test_base.py

from collections import defaultdict
from datetime import datetime, timedelta
from io import StringIO
import math
import operator
import re

import numpy as np
import pytest

import pandas._config.config as cf

from pandas._libs.tslib import Timestamp
from pandas.compat.numpy import np_datetime64_compat
from pandas.util._test_decorators import async_mark

from pandas.core.dtypes.generic import ABCIndex

import pandas as pd
from pandas import (
    CategoricalIndex,
    DataFrame,
    DatetimeIndex,
    Float64Index,
    Int64Index,
    PeriodIndex,
    RangeIndex,
    Series,
    TimedeltaIndex,
    UInt64Index,
    date_range,
    isna,
    period_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.tests.indexes.common import Base


class TestIndex(Base):
    _holder = Index

    def create_index(self) -> Index:
        return Index(list("abcde"))

    def test_can_hold_identifiers(self):
        index = self.create_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):
        with tm.assert_produces_warning(FutureWarning):
            # GH#30588 multi-dimensional indexing deprecated
            new_index = index[None, :]
        assert new_index.ndim == 2
        assert isinstance(new_index, np.ndarray)

    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):
        # copy
        # index = self.create_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"

        # FIXME: dont leave commented-out
        # what to do here?
        # arr = np.array(5.)
        # pytest.raises(Exception, arr.view, Index)

    @pytest.mark.parametrize("cast_as_obj", [True, False])
    @pytest.mark.parametrize(
        "index",
        [
            pd.date_range(
                "2015-01-01 10:00",
                freq="D",
                periods=3,
                tz="US/Eastern",
                name="Green Eggs & Ham",
            ),  # DTI with tz
            pd.date_range("2015-01-01 10:00", freq="D", periods=3),  # DTI no tz
            pd.timedelta_range("1 days", freq="D", periods=3),  # td
            pd.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 = pd.Index(index.astype(object))
        else:
            result = pd.Index(index)

        tm.assert_index_equal(result, index)

        if isinstance(index, pd.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 = pd.Index(index, dtype=object)
                assert result.dtype == np.object_
                assert list(result) == list(index)

    @pytest.mark.parametrize(
        "index,has_tz",
        [
            (
                pd.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
            (pd.period_range("2015-01-01", freq="D", periods=3), False),  # period
        ],
    )
    def test_constructor_from_series_dtlike(self, index, has_tz):
        result = pd.Index(pd.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 = pd.DataFrame(np.random.rand(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 = pd.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"

    @pytest.mark.parametrize(
        "array",
        [
            np.arange(5),
            np.array(["a", "b", "c"]),
            date_range("2000-01-01", periods=3).values,
        ],
    )
    def test_constructor_ndarray_like(self, array):
        # GH 5460#issuecomment-44474502
        # it should be possible to convert any object that satisfies the numpy
        # ndarray interface directly into an Index
        class ArrayLike:
            def __init__(self, array):
                self.array = array

            def __array__(self, dtype=None) -> np.ndarray:
                return self.array

        expected = pd.Index(array)
        result = pd.Index(ArrayLike(array))
        tm.assert_index_equal(result, expected)

    def test_constructor_int_dtype_nan(self):
        # see gh-15187
        data = [np.nan]
        expected = Float64Index(data)
        result = Index(data, dtype="float")
        tm.assert_index_equal(result, expected)

    @pytest.mark.parametrize("dtype", ["int64", "uint64"])
    def test_constructor_int_dtype_nan_raises(self, dtype):
        # see gh-15187
        data = [np.nan]
        msg = "cannot convert"
        with pytest.raises(ValueError, match=msg):
            Index(data, dtype=dtype)

    def test_constructor_no_pandas_array(self):
        ser = pd.Series([1, 2, 3])
        result = pd.Index(ser.array)
        expected = pd.Index([1, 2, 3])
        tm.assert_index_equal(result, expected)

    @pytest.mark.parametrize(
        "klass,dtype,na_val",
        [
            (pd.Float64Index, np.float64, np.nan),
            (pd.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(
        "vals",
        [
            [1, 2, 3],
            np.array([1, 2, 3]),
            np.array([1, 2, 3], dtype=int),
            # below should coerce
            [1.0, 2.0, 3.0],
            np.array([1.0, 2.0, 3.0], dtype=float),
        ],
    )
    def test_constructor_dtypes_to_int64(self, vals):
        index = Index(vals, dtype=int)
        assert isinstance(index, Int64Index)

    @pytest.mark.parametrize(
        "vals",
        [
            [1, 2, 3],
            [1.0, 2.0, 3.0],
            np.array([1.0, 2.0, 3.0]),
            np.array([1, 2, 3], dtype=int),
            np.array([1.0, 2.0, 3.0], dtype=float),
        ],
    )
    def test_constructor_dtypes_to_float64(self, vals):
        index = Index(vals, dtype=float)
        assert isinstance(index, Float64Index)

    @pytest.mark.parametrize(
        "vals",
        [
            [1, 2, 3],
            np.array([1, 2, 3], dtype=int),
            np.array(
                [np_datetime64_compat("2011-01-01"), np_datetime64_compat("2011-01-02")]
            ),
            [datetime(2011, 1, 1), datetime(2011, 1, 2)],
        ],
    )
    def test_constructor_dtypes_to_categorical(self, vals):
        index = Index(vals, dtype="category")
        assert isinstance(index, CategoricalIndex)

    @pytest.mark.parametrize("cast_index", [True, False])
    @pytest.mark.parametrize(
        "vals",
        [
            Index(
                np.array(
                    [
                        np_datetime64_compat("2011-01-01"),
                        np_datetime64_compat("2011-01-02"),
                    ]
                )
            ),
            Index([datetime(2011, 1, 1), datetime(2011, 1, 2)]),
        ],
    )
    def test_constructor_dtypes_to_datetime(self, cast_index, vals):
        if cast_index:
            index = Index(vals, dtype=object)
            assert isinstance(index, Index)
            assert index.dtype == object
        else:
            index = Index(vals)
            assert isinstance(index, DatetimeIndex)

    @pytest.mark.parametrize("cast_index", [True, False])
    @pytest.mark.parametrize(
        "vals",
        [
            np.array([np.timedelta64(1, "D"), np.timedelta64(1, "D")]),
            [timedelta(1), timedelta(1)],
        ],
    )
    def test_constructor_dtypes_to_timedelta(self, cast_index, vals):
        if cast_index:
            index = Index(vals, dtype=object)
            assert isinstance(index, Index)
            assert index.dtype == object
        else:
            index = Index(vals)
            assert isinstance(index, TimedeltaIndex)

    @pytest.mark.parametrize("attr", ["values", "asi8"])
    @pytest.mark.parametrize("klass", [pd.Index, pd.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 = pd.date_range("2011-01-01", periods=5)
        arg = getattr(index, attr)
        index = index.tz_localize(tz_naive_fixture)
        dtype = index.dtype

        if attr == "asi8":
            result = pd.DatetimeIndex(arg).tz_localize(tz_naive_fixture)
        else:
            result = klass(arg, tz=tz_naive_fixture)
        tm.assert_index_equal(result, index)

        if attr == "asi8":
            result = pd.DatetimeIndex(arg).astype(dtype)
        else:
            result = klass(arg, dtype=dtype)
        tm.assert_index_equal(result, index)

        if attr == "asi8":
            result = pd.DatetimeIndex(list(arg)).tz_localize(tz_naive_fixture)
        else:
            result = klass(list(arg), tz=tz_naive_fixture)
        tm.assert_index_equal(result, index)

        if attr == "asi8":
            result = pd.DatetimeIndex(list(arg)).astype(dtype)
        else:
            result = klass(list(arg), dtype=dtype)
        tm.assert_index_equal(result, index)

