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diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_append.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_append.py
new file mode 100644
index 0000000000000000000000000000000000000000..b48c3219f5111a7a1226d09ce4625c723c4168fb
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_append.py
@@ -0,0 +1,62 @@
+import pytest
+
+from pandas import (
+    CategoricalIndex,
+    Index,
+)
+import pandas._testing as tm
+
+
+class TestAppend:
+    @pytest.fixture
+    def ci(self):
+        categories = list("cab")
+        return CategoricalIndex(list("aabbca"), categories=categories, ordered=False)
+
+    def test_append(self, ci):
+        # append cats with the same categories
+        result = ci[:3].append(ci[3:])
+        tm.assert_index_equal(result, ci, exact=True)
+
+        foos = [ci[:1], ci[1:3], ci[3:]]
+        result = foos[0].append(foos[1:])
+        tm.assert_index_equal(result, ci, exact=True)
+
+    def test_append_empty(self, ci):
+        # empty
+        result = ci.append([])
+        tm.assert_index_equal(result, ci, exact=True)
+
+    def test_append_mismatched_categories(self, ci):
+        # appending with different categories or reordered is not ok
+        msg = "all inputs must be Index"
+        with pytest.raises(TypeError, match=msg):
+            ci.append(ci.values.set_categories(list("abcd")))
+        with pytest.raises(TypeError, match=msg):
+            ci.append(ci.values.reorder_categories(list("abc")))
+
+    def test_append_category_objects(self, ci):
+        # with objects
+        result = ci.append(Index(["c", "a"]))
+        expected = CategoricalIndex(list("aabbcaca"), categories=ci.categories)
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_append_non_categories(self, ci):
+        # invalid objects -> cast to object via concat_compat
+        result = ci.append(Index(["a", "d"]))
+        expected = Index(["a", "a", "b", "b", "c", "a", "a", "d"])
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_append_object(self, ci):
+        # GH#14298 - if base object is not categorical -> coerce to object
+        result = Index(["c", "a"]).append(ci)
+        expected = Index(list("caaabbca"))
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_append_to_another(self):
+        # hits Index._concat
+        fst = Index(["a", "b"])
+        snd = CategoricalIndex(["d", "e"])
+        result = fst.append(snd)
+        expected = Index(["a", "b", "d", "e"])
+        tm.assert_index_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_astype.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_astype.py
new file mode 100644
index 0000000000000000000000000000000000000000..a17627b7515b26b1fcfdca0feec376f03a018e83
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_astype.py
@@ -0,0 +1,90 @@
+from datetime import date
+
+import numpy as np
+import pytest
+
+from pandas import (
+    Categorical,
+    CategoricalDtype,
+    CategoricalIndex,
+    Index,
+    IntervalIndex,
+)
+import pandas._testing as tm
+
+
+class TestAstype:
+    def test_astype(self):
+        ci = CategoricalIndex(list("aabbca"), categories=list("cab"), ordered=False)
+
+        result = ci.astype(object)
+        tm.assert_index_equal(result, Index(np.array(ci), dtype=object))
+
+        # this IS equal, but not the same class
+        assert result.equals(ci)
+        assert isinstance(result, Index)
+        assert not isinstance(result, CategoricalIndex)
+
+        # interval
+        ii = IntervalIndex.from_arrays(left=[-0.001, 2.0], right=[2, 4], closed="right")
+
+        ci = CategoricalIndex(
+            Categorical.from_codes([0, 1, -1], categories=ii, ordered=True)
+        )
+
+        result = ci.astype("interval")
+        expected = ii.take([0, 1, -1], allow_fill=True, fill_value=np.nan)
+        tm.assert_index_equal(result, expected)
+
+        result = IntervalIndex(result.values)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("name", [None, "foo"])
+    @pytest.mark.parametrize("dtype_ordered", [True, False])
+    @pytest.mark.parametrize("index_ordered", [True, False])
+    def test_astype_category(self, name, dtype_ordered, index_ordered):
+        # GH#18630
+        index = CategoricalIndex(
+            list("aabbca"), categories=list("cab"), ordered=index_ordered
+        )
+        if name:
+            index = index.rename(name)
+
+        # standard categories
+        dtype = CategoricalDtype(ordered=dtype_ordered)
+        result = index.astype(dtype)
+        expected = CategoricalIndex(
+            index.tolist(),
+            name=name,
+            categories=index.categories,
+            ordered=dtype_ordered,
+        )
+        tm.assert_index_equal(result, expected)
+
+        # non-standard categories
+        dtype = CategoricalDtype(index.unique().tolist()[:-1], dtype_ordered)
+        result = index.astype(dtype)
+        expected = CategoricalIndex(index.tolist(), name=name, dtype=dtype)
+        tm.assert_index_equal(result, expected)
+
+        if dtype_ordered is False:
+            # dtype='category' can't specify ordered, so only test once
+            result = index.astype("category")
+            expected = index
+            tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("box", [True, False])
+    def test_categorical_date_roundtrip(self, box):
+        # astype to categorical and back should preserve date objects
+        v = date.today()
+
+        obj = Index([v, v])
+        assert obj.dtype == object
+        if box:
+            obj = obj.array
+
+        cat = obj.astype("category")
+
+        rtrip = cat.astype(object)
+        assert rtrip.dtype == object
+        assert type(rtrip[0]) is date
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_category.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_category.py
new file mode 100644
index 0000000000000000000000000000000000000000..03a298a13dc2b45b3e78ec2d6390741709e42590
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_category.py
@@ -0,0 +1,394 @@
+import numpy as np
+import pytest
+
+from pandas._config import using_pyarrow_string_dtype
+
+from pandas._libs import index as libindex
+from pandas._libs.arrays import NDArrayBacked
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    CategoricalDtype,
+)
+import pandas._testing as tm
+from pandas.core.indexes.api import (
+    CategoricalIndex,
+    Index,
+)
+
+
+class TestCategoricalIndex:
+    @pytest.fixture
+    def simple_index(self) -> CategoricalIndex:
+        return CategoricalIndex(list("aabbca"), categories=list("cab"), ordered=False)
+
+    def test_can_hold_identifiers(self):
+        idx = CategoricalIndex(list("aabbca"), categories=None, ordered=False)
+        key = idx[0]
+        assert idx._can_hold_identifiers_and_holds_name(key) is True
+
+    def test_insert(self, simple_index):
+        ci = simple_index
+        categories = ci.categories
+
+        # test 0th element
+        result = ci.insert(0, "a")
+        expected = CategoricalIndex(list("aaabbca"), categories=categories)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        # test Nth element that follows Python list behavior
+        result = ci.insert(-1, "a")
+        expected = CategoricalIndex(list("aabbcaa"), categories=categories)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        # test empty
+        result = CategoricalIndex([], categories=categories).insert(0, "a")
+        expected = CategoricalIndex(["a"], categories=categories)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        # invalid -> cast to object
+        expected = ci.astype(object).insert(0, "d")
+        result = ci.insert(0, "d").astype(object)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        # GH 18295 (test missing)
+        expected = CategoricalIndex(["a", np.nan, "a", "b", "c", "b"])
+        for na in (np.nan, pd.NaT, None):
+            result = CategoricalIndex(list("aabcb")).insert(1, na)
+            tm.assert_index_equal(result, expected)
+
+    def test_insert_na_mismatched_dtype(self):
+        ci = CategoricalIndex([0, 1, 1])
+        result = ci.insert(0, pd.NaT)
+        expected = Index([pd.NaT, 0, 1, 1], dtype=object)
+        tm.assert_index_equal(result, expected)
+
+    def test_delete(self, simple_index):
+        ci = simple_index
+        categories = ci.categories
+
+        result = ci.delete(0)
+        expected = CategoricalIndex(list("abbca"), categories=categories)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        result = ci.delete(-1)
+        expected = CategoricalIndex(list("aabbc"), categories=categories)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        with tm.external_error_raised((IndexError, ValueError)):
+            # Either depending on NumPy version
+            ci.delete(10)
+
+    @pytest.mark.parametrize(
+        "data, non_lexsorted_data",
+        [[[1, 2, 3], [9, 0, 1, 2, 3]], [list("abc"), list("fabcd")]],
+    )
+    def test_is_monotonic(self, data, non_lexsorted_data):
+        c = CategoricalIndex(data)
+        assert c.is_monotonic_increasing is True
+        assert c.is_monotonic_decreasing is False
+
+        c = CategoricalIndex(data, ordered=True)
+        assert c.is_monotonic_increasing is True
+        assert c.is_monotonic_decreasing is False
+
+        c = CategoricalIndex(data, categories=reversed(data))
+        assert c.is_monotonic_increasing is False
+        assert c.is_monotonic_decreasing is True
+
+        c = CategoricalIndex(data, categories=reversed(data), ordered=True)
+        assert c.is_monotonic_increasing is False
+        assert c.is_monotonic_decreasing is True
+
+        # test when data is neither monotonic increasing nor decreasing
+        reordered_data = [data[0], data[2], data[1]]
+        c = CategoricalIndex(reordered_data, categories=reversed(data))
+        assert c.is_monotonic_increasing is False
+        assert c.is_monotonic_decreasing is False
+
+        # non lexsorted categories
+        categories = non_lexsorted_data
+
+        c = CategoricalIndex(categories[:2], categories=categories)
+        assert c.is_monotonic_increasing is True
+        assert c.is_monotonic_decreasing is False
+
+        c = CategoricalIndex(categories[1:3], categories=categories)
+        assert c.is_monotonic_increasing is True
+        assert c.is_monotonic_decreasing is False
+
+    def test_has_duplicates(self):
+        idx = CategoricalIndex([0, 0, 0], name="foo")
+        assert idx.is_unique is False
+        assert idx.has_duplicates is True
+
+        idx = CategoricalIndex([0, 1], categories=[2, 3], name="foo")
+        assert idx.is_unique is False
+        assert idx.has_duplicates is True
+
+        idx = CategoricalIndex([0, 1, 2, 3], categories=[1, 2, 3], name="foo")
+        assert idx.is_unique is True
+        assert idx.has_duplicates is False
+
+    @pytest.mark.parametrize(
+        "data, categories, expected",
+        [
+            (
+                [1, 1, 1],
+                [1, 2, 3],
+                {
+                    "first": np.array([False, True, True]),
+                    "last": np.array([True, True, False]),
+                    False: np.array([True, True, True]),
+                },
+            ),
+            (
+                [1, 1, 1],
+                list("abc"),
+                {
+                    "first": np.array([False, True, True]),
+                    "last": np.array([True, True, False]),
+                    False: np.array([True, True, True]),
+                },
+            ),
+            (
+                [2, "a", "b"],
+                list("abc"),
+                {
+                    "first": np.zeros(shape=(3), dtype=np.bool_),
+                    "last": np.zeros(shape=(3), dtype=np.bool_),
+                    False: np.zeros(shape=(3), dtype=np.bool_),
+                },
+            ),
+            (
+                list("abb"),
+                list("abc"),
+                {
+                    "first": np.array([False, False, True]),
+                    "last": np.array([False, True, False]),
+                    False: np.array([False, True, True]),
+                },
+            ),
+        ],
+    )
+    def test_drop_duplicates(self, data, categories, expected):
+        idx = CategoricalIndex(data, categories=categories, name="foo")
+        for keep, e in expected.items():
+            tm.assert_numpy_array_equal(idx.duplicated(keep=keep), e)
+            e = idx[~e]
+            result = idx.drop_duplicates(keep=keep)
+            tm.assert_index_equal(result, e)
+
+    @pytest.mark.parametrize(
+        "data, categories, expected_data",
+        [
+            ([1, 1, 1], [1, 2, 3], [1]),
+            ([1, 1, 1], list("abc"), [np.nan]),
+            ([1, 2, "a"], [1, 2, 3], [1, 2, np.nan]),
+            ([2, "a", "b"], list("abc"), [np.nan, "a", "b"]),
+        ],
+    )
+    def test_unique(self, data, categories, expected_data, ordered):
+        dtype = CategoricalDtype(categories, ordered=ordered)
+
+        idx = CategoricalIndex(data, dtype=dtype)
+        expected = CategoricalIndex(expected_data, dtype=dtype)
+        tm.assert_index_equal(idx.unique(), expected)
+
+    @pytest.mark.xfail(using_pyarrow_string_dtype(), reason="repr doesn't roundtrip")
+    def test_repr_roundtrip(self):
+        ci = CategoricalIndex(["a", "b"], categories=["a", "b"], ordered=True)
+        str(ci)
+        tm.assert_index_equal(eval(repr(ci)), ci, exact=True)
+
+        # formatting
+        str(ci)
+
+        # long format
+        # this is not reprable
+        ci = CategoricalIndex(np.random.default_rng(2).integers(0, 5, size=100))
+        str(ci)
+
+    def test_isin(self):
+        ci = CategoricalIndex(list("aabca") + [np.nan], categories=["c", "a", "b"])
+        tm.assert_numpy_array_equal(
+            ci.isin(["c"]), np.array([False, False, False, True, False, False])
+        )
+        tm.assert_numpy_array_equal(
+            ci.isin(["c", "a", "b"]), np.array([True] * 5 + [False])
+        )
+        tm.assert_numpy_array_equal(
+            ci.isin(["c", "a", "b", np.nan]), np.array([True] * 6)
+        )
+
+        # mismatched categorical -> coerced to ndarray so doesn't matter
+        result = ci.isin(ci.set_categories(list("abcdefghi")))
+        expected = np.array([True] * 6)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = ci.isin(ci.set_categories(list("defghi")))
+        expected = np.array([False] * 5 + [True])
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_isin_overlapping_intervals(self):
+        # GH 34974
+        idx = pd.IntervalIndex([pd.Interval(0, 2), pd.Interval(0, 1)])
+        result = CategoricalIndex(idx).isin(idx)
+        expected = np.array([True, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_identical(self):
+        ci1 = CategoricalIndex(["a", "b"], categories=["a", "b"], ordered=True)
+        ci2 = CategoricalIndex(["a", "b"], categories=["a", "b", "c"], ordered=True)
+        assert ci1.identical(ci1)
+        assert ci1.identical(ci1.copy())
+        assert not ci1.identical(ci2)
+
+    def test_ensure_copied_data(self):
+        # gh-12309: Check the "copy" argument of each
+        # Index.__new__ is honored.
+        #
+        # Must be tested separately from other indexes because
+        # self.values is not an ndarray.
+        index = CategoricalIndex(list("ab") * 5)
+
+        result = CategoricalIndex(index.values, copy=True)
+        tm.assert_index_equal(index, result)
+        assert not np.shares_memory(result._data._codes, index._data._codes)
+
+        result = CategoricalIndex(index.values, copy=False)
+        assert result._data._codes is index._data._codes
+
+
+class TestCategoricalIndex2:
+    def test_view_i8(self):
+        # GH#25464
+        ci = CategoricalIndex(list("ab") * 50)
+        msg = "When changing to a larger dtype, its size must be a divisor"
+        with pytest.raises(ValueError, match=msg):
+            ci.view("i8")
+        with pytest.raises(ValueError, match=msg):
+            ci._data.view("i8")
+
+        ci = ci[:-4]  # length divisible by 8
+
+        res = ci.view("i8")
+        expected = ci._data.codes.view("i8")
+        tm.assert_numpy_array_equal(res, expected)
+
+        cat = ci._data
+        tm.assert_numpy_array_equal(cat.view("i8"), expected)
+
+    @pytest.mark.parametrize(
+        "dtype, engine_type",
+        [
+            (np.int8, libindex.Int8Engine),
+            (np.int16, libindex.Int16Engine),
+            (np.int32, libindex.Int32Engine),
+            (np.int64, libindex.Int64Engine),
+        ],
+    )
+    def test_engine_type(self, dtype, engine_type):
+        if dtype != np.int64:
+            # num. of uniques required to push CategoricalIndex.codes to a
+            # dtype (128 categories required for .codes dtype to be int16 etc.)
+            num_uniques = {np.int8: 1, np.int16: 128, np.int32: 32768}[dtype]
+            ci = CategoricalIndex(range(num_uniques))
+        else:
+            # having 2**32 - 2**31 categories would be very memory-intensive,
+            # so we cheat a bit with the dtype
+            ci = CategoricalIndex(range(32768))  # == 2**16 - 2**(16 - 1)
+            arr = ci.values._ndarray.astype("int64")
+            NDArrayBacked.__init__(ci._data, arr, ci.dtype)
+        assert np.issubdtype(ci.codes.dtype, dtype)
+        assert isinstance(ci._engine, engine_type)
+
+    @pytest.mark.parametrize(
+        "func,op_name",
+        [
+            (lambda idx: idx - idx, "__sub__"),
+            (lambda idx: idx + idx, "__add__"),
+            (lambda idx: idx - ["a", "b"], "__sub__"),
+            (lambda idx: idx + ["a", "b"], "__add__"),
+            (lambda idx: ["a", "b"] - idx, "__rsub__"),
+            (lambda idx: ["a", "b"] + idx, "__radd__"),
+        ],
+    )
+    def test_disallow_addsub_ops(self, func, op_name):
+        # GH 10039
+        # set ops (+/-) raise TypeError
+        idx = Index(Categorical(["a", "b"]))
+        cat_or_list = "'(Categorical|list)' and '(Categorical|list)'"
+        msg = "|".join(
+            [
+                f"cannot perform {op_name} with this index type: CategoricalIndex",
+                "can only concatenate list",
+                rf"unsupported operand type\(s\) for [\+-]: {cat_or_list}",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            func(idx)
+
+    def test_method_delegation(self):
+        ci = CategoricalIndex(list("aabbca"), categories=list("cabdef"))
+        result = ci.set_categories(list("cab"))
+        tm.assert_index_equal(
+            result, CategoricalIndex(list("aabbca"), categories=list("cab"))
+        )
+
+        ci = CategoricalIndex(list("aabbca"), categories=list("cab"))
+        result = ci.rename_categories(list("efg"))
+        tm.assert_index_equal(
+            result, CategoricalIndex(list("ffggef"), categories=list("efg"))
+        )
+
+        # GH18862 (let rename_categories take callables)
+        result = ci.rename_categories(lambda x: x.upper())
+        tm.assert_index_equal(
+            result, CategoricalIndex(list("AABBCA"), categories=list("CAB"))
+        )
+
+        ci = CategoricalIndex(list("aabbca"), categories=list("cab"))
+        result = ci.add_categories(["d"])
+        tm.assert_index_equal(
+            result, CategoricalIndex(list("aabbca"), categories=list("cabd"))
+        )
+
+        ci = CategoricalIndex(list("aabbca"), categories=list("cab"))
+        result = ci.remove_categories(["c"])
+        tm.assert_index_equal(
+            result,
+            CategoricalIndex(list("aabb") + [np.nan] + ["a"], categories=list("ab")),
+        )
+
+        ci = CategoricalIndex(list("aabbca"), categories=list("cabdef"))
+        result = ci.as_unordered()
+        tm.assert_index_equal(result, ci)
+
+        ci = CategoricalIndex(list("aabbca"), categories=list("cabdef"))
+        result = ci.as_ordered()
+        tm.assert_index_equal(
+            result,
+            CategoricalIndex(list("aabbca"), categories=list("cabdef"), ordered=True),
+        )
+
+        # invalid
+        msg = "cannot use inplace with CategoricalIndex"
+        with pytest.raises(ValueError, match=msg):
+            ci.set_categories(list("cab"), inplace=True)
+
+    def test_remove_maintains_order(self):
+        ci = CategoricalIndex(list("abcdda"), categories=list("abcd"))
+        result = ci.reorder_categories(["d", "c", "b", "a"], ordered=True)
+        tm.assert_index_equal(
+            result,
+            CategoricalIndex(list("abcdda"), categories=list("dcba"), ordered=True),
+        )
+        result = result.remove_categories(["c"])
+        tm.assert_index_equal(
+            result,
+            CategoricalIndex(
+                ["a", "b", np.nan, "d", "d", "a"], categories=list("dba"), ordered=True
+            ),
+        )
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_constructors.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_constructors.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0c5307fc5c641ff25d26bd2bd8a158b43dd6a6d
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_constructors.py
@@ -0,0 +1,142 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Categorical,
+    CategoricalDtype,
+    CategoricalIndex,
+    Index,
+)
+import pandas._testing as tm
+
+
+class TestCategoricalIndexConstructors:
+    def test_construction_disallows_scalar(self):
+        msg = "must be called with a collection of some kind"
+        with pytest.raises(TypeError, match=msg):
+            CategoricalIndex(data=1, categories=list("abcd"), ordered=False)
+        with pytest.raises(TypeError, match=msg):
+            CategoricalIndex(categories=list("abcd"), ordered=False)
+
+    def test_construction(self):
+        ci = CategoricalIndex(list("aabbca"), categories=list("abcd"), ordered=False)
+        categories = ci.categories
+
+        result = Index(ci)
+        tm.assert_index_equal(result, ci, exact=True)
+        assert not result.ordered
+
+        result = Index(ci.values)
+        tm.assert_index_equal(result, ci, exact=True)
+        assert not result.ordered
+
+        # empty
+        result = CategoricalIndex([], categories=categories)
+        tm.assert_index_equal(result.categories, Index(categories))
+        tm.assert_numpy_array_equal(result.codes, np.array([], dtype="int8"))
+        assert not result.ordered
+
+        # passing categories
+        result = CategoricalIndex(list("aabbca"), categories=categories)
+        tm.assert_index_equal(result.categories, Index(categories))
+        tm.assert_numpy_array_equal(
+            result.codes, np.array([0, 0, 1, 1, 2, 0], dtype="int8")
+        )
+
+        c = Categorical(list("aabbca"))
+        result = CategoricalIndex(c)
+        tm.assert_index_equal(result.categories, Index(list("abc")))
+        tm.assert_numpy_array_equal(
+            result.codes, np.array([0, 0, 1, 1, 2, 0], dtype="int8")
+        )
+        assert not result.ordered
+
+        result = CategoricalIndex(c, categories=categories)
+        tm.assert_index_equal(result.categories, Index(categories))
+        tm.assert_numpy_array_equal(
+            result.codes, np.array([0, 0, 1, 1, 2, 0], dtype="int8")
+        )
+        assert not result.ordered
+
+        ci = CategoricalIndex(c, categories=list("abcd"))
+        result = CategoricalIndex(ci)
+        tm.assert_index_equal(result.categories, Index(categories))
+        tm.assert_numpy_array_equal(
+            result.codes, np.array([0, 0, 1, 1, 2, 0], dtype="int8")
+        )
+        assert not result.ordered
+
+        result = CategoricalIndex(ci, categories=list("ab"))
+        tm.assert_index_equal(result.categories, Index(list("ab")))
+        tm.assert_numpy_array_equal(
+            result.codes, np.array([0, 0, 1, 1, -1, 0], dtype="int8")
+        )
+        assert not result.ordered
+
+        result = CategoricalIndex(ci, categories=list("ab"), ordered=True)
+        tm.assert_index_equal(result.categories, Index(list("ab")))
+        tm.assert_numpy_array_equal(
+            result.codes, np.array([0, 0, 1, 1, -1, 0], dtype="int8")
+        )
+        assert result.ordered
+
+        result = CategoricalIndex(ci, categories=list("ab"), ordered=True)
+        expected = CategoricalIndex(
+            ci, categories=list("ab"), ordered=True, dtype="category"
+        )
+        tm.assert_index_equal(result, expected, exact=True)
+
+        # turn me to an Index
+        result = Index(np.array(ci))
+        assert isinstance(result, Index)
+        assert not isinstance(result, CategoricalIndex)
+
+    def test_construction_with_dtype(self):
+        # specify dtype
+        ci = CategoricalIndex(list("aabbca"), categories=list("abc"), ordered=False)
+
+        result = Index(np.array(ci), dtype="category")
+        tm.assert_index_equal(result, ci, exact=True)
+
+        result = Index(np.array(ci).tolist(), dtype="category")
+        tm.assert_index_equal(result, ci, exact=True)
+
+        # these are generally only equal when the categories are reordered
+        ci = CategoricalIndex(list("aabbca"), categories=list("cab"), ordered=False)
+
+        result = Index(np.array(ci), dtype="category").reorder_categories(ci.categories)
+        tm.assert_index_equal(result, ci, exact=True)
+
+        # make sure indexes are handled
+        idx = Index(range(3))
+        expected = CategoricalIndex([0, 1, 2], categories=idx, ordered=True)
+        result = CategoricalIndex(idx, categories=idx, ordered=True)
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_construction_empty_with_bool_categories(self):
+        # see GH#22702
+        cat = CategoricalIndex([], categories=[True, False])
+        categories = sorted(cat.categories.tolist())
+        assert categories == [False, True]
+
+    def test_construction_with_categorical_dtype(self):
+        # construction with CategoricalDtype
+        # GH#18109
+        data, cats, ordered = "a a b b".split(), "c b a".split(), True
+        dtype = CategoricalDtype(categories=cats, ordered=ordered)
+
+        result = CategoricalIndex(data, dtype=dtype)
+        expected = CategoricalIndex(data, categories=cats, ordered=ordered)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        # GH#19032
+        result = Index(data, dtype=dtype)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        # error when combining categories/ordered and dtype kwargs
+        msg = "Cannot specify `categories` or `ordered` together with `dtype`."
+        with pytest.raises(ValueError, match=msg):
+            CategoricalIndex(data, categories=cats, dtype=dtype)
+
+        with pytest.raises(ValueError, match=msg):
+            CategoricalIndex(data, ordered=ordered, dtype=dtype)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_equals.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_equals.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8353f301a3c39a50b2a0c5541722551ff660e30
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_equals.py
@@ -0,0 +1,96 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Categorical,
+    CategoricalIndex,
+    Index,
+    MultiIndex,
+)
+
+
+class TestEquals:
+    def test_equals_categorical(self):
+        ci1 = CategoricalIndex(["a", "b"], categories=["a", "b"], ordered=True)
+        ci2 = CategoricalIndex(["a", "b"], categories=["a", "b", "c"], ordered=True)
+
+        assert ci1.equals(ci1)
+        assert not ci1.equals(ci2)
+        assert ci1.equals(ci1.astype(object))
+        assert ci1.astype(object).equals(ci1)
+
+        assert (ci1 == ci1).all()
+        assert not (ci1 != ci1).all()
+        assert not (ci1 > ci1).all()
+        assert not (ci1 < ci1).all()
+        assert (ci1 <= ci1).all()
+        assert (ci1 >= ci1).all()
+
+        assert not (ci1 == 1).all()
+        assert (ci1 == Index(["a", "b"])).all()
+        assert (ci1 == ci1.values).all()
+
+        # invalid comparisons
+        with pytest.raises(ValueError, match="Lengths must match"):
+            ci1 == Index(["a", "b", "c"])
+
+        msg = "Categoricals can only be compared if 'categories' are the same"
+        with pytest.raises(TypeError, match=msg):
+            ci1 == ci2
+        with pytest.raises(TypeError, match=msg):
+            ci1 == Categorical(ci1.values, ordered=False)
+        with pytest.raises(TypeError, match=msg):
+            ci1 == Categorical(ci1.values, categories=list("abc"))
+
+        # tests
+        # make sure that we are testing for category inclusion properly
+        ci = CategoricalIndex(list("aabca"), categories=["c", "a", "b"])
+        assert not ci.equals(list("aabca"))
+        # Same categories, but different order
+        # Unordered
+        assert ci.equals(CategoricalIndex(list("aabca")))
+        # Ordered
+        assert not ci.equals(CategoricalIndex(list("aabca"), ordered=True))
+        assert ci.equals(ci.copy())
+
+        ci = CategoricalIndex(list("aabca") + [np.nan], categories=["c", "a", "b"])
+        assert not ci.equals(list("aabca"))
+        assert not ci.equals(CategoricalIndex(list("aabca")))
+        assert ci.equals(ci.copy())
+
+        ci = CategoricalIndex(list("aabca") + [np.nan], categories=["c", "a", "b"])
+        assert not ci.equals(list("aabca") + [np.nan])
+        assert ci.equals(CategoricalIndex(list("aabca") + [np.nan]))
+        assert not ci.equals(CategoricalIndex(list("aabca") + [np.nan], ordered=True))
+        assert ci.equals(ci.copy())
+
+    def test_equals_categorical_unordered(self):
+        # https://github.com/pandas-dev/pandas/issues/16603
+        a = CategoricalIndex(["A"], categories=["A", "B"])
+        b = CategoricalIndex(["A"], categories=["B", "A"])
+        c = CategoricalIndex(["C"], categories=["B", "A"])
+        assert a.equals(b)
+        assert not a.equals(c)
+        assert not b.equals(c)
+
+    def test_equals_non_category(self):
+        # GH#37667 Case where other contains a value not among ci's
+        #  categories ("D") and also contains np.nan
+        ci = CategoricalIndex(["A", "B", np.nan, np.nan])
+        other = Index(["A", "B", "D", np.nan])
+
+        assert not ci.equals(other)
+
+    def test_equals_multiindex(self):
+        # dont raise NotImplementedError when calling is_dtype_compat
+
+        mi = MultiIndex.from_arrays([["A", "B", "C", "D"], range(4)])
+        ci = mi.to_flat_index().astype("category")
+
+        assert not ci.equals(mi)
+
+    def test_equals_string_dtype(self, any_string_dtype):
+        # GH#55364
+        idx = CategoricalIndex(list("abc"), name="B")
+        other = Index(["a", "b", "c"], name="B", dtype=any_string_dtype)
+        assert idx.equals(other)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_fillna.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_fillna.py
new file mode 100644
index 0000000000000000000000000000000000000000..09de578f3c649e5a90278f11b1e3cd5b1d0646d5
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_fillna.py
@@ -0,0 +1,54 @@
+import numpy as np
+import pytest
+
+from pandas import CategoricalIndex
+import pandas._testing as tm
+
+
+class TestFillNA:
+    def test_fillna_categorical(self):
+        # GH#11343
+        idx = CategoricalIndex([1.0, np.nan, 3.0, 1.0], name="x")
+        # fill by value in categories
+        exp = CategoricalIndex([1.0, 1.0, 3.0, 1.0], name="x")
+        tm.assert_index_equal(idx.fillna(1.0), exp)
+
+        cat = idx._data
+
+        # fill by value not in categories raises TypeError on EA, casts on CI
+        msg = "Cannot setitem on a Categorical with a new category"
+        with pytest.raises(TypeError, match=msg):
+            cat.fillna(2.0)
+
+        result = idx.fillna(2.0)
+        expected = idx.astype(object).fillna(2.0)
+        tm.assert_index_equal(result, expected)
+
+    def test_fillna_copies_with_no_nas(self):
+        # Nothing to fill, should still get a copy for the Categorical method,
+        #  but OK to get a view on CategoricalIndex method
+        ci = CategoricalIndex([0, 1, 1])
+        result = ci.fillna(0)
+        assert result is not ci
+        assert tm.shares_memory(result, ci)
+
+        # But at the EA level we always get a copy.
+        cat = ci._data
+        result = cat.fillna(0)
+        assert result._ndarray is not cat._ndarray
+        assert result._ndarray.base is None
+        assert not tm.shares_memory(result, cat)
+
+    def test_fillna_validates_with_no_nas(self):
+        # We validate the fill value even if fillna is a no-op
+        ci = CategoricalIndex([2, 3, 3])
+        cat = ci._data
+
+        msg = "Cannot setitem on a Categorical with a new category"
+        res = ci.fillna(False)
+        # nothing to fill, so we dont cast
+        tm.assert_index_equal(res, ci)
+
+        # Same check directly on the Categorical
+        with pytest.raises(TypeError, match=msg):
+            cat.fillna(False)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_formats.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_formats.py
new file mode 100644
index 0000000000000000000000000000000000000000..522ca1bc2afde451e2d6feff780a973aceb4c39f
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_formats.py
@@ -0,0 +1,120 @@
+"""
+Tests for CategoricalIndex.__repr__ and related methods.
+"""
+import pytest
+
+from pandas._config import using_pyarrow_string_dtype
+import pandas._config.config as cf
+
+from pandas import CategoricalIndex
+import pandas._testing as tm
+
+
+class TestCategoricalIndexRepr:
+    def test_format_different_scalar_lengths(self):
+        # GH#35439
+        idx = CategoricalIndex(["aaaaaaaaa", "b"])
+        expected = ["aaaaaaaaa", "b"]
+        msg = r"CategoricalIndex\.format is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert idx.format() == expected
+
+    @pytest.mark.xfail(using_pyarrow_string_dtype(), reason="repr different")
+    def test_string_categorical_index_repr(self):
+        # short
+        idx = CategoricalIndex(["a", "bb", "ccc"])
+        expected = """CategoricalIndex(['a', 'bb', 'ccc'], categories=['a', 'bb', 'ccc'], ordered=False, dtype='category')"""  # noqa: E501
+        assert repr(idx) == expected
+
+        # multiple lines
+        idx = CategoricalIndex(["a", "bb", "ccc"] * 10)
+        expected = """CategoricalIndex(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a',
+                  'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb',
+                  'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'],
+                 categories=['a', 'bb', 'ccc'], ordered=False, dtype='category')"""  # noqa: E501
+
+        assert repr(idx) == expected
+
+        # truncated
+        idx = CategoricalIndex(["a", "bb", "ccc"] * 100)
+        expected = """CategoricalIndex(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a',
+                  ...
+                  'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'],
+                 categories=['a', 'bb', 'ccc'], ordered=False, dtype='category', length=300)"""  # noqa: E501
+
+        assert repr(idx) == expected
+
+        # larger categories
+        idx = CategoricalIndex(list("abcdefghijklmmo"))
+        expected = """CategoricalIndex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l',
+                  'm', 'm', 'o'],
+                 categories=['a', 'b', 'c', 'd', ..., 'k', 'l', 'm', 'o'], ordered=False, dtype='category')"""  # noqa: E501
+
+        assert repr(idx) == expected
+
+        # short
+        idx = CategoricalIndex(["あ", "いい", "ううう"])
+        expected = """CategoricalIndex(['あ', 'いい', 'ううう'], categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category')"""  # noqa: E501
+        assert repr(idx) == expected
+
+        # multiple lines
+        idx = CategoricalIndex(["あ", "いい", "ううう"] * 10)
+        expected = """CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ',
+                  'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい',
+                  'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'],
+                 categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category')"""  # noqa: E501
+
+        assert repr(idx) == expected
+
+        # truncated
+        idx = CategoricalIndex(["あ", "いい", "ううう"] * 100)
+        expected = """CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ',
+                  ...
+                  'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'],
+                 categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category', length=300)"""  # noqa: E501
+
+        assert repr(idx) == expected
+
+        # larger categories
+        idx = CategoricalIndex(list("あいうえおかきくけこさしすせそ"))
+        expected = """CategoricalIndex(['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', 'け', 'こ', 'さ', 'し',
+                  'す', 'せ', 'そ'],
+                 categories=['あ', 'い', 'う', 'え', ..., 'し', 'す', 'せ', 'そ'], ordered=False, dtype='category')"""  # noqa: E501
+
+        assert repr(idx) == expected
+
+        # Enable Unicode option -----------------------------------------
+        with cf.option_context("display.unicode.east_asian_width", True):
+            # short
+            idx = CategoricalIndex(["あ", "いい", "ううう"])
+            expected = """CategoricalIndex(['あ', 'いい', 'ううう'], categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category')"""  # noqa: E501
+            assert repr(idx) == expected
+
+            # multiple lines
+            idx = CategoricalIndex(["あ", "いい", "ううう"] * 10)
+            expected = """CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい',
+                  'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう',
+                  'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい',
+                  'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'],
+                 categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category')"""  # noqa: E501
+
+            assert repr(idx) == expected
+
+            # truncated
+            idx = CategoricalIndex(["あ", "いい", "ううう"] * 100)
+            expected = """CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい',
+                  'ううう', 'あ',
+                  ...
