forward", allow_exact_matches=False
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_allow_exact_matches_nearest(self):
+        # GH14887
+
+        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
+        right = pd.DataFrame({"a": [1, 2, 3, 7, 11], "right_val": [1, 2, 3, 7, 11]})
+
+        expected = pd.DataFrame(
+            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [2, 3, 11]}
+        )
+
+        result = merge_asof(
+            left, right, on="a", direction="nearest", allow_exact_matches=False
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_allow_exact_matches_and_tolerance(
+        self, trades, quotes, allow_exact_matches_and_tolerance
+    ):
+        result = merge_asof(
+            trades,
+            quotes,
+            on="time",
+            by="ticker",
+            tolerance=Timedelta("100ms"),
+            allow_exact_matches=False,
+        )
+        expected = allow_exact_matches_and_tolerance
+        tm.assert_frame_equal(result, expected)
+
+    def test_allow_exact_matches_and_tolerance2(self):
+        # GH 13695
+        df1 = pd.DataFrame(
+            {"time": to_datetime(["2016-07-15 13:30:00.030"]), "username": ["bob"]}
+        )
+        df2 = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    ["2016-07-15 13:30:00.000", "2016-07-15 13:30:00.030"]
+                ),
+                "version": [1, 2],
+            }
+        )
+
+        result = merge_asof(df1, df2, on="time")
+        expected = pd.DataFrame(
+            {
+                "time": to_datetime(["2016-07-15 13:30:00.030"]),
+                "username": ["bob"],
+                "version": [2],
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = merge_asof(df1, df2, on="time", allow_exact_matches=False)
+        expected = pd.DataFrame(
+            {
+                "time": to_datetime(["2016-07-15 13:30:00.030"]),
+                "username": ["bob"],
+                "version": [1],
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = merge_asof(
+            df1,
+            df2,
+            on="time",
+            allow_exact_matches=False,
+            tolerance=Timedelta("10ms"),
+        )
+        expected = pd.DataFrame(
+            {
+                "time": to_datetime(["2016-07-15 13:30:00.030"]),
+                "username": ["bob"],
+                "version": [np.nan],
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_allow_exact_matches_and_tolerance3(self):
+        # GH 13709
+        df1 = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    ["2016-07-15 13:30:00.030", "2016-07-15 13:30:00.030"]
+                ),
+                "username": ["bob", "charlie"],
+            }
+        )
+        df2 = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    ["2016-07-15 13:30:00.000", "2016-07-15 13:30:00.030"]
+                ),
+                "version": [1, 2],
+            }
+        )
+
+        result = merge_asof(
+            df1,
+            df2,
+            on="time",
+            allow_exact_matches=False,
+            tolerance=Timedelta("10ms"),
+        )
+        expected = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    ["2016-07-15 13:30:00.030", "2016-07-15 13:30:00.030"]
+                ),
+                "username": ["bob", "charlie"],
+                "version": [np.nan, np.nan],
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_allow_exact_matches_and_tolerance_forward(self):
+        # GH14887
+
+        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
+        right = pd.DataFrame({"a": [1, 3, 4, 6, 11], "right_val": [1, 3, 4, 6, 11]})
+
+        expected = pd.DataFrame(
+            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [np.nan, 6, 11]}
+        )
+
+        result = merge_asof(
+            left,
+            right,
+            on="a",
+            direction="forward",
+            allow_exact_matches=False,
+            tolerance=1,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_allow_exact_matches_and_tolerance_nearest(self):
+        # GH14887
+
+        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
+        right = pd.DataFrame({"a": [1, 3, 4, 6, 11], "right_val": [1, 3, 4, 7, 11]})
+
+        expected = pd.DataFrame(
+            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [np.nan, 4, 11]}
+        )
+
+        result = merge_asof(
+            left,
+            right,
+            on="a",
+            direction="nearest",
+            allow_exact_matches=False,
+            tolerance=1,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_forward_by(self):
+        # GH14887
+
+        left = pd.DataFrame(
+            {
+                "a": [1, 5, 10, 12, 15],
+                "b": ["X", "X", "Y", "Z", "Y"],
+                "left_val": ["a", "b", "c", "d", "e"],
+            }
+        )
+        right = pd.DataFrame(
+            {
+                "a": [1, 6, 11, 15, 16],
+                "b": ["X", "Z", "Y", "Z", "Y"],
+                "right_val": [1, 6, 11, 15, 16],
+            }
+        )
+
+        expected = pd.DataFrame(
+            {
+                "a": [1, 5, 10, 12, 15],
+                "b": ["X", "X", "Y", "Z", "Y"],
+                "left_val": ["a", "b", "c", "d", "e"],
+                "right_val": [1, np.nan, 11, 15, 16],
+            }
+        )
+
+        result = merge_asof(left, right, on="a", by="b", direction="forward")
+        tm.assert_frame_equal(result, expected)
+
+    def test_nearest_by(self):
+        # GH14887
+
+        left = pd.DataFrame(
+            {
+                "a": [1, 5, 10, 12, 15],
+                "b": ["X", "X", "Z", "Z", "Y"],
+                "left_val": ["a", "b", "c", "d", "e"],
+            }
+        )
+        right = pd.DataFrame(
+            {
+                "a": [1, 6, 11, 15, 16],
+                "b": ["X", "Z", "Z", "Z", "Y"],
+                "right_val": [1, 6, 11, 15, 16],
+            }
+        )
+
+        expected = pd.DataFrame(
+            {
+                "a": [1, 5, 10, 12, 15],
+                "b": ["X", "X", "Z", "Z", "Y"],
+                "left_val": ["a", "b", "c", "d", "e"],
+                "right_val": [1, 1, 11, 11, 16],
+            }
+        )
+
+        result = merge_asof(left, right, on="a", by="b", direction="nearest")
+        tm.assert_frame_equal(result, expected)
+
+    def test_by_int(self):
+        # we specialize by type, so test that this is correct
+        df1 = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    [
+                        "20160525 13:30:00.020",
+                        "20160525 13:30:00.030",
+                        "20160525 13:30:00.040",
+                        "20160525 13:30:00.050",
+                        "20160525 13:30:00.060",
+                    ]
+                ),
+                "key": [1, 2, 1, 3, 2],
+                "value1": [1.1, 1.2, 1.3, 1.4, 1.5],
+            },
+            columns=["time", "key", "value1"],
+        )
+
+        df2 = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    [
+                        "20160525 13:30:00.015",
+                        "20160525 13:30:00.020",
+                        "20160525 13:30:00.025",
+                        "20160525 13:30:00.035",
+                        "20160525 13:30:00.040",
+                        "20160525 13:30:00.055",
+                        "20160525 13:30:00.060",
+                        "20160525 13:30:00.065",
+                    ]
+                ),
+                "key": [2, 1, 1, 3, 2, 1, 2, 3],
+                "value2": [2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8],
+            },
+            columns=["time", "key", "value2"],
+        )
+
+        result = merge_asof(df1, df2, on="time", by="key")
+
+        expected = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    [
+                        "20160525 13:30:00.020",
+                        "20160525 13:30:00.030",
+                        "20160525 13:30:00.040",
+                        "20160525 13:30:00.050",
+                        "20160525 13:30:00.060",
+                    ]
+                ),
+                "key": [1, 2, 1, 3, 2],
+                "value1": [1.1, 1.2, 1.3, 1.4, 1.5],
+                "value2": [2.2, 2.1, 2.3, 2.4, 2.7],
+            },
+            columns=["time", "key", "value1", "value2"],
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_on_float(self):
+        # mimics how to determine the minimum-price variation
+        df1 = pd.DataFrame(
+            {
+                "price": [5.01, 0.0023, 25.13, 340.05, 30.78, 1040.90, 0.0078],
+                "symbol": list("ABCDEFG"),
+            },
+            columns=["symbol", "price"],
+        )
+
+        df2 = pd.DataFrame(
+            {"price": [0.0, 1.0, 100.0], "mpv": [0.0001, 0.01, 0.05]},
+            columns=["price", "mpv"],
+        )
+
+        df1 = df1.sort_values("price").reset_index(drop=True)
+
+        result = merge_asof(df1, df2, on="price")
+
+        expected = pd.DataFrame(
+            {
+                "symbol": list("BGACEDF"),
+                "price": [0.0023, 0.0078, 5.01, 25.13, 30.78, 340.05, 1040.90],
+                "mpv": [0.0001, 0.0001, 0.01, 0.01, 0.01, 0.05, 0.05],
+            },
+            columns=["symbol", "price", "mpv"],
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_on_specialized_type(self, any_real_numpy_dtype):
+        # see gh-13936
+        dtype = np.dtype(any_real_numpy_dtype).type
+
+        df1 = pd.DataFrame(
+            {"value": [5, 2, 25, 100, 78, 120, 79], "symbol": list("ABCDEFG")},
+            columns=["symbol", "value"],
+        )
+        df1.value = dtype(df1.value)
+
+        df2 = pd.DataFrame(
+            {"value": [0, 80, 120, 125], "result": list("xyzw")},
+            columns=["value", "result"],
+        )
+        df2.value = dtype(df2.value)
+
+        df1 = df1.sort_values("value").reset_index(drop=True)
+        result = merge_asof(df1, df2, on="value")
+
+        expected = pd.DataFrame(
+            {
+                "symbol": list("BACEGDF"),
+                "value": [2, 5, 25, 78, 79, 100, 120],
+                "result": list("xxxxxyz"),
+            },
+            columns=["symbol", "value", "result"],
+        )
+        expected.value = dtype(expected.value)
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_on_specialized_type_by_int(self, any_real_numpy_dtype):
+        # see gh-13936
+        dtype = np.dtype(any_real_numpy_dtype).type
+
+        df1 = pd.DataFrame(
+            {
+                "value": [5, 2, 25, 100, 78, 120, 79],
+                "key": [1, 2, 3, 2, 3, 1, 2],
+                "symbol": list("ABCDEFG"),
+            },
+            columns=["symbol", "key", "value"],
+        )
+        df1.value = dtype(df1.value)
+
+        df2 = pd.DataFrame(
+            {"value": [0, 80, 120, 125], "key": [1, 2, 2, 3], "result": list("xyzw")},
+            columns=["value", "key", "result"],
+        )
+        df2.value = dtype(df2.value)
+
+        df1 = df1.sort_values("value").reset_index(drop=True)
+        result = merge_asof(df1, df2, on="value", by="key")
+
+        expected = pd.DataFrame(
+            {
+                "symbol": list("BACEGDF"),
+                "key": [2, 1, 3, 3, 2, 2, 1],
+                "value": [2, 5, 25, 78, 79, 100, 120],
+                "result": [np.nan, "x", np.nan, np.nan, np.nan, "y", "x"],
+            },
+            columns=["symbol", "key", "value", "result"],
+        )
+        expected.value = dtype(expected.value)
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_on_float_by_int(self):
+        # type specialize both "by" and "on" parameters
+        df1 = pd.DataFrame(
+            {
+                "symbol": list("AAABBBCCC"),
+                "exch": [1, 2, 3, 1, 2, 3, 1, 2, 3],
+                "price": [
+                    3.26,
+                    3.2599,
+                    3.2598,
+                    12.58,
+                    12.59,
+                    12.5,
+                    378.15,
+                    378.2,
+                    378.25,
+                ],
+            },
+            columns=["symbol", "exch", "price"],
+        )
+
+        df2 = pd.DataFrame(
+            {
+                "exch": [1, 1, 1, 2, 2, 2, 3, 3, 3],
+                "price": [0.0, 1.0, 100.0, 0.0, 5.0, 100.0, 0.0, 5.0, 1000.0],
+                "mpv": [0.0001, 0.01, 0.05, 0.0001, 0.01, 0.1, 0.0001, 0.25, 1.0],
+            },
+            columns=["exch", "price", "mpv"],
+        )
+
+        df1 = df1.sort_values("price").reset_index(drop=True)
+        df2 = df2.sort_values("price").reset_index(drop=True)
+
+        result = merge_asof(df1, df2, on="price", by="exch")
+
+        expected = pd.DataFrame(
+            {
+                "symbol": list("AAABBBCCC"),
+                "exch": [3, 2, 1, 3, 1, 2, 1, 2, 3],
+                "price": [
+                    3.2598,
+                    3.2599,
+                    3.26,
+                    12.5,
+                    12.58,
+                    12.59,
+                    378.15,
+                    378.2,
+                    378.25,
+                ],
+                "mpv": [0.0001, 0.0001, 0.01, 0.25, 0.01, 0.01, 0.05, 0.1, 0.25],
+            },
+            columns=["symbol", "exch", "price", "mpv"],
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_datatype_error_raises(self, using_infer_string):
+        if using_infer_string:
+            msg = "incompatible merge keys"
+        else:
+            msg = r"Incompatible merge dtype, .*, both sides must have numeric dtype"
+
+        left = pd.DataFrame({"left_val": [1, 5, 10], "a": ["a", "b", "c"]})
+        right = pd.DataFrame({"right_val": [1, 2, 3, 6, 7], "a": [1, 2, 3, 6, 7]})
+
+        with pytest.raises(MergeError, match=msg):
+            merge_asof(left, right, on="a")
+
+    def test_merge_datatype_categorical_error_raises(self):
+        msg = (
+            r"incompatible merge keys \[0\] .* both sides category, "
+            "but not equal ones"
+        )
+
+        left = pd.DataFrame(
+            {"left_val": [1, 5, 10], "a": pd.Categorical(["a", "b", "c"])}
+        )
+        right = pd.DataFrame(
+            {
+                "right_val": [1, 2, 3, 6, 7],
+                "a": pd.Categorical(["a", "X", "c", "X", "b"]),
+            }
+        )
+
+        with pytest.raises(MergeError, match=msg):
+            merge_asof(left, right, on="a")
+
+    def test_merge_groupby_multiple_column_with_categorical_column(self):
+        # GH 16454
+        df = pd.DataFrame({"x": [0], "y": [0], "z": pd.Categorical([0])})
+        result = merge_asof(df, df, on="x", by=["y", "z"])
+        expected = pd.DataFrame({"x": [0], "y": [0], "z": pd.Categorical([0])})
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "func", [lambda x: x, lambda x: to_datetime(x)], ids=["numeric", "datetime"]
+    )
+    @pytest.mark.parametrize("side", ["left", "right"])
+    def test_merge_on_nans(self, func, side):
+        # GH 23189
+        msg = f"Merge keys contain null values on {side} side"
+        nulls = func([1.0, 5.0, np.nan])
+        non_nulls = func([1.0, 5.0, 10.0])
+        df_null = pd.DataFrame({"a": nulls, "left_val": ["a", "b", "c"]})
+        df = pd.DataFrame({"a": non_nulls, "right_val": [1, 6, 11]})
+
+        with pytest.raises(ValueError, match=msg):
+            if side == "left":
+                merge_asof(df_null, df, on="a")
+            else:
+                merge_asof(df, df_null, on="a")
+
+    def test_by_nullable(self, any_numeric_ea_dtype, using_infer_string):
+        # Note: this test passes if instead of using pd.array we use
+        #  np.array([np.nan, 1]).  Other than that, I (@jbrockmendel)
+        #  have NO IDEA what the expected behavior is.
+        # TODO(GH#32306): may be relevant to the expected behavior here.
+
+        arr = pd.array([pd.NA, 0, 1], dtype=any_numeric_ea_dtype)
+        if arr.dtype.kind in ["i", "u"]:
+            max_val = np.iinfo(arr.dtype.numpy_dtype).max
+        else:
+            max_val = np.finfo(arr.dtype.numpy_dtype).max
+        # set value s.t. (at least for integer dtypes) arr._values_for_argsort
+        #  is not an injection
+        arr[2] = max_val
+
+        left = pd.DataFrame(
+            {
+                "by_col1": arr,
+                "by_col2": ["HELLO", "To", "You"],
+                "on_col": [2, 4, 6],
+                "value": ["a", "c", "e"],
+            }
+        )
+        right = pd.DataFrame(
+            {
+                "by_col1": arr,
+                "by_col2": ["WORLD", "Wide", "Web"],
+                "on_col": [1, 2, 6],
+                "value": ["b", "d", "f"],
+            }
+        )
+
+        result = merge_asof(left, right, by=["by_col1", "by_col2"], on="on_col")
+        expected = pd.DataFrame(
+            {
+                "by_col1": arr,
+                "by_col2": ["HELLO", "To", "You"],
+                "on_col": [2, 4, 6],
+                "value_x": ["a", "c", "e"],
+            }
+        )
+        expected["value_y"] = np.array([np.nan, np.nan, np.nan], dtype=object)
+        if using_infer_string:
+            expected["value_y"] = expected["value_y"].astype("string[pyarrow_numpy]")
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_by_col_tz_aware(self):
+        # GH 21184
+        left = pd.DataFrame(
+            {
+                "by_col": pd.DatetimeIndex(["2018-01-01"]).tz_localize("UTC"),
+                "on_col": [2],
+                "values": ["a"],
+            }
+        )
+        right = pd.DataFrame(
+            {
+                "by_col": pd.DatetimeIndex(["2018-01-01"]).tz_localize("UTC"),
+                "on_col": [1],
+                "values": ["b"],
+            }
+        )
+        result = merge_asof(left, right, by="by_col", on="on_col")
+        expected = pd.DataFrame(
+            [[pd.Timestamp("2018-01-01", tz="UTC"), 2, "a", "b"]],
+            columns=["by_col", "on_col", "values_x", "values_y"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_by_mixed_tz_aware(self, using_infer_string):
+        # GH 26649
+        left = pd.DataFrame(
+            {
+                "by_col1": pd.DatetimeIndex(["2018-01-01"]).tz_localize("UTC"),
+                "by_col2": ["HELLO"],
+                "on_col": [2],
+                "value": ["a"],
+            }
+        )
+        right = pd.DataFrame(
+            {
+                "by_col1": pd.DatetimeIndex(["2018-01-01"]).tz_localize("UTC"),
+                "by_col2": ["WORLD"],
+                "on_col": [1],
+                "value": ["b"],
+            }
+        )
+        result = merge_asof(left, right, by=["by_col1", "by_col2"], on="on_col")
+        expected = pd.DataFrame(
+            [[pd.Timestamp("2018-01-01", tz="UTC"), "HELLO", 2, "a"]],
+            columns=["by_col1", "by_col2", "on_col", "value_x"],
+        )
+        expected["value_y"] = np.array([np.nan], dtype=object)
+        if using_infer_string:
+            expected["value_y"] = expected["value_y"].astype("string[pyarrow_numpy]")
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", ["float64", "int16", "m8[ns]", "M8[us]"])
+    def test_by_dtype(self, dtype):
+        # GH 55453, GH 22794
+        left = pd.DataFrame(
+            {
+                "by_col": np.array([1], dtype=dtype),
+                "on_col": [2],
+                "value": ["a"],
+            }
+        )
+        right = pd.DataFrame(
+            {
+                "by_col": np.array([1], dtype=dtype),
+                "on_col": [1],
+                "value": ["b"],
+            }
+        )
+        result = merge_asof(left, right, by="by_col", on="on_col")
+        expected = pd.DataFrame(
+            {
+                "by_col": np.array([1], dtype=dtype),
+                "on_col": [2],
+                "value_x": ["a"],
+                "value_y": ["b"],
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_timedelta_tolerance_nearest(self, unit):
+        # GH 27642
+        if unit == "s":
+            pytest.skip(
+                "This test is invalid with unit='s' because that would "
+                "round left['time']"
+            )
+
+        left = pd.DataFrame(
+            list(zip([0, 5, 10, 15, 20, 25], [0, 1, 2, 3, 4, 5])),
+            columns=["time", "left"],
+        )
+
+        left["time"] = pd.to_timedelta(left["time"], "ms").astype(f"m8[{unit}]")
+
+        right = pd.DataFrame(
+            list(zip([0, 3, 9, 12, 15, 18], [0, 1, 2, 3, 4, 5])),
+            columns=["time", "right"],
+        )
+
+        right["time"] = pd.to_timedelta(right["time"], "ms").astype(f"m8[{unit}]")
+
+        expected = pd.DataFrame(
+            list(
+                zip(
+                    [0, 5, 10, 15, 20, 25],
+                    [0, 1, 2, 3, 4, 5],
+                    [0, np.nan, 2, 4, np.nan, np.nan],
+                )
+            ),
+            columns=["time", "left", "right"],
+        )
+
+        expected["time"] = pd.to_timedelta(expected["time"], "ms").astype(f"m8[{unit}]")
+
+        result = merge_asof(
+            left, right, on="time", tolerance=Timedelta("1ms"), direction="nearest"
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_int_type_tolerance(self, any_int_dtype):
+        # GH #28870
+
+        left = pd.DataFrame({"a": [0, 10, 20], "left_val": [1, 2, 3]})
+        right = pd.DataFrame({"a": [5, 15, 25], "right_val": [1, 2, 3]})
+        left["a"] = left["a"].astype(any_int_dtype)
+        right["a"] = right["a"].astype(any_int_dtype)
+
+        expected = pd.DataFrame(
+            {"a": [0, 10, 20], "left_val": [1, 2, 3], "right_val": [np.nan, 1.0, 2.0]}
+        )
+        expected["a"] = expected["a"].astype(any_int_dtype)
+
+        result = merge_asof(left, right, on="a", tolerance=10)
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_index_column_tz(self):
+        # GH 29864
+        index = pd.date_range("2019-10-01", freq="30min", periods=5, tz="UTC")
+        left = pd.DataFrame([0.9, 0.8, 0.7, 0.6], columns=["xyz"], index=index[1:])
+        right = pd.DataFrame({"from_date": index, "abc": [2.46] * 4 + [2.19]})
+        result = merge_asof(
+            left=left, right=right, left_index=True, right_on=["from_date"]
+        )
+        expected = pd.DataFrame(
+            {
+                "xyz": [0.9, 0.8, 0.7, 0.6],
+                "from_date": index[1:],
+                "abc": [2.46] * 3 + [2.19],
+            },
+            index=pd.date_range(
+                "2019-10-01 00:30:00", freq="30min", periods=4, tz="UTC"
+            ),
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = merge_asof(
+            left=right, right=left, right_index=True, left_on=["from_date"]
+        )
+        expected = pd.DataFrame(
+            {
+                "from_date": index,
+                "abc": [2.46] * 4 + [2.19],
+                "xyz": [np.nan, 0.9, 0.8, 0.7, 0.6],
+            },
+            index=Index([0, 1, 2, 3, 4]),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_left_index_right_index_tolerance(self, unit):
+        # https://github.com/pandas-dev/pandas/issues/35558
+        if unit == "s":
+            pytest.skip(
+                "This test is invalid with unit='s' because that would round dr1"
+            )
+
+        dr1 = pd.date_range(
+            start="1/1/2020", end="1/20/2020", freq="2D", unit=unit
+        ) + Timedelta(seconds=0.4).as_unit(unit)
+        dr2 = pd.date_range(start="1/1/2020", end="2/1/2020", unit=unit)
+
+        df1 = pd.DataFrame({"val1": "foo"}, index=pd.DatetimeIndex(dr1))
+        df2 = pd.DataFrame({"val2": "bar"}, index=pd.DatetimeIndex(dr2))
+
+        expected = pd.DataFrame(
+            {"val1": "foo", "val2": "bar"}, index=pd.DatetimeIndex(dr1)
+        )
+        result = merge_asof(
+            df1,
+            df2,
+            left_index=True,
+            right_index=True,
+            tolerance=Timedelta(seconds=0.5),
+        )
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "infer_string", [False, pytest.param(True, marks=td.skip_if_no("pyarrow"))]
+)
+@pytest.mark.parametrize(
+    "kwargs", [{"on": "x"}, {"left_index": True, "right_index": True}]
+)
+@pytest.mark.parametrize(
+    "data",
+    [["2019-06-01 00:09:12", "2019-06-01 00:10:29"], [1.0, "2019-06-01 00:10:29"]],
+)
+def test_merge_asof_non_numerical_dtype(kwargs, data, infer_string):
+    # GH#29130
+    with option_context("future.infer_string", infer_string):
+        left = pd.DataFrame({"x": data}, index=data)
+        right = pd.DataFrame({"x": data}, index=data)
+        with pytest.raises(
+            MergeError,
+            match=r"Incompatible merge dtype, .*, both sides must have numeric dtype",
+        ):
+            merge_asof(left, right, **kwargs)
+
+
+def test_merge_asof_non_numerical_dtype_object():
+    # GH#29130
+    left = pd.DataFrame({"a": ["12", "13", "15"], "left_val1": ["a", "b", "c"]})
+    right = pd.DataFrame({"a": ["a", "b", "c"], "left_val": ["d", "e", "f"]})
+    with pytest.raises(
+        MergeError,
+        match=r"Incompatible merge dtype, .*, both sides must have numeric dtype",
+    ):
+        merge_asof(
+            left,
+            right,
+            left_on="left_val1",
+            right_on="a",
+            left_by="a",
+            right_by="left_val",
+        )
+
+
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {"right_index": True, "left_index": True},
+        {"left_on": "left_time", "right_index": True},
+        {"left_index": True, "right_on": "right"},
+    ],
+)
+def test_merge_asof_index_behavior(kwargs):
+    # GH 33463
+    index = Index([1, 5, 10], name="test")
+    left = pd.DataFrame({"left": ["a", "b", "c"], "left_time": [1, 4, 10]}, index=index)
+    right = pd.DataFrame({"right": [1, 2, 3, 6, 7]}, index=[1, 2, 3, 6, 7])
+    result = merge_asof(left, right, **kwargs)
+
+    expected = pd.DataFrame(
+        {"left": ["a", "b", "c"], "left_time": [1, 4, 10], "right": [1, 3, 7]},
+        index=index,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_asof_numeric_column_in_index():
+    # GH#34488
+    left = pd.DataFrame({"b": [10, 11, 12]}, index=Index([1, 2, 3], name="a"))
+    right = pd.DataFrame({"c": [20, 21, 22]}, index=Index([0, 2, 3], name="a"))
+
+    result = merge_asof(left, right, left_on="a", right_on="a")
+    expected = pd.DataFrame({"a": [1, 2, 3], "b": [10, 11, 12], "c": [20, 21, 22]})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_asof_numeric_column_in_multiindex():
+    # GH#34488
+    left = pd.DataFrame(
+        {"b": [10, 11, 12]},
+        index=pd.MultiIndex.from_arrays([[1, 2, 3], ["a", "b", "c"]], names=["a", "z"]),
+    )
+    right = pd.DataFrame(
+        {"c": [20, 21, 22]},
+        index=pd.MultiIndex.from_arrays([[1, 2, 3], ["x", "y", "z"]], names=["a", "y"]),
+    )
+
+    result = merge_asof(left, right, left_on="a", right_on="a")
+    expected = pd.DataFrame({"a": [1, 2, 3], "b": [10, 11, 12], "c": [20, 21, 22]})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_asof_numeri_column_in_index_object_dtype():
+    # GH#34488
+    left = pd.DataFrame({"b": [10, 11, 12]}, index=Index(["1", "2", "3"], name="a"))
+    right = pd.DataFrame({"c": [20, 21, 22]}, index=Index(["m", "n", "o"], name="a"))
+
+    with pytest.raises(
+        MergeError,
+        match=r"Incompatible merge dtype, .*, both sides must have numeric dtype",
+    ):
+        merge_asof(left, right, left_on="a", right_on="a")
+
+    left = left.reset_index().set_index(["a", "b"])
+    right = right.reset_index().set_index(["a", "c"])
+
+    with pytest.raises(
+        MergeError,
+        match=r"Incompatible merge dtype, .*, both sides must have numeric dtype",
+    ):
+        merge_asof(left, right, left_on="a", right_on="a")
+
+
+def test_merge_asof_array_as_on(unit):
+    # GH#42844
+    dti = pd.DatetimeIndex(
+        ["2021/01/01 00:37", "2021/01/01 01:40"], dtype=f"M8[{unit}]"
+    )
+    right = pd.DataFrame(
+        {
+            "a": [2, 6],
+            "ts": dti,
+        }
+    )
+    ts_merge = pd.date_range(
+        start=pd.Timestamp("2021/01/01 00:00"), periods=3, freq="1h", unit=unit
+    )
+    left = pd.DataFrame({"b": [4, 8, 7]})
+    result = merge_asof(
+        left,
+        right,
+        left_on=ts_merge,
+        right_on="ts",
+        allow_exact_matches=False,
+        direction="backward",
+    )
+    expected = pd.DataFrame({"b": [4, 8, 7], "a": [np.nan, 2, 6], "ts": ts_merge})
+    tm.assert_frame_equal(result, expected)
+
+    result = merge_asof(
+        right,
+        left,
+        left_on="ts",
+        right_on=ts_merge,
+        allow_exact_matches=False,
+        direction="backward",
+    )
+    expected = pd.DataFrame(
+        {
+            "a": [2, 6],
+            "ts": dti,
+            "b": [4, 8],
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_asof_raise_for_duplicate_columns():
+    # GH#50102
+    left = pd.DataFrame([[1, 2, "a"]], columns=["a", "a", "left_val"])
+    right = pd.DataFrame([[1, 1, 1]], columns=["a", "a", "right_val"])
+
+    with pytest.raises(ValueError, match="column label 'a'"):
+        merge_asof(left, right, on="a")
+
+    with pytest.raises(ValueError, match="column label 'a'"):
+        merge_asof(left, right, left_on="a", right_on="right_val")
+
+    with pytest.raises(ValueError, match="column label 'a'"):
+        merge_asof(left, right, left_on="left_val", right_on="a")
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        "Int64",
+        pytest.param("int64[pyarrow]", marks=td.skip_if_no("pyarrow")),
+        pytest.param("timestamp[s][pyarrow]", marks=td.skip_if_no("pyarrow")),
+    ],
+)
+def test_merge_asof_extension_dtype(dtype):
+    # GH 52904
+    left = pd.DataFrame(
+        {
+            "join_col": [1, 3, 5],
+            "left_val": [1, 2, 3],
+        }
+    )
+    right = pd.DataFrame(
+        {
+            "join_col": [2, 3, 4],
+            "right_val": [1, 2, 3],
+        }
+    )
+    left = left.astype({"join_col": dtype})
+    right = right.astype({"join_col": dtype})
+    result = merge_asof(left, right, on="join_col")
+    expected = pd.DataFrame(
+        {
+            "join_col": [1, 3, 5],
+            "left_val": [1, 2, 3],
+            "right_val": [np.nan, 2.0, 3.0],
+        }
+    )
+    expected = expected.astype({"join_col": dtype})
+    tm.assert_frame_equal(result, expected)
+
+
+@td.skip_if_no("pyarrow")
+def test_merge_asof_pyarrow_td_tolerance():
+    # GH 56486
+    ser = pd.Series(
+        [datetime.datetime(2023, 1, 1)], dtype="timestamp[us, UTC][pyarrow]"
+    )
+    df = pd.DataFrame(
+        {
+            "timestamp": ser,
+            "value": [1],
+        }
+    )
+    result = merge_asof(df, df, on="timestamp", tolerance=Timedelta("1s"))
+    expected = pd.DataFrame(
+        {
+            "timestamp": ser,
+            "value_x": [1],
+            "value_y": [1],
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_asof_read_only_ndarray():
+    # GH 53513
+    left = pd.Series([2], index=[2], name="left")
+    right = pd.Series([1], index=[1], name="right")
+    # set to read-only
+    left.index.values.flags.writeable = False
+    right.index.values.flags.writeable = False
+    result = merge_asof(left, right, left_index=True, right_index=True)
+    expected = pd.DataFrame({"left": [2], "right": [1]}, index=[2])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_asof_multiby_with_categorical():
+    # GH 43541
+    left = pd.DataFrame(
+        {
+            "c1": pd.Categorical(["a", "a", "b", "b"], categories=["a", "b"]),
+            "c2": ["x"] * 4,
+            "t": [1] * 4,
+            "v": range(4),
+        }
+    )
+    right = pd.DataFrame(
+        {
+            "c1": pd.Categorical(["b", "b"], categories=["b", "a"]),
+            "c2": ["x"] * 2,
+            "t": [1, 2],
+            "v": range(2),
+        }
+    )
+    result = merge_asof(
+        left,
+        right,
+        by=["c1", "c2"],
+        on="t",
+        direction="forward",
+        suffixes=["_left", "_right"],
+    )
+    expected = pd.DataFrame(
+        {
+            "c1": pd.Categorical(["a", "a", "b", "b"], categories=["a", "b"]),
+            "c2": ["x"] * 4,
+            "t": [1] * 4,
+            "v_left": range(4),
+            "v_right": [np.nan, np.nan, 0.0, 0.0],
+        }
+    )
+    tm.assert_frame_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_merge_cross.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_merge_cross.py
new file mode 100644
index 0000000000000000000000000000000000000000..14f9036e43fce13580916dacfd6409e4639d025e
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_merge_cross.py
@@ -0,0 +1,111 @@
+import pytest
+
+from pandas import (
+    DataFrame,
+    Series,
+)
+import pandas._testing as tm
+from pandas.core.reshape.merge import (
+    MergeError,
+    merge,
+)
+
+
+@pytest.mark.parametrize(
+    ("input_col", "output_cols"), [("b", ["a", "b"]), ("a", ["a_x", "a_y"])]
+)
+def test_merge_cross(input_col, output_cols):
+    # GH#5401
+    left = DataFrame({"a": [1, 3]})
+    right = DataFrame({input_col: [3, 4]})
+    left_copy = left.copy()
+    right_copy = right.copy()
+    result = merge(left, right, how="cross")
+    expected = DataFrame({output_cols[0]: [1, 1, 3, 3], output_cols[1]: [3, 4, 3, 4]})
+    tm.assert_frame_equal(result, expected)
+    tm.assert_frame_equal(left, left_copy)
+    tm.assert_frame_equal(right, right_copy)
+
+
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {"left_index": True},
+        {"right_index": True},
+        {"on": "a"},
+        {"left_on": "a"},
+        {"right_on": "b"},
+    ],
+)
+def test_merge_cross_error_reporting(kwargs):
+    # GH#5401
+    left = DataFrame({"a": [1, 3]})
+    right = DataFrame({"b": [3, 4]})
+    msg = (
+        "Can not pass on, right_on, left_on or set right_index=True or "
+        "left_index=True"
+    )
+    with pytest.raises(MergeError, match=msg):
+        merge(left, right, how="cross", **kwargs)
+
+
+def test_merge_cross_mixed_dtypes():
+    # GH#5401
+    left = DataFrame(["a", "b", "c"], columns=["A"])
+    right = DataFrame(range(2), columns=["B"])
+    result = merge(left, right, how="cross")
+    expected = DataFrame({"A": ["a", "a", "b", "b", "c", "c"], "B": [0, 1, 0, 1, 0, 1]})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_cross_more_than_one_column():
+    # GH#5401
+    left = DataFrame({"A": list("ab"), "B": [2, 1]})
+    right = DataFrame({"C": range(2), "D": range(4, 6)})
+    result = merge(left, right, how="cross")
+    expected = DataFrame(
+        {
+            "A": ["a", "a", "b", "b"],
+            "B": [2, 2, 1, 1],
+            "C": [0, 1, 0, 1],
+            "D": [4, 5, 4, 5],
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_cross_null_values(nulls_fixture):
+    # GH#5401
+    left = DataFrame({"a": [1, nulls_fixture]})
+    right = DataFrame({"b": ["a", "b"], "c": [1.0, 2.0]})
+    result = merge(left, right, how="cross")
+    expected = DataFrame(
+        {
+            "a": [1, 1, nulls_fixture, nulls_fixture],
+            "b": ["a", "b", "a", "b"],
+            "c": [1.0, 2.0, 1.0, 2.0],
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_join_cross_error_reporting():
+    # GH#5401
+    left = DataFrame({"a": [1, 3]})
+    right = DataFrame({"a": [3, 4]})
+    msg = (
+        "Can not pass on, right_on, left_on or set right_index=True or "
+        "left_index=True"
+    )
+    with pytest.raises(MergeError, match=msg):
+        left.join(right, how="cross", on="a")
+
+
+def test_merge_cross_series():
+    # GH#54055
+    ls = Series([1, 2, 3, 4], index=[1, 2, 3, 4], name="left")
+    rs = Series([3, 4, 5, 6], index=[3, 4, 5, 6], name="right")
+    res = merge(ls, rs, how="cross")
+
+    expected = merge(ls.to_frame(), rs.to_frame(), how="cross")
+    tm.assert_frame_equal(res, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_merge_index_as_string.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_merge_index_as_string.py
new file mode 100644
index 0000000000000000000000000000000000000000..b4271d4face4fb4e90477196cec7919845f37813
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_merge_index_as_string.py
@@ -0,0 +1,186 @@
+import numpy as np
+import pytest
+
+from pandas import DataFrame
+import pandas._testing as tm
+
+
+@pytest.fixture
+def df1():
+    return DataFrame(
+        {
+            "outer": [1, 1, 1, 2, 2, 2, 2, 3, 3, 4, 4],
+            "inner": [1, 2, 3, 1, 2, 3, 4, 1, 2, 1, 2],
+            "v1": np.linspace(0, 1, 11),
+        }
+    )
+
+
+@pytest.fixture
+def df2():
+    return DataFrame(
+        {
+            "outer": [1, 1, 1, 1, 1, 1, 2, 2, 3, 3, 3, 3],
+            "inner": [1, 2, 2, 3, 3, 4, 2, 3, 1, 1, 2, 3],
+            "v2": np.linspace(10, 11, 12),
+        }
+    )
+
+
+@pytest.fixture(params=[[], ["outer"], ["outer", "inner"]])
+def left_df(request, df1):
+    """Construct left test DataFrame with specified levels
+    (any of 'outer', 'inner', and 'v1')
+    """
+    levels = request.param
+    if levels:
+        df1 = df1.set_index(levels)
+
+    return df1
+
+
+@pytest.fixture(params=[[], ["outer"], ["outer", "inner"]])
+def right_df(request, df2):
+    """Construct right test DataFrame with specified levels
+    (any of 'outer', 'inner', and 'v2')
+    """
+    levels = request.param
+
+    if levels:
+        df2 = df2.set_index(levels)
+
+    return df2
+
+
+def compute_expected(df_left, df_right, on=None, left_on=None, right_on=None, how=None):
+    """
+    Compute the expected merge result for the test case.
+
+    This method computes the expected result of merging two DataFrames on
+    a combination of their columns and index levels. It does so by
+    explicitly dropping/resetting their named index levels, performing a
+    merge on their columns, and then finally restoring the appropriate
+    index in the result.