    @pytest.mark.parametrize("attr", ["values", "asi8"])
    @pytest.mark.parametrize("klass", [pd.Index, pd.TimedeltaIndex])
    def test_constructor_dtypes_timedelta(self, attr, klass):
        index = pd.timedelta_range("1 days", periods=5)
        index = index._with_freq(None)  # wont 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,
            Float64Index,
            Int64Index,
            UInt64Index,
            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="B"), PeriodIndex),
            (PeriodIndex(iter([]), freq="B"), PeriodIndex),
            (PeriodIndex((_ for _ in []), freq="B"), PeriodIndex),
            (RangeIndex(step=1), pd.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)

    def test_constructor_overflow_int64(self):
        # see gh-15832
        msg = (
            "The elements provided in the data cannot "
            "all be casted to the dtype int64"
        )
        with pytest.raises(OverflowError, match=msg):
            Index([np.iinfo(np.uint64).max - 1], dtype="int64")

    @pytest.mark.parametrize(
        "index",
        [
            "datetime",
            "float",
            "int",
            "period",
            "range",
            "repeats",
            "timedelta",
            "tuples",
            "uint",
        ],
        indirect=True,
    )
    def test_view_with_args(self, index):
        index.view("i8")

    @pytest.mark.parametrize(
        "index",
        [
            "unicode",
            "string",
            pytest.param("categorical", marks=pytest.mark.xfail(reason="gh-25464")),
            "bool",
            "empty",
        ],
        indirect=True,
    )
    def test_view_with_args_object_array_raises(self, index):
        msg = "Cannot change data-type for object array"
        with pytest.raises(TypeError, match=msg):
            index.view("i8")

    @pytest.mark.parametrize("index", ["int", "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_insert_missing(self, nulls_fixture):
        # GH 22295
        # test there is no mangling of NA values
        expected = Index(["a", nulls_fixture, "b", "c"])
        result = Index(list("abc")).insert(1, nulls_fixture)
        tm.assert_index_equal(result, expected)

    def test_delete_raises(self):
        index = Index(["a", "b", "c", "d"], name="index")
        msg = "index 5 is out of bounds for axis 0 with size 4"
        with pytest.raises(IndexError, match=msg):
            index.delete(5)

    def test_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)

    @pytest.mark.parametrize("index", ["datetime"], indirect=True)
    def test_asof(self, index):
        d = index[0]
        assert index.asof(d) == d
        assert isna(index.asof(d - timedelta(1)))

        d = index[-1]
        assert index.asof(d + timedelta(1)) == d

        d = index[0].to_pydatetime()
        assert isinstance(index.asof(d), Timestamp)

    def test_asof_datetime_partial(self):
        index = pd.date_range("2010-01-01", periods=2, freq="m")
        expected = Timestamp("2010-02-28")
        result = index.asof("2010-02")
        assert result == expected
        assert not isinstance(result, Index)

    def test_nanosecond_index_access(self):
        s = Series([Timestamp("20130101")]).values.view("i8")[0]
        r = DatetimeIndex([s + 50 + i for i in range(100)])
        x = Series(np.random.randn(100), index=r)

        first_value = x.asof(x.index[0])

        # this does not yet work, as parsing strings is done via dateutil
        # assert first_value == x['2013-01-01 00:00:00.000000050+0000']

        expected_ts = np_datetime64_compat("2013-01-01 00:00:00.000000050+0000", "ns")
        assert first_value == x[Timestamp(expected_ts)]

    @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):
        index = self.create_index()
        sl = index[[1, 2, 3]]
        for i in sl:
            assert i == sl[sl.get_loc(i)]

    @pytest.mark.parametrize("index", ["string", "int", "float"], indirect=True)
    @pytest.mark.parametrize("dtype", [np.int_, np.bool_])
    def test_empty_fancy(self, index, dtype):
        empty_arr = np.array([], dtype=dtype)
        empty_index = type(index)([])

        assert index[[]].identical(empty_index)
        assert index[empty_arr].identical(empty_index)

    @pytest.mark.parametrize("index", ["string", "int", "float"], indirect=True)
    def test_empty_fancy_raises(self, index):
        # pd.DatetimeIndex is excluded, because it overrides getitem and should
        # be tested separately.
        empty_farr = np.array([], dtype=np.float_)
        empty_index = type(index)([])

        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]

    @pytest.mark.parametrize("index", ["string"], indirect=True)
    def test_intersection(self, index, sort):
        first = index[:20]
        second = index[:10]
        intersect = first.intersection(second, sort=sort)
        if sort is None:
            tm.assert_index_equal(intersect, second.sort_values())
        assert tm.equalContents(intersect, second)

        # Corner cases
        inter = first.intersection(first, sort=sort)
        assert inter is first

    @pytest.mark.parametrize(
        "index2,keeps_name",
        [
            (Index([3, 4, 5, 6, 7], name="index"), True),  # preserve same name
            (Index([3, 4, 5, 6, 7], name="other"), False),  # drop diff names
            (Index([3, 4, 5, 6, 7]), False),
        ],
    )
    def test_intersection_name_preservation(self, index2, keeps_name, sort):
        index1 = Index([1, 2, 3, 4, 5], name="index")
        expected = Index([3, 4, 5])
        result = index1.intersection(index2, sort)

        if keeps_name:
            expected.name = "index"

        assert result.name == expected.name
        tm.assert_index_equal(result, expected)

    @pytest.mark.parametrize("index", ["string"], indirect=True)
    @pytest.mark.parametrize(
        "first_name,second_name,expected_name",
        [("A", "A", "A"), ("A", "B", None), (None, "B", None)],
    )
    def test_intersection_name_preservation2(
        self, index, first_name, second_name, expected_name, sort
    ):
        first = index[5:20]
        second = index[:10]
        first.name = first_name
        second.name = second_name
        intersect = first.intersection(second, sort=sort)
        assert intersect.name == expected_name

    @pytest.mark.parametrize(
        "index2,keeps_name",
        [
            (Index([4, 7, 6, 5, 3], name="index"), True),
            (Index([4, 7, 6, 5, 3], name="other"), False),
        ],
    )
    def test_intersection_monotonic(self, index2, keeps_name, sort):
        index1 = Index([5, 3, 2, 4, 1], name="index")
        expected = Index([5, 3, 4])

        if keeps_name:
            expected.name = "index"

        result = index1.intersection(index2, sort=sort)
        if sort is None:
            expected = expected.sort_values()
        tm.assert_index_equal(result, expected)

    @pytest.mark.parametrize(
        "index2,expected_arr",
        [(Index(["B", "D"]), ["B"]), (Index(["B", "D", "A"]), ["A", "B", "A"])],
    )
    def test_intersection_non_monotonic_non_unique(self, index2, expected_arr, sort):
        # non-monotonic non-unique
        index1 = Index(["A", "B", "A", "C"])
        expected = Index(expected_arr, dtype="object")
        result = index1.intersection(index2, sort=sort)
        if sort is None:
            expected = expected.sort_values()
        tm.assert_index_equal(result, expected)

    def test_intersect_str_dates(self, sort):
        dt_dates = [datetime(2012, 2, 9), datetime(2012, 2, 22)]

        i1 = Index(dt_dates, dtype=object)
        i2 = Index(["aa"], dtype=object)
        result = i2.intersection(i1, sort=sort)

        assert len(result) == 0

    @pytest.mark.xfail(reason="Not implemented")
    def test_intersection_equal_sort_true(self):
        # TODO decide on True behaviour
        idx = pd.Index(["c", "a", "b"])
        sorted_ = pd.Index(["a", "b", "c"])
        tm.assert_index_equal(idx.intersection(idx, sort=True), sorted_)

    def test_chained_union(self, sort):
        # Chained unions handles names correctly
        i1 = Index([1, 2], name="i1")
        i2 = Index([5, 6], name="i2")
        i3 = Index([3, 4], name="i3")
        union = i1.union(i2.union(i3, sort=sort), sort=sort)
        expected = i1.union(i2, sort=sort).union(i3, sort=sort)
        tm.assert_index_equal(union, expected)

        j1 = Index([1, 2], name="j1")
        j2 = Index([], name="j2")
        j3 = Index([], name="j3")
        union = j1.union(j2.union(j3, sort=sort), sort=sort)
        expected = j1.union(j2, sort=sort).union(j3, sort=sort)
        tm.assert_index_equal(union, expected)