+                  'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう',
+                  'あ', 'いい', 'ううう'],
+                 categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category', length=300)"""  # noqa: E501
+
+            assert repr(idx) == expected
+
+            # larger categories
+            idx = CategoricalIndex(list("あいうえおかきくけこさしすせそ"))
+            expected = """CategoricalIndex(['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', 'け', 'こ',
+                  'さ', 'し', 'す', 'せ', 'そ'],
+                 categories=['あ', 'い', 'う', 'え', ..., 'し', 'す', 'せ', 'そ'], ordered=False, dtype='category')"""  # noqa: E501
+
+            assert repr(idx) == expected
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_indexing.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_indexing.py
new file mode 100644
index 0000000000000000000000000000000000000000..49eb79da616e7603b70ee3189e9004dd51fb33e7
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_indexing.py
@@ -0,0 +1,420 @@
+import numpy as np
+import pytest
+
+from pandas.errors import InvalidIndexError
+
+import pandas as pd
+from pandas import (
+    CategoricalIndex,
+    Index,
+    IntervalIndex,
+    Timestamp,
+)
+import pandas._testing as tm
+
+
+class TestTake:
+    def test_take_fill_value(self):
+        # GH 12631
+
+        # numeric category
+        idx = CategoricalIndex([1, 2, 3], name="xxx")
+        result = idx.take(np.array([1, 0, -1]))
+        expected = CategoricalIndex([2, 1, 3], name="xxx")
+        tm.assert_index_equal(result, expected)
+        tm.assert_categorical_equal(result.values, expected.values)
+
+        # fill_value
+        result = idx.take(np.array([1, 0, -1]), fill_value=True)
+        expected = CategoricalIndex([2, 1, np.nan], categories=[1, 2, 3], name="xxx")
+        tm.assert_index_equal(result, expected)
+        tm.assert_categorical_equal(result.values, expected.values)
+
+        # allow_fill=False
+        result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True)
+        expected = CategoricalIndex([2, 1, 3], name="xxx")
+        tm.assert_index_equal(result, expected)
+        tm.assert_categorical_equal(result.values, expected.values)
+
+        # object category
+        idx = CategoricalIndex(
+            list("CBA"), categories=list("ABC"), ordered=True, name="xxx"
+        )
+        result = idx.take(np.array([1, 0, -1]))
+        expected = CategoricalIndex(
+            list("BCA"), categories=list("ABC"), ordered=True, name="xxx"
+        )
+        tm.assert_index_equal(result, expected)
+        tm.assert_categorical_equal(result.values, expected.values)
+
+        # fill_value
+        result = idx.take(np.array([1, 0, -1]), fill_value=True)
+        expected = CategoricalIndex(
+            ["B", "C", np.nan], categories=list("ABC"), ordered=True, name="xxx"
+        )
+        tm.assert_index_equal(result, expected)
+        tm.assert_categorical_equal(result.values, expected.values)
+
+        # allow_fill=False
+        result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True)
+        expected = CategoricalIndex(
+            list("BCA"), categories=list("ABC"), ordered=True, name="xxx"
+        )
+        tm.assert_index_equal(result, expected)
+        tm.assert_categorical_equal(result.values, expected.values)
+
+        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]))
+
+    def test_take_fill_value_datetime(self):
+        # datetime category
+        idx = pd.DatetimeIndex(["2011-01-01", "2011-02-01", "2011-03-01"], name="xxx")
+        idx = CategoricalIndex(idx)
+        result = idx.take(np.array([1, 0, -1]))
+        expected = pd.DatetimeIndex(
+            ["2011-02-01", "2011-01-01", "2011-03-01"], name="xxx"
+        )
+        expected = CategoricalIndex(expected)
+        tm.assert_index_equal(result, expected)
+
+        # fill_value
+        result = idx.take(np.array([1, 0, -1]), fill_value=True)
+        expected = pd.DatetimeIndex(["2011-02-01", "2011-01-01", "NaT"], name="xxx")
+        exp_cats = pd.DatetimeIndex(["2011-01-01", "2011-02-01", "2011-03-01"])
+        expected = CategoricalIndex(expected, categories=exp_cats)
+        tm.assert_index_equal(result, expected)
+
+        # allow_fill=False
+        result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True)
+        expected = pd.DatetimeIndex(
+            ["2011-02-01", "2011-01-01", "2011-03-01"], name="xxx"
+        )
+        expected = CategoricalIndex(expected)
+        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]))
+
+    def test_take_invalid_kwargs(self):
+        idx = CategoricalIndex([1, 2, 3], name="foo")
+        indices = [1, 0, -1]
+
+        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")
+
+
+class TestGetLoc:
+    def test_get_loc(self):
+        # GH 12531
+        cidx1 = CategoricalIndex(list("abcde"), categories=list("edabc"))
+        idx1 = Index(list("abcde"))
+        assert cidx1.get_loc("a") == idx1.get_loc("a")
+        assert cidx1.get_loc("e") == idx1.get_loc("e")
+
+        for i in [cidx1, idx1]:
+            with pytest.raises(KeyError, match="'NOT-EXIST'"):
+                i.get_loc("NOT-EXIST")
+
+        # non-unique
+        cidx2 = CategoricalIndex(list("aacded"), categories=list("edabc"))
+        idx2 = Index(list("aacded"))
+
+        # results in bool array
+        res = cidx2.get_loc("d")
+        tm.assert_numpy_array_equal(res, idx2.get_loc("d"))
+        tm.assert_numpy_array_equal(
+            res, np.array([False, False, False, True, False, True])
+        )
+        # unique element results in scalar
+        res = cidx2.get_loc("e")
+        assert res == idx2.get_loc("e")
+        assert res == 4
+
+        for i in [cidx2, idx2]:
+            with pytest.raises(KeyError, match="'NOT-EXIST'"):
+                i.get_loc("NOT-EXIST")
+
+        # non-unique, sliceable
+        cidx3 = CategoricalIndex(list("aabbb"), categories=list("abc"))
+        idx3 = Index(list("aabbb"))
+
+        # results in slice
+        res = cidx3.get_loc("a")
+        assert res == idx3.get_loc("a")
+        assert res == slice(0, 2, None)
+
+        res = cidx3.get_loc("b")
+        assert res == idx3.get_loc("b")
+        assert res == slice(2, 5, None)
+
+        for i in [cidx3, idx3]:
+            with pytest.raises(KeyError, match="'c'"):
+                i.get_loc("c")
+
+    def test_get_loc_unique(self):
+        cidx = CategoricalIndex(list("abc"))
+        result = cidx.get_loc("b")
+        assert result == 1
+
+    def test_get_loc_monotonic_nonunique(self):
+        cidx = CategoricalIndex(list("abbc"))
+        result = cidx.get_loc("b")
+        expected = slice(1, 3, None)
+        assert result == expected
+
+    def test_get_loc_nonmonotonic_nonunique(self):
+        cidx = CategoricalIndex(list("abcb"))
+        result = cidx.get_loc("b")
+        expected = np.array([False, True, False, True], dtype=bool)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_get_loc_nan(self):
+        # GH#41933
+        ci = CategoricalIndex(["A", "B", np.nan])
+        res = ci.get_loc(np.nan)
+
+        assert res == 2
+
+
+class TestGetIndexer:
+    def test_get_indexer_base(self):
+        # Determined by cat ordering.
+        idx = CategoricalIndex(list("cab"), categories=list("cab"))
+        expected = np.arange(len(idx), dtype=np.intp)
+
+        actual = idx.get_indexer(idx)
+        tm.assert_numpy_array_equal(expected, actual)
+
+        with pytest.raises(ValueError, match="Invalid fill method"):
+            idx.get_indexer(idx, method="invalid")
+
+    def test_get_indexer_requires_unique(self):
+        ci = CategoricalIndex(list("aabbca"), categories=list("cab"), ordered=False)
+        oidx = Index(np.array(ci))
+
+        msg = "Reindexing only valid with uniquely valued Index objects"
+
+        for n in [1, 2, 5, len(ci)]:
+            finder = oidx[np.random.default_rng(2).integers(0, len(ci), size=n)]
+
+            with pytest.raises(InvalidIndexError, match=msg):
+                ci.get_indexer(finder)
+
+        # see gh-17323
+        #
+        # Even when indexer is equal to the
+        # members in the index, we should
+        # respect duplicates instead of taking
+        # the fast-track path.
+        for finder in [list("aabbca"), list("aababca")]:
+            with pytest.raises(InvalidIndexError, match=msg):
+                ci.get_indexer(finder)
+
+    def test_get_indexer_non_unique(self):
+        idx1 = CategoricalIndex(list("aabcde"), categories=list("edabc"))
+        idx2 = CategoricalIndex(list("abf"))
+
+        for indexer in [idx2, list("abf"), Index(list("abf"))]:
+            msg = "Reindexing only valid with uniquely valued Index objects"
+            with pytest.raises(InvalidIndexError, match=msg):
+                idx1.get_indexer(indexer)
+
+            r1, _ = idx1.get_indexer_non_unique(indexer)
+            expected = np.array([0, 1, 2, -1], dtype=np.intp)
+            tm.assert_almost_equal(r1, expected)
+
+    def test_get_indexer_method(self):
+        idx1 = CategoricalIndex(list("aabcde"), categories=list("edabc"))
+        idx2 = CategoricalIndex(list("abf"))
+
+        msg = "method pad not yet implemented for CategoricalIndex"
+        with pytest.raises(NotImplementedError, match=msg):
+            idx2.get_indexer(idx1, method="pad")
+        msg = "method backfill not yet implemented for CategoricalIndex"
+        with pytest.raises(NotImplementedError, match=msg):
+            idx2.get_indexer(idx1, method="backfill")
+
+        msg = "method nearest not yet implemented for CategoricalIndex"
+        with pytest.raises(NotImplementedError, match=msg):
+            idx2.get_indexer(idx1, method="nearest")
+
+    def test_get_indexer_array(self):
+        arr = np.array(
+            [Timestamp("1999-12-31 00:00:00"), Timestamp("2000-12-31 00:00:00")],
+            dtype=object,
+        )
+        cats = [Timestamp("1999-12-31 00:00:00"), Timestamp("2000-12-31 00:00:00")]
+        ci = CategoricalIndex(cats, categories=cats, ordered=False, dtype="category")
+        result = ci.get_indexer(arr)
+        expected = np.array([0, 1], dtype="intp")
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_get_indexer_same_categories_same_order(self):
+        ci = CategoricalIndex(["a", "b"], categories=["a", "b"])
+
+        result = ci.get_indexer(CategoricalIndex(["b", "b"], categories=["a", "b"]))
+        expected = np.array([1, 1], dtype="intp")
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_get_indexer_same_categories_different_order(self):
+        # https://github.com/pandas-dev/pandas/issues/19551
+        ci = CategoricalIndex(["a", "b"], categories=["a", "b"])
+
+        result = ci.get_indexer(CategoricalIndex(["b", "b"], categories=["b", "a"]))
+        expected = np.array([1, 1], dtype="intp")
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_get_indexer_nans_in_index_and_target(self):
+        # GH 45361
+        ci = CategoricalIndex([1, 2, np.nan, 3])
+        other1 = [2, 3, 4, np.nan]
+        res1 = ci.get_indexer(other1)
+        expected1 = np.array([1, 3, -1, 2], dtype=np.intp)
+        tm.assert_numpy_array_equal(res1, expected1)
+        other2 = [1, 4, 2, 3]
+        res2 = ci.get_indexer(other2)
+        expected2 = np.array([0, -1, 1, 3], dtype=np.intp)
+        tm.assert_numpy_array_equal(res2, expected2)
+
+
+class TestWhere:
+    def test_where(self, listlike_box):
+        klass = listlike_box
+
+        i = CategoricalIndex(list("aabbca"), categories=list("cab"), ordered=False)
+        cond = [True] * len(i)
+        expected = i
+        result = i.where(klass(cond))
+        tm.assert_index_equal(result, expected)
+
+        cond = [False] + [True] * (len(i) - 1)
+        expected = CategoricalIndex([np.nan] + i[1:].tolist(), categories=i.categories)
+        result = i.where(klass(cond))
+        tm.assert_index_equal(result, expected)
+
+    def test_where_non_categories(self):
+        ci = CategoricalIndex(["a", "b", "c", "d"])
+        mask = np.array([True, False, True, False])
+
+        result = ci.where(mask, 2)
+        expected = Index(["a", 2, "c", 2], dtype=object)
+        tm.assert_index_equal(result, expected)
+
+        msg = "Cannot setitem on a Categorical with a new category"
+        with pytest.raises(TypeError, match=msg):
+            # Test the Categorical method directly
+            ci._data._where(mask, 2)
+
+
+class TestContains:
+    def test_contains(self):
+        ci = CategoricalIndex(list("aabbca"), categories=list("cabdef"), ordered=False)
+
+        assert "a" in ci
+        assert "z" not in ci
+        assert "e" not in ci
+        assert np.nan not in ci
+
+        # assert codes NOT in index
+        assert 0 not in ci
+        assert 1 not in ci
+
+    def test_contains_nan(self):
+        ci = CategoricalIndex(list("aabbca") + [np.nan], categories=list("cabdef"))
+        assert np.nan in ci
+
+    @pytest.mark.parametrize("unwrap", [True, False])
+    def test_contains_na_dtype(self, unwrap):
+        dti = pd.date_range("2016-01-01", periods=100).insert(0, pd.NaT)
+        pi = dti.to_period("D")
+        tdi = dti - dti[-1]
+        ci = CategoricalIndex(dti)
+
+        obj = ci
+        if unwrap:
+            obj = ci._data
+
+        assert np.nan in obj
+        assert None in obj
+        assert pd.NaT in obj
+        assert np.datetime64("NaT") in obj
+        assert np.timedelta64("NaT") not in obj
+
+        obj2 = CategoricalIndex(tdi)
+        if unwrap:
+            obj2 = obj2._data
+
+        assert np.nan in obj2
+        assert None in obj2
+        assert pd.NaT in obj2
+        assert np.datetime64("NaT") not in obj2
+        assert np.timedelta64("NaT") in obj2
+
+        obj3 = CategoricalIndex(pi)
+        if unwrap:
+            obj3 = obj3._data
+
+        assert np.nan in obj3
+        assert None in obj3
+        assert pd.NaT in obj3
+        assert np.datetime64("NaT") not in obj3
+        assert np.timedelta64("NaT") not in obj3
+
+    @pytest.mark.parametrize(
+        "item, expected",
+        [
+            (pd.Interval(0, 1), True),
+            (1.5, True),
+            (pd.Interval(0.5, 1.5), False),
+            ("a", False),
+            (Timestamp(1), False),
+            (pd.Timedelta(1), False),
+        ],
+        ids=str,
+    )
+    def test_contains_interval(self, item, expected):
+        # GH 23705
+        ci = CategoricalIndex(IntervalIndex.from_breaks(range(3)))
+        result = item in ci
+        assert result is expected
+
+    def test_contains_list(self):
+        # GH#21729
+        idx = CategoricalIndex([1, 2, 3])
+
+        assert "a" not in idx
+
+        with pytest.raises(TypeError, match="unhashable type"):
+            ["a"] in idx
+
+        with pytest.raises(TypeError, match="unhashable type"):
+            ["a", "b"] in idx
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_map.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_map.py
new file mode 100644
index 0000000000000000000000000000000000000000..baf836594dfb5e03332b57522f39a679ee5b1e40
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_map.py
@@ -0,0 +1,144 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    CategoricalIndex,
+    Index,
+    Series,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize(
+    "data, categories",
+    [
+        (list("abcbca"), list("cab")),
+        (pd.interval_range(0, 3).repeat(3), pd.interval_range(0, 3)),
+    ],
+    ids=["string", "interval"],
+)
+def test_map_str(data, categories, ordered):
+    # GH 31202 - override base class since we want to maintain categorical/ordered
+    index = CategoricalIndex(data, categories=categories, ordered=ordered)
+    result = index.map(str)
+    expected = CategoricalIndex(
+        map(str, data), categories=map(str, categories), ordered=ordered
+    )
+    tm.assert_index_equal(result, expected)
+
+
+def test_map():
+    ci = CategoricalIndex(list("ABABC"), categories=list("CBA"), ordered=True)
+    result = ci.map(lambda x: x.lower())
+    exp = CategoricalIndex(list("ababc"), categories=list("cba"), ordered=True)
+    tm.assert_index_equal(result, exp)
+
+    ci = CategoricalIndex(
+        list("ABABC"), categories=list("BAC"), ordered=False, name="XXX"
+    )
+    result = ci.map(lambda x: x.lower())
+    exp = CategoricalIndex(
+        list("ababc"), categories=list("bac"), ordered=False, name="XXX"
+    )
+    tm.assert_index_equal(result, exp)
+
+    # GH 12766: Return an index not an array
+    tm.assert_index_equal(
+        ci.map(lambda x: 1), Index(np.array([1] * 5, dtype=np.int64), name="XXX")
+    )
+
+    # change categories dtype
+    ci = CategoricalIndex(list("ABABC"), categories=list("BAC"), ordered=False)
+
+    def f(x):
+        return {"A": 10, "B": 20, "C": 30}.get(x)
+
+    result = ci.map(f)
+    exp = CategoricalIndex([10, 20, 10, 20, 30], categories=[20, 10, 30], ordered=False)
+    tm.assert_index_equal(result, exp)
+
+    result = ci.map(Series([10, 20, 30], index=["A", "B", "C"]))
+    tm.assert_index_equal(result, exp)
+
+    result = ci.map({"A": 10, "B": 20, "C": 30})
+    tm.assert_index_equal(result, exp)
+
+
+def test_map_with_categorical_series():
+    # GH 12756
+    a = Index([1, 2, 3, 4])
+    b = Series(["even", "odd", "even", "odd"], dtype="category")
+    c = Series(["even", "odd", "even", "odd"])
+
+    exp = CategoricalIndex(["odd", "even", "odd", np.nan])
+    tm.assert_index_equal(a.map(b), exp)
+    exp = Index(["odd", "even", "odd", np.nan])
+    tm.assert_index_equal(a.map(c), exp)
+
+
+@pytest.mark.parametrize(
+    ("data", "f", "expected"),
+    (
+        ([1, 1, np.nan], pd.isna, CategoricalIndex([False, False, np.nan])),
+        ([1, 2, np.nan], pd.isna, Index([False, False, np.nan])),
+        ([1, 1, np.nan], {1: False}, CategoricalIndex([False, False, np.nan])),
+        ([1, 2, np.nan], {1: False, 2: False}, Index([False, False, np.nan])),
+        (
+            [1, 1, np.nan],
+            Series([False, False]),
+            CategoricalIndex([False, False, np.nan]),
+        ),
+        (
+            [1, 2, np.nan],
+            Series([False, False, False]),
+            Index([False, False, np.nan]),
+        ),
+    ),
+)
+def test_map_with_nan_ignore(data, f, expected):  # GH 24241
+    values = CategoricalIndex(data)
+    result = values.map(f, na_action="ignore")
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    ("data", "f", "expected"),
+    (
+        ([1, 1, np.nan], pd.isna, Index([False, False, True])),
+        ([1, 2, np.nan], pd.isna, Index([False, False, True])),
+        ([1, 1, np.nan], {1: False}, CategoricalIndex([False, False, np.nan])),
+        ([1, 2, np.nan], {1: False, 2: False}, Index([False, False, np.nan])),
+        (
+            [1, 1, np.nan],
+            Series([False, False]),
+            CategoricalIndex([False, False, np.nan]),
+        ),
+        (
+            [1, 2, np.nan],
+            Series([False, False, False]),
+            Index([False, False, np.nan]),
+        ),
+    ),
+)
+def test_map_with_nan_none(data, f, expected):  # GH 24241
+    values = CategoricalIndex(data)
+    result = values.map(f, na_action=None)
+    tm.assert_index_equal(result, expected)
+
+
+def test_map_with_dict_or_series():
+    orig_values = ["a", "B", 1, "a"]
+    new_values = ["one", 2, 3.0, "one"]
+    cur_index = CategoricalIndex(orig_values, name="XXX")
+    expected = CategoricalIndex(new_values, name="XXX", categories=[3.0, 2, "one"])
+
+    mapper = Series(new_values[:-1], index=orig_values[:-1])
+    result = cur_index.map(mapper)
+    # Order of categories in result can be different
+    tm.assert_index_equal(result, expected)
+
+    mapper = dict(zip(orig_values[:-1], new_values[:-1]))
+    result = cur_index.map(mapper)
+    # Order of categories in result can be different
+    tm.assert_index_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_reindex.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_reindex.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b1f2b9fb159a6873c83e0a0a4e777913bb99fee
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_reindex.py
@@ -0,0 +1,78 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Categorical,
+    CategoricalIndex,
+    Index,
+    Interval,
+)
+import pandas._testing as tm
+
+
+class TestReindex:
+    def test_reindex_list_non_unique(self):
+        # GH#11586
+        msg = "cannot reindex on an axis with duplicate labels"
+        ci = CategoricalIndex(["a", "b", "c", "a"])
+        with pytest.raises(ValueError, match=msg):
+            ci.reindex(["a", "c"])
+
+    def test_reindex_categorical_non_unique(self):
+        msg = "cannot reindex on an axis with duplicate labels"
+        ci = CategoricalIndex(["a", "b", "c", "a"])
+        with pytest.raises(ValueError, match=msg):
+            ci.reindex(Categorical(["a", "c"]))
+
+    def test_reindex_list_non_unique_unused_category(self):
+        msg = "cannot reindex on an axis with duplicate labels"
+        ci = CategoricalIndex(["a", "b", "c", "a"], categories=["a", "b", "c", "d"])
+        with pytest.raises(ValueError, match=msg):
+            ci.reindex(["a", "c"])
+
+    def test_reindex_categorical_non_unique_unused_category(self):
+        msg = "cannot reindex on an axis with duplicate labels"
+        ci = CategoricalIndex(["a", "b", "c", "a"], categories=["a", "b", "c", "d"])
+        with pytest.raises(ValueError, match=msg):
+            ci.reindex(Categorical(["a", "c"]))
+
+    def test_reindex_duplicate_target(self):
+        # See GH25459
+        cat = CategoricalIndex(["a", "b", "c"], categories=["a", "b", "c", "d"])
+        res, indexer = cat.reindex(["a", "c", "c"])
+        exp = Index(["a", "c", "c"])
+        tm.assert_index_equal(res, exp, exact=True)
+        tm.assert_numpy_array_equal(indexer, np.array([0, 2, 2], dtype=np.intp))
+
+        res, indexer = cat.reindex(
+            CategoricalIndex(["a", "c", "c"], categories=["a", "b", "c", "d"])
+        )
+        exp = CategoricalIndex(["a", "c", "c"], categories=["a", "b", "c", "d"])
+        tm.assert_index_equal(res, exp, exact=True)
+        tm.assert_numpy_array_equal(indexer, np.array([0, 2, 2], dtype=np.intp))
+
+    def test_reindex_empty_index(self):
+        # See GH16770
+        c = CategoricalIndex([])
+        res, indexer = c.reindex(["a", "b"])
+        tm.assert_index_equal(res, Index(["a", "b"]), exact=True)
+        tm.assert_numpy_array_equal(indexer, np.array([-1, -1], dtype=np.intp))
+
+    def test_reindex_categorical_added_category(self):
+        # GH 42424
+        ci = CategoricalIndex(
+            [Interval(0, 1, closed="right"), Interval(1, 2, closed="right")],
+            ordered=True,
+        )
+        ci_add = CategoricalIndex(
+            [
+                Interval(0, 1, closed="right"),
+                Interval(1, 2, closed="right"),
+                Interval(2, 3, closed="right"),
+                Interval(3, 4, closed="right"),
+            ],
+            ordered=True,
+        )
+        result, _ = ci.reindex(ci_add)
+        expected = ci_add
+        tm.assert_index_equal(expected, result)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_setops.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_setops.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e87b90efd54c8fcc4dcab7ec538d461add370de
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_setops.py
@@ -0,0 +1,18 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    CategoricalIndex,
+    Index,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize("na_value", [None, np.nan])
+def test_difference_with_na(na_value):
+    # GH 57318
+    ci = CategoricalIndex(["a", "b", "c", None])
+    other = Index(["c", na_value])
+    result = ci.difference(other)
+    expected = CategoricalIndex(["a", "b"], categories=["a", "b", "c"])
+    tm.assert_index_equal(result, expected)
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diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/methods/test_asof.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/methods/test_asof.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc92f533087bc3226727fac1810269520e1c4d1f
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/methods/test_asof.py
@@ -0,0 +1,30 @@
+from datetime import timedelta
+
+from pandas import (
+    Index,
+    Timestamp,
+    date_range,
+    isna,
+)
+
+
+class TestAsOf:
+    def test_asof_partial(self):
+        index = date_range("2010-01-01", periods=2, freq="ME")
+        expected = Timestamp("2010-02-28")
+        result = index.asof("2010-02")
+        assert result == expected
+        assert not isinstance(result, Index)
+
+    def test_asof(self):
+        index = date_range("2020-01-01", periods=10)
+
+        dt = index[0]
+        assert index.asof(dt) == dt
+        assert isna(index.asof(dt - timedelta(1)))
+
+        dt = index[-1]
+        assert index.asof(dt + timedelta(1)) == dt
+
+        dt = index[0].to_pydatetime()
+        assert isinstance(index.asof(dt), Timestamp)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/methods/test_astype.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/methods/test_astype.py
new file mode 100644
index 0000000000000000000000000000000000000000..c0bc6601769b1dce195f0af7303e5015bcddf589
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/methods/test_astype.py
@@ -0,0 +1,335 @@
+from datetime import datetime
+
+import dateutil
+import numpy as np
+import pytest
+import pytz
+
+import pandas as pd
+from pandas import (
+    DatetimeIndex,
+    Index,
+    NaT,
+    PeriodIndex,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestDatetimeIndex:
+    @pytest.mark.parametrize("tzstr", ["US/Eastern", "dateutil/US/Eastern"])
+    def test_dti_astype_asobject_around_dst_transition(self, tzstr):
+        # GH#1345
+
+        # dates around a dst transition
+        rng = date_range("2/13/2010", "5/6/2010", tz=tzstr)
+
+        objs = rng.astype(object)
+        for i, x in enumerate(objs):
+            exval = rng[i]
+            assert x == exval
+            assert x.tzinfo == exval.tzinfo
+
+        objs = rng.astype(object)
+        for i, x in enumerate(objs):
+            exval = rng[i]
+            assert x == exval
+            assert x.tzinfo == exval.tzinfo
+
+    def test_astype(self):
+        # GH 13149, GH 13209
+        idx = DatetimeIndex(
+            ["2016-05-16", "NaT", NaT, np.nan], dtype="M8[ns]", name="idx"
+        )
+
+        result = idx.astype(object)
+        expected = Index(
+            [Timestamp("2016-05-16")] + [NaT] * 3, dtype=object, name="idx"
+        )
+        tm.assert_index_equal(result, expected)
+
+        result = idx.astype(np.int64)
+        expected = Index(
+            [1463356800000000000] + [-9223372036854775808] * 3,
+            dtype=np.int64,
+            name="idx",
+        )
+        tm.assert_index_equal(result, expected)
+
+    def test_astype2(self):
+        rng = date_range("1/1/2000", periods=10, name="idx")
+        result = rng.astype("i8")
+        tm.assert_index_equal(result, Index(rng.asi8, name="idx"))
+        tm.assert_numpy_array_equal(result.values, rng.asi8)
+
+    def test_astype_uint(self):
+        arr = date_range("2000", periods=2, name="idx")
+
+        with pytest.raises(TypeError, match=r"Do obj.astype\('int64'\)"):
+            arr.astype("uint64")
+        with pytest.raises(TypeError, match=r"Do obj.astype\('int64'\)"):
+            arr.astype("uint32")
+
+    def test_astype_with_tz(self):
+        # with tz
+        rng = date_range("1/1/2000", periods=10, tz="US/Eastern")
+        msg = "Cannot use .astype to convert from timezone-aware"
+        with pytest.raises(TypeError, match=msg):
+            # deprecated
+            rng.astype("datetime64[ns]")
+        with pytest.raises(TypeError, match=msg):
+            # check DatetimeArray while we're here deprecated
+            rng._data.astype("datetime64[ns]")
+
+    def test_astype_tzaware_to_tzaware(self):
+        # GH 18951: tz-aware to tz-aware
+        idx = date_range("20170101", periods=4, tz="US/Pacific")
+        result = idx.astype("datetime64[ns, US/Eastern]")
+        expected = date_range("20170101 03:00:00", periods=4, tz="US/Eastern")
+        tm.assert_index_equal(result, expected)
+        assert result.freq == expected.freq
+
+    def test_astype_tznaive_to_tzaware(self):
+        # GH 18951: tz-naive to tz-aware
+        idx = date_range("20170101", periods=4)
+        idx = idx._with_freq(None)  # tz_localize does not preserve freq
+        msg = "Cannot use .astype to convert from timezone-naive"
+        with pytest.raises(TypeError, match=msg):
+            # dt64->dt64tz deprecated
+            idx.astype("datetime64[ns, US/Eastern]")
+        with pytest.raises(TypeError, match=msg):
+            # dt64->dt64tz deprecated
+            idx._data.astype("datetime64[ns, US/Eastern]")
+
+    def test_astype_str_nat(self):
+        # GH 13149, GH 13209
+        # verify that we are returning NaT as a string (and not unicode)
+
+        idx = DatetimeIndex(["2016-05-16", "NaT", NaT, np.nan])
+        result = idx.astype(str)
+        expected = Index(["2016-05-16", "NaT", "NaT", "NaT"], dtype=object)
+        tm.assert_index_equal(result, expected)
+
+    def test_astype_str(self):
+        # test astype string - #10442
+        dti = date_range("2012-01-01", periods=4, name="test_name")
+        result = dti.astype(str)
+        expected = Index(
+            ["2012-01-01", "2012-01-02", "2012-01-03", "2012-01-04"],
+            name="test_name",
+            dtype=object,
+        )
+        tm.assert_index_equal(result, expected)
+
+    def test_astype_str_tz_and_name(self):
+        # test astype string with tz and name
+        dti = date_range("2012-01-01", periods=3, name="test_name", tz="US/Eastern")
+        result = dti.astype(str)
+        expected = Index(
+            [
+                "2012-01-01 00:00:00-05:00",
+                "2012-01-02 00:00:00-05:00",
+                "2012-01-03 00:00:00-05:00",
+            ],
+            name="test_name",
+            dtype=object,
+        )
+        tm.assert_index_equal(result, expected)
+
+    def test_astype_str_freq_and_name(self):
+        # test astype string with freqH and name
+        dti = date_range("1/1/2011", periods=3, freq="h", name="test_name")
+        result = dti.astype(str)
+        expected = Index(
+            ["2011-01-01 00:00:00", "2011-01-01 01:00:00", "2011-01-01 02:00:00"],
+            name="test_name",
+            dtype=object,
+        )
+        tm.assert_index_equal(result, expected)
+
+    def test_astype_str_freq_and_tz(self):
+        # test astype string with freqH and timezone
+        dti = date_range(
+            "3/6/2012 00:00", periods=2, freq="h", tz="Europe/London", name="test_name"
+        )
+        result = dti.astype(str)
+        expected = Index(
+            ["2012-03-06 00:00:00+00:00", "2012-03-06 01:00:00+00:00"],
+            dtype=object,
+            name="test_name",
+        )
+        tm.assert_index_equal(result, expected)
+
+    def test_astype_datetime64(self):
+        # GH 13149, GH 13209
+        idx = DatetimeIndex(
+            ["2016-05-16", "NaT", NaT, np.nan], dtype="M8[ns]", name="idx"
+        )
+
+        result = idx.astype("datetime64[ns]")
+        tm.assert_index_equal(result, idx)
+        assert result is not idx
+
+        result = idx.astype("datetime64[ns]", copy=False)
+        tm.assert_index_equal(result, idx)
+        assert result is idx
+
+        idx_tz = DatetimeIndex(["2016-05-16", "NaT", NaT, np.nan], tz="EST", name="idx")
+        msg = "Cannot use .astype to convert from timezone-aware"
+        with pytest.raises(TypeError, match=msg):
+            # dt64tz->dt64 deprecated
+            result = idx_tz.astype("datetime64[ns]")
+
+    def test_astype_object(self):
+        rng = date_range("1/1/2000", periods=20)
+
+        casted = rng.astype("O")
+        exp_values = list(rng)
+
+        tm.assert_index_equal(casted, Index(exp_values, dtype=np.object_))
+        assert casted.tolist() == exp_values
+
+    @pytest.mark.parametrize("tz", [None, "Asia/Tokyo"])
+    def test_astype_object_tz(self, tz):
+        idx = date_range(start="2013-01-01", periods=4, freq="ME", name="idx", tz=tz)
+        expected_list = [
+            Timestamp("2013-01-31", tz=tz),
+            Timestamp("2013-02-28", tz=tz),
+            Timestamp("2013-03-31", tz=tz),
+            Timestamp("2013-04-30", tz=tz),
+        ]
+        expected = Index(expected_list, dtype=object, name="idx")
+        result = idx.astype(object)
+        tm.assert_index_equal(result, expected)
+        assert idx.tolist() == expected_list
+
+    def test_astype_object_with_nat(self):
+        idx = DatetimeIndex(
+            [datetime(2013, 1, 1), datetime(2013, 1, 2), NaT, datetime(2013, 1, 4)],
+            name="idx",
+        )
+        expected_list = [
+            Timestamp("2013-01-01"),
+            Timestamp("2013-01-02"),
+            NaT,
+            Timestamp("2013-01-04"),
+        ]
+        expected = Index(expected_list, dtype=object, name="idx")
+        result = idx.astype(object)
+        tm.assert_index_equal(result, expected)
+        assert idx.tolist() == expected_list
+
+    @pytest.mark.parametrize(
+        "dtype",
+        [float, "timedelta64", "timedelta64[ns]", "datetime64", "datetime64[D]"],
+    )
+    def test_astype_raises(self, dtype):
+        # GH 13149, GH 13209
+        idx = DatetimeIndex(["2016-05-16", "NaT", NaT, np.nan])
+        msg = "Cannot cast DatetimeIndex to dtype"
+        if dtype == "datetime64":
+            msg = "Casting to unit-less dtype 'datetime64' is not supported"
+        with pytest.raises(TypeError, match=msg):
+            idx.astype(dtype)
+
+    def test_index_convert_to_datetime_array(self):
+        def _check_rng(rng):
+            converted = rng.to_pydatetime()
+            assert isinstance(converted, np.ndarray)
+            for x, stamp in zip(converted, rng):
+                assert isinstance(x, datetime)
+                assert x == stamp.to_pydatetime()
+                assert x.tzinfo == stamp.tzinfo
+
+        rng = date_range("20090415", "20090519")
+        rng_eastern = date_range("20090415", "20090519", tz="US/Eastern")
+        rng_utc = date_range("20090415", "20090519", tz="utc")
+
+        _check_rng(rng)
+        _check_rng(rng_eastern)
+        _check_rng(rng_utc)
+
+    def test_index_convert_to_datetime_array_explicit_pytz(self):
+        def _check_rng(rng):
+            converted = rng.to_pydatetime()
+            assert isinstance(converted, np.ndarray)
+            for x, stamp in zip(converted, rng):
+                assert isinstance(x, datetime)
+                assert x == stamp.to_pydatetime()
+                assert x.tzinfo == stamp.tzinfo
+
+        rng = date_range("20090415", "20090519")
+        rng_eastern = date_range("20090415", "20090519", tz=pytz.timezone("US/Eastern"))
+        rng_utc = date_range("20090415", "20090519", tz=pytz.utc)
+
+        _check_rng(rng)
+        _check_rng(rng_eastern)
+        _check_rng(rng_utc)
+
+    def test_index_convert_to_datetime_array_dateutil(self):
+        def _check_rng(rng):
+            converted = rng.to_pydatetime()
+            assert isinstance(converted, np.ndarray)
+            for x, stamp in zip(converted, rng):
+                assert isinstance(x, datetime)
+                assert x == stamp.to_pydatetime()
+                assert x.tzinfo == stamp.tzinfo
+
+        rng = date_range("20090415", "20090519")
+        rng_eastern = date_range("20090415", "20090519", tz="dateutil/US/Eastern")
+        rng_utc = date_range("20090415", "20090519", tz=dateutil.tz.tzutc())
+
+        _check_rng(rng)
+        _check_rng(rng_eastern)
+        _check_rng(rng_utc)
+
+    @pytest.mark.parametrize(
+        "tz, dtype",
+        [["US/Pacific", "datetime64[ns, US/Pacific]"], [None, "datetime64[ns]"]],
+    )
+    def test_integer_index_astype_datetime(self, tz, dtype):
+        # GH 20997, 20964, 24559
+        val = [Timestamp("2018-01-01", tz=tz).as_unit("ns")._value]
+        result = Index(val, name="idx").astype(dtype)
+        expected = DatetimeIndex(["2018-01-01"], tz=tz, name="idx").as_unit("ns")
+        tm.assert_index_equal(result, expected)
+
+    def test_dti_astype_period(self):
+        idx = DatetimeIndex([NaT, "2011-01-01", "2011-02-01"], name="idx")
+
+        res = idx.astype("period[M]")
+        exp = PeriodIndex(["NaT", "2011-01", "2011-02"], freq="M", name="idx")
+        tm.assert_index_equal(res, exp)
+
+        res = idx.astype("period[3M]")
+        exp = PeriodIndex(["NaT", "2011-01", "2011-02"], freq="3M", name="idx")
+        tm.assert_index_equal(res, exp)
+
+
+class TestAstype:
+    @pytest.mark.parametrize("tz", [None, "US/Central"])
+    def test_astype_category(self, tz):
+        obj = date_range("2000", periods=2, tz=tz, name="idx")
+        result = obj.astype("category")
+        dti = DatetimeIndex(["2000-01-01", "2000-01-02"], tz=tz).as_unit("ns")
+        expected = pd.CategoricalIndex(
+            dti,
+            name="idx",
+        )
+        tm.assert_index_equal(result, expected)
+
+        result = obj._data.astype("category")
+        expected = expected.values
+        tm.assert_categorical_equal(result, expected)
+
+    @pytest.mark.parametrize("tz", [None, "US/Central"])
+    def test_astype_array_fallback(self, tz):
+        obj = date_range("2000", periods=2, tz=tz, name="idx")
+        result = obj.astype(bool)
+        expected = Index(np.array([True, True]), name="idx")
+        tm.assert_index_equal(result, expected)
+
+        result = obj._data.astype(bool)
+        expected = np.array([True, True])
+        tm.assert_numpy_array_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/methods/test_delete.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/methods/test_delete.py
new file mode 100644
index 0000000000000000000000000000000000000000..2341499977f2247dc42c30470795378515f49dc8
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/methods/test_delete.py
@@ -0,0 +1,141 @@
+import pytest
+
+from pandas import (
+    DatetimeIndex,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestDelete:
+    def test_delete(self, unit):
+        idx = date_range(
+            start="2000-01-01", periods=5, freq="ME", name="idx", unit=unit
+        )
+
+        # preserve freq
+        expected_0 = date_range(
+            start="2000-02-01", periods=4, freq="ME", name="idx", unit=unit
+        )
+        expected_4 = date_range(
+            start="2000-01-01", periods=4, freq="ME", name="idx", unit=unit
+        )
+
+        # reset freq to None
+        expected_1 = DatetimeIndex(
+            ["2000-01-31", "2000-03-31", "2000-04-30", "2000-05-31"],
+            freq=None,
+            name="idx",
+        ).as_unit(unit)
+
+        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 pytest.raises((IndexError, ValueError), match="out of bounds"):
+            # either depending on numpy version
+            idx.delete(5)
+
+    @pytest.mark.parametrize("tz", [None, "Asia/Tokyo", "US/Pacific"])
+    def test_delete2(self, tz):
+        idx = date_range(
+            start="2000-01-01 09:00", periods=10, freq="h", name="idx", tz=tz
+        )
+
+        expected = date_range(
+            start="2000-01-01 10:00", periods=9, freq="h", name="idx", tz=tz
+        )
+        result = idx.delete(0)
+        tm.assert_index_equal(result, expected)
+        assert result.name == expected.name
+        assert result.freqstr == "h"
+        assert result.tz == expected.tz
+
+        expected = date_range(
+            start="2000-01-01 09:00", periods=9, freq="h", name="idx", tz=tz
+        )
+        result = idx.delete(-1)
+        tm.assert_index_equal(result, expected)
+        assert result.name == expected.name
+        assert result.freqstr == "h"
+        assert result.tz == expected.tz
+
+    def test_delete_slice(self, unit):
+        idx = date_range(
+            start="2000-01-01", periods=10, freq="D", name="idx", unit=unit
+        )
+
+        # preserve freq
+        expected_0_2 = date_range(
+            start="2000-01-04", periods=7, freq="D", name="idx", unit=unit
+        )
+        expected_7_9 = date_range(
+            start="2000-01-01", periods=7, freq="D", name="idx", unit=unit
+        )
+
+        # reset freq to None
+        expected_3_5 = DatetimeIndex(
+            [
+                "2000-01-01",
+                "2000-01-02",
+                "2000-01-03",
+                "2000-01-07",
+                "2000-01-08",
+                "2000-01-09",
+                "2000-01-10",
+            ],
+            freq=None,
+            name="idx",
+        ).as_unit(unit)
+
+        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
+
+    # TODO: belongs in Series.drop tests?
