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ckpts/universal/global_step80/zero/19.attention.query_key_value.weight/exp_avg_sq.pt

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ckpts/universal/global_step80/zero/23.mlp.dense_h_to_4h_swiglu.weight/exp_avg_sq.pt

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ckpts/universal/global_step80/zero/23.mlp.dense_h_to_4h_swiglu.weight/fp32.pt

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ckpts/universal/global_step80/zero/4.input_layernorm.weight/exp_avg.pt

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ckpts/universal/global_step80/zero/4.input_layernorm.weight/exp_avg_sq.pt

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ckpts/universal/global_step80/zero/4.input_layernorm.weight/fp32.pt

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venv/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

venv/lib/python3.10/site-packages/pandas/tests/indexes/numeric/test_numeric.py

@@ -0,0 +1,553 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Index,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestFloatNumericIndex:
+    @pytest.fixture(params=[np.float64, np.float32])
+    def dtype(self, request):
+        return request.param
+
+    @pytest.fixture
+    def simple_index(self, dtype):
+        values = np.arange(5, dtype=dtype)
+        return Index(values)
+
+    @pytest.fixture(
+        params=[
+            [1.5, 2, 3, 4, 5],
+            [0.0, 2.5, 5.0, 7.5, 10.0],
+            [5, 4, 3, 2, 1.5],
+            [10.0, 7.5, 5.0, 2.5, 0.0],
+        ],
+        ids=["mixed", "float", "mixed_dec", "float_dec"],
+    )
+    def index(self, request, dtype):
+        return Index(request.param, dtype=dtype)
+
+    @pytest.fixture
+    def mixed_index(self, dtype):
+        return Index([1.5, 2, 3, 4, 5], dtype=dtype)
+
+    @pytest.fixture
+    def float_index(self, dtype):
+        return Index([0.0, 2.5, 5.0, 7.5, 10.0], dtype=dtype)
+
+    def test_repr_roundtrip(self, index):
+        tm.assert_index_equal(eval(repr(index)), index, exact=True)
+
+    def check_coerce(self, a, b, is_float_index=True):
+        assert a.equals(b)
+        tm.assert_index_equal(a, b, exact=False)
+        if is_float_index:
+            assert isinstance(b, Index)
+        else:
+            assert type(b) is Index
+
+    def test_constructor_from_list_no_dtype(self):
+        index = Index([1.5, 2.5, 3.5])
+        assert index.dtype == np.float64
+
+    def test_constructor(self, dtype):
+        index_cls = Index
+
+        # explicit construction
+        index = index_cls([1, 2, 3, 4, 5], dtype=dtype)
+
+        assert isinstance(index, index_cls)
+        assert index.dtype == dtype
+
+        expected = np.array([1, 2, 3, 4, 5], dtype=dtype)
+        tm.assert_numpy_array_equal(index.values, expected)
+
+        index = index_cls(np.array([1, 2, 3, 4, 5]), dtype=dtype)
+        assert isinstance(index, index_cls)
+        assert index.dtype == dtype
+
+        index = index_cls([1.0, 2, 3, 4, 5], dtype=dtype)
+        assert isinstance(index, index_cls)
+        assert index.dtype == dtype
+
+        index = index_cls(np.array([1.0, 2, 3, 4, 5]), dtype=dtype)
+        assert isinstance(index, index_cls)
+        assert index.dtype == dtype
+
+        index = index_cls([1.0, 2, 3, 4, 5], dtype=dtype)
+        assert isinstance(index, index_cls)
+        assert index.dtype == dtype
+
+        index = index_cls(np.array([1.0, 2, 3, 4, 5]), dtype=dtype)
+        assert isinstance(index, index_cls)
+        assert index.dtype == dtype
+
+        # nan handling
+        result = index_cls([np.nan, np.nan], dtype=dtype)
+        assert pd.isna(result.values).all()
+
+        result = index_cls(np.array([np.nan]), dtype=dtype)
+        assert pd.isna(result.values).all()
+
+    def test_constructor_invalid(self):
+        index_cls = Index
+        cls_name = index_cls.__name__
+        # invalid
+        msg = (
+            rf"{cls_name}\(\.\.\.\) must be called with a collection of "
+            r"some kind, 0\.0 was passed"
+        )
+        with pytest.raises(TypeError, match=msg):
+            index_cls(0.0)
+
+    def test_constructor_coerce(self, mixed_index, float_index):
+        self.check_coerce(mixed_index, Index([1.5, 2, 3, 4, 5]))
+        self.check_coerce(float_index, Index(np.arange(5) * 2.5))
+
+        result = Index(np.array(np.arange(5) * 2.5, dtype=object))
+        assert result.dtype == object  # as of 2.0 to match Series
+        self.check_coerce(float_index, result.astype("float64"))
+
+    def test_constructor_explicit(self, mixed_index, float_index):
+        # these don't auto convert
+        self.check_coerce(
+            float_index, Index((np.arange(5) * 2.5), dtype=object), is_float_index=False
+        )
+        self.check_coerce(
+            mixed_index, Index([1.5, 2, 3, 4, 5], dtype=object), is_float_index=False
+        )
+
+    def test_type_coercion_fail(self, any_int_numpy_dtype):
+        # see gh-15832
+        msg = "Trying to coerce float values to integers"
+        with pytest.raises(ValueError, match=msg):
+            Index([1, 2, 3.5], dtype=any_int_numpy_dtype)
+
+    def test_equals_numeric(self):
+        index_cls = Index
+
+        idx = index_cls([1.0, 2.0])
+        assert idx.equals(idx)
+        assert idx.identical(idx)
+
+        idx2 = index_cls([1.0, 2.0])
+        assert idx.equals(idx2)
+
+        idx = index_cls([1.0, np.nan])
+        assert idx.equals(idx)
+        assert idx.identical(idx)
+
+        idx2 = index_cls([1.0, np.nan])
+        assert idx.equals(idx2)
+
+    @pytest.mark.parametrize(
+        "other",
+        (
+            Index([1, 2], dtype=np.int64),
+            Index([1.0, 2.0], dtype=object),
+            Index([1, 2], dtype=object),
+        ),
+    )
+    def test_equals_numeric_other_index_type(self, other):
+        idx = Index([1.0, 2.0])
+        assert idx.equals(other)
+        assert other.equals(idx)
+
+    @pytest.mark.parametrize(
+        "vals",
+        [
+            pd.date_range("2016-01-01", periods=3),
+            pd.timedelta_range("1 Day", periods=3),
+        ],
+    )
+    def test_lookups_datetimelike_values(self, vals, dtype):
+        # If we have datetime64 or timedelta64 values, make sure they are
+        #  wrapped correctly  GH#31163
+        ser = Series(vals, index=range(3, 6))
+        ser.index = ser.index.astype(dtype)
+
+        expected = vals[1]
+
+        result = ser[4.0]
+        assert isinstance(result, type(expected)) and result == expected
+        result = ser[4]
+        assert isinstance(result, type(expected)) and result == expected
+
+        result = ser.loc[4.0]
+        assert isinstance(result, type(expected)) and result == expected
+        result = ser.loc[4]
+        assert isinstance(result, type(expected)) and result == expected
+
+        result = ser.at[4.0]
+        assert isinstance(result, type(expected)) and result == expected
+        # GH#31329 .at[4] should cast to 4.0, matching .loc behavior
+        result = ser.at[4]
+        assert isinstance(result, type(expected)) and result == expected
+
+        result = ser.iloc[1]
+        assert isinstance(result, type(expected)) and result == expected
+
+        result = ser.iat[1]
+        assert isinstance(result, type(expected)) and result == expected
+
+    def test_doesnt_contain_all_the_things(self):
+        idx = Index([np.nan])
+        assert not idx.isin([0]).item()
+        assert not idx.isin([1]).item()
+        assert idx.isin([np.nan]).item()
+
+    def test_nan_multiple_containment(self):
+        index_cls = Index
+
+        idx = index_cls([1.0, np.nan])
+        tm.assert_numpy_array_equal(idx.isin([1.0]), np.array([True, False]))
+        tm.assert_numpy_array_equal(idx.isin([2.0, np.pi]), np.array([False, False]))
+        tm.assert_numpy_array_equal(idx.isin([np.nan]), np.array([False, True]))
+        tm.assert_numpy_array_equal(idx.isin([1.0, np.nan]), np.array([True, True]))
+        idx = index_cls([1.0, 2.0])
+        tm.assert_numpy_array_equal(idx.isin([np.nan]), np.array([False, False]))
+
+    def test_fillna_float64(self):
+        index_cls = Index
+        # GH 11343
+        idx = Index([1.0, np.nan, 3.0], dtype=float, name="x")
+        # can't downcast
+        exp = Index([1.0, 0.1, 3.0], name="x")
+        tm.assert_index_equal(idx.fillna(0.1), exp, exact=True)
+
+        # downcast
+        exp = index_cls([1.0, 2.0, 3.0], name="x")
+        tm.assert_index_equal(idx.fillna(2), exp)
+
+        # object
+        exp = Index([1.0, "obj", 3.0], name="x")
+        tm.assert_index_equal(idx.fillna("obj"), exp, exact=True)
+
+    def test_logical_compat(self, simple_index):
+        idx = simple_index
+        assert idx.all() == idx.values.all()
+        assert idx.any() == idx.values.any()
+
+        assert idx.all() == idx.to_series().all()
+        assert idx.any() == idx.to_series().any()
+
+
+class TestNumericInt:
+    @pytest.fixture(params=[np.int64, np.int32, np.int16, np.int8, np.uint64])
+    def dtype(self, request):
+        return request.param
+
+    @pytest.fixture
+    def simple_index(self, dtype):
+        return Index(range(0, 20, 2), dtype=dtype)
+
+    def test_is_monotonic(self):
+        index_cls = Index
+
+        index = index_cls([1, 2, 3, 4])
+        assert index.is_monotonic_increasing is True
+        assert index.is_monotonic_increasing is True
+        assert index._is_strictly_monotonic_increasing is True
+        assert index.is_monotonic_decreasing is False
+        assert index._is_strictly_monotonic_decreasing is False
+
+        index = index_cls([4, 3, 2, 1])
+        assert index.is_monotonic_increasing is False
+        assert index._is_strictly_monotonic_increasing is False
+        assert index._is_strictly_monotonic_decreasing is True
+
+        index = index_cls([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
+
+    def test_is_strictly_monotonic(self):
+        index_cls = Index
+
+        index = index_cls([1, 1, 2, 3])
+        assert index.is_monotonic_increasing is True
+        assert index._is_strictly_monotonic_increasing is False
+
+        index = index_cls([3, 2, 1, 1])
+        assert index.is_monotonic_decreasing is True
+        assert index._is_strictly_monotonic_decreasing is False
+
+        index = index_cls([1, 1])
+        assert index.is_monotonic_increasing
+        assert index.is_monotonic_decreasing
+        assert not index._is_strictly_monotonic_increasing
+        assert not index._is_strictly_monotonic_decreasing
+
+    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, dtype):
+        index = simple_index
+
+        idx = Index(index.copy())
+        assert idx.identical(index)
+
+        same_values_different_type = Index(idx, dtype=object)
+        assert not idx.identical(same_values_different_type)
+
+        idx = index.astype(dtype=object)
+        idx = idx.rename("foo")
+        same_values = Index(idx, dtype=object)
+        assert same_values.identical(idx)
+
+        assert not idx.identical(index)
+        assert Index(same_values, name="foo", dtype=object).identical(idx)
+
+        assert not index.astype(dtype=object).identical(index.astype(dtype=dtype))
+
+    def test_cant_or_shouldnt_cast(self, dtype):
+        msg = r"invalid literal for int\(\) with base 10: 'foo'"
+
+        # can't
+        data = ["foo", "bar", "baz"]
+        with pytest.raises(ValueError, match=msg):
+            Index(data, dtype=dtype)
+
+    def test_view_index(self, simple_index):
+        index = simple_index
+        msg = "Passing a type in .*Index.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_
+
+
+class TestIntNumericIndex:
+    @pytest.fixture(params=[np.int64, np.int32, np.int16, np.int8])
+    def dtype(self, request):
+        return request.param
+
+    def test_constructor_from_list_no_dtype(self):
+        index = Index([1, 2, 3])
+        assert index.dtype == np.int64
+
+    def test_constructor(self, dtype):
+        index_cls = Index
+
+        # scalar raise Exception
+        msg = (
+            rf"{index_cls.__name__}\(\.\.\.\) must be called with a collection of some "
+            "kind, 5 was passed"
+        )
+        with pytest.raises(TypeError, match=msg):
+            index_cls(5)
+
+        # copy
+        # pass list, coerce fine
+        index = index_cls([-5, 0, 1, 2], dtype=dtype)
+        arr = index.values.copy()
+        new_index = index_cls(arr, copy=True)
+        tm.assert_index_equal(new_index, index, exact=True)
+        val = int(arr[0]) + 3000
+
+        # this should not change index
+        if dtype != np.int8:
+            # NEP 50 won't allow assignment that would overflow
+            arr[0] = val
+            assert new_index[0] != val
+
+        if dtype == np.int64:
+            # pass list, coerce fine
+            index = index_cls([-5, 0, 1, 2], dtype=dtype)
+            expected = Index([-5, 0, 1, 2], dtype=dtype)
+            tm.assert_index_equal(index, expected)
+
+            # from iterable
+            index = index_cls(iter([-5, 0, 1, 2]), dtype=dtype)
+            expected = index_cls([-5, 0, 1, 2], dtype=dtype)
+            tm.assert_index_equal(index, expected, exact=True)
+
+            # interpret list-like
+            expected = index_cls([5, 0], dtype=dtype)
+            for cls in [Index, index_cls]:
+                for idx in [
+                    cls([5, 0], dtype=dtype),
+                    cls(np.array([5, 0]), dtype=dtype),
+                    cls(Series([5, 0]), dtype=dtype),
+                ]:
+                    tm.assert_index_equal(idx, expected)
+
+    def test_constructor_corner(self, dtype):
+        index_cls = Index
+
+        arr = np.array([1, 2, 3, 4], dtype=object)
+
+        index = index_cls(arr, dtype=dtype)
+        assert index.values.dtype == index.dtype
+        if dtype == np.int64:
+            without_dtype = Index(arr)
+            # as of 2.0 we do not infer a dtype when we get an object-dtype
+            #  ndarray of numbers, matching Series behavior
+            assert without_dtype.dtype == object
+
+            tm.assert_index_equal(index, without_dtype.astype(np.int64))
+
+        # preventing casting
+        arr = np.array([1, "2", 3, "4"], dtype=object)
+        msg = "Trying to coerce float values to integers"
+        with pytest.raises(ValueError, match=msg):
+            index_cls(arr, dtype=dtype)
+
+    def test_constructor_coercion_signed_to_unsigned(
+        self,
+        any_unsigned_int_numpy_dtype,
+    ):
+        # see gh-15832
+        msg = "|".join(
+            [
+                "Trying to coerce negative values to unsigned integers",
+                "The elements provided in the data cannot all be casted",
+            ]
+        )
+        with pytest.raises(OverflowError, match=msg):
+            Index([-1], dtype=any_unsigned_int_numpy_dtype)
+
+    def test_constructor_np_signed(self, any_signed_int_numpy_dtype):
+        # GH#47475
+        scalar = np.dtype(any_signed_int_numpy_dtype).type(1)
+        result = Index([scalar])
+        expected = Index([1], dtype=any_signed_int_numpy_dtype)
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_constructor_np_unsigned(self, any_unsigned_int_numpy_dtype):
+        # GH#47475
+        scalar = np.dtype(any_unsigned_int_numpy_dtype).type(1)
+        result = Index([scalar])
+        expected = Index([1], dtype=any_unsigned_int_numpy_dtype)
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_coerce_list(self):
+        # coerce things
+        arr = Index([1, 2, 3, 4])
+        assert isinstance(arr, Index)
+
+        # but not if explicit dtype passed
+        arr = Index([1, 2, 3, 4], dtype=object)
+        assert type(arr) is Index
+
+
+class TestFloat16Index:
+    # float 16 indexes not supported
+    # GH 49535
+    def test_constructor(self):
+        index_cls = Index
+        dtype = np.float16
+
+        msg = "float16 indexes are not supported"
+
+        # explicit construction
+        with pytest.raises(NotImplementedError, match=msg):
+            index_cls([1, 2, 3, 4, 5], dtype=dtype)
+
+        with pytest.raises(NotImplementedError, match=msg):
+            index_cls(np.array([1, 2, 3, 4, 5]), dtype=dtype)
+
+        with pytest.raises(NotImplementedError, match=msg):
+            index_cls([1.0, 2, 3, 4, 5], dtype=dtype)
+
+        with pytest.raises(NotImplementedError, match=msg):
+            index_cls(np.array([1.0, 2, 3, 4, 5]), dtype=dtype)
+
+        with pytest.raises(NotImplementedError, match=msg):
+            index_cls([1.0, 2, 3, 4, 5], dtype=dtype)
+
+        with pytest.raises(NotImplementedError, match=msg):
+            index_cls(np.array([1.0, 2, 3, 4, 5]), dtype=dtype)
+
+        # nan handling
+        with pytest.raises(NotImplementedError, match=msg):
+            index_cls([np.nan, np.nan], dtype=dtype)
+
+        with pytest.raises(NotImplementedError, match=msg):
+            index_cls(np.array([np.nan]), dtype=dtype)
+
+
+@pytest.mark.parametrize(
+    "box",
+    [list, lambda x: np.array(x, dtype=object), lambda x: Index(x, dtype=object)],
+)
+def test_uint_index_does_not_convert_to_float64(box):
+    # https://github.com/pandas-dev/pandas/issues/28279
+    # https://github.com/pandas-dev/pandas/issues/28023
+    series = Series(
+        [0, 1, 2, 3, 4, 5],
+        index=[
+            7606741985629028552,
+            17876870360202815256,
+            17876870360202815256,
+            13106359306506049338,
+            8991270399732411471,
+            8991270399732411472,
+        ],
+    )
+
+    result = series.loc[box([7606741985629028552, 17876870360202815256])]
+
+    expected = Index(
+        [7606741985629028552, 17876870360202815256, 17876870360202815256],
+        dtype="uint64",
+    )
+    tm.assert_index_equal(result.index, expected)
+
+    tm.assert_equal(result, series.iloc[:3])
+
+
+def test_float64_index_equals():
+    # https://github.com/pandas-dev/pandas/issues/35217
+    float_index = Index([1.0, 2, 3])
+    string_index = Index(["1", "2", "3"])
+
+    result = float_index.equals(string_index)
+    assert result is False
+
+    result = string_index.equals(float_index)
+    assert result is False
+
+
+def test_map_dtype_inference_unsigned_to_signed():
+    # GH#44609 cases where we don't retain dtype
+    idx = Index([1, 2, 3], dtype=np.uint64)
+    result = idx.map(lambda x: -x)
+    expected = Index([-1, -2, -3], dtype=np.int64)
+    tm.assert_index_equal(result, expected)
+
+
+def test_map_dtype_inference_overflows():
+    # GH#44609 case where we have to upcast
+    idx = Index(np.array([1, 2, 3], dtype=np.int8))
+    result = idx.map(lambda x: x * 1000)
+    # TODO: we could plausibly try to infer down to int16 here
+    expected = Index([1000, 2000, 3000], dtype=np.int64)
+    tm.assert_index_equal(result, expected)
+
+
+def test_view_to_datetimelike():
+    # GH#55710
+    idx = Index([1, 2, 3])
+    res = idx.view("m8[s]")
+    expected = pd.TimedeltaIndex(idx.values.view("m8[s]"))
+    tm.assert_index_equal(res, expected)
+
+    res2 = idx.view("m8[D]")
+    expected2 = idx.values.view("m8[D]")
+    tm.assert_numpy_array_equal(res2, expected2)
+
+    res3 = idx.view("M8[h]")
+    expected3 = idx.values.view("M8[h]")
+    tm.assert_numpy_array_equal(res3, expected3)

venv/lib/python3.10/site-packages/pandas/tests/indexes/test_any_index.py

@@ -0,0 +1,172 @@
+"""
+Tests that can be parametrized over _any_ Index object.
+"""
+import re
+
+import numpy as np
+import pytest
+
+from pandas.errors import InvalidIndexError
+
+import pandas._testing as tm
+
+
+def test_boolean_context_compat(index):
+    # GH#7897
+    with pytest.raises(ValueError, match="The truth value of a"):
+        if index:
+            pass
+
+    with pytest.raises(ValueError, match="The truth value of a"):
+        bool(index)
+
+
+def test_sort(index):
+    msg = "cannot sort an Index object in-place, use sort_values instead"
+    with pytest.raises(TypeError, match=msg):
+        index.sort()
+
+
+def test_hash_error(index):
+    with pytest.raises(TypeError, match=f"unhashable type: '{type(index).__name__}'"):
+        hash(index)
+
+
+def test_mutability(index):
+    if not len(index):
+        pytest.skip("Test doesn't make sense for empty index")
+    msg = "Index does not support mutable operations"
+    with pytest.raises(TypeError, match=msg):
+        index[0] = index[0]
+
+
+@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+def test_map_identity_mapping(index, request):
+    # GH#12766
+
+    result = index.map(lambda x: x)
+    if index.dtype == object and result.dtype == bool:
+        assert (index == result).all()
+        # TODO: could work that into the 'exact="equiv"'?
+        return  # FIXME: doesn't belong in this file anymore!
+    tm.assert_index_equal(result, index, exact="equiv")
+
+
+def test_wrong_number_names(index):
+    names = index.nlevels * ["apple", "banana", "carrot"]
+    with pytest.raises(ValueError, match="^Length"):
+        index.names = names
+
+
+def test_view_preserves_name(index):
+    assert index.view().name == index.name
+
+
+def test_ravel(index):
+    # GH#19956 ravel returning ndarray is deprecated, in 2.0 returns a view on self
+    res = index.ravel()
+    tm.assert_index_equal(res, index)
+
+
+class TestConversion:
+    def test_to_series(self, index):
+        # assert that we are creating a copy of the index
+
+        ser = index.to_series()
+        assert ser.values is not index.values
+        assert ser.index is not index
+        assert ser.name == index.name
+
+    def test_to_series_with_arguments(self, index):
+        # GH#18699
+
+        # index kwarg
+        ser = index.to_series(index=index)
+
+        assert ser.values is not index.values
+        assert ser.index is index
+        assert ser.name == index.name
+
+        # name kwarg
+        ser = index.to_series(name="__test")
+
+        assert ser.values is not index.values
+        assert ser.index is not index
+        assert ser.name != index.name
+
+    def test_tolist_matches_list(self, index):
+        assert index.tolist() == list(index)
+
+
+class TestRoundTrips:
+    def test_pickle_roundtrip(self, index):
+        result = tm.round_trip_pickle(index)
+        tm.assert_index_equal(result, index, exact=True)
+        if result.nlevels > 1:
+            # GH#8367 round-trip with timezone
+            assert index.equal_levels(result)
+
+    def test_pickle_preserves_name(self, index):
+        original_name, index.name = index.name, "foo"
+        unpickled = tm.round_trip_pickle(index)
+        assert index.equals(unpickled)
+        index.name = original_name
+
+
+class TestIndexing:
+    def test_get_loc_listlike_raises_invalid_index_error(self, index):
+        # and never TypeError
+        key = np.array([0, 1], dtype=np.intp)
+
+        with pytest.raises(InvalidIndexError, match=r"\[0 1\]"):
+            index.get_loc(key)
+
+        with pytest.raises(InvalidIndexError, match=r"\[False  True\]"):
+            index.get_loc(key.astype(bool))
+
+    def test_getitem_ellipsis(self, index):
+        # GH#21282
+        result = index[...]
+        assert result.equals(index)
+        assert result is not index
+
+    def test_slice_keeps_name(self, index):
+        assert index.name == index[1:].name
+
+    @pytest.mark.parametrize("item", [101, "no_int", 2.5])
+    def test_getitem_error(self, index, item):
+        msg = "|".join(
+            [
+                r"index 101 is out of bounds for axis 0 with size [\d]+",
+                re.escape(
+                    "only integers, slices (`:`), ellipsis (`...`), "
+                    "numpy.newaxis (`None`) and integer or boolean arrays "
+                    "are valid indices"
+                ),
+                "index out of bounds",  # string[pyarrow]
+            ]
+        )
+        with pytest.raises(IndexError, match=msg):
+            index[item]
+
+
+class TestRendering:
+    def test_str(self, index):
+        # test the string repr
+        index.name = "foo"
+        assert "'foo'" in str(index)
+        assert type(index).__name__ in str(index)
+
+
+class TestReductions:
+    def test_argmax_axis_invalid(self, index):
+        # GH#23081
+        msg = r"`axis` must be fewer than the number of dimensions \(1\)"
+        with pytest.raises(ValueError, match=msg):
+            index.argmax(axis=1)
+        with pytest.raises(ValueError, match=msg):
+            index.argmin(axis=2)
+        with pytest.raises(ValueError, match=msg):
+            index.min(axis=-2)
+        with pytest.raises(ValueError, match=msg):
+            index.max(axis=-3)