    @pytest.mark.parametrize("index", ["string"], indirect=True)
    def test_union(self, index, sort):
        first = index[5:20]
        second = index[:10]
        everything = index[:20]

        union = first.union(second, sort=sort)
        if sort is None:
            tm.assert_index_equal(union, everything.sort_values())
        assert tm.equalContents(union, everything)

    @pytest.mark.parametrize("slice_", [slice(None), slice(0)])
    def test_union_sort_other_special(self, slice_):
        # https://github.com/pandas-dev/pandas/issues/24959

        idx = pd.Index([1, 0, 2])
        # default, sort=None
        other = idx[slice_]
        tm.assert_index_equal(idx.union(other), idx)
        tm.assert_index_equal(other.union(idx), idx)

        # sort=False
        tm.assert_index_equal(idx.union(other, sort=False), idx)

    @pytest.mark.xfail(reason="Not implemented")
    @pytest.mark.parametrize("slice_", [slice(None), slice(0)])
    def test_union_sort_special_true(self, slice_):
        # TODO decide on True behaviour
        # sort=True
        idx = pd.Index([1, 0, 2])
        # default, sort=None
        other = idx[slice_]

        result = idx.union(other, sort=True)
        expected = pd.Index([0, 1, 2])
        tm.assert_index_equal(result, expected)

    @pytest.mark.parametrize("klass", [np.array, Series, list])
    @pytest.mark.parametrize("index", ["string"], indirect=True)
    def test_union_from_iterables(self, index, klass, sort):
        # GH 10149
        first = index[5:20]
        second = index[:10]
        everything = index[:20]

        case = klass(second.values)
        result = first.union(case, sort=sort)
        if sort is None:
            tm.assert_index_equal(result, everything.sort_values())
        assert tm.equalContents(result, everything)

    @pytest.mark.parametrize("index", ["string"], indirect=True)
    def test_union_identity(self, index, sort):
        first = index[5:20]

        union = first.union(first, sort=sort)
        # i.e. identity is not preserved when sort is True
        assert (union is first) is (not sort)

        # This should no longer be the same object, since [] is not consistent,
        # both objects will be recast to dtype('O')
        union = first.union([], sort=sort)
        assert (union is first) is (not sort)

        union = Index([]).union(first, sort=sort)
        assert (union is first) is (not sort)

    @pytest.mark.parametrize("first_list", [list("ba"), list()])
    @pytest.mark.parametrize("second_list", [list("ab"), list()])
    @pytest.mark.parametrize(
        "first_name, second_name, expected_name",
        [("A", "B", None), (None, "B", None), ("A", None, None)],
    )
    def test_union_name_preservation(
        self, first_list, second_list, first_name, second_name, expected_name, sort
    ):
        first = Index(first_list, name=first_name)
        second = Index(second_list, name=second_name)
        union = first.union(second, sort=sort)

        vals = set(first_list).union(second_list)

        if sort is None and len(first_list) > 0 and len(second_list) > 0:
            expected = Index(sorted(vals), name=expected_name)
            tm.assert_index_equal(union, expected)
        else:
            expected = Index(vals, name=expected_name)
            assert tm.equalContents(union, expected)

    def test_union_dt_as_obj(self, sort):
        # TODO: Replace with fixturesult
        index = self.create_index()
        date_index = pd.date_range("2019-01-01", periods=10)
        first_cat = index.union(date_index)
        second_cat = index.union(index)

        if date_index.dtype == np.object_:
            appended = np.append(index, date_index)
        else:
            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_identity_mapping(self, index):
        # GH 12766
        tm.assert_index_equal(index, index.map(lambda x: x))

    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")
        expected = Index(range(24), name="hourly")
        tm.assert_index_equal(expected, date_index.map(lambda x: x.hour))

    @pytest.mark.parametrize(
        "mapper",
        [
            lambda values, index: {i: e for e, i in zip(values, index)},
            lambda values, index: pd.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: pd.Series(values, index),
        ],
    )
    def test_map_dictlike(self, index, mapper):
        # GH 12756
        if isinstance(index, CategoricalIndex):
            # Tested in test_categorical
            return
        elif not index.is_unique:
            # Cannot map duplicated index
            return

        if index.empty:
            # to match proper result coercion for uints
            expected = Index([])
        else:
            expected = Index(np.arange(len(index), 0, -1))

        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", ["string"], indirect=True)
    @pytest.mark.parametrize("second_name,expected", [(None, None), ("name", "name")])
    def test_difference_name_preservation(self, index, second_name, expected, sort):
        first = index[5:20]
        second = index[:10]
        answer = index[10:20]

        first.name = "name"
        second.name = second_name
        result = first.difference(second, sort=sort)

        assert tm.equalContents(result, answer)

        if expected is None:
            assert result.name is None
        else:
            assert result.name == expected

    @pytest.mark.parametrize("index", ["string"], indirect=True)
    def test_difference_empty_arg(self, index, sort):
        first = index[5:20]
        first.name = "name"
        result = first.difference([], sort)

        assert tm.equalContents(result, first)
        assert result.name == first.name

    @pytest.mark.parametrize("index", ["string"], indirect=True)
    def test_difference_identity(self, index, sort):
        first = index[5:20]
        first.name = "name"
        result = first.difference(first, sort)

        assert len(result) == 0
        assert result.name == first.name

    @pytest.mark.parametrize("index", ["string"], indirect=True)
    def test_difference_sort(self, index, sort):
        first = index[5:20]
        second = index[:10]

        result = first.difference(second, sort)
        expected = index[10:20]

        if sort is None:
            expected = expected.sort_values()

        tm.assert_index_equal(result, expected)

    def test_symmetric_difference(self, sort):
        # smoke
        index1 = Index([5, 2, 3, 4], name="index1")
        index2 = Index([2, 3, 4, 1])
        result = index1.symmetric_difference(index2, sort=sort)
        expected = Index([5, 1])
        assert tm.equalContents(result, expected)
        assert result.name is None
        if sort is None:
            expected = expected.sort_values()
        tm.assert_index_equal(result, expected)

        # __xor__ syntax
        expected = index1 ^ index2
        assert tm.equalContents(result, expected)
        assert result.name is None

    @pytest.mark.parametrize("opname", ["difference", "symmetric_difference"])
    def test_difference_incomparable(self, opname):
        a = pd.Index([3, pd.Timestamp("2000"), 1])
        b = pd.Index([2, pd.Timestamp("1999"), 1])
        op = operator.methodcaller(opname, b)

        # sort=None, the default
        result = op(a)
        expected = pd.Index([3, pd.Timestamp("2000"), 2, pd.Timestamp("1999")])
        if opname == "difference":
            expected = expected[:2]
        tm.assert_index_equal(result, expected)

        # sort=False
        op = operator.methodcaller(opname, b, sort=False)
        result = op(a)
        tm.assert_index_equal(result, expected)