+    @pytest.mark.parametrize("tz", [None, "Asia/Tokyo", "US/Pacific"])
+    def test_delete_slice2(self, tz, unit):
+        dti = date_range(
+            "2000-01-01 09:00", periods=10, freq="h", name="idx", tz=tz, unit=unit
+        )
+        ts = Series(
+            1,
+            index=dti,
+        )
+        # preserve freq
+        result = ts.drop(ts.index[:5]).index
+        expected = dti[5:]
+        tm.assert_index_equal(result, expected)
+        assert result.name == expected.name
+        assert result.freq == expected.freq
+        assert result.tz == expected.tz
+
+        # reset freq to None
+        result = ts.drop(ts.index[[1, 3, 5, 7, 9]]).index
+        expected = dti[::2]._with_freq(None)
+        tm.assert_index_equal(result, expected)
+        assert result.name == expected.name
+        assert result.freq == expected.freq
+        assert result.tz == expected.tz
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/methods/test_insert.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/methods/test_insert.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebfe490e0e067807f7a38d3f8f285aee76718fcf
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/methods/test_insert.py
@@ -0,0 +1,265 @@
+from datetime import datetime
+
+import numpy as np
+import pytest
+import pytz
+
+from pandas import (
+    NA,
+    DatetimeIndex,
+    Index,
+    NaT,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestInsert:
+    @pytest.mark.parametrize("null", [None, np.nan, np.datetime64("NaT"), NaT, NA])
+    @pytest.mark.parametrize("tz", [None, "UTC", "US/Eastern"])
+    def test_insert_nat(self, tz, null):
+        # GH#16537, GH#18295 (test missing)
+
+        idx = DatetimeIndex(["2017-01-01"], tz=tz)
+        expected = DatetimeIndex(["NaT", "2017-01-01"], tz=tz)
+        if tz is not None and isinstance(null, np.datetime64):
+            expected = Index([null, idx[0]], dtype=object)
+
+        res = idx.insert(0, null)
+        tm.assert_index_equal(res, expected)
+
+    @pytest.mark.parametrize("tz", [None, "UTC", "US/Eastern"])
+    def test_insert_invalid_na(self, tz):
+        idx = DatetimeIndex(["2017-01-01"], tz=tz)
+
+        item = np.timedelta64("NaT")
+        result = idx.insert(0, item)
+        expected = Index([item] + list(idx), dtype=object)
+        tm.assert_index_equal(result, expected)
+
+    def test_insert_empty_preserves_freq(self, tz_naive_fixture):
+        # GH#33573
+        tz = tz_naive_fixture
+        dti = DatetimeIndex([], tz=tz, freq="D")
+        item = Timestamp("2017-04-05").tz_localize(tz)
+
+        result = dti.insert(0, item)
+        assert result.freq == dti.freq
+
+        # But not when we insert an item that doesn't conform to freq
+        dti = DatetimeIndex([], tz=tz, freq="W-THU")
+        result = dti.insert(0, item)
+        assert result.freq is None
+
+    def test_insert(self, unit):
+        idx = DatetimeIndex(
+            ["2000-01-04", "2000-01-01", "2000-01-02"], name="idx"
+        ).as_unit(unit)
+
+        result = idx.insert(2, datetime(2000, 1, 5))
+        exp = DatetimeIndex(
+            ["2000-01-04", "2000-01-01", "2000-01-05", "2000-01-02"], name="idx"
+        ).as_unit(unit)
+        tm.assert_index_equal(result, exp)
+
+        # insertion of non-datetime should coerce to object index
+        result = idx.insert(1, "inserted")
+        expected = Index(
+            [
+                datetime(2000, 1, 4),
+                "inserted",
+                datetime(2000, 1, 1),
+                datetime(2000, 1, 2),
+            ],
+            name="idx",
+        )
+        assert not isinstance(result, DatetimeIndex)
+        tm.assert_index_equal(result, expected)
+        assert result.name == expected.name
+
+    def test_insert2(self, unit):
+        idx = date_range("1/1/2000", periods=3, freq="ME", name="idx", unit=unit)
+
+        # preserve freq
+        expected_0 = DatetimeIndex(
+            ["1999-12-31", "2000-01-31", "2000-02-29", "2000-03-31"],
+            name="idx",
+            freq="ME",
+        ).as_unit(unit)
+        expected_3 = DatetimeIndex(
+            ["2000-01-31", "2000-02-29", "2000-03-31", "2000-04-30"],
+            name="idx",
+            freq="ME",
+        ).as_unit(unit)
+
+        # reset freq to None
+        expected_1_nofreq = DatetimeIndex(
+            ["2000-01-31", "2000-01-31", "2000-02-29", "2000-03-31"],
+            name="idx",
+            freq=None,
+        ).as_unit(unit)
+        expected_3_nofreq = DatetimeIndex(
+            ["2000-01-31", "2000-02-29", "2000-03-31", "2000-01-02"],
+            name="idx",
+            freq=None,
+        ).as_unit(unit)
+
+        cases = [
+            (0, datetime(1999, 12, 31), expected_0),
+            (-3, datetime(1999, 12, 31), expected_0),
+            (3, datetime(2000, 4, 30), expected_3),
+            (1, datetime(2000, 1, 31), expected_1_nofreq),
+            (3, datetime(2000, 1, 2), expected_3_nofreq),
+        ]
+
+        for n, d, expected in cases:
+            result = idx.insert(n, d)
+            tm.assert_index_equal(result, expected)
+            assert result.name == expected.name
+            assert result.freq == expected.freq
+
+    def test_insert3(self, unit):
+        idx = date_range("1/1/2000", periods=3, freq="ME", name="idx", unit=unit)
+
+        # reset freq to None
+        result = idx.insert(3, datetime(2000, 1, 2))
+        expected = DatetimeIndex(
+            ["2000-01-31", "2000-02-29", "2000-03-31", "2000-01-02"],
+            name="idx",
+            freq=None,
+        ).as_unit(unit)
+        tm.assert_index_equal(result, expected)
+        assert result.name == expected.name
+        assert result.freq is None
+
+    def test_insert4(self, unit):
+        for tz in ["US/Pacific", "Asia/Singapore"]:
+            idx = date_range(
+                "1/1/2000 09:00", periods=6, freq="h", tz=tz, name="idx", unit=unit
+            )
+            # preserve freq
+            expected = date_range(
+                "1/1/2000 09:00", periods=7, freq="h", tz=tz, name="idx", unit=unit
+            )
+            for d in [
+                Timestamp("2000-01-01 15:00", tz=tz),
+                pytz.timezone(tz).localize(datetime(2000, 1, 1, 15)),
+            ]:
+                result = idx.insert(6, d)
+                tm.assert_index_equal(result, expected)
+                assert result.name == expected.name
+                assert result.freq == expected.freq
+                assert result.tz == expected.tz
+
+            expected = DatetimeIndex(
+                [
+                    "2000-01-01 09:00",
+                    "2000-01-01 10:00",
+                    "2000-01-01 11:00",
+                    "2000-01-01 12:00",
+                    "2000-01-01 13:00",
+                    "2000-01-01 14:00",
+                    "2000-01-01 10:00",
+                ],
+                name="idx",
+                tz=tz,
+                freq=None,
+            ).as_unit(unit)
+            # reset freq to None
+            for d in [
+                Timestamp("2000-01-01 10:00", tz=tz),
+                pytz.timezone(tz).localize(datetime(2000, 1, 1, 10)),
+            ]:
+                result = idx.insert(6, d)
+                tm.assert_index_equal(result, expected)
+                assert result.name == expected.name
+                assert result.tz == expected.tz
+                assert result.freq is None
+
+    # TODO: also changes DataFrame.__setitem__ with expansion
+    def test_insert_mismatched_tzawareness(self):
+        # see GH#7299
+        idx = date_range("1/1/2000", periods=3, freq="D", tz="Asia/Tokyo", name="idx")
+
+        # mismatched tz-awareness
+        item = Timestamp("2000-01-04")
+        result = idx.insert(3, item)
+        expected = Index(
+            list(idx[:3]) + [item] + list(idx[3:]), dtype=object, name="idx"
+        )
+        tm.assert_index_equal(result, expected)
+
+        # mismatched tz-awareness
+        item = datetime(2000, 1, 4)
+        result = idx.insert(3, item)
+        expected = Index(
+            list(idx[:3]) + [item] + list(idx[3:]), dtype=object, name="idx"
+        )
+        tm.assert_index_equal(result, expected)
+
+    # TODO: also changes DataFrame.__setitem__ with expansion
+    def test_insert_mismatched_tz(self):
+        # see GH#7299
+        # pre-2.0 with mismatched tzs we would cast to object
+        idx = date_range("1/1/2000", periods=3, freq="D", tz="Asia/Tokyo", name="idx")
+
+        # mismatched tz -> cast to object (could reasonably cast to same tz or UTC)
+        item = Timestamp("2000-01-04", tz="US/Eastern")
+        result = idx.insert(3, item)
+        expected = Index(
+            list(idx[:3]) + [item.tz_convert(idx.tz)] + list(idx[3:]),
+            name="idx",
+        )
+        assert expected.dtype == idx.dtype
+        tm.assert_index_equal(result, expected)
+
+        item = datetime(2000, 1, 4, tzinfo=pytz.timezone("US/Eastern"))
+        result = idx.insert(3, item)
+        expected = Index(
+            list(idx[:3]) + [item.astimezone(idx.tzinfo)] + list(idx[3:]),
+            name="idx",
+        )
+        assert expected.dtype == idx.dtype
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "item", [0, np.int64(0), np.float64(0), np.array(0), np.timedelta64(456)]
+    )
+    def test_insert_mismatched_types_raises(self, tz_aware_fixture, item):
+        # GH#33703 dont cast these to dt64
+        tz = tz_aware_fixture
+        dti = date_range("2019-11-04", periods=9, freq="-1D", name=9, tz=tz)
+
+        result = dti.insert(1, item)
+
+        if isinstance(item, np.ndarray):
+            assert item.item() == 0
+            expected = Index([dti[0], 0] + list(dti[1:]), dtype=object, name=9)
+        else:
+            expected = Index([dti[0], item] + list(dti[1:]), dtype=object, name=9)
+
+        tm.assert_index_equal(result, expected)
+
+    def test_insert_castable_str(self, tz_aware_fixture):
+        # GH#33703
+        tz = tz_aware_fixture
+        dti = date_range("2019-11-04", periods=3, freq="-1D", name=9, tz=tz)
+
+        value = "2019-11-05"
+        result = dti.insert(0, value)
+
+        ts = Timestamp(value).tz_localize(tz)
+        expected = DatetimeIndex([ts] + list(dti), dtype=dti.dtype, name=9)
+        tm.assert_index_equal(result, expected)
+
+    def test_insert_non_castable_str(self, tz_aware_fixture):
+        # GH#33703
+        tz = tz_aware_fixture
+        dti = date_range("2019-11-04", periods=3, freq="-1D", name=9, tz=tz)
+
+        value = "foo"
+        result = dti.insert(0, value)
+
+        expected = Index(["foo"] + list(dti), dtype=object, name=9)
+        tm.assert_index_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/methods/test_normalize.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/methods/test_normalize.py
new file mode 100644
index 0000000000000000000000000000000000000000..74711f67e64465c5592e562fcc94202666d0ad67
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/methods/test_normalize.py
@@ -0,0 +1,95 @@
+from dateutil.tz import tzlocal
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+from pandas import (
+    DatetimeIndex,
+    NaT,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestNormalize:
+    def test_normalize(self):
+        rng = date_range("1/1/2000 9:30", periods=10, freq="D")
+
+        result = rng.normalize()
+        expected = date_range("1/1/2000", periods=10, freq="D")
+        tm.assert_index_equal(result, expected)
+
+        arr_ns = np.array([1380585623454345752, 1380585612343234312]).astype(
+            "datetime64[ns]"
+        )
+        rng_ns = DatetimeIndex(arr_ns)
+        rng_ns_normalized = rng_ns.normalize()
+
+        arr_ns = np.array([1380585600000000000, 1380585600000000000]).astype(
+            "datetime64[ns]"
+        )
+        expected = DatetimeIndex(arr_ns)
+        tm.assert_index_equal(rng_ns_normalized, expected)
+
+        assert result.is_normalized
+        assert not rng.is_normalized
+
+    def test_normalize_nat(self):
+        dti = DatetimeIndex([NaT, Timestamp("2018-01-01 01:00:00")])
+        result = dti.normalize()
+        expected = DatetimeIndex([NaT, Timestamp("2018-01-01")])
+        tm.assert_index_equal(result, expected)
+
+    def test_normalize_tz(self):
+        rng = date_range("1/1/2000 9:30", periods=10, freq="D", tz="US/Eastern")
+
+        result = rng.normalize()  # does not preserve freq
+        expected = date_range("1/1/2000", periods=10, freq="D", tz="US/Eastern")
+        tm.assert_index_equal(result, expected._with_freq(None))
+
+        assert result.is_normalized
+        assert not rng.is_normalized
+
+        rng = date_range("1/1/2000 9:30", periods=10, freq="D", tz="UTC")
+
+        result = rng.normalize()
+        expected = date_range("1/1/2000", periods=10, freq="D", tz="UTC")
+        tm.assert_index_equal(result, expected)
+
+        assert result.is_normalized
+        assert not rng.is_normalized
+
+        rng = date_range("1/1/2000 9:30", periods=10, freq="D", tz=tzlocal())
+        result = rng.normalize()  # does not preserve freq
+        expected = date_range("1/1/2000", periods=10, freq="D", tz=tzlocal())
+        tm.assert_index_equal(result, expected._with_freq(None))
+
+        assert result.is_normalized
+        assert not rng.is_normalized
+
+    @td.skip_if_windows
+    @pytest.mark.parametrize(
+        "timezone",
+        [
+            "US/Pacific",
+            "US/Eastern",
+            "UTC",
+            "Asia/Kolkata",
+            "Asia/Shanghai",
+            "Australia/Canberra",
+        ],
+    )
+    def test_normalize_tz_local(self, timezone):
+        # GH#13459
+        with tm.set_timezone(timezone):
+            rng = date_range("1/1/2000 9:30", periods=10, freq="D", tz=tzlocal())
+
+            result = rng.normalize()
+            expected = date_range("1/1/2000", periods=10, freq="D", tz=tzlocal())
+            expected = expected._with_freq(None)
+            tm.assert_index_equal(result, expected)
+
+            assert result.is_normalized
+            assert not rng.is_normalized
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/methods/test_to_period.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/methods/test_to_period.py
new file mode 100644
index 0000000000000000000000000000000000000000..de8d32f64cde26b2fa0a0720cbdacc56f6c2e983
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/methods/test_to_period.py
@@ -0,0 +1,225 @@
+import dateutil.tz
+from dateutil.tz import tzlocal
+import pytest
+import pytz
+
+from pandas._libs.tslibs.ccalendar import MONTHS
+from pandas._libs.tslibs.offsets import MonthEnd
+from pandas._libs.tslibs.period import INVALID_FREQ_ERR_MSG
+
+from pandas import (
+    DatetimeIndex,
+    Period,
+    PeriodIndex,
+    Timestamp,
+    date_range,
+    period_range,
+)
+import pandas._testing as tm
+
+
+class TestToPeriod:
+    def test_dti_to_period(self):
+        dti = date_range(start="1/1/2005", end="12/1/2005", freq="ME")
+        pi1 = dti.to_period()
+        pi2 = dti.to_period(freq="D")
+        pi3 = dti.to_period(freq="3D")
+
+        assert pi1[0] == Period("Jan 2005", freq="M")
+        assert pi2[0] == Period("1/31/2005", freq="D")
+        assert pi3[0] == Period("1/31/2005", freq="3D")
+
+        assert pi1[-1] == Period("Nov 2005", freq="M")
+        assert pi2[-1] == Period("11/30/2005", freq="D")
+        assert pi3[-1], Period("11/30/2005", freq="3D")
+
+        tm.assert_index_equal(pi1, period_range("1/1/2005", "11/1/2005", freq="M"))
+        tm.assert_index_equal(
+            pi2, period_range("1/1/2005", "11/1/2005", freq="M").asfreq("D")
+        )
+        tm.assert_index_equal(
+            pi3, period_range("1/1/2005", "11/1/2005", freq="M").asfreq("3D")
+        )
+
+    @pytest.mark.parametrize("month", MONTHS)
+    def test_to_period_quarterly(self, month):
+        # make sure we can make the round trip
+        freq = f"Q-{month}"
+        rng = period_range("1989Q3", "1991Q3", freq=freq)
+        stamps = rng.to_timestamp()
+        result = stamps.to_period(freq)
+        tm.assert_index_equal(rng, result)
+
+    @pytest.mark.parametrize("off", ["BQE", "QS", "BQS"])
+    def test_to_period_quarterlyish(self, off):
+        rng = date_range("01-Jan-2012", periods=8, freq=off)
+        prng = rng.to_period()
+        assert prng.freq == "QE-DEC"
+
+    @pytest.mark.parametrize("off", ["BYE", "YS", "BYS"])
+    def test_to_period_annualish(self, off):
+        rng = date_range("01-Jan-2012", periods=8, freq=off)
+        prng = rng.to_period()
+        assert prng.freq == "YE-DEC"
+
+    def test_to_period_monthish(self):
+        offsets = ["MS", "BME"]
+        for off in offsets:
+            rng = date_range("01-Jan-2012", periods=8, freq=off)
+            prng = rng.to_period()
+            assert prng.freqstr == "M"
+
+        rng = date_range("01-Jan-2012", periods=8, freq="ME")
+        prng = rng.to_period()
+        assert prng.freqstr == "M"
+
+        with pytest.raises(ValueError, match=INVALID_FREQ_ERR_MSG):
+            date_range("01-Jan-2012", periods=8, freq="EOM")
+
+    @pytest.mark.parametrize(
+        "freq_offset, freq_period",
+        [
+            ("2ME", "2M"),
+            (MonthEnd(2), MonthEnd(2)),
+        ],
+    )
+    def test_dti_to_period_2monthish(self, freq_offset, freq_period):
+        dti = date_range("2020-01-01", periods=3, freq=freq_offset)
+        pi = dti.to_period()
+
+        tm.assert_index_equal(pi, period_range("2020-01", "2020-05", freq=freq_period))
+
+    @pytest.mark.parametrize(
+        "freq, freq_depr",
+        [
+            ("2ME", "2M"),
+            ("2QE", "2Q"),
+            ("2QE-SEP", "2Q-SEP"),
+            ("1YE", "1Y"),
+            ("2YE-MAR", "2Y-MAR"),
+            ("1YE", "1A"),
+            ("2YE-MAR", "2A-MAR"),
+        ],
+    )
+    def test_to_period_frequency_M_Q_Y_A_deprecated(self, freq, freq_depr):
+        # GH#9586
+        msg = f"'{freq_depr[1:]}' is deprecated and will be removed "
+        f"in a future version, please use '{freq[1:]}' instead."
+
+        rng = date_range("01-Jan-2012", periods=8, freq=freq)
+        prng = rng.to_period()
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert prng.freq == freq_depr
+
+    def test_to_period_infer(self):
+        # https://github.com/pandas-dev/pandas/issues/33358
+        rng = date_range(
+            start="2019-12-22 06:40:00+00:00",
+            end="2019-12-22 08:45:00+00:00",
+            freq="5min",
+        )
+
+        with tm.assert_produces_warning(UserWarning):
+            pi1 = rng.to_period("5min")
+
+        with tm.assert_produces_warning(UserWarning):
+            pi2 = rng.to_period()
+
+        tm.assert_index_equal(pi1, pi2)
+
+    @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+    def test_period_dt64_round_trip(self):
+        dti = date_range("1/1/2000", "1/7/2002", freq="B")
+        pi = dti.to_period()
+        tm.assert_index_equal(pi.to_timestamp(), dti)
+
+        dti = date_range("1/1/2000", "1/7/2002", freq="B")
+        pi = dti.to_period(freq="h")
+        tm.assert_index_equal(pi.to_timestamp(), dti)
+
+    def test_to_period_millisecond(self):
+        index = DatetimeIndex(
+            [
+                Timestamp("2007-01-01 10:11:12.123456Z"),
+                Timestamp("2007-01-01 10:11:13.789123Z"),
+            ]
+        )
+
+        with tm.assert_produces_warning(UserWarning):
+            # warning that timezone info will be lost
+            period = index.to_period(freq="ms")
+        assert 2 == len(period)
+        assert period[0] == Period("2007-01-01 10:11:12.123Z", "ms")
+        assert period[1] == Period("2007-01-01 10:11:13.789Z", "ms")
+
+    def test_to_period_microsecond(self):
+        index = DatetimeIndex(
+            [
+                Timestamp("2007-01-01 10:11:12.123456Z"),
+                Timestamp("2007-01-01 10:11:13.789123Z"),
+            ]
+        )
+
+        with tm.assert_produces_warning(UserWarning):
+            # warning that timezone info will be lost
+            period = index.to_period(freq="us")
+        assert 2 == len(period)
+        assert period[0] == Period("2007-01-01 10:11:12.123456Z", "us")
+        assert period[1] == Period("2007-01-01 10:11:13.789123Z", "us")
+
+    @pytest.mark.parametrize(
+        "tz",
+        ["US/Eastern", pytz.utc, tzlocal(), "dateutil/US/Eastern", dateutil.tz.tzutc()],
+    )
+    def test_to_period_tz(self, tz):
+        ts = date_range("1/1/2000", "2/1/2000", tz=tz)
+
+        with tm.assert_produces_warning(UserWarning):
+            # GH#21333 warning that timezone info will be lost
+            # filter warning about freq deprecation
+
+            result = ts.to_period()[0]
+            expected = ts[0].to_period(ts.freq)
+
+        assert result == expected
+
+        expected = date_range("1/1/2000", "2/1/2000").to_period()
+
+        with tm.assert_produces_warning(UserWarning):
+            # GH#21333 warning that timezone info will be lost
+            result = ts.to_period(ts.freq)
+
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("tz", ["Etc/GMT-1", "Etc/GMT+1"])
+    def test_to_period_tz_utc_offset_consistency(self, tz):
+        # GH#22905
+        ts = date_range("1/1/2000", "2/1/2000", tz="Etc/GMT-1")
+        with tm.assert_produces_warning(UserWarning):
+            result = ts.to_period()[0]
+            expected = ts[0].to_period(ts.freq)
+            assert result == expected
+
+    def test_to_period_nofreq(self):
+        idx = DatetimeIndex(["2000-01-01", "2000-01-02", "2000-01-04"])
+        msg = "You must pass a freq argument as current index has none."
+        with pytest.raises(ValueError, match=msg):
+            idx.to_period()
+
+        idx = DatetimeIndex(["2000-01-01", "2000-01-02", "2000-01-03"], freq="infer")
+        assert idx.freqstr == "D"
+        expected = PeriodIndex(["2000-01-01", "2000-01-02", "2000-01-03"], freq="D")
+        tm.assert_index_equal(idx.to_period(), expected)
+
+        # GH#7606
+        idx = DatetimeIndex(["2000-01-01", "2000-01-02", "2000-01-03"])
+        assert idx.freqstr is None
+        tm.assert_index_equal(idx.to_period(), expected)
+
+    @pytest.mark.parametrize("freq", ["2BMS", "1SME-15"])
+    def test_to_period_offsets_not_supported(self, freq):
+        # GH#56243
+        msg = f"{freq[1:]} is not supported as period frequency"
+        ts = date_range("1/1/2012", periods=4, freq=freq)
+        with pytest.raises(ValueError, match=msg):
+            ts.to_period()
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/methods/test_to_series.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/methods/test_to_series.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c397c8ab2cd310a2d4fdf59992ea4d123370ee0
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/methods/test_to_series.py
@@ -0,0 +1,18 @@
+import numpy as np
+
+from pandas import (
+    DatetimeIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestToSeries:
+    def test_to_series(self):
+        naive = DatetimeIndex(["2013-1-1 13:00", "2013-1-2 14:00"], name="B")
+        idx = naive.tz_localize("US/Pacific")
+
+        expected = Series(np.array(idx.tolist(), dtype="object"), name="B")
+        result = idx.to_series(index=[0, 1])
+        assert expected.dtype == idx.dtype
+        tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/methods/test_unique.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/methods/test_unique.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c419b23c749a16e66458b334b3aec34521c2241
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/methods/test_unique.py
@@ -0,0 +1,77 @@
+from datetime import (
+    datetime,
+    timedelta,
+)
+
+from pandas import (
+    DatetimeIndex,
+    NaT,
+    Timestamp,
+)
+import pandas._testing as tm
+
+
+def test_unique(tz_naive_fixture):
+    idx = DatetimeIndex(["2017"] * 2, tz=tz_naive_fixture)
+    expected = idx[:1]
+
+    result = idx.unique()
+    tm.assert_index_equal(result, expected)
+    # GH#21737
+    # Ensure the underlying data is consistent
+    assert result[0] == expected[0]
+
+
+def test_index_unique(rand_series_with_duplicate_datetimeindex):
+    dups = rand_series_with_duplicate_datetimeindex
+    index = dups.index
+
+    uniques = index.unique()
+    expected = DatetimeIndex(
+        [
+            datetime(2000, 1, 2),
+            datetime(2000, 1, 3),
+            datetime(2000, 1, 4),
+            datetime(2000, 1, 5),
+        ],
+        dtype=index.dtype,
+    )
+    assert uniques.dtype == index.dtype  # sanity
+    tm.assert_index_equal(uniques, expected)
+    assert index.nunique() == 4
+
+    # GH#2563
+    assert isinstance(uniques, DatetimeIndex)
+
+    dups_local = index.tz_localize("US/Eastern")
+    dups_local.name = "foo"
+    result = dups_local.unique()
+    expected = DatetimeIndex(expected, name="foo")
+    expected = expected.tz_localize("US/Eastern")
+    assert result.tz is not None
+    assert result.name == "foo"
+    tm.assert_index_equal(result, expected)
+
+
+def test_index_unique2():
+    # NaT, note this is excluded
+    arr = [1370745748 + t for t in range(20)] + [NaT._value]
+    idx = DatetimeIndex(arr * 3)
+    tm.assert_index_equal(idx.unique(), DatetimeIndex(arr))
+    assert idx.nunique() == 20
+    assert idx.nunique(dropna=False) == 21
+
+
+def test_index_unique3():
+    arr = [
+        Timestamp("2013-06-09 02:42:28") + timedelta(seconds=t) for t in range(20)
+    ] + [NaT]
+    idx = DatetimeIndex(arr * 3)
+    tm.assert_index_equal(idx.unique(), DatetimeIndex(arr))
+    assert idx.nunique() == 20
+    assert idx.nunique(dropna=False) == 21
+
+
+def test_is_unique_monotonic(rand_series_with_duplicate_datetimeindex):
+    index = rand_series_with_duplicate_datetimeindex.index
+    assert not index.is_unique
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/test_partial_slicing.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/test_partial_slicing.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b493fc61cb5873532e2e8393007533ee6cb8e4f
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/datetimes/test_partial_slicing.py
@@ -0,0 +1,466 @@
+""" test partial slicing on Series/Frame """
+
+from datetime import datetime
+
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    MultiIndex,
+    Series,
+    Timedelta,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestSlicing:
+    def test_string_index_series_name_converted(self):
+        # GH#1644
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 4)),
+            index=date_range("1/1/2000", periods=10),
+        )
+
+        result = df.loc["1/3/2000"]
+        assert result.name == df.index[2]
+
+        result = df.T["1/3/2000"]
+        assert result.name == df.index[2]
+
+    def test_stringified_slice_with_tz(self):
+        # GH#2658
+        start = "2013-01-07"
+        idx = date_range(start=start, freq="1d", periods=10, tz="US/Eastern")
+        df = DataFrame(np.arange(10), index=idx)
+        df["2013-01-14 23:44:34.437768-05:00":]  # no exception here
+
+    def test_return_type_doesnt_depend_on_monotonicity(self):
+        # GH#24892 we get Series back regardless of whether our DTI is monotonic
+        dti = date_range(start="2015-5-13 23:59:00", freq="min", periods=3)
+        ser = Series(range(3), index=dti)
+
+        # non-monotonic index
+        ser2 = Series(range(3), index=[dti[1], dti[0], dti[2]])
+
+        # key with resolution strictly lower than "min"
+        key = "2015-5-14 00"
+
+        # monotonic increasing index
+        result = ser.loc[key]
+        expected = ser.iloc[1:]
+        tm.assert_series_equal(result, expected)
+
+        # monotonic decreasing index
+        result = ser.iloc[::-1].loc[key]
+        expected = ser.iloc[::-1][:-1]
+        tm.assert_series_equal(result, expected)
+
+        # non-monotonic index
+        result2 = ser2.loc[key]
+        expected2 = ser2.iloc[::2]
+        tm.assert_series_equal(result2, expected2)
+
+    def test_return_type_doesnt_depend_on_monotonicity_higher_reso(self):
+        # GH#24892 we get Series back regardless of whether our DTI is monotonic
+        dti = date_range(start="2015-5-13 23:59:00", freq="min", periods=3)
+        ser = Series(range(3), index=dti)
+
+        # non-monotonic index
+        ser2 = Series(range(3), index=[dti[1], dti[0], dti[2]])
+
+        # key with resolution strictly *higher) than "min"
+        key = "2015-5-14 00:00:00"
+
+        # monotonic increasing index
+        result = ser.loc[key]
+        assert result == 1
+
+        # monotonic decreasing index
+        result = ser.iloc[::-1].loc[key]
+        assert result == 1
+
+        # non-monotonic index
+        result2 = ser2.loc[key]
+        assert result2 == 0
+
+    def test_monotone_DTI_indexing_bug(self):
+        # GH 19362
+        # Testing accessing the first element in a monotonic descending
+        # partial string indexing.
+
+        df = DataFrame(list(range(5)))
+        date_list = [
+            "2018-01-02",
+            "2017-02-10",
+            "2016-03-10",
+            "2015-03-15",
+            "2014-03-16",
+        ]
+        date_index = DatetimeIndex(date_list)
+        df["date"] = date_index
+        expected = DataFrame({0: list(range(5)), "date": date_index})
+        tm.assert_frame_equal(df, expected)
+
+        # We get a slice because df.index's resolution is hourly and we
+        #  are slicing with a daily-resolution string.  If both were daily,
+        #  we would get a single item back
+        dti = date_range("20170101 01:00:00", periods=3)
+        df = DataFrame({"A": [1, 2, 3]}, index=dti[::-1])
+
+        expected = DataFrame({"A": 1}, index=dti[-1:][::-1])
+        result = df.loc["2017-01-03"]
+        tm.assert_frame_equal(result, expected)
+
+        result2 = df.iloc[::-1].loc["2017-01-03"]
+        expected2 = expected.iloc[::-1]
+        tm.assert_frame_equal(result2, expected2)
+
+    def test_slice_year(self):
+        dti = date_range(freq="B", start=datetime(2005, 1, 1), periods=500)
+
+        s = Series(np.arange(len(dti)), index=dti)
+        result = s["2005"]
+        expected = s[s.index.year == 2005]
+        tm.assert_series_equal(result, expected)
+
+        df = DataFrame(np.random.default_rng(2).random((len(dti), 5)), index=dti)
+        result = df.loc["2005"]
+        expected = df[df.index.year == 2005]
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "partial_dtime",
+        [
+            "2019",
+            "2019Q4",
+            "Dec 2019",
+            "2019-12-31",
+            "2019-12-31 23",
+            "2019-12-31 23:59",
+        ],
+    )
+    def test_slice_end_of_period_resolution(self, partial_dtime):
+        # GH#31064
+        dti = date_range("2019-12-31 23:59:55.999999999", periods=10, freq="s")
+
+        ser = Series(range(10), index=dti)
+        result = ser[partial_dtime]
+        expected = ser.iloc[:5]
+        tm.assert_series_equal(result, expected)
+
+    def test_slice_quarter(self):
+        dti = date_range(freq="D", start=datetime(2000, 6, 1), periods=500)
+
+        s = Series(np.arange(len(dti)), index=dti)
+        assert len(s["2001Q1"]) == 90
+
+        df = DataFrame(np.random.default_rng(2).random((len(dti), 5)), index=dti)
+        assert len(df.loc["1Q01"]) == 90
+
+    def test_slice_month(self):
+        dti = date_range(freq="D", start=datetime(2005, 1, 1), periods=500)
+        s = Series(np.arange(len(dti)), index=dti)
+        assert len(s["2005-11"]) == 30
+
+        df = DataFrame(np.random.default_rng(2).random((len(dti), 5)), index=dti)
+        assert len(df.loc["2005-11"]) == 30
+
+        tm.assert_series_equal(s["2005-11"], s["11-2005"])
+
+    def test_partial_slice(self):
+        rng = date_range(freq="D", start=datetime(2005, 1, 1), periods=500)
+        s = Series(np.arange(len(rng)), index=rng)
+
+        result = s["2005-05":"2006-02"]
+        expected = s["20050501":"20060228"]
+        tm.assert_series_equal(result, expected)
+
+        result = s["2005-05":]
+        expected = s["20050501":]
+        tm.assert_series_equal(result, expected)
+
+        result = s[:"2006-02"]
+        expected = s[:"20060228"]
+        tm.assert_series_equal(result, expected)
+
+        result = s["2005-1-1"]
+        assert result == s.iloc[0]
+
+        with pytest.raises(KeyError, match=r"^'2004-12-31'$"):
+            s["2004-12-31"]
+
+    def test_partial_slice_daily(self):
+        rng = date_range(freq="h", start=datetime(2005, 1, 31), periods=500)
+        s = Series(np.arange(len(rng)), index=rng)
+
+        result = s["2005-1-31"]
+        tm.assert_series_equal(result, s.iloc[:24])
+
+        with pytest.raises(KeyError, match=r"^'2004-12-31 00'$"):
+            s["2004-12-31 00"]
+
+    def test_partial_slice_hourly(self):
+        rng = date_range(freq="min", start=datetime(2005, 1, 1, 20, 0, 0), periods=500)
+        s = Series(np.arange(len(rng)), index=rng)
+
+        result = s["2005-1-1"]
+        tm.assert_series_equal(result, s.iloc[: 60 * 4])
+
+        result = s["2005-1-1 20"]
+        tm.assert_series_equal(result, s.iloc[:60])
+
+        assert s["2005-1-1 20:00"] == s.iloc[0]
+        with pytest.raises(KeyError, match=r"^'2004-12-31 00:15'$"):
+            s["2004-12-31 00:15"]
+
+    def test_partial_slice_minutely(self):
+        rng = date_range(freq="s", start=datetime(2005, 1, 1, 23, 59, 0), periods=500)
+        s = Series(np.arange(len(rng)), index=rng)
+
+        result = s["2005-1-1 23:59"]
+        tm.assert_series_equal(result, s.iloc[:60])
+
+        result = s["2005-1-1"]
+        tm.assert_series_equal(result, s.iloc[:60])
+
+        assert s[Timestamp("2005-1-1 23:59:00")] == s.iloc[0]
+        with pytest.raises(KeyError, match=r"^'2004-12-31 00:00:00'$"):
+            s["2004-12-31 00:00:00"]
+
+    def test_partial_slice_second_precision(self):
+        rng = date_range(
+            start=datetime(2005, 1, 1, 0, 0, 59, microsecond=999990),
+            periods=20,
+            freq="us",
+        )
+        s = Series(np.arange(20), rng)
+
+        tm.assert_series_equal(s["2005-1-1 00:00"], s.iloc[:10])
+        tm.assert_series_equal(s["2005-1-1 00:00:59"], s.iloc[:10])
+
+        tm.assert_series_equal(s["2005-1-1 00:01"], s.iloc[10:])
+        tm.assert_series_equal(s["2005-1-1 00:01:00"], s.iloc[10:])
+
+        assert s[Timestamp("2005-1-1 00:00:59.999990")] == s.iloc[0]
+        with pytest.raises(KeyError, match="2005-1-1 00:00:00"):
+            s["2005-1-1 00:00:00"]
+
+    def test_partial_slicing_dataframe(self):
+        # GH14856
+        # Test various combinations of string slicing resolution vs.