+
+    Parameters
+    ----------
+    df_left : DataFrame
+        The left DataFrame (may have zero or more named index levels)
+    df_right : DataFrame
+        The right DataFrame (may have zero or more named index levels)
+    on : list of str
+        The on parameter to the merge operation
+    left_on : list of str
+        The left_on parameter to the merge operation
+    right_on : list of str
+        The right_on parameter to the merge operation
+    how : str
+        The how parameter to the merge operation
+
+    Returns
+    -------
+    DataFrame
+        The expected merge result
+    """
+    # Handle on param if specified
+    if on is not None:
+        left_on, right_on = on, on
+
+    # Compute input named index levels
+    left_levels = [n for n in df_left.index.names if n is not None]
+    right_levels = [n for n in df_right.index.names if n is not None]
+
+    # Compute output named index levels
+    output_levels = [i for i in left_on if i in right_levels and i in left_levels]
+
+    # Drop index levels that aren't involved in the merge
+    drop_left = [n for n in left_levels if n not in left_on]
+    if drop_left:
+        df_left = df_left.reset_index(drop_left, drop=True)
+
+    drop_right = [n for n in right_levels if n not in right_on]
+    if drop_right:
+        df_right = df_right.reset_index(drop_right, drop=True)
+
+    # Convert remaining index levels to columns
+    reset_left = [n for n in left_levels if n in left_on]
+    if reset_left:
+        df_left = df_left.reset_index(level=reset_left)
+
+    reset_right = [n for n in right_levels if n in right_on]
+    if reset_right:
+        df_right = df_right.reset_index(level=reset_right)
+
+    # Perform merge
+    expected = df_left.merge(df_right, left_on=left_on, right_on=right_on, how=how)
+
+    # Restore index levels
+    if output_levels:
+        expected = expected.set_index(output_levels)
+
+    return expected
+
+
+@pytest.mark.parametrize(
+    "on,how",
+    [
+        (["outer"], "inner"),
+        (["inner"], "left"),
+        (["outer", "inner"], "right"),
+        (["inner", "outer"], "outer"),
+    ],
+)
+def test_merge_indexes_and_columns_on(left_df, right_df, on, how):
+    # Construct expected result
+    expected = compute_expected(left_df, right_df, on=on, how=how)
+
+    # Perform merge
+    result = left_df.merge(right_df, on=on, how=how)
+    tm.assert_frame_equal(result, expected, check_like=True)
+
+
+@pytest.mark.parametrize(
+    "left_on,right_on,how",
+    [
+        (["outer"], ["outer"], "inner"),
+        (["inner"], ["inner"], "right"),
+        (["outer", "inner"], ["outer", "inner"], "left"),
+        (["inner", "outer"], ["inner", "outer"], "outer"),
+    ],
+)
+def test_merge_indexes_and_columns_lefton_righton(
+    left_df, right_df, left_on, right_on, how
+):
+    # Construct expected result
+    expected = compute_expected(
+        left_df, right_df, left_on=left_on, right_on=right_on, how=how
+    )
+
+    # Perform merge
+    result = left_df.merge(right_df, left_on=left_on, right_on=right_on, how=how)
+    tm.assert_frame_equal(result, expected, check_like=True)
+
+
+@pytest.mark.parametrize("left_index", ["inner", ["inner", "outer"]])
+def test_join_indexes_and_columns_on(df1, df2, left_index, join_type):
+    # Construct left_df
+    left_df = df1.set_index(left_index)
+
+    # Construct right_df
+    right_df = df2.set_index(["outer", "inner"])
+
+    # Result
+    expected = (
+        left_df.reset_index()
+        .join(
+            right_df, on=["outer", "inner"], how=join_type, lsuffix="_x", rsuffix="_y"
+        )
+        .set_index(left_index)
+    )
+
+    # Perform join
+    result = left_df.join(
+        right_df, on=["outer", "inner"], how=join_type, lsuffix="_x", rsuffix="_y"
+    )
+
+    tm.assert_frame_equal(result, expected, check_like=True)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_merge_ordered.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_merge_ordered.py
new file mode 100644
index 0000000000000000000000000000000000000000..0bd3ca3cf2c1bd8ceae38d8a04cd6d48c4c69f68
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_merge_ordered.py
@@ -0,0 +1,244 @@
+import re
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    merge_ordered,
+)
+import pandas._testing as tm
+
+
+@pytest.fixture
+def left():
+    return DataFrame({"key": ["a", "c", "e"], "lvalue": [1, 2.0, 3]})
+
+
+@pytest.fixture
+def right():
+    return DataFrame({"key": ["b", "c", "d", "f"], "rvalue": [1, 2, 3.0, 4]})
+
+
+class TestMergeOrdered:
+    def test_basic(self, left, right):
+        result = merge_ordered(left, right, on="key")
+        expected = DataFrame(
+            {
+                "key": ["a", "b", "c", "d", "e", "f"],
+                "lvalue": [1, np.nan, 2, np.nan, 3, np.nan],
+                "rvalue": [np.nan, 1, 2, 3, np.nan, 4],
+            }
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_ffill(self, left, right):
+        result = merge_ordered(left, right, on="key", fill_method="ffill")
+        expected = DataFrame(
+            {
+                "key": ["a", "b", "c", "d", "e", "f"],
+                "lvalue": [1.0, 1, 2, 2, 3, 3.0],
+                "rvalue": [np.nan, 1, 2, 3, 3, 4],
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_multigroup(self, left, right):
+        left = pd.concat([left, left], ignore_index=True)
+
+        left["group"] = ["a"] * 3 + ["b"] * 3
+
+        result = merge_ordered(
+            left, right, on="key", left_by="group", fill_method="ffill"
+        )
+        expected = DataFrame(
+            {
+                "key": ["a", "b", "c", "d", "e", "f"] * 2,
+                "lvalue": [1.0, 1, 2, 2, 3, 3.0] * 2,
+                "rvalue": [np.nan, 1, 2, 3, 3, 4] * 2,
+            }
+        )
+        expected["group"] = ["a"] * 6 + ["b"] * 6
+
+        tm.assert_frame_equal(result, expected.loc[:, result.columns])
+
+        result2 = merge_ordered(
+            right, left, on="key", right_by="group", fill_method="ffill"
+        )
+        tm.assert_frame_equal(result, result2.loc[:, result.columns])
+
+        result = merge_ordered(left, right, on="key", left_by="group")
+        assert result["group"].notna().all()
+
+    @pytest.mark.filterwarnings(
+        "ignore:Passing a BlockManager|Passing a SingleBlockManager:DeprecationWarning"
+    )
+    def test_merge_type(self, left, right):
+        class NotADataFrame(DataFrame):
+            @property
+            def _constructor(self):
+                return NotADataFrame
+
+        nad = NotADataFrame(left)
+        result = nad.merge(right, on="key")
+
+        assert isinstance(result, NotADataFrame)
+
+    @pytest.mark.parametrize(
+        "df_seq, pattern",
+        [
+            ((), "[Nn]o objects"),
+            ([], "[Nn]o objects"),
+            ({}, "[Nn]o objects"),
+            ([None], "objects.*None"),
+            ([None, None], "objects.*None"),
+        ],
+    )
+    def test_empty_sequence_concat(self, df_seq, pattern):
+        # GH 9157
+        with pytest.raises(ValueError, match=pattern):
+            pd.concat(df_seq)
+
+    @pytest.mark.parametrize(
+        "arg", [[DataFrame()], [None, DataFrame()], [DataFrame(), None]]
+    )
+    def test_empty_sequence_concat_ok(self, arg):
+        pd.concat(arg)
+
+    def test_doc_example(self):
+        left = DataFrame(
+            {
+                "group": list("aaabbb"),
+                "key": ["a", "c", "e", "a", "c", "e"],
+                "lvalue": [1, 2, 3] * 2,
+            }
+        )
+
+        right = DataFrame({"key": ["b", "c", "d"], "rvalue": [1, 2, 3]})
+
+        result = merge_ordered(left, right, fill_method="ffill", left_by="group")
+
+        expected = DataFrame(
+            {
+                "group": list("aaaaabbbbb"),
+                "key": ["a", "b", "c", "d", "e"] * 2,
+                "lvalue": [1, 1, 2, 2, 3] * 2,
+                "rvalue": [np.nan, 1, 2, 3, 3] * 2,
+            }
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "left, right, on, left_by, right_by, expected",
+        [
+            (
+                DataFrame({"G": ["g", "g"], "H": ["h", "h"], "T": [1, 3]}),
+                DataFrame({"T": [2], "E": [1]}),
+                ["T"],
+                ["G", "H"],
+                None,
+                DataFrame(
+                    {
+                        "G": ["g"] * 3,
+                        "H": ["h"] * 3,
+                        "T": [1, 2, 3],
+                        "E": [np.nan, 1.0, np.nan],
+                    }
+                ),
+            ),
+            (
+                DataFrame({"G": ["g", "g"], "H": ["h", "h"], "T": [1, 3]}),
+                DataFrame({"T": [2], "E": [1]}),
+                "T",
+                ["G", "H"],
+                None,
+                DataFrame(
+                    {
+                        "G": ["g"] * 3,
+                        "H": ["h"] * 3,
+                        "T": [1, 2, 3],
+                        "E": [np.nan, 1.0, np.nan],
+                    }
+                ),
+            ),
+            (
+                DataFrame({"T": [2], "E": [1]}),
+                DataFrame({"G": ["g", "g"], "H": ["h", "h"], "T": [1, 3]}),
+                ["T"],
+                None,
+                ["G", "H"],
+                DataFrame(
+                    {
+                        "T": [1, 2, 3],
+                        "E": [np.nan, 1.0, np.nan],
+                        "G": ["g"] * 3,
+                        "H": ["h"] * 3,
+                    }
+                ),
+            ),
+        ],
+    )
+    def test_list_type_by(self, left, right, on, left_by, right_by, expected):
+        # GH 35269
+        result = merge_ordered(
+            left=left,
+            right=right,
+            on=on,
+            left_by=left_by,
+            right_by=right_by,
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_left_by_length_equals_to_right_shape0(self):
+        # GH 38166
+        left = DataFrame([["g", "h", 1], ["g", "h", 3]], columns=list("GHE"))
+        right = DataFrame([[2, 1]], columns=list("ET"))
+        result = merge_ordered(left, right, on="E", left_by=["G", "H"])
+        expected = DataFrame(
+            {"G": ["g"] * 3, "H": ["h"] * 3, "E": [1, 2, 3], "T": [np.nan, 1.0, np.nan]}
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_elements_not_in_by_but_in_df(self):
+        # GH 38167
+        left = DataFrame([["g", "h", 1], ["g", "h", 3]], columns=list("GHE"))
+        right = DataFrame([[2, 1]], columns=list("ET"))
+        msg = r"\{'h'\} not found in left columns"
+        with pytest.raises(KeyError, match=msg):
+            merge_ordered(left, right, on="E", left_by=["G", "h"])
+
+    @pytest.mark.parametrize("invalid_method", ["linear", "carrot"])
+    def test_ffill_validate_fill_method(self, left, right, invalid_method):
+        # GH 55884
+        with pytest.raises(
+            ValueError, match=re.escape("fill_method must be 'ffill' or None")
+        ):
+            merge_ordered(left, right, on="key", fill_method=invalid_method)
+
+    def test_ffill_left_merge(self):
+        # GH 57010
+        df1 = DataFrame(
+            {
+                "key": ["a", "c", "e", "a", "c", "e"],
+                "lvalue": [1, 2, 3, 1, 2, 3],
+                "group": ["a", "a", "a", "b", "b", "b"],
+            }
+        )
+        df2 = DataFrame({"key": ["b", "c", "d"], "rvalue": [1, 2, 3]})
+        result = merge_ordered(
+            df1, df2, fill_method="ffill", left_by="group", how="left"
+        )
+        expected = DataFrame(
+            {
+                "key": ["a", "c", "e", "a", "c", "e"],
+                "lvalue": [1, 2, 3, 1, 2, 3],
+                "group": ["a", "a", "a", "b", "b", "b"],
+                "rvalue": [np.nan, 2.0, 2.0, np.nan, 2.0, 2.0],
+            }
+        )
+        tm.assert_frame_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_multi.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_multi.py
new file mode 100644
index 0000000000000000000000000000000000000000..402ff049884ba25e557d8c415fa9b50030d13b06
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_multi.py
@@ -0,0 +1,934 @@
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    RangeIndex,
+    Series,
+    Timestamp,
+    option_context,
+)
+import pandas._testing as tm
+from pandas.core.reshape.concat import concat
+from pandas.core.reshape.merge import merge
+
+
+@pytest.fixture
+def left():
+    """left dataframe (not multi-indexed) for multi-index join tests"""
+    # a little relevant example with NAs
+    key1 = ["bar", "bar", "bar", "foo", "foo", "baz", "baz", "qux", "qux", "snap"]
+    key2 = ["two", "one", "three", "one", "two", "one", "two", "two", "three", "one"]
+
+    data = np.random.default_rng(2).standard_normal(len(key1))
+    return DataFrame({"key1": key1, "key2": key2, "data": data})
+
+
+@pytest.fixture
+def right(multiindex_dataframe_random_data):
+    """right dataframe (multi-indexed) for multi-index join tests"""
+    df = multiindex_dataframe_random_data
+    df.index.names = ["key1", "key2"]
+
+    df.columns = ["j_one", "j_two", "j_three"]
+    return df
+
+
+@pytest.fixture
+def left_multi():
+    return DataFrame(
+        {
+            "Origin": ["A", "A", "B", "B", "C"],
+            "Destination": ["A", "B", "A", "C", "A"],
+            "Period": ["AM", "AM", "IP", "AM", "OP"],
+            "TripPurp": ["hbw", "nhb", "hbo", "nhb", "hbw"],
+            "Trips": [1987, 3647, 2470, 4296, 4444],
+        },
+        columns=["Origin", "Destination", "Period", "TripPurp", "Trips"],
+    ).set_index(["Origin", "Destination", "Period", "TripPurp"])
+
+
+@pytest.fixture
+def right_multi():
+    return DataFrame(
+        {
+            "Origin": ["A", "A", "B", "B", "C", "C", "E"],
+            "Destination": ["A", "B", "A", "B", "A", "B", "F"],
+            "Period": ["AM", "AM", "IP", "AM", "OP", "IP", "AM"],
+            "LinkType": ["a", "b", "c", "b", "a", "b", "a"],
+            "Distance": [100, 80, 90, 80, 75, 35, 55],
+        },
+        columns=["Origin", "Destination", "Period", "LinkType", "Distance"],
+    ).set_index(["Origin", "Destination", "Period", "LinkType"])
+
+
+@pytest.fixture
+def on_cols_multi():
+    return ["Origin", "Destination", "Period"]
+
+
+class TestMergeMulti:
+    def test_merge_on_multikey(self, left, right, join_type):
+        on_cols = ["key1", "key2"]
+        result = left.join(right, on=on_cols, how=join_type).reset_index(drop=True)
+
+        expected = merge(left, right.reset_index(), on=on_cols, how=join_type)
+
+        tm.assert_frame_equal(result, expected)
+
+        result = left.join(right, on=on_cols, how=join_type, sort=True).reset_index(
+            drop=True
+        )
+
+        expected = merge(
+            left, right.reset_index(), on=on_cols, how=join_type, sort=True
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "infer_string", [False, pytest.param(True, marks=td.skip_if_no("pyarrow"))]
+    )
+    @pytest.mark.parametrize("sort", [True, False])
+    def test_left_join_multi_index(self, sort, infer_string):
+        with option_context("future.infer_string", infer_string):
+            icols = ["1st", "2nd", "3rd"]
+
+            def bind_cols(df):
+                iord = lambda a: 0 if a != a else ord(a)
+                f = lambda ts: ts.map(iord) - ord("a")
+                return f(df["1st"]) + f(df["3rd"]) * 1e2 + df["2nd"].fillna(0) * 10
+
+            def run_asserts(left, right, sort):
+                res = left.join(right, on=icols, how="left", sort=sort)
+
+                assert len(left) < len(res) + 1
+                assert not res["4th"].isna().any()
+                assert not res["5th"].isna().any()
+
+                tm.assert_series_equal(res["4th"], -res["5th"], check_names=False)
+                result = bind_cols(res.iloc[:, :-2])
+                tm.assert_series_equal(res["4th"], result, check_names=False)
+                assert result.name is None
+
+                if sort:
+                    tm.assert_frame_equal(res, res.sort_values(icols, kind="mergesort"))
+
+                out = merge(left, right.reset_index(), on=icols, sort=sort, how="left")
+
+                res.index = RangeIndex(len(res))
+                tm.assert_frame_equal(out, res)
+
+            lc = list(map(chr, np.arange(ord("a"), ord("z") + 1)))
+            left = DataFrame(
+                np.random.default_rng(2).choice(lc, (50, 2)), columns=["1st", "3rd"]
+            )
+            # Explicit cast to float to avoid implicit cast when setting nan
+            left.insert(
+                1,
+                "2nd",
+                np.random.default_rng(2).integers(0, 10, len(left)).astype("float"),
+            )
+
+            i = np.random.default_rng(2).permutation(len(left))
+            right = left.iloc[i].copy()
+
+            left["4th"] = bind_cols(left)
+            right["5th"] = -bind_cols(right)
+            right.set_index(icols, inplace=True)
+
+            run_asserts(left, right, sort)
+
+            # inject some nulls
+            left.loc[1::4, "1st"] = np.nan
+            left.loc[2::5, "2nd"] = np.nan
+            left.loc[3::6, "3rd"] = np.nan
+            left["4th"] = bind_cols(left)
+
+            i = np.random.default_rng(2).permutation(len(left))
+            right = left.iloc[i, :-1]
+            right["5th"] = -bind_cols(right)
+            right.set_index(icols, inplace=True)
+
+            run_asserts(left, right, sort)
+
+    @pytest.mark.parametrize("sort", [False, True])
+    def test_merge_right_vs_left(self, left, right, sort):
+        # compare left vs right merge with multikey
+        on_cols = ["key1", "key2"]
+        merged_left_right = left.merge(
+            right, left_on=on_cols, right_index=True, how="left", sort=sort
+        )
+
+        merge_right_left = right.merge(
+            left, right_on=on_cols, left_index=True, how="right", sort=sort
+        )
+
+        # Reorder columns
+        merge_right_left = merge_right_left[merged_left_right.columns]
+
+        tm.assert_frame_equal(merged_left_right, merge_right_left)
+
+    def test_merge_multiple_cols_with_mixed_cols_index(self):
+        # GH29522
+        s = Series(
+            range(6),
+            MultiIndex.from_product([["A", "B"], [1, 2, 3]], names=["lev1", "lev2"]),
+            name="Amount",
+        )
+        df = DataFrame({"lev1": list("AAABBB"), "lev2": [1, 2, 3, 1, 2, 3], "col": 0})
+        result = merge(df, s.reset_index(), on=["lev1", "lev2"])
+        expected = DataFrame(
+            {
+                "lev1": list("AAABBB"),
+                "lev2": [1, 2, 3, 1, 2, 3],
+                "col": [0] * 6,
+                "Amount": range(6),
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_compress_group_combinations(self):
+        # ~ 40000000 possible unique groups
+        key1 = [str(i) for i in range(10000)]
+        key1 = np.tile(key1, 2)
+        key2 = key1[::-1]
+
+        df = DataFrame(
+            {
+                "key1": key1,
+                "key2": key2,
+                "value1": np.random.default_rng(2).standard_normal(20000),
+            }
+        )
+
+        df2 = DataFrame(
+            {
+                "key1": key1[::2],
+                "key2": key2[::2],
+                "value2": np.random.default_rng(2).standard_normal(10000),
+            }
+        )
+
+        # just to hit the label compression code path
+        merge(df, df2, how="outer")
+
+    def test_left_join_index_preserve_order(self):
+        on_cols = ["k1", "k2"]
+        left = DataFrame(
+            {
+                "k1": [0, 1, 2] * 8,
+                "k2": ["foo", "bar"] * 12,
+                "v": np.array(np.arange(24), dtype=np.int64),
+            }
+        )
+
+        index = MultiIndex.from_tuples([(2, "bar"), (1, "foo")])
+        right = DataFrame({"v2": [5, 7]}, index=index)
+
+        result = left.join(right, on=on_cols)
+
+        expected = left.copy()
+        expected["v2"] = np.nan
+        expected.loc[(expected.k1 == 2) & (expected.k2 == "bar"), "v2"] = 5
+        expected.loc[(expected.k1 == 1) & (expected.k2 == "foo"), "v2"] = 7
+
+        tm.assert_frame_equal(result, expected)
+
+        result.sort_values(on_cols, kind="mergesort", inplace=True)
+        expected = left.join(right, on=on_cols, sort=True)
+
+        tm.assert_frame_equal(result, expected)
+
+        # test join with multi dtypes blocks
+        left = DataFrame(
+            {
+                "k1": [0, 1, 2] * 8,
+                "k2": ["foo", "bar"] * 12,
+                "k3": np.array([0, 1, 2] * 8, dtype=np.float32),
+                "v": np.array(np.arange(24), dtype=np.int32),
+            }
+        )
+
+        index = MultiIndex.from_tuples([(2, "bar"), (1, "foo")])
+        right = DataFrame({"v2": [5, 7]}, index=index)
+
+        result = left.join(right, on=on_cols)
+
+        expected = left.copy()
+        expected["v2"] = np.nan
+        expected.loc[(expected.k1 == 2) & (expected.k2 == "bar"), "v2"] = 5
+        expected.loc[(expected.k1 == 1) & (expected.k2 == "foo"), "v2"] = 7
+
+        tm.assert_frame_equal(result, expected)
+
+        result = result.sort_values(on_cols, kind="mergesort")
+        expected = left.join(right, on=on_cols, sort=True)
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_left_join_index_multi_match_multiindex(self):
+        left = DataFrame(
+            [
+                ["X", "Y", "C", "a"],
+                ["W", "Y", "C", "e"],
+                ["V", "Q", "A", "h"],
+                ["V", "R", "D", "i"],
+                ["X", "Y", "D", "b"],
+                ["X", "Y", "A", "c"],
+                ["W", "Q", "B", "f"],
+                ["W", "R", "C", "g"],
+                ["V", "Y", "C", "j"],
+                ["X", "Y", "B", "d"],
+            ],
+            columns=["cola", "colb", "colc", "tag"],
+            index=[3, 2, 0, 1, 7, 6, 4, 5, 9, 8],
+        )
+
+        right = DataFrame(
+            [
+                ["W", "R", "C", 0],
+                ["W", "Q", "B", 3],
+                ["W", "Q", "B", 8],
+                ["X", "Y", "A", 1],
+                ["X", "Y", "A", 4],
+                ["X", "Y", "B", 5],
+                ["X", "Y", "C", 6],
+                ["X", "Y", "C", 9],
+                ["X", "Q", "C", -6],
+                ["X", "R", "C", -9],
+                ["V", "Y", "C", 7],
+                ["V", "R", "D", 2],
+                ["V", "R", "D", -1],
+                ["V", "Q", "A", -3],
+            ],
+            columns=["col1", "col2", "col3", "val"],
+        ).set_index(["col1", "col2", "col3"])
+
+        result = left.join(right, on=["cola", "colb", "colc"], how="left")
+
+        expected = DataFrame(
+            [
+                ["X", "Y", "C", "a", 6],
+                ["X", "Y", "C", "a", 9],
+                ["W", "Y", "C", "e", np.nan],
+                ["V", "Q", "A", "h", -3],
+                ["V", "R", "D", "i", 2],
+                ["V", "R", "D", "i", -1],
+                ["X", "Y", "D", "b", np.nan],
+                ["X", "Y", "A", "c", 1],
+                ["X", "Y", "A", "c", 4],
+                ["W", "Q", "B", "f", 3],
+                ["W", "Q", "B", "f", 8],
+                ["W", "R", "C", "g", 0],
+                ["V", "Y", "C", "j", 7],
+                ["X", "Y", "B", "d", 5],
+            ],
+            columns=["cola", "colb", "colc", "tag", "val"],
+            index=[3, 3, 2, 0, 1, 1, 7, 6, 6, 4, 4, 5, 9, 8],
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+        result = left.join(right, on=["cola", "colb", "colc"], how="left", sort=True)
+
+        expected = expected.sort_values(["cola", "colb", "colc"], kind="mergesort")
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_left_join_index_multi_match(self):
+        left = DataFrame(
+            [["c", 0], ["b", 1], ["a", 2], ["b", 3]],
+            columns=["tag", "val"],
+            index=[2, 0, 1, 3],
+        )
+
+        right = DataFrame(
+            [
+                ["a", "v"],
+                ["c", "w"],
+                ["c", "x"],
+                ["d", "y"],
+                ["a", "z"],
+                ["c", "r"],
+                ["e", "q"],
+                ["c", "s"],
+            ],
+            columns=["tag", "char"],
+        ).set_index("tag")
+
+        result = left.join(right, on="tag", how="left")
+
+        expected = DataFrame(
+            [
+                ["c", 0, "w"],
+                ["c", 0, "x"],
+                ["c", 0, "r"],
+                ["c", 0, "s"],
+                ["b", 1, np.nan],
+                ["a", 2, "v"],
+                ["a", 2, "z"],
+                ["b", 3, np.nan],
+            ],
+            columns=["tag", "val", "char"],
+            index=[2, 2, 2, 2, 0, 1, 1, 3],
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+        result = left.join(right, on="tag", how="left", sort=True)
+        expected2 = expected.sort_values("tag", kind="mergesort")
+
+        tm.assert_frame_equal(result, expected2)
+
+        # GH7331 - maintain left frame order in left merge
+        result = merge(left, right.reset_index(), how="left", on="tag")
+        expected.index = RangeIndex(len(expected))
+        tm.assert_frame_equal(result, expected)
+
+    def test_left_merge_na_buglet(self):
+        left = DataFrame(
+            {
+                "id": list("abcde"),
+                "v1": np.random.default_rng(2).standard_normal(5),
+                "v2": np.random.default_rng(2).standard_normal(5),
+                "dummy": list("abcde"),
+                "v3": np.random.default_rng(2).standard_normal(5),
+            },
+            columns=["id", "v1", "v2", "dummy", "v3"],
+        )
+        right = DataFrame(
+            {
+                "id": ["a", "b", np.nan, np.nan, np.nan],
+                "sv3": [1.234, 5.678, np.nan, np.nan, np.nan],
+            }
+        )
+
+        result = merge(left, right, on="id", how="left")
+
+        rdf = right.drop(["id"], axis=1)
+        expected = left.join(rdf)
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_na_keys(self):
+        data = [
+            [1950, "A", 1.5],
+            [1950, "B", 1.5],
+            [1955, "B", 1.5],
+            [1960, "B", np.nan],
+            [1970, "B", 4.0],
+            [1950, "C", 4.0],
+            [1960, "C", np.nan],
+            [1965, "C", 3.0],
+            [1970, "C", 4.0],
+        ]
+
+        frame = DataFrame(data, columns=["year", "panel", "data"])
+
+        other_data = [
+            [1960, "A", np.nan],
+            [1970, "A", np.nan],
+            [1955, "A", np.nan],
+            [1965, "A", np.nan],
+            [1965, "B", np.nan],
+            [1955, "C", np.nan],
+        ]
+        other = DataFrame(other_data, columns=["year", "panel", "data"])
+
+        result = frame.merge(other, how="outer")
+
+        expected = frame.fillna(-999).merge(other.fillna(-999), how="outer")
+        expected = expected.replace(-999, np.nan)
+
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("klass", [None, np.asarray, Series, Index])
+    def test_merge_datetime_index(self, klass):
+        # see gh-19038
+        df = DataFrame(
+            [1, 2, 3], ["2016-01-01", "2017-01-01", "2018-01-01"], columns=["a"]
+        )
+        df.index = pd.to_datetime(df.index)
+        on_vector = df.index.year
+
+        if klass is not None:
+            on_vector = klass(on_vector)
+
+        exp_years = np.array([2016, 2017, 2018], dtype=np.int32)
+        expected = DataFrame({"a": [1, 2, 3], "key_1": exp_years})
+
+        result = df.merge(df, on=["a", on_vector], how="inner")
+        tm.assert_frame_equal(result, expected)
+
+        expected = DataFrame({"key_0": exp_years, "a_x": [1, 2, 3], "a_y": [1, 2, 3]})
+
+        result = df.merge(df, on=[df.index.year], how="inner")
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("merge_type", ["left", "right"])
+    def test_merge_datetime_multi_index_empty_df(self, merge_type):
+        # see gh-36895
+
+        left = DataFrame(
+            data={
+                "data": [1.5, 1.5],
+            },
+            index=MultiIndex.from_tuples(
+                [[Timestamp("1950-01-01"), "A"], [Timestamp("1950-01-02"), "B"]],
+                names=["date", "panel"],
+            ),
+        )
+
+        right = DataFrame(
+            index=MultiIndex.from_tuples([], names=["date", "panel"]), columns=["state"]
+        )
+
+        expected_index = MultiIndex.from_tuples(
+            [[Timestamp("1950-01-01"), "A"], [Timestamp("1950-01-02"), "B"]],
+            names=["date", "panel"],
+        )
+
+        if merge_type == "left":
+            expected = DataFrame(
+                data={
+                    "data": [1.5, 1.5],
+                    "state": np.array([np.nan, np.nan], dtype=object),
+                },
+                index=expected_index,
+            )
+            results_merge = left.merge(right, how="left", on=["date", "panel"])
+            results_join = left.join(right, how="left")
+        else:
+            expected = DataFrame(
+                data={
+                    "state": np.array([np.nan, np.nan], dtype=object),
+                    "data": [1.5, 1.5],
+                },
+                index=expected_index,
+            )
+            results_merge = right.merge(left, how="right", on=["date", "panel"])
+            results_join = right.join(left, how="right")
+
+        tm.assert_frame_equal(results_merge, expected)
+        tm.assert_frame_equal(results_join, expected)
+
+    @pytest.fixture
+    def household(self):
+        household = DataFrame(
+            {
+                "household_id": [1, 2, 3],
+                "male": [0, 1, 0],
+                "wealth": [196087.3, 316478.7, 294750],
+            },
+            columns=["household_id", "male", "wealth"],
+        ).set_index("household_id")
+        return household
+
+    @pytest.fixture
+    def portfolio(self):
+        portfolio = DataFrame(
+            {
+                "household_id": [1, 2, 2, 3, 3, 3, 4],
+                "asset_id": [
+                    "nl0000301109",
+                    "nl0000289783",
+                    "gb00b03mlx29",
+                    "gb00b03mlx29",
+                    "lu0197800237",
+                    "nl0000289965",
+                    np.nan,
+                ],
+                "name": [
+                    "ABN Amro",
+                    "Robeco",
+                    "Royal Dutch Shell",
+                    "Royal Dutch Shell",
+                    "AAB Eastern Europe Equity Fund",
+                    "Postbank BioTech Fonds",
+                    np.nan,
+                ],
+                "share": [1.0, 0.4, 0.6, 0.15, 0.6, 0.25, 1.0],
+            },
+            columns=["household_id", "asset_id", "name", "share"],
+        ).set_index(["household_id", "asset_id"])
+        return portfolio
+
+    @pytest.fixture
+    def expected(self):
+        expected = (
+            DataFrame(
+                {
+                    "male": [0, 1, 1, 0, 0, 0],
+                    "wealth": [
+                        196087.3,
+                        316478.7,
+                        316478.7,
+                        294750.0,
+                        294750.0,
+                        294750.0,
+                    ],
+                    "name": [
+                        "ABN Amro",
+                        "Robeco",
+                        "Royal Dutch Shell",
+                        "Royal Dutch Shell",
+                        "AAB Eastern Europe Equity Fund",
+                        "Postbank BioTech Fonds",
+                    ],
+                    "share": [1.00, 0.40, 0.60, 0.15, 0.60, 0.25],
+                    "household_id": [1, 2, 2, 3, 3, 3],
+                    "asset_id": [
+                        "nl0000301109",
+                        "nl0000289783",
+                        "gb00b03mlx29",
+                        "gb00b03mlx29",
+                        "lu0197800237",
+                        "nl0000289965",
+                    ],
+                }
+            )
+            .set_index(["household_id", "asset_id"])
+            .reindex(columns=["male", "wealth", "name", "share"])
+        )
+        return expected
+
+    def test_join_multi_levels(self, portfolio, household, expected):
+        portfolio = portfolio.copy()
+        household = household.copy()
+
+        # GH 3662
+        # merge multi-levels
+        result = household.join(portfolio, how="inner")
+        tm.assert_frame_equal(result, expected)
+
+    def test_join_multi_levels_merge_equivalence(self, portfolio, household, expected):
+        portfolio = portfolio.copy()
+        household = household.copy()
+
+        # equivalency
+        result = merge(
+            household.reset_index(),
+            portfolio.reset_index(),
+            on=["household_id"],
+            how="inner",
+        ).set_index(["household_id", "asset_id"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_join_multi_levels_outer(self, portfolio, household, expected):
+        portfolio = portfolio.copy()
+        household = household.copy()
+
+        result = household.join(portfolio, how="outer")
+        expected = concat(
+            [
+                expected,
+                (
+                    DataFrame(
+                        {"share": [1.00]},
+                        index=MultiIndex.from_tuples(
+                            [(4, np.nan)], names=["household_id", "asset_id"]
+                        ),
+                    )
+                ),
+            ],
+            axis=0,
+            sort=True,
+        ).reindex(columns=expected.columns)
+        tm.assert_frame_equal(result, expected, check_index_type=False)
+
+    def test_join_multi_levels_invalid(self, portfolio, household):
+        portfolio = portfolio.copy()
+        household = household.copy()
+
+        # invalid cases
+        household.index.name = "foo"
+
+        with pytest.raises(
+            ValueError, match="cannot join with no overlapping index names"
+        ):
+            household.join(portfolio, how="inner")
+
+        portfolio2 = portfolio.copy()
+        portfolio2.index.set_names(["household_id", "foo"])
+
+        with pytest.raises(ValueError, match="columns overlap but no suffix specified"):
+            portfolio2.join(portfolio, how="inner")
+
+    def test_join_multi_levels2(self):
+        # some more advanced merges
+        # GH6360
+        household = DataFrame(
+            {
+                "household_id": [1, 2, 2, 3, 3, 3, 4],
+                "asset_id": [
+                    "nl0000301109",
+                    "nl0000301109",
+                    "gb00b03mlx29",
+                    "gb00b03mlx29",
+                    "lu0197800237",
+                    "nl0000289965",
+                    np.nan,
+                ],
+                "share": [1.0, 0.4, 0.6, 0.15, 0.6, 0.25, 1.0],
+            },
+            columns=["household_id", "asset_id", "share"],
+        ).set_index(["household_id", "asset_id"])
+
+        log_return = DataFrame(
+            {
+                "asset_id": [
+                    "gb00b03mlx29",
+                    "gb00b03mlx29",
+                    "gb00b03mlx29",
+                    "lu0197800237",
+                    "lu0197800237",
+                ],
+                "t": [233, 234, 235, 180, 181],
+                "log_return": [
+                    0.09604978,
+                    -0.06524096,
+                    0.03532373,
+                    0.03025441,
+                    0.036997,
+                ],
+            }
+        ).set_index(["asset_id", "t"])
+
+        expected = (
+            DataFrame(
+                {
+                    "household_id": [2, 2, 2, 3, 3, 3, 3, 3],
+                    "asset_id": [
+                        "gb00b03mlx29",
+                        "gb00b03mlx29",
+                        "gb00b03mlx29",
+                        "gb00b03mlx29",
+                        "gb00b03mlx29",
+                        "gb00b03mlx29",
+                        "lu0197800237",
+                        "lu0197800237",
+                    ],
+                    "t": [233, 234, 235, 233, 234, 235, 180, 181],
+                    "share": [0.6, 0.6, 0.6, 0.15, 0.15, 0.15, 0.6, 0.6],
+                    "log_return": [
+                        0.09604978,
+                        -0.06524096,
+                        0.03532373,
+                        0.09604978,
+                        -0.06524096,
+                        0.03532373,
+                        0.03025441,
+                        0.036997,
+                    ],
+                }
+            )
+            .set_index(["household_id", "asset_id", "t"])
+            .reindex(columns=["share", "log_return"])
+        )
+
+        # this is the equivalency
+        result = merge(
+            household.reset_index(),
+            log_return.reset_index(),
+            on=["asset_id"],
+            how="inner",
+        ).set_index(["household_id", "asset_id", "t"])
+        tm.assert_frame_equal(result, expected)
+
+        expected = (
+            DataFrame(
+                {
+                    "household_id": [2, 2, 2, 3, 3, 3, 3, 3, 3, 1, 2, 4],
+                    "asset_id": [
+                        "gb00b03mlx29",
+                        "gb00b03mlx29",
+                        "gb00b03mlx29",
+                        "gb00b03mlx29",
+                        "gb00b03mlx29",
+                        "gb00b03mlx29",
+                        "lu0197800237",
+                        "lu0197800237",
+                        "nl0000289965",
+                        "nl0000301109",
+                        "nl0000301109",
+                        None,
+                    ],
+                    "t": [
+                        233,
+                        234,
+                        235,
+                        233,
+                        234,
+                        235,
+                        180,
+                        181,
+                        None,
+                        None,
+                        None,
+                        None,
+                    ],
+                    "share": [
+                        0.6,
+                        0.6,
+                        0.6,
+                        0.15,
+                        0.15,
+                        0.15,
+                        0.6,
+                        0.6,
+                        0.25,
+                        1.0,
+                        0.4,
+                        1.0,
+                    ],
+                    "log_return": [
+                        0.09604978,
+                        -0.06524096,
+                        0.03532373,
+                        0.09604978,
+                        -0.06524096,
+                        0.03532373,
+                        0.03025441,
+                        0.036997,
+                        None,
+                        None,
+                        None,
+                        None,
+                    ],
+                }
+            )
+            .set_index(["household_id", "asset_id", "t"])
+            .reindex(columns=["share", "log_return"])
+        )
+
+        result = merge(
+            household.reset_index(),
+            log_return.reset_index(),
+            on=["asset_id"],
+            how="outer",
+        ).set_index(["household_id", "asset_id", "t"])
+
+        tm.assert_frame_equal(result, expected)
+
+
+class TestJoinMultiMulti:
+    def test_join_multi_multi(self, left_multi, right_multi, join_type, on_cols_multi):
+        left_names = left_multi.index.names
+        right_names = right_multi.index.names
+        if join_type == "right":
+            level_order = right_names + left_names.difference(right_names)
+        else:
+            level_order = left_names + right_names.difference(left_names)
+        # Multi-index join tests
+        expected = (
+            merge(
+                left_multi.reset_index(),
+                right_multi.reset_index(),
+                how=join_type,
+                on=on_cols_multi,
+            )
+            .set_index(level_order)
+            .sort_index()
+        )
+
+        result = left_multi.join(right_multi, how=join_type).sort_index()
+        tm.assert_frame_equal(result, expected)
+
+    def test_join_multi_empty_frames(
+        self, left_multi, right_multi, join_type, on_cols_multi
+    ):
+        left_multi = left_multi.drop(columns=left_multi.columns)
+        right_multi = right_multi.drop(columns=right_multi.columns)
+
+        left_names = left_multi.index.names
+        right_names = right_multi.index.names
+        if join_type == "right":
+            level_order = right_names + left_names.difference(right_names)
+        else:
+            level_order = left_names + right_names.difference(left_names)
+
+        expected = (
+            merge(
+                left_multi.reset_index(),
+                right_multi.reset_index(),
+                how=join_type,
+                on=on_cols_multi,
+            )
+            .set_index(level_order)
+            .sort_index()
+        )
+
+        result = left_multi.join(right_multi, how=join_type).sort_index()
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("box", [None, np.asarray, Series, Index])
+    def test_merge_datetime_index(self, box):
+        # see gh-19038
+        df = DataFrame(
+            [1, 2, 3], ["2016-01-01", "2017-01-01", "2018-01-01"], columns=["a"]
+        )
+        df.index = pd.to_datetime(df.index)
+        on_vector = df.index.year
+
+        if box is not None:
+            on_vector = box(on_vector)
+
+        exp_years = np.array([2016, 2017, 2018], dtype=np.int32)
+        expected = DataFrame({"a": [1, 2, 3], "key_1": exp_years})
+
+        result = df.merge(df, on=["a", on_vector], how="inner")
+        tm.assert_frame_equal(result, expected)
+
+        expected = DataFrame({"key_0": exp_years, "a_x": [1, 2, 3], "a_y": [1, 2, 3]})
+
+        result = df.merge(df, on=[df.index.year], how="inner")
+        tm.assert_frame_equal(result, expected)
+
+    def test_single_common_level(self):
+        index_left = MultiIndex.from_tuples(
+            [("K0", "X0"), ("K0", "X1"), ("K1", "X2")], names=["key", "X"]
+        )
+
+        left = DataFrame(
+            {"A": ["A0", "A1", "A2"], "B": ["B0", "B1", "B2"]}, index=index_left
+        )
+
+        index_right = MultiIndex.from_tuples(
+            [("K0", "Y0"), ("K1", "Y1"), ("K2", "Y2"), ("K2", "Y3")], names=["key", "Y"]
+        )
+
+        right = DataFrame(
+            {"C": ["C0", "C1", "C2", "C3"], "D": ["D0", "D1", "D2", "D3"]},
+            index=index_right,
+        )
+
+        result = left.join(right)
+        expected = merge(
+            left.reset_index(), right.reset_index(), on=["key"], how="inner"
+        ).set_index(["key", "X", "Y"])
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_join_multi_wrong_order(self):
+        # GH 25760
+        # GH 28956
+
+        midx1 = MultiIndex.from_product([[1, 2], [3, 4]], names=["a", "b"])
+        midx3 = MultiIndex.from_tuples([(4, 1), (3, 2), (3, 1)], names=["b", "a"])
+
+        left = DataFrame(index=midx1, data={"x": [10, 20, 30, 40]})
+        right = DataFrame(index=midx3, data={"y": ["foo", "bar", "fing"]})
+
+        result = left.join(right)
+
+        expected = DataFrame(
+            index=midx1,