    @pytest.mark.xfail(reason="Not implemented")
    @pytest.mark.parametrize("opname", ["difference", "symmetric_difference"])
    def test_difference_incomparable_true(self, opname):
        # TODO decide on True behaviour
        # # sort=True, raises
        a = pd.Index([3, pd.Timestamp("2000"), 1])
        b = pd.Index([2, pd.Timestamp("1999"), 1])
        op = operator.methodcaller(opname, b, sort=True)

        with pytest.raises(TypeError, match="Cannot compare"):
            op(a)

    def test_symmetric_difference_mi(self, sort):
        index1 = MultiIndex.from_tuples(zip(["foo", "bar", "baz"], [1, 2, 3]))
        index2 = MultiIndex.from_tuples([("foo", 1), ("bar", 3)])
        result = index1.symmetric_difference(index2, sort=sort)
        expected = MultiIndex.from_tuples([("bar", 2), ("baz", 3), ("bar", 3)])
        if sort is None:
            expected = expected.sort_values()
        tm.assert_index_equal(result, expected)
        assert tm.equalContents(result, expected)

    @pytest.mark.parametrize(
        "index2,expected",
        [
            (Index([0, 1, np.nan]), Index([2.0, 3.0, 0.0])),
            (Index([0, 1]), Index([np.nan, 2.0, 3.0, 0.0])),
        ],
    )
    def test_symmetric_difference_missing(self, index2, expected, sort):
        # GH 13514 change: {nan} - {nan} == {}
        # (GH 6444, sorting of nans, is no longer an issue)
        index1 = Index([1, np.nan, 2, 3])

        result = index1.symmetric_difference(index2, sort=sort)
        if sort is None:
            expected = expected.sort_values()
        tm.assert_index_equal(result, expected)

    def test_symmetric_difference_non_index(self, sort):
        index1 = Index([1, 2, 3, 4], name="index1")
        index2 = np.array([2, 3, 4, 5])
        expected = Index([1, 5])
        result = index1.symmetric_difference(index2, sort=sort)
        assert tm.equalContents(result, expected)
        assert result.name == "index1"

        result = index1.symmetric_difference(index2, result_name="new_name", sort=sort)
        assert tm.equalContents(result, expected)
        assert result.name == "new_name"

    def test_difference_type(self, index, sort):
        # GH 20040
        # If taking difference of a set and itself, it
        # needs to preserve the type of the index
        if not index.is_unique:
            return
        result = index.difference(index, sort=sort)
        expected = index.drop(index)
        tm.assert_index_equal(result, expected)

    def test_intersection_difference(self, index, sort):
        # GH 20040
        # Test that the intersection of an index with an
        # empty index produces the same index as the difference
        # of an index with itself.  Test for all types
        if not index.is_unique:
            return
        inter = index.intersection(index.drop(index))
        diff = index.difference(index, sort=sort)
        tm.assert_index_equal(inter, diff)

    def test_is_mixed_deprecated(self):
        # GH#32922
        index = self.create_index()
        with tm.assert_produces_warning(FutureWarning):
            index.is_mixed()

    @pytest.mark.parametrize(
        "index, expected",
        [
            ("string", False),
            ("bool", False),
            ("categorical", False),
            ("int", True),
            ("datetime", False),
            ("float", True),
        ],
        indirect=["index"],
    )
    def test_is_numeric(self, index, expected):
        assert index.is_numeric() is expected

    @pytest.mark.parametrize(
        "index, expected",
        [
            ("string", True),
            ("bool", True),
            ("categorical", False),
            ("int", False),
            ("datetime", False),
            ("float", False),
        ],
        indirect=["index"],
    )
    def test_is_object(self, index, expected):
        assert index.is_object() is expected

    @pytest.mark.parametrize(
        "index, expected",
        [
            ("string", False),
            ("bool", False),
            ("categorical", False),
            ("int", False),
            ("datetime", True),
            ("float", False),
        ],
        indirect=["index"],
    )
    def test_is_all_dates(self, index, expected):
        assert index.is_all_dates is expected

    def test_summary(self, index):
        self._check_method_works(Index._summary, index)

    def test_summary_bug(self):
        # GH3869`
        ind = Index(["{other}%s", "~:{range}:0"], name="A")
        result = ind._summary()
        # shouldn't be formatted accidentally.
        assert "~:{range}:0" in result
        assert "{other}%s" in result

    def test_format_different_scalar_lengths(self):
        # GH35439
        idx = Index(["aaaaaaaaa", "b"])
        expected = ["aaaaaaaaa", "b"]
        assert idx.format() == expected

    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()
        if not str(now).endswith("000"):
            index = Index([now])
            formatted = index.format()
            expected = [str(index[0])]
            assert formatted == expected

        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)

        formatted = index.format()
        expected = [str(index[0]), str(index[1]), str(index[2]), "NaN"]

        assert formatted == expected
        assert index[3] is nulls_fixture

    def test_format_with_name_time_info(self):
        # bug I fixed 12/20/2011
        dates = date_range("2011-01-01 04:00:00", periods=10, name="something")

        formatted = dates.format(name=True)
        assert formatted[0] == "something"

    def test_format_datetime_with_time(self):
        t = Index([datetime(2012, 2, 7), datetime(2012, 2, 7, 23)])

        result = t.format()
        expected = ["2012-02-07 00:00:00", "2012-02-07 23:00:00"]
        assert len(result) == 2
        assert result == expected

    @pytest.mark.parametrize("op", ["any", "all"])
    def test_logical_compat(self, op):
        index = self.create_index()
        assert getattr(index, op)() == getattr(index.values, op)()

    def _check_method_works(self, method, index):
        method(index)

    def test_get_indexer(self):
        index1 = Index([1, 2, 3, 4, 5])
        index2 = Index([2, 4, 6])

        r1 = index1.get_indexer(index2)
        e1 = np.array([1, 3, -1], dtype=np.intp)
        tm.assert_almost_equal(r1, e1)

    @pytest.mark.parametrize("reverse", [True, False])
    @pytest.mark.parametrize(
        "expected,method",
        [
            (np.array([-1, 0, 0, 1, 1], dtype=np.intp), "pad"),
            (np.array([-1, 0, 0, 1, 1], dtype=np.intp), "ffill"),
            (np.array([0, 0, 1, 1, 2], dtype=np.intp), "backfill"),
            (np.array([0, 0, 1, 1, 2], dtype=np.intp), "bfill"),
        ],
    )
    def test_get_indexer_methods(self, reverse, expected, method):
        index1 = Index([1, 2, 3, 4, 5])
        index2 = Index([2, 4, 6])

        if reverse:
            index1 = index1[::-1]
            expected = expected[::-1]

        result = index2.get_indexer(index1, method=method)
        tm.assert_almost_equal(result, expected)

    def test_get_indexer_invalid(self):
        # GH10411
        index = Index(np.arange(10))

        with pytest.raises(ValueError, match="tolerance argument"):
            index.get_indexer([1, 0], tolerance=1)

        with pytest.raises(ValueError, match="limit argument"):
            index.get_indexer([1, 0], limit=1)

    @pytest.mark.parametrize(
        "method, tolerance, indexer, expected",
        [
            ("pad", None, [0, 5, 9], [0, 5, 9]),
            ("backfill", None, [0, 5, 9], [0, 5, 9]),
            ("nearest", None, [0, 5, 9], [0, 5, 9]),
            ("pad", 0, [0, 5, 9], [0, 5, 9]),
            ("backfill", 0, [0, 5, 9], [0, 5, 9]),
            ("nearest", 0, [0, 5, 9], [0, 5, 9]),
            ("pad", None, [0.2, 1.8, 8.5], [0, 1, 8]),
            ("backfill", None, [0.2, 1.8, 8.5], [1, 2, 9]),
            ("nearest", None, [0.2, 1.8, 8.5], [0, 2, 9]),
            ("pad", 1, [0.2, 1.8, 8.5], [0, 1, 8]),
            ("backfill", 1, [0.2, 1.8, 8.5], [1, 2, 9]),
            ("nearest", 1, [0.2, 1.8, 8.5], [0, 2, 9]),
            ("pad", 0.2, [0.2, 1.8, 8.5], [0, -1, -1]),
            ("backfill", 0.2, [0.2, 1.8, 8.5], [-1, 2, -1]),
            ("nearest", 0.2, [0.2, 1.8, 8.5], [0, 2, -1]),
        ],
    )
    def test_get_indexer_nearest(self, method, tolerance, indexer, expected):
        index = Index(np.arange(10))

        actual = index.get_indexer(indexer, method=method, tolerance=tolerance)
        tm.assert_numpy_array_equal(actual, np.array(expected, dtype=np.intp))

