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llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_constructors.py

@@ -0,0 +1,80 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Index,
+    MultiIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestIndexConstructor:
+    # Tests for the Index constructor, specifically for cases that do
+    #  not return a subclass
+
+    @pytest.mark.parametrize("value", [1, np.int64(1)])
+    def test_constructor_corner(self, value):
+        # corner case
+        msg = (
+            r"Index\(\.\.\.\) must be called with a collection of some "
+            f"kind, {value} was passed"
+        )
+        with pytest.raises(TypeError, match=msg):
+            Index(value)
+
+    @pytest.mark.parametrize("index_vals", [[("A", 1), "B"], ["B", ("A", 1)]])
+    def test_construction_list_mixed_tuples(self, index_vals):
+        # see gh-10697: if we are constructing from a mixed list of tuples,
+        # make sure that we are independent of the sorting order.
+        index = Index(index_vals)
+        assert isinstance(index, Index)
+        assert not isinstance(index, MultiIndex)
+
+    def test_constructor_cast(self):
+        msg = "could not convert string to float"
+        with pytest.raises(ValueError, match=msg):
+            Index(["a", "b", "c"], dtype=float)
+
+    @pytest.mark.parametrize("tuple_list", [[()], [(), ()]])
+    def test_construct_empty_tuples(self, tuple_list):
+        # GH #45608
+        result = Index(tuple_list)
+        expected = MultiIndex.from_tuples(tuple_list)
+
+        tm.assert_index_equal(result, expected)
+
+    def test_index_string_inference(self):
+        # GH#54430
+        pytest.importorskip("pyarrow")
+        dtype = "string[pyarrow_numpy]"
+        expected = Index(["a", "b"], dtype=dtype)
+        with pd.option_context("future.infer_string", True):
+            ser = Index(["a", "b"])
+        tm.assert_index_equal(ser, expected)
+
+        expected = Index(["a", 1], dtype="object")
+        with pd.option_context("future.infer_string", True):
+            ser = Index(["a", 1])
+        tm.assert_index_equal(ser, expected)
+
+    def test_inference_on_pandas_objects(self):
+        # GH#56012
+        idx = Index([pd.Timestamp("2019-12-31")], dtype=object)
+        with tm.assert_produces_warning(FutureWarning, match="Dtype inference"):
+            result = Index(idx)
+        assert result.dtype != np.object_
+
+        ser = Series([pd.Timestamp("2019-12-31")], dtype=object)
+
+        with tm.assert_produces_warning(FutureWarning, match="Dtype inference"):
+            result = Index(ser)
+        assert result.dtype != np.object_
+
+    def test_constructor_not_read_only(self):
+        # GH#57130
+        ser = Series([1, 2], dtype=object)
+        with pd.option_context("mode.copy_on_write", True):
+            idx = Index(ser)
+            assert idx._values.flags.writeable

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_formats.py

@@ -0,0 +1,163 @@
+import numpy as np
+import pytest
+
+from pandas._config import using_pyarrow_string_dtype
+import pandas._config.config as cf
+
+from pandas import Index
+import pandas._testing as tm
+
+
+class TestIndexRendering:
+    def test_repr_is_valid_construction_code(self):
+        # for the case of Index, where the repr is traditional rather than
+        # stylized
+        idx = Index(["a", "b"])
+        res = eval(repr(idx))
+        tm.assert_index_equal(res, idx)
+
+    @pytest.mark.xfail(using_pyarrow_string_dtype(), reason="repr different")
+    @pytest.mark.parametrize(
+        "index,expected",
+        [
+            # ASCII
+            # short
+            (
+                Index(["a", "bb", "ccc"]),
+                """Index(['a', 'bb', 'ccc'], dtype='object')""",
+            ),
+            # multiple lines
+            (
+                Index(["a", "bb", "ccc"] * 10),
+                "Index(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', "
+                "'bb', 'ccc', 'a', 'bb', 'ccc',\n"
+                "       'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', "
+                "'bb', 'ccc', 'a', 'bb', 'ccc',\n"
+                "       'a', 'bb', 'ccc', 'a', 'bb', 'ccc'],\n"
+                "      dtype='object')",
+            ),
+            # truncated
+            (
+                Index(["a", "bb", "ccc"] * 100),
+                "Index(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a',\n"
+                "       ...\n"
+                "       'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'],\n"
+                "      dtype='object', length=300)",
+            ),
+            # Non-ASCII
+            # short
+            (
+                Index(["あ", "いい", "ううう"]),
+                """Index(['あ', 'いい', 'ううう'], dtype='object')""",
+            ),
+            # multiple lines
+            (
+                Index(["あ", "いい", "ううう"] * 10),
+                (
+                    "Index(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', "
+                    "'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう',\n"
+                    "       'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', "
+                    "'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう',\n"
+                    "       'あ', 'いい', 'ううう', 'あ', 'いい', "
+                    "'ううう'],\n"
+                    "      dtype='object')"
+                ),
+            ),
+            # truncated
+            (
+                Index(["あ", "いい", "ううう"] * 100),
+                (
+                    "Index(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', "
+                    "'あ', 'いい', 'ううう', 'あ',\n"
+                    "       ...\n"
+                    "       'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', "
+                    "'ううう', 'あ', 'いい', 'ううう'],\n"
+                    "      dtype='object', length=300)"
+                ),
+            ),
+        ],
+    )
+    def test_string_index_repr(self, index, expected):
+        result = repr(index)
+        assert result == expected
+
+    @pytest.mark.xfail(using_pyarrow_string_dtype(), reason="repr different")
+    @pytest.mark.parametrize(
+        "index,expected",
+        [
+            # short
+            (
+                Index(["あ", "いい", "ううう"]),
+                ("Index(['あ', 'いい', 'ううう'], dtype='object')"),
+            ),
+            # multiple lines
+            (
+                Index(["あ", "いい", "ううう"] * 10),
+                (
+                    "Index(['あ', 'いい', 'ううう', 'あ', 'いい', "
+                    "'ううう', 'あ', 'いい', 'ううう',\n"
+                    "       'あ', 'いい', 'ううう', 'あ', 'いい', "
+                    "'ううう', 'あ', 'いい', 'ううう',\n"
+                    "       'あ', 'いい', 'ううう', 'あ', 'いい', "
+                    "'ううう', 'あ', 'いい', 'ううう',\n"
+                    "       'あ', 'いい', 'ううう'],\n"
+                    "      dtype='object')"
+                    ""
+                ),
+            ),
+            # truncated
+            (
+                Index(["あ", "いい", "ううう"] * 100),
+                (
+                    "Index(['あ', 'いい', 'ううう', 'あ', 'いい', "
+                    "'ううう', 'あ', 'いい', 'ううう',\n"
+                    "       'あ',\n"
+                    "       ...\n"
+                    "       'ううう', 'あ', 'いい', 'ううう', 'あ', "
+                    "'いい', 'ううう', 'あ', 'いい',\n"
+                    "       'ううう'],\n"
+                    "      dtype='object', length=300)"
+                ),
+            ),
+        ],
+    )
+    def test_string_index_repr_with_unicode_option(self, index, expected):
+        # Enable Unicode option -----------------------------------------
+        with cf.option_context("display.unicode.east_asian_width", True):
+            result = repr(index)
+            assert result == expected
+
+    def test_repr_summary(self):
+        with cf.option_context("display.max_seq_items", 10):
+            result = repr(Index(np.arange(1000)))
+            assert len(result) < 200
+            assert "..." in result
+
+    def test_summary_bug(self):
+        # GH#3869
+        ind = Index(["{other}%s", "~:{range}:0"], name="A")
+        result = ind._summary()
+        # shouldn't be formatted accidentally.
+        assert "~:{range}:0" in result
+        assert "{other}%s" in result
+
+    def test_index_repr_bool_nan(self):
+        # GH32146
+        arr = Index([True, False, np.nan], dtype=object)
+        msg = "Index.format is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            exp1 = arr.format()
+        out1 = ["True", "False", "NaN"]
+        assert out1 == exp1
+
+        exp2 = repr(arr)
+        out2 = "Index([True, False, nan], dtype='object')"
+        assert out2 == exp2
+
+    def test_format_different_scalar_lengths(self):
+        # GH#35439
+        idx = Index(["aaaaaaaaa", "b"])
+        expected = ["aaaaaaaaa", "b"]
+        msg = r"Index\.format is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert idx.format() == expected

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_indexing.py

@@ -0,0 +1,104 @@
+import numpy as np
+import pytest
+
+from pandas._libs import index as libindex
+
+import pandas as pd
+from pandas import (
+    Index,
+    NaT,
+)
+import pandas._testing as tm
+
+
+class TestGetSliceBounds:
+    @pytest.mark.parametrize("side, expected", [("left", 4), ("right", 5)])
+    def test_get_slice_bounds_within(self, side, expected):
+        index = Index(list("abcdef"))
+        result = index.get_slice_bound("e", side=side)
+        assert result == expected
+
+    @pytest.mark.parametrize("side", ["left", "right"])
+    @pytest.mark.parametrize(
+        "data, bound, expected", [(list("abcdef"), "x", 6), (list("bcdefg"), "a", 0)]
+    )
+    def test_get_slice_bounds_outside(self, side, expected, data, bound):
+        index = Index(data)
+        result = index.get_slice_bound(bound, side=side)
+        assert result == expected
+
+    def test_get_slice_bounds_invalid_side(self):
+        with pytest.raises(ValueError, match="Invalid value for side kwarg"):
+            Index([]).get_slice_bound("a", side="middle")
+
+
+class TestGetIndexerNonUnique:
+    def test_get_indexer_non_unique_dtype_mismatch(self):
+        # GH#25459
+        indexes, missing = Index(["A", "B"]).get_indexer_non_unique(Index([0]))
+        tm.assert_numpy_array_equal(np.array([-1], dtype=np.intp), indexes)
+        tm.assert_numpy_array_equal(np.array([0], dtype=np.intp), missing)
+
+    @pytest.mark.parametrize(
+        "idx_values,idx_non_unique",
+        [
+            ([np.nan, 100, 200, 100], [np.nan, 100]),
+            ([np.nan, 100.0, 200.0, 100.0], [np.nan, 100.0]),
+        ],
+    )
+    def test_get_indexer_non_unique_int_index(self, idx_values, idx_non_unique):
+        indexes, missing = Index(idx_values).get_indexer_non_unique(Index([np.nan]))
+        tm.assert_numpy_array_equal(np.array([0], dtype=np.intp), indexes)
+        tm.assert_numpy_array_equal(np.array([], dtype=np.intp), missing)
+
+        indexes, missing = Index(idx_values).get_indexer_non_unique(
+            Index(idx_non_unique)
+        )
+        tm.assert_numpy_array_equal(np.array([0, 1, 3], dtype=np.intp), indexes)
+        tm.assert_numpy_array_equal(np.array([], dtype=np.intp), missing)
+
+
+class TestGetLoc:
+    @pytest.mark.slow  # to_flat_index takes a while
+    def test_get_loc_tuple_monotonic_above_size_cutoff(self, monkeypatch):
+        # Go through the libindex path for which using
+        # _bin_search vs ndarray.searchsorted makes a difference
+
+        with monkeypatch.context():
+            monkeypatch.setattr(libindex, "_SIZE_CUTOFF", 100)
+            lev = list("ABCD")
+            dti = pd.date_range("2016-01-01", periods=10)
+
+            mi = pd.MultiIndex.from_product([lev, range(5), dti])
+            oidx = mi.to_flat_index()
+
+            loc = len(oidx) // 2
+            tup = oidx[loc]
+
+            res = oidx.get_loc(tup)
+        assert res == loc
+
+    def test_get_loc_nan_object_dtype_nonmonotonic_nonunique(self):
+        # case that goes through _maybe_get_bool_indexer
+        idx = Index(["foo", np.nan, None, "foo", 1.0, None], dtype=object)
+
+        # we dont raise KeyError on nan
+        res = idx.get_loc(np.nan)
+        assert res == 1
+
+        # we only match on None, not on np.nan
+        res = idx.get_loc(None)
+        expected = np.array([False, False, True, False, False, True])
+        tm.assert_numpy_array_equal(res, expected)
+
+        # we don't match at all on mismatched NA
+        with pytest.raises(KeyError, match="NaT"):
+            idx.get_loc(NaT)
+
+
+def test_getitem_boolean_ea_indexer():
+    # GH#45806
+    ser = pd.Series([True, False, pd.NA], dtype="boolean")
+    result = ser.index[ser]
+    expected = Index([0])
+    tm.assert_index_equal(result, expected)

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_pickle.py

@@ -0,0 +1,11 @@
+from pandas import Index
+import pandas._testing as tm
+
+
+def test_pickle_preserves_object_dtype():
+    # GH#43188, GH#43155 don't infer numeric dtype
+    index = Index([1, 2, 3], dtype=object)
+
+    result = tm.round_trip_pickle(index)
+    assert result.dtype == object
+    tm.assert_index_equal(index, result)

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_reshape.py

@@ -0,0 +1,95 @@
+"""
+Tests for ndarray-like method on the base Index class
+"""
+import numpy as np
+import pytest
+
+from pandas import Index
+import pandas._testing as tm
+
+
+class TestReshape:
+    def test_repeat(self):
+        repeats = 2
+        index = Index([1, 2, 3])
+        expected = Index([1, 1, 2, 2, 3, 3])
+
+        result = index.repeat(repeats)
+        tm.assert_index_equal(result, expected)
+
+    def test_insert(self):
+        # GH 7256
+        # validate neg/pos inserts
+        result = Index(["b", "c", "d"])
+
+        # test 0th element
+        tm.assert_index_equal(Index(["a", "b", "c", "d"]), result.insert(0, "a"))
+
+        # test Nth element that follows Python list behavior
+        tm.assert_index_equal(Index(["b", "c", "e", "d"]), result.insert(-1, "e"))
+
+        # test loc +/- neq (0, -1)
+        tm.assert_index_equal(result.insert(1, "z"), result.insert(-2, "z"))
+
+        # test empty
+        null_index = Index([])
+        tm.assert_index_equal(Index(["a"], dtype=object), null_index.insert(0, "a"))
+
+    def test_insert_missing(self, nulls_fixture, using_infer_string):
+        # GH#22295
+        # test there is no mangling of NA values
+        expected = Index(["a", nulls_fixture, "b", "c"], dtype=object)
+        result = Index(list("abc"), dtype=object).insert(
+            1, Index([nulls_fixture], dtype=object)
+        )
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "val", [(1, 2), np.datetime64("2019-12-31"), np.timedelta64(1, "D")]
+    )
+    @pytest.mark.parametrize("loc", [-1, 2])
+    def test_insert_datetime_into_object(self, loc, val):
+        # GH#44509
+        idx = Index(["1", "2", "3"])
+        result = idx.insert(loc, val)
+        expected = Index(["1", "2", val, "3"])
+        tm.assert_index_equal(result, expected)
+        assert type(expected[2]) is type(val)
+
+    def test_insert_none_into_string_numpy(self):
+        # GH#55365
+        pytest.importorskip("pyarrow")
+        index = Index(["a", "b", "c"], dtype="string[pyarrow_numpy]")
+        result = index.insert(-1, None)
+        expected = Index(["a", "b", None, "c"], dtype="string[pyarrow_numpy]")
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "pos,expected",
+        [
+            (0, Index(["b", "c", "d"], name="index")),
+            (-1, Index(["a", "b", "c"], name="index")),
+        ],
+    )
+    def test_delete(self, pos, expected):
+        index = Index(["a", "b", "c", "d"], name="index")
+        result = index.delete(pos)
+        tm.assert_index_equal(result, expected)
+        assert result.name == expected.name
+
+    def test_delete_raises(self):
+        index = Index(["a", "b", "c", "d"], name="index")
+        msg = "index 5 is out of bounds for axis 0 with size 4"
+        with pytest.raises(IndexError, match=msg):
+            index.delete(5)
+
+    def test_append_multiple(self):
+        index = Index(["a", "b", "c", "d", "e", "f"])
+
+        foos = [index[:2], index[2:4], index[4:]]
+        result = foos[0].append(foos[1:])
+        tm.assert_index_equal(result, index)
+
+        # empty
+        result = index.append([])
+        tm.assert_index_equal(result, index)