+            data={"x": [10, 20, 30, 40], "y": ["fing", "foo", "bar", np.nan]},
+        )
+
+        tm.assert_frame_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/test_crosstab.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/test_crosstab.py
new file mode 100644
index 0000000000000000000000000000000000000000..136e76986df9d86afbf5a6ca8e3650d7745dae3a
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/test_crosstab.py
@@ -0,0 +1,886 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    CategoricalDtype,
+    CategoricalIndex,
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    crosstab,
+)
+import pandas._testing as tm
+
+
+@pytest.fixture
+def df():
+    df = DataFrame(
+        {
+            "A": [
+                "foo",
+                "foo",
+                "foo",
+                "foo",
+                "bar",
+                "bar",
+                "bar",
+                "bar",
+                "foo",
+                "foo",
+                "foo",
+            ],
+            "B": [
+                "one",
+                "one",
+                "one",
+                "two",
+                "one",
+                "one",
+                "one",
+                "two",
+                "two",
+                "two",
+                "one",
+            ],
+            "C": [
+                "dull",
+                "dull",
+                "shiny",
+                "dull",
+                "dull",
+                "shiny",
+                "shiny",
+                "dull",
+                "shiny",
+                "shiny",
+                "shiny",
+            ],
+            "D": np.random.default_rng(2).standard_normal(11),
+            "E": np.random.default_rng(2).standard_normal(11),
+            "F": np.random.default_rng(2).standard_normal(11),
+        }
+    )
+
+    return pd.concat([df, df], ignore_index=True)
+
+
+class TestCrosstab:
+    def test_crosstab_single(self, df):
+        result = crosstab(df["A"], df["C"])
+        expected = df.groupby(["A", "C"]).size().unstack()
+        tm.assert_frame_equal(result, expected.fillna(0).astype(np.int64))
+
+    def test_crosstab_multiple(self, df):
+        result = crosstab(df["A"], [df["B"], df["C"]])
+        expected = df.groupby(["A", "B", "C"]).size()
+        expected = expected.unstack("B").unstack("C").fillna(0).astype(np.int64)
+        tm.assert_frame_equal(result, expected)
+
+        result = crosstab([df["B"], df["C"]], df["A"])
+        expected = df.groupby(["B", "C", "A"]).size()
+        expected = expected.unstack("A").fillna(0).astype(np.int64)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("box", [np.array, list, tuple])
+    def test_crosstab_ndarray(self, box):
+        # GH 44076
+        a = box(np.random.default_rng(2).integers(0, 5, size=100))
+        b = box(np.random.default_rng(2).integers(0, 3, size=100))
+        c = box(np.random.default_rng(2).integers(0, 10, size=100))
+
+        df = DataFrame({"a": a, "b": b, "c": c})
+
+        result = crosstab(a, [b, c], rownames=["a"], colnames=("b", "c"))
+        expected = crosstab(df["a"], [df["b"], df["c"]])
+        tm.assert_frame_equal(result, expected)
+
+        result = crosstab([b, c], a, colnames=["a"], rownames=("b", "c"))
+        expected = crosstab([df["b"], df["c"]], df["a"])
+        tm.assert_frame_equal(result, expected)
+
+        # assign arbitrary names
+        result = crosstab(a, c)
+        expected = crosstab(df["a"], df["c"])
+        expected.index.names = ["row_0"]
+        expected.columns.names = ["col_0"]
+        tm.assert_frame_equal(result, expected)
+
+    def test_crosstab_non_aligned(self):
+        # GH 17005
+        a = Series([0, 1, 1], index=["a", "b", "c"])
+        b = Series([3, 4, 3, 4, 3], index=["a", "b", "c", "d", "f"])
+        c = np.array([3, 4, 3], dtype=np.int64)
+
+        expected = DataFrame(
+            [[1, 0], [1, 1]],
+            index=Index([0, 1], name="row_0"),
+            columns=Index([3, 4], name="col_0"),
+        )
+
+        result = crosstab(a, b)
+        tm.assert_frame_equal(result, expected)
+
+        result = crosstab(a, c)
+        tm.assert_frame_equal(result, expected)
+
+    def test_crosstab_margins(self):
+        a = np.random.default_rng(2).integers(0, 7, size=100)
+        b = np.random.default_rng(2).integers(0, 3, size=100)
+        c = np.random.default_rng(2).integers(0, 5, size=100)
+
+        df = DataFrame({"a": a, "b": b, "c": c})
+
+        result = crosstab(a, [b, c], rownames=["a"], colnames=("b", "c"), margins=True)
+
+        assert result.index.names == ("a",)
+        assert result.columns.names == ["b", "c"]
+
+        all_cols = result["All", ""]
+        exp_cols = df.groupby(["a"]).size().astype("i8")
+        # to keep index.name
+        exp_margin = Series([len(df)], index=Index(["All"], name="a"))
+        exp_cols = pd.concat([exp_cols, exp_margin])
+        exp_cols.name = ("All", "")
+
+        tm.assert_series_equal(all_cols, exp_cols)
+
+        all_rows = result.loc["All"]
+        exp_rows = df.groupby(["b", "c"]).size().astype("i8")
+        exp_rows = pd.concat([exp_rows, Series([len(df)], index=[("All", "")])])
+        exp_rows.name = "All"
+
+        exp_rows = exp_rows.reindex(all_rows.index)
+        exp_rows = exp_rows.fillna(0).astype(np.int64)
+        tm.assert_series_equal(all_rows, exp_rows)
+
+    def test_crosstab_margins_set_margin_name(self):
+        # GH 15972
+        a = np.random.default_rng(2).integers(0, 7, size=100)
+        b = np.random.default_rng(2).integers(0, 3, size=100)
+        c = np.random.default_rng(2).integers(0, 5, size=100)
+
+        df = DataFrame({"a": a, "b": b, "c": c})
+
+        result = crosstab(
+            a,
+            [b, c],
+            rownames=["a"],
+            colnames=("b", "c"),
+            margins=True,
+            margins_name="TOTAL",
+        )
+
+        assert result.index.names == ("a",)
+        assert result.columns.names == ["b", "c"]
+
+        all_cols = result["TOTAL", ""]
+        exp_cols = df.groupby(["a"]).size().astype("i8")
+        # to keep index.name
+        exp_margin = Series([len(df)], index=Index(["TOTAL"], name="a"))
+        exp_cols = pd.concat([exp_cols, exp_margin])
+        exp_cols.name = ("TOTAL", "")
+
+        tm.assert_series_equal(all_cols, exp_cols)
+
+        all_rows = result.loc["TOTAL"]
+        exp_rows = df.groupby(["b", "c"]).size().astype("i8")
+        exp_rows = pd.concat([exp_rows, Series([len(df)], index=[("TOTAL", "")])])
+        exp_rows.name = "TOTAL"
+
+        exp_rows = exp_rows.reindex(all_rows.index)
+        exp_rows = exp_rows.fillna(0).astype(np.int64)
+        tm.assert_series_equal(all_rows, exp_rows)
+
+        msg = "margins_name argument must be a string"
+        for margins_name in [666, None, ["a", "b"]]:
+            with pytest.raises(ValueError, match=msg):
+                crosstab(
+                    a,
+                    [b, c],
+                    rownames=["a"],
+                    colnames=("b", "c"),
+                    margins=True,
+                    margins_name=margins_name,
+                )
+
+    def test_crosstab_pass_values(self):
+        a = np.random.default_rng(2).integers(0, 7, size=100)
+        b = np.random.default_rng(2).integers(0, 3, size=100)
+        c = np.random.default_rng(2).integers(0, 5, size=100)
+        values = np.random.default_rng(2).standard_normal(100)
+
+        table = crosstab(
+            [a, b], c, values, aggfunc="sum", rownames=["foo", "bar"], colnames=["baz"]
+        )
+
+        df = DataFrame({"foo": a, "bar": b, "baz": c, "values": values})
+
+        expected = df.pivot_table(
+            "values", index=["foo", "bar"], columns="baz", aggfunc="sum"
+        )
+        tm.assert_frame_equal(table, expected)
+
+    def test_crosstab_dropna(self):
+        # GH 3820
+        a = np.array(["foo", "foo", "foo", "bar", "bar", "foo", "foo"], dtype=object)
+        b = np.array(["one", "one", "two", "one", "two", "two", "two"], dtype=object)
+        c = np.array(
+            ["dull", "dull", "dull", "dull", "dull", "shiny", "shiny"], dtype=object
+        )
+        res = crosstab(a, [b, c], rownames=["a"], colnames=["b", "c"], dropna=False)
+        m = MultiIndex.from_tuples(
+            [("one", "dull"), ("one", "shiny"), ("two", "dull"), ("two", "shiny")],
+            names=["b", "c"],
+        )
+        tm.assert_index_equal(res.columns, m)
+
+    def test_crosstab_no_overlap(self):
+        # GS 10291
+
+        s1 = Series([1, 2, 3], index=[1, 2, 3])
+        s2 = Series([4, 5, 6], index=[4, 5, 6])
+
+        actual = crosstab(s1, s2)
+        expected = DataFrame(
+            index=Index([], dtype="int64", name="row_0"),
+            columns=Index([], dtype="int64", name="col_0"),
+        )
+
+        tm.assert_frame_equal(actual, expected)
+
+    def test_margin_dropna(self):
+        # GH 12577
+        # pivot_table counts null into margin ('All')
+        # when margins=true and dropna=true
+
+        df = DataFrame({"a": [1, 2, 2, 2, 2, np.nan], "b": [3, 3, 4, 4, 4, 4]})
+        actual = crosstab(df.a, df.b, margins=True, dropna=True)
+        expected = DataFrame([[1, 0, 1], [1, 3, 4], [2, 3, 5]])
+        expected.index = Index([1.0, 2.0, "All"], name="a")
+        expected.columns = Index([3, 4, "All"], name="b")
+        tm.assert_frame_equal(actual, expected)
+
+    def test_margin_dropna2(self):
+        df = DataFrame(
+            {"a": [1, np.nan, np.nan, np.nan, 2, np.nan], "b": [3, np.nan, 4, 4, 4, 4]}
+        )
+        actual = crosstab(df.a, df.b, margins=True, dropna=True)
+        expected = DataFrame([[1, 0, 1], [0, 1, 1], [1, 1, 2]])
+        expected.index = Index([1.0, 2.0, "All"], name="a")
+        expected.columns = Index([3.0, 4.0, "All"], name="b")
+        tm.assert_frame_equal(actual, expected)
+
+    def test_margin_dropna3(self):
+        df = DataFrame(
+            {"a": [1, np.nan, np.nan, np.nan, np.nan, 2], "b": [3, 3, 4, 4, 4, 4]}
+        )
+        actual = crosstab(df.a, df.b, margins=True, dropna=True)
+        expected = DataFrame([[1, 0, 1], [0, 1, 1], [1, 1, 2]])
+        expected.index = Index([1.0, 2.0, "All"], name="a")
+        expected.columns = Index([3, 4, "All"], name="b")
+        tm.assert_frame_equal(actual, expected)
+
+    def test_margin_dropna4(self):
+        # GH 12642
+        # _add_margins raises KeyError: Level None not found
+        # when margins=True and dropna=False
+        # GH: 10772: Keep np.nan in result with dropna=False
+        df = DataFrame({"a": [1, 2, 2, 2, 2, np.nan], "b": [3, 3, 4, 4, 4, 4]})
+        actual = crosstab(df.a, df.b, margins=True, dropna=False)
+        expected = DataFrame([[1, 0, 1.0], [1, 3, 4.0], [0, 1, np.nan], [2, 4, 6.0]])
+        expected.index = Index([1.0, 2.0, np.nan, "All"], name="a")
+        expected.columns = Index([3, 4, "All"], name="b")
+        tm.assert_frame_equal(actual, expected)
+
+    def test_margin_dropna5(self):
+        # GH: 10772: Keep np.nan in result with dropna=False
+        df = DataFrame(
+            {"a": [1, np.nan, np.nan, np.nan, 2, np.nan], "b": [3, np.nan, 4, 4, 4, 4]}
+        )
+        actual = crosstab(df.a, df.b, margins=True, dropna=False)
+        expected = DataFrame(
+            [[1, 0, 0, 1.0], [0, 1, 0, 1.0], [0, 3, 1, np.nan], [1, 4, 0, 6.0]]
+        )
+        expected.index = Index([1.0, 2.0, np.nan, "All"], name="a")
+        expected.columns = Index([3.0, 4.0, np.nan, "All"], name="b")
+        tm.assert_frame_equal(actual, expected)
+
+    def test_margin_dropna6(self):
+        # GH: 10772: Keep np.nan in result with dropna=False
+        a = np.array(["foo", "foo", "foo", "bar", "bar", "foo", "foo"], dtype=object)
+        b = np.array(["one", "one", "two", "one", "two", np.nan, "two"], dtype=object)
+        c = np.array(
+            ["dull", "dull", "dull", "dull", "dull", "shiny", "shiny"], dtype=object
+        )
+
+        actual = crosstab(
+            a, [b, c], rownames=["a"], colnames=["b", "c"], margins=True, dropna=False
+        )
+        m = MultiIndex.from_arrays(
+            [
+                ["one", "one", "two", "two", np.nan, np.nan, "All"],
+                ["dull", "shiny", "dull", "shiny", "dull", "shiny", ""],
+            ],
+            names=["b", "c"],
+        )
+        expected = DataFrame(
+            [[1, 0, 1, 0, 0, 0, 2], [2, 0, 1, 1, 0, 1, 5], [3, 0, 2, 1, 0, 0, 7]],
+            columns=m,
+        )
+        expected.index = Index(["bar", "foo", "All"], name="a")
+        tm.assert_frame_equal(actual, expected)
+
+        actual = crosstab(
+            [a, b], c, rownames=["a", "b"], colnames=["c"], margins=True, dropna=False
+        )
+        m = MultiIndex.from_arrays(
+            [
+                ["bar", "bar", "bar", "foo", "foo", "foo", "All"],
+                ["one", "two", np.nan, "one", "two", np.nan, ""],
+            ],
+            names=["a", "b"],
+        )
+        expected = DataFrame(
+            [
+                [1, 0, 1.0],
+                [1, 0, 1.0],
+                [0, 0, np.nan],
+                [2, 0, 2.0],
+                [1, 1, 2.0],
+                [0, 1, np.nan],
+                [5, 2, 7.0],
+            ],
+            index=m,
+        )
+        expected.columns = Index(["dull", "shiny", "All"], name="c")
+        tm.assert_frame_equal(actual, expected)
+
+        actual = crosstab(
+            [a, b], c, rownames=["a", "b"], colnames=["c"], margins=True, dropna=True
+        )
+        m = MultiIndex.from_arrays(
+            [["bar", "bar", "foo", "foo", "All"], ["one", "two", "one", "two", ""]],
+            names=["a", "b"],
+        )
+        expected = DataFrame(
+            [[1, 0, 1], [1, 0, 1], [2, 0, 2], [1, 1, 2], [5, 1, 6]], index=m
+        )
+        expected.columns = Index(["dull", "shiny", "All"], name="c")
+        tm.assert_frame_equal(actual, expected)
+
+    def test_crosstab_normalize(self):
+        # Issue 12578
+        df = DataFrame(
+            {"a": [1, 2, 2, 2, 2], "b": [3, 3, 4, 4, 4], "c": [1, 1, np.nan, 1, 1]}
+        )
+
+        rindex = Index([1, 2], name="a")
+        cindex = Index([3, 4], name="b")
+        full_normal = DataFrame([[0.2, 0], [0.2, 0.6]], index=rindex, columns=cindex)
+        row_normal = DataFrame([[1.0, 0], [0.25, 0.75]], index=rindex, columns=cindex)
+        col_normal = DataFrame([[0.5, 0], [0.5, 1.0]], index=rindex, columns=cindex)
+
+        # Check all normalize args
+        tm.assert_frame_equal(crosstab(df.a, df.b, normalize="all"), full_normal)
+        tm.assert_frame_equal(crosstab(df.a, df.b, normalize=True), full_normal)
+        tm.assert_frame_equal(crosstab(df.a, df.b, normalize="index"), row_normal)
+        tm.assert_frame_equal(crosstab(df.a, df.b, normalize="columns"), col_normal)
+        tm.assert_frame_equal(
+            crosstab(df.a, df.b, normalize=1),
+            crosstab(df.a, df.b, normalize="columns"),
+        )
+        tm.assert_frame_equal(
+            crosstab(df.a, df.b, normalize=0), crosstab(df.a, df.b, normalize="index")
+        )
+
+        row_normal_margins = DataFrame(
+            [[1.0, 0], [0.25, 0.75], [0.4, 0.6]],
+            index=Index([1, 2, "All"], name="a", dtype="object"),
+            columns=Index([3, 4], name="b", dtype="object"),
+        )
+        col_normal_margins = DataFrame(
+            [[0.5, 0, 0.2], [0.5, 1.0, 0.8]],
+            index=Index([1, 2], name="a", dtype="object"),
+            columns=Index([3, 4, "All"], name="b", dtype="object"),
+        )
+
+        all_normal_margins = DataFrame(
+            [[0.2, 0, 0.2], [0.2, 0.6, 0.8], [0.4, 0.6, 1]],
+            index=Index([1, 2, "All"], name="a", dtype="object"),
+            columns=Index([3, 4, "All"], name="b", dtype="object"),
+        )
+        tm.assert_frame_equal(
+            crosstab(df.a, df.b, normalize="index", margins=True), row_normal_margins
+        )
+        tm.assert_frame_equal(
+            crosstab(df.a, df.b, normalize="columns", margins=True), col_normal_margins
+        )
+        tm.assert_frame_equal(
+            crosstab(df.a, df.b, normalize=True, margins=True), all_normal_margins
+        )
+
+    def test_crosstab_normalize_arrays(self):
+        # GH#12578
+        df = DataFrame(
+            {"a": [1, 2, 2, 2, 2], "b": [3, 3, 4, 4, 4], "c": [1, 1, np.nan, 1, 1]}
+        )
+
+        # Test arrays
+        crosstab(
+            [np.array([1, 1, 2, 2]), np.array([1, 2, 1, 2])], np.array([1, 2, 1, 2])
+        )
+
+        # Test with aggfunc
+        norm_counts = DataFrame(
+            [[0.25, 0, 0.25], [0.25, 0.5, 0.75], [0.5, 0.5, 1]],
+            index=Index([1, 2, "All"], name="a", dtype="object"),
+            columns=Index([3, 4, "All"], name="b"),
+        )
+        test_case = crosstab(
+            df.a, df.b, df.c, aggfunc="count", normalize="all", margins=True
+        )
+        tm.assert_frame_equal(test_case, norm_counts)
+
+        df = DataFrame(
+            {"a": [1, 2, 2, 2, 2], "b": [3, 3, 4, 4, 4], "c": [0, 4, np.nan, 3, 3]}
+        )
+
+        norm_sum = DataFrame(
+            [[0, 0, 0.0], [0.4, 0.6, 1], [0.4, 0.6, 1]],
+            index=Index([1, 2, "All"], name="a", dtype="object"),
+            columns=Index([3, 4, "All"], name="b", dtype="object"),
+        )
+        msg = "using DataFrameGroupBy.sum"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            test_case = crosstab(
+                df.a, df.b, df.c, aggfunc=np.sum, normalize="all", margins=True
+            )
+        tm.assert_frame_equal(test_case, norm_sum)
+
+    def test_crosstab_with_empties(self, using_array_manager):
+        # Check handling of empties
+        df = DataFrame(
+            {
+                "a": [1, 2, 2, 2, 2],
+                "b": [3, 3, 4, 4, 4],
+                "c": [np.nan, np.nan, np.nan, np.nan, np.nan],
+            }
+        )
+
+        empty = DataFrame(
+            [[0.0, 0.0], [0.0, 0.0]],
+            index=Index([1, 2], name="a", dtype="int64"),
+            columns=Index([3, 4], name="b"),
+        )
+
+        for i in [True, "index", "columns"]:
+            calculated = crosstab(df.a, df.b, values=df.c, aggfunc="count", normalize=i)
+            tm.assert_frame_equal(empty, calculated)
+
+        nans = DataFrame(
+            [[0.0, np.nan], [0.0, 0.0]],
+            index=Index([1, 2], name="a", dtype="int64"),
+            columns=Index([3, 4], name="b"),
+        )
+        if using_array_manager:
+            # INFO(ArrayManager) column without NaNs can preserve int dtype
+            nans[3] = nans[3].astype("int64")
+
+        calculated = crosstab(df.a, df.b, values=df.c, aggfunc="count", normalize=False)
+        tm.assert_frame_equal(nans, calculated)
+
+    def test_crosstab_errors(self):
+        # Issue 12578
+
+        df = DataFrame(
+            {"a": [1, 2, 2, 2, 2], "b": [3, 3, 4, 4, 4], "c": [1, 1, np.nan, 1, 1]}
+        )
+
+        error = "values cannot be used without an aggfunc."
+        with pytest.raises(ValueError, match=error):
+            crosstab(df.a, df.b, values=df.c)
+
+        error = "aggfunc cannot be used without values"
+        with pytest.raises(ValueError, match=error):
+            crosstab(df.a, df.b, aggfunc=np.mean)
+
+        error = "Not a valid normalize argument"
+        with pytest.raises(ValueError, match=error):
+            crosstab(df.a, df.b, normalize="42")
+
+        with pytest.raises(ValueError, match=error):
+            crosstab(df.a, df.b, normalize=42)
+
+        error = "Not a valid margins argument"
+        with pytest.raises(ValueError, match=error):
+            crosstab(df.a, df.b, normalize="all", margins=42)
+
+    def test_crosstab_with_categorial_columns(self):
+        # GH 8860
+        df = DataFrame(
+            {
+                "MAKE": ["Honda", "Acura", "Tesla", "Honda", "Honda", "Acura"],
+                "MODEL": ["Sedan", "Sedan", "Electric", "Pickup", "Sedan", "Sedan"],
+            }
+        )
+        categories = ["Sedan", "Electric", "Pickup"]
+        df["MODEL"] = df["MODEL"].astype("category").cat.set_categories(categories)
+        result = crosstab(df["MAKE"], df["MODEL"])
+
+        expected_index = Index(["Acura", "Honda", "Tesla"], name="MAKE")
+        expected_columns = CategoricalIndex(
+            categories, categories=categories, ordered=False, name="MODEL"
+        )
+        expected_data = [[2, 0, 0], [2, 0, 1], [0, 1, 0]]
+        expected = DataFrame(
+            expected_data, index=expected_index, columns=expected_columns
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_crosstab_with_numpy_size(self):
+        # GH 4003
+        df = DataFrame(
+            {
+                "A": ["one", "one", "two", "three"] * 6,
+                "B": ["A", "B", "C"] * 8,
+                "C": ["foo", "foo", "foo", "bar", "bar", "bar"] * 4,
+                "D": np.random.default_rng(2).standard_normal(24),
+                "E": np.random.default_rng(2).standard_normal(24),
+            }
+        )
+        result = crosstab(
+            index=[df["A"], df["B"]],
+            columns=[df["C"]],
+            margins=True,
+            aggfunc=np.size,
+            values=df["D"],
+        )
+        expected_index = MultiIndex(
+            levels=[["All", "one", "three", "two"], ["", "A", "B", "C"]],
+            codes=[[1, 1, 1, 2, 2, 2, 3, 3, 3, 0], [1, 2, 3, 1, 2, 3, 1, 2, 3, 0]],
+            names=["A", "B"],
+        )
+        expected_column = Index(["bar", "foo", "All"], name="C")
+        expected_data = np.array(
+            [
+                [2.0, 2.0, 4.0],
+                [2.0, 2.0, 4.0],
+                [2.0, 2.0, 4.0],
+                [2.0, np.nan, 2.0],
+                [np.nan, 2.0, 2.0],
+                [2.0, np.nan, 2.0],
+                [np.nan, 2.0, 2.0],
+                [2.0, np.nan, 2.0],
+                [np.nan, 2.0, 2.0],
+                [12.0, 12.0, 24.0],
+            ]
+        )
+        expected = DataFrame(
+            expected_data, index=expected_index, columns=expected_column
+        )
+        # aggfunc is np.size, resulting in integers
+        expected["All"] = expected["All"].astype("int64")
+        tm.assert_frame_equal(result, expected)
+
+    def test_crosstab_duplicate_names(self):
+        # GH 13279 / 22529
+
+        s1 = Series(range(3), name="foo")
+        s2_foo = Series(range(1, 4), name="foo")
+        s2_bar = Series(range(1, 4), name="bar")
+        s3 = Series(range(3), name="waldo")
+
+        # check result computed with duplicate labels against
+        # result computed with unique labels, then relabelled
+        mapper = {"bar": "foo"}
+
+        # duplicate row, column labels
+        result = crosstab(s1, s2_foo)
+        expected = crosstab(s1, s2_bar).rename_axis(columns=mapper, axis=1)
+        tm.assert_frame_equal(result, expected)
+
+        # duplicate row, unique column labels
+        result = crosstab([s1, s2_foo], s3)
+        expected = crosstab([s1, s2_bar], s3).rename_axis(index=mapper, axis=0)
+        tm.assert_frame_equal(result, expected)
+
+        # unique row, duplicate column labels
+        result = crosstab(s3, [s1, s2_foo])
+        expected = crosstab(s3, [s1, s2_bar]).rename_axis(columns=mapper, axis=1)
+
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("names", [["a", ("b", "c")], [("a", "b"), "c"]])
+    def test_crosstab_tuple_name(self, names):
+        s1 = Series(range(3), name=names[0])
+        s2 = Series(range(1, 4), name=names[1])
+
+        mi = MultiIndex.from_arrays([range(3), range(1, 4)], names=names)
+        expected = Series(1, index=mi).unstack(1, fill_value=0)
+
+        result = crosstab(s1, s2)
+        tm.assert_frame_equal(result, expected)
+
+    def test_crosstab_both_tuple_names(self):
+        # GH 18321
+        s1 = Series(range(3), name=("a", "b"))
+        s2 = Series(range(3), name=("c", "d"))
+
+        expected = DataFrame(
+            np.eye(3, dtype="int64"),
+            index=Index(range(3), name=("a", "b")),
+            columns=Index(range(3), name=("c", "d")),
+        )
+        result = crosstab(s1, s2)
+        tm.assert_frame_equal(result, expected)
+
+    def test_crosstab_unsorted_order(self):
+        df = DataFrame({"b": [3, 1, 2], "a": [5, 4, 6]}, index=["C", "A", "B"])
+        result = crosstab(df.index, [df.b, df.a])
+        e_idx = Index(["A", "B", "C"], name="row_0")
+        e_columns = MultiIndex.from_tuples([(1, 4), (2, 6), (3, 5)], names=["b", "a"])
+        expected = DataFrame(
+            [[1, 0, 0], [0, 1, 0], [0, 0, 1]], index=e_idx, columns=e_columns
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_crosstab_normalize_multiple_columns(self):
+        # GH 15150
+        df = DataFrame(
+            {
+                "A": ["one", "one", "two", "three"] * 6,
+                "B": ["A", "B", "C"] * 8,
+                "C": ["foo", "foo", "foo", "bar", "bar", "bar"] * 4,
+                "D": [0] * 24,
+                "E": [0] * 24,
+            }
+        )
+
+        msg = "using DataFrameGroupBy.sum"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = crosstab(
+                [df.A, df.B],
+                df.C,
+                values=df.D,
+                aggfunc=np.sum,
+                normalize=True,
+                margins=True,
+            )
+        expected = DataFrame(
+            np.array([0] * 29 + [1], dtype=float).reshape(10, 3),
+            columns=Index(["bar", "foo", "All"], name="C"),
+            index=MultiIndex.from_tuples(
+                [
+                    ("one", "A"),
+                    ("one", "B"),
+                    ("one", "C"),
+                    ("three", "A"),
+                    ("three", "B"),
+                    ("three", "C"),
+                    ("two", "A"),
+                    ("two", "B"),
+                    ("two", "C"),
+                    ("All", ""),
+                ],
+                names=["A", "B"],
+            ),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_margin_normalize(self):
+        # GH 27500
+        df = DataFrame(
+            {
+                "A": ["foo", "foo", "foo", "foo", "foo", "bar", "bar", "bar", "bar"],
+                "B": ["one", "one", "one", "two", "two", "one", "one", "two", "two"],
+                "C": [
+                    "small",
+                    "large",
+                    "large",
+                    "small",
+                    "small",
+                    "large",
+                    "small",
+                    "small",
+                    "large",
+                ],
+                "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
+                "E": [2, 4, 5, 5, 6, 6, 8, 9, 9],
+            }
+        )
+        # normalize on index
+        result = crosstab(
+            [df.A, df.B], df.C, margins=True, margins_name="Sub-Total", normalize=0
+        )
+        expected = DataFrame(
+            [[0.5, 0.5], [0.5, 0.5], [0.666667, 0.333333], [0, 1], [0.444444, 0.555556]]
+        )
+        expected.index = MultiIndex(
+            levels=[["Sub-Total", "bar", "foo"], ["", "one", "two"]],
+            codes=[[1, 1, 2, 2, 0], [1, 2, 1, 2, 0]],
+            names=["A", "B"],
+        )
+        expected.columns = Index(["large", "small"], name="C")
+        tm.assert_frame_equal(result, expected)
+
+        # normalize on columns
+        result = crosstab(
+            [df.A, df.B], df.C, margins=True, margins_name="Sub-Total", normalize=1
+        )
+        expected = DataFrame(
+            [
+                [0.25, 0.2, 0.222222],
+                [0.25, 0.2, 0.222222],
+                [0.5, 0.2, 0.333333],
+                [0, 0.4, 0.222222],
+            ]
+        )
+        expected.columns = Index(["large", "small", "Sub-Total"], name="C")
+        expected.index = MultiIndex(
+            levels=[["bar", "foo"], ["one", "two"]],
+            codes=[[0, 0, 1, 1], [0, 1, 0, 1]],
+            names=["A", "B"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # normalize on both index and column
+        result = crosstab(
+            [df.A, df.B], df.C, margins=True, margins_name="Sub-Total", normalize=True
+        )
+        expected = DataFrame(
+            [
+                [0.111111, 0.111111, 0.222222],
+                [0.111111, 0.111111, 0.222222],
+                [0.222222, 0.111111, 0.333333],
+                [0.000000, 0.222222, 0.222222],
+                [0.444444, 0.555555, 1],
+            ]
+        )
+        expected.columns = Index(["large", "small", "Sub-Total"], name="C")
+        expected.index = MultiIndex(
+            levels=[["Sub-Total", "bar", "foo"], ["", "one", "two"]],
+            codes=[[1, 1, 2, 2, 0], [1, 2, 1, 2, 0]],
+            names=["A", "B"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_margin_normalize_multiple_columns(self):
+        # GH 35144
+        # use multiple columns with margins and normalization
+        df = DataFrame(
+            {
+                "A": ["foo", "foo", "foo", "foo", "foo", "bar", "bar", "bar", "bar"],
+                "B": ["one", "one", "one", "two", "two", "one", "one", "two", "two"],
+                "C": [
+                    "small",
+                    "large",
+                    "large",
+                    "small",
+                    "small",
+                    "large",
+                    "small",
+                    "small",
+                    "large",
+                ],
+                "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
+                "E": [2, 4, 5, 5, 6, 6, 8, 9, 9],
+            }
+        )
+        result = crosstab(
+            index=df.C,
+            columns=[df.A, df.B],
+            margins=True,
+            margins_name="margin",
+            normalize=True,
+        )
+        expected = DataFrame(
+            [
+                [0.111111, 0.111111, 0.222222, 0.000000, 0.444444],
+                [0.111111, 0.111111, 0.111111, 0.222222, 0.555556],
+                [0.222222, 0.222222, 0.333333, 0.222222, 1.0],
+            ],
+            index=["large", "small", "margin"],
+        )
+        expected.columns = MultiIndex(
+            levels=[["bar", "foo", "margin"], ["", "one", "two"]],
+            codes=[[0, 0, 1, 1, 2], [1, 2, 1, 2, 0]],
+            names=["A", "B"],
+        )
+        expected.index.name = "C"
+        tm.assert_frame_equal(result, expected)
+
+    def test_margin_support_Float(self):
+        # GH 50313
+        # use Float64 formats and function aggfunc with margins
+        df = DataFrame(
+            {"A": [1, 2, 2, 1], "B": [3, 3, 4, 5], "C": [-1.0, 10.0, 1.0, 10.0]},
+            dtype="Float64",
+        )
+        result = crosstab(
+            df["A"],
+            df["B"],
+            values=df["C"],
+            aggfunc="sum",
+            margins=True,
+        )
+        expected = DataFrame(
+            [
+                [-1.0, pd.NA, 10.0, 9.0],
+                [10.0, 1.0, pd.NA, 11.0],
+                [9.0, 1.0, 10.0, 20.0],
+            ],
+            index=Index([1.0, 2.0, "All"], dtype="object", name="A"),
+            columns=Index([3.0, 4.0, 5.0, "All"], dtype="object", name="B"),
+            dtype="Float64",
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_margin_with_ordered_categorical_column(self):
+        # GH 25278
+        df = DataFrame(
+            {
+                "First": ["B", "B", "C", "A", "B", "C"],
+                "Second": ["C", "B", "B", "B", "C", "A"],
+            }
+        )
+        df["First"] = df["First"].astype(CategoricalDtype(ordered=True))
+        customized_categories_order = ["C", "A", "B"]
+        df["First"] = df["First"].cat.reorder_categories(customized_categories_order)
+        result = crosstab(df["First"], df["Second"], margins=True)
+
+        expected_index = Index(["C", "A", "B", "All"], name="First")
+        expected_columns = Index(["A", "B", "C", "All"], name="Second")
+        expected_data = [[1, 1, 0, 2], [0, 1, 0, 1], [0, 1, 2, 3], [1, 3, 2, 6]]
+        expected = DataFrame(
+            expected_data, index=expected_index, columns=expected_columns
+        )
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("a_dtype", ["category", "int64"])
+@pytest.mark.parametrize("b_dtype", ["category", "int64"])
+def test_categoricals(a_dtype, b_dtype):
+    # https://github.com/pandas-dev/pandas/issues/37465
+    g = np.random.default_rng(2)
+    a = Series(g.integers(0, 3, size=100)).astype(a_dtype)
+    b = Series(g.integers(0, 2, size=100)).astype(b_dtype)
+    result = crosstab(a, b, margins=True, dropna=False)
+    columns = Index([0, 1, "All"], dtype="object", name="col_0")
+    index = Index([0, 1, 2, "All"], dtype="object", name="row_0")
+    values = [[10, 18, 28], [23, 16, 39], [17, 16, 33], [50, 50, 100]]
+    expected = DataFrame(values, index, columns)
+    tm.assert_frame_equal(result, expected)
+
+    # Verify when categorical does not have all values present
+    a.loc[a == 1] = 2
+    a_is_cat = isinstance(a.dtype, CategoricalDtype)
+    assert not a_is_cat or a.value_counts().loc[1] == 0
+    result = crosstab(a, b, margins=True, dropna=False)
+    values = [[10, 18, 28], [0, 0, 0], [40, 32, 72], [50, 50, 100]]
+    expected = DataFrame(values, index, columns)
+    if not a_is_cat:
+        expected = expected.loc[[0, 2, "All"]]
+        expected["All"] = expected["All"].astype("int64")
+    tm.assert_frame_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/test_cut.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/test_cut.py
new file mode 100644
index 0000000000000000000000000000000000000000..0811c69859c0dadf9b3b909437d9a6a16a584c24
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/test_cut.py
@@ -0,0 +1,791 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    Interval,
+    IntervalIndex,
+    Series,
+    TimedeltaIndex,
+    Timestamp,
+    cut,
+    date_range,
+    interval_range,
+    isna,
+    qcut,
+    timedelta_range,
+    to_datetime,
+)
+import pandas._testing as tm
+from pandas.api.types import CategoricalDtype
+import pandas.core.reshape.tile as tmod
+
+
+def test_simple():
+    data = np.ones(5, dtype="int64")
+    result = cut(data, 4, labels=False)
+
+    expected = np.array([1, 1, 1, 1, 1])
+    tm.assert_numpy_array_equal(result, expected, check_dtype=False)
+
+
+@pytest.mark.parametrize("func", [list, np.array])
+def test_bins(func):
+    data = func([0.2, 1.4, 2.5, 6.2, 9.7, 2.1])
+    result, bins = cut(data, 3, retbins=True)
+
+    intervals = IntervalIndex.from_breaks(bins.round(3))
+    intervals = intervals.take([0, 0, 0, 1, 2, 0])
+    expected = Categorical(intervals, ordered=True)
+
+    tm.assert_categorical_equal(result, expected)
+    tm.assert_almost_equal(bins, np.array([0.1905, 3.36666667, 6.53333333, 9.7]))
+
+
+def test_right():
+    data = np.array([0.2, 1.4, 2.5, 6.2, 9.7, 2.1, 2.575])
+    result, bins = cut(data, 4, right=True, retbins=True)
+
+    intervals = IntervalIndex.from_breaks(bins.round(3))
+    expected = Categorical(intervals, ordered=True)
+    expected = expected.take([0, 0, 0, 2, 3, 0, 0])
+
+    tm.assert_categorical_equal(result, expected)
+    tm.assert_almost_equal(bins, np.array([0.1905, 2.575, 4.95, 7.325, 9.7]))
+
+
+def test_no_right():
+    data = np.array([0.2, 1.4, 2.5, 6.2, 9.7, 2.1, 2.575])
+    result, bins = cut(data, 4, right=False, retbins=True)
+
+    intervals = IntervalIndex.from_breaks(bins.round(3), closed="left")
+    intervals = intervals.take([0, 0, 0, 2, 3, 0, 1])
+    expected = Categorical(intervals, ordered=True)
+
+    tm.assert_categorical_equal(result, expected)
+    tm.assert_almost_equal(bins, np.array([0.2, 2.575, 4.95, 7.325, 9.7095]))
+
+
+def test_bins_from_interval_index():
+    c = cut(range(5), 3)
+    expected = c
+    result = cut(range(5), bins=expected.categories)
+    tm.assert_categorical_equal(result, expected)
+
+    expected = Categorical.from_codes(
+        np.append(c.codes, -1), categories=c.categories, ordered=True
+    )
+    result = cut(range(6), bins=expected.categories)
+    tm.assert_categorical_equal(result, expected)
+
+
+def test_bins_from_interval_index_doc_example():
+    # Make sure we preserve the bins.
+    ages = np.array([10, 15, 13, 12, 23, 25, 28, 59, 60])
+    c = cut(ages, bins=[0, 18, 35, 70])
+    expected = IntervalIndex.from_tuples([(0, 18), (18, 35), (35, 70)])
+    tm.assert_index_equal(c.categories, expected)
+
+    result = cut([25, 20, 50], bins=c.categories)
+    tm.assert_index_equal(result.categories, expected)
+    tm.assert_numpy_array_equal(result.codes, np.array([1, 1, 2], dtype="int8"))
+
+
+def test_bins_not_overlapping_from_interval_index():
+    # see gh-23980
+    msg = "Overlapping IntervalIndex is not accepted"
+    ii = IntervalIndex.from_tuples([(0, 10), (2, 12), (4, 14)])
+
+    with pytest.raises(ValueError, match=msg):
+        cut([5, 6], bins=ii)
+
+
+def test_bins_not_monotonic():
+    msg = "bins must increase monotonically"
+    data = [0.2, 1.4, 2.5, 6.2, 9.7, 2.1]
+
+    with pytest.raises(ValueError, match=msg):
+        cut(data, [0.1, 1.5, 1, 10])
+
+
+@pytest.mark.parametrize(
+    "x, bins, expected",
+    [
+        (
+            date_range("2017-12-31", periods=3),
+            [Timestamp.min, Timestamp("2018-01-01"), Timestamp.max],
+            IntervalIndex.from_tuples(
+                [
+                    (Timestamp.min, Timestamp("2018-01-01")),
+                    (Timestamp("2018-01-01"), Timestamp.max),
+                ]
+            ),
+        ),
+        (
+            [-1, 0, 1],
+            np.array(
+                [np.iinfo(np.int64).min, 0, np.iinfo(np.int64).max], dtype="int64"
+            ),
+            IntervalIndex.from_tuples(
+                [(np.iinfo(np.int64).min, 0), (0, np.iinfo(np.int64).max)]
+            ),
+        ),
+        (
+            [
+                np.timedelta64(-1, "ns"),
+                np.timedelta64(0, "ns"),
+                np.timedelta64(1, "ns"),
+            ],
+            np.array(
+                [
+                    np.timedelta64(-np.iinfo(np.int64).max, "ns"),
+                    np.timedelta64(0, "ns"),
+                    np.timedelta64(np.iinfo(np.int64).max, "ns"),
+                ]
+            ),
+            IntervalIndex.from_tuples(
+                [
+                    (
+                        np.timedelta64(-np.iinfo(np.int64).max, "ns"),
+                        np.timedelta64(0, "ns"),
+                    ),
+                    (
+                        np.timedelta64(0, "ns"),
+                        np.timedelta64(np.iinfo(np.int64).max, "ns"),
+                    ),
+                ]
+            ),
+        ),
+    ],
+)
+def test_bins_monotonic_not_overflowing(x, bins, expected):
+    # GH 26045
+    result = cut(x, bins)
+    tm.assert_index_equal(result.categories, expected)
+
+
+def test_wrong_num_labels():
+    msg = "Bin labels must be one fewer than the number of bin edges"
+    data = [0.2, 1.4, 2.5, 6.2, 9.7, 2.1]
+
+    with pytest.raises(ValueError, match=msg):
+        cut(data, [0, 1, 10], labels=["foo", "bar", "baz"])
+
+
+@pytest.mark.parametrize(
+    "x,bins,msg",
+    [
+        ([], 2, "Cannot cut empty array"),
+        ([1, 2, 3], 0.5, "`bins` should be a positive integer"),
+    ],
+)
+def test_cut_corner(x, bins, msg):
+    with pytest.raises(ValueError, match=msg):
+        cut(x, bins)
+
+
+@pytest.mark.parametrize("arg", [2, np.eye(2), DataFrame(np.eye(2))])
+@pytest.mark.parametrize("cut_func", [cut, qcut])
+def test_cut_not_1d_arg(arg, cut_func):
+    msg = "Input array must be 1 dimensional"
+    with pytest.raises(ValueError, match=msg):
+        cut_func(arg, 2)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [0, 1, 2, 3, 4, np.inf],
+        [-np.inf, 0, 1, 2, 3, 4],
+        [-np.inf, 0, 1, 2, 3, 4, np.inf],
+    ],
+)
+def test_int_bins_with_inf(data):
+    # GH 24314
+    msg = "cannot specify integer `bins` when input data contains infinity"
+    with pytest.raises(ValueError, match=msg):
+        cut(data, bins=3)
+
+
+def test_cut_out_of_range_more():
+    # see gh-1511
+    name = "x"
+
+    ser = Series([0, -1, 0, 1, -3], name=name)
+    ind = cut(ser, [0, 1], labels=False)
+
+    exp = Series([np.nan, np.nan, np.nan, 0, np.nan], name=name)
+    tm.assert_series_equal(ind, exp)
+
+
+@pytest.mark.parametrize(
+    "right,breaks,closed",
+    [
+        (True, [-1e-3, 0.25, 0.5, 0.75, 1], "right"),
+        (False, [0, 0.25, 0.5, 0.75, 1 + 1e-3], "left"),
+    ],
+)
+def test_labels(right, breaks, closed):
+    arr = np.tile(np.arange(0, 1.01, 0.1), 4)
+
+    result, bins = cut(arr, 4, retbins=True, right=right)
+    ex_levels = IntervalIndex.from_breaks(breaks, closed=closed)
+    tm.assert_index_equal(result.categories, ex_levels)
+
+
+def test_cut_pass_series_name_to_factor():
+    name = "foo"
+    ser = Series(np.random.default_rng(2).standard_normal(100), name=name)
+
+    factor = cut(ser, 4)
+    assert factor.name == name
+
+
+def test_label_precision():
+    arr = np.arange(0, 0.73, 0.01)
+    result = cut(arr, 4, precision=2)
+
+    ex_levels = IntervalIndex.from_breaks([-0.00072, 0.18, 0.36, 0.54, 0.72])
+    tm.assert_index_equal(result.categories, ex_levels)
+
+
+@pytest.mark.parametrize("labels", [None, False])
+def test_na_handling(labels):
+    arr = np.arange(0, 0.75, 0.01)
+    arr[::3] = np.nan
+
+    result = cut(arr, 4, labels=labels)
+    result = np.asarray(result)
+
+    expected = np.where(isna(arr), np.nan, result)
+    tm.assert_almost_equal(result, expected)
+
+
+def test_inf_handling():
+    data = np.arange(6)
+    data_ser = Series(data, dtype="int64")
+
+    bins = [-np.inf, 2, 4, np.inf]
+    result = cut(data, bins)
+    result_ser = cut(data_ser, bins)
+
+    ex_uniques = IntervalIndex.from_breaks(bins)
+    tm.assert_index_equal(result.categories, ex_uniques)
+
+    assert result[5] == Interval(4, np.inf)
+    assert result[0] == Interval(-np.inf, 2)
+    assert result_ser[5] == Interval(4, np.inf)
+    assert result_ser[0] == Interval(-np.inf, 2)
+
+
+def test_cut_out_of_bounds():
+    arr = np.random.default_rng(2).standard_normal(100)
+    result = cut(arr, [-1, 0, 1])
+
+    mask = isna(result)
+    ex_mask = (arr < -1) | (arr > 1)
+    tm.assert_numpy_array_equal(mask, ex_mask)
+
+
+@pytest.mark.parametrize(
+    "get_labels,get_expected",
+    [
+        (
+            lambda labels: labels,
+            lambda labels: Categorical(
+                ["Medium"] + 4 * ["Small"] + ["Medium", "Large"],
+                categories=labels,
+                ordered=True,
+            ),
+        ),
+        (
+            lambda labels: Categorical.from_codes([0, 1, 2], labels),
+            lambda labels: Categorical.from_codes([1] + 4 * [0] + [1, 2], labels),
+        ),
+    ],
+)
+def test_cut_pass_labels(get_labels, get_expected):
+    bins = [0, 25, 50, 100]
+    arr = [50, 5, 10, 15, 20, 30, 70]
+    labels = ["Small", "Medium", "Large"]
+
+    result = cut(arr, bins, labels=get_labels(labels))
+    tm.assert_categorical_equal(result, get_expected(labels))
+
+
+def test_cut_pass_labels_compat():
+    # see gh-16459
+    arr = [50, 5, 10, 15, 20, 30, 70]
+    labels = ["Good", "Medium", "Bad"]
+
+    result = cut(arr, 3, labels=labels)
+    exp = cut(arr, 3, labels=Categorical(labels, categories=labels, ordered=True))
+    tm.assert_categorical_equal(result, exp)
+
+
+@pytest.mark.parametrize("x", [np.arange(11.0), np.arange(11.0) / 1e10])
+def test_round_frac_just_works(x):
+    # It works.