    @pytest.mark.parametrize("listtype", [list, tuple, Series, np.array])
    @pytest.mark.parametrize(
        "tolerance, expected",
        list(
            zip(
                [[0.3, 0.3, 0.1], [0.2, 0.1, 0.1], [0.1, 0.5, 0.5]],
                [[0, 2, -1], [0, -1, -1], [-1, 2, 9]],
            )
        ),
    )
    def test_get_indexer_nearest_listlike_tolerance(
        self, tolerance, expected, listtype
    ):
        index = Index(np.arange(10))

        actual = index.get_indexer(
            [0.2, 1.8, 8.5], method="nearest", tolerance=listtype(tolerance)
        )
        tm.assert_numpy_array_equal(actual, np.array(expected, dtype=np.intp))

    def test_get_indexer_nearest_error(self):
        index = Index(np.arange(10))
        with pytest.raises(ValueError, match="limit argument"):
            index.get_indexer([1, 0], method="nearest", limit=1)

        with pytest.raises(ValueError, match="tolerance size must match"):
            index.get_indexer([1, 0], method="nearest", tolerance=[1, 2, 3])

    @pytest.mark.parametrize(
        "method,expected",
        [("pad", [8, 7, 0]), ("backfill", [9, 8, 1]), ("nearest", [9, 7, 0])],
    )
    def test_get_indexer_nearest_decreasing(self, method, expected):
        index = Index(np.arange(10))[::-1]

        actual = index.get_indexer([0, 5, 9], method=method)
        tm.assert_numpy_array_equal(actual, np.array([9, 4, 0], dtype=np.intp))

        actual = index.get_indexer([0.2, 1.8, 8.5], method=method)
        tm.assert_numpy_array_equal(actual, np.array(expected, dtype=np.intp))

    @pytest.mark.parametrize(
        "method,expected",
        [
            ("pad", np.array([-1, 0, 1, 1], dtype=np.intp)),
            ("backfill", np.array([0, 0, 1, -1], dtype=np.intp)),
        ],
    )
    def test_get_indexer_strings(self, method, expected):
        index = pd.Index(["b", "c"])
        actual = index.get_indexer(["a", "b", "c", "d"], method=method)

        tm.assert_numpy_array_equal(actual, expected)

    def test_get_indexer_strings_raises(self):
        index = pd.Index(["b", "c"])

        msg = r"unsupported operand type\(s\) for -: 'str' and 'str'"
        with pytest.raises(TypeError, match=msg):
            index.get_indexer(["a", "b", "c", "d"], method="nearest")

        with pytest.raises(TypeError, match=msg):
            index.get_indexer(["a", "b", "c", "d"], method="pad", tolerance=2)

        with pytest.raises(TypeError, match=msg):
            index.get_indexer(
                ["a", "b", "c", "d"], method="pad", tolerance=[2, 2, 2, 2]
            )

    @pytest.mark.parametrize("idx_class", [Int64Index, RangeIndex, Float64Index])
    def test_get_indexer_numeric_index_boolean_target(self, idx_class):
        # GH 16877

        numeric_index = idx_class(RangeIndex(4))
        result = numeric_index.get_indexer([True, False, True])
        expected = np.array([-1, -1, -1], dtype=np.intp)
        tm.assert_numpy_array_equal(result, expected)

    def test_get_indexer_with_NA_values(
        self, unique_nulls_fixture, unique_nulls_fixture2
    ):
        # GH 22332
        # check pairwise, that no pair of na values
        # is mangled
        if unique_nulls_fixture is unique_nulls_fixture2:
            return  # skip it, values are not unique
        arr = np.array([unique_nulls_fixture, unique_nulls_fixture2], dtype=object)
        index = pd.Index(arr, dtype=object)
        result = index.get_indexer(
            [unique_nulls_fixture, unique_nulls_fixture2, "Unknown"]
        )
        expected = np.array([0, 1, -1], dtype=np.intp)
        tm.assert_numpy_array_equal(result, expected)

    @pytest.mark.parametrize("method", [None, "pad", "backfill", "nearest"])
    def test_get_loc(self, method):
        index = pd.Index([0, 1, 2])
        assert index.get_loc(1, method=method) == 1

        if method:
            assert index.get_loc(1, method=method, tolerance=0) == 1

    @pytest.mark.parametrize("method", [None, "pad", "backfill", "nearest"])
    def test_get_loc_raises_bad_label(self, method):
        index = pd.Index([0, 1, 2])
        if method:
            msg = "not supported between"
        else:
            msg = "invalid key"

        with pytest.raises(TypeError, match=msg):
            index.get_loc([1, 2], method=method)

    @pytest.mark.parametrize(
        "method,loc", [("pad", 1), ("backfill", 2), ("nearest", 1)]
    )
    def test_get_loc_tolerance(self, method, loc):
        index = pd.Index([0, 1, 2])
        assert index.get_loc(1.1, method) == loc
        assert index.get_loc(1.1, method, tolerance=1) == loc

    @pytest.mark.parametrize("method", ["pad", "backfill", "nearest"])
    def test_get_loc_outside_tolerance_raises(self, method):
        index = pd.Index([0, 1, 2])
        with pytest.raises(KeyError, match="1.1"):
            index.get_loc(1.1, method, tolerance=0.05)

    def test_get_loc_bad_tolerance_raises(self):
        index = pd.Index([0, 1, 2])
        with pytest.raises(ValueError, match="must be numeric"):
            index.get_loc(1.1, "nearest", tolerance="invalid")

    def test_get_loc_tolerance_no_method_raises(self):
        index = pd.Index([0, 1, 2])
        with pytest.raises(ValueError, match="tolerance .* valid if"):
            index.get_loc(1.1, tolerance=1)

    def test_get_loc_raises_missized_tolerance(self):
        index = pd.Index([0, 1, 2])
        with pytest.raises(ValueError, match="tolerance size must match"):
            index.get_loc(1.1, "nearest", tolerance=[1, 1])

    def test_get_loc_raises_object_nearest(self):
        index = pd.Index(["a", "c"])
        with pytest.raises(TypeError, match="unsupported operand type"):
            index.get_loc("a", method="nearest")

    def test_get_loc_raises_object_tolerance(self):
        index = pd.Index(["a", "c"])
        with pytest.raises(TypeError, match="unsupported operand type"):
            index.get_loc("a", method="pad", tolerance="invalid")

    @pytest.mark.parametrize("dtype", [int, float])
    def test_slice_locs(self, dtype):
        index = Index(np.array([0, 1, 2, 5, 6, 7, 9, 10], dtype=dtype))
        n = len(index)

        assert index.slice_locs(start=2) == (2, n)
        assert index.slice_locs(start=3) == (3, n)
        assert index.slice_locs(3, 8) == (3, 6)
        assert index.slice_locs(5, 10) == (3, n)
        assert index.slice_locs(end=8) == (0, 6)
        assert index.slice_locs(end=9) == (0, 7)