+        # index resolution
+        # - If string resolution is less precise than index resolution,
+        # string is considered a slice
+        # - If string resolution is equal to or more precise than index
+        # resolution, string is considered an exact match
+        formats = [
+            "%Y",
+            "%Y-%m",
+            "%Y-%m-%d",
+            "%Y-%m-%d %H",
+            "%Y-%m-%d %H:%M",
+            "%Y-%m-%d %H:%M:%S",
+        ]
+        resolutions = ["year", "month", "day", "hour", "minute", "second"]
+        for rnum, resolution in enumerate(resolutions[2:], 2):
+            # we check only 'day', 'hour', 'minute' and 'second'
+            unit = Timedelta("1 " + resolution)
+            middate = datetime(2012, 1, 1, 0, 0, 0)
+            index = DatetimeIndex([middate - unit, middate, middate + unit])
+            values = [1, 2, 3]
+            df = DataFrame({"a": values}, index, dtype=np.int64)
+            assert df.index.resolution == resolution
+
+            # Timestamp with the same resolution as index
+            # Should be exact match for Series (return scalar)
+            # and raise KeyError for Frame
+            for timestamp, expected in zip(index, values):
+                ts_string = timestamp.strftime(formats[rnum])
+                # make ts_string as precise as index
+                result = df["a"][ts_string]
+                assert isinstance(result, np.int64)
+                assert result == expected
+                msg = rf"^'{ts_string}'$"
+                with pytest.raises(KeyError, match=msg):
+                    df[ts_string]
+
+            # Timestamp with resolution less precise than index
+            for fmt in formats[:rnum]:
+                for element, theslice in [[0, slice(None, 1)], [1, slice(1, None)]]:
+                    ts_string = index[element].strftime(fmt)
+
+                    # Series should return slice
+                    result = df["a"][ts_string]
+                    expected = df["a"][theslice]
+                    tm.assert_series_equal(result, expected)
+
+                    # pre-2.0 df[ts_string] was overloaded to interpret this
+                    #  as slicing along index
+                    with pytest.raises(KeyError, match=ts_string):
+                        df[ts_string]
+
+            # Timestamp with resolution more precise than index
+            # Compatible with existing key
+            # Should return scalar for Series
+            # and raise KeyError for Frame
+            for fmt in formats[rnum + 1 :]:
+                ts_string = index[1].strftime(fmt)
+                result = df["a"][ts_string]
+                assert isinstance(result, np.int64)
+                assert result == 2
+                msg = rf"^'{ts_string}'$"
+                with pytest.raises(KeyError, match=msg):
+                    df[ts_string]
+
+            # Not compatible with existing key
+            # Should raise KeyError
+            for fmt, res in list(zip(formats, resolutions))[rnum + 1 :]:
+                ts = index[1] + Timedelta("1 " + res)
+                ts_string = ts.strftime(fmt)
+                msg = rf"^'{ts_string}'$"
+                with pytest.raises(KeyError, match=msg):
+                    df["a"][ts_string]
+                with pytest.raises(KeyError, match=msg):
+                    df[ts_string]
+
+    def test_partial_slicing_with_multiindex(self):
+        # GH 4758
+        # partial string indexing with a multi-index buggy
+        df = DataFrame(
+            {
+                "ACCOUNT": ["ACCT1", "ACCT1", "ACCT1", "ACCT2"],
+                "TICKER": ["ABC", "MNP", "XYZ", "XYZ"],
+                "val": [1, 2, 3, 4],
+            },
+            index=date_range("2013-06-19 09:30:00", periods=4, freq="5min"),
+        )
+        df_multi = df.set_index(["ACCOUNT", "TICKER"], append=True)
+
+        expected = DataFrame(
+            [[1]], index=Index(["ABC"], name="TICKER"), columns=["val"]
+        )
+        result = df_multi.loc[("2013-06-19 09:30:00", "ACCT1")]
+        tm.assert_frame_equal(result, expected)
+
+        expected = df_multi.loc[
+            (Timestamp("2013-06-19 09:30:00", tz=None), "ACCT1", "ABC")
+        ]
+        result = df_multi.loc[("2013-06-19 09:30:00", "ACCT1", "ABC")]
+        tm.assert_series_equal(result, expected)
+
+        # partial string indexing on first level, scalar indexing on the other two
+        result = df_multi.loc[("2013-06-19", "ACCT1", "ABC")]
+        expected = df_multi.iloc[:1].droplevel([1, 2])
+        tm.assert_frame_equal(result, expected)
+
+    def test_partial_slicing_with_multiindex_series(self):
+        # GH 4294
+        # partial slice on a series mi
+        ser = Series(
+            range(250),
+            index=MultiIndex.from_product(
+                [date_range("2000-1-1", periods=50), range(5)]
+            ),
+        )
+
+        s2 = ser[:-1].copy()
+        expected = s2["2000-1-4"]
+        result = s2[Timestamp("2000-1-4")]
+        tm.assert_series_equal(result, expected)
+
+        result = ser[Timestamp("2000-1-4")]
+        expected = ser["2000-1-4"]
+        tm.assert_series_equal(result, expected)
+
+        df2 = DataFrame(ser)
+        expected = df2.xs("2000-1-4")
+        result = df2.loc[Timestamp("2000-1-4")]
+        tm.assert_frame_equal(result, expected)
+
+    def test_partial_slice_requires_monotonicity(self):
+        # Disallowed since 2.0 (GH 37819)
+        ser = Series(np.arange(10), date_range("2014-01-01", periods=10))
+
+        nonmonotonic = ser.iloc[[3, 5, 4]]
+        timestamp = Timestamp("2014-01-10")
+        with pytest.raises(
+            KeyError, match="Value based partial slicing on non-monotonic"
+        ):
+            nonmonotonic["2014-01-10":]
+
+        with pytest.raises(KeyError, match=r"Timestamp\('2014-01-10 00:00:00'\)"):
+            nonmonotonic[timestamp:]
+
+        with pytest.raises(
+            KeyError, match="Value based partial slicing on non-monotonic"
+        ):
+            nonmonotonic.loc["2014-01-10":]
+
+        with pytest.raises(KeyError, match=r"Timestamp\('2014-01-10 00:00:00'\)"):
+            nonmonotonic.loc[timestamp:]
+
+    def test_loc_datetime_length_one(self):
+        # GH16071
+        df = DataFrame(
+            columns=["1"],
+            index=date_range("2016-10-01T00:00:00", "2016-10-01T23:59:59"),
+        )
+        result = df.loc[datetime(2016, 10, 1) :]
+        tm.assert_frame_equal(result, df)
+
+        result = df.loc["2016-10-01T00:00:00":]
+        tm.assert_frame_equal(result, df)
+
+    @pytest.mark.parametrize(
+        "start",
+        [
+            "2018-12-02 21:50:00+00:00",
+            Timestamp("2018-12-02 21:50:00+00:00"),
+            Timestamp("2018-12-02 21:50:00+00:00").to_pydatetime(),
+        ],
+    )
+    @pytest.mark.parametrize(
+        "end",
+        [
+            "2018-12-02 21:52:00+00:00",
+            Timestamp("2018-12-02 21:52:00+00:00"),
+            Timestamp("2018-12-02 21:52:00+00:00").to_pydatetime(),
+        ],
+    )
+    def test_getitem_with_datestring_with_UTC_offset(self, start, end):
+        # GH 24076
+        idx = date_range(
+            start="2018-12-02 14:50:00-07:00",
+            end="2018-12-02 14:50:00-07:00",
+            freq="1min",
+        )
+        df = DataFrame(1, index=idx, columns=["A"])
+        result = df[start:end]
+        expected = df.iloc[0:3, :]
+        tm.assert_frame_equal(result, expected)
+
+        # GH 16785
+        start = str(start)
+        end = str(end)
+        with pytest.raises(ValueError, match="Both dates must"):
+            df[start : end[:-4] + "1:00"]
+
+        with pytest.raises(ValueError, match="The index must be timezone"):
+            df = df.tz_localize(None)
+            df[start:end]
+
+    def test_slice_reduce_to_series(self):
+        # GH 27516
+        df = DataFrame(
+            {"A": range(24)}, index=date_range("2000", periods=24, freq="ME")
+        )
+        expected = Series(
+            range(12), index=date_range("2000", periods=12, freq="ME"), name="A"
+        )
+        result = df.loc["2000", "A"]
+        tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/conftest.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..15062aee56e3a1b91d1f6eb76a4f86e381e0ad44
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/conftest.py
@@ -0,0 +1,27 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Index,
+    MultiIndex,
+)
+
+
+# Note: identical the "multi" entry in the top-level "index" fixture
+@pytest.fixture
+def idx():
+    # a MultiIndex used to test the general functionality of the
+    # general functionality of this object
+    major_axis = Index(["foo", "bar", "baz", "qux"])
+    minor_axis = Index(["one", "two"])
+
+    major_codes = np.array([0, 0, 1, 2, 3, 3])
+    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
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_compat.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_compat.py
new file mode 100644
index 0000000000000000000000000000000000000000..27a8c6e9b715880a57e711e8eab457ae553a4867
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_compat.py
@@ -0,0 +1,122 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import MultiIndex
+import pandas._testing as tm
+
+
+def test_numeric_compat(idx):
+    with pytest.raises(TypeError, match="cannot perform __mul__"):
+        idx * 1
+
+    with pytest.raises(TypeError, match="cannot perform __rmul__"):
+        1 * idx
+
+    div_err = "cannot perform __truediv__"
+    with pytest.raises(TypeError, match=div_err):
+        idx / 1
+
+    div_err = div_err.replace(" __", " __r")
+    with pytest.raises(TypeError, match=div_err):
+        1 / idx
+
+    with pytest.raises(TypeError, match="cannot perform __floordiv__"):
+        idx // 1
+
+    with pytest.raises(TypeError, match="cannot perform __rfloordiv__"):
+        1 // idx
+
+
+@pytest.mark.parametrize("method", ["all", "any", "__invert__"])
+def test_logical_compat(idx, method):
+    msg = f"cannot perform {method}"
+
+    with pytest.raises(TypeError, match=msg):
+        getattr(idx, method)()
+
+
+def test_inplace_mutation_resets_values():
+    levels = [["a", "b", "c"], [4]]
+    levels2 = [[1, 2, 3], ["a"]]
+    codes = [[0, 1, 0, 2, 2, 0], [0, 0, 0, 0, 0, 0]]
+
+    mi1 = MultiIndex(levels=levels, codes=codes)
+    mi2 = MultiIndex(levels=levels2, codes=codes)
+
+    # instantiating MultiIndex should not access/cache _.values
+    assert "_values" not in mi1._cache
+    assert "_values" not in mi2._cache
+
+    vals = mi1.values.copy()
+    vals2 = mi2.values.copy()
+
+    # accessing .values should cache ._values
+    assert mi1._values is mi1._cache["_values"]
+    assert mi1.values is mi1._cache["_values"]
+    assert isinstance(mi1._cache["_values"], np.ndarray)
+
+    # Make sure level setting works
+    new_vals = mi1.set_levels(levels2).values
+    tm.assert_almost_equal(vals2, new_vals)
+
+    #  Doesn't drop _values from _cache [implementation detail]
+    tm.assert_almost_equal(mi1._cache["_values"], vals)
+
+    # ...and values is still same too
+    tm.assert_almost_equal(mi1.values, vals)
+
+    # Make sure label setting works too
+    codes2 = [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]
+    exp_values = np.empty((6,), dtype=object)
+    exp_values[:] = [(1, "a")] * 6
+
+    # Must be 1d array of tuples
+    assert exp_values.shape == (6,)
+
+    new_mi = mi2.set_codes(codes2)
+    assert "_values" not in new_mi._cache
+    new_values = new_mi.values
+    assert "_values" in new_mi._cache
+
+    # Shouldn't change cache
+    tm.assert_almost_equal(mi2._cache["_values"], vals2)
+
+    # Should have correct values
+    tm.assert_almost_equal(exp_values, new_values)
+
+
+def test_boxable_categorical_values():
+    cat = pd.Categorical(pd.date_range("2012-01-01", periods=3, freq="h"))
+    result = MultiIndex.from_product([["a", "b", "c"], cat]).values
+    expected = pd.Series(
+        [
+            ("a", pd.Timestamp("2012-01-01 00:00:00")),
+            ("a", pd.Timestamp("2012-01-01 01:00:00")),
+            ("a", pd.Timestamp("2012-01-01 02:00:00")),
+            ("b", pd.Timestamp("2012-01-01 00:00:00")),
+            ("b", pd.Timestamp("2012-01-01 01:00:00")),
+            ("b", pd.Timestamp("2012-01-01 02:00:00")),
+            ("c", pd.Timestamp("2012-01-01 00:00:00")),
+            ("c", pd.Timestamp("2012-01-01 01:00:00")),
+            ("c", pd.Timestamp("2012-01-01 02:00:00")),
+        ]
+    ).values
+    tm.assert_numpy_array_equal(result, expected)
+    result = pd.DataFrame({"a": ["a", "b", "c"], "b": cat, "c": np.array(cat)}).values
+    expected = pd.DataFrame(
+        {
+            "a": ["a", "b", "c"],
+            "b": [
+                pd.Timestamp("2012-01-01 00:00:00"),
+                pd.Timestamp("2012-01-01 01:00:00"),
+                pd.Timestamp("2012-01-01 02:00:00"),
+            ],
+            "c": [
+                pd.Timestamp("2012-01-01 00:00:00"),
+                pd.Timestamp("2012-01-01 01:00:00"),
+                pd.Timestamp("2012-01-01 02:00:00"),
+            ],
+        }
+    ).values
+    tm.assert_numpy_array_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_constructors.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_constructors.py
new file mode 100644
index 0000000000000000000000000000000000000000..8456e6a7acba5a160556f6f3192fa795ce678786
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_constructors.py
@@ -0,0 +1,860 @@
+from datetime import (
+    date,
+    datetime,
+)
+import itertools
+
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
+
+import pandas as pd
+from pandas import (
+    Index,
+    MultiIndex,
+    Series,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+
+
+def test_constructor_single_level():
+    result = MultiIndex(
+        levels=[["foo", "bar", "baz", "qux"]], codes=[[0, 1, 2, 3]], names=["first"]
+    )
+    assert isinstance(result, MultiIndex)
+    expected = Index(["foo", "bar", "baz", "qux"], name="first")
+    tm.assert_index_equal(result.levels[0], expected)
+    assert result.names == ["first"]
+
+
+def test_constructor_no_levels():
+    msg = "non-zero number of levels/codes"
+    with pytest.raises(ValueError, match=msg):
+        MultiIndex(levels=[], codes=[])
+
+    msg = "Must pass both levels and codes"
+    with pytest.raises(TypeError, match=msg):
+        MultiIndex(levels=[])
+    with pytest.raises(TypeError, match=msg):
+        MultiIndex(codes=[])
+
+
+def test_constructor_nonhashable_names():
+    # GH 20527
+    levels = [[1, 2], ["one", "two"]]
+    codes = [[0, 0, 1, 1], [0, 1, 0, 1]]
+    names = (["foo"], ["bar"])
+    msg = r"MultiIndex\.name must be a hashable type"
+    with pytest.raises(TypeError, match=msg):
+        MultiIndex(levels=levels, codes=codes, names=names)
+
+    # With .rename()
+    mi = MultiIndex(
+        levels=[[1, 2], ["one", "two"]],
+        codes=[[0, 0, 1, 1], [0, 1, 0, 1]],
+        names=("foo", "bar"),
+    )
+    renamed = [["fooo"], ["barr"]]
+    with pytest.raises(TypeError, match=msg):
+        mi.rename(names=renamed)
+
+    # With .set_names()
+    with pytest.raises(TypeError, match=msg):
+        mi.set_names(names=renamed)
+
+
+def test_constructor_mismatched_codes_levels(idx):
+    codes = [np.array([1]), np.array([2]), np.array([3])]
+    levels = ["a"]
+
+    msg = "Length of levels and codes must be the same"
+    with pytest.raises(ValueError, match=msg):
+        MultiIndex(levels=levels, codes=codes)
+
+    length_error = (
+        r"On level 0, code max \(3\) >= length of level \(1\)\. "
+        "NOTE: this index is in an inconsistent state"
+    )
+    label_error = r"Unequal code lengths: \[4, 2\]"
+    code_value_error = r"On level 0, code value \(-2\) < -1"
+
+    # important to check that it's looking at the right thing.
+    with pytest.raises(ValueError, match=length_error):
+        MultiIndex(levels=[["a"], ["b"]], codes=[[0, 1, 2, 3], [0, 3, 4, 1]])
+
+    with pytest.raises(ValueError, match=label_error):
+        MultiIndex(levels=[["a"], ["b"]], codes=[[0, 0, 0, 0], [0, 0]])
+
+    # external API
+    with pytest.raises(ValueError, match=length_error):
+        idx.copy().set_levels([["a"], ["b"]])
+
+    with pytest.raises(ValueError, match=label_error):
+        idx.copy().set_codes([[0, 0, 0, 0], [0, 0]])
+
+    # test set_codes with verify_integrity=False
+    # the setting should not raise any value error
+    idx.copy().set_codes(codes=[[0, 0, 0, 0], [0, 0]], verify_integrity=False)
+
+    # code value smaller than -1
+    with pytest.raises(ValueError, match=code_value_error):
+        MultiIndex(levels=[["a"], ["b"]], codes=[[0, -2], [0, 0]])
+
+
+def test_na_levels():
+    # GH26408
+    # test if codes are re-assigned value -1 for levels
+    # with missing values (NaN, NaT, None)
+    result = MultiIndex(
+        levels=[[np.nan, None, pd.NaT, 128, 2]], codes=[[0, -1, 1, 2, 3, 4]]
+    )
+    expected = MultiIndex(
+        levels=[[np.nan, None, pd.NaT, 128, 2]], codes=[[-1, -1, -1, -1, 3, 4]]
+    )
+    tm.assert_index_equal(result, expected)
+
+    result = MultiIndex(
+        levels=[[np.nan, "s", pd.NaT, 128, None]], codes=[[0, -1, 1, 2, 3, 4]]
+    )
+    expected = MultiIndex(
+        levels=[[np.nan, "s", pd.NaT, 128, None]], codes=[[-1, -1, 1, -1, 3, -1]]
+    )
+    tm.assert_index_equal(result, expected)
+
+    # verify set_levels and set_codes
+    result = MultiIndex(
+        levels=[[1, 2, 3, 4, 5]], codes=[[0, -1, 1, 2, 3, 4]]
+    ).set_levels([[np.nan, "s", pd.NaT, 128, None]])
+    tm.assert_index_equal(result, expected)
+
+    result = MultiIndex(
+        levels=[[np.nan, "s", pd.NaT, 128, None]], codes=[[1, 2, 2, 2, 2, 2]]
+    ).set_codes([[0, -1, 1, 2, 3, 4]])
+    tm.assert_index_equal(result, expected)
+
+
+def test_copy_in_constructor():
+    levels = np.array(["a", "b", "c"])
+    codes = np.array([1, 1, 2, 0, 0, 1, 1])
+    val = codes[0]
+    mi = MultiIndex(levels=[levels, levels], codes=[codes, codes], copy=True)
+    assert mi.codes[0][0] == val
+    codes[0] = 15
+    assert mi.codes[0][0] == val
+    val = levels[0]
+    levels[0] = "PANDA"
+    assert mi.levels[0][0] == val
+
+
+# ----------------------------------------------------------------------------
+# from_arrays
+# ----------------------------------------------------------------------------
+def test_from_arrays(idx):
+    arrays = [
+        np.asarray(lev).take(level_codes)
+        for lev, level_codes in zip(idx.levels, idx.codes)
+    ]
+
+    # list of arrays as input
+    result = MultiIndex.from_arrays(arrays, names=idx.names)
+    tm.assert_index_equal(result, idx)
+
+    # infer correctly
+    result = MultiIndex.from_arrays([[pd.NaT, Timestamp("20130101")], ["a", "b"]])
+    assert result.levels[0].equals(Index([Timestamp("20130101")]))
+    assert result.levels[1].equals(Index(["a", "b"]))
+
+
+def test_from_arrays_iterator(idx):
+    # GH 18434
+    arrays = [
+        np.asarray(lev).take(level_codes)
+        for lev, level_codes in zip(idx.levels, idx.codes)
+    ]
+
+    # iterator as input
+    result = MultiIndex.from_arrays(iter(arrays), names=idx.names)
+    tm.assert_index_equal(result, idx)
+
+    # invalid iterator input
+    msg = "Input must be a list / sequence of array-likes."
+    with pytest.raises(TypeError, match=msg):
+        MultiIndex.from_arrays(0)
+
+
+def test_from_arrays_tuples(idx):
+    arrays = tuple(
+        tuple(np.asarray(lev).take(level_codes))
+        for lev, level_codes in zip(idx.levels, idx.codes)
+    )
+
+    # tuple of tuples as input
+    result = MultiIndex.from_arrays(arrays, names=idx.names)
+    tm.assert_index_equal(result, idx)
+
+
+@pytest.mark.parametrize(
+    ("idx1", "idx2"),
+    [
+        (
+            pd.period_range("2011-01-01", freq="D", periods=3),
+            pd.period_range("2015-01-01", freq="h", periods=3),
+        ),
+        (
+            date_range("2015-01-01 10:00", freq="D", periods=3, tz="US/Eastern"),
+            date_range("2015-01-01 10:00", freq="h", periods=3, tz="Asia/Tokyo"),
+        ),
+        (
+            pd.timedelta_range("1 days", freq="D", periods=3),
+            pd.timedelta_range("2 hours", freq="h", periods=3),
+        ),
+    ],
+)
+def test_from_arrays_index_series_period_datetimetz_and_timedelta(idx1, idx2):
+    result = MultiIndex.from_arrays([idx1, idx2])
+    tm.assert_index_equal(result.get_level_values(0), idx1)
+    tm.assert_index_equal(result.get_level_values(1), idx2)
+
+    result2 = MultiIndex.from_arrays([Series(idx1), Series(idx2)])
+    tm.assert_index_equal(result2.get_level_values(0), idx1)
+    tm.assert_index_equal(result2.get_level_values(1), idx2)
+
+    tm.assert_index_equal(result, result2)
+
+
+def test_from_arrays_index_datetimelike_mixed():
+    idx1 = date_range("2015-01-01 10:00", freq="D", periods=3, tz="US/Eastern")
+    idx2 = date_range("2015-01-01 10:00", freq="h", periods=3)
+    idx3 = pd.timedelta_range("1 days", freq="D", periods=3)
+    idx4 = pd.period_range("2011-01-01", freq="D", periods=3)
+
+    result = MultiIndex.from_arrays([idx1, idx2, idx3, idx4])
+    tm.assert_index_equal(result.get_level_values(0), idx1)
+    tm.assert_index_equal(result.get_level_values(1), idx2)
+    tm.assert_index_equal(result.get_level_values(2), idx3)
+    tm.assert_index_equal(result.get_level_values(3), idx4)
+
+    result2 = MultiIndex.from_arrays(
+        [Series(idx1), Series(idx2), Series(idx3), Series(idx4)]
+    )
+    tm.assert_index_equal(result2.get_level_values(0), idx1)
+    tm.assert_index_equal(result2.get_level_values(1), idx2)
+    tm.assert_index_equal(result2.get_level_values(2), idx3)
+    tm.assert_index_equal(result2.get_level_values(3), idx4)
+
+    tm.assert_index_equal(result, result2)
+
+
+def test_from_arrays_index_series_categorical():
+    # GH13743
+    idx1 = pd.CategoricalIndex(list("abcaab"), categories=list("bac"), ordered=False)
+    idx2 = pd.CategoricalIndex(list("abcaab"), categories=list("bac"), ordered=True)
+
+    result = MultiIndex.from_arrays([idx1, idx2])
+    tm.assert_index_equal(result.get_level_values(0), idx1)
+    tm.assert_index_equal(result.get_level_values(1), idx2)
+
+    result2 = MultiIndex.from_arrays([Series(idx1), Series(idx2)])
+    tm.assert_index_equal(result2.get_level_values(0), idx1)
+    tm.assert_index_equal(result2.get_level_values(1), idx2)
+
+    result3 = MultiIndex.from_arrays([idx1.values, idx2.values])
+    tm.assert_index_equal(result3.get_level_values(0), idx1)
+    tm.assert_index_equal(result3.get_level_values(1), idx2)
+
+
+def test_from_arrays_empty():
+    # 0 levels
+    msg = "Must pass non-zero number of levels/codes"
+    with pytest.raises(ValueError, match=msg):
+        MultiIndex.from_arrays(arrays=[])
+
+    # 1 level
+    result = MultiIndex.from_arrays(arrays=[[]], names=["A"])
+    assert isinstance(result, MultiIndex)
+    expected = Index([], name="A")
+    tm.assert_index_equal(result.levels[0], expected)
+    assert result.names == ["A"]
+
+    # N levels
+    for N in [2, 3]:
+        arrays = [[]] * N
+        names = list("ABC")[:N]
+        result = MultiIndex.from_arrays(arrays=arrays, names=names)
+        expected = MultiIndex(levels=[[]] * N, codes=[[]] * N, names=names)
+        tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "invalid_sequence_of_arrays",
+    [
+        1,
+        [1],
+        [1, 2],
+        [[1], 2],
+        [1, [2]],
+        "a",
+        ["a"],
+        ["a", "b"],
+        [["a"], "b"],
+        (1,),
+        (1, 2),
+        ([1], 2),
+        (1, [2]),
+        "a",
+        ("a",),
+        ("a", "b"),
+        (["a"], "b"),
+        [(1,), 2],
+        [1, (2,)],
+        [("a",), "b"],
+        ((1,), 2),
+        (1, (2,)),
+        (("a",), "b"),
+    ],
+)
+def test_from_arrays_invalid_input(invalid_sequence_of_arrays):
+    msg = "Input must be a list / sequence of array-likes"
+    with pytest.raises(TypeError, match=msg):
+        MultiIndex.from_arrays(arrays=invalid_sequence_of_arrays)
+
+
+@pytest.mark.parametrize(
+    "idx1, idx2", [([1, 2, 3], ["a", "b"]), ([], ["a", "b"]), ([1, 2, 3], [])]
+)
+def test_from_arrays_different_lengths(idx1, idx2):
+    # see gh-13599
+    msg = "^all arrays must be same length$"
+    with pytest.raises(ValueError, match=msg):
+        MultiIndex.from_arrays([idx1, idx2])
+
+
+def test_from_arrays_respects_none_names():
+    # GH27292
+    a = Series([1, 2, 3], name="foo")
+    b = Series(["a", "b", "c"], name="bar")
+
+    result = MultiIndex.from_arrays([a, b], names=None)
+    expected = MultiIndex(
+        levels=[[1, 2, 3], ["a", "b", "c"]], codes=[[0, 1, 2], [0, 1, 2]], names=None
+    )
+
+    tm.assert_index_equal(result, expected)
+
+
+# ----------------------------------------------------------------------------
+# from_tuples
+# ----------------------------------------------------------------------------
+def test_from_tuples():
+    msg = "Cannot infer number of levels from empty list"
+    with pytest.raises(TypeError, match=msg):
+        MultiIndex.from_tuples([])
+
+    expected = MultiIndex(
+        levels=[[1, 3], [2, 4]], codes=[[0, 1], [0, 1]], names=["a", "b"]
+    )
+
+    # input tuples
+    result = MultiIndex.from_tuples(((1, 2), (3, 4)), names=["a", "b"])
+    tm.assert_index_equal(result, expected)
+
+
+def test_from_tuples_iterator():
+    # GH 18434
+    # input iterator for tuples
+    expected = MultiIndex(
+        levels=[[1, 3], [2, 4]], codes=[[0, 1], [0, 1]], names=["a", "b"]
+    )
+
+    result = MultiIndex.from_tuples(zip([1, 3], [2, 4]), names=["a", "b"])
+    tm.assert_index_equal(result, expected)
+
+    # input non-iterables
+    msg = "Input must be a list / sequence of tuple-likes."
+    with pytest.raises(TypeError, match=msg):
+        MultiIndex.from_tuples(0)
+
+
+def test_from_tuples_empty():
+    # GH 16777
+    result = MultiIndex.from_tuples([], names=["a", "b"])
+    expected = MultiIndex.from_arrays(arrays=[[], []], names=["a", "b"])
+    tm.assert_index_equal(result, expected)
+
+
+def test_from_tuples_index_values(idx):
+    result = MultiIndex.from_tuples(idx)
+    assert (result.values == idx.values).all()
+
+
+def test_tuples_with_name_string():
+    # GH 15110 and GH 14848
+
+    li = [(0, 0, 1), (0, 1, 0), (1, 0, 0)]
+    msg = "Names should be list-like for a MultiIndex"
+    with pytest.raises(ValueError, match=msg):
+        Index(li, name="abc")
+    with pytest.raises(ValueError, match=msg):
+        Index(li, name="a")
+
+
+def test_from_tuples_with_tuple_label():
+    # GH 15457
+    expected = pd.DataFrame(
+        [[2, 1, 2], [4, (1, 2), 3]], columns=["a", "b", "c"]
+    ).set_index(["a", "b"])
+    idx = MultiIndex.from_tuples([(2, 1), (4, (1, 2))], names=("a", "b"))
+    result = pd.DataFrame([2, 3], columns=["c"], index=idx)
+    tm.assert_frame_equal(expected, result)
+
+
+# ----------------------------------------------------------------------------
+# from_product
+# ----------------------------------------------------------------------------
+def test_from_product_empty_zero_levels():
+    # 0 levels
+    msg = "Must pass non-zero number of levels/codes"
+    with pytest.raises(ValueError, match=msg):
+        MultiIndex.from_product([])
+
+
+def test_from_product_empty_one_level():
+    result = MultiIndex.from_product([[]], names=["A"])
+    expected = Index([], name="A")
+    tm.assert_index_equal(result.levels[0], expected)
+    assert result.names == ["A"]
+
+
+@pytest.mark.parametrize(
+    "first, second", [([], []), (["foo", "bar", "baz"], []), ([], ["a", "b", "c"])]
+)
+def test_from_product_empty_two_levels(first, second):
+    names = ["A", "B"]
+    result = MultiIndex.from_product([first, second], names=names)
+    expected = MultiIndex(levels=[first, second], codes=[[], []], names=names)
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("N", list(range(4)))
+def test_from_product_empty_three_levels(N):
+    # GH12258
+    names = ["A", "B", "C"]
+    lvl2 = list(range(N))
+    result = MultiIndex.from_product([[], lvl2, []], names=names)
+    expected = MultiIndex(levels=[[], lvl2, []], codes=[[], [], []], names=names)
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "invalid_input", [1, [1], [1, 2], [[1], 2], "a", ["a"], ["a", "b"], [["a"], "b"]]
+)
+def test_from_product_invalid_input(invalid_input):
+    msg = r"Input must be a list / sequence of iterables|Input must be list-like"
+    with pytest.raises(TypeError, match=msg):
+        MultiIndex.from_product(iterables=invalid_input)
+
+
+def test_from_product_datetimeindex():
+    dt_index = date_range("2000-01-01", periods=2)
+    mi = MultiIndex.from_product([[1, 2], dt_index])
+    etalon = construct_1d_object_array_from_listlike(
+        [
+            (1, Timestamp("2000-01-01")),
+            (1, Timestamp("2000-01-02")),
+            (2, Timestamp("2000-01-01")),
+            (2, Timestamp("2000-01-02")),
+        ]
+    )
+    tm.assert_numpy_array_equal(mi.values, etalon)
+
+
+def test_from_product_rangeindex():
+    # RangeIndex is preserved by factorize, so preserved in levels
+    rng = Index(range(5))
+    other = ["a", "b"]
+    mi = MultiIndex.from_product([rng, other])
+    tm.assert_index_equal(mi._levels[0], rng, exact=True)
+
+
+@pytest.mark.parametrize("ordered", [False, True])
+@pytest.mark.parametrize("f", [lambda x: x, lambda x: Series(x), lambda x: x.values])
+def test_from_product_index_series_categorical(ordered, f):
+    # GH13743
+    first = ["foo", "bar"]
+
+    idx = pd.CategoricalIndex(list("abcaab"), categories=list("bac"), ordered=ordered)
+    expected = pd.CategoricalIndex(
+        list("abcaab") + list("abcaab"), categories=list("bac"), ordered=ordered
+    )
+
+    result = MultiIndex.from_product([first, f(idx)])
+    tm.assert_index_equal(result.get_level_values(1), expected)
+
+
+def test_from_product():
+    first = ["foo", "bar", "buz"]
+    second = ["a", "b", "c"]
+    names = ["first", "second"]
+    result = MultiIndex.from_product([first, second], names=names)
+
+    tuples = [
+        ("foo", "a"),
+        ("foo", "b"),
+        ("foo", "c"),
+        ("bar", "a"),
+        ("bar", "b"),
+        ("bar", "c"),
+        ("buz", "a"),
+        ("buz", "b"),
+        ("buz", "c"),
+    ]
+    expected = MultiIndex.from_tuples(tuples, names=names)
+
+    tm.assert_index_equal(result, expected)
+
+
+def test_from_product_iterator():
+    # GH 18434
+    first = ["foo", "bar", "buz"]
+    second = ["a", "b", "c"]
+    names = ["first", "second"]
+    tuples = [
+        ("foo", "a"),
+        ("foo", "b"),
+        ("foo", "c"),
+        ("bar", "a"),
+        ("bar", "b"),
+        ("bar", "c"),
+        ("buz", "a"),
+        ("buz", "b"),
+        ("buz", "c"),
+    ]
+    expected = MultiIndex.from_tuples(tuples, names=names)
+
+    # iterator as input
+    result = MultiIndex.from_product(iter([first, second]), names=names)
+    tm.assert_index_equal(result, expected)
+
+    # Invalid non-iterable input
+    msg = "Input must be a list / sequence of iterables."