+    cut(x, 2)
+
+
+@pytest.mark.parametrize(
+    "val,precision,expected",
+    [
+        (-117.9998, 3, -118),
+        (117.9998, 3, 118),
+        (117.9998, 2, 118),
+        (0.000123456, 2, 0.00012),
+    ],
+)
+def test_round_frac(val, precision, expected):
+    # see gh-1979
+    result = tmod._round_frac(val, precision=precision)
+    assert result == expected
+
+
+def test_cut_return_intervals():
+    ser = Series([0, 1, 2, 3, 4, 5, 6, 7, 8])
+    result = cut(ser, 3)
+
+    exp_bins = np.linspace(0, 8, num=4).round(3)
+    exp_bins[0] -= 0.008
+
+    expected = Series(
+        IntervalIndex.from_breaks(exp_bins, closed="right").take(
+            [0, 0, 0, 1, 1, 1, 2, 2, 2]
+        )
+    ).astype(CategoricalDtype(ordered=True))
+    tm.assert_series_equal(result, expected)
+
+
+def test_series_ret_bins():
+    # see gh-8589
+    ser = Series(np.arange(4))
+    result, bins = cut(ser, 2, retbins=True)
+
+    expected = Series(
+        IntervalIndex.from_breaks([-0.003, 1.5, 3], closed="right").repeat(2)
+    ).astype(CategoricalDtype(ordered=True))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "kwargs,msg",
+    [
+        ({"duplicates": "drop"}, None),
+        ({}, "Bin edges must be unique"),
+        ({"duplicates": "raise"}, "Bin edges must be unique"),
+        ({"duplicates": "foo"}, "invalid value for 'duplicates' parameter"),
+    ],
+)
+def test_cut_duplicates_bin(kwargs, msg):
+    # see gh-20947
+    bins = [0, 2, 4, 6, 10, 10]
+    values = Series(np.array([1, 3, 5, 7, 9]), index=["a", "b", "c", "d", "e"])
+
+    if msg is not None:
+        with pytest.raises(ValueError, match=msg):
+            cut(values, bins, **kwargs)
+    else:
+        result = cut(values, bins, **kwargs)
+        expected = cut(values, pd.unique(np.asarray(bins)))
+        tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("data", [9.0, -9.0, 0.0])
+@pytest.mark.parametrize("length", [1, 2])
+def test_single_bin(data, length):
+    # see gh-14652, gh-15428
+    ser = Series([data] * length)
+    result = cut(ser, 1, labels=False)
+
+    expected = Series([0] * length, dtype=np.intp)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "array_1_writeable,array_2_writeable", [(True, True), (True, False), (False, False)]
+)
+def test_cut_read_only(array_1_writeable, array_2_writeable):
+    # issue 18773
+    array_1 = np.arange(0, 100, 10)
+    array_1.flags.writeable = array_1_writeable
+
+    array_2 = np.arange(0, 100, 10)
+    array_2.flags.writeable = array_2_writeable
+
+    hundred_elements = np.arange(100)
+    tm.assert_categorical_equal(
+        cut(hundred_elements, array_1), cut(hundred_elements, array_2)
+    )
+
+
+@pytest.mark.parametrize(
+    "conv",
+    [
+        lambda v: Timestamp(v),
+        lambda v: to_datetime(v),
+        lambda v: np.datetime64(v),
+        lambda v: Timestamp(v).to_pydatetime(),
+    ],
+)
+def test_datetime_bin(conv):
+    data = [np.datetime64("2012-12-13"), np.datetime64("2012-12-15")]
+    bin_data = ["2012-12-12", "2012-12-14", "2012-12-16"]
+
+    expected = Series(
+        IntervalIndex(
+            [
+                Interval(Timestamp(bin_data[0]), Timestamp(bin_data[1])),
+                Interval(Timestamp(bin_data[1]), Timestamp(bin_data[2])),
+            ]
+        )
+    ).astype(CategoricalDtype(ordered=True))
+
+    bins = [conv(v) for v in bin_data]
+    result = Series(cut(data, bins=bins))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("box", [Series, Index, np.array, list])
+def test_datetime_cut(unit, box):
+    # see gh-14714
+    #
+    # Testing time data when it comes in various collection types.
+    data = to_datetime(["2013-01-01", "2013-01-02", "2013-01-03"]).astype(f"M8[{unit}]")
+    data = box(data)
+    result, _ = cut(data, 3, retbins=True)
+
+    if box is list:
+        # We don't (yet) do inference on these, so get nanos
+        unit = "ns"
+
+    if unit == "s":
+        # See https://github.com/pandas-dev/pandas/pull/56101#discussion_r1405325425
+        # for why we round to 8 seconds instead of 7
+        left = DatetimeIndex(
+            ["2012-12-31 23:57:08", "2013-01-01 16:00:00", "2013-01-02 08:00:00"],
+            dtype=f"M8[{unit}]",
+        )
+    else:
+        left = DatetimeIndex(
+            [
+                "2012-12-31 23:57:07.200000",
+                "2013-01-01 16:00:00",
+                "2013-01-02 08:00:00",
+            ],
+            dtype=f"M8[{unit}]",
+        )
+    right = DatetimeIndex(
+        ["2013-01-01 16:00:00", "2013-01-02 08:00:00", "2013-01-03 00:00:00"],
+        dtype=f"M8[{unit}]",
+    )
+
+    exp_intervals = IntervalIndex.from_arrays(left, right)
+    expected = Series(exp_intervals).astype(CategoricalDtype(ordered=True))
+    tm.assert_series_equal(Series(result), expected)
+
+
+@pytest.mark.parametrize("box", [list, np.array, Index, Series])
+def test_datetime_tz_cut_mismatched_tzawareness(box):
+    # GH#54964
+    bins = box(
+        [
+            Timestamp("2013-01-01 04:57:07.200000"),
+            Timestamp("2013-01-01 21:00:00"),
+            Timestamp("2013-01-02 13:00:00"),
+            Timestamp("2013-01-03 05:00:00"),
+        ]
+    )
+    ser = Series(date_range("20130101", periods=3, tz="US/Eastern"))
+
+    msg = "Cannot use timezone-naive bins with timezone-aware values"
+    with pytest.raises(ValueError, match=msg):
+        cut(ser, bins)
+
+
+@pytest.mark.parametrize(
+    "bins",
+    [
+        3,
+        [
+            Timestamp("2013-01-01 04:57:07.200000", tz="UTC").tz_convert("US/Eastern"),
+            Timestamp("2013-01-01 21:00:00", tz="UTC").tz_convert("US/Eastern"),
+            Timestamp("2013-01-02 13:00:00", tz="UTC").tz_convert("US/Eastern"),
+            Timestamp("2013-01-03 05:00:00", tz="UTC").tz_convert("US/Eastern"),
+        ],
+    ],
+)
+@pytest.mark.parametrize("box", [list, np.array, Index, Series])
+def test_datetime_tz_cut(bins, box):
+    # see gh-19872
+    tz = "US/Eastern"
+    ser = Series(date_range("20130101", periods=3, tz=tz))
+
+    if not isinstance(bins, int):
+        bins = box(bins)
+
+    result = cut(ser, bins)
+    expected = Series(
+        IntervalIndex(
+            [
+                Interval(
+                    Timestamp("2012-12-31 23:57:07.200000", tz=tz),
+                    Timestamp("2013-01-01 16:00:00", tz=tz),
+                ),
+                Interval(
+                    Timestamp("2013-01-01 16:00:00", tz=tz),
+                    Timestamp("2013-01-02 08:00:00", tz=tz),
+                ),
+                Interval(
+                    Timestamp("2013-01-02 08:00:00", tz=tz),
+                    Timestamp("2013-01-03 00:00:00", tz=tz),
+                ),
+            ]
+        )
+    ).astype(CategoricalDtype(ordered=True))
+    tm.assert_series_equal(result, expected)
+
+
+def test_datetime_nan_error():
+    msg = "bins must be of datetime64 dtype"
+
+    with pytest.raises(ValueError, match=msg):
+        cut(date_range("20130101", periods=3), bins=[0, 2, 4])
+
+
+def test_datetime_nan_mask():
+    result = cut(
+        date_range("20130102", periods=5), bins=date_range("20130101", periods=2)
+    )
+
+    mask = result.categories.isna()
+    tm.assert_numpy_array_equal(mask, np.array([False]))
+
+    mask = result.isna()
+    tm.assert_numpy_array_equal(mask, np.array([False, True, True, True, True]))
+
+
+@pytest.mark.parametrize("tz", [None, "UTC", "US/Pacific"])
+def test_datetime_cut_roundtrip(tz, unit):
+    # see gh-19891
+    ser = Series(date_range("20180101", periods=3, tz=tz, unit=unit))
+    result, result_bins = cut(ser, 2, retbins=True)
+
+    expected = cut(ser, result_bins)
+    tm.assert_series_equal(result, expected)
+
+    if unit == "s":
+        # TODO: constructing DatetimeIndex with dtype="M8[s]" without truncating
+        #  the first entry here raises in array_to_datetime. Should truncate
+        #  instead of raising?
+        # See https://github.com/pandas-dev/pandas/pull/56101#discussion_r1405325425
+        # for why we round to 8 seconds instead of 7
+        expected_bins = DatetimeIndex(
+            ["2017-12-31 23:57:08", "2018-01-02 00:00:00", "2018-01-03 00:00:00"],
+            dtype=f"M8[{unit}]",
+        )
+    else:
+        expected_bins = DatetimeIndex(
+            [
+                "2017-12-31 23:57:07.200000",
+                "2018-01-02 00:00:00",
+                "2018-01-03 00:00:00",
+            ],
+            dtype=f"M8[{unit}]",
+        )
+    expected_bins = expected_bins.tz_localize(tz)
+    tm.assert_index_equal(result_bins, expected_bins)
+
+
+def test_timedelta_cut_roundtrip():
+    # see gh-19891
+    ser = Series(timedelta_range("1day", periods=3))
+    result, result_bins = cut(ser, 2, retbins=True)
+
+    expected = cut(ser, result_bins)
+    tm.assert_series_equal(result, expected)
+
+    expected_bins = TimedeltaIndex(
+        ["0 days 23:57:07.200000", "2 days 00:00:00", "3 days 00:00:00"]
+    )
+    tm.assert_index_equal(result_bins, expected_bins)
+
+
+@pytest.mark.parametrize("bins", [6, 7])
+@pytest.mark.parametrize(
+    "box, compare",
+    [
+        (Series, tm.assert_series_equal),
+        (np.array, tm.assert_categorical_equal),
+        (list, tm.assert_equal),
+    ],
+)
+def test_cut_bool_coercion_to_int(bins, box, compare):
+    # issue 20303
+    data_expected = box([0, 1, 1, 0, 1] * 10)
+    data_result = box([False, True, True, False, True] * 10)
+    expected = cut(data_expected, bins, duplicates="drop")
+    result = cut(data_result, bins, duplicates="drop")
+    compare(result, expected)
+
+
+@pytest.mark.parametrize("labels", ["foo", 1, True])
+def test_cut_incorrect_labels(labels):
+    # GH 13318
+    values = range(5)
+    msg = "Bin labels must either be False, None or passed in as a list-like argument"
+    with pytest.raises(ValueError, match=msg):
+        cut(values, 4, labels=labels)
+
+
+@pytest.mark.parametrize("bins", [3, [0, 5, 15]])
+@pytest.mark.parametrize("right", [True, False])
+@pytest.mark.parametrize("include_lowest", [True, False])
+def test_cut_nullable_integer(bins, right, include_lowest):
+    a = np.random.default_rng(2).integers(0, 10, size=50).astype(float)
+    a[::2] = np.nan
+    result = cut(
+        pd.array(a, dtype="Int64"), bins, right=right, include_lowest=include_lowest
+    )
+    expected = cut(a, bins, right=right, include_lowest=include_lowest)
+    tm.assert_categorical_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data, bins, labels, expected_codes, expected_labels",
+    [
+        ([15, 17, 19], [14, 16, 18, 20], ["A", "B", "A"], [0, 1, 0], ["A", "B"]),
+        ([1, 3, 5], [0, 2, 4, 6, 8], [2, 0, 1, 2], [2, 0, 1], [0, 1, 2]),
+    ],
+)
+def test_cut_non_unique_labels(data, bins, labels, expected_codes, expected_labels):
+    # GH 33141
+    result = cut(data, bins=bins, labels=labels, ordered=False)
+    expected = Categorical.from_codes(
+        expected_codes, categories=expected_labels, ordered=False
+    )
+    tm.assert_categorical_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data, bins, labels, expected_codes, expected_labels",
+    [
+        ([15, 17, 19], [14, 16, 18, 20], ["C", "B", "A"], [0, 1, 2], ["C", "B", "A"]),
+        ([1, 3, 5], [0, 2, 4, 6, 8], [3, 0, 1, 2], [0, 1, 2], [3, 0, 1, 2]),
+    ],
+)
+def test_cut_unordered_labels(data, bins, labels, expected_codes, expected_labels):
+    # GH 33141
+    result = cut(data, bins=bins, labels=labels, ordered=False)
+    expected = Categorical.from_codes(
+        expected_codes, categories=expected_labels, ordered=False
+    )
+    tm.assert_categorical_equal(result, expected)
+
+
+def test_cut_unordered_with_missing_labels_raises_error():
+    # GH 33141
+    msg = "'labels' must be provided if 'ordered = False'"
+    with pytest.raises(ValueError, match=msg):
+        cut([0.5, 3], bins=[0, 1, 2], ordered=False)
+
+
+def test_cut_unordered_with_series_labels():
+    # https://github.com/pandas-dev/pandas/issues/36603
+    ser = Series([1, 2, 3, 4, 5])
+    bins = Series([0, 2, 4, 6])
+    labels = Series(["a", "b", "c"])
+    result = cut(ser, bins=bins, labels=labels, ordered=False)
+    expected = Series(["a", "a", "b", "b", "c"], dtype="category")
+    tm.assert_series_equal(result, expected)
+
+
+def test_cut_no_warnings():
+    df = DataFrame({"value": np.random.default_rng(2).integers(0, 100, 20)})
+    labels = [f"{i} - {i + 9}" for i in range(0, 100, 10)]
+    with tm.assert_produces_warning(False):
+        df["group"] = cut(df.value, range(0, 105, 10), right=False, labels=labels)
+
+
+def test_cut_with_duplicated_index_lowest_included():
+    # GH 42185
+    expected = Series(
+        [Interval(-0.001, 2, closed="right")] * 3
+        + [Interval(2, 4, closed="right"), Interval(-0.001, 2, closed="right")],
+        index=[0, 1, 2, 3, 0],
+        dtype="category",
+    ).cat.as_ordered()
+
+    ser = Series([0, 1, 2, 3, 0], index=[0, 1, 2, 3, 0])
+    result = cut(ser, bins=[0, 2, 4], include_lowest=True)
+    tm.assert_series_equal(result, expected)
+
+
+def test_cut_with_nonexact_categorical_indices():
+    # GH 42424
+
+    ser = Series(range(100))
+    ser1 = cut(ser, 10).value_counts().head(5)
+    ser2 = cut(ser, 10).value_counts().tail(5)
+    result = DataFrame({"1": ser1, "2": ser2})
+
+    index = pd.CategoricalIndex(
+        [
+            Interval(-0.099, 9.9, closed="right"),
+            Interval(9.9, 19.8, closed="right"),
+            Interval(19.8, 29.7, closed="right"),
+            Interval(29.7, 39.6, closed="right"),
+            Interval(39.6, 49.5, closed="right"),
+            Interval(49.5, 59.4, closed="right"),
+            Interval(59.4, 69.3, closed="right"),
+            Interval(69.3, 79.2, closed="right"),
+            Interval(79.2, 89.1, closed="right"),
+            Interval(89.1, 99, closed="right"),
+        ],
+        ordered=True,
+    )
+
+    expected = DataFrame(
+        {"1": [10] * 5 + [np.nan] * 5, "2": [np.nan] * 5 + [10] * 5}, index=index
+    )
+
+    tm.assert_frame_equal(expected, result)
+
+
+def test_cut_with_timestamp_tuple_labels():
+    # GH 40661
+    labels = [(Timestamp(10),), (Timestamp(20),), (Timestamp(30),)]
+    result = cut([2, 4, 6], bins=[1, 3, 5, 7], labels=labels)
+
+    expected = Categorical.from_codes([0, 1, 2], labels, ordered=True)
+    tm.assert_categorical_equal(result, expected)
+
+
+def test_cut_bins_datetime_intervalindex():
+    # https://github.com/pandas-dev/pandas/issues/46218
+    bins = interval_range(Timestamp("2022-02-25"), Timestamp("2022-02-27"), freq="1D")
+    # passing Series instead of list is important to trigger bug
+    result = cut(Series([Timestamp("2022-02-26")]).astype("M8[ns]"), bins=bins)
+    expected = Categorical.from_codes([0], bins, ordered=True)
+    tm.assert_categorical_equal(result.array, expected)
+
+
+def test_cut_with_nullable_int64():
+    # GH 30787
+    series = Series([0, 1, 2, 3, 4, pd.NA, 6, 7], dtype="Int64")
+    bins = [0, 2, 4, 6, 8]
+    intervals = IntervalIndex.from_breaks(bins)
+
+    expected = Series(
+        Categorical.from_codes([-1, 0, 0, 1, 1, -1, 2, 3], intervals, ordered=True)
+    )
+
+    result = cut(series, bins=bins)
+
+    tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/test_get_dummies.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/test_get_dummies.py
new file mode 100644
index 0000000000000000000000000000000000000000..31260e4dcb7d2ecf5f003ce4aadf890493b59887
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/test_get_dummies.py
@@ -0,0 +1,743 @@
+import re
+import unicodedata
+
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+from pandas.core.dtypes.common import is_integer_dtype
+
+import pandas as pd
+from pandas import (
+    ArrowDtype,
+    Categorical,
+    CategoricalDtype,
+    CategoricalIndex,
+    DataFrame,
+    Index,
+    RangeIndex,
+    Series,
+    SparseDtype,
+    get_dummies,
+)
+import pandas._testing as tm
+from pandas.core.arrays.sparse import SparseArray
+
+try:
+    import pyarrow as pa
+except ImportError:
+    pa = None
+
+
+class TestGetDummies:
+    @pytest.fixture
+    def df(self):
+        return DataFrame({"A": ["a", "b", "a"], "B": ["b", "b", "c"], "C": [1, 2, 3]})
+
+    @pytest.fixture(params=["uint8", "i8", np.float64, bool, None])
+    def dtype(self, request):
+        return np.dtype(request.param)
+
+    @pytest.fixture(params=["dense", "sparse"])
+    def sparse(self, request):
+        # params are strings to simplify reading test results,
+        # e.g. TestGetDummies::test_basic[uint8-sparse] instead of [uint8-True]
+        return request.param == "sparse"
+
+    def effective_dtype(self, dtype):
+        if dtype is None:
+            return np.uint8
+        return dtype
+
+    def test_get_dummies_raises_on_dtype_object(self, df):
+        msg = "dtype=object is not a valid dtype for get_dummies"
+        with pytest.raises(ValueError, match=msg):
+            get_dummies(df, dtype="object")
+
+    def test_get_dummies_basic(self, sparse, dtype):
+        s_list = list("abc")
+        s_series = Series(s_list)
+        s_series_index = Series(s_list, list("ABC"))
+
+        expected = DataFrame(
+            {"a": [1, 0, 0], "b": [0, 1, 0], "c": [0, 0, 1]},
+            dtype=self.effective_dtype(dtype),
+        )
+        if sparse:
+            if dtype.kind == "b":
+                expected = expected.apply(SparseArray, fill_value=False)
+            else:
+                expected = expected.apply(SparseArray, fill_value=0.0)
+        result = get_dummies(s_list, sparse=sparse, dtype=dtype)
+        tm.assert_frame_equal(result, expected)
+
+        result = get_dummies(s_series, sparse=sparse, dtype=dtype)
+        tm.assert_frame_equal(result, expected)
+
+        expected.index = list("ABC")
+        result = get_dummies(s_series_index, sparse=sparse, dtype=dtype)
+        tm.assert_frame_equal(result, expected)
+
+    def test_get_dummies_basic_types(self, sparse, dtype, using_infer_string):
+        # GH 10531
+        s_list = list("abc")
+        s_series = Series(s_list)
+        s_df = DataFrame(
+            {"a": [0, 1, 0, 1, 2], "b": ["A", "A", "B", "C", "C"], "c": [2, 3, 3, 3, 2]}
+        )
+
+        expected = DataFrame(
+            {"a": [1, 0, 0], "b": [0, 1, 0], "c": [0, 0, 1]},
+            dtype=self.effective_dtype(dtype),
+            columns=list("abc"),
+        )
+        if sparse:
+            if is_integer_dtype(dtype):
+                fill_value = 0
+            elif dtype == bool:
+                fill_value = False
+            else:
+                fill_value = 0.0
+
+            expected = expected.apply(SparseArray, fill_value=fill_value)
+        result = get_dummies(s_list, sparse=sparse, dtype=dtype)
+        tm.assert_frame_equal(result, expected)
+
+        result = get_dummies(s_series, sparse=sparse, dtype=dtype)
+        tm.assert_frame_equal(result, expected)
+
+        result = get_dummies(s_df, columns=s_df.columns, sparse=sparse, dtype=dtype)
+        if sparse:
+            dtype_name = f"Sparse[{self.effective_dtype(dtype).name}, {fill_value}]"
+        else:
+            dtype_name = self.effective_dtype(dtype).name
+
+        expected = Series({dtype_name: 8}, name="count")
+        result = result.dtypes.value_counts()
+        result.index = [str(i) for i in result.index]
+        tm.assert_series_equal(result, expected)
+
+        result = get_dummies(s_df, columns=["a"], sparse=sparse, dtype=dtype)
+
+        key = "string" if using_infer_string else "object"
+        expected_counts = {"int64": 1, key: 1}
+        expected_counts[dtype_name] = 3 + expected_counts.get(dtype_name, 0)
+
+        expected = Series(expected_counts, name="count").sort_index()
+        result = result.dtypes.value_counts()
+        result.index = [str(i) for i in result.index]
+        result = result.sort_index()
+        tm.assert_series_equal(result, expected)
+
+    def test_get_dummies_just_na(self, sparse):
+        just_na_list = [np.nan]
+        just_na_series = Series(just_na_list)
+        just_na_series_index = Series(just_na_list, index=["A"])
+
+        res_list = get_dummies(just_na_list, sparse=sparse)
+        res_series = get_dummies(just_na_series, sparse=sparse)
+        res_series_index = get_dummies(just_na_series_index, sparse=sparse)
+
+        assert res_list.empty
+        assert res_series.empty
+        assert res_series_index.empty
+
+        assert res_list.index.tolist() == [0]
+        assert res_series.index.tolist() == [0]
+        assert res_series_index.index.tolist() == ["A"]
+
+    def test_get_dummies_include_na(self, sparse, dtype):
+        s = ["a", "b", np.nan]
+        res = get_dummies(s, sparse=sparse, dtype=dtype)
+        exp = DataFrame(
+            {"a": [1, 0, 0], "b": [0, 1, 0]}, dtype=self.effective_dtype(dtype)
+        )
+        if sparse:
+            if dtype.kind == "b":
+                exp = exp.apply(SparseArray, fill_value=False)
+            else:
+                exp = exp.apply(SparseArray, fill_value=0.0)
+        tm.assert_frame_equal(res, exp)
+
+        # Sparse dataframes do not allow nan labelled columns, see #GH8822
+        res_na = get_dummies(s, dummy_na=True, sparse=sparse, dtype=dtype)
+        exp_na = DataFrame(
+            {np.nan: [0, 0, 1], "a": [1, 0, 0], "b": [0, 1, 0]},
+            dtype=self.effective_dtype(dtype),
+        )
+        exp_na = exp_na.reindex(["a", "b", np.nan], axis=1)
+        # hack (NaN handling in assert_index_equal)
+        exp_na.columns = res_na.columns
+        if sparse:
+            if dtype.kind == "b":
+                exp_na = exp_na.apply(SparseArray, fill_value=False)
+            else:
+                exp_na = exp_na.apply(SparseArray, fill_value=0.0)
+        tm.assert_frame_equal(res_na, exp_na)
+
+        res_just_na = get_dummies([np.nan], dummy_na=True, sparse=sparse, dtype=dtype)
+        exp_just_na = DataFrame(
+            Series(1, index=[0]), columns=[np.nan], dtype=self.effective_dtype(dtype)
+        )
+        tm.assert_numpy_array_equal(res_just_na.values, exp_just_na.values)
+
+    def test_get_dummies_unicode(self, sparse):
+        # See GH 6885 - get_dummies chokes on unicode values
+        e = "e"
+        eacute = unicodedata.lookup("LATIN SMALL LETTER E WITH ACUTE")
+        s = [e, eacute, eacute]
+        res = get_dummies(s, prefix="letter", sparse=sparse)
+        exp = DataFrame(
+            {"letter_e": [True, False, False], f"letter_{eacute}": [False, True, True]}
+        )
+        if sparse:
+            exp = exp.apply(SparseArray, fill_value=False)
+        tm.assert_frame_equal(res, exp)
+
+    def test_dataframe_dummies_all_obj(self, df, sparse):
+        df = df[["A", "B"]]
+        result = get_dummies(df, sparse=sparse)
+        expected = DataFrame(
+            {"A_a": [1, 0, 1], "A_b": [0, 1, 0], "B_b": [1, 1, 0], "B_c": [0, 0, 1]},
+            dtype=bool,
+        )
+        if sparse:
+            expected = DataFrame(
+                {
+                    "A_a": SparseArray([1, 0, 1], dtype="bool"),
+                    "A_b": SparseArray([0, 1, 0], dtype="bool"),
+                    "B_b": SparseArray([1, 1, 0], dtype="bool"),
+                    "B_c": SparseArray([0, 0, 1], dtype="bool"),
+                }
+            )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_dataframe_dummies_string_dtype(self, df, using_infer_string):
+        # GH44965
+        df = df[["A", "B"]]
+        df = df.astype({"A": "object", "B": "string"})
+        result = get_dummies(df)
+        expected = DataFrame(
+            {
+                "A_a": [1, 0, 1],
+                "A_b": [0, 1, 0],
+                "B_b": [1, 1, 0],
+                "B_c": [0, 0, 1],
+            },
+            dtype=bool,
+        )
+        if not using_infer_string:
+            # infer_string returns numpy bools
+            expected[["B_b", "B_c"]] = expected[["B_b", "B_c"]].astype("boolean")
+        tm.assert_frame_equal(result, expected)
+
+    def test_dataframe_dummies_mix_default(self, df, sparse, dtype):
+        result = get_dummies(df, sparse=sparse, dtype=dtype)
+        if sparse:
+            arr = SparseArray
+            if dtype.kind == "b":
+                typ = SparseDtype(dtype, False)
+            else:
+                typ = SparseDtype(dtype, 0)
+        else:
+            arr = np.array
+            typ = dtype
+        expected = DataFrame(
+            {
+                "C": [1, 2, 3],
+                "A_a": arr([1, 0, 1], dtype=typ),
+                "A_b": arr([0, 1, 0], dtype=typ),
+                "B_b": arr([1, 1, 0], dtype=typ),
+                "B_c": arr([0, 0, 1], dtype=typ),
+            }
+        )
+        expected = expected[["C", "A_a", "A_b", "B_b", "B_c"]]
+        tm.assert_frame_equal(result, expected)
+
+    def test_dataframe_dummies_prefix_list(self, df, sparse):
+        prefixes = ["from_A", "from_B"]
+        result = get_dummies(df, prefix=prefixes, sparse=sparse)
+        expected = DataFrame(
+            {
+                "C": [1, 2, 3],
+                "from_A_a": [True, False, True],
+                "from_A_b": [False, True, False],
+                "from_B_b": [True, True, False],
+                "from_B_c": [False, False, True],
+            },
+        )
+        expected[["C"]] = df[["C"]]
+        cols = ["from_A_a", "from_A_b", "from_B_b", "from_B_c"]
+        expected = expected[["C"] + cols]
+
+        typ = SparseArray if sparse else Series
+        expected[cols] = expected[cols].apply(lambda x: typ(x))
+        tm.assert_frame_equal(result, expected)
+
+    def test_dataframe_dummies_prefix_str(self, df, sparse):
+        # not that you should do this...
+        result = get_dummies(df, prefix="bad", sparse=sparse)
+        bad_columns = ["bad_a", "bad_b", "bad_b", "bad_c"]
+        expected = DataFrame(
+            [
+                [1, True, False, True, False],
+                [2, False, True, True, False],
+                [3, True, False, False, True],
+            ],
+            columns=["C"] + bad_columns,
+        )
+        expected = expected.astype({"C": np.int64})
+        if sparse:
+            # work around astyping & assigning with duplicate columns
+            # https://github.com/pandas-dev/pandas/issues/14427
+            expected = pd.concat(
+                [
+                    Series([1, 2, 3], name="C"),
+                    Series([True, False, True], name="bad_a", dtype="Sparse[bool]"),
+                    Series([False, True, False], name="bad_b", dtype="Sparse[bool]"),
+                    Series([True, True, False], name="bad_b", dtype="Sparse[bool]"),
+                    Series([False, False, True], name="bad_c", dtype="Sparse[bool]"),
+                ],
+                axis=1,
+            )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_dataframe_dummies_subset(self, df, sparse):
+        result = get_dummies(df, prefix=["from_A"], columns=["A"], sparse=sparse)
+        expected = DataFrame(
+            {
+                "B": ["b", "b", "c"],
+                "C": [1, 2, 3],
+                "from_A_a": [1, 0, 1],
+                "from_A_b": [0, 1, 0],
+            },
+        )
+        cols = expected.columns
+        expected[cols[1:]] = expected[cols[1:]].astype(bool)
+        expected[["C"]] = df[["C"]]
+        if sparse:
+            cols = ["from_A_a", "from_A_b"]
+            expected[cols] = expected[cols].astype(SparseDtype("bool", False))
+        tm.assert_frame_equal(result, expected)
+
+    def test_dataframe_dummies_prefix_sep(self, df, sparse):
+        result = get_dummies(df, prefix_sep="..", sparse=sparse)
+        expected = DataFrame(
+            {
+                "C": [1, 2, 3],
+                "A..a": [True, False, True],
+                "A..b": [False, True, False],
+                "B..b": [True, True, False],
+                "B..c": [False, False, True],
+            },
+        )
+        expected[["C"]] = df[["C"]]
+        expected = expected[["C", "A..a", "A..b", "B..b", "B..c"]]
+        if sparse:
+            cols = ["A..a", "A..b", "B..b", "B..c"]
+            expected[cols] = expected[cols].astype(SparseDtype("bool", False))
+
+        tm.assert_frame_equal(result, expected)
+
+        result = get_dummies(df, prefix_sep=["..", "__"], sparse=sparse)
+        expected = expected.rename(columns={"B..b": "B__b", "B..c": "B__c"})
+        tm.assert_frame_equal(result, expected)
+
+        result = get_dummies(df, prefix_sep={"A": "..", "B": "__"}, sparse=sparse)
+        tm.assert_frame_equal(result, expected)
+
+    def test_dataframe_dummies_prefix_bad_length(self, df, sparse):
+        msg = re.escape(
+            "Length of 'prefix' (1) did not match the length of the columns being "
+            "encoded (2)"
+        )
+        with pytest.raises(ValueError, match=msg):
+            get_dummies(df, prefix=["too few"], sparse=sparse)
+
+    def test_dataframe_dummies_prefix_sep_bad_length(self, df, sparse):
+        msg = re.escape(
+            "Length of 'prefix_sep' (1) did not match the length of the columns being "
+            "encoded (2)"
+        )
+        with pytest.raises(ValueError, match=msg):
+            get_dummies(df, prefix_sep=["bad"], sparse=sparse)
+
+    def test_dataframe_dummies_prefix_dict(self, sparse):
+        prefixes = {"A": "from_A", "B": "from_B"}
+        df = DataFrame({"C": [1, 2, 3], "A": ["a", "b", "a"], "B": ["b", "b", "c"]})
+        result = get_dummies(df, prefix=prefixes, sparse=sparse)
+
+        expected = DataFrame(
+            {
+                "C": [1, 2, 3],
+                "from_A_a": [1, 0, 1],
+                "from_A_b": [0, 1, 0],
+                "from_B_b": [1, 1, 0],
+                "from_B_c": [0, 0, 1],
+            }
+        )
+
+        columns = ["from_A_a", "from_A_b", "from_B_b", "from_B_c"]
+        expected[columns] = expected[columns].astype(bool)
+        if sparse:
+            expected[columns] = expected[columns].astype(SparseDtype("bool", False))
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_dataframe_dummies_with_na(self, df, sparse, dtype):
+        df.loc[3, :] = [np.nan, np.nan, np.nan]
+        result = get_dummies(df, dummy_na=True, sparse=sparse, dtype=dtype).sort_index(
+            axis=1
+        )
+
+        if sparse:
+            arr = SparseArray
+            if dtype.kind == "b":
+                typ = SparseDtype(dtype, False)
+            else:
+                typ = SparseDtype(dtype, 0)
+        else:
+            arr = np.array
+            typ = dtype
+
+        expected = DataFrame(
+            {
+                "C": [1, 2, 3, np.nan],
+                "A_a": arr([1, 0, 1, 0], dtype=typ),
+                "A_b": arr([0, 1, 0, 0], dtype=typ),
+                "A_nan": arr([0, 0, 0, 1], dtype=typ),
+                "B_b": arr([1, 1, 0, 0], dtype=typ),
+                "B_c": arr([0, 0, 1, 0], dtype=typ),
+                "B_nan": arr([0, 0, 0, 1], dtype=typ),
+            }
+        ).sort_index(axis=1)
+
+        tm.assert_frame_equal(result, expected)
+
+        result = get_dummies(df, dummy_na=False, sparse=sparse, dtype=dtype)
+        expected = expected[["C", "A_a", "A_b", "B_b", "B_c"]]
+        tm.assert_frame_equal(result, expected)
+
+    def test_dataframe_dummies_with_categorical(self, df, sparse, dtype):
+        df["cat"] = Categorical(["x", "y", "y"])
+        result = get_dummies(df, sparse=sparse, dtype=dtype).sort_index(axis=1)
+        if sparse:
+            arr = SparseArray
+            if dtype.kind == "b":
+                typ = SparseDtype(dtype, False)
+            else:
+                typ = SparseDtype(dtype, 0)
+        else:
+            arr = np.array
+            typ = dtype
+
+        expected = DataFrame(
+            {
+                "C": [1, 2, 3],
+                "A_a": arr([1, 0, 1], dtype=typ),
+                "A_b": arr([0, 1, 0], dtype=typ),
+                "B_b": arr([1, 1, 0], dtype=typ),
+                "B_c": arr([0, 0, 1], dtype=typ),
+                "cat_x": arr([1, 0, 0], dtype=typ),
+                "cat_y": arr([0, 1, 1], dtype=typ),
+            }
+        ).sort_index(axis=1)
+
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "get_dummies_kwargs,expected",
+        [
+            (
+                {"data": DataFrame({"ä": ["a"]})},
+                DataFrame({"ä_a": [True]}),
+            ),
+            (
+                {"data": DataFrame({"x": ["ä"]})},
+                DataFrame({"x_ä": [True]}),
+            ),
+            (
+                {"data": DataFrame({"x": ["a"]}), "prefix": "ä"},
+                DataFrame({"ä_a": [True]}),
+            ),
+            (
+                {"data": DataFrame({"x": ["a"]}), "prefix_sep": "ä"},
+                DataFrame({"xäa": [True]}),
+            ),
+        ],
+    )
+    def test_dataframe_dummies_unicode(self, get_dummies_kwargs, expected):
+        # GH22084 get_dummies incorrectly encodes unicode characters
+        # in dataframe column names
+        result = get_dummies(**get_dummies_kwargs)
+        tm.assert_frame_equal(result, expected)
+
+    def test_get_dummies_basic_drop_first(self, sparse):
+        # GH12402 Add a new parameter `drop_first` to avoid collinearity
+        # Basic case
+        s_list = list("abc")
+        s_series = Series(s_list)
+        s_series_index = Series(s_list, list("ABC"))
+
+        expected = DataFrame({"b": [0, 1, 0], "c": [0, 0, 1]}, dtype=bool)
+
+        result = get_dummies(s_list, drop_first=True, sparse=sparse)
+        if sparse:
+            expected = expected.apply(SparseArray, fill_value=False)
+        tm.assert_frame_equal(result, expected)
+
+        result = get_dummies(s_series, drop_first=True, sparse=sparse)
+        tm.assert_frame_equal(result, expected)
+
+        expected.index = list("ABC")
+        result = get_dummies(s_series_index, drop_first=True, sparse=sparse)
+        tm.assert_frame_equal(result, expected)
+
+    def test_get_dummies_basic_drop_first_one_level(self, sparse):
+        # Test the case that categorical variable only has one level.