        # reversed
        index2 = index[::-1]
        assert index2.slice_locs(8, 2) == (2, 6)
        assert index2.slice_locs(7, 3) == (2, 5)

    @pytest.mark.parametrize("dtype", [int, float])
    def test_slice_float_locs(self, dtype):
        index = Index(np.array([0, 1, 2, 5, 6, 7, 9, 10], dtype=dtype))
        n = len(index)
        assert index.slice_locs(5.0, 10.0) == (3, n)
        assert index.slice_locs(4.5, 10.5) == (3, 8)

        index2 = index[::-1]
        assert index2.slice_locs(8.5, 1.5) == (2, 6)
        assert index2.slice_locs(10.5, -1) == (0, n)

    def test_slice_locs_dup(self):
        index = Index(["a", "a", "b", "c", "d", "d"])
        assert index.slice_locs("a", "d") == (0, 6)
        assert index.slice_locs(end="d") == (0, 6)
        assert index.slice_locs("a", "c") == (0, 4)
        assert index.slice_locs("b", "d") == (2, 6)

        index2 = index[::-1]
        assert index2.slice_locs("d", "a") == (0, 6)
        assert index2.slice_locs(end="a") == (0, 6)
        assert index2.slice_locs("d", "b") == (0, 4)
        assert index2.slice_locs("c", "a") == (2, 6)

    @pytest.mark.parametrize("dtype", [int, float])
    def test_slice_locs_dup_numeric(self, dtype):
        index = Index(np.array([10, 12, 12, 14], dtype=dtype))
        assert index.slice_locs(12, 12) == (1, 3)
        assert index.slice_locs(11, 13) == (1, 3)

        index2 = index[::-1]
        assert index2.slice_locs(12, 12) == (1, 3)
        assert index2.slice_locs(13, 11) == (1, 3)

    def test_slice_locs_na(self):
        index = Index([np.nan, 1, 2])
        assert index.slice_locs(1) == (1, 3)
        assert index.slice_locs(np.nan) == (0, 3)

        index = Index([0, np.nan, np.nan, 1, 2])
        assert index.slice_locs(np.nan) == (1, 5)

    def test_slice_locs_na_raises(self):
        index = Index([np.nan, 1, 2])
        with pytest.raises(KeyError, match=""):
            index.slice_locs(start=1.5)

        with pytest.raises(KeyError, match=""):
            index.slice_locs(end=1.5)

    @pytest.mark.parametrize(
        "in_slice,expected",
        [
            # error: Slice index must be an integer or None
            (pd.IndexSlice[::-1], "yxdcb"),
            (pd.IndexSlice["b":"y":-1], ""),  # type: ignore[misc]
            (pd.IndexSlice["b"::-1], "b"),  # type: ignore[misc]
            (pd.IndexSlice[:"b":-1], "yxdcb"),  # type: ignore[misc]
            (pd.IndexSlice[:"y":-1], "y"),  # type: ignore[misc]
            (pd.IndexSlice["y"::-1], "yxdcb"),  # type: ignore[misc]
            (pd.IndexSlice["y"::-4], "yb"),  # type: ignore[misc]
            # absent labels
            (pd.IndexSlice[:"a":-1], "yxdcb"),  # type: ignore[misc]
            (pd.IndexSlice[:"a":-2], "ydb"),  # type: ignore[misc]
            (pd.IndexSlice["z"::-1], "yxdcb"),  # type: ignore[misc]
            (pd.IndexSlice["z"::-3], "yc"),  # type: ignore[misc]
            (pd.IndexSlice["m"::-1], "dcb"),  # type: ignore[misc]
            (pd.IndexSlice[:"m":-1], "yx"),  # type: ignore[misc]
            (pd.IndexSlice["a":"a":-1], ""),  # type: ignore[misc]
            (pd.IndexSlice["z":"z":-1], ""),  # type: ignore[misc]
            (pd.IndexSlice["m":"m":-1], ""),  # type: ignore[misc]
        ],
    )
    def test_slice_locs_negative_step(self, in_slice, expected):
        index = Index(list("bcdxy"))

        s_start, s_stop = index.slice_locs(in_slice.start, in_slice.stop, in_slice.step)
        result = index[s_start : s_stop : in_slice.step]
        expected = pd.Index(list(expected))
        tm.assert_index_equal(result, expected)

    @pytest.mark.parametrize("index", ["string", "int", "float"], 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", "int", "float"], 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", "int", "float"], 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 = pd.Index(values)
        expected = pd.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 = fr"\"\[{re.escape(to_drop[1].__repr__())}\] 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.parametrize(
        "method,expected,sort",
        [
            (
                "intersection",
                np.array(
                    [(1, "A"), (2, "A"), (1, "B"), (2, "B")],
                    dtype=[("num", int), ("let", "a1")],
                ),
                False,
            ),
            (
                "intersection",
                np.array(
                    [(1, "A"), (1, "B"), (2, "A"), (2, "B")],
                    dtype=[("num", int), ("let", "a1")],
                ),
                None,
            ),
            (
                "union",
                np.array(
                    [(1, "A"), (1, "B"), (1, "C"), (2, "A"), (2, "B"), (2, "C")],
                    dtype=[("num", int), ("let", "a1")],
                ),
                None,
            ),
        ],
    )
    def test_tuple_union_bug(self, method, expected, sort):
        index1 = Index(
            np.array(
                [(1, "A"), (2, "A"), (1, "B"), (2, "B")],
                dtype=[("num", int), ("let", "a1")],
            )
        )
        index2 = Index(
            np.array(
                [(1, "A"), (2, "A"), (1, "B"), (2, "B"), (1, "C"), (2, "C")],
                dtype=[("num", int), ("let", "a1")],
            )
        )

        result = getattr(index1, method)(index2, sort=sort)
        assert result.ndim == 1

        expected = Index(expected)
        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)

    def test_set_value_deprecated(self):
        # GH 28621
        idx = self.create_index()
        arr = np.array([1, 2, 3])
        with tm.assert_produces_warning(FutureWarning):
            idx.set_value(arr, idx[1], 80)
        assert arr[1] == 80

    @pytest.mark.parametrize(
        "index", ["string", "int", "datetime", "timedelta"], indirect=True
    )
    def test_get_value(self, index):
        # TODO: Remove function? GH 19728
        values = np.random.randn(100)
        value = index[67]

        with pytest.raises(AttributeError, match="has no attribute '_values'"):
            # Index.get_value requires a Series, not an ndarray
            with tm.assert_produces_warning(FutureWarning):
                index.get_value(values, value)

        with tm.assert_produces_warning(FutureWarning):
            result = index.get_value(Series(values, index=values), value)
        tm.assert_almost_equal(result, values[67])

    @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):
        if nulls_fixture is pd.NaT:
            pytest.skip("pd.NaT not compatible with Float64Index")

        # Float64Index overrides isin, so must be checked separately
        if nulls_fixture is pd.NA:
            pytest.xfail("Float64Index cannot contain pd.NA")

        tm.assert_numpy_array_equal(
            Float64Index([1.0, nulls_fixture]).isin([np.nan]), np.array([False, True])
        )

        # we cannot compare NaT with NaN
        tm.assert_numpy_array_equal(
            Float64Index([1.0, nulls_fixture]).isin([pd.NaT]), np.array([False, False])
        )

    @pytest.mark.parametrize("level", [0, -1])
    @pytest.mark.parametrize(
        "index",
        [
            Index(["qux", "baz", "foo", "bar"]),
            # Float64Index overrides isin, so must be checked separately
            Float64Index([1.0, 2.0, 3.0, 4.0]),
        ],
    )
    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 = fr"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)