venv/lib/python3.10/site-packages/pandas/tests/indexes/test_base.py

@@ -0,0 +1,1737 @@
+from collections import defaultdict
+from datetime import datetime
+from functools import partial
+import math
+import operator
+import re
+
+import numpy as np
+import pytest
+
+from pandas.compat import IS64
+from pandas.errors import InvalidIndexError
+import pandas.util._test_decorators as td
+
+from pandas.core.dtypes.common import (
+    is_any_real_numeric_dtype,
+    is_numeric_dtype,
+    is_object_dtype,
+)
+
+import pandas as pd
+from pandas import (
+    CategoricalIndex,
+    DataFrame,
+    DatetimeIndex,
+    IntervalIndex,
+    PeriodIndex,
+    RangeIndex,
+    Series,
+    TimedeltaIndex,
+    date_range,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core.indexes.api import (
+    Index,
+    MultiIndex,
+    _get_combined_index,
+    ensure_index,
+    ensure_index_from_sequences,
+)
+
+
+class TestIndex:
+    @pytest.fixture
+    def simple_index(self) -> Index:
+        return Index(list("abcde"))
+
+    def test_can_hold_identifiers(self, simple_index):
+        index = simple_index
+        key = index[0]
+        assert index._can_hold_identifiers_and_holds_name(key) is True
+
+    @pytest.mark.parametrize("index", ["datetime"], indirect=True)
+    def test_new_axis(self, index):
+        # TODO: a bunch of scattered tests check this deprecation is enforced.
+        #  de-duplicate/centralize them.
+        with pytest.raises(ValueError, match="Multi-dimensional indexing"):
+            # GH#30588 multi-dimensional indexing deprecated
+            index[None, :]
+
+    def test_constructor_regular(self, index):
+        tm.assert_contains_all(index, index)
+
+    @pytest.mark.parametrize("index", ["string"], indirect=True)
+    def test_constructor_casting(self, index):
+        # casting
+        arr = np.array(index)
+        new_index = Index(arr)
+        tm.assert_contains_all(arr, new_index)
+        tm.assert_index_equal(index, new_index)
+
+    @pytest.mark.parametrize("index", ["string"], indirect=True)
+    def test_constructor_copy(self, index, using_infer_string):
+        arr = np.array(index)
+        new_index = Index(arr, copy=True, name="name")
+        assert isinstance(new_index, Index)
+        assert new_index.name == "name"
+        if using_infer_string:
+            tm.assert_extension_array_equal(
+                new_index.values, pd.array(arr, dtype="string[pyarrow_numpy]")
+            )
+        else:
+            tm.assert_numpy_array_equal(arr, new_index.values)
+        arr[0] = "SOMEBIGLONGSTRING"
+        assert new_index[0] != "SOMEBIGLONGSTRING"
+
+    @pytest.mark.parametrize("cast_as_obj", [True, False])
+    @pytest.mark.parametrize(
+        "index",
+        [
+            date_range(
+                "2015-01-01 10:00",
+                freq="D",
+                periods=3,
+                tz="US/Eastern",
+                name="Green Eggs & Ham",
+            ),  # DTI with tz
+            date_range("2015-01-01 10:00", freq="D", periods=3),  # DTI no tz
+            timedelta_range("1 days", freq="D", periods=3),  # td
+            period_range("2015-01-01", freq="D", periods=3),  # period
+        ],
+    )
+    def test_constructor_from_index_dtlike(self, cast_as_obj, index):
+        if cast_as_obj:
+            with tm.assert_produces_warning(FutureWarning, match="Dtype inference"):
+                result = Index(index.astype(object))
+        else:
+            result = Index(index)
+
+        tm.assert_index_equal(result, index)
+
+        if isinstance(index, DatetimeIndex):
+            assert result.tz == index.tz
+            if cast_as_obj:
+                # GH#23524 check that Index(dti, dtype=object) does not
+                #  incorrectly raise ValueError, and that nanoseconds are not
+                #  dropped
+                index += pd.Timedelta(nanoseconds=50)
+                result = Index(index, dtype=object)
+                assert result.dtype == np.object_
+                assert list(result) == list(index)
+
+    @pytest.mark.parametrize(
+        "index,has_tz",
+        [
+            (
+                date_range("2015-01-01 10:00", freq="D", periods=3, tz="US/Eastern"),
+                True,
+            ),  # datetimetz
+            (timedelta_range("1 days", freq="D", periods=3), False),  # td
+            (period_range("2015-01-01", freq="D", periods=3), False),  # period
+        ],
+    )
+    def test_constructor_from_series_dtlike(self, index, has_tz):
+        result = Index(Series(index))
+        tm.assert_index_equal(result, index)
+
+        if has_tz:
+            assert result.tz == index.tz
+
+    def test_constructor_from_series_freq(self):
+        # GH 6273
+        # create from a series, passing a freq
+        dts = ["1-1-1990", "2-1-1990", "3-1-1990", "4-1-1990", "5-1-1990"]
+        expected = DatetimeIndex(dts, freq="MS")
+
+        s = Series(pd.to_datetime(dts))
+        result = DatetimeIndex(s, freq="MS")
+
+        tm.assert_index_equal(result, expected)
+
+    def test_constructor_from_frame_series_freq(self, using_infer_string):
+        # GH 6273
+        # create from a series, passing a freq
+        dts = ["1-1-1990", "2-1-1990", "3-1-1990", "4-1-1990", "5-1-1990"]
+        expected = DatetimeIndex(dts, freq="MS")
+
+        df = DataFrame(np.random.default_rng(2).random((5, 3)))
+        df["date"] = dts
+        result = DatetimeIndex(df["date"], freq="MS")
+        dtype = object if not using_infer_string else "string"
+        assert df["date"].dtype == dtype
+        expected.name = "date"
+        tm.assert_index_equal(result, expected)
+
+        expected = Series(dts, name="date")
+        tm.assert_series_equal(df["date"], expected)
+
+        # GH 6274
+        # infer freq of same
+        if not using_infer_string:
+            # Doesn't work with arrow strings
+            freq = pd.infer_freq(df["date"])
+            assert freq == "MS"
+
+    def test_constructor_int_dtype_nan(self):
+        # see gh-15187
+        data = [np.nan]
+        expected = Index(data, dtype=np.float64)
+        result = Index(data, dtype="float")
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "klass,dtype,na_val",
+        [
+            (Index, np.float64, np.nan),
+            (DatetimeIndex, "datetime64[ns]", pd.NaT),
+        ],
+    )
+    def test_index_ctor_infer_nan_nat(self, klass, dtype, na_val):
+        # GH 13467
+        na_list = [na_val, na_val]
+        expected = klass(na_list)
+        assert expected.dtype == dtype
+
+        result = Index(na_list)
+        tm.assert_index_equal(result, expected)
+
+        result = Index(np.array(na_list))
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "vals,dtype",
+        [
+            ([1, 2, 3, 4, 5], "int"),
+            ([1.1, np.nan, 2.2, 3.0], "float"),
+            (["A", "B", "C", np.nan], "obj"),
+        ],
+    )
+    def test_constructor_simple_new(self, vals, dtype):
+        index = Index(vals, name=dtype)
+        result = index._simple_new(index.values, dtype)
+        tm.assert_index_equal(result, index)
+
+    @pytest.mark.parametrize("attr", ["values", "asi8"])
+    @pytest.mark.parametrize("klass", [Index, DatetimeIndex])
+    def test_constructor_dtypes_datetime(self, tz_naive_fixture, attr, klass):
+        # Test constructing with a datetimetz dtype
+        # .values produces numpy datetimes, so these are considered naive
+        # .asi8 produces integers, so these are considered epoch timestamps
+        # ^the above will be true in a later version. Right now we `.view`
+        # the i8 values as NS_DTYPE, effectively treating them as wall times.
+        index = date_range("2011-01-01", periods=5)
+        arg = getattr(index, attr)
+        index = index.tz_localize(tz_naive_fixture)
+        dtype = index.dtype
+
+        # As of 2.0 astype raises on dt64.astype(dt64tz)
+        err = tz_naive_fixture is not None
+        msg = "Cannot use .astype to convert from timezone-naive dtype to"
+
+        if attr == "asi8":
+            result = DatetimeIndex(arg).tz_localize(tz_naive_fixture)
+            tm.assert_index_equal(result, index)
+        elif klass is Index:
+            with pytest.raises(TypeError, match="unexpected keyword"):
+                klass(arg, tz=tz_naive_fixture)
+        else:
+            result = klass(arg, tz=tz_naive_fixture)
+            tm.assert_index_equal(result, index)
+
+        if attr == "asi8":
+            if err:
+                with pytest.raises(TypeError, match=msg):
+                    DatetimeIndex(arg).astype(dtype)
+            else:
+                result = DatetimeIndex(arg).astype(dtype)
+                tm.assert_index_equal(result, index)
+        else:
+            result = klass(arg, dtype=dtype)
+            tm.assert_index_equal(result, index)
+
+        if attr == "asi8":
+            result = DatetimeIndex(list(arg)).tz_localize(tz_naive_fixture)
+            tm.assert_index_equal(result, index)
+        elif klass is Index:
+            with pytest.raises(TypeError, match="unexpected keyword"):
+                klass(arg, tz=tz_naive_fixture)
+        else:
+            result = klass(list(arg), tz=tz_naive_fixture)
+            tm.assert_index_equal(result, index)
+
+        if attr == "asi8":
+            if err:
+                with pytest.raises(TypeError, match=msg):
+                    DatetimeIndex(list(arg)).astype(dtype)
+            else:
+                result = DatetimeIndex(list(arg)).astype(dtype)
+                tm.assert_index_equal(result, index)
+        else:
+            result = klass(list(arg), dtype=dtype)
+            tm.assert_index_equal(result, index)
+
+    @pytest.mark.parametrize("attr", ["values", "asi8"])
+    @pytest.mark.parametrize("klass", [Index, TimedeltaIndex])
+    def test_constructor_dtypes_timedelta(self, attr, klass):
+        index = timedelta_range("1 days", periods=5)
+        index = index._with_freq(None)  # won't be preserved by constructors
+        dtype = index.dtype
+
+        values = getattr(index, attr)
+
+        result = klass(values, dtype=dtype)
+        tm.assert_index_equal(result, index)
+
+        result = klass(list(values), dtype=dtype)
+        tm.assert_index_equal(result, index)
+
+    @pytest.mark.parametrize("value", [[], iter([]), (_ for _ in [])])
+    @pytest.mark.parametrize(
+        "klass",
+        [
+            Index,
+            CategoricalIndex,
+            DatetimeIndex,
+            TimedeltaIndex,
+        ],
+    )
+    def test_constructor_empty(self, value, klass):
+        empty = klass(value)
+        assert isinstance(empty, klass)
+        assert not len(empty)
+
+    @pytest.mark.parametrize(
+        "empty,klass",
+        [
+            (PeriodIndex([], freq="D"), PeriodIndex),
+            (PeriodIndex(iter([]), freq="D"), PeriodIndex),
+            (PeriodIndex((_ for _ in []), freq="D"), PeriodIndex),
+            (RangeIndex(step=1), RangeIndex),
+            (MultiIndex(levels=[[1, 2], ["blue", "red"]], codes=[[], []]), MultiIndex),
+        ],
+    )
+    def test_constructor_empty_special(self, empty, klass):
+        assert isinstance(empty, klass)
+        assert not len(empty)
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            "datetime",
+            "float64",
+            "float32",
+            "int64",
+            "int32",
+            "period",
+            "range",
+            "repeats",
+            "timedelta",
+            "tuples",
+            "uint64",
+            "uint32",
+        ],
+        indirect=True,
+    )
+    def test_view_with_args(self, index):
+        index.view("i8")
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            "string",
+            pytest.param("categorical", marks=pytest.mark.xfail(reason="gh-25464")),
+            "bool-object",
+            "bool-dtype",
+            "empty",
+        ],
+        indirect=True,
+    )
+    def test_view_with_args_object_array_raises(self, index):
+        if index.dtype == bool:
+            msg = "When changing to a larger dtype"
+            with pytest.raises(ValueError, match=msg):
+                index.view("i8")
+        elif index.dtype == "string":
+            with pytest.raises(NotImplementedError, match="i8"):
+                index.view("i8")
+        else:
+            msg = (
+                "Cannot change data-type for array of references|"
+                "Cannot change data-type for object array|"
+            )
+            with pytest.raises(TypeError, match=msg):
+                index.view("i8")
+
+    @pytest.mark.parametrize(
+        "index",
+        ["int64", "int32", "range"],
+        indirect=True,
+    )
+    def test_astype(self, index):
+        casted = index.astype("i8")
+
+        # it works!
+        casted.get_loc(5)
+
+        # pass on name
+        index.name = "foobar"
+        casted = index.astype("i8")
+        assert casted.name == "foobar"
+
+    def test_equals_object(self):
+        # same
+        assert Index(["a", "b", "c"]).equals(Index(["a", "b", "c"]))
+
+    @pytest.mark.parametrize(
+        "comp", [Index(["a", "b"]), Index(["a", "b", "d"]), ["a", "b", "c"]]
+    )
+    def test_not_equals_object(self, comp):
+        assert not Index(["a", "b", "c"]).equals(comp)
+
+    def test_identical(self):
+        # index
+        i1 = Index(["a", "b", "c"])
+        i2 = Index(["a", "b", "c"])
+
+        assert i1.identical(i2)
+
+        i1 = i1.rename("foo")
+        assert i1.equals(i2)
+        assert not i1.identical(i2)
+
+        i2 = i2.rename("foo")
+        assert i1.identical(i2)
+
+        i3 = Index([("a", "a"), ("a", "b"), ("b", "a")])
+        i4 = Index([("a", "a"), ("a", "b"), ("b", "a")], tupleize_cols=False)
+        assert not i3.identical(i4)
+
+    def test_is_(self):
+        ind = Index(range(10))
+        assert ind.is_(ind)
+        assert ind.is_(ind.view().view().view().view())
+        assert not ind.is_(Index(range(10)))
+        assert not ind.is_(ind.copy())
+        assert not ind.is_(ind.copy(deep=False))
+        assert not ind.is_(ind[:])
+        assert not ind.is_(np.array(range(10)))
+
+        # quasi-implementation dependent
+        assert ind.is_(ind.view())
+        ind2 = ind.view()
+        ind2.name = "bob"
+        assert ind.is_(ind2)
+        assert ind2.is_(ind)
+        # doesn't matter if Indices are *actually* views of underlying data,
+        assert not ind.is_(Index(ind.values))
+        arr = np.array(range(1, 11))
+        ind1 = Index(arr, copy=False)
+        ind2 = Index(arr, copy=False)
+        assert not ind1.is_(ind2)
+
+    def test_asof_numeric_vs_bool_raises(self):
+        left = Index([1, 2, 3])
+        right = Index([True, False], dtype=object)
+
+        msg = "Cannot compare dtypes int64 and bool"
+        with pytest.raises(TypeError, match=msg):
+            left.asof(right[0])
+        # TODO: should right.asof(left[0]) also raise?
+
+        with pytest.raises(InvalidIndexError, match=re.escape(str(right))):
+            left.asof(right)
+
+        with pytest.raises(InvalidIndexError, match=re.escape(str(left))):
+            right.asof(left)
+
+    @pytest.mark.parametrize("index", ["string"], indirect=True)
+    def test_booleanindex(self, index):
+        bool_index = np.ones(len(index), dtype=bool)
+        bool_index[5:30:2] = False
+
+        sub_index = index[bool_index]
+
+        for i, val in enumerate(sub_index):
+            assert sub_index.get_loc(val) == i
+
+        sub_index = index[list(bool_index)]
+        for i, val in enumerate(sub_index):
+            assert sub_index.get_loc(val) == i
+
+    def test_fancy(self, simple_index):
+        index = simple_index
+        sl = index[[1, 2, 3]]
+        for i in sl:
+            assert i == sl[sl.get_loc(i)]
+
+    @pytest.mark.parametrize(
+        "index",
+        ["string", "int64", "int32", "uint64", "uint32", "float64", "float32"],
+        indirect=True,
+    )
+    @pytest.mark.parametrize("dtype", [int, np.bool_])
+    def test_empty_fancy(self, index, dtype, request, using_infer_string):
+        if dtype is np.bool_ and using_infer_string and index.dtype == "string":
+            request.applymarker(pytest.mark.xfail(reason="numpy behavior is buggy"))
+        empty_arr = np.array([], dtype=dtype)
+        empty_index = type(index)([], dtype=index.dtype)
+
+        assert index[[]].identical(empty_index)
+        if dtype == np.bool_:
+            with tm.assert_produces_warning(FutureWarning, match="is deprecated"):
+                assert index[empty_arr].identical(empty_index)
+        else:
+            assert index[empty_arr].identical(empty_index)
+
+    @pytest.mark.parametrize(
+        "index",
+        ["string", "int64", "int32", "uint64", "uint32", "float64", "float32"],
+        indirect=True,
+    )
+    def test_empty_fancy_raises(self, index):
+        # DatetimeIndex is excluded, because it overrides getitem and should
+        # be tested separately.
+        empty_farr = np.array([], dtype=np.float64)
+        empty_index = type(index)([], dtype=index.dtype)
+
+        assert index[[]].identical(empty_index)
+        # np.ndarray only accepts ndarray of int & bool dtypes, so should Index
+        msg = r"arrays used as indices must be of integer"
+        with pytest.raises(IndexError, match=msg):
+            index[empty_farr]
+
+    def test_union_dt_as_obj(self, simple_index):
+        # TODO: Replace with fixturesult
+        index = simple_index
+        date_index = date_range("2019-01-01", periods=10)
+        first_cat = index.union(date_index)
+        second_cat = index.union(index)
+
+        appended = Index(np.append(index, date_index.astype("O")))
+
+        tm.assert_index_equal(first_cat, appended)
+        tm.assert_index_equal(second_cat, index)
+        tm.assert_contains_all(index, first_cat)
+        tm.assert_contains_all(index, second_cat)
+        tm.assert_contains_all(date_index, first_cat)
+
+    def test_map_with_tuples(self):
+        # GH 12766
+
+        # Test that returning a single tuple from an Index
+        #   returns an Index.
+        index = Index(np.arange(3), dtype=np.int64)
+        result = index.map(lambda x: (x,))
+        expected = Index([(i,) for i in index])
+        tm.assert_index_equal(result, expected)
+
+        # Test that returning a tuple from a map of a single index
+        #   returns a MultiIndex object.
+        result = index.map(lambda x: (x, x == 1))
+        expected = MultiIndex.from_tuples([(i, i == 1) for i in index])
+        tm.assert_index_equal(result, expected)
+
+    def test_map_with_tuples_mi(self):
+        # Test that returning a single object from a MultiIndex
+        #   returns an Index.
+        first_level = ["foo", "bar", "baz"]
+        multi_index = MultiIndex.from_tuples(zip(first_level, [1, 2, 3]))
+        reduced_index = multi_index.map(lambda x: x[0])
+        tm.assert_index_equal(reduced_index, Index(first_level))
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            date_range("2020-01-01", freq="D", periods=10),
+            period_range("2020-01-01", freq="D", periods=10),
+            timedelta_range("1 day", periods=10),
+        ],
+    )
+    def test_map_tseries_indices_return_index(self, index):
+        expected = Index([1] * 10)
+        result = index.map(lambda x: 1)
+        tm.assert_index_equal(expected, result)
+
+    def test_map_tseries_indices_accsr_return_index(self):
+        date_index = DatetimeIndex(
+            date_range("2020-01-01", periods=24, freq="h"), name="hourly"
+        )
+        result = date_index.map(lambda x: x.hour)
+        expected = Index(np.arange(24, dtype="int64"), name="hourly")
+        tm.assert_index_equal(result, expected, exact=True)
+
+    @pytest.mark.parametrize(
+        "mapper",
+        [
+            lambda values, index: {i: e for e, i in zip(values, index)},
+            lambda values, index: Series(values, index),
+        ],
+    )
+    def test_map_dictlike_simple(self, mapper):
+        # GH 12756
+        expected = Index(["foo", "bar", "baz"])
+        index = Index(np.arange(3), dtype=np.int64)
+        result = index.map(mapper(expected.values, index))
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "mapper",
+        [
+            lambda values, index: {i: e for e, i in zip(values, index)},
+            lambda values, index: Series(values, index),
+        ],
+    )
+    @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+    def test_map_dictlike(self, index, mapper, request):
+        # GH 12756
+        if isinstance(index, CategoricalIndex):
+            pytest.skip("Tested in test_categorical")
+        elif not index.is_unique:
+            pytest.skip("Cannot map duplicated index")
+
+        rng = np.arange(len(index), 0, -1, dtype=np.int64)
+
+        if index.empty:
+            # to match proper result coercion for uints
+            expected = Index([])
+        elif is_numeric_dtype(index.dtype):
+            expected = index._constructor(rng, dtype=index.dtype)
+        elif type(index) is Index and index.dtype != object:
+            # i.e. EA-backed, for now just Nullable
+            expected = Index(rng, dtype=index.dtype)
+        else:
+            expected = Index(rng)
+
+        result = index.map(mapper(expected, index))
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "mapper",
+        [Series(["foo", 2.0, "baz"], index=[0, 2, -1]), {0: "foo", 2: 2.0, -1: "baz"}],
+    )
+    def test_map_with_non_function_missing_values(self, mapper):
+        # GH 12756
+        expected = Index([2.0, np.nan, "foo"])
+        result = Index([2, 1, 0]).map(mapper)
+
+        tm.assert_index_equal(expected, result)
+
+    def test_map_na_exclusion(self):
+        index = Index([1.5, np.nan, 3, np.nan, 5])
+
+        result = index.map(lambda x: x * 2, na_action="ignore")
+        expected = index * 2
+        tm.assert_index_equal(result, expected)
+
+    def test_map_defaultdict(self):
+        index = Index([1, 2, 3])
+        default_dict = defaultdict(lambda: "blank")
+        default_dict[1] = "stuff"
+        result = index.map(default_dict)
+        expected = Index(["stuff", "blank", "blank"])
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("name,expected", [("foo", "foo"), ("bar", None)])
+    def test_append_empty_preserve_name(self, name, expected):
+        left = Index([], name="foo")
+        right = Index([1, 2, 3], name=name)
+
+        msg = "The behavior of array concatenation with empty entries is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = left.append(right)
+        assert result.name == expected
+
+    @pytest.mark.parametrize(
+        "index, expected",
+        [
+            ("string", False),
+            ("bool-object", False),
+            ("bool-dtype", False),
+            ("categorical", False),
+            ("int64", True),
+            ("int32", True),
+            ("uint64", True),
+            ("uint32", True),
+            ("datetime", False),
+            ("float64", True),
+            ("float32", True),
+        ],
+        indirect=["index"],
+    )
+    def test_is_numeric(self, index, expected):
+        assert is_any_real_numeric_dtype(index) is expected
+
+    @pytest.mark.parametrize(
+        "index, expected",
+        [
+            ("string", True),
+            ("bool-object", True),
+            ("bool-dtype", False),
+            ("categorical", False),
+            ("int64", False),
+            ("int32", False),
+            ("uint64", False),
+            ("uint32", False),
+            ("datetime", False),
+            ("float64", False),
+            ("float32", False),
+        ],
+        indirect=["index"],
+    )
+    def test_is_object(self, index, expected, using_infer_string):
+        if using_infer_string and index.dtype == "string" and expected:
+            expected = False
+        assert is_object_dtype(index) is expected
+
+    def test_summary(self, index):
+        index._summary()
+
+    def test_format_bug(self):
+        # GH 14626
+        # windows has different precision on datetime.datetime.now (it doesn't
+        # include us since the default for Timestamp shows these but Index
+        # formatting does not we are skipping)
+        now = datetime.now()
+        msg = r"Index\.format is deprecated"
+
+        if not str(now).endswith("000"):
+            index = Index([now])
+            with tm.assert_produces_warning(FutureWarning, match=msg):
+                formatted = index.format()
+            expected = [str(index[0])]
+            assert formatted == expected
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            Index([]).format()
+
+    @pytest.mark.parametrize("vals", [[1, 2.0 + 3.0j, 4.0], ["a", "b", "c"]])
+    def test_format_missing(self, vals, nulls_fixture):
+        # 2845
+        vals = list(vals)  # Copy for each iteration
+        vals.append(nulls_fixture)
+        index = Index(vals, dtype=object)
+        # TODO: case with complex dtype?
+
+        msg = r"Index\.format is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            formatted = index.format()
+        null_repr = "NaN" if isinstance(nulls_fixture, float) else str(nulls_fixture)
+        expected = [str(index[0]), str(index[1]), str(index[2]), null_repr]
+
+        assert formatted == expected
+        assert index[3] is nulls_fixture
+
+    @pytest.mark.parametrize("op", ["any", "all"])
+    def test_logical_compat(self, op, simple_index):
+        index = simple_index
+        left = getattr(index, op)()
+        assert left == getattr(index.values, op)()
+        right = getattr(index.to_series(), op)()
+        # left might not match right exactly in e.g. string cases where the
+        # because we use np.any/all instead of .any/all
+        assert bool(left) == bool(right)
+
+    @pytest.mark.parametrize(
+        "index", ["string", "int64", "int32", "float64", "float32"], indirect=True
+    )
+    def test_drop_by_str_label(self, index):
+        n = len(index)
+        drop = index[list(range(5, 10))]
+        dropped = index.drop(drop)
+
+        expected = index[list(range(5)) + list(range(10, n))]
+        tm.assert_index_equal(dropped, expected)
+
+        dropped = index.drop(index[0])
+        expected = index[1:]
+        tm.assert_index_equal(dropped, expected)
+
+    @pytest.mark.parametrize(
+        "index", ["string", "int64", "int32", "float64", "float32"], indirect=True
+    )
+    @pytest.mark.parametrize("keys", [["foo", "bar"], ["1", "bar"]])
+    def test_drop_by_str_label_raises_missing_keys(self, index, keys):
+        with pytest.raises(KeyError, match=""):
+            index.drop(keys)
+
+    @pytest.mark.parametrize(
+        "index", ["string", "int64", "int32", "float64", "float32"], indirect=True
+    )
+    def test_drop_by_str_label_errors_ignore(self, index):
+        n = len(index)
+        drop = index[list(range(5, 10))]
+        mixed = drop.tolist() + ["foo"]
+        dropped = index.drop(mixed, errors="ignore")
+
+        expected = index[list(range(5)) + list(range(10, n))]
+        tm.assert_index_equal(dropped, expected)
+
+        dropped = index.drop(["foo", "bar"], errors="ignore")
+        expected = index[list(range(n))]
+        tm.assert_index_equal(dropped, expected)
+
+    def test_drop_by_numeric_label_loc(self):
+        # TODO: Parametrize numeric and str tests after self.strIndex fixture
+        index = Index([1, 2, 3])
+        dropped = index.drop(1)
+        expected = Index([2, 3])
+
+        tm.assert_index_equal(dropped, expected)
+
+    def test_drop_by_numeric_label_raises_missing_keys(self):
+        index = Index([1, 2, 3])
+        with pytest.raises(KeyError, match=""):
+            index.drop([3, 4])
+
+    @pytest.mark.parametrize(
+        "key,expected", [(4, Index([1, 2, 3])), ([3, 4, 5], Index([1, 2]))]
+    )
+    def test_drop_by_numeric_label_errors_ignore(self, key, expected):
+        index = Index([1, 2, 3])
+        dropped = index.drop(key, errors="ignore")
+
+        tm.assert_index_equal(dropped, expected)
+
+    @pytest.mark.parametrize(
+        "values",
+        [["a", "b", ("c", "d")], ["a", ("c", "d"), "b"], [("c", "d"), "a", "b"]],
+    )
+    @pytest.mark.parametrize("to_drop", [[("c", "d"), "a"], ["a", ("c", "d")]])
+    def test_drop_tuple(self, values, to_drop):
+        # GH 18304
+        index = Index(values)
+        expected = Index(["b"], dtype=object)
+
+        result = index.drop(to_drop)
+        tm.assert_index_equal(result, expected)
+
+        removed = index.drop(to_drop[0])
+        for drop_me in to_drop[1], [to_drop[1]]:
+            result = removed.drop(drop_me)
+            tm.assert_index_equal(result, expected)
+
+        removed = index.drop(to_drop[1])
+        msg = rf"\"\[{re.escape(to_drop[1].__repr__())}\] not found in axis\""
+        for drop_me in to_drop[1], [to_drop[1]]:
+            with pytest.raises(KeyError, match=msg):
+                removed.drop(drop_me)
+
+    @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+    def test_drop_with_duplicates_in_index(self, index):
+        # GH38051
+        if len(index) == 0 or isinstance(index, MultiIndex):
+            pytest.skip("Test doesn't make sense for empty MultiIndex")
+        if isinstance(index, IntervalIndex) and not IS64:
+            pytest.skip("Cannot test IntervalIndex with int64 dtype on 32 bit platform")
+        index = index.unique().repeat(2)
+        expected = index[2:]
+        result = index.drop(index[0])
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "attr",
+        [
+            "is_monotonic_increasing",
+            "is_monotonic_decreasing",
+            "_is_strictly_monotonic_increasing",
+            "_is_strictly_monotonic_decreasing",
+        ],
+    )
+    def test_is_monotonic_incomparable(self, attr):
+        index = Index([5, datetime.now(), 7])
+        assert not getattr(index, attr)
+
+    @pytest.mark.parametrize("values", [["foo", "bar", "quux"], {"foo", "bar", "quux"}])
+    @pytest.mark.parametrize(
+        "index,expected",
+        [
+            (Index(["qux", "baz", "foo", "bar"]), np.array([False, False, True, True])),
+            (Index([]), np.array([], dtype=bool)),  # empty
+        ],
+    )
+    def test_isin(self, values, index, expected):
+        result = index.isin(values)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_isin_nan_common_object(
+        self, nulls_fixture, nulls_fixture2, using_infer_string
+    ):
+        # Test cartesian product of null fixtures and ensure that we don't
+        # mangle the various types (save a corner case with PyPy)
+        idx = Index(["a", nulls_fixture])
+
+        # all nans are the same
+        if (
+            isinstance(nulls_fixture, float)
+            and isinstance(nulls_fixture2, float)
+            and math.isnan(nulls_fixture)
+            and math.isnan(nulls_fixture2)
+        ):
+            tm.assert_numpy_array_equal(
+                idx.isin([nulls_fixture2]),
+                np.array([False, True]),
+            )
+
+        elif nulls_fixture is nulls_fixture2:  # should preserve NA type
+            tm.assert_numpy_array_equal(
+                idx.isin([nulls_fixture2]),
+                np.array([False, True]),
+            )
+
+        elif using_infer_string and idx.dtype == "string":
+            tm.assert_numpy_array_equal(
+                idx.isin([nulls_fixture2]),
+                np.array([False, True]),
+            )
+
+        else:
+            tm.assert_numpy_array_equal(
+                idx.isin([nulls_fixture2]),
+                np.array([False, False]),
+            )
+
+    def test_isin_nan_common_float64(self, nulls_fixture, float_numpy_dtype):
+        dtype = float_numpy_dtype
+
+        if nulls_fixture is pd.NaT or nulls_fixture is pd.NA:
+            # Check 1) that we cannot construct a float64 Index with this value
+            #  and 2) that with an NaN we do not have .isin(nulls_fixture)
+            msg = (
+                r"float\(\) argument must be a string or a (real )?number, "
+                f"not {repr(type(nulls_fixture).__name__)}"
+            )
+            with pytest.raises(TypeError, match=msg):
+                Index([1.0, nulls_fixture], dtype=dtype)
+
+            idx = Index([1.0, np.nan], dtype=dtype)
+            assert not idx.isin([nulls_fixture]).any()
+            return
+
+        idx = Index([1.0, nulls_fixture], dtype=dtype)
+        res = idx.isin([np.nan])
+        tm.assert_numpy_array_equal(res, np.array([False, True]))
+
+        # we cannot compare NaT with NaN
+        res = idx.isin([pd.NaT])
+        tm.assert_numpy_array_equal(res, np.array([False, False]))
+
+    @pytest.mark.parametrize("level", [0, -1])
+    @pytest.mark.parametrize(
+        "index",
+        [
+            Index(["qux", "baz", "foo", "bar"]),
+            Index([1.0, 2.0, 3.0, 4.0], dtype=np.float64),
+        ],
+    )
+    def test_isin_level_kwarg(self, level, index):
+        values = index.tolist()[-2:] + ["nonexisting"]
+
+        expected = np.array([False, False, True, True])
+        tm.assert_numpy_array_equal(expected, index.isin(values, level=level))
+
+        index.name = "foobar"
+        tm.assert_numpy_array_equal(expected, index.isin(values, level="foobar"))
+
+    def test_isin_level_kwarg_bad_level_raises(self, index):
+        for level in [10, index.nlevels, -(index.nlevels + 1)]:
+            with pytest.raises(IndexError, match="Too many levels"):
+                index.isin([], level=level)
+
+    @pytest.mark.parametrize("label", [1.0, "foobar", "xyzzy", np.nan])
+    def test_isin_level_kwarg_bad_label_raises(self, label, index):
+        if isinstance(index, MultiIndex):
+            index = index.rename(["foo", "bar"] + index.names[2:])
+            msg = f"'Level {label} not found'"
+        else:
+            index = index.rename("foo")
+            msg = rf"Requested level \({label}\) does not match index name \(foo\)"
+        with pytest.raises(KeyError, match=msg):
+            index.isin([], level=label)
+
+    @pytest.mark.parametrize("empty", [[], Series(dtype=object), np.array([])])
+    def test_isin_empty(self, empty):
+        # see gh-16991
+        index = Index(["a", "b"])
+        expected = np.array([False, False])
+
+        result = index.isin(empty)
+        tm.assert_numpy_array_equal(expected, result)
+
+    @td.skip_if_no("pyarrow")
+    def test_isin_arrow_string_null(self):
+        # GH#55821
+        index = Index(["a", "b"], dtype="string[pyarrow_numpy]")
+        result = index.isin([None])
+        expected = np.array([False, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "values",
+        [
+            [1, 2, 3, 4],
+            [1.0, 2.0, 3.0, 4.0],
+            [True, True, True, True],
+            ["foo", "bar", "baz", "qux"],
+            date_range("2018-01-01", freq="D", periods=4),
+        ],
+    )
+    def test_boolean_cmp(self, values):
+        index = Index(values)
+        result = index == values
+        expected = np.array([True, True, True, True], dtype=bool)
+
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("index", ["string"], indirect=True)
+    @pytest.mark.parametrize("name,level", [(None, 0), ("a", "a")])
+    def test_get_level_values(self, index, name, level):
+        expected = index.copy()
+        if name:
+            expected.name = name
+
+        result = expected.get_level_values(level)
+        tm.assert_index_equal(result, expected)
+
+    def test_slice_keep_name(self):
+        index = Index(["a", "b"], name="asdf")
+        assert index.name == index[1:].name
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            "string",
+            "datetime",
+            "int64",
+            "int32",
+            "uint64",
+            "uint32",
+            "float64",
+            "float32",
+        ],
+        indirect=True,
+    )
+    def test_join_self(self, index, join_type):
+        result = index.join(index, how=join_type)
+        expected = index
+        if join_type == "outer":
+            expected = expected.sort_values()
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("method", ["strip", "rstrip", "lstrip"])
+    def test_str_attribute(self, method):
+        # GH9068
+        index = Index([" jack", "jill ", " jesse ", "frank"])
+        expected = Index([getattr(str, method)(x) for x in index.values])
+
+        result = getattr(index.str, method)()
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            Index(range(5)),
+            date_range("2020-01-01", periods=10),
+            MultiIndex.from_tuples([("foo", "1"), ("bar", "3")]),
+            period_range(start="2000", end="2010", freq="Y"),
+        ],
+    )
+    def test_str_attribute_raises(self, index):
+        with pytest.raises(AttributeError, match="only use .str accessor"):
+            index.str.repeat(2)
+
+    @pytest.mark.parametrize(
+        "expand,expected",
+        [
+            (None, Index([["a", "b", "c"], ["d", "e"], ["f"]])),
+            (False, Index([["a", "b", "c"], ["d", "e"], ["f"]])),
+            (
+                True,
+                MultiIndex.from_tuples(
+                    [("a", "b", "c"), ("d", "e", np.nan), ("f", np.nan, np.nan)]
+                ),
+            ),
+        ],
+    )
+    def test_str_split(self, expand, expected):
+        index = Index(["a b c", "d e", "f"])
+        if expand is not None:
+            result = index.str.split(expand=expand)
+        else:
+            result = index.str.split()
+
+        tm.assert_index_equal(result, expected)
+
+    def test_str_bool_return(self):
+        # test boolean case, should return np.array instead of boolean Index
+        index = Index(["a1", "a2", "b1", "b2"])
+        result = index.str.startswith("a")
+        expected = np.array([True, True, False, False])
+
+        tm.assert_numpy_array_equal(result, expected)
+        assert isinstance(result, np.ndarray)
+
+    def test_str_bool_series_indexing(self):
+        index = Index(["a1", "a2", "b1", "b2"])
+        s = Series(range(4), index=index)
+
+        result = s[s.index.str.startswith("a")]
+        expected = Series(range(2), index=["a1", "a2"])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "index,expected", [(Index(list("abcd")), True), (Index(range(4)), False)]
+    )
+    def test_tab_completion(self, index, expected):
+        # GH 9910
+        result = "str" in dir(index)
+        assert result == expected
+
+    def test_indexing_doesnt_change_class(self):
+        index = Index([1, 2, 3, "a", "b", "c"])
+
+        assert index[1:3].identical(Index([2, 3], dtype=np.object_))
+        assert index[[0, 1]].identical(Index([1, 2], dtype=np.object_))
+
+    def test_outer_join_sort(self):
+        left_index = Index(np.random.default_rng(2).permutation(15))
+        right_index = date_range("2020-01-01", periods=10)
+
+        with tm.assert_produces_warning(RuntimeWarning):
+            result = left_index.join(right_index, how="outer")
+
+        with tm.assert_produces_warning(RuntimeWarning):
+            expected = left_index.astype(object).union(right_index.astype(object))
+
+        tm.assert_index_equal(result, expected)
+
+    def test_take_fill_value(self):
+        # GH 12631
+        index = Index(list("ABC"), name="xxx")
+        result = index.take(np.array([1, 0, -1]))
+        expected = Index(list("BAC"), name="xxx")
+        tm.assert_index_equal(result, expected)
+
+        # fill_value
+        result = index.take(np.array([1, 0, -1]), fill_value=True)
+        expected = Index(["B", "A", np.nan], name="xxx")
+        tm.assert_index_equal(result, expected)
+
+        # allow_fill=False
+        result = index.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True)
+        expected = Index(["B", "A", "C"], name="xxx")
+        tm.assert_index_equal(result, expected)
+
+    def test_take_fill_value_none_raises(self):
+        index = Index(list("ABC"), name="xxx")
+        msg = (
+            "When allow_fill=True and fill_value is not None, "
+            "all indices must be >= -1"
+        )
+
+        with pytest.raises(ValueError, match=msg):
+            index.take(np.array([1, 0, -2]), fill_value=True)
+        with pytest.raises(ValueError, match=msg):
+            index.take(np.array([1, 0, -5]), fill_value=True)
+
+    def test_take_bad_bounds_raises(self):
+        index = Index(list("ABC"), name="xxx")
+        with pytest.raises(IndexError, match="out of bounds"):
+            index.take(np.array([1, -5]))
+
+    @pytest.mark.parametrize("name", [None, "foobar"])
+    @pytest.mark.parametrize(
+        "labels",
+        [
+            [],
+            np.array([]),
+            ["A", "B", "C"],
+            ["C", "B", "A"],
+            np.array(["A", "B", "C"]),
+            np.array(["C", "B", "A"]),
+            # Must preserve name even if dtype changes
+            date_range("20130101", periods=3).values,
+            date_range("20130101", periods=3).tolist(),
+        ],
+    )
+    def test_reindex_preserves_name_if_target_is_list_or_ndarray(self, name, labels):
+        # GH6552
+        index = Index([0, 1, 2])
+        index.name = name
+        assert index.reindex(labels)[0].name == name
+
+    @pytest.mark.parametrize("labels", [[], np.array([]), np.array([], dtype=np.int64)])
+    def test_reindex_preserves_type_if_target_is_empty_list_or_array(self, labels):
+        # GH7774
+        index = Index(list("abc"))
+        assert index.reindex(labels)[0].dtype.type == index.dtype.type
+
+    @pytest.mark.parametrize(
+        "labels,dtype",
+        [
+            (DatetimeIndex([]), np.datetime64),
+        ],
+    )
+    def test_reindex_doesnt_preserve_type_if_target_is_empty_index(self, labels, dtype):
+        # GH7774
+        index = Index(list("abc"))
+        assert index.reindex(labels)[0].dtype.type == dtype
+
+    def test_reindex_doesnt_preserve_type_if_target_is_empty_index_numeric(
+        self, any_real_numpy_dtype
+    ):
+        # GH7774
+        dtype = any_real_numpy_dtype
+        index = Index(list("abc"))
+        labels = Index([], dtype=dtype)
+        assert index.reindex(labels)[0].dtype == dtype
+
+    def test_reindex_no_type_preserve_target_empty_mi(self):
+        index = Index(list("abc"))
+        result = index.reindex(
+            MultiIndex([Index([], np.int64), Index([], np.float64)], [[], []])
+        )[0]
+        assert result.levels[0].dtype.type == np.int64
+        assert result.levels[1].dtype.type == np.float64
+
+    def test_reindex_ignoring_level(self):
+        # GH#35132
+        idx = Index([1, 2, 3], name="x")
+        idx2 = Index([1, 2, 3, 4], name="x")
+        expected = Index([1, 2, 3, 4], name="x")
+        result, _ = idx.reindex(idx2, level="x")
+        tm.assert_index_equal(result, expected)
+
+    def test_groupby(self):
+        index = Index(range(5))
+        result = index.groupby(np.array([1, 1, 2, 2, 2]))
+        expected = {1: Index([0, 1]), 2: Index([2, 3, 4])}
+
+        tm.assert_dict_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "mi,expected",
+        [
+            (MultiIndex.from_tuples([(1, 2), (4, 5)]), np.array([True, True])),
+            (MultiIndex.from_tuples([(1, 2), (4, 6)]), np.array([True, False])),
+        ],
+    )
+    def test_equals_op_multiindex(self, mi, expected):
+        # GH9785
+        # test comparisons of multiindex
+        df = DataFrame(
+            [3, 6],
+            columns=["c"],
+            index=MultiIndex.from_arrays([[1, 4], [2, 5]], names=["a", "b"]),
+        )
+
+        result = df.index == mi
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_equals_op_multiindex_identify(self):
+        df = DataFrame(
+            [3, 6],
+            columns=["c"],
+            index=MultiIndex.from_arrays([[1, 4], [2, 5]], names=["a", "b"]),
+        )
+
+        result = df.index == df.index
+        expected = np.array([True, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            MultiIndex.from_tuples([(1, 2), (4, 5), (8, 9)]),
+            Index(["foo", "bar", "baz"]),
+        ],
+    )
+    def test_equals_op_mismatched_multiindex_raises(self, index):
+        df = DataFrame(
+            [3, 6],
+            columns=["c"],
+            index=MultiIndex.from_arrays([[1, 4], [2, 5]], names=["a", "b"]),
+        )
+
+        with pytest.raises(ValueError, match="Lengths must match"):
+            df.index == index
+
+    def test_equals_op_index_vs_mi_same_length(self, using_infer_string):
+        mi = MultiIndex.from_tuples([(1, 2), (4, 5), (8, 9)])
+        index = Index(["foo", "bar", "baz"])
+
+        result = mi == index
+        expected = np.array([False, False, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "dt_conv, arg",
+        [
+            (pd.to_datetime, ["2000-01-01", "2000-01-02"]),
+            (pd.to_timedelta, ["01:02:03", "01:02:04"]),
+        ],
+    )
+    def test_dt_conversion_preserves_name(self, dt_conv, arg):
+        # GH 10875
+        index = Index(arg, name="label")
+        assert index.name == dt_conv(index).name
+
+    def test_cached_properties_not_settable(self):
+        index = Index([1, 2, 3])
+        with pytest.raises(AttributeError, match="Can't set attribute"):
+            index.is_unique = False
+
+    def test_tab_complete_warning(self, ip):
+        # https://github.com/pandas-dev/pandas/issues/16409
+        pytest.importorskip("IPython", minversion="6.0.0")
+        from IPython.core.completer import provisionalcompleter
+
+        code = "import pandas as pd; idx = pd.Index([1, 2])"
+        ip.run_cell(code)
+
+        # GH 31324 newer jedi version raises Deprecation warning;
+        #  appears resolved 2021-02-02
+        with tm.assert_produces_warning(None, raise_on_extra_warnings=False):
+            with provisionalcompleter("ignore"):
+                list(ip.Completer.completions("idx.", 4))
+
+    def test_contains_method_removed(self, index):
+        # GH#30103 method removed for all types except IntervalIndex
+        if isinstance(index, IntervalIndex):
+            index.contains(1)
+        else:
+            msg = f"'{type(index).__name__}' object has no attribute 'contains'"
+            with pytest.raises(AttributeError, match=msg):
+                index.contains(1)
+
+    def test_sortlevel(self):
+        index = Index([5, 4, 3, 2, 1])
+        with pytest.raises(Exception, match="ascending must be a single bool value or"):
+            index.sortlevel(ascending="True")
+
+        with pytest.raises(
+            Exception, match="ascending must be a list of bool values of length 1"
+        ):
+            index.sortlevel(ascending=[True, True])
+
+        with pytest.raises(Exception, match="ascending must be a bool value"):
+            index.sortlevel(ascending=["True"])
+
+        expected = Index([1, 2, 3, 4, 5])
+        result = index.sortlevel(ascending=[True])
+        tm.assert_index_equal(result[0], expected)
+
+        expected = Index([1, 2, 3, 4, 5])
+        result = index.sortlevel(ascending=True)
+        tm.assert_index_equal(result[0], expected)
+
+        expected = Index([5, 4, 3, 2, 1])
+        result = index.sortlevel(ascending=False)
+        tm.assert_index_equal(result[0], expected)
+
+    def test_sortlevel_na_position(self):
+        # GH#51612
+        idx = Index([1, np.nan])
+        result = idx.sortlevel(na_position="first")[0]
+        expected = Index([np.nan, 1])
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "periods, expected_results",
+        [
+            (1, [np.nan, 10, 10, 10, 10]),
+            (2, [np.nan, np.nan, 20, 20, 20]),
+            (3, [np.nan, np.nan, np.nan, 30, 30]),
+        ],
+    )
+    def test_index_diff(self, periods, expected_results):
+        # GH#19708
+        idx = Index([10, 20, 30, 40, 50])
+        result = idx.diff(periods)
+        expected = Index(expected_results)
+
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "decimals, expected_results",
+        [
+            (0, [1.0, 2.0, 3.0]),
+            (1, [1.2, 2.3, 3.5]),
+            (2, [1.23, 2.35, 3.46]),
+        ],
+    )
+    def test_index_round(self, decimals, expected_results):
+        # GH#19708
+        idx = Index([1.234, 2.345, 3.456])
+        result = idx.round(decimals)
+        expected = Index(expected_results)
+
+        tm.assert_index_equal(result, expected)
+
+
+class TestMixedIntIndex:
+    # Mostly the tests from common.py for which the results differ
+    # in py2 and py3 because ints and strings are uncomparable in py3
+    # (GH 13514)
+    @pytest.fixture
+    def simple_index(self) -> Index:
+        return Index([0, "a", 1, "b", 2, "c"])
+
+    def test_argsort(self, simple_index):
+        index = simple_index
+        with pytest.raises(TypeError, match="'>|<' not supported"):
+            index.argsort()
+
+    def test_numpy_argsort(self, simple_index):
+        index = simple_index
+        with pytest.raises(TypeError, match="'>|<' not supported"):
+            np.argsort(index)
+
+    def test_copy_name(self, simple_index):
+        # Check that "name" argument passed at initialization is honoured
+        # GH12309
+        index = simple_index
+
+        first = type(index)(index, copy=True, name="mario")
+        second = type(first)(first, copy=False)
+
+        # Even though "copy=False", we want a new object.
+        assert first is not second
+        tm.assert_index_equal(first, second)
+
+        assert first.name == "mario"
+        assert second.name == "mario"
+
+        s1 = Series(2, index=first)
+        s2 = Series(3, index=second[:-1])
+
+        s3 = s1 * s2
+
+        assert s3.index.name == "mario"
+
+    def test_copy_name2(self):
+        # Check that adding a "name" parameter to the copy is honored
+        # GH14302
+        index = Index([1, 2], name="MyName")
+        index1 = index.copy()
+
+        tm.assert_index_equal(index, index1)
+
+        index2 = index.copy(name="NewName")
+        tm.assert_index_equal(index, index2, check_names=False)
+        assert index.name == "MyName"
+        assert index2.name == "NewName"
+
+    def test_unique_na(self):
+        idx = Index([2, np.nan, 2, 1], name="my_index")
+        expected = Index([2, np.nan, 1], name="my_index")
+        result = idx.unique()
+        tm.assert_index_equal(result, expected)
+
+    def test_logical_compat(self, simple_index):
+        index = simple_index
+        assert index.all() == index.values.all()
+        assert index.any() == index.values.any()
+
+    @pytest.mark.parametrize("how", ["any", "all"])
+    @pytest.mark.parametrize("dtype", [None, object, "category"])
+    @pytest.mark.parametrize(
+        "vals,expected",
+        [
+            ([1, 2, 3], [1, 2, 3]),
+            ([1.0, 2.0, 3.0], [1.0, 2.0, 3.0]),
+            ([1.0, 2.0, np.nan, 3.0], [1.0, 2.0, 3.0]),
+            (["A", "B", "C"], ["A", "B", "C"]),
+            (["A", np.nan, "B", "C"], ["A", "B", "C"]),
+        ],
+    )
+    def test_dropna(self, how, dtype, vals, expected):
+        # GH 6194
+        index = Index(vals, dtype=dtype)
+        result = index.dropna(how=how)
+        expected = Index(expected, dtype=dtype)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("how", ["any", "all"])
+    @pytest.mark.parametrize(
+        "index,expected",
+        [
+            (
+                DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"]),
+                DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"]),
+            ),
+            (
+                DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03", pd.NaT]),
+                DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"]),
+            ),
+            (
+                TimedeltaIndex(["1 days", "2 days", "3 days"]),
+                TimedeltaIndex(["1 days", "2 days", "3 days"]),
+            ),
+            (
+                TimedeltaIndex([pd.NaT, "1 days", "2 days", "3 days", pd.NaT]),
+                TimedeltaIndex(["1 days", "2 days", "3 days"]),
+            ),
+            (
+                PeriodIndex(["2012-02", "2012-04", "2012-05"], freq="M"),
+                PeriodIndex(["2012-02", "2012-04", "2012-05"], freq="M"),
+            ),
+            (
+                PeriodIndex(["2012-02", "2012-04", "NaT", "2012-05"], freq="M"),
+                PeriodIndex(["2012-02", "2012-04", "2012-05"], freq="M"),
+            ),
+        ],
+    )
+    def test_dropna_dt_like(self, how, index, expected):
+        result = index.dropna(how=how)
+        tm.assert_index_equal(result, expected)
+
+    def test_dropna_invalid_how_raises(self):
+        msg = "invalid how option: xxx"
+        with pytest.raises(ValueError, match=msg):
+            Index([1, 2, 3]).dropna(how="xxx")
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            Index([np.nan]),
+            Index([np.nan, 1]),
+            Index([1, 2, np.nan]),
+            Index(["a", "b", np.nan]),
+            pd.to_datetime(["NaT"]),
+            pd.to_datetime(["NaT", "2000-01-01"]),
+            pd.to_datetime(["2000-01-01", "NaT", "2000-01-02"]),
+            pd.to_timedelta(["1 day", "NaT"]),
+        ],
+    )
+    def test_is_monotonic_na(self, index):
+        assert index.is_monotonic_increasing is False
+        assert index.is_monotonic_decreasing is False
+        assert index._is_strictly_monotonic_increasing is False
+        assert index._is_strictly_monotonic_decreasing is False
+
+    @pytest.mark.parametrize("dtype", ["f8", "m8[ns]", "M8[us]"])
+    @pytest.mark.parametrize("unique_first", [True, False])
+    def test_is_monotonic_unique_na(self, dtype, unique_first):
+        # GH 55755
+        index = Index([None, 1, 1], dtype=dtype)
+        if unique_first:
+            assert index.is_unique is False
+            assert index.is_monotonic_increasing is False
+            assert index.is_monotonic_decreasing is False
+        else:
+            assert index.is_monotonic_increasing is False
+            assert index.is_monotonic_decreasing is False
+            assert index.is_unique is False
+
+    def test_int_name_format(self, frame_or_series):
+        index = Index(["a", "b", "c"], name=0)
+        result = frame_or_series(list(range(3)), index=index)
+        assert "0" in repr(result)
+
+    def test_str_to_bytes_raises(self):
+        # GH 26447
+        index = Index([str(x) for x in range(10)])
+        msg = "^'str' object cannot be interpreted as an integer$"
+        with pytest.raises(TypeError, match=msg):
+            bytes(index)
+
+    @pytest.mark.filterwarnings("ignore:elementwise comparison failed:FutureWarning")
+    def test_index_with_tuple_bool(self):
+        # GH34123
+        # TODO: also this op right now produces FutureWarning from numpy
+        #  https://github.com/numpy/numpy/issues/11521
+        idx = Index([("a", "b"), ("b", "c"), ("c", "a")])
+        result = idx == ("c", "a")
+        expected = np.array([False, False, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+
+class TestIndexUtils:
+    @pytest.mark.parametrize(
+        "data, names, expected",
+        [
+            ([[1, 2, 3]], None, Index([1, 2, 3])),
+            ([[1, 2, 3]], ["name"], Index([1, 2, 3], name="name")),
+            (
+                [["a", "a"], ["c", "d"]],
+                None,
+                MultiIndex([["a"], ["c", "d"]], [[0, 0], [0, 1]]),
+            ),
+            (
+                [["a", "a"], ["c", "d"]],
+                ["L1", "L2"],
+                MultiIndex([["a"], ["c", "d"]], [[0, 0], [0, 1]], names=["L1", "L2"]),
+            ),
+        ],
+    )
+    def test_ensure_index_from_sequences(self, data, names, expected):
+        result = ensure_index_from_sequences(data, names)
+        tm.assert_index_equal(result, expected)
+
+    def test_ensure_index_mixed_closed_intervals(self):
+        # GH27172
+        intervals = [
+            pd.Interval(0, 1, closed="left"),
+            pd.Interval(1, 2, closed="right"),
+            pd.Interval(2, 3, closed="neither"),
+            pd.Interval(3, 4, closed="both"),
+        ]
+        result = ensure_index(intervals)
+        expected = Index(intervals, dtype=object)
+        tm.assert_index_equal(result, expected)
+
+    def test_ensure_index_uint64(self):
+        # with both 0 and a large-uint64, np.array will infer to float64
+        #  https://github.com/numpy/numpy/issues/19146
+        #  but a more accurate choice would be uint64
+        values = [0, np.iinfo(np.uint64).max]
+
+        result = ensure_index(values)
+        assert list(result) == values
+
+        expected = Index(values, dtype="uint64")
+        tm.assert_index_equal(result, expected)
+
+    def test_get_combined_index(self):
+        result = _get_combined_index([])
+        expected = Index([])
+        tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "opname",
+    [
+        "eq",
+        "ne",
+        "le",
+        "lt",
+        "ge",
+        "gt",
+        "add",
+        "radd",
+        "sub",
+        "rsub",
+        "mul",
+        "rmul",
+        "truediv",
+        "rtruediv",
+        "floordiv",
+        "rfloordiv",
+        "pow",
+        "rpow",
+        "mod",
+        "divmod",
+    ],
+)
+def test_generated_op_names(opname, index):
+    opname = f"__{opname}__"
+    method = getattr(index, opname)
+    assert method.__name__ == opname
+
+
+@pytest.mark.parametrize(
+    "klass",
+    [
+        partial(CategoricalIndex, data=[1]),
+        partial(DatetimeIndex, data=["2020-01-01"]),
+        partial(PeriodIndex, data=["2020-01-01"]),
+        partial(TimedeltaIndex, data=["1 day"]),
+        partial(RangeIndex, data=range(1)),
+        partial(IntervalIndex, data=[pd.Interval(0, 1)]),
+        partial(Index, data=["a"], dtype=object),
+        partial(MultiIndex, levels=[1], codes=[0]),
+    ],
+)
+def test_index_subclass_constructor_wrong_kwargs(klass):
+    # GH #19348
+    with pytest.raises(TypeError, match="unexpected keyword argument"):
+        klass(foo="bar")
+
+
+def test_deprecated_fastpath():
+    msg = "[Uu]nexpected keyword argument"
+    with pytest.raises(TypeError, match=msg):
+        Index(np.array(["a", "b"], dtype=object), name="test", fastpath=True)
+
+    with pytest.raises(TypeError, match=msg):
+        Index(np.array([1, 2, 3], dtype="int64"), name="test", fastpath=True)
+
+    with pytest.raises(TypeError, match=msg):
+        RangeIndex(0, 5, 2, name="test", fastpath=True)
+
+    with pytest.raises(TypeError, match=msg):
+        CategoricalIndex(["a", "b", "c"], name="test", fastpath=True)
+
+
+def test_shape_of_invalid_index():
+    # Pre-2.0, it was possible to create "invalid" index objects backed by
+    # a multi-dimensional array (see https://github.com/pandas-dev/pandas/issues/27125
+    # about this). However, as long as this is not solved in general,this test ensures
+    # that the returned shape is consistent with this underlying array for
+    # compat with matplotlib (see https://github.com/pandas-dev/pandas/issues/27775)
+    idx = Index([0, 1, 2, 3])
+    with pytest.raises(ValueError, match="Multi-dimensional indexing"):
+        # GH#30588 multi-dimensional indexing deprecated
+        idx[:, None]
+
+
+@pytest.mark.parametrize("dtype", [None, np.int64, np.uint64, np.float64])
+def test_validate_1d_input(dtype):
+    # GH#27125 check that we do not have >1-dimensional input
+    msg = "Index data must be 1-dimensional"
+
+    arr = np.arange(8).reshape(2, 2, 2)
+    with pytest.raises(ValueError, match=msg):
+        Index(arr, dtype=dtype)
+
+    df = DataFrame(arr.reshape(4, 2))
+    with pytest.raises(ValueError, match=msg):
+        Index(df, dtype=dtype)
+
+    # GH#13601 trying to assign a multi-dimensional array to an index is not allowed
+    ser = Series(0, range(4))
+    with pytest.raises(ValueError, match=msg):
+        ser.index = np.array([[2, 3]] * 4, dtype=dtype)
+
+
+@pytest.mark.parametrize(
+    "klass, extra_kwargs",
+    [
+        [Index, {}],
+        *[[lambda x: Index(x, dtype=dtyp), {}] for dtyp in tm.ALL_REAL_NUMPY_DTYPES],
+        [DatetimeIndex, {}],
+        [TimedeltaIndex, {}],
+        [PeriodIndex, {"freq": "Y"}],
+    ],
+)
+def test_construct_from_memoryview(klass, extra_kwargs):
+    # GH 13120
+    result = klass(memoryview(np.arange(2000, 2005)), **extra_kwargs)
+    expected = klass(list(range(2000, 2005)), **extra_kwargs)
+    tm.assert_index_equal(result, expected, exact=True)
+
+
+@pytest.mark.parametrize("op", [operator.lt, operator.gt])
+def test_nan_comparison_same_object(op):
+    # GH#47105
+    idx = Index([np.nan])
+    expected = np.array([False])
+
+    result = op(idx, idx)
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = op(idx, idx.copy())
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@td.skip_if_no("pyarrow")
+def test_is_monotonic_pyarrow_list_type():
+    # GH 57333
+    import pyarrow as pa
+
+    idx = Index([[1], [2, 3]], dtype=pd.ArrowDtype(pa.list_(pa.int64())))
+    assert not idx.is_monotonic_increasing
+    assert not idx.is_monotonic_decreasing