+    with pytest.raises(TypeError, match=msg):
+        MultiIndex.from_product(0)
+
+
+@pytest.mark.parametrize(
+    "a, b, expected_names",
+    [
+        (
+            Series([1, 2, 3], name="foo"),
+            Series(["a", "b"], name="bar"),
+            ["foo", "bar"],
+        ),
+        (Series([1, 2, 3], name="foo"), ["a", "b"], ["foo", None]),
+        ([1, 2, 3], ["a", "b"], None),
+    ],
+)
+def test_from_product_infer_names(a, b, expected_names):
+    # GH27292
+    result = MultiIndex.from_product([a, b])
+    expected = MultiIndex(
+        levels=[[1, 2, 3], ["a", "b"]],
+        codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]],
+        names=expected_names,
+    )
+    tm.assert_index_equal(result, expected)
+
+
+def test_from_product_respects_none_names():
+    # GH27292
+    a = Series([1, 2, 3], name="foo")
+    b = Series(["a", "b"], name="bar")
+
+    result = MultiIndex.from_product([a, b], names=None)
+    expected = MultiIndex(
+        levels=[[1, 2, 3], ["a", "b"]],
+        codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]],
+        names=None,
+    )
+    tm.assert_index_equal(result, expected)
+
+
+def test_from_product_readonly():
+    # GH#15286 passing read-only array to from_product
+    a = np.array(range(3))
+    b = ["a", "b"]
+    expected = MultiIndex.from_product([a, b])
+
+    a.setflags(write=False)
+    result = MultiIndex.from_product([a, b])
+    tm.assert_index_equal(result, expected)
+
+
+def test_create_index_existing_name(idx):
+    # GH11193, when an existing index is passed, and a new name is not
+    # specified, the new index should inherit the previous object name
+    index = idx
+    index.names = ["foo", "bar"]
+    result = Index(index)
+    expected = Index(
+        Index(
+            [
+                ("foo", "one"),
+                ("foo", "two"),
+                ("bar", "one"),
+                ("baz", "two"),
+                ("qux", "one"),
+                ("qux", "two"),
+            ],
+            dtype="object",
+        )
+    )
+    tm.assert_index_equal(result, expected)
+
+    result = Index(index, name="A")
+    expected = Index(
+        Index(
+            [
+                ("foo", "one"),
+                ("foo", "two"),
+                ("bar", "one"),
+                ("baz", "two"),
+                ("qux", "one"),
+                ("qux", "two"),
+            ],
+            dtype="object",
+        ),
+        name="A",
+    )
+    tm.assert_index_equal(result, expected)
+
+
+# ----------------------------------------------------------------------------
+# from_frame
+# ----------------------------------------------------------------------------
+def test_from_frame():
+    # GH 22420
+    df = pd.DataFrame(
+        [["a", "a"], ["a", "b"], ["b", "a"], ["b", "b"]], columns=["L1", "L2"]
+    )
+    expected = MultiIndex.from_tuples(
+        [("a", "a"), ("a", "b"), ("b", "a"), ("b", "b")], names=["L1", "L2"]
+    )
+    result = MultiIndex.from_frame(df)
+    tm.assert_index_equal(expected, result)
+
+
+def test_from_frame_missing_values_multiIndex():
+    # GH 39984
+    pa = pytest.importorskip("pyarrow")
+
+    df = pd.DataFrame(
+        {
+            "a": Series([1, 2, None], dtype="Int64"),
+            "b": pd.Float64Dtype().__from_arrow__(pa.array([0.2, np.nan, None])),
+        }
+    )
+    multi_indexed = MultiIndex.from_frame(df)
+    expected = MultiIndex.from_arrays(
+        [
+            Series([1, 2, None]).astype("Int64"),
+            pd.Float64Dtype().__from_arrow__(pa.array([0.2, np.nan, None])),
+        ],
+        names=["a", "b"],
+    )
+    tm.assert_index_equal(multi_indexed, expected)
+
+
+@pytest.mark.parametrize(
+    "non_frame",
+    [
+        Series([1, 2, 3, 4]),
+        [1, 2, 3, 4],
+        [[1, 2], [3, 4], [5, 6]],
+        Index([1, 2, 3, 4]),
+        np.array([[1, 2], [3, 4], [5, 6]]),
+        27,
+    ],
+)
+def test_from_frame_error(non_frame):
+    # GH 22420
+    with pytest.raises(TypeError, match="Input must be a DataFrame"):
+        MultiIndex.from_frame(non_frame)
+
+
+def test_from_frame_dtype_fidelity():
+    # GH 22420
+    df = pd.DataFrame(
+        {
+            "dates": date_range("19910905", periods=6, tz="US/Eastern"),
+            "a": [1, 1, 1, 2, 2, 2],
+            "b": pd.Categorical(["a", "a", "b", "b", "c", "c"], ordered=True),
+            "c": ["x", "x", "y", "z", "x", "y"],
+        }
+    )
+    original_dtypes = df.dtypes.to_dict()
+
+    expected_mi = MultiIndex.from_arrays(
+        [
+            date_range("19910905", periods=6, tz="US/Eastern"),
+            [1, 1, 1, 2, 2, 2],
+            pd.Categorical(["a", "a", "b", "b", "c", "c"], ordered=True),
+            ["x", "x", "y", "z", "x", "y"],
+        ],
+        names=["dates", "a", "b", "c"],
+    )
+    mi = MultiIndex.from_frame(df)
+    mi_dtypes = {name: mi.levels[i].dtype for i, name in enumerate(mi.names)}
+
+    tm.assert_index_equal(expected_mi, mi)
+    assert original_dtypes == mi_dtypes
+
+
+@pytest.mark.parametrize(
+    "names_in,names_out", [(None, [("L1", "x"), ("L2", "y")]), (["x", "y"], ["x", "y"])]
+)
+def test_from_frame_valid_names(names_in, names_out):
+    # GH 22420
+    df = pd.DataFrame(
+        [["a", "a"], ["a", "b"], ["b", "a"], ["b", "b"]],
+        columns=MultiIndex.from_tuples([("L1", "x"), ("L2", "y")]),
+    )
+    mi = MultiIndex.from_frame(df, names=names_in)
+    assert mi.names == names_out
+
+
+@pytest.mark.parametrize(
+    "names,expected_error_msg",
+    [
+        ("bad_input", "Names should be list-like for a MultiIndex"),
+        (["a", "b", "c"], "Length of names must match number of levels in MultiIndex"),
+    ],
+)
+def test_from_frame_invalid_names(names, expected_error_msg):
+    # GH 22420
+    df = pd.DataFrame(
+        [["a", "a"], ["a", "b"], ["b", "a"], ["b", "b"]],
+        columns=MultiIndex.from_tuples([("L1", "x"), ("L2", "y")]),
+    )
+    with pytest.raises(ValueError, match=expected_error_msg):
+        MultiIndex.from_frame(df, names=names)
+
+
+def test_index_equal_empty_iterable():
+    # #16844
+    a = MultiIndex(levels=[[], []], codes=[[], []], names=["a", "b"])
+    b = MultiIndex.from_arrays(arrays=[[], []], names=["a", "b"])
+    tm.assert_index_equal(a, b)
+
+
+def test_raise_invalid_sortorder():
+    # Test that the MultiIndex constructor raise when a incorrect sortorder is given
+    # GH#28518
+
+    levels = [[0, 1], [0, 1, 2]]
+
+    # Correct sortorder
+    MultiIndex(
+        levels=levels, codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]], sortorder=2
+    )
+
+    with pytest.raises(ValueError, match=r".* sortorder 2 with lexsort_depth 1.*"):
+        MultiIndex(
+            levels=levels, codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 2, 1]], sortorder=2
+        )
+
+    with pytest.raises(ValueError, match=r".* sortorder 1 with lexsort_depth 0.*"):
+        MultiIndex(
+            levels=levels, codes=[[0, 0, 1, 0, 1, 1], [0, 1, 0, 2, 2, 1]], sortorder=1
+        )
+
+
+def test_datetimeindex():
+    idx1 = pd.DatetimeIndex(
+        ["2013-04-01 9:00", "2013-04-02 9:00", "2013-04-03 9:00"] * 2, tz="Asia/Tokyo"
+    )
+    idx2 = date_range("2010/01/01", periods=6, freq="ME", tz="US/Eastern")
+    idx = MultiIndex.from_arrays([idx1, idx2])
+
+    expected1 = pd.DatetimeIndex(
+        ["2013-04-01 9:00", "2013-04-02 9:00", "2013-04-03 9:00"], tz="Asia/Tokyo"
+    )
+
+    tm.assert_index_equal(idx.levels[0], expected1)
+    tm.assert_index_equal(idx.levels[1], idx2)
+
+    # from datetime combos
+    # GH 7888
+    date1 = np.datetime64("today")
+    date2 = datetime.today()
+    date3 = Timestamp.today()
+
+    for d1, d2 in itertools.product([date1, date2, date3], [date1, date2, date3]):
+        index = MultiIndex.from_product([[d1], [d2]])
+        assert isinstance(index.levels[0], pd.DatetimeIndex)
+        assert isinstance(index.levels[1], pd.DatetimeIndex)
+
+    # but NOT date objects, matching Index behavior
+    date4 = date.today()
+    index = MultiIndex.from_product([[date4], [date2]])
+    assert not isinstance(index.levels[0], pd.DatetimeIndex)
+    assert isinstance(index.levels[1], pd.DatetimeIndex)
+
+
+def test_constructor_with_tz():
+    index = pd.DatetimeIndex(
+        ["2013/01/01 09:00", "2013/01/02 09:00"], name="dt1", tz="US/Pacific"
+    )
+    columns = pd.DatetimeIndex(
+        ["2014/01/01 09:00", "2014/01/02 09:00"], name="dt2", tz="Asia/Tokyo"
+    )
+
+    result = MultiIndex.from_arrays([index, columns])
+
+    assert result.names == ["dt1", "dt2"]
+    tm.assert_index_equal(result.levels[0], index)
+    tm.assert_index_equal(result.levels[1], columns)
+
+    result = MultiIndex.from_arrays([Series(index), Series(columns)])
+
+    assert result.names == ["dt1", "dt2"]
+    tm.assert_index_equal(result.levels[0], index)
+    tm.assert_index_equal(result.levels[1], columns)
+
+
+def test_multiindex_inference_consistency():
+    # check that inference behavior matches the base class
+
+    v = date.today()
+
+    arr = [v, v]
+
+    idx = Index(arr)
+    assert idx.dtype == object
+
+    mi = MultiIndex.from_arrays([arr])
+    lev = mi.levels[0]
+    assert lev.dtype == object
+
+    mi = MultiIndex.from_product([arr])
+    lev = mi.levels[0]
+    assert lev.dtype == object
+
+    mi = MultiIndex.from_tuples([(x,) for x in arr])
+    lev = mi.levels[0]
+    assert lev.dtype == object
+
+
+def test_dtype_representation(using_infer_string):
+    # GH#46900
+    pmidx = MultiIndex.from_arrays([[1], ["a"]], names=[("a", "b"), ("c", "d")])
+    result = pmidx.dtypes
+    exp = "object" if not using_infer_string else "string"
+    expected = Series(
+        ["int64", exp],
+        index=MultiIndex.from_tuples([("a", "b"), ("c", "d")]),
+        dtype=object,
+    )
+    tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_conversion.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_conversion.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c2ca045d6f990837fae4d2b3d7bcbbf40e175e9
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_conversion.py
@@ -0,0 +1,164 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    MultiIndex,
+)
+import pandas._testing as tm
+
+
+def test_to_numpy(idx):
+    result = idx.to_numpy()
+    exp = idx.values
+    tm.assert_numpy_array_equal(result, exp)
+
+
+def test_to_frame():
+    tuples = [(1, "one"), (1, "two"), (2, "one"), (2, "two")]
+
+    index = MultiIndex.from_tuples(tuples)
+    result = index.to_frame(index=False)
+    expected = DataFrame(tuples)
+    tm.assert_frame_equal(result, expected)
+
+    result = index.to_frame()
+    expected.index = index
+    tm.assert_frame_equal(result, expected)
+
+    tuples = [(1, "one"), (1, "two"), (2, "one"), (2, "two")]
+    index = MultiIndex.from_tuples(tuples, names=["first", "second"])
+    result = index.to_frame(index=False)
+    expected = DataFrame(tuples)
+    expected.columns = ["first", "second"]
+    tm.assert_frame_equal(result, expected)
+
+    result = index.to_frame()
+    expected.index = index
+    tm.assert_frame_equal(result, expected)
+
+    # See GH-22580
+    index = MultiIndex.from_tuples(tuples)
+    result = index.to_frame(index=False, name=["first", "second"])
+    expected = DataFrame(tuples)
+    expected.columns = ["first", "second"]
+    tm.assert_frame_equal(result, expected)
+
+    result = index.to_frame(name=["first", "second"])
+    expected.index = index
+    expected.columns = ["first", "second"]
+    tm.assert_frame_equal(result, expected)
+
+    msg = "'name' must be a list / sequence of column names."
+    with pytest.raises(TypeError, match=msg):
+        index.to_frame(name="first")
+
+    msg = "'name' should have same length as number of levels on index."
+    with pytest.raises(ValueError, match=msg):
+        index.to_frame(name=["first"])
+
+    # Tests for datetime index
+    index = MultiIndex.from_product([range(5), pd.date_range("20130101", periods=3)])
+    result = index.to_frame(index=False)
+    expected = DataFrame(
+        {
+            0: np.repeat(np.arange(5, dtype="int64"), 3),
+            1: np.tile(pd.date_range("20130101", periods=3), 5),
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+    result = index.to_frame()
+    expected.index = index
+    tm.assert_frame_equal(result, expected)
+
+    # See GH-22580
+    result = index.to_frame(index=False, name=["first", "second"])
+    expected = DataFrame(
+        {
+            "first": np.repeat(np.arange(5, dtype="int64"), 3),
+            "second": np.tile(pd.date_range("20130101", periods=3), 5),
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+    result = index.to_frame(name=["first", "second"])
+    expected.index = index
+    tm.assert_frame_equal(result, expected)
+
+
+def test_to_frame_dtype_fidelity():
+    # GH 22420
+    mi = MultiIndex.from_arrays(
+        [
+            pd.date_range("19910905", periods=6, tz="US/Eastern"),
+            [1, 1, 1, 2, 2, 2],
+            pd.Categorical(["a", "a", "b", "b", "c", "c"], ordered=True),
+            ["x", "x", "y", "z", "x", "y"],
+        ],
+        names=["dates", "a", "b", "c"],
+    )
+    original_dtypes = {name: mi.levels[i].dtype for i, name in enumerate(mi.names)}
+
+    expected_df = DataFrame(
+        {
+            "dates": pd.date_range("19910905", periods=6, tz="US/Eastern"),
+            "a": [1, 1, 1, 2, 2, 2],
+            "b": pd.Categorical(["a", "a", "b", "b", "c", "c"], ordered=True),
+            "c": ["x", "x", "y", "z", "x", "y"],
+        }
+    )
+    df = mi.to_frame(index=False)
+    df_dtypes = df.dtypes.to_dict()
+
+    tm.assert_frame_equal(df, expected_df)
+    assert original_dtypes == df_dtypes
+
+
+def test_to_frame_resulting_column_order():
+    # GH 22420
+    expected = ["z", 0, "a"]
+    mi = MultiIndex.from_arrays(
+        [["a", "b", "c"], ["x", "y", "z"], ["q", "w", "e"]], names=expected
+    )
+    result = mi.to_frame().columns.tolist()
+    assert result == expected
+
+
+def test_to_frame_duplicate_labels():
+    # GH 45245
+    data = [(1, 2), (3, 4)]
+    names = ["a", "a"]
+    index = MultiIndex.from_tuples(data, names=names)
+    with pytest.raises(ValueError, match="Cannot create duplicate column labels"):
+        index.to_frame()
+
+    result = index.to_frame(allow_duplicates=True)
+    expected = DataFrame(data, index=index, columns=names)
+    tm.assert_frame_equal(result, expected)
+
+    names = [None, 0]
+    index = MultiIndex.from_tuples(data, names=names)
+    with pytest.raises(ValueError, match="Cannot create duplicate column labels"):
+        index.to_frame()
+
+    result = index.to_frame(allow_duplicates=True)
+    expected = DataFrame(data, index=index, columns=[0, 0])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_to_flat_index(idx):
+    expected = pd.Index(
+        (
+            ("foo", "one"),
+            ("foo", "two"),
+            ("bar", "one"),
+            ("baz", "two"),
+            ("qux", "one"),
+            ("qux", "two"),
+        ),
+        tupleize_cols=False,
+    )
+    result = idx.to_flat_index()
+    tm.assert_index_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_copy.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_copy.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e09a580f9528bc8197d55c6a7533098e0129fa2
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_copy.py
@@ -0,0 +1,96 @@
+from copy import (
+    copy,
+    deepcopy,
+)
+
+import pytest
+
+from pandas import MultiIndex
+import pandas._testing as tm
+
+
+def assert_multiindex_copied(copy, original):
+    # Levels should be (at least, shallow copied)
+    tm.assert_copy(copy.levels, original.levels)
+    tm.assert_almost_equal(copy.codes, original.codes)
+
+    # Labels doesn't matter which way copied
+    tm.assert_almost_equal(copy.codes, original.codes)
+    assert copy.codes is not original.codes
+
+    # Names doesn't matter which way copied
+    assert copy.names == original.names
+    assert copy.names is not original.names
+
+    # Sort order should be copied
+    assert copy.sortorder == original.sortorder
+
+
+def test_copy(idx):
+    i_copy = idx.copy()
+
+    assert_multiindex_copied(i_copy, idx)
+
+
+def test_shallow_copy(idx):
+    i_copy = idx._view()
+
+    assert_multiindex_copied(i_copy, idx)
+
+
+def test_view(idx):
+    i_view = idx.view()
+    assert_multiindex_copied(i_view, idx)
+
+
+@pytest.mark.parametrize("func", [copy, deepcopy])
+def test_copy_and_deepcopy(func):
+    idx = MultiIndex(
+        levels=[["foo", "bar"], ["fizz", "buzz"]],
+        codes=[[0, 0, 0, 1], [0, 0, 1, 1]],
+        names=["first", "second"],
+    )
+    idx_copy = func(idx)
+    assert idx_copy is not idx
+    assert idx_copy.equals(idx)
+
+
+@pytest.mark.parametrize("deep", [True, False])
+def test_copy_method(deep):
+    idx = MultiIndex(
+        levels=[["foo", "bar"], ["fizz", "buzz"]],
+        codes=[[0, 0, 0, 1], [0, 0, 1, 1]],
+        names=["first", "second"],
+    )
+    idx_copy = idx.copy(deep=deep)
+    assert idx_copy.equals(idx)
+
+
+@pytest.mark.parametrize("deep", [True, False])
+@pytest.mark.parametrize(
+    "kwarg, value",
+    [
+        ("names", ["third", "fourth"]),
+    ],
+)
+def test_copy_method_kwargs(deep, kwarg, value):
+    # gh-12309: Check that the "name" argument as well other kwargs are honored
+    idx = MultiIndex(
+        levels=[["foo", "bar"], ["fizz", "buzz"]],
+        codes=[[0, 0, 0, 1], [0, 0, 1, 1]],
+        names=["first", "second"],
+    )
+    idx_copy = idx.copy(**{kwarg: value, "deep": deep})
+    assert getattr(idx_copy, kwarg) == value
+
+
+def test_copy_deep_false_retains_id():
+    # GH#47878
+    idx = MultiIndex(
+        levels=[["foo", "bar"], ["fizz", "buzz"]],
+        codes=[[0, 0, 0, 1], [0, 0, 1, 1]],
+        names=["first", "second"],
+    )
+
+    res = idx.copy(deep=False)
+    assert res._id is idx._id
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_drop.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_drop.py
new file mode 100644
index 0000000000000000000000000000000000000000..99c8ebb1e57b22059d5a545a79de7b8348d73b14
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_drop.py
@@ -0,0 +1,190 @@
+import numpy as np
+import pytest
+
+from pandas.errors import PerformanceWarning
+
+import pandas as pd
+from pandas import (
+    Index,
+    MultiIndex,
+)
+import pandas._testing as tm
+
+
+def test_drop(idx):
+    dropped = idx.drop([("foo", "two"), ("qux", "one")])
+
+    index = MultiIndex.from_tuples([("foo", "two"), ("qux", "one")])
+    dropped2 = idx.drop(index)
+
+    expected = idx[[0, 2, 3, 5]]
+    tm.assert_index_equal(dropped, expected)
+    tm.assert_index_equal(dropped2, expected)
+
+    dropped = idx.drop(["bar"])
+    expected = idx[[0, 1, 3, 4, 5]]
+    tm.assert_index_equal(dropped, expected)
+
+    dropped = idx.drop("foo")
+    expected = idx[[2, 3, 4, 5]]
+    tm.assert_index_equal(dropped, expected)
+
+    index = MultiIndex.from_tuples([("bar", "two")])
+    with pytest.raises(KeyError, match=r"^\('bar', 'two'\)$"):
+        idx.drop([("bar", "two")])
+    with pytest.raises(KeyError, match=r"^\('bar', 'two'\)$"):
+        idx.drop(index)
+    with pytest.raises(KeyError, match=r"^'two'$"):
+        idx.drop(["foo", "two"])
+
+    # partially correct argument
+    mixed_index = MultiIndex.from_tuples([("qux", "one"), ("bar", "two")])
+    with pytest.raises(KeyError, match=r"^\('bar', 'two'\)$"):
+        idx.drop(mixed_index)
+
+    # error='ignore'
+    dropped = idx.drop(index, errors="ignore")
+    expected = idx[[0, 1, 2, 3, 4, 5]]
+    tm.assert_index_equal(dropped, expected)
+
+    dropped = idx.drop(mixed_index, errors="ignore")
+    expected = idx[[0, 1, 2, 3, 5]]
+    tm.assert_index_equal(dropped, expected)
+
+    dropped = idx.drop(["foo", "two"], errors="ignore")
+    expected = idx[[2, 3, 4, 5]]
+    tm.assert_index_equal(dropped, expected)
+
+    # mixed partial / full drop
+    dropped = idx.drop(["foo", ("qux", "one")])
+    expected = idx[[2, 3, 5]]
+    tm.assert_index_equal(dropped, expected)
+
+    # mixed partial / full drop / error='ignore'
+    mixed_index = ["foo", ("qux", "one"), "two"]
+    with pytest.raises(KeyError, match=r"^'two'$"):
+        idx.drop(mixed_index)
+    dropped = idx.drop(mixed_index, errors="ignore")
+    expected = idx[[2, 3, 5]]
+    tm.assert_index_equal(dropped, expected)
+
+
+def test_droplevel_with_names(idx):
+    index = idx[idx.get_loc("foo")]
+    dropped = index.droplevel(0)
+    assert dropped.name == "second"
+
+    index = MultiIndex(
+        levels=[Index(range(4)), Index(range(4)), Index(range(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]),
+        ],
+        names=["one", "two", "three"],
+    )
+    dropped = index.droplevel(0)
+    assert dropped.names == ("two", "three")
+
+    dropped = index.droplevel("two")
+    expected = index.droplevel(1)
+    assert dropped.equals(expected)
+
+
+def test_droplevel_list():
+    index = MultiIndex(
+        levels=[Index(range(4)), Index(range(4)), Index(range(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]),
+        ],
+        names=["one", "two", "three"],
+    )
+
+    dropped = index[:2].droplevel(["three", "one"])
+    expected = index[:2].droplevel(2).droplevel(0)
+    assert dropped.equals(expected)
+
+    dropped = index[:2].droplevel([])
+    expected = index[:2]
+    assert dropped.equals(expected)
+
+    msg = (
+        "Cannot remove 3 levels from an index with 3 levels: "
+        "at least one level must be left"
+    )
+    with pytest.raises(ValueError, match=msg):
+        index[:2].droplevel(["one", "two", "three"])
+
+    with pytest.raises(KeyError, match="'Level four not found'"):
+        index[:2].droplevel(["one", "four"])
+
+
+def test_drop_not_lexsorted():
+    # GH 12078
+
+    # define the lexsorted version of the multi-index
+    tuples = [("a", ""), ("b1", "c1"), ("b2", "c2")]
+    lexsorted_mi = MultiIndex.from_tuples(tuples, names=["b", "c"])
+    assert lexsorted_mi._is_lexsorted()
+
+    # and the not-lexsorted version
+    df = pd.DataFrame(
+        columns=["a", "b", "c", "d"], data=[[1, "b1", "c1", 3], [1, "b2", "c2", 4]]
+    )
+    df = df.pivot_table(index="a", columns=["b", "c"], values="d")
+    df = df.reset_index()
+    not_lexsorted_mi = df.columns
+    assert not not_lexsorted_mi._is_lexsorted()
+
+    # compare the results
+    tm.assert_index_equal(lexsorted_mi, not_lexsorted_mi)
+    with tm.assert_produces_warning(PerformanceWarning):
+        tm.assert_index_equal(lexsorted_mi.drop("a"), not_lexsorted_mi.drop("a"))
+
+
+def test_drop_with_nan_in_index(nulls_fixture):
+    # GH#18853
+    mi = MultiIndex.from_tuples([("blah", nulls_fixture)], names=["name", "date"])
+    msg = r"labels \[Timestamp\('2001-01-01 00:00:00'\)\] not found in level"
+    with pytest.raises(KeyError, match=msg):
+        mi.drop(pd.Timestamp("2001"), level="date")
+
+
+@pytest.mark.filterwarnings("ignore::pandas.errors.PerformanceWarning")
+def test_drop_with_non_monotonic_duplicates():
+    # GH#33494
+    mi = MultiIndex.from_tuples([(1, 2), (2, 3), (1, 2)])
+    result = mi.drop((1, 2))
+    expected = MultiIndex.from_tuples([(2, 3)])
+    tm.assert_index_equal(result, expected)
+
+
+def test_single_level_drop_partially_missing_elements():
+    # GH 37820
+
+    mi = MultiIndex.from_tuples([(1, 2), (2, 2), (3, 2)])
+    msg = r"labels \[4\] not found in level"
+    with pytest.raises(KeyError, match=msg):
+        mi.drop(4, level=0)
+    with pytest.raises(KeyError, match=msg):
+        mi.drop([1, 4], level=0)
+    msg = r"labels \[nan\] not found in level"
+    with pytest.raises(KeyError, match=msg):
+        mi.drop([np.nan], level=0)
+    with pytest.raises(KeyError, match=msg):
+        mi.drop([np.nan, 1, 2, 3], level=0)
+
+    mi = MultiIndex.from_tuples([(np.nan, 1), (1, 2)])
+    msg = r"labels \['a'\] not found in level"
+    with pytest.raises(KeyError, match=msg):
+        mi.drop([np.nan, 1, "a"], level=0)
+
+
+def test_droplevel_multiindex_one_level():
+    # GH#37208
+    index = MultiIndex.from_tuples([(2,)], names=("b",))
+    result = index.droplevel([])
+    expected = Index([2], name="b")
+    tm.assert_index_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_equivalence.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_equivalence.py
new file mode 100644
index 0000000000000000000000000000000000000000..9babbd5b8d56d64d704978758efb81f8d730274f
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_equivalence.py
@@ -0,0 +1,284 @@
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.common import is_any_real_numeric_dtype
+
+import pandas as pd
+from pandas import (
+    Index,
+    MultiIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+def test_equals(idx):
+    assert idx.equals(idx)
+    assert idx.equals(idx.copy())
+    assert idx.equals(idx.astype(object))
+    assert idx.equals(idx.to_flat_index())
+    assert idx.equals(idx.to_flat_index().astype("category"))
+
+    assert not idx.equals(list(idx))
+    assert not idx.equals(np.array(idx))
+
+    same_values = Index(idx, dtype=object)
+    assert idx.equals(same_values)
+    assert same_values.equals(idx)
+
+    if idx.nlevels == 1:
+        # do not test MultiIndex
+        assert not idx.equals(Series(idx))
+
+
+def test_equals_op(idx):
+    # GH9947, GH10637
+    index_a = idx
+
+    n = len(index_a)
+    index_b = index_a[0:-1]
+    index_c = index_a[0:-1].append(index_a[-2:-1])
+    index_d = index_a[0:1]
+    with pytest.raises(ValueError, match="Lengths must match"):
+        index_a == index_b
+    expected1 = np.array([True] * n)
+    expected2 = np.array([True] * (n - 1) + [False])
+    tm.assert_numpy_array_equal(index_a == index_a, expected1)
+    tm.assert_numpy_array_equal(index_a == index_c, expected2)
+
+    # test comparisons with numpy arrays
+    array_a = np.array(index_a)
+    array_b = np.array(index_a[0:-1])
+    array_c = np.array(index_a[0:-1].append(index_a[-2:-1]))
+    array_d = np.array(index_a[0:1])
+    with pytest.raises(ValueError, match="Lengths must match"):
+        index_a == array_b
+    tm.assert_numpy_array_equal(index_a == array_a, expected1)
+    tm.assert_numpy_array_equal(index_a == array_c, expected2)
+
+    # test comparisons with Series
+    series_a = Series(array_a)
+    series_b = Series(array_b)
+    series_c = Series(array_c)
+    series_d = Series(array_d)
+    with pytest.raises(ValueError, match="Lengths must match"):
+        index_a == series_b
+
+    tm.assert_numpy_array_equal(index_a == series_a, expected1)
+    tm.assert_numpy_array_equal(index_a == series_c, expected2)
+
+    # cases where length is 1 for one of them
+    with pytest.raises(ValueError, match="Lengths must match"):
+        index_a == index_d
+    with pytest.raises(ValueError, match="Lengths must match"):
+        index_a == series_d
+    with pytest.raises(ValueError, match="Lengths must match"):
+        index_a == array_d
+    msg = "Can only compare identically-labeled Series objects"
+    with pytest.raises(ValueError, match=msg):
+        series_a == series_d
+    with pytest.raises(ValueError, match="Lengths must match"):
+        series_a == array_d
+
+    # comparing with a scalar should broadcast; note that we are excluding
+    # MultiIndex because in this case each item in the index is a tuple of
+    # length 2, and therefore is considered an array of length 2 in the
+    # comparison instead of a scalar
+    if not isinstance(index_a, MultiIndex):
+        expected3 = np.array([False] * (len(index_a) - 2) + [True, False])
+        # assuming the 2nd to last item is unique in the data
+        item = index_a[-2]
+        tm.assert_numpy_array_equal(index_a == item, expected3)
+        tm.assert_series_equal(series_a == item, Series(expected3))
+
+
+def test_compare_tuple():
+    # GH#21517
+    mi = MultiIndex.from_product([[1, 2]] * 2)
+
+    all_false = np.array([False, False, False, False])
+
+    result = mi == mi[0]
+    expected = np.array([True, False, False, False])
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = mi != mi[0]
+    tm.assert_numpy_array_equal(result, ~expected)
+
+    result = mi < mi[0]
+    tm.assert_numpy_array_equal(result, all_false)
+
+    result = mi <= mi[0]
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = mi > mi[0]
+    tm.assert_numpy_array_equal(result, ~expected)
+
+    result = mi >= mi[0]
+    tm.assert_numpy_array_equal(result, ~all_false)
+
+
+def test_compare_tuple_strs():
+    # GH#34180
+
+    mi = MultiIndex.from_tuples([("a", "b"), ("b", "c"), ("c", "a")])
+
+    result = mi == ("c", "a")
+    expected = np.array([False, False, True])
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = mi == ("c",)
+    expected = np.array([False, False, False])
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_equals_multi(idx):
+    assert idx.equals(idx)
+    assert not idx.equals(idx.values)
+    assert idx.equals(Index(idx.values))
+
+    assert idx.equal_levels(idx)
+    assert not idx.equals(idx[:-1])
+    assert not idx.equals(idx[-1])
+
+    # different number of levels
+    index = MultiIndex(
+        levels=[Index(list(range(4))), Index(list(range(4))), Index(list(range(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]),
+        ],
+    )
+
+    index2 = MultiIndex(levels=index.levels[:-1], codes=index.codes[:-1])
+    assert not index.equals(index2)
+    assert not index.equal_levels(index2)
+
+    # levels are different
+    major_axis = Index(list(range(4)))
+    minor_axis = Index(list(range(2)))
+
+    major_codes = np.array([0, 0, 1, 2, 2, 3])
+    minor_codes = np.array([0, 1, 0, 0, 1, 0])
+
+    index = MultiIndex(
+        levels=[major_axis, minor_axis], codes=[major_codes, minor_codes]
+    )
+    assert not idx.equals(index)
+    assert not idx.equal_levels(index)
+
+    # some of the labels are different
+    major_axis = Index(["foo", "bar", "baz", "qux"])
+    minor_axis = Index(["one", "two"])
+
+    major_codes = np.array([0, 0, 2, 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]
+    )
+    assert not idx.equals(index)
+
+
+def test_identical(idx):
+    mi = idx.copy()
+    mi2 = idx.copy()
+    assert mi.identical(mi2)
+
+    mi = mi.set_names(["new1", "new2"])
+    assert mi.equals(mi2)
+    assert not mi.identical(mi2)
+
+    mi2 = mi2.set_names(["new1", "new2"])
+    assert mi.identical(mi2)
+
+    mi4 = Index(mi.tolist(), tupleize_cols=False)
+    assert not mi.identical(mi4)
+    assert mi.equals(mi4)
+
+
+def test_equals_operator(idx):
+    # GH9785
+    assert (idx == idx).all()
+
+
+def test_equals_missing_values():
+    # make sure take is not using -1
+    i = MultiIndex.from_tuples([(0, pd.NaT), (0, pd.Timestamp("20130101"))])
+    result = i[0:1].equals(i[0])
+    assert not result
+    result = i[1:2].equals(i[1])
+    assert not result
+
+
+def test_equals_missing_values_differently_sorted():
+    # GH#38439
+    mi1 = MultiIndex.from_tuples([(81.0, np.nan), (np.nan, np.nan)])
+    mi2 = MultiIndex.from_tuples([(np.nan, np.nan), (81.0, np.nan)])
+    assert not mi1.equals(mi2)
+
+    mi2 = MultiIndex.from_tuples([(81.0, np.nan), (np.nan, np.nan)])
+    assert mi1.equals(mi2)
+
+
+def test_is_():
+    mi = MultiIndex.from_tuples(zip(range(10), range(10)))
+    assert mi.is_(mi)
+    assert mi.is_(mi.view())
+    assert mi.is_(mi.view().view().view().view())
+    mi2 = mi.view()
+    # names are metadata, they don't change id
+    mi2.names = ["A", "B"]
+    assert mi2.is_(mi)
+    assert mi.is_(mi2)
+
+    assert not mi.is_(mi.set_names(["C", "D"]))
+    # levels are inherent properties, they change identity
+    mi3 = mi2.set_levels([list(range(10)), list(range(10))])
+    assert not mi3.is_(mi2)
+    # shouldn't change
+    assert mi2.is_(mi)
+    mi4 = mi3.view()
+
+    # GH 17464 - Remove duplicate MultiIndex levels
+    mi4 = mi4.set_levels([list(range(10)), list(range(10))])
+    assert not mi4.is_(mi3)
+    mi5 = mi.view()
+    mi5 = mi5.set_levels(mi5.levels)
+    assert not mi5.is_(mi)
+
+
+def test_is_all_dates(idx):
+    assert not idx._is_all_dates
+
+
+def test_is_numeric(idx):
+    # MultiIndex is never numeric
+    assert not is_any_real_numeric_dtype(idx)
+
+
+def test_multiindex_compare():
+    # GH 21149
+    # Ensure comparison operations for MultiIndex with nlevels == 1
+    # behave consistently with those for MultiIndex with nlevels > 1
+
+    midx = MultiIndex.from_product([[0, 1]])
+
+    # Equality self-test: MultiIndex object vs self
+    expected = Series([True, True])
+    result = Series(midx == midx)
+    tm.assert_series_equal(result, expected)
+
+    # Greater than comparison: MultiIndex object vs self
+    expected = Series([False, False])
+    result = Series(midx > midx)
+    tm.assert_series_equal(result, expected)
+
+
+def test_equals_ea_int_regular_int():
+    # GH#46026
+    mi1 = MultiIndex.from_arrays([Index([1, 2], dtype="Int64"), [3, 4]])
+    mi2 = MultiIndex.from_arrays([[1, 2], [3, 4]])
+    assert not mi1.equals(mi2)
+    assert not mi2.equals(mi1)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_formats.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_formats.py
new file mode 100644
index 0000000000000000000000000000000000000000..52ff3109128f24f43d9a12527d08770b463459a5
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_formats.py
@@ -0,0 +1,249 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Index,
+    MultiIndex,
+)
+import pandas._testing as tm
+
+
+def test_format(idx):
+    msg = "MultiIndex.format is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        idx.format()
+        idx[:0].format()
+
+
+def test_format_integer_names():
+    index = MultiIndex(
+        levels=[[0, 1], [0, 1]], codes=[[0, 0, 1, 1], [0, 1, 0, 1]], names=[0, 1]
+    )
+    msg = "MultiIndex.format is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        index.format(names=True)
+
+
+def test_format_sparse_config(idx):
+    # GH1538
+    msg = "MultiIndex.format is deprecated"
+    with pd.option_context("display.multi_sparse", False):
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = idx.format()
+    assert result[1] == "foo  two"
+
+
+def test_format_sparse_display():
+    index = MultiIndex(
+        levels=[[0, 1], [0, 1], [0, 1], [0]],
+        codes=[
+            [0, 0, 0, 1, 1, 1],
+            [0, 0, 1, 0, 0, 1],
+            [0, 1, 0, 0, 1, 0],
+            [0, 0, 0, 0, 0, 0],
+        ],
+    )
+    msg = "MultiIndex.format is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = index.format()
+    assert result[3] == "1  0  0  0"
+
+
+def test_repr_with_unicode_data():
+    with pd.option_context("display.encoding", "UTF-8"):
+        d = {"a": ["\u05d0", 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}
+        index = pd.DataFrame(d).set_index(["a", "b"]).index
+        assert "\\" not in repr(index)  # we don't want unicode-escaped
+
+
+def test_repr_roundtrip_raises():
+    mi = MultiIndex.from_product([list("ab"), range(3)], names=["first", "second"])
+    msg = "Must pass both levels and codes"
+    with pytest.raises(TypeError, match=msg):
+        eval(repr(mi))
+
+
+def test_unicode_string_with_unicode():
+    d = {"a": ["\u05d0", 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}
+    idx = pd.DataFrame(d).set_index(["a", "b"]).index
+    str(idx)
+
+
+def test_repr_max_seq_item_setting(idx):
+    # GH10182
+    idx = idx.repeat(50)
+    with pd.option_context("display.max_seq_items", None):
+        repr(idx)
+        assert "..." not in str(idx)
+
+
+class TestRepr:
+    def test_unicode_repr_issues(self):
+        levels = [Index(["a/\u03c3", "b/\u03c3", "c/\u03c3"]), Index([0, 1])]
+        codes = [np.arange(3).repeat(2), np.tile(np.arange(2), 3)]
+        index = MultiIndex(levels=levels, codes=codes)
+
+        repr(index.levels)
+        repr(index.get_level_values(1))
+
+    def test_repr_max_seq_items_equal_to_n(self, idx):
+        # display.max_seq_items == n
+        with pd.option_context("display.max_seq_items", 6):
+            result = idx.__repr__()
+            expected = """\
+MultiIndex([('foo', 'one'),
+            ('foo', 'two'),
+            ('bar', 'one'),
+            ('baz', 'two'),
+            ('qux', 'one'),
+            ('qux', 'two')],
+           names=['first', 'second'])"""
+            assert result == expected
+
+    def test_repr(self, idx):
+        result = idx[:1].__repr__()
+        expected = """\
+MultiIndex([('foo', 'one')],
+           names=['first', 'second'])"""
+        assert result == expected
+
+        result = idx.__repr__()
+        expected = """\
+MultiIndex([('foo', 'one'),
+            ('foo', 'two'),
+            ('bar', 'one'),
+            ('baz', 'two'),
+            ('qux', 'one'),
+            ('qux', 'two')],
+           names=['first', 'second'])"""
+        assert result == expected
+
+        with pd.option_context("display.max_seq_items", 5):
+            result = idx.__repr__()
+            expected = """\
+MultiIndex([('foo', 'one'),
+            ('foo', 'two'),
+            ...