    @pytest.mark.parametrize(
        "values",
        [
            [1, 2, 3, 4],
            [1.0, 2.0, 3.0, 4.0],
            [True, True, True, True],
            ["foo", "bar", "baz", "qux"],
            pd.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",
        ["unicode", "string", "datetime", "int", "uint", "float"],
        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="A"),
        ],
    )
    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(pd.Index([2, 3], dtype=np.object_))
        assert index[[0, 1]].identical(pd.Index([1, 2], dtype=np.object_))

    def test_outer_join_sort(self):
        left_index = Index(np.random.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 Int64Index
        with tm.assert_produces_warning(RuntimeWarning):
            expected = right_index.astype(object).union(left_index.astype(object))

        tm.assert_index_equal(result, expected)

    def test_nan_first_take_datetime(self):
        index = Index([pd.NaT, Timestamp("20130101"), Timestamp("20130102")])
        result = index.take([-1, 0, 1])
        expected = Index([index[-1], index[0], index[1]])
        tm.assert_index_equal(result, expected)

    def test_take_fill_value(self):
        # GH 12631
        index = pd.Index(list("ABC"), name="xxx")
        result = index.take(np.array([1, 0, -1]))
        expected = pd.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 = pd.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 = pd.Index(["B", "A", "C"], name="xxx")
        tm.assert_index_equal(result, expected)

    def test_take_fill_value_none_raises(self):
        index = pd.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 = pd.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
            pd.date_range("20130101", periods=3).values,
            pd.date_range("20130101", periods=3).tolist(),
        ],
    )
    def test_reindex_preserves_name_if_target_is_list_or_ndarray(self, name, labels):
        # GH6552
        index = pd.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 = pd.Index(list("abc"))
        assert index.reindex(labels)[0].dtype.type == np.object_

    @pytest.mark.parametrize(
        "labels,dtype",
        [
            (pd.Int64Index([]), np.int64),
            (pd.Float64Index([]), np.float64),
            (pd.DatetimeIndex([]), np.datetime64),
        ],
    )
    def test_reindex_doesnt_preserve_type_if_target_is_empty_index(self, labels, dtype):
        # GH7774
        index = pd.Index(list("abc"))
        assert index.reindex(labels)[0].dtype.type == dtype

    def test_reindex_no_type_preserve_target_empty_mi(self):
        index = pd.Index(list("abc"))
        result = index.reindex(
            pd.MultiIndex([pd.Int64Index([]), pd.Float64Index([])], [[], []])
        )[0]
        assert result.levels[0].dtype.type == np.int64
        assert result.levels[1].dtype.type == np.float64

    def test_groupby(self):
        index = Index(range(5))
        result = index.groupby(np.array([1, 1, 2, 2, 2]))
        expected = {1: pd.Index([0, 1]), 2: pd.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 = pd.read_csv(StringIO("a,b,c\n1,2,3\n4,5,6"), index_col=[0, 1])

        result = df.index == mi
        tm.assert_numpy_array_equal(result, expected)

    def test_equals_op_multiindex_identify(self):
        df = pd.read_csv(StringIO("a,b,c\n1,2,3\n4,5,6"), index_col=[0, 1])

        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 = pd.read_csv(StringIO("a,b,c\n1,2,3\n4,5,6"), index_col=[0, 1])

        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", [pd.to_datetime, pd.to_timedelta])
    def test_dt_conversion_preserves_name(self, dt_conv):
        # GH 10875
        index = pd.Index(["01:02:03", "01:02:04"], name="label")
        assert index.name == dt_conv(index).name

    @pytest.mark.parametrize(
        "index,expected",
        [
            # ASCII
            # short
            (
                pd.Index(["a", "bb", "ccc"]),
                """Index(['a', 'bb', 'ccc'], dtype='object')""",
            ),
            # multiple lines
            (
                pd.Index(["a", "bb", "ccc"] * 10),
                """\
Index(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc',
       'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc',
       'a', 'bb', 'ccc', 'a', 'bb', 'ccc'],
      dtype='object')""",
            ),
            # truncated
            (
                pd.Index(["a", "bb", "ccc"] * 100),
                """\
Index(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a',
       ...
       'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'],
      dtype='object', length=300)""",
            ),
            # Non-ASCII
            # short
            (
                pd.Index(["あ", "いい", "ううう"]),
                """Index(['あ', 'いい', 'ううう'], dtype='object')""",
            ),
            # multiple lines
            (
                pd.Index(["あ", "いい", "ううう"] * 10),
                (
                    "Index(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', "
                    "'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう',\n"
                    "       'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', "
                    "'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう',\n"
                    "       'あ', 'いい', 'ううう', 'あ', 'いい', "
                    "'ううう'],\n"
                    "      dtype='object')"
                ),
            ),
            # truncated
            (
                pd.Index(["あ", "いい", "ううう"] * 100),
                (
                    "Index(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', "
                    "'あ', 'いい', 'ううう', 'あ',\n"
                    "       ...\n"
                    "       'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', "
                    "'ううう', 'あ', 'いい', 'ううう'],\n"
                    "      dtype='object', length=300)"
                ),
            ),
        ],
    )
    def test_string_index_repr(self, index, expected):
        result = repr(index)
        assert result == expected

    @pytest.mark.parametrize(
        "index,expected",
        [
            # short
            (
                pd.Index(["あ", "いい", "ううう"]),
                ("Index(['あ', 'いい', 'ううう'], dtype='object')"),
            ),
            # multiple lines
            (
                pd.Index(["あ", "いい", "ううう"] * 10),
                (
                    "Index(['あ', 'いい', 'ううう', 'あ', 'いい', "
                    "'ううう', 'あ', 'いい', 'ううう',\n"
                    "       'あ', 'いい', 'ううう', 'あ', 'いい', "
                    "'ううう', 'あ', 'いい', 'ううう',\n"
                    "       'あ', 'いい', 'ううう', 'あ', 'いい', "
                    "'ううう', 'あ', 'いい', 'ううう',\n"
                    "       'あ', 'いい', 'ううう'],\n"
                    "      dtype='object')"
                    ""
                ),
            ),
            # truncated
            (
                pd.Index(["あ", "いい", "ううう"] * 100),
                (
                    "Index(['あ', 'いい', 'ううう', 'あ', 'いい', "
                    "'ううう', 'あ', 'いい', 'ううう',\n"
                    "       'あ',\n"
                    "       ...\n"
                    "       'ううう', 'あ', 'いい', 'ううう', 'あ', "
                    "'いい', 'ううう', 'あ', 'いい',\n"
                    "       'ううう'],\n"
                    "      dtype='object', length=300)"
                ),
            ),
        ],
    )
    def test_string_index_repr_with_unicode_option(self, index, expected):
        # Enable Unicode option -----------------------------------------
        with cf.option_context("display.unicode.east_asian_width", True):
            result = repr(index)
            assert result == expected

    def test_cached_properties_not_settable(self):
        index = pd.Index([1, 2, 3])
        with pytest.raises(AttributeError, match="Can't set attribute"):
            index.is_unique = False

    @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
        import jedi

        if jedi.__version__ < "0.16.0":
            warning = tm.assert_produces_warning(None)
        else:
            warning = tm.assert_produces_warning(
                DeprecationWarning, check_stacklevel=False
            )
        with warning:
            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, pd.IntervalIndex):
            index.contains(1)
        else:
            msg = f"'{type(index).__name__}' object has no attribute 'contains'"
            with pytest.raises(AttributeError, match=msg):
                index.contains(1)


class TestMixedIntIndex(Base):
    # 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)
    _holder = Index

