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diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_constructors.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_constructors.py
new file mode 100644
index 0000000000000000000000000000000000000000..338509dd239e63f7fde17fe377a697429f1405e2
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_constructors.py
@@ -0,0 +1,80 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Index,
+    MultiIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestIndexConstructor:
+    # Tests for the Index constructor, specifically for cases that do
+    #  not return a subclass
+
+    @pytest.mark.parametrize("value", [1, np.int64(1)])
+    def test_constructor_corner(self, value):
+        # corner case
+        msg = (
+            r"Index\(\.\.\.\) must be called with a collection of some "
+            f"kind, {value} was passed"
+        )
+        with pytest.raises(TypeError, match=msg):
+            Index(value)
+
+    @pytest.mark.parametrize("index_vals", [[("A", 1), "B"], ["B", ("A", 1)]])
+    def test_construction_list_mixed_tuples(self, index_vals):
+        # see gh-10697: if we are constructing from a mixed list of tuples,
+        # make sure that we are independent of the sorting order.
+        index = Index(index_vals)
+        assert isinstance(index, Index)
+        assert not isinstance(index, MultiIndex)
+
+    def test_constructor_cast(self):
+        msg = "could not convert string to float"
+        with pytest.raises(ValueError, match=msg):
+            Index(["a", "b", "c"], dtype=float)
+
+    @pytest.mark.parametrize("tuple_list", [[()], [(), ()]])
+    def test_construct_empty_tuples(self, tuple_list):
+        # GH #45608
+        result = Index(tuple_list)
+        expected = MultiIndex.from_tuples(tuple_list)
+
+        tm.assert_index_equal(result, expected)
+
+    def test_index_string_inference(self):
+        # GH#54430
+        pytest.importorskip("pyarrow")
+        dtype = "string[pyarrow_numpy]"
+        expected = Index(["a", "b"], dtype=dtype)
+        with pd.option_context("future.infer_string", True):
+            ser = Index(["a", "b"])
+        tm.assert_index_equal(ser, expected)
+
+        expected = Index(["a", 1], dtype="object")
+        with pd.option_context("future.infer_string", True):
+            ser = Index(["a", 1])
+        tm.assert_index_equal(ser, expected)
+
+    def test_inference_on_pandas_objects(self):
+        # GH#56012
+        idx = Index([pd.Timestamp("2019-12-31")], dtype=object)
+        with tm.assert_produces_warning(FutureWarning, match="Dtype inference"):
+            result = Index(idx)
+        assert result.dtype != np.object_
+
+        ser = Series([pd.Timestamp("2019-12-31")], dtype=object)
+
+        with tm.assert_produces_warning(FutureWarning, match="Dtype inference"):
+            result = Index(ser)
+        assert result.dtype != np.object_
+
+    def test_constructor_not_read_only(self):
+        # GH#57130
+        ser = Series([1, 2], dtype=object)
+        with pd.option_context("mode.copy_on_write", True):
+            idx = Index(ser)
+            assert idx._values.flags.writeable
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_formats.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_formats.py
new file mode 100644
index 0000000000000000000000000000000000000000..f30b578cfcf566ac577d15a1bc15880bea5d7af0
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_formats.py
@@ -0,0 +1,163 @@
+import numpy as np
+import pytest
+
+from pandas._config import using_pyarrow_string_dtype
+import pandas._config.config as cf
+
+from pandas import Index
+import pandas._testing as tm
+
+
+class TestIndexRendering:
+    def test_repr_is_valid_construction_code(self):
+        # for the case of Index, where the repr is traditional rather than
+        # stylized
+        idx = Index(["a", "b"])
+        res = eval(repr(idx))
+        tm.assert_index_equal(res, idx)
+
+    @pytest.mark.xfail(using_pyarrow_string_dtype(), reason="repr different")
+    @pytest.mark.parametrize(
+        "index,expected",
+        [
+            # ASCII
+            # short
+            (
+                Index(["a", "bb", "ccc"]),
+                """Index(['a', 'bb', 'ccc'], dtype='object')""",
+            ),
+            # multiple lines
+            (
+                Index(["a", "bb", "ccc"] * 10),
+                "Index(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', "
+                "'bb', 'ccc', 'a', 'bb', 'ccc',\n"
+                "       'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', "
+                "'bb', 'ccc', 'a', 'bb', 'ccc',\n"
+                "       'a', 'bb', 'ccc', 'a', 'bb', 'ccc'],\n"
+                "      dtype='object')",
+            ),
+            # truncated
+            (
+                Index(["a", "bb", "ccc"] * 100),
+                "Index(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a',\n"
+                "       ...\n"
+                "       'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'],\n"
+                "      dtype='object', length=300)",
+            ),
+            # Non-ASCII
+            # short
+            (
+                Index(["あ", "いい", "ううう"]),
+                """Index(['あ', 'いい', 'ううう'], dtype='object')""",
+            ),
+            # multiple lines
+            (
+                Index(["あ", "いい", "ううう"] * 10),
+                (
+                    "Index(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', "
+                    "'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう',\n"
+                    "       'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', "
+                    "'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう',\n"
+                    "       'あ', 'いい', 'ううう', 'あ', 'いい', "
+                    "'ううう'],\n"
+                    "      dtype='object')"
+                ),
+            ),
+            # truncated
+            (
+                Index(["あ", "いい", "ううう"] * 100),
+                (
+                    "Index(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', "
+                    "'あ', 'いい', 'ううう', 'あ',\n"
+                    "       ...\n"
+                    "       'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', "
+                    "'ううう', 'あ', 'いい', 'ううう'],\n"
+                    "      dtype='object', length=300)"
+                ),
+            ),
+        ],
+    )
+    def test_string_index_repr(self, index, expected):
+        result = repr(index)
+        assert result == expected
+
+    @pytest.mark.xfail(using_pyarrow_string_dtype(), reason="repr different")
+    @pytest.mark.parametrize(
+        "index,expected",
+        [
+            # short
+            (
+                Index(["あ", "いい", "ううう"]),
+                ("Index(['あ', 'いい', 'ううう'], dtype='object')"),
+            ),
+            # multiple lines
+            (
+                Index(["あ", "いい", "ううう"] * 10),
+                (
+                    "Index(['あ', 'いい', 'ううう', 'あ', 'いい', "
+                    "'ううう', 'あ', 'いい', 'ううう',\n"
+                    "       'あ', 'いい', 'ううう', 'あ', 'いい', "
+                    "'ううう', 'あ', 'いい', 'ううう',\n"
+                    "       'あ', 'いい', 'ううう', 'あ', 'いい', "
+                    "'ううう', 'あ', 'いい', 'ううう',\n"
+                    "       'あ', 'いい', 'ううう'],\n"
+                    "      dtype='object')"
+                    ""
+                ),
+            ),
+            # truncated
+            (
+                Index(["あ", "いい", "ううう"] * 100),
+                (
+                    "Index(['あ', 'いい', 'ううう', 'あ', 'いい', "
+                    "'ううう', 'あ', 'いい', 'ううう',\n"
+                    "       'あ',\n"
+                    "       ...\n"
+                    "       'ううう', 'あ', 'いい', 'ううう', 'あ', "
+                    "'いい', 'ううう', 'あ', 'いい',\n"
+                    "       'ううう'],\n"
+                    "      dtype='object', length=300)"
+                ),
+            ),
+        ],
+    )
+    def test_string_index_repr_with_unicode_option(self, index, expected):
+        # Enable Unicode option -----------------------------------------
+        with cf.option_context("display.unicode.east_asian_width", True):
+            result = repr(index)
+            assert result == expected
+
+    def test_repr_summary(self):
+        with cf.option_context("display.max_seq_items", 10):
+            result = repr(Index(np.arange(1000)))
+            assert len(result) < 200
+            assert "..." in result
+
+    def test_summary_bug(self):
+        # GH#3869
+        ind = Index(["{other}%s", "~:{range}:0"], name="A")
+        result = ind._summary()
+        # shouldn't be formatted accidentally.
+        assert "~:{range}:0" in result
+        assert "{other}%s" in result
+
+    def test_index_repr_bool_nan(self):
+        # GH32146
+        arr = Index([True, False, np.nan], dtype=object)
+        msg = "Index.format is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            exp1 = arr.format()
+        out1 = ["True", "False", "NaN"]
+        assert out1 == exp1
+
+        exp2 = repr(arr)
+        out2 = "Index([True, False, nan], dtype='object')"
+        assert out2 == exp2
+
+    def test_format_different_scalar_lengths(self):
+        # GH#35439
+        idx = Index(["aaaaaaaaa", "b"])
+        expected = ["aaaaaaaaa", "b"]
+        msg = r"Index\.format is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert idx.format() == expected
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_indexing.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_indexing.py
new file mode 100644
index 0000000000000000000000000000000000000000..2988fa7d1baa1e0bc0f6cc4b6dc32e5d12f332cf
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_indexing.py
@@ -0,0 +1,104 @@
+import numpy as np
+import pytest
+
+from pandas._libs import index as libindex
+
+import pandas as pd
+from pandas import (
+    Index,
+    NaT,
+)
+import pandas._testing as tm
+
+
+class TestGetSliceBounds:
+    @pytest.mark.parametrize("side, expected", [("left", 4), ("right", 5)])
+    def test_get_slice_bounds_within(self, side, expected):
+        index = Index(list("abcdef"))
+        result = index.get_slice_bound("e", side=side)
+        assert result == expected
+
+    @pytest.mark.parametrize("side", ["left", "right"])
+    @pytest.mark.parametrize(
+        "data, bound, expected", [(list("abcdef"), "x", 6), (list("bcdefg"), "a", 0)]
+    )
+    def test_get_slice_bounds_outside(self, side, expected, data, bound):
+        index = Index(data)
+        result = index.get_slice_bound(bound, side=side)
+        assert result == expected
+
+    def test_get_slice_bounds_invalid_side(self):
+        with pytest.raises(ValueError, match="Invalid value for side kwarg"):
+            Index([]).get_slice_bound("a", side="middle")
+
+
+class TestGetIndexerNonUnique:
+    def test_get_indexer_non_unique_dtype_mismatch(self):
+        # GH#25459
+        indexes, missing = Index(["A", "B"]).get_indexer_non_unique(Index([0]))
+        tm.assert_numpy_array_equal(np.array([-1], dtype=np.intp), indexes)
+        tm.assert_numpy_array_equal(np.array([0], dtype=np.intp), missing)
+
+    @pytest.mark.parametrize(
+        "idx_values,idx_non_unique",
+        [
+            ([np.nan, 100, 200, 100], [np.nan, 100]),
+            ([np.nan, 100.0, 200.0, 100.0], [np.nan, 100.0]),
+        ],
+    )
+    def test_get_indexer_non_unique_int_index(self, idx_values, idx_non_unique):
+        indexes, missing = Index(idx_values).get_indexer_non_unique(Index([np.nan]))
+        tm.assert_numpy_array_equal(np.array([0], dtype=np.intp), indexes)
+        tm.assert_numpy_array_equal(np.array([], dtype=np.intp), missing)
+
+        indexes, missing = Index(idx_values).get_indexer_non_unique(
+            Index(idx_non_unique)
+        )
+        tm.assert_numpy_array_equal(np.array([0, 1, 3], dtype=np.intp), indexes)
+        tm.assert_numpy_array_equal(np.array([], dtype=np.intp), missing)
+
+
+class TestGetLoc:
+    @pytest.mark.slow  # to_flat_index takes a while
+    def test_get_loc_tuple_monotonic_above_size_cutoff(self, monkeypatch):
+        # Go through the libindex path for which using
+        # _bin_search vs ndarray.searchsorted makes a difference
+
+        with monkeypatch.context():
+            monkeypatch.setattr(libindex, "_SIZE_CUTOFF", 100)
+            lev = list("ABCD")
+            dti = pd.date_range("2016-01-01", periods=10)
+
+            mi = pd.MultiIndex.from_product([lev, range(5), dti])
+            oidx = mi.to_flat_index()
+
+            loc = len(oidx) // 2
+            tup = oidx[loc]
+
+            res = oidx.get_loc(tup)
+        assert res == loc
+
+    def test_get_loc_nan_object_dtype_nonmonotonic_nonunique(self):
+        # case that goes through _maybe_get_bool_indexer
+        idx = Index(["foo", np.nan, None, "foo", 1.0, None], dtype=object)
+
+        # we dont raise KeyError on nan
+        res = idx.get_loc(np.nan)
+        assert res == 1
+
+        # we only match on None, not on np.nan
+        res = idx.get_loc(None)
+        expected = np.array([False, False, True, False, False, True])
+        tm.assert_numpy_array_equal(res, expected)
+
+        # we don't match at all on mismatched NA
+        with pytest.raises(KeyError, match="NaT"):
+            idx.get_loc(NaT)
+
+
+def test_getitem_boolean_ea_indexer():
+    # GH#45806
+    ser = pd.Series([True, False, pd.NA], dtype="boolean")
+    result = ser.index[ser]
+    expected = Index([0])
+    tm.assert_index_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_pickle.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_pickle.py
new file mode 100644
index 0000000000000000000000000000000000000000..c670921decb78808fa54a35c45e3d2d15ab57a67
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_pickle.py
@@ -0,0 +1,11 @@
+from pandas import Index
+import pandas._testing as tm
+
+
+def test_pickle_preserves_object_dtype():
+    # GH#43188, GH#43155 don't infer numeric dtype
+    index = Index([1, 2, 3], dtype=object)
+
+    result = tm.round_trip_pickle(index)
+    assert result.dtype == object
+    tm.assert_index_equal(index, result)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_reshape.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_reshape.py
new file mode 100644
index 0000000000000000000000000000000000000000..814a6a516904b6c31d3ef38fa91f23f69fa6ed7e
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_reshape.py
@@ -0,0 +1,95 @@
+"""
+Tests for ndarray-like method on the base Index class
+"""
+import numpy as np
+import pytest
+
+from pandas import Index
+import pandas._testing as tm
+
+
+class TestReshape:
+    def test_repeat(self):
+        repeats = 2
+        index = Index([1, 2, 3])
+        expected = Index([1, 1, 2, 2, 3, 3])
+
+        result = index.repeat(repeats)
+        tm.assert_index_equal(result, expected)
+
+    def test_insert(self):
+        # GH 7256
+        # validate neg/pos inserts
+        result = Index(["b", "c", "d"])
+
+        # test 0th element
+        tm.assert_index_equal(Index(["a", "b", "c", "d"]), result.insert(0, "a"))
+
+        # test Nth element that follows Python list behavior
+        tm.assert_index_equal(Index(["b", "c", "e", "d"]), result.insert(-1, "e"))
+
+        # test loc +/- neq (0, -1)
+        tm.assert_index_equal(result.insert(1, "z"), result.insert(-2, "z"))
+
+        # test empty
+        null_index = Index([])
+        tm.assert_index_equal(Index(["a"], dtype=object), null_index.insert(0, "a"))
+
+    def test_insert_missing(self, nulls_fixture, using_infer_string):
+        # GH#22295
+        # test there is no mangling of NA values
+        expected = Index(["a", nulls_fixture, "b", "c"], dtype=object)
+        result = Index(list("abc"), dtype=object).insert(
+            1, Index([nulls_fixture], dtype=object)
+        )
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "val", [(1, 2), np.datetime64("2019-12-31"), np.timedelta64(1, "D")]
+    )
+    @pytest.mark.parametrize("loc", [-1, 2])
+    def test_insert_datetime_into_object(self, loc, val):
+        # GH#44509
+        idx = Index(["1", "2", "3"])
+        result = idx.insert(loc, val)
+        expected = Index(["1", "2", val, "3"])
+        tm.assert_index_equal(result, expected)
+        assert type(expected[2]) is type(val)
+
+    def test_insert_none_into_string_numpy(self):
+        # GH#55365
+        pytest.importorskip("pyarrow")
+        index = Index(["a", "b", "c"], dtype="string[pyarrow_numpy]")
+        result = index.insert(-1, None)
+        expected = Index(["a", "b", None, "c"], dtype="string[pyarrow_numpy]")
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "pos,expected",
+        [
+            (0, Index(["b", "c", "d"], name="index")),
+            (-1, Index(["a", "b", "c"], name="index")),
+        ],
+    )
+    def test_delete(self, pos, expected):
+        index = Index(["a", "b", "c", "d"], name="index")
+        result = index.delete(pos)
+        tm.assert_index_equal(result, expected)
+        assert result.name == expected.name
+
+    def test_delete_raises(self):
+        index = Index(["a", "b", "c", "d"], name="index")
+        msg = "index 5 is out of bounds for axis 0 with size 4"
+        with pytest.raises(IndexError, match=msg):
+            index.delete(5)
+
+    def test_append_multiple(self):
+        index = Index(["a", "b", "c", "d", "e", "f"])
+
+        foos = [index[:2], index[2:4], index[4:]]
+        result = foos[0].append(foos[1:])
+        tm.assert_index_equal(result, index)
+
+        # empty
+        result = index.append([])
+        tm.assert_index_equal(result, index)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_setops.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_setops.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ef3f3ad4d3a20bd2e6303d781590396cbc00ae0
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_setops.py
@@ -0,0 +1,266 @@
+from datetime import datetime
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Index,
+    Series,
+)
+import pandas._testing as tm
+from pandas.core.algorithms import safe_sort
+
+
+def equal_contents(arr1, arr2) -> bool:
+    """
+    Checks if the set of unique elements of arr1 and arr2 are equivalent.
+    """
+    return frozenset(arr1) == frozenset(arr2)
+
+
+class TestIndexSetOps:
+    @pytest.mark.parametrize(
+        "method", ["union", "intersection", "difference", "symmetric_difference"]
+    )
+    def test_setops_sort_validation(self, method):
+        idx1 = Index(["a", "b"])
+        idx2 = Index(["b", "c"])
+
+        with pytest.raises(ValueError, match="The 'sort' keyword only takes"):
+            getattr(idx1, method)(idx2, sort=2)
+
+        # sort=True is supported as of GH#??