+            ('qux', 'one'),
+            ('qux', 'two')],
+           names=['first', 'second'], length=6)"""
+            assert result == expected
+
+        # display.max_seq_items == 1
+        with pd.option_context("display.max_seq_items", 1):
+            result = idx.__repr__()
+            expected = """\
+MultiIndex([...
+            ('qux', 'two')],
+           names=['first', ...], length=6)"""
+            assert result == expected
+
+    def test_rjust(self):
+        n = 1000
+        ci = pd.CategoricalIndex(list("a" * n) + (["abc"] * n))
+        dti = pd.date_range("2000-01-01", freq="s", periods=n * 2)
+        mi = MultiIndex.from_arrays([ci, ci.codes + 9, dti], names=["a", "b", "dti"])
+        result = mi[:1].__repr__()
+        expected = """\
+MultiIndex([('a', 9, '2000-01-01 00:00:00')],
+           names=['a', 'b', 'dti'])"""
+        assert result == expected
+
+        result = mi[::500].__repr__()
+        expected = """\
+MultiIndex([(  'a',  9, '2000-01-01 00:00:00'),
+            (  'a',  9, '2000-01-01 00:08:20'),
+            ('abc', 10, '2000-01-01 00:16:40'),
+            ('abc', 10, '2000-01-01 00:25:00')],
+           names=['a', 'b', 'dti'])"""
+        assert result == expected
+
+        result = mi.__repr__()
+        expected = """\
+MultiIndex([(  'a',  9, '2000-01-01 00:00:00'),
+            (  'a',  9, '2000-01-01 00:00:01'),
+            (  'a',  9, '2000-01-01 00:00:02'),
+            (  'a',  9, '2000-01-01 00:00:03'),
+            (  'a',  9, '2000-01-01 00:00:04'),
+            (  'a',  9, '2000-01-01 00:00:05'),
+            (  'a',  9, '2000-01-01 00:00:06'),
+            (  'a',  9, '2000-01-01 00:00:07'),
+            (  'a',  9, '2000-01-01 00:00:08'),
+            (  'a',  9, '2000-01-01 00:00:09'),
+            ...
+            ('abc', 10, '2000-01-01 00:33:10'),
+            ('abc', 10, '2000-01-01 00:33:11'),
+            ('abc', 10, '2000-01-01 00:33:12'),
+            ('abc', 10, '2000-01-01 00:33:13'),
+            ('abc', 10, '2000-01-01 00:33:14'),
+            ('abc', 10, '2000-01-01 00:33:15'),
+            ('abc', 10, '2000-01-01 00:33:16'),
+            ('abc', 10, '2000-01-01 00:33:17'),
+            ('abc', 10, '2000-01-01 00:33:18'),
+            ('abc', 10, '2000-01-01 00:33:19')],
+           names=['a', 'b', 'dti'], length=2000)"""
+        assert result == expected
+
+    def test_tuple_width(self):
+        n = 1000
+        ci = pd.CategoricalIndex(list("a" * n) + (["abc"] * n))
+        dti = pd.date_range("2000-01-01", freq="s", periods=n * 2)
+        levels = [ci, ci.codes + 9, dti, dti, dti]
+        names = ["a", "b", "dti_1", "dti_2", "dti_3"]
+        mi = MultiIndex.from_arrays(levels, names=names)
+        result = mi[:1].__repr__()
+        expected = """MultiIndex([('a', 9, '2000-01-01 00:00:00', '2000-01-01 00:00:00', ...)],
+           names=['a', 'b', 'dti_1', 'dti_2', 'dti_3'])"""  # noqa: E501
+        assert result == expected
+
+        result = mi[:10].__repr__()
+        expected = """\
+MultiIndex([('a', 9, '2000-01-01 00:00:00', '2000-01-01 00:00:00', ...),
+            ('a', 9, '2000-01-01 00:00:01', '2000-01-01 00:00:01', ...),
+            ('a', 9, '2000-01-01 00:00:02', '2000-01-01 00:00:02', ...),
+            ('a', 9, '2000-01-01 00:00:03', '2000-01-01 00:00:03', ...),
+            ('a', 9, '2000-01-01 00:00:04', '2000-01-01 00:00:04', ...),
+            ('a', 9, '2000-01-01 00:00:05', '2000-01-01 00:00:05', ...),
+            ('a', 9, '2000-01-01 00:00:06', '2000-01-01 00:00:06', ...),
+            ('a', 9, '2000-01-01 00:00:07', '2000-01-01 00:00:07', ...),
+            ('a', 9, '2000-01-01 00:00:08', '2000-01-01 00:00:08', ...),
+            ('a', 9, '2000-01-01 00:00:09', '2000-01-01 00:00:09', ...)],
+           names=['a', 'b', 'dti_1', 'dti_2', 'dti_3'])"""
+        assert result == expected
+
+        result = mi.__repr__()
+        expected = """\
+MultiIndex([(  'a',  9, '2000-01-01 00:00:00', '2000-01-01 00:00:00', ...),
+            (  'a',  9, '2000-01-01 00:00:01', '2000-01-01 00:00:01', ...),
+            (  'a',  9, '2000-01-01 00:00:02', '2000-01-01 00:00:02', ...),
+            (  'a',  9, '2000-01-01 00:00:03', '2000-01-01 00:00:03', ...),
+            (  'a',  9, '2000-01-01 00:00:04', '2000-01-01 00:00:04', ...),
+            (  'a',  9, '2000-01-01 00:00:05', '2000-01-01 00:00:05', ...),
+            (  'a',  9, '2000-01-01 00:00:06', '2000-01-01 00:00:06', ...),
+            (  'a',  9, '2000-01-01 00:00:07', '2000-01-01 00:00:07', ...),
+            (  'a',  9, '2000-01-01 00:00:08', '2000-01-01 00:00:08', ...),
+            (  'a',  9, '2000-01-01 00:00:09', '2000-01-01 00:00:09', ...),
+            ...
+            ('abc', 10, '2000-01-01 00:33:10', '2000-01-01 00:33:10', ...),
+            ('abc', 10, '2000-01-01 00:33:11', '2000-01-01 00:33:11', ...),
+            ('abc', 10, '2000-01-01 00:33:12', '2000-01-01 00:33:12', ...),
+            ('abc', 10, '2000-01-01 00:33:13', '2000-01-01 00:33:13', ...),
+            ('abc', 10, '2000-01-01 00:33:14', '2000-01-01 00:33:14', ...),
+            ('abc', 10, '2000-01-01 00:33:15', '2000-01-01 00:33:15', ...),
+            ('abc', 10, '2000-01-01 00:33:16', '2000-01-01 00:33:16', ...),
+            ('abc', 10, '2000-01-01 00:33:17', '2000-01-01 00:33:17', ...),
+            ('abc', 10, '2000-01-01 00:33:18', '2000-01-01 00:33:18', ...),
+            ('abc', 10, '2000-01-01 00:33:19', '2000-01-01 00:33:19', ...)],
+           names=['a', 'b', 'dti_1', 'dti_2', 'dti_3'], length=2000)"""
+        assert result == expected
+
+    def test_multiindex_long_element(self):
+        # Non-regression test towards GH#52960
+        data = MultiIndex.from_tuples([("c" * 62,)])
+
+        expected = (
+            "MultiIndex([('cccccccccccccccccccccccccccccccccccccccc"
+            "cccccccccccccccccccccc',)],\n           )"
+        )
+        assert str(data) == expected
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_get_level_values.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_get_level_values.py
new file mode 100644
index 0000000000000000000000000000000000000000..28c77e78924cbc35feed4ae838b81f6be38478b5
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_get_level_values.py
@@ -0,0 +1,124 @@
+import numpy as np
+
+import pandas as pd
+from pandas import (
+    CategoricalIndex,
+    Index,
+    MultiIndex,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestGetLevelValues:
+    def test_get_level_values_box_datetime64(self):
+        dates = date_range("1/1/2000", periods=4)
+        levels = [dates, [0, 1]]
+        codes = [[0, 0, 1, 1, 2, 2, 3, 3], [0, 1, 0, 1, 0, 1, 0, 1]]
+
+        index = MultiIndex(levels=levels, codes=codes)
+
+        assert isinstance(index.get_level_values(0)[0], Timestamp)
+
+
+def test_get_level_values(idx):
+    result = idx.get_level_values(0)
+    expected = Index(["foo", "foo", "bar", "baz", "qux", "qux"], name="first")
+    tm.assert_index_equal(result, expected)
+    assert result.name == "first"
+
+    result = idx.get_level_values("first")
+    expected = idx.get_level_values(0)
+    tm.assert_index_equal(result, expected)
+
+    # GH 10460
+    index = MultiIndex(
+        levels=[CategoricalIndex(["A", "B"]), CategoricalIndex([1, 2, 3])],
+        codes=[np.array([0, 0, 0, 1, 1, 1]), np.array([0, 1, 2, 0, 1, 2])],
+    )
+
+    exp = CategoricalIndex(["A", "A", "A", "B", "B", "B"])
+    tm.assert_index_equal(index.get_level_values(0), exp)
+    exp = CategoricalIndex([1, 2, 3, 1, 2, 3])
+    tm.assert_index_equal(index.get_level_values(1), exp)
+
+
+def test_get_level_values_all_na():
+    # GH#17924 when level entirely consists of nan
+    arrays = [[np.nan, np.nan, np.nan], ["a", np.nan, 1]]
+    index = MultiIndex.from_arrays(arrays)
+    result = index.get_level_values(0)
+    expected = Index([np.nan, np.nan, np.nan], dtype=np.float64)
+    tm.assert_index_equal(result, expected)
+
+    result = index.get_level_values(1)
+    expected = Index(["a", np.nan, 1], dtype=object)
+    tm.assert_index_equal(result, expected)
+
+
+def test_get_level_values_int_with_na():
+    # GH#17924
+    arrays = [["a", "b", "b"], [1, np.nan, 2]]
+    index = MultiIndex.from_arrays(arrays)
+    result = index.get_level_values(1)
+    expected = Index([1, np.nan, 2])
+    tm.assert_index_equal(result, expected)
+
+    arrays = [["a", "b", "b"], [np.nan, np.nan, 2]]
+    index = MultiIndex.from_arrays(arrays)
+    result = index.get_level_values(1)
+    expected = Index([np.nan, np.nan, 2])
+    tm.assert_index_equal(result, expected)
+
+
+def test_get_level_values_na():
+    arrays = [[np.nan, np.nan, np.nan], ["a", np.nan, 1]]
+    index = MultiIndex.from_arrays(arrays)
+    result = index.get_level_values(0)
+    expected = Index([np.nan, np.nan, np.nan])
+    tm.assert_index_equal(result, expected)
+
+    result = index.get_level_values(1)
+    expected = Index(["a", np.nan, 1])
+    tm.assert_index_equal(result, expected)
+
+    arrays = [["a", "b", "b"], pd.DatetimeIndex([0, 1, pd.NaT])]
+    index = MultiIndex.from_arrays(arrays)
+    result = index.get_level_values(1)
+    expected = pd.DatetimeIndex([0, 1, pd.NaT])
+    tm.assert_index_equal(result, expected)
+
+    arrays = [[], []]
+    index = MultiIndex.from_arrays(arrays)
+    result = index.get_level_values(0)
+    expected = Index([], dtype=object)
+    tm.assert_index_equal(result, expected)
+
+
+def test_get_level_values_when_periods():
+    # GH33131. See also discussion in GH32669.
+    # This test can probably be removed when PeriodIndex._engine is removed.
+    from pandas import (
+        Period,
+        PeriodIndex,
+    )
+
+    idx = MultiIndex.from_arrays(
+        [PeriodIndex([Period("2019Q1"), Period("2019Q2")], name="b")]
+    )
+    idx2 = MultiIndex.from_arrays(
+        [idx._get_level_values(level) for level in range(idx.nlevels)]
+    )
+    assert all(x.is_monotonic_increasing for x in idx2.levels)
+
+
+def test_values_loses_freq_of_underlying_index():
+    # GH#49054
+    idx = pd.DatetimeIndex(date_range("20200101", periods=3, freq="BME"))
+    expected = idx.copy(deep=True)
+    idx2 = Index([1, 2, 3])
+    midx = MultiIndex(levels=[idx, idx2], codes=[[0, 1, 2], [0, 1, 2]])
+    midx.values
+    assert idx.freq is not None
+    tm.assert_index_equal(idx, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_get_set.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_get_set.py
new file mode 100644
index 0000000000000000000000000000000000000000..6eeaeb6711d03c981fb190964de2ec5501380378
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_get_set.py
@@ -0,0 +1,384 @@
+import numpy as np
+import pytest
+
+from pandas.compat import PY311
+
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
+
+import pandas as pd
+from pandas import (
+    CategoricalIndex,
+    MultiIndex,
+)
+import pandas._testing as tm
+
+
+def assert_matching(actual, expected, check_dtype=False):
+    # avoid specifying internal representation
+    # as much as possible
+    assert len(actual) == len(expected)
+    for act, exp in zip(actual, expected):
+        act = np.asarray(act)
+        exp = np.asarray(exp)
+        tm.assert_numpy_array_equal(act, exp, check_dtype=check_dtype)
+
+
+def test_get_level_number_integer(idx):
+    idx.names = [1, 0]
+    assert idx._get_level_number(1) == 0
+    assert idx._get_level_number(0) == 1
+    msg = "Too many levels: Index has only 2 levels, not 3"
+    with pytest.raises(IndexError, match=msg):
+        idx._get_level_number(2)
+    with pytest.raises(KeyError, match="Level fourth not found"):
+        idx._get_level_number("fourth")
+
+
+def test_get_dtypes(using_infer_string):
+    # Test MultiIndex.dtypes (# Gh37062)
+    idx_multitype = MultiIndex.from_product(
+        [[1, 2, 3], ["a", "b", "c"], pd.date_range("20200101", periods=2, tz="UTC")],
+        names=["int", "string", "dt"],
+    )
+
+    exp = "object" if not using_infer_string else "string"
+    expected = pd.Series(
+        {
+            "int": np.dtype("int64"),
+            "string": exp,
+            "dt": DatetimeTZDtype(tz="utc"),
+        }
+    )
+    tm.assert_series_equal(expected, idx_multitype.dtypes)
+
+
+def test_get_dtypes_no_level_name(using_infer_string):
+    # Test MultiIndex.dtypes (# GH38580 )
+    idx_multitype = MultiIndex.from_product(
+        [
+            [1, 2, 3],
+            ["a", "b", "c"],
+            pd.date_range("20200101", periods=2, tz="UTC"),
+        ],
+    )
+    exp = "object" if not using_infer_string else "string"
+    expected = pd.Series(
+        {
+            "level_0": np.dtype("int64"),
+            "level_1": exp,
+            "level_2": DatetimeTZDtype(tz="utc"),
+        }
+    )
+    tm.assert_series_equal(expected, idx_multitype.dtypes)
+
+
+def test_get_dtypes_duplicate_level_names(using_infer_string):
+    # Test MultiIndex.dtypes with non-unique level names (# GH45174)
+    result = MultiIndex.from_product(
+        [
+            [1, 2, 3],
+            ["a", "b", "c"],
+            pd.date_range("20200101", periods=2, tz="UTC"),
+        ],
+        names=["A", "A", "A"],
+    ).dtypes
+    exp = "object" if not using_infer_string else "string"
+    expected = pd.Series(
+        [np.dtype("int64"), exp, DatetimeTZDtype(tz="utc")],
+        index=["A", "A", "A"],
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_get_level_number_out_of_bounds(multiindex_dataframe_random_data):
+    frame = multiindex_dataframe_random_data
+
+    with pytest.raises(IndexError, match="Too many levels"):
+        frame.index._get_level_number(2)
+    with pytest.raises(IndexError, match="not a valid level number"):
+        frame.index._get_level_number(-3)
+
+
+def test_set_name_methods(idx):
+    # so long as these are synonyms, we don't need to test set_names
+    index_names = ["first", "second"]
+    assert idx.rename == idx.set_names
+    new_names = [name + "SUFFIX" for name in index_names]
+    ind = idx.set_names(new_names)
+    assert idx.names == index_names
+    assert ind.names == new_names
+    msg = "Length of names must match number of levels in MultiIndex"
+    with pytest.raises(ValueError, match=msg):
+        ind.set_names(new_names + new_names)
+    new_names2 = [name + "SUFFIX2" for name in new_names]
+    res = ind.set_names(new_names2, inplace=True)
+    assert res is None
+    assert ind.names == new_names2
+
+    # set names for specific level (# GH7792)
+    ind = idx.set_names(new_names[0], level=0)
+    assert idx.names == index_names
+    assert ind.names == [new_names[0], index_names[1]]
+
+    res = ind.set_names(new_names2[0], level=0, inplace=True)
+    assert res is None
+    assert ind.names == [new_names2[0], index_names[1]]
+
+    # set names for multiple levels
+    ind = idx.set_names(new_names, level=[0, 1])
+    assert idx.names == index_names
+    assert ind.names == new_names
+
+    res = ind.set_names(new_names2, level=[0, 1], inplace=True)
+    assert res is None
+    assert ind.names == new_names2
+
+
+def test_set_levels_codes_directly(idx):
+    # setting levels/codes directly raises AttributeError
+
+    levels = idx.levels
+    new_levels = [[lev + "a" for lev in level] for level in levels]
+
+    codes = idx.codes
+    major_codes, minor_codes = codes
+    major_codes = [(x + 1) % 3 for x in major_codes]
+    minor_codes = [(x + 1) % 1 for x in minor_codes]
+    new_codes = [major_codes, minor_codes]
+
+    msg = "Can't set attribute"
+    with pytest.raises(AttributeError, match=msg):
+        idx.levels = new_levels
+
+    msg = (
+        "property 'codes' of 'MultiIndex' object has no setter"
+        if PY311
+        else "can't set attribute"
+    )
+    with pytest.raises(AttributeError, match=msg):
+        idx.codes = new_codes
+
+
+def test_set_levels(idx):
+    # side note - you probably wouldn't want to use levels and codes
+    # directly like this - but it is possible.
+    levels = idx.levels
+    new_levels = [[lev + "a" for lev in level] for level in levels]
+
+    # level changing [w/o mutation]
+    ind2 = idx.set_levels(new_levels)
+    assert_matching(ind2.levels, new_levels)
+    assert_matching(idx.levels, levels)
+
+    # level changing specific level [w/o mutation]
+    ind2 = idx.set_levels(new_levels[0], level=0)
+    assert_matching(ind2.levels, [new_levels[0], levels[1]])
+    assert_matching(idx.levels, levels)
+
+    ind2 = idx.set_levels(new_levels[1], level=1)
+    assert_matching(ind2.levels, [levels[0], new_levels[1]])
+    assert_matching(idx.levels, levels)
+
+    # level changing multiple levels [w/o mutation]
+    ind2 = idx.set_levels(new_levels, level=[0, 1])
+    assert_matching(ind2.levels, new_levels)
+    assert_matching(idx.levels, levels)
+
+    # illegal level changing should not change levels
+    # GH 13754
+    original_index = idx.copy()
+    with pytest.raises(ValueError, match="^On"):
+        idx.set_levels(["c"], level=0)
+    assert_matching(idx.levels, original_index.levels, check_dtype=True)
+
+    with pytest.raises(ValueError, match="^On"):
+        idx.set_codes([0, 1, 2, 3, 4, 5], level=0)
+    assert_matching(idx.codes, original_index.codes, check_dtype=True)
+
+    with pytest.raises(TypeError, match="^Levels"):
+        idx.set_levels("c", level=0)
+    assert_matching(idx.levels, original_index.levels, check_dtype=True)
+
+    with pytest.raises(TypeError, match="^Codes"):
+        idx.set_codes(1, level=0)
+    assert_matching(idx.codes, original_index.codes, check_dtype=True)
+
+
+def test_set_codes(idx):
+    # side note - you probably wouldn't want to use levels and codes
+    # directly like this - but it is possible.
+    codes = idx.codes
+    major_codes, minor_codes = codes
+    major_codes = [(x + 1) % 3 for x in major_codes]
+    minor_codes = [(x + 1) % 1 for x in minor_codes]
+    new_codes = [major_codes, minor_codes]
+
+    # changing codes w/o mutation
+    ind2 = idx.set_codes(new_codes)
+    assert_matching(ind2.codes, new_codes)
+    assert_matching(idx.codes, codes)
+
+    # codes changing specific level w/o mutation
+    ind2 = idx.set_codes(new_codes[0], level=0)
+    assert_matching(ind2.codes, [new_codes[0], codes[1]])
+    assert_matching(idx.codes, codes)
+
+    ind2 = idx.set_codes(new_codes[1], level=1)
+    assert_matching(ind2.codes, [codes[0], new_codes[1]])
+    assert_matching(idx.codes, codes)
+
+    # codes changing multiple levels w/o mutation
+    ind2 = idx.set_codes(new_codes, level=[0, 1])
+    assert_matching(ind2.codes, new_codes)
+    assert_matching(idx.codes, codes)
+
+    # label changing for levels of different magnitude of categories
+    ind = MultiIndex.from_tuples([(0, i) for i in range(130)])
+    new_codes = range(129, -1, -1)
+    expected = MultiIndex.from_tuples([(0, i) for i in new_codes])
+
+    # [w/o mutation]
+    result = ind.set_codes(codes=new_codes, level=1)
+    assert result.equals(expected)
+
+
+def test_set_levels_codes_names_bad_input(idx):
+    levels, codes = idx.levels, idx.codes
+    names = idx.names
+
+    with pytest.raises(ValueError, match="Length of levels"):
+        idx.set_levels([levels[0]])
+
+    with pytest.raises(ValueError, match="Length of codes"):
+        idx.set_codes([codes[0]])
+
+    with pytest.raises(ValueError, match="Length of names"):
+        idx.set_names([names[0]])
+
+    # shouldn't scalar data error, instead should demand list-like
+    with pytest.raises(TypeError, match="list of lists-like"):
+        idx.set_levels(levels[0])
+
+    # shouldn't scalar data error, instead should demand list-like
+    with pytest.raises(TypeError, match="list of lists-like"):
+        idx.set_codes(codes[0])
+
+    # shouldn't scalar data error, instead should demand list-like
+    with pytest.raises(TypeError, match="list-like"):
+        idx.set_names(names[0])
+
+    # should have equal lengths
+    with pytest.raises(TypeError, match="list of lists-like"):
+        idx.set_levels(levels[0], level=[0, 1])
+
+    with pytest.raises(TypeError, match="list-like"):
+        idx.set_levels(levels, level=0)
+
+    # should have equal lengths
+    with pytest.raises(TypeError, match="list of lists-like"):
+        idx.set_codes(codes[0], level=[0, 1])
+
+    with pytest.raises(TypeError, match="list-like"):
+        idx.set_codes(codes, level=0)
+
+    # should have equal lengths
+    with pytest.raises(ValueError, match="Length of names"):
+        idx.set_names(names[0], level=[0, 1])
+
+    with pytest.raises(TypeError, match="Names must be a"):
+        idx.set_names(names, level=0)
+
+
+@pytest.mark.parametrize("inplace", [True, False])
+def test_set_names_with_nlevel_1(inplace):
+    # GH 21149
+    # Ensure that .set_names for MultiIndex with
+    # nlevels == 1 does not raise any errors
+    expected = MultiIndex(levels=[[0, 1]], codes=[[0, 1]], names=["first"])
+    m = MultiIndex.from_product([[0, 1]])
+    result = m.set_names("first", level=0, inplace=inplace)
+
+    if inplace:
+        result = m
+
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("ordered", [True, False])
+def test_set_levels_categorical(ordered):
+    # GH13854
+    index = MultiIndex.from_arrays([list("xyzx"), [0, 1, 2, 3]])
+
+    cidx = CategoricalIndex(list("bac"), ordered=ordered)
+    result = index.set_levels(cidx, level=0)
+    expected = MultiIndex(levels=[cidx, [0, 1, 2, 3]], codes=index.codes)
+    tm.assert_index_equal(result, expected)
+
+    result_lvl = result.get_level_values(0)
+    expected_lvl = CategoricalIndex(
+        list("bacb"), categories=cidx.categories, ordered=cidx.ordered
+    )
+    tm.assert_index_equal(result_lvl, expected_lvl)
+
+
+def test_set_value_keeps_names():
+    # motivating example from #3742
+    lev1 = ["hans", "hans", "hans", "grethe", "grethe", "grethe"]
+    lev2 = ["1", "2", "3"] * 2
+    idx = MultiIndex.from_arrays([lev1, lev2], names=["Name", "Number"])
+    df = pd.DataFrame(
+        np.random.default_rng(2).standard_normal((6, 4)),
+        columns=["one", "two", "three", "four"],
+        index=idx,
+    )
+    df = df.sort_index()
+    assert df._is_copy is None
+    assert df.index.names == ("Name", "Number")
+    df.at[("grethe", "4"), "one"] = 99.34
+    assert df._is_copy is None
+    assert df.index.names == ("Name", "Number")
+
+
+def test_set_levels_with_iterable():
+    # GH23273
+    sizes = [1, 2, 3]
+    colors = ["black"] * 3
+    index = MultiIndex.from_arrays([sizes, colors], names=["size", "color"])
+
+    result = index.set_levels(map(int, ["3", "2", "1"]), level="size")
+
+    expected_sizes = [3, 2, 1]
+    expected = MultiIndex.from_arrays([expected_sizes, colors], names=["size", "color"])
+    tm.assert_index_equal(result, expected)
+
+
+def test_set_empty_level():
+    # GH#48636
+    midx = MultiIndex.from_arrays([[]], names=["A"])
+    result = midx.set_levels(pd.DatetimeIndex([]), level=0)
+    expected = MultiIndex.from_arrays([pd.DatetimeIndex([])], names=["A"])
+    tm.assert_index_equal(result, expected)
+
+
+def test_set_levels_pos_args_removal():
+    # https://github.com/pandas-dev/pandas/issues/41485
+    idx = MultiIndex.from_tuples(
+        [
+            (1, "one"),
+            (3, "one"),
+        ],
+        names=["foo", "bar"],
+    )
+    with pytest.raises(TypeError, match="positional arguments"):
+        idx.set_levels(["a", "b", "c"], 0)
+
+    with pytest.raises(TypeError, match="positional arguments"):
+        idx.set_codes([[0, 1], [1, 0]], 0)
+
+
+def test_set_levels_categorical_keep_dtype():
+    # GH#52125
+    midx = MultiIndex.from_arrays([[5, 6]])
+    result = midx.set_levels(levels=pd.Categorical([1, 2]), level=0)
+    expected = MultiIndex.from_arrays([pd.Categorical([1, 2])])
+    tm.assert_index_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_integrity.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_integrity.py
new file mode 100644
index 0000000000000000000000000000000000000000..d956747cbc859f40b69e52ea78c85ebce31f3427
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_integrity.py
@@ -0,0 +1,289 @@
+import re
+
+import numpy as np
+import pytest
+
+from pandas._libs import index as libindex
+
+from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
+
+import pandas as pd
+from pandas import (
+    Index,
+    IntervalIndex,
+    MultiIndex,
+    RangeIndex,
+)
+import pandas._testing as tm
+
+
+def test_labels_dtypes():
+    # GH 8456
+    i = MultiIndex.from_tuples([("A", 1), ("A", 2)])
+    assert i.codes[0].dtype == "int8"
+    assert i.codes[1].dtype == "int8"
+
+    i = MultiIndex.from_product([["a"], range(40)])
+    assert i.codes[1].dtype == "int8"
+    i = MultiIndex.from_product([["a"], range(400)])
+    assert i.codes[1].dtype == "int16"
+    i = MultiIndex.from_product([["a"], range(40000)])
+    assert i.codes[1].dtype == "int32"
+
+    i = MultiIndex.from_product([["a"], range(1000)])
+    assert (i.codes[0] >= 0).all()
+    assert (i.codes[1] >= 0).all()
+
+
+def test_values_boxed():
+    tuples = [
+        (1, pd.Timestamp("2000-01-01")),
+        (2, pd.NaT),
+        (3, pd.Timestamp("2000-01-03")),
+        (1, pd.Timestamp("2000-01-04")),
+        (2, pd.Timestamp("2000-01-02")),
+        (3, pd.Timestamp("2000-01-03")),
+    ]
+    result = MultiIndex.from_tuples(tuples)
+    expected = construct_1d_object_array_from_listlike(tuples)
+    tm.assert_numpy_array_equal(result.values, expected)
+    # Check that code branches for boxed values produce identical results
+    tm.assert_numpy_array_equal(result.values[:4], result[:4].values)
+
+
+def test_values_multiindex_datetimeindex():
+    # Test to ensure we hit the boxing / nobox part of MI.values
+    ints = np.arange(10**18, 10**18 + 5)
+    naive = pd.DatetimeIndex(ints)
+
+    aware = pd.DatetimeIndex(ints, tz="US/Central")
+
+    idx = MultiIndex.from_arrays([naive, aware])
+    result = idx.values
+
+    outer = pd.DatetimeIndex([x[0] for x in result])
+    tm.assert_index_equal(outer, naive)
+
+    inner = pd.DatetimeIndex([x[1] for x in result])
+    tm.assert_index_equal(inner, aware)
+
+    # n_lev > n_lab
+    result = idx[:2].values
+
+    outer = pd.DatetimeIndex([x[0] for x in result])
+    tm.assert_index_equal(outer, naive[:2])
+
+    inner = pd.DatetimeIndex([x[1] for x in result])
+    tm.assert_index_equal(inner, aware[:2])
+
+
+def test_values_multiindex_periodindex():
+    # Test to ensure we hit the boxing / nobox part of MI.values
+    ints = np.arange(2007, 2012)
+    pidx = pd.PeriodIndex(ints, freq="D")
+
+    idx = MultiIndex.from_arrays([ints, pidx])
+    result = idx.values
+
+    outer = Index([x[0] for x in result])
+    tm.assert_index_equal(outer, Index(ints, dtype=np.int64))
+
+    inner = pd.PeriodIndex([x[1] for x in result])
+    tm.assert_index_equal(inner, pidx)
+
+    # n_lev > n_lab
+    result = idx[:2].values
+
+    outer = Index([x[0] for x in result])
+    tm.assert_index_equal(outer, Index(ints[:2], dtype=np.int64))
+
+    inner = pd.PeriodIndex([x[1] for x in result])
+    tm.assert_index_equal(inner, pidx[:2])
+
+
+def test_consistency():
+    # need to construct an overflow
+    major_axis = list(range(70000))
+    minor_axis = list(range(10))
+
+    major_codes = np.arange(70000)
+    minor_codes = np.repeat(range(10), 7000)
+
+    # the fact that is works means it's consistent
+    index = MultiIndex(
+        levels=[major_axis, minor_axis], codes=[major_codes, minor_codes]
+    )
+
+    # inconsistent
+    major_codes = np.array([0, 0, 1, 1, 1, 2, 2, 3, 3])
+    minor_codes = np.array([0, 1, 0, 1, 1, 0, 1, 0, 1])
+    index = MultiIndex(
+        levels=[major_axis, minor_axis], codes=[major_codes, minor_codes]
+    )
+
+    assert index.is_unique is False
+
+
+@pytest.mark.slow
+def test_hash_collisions(monkeypatch):
+    # non-smoke test that we don't get hash collisions
+    size_cutoff = 50
+    with monkeypatch.context() as m:
+        m.setattr(libindex, "_SIZE_CUTOFF", size_cutoff)
+        index = MultiIndex.from_product(
+            [np.arange(8), np.arange(8)], names=["one", "two"]
+        )
+        result = index.get_indexer(index.values)
+        tm.assert_numpy_array_equal(result, np.arange(len(index), dtype="intp"))
+
+        for i in [0, 1, len(index) - 2, len(index) - 1]:
+            result = index.get_loc(index[i])
+            assert result == i
+
+
+def test_dims():
+    pass
+
+
+def test_take_invalid_kwargs():
+    vals = [["A", "B"], [pd.Timestamp("2011-01-01"), pd.Timestamp("2011-01-02")]]
+    idx = MultiIndex.from_product(vals, names=["str", "dt"])
+    indices = [1, 2]
+
+    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_isna_behavior(idx):
+    # should not segfault GH5123
+    # NOTE: if MI representation changes, may make sense to allow
+    # isna(MI)
+    msg = "isna is not defined for MultiIndex"
+    with pytest.raises(NotImplementedError, match=msg):
+        pd.isna(idx)
+
+
+def test_large_multiindex_error(monkeypatch):
+    # GH12527
+    size_cutoff = 50
+    with monkeypatch.context() as m:
+        m.setattr(libindex, "_SIZE_CUTOFF", size_cutoff)
+        df_below_cutoff = pd.DataFrame(
+            1,
+            index=MultiIndex.from_product([[1, 2], range(size_cutoff - 1)]),
+            columns=["dest"],
+        )
+        with pytest.raises(KeyError, match=r"^\(-1, 0\)$"):
+            df_below_cutoff.loc[(-1, 0), "dest"]
+        with pytest.raises(KeyError, match=r"^\(3, 0\)$"):
+            df_below_cutoff.loc[(3, 0), "dest"]
+        df_above_cutoff = pd.DataFrame(
+            1,
+            index=MultiIndex.from_product([[1, 2], range(size_cutoff + 1)]),
+            columns=["dest"],
+        )
+        with pytest.raises(KeyError, match=r"^\(-1, 0\)$"):
+            df_above_cutoff.loc[(-1, 0), "dest"]
+        with pytest.raises(KeyError, match=r"^\(3, 0\)$"):
+            df_above_cutoff.loc[(3, 0), "dest"]
+
+
+def test_mi_hashtable_populated_attribute_error(monkeypatch):
+    # GH 18165
+    monkeypatch.setattr(libindex, "_SIZE_CUTOFF", 50)
+    r = range(50)
+    df = pd.DataFrame({"a": r, "b": r}, index=MultiIndex.from_arrays([r, r]))
+
+    msg = "'Series' object has no attribute 'foo'"
+    with pytest.raises(AttributeError, match=msg):
+        df["a"].foo()
+
+
+def test_can_hold_identifiers(idx):
+    key = idx[0]
+    assert idx._can_hold_identifiers_and_holds_name(key) is True
+
+
+def test_metadata_immutable(idx):
+    levels, codes = idx.levels, idx.codes
+    # shouldn't be able to set at either the top level or base level
+    mutable_regex = re.compile("does not support mutable operations")
+    with pytest.raises(TypeError, match=mutable_regex):
+        levels[0] = levels[0]
+    with pytest.raises(TypeError, match=mutable_regex):
+        levels[0][0] = levels[0][0]
+    # ditto for labels
+    with pytest.raises(TypeError, match=mutable_regex):
+        codes[0] = codes[0]
+    with pytest.raises(ValueError, match="assignment destination is read-only"):
+        codes[0][0] = codes[0][0]
+    # and for names
+    names = idx.names
+    with pytest.raises(TypeError, match=mutable_regex):
+        names[0] = names[0]
+
+
+def test_level_setting_resets_attributes():
+    ind = MultiIndex.from_arrays([["A", "A", "B", "B", "B"], [1, 2, 1, 2, 3]])
+    assert ind.is_monotonic_increasing
+    ind = ind.set_levels([["A", "B"], [1, 3, 2]])
+    # if this fails, probably didn't reset the cache correctly.