venv/lib/python3.10/site-packages/pandas/tests/indexes/test_common.py

@@ -0,0 +1,511 @@
+"""
+Collection of tests asserting things that should be true for
+any index subclass except for MultiIndex. Makes use of the `index_flat`
+fixture defined in pandas/conftest.py.
+"""
+from copy import (
+    copy,
+    deepcopy,
+)
+import re
+
+import numpy as np
+import pytest
+
+from pandas.compat import IS64
+from pandas.compat.numpy import np_version_gte1p25
+
+from pandas.core.dtypes.common import (
+    is_integer_dtype,
+    is_numeric_dtype,
+)
+
+import pandas as pd
+from pandas import (
+    CategoricalIndex,
+    MultiIndex,
+    PeriodIndex,
+    RangeIndex,
+)
+import pandas._testing as tm
+
+
+class TestCommon:
+    @pytest.mark.parametrize("name", [None, "new_name"])
+    def test_to_frame(self, name, index_flat, using_copy_on_write):
+        # see GH#15230, GH#22580
+        idx = index_flat
+
+        if name:
+            idx_name = name
+        else:
+            idx_name = idx.name or 0
+
+        df = idx.to_frame(name=idx_name)
+
+        assert df.index is idx
+        assert len(df.columns) == 1
+        assert df.columns[0] == idx_name
+        if not using_copy_on_write:
+            assert df[idx_name].values is not idx.values
+
+        df = idx.to_frame(index=False, name=idx_name)
+        assert df.index is not idx
+
+    def test_droplevel(self, index_flat):
+        # GH 21115
+        # MultiIndex is tested separately in test_multi.py
+        index = index_flat
+
+        assert index.droplevel([]).equals(index)
+
+        for level in [index.name, [index.name]]:
+            if isinstance(index.name, tuple) and level is index.name:
+                # GH 21121 : droplevel with tuple name
+                continue
+            msg = (
+                "Cannot remove 1 levels from an index with 1 levels: at least one "
+                "level must be left."
+            )
+            with pytest.raises(ValueError, match=msg):
+                index.droplevel(level)
+
+        for level in "wrong", ["wrong"]:
+            with pytest.raises(
+                KeyError,
+                match=r"'Requested level \(wrong\) does not match index name \(None\)'",
+            ):
+                index.droplevel(level)
+
+    def test_constructor_non_hashable_name(self, index_flat):
+        # GH 20527
+        index = index_flat
+
+        message = "Index.name must be a hashable type"
+        renamed = [["1"]]
+
+        # With .rename()
+        with pytest.raises(TypeError, match=message):
+            index.rename(name=renamed)
+
+        # With .set_names()
+        with pytest.raises(TypeError, match=message):
+            index.set_names(names=renamed)
+
+    def test_constructor_unwraps_index(self, index_flat):
+        a = index_flat
+        # Passing dtype is necessary for Index([True, False], dtype=object)
+        #  case.
+        b = type(a)(a, dtype=a.dtype)
+        tm.assert_equal(a._data, b._data)
+
+    def test_to_flat_index(self, index_flat):
+        # 22866
+        index = index_flat
+
+        result = index.to_flat_index()
+        tm.assert_index_equal(result, index)
+
+    def test_set_name_methods(self, index_flat):
+        # MultiIndex tested separately
+        index = index_flat
+        new_name = "This is the new name for this index"
+
+        original_name = index.name
+        new_ind = index.set_names([new_name])
+        assert new_ind.name == new_name
+        assert index.name == original_name
+        res = index.rename(new_name, inplace=True)
+
+        # should return None
+        assert res is None
+        assert index.name == new_name
+        assert index.names == [new_name]
+        with pytest.raises(ValueError, match="Level must be None"):
+            index.set_names("a", level=0)
+
+        # rename in place just leaves tuples and other containers alone
+        name = ("A", "B")
+        index.rename(name, inplace=True)
+        assert index.name == name
+        assert index.names == [name]
+
+    @pytest.mark.xfail
+    def test_set_names_single_label_no_level(self, index_flat):
+        with pytest.raises(TypeError, match="list-like"):
+            # should still fail even if it would be the right length
+            index_flat.set_names("a")
+
+    def test_copy_and_deepcopy(self, index_flat):
+        index = index_flat
+
+        for func in (copy, deepcopy):
+            idx_copy = func(index)
+            assert idx_copy is not index
+            assert idx_copy.equals(index)
+
+        new_copy = index.copy(deep=True, name="banana")
+        assert new_copy.name == "banana"
+
+    def test_copy_name(self, index_flat):
+        # GH#12309: Check that the "name" argument
+        # passed at initialization is honored.
+        index = index_flat
+
+        first = type(index)(index, copy=True, name="mario")
+        second = type(first)(first, copy=False)
+
+        # Even though "copy=False", we want a new object.
+        assert first is not second
+        tm.assert_index_equal(first, second)
+
+        # Not using tm.assert_index_equal() since names differ.
+        assert index.equals(first)
+
+        assert first.name == "mario"
+        assert second.name == "mario"
+
+        # TODO: belongs in series arithmetic tests?
+        s1 = pd.Series(2, index=first)
+        s2 = pd.Series(3, index=second[:-1])
+        # See GH#13365
+        s3 = s1 * s2
+        assert s3.index.name == "mario"
+
+    def test_copy_name2(self, index_flat):
+        # GH#35592
+        index = index_flat
+
+        assert index.copy(name="mario").name == "mario"
+
+        with pytest.raises(ValueError, match="Length of new names must be 1, got 2"):
+            index.copy(name=["mario", "luigi"])
+
+        msg = f"{type(index).__name__}.name must be a hashable type"
+        with pytest.raises(TypeError, match=msg):
+            index.copy(name=[["mario"]])
+
+    def test_unique_level(self, index_flat):
+        # don't test a MultiIndex here (as its tested separated)
+        index = index_flat
+
+        # GH 17896
+        expected = index.drop_duplicates()
+        for level in [0, index.name, None]:
+            result = index.unique(level=level)
+            tm.assert_index_equal(result, expected)
+
+        msg = "Too many levels: Index has only 1 level, not 4"
+        with pytest.raises(IndexError, match=msg):
+            index.unique(level=3)
+
+        msg = (
+            rf"Requested level \(wrong\) does not match index name "
+            rf"\({re.escape(index.name.__repr__())}\)"
+        )
+        with pytest.raises(KeyError, match=msg):
+            index.unique(level="wrong")
+
+    def test_unique(self, index_flat):
+        # MultiIndex tested separately
+        index = index_flat
+        if not len(index):
+            pytest.skip("Skip check for empty Index and MultiIndex")
+
+        idx = index[[0] * 5]
+        idx_unique = index[[0]]
+
+        # We test against `idx_unique`, so first we make sure it's unique
+        # and doesn't contain nans.
+        assert idx_unique.is_unique is True
+        try:
+            assert idx_unique.hasnans is False
+        except NotImplementedError:
+            pass
+
+        result = idx.unique()
+        tm.assert_index_equal(result, idx_unique)
+
+        # nans:
+        if not index._can_hold_na:
+            pytest.skip("Skip na-check if index cannot hold na")
+
+        vals = index._values[[0] * 5]
+        vals[0] = np.nan
+
+        vals_unique = vals[:2]
+        idx_nan = index._shallow_copy(vals)
+        idx_unique_nan = index._shallow_copy(vals_unique)
+        assert idx_unique_nan.is_unique is True
+
+        assert idx_nan.dtype == index.dtype
+        assert idx_unique_nan.dtype == index.dtype
+
+        expected = idx_unique_nan
+        for pos, i in enumerate([idx_nan, idx_unique_nan]):
+            result = i.unique()
+            tm.assert_index_equal(result, expected)
+
+    @pytest.mark.filterwarnings("ignore:Period with BDay freq:FutureWarning")
+    @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+    def test_searchsorted_monotonic(self, index_flat, request):
+        # GH17271
+        index = index_flat
+        # not implemented for tuple searches in MultiIndex
+        # or Intervals searches in IntervalIndex
+        if isinstance(index, pd.IntervalIndex):
+            mark = pytest.mark.xfail(
+                reason="IntervalIndex.searchsorted does not support Interval arg",
+                raises=NotImplementedError,
+            )
+            request.applymarker(mark)
+
+        # nothing to test if the index is empty
+        if index.empty:
+            pytest.skip("Skip check for empty Index")
+        value = index[0]
+
+        # determine the expected results (handle dupes for 'right')
+        expected_left, expected_right = 0, (index == value).argmin()
+        if expected_right == 0:
+            # all values are the same, expected_right should be length
+            expected_right = len(index)
+
+        # test _searchsorted_monotonic in all cases
+        # test searchsorted only for increasing
+        if index.is_monotonic_increasing:
+            ssm_left = index._searchsorted_monotonic(value, side="left")
+            assert expected_left == ssm_left
+
+            ssm_right = index._searchsorted_monotonic(value, side="right")
+            assert expected_right == ssm_right
+
+            ss_left = index.searchsorted(value, side="left")
+            assert expected_left == ss_left
+
+            ss_right = index.searchsorted(value, side="right")
+            assert expected_right == ss_right
+
+        elif index.is_monotonic_decreasing:
+            ssm_left = index._searchsorted_monotonic(value, side="left")
+            assert expected_left == ssm_left
+
+            ssm_right = index._searchsorted_monotonic(value, side="right")
+            assert expected_right == ssm_right
+        else:
+            # non-monotonic should raise.
+            msg = "index must be monotonic increasing or decreasing"
+            with pytest.raises(ValueError, match=msg):
+                index._searchsorted_monotonic(value, side="left")
+
+    @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+    def test_drop_duplicates(self, index_flat, keep):
+        # MultiIndex is tested separately
+        index = index_flat
+        if isinstance(index, RangeIndex):
+            pytest.skip(
+                "RangeIndex is tested in test_drop_duplicates_no_duplicates "
+                "as it cannot hold duplicates"
+            )
+        if len(index) == 0:
+            pytest.skip(
+                "empty index is tested in test_drop_duplicates_no_duplicates "
+                "as it cannot hold duplicates"
+            )
+
+        # make unique index
+        holder = type(index)
+        unique_values = list(set(index))
+        dtype = index.dtype if is_numeric_dtype(index) else None
+        unique_idx = holder(unique_values, dtype=dtype)
+
+        # make duplicated index
+        n = len(unique_idx)
+        duplicated_selection = np.random.default_rng(2).choice(n, int(n * 1.5))
+        idx = holder(unique_idx.values[duplicated_selection])
+
+        # Series.duplicated is tested separately
+        expected_duplicated = (
+            pd.Series(duplicated_selection).duplicated(keep=keep).values
+        )
+        tm.assert_numpy_array_equal(idx.duplicated(keep=keep), expected_duplicated)
+
+        # Series.drop_duplicates is tested separately
+        expected_dropped = holder(pd.Series(idx).drop_duplicates(keep=keep))
+        tm.assert_index_equal(idx.drop_duplicates(keep=keep), expected_dropped)
+
+    @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+    def test_drop_duplicates_no_duplicates(self, index_flat):
+        # MultiIndex is tested separately
+        index = index_flat
+
+        # make unique index
+        if isinstance(index, RangeIndex):
+            # RangeIndex cannot have duplicates
+            unique_idx = index
+        else:
+            holder = type(index)
+            unique_values = list(set(index))
+            dtype = index.dtype if is_numeric_dtype(index) else None
+            unique_idx = holder(unique_values, dtype=dtype)
+
+        # check on unique index
+        expected_duplicated = np.array([False] * len(unique_idx), dtype="bool")
+        tm.assert_numpy_array_equal(unique_idx.duplicated(), expected_duplicated)
+        result_dropped = unique_idx.drop_duplicates()
+        tm.assert_index_equal(result_dropped, unique_idx)
+        # validate shallow copy
+        assert result_dropped is not unique_idx
+
+    def test_drop_duplicates_inplace(self, index):
+        msg = r"drop_duplicates\(\) got an unexpected keyword argument"
+        with pytest.raises(TypeError, match=msg):
+            index.drop_duplicates(inplace=True)
+
+    @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+    def test_has_duplicates(self, index_flat):
+        # MultiIndex tested separately in:
+        #   tests/indexes/multi/test_unique_and_duplicates.
+        index = index_flat
+        holder = type(index)
+        if not len(index) or isinstance(index, RangeIndex):
+            # MultiIndex tested separately in:
+            #   tests/indexes/multi/test_unique_and_duplicates.
+            # RangeIndex is unique by definition.
+            pytest.skip("Skip check for empty Index, MultiIndex, and RangeIndex")
+
+        idx = holder([index[0]] * 5)
+        assert idx.is_unique is False
+        assert idx.has_duplicates is True
+
+    @pytest.mark.parametrize(
+        "dtype",
+        ["int64", "uint64", "float64", "category", "datetime64[ns]", "timedelta64[ns]"],
+    )
+    def test_astype_preserves_name(self, index, dtype):
+        # https://github.com/pandas-dev/pandas/issues/32013
+        if isinstance(index, MultiIndex):
+            index.names = ["idx" + str(i) for i in range(index.nlevels)]
+        else:
+            index.name = "idx"
+
+        warn = None
+        if index.dtype.kind == "c" and dtype in ["float64", "int64", "uint64"]:
+            # imaginary components discarded
+            if np_version_gte1p25:
+                warn = np.exceptions.ComplexWarning
+            else:
+                warn = np.ComplexWarning
+
+        is_pyarrow_str = str(index.dtype) == "string[pyarrow]" and dtype == "category"
+        try:
+            # Some of these conversions cannot succeed so we use a try / except
+            with tm.assert_produces_warning(
+                warn,
+                raise_on_extra_warnings=is_pyarrow_str,
+                check_stacklevel=False,
+            ):
+                result = index.astype(dtype)
+        except (ValueError, TypeError, NotImplementedError, SystemError):
+            return
+
+        if isinstance(index, MultiIndex):
+            assert result.names == index.names
+        else:
+            assert result.name == index.name
+
+    def test_hasnans_isnans(self, index_flat):
+        # GH#11343, added tests for hasnans / isnans
+        index = index_flat
+
+        # cases in indices doesn't include NaN
+        idx = index.copy(deep=True)
+        expected = np.array([False] * len(idx), dtype=bool)
+        tm.assert_numpy_array_equal(idx._isnan, expected)
+        assert idx.hasnans is False
+
+        idx = index.copy(deep=True)
+        values = idx._values
+
+        if len(index) == 0:
+            return
+        elif is_integer_dtype(index.dtype):
+            return
+        elif index.dtype == bool:
+            # values[1] = np.nan below casts to True!
+            return
+
+        values[1] = np.nan
+
+        idx = type(index)(values)
+
+        expected = np.array([False] * len(idx), dtype=bool)
+        expected[1] = True
+        tm.assert_numpy_array_equal(idx._isnan, expected)
+        assert idx.hasnans is True
+
+
+@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+@pytest.mark.parametrize("na_position", [None, "middle"])
+def test_sort_values_invalid_na_position(index_with_missing, na_position):
+    with pytest.raises(ValueError, match=f"invalid na_position: {na_position}"):
+        index_with_missing.sort_values(na_position=na_position)
+
+
+@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+@pytest.mark.parametrize("na_position", ["first", "last"])
+def test_sort_values_with_missing(index_with_missing, na_position, request):
+    # GH 35584. Test that sort_values works with missing values,
+    # sort non-missing and place missing according to na_position
+
+    if isinstance(index_with_missing, CategoricalIndex):
+        request.applymarker(
+            pytest.mark.xfail(
+                reason="missing value sorting order not well-defined", strict=False
+            )
+        )
+
+    missing_count = np.sum(index_with_missing.isna())
+    not_na_vals = index_with_missing[index_with_missing.notna()].values
+    sorted_values = np.sort(not_na_vals)
+    if na_position == "first":
+        sorted_values = np.concatenate([[None] * missing_count, sorted_values])
+    else:
+        sorted_values = np.concatenate([sorted_values, [None] * missing_count])
+
+    # Explicitly pass dtype needed for Index backed by EA e.g. IntegerArray
+    expected = type(index_with_missing)(sorted_values, dtype=index_with_missing.dtype)
+
+    result = index_with_missing.sort_values(na_position=na_position)
+    tm.assert_index_equal(result, expected)
+
+
+def test_ndarray_compat_properties(index):
+    if isinstance(index, PeriodIndex) and not IS64:
+        pytest.skip("Overflow")
+    idx = index
+    assert idx.T.equals(idx)
+    assert idx.transpose().equals(idx)
+
+    values = idx.values
+
+    assert idx.shape == values.shape
+    assert idx.ndim == values.ndim
+    assert idx.size == values.size
+
+    if not isinstance(index, (RangeIndex, MultiIndex)):
+        # These two are not backed by an ndarray
+        assert idx.nbytes == values.nbytes
+
+    # test for validity
+    idx.nbytes
+    idx.values.nbytes
+
+
+def test_compare_read_only_array():
+    # GH#57130
+    arr = np.array([], dtype=object)
+    arr.flags.writeable = False
+    idx = pd.Index(arr)
+    result = idx > 69
+    assert result.dtype == bool