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_setops.py

@@ -0,0 +1,266 @@
+from datetime import datetime
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Index,
+    Series,
+)
+import pandas._testing as tm
+from pandas.core.algorithms import safe_sort
+
+
+def equal_contents(arr1, arr2) -> bool:
+    """
+    Checks if the set of unique elements of arr1 and arr2 are equivalent.
+    """
+    return frozenset(arr1) == frozenset(arr2)
+
+
+class TestIndexSetOps:
+    @pytest.mark.parametrize(
+        "method", ["union", "intersection", "difference", "symmetric_difference"]
+    )
+    def test_setops_sort_validation(self, method):
+        idx1 = Index(["a", "b"])
+        idx2 = Index(["b", "c"])
+
+        with pytest.raises(ValueError, match="The 'sort' keyword only takes"):
+            getattr(idx1, method)(idx2, sort=2)
+
+        # sort=True is supported as of GH#??
+        getattr(idx1, method)(idx2, sort=True)
+
+    def test_setops_preserve_object_dtype(self):
+        idx = Index([1, 2, 3], dtype=object)
+        result = idx.intersection(idx[1:])
+        expected = idx[1:]
+        tm.assert_index_equal(result, expected)
+
+        # if other is not monotonic increasing, intersection goes through
+        #  a different route
+        result = idx.intersection(idx[1:][::-1])
+        tm.assert_index_equal(result, expected)
+
+        result = idx._union(idx[1:], sort=None)
+        expected = idx
+        tm.assert_numpy_array_equal(result, expected.values)
+
+        result = idx.union(idx[1:], sort=None)
+        tm.assert_index_equal(result, expected)
+
+        # if other is not monotonic increasing, _union goes through
+        #  a different route
+        result = idx._union(idx[1:][::-1], sort=None)
+        tm.assert_numpy_array_equal(result, expected.values)
+
+        result = idx.union(idx[1:][::-1], sort=None)
+        tm.assert_index_equal(result, expected)
+
+    def test_union_base(self):
+        index = Index([0, "a", 1, "b", 2, "c"])
+        first = index[3:]
+        second = index[:5]
+
+        result = first.union(second)
+
+        expected = Index([0, 1, 2, "a", "b", "c"])
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("klass", [np.array, Series, list])
+    def test_union_different_type_base(self, klass):
+        # GH 10149
+        index = Index([0, "a", 1, "b", 2, "c"])
+        first = index[3:]
+        second = index[:5]
+
+        result = first.union(klass(second.values))
+
+        assert equal_contents(result, index)
+
+    def test_union_sort_other_incomparable(self):
+        # https://github.com/pandas-dev/pandas/issues/24959
+        idx = Index([1, pd.Timestamp("2000")])
+        # default (sort=None)
+        with tm.assert_produces_warning(RuntimeWarning):
+            result = idx.union(idx[:1])
+
+        tm.assert_index_equal(result, idx)
+
+        # sort=None
+        with tm.assert_produces_warning(RuntimeWarning):
+            result = idx.union(idx[:1], sort=None)
+        tm.assert_index_equal(result, idx)
+
+        # sort=False
+        result = idx.union(idx[:1], sort=False)
+        tm.assert_index_equal(result, idx)
+
+    def test_union_sort_other_incomparable_true(self):
+        idx = Index([1, pd.Timestamp("2000")])
+        with pytest.raises(TypeError, match=".*"):
+            idx.union(idx[:1], sort=True)
+
+    def test_intersection_equal_sort_true(self):
+        idx = Index(["c", "a", "b"])
+        sorted_ = Index(["a", "b", "c"])
+        tm.assert_index_equal(idx.intersection(idx, sort=True), sorted_)
+
+    def test_intersection_base(self, sort):
+        # (same results for py2 and py3 but sortedness not tested elsewhere)
+        index = Index([0, "a", 1, "b", 2, "c"])
+        first = index[:5]
+        second = index[:3]
+
+        expected = Index([0, 1, "a"]) if sort is None else Index([0, "a", 1])
+        result = first.intersection(second, sort=sort)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("klass", [np.array, Series, list])
+    def test_intersection_different_type_base(self, klass, sort):
+        # GH 10149
+        index = Index([0, "a", 1, "b", 2, "c"])
+        first = index[:5]
+        second = index[:3]
+
+        result = first.intersection(klass(second.values), sort=sort)
+        assert equal_contents(result, second)
+
+    def test_intersection_nosort(self):
+        result = Index(["c", "b", "a"]).intersection(["b", "a"])
+        expected = Index(["b", "a"])
+        tm.assert_index_equal(result, expected)
+
+    def test_intersection_equal_sort(self):
+        idx = Index(["c", "a", "b"])
+        tm.assert_index_equal(idx.intersection(idx, sort=False), idx)
+        tm.assert_index_equal(idx.intersection(idx, sort=None), idx)
+
+    def test_intersection_str_dates(self, sort):
+        dt_dates = [datetime(2012, 2, 9), datetime(2012, 2, 22)]
+
+        i1 = Index(dt_dates, dtype=object)
+        i2 = Index(["aa"], dtype=object)
+        result = i2.intersection(i1, sort=sort)
+
+        assert len(result) == 0
+
+    @pytest.mark.parametrize(
+        "index2,expected_arr",
+        [(Index(["B", "D"]), ["B"]), (Index(["B", "D", "A"]), ["A", "B"])],
+    )
+    def test_intersection_non_monotonic_non_unique(self, index2, expected_arr, sort):
+        # non-monotonic non-unique
+        index1 = Index(["A", "B", "A", "C"])
+        expected = Index(expected_arr)
+        result = index1.intersection(index2, sort=sort)
+        if sort is None:
+            expected = expected.sort_values()
+        tm.assert_index_equal(result, expected)
+
+    def test_difference_base(self, sort):
+        # (same results for py2 and py3 but sortedness not tested elsewhere)
+        index = Index([0, "a", 1, "b", 2, "c"])
+        first = index[:4]
+        second = index[3:]
+
+        result = first.difference(second, sort)
+        expected = Index([0, "a", 1])
+        if sort is None:
+            expected = Index(safe_sort(expected))
+        tm.assert_index_equal(result, expected)
+
+    def test_symmetric_difference(self):
+        # (same results for py2 and py3 but sortedness not tested elsewhere)
+        index = Index([0, "a", 1, "b", 2, "c"])
+        first = index[:4]
+        second = index[3:]
+
+        result = first.symmetric_difference(second)
+        expected = Index([0, 1, 2, "a", "c"])
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "method,expected,sort",
+        [
+            (
+                "intersection",
+                np.array(
+                    [(1, "A"), (2, "A"), (1, "B"), (2, "B")],
+                    dtype=[("num", int), ("let", "S1")],
+                ),
+                False,
+            ),
+            (
+                "intersection",
+                np.array(
+                    [(1, "A"), (1, "B"), (2, "A"), (2, "B")],
+                    dtype=[("num", int), ("let", "S1")],
+                ),
+                None,
+            ),
+            (
+                "union",
+                np.array(
+                    [(1, "A"), (1, "B"), (1, "C"), (2, "A"), (2, "B"), (2, "C")],
+                    dtype=[("num", int), ("let", "S1")],
+                ),
+                None,
+            ),
+        ],
+    )
+    def test_tuple_union_bug(self, method, expected, sort):
+        index1 = Index(
+            np.array(
+                [(1, "A"), (2, "A"), (1, "B"), (2, "B")],
+                dtype=[("num", int), ("let", "S1")],
+            )
+        )
+        index2 = Index(
+            np.array(
+                [(1, "A"), (2, "A"), (1, "B"), (2, "B"), (1, "C"), (2, "C")],
+                dtype=[("num", int), ("let", "S1")],
+            )
+        )
+
+        result = getattr(index1, method)(index2, sort=sort)
+        assert result.ndim == 1
+
+        expected = Index(expected)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("first_list", [["b", "a"], []])
+    @pytest.mark.parametrize("second_list", [["a", "b"], []])
+    @pytest.mark.parametrize(
+        "first_name, second_name, expected_name",
+        [("A", "B", None), (None, "B", None), ("A", None, None)],
+    )
+    def test_union_name_preservation(
+        self, first_list, second_list, first_name, second_name, expected_name, sort
+    ):
+        first = Index(first_list, name=first_name)
+        second = Index(second_list, name=second_name)
+        union = first.union(second, sort=sort)
+
+        vals = set(first_list).union(second_list)
+
+        if sort is None and len(first_list) > 0 and len(second_list) > 0:
+            expected = Index(sorted(vals), name=expected_name)
+            tm.assert_index_equal(union, expected)
+        else:
+            expected = Index(vals, name=expected_name)
+            tm.assert_index_equal(union.sort_values(), expected.sort_values())
+
+    @pytest.mark.parametrize(
+        "diff_type, expected",
+        [["difference", [1, "B"]], ["symmetric_difference", [1, 2, "B", "C"]]],
+    )
+    def test_difference_object_type(self, diff_type, expected):
+        # GH 13432
+        idx1 = Index([0, 1, "A", "B"])
+        idx2 = Index([0, 2, "A", "C"])
+        result = getattr(idx1, diff_type)(idx2)
+        expected = Index(expected)
+        tm.assert_index_equal(result, expected)

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_where.py

@@ -0,0 +1,13 @@
+import numpy as np
+
+from pandas import Index
+import pandas._testing as tm
+
+
+class TestWhere:
+    def test_where_intlike_str_doesnt_cast_ints(self):
+        idx = Index(range(3))
+        mask = np.array([True, False, True])
+        res = idx.where(mask, "2")
+        expected = Index([0, "2", 2])
+        tm.assert_index_equal(res, expected)

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/conftest.py

@@ -0,0 +1,41 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Series,
+    array,
+)
+
+
+@pytest.fixture(params=[None, False])
+def sort(request):
+    """
+    Valid values for the 'sort' parameter used in the Index
+    setops methods (intersection, union, etc.)
+
+    Caution:
+        Don't confuse this one with the "sort" fixture used
+        for DataFrame.append or concat. That one has
+        parameters [True, False].
+
+        We can't combine them as sort=True is not permitted
+        in the Index setops methods.
+    """
+    return request.param
+
+
+@pytest.fixture(params=["D", "3D", "-3D", "h", "2h", "-2h", "min", "2min", "s", "-3s"])
+def freq_sample(request):
+    """
+    Valid values for 'freq' parameter used to create date_range and
+    timedelta_range..
+    """
+    return request.param
+
+
+@pytest.fixture(params=[list, tuple, np.array, array, Series])
+def listlike_box(request):
+    """
+    Types that may be passed as the indexer to searchsorted.
+    """
+    return request.param

llmeval-env/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

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_astype.py

@@ -0,0 +1,30 @@
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+import pandas._testing as tm
+
+
+def test_astype(idx):
+    expected = idx.copy()
+    actual = idx.astype("O")
+    tm.assert_copy(actual.levels, expected.levels)
+    tm.assert_copy(actual.codes, expected.codes)
+    assert actual.names == list(expected.names)
+
+    with pytest.raises(TypeError, match="^Setting.*dtype.*object"):
+        idx.astype(np.dtype(int))
+
+
+@pytest.mark.parametrize("ordered", [True, False])
+def test_astype_category(idx, ordered):
+    # GH 18630
+    msg = "> 1 ndim Categorical are not supported at this time"
+    with pytest.raises(NotImplementedError, match=msg):
+        idx.astype(CategoricalDtype(ordered=ordered))
+
+    if ordered is False:
+        # dtype='category' defaults to ordered=False, so only test once
+        with pytest.raises(NotImplementedError, match=msg):
+            idx.astype("category")

llmeval-env/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

llmeval-env/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)

llmeval-env/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)