+    assert not ind.is_monotonic_increasing
+
+
+def test_rangeindex_fallback_coercion_bug():
+    # GH 12893
+    df1 = pd.DataFrame(np.arange(100).reshape((10, 10)))
+    df2 = pd.DataFrame(np.arange(100).reshape((10, 10)))
+    df = pd.concat(
+        {"df1": df1.stack(future_stack=True), "df2": df2.stack(future_stack=True)},
+        axis=1,
+    )
+    df.index.names = ["fizz", "buzz"]
+
+    expected = pd.DataFrame(
+        {"df2": np.arange(100), "df1": np.arange(100)},
+        index=MultiIndex.from_product([range(10), range(10)], names=["fizz", "buzz"]),
+    )
+    tm.assert_frame_equal(df, expected, check_like=True)
+
+    result = df.index.get_level_values("fizz")
+    expected = Index(np.arange(10, dtype=np.int64), name="fizz").repeat(10)
+    tm.assert_index_equal(result, expected)
+
+    result = df.index.get_level_values("buzz")
+    expected = Index(np.tile(np.arange(10, dtype=np.int64), 10), name="buzz")
+    tm.assert_index_equal(result, expected)
+
+
+def test_memory_usage(idx):
+    result = idx.memory_usage()
+    if len(idx):
+        idx.get_loc(idx[0])
+        result2 = idx.memory_usage()
+        result3 = idx.memory_usage(deep=True)
+
+        # RangeIndex, IntervalIndex
+        # don't have engines
+        if not isinstance(idx, (RangeIndex, IntervalIndex)):
+            assert result2 > result
+
+        if idx.inferred_type == "object":
+            assert result3 > result2
+
+    else:
+        # we report 0 for no-length
+        assert result == 0
+
+
+def test_nlevels(idx):
+    assert idx.nlevels == 2
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_isin.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_isin.py
new file mode 100644
index 0000000000000000000000000000000000000000..68fdf25359f1bbada24f6a2403d5a04331bee84c
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_isin.py
@@ -0,0 +1,103 @@
+import numpy as np
+import pytest
+
+from pandas import MultiIndex
+import pandas._testing as tm
+
+
+def test_isin_nan():
+    idx = MultiIndex.from_arrays([["foo", "bar"], [1.0, np.nan]])
+    tm.assert_numpy_array_equal(idx.isin([("bar", np.nan)]), np.array([False, True]))
+    tm.assert_numpy_array_equal(
+        idx.isin([("bar", float("nan"))]), np.array([False, True])
+    )
+
+
+def test_isin_missing(nulls_fixture):
+    # GH48905
+    mi1 = MultiIndex.from_tuples([(1, nulls_fixture)])
+    mi2 = MultiIndex.from_tuples([(1, 1), (1, 2)])
+    result = mi2.isin(mi1)
+    expected = np.array([False, False])
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_isin():
+    values = [("foo", 2), ("bar", 3), ("quux", 4)]
+
+    idx = MultiIndex.from_arrays([["qux", "baz", "foo", "bar"], np.arange(4)])
+    result = idx.isin(values)
+    expected = np.array([False, False, True, True])
+    tm.assert_numpy_array_equal(result, expected)
+
+    # empty, return dtype bool
+    idx = MultiIndex.from_arrays([[], []])
+    result = idx.isin(values)
+    assert len(result) == 0
+    assert result.dtype == np.bool_
+
+
+def test_isin_level_kwarg():
+    idx = MultiIndex.from_arrays([["qux", "baz", "foo", "bar"], np.arange(4)])
+
+    vals_0 = ["foo", "bar", "quux"]
+    vals_1 = [2, 3, 10]
+
+    expected = np.array([False, False, True, True])
+    tm.assert_numpy_array_equal(expected, idx.isin(vals_0, level=0))
+    tm.assert_numpy_array_equal(expected, idx.isin(vals_0, level=-2))
+
+    tm.assert_numpy_array_equal(expected, idx.isin(vals_1, level=1))
+    tm.assert_numpy_array_equal(expected, idx.isin(vals_1, level=-1))
+
+    msg = "Too many levels: Index has only 2 levels, not 6"
+    with pytest.raises(IndexError, match=msg):
+        idx.isin(vals_0, level=5)
+    msg = "Too many levels: Index has only 2 levels, -5 is not a valid level number"
+    with pytest.raises(IndexError, match=msg):
+        idx.isin(vals_0, level=-5)
+
+    with pytest.raises(KeyError, match=r"'Level 1\.0 not found'"):
+        idx.isin(vals_0, level=1.0)
+    with pytest.raises(KeyError, match=r"'Level -1\.0 not found'"):
+        idx.isin(vals_1, level=-1.0)
+    with pytest.raises(KeyError, match="'Level A not found'"):
+        idx.isin(vals_1, level="A")
+
+    idx.names = ["A", "B"]
+    tm.assert_numpy_array_equal(expected, idx.isin(vals_0, level="A"))
+    tm.assert_numpy_array_equal(expected, idx.isin(vals_1, level="B"))
+
+    with pytest.raises(KeyError, match="'Level C not found'"):
+        idx.isin(vals_1, level="C")
+
+
+@pytest.mark.parametrize(
+    "labels,expected,level",
+    [
+        ([("b", np.nan)], np.array([False, False, True]), None),
+        ([np.nan, "a"], np.array([True, True, False]), 0),
+        (["d", np.nan], np.array([False, True, True]), 1),
+    ],
+)
+def test_isin_multi_index_with_missing_value(labels, expected, level):
+    # GH 19132
+    midx = MultiIndex.from_arrays([[np.nan, "a", "b"], ["c", "d", np.nan]])
+    result = midx.isin(labels, level=level)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_isin_empty():
+    # GH#51599
+    midx = MultiIndex.from_arrays([[1, 2], [3, 4]])
+    result = midx.isin([])
+    expected = np.array([False, False])
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_isin_generator():
+    # GH#52568
+    midx = MultiIndex.from_tuples([(1, 2)])
+    result = midx.isin(x for x in [(1, 2)])
+    expected = np.array([True])
+    tm.assert_numpy_array_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_join.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_join.py
new file mode 100644
index 0000000000000000000000000000000000000000..edd0feaaa1159ff8340af772d27f2a7af09ceb87
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_join.py
@@ -0,0 +1,268 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    Index,
+    Interval,
+    MultiIndex,
+    Series,
+    StringDtype,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize(
+    "other", [Index(["three", "one", "two"]), Index(["one"]), Index(["one", "three"])]
+)
+def test_join_level(idx, other, join_type):
+    join_index, lidx, ridx = other.join(
+        idx, how=join_type, level="second", return_indexers=True
+    )
+
+    exp_level = other.join(idx.levels[1], how=join_type)
+    assert join_index.levels[0].equals(idx.levels[0])
+    assert join_index.levels[1].equals(exp_level)
+
+    # pare down levels
+    mask = np.array([x[1] in exp_level for x in idx], dtype=bool)
+    exp_values = idx.values[mask]
+    tm.assert_numpy_array_equal(join_index.values, exp_values)
+
+    if join_type in ("outer", "inner"):
+        join_index2, ridx2, lidx2 = idx.join(
+            other, how=join_type, level="second", return_indexers=True
+        )
+
+        assert join_index.equals(join_index2)
+        tm.assert_numpy_array_equal(lidx, lidx2)
+        tm.assert_numpy_array_equal(ridx, ridx2)
+        tm.assert_numpy_array_equal(join_index2.values, exp_values)
+
+
+def test_join_level_corner_case(idx):
+    # some corner cases
+    index = Index(["three", "one", "two"])
+    result = index.join(idx, level="second")
+    assert isinstance(result, MultiIndex)
+
+    with pytest.raises(TypeError, match="Join.*MultiIndex.*ambiguous"):
+        idx.join(idx, level=1)
+
+
+def test_join_self(idx, join_type):
+    result = idx.join(idx, how=join_type)
+    expected = idx
+    if join_type == "outer":
+        expected = expected.sort_values()
+    tm.assert_index_equal(result, expected)
+
+
+def test_join_multi():
+    # GH 10665
+    midx = MultiIndex.from_product([np.arange(4), np.arange(4)], names=["a", "b"])
+    idx = Index([1, 2, 5], name="b")
+
+    # inner
+    jidx, lidx, ridx = midx.join(idx, how="inner", return_indexers=True)
+    exp_idx = MultiIndex.from_product([np.arange(4), [1, 2]], names=["a", "b"])
+    exp_lidx = np.array([1, 2, 5, 6, 9, 10, 13, 14], dtype=np.intp)
+    exp_ridx = np.array([0, 1, 0, 1, 0, 1, 0, 1], dtype=np.intp)
+    tm.assert_index_equal(jidx, exp_idx)
+    tm.assert_numpy_array_equal(lidx, exp_lidx)
+    tm.assert_numpy_array_equal(ridx, exp_ridx)
+    # flip
+    jidx, ridx, lidx = idx.join(midx, how="inner", return_indexers=True)
+    tm.assert_index_equal(jidx, exp_idx)
+    tm.assert_numpy_array_equal(lidx, exp_lidx)
+    tm.assert_numpy_array_equal(ridx, exp_ridx)
+
+    # keep MultiIndex
+    jidx, lidx, ridx = midx.join(idx, how="left", return_indexers=True)
+    exp_ridx = np.array(
+        [-1, 0, 1, -1, -1, 0, 1, -1, -1, 0, 1, -1, -1, 0, 1, -1], dtype=np.intp
+    )
+    tm.assert_index_equal(jidx, midx)
+    assert lidx is None
+    tm.assert_numpy_array_equal(ridx, exp_ridx)
+    # flip
+    jidx, ridx, lidx = idx.join(midx, how="right", return_indexers=True)
+    tm.assert_index_equal(jidx, midx)
+    assert lidx is None
+    tm.assert_numpy_array_equal(ridx, exp_ridx)
+
+
+def test_join_multi_wrong_order():
+    # GH 25760
+    # GH 28956
+
+    midx1 = MultiIndex.from_product([[1, 2], [3, 4]], names=["a", "b"])
+    midx2 = MultiIndex.from_product([[1, 2], [3, 4]], names=["b", "a"])
+
+    join_idx, lidx, ridx = midx1.join(midx2, return_indexers=True)
+
+    exp_ridx = np.array([-1, -1, -1, -1], dtype=np.intp)
+
+    tm.assert_index_equal(midx1, join_idx)
+    assert lidx is None
+    tm.assert_numpy_array_equal(ridx, exp_ridx)
+
+
+def test_join_multi_return_indexers():
+    # GH 34074
+
+    midx1 = MultiIndex.from_product([[1, 2], [3, 4], [5, 6]], names=["a", "b", "c"])
+    midx2 = MultiIndex.from_product([[1, 2], [3, 4]], names=["a", "b"])
+
+    result = midx1.join(midx2, return_indexers=False)
+    tm.assert_index_equal(result, midx1)
+
+
+def test_join_overlapping_interval_level():
+    # GH 44096
+    idx_1 = MultiIndex.from_tuples(
+        [
+            (1, Interval(0.0, 1.0)),
+            (1, Interval(1.0, 2.0)),
+            (1, Interval(2.0, 5.0)),
+            (2, Interval(0.0, 1.0)),
+            (2, Interval(1.0, 3.0)),  # interval limit is here at 3.0, not at 2.0
+            (2, Interval(3.0, 5.0)),
+        ],
+        names=["num", "interval"],
+    )
+
+    idx_2 = MultiIndex.from_tuples(
+        [
+            (1, Interval(2.0, 5.0)),
+            (1, Interval(0.0, 1.0)),
+            (1, Interval(1.0, 2.0)),
+            (2, Interval(3.0, 5.0)),
+            (2, Interval(0.0, 1.0)),
+            (2, Interval(1.0, 3.0)),
+        ],
+        names=["num", "interval"],
+    )
+
+    expected = MultiIndex.from_tuples(
+        [
+            (1, Interval(0.0, 1.0)),
+            (1, Interval(1.0, 2.0)),
+            (1, Interval(2.0, 5.0)),
+            (2, Interval(0.0, 1.0)),
+            (2, Interval(1.0, 3.0)),
+            (2, Interval(3.0, 5.0)),
+        ],
+        names=["num", "interval"],
+    )
+    result = idx_1.join(idx_2, how="outer")
+
+    tm.assert_index_equal(result, expected)
+
+
+def test_join_midx_ea():
+    # GH#49277
+    midx = MultiIndex.from_arrays(
+        [Series([1, 1, 3], dtype="Int64"), Series([1, 2, 3], dtype="Int64")],
+        names=["a", "b"],
+    )
+    midx2 = MultiIndex.from_arrays(
+        [Series([1], dtype="Int64"), Series([3], dtype="Int64")], names=["a", "c"]
+    )
+    result = midx.join(midx2, how="inner")
+    expected = MultiIndex.from_arrays(
+        [
+            Series([1, 1], dtype="Int64"),
+            Series([1, 2], dtype="Int64"),
+            Series([3, 3], dtype="Int64"),
+        ],
+        names=["a", "b", "c"],
+    )
+    tm.assert_index_equal(result, expected)
+
+
+def test_join_midx_string():
+    # GH#49277
+    midx = MultiIndex.from_arrays(
+        [
+            Series(["a", "a", "c"], dtype=StringDtype()),
+            Series(["a", "b", "c"], dtype=StringDtype()),
+        ],
+        names=["a", "b"],
+    )
+    midx2 = MultiIndex.from_arrays(
+        [Series(["a"], dtype=StringDtype()), Series(["c"], dtype=StringDtype())],
+        names=["a", "c"],
+    )
+    result = midx.join(midx2, how="inner")
+    expected = MultiIndex.from_arrays(
+        [
+            Series(["a", "a"], dtype=StringDtype()),
+            Series(["a", "b"], dtype=StringDtype()),
+            Series(["c", "c"], dtype=StringDtype()),
+        ],
+        names=["a", "b", "c"],
+    )
+    tm.assert_index_equal(result, expected)
+
+
+def test_join_multi_with_nan():
+    # GH29252
+    df1 = DataFrame(
+        data={"col1": [1.1, 1.2]},
+        index=MultiIndex.from_product([["A"], [1.0, 2.0]], names=["id1", "id2"]),
+    )
+    df2 = DataFrame(
+        data={"col2": [2.1, 2.2]},
+        index=MultiIndex.from_product([["A"], [np.nan, 2.0]], names=["id1", "id2"]),
+    )
+    result = df1.join(df2)
+    expected = DataFrame(
+        data={"col1": [1.1, 1.2], "col2": [np.nan, 2.2]},
+        index=MultiIndex.from_product([["A"], [1.0, 2.0]], names=["id1", "id2"]),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("val", [0, 5])
+def test_join_dtypes(any_numeric_ea_dtype, val):
+    # GH#49830
+    midx = MultiIndex.from_arrays([Series([1, 2], dtype=any_numeric_ea_dtype), [3, 4]])
+    midx2 = MultiIndex.from_arrays(
+        [Series([1, val, val], dtype=any_numeric_ea_dtype), [3, 4, 4]]
+    )
+    result = midx.join(midx2, how="outer")
+    expected = MultiIndex.from_arrays(
+        [Series([val, val, 1, 2], dtype=any_numeric_ea_dtype), [4, 4, 3, 4]]
+    ).sort_values()
+    tm.assert_index_equal(result, expected)
+
+
+def test_join_dtypes_all_nan(any_numeric_ea_dtype):
+    # GH#49830
+    midx = MultiIndex.from_arrays(
+        [Series([1, 2], dtype=any_numeric_ea_dtype), [np.nan, np.nan]]
+    )
+    midx2 = MultiIndex.from_arrays(
+        [Series([1, 0, 0], dtype=any_numeric_ea_dtype), [np.nan, np.nan, np.nan]]
+    )
+    result = midx.join(midx2, how="outer")
+    expected = MultiIndex.from_arrays(
+        [
+            Series([0, 0, 1, 2], dtype=any_numeric_ea_dtype),
+            [np.nan, np.nan, np.nan, np.nan],
+        ]
+    )
+    tm.assert_index_equal(result, expected)
+
+
+def test_join_index_levels():
+    # GH#53093
+    midx = midx = MultiIndex.from_tuples([("a", "2019-02-01"), ("a", "2019-02-01")])
+    midx2 = MultiIndex.from_tuples([("a", "2019-01-31")])
+    result = midx.join(midx2, how="outer")
+    expected = MultiIndex.from_tuples(
+        [("a", "2019-01-31"), ("a", "2019-02-01"), ("a", "2019-02-01")]
+    )
+    tm.assert_index_equal(result.levels[1], expected.levels[1])
+    tm.assert_index_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_lexsort.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_lexsort.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc16a4197a3a4daf65de6f58d85d13883d535d41
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_lexsort.py
@@ -0,0 +1,46 @@
+from pandas import MultiIndex
+
+
+class TestIsLexsorted:
+    def test_is_lexsorted(self):
+        levels = [[0, 1], [0, 1, 2]]
+
+        index = MultiIndex(
+            levels=levels, codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]]
+        )
+        assert index._is_lexsorted()
+
+        index = MultiIndex(
+            levels=levels, codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 2, 1]]
+        )
+        assert not index._is_lexsorted()
+
+        index = MultiIndex(
+            levels=levels, codes=[[0, 0, 1, 0, 1, 1], [0, 1, 0, 2, 2, 1]]
+        )
+        assert not index._is_lexsorted()
+        assert index._lexsort_depth == 0
+
+
+class TestLexsortDepth:
+    def test_lexsort_depth(self):
+        # Test that lexsort_depth return the correct sortorder
+        # when it was given to the MultiIndex const.
+        # GH#28518
+
+        levels = [[0, 1], [0, 1, 2]]
+
+        index = MultiIndex(
+            levels=levels, codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]], sortorder=2
+        )
+        assert index._lexsort_depth == 2
+
+        index = MultiIndex(
+            levels=levels, codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 2, 1]], sortorder=1
+        )
+        assert index._lexsort_depth == 1
+
+        index = MultiIndex(
+            levels=levels, codes=[[0, 0, 1, 0, 1, 1], [0, 1, 0, 2, 2, 1]], sortorder=0
+        )
+        assert index._lexsort_depth == 0
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_missing.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_missing.py
new file mode 100644
index 0000000000000000000000000000000000000000..14ffc42fb4b59074c3c830a83ff6bdc36bdf099e
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_missing.py
@@ -0,0 +1,111 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import MultiIndex
+import pandas._testing as tm
+
+
+def test_fillna(idx):
+    # GH 11343
+    msg = "isna is not defined for MultiIndex"
+    with pytest.raises(NotImplementedError, match=msg):
+        idx.fillna(idx[0])
+
+
+def test_dropna():
+    # GH 6194
+    idx = MultiIndex.from_arrays(
+        [
+            [1, np.nan, 3, np.nan, 5],
+            [1, 2, np.nan, np.nan, 5],
+            ["a", "b", "c", np.nan, "e"],
+        ]
+    )
+
+    exp = MultiIndex.from_arrays([[1, 5], [1, 5], ["a", "e"]])
+    tm.assert_index_equal(idx.dropna(), exp)
+    tm.assert_index_equal(idx.dropna(how="any"), exp)
+
+    exp = MultiIndex.from_arrays(
+        [[1, np.nan, 3, 5], [1, 2, np.nan, 5], ["a", "b", "c", "e"]]
+    )
+    tm.assert_index_equal(idx.dropna(how="all"), exp)
+
+    msg = "invalid how option: xxx"
+    with pytest.raises(ValueError, match=msg):
+        idx.dropna(how="xxx")
+
+    # GH26408
+    # test if missing values are dropped for multiindex constructed
+    # from codes and values
+    idx = MultiIndex(
+        levels=[[np.nan, None, pd.NaT, "128", 2], [np.nan, None, pd.NaT, "128", 2]],
+        codes=[[0, -1, 1, 2, 3, 4], [0, -1, 3, 3, 3, 4]],
+    )
+    expected = MultiIndex.from_arrays([["128", 2], ["128", 2]])
+    tm.assert_index_equal(idx.dropna(), expected)
+    tm.assert_index_equal(idx.dropna(how="any"), expected)
+
+    expected = MultiIndex.from_arrays(
+        [[np.nan, np.nan, "128", 2], ["128", "128", "128", 2]]
+    )
+    tm.assert_index_equal(idx.dropna(how="all"), expected)
+
+
+def test_nulls(idx):
+    # this is really a smoke test for the methods
+    # as these are adequately tested for function elsewhere
+
+    msg = "isna is not defined for MultiIndex"
+    with pytest.raises(NotImplementedError, match=msg):
+        idx.isna()
+
+
+@pytest.mark.xfail(reason="isna is not defined for MultiIndex")
+def test_hasnans_isnans(idx):
+    # GH 11343, added tests for hasnans / isnans
+    index = idx.copy()
+
+    # cases in indices doesn't include NaN
+    expected = np.array([False] * len(index), dtype=bool)
+    tm.assert_numpy_array_equal(index._isnan, expected)
+    assert index.hasnans is False
+
+    index = idx.copy()
+    values = index.values
+    values[1] = np.nan
+
+    index = type(idx)(values)
+
+    expected = np.array([False] * len(index), dtype=bool)
+    expected[1] = True
+    tm.assert_numpy_array_equal(index._isnan, expected)
+    assert index.hasnans is True
+
+
+def test_nan_stays_float():
+    # GH 7031
+    idx0 = MultiIndex(levels=[["A", "B"], []], codes=[[1, 0], [-1, -1]], names=[0, 1])
+    idx1 = MultiIndex(levels=[["C"], ["D"]], codes=[[0], [0]], names=[0, 1])
+    idxm = idx0.join(idx1, how="outer")
+    assert pd.isna(idx0.get_level_values(1)).all()
+    # the following failed in 0.14.1
+    assert pd.isna(idxm.get_level_values(1)[:-1]).all()
+
+    df0 = pd.DataFrame([[1, 2]], index=idx0)
+    df1 = pd.DataFrame([[3, 4]], index=idx1)
+    dfm = df0 - df1
+    assert pd.isna(df0.index.get_level_values(1)).all()
+    # the following failed in 0.14.1
+    assert pd.isna(dfm.index.get_level_values(1)[:-1]).all()
+
+
+def test_tuples_have_na():
+    index = MultiIndex(
+        levels=[[1, 0], [0, 1, 2, 3]],
+        codes=[[1, 1, 1, 1, -1, 0, 0, 0], [0, 1, 2, 3, 0, 1, 2, 3]],
+    )
+
+    assert pd.isna(index[4][0])
+    assert pd.isna(index.values[4][0])
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_names.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_names.py
new file mode 100644
index 0000000000000000000000000000000000000000..45f19b4d70fb95cb2aee459a54d2ad53790b7df8
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_names.py
@@ -0,0 +1,201 @@
+import pytest
+
+import pandas as pd
+from pandas import MultiIndex
+import pandas._testing as tm
+
+
+def check_level_names(index, names):
+    assert [level.name for level in index.levels] == list(names)
+
+
+def test_slice_keep_name():
+    x = MultiIndex.from_tuples([("a", "b"), (1, 2), ("c", "d")], names=["x", "y"])
+    assert x[1:].names == x.names
+
+
+def test_index_name_retained():
+    # GH9857
+    result = pd.DataFrame({"x": [1, 2, 6], "y": [2, 2, 8], "z": [-5, 0, 5]})
+    result = result.set_index("z")
+    result.loc[10] = [9, 10]
+    df_expected = pd.DataFrame(
+        {"x": [1, 2, 6, 9], "y": [2, 2, 8, 10], "z": [-5, 0, 5, 10]}
+    )
+    df_expected = df_expected.set_index("z")
+    tm.assert_frame_equal(result, df_expected)
+
+
+def test_changing_names(idx):
+    assert [level.name for level in idx.levels] == ["first", "second"]
+
+    view = idx.view()
+    copy = idx.copy()
+    shallow_copy = idx._view()
+
+    # changing names should not change level names on object
+    new_names = [name + "a" for name in idx.names]
+    idx.names = new_names
+    check_level_names(idx, ["firsta", "seconda"])
+
+    # and not on copies
+    check_level_names(view, ["first", "second"])
+    check_level_names(copy, ["first", "second"])
+    check_level_names(shallow_copy, ["first", "second"])
+
+    # and copies shouldn't change original
+    shallow_copy.names = [name + "c" for name in shallow_copy.names]
+    check_level_names(idx, ["firsta", "seconda"])
+
+
+def test_take_preserve_name(idx):
+    taken = idx.take([3, 0, 1])
+    assert taken.names == idx.names
+
+
+def test_copy_names():
+    # Check that adding a "names" parameter to the copy is honored
+    # GH14302
+    multi_idx = MultiIndex.from_tuples([(1, 2), (3, 4)], names=["MyName1", "MyName2"])
+    multi_idx1 = multi_idx.copy()
+
+    assert multi_idx.equals(multi_idx1)
+    assert multi_idx.names == ["MyName1", "MyName2"]
+    assert multi_idx1.names == ["MyName1", "MyName2"]
+
+    multi_idx2 = multi_idx.copy(names=["NewName1", "NewName2"])
+
+    assert multi_idx.equals(multi_idx2)
+    assert multi_idx.names == ["MyName1", "MyName2"]
+    assert multi_idx2.names == ["NewName1", "NewName2"]
+
+    multi_idx3 = multi_idx.copy(name=["NewName1", "NewName2"])
+
+    assert multi_idx.equals(multi_idx3)
+    assert multi_idx.names == ["MyName1", "MyName2"]
+    assert multi_idx3.names == ["NewName1", "NewName2"]
+
+    # gh-35592
+    with pytest.raises(ValueError, match="Length of new names must be 2, got 1"):
+        multi_idx.copy(names=["mario"])
+
+    with pytest.raises(TypeError, match="MultiIndex.name must be a hashable type"):
+        multi_idx.copy(names=[["mario"], ["luigi"]])
+
+
+def test_names(idx):
+    # names are assigned in setup
+    assert idx.names == ["first", "second"]
+    level_names = [level.name for level in idx.levels]
+    assert level_names == idx.names
+
+    # setting bad names on existing
+    index = idx
+    with pytest.raises(ValueError, match="^Length of names"):
+        setattr(index, "names", list(index.names) + ["third"])
+    with pytest.raises(ValueError, match="^Length of names"):
+        setattr(index, "names", [])
+
+    # initializing with bad names (should always be equivalent)
+    major_axis, minor_axis = idx.levels
+    major_codes, minor_codes = idx.codes
+    with pytest.raises(ValueError, match="^Length of names"):
+        MultiIndex(
+            levels=[major_axis, minor_axis],
+            codes=[major_codes, minor_codes],
+            names=["first"],
+        )
+    with pytest.raises(ValueError, match="^Length of names"):
+        MultiIndex(
+            levels=[major_axis, minor_axis],
+            codes=[major_codes, minor_codes],
+            names=["first", "second", "third"],
+        )
+
+    # names are assigned on index, but not transferred to the levels
+    index.names = ["a", "b"]
+    level_names = [level.name for level in index.levels]
+    assert level_names == ["a", "b"]
+
+
+def test_duplicate_level_names_access_raises(idx):
+    # GH19029
+    idx.names = ["foo", "foo"]
+    with pytest.raises(ValueError, match="name foo occurs multiple times"):
+        idx._get_level_number("foo")
+
+
+def test_get_names_from_levels():
+    idx = MultiIndex.from_product([["a"], [1, 2]], names=["a", "b"])
+
+    assert idx.levels[0].name == "a"
+    assert idx.levels[1].name == "b"
+
+
+def test_setting_names_from_levels_raises():
+    idx = MultiIndex.from_product([["a"], [1, 2]], names=["a", "b"])
+    with pytest.raises(RuntimeError, match="set_names"):
+        idx.levels[0].name = "foo"
+
+    with pytest.raises(RuntimeError, match="set_names"):
+        idx.levels[1].name = "foo"
+
+    new = pd.Series(1, index=idx.levels[0])
+    with pytest.raises(RuntimeError, match="set_names"):
+        new.index.name = "bar"
+
+    assert pd.Index._no_setting_name is False
+    assert pd.RangeIndex._no_setting_name is False
+
+
+@pytest.mark.parametrize("func", ["rename", "set_names"])
+@pytest.mark.parametrize(
+    "rename_dict, exp_names",
+    [
+        ({"x": "z"}, ["z", "y", "z"]),
+        ({"x": "z", "y": "x"}, ["z", "x", "z"]),
+        ({"y": "z"}, ["x", "z", "x"]),
+        ({}, ["x", "y", "x"]),
+        ({"z": "a"}, ["x", "y", "x"]),
+        ({"y": "z", "a": "b"}, ["x", "z", "x"]),
+    ],
+)
+def test_name_mi_with_dict_like_duplicate_names(func, rename_dict, exp_names):
+    # GH#20421
+    mi = MultiIndex.from_arrays([[1, 2], [3, 4], [5, 6]], names=["x", "y", "x"])
+    result = getattr(mi, func)(rename_dict)
+    expected = MultiIndex.from_arrays([[1, 2], [3, 4], [5, 6]], names=exp_names)
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("func", ["rename", "set_names"])
+@pytest.mark.parametrize(
+    "rename_dict, exp_names",
+    [
+        ({"x": "z"}, ["z", "y"]),
+        ({"x": "z", "y": "x"}, ["z", "x"]),
+        ({"a": "z"}, ["x", "y"]),
+        ({}, ["x", "y"]),
+    ],
+)
+def test_name_mi_with_dict_like(func, rename_dict, exp_names):
+    # GH#20421
+    mi = MultiIndex.from_arrays([[1, 2], [3, 4]], names=["x", "y"])
+    result = getattr(mi, func)(rename_dict)
+    expected = MultiIndex.from_arrays([[1, 2], [3, 4]], names=exp_names)
+    tm.assert_index_equal(result, expected)
+
+
+def test_index_name_with_dict_like_raising():
+    # GH#20421
+    ix = pd.Index([1, 2])
+    msg = "Can only pass dict-like as `names` for MultiIndex."
+    with pytest.raises(TypeError, match=msg):
+        ix.set_names({"x": "z"})
+
+
+def test_multiindex_name_and_level_raising():
+    # GH#20421
+    mi = MultiIndex.from_arrays([[1, 2], [3, 4]], names=["x", "y"])
+    with pytest.raises(TypeError, match="Can not pass level for dictlike `names`."):
+        mi.set_names(names={"x": "z"}, level={"x": "z"})
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_partial_indexing.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_partial_indexing.py
new file mode 100644
index 0000000000000000000000000000000000000000..64cc1fa621b3195727cbfb3e62a8b6a6acf4dfaf
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_partial_indexing.py
@@ -0,0 +1,148 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    IndexSlice,
+    MultiIndex,
+    date_range,
+)
+import pandas._testing as tm
+
+
+@pytest.fixture
+def df():
+    #                        c1
+    # 2016-01-01 00:00:00 a   0
+    #                     b   1
+    #                     c   2
+    # 2016-01-01 12:00:00 a   3
+    #                     b   4
+    #                     c   5
+    # 2016-01-02 00:00:00 a   6
+    #                     b   7
+    #                     c   8
+    # 2016-01-02 12:00:00 a   9
+    #                     b  10
+    #                     c  11
+    # 2016-01-03 00:00:00 a  12
+    #                     b  13
+    #                     c  14
+    dr = date_range("2016-01-01", "2016-01-03", freq="12h")
+    abc = ["a", "b", "c"]
+    mi = MultiIndex.from_product([dr, abc])
+    frame = DataFrame({"c1": range(15)}, index=mi)
+    return frame
+
+
+def test_partial_string_matching_single_index(df):
+    # partial string matching on a single index
+    for df_swap in [df.swaplevel(), df.swaplevel(0), df.swaplevel(0, 1)]:
+        df_swap = df_swap.sort_index()
+        just_a = df_swap.loc["a"]
+        result = just_a.loc["2016-01-01"]
+        expected = df.loc[IndexSlice[:, "a"], :].iloc[0:2]
+        expected.index = expected.index.droplevel(1)
+        tm.assert_frame_equal(result, expected)
+
+
+def test_get_loc_partial_timestamp_multiindex(df):
+    mi = df.index
+    key = ("2016-01-01", "a")
+    loc = mi.get_loc(key)
+
+    expected = np.zeros(len(mi), dtype=bool)
+    expected[[0, 3]] = True
+    tm.assert_numpy_array_equal(loc, expected)
+
+    key2 = ("2016-01-02", "a")
+    loc2 = mi.get_loc(key2)
+    expected2 = np.zeros(len(mi), dtype=bool)
+    expected2[[6, 9]] = True
+    tm.assert_numpy_array_equal(loc2, expected2)
+
+    key3 = ("2016-01", "a")
+    loc3 = mi.get_loc(key3)
+    expected3 = np.zeros(len(mi), dtype=bool)
+    expected3[mi.get_level_values(1).get_loc("a")] = True
+    tm.assert_numpy_array_equal(loc3, expected3)
+
+    key4 = ("2016", "a")
+    loc4 = mi.get_loc(key4)
+    expected4 = expected3
+    tm.assert_numpy_array_equal(loc4, expected4)
+
+    # non-monotonic
+    taker = np.arange(len(mi), dtype=np.intp)
+    taker[::2] = taker[::-2]
+    mi2 = mi.take(taker)
+    loc5 = mi2.get_loc(key)
+    expected5 = np.zeros(len(mi2), dtype=bool)
+    expected5[[3, 14]] = True
+    tm.assert_numpy_array_equal(loc5, expected5)
+
+
+def test_partial_string_timestamp_multiindex(df):
+    # GH10331
+    df_swap = df.swaplevel(0, 1).sort_index()
+    SLC = IndexSlice
+
+    # indexing with IndexSlice
+    result = df.loc[SLC["2016-01-01":"2016-02-01", :], :]
+    expected = df
+    tm.assert_frame_equal(result, expected)
+
+    # match on secondary index
+    result = df_swap.loc[SLC[:, "2016-01-01":"2016-01-01"], :]
+    expected = df_swap.iloc[[0, 1, 5, 6, 10, 11]]
+    tm.assert_frame_equal(result, expected)
+
+    # partial string match on year only
+    result = df.loc["2016"]
+    expected = df
+    tm.assert_frame_equal(result, expected)
+
+    # partial string match on date
+    result = df.loc["2016-01-01"]
+    expected = df.iloc[0:6]
+    tm.assert_frame_equal(result, expected)
+
+    # partial string match on date and hour, from middle
+    result = df.loc["2016-01-02 12"]
+    # hourly resolution, same as index.levels[0], so we are _not_ slicing on
+    #  that level, so that level gets dropped
+    expected = df.iloc[9:12].droplevel(0)
+    tm.assert_frame_equal(result, expected)
+
+    # partial string match on secondary index
+    result = df_swap.loc[SLC[:, "2016-01-02"], :]
+    expected = df_swap.iloc[[2, 3, 7, 8, 12, 13]]
+    tm.assert_frame_equal(result, expected)
+
+    # tuple selector with partial string match on date
+    # "2016-01-01" has daily resolution, so _is_ a slice on the first level.
+    result = df.loc[("2016-01-01", "a"), :]
+    expected = df.iloc[[0, 3]]
+    expected = df.iloc[[0, 3]].droplevel(1)
+    tm.assert_frame_equal(result, expected)
+
+    # Slicing date on first level should break (of course) bc the DTI is the
+    #  second level on df_swap
+    with pytest.raises(KeyError, match="'2016-01-01'"):
+        df_swap.loc["2016-01-01"]
+
+
+def test_partial_string_timestamp_multiindex_str_key_raises(df):
+    # Even though this syntax works on a single index, this is somewhat
+    # ambiguous and we don't want to extend this behavior forward to work
+    # in multi-indexes. This would amount to selecting a scalar from a
+    # column.