+        getattr(idx1, method)(idx2, sort=True)
+
+    def test_setops_preserve_object_dtype(self):
+        idx = Index([1, 2, 3], dtype=object)
+        result = idx.intersection(idx[1:])
+        expected = idx[1:]
+        tm.assert_index_equal(result, expected)
+
+        # if other is not monotonic increasing, intersection goes through
+        #  a different route
+        result = idx.intersection(idx[1:][::-1])
+        tm.assert_index_equal(result, expected)
+
+        result = idx._union(idx[1:], sort=None)
+        expected = idx
+        tm.assert_numpy_array_equal(result, expected.values)
+
+        result = idx.union(idx[1:], sort=None)
+        tm.assert_index_equal(result, expected)
+
+        # if other is not monotonic increasing, _union goes through
+        #  a different route
+        result = idx._union(idx[1:][::-1], sort=None)
+        tm.assert_numpy_array_equal(result, expected.values)
+
+        result = idx.union(idx[1:][::-1], sort=None)
+        tm.assert_index_equal(result, expected)
+
+    def test_union_base(self):
+        index = Index([0, "a", 1, "b", 2, "c"])
+        first = index[3:]
+        second = index[:5]
+
+        result = first.union(second)
+
+        expected = Index([0, 1, 2, "a", "b", "c"])
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("klass", [np.array, Series, list])
+    def test_union_different_type_base(self, klass):
+        # GH 10149
+        index = Index([0, "a", 1, "b", 2, "c"])
+        first = index[3:]
+        second = index[:5]
+
+        result = first.union(klass(second.values))
+
+        assert equal_contents(result, index)
+
+    def test_union_sort_other_incomparable(self):
+        # https://github.com/pandas-dev/pandas/issues/24959
+        idx = Index([1, pd.Timestamp("2000")])
+        # default (sort=None)
+        with tm.assert_produces_warning(RuntimeWarning):
+            result = idx.union(idx[:1])
+
+        tm.assert_index_equal(result, idx)
+
+        # sort=None
+        with tm.assert_produces_warning(RuntimeWarning):
+            result = idx.union(idx[:1], sort=None)
+        tm.assert_index_equal(result, idx)
+
+        # sort=False
+        result = idx.union(idx[:1], sort=False)
+        tm.assert_index_equal(result, idx)
+
+    def test_union_sort_other_incomparable_true(self):
+        idx = Index([1, pd.Timestamp("2000")])
+        with pytest.raises(TypeError, match=".*"):
+            idx.union(idx[:1], sort=True)
+
+    def test_intersection_equal_sort_true(self):
+        idx = Index(["c", "a", "b"])
+        sorted_ = Index(["a", "b", "c"])
+        tm.assert_index_equal(idx.intersection(idx, sort=True), sorted_)
+
+    def test_intersection_base(self, sort):
+        # (same results for py2 and py3 but sortedness not tested elsewhere)
+        index = Index([0, "a", 1, "b", 2, "c"])
+        first = index[:5]
+        second = index[:3]
+
+        expected = Index([0, 1, "a"]) if sort is None else Index([0, "a", 1])
+        result = first.intersection(second, sort=sort)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("klass", [np.array, Series, list])
+    def test_intersection_different_type_base(self, klass, sort):
+        # GH 10149
+        index = Index([0, "a", 1, "b", 2, "c"])
+        first = index[:5]
+        second = index[:3]
+
+        result = first.intersection(klass(second.values), sort=sort)
+        assert equal_contents(result, second)
+
+    def test_intersection_nosort(self):
+        result = Index(["c", "b", "a"]).intersection(["b", "a"])
+        expected = Index(["b", "a"])
+        tm.assert_index_equal(result, expected)
+
+    def test_intersection_equal_sort(self):
+        idx = Index(["c", "a", "b"])
+        tm.assert_index_equal(idx.intersection(idx, sort=False), idx)
+        tm.assert_index_equal(idx.intersection(idx, sort=None), idx)
+
+    def test_intersection_str_dates(self, sort):
+        dt_dates = [datetime(2012, 2, 9), datetime(2012, 2, 22)]
+
+        i1 = Index(dt_dates, dtype=object)
+        i2 = Index(["aa"], dtype=object)
+        result = i2.intersection(i1, sort=sort)
+
+        assert len(result) == 0
+
+    @pytest.mark.parametrize(
+        "index2,expected_arr",
+        [(Index(["B", "D"]), ["B"]), (Index(["B", "D", "A"]), ["A", "B"])],
+    )
+    def test_intersection_non_monotonic_non_unique(self, index2, expected_arr, sort):
+        # non-monotonic non-unique
+        index1 = Index(["A", "B", "A", "C"])
+        expected = Index(expected_arr)
+        result = index1.intersection(index2, sort=sort)
+        if sort is None:
+            expected = expected.sort_values()
+        tm.assert_index_equal(result, expected)
+
+    def test_difference_base(self, sort):
+        # (same results for py2 and py3 but sortedness not tested elsewhere)
+        index = Index([0, "a", 1, "b", 2, "c"])
+        first = index[:4]
+        second = index[3:]
+
+        result = first.difference(second, sort)
+        expected = Index([0, "a", 1])
+        if sort is None:
+            expected = Index(safe_sort(expected))
+        tm.assert_index_equal(result, expected)
+
+    def test_symmetric_difference(self):
+        # (same results for py2 and py3 but sortedness not tested elsewhere)
+        index = Index([0, "a", 1, "b", 2, "c"])
+        first = index[:4]
+        second = index[3:]
+
+        result = first.symmetric_difference(second)
+        expected = Index([0, 1, 2, "a", "c"])
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "method,expected,sort",
+        [
+            (
+                "intersection",
+                np.array(
+                    [(1, "A"), (2, "A"), (1, "B"), (2, "B")],
+                    dtype=[("num", int), ("let", "S1")],
+                ),
+                False,
+            ),
+            (
+                "intersection",
+                np.array(
+                    [(1, "A"), (1, "B"), (2, "A"), (2, "B")],
+                    dtype=[("num", int), ("let", "S1")],
+                ),
+                None,
+            ),
+            (
+                "union",
+                np.array(
+                    [(1, "A"), (1, "B"), (1, "C"), (2, "A"), (2, "B"), (2, "C")],
+                    dtype=[("num", int), ("let", "S1")],
+                ),
+                None,
+            ),
+        ],
+    )
+    def test_tuple_union_bug(self, method, expected, sort):
+        index1 = Index(
+            np.array(
+                [(1, "A"), (2, "A"), (1, "B"), (2, "B")],
+                dtype=[("num", int), ("let", "S1")],
+            )
+        )
+        index2 = Index(
+            np.array(
+                [(1, "A"), (2, "A"), (1, "B"), (2, "B"), (1, "C"), (2, "C")],
+                dtype=[("num", int), ("let", "S1")],
+            )
+        )
+
+        result = getattr(index1, method)(index2, sort=sort)
+        assert result.ndim == 1
+
+        expected = Index(expected)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("first_list", [["b", "a"], []])
+    @pytest.mark.parametrize("second_list", [["a", "b"], []])
+    @pytest.mark.parametrize(
+        "first_name, second_name, expected_name",
+        [("A", "B", None), (None, "B", None), ("A", None, None)],
+    )
+    def test_union_name_preservation(
+        self, first_list, second_list, first_name, second_name, expected_name, sort
+    ):
+        first = Index(first_list, name=first_name)
+        second = Index(second_list, name=second_name)
+        union = first.union(second, sort=sort)
+
+        vals = set(first_list).union(second_list)
+
+        if sort is None and len(first_list) > 0 and len(second_list) > 0:
+            expected = Index(sorted(vals), name=expected_name)
+            tm.assert_index_equal(union, expected)
+        else:
+            expected = Index(vals, name=expected_name)
+            tm.assert_index_equal(union.sort_values(), expected.sort_values())
+
+    @pytest.mark.parametrize(
+        "diff_type, expected",
+        [["difference", [1, "B"]], ["symmetric_difference", [1, 2, "B", "C"]]],
+    )
+    def test_difference_object_type(self, diff_type, expected):
+        # GH 13432
+        idx1 = Index([0, 1, "A", "B"])
+        idx2 = Index([0, 2, "A", "C"])
+        result = getattr(idx1, diff_type)(idx2)
+        expected = Index(expected)
+        tm.assert_index_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_where.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_where.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c8969735e14e2741bc029b499024af3ec378a92
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_where.py
@@ -0,0 +1,13 @@
+import numpy as np
+
+from pandas import Index
+import pandas._testing as tm
+
+
+class TestWhere:
+    def test_where_intlike_str_doesnt_cast_ints(self):
+        idx = Index(range(3))
+        mask = np.array([True, False, True])
+        res = idx.where(mask, "2")
+        expected = Index([0, "2", 2])
+        tm.assert_index_equal(res, expected)
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diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_analytics.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_analytics.py
new file mode 100644
index 0000000000000000000000000000000000000000..87f1439db5fc87c3be08e3675df1dae0fdb5554d
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_analytics.py
@@ -0,0 +1,263 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Index,
+    MultiIndex,
+    date_range,
+    period_range,
+)
+import pandas._testing as tm
+
+
+def test_infer_objects(idx):
+    with pytest.raises(NotImplementedError, match="to_frame"):
+        idx.infer_objects()
+
+
+def test_shift(idx):
+    # GH8083 test the base class for shift
+    msg = (
+        "This method is only implemented for DatetimeIndex, PeriodIndex and "
+        "TimedeltaIndex; Got type MultiIndex"
+    )
+    with pytest.raises(NotImplementedError, match=msg):
+        idx.shift(1)
+    with pytest.raises(NotImplementedError, match=msg):
+        idx.shift(1, 2)
+
+
+def test_groupby(idx):
+    groups = idx.groupby(np.array([1, 1, 1, 2, 2, 2]))
+    labels = idx.tolist()
+    exp = {1: labels[:3], 2: labels[3:]}
+    tm.assert_dict_equal(groups, exp)
+
+    # GH5620
+    groups = idx.groupby(idx)
+    exp = {key: [key] for key in idx}
+    tm.assert_dict_equal(groups, exp)
+
+
+def test_truncate_multiindex():
+    # GH 34564 for MultiIndex level names check
+    major_axis = Index(list(range(4)))
+    minor_axis = Index(list(range(2)))
+
+    major_codes = np.array([0, 0, 1, 2, 3, 3])
+    minor_codes = np.array([0, 1, 0, 1, 0, 1])
+
+    index = MultiIndex(
+        levels=[major_axis, minor_axis],
+        codes=[major_codes, minor_codes],
+        names=["L1", "L2"],
+    )
+
+    result = index.truncate(before=1)
+    assert "foo" not in result.levels[0]
+    assert 1 in result.levels[0]
+    assert index.names == result.names
+
+    result = index.truncate(after=1)
+    assert 2 not in result.levels[0]
+    assert 1 in result.levels[0]
+    assert index.names == result.names
+
+    result = index.truncate(before=1, after=2)
+    assert len(result.levels[0]) == 2
+    assert index.names == result.names
+
+    msg = "after < before"
+    with pytest.raises(ValueError, match=msg):
+        index.truncate(3, 1)
+
+
+# TODO: reshape
+
+
+def test_reorder_levels(idx):
+    # this blows up
+    with pytest.raises(IndexError, match="^Too many levels"):
+        idx.reorder_levels([2, 1, 0])
+
+
+def test_numpy_repeat():
+    reps = 2
+    numbers = [1, 2, 3]
+    names = np.array(["foo", "bar"])
+
+    m = MultiIndex.from_product([numbers, names], names=names)
+    expected = MultiIndex.from_product([numbers, names.repeat(reps)], names=names)
+    tm.assert_index_equal(np.repeat(m, reps), expected)
+
+    msg = "the 'axis' parameter is not supported"
+    with pytest.raises(ValueError, match=msg):
+        np.repeat(m, reps, axis=1)
+
+
+def test_append_mixed_dtypes():
+    # GH 13660
+    dti = date_range("2011-01-01", freq="ME", periods=3)
+    dti_tz = date_range("2011-01-01", freq="ME", periods=3, tz="US/Eastern")
+    pi = period_range("2011-01", freq="M", periods=3)
+
+    mi = MultiIndex.from_arrays(
+        [[1, 2, 3], [1.1, np.nan, 3.3], ["a", "b", "c"], dti, dti_tz, pi]
+    )
+    assert mi.nlevels == 6
+
+    res = mi.append(mi)
+    exp = MultiIndex.from_arrays(
+        [
+            [1, 2, 3, 1, 2, 3],
+            [1.1, np.nan, 3.3, 1.1, np.nan, 3.3],
+            ["a", "b", "c", "a", "b", "c"],
+            dti.append(dti),
+            dti_tz.append(dti_tz),
+            pi.append(pi),
+        ]
+    )
+    tm.assert_index_equal(res, exp)
+
+    other = MultiIndex.from_arrays(
+        [
+            ["x", "y", "z"],
+            ["x", "y", "z"],
+            ["x", "y", "z"],
+            ["x", "y", "z"],
+            ["x", "y", "z"],
+            ["x", "y", "z"],
+        ]
+    )
+
+    res = mi.append(other)
+    exp = MultiIndex.from_arrays(
+        [
+            [1, 2, 3, "x", "y", "z"],
+            [1.1, np.nan, 3.3, "x", "y", "z"],
+            ["a", "b", "c", "x", "y", "z"],
+            dti.append(Index(["x", "y", "z"])),
+            dti_tz.append(Index(["x", "y", "z"])),
+            pi.append(Index(["x", "y", "z"])),
+        ]
+    )
+    tm.assert_index_equal(res, exp)
+
+
+def test_iter(idx):
+    result = list(idx)
+    expected = [
+        ("foo", "one"),
+        ("foo", "two"),
+        ("bar", "one"),
+        ("baz", "two"),
+        ("qux", "one"),
+        ("qux", "two"),
+    ]
+    assert result == expected
+
+
+def test_sub(idx):
+    first = idx
+
+    # - now raises (previously was set op difference)
+    msg = "cannot perform __sub__ with this index type: MultiIndex"
+    with pytest.raises(TypeError, match=msg):
+        first - idx[-3:]
+    with pytest.raises(TypeError, match=msg):
+        idx[-3:] - first
+    with pytest.raises(TypeError, match=msg):
+        idx[-3:] - first.tolist()
+    msg = "cannot perform __rsub__ with this index type: MultiIndex"
+    with pytest.raises(TypeError, match=msg):
+        first.tolist() - idx[-3:]
+
+
+def test_map(idx):
+    # callable
+    index = idx
+
+    result = index.map(lambda x: x)
+    tm.assert_index_equal(result, index)
+
+
+@pytest.mark.parametrize(
+    "mapper",
+    [
+        lambda values, idx: {i: e for e, i in zip(values, idx)},
+        lambda values, idx: pd.Series(values, idx),
+    ],
+)
+def test_map_dictlike(idx, mapper):
+    identity = mapper(idx.values, idx)
+
+    # we don't infer to uint64 dtype for a dict
+    if idx.dtype == np.uint64 and isinstance(identity, dict):
+        expected = idx.astype("int64")
+    else:
+        expected = idx
+
+    result = idx.map(identity)
+    tm.assert_index_equal(result, expected)
+
+    # empty mappable
+    expected = Index([np.nan] * len(idx))
+    result = idx.map(mapper(expected, idx))
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        np.exp,
+        np.exp2,
+        np.expm1,
+        np.log,
+        np.log2,
+        np.log10,
+        np.log1p,
+        np.sqrt,
+        np.sin,
+        np.cos,
+        np.tan,
+        np.arcsin,
+        np.arccos,
+        np.arctan,
+        np.sinh,
+        np.cosh,
+        np.tanh,
+        np.arcsinh,
+        np.arccosh,
+        np.arctanh,
+        np.deg2rad,
+        np.rad2deg,
+    ],
+    ids=lambda func: func.__name__,
+)
+def test_numpy_ufuncs(idx, func):
+    # test ufuncs of numpy. see:
+    # https://numpy.org/doc/stable/reference/ufuncs.html
+
+    expected_exception = TypeError
+    msg = (
+        "loop of ufunc does not support argument 0 of type tuple which "
+        f"has no callable {func.__name__} method"
+    )
+    with pytest.raises(expected_exception, match=msg):
+        func(idx)
+
+
+@pytest.mark.parametrize(
+    "func",
+    [np.isfinite, np.isinf, np.isnan, np.signbit],
+    ids=lambda func: func.__name__,
+)
+def test_numpy_type_funcs(idx, func):
+    msg = (
+        f"ufunc '{func.__name__}' not supported for the input types, and the inputs "
+        "could not be safely coerced to any supported types according to "
+        "the casting rule ''safe''"
+    )
+    with pytest.raises(TypeError, match=msg):
+        func(idx)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_astype.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_astype.py
new file mode 100644
index 0000000000000000000000000000000000000000..29908537fbe590328ac586e05f90f3cc24cab9ab
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_astype.py
@@ -0,0 +1,30 @@
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+import pandas._testing as tm
+
+
+def test_astype(idx):
+    expected = idx.copy()
+    actual = idx.astype("O")
+    tm.assert_copy(actual.levels, expected.levels)
+    tm.assert_copy(actual.codes, expected.codes)
+    assert actual.names == list(expected.names)
+
+    with pytest.raises(TypeError, match="^Setting.*dtype.*object"):
+        idx.astype(np.dtype(int))
+
+
+@pytest.mark.parametrize("ordered", [True, False])
+def test_astype_category(idx, ordered):
+    # GH 18630
+    msg = "> 1 ndim Categorical are not supported at this time"
+    with pytest.raises(NotImplementedError, match=msg):
+        idx.astype(CategoricalDtype(ordered=ordered))
+
+    if ordered is False:
+        # dtype='category' defaults to ordered=False, so only test once
+        with pytest.raises(NotImplementedError, match=msg):
+            idx.astype("category")
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_duplicates.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_duplicates.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c6d9022b1af31e905b5ec739753af77a52f438b
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_duplicates.py
@@ -0,0 +1,363 @@
+from itertools import product
+
+import numpy as np
+import pytest
+
+from pandas._libs import (
+    hashtable,
+    index as libindex,
+)
+
+from pandas import (
+    NA,
+    DatetimeIndex,
+    Index,
+    MultiIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+@pytest.fixture
+def idx_dup():
+    # compare tests/indexes/multi/conftest.py
+    major_axis = Index(["foo", "bar", "baz", "qux"])
+    minor_axis = Index(["one", "two"])
+
+    major_codes = np.array([0, 0, 1, 0, 1, 1])
+    minor_codes = np.array([0, 1, 0, 1, 0, 1])
+    index_names = ["first", "second"]
+    mi = MultiIndex(
+        levels=[major_axis, minor_axis],
+        codes=[major_codes, minor_codes],
+        names=index_names,
+        verify_integrity=False,
+    )
+    return mi
+
+
+@pytest.mark.parametrize("names", [None, ["first", "second"]])
+def test_unique(names):
+    mi = MultiIndex.from_arrays([[1, 2, 1, 2], [1, 1, 1, 2]], names=names)
+
+    res = mi.unique()
+    exp = MultiIndex.from_arrays([[1, 2, 2], [1, 1, 2]], names=mi.names)
+    tm.assert_index_equal(res, exp)
+
+    mi = MultiIndex.from_arrays([list("aaaa"), list("abab")], names=names)
+    res = mi.unique()
+    exp = MultiIndex.from_arrays([list("aa"), list("ab")], names=mi.names)
+    tm.assert_index_equal(res, exp)
+
+    mi = MultiIndex.from_arrays([list("aaaa"), list("aaaa")], names=names)
+    res = mi.unique()
+    exp = MultiIndex.from_arrays([["a"], ["a"]], names=mi.names)
+    tm.assert_index_equal(res, exp)
+
+    # GH #20568 - empty MI
+    mi = MultiIndex.from_arrays([[], []], names=names)
+    res = mi.unique()
+    tm.assert_index_equal(mi, res)
+
+
+def test_unique_datetimelike():
+    idx1 = DatetimeIndex(
+        ["2015-01-01", "2015-01-01", "2015-01-01", "2015-01-01", "NaT", "NaT"]
+    )
+    idx2 = DatetimeIndex(
+        ["2015-01-01", "2015-01-01", "2015-01-02", "2015-01-02", "NaT", "2015-01-01"],
+        tz="Asia/Tokyo",
+    )
+    result = MultiIndex.from_arrays([idx1, idx2]).unique()
+
+    eidx1 = DatetimeIndex(["2015-01-01", "2015-01-01", "NaT", "NaT"])
+    eidx2 = DatetimeIndex(
+        ["2015-01-01", "2015-01-02", "NaT", "2015-01-01"], tz="Asia/Tokyo"
+    )
+    exp = MultiIndex.from_arrays([eidx1, eidx2])
+    tm.assert_index_equal(result, exp)
+
+
+@pytest.mark.parametrize("level", [0, "first", 1, "second"])
+def test_unique_level(idx, level):
+    # GH #17896 - with level= argument
+    result = idx.unique(level=level)
+    expected = idx.get_level_values(level).unique()
+    tm.assert_index_equal(result, expected)
+
+    # With already unique level
+    mi = MultiIndex.from_arrays([[1, 3, 2, 4], [1, 3, 2, 5]], names=["first", "second"])
+    result = mi.unique(level=level)
+    expected = mi.get_level_values(level)
+    tm.assert_index_equal(result, expected)
+
+    # With empty MI
+    mi = MultiIndex.from_arrays([[], []], names=["first", "second"])
+    result = mi.unique(level=level)
+    expected = mi.get_level_values(level)
+    tm.assert_index_equal(result, expected)
+
+
+def test_duplicate_multiindex_codes():
+    # GH 17464
+    # Make sure that a MultiIndex with duplicate levels throws a ValueError
+    msg = r"Level values must be unique: \[[A', ]+\] on level 0"
+    with pytest.raises(ValueError, match=msg):
+        mi = MultiIndex([["A"] * 10, range(10)], [[0] * 10, range(10)])
+
+    # And that using set_levels with duplicate levels fails
+    mi = MultiIndex.from_arrays([["A", "A", "B", "B", "B"], [1, 2, 1, 2, 3]])
+    msg = r"Level values must be unique: \[[AB', ]+\] on level 0"
+    with pytest.raises(ValueError, match=msg):
+        mi.set_levels([["A", "B", "A", "A", "B"], [2, 1, 3, -2, 5]])
+
+
+@pytest.mark.parametrize("names", [["a", "b", "a"], [1, 1, 2], [1, "a", 1]])
+def test_duplicate_level_names(names):
+    # GH18872, GH19029
+    mi = MultiIndex.from_product([[0, 1]] * 3, names=names)
+    assert mi.names == names
+
+    # With .rename()
+    mi = MultiIndex.from_product([[0, 1]] * 3)