+        s_list = list("aaa")
+        s_series = Series(s_list)
+        s_series_index = Series(s_list, list("ABC"))
+
+        expected = DataFrame(index=RangeIndex(3))
+
+        result = get_dummies(s_list, drop_first=True, sparse=sparse)
+        tm.assert_frame_equal(result, expected)
+
+        result = get_dummies(s_series, drop_first=True, sparse=sparse)
+        tm.assert_frame_equal(result, expected)
+
+        expected = DataFrame(index=list("ABC"))
+        result = get_dummies(s_series_index, drop_first=True, sparse=sparse)
+        tm.assert_frame_equal(result, expected)
+
+    def test_get_dummies_basic_drop_first_NA(self, sparse):
+        # Test NA handling together with drop_first
+        s_NA = ["a", "b", np.nan]
+        res = get_dummies(s_NA, drop_first=True, sparse=sparse)
+        exp = DataFrame({"b": [0, 1, 0]}, dtype=bool)
+        if sparse:
+            exp = exp.apply(SparseArray, fill_value=False)
+
+        tm.assert_frame_equal(res, exp)
+
+        res_na = get_dummies(s_NA, dummy_na=True, drop_first=True, sparse=sparse)
+        exp_na = DataFrame({"b": [0, 1, 0], np.nan: [0, 0, 1]}, dtype=bool).reindex(
+            ["b", np.nan], axis=1
+        )
+        if sparse:
+            exp_na = exp_na.apply(SparseArray, fill_value=False)
+        tm.assert_frame_equal(res_na, exp_na)
+
+        res_just_na = get_dummies(
+            [np.nan], dummy_na=True, drop_first=True, sparse=sparse
+        )
+        exp_just_na = DataFrame(index=RangeIndex(1))
+        tm.assert_frame_equal(res_just_na, exp_just_na)
+
+    def test_dataframe_dummies_drop_first(self, df, sparse):
+        df = df[["A", "B"]]
+        result = get_dummies(df, drop_first=True, sparse=sparse)
+        expected = DataFrame({"A_b": [0, 1, 0], "B_c": [0, 0, 1]}, dtype=bool)
+        if sparse:
+            expected = expected.apply(SparseArray, fill_value=False)
+        tm.assert_frame_equal(result, expected)
+
+    def test_dataframe_dummies_drop_first_with_categorical(self, df, sparse, dtype):
+        df["cat"] = Categorical(["x", "y", "y"])
+        result = get_dummies(df, drop_first=True, sparse=sparse)
+        expected = DataFrame(
+            {"C": [1, 2, 3], "A_b": [0, 1, 0], "B_c": [0, 0, 1], "cat_y": [0, 1, 1]}
+        )
+        cols = ["A_b", "B_c", "cat_y"]
+        expected[cols] = expected[cols].astype(bool)
+        expected = expected[["C", "A_b", "B_c", "cat_y"]]
+        if sparse:
+            for col in cols:
+                expected[col] = SparseArray(expected[col])
+        tm.assert_frame_equal(result, expected)
+
+    def test_dataframe_dummies_drop_first_with_na(self, df, sparse):
+        df.loc[3, :] = [np.nan, np.nan, np.nan]
+        result = get_dummies(
+            df, dummy_na=True, drop_first=True, sparse=sparse
+        ).sort_index(axis=1)
+        expected = DataFrame(
+            {
+                "C": [1, 2, 3, np.nan],
+                "A_b": [0, 1, 0, 0],
+                "A_nan": [0, 0, 0, 1],
+                "B_c": [0, 0, 1, 0],
+                "B_nan": [0, 0, 0, 1],
+            }
+        )
+        cols = ["A_b", "A_nan", "B_c", "B_nan"]
+        expected[cols] = expected[cols].astype(bool)
+        expected = expected.sort_index(axis=1)
+        if sparse:
+            for col in cols:
+                expected[col] = SparseArray(expected[col])
+
+        tm.assert_frame_equal(result, expected)
+
+        result = get_dummies(df, dummy_na=False, drop_first=True, sparse=sparse)
+        expected = expected[["C", "A_b", "B_c"]]
+        tm.assert_frame_equal(result, expected)
+
+    def test_get_dummies_int_int(self):
+        data = Series([1, 2, 1])
+        result = get_dummies(data)
+        expected = DataFrame([[1, 0], [0, 1], [1, 0]], columns=[1, 2], dtype=bool)
+        tm.assert_frame_equal(result, expected)
+
+        data = Series(Categorical(["a", "b", "a"]))
+        result = get_dummies(data)
+        expected = DataFrame(
+            [[1, 0], [0, 1], [1, 0]], columns=Categorical(["a", "b"]), dtype=bool
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_get_dummies_int_df(self, dtype):
+        data = DataFrame(
+            {
+                "A": [1, 2, 1],
+                "B": Categorical(["a", "b", "a"]),
+                "C": [1, 2, 1],
+                "D": [1.0, 2.0, 1.0],
+            }
+        )
+        columns = ["C", "D", "A_1", "A_2", "B_a", "B_b"]
+        expected = DataFrame(
+            [[1, 1.0, 1, 0, 1, 0], [2, 2.0, 0, 1, 0, 1], [1, 1.0, 1, 0, 1, 0]],
+            columns=columns,
+        )
+        expected[columns[2:]] = expected[columns[2:]].astype(dtype)
+        result = get_dummies(data, columns=["A", "B"], dtype=dtype)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("ordered", [True, False])
+    def test_dataframe_dummies_preserve_categorical_dtype(self, dtype, ordered):
+        # GH13854
+        cat = Categorical(list("xy"), categories=list("xyz"), ordered=ordered)
+        result = get_dummies(cat, dtype=dtype)
+
+        data = np.array([[1, 0, 0], [0, 1, 0]], dtype=self.effective_dtype(dtype))
+        cols = CategoricalIndex(
+            cat.categories, categories=cat.categories, ordered=ordered
+        )
+        expected = DataFrame(data, columns=cols, dtype=self.effective_dtype(dtype))
+
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("sparse", [True, False])
+    def test_get_dummies_dont_sparsify_all_columns(self, sparse):
+        # GH18914
+        df = DataFrame.from_dict({"GDP": [1, 2], "Nation": ["AB", "CD"]})
+        df = get_dummies(df, columns=["Nation"], sparse=sparse)
+        df2 = df.reindex(columns=["GDP"])
+
+        tm.assert_frame_equal(df[["GDP"]], df2)
+
+    def test_get_dummies_duplicate_columns(self, df):
+        # GH20839
+        df.columns = ["A", "A", "A"]
+        result = get_dummies(df).sort_index(axis=1)
+
+        expected = DataFrame(
+            [
+                [1, True, False, True, False],
+                [2, False, True, True, False],
+                [3, True, False, False, True],
+            ],
+            columns=["A", "A_a", "A_b", "A_b", "A_c"],
+        ).sort_index(axis=1)
+
+        expected = expected.astype({"A": np.int64})
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_get_dummies_all_sparse(self):
+        df = DataFrame({"A": [1, 2]})
+        result = get_dummies(df, columns=["A"], sparse=True)
+        dtype = SparseDtype("bool", False)
+        expected = DataFrame(
+            {
+                "A_1": SparseArray([1, 0], dtype=dtype),
+                "A_2": SparseArray([0, 1], dtype=dtype),
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("values", ["baz"])
+    def test_get_dummies_with_string_values(self, values):
+        # issue #28383
+        df = DataFrame(
+            {
+                "bar": [1, 2, 3, 4, 5, 6],
+                "foo": ["one", "one", "one", "two", "two", "two"],
+                "baz": ["A", "B", "C", "A", "B", "C"],
+                "zoo": ["x", "y", "z", "q", "w", "t"],
+            }
+        )
+
+        msg = "Input must be a list-like for parameter `columns`"
+
+        with pytest.raises(TypeError, match=msg):
+            get_dummies(df, columns=values)
+
+    def test_get_dummies_ea_dtype_series(self, any_numeric_ea_and_arrow_dtype):
+        # GH#32430
+        ser = Series(list("abca"))
+        result = get_dummies(ser, dtype=any_numeric_ea_and_arrow_dtype)
+        expected = DataFrame(
+            {"a": [1, 0, 0, 1], "b": [0, 1, 0, 0], "c": [0, 0, 1, 0]},
+            dtype=any_numeric_ea_and_arrow_dtype,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_get_dummies_ea_dtype_dataframe(self, any_numeric_ea_and_arrow_dtype):
+        # GH#32430
+        df = DataFrame({"x": list("abca")})
+        result = get_dummies(df, dtype=any_numeric_ea_and_arrow_dtype)
+        expected = DataFrame(
+            {"x_a": [1, 0, 0, 1], "x_b": [0, 1, 0, 0], "x_c": [0, 0, 1, 0]},
+            dtype=any_numeric_ea_and_arrow_dtype,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @td.skip_if_no("pyarrow")
+    def test_get_dummies_ea_dtype(self):
+        # GH#56273
+        for dtype, exp_dtype in [
+            ("string[pyarrow]", "boolean"),
+            ("string[pyarrow_numpy]", "bool"),
+            (CategoricalDtype(Index(["a"], dtype="string[pyarrow]")), "boolean"),
+            (CategoricalDtype(Index(["a"], dtype="string[pyarrow_numpy]")), "bool"),
+        ]:
+            df = DataFrame({"name": Series(["a"], dtype=dtype), "x": 1})
+            result = get_dummies(df)
+            expected = DataFrame({"x": 1, "name_a": Series([True], dtype=exp_dtype)})
+            tm.assert_frame_equal(result, expected)
+
+    @td.skip_if_no("pyarrow")
+    def test_get_dummies_arrow_dtype(self):
+        # GH#56273
+        df = DataFrame({"name": Series(["a"], dtype=ArrowDtype(pa.string())), "x": 1})
+        result = get_dummies(df)
+        expected = DataFrame({"x": 1, "name_a": Series([True], dtype="bool[pyarrow]")})
+        tm.assert_frame_equal(result, expected)
+
+        df = DataFrame(
+            {
+                "name": Series(
+                    ["a"],
+                    dtype=CategoricalDtype(Index(["a"], dtype=ArrowDtype(pa.string()))),
+                ),
+                "x": 1,
+            }
+        )
+        result = get_dummies(df)
+        tm.assert_frame_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/test_melt.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/test_melt.py
new file mode 100644
index 0000000000000000000000000000000000000000..272c5b34032937215b3ffff45ea10a9e31048041
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/test_melt.py
@@ -0,0 +1,1252 @@
+import re
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    date_range,
+    lreshape,
+    melt,
+    wide_to_long,
+)
+import pandas._testing as tm
+
+
+@pytest.fixture
+def df():
+    res = DataFrame(
+        np.random.default_rng(2).standard_normal((10, 4)),
+        columns=Index(list("ABCD"), dtype=object),
+        index=date_range("2000-01-01", periods=10, freq="B"),
+    )
+    res["id1"] = (res["A"] > 0).astype(np.int64)
+    res["id2"] = (res["B"] > 0).astype(np.int64)
+    return res
+
+
+@pytest.fixture
+def df1():
+    res = DataFrame(
+        [
+            [1.067683, -1.110463, 0.20867],
+            [-1.321405, 0.368915, -1.055342],
+            [-0.807333, 0.08298, -0.873361],
+        ]
+    )
+    res.columns = [list("ABC"), list("abc")]
+    res.columns.names = ["CAP", "low"]
+    return res
+
+
+@pytest.fixture
+def var_name():
+    return "var"
+
+
+@pytest.fixture
+def value_name():
+    return "val"
+
+
+class TestMelt:
+    def test_top_level_method(self, df):
+        result = melt(df)
+        assert result.columns.tolist() == ["variable", "value"]
+
+    def test_method_signatures(self, df, df1, var_name, value_name):
+        tm.assert_frame_equal(df.melt(), melt(df))
+
+        tm.assert_frame_equal(
+            df.melt(id_vars=["id1", "id2"], value_vars=["A", "B"]),
+            melt(df, id_vars=["id1", "id2"], value_vars=["A", "B"]),
+        )
+
+        tm.assert_frame_equal(
+            df.melt(var_name=var_name, value_name=value_name),
+            melt(df, var_name=var_name, value_name=value_name),
+        )
+
+        tm.assert_frame_equal(df1.melt(col_level=0), melt(df1, col_level=0))
+
+    def test_default_col_names(self, df):
+        result = df.melt()
+        assert result.columns.tolist() == ["variable", "value"]
+
+        result1 = df.melt(id_vars=["id1"])
+        assert result1.columns.tolist() == ["id1", "variable", "value"]
+
+        result2 = df.melt(id_vars=["id1", "id2"])
+        assert result2.columns.tolist() == ["id1", "id2", "variable", "value"]
+
+    def test_value_vars(self, df):
+        result3 = df.melt(id_vars=["id1", "id2"], value_vars="A")
+        assert len(result3) == 10
+
+        result4 = df.melt(id_vars=["id1", "id2"], value_vars=["A", "B"])
+        expected4 = DataFrame(
+            {
+                "id1": df["id1"].tolist() * 2,
+                "id2": df["id2"].tolist() * 2,
+                "variable": ["A"] * 10 + ["B"] * 10,
+                "value": (df["A"].tolist() + df["B"].tolist()),
+            },
+            columns=["id1", "id2", "variable", "value"],
+        )
+        tm.assert_frame_equal(result4, expected4)
+
+    @pytest.mark.parametrize("type_", (tuple, list, np.array))
+    def test_value_vars_types(self, type_, df):
+        # GH 15348
+        expected = DataFrame(
+            {
+                "id1": df["id1"].tolist() * 2,
+                "id2": df["id2"].tolist() * 2,
+                "variable": ["A"] * 10 + ["B"] * 10,
+                "value": (df["A"].tolist() + df["B"].tolist()),
+            },
+            columns=["id1", "id2", "variable", "value"],
+        )
+        result = df.melt(id_vars=["id1", "id2"], value_vars=type_(("A", "B")))
+        tm.assert_frame_equal(result, expected)
+
+    def test_vars_work_with_multiindex(self, df1):
+        expected = DataFrame(
+            {
+                ("A", "a"): df1[("A", "a")],
+                "CAP": ["B"] * len(df1),
+                "low": ["b"] * len(df1),
+                "value": df1[("B", "b")],
+            },
+            columns=[("A", "a"), "CAP", "low", "value"],
+        )
+
+        result = df1.melt(id_vars=[("A", "a")], value_vars=[("B", "b")])
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "id_vars, value_vars, col_level, expected",
+        [
+            (
+                ["A"],
+                ["B"],
+                0,
+                DataFrame(
+                    {
+                        "A": {0: 1.067683, 1: -1.321405, 2: -0.807333},
+                        "CAP": {0: "B", 1: "B", 2: "B"},
+                        "value": {0: -1.110463, 1: 0.368915, 2: 0.08298},
+                    }
+                ),
+            ),
+            (
+                ["a"],
+                ["b"],
+                1,
+                DataFrame(
+                    {
+                        "a": {0: 1.067683, 1: -1.321405, 2: -0.807333},
+                        "low": {0: "b", 1: "b", 2: "b"},
+                        "value": {0: -1.110463, 1: 0.368915, 2: 0.08298},
+                    }
+                ),
+            ),
+        ],
+    )
+    def test_single_vars_work_with_multiindex(
+        self, id_vars, value_vars, col_level, expected, df1
+    ):
+        result = df1.melt(id_vars, value_vars, col_level=col_level)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "id_vars, value_vars",
+        [
+            [("A", "a"), [("B", "b")]],
+            [[("A", "a")], ("B", "b")],
+            [("A", "a"), ("B", "b")],
+        ],
+    )
+    def test_tuple_vars_fail_with_multiindex(self, id_vars, value_vars, df1):
+        # melt should fail with an informative error message if
+        # the columns have a MultiIndex and a tuple is passed
+        # for id_vars or value_vars.
+        msg = r"(id|value)_vars must be a list of tuples when columns are a MultiIndex"
+        with pytest.raises(ValueError, match=msg):
+            df1.melt(id_vars=id_vars, value_vars=value_vars)
+
+    def test_custom_var_name(self, df, var_name):
+        result5 = df.melt(var_name=var_name)
+        assert result5.columns.tolist() == ["var", "value"]
+
+        result6 = df.melt(id_vars=["id1"], var_name=var_name)
+        assert result6.columns.tolist() == ["id1", "var", "value"]
+
+        result7 = df.melt(id_vars=["id1", "id2"], var_name=var_name)
+        assert result7.columns.tolist() == ["id1", "id2", "var", "value"]
+
+        result8 = df.melt(id_vars=["id1", "id2"], value_vars="A", var_name=var_name)
+        assert result8.columns.tolist() == ["id1", "id2", "var", "value"]
+
+        result9 = df.melt(
+            id_vars=["id1", "id2"], value_vars=["A", "B"], var_name=var_name
+        )
+        expected9 = DataFrame(
+            {
+                "id1": df["id1"].tolist() * 2,
+                "id2": df["id2"].tolist() * 2,
+                var_name: ["A"] * 10 + ["B"] * 10,
+                "value": (df["A"].tolist() + df["B"].tolist()),
+            },
+            columns=["id1", "id2", var_name, "value"],
+        )
+        tm.assert_frame_equal(result9, expected9)
+
+    def test_custom_value_name(self, df, value_name):
+        result10 = df.melt(value_name=value_name)
+        assert result10.columns.tolist() == ["variable", "val"]
+
+        result11 = df.melt(id_vars=["id1"], value_name=value_name)
+        assert result11.columns.tolist() == ["id1", "variable", "val"]
+
+        result12 = df.melt(id_vars=["id1", "id2"], value_name=value_name)
+        assert result12.columns.tolist() == ["id1", "id2", "variable", "val"]
+
+        result13 = df.melt(
+            id_vars=["id1", "id2"], value_vars="A", value_name=value_name
+        )
+        assert result13.columns.tolist() == ["id1", "id2", "variable", "val"]
+
+        result14 = df.melt(
+            id_vars=["id1", "id2"], value_vars=["A", "B"], value_name=value_name
+        )
+        expected14 = DataFrame(
+            {
+                "id1": df["id1"].tolist() * 2,
+                "id2": df["id2"].tolist() * 2,
+                "variable": ["A"] * 10 + ["B"] * 10,
+                value_name: (df["A"].tolist() + df["B"].tolist()),
+            },
+            columns=["id1", "id2", "variable", value_name],
+        )
+        tm.assert_frame_equal(result14, expected14)
+
+    def test_custom_var_and_value_name(self, df, value_name, var_name):
+        result15 = df.melt(var_name=var_name, value_name=value_name)
+        assert result15.columns.tolist() == ["var", "val"]
+
+        result16 = df.melt(id_vars=["id1"], var_name=var_name, value_name=value_name)
+        assert result16.columns.tolist() == ["id1", "var", "val"]
+
+        result17 = df.melt(
+            id_vars=["id1", "id2"], var_name=var_name, value_name=value_name
+        )
+        assert result17.columns.tolist() == ["id1", "id2", "var", "val"]
+
+        result18 = df.melt(
+            id_vars=["id1", "id2"],
+            value_vars="A",
+            var_name=var_name,
+            value_name=value_name,
+        )
+        assert result18.columns.tolist() == ["id1", "id2", "var", "val"]
+
+        result19 = df.melt(
+            id_vars=["id1", "id2"],
+            value_vars=["A", "B"],
+            var_name=var_name,
+            value_name=value_name,
+        )
+        expected19 = DataFrame(
+            {
+                "id1": df["id1"].tolist() * 2,
+                "id2": df["id2"].tolist() * 2,
+                var_name: ["A"] * 10 + ["B"] * 10,
+                value_name: (df["A"].tolist() + df["B"].tolist()),
+            },
+            columns=["id1", "id2", var_name, value_name],
+        )
+        tm.assert_frame_equal(result19, expected19)
+
+        df20 = df.copy()
+        df20.columns.name = "foo"
+        result20 = df20.melt()
+        assert result20.columns.tolist() == ["foo", "value"]
+
+    @pytest.mark.parametrize("col_level", [0, "CAP"])
+    def test_col_level(self, col_level, df1):
+        res = df1.melt(col_level=col_level)
+        assert res.columns.tolist() == ["CAP", "value"]
+
+    def test_multiindex(self, df1):
+        res = df1.melt()
+        assert res.columns.tolist() == ["CAP", "low", "value"]
+
+    @pytest.mark.parametrize(
+        "col",
+        [
+            pd.Series(date_range("2010", periods=5, tz="US/Pacific")),
+            pd.Series(["a", "b", "c", "a", "d"], dtype="category"),
+            pd.Series([0, 1, 0, 0, 0]),
+        ],
+    )
+    def test_pandas_dtypes(self, col):
+        # GH 15785
+        df = DataFrame(
+            {"klass": range(5), "col": col, "attr1": [1, 0, 0, 0, 0], "attr2": col}
+        )
+        expected_value = pd.concat([pd.Series([1, 0, 0, 0, 0]), col], ignore_index=True)
+        result = melt(
+            df, id_vars=["klass", "col"], var_name="attribute", value_name="value"
+        )
+        expected = DataFrame(
+            {
+                0: list(range(5)) * 2,
+                1: pd.concat([col] * 2, ignore_index=True),
+                2: ["attr1"] * 5 + ["attr2"] * 5,
+                3: expected_value,
+            }
+        )
+        expected.columns = ["klass", "col", "attribute", "value"]
+        tm.assert_frame_equal(result, expected)
+
+    def test_preserve_category(self):
+        # GH 15853
+        data = DataFrame({"A": [1, 2], "B": pd.Categorical(["X", "Y"])})
+        result = melt(data, ["B"], ["A"])
+        expected = DataFrame(
+            {"B": pd.Categorical(["X", "Y"]), "variable": ["A", "A"], "value": [1, 2]}
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_melt_missing_columns_raises(self):
+        # GH-23575
+        # This test is to ensure that pandas raises an error if melting is
+        # attempted with column names absent from the dataframe
+
+        # Generate data
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 4)), columns=list("abcd")
+        )
+
+        # Try to melt with missing `value_vars` column name
+        msg = "The following id_vars or value_vars are not present in the DataFrame:"
+        with pytest.raises(KeyError, match=msg):
+            df.melt(["a", "b"], ["C", "d"])
+
+        # Try to melt with missing `id_vars` column name
+        with pytest.raises(KeyError, match=msg):
+            df.melt(["A", "b"], ["c", "d"])
+
+        # Multiple missing
+        with pytest.raises(
+            KeyError,
+            match=msg,
+        ):
+            df.melt(["a", "b", "not_here", "or_there"], ["c", "d"])
+
+        # Multiindex melt fails if column is missing from multilevel melt
+        multi = df.copy()
+        multi.columns = [list("ABCD"), list("abcd")]
+        with pytest.raises(KeyError, match=msg):
+            multi.melt([("E", "a")], [("B", "b")])
+        # Multiindex fails if column is missing from single level melt
+        with pytest.raises(KeyError, match=msg):
+            multi.melt(["A"], ["F"], col_level=0)
+
+    def test_melt_mixed_int_str_id_vars(self):
+        # GH 29718
+        df = DataFrame({0: ["foo"], "a": ["bar"], "b": [1], "d": [2]})
+        result = melt(df, id_vars=[0, "a"], value_vars=["b", "d"])
+        expected = DataFrame(
+            {0: ["foo"] * 2, "a": ["bar"] * 2, "variable": list("bd"), "value": [1, 2]}
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_melt_mixed_int_str_value_vars(self):
+        # GH 29718
+        df = DataFrame({0: ["foo"], "a": ["bar"]})
+        result = melt(df, value_vars=[0, "a"])
+        expected = DataFrame({"variable": [0, "a"], "value": ["foo", "bar"]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_ignore_index(self):
+        # GH 17440
+        df = DataFrame({"foo": [0], "bar": [1]}, index=["first"])
+        result = melt(df, ignore_index=False)
+        expected = DataFrame(
+            {"variable": ["foo", "bar"], "value": [0, 1]}, index=["first", "first"]
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_ignore_multiindex(self):
+        # GH 17440
+        index = pd.MultiIndex.from_tuples(
+            [("first", "second"), ("first", "third")], names=["baz", "foobar"]
+        )
+        df = DataFrame({"foo": [0, 1], "bar": [2, 3]}, index=index)
+        result = melt(df, ignore_index=False)
+
+        expected_index = pd.MultiIndex.from_tuples(
+            [("first", "second"), ("first", "third")] * 2, names=["baz", "foobar"]
+        )
+        expected = DataFrame(
+            {"variable": ["foo"] * 2 + ["bar"] * 2, "value": [0, 1, 2, 3]},
+            index=expected_index,
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_ignore_index_name_and_type(self):
+        # GH 17440
+        index = Index(["foo", "bar"], dtype="category", name="baz")
+        df = DataFrame({"x": [0, 1], "y": [2, 3]}, index=index)
+        result = melt(df, ignore_index=False)
+
+        expected_index = Index(["foo", "bar"] * 2, dtype="category", name="baz")
+        expected = DataFrame(
+            {"variable": ["x", "x", "y", "y"], "value": [0, 1, 2, 3]},
+            index=expected_index,
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_melt_with_duplicate_columns(self):
+        # GH#41951
+        df = DataFrame([["id", 2, 3]], columns=["a", "b", "b"])
+        result = df.melt(id_vars=["a"], value_vars=["b"])
+        expected = DataFrame(
+            [["id", "b", 2], ["id", "b", 3]], columns=["a", "variable", "value"]
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", ["Int8", "Int64"])
+    def test_melt_ea_dtype(self, dtype):
+        # GH#41570
+        df = DataFrame(
+            {
+                "a": pd.Series([1, 2], dtype="Int8"),
+                "b": pd.Series([3, 4], dtype=dtype),
+            }
+        )
+        result = df.melt()
+        expected = DataFrame(
+            {
+                "variable": ["a", "a", "b", "b"],
+                "value": pd.Series([1, 2, 3, 4], dtype=dtype),
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_melt_ea_columns(self):
+        # GH 54297
+        df = DataFrame(
+            {
+                "A": {0: "a", 1: "b", 2: "c"},
+                "B": {0: 1, 1: 3, 2: 5},
+                "C": {0: 2, 1: 4, 2: 6},
+            }
+        )
+        df.columns = df.columns.astype("string[python]")
+        result = df.melt(id_vars=["A"], value_vars=["B"])
+        expected = DataFrame(
+            {
+                "A": list("abc"),
+                "variable": pd.Series(["B"] * 3, dtype="string[python]"),
+                "value": [1, 3, 5],
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_melt_preserves_datetime(self):
+        df = DataFrame(
+            data=[
+                {
+                    "type": "A0",
+                    "start_date": pd.Timestamp("2023/03/01", tz="Asia/Tokyo"),
+                    "end_date": pd.Timestamp("2023/03/10", tz="Asia/Tokyo"),
+                },
+                {
+                    "type": "A1",
+                    "start_date": pd.Timestamp("2023/03/01", tz="Asia/Tokyo"),
+                    "end_date": pd.Timestamp("2023/03/11", tz="Asia/Tokyo"),
+                },
+            ],
+            index=["aaaa", "bbbb"],
+        )
+        result = df.melt(
+            id_vars=["type"],
+            value_vars=["start_date", "end_date"],
+            var_name="start/end",
+            value_name="date",
+        )
+        expected = DataFrame(
+            {
+                "type": {0: "A0", 1: "A1", 2: "A0", 3: "A1"},
+                "start/end": {
+                    0: "start_date",
+                    1: "start_date",
+                    2: "end_date",
+                    3: "end_date",
+                },
+                "date": {
+                    0: pd.Timestamp("2023-03-01 00:00:00+0900", tz="Asia/Tokyo"),
+                    1: pd.Timestamp("2023-03-01 00:00:00+0900", tz="Asia/Tokyo"),
+                    2: pd.Timestamp("2023-03-10 00:00:00+0900", tz="Asia/Tokyo"),
+                    3: pd.Timestamp("2023-03-11 00:00:00+0900", tz="Asia/Tokyo"),
+                },
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_melt_allows_non_scalar_id_vars(self):
+        df = DataFrame(
+            data={"a": [1, 2, 3], "b": [4, 5, 6]},
+            index=["11", "22", "33"],
+        )
+        result = df.melt(
+            id_vars="a",
+            var_name=0,
+            value_name=1,
+        )
+        expected = DataFrame({"a": [1, 2, 3], 0: ["b"] * 3, 1: [4, 5, 6]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_melt_allows_non_string_var_name(self):
+        df = DataFrame(
+            data={"a": [1, 2, 3], "b": [4, 5, 6]},
+            index=["11", "22", "33"],
+        )
+        result = df.melt(
+            id_vars=["a"],
+            var_name=0,
+            value_name=1,
+        )
+        expected = DataFrame({"a": [1, 2, 3], 0: ["b"] * 3, 1: [4, 5, 6]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_melt_non_scalar_var_name_raises(self):
+        df = DataFrame(
+            data={"a": [1, 2, 3], "b": [4, 5, 6]},
+            index=["11", "22", "33"],
+        )
+        with pytest.raises(ValueError, match=r".* must be a scalar."):
+            df.melt(id_vars=["a"], var_name=[1, 2])
+
+
+class TestLreshape:
+    def test_pairs(self):
+        data = {
+            "birthdt": [
+                "08jan2009",
+                "20dec2008",
+                "30dec2008",
+                "21dec2008",
+                "11jan2009",
+            ],
+            "birthwt": [1766, 3301, 1454, 3139, 4133],
+            "id": [101, 102, 103, 104, 105],
+            "sex": ["Male", "Female", "Female", "Female", "Female"],
+            "visitdt1": [
+                "11jan2009",
+                "22dec2008",
+                "04jan2009",
+                "29dec2008",
+                "20jan2009",
+            ],
+            "visitdt2": ["21jan2009", np.nan, "22jan2009", "31dec2008", "03feb2009"],
+            "visitdt3": ["05feb2009", np.nan, np.nan, "02jan2009", "15feb2009"],
+            "wt1": [1823, 3338, 1549, 3298, 4306],
+            "wt2": [2011.0, np.nan, 1892.0, 3338.0, 4575.0],
+            "wt3": [2293.0, np.nan, np.nan, 3377.0, 4805.0],
+        }
+
+        df = DataFrame(data)
+
+        spec = {
+            "visitdt": [f"visitdt{i:d}" for i in range(1, 4)],
+            "wt": [f"wt{i:d}" for i in range(1, 4)],
+        }
+        result = lreshape(df, spec)
+
+        exp_data = {
+            "birthdt": [
+                "08jan2009",
+                "20dec2008",
+                "30dec2008",
+                "21dec2008",
+                "11jan2009",
+                "08jan2009",
+                "30dec2008",
+                "21dec2008",
+                "11jan2009",
+                "08jan2009",
+                "21dec2008",
+                "11jan2009",
+            ],
+            "birthwt": [
+                1766,
+                3301,
+                1454,
+                3139,
+                4133,
+                1766,
+                1454,
+                3139,
+                4133,
+                1766,
+                3139,
+                4133,
+            ],
+            "id": [101, 102, 103, 104, 105, 101, 103, 104, 105, 101, 104, 105],
+            "sex": [
+                "Male",
+                "Female",
+                "Female",
+                "Female",
+                "Female",
+                "Male",
+                "Female",
+                "Female",
+                "Female",
+                "Male",
+                "Female",
+                "Female",
+            ],
+            "visitdt": [
+                "11jan2009",
+                "22dec2008",
+                "04jan2009",
+                "29dec2008",
+                "20jan2009",
+                "21jan2009",
+                "22jan2009",
+                "31dec2008",
+                "03feb2009",
+                "05feb2009",
+                "02jan2009",
+                "15feb2009",
+            ],
+            "wt": [
+                1823.0,
+                3338.0,
+                1549.0,
+                3298.0,
+                4306.0,
+                2011.0,
+                1892.0,
+                3338.0,
+                4575.0,
+                2293.0,
+                3377.0,
+                4805.0,
+            ],
+        }
+        exp = DataFrame(exp_data, columns=result.columns)
+        tm.assert_frame_equal(result, exp)
+
+        result = lreshape(df, spec, dropna=False)
+        exp_data = {
+            "birthdt": [
+                "08jan2009",
+                "20dec2008",
+                "30dec2008",
+                "21dec2008",
+                "11jan2009",
+                "08jan2009",
+                "20dec2008",
+                "30dec2008",
+                "21dec2008",
+                "11jan2009",
+                "08jan2009",
+                "20dec2008",
+                "30dec2008",
+                "21dec2008",
+                "11jan2009",
+            ],
+            "birthwt": [
+                1766,
+                3301,
+                1454,
+                3139,
+                4133,
+                1766,
+                3301,
+                1454,
+                3139,
+                4133,
+                1766,
+                3301,
+                1454,
+                3139,
+                4133,
+            ],
+            "id": [
+                101,
+                102,
+                103,
+                104,
+                105,
+                101,
+                102,
+                103,
+                104,
+                105,
+                101,
+                102,
+                103,
+                104,
+                105,
+            ],
+            "sex": [
+                "Male",
+                "Female",
+                "Female",
+                "Female",
+                "Female",
+                "Male",
+                "Female",
+                "Female",
+                "Female",
+                "Female",
+                "Male",
+                "Female",
+                "Female",
+                "Female",
+                "Female",
+            ],
+            "visitdt": [
+                "11jan2009",
+                "22dec2008",
+                "04jan2009",
+                "29dec2008",
+                "20jan2009",
+                "21jan2009",
+                np.nan,
+                "22jan2009",
+                "31dec2008",
+                "03feb2009",
+                "05feb2009",
+                np.nan,
+                np.nan,
+                "02jan2009",
+                "15feb2009",
+            ],
+            "wt": [
+                1823.0,
+                3338.0,
+                1549.0,
+                3298.0,
+                4306.0,
+                2011.0,
+                np.nan,
+                1892.0,
+                3338.0,
+                4575.0,
+                2293.0,
+                np.nan,
+                np.nan,
+                3377.0,
+                4805.0,
+            ],
+        }
+        exp = DataFrame(exp_data, columns=result.columns)
+        tm.assert_frame_equal(result, exp)
+
+        spec = {
+            "visitdt": [f"visitdt{i:d}" for i in range(1, 3)],
+            "wt": [f"wt{i:d}" for i in range(1, 4)],
+        }
+        msg = "All column lists must be same length"
+        with pytest.raises(ValueError, match=msg):
+            lreshape(df, spec)
+
+
+class TestWideToLong:
+    def test_simple(self):
+        x = np.random.default_rng(2).standard_normal(3)
+        df = DataFrame(
+            {
+                "A1970": {0: "a", 1: "b", 2: "c"},
+                "A1980": {0: "d", 1: "e", 2: "f"},
+                "B1970": {0: 2.5, 1: 1.2, 2: 0.7},
+                "B1980": {0: 3.2, 1: 1.3, 2: 0.1},
+                "X": dict(zip(range(3), x)),
+            }
+        )
+        df["id"] = df.index
+        exp_data = {
+            "X": x.tolist() + x.tolist(),
+            "A": ["a", "b", "c", "d", "e", "f"],
+            "B": [2.5, 1.2, 0.7, 3.2, 1.3, 0.1],
+            "year": [1970, 1970, 1970, 1980, 1980, 1980],
+            "id": [0, 1, 2, 0, 1, 2],
+        }
+        expected = DataFrame(exp_data)
+        expected = expected.set_index(["id", "year"])[["X", "A", "B"]]
+        result = wide_to_long(df, ["A", "B"], i="id", j="year")
+        tm.assert_frame_equal(result, expected)
+
+    def test_stubs(self):
+        # GH9204 wide_to_long call should not modify 'stubs' list
+        df = DataFrame([[0, 1, 2, 3, 8], [4, 5, 6, 7, 9]])
+        df.columns = ["id", "inc1", "inc2", "edu1", "edu2"]
+        stubs = ["inc", "edu"]
+
+        wide_to_long(df, stubs, i="id", j="age")
+
+        assert stubs == ["inc", "edu"]
+
+    def test_separating_character(self):
+        # GH14779
+
+        x = np.random.default_rng(2).standard_normal(3)
+        df = DataFrame(
+            {
+                "A.1970": {0: "a", 1: "b", 2: "c"},
+                "A.1980": {0: "d", 1: "e", 2: "f"},
+                "B.1970": {0: 2.5, 1: 1.2, 2: 0.7},
+                "B.1980": {0: 3.2, 1: 1.3, 2: 0.1},
+                "X": dict(zip(range(3), x)),
+            }
+        )
+        df["id"] = df.index
+        exp_data = {
+            "X": x.tolist() + x.tolist(),
+            "A": ["a", "b", "c", "d", "e", "f"],
+            "B": [2.5, 1.2, 0.7, 3.2, 1.3, 0.1],
+            "year": [1970, 1970, 1970, 1980, 1980, 1980],
+            "id": [0, 1, 2, 0, 1, 2],
+        }
+        expected = DataFrame(exp_data)
+        expected = expected.set_index(["id", "year"])[["X", "A", "B"]]
+        result = wide_to_long(df, ["A", "B"], i="id", j="year", sep=".")
+        tm.assert_frame_equal(result, expected)
+
+    def test_escapable_characters(self):
+        x = np.random.default_rng(2).standard_normal(3)
+        df = DataFrame(
+            {
+                "A(quarterly)1970": {0: "a", 1: "b", 2: "c"},
+                "A(quarterly)1980": {0: "d", 1: "e", 2: "f"},
+                "B(quarterly)1970": {0: 2.5, 1: 1.2, 2: 0.7},
+                "B(quarterly)1980": {0: 3.2, 1: 1.3, 2: 0.1},
+                "X": dict(zip(range(3), x)),
+            }
+        )
+        df["id"] = df.index
+        exp_data = {
+            "X": x.tolist() + x.tolist(),
+            "A(quarterly)": ["a", "b", "c", "d", "e", "f"],
+            "B(quarterly)": [2.5, 1.2, 0.7, 3.2, 1.3, 0.1],
+            "year": [1970, 1970, 1970, 1980, 1980, 1980],
+            "id": [0, 1, 2, 0, 1, 2],
+        }
+        expected = DataFrame(exp_data)
+        expected = expected.set_index(["id", "year"])[
+            ["X", "A(quarterly)", "B(quarterly)"]
+        ]
+        result = wide_to_long(df, ["A(quarterly)", "B(quarterly)"], i="id", j="year")
+        tm.assert_frame_equal(result, expected)
+
+    def test_unbalanced(self):
+        # test that we can have a varying amount of time variables
+        df = DataFrame(
+            {
+                "A2010": [1.0, 2.0],
+                "A2011": [3.0, 4.0],
+                "B2010": [5.0, 6.0],
+                "X": ["X1", "X2"],
+            }
+        )
+        df["id"] = df.index
+        exp_data = {
+            "X": ["X1", "X2", "X1", "X2"],
+            "A": [1.0, 2.0, 3.0, 4.0],
+            "B": [5.0, 6.0, np.nan, np.nan],
+            "id": [0, 1, 0, 1],
+            "year": [2010, 2010, 2011, 2011],
+        }
+        expected = DataFrame(exp_data)
+        expected = expected.set_index(["id", "year"])[["X", "A", "B"]]
+        result = wide_to_long(df, ["A", "B"], i="id", j="year")
+        tm.assert_frame_equal(result, expected)
+
+    def test_character_overlap(self):
+        # Test we handle overlapping characters in both id_vars and value_vars
+        df = DataFrame(
+            {
+                "A11": ["a11", "a22", "a33"],
+                "A12": ["a21", "a22", "a23"],
+                "B11": ["b11", "b12", "b13"],
+                "B12": ["b21", "b22", "b23"],
+                "BB11": [1, 2, 3],
+                "BB12": [4, 5, 6],
+                "BBBX": [91, 92, 93],
+                "BBBZ": [91, 92, 93],
+            }
+        )
+        df["id"] = df.index
+        expected = DataFrame(
+            {
+                "BBBX": [91, 92, 93, 91, 92, 93],
+                "BBBZ": [91, 92, 93, 91, 92, 93],
+                "A": ["a11", "a22", "a33", "a21", "a22", "a23"],
+                "B": ["b11", "b12", "b13", "b21", "b22", "b23"],
+                "BB": [1, 2, 3, 4, 5, 6],
+                "id": [0, 1, 2, 0, 1, 2],
+                "year": [11, 11, 11, 12, 12, 12],
+            }
+        )
+        expected = expected.set_index(["id", "year"])[["BBBX", "BBBZ", "A", "B", "BB"]]
+        result = wide_to_long(df, ["A", "B", "BB"], i="id", j="year")
+        tm.assert_frame_equal(result.sort_index(axis=1), expected.sort_index(axis=1))
+
+    def test_invalid_separator(self):
+        # if an invalid separator is supplied a empty data frame is returned
+        sep = "nope!"