    @pytest.fixture(params=[[0, "a", 1, "b", 2, "c"]], ids=["mixedIndex"])
    def index(self, request):
        return Index(request.param)

    def create_index(self) -> Index:
        return Index([0, "a", 1, "b", 2, "c"])

    def test_argsort(self):
        index = self.create_index()
        with pytest.raises(TypeError, match="'>|<' not supported"):
            index.argsort()

    def test_numpy_argsort(self):
        index = self.create_index()
        with pytest.raises(TypeError, match="'>|<' not supported"):
            np.argsort(index)

    def test_copy_name(self):
        # Check that "name" argument passed at initialization is honoured
        # GH12309
        index = self.create_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 = pd.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"

        index3 = index.copy(names=["NewName"])
        tm.assert_index_equal(index, index3, check_names=False)
        assert index.name == "MyName"
        assert index.names == ["MyName"]
        assert index3.name == "NewName"
        assert index3.names == ["NewName"]

    def test_unique_na(self):
        idx = pd.Index([2, np.nan, 2, 1], name="my_index")
        expected = pd.Index([2, np.nan, 1], name="my_index")
        result = idx.unique()
        tm.assert_index_equal(result, expected)

    def test_logical_compat(self):
        index = self.create_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 = pd.Index(vals, dtype=dtype)
        result = index.dropna(how=how)
        expected = pd.Index(expected, dtype=dtype)
        tm.assert_index_equal(result, expected)

    @pytest.mark.parametrize("how", ["any", "all"])
    @pytest.mark.parametrize(
        "index,expected",
        [
            (
                pd.DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"]),
                pd.DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"]),
            ),
            (
                pd.DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03", pd.NaT]),
                pd.DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"]),
            ),
            (
                pd.TimedeltaIndex(["1 days", "2 days", "3 days"]),
                pd.TimedeltaIndex(["1 days", "2 days", "3 days"]),
            ),
            (
                pd.TimedeltaIndex([pd.NaT, "1 days", "2 days", "3 days", pd.NaT]),
                pd.TimedeltaIndex(["1 days", "2 days", "3 days"]),
            ),
            (
                pd.PeriodIndex(["2012-02", "2012-04", "2012-05"], freq="M"),
                pd.PeriodIndex(["2012-02", "2012-04", "2012-05"], freq="M"),
            ),
            (
                pd.PeriodIndex(["2012-02", "2012-04", "NaT", "2012-05"], freq="M"),
                pd.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):
            pd.Index([1, 2, 3]).dropna(how="xxx")

    def test_get_combined_index(self):
        result = _get_combined_index([])
        expected = Index([])
        tm.assert_index_equal(result, expected)

    @pytest.mark.parametrize(
        "index",
        [
            pd.Index([np.nan]),
            pd.Index([np.nan, 1]),
            pd.Index([1, 2, np.nan]),
            pd.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

    def test_repr_summary(self):
        with cf.option_context("display.max_seq_items", 10):
            result = repr(pd.Index(np.arange(1000)))
            assert len(result) < 200
            assert "..." in result

    @pytest.mark.parametrize("klass", [Series, DataFrame])
    def test_int_name_format(self, klass):
        index = Index(["a", "b", "c"], name=0)
        result = klass(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)

    def test_intersect_str_dates(self):
        dt_dates = [datetime(2012, 2, 9), datetime(2012, 2, 22)]

        index1 = Index(dt_dates, dtype=object)
        index2 = Index(["aa"], dtype=object)
        result = index2.intersection(index1)

        expected = Index([], dtype=object)
        tm.assert_index_equal(result, expected)

    def test_index_repr_bool_nan(self):
        # GH32146
        arr = Index([True, False, np.nan], dtype=object)
        exp1 = arr.format()
        out1 = ["True", "False", "NaN"]
        assert out1 == exp1

        exp2 = repr(arr)
        out2 = "Index([True, False, nan], dtype='object')"
        assert out2 == exp2

    @pytest.mark.filterwarnings("ignore:elementwise comparison failed:FutureWarning")
    def test_index_with_tuple_bool(self):
        # GH34123
        # TODO: remove tupleize_cols=False once correct behaviour is restored
        # TODO: also this op right now produces FutureWarning from numpy
        idx = Index([("a", "b"), ("b", "c"), ("c", "a")], tupleize_cols=False)
        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)


@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):
    if isinstance(index, ABCIndex) and opname == "rsub":
        # pd.Index.__rsub__ does not exist; though the method does exist
        # for subclasses.  see GH#19723
        return
    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):
        pd.Index(np.array(["a", "b"], dtype=object), name="test", fastpath=True)

    with pytest.raises(TypeError, match=msg):
        pd.Int64Index(np.array([1, 2, 3], dtype="int64"), name="test", fastpath=True)

    with pytest.raises(TypeError, match=msg):
        pd.RangeIndex(0, 5, 2, name="test", fastpath=True)

    with pytest.raises(TypeError, match=msg):
        pd.CategoricalIndex(["a", "b", "c"], name="test", fastpath=True)


def test_shape_of_invalid_index():
    # Currently, it is 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 = pd.Index([0, 1, 2, 3])
    with tm.assert_produces_warning(FutureWarning):
        # GH#30588 multi-dimensional indexing deprecated
        assert idx[:, None].shape == (4, 1)


def test_validate_1d_input():
    # 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):
        pd.Index(arr)

    with pytest.raises(ValueError, match=msg):
        pd.Float64Index(arr.astype(np.float64))

    with pytest.raises(ValueError, match=msg):
        pd.Int64Index(arr.astype(np.int64))

    with pytest.raises(ValueError, match=msg):
        pd.UInt64Index(arr.astype(np.uint64))

    df = pd.DataFrame(arr.reshape(4, 2))
    with pytest.raises(ValueError, match=msg):
        pd.Index(df)

    # GH#13601 trying to assign a multi-dimensional array to an index is not
    #  allowed
    ser = pd.Series(0, range(4))
    with pytest.raises(ValueError, match=msg):
        ser.index = np.array([[2, 3]] * 4)


def test_convert_almost_null_slice(index):
    # slice with None at both ends, but not step

    key = slice(None, None, "foo")

    if isinstance(index, pd.IntervalIndex):
        msg = "label-based slicing with step!=1 is not supported for IntervalIndex"
        with pytest.raises(ValueError, match=msg):
            index._convert_slice_indexer(key, "loc")
    else:
        msg = "'>=' not supported between instances of 'str' and 'int'"
        with pytest.raises(TypeError, match=msg):
            index._convert_slice_indexer(key, "loc")


dtlike_dtypes = [
    np.dtype("timedelta64[ns]"),
    np.dtype("datetime64[ns]"),
    pd.DatetimeTZDtype("ns", "Asia/Tokyo"),
    pd.PeriodDtype("ns"),
]


@pytest.mark.parametrize("ldtype", dtlike_dtypes)
@pytest.mark.parametrize("rdtype", dtlike_dtypes)
def test_get_indexer_non_unique_wrong_dtype(ldtype, rdtype):

    vals = np.tile(3600 * 10 ** 9 * np.arange(3), 2)

    def construct(dtype):
        if dtype is dtlike_dtypes[-1]:
            # PeriodArray will try to cast ints to strings
            return pd.DatetimeIndex(vals).astype(dtype)
        return pd.Index(vals, dtype=dtype)

    left = construct(ldtype)
    right = construct(rdtype)

    result = left.get_indexer_non_unique(right)

    if ldtype is rdtype:
        ex1 = np.array([0, 3, 1, 4, 2, 5] * 2, dtype=np.intp)
        ex2 = np.array([], dtype=np.intp)
        tm.assert_numpy_array_equal(result[0], ex1)
        tm.assert_numpy_array_equal(result[1], ex2.astype(np.int64))

    else:
        no_matches = np.array([-1] * 6, dtype=np.intp)
        tm.assert_numpy_array_equal(result[0], no_matches)
        tm.assert_numpy_array_equal(result[1], no_matches)