+    mi = mi.rename(names)
+    assert mi.names == names
+
+    # With .rename(., level=)
+    mi.rename(names[1], level=1, inplace=True)
+    mi = mi.rename([names[0], names[2]], level=[0, 2])
+    assert mi.names == names
+
+
+def test_duplicate_meta_data():
+    # GH 10115
+    mi = MultiIndex(
+        levels=[[0, 1], [0, 1, 2]], codes=[[0, 0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 0, 1, 2]]
+    )
+
+    for idx in [
+        mi,
+        mi.set_names([None, None]),
+        mi.set_names([None, "Num"]),
+        mi.set_names(["Upper", "Num"]),
+    ]:
+        assert idx.has_duplicates
+        assert idx.drop_duplicates().names == idx.names
+
+
+def test_has_duplicates(idx, idx_dup):
+    # see fixtures
+    assert idx.is_unique is True
+    assert idx.has_duplicates is False
+    assert idx_dup.is_unique is False
+    assert idx_dup.has_duplicates is True
+
+    mi = MultiIndex(
+        levels=[[0, 1], [0, 1, 2]], codes=[[0, 0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 0, 1, 2]]
+    )
+    assert mi.is_unique is False
+    assert mi.has_duplicates is True
+
+    # single instance of NaN
+    mi_nan = MultiIndex(
+        levels=[["a", "b"], [0, 1]], codes=[[-1, 0, 0, 1, 1], [-1, 0, 1, 0, 1]]
+    )
+    assert mi_nan.is_unique is True
+    assert mi_nan.has_duplicates is False
+
+    # multiple instances of NaN
+    mi_nan_dup = MultiIndex(
+        levels=[["a", "b"], [0, 1]], codes=[[-1, -1, 0, 0, 1, 1], [-1, -1, 0, 1, 0, 1]]
+    )
+    assert mi_nan_dup.is_unique is False
+    assert mi_nan_dup.has_duplicates is True
+
+
+def test_has_duplicates_from_tuples():
+    # GH 9075
+    t = [
+        ("x", "out", "z", 5, "y", "in", "z", 169),
+        ("x", "out", "z", 7, "y", "in", "z", 119),
+        ("x", "out", "z", 9, "y", "in", "z", 135),
+        ("x", "out", "z", 13, "y", "in", "z", 145),
+        ("x", "out", "z", 14, "y", "in", "z", 158),
+        ("x", "out", "z", 16, "y", "in", "z", 122),
+        ("x", "out", "z", 17, "y", "in", "z", 160),
+        ("x", "out", "z", 18, "y", "in", "z", 180),
+        ("x", "out", "z", 20, "y", "in", "z", 143),
+        ("x", "out", "z", 21, "y", "in", "z", 128),
+        ("x", "out", "z", 22, "y", "in", "z", 129),
+        ("x", "out", "z", 25, "y", "in", "z", 111),
+        ("x", "out", "z", 28, "y", "in", "z", 114),
+        ("x", "out", "z", 29, "y", "in", "z", 121),
+        ("x", "out", "z", 31, "y", "in", "z", 126),
+        ("x", "out", "z", 32, "y", "in", "z", 155),
+        ("x", "out", "z", 33, "y", "in", "z", 123),
+        ("x", "out", "z", 12, "y", "in", "z", 144),
+    ]
+
+    mi = MultiIndex.from_tuples(t)
+    assert not mi.has_duplicates
+
+
+@pytest.mark.parametrize("nlevels", [4, 8])
+@pytest.mark.parametrize("with_nulls", [True, False])
+def test_has_duplicates_overflow(nlevels, with_nulls):
+    # handle int64 overflow if possible
+    # no overflow with 4
+    # overflow possible with 8
+    codes = np.tile(np.arange(500), 2)
+    level = np.arange(500)
+
+    if with_nulls:  # inject some null values
+        codes[500] = -1  # common nan value
+        codes = [codes.copy() for i in range(nlevels)]
+        for i in range(nlevels):
+            codes[i][500 + i - nlevels // 2] = -1
+
+        codes += [np.array([-1, 1]).repeat(500)]
+    else:
+        codes = [codes] * nlevels + [np.arange(2).repeat(500)]
+
+    levels = [level] * nlevels + [[0, 1]]
+
+    # no dups
+    mi = MultiIndex(levels=levels, codes=codes)
+    assert not mi.has_duplicates
+
+    # with a dup
+    if with_nulls:
+
+        def f(a):
+            return np.insert(a, 1000, a[0])
+
+        codes = list(map(f, codes))
+        mi = MultiIndex(levels=levels, codes=codes)
+    else:
+        values = mi.values.tolist()
+        mi = MultiIndex.from_tuples(values + [values[0]])
+
+    assert mi.has_duplicates
+
+
+@pytest.mark.parametrize(
+    "keep, expected",
+    [
+        ("first", np.array([False, False, False, True, True, False])),
+        ("last", np.array([False, True, True, False, False, False])),
+        (False, np.array([False, True, True, True, True, False])),
+    ],
+)
+def test_duplicated(idx_dup, keep, expected):
+    result = idx_dup.duplicated(keep=keep)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.arm_slow
+def test_duplicated_hashtable_impl(keep, monkeypatch):
+    # GH 9125
+    n, k = 6, 10
+    levels = [np.arange(n), [str(i) for i in range(n)], 1000 + np.arange(n)]
+    codes = [np.random.default_rng(2).choice(n, k * n) for _ in levels]
+    with monkeypatch.context() as m:
+        m.setattr(libindex, "_SIZE_CUTOFF", 50)
+        mi = MultiIndex(levels=levels, codes=codes)
+
+        result = mi.duplicated(keep=keep)
+        expected = hashtable.duplicated(mi.values, keep=keep)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("val", [101, 102])
+def test_duplicated_with_nan(val):
+    # GH5873
+    mi = MultiIndex.from_arrays([[101, val], [3.5, np.nan]])
+    assert not mi.has_duplicates
+
+    tm.assert_numpy_array_equal(mi.duplicated(), np.zeros(2, dtype="bool"))
+
+
+@pytest.mark.parametrize("n", range(1, 6))
+@pytest.mark.parametrize("m", range(1, 5))
+def test_duplicated_with_nan_multi_shape(n, m):
+    # GH5873
+    # all possible unique combinations, including nan
+    codes = product(range(-1, n), range(-1, m))
+    mi = MultiIndex(
+        levels=[list("abcde")[:n], list("WXYZ")[:m]],
+        codes=np.random.default_rng(2).permutation(list(codes)).T,
+    )
+    assert len(mi) == (n + 1) * (m + 1)
+    assert not mi.has_duplicates
+
+    tm.assert_numpy_array_equal(mi.duplicated(), np.zeros(len(mi), dtype="bool"))
+
+
+def test_duplicated_drop_duplicates():
+    # GH#4060
+    idx = MultiIndex.from_arrays(([1, 2, 3, 1, 2, 3], [1, 1, 1, 1, 2, 2]))
+
+    expected = np.array([False, False, False, True, False, False], dtype=bool)
+    duplicated = idx.duplicated()
+    tm.assert_numpy_array_equal(duplicated, expected)
+    assert duplicated.dtype == bool
+    expected = MultiIndex.from_arrays(([1, 2, 3, 2, 3], [1, 1, 1, 2, 2]))
+    tm.assert_index_equal(idx.drop_duplicates(), expected)
+
+    expected = np.array([True, False, False, False, False, False])
+    duplicated = idx.duplicated(keep="last")
+    tm.assert_numpy_array_equal(duplicated, expected)
+    assert duplicated.dtype == bool
+    expected = MultiIndex.from_arrays(([2, 3, 1, 2, 3], [1, 1, 1, 2, 2]))
+    tm.assert_index_equal(idx.drop_duplicates(keep="last"), expected)
+
+    expected = np.array([True, False, False, True, False, False])
+    duplicated = idx.duplicated(keep=False)
+    tm.assert_numpy_array_equal(duplicated, expected)
+    assert duplicated.dtype == bool
+    expected = MultiIndex.from_arrays(([2, 3, 2, 3], [1, 1, 2, 2]))
+    tm.assert_index_equal(idx.drop_duplicates(keep=False), expected)
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        np.complex64,
+        np.complex128,
+    ],
+)
+def test_duplicated_series_complex_numbers(dtype):
+    # GH 17927
+    expected = Series(
+        [False, False, False, True, False, False, False, True, False, True],
+        dtype=bool,
+    )
+    result = Series(
+        [
+            np.nan + np.nan * 1j,
+            0,
+            1j,
+            1j,
+            1,
+            1 + 1j,
+            1 + 2j,
+            1 + 1j,
+            np.nan,
+            np.nan + np.nan * 1j,
+        ],
+        dtype=dtype,
+    ).duplicated()
+    tm.assert_series_equal(result, expected)
+
+
+def test_midx_unique_ea_dtype():
+    # GH#48335
+    vals_a = Series([1, 2, NA, NA], dtype="Int64")
+    vals_b = np.array([1, 2, 3, 3])
+    midx = MultiIndex.from_arrays([vals_a, vals_b], names=["a", "b"])
+    result = midx.unique()
+
+    exp_vals_a = Series([1, 2, NA], dtype="Int64")
+    exp_vals_b = np.array([1, 2, 3])
+    expected = MultiIndex.from_arrays([exp_vals_a, exp_vals_b], names=["a", "b"])
+    tm.assert_index_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_indexing.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_indexing.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e2d3c23da6452a4155af2674b7ce4a6dd7d2680
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_indexing.py
@@ -0,0 +1,1001 @@
+from datetime import timedelta
+import re
+
+import numpy as np
+import pytest
+
+from pandas._libs import index as libindex
+from pandas.errors import (
+    InvalidIndexError,
+    PerformanceWarning,
+)
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    Index,
+    MultiIndex,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestSliceLocs:
+    def test_slice_locs_partial(self, idx):
+        sorted_idx, _ = idx.sortlevel(0)
+
+        result = sorted_idx.slice_locs(("foo", "two"), ("qux", "one"))
+        assert result == (1, 5)
+
+        result = sorted_idx.slice_locs(None, ("qux", "one"))
+        assert result == (0, 5)
+
+        result = sorted_idx.slice_locs(("foo", "two"), None)
+        assert result == (1, len(sorted_idx))
+
+        result = sorted_idx.slice_locs("bar", "baz")
+        assert result == (2, 4)
+
+    def test_slice_locs(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((50, 4)),
+            columns=Index(list("ABCD"), dtype=object),
+            index=date_range("2000-01-01", periods=50, freq="B"),
+        )
+        stacked = df.stack(future_stack=True)
+        idx = stacked.index
+
+        slob = slice(*idx.slice_locs(df.index[5], df.index[15]))
+        sliced = stacked[slob]
+        expected = df[5:16].stack(future_stack=True)
+        tm.assert_almost_equal(sliced.values, expected.values)
+
+        slob = slice(
+            *idx.slice_locs(
+                df.index[5] + timedelta(seconds=30),
+                df.index[15] - timedelta(seconds=30),
+            )
+        )
+        sliced = stacked[slob]
+        expected = df[6:15].stack(future_stack=True)
+        tm.assert_almost_equal(sliced.values, expected.values)
+
+    def test_slice_locs_with_type_mismatch(self):
+        df = 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"),
+        )
+        stacked = df.stack(future_stack=True)
+        idx = stacked.index
+        with pytest.raises(TypeError, match="^Level type mismatch"):
+            idx.slice_locs((1, 3))
+        with pytest.raises(TypeError, match="^Level type mismatch"):
+            idx.slice_locs(df.index[5] + timedelta(seconds=30), (5, 2))
+        df = DataFrame(
+            np.ones((5, 5)),
+            index=Index([f"i-{i}" for i in range(5)], name="a"),
+            columns=Index([f"i-{i}" for i in range(5)], name="a"),
+        )
+        stacked = df.stack(future_stack=True)
+        idx = stacked.index
+        with pytest.raises(TypeError, match="^Level type mismatch"):
+            idx.slice_locs(timedelta(seconds=30))
+        # TODO: Try creating a UnicodeDecodeError in exception message
+        with pytest.raises(TypeError, match="^Level type mismatch"):
+            idx.slice_locs(df.index[1], (16, "a"))
+
+    def test_slice_locs_not_sorted(self):
+        index = MultiIndex(
+            levels=[Index(np.arange(4)), Index(np.arange(4)), Index(np.arange(4))],
+            codes=[
+                np.array([0, 0, 1, 2, 2, 2, 3, 3]),
+                np.array([0, 1, 0, 0, 0, 1, 0, 1]),
+                np.array([1, 0, 1, 1, 0, 0, 1, 0]),
+            ],
+        )
+        msg = "[Kk]ey length.*greater than MultiIndex lexsort depth"
+        with pytest.raises(KeyError, match=msg):
+            index.slice_locs((1, 0, 1), (2, 1, 0))
+
+        # works
+        sorted_index, _ = index.sortlevel(0)
+        # should there be a test case here???
+        sorted_index.slice_locs((1, 0, 1), (2, 1, 0))
+
+    def test_slice_locs_not_contained(self):
+        # some searchsorted action
+
+        index = MultiIndex(
+            levels=[[0, 2, 4, 6], [0, 2, 4]],
+            codes=[[0, 0, 0, 1, 1, 2, 3, 3, 3], [0, 1, 2, 1, 2, 2, 0, 1, 2]],
+        )
+
+        result = index.slice_locs((1, 0), (5, 2))
+        assert result == (3, 6)
+
+        result = index.slice_locs(1, 5)
+        assert result == (3, 6)
+
+        result = index.slice_locs((2, 2), (5, 2))
+        assert result == (3, 6)
+
+        result = index.slice_locs(2, 5)
+        assert result == (3, 6)
+
+        result = index.slice_locs((1, 0), (6, 3))
+        assert result == (3, 8)
+
+        result = index.slice_locs(-1, 10)
+        assert result == (0, len(index))
+
+    @pytest.mark.parametrize(
+        "index_arr,expected,start_idx,end_idx",
+        [
+            ([[np.nan, "a", "b"], ["c", "d", "e"]], (0, 3), np.nan, None),
+            ([[np.nan, "a", "b"], ["c", "d", "e"]], (0, 3), np.nan, "b"),
+            ([[np.nan, "a", "b"], ["c", "d", "e"]], (0, 3), np.nan, ("b", "e")),
+            ([["a", "b", "c"], ["d", np.nan, "e"]], (1, 3), ("b", np.nan), None),
+            ([["a", "b", "c"], ["d", np.nan, "e"]], (1, 3), ("b", np.nan), "c"),
+            ([["a", "b", "c"], ["d", np.nan, "e"]], (1, 3), ("b", np.nan), ("c", "e")),
+        ],
+    )
+    def test_slice_locs_with_missing_value(
+        self, index_arr, expected, start_idx, end_idx
+    ):
+        # issue 19132
+        idx = MultiIndex.from_arrays(index_arr)
+        result = idx.slice_locs(start=start_idx, end=end_idx)
+        assert result == expected
+
+
+class TestPutmask:
+    def test_putmask_with_wrong_mask(self, idx):
+        # GH18368
+
+        msg = "putmask: mask and data must be the same size"
+        with pytest.raises(ValueError, match=msg):
+            idx.putmask(np.ones(len(idx) + 1, np.bool_), 1)
+
+        with pytest.raises(ValueError, match=msg):
+            idx.putmask(np.ones(len(idx) - 1, np.bool_), 1)
+
+        with pytest.raises(ValueError, match=msg):
+            idx.putmask("foo", 1)
+
+    def test_putmask_multiindex_other(self):
+        # GH#43212 `value` is also a MultiIndex
+
+        left = MultiIndex.from_tuples([(np.nan, 6), (np.nan, 6), ("a", 4)])
+        right = MultiIndex.from_tuples([("a", 1), ("a", 1), ("d", 1)])
+        mask = np.array([True, True, False])
+
+        result = left.putmask(mask, right)
+
+        expected = MultiIndex.from_tuples([right[0], right[1], left[2]])
+        tm.assert_index_equal(result, expected)
+
+    def test_putmask_keep_dtype(self, any_numeric_ea_dtype):
+        # GH#49830
+        midx = MultiIndex.from_arrays(
+            [pd.Series([1, 2, 3], dtype=any_numeric_ea_dtype), [10, 11, 12]]
+        )
+        midx2 = MultiIndex.from_arrays(
+            [pd.Series([5, 6, 7], dtype=any_numeric_ea_dtype), [-1, -2, -3]]
+        )
+        result = midx.putmask([True, False, False], midx2)
+        expected = MultiIndex.from_arrays(
+            [pd.Series([5, 2, 3], dtype=any_numeric_ea_dtype), [-1, 11, 12]]
+        )
+        tm.assert_index_equal(result, expected)
+
+    def test_putmask_keep_dtype_shorter_value(self, any_numeric_ea_dtype):
+        # GH#49830
+        midx = MultiIndex.from_arrays(
+            [pd.Series([1, 2, 3], dtype=any_numeric_ea_dtype), [10, 11, 12]]
+        )
+        midx2 = MultiIndex.from_arrays(
+            [pd.Series([5], dtype=any_numeric_ea_dtype), [-1]]
+        )
+        result = midx.putmask([True, False, False], midx2)
+        expected = MultiIndex.from_arrays(
+            [pd.Series([5, 2, 3], dtype=any_numeric_ea_dtype), [-1, 11, 12]]
+        )
+        tm.assert_index_equal(result, expected)
+
+
+class TestGetIndexer:
+    def test_get_indexer(self):
+        major_axis = Index(np.arange(4))
+        minor_axis = Index(np.arange(2))
+
+        major_codes = np.array([0, 0, 1, 2, 2, 3, 3], dtype=np.intp)
+        minor_codes = np.array([0, 1, 0, 0, 1, 0, 1], dtype=np.intp)
+
+        index = MultiIndex(
+            levels=[major_axis, minor_axis], codes=[major_codes, minor_codes]
+        )
+        idx1 = index[:5]
+        idx2 = index[[1, 3, 5]]
+
+        r1 = idx1.get_indexer(idx2)
+        tm.assert_almost_equal(r1, np.array([1, 3, -1], dtype=np.intp))
+
+        r1 = idx2.get_indexer(idx1, method="pad")
+        e1 = np.array([-1, 0, 0, 1, 1], dtype=np.intp)
+        tm.assert_almost_equal(r1, e1)
+
+        r2 = idx2.get_indexer(idx1[::-1], method="pad")
+        tm.assert_almost_equal(r2, e1[::-1])
+
+        rffill1 = idx2.get_indexer(idx1, method="ffill")
+        tm.assert_almost_equal(r1, rffill1)
+
+        r1 = idx2.get_indexer(idx1, method="backfill")
+        e1 = np.array([0, 0, 1, 1, 2], dtype=np.intp)
+        tm.assert_almost_equal(r1, e1)
+
+        r2 = idx2.get_indexer(idx1[::-1], method="backfill")
+        tm.assert_almost_equal(r2, e1[::-1])
+
+        rbfill1 = idx2.get_indexer(idx1, method="bfill")
+        tm.assert_almost_equal(r1, rbfill1)
+
+        # pass non-MultiIndex
+        r1 = idx1.get_indexer(idx2.values)
+        rexp1 = idx1.get_indexer(idx2)
+        tm.assert_almost_equal(r1, rexp1)
+
+        r1 = idx1.get_indexer([1, 2, 3])
+        assert (r1 == [-1, -1, -1]).all()
+
+        # create index with duplicates
+        idx1 = Index(list(range(10)) + list(range(10)))
+        idx2 = Index(list(range(20)))
+
+        msg = "Reindexing only valid with uniquely valued Index objects"
+        with pytest.raises(InvalidIndexError, match=msg):
+            idx1.get_indexer(idx2)
+
+    def test_get_indexer_nearest(self):
+        midx = MultiIndex.from_tuples([("a", 1), ("b", 2)])
+        msg = (
+            "method='nearest' not implemented yet for MultiIndex; "
+            "see GitHub issue 9365"
+        )
+        with pytest.raises(NotImplementedError, match=msg):
+            midx.get_indexer(["a"], method="nearest")
+        msg = "tolerance not implemented yet for MultiIndex"
+        with pytest.raises(NotImplementedError, match=msg):
+            midx.get_indexer(["a"], method="pad", tolerance=2)
+
+    def test_get_indexer_categorical_time(self):
+        # https://github.com/pandas-dev/pandas/issues/21390
+        midx = MultiIndex.from_product(
+            [
+                Categorical(["a", "b", "c"]),
+                Categorical(date_range("2012-01-01", periods=3, freq="h")),
+            ]
+        )
+        result = midx.get_indexer(midx)
+        tm.assert_numpy_array_equal(result, np.arange(9, dtype=np.intp))
+
+    @pytest.mark.parametrize(
+        "index_arr,labels,expected",
+        [
+            (
+                [[1, np.nan, 2], [3, 4, 5]],
+                [1, np.nan, 2],
+                np.array([-1, -1, -1], dtype=np.intp),
+            ),
+            ([[1, np.nan, 2], [3, 4, 5]], [(np.nan, 4)], np.array([1], dtype=np.intp)),
+            ([[1, 2, 3], [np.nan, 4, 5]], [(1, np.nan)], np.array([0], dtype=np.intp)),
+            (
+                [[1, 2, 3], [np.nan, 4, 5]],
+                [np.nan, 4, 5],
+                np.array([-1, -1, -1], dtype=np.intp),
+            ),
+        ],
+    )
+    def test_get_indexer_with_missing_value(self, index_arr, labels, expected):
+        # issue 19132
+        idx = MultiIndex.from_arrays(index_arr)
+        result = idx.get_indexer(labels)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_get_indexer_methods(self):
+        # https://github.com/pandas-dev/pandas/issues/29896
+        # test getting an indexer for another index with different methods
+        # confirms that getting an indexer without a filling method, getting an
+        # indexer and backfilling, and getting an indexer and padding all behave
+        # correctly in the case where all of the target values fall in between
+        # several levels in the MultiIndex into which they are getting an indexer
+        #
+        # visually, the MultiIndexes used in this test are:
+        # mult_idx_1:
+        #  0: -1 0
+        #  1:    2
+        #  2:    3
+        #  3:    4
+        #  4:  0 0
+        #  5:    2
+        #  6:    3
+        #  7:    4
+        #  8:  1 0
+        #  9:    2
+        # 10:    3
+        # 11:    4
+        #
+        # mult_idx_2:
+        #  0: 0 1
+        #  1:   3
+        #  2:   4
+        mult_idx_1 = MultiIndex.from_product([[-1, 0, 1], [0, 2, 3, 4]])
+        mult_idx_2 = MultiIndex.from_product([[0], [1, 3, 4]])
+
+        indexer = mult_idx_1.get_indexer(mult_idx_2)
+        expected = np.array([-1, 6, 7], dtype=indexer.dtype)
+        tm.assert_almost_equal(expected, indexer)
+
+        backfill_indexer = mult_idx_1.get_indexer(mult_idx_2, method="backfill")
+        expected = np.array([5, 6, 7], dtype=backfill_indexer.dtype)
+        tm.assert_almost_equal(expected, backfill_indexer)
+
+        # ensure the legacy "bfill" option functions identically to "backfill"
+        backfill_indexer = mult_idx_1.get_indexer(mult_idx_2, method="bfill")
+        expected = np.array([5, 6, 7], dtype=backfill_indexer.dtype)
+        tm.assert_almost_equal(expected, backfill_indexer)
+
+        pad_indexer = mult_idx_1.get_indexer(mult_idx_2, method="pad")
+        expected = np.array([4, 6, 7], dtype=pad_indexer.dtype)
+        tm.assert_almost_equal(expected, pad_indexer)
+
+        # ensure the legacy "ffill" option functions identically to "pad"
+        pad_indexer = mult_idx_1.get_indexer(mult_idx_2, method="ffill")
+        expected = np.array([4, 6, 7], dtype=pad_indexer.dtype)
+        tm.assert_almost_equal(expected, pad_indexer)
+
+    @pytest.mark.parametrize("method", ["pad", "ffill", "backfill", "bfill", "nearest"])
+    def test_get_indexer_methods_raise_for_non_monotonic(self, method):
+        # 53452
+        mi = MultiIndex.from_arrays([[0, 4, 2], [0, 4, 2]])
+        if method == "nearest":
+            err = NotImplementedError
+            msg = "not implemented yet for MultiIndex"
+        else:
+            err = ValueError
+            msg = "index must be monotonic increasing or decreasing"