venv/lib/python3.10/site-packages/pandas/tests/indexes/test_datetimelike.py

@@ -0,0 +1,171 @@
+""" generic datetimelike tests """
+
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+class TestDatetimeLike:
+    @pytest.fixture(
+        params=[
+            pd.period_range("20130101", periods=5, freq="D"),
+            pd.TimedeltaIndex(
+                [
+                    "0 days 01:00:00",
+                    "1 days 01:00:00",
+                    "2 days 01:00:00",
+                    "3 days 01:00:00",
+                    "4 days 01:00:00",
+                ],
+                dtype="timedelta64[ns]",
+                freq="D",
+            ),
+            pd.DatetimeIndex(
+                ["2013-01-01", "2013-01-02", "2013-01-03", "2013-01-04", "2013-01-05"],
+                dtype="datetime64[ns]",
+                freq="D",
+            ),
+        ]
+    )
+    def simple_index(self, request):
+        return request.param
+
+    def test_isin(self, simple_index):
+        index = simple_index[:4]
+        result = index.isin(index)
+        assert result.all()
+
+        result = index.isin(list(index))
+        assert result.all()
+
+        result = index.isin([index[2], 5])
+        expected = np.array([False, False, True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_argsort_matches_array(self, simple_index):
+        idx = simple_index
+        idx = idx.insert(1, pd.NaT)
+
+        result = idx.argsort()
+        expected = idx._data.argsort()
+        tm.assert_numpy_array_equal(result, 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_shift_identity(self, simple_index):
+        idx = simple_index
+        tm.assert_index_equal(idx, idx.shift(0))
+
+    def test_shift_empty(self, simple_index):
+        # GH#14811
+        idx = simple_index[:0]
+        tm.assert_index_equal(idx, idx.shift(1))
+
+    def test_str(self, simple_index):
+        # test the string repr
+        idx = simple_index.copy()
+        idx.name = "foo"
+        assert f"length={len(idx)}" not in str(idx)
+        assert "'foo'" in str(idx)
+        assert type(idx).__name__ in str(idx)
+
+        if hasattr(idx, "tz"):
+            if idx.tz is not None:
+                assert idx.tz in str(idx)
+        if isinstance(idx, pd.PeriodIndex):
+            assert f"dtype='period[{idx.freqstr}]'" in str(idx)
+        else:
+            assert f"freq='{idx.freqstr}'" in str(idx)
+
+    def test_view(self, simple_index):
+        idx = simple_index
+
+        idx_view = idx.view("i8")
+        result = type(simple_index)(idx)
+        tm.assert_index_equal(result, idx)
+
+        msg = "Passing a type in .*Index.view is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            idx_view = idx.view(type(simple_index))
+        result = type(simple_index)(idx)
+        tm.assert_index_equal(result, idx_view)
+
+    def test_map_callable(self, simple_index):
+        index = simple_index
+        expected = index + index.freq
+        result = index.map(lambda x: x + index.freq)
+        tm.assert_index_equal(result, expected)
+
+        # map to NaT
+        result = index.map(lambda x: pd.NaT if x == index[0] else x)
+        expected = pd.Index([pd.NaT] + index[1:].tolist())
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "mapper",
+        [
+            lambda values, index: {i: e for e, i in zip(values, index)},
+            lambda values, index: pd.Series(values, index, dtype=object),
+        ],
+    )
+    @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+    def test_map_dictlike(self, mapper, simple_index):
+        index = simple_index
+        expected = index + index.freq
+
+        # don't compare the freqs
+        if isinstance(expected, (pd.DatetimeIndex, pd.TimedeltaIndex)):
+            expected = expected._with_freq(None)
+
+        result = index.map(mapper(expected, index))
+        tm.assert_index_equal(result, expected)
+
+        expected = pd.Index([pd.NaT] + index[1:].tolist())
+        result = index.map(mapper(expected, index))
+        tm.assert_index_equal(result, expected)
+
+        # empty map; these map to np.nan because we cannot know
+        # to re-infer things
+        expected = pd.Index([np.nan] * len(index))
+        result = index.map(mapper([], []))
+        tm.assert_index_equal(result, expected)
+
+    def test_getitem_preserves_freq(self, simple_index):
+        index = simple_index
+        assert index.freq is not None
+
+        result = index[:]
+        assert result.freq == index.freq
+
+    def test_where_cast_str(self, simple_index):
+        index = simple_index
+
+        mask = np.ones(len(index), dtype=bool)
+        mask[-1] = False
+
+        result = index.where(mask, str(index[0]))
+        expected = index.where(mask, index[0])
+        tm.assert_index_equal(result, expected)
+
+        result = index.where(mask, [str(index[0])])
+        tm.assert_index_equal(result, expected)
+
+        expected = index.astype(object).where(mask, "foo")
+        result = index.where(mask, "foo")
+        tm.assert_index_equal(result, expected)
+
+        result = index.where(mask, ["foo"])
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("unit", ["ns", "us", "ms", "s"])
+    def test_diff(self, unit):
+        # GH 55080
+        dti = pd.to_datetime([10, 20, 30], unit=unit).as_unit(unit)
+        result = dti.diff(1)
+        expected = pd.to_timedelta([pd.NaT, 10, 10], unit=unit).as_unit(unit)
+        tm.assert_index_equal(result, expected)

venv/lib/python3.10/site-packages/pandas/tests/indexes/test_engines.py

@@ -0,0 +1,192 @@
+import re
+
+import numpy as np
+import pytest
+
+from pandas._libs import index as libindex
+
+import pandas as pd
+
+
+@pytest.fixture(
+    params=[
+        (libindex.Int64Engine, np.int64),
+        (libindex.Int32Engine, np.int32),
+        (libindex.Int16Engine, np.int16),
+        (libindex.Int8Engine, np.int8),
+        (libindex.UInt64Engine, np.uint64),
+        (libindex.UInt32Engine, np.uint32),
+        (libindex.UInt16Engine, np.uint16),
+        (libindex.UInt8Engine, np.uint8),
+        (libindex.Float64Engine, np.float64),
+        (libindex.Float32Engine, np.float32),
+    ],
+    ids=lambda x: x[0].__name__,
+)
+def numeric_indexing_engine_type_and_dtype(request):
+    return request.param
+
+
+class TestDatetimeEngine:
+    @pytest.mark.parametrize(
+        "scalar",
+        [
+            pd.Timedelta(pd.Timestamp("2016-01-01").asm8.view("m8[ns]")),
+            pd.Timestamp("2016-01-01")._value,
+            pd.Timestamp("2016-01-01").to_pydatetime(),
+            pd.Timestamp("2016-01-01").to_datetime64(),
+        ],
+    )
+    def test_not_contains_requires_timestamp(self, scalar):
+        dti1 = pd.date_range("2016-01-01", periods=3)
+        dti2 = dti1.insert(1, pd.NaT)  # non-monotonic
+        dti3 = dti1.insert(3, dti1[0])  # non-unique
+        dti4 = pd.date_range("2016-01-01", freq="ns", periods=2_000_000)
+        dti5 = dti4.insert(0, dti4[0])  # over size threshold, not unique
+
+        msg = "|".join([re.escape(str(scalar)), re.escape(repr(scalar))])
+        for dti in [dti1, dti2, dti3, dti4, dti5]:
+            with pytest.raises(TypeError, match=msg):
+                scalar in dti._engine
+
+            with pytest.raises(KeyError, match=msg):
+                dti._engine.get_loc(scalar)
+
+
+class TestTimedeltaEngine:
+    @pytest.mark.parametrize(
+        "scalar",
+        [
+            pd.Timestamp(pd.Timedelta(days=42).asm8.view("datetime64[ns]")),
+            pd.Timedelta(days=42)._value,
+            pd.Timedelta(days=42).to_pytimedelta(),
+            pd.Timedelta(days=42).to_timedelta64(),
+        ],
+    )
+    def test_not_contains_requires_timedelta(self, scalar):
+        tdi1 = pd.timedelta_range("42 days", freq="9h", periods=1234)
+        tdi2 = tdi1.insert(1, pd.NaT)  # non-monotonic
+        tdi3 = tdi1.insert(3, tdi1[0])  # non-unique
+        tdi4 = pd.timedelta_range("42 days", freq="ns", periods=2_000_000)
+        tdi5 = tdi4.insert(0, tdi4[0])  # over size threshold, not unique
+
+        msg = "|".join([re.escape(str(scalar)), re.escape(repr(scalar))])
+        for tdi in [tdi1, tdi2, tdi3, tdi4, tdi5]:
+            with pytest.raises(TypeError, match=msg):
+                scalar in tdi._engine
+
+            with pytest.raises(KeyError, match=msg):
+                tdi._engine.get_loc(scalar)
+
+
+class TestNumericEngine:
+    def test_is_monotonic(self, numeric_indexing_engine_type_and_dtype):
+        engine_type, dtype = numeric_indexing_engine_type_and_dtype
+        num = 1000
+        arr = np.array([1] * num + [2] * num + [3] * num, dtype=dtype)
+
+        # monotonic increasing
+        engine = engine_type(arr)
+        assert engine.is_monotonic_increasing is True
+        assert engine.is_monotonic_decreasing is False
+
+        # monotonic decreasing
+        engine = engine_type(arr[::-1])
+        assert engine.is_monotonic_increasing is False
+        assert engine.is_monotonic_decreasing is True
+
+        # neither monotonic increasing or decreasing
+        arr = np.array([1] * num + [2] * num + [1] * num, dtype=dtype)
+        engine = engine_type(arr[::-1])
+        assert engine.is_monotonic_increasing is False
+        assert engine.is_monotonic_decreasing is False
+
+    def test_is_unique(self, numeric_indexing_engine_type_and_dtype):
+        engine_type, dtype = numeric_indexing_engine_type_and_dtype
+
+        # unique
+        arr = np.array([1, 3, 2], dtype=dtype)
+        engine = engine_type(arr)
+        assert engine.is_unique is True
+
+        # not unique
+        arr = np.array([1, 2, 1], dtype=dtype)
+        engine = engine_type(arr)
+        assert engine.is_unique is False
+
+    def test_get_loc(self, numeric_indexing_engine_type_and_dtype):
+        engine_type, dtype = numeric_indexing_engine_type_and_dtype
+
+        # unique
+        arr = np.array([1, 2, 3], dtype=dtype)
+        engine = engine_type(arr)
+        assert engine.get_loc(2) == 1
+
+        # monotonic
+        num = 1000
+        arr = np.array([1] * num + [2] * num + [3] * num, dtype=dtype)
+        engine = engine_type(arr)
+        assert engine.get_loc(2) == slice(1000, 2000)
+
+        # not monotonic
+        arr = np.array([1, 2, 3] * num, dtype=dtype)
+        engine = engine_type(arr)
+        expected = np.array([False, True, False] * num, dtype=bool)
+        result = engine.get_loc(2)
+        assert (result == expected).all()
+
+
+class TestObjectEngine:
+    engine_type = libindex.ObjectEngine
+    dtype = np.object_
+    values = list("abc")
+
+    def test_is_monotonic(self):
+        num = 1000
+        arr = np.array(["a"] * num + ["a"] * num + ["c"] * num, dtype=self.dtype)
+
+        # monotonic increasing
+        engine = self.engine_type(arr)
+        assert engine.is_monotonic_increasing is True
+        assert engine.is_monotonic_decreasing is False
+
+        # monotonic decreasing
+        engine = self.engine_type(arr[::-1])
+        assert engine.is_monotonic_increasing is False
+        assert engine.is_monotonic_decreasing is True
+
+        # neither monotonic increasing or decreasing
+        arr = np.array(["a"] * num + ["b"] * num + ["a"] * num, dtype=self.dtype)
+        engine = self.engine_type(arr[::-1])
+        assert engine.is_monotonic_increasing is False
+        assert engine.is_monotonic_decreasing is False
+
+    def test_is_unique(self):
+        # unique
+        arr = np.array(self.values, dtype=self.dtype)
+        engine = self.engine_type(arr)
+        assert engine.is_unique is True
+
+        # not unique
+        arr = np.array(["a", "b", "a"], dtype=self.dtype)
+        engine = self.engine_type(arr)
+        assert engine.is_unique is False
+
+    def test_get_loc(self):
+        # unique
+        arr = np.array(self.values, dtype=self.dtype)
+        engine = self.engine_type(arr)
+        assert engine.get_loc("b") == 1
+
+        # monotonic
+        num = 1000
+        arr = np.array(["a"] * num + ["b"] * num + ["c"] * num, dtype=self.dtype)
+        engine = self.engine_type(arr)
+        assert engine.get_loc("b") == slice(1000, 2000)
+
+        # not monotonic
+        arr = np.array(self.values * num, dtype=self.dtype)
+        engine = self.engine_type(arr)
+        expected = np.array([False, True, False] * num, dtype=bool)
+        result = engine.get_loc("b")
+        assert (result == expected).all()

venv/lib/python3.10/site-packages/pandas/tests/indexes/test_frozen.py

@@ -0,0 +1,113 @@
+import re
+
+import pytest
+
+from pandas.core.indexes.frozen import FrozenList
+
+
+@pytest.fixture
+def lst():
+    return [1, 2, 3, 4, 5]
+
+
+@pytest.fixture
+def container(lst):
+    return FrozenList(lst)
+
+
+@pytest.fixture
+def unicode_container():
+    return FrozenList(["\u05d0", "\u05d1", "c"])
+
+
+class TestFrozenList:
+    def check_mutable_error(self, *args, **kwargs):
+        # Pass whatever function you normally would to pytest.raises
+        # (after the Exception kind).
+        mutable_regex = re.compile("does not support mutable operations")
+        msg = "'(_s)?re.(SRE_)?Pattern' object is not callable"
+        with pytest.raises(TypeError, match=msg):
+            mutable_regex(*args, **kwargs)
+
+    def test_no_mutable_funcs(self, container):
+        def setitem():
+            container[0] = 5
+
+        self.check_mutable_error(setitem)
+
+        def setslice():
+            container[1:2] = 3
+
+        self.check_mutable_error(setslice)
+
+        def delitem():
+            del container[0]
+
+        self.check_mutable_error(delitem)
+
+        def delslice():
+            del container[0:3]
+
+        self.check_mutable_error(delslice)
+
+        mutable_methods = ("extend", "pop", "remove", "insert")
+
+        for meth in mutable_methods:
+            self.check_mutable_error(getattr(container, meth))
+
+    def test_slicing_maintains_type(self, container, lst):
+        result = container[1:2]
+        expected = lst[1:2]
+        self.check_result(result, expected)
+
+    def check_result(self, result, expected):
+        assert isinstance(result, FrozenList)
+        assert result == expected
+
+    def test_string_methods_dont_fail(self, container):
+        repr(container)
+        str(container)
+        bytes(container)
+
+    def test_tricky_container(self, unicode_container):
+        repr(unicode_container)
+        str(unicode_container)
+
+    def test_add(self, container, lst):
+        result = container + (1, 2, 3)
+        expected = FrozenList(lst + [1, 2, 3])
+        self.check_result(result, expected)
+
+        result = (1, 2, 3) + container
+        expected = FrozenList([1, 2, 3] + lst)
+        self.check_result(result, expected)
+
+    def test_iadd(self, container, lst):
+        q = r = container
+
+        q += [5]
+        self.check_result(q, lst + [5])
+
+        # Other shouldn't be mutated.
+        self.check_result(r, lst)
+
+    def test_union(self, container, lst):
+        result = container.union((1, 2, 3))
+        expected = FrozenList(lst + [1, 2, 3])
+        self.check_result(result, expected)
+
+    def test_difference(self, container):
+        result = container.difference([2])
+        expected = FrozenList([1, 3, 4, 5])
+        self.check_result(result, expected)
+
+    def test_difference_dupe(self):
+        result = FrozenList([1, 2, 3, 2]).difference([2])
+        expected = FrozenList([1, 3])
+        self.check_result(result, expected)
+
+    def test_tricky_container_to_bytes_raises(self, unicode_container):
+        # GH 26447
+        msg = "^'str' object cannot be interpreted as an integer$"
+        with pytest.raises(TypeError, match=msg):
+            bytes(unicode_container)

venv/lib/python3.10/site-packages/pandas/tests/indexes/test_index_new.py

@@ -0,0 +1,432 @@
+"""
+Tests for the Index constructor conducting inference.
+"""
+from datetime import (
+    datetime,
+    timedelta,
+    timezone,
+)
+from decimal import Decimal
+
+import numpy as np
+import pytest
+
+from pandas._libs.tslibs.timezones import maybe_get_tz
+
+from pandas import (
+    NA,
+    Categorical,
+    CategoricalIndex,
+    DatetimeIndex,
+    Index,
+    IntervalIndex,
+    MultiIndex,
+    NaT,
+    PeriodIndex,
+    Series,
+    TimedeltaIndex,
+    Timestamp,
+    array,
+    date_range,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+class TestIndexConstructorInference:
+    def test_object_all_bools(self):
+        # GH#49594 match Series behavior on ndarray[object] of all bools
+        arr = np.array([True, False], dtype=object)
+        res = Index(arr)
+        assert res.dtype == object
+
+        # since the point is matching Series behavior, let's double check
+        assert Series(arr).dtype == object
+
+    def test_object_all_complex(self):
+        # GH#49594 match Series behavior on ndarray[object] of all complex
+        arr = np.array([complex(1), complex(2)], dtype=object)
+        res = Index(arr)
+        assert res.dtype == object
+
+        # since the point is matching Series behavior, let's double check
+        assert Series(arr).dtype == object
+
+    @pytest.mark.parametrize("val", [NaT, None, np.nan, float("nan")])
+    def test_infer_nat(self, val):
+        # GH#49340 all NaT/None/nan and at least 1 NaT -> datetime64[ns],
+        #  matching Series behavior
+        values = [NaT, val]
+
+        idx = Index(values)
+        assert idx.dtype == "datetime64[ns]" and idx.isna().all()
+
+        idx = Index(values[::-1])
+        assert idx.dtype == "datetime64[ns]" and idx.isna().all()
+
+        idx = Index(np.array(values, dtype=object))
+        assert idx.dtype == "datetime64[ns]" and idx.isna().all()
+
+        idx = Index(np.array(values, dtype=object)[::-1])
+        assert idx.dtype == "datetime64[ns]" and idx.isna().all()
+
+    @pytest.mark.parametrize("na_value", [None, np.nan])
+    @pytest.mark.parametrize("vtype", [list, tuple, iter])
+    def test_construction_list_tuples_nan(self, na_value, vtype):
+        # GH#18505 : valid tuples containing NaN
+        values = [(1, "two"), (3.0, na_value)]
+        result = Index(vtype(values))
+        expected = MultiIndex.from_tuples(values)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "dtype",
+        [int, "int64", "int32", "int16", "int8", "uint64", "uint32", "uint16", "uint8"],
+    )
+    def test_constructor_int_dtype_float(self, dtype):
+        # GH#18400
+        expected = Index([0, 1, 2, 3], dtype=dtype)
+        result = Index([0.0, 1.0, 2.0, 3.0], dtype=dtype)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("cast_index", [True, False])
+    @pytest.mark.parametrize(
+        "vals", [[True, False, True], np.array([True, False, True], dtype=bool)]
+    )
+    def test_constructor_dtypes_to_object(self, cast_index, vals):
+        if cast_index:
+            index = Index(vals, dtype=bool)
+        else:
+            index = Index(vals)
+
+        assert type(index) is Index
+        assert index.dtype == bool
+
+    def test_constructor_categorical_to_object(self):
+        # GH#32167 Categorical data and dtype=object should return object-dtype
+        ci = CategoricalIndex(range(5))
+        result = Index(ci, dtype=object)
+        assert not isinstance(result, CategoricalIndex)
+
+    def test_constructor_infer_periodindex(self):
+        xp = period_range("2012-1-1", freq="M", periods=3)
+        rs = Index(xp)
+        tm.assert_index_equal(rs, xp)
+        assert isinstance(rs, PeriodIndex)
+
+    def test_from_list_of_periods(self):
+        rng = period_range("1/1/2000", periods=20, freq="D")
+        periods = list(rng)
+
+        result = Index(periods)
+        assert isinstance(result, PeriodIndex)
+
+    @pytest.mark.parametrize("pos", [0, 1])
+    @pytest.mark.parametrize(
+        "klass,dtype,ctor",
+        [
+            (DatetimeIndex, "datetime64[ns]", np.datetime64("nat")),
+            (TimedeltaIndex, "timedelta64[ns]", np.timedelta64("nat")),
+        ],
+    )
+    def test_constructor_infer_nat_dt_like(
+        self, pos, klass, dtype, ctor, nulls_fixture, request
+    ):
+        if isinstance(nulls_fixture, Decimal):
+            # We dont cast these to datetime64/timedelta64
+            pytest.skip(
+                f"We don't cast {type(nulls_fixture).__name__} to "
+                "datetime64/timedelta64"
+            )
+
+        expected = klass([NaT, NaT])
+        assert expected.dtype == dtype
+        data = [ctor]
+        data.insert(pos, nulls_fixture)
+
+        warn = None
+        if nulls_fixture is NA:
+            expected = Index([NA, NaT])
+            mark = pytest.mark.xfail(reason="Broken with np.NaT ctor; see GH 31884")
+            request.applymarker(mark)
+            # GH#35942 numpy will emit a DeprecationWarning within the
+            #  assert_index_equal calls.  Since we can't do anything
+            #  about it until GH#31884 is fixed, we suppress that warning.
+            warn = DeprecationWarning
+
+        result = Index(data)
+
+        with tm.assert_produces_warning(warn):
+            tm.assert_index_equal(result, expected)
+
+        result = Index(np.array(data, dtype=object))
+
+        with tm.assert_produces_warning(warn):
+            tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("swap_objs", [True, False])
+    def test_constructor_mixed_nat_objs_infers_object(self, swap_objs):
+        # mixed np.datetime64/timedelta64 nat results in object
+        data = [np.datetime64("nat"), np.timedelta64("nat")]
+        if swap_objs:
+            data = data[::-1]
+
+        expected = Index(data, dtype=object)
+        tm.assert_index_equal(Index(data), expected)
+        tm.assert_index_equal(Index(np.array(data, dtype=object)), expected)
+
+    @pytest.mark.parametrize("swap_objs", [True, False])
+    def test_constructor_datetime_and_datetime64(self, swap_objs):
+        data = [Timestamp(2021, 6, 8, 9, 42), np.datetime64("now")]
+        if swap_objs:
+            data = data[::-1]
+        expected = DatetimeIndex(data)
+
+        tm.assert_index_equal(Index(data), expected)
+        tm.assert_index_equal(Index(np.array(data, dtype=object)), expected)
+
+    def test_constructor_datetimes_mixed_tzs(self):
+        # https://github.com/pandas-dev/pandas/pull/55793/files#r1383719998
+        tz = maybe_get_tz("US/Central")
+        dt1 = datetime(2020, 1, 1, tzinfo=tz)
+        dt2 = datetime(2020, 1, 1, tzinfo=timezone.utc)
+        result = Index([dt1, dt2])
+        expected = Index([dt1, dt2], dtype=object)
+        tm.assert_index_equal(result, expected)
+
+
+class TestDtypeEnforced:
+    # check we don't silently ignore the dtype keyword
+
+    def test_constructor_object_dtype_with_ea_data(self, any_numeric_ea_dtype):
+        # GH#45206
+        arr = array([0], dtype=any_numeric_ea_dtype)
+
+        idx = Index(arr, dtype=object)
+        assert idx.dtype == object
+
+    @pytest.mark.parametrize("dtype", [object, "float64", "uint64", "category"])
+    def test_constructor_range_values_mismatched_dtype(self, dtype):
+        rng = Index(range(5))
+
+        result = Index(rng, dtype=dtype)
+        assert result.dtype == dtype
+
+        result = Index(range(5), dtype=dtype)
+        assert result.dtype == dtype
+
+    @pytest.mark.parametrize("dtype", [object, "float64", "uint64", "category"])
+    def test_constructor_categorical_values_mismatched_non_ea_dtype(self, dtype):
+        cat = Categorical([1, 2, 3])
+
+        result = Index(cat, dtype=dtype)
+        assert result.dtype == dtype
+
+    def test_constructor_categorical_values_mismatched_dtype(self):
+        dti = date_range("2016-01-01", periods=3)
+        cat = Categorical(dti)
+        result = Index(cat, dti.dtype)
+        tm.assert_index_equal(result, dti)
+
+        dti2 = dti.tz_localize("Asia/Tokyo")
+        cat2 = Categorical(dti2)
+        result = Index(cat2, dti2.dtype)
+        tm.assert_index_equal(result, dti2)
+
+        ii = IntervalIndex.from_breaks(range(5))
+        cat3 = Categorical(ii)
+        result = Index(cat3, dtype=ii.dtype)
+        tm.assert_index_equal(result, ii)
+
+    def test_constructor_ea_values_mismatched_categorical_dtype(self):
+        dti = date_range("2016-01-01", periods=3)
+        result = Index(dti, dtype="category")
+        expected = CategoricalIndex(dti)
+        tm.assert_index_equal(result, expected)
+
+        dti2 = date_range("2016-01-01", periods=3, tz="US/Pacific")
+        result = Index(dti2, dtype="category")
+        expected = CategoricalIndex(dti2)
+        tm.assert_index_equal(result, expected)
+
+    def test_constructor_period_values_mismatched_dtype(self):
+        pi = period_range("2016-01-01", periods=3, freq="D")
+        result = Index(pi, dtype="category")
+        expected = CategoricalIndex(pi)
+        tm.assert_index_equal(result, expected)
+
+    def test_constructor_timedelta64_values_mismatched_dtype(self):
+        # check we don't silently ignore the dtype keyword
+        tdi = timedelta_range("4 Days", periods=5)
+        result = Index(tdi, dtype="category")
+        expected = CategoricalIndex(tdi)
+        tm.assert_index_equal(result, expected)
+
+    def test_constructor_interval_values_mismatched_dtype(self):
+        dti = date_range("2016-01-01", periods=3)
+        ii = IntervalIndex.from_breaks(dti)
+        result = Index(ii, dtype="category")
+        expected = CategoricalIndex(ii)
+        tm.assert_index_equal(result, expected)
+
+    def test_constructor_datetime64_values_mismatched_period_dtype(self):
+        dti = date_range("2016-01-01", periods=3)
+        result = Index(dti, dtype="Period[D]")
+        expected = dti.to_period("D")
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", ["int64", "uint64"])
+    def test_constructor_int_dtype_nan_raises(self, dtype):
+        # see GH#15187
+        data = [np.nan]
+        msg = "cannot convert"
+        with pytest.raises(ValueError, match=msg):
+            Index(data, dtype=dtype)
+
+    @pytest.mark.parametrize(
+        "vals",
+        [
+            [1, 2, 3],
+            np.array([1, 2, 3]),
+            np.array([1, 2, 3], dtype=int),
+            # below should coerce
+            [1.0, 2.0, 3.0],
+            np.array([1.0, 2.0, 3.0], dtype=float),
+        ],
+    )
+    def test_constructor_dtypes_to_int(self, vals, any_int_numpy_dtype):
+        dtype = any_int_numpy_dtype
+        index = Index(vals, dtype=dtype)
+        assert index.dtype == dtype
+
+    @pytest.mark.parametrize(
+        "vals",
+        [
+            [1, 2, 3],
+            [1.0, 2.0, 3.0],
+            np.array([1.0, 2.0, 3.0]),
+            np.array([1, 2, 3], dtype=int),
+            np.array([1.0, 2.0, 3.0], dtype=float),
+        ],
+    )
+    def test_constructor_dtypes_to_float(self, vals, float_numpy_dtype):
+        dtype = float_numpy_dtype
+        index = Index(vals, dtype=dtype)
+        assert index.dtype == dtype
+
+    @pytest.mark.parametrize(
+        "vals",
+        [
+            [1, 2, 3],
+            np.array([1, 2, 3], dtype=int),
+            np.array(["2011-01-01", "2011-01-02"], dtype="datetime64[ns]"),
+            [datetime(2011, 1, 1), datetime(2011, 1, 2)],
+        ],
+    )
+    def test_constructor_dtypes_to_categorical(self, vals):
+        index = Index(vals, dtype="category")
+        assert isinstance(index, CategoricalIndex)
+
+    @pytest.mark.parametrize("cast_index", [True, False])
+    @pytest.mark.parametrize(
+        "vals",
+        [
+            Index(np.array([np.datetime64("2011-01-01"), np.datetime64("2011-01-02")])),
+            Index([datetime(2011, 1, 1), datetime(2011, 1, 2)]),
+        ],
+    )
+    def test_constructor_dtypes_to_datetime(self, cast_index, vals):
+        if cast_index:
+            index = Index(vals, dtype=object)
+            assert isinstance(index, Index)
+            assert index.dtype == object
+        else:
+            index = Index(vals)
+            assert isinstance(index, DatetimeIndex)
+
+    @pytest.mark.parametrize("cast_index", [True, False])
+    @pytest.mark.parametrize(
+        "vals",
+        [
+            np.array([np.timedelta64(1, "D"), np.timedelta64(1, "D")]),
+            [timedelta(1), timedelta(1)],
+        ],
+    )
+    def test_constructor_dtypes_to_timedelta(self, cast_index, vals):
+        if cast_index:
+            index = Index(vals, dtype=object)
+            assert isinstance(index, Index)
+            assert index.dtype == object
+        else:
+            index = Index(vals)
+            assert isinstance(index, TimedeltaIndex)
+
+    def test_pass_timedeltaindex_to_index(self):
+        rng = timedelta_range("1 days", "10 days")
+        idx = Index(rng, dtype=object)
+
+        expected = Index(rng.to_pytimedelta(), dtype=object)
+
+        tm.assert_numpy_array_equal(idx.values, expected.values)
+
+    def test_pass_datetimeindex_to_index(self):
+        # GH#1396
+        rng = date_range("1/1/2000", "3/1/2000")
+        idx = Index(rng, dtype=object)
+
+        expected = Index(rng.to_pydatetime(), dtype=object)
+
+        tm.assert_numpy_array_equal(idx.values, expected.values)
+
+
+class TestIndexConstructorUnwrapping:
+    # Test passing different arraylike values to pd.Index
+
+    @pytest.mark.parametrize("klass", [Index, DatetimeIndex])
+    def test_constructor_from_series_dt64(self, klass):
+        stamps = [Timestamp("20110101"), Timestamp("20120101"), Timestamp("20130101")]
+        expected = DatetimeIndex(stamps)
+        ser = Series(stamps)
+        result = klass(ser)
+        tm.assert_index_equal(result, expected)
+
+    def test_constructor_no_pandas_array(self):
+        ser = Series([1, 2, 3])
+        result = Index(ser.array)
+        expected = Index([1, 2, 3])
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "array",
+        [
+            np.arange(5),
+            np.array(["a", "b", "c"]),
+            date_range("2000-01-01", periods=3).values,
+        ],
+    )
+    def test_constructor_ndarray_like(self, array):
+        # GH#5460#issuecomment-44474502
+        # it should be possible to convert any object that satisfies the numpy
+        # ndarray interface directly into an Index
+        class ArrayLike:
+            def __init__(self, array) -> None:
+                self.array = array
+
+            def __array__(self, dtype=None, copy=None) -> np.ndarray:
+                return self.array
+
+        expected = Index(array)
+        result = Index(ArrayLike(array))
+        tm.assert_index_equal(result, expected)
+
+
+class TestIndexConstructionErrors:
+    def test_constructor_overflow_int64(self):
+        # see GH#15832
+        msg = (
+            "The elements provided in the data cannot "
+            "all be casted to the dtype int64"
+        )
+        with pytest.raises(OverflowError, match=msg):
+            Index([np.iinfo(np.uint64).max - 1], dtype="int64")