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_indexing.py

@@ -0,0 +1,1001 @@
+from datetime import timedelta
+import re
+
+import numpy as np
+import pytest
+
+from pandas._libs import index as libindex
+from pandas.errors import (
+    InvalidIndexError,
+    PerformanceWarning,
+)
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    Index,
+    MultiIndex,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestSliceLocs:
+    def test_slice_locs_partial(self, idx):
+        sorted_idx, _ = idx.sortlevel(0)
+
+        result = sorted_idx.slice_locs(("foo", "two"), ("qux", "one"))
+        assert result == (1, 5)
+
+        result = sorted_idx.slice_locs(None, ("qux", "one"))
+        assert result == (0, 5)
+
+        result = sorted_idx.slice_locs(("foo", "two"), None)
+        assert result == (1, len(sorted_idx))
+
+        result = sorted_idx.slice_locs("bar", "baz")
+        assert result == (2, 4)
+
+    def test_slice_locs(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((50, 4)),
+            columns=Index(list("ABCD"), dtype=object),
+            index=date_range("2000-01-01", periods=50, freq="B"),
+        )
+        stacked = df.stack(future_stack=True)
+        idx = stacked.index
+
+        slob = slice(*idx.slice_locs(df.index[5], df.index[15]))
+        sliced = stacked[slob]
+        expected = df[5:16].stack(future_stack=True)
+        tm.assert_almost_equal(sliced.values, expected.values)
+
+        slob = slice(
+            *idx.slice_locs(
+                df.index[5] + timedelta(seconds=30),
+                df.index[15] - timedelta(seconds=30),
+            )
+        )
+        sliced = stacked[slob]
+        expected = df[6:15].stack(future_stack=True)
+        tm.assert_almost_equal(sliced.values, expected.values)
+
+    def test_slice_locs_with_type_mismatch(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 4)),
+            columns=Index(list("ABCD"), dtype=object),
+            index=date_range("2000-01-01", periods=10, freq="B"),
+        )
+        stacked = df.stack(future_stack=True)
+        idx = stacked.index
+        with pytest.raises(TypeError, match="^Level type mismatch"):
+            idx.slice_locs((1, 3))
+        with pytest.raises(TypeError, match="^Level type mismatch"):
+            idx.slice_locs(df.index[5] + timedelta(seconds=30), (5, 2))
+        df = DataFrame(
+            np.ones((5, 5)),
+            index=Index([f"i-{i}" for i in range(5)], name="a"),
+            columns=Index([f"i-{i}" for i in range(5)], name="a"),
+        )
+        stacked = df.stack(future_stack=True)
+        idx = stacked.index
+        with pytest.raises(TypeError, match="^Level type mismatch"):
+            idx.slice_locs(timedelta(seconds=30))
+        # TODO: Try creating a UnicodeDecodeError in exception message
+        with pytest.raises(TypeError, match="^Level type mismatch"):
+            idx.slice_locs(df.index[1], (16, "a"))
+
+    def test_slice_locs_not_sorted(self):
+        index = MultiIndex(
+            levels=[Index(np.arange(4)), Index(np.arange(4)), Index(np.arange(4))],
+            codes=[
+                np.array([0, 0, 1, 2, 2, 2, 3, 3]),
+                np.array([0, 1, 0, 0, 0, 1, 0, 1]),
+                np.array([1, 0, 1, 1, 0, 0, 1, 0]),
+            ],
+        )
+        msg = "[Kk]ey length.*greater than MultiIndex lexsort depth"
+        with pytest.raises(KeyError, match=msg):
+            index.slice_locs((1, 0, 1), (2, 1, 0))
+
+        # works
+        sorted_index, _ = index.sortlevel(0)
+        # should there be a test case here???
+        sorted_index.slice_locs((1, 0, 1), (2, 1, 0))
+
+    def test_slice_locs_not_contained(self):
+        # some searchsorted action
+
+        index = MultiIndex(
+            levels=[[0, 2, 4, 6], [0, 2, 4]],
+            codes=[[0, 0, 0, 1, 1, 2, 3, 3, 3], [0, 1, 2, 1, 2, 2, 0, 1, 2]],
+        )
+
+        result = index.slice_locs((1, 0), (5, 2))
+        assert result == (3, 6)
+
+        result = index.slice_locs(1, 5)
+        assert result == (3, 6)
+
+        result = index.slice_locs((2, 2), (5, 2))
+        assert result == (3, 6)
+
+        result = index.slice_locs(2, 5)
+        assert result == (3, 6)
+
+        result = index.slice_locs((1, 0), (6, 3))
+        assert result == (3, 8)
+
+        result = index.slice_locs(-1, 10)
+        assert result == (0, len(index))
+
+    @pytest.mark.parametrize(
+        "index_arr,expected,start_idx,end_idx",
+        [
+            ([[np.nan, "a", "b"], ["c", "d", "e"]], (0, 3), np.nan, None),
+            ([[np.nan, "a", "b"], ["c", "d", "e"]], (0, 3), np.nan, "b"),
+            ([[np.nan, "a", "b"], ["c", "d", "e"]], (0, 3), np.nan, ("b", "e")),
+            ([["a", "b", "c"], ["d", np.nan, "e"]], (1, 3), ("b", np.nan), None),
+            ([["a", "b", "c"], ["d", np.nan, "e"]], (1, 3), ("b", np.nan), "c"),
+            ([["a", "b", "c"], ["d", np.nan, "e"]], (1, 3), ("b", np.nan), ("c", "e")),
+        ],
+    )
+    def test_slice_locs_with_missing_value(
+        self, index_arr, expected, start_idx, end_idx
+    ):
+        # issue 19132
+        idx = MultiIndex.from_arrays(index_arr)
+        result = idx.slice_locs(start=start_idx, end=end_idx)
+        assert result == expected
+
+
+class TestPutmask:
+    def test_putmask_with_wrong_mask(self, idx):
+        # GH18368
+
+        msg = "putmask: mask and data must be the same size"
+        with pytest.raises(ValueError, match=msg):
+            idx.putmask(np.ones(len(idx) + 1, np.bool_), 1)
+
+        with pytest.raises(ValueError, match=msg):
+            idx.putmask(np.ones(len(idx) - 1, np.bool_), 1)
+
+        with pytest.raises(ValueError, match=msg):
+            idx.putmask("foo", 1)
+
+    def test_putmask_multiindex_other(self):
+        # GH#43212 `value` is also a MultiIndex
+
+        left = MultiIndex.from_tuples([(np.nan, 6), (np.nan, 6), ("a", 4)])
+        right = MultiIndex.from_tuples([("a", 1), ("a", 1), ("d", 1)])
+        mask = np.array([True, True, False])
+
+        result = left.putmask(mask, right)
+
+        expected = MultiIndex.from_tuples([right[0], right[1], left[2]])
+        tm.assert_index_equal(result, expected)
+
+    def test_putmask_keep_dtype(self, any_numeric_ea_dtype):
+        # GH#49830
+        midx = MultiIndex.from_arrays(
+            [pd.Series([1, 2, 3], dtype=any_numeric_ea_dtype), [10, 11, 12]]
+        )
+        midx2 = MultiIndex.from_arrays(
+            [pd.Series([5, 6, 7], dtype=any_numeric_ea_dtype), [-1, -2, -3]]
+        )
+        result = midx.putmask([True, False, False], midx2)
+        expected = MultiIndex.from_arrays(
+            [pd.Series([5, 2, 3], dtype=any_numeric_ea_dtype), [-1, 11, 12]]
+        )
+        tm.assert_index_equal(result, expected)
+
+    def test_putmask_keep_dtype_shorter_value(self, any_numeric_ea_dtype):
+        # GH#49830
+        midx = MultiIndex.from_arrays(
+            [pd.Series([1, 2, 3], dtype=any_numeric_ea_dtype), [10, 11, 12]]
+        )
+        midx2 = MultiIndex.from_arrays(
+            [pd.Series([5], dtype=any_numeric_ea_dtype), [-1]]
+        )
+        result = midx.putmask([True, False, False], midx2)
+        expected = MultiIndex.from_arrays(
+            [pd.Series([5, 2, 3], dtype=any_numeric_ea_dtype), [-1, 11, 12]]
+        )
+        tm.assert_index_equal(result, expected)
+
+
+class TestGetIndexer:
+    def test_get_indexer(self):
+        major_axis = Index(np.arange(4))
+        minor_axis = Index(np.arange(2))
+
+        major_codes = np.array([0, 0, 1, 2, 2, 3, 3], dtype=np.intp)
+        minor_codes = np.array([0, 1, 0, 0, 1, 0, 1], dtype=np.intp)
+
+        index = MultiIndex(
+            levels=[major_axis, minor_axis], codes=[major_codes, minor_codes]
+        )
+        idx1 = index[:5]
+        idx2 = index[[1, 3, 5]]
+
+        r1 = idx1.get_indexer(idx2)
+        tm.assert_almost_equal(r1, np.array([1, 3, -1], dtype=np.intp))
+
+        r1 = idx2.get_indexer(idx1, method="pad")
+        e1 = np.array([-1, 0, 0, 1, 1], dtype=np.intp)
+        tm.assert_almost_equal(r1, e1)
+
+        r2 = idx2.get_indexer(idx1[::-1], method="pad")
+        tm.assert_almost_equal(r2, e1[::-1])
+
+        rffill1 = idx2.get_indexer(idx1, method="ffill")
+        tm.assert_almost_equal(r1, rffill1)
+
+        r1 = idx2.get_indexer(idx1, method="backfill")
+        e1 = np.array([0, 0, 1, 1, 2], dtype=np.intp)
+        tm.assert_almost_equal(r1, e1)
+
+        r2 = idx2.get_indexer(idx1[::-1], method="backfill")
+        tm.assert_almost_equal(r2, e1[::-1])
+
+        rbfill1 = idx2.get_indexer(idx1, method="bfill")
+        tm.assert_almost_equal(r1, rbfill1)
+
+        # pass non-MultiIndex
+        r1 = idx1.get_indexer(idx2.values)
+        rexp1 = idx1.get_indexer(idx2)
+        tm.assert_almost_equal(r1, rexp1)
+
+        r1 = idx1.get_indexer([1, 2, 3])
+        assert (r1 == [-1, -1, -1]).all()
+
+        # create index with duplicates
+        idx1 = Index(list(range(10)) + list(range(10)))
+        idx2 = Index(list(range(20)))
+
+        msg = "Reindexing only valid with uniquely valued Index objects"
+        with pytest.raises(InvalidIndexError, match=msg):
+            idx1.get_indexer(idx2)
+
+    def test_get_indexer_nearest(self):
+        midx = MultiIndex.from_tuples([("a", 1), ("b", 2)])
+        msg = (
+            "method='nearest' not implemented yet for MultiIndex; "
+            "see GitHub issue 9365"
+        )
+        with pytest.raises(NotImplementedError, match=msg):
+            midx.get_indexer(["a"], method="nearest")
+        msg = "tolerance not implemented yet for MultiIndex"
+        with pytest.raises(NotImplementedError, match=msg):
+            midx.get_indexer(["a"], method="pad", tolerance=2)
+
+    def test_get_indexer_categorical_time(self):
+        # https://github.com/pandas-dev/pandas/issues/21390
+        midx = MultiIndex.from_product(
+            [
+                Categorical(["a", "b", "c"]),
+                Categorical(date_range("2012-01-01", periods=3, freq="h")),
+            ]
+        )
+        result = midx.get_indexer(midx)
+        tm.assert_numpy_array_equal(result, np.arange(9, dtype=np.intp))
+
+    @pytest.mark.parametrize(
+        "index_arr,labels,expected",
+        [
+            (
+                [[1, np.nan, 2], [3, 4, 5]],
+                [1, np.nan, 2],
+                np.array([-1, -1, -1], dtype=np.intp),
+            ),
+            ([[1, np.nan, 2], [3, 4, 5]], [(np.nan, 4)], np.array([1], dtype=np.intp)),
+            ([[1, 2, 3], [np.nan, 4, 5]], [(1, np.nan)], np.array([0], dtype=np.intp)),
+            (
+                [[1, 2, 3], [np.nan, 4, 5]],
+                [np.nan, 4, 5],
+                np.array([-1, -1, -1], dtype=np.intp),
+            ),
+        ],
+    )
+    def test_get_indexer_with_missing_value(self, index_arr, labels, expected):
+        # issue 19132
+        idx = MultiIndex.from_arrays(index_arr)
+        result = idx.get_indexer(labels)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_get_indexer_methods(self):
+        # https://github.com/pandas-dev/pandas/issues/29896
+        # test getting an indexer for another index with different methods
+        # confirms that getting an indexer without a filling method, getting an
+        # indexer and backfilling, and getting an indexer and padding all behave
+        # correctly in the case where all of the target values fall in between
+        # several levels in the MultiIndex into which they are getting an indexer
+        #
+        # visually, the MultiIndexes used in this test are:
+        # mult_idx_1:
+        #  0: -1 0
+        #  1:    2
+        #  2:    3
+        #  3:    4
+        #  4:  0 0
+        #  5:    2
+        #  6:    3
+        #  7:    4
+        #  8:  1 0
+        #  9:    2
+        # 10:    3
+        # 11:    4
+        #
+        # mult_idx_2:
+        #  0: 0 1
+        #  1:   3
+        #  2:   4
+        mult_idx_1 = MultiIndex.from_product([[-1, 0, 1], [0, 2, 3, 4]])
+        mult_idx_2 = MultiIndex.from_product([[0], [1, 3, 4]])
+
+        indexer = mult_idx_1.get_indexer(mult_idx_2)
+        expected = np.array([-1, 6, 7], dtype=indexer.dtype)
+        tm.assert_almost_equal(expected, indexer)
+
+        backfill_indexer = mult_idx_1.get_indexer(mult_idx_2, method="backfill")
+        expected = np.array([5, 6, 7], dtype=backfill_indexer.dtype)
+        tm.assert_almost_equal(expected, backfill_indexer)
+
+        # ensure the legacy "bfill" option functions identically to "backfill"
+        backfill_indexer = mult_idx_1.get_indexer(mult_idx_2, method="bfill")
+        expected = np.array([5, 6, 7], dtype=backfill_indexer.dtype)
+        tm.assert_almost_equal(expected, backfill_indexer)
+
+        pad_indexer = mult_idx_1.get_indexer(mult_idx_2, method="pad")
+        expected = np.array([4, 6, 7], dtype=pad_indexer.dtype)
+        tm.assert_almost_equal(expected, pad_indexer)
+
+        # ensure the legacy "ffill" option functions identically to "pad"
+        pad_indexer = mult_idx_1.get_indexer(mult_idx_2, method="ffill")
+        expected = np.array([4, 6, 7], dtype=pad_indexer.dtype)
+        tm.assert_almost_equal(expected, pad_indexer)
+
+    @pytest.mark.parametrize("method", ["pad", "ffill", "backfill", "bfill", "nearest"])
+    def test_get_indexer_methods_raise_for_non_monotonic(self, method):
+        # 53452
+        mi = MultiIndex.from_arrays([[0, 4, 2], [0, 4, 2]])
+        if method == "nearest":
+            err = NotImplementedError
+            msg = "not implemented yet for MultiIndex"
+        else:
+            err = ValueError
+            msg = "index must be monotonic increasing or decreasing"
+        with pytest.raises(err, match=msg):
+            mi.get_indexer([(1, 1)], method=method)
+
+    def test_get_indexer_three_or_more_levels(self):
+        # https://github.com/pandas-dev/pandas/issues/29896
+        # tests get_indexer() on MultiIndexes with 3+ levels
+        # visually, these are
+        # mult_idx_1:
+        #  0: 1 2 5
+        #  1:     7
+        #  2:   4 5
+        #  3:     7
+        #  4:   6 5
+        #  5:     7
+        #  6: 3 2 5
+        #  7:     7
+        #  8:   4 5
+        #  9:     7
+        # 10:   6 5
+        # 11:     7
+        #
+        # mult_idx_2:
+        #  0: 1 1 8
+        #  1: 1 5 9
+        #  2: 1 6 7
+        #  3: 2 1 6
+        #  4: 2 7 6
+        #  5: 2 7 8
+        #  6: 3 6 8
+        mult_idx_1 = MultiIndex.from_product([[1, 3], [2, 4, 6], [5, 7]])
+        mult_idx_2 = MultiIndex.from_tuples(
+            [
+                (1, 1, 8),
+                (1, 5, 9),
+                (1, 6, 7),
+                (2, 1, 6),
+                (2, 7, 7),
+                (2, 7, 8),
+                (3, 6, 8),
+            ]
+        )
+        # sanity check
+        assert mult_idx_1.is_monotonic_increasing
+        assert mult_idx_1.is_unique
+        assert mult_idx_2.is_monotonic_increasing
+        assert mult_idx_2.is_unique
+
+        # show the relationships between the two
+        assert mult_idx_2[0] < mult_idx_1[0]
+        assert mult_idx_1[3] < mult_idx_2[1] < mult_idx_1[4]
+        assert mult_idx_1[5] == mult_idx_2[2]
+        assert mult_idx_1[5] < mult_idx_2[3] < mult_idx_1[6]
+        assert mult_idx_1[5] < mult_idx_2[4] < mult_idx_1[6]
+        assert mult_idx_1[5] < mult_idx_2[5] < mult_idx_1[6]
+        assert mult_idx_1[-1] < mult_idx_2[6]
+
+        indexer_no_fill = mult_idx_1.get_indexer(mult_idx_2)
+        expected = np.array([-1, -1, 5, -1, -1, -1, -1], dtype=indexer_no_fill.dtype)
+        tm.assert_almost_equal(expected, indexer_no_fill)
+
+        # test with backfilling
+        indexer_backfilled = mult_idx_1.get_indexer(mult_idx_2, method="backfill")
+        expected = np.array([0, 4, 5, 6, 6, 6, -1], dtype=indexer_backfilled.dtype)
+        tm.assert_almost_equal(expected, indexer_backfilled)
+
+        # now, the same thing, but forward-filled (aka "padded")
+        indexer_padded = mult_idx_1.get_indexer(mult_idx_2, method="pad")
+        expected = np.array([-1, 3, 5, 5, 5, 5, 11], dtype=indexer_padded.dtype)
+        tm.assert_almost_equal(expected, indexer_padded)
+
+        # now, do the indexing in the other direction
+        assert mult_idx_2[0] < mult_idx_1[0] < mult_idx_2[1]
+        assert mult_idx_2[0] < mult_idx_1[1] < mult_idx_2[1]
+        assert mult_idx_2[0] < mult_idx_1[2] < mult_idx_2[1]
+        assert mult_idx_2[0] < mult_idx_1[3] < mult_idx_2[1]
+        assert mult_idx_2[1] < mult_idx_1[4] < mult_idx_2[2]
+        assert mult_idx_2[2] == mult_idx_1[5]
+        assert mult_idx_2[5] < mult_idx_1[6] < mult_idx_2[6]
+        assert mult_idx_2[5] < mult_idx_1[7] < mult_idx_2[6]
+        assert mult_idx_2[5] < mult_idx_1[8] < mult_idx_2[6]
+        assert mult_idx_2[5] < mult_idx_1[9] < mult_idx_2[6]
+        assert mult_idx_2[5] < mult_idx_1[10] < mult_idx_2[6]
+        assert mult_idx_2[5] < mult_idx_1[11] < mult_idx_2[6]
+
+        indexer = mult_idx_2.get_indexer(mult_idx_1)
+        expected = np.array(
+            [-1, -1, -1, -1, -1, 2, -1, -1, -1, -1, -1, -1], dtype=indexer.dtype
+        )
+        tm.assert_almost_equal(expected, indexer)
+
+        backfill_indexer = mult_idx_2.get_indexer(mult_idx_1, method="bfill")
+        expected = np.array(
+            [1, 1, 1, 1, 2, 2, 6, 6, 6, 6, 6, 6], dtype=backfill_indexer.dtype
+        )
+        tm.assert_almost_equal(expected, backfill_indexer)
+
+        pad_indexer = mult_idx_2.get_indexer(mult_idx_1, method="pad")
+        expected = np.array(
+            [0, 0, 0, 0, 1, 2, 5, 5, 5, 5, 5, 5], dtype=pad_indexer.dtype
+        )
+        tm.assert_almost_equal(expected, pad_indexer)
+
+    def test_get_indexer_crossing_levels(self):
+        # https://github.com/pandas-dev/pandas/issues/29896
+        # tests a corner case with get_indexer() with MultiIndexes where, when we
+        # need to "carry" across levels, proper tuple ordering is respected
+        #
+        # the MultiIndexes used in this test, visually, are:
+        # mult_idx_1:
+        #  0: 1 1 1 1
+        #  1:       2
+        #  2:     2 1
+        #  3:       2
+        #  4: 1 2 1 1
+        #  5:       2
+        #  6:     2 1
+        #  7:       2
+        #  8: 2 1 1 1
+        #  9:       2
+        # 10:     2 1
+        # 11:       2
+        # 12: 2 2 1 1
+        # 13:       2
+        # 14:     2 1
+        # 15:       2
+        #
+        # mult_idx_2:
+        #  0: 1 3 2 2
+        #  1: 2 3 2 2
+        mult_idx_1 = MultiIndex.from_product([[1, 2]] * 4)