+        df = DataFrame(
+            {
+                "A2010": [1.0, 2.0],
+                "A2011": [3.0, 4.0],
+                "B2010": [5.0, 6.0],
+                "X": ["X1", "X2"],
+            }
+        )
+        df["id"] = df.index
+        exp_data = {
+            "X": "",
+            "A2010": [],
+            "A2011": [],
+            "B2010": [],
+            "id": [],
+            "year": [],
+            "A": [],
+            "B": [],
+        }
+        expected = DataFrame(exp_data).astype({"year": np.int64})
+        expected = expected.set_index(["id", "year"])[
+            ["X", "A2010", "A2011", "B2010", "A", "B"]
+        ]
+        expected.index = expected.index.set_levels([0, 1], level=0)
+        result = wide_to_long(df, ["A", "B"], i="id", j="year", sep=sep)
+        tm.assert_frame_equal(result.sort_index(axis=1), expected.sort_index(axis=1))
+
+    def test_num_string_disambiguation(self):
+        # Test that we can disambiguate number value_vars from
+        # string value_vars
+        df = DataFrame(
+            {
+                "A11": ["a11", "a22", "a33"],
+                "A12": ["a21", "a22", "a23"],
+                "B11": ["b11", "b12", "b13"],
+                "B12": ["b21", "b22", "b23"],
+                "BB11": [1, 2, 3],
+                "BB12": [4, 5, 6],
+                "Arating": [91, 92, 93],
+                "Arating_old": [91, 92, 93],
+            }
+        )
+        df["id"] = df.index
+        expected = DataFrame(
+            {
+                "Arating": [91, 92, 93, 91, 92, 93],
+                "Arating_old": [91, 92, 93, 91, 92, 93],
+                "A": ["a11", "a22", "a33", "a21", "a22", "a23"],
+                "B": ["b11", "b12", "b13", "b21", "b22", "b23"],
+                "BB": [1, 2, 3, 4, 5, 6],
+                "id": [0, 1, 2, 0, 1, 2],
+                "year": [11, 11, 11, 12, 12, 12],
+            }
+        )
+        expected = expected.set_index(["id", "year"])[
+            ["Arating", "Arating_old", "A", "B", "BB"]
+        ]
+        result = wide_to_long(df, ["A", "B", "BB"], i="id", j="year")
+        tm.assert_frame_equal(result.sort_index(axis=1), expected.sort_index(axis=1))
+
+    def test_invalid_suffixtype(self):
+        # If all stubs names end with a string, but a numeric suffix is
+        # assumed,  an empty data frame is returned
+        df = DataFrame(
+            {
+                "Aone": [1.0, 2.0],
+                "Atwo": [3.0, 4.0],
+                "Bone": [5.0, 6.0],
+                "X": ["X1", "X2"],
+            }
+        )
+        df["id"] = df.index
+        exp_data = {
+            "X": "",
+            "Aone": [],
+            "Atwo": [],
+            "Bone": [],
+            "id": [],
+            "year": [],
+            "A": [],
+            "B": [],
+        }
+        expected = DataFrame(exp_data).astype({"year": np.int64})
+
+        expected = expected.set_index(["id", "year"])
+        expected.index = expected.index.set_levels([0, 1], level=0)
+        result = wide_to_long(df, ["A", "B"], i="id", j="year")
+        tm.assert_frame_equal(result.sort_index(axis=1), expected.sort_index(axis=1))
+
+    def test_multiple_id_columns(self):
+        # Taken from http://www.ats.ucla.edu/stat/stata/modules/reshapel.htm
+        df = DataFrame(
+            {
+                "famid": [1, 1, 1, 2, 2, 2, 3, 3, 3],
+                "birth": [1, 2, 3, 1, 2, 3, 1, 2, 3],
+                "ht1": [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
+                "ht2": [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9],
+            }
+        )
+        expected = DataFrame(
+            {
+                "ht": [
+                    2.8,
+                    3.4,
+                    2.9,
+                    3.8,
+                    2.2,
+                    2.9,
+                    2.0,
+                    3.2,
+                    1.8,
+                    2.8,
+                    1.9,
+                    2.4,
+                    2.2,
+                    3.3,
+                    2.3,
+                    3.4,
+                    2.1,
+                    2.9,
+                ],
+                "famid": [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3],
+                "birth": [1, 1, 2, 2, 3, 3, 1, 1, 2, 2, 3, 3, 1, 1, 2, 2, 3, 3],
+                "age": [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2],
+            }
+        )
+        expected = expected.set_index(["famid", "birth", "age"])[["ht"]]
+        result = wide_to_long(df, "ht", i=["famid", "birth"], j="age")
+        tm.assert_frame_equal(result, expected)
+
+    def test_non_unique_idvars(self):
+        # GH16382
+        # Raise an error message if non unique id vars (i) are passed
+        df = DataFrame(
+            {"A_A1": [1, 2, 3, 4, 5], "B_B1": [1, 2, 3, 4, 5], "x": [1, 1, 1, 1, 1]}
+        )
+        msg = "the id variables need to uniquely identify each row"
+        with pytest.raises(ValueError, match=msg):
+            wide_to_long(df, ["A_A", "B_B"], i="x", j="colname")
+
+    def test_cast_j_int(self):
+        df = DataFrame(
+            {
+                "actor_1": ["CCH Pounder", "Johnny Depp", "Christoph Waltz"],
+                "actor_2": ["Joel David Moore", "Orlando Bloom", "Rory Kinnear"],
+                "actor_fb_likes_1": [1000.0, 40000.0, 11000.0],
+                "actor_fb_likes_2": [936.0, 5000.0, 393.0],
+                "title": ["Avatar", "Pirates of the Caribbean", "Spectre"],
+            }
+        )
+
+        expected = DataFrame(
+            {
+                "actor": [
+                    "CCH Pounder",
+                    "Johnny Depp",
+                    "Christoph Waltz",
+                    "Joel David Moore",
+                    "Orlando Bloom",
+                    "Rory Kinnear",
+                ],
+                "actor_fb_likes": [1000.0, 40000.0, 11000.0, 936.0, 5000.0, 393.0],
+                "num": [1, 1, 1, 2, 2, 2],
+                "title": [
+                    "Avatar",
+                    "Pirates of the Caribbean",
+                    "Spectre",
+                    "Avatar",
+                    "Pirates of the Caribbean",
+                    "Spectre",
+                ],
+            }
+        ).set_index(["title", "num"])
+        result = wide_to_long(
+            df, ["actor", "actor_fb_likes"], i="title", j="num", sep="_"
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_identical_stubnames(self):
+        df = DataFrame(
+            {
+                "A2010": [1.0, 2.0],
+                "A2011": [3.0, 4.0],
+                "B2010": [5.0, 6.0],
+                "A": ["X1", "X2"],
+            }
+        )
+        msg = "stubname can't be identical to a column name"
+        with pytest.raises(ValueError, match=msg):
+            wide_to_long(df, ["A", "B"], i="A", j="colname")
+
+    def test_nonnumeric_suffix(self):
+        df = DataFrame(
+            {
+                "treatment_placebo": [1.0, 2.0],
+                "treatment_test": [3.0, 4.0],
+                "result_placebo": [5.0, 6.0],
+                "A": ["X1", "X2"],
+            }
+        )
+        expected = DataFrame(
+            {
+                "A": ["X1", "X2", "X1", "X2"],
+                "colname": ["placebo", "placebo", "test", "test"],
+                "result": [5.0, 6.0, np.nan, np.nan],
+                "treatment": [1.0, 2.0, 3.0, 4.0],
+            }
+        )
+        expected = expected.set_index(["A", "colname"])
+        result = wide_to_long(
+            df, ["result", "treatment"], i="A", j="colname", suffix="[a-z]+", sep="_"
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_mixed_type_suffix(self):
+        df = DataFrame(
+            {
+                "A": ["X1", "X2"],
+                "result_1": [0, 9],
+                "result_foo": [5.0, 6.0],
+                "treatment_1": [1.0, 2.0],
+                "treatment_foo": [3.0, 4.0],
+            }
+        )
+        expected = DataFrame(
+            {
+                "A": ["X1", "X2", "X1", "X2"],
+                "colname": ["1", "1", "foo", "foo"],
+                "result": [0.0, 9.0, 5.0, 6.0],
+                "treatment": [1.0, 2.0, 3.0, 4.0],
+            }
+        ).set_index(["A", "colname"])
+        result = wide_to_long(
+            df, ["result", "treatment"], i="A", j="colname", suffix=".+", sep="_"
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_float_suffix(self):
+        df = DataFrame(
+            {
+                "treatment_1.1": [1.0, 2.0],
+                "treatment_2.1": [3.0, 4.0],
+                "result_1.2": [5.0, 6.0],
+                "result_1": [0, 9],
+                "A": ["X1", "X2"],
+            }
+        )
+        expected = DataFrame(
+            {
+                "A": ["X1", "X2", "X1", "X2", "X1", "X2", "X1", "X2"],
+                "colname": [1.2, 1.2, 1.0, 1.0, 1.1, 1.1, 2.1, 2.1],
+                "result": [5.0, 6.0, 0.0, 9.0, np.nan, np.nan, np.nan, np.nan],
+                "treatment": [np.nan, np.nan, np.nan, np.nan, 1.0, 2.0, 3.0, 4.0],
+            }
+        )
+        expected = expected.set_index(["A", "colname"])
+        result = wide_to_long(
+            df, ["result", "treatment"], i="A", j="colname", suffix="[0-9.]+", sep="_"
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_col_substring_of_stubname(self):
+        # GH22468
+        # Don't raise ValueError when a column name is a substring
+        # of a stubname that's been passed as a string
+        wide_data = {
+            "node_id": {0: 0, 1: 1, 2: 2, 3: 3, 4: 4},
+            "A": {0: 0.80, 1: 0.0, 2: 0.25, 3: 1.0, 4: 0.81},
+            "PA0": {0: 0.74, 1: 0.56, 2: 0.56, 3: 0.98, 4: 0.6},
+            "PA1": {0: 0.77, 1: 0.64, 2: 0.52, 3: 0.98, 4: 0.67},
+            "PA3": {0: 0.34, 1: 0.70, 2: 0.52, 3: 0.98, 4: 0.67},
+        }
+        wide_df = DataFrame.from_dict(wide_data)
+        expected = wide_to_long(wide_df, stubnames=["PA"], i=["node_id", "A"], j="time")
+        result = wide_to_long(wide_df, stubnames="PA", i=["node_id", "A"], j="time")
+        tm.assert_frame_equal(result, expected)
+
+    def test_raise_of_column_name_value(self):
+        # GH34731, enforced in 2.0
+        # raise a ValueError if the resultant value column name matches
+        # a name in the dataframe already (default name is "value")
+        df = DataFrame({"col": list("ABC"), "value": range(10, 16, 2)})
+
+        with pytest.raises(
+            ValueError, match=re.escape("value_name (value) cannot match")
+        ):
+            df.melt(id_vars="value", value_name="value")
+
+    @pytest.mark.parametrize("dtype", ["O", "string"])
+    def test_missing_stubname(self, dtype):
+        # GH46044
+        df = DataFrame({"id": ["1", "2"], "a-1": [100, 200], "a-2": [300, 400]})
+        df = df.astype({"id": dtype})
+        result = wide_to_long(
+            df,
+            stubnames=["a", "b"],
+            i="id",
+            j="num",
+            sep="-",
+        )
+        index = Index(
+            [("1", 1), ("2", 1), ("1", 2), ("2", 2)],
+            name=("id", "num"),
+        )
+        expected = DataFrame(
+            {"a": [100, 200, 300, 400], "b": [np.nan] * 4},
+            index=index,
+        )
+        new_level = expected.index.levels[0].astype(dtype)
+        expected.index = expected.index.set_levels(new_level, level=0)
+        tm.assert_frame_equal(result, expected)
+
+
+def test_wide_to_long_pyarrow_string_columns():
+    # GH 57066
+    pytest.importorskip("pyarrow")
+    df = DataFrame(
+        {
+            "ID": {0: 1},
+            "R_test1": {0: 1},
+            "R_test2": {0: 1},
+            "R_test3": {0: 2},
+            "D": {0: 1},
+        }
+    )
+    df.columns = df.columns.astype("string[pyarrow_numpy]")
+    result = wide_to_long(
+        df, stubnames="R", i="ID", j="UNPIVOTED", sep="_", suffix=".*"
+    )
+    expected = DataFrame(
+        [[1, 1], [1, 1], [1, 2]],
+        columns=Index(["D", "R"], dtype=object),
+        index=pd.MultiIndex.from_arrays(
+            [
+                [1, 1, 1],
+                Index(["test1", "test2", "test3"], dtype="string[pyarrow_numpy]"),
+            ],
+            names=["ID", "UNPIVOTED"],
+        ),
+    )
+    tm.assert_frame_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/test_pivot.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/test_pivot.py
new file mode 100644
index 0000000000000000000000000000000000000000..18a449b4d0c67b55f24aa590229dddb0ed456054
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/test_pivot.py
@@ -0,0 +1,2714 @@
+from datetime import (
+    date,
+    datetime,
+    timedelta,
+)
+from itertools import product
+import re
+
+import numpy as np
+import pytest
+
+from pandas._config import using_pyarrow_string_dtype
+
+from pandas.errors import PerformanceWarning
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    Grouper,
+    Index,
+    MultiIndex,
+    Series,
+    concat,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.api.types import CategoricalDtype
+from pandas.core.reshape import reshape as reshape_lib
+from pandas.core.reshape.pivot import pivot_table
+
+
+@pytest.fixture(params=[True, False])
+def dropna(request):
+    return request.param
+
+
+@pytest.fixture(params=[([0] * 4, [1] * 4), (range(3), range(1, 4))])
+def interval_values(request, closed):
+    left, right = request.param
+    return Categorical(pd.IntervalIndex.from_arrays(left, right, closed))
+
+
+class TestPivotTable:
+    @pytest.fixture
+    def data(self):
+        return DataFrame(
+            {
+                "A": [
+                    "foo",
+                    "foo",
+                    "foo",
+                    "foo",
+                    "bar",
+                    "bar",
+                    "bar",
+                    "bar",
+                    "foo",
+                    "foo",
+                    "foo",
+                ],
+                "B": [
+                    "one",
+                    "one",
+                    "one",
+                    "two",
+                    "one",
+                    "one",
+                    "one",
+                    "two",
+                    "two",
+                    "two",
+                    "one",
+                ],
+                "C": [
+                    "dull",
+                    "dull",
+                    "shiny",
+                    "dull",
+                    "dull",
+                    "shiny",
+                    "shiny",
+                    "dull",
+                    "shiny",
+                    "shiny",
+                    "shiny",
+                ],
+                "D": np.random.default_rng(2).standard_normal(11),
+                "E": np.random.default_rng(2).standard_normal(11),
+                "F": np.random.default_rng(2).standard_normal(11),
+            }
+        )
+
+    def test_pivot_table(self, observed, data):
+        index = ["A", "B"]
+        columns = "C"
+        table = pivot_table(
+            data, values="D", index=index, columns=columns, observed=observed
+        )
+
+        table2 = data.pivot_table(
+            values="D", index=index, columns=columns, observed=observed
+        )
+        tm.assert_frame_equal(table, table2)
+
+        # this works
+        pivot_table(data, values="D", index=index, observed=observed)
+
+        if len(index) > 1:
+            assert table.index.names == tuple(index)
+        else:
+            assert table.index.name == index[0]
+
+        if len(columns) > 1:
+            assert table.columns.names == columns
+        else:
+            assert table.columns.name == columns[0]
+
+        expected = data.groupby(index + [columns])["D"].agg("mean").unstack()
+        tm.assert_frame_equal(table, expected)
+
+    def test_pivot_table_categorical_observed_equal(self, observed):
+        # issue #24923
+        df = DataFrame(
+            {"col1": list("abcde"), "col2": list("fghij"), "col3": [1, 2, 3, 4, 5]}
+        )
+
+        expected = df.pivot_table(
+            index="col1", values="col3", columns="col2", aggfunc="sum", fill_value=0
+        )
+
+        expected.index = expected.index.astype("category")
+        expected.columns = expected.columns.astype("category")
+
+        df.col1 = df.col1.astype("category")
+        df.col2 = df.col2.astype("category")
+
+        result = df.pivot_table(
+            index="col1",
+            values="col3",
+            columns="col2",
+            aggfunc="sum",
+            fill_value=0,
+            observed=observed,
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_table_nocols(self):
+        df = DataFrame(
+            {"rows": ["a", "b", "c"], "cols": ["x", "y", "z"], "values": [1, 2, 3]}
+        )
+        rs = df.pivot_table(columns="cols", aggfunc="sum")
+        xp = df.pivot_table(index="cols", aggfunc="sum").T
+        tm.assert_frame_equal(rs, xp)
+
+        rs = df.pivot_table(columns="cols", aggfunc={"values": "mean"})
+        xp = df.pivot_table(index="cols", aggfunc={"values": "mean"}).T
+        tm.assert_frame_equal(rs, xp)
+
+    def test_pivot_table_dropna(self):
+        df = DataFrame(
+            {
+                "amount": {0: 60000, 1: 100000, 2: 50000, 3: 30000},
+                "customer": {0: "A", 1: "A", 2: "B", 3: "C"},
+                "month": {0: 201307, 1: 201309, 2: 201308, 3: 201310},
+                "product": {0: "a", 1: "b", 2: "c", 3: "d"},
+                "quantity": {0: 2000000, 1: 500000, 2: 1000000, 3: 1000000},
+            }
+        )
+        pv_col = df.pivot_table(
+            "quantity", "month", ["customer", "product"], dropna=False
+        )
+        pv_ind = df.pivot_table(
+            "quantity", ["customer", "product"], "month", dropna=False
+        )
+
+        m = MultiIndex.from_tuples(
+            [
+                ("A", "a"),
+                ("A", "b"),
+                ("A", "c"),
+                ("A", "d"),
+                ("B", "a"),
+                ("B", "b"),
+                ("B", "c"),
+                ("B", "d"),
+                ("C", "a"),
+                ("C", "b"),
+                ("C", "c"),
+                ("C", "d"),
+            ],
+            names=["customer", "product"],
+        )
+        tm.assert_index_equal(pv_col.columns, m)
+        tm.assert_index_equal(pv_ind.index, m)
+
+    def test_pivot_table_categorical(self):
+        cat1 = Categorical(
+            ["a", "a", "b", "b"], categories=["a", "b", "z"], ordered=True
+        )
+        cat2 = Categorical(
+            ["c", "d", "c", "d"], categories=["c", "d", "y"], ordered=True
+        )
+        df = DataFrame({"A": cat1, "B": cat2, "values": [1, 2, 3, 4]})
+        msg = "The default value of observed=False is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = pivot_table(df, values="values", index=["A", "B"], dropna=True)
+
+        exp_index = MultiIndex.from_arrays([cat1, cat2], names=["A", "B"])
+        expected = DataFrame({"values": [1.0, 2.0, 3.0, 4.0]}, index=exp_index)
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_table_dropna_categoricals(self, dropna):
+        # GH 15193
+        categories = ["a", "b", "c", "d"]
+
+        df = DataFrame(
+            {
+                "A": ["a", "a", "a", "b", "b", "b", "c", "c", "c"],
+                "B": [1, 2, 3, 1, 2, 3, 1, 2, 3],
+                "C": range(9),
+            }
+        )
+
+        df["A"] = df["A"].astype(CategoricalDtype(categories, ordered=False))
+        msg = "The default value of observed=False is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = df.pivot_table(index="B", columns="A", values="C", dropna=dropna)
+        expected_columns = Series(["a", "b", "c"], name="A")
+        expected_columns = expected_columns.astype(
+            CategoricalDtype(categories, ordered=False)
+        )
+        expected_index = Series([1, 2, 3], name="B")
+        expected = DataFrame(
+            [[0.0, 3.0, 6.0], [1.0, 4.0, 7.0], [2.0, 5.0, 8.0]],
+            index=expected_index,
+            columns=expected_columns,
+        )
+        if not dropna:
+            # add back the non observed to compare
+            expected = expected.reindex(columns=Categorical(categories)).astype("float")
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_with_non_observable_dropna(self, dropna):
+        # gh-21133
+        df = DataFrame(
+            {
+                "A": Categorical(
+                    [np.nan, "low", "high", "low", "high"],
+                    categories=["low", "high"],
+                    ordered=True,
+                ),
+                "B": [0.0, 1.0, 2.0, 3.0, 4.0],
+            }
+        )
+
+        msg = "The default value of observed=False is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = df.pivot_table(index="A", values="B", dropna=dropna)
+        if dropna:
+            values = [2.0, 3.0]
+            codes = [0, 1]
+        else:
+            # GH: 10772
+            values = [2.0, 3.0, 0.0]
+            codes = [0, 1, -1]
+        expected = DataFrame(
+            {"B": values},
+            index=Index(
+                Categorical.from_codes(
+                    codes, categories=["low", "high"], ordered=dropna
+                ),
+                name="A",
+            ),
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_with_non_observable_dropna_multi_cat(self, dropna):
+        # gh-21378
+        df = DataFrame(
+            {
+                "A": Categorical(
+                    ["left", "low", "high", "low", "high"],
+                    categories=["low", "high", "left"],
+                    ordered=True,
+                ),
+                "B": range(5),
+            }
+        )
+
+        msg = "The default value of observed=False is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = df.pivot_table(index="A", values="B", dropna=dropna)
+        expected = DataFrame(
+            {"B": [2.0, 3.0, 0.0]},
+            index=Index(
+                Categorical.from_codes(
+                    [0, 1, 2], categories=["low", "high", "left"], ordered=True
+                ),
+                name="A",
+            ),
+        )
+        if not dropna:
+            expected["B"] = expected["B"].astype(float)
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_with_interval_index(self, interval_values, dropna):
+        # GH 25814
+        df = DataFrame({"A": interval_values, "B": 1})
+
+        msg = "The default value of observed=False is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = df.pivot_table(index="A", values="B", dropna=dropna)
+        expected = DataFrame(
+            {"B": 1.0}, index=Index(interval_values.unique(), name="A")
+        )
+        if not dropna:
+            expected = expected.astype(float)
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_with_interval_index_margins(self):
+        # GH 25815
+        ordered_cat = pd.IntervalIndex.from_arrays([0, 0, 1, 1], [1, 1, 2, 2])
+        df = DataFrame(
+            {
+                "A": np.arange(4, 0, -1, dtype=np.intp),
+                "B": ["a", "b", "a", "b"],
+                "C": Categorical(ordered_cat, ordered=True).sort_values(
+                    ascending=False
+                ),
+            }
+        )
+
+        msg = "The default value of observed=False is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            pivot_tab = pivot_table(
+                df, index="C", columns="B", values="A", aggfunc="sum", margins=True
+            )
+
+        result = pivot_tab["All"]
+        expected = Series(
+            [3, 7, 10],
+            index=Index([pd.Interval(0, 1), pd.Interval(1, 2), "All"], name="C"),
+            name="All",
+            dtype=np.intp,
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_pass_array(self, data):
+        result = data.pivot_table("D", index=data.A, columns=data.C)
+        expected = data.pivot_table("D", index="A", columns="C")
+        tm.assert_frame_equal(result, expected)
+
+    def test_pass_function(self, data):
+        result = data.pivot_table("D", index=lambda x: x // 5, columns=data.C)
+        expected = data.pivot_table("D", index=data.index // 5, columns="C")
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_table_multiple(self, data):
+        index = ["A", "B"]
+        columns = "C"
+        table = pivot_table(data, index=index, columns=columns)
+        expected = data.groupby(index + [columns]).agg("mean").unstack()
+        tm.assert_frame_equal(table, expected)
+
+    def test_pivot_dtypes(self):
+        # can convert dtypes
+        f = DataFrame(
+            {
+                "a": ["cat", "bat", "cat", "bat"],
+                "v": [1, 2, 3, 4],
+                "i": ["a", "b", "a", "b"],
+            }
+        )
+        assert f.dtypes["v"] == "int64"
+
+        z = pivot_table(
+            f, values="v", index=["a"], columns=["i"], fill_value=0, aggfunc="sum"
+        )
+        result = z.dtypes
+        expected = Series([np.dtype("int64")] * 2, index=Index(list("ab"), name="i"))
+        tm.assert_series_equal(result, expected)
+
+        # cannot convert dtypes
+        f = DataFrame(
+            {
+                "a": ["cat", "bat", "cat", "bat"],
+                "v": [1.5, 2.5, 3.5, 4.5],
+                "i": ["a", "b", "a", "b"],
+            }
+        )
+        assert f.dtypes["v"] == "float64"
+
+        z = pivot_table(
+            f, values="v", index=["a"], columns=["i"], fill_value=0, aggfunc="mean"
+        )
+        result = z.dtypes
+        expected = Series([np.dtype("float64")] * 2, index=Index(list("ab"), name="i"))
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "columns,values",
+        [
+            ("bool1", ["float1", "float2"]),
+            ("bool1", ["float1", "float2", "bool1"]),
+            ("bool2", ["float1", "float2", "bool1"]),
+        ],
+    )
+    def test_pivot_preserve_dtypes(self, columns, values):
+        # GH 7142 regression test
+        v = np.arange(5, dtype=np.float64)
+        df = DataFrame(
+            {"float1": v, "float2": v + 2.0, "bool1": v <= 2, "bool2": v <= 3}
+        )
+
+        df_res = df.reset_index().pivot_table(
+            index="index", columns=columns, values=values
+        )
+
+        result = dict(df_res.dtypes)
+        expected = {col: np.dtype("float64") for col in df_res}
+        assert result == expected
+
+    def test_pivot_no_values(self):
+        # GH 14380
+        idx = pd.DatetimeIndex(
+            ["2011-01-01", "2011-02-01", "2011-01-02", "2011-01-01", "2011-01-02"]
+        )
+        df = DataFrame({"A": [1, 2, 3, 4, 5]}, index=idx)
+        res = df.pivot_table(index=df.index.month, columns=df.index.day)
+
+        exp_columns = MultiIndex.from_tuples([("A", 1), ("A", 2)])
+        exp_columns = exp_columns.set_levels(
+            exp_columns.levels[1].astype(np.int32), level=1
+        )
+        exp = DataFrame(
+            [[2.5, 4.0], [2.0, np.nan]],
+            index=Index([1, 2], dtype=np.int32),
+            columns=exp_columns,
+        )
+        tm.assert_frame_equal(res, exp)
+
+        df = DataFrame(
+            {
+                "A": [1, 2, 3, 4, 5],
+                "dt": date_range("2011-01-01", freq="D", periods=5),
+            },
+            index=idx,
+        )
+        res = df.pivot_table(index=df.index.month, columns=Grouper(key="dt", freq="ME"))
+        exp_columns = MultiIndex.from_arrays(
+            [["A"], pd.DatetimeIndex(["2011-01-31"], dtype="M8[ns]")],
+            names=[None, "dt"],
+        )
+        exp = DataFrame(
+            [3.25, 2.0], index=Index([1, 2], dtype=np.int32), columns=exp_columns
+        )
+        tm.assert_frame_equal(res, exp)
+
+        res = df.pivot_table(
+            index=Grouper(freq="YE"), columns=Grouper(key="dt", freq="ME")
+        )
+        exp = DataFrame(
+            [3.0],
+            index=pd.DatetimeIndex(["2011-12-31"], freq="YE"),
+            columns=exp_columns,
+        )
+        tm.assert_frame_equal(res, exp)
+
+    def test_pivot_multi_values(self, data):
+        result = pivot_table(
+            data, values=["D", "E"], index="A", columns=["B", "C"], fill_value=0
+        )
+        expected = pivot_table(
+            data.drop(["F"], axis=1), index="A", columns=["B", "C"], fill_value=0
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_multi_functions(self, data):
+        f = lambda func: pivot_table(
+            data, values=["D", "E"], index=["A", "B"], columns="C", aggfunc=func
+        )
+        result = f(["mean", "std"])
+        means = f("mean")
+        stds = f("std")
+        expected = concat([means, stds], keys=["mean", "std"], axis=1)
+        tm.assert_frame_equal(result, expected)
+
+        # margins not supported??
+        f = lambda func: pivot_table(
+            data,
+            values=["D", "E"],
+            index=["A", "B"],
+            columns="C",
+            aggfunc=func,
+            margins=True,
+        )
+        result = f(["mean", "std"])
+        means = f("mean")
+        stds = f("std")
+        expected = concat([means, stds], keys=["mean", "std"], axis=1)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("method", [True, False])
+    def test_pivot_index_with_nan(self, method):
+        # GH 3588
+        nan = np.nan
+        df = DataFrame(
+            {
+                "a": ["R1", "R2", nan, "R4"],
+                "b": ["C1", "C2", "C3", "C4"],
+                "c": [10, 15, 17, 20],
+            }
+        )
+        if method:
+            result = df.pivot(index="a", columns="b", values="c")
+        else:
+            result = pd.pivot(df, index="a", columns="b", values="c")
+        expected = DataFrame(
+            [
+                [nan, nan, 17, nan],
+                [10, nan, nan, nan],
+                [nan, 15, nan, nan],
+                [nan, nan, nan, 20],
+            ],
+            index=Index([nan, "R1", "R2", "R4"], name="a"),
+            columns=Index(["C1", "C2", "C3", "C4"], name="b"),
+        )
+        tm.assert_frame_equal(result, expected)
+        tm.assert_frame_equal(df.pivot(index="b", columns="a", values="c"), expected.T)
+
+    @pytest.mark.parametrize("method", [True, False])
+    def test_pivot_index_with_nan_dates(self, method):
+        # GH9491
+        df = DataFrame(
+            {
+                "a": date_range("2014-02-01", periods=6, freq="D"),
+                "c": 100 + np.arange(6),
+            }
+        )
+        df["b"] = df["a"] - pd.Timestamp("2014-02-02")
+        df.loc[1, "a"] = df.loc[3, "a"] = np.nan
+        df.loc[1, "b"] = df.loc[4, "b"] = np.nan
+
+        if method:
+            pv = df.pivot(index="a", columns="b", values="c")
+        else:
+            pv = pd.pivot(df, index="a", columns="b", values="c")
+        assert pv.notna().values.sum() == len(df)
+
+        for _, row in df.iterrows():
+            assert pv.loc[row["a"], row["b"]] == row["c"]
+
+        if method:
+            result = df.pivot(index="b", columns="a", values="c")
+        else:
+            result = pd.pivot(df, index="b", columns="a", values="c")
+        tm.assert_frame_equal(result, pv.T)
+
+    @pytest.mark.parametrize("method", [True, False])
+    def test_pivot_with_tz(self, method, unit):
+        # GH 5878
+        df = DataFrame(
+            {
+                "dt1": pd.DatetimeIndex(
+                    [
+                        datetime(2013, 1, 1, 9, 0),
+                        datetime(2013, 1, 2, 9, 0),
+                        datetime(2013, 1, 1, 9, 0),
+                        datetime(2013, 1, 2, 9, 0),
+                    ],
+                    dtype=f"M8[{unit}, US/Pacific]",
+                ),
+                "dt2": pd.DatetimeIndex(
+                    [
+                        datetime(2014, 1, 1, 9, 0),
+                        datetime(2014, 1, 1, 9, 0),
+                        datetime(2014, 1, 2, 9, 0),
+                        datetime(2014, 1, 2, 9, 0),
+                    ],
+                    dtype=f"M8[{unit}, Asia/Tokyo]",
+                ),
+                "data1": np.arange(4, dtype="int64"),
+                "data2": np.arange(4, dtype="int64"),
+            }
+        )
+
+        exp_col1 = Index(["data1", "data1", "data2", "data2"])
+        exp_col2 = pd.DatetimeIndex(
+            ["2014/01/01 09:00", "2014/01/02 09:00"] * 2,
+            name="dt2",
+            dtype=f"M8[{unit}, Asia/Tokyo]",
+        )
+        exp_col = MultiIndex.from_arrays([exp_col1, exp_col2])
+        exp_idx = pd.DatetimeIndex(
+            ["2013/01/01 09:00", "2013/01/02 09:00"],
+            name="dt1",
+            dtype=f"M8[{unit}, US/Pacific]",
+        )
+        expected = DataFrame(
+            [[0, 2, 0, 2], [1, 3, 1, 3]],
+            index=exp_idx,
+            columns=exp_col,
+        )
+
+        if method:
+            pv = df.pivot(index="dt1", columns="dt2")
+        else:
+            pv = pd.pivot(df, index="dt1", columns="dt2")
+        tm.assert_frame_equal(pv, expected)
+
+        expected = DataFrame(
+            [[0, 2], [1, 3]],
+            index=exp_idx,
+            columns=exp_col2[:2],
+        )
+
+        if method:
+            pv = df.pivot(index="dt1", columns="dt2", values="data1")
+        else:
+            pv = pd.pivot(df, index="dt1", columns="dt2", values="data1")
+        tm.assert_frame_equal(pv, expected)
+
+    def test_pivot_tz_in_values(self):
+        # GH 14948
+        df = DataFrame(
+            [
+                {
+                    "uid": "aa",
+                    "ts": pd.Timestamp("2016-08-12 13:00:00-0700", tz="US/Pacific"),
+                },
+                {
+                    "uid": "aa",
+                    "ts": pd.Timestamp("2016-08-12 08:00:00-0700", tz="US/Pacific"),
+                },
+                {
+                    "uid": "aa",
+                    "ts": pd.Timestamp("2016-08-12 14:00:00-0700", tz="US/Pacific"),
+                },
+                {
+                    "uid": "aa",
+                    "ts": pd.Timestamp("2016-08-25 11:00:00-0700", tz="US/Pacific"),
+                },
+                {
+                    "uid": "aa",
+                    "ts": pd.Timestamp("2016-08-25 13:00:00-0700", tz="US/Pacific"),
+                },
+            ]
+        )
+
+        df = df.set_index("ts").reset_index()
+        mins = df.ts.map(lambda x: x.replace(hour=0, minute=0, second=0, microsecond=0))
+
+        result = pivot_table(
+            df.set_index("ts").reset_index(),
+            values="ts",
+            index=["uid"],
+            columns=[mins],
+            aggfunc="min",
+        )
+        expected = DataFrame(
+            [
+                [
+                    pd.Timestamp("2016-08-12 08:00:00-0700", tz="US/Pacific"),
+                    pd.Timestamp("2016-08-25 11:00:00-0700", tz="US/Pacific"),
+                ]
+            ],
+            index=Index(["aa"], name="uid"),
+            columns=pd.DatetimeIndex(
+                [
+                    pd.Timestamp("2016-08-12 00:00:00", tz="US/Pacific"),
+                    pd.Timestamp("2016-08-25 00:00:00", tz="US/Pacific"),
+                ],
+                name="ts",
+            ),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("method", [True, False])
+    def test_pivot_periods(self, method):
+        df = DataFrame(
+            {
+                "p1": [
+                    pd.Period("2013-01-01", "D"),
+                    pd.Period("2013-01-02", "D"),
+                    pd.Period("2013-01-01", "D"),
+                    pd.Period("2013-01-02", "D"),
+                ],
+                "p2": [
+                    pd.Period("2013-01", "M"),
+                    pd.Period("2013-01", "M"),
+                    pd.Period("2013-02", "M"),
+                    pd.Period("2013-02", "M"),
+                ],
+                "data1": np.arange(4, dtype="int64"),
+                "data2": np.arange(4, dtype="int64"),
+            }
+        )
+
+        exp_col1 = Index(["data1", "data1", "data2", "data2"])
+        exp_col2 = pd.PeriodIndex(["2013-01", "2013-02"] * 2, name="p2", freq="M")
+        exp_col = MultiIndex.from_arrays([exp_col1, exp_col2])
+        expected = DataFrame(
+            [[0, 2, 0, 2], [1, 3, 1, 3]],
+            index=pd.PeriodIndex(["2013-01-01", "2013-01-02"], name="p1", freq="D"),
+            columns=exp_col,
+        )
+        if method:
+            pv = df.pivot(index="p1", columns="p2")
+        else:
+            pv = pd.pivot(df, index="p1", columns="p2")
+        tm.assert_frame_equal(pv, expected)
+
+        expected = DataFrame(
+            [[0, 2], [1, 3]],
+            index=pd.PeriodIndex(["2013-01-01", "2013-01-02"], name="p1", freq="D"),
+            columns=pd.PeriodIndex(["2013-01", "2013-02"], name="p2", freq="M"),
+        )
+        if method:
+            pv = df.pivot(index="p1", columns="p2", values="data1")
+        else:
+            pv = pd.pivot(df, index="p1", columns="p2", values="data1")
+        tm.assert_frame_equal(pv, expected)
+
+    def test_pivot_periods_with_margins(self):
+        # GH 28323
+        df = DataFrame(
+            {
+                "a": [1, 1, 2, 2],
+                "b": [
+                    pd.Period("2019Q1"),
+                    pd.Period("2019Q2"),
+                    pd.Period("2019Q1"),
+                    pd.Period("2019Q2"),
+                ],
+                "x": 1.0,
+            }
+        )
+
+        expected = DataFrame(
+            data=1.0,
+            index=Index([1, 2, "All"], name="a"),
+            columns=Index([pd.Period("2019Q1"), pd.Period("2019Q2"), "All"], name="b"),
+        )
+
+        result = df.pivot_table(index="a", columns="b", values="x", margins=True)
+        tm.assert_frame_equal(expected, result)
+
+    @pytest.mark.parametrize(
+        "values",
+        [
+            ["baz", "zoo"],
+            np.array(["baz", "zoo"]),
+            Series(["baz", "zoo"]),
+            Index(["baz", "zoo"]),
+        ],
+    )
+    @pytest.mark.parametrize("method", [True, False])
+    def test_pivot_with_list_like_values(self, values, method):
+        # issue #17160
+        df = DataFrame(
+            {
+                "foo": ["one", "one", "one", "two", "two", "two"],
+                "bar": ["A", "B", "C", "A", "B", "C"],
+                "baz": [1, 2, 3, 4, 5, 6],
+                "zoo": ["x", "y", "z", "q", "w", "t"],
+            }
+        )
+
+        if method:
+            result = df.pivot(index="foo", columns="bar", values=values)
+        else:
+            result = pd.pivot(df, index="foo", columns="bar", values=values)
+
+        data = [[1, 2, 3, "x", "y", "z"], [4, 5, 6, "q", "w", "t"]]
+        index = Index(data=["one", "two"], name="foo")
+        columns = MultiIndex(
+            levels=[["baz", "zoo"], ["A", "B", "C"]],
+            codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]],
+            names=[None, "bar"],
+        )
+        expected = DataFrame(data=data, index=index, columns=columns)
+        expected["baz"] = expected["baz"].astype(object)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "values",
+        [
+            ["bar", "baz"],
+            np.array(["bar", "baz"]),
+            Series(["bar", "baz"]),
+            Index(["bar", "baz"]),
+        ],
+    )
+    @pytest.mark.parametrize("method", [True, False])
+    def test_pivot_with_list_like_values_nans(self, values, method):
+        # issue #17160
+        df = DataFrame(
+            {
+                "foo": ["one", "one", "one", "two", "two", "two"],
+                "bar": ["A", "B", "C", "A", "B", "C"],
+                "baz": [1, 2, 3, 4, 5, 6],
+                "zoo": ["x", "y", "z", "q", "w", "t"],
+            }
+        )
+
+        if method:
+            result = df.pivot(index="zoo", columns="foo", values=values)
+        else:
+            result = pd.pivot(df, index="zoo", columns="foo", values=values)
+
+        data = [
+            [np.nan, "A", np.nan, 4],
+            [np.nan, "C", np.nan, 6],
+            [np.nan, "B", np.nan, 5],
+            ["A", np.nan, 1, np.nan],
+            ["B", np.nan, 2, np.nan],
+            ["C", np.nan, 3, np.nan],
+        ]
+        index = Index(data=["q", "t", "w", "x", "y", "z"], name="zoo")
+        columns = MultiIndex(
+            levels=[["bar", "baz"], ["one", "two"]],
+            codes=[[0, 0, 1, 1], [0, 1, 0, 1]],
+            names=[None, "foo"],
+        )
+        expected = DataFrame(data=data, index=index, columns=columns)
+        expected["baz"] = expected["baz"].astype(object)
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_columns_none_raise_error(self):
+        # GH 30924
+        df = DataFrame({"col1": ["a", "b", "c"], "col2": [1, 2, 3], "col3": [1, 2, 3]})
+        msg = r"pivot\(\) missing 1 required keyword-only argument: 'columns'"
+        with pytest.raises(TypeError, match=msg):
+            df.pivot(index="col1", values="col3")  # pylint: disable=missing-kwoa
+
+    @pytest.mark.xfail(
+        reason="MultiIndexed unstack with tuple names fails with KeyError GH#19966"
+    )
+    @pytest.mark.parametrize("method", [True, False])
+    def test_pivot_with_multiindex(self, method):
+        # issue #17160
+        index = Index(data=[0, 1, 2, 3, 4, 5])
+        data = [
+            ["one", "A", 1, "x"],
+            ["one", "B", 2, "y"],
+            ["one", "C", 3, "z"],
+            ["two", "A", 4, "q"],
+            ["two", "B", 5, "w"],
+            ["two", "C", 6, "t"],
+        ]
+        columns = MultiIndex(
+            levels=[["bar", "baz"], ["first", "second"]],
+            codes=[[0, 0, 1, 1], [0, 1, 0, 1]],
+        )
+        df = DataFrame(data=data, index=index, columns=columns, dtype="object")
+        if method:
+            result = df.pivot(
+                index=("bar", "first"),
+                columns=("bar", "second"),
+                values=("baz", "first"),
+            )
+        else:
+            result = pd.pivot(
+                df,
+                index=("bar", "first"),
+                columns=("bar", "second"),
+                values=("baz", "first"),
+            )
+
+        data = {
+            "A": Series([1, 4], index=["one", "two"]),
+            "B": Series([2, 5], index=["one", "two"]),
+            "C": Series([3, 6], index=["one", "two"]),
+        }
+        expected = DataFrame(data)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("method", [True, False])
+    def test_pivot_with_tuple_of_values(self, method):
+        # issue #17160
+        df = DataFrame(
+            {
+                "foo": ["one", "one", "one", "two", "two", "two"],
+                "bar": ["A", "B", "C", "A", "B", "C"],
+                "baz": [1, 2, 3, 4, 5, 6],
+                "zoo": ["x", "y", "z", "q", "w", "t"],
+            }
+        )
+        with pytest.raises(KeyError, match=r"^\('bar', 'baz'\)$"):
+            # tuple is seen as a single column name
+            if method:
+                df.pivot(index="zoo", columns="foo", values=("bar", "baz"))
+            else:
+                pd.pivot(df, index="zoo", columns="foo", values=("bar", "baz"))
+
+    def _check_output(
+        self,
+        result,
+        values_col,
+        data,
+        index=["A", "B"],
+        columns=["C"],
+        margins_col="All",
+    ):
+        col_margins = result.loc[result.index[:-1], margins_col]
+        expected_col_margins = data.groupby(index)[values_col].mean()
+        tm.assert_series_equal(col_margins, expected_col_margins, check_names=False)
+        assert col_margins.name == margins_col
+
+        result = result.sort_index()
+        index_margins = result.loc[(margins_col, "")].iloc[:-1]
+
+        expected_ix_margins = data.groupby(columns)[values_col].mean()
+        tm.assert_series_equal(index_margins, expected_ix_margins, check_names=False)
+        assert index_margins.name == (margins_col, "")
+
+        grand_total_margins = result.loc[(margins_col, ""), margins_col]
+        expected_total_margins = data[values_col].mean()
+        assert grand_total_margins == expected_total_margins
+
+    def test_margins(self, data):
+        # column specified
+        result = data.pivot_table(
+            values="D", index=["A", "B"], columns="C", margins=True, aggfunc="mean"
+        )
+        self._check_output(result, "D", data)
+
+        # Set a different margins_name (not 'All')
+        result = data.pivot_table(
+            values="D",
+            index=["A", "B"],
+            columns="C",
+            margins=True,
+            aggfunc="mean",
+            margins_name="Totals",
+        )
+        self._check_output(result, "D", data, margins_col="Totals")
+
+        # no column specified
+        table = data.pivot_table(
+            index=["A", "B"], columns="C", margins=True, aggfunc="mean"
+        )
+        for value_col in table.columns.levels[0]:
+            self._check_output(table[value_col], value_col, data)
+
+    def test_no_col(self, data):
+        # no col
+
+        # to help with a buglet
+        data.columns = [k * 2 for k in data.columns]
+        msg = re.escape("agg function failed [how->mean,dtype->")
+        with pytest.raises(TypeError, match=msg):
+            data.pivot_table(index=["AA", "BB"], margins=True, aggfunc="mean")
+        table = data.drop(columns="CC").pivot_table(
+            index=["AA", "BB"], margins=True, aggfunc="mean"
+        )
+        for value_col in table.columns:
+            totals = table.loc[("All", ""), value_col]
+            assert totals == data[value_col].mean()
+
+        with pytest.raises(TypeError, match=msg):
+            data.pivot_table(index=["AA", "BB"], margins=True, aggfunc="mean")
+        table = data.drop(columns="CC").pivot_table(
+            index=["AA", "BB"], margins=True, aggfunc="mean"
+        )
+        for item in ["DD", "EE", "FF"]:
+            totals = table.loc[("All", ""), item]
+            assert totals == data[item].mean()
+
+    @pytest.mark.parametrize(
+        "columns, aggfunc, values, expected_columns",
+        [
+            (
+                "A",
+                "mean",
+                [[5.5, 5.5, 2.2, 2.2], [8.0, 8.0, 4.4, 4.4]],
+                Index(["bar", "All", "foo", "All"], name="A"),
+            ),
+            (
+                ["A", "B"],
+                "sum",
+                [
+                    [9, 13, 22, 5, 6, 11],
+                    [14, 18, 32, 11, 11, 22],
+                ],
+                MultiIndex.from_tuples(
+                    [
+                        ("bar", "one"),
+                        ("bar", "two"),
+                        ("bar", "All"),
+                        ("foo", "one"),
+                        ("foo", "two"),
+                        ("foo", "All"),
+                    ],
+                    names=["A", "B"],
+                ),
+            ),
+        ],
+    )
+    def test_margin_with_only_columns_defined(
+        self, columns, aggfunc, values, expected_columns
+    ):
+        # GH 31016
+        df = DataFrame(
+            {
+                "A": ["foo", "foo", "foo", "foo", "foo", "bar", "bar", "bar", "bar"],
+                "B": ["one", "one", "one", "two", "two", "one", "one", "two", "two"],
+                "C": [
+                    "small",
+                    "large",
+                    "large",
+                    "small",
+                    "small",
+                    "large",
+                    "small",
+                    "small",
+                    "large",
+                ],
+                "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
+                "E": [2, 4, 5, 5, 6, 6, 8, 9, 9],
+            }
+        )
+        if aggfunc != "sum":
+            msg = re.escape("agg function failed [how->mean,dtype->")
+            with pytest.raises(TypeError, match=msg):
+                df.pivot_table(columns=columns, margins=True, aggfunc=aggfunc)
+        if "B" not in columns:
+            df = df.drop(columns="B")
+        result = df.drop(columns="C").pivot_table(
+            columns=columns, margins=True, aggfunc=aggfunc
+        )
+        expected = DataFrame(values, index=Index(["D", "E"]), columns=expected_columns)
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_margins_dtype(self, data):
+        # GH 17013
+
+        df = data.copy()
+        df[["D", "E", "F"]] = np.arange(len(df) * 3).reshape(len(df), 3).astype("i8")
+
+        mi_val = list(product(["bar", "foo"], ["one", "two"])) + [("All", "")]
+        mi = MultiIndex.from_tuples(mi_val, names=("A", "B"))
+        expected = DataFrame(
+            {"dull": [12, 21, 3, 9, 45], "shiny": [33, 0, 36, 51, 120]}, index=mi
+        ).rename_axis("C", axis=1)
+        expected["All"] = expected["dull"] + expected["shiny"]
+
+        result = df.pivot_table(
+            values="D",
+            index=["A", "B"],
+            columns="C",
+            margins=True,
+            aggfunc="sum",
+            fill_value=0,
+        )
+
+        tm.assert_frame_equal(expected, result)
+
+    def test_margins_dtype_len(self, data):
+        mi_val = list(product(["bar", "foo"], ["one", "two"])) + [("All", "")]
+        mi = MultiIndex.from_tuples(mi_val, names=("A", "B"))
+        expected = DataFrame(
+            {"dull": [1, 1, 2, 1, 5], "shiny": [2, 0, 2, 2, 6]}, index=mi
+        ).rename_axis("C", axis=1)
+        expected["All"] = expected["dull"] + expected["shiny"]
+
+        result = data.pivot_table(
+            values="D",
+            index=["A", "B"],
+            columns="C",
+            margins=True,
+            aggfunc=len,
+            fill_value=0,
+        )
+
+        tm.assert_frame_equal(expected, result)
+
+    @pytest.mark.parametrize("cols", [(1, 2), ("a", "b"), (1, "b"), ("a", 1)])
+    def test_pivot_table_multiindex_only(self, cols):
+        # GH 17038
+        df2 = DataFrame({cols[0]: [1, 2, 3], cols[1]: [1, 2, 3], "v": [4, 5, 6]})
+
+        result = df2.pivot_table(values="v", columns=cols)
+        expected = DataFrame(
+            [[4.0, 5.0, 6.0]],
+            columns=MultiIndex.from_tuples([(1, 1), (2, 2), (3, 3)], names=cols),
+            index=Index(["v"], dtype=object),
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_table_retains_tz(self):
+        dti = date_range("2016-01-01", periods=3, tz="Europe/Amsterdam")
+        df = DataFrame(
+            {
+                "A": np.random.default_rng(2).standard_normal(3),
+                "B": np.random.default_rng(2).standard_normal(3),
+                "C": dti,
+            }
+        )
+        result = df.pivot_table(index=["B", "C"], dropna=False)
+
+        # check tz retention
+        assert result.index.levels[1].equals(dti)
+
+    def test_pivot_integer_columns(self):
+        # caused by upstream bug in unstack
+
+        d = date.min
+        data = list(
+            product(
+                ["foo", "bar"],
+                ["A", "B", "C"],
+                ["x1", "x2"],
+                [d + timedelta(i) for i in range(20)],
+                [1.0],
+            )
+        )
+        df = DataFrame(data)
+        table = df.pivot_table(values=4, index=[0, 1, 3], columns=[2])
+
+        df2 = df.rename(columns=str)
+        table2 = df2.pivot_table(values="4", index=["0", "1", "3"], columns=["2"])
+
+        tm.assert_frame_equal(table, table2, check_names=False)
+
+    def test_pivot_no_level_overlap(self):
+        # GH #1181
+
+        data = DataFrame(
+            {
+                "a": ["a", "a", "a", "a", "b", "b", "b", "b"] * 2,
+                "b": [0, 0, 0, 0, 1, 1, 1, 1] * 2,
+                "c": (["foo"] * 4 + ["bar"] * 4) * 2,
+                "value": np.random.default_rng(2).standard_normal(16),
+            }
+        )
+
+        table = data.pivot_table("value", index="a", columns=["b", "c"])
+
+        grouped = data.groupby(["a", "b", "c"])["value"].mean()
+        expected = grouped.unstack("b").unstack("c").dropna(axis=1, how="all")
+        tm.assert_frame_equal(table, expected)
+
+    def test_pivot_columns_lexsorted(self):
+        n = 10000
+
+        dtype = np.dtype(
+            [
+                ("Index", object),
+                ("Symbol", object),
+                ("Year", int),
+                ("Month", int),
+                ("Day", int),
+                ("Quantity", int),
+                ("Price", float),
+            ]
+        )
+
+        products = np.array(
+            [
+                ("SP500", "ADBE"),
+                ("SP500", "NVDA"),
+                ("SP500", "ORCL"),
+                ("NDQ100", "AAPL"),
+                ("NDQ100", "MSFT"),
+                ("NDQ100", "GOOG"),
+                ("FTSE", "DGE.L"),
+                ("FTSE", "TSCO.L"),
+                ("FTSE", "GSK.L"),
+            ],
+            dtype=[("Index", object), ("Symbol", object)],
+        )
+        items = np.empty(n, dtype=dtype)
+        iproduct = np.random.default_rng(2).integers(0, len(products), n)
+        items["Index"] = products["Index"][iproduct]
+        items["Symbol"] = products["Symbol"][iproduct]
+        dr = date_range(date(2000, 1, 1), date(2010, 12, 31))
+        dates = dr[np.random.default_rng(2).integers(0, len(dr), n)]
+        items["Year"] = dates.year
+        items["Month"] = dates.month
+        items["Day"] = dates.day
+        items["Price"] = np.random.default_rng(2).lognormal(4.0, 2.0, n)
+
+        df = DataFrame(items)
+
+        pivoted = df.pivot_table(
+            "Price",
+            index=["Month", "Day"],
+            columns=["Index", "Symbol", "Year"],
+            aggfunc="mean",
+        )
+
+        assert pivoted.columns.is_monotonic_increasing
+
+    def test_pivot_complex_aggfunc(self, data):
+        f = {"D": ["std"], "E": ["sum"]}
+        expected = data.groupby(["A", "B"]).agg(f).unstack("B")
+        result = data.pivot_table(index="A", columns="B", aggfunc=f)
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_margins_no_values_no_cols(self, data):
+        # Regression test on pivot table: no values or cols passed.
