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,
)
from pandas.testing import assert_series_equal


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)

    def test_constructor_copy(self, using_infer_string):
        index = Index(list("abc"), name="name")
        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="str")
            )
        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:
            result = Index(index.astype(object))
            assert result.dtype == np.dtype(object)
            if isinstance(index, DatetimeIndex):
                # 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)
        else:
            result = Index(index)

            tm.assert_index_equal(result, 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 "str"
        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[s]", 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, unit="ns")
        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, unit="ns")
        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 = (
                r"Cannot change data-type for array of references\.|"
                r"Cannot change data-type for object array\.|"
                r"Cannot change data-type for array of strings\.|"
            )
            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 pytest.raises(ValueError, match="length of the boolean indexer"):
                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, using_infer_string):
        # GH 12756
        if isinstance(index, CategoricalIndex):
            pytest.skip("Tested in test_categorical")
        elif not index.is_unique:
            pytest.skip("Cannot map duplicated index")
        if (
            not using_infer_string
            and isinstance(index.dtype, pd.StringDtype)
            and index.dtype.storage == "python"
        ):
            mark = pytest.mark.xfail(reason="map does not retain dtype")
            request.applymarker(mark)

        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)

        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_logical_compat(self, all_boolean_reductions, simple_index):
        index = simple_index
        left = getattr(index, all_boolean_reductions)()
        assert left == getattr(index.values, all_boolean_reductions)()
        right = getattr(index.to_series(), all_boolean_reductions)()
        # 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=".* not found in axis"):
            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=re.escape("[4] not found in axis")):
            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",
        [
            (["qux", "baz", "foo", "bar"], [False, False, True, True]),
            ([], []),  # empty
        ],
    )
    def test_isin(self, values, index, expected):
        index = Index(index)
        result = index.isin(values)
        expected = np.array(expected, dtype=bool)
        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 a NaN we do not have .isin(nulls_fixture)
            msg = (
                r"float\(\) argument must be a string or a (real )?number, "
                f"not {type(nulls_fixture).__name__!r}"
            )
            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",
        [
            ["qux", "baz", "foo", "bar"],
            np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float64),
        ],
    )
    def test_isin_level_kwarg(self, level, index):
        index = Index(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)

    def test_isin_string_null(self, string_dtype_no_object):
        # GH#55821
        index = Index(["a", "b"], dtype=string_dtype_no_object)
        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

    def test_slice_is_unique(self):
        # GH 57911
        index = Index([1, 1, 2, 3, 4])
        assert not index.is_unique
        filtered_index = index[2:].copy()
        assert filtered_index.is_unique

    def test_slice_is_montonic(self):
        """Test that is_monotonic_decreasing is correct on slices."""
        # GH 57911
        index = Index([1, 2, 3, 3])
        assert not index.is_monotonic_decreasing

        filtered_index = index[2:].copy()
        assert filtered_index.is_monotonic_decreasing
        assert filtered_index.is_monotonic_increasing

        filtered_index = index[1:].copy()
        assert not filtered_index.is_monotonic_decreasing
        assert filtered_index.is_monotonic_increasing

        filtered_index = index[:].copy()
        assert not filtered_index.is_monotonic_decreasing
        assert filtered_index.is_monotonic_increasing

    @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", [(list("abcd"), True), (range(4), False)]
    )
    def test_tab_completion(self, index, expected):
        # GH 9910
        index = Index(index)
        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, match="not supported between"):
            result = left_index.join(right_index, how="outer")

        with tm.assert_produces_warning(RuntimeWarning, match="not supported between"):
            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

    def test_reindex_doesnt_preserve_type_if_target_is_empty_index(self):
        # GH7774
        index = Index(list("abc"))
        labels = DatetimeIndex([])
        dtype = np.datetime64
        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, 4]], None, Index([1, 2, 4])),
            ([[1, 2, 4]], ["name"], Index([1, 2, 4], name="name")),
            ([[1, 2, 3]], None, RangeIndex(1, 4)),
            ([[1, 2, 3]], ["name"], RangeIndex(1, 4, 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, exact=True)

    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 = RangeIndex(0)
        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, start=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


def test_index_equals_different_string_dtype(string_dtype_no_object):
    # GH 61099
    idx_obj = Index(["a", "b", "c"])
    idx_str = Index(["a", "b", "c"], dtype=string_dtype_no_object)

    assert idx_obj.equals(idx_str)
    assert idx_str.equals(idx_obj)


def test_index_comparison_different_string_dtype(string_dtype_no_object):
    # GH 61099
    idx = Index(["a", "b", "c"])
    s_obj = Series([1, 2, 3], index=idx)
    s_str = Series([4, 5, 6], index=idx.astype(string_dtype_no_object))

    expected = Series([True, True, True], index=["a", "b", "c"])
    result = s_obj < s_str
    assert_series_equal(result, expected)

    result = s_str > s_obj
    expected.index = idx.astype(string_dtype_no_object)
    assert_series_equal(result, expected)