+        mult_idx_2 = MultiIndex.from_tuples([(1, 3, 2, 2), (2, 3, 2, 2)])
+
+        # show the tuple orderings, which get_indexer() should respect
+        assert mult_idx_1[7] < mult_idx_2[0] < mult_idx_1[8]
+        assert mult_idx_1[-1] < mult_idx_2[1]
+
+        indexer = mult_idx_1.get_indexer(mult_idx_2)
+        expected = np.array([-1, -1], dtype=indexer.dtype)
+        tm.assert_almost_equal(expected, indexer)
+
+        backfill_indexer = mult_idx_1.get_indexer(mult_idx_2, method="bfill")
+        expected = np.array([8, -1], dtype=backfill_indexer.dtype)
+        tm.assert_almost_equal(expected, backfill_indexer)
+
+        pad_indexer = mult_idx_1.get_indexer(mult_idx_2, method="ffill")
+        expected = np.array([7, 15], dtype=pad_indexer.dtype)
+        tm.assert_almost_equal(expected, pad_indexer)
+
+    def test_get_indexer_kwarg_validation(self):
+        # GH#41918
+        mi = MultiIndex.from_product([range(3), ["A", "B"]])
+
+        msg = "limit argument only valid if doing pad, backfill or nearest"
+        with pytest.raises(ValueError, match=msg):
+            mi.get_indexer(mi[:-1], limit=4)
+
+        msg = "tolerance argument only valid if doing pad, backfill or nearest"
+        with pytest.raises(ValueError, match=msg):
+            mi.get_indexer(mi[:-1], tolerance="piano")
+
+    def test_get_indexer_nan(self):
+        # GH#37222
+        idx1 = MultiIndex.from_product([["A"], [1.0, 2.0]], names=["id1", "id2"])
+        idx2 = MultiIndex.from_product([["A"], [np.nan, 2.0]], names=["id1", "id2"])
+        expected = np.array([-1, 1])
+        result = idx2.get_indexer(idx1)
+        tm.assert_numpy_array_equal(result, expected, check_dtype=False)
+        result = idx1.get_indexer(idx2)
+        tm.assert_numpy_array_equal(result, expected, check_dtype=False)
+
+
+def test_getitem(idx):
+    # scalar
+    assert idx[2] == ("bar", "one")
+
+    # slice
+    result = idx[2:5]
+    expected = idx[[2, 3, 4]]
+    assert result.equals(expected)
+
+    # boolean
+    result = idx[[True, False, True, False, True, True]]
+    result2 = idx[np.array([True, False, True, False, True, True])]
+    expected = idx[[0, 2, 4, 5]]
+    assert result.equals(expected)
+    assert result2.equals(expected)
+
+
+def test_getitem_group_select(idx):
+    sorted_idx, _ = idx.sortlevel(0)
+    assert sorted_idx.get_loc("baz") == slice(3, 4)
+    assert sorted_idx.get_loc("foo") == slice(0, 2)
+
+
+@pytest.mark.parametrize("ind1", [[True] * 5, Index([True] * 5)])
+@pytest.mark.parametrize(
+    "ind2",
+    [[True, False, True, False, False], Index([True, False, True, False, False])],
+)
+def test_getitem_bool_index_all(ind1, ind2):
+    # GH#22533
+    idx = MultiIndex.from_tuples([(10, 1), (20, 2), (30, 3), (40, 4), (50, 5)])
+    tm.assert_index_equal(idx[ind1], idx)
+
+    expected = MultiIndex.from_tuples([(10, 1), (30, 3)])
+    tm.assert_index_equal(idx[ind2], expected)
+
+
+@pytest.mark.parametrize("ind1", [[True], Index([True])])
+@pytest.mark.parametrize("ind2", [[False], Index([False])])
+def test_getitem_bool_index_single(ind1, ind2):
+    # GH#22533
+    idx = MultiIndex.from_tuples([(10, 1)])
+    tm.assert_index_equal(idx[ind1], idx)
+
+    expected = MultiIndex(
+        levels=[np.array([], dtype=np.int64), np.array([], dtype=np.int64)],
+        codes=[[], []],
+    )
+    tm.assert_index_equal(idx[ind2], expected)
+
+
+class TestGetLoc:
+    def test_get_loc(self, idx):
+        assert idx.get_loc(("foo", "two")) == 1
+        assert idx.get_loc(("baz", "two")) == 3
+        with pytest.raises(KeyError, match=r"^\('bar', 'two'\)$"):
+            idx.get_loc(("bar", "two"))
+        with pytest.raises(KeyError, match=r"^'quux'$"):
+            idx.get_loc("quux")
+
+        # 3 levels
+        index = MultiIndex(
+            levels=[Index(np.arange(4)), Index(np.arange(4)), Index(np.arange(4))],
+            codes=[
+                np.array([0, 0, 1, 2, 2, 2, 3, 3]),
+                np.array([0, 1, 0, 0, 0, 1, 0, 1]),
+                np.array([1, 0, 1, 1, 0, 0, 1, 0]),
+            ],
+        )
+        with pytest.raises(KeyError, match=r"^\(1, 1\)$"):
+            index.get_loc((1, 1))
+        assert index.get_loc((2, 0)) == slice(3, 5)
+
+    def test_get_loc_duplicates(self):
+        index = Index([2, 2, 2, 2])
+        result = index.get_loc(2)
+        expected = slice(0, 4)
+        assert result == expected
+
+        index = Index(["c", "a", "a", "b", "b"])
+        rs = index.get_loc("c")
+        xp = 0
+        assert rs == xp
+
+        with pytest.raises(KeyError, match="2"):
+            index.get_loc(2)
+
+    def test_get_loc_level(self):
+        index = MultiIndex(
+            levels=[Index(np.arange(4)), Index(np.arange(4)), Index(np.arange(4))],
+            codes=[
+                np.array([0, 0, 1, 2, 2, 2, 3, 3]),
+                np.array([0, 1, 0, 0, 0, 1, 0, 1]),
+                np.array([1, 0, 1, 1, 0, 0, 1, 0]),
+            ],
+        )
+        loc, new_index = index.get_loc_level((0, 1))
+        expected = slice(1, 2)
+        exp_index = index[expected].droplevel(0).droplevel(0)
+        assert loc == expected
+        assert new_index.equals(exp_index)
+
+        loc, new_index = index.get_loc_level((0, 1, 0))
+        expected = 1
+        assert loc == expected
+        assert new_index is None
+
+        with pytest.raises(KeyError, match=r"^\(2, 2\)$"):
+            index.get_loc_level((2, 2))
+        # GH 22221: unused label
+        with pytest.raises(KeyError, match=r"^2$"):
+            index.drop(2).get_loc_level(2)
+        # Unused label on unsorted level:
+        with pytest.raises(KeyError, match=r"^2$"):
+            index.drop(1, level=2).get_loc_level(2, level=2)
+
+        index = MultiIndex(
+            levels=[[2000], list(range(4))],
+            codes=[np.array([0, 0, 0, 0]), np.array([0, 1, 2, 3])],
+        )
+        result, new_index = index.get_loc_level((2000, slice(None, None)))
+        expected = slice(None, None)
+        assert result == expected
+        assert new_index.equals(index.droplevel(0))
+
+    @pytest.mark.parametrize("dtype1", [int, float, bool, str])
+    @pytest.mark.parametrize("dtype2", [int, float, bool, str])
+    def test_get_loc_multiple_dtypes(self, dtype1, dtype2):
+        # GH 18520
+        levels = [np.array([0, 1]).astype(dtype1), np.array([0, 1]).astype(dtype2)]
+        idx = MultiIndex.from_product(levels)
+        assert idx.get_loc(idx[2]) == 2
+
+    @pytest.mark.parametrize("level", [0, 1])
+    @pytest.mark.parametrize("dtypes", [[int, float], [float, int]])
+    def test_get_loc_implicit_cast(self, level, dtypes):
+        # GH 18818, GH 15994 : as flat index, cast int to float and vice-versa
+        levels = [["a", "b"], ["c", "d"]]
+        key = ["b", "d"]
+        lev_dtype, key_dtype = dtypes
+        levels[level] = np.array([0, 1], dtype=lev_dtype)
+        key[level] = key_dtype(1)
+        idx = MultiIndex.from_product(levels)
+        assert idx.get_loc(tuple(key)) == 3
+
+    @pytest.mark.parametrize("dtype", [bool, object])
+    def test_get_loc_cast_bool(self, dtype):
+        # GH 19086 : int is casted to bool, but not vice-versa (for object dtype)
+        #  With bool dtype, we don't cast in either direction.
+        levels = [Index([False, True], dtype=dtype), np.arange(2, dtype="int64")]
+        idx = MultiIndex.from_product(levels)
+
+        if dtype is bool:
+            with pytest.raises(KeyError, match=r"^\(0, 1\)$"):
+                assert idx.get_loc((0, 1)) == 1
+            with pytest.raises(KeyError, match=r"^\(1, 0\)$"):
+                assert idx.get_loc((1, 0)) == 2
+        else:
+            # We use python object comparisons, which treat 0 == False and 1 == True
+            assert idx.get_loc((0, 1)) == 1
+            assert idx.get_loc((1, 0)) == 2
+
+        with pytest.raises(KeyError, match=r"^\(False, True\)$"):
+            idx.get_loc((False, True))
+        with pytest.raises(KeyError, match=r"^\(True, False\)$"):
+            idx.get_loc((True, False))
+
+    @pytest.mark.parametrize("level", [0, 1])
+    def test_get_loc_nan(self, level, nulls_fixture):
+        # GH 18485 : NaN in MultiIndex
+        levels = [["a", "b"], ["c", "d"]]
+        key = ["b", "d"]
+        levels[level] = np.array([0, nulls_fixture], dtype=type(nulls_fixture))
+        key[level] = nulls_fixture
+        idx = MultiIndex.from_product(levels)
+        assert idx.get_loc(tuple(key)) == 3
+
+    def test_get_loc_missing_nan(self):
+        # GH 8569
+        idx = MultiIndex.from_arrays([[1.0, 2.0], [3.0, 4.0]])
+        assert isinstance(idx.get_loc(1), slice)
+        with pytest.raises(KeyError, match=r"^3$"):
+            idx.get_loc(3)
+        with pytest.raises(KeyError, match=r"^nan$"):
+            idx.get_loc(np.nan)
+        with pytest.raises(InvalidIndexError, match=r"\[nan\]"):
+            # listlike/non-hashable raises TypeError
+            idx.get_loc([np.nan])
+
+    def test_get_loc_with_values_including_missing_values(self):
+        # issue 19132
+        idx = MultiIndex.from_product([[np.nan, 1]] * 2)
+        expected = slice(0, 2, None)
+        assert idx.get_loc(np.nan) == expected
+
+        idx = MultiIndex.from_arrays([[np.nan, 1, 2, np.nan]])
+        expected = np.array([True, False, False, True])
+        tm.assert_numpy_array_equal(idx.get_loc(np.nan), expected)
+
+        idx = MultiIndex.from_product([[np.nan, 1]] * 3)
+        expected = slice(2, 4, None)
+        assert idx.get_loc((np.nan, 1)) == expected
+
+    def test_get_loc_duplicates2(self):
+        # TODO: de-duplicate with test_get_loc_duplicates above?
+        index = MultiIndex(
+            levels=[["D", "B", "C"], [0, 26, 27, 37, 57, 67, 75, 82]],
+            codes=[[0, 0, 0, 1, 2, 2, 2, 2, 2, 2], [1, 3, 4, 6, 0, 2, 2, 3, 5, 7]],
+            names=["tag", "day"],
+        )
+
+        assert index.get_loc("D") == slice(0, 3)
+
+    def test_get_loc_past_lexsort_depth(self):
+        # GH#30053
+        idx = MultiIndex(
+            levels=[["a"], [0, 7], [1]],
+            codes=[[0, 0], [1, 0], [0, 0]],
+            names=["x", "y", "z"],
+            sortorder=0,
+        )
+        key = ("a", 7)
+
+        with tm.assert_produces_warning(PerformanceWarning):
+            # PerformanceWarning: indexing past lexsort depth may impact performance
+            result = idx.get_loc(key)
+
+        assert result == slice(0, 1, None)
+
+    def test_multiindex_get_loc_list_raises(self):
+        # GH#35878
+        idx = MultiIndex.from_tuples([("a", 1), ("b", 2)])
+        msg = r"\[\]"
+        with pytest.raises(InvalidIndexError, match=msg):
+            idx.get_loc([])
+
+    def test_get_loc_nested_tuple_raises_keyerror(self):
+        # raise KeyError, not TypeError
+        mi = MultiIndex.from_product([range(3), range(4), range(5), range(6)])
+        key = ((2, 3, 4), "foo")
+
+        with pytest.raises(KeyError, match=re.escape(str(key))):
+            mi.get_loc(key)
+
+
+class TestWhere:
+    def test_where(self):
+        i = MultiIndex.from_tuples([("A", 1), ("A", 2)])
+
+        msg = r"\.where is not supported for MultiIndex operations"
+        with pytest.raises(NotImplementedError, match=msg):
+            i.where(True)
+
+    def test_where_array_like(self, listlike_box):
+        mi = MultiIndex.from_tuples([("A", 1), ("A", 2)])
+        cond = [False, True]
+        msg = r"\.where is not supported for MultiIndex operations"
+        with pytest.raises(NotImplementedError, match=msg):
+            mi.where(listlike_box(cond))
+
+
+class TestContains:
+    def test_contains_top_level(self):
+        midx = MultiIndex.from_product([["A", "B"], [1, 2]])
+        assert "A" in midx
+        assert "A" not in midx._engine
+
+    def test_contains_with_nat(self):
+        # MI with a NaT
+        mi = MultiIndex(
+            levels=[["C"], date_range("2012-01-01", periods=5)],
+            codes=[[0, 0, 0, 0, 0, 0], [-1, 0, 1, 2, 3, 4]],
+            names=[None, "B"],
+        )
+        assert ("C", pd.Timestamp("2012-01-01")) in mi
+        for val in mi.values:
+            assert val in mi
+
+    def test_contains(self, idx):
+        assert ("foo", "two") in idx
+        assert ("bar", "two") not in idx
+        assert None not in idx
+
+    def test_contains_with_missing_value(self):
+        # GH#19132
+        idx = MultiIndex.from_arrays([[1, np.nan, 2]])
+        assert np.nan in idx
+
+        idx = MultiIndex.from_arrays([[1, 2], [np.nan, 3]])
+        assert np.nan not in idx
+        assert (1, np.nan) in idx
+
+    def test_multiindex_contains_dropped(self):
+        # GH#19027
+        # test that dropped MultiIndex levels are not in the MultiIndex
+        # despite continuing to be in the MultiIndex's levels
+        idx = MultiIndex.from_product([[1, 2], [3, 4]])
+        assert 2 in idx
+        idx = idx.drop(2)
+
+        # drop implementation keeps 2 in the levels
+        assert 2 in idx.levels[0]
+        # but it should no longer be in the index itself
+        assert 2 not in idx
+
+        # also applies to strings
+        idx = MultiIndex.from_product([["a", "b"], ["c", "d"]])
+        assert "a" in idx
+        idx = idx.drop("a")
+        assert "a" in idx.levels[0]
+        assert "a" not in idx
+
+    def test_contains_td64_level(self):
+        # GH#24570
+        tx = pd.timedelta_range("09:30:00", "16:00:00", freq="30 min")
+        idx = MultiIndex.from_arrays([tx, np.arange(len(tx))])
+        assert tx[0] in idx
+        assert "element_not_exit" not in idx
+        assert "0 day 09:30:00" in idx
+
+    def test_large_mi_contains(self, monkeypatch):
+        # GH#10645
+        with monkeypatch.context():
+            monkeypatch.setattr(libindex, "_SIZE_CUTOFF", 10)
+            result = MultiIndex.from_arrays([range(10), range(10)])
+            assert (10, 0) not in result
+
+
+def test_timestamp_multiindex_indexer():
+    # https://github.com/pandas-dev/pandas/issues/26944
+    idx = MultiIndex.from_product(
+        [
+            date_range("2019-01-01T00:15:33", periods=100, freq="h", name="date"),
+            ["x"],
+            [3],
+        ]
+    )
+    df = DataFrame({"foo": np.arange(len(idx))}, idx)
+    result = df.loc[pd.IndexSlice["2019-1-2":, "x", :], "foo"]
+    qidx = MultiIndex.from_product(
+        [
+            date_range(
+                start="2019-01-02T00:15:33",
+                end="2019-01-05T03:15:33",
+                freq="h",
+                name="date",
+            ),
+            ["x"],
+            [3],
+        ]
+    )
+    should_be = pd.Series(data=np.arange(24, len(qidx) + 24), index=qidx, name="foo")
+    tm.assert_series_equal(result, should_be)
+
+
+@pytest.mark.parametrize(
+    "index_arr,expected,target,algo",
+    [
+        ([[np.nan, "a", "b"], ["c", "d", "e"]], 0, np.nan, "left"),
+        ([[np.nan, "a", "b"], ["c", "d", "e"]], 1, (np.nan, "c"), "right"),
+        ([["a", "b", "c"], ["d", np.nan, "d"]], 1, ("b", np.nan), "left"),
+    ],
+)
+def test_get_slice_bound_with_missing_value(index_arr, expected, target, algo):
+    # issue 19132
+    idx = MultiIndex.from_arrays(index_arr)
+    result = idx.get_slice_bound(target, side=algo)
+    assert result == expected
+
+
+@pytest.mark.parametrize(
+    "index_arr,expected,start_idx,end_idx",
+    [
+        ([[np.nan, 1, 2], [3, 4, 5]], slice(0, 2, None), np.nan, 1),
+        ([[np.nan, 1, 2], [3, 4, 5]], slice(0, 3, None), np.nan, (2, 5)),
+        ([[1, 2, 3], [4, np.nan, 5]], slice(1, 3, None), (2, np.nan), 3),
+        ([[1, 2, 3], [4, np.nan, 5]], slice(1, 3, None), (2, np.nan), (3, 5)),
+    ],
+)
+def test_slice_indexer_with_missing_value(index_arr, expected, start_idx, end_idx):
+    # issue 19132
+    idx = MultiIndex.from_arrays(index_arr)
+    result = idx.slice_indexer(start=start_idx, end=end_idx)
+    assert result == expected
+
+
+def test_pyint_engine():
+    # GH#18519 : when combinations of codes cannot be represented in 64
+    # bits, the index underlying the MultiIndex engine works with Python
+    # integers, rather than uint64.
+    N = 5
+    keys = [
+        tuple(arr)
+        for arr in [
+            [0] * 10 * N,
+            [1] * 10 * N,
+            [2] * 10 * N,
+            [np.nan] * N + [2] * 9 * N,
+            [0] * N + [2] * 9 * N,
+            [np.nan] * N + [2] * 8 * N + [0] * N,
+        ]
+    ]
+    # Each level contains 4 elements (including NaN), so it is represented
+    # in 2 bits, for a total of 2*N*10 = 100 > 64 bits. If we were using a
+    # 64 bit engine and truncating the first levels, the fourth and fifth
+    # keys would collide; if truncating the last levels, the fifth and
+    # sixth; if rotating bits rather than shifting, the third and fifth.
+
+    for idx, key_value in enumerate(keys):
+        index = MultiIndex.from_tuples(keys)
+        assert index.get_loc(key_value) == idx
+
+        expected = np.arange(idx + 1, dtype=np.intp)
+        result = index.get_indexer([keys[i] for i in expected])
+        tm.assert_numpy_array_equal(result, expected)
+
+    # With missing key:
+    idces = range(len(keys))
+    expected = np.array([-1] + list(idces), dtype=np.intp)
+    missing = tuple([0, 1] * 5 * N)
+    result = index.get_indexer([missing] + [keys[i] for i in idces])
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "keys,expected",
+    [
+        ((slice(None), [5, 4]), [1, 0]),
+        ((slice(None), [4, 5]), [0, 1]),
+        (([True, False, True], [4, 6]), [0, 2]),
+        (([True, False, True], [6, 4]), [0, 2]),
+        ((2, [4, 5]), [0, 1]),
+        ((2, [5, 4]), [1, 0]),
+        (([2], [4, 5]), [0, 1]),
+        (([2], [5, 4]), [1, 0]),
+    ],
+)
+def test_get_locs_reordering(keys, expected):
+    # GH48384
+    idx = MultiIndex.from_arrays(
+        [
+            [2, 2, 1],
+            [4, 5, 6],
+        ]
+    )
+    result = idx.get_locs(keys)
+    expected = np.array(expected, dtype=np.intp)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_get_indexer_for_multiindex_with_nans(nulls_fixture):
+    # GH37222
+    idx1 = MultiIndex.from_product([["A"], [1.0, 2.0]], names=["id1", "id2"])
+    idx2 = MultiIndex.from_product([["A"], [nulls_fixture, 2.0]], names=["id1", "id2"])
+
+    result = idx2.get_indexer(idx1)
+    expected = np.array([-1, 1], dtype=np.intp)
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = idx1.get_indexer(idx2)
+    expected = np.array([-1, 1], dtype=np.intp)
+    tm.assert_numpy_array_equal(result, expected)