+        result = data[["A", "B"]].pivot_table(
+            index=["A", "B"], aggfunc=len, margins=True
+        )
+        result_list = result.tolist()
+        assert sum(result_list[:-1]) == result_list[-1]
+
+    def test_margins_no_values_two_rows(self, data):
+        # Regression test on pivot table: no values passed but rows are a
+        # multi-index
+        result = data[["A", "B", "C"]].pivot_table(
+            index=["A", "B"], columns="C", aggfunc=len, margins=True
+        )
+        assert result.All.tolist() == [3.0, 1.0, 4.0, 3.0, 11.0]
+
+    def test_margins_no_values_one_row_one_col(self, data):
+        # Regression test on pivot table: no values passed but row and col
+        # defined
+        result = data[["A", "B"]].pivot_table(
+            index="A", columns="B", aggfunc=len, margins=True
+        )
+        assert result.All.tolist() == [4.0, 7.0, 11.0]
+
+    def test_margins_no_values_two_row_two_cols(self, data):
+        # Regression test on pivot table: no values passed but rows and cols
+        # are multi-indexed
+        data["D"] = ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k"]
+        result = data[["A", "B", "C", "D"]].pivot_table(
+            index=["A", "B"], columns=["C", "D"], aggfunc=len, margins=True
+        )
+        assert result.All.tolist() == [3.0, 1.0, 4.0, 3.0, 11.0]
+
+    @pytest.mark.parametrize("margin_name", ["foo", "one", 666, None, ["a", "b"]])
+    def test_pivot_table_with_margins_set_margin_name(self, margin_name, data):
+        # see gh-3335
+        msg = (
+            f'Conflicting name "{margin_name}" in margins|'
+            "margins_name argument must be a string"
+        )
+        with pytest.raises(ValueError, match=msg):
+            # multi-index index
+            pivot_table(
+                data,
+                values="D",
+                index=["A", "B"],
+                columns=["C"],
+                margins=True,
+                margins_name=margin_name,
+            )
+        with pytest.raises(ValueError, match=msg):
+            # multi-index column
+            pivot_table(
+                data,
+                values="D",
+                index=["C"],
+                columns=["A", "B"],
+                margins=True,
+                margins_name=margin_name,
+            )
+        with pytest.raises(ValueError, match=msg):
+            # non-multi-index index/column
+            pivot_table(
+                data,
+                values="D",
+                index=["A"],
+                columns=["B"],
+                margins=True,
+                margins_name=margin_name,
+            )
+
+    def test_pivot_timegrouper(self, using_array_manager):
+        df = DataFrame(
+            {
+                "Branch": "A A A A A A A B".split(),
+                "Buyer": "Carl Mark Carl Carl Joe Joe Joe Carl".split(),
+                "Quantity": [1, 3, 5, 1, 8, 1, 9, 3],
+                "Date": [
+                    datetime(2013, 1, 1),
+                    datetime(2013, 1, 1),
+                    datetime(2013, 10, 1),
+                    datetime(2013, 10, 2),
+                    datetime(2013, 10, 1),
+                    datetime(2013, 10, 2),
+                    datetime(2013, 12, 2),
+                    datetime(2013, 12, 2),
+                ],
+            }
+        ).set_index("Date")
+
+        expected = DataFrame(
+            np.array([10, 18, 3], dtype="int64").reshape(1, 3),
+            index=pd.DatetimeIndex([datetime(2013, 12, 31)], freq="YE"),
+            columns="Carl Joe Mark".split(),
+        )
+        expected.index.name = "Date"
+        expected.columns.name = "Buyer"
+
+        result = pivot_table(
+            df,
+            index=Grouper(freq="YE"),
+            columns="Buyer",
+            values="Quantity",
+            aggfunc="sum",
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = pivot_table(
+            df,
+            index="Buyer",
+            columns=Grouper(freq="YE"),
+            values="Quantity",
+            aggfunc="sum",
+        )
+        tm.assert_frame_equal(result, expected.T)
+
+        expected = DataFrame(
+            np.array([1, np.nan, 3, 9, 18, np.nan]).reshape(2, 3),
+            index=pd.DatetimeIndex(
+                [datetime(2013, 1, 1), datetime(2013, 7, 1)], freq="6MS"
+            ),
+            columns="Carl Joe Mark".split(),
+        )
+        expected.index.name = "Date"
+        expected.columns.name = "Buyer"
+        if using_array_manager:
+            # INFO(ArrayManager) column without NaNs can preserve int dtype
+            expected["Carl"] = expected["Carl"].astype("int64")
+
+        result = pivot_table(
+            df,
+            index=Grouper(freq="6MS"),
+            columns="Buyer",
+            values="Quantity",
+            aggfunc="sum",
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = pivot_table(
+            df,
+            index="Buyer",
+            columns=Grouper(freq="6MS"),
+            values="Quantity",
+            aggfunc="sum",
+        )
+        tm.assert_frame_equal(result, expected.T)
+
+        # passing the name
+        df = df.reset_index()
+        result = pivot_table(
+            df,
+            index=Grouper(freq="6MS", key="Date"),
+            columns="Buyer",
+            values="Quantity",
+            aggfunc="sum",
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = pivot_table(
+            df,
+            index="Buyer",
+            columns=Grouper(freq="6MS", key="Date"),
+            values="Quantity",
+            aggfunc="sum",
+        )
+        tm.assert_frame_equal(result, expected.T)
+
+        msg = "'The grouper name foo is not found'"
+        with pytest.raises(KeyError, match=msg):
+            pivot_table(
+                df,
+                index=Grouper(freq="6MS", key="foo"),
+                columns="Buyer",
+                values="Quantity",
+                aggfunc="sum",
+            )
+        with pytest.raises(KeyError, match=msg):
+            pivot_table(
+                df,
+                index="Buyer",
+                columns=Grouper(freq="6MS", key="foo"),
+                values="Quantity",
+                aggfunc="sum",
+            )
+
+        # passing the level
+        df = df.set_index("Date")
+        result = pivot_table(
+            df,
+            index=Grouper(freq="6MS", level="Date"),
+            columns="Buyer",
+            values="Quantity",
+            aggfunc="sum",
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = pivot_table(
+            df,
+            index="Buyer",
+            columns=Grouper(freq="6MS", level="Date"),
+            values="Quantity",
+            aggfunc="sum",
+        )
+        tm.assert_frame_equal(result, expected.T)
+
+        msg = "The level foo is not valid"
+        with pytest.raises(ValueError, match=msg):
+            pivot_table(
+                df,
+                index=Grouper(freq="6MS", level="foo"),
+                columns="Buyer",
+                values="Quantity",
+                aggfunc="sum",
+            )
+        with pytest.raises(ValueError, match=msg):
+            pivot_table(
+                df,
+                index="Buyer",
+                columns=Grouper(freq="6MS", level="foo"),
+                values="Quantity",
+                aggfunc="sum",
+            )
+
+    def test_pivot_timegrouper_double(self):
+        # double grouper
+        df = DataFrame(
+            {
+                "Branch": "A A A A A A A B".split(),
+                "Buyer": "Carl Mark Carl Carl Joe Joe Joe Carl".split(),
+                "Quantity": [1, 3, 5, 1, 8, 1, 9, 3],
+                "Date": [
+                    datetime(2013, 11, 1, 13, 0),
+                    datetime(2013, 9, 1, 13, 5),
+                    datetime(2013, 10, 1, 20, 0),
+                    datetime(2013, 10, 2, 10, 0),
+                    datetime(2013, 11, 1, 20, 0),
+                    datetime(2013, 10, 2, 10, 0),
+                    datetime(2013, 10, 2, 12, 0),
+                    datetime(2013, 12, 5, 14, 0),
+                ],
+                "PayDay": [
+                    datetime(2013, 10, 4, 0, 0),
+                    datetime(2013, 10, 15, 13, 5),
+                    datetime(2013, 9, 5, 20, 0),
+                    datetime(2013, 11, 2, 10, 0),
+                    datetime(2013, 10, 7, 20, 0),
+                    datetime(2013, 9, 5, 10, 0),
+                    datetime(2013, 12, 30, 12, 0),
+                    datetime(2013, 11, 20, 14, 0),
+                ],
+            }
+        )
+
+        result = pivot_table(
+            df,
+            index=Grouper(freq="ME", key="Date"),
+            columns=Grouper(freq="ME", key="PayDay"),
+            values="Quantity",
+            aggfunc="sum",
+        )
+        expected = DataFrame(
+            np.array(
+                [
+                    np.nan,
+                    3,
+                    np.nan,
+                    np.nan,
+                    6,
+                    np.nan,
+                    1,
+                    9,
+                    np.nan,
+                    9,
+                    np.nan,
+                    np.nan,
+                    np.nan,
+                    np.nan,
+                    3,
+                    np.nan,
+                ]
+            ).reshape(4, 4),
+            index=pd.DatetimeIndex(
+                [
+                    datetime(2013, 9, 30),
+                    datetime(2013, 10, 31),
+                    datetime(2013, 11, 30),
+                    datetime(2013, 12, 31),
+                ],
+                freq="ME",
+            ),
+            columns=pd.DatetimeIndex(
+                [
+                    datetime(2013, 9, 30),
+                    datetime(2013, 10, 31),
+                    datetime(2013, 11, 30),
+                    datetime(2013, 12, 31),
+                ],
+                freq="ME",
+            ),
+        )
+        expected.index.name = "Date"
+        expected.columns.name = "PayDay"
+
+        tm.assert_frame_equal(result, expected)
+
+        result = pivot_table(
+            df,
+            index=Grouper(freq="ME", key="PayDay"),
+            columns=Grouper(freq="ME", key="Date"),
+            values="Quantity",
+            aggfunc="sum",
+        )
+        tm.assert_frame_equal(result, expected.T)
+
+        tuples = [
+            (datetime(2013, 9, 30), datetime(2013, 10, 31)),
+            (datetime(2013, 10, 31), datetime(2013, 9, 30)),
+            (datetime(2013, 10, 31), datetime(2013, 11, 30)),
+            (datetime(2013, 10, 31), datetime(2013, 12, 31)),
+            (datetime(2013, 11, 30), datetime(2013, 10, 31)),
+            (datetime(2013, 12, 31), datetime(2013, 11, 30)),
+        ]
+        idx = MultiIndex.from_tuples(tuples, names=["Date", "PayDay"])
+        expected = DataFrame(
+            np.array(
+                [3, np.nan, 6, np.nan, 1, np.nan, 9, np.nan, 9, np.nan, np.nan, 3]
+            ).reshape(6, 2),
+            index=idx,
+            columns=["A", "B"],
+        )
+        expected.columns.name = "Branch"
+
+        result = pivot_table(
+            df,
+            index=[Grouper(freq="ME", key="Date"), Grouper(freq="ME", key="PayDay")],
+            columns=["Branch"],
+            values="Quantity",
+            aggfunc="sum",
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = pivot_table(
+            df,
+            index=["Branch"],
+            columns=[Grouper(freq="ME", key="Date"), Grouper(freq="ME", key="PayDay")],
+            values="Quantity",
+            aggfunc="sum",
+        )
+        tm.assert_frame_equal(result, expected.T)
+
+    def test_pivot_datetime_tz(self):
+        dates1 = pd.DatetimeIndex(
+            [
+                "2011-07-19 07:00:00",
+                "2011-07-19 08:00:00",
+                "2011-07-19 09:00:00",
+                "2011-07-19 07:00:00",
+                "2011-07-19 08:00:00",
+                "2011-07-19 09:00:00",
+            ],
+            dtype="M8[ns, US/Pacific]",
+            name="dt1",
+        )
+        dates2 = pd.DatetimeIndex(
+            [
+                "2013-01-01 15:00:00",
+                "2013-01-01 15:00:00",
+                "2013-01-01 15:00:00",
+                "2013-02-01 15:00:00",
+                "2013-02-01 15:00:00",
+                "2013-02-01 15:00:00",
+            ],
+            dtype="M8[ns, Asia/Tokyo]",
+        )
+        df = DataFrame(
+            {
+                "label": ["a", "a", "a", "b", "b", "b"],
+                "dt1": dates1,
+                "dt2": dates2,
+                "value1": np.arange(6, dtype="int64"),
+                "value2": [1, 2] * 3,
+            }
+        )
+
+        exp_idx = dates1[:3]
+        exp_col1 = Index(["value1", "value1"])
+        exp_col2 = Index(["a", "b"], name="label")
+        exp_col = MultiIndex.from_arrays([exp_col1, exp_col2])
+        expected = DataFrame(
+            [[0.0, 3.0], [1.0, 4.0], [2.0, 5.0]], index=exp_idx, columns=exp_col
+        )
+        result = pivot_table(df, index=["dt1"], columns=["label"], values=["value1"])
+        tm.assert_frame_equal(result, expected)
+
+        exp_col1 = Index(["sum", "sum", "sum", "sum", "mean", "mean", "mean", "mean"])
+        exp_col2 = Index(["value1", "value1", "value2", "value2"] * 2)
+        exp_col3 = pd.DatetimeIndex(
+            ["2013-01-01 15:00:00", "2013-02-01 15:00:00"] * 4,
+            dtype="M8[ns, Asia/Tokyo]",
+            name="dt2",
+        )
+        exp_col = MultiIndex.from_arrays([exp_col1, exp_col2, exp_col3])
+        expected1 = DataFrame(
+            np.array(
+                [
+                    [
+                        0,
+                        3,
+                        1,
+                        2,
+                    ],
+                    [1, 4, 2, 1],
+                    [2, 5, 1, 2],
+                ],
+                dtype="int64",
+            ),
+            index=exp_idx,
+            columns=exp_col[:4],
+        )
+        expected2 = DataFrame(
+            np.array(
+                [
+                    [0.0, 3.0, 1.0, 2.0],
+                    [1.0, 4.0, 2.0, 1.0],
+                    [2.0, 5.0, 1.0, 2.0],
+                ],
+            ),
+            index=exp_idx,
+            columns=exp_col[4:],
+        )
+        expected = concat([expected1, expected2], axis=1)
+
+        result = pivot_table(
+            df,
+            index=["dt1"],
+            columns=["dt2"],
+            values=["value1", "value2"],
+            aggfunc=["sum", "mean"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_dtaccessor(self):
+        # GH 8103
+        dates1 = pd.DatetimeIndex(
+            [
+                "2011-07-19 07:00:00",
+                "2011-07-19 08:00:00",
+                "2011-07-19 09:00:00",
+                "2011-07-19 07:00:00",
+                "2011-07-19 08:00:00",
+                "2011-07-19 09:00:00",
+            ]
+        )
+        dates2 = pd.DatetimeIndex(
+            [
+                "2013-01-01 15:00:00",
+                "2013-01-01 15:00:00",
+                "2013-01-01 15:00:00",
+                "2013-02-01 15:00:00",
+                "2013-02-01 15:00:00",
+                "2013-02-01 15:00:00",
+            ]
+        )
+        df = DataFrame(
+            {
+                "label": ["a", "a", "a", "b", "b", "b"],
+                "dt1": dates1,
+                "dt2": dates2,
+                "value1": np.arange(6, dtype="int64"),
+                "value2": [1, 2] * 3,
+            }
+        )
+
+        result = pivot_table(
+            df, index="label", columns=df["dt1"].dt.hour, values="value1"
+        )
+
+        exp_idx = Index(["a", "b"], name="label")
+        expected = DataFrame(
+            {7: [0.0, 3.0], 8: [1.0, 4.0], 9: [2.0, 5.0]},
+            index=exp_idx,
+            columns=Index([7, 8, 9], dtype=np.int32, name="dt1"),
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = pivot_table(
+            df, index=df["dt2"].dt.month, columns=df["dt1"].dt.hour, values="value1"
+        )
+
+        expected = DataFrame(
+            {7: [0.0, 3.0], 8: [1.0, 4.0], 9: [2.0, 5.0]},
+            index=Index([1, 2], dtype=np.int32, name="dt2"),
+            columns=Index([7, 8, 9], dtype=np.int32, name="dt1"),
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = pivot_table(
+            df,
+            index=df["dt2"].dt.year.values,
+            columns=[df["dt1"].dt.hour, df["dt2"].dt.month],
+            values="value1",
+        )
+
+        exp_col = MultiIndex.from_arrays(
+            [
+                np.array([7, 7, 8, 8, 9, 9], dtype=np.int32),
+                np.array([1, 2] * 3, dtype=np.int32),
+            ],
+            names=["dt1", "dt2"],
+        )
+        expected = DataFrame(
+            np.array([[0.0, 3.0, 1.0, 4.0, 2.0, 5.0]]),
+            index=Index([2013], dtype=np.int32),
+            columns=exp_col,
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = pivot_table(
+            df,
+            index=np.array(["X", "X", "X", "X", "Y", "Y"]),
+            columns=[df["dt1"].dt.hour, df["dt2"].dt.month],
+            values="value1",
+        )
+        expected = DataFrame(
+            np.array(
+                [[0, 3, 1, np.nan, 2, np.nan], [np.nan, np.nan, np.nan, 4, np.nan, 5]]
+            ),
+            index=["X", "Y"],
+            columns=exp_col,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_daily(self):
+        rng = date_range("1/1/2000", "12/31/2004", freq="D")
+        ts = Series(np.arange(len(rng)), index=rng)
+
+        result = pivot_table(
+            DataFrame(ts), index=ts.index.year, columns=ts.index.dayofyear
+        )
+        result.columns = result.columns.droplevel(0)
+
+        doy = np.asarray(ts.index.dayofyear)
+
+        expected = {}
+        for y in ts.index.year.unique().values:
+            mask = ts.index.year == y
+            expected[y] = Series(ts.values[mask], index=doy[mask])
+        expected = DataFrame(expected, dtype=float).T
+        tm.assert_frame_equal(result, expected)
+
+    def test_monthly(self):
+        rng = date_range("1/1/2000", "12/31/2004", freq="ME")
+        ts = Series(np.arange(len(rng)), index=rng)
+
+        result = pivot_table(DataFrame(ts), index=ts.index.year, columns=ts.index.month)
+        result.columns = result.columns.droplevel(0)
+
+        month = np.asarray(ts.index.month)
+        expected = {}
+        for y in ts.index.year.unique().values:
+            mask = ts.index.year == y
+            expected[y] = Series(ts.values[mask], index=month[mask])
+        expected = DataFrame(expected, dtype=float).T
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_table_with_iterator_values(self, data):
+        # GH 12017
+        aggs = {"D": "sum", "E": "mean"}
+
+        pivot_values_list = pivot_table(
+            data, index=["A"], values=list(aggs.keys()), aggfunc=aggs
+        )
+
+        pivot_values_keys = pivot_table(
+            data, index=["A"], values=aggs.keys(), aggfunc=aggs
+        )
+        tm.assert_frame_equal(pivot_values_keys, pivot_values_list)
+
+        agg_values_gen = (value for value in aggs)
+        pivot_values_gen = pivot_table(
+            data, index=["A"], values=agg_values_gen, aggfunc=aggs
+        )
+        tm.assert_frame_equal(pivot_values_gen, pivot_values_list)
+
+    def test_pivot_table_margins_name_with_aggfunc_list(self):
+        # GH 13354
+        margins_name = "Weekly"
+        costs = DataFrame(
+            {
+                "item": ["bacon", "cheese", "bacon", "cheese"],
+                "cost": [2.5, 4.5, 3.2, 3.3],
+                "day": ["ME", "ME", "T", "T"],
+            }
+        )
+        table = costs.pivot_table(
+            index="item",
+            columns="day",
+            margins=True,
+            margins_name=margins_name,
+            aggfunc=["mean", "max"],
+        )
+        ix = Index(["bacon", "cheese", margins_name], name="item")
+        tups = [
+            ("mean", "cost", "ME"),
+            ("mean", "cost", "T"),
+            ("mean", "cost", margins_name),
+            ("max", "cost", "ME"),
+            ("max", "cost", "T"),
+            ("max", "cost", margins_name),
+        ]
+        cols = MultiIndex.from_tuples(tups, names=[None, None, "day"])
+        expected = DataFrame(table.values, index=ix, columns=cols)
+        tm.assert_frame_equal(table, expected)
+
+    def test_categorical_margins(self, observed):
+        # GH 10989
+        df = DataFrame(
+            {"x": np.arange(8), "y": np.arange(8) // 4, "z": np.arange(8) % 2}
+        )
+
+        expected = DataFrame([[1.0, 2.0, 1.5], [5, 6, 5.5], [3, 4, 3.5]])
+        expected.index = Index([0, 1, "All"], name="y")
+        expected.columns = Index([0, 1, "All"], name="z")
+
+        table = df.pivot_table("x", "y", "z", dropna=observed, margins=True)
+        tm.assert_frame_equal(table, expected)
+
+    def test_categorical_margins_category(self, observed):
+        df = DataFrame(
+            {"x": np.arange(8), "y": np.arange(8) // 4, "z": np.arange(8) % 2}
+        )
+
+        expected = DataFrame([[1.0, 2.0, 1.5], [5, 6, 5.5], [3, 4, 3.5]])
+        expected.index = Index([0, 1, "All"], name="y")
+        expected.columns = Index([0, 1, "All"], name="z")
+
+        df.y = df.y.astype("category")
+        df.z = df.z.astype("category")
+        msg = "The default value of observed=False is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            table = df.pivot_table("x", "y", "z", dropna=observed, margins=True)
+        tm.assert_frame_equal(table, expected)
+
+    def test_margins_casted_to_float(self):
+        # GH 24893
+        df = DataFrame(
+            {
+                "A": [2, 4, 6, 8],
+                "B": [1, 4, 5, 8],
+                "C": [1, 3, 4, 6],
+                "D": ["X", "X", "Y", "Y"],
+            }
+        )
+
+        result = pivot_table(df, index="D", margins=True)
+        expected = DataFrame(
+            {"A": [3.0, 7.0, 5], "B": [2.5, 6.5, 4.5], "C": [2.0, 5.0, 3.5]},
+            index=Index(["X", "Y", "All"], name="D"),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_with_categorical(self, observed, ordered):
+        # gh-21370
+        idx = [np.nan, "low", "high", "low", np.nan]
+        col = [np.nan, "A", "B", np.nan, "A"]
+        df = DataFrame(
+            {
+                "In": Categorical(idx, categories=["low", "high"], ordered=ordered),
+                "Col": Categorical(col, categories=["A", "B"], ordered=ordered),
+                "Val": range(1, 6),
+            }
+        )
+        # case with index/columns/value
+        result = df.pivot_table(
+            index="In", columns="Col", values="Val", observed=observed
+        )
+
+        expected_cols = pd.CategoricalIndex(["A", "B"], ordered=ordered, name="Col")
+
+        expected = DataFrame(data=[[2.0, np.nan], [np.nan, 3.0]], columns=expected_cols)
+        expected.index = Index(
+            Categorical(["low", "high"], categories=["low", "high"], ordered=ordered),
+            name="In",
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+        # case with columns/value
+        result = df.pivot_table(columns="Col", values="Val", observed=observed)
+
+        expected = DataFrame(
+            data=[[3.5, 3.0]], columns=expected_cols, index=Index(["Val"])
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_categorical_aggfunc(self, observed):
+        # GH 9534
+        df = DataFrame(
+            {"C1": ["A", "B", "C", "C"], "C2": ["a", "a", "b", "b"], "V": [1, 2, 3, 4]}
+        )
+        df["C1"] = df["C1"].astype("category")
+        msg = "The default value of observed=False is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = df.pivot_table(
+                "V", index="C1", columns="C2", dropna=observed, aggfunc="count"
+            )
+
+        expected_index = pd.CategoricalIndex(
+            ["A", "B", "C"], categories=["A", "B", "C"], ordered=False, name="C1"
+        )
+        expected_columns = Index(["a", "b"], name="C2")
+        expected_data = np.array([[1, 0], [1, 0], [0, 2]], dtype=np.int64)
+        expected = DataFrame(
+            expected_data, index=expected_index, columns=expected_columns
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_categorical_pivot_index_ordering(self, observed):
+        # GH 8731
+        df = DataFrame(
+            {
+                "Sales": [100, 120, 220],
+                "Month": ["January", "January", "January"],
+                "Year": [2013, 2014, 2013],
+            }
+        )
+        months = [
+            "January",
+            "February",
+            "March",
+            "April",
+            "May",
+            "June",
+            "July",
+            "August",
+            "September",
+            "October",
+            "November",
+            "December",
+        ]
+        df["Month"] = df["Month"].astype("category").cat.set_categories(months)
+        result = df.pivot_table(
+            values="Sales",
+            index="Month",
+            columns="Year",
+            observed=observed,
+            aggfunc="sum",
+        )
+        expected_columns = Index([2013, 2014], name="Year", dtype="int64")
+        expected_index = pd.CategoricalIndex(
+            months, categories=months, ordered=False, name="Month"
+        )
+        expected_data = [[320, 120]] + [[0, 0]] * 11
+        expected = DataFrame(
+            expected_data, index=expected_index, columns=expected_columns
+        )
+        if observed:
+            expected = expected.loc[["January"]]
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_table_not_series(self):
+        # GH 4386
+        # pivot_table always returns a DataFrame
+        # when values is not list like and columns is None
+        # and aggfunc is not instance of list
+        df = DataFrame({"col1": [3, 4, 5], "col2": ["C", "D", "E"], "col3": [1, 3, 9]})
+
+        result = df.pivot_table("col1", index=["col3", "col2"], aggfunc="sum")
+        m = MultiIndex.from_arrays([[1, 3, 9], ["C", "D", "E"]], names=["col3", "col2"])
+        expected = DataFrame([3, 4, 5], index=m, columns=["col1"])
+
+        tm.assert_frame_equal(result, expected)
+
+        result = df.pivot_table("col1", index="col3", columns="col2", aggfunc="sum")
+        expected = DataFrame(
+            [[3, np.nan, np.nan], [np.nan, 4, np.nan], [np.nan, np.nan, 5]],
+            index=Index([1, 3, 9], name="col3"),
+            columns=Index(["C", "D", "E"], name="col2"),
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+        result = df.pivot_table("col1", index="col3", aggfunc=["sum"])
+        m = MultiIndex.from_arrays([["sum"], ["col1"]])
+        expected = DataFrame([3, 4, 5], index=Index([1, 3, 9], name="col3"), columns=m)
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_margins_name_unicode(self):
+        # issue #13292
+        greek = "\u0394\u03bf\u03ba\u03b9\u03bc\u03ae"
+        frame = DataFrame({"foo": [1, 2, 3]}, columns=Index(["foo"], dtype=object))
+        table = pivot_table(
+            frame, index=["foo"], aggfunc=len, margins=True, margins_name=greek
+        )
+        index = Index([1, 2, 3, greek], dtype="object", name="foo")
+        expected = DataFrame(index=index, columns=[])
+        tm.assert_frame_equal(table, expected)
+
+    def test_pivot_string_as_func(self):
+        # GH #18713
+        # for correctness purposes
+        data = DataFrame(
+            {
+                "A": [
+                    "foo",
+                    "foo",
+                    "foo",
+                    "foo",
+                    "bar",
+                    "bar",
+                    "bar",
+                    "bar",
+                    "foo",
+                    "foo",
+                    "foo",
+                ],
+                "B": [
+                    "one",
+                    "one",
+                    "one",
+                    "two",
+                    "one",
+                    "one",
+                    "one",
+                    "two",
+                    "two",
+                    "two",
+                    "one",
+                ],
+                "C": range(11),
+            }
+        )
+
+        result = pivot_table(data, index="A", columns="B", aggfunc="sum")
+        mi = MultiIndex(
+            levels=[["C"], ["one", "two"]], codes=[[0, 0], [0, 1]], names=[None, "B"]
+        )
+        expected = DataFrame(
+            {("C", "one"): {"bar": 15, "foo": 13}, ("C", "two"): {"bar": 7, "foo": 20}},
+            columns=mi,
+        ).rename_axis("A")
+        tm.assert_frame_equal(result, expected)
+
+        result = pivot_table(data, index="A", columns="B", aggfunc=["sum", "mean"])
+        mi = MultiIndex(
+            levels=[["sum", "mean"], ["C"], ["one", "two"]],
+            codes=[[0, 0, 1, 1], [0, 0, 0, 0], [0, 1, 0, 1]],
+            names=[None, None, "B"],
+        )
+        expected = DataFrame(
+            {
+                ("mean", "C", "one"): {"bar": 5.0, "foo": 3.25},
+                ("mean", "C", "two"): {"bar": 7.0, "foo": 6.666666666666667},
+                ("sum", "C", "one"): {"bar": 15, "foo": 13},
+                ("sum", "C", "two"): {"bar": 7, "foo": 20},
+            },
+            columns=mi,
+        ).rename_axis("A")
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "f, f_numpy",
+        [
+            ("sum", np.sum),
+            ("mean", np.mean),
+            ("std", np.std),
+            (["sum", "mean"], [np.sum, np.mean]),
+            (["sum", "std"], [np.sum, np.std]),
+            (["std", "mean"], [np.std, np.mean]),
+        ],
+    )
+    def test_pivot_string_func_vs_func(self, f, f_numpy, data):
+        # GH #18713
+        # for consistency purposes
+        data = data.drop(columns="C")
+        result = pivot_table(data, index="A", columns="B", aggfunc=f)
+        ops = "|".join(f) if isinstance(f, list) else f
+        msg = f"using DataFrameGroupBy.[{ops}]"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            expected = pivot_table(data, index="A", columns="B", aggfunc=f_numpy)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.slow
+    def test_pivot_number_of_levels_larger_than_int32(self, monkeypatch):
+        # GH 20601
+        # GH 26314: Change ValueError to PerformanceWarning
+        class MockUnstacker(reshape_lib._Unstacker):
+            def __init__(self, *args, **kwargs) -> None:
+                # __init__ will raise the warning
+                super().__init__(*args, **kwargs)
+                raise Exception("Don't compute final result.")