+    with pytest.raises(KeyError, match="'2016-01-01'"):
+        df["2016-01-01"]
+
+
+def test_partial_string_timestamp_multiindex_daily_resolution(df):
+    # GH12685 (partial string with daily resolution or below)
+    result = df.loc[IndexSlice["2013-03":"2013-03", :], :]
+    expected = df.iloc[118:180]
+    tm.assert_frame_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_pickle.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_pickle.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d8b72140442159fa0b8c608022d167bddd95db4
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_pickle.py
@@ -0,0 +1,10 @@
+import pytest
+
+from pandas import MultiIndex
+
+
+def test_pickle_compat_construction():
+    # this is testing for pickle compat
+    # need an object to create with
+    with pytest.raises(TypeError, match="Must pass both levels and codes"):
+        MultiIndex()
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_reindex.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_reindex.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1b4fe8b98760a0b776c5d81d471a7745e8407de
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_reindex.py
@@ -0,0 +1,174 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Index,
+    MultiIndex,
+)
+import pandas._testing as tm
+
+
+def test_reindex(idx):
+    result, indexer = idx.reindex(list(idx[:4]))
+    assert isinstance(result, MultiIndex)
+    assert result.names == ["first", "second"]
+    assert [level.name for level in result.levels] == ["first", "second"]
+
+    result, indexer = idx.reindex(list(idx))
+    assert isinstance(result, MultiIndex)
+    assert indexer is None
+    assert result.names == ["first", "second"]
+    assert [level.name for level in result.levels] == ["first", "second"]
+
+
+def test_reindex_level(idx):
+    index = Index(["one"])
+
+    target, indexer = idx.reindex(index, level="second")
+    target2, indexer2 = index.reindex(idx, level="second")
+
+    exp_index = idx.join(index, level="second", how="right")
+    exp_index2 = idx.join(index, level="second", how="left")
+
+    assert target.equals(exp_index)
+    exp_indexer = np.array([0, 2, 4])
+    tm.assert_numpy_array_equal(indexer, exp_indexer, check_dtype=False)
+
+    assert target2.equals(exp_index2)
+    exp_indexer2 = np.array([0, -1, 0, -1, 0, -1])
+    tm.assert_numpy_array_equal(indexer2, exp_indexer2, check_dtype=False)
+
+    with pytest.raises(TypeError, match="Fill method not supported"):
+        idx.reindex(idx, method="pad", level="second")
+
+
+def test_reindex_preserves_names_when_target_is_list_or_ndarray(idx):
+    # GH6552
+    idx = idx.copy()
+    target = idx.copy()
+    idx.names = target.names = [None, None]
+
+    other_dtype = MultiIndex.from_product([[1, 2], [3, 4]])
+
+    # list & ndarray cases
+    assert idx.reindex([])[0].names == [None, None]
+    assert idx.reindex(np.array([]))[0].names == [None, None]
+    assert idx.reindex(target.tolist())[0].names == [None, None]
+    assert idx.reindex(target.values)[0].names == [None, None]
+    assert idx.reindex(other_dtype.tolist())[0].names == [None, None]
+    assert idx.reindex(other_dtype.values)[0].names == [None, None]
+
+    idx.names = ["foo", "bar"]
+    assert idx.reindex([])[0].names == ["foo", "bar"]
+    assert idx.reindex(np.array([]))[0].names == ["foo", "bar"]
+    assert idx.reindex(target.tolist())[0].names == ["foo", "bar"]
+    assert idx.reindex(target.values)[0].names == ["foo", "bar"]
+    assert idx.reindex(other_dtype.tolist())[0].names == ["foo", "bar"]
+    assert idx.reindex(other_dtype.values)[0].names == ["foo", "bar"]
+
+
+def test_reindex_lvl_preserves_names_when_target_is_list_or_array():
+    # GH7774
+    idx = MultiIndex.from_product([[0, 1], ["a", "b"]], names=["foo", "bar"])
+    assert idx.reindex([], level=0)[0].names == ["foo", "bar"]
+    assert idx.reindex([], level=1)[0].names == ["foo", "bar"]
+
+
+def test_reindex_lvl_preserves_type_if_target_is_empty_list_or_array(
+    using_infer_string,
+):
+    # GH7774
+    idx = MultiIndex.from_product([[0, 1], ["a", "b"]])
+    assert idx.reindex([], level=0)[0].levels[0].dtype.type == np.int64
+    exp = np.object_ if not using_infer_string else str
+    assert idx.reindex([], level=1)[0].levels[1].dtype.type == exp
+
+    # case with EA levels
+    cat = pd.Categorical(["foo", "bar"])
+    dti = pd.date_range("2016-01-01", periods=2, tz="US/Pacific")
+    mi = MultiIndex.from_product([cat, dti])
+    assert mi.reindex([], level=0)[0].levels[0].dtype == cat.dtype
+    assert mi.reindex([], level=1)[0].levels[1].dtype == dti.dtype
+
+
+def test_reindex_base(idx):
+    expected = np.arange(idx.size, dtype=np.intp)
+
+    actual = idx.get_indexer(idx)
+    tm.assert_numpy_array_equal(expected, actual)
+
+    with pytest.raises(ValueError, match="Invalid fill method"):
+        idx.get_indexer(idx, method="invalid")
+
+
+def test_reindex_non_unique():
+    idx = MultiIndex.from_tuples([(0, 0), (1, 1), (1, 1), (2, 2)])
+    a = pd.Series(np.arange(4), index=idx)
+    new_idx = MultiIndex.from_tuples([(0, 0), (1, 1), (2, 2)])
+
+    msg = "cannot handle a non-unique multi-index!"
+    with pytest.raises(ValueError, match=msg):
+        a.reindex(new_idx)
+
+
+@pytest.mark.parametrize("values", [[["a"], ["x"]], [[], []]])
+def test_reindex_empty_with_level(values):
+    # GH41170
+    idx = MultiIndex.from_arrays(values)
+    result, result_indexer = idx.reindex(np.array(["b"]), level=0)
+    expected = MultiIndex(levels=[["b"], values[1]], codes=[[], []])
+    expected_indexer = np.array([], dtype=result_indexer.dtype)
+    tm.assert_index_equal(result, expected)
+    tm.assert_numpy_array_equal(result_indexer, expected_indexer)
+
+
+def test_reindex_not_all_tuples():
+    keys = [("i", "i"), ("i", "j"), ("j", "i"), "j"]
+    mi = MultiIndex.from_tuples(keys[:-1])
+    idx = Index(keys)
+    res, indexer = mi.reindex(idx)
+
+    tm.assert_index_equal(res, idx)
+    expected = np.array([0, 1, 2, -1], dtype=np.intp)
+    tm.assert_numpy_array_equal(indexer, expected)
+
+
+def test_reindex_limit_arg_with_multiindex():
+    # GH21247
+
+    idx = MultiIndex.from_tuples([(3, "A"), (4, "A"), (4, "B")])
+
+    df = pd.Series([0.02, 0.01, 0.012], index=idx)
+
+    new_idx = MultiIndex.from_tuples(
+        [
+            (3, "A"),
+            (3, "B"),
+            (4, "A"),
+            (4, "B"),
+            (4, "C"),
+            (5, "B"),
+            (5, "C"),
+            (6, "B"),
+            (6, "C"),
+        ]
+    )
+
+    with pytest.raises(
+        ValueError,
+        match="limit argument only valid if doing pad, backfill or nearest reindexing",
+    ):
+        df.reindex(new_idx, fill_value=0, limit=1)
+
+
+def test_reindex_with_none_in_nested_multiindex():
+    # GH42883
+    index = MultiIndex.from_tuples([(("a", None), 1), (("b", None), 2)])
+    index2 = MultiIndex.from_tuples([(("b", None), 2), (("a", None), 1)])
+    df1_dtype = pd.DataFrame([1, 2], index=index)
+    df2_dtype = pd.DataFrame([2, 1], index=index2)
+
+    result = df1_dtype.reindex_like(df2_dtype)
+    expected = df2_dtype
+    tm.assert_frame_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_setops.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_setops.py
new file mode 100644
index 0000000000000000000000000000000000000000..0abb56ecf9de7182dd38e45d5a8938cb81248034
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_setops.py
@@ -0,0 +1,772 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    CategoricalIndex,
+    DataFrame,
+    Index,
+    IntervalIndex,
+    MultiIndex,
+    Series,
+)
+import pandas._testing as tm
+from pandas.api.types import (
+    is_float_dtype,
+    is_unsigned_integer_dtype,
+)
+
+
+@pytest.mark.parametrize("case", [0.5, "xxx"])
+@pytest.mark.parametrize(
+    "method", ["intersection", "union", "difference", "symmetric_difference"]
+)
+def test_set_ops_error_cases(idx, case, sort, method):
+    # non-iterable input
+    msg = "Input must be Index or array-like"
+    with pytest.raises(TypeError, match=msg):
+        getattr(idx, method)(case, sort=sort)
+
+
+@pytest.mark.parametrize("klass", [MultiIndex, np.array, Series, list])
+def test_intersection_base(idx, sort, klass):
+    first = idx[2::-1]  # first 3 elements reversed
+    second = idx[:5]
+
+    if klass is not MultiIndex:
+        second = klass(second.values)
+
+    intersect = first.intersection(second, sort=sort)
+    if sort is None:
+        expected = first.sort_values()
+    else:
+        expected = first
+    tm.assert_index_equal(intersect, expected)
+
+    msg = "other must be a MultiIndex or a list of tuples"
+    with pytest.raises(TypeError, match=msg):
+        first.intersection([1, 2, 3], sort=sort)
+
+
+@pytest.mark.arm_slow
+@pytest.mark.parametrize("klass", [MultiIndex, np.array, Series, list])
+def test_union_base(idx, sort, klass):
+    first = idx[::-1]
+    second = idx[:5]
+
+    if klass is not MultiIndex:
+        second = klass(second.values)
+
+    union = first.union(second, sort=sort)
+    if sort is None:
+        expected = first.sort_values()
+    else:
+        expected = first
+    tm.assert_index_equal(union, expected)
+
+    msg = "other must be a MultiIndex or a list of tuples"
+    with pytest.raises(TypeError, match=msg):
+        first.union([1, 2, 3], sort=sort)
+
+
+def test_difference_base(idx, sort):
+    second = idx[4:]
+    answer = idx[:4]
+    result = idx.difference(second, sort=sort)
+
+    if sort is None:
+        answer = answer.sort_values()
+
+    assert result.equals(answer)
+    tm.assert_index_equal(result, answer)
+
+    # GH 10149
+    cases = [klass(second.values) for klass in [np.array, Series, list]]
+    for case in cases:
+        result = idx.difference(case, sort=sort)
+        tm.assert_index_equal(result, answer)
+
+    msg = "other must be a MultiIndex or a list of tuples"
+    with pytest.raises(TypeError, match=msg):
+        idx.difference([1, 2, 3], sort=sort)
+
+
+def test_symmetric_difference(idx, sort):
+    first = idx[1:]
+    second = idx[:-1]
+    answer = idx[[-1, 0]]
+    result = first.symmetric_difference(second, sort=sort)
+
+    if sort is None:
+        answer = answer.sort_values()
+
+    tm.assert_index_equal(result, answer)
+
+    # GH 10149
+    cases = [klass(second.values) for klass in [np.array, Series, list]]
+    for case in cases:
+        result = first.symmetric_difference(case, sort=sort)
+        tm.assert_index_equal(result, answer)
+
+    msg = "other must be a MultiIndex or a list of tuples"
+    with pytest.raises(TypeError, match=msg):
+        first.symmetric_difference([1, 2, 3], sort=sort)
+
+
+def test_multiindex_symmetric_difference():
+    # GH 13490
+    idx = MultiIndex.from_product([["a", "b"], ["A", "B"]], names=["a", "b"])
+    result = idx.symmetric_difference(idx)
+    assert result.names == idx.names
+
+    idx2 = idx.copy().rename(["A", "B"])
+    result = idx.symmetric_difference(idx2)
+    assert result.names == [None, None]
+
+
+def test_empty(idx):
+    # GH 15270
+    assert not idx.empty
+    assert idx[:0].empty
+
+
+def test_difference(idx, sort):
+    first = idx
+    result = first.difference(idx[-3:], sort=sort)
+    vals = idx[:-3].values
+
+    if sort is None:
+        vals = sorted(vals)
+
+    expected = MultiIndex.from_tuples(vals, sortorder=0, names=idx.names)
+
+    assert isinstance(result, MultiIndex)
+    assert result.equals(expected)
+    assert result.names == idx.names
+    tm.assert_index_equal(result, expected)
+
+    # empty difference: reflexive
+    result = idx.difference(idx, sort=sort)
+    expected = idx[:0]
+    assert result.equals(expected)
+    assert result.names == idx.names
+
+    # empty difference: superset
+    result = idx[-3:].difference(idx, sort=sort)
+    expected = idx[:0]
+    assert result.equals(expected)
+    assert result.names == idx.names
+
+    # empty difference: degenerate
+    result = idx[:0].difference(idx, sort=sort)
+    expected = idx[:0]
+    assert result.equals(expected)
+    assert result.names == idx.names
+
+    # names not the same
+    chunklet = idx[-3:]
+    chunklet.names = ["foo", "baz"]
+    result = first.difference(chunklet, sort=sort)
+    assert result.names == (None, None)
+
+    # empty, but non-equal
+    result = idx.difference(idx.sortlevel(1)[0], sort=sort)
+    assert len(result) == 0
+
+    # raise Exception called with non-MultiIndex
+    result = first.difference(first.values, sort=sort)
+    assert result.equals(first[:0])
+
+    # name from empty array
+    result = first.difference([], sort=sort)
+    assert first.equals(result)
+    assert first.names == result.names
+
+    # name from non-empty array
+    result = first.difference([("foo", "one")], sort=sort)
+    expected = MultiIndex.from_tuples(
+        [("bar", "one"), ("baz", "two"), ("foo", "two"), ("qux", "one"), ("qux", "two")]
+    )
+    expected.names = first.names
+    assert first.names == result.names
+
+    msg = "other must be a MultiIndex or a list of tuples"
+    with pytest.raises(TypeError, match=msg):
+        first.difference([1, 2, 3, 4, 5], sort=sort)
+
+
+def test_difference_sort_special():
+    # GH-24959
+    idx = MultiIndex.from_product([[1, 0], ["a", "b"]])
+    # sort=None, the default
+    result = idx.difference([])
+    tm.assert_index_equal(result, idx)
+
+
+def test_difference_sort_special_true():
+    idx = MultiIndex.from_product([[1, 0], ["a", "b"]])
+    result = idx.difference([], sort=True)
+    expected = MultiIndex.from_product([[0, 1], ["a", "b"]])
+    tm.assert_index_equal(result, expected)
+
+
+def test_difference_sort_incomparable():
+    # GH-24959
+    idx = MultiIndex.from_product([[1, pd.Timestamp("2000"), 2], ["a", "b"]])
+
+    other = MultiIndex.from_product([[3, pd.Timestamp("2000"), 4], ["c", "d"]])
+    # sort=None, the default
+    msg = "sort order is undefined for incomparable objects"
+    with tm.assert_produces_warning(RuntimeWarning, match=msg):
+        result = idx.difference(other)
+    tm.assert_index_equal(result, idx)
+
+    # sort=False
+    result = idx.difference(other, sort=False)
+    tm.assert_index_equal(result, idx)
+
+
+def test_difference_sort_incomparable_true():
+    idx = MultiIndex.from_product([[1, pd.Timestamp("2000"), 2], ["a", "b"]])
+    other = MultiIndex.from_product([[3, pd.Timestamp("2000"), 4], ["c", "d"]])
+
+    # TODO: this is raising in constructing a Categorical when calling
+    #  algos.safe_sort. Should we catch and re-raise with a better message?
+    msg = "'values' is not ordered, please explicitly specify the categories order "
+    with pytest.raises(TypeError, match=msg):
+        idx.difference(other, sort=True)
+
+
+def test_union(idx, sort):
+    piece1 = idx[:5][::-1]
+    piece2 = idx[3:]
+
+    the_union = piece1.union(piece2, sort=sort)
+
+    if sort in (None, False):
+        tm.assert_index_equal(the_union.sort_values(), idx.sort_values())
+    else:
+        tm.assert_index_equal(the_union, idx)
+
+    # corner case, pass self or empty thing:
+    the_union = idx.union(idx, sort=sort)
+    tm.assert_index_equal(the_union, idx)
+
+    the_union = idx.union(idx[:0], sort=sort)
+    tm.assert_index_equal(the_union, idx)
+
+    tuples = idx.values
+    result = idx[:4].union(tuples[4:], sort=sort)
+    if sort is None:
+        tm.assert_index_equal(result.sort_values(), idx.sort_values())
+    else:
+        assert result.equals(idx)
+
+
+def test_union_with_regular_index(idx, using_infer_string):
+    other = Index(["A", "B", "C"])
+
+    result = other.union(idx)
+    assert ("foo", "one") in result
+    assert "B" in result
+
+    if using_infer_string:
+        with pytest.raises(NotImplementedError, match="Can only union"):
+            idx.union(other)
+    else:
+        msg = "The values in the array are unorderable"
+        with tm.assert_produces_warning(RuntimeWarning, match=msg):
+            result2 = idx.union(other)
+        # This is more consistent now, if sorting fails then we don't sort at all
+        # in the MultiIndex case.
+        assert not result.equals(result2)
+
+
+def test_intersection(idx, sort):
+    piece1 = idx[:5][::-1]
+    piece2 = idx[3:]
+
+    the_int = piece1.intersection(piece2, sort=sort)
+
+    if sort in (None, True):
+        tm.assert_index_equal(the_int, idx[3:5])
+    else:
+        tm.assert_index_equal(the_int.sort_values(), idx[3:5])
+
+    # corner case, pass self
+    the_int = idx.intersection(idx, sort=sort)
+    tm.assert_index_equal(the_int, idx)
+
+    # empty intersection: disjoint
+    empty = idx[:2].intersection(idx[2:], sort=sort)
+    expected = idx[:0]
+    assert empty.equals(expected)
+
+    tuples = idx.values
+    result = idx.intersection(tuples)
+    assert result.equals(idx)
+
+
+@pytest.mark.parametrize(
+    "method", ["intersection", "union", "difference", "symmetric_difference"]
+)
+def test_setop_with_categorical(idx, sort, method):
+    other = idx.to_flat_index().astype("category")
+    res_names = [None] * idx.nlevels
+
+    result = getattr(idx, method)(other, sort=sort)
+    expected = getattr(idx, method)(idx, sort=sort).rename(res_names)
+    tm.assert_index_equal(result, expected)
+
+    result = getattr(idx, method)(other[:5], sort=sort)
+    expected = getattr(idx, method)(idx[:5], sort=sort).rename(res_names)
+    tm.assert_index_equal(result, expected)
+
+
+def test_intersection_non_object(idx, sort):
+    other = Index(range(3), name="foo")
+
+    result = idx.intersection(other, sort=sort)
+    expected = MultiIndex(levels=idx.levels, codes=[[]] * idx.nlevels, names=None)
+    tm.assert_index_equal(result, expected, exact=True)
+
+    # if we pass a length-0 ndarray (i.e. no name, we retain our idx.name)
+    result = idx.intersection(np.asarray(other)[:0], sort=sort)
+    expected = MultiIndex(levels=idx.levels, codes=[[]] * idx.nlevels, names=idx.names)
+    tm.assert_index_equal(result, expected, exact=True)
+
+    msg = "other must be a MultiIndex or a list of tuples"
+    with pytest.raises(TypeError, match=msg):
+        # With non-zero length non-index, we try and fail to convert to tuples
+        idx.intersection(np.asarray(other), sort=sort)
+
+
+def test_intersect_equal_sort():
+    # GH-24959
+    idx = MultiIndex.from_product([[1, 0], ["a", "b"]])
+    tm.assert_index_equal(idx.intersection(idx, sort=False), idx)
+    tm.assert_index_equal(idx.intersection(idx, sort=None), idx)
+
+
+def test_intersect_equal_sort_true():
+    idx = MultiIndex.from_product([[1, 0], ["a", "b"]])
+    expected = MultiIndex.from_product([[0, 1], ["a", "b"]])
+    result = idx.intersection(idx, sort=True)
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("slice_", [slice(None), slice(0)])
+def test_union_sort_other_empty(slice_):
+    # https://github.com/pandas-dev/pandas/issues/24959
+    idx = MultiIndex.from_product([[1, 0], ["a", "b"]])
+
+    # default, sort=None
+    other = idx[slice_]
+    tm.assert_index_equal(idx.union(other), idx)
+    tm.assert_index_equal(other.union(idx), idx)
+
+    # sort=False
+    tm.assert_index_equal(idx.union(other, sort=False), idx)
+
+
+def test_union_sort_other_empty_sort():
+    idx = MultiIndex.from_product([[1, 0], ["a", "b"]])
+    other = idx[:0]
+    result = idx.union(other, sort=True)
+    expected = MultiIndex.from_product([[0, 1], ["a", "b"]])
+    tm.assert_index_equal(result, expected)
+
+
+def test_union_sort_other_incomparable():
+    # https://github.com/pandas-dev/pandas/issues/24959
+    idx = MultiIndex.from_product([[1, pd.Timestamp("2000")], ["a", "b"]])
+
+    # default, sort=None
+    with tm.assert_produces_warning(RuntimeWarning):
+        result = idx.union(idx[:1])
+    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_sort():
+    idx = MultiIndex.from_product([[1, pd.Timestamp("2000")], ["a", "b"]])
+    msg = "'<' not supported between instances of 'Timestamp' and 'int'"
+    with pytest.raises(TypeError, match=msg):
+        idx.union(idx[:1], sort=True)
+
+
+def test_union_non_object_dtype_raises():
+    # GH#32646 raise NotImplementedError instead of less-informative error
+    mi = MultiIndex.from_product([["a", "b"], [1, 2]])
+
+    idx = mi.levels[1]
+
+    msg = "Can only union MultiIndex with MultiIndex or Index of tuples"
+    with pytest.raises(NotImplementedError, match=msg):
+        mi.union(idx)
+
+
+def test_union_empty_self_different_names():
+    # GH#38423
+    mi = MultiIndex.from_arrays([[]])
+    mi2 = MultiIndex.from_arrays([[1, 2], [3, 4]], names=["a", "b"])
+    result = mi.union(mi2)
+    expected = MultiIndex.from_arrays([[1, 2], [3, 4]])
+    tm.assert_index_equal(result, expected)
+
+
+def test_union_multiindex_empty_rangeindex():
+    # GH#41234
+    mi = MultiIndex.from_arrays([[1, 2], [3, 4]], names=["a", "b"])
+    ri = pd.RangeIndex(0)
+
+    result_left = mi.union(ri)
+    tm.assert_index_equal(mi, result_left, check_names=False)
+
+    result_right = ri.union(mi)
+    tm.assert_index_equal(mi, result_right, check_names=False)
+
+
+@pytest.mark.parametrize(
+    "method", ["union", "intersection", "difference", "symmetric_difference"]
+)
+def test_setops_sort_validation(method):
+    idx1 = MultiIndex.from_product([["a", "b"], [1, 2]])
+    idx2 = MultiIndex.from_product([["b", "c"], [1, 2]])
+
+    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)
+
+
+@pytest.mark.parametrize("val", [pd.NA, 100])
+def test_difference_keep_ea_dtypes(any_numeric_ea_dtype, val):
+    # GH#48606
+    midx = MultiIndex.from_arrays(
+        [Series([1, 2], dtype=any_numeric_ea_dtype), [2, 1]], names=["a", None]
+    )
+    midx2 = MultiIndex.from_arrays(
+        [Series([1, 2, val], dtype=any_numeric_ea_dtype), [1, 1, 3]]
+    )
+    result = midx.difference(midx2)
+    expected = MultiIndex.from_arrays([Series([1], dtype=any_numeric_ea_dtype), [2]])
+    tm.assert_index_equal(result, expected)
+
+    result = midx.difference(midx.sort_values(ascending=False))
+    expected = MultiIndex.from_arrays(
+        [Series([], dtype=any_numeric_ea_dtype), Series([], dtype=np.int64)],
+        names=["a", None],
+    )
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("val", [pd.NA, 5])
+def test_symmetric_difference_keeping_ea_dtype(any_numeric_ea_dtype, val):
+    # GH#48607
+    midx = MultiIndex.from_arrays(
+        [Series([1, 2], dtype=any_numeric_ea_dtype), [2, 1]], names=["a", None]
+    )
+    midx2 = MultiIndex.from_arrays(
+        [Series([1, 2, val], dtype=any_numeric_ea_dtype), [1, 1, 3]]
+    )
+    result = midx.symmetric_difference(midx2)
+    expected = MultiIndex.from_arrays(
+        [Series([1, 1, val], dtype=any_numeric_ea_dtype), [1, 2, 3]]
+    )
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    ("tuples", "exp_tuples"),
+    [
+        ([("val1", "test1")], [("val1", "test1")]),
+        ([("val1", "test1"), ("val1", "test1")], [("val1", "test1")]),
+        (
+            [("val2", "test2"), ("val1", "test1")],
+            [("val2", "test2"), ("val1", "test1")],
+        ),
+    ],
+)
+def test_intersect_with_duplicates(tuples, exp_tuples):
+    # GH#36915
+    left = MultiIndex.from_tuples(tuples, names=["first", "second"])
+    right = MultiIndex.from_tuples(
+        [("val1", "test1"), ("val1", "test1"), ("val2", "test2")],
+        names=["first", "second"],
+    )
+    result = left.intersection(right)
+    expected = MultiIndex.from_tuples(exp_tuples, names=["first", "second"])
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data, names, expected",
+    [
+        ((1,), None, [None, None]),
+        ((1,), ["a"], [None, None]),
+        ((1,), ["b"], [None, None]),
+        ((1, 2), ["c", "d"], [None, None]),
+        ((1, 2), ["b", "a"], [None, None]),
+        ((1, 2, 3), ["a", "b", "c"], [None, None]),
+        ((1, 2), ["a", "c"], ["a", None]),
+        ((1, 2), ["c", "b"], [None, "b"]),
+        ((1, 2), ["a", "b"], ["a", "b"]),
+        ((1, 2), [None, "b"], [None, "b"]),
+    ],
+)
+def test_maybe_match_names(data, names, expected):
+    # GH#38323
+    mi = MultiIndex.from_tuples([], names=["a", "b"])
+    mi2 = MultiIndex.from_tuples([data], names=names)
+    result = mi._maybe_match_names(mi2)
+    assert result == expected
+
+
+def test_intersection_equal_different_names():
+    # GH#30302
+    mi1 = MultiIndex.from_arrays([[1, 2], [3, 4]], names=["c", "b"])
+    mi2 = MultiIndex.from_arrays([[1, 2], [3, 4]], names=["a", "b"])
+
+    result = mi1.intersection(mi2)
+    expected = MultiIndex.from_arrays([[1, 2], [3, 4]], names=[None, "b"])
+    tm.assert_index_equal(result, expected)
+
+
+def test_intersection_different_names():
+    # GH#38323
+    mi = MultiIndex.from_arrays([[1], [3]], names=["c", "b"])
+    mi2 = MultiIndex.from_arrays([[1], [3]])
+    result = mi.intersection(mi2)
+    tm.assert_index_equal(result, mi2)
+
+
+def test_intersection_with_missing_values_on_both_sides(nulls_fixture):
+    # GH#38623
+    mi1 = MultiIndex.from_arrays([[3, nulls_fixture, 4, nulls_fixture], [1, 2, 4, 2]])
+    mi2 = MultiIndex.from_arrays([[3, nulls_fixture, 3], [1, 2, 4]])
+    result = mi1.intersection(mi2)
+    expected = MultiIndex.from_arrays([[3, nulls_fixture], [1, 2]])
+    tm.assert_index_equal(result, expected)
+
+
+def test_union_with_missing_values_on_both_sides(nulls_fixture):
+    # GH#38623
+    mi1 = MultiIndex.from_arrays([[1, nulls_fixture]])
+    mi2 = MultiIndex.from_arrays([[1, nulls_fixture, 3]])
+    result = mi1.union(mi2)
+    expected = MultiIndex.from_arrays([[1, 3, nulls_fixture]])
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["float64", "Float64"])
+@pytest.mark.parametrize("sort", [None, False])
+def test_union_nan_got_duplicated(dtype, sort):
+    # GH#38977, GH#49010
+    mi1 = MultiIndex.from_arrays([pd.array([1.0, np.nan], dtype=dtype), [2, 3]])
+    mi2 = MultiIndex.from_arrays([pd.array([1.0, np.nan, 3.0], dtype=dtype), [2, 3, 4]])
+    result = mi1.union(mi2, sort=sort)
+    if sort is None:
+        expected = MultiIndex.from_arrays(
+            [pd.array([1.0, 3.0, np.nan], dtype=dtype), [2, 4, 3]]
+        )
+    else:
+        expected = mi2
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("val", [4, 1])
+def test_union_keep_ea_dtype(any_numeric_ea_dtype, val):
+    # GH#48505
+
+    arr1 = Series([val, 2], dtype=any_numeric_ea_dtype)
+    arr2 = Series([2, 1], dtype=any_numeric_ea_dtype)
+    midx = MultiIndex.from_arrays([arr1, [1, 2]], names=["a", None])
+    midx2 = MultiIndex.from_arrays([arr2, [2, 1]])
+    result = midx.union(midx2)
+    if val == 4:
+        expected = MultiIndex.from_arrays(
+            [Series([1, 2, 4], dtype=any_numeric_ea_dtype), [1, 2, 1]]
+        )
+    else:
+        expected = MultiIndex.from_arrays(
+            [Series([1, 2], dtype=any_numeric_ea_dtype), [1, 2]]
+        )
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("dupe_val", [3, pd.NA])
+def test_union_with_duplicates_keep_ea_dtype(dupe_val, any_numeric_ea_dtype):
+    # GH48900
+    mi1 = MultiIndex.from_arrays(
+        [
+            Series([1, dupe_val, 2], dtype=any_numeric_ea_dtype),
+            Series([1, dupe_val, 2], dtype=any_numeric_ea_dtype),
+        ]
+    )
+    mi2 = MultiIndex.from_arrays(
+        [
+            Series([2, dupe_val, dupe_val], dtype=any_numeric_ea_dtype),
+            Series([2, dupe_val, dupe_val], dtype=any_numeric_ea_dtype),
+        ]
+    )
+    result = mi1.union(mi2)
+    expected = MultiIndex.from_arrays(
+        [
+            Series([1, 2, dupe_val, dupe_val], dtype=any_numeric_ea_dtype),
+            Series([1, 2, dupe_val, dupe_val], dtype=any_numeric_ea_dtype),
+        ]
+    )
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+def test_union_duplicates(index, request):
+    # GH#38977
+    if index.empty or isinstance(index, (IntervalIndex, CategoricalIndex)):
+        pytest.skip(f"No duplicates in an empty {type(index).__name__}")
+
+    values = index.unique().values.tolist()
+    mi1 = MultiIndex.from_arrays([values, [1] * len(values)])
+    mi2 = MultiIndex.from_arrays([[values[0]] + values, [1] * (len(values) + 1)])
+    result = mi2.union(mi1)
+    expected = mi2.sort_values()
+    tm.assert_index_equal(result, expected)
+
+    if (
+        is_unsigned_integer_dtype(mi2.levels[0])
+        and (mi2.get_level_values(0) < 2**63).all()
+    ):
+        # GH#47294 - union uses lib.fast_zip, converting data to Python integers
+        # and loses type information. Result is then unsigned only when values are
+        # sufficiently large to require unsigned dtype. This happens only if other
+        # has dups or one of both have missing values
+        expected = expected.set_levels(
+            [expected.levels[0].astype(np.int64), expected.levels[1]]
+        )
+    elif is_float_dtype(mi2.levels[0]):
+        # mi2 has duplicates witch is a different path than above, Fix that path
+        # to use correct float dtype?
+        expected = expected.set_levels(
+            [expected.levels[0].astype(float), expected.levels[1]]
+        )
+
+    result = mi1.union(mi2)
+    tm.assert_index_equal(result, expected)
+
+
+def test_union_keep_dtype_precision(any_real_numeric_dtype):
+    # GH#48498
+    arr1 = Series([4, 1, 1], dtype=any_real_numeric_dtype)
+    arr2 = Series([1, 4], dtype=any_real_numeric_dtype)
+    midx = MultiIndex.from_arrays([arr1, [2, 1, 1]], names=["a", None])
+    midx2 = MultiIndex.from_arrays([arr2, [1, 2]], names=["a", None])
+
+    result = midx.union(midx2)
+    expected = MultiIndex.from_arrays(
+        ([Series([1, 1, 4], dtype=any_real_numeric_dtype), [1, 1, 2]]),
+        names=["a", None],
+    )
+    tm.assert_index_equal(result, expected)
+
+
+def test_union_keep_ea_dtype_with_na(any_numeric_ea_dtype):
+    # GH#48498
+    arr1 = Series([4, pd.NA], dtype=any_numeric_ea_dtype)
+    arr2 = Series([1, pd.NA], dtype=any_numeric_ea_dtype)
+    midx = MultiIndex.from_arrays([arr1, [2, 1]], names=["a", None])
+    midx2 = MultiIndex.from_arrays([arr2, [1, 2]])
+    result = midx.union(midx2)
+    expected = MultiIndex.from_arrays(
+        [Series([1, 4, pd.NA, pd.NA], dtype=any_numeric_ea_dtype), [1, 2, 1, 2]]
+    )
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "levels1, levels2, codes1, codes2, names",
+    [
+        (
+            [["a", "b", "c"], [0, ""]],
+            [["c", "d", "b"], [""]],
+            [[0, 1, 2], [1, 1, 1]],
+            [[0, 1, 2], [0, 0, 0]],
+            ["name1", "name2"],
+        ),
+    ],
+)
+def test_intersection_lexsort_depth(levels1, levels2, codes1, codes2, names):
+    # GH#25169
+    mi1 = MultiIndex(levels=levels1, codes=codes1, names=names)
+    mi2 = MultiIndex(levels=levels2, codes=codes2, names=names)
+    mi_int = mi1.intersection(mi2)
+    assert mi_int._lexsort_depth == 2
+
+
+@pytest.mark.parametrize(
+    "a",
+    [pd.Categorical(["a", "b"], categories=["a", "b"]), ["a", "b"]],
+)
+@pytest.mark.parametrize(
+    "b",
+    [
+        pd.Categorical(["a", "b"], categories=["b", "a"], ordered=True),
+        pd.Categorical(["a", "b"], categories=["b", "a"]),
+    ],
+)
+def test_intersection_with_non_lex_sorted_categories(a, b):
+    # GH#49974
+    other = ["1", "2"]
+
+    df1 = DataFrame({"x": a, "y": other})
+    df2 = DataFrame({"x": b, "y": other})
+
+    expected = MultiIndex.from_arrays([a, other], names=["x", "y"])
+
+    res1 = MultiIndex.from_frame(df1).intersection(
+        MultiIndex.from_frame(df2.sort_values(["x", "y"]))
+    )
+    res2 = MultiIndex.from_frame(df1).intersection(MultiIndex.from_frame(df2))
+    res3 = MultiIndex.from_frame(df1.sort_values(["x", "y"])).intersection(
+        MultiIndex.from_frame(df2)
+    )
+    res4 = MultiIndex.from_frame(df1.sort_values(["x", "y"])).intersection(
+        MultiIndex.from_frame(df2.sort_values(["x", "y"]))
+    )
+
+    tm.assert_index_equal(res1, expected)
+    tm.assert_index_equal(res2, expected)
+    tm.assert_index_equal(res3, expected)
+    tm.assert_index_equal(res4, expected)
+
+
+@pytest.mark.parametrize("val", [pd.NA, 100])
+def test_intersection_keep_ea_dtypes(val, any_numeric_ea_dtype):
+    # GH#48604
+    midx = MultiIndex.from_arrays(
+        [Series([1, 2], dtype=any_numeric_ea_dtype), [2, 1]], names=["a", None]
+    )
+    midx2 = MultiIndex.from_arrays(
+        [Series([1, 2, val], dtype=any_numeric_ea_dtype), [1, 1, 3]]
+    )
+    result = midx.intersection(midx2)
+    expected = MultiIndex.from_arrays([Series([2], dtype=any_numeric_ea_dtype), [1]])
+    tm.assert_index_equal(result, expected)
+
+
+def test_union_with_na_when_constructing_dataframe():
+    # GH43222
+    series1 = Series(
+        (1,),
+        index=MultiIndex.from_arrays(
+            [Series([None], dtype="string"), Series([None], dtype="string")]
+        ),
+    )
+    series2 = Series((10, 20), index=MultiIndex.from_tuples(((None, None), ("a", "b"))))
+    result = DataFrame([series1, series2])
+    expected = DataFrame({(np.nan, np.nan): [1.0, 10.0], ("a", "b"): [np.nan, 20.0]})
+    tm.assert_frame_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_take.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_take.py
new file mode 100644
index 0000000000000000000000000000000000000000..543cba25c373b71b8c79c7fe0ea5ae2fb7f40b18
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_take.py
@@ -0,0 +1,78 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+def test_take(idx):
+    indexer = [4, 3, 0, 2]
+    result = idx.take(indexer)
+    expected = idx[indexer]
+    assert result.equals(expected)
+
+    # GH 10791
+    msg = "'MultiIndex' object has no attribute 'freq'"
+    with pytest.raises(AttributeError, match=msg):
+        idx.freq
+
+
+def test_take_invalid_kwargs(idx):
+    indices = [1, 2]
+
+    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_fill_value():
+    # GH 12631
+    vals = [["A", "B"], [pd.Timestamp("2011-01-01"), pd.Timestamp("2011-01-02")]]
+    idx = pd.MultiIndex.from_product(vals, names=["str", "dt"])
+
+    result = idx.take(np.array([1, 0, -1]))
+    exp_vals = [
+        ("A", pd.Timestamp("2011-01-02")),
+        ("A", pd.Timestamp("2011-01-01")),
+        ("B", pd.Timestamp("2011-01-02")),
+    ]
+    expected = pd.MultiIndex.from_tuples(exp_vals, names=["str", "dt"])
+    tm.assert_index_equal(result, expected)
+
+    # fill_value
+    result = idx.take(np.array([1, 0, -1]), fill_value=True)
+    exp_vals = [
+        ("A", pd.Timestamp("2011-01-02")),
+        ("A", pd.Timestamp("2011-01-01")),
+        (np.nan, pd.NaT),
+    ]
+    expected = pd.MultiIndex.from_tuples(exp_vals, names=["str", "dt"])
+    tm.assert_index_equal(result, expected)
+
+    # allow_fill=False
+    result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True)
+    exp_vals = [
+        ("A", pd.Timestamp("2011-01-02")),
+        ("A", pd.Timestamp("2011-01-01")),
+        ("B", pd.Timestamp("2011-01-02")),
+    ]
+    expected = pd.MultiIndex.from_tuples(exp_vals, names=["str", "dt"])
+    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 4"
+    with pytest.raises(IndexError, match=msg):
+        idx.take(np.array([1, -5]))