llmeval-env/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

llmeval-env/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

llmeval-env/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])

llmeval-env/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)

llmeval-env/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()

llmeval-env/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)

llmeval-env/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)

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_sorting.py

@@ -0,0 +1,349 @@
+import numpy as np
+import pytest
+
+from pandas.errors import (
+    PerformanceWarning,
+    UnsortedIndexError,
+)
+
+from pandas import (
+    CategoricalIndex,
+    DataFrame,
+    Index,
+    MultiIndex,
+    RangeIndex,
+    Series,
+    Timestamp,
+)
+import pandas._testing as tm
+from pandas.core.indexes.frozen import FrozenList
+
+
+def test_sortlevel(idx):
+    tuples = list(idx)
+    np.random.default_rng(2).shuffle(tuples)
+
+    index = MultiIndex.from_tuples(tuples)
+
+    sorted_idx, _ = index.sortlevel(0)
+    expected = MultiIndex.from_tuples(sorted(tuples))
+    assert sorted_idx.equals(expected)
+
+    sorted_idx, _ = index.sortlevel(0, ascending=False)
+    assert sorted_idx.equals(expected[::-1])
+
+    sorted_idx, _ = index.sortlevel(1)
+    by1 = sorted(tuples, key=lambda x: (x[1], x[0]))
+    expected = MultiIndex.from_tuples(by1)
+    assert sorted_idx.equals(expected)
+
+    sorted_idx, _ = index.sortlevel(1, ascending=False)
+    assert sorted_idx.equals(expected[::-1])
+
+
+def test_sortlevel_not_sort_remaining():
+    mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list("ABC"))
+    sorted_idx, _ = mi.sortlevel("A", sort_remaining=False)
+    assert sorted_idx.equals(mi)
+
+
+def test_sortlevel_deterministic():
+    tuples = [
+        ("bar", "one"),
+        ("foo", "two"),
+        ("qux", "two"),
+        ("foo", "one"),
+        ("baz", "two"),
+        ("qux", "one"),
+    ]
+
+    index = MultiIndex.from_tuples(tuples)
+
+    sorted_idx, _ = index.sortlevel(0)
+    expected = MultiIndex.from_tuples(sorted(tuples))
+    assert sorted_idx.equals(expected)
+
+    sorted_idx, _ = index.sortlevel(0, ascending=False)
+    assert sorted_idx.equals(expected[::-1])
+
+    sorted_idx, _ = index.sortlevel(1)
+    by1 = sorted(tuples, key=lambda x: (x[1], x[0]))
+    expected = MultiIndex.from_tuples(by1)
+    assert sorted_idx.equals(expected)
+
+    sorted_idx, _ = index.sortlevel(1, ascending=False)
+    assert sorted_idx.equals(expected[::-1])
+
+
+def test_sortlevel_na_position():
+    # GH#51612
+    midx = MultiIndex.from_tuples([(1, np.nan), (1, 1)])
+    result = midx.sortlevel(level=[0, 1], na_position="last")[0]
+    expected = MultiIndex.from_tuples([(1, 1), (1, np.nan)])
+    tm.assert_index_equal(result, expected)
+
+
+def test_numpy_argsort(idx):
+    result = np.argsort(idx)
+    expected = idx.argsort()
+    tm.assert_numpy_array_equal(result, expected)
+
+    # these are the only two types that perform
+    # pandas compatibility input validation - the
+    # rest already perform separate (or no) such
+    # validation via their 'values' attribute as
+    # defined in pandas.core.indexes/base.py - they
+    # cannot be changed at the moment due to
+    # backwards compatibility concerns
+    if isinstance(type(idx), (CategoricalIndex, RangeIndex)):
+        msg = "the 'axis' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            np.argsort(idx, axis=1)
+
+        msg = "the 'kind' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            np.argsort(idx, kind="mergesort")
+
+        msg = "the 'order' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            np.argsort(idx, order=("a", "b"))
+
+
+def test_unsortedindex():
+    # GH 11897
+    mi = MultiIndex.from_tuples(
+        [("z", "a"), ("x", "a"), ("y", "b"), ("x", "b"), ("y", "a"), ("z", "b")],
+        names=["one", "two"],
+    )
+    df = DataFrame([[i, 10 * i] for i in range(6)], index=mi, columns=["one", "two"])
+
+    # GH 16734: not sorted, but no real slicing
+    result = df.loc(axis=0)["z", "a"]
+    expected = df.iloc[0]
+    tm.assert_series_equal(result, expected)
+
+    msg = (
+        "MultiIndex slicing requires the index to be lexsorted: "
+        r"slicing on levels \[1\], lexsort depth 0"
+    )
+    with pytest.raises(UnsortedIndexError, match=msg):
+        df.loc(axis=0)["z", slice("a")]
+    df.sort_index(inplace=True)
+    assert len(df.loc(axis=0)["z", :]) == 2
+
+    with pytest.raises(KeyError, match="'q'"):
+        df.loc(axis=0)["q", :]
+
+
+def test_unsortedindex_doc_examples():
+    # https://pandas.pydata.org/pandas-docs/stable/advanced.html#sorting-a-multiindex
+    dfm = DataFrame(
+        {
+            "jim": [0, 0, 1, 1],
+            "joe": ["x", "x", "z", "y"],
+            "jolie": np.random.default_rng(2).random(4),
+        }
+    )
+
+    dfm = dfm.set_index(["jim", "joe"])
+    with tm.assert_produces_warning(PerformanceWarning):
+        dfm.loc[(1, "z")]
+
+    msg = r"Key length \(2\) was greater than MultiIndex lexsort depth \(1\)"
+    with pytest.raises(UnsortedIndexError, match=msg):
+        dfm.loc[(0, "y"):(1, "z")]
+
+    assert not dfm.index._is_lexsorted()
+    assert dfm.index._lexsort_depth == 1
+
+    # sort it
+    dfm = dfm.sort_index()
+    dfm.loc[(1, "z")]
+    dfm.loc[(0, "y"):(1, "z")]
+
+    assert dfm.index._is_lexsorted()
+    assert dfm.index._lexsort_depth == 2
+
+
+def test_reconstruct_sort():
+    # starts off lexsorted & monotonic
+    mi = MultiIndex.from_arrays([["A", "A", "B", "B", "B"], [1, 2, 1, 2, 3]])
+    assert mi.is_monotonic_increasing
+    recons = mi._sort_levels_monotonic()
+    assert recons.is_monotonic_increasing
+    assert mi is recons
+
+    assert mi.equals(recons)
+    assert Index(mi.values).equals(Index(recons.values))
+
+    # cannot convert to lexsorted
+    mi = MultiIndex.from_tuples(
+        [("z", "a"), ("x", "a"), ("y", "b"), ("x", "b"), ("y", "a"), ("z", "b")],
+        names=["one", "two"],
+    )
+    assert not mi.is_monotonic_increasing
+    recons = mi._sort_levels_monotonic()
+    assert not recons.is_monotonic_increasing
+    assert mi.equals(recons)
+    assert Index(mi.values).equals(Index(recons.values))
+
+    # cannot convert to lexsorted
+    mi = MultiIndex(
+        levels=[["b", "d", "a"], [1, 2, 3]],
+        codes=[[0, 1, 0, 2], [2, 0, 0, 1]],
+        names=["col1", "col2"],
+    )
+    assert not mi.is_monotonic_increasing
+    recons = mi._sort_levels_monotonic()
+    assert not recons.is_monotonic_increasing
+    assert mi.equals(recons)
+    assert Index(mi.values).equals(Index(recons.values))
+
+
+def test_reconstruct_remove_unused():
+    # xref to GH 2770
+    df = DataFrame(
+        [["deleteMe", 1, 9], ["keepMe", 2, 9], ["keepMeToo", 3, 9]],
+        columns=["first", "second", "third"],
+    )
+    df2 = df.set_index(["first", "second"], drop=False)
+    df2 = df2[df2["first"] != "deleteMe"]
+
+    # removed levels are there
+    expected = MultiIndex(
+        levels=[["deleteMe", "keepMe", "keepMeToo"], [1, 2, 3]],
+        codes=[[1, 2], [1, 2]],
+        names=["first", "second"],
+    )
+    result = df2.index
+    tm.assert_index_equal(result, expected)
+
+    expected = MultiIndex(
+        levels=[["keepMe", "keepMeToo"], [2, 3]],
+        codes=[[0, 1], [0, 1]],
+        names=["first", "second"],
+    )
+    result = df2.index.remove_unused_levels()
+    tm.assert_index_equal(result, expected)
+
+    # idempotent
+    result2 = result.remove_unused_levels()
+    tm.assert_index_equal(result2, expected)
+    assert result2.is_(result)
+
+
+@pytest.mark.parametrize(
+    "first_type,second_type", [("int64", "int64"), ("datetime64[D]", "str")]
+)
+def test_remove_unused_levels_large(first_type, second_type):
+    # GH16556
+
+    # because tests should be deterministic (and this test in particular
+    # checks that levels are removed, which is not the case for every
+    # random input):
+    rng = np.random.default_rng(10)  # seed is arbitrary value that works
+
+    size = 1 << 16
+    df = DataFrame(
+        {
+            "first": rng.integers(0, 1 << 13, size).astype(first_type),
+            "second": rng.integers(0, 1 << 10, size).astype(second_type),
+            "third": rng.random(size),
+        }
+    )
+    df = df.groupby(["first", "second"]).sum()
+    df = df[df.third < 0.1]
+
+    result = df.index.remove_unused_levels()
+    assert len(result.levels[0]) < len(df.index.levels[0])
+    assert len(result.levels[1]) < len(df.index.levels[1])
+    assert result.equals(df.index)
+
+    expected = df.reset_index().set_index(["first", "second"]).index
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("level0", [["a", "d", "b"], ["a", "d", "b", "unused"]])
+@pytest.mark.parametrize(
+    "level1", [["w", "x", "y", "z"], ["w", "x", "y", "z", "unused"]]
+)
+def test_remove_unused_nan(level0, level1):
+    # GH 18417
+    mi = MultiIndex(levels=[level0, level1], codes=[[0, 2, -1, 1, -1], [0, 1, 2, 3, 2]])
+
+    result = mi.remove_unused_levels()
+    tm.assert_index_equal(result, mi)
+    for level in 0, 1:
+        assert "unused" not in result.levels[level]
+
+
+def test_argsort(idx):
+    result = idx.argsort()
+    expected = idx.values.argsort()
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_remove_unused_levels_with_nan():
+    # GH 37510
+    idx = Index([(1, np.nan), (3, 4)]).rename(["id1", "id2"])
+    idx = idx.set_levels(["a", np.nan], level="id1")
+    idx = idx.remove_unused_levels()
+    result = idx.levels
+    expected = FrozenList([["a", np.nan], [4]])
+    assert str(result) == str(expected)
+
+
+def test_sort_values_nan():
+    # GH48495, GH48626
+    midx = MultiIndex(levels=[["A", "B", "C"], ["D"]], codes=[[1, 0, 2], [-1, -1, 0]])
+    result = midx.sort_values()
+    expected = MultiIndex(
+        levels=[["A", "B", "C"], ["D"]], codes=[[0, 1, 2], [-1, -1, 0]]
+    )
+    tm.assert_index_equal(result, expected)
+
+
+def test_sort_values_incomparable():
+    # GH48495
+    mi = MultiIndex.from_arrays(
+        [
+            [1, Timestamp("2000-01-01")],
+            [3, 4],
+        ]
+    )
+    match = "'<' not supported between instances of 'Timestamp' and 'int'"
+    with pytest.raises(TypeError, match=match):
+        mi.sort_values()
+
+
+@pytest.mark.parametrize("na_position", ["first", "last"])
+@pytest.mark.parametrize("dtype", ["float64", "Int64", "Float64"])
+def test_sort_values_with_na_na_position(dtype, na_position):
+    # 51612
+    arrays = [
+        Series([1, 1, 2], dtype=dtype),
+        Series([1, None, 3], dtype=dtype),
+    ]
+    index = MultiIndex.from_arrays(arrays)
+    result = index.sort_values(na_position=na_position)
+    if na_position == "first":
+        arrays = [
+            Series([1, 1, 2], dtype=dtype),
+            Series([None, 1, 3], dtype=dtype),
+        ]
+    else:
+        arrays = [
+            Series([1, 1, 2], dtype=dtype),
+            Series([1, None, 3], dtype=dtype),
+        ]
+    expected = MultiIndex.from_arrays(arrays)
+    tm.assert_index_equal(result, expected)
+
+
+def test_sort_unnecessary_warning():
+    # GH#55386
+    midx = MultiIndex.from_tuples([(1.5, 2), (3.5, 3), (0, 1)])
+    midx = midx.set_levels([2.5, np.nan, 1], level=0)
+    result = midx.sort_values()
+    expected = MultiIndex.from_tuples([(1, 3), (2.5, 1), (np.nan, 2)])
+    tm.assert_index_equal(result, expected)

llmeval-env/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]))

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/object/test_astype.py

@@ -0,0 +1,33 @@
+import pytest
+
+from pandas import (
+    Index,
+    NaT,
+    Series,
+)
+import pandas._testing as tm
+
+
+def test_astype_str_from_bytes():
+    # https://github.com/pandas-dev/pandas/issues/38607
+    # GH#49658 pre-2.0 Index called .values.astype(str) here, which effectively
+    #  did a .decode() on the bytes object.  In 2.0 we go through
+    #  ensure_string_array which does f"{val}"
+    idx = Index(["あ", b"a"], dtype="object")
+    result = idx.astype(str)
+    expected = Index(["あ", "a"], dtype="object")
+    tm.assert_index_equal(result, expected)
+
+    # while we're here, check that Series.astype behaves the same
+    result = Series(idx).astype(str)
+    expected = Series(expected, dtype=object)
+    tm.assert_series_equal(result, expected)
+
+
+def test_astype_invalid_nas_to_tdt64_raises():
+    # GH#45722 don't cast np.datetime64 NaTs to timedelta64 NaT
+    idx = Index([NaT.asm8] * 2, dtype=object)
+
+    msg = r"Invalid type for timedelta scalar: <class 'numpy.datetime64'>"
+    with pytest.raises(TypeError, match=msg):
+        idx.astype("m8[ns]")