+
+        with monkeypatch.context() as m:
+            m.setattr(reshape_lib, "_Unstacker", MockUnstacker)
+            df = DataFrame(
+                {"ind1": np.arange(2**16), "ind2": np.arange(2**16), "count": 0}
+            )
+
+            msg = "The following operation may generate"
+            with tm.assert_produces_warning(PerformanceWarning, match=msg):
+                with pytest.raises(Exception, match="Don't compute final result."):
+                    df.pivot_table(
+                        index="ind1", columns="ind2", values="count", aggfunc="count"
+                    )
+
+    def test_pivot_table_aggfunc_dropna(self, dropna):
+        # GH 22159
+        df = DataFrame(
+            {
+                "fruit": ["apple", "peach", "apple"],
+                "size": [1, 1, 2],
+                "taste": [7, 6, 6],
+            }
+        )
+
+        def ret_one(x):
+            return 1
+
+        def ret_sum(x):
+            return sum(x)
+
+        def ret_none(x):
+            return np.nan
+
+        result = pivot_table(
+            df, columns="fruit", aggfunc=[ret_sum, ret_none, ret_one], dropna=dropna
+        )
+
+        data = [[3, 1, np.nan, np.nan, 1, 1], [13, 6, np.nan, np.nan, 1, 1]]
+        col = MultiIndex.from_product(
+            [["ret_sum", "ret_none", "ret_one"], ["apple", "peach"]],
+            names=[None, "fruit"],
+        )
+        expected = DataFrame(data, index=["size", "taste"], columns=col)
+
+        if dropna:
+            expected = expected.dropna(axis="columns")
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_table_aggfunc_scalar_dropna(self, dropna):
+        # GH 22159
+        df = DataFrame(
+            {"A": ["one", "two", "one"], "x": [3, np.nan, 2], "y": [1, np.nan, np.nan]}
+        )
+
+        result = pivot_table(df, columns="A", aggfunc="mean", dropna=dropna)
+
+        data = [[2.5, np.nan], [1, np.nan]]
+        col = Index(["one", "two"], name="A")
+        expected = DataFrame(data, index=["x", "y"], columns=col)
+
+        if dropna:
+            expected = expected.dropna(axis="columns")
+
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("margins", [True, False])
+    def test_pivot_table_empty_aggfunc(self, margins):
+        # GH 9186 & GH 13483 & GH 49240
+        df = DataFrame(
+            {
+                "A": [2, 2, 3, 3, 2],
+                "id": [5, 6, 7, 8, 9],
+                "C": ["p", "q", "q", "p", "q"],
+                "D": [None, None, None, None, None],
+            }
+        )
+        result = df.pivot_table(
+            index="A", columns="D", values="id", aggfunc=np.size, margins=margins
+        )
+        exp_cols = Index([], name="D")
+        expected = DataFrame(index=Index([], dtype="int64", name="A"), columns=exp_cols)
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_table_no_column_raises(self):
+        # GH 10326
+        def agg(arr):
+            return np.mean(arr)
+
+        df = DataFrame({"X": [0, 0, 1, 1], "Y": [0, 1, 0, 1], "Z": [10, 20, 30, 40]})
+        with pytest.raises(KeyError, match="notpresent"):
+            df.pivot_table("notpresent", "X", "Y", aggfunc=agg)
+
+    def test_pivot_table_multiindex_columns_doctest_case(self):
+        # The relevant characteristic is that the call
+        #  to maybe_downcast_to_dtype(agged[v], data[v].dtype) in
+        #  __internal_pivot_table has `agged[v]` a DataFrame instead of Series,
+        #  In this case this is because agged.columns is a MultiIndex and 'v'
+        #  is only indexing on its first level.
+        df = DataFrame(
+            {
+                "A": ["foo", "foo", "foo", "foo", "foo", "bar", "bar", "bar", "bar"],
+                "B": ["one", "one", "one", "two", "two", "one", "one", "two", "two"],
+                "C": [
+                    "small",
+                    "large",
+                    "large",
+                    "small",
+                    "small",
+                    "large",
+                    "small",
+                    "small",
+                    "large",
+                ],
+                "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
+                "E": [2, 4, 5, 5, 6, 6, 8, 9, 9],
+            }
+        )
+
+        table = pivot_table(
+            df,
+            values=["D", "E"],
+            index=["A", "C"],
+            aggfunc={"D": "mean", "E": ["min", "max", "mean"]},
+        )
+        cols = MultiIndex.from_tuples(
+            [("D", "mean"), ("E", "max"), ("E", "mean"), ("E", "min")]
+        )
+        index = MultiIndex.from_tuples(
+            [("bar", "large"), ("bar", "small"), ("foo", "large"), ("foo", "small")],
+            names=["A", "C"],
+        )
+        vals = np.array(
+            [
+                [5.5, 9.0, 7.5, 6.0],
+                [5.5, 9.0, 8.5, 8.0],
+                [2.0, 5.0, 4.5, 4.0],
+                [2.33333333, 6.0, 4.33333333, 2.0],
+            ]
+        )
+        expected = DataFrame(vals, columns=cols, index=index)
+        expected[("E", "min")] = expected[("E", "min")].astype(np.int64)
+        expected[("E", "max")] = expected[("E", "max")].astype(np.int64)
+        tm.assert_frame_equal(table, expected)
+
+    def test_pivot_table_sort_false(self):
+        # GH#39143
+        df = DataFrame(
+            {
+                "a": ["d1", "d4", "d3"],
+                "col": ["a", "b", "c"],
+                "num": [23, 21, 34],
+                "year": ["2018", "2018", "2019"],
+            }
+        )
+        result = df.pivot_table(
+            index=["a", "col"], columns="year", values="num", aggfunc="sum", sort=False
+        )
+        expected = DataFrame(
+            [[23, np.nan], [21, np.nan], [np.nan, 34]],
+            columns=Index(["2018", "2019"], name="year"),
+            index=MultiIndex.from_arrays(
+                [["d1", "d4", "d3"], ["a", "b", "c"]], names=["a", "col"]
+            ),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_table_nullable_margins(self):
+        # GH#48681
+        df = DataFrame(
+            {"a": "A", "b": [1, 2], "sales": Series([10, 11], dtype="Int64")}
+        )
+
+        result = df.pivot_table(index="b", columns="a", margins=True, aggfunc="sum")
+        expected = DataFrame(
+            [[10, 10], [11, 11], [21, 21]],
+            index=Index([1, 2, "All"], name="b"),
+            columns=MultiIndex.from_tuples(
+                [("sales", "A"), ("sales", "All")], names=[None, "a"]
+            ),
+            dtype="Int64",
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_table_sort_false_with_multiple_values(self):
+        df = DataFrame(
+            {
+                "firstname": ["John", "Michael"],
+                "lastname": ["Foo", "Bar"],
+                "height": [173, 182],
+                "age": [47, 33],
+            }
+        )
+        result = df.pivot_table(
+            index=["lastname", "firstname"], values=["height", "age"], sort=False
+        )
+        expected = DataFrame(
+            [[173.0, 47.0], [182.0, 33.0]],
+            columns=["height", "age"],
+            index=MultiIndex.from_tuples(
+                [("Foo", "John"), ("Bar", "Michael")],
+                names=["lastname", "firstname"],
+            ),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_table_with_margins_and_numeric_columns(self):
+        # GH 26568
+        df = DataFrame([["a", "x", 1], ["a", "y", 2], ["b", "y", 3], ["b", "z", 4]])
+        df.columns = [10, 20, 30]
+
+        result = df.pivot_table(
+            index=10, columns=20, values=30, aggfunc="sum", fill_value=0, margins=True
+        )
+
+        expected = DataFrame([[1, 2, 0, 3], [0, 3, 4, 7], [1, 5, 4, 10]])
+        expected.columns = ["x", "y", "z", "All"]
+        expected.index = ["a", "b", "All"]
+        expected.columns.name = 20
+        expected.index.name = 10
+
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("dropna", [True, False])
+    def test_pivot_ea_dtype_dropna(self, dropna):
+        # GH#47477
+        df = DataFrame({"x": "a", "y": "b", "age": Series([20, 40], dtype="Int64")})
+        result = df.pivot_table(
+            index="x", columns="y", values="age", aggfunc="mean", dropna=dropna
+        )
+        expected = DataFrame(
+            [[30]],
+            index=Index(["a"], name="x"),
+            columns=Index(["b"], name="y"),
+            dtype="Float64",
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_table_datetime_warning(self):
+        # GH#48683
+        df = DataFrame(
+            {
+                "a": "A",
+                "b": [1, 2],
+                "date": pd.Timestamp("2019-12-31"),
+                "sales": [10.0, 11],
+            }
+        )
+        with tm.assert_produces_warning(None):
+            result = df.pivot_table(
+                index=["b", "date"], columns="a", margins=True, aggfunc="sum"
+            )
+        expected = DataFrame(
+            [[10.0, 10.0], [11.0, 11.0], [21.0, 21.0]],
+            index=MultiIndex.from_arrays(
+                [
+                    Index([1, 2, "All"], name="b"),
+                    Index(
+                        [pd.Timestamp("2019-12-31"), pd.Timestamp("2019-12-31"), ""],
+                        dtype=object,
+                        name="date",
+                    ),
+                ]
+            ),
+            columns=MultiIndex.from_tuples(
+                [("sales", "A"), ("sales", "All")], names=[None, "a"]
+            ),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_table_with_mixed_nested_tuples(self, using_array_manager):
+        # GH 50342
+        df = DataFrame(
+            {
+                "A": ["foo", "foo", "foo", "foo", "foo", "bar", "bar", "bar", "bar"],
+                "B": ["one", "one", "one", "two", "two", "one", "one", "two", "two"],
+                "C": [
+                    "small",
+                    "large",
+                    "large",
+                    "small",
+                    "small",
+                    "large",
+                    "small",
+                    "small",
+                    "large",
+                ],
+                "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
+                "E": [2, 4, 5, 5, 6, 6, 8, 9, 9],
+                ("col5",): [
+                    "foo",
+                    "foo",
+                    "foo",
+                    "foo",
+                    "foo",
+                    "bar",
+                    "bar",
+                    "bar",
+                    "bar",
+                ],
+                ("col6", 6): [
+                    "one",
+                    "one",
+                    "one",
+                    "two",
+                    "two",
+                    "one",
+                    "one",
+                    "two",
+                    "two",
+                ],
+                (7, "seven"): [
+                    "small",
+                    "large",
+                    "large",
+                    "small",
+                    "small",
+                    "large",
+                    "small",
+                    "small",
+                    "large",
+                ],
+            }
+        )
+        result = pivot_table(
+            df, values="D", index=["A", "B"], columns=[(7, "seven")], aggfunc="sum"
+        )
+        expected = DataFrame(
+            [[4.0, 5.0], [7.0, 6.0], [4.0, 1.0], [np.nan, 6.0]],
+            columns=Index(["large", "small"], name=(7, "seven")),
+            index=MultiIndex.from_arrays(
+                [["bar", "bar", "foo", "foo"], ["one", "two"] * 2], names=["A", "B"]
+            ),
+        )
+        if using_array_manager:
+            # INFO(ArrayManager) column without NaNs can preserve int dtype
+            expected["small"] = expected["small"].astype("int64")
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_table_aggfunc_nunique_with_different_values(self):
+        test = DataFrame(
+            {
+                "a": range(10),
+                "b": range(10),
+                "c": range(10),
+                "d": range(10),
+            }
+        )
+
+        columnval = MultiIndex.from_arrays(
+            [
+                ["nunique" for i in range(10)],
+                ["c" for i in range(10)],
+                range(10),
+            ],
+            names=(None, None, "b"),
+        )
+        nparr = np.full((10, 10), np.nan)
+        np.fill_diagonal(nparr, 1.0)
+
+        expected = DataFrame(nparr, index=Index(range(10), name="a"), columns=columnval)
+        result = test.pivot_table(
+            index=[
+                "a",
+            ],
+            columns=[
+                "b",
+            ],
+            values=[
+                "c",
+            ],
+            aggfunc=["nunique"],
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+
+class TestPivot:
+    def test_pivot(self):
+        data = {
+            "index": ["A", "B", "C", "C", "B", "A"],
+            "columns": ["One", "One", "One", "Two", "Two", "Two"],
+            "values": [1.0, 2.0, 3.0, 3.0, 2.0, 1.0],
+        }
+
+        frame = DataFrame(data)
+        pivoted = frame.pivot(index="index", columns="columns", values="values")
+
+        expected = DataFrame(
+            {
+                "One": {"A": 1.0, "B": 2.0, "C": 3.0},
+                "Two": {"A": 1.0, "B": 2.0, "C": 3.0},
+            }
+        )
+
+        expected.index.name, expected.columns.name = "index", "columns"
+        tm.assert_frame_equal(pivoted, expected)
+
+        # name tracking
+        assert pivoted.index.name == "index"
+        assert pivoted.columns.name == "columns"
+
+        # don't specify values
+        pivoted = frame.pivot(index="index", columns="columns")
+        assert pivoted.index.name == "index"
+        assert pivoted.columns.names == (None, "columns")
+
+    def test_pivot_duplicates(self):
+        data = DataFrame(
+            {
+                "a": ["bar", "bar", "foo", "foo", "foo"],
+                "b": ["one", "two", "one", "one", "two"],
+                "c": [1.0, 2.0, 3.0, 3.0, 4.0],
+            }
+        )
+        with pytest.raises(ValueError, match="duplicate entries"):
+            data.pivot(index="a", columns="b", values="c")
+
+    def test_pivot_empty(self):
+        df = DataFrame(columns=["a", "b", "c"])
+        result = df.pivot(index="a", columns="b", values="c")
+        expected = DataFrame(index=[], columns=[])
+        tm.assert_frame_equal(result, expected, check_names=False)
+
+    @pytest.mark.parametrize("dtype", [object, "string"])
+    def test_pivot_integer_bug(self, dtype):
+        df = DataFrame(data=[("A", "1", "A1"), ("B", "2", "B2")], dtype=dtype)
+
+        result = df.pivot(index=1, columns=0, values=2)
+        tm.assert_index_equal(result.columns, Index(["A", "B"], name=0, dtype=dtype))
+
+    def test_pivot_index_none(self):
+        # GH#3962
+        data = {
+            "index": ["A", "B", "C", "C", "B", "A"],
+            "columns": ["One", "One", "One", "Two", "Two", "Two"],
+            "values": [1.0, 2.0, 3.0, 3.0, 2.0, 1.0],
+        }
+
+        frame = DataFrame(data).set_index("index")
+        result = frame.pivot(columns="columns", values="values")
+        expected = DataFrame(
+            {
+                "One": {"A": 1.0, "B": 2.0, "C": 3.0},
+                "Two": {"A": 1.0, "B": 2.0, "C": 3.0},
+            }
+        )
+
+        expected.index.name, expected.columns.name = "index", "columns"
+        tm.assert_frame_equal(result, expected)
+
+        # omit values
+        result = frame.pivot(columns="columns")
+
+        expected.columns = MultiIndex.from_tuples(
+            [("values", "One"), ("values", "Two")], names=[None, "columns"]
+        )
+        expected.index.name = "index"
+        tm.assert_frame_equal(result, expected, check_names=False)
+        assert result.index.name == "index"
+        assert result.columns.names == (None, "columns")
+        expected.columns = expected.columns.droplevel(0)
+        result = frame.pivot(columns="columns", values="values")
+
+        expected.columns.name = "columns"
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_index_list_values_none_immutable_args(self):
+        # GH37635
+        df = DataFrame(
+            {
+                "lev1": [1, 1, 1, 2, 2, 2],
+                "lev2": [1, 1, 2, 1, 1, 2],
+                "lev3": [1, 2, 1, 2, 1, 2],
+                "lev4": [1, 2, 3, 4, 5, 6],
+                "values": [0, 1, 2, 3, 4, 5],
+            }
+        )
+        index = ["lev1", "lev2"]
+        columns = ["lev3"]
+        result = df.pivot(index=index, columns=columns)
+
+        expected = DataFrame(
+            np.array(
+                [
+                    [1.0, 2.0, 0.0, 1.0],
+                    [3.0, np.nan, 2.0, np.nan],
+                    [5.0, 4.0, 4.0, 3.0],
+                    [np.nan, 6.0, np.nan, 5.0],
+                ]
+            ),
+            index=MultiIndex.from_arrays(
+                [(1, 1, 2, 2), (1, 2, 1, 2)], names=["lev1", "lev2"]
+            ),
+            columns=MultiIndex.from_arrays(
+                [("lev4", "lev4", "values", "values"), (1, 2, 1, 2)],
+                names=[None, "lev3"],
+            ),
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+        assert index == ["lev1", "lev2"]
+        assert columns == ["lev3"]
+
+    def test_pivot_columns_not_given(self):
+        # GH#48293
+        df = DataFrame({"a": [1], "b": 1})
+        with pytest.raises(TypeError, match="missing 1 required keyword-only argument"):
+            df.pivot()  # pylint: disable=missing-kwoa
+
+    @pytest.mark.xfail(using_pyarrow_string_dtype(), reason="None is cast to NaN")
+    def test_pivot_columns_is_none(self):
+        # GH#48293
+        df = DataFrame({None: [1], "b": 2, "c": 3})
+        result = df.pivot(columns=None)
+        expected = DataFrame({("b", 1): [2], ("c", 1): 3})
+        tm.assert_frame_equal(result, expected)
+
+        result = df.pivot(columns=None, index="b")
+        expected = DataFrame({("c", 1): 3}, index=Index([2], name="b"))
+        tm.assert_frame_equal(result, expected)
+
+        result = df.pivot(columns=None, index="b", values="c")
+        expected = DataFrame({1: 3}, index=Index([2], name="b"))
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.xfail(using_pyarrow_string_dtype(), reason="None is cast to NaN")
+    def test_pivot_index_is_none(self):
+        # GH#48293
+        df = DataFrame({None: [1], "b": 2, "c": 3})
+
+        result = df.pivot(columns="b", index=None)
+        expected = DataFrame({("c", 2): 3}, index=[1])
+        expected.columns.names = [None, "b"]
+        tm.assert_frame_equal(result, expected)
+
+        result = df.pivot(columns="b", index=None, values="c")
+        expected = DataFrame(3, index=[1], columns=Index([2], name="b"))
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.xfail(using_pyarrow_string_dtype(), reason="None is cast to NaN")
+    def test_pivot_values_is_none(self):
+        # GH#48293
+        df = DataFrame({None: [1], "b": 2, "c": 3})
+
+        result = df.pivot(columns="b", index="c", values=None)
+        expected = DataFrame(
+            1, index=Index([3], name="c"), columns=Index([2], name="b")
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = df.pivot(columns="b", values=None)
+        expected = DataFrame(1, index=[0], columns=Index([2], name="b"))
+        tm.assert_frame_equal(result, expected)
+
+    def test_pivot_not_changing_index_name(self):
+        # GH#52692
+        df = DataFrame({"one": ["a"], "two": 0, "three": 1})
+        expected = df.copy(deep=True)
+        df.pivot(index="one", columns="two", values="three")
+        tm.assert_frame_equal(df, expected)
+
+    def test_pivot_table_empty_dataframe_correct_index(self):
+        # GH 21932
+        df = DataFrame([], columns=["a", "b", "value"])
+        pivot = df.pivot_table(index="a", columns="b", values="value", aggfunc="count")
+
+        expected = Index([], dtype="object", name="b")
+        tm.assert_index_equal(pivot.columns, expected)
+
+    def test_pivot_table_handles_explicit_datetime_types(self):
+        # GH#43574
+        df = DataFrame(
+            [
+                {"a": "x", "date_str": "2023-01-01", "amount": 1},
+                {"a": "y", "date_str": "2023-01-02", "amount": 2},
+                {"a": "z", "date_str": "2023-01-03", "amount": 3},
+            ]
+        )
+        df["date"] = pd.to_datetime(df["date_str"])
+
+        with tm.assert_produces_warning(False):
+            pivot = df.pivot_table(
+                index=["a", "date"], values=["amount"], aggfunc="sum", margins=True
+            )
+
+        expected = MultiIndex.from_tuples(
+            [
+                ("x", datetime.strptime("2023-01-01 00:00:00", "%Y-%m-%d %H:%M:%S")),
+                ("y", datetime.strptime("2023-01-02 00:00:00", "%Y-%m-%d %H:%M:%S")),
+                ("z", datetime.strptime("2023-01-03 00:00:00", "%Y-%m-%d %H:%M:%S")),
+                ("All", ""),
+            ],
+            names=["a", "date"],
+        )
+        tm.assert_index_equal(pivot.index, expected)
+
+    def test_pivot_table_with_margins_and_numeric_column_names(self):
+        # GH#26568
+        df = DataFrame([["a", "x", 1], ["a", "y", 2], ["b", "y", 3], ["b", "z", 4]])
+
+        result = df.pivot_table(
+            index=0, columns=1, values=2, aggfunc="sum", fill_value=0, margins=True
+        )
+
+        expected = DataFrame(
+            [[1, 2, 0, 3], [0, 3, 4, 7], [1, 5, 4, 10]],
+            columns=Index(["x", "y", "z", "All"], name=1),
+            index=Index(["a", "b", "All"], name=0),
+        )
+        tm.assert_frame_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/test_union_categoricals.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/test_union_categoricals.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d78d34e936f0eba01bcd2b2a1135271f7e20918
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/test_union_categoricals.py
@@ -0,0 +1,365 @@
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.concat import union_categoricals
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    CategoricalIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestUnionCategoricals:
+    @pytest.mark.parametrize(
+        "a, b, combined",
+        [
+            (list("abc"), list("abd"), list("abcabd")),
+            ([0, 1, 2], [2, 3, 4], [0, 1, 2, 2, 3, 4]),
+            ([0, 1.2, 2], [2, 3.4, 4], [0, 1.2, 2, 2, 3.4, 4]),
+            (
+                ["b", "b", np.nan, "a"],
+                ["a", np.nan, "c"],
+                ["b", "b", np.nan, "a", "a", np.nan, "c"],
+            ),
+            (
+                pd.date_range("2014-01-01", "2014-01-05"),
+                pd.date_range("2014-01-06", "2014-01-07"),
+                pd.date_range("2014-01-01", "2014-01-07"),
+            ),
+            (
+                pd.date_range("2014-01-01", "2014-01-05", tz="US/Central"),
+                pd.date_range("2014-01-06", "2014-01-07", tz="US/Central"),
+                pd.date_range("2014-01-01", "2014-01-07", tz="US/Central"),
+            ),
+            (
+                pd.period_range("2014-01-01", "2014-01-05"),
+                pd.period_range("2014-01-06", "2014-01-07"),
+                pd.period_range("2014-01-01", "2014-01-07"),
+            ),
+        ],
+    )
+    @pytest.mark.parametrize("box", [Categorical, CategoricalIndex, Series])
+    def test_union_categorical(self, a, b, combined, box):
+        # GH 13361
+        result = union_categoricals([box(Categorical(a)), box(Categorical(b))])
+        expected = Categorical(combined)
+        tm.assert_categorical_equal(result, expected)
+
+    def test_union_categorical_ordered_appearance(self):
+        # new categories ordered by appearance
+        s = Categorical(["x", "y", "z"])
+        s2 = Categorical(["a", "b", "c"])
+        result = union_categoricals([s, s2])
+        expected = Categorical(
+            ["x", "y", "z", "a", "b", "c"], categories=["x", "y", "z", "a", "b", "c"]
+        )
+        tm.assert_categorical_equal(result, expected)
+
+    def test_union_categorical_ordered_true(self):
+        s = Categorical([0, 1.2, 2], ordered=True)
+        s2 = Categorical([0, 1.2, 2], ordered=True)
+        result = union_categoricals([s, s2])
+        expected = Categorical([0, 1.2, 2, 0, 1.2, 2], ordered=True)
+        tm.assert_categorical_equal(result, expected)
+
+    def test_union_categorical_match_types(self):
+        # must exactly match types
+        s = Categorical([0, 1.2, 2])
+        s2 = Categorical([2, 3, 4])
+        msg = "dtype of categories must be the same"
+        with pytest.raises(TypeError, match=msg):
+            union_categoricals([s, s2])
+
+    def test_union_categorical_empty(self):
+        msg = "No Categoricals to union"
+        with pytest.raises(ValueError, match=msg):
+            union_categoricals([])
+
+    def test_union_categoricals_nan(self):
+        # GH 13759
+        res = union_categoricals(
+            [Categorical([1, 2, np.nan]), Categorical([3, 2, np.nan])]
+        )
+        exp = Categorical([1, 2, np.nan, 3, 2, np.nan])
+        tm.assert_categorical_equal(res, exp)
+
+        res = union_categoricals(
+            [Categorical(["A", "B"]), Categorical(["B", "B", np.nan])]
+        )
+        exp = Categorical(["A", "B", "B", "B", np.nan])
+        tm.assert_categorical_equal(res, exp)
+
+        val1 = [pd.Timestamp("2011-01-01"), pd.Timestamp("2011-03-01"), pd.NaT]
+        val2 = [pd.NaT, pd.Timestamp("2011-01-01"), pd.Timestamp("2011-02-01")]
+
+        res = union_categoricals([Categorical(val1), Categorical(val2)])
+        exp = Categorical(
+            val1 + val2,
+            categories=[
+                pd.Timestamp("2011-01-01"),
+                pd.Timestamp("2011-03-01"),
+                pd.Timestamp("2011-02-01"),
+            ],
+        )
+        tm.assert_categorical_equal(res, exp)
+
+        # all NaN
+        res = union_categoricals(
+            [
+                Categorical(np.array([np.nan, np.nan], dtype=object)),
+                Categorical(["X"], categories=pd.Index(["X"], dtype=object)),
+            ]
+        )
+        exp = Categorical([np.nan, np.nan, "X"])
+        tm.assert_categorical_equal(res, exp)
+
+        res = union_categoricals(
+            [Categorical([np.nan, np.nan]), Categorical([np.nan, np.nan])]
+        )
+        exp = Categorical([np.nan, np.nan, np.nan, np.nan])
+        tm.assert_categorical_equal(res, exp)
+
+    @pytest.mark.parametrize("val", [[], ["1"]])
+    def test_union_categoricals_empty(self, val, request, using_infer_string):
+        # GH 13759
+        if using_infer_string and val == ["1"]:
+            request.applymarker(pytest.mark.xfail("object and strings dont match"))
+        res = union_categoricals([Categorical([]), Categorical(val)])
+        exp = Categorical(val)
+        tm.assert_categorical_equal(res, exp)
+
+    def test_union_categorical_same_category(self):
+        # check fastpath
+        c1 = Categorical([1, 2, 3, 4], categories=[1, 2, 3, 4])
+        c2 = Categorical([3, 2, 1, np.nan], categories=[1, 2, 3, 4])
+        res = union_categoricals([c1, c2])
+        exp = Categorical([1, 2, 3, 4, 3, 2, 1, np.nan], categories=[1, 2, 3, 4])
+        tm.assert_categorical_equal(res, exp)
+
+    def test_union_categorical_same_category_str(self):
+        c1 = Categorical(["z", "z", "z"], categories=["x", "y", "z"])
+        c2 = Categorical(["x", "x", "x"], categories=["x", "y", "z"])
+        res = union_categoricals([c1, c2])
+        exp = Categorical(["z", "z", "z", "x", "x", "x"], categories=["x", "y", "z"])
+        tm.assert_categorical_equal(res, exp)
+
+    def test_union_categorical_same_categories_different_order(self):
+        # https://github.com/pandas-dev/pandas/issues/19096
+        c1 = Categorical(["a", "b", "c"], categories=["a", "b", "c"])
+        c2 = Categorical(["a", "b", "c"], categories=["b", "a", "c"])
+        result = union_categoricals([c1, c2])
+        expected = Categorical(
+            ["a", "b", "c", "a", "b", "c"], categories=["a", "b", "c"]
+        )
+        tm.assert_categorical_equal(result, expected)
+
+    def test_union_categoricals_ordered(self):
+        c1 = Categorical([1, 2, 3], ordered=True)
+        c2 = Categorical([1, 2, 3], ordered=False)
+
+        msg = "Categorical.ordered must be the same"
+        with pytest.raises(TypeError, match=msg):
+            union_categoricals([c1, c2])
+
+        res = union_categoricals([c1, c1])
+        exp = Categorical([1, 2, 3, 1, 2, 3], ordered=True)
+        tm.assert_categorical_equal(res, exp)
+
+        c1 = Categorical([1, 2, 3, np.nan], ordered=True)
+        c2 = Categorical([3, 2], categories=[1, 2, 3], ordered=True)
+
+        res = union_categoricals([c1, c2])
+        exp = Categorical([1, 2, 3, np.nan, 3, 2], ordered=True)
+        tm.assert_categorical_equal(res, exp)
+
+        c1 = Categorical([1, 2, 3], ordered=True)
+        c2 = Categorical([1, 2, 3], categories=[3, 2, 1], ordered=True)
+
+        msg = "to union ordered Categoricals, all categories must be the same"
+        with pytest.raises(TypeError, match=msg):
+            union_categoricals([c1, c2])
+
+    def test_union_categoricals_ignore_order(self):
+        # GH 15219
+        c1 = Categorical([1, 2, 3], ordered=True)
+        c2 = Categorical([1, 2, 3], ordered=False)
+
+        res = union_categoricals([c1, c2], ignore_order=True)
+        exp = Categorical([1, 2, 3, 1, 2, 3])
+        tm.assert_categorical_equal(res, exp)
+
+        msg = "Categorical.ordered must be the same"
+        with pytest.raises(TypeError, match=msg):
+            union_categoricals([c1, c2], ignore_order=False)
+
+        res = union_categoricals([c1, c1], ignore_order=True)
+        exp = Categorical([1, 2, 3, 1, 2, 3])
+        tm.assert_categorical_equal(res, exp)
+
+        res = union_categoricals([c1, c1], ignore_order=False)
+        exp = Categorical([1, 2, 3, 1, 2, 3], categories=[1, 2, 3], ordered=True)
+        tm.assert_categorical_equal(res, exp)
+
+        c1 = Categorical([1, 2, 3, np.nan], ordered=True)
+        c2 = Categorical([3, 2], categories=[1, 2, 3], ordered=True)
+
+        res = union_categoricals([c1, c2], ignore_order=True)
+        exp = Categorical([1, 2, 3, np.nan, 3, 2])
+        tm.assert_categorical_equal(res, exp)
+
+        c1 = Categorical([1, 2, 3], ordered=True)
+        c2 = Categorical([1, 2, 3], categories=[3, 2, 1], ordered=True)
+
+        res = union_categoricals([c1, c2], ignore_order=True)
+        exp = Categorical([1, 2, 3, 1, 2, 3])
+        tm.assert_categorical_equal(res, exp)
+
+        res = union_categoricals([c2, c1], ignore_order=True, sort_categories=True)
+        exp = Categorical([1, 2, 3, 1, 2, 3], categories=[1, 2, 3])
+        tm.assert_categorical_equal(res, exp)
+
+        c1 = Categorical([1, 2, 3], ordered=True)
+        c2 = Categorical([4, 5, 6], ordered=True)
+        result = union_categoricals([c1, c2], ignore_order=True)
+        expected = Categorical([1, 2, 3, 4, 5, 6])
+        tm.assert_categorical_equal(result, expected)
+
+        msg = "to union ordered Categoricals, all categories must be the same"
+        with pytest.raises(TypeError, match=msg):
+            union_categoricals([c1, c2], ignore_order=False)
+
+        with pytest.raises(TypeError, match=msg):
+            union_categoricals([c1, c2])
+
+    def test_union_categoricals_sort(self):
+        # GH 13846
+        c1 = Categorical(["x", "y", "z"])
+        c2 = Categorical(["a", "b", "c"])
+        result = union_categoricals([c1, c2], sort_categories=True)
+        expected = Categorical(
+            ["x", "y", "z", "a", "b", "c"], categories=["a", "b", "c", "x", "y", "z"]
+        )
+        tm.assert_categorical_equal(result, expected)
+
+        # fastpath
+        c1 = Categorical(["a", "b"], categories=["b", "a", "c"])
+        c2 = Categorical(["b", "c"], categories=["b", "a", "c"])
+        result = union_categoricals([c1, c2], sort_categories=True)
+        expected = Categorical(["a", "b", "b", "c"], categories=["a", "b", "c"])
+        tm.assert_categorical_equal(result, expected)
+
+        c1 = Categorical(["a", "b"], categories=["c", "a", "b"])
+        c2 = Categorical(["b", "c"], categories=["c", "a", "b"])
+        result = union_categoricals([c1, c2], sort_categories=True)
+        expected = Categorical(["a", "b", "b", "c"], categories=["a", "b", "c"])
+        tm.assert_categorical_equal(result, expected)
+
+        # fastpath - skip resort
+        c1 = Categorical(["a", "b"], categories=["a", "b", "c"])
+        c2 = Categorical(["b", "c"], categories=["a", "b", "c"])
+        result = union_categoricals([c1, c2], sort_categories=True)
+        expected = Categorical(["a", "b", "b", "c"], categories=["a", "b", "c"])
+        tm.assert_categorical_equal(result, expected)
+
+        c1 = Categorical(["x", np.nan])
+        c2 = Categorical([np.nan, "b"])
+        result = union_categoricals([c1, c2], sort_categories=True)
+        expected = Categorical(["x", np.nan, np.nan, "b"], categories=["b", "x"])
+        tm.assert_categorical_equal(result, expected)
+
+        c1 = Categorical([np.nan])
+        c2 = Categorical([np.nan])
+        result = union_categoricals([c1, c2], sort_categories=True)
+        expected = Categorical([np.nan, np.nan])
+        tm.assert_categorical_equal(result, expected)
+
+        c1 = Categorical([])
+        c2 = Categorical([])
+        result = union_categoricals([c1, c2], sort_categories=True)
+        expected = Categorical([])
+        tm.assert_categorical_equal(result, expected)
+
+        c1 = Categorical(["b", "a"], categories=["b", "a", "c"], ordered=True)
+        c2 = Categorical(["a", "c"], categories=["b", "a", "c"], ordered=True)
+        msg = "Cannot use sort_categories=True with ordered Categoricals"
+        with pytest.raises(TypeError, match=msg):
+            union_categoricals([c1, c2], sort_categories=True)
+
+    def test_union_categoricals_sort_false(self):
+        # GH 13846
+        c1 = Categorical(["x", "y", "z"])
+        c2 = Categorical(["a", "b", "c"])
+        result = union_categoricals([c1, c2], sort_categories=False)
+        expected = Categorical(
+            ["x", "y", "z", "a", "b", "c"], categories=["x", "y", "z", "a", "b", "c"]
+        )
+        tm.assert_categorical_equal(result, expected)
+
+    def test_union_categoricals_sort_false_fastpath(self):
+        # fastpath
+        c1 = Categorical(["a", "b"], categories=["b", "a", "c"])
+        c2 = Categorical(["b", "c"], categories=["b", "a", "c"])
+        result = union_categoricals([c1, c2], sort_categories=False)
+        expected = Categorical(["a", "b", "b", "c"], categories=["b", "a", "c"])
+        tm.assert_categorical_equal(result, expected)
+
+    def test_union_categoricals_sort_false_skipresort(self):
+        # fastpath - skip resort
+        c1 = Categorical(["a", "b"], categories=["a", "b", "c"])
+        c2 = Categorical(["b", "c"], categories=["a", "b", "c"])
+        result = union_categoricals([c1, c2], sort_categories=False)
+        expected = Categorical(["a", "b", "b", "c"], categories=["a", "b", "c"])
+        tm.assert_categorical_equal(result, expected)
+
+    def test_union_categoricals_sort_false_one_nan(self):
+        c1 = Categorical(["x", np.nan])
+        c2 = Categorical([np.nan, "b"])
+        result = union_categoricals([c1, c2], sort_categories=False)
+        expected = Categorical(["x", np.nan, np.nan, "b"], categories=["x", "b"])
+        tm.assert_categorical_equal(result, expected)
+
+    def test_union_categoricals_sort_false_only_nan(self):
+        c1 = Categorical([np.nan])
+        c2 = Categorical([np.nan])
+        result = union_categoricals([c1, c2], sort_categories=False)
+        expected = Categorical([np.nan, np.nan])
+        tm.assert_categorical_equal(result, expected)
+
+    def test_union_categoricals_sort_false_empty(self):
+        c1 = Categorical([])
+        c2 = Categorical([])
+        result = union_categoricals([c1, c2], sort_categories=False)
+        expected = Categorical([])
+        tm.assert_categorical_equal(result, expected)
+
+    def test_union_categoricals_sort_false_ordered_true(self):
+        c1 = Categorical(["b", "a"], categories=["b", "a", "c"], ordered=True)
+        c2 = Categorical(["a", "c"], categories=["b", "a", "c"], ordered=True)
+        result = union_categoricals([c1, c2], sort_categories=False)
+        expected = Categorical(
+            ["b", "a", "a", "c"], categories=["b", "a", "c"], ordered=True
+        )
+        tm.assert_categorical_equal(result, expected)
+
+    def test_union_categorical_unwrap(self):
+        # GH 14173
+        c1 = Categorical(["a", "b"])
+        c2 = Series(["b", "c"], dtype="category")
+        result = union_categoricals([c1, c2])
+        expected = Categorical(["a", "b", "b", "c"])
+        tm.assert_categorical_equal(result, expected)
+
+        c2 = CategoricalIndex(c2)
+        result = union_categoricals([c1, c2])
+        tm.assert_categorical_equal(result, expected)
+
+        c1 = Series(c1)
+        result = union_categoricals([c1, c2])
+        tm.assert_categorical_equal(result, expected)
+
+        msg = "all components to combine must be Categorical"
+        with pytest.raises(TypeError, match=msg):
+            union_categoricals([c1, ["a", "b", "c"]])
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/test_util.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/test_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d0be7464cb3d97697323faef5b4e7cd0d9b6df0
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/reshape/test_util.py
@@ -0,0 +1,79 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Index,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.core.reshape.util import cartesian_product
+
+
+class TestCartesianProduct:
+    def test_simple(self):
+        x, y = list("ABC"), [1, 22]
+        result1, result2 = cartesian_product([x, y])
+        expected1 = np.array(["A", "A", "B", "B", "C", "C"])
+        expected2 = np.array([1, 22, 1, 22, 1, 22])
+        tm.assert_numpy_array_equal(result1, expected1)
+        tm.assert_numpy_array_equal(result2, expected2)
+
+    def test_datetimeindex(self):
+        # regression test for GitHub issue #6439
+        # make sure that the ordering on datetimeindex is consistent
+        x = date_range("2000-01-01", periods=2)
+        result1, result2 = (Index(y).day for y in cartesian_product([x, x]))
+        expected1 = Index([1, 1, 2, 2], dtype=np.int32)
+        expected2 = Index([1, 2, 1, 2], dtype=np.int32)
+        tm.assert_index_equal(result1, expected1)
+        tm.assert_index_equal(result2, expected2)
+
+    def test_tzaware_retained(self):
+        x = date_range("2000-01-01", periods=2, tz="US/Pacific")
+        y = np.array([3, 4])
+        result1, result2 = cartesian_product([x, y])
+
+        expected = x.repeat(2)
+        tm.assert_index_equal(result1, expected)
+
+    def test_tzaware_retained_categorical(self):
+        x = date_range("2000-01-01", periods=2, tz="US/Pacific").astype("category")
+        y = np.array([3, 4])
+        result1, result2 = cartesian_product([x, y])
+
+        expected = x.repeat(2)
+        tm.assert_index_equal(result1, expected)
+
+    @pytest.mark.parametrize("x, y", [[[], []], [[0, 1], []], [[], ["a", "b", "c"]]])
+    def test_empty(self, x, y):
+        # product of empty factors
+        expected1 = np.array([], dtype=np.asarray(x).dtype)
+        expected2 = np.array([], dtype=np.asarray(y).dtype)
+        result1, result2 = cartesian_product([x, y])
+        tm.assert_numpy_array_equal(result1, expected1)
+        tm.assert_numpy_array_equal(result2, expected2)
+
+    def test_empty_input(self):
+        # empty product (empty input):
+        result = cartesian_product([])
+        expected = []
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "X", [1, [1], [1, 2], [[1], 2], "a", ["a"], ["a", "b"], [["a"], "b"]]
+    )
+    def test_invalid_input(self, X):
+        msg = "Input must be a list-like of list-likes"
+
+        with pytest.raises(TypeError, match=msg):
+            cartesian_product(X=X)
+
+    def test_exceed_product_space(self):
+        # GH31355: raise useful error when produce space is too large
+        msg = "Product space too large to allocate arrays!"
+
+        with pytest.raises(ValueError, match=msg):
+            dims = [np.arange(0, 22, dtype=np.int16) for i in range(12)] + [
+                (np.arange(15128, dtype=np.int16)),
+            ]
+            cartesian_product(X=dims)