+        with pytest.raises(err, match=msg):
+            mi.get_indexer([(1, 1)], method=method)
+
+    def test_get_indexer_three_or_more_levels(self):
+        # https://github.com/pandas-dev/pandas/issues/29896
+        # tests get_indexer() on MultiIndexes with 3+ levels
+        # visually, these are
+        # mult_idx_1:
+        #  0: 1 2 5
+        #  1:     7
+        #  2:   4 5
+        #  3:     7
+        #  4:   6 5
+        #  5:     7
+        #  6: 3 2 5
+        #  7:     7
+        #  8:   4 5
+        #  9:     7
+        # 10:   6 5
+        # 11:     7
+        #
+        # mult_idx_2:
+        #  0: 1 1 8
+        #  1: 1 5 9
+        #  2: 1 6 7
+        #  3: 2 1 6
+        #  4: 2 7 6
+        #  5: 2 7 8
+        #  6: 3 6 8
+        mult_idx_1 = MultiIndex.from_product([[1, 3], [2, 4, 6], [5, 7]])
+        mult_idx_2 = MultiIndex.from_tuples(
+            [
+                (1, 1, 8),
+                (1, 5, 9),
+                (1, 6, 7),
+                (2, 1, 6),
+                (2, 7, 7),
+                (2, 7, 8),
+                (3, 6, 8),
+            ]
+        )
+        # sanity check
+        assert mult_idx_1.is_monotonic_increasing
+        assert mult_idx_1.is_unique
+        assert mult_idx_2.is_monotonic_increasing
+        assert mult_idx_2.is_unique
+
+        # show the relationships between the two
+        assert mult_idx_2[0] < mult_idx_1[0]
+        assert mult_idx_1[3] < mult_idx_2[1] < mult_idx_1[4]
+        assert mult_idx_1[5] == mult_idx_2[2]
+        assert mult_idx_1[5] < mult_idx_2[3] < mult_idx_1[6]
+        assert mult_idx_1[5] < mult_idx_2[4] < mult_idx_1[6]
+        assert mult_idx_1[5] < mult_idx_2[5] < mult_idx_1[6]
+        assert mult_idx_1[-1] < mult_idx_2[6]
+
+        indexer_no_fill = mult_idx_1.get_indexer(mult_idx_2)
+        expected = np.array([-1, -1, 5, -1, -1, -1, -1], dtype=indexer_no_fill.dtype)
+        tm.assert_almost_equal(expected, indexer_no_fill)
+
+        # test with backfilling
+        indexer_backfilled = mult_idx_1.get_indexer(mult_idx_2, method="backfill")
+        expected = np.array([0, 4, 5, 6, 6, 6, -1], dtype=indexer_backfilled.dtype)
+        tm.assert_almost_equal(expected, indexer_backfilled)
+
+        # now, the same thing, but forward-filled (aka "padded")
+        indexer_padded = mult_idx_1.get_indexer(mult_idx_2, method="pad")
+        expected = np.array([-1, 3, 5, 5, 5, 5, 11], dtype=indexer_padded.dtype)
+        tm.assert_almost_equal(expected, indexer_padded)
+
+        # now, do the indexing in the other direction
+        assert mult_idx_2[0] < mult_idx_1[0] < mult_idx_2[1]
+        assert mult_idx_2[0] < mult_idx_1[1] < mult_idx_2[1]
+        assert mult_idx_2[0] < mult_idx_1[2] < mult_idx_2[1]
+        assert mult_idx_2[0] < mult_idx_1[3] < mult_idx_2[1]
+        assert mult_idx_2[1] < mult_idx_1[4] < mult_idx_2[2]
+        assert mult_idx_2[2] == mult_idx_1[5]
+        assert mult_idx_2[5] < mult_idx_1[6] < mult_idx_2[6]
+        assert mult_idx_2[5] < mult_idx_1[7] < mult_idx_2[6]
+        assert mult_idx_2[5] < mult_idx_1[8] < mult_idx_2[6]
+        assert mult_idx_2[5] < mult_idx_1[9] < mult_idx_2[6]
+        assert mult_idx_2[5] < mult_idx_1[10] < mult_idx_2[6]
+        assert mult_idx_2[5] < mult_idx_1[11] < mult_idx_2[6]
+
+        indexer = mult_idx_2.get_indexer(mult_idx_1)
+        expected = np.array(
+            [-1, -1, -1, -1, -1, 2, -1, -1, -1, -1, -1, -1], dtype=indexer.dtype
+        )
+        tm.assert_almost_equal(expected, indexer)
+
+        backfill_indexer = mult_idx_2.get_indexer(mult_idx_1, method="bfill")
+        expected = np.array(
+            [1, 1, 1, 1, 2, 2, 6, 6, 6, 6, 6, 6], dtype=backfill_indexer.dtype
+        )
+        tm.assert_almost_equal(expected, backfill_indexer)
+
+        pad_indexer = mult_idx_2.get_indexer(mult_idx_1, method="pad")
+        expected = np.array(
+            [0, 0, 0, 0, 1, 2, 5, 5, 5, 5, 5, 5], dtype=pad_indexer.dtype
+        )
+        tm.assert_almost_equal(expected, pad_indexer)
+
+    def test_get_indexer_crossing_levels(self):
+        # https://github.com/pandas-dev/pandas/issues/29896
+        # tests a corner case with get_indexer() with MultiIndexes where, when we
+        # need to "carry" across levels, proper tuple ordering is respected
+        #
+        # the MultiIndexes used in this test, visually, are:
+        # mult_idx_1:
+        #  0: 1 1 1 1
+        #  1:       2
+        #  2:     2 1
+        #  3:       2
+        #  4: 1 2 1 1
+        #  5:       2
+        #  6:     2 1
+        #  7:       2
+        #  8: 2 1 1 1
+        #  9:       2
+        # 10:     2 1
+        # 11:       2
+        # 12: 2 2 1 1
+        # 13:       2
+        # 14:     2 1
+        # 15:       2
+        #
+        # mult_idx_2:
+        #  0: 1 3 2 2
+        #  1: 2 3 2 2
+        mult_idx_1 = MultiIndex.from_product([[1, 2]] * 4)
+        mult_idx_2 = MultiIndex.from_tuples([(1, 3, 2, 2), (2, 3, 2, 2)])
+
+        # show the tuple orderings, which get_indexer() should respect
+        assert mult_idx_1[7] < mult_idx_2[0] < mult_idx_1[8]
+        assert mult_idx_1[-1] < mult_idx_2[1]
+
+        indexer = mult_idx_1.get_indexer(mult_idx_2)
+        expected = np.array([-1, -1], dtype=indexer.dtype)
+        tm.assert_almost_equal(expected, indexer)
+
+        backfill_indexer = mult_idx_1.get_indexer(mult_idx_2, method="bfill")
+        expected = np.array([8, -1], dtype=backfill_indexer.dtype)
+        tm.assert_almost_equal(expected, backfill_indexer)
+
+        pad_indexer = mult_idx_1.get_indexer(mult_idx_2, method="ffill")
+        expected = np.array([7, 15], dtype=pad_indexer.dtype)
+        tm.assert_almost_equal(expected, pad_indexer)
+
+    def test_get_indexer_kwarg_validation(self):
+        # GH#41918
+        mi = MultiIndex.from_product([range(3), ["A", "B"]])
+
+        msg = "limit argument only valid if doing pad, backfill or nearest"
+        with pytest.raises(ValueError, match=msg):
+            mi.get_indexer(mi[:-1], limit=4)
+
+        msg = "tolerance argument only valid if doing pad, backfill or nearest"
+        with pytest.raises(ValueError, match=msg):
+            mi.get_indexer(mi[:-1], tolerance="piano")
+
+    def test_get_indexer_nan(self):
+        # GH#37222
+        idx1 = MultiIndex.from_product([["A"], [1.0, 2.0]], names=["id1", "id2"])
+        idx2 = MultiIndex.from_product([["A"], [np.nan, 2.0]], names=["id1", "id2"])
+        expected = np.array([-1, 1])
+        result = idx2.get_indexer(idx1)
+        tm.assert_numpy_array_equal(result, expected, check_dtype=False)
+        result = idx1.get_indexer(idx2)
+        tm.assert_numpy_array_equal(result, expected, check_dtype=False)
+
+
+def test_getitem(idx):
+    # scalar
+    assert idx[2] == ("bar", "one")
+
+    # slice
+    result = idx[2:5]
+    expected = idx[[2, 3, 4]]
+    assert result.equals(expected)
+
+    # boolean
+    result = idx[[True, False, True, False, True, True]]
+    result2 = idx[np.array([True, False, True, False, True, True])]
+    expected = idx[[0, 2, 4, 5]]
+    assert result.equals(expected)
+    assert result2.equals(expected)
+
+
+def test_getitem_group_select(idx):
+    sorted_idx, _ = idx.sortlevel(0)
+    assert sorted_idx.get_loc("baz") == slice(3, 4)
+    assert sorted_idx.get_loc("foo") == slice(0, 2)
+
+
+@pytest.mark.parametrize("ind1", [[True] * 5, Index([True] * 5)])
+@pytest.mark.parametrize(
+    "ind2",
+    [[True, False, True, False, False], Index([True, False, True, False, False])],
+)
+def test_getitem_bool_index_all(ind1, ind2):
+    # GH#22533
+    idx = MultiIndex.from_tuples([(10, 1), (20, 2), (30, 3), (40, 4), (50, 5)])
+    tm.assert_index_equal(idx[ind1], idx)
+
+    expected = MultiIndex.from_tuples([(10, 1), (30, 3)])
+    tm.assert_index_equal(idx[ind2], expected)
+
+
+@pytest.mark.parametrize("ind1", [[True], Index([True])])
+@pytest.mark.parametrize("ind2", [[False], Index([False])])
+def test_getitem_bool_index_single(ind1, ind2):
+    # GH#22533
+    idx = MultiIndex.from_tuples([(10, 1)])
+    tm.assert_index_equal(idx[ind1], idx)
+
+    expected = MultiIndex(
+        levels=[np.array([], dtype=np.int64), np.array([], dtype=np.int64)],
+        codes=[[], []],
+    )
+    tm.assert_index_equal(idx[ind2], expected)
+
+
+class TestGetLoc:
+    def test_get_loc(self, idx):
+        assert idx.get_loc(("foo", "two")) == 1
+        assert idx.get_loc(("baz", "two")) == 3
+        with pytest.raises(KeyError, match=r"^\('bar', 'two'\)$"):
+            idx.get_loc(("bar", "two"))
+        with pytest.raises(KeyError, match=r"^'quux'$"):
+            idx.get_loc("quux")
+
+        # 3 levels
+        index = MultiIndex(
+            levels=[Index(np.arange(4)), Index(np.arange(4)), Index(np.arange(4))],
+            codes=[
+                np.array([0, 0, 1, 2, 2, 2, 3, 3]),
+                np.array([0, 1, 0, 0, 0, 1, 0, 1]),
+                np.array([1, 0, 1, 1, 0, 0, 1, 0]),
+            ],
+        )
+        with pytest.raises(KeyError, match=r"^\(1, 1\)$"):
+            index.get_loc((1, 1))
+        assert index.get_loc((2, 0)) == slice(3, 5)
+
+    def test_get_loc_duplicates(self):
+        index = Index([2, 2, 2, 2])
+        result = index.get_loc(2)
+        expected = slice(0, 4)
+        assert result == expected
+
+        index = Index(["c", "a", "a", "b", "b"])
+        rs = index.get_loc("c")
+        xp = 0
+        assert rs == xp
+
+        with pytest.raises(KeyError, match="2"):
+            index.get_loc(2)
+
+    def test_get_loc_level(self):
+        index = MultiIndex(
+            levels=[Index(np.arange(4)), Index(np.arange(4)), Index(np.arange(4))],
+            codes=[
+                np.array([0, 0, 1, 2, 2, 2, 3, 3]),
+                np.array([0, 1, 0, 0, 0, 1, 0, 1]),
+                np.array([1, 0, 1, 1, 0, 0, 1, 0]),
+            ],
+        )
+        loc, new_index = index.get_loc_level((0, 1))
+        expected = slice(1, 2)
+        exp_index = index[expected].droplevel(0).droplevel(0)
+        assert loc == expected
+        assert new_index.equals(exp_index)
+
+        loc, new_index = index.get_loc_level((0, 1, 0))
+        expected = 1
+        assert loc == expected
+        assert new_index is None
+
+        with pytest.raises(KeyError, match=r"^\(2, 2\)$"):
+            index.get_loc_level((2, 2))
+        # GH 22221: unused label
+        with pytest.raises(KeyError, match=r"^2$"):
+            index.drop(2).get_loc_level(2)
+        # Unused label on unsorted level:
+        with pytest.raises(KeyError, match=r"^2$"):
+            index.drop(1, level=2).get_loc_level(2, level=2)
+
+        index = MultiIndex(
+            levels=[[2000], list(range(4))],
+            codes=[np.array([0, 0, 0, 0]), np.array([0, 1, 2, 3])],
+        )
+        result, new_index = index.get_loc_level((2000, slice(None, None)))
+        expected = slice(None, None)
+        assert result == expected
+        assert new_index.equals(index.droplevel(0))
+
+    @pytest.mark.parametrize("dtype1", [int, float, bool, str])
+    @pytest.mark.parametrize("dtype2", [int, float, bool, str])
+    def test_get_loc_multiple_dtypes(self, dtype1, dtype2):
+        # GH 18520
+        levels = [np.array([0, 1]).astype(dtype1), np.array([0, 1]).astype(dtype2)]
+        idx = MultiIndex.from_product(levels)
+        assert idx.get_loc(idx[2]) == 2
+
+    @pytest.mark.parametrize("level", [0, 1])
+    @pytest.mark.parametrize("dtypes", [[int, float], [float, int]])
+    def test_get_loc_implicit_cast(self, level, dtypes):
+        # GH 18818, GH 15994 : as flat index, cast int to float and vice-versa
+        levels = [["a", "b"], ["c", "d"]]
+        key = ["b", "d"]
+        lev_dtype, key_dtype = dtypes
+        levels[level] = np.array([0, 1], dtype=lev_dtype)
+        key[level] = key_dtype(1)
+        idx = MultiIndex.from_product(levels)
+        assert idx.get_loc(tuple(key)) == 3
+
+    @pytest.mark.parametrize("dtype", [bool, object])
+    def test_get_loc_cast_bool(self, dtype):
+        # GH 19086 : int is casted to bool, but not vice-versa (for object dtype)
+        #  With bool dtype, we don't cast in either direction.
+        levels = [Index([False, True], dtype=dtype), np.arange(2, dtype="int64")]
+        idx = MultiIndex.from_product(levels)
+
+        if dtype is bool:
+            with pytest.raises(KeyError, match=r"^\(0, 1\)$"):
+                assert idx.get_loc((0, 1)) == 1
+            with pytest.raises(KeyError, match=r"^\(1, 0\)$"):
+                assert idx.get_loc((1, 0)) == 2
+        else:
+            # We use python object comparisons, which treat 0 == False and 1 == True
+            assert idx.get_loc((0, 1)) == 1
+            assert idx.get_loc((1, 0)) == 2
+
+        with pytest.raises(KeyError, match=r"^\(False, True\)$"):
+            idx.get_loc((False, True))
+        with pytest.raises(KeyError, match=r"^\(True, False\)$"):
+            idx.get_loc((True, False))
+
+    @pytest.mark.parametrize("level", [0, 1])
+    def test_get_loc_nan(self, level, nulls_fixture):
+        # GH 18485 : NaN in MultiIndex
+        levels = [["a", "b"], ["c", "d"]]
+        key = ["b", "d"]
+        levels[level] = np.array([0, nulls_fixture], dtype=type(nulls_fixture))
+        key[level] = nulls_fixture
+        idx = MultiIndex.from_product(levels)
+        assert idx.get_loc(tuple(key)) == 3
+
+    def test_get_loc_missing_nan(self):
+        # GH 8569
+        idx = MultiIndex.from_arrays([[1.0, 2.0], [3.0, 4.0]])
+        assert isinstance(idx.get_loc(1), slice)
+        with pytest.raises(KeyError, match=r"^3$"):
+            idx.get_loc(3)
+        with pytest.raises(KeyError, match=r"^nan$"):
+            idx.get_loc(np.nan)
+        with pytest.raises(InvalidIndexError, match=r"\[nan\]"):
+            # listlike/non-hashable raises TypeError
+            idx.get_loc([np.nan])
+
+    def test_get_loc_with_values_including_missing_values(self):
+        # issue 19132
+        idx = MultiIndex.from_product([[np.nan, 1]] * 2)
+        expected = slice(0, 2, None)
+        assert idx.get_loc(np.nan) == expected
+
+        idx = MultiIndex.from_arrays([[np.nan, 1, 2, np.nan]])
+        expected = np.array([True, False, False, True])
+        tm.assert_numpy_array_equal(idx.get_loc(np.nan), expected)
+
+        idx = MultiIndex.from_product([[np.nan, 1]] * 3)
+        expected = slice(2, 4, None)
+        assert idx.get_loc((np.nan, 1)) == expected
+
+    def test_get_loc_duplicates2(self):
+        # TODO: de-duplicate with test_get_loc_duplicates above?
+        index = MultiIndex(
+            levels=[["D", "B", "C"], [0, 26, 27, 37, 57, 67, 75, 82]],
+            codes=[[0, 0, 0, 1, 2, 2, 2, 2, 2, 2], [1, 3, 4, 6, 0, 2, 2, 3, 5, 7]],
+            names=["tag", "day"],
+        )
+
+        assert index.get_loc("D") == slice(0, 3)
+
+    def test_get_loc_past_lexsort_depth(self):
+        # GH#30053
+        idx = MultiIndex(
+            levels=[["a"], [0, 7], [1]],
+            codes=[[0, 0], [1, 0], [0, 0]],
+            names=["x", "y", "z"],
+            sortorder=0,
+        )
+        key = ("a", 7)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            # PerformanceWarning: indexing past lexsort depth may impact performance
+            result = idx.get_loc(key)
+
+        assert result == slice(0, 1, None)
+
+    def test_multiindex_get_loc_list_raises(self):
+        # GH#35878
+        idx = MultiIndex.from_tuples([("a", 1), ("b", 2)])
+        msg = r"\[\]"
+        with pytest.raises(InvalidIndexError, match=msg):
+            idx.get_loc([])
+
+    def test_get_loc_nested_tuple_raises_keyerror(self):
+        # raise KeyError, not TypeError
+        mi = MultiIndex.from_product([range(3), range(4), range(5), range(6)])
+        key = ((2, 3, 4), "foo")
+
+        with pytest.raises(KeyError, match=re.escape(str(key))):
+            mi.get_loc(key)
+
+
+class TestWhere:
+    def test_where(self):
+        i = MultiIndex.from_tuples([("A", 1), ("A", 2)])
+
+        msg = r"\.where is not supported for MultiIndex operations"
+        with pytest.raises(NotImplementedError, match=msg):
+            i.where(True)
+
+    def test_where_array_like(self, listlike_box):
+        mi = MultiIndex.from_tuples([("A", 1), ("A", 2)])
+        cond = [False, True]
+        msg = r"\.where is not supported for MultiIndex operations"
+        with pytest.raises(NotImplementedError, match=msg):
+            mi.where(listlike_box(cond))
+
+
+class TestContains:
+    def test_contains_top_level(self):
+        midx = MultiIndex.from_product([["A", "B"], [1, 2]])
+        assert "A" in midx
+        assert "A" not in midx._engine
+
+    def test_contains_with_nat(self):
+        # MI with a NaT
+        mi = MultiIndex(
+            levels=[["C"], date_range("2012-01-01", periods=5)],
+            codes=[[0, 0, 0, 0, 0, 0], [-1, 0, 1, 2, 3, 4]],
+            names=[None, "B"],
+        )
+        assert ("C", pd.Timestamp("2012-01-01")) in mi
+        for val in mi.values:
+            assert val in mi
+
+    def test_contains(self, idx):
+        assert ("foo", "two") in idx
+        assert ("bar", "two") not in idx
+        assert None not in idx
+
+    def test_contains_with_missing_value(self):
+        # GH#19132
+        idx = MultiIndex.from_arrays([[1, np.nan, 2]])
+        assert np.nan in idx
+
+        idx = MultiIndex.from_arrays([[1, 2], [np.nan, 3]])
+        assert np.nan not in idx
+        assert (1, np.nan) in idx
+
+    def test_multiindex_contains_dropped(self):
+        # GH#19027
+        # test that dropped MultiIndex levels are not in the MultiIndex
+        # despite continuing to be in the MultiIndex's levels
+        idx = MultiIndex.from_product([[1, 2], [3, 4]])
+        assert 2 in idx
+        idx = idx.drop(2)
+
+        # drop implementation keeps 2 in the levels
+        assert 2 in idx.levels[0]
+        # but it should no longer be in the index itself
+        assert 2 not in idx
+
+        # also applies to strings
+        idx = MultiIndex.from_product([["a", "b"], ["c", "d"]])
+        assert "a" in idx
+        idx = idx.drop("a")
+        assert "a" in idx.levels[0]
+        assert "a" not in idx
+
+    def test_contains_td64_level(self):
+        # GH#24570
+        tx = pd.timedelta_range("09:30:00", "16:00:00", freq="30 min")
+        idx = MultiIndex.from_arrays([tx, np.arange(len(tx))])
+        assert tx[0] in idx
+        assert "element_not_exit" not in idx
+        assert "0 day 09:30:00" in idx
+
+    def test_large_mi_contains(self, monkeypatch):
+        # GH#10645
+        with monkeypatch.context():
+            monkeypatch.setattr(libindex, "_SIZE_CUTOFF", 10)
+            result = MultiIndex.from_arrays([range(10), range(10)])
+            assert (10, 0) not in result
+
+
+def test_timestamp_multiindex_indexer():
+    # https://github.com/pandas-dev/pandas/issues/26944
+    idx = MultiIndex.from_product(
+        [
+            date_range("2019-01-01T00:15:33", periods=100, freq="h", name="date"),
+            ["x"],
+            [3],
+        ]
+    )
+    df = DataFrame({"foo": np.arange(len(idx))}, idx)
+    result = df.loc[pd.IndexSlice["2019-1-2":, "x", :], "foo"]
+    qidx = MultiIndex.from_product(
+        [
+            date_range(
+                start="2019-01-02T00:15:33",
+                end="2019-01-05T03:15:33",
+                freq="h",
+                name="date",
+            ),
+            ["x"],
+            [3],
+        ]
+    )
+    should_be = pd.Series(data=np.arange(24, len(qidx) + 24), index=qidx, name="foo")
+    tm.assert_series_equal(result, should_be)
+
+
+@pytest.mark.parametrize(
+    "index_arr,expected,target,algo",
+    [
+        ([[np.nan, "a", "b"], ["c", "d", "e"]], 0, np.nan, "left"),
+        ([[np.nan, "a", "b"], ["c", "d", "e"]], 1, (np.nan, "c"), "right"),
+        ([["a", "b", "c"], ["d", np.nan, "d"]], 1, ("b", np.nan), "left"),
+    ],
+)
+def test_get_slice_bound_with_missing_value(index_arr, expected, target, algo):
+    # issue 19132
+    idx = MultiIndex.from_arrays(index_arr)
+    result = idx.get_slice_bound(target, side=algo)
+    assert result == expected
+
+
+@pytest.mark.parametrize(
+    "index_arr,expected,start_idx,end_idx",
+    [
+        ([[np.nan, 1, 2], [3, 4, 5]], slice(0, 2, None), np.nan, 1),
+        ([[np.nan, 1, 2], [3, 4, 5]], slice(0, 3, None), np.nan, (2, 5)),
+        ([[1, 2, 3], [4, np.nan, 5]], slice(1, 3, None), (2, np.nan), 3),
+        ([[1, 2, 3], [4, np.nan, 5]], slice(1, 3, None), (2, np.nan), (3, 5)),
+    ],
+)
+def test_slice_indexer_with_missing_value(index_arr, expected, start_idx, end_idx):
+    # issue 19132
+    idx = MultiIndex.from_arrays(index_arr)
+    result = idx.slice_indexer(start=start_idx, end=end_idx)
+    assert result == expected
+
+
+def test_pyint_engine():
+    # GH#18519 : when combinations of codes cannot be represented in 64
+    # bits, the index underlying the MultiIndex engine works with Python
+    # integers, rather than uint64.
+    N = 5
+    keys = [
+        tuple(arr)
+        for arr in [
+            [0] * 10 * N,
+            [1] * 10 * N,
+            [2] * 10 * N,
+            [np.nan] * N + [2] * 9 * N,
+            [0] * N + [2] * 9 * N,
+            [np.nan] * N + [2] * 8 * N + [0] * N,
+        ]
+    ]
+    # Each level contains 4 elements (including NaN), so it is represented
+    # in 2 bits, for a total of 2*N*10 = 100 > 64 bits. If we were using a
+    # 64 bit engine and truncating the first levels, the fourth and fifth
+    # keys would collide; if truncating the last levels, the fifth and
+    # sixth; if rotating bits rather than shifting, the third and fifth.