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/object/test_indexing.py

@@ -0,0 +1,243 @@
+from decimal import Decimal
+
+import numpy as np
+import pytest
+
+from pandas._libs.missing import (
+    NA,
+    is_matching_na,
+)
+from pandas.compat import pa_version_under16p0
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import Index
+import pandas._testing as tm
+
+
+class TestGetIndexer:
+    @pytest.mark.parametrize(
+        "method,expected",
+        [
+            ("pad", np.array([-1, 0, 1, 1], dtype=np.intp)),
+            ("backfill", np.array([0, 0, 1, -1], dtype=np.intp)),
+        ],
+    )
+    def test_get_indexer_strings(self, method, expected):
+        index = Index(["b", "c"])
+        actual = index.get_indexer(["a", "b", "c", "d"], method=method)
+
+        tm.assert_numpy_array_equal(actual, expected)
+
+    def test_get_indexer_strings_raises(self, using_infer_string):
+        index = Index(["b", "c"])
+
+        if using_infer_string:
+            import pyarrow as pa
+
+            msg = "has no kernel"
+            with pytest.raises(pa.lib.ArrowNotImplementedError, match=msg):
+                index.get_indexer(["a", "b", "c", "d"], method="nearest")
+
+            with pytest.raises(pa.lib.ArrowNotImplementedError, match=msg):
+                index.get_indexer(["a", "b", "c", "d"], method="pad", tolerance=2)
+
+            with pytest.raises(pa.lib.ArrowNotImplementedError, match=msg):
+                index.get_indexer(
+                    ["a", "b", "c", "d"], method="pad", tolerance=[2, 2, 2, 2]
+                )
+
+        else:
+            msg = r"unsupported operand type\(s\) for -: 'str' and 'str'"
+            with pytest.raises(TypeError, match=msg):
+                index.get_indexer(["a", "b", "c", "d"], method="nearest")
+
+            with pytest.raises(TypeError, match=msg):
+                index.get_indexer(["a", "b", "c", "d"], method="pad", tolerance=2)
+
+            with pytest.raises(TypeError, match=msg):
+                index.get_indexer(
+                    ["a", "b", "c", "d"], method="pad", tolerance=[2, 2, 2, 2]
+                )
+
+    def test_get_indexer_with_NA_values(
+        self, unique_nulls_fixture, unique_nulls_fixture2
+    ):
+        # GH#22332
+        # check pairwise, that no pair of na values
+        # is mangled
+        if unique_nulls_fixture is unique_nulls_fixture2:
+            return  # skip it, values are not unique
+        arr = np.array([unique_nulls_fixture, unique_nulls_fixture2], dtype=object)
+        index = Index(arr, dtype=object)
+        result = index.get_indexer(
+            Index(
+                [unique_nulls_fixture, unique_nulls_fixture2, "Unknown"], dtype=object
+            )
+        )
+        expected = np.array([0, 1, -1], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+
+class TestGetIndexerNonUnique:
+    def test_get_indexer_non_unique_nas(
+        self, nulls_fixture, request, using_infer_string
+    ):
+        # even though this isn't non-unique, this should still work
+        if using_infer_string and (nulls_fixture is None or nulls_fixture is NA):
+            request.applymarker(pytest.mark.xfail(reason="NAs are cast to NaN"))
+        index = Index(["a", "b", nulls_fixture])
+        indexer, missing = index.get_indexer_non_unique([nulls_fixture])
+
+        expected_indexer = np.array([2], dtype=np.intp)
+        expected_missing = np.array([], dtype=np.intp)
+        tm.assert_numpy_array_equal(indexer, expected_indexer)
+        tm.assert_numpy_array_equal(missing, expected_missing)
+
+        # actually non-unique
+        index = Index(["a", nulls_fixture, "b", nulls_fixture])
+        indexer, missing = index.get_indexer_non_unique([nulls_fixture])
+
+        expected_indexer = np.array([1, 3], dtype=np.intp)
+        tm.assert_numpy_array_equal(indexer, expected_indexer)
+        tm.assert_numpy_array_equal(missing, expected_missing)
+
+        # matching-but-not-identical nans
+        if is_matching_na(nulls_fixture, float("NaN")):
+            index = Index(["a", float("NaN"), "b", float("NaN")])
+            match_but_not_identical = True
+        elif is_matching_na(nulls_fixture, Decimal("NaN")):
+            index = Index(["a", Decimal("NaN"), "b", Decimal("NaN")])
+            match_but_not_identical = True
+        else:
+            match_but_not_identical = False
+
+        if match_but_not_identical:
+            indexer, missing = index.get_indexer_non_unique([nulls_fixture])
+
+            expected_indexer = np.array([1, 3], dtype=np.intp)
+            tm.assert_numpy_array_equal(indexer, expected_indexer)
+            tm.assert_numpy_array_equal(missing, expected_missing)
+
+    @pytest.mark.filterwarnings("ignore:elementwise comp:DeprecationWarning")
+    def test_get_indexer_non_unique_np_nats(self, np_nat_fixture, np_nat_fixture2):
+        expected_missing = np.array([], dtype=np.intp)
+        # matching-but-not-identical nats
+        if is_matching_na(np_nat_fixture, np_nat_fixture2):
+            # ensure nats are different objects
+            index = Index(
+                np.array(
+                    ["2021-10-02", np_nat_fixture.copy(), np_nat_fixture2.copy()],
+                    dtype=object,
+                ),
+                dtype=object,
+            )
+            # pass as index to prevent target from being casted to DatetimeIndex
+            indexer, missing = index.get_indexer_non_unique(
+                Index([np_nat_fixture], dtype=object)
+            )
+            expected_indexer = np.array([1, 2], dtype=np.intp)
+            tm.assert_numpy_array_equal(indexer, expected_indexer)
+            tm.assert_numpy_array_equal(missing, expected_missing)
+        # dt64nat vs td64nat
+        else:
+            try:
+                np_nat_fixture == np_nat_fixture2
+            except (TypeError, OverflowError):
+                # Numpy will raise on uncomparable types, like
+                # np.datetime64('NaT', 'Y') and np.datetime64('NaT', 'ps')
+                # https://github.com/numpy/numpy/issues/22762
+                return
+            index = Index(
+                np.array(
+                    [
+                        "2021-10-02",
+                        np_nat_fixture,
+                        np_nat_fixture2,
+                        np_nat_fixture,
+                        np_nat_fixture2,
+                    ],
+                    dtype=object,
+                ),
+                dtype=object,
+            )
+            # pass as index to prevent target from being casted to DatetimeIndex
+            indexer, missing = index.get_indexer_non_unique(
+                Index([np_nat_fixture], dtype=object)
+            )
+            expected_indexer = np.array([1, 3], dtype=np.intp)
+            tm.assert_numpy_array_equal(indexer, expected_indexer)
+            tm.assert_numpy_array_equal(missing, expected_missing)
+
+
+class TestSliceLocs:
+    @pytest.mark.parametrize(
+        "dtype",
+        [
+            "object",
+            pytest.param("string[pyarrow_numpy]", marks=td.skip_if_no("pyarrow")),
+        ],
+    )
+    @pytest.mark.parametrize(
+        "in_slice,expected",
+        [
+            # error: Slice index must be an integer or None
+            (pd.IndexSlice[::-1], "yxdcb"),
+            (pd.IndexSlice["b":"y":-1], ""),  # type: ignore[misc]
+            (pd.IndexSlice["b"::-1], "b"),  # type: ignore[misc]
+            (pd.IndexSlice[:"b":-1], "yxdcb"),  # type: ignore[misc]
+            (pd.IndexSlice[:"y":-1], "y"),  # type: ignore[misc]
+            (pd.IndexSlice["y"::-1], "yxdcb"),  # type: ignore[misc]
+            (pd.IndexSlice["y"::-4], "yb"),  # type: ignore[misc]
+            # absent labels
+            (pd.IndexSlice[:"a":-1], "yxdcb"),  # type: ignore[misc]
+            (pd.IndexSlice[:"a":-2], "ydb"),  # type: ignore[misc]
+            (pd.IndexSlice["z"::-1], "yxdcb"),  # type: ignore[misc]
+            (pd.IndexSlice["z"::-3], "yc"),  # type: ignore[misc]
+            (pd.IndexSlice["m"::-1], "dcb"),  # type: ignore[misc]
+            (pd.IndexSlice[:"m":-1], "yx"),  # type: ignore[misc]
+            (pd.IndexSlice["a":"a":-1], ""),  # type: ignore[misc]
+            (pd.IndexSlice["z":"z":-1], ""),  # type: ignore[misc]
+            (pd.IndexSlice["m":"m":-1], ""),  # type: ignore[misc]
+        ],
+    )
+    def test_slice_locs_negative_step(self, in_slice, expected, dtype, request):
+        if (
+            not pa_version_under16p0
+            and dtype == "string[pyarrow_numpy]"
+            and in_slice == slice("a", "a", -1)
+        ):
+            request.applymarker(
+                pytest.mark.xfail(reason="https://github.com/apache/arrow/issues/40642")
+            )
+
+        index = Index(list("bcdxy"), dtype=dtype)
+
+        s_start, s_stop = index.slice_locs(in_slice.start, in_slice.stop, in_slice.step)
+        result = index[s_start : s_stop : in_slice.step]
+        expected = Index(list(expected), dtype=dtype)
+        tm.assert_index_equal(result, expected)
+
+    @td.skip_if_no("pyarrow")
+    def test_slice_locs_negative_step_oob(self):
+        index = Index(list("bcdxy"), dtype="string[pyarrow_numpy]")
+
+        result = index[-10:5:1]
+        tm.assert_index_equal(result, index)
+
+        result = index[4:-10:-1]
+        expected = Index(list("yxdcb"), dtype="string[pyarrow_numpy]")
+        tm.assert_index_equal(result, expected)
+
+    def test_slice_locs_dup(self):
+        index = Index(["a", "a", "b", "c", "d", "d"])
+        assert index.slice_locs("a", "d") == (0, 6)
+        assert index.slice_locs(end="d") == (0, 6)
+        assert index.slice_locs("a", "c") == (0, 4)
+        assert index.slice_locs("b", "d") == (2, 6)
+
+        index2 = index[::-1]
+        assert index2.slice_locs("d", "a") == (0, 6)
+        assert index2.slice_locs(end="a") == (0, 6)
+        assert index2.slice_locs("d", "b") == (0, 4)
+        assert index2.slice_locs("c", "a") == (2, 6)

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/ranges/test_constructors.py

@@ -0,0 +1,164 @@
+from datetime import datetime
+
+import numpy as np
+import pytest
+
+from pandas import (
+    Index,
+    RangeIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestRangeIndexConstructors:
+    @pytest.mark.parametrize("name", [None, "foo"])
+    @pytest.mark.parametrize(
+        "args, kwargs, start, stop, step",
+        [
+            ((5,), {}, 0, 5, 1),
+            ((1, 5), {}, 1, 5, 1),
+            ((1, 5, 2), {}, 1, 5, 2),
+            ((0,), {}, 0, 0, 1),
+            ((0, 0), {}, 0, 0, 1),
+            ((), {"start": 0}, 0, 0, 1),
+            ((), {"stop": 0}, 0, 0, 1),
+        ],
+    )
+    def test_constructor(self, args, kwargs, start, stop, step, name):
+        result = RangeIndex(*args, name=name, **kwargs)
+        expected = Index(np.arange(start, stop, step, dtype=np.int64), name=name)
+        assert isinstance(result, RangeIndex)
+        assert result.name is name
+        assert result._range == range(start, stop, step)
+        tm.assert_index_equal(result, expected, exact="equiv")
+
+    def test_constructor_invalid_args(self):
+        msg = "RangeIndex\\(\\.\\.\\.\\) must be called with integers"
+        with pytest.raises(TypeError, match=msg):
+            RangeIndex()
+
+        with pytest.raises(TypeError, match=msg):
+            RangeIndex(name="Foo")
+
+        # we don't allow on a bare Index
+        msg = (
+            r"Index\(\.\.\.\) must be called with a collection of some "
+            r"kind, 0 was passed"
+        )
+        with pytest.raises(TypeError, match=msg):
+            Index(0)
+
+    @pytest.mark.parametrize(
+        "args",
+        [
+            Index(["a", "b"]),
+            Series(["a", "b"]),
+            np.array(["a", "b"]),
+            [],
+            np.arange(0, 10),
+            np.array([1]),
+            [1],
+        ],
+    )
+    def test_constructor_additional_invalid_args(self, args):
+        msg = f"Value needs to be a scalar value, was type {type(args).__name__}"
+        with pytest.raises(TypeError, match=msg):
+            RangeIndex(args)
+
+    @pytest.mark.parametrize("args", ["foo", datetime(2000, 1, 1, 0, 0)])
+    def test_constructor_invalid_args_wrong_type(self, args):
+        msg = f"Wrong type {type(args)} for value {args}"
+        with pytest.raises(TypeError, match=msg):
+            RangeIndex(args)
+
+    def test_constructor_same(self):
+        # pass thru w and w/o copy
+        index = RangeIndex(1, 5, 2)
+        result = RangeIndex(index, copy=False)
+        assert result.identical(index)
+
+        result = RangeIndex(index, copy=True)
+        tm.assert_index_equal(result, index, exact=True)
+
+        result = RangeIndex(index)
+        tm.assert_index_equal(result, index, exact=True)
+
+        with pytest.raises(
+            ValueError,
+            match="Incorrect `dtype` passed: expected signed integer, received float64",
+        ):
+            RangeIndex(index, dtype="float64")
+
+    def test_constructor_range_object(self):
+        result = RangeIndex(range(1, 5, 2))
+        expected = RangeIndex(1, 5, 2)
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_constructor_range(self):
+        result = RangeIndex.from_range(range(1, 5, 2))
+        expected = RangeIndex(1, 5, 2)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        result = RangeIndex.from_range(range(5, 6))
+        expected = RangeIndex(5, 6, 1)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        # an invalid range
+        result = RangeIndex.from_range(range(5, 1))
+        expected = RangeIndex(0, 0, 1)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        result = RangeIndex.from_range(range(5))
+        expected = RangeIndex(0, 5, 1)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        result = Index(range(1, 5, 2))
+        expected = RangeIndex(1, 5, 2)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        msg = (
+            r"(RangeIndex.)?from_range\(\) got an unexpected keyword argument( 'copy')?"
+        )
+        with pytest.raises(TypeError, match=msg):
+            RangeIndex.from_range(range(10), copy=True)
+
+    def test_constructor_name(self):
+        # GH#12288
+        orig = RangeIndex(10)
+        orig.name = "original"
+
+        copy = RangeIndex(orig)
+        copy.name = "copy"
+
+        assert orig.name == "original"
+        assert copy.name == "copy"
+
+        new = Index(copy)
+        assert new.name == "copy"
+
+        new.name = "new"
+        assert orig.name == "original"
+        assert copy.name == "copy"
+        assert new.name == "new"
+
+    def test_constructor_corner(self):
+        arr = np.array([1, 2, 3, 4], dtype=object)
+        index = RangeIndex(1, 5)
+        assert index.values.dtype == np.int64
+        expected = Index(arr).astype("int64")
+
+        tm.assert_index_equal(index, expected, exact="equiv")
+
+        # non-int raise Exception
+        with pytest.raises(TypeError, match=r"Wrong type \<class 'str'\>"):
+            RangeIndex("1", "10", "1")
+        with pytest.raises(TypeError, match=r"Wrong type \<class 'float'\>"):
+            RangeIndex(1.1, 10.2, 1.3)
+
+        # invalid passed type
+        with pytest.raises(
+            ValueError,
+            match="Incorrect `dtype` passed: expected signed integer, received float64",
+        ):
+            RangeIndex(1, 5, dtype="float64")