venv/lib/python3.10/site-packages/pandas/tests/indexes/test_indexing.py

@@ -0,0 +1,357 @@
+"""
+test_indexing tests the following Index methods:
+    __getitem__
+    get_loc
+    get_value
+    __contains__
+    take
+    where
+    get_indexer
+    get_indexer_for
+    slice_locs
+    asof_locs
+
+The corresponding tests.indexes.[index_type].test_indexing files
+contain tests for the corresponding methods specific to those Index subclasses.
+"""
+import numpy as np
+import pytest
+
+from pandas.errors import InvalidIndexError
+
+from pandas.core.dtypes.common import (
+    is_float_dtype,
+    is_scalar,
+)
+
+from pandas import (
+    NA,
+    DatetimeIndex,
+    Index,
+    IntervalIndex,
+    MultiIndex,
+    NaT,
+    PeriodIndex,
+    TimedeltaIndex,
+)
+import pandas._testing as tm
+
+
+class TestTake:
+    def test_take_invalid_kwargs(self, index):
+        indices = [1, 2]
+
+        msg = r"take\(\) got an unexpected keyword argument 'foo'"
+        with pytest.raises(TypeError, match=msg):
+            index.take(indices, foo=2)
+
+        msg = "the 'out' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            index.take(indices, out=indices)
+
+        msg = "the 'mode' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            index.take(indices, mode="clip")
+
+    def test_take(self, index):
+        indexer = [4, 3, 0, 2]
+        if len(index) < 5:
+            pytest.skip("Test doesn't make sense since not enough elements")
+
+        result = index.take(indexer)
+        expected = index[indexer]
+        assert result.equals(expected)
+
+        if not isinstance(index, (DatetimeIndex, PeriodIndex, TimedeltaIndex)):
+            # GH 10791
+            msg = r"'(.*Index)' object has no attribute 'freq'"
+            with pytest.raises(AttributeError, match=msg):
+                index.freq
+
+    def test_take_indexer_type(self):
+        # GH#42875
+        integer_index = Index([0, 1, 2, 3])
+        scalar_index = 1
+        msg = "Expected indices to be array-like"
+        with pytest.raises(TypeError, match=msg):
+            integer_index.take(scalar_index)
+
+    def test_take_minus1_without_fill(self, index):
+        # -1 does not get treated as NA unless allow_fill=True is passed
+        if len(index) == 0:
+            # Test is not applicable
+            pytest.skip("Test doesn't make sense for empty index")
+
+        result = index.take([0, 0, -1])
+
+        expected = index.take([0, 0, len(index) - 1])
+        tm.assert_index_equal(result, expected)
+
+
+class TestContains:
+    @pytest.mark.parametrize(
+        "index,val",
+        [
+            (Index([0, 1, 2]), 2),
+            (Index([0, 1, "2"]), "2"),
+            (Index([0, 1, 2, np.inf, 4]), 4),
+            (Index([0, 1, 2, np.nan, 4]), 4),
+            (Index([0, 1, 2, np.inf]), np.inf),
+            (Index([0, 1, 2, np.nan]), np.nan),
+        ],
+    )
+    def test_index_contains(self, index, val):
+        assert val in index
+
+    @pytest.mark.parametrize(
+        "index,val",
+        [
+            (Index([0, 1, 2]), "2"),
+            (Index([0, 1, "2"]), 2),
+            (Index([0, 1, 2, np.inf]), 4),
+            (Index([0, 1, 2, np.nan]), 4),
+            (Index([0, 1, 2, np.inf]), np.nan),
+            (Index([0, 1, 2, np.nan]), np.inf),
+            # Checking if np.inf in int64 Index should not cause an OverflowError
+            # Related to GH 16957
+            (Index([0, 1, 2], dtype=np.int64), np.inf),
+            (Index([0, 1, 2], dtype=np.int64), np.nan),
+            (Index([0, 1, 2], dtype=np.uint64), np.inf),
+            (Index([0, 1, 2], dtype=np.uint64), np.nan),
+        ],
+    )
+    def test_index_not_contains(self, index, val):
+        assert val not in index
+
+    @pytest.mark.parametrize(
+        "index,val", [(Index([0, 1, "2"]), 0), (Index([0, 1, "2"]), "2")]
+    )
+    def test_mixed_index_contains(self, index, val):
+        # GH#19860
+        assert val in index
+
+    @pytest.mark.parametrize(
+        "index,val", [(Index([0, 1, "2"]), "1"), (Index([0, 1, "2"]), 2)]
+    )
+    def test_mixed_index_not_contains(self, index, val):
+        # GH#19860
+        assert val not in index
+
+    def test_contains_with_float_index(self, any_real_numpy_dtype):
+        # GH#22085
+        dtype = any_real_numpy_dtype
+        data = [0, 1, 2, 3] if not is_float_dtype(dtype) else [0.1, 1.1, 2.2, 3.3]
+        index = Index(data, dtype=dtype)
+
+        if not is_float_dtype(index.dtype):
+            assert 1.1 not in index
+            assert 1.0 in index
+            assert 1 in index
+        else:
+            assert 1.1 in index
+            assert 1.0 not in index
+            assert 1 not in index
+
+    def test_contains_requires_hashable_raises(self, index):
+        if isinstance(index, MultiIndex):
+            return  # TODO: do we want this to raise?
+
+        msg = "unhashable type: 'list'"
+        with pytest.raises(TypeError, match=msg):
+            [] in index
+
+        msg = "|".join(
+            [
+                r"unhashable type: 'dict'",
+                r"must be real number, not dict",
+                r"an integer is required",
+                r"\{\}",
+                r"pandas\._libs\.interval\.IntervalTree' is not iterable",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            {} in index._engine
+
+
+class TestGetLoc:
+    def test_get_loc_non_hashable(self, index):
+        with pytest.raises(InvalidIndexError, match="[0, 1]"):
+            index.get_loc([0, 1])
+
+    def test_get_loc_non_scalar_hashable(self, index):
+        # GH52877
+        from enum import Enum
+
+        class E(Enum):
+            X1 = "x1"
+
+        assert not is_scalar(E.X1)
+
+        exc = KeyError
+        msg = "<E.X1: 'x1'>"
+        if isinstance(
+            index,
+            (
+                DatetimeIndex,
+                TimedeltaIndex,
+                PeriodIndex,
+                IntervalIndex,
+            ),
+        ):
+            # TODO: make these more consistent?
+            exc = InvalidIndexError
+            msg = "E.X1"
+        with pytest.raises(exc, match=msg):
+            index.get_loc(E.X1)
+
+    def test_get_loc_generator(self, index):
+        exc = KeyError
+        if isinstance(
+            index,
+            (
+                DatetimeIndex,
+                TimedeltaIndex,
+                PeriodIndex,
+                IntervalIndex,
+                MultiIndex,
+            ),
+        ):
+            # TODO: make these more consistent?
+            exc = InvalidIndexError
+        with pytest.raises(exc, match="generator object"):
+            # MultiIndex specifically checks for generator; others for scalar
+            index.get_loc(x for x in range(5))
+
+    def test_get_loc_masked_duplicated_na(self):
+        # GH#48411
+        idx = Index([1, 2, NA, NA], dtype="Int64")
+        result = idx.get_loc(NA)
+        expected = np.array([False, False, True, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+
+class TestGetIndexer:
+    def test_get_indexer_base(self, index):
+        if index._index_as_unique:
+            expected = np.arange(index.size, dtype=np.intp)
+            actual = index.get_indexer(index)
+            tm.assert_numpy_array_equal(expected, actual)
+        else:
+            msg = "Reindexing only valid with uniquely valued Index objects"
+            with pytest.raises(InvalidIndexError, match=msg):
+                index.get_indexer(index)
+
+        with pytest.raises(ValueError, match="Invalid fill method"):
+            index.get_indexer(index, method="invalid")
+
+    def test_get_indexer_consistency(self, index):
+        # See GH#16819
+
+        if index._index_as_unique:
+            indexer = index.get_indexer(index[0:2])
+            assert isinstance(indexer, np.ndarray)
+            assert indexer.dtype == np.intp
+        else:
+            msg = "Reindexing only valid with uniquely valued Index objects"
+            with pytest.raises(InvalidIndexError, match=msg):
+                index.get_indexer(index[0:2])
+
+        indexer, _ = index.get_indexer_non_unique(index[0:2])
+        assert isinstance(indexer, np.ndarray)
+        assert indexer.dtype == np.intp
+
+    def test_get_indexer_masked_duplicated_na(self):
+        # GH#48411
+        idx = Index([1, 2, NA, NA], dtype="Int64")
+        result = idx.get_indexer_for(Index([1, NA], dtype="Int64"))
+        expected = np.array([0, 2, 3], dtype=result.dtype)
+        tm.assert_numpy_array_equal(result, expected)
+
+
+class TestConvertSliceIndexer:
+    def test_convert_almost_null_slice(self, index):
+        # slice with None at both ends, but not step
+
+        key = slice(None, None, "foo")
+
+        if isinstance(index, IntervalIndex):
+            msg = "label-based slicing with step!=1 is not supported for IntervalIndex"
+            with pytest.raises(ValueError, match=msg):
+                index._convert_slice_indexer(key, "loc")
+        else:
+            msg = "'>=' not supported between instances of 'str' and 'int'"
+            with pytest.raises(TypeError, match=msg):
+                index._convert_slice_indexer(key, "loc")
+
+
+class TestPutmask:
+    def test_putmask_with_wrong_mask(self, index):
+        # GH#18368
+        if not len(index):
+            pytest.skip("Test doesn't make sense for empty index")
+
+        fill = index[0]
+
+        msg = "putmask: mask and data must be the same size"
+        with pytest.raises(ValueError, match=msg):
+            index.putmask(np.ones(len(index) + 1, np.bool_), fill)
+
+        with pytest.raises(ValueError, match=msg):
+            index.putmask(np.ones(len(index) - 1, np.bool_), fill)
+
+        with pytest.raises(ValueError, match=msg):
+            index.putmask("foo", fill)
+
+
+@pytest.mark.parametrize(
+    "idx", [Index([1, 2, 3]), Index([0.1, 0.2, 0.3]), Index(["a", "b", "c"])]
+)
+def test_getitem_deprecated_float(idx):
+    # https://github.com/pandas-dev/pandas/issues/34191
+
+    msg = "Indexing with a float is no longer supported"
+    with pytest.raises(IndexError, match=msg):
+        idx[1.0]
+
+
+@pytest.mark.parametrize(
+    "idx,target,expected",
+    [
+        ([np.nan, "var1", np.nan], [np.nan], np.array([0, 2], dtype=np.intp)),
+        (
+            [np.nan, "var1", np.nan],
+            [np.nan, "var1"],
+            np.array([0, 2, 1], dtype=np.intp),
+        ),
+        (
+            np.array([np.nan, "var1", np.nan], dtype=object),
+            [np.nan],
+            np.array([0, 2], dtype=np.intp),
+        ),
+        (
+            DatetimeIndex(["2020-08-05", NaT, NaT]),
+            [NaT],
+            np.array([1, 2], dtype=np.intp),
+        ),
+        (["a", "b", "a", np.nan], [np.nan], np.array([3], dtype=np.intp)),
+        (
+            np.array(["b", np.nan, float("NaN"), "b"], dtype=object),
+            Index([np.nan], dtype=object),
+            np.array([1, 2], dtype=np.intp),
+        ),
+    ],
+)
+def test_get_indexer_non_unique_multiple_nans(idx, target, expected):
+    # GH 35392
+    axis = Index(idx)
+    actual = axis.get_indexer_for(target)
+    tm.assert_numpy_array_equal(actual, expected)
+
+
+def test_get_indexer_non_unique_nans_in_object_dtype_target(nulls_fixture):
+    idx = Index([1.0, 2.0])
+    target = Index([1, nulls_fixture], dtype="object")
+
+    result_idx, result_missing = idx.get_indexer_non_unique(target)
+    tm.assert_numpy_array_equal(result_idx, np.array([0, -1], dtype=np.intp))
+    tm.assert_numpy_array_equal(result_missing, np.array([1], dtype=np.intp))

venv/lib/python3.10/site-packages/pandas/tests/indexes/test_numpy_compat.py

@@ -0,0 +1,189 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    CategoricalIndex,
+    DatetimeIndex,
+    Index,
+    PeriodIndex,
+    TimedeltaIndex,
+    isna,
+)
+import pandas._testing as tm
+from pandas.api.types import (
+    is_complex_dtype,
+    is_numeric_dtype,
+)
+from pandas.core.arrays import BooleanArray
+from pandas.core.indexes.datetimelike import DatetimeIndexOpsMixin
+
+
+def test_numpy_ufuncs_out(index):
+    result = index == index
+
+    out = np.empty(index.shape, dtype=bool)
+    np.equal(index, index, out=out)
+    tm.assert_numpy_array_equal(out, result)
+
+    if not index._is_multi:
+        # same thing on the ExtensionArray
+        out = np.empty(index.shape, dtype=bool)
+        np.equal(index.array, index.array, out=out)
+        tm.assert_numpy_array_equal(out, result)
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        np.exp,
+        np.exp2,
+        np.expm1,
+        np.log,
+        np.log2,
+        np.log10,
+        np.log1p,
+        np.sqrt,
+        np.sin,
+        np.cos,
+        np.tan,
+        np.arcsin,
+        np.arccos,
+        np.arctan,
+        np.sinh,
+        np.cosh,
+        np.tanh,
+        np.arcsinh,
+        np.arccosh,
+        np.arctanh,
+        np.deg2rad,
+        np.rad2deg,
+    ],
+    ids=lambda x: x.__name__,
+)
+def test_numpy_ufuncs_basic(index, func):
+    # test ufuncs of numpy, see:
+    # https://numpy.org/doc/stable/reference/ufuncs.html
+
+    if isinstance(index, DatetimeIndexOpsMixin):
+        with tm.external_error_raised((TypeError, AttributeError)):
+            with np.errstate(all="ignore"):
+                func(index)
+    elif is_numeric_dtype(index) and not (
+        is_complex_dtype(index) and func in [np.deg2rad, np.rad2deg]
+    ):
+        # coerces to float (e.g. np.sin)
+        with np.errstate(all="ignore"):
+            result = func(index)
+            arr_result = func(index.values)
+            if arr_result.dtype == np.float16:
+                arr_result = arr_result.astype(np.float32)
+            exp = Index(arr_result, name=index.name)
+
+        tm.assert_index_equal(result, exp)
+        if isinstance(index.dtype, np.dtype) and is_numeric_dtype(index):
+            if is_complex_dtype(index):
+                assert result.dtype == index.dtype
+            elif index.dtype in ["bool", "int8", "uint8"]:
+                assert result.dtype in ["float16", "float32"]
+            elif index.dtype in ["int16", "uint16", "float32"]:
+                assert result.dtype == "float32"
+            else:
+                assert result.dtype == "float64"
+        else:
+            # e.g. np.exp with Int64 -> Float64
+            assert type(result) is Index
+    # raise AttributeError or TypeError
+    elif len(index) == 0:
+        pass
+    else:
+        with tm.external_error_raised((TypeError, AttributeError)):
+            with np.errstate(all="ignore"):
+                func(index)
+
+
+@pytest.mark.parametrize(
+    "func", [np.isfinite, np.isinf, np.isnan, np.signbit], ids=lambda x: x.__name__
+)
+def test_numpy_ufuncs_other(index, func):
+    # test ufuncs of numpy, see:
+    # https://numpy.org/doc/stable/reference/ufuncs.html
+    if isinstance(index, (DatetimeIndex, TimedeltaIndex)):
+        if func in (np.isfinite, np.isinf, np.isnan):
+            # numpy 1.18 changed isinf and isnan to not raise on dt64/td64
+            result = func(index)
+            assert isinstance(result, np.ndarray)
+
+            out = np.empty(index.shape, dtype=bool)
+            func(index, out=out)
+            tm.assert_numpy_array_equal(out, result)
+        else:
+            with tm.external_error_raised(TypeError):
+                func(index)
+
+    elif isinstance(index, PeriodIndex):
+        with tm.external_error_raised(TypeError):
+            func(index)
+
+    elif is_numeric_dtype(index) and not (
+        is_complex_dtype(index) and func is np.signbit
+    ):
+        # Results in bool array
+        result = func(index)
+        if not isinstance(index.dtype, np.dtype):
+            # e.g. Int64 we expect to get BooleanArray back
+            assert isinstance(result, BooleanArray)
+        else:
+            assert isinstance(result, np.ndarray)
+
+        out = np.empty(index.shape, dtype=bool)
+        func(index, out=out)
+
+        if not isinstance(index.dtype, np.dtype):
+            tm.assert_numpy_array_equal(out, result._data)
+        else:
+            tm.assert_numpy_array_equal(out, result)
+
+    elif len(index) == 0:
+        pass
+    else:
+        with tm.external_error_raised(TypeError):
+            func(index)
+
+
+@pytest.mark.parametrize("func", [np.maximum, np.minimum])
+def test_numpy_ufuncs_reductions(index, func, request):
+    # TODO: overlap with tests.series.test_ufunc.test_reductions
+    if len(index) == 0:
+        pytest.skip("Test doesn't make sense for empty index.")
+
+    if isinstance(index, CategoricalIndex) and index.dtype.ordered is False:
+        with pytest.raises(TypeError, match="is not ordered for"):
+            func.reduce(index)
+        return
+    else:
+        result = func.reduce(index)
+
+    if func is np.maximum:
+        expected = index.max(skipna=False)
+    else:
+        expected = index.min(skipna=False)
+        # TODO: do we have cases both with and without NAs?
+
+    assert type(result) is type(expected)
+    if isna(result):
+        assert isna(expected)
+    else:
+        assert result == expected
+
+
+@pytest.mark.parametrize("func", [np.bitwise_and, np.bitwise_or, np.bitwise_xor])
+def test_numpy_ufuncs_bitwise(func):
+    # https://github.com/pandas-dev/pandas/issues/46769
+    idx1 = Index([1, 2, 3, 4], dtype="int64")
+    idx2 = Index([3, 4, 5, 6], dtype="int64")
+
+    with tm.assert_produces_warning(None):
+        result = func(idx1, idx2)
+
+    expected = Index(func(idx1.values, idx2.values))
+    tm.assert_index_equal(result, expected)