+
+    for idx, key_value in enumerate(keys):
+        index = MultiIndex.from_tuples(keys)
+        assert index.get_loc(key_value) == idx
+
+        expected = np.arange(idx + 1, dtype=np.intp)
+        result = index.get_indexer([keys[i] for i in expected])
+        tm.assert_numpy_array_equal(result, expected)
+
+    # With missing key:
+    idces = range(len(keys))
+    expected = np.array([-1] + list(idces), dtype=np.intp)
+    missing = tuple([0, 1] * 5 * N)
+    result = index.get_indexer([missing] + [keys[i] for i in idces])
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "keys,expected",
+    [
+        ((slice(None), [5, 4]), [1, 0]),
+        ((slice(None), [4, 5]), [0, 1]),
+        (([True, False, True], [4, 6]), [0, 2]),
+        (([True, False, True], [6, 4]), [0, 2]),
+        ((2, [4, 5]), [0, 1]),
+        ((2, [5, 4]), [1, 0]),
+        (([2], [4, 5]), [0, 1]),
+        (([2], [5, 4]), [1, 0]),
+    ],
+)
+def test_get_locs_reordering(keys, expected):
+    # GH48384
+    idx = MultiIndex.from_arrays(
+        [
+            [2, 2, 1],
+            [4, 5, 6],
+        ]
+    )
+    result = idx.get_locs(keys)
+    expected = np.array(expected, dtype=np.intp)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_get_indexer_for_multiindex_with_nans(nulls_fixture):
+    # GH37222
+    idx1 = MultiIndex.from_product([["A"], [1.0, 2.0]], names=["id1", "id2"])
+    idx2 = MultiIndex.from_product([["A"], [nulls_fixture, 2.0]], names=["id1", "id2"])
+
+    result = idx2.get_indexer(idx1)
+    expected = np.array([-1, 1], dtype=np.intp)
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = idx1.get_indexer(idx2)
+    expected = np.array([-1, 1], dtype=np.intp)
+    tm.assert_numpy_array_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_monotonic.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_monotonic.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b0b3f7cb36d72abedc538eda9e6a85eb45067e2
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_monotonic.py
@@ -0,0 +1,188 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Index,
+    MultiIndex,
+)
+
+
+def test_is_monotonic_increasing_lexsorted(lexsorted_two_level_string_multiindex):
+    # string ordering
+    mi = lexsorted_two_level_string_multiindex
+    assert mi.is_monotonic_increasing is False
+    assert Index(mi.values).is_monotonic_increasing is False
+    assert mi._is_strictly_monotonic_increasing is False
+    assert Index(mi.values)._is_strictly_monotonic_increasing is False
+
+
+def test_is_monotonic_increasing():
+    i = MultiIndex.from_product([np.arange(10), np.arange(10)], names=["one", "two"])
+    assert i.is_monotonic_increasing is True
+    assert i._is_strictly_monotonic_increasing is True
+    assert Index(i.values).is_monotonic_increasing is True
+    assert i._is_strictly_monotonic_increasing is True
+
+    i = MultiIndex.from_product(
+        [np.arange(10, 0, -1), np.arange(10)], names=["one", "two"]
+    )
+    assert i.is_monotonic_increasing is False
+    assert i._is_strictly_monotonic_increasing is False
+    assert Index(i.values).is_monotonic_increasing is False
+    assert Index(i.values)._is_strictly_monotonic_increasing is False
+
+    i = MultiIndex.from_product(
+        [np.arange(10), np.arange(10, 0, -1)], names=["one", "two"]
+    )
+    assert i.is_monotonic_increasing is False
+    assert i._is_strictly_monotonic_increasing is False
+    assert Index(i.values).is_monotonic_increasing is False
+    assert Index(i.values)._is_strictly_monotonic_increasing is False
+
+    i = MultiIndex.from_product([[1.0, np.nan, 2.0], ["a", "b", "c"]])
+    assert i.is_monotonic_increasing is False
+    assert i._is_strictly_monotonic_increasing is False
+    assert Index(i.values).is_monotonic_increasing is False
+    assert Index(i.values)._is_strictly_monotonic_increasing is False
+
+    i = MultiIndex(
+        levels=[["bar", "baz", "foo", "qux"], ["mom", "next", "zenith"]],
+        codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
+        names=["first", "second"],
+    )
+    assert i.is_monotonic_increasing is True
+    assert Index(i.values).is_monotonic_increasing is True
+    assert i._is_strictly_monotonic_increasing is True
+    assert Index(i.values)._is_strictly_monotonic_increasing is True
+
+    # mixed levels, hits the TypeError
+    i = MultiIndex(
+        levels=[
+            [1, 2, 3, 4],
+            [
+                "gb00b03mlx29",
+                "lu0197800237",
+                "nl0000289783",
+                "nl0000289965",
+                "nl0000301109",
+            ],
+        ],
+        codes=[[0, 1, 1, 2, 2, 2, 3], [4, 2, 0, 0, 1, 3, -1]],
+        names=["household_id", "asset_id"],
+    )
+
+    assert i.is_monotonic_increasing is False
+    assert i._is_strictly_monotonic_increasing is False
+
+    # empty
+    i = MultiIndex.from_arrays([[], []])
+    assert i.is_monotonic_increasing is True
+    assert Index(i.values).is_monotonic_increasing is True
+    assert i._is_strictly_monotonic_increasing is True
+    assert Index(i.values)._is_strictly_monotonic_increasing is True
+
+
+def test_is_monotonic_decreasing():
+    i = MultiIndex.from_product(
+        [np.arange(9, -1, -1), np.arange(9, -1, -1)], names=["one", "two"]
+    )
+    assert i.is_monotonic_decreasing is True
+    assert i._is_strictly_monotonic_decreasing is True
+    assert Index(i.values).is_monotonic_decreasing is True
+    assert i._is_strictly_monotonic_decreasing is True
+
+    i = MultiIndex.from_product(
+        [np.arange(10), np.arange(10, 0, -1)], names=["one", "two"]
+    )
+    assert i.is_monotonic_decreasing is False
+    assert i._is_strictly_monotonic_decreasing is False
+    assert Index(i.values).is_monotonic_decreasing is False
+    assert Index(i.values)._is_strictly_monotonic_decreasing is False
+
+    i = MultiIndex.from_product(
+        [np.arange(10, 0, -1), np.arange(10)], names=["one", "two"]
+    )
+    assert i.is_monotonic_decreasing is False
+    assert i._is_strictly_monotonic_decreasing is False
+    assert Index(i.values).is_monotonic_decreasing is False
+    assert Index(i.values)._is_strictly_monotonic_decreasing is False
+
+    i = MultiIndex.from_product([[2.0, np.nan, 1.0], ["c", "b", "a"]])
+    assert i.is_monotonic_decreasing is False
+    assert i._is_strictly_monotonic_decreasing is False
+    assert Index(i.values).is_monotonic_decreasing is False
+    assert Index(i.values)._is_strictly_monotonic_decreasing is False
+
+    # string ordering
+    i = MultiIndex(
+        levels=[["qux", "foo", "baz", "bar"], ["three", "two", "one"]],
+        codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
+        names=["first", "second"],
+    )
+    assert i.is_monotonic_decreasing is False
+    assert Index(i.values).is_monotonic_decreasing is False
+    assert i._is_strictly_monotonic_decreasing is False
+    assert Index(i.values)._is_strictly_monotonic_decreasing is False
+
+    i = MultiIndex(
+        levels=[["qux", "foo", "baz", "bar"], ["zenith", "next", "mom"]],
+        codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
+        names=["first", "second"],
+    )
+    assert i.is_monotonic_decreasing is True
+    assert Index(i.values).is_monotonic_decreasing is True
+    assert i._is_strictly_monotonic_decreasing is True
+    assert Index(i.values)._is_strictly_monotonic_decreasing is True
+
+    # mixed levels, hits the TypeError
+    i = MultiIndex(
+        levels=[
+            [4, 3, 2, 1],
+            [
+                "nl0000301109",
+                "nl0000289965",
+                "nl0000289783",
+                "lu0197800237",
+                "gb00b03mlx29",
+            ],
+        ],
+        codes=[[0, 1, 1, 2, 2, 2, 3], [4, 2, 0, 0, 1, 3, -1]],
+        names=["household_id", "asset_id"],
+    )
+
+    assert i.is_monotonic_decreasing is False
+    assert i._is_strictly_monotonic_decreasing is False
+
+    # empty
+    i = MultiIndex.from_arrays([[], []])
+    assert i.is_monotonic_decreasing is True
+    assert Index(i.values).is_monotonic_decreasing is True
+    assert i._is_strictly_monotonic_decreasing is True
+    assert Index(i.values)._is_strictly_monotonic_decreasing is True
+
+
+def test_is_strictly_monotonic_increasing():
+    idx = MultiIndex(
+        levels=[["bar", "baz"], ["mom", "next"]], codes=[[0, 0, 1, 1], [0, 0, 0, 1]]
+    )
+    assert idx.is_monotonic_increasing is True
+    assert idx._is_strictly_monotonic_increasing is False
+
+
+def test_is_strictly_monotonic_decreasing():
+    idx = MultiIndex(
+        levels=[["baz", "bar"], ["next", "mom"]], codes=[[0, 0, 1, 1], [0, 0, 0, 1]]
+    )
+    assert idx.is_monotonic_decreasing is True
+    assert idx._is_strictly_monotonic_decreasing is False
+
+
+@pytest.mark.parametrize("attr", ["is_monotonic_increasing", "is_monotonic_decreasing"])
+@pytest.mark.parametrize(
+    "values",
+    [[(np.nan,), (1,), (2,)], [(1,), (np.nan,), (2,)], [(1,), (2,), (np.nan,)]],
+)
+def test_is_monotonic_with_nans(values, attr):
+    # GH: 37220
+    idx = MultiIndex.from_tuples(values, names=["test"])
+    assert getattr(idx, attr) is False
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_reshape.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_reshape.py
new file mode 100644
index 0000000000000000000000000000000000000000..06dbb33aadf97a54e4bb283d3aed8fe1169164b3
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_reshape.py
@@ -0,0 +1,224 @@
+from datetime import datetime
+
+import numpy as np
+import pytest
+import pytz
+
+import pandas as pd
+from pandas import (
+    Index,
+    MultiIndex,
+)
+import pandas._testing as tm
+
+
+def test_insert(idx):
+    # key contained in all levels
+    new_index = idx.insert(0, ("bar", "two"))
+    assert new_index.equal_levels(idx)
+    assert new_index[0] == ("bar", "two")
+
+    # key not contained in all levels
+    new_index = idx.insert(0, ("abc", "three"))
+
+    exp0 = Index(list(idx.levels[0]) + ["abc"], name="first")
+    tm.assert_index_equal(new_index.levels[0], exp0)
+    assert new_index.names == ["first", "second"]
+
+    exp1 = Index(list(idx.levels[1]) + ["three"], name="second")
+    tm.assert_index_equal(new_index.levels[1], exp1)
+    assert new_index[0] == ("abc", "three")
+
+    # key wrong length
+    msg = "Item must have length equal to number of levels"
+    with pytest.raises(ValueError, match=msg):
+        idx.insert(0, ("foo2",))
+
+    left = pd.DataFrame([["a", "b", 0], ["b", "d", 1]], columns=["1st", "2nd", "3rd"])
+    left.set_index(["1st", "2nd"], inplace=True)
+    ts = left["3rd"].copy(deep=True)
+
+    left.loc[("b", "x"), "3rd"] = 2
+    left.loc[("b", "a"), "3rd"] = -1
+    left.loc[("b", "b"), "3rd"] = 3
+    left.loc[("a", "x"), "3rd"] = 4
+    left.loc[("a", "w"), "3rd"] = 5
+    left.loc[("a", "a"), "3rd"] = 6
+
+    ts.loc[("b", "x")] = 2
+    ts.loc["b", "a"] = -1
+    ts.loc[("b", "b")] = 3
+    ts.loc["a", "x"] = 4
+    ts.loc[("a", "w")] = 5
+    ts.loc["a", "a"] = 6
+
+    right = pd.DataFrame(
+        [
+            ["a", "b", 0],
+            ["b", "d", 1],
+            ["b", "x", 2],
+            ["b", "a", -1],
+            ["b", "b", 3],
+            ["a", "x", 4],
+            ["a", "w", 5],
+            ["a", "a", 6],
+        ],
+        columns=["1st", "2nd", "3rd"],
+    )
+    right.set_index(["1st", "2nd"], inplace=True)
+    # FIXME data types changes to float because
+    # of intermediate nan insertion;
+    tm.assert_frame_equal(left, right, check_dtype=False)
+    tm.assert_series_equal(ts, right["3rd"])
+
+
+def test_insert2():
+    # GH9250
+    idx = (
+        [("test1", i) for i in range(5)]
+        + [("test2", i) for i in range(6)]
+        + [("test", 17), ("test", 18)]
+    )
+
+    left = pd.Series(np.linspace(0, 10, 11), MultiIndex.from_tuples(idx[:-2]))
+
+    left.loc[("test", 17)] = 11
+    left.loc[("test", 18)] = 12
+
+    right = pd.Series(np.linspace(0, 12, 13), MultiIndex.from_tuples(idx))
+
+    tm.assert_series_equal(left, right)
+
+
+def test_append(idx):
+    result = idx[:3].append(idx[3:])
+    assert result.equals(idx)
+
+    foos = [idx[:1], idx[1:3], idx[3:]]
+    result = foos[0].append(foos[1:])
+    assert result.equals(idx)
+
+    # empty
+    result = idx.append([])
+    assert result.equals(idx)
+
+
+def test_append_index():
+    idx1 = Index([1.1, 1.2, 1.3])
+    idx2 = pd.date_range("2011-01-01", freq="D", periods=3, tz="Asia/Tokyo")
+    idx3 = Index(["A", "B", "C"])
+
+    midx_lv2 = MultiIndex.from_arrays([idx1, idx2])
+    midx_lv3 = MultiIndex.from_arrays([idx1, idx2, idx3])
+
+    result = idx1.append(midx_lv2)
+
+    # see gh-7112
+    tz = pytz.timezone("Asia/Tokyo")
+    expected_tuples = [
+        (1.1, tz.localize(datetime(2011, 1, 1))),
+        (1.2, tz.localize(datetime(2011, 1, 2))),
+        (1.3, tz.localize(datetime(2011, 1, 3))),
+    ]
+    expected = Index([1.1, 1.2, 1.3] + expected_tuples)
+    tm.assert_index_equal(result, expected)
+
+    result = midx_lv2.append(idx1)
+    expected = Index(expected_tuples + [1.1, 1.2, 1.3])
+    tm.assert_index_equal(result, expected)
+
+    result = midx_lv2.append(midx_lv2)
+    expected = MultiIndex.from_arrays([idx1.append(idx1), idx2.append(idx2)])
+    tm.assert_index_equal(result, expected)
+
+    result = midx_lv2.append(midx_lv3)
+    tm.assert_index_equal(result, expected)
+
+    result = midx_lv3.append(midx_lv2)
+    expected = Index._simple_new(
+        np.array(
+            [
+                (1.1, tz.localize(datetime(2011, 1, 1)), "A"),
+                (1.2, tz.localize(datetime(2011, 1, 2)), "B"),
+                (1.3, tz.localize(datetime(2011, 1, 3)), "C"),
+            ]
+            + expected_tuples,
+            dtype=object,
+        ),
+        None,
+    )
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("name, exp", [("b", "b"), ("c", None)])
+def test_append_names_match(name, exp):
+    # GH#48288
+    midx = MultiIndex.from_arrays([[1, 2], [3, 4]], names=["a", "b"])
+    midx2 = MultiIndex.from_arrays([[3], [5]], names=["a", name])
+    result = midx.append(midx2)
+    expected = MultiIndex.from_arrays([[1, 2, 3], [3, 4, 5]], names=["a", exp])
+    tm.assert_index_equal(result, expected)
+
+
+def test_append_names_dont_match():
+    # GH#48288
+    midx = MultiIndex.from_arrays([[1, 2], [3, 4]], names=["a", "b"])
+    midx2 = MultiIndex.from_arrays([[3], [5]], names=["x", "y"])
+    result = midx.append(midx2)
+    expected = MultiIndex.from_arrays([[1, 2, 3], [3, 4, 5]], names=None)
+    tm.assert_index_equal(result, expected)
+
+
+def test_append_overlapping_interval_levels():
+    # GH 54934
+    ivl1 = pd.IntervalIndex.from_breaks([0.0, 1.0, 2.0])
+    ivl2 = pd.IntervalIndex.from_breaks([0.5, 1.5, 2.5])
+    mi1 = MultiIndex.from_product([ivl1, ivl1])
+    mi2 = MultiIndex.from_product([ivl2, ivl2])
+    result = mi1.append(mi2)
+    expected = MultiIndex.from_tuples(
+        [
+            (pd.Interval(0.0, 1.0), pd.Interval(0.0, 1.0)),
+            (pd.Interval(0.0, 1.0), pd.Interval(1.0, 2.0)),
+            (pd.Interval(1.0, 2.0), pd.Interval(0.0, 1.0)),
+            (pd.Interval(1.0, 2.0), pd.Interval(1.0, 2.0)),
+            (pd.Interval(0.5, 1.5), pd.Interval(0.5, 1.5)),
+            (pd.Interval(0.5, 1.5), pd.Interval(1.5, 2.5)),
+            (pd.Interval(1.5, 2.5), pd.Interval(0.5, 1.5)),
+            (pd.Interval(1.5, 2.5), pd.Interval(1.5, 2.5)),
+        ]
+    )
+    tm.assert_index_equal(result, expected)
+
+
+def test_repeat():
+    reps = 2
+    numbers = [1, 2, 3]
+    names = np.array(["foo", "bar"])
+
+    m = MultiIndex.from_product([numbers, names], names=names)
+    expected = MultiIndex.from_product([numbers, names.repeat(reps)], names=names)
+    tm.assert_index_equal(m.repeat(reps), expected)
+
+
+def test_insert_base(idx):
+    result = idx[1:4]
+
+    # test 0th element
+    assert idx[0:4].equals(result.insert(0, idx[0]))
+
+
+def test_delete_base(idx):
+    expected = idx[1:]
+    result = idx.delete(0)
+    assert result.equals(expected)
+    assert result.name == expected.name
+
+    expected = idx[:-1]
+    result = idx.delete(-1)
+    assert result.equals(expected)
+    assert result.name == expected.name
+
+    msg = "index 6 is out of bounds for axis 0 with size 6"
+    with pytest.raises(IndexError, match=msg):
+        idx.delete(len(idx))
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_sorting.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_sorting.py
new file mode 100644
index 0000000000000000000000000000000000000000..b4dcef71dcf50724c90599b03d1c1c5aa99b7916
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_sorting.py
@@ -0,0 +1,349 @@
+import numpy as np
+import pytest
+
+from pandas.errors import (
+    PerformanceWarning,
+    UnsortedIndexError,
+)
+
+from pandas import (
+    CategoricalIndex,
+    DataFrame,
+    Index,
+    MultiIndex,
+    RangeIndex,
+    Series,
+    Timestamp,
+)
+import pandas._testing as tm
+from pandas.core.indexes.frozen import FrozenList
+
+
+def test_sortlevel(idx):
+    tuples = list(idx)
+    np.random.default_rng(2).shuffle(tuples)
+
+    index = MultiIndex.from_tuples(tuples)
+
+    sorted_idx, _ = index.sortlevel(0)
+    expected = MultiIndex.from_tuples(sorted(tuples))
+    assert sorted_idx.equals(expected)
+
+    sorted_idx, _ = index.sortlevel(0, ascending=False)
+    assert sorted_idx.equals(expected[::-1])
+
+    sorted_idx, _ = index.sortlevel(1)
+    by1 = sorted(tuples, key=lambda x: (x[1], x[0]))
+    expected = MultiIndex.from_tuples(by1)
+    assert sorted_idx.equals(expected)
+
+    sorted_idx, _ = index.sortlevel(1, ascending=False)
+    assert sorted_idx.equals(expected[::-1])
+
+
+def test_sortlevel_not_sort_remaining():
+    mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list("ABC"))
+    sorted_idx, _ = mi.sortlevel("A", sort_remaining=False)
+    assert sorted_idx.equals(mi)
+
+
+def test_sortlevel_deterministic():
+    tuples = [
+        ("bar", "one"),
+        ("foo", "two"),
+        ("qux", "two"),
+        ("foo", "one"),
+        ("baz", "two"),
+        ("qux", "one"),
+    ]
+
+    index = MultiIndex.from_tuples(tuples)
+
+    sorted_idx, _ = index.sortlevel(0)
+    expected = MultiIndex.from_tuples(sorted(tuples))
+    assert sorted_idx.equals(expected)
+
+    sorted_idx, _ = index.sortlevel(0, ascending=False)
+    assert sorted_idx.equals(expected[::-1])
+
+    sorted_idx, _ = index.sortlevel(1)
+    by1 = sorted(tuples, key=lambda x: (x[1], x[0]))
+    expected = MultiIndex.from_tuples(by1)
+    assert sorted_idx.equals(expected)
+
+    sorted_idx, _ = index.sortlevel(1, ascending=False)
+    assert sorted_idx.equals(expected[::-1])
+
+
+def test_sortlevel_na_position():
+    # GH#51612
+    midx = MultiIndex.from_tuples([(1, np.nan), (1, 1)])
+    result = midx.sortlevel(level=[0, 1], na_position="last")[0]
+    expected = MultiIndex.from_tuples([(1, 1), (1, np.nan)])
+    tm.assert_index_equal(result, expected)
+
+
+def test_numpy_argsort(idx):
+    result = np.argsort(idx)
+    expected = idx.argsort()
+    tm.assert_numpy_array_equal(result, expected)
+
+    # these are the only two types that perform
+    # pandas compatibility input validation - the
+    # rest already perform separate (or no) such
+    # validation via their 'values' attribute as
+    # defined in pandas.core.indexes/base.py - they
+    # cannot be changed at the moment due to
+    # backwards compatibility concerns
+    if isinstance(type(idx), (CategoricalIndex, RangeIndex)):
+        msg = "the 'axis' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            np.argsort(idx, axis=1)
+
+        msg = "the 'kind' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            np.argsort(idx, kind="mergesort")
+
+        msg = "the 'order' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            np.argsort(idx, order=("a", "b"))
+
+
+def test_unsortedindex():
+    # GH 11897
+    mi = MultiIndex.from_tuples(
+        [("z", "a"), ("x", "a"), ("y", "b"), ("x", "b"), ("y", "a"), ("z", "b")],
+        names=["one", "two"],
+    )
+    df = DataFrame([[i, 10 * i] for i in range(6)], index=mi, columns=["one", "two"])
+
+    # GH 16734: not sorted, but no real slicing
+    result = df.loc(axis=0)["z", "a"]
+    expected = df.iloc[0]
+    tm.assert_series_equal(result, expected)
+
+    msg = (
+        "MultiIndex slicing requires the index to be lexsorted: "
+        r"slicing on levels \[1\], lexsort depth 0"
+    )
+    with pytest.raises(UnsortedIndexError, match=msg):
+        df.loc(axis=0)["z", slice("a")]
+    df.sort_index(inplace=True)
+    assert len(df.loc(axis=0)["z", :]) == 2
+
+    with pytest.raises(KeyError, match="'q'"):
+        df.loc(axis=0)["q", :]
+
+
+def test_unsortedindex_doc_examples():
+    # https://pandas.pydata.org/pandas-docs/stable/advanced.html#sorting-a-multiindex
+    dfm = DataFrame(
+        {
+            "jim": [0, 0, 1, 1],
+            "joe": ["x", "x", "z", "y"],
+            "jolie": np.random.default_rng(2).random(4),
+        }
+    )
+
+    dfm = dfm.set_index(["jim", "joe"])
+    with tm.assert_produces_warning(PerformanceWarning):
+        dfm.loc[(1, "z")]
+