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/ranges/test_indexing.py

@@ -0,0 +1,137 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Index,
+    RangeIndex,
+)
+import pandas._testing as tm
+
+
+class TestGetIndexer:
+    def test_get_indexer(self):
+        index = RangeIndex(start=0, stop=20, step=2)
+        target = RangeIndex(10)
+        indexer = index.get_indexer(target)
+        expected = np.array([0, -1, 1, -1, 2, -1, 3, -1, 4, -1], dtype=np.intp)
+        tm.assert_numpy_array_equal(indexer, expected)
+
+    def test_get_indexer_pad(self):
+        index = RangeIndex(start=0, stop=20, step=2)
+        target = RangeIndex(10)
+        indexer = index.get_indexer(target, method="pad")
+        expected = np.array([0, 0, 1, 1, 2, 2, 3, 3, 4, 4], dtype=np.intp)
+        tm.assert_numpy_array_equal(indexer, expected)
+
+    def test_get_indexer_backfill(self):
+        index = RangeIndex(start=0, stop=20, step=2)
+        target = RangeIndex(10)
+        indexer = index.get_indexer(target, method="backfill")
+        expected = np.array([0, 1, 1, 2, 2, 3, 3, 4, 4, 5], dtype=np.intp)
+        tm.assert_numpy_array_equal(indexer, expected)
+
+    def test_get_indexer_limit(self):
+        # GH#28631
+        idx = RangeIndex(4)
+        target = RangeIndex(6)
+        result = idx.get_indexer(target, method="pad", limit=1)
+        expected = np.array([0, 1, 2, 3, 3, -1], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("stop", [0, -1, -2])
+    def test_get_indexer_decreasing(self, stop):
+        # GH#28678
+        index = RangeIndex(7, stop, -3)
+        result = index.get_indexer(range(9))
+        expected = np.array([-1, 2, -1, -1, 1, -1, -1, 0, -1], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+
+class TestTake:
+    def test_take_preserve_name(self):
+        index = RangeIndex(1, 5, name="foo")
+        taken = index.take([3, 0, 1])
+        assert index.name == taken.name
+
+    def test_take_fill_value(self):
+        # GH#12631
+        idx = RangeIndex(1, 4, name="xxx")
+        result = idx.take(np.array([1, 0, -1]))
+        expected = Index([2, 1, 3], dtype=np.int64, name="xxx")
+        tm.assert_index_equal(result, expected)
+
+        # fill_value
+        msg = "Unable to fill values because RangeIndex cannot contain NA"
+        with pytest.raises(ValueError, match=msg):
+            idx.take(np.array([1, 0, -1]), fill_value=True)
+
+        # allow_fill=False
+        result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True)
+        expected = Index([2, 1, 3], dtype=np.int64, name="xxx")
+        tm.assert_index_equal(result, expected)
+
+        msg = "Unable to fill values because RangeIndex cannot contain NA"
+        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)
+
+    def test_take_raises_index_error(self):
+        idx = RangeIndex(1, 4, name="xxx")
+
+        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]))
+
+        msg = "index -4 is out of bounds for (axis 0 with )?size 3"
+        with pytest.raises(IndexError, match=msg):
+            idx.take(np.array([1, -4]))
+
+        # no errors
+        result = idx.take(np.array([1, -3]))
+        expected = Index([2, 1], dtype=np.int64, name="xxx")
+        tm.assert_index_equal(result, expected)
+
+    def test_take_accepts_empty_array(self):
+        idx = RangeIndex(1, 4, name="foo")
+        result = idx.take(np.array([]))
+        expected = Index([], dtype=np.int64, name="foo")
+        tm.assert_index_equal(result, expected)
+
+        # empty index
+        idx = RangeIndex(0, name="foo")
+        result = idx.take(np.array([]))
+        expected = Index([], dtype=np.int64, name="foo")
+        tm.assert_index_equal(result, expected)
+
+    def test_take_accepts_non_int64_array(self):
+        idx = RangeIndex(1, 4, name="foo")
+        result = idx.take(np.array([2, 1], dtype=np.uint32))
+        expected = Index([3, 2], dtype=np.int64, name="foo")
+        tm.assert_index_equal(result, expected)
+
+    def test_take_when_index_has_step(self):
+        idx = RangeIndex(1, 11, 3, name="foo")  # [1, 4, 7, 10]
+        result = idx.take(np.array([1, 0, -1, -4]))
+        expected = Index([4, 1, 10, 1], dtype=np.int64, name="foo")
+        tm.assert_index_equal(result, expected)
+
+    def test_take_when_index_has_negative_step(self):
+        idx = RangeIndex(11, -4, -2, name="foo")  # [11, 9, 7, 5, 3, 1, -1, -3]
+        result = idx.take(np.array([1, 0, -1, -8]))
+        expected = Index([9, 11, -3, 11], dtype=np.int64, name="foo")
+        tm.assert_index_equal(result, expected)
+
+
+class TestWhere:
+    def test_where_putmask_range_cast(self):
+        # GH#43240
+        idx = RangeIndex(0, 5, name="test")
+
+        mask = np.array([True, True, False, False, False])
+        result = idx.putmask(mask, 10)
+        expected = Index([10, 10, 2, 3, 4], dtype=np.int64, name="test")
+        tm.assert_index_equal(result, expected)
+
+        result = idx.where(~mask, 10)
+        tm.assert_index_equal(result, expected)

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/ranges/test_join.py

@@ -0,0 +1,177 @@
+import numpy as np
+
+from pandas import (
+    Index,
+    RangeIndex,
+)
+import pandas._testing as tm
+
+
+class TestJoin:
+    def test_join_outer(self):
+        # join with Index[int64]
+        index = RangeIndex(start=0, stop=20, step=2)
+        other = Index(np.arange(25, 14, -1, dtype=np.int64))
+
+        res, lidx, ridx = index.join(other, how="outer", return_indexers=True)
+        noidx_res = index.join(other, how="outer")
+        tm.assert_index_equal(res, noidx_res)
+
+        eres = Index(
+            [0, 2, 4, 6, 8, 10, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25]
+        )
+        elidx = np.array(
+            [0, 1, 2, 3, 4, 5, 6, 7, -1, 8, -1, 9, -1, -1, -1, -1, -1, -1, -1],
+            dtype=np.intp,
+        )
+        eridx = np.array(
+            [-1, -1, -1, -1, -1, -1, -1, -1, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0],
+            dtype=np.intp,
+        )
+
+        assert isinstance(res, Index) and res.dtype == np.dtype(np.int64)
+        assert not isinstance(res, RangeIndex)
+        tm.assert_index_equal(res, eres, exact=True)
+        tm.assert_numpy_array_equal(lidx, elidx)
+        tm.assert_numpy_array_equal(ridx, eridx)
+
+        # join with RangeIndex
+        other = RangeIndex(25, 14, -1)
+
+        res, lidx, ridx = index.join(other, how="outer", return_indexers=True)
+        noidx_res = index.join(other, how="outer")
+        tm.assert_index_equal(res, noidx_res)
+
+        assert isinstance(res, Index) and res.dtype == np.int64
+        assert not isinstance(res, RangeIndex)
+        tm.assert_index_equal(res, eres)
+        tm.assert_numpy_array_equal(lidx, elidx)
+        tm.assert_numpy_array_equal(ridx, eridx)
+
+    def test_join_inner(self):
+        # Join with non-RangeIndex
+        index = RangeIndex(start=0, stop=20, step=2)
+        other = Index(np.arange(25, 14, -1, dtype=np.int64))
+
+        res, lidx, ridx = index.join(other, how="inner", return_indexers=True)
+
+        # no guarantee of sortedness, so sort for comparison purposes
+        ind = res.argsort()
+        res = res.take(ind)
+        lidx = lidx.take(ind)
+        ridx = ridx.take(ind)
+
+        eres = Index([16, 18])
+        elidx = np.array([8, 9], dtype=np.intp)
+        eridx = np.array([9, 7], dtype=np.intp)
+
+        assert isinstance(res, Index) and res.dtype == np.int64
+        tm.assert_index_equal(res, eres)
+        tm.assert_numpy_array_equal(lidx, elidx)
+        tm.assert_numpy_array_equal(ridx, eridx)
+
+        # Join two RangeIndex
+        other = RangeIndex(25, 14, -1)
+
+        res, lidx, ridx = index.join(other, how="inner", return_indexers=True)
+
+        assert isinstance(res, RangeIndex)
+        tm.assert_index_equal(res, eres, exact="equiv")
+        tm.assert_numpy_array_equal(lidx, elidx)
+        tm.assert_numpy_array_equal(ridx, eridx)
+
+    def test_join_left(self):
+        # Join with Index[int64]
+        index = RangeIndex(start=0, stop=20, step=2)
+        other = Index(np.arange(25, 14, -1, dtype=np.int64))
+
+        res, lidx, ridx = index.join(other, how="left", return_indexers=True)
+        eres = index
+        eridx = np.array([-1, -1, -1, -1, -1, -1, -1, -1, 9, 7], dtype=np.intp)
+
+        assert isinstance(res, RangeIndex)
+        tm.assert_index_equal(res, eres)
+        assert lidx is None
+        tm.assert_numpy_array_equal(ridx, eridx)
+
+        # Join withRangeIndex
+        other = Index(np.arange(25, 14, -1, dtype=np.int64))
+
+        res, lidx, ridx = index.join(other, how="left", return_indexers=True)
+
+        assert isinstance(res, RangeIndex)
+        tm.assert_index_equal(res, eres)
+        assert lidx is None
+        tm.assert_numpy_array_equal(ridx, eridx)
+
+    def test_join_right(self):
+        # Join with Index[int64]
+        index = RangeIndex(start=0, stop=20, step=2)
+        other = Index(np.arange(25, 14, -1, dtype=np.int64))
+
+        res, lidx, ridx = index.join(other, how="right", return_indexers=True)
+        eres = other
+        elidx = np.array([-1, -1, -1, -1, -1, -1, -1, 9, -1, 8, -1], dtype=np.intp)
+
+        assert isinstance(other, Index) and other.dtype == np.int64
+        tm.assert_index_equal(res, eres)
+        tm.assert_numpy_array_equal(lidx, elidx)
+        assert ridx is None
+
+        # Join withRangeIndex
+        other = RangeIndex(25, 14, -1)
+
+        res, lidx, ridx = index.join(other, how="right", return_indexers=True)
+        eres = other
+
+        assert isinstance(other, RangeIndex)
+        tm.assert_index_equal(res, eres)
+        tm.assert_numpy_array_equal(lidx, elidx)
+        assert ridx is None
+
+    def test_join_non_int_index(self):
+        index = RangeIndex(start=0, stop=20, step=2)
+        other = Index([3, 6, 7, 8, 10], dtype=object)
+
+        outer = index.join(other, how="outer")
+        outer2 = other.join(index, how="outer")
+        expected = Index([0, 2, 3, 4, 6, 7, 8, 10, 12, 14, 16, 18])
+        tm.assert_index_equal(outer, outer2)
+        tm.assert_index_equal(outer, expected)
+
+        inner = index.join(other, how="inner")
+        inner2 = other.join(index, how="inner")
+        expected = Index([6, 8, 10])
+        tm.assert_index_equal(inner, inner2)
+        tm.assert_index_equal(inner, expected)
+
+        left = index.join(other, how="left")
+        tm.assert_index_equal(left, index.astype(object))
+
+        left2 = other.join(index, how="left")
+        tm.assert_index_equal(left2, other)
+
+        right = index.join(other, how="right")
+        tm.assert_index_equal(right, other)
+
+        right2 = other.join(index, how="right")
+        tm.assert_index_equal(right2, index.astype(object))
+
+    def test_join_non_unique(self):
+        index = RangeIndex(start=0, stop=20, step=2)
+        other = Index([4, 4, 3, 3])
+
+        res, lidx, ridx = index.join(other, return_indexers=True)
+
+        eres = Index([0, 2, 4, 4, 6, 8, 10, 12, 14, 16, 18])
+        elidx = np.array([0, 1, 2, 2, 3, 4, 5, 6, 7, 8, 9], dtype=np.intp)
+        eridx = np.array([-1, -1, 0, 1, -1, -1, -1, -1, -1, -1, -1], dtype=np.intp)
+
+        tm.assert_index_equal(res, eres)
+        tm.assert_numpy_array_equal(lidx, elidx)
+        tm.assert_numpy_array_equal(ridx, eridx)
+
+    def test_join_self(self, join_type):
+        index = RangeIndex(start=0, stop=20, step=2)
+        joined = index.join(index, how=join_type)
+        assert index is joined