venv/lib/python3.10/site-packages/pandas/tests/indexes/test_old_base.py

@@ -0,0 +1,1061 @@
+from __future__ import annotations
+
+from datetime import datetime
+import weakref
+
+import numpy as np
+import pytest
+
+from pandas._config import using_pyarrow_string_dtype
+
+from pandas._libs.tslibs import Timestamp
+
+from pandas.core.dtypes.common import (
+    is_integer_dtype,
+    is_numeric_dtype,
+)
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+import pandas as pd
+from pandas import (
+    CategoricalIndex,
+    DatetimeIndex,
+    DatetimeTZDtype,
+    Index,
+    IntervalIndex,
+    MultiIndex,
+    PeriodIndex,
+    RangeIndex,
+    Series,
+    TimedeltaIndex,
+    isna,
+    period_range,
+)
+import pandas._testing as tm
+import pandas.core.algorithms as algos
+from pandas.core.arrays import BaseMaskedArray
+
+
+class TestBase:
+    @pytest.fixture(
+        params=[
+            RangeIndex(start=0, stop=20, step=2),
+            Index(np.arange(5, dtype=np.float64)),
+            Index(np.arange(5, dtype=np.float32)),
+            Index(np.arange(5, dtype=np.uint64)),
+            Index(range(0, 20, 2), dtype=np.int64),
+            Index(range(0, 20, 2), dtype=np.int32),
+            Index(range(0, 20, 2), dtype=np.int16),
+            Index(range(0, 20, 2), dtype=np.int8),
+            Index(list("abcde")),
+            Index([0, "a", 1, "b", 2, "c"]),
+            period_range("20130101", periods=5, freq="D"),
+            TimedeltaIndex(
+                [
+                    "0 days 01:00:00",
+                    "1 days 01:00:00",
+                    "2 days 01:00:00",
+                    "3 days 01:00:00",
+                    "4 days 01:00:00",
+                ],
+                dtype="timedelta64[ns]",
+                freq="D",
+            ),
+            DatetimeIndex(
+                ["2013-01-01", "2013-01-02", "2013-01-03", "2013-01-04", "2013-01-05"],
+                dtype="datetime64[ns]",
+                freq="D",
+            ),
+            IntervalIndex.from_breaks(range(11), closed="right"),
+        ]
+    )
+    def simple_index(self, request):
+        return request.param
+
+    def test_pickle_compat_construction(self, simple_index):
+        # need an object to create with
+        if isinstance(simple_index, RangeIndex):
+            pytest.skip("RangeIndex() is a valid constructor")
+        msg = "|".join(
+            [
+                r"Index\(\.\.\.\) must be called with a collection of some "
+                r"kind, None was passed",
+                r"DatetimeIndex\(\) must be called with a collection of some "
+                r"kind, None was passed",
+                r"TimedeltaIndex\(\) must be called with a collection of some "
+                r"kind, None was passed",
+                r"__new__\(\) missing 1 required positional argument: 'data'",
+                r"__new__\(\) takes at least 2 arguments \(1 given\)",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            type(simple_index)()
+
+    def test_shift(self, simple_index):
+        # GH8083 test the base class for shift
+        if isinstance(simple_index, (DatetimeIndex, TimedeltaIndex, PeriodIndex)):
+            pytest.skip("Tested in test_ops/test_arithmetic")
+        idx = simple_index
+        msg = (
+            f"This method is only implemented for DatetimeIndex, PeriodIndex and "
+            f"TimedeltaIndex; Got type {type(idx).__name__}"
+        )
+        with pytest.raises(NotImplementedError, match=msg):
+            idx.shift(1)
+        with pytest.raises(NotImplementedError, match=msg):
+            idx.shift(1, 2)
+
+    def test_constructor_name_unhashable(self, simple_index):
+        # GH#29069 check that name is hashable
+        # See also same-named test in tests.series.test_constructors
+        idx = simple_index
+        with pytest.raises(TypeError, match="Index.name must be a hashable type"):
+            type(idx)(idx, name=[])
+
+    def test_create_index_existing_name(self, simple_index):
+        # GH11193, when an existing index is passed, and a new name is not
+        # specified, the new index should inherit the previous object name
+        expected = simple_index.copy()
+        if not isinstance(expected, MultiIndex):
+            expected.name = "foo"
+            result = Index(expected)
+            tm.assert_index_equal(result, expected)
+
+            result = Index(expected, name="bar")
+            expected.name = "bar"
+            tm.assert_index_equal(result, expected)
+        else:
+            expected.names = ["foo", "bar"]
+            result = Index(expected)
+            tm.assert_index_equal(
+                result,
+                Index(
+                    Index(
+                        [
+                            ("foo", "one"),
+                            ("foo", "two"),
+                            ("bar", "one"),
+                            ("baz", "two"),
+                            ("qux", "one"),
+                            ("qux", "two"),
+                        ],
+                        dtype="object",
+                    ),
+                    names=["foo", "bar"],
+                ),
+            )
+
+            result = Index(expected, names=["A", "B"])
+            tm.assert_index_equal(
+                result,
+                Index(
+                    Index(
+                        [
+                            ("foo", "one"),
+                            ("foo", "two"),
+                            ("bar", "one"),
+                            ("baz", "two"),
+                            ("qux", "one"),
+                            ("qux", "two"),
+                        ],
+                        dtype="object",
+                    ),
+                    names=["A", "B"],
+                ),
+            )
+
+    def test_numeric_compat(self, simple_index):
+        idx = simple_index
+        # Check that this doesn't cover MultiIndex case, if/when it does,
+        #  we can remove multi.test_compat.test_numeric_compat
+        assert not isinstance(idx, MultiIndex)
+        if type(idx) is Index:
+            pytest.skip("Not applicable for Index")
+        if is_numeric_dtype(simple_index.dtype) or isinstance(
+            simple_index, TimedeltaIndex
+        ):
+            pytest.skip("Tested elsewhere.")
+
+        typ = type(idx._data).__name__
+        cls = type(idx).__name__
+        lmsg = "|".join(
+            [
+                rf"unsupported operand type\(s\) for \*: '{typ}' and 'int'",
+                "cannot perform (__mul__|__truediv__|__floordiv__) with "
+                f"this index type: ({cls}|{typ})",
+            ]
+        )
+        with pytest.raises(TypeError, match=lmsg):
+            idx * 1
+        rmsg = "|".join(
+            [
+                rf"unsupported operand type\(s\) for \*: 'int' and '{typ}'",
+                "cannot perform (__rmul__|__rtruediv__|__rfloordiv__) with "
+                f"this index type: ({cls}|{typ})",
+            ]
+        )
+        with pytest.raises(TypeError, match=rmsg):
+            1 * idx
+
+        div_err = lmsg.replace("*", "/")
+        with pytest.raises(TypeError, match=div_err):
+            idx / 1
+        div_err = rmsg.replace("*", "/")
+        with pytest.raises(TypeError, match=div_err):
+            1 / idx
+
+        floordiv_err = lmsg.replace("*", "//")
+        with pytest.raises(TypeError, match=floordiv_err):
+            idx // 1
+        floordiv_err = rmsg.replace("*", "//")
+        with pytest.raises(TypeError, match=floordiv_err):
+            1 // idx
+
+    def test_logical_compat(self, simple_index):
+        if simple_index.dtype in (object, "string"):
+            pytest.skip("Tested elsewhere.")
+        idx = simple_index
+        if idx.dtype.kind in "iufcbm":
+            assert idx.all() == idx._values.all()
+            assert idx.all() == idx.to_series().all()
+            assert idx.any() == idx._values.any()
+            assert idx.any() == idx.to_series().any()
+        else:
+            msg = "cannot perform (any|all)"
+            if isinstance(idx, IntervalIndex):
+                msg = (
+                    r"'IntervalArray' with dtype interval\[.*\] does "
+                    "not support reduction '(any|all)'"
+                )
+            with pytest.raises(TypeError, match=msg):
+                idx.all()
+            with pytest.raises(TypeError, match=msg):
+                idx.any()
+
+    def test_repr_roundtrip(self, simple_index):
+        if isinstance(simple_index, IntervalIndex):
+            pytest.skip(f"Not a valid repr for {type(simple_index).__name__}")
+        idx = simple_index
+        tm.assert_index_equal(eval(repr(idx)), idx)
+
+    def test_repr_max_seq_item_setting(self, simple_index):
+        # GH10182
+        if isinstance(simple_index, IntervalIndex):
+            pytest.skip(f"Not a valid repr for {type(simple_index).__name__}")
+        idx = simple_index
+        idx = idx.repeat(50)
+        with pd.option_context("display.max_seq_items", None):
+            repr(idx)
+            assert "..." not in str(idx)
+
+    @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+    def test_ensure_copied_data(self, index):
+        # Check the "copy" argument of each Index.__new__ is honoured
+        # GH12309
+        init_kwargs = {}
+        if isinstance(index, PeriodIndex):
+            # Needs "freq" specification:
+            init_kwargs["freq"] = index.freq
+        elif isinstance(index, (RangeIndex, MultiIndex, CategoricalIndex)):
+            pytest.skip(
+                "RangeIndex cannot be initialized from data, "
+                "MultiIndex and CategoricalIndex are tested separately"
+            )
+        elif index.dtype == object and index.inferred_type == "boolean":
+            init_kwargs["dtype"] = index.dtype
+
+        index_type = type(index)
+        result = index_type(index.values, copy=True, **init_kwargs)
+        if isinstance(index.dtype, DatetimeTZDtype):
+            result = result.tz_localize("UTC").tz_convert(index.tz)
+        if isinstance(index, (DatetimeIndex, TimedeltaIndex)):
+            index = index._with_freq(None)
+
+        tm.assert_index_equal(index, result)
+
+        if isinstance(index, PeriodIndex):
+            # .values an object array of Period, thus copied
+            depr_msg = "The 'ordinal' keyword in PeriodIndex is deprecated"
+            with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+                result = index_type(ordinal=index.asi8, copy=False, **init_kwargs)
+            tm.assert_numpy_array_equal(index.asi8, result.asi8, check_same="same")
+        elif isinstance(index, IntervalIndex):
+            # checked in test_interval.py
+            pass
+        elif type(index) is Index and not isinstance(index.dtype, np.dtype):
+            result = index_type(index.values, copy=False, **init_kwargs)
+            tm.assert_index_equal(result, index)
+
+            if isinstance(index._values, BaseMaskedArray):
+                assert np.shares_memory(index._values._data, result._values._data)
+                tm.assert_numpy_array_equal(
+                    index._values._data, result._values._data, check_same="same"
+                )
+                assert np.shares_memory(index._values._mask, result._values._mask)
+                tm.assert_numpy_array_equal(
+                    index._values._mask, result._values._mask, check_same="same"
+                )
+            elif index.dtype == "string[python]":
+                assert np.shares_memory(index._values._ndarray, result._values._ndarray)
+                tm.assert_numpy_array_equal(
+                    index._values._ndarray, result._values._ndarray, check_same="same"
+                )
+            elif index.dtype in ("string[pyarrow]", "string[pyarrow_numpy]"):
+                assert tm.shares_memory(result._values, index._values)
+            else:
+                raise NotImplementedError(index.dtype)
+        else:
+            result = index_type(index.values, copy=False, **init_kwargs)
+            tm.assert_numpy_array_equal(index.values, result.values, check_same="same")
+
+    def test_memory_usage(self, index):
+        index._engine.clear_mapping()
+        result = index.memory_usage()
+        if index.empty:
+            # we report 0 for no-length
+            assert result == 0
+            return
+
+        # non-zero length
+        index.get_loc(index[0])
+        result2 = index.memory_usage()
+        result3 = index.memory_usage(deep=True)
+
+        # RangeIndex, IntervalIndex
+        # don't have engines
+        # Index[EA] has engine but it does not have a Hashtable .mapping
+        if not isinstance(index, (RangeIndex, IntervalIndex)) and not (
+            type(index) is Index and not isinstance(index.dtype, np.dtype)
+        ):
+            assert result2 > result
+
+        if index.inferred_type == "object":
+            assert result3 > result2
+
+    def test_argsort(self, index):
+        if isinstance(index, CategoricalIndex):
+            pytest.skip(f"{type(self).__name__} separately tested")
+
+        result = index.argsort()
+        expected = np.array(index).argsort()
+        tm.assert_numpy_array_equal(result, expected, check_dtype=False)
+
+    def test_numpy_argsort(self, index):
+        result = np.argsort(index)
+        expected = index.argsort()
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = np.argsort(index, kind="mergesort")
+        expected = index.argsort(kind="mergesort")
+        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(index, (CategoricalIndex, RangeIndex)):
+            msg = "the 'axis' parameter is not supported"
+            with pytest.raises(ValueError, match=msg):
+                np.argsort(index, axis=1)
+
+            msg = "the 'order' parameter is not supported"
+            with pytest.raises(ValueError, match=msg):
+                np.argsort(index, order=("a", "b"))
+
+    def test_repeat(self, simple_index):
+        rep = 2
+        idx = simple_index.copy()
+        new_index_cls = idx._constructor
+        expected = new_index_cls(idx.values.repeat(rep), name=idx.name)
+        tm.assert_index_equal(idx.repeat(rep), expected)
+
+        idx = simple_index
+        rep = np.arange(len(idx))
+        expected = new_index_cls(idx.values.repeat(rep), name=idx.name)
+        tm.assert_index_equal(idx.repeat(rep), expected)
+
+    def test_numpy_repeat(self, simple_index):
+        rep = 2
+        idx = simple_index
+        expected = idx.repeat(rep)
+        tm.assert_index_equal(np.repeat(idx, rep), expected)
+
+        msg = "the 'axis' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            np.repeat(idx, rep, axis=0)
+
+    def test_where(self, listlike_box, simple_index):
+        if isinstance(simple_index, (IntervalIndex, PeriodIndex)) or is_numeric_dtype(
+            simple_index.dtype
+        ):
+            pytest.skip("Tested elsewhere.")
+        klass = listlike_box
+
+        idx = simple_index
+        if isinstance(idx, (DatetimeIndex, TimedeltaIndex)):
+            # where does not preserve freq
+            idx = idx._with_freq(None)
+
+        cond = [True] * len(idx)
+        result = idx.where(klass(cond))
+        expected = idx
+        tm.assert_index_equal(result, expected)
+
+        cond = [False] + [True] * len(idx[1:])
+        expected = Index([idx._na_value] + idx[1:].tolist(), dtype=idx.dtype)
+        result = idx.where(klass(cond))
+        tm.assert_index_equal(result, expected)
+
+    def test_insert_base(self, index):
+        trimmed = index[1:4]
+
+        if not len(index):
+            pytest.skip("Not applicable for empty index")
+
+        # test 0th element
+        warn = None
+        if index.dtype == object and index.inferred_type == "boolean":
+            # GH#51363
+            warn = FutureWarning
+        msg = "The behavior of Index.insert with object-dtype is deprecated"
+        with tm.assert_produces_warning(warn, match=msg):
+            result = trimmed.insert(0, index[0])
+        assert index[0:4].equals(result)
+
+    @pytest.mark.skipif(
+        using_pyarrow_string_dtype(),
+        reason="completely different behavior, tested elsewher",
+    )
+    def test_insert_out_of_bounds(self, index):
+        # TypeError/IndexError matches what np.insert raises in these cases
+
+        if len(index) > 0:
+            err = TypeError
+        else:
+            err = IndexError
+        if len(index) == 0:
+            # 0 vs 0.5 in error message varies with numpy version
+            msg = "index (0|0.5) is out of bounds for axis 0 with size 0"
+        else:
+            msg = "slice indices must be integers or None or have an __index__ method"
+        with pytest.raises(err, match=msg):
+            index.insert(0.5, "foo")
+
+        msg = "|".join(
+            [
+                r"index -?\d+ is out of bounds for axis 0 with size \d+",
+                "loc must be an integer between",
+            ]
+        )
+        with pytest.raises(IndexError, match=msg):
+            index.insert(len(index) + 1, 1)
+
+        with pytest.raises(IndexError, match=msg):
+            index.insert(-len(index) - 1, 1)
+
+    def test_delete_base(self, index):
+        if not len(index):
+            pytest.skip("Not applicable for empty index")
+
+        if isinstance(index, RangeIndex):
+            # tested in class
+            pytest.skip(f"{type(self).__name__} tested elsewhere")
+
+        expected = index[1:]
+        result = index.delete(0)
+        assert result.equals(expected)
+        assert result.name == expected.name
+
+        expected = index[:-1]
+        result = index.delete(-1)
+        assert result.equals(expected)
+        assert result.name == expected.name
+
+        length = len(index)
+        msg = f"index {length} is out of bounds for axis 0 with size {length}"
+        with pytest.raises(IndexError, match=msg):
+            index.delete(length)
+
+    @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+    def test_equals(self, index):
+        if isinstance(index, IntervalIndex):
+            pytest.skip(f"{type(index).__name__} tested elsewhere")
+
+        is_ea_idx = type(index) is Index and not isinstance(index.dtype, np.dtype)
+
+        assert index.equals(index)
+        assert index.equals(index.copy())
+        if not is_ea_idx:
+            # doesn't hold for e.g. IntegerDtype
+            assert index.equals(index.astype(object))
+
+        assert not index.equals(list(index))
+        assert not index.equals(np.array(index))
+
+        # Cannot pass in non-int64 dtype to RangeIndex
+        if not isinstance(index, RangeIndex) and not is_ea_idx:
+            same_values = Index(index, dtype=object)
+            assert index.equals(same_values)
+            assert same_values.equals(index)
+
+        if index.nlevels == 1:
+            # do not test MultiIndex
+            assert not index.equals(Series(index))
+
+    def test_equals_op(self, simple_index):
+        # GH9947, GH10637
+        index_a = simple_index
+
+        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]
+
+        msg = "Lengths must match|could not be broadcast"
+        with pytest.raises(ValueError, match=msg):
+            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=msg):
+            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=msg):
+            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_format(self, simple_index):
+        # GH35439
+        if is_numeric_dtype(simple_index.dtype) or isinstance(
+            simple_index, DatetimeIndex
+        ):
+            pytest.skip("Tested elsewhere.")
+        idx = simple_index
+        expected = [str(x) for x in idx]
+        msg = r"Index\.format is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert idx.format() == expected
+
+    def test_format_empty(self, simple_index):
+        # GH35712
+        if isinstance(simple_index, (PeriodIndex, RangeIndex)):
+            pytest.skip("Tested elsewhere")
+        empty_idx = type(simple_index)([])
+        msg = r"Index\.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) == [""]
+
+    def test_fillna(self, index):
+        # GH 11343
+        if len(index) == 0:
+            pytest.skip("Not relevant for empty index")
+        elif index.dtype == bool:
+            pytest.skip(f"{index.dtype} cannot hold NAs")
+        elif isinstance(index, Index) and is_integer_dtype(index.dtype):
+            pytest.skip(f"Not relevant for Index with {index.dtype}")
+        elif isinstance(index, MultiIndex):
+            idx = index.copy(deep=True)
+            msg = "isna is not defined for MultiIndex"
+            with pytest.raises(NotImplementedError, match=msg):
+                idx.fillna(idx[0])
+        else:
+            idx = index.copy(deep=True)
+            result = idx.fillna(idx[0])
+            tm.assert_index_equal(result, idx)
+            assert result is not idx
+
+            msg = "'value' must be a scalar, passed: "
+            with pytest.raises(TypeError, match=msg):
+                idx.fillna([idx[0]])
+
+            idx = index.copy(deep=True)
+            values = idx._values
+
+            values[1] = np.nan
+
+            idx = type(index)(values)
+
+            msg = "does not support 'downcast'"
+            msg2 = r"The 'downcast' keyword in .*Index\.fillna is deprecated"
+            with tm.assert_produces_warning(FutureWarning, match=msg2):
+                with pytest.raises(NotImplementedError, match=msg):
+                    # For now at least, we only raise if there are NAs present
+                    idx.fillna(idx[0], downcast="infer")
+
+            expected = np.array([False] * len(idx), dtype=bool)
+            expected[1] = True
+            tm.assert_numpy_array_equal(idx._isnan, expected)
+            assert idx.hasnans is True
+
+    def test_nulls(self, index):
+        # this is really a smoke test for the methods
+        # as these are adequately tested for function elsewhere
+        if len(index) == 0:
+            tm.assert_numpy_array_equal(index.isna(), np.array([], dtype=bool))
+        elif isinstance(index, MultiIndex):
+            idx = index.copy()
+            msg = "isna is not defined for MultiIndex"
+            with pytest.raises(NotImplementedError, match=msg):
+                idx.isna()
+        elif not index.hasnans:
+            tm.assert_numpy_array_equal(index.isna(), np.zeros(len(index), dtype=bool))
+            tm.assert_numpy_array_equal(index.notna(), np.ones(len(index), dtype=bool))
+        else:
+            result = isna(index)
+            tm.assert_numpy_array_equal(index.isna(), result)
+            tm.assert_numpy_array_equal(index.notna(), ~result)
+
+    def test_empty(self, simple_index):
+        # GH 15270
+        idx = simple_index
+        assert not idx.empty
+        assert idx[:0].empty
+
+    def test_join_self_unique(self, join_type, simple_index):
+        idx = simple_index
+        if idx.is_unique:
+            joined = idx.join(idx, how=join_type)
+            expected = simple_index
+            if join_type == "outer":
+                expected = algos.safe_sort(expected)
+            tm.assert_index_equal(joined, expected)
+
+    def test_map(self, simple_index):
+        # callable
+        if isinstance(simple_index, (TimedeltaIndex, PeriodIndex)):
+            pytest.skip("Tested elsewhere.")
+        idx = simple_index
+
+        result = idx.map(lambda x: x)
+        # RangeIndex are equivalent to the similar Index with int64 dtype
+        tm.assert_index_equal(result, idx, exact="equiv")
+
+    @pytest.mark.parametrize(
+        "mapper",
+        [
+            lambda values, index: {i: e for e, i in zip(values, index)},
+            lambda values, index: Series(values, index),
+        ],
+    )
+    @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+    def test_map_dictlike(self, mapper, simple_index, request):
+        idx = simple_index
+        if isinstance(idx, (DatetimeIndex, TimedeltaIndex, PeriodIndex)):
+            pytest.skip("Tested elsewhere.")
+
+        identity = mapper(idx.values, idx)
+
+        result = idx.map(identity)
+        # RangeIndex are equivalent to the similar Index with int64 dtype
+        tm.assert_index_equal(result, idx, exact="equiv")
+
+        # empty mappable
+        dtype = None
+        if idx.dtype.kind == "f":
+            dtype = idx.dtype
+
+        expected = Index([np.nan] * len(idx), dtype=dtype)
+        result = idx.map(mapper(expected, idx))
+        tm.assert_index_equal(result, expected)
+
+    def test_map_str(self, simple_index):
+        # GH 31202
+        if isinstance(simple_index, CategoricalIndex):
+            pytest.skip("See test_map.py")
+        idx = simple_index
+        result = idx.map(str)
+        expected = Index([str(x) for x in idx])
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("copy", [True, False])
+    @pytest.mark.parametrize("name", [None, "foo"])
+    @pytest.mark.parametrize("ordered", [True, False])
+    def test_astype_category(self, copy, name, ordered, simple_index):
+        # GH 18630
+        idx = simple_index
+        if name:
+            idx = idx.rename(name)
+
+        # standard categories
+        dtype = CategoricalDtype(ordered=ordered)
+        result = idx.astype(dtype, copy=copy)
+        expected = CategoricalIndex(idx, name=name, ordered=ordered)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        # non-standard categories
+        dtype = CategoricalDtype(idx.unique().tolist()[:-1], ordered)
+        result = idx.astype(dtype, copy=copy)
+        expected = CategoricalIndex(idx, name=name, dtype=dtype)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        if ordered is False:
+            # dtype='category' defaults to ordered=False, so only test once
+            result = idx.astype("category", copy=copy)
+            expected = CategoricalIndex(idx, name=name)
+            tm.assert_index_equal(result, expected, exact=True)
+
+    def test_is_unique(self, simple_index):
+        # initialize a unique index
+        index = simple_index.drop_duplicates()
+        assert index.is_unique is True
+
+        # empty index should be unique
+        index_empty = index[:0]
+        assert index_empty.is_unique is True
+
+        # test basic dupes
+        index_dup = index.insert(0, index[0])
+        assert index_dup.is_unique is False
+
+        # single NA should be unique
+        index_na = index.insert(0, np.nan)
+        assert index_na.is_unique is True
+
+        # multiple NA should not be unique
+        index_na_dup = index_na.insert(0, np.nan)
+        assert index_na_dup.is_unique is False
+
+    @pytest.mark.arm_slow
+    def test_engine_reference_cycle(self, simple_index):
+        # GH27585
+        index = simple_index.copy()
+        ref = weakref.ref(index)
+        index._engine
+        del index
+        assert ref() is None
+
+    def test_getitem_2d_deprecated(self, simple_index):
+        # GH#30588, GH#31479
+        if isinstance(simple_index, IntervalIndex):
+            pytest.skip("Tested elsewhere")
+        idx = simple_index
+        msg = "Multi-dimensional indexing|too many|only"
+        with pytest.raises((ValueError, IndexError), match=msg):
+            idx[:, None]
+
+        if not isinstance(idx, RangeIndex):
+            # GH#44051 RangeIndex already raised pre-2.0 with a different message
+            with pytest.raises((ValueError, IndexError), match=msg):
+                idx[True]
+            with pytest.raises((ValueError, IndexError), match=msg):
+                idx[False]
+        else:
+            msg = "only integers, slices"
+            with pytest.raises(IndexError, match=msg):
+                idx[True]
+            with pytest.raises(IndexError, match=msg):
+                idx[False]
+
+    def test_copy_shares_cache(self, simple_index):
+        # GH32898, GH36840
+        idx = simple_index
+        idx.get_loc(idx[0])  # populates the _cache.
+        copy = idx.copy()
+
+        assert copy._cache is idx._cache
+
+    def test_shallow_copy_shares_cache(self, simple_index):
+        # GH32669, GH36840
+        idx = simple_index
+        idx.get_loc(idx[0])  # populates the _cache.
+        shallow_copy = idx._view()
+
+        assert shallow_copy._cache is idx._cache
+
+        shallow_copy = idx._shallow_copy(idx._data)
+        assert shallow_copy._cache is not idx._cache
+        assert shallow_copy._cache == {}
+
+    def test_index_groupby(self, simple_index):
+        idx = simple_index[:5]
+        to_groupby = np.array([1, 2, np.nan, 2, 1])
+        tm.assert_dict_equal(
+            idx.groupby(to_groupby), {1.0: idx[[0, 4]], 2.0: idx[[1, 3]]}
+        )
+
+        to_groupby = DatetimeIndex(
+            [
+                datetime(2011, 11, 1),
+                datetime(2011, 12, 1),
+                pd.NaT,
+                datetime(2011, 12, 1),
+                datetime(2011, 11, 1),
+            ],
+            tz="UTC",
+        ).values
+
+        ex_keys = [Timestamp("2011-11-01"), Timestamp("2011-12-01")]
+        expected = {ex_keys[0]: idx[[0, 4]], ex_keys[1]: idx[[1, 3]]}
+        tm.assert_dict_equal(idx.groupby(to_groupby), expected)
+
+    def test_append_preserves_dtype(self, simple_index):
+        # In particular Index with dtype float32
+        index = simple_index
+        N = len(index)
+
+        result = index.append(index)
+        assert result.dtype == index.dtype
+        tm.assert_index_equal(result[:N], index, check_exact=True)
+        tm.assert_index_equal(result[N:], index, check_exact=True)
+
+        alt = index.take(list(range(N)) * 2)
+        tm.assert_index_equal(result, alt, check_exact=True)
+
+    def test_inv(self, simple_index, using_infer_string):
+        idx = simple_index
+
+        if idx.dtype.kind in ["i", "u"]:
+            res = ~idx
+            expected = Index(~idx.values, name=idx.name)
+            tm.assert_index_equal(res, expected)
+
+            # check that we are matching Series behavior
+            res2 = ~Series(idx)
+            tm.assert_series_equal(res2, Series(expected))
+        else:
+            if idx.dtype.kind == "f":
+                err = TypeError
+                msg = "ufunc 'invert' not supported for the input types"
+            elif using_infer_string and idx.dtype == "string":
+                import pyarrow as pa
+
+                err = pa.lib.ArrowNotImplementedError
+                msg = "has no kernel"
+            else:
+                err = TypeError
+                msg = "bad operand"
+            with pytest.raises(err, match=msg):
+                ~idx
+
+            # check that we get the same behavior with Series
+            with pytest.raises(err, match=msg):
+                ~Series(idx)
+
+    def test_is_boolean_is_deprecated(self, simple_index):
+        # GH50042
+        idx = simple_index
+        with tm.assert_produces_warning(FutureWarning):
+            idx.is_boolean()
+
+    def test_is_floating_is_deprecated(self, simple_index):
+        # GH50042
+        idx = simple_index
+        with tm.assert_produces_warning(FutureWarning):
+            idx.is_floating()
+
+    def test_is_integer_is_deprecated(self, simple_index):
+        # GH50042
+        idx = simple_index
+        with tm.assert_produces_warning(FutureWarning):
+            idx.is_integer()
+
+    def test_holds_integer_deprecated(self, simple_index):
+        # GH50243
+        idx = simple_index
+        msg = f"{type(idx).__name__}.holds_integer is deprecated. "
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            idx.holds_integer()
+
+    def test_is_numeric_is_deprecated(self, simple_index):
+        # GH50042
+        idx = simple_index
+        with tm.assert_produces_warning(
+            FutureWarning,
+            match=f"{type(idx).__name__}.is_numeric is deprecated. ",
+        ):
+            idx.is_numeric()
+
+    def test_is_categorical_is_deprecated(self, simple_index):
+        # GH50042
+        idx = simple_index
+        with tm.assert_produces_warning(
+            FutureWarning,
+            match=r"Use pandas\.api\.types\.is_categorical_dtype instead",
+        ):
+            idx.is_categorical()
+
+    def test_is_interval_is_deprecated(self, simple_index):
+        # GH50042
+        idx = simple_index
+        with tm.assert_produces_warning(FutureWarning):
+            idx.is_interval()
+
+    def test_is_object_is_deprecated(self, simple_index):
+        # GH50042
+        idx = simple_index
+        with tm.assert_produces_warning(FutureWarning):
+            idx.is_object()
+
+
+class TestNumericBase:
+    @pytest.fixture(
+        params=[
+            RangeIndex(start=0, stop=20, step=2),
+            Index(np.arange(5, dtype=np.float64)),
+            Index(np.arange(5, dtype=np.float32)),
+            Index(np.arange(5, dtype=np.uint64)),
+            Index(range(0, 20, 2), dtype=np.int64),
+            Index(range(0, 20, 2), dtype=np.int32),
+            Index(range(0, 20, 2), dtype=np.int16),
+            Index(range(0, 20, 2), dtype=np.int8),
+        ]
+    )
+    def simple_index(self, request):
+        return request.param
+
+    def test_constructor_unwraps_index(self, simple_index):
+        if isinstance(simple_index, RangeIndex):
+            pytest.skip("Tested elsewhere.")
+        index_cls = type(simple_index)
+        dtype = simple_index.dtype
+
+        idx = Index([1, 2], dtype=dtype)
+        result = index_cls(idx)
+        expected = np.array([1, 2], dtype=idx.dtype)
+        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_view(self, simple_index):
+        if isinstance(simple_index, RangeIndex):
+            pytest.skip("Tested elsewhere.")
+        index_cls = type(simple_index)
+        dtype = simple_index.dtype
+
+        idx = index_cls([], dtype=dtype, name="Foo")
+        idx_view = idx.view()
+        assert idx_view.name == "Foo"
+
+        idx_view = idx.view(dtype)
+        tm.assert_index_equal(idx, index_cls(idx_view, name="Foo"), exact=True)
+
+        msg = "Passing a type in .*Index.view is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            idx_view = idx.view(index_cls)
+        tm.assert_index_equal(idx, index_cls(idx_view, name="Foo"), exact=True)
+
+    def test_format(self, simple_index):
+        # GH35439
+        if isinstance(simple_index, DatetimeIndex):
+            pytest.skip("Tested elsewhere")
+        idx = simple_index
+        max_width = max(len(str(x)) for x in idx)
+        expected = [str(x).ljust(max_width) for x in idx]
+        msg = r"Index\.format is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert idx.format() == expected
+
+    def test_insert_non_na(self, simple_index):
+        # GH#43921 inserting an element that we know we can hold should
+        #  not change dtype or type (except for RangeIndex)
+        index = simple_index
+
+        result = index.insert(0, index[0])
+
+        expected = Index([index[0]] + list(index), dtype=index.dtype)
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_insert_na(self, nulls_fixture, simple_index):
+        # GH 18295 (test missing)
+        index = simple_index
+        na_val = nulls_fixture
+
+        if na_val is pd.NaT:
+            expected = Index([index[0], pd.NaT] + list(index[1:]), dtype=object)
+        else:
+            expected = Index([index[0], np.nan] + list(index[1:]))
+            # GH#43921 we preserve float dtype
+            if index.dtype.kind == "f":
+                expected = Index(expected, dtype=index.dtype)
+
+        result = index.insert(1, na_val)
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_arithmetic_explicit_conversions(self, simple_index):
+        # GH 8608
+        # add/sub are overridden explicitly for Float/Int Index
+        index_cls = type(simple_index)
+        if index_cls is RangeIndex:
+            idx = RangeIndex(5)
+        else:
+            idx = index_cls(np.arange(5, dtype="int64"))
+
+        # float conversions
+        arr = np.arange(5, dtype="int64") * 3.2
+        expected = Index(arr, dtype=np.float64)
+        fidx = idx * 3.2
+        tm.assert_index_equal(fidx, expected)
+        fidx = 3.2 * idx
+        tm.assert_index_equal(fidx, expected)
+
+        # interops with numpy arrays
+        expected = Index(arr, dtype=np.float64)
+        a = np.zeros(5, dtype="float64")
+        result = fidx - a
+        tm.assert_index_equal(result, expected)
+
+        expected = Index(-arr, dtype=np.float64)
+        a = np.zeros(5, dtype="float64")
+        result = a - fidx
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("complex_dtype", [np.complex64, np.complex128])
+    def test_astype_to_complex(self, complex_dtype, simple_index):
+        result = simple_index.astype(complex_dtype)
+
+        assert type(result) is Index and result.dtype == complex_dtype
+
+    def test_cast_string(self, simple_index):
+        if isinstance(simple_index, RangeIndex):
+            pytest.skip("casting of strings not relevant for RangeIndex")
+        result = type(simple_index)(["0", "1", "2"], dtype=simple_index.dtype)
+        expected = type(simple_index)([0, 1, 2], dtype=simple_index.dtype)
+        tm.assert_index_equal(result, expected)

venv/lib/python3.10/site-packages/pandas/tests/indexes/test_setops.py

@@ -0,0 +1,959 @@
+"""
+The tests in this package are to ensure the proper resultant dtypes of
+set operations.
+"""
+from datetime import datetime
+import operator
+
+import numpy as np
+import pytest
+
+from pandas._libs import lib
+
+from pandas.core.dtypes.cast import find_common_type
+
+from pandas import (
+    CategoricalDtype,
+    CategoricalIndex,
+    DatetimeTZDtype,
+    Index,
+    MultiIndex,
+    PeriodDtype,
+    RangeIndex,
+    Series,
+    Timestamp,
+)
+import pandas._testing as tm
+from pandas.api.types import (
+    is_signed_integer_dtype,
+    pandas_dtype,
+)
+
+
+def equal_contents(arr1, arr2) -> bool:
+    """
+    Checks if the set of unique elements of arr1 and arr2 are equivalent.
+    """
+    return frozenset(arr1) == frozenset(arr2)
+
+
+@pytest.fixture(
+    params=tm.ALL_REAL_NUMPY_DTYPES
+    + [
+        "object",
+        "category",
+        "datetime64[ns]",
+        "timedelta64[ns]",
+    ]
+)
+def any_dtype_for_small_pos_integer_indexes(request):
+    """
+    Dtypes that can be given to an Index with small positive integers.
+
+    This means that for any dtype `x` in the params list, `Index([1, 2, 3], dtype=x)` is
+    valid and gives the correct Index (sub-)class.
+    """
+    return request.param
+
+
+def test_union_same_types(index):
+    # Union with a non-unique, non-monotonic index raises error
+    # Only needed for bool index factory
+    idx1 = index.sort_values()
+    idx2 = index.sort_values()
+    assert idx1.union(idx2).dtype == idx1.dtype
+
+
+def test_union_different_types(index_flat, index_flat2, request):
+    # This test only considers combinations of indices
+    # GH 23525
+    idx1 = index_flat
+    idx2 = index_flat2
+
+    if (
+        not idx1.is_unique
+        and not idx2.is_unique
+        and idx1.dtype.kind == "i"
+        and idx2.dtype.kind == "b"
+    ) or (
+        not idx2.is_unique
+        and not idx1.is_unique
+        and idx2.dtype.kind == "i"
+        and idx1.dtype.kind == "b"
+    ):
+        # Each condition had idx[1|2].is_monotonic_decreasing
+        # but failed when e.g.
+        # idx1 = Index(
+        # [True, True, True, True, True, True, True, True, False, False], dtype='bool'
+        # )
+        # idx2 = Index([0, 0, 1, 1, 2, 2], dtype='int64')
+        mark = pytest.mark.xfail(
+            reason="GH#44000 True==1", raises=ValueError, strict=False
+        )
+        request.applymarker(mark)
+
+    common_dtype = find_common_type([idx1.dtype, idx2.dtype])
+
+    warn = None
+    msg = "'<' not supported between"
+    if not len(idx1) or not len(idx2):
+        pass
+    elif (idx1.dtype.kind == "c" and (not lib.is_np_dtype(idx2.dtype, "iufc"))) or (
+        idx2.dtype.kind == "c" and (not lib.is_np_dtype(idx1.dtype, "iufc"))
+    ):
+        # complex objects non-sortable
+        warn = RuntimeWarning
+    elif (
+        isinstance(idx1.dtype, PeriodDtype) and isinstance(idx2.dtype, CategoricalDtype)
+    ) or (
+        isinstance(idx2.dtype, PeriodDtype) and isinstance(idx1.dtype, CategoricalDtype)
+    ):
+        warn = FutureWarning
+        msg = r"PeriodDtype\[B\] is deprecated"
+        mark = pytest.mark.xfail(
+            reason="Warning not produced on all builds",
+            raises=AssertionError,
+            strict=False,
+        )
+        request.applymarker(mark)
+
+    any_uint64 = np.uint64 in (idx1.dtype, idx2.dtype)
+    idx1_signed = is_signed_integer_dtype(idx1.dtype)
+    idx2_signed = is_signed_integer_dtype(idx2.dtype)
+
+    # Union with a non-unique, non-monotonic index raises error
+    # This applies to the boolean index
+    idx1 = idx1.sort_values()
+    idx2 = idx2.sort_values()
+
+    with tm.assert_produces_warning(warn, match=msg):
+        res1 = idx1.union(idx2)
+        res2 = idx2.union(idx1)
+
+    if any_uint64 and (idx1_signed or idx2_signed):
+        assert res1.dtype == np.dtype("O")
+        assert res2.dtype == np.dtype("O")
+    else:
+        assert res1.dtype == common_dtype
+        assert res2.dtype == common_dtype
+
+
+@pytest.mark.parametrize(
+    "idx1,idx2",
+    [
+        (Index(np.arange(5), dtype=np.int64), RangeIndex(5)),
+        (Index(np.arange(5), dtype=np.float64), Index(np.arange(5), dtype=np.int64)),
+        (Index(np.arange(5), dtype=np.float64), RangeIndex(5)),
+        (Index(np.arange(5), dtype=np.float64), Index(np.arange(5), dtype=np.uint64)),
+    ],
+)
+def test_compatible_inconsistent_pairs(idx1, idx2):
+    # GH 23525
+    res1 = idx1.union(idx2)
+    res2 = idx2.union(idx1)
+
+    assert res1.dtype in (idx1.dtype, idx2.dtype)
+    assert res2.dtype in (idx1.dtype, idx2.dtype)
+
+
+@pytest.mark.parametrize(
+    "left, right, expected",
+    [
+        ("int64", "int64", "int64"),
+        ("int64", "uint64", "object"),
+        ("int64", "float64", "float64"),
+        ("uint64", "float64", "float64"),
+        ("uint64", "uint64", "uint64"),
+        ("float64", "float64", "float64"),
+        ("datetime64[ns]", "int64", "object"),
+        ("datetime64[ns]", "uint64", "object"),
+        ("datetime64[ns]", "float64", "object"),
+        ("datetime64[ns, CET]", "int64", "object"),
+        ("datetime64[ns, CET]", "uint64", "object"),
+        ("datetime64[ns, CET]", "float64", "object"),
+        ("Period[D]", "int64", "object"),
+        ("Period[D]", "uint64", "object"),
+        ("Period[D]", "float64", "object"),
+    ],
+)
+@pytest.mark.parametrize("names", [("foo", "foo", "foo"), ("foo", "bar", None)])
+def test_union_dtypes(left, right, expected, names):
+    left = pandas_dtype(left)
+    right = pandas_dtype(right)
+    a = Index([], dtype=left, name=names[0])
+    b = Index([], dtype=right, name=names[1])
+    result = a.union(b)
+    assert result.dtype == expected
+    assert result.name == names[2]
+
+    # Testing name retention
+    # TODO: pin down desired dtype; do we want it to be commutative?
+    result = a.intersection(b)
+    assert result.name == names[2]
+
+
+@pytest.mark.parametrize("values", [[1, 2, 2, 3], [3, 3]])
+def test_intersection_duplicates(values):
+    # GH#31326
+    a = Index(values)
+    b = Index([3, 3])
+    result = a.intersection(b)
+    expected = Index([3])
+    tm.assert_index_equal(result, expected)
+
+
+class TestSetOps:
+    # Set operation tests shared by all indexes in the `index` fixture
+    @pytest.mark.parametrize("case", [0.5, "xxx"])
+    @pytest.mark.parametrize(
+        "method", ["intersection", "union", "difference", "symmetric_difference"]
+    )
+    def test_set_ops_error_cases(self, case, method, index):
+        # non-iterable input
+        msg = "Input must be Index or array-like"
+        with pytest.raises(TypeError, match=msg):
+            getattr(index, method)(case)
+
+    @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+    def test_intersection_base(self, index):
+        if isinstance(index, CategoricalIndex):
+            pytest.skip(f"Not relevant for {type(index).__name__}")
+
+        first = index[:5].unique()
+        second = index[:3].unique()
+        intersect = first.intersection(second)
+        tm.assert_index_equal(intersect, second)
+
+        if isinstance(index.dtype, DatetimeTZDtype):
+            # The second.values below will drop tz, so the rest of this test
+            #  is not applicable.
+            return
+
+        # GH#10149
+        cases = [second.to_numpy(), second.to_series(), second.to_list()]
+        for case in cases:
+            result = first.intersection(case)
+            assert equal_contents(result, second)
+
+        if isinstance(index, MultiIndex):
+            msg = "other must be a MultiIndex or a list of tuples"
+            with pytest.raises(TypeError, match=msg):
+                first.intersection([1, 2, 3])
+
+    @pytest.mark.filterwarnings(
+        "ignore:Falling back on a non-pyarrow:pandas.errors.PerformanceWarning"
+    )
+    @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+    def test_union_base(self, index):
+        index = index.unique()
+        first = index[3:]
+        second = index[:5]
+        everything = index
+
+        union = first.union(second)
+        tm.assert_index_equal(union.sort_values(), everything.sort_values())
+
+        if isinstance(index.dtype, DatetimeTZDtype):
+            # The second.values below will drop tz, so the rest of this test
+            #  is not applicable.
+            return
+
+        # GH#10149
+        cases = [second.to_numpy(), second.to_series(), second.to_list()]
+        for case in cases:
+            result = first.union(case)
+            assert equal_contents(result, everything)
+
+        if isinstance(index, MultiIndex):
+            msg = "other must be a MultiIndex or a list of tuples"
+            with pytest.raises(TypeError, match=msg):
+                first.union([1, 2, 3])
+
+    @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+    @pytest.mark.filterwarnings(
+        "ignore:Falling back on a non-pyarrow:pandas.errors.PerformanceWarning"
+    )
+    def test_difference_base(self, sort, index):
+        first = index[2:]
+        second = index[:4]
+        if index.inferred_type == "boolean":
+            # i think (TODO: be sure) there assumptions baked in about
+            #  the index fixture that don't hold here?
+            answer = set(first).difference(set(second))
+        elif isinstance(index, CategoricalIndex):
+            answer = []
+        else:
+            answer = index[4:]
+        result = first.difference(second, sort)
+        assert equal_contents(result, answer)
+
+        # GH#10149
+        cases = [second.to_numpy(), second.to_series(), second.to_list()]
+        for case in cases:
+            result = first.difference(case, sort)
+            assert equal_contents(result, answer)
+
+        if isinstance(index, MultiIndex):
+            msg = "other must be a MultiIndex or a list of tuples"
+            with pytest.raises(TypeError, match=msg):
+                first.difference([1, 2, 3], sort)
+
+    @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+    @pytest.mark.filterwarnings(
+        "ignore:Falling back on a non-pyarrow:pandas.errors.PerformanceWarning"
+    )
+    def test_symmetric_difference(self, index):
+        if isinstance(index, CategoricalIndex):
+            pytest.skip(f"Not relevant for {type(index).__name__}")
+        if len(index) < 2:
+            pytest.skip("Too few values for test")
+        if index[0] in index[1:] or index[-1] in index[:-1]:
+            # index fixture has e.g. an index of bools that does not satisfy this,
+            #  another with [0, 0, 1, 1, 2, 2]
+            pytest.skip("Index values no not satisfy test condition.")
+
+        first = index[1:]
+        second = index[:-1]
+        answer = index[[0, -1]]
+        result = first.symmetric_difference(second)
+        tm.assert_index_equal(result.sort_values(), answer.sort_values())
+
+        # GH#10149
+        cases = [second.to_numpy(), second.to_series(), second.to_list()]
+        for case in cases:
+            result = first.symmetric_difference(case)
+            assert equal_contents(result, answer)
+
+        if isinstance(index, MultiIndex):
+            msg = "other must be a MultiIndex or a list of tuples"
+            with pytest.raises(TypeError, match=msg):
+                first.symmetric_difference([1, 2, 3])
+
+    @pytest.mark.parametrize(
+        "fname, sname, expected_name",
+        [
+            ("A", "A", "A"),
+            ("A", "B", None),
+            ("A", None, None),
+            (None, "B", None),
+            (None, None, None),
+        ],
+    )
+    def test_corner_union(self, index_flat, fname, sname, expected_name):
+        # GH#9943, GH#9862
+        # Test unions with various name combinations
+        # Do not test MultiIndex or repeats
+        if not index_flat.is_unique:
+            index = index_flat.unique()
+        else:
+            index = index_flat
+
+        # Test copy.union(copy)
+        first = index.copy().set_names(fname)
+        second = index.copy().set_names(sname)
+        union = first.union(second)
+        expected = index.copy().set_names(expected_name)
+        tm.assert_index_equal(union, expected)
+
+        # Test copy.union(empty)
+        first = index.copy().set_names(fname)
+        second = index.drop(index).set_names(sname)
+        union = first.union(second)
+        expected = index.copy().set_names(expected_name)
+        tm.assert_index_equal(union, expected)
+
+        # Test empty.union(copy)
+        first = index.drop(index).set_names(fname)
+        second = index.copy().set_names(sname)
+        union = first.union(second)
+        expected = index.copy().set_names(expected_name)
+        tm.assert_index_equal(union, expected)
+
+        # Test empty.union(empty)
+        first = index.drop(index).set_names(fname)
+        second = index.drop(index).set_names(sname)
+        union = first.union(second)
+        expected = index.drop(index).set_names(expected_name)
+        tm.assert_index_equal(union, expected)
+
+    @pytest.mark.parametrize(
+        "fname, sname, expected_name",
+        [
+            ("A", "A", "A"),
+            ("A", "B", None),
+            ("A", None, None),
+            (None, "B", None),
+            (None, None, None),
+        ],
+    )
+    def test_union_unequal(self, index_flat, fname, sname, expected_name):
+        if not index_flat.is_unique:
+            index = index_flat.unique()
+        else:
+            index = index_flat
+
+        # test copy.union(subset) - need sort for unicode and string
+        first = index.copy().set_names(fname)
+        second = index[1:].set_names(sname)
+        union = first.union(second).sort_values()
+        expected = index.set_names(expected_name).sort_values()
+        tm.assert_index_equal(union, expected)
+
+    @pytest.mark.parametrize(
+        "fname, sname, expected_name",
+        [
+            ("A", "A", "A"),
+            ("A", "B", None),
+            ("A", None, None),
+            (None, "B", None),
+            (None, None, None),
+        ],
+    )
+    def test_corner_intersect(self, index_flat, fname, sname, expected_name):
+        # GH#35847
+        # Test intersections with various name combinations
+        if not index_flat.is_unique:
+            index = index_flat.unique()
+        else:
+            index = index_flat
+
+        # Test copy.intersection(copy)
+        first = index.copy().set_names(fname)
+        second = index.copy().set_names(sname)
+        intersect = first.intersection(second)
+        expected = index.copy().set_names(expected_name)
+        tm.assert_index_equal(intersect, expected)
+
+        # Test copy.intersection(empty)
+        first = index.copy().set_names(fname)
+        second = index.drop(index).set_names(sname)
+        intersect = first.intersection(second)
+        expected = index.drop(index).set_names(expected_name)
+        tm.assert_index_equal(intersect, expected)
+
+        # Test empty.intersection(copy)
+        first = index.drop(index).set_names(fname)
+        second = index.copy().set_names(sname)
+        intersect = first.intersection(second)
+        expected = index.drop(index).set_names(expected_name)
+        tm.assert_index_equal(intersect, expected)
+
+        # Test empty.intersection(empty)
+        first = index.drop(index).set_names(fname)
+        second = index.drop(index).set_names(sname)
+        intersect = first.intersection(second)
+        expected = index.drop(index).set_names(expected_name)
+        tm.assert_index_equal(intersect, expected)
+
+    @pytest.mark.parametrize(
+        "fname, sname, expected_name",
+        [
+            ("A", "A", "A"),
+            ("A", "B", None),
+            ("A", None, None),
+            (None, "B", None),
+            (None, None, None),
+        ],
+    )
+    def test_intersect_unequal(self, index_flat, fname, sname, expected_name):
+        if not index_flat.is_unique:
+            index = index_flat.unique()
+        else:
+            index = index_flat
+
+        # test copy.intersection(subset) - need sort for unicode and string
+        first = index.copy().set_names(fname)
+        second = index[1:].set_names(sname)
+        intersect = first.intersection(second).sort_values()
+        expected = index[1:].set_names(expected_name).sort_values()
+        tm.assert_index_equal(intersect, expected)
+
+    @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+    def test_intersection_name_retention_with_nameless(self, index):
+        if isinstance(index, MultiIndex):
+            index = index.rename(list(range(index.nlevels)))
+        else:
+            index = index.rename("foo")
+
+        other = np.asarray(index)
+
+        result = index.intersection(other)
+        assert result.name == index.name
+
+        # empty other, same dtype
+        result = index.intersection(other[:0])
+        assert result.name == index.name
+
+        # empty `self`
+        result = index[:0].intersection(other)
+        assert result.name == index.name
+
+    def test_difference_preserves_type_empty(self, index, sort):
+        # GH#20040
+        # If taking difference of a set and itself, it
+        # needs to preserve the type of the index
+        if not index.is_unique:
+            pytest.skip("Not relevant since index is not unique")
+        result = index.difference(index, sort=sort)
+        expected = index[:0]
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_difference_name_retention_equals(self, index, names):
+        if isinstance(index, MultiIndex):
+            names = [[x] * index.nlevels for x in names]
+        index = index.rename(names[0])
+        other = index.rename(names[1])
+
+        assert index.equals(other)
+
+        result = index.difference(other)
+        expected = index[:0].rename(names[2])
+        tm.assert_index_equal(result, expected)
+
+    def test_intersection_difference_match_empty(self, index, sort):
+        # GH#20040
+        # Test that the intersection of an index with an
+        # empty index produces the same index as the difference
+        # of an index with itself.  Test for all types
+        if not index.is_unique:
+            pytest.skip("Not relevant because index is not unique")
+        inter = index.intersection(index[:0])
+        diff = index.difference(index, sort=sort)
+        tm.assert_index_equal(inter, diff, exact=True)
+
+
+@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+@pytest.mark.filterwarnings(
+    "ignore:Falling back on a non-pyarrow:pandas.errors.PerformanceWarning"
+)
+@pytest.mark.parametrize(
+    "method", ["intersection", "union", "difference", "symmetric_difference"]
+)
+def test_setop_with_categorical(index_flat, sort, method):
+    # MultiIndex tested separately in tests.indexes.multi.test_setops
+    index = index_flat
+
+    other = index.astype("category")
+    exact = "equiv" if isinstance(index, RangeIndex) else True
+
+    result = getattr(index, method)(other, sort=sort)
+    expected = getattr(index, method)(index, sort=sort)
+    tm.assert_index_equal(result, expected, exact=exact)
+
+    result = getattr(index, method)(other[:5], sort=sort)
+    expected = getattr(index, method)(index[:5], sort=sort)
+    tm.assert_index_equal(result, expected, exact=exact)
+
+
+def test_intersection_duplicates_all_indexes(index):
+    # GH#38743
+    if index.empty:
+        # No duplicates in empty indexes
+        pytest.skip("Not relevant for empty Index")
+
+    idx = index
+    idx_non_unique = idx[[0, 0, 1, 2]]
+
+    assert idx.intersection(idx_non_unique).equals(idx_non_unique.intersection(idx))
+    assert idx.intersection(idx_non_unique).is_unique
+
+
+def test_union_duplicate_index_subsets_of_each_other(
+    any_dtype_for_small_pos_integer_indexes,
+):
+    # GH#31326
+    dtype = any_dtype_for_small_pos_integer_indexes
+    a = Index([1, 2, 2, 3], dtype=dtype)
+    b = Index([3, 3, 4], dtype=dtype)
+
+    expected = Index([1, 2, 2, 3, 3, 4], dtype=dtype)
+    if isinstance(a, CategoricalIndex):
+        expected = Index([1, 2, 2, 3, 3, 4])
+    result = a.union(b)
+    tm.assert_index_equal(result, expected)
+    result = a.union(b, sort=False)
+    tm.assert_index_equal(result, expected)
+
+
+def test_union_with_duplicate_index_and_non_monotonic(
+    any_dtype_for_small_pos_integer_indexes,
+):
+    # GH#36289
+    dtype = any_dtype_for_small_pos_integer_indexes
+    a = Index([1, 0, 0], dtype=dtype)
+    b = Index([0, 1], dtype=dtype)
+    expected = Index([0, 0, 1], dtype=dtype)
+
+    result = a.union(b)
+    tm.assert_index_equal(result, expected)
+
+    result = b.union(a)
+    tm.assert_index_equal(result, expected)
+
+
+def test_union_duplicate_index_different_dtypes():
+    # GH#36289
+    a = Index([1, 2, 2, 3])
+    b = Index(["1", "0", "0"])
+    expected = Index([1, 2, 2, 3, "1", "0", "0"])
+    result = a.union(b, sort=False)
+    tm.assert_index_equal(result, expected)
+
+
+def test_union_same_value_duplicated_in_both():
+    # GH#36289
+    a = Index([0, 0, 1])
+    b = Index([0, 0, 1, 2])
+    result = a.union(b)
+    expected = Index([0, 0, 1, 2])
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("dup", [1, np.nan])
+def test_union_nan_in_both(dup):
+    # GH#36289
+    a = Index([np.nan, 1, 2, 2])
+    b = Index([np.nan, dup, 1, 2])
+    result = a.union(b, sort=False)
+    expected = Index([np.nan, dup, 1.0, 2.0, 2.0])
+    tm.assert_index_equal(result, expected)
+
+
+def test_union_rangeindex_sort_true():
+    # GH 53490
+    idx1 = RangeIndex(1, 100, 6)
+    idx2 = RangeIndex(1, 50, 3)
+    result = idx1.union(idx2, sort=True)
+    expected = Index(
+        [
+            1,
+            4,
+            7,
+            10,
+            13,
+            16,
+            19,
+            22,
+            25,
+            28,
+            31,
+            34,
+            37,
+            40,
+            43,
+            46,
+            49,
+            55,
+            61,
+            67,
+            73,
+            79,
+            85,
+            91,
+            97,
+        ]
+    )
+    tm.assert_index_equal(result, expected)
+
+
+def test_union_with_duplicate_index_not_subset_and_non_monotonic(
+    any_dtype_for_small_pos_integer_indexes,
+):
+    # GH#36289
+    dtype = any_dtype_for_small_pos_integer_indexes
+    a = Index([1, 0, 2], dtype=dtype)
+    b = Index([0, 0, 1], dtype=dtype)
+    expected = Index([0, 0, 1, 2], dtype=dtype)
+    if isinstance(a, CategoricalIndex):
+        expected = Index([0, 0, 1, 2])
+
+    result = a.union(b)
+    tm.assert_index_equal(result, expected)
+
+    result = b.union(a)
+    tm.assert_index_equal(result, expected)
+
+
+def test_union_int_categorical_with_nan():
+    ci = CategoricalIndex([1, 2, np.nan])
+    assert ci.categories.dtype.kind == "i"
+
+    idx = Index([1, 2])
+
+    result = idx.union(ci)
+    expected = Index([1, 2, np.nan], dtype=np.float64)
+    tm.assert_index_equal(result, expected)
+
+    result = ci.union(idx)
+    tm.assert_index_equal(result, expected)
+
+
+class TestSetOpsUnsorted:
+    # These may eventually belong in a dtype-specific test_setops, or
+    #  parametrized over a more general fixture
+    def test_intersect_str_dates(self):
+        dt_dates = [datetime(2012, 2, 9), datetime(2012, 2, 22)]
+
+        index1 = Index(dt_dates, dtype=object)
+        index2 = Index(["aa"], dtype=object)
+        result = index2.intersection(index1)
+
+        expected = Index([], dtype=object)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("index", ["string"], indirect=True)
+    def test_intersection(self, index, sort):
+        first = index[:20]
+        second = index[:10]
+        intersect = first.intersection(second, sort=sort)
+        if sort in (None, False):
+            tm.assert_index_equal(intersect.sort_values(), second.sort_values())
+        else:
+            tm.assert_index_equal(intersect, second)
+
+        # Corner cases
+        inter = first.intersection(first, sort=sort)
+        assert inter is first
+
+    @pytest.mark.parametrize(
+        "index2,keeps_name",
+        [
+            (Index([3, 4, 5, 6, 7], name="index"), True),  # preserve same name
+            (Index([3, 4, 5, 6, 7], name="other"), False),  # drop diff names
+            (Index([3, 4, 5, 6, 7]), False),
+        ],
+    )
+    def test_intersection_name_preservation(self, index2, keeps_name, sort):
+        index1 = Index([1, 2, 3, 4, 5], name="index")
+        expected = Index([3, 4, 5])
+        result = index1.intersection(index2, sort)
+
+        if keeps_name:
+            expected.name = "index"
+
+        assert result.name == expected.name
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("index", ["string"], indirect=True)
+    @pytest.mark.parametrize(
+        "first_name,second_name,expected_name",
+        [("A", "A", "A"), ("A", "B", None), (None, "B", None)],
+    )
+    def test_intersection_name_preservation2(
+        self, index, first_name, second_name, expected_name, sort
+    ):
+        first = index[5:20]
+        second = index[:10]
+        first.name = first_name
+        second.name = second_name
+        intersect = first.intersection(second, sort=sort)
+        assert intersect.name == expected_name
+
+    def test_chained_union(self, sort):
+        # Chained unions handles names correctly
+        i1 = Index([1, 2], name="i1")
+        i2 = Index([5, 6], name="i2")
+        i3 = Index([3, 4], name="i3")
+        union = i1.union(i2.union(i3, sort=sort), sort=sort)
+        expected = i1.union(i2, sort=sort).union(i3, sort=sort)
+        tm.assert_index_equal(union, expected)
+
+        j1 = Index([1, 2], name="j1")
+        j2 = Index([], name="j2")
+        j3 = Index([], name="j3")
+        union = j1.union(j2.union(j3, sort=sort), sort=sort)
+        expected = j1.union(j2, sort=sort).union(j3, sort=sort)
+        tm.assert_index_equal(union, expected)
+
+    @pytest.mark.parametrize("index", ["string"], indirect=True)
+    def test_union(self, index, sort):
+        first = index[5:20]
+        second = index[:10]
+        everything = index[:20]
+
+        union = first.union(second, sort=sort)
+        if sort in (None, False):
+            tm.assert_index_equal(union.sort_values(), everything.sort_values())
+        else:
+            tm.assert_index_equal(union, everything)
+
+    @pytest.mark.parametrize("klass", [np.array, Series, list])
+    @pytest.mark.parametrize("index", ["string"], indirect=True)
+    def test_union_from_iterables(self, index, klass, sort):
+        # GH#10149
+        first = index[5:20]
+        second = index[:10]
+        everything = index[:20]
+
+        case = klass(second.values)
+        result = first.union(case, sort=sort)
+        if sort in (None, False):
+            tm.assert_index_equal(result.sort_values(), everything.sort_values())
+        else:
+            tm.assert_index_equal(result, everything)
+
+    @pytest.mark.parametrize("index", ["string"], indirect=True)
+    def test_union_identity(self, index, sort):
+        first = index[5:20]
+
+        union = first.union(first, sort=sort)
+        # i.e. identity is not preserved when sort is True
+        assert (union is first) is (not sort)
+
+        # This should no longer be the same object, since [] is not consistent,
+        # both objects will be recast to dtype('O')
+        union = first.union(Index([], dtype=first.dtype), sort=sort)
+        assert (union is first) is (not sort)
+
+        union = Index([], dtype=first.dtype).union(first, sort=sort)
+        assert (union is first) is (not sort)
+
+    @pytest.mark.parametrize("index", ["string"], indirect=True)
+    @pytest.mark.parametrize("second_name,expected", [(None, None), ("name", "name")])
+    def test_difference_name_preservation(self, index, second_name, expected, sort):
+        first = index[5:20]
+        second = index[:10]
+        answer = index[10:20]
+
+        first.name = "name"
+        second.name = second_name
+        result = first.difference(second, sort=sort)
+
+        if sort is True:
+            tm.assert_index_equal(result, answer)
+        else:
+            answer.name = second_name
+            tm.assert_index_equal(result.sort_values(), answer.sort_values())
+
+        if expected is None:
+            assert result.name is None
+        else:
+            assert result.name == expected
+
+    def test_difference_empty_arg(self, index, sort):
+        first = index.copy()
+        first = first[5:20]
+        first.name = "name"
+        result = first.difference([], sort)
+        expected = index[5:20].unique()
+        expected.name = "name"
+        tm.assert_index_equal(result, expected)
+
+    def test_difference_should_not_compare(self):
+        # GH 55113
+        left = Index([1, 1])
+        right = Index([True])
+        result = left.difference(right)
+        expected = Index([1])
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("index", ["string"], indirect=True)
+    def test_difference_identity(self, index, sort):
+        first = index[5:20]
+        first.name = "name"
+        result = first.difference(first, sort)
+
+        assert len(result) == 0
+        assert result.name == first.name
+
+    @pytest.mark.parametrize("index", ["string"], indirect=True)
+    def test_difference_sort(self, index, sort):
+        first = index[5:20]
+        second = index[:10]
+
+        result = first.difference(second, sort)
+        expected = index[10:20]
+
+        if sort is None:
+            expected = expected.sort_values()
+
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("opname", ["difference", "symmetric_difference"])
+    def test_difference_incomparable(self, opname):
+        a = Index([3, Timestamp("2000"), 1])
+        b = Index([2, Timestamp("1999"), 1])
+        op = operator.methodcaller(opname, b)
+
+        with tm.assert_produces_warning(RuntimeWarning):
+            # sort=None, the default
+            result = op(a)
+        expected = Index([3, Timestamp("2000"), 2, Timestamp("1999")])
+        if opname == "difference":
+            expected = expected[:2]
+        tm.assert_index_equal(result, expected)
+
+        # sort=False
+        op = operator.methodcaller(opname, b, sort=False)
+        result = op(a)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("opname", ["difference", "symmetric_difference"])
+    def test_difference_incomparable_true(self, opname):
+        a = Index([3, Timestamp("2000"), 1])
+        b = Index([2, Timestamp("1999"), 1])
+        op = operator.methodcaller(opname, b, sort=True)
+
+        msg = "'<' not supported between instances of 'Timestamp' and 'int'"
+        with pytest.raises(TypeError, match=msg):
+            op(a)
+
+    def test_symmetric_difference_mi(self, sort):
+        index1 = MultiIndex.from_tuples(zip(["foo", "bar", "baz"], [1, 2, 3]))
+        index2 = MultiIndex.from_tuples([("foo", 1), ("bar", 3)])
+        result = index1.symmetric_difference(index2, sort=sort)
+        expected = MultiIndex.from_tuples([("bar", 2), ("baz", 3), ("bar", 3)])
+        if sort is None:
+            expected = expected.sort_values()
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "index2,expected",
+        [
+            (Index([0, 1, np.nan]), Index([2.0, 3.0, 0.0])),
+            (Index([0, 1]), Index([np.nan, 2.0, 3.0, 0.0])),
+        ],
+    )
+    def test_symmetric_difference_missing(self, index2, expected, sort):
+        # GH#13514 change: {nan} - {nan} == {}
+        # (GH#6444, sorting of nans, is no longer an issue)
+        index1 = Index([1, np.nan, 2, 3])
+
+        result = index1.symmetric_difference(index2, sort=sort)
+        if sort is None:
+            expected = expected.sort_values()
+        tm.assert_index_equal(result, expected)
+
+    def test_symmetric_difference_non_index(self, sort):
+        index1 = Index([1, 2, 3, 4], name="index1")
+        index2 = np.array([2, 3, 4, 5])
+        expected = Index([1, 5], name="index1")
+        result = index1.symmetric_difference(index2, sort=sort)
+        if sort in (None, True):
+            tm.assert_index_equal(result, expected)
+        else:
+            tm.assert_index_equal(result.sort_values(), expected)
+        assert result.name == "index1"
+
+        result = index1.symmetric_difference(index2, result_name="new_name", sort=sort)
+        expected.name = "new_name"
+        if sort in (None, True):
+            tm.assert_index_equal(result, expected)
+        else:
+            tm.assert_index_equal(result.sort_values(), expected)
+        assert result.name == "new_name"
+
+    def test_union_ea_dtypes(self, any_numeric_ea_and_arrow_dtype):
+        # GH#51365
+        idx = Index([1, 2, 3], dtype=any_numeric_ea_and_arrow_dtype)
+        idx2 = Index([3, 4, 5], dtype=any_numeric_ea_and_arrow_dtype)
+        result = idx.union(idx2)
+        expected = Index([1, 2, 3, 4, 5], dtype=any_numeric_ea_and_arrow_dtype)
+        tm.assert_index_equal(result, expected)
+
+    def test_union_string_array(self, any_string_dtype):
+        idx1 = Index(["a"], dtype=any_string_dtype)
+        idx2 = Index(["b"], dtype=any_string_dtype)
+        result = idx1.union(idx2)
+        expected = Index(["a", "b"], dtype=any_string_dtype)
+        tm.assert_index_equal(result, expected)

venv/lib/python3.10/site-packages/pandas/tests/indexes/test_subclass.py

@@ -0,0 +1,40 @@
+"""
+Tests involving custom Index subclasses
+"""
+import numpy as np
+
+from pandas import (
+    DataFrame,
+    Index,
+)
+import pandas._testing as tm
+
+
+class CustomIndex(Index):
+    def __new__(cls, data, name=None):
+        # assert that this index class cannot hold strings
+        if any(isinstance(val, str) for val in data):
+            raise TypeError("CustomIndex cannot hold strings")
+
+        if name is None and hasattr(data, "name"):
+            name = data.name
+        data = np.array(data, dtype="O")
+
+        return cls._simple_new(data, name)
+
+
+def test_insert_fallback_to_base_index():
+    # https://github.com/pandas-dev/pandas/issues/47071
+
+    idx = CustomIndex([1, 2, 3])
+    result = idx.insert(0, "string")
+    expected = Index(["string", 1, 2, 3], dtype=object)
+    tm.assert_index_equal(result, expected)
+
+    df = DataFrame(
+        np.random.default_rng(2).standard_normal((2, 3)),
+        columns=idx,
+        index=Index([1, 2], name="string"),
+    )
+    result = df.reset_index()
+    tm.assert_index_equal(result.columns, expected)

venv/lib/python3.10/site-packages/pandas/tests/reshape/concat/conftest.py

@@ -0,0 +1,7 @@
+import pytest
+
+
+@pytest.fixture(params=[True, False])
+def sort(request):
+    """Boolean sort keyword for concat and DataFrame.append."""
+    return request.param

venv/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_append.py

@@ -0,0 +1,389 @@
+import datetime as dt
+from itertools import combinations
+
+import dateutil
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+    Timestamp,
+    concat,
+    isna,
+)
+import pandas._testing as tm
+
+
+class TestAppend:
+    def test_append(self, sort, float_frame):
+        mixed_frame = float_frame.copy()
+        mixed_frame["foo"] = "bar"
+
+        begin_index = float_frame.index[:5]
+        end_index = float_frame.index[5:]
+
+        begin_frame = float_frame.reindex(begin_index)
+        end_frame = float_frame.reindex(end_index)
+
+        appended = begin_frame._append(end_frame)
+        tm.assert_almost_equal(appended["A"], float_frame["A"])
+
+        del end_frame["A"]
+        partial_appended = begin_frame._append(end_frame, sort=sort)
+        assert "A" in partial_appended
+
+        partial_appended = end_frame._append(begin_frame, sort=sort)
+        assert "A" in partial_appended
+
+        # mixed type handling
+        appended = mixed_frame[:5]._append(mixed_frame[5:])
+        tm.assert_frame_equal(appended, mixed_frame)
+
+        # what to test here
+        mixed_appended = mixed_frame[:5]._append(float_frame[5:], sort=sort)
+        mixed_appended2 = float_frame[:5]._append(mixed_frame[5:], sort=sort)
+
+        # all equal except 'foo' column
+        tm.assert_frame_equal(
+            mixed_appended.reindex(columns=["A", "B", "C", "D"]),
+            mixed_appended2.reindex(columns=["A", "B", "C", "D"]),
+        )
+
+    def test_append_empty(self, float_frame):
+        empty = DataFrame()
+
+        appended = float_frame._append(empty)
+        tm.assert_frame_equal(float_frame, appended)
+        assert appended is not float_frame
+
+        appended = empty._append(float_frame)
+        tm.assert_frame_equal(float_frame, appended)
+        assert appended is not float_frame
+
+    def test_append_overlap_raises(self, float_frame):
+        msg = "Indexes have overlapping values"
+        with pytest.raises(ValueError, match=msg):
+            float_frame._append(float_frame, verify_integrity=True)
+
+    def test_append_new_columns(self):
+        # see gh-6129: new columns
+        df = DataFrame({"a": {"x": 1, "y": 2}, "b": {"x": 3, "y": 4}})
+        row = Series([5, 6, 7], index=["a", "b", "c"], name="z")
+        expected = DataFrame(
+            {
+                "a": {"x": 1, "y": 2, "z": 5},
+                "b": {"x": 3, "y": 4, "z": 6},
+                "c": {"z": 7},
+            }
+        )
+        result = df._append(row)
+        tm.assert_frame_equal(result, expected)
+
+    def test_append_length0_frame(self, sort):
+        df = DataFrame(columns=["A", "B", "C"])
+        df3 = DataFrame(index=[0, 1], columns=["A", "B"])
+        df5 = df._append(df3, sort=sort)
+
+        expected = DataFrame(index=[0, 1], columns=["A", "B", "C"])
+        tm.assert_frame_equal(df5, expected)
+
+    def test_append_records(self):
+        arr1 = np.zeros((2,), dtype=("i4,f4,S10"))
+        arr1[:] = [(1, 2.0, "Hello"), (2, 3.0, "World")]
+
+        arr2 = np.zeros((3,), dtype=("i4,f4,S10"))
+        arr2[:] = [(3, 4.0, "foo"), (5, 6.0, "bar"), (7.0, 8.0, "baz")]
+
+        df1 = DataFrame(arr1)
+        df2 = DataFrame(arr2)
+
+        result = df1._append(df2, ignore_index=True)
+        expected = DataFrame(np.concatenate((arr1, arr2)))
+        tm.assert_frame_equal(result, expected)
+
+    # rewrite sort fixture, since we also want to test default of None
+    def test_append_sorts(self, sort):
+        df1 = DataFrame({"a": [1, 2], "b": [1, 2]}, columns=["b", "a"])
+        df2 = DataFrame({"a": [1, 2], "c": [3, 4]}, index=[2, 3])
+
+        result = df1._append(df2, sort=sort)
+
+        # for None / True
+        expected = DataFrame(
+            {"b": [1, 2, None, None], "a": [1, 2, 1, 2], "c": [None, None, 3, 4]},
+            columns=["a", "b", "c"],
+        )
+        if sort is False:
+            expected = expected[["b", "a", "c"]]
+        tm.assert_frame_equal(result, expected)
+
+    def test_append_different_columns(self, sort):
+        df = DataFrame(
+            {
+                "bools": np.random.default_rng(2).standard_normal(10) > 0,
+                "ints": np.random.default_rng(2).integers(0, 10, 10),
+                "floats": np.random.default_rng(2).standard_normal(10),
+                "strings": ["foo", "bar"] * 5,
+            }
+        )
+
+        a = df[:5].loc[:, ["bools", "ints", "floats"]]
+        b = df[5:].loc[:, ["strings", "ints", "floats"]]
+
+        appended = a._append(b, sort=sort)
+        assert isna(appended["strings"][0:4]).all()
+        assert isna(appended["bools"][5:]).all()
+
+    def test_append_many(self, sort, float_frame):
+        chunks = [
+            float_frame[:5],
+            float_frame[5:10],
+            float_frame[10:15],
+            float_frame[15:],
+        ]
+
+        result = chunks[0]._append(chunks[1:])
+        tm.assert_frame_equal(result, float_frame)
+
+        chunks[-1] = chunks[-1].copy()
+        chunks[-1]["foo"] = "bar"
+        result = chunks[0]._append(chunks[1:], sort=sort)
+        tm.assert_frame_equal(result.loc[:, float_frame.columns], float_frame)
+        assert (result["foo"][15:] == "bar").all()
+        assert result["foo"][:15].isna().all()
+
+    def test_append_preserve_index_name(self):
+        # #980
+        df1 = DataFrame(columns=["A", "B", "C"])
+        df1 = df1.set_index(["A"])
+        df2 = DataFrame(data=[[1, 4, 7], [2, 5, 8], [3, 6, 9]], columns=["A", "B", "C"])
+        df2 = df2.set_index(["A"])
+
+        msg = "The behavior of array concatenation with empty entries is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = df1._append(df2)
+        assert result.index.name == "A"
+
+    indexes_can_append = [
+        pd.RangeIndex(3),
+        Index([4, 5, 6]),
+        Index([4.5, 5.5, 6.5]),
+        Index(list("abc")),
+        pd.CategoricalIndex("A B C".split()),
+        pd.CategoricalIndex("D E F".split(), ordered=True),
+        pd.IntervalIndex.from_breaks([7, 8, 9, 10]),
+        pd.DatetimeIndex(
+            [
+                dt.datetime(2013, 1, 3, 0, 0),
+                dt.datetime(2013, 1, 3, 6, 10),
+                dt.datetime(2013, 1, 3, 7, 12),
+            ]
+        ),
+        pd.MultiIndex.from_arrays(["A B C".split(), "D E F".split()]),
+    ]
+
+    @pytest.mark.parametrize(
+        "index", indexes_can_append, ids=lambda x: type(x).__name__
+    )
+    def test_append_same_columns_type(self, index):
+        # GH18359
+
+        # df wider than ser
+        df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=index)
+        ser_index = index[:2]
+        ser = Series([7, 8], index=ser_index, name=2)
+        result = df._append(ser)
+        expected = DataFrame(
+            [[1, 2, 3.0], [4, 5, 6], [7, 8, np.nan]], index=[0, 1, 2], columns=index
+        )
+        # integer dtype is preserved for columns present in ser.index
+        assert expected.dtypes.iloc[0].kind == "i"
+        assert expected.dtypes.iloc[1].kind == "i"
+
+        tm.assert_frame_equal(result, expected)
+
+        # ser wider than df
+        ser_index = index
+        index = index[:2]
+        df = DataFrame([[1, 2], [4, 5]], columns=index)
+        ser = Series([7, 8, 9], index=ser_index, name=2)
+        result = df._append(ser)
+        expected = DataFrame(
+            [[1, 2, np.nan], [4, 5, np.nan], [7, 8, 9]],
+            index=[0, 1, 2],
+            columns=ser_index,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "df_columns, series_index",
+        combinations(indexes_can_append, r=2),
+        ids=lambda x: type(x).__name__,
+    )
+    def test_append_different_columns_types(self, df_columns, series_index):
+        # GH18359
+        # See also test 'test_append_different_columns_types_raises' below
+        # for errors raised when appending
+
+        df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=df_columns)
+        ser = Series([7, 8, 9], index=series_index, name=2)
+
+        result = df._append(ser)
+        idx_diff = ser.index.difference(df_columns)
+        combined_columns = Index(df_columns.tolist()).append(idx_diff)
+        expected = DataFrame(
+            [
+                [1.0, 2.0, 3.0, np.nan, np.nan, np.nan],
+                [4, 5, 6, np.nan, np.nan, np.nan],
+                [np.nan, np.nan, np.nan, 7, 8, 9],
+            ],
+            index=[0, 1, 2],
+            columns=combined_columns,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_append_dtype_coerce(self, sort):
+        # GH 4993
+        # appending with datetime will incorrectly convert datetime64
+
+        df1 = DataFrame(
+            index=[1, 2],
+            data=[dt.datetime(2013, 1, 1, 0, 0), dt.datetime(2013, 1, 2, 0, 0)],
+            columns=["start_time"],
+        )
+        df2 = DataFrame(
+            index=[4, 5],
+            data=[
+                [dt.datetime(2013, 1, 3, 0, 0), dt.datetime(2013, 1, 3, 6, 10)],
+                [dt.datetime(2013, 1, 4, 0, 0), dt.datetime(2013, 1, 4, 7, 10)],
+            ],
+            columns=["start_time", "end_time"],
+        )
+
+        expected = concat(
+            [
+                Series(
+                    [
+                        pd.NaT,
+                        pd.NaT,
+                        dt.datetime(2013, 1, 3, 6, 10),
+                        dt.datetime(2013, 1, 4, 7, 10),
+                    ],
+                    name="end_time",
+                ),
+                Series(
+                    [
+                        dt.datetime(2013, 1, 1, 0, 0),
+                        dt.datetime(2013, 1, 2, 0, 0),
+                        dt.datetime(2013, 1, 3, 0, 0),
+                        dt.datetime(2013, 1, 4, 0, 0),
+                    ],
+                    name="start_time",
+                ),
+            ],
+            axis=1,
+            sort=sort,
+        )
+        result = df1._append(df2, ignore_index=True, sort=sort)
+        if sort:
+            expected = expected[["end_time", "start_time"]]
+        else:
+            expected = expected[["start_time", "end_time"]]
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_append_missing_column_proper_upcast(self, sort):
+        df1 = DataFrame({"A": np.array([1, 2, 3, 4], dtype="i8")})
+        df2 = DataFrame({"B": np.array([True, False, True, False], dtype=bool)})
+
+        appended = df1._append(df2, ignore_index=True, sort=sort)
+        assert appended["A"].dtype == "f8"
+        assert appended["B"].dtype == "O"
+
+    def test_append_empty_frame_to_series_with_dateutil_tz(self):
+        # GH 23682
+        date = Timestamp("2018-10-24 07:30:00", tz=dateutil.tz.tzutc())
+        ser = Series({"a": 1.0, "b": 2.0, "date": date})
+        df = DataFrame(columns=["c", "d"])
+        result_a = df._append(ser, ignore_index=True)
+        expected = DataFrame(
+            [[np.nan, np.nan, 1.0, 2.0, date]], columns=["c", "d", "a", "b", "date"]
+        )
+        # These columns get cast to object after append
+        expected["c"] = expected["c"].astype(object)
+        expected["d"] = expected["d"].astype(object)
+        tm.assert_frame_equal(result_a, expected)
+
+        expected = DataFrame(
+            [[np.nan, np.nan, 1.0, 2.0, date]] * 2, columns=["c", "d", "a", "b", "date"]
+        )
+        expected["c"] = expected["c"].astype(object)
+        expected["d"] = expected["d"].astype(object)
+        result_b = result_a._append(ser, ignore_index=True)
+        tm.assert_frame_equal(result_b, expected)
+
+        result = df._append([ser, ser], ignore_index=True)
+        tm.assert_frame_equal(result, expected)
+
+    def test_append_empty_tz_frame_with_datetime64ns(self, using_array_manager):
+        # https://github.com/pandas-dev/pandas/issues/35460
+        df = DataFrame(columns=["a"]).astype("datetime64[ns, UTC]")
+
+        # pd.NaT gets inferred as tz-naive, so append result is tz-naive
+        result = df._append({"a": pd.NaT}, ignore_index=True)
+        if using_array_manager:
+            expected = DataFrame({"a": [pd.NaT]}, dtype=object)
+        else:
+            expected = DataFrame({"a": [np.nan]}, dtype=object)
+        tm.assert_frame_equal(result, expected)
+
+        # also test with typed value to append
+        df = DataFrame(columns=["a"]).astype("datetime64[ns, UTC]")
+        other = Series({"a": pd.NaT}, dtype="datetime64[ns]")
+        result = df._append(other, ignore_index=True)
+        tm.assert_frame_equal(result, expected)
+
+        # mismatched tz
+        other = Series({"a": pd.NaT}, dtype="datetime64[ns, US/Pacific]")
+        result = df._append(other, ignore_index=True)
+        expected = DataFrame({"a": [pd.NaT]}).astype(object)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "dtype_str", ["datetime64[ns, UTC]", "datetime64[ns]", "Int64", "int64"]
+    )
+    @pytest.mark.parametrize("val", [1, "NaT"])
+    def test_append_empty_frame_with_timedelta64ns_nat(
+        self, dtype_str, val, using_array_manager
+    ):
+        # https://github.com/pandas-dev/pandas/issues/35460
+        df = DataFrame(columns=["a"]).astype(dtype_str)
+
+        other = DataFrame({"a": [np.timedelta64(val, "ns")]})
+        result = df._append(other, ignore_index=True)
+
+        expected = other.astype(object)
+        if isinstance(val, str) and dtype_str != "int64" and not using_array_manager:
+            # TODO: expected used to be `other.astype(object)` which is a more
+            #  reasonable result.  This was changed when tightening
+            #  assert_frame_equal's treatment of mismatched NAs to match the
+            #  existing behavior.
+            expected = DataFrame({"a": [np.nan]}, dtype=object)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "dtype_str", ["datetime64[ns, UTC]", "datetime64[ns]", "Int64", "int64"]
+    )
+    @pytest.mark.parametrize("val", [1, "NaT"])
+    def test_append_frame_with_timedelta64ns_nat(self, dtype_str, val):
+        # https://github.com/pandas-dev/pandas/issues/35460
+        df = DataFrame({"a": pd.array([1], dtype=dtype_str)})
+
+        other = DataFrame({"a": [np.timedelta64(val, "ns")]})
+        result = df._append(other, ignore_index=True)
+
+        expected = DataFrame({"a": [df.iloc[0, 0], other.iloc[0, 0]]}, dtype=object)
+        tm.assert_frame_equal(result, expected)

venv/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_append_common.py

@@ -0,0 +1,753 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    Index,
+    Series,
+)
+import pandas._testing as tm
+
+
+@pytest.fixture(
+    params=list(
+        {
+            "bool": [True, False, True],
+            "int64": [1, 2, 3],
+            "float64": [1.1, np.nan, 3.3],
+            "category": Categorical(["X", "Y", "Z"]),
+            "object": ["a", "b", "c"],
+            "datetime64[ns]": [
+                pd.Timestamp("2011-01-01"),
+                pd.Timestamp("2011-01-02"),
+                pd.Timestamp("2011-01-03"),
+            ],
+            "datetime64[ns, US/Eastern]": [
+                pd.Timestamp("2011-01-01", tz="US/Eastern"),
+                pd.Timestamp("2011-01-02", tz="US/Eastern"),
+                pd.Timestamp("2011-01-03", tz="US/Eastern"),
+            ],
+            "timedelta64[ns]": [
+                pd.Timedelta("1 days"),
+                pd.Timedelta("2 days"),
+                pd.Timedelta("3 days"),
+            ],
+            "period[M]": [
+                pd.Period("2011-01", freq="M"),
+                pd.Period("2011-02", freq="M"),
+                pd.Period("2011-03", freq="M"),
+            ],
+        }.items()
+    )
+)
+def item(request):
+    key, data = request.param
+    return key, data
+
+
+@pytest.fixture
+def item2(item):
+    return item
+
+
+class TestConcatAppendCommon:
+    """
+    Test common dtype coercion rules between concat and append.
+    """
+
+    def test_dtypes(self, item, index_or_series, using_infer_string):
+        # to confirm test case covers intended dtypes
+        typ, vals = item
+        obj = index_or_series(vals)
+        if typ == "object" and using_infer_string:
+            typ = "string"
+        if isinstance(obj, Index):
+            assert obj.dtype == typ
+        elif isinstance(obj, Series):
+            if typ.startswith("period"):
+                assert obj.dtype == "Period[M]"
+            else:
+                assert obj.dtype == typ
+
+    def test_concatlike_same_dtypes(self, item):
+        # GH 13660
+        typ1, vals1 = item
+
+        vals2 = vals1
+        vals3 = vals1
+
+        if typ1 == "category":
+            exp_data = Categorical(list(vals1) + list(vals2))
+            exp_data3 = Categorical(list(vals1) + list(vals2) + list(vals3))
+        else:
+            exp_data = vals1 + vals2
+            exp_data3 = vals1 + vals2 + vals3
+
+        # ----- Index ----- #
+
+        # index.append
+        res = Index(vals1).append(Index(vals2))
+        exp = Index(exp_data)
+        tm.assert_index_equal(res, exp)
+
+        # 3 elements
+        res = Index(vals1).append([Index(vals2), Index(vals3)])
+        exp = Index(exp_data3)
+        tm.assert_index_equal(res, exp)
+
+        # index.append name mismatch
+        i1 = Index(vals1, name="x")
+        i2 = Index(vals2, name="y")
+        res = i1.append(i2)
+        exp = Index(exp_data)
+        tm.assert_index_equal(res, exp)
+
+        # index.append name match
+        i1 = Index(vals1, name="x")
+        i2 = Index(vals2, name="x")
+        res = i1.append(i2)
+        exp = Index(exp_data, name="x")
+        tm.assert_index_equal(res, exp)
+
+        # cannot append non-index
+        with pytest.raises(TypeError, match="all inputs must be Index"):
+            Index(vals1).append(vals2)
+
+        with pytest.raises(TypeError, match="all inputs must be Index"):
+            Index(vals1).append([Index(vals2), vals3])
+
+        # ----- Series ----- #
+
+        # series.append
+        res = Series(vals1)._append(Series(vals2), ignore_index=True)
+        exp = Series(exp_data)
+        tm.assert_series_equal(res, exp, check_index_type=True)
+
+        # concat
+        res = pd.concat([Series(vals1), Series(vals2)], ignore_index=True)
+        tm.assert_series_equal(res, exp, check_index_type=True)
+
+        # 3 elements
+        res = Series(vals1)._append([Series(vals2), Series(vals3)], ignore_index=True)
+        exp = Series(exp_data3)
+        tm.assert_series_equal(res, exp)
+
+        res = pd.concat(
+            [Series(vals1), Series(vals2), Series(vals3)],
+            ignore_index=True,
+        )
+        tm.assert_series_equal(res, exp)
+
+        # name mismatch
+        s1 = Series(vals1, name="x")
+        s2 = Series(vals2, name="y")
+        res = s1._append(s2, ignore_index=True)
+        exp = Series(exp_data)
+        tm.assert_series_equal(res, exp, check_index_type=True)
+
+        res = pd.concat([s1, s2], ignore_index=True)
+        tm.assert_series_equal(res, exp, check_index_type=True)
+
+        # name match
+        s1 = Series(vals1, name="x")
+        s2 = Series(vals2, name="x")
+        res = s1._append(s2, ignore_index=True)
+        exp = Series(exp_data, name="x")
+        tm.assert_series_equal(res, exp, check_index_type=True)
+
+        res = pd.concat([s1, s2], ignore_index=True)
+        tm.assert_series_equal(res, exp, check_index_type=True)
+
+        # cannot append non-index
+        msg = (
+            r"cannot concatenate object of type '.+'; "
+            "only Series and DataFrame objs are valid"
+        )
+        with pytest.raises(TypeError, match=msg):
+            Series(vals1)._append(vals2)
+
+        with pytest.raises(TypeError, match=msg):
+            Series(vals1)._append([Series(vals2), vals3])
+
+        with pytest.raises(TypeError, match=msg):
+            pd.concat([Series(vals1), vals2])
+
+        with pytest.raises(TypeError, match=msg):
+            pd.concat([Series(vals1), Series(vals2), vals3])
+
+    def test_concatlike_dtypes_coercion(self, item, item2, request):
+        # GH 13660
+        typ1, vals1 = item
+        typ2, vals2 = item2
+
+        vals3 = vals2
+
+        # basically infer
+        exp_index_dtype = None
+        exp_series_dtype = None
+
+        if typ1 == typ2:
+            pytest.skip("same dtype is tested in test_concatlike_same_dtypes")
+        elif typ1 == "category" or typ2 == "category":
+            pytest.skip("categorical type tested elsewhere")
+
+        # specify expected dtype
+        if typ1 == "bool" and typ2 in ("int64", "float64"):
+            # series coerces to numeric based on numpy rule
+            # index doesn't because bool is object dtype
+            exp_series_dtype = typ2
+            mark = pytest.mark.xfail(reason="GH#39187 casting to object")
+            request.applymarker(mark)
+        elif typ2 == "bool" and typ1 in ("int64", "float64"):
+            exp_series_dtype = typ1
+            mark = pytest.mark.xfail(reason="GH#39187 casting to object")
+            request.applymarker(mark)
+        elif typ1 in {"datetime64[ns, US/Eastern]", "timedelta64[ns]"} or typ2 in {
+            "datetime64[ns, US/Eastern]",
+            "timedelta64[ns]",
+        }:
+            exp_index_dtype = object
+            exp_series_dtype = object
+
+        exp_data = vals1 + vals2
+        exp_data3 = vals1 + vals2 + vals3
+
+        # ----- Index ----- #
+
+        # index.append
+        # GH#39817
+        res = Index(vals1).append(Index(vals2))
+        exp = Index(exp_data, dtype=exp_index_dtype)
+        tm.assert_index_equal(res, exp)
+
+        # 3 elements
+        res = Index(vals1).append([Index(vals2), Index(vals3)])
+        exp = Index(exp_data3, dtype=exp_index_dtype)
+        tm.assert_index_equal(res, exp)
+
+        # ----- Series ----- #
+
+        # series._append
+        # GH#39817
+        res = Series(vals1)._append(Series(vals2), ignore_index=True)
+        exp = Series(exp_data, dtype=exp_series_dtype)
+        tm.assert_series_equal(res, exp, check_index_type=True)
+
+        # concat
+        # GH#39817
+        res = pd.concat([Series(vals1), Series(vals2)], ignore_index=True)
+        tm.assert_series_equal(res, exp, check_index_type=True)
+
+        # 3 elements
+        # GH#39817
+        res = Series(vals1)._append([Series(vals2), Series(vals3)], ignore_index=True)
+        exp = Series(exp_data3, dtype=exp_series_dtype)
+        tm.assert_series_equal(res, exp)
+
+        # GH#39817
+        res = pd.concat(
+            [Series(vals1), Series(vals2), Series(vals3)],
+            ignore_index=True,
+        )
+        tm.assert_series_equal(res, exp)
+
+    def test_concatlike_common_coerce_to_pandas_object(self):
+        # GH 13626
+        # result must be Timestamp/Timedelta, not datetime.datetime/timedelta
+        dti = pd.DatetimeIndex(["2011-01-01", "2011-01-02"])
+        tdi = pd.TimedeltaIndex(["1 days", "2 days"])
+
+        exp = Index(
+            [
+                pd.Timestamp("2011-01-01"),
+                pd.Timestamp("2011-01-02"),
+                pd.Timedelta("1 days"),
+                pd.Timedelta("2 days"),
+            ]
+        )
+
+        res = dti.append(tdi)
+        tm.assert_index_equal(res, exp)
+        assert isinstance(res[0], pd.Timestamp)
+        assert isinstance(res[-1], pd.Timedelta)
+
+        dts = Series(dti)
+        tds = Series(tdi)
+        res = dts._append(tds)
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+        assert isinstance(res.iloc[0], pd.Timestamp)
+        assert isinstance(res.iloc[-1], pd.Timedelta)
+
+        res = pd.concat([dts, tds])
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+        assert isinstance(res.iloc[0], pd.Timestamp)
+        assert isinstance(res.iloc[-1], pd.Timedelta)
+
+    def test_concatlike_datetimetz(self, tz_aware_fixture):
+        tz = tz_aware_fixture
+        # GH 7795
+        dti1 = pd.DatetimeIndex(["2011-01-01", "2011-01-02"], tz=tz)
+        dti2 = pd.DatetimeIndex(["2012-01-01", "2012-01-02"], tz=tz)
+
+        exp = pd.DatetimeIndex(
+            ["2011-01-01", "2011-01-02", "2012-01-01", "2012-01-02"], tz=tz
+        )
+
+        res = dti1.append(dti2)
+        tm.assert_index_equal(res, exp)
+
+        dts1 = Series(dti1)
+        dts2 = Series(dti2)
+        res = dts1._append(dts2)
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        res = pd.concat([dts1, dts2])
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+    @pytest.mark.parametrize("tz", ["UTC", "US/Eastern", "Asia/Tokyo", "EST5EDT"])
+    def test_concatlike_datetimetz_short(self, tz):
+        # GH#7795
+        ix1 = pd.date_range(start="2014-07-15", end="2014-07-17", freq="D", tz=tz)
+        ix2 = pd.DatetimeIndex(["2014-07-11", "2014-07-21"], tz=tz)
+        df1 = DataFrame(0, index=ix1, columns=["A", "B"])
+        df2 = DataFrame(0, index=ix2, columns=["A", "B"])
+
+        exp_idx = pd.DatetimeIndex(
+            ["2014-07-15", "2014-07-16", "2014-07-17", "2014-07-11", "2014-07-21"],
+            tz=tz,
+        ).as_unit("ns")
+        exp = DataFrame(0, index=exp_idx, columns=["A", "B"])
+
+        tm.assert_frame_equal(df1._append(df2), exp)
+        tm.assert_frame_equal(pd.concat([df1, df2]), exp)
+
+    def test_concatlike_datetimetz_to_object(self, tz_aware_fixture):
+        tz = tz_aware_fixture
+        # GH 13660
+
+        # different tz coerces to object
+        dti1 = pd.DatetimeIndex(["2011-01-01", "2011-01-02"], tz=tz)
+        dti2 = pd.DatetimeIndex(["2012-01-01", "2012-01-02"])
+
+        exp = Index(
+            [
+                pd.Timestamp("2011-01-01", tz=tz),
+                pd.Timestamp("2011-01-02", tz=tz),
+                pd.Timestamp("2012-01-01"),
+                pd.Timestamp("2012-01-02"),
+            ],
+            dtype=object,
+        )
+
+        res = dti1.append(dti2)
+        tm.assert_index_equal(res, exp)
+
+        dts1 = Series(dti1)
+        dts2 = Series(dti2)
+        res = dts1._append(dts2)
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        res = pd.concat([dts1, dts2])
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        # different tz
+        dti3 = pd.DatetimeIndex(["2012-01-01", "2012-01-02"], tz="US/Pacific")
+
+        exp = Index(
+            [
+                pd.Timestamp("2011-01-01", tz=tz),
+                pd.Timestamp("2011-01-02", tz=tz),
+                pd.Timestamp("2012-01-01", tz="US/Pacific"),
+                pd.Timestamp("2012-01-02", tz="US/Pacific"),
+            ],
+            dtype=object,
+        )
+
+        res = dti1.append(dti3)
+        tm.assert_index_equal(res, exp)
+
+        dts1 = Series(dti1)
+        dts3 = Series(dti3)
+        res = dts1._append(dts3)
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        res = pd.concat([dts1, dts3])
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+    def test_concatlike_common_period(self):
+        # GH 13660
+        pi1 = pd.PeriodIndex(["2011-01", "2011-02"], freq="M")
+        pi2 = pd.PeriodIndex(["2012-01", "2012-02"], freq="M")
+
+        exp = pd.PeriodIndex(["2011-01", "2011-02", "2012-01", "2012-02"], freq="M")
+
+        res = pi1.append(pi2)
+        tm.assert_index_equal(res, exp)
+
+        ps1 = Series(pi1)
+        ps2 = Series(pi2)
+        res = ps1._append(ps2)
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        res = pd.concat([ps1, ps2])
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+    def test_concatlike_common_period_diff_freq_to_object(self):
+        # GH 13221
+        pi1 = pd.PeriodIndex(["2011-01", "2011-02"], freq="M")
+        pi2 = pd.PeriodIndex(["2012-01-01", "2012-02-01"], freq="D")
+
+        exp = Index(
+            [
+                pd.Period("2011-01", freq="M"),
+                pd.Period("2011-02", freq="M"),
+                pd.Period("2012-01-01", freq="D"),
+                pd.Period("2012-02-01", freq="D"),
+            ],
+            dtype=object,
+        )
+
+        res = pi1.append(pi2)
+        tm.assert_index_equal(res, exp)
+
+        ps1 = Series(pi1)
+        ps2 = Series(pi2)
+        res = ps1._append(ps2)
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        res = pd.concat([ps1, ps2])
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+    def test_concatlike_common_period_mixed_dt_to_object(self):
+        # GH 13221
+        # different datetimelike
+        pi1 = pd.PeriodIndex(["2011-01", "2011-02"], freq="M")
+        tdi = pd.TimedeltaIndex(["1 days", "2 days"])
+        exp = Index(
+            [
+                pd.Period("2011-01", freq="M"),
+                pd.Period("2011-02", freq="M"),
+                pd.Timedelta("1 days"),
+                pd.Timedelta("2 days"),
+            ],
+            dtype=object,
+        )
+
+        res = pi1.append(tdi)
+        tm.assert_index_equal(res, exp)
+
+        ps1 = Series(pi1)
+        tds = Series(tdi)
+        res = ps1._append(tds)
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        res = pd.concat([ps1, tds])
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        # inverse
+        exp = Index(
+            [
+                pd.Timedelta("1 days"),
+                pd.Timedelta("2 days"),
+                pd.Period("2011-01", freq="M"),
+                pd.Period("2011-02", freq="M"),
+            ],
+            dtype=object,
+        )
+
+        res = tdi.append(pi1)
+        tm.assert_index_equal(res, exp)
+
+        ps1 = Series(pi1)
+        tds = Series(tdi)
+        res = tds._append(ps1)
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        res = pd.concat([tds, ps1])
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+    def test_concat_categorical(self):
+        # GH 13524
+
+        # same categories -> category
+        s1 = Series([1, 2, np.nan], dtype="category")
+        s2 = Series([2, 1, 2], dtype="category")
+
+        exp = Series([1, 2, np.nan, 2, 1, 2], dtype="category")
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        # partially different categories => not-category
+        s1 = Series([3, 2], dtype="category")
+        s2 = Series([2, 1], dtype="category")
+
+        exp = Series([3, 2, 2, 1])
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        # completely different categories (same dtype) => not-category
+        s1 = Series([10, 11, np.nan], dtype="category")
+        s2 = Series([np.nan, 1, 3, 2], dtype="category")
+
+        exp = Series([10, 11, np.nan, np.nan, 1, 3, 2], dtype=np.float64)
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+    def test_union_categorical_same_categories_different_order(self):
+        # https://github.com/pandas-dev/pandas/issues/19096
+        a = Series(Categorical(["a", "b", "c"], categories=["a", "b", "c"]))
+        b = Series(Categorical(["a", "b", "c"], categories=["b", "a", "c"]))
+        result = pd.concat([a, b], ignore_index=True)
+        expected = Series(
+            Categorical(["a", "b", "c", "a", "b", "c"], categories=["a", "b", "c"])
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_concat_categorical_coercion(self):
+        # GH 13524
+
+        # category + not-category => not-category
+        s1 = Series([1, 2, np.nan], dtype="category")
+        s2 = Series([2, 1, 2])
+
+        exp = Series([1, 2, np.nan, 2, 1, 2], dtype=np.float64)
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        # result shouldn't be affected by 1st elem dtype
+        exp = Series([2, 1, 2, 1, 2, np.nan], dtype=np.float64)
+        tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
+        tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
+
+        # all values are not in category => not-category
+        s1 = Series([3, 2], dtype="category")
+        s2 = Series([2, 1])
+
+        exp = Series([3, 2, 2, 1])
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        exp = Series([2, 1, 3, 2])
+        tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
+        tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
+
+        # completely different categories => not-category
+        s1 = Series([10, 11, np.nan], dtype="category")
+        s2 = Series([1, 3, 2])
+
+        exp = Series([10, 11, np.nan, 1, 3, 2], dtype=np.float64)
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        exp = Series([1, 3, 2, 10, 11, np.nan], dtype=np.float64)
+        tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
+        tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
+
+        # different dtype => not-category
+        s1 = Series([10, 11, np.nan], dtype="category")
+        s2 = Series(["a", "b", "c"])
+
+        exp = Series([10, 11, np.nan, "a", "b", "c"])
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        exp = Series(["a", "b", "c", 10, 11, np.nan])
+        tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
+        tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
+
+        # if normal series only contains NaN-likes => not-category
+        s1 = Series([10, 11], dtype="category")
+        s2 = Series([np.nan, np.nan, np.nan])
+
+        exp = Series([10, 11, np.nan, np.nan, np.nan])
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        exp = Series([np.nan, np.nan, np.nan, 10, 11])
+        tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
+        tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
+
+    def test_concat_categorical_3elem_coercion(self):
+        # GH 13524
+
+        # mixed dtypes => not-category
+        s1 = Series([1, 2, np.nan], dtype="category")
+        s2 = Series([2, 1, 2], dtype="category")
+        s3 = Series([1, 2, 1, 2, np.nan])
+
+        exp = Series([1, 2, np.nan, 2, 1, 2, 1, 2, 1, 2, np.nan], dtype="float")
+        tm.assert_series_equal(pd.concat([s1, s2, s3], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append([s2, s3], ignore_index=True), exp)
+
+        exp = Series([1, 2, 1, 2, np.nan, 1, 2, np.nan, 2, 1, 2], dtype="float")
+        tm.assert_series_equal(pd.concat([s3, s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s3._append([s1, s2], ignore_index=True), exp)
+
+        # values are all in either category => not-category
+        s1 = Series([4, 5, 6], dtype="category")
+        s2 = Series([1, 2, 3], dtype="category")
+        s3 = Series([1, 3, 4])
+
+        exp = Series([4, 5, 6, 1, 2, 3, 1, 3, 4])
+        tm.assert_series_equal(pd.concat([s1, s2, s3], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append([s2, s3], ignore_index=True), exp)
+
+        exp = Series([1, 3, 4, 4, 5, 6, 1, 2, 3])
+        tm.assert_series_equal(pd.concat([s3, s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s3._append([s1, s2], ignore_index=True), exp)
+
+        # values are all in either category => not-category
+        s1 = Series([4, 5, 6], dtype="category")
+        s2 = Series([1, 2, 3], dtype="category")
+        s3 = Series([10, 11, 12])
+
+        exp = Series([4, 5, 6, 1, 2, 3, 10, 11, 12])
+        tm.assert_series_equal(pd.concat([s1, s2, s3], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append([s2, s3], ignore_index=True), exp)
+
+        exp = Series([10, 11, 12, 4, 5, 6, 1, 2, 3])
+        tm.assert_series_equal(pd.concat([s3, s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s3._append([s1, s2], ignore_index=True), exp)
+
+    def test_concat_categorical_multi_coercion(self):
+        # GH 13524
+
+        s1 = Series([1, 3], dtype="category")
+        s2 = Series([3, 4], dtype="category")
+        s3 = Series([2, 3])
+        s4 = Series([2, 2], dtype="category")
+        s5 = Series([1, np.nan])
+        s6 = Series([1, 3, 2], dtype="category")
+
+        # mixed dtype, values are all in categories => not-category
+        exp = Series([1, 3, 3, 4, 2, 3, 2, 2, 1, np.nan, 1, 3, 2])
+        res = pd.concat([s1, s2, s3, s4, s5, s6], ignore_index=True)
+        tm.assert_series_equal(res, exp)
+        res = s1._append([s2, s3, s4, s5, s6], ignore_index=True)
+        tm.assert_series_equal(res, exp)
+
+        exp = Series([1, 3, 2, 1, np.nan, 2, 2, 2, 3, 3, 4, 1, 3])
+        res = pd.concat([s6, s5, s4, s3, s2, s1], ignore_index=True)
+        tm.assert_series_equal(res, exp)
+        res = s6._append([s5, s4, s3, s2, s1], ignore_index=True)
+        tm.assert_series_equal(res, exp)
+
+    def test_concat_categorical_ordered(self):
+        # GH 13524
+
+        s1 = Series(Categorical([1, 2, np.nan], ordered=True))
+        s2 = Series(Categorical([2, 1, 2], ordered=True))
+
+        exp = Series(Categorical([1, 2, np.nan, 2, 1, 2], ordered=True))
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        exp = Series(Categorical([1, 2, np.nan, 2, 1, 2, 1, 2, np.nan], ordered=True))
+        tm.assert_series_equal(pd.concat([s1, s2, s1], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append([s2, s1], ignore_index=True), exp)
+
+    def test_concat_categorical_coercion_nan(self):
+        # GH 13524
+
+        # some edge cases
+        # category + not-category => not category
+        s1 = Series(np.array([np.nan, np.nan], dtype=np.float64), dtype="category")
+        s2 = Series([np.nan, 1])
+
+        exp = Series([np.nan, np.nan, np.nan, 1])
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        s1 = Series([1, np.nan], dtype="category")
+        s2 = Series([np.nan, np.nan])
+
+        exp = Series([1, np.nan, np.nan, np.nan], dtype="float")
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        # mixed dtype, all nan-likes => not-category
+        s1 = Series([np.nan, np.nan], dtype="category")
+        s2 = Series([np.nan, np.nan])
+
+        exp = Series([np.nan, np.nan, np.nan, np.nan])
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+        tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
+        tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
+
+        # all category nan-likes => category
+        s1 = Series([np.nan, np.nan], dtype="category")
+        s2 = Series([np.nan, np.nan], dtype="category")
+
+        exp = Series([np.nan, np.nan, np.nan, np.nan], dtype="category")
+
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+    def test_concat_categorical_empty(self):
+        # GH 13524
+
+        s1 = Series([], dtype="category")
+        s2 = Series([1, 2], dtype="category")
+
+        msg = "The behavior of array concatenation with empty entries is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), s2)
+            tm.assert_series_equal(s1._append(s2, ignore_index=True), s2)
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), s2)
+            tm.assert_series_equal(s2._append(s1, ignore_index=True), s2)
+
+        s1 = Series([], dtype="category")
+        s2 = Series([], dtype="category")
+
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), s2)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), s2)
+
+        s1 = Series([], dtype="category")
+        s2 = Series([], dtype="object")
+
+        # different dtype => not-category
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), s2)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), s2)
+        tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), s2)
+        tm.assert_series_equal(s2._append(s1, ignore_index=True), s2)
+
+        s1 = Series([], dtype="category")
+        s2 = Series([np.nan, np.nan])
+
+        # empty Series is ignored
+        exp = Series([np.nan, np.nan])
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+            tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
+            tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
+
+    def test_categorical_concat_append(self):
+        cat = Categorical(["a", "b"], categories=["a", "b"])
+        vals = [1, 2]
+        df = DataFrame({"cats": cat, "vals": vals})
+        cat2 = Categorical(["a", "b", "a", "b"], categories=["a", "b"])
+        vals2 = [1, 2, 1, 2]
+        exp = DataFrame({"cats": cat2, "vals": vals2}, index=Index([0, 1, 0, 1]))
+
+        tm.assert_frame_equal(pd.concat([df, df]), exp)
+        tm.assert_frame_equal(df._append(df), exp)
+
+        # GH 13524 can concat different categories
+        cat3 = Categorical(["a", "b"], categories=["a", "b", "c"])
+        vals3 = [1, 2]
+        df_different_categories = DataFrame({"cats": cat3, "vals": vals3})
+
+        res = pd.concat([df, df_different_categories], ignore_index=True)
+        exp = DataFrame({"cats": list("abab"), "vals": [1, 2, 1, 2]})
+        tm.assert_frame_equal(res, exp)
+
+        res = df._append(df_different_categories, ignore_index=True)
+        tm.assert_frame_equal(res, exp)