+    msg = r"Key length \(2\) was greater than MultiIndex lexsort depth \(1\)"
+    with pytest.raises(UnsortedIndexError, match=msg):
+        dfm.loc[(0, "y"):(1, "z")]
+
+    assert not dfm.index._is_lexsorted()
+    assert dfm.index._lexsort_depth == 1
+
+    # sort it
+    dfm = dfm.sort_index()
+    dfm.loc[(1, "z")]
+    dfm.loc[(0, "y"):(1, "z")]
+
+    assert dfm.index._is_lexsorted()
+    assert dfm.index._lexsort_depth == 2
+
+
+def test_reconstruct_sort():
+    # starts off lexsorted & monotonic
+    mi = MultiIndex.from_arrays([["A", "A", "B", "B", "B"], [1, 2, 1, 2, 3]])
+    assert mi.is_monotonic_increasing
+    recons = mi._sort_levels_monotonic()
+    assert recons.is_monotonic_increasing
+    assert mi is recons
+
+    assert mi.equals(recons)
+    assert Index(mi.values).equals(Index(recons.values))
+
+    # cannot convert to lexsorted
+    mi = MultiIndex.from_tuples(
+        [("z", "a"), ("x", "a"), ("y", "b"), ("x", "b"), ("y", "a"), ("z", "b")],
+        names=["one", "two"],
+    )
+    assert not mi.is_monotonic_increasing
+    recons = mi._sort_levels_monotonic()
+    assert not recons.is_monotonic_increasing
+    assert mi.equals(recons)
+    assert Index(mi.values).equals(Index(recons.values))
+
+    # cannot convert to lexsorted
+    mi = MultiIndex(
+        levels=[["b", "d", "a"], [1, 2, 3]],
+        codes=[[0, 1, 0, 2], [2, 0, 0, 1]],
+        names=["col1", "col2"],
+    )
+    assert not mi.is_monotonic_increasing
+    recons = mi._sort_levels_monotonic()
+    assert not recons.is_monotonic_increasing
+    assert mi.equals(recons)
+    assert Index(mi.values).equals(Index(recons.values))
+
+
+def test_reconstruct_remove_unused():
+    # xref to GH 2770
+    df = DataFrame(
+        [["deleteMe", 1, 9], ["keepMe", 2, 9], ["keepMeToo", 3, 9]],
+        columns=["first", "second", "third"],
+    )
+    df2 = df.set_index(["first", "second"], drop=False)
+    df2 = df2[df2["first"] != "deleteMe"]
+
+    # removed levels are there
+    expected = MultiIndex(
+        levels=[["deleteMe", "keepMe", "keepMeToo"], [1, 2, 3]],
+        codes=[[1, 2], [1, 2]],
+        names=["first", "second"],
+    )
+    result = df2.index
+    tm.assert_index_equal(result, expected)
+
+    expected = MultiIndex(
+        levels=[["keepMe", "keepMeToo"], [2, 3]],
+        codes=[[0, 1], [0, 1]],
+        names=["first", "second"],
+    )
+    result = df2.index.remove_unused_levels()
+    tm.assert_index_equal(result, expected)
+
+    # idempotent
+    result2 = result.remove_unused_levels()
+    tm.assert_index_equal(result2, expected)
+    assert result2.is_(result)
+
+
+@pytest.mark.parametrize(
+    "first_type,second_type", [("int64", "int64"), ("datetime64[D]", "str")]
+)
+def test_remove_unused_levels_large(first_type, second_type):
+    # GH16556
+
+    # because tests should be deterministic (and this test in particular
+    # checks that levels are removed, which is not the case for every
+    # random input):
+    rng = np.random.default_rng(10)  # seed is arbitrary value that works
+
+    size = 1 << 16
+    df = DataFrame(
+        {
+            "first": rng.integers(0, 1 << 13, size).astype(first_type),
+            "second": rng.integers(0, 1 << 10, size).astype(second_type),
+            "third": rng.random(size),
+        }
+    )
+    df = df.groupby(["first", "second"]).sum()
+    df = df[df.third < 0.1]
+
+    result = df.index.remove_unused_levels()
+    assert len(result.levels[0]) < len(df.index.levels[0])
+    assert len(result.levels[1]) < len(df.index.levels[1])
+    assert result.equals(df.index)
+
+    expected = df.reset_index().set_index(["first", "second"]).index
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("level0", [["a", "d", "b"], ["a", "d", "b", "unused"]])
+@pytest.mark.parametrize(
+    "level1", [["w", "x", "y", "z"], ["w", "x", "y", "z", "unused"]]
+)
+def test_remove_unused_nan(level0, level1):
+    # GH 18417
+    mi = MultiIndex(levels=[level0, level1], codes=[[0, 2, -1, 1, -1], [0, 1, 2, 3, 2]])
+
+    result = mi.remove_unused_levels()
+    tm.assert_index_equal(result, mi)
+    for level in 0, 1:
+        assert "unused" not in result.levels[level]
+
+
+def test_argsort(idx):
+    result = idx.argsort()
+    expected = idx.values.argsort()
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_remove_unused_levels_with_nan():
+    # GH 37510
+    idx = Index([(1, np.nan), (3, 4)]).rename(["id1", "id2"])
+    idx = idx.set_levels(["a", np.nan], level="id1")
+    idx = idx.remove_unused_levels()
+    result = idx.levels
+    expected = FrozenList([["a", np.nan], [4]])
+    assert str(result) == str(expected)
+
+
+def test_sort_values_nan():
+    # GH48495, GH48626
+    midx = MultiIndex(levels=[["A", "B", "C"], ["D"]], codes=[[1, 0, 2], [-1, -1, 0]])
+    result = midx.sort_values()
+    expected = MultiIndex(
+        levels=[["A", "B", "C"], ["D"]], codes=[[0, 1, 2], [-1, -1, 0]]
+    )
+    tm.assert_index_equal(result, expected)
+
+
+def test_sort_values_incomparable():
+    # GH48495
+    mi = MultiIndex.from_arrays(
+        [
+            [1, Timestamp("2000-01-01")],
+            [3, 4],
+        ]
+    )
+    match = "'<' not supported between instances of 'Timestamp' and 'int'"
+    with pytest.raises(TypeError, match=match):
+        mi.sort_values()
+
+
+@pytest.mark.parametrize("na_position", ["first", "last"])
+@pytest.mark.parametrize("dtype", ["float64", "Int64", "Float64"])
+def test_sort_values_with_na_na_position(dtype, na_position):
+    # 51612
+    arrays = [
+        Series([1, 1, 2], dtype=dtype),
+        Series([1, None, 3], dtype=dtype),
+    ]
+    index = MultiIndex.from_arrays(arrays)
+    result = index.sort_values(na_position=na_position)
+    if na_position == "first":
+        arrays = [
+            Series([1, 1, 2], dtype=dtype),
+            Series([None, 1, 3], dtype=dtype),
+        ]
+    else:
+        arrays = [
+            Series([1, 1, 2], dtype=dtype),
+            Series([1, None, 3], dtype=dtype),
+        ]
+    expected = MultiIndex.from_arrays(arrays)
+    tm.assert_index_equal(result, expected)
+
+
+def test_sort_unnecessary_warning():
+    # GH#55386
+    midx = MultiIndex.from_tuples([(1.5, 2), (3.5, 3), (0, 1)])
+    midx = midx.set_levels([2.5, np.nan, 1], level=0)
+    result = midx.sort_values()
+    expected = MultiIndex.from_tuples([(1, 3), (2.5, 1), (np.nan, 2)])
+    tm.assert_index_equal(result, expected)
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diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/object/test_astype.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/object/test_astype.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c1ef302c5b51cad92e2c0b1996d4d58d90e5ec6
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/object/test_astype.py
@@ -0,0 +1,33 @@
+import pytest
+
+from pandas import (
+    Index,
+    NaT,
+    Series,
+)
+import pandas._testing as tm
+
+
+def test_astype_str_from_bytes():
+    # https://github.com/pandas-dev/pandas/issues/38607
+    # GH#49658 pre-2.0 Index called .values.astype(str) here, which effectively
+    #  did a .decode() on the bytes object.  In 2.0 we go through
+    #  ensure_string_array which does f"{val}"
+    idx = Index(["あ", b"a"], dtype="object")
+    result = idx.astype(str)
+    expected = Index(["あ", "a"], dtype="object")
+    tm.assert_index_equal(result, expected)
+
+    # while we're here, check that Series.astype behaves the same
+    result = Series(idx).astype(str)
+    expected = Series(expected, dtype=object)
+    tm.assert_series_equal(result, expected)
+
+
+def test_astype_invalid_nas_to_tdt64_raises():
+    # GH#45722 don't cast np.datetime64 NaTs to timedelta64 NaT
+    idx = Index([NaT.asm8] * 2, dtype=object)
+
+    msg = r"Invalid type for timedelta scalar: <class 'numpy.datetime64'>"
+    with pytest.raises(TypeError, match=msg):
+        idx.astype("m8[ns]")
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/object/test_indexing.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/object/test_indexing.py
new file mode 100644
index 0000000000000000000000000000000000000000..443cacf94d23923fd2fce28eec45f0626fe5745d
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/object/test_indexing.py
@@ -0,0 +1,243 @@
+from decimal import Decimal
+
+import numpy as np
+import pytest
+
+from pandas._libs.missing import (
+    NA,
+    is_matching_na,
+)
+from pandas.compat import pa_version_under16p0
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import Index
+import pandas._testing as tm
+
+
+class TestGetIndexer:
+    @pytest.mark.parametrize(
+        "method,expected",
+        [
+            ("pad", np.array([-1, 0, 1, 1], dtype=np.intp)),
+            ("backfill", np.array([0, 0, 1, -1], dtype=np.intp)),
+        ],
+    )
+    def test_get_indexer_strings(self, method, expected):
+        index = Index(["b", "c"])
+        actual = index.get_indexer(["a", "b", "c", "d"], method=method)
+
+        tm.assert_numpy_array_equal(actual, expected)
+
+    def test_get_indexer_strings_raises(self, using_infer_string):
+        index = Index(["b", "c"])
+
+        if using_infer_string:
+            import pyarrow as pa
+
+            msg = "has no kernel"
+            with pytest.raises(pa.lib.ArrowNotImplementedError, match=msg):
+                index.get_indexer(["a", "b", "c", "d"], method="nearest")
+
+            with pytest.raises(pa.lib.ArrowNotImplementedError, match=msg):
+                index.get_indexer(["a", "b", "c", "d"], method="pad", tolerance=2)
+
+            with pytest.raises(pa.lib.ArrowNotImplementedError, match=msg):
+                index.get_indexer(
+                    ["a", "b", "c", "d"], method="pad", tolerance=[2, 2, 2, 2]
+                )
+
+        else:
+            msg = r"unsupported operand type\(s\) for -: 'str' and 'str'"
+            with pytest.raises(TypeError, match=msg):
+                index.get_indexer(["a", "b", "c", "d"], method="nearest")
+
+            with pytest.raises(TypeError, match=msg):
+                index.get_indexer(["a", "b", "c", "d"], method="pad", tolerance=2)
+
+            with pytest.raises(TypeError, match=msg):
+                index.get_indexer(
+                    ["a", "b", "c", "d"], method="pad", tolerance=[2, 2, 2, 2]
+                )
+
+    def test_get_indexer_with_NA_values(
+        self, unique_nulls_fixture, unique_nulls_fixture2
+    ):
+        # GH#22332
+        # check pairwise, that no pair of na values
+        # is mangled
+        if unique_nulls_fixture is unique_nulls_fixture2:
+            return  # skip it, values are not unique
+        arr = np.array([unique_nulls_fixture, unique_nulls_fixture2], dtype=object)
+        index = Index(arr, dtype=object)
+        result = index.get_indexer(
+            Index(
+                [unique_nulls_fixture, unique_nulls_fixture2, "Unknown"], dtype=object
+            )
+        )
+        expected = np.array([0, 1, -1], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+
+class TestGetIndexerNonUnique:
+    def test_get_indexer_non_unique_nas(
+        self, nulls_fixture, request, using_infer_string
+    ):
+        # even though this isn't non-unique, this should still work
+        if using_infer_string and (nulls_fixture is None or nulls_fixture is NA):
+            request.applymarker(pytest.mark.xfail(reason="NAs are cast to NaN"))
+        index = Index(["a", "b", nulls_fixture])
+        indexer, missing = index.get_indexer_non_unique([nulls_fixture])
+
+        expected_indexer = np.array([2], dtype=np.intp)
+        expected_missing = np.array([], dtype=np.intp)
+        tm.assert_numpy_array_equal(indexer, expected_indexer)
+        tm.assert_numpy_array_equal(missing, expected_missing)
+
+        # actually non-unique
+        index = Index(["a", nulls_fixture, "b", nulls_fixture])
+        indexer, missing = index.get_indexer_non_unique([nulls_fixture])
+
+        expected_indexer = np.array([1, 3], dtype=np.intp)
+        tm.assert_numpy_array_equal(indexer, expected_indexer)
+        tm.assert_numpy_array_equal(missing, expected_missing)
+
+        # matching-but-not-identical nans
+        if is_matching_na(nulls_fixture, float("NaN")):
+            index = Index(["a", float("NaN"), "b", float("NaN")])
+            match_but_not_identical = True
+        elif is_matching_na(nulls_fixture, Decimal("NaN")):
+            index = Index(["a", Decimal("NaN"), "b", Decimal("NaN")])
+            match_but_not_identical = True
+        else:
+            match_but_not_identical = False
+
+        if match_but_not_identical:
+            indexer, missing = index.get_indexer_non_unique([nulls_fixture])
+
+            expected_indexer = np.array([1, 3], dtype=np.intp)
+            tm.assert_numpy_array_equal(indexer, expected_indexer)
+            tm.assert_numpy_array_equal(missing, expected_missing)
+
+    @pytest.mark.filterwarnings("ignore:elementwise comp:DeprecationWarning")
+    def test_get_indexer_non_unique_np_nats(self, np_nat_fixture, np_nat_fixture2):
+        expected_missing = np.array([], dtype=np.intp)
+        # matching-but-not-identical nats
+        if is_matching_na(np_nat_fixture, np_nat_fixture2):
+            # ensure nats are different objects
+            index = Index(
+                np.array(
+                    ["2021-10-02", np_nat_fixture.copy(), np_nat_fixture2.copy()],
+                    dtype=object,
+                ),
+                dtype=object,
+            )
+            # pass as index to prevent target from being casted to DatetimeIndex
+            indexer, missing = index.get_indexer_non_unique(
+                Index([np_nat_fixture], dtype=object)
+            )
+            expected_indexer = np.array([1, 2], dtype=np.intp)
+            tm.assert_numpy_array_equal(indexer, expected_indexer)
+            tm.assert_numpy_array_equal(missing, expected_missing)
+        # dt64nat vs td64nat
+        else:
+            try:
+                np_nat_fixture == np_nat_fixture2
+            except (TypeError, OverflowError):
+                # Numpy will raise on uncomparable types, like
+                # np.datetime64('NaT', 'Y') and np.datetime64('NaT', 'ps')
+                # https://github.com/numpy/numpy/issues/22762
+                return
+            index = Index(
+                np.array(
+                    [
+                        "2021-10-02",
+                        np_nat_fixture,
+                        np_nat_fixture2,
+                        np_nat_fixture,
+                        np_nat_fixture2,
+                    ],
+                    dtype=object,
+                ),
+                dtype=object,
+            )
+            # pass as index to prevent target from being casted to DatetimeIndex
+            indexer, missing = index.get_indexer_non_unique(
+                Index([np_nat_fixture], dtype=object)
+            )
+            expected_indexer = np.array([1, 3], dtype=np.intp)
+            tm.assert_numpy_array_equal(indexer, expected_indexer)
+            tm.assert_numpy_array_equal(missing, expected_missing)
+
+
+class TestSliceLocs:
+    @pytest.mark.parametrize(
+        "dtype",
+        [
+            "object",
+            pytest.param("string[pyarrow_numpy]", marks=td.skip_if_no("pyarrow")),
+        ],
+    )
+    @pytest.mark.parametrize(
+        "in_slice,expected",
+        [
+            # error: Slice index must be an integer or None
+            (pd.IndexSlice[::-1], "yxdcb"),
+            (pd.IndexSlice["b":"y":-1], ""),  # type: ignore[misc]
+            (pd.IndexSlice["b"::-1], "b"),  # type: ignore[misc]
+            (pd.IndexSlice[:"b":-1], "yxdcb"),  # type: ignore[misc]
+            (pd.IndexSlice[:"y":-1], "y"),  # type: ignore[misc]
+            (pd.IndexSlice["y"::-1], "yxdcb"),  # type: ignore[misc]
+            (pd.IndexSlice["y"::-4], "yb"),  # type: ignore[misc]
+            # absent labels
+            (pd.IndexSlice[:"a":-1], "yxdcb"),  # type: ignore[misc]
+            (pd.IndexSlice[:"a":-2], "ydb"),  # type: ignore[misc]
+            (pd.IndexSlice["z"::-1], "yxdcb"),  # type: ignore[misc]
+            (pd.IndexSlice["z"::-3], "yc"),  # type: ignore[misc]
+            (pd.IndexSlice["m"::-1], "dcb"),  # type: ignore[misc]
+            (pd.IndexSlice[:"m":-1], "yx"),  # type: ignore[misc]
+            (pd.IndexSlice["a":"a":-1], ""),  # type: ignore[misc]
+            (pd.IndexSlice["z":"z":-1], ""),  # type: ignore[misc]
+            (pd.IndexSlice["m":"m":-1], ""),  # type: ignore[misc]
+        ],
+    )
+    def test_slice_locs_negative_step(self, in_slice, expected, dtype, request):
+        if (
+            not pa_version_under16p0
+            and dtype == "string[pyarrow_numpy]"
+            and in_slice == slice("a", "a", -1)
+        ):
+            request.applymarker(
+                pytest.mark.xfail(reason="https://github.com/apache/arrow/issues/40642")
+            )
+
+        index = Index(list("bcdxy"), dtype=dtype)
+
+        s_start, s_stop = index.slice_locs(in_slice.start, in_slice.stop, in_slice.step)
+        result = index[s_start : s_stop : in_slice.step]
+        expected = Index(list(expected), dtype=dtype)
+        tm.assert_index_equal(result, expected)
+
+    @td.skip_if_no("pyarrow")
+    def test_slice_locs_negative_step_oob(self):
+        index = Index(list("bcdxy"), dtype="string[pyarrow_numpy]")
+
+        result = index[-10:5:1]
+        tm.assert_index_equal(result, index)
+
+        result = index[4:-10:-1]
+        expected = Index(list("yxdcb"), dtype="string[pyarrow_numpy]")
+        tm.assert_index_equal(result, expected)
+
+    def test_slice_locs_dup(self):
+        index = Index(["a", "a", "b", "c", "d", "d"])
+        assert index.slice_locs("a", "d") == (0, 6)
+        assert index.slice_locs(end="d") == (0, 6)
+        assert index.slice_locs("a", "c") == (0, 4)
+        assert index.slice_locs("b", "d") == (2, 6)
+
+        index2 = index[::-1]
+        assert index2.slice_locs("d", "a") == (0, 6)
+        assert index2.slice_locs(end="a") == (0, 6)
+        assert index2.slice_locs("d", "b") == (0, 4)
+        assert index2.slice_locs("c", "a") == (2, 6)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_factorize.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_factorize.py
new file mode 100644
index 0000000000000000000000000000000000000000..24ab3888412d08b54543ed22910c67ce9bdf328f
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_factorize.py
@@ -0,0 +1,40 @@
+import numpy as np
+
+from pandas import (
+    TimedeltaIndex,
+    factorize,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+class TestTimedeltaIndexFactorize:
+    def test_factorize(self):
+        idx1 = TimedeltaIndex(["1 day", "1 day", "2 day", "2 day", "3 day", "3 day"])
+
+        exp_arr = np.array([0, 0, 1, 1, 2, 2], dtype=np.intp)
+        exp_idx = TimedeltaIndex(["1 day", "2 day", "3 day"])
+
+        arr, idx = idx1.factorize()
+        tm.assert_numpy_array_equal(arr, exp_arr)
+        tm.assert_index_equal(idx, exp_idx)
+        assert idx.freq == exp_idx.freq
+
+        arr, idx = idx1.factorize(sort=True)
+        tm.assert_numpy_array_equal(arr, exp_arr)
+        tm.assert_index_equal(idx, exp_idx)
+        assert idx.freq == exp_idx.freq
+
+    def test_factorize_preserves_freq(self):
+        # GH#38120 freq should be preserved
+        idx3 = timedelta_range("1 day", periods=4, freq="s")
+        exp_arr = np.array([0, 1, 2, 3], dtype=np.intp)
+        arr, idx = idx3.factorize()
+        tm.assert_numpy_array_equal(arr, exp_arr)
+        tm.assert_index_equal(idx, idx3)
+        assert idx.freq == idx3.freq
+
+        arr, idx = factorize(idx3)
+        tm.assert_numpy_array_equal(arr, exp_arr)
+        tm.assert_index_equal(idx, idx3)
+        assert idx.freq == idx3.freq
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_fillna.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_fillna.py
new file mode 100644
index 0000000000000000000000000000000000000000..40aa95d0a46058d2dc3fc5208ca39328d96b23fb
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_fillna.py
@@ -0,0 +1,22 @@
+from pandas import (
+    Index,
+    NaT,
+    Timedelta,
+    TimedeltaIndex,
+)
+import pandas._testing as tm
+
+
+class TestFillNA:
+    def test_fillna_timedelta(self):
+        # GH#11343
+        idx = TimedeltaIndex(["1 day", NaT, "3 day"])
+
+        exp = TimedeltaIndex(["1 day", "2 day", "3 day"])
+        tm.assert_index_equal(idx.fillna(Timedelta("2 day")), exp)
+
+        exp = TimedeltaIndex(["1 day", "3 hour", "3 day"])
+        idx.fillna(Timedelta("3 hour"))
+
+        exp = Index([Timedelta("1 day"), "x", Timedelta("3 day")], dtype=object)
+        tm.assert_index_equal(idx.fillna("x"), exp)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_arithmetic.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_arithmetic.py
new file mode 100644
index 0000000000000000000000000000000000000000..a431e10dc18ab15da0fd07f798d54b6dead26073
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_arithmetic.py
@@ -0,0 +1,51 @@
+# Arithmetic tests for TimedeltaIndex are generally about the result's `freq` attribute.
+# Other cases can be shared in tests.arithmetic.test_timedelta64
+import numpy as np
+
+from pandas import (
+    NaT,
+    Timedelta,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+class TestTimedeltaIndexArithmetic:
+    def test_arithmetic_zero_freq(self):
+        # GH#51575 don't get a .freq with freq.n = 0
+        tdi = timedelta_range(0, periods=100, freq="ns")
+        result = tdi / 2
+        assert result.freq is None
+        expected = tdi[:50].repeat(2)
+        tm.assert_index_equal(result, expected)
+
+        result2 = tdi // 2
+        assert result2.freq is None
+        expected2 = expected
+        tm.assert_index_equal(result2, expected2)
+
+        result3 = tdi * 0
+        assert result3.freq is None
+        expected3 = tdi[:1].repeat(100)
+        tm.assert_index_equal(result3, expected3)
+
+    def test_tdi_division(self, index_or_series):
+        # doc example
+
+        scalar = Timedelta(days=31)
+        td = index_or_series(
+            [scalar, scalar, scalar + Timedelta(minutes=5, seconds=3), NaT],
+            dtype="m8[ns]",
+        )
+
+        result = td / np.timedelta64(1, "D")
+        expected = index_or_series(
+            [31, 31, (31 * 86400 + 5 * 60 + 3) / 86400.0, np.nan]
+        )
+        tm.assert_equal(result, expected)
+
+        result = td / np.timedelta64(1, "s")
+        expected = index_or_series(
+            [31 * 86400, 31 * 86400, 31 * 86400 + 5 * 60 + 3, np.nan]
+        )
+        tm.assert_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_constructors.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_constructors.py
new file mode 100644
index 0000000000000000000000000000000000000000..0510700bb64d7a626761a67d72ecfa6ecfba9ac4
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_constructors.py
@@ -0,0 +1,291 @@
+from datetime import timedelta
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Timedelta,
+    TimedeltaIndex,
+    timedelta_range,
+    to_timedelta,
+)
+import pandas._testing as tm
+from pandas.core.arrays.timedeltas import TimedeltaArray
+
+
+class TestTimedeltaIndex:
+    def test_closed_deprecated(self):
+        # GH#52628
+        msg = "The 'closed' keyword"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            TimedeltaIndex([], closed=True)
+
+    def test_array_of_dt64_nat_raises(self):
+        # GH#39462
+        nat = np.datetime64("NaT", "ns")
+        arr = np.array([nat], dtype=object)
+
+        msg = "Invalid type for timedelta scalar"
+        with pytest.raises(TypeError, match=msg):
+            TimedeltaIndex(arr)
+
+        with pytest.raises(TypeError, match=msg):
+            TimedeltaArray._from_sequence(arr, dtype="m8[ns]")
+
+        with pytest.raises(TypeError, match=msg):
+            to_timedelta(arr)
+
+    @pytest.mark.parametrize("unit", ["Y", "y", "M"])
+    def test_unit_m_y_raises(self, unit):
+        msg = "Units 'M', 'Y', and 'y' are no longer supported"
+        depr_msg = "The 'unit' keyword in TimedeltaIndex construction is deprecated"
+        with pytest.raises(ValueError, match=msg):
+            with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+                TimedeltaIndex([1, 3, 7], unit)
+
+    def test_int64_nocopy(self):
+        # GH#23539 check that a copy isn't made when we pass int64 data
+        #  and copy=False
+        arr = np.arange(10, dtype=np.int64)
+        tdi = TimedeltaIndex(arr, copy=False)
+        assert tdi._data._ndarray.base is arr
+
+    def test_infer_from_tdi(self):
+        # GH#23539
+        # fast-path for inferring a frequency if the passed data already
+        #  has one
+        tdi = timedelta_range("1 second", periods=10**7, freq="1s")
+
+        result = TimedeltaIndex(tdi, freq="infer")
+        assert result.freq == tdi.freq
+
+        # check that inferred_freq was not called by checking that the
+        #  value has not been cached
+        assert "inferred_freq" not in getattr(result, "_cache", {})
+
+    def test_infer_from_tdi_mismatch(self):
+        # GH#23539
+        # fast-path for invalidating a frequency if the passed data already
+        #  has one and it does not match the `freq` input
+        tdi = timedelta_range("1 second", periods=100, freq="1s")
+
+        depr_msg = "TimedeltaArray.__init__ is deprecated"
+        msg = (
+            "Inferred frequency .* from passed values does "
+            "not conform to passed frequency"
+        )
+        with pytest.raises(ValueError, match=msg):
+            TimedeltaIndex(tdi, freq="D")
+
+        with pytest.raises(ValueError, match=msg):
+            # GH#23789
+            with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+                TimedeltaArray(tdi, freq="D")
+
+        with pytest.raises(ValueError, match=msg):
+            TimedeltaIndex(tdi._data, freq="D")
+
+        with pytest.raises(ValueError, match=msg):
+            with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+                TimedeltaArray(tdi._data, freq="D")
+
+    def test_dt64_data_invalid(self):
+        # GH#23539
+        # passing tz-aware DatetimeIndex raises, naive or ndarray[datetime64]
+        #  raise as of GH#29794
+        dti = pd.date_range("2016-01-01", periods=3)
+
+        msg = "cannot be converted to timedelta64"
+        with pytest.raises(TypeError, match=msg):
+            TimedeltaIndex(dti.tz_localize("Europe/Brussels"))
+
+        with pytest.raises(TypeError, match=msg):
+            TimedeltaIndex(dti)
+
+        with pytest.raises(TypeError, match=msg):
+            TimedeltaIndex(np.asarray(dti))
+
+    def test_float64_ns_rounded(self):
+        # GH#23539 without specifying a unit, floats are regarded as nanos,
+        #  and fractional portions are truncated
+        tdi = TimedeltaIndex([2.3, 9.7])
+        expected = TimedeltaIndex([2, 9])
+        tm.assert_index_equal(tdi, expected)
+
+        # integral floats are non-lossy
+        tdi = TimedeltaIndex([2.0, 9.0])
+        expected = TimedeltaIndex([2, 9])
+        tm.assert_index_equal(tdi, expected)
+
+        # NaNs get converted to NaT
+        tdi = TimedeltaIndex([2.0, np.nan])
+        expected = TimedeltaIndex([Timedelta(nanoseconds=2), pd.NaT])
+        tm.assert_index_equal(tdi, expected)
+
+    def test_float64_unit_conversion(self):
+        # GH#23539
+        tdi = to_timedelta([1.5, 2.25], unit="D")
+        expected = TimedeltaIndex([Timedelta(days=1.5), Timedelta(days=2.25)])
+        tm.assert_index_equal(tdi, expected)
+
+    def test_construction_base_constructor(self):
+        arr = [Timedelta("1 days"), pd.NaT, Timedelta("3 days")]
+        tm.assert_index_equal(pd.Index(arr), TimedeltaIndex(arr))
+        tm.assert_index_equal(pd.Index(np.array(arr)), TimedeltaIndex(np.array(arr)))
+
+        arr = [np.nan, pd.NaT, Timedelta("1 days")]
+        tm.assert_index_equal(pd.Index(arr), TimedeltaIndex(arr))
+        tm.assert_index_equal(pd.Index(np.array(arr)), TimedeltaIndex(np.array(arr)))
+
+    @pytest.mark.filterwarnings(
+        "ignore:The 'unit' keyword in TimedeltaIndex construction:FutureWarning"
+    )
+    def test_constructor(self):
+        expected = TimedeltaIndex(
+            [
+                "1 days",
+                "1 days 00:00:05",
+                "2 days",
+                "2 days 00:00:02",
+                "0 days 00:00:03",
+            ]
+        )
+        result = TimedeltaIndex(
+            [
+                "1 days",
+                "1 days, 00:00:05",
+                np.timedelta64(2, "D"),
+                timedelta(days=2, seconds=2),
+                pd.offsets.Second(3),
+            ]
+        )
+        tm.assert_index_equal(result, expected)
+
+        expected = TimedeltaIndex(
+            ["0 days 00:00:00", "0 days 00:00:01", "0 days 00:00:02"]
+        )
+        result = TimedeltaIndex(range(3), unit="s")
+        tm.assert_index_equal(result, expected)
+        expected = TimedeltaIndex(
+            ["0 days 00:00:00", "0 days 00:00:05", "0 days 00:00:09"]
+        )
+        result = TimedeltaIndex([0, 5, 9], unit="s")
+        tm.assert_index_equal(result, expected)
+        expected = TimedeltaIndex(
+            ["0 days 00:00:00.400", "0 days 00:00:00.450", "0 days 00:00:01.200"]
+        )
+        result = TimedeltaIndex([400, 450, 1200], unit="ms")
+        tm.assert_index_equal(result, expected)
+
+    def test_constructor_iso(self):
+        # GH #21877
+        expected = timedelta_range("1s", periods=9, freq="s")
+        durations = [f"P0DT0H0M{i}S" for i in range(1, 10)]
+        result = to_timedelta(durations)
+        tm.assert_index_equal(result, expected)
+
+    def test_timedelta_range_fractional_period(self):
+        msg = "Non-integer 'periods' in pd.date_range, pd.timedelta_range"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            rng = timedelta_range("1 days", periods=10.5)
+        exp = timedelta_range("1 days", periods=10)
+        tm.assert_index_equal(rng, exp)
+
+    def test_constructor_coverage(self):
+        msg = "periods must be a number, got foo"
+        with pytest.raises(TypeError, match=msg):
+            timedelta_range(start="1 days", periods="foo", freq="D")
+
+        msg = (
+            r"TimedeltaIndex\(\.\.\.\) must be called with a collection of some kind, "
+            "'1 days' was passed"
+        )
+        with pytest.raises(TypeError, match=msg):
+            TimedeltaIndex("1 days")
+
+        # generator expression
+        gen = (timedelta(i) for i in range(10))
+        result = TimedeltaIndex(gen)
+        expected = TimedeltaIndex([timedelta(i) for i in range(10)])
+        tm.assert_index_equal(result, expected)
+
+        # NumPy string array
+        strings = np.array(["1 days", "2 days", "3 days"])
+        result = TimedeltaIndex(strings)
+        expected = to_timedelta([1, 2, 3], unit="d")
+        tm.assert_index_equal(result, expected)
+
+        from_ints = TimedeltaIndex(expected.asi8)
+        tm.assert_index_equal(from_ints, expected)
+
+        # non-conforming freq
+        msg = (
+            "Inferred frequency None from passed values does not conform to "
+            "passed frequency D"
+        )
+        with pytest.raises(ValueError, match=msg):
+            TimedeltaIndex(["1 days", "2 days", "4 days"], freq="D")
+
+        msg = (
+            "Of the four parameters: start, end, periods, and freq, exactly "
+            "three must be specified"
+        )
+        with pytest.raises(ValueError, match=msg):
+            timedelta_range(periods=10, freq="D")
+
+    def test_constructor_name(self):
+        idx = timedelta_range(start="1 days", periods=1, freq="D", name="TEST")
+        assert idx.name == "TEST"
+
+        # GH10025
+        idx2 = TimedeltaIndex(idx, name="something else")
+        assert idx2.name == "something else"
+
+    def test_constructor_no_precision_raises(self):
+        # GH-24753, GH-24739
+
+        msg = "with no precision is not allowed"
+        with pytest.raises(ValueError, match=msg):
+            TimedeltaIndex(["2000"], dtype="timedelta64")
+
+        msg = "The 'timedelta64' dtype has no unit. Please pass in"
+        with pytest.raises(ValueError, match=msg):
+            pd.Index(["2000"], dtype="timedelta64")
+
+    def test_constructor_wrong_precision_raises(self):
+        msg = "Supported timedelta64 resolutions are 's', 'ms', 'us', 'ns'"
+        with pytest.raises(ValueError, match=msg):
+            TimedeltaIndex(["2000"], dtype="timedelta64[D]")
+
+        # "timedelta64[us]" was unsupported pre-2.0, but now this works.
+        tdi = TimedeltaIndex(["2000"], dtype="timedelta64[us]")
+        assert tdi.dtype == "m8[us]"
+
+    def test_explicit_none_freq(self):
+        # Explicitly passing freq=None is respected
+        tdi = timedelta_range(1, periods=5)
+        assert tdi.freq is not None
+
+        result = TimedeltaIndex(tdi, freq=None)
+        assert result.freq is None
+
+        result = TimedeltaIndex(tdi._data, freq=None)
+        assert result.freq is None
+
+        msg = "TimedeltaArray.__init__ is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            tda = TimedeltaArray(tdi, freq=None)
+        assert tda.freq is None
+
+    def test_from_categorical(self):
+        tdi = timedelta_range(1, periods=5)
+
+        cat = pd.Categorical(tdi)
+
+        result = TimedeltaIndex(cat)
+        tm.assert_index_equal(result, tdi)
+
+        ci = pd.CategoricalIndex(tdi)
+        result = TimedeltaIndex(ci)
+        tm.assert_index_equal(result, tdi)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_delete.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_delete.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e6f54702ce1a09c0fccb0c44d0cd4a474c46a8c
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_delete.py
@@ -0,0 +1,71 @@
+from pandas import (
+    TimedeltaIndex,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+class TestTimedeltaIndexDelete:
+    def test_delete(self):
+        idx = timedelta_range(start="1 Days", periods=5, freq="D", name="idx")
+
+        # preserve freq
+        expected_0 = timedelta_range(start="2 Days", periods=4, freq="D", name="idx")
+        expected_4 = timedelta_range(start="1 Days", periods=4, freq="D", name="idx")
+
+        # reset freq to None
+        expected_1 = TimedeltaIndex(
+            ["1 day", "3 day", "4 day", "5 day"], freq=None, name="idx"
+        )
+
+        cases = {
+            0: expected_0,
+            -5: expected_0,
+            -1: expected_4,
+            4: expected_4,
+            1: expected_1,
+        }
+        for n, expected in cases.items():
+            result = idx.delete(n)
+            tm.assert_index_equal(result, expected)
+            assert result.name == expected.name
+            assert result.freq == expected.freq
+
+        with tm.external_error_raised((IndexError, ValueError)):
+            # either depending on numpy version
+            idx.delete(5)
+
+    def test_delete_slice(self):
+        idx = timedelta_range(start="1 days", periods=10, freq="D", name="idx")
+
+        # preserve freq
+        expected_0_2 = timedelta_range(start="4 days", periods=7, freq="D", name="idx")
+        expected_7_9 = timedelta_range(start="1 days", periods=7, freq="D", name="idx")
+
+        # reset freq to None
+        expected_3_5 = TimedeltaIndex(
+            ["1 d", "2 d", "3 d", "7 d", "8 d", "9 d", "10d"], freq=None, name="idx"
+        )
+
+        cases = {
+            (0, 1, 2): expected_0_2,
+            (7, 8, 9): expected_7_9,
+            (3, 4, 5): expected_3_5,
+        }
+        for n, expected in cases.items():
+            result = idx.delete(n)
+            tm.assert_index_equal(result, expected)
+            assert result.name == expected.name
+            assert result.freq == expected.freq
+
+            result = idx.delete(slice(n[0], n[-1] + 1))
+            tm.assert_index_equal(result, expected)
+            assert result.name == expected.name
+            assert result.freq == expected.freq
+
+    def test_delete_doesnt_infer_freq(self):
+        # GH#30655 behavior matches DatetimeIndex
+
+        tdi = TimedeltaIndex(["1 Day", "2 Days", None, "3 Days", "4 Days"])
+        result = tdi.delete(2)
+        assert result.freq is None
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_formats.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_formats.py
new file mode 100644
index 0000000000000000000000000000000000000000..607336060cbbc2093e224e31614e26a2c03bd72f
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_formats.py
@@ -0,0 +1,106 @@
+import pytest
+
+import pandas as pd
+from pandas import (
+    Series,
+    TimedeltaIndex,
+)
+
+
+class TestTimedeltaIndexRendering:
+    def test_repr_round_days_non_nano(self):
+        # GH#55405
+        # we should get "1 days", not "1 days 00:00:00" with non-nano
+        tdi = TimedeltaIndex(["1 days"], freq="D").as_unit("s")
+        result = repr(tdi)
+        expected = "TimedeltaIndex(['1 days'], dtype='timedelta64[s]', freq='D')"
+        assert result == expected
+
+        result2 = repr(Series(tdi))
+        expected2 = "0   1 days\ndtype: timedelta64[s]"
+        assert result2 == expected2
+
+    @pytest.mark.parametrize("method", ["__repr__", "__str__"])
+    def test_representation(self, method):
+        idx1 = TimedeltaIndex([], freq="D")
+        idx2 = TimedeltaIndex(["1 days"], freq="D")
+        idx3 = TimedeltaIndex(["1 days", "2 days"], freq="D")
+        idx4 = TimedeltaIndex(["1 days", "2 days", "3 days"], freq="D")
+        idx5 = TimedeltaIndex(["1 days 00:00:01", "2 days", "3 days"])
+
+        exp1 = "TimedeltaIndex([], dtype='timedelta64[ns]', freq='D')"
+
+        exp2 = "TimedeltaIndex(['1 days'], dtype='timedelta64[ns]', freq='D')"
+
+        exp3 = "TimedeltaIndex(['1 days', '2 days'], dtype='timedelta64[ns]', freq='D')"
+
+        exp4 = (
+            "TimedeltaIndex(['1 days', '2 days', '3 days'], "
+            "dtype='timedelta64[ns]', freq='D')"
+        )
+
+        exp5 = (
+            "TimedeltaIndex(['1 days 00:00:01', '2 days 00:00:00', "
+            "'3 days 00:00:00'], dtype='timedelta64[ns]', freq=None)"
+        )
+
+        with pd.option_context("display.width", 300):
+            for idx, expected in zip(
+                [idx1, idx2, idx3, idx4, idx5], [exp1, exp2, exp3, exp4, exp5]
+            ):
+                result = getattr(idx, method)()
+                assert result == expected
+
+    # TODO: this is a Series.__repr__ test
+    def test_representation_to_series(self):
+        idx1 = TimedeltaIndex([], freq="D")
+        idx2 = TimedeltaIndex(["1 days"], freq="D")
+        idx3 = TimedeltaIndex(["1 days", "2 days"], freq="D")
+        idx4 = TimedeltaIndex(["1 days", "2 days", "3 days"], freq="D")
+        idx5 = TimedeltaIndex(["1 days 00:00:01", "2 days", "3 days"])
+
+        exp1 = """Series([], dtype: timedelta64[ns])"""
+
+        exp2 = "0   1 days\ndtype: timedelta64[ns]"
+
+        exp3 = "0   1 days\n1   2 days\ndtype: timedelta64[ns]"
+
+        exp4 = "0   1 days\n1   2 days\n2   3 days\ndtype: timedelta64[ns]"
+
+        exp5 = (
+            "0   1 days 00:00:01\n"
+            "1   2 days 00:00:00\n"
+            "2   3 days 00:00:00\n"
+            "dtype: timedelta64[ns]"
+        )
+
+        with pd.option_context("display.width", 300):
+            for idx, expected in zip(
+                [idx1, idx2, idx3, idx4, idx5], [exp1, exp2, exp3, exp4, exp5]
+            ):
+                result = repr(Series(idx))
+                assert result == expected
+
+    def test_summary(self):
+        # GH#9116
+        idx1 = TimedeltaIndex([], freq="D")
+        idx2 = TimedeltaIndex(["1 days"], freq="D")
+        idx3 = TimedeltaIndex(["1 days", "2 days"], freq="D")
+        idx4 = TimedeltaIndex(["1 days", "2 days", "3 days"], freq="D")
+        idx5 = TimedeltaIndex(["1 days 00:00:01", "2 days", "3 days"])
+
+        exp1 = "TimedeltaIndex: 0 entries\nFreq: D"
+
+        exp2 = "TimedeltaIndex: 1 entries, 1 days to 1 days\nFreq: D"
+
+        exp3 = "TimedeltaIndex: 2 entries, 1 days to 2 days\nFreq: D"
+
+        exp4 = "TimedeltaIndex: 3 entries, 1 days to 3 days\nFreq: D"
+
+        exp5 = "TimedeltaIndex: 3 entries, 1 days 00:00:01 to 3 days 00:00:00"
+
+        for idx, expected in zip(
+            [idx1, idx2, idx3, idx4, idx5], [exp1, exp2, exp3, exp4, exp5]
+        ):
+            result = idx._summary()
+            assert result == expected
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_freq_attr.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_freq_attr.py
new file mode 100644
index 0000000000000000000000000000000000000000..1912c49d3000fcbef45dd081213778bfb387e38e
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_freq_attr.py
@@ -0,0 +1,72 @@
+import pytest
+
+from pandas import TimedeltaIndex
+
+from pandas.tseries.offsets import (
+    DateOffset,
+    Day,
+    Hour,
+    MonthEnd,
+)
+
+
+class TestFreq:
+    @pytest.mark.parametrize("values", [["0 days", "2 days", "4 days"], []])
+    @pytest.mark.parametrize("freq", ["2D", Day(2), "48h", Hour(48)])
+    def test_freq_setter(self, values, freq):
+        # GH#20678
+        idx = TimedeltaIndex(values)
+
+        # can set to an offset, converting from string if necessary
+        idx._data.freq = freq
+        assert idx.freq == freq
+        assert isinstance(idx.freq, DateOffset)
+
+        # can reset to None
+        idx._data.freq = None
+        assert idx.freq is None
+
+    def test_with_freq_empty_requires_tick(self):
+        idx = TimedeltaIndex([])
+
+        off = MonthEnd(1)
+        msg = "TimedeltaArray/Index freq must be a Tick"
+        with pytest.raises(TypeError, match=msg):
+            idx._with_freq(off)
+        with pytest.raises(TypeError, match=msg):
+            idx._data._with_freq(off)
+
+    def test_freq_setter_errors(self):
+        # GH#20678
+        idx = TimedeltaIndex(["0 days", "2 days", "4 days"])
+
+        # setting with an incompatible freq
+        msg = (
+            "Inferred frequency 2D from passed values does not conform to "
+            "passed frequency 5D"
+        )
+        with pytest.raises(ValueError, match=msg):
+            idx._data.freq = "5D"
+
+        # setting with a non-fixed frequency
+        msg = r"<2 \* BusinessDays> is a non-fixed frequency"
+        with pytest.raises(ValueError, match=msg):
+            idx._data.freq = "2B"
+
+        # setting with non-freq string
+        with pytest.raises(ValueError, match="Invalid frequency"):
+            idx._data.freq = "foo"
+
+    def test_freq_view_safe(self):
+        # Setting the freq for one TimedeltaIndex shouldn't alter the freq
+        #  for another that views the same data
+
+        tdi = TimedeltaIndex(["0 days", "2 days", "4 days"], freq="2D")
+        tda = tdi._data
+
+        tdi2 = TimedeltaIndex(tda)._with_freq(None)
+        assert tdi2.freq is None
+
+        # Original was not altered
+        assert tdi.freq == "2D"
+        assert tda.freq == "2D"
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_indexing.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_indexing.py
new file mode 100644
index 0000000000000000000000000000000000000000..397f9d9e183319f6df9335fbae7f8cb7401d6ac1
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_indexing.py
@@ -0,0 +1,347 @@
+from datetime import datetime
+import re
+
+import numpy as np
+import pytest
+
+from pandas import (
+    Index,
+    NaT,
+    Timedelta,
+    TimedeltaIndex,
+    Timestamp,
+    notna,
+    offsets,
+    timedelta_range,
+    to_timedelta,
+)
+import pandas._testing as tm
+
+
+class TestGetItem:
+    def test_getitem_slice_keeps_name(self):
+        # GH#4226
+        tdi = timedelta_range("1d", "5d", freq="h", name="timebucket")
+        assert tdi[1:].name == tdi.name
+
+    def test_getitem(self):
+        idx1 = timedelta_range("1 day", "31 day", freq="D", name="idx")
+
+        for idx in [idx1]:
+            result = idx[0]
+            assert result == Timedelta("1 day")
+
+            result = idx[0:5]
+            expected = timedelta_range("1 day", "5 day", freq="D", name="idx")
+            tm.assert_index_equal(result, expected)
+            assert result.freq == expected.freq
+
+            result = idx[0:10:2]
+            expected = timedelta_range("1 day", "9 day", freq="2D", name="idx")
+            tm.assert_index_equal(result, expected)
+            assert result.freq == expected.freq
+
+            result = idx[-20:-5:3]
+            expected = timedelta_range("12 day", "24 day", freq="3D", name="idx")
+            tm.assert_index_equal(result, expected)
+            assert result.freq == expected.freq
+
+            result = idx[4::-1]
+            expected = TimedeltaIndex(
+                ["5 day", "4 day", "3 day", "2 day", "1 day"], freq="-1D", name="idx"
+            )
+            tm.assert_index_equal(result, expected)
+            assert result.freq == expected.freq
+
+    @pytest.mark.parametrize(
+        "key",
+        [
+            Timestamp("1970-01-01"),
+            Timestamp("1970-01-02"),
+            datetime(1970, 1, 1),
+            Timestamp("1970-01-03").to_datetime64(),
+            # non-matching NA values
+            np.datetime64("NaT"),
+        ],
+    )
+    def test_timestamp_invalid_key(self, key):
+        # GH#20464
+        tdi = timedelta_range(0, periods=10)
+        with pytest.raises(KeyError, match=re.escape(repr(key))):
+            tdi.get_loc(key)
+
+
+class TestGetLoc:
+    def test_get_loc_key_unit_mismatch(self):
+        idx = to_timedelta(["0 days", "1 days", "2 days"])
+        key = idx[1].as_unit("ms")
+        loc = idx.get_loc(key)
+        assert loc == 1
+
+    def test_get_loc_key_unit_mismatch_not_castable(self):
+        tdi = to_timedelta(["0 days", "1 days", "2 days"]).astype("m8[s]")
+        assert tdi.dtype == "m8[s]"
+        key = tdi[0].as_unit("ns") + Timedelta(1)
+
+        with pytest.raises(KeyError, match=r"Timedelta\('0 days 00:00:00.000000001'\)"):
+            tdi.get_loc(key)
+
+        assert key not in tdi
+
+    def test_get_loc(self):
+        idx = to_timedelta(["0 days", "1 days", "2 days"])
+
+        # GH 16909
+        assert idx.get_loc(idx[1].to_timedelta64()) == 1
+
+        # GH 16896
+        assert idx.get_loc("0 days") == 0
+
+    def test_get_loc_nat(self):
+        tidx = TimedeltaIndex(["1 days 01:00:00", "NaT", "2 days 01:00:00"])
+
+        assert tidx.get_loc(NaT) == 1
+        assert tidx.get_loc(None) == 1
+        assert tidx.get_loc(float("nan")) == 1
+        assert tidx.get_loc(np.nan) == 1
+
+
+class TestGetIndexer:
+    def test_get_indexer(self):
+        idx = to_timedelta(["0 days", "1 days", "2 days"])
+        tm.assert_numpy_array_equal(
+            idx.get_indexer(idx), np.array([0, 1, 2], dtype=np.intp)
+        )
+
+        target = to_timedelta(["-1 hour", "12 hours", "1 day 1 hour"])
+        tm.assert_numpy_array_equal(
+            idx.get_indexer(target, "pad"), np.array([-1, 0, 1], dtype=np.intp)
+        )
+        tm.assert_numpy_array_equal(
+            idx.get_indexer(target, "backfill"), np.array([0, 1, 2], dtype=np.intp)
+        )
+        tm.assert_numpy_array_equal(
+            idx.get_indexer(target, "nearest"), np.array([0, 1, 1], dtype=np.intp)
+        )
+
+        res = idx.get_indexer(target, "nearest", tolerance=Timedelta("1 hour"))
+        tm.assert_numpy_array_equal(res, np.array([0, -1, 1], dtype=np.intp))
+
+
+class TestWhere:
+    def test_where_doesnt_retain_freq(self):
+        tdi = timedelta_range("1 day", periods=3, freq="D", name="idx")
+        cond = [True, True, False]
+        expected = TimedeltaIndex([tdi[0], tdi[1], tdi[0]], freq=None, name="idx")
+
+        result = tdi.where(cond, tdi[::-1])
+        tm.assert_index_equal(result, expected)
+
+    def test_where_invalid_dtypes(self, fixed_now_ts):
+        tdi = timedelta_range("1 day", periods=3, freq="D", name="idx")
+
+        tail = tdi[2:].tolist()
+        i2 = Index([NaT, NaT] + tail)
+        mask = notna(i2)
+
+        expected = Index([NaT._value, NaT._value] + tail, dtype=object, name="idx")
+        assert isinstance(expected[0], int)
+        result = tdi.where(mask, i2.asi8)
+        tm.assert_index_equal(result, expected)
+
+        ts = i2 + fixed_now_ts
+        expected = Index([ts[0], ts[1]] + tail, dtype=object, name="idx")
+        result = tdi.where(mask, ts)
+        tm.assert_index_equal(result, expected)
+
+        per = (i2 + fixed_now_ts).to_period("D")
+        expected = Index([per[0], per[1]] + tail, dtype=object, name="idx")
+        result = tdi.where(mask, per)
+        tm.assert_index_equal(result, expected)
+
+        ts = fixed_now_ts
+        expected = Index([ts, ts] + tail, dtype=object, name="idx")
+        result = tdi.where(mask, ts)
+        tm.assert_index_equal(result, expected)
+
+    def test_where_mismatched_nat(self):
+        tdi = timedelta_range("1 day", periods=3, freq="D", name="idx")
+        cond = np.array([True, False, False])
+
+        dtnat = np.datetime64("NaT", "ns")
+        expected = Index([tdi[0], dtnat, dtnat], dtype=object, name="idx")
+        assert expected[2] is dtnat
+        result = tdi.where(cond, dtnat)
+        tm.assert_index_equal(result, expected)
+
+
+class TestTake:
+    def test_take(self):
+        # GH 10295
+        idx1 = timedelta_range("1 day", "31 day", freq="D", name="idx")
+
+        for idx in [idx1]:
+            result = idx.take([0])
+            assert result == Timedelta("1 day")
+
+            result = idx.take([-1])
+            assert result == Timedelta("31 day")
+
+            result = idx.take([0, 1, 2])
+            expected = timedelta_range("1 day", "3 day", freq="D", name="idx")
+            tm.assert_index_equal(result, expected)
+            assert result.freq == expected.freq
+
+            result = idx.take([0, 2, 4])
+            expected = timedelta_range("1 day", "5 day", freq="2D", name="idx")
+            tm.assert_index_equal(result, expected)
+            assert result.freq == expected.freq
+
+            result = idx.take([7, 4, 1])
+            expected = timedelta_range("8 day", "2 day", freq="-3D", name="idx")
+            tm.assert_index_equal(result, expected)
+            assert result.freq == expected.freq
+
+            result = idx.take([3, 2, 5])
+            expected = TimedeltaIndex(["4 day", "3 day", "6 day"], name="idx")
+            tm.assert_index_equal(result, expected)
+            assert result.freq is None
+
+            result = idx.take([-3, 2, 5])
+            expected = TimedeltaIndex(["29 day", "3 day", "6 day"], name="idx")
+            tm.assert_index_equal(result, expected)
+            assert result.freq is None
+
+    def test_take_invalid_kwargs(self):
+        idx = timedelta_range("1 day", "31 day", freq="D", name="idx")
+        indices = [1, 6, 5, 9, 10, 13, 15, 3]
+
+        msg = r"take\(\) got an unexpected keyword argument 'foo'"
+        with pytest.raises(TypeError, match=msg):
+            idx.take(indices, foo=2)
+
+        msg = "the 'out' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            idx.take(indices, out=indices)
+
+        msg = "the 'mode' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            idx.take(indices, mode="clip")
+
+    def test_take_equiv_getitem(self):
+        tds = ["1day 02:00:00", "1 day 04:00:00", "1 day 10:00:00"]
+        idx = timedelta_range(start="1d", end="2d", freq="h", name="idx")
+        expected = TimedeltaIndex(tds, freq=None, name="idx")
+
+        taken1 = idx.take([2, 4, 10])
+        taken2 = idx[[2, 4, 10]]
+
+        for taken in [taken1, taken2]:
+            tm.assert_index_equal(taken, expected)
+            assert isinstance(taken, TimedeltaIndex)
+            assert taken.freq is None
+            assert taken.name == expected.name
+
+    def test_take_fill_value(self):
+        # GH 12631
+        idx = TimedeltaIndex(["1 days", "2 days", "3 days"], name="xxx")
+        result = idx.take(np.array([1, 0, -1]))
+        expected = TimedeltaIndex(["2 days", "1 days", "3 days"], name="xxx")
+        tm.assert_index_equal(result, expected)
+
+        # fill_value
+        result = idx.take(np.array([1, 0, -1]), fill_value=True)
+        expected = TimedeltaIndex(["2 days", "1 days", "NaT"], name="xxx")
+        tm.assert_index_equal(result, expected)
+
+        # allow_fill=False
+        result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True)
+        expected = TimedeltaIndex(["2 days", "1 days", "3 days"], name="xxx")
+        tm.assert_index_equal(result, expected)
+
+        msg = (
+            "When allow_fill=True and fill_value is not None, "
+            "all indices must be >= -1"
+        )
+        with pytest.raises(ValueError, match=msg):
+            idx.take(np.array([1, 0, -2]), fill_value=True)
+        with pytest.raises(ValueError, match=msg):
+            idx.take(np.array([1, 0, -5]), fill_value=True)
+
+        msg = "index -5 is out of bounds for (axis 0 with )?size 3"
+        with pytest.raises(IndexError, match=msg):
+            idx.take(np.array([1, -5]))
+
+
+class TestMaybeCastSliceBound:
+    @pytest.fixture(params=["increasing", "decreasing", None])
+    def monotonic(self, request):
+        return request.param
+
+    @pytest.fixture
+    def tdi(self, monotonic):
+        tdi = timedelta_range("1 Day", periods=10)
+        if monotonic == "decreasing":
+            tdi = tdi[::-1]
+        elif monotonic is None:
+            taker = np.arange(10, dtype=np.intp)
+            np.random.default_rng(2).shuffle(taker)
+            tdi = tdi.take(taker)
+        return tdi
+
+    def test_maybe_cast_slice_bound_invalid_str(self, tdi):
+        # test the low-level _maybe_cast_slice_bound and that we get the
+        #  expected exception+message all the way up the stack
+        msg = (
+            "cannot do slice indexing on TimedeltaIndex with these "
+            r"indexers \[foo\] of type str"
+        )
+        with pytest.raises(TypeError, match=msg):
+            tdi._maybe_cast_slice_bound("foo", side="left")
+        with pytest.raises(TypeError, match=msg):
+            tdi.get_slice_bound("foo", side="left")
+        with pytest.raises(TypeError, match=msg):
+            tdi.slice_locs("foo", None, None)
+
+    def test_slice_invalid_str_with_timedeltaindex(
+        self, tdi, frame_or_series, indexer_sl
+    ):
+        obj = frame_or_series(range(10), index=tdi)
+
+        msg = (
+            "cannot do slice indexing on TimedeltaIndex with these "
+            r"indexers \[foo\] of type str"
+        )
+        with pytest.raises(TypeError, match=msg):
+            indexer_sl(obj)["foo":]
+        with pytest.raises(TypeError, match=msg):
+            indexer_sl(obj)["foo":-1]
+        with pytest.raises(TypeError, match=msg):
+            indexer_sl(obj)[:"foo"]
+        with pytest.raises(TypeError, match=msg):
+            indexer_sl(obj)[tdi[0] : "foo"]
+
+
+class TestContains:
+    def test_contains_nonunique(self):
+        # GH#9512
+        for vals in (
+            [0, 1, 0],
+            [0, 0, -1],
+            [0, -1, -1],
+            ["00:01:00", "00:01:00", "00:02:00"],
+            ["00:01:00", "00:01:00", "00:00:01"],
+        ):
+            idx = TimedeltaIndex(vals)
+            assert idx[0] in idx
+
+    def test_contains(self):
+        # Checking for any NaT-like objects
+        # GH#13603
+        td = to_timedelta(range(5), unit="d") + offsets.Hour(1)
+        for v in [NaT, None, float("nan"), np.nan]:
+            assert v not in td
+
+        td = to_timedelta([NaT])
+        for v in [NaT, None, float("nan"), np.nan]:
+            assert v in td
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_join.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_join.py
new file mode 100644
index 0000000000000000000000000000000000000000..cbd7a5de71b10a6004cd7a3f798fecd8e7631750
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_join.py
@@ -0,0 +1,47 @@
+import numpy as np
+
+from pandas import (
+    DataFrame,
+    Index,
+    Timedelta,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+class TestJoin:
+    def test_append_join_nondatetimeindex(self):
+        rng = timedelta_range("1 days", periods=10)
+        idx = Index(["a", "b", "c", "d"])
+
+        result = rng.append(idx)
+        assert isinstance(result[0], Timedelta)
+
+        # it works
+        rng.join(idx, how="outer")
+
+    def test_join_self(self, join_type):
+        index = timedelta_range("1 day", periods=10)
+        joined = index.join(index, how=join_type)
+        tm.assert_index_equal(index, joined)
+
+    def test_does_not_convert_mixed_integer(self):
+        df = DataFrame(np.ones((5, 5)), columns=timedelta_range("1 day", periods=5))
+
+        cols = df.columns.join(df.index, how="outer")
+        joined = cols.join(df.columns)
+        assert cols.dtype == np.dtype("O")
+        assert cols.dtype == joined.dtype
+        tm.assert_index_equal(cols, joined)
+
+    def test_join_preserves_freq(self):
+        # GH#32157
+        tdi = timedelta_range("1 day", periods=10)
+        result = tdi[:5].join(tdi[5:], how="outer")
+        assert result.freq == tdi.freq
+        tm.assert_index_equal(result, tdi)
+
+        result = tdi[:5].join(tdi[6:], how="outer")
+        assert result.freq is None
+        expected = tdi.delete(5)
+        tm.assert_index_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_pickle.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_pickle.py
new file mode 100644
index 0000000000000000000000000000000000000000..befe709728bdd4e9fac3c626f4e33986d671c86d
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_pickle.py
@@ -0,0 +1,11 @@
+from pandas import timedelta_range
+import pandas._testing as tm
+
+
+class TestPickle:
+    def test_pickle_after_set_freq(self):
+        tdi = timedelta_range("1 day", periods=4, freq="s")
+        tdi = tdi._with_freq(None)
+
+        res = tm.round_trip_pickle(tdi)
+        tm.assert_index_equal(res, tdi)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_scalar_compat.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_scalar_compat.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f0552f8baa901addaae9b4ca0890f6edb272715
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_scalar_compat.py
@@ -0,0 +1,142 @@
+"""
+Tests for TimedeltaIndex methods behaving like their Timedelta counterparts
+"""
+
+import numpy as np
+import pytest
+
+from pandas._libs.tslibs.offsets import INVALID_FREQ_ERR_MSG
+
+from pandas import (
+    Index,
+    Series,
+    Timedelta,
+    TimedeltaIndex,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+class TestVectorizedTimedelta:
+    def test_tdi_total_seconds(self):
+        # GH#10939
+        # test index
+        rng = timedelta_range("1 days, 10:11:12.100123456", periods=2, freq="s")
+        expt = [
+            1 * 86400 + 10 * 3600 + 11 * 60 + 12 + 100123456.0 / 1e9,
+            1 * 86400 + 10 * 3600 + 11 * 60 + 13 + 100123456.0 / 1e9,
+        ]
+        tm.assert_almost_equal(rng.total_seconds(), Index(expt))
+
+        # test Series
+        ser = Series(rng)
+        s_expt = Series(expt, index=[0, 1])
+        tm.assert_series_equal(ser.dt.total_seconds(), s_expt)
+
+        # with nat
+        ser[1] = np.nan
+        s_expt = Series(
+            [1 * 86400 + 10 * 3600 + 11 * 60 + 12 + 100123456.0 / 1e9, np.nan],
+            index=[0, 1],
+        )
+        tm.assert_series_equal(ser.dt.total_seconds(), s_expt)
+
+    def test_tdi_total_seconds_all_nat(self):
+        # with both nat
+        ser = Series([np.nan, np.nan], dtype="timedelta64[ns]")
+        result = ser.dt.total_seconds()
+        expected = Series([np.nan, np.nan])
+        tm.assert_series_equal(result, expected)
+
+    def test_tdi_round(self):
+        td = timedelta_range(start="16801 days", periods=5, freq="30Min")
+        elt = td[1]
+
+        expected_rng = TimedeltaIndex(
+            [
+                Timedelta("16801 days 00:00:00"),
+                Timedelta("16801 days 00:00:00"),
+                Timedelta("16801 days 01:00:00"),
+                Timedelta("16801 days 02:00:00"),
+                Timedelta("16801 days 02:00:00"),
+            ]
+        )
+        expected_elt = expected_rng[1]
+
+        tm.assert_index_equal(td.round(freq="h"), expected_rng)
+        assert elt.round(freq="h") == expected_elt
+
+        msg = INVALID_FREQ_ERR_MSG
+        with pytest.raises(ValueError, match=msg):
+            td.round(freq="foo")
+        with pytest.raises(ValueError, match=msg):
+            elt.round(freq="foo")
+
+        msg = "<MonthEnd> is a non-fixed frequency"
+        with pytest.raises(ValueError, match=msg):
+            td.round(freq="ME")
+        with pytest.raises(ValueError, match=msg):
+            elt.round(freq="ME")
+
+    @pytest.mark.parametrize(
+        "freq,msg",
+        [
+            ("YE", "<YearEnd: month=12> is a non-fixed frequency"),
+            ("ME", "<MonthEnd> is a non-fixed frequency"),
+            ("foobar", "Invalid frequency: foobar"),
+        ],
+    )
+    def test_tdi_round_invalid(self, freq, msg):
+        t1 = timedelta_range("1 days", periods=3, freq="1 min 2 s 3 us")
+
+        with pytest.raises(ValueError, match=msg):
+            t1.round(freq)
+        with pytest.raises(ValueError, match=msg):
+            # Same test for TimedeltaArray
+            t1._data.round(freq)
+
+    # TODO: de-duplicate with test_tdi_round
+    def test_round(self):
+        t1 = timedelta_range("1 days", periods=3, freq="1 min 2 s 3 us")
+        t2 = -1 * t1
+        t1a = timedelta_range("1 days", periods=3, freq="1 min 2 s")
+        t1c = TimedeltaIndex(np.array([1, 1, 1], "m8[D]")).as_unit("ns")
+
+        # note that negative times round DOWN! so don't give whole numbers
+        for freq, s1, s2 in [
+            ("ns", t1, t2),
+            ("us", t1, t2),
+            (
+                "ms",
+                t1a,
+                TimedeltaIndex(
+                    ["-1 days +00:00:00", "-2 days +23:58:58", "-2 days +23:57:56"]
+                ),
+            ),
+            (
+                "s",
+                t1a,
+                TimedeltaIndex(
+                    ["-1 days +00:00:00", "-2 days +23:58:58", "-2 days +23:57:56"]
+                ),
+            ),
+            ("12min", t1c, TimedeltaIndex(["-1 days", "-1 days", "-1 days"])),
+            ("h", t1c, TimedeltaIndex(["-1 days", "-1 days", "-1 days"])),
+            ("d", t1c, -1 * t1c),
+        ]:
+            r1 = t1.round(freq)
+            tm.assert_index_equal(r1, s1)
+            r2 = t2.round(freq)
+            tm.assert_index_equal(r2, s2)
+
+    def test_components(self):
+        rng = timedelta_range("1 days, 10:11:12", periods=2, freq="s")
+        rng.components
+
+        # with nat
+        s = Series(rng)
+        s[1] = np.nan
+
+        result = s.dt.components
+        assert not result.iloc[0].isna().all()
+        assert result.iloc[1].isna().all()
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_timedelta.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_timedelta.py
new file mode 100644
index 0000000000000000000000000000000000000000..3120066741ffa292dc1533056438ebf481cb1849
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_timedelta.py
@@ -0,0 +1,61 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Index,
+    Series,
+    Timedelta,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+class TestTimedeltaIndex:
+    def test_misc_coverage(self):
+        rng = timedelta_range("1 day", periods=5)
+        result = rng.groupby(rng.days)
+        assert isinstance(next(iter(result.values()))[0], Timedelta)
+
+    def test_map(self):
+        # test_map_dictlike generally tests
+
+        rng = timedelta_range("1 day", periods=10)
+
+        f = lambda x: x.days
+        result = rng.map(f)
+        exp = Index([f(x) for x in rng], dtype=np.int64)
+        tm.assert_index_equal(result, exp)
+
+    def test_fields(self):
+        rng = timedelta_range("1 days, 10:11:12.100123456", periods=2, freq="s")
+        tm.assert_index_equal(rng.days, Index([1, 1], dtype=np.int64))
+        tm.assert_index_equal(
+            rng.seconds,
+            Index([10 * 3600 + 11 * 60 + 12, 10 * 3600 + 11 * 60 + 13], dtype=np.int32),
+        )
+        tm.assert_index_equal(
+            rng.microseconds,
+            Index([100 * 1000 + 123, 100 * 1000 + 123], dtype=np.int32),
+        )
+        tm.assert_index_equal(rng.nanoseconds, Index([456, 456], dtype=np.int32))
+
+        msg = "'TimedeltaIndex' object has no attribute '{}'"
+        with pytest.raises(AttributeError, match=msg.format("hours")):
+            rng.hours
+        with pytest.raises(AttributeError, match=msg.format("minutes")):
+            rng.minutes
+        with pytest.raises(AttributeError, match=msg.format("milliseconds")):
+            rng.milliseconds
+
+        # with nat
+        s = Series(rng)
+        s[1] = np.nan
+
+        tm.assert_series_equal(s.dt.days, Series([1, np.nan], index=[0, 1]))
+        tm.assert_series_equal(
+            s.dt.seconds, Series([10 * 3600 + 11 * 60 + 12, np.nan], index=[0, 1])
+        )
+
+        # preserve name (GH15589)
+        rng.name = "name"
+        assert rng.days.name == "name"