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/ranges/test_range.py

@@ -0,0 +1,622 @@
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.common import ensure_platform_int
+
+import pandas as pd
+from pandas import (
+    Index,
+    RangeIndex,
+)
+import pandas._testing as tm
+
+
+class TestRangeIndex:
+    @pytest.fixture
+    def simple_index(self):
+        return RangeIndex(start=0, stop=20, step=2)
+
+    def test_constructor_unwraps_index(self):
+        result = RangeIndex(1, 3)
+        expected = np.array([1, 2], dtype=np.int64)
+        tm.assert_numpy_array_equal(result._data, expected)
+
+    def test_can_hold_identifiers(self, simple_index):
+        idx = simple_index
+        key = idx[0]
+        assert idx._can_hold_identifiers_and_holds_name(key) is False
+
+    def test_too_many_names(self, simple_index):
+        index = simple_index
+        with pytest.raises(ValueError, match="^Length"):
+            index.names = ["roger", "harold"]
+
+    @pytest.mark.parametrize(
+        "index, start, stop, step",
+        [
+            (RangeIndex(5), 0, 5, 1),
+            (RangeIndex(0, 5), 0, 5, 1),
+            (RangeIndex(5, step=2), 0, 5, 2),
+            (RangeIndex(1, 5, 2), 1, 5, 2),
+        ],
+    )
+    def test_start_stop_step_attrs(self, index, start, stop, step):
+        # GH 25710
+        assert index.start == start
+        assert index.stop == stop
+        assert index.step == step
+
+    def test_copy(self):
+        i = RangeIndex(5, name="Foo")
+        i_copy = i.copy()
+        assert i_copy is not i
+        assert i_copy.identical(i)
+        assert i_copy._range == range(0, 5, 1)
+        assert i_copy.name == "Foo"
+
+    def test_repr(self):
+        i = RangeIndex(5, name="Foo")
+        result = repr(i)
+        expected = "RangeIndex(start=0, stop=5, step=1, name='Foo')"
+        assert result == expected
+
+        result = eval(result)
+        tm.assert_index_equal(result, i, exact=True)
+
+        i = RangeIndex(5, 0, -1)
+        result = repr(i)
+        expected = "RangeIndex(start=5, stop=0, step=-1)"
+        assert result == expected
+
+        result = eval(result)
+        tm.assert_index_equal(result, i, exact=True)
+
+    def test_insert(self):
+        idx = RangeIndex(5, name="Foo")
+        result = idx[1:4]
+
+        # test 0th element
+        tm.assert_index_equal(idx[0:4], result.insert(0, idx[0]), exact="equiv")
+
+        # GH 18295 (test missing)
+        expected = Index([0, np.nan, 1, 2, 3, 4], dtype=np.float64)
+        for na in [np.nan, None, pd.NA]:
+            result = RangeIndex(5).insert(1, na)
+            tm.assert_index_equal(result, expected)
+
+        result = RangeIndex(5).insert(1, pd.NaT)
+        expected = Index([0, pd.NaT, 1, 2, 3, 4], dtype=object)
+        tm.assert_index_equal(result, expected)
+
+    def test_insert_edges_preserves_rangeindex(self):
+        idx = Index(range(4, 9, 2))
+
+        result = idx.insert(0, 2)
+        expected = Index(range(2, 9, 2))
+        tm.assert_index_equal(result, expected, exact=True)
+
+        result = idx.insert(3, 10)
+        expected = Index(range(4, 11, 2))
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_insert_middle_preserves_rangeindex(self):
+        # insert in the middle
+        idx = Index(range(0, 3, 2))
+        result = idx.insert(1, 1)
+        expected = Index(range(3))
+        tm.assert_index_equal(result, expected, exact=True)
+
+        idx = idx * 2
+        result = idx.insert(1, 2)
+        expected = expected * 2
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_delete(self):
+        idx = RangeIndex(5, name="Foo")
+        expected = idx[1:]
+        result = idx.delete(0)
+        tm.assert_index_equal(result, expected, exact=True)
+        assert result.name == expected.name
+
+        expected = idx[:-1]
+        result = idx.delete(-1)
+        tm.assert_index_equal(result, expected, exact=True)
+        assert result.name == expected.name
+
+        msg = "index 5 is out of bounds for axis 0 with size 5"
+        with pytest.raises((IndexError, ValueError), match=msg):
+            # either depending on numpy version
+            result = idx.delete(len(idx))
+
+    def test_delete_preserves_rangeindex(self):
+        idx = Index(range(2), name="foo")
+
+        result = idx.delete([1])
+        expected = Index(range(1), name="foo")
+        tm.assert_index_equal(result, expected, exact=True)
+
+        result = idx.delete(1)
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_delete_preserves_rangeindex_middle(self):
+        idx = Index(range(3), name="foo")
+        result = idx.delete(1)
+        expected = idx[::2]
+        tm.assert_index_equal(result, expected, exact=True)
+
+        result = idx.delete(-2)
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_delete_preserves_rangeindex_list_at_end(self):
+        idx = RangeIndex(0, 6, 1)
+
+        loc = [2, 3, 4, 5]
+        result = idx.delete(loc)
+        expected = idx[:2]
+        tm.assert_index_equal(result, expected, exact=True)
+
+        result = idx.delete(loc[::-1])
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_delete_preserves_rangeindex_list_middle(self):
+        idx = RangeIndex(0, 6, 1)
+
+        loc = [1, 2, 3, 4]
+        result = idx.delete(loc)
+        expected = RangeIndex(0, 6, 5)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        result = idx.delete(loc[::-1])
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_delete_all_preserves_rangeindex(self):
+        idx = RangeIndex(0, 6, 1)
+
+        loc = [0, 1, 2, 3, 4, 5]
+        result = idx.delete(loc)
+        expected = idx[:0]
+        tm.assert_index_equal(result, expected, exact=True)
+
+        result = idx.delete(loc[::-1])
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_delete_not_preserving_rangeindex(self):
+        idx = RangeIndex(0, 6, 1)
+
+        loc = [0, 3, 5]
+        result = idx.delete(loc)
+        expected = Index([1, 2, 4])
+        tm.assert_index_equal(result, expected, exact=True)
+
+        result = idx.delete(loc[::-1])
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_view(self):
+        i = RangeIndex(0, name="Foo")
+        i_view = i.view()
+        assert i_view.name == "Foo"
+
+        i_view = i.view("i8")
+        tm.assert_numpy_array_equal(i.values, i_view)
+
+        msg = "Passing a type in RangeIndex.view is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            i_view = i.view(RangeIndex)
+        tm.assert_index_equal(i, i_view)
+
+    def test_dtype(self, simple_index):
+        index = simple_index
+        assert index.dtype == np.int64
+
+    def test_cache(self):
+        # GH 26565, GH26617, GH35432, GH53387
+        # This test checks whether _cache has been set.
+        # Calling RangeIndex._cache["_data"] creates an int64 array of the same length
+        # as the RangeIndex and stores it in _cache.
+        idx = RangeIndex(0, 100, 10)
+
+        assert idx._cache == {}
+
+        repr(idx)
+        assert idx._cache == {}
+
+        str(idx)
+        assert idx._cache == {}
+
+        idx.get_loc(20)
+        assert idx._cache == {}
+
+        90 in idx  # True
+        assert idx._cache == {}
+
+        91 in idx  # False
+        assert idx._cache == {}
+
+        idx.all()
+        assert idx._cache == {}
+
+        idx.any()
+        assert idx._cache == {}
+
+        for _ in idx:
+            pass
+        assert idx._cache == {}
+
+        msg = "RangeIndex.format is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            idx.format()
+        assert idx._cache == {}
+
+        df = pd.DataFrame({"a": range(10)}, index=idx)
+
+        # df.__repr__ should not populate index cache
+        str(df)
+        assert idx._cache == {}
+
+        df.loc[50]
+        assert idx._cache == {}
+
+        with pytest.raises(KeyError, match="51"):
+            df.loc[51]
+        assert idx._cache == {}
+
+        df.loc[10:50]
+        assert idx._cache == {}
+
+        df.iloc[5:10]
+        assert idx._cache == {}
+
+        # after calling take, _cache may contain other keys, but not "_data"
+        idx.take([3, 0, 1])
+        assert "_data" not in idx._cache
+
+        df.loc[[50]]
+        assert "_data" not in idx._cache
+
+        df.iloc[[5, 6, 7, 8, 9]]
+        assert "_data" not in idx._cache
+
+        # idx._cache should contain a _data entry after call to idx._data
+        idx._data
+        assert isinstance(idx._data, np.ndarray)
+        assert idx._data is idx._data  # check cached value is reused
+        assert "_data" in idx._cache
+        expected = np.arange(0, 100, 10, dtype="int64")
+        tm.assert_numpy_array_equal(idx._cache["_data"], expected)
+
+    def test_is_monotonic(self):
+        index = RangeIndex(0, 20, 2)
+        assert index.is_monotonic_increasing is True
+        assert index.is_monotonic_increasing is True
+        assert index.is_monotonic_decreasing is False
+        assert index._is_strictly_monotonic_increasing is True
+        assert index._is_strictly_monotonic_decreasing is False
+
+        index = RangeIndex(4, 0, -1)
+        assert index.is_monotonic_increasing is False
+        assert index._is_strictly_monotonic_increasing is False
+        assert index.is_monotonic_decreasing is True
+        assert index._is_strictly_monotonic_decreasing is True
+
+        index = RangeIndex(1, 2)
+        assert index.is_monotonic_increasing is True
+        assert index.is_monotonic_increasing is True
+        assert index.is_monotonic_decreasing is True
+        assert index._is_strictly_monotonic_increasing is True
+        assert index._is_strictly_monotonic_decreasing is True
+
+        index = RangeIndex(2, 1)
+        assert index.is_monotonic_increasing is True
+        assert index.is_monotonic_increasing is True
+        assert index.is_monotonic_decreasing is True
+        assert index._is_strictly_monotonic_increasing is True
+        assert index._is_strictly_monotonic_decreasing is True
+
+        index = RangeIndex(1, 1)
+        assert index.is_monotonic_increasing is True
+        assert index.is_monotonic_increasing is True
+        assert index.is_monotonic_decreasing is True
+        assert index._is_strictly_monotonic_increasing is True
+        assert index._is_strictly_monotonic_decreasing is True
+
+    @pytest.mark.parametrize(
+        "left,right",
+        [
+            (RangeIndex(0, 9, 2), RangeIndex(0, 10, 2)),
+            (RangeIndex(0), RangeIndex(1, -1, 3)),
+            (RangeIndex(1, 2, 3), RangeIndex(1, 3, 4)),
+            (RangeIndex(0, -9, -2), RangeIndex(0, -10, -2)),
+        ],
+    )
+    def test_equals_range(self, left, right):
+        assert left.equals(right)
+        assert right.equals(left)
+
+    def test_logical_compat(self, simple_index):
+        idx = simple_index
+        assert idx.all() == idx.values.all()
+        assert idx.any() == idx.values.any()
+
+    def test_identical(self, simple_index):
+        index = simple_index
+        i = Index(index.copy())
+        assert i.identical(index)
+
+        # we don't allow object dtype for RangeIndex
+        if isinstance(index, RangeIndex):
+            return
+
+        same_values_different_type = Index(i, dtype=object)
+        assert not i.identical(same_values_different_type)
+
+        i = index.copy(dtype=object)
+        i = i.rename("foo")
+        same_values = Index(i, dtype=object)
+        assert same_values.identical(index.copy(dtype=object))
+
+        assert not i.identical(index)
+        assert Index(same_values, name="foo", dtype=object).identical(i)
+
+        assert not index.copy(dtype=object).identical(index.copy(dtype="int64"))
+
+    def test_nbytes(self):
+        # memory savings vs int index
+        idx = RangeIndex(0, 1000)
+        assert idx.nbytes < Index(idx._values).nbytes / 10
+
+        # constant memory usage
+        i2 = RangeIndex(0, 10)
+        assert idx.nbytes == i2.nbytes
+
+    @pytest.mark.parametrize(
+        "start,stop,step",
+        [
+            # can't
+            ("foo", "bar", "baz"),
+            # shouldn't
+            ("0", "1", "2"),
+        ],
+    )
+    def test_cant_or_shouldnt_cast(self, start, stop, step):
+        msg = f"Wrong type {type(start)} for value {start}"
+        with pytest.raises(TypeError, match=msg):
+            RangeIndex(start, stop, step)
+
+    def test_view_index(self, simple_index):
+        index = simple_index
+        msg = "Passing a type in RangeIndex.view is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            index.view(Index)
+
+    def test_prevent_casting(self, simple_index):
+        index = simple_index
+        result = index.astype("O")
+        assert result.dtype == np.object_
+
+    def test_repr_roundtrip(self, simple_index):
+        index = simple_index
+        tm.assert_index_equal(eval(repr(index)), index)
+
+    def test_slice_keep_name(self):
+        idx = RangeIndex(1, 2, name="asdf")
+        assert idx.name == idx[1:].name
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            RangeIndex(start=0, stop=20, step=2, name="foo"),
+            RangeIndex(start=18, stop=-1, step=-2, name="bar"),
+        ],
+        ids=["index_inc", "index_dec"],
+    )
+    def test_has_duplicates(self, index):
+        assert index.is_unique
+        assert not index.has_duplicates
+
+    def test_extended_gcd(self, simple_index):
+        index = simple_index
+        result = index._extended_gcd(6, 10)
+        assert result[0] == result[1] * 6 + result[2] * 10
+        assert 2 == result[0]
+
+        result = index._extended_gcd(10, 6)
+        assert 2 == result[1] * 10 + result[2] * 6
+        assert 2 == result[0]
+
+    def test_min_fitting_element(self):
+        result = RangeIndex(0, 20, 2)._min_fitting_element(1)
+        assert 2 == result
+
+        result = RangeIndex(1, 6)._min_fitting_element(1)
+        assert 1 == result
+
+        result = RangeIndex(18, -2, -2)._min_fitting_element(1)
+        assert 2 == result
+
+        result = RangeIndex(5, 0, -1)._min_fitting_element(1)
+        assert 1 == result
+
+        big_num = 500000000000000000000000
+
+        result = RangeIndex(5, big_num * 2, 1)._min_fitting_element(big_num)
+        assert big_num == result
+
+    def test_slice_specialised(self, simple_index):
+        index = simple_index
+        index.name = "foo"
+
+        # scalar indexing
+        res = index[1]
+        expected = 2
+        assert res == expected
+
+        res = index[-1]
+        expected = 18
+        assert res == expected
+
+        # slicing
+        # slice value completion
+        index_slice = index[:]
+        expected = index
+        tm.assert_index_equal(index_slice, expected)
+
+        # positive slice values
+        index_slice = index[7:10:2]
+        expected = Index([14, 18], name="foo")
+        tm.assert_index_equal(index_slice, expected, exact="equiv")
+
+        # negative slice values
+        index_slice = index[-1:-5:-2]
+        expected = Index([18, 14], name="foo")
+        tm.assert_index_equal(index_slice, expected, exact="equiv")
+
+        # stop overshoot
+        index_slice = index[2:100:4]
+        expected = Index([4, 12], name="foo")
+        tm.assert_index_equal(index_slice, expected, exact="equiv")
+
+        # reverse
+        index_slice = index[::-1]
+        expected = Index(index.values[::-1], name="foo")
+        tm.assert_index_equal(index_slice, expected, exact="equiv")
+
+        index_slice = index[-8::-1]
+        expected = Index([4, 2, 0], name="foo")
+        tm.assert_index_equal(index_slice, expected, exact="equiv")
+
+        index_slice = index[-40::-1]
+        expected = Index(np.array([], dtype=np.int64), name="foo")
+        tm.assert_index_equal(index_slice, expected, exact="equiv")
+
+        index_slice = index[40::-1]
+        expected = Index(index.values[40::-1], name="foo")
+        tm.assert_index_equal(index_slice, expected, exact="equiv")
+
+        index_slice = index[10::-1]
+        expected = Index(index.values[::-1], name="foo")
+        tm.assert_index_equal(index_slice, expected, exact="equiv")
+
+    @pytest.mark.parametrize("step", set(range(-5, 6)) - {0})
+    def test_len_specialised(self, step):
+        # make sure that our len is the same as np.arange calc
+        start, stop = (0, 5) if step > 0 else (5, 0)
+
+        arr = np.arange(start, stop, step)
+        index = RangeIndex(start, stop, step)
+        assert len(index) == len(arr)
+
+        index = RangeIndex(stop, start, step)
+        assert len(index) == 0
+
+    @pytest.mark.parametrize(
+        "indices, expected",
+        [
+            ([RangeIndex(1, 12, 5)], RangeIndex(1, 12, 5)),
+            ([RangeIndex(0, 6, 4)], RangeIndex(0, 6, 4)),
+            ([RangeIndex(1, 3), RangeIndex(3, 7)], RangeIndex(1, 7)),
+            ([RangeIndex(1, 5, 2), RangeIndex(5, 6)], RangeIndex(1, 6, 2)),
+            ([RangeIndex(1, 3, 2), RangeIndex(4, 7, 3)], RangeIndex(1, 7, 3)),
+            ([RangeIndex(-4, 3, 2), RangeIndex(4, 7, 2)], RangeIndex(-4, 7, 2)),
+            ([RangeIndex(-4, -8), RangeIndex(-8, -12)], RangeIndex(0, 0)),
+            ([RangeIndex(-4, -8), RangeIndex(3, -4)], RangeIndex(0, 0)),
+            ([RangeIndex(-4, -8), RangeIndex(3, 5)], RangeIndex(3, 5)),
+            ([RangeIndex(-4, -2), RangeIndex(3, 5)], Index([-4, -3, 3, 4])),
+            ([RangeIndex(-2), RangeIndex(3, 5)], RangeIndex(3, 5)),
+            ([RangeIndex(2), RangeIndex(2)], Index([0, 1, 0, 1])),
+            ([RangeIndex(2), RangeIndex(2, 5), RangeIndex(5, 8, 4)], RangeIndex(0, 6)),
+            (
+                [RangeIndex(2), RangeIndex(3, 5), RangeIndex(5, 8, 4)],
+                Index([0, 1, 3, 4, 5]),
+            ),
+            (
+                [RangeIndex(-2, 2), RangeIndex(2, 5), RangeIndex(5, 8, 4)],
+                RangeIndex(-2, 6),
+            ),
+            ([RangeIndex(3), Index([-1, 3, 15])], Index([0, 1, 2, -1, 3, 15])),
+            ([RangeIndex(3), Index([-1, 3.1, 15.0])], Index([0, 1, 2, -1, 3.1, 15.0])),
+            ([RangeIndex(3), Index(["a", None, 14])], Index([0, 1, 2, "a", None, 14])),
+            ([RangeIndex(3, 1), Index(["a", None, 14])], Index(["a", None, 14])),
+        ],
+    )
+    def test_append(self, indices, expected):
+        # GH16212
+        result = indices[0].append(indices[1:])
+        tm.assert_index_equal(result, expected, exact=True)
+
+        if len(indices) == 2:
+            # Append single item rather than list
+            result2 = indices[0].append(indices[1])
+            tm.assert_index_equal(result2, expected, exact=True)
+
+    def test_engineless_lookup(self):
+        # GH 16685
+        # Standard lookup on RangeIndex should not require the engine to be
+        # created
+        idx = RangeIndex(2, 10, 3)
+
+        assert idx.get_loc(5) == 1
+        tm.assert_numpy_array_equal(
+            idx.get_indexer([2, 8]), ensure_platform_int(np.array([0, 2]))
+        )
+        with pytest.raises(KeyError, match="3"):
+            idx.get_loc(3)
+
+        assert "_engine" not in idx._cache
+
+        # Different types of scalars can be excluded immediately, no need to
+        #  use the _engine
+        with pytest.raises(KeyError, match="'a'"):
+            idx.get_loc("a")
+
+        assert "_engine" not in idx._cache
+
+    def test_format_empty(self):
+        # GH35712
+        empty_idx = RangeIndex(0)
+        msg = r"RangeIndex\.format is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert empty_idx.format() == []
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert empty_idx.format(name=True) == [""]
+
+    @pytest.mark.parametrize(
+        "ri",
+        [
+            RangeIndex(0, -1, -1),
+            RangeIndex(0, 1, 1),
+            RangeIndex(1, 3, 2),
+            RangeIndex(0, -1, -2),
+            RangeIndex(-3, -5, -2),
+        ],
+    )
+    def test_append_len_one(self, ri):
+        # GH39401
+        result = ri.append([])
+        tm.assert_index_equal(result, ri, exact=True)
+
+    @pytest.mark.parametrize("base", [RangeIndex(0, 2), Index([0, 1])])
+    def test_isin_range(self, base):
+        # GH#41151
+        values = RangeIndex(0, 1)
+        result = base.isin(values)
+        expected = np.array([True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_sort_values_key(self):
+        # GH#43666, GH#52764
+        sort_order = {8: 2, 6: 0, 4: 8, 2: 10, 0: 12}
+        values = RangeIndex(0, 10, 2)
+        result = values.sort_values(key=lambda x: x.map(sort_order))
+        expected = Index([6, 8, 4, 2, 0], dtype="int64")
+        tm.assert_index_equal(result, expected, check_exact=True)
+
+        # check this matches the Series.sort_values behavior
+        ser = values.to_series()
+        result2 = ser.sort_values(key=lambda x: x.map(sort_order))
+        tm.assert_series_equal(result2, expected.to_series(), check_exact=True)
+
+    def test_range_index_rsub_by_const(self):
+        # GH#53255
+        result = 3 - RangeIndex(0, 4, 1)
+        expected = RangeIndex(3, -1, -1)
+        tm.assert_index_equal(result, expected)