import numpy as np
import pytest

import pandas as pd
from pandas import (
    Categorical,
    DataFrame,
    DatetimeIndex,
    Index,
    Interval,
    IntervalIndex,
    Series,
    TimedeltaIndex,
    Timestamp,
    cut,
    date_range,
    interval_range,
    isna,
    qcut,
    timