test_base.py

from collections import defaultdict
from datetime import datetime
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,
)
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):
        arr = np.array(index)
        new_index = Index(arr, copy=True, name="name")
        assert isinstance(new_index, Index)
        assert new_index.name == "name"
        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
            pd.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:
            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
            (pd.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):
        # 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")

        assert df["date"].dtype == object
        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
        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 = pd.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")
        else:
            msg = "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):
        empty_arr = np.array([], dtype=dtype)
        empty_index = type(index)([], dtype=index.dtype)

        assert index[[]].identical(empty_index)
        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 \(or boolean\) type"
        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 = np.append(index, date_index.astype("O"))

        assert tm.equalContents(first_cat, appended)
        assert tm.equalContents(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 = tm.makeIntIndex(3)
        result = tm.makeIntIndex(3).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(
        "attr", ["makeDateIndex", "makePeriodIndex", "makeTimedeltaIndex"]
    )
    def test_map_tseries_indices_return_index(self, attr):
        index = getattr(tm, attr)(10)
        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 = tm.makeDateIndex(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 = tm.makeIntIndex(3)
        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):
        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"])

        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):
        # Test cartesian product of null fixtures and ensure that we don't
        # mangle the various types (save a corner case with PyPy)

        # 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(
                Index(["a", nulls_fixture]).isin([nulls_fixture2]),
                np.array([False, True]),
            )

        elif nulls_fixture is nulls_fixture2:  # should preserve NA type
            tm.assert_numpy_array_equal(
                Index(["a", nulls_fixture]).isin([nulls_fixture2]),
                np.array([False, True]),
            )

        else:
            tm.assert_numpy_array_equal(
                Index(["a", nulls_fixture]).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):
        joined = index.join(index, how=join_type)
        assert index is joined

    @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)),
            tm.makeDateIndex(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 = tm.makeDateIndex(10)

        with tm.assert_produces_warning(RuntimeWarning):
            result = left_index.join(right_index, how="outer")

        # right_index in this case because DatetimeIndex has join precedence
        # over int64 Index
        with tm.assert_produces_warning(RuntimeWarning):
            expected = right_index.astype(object).union(left_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 == np.object_

    @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):
        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

    @td.async_mark()
    async 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])"
        await ip.run_code(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("index_maker", tm.index_subclass_makers_generator())
def test_index_subclass_constructor_wrong_kwargs(index_maker):
    # GH #19348
    with pytest.raises(TypeError, match="unexpected keyword argument"):
        index_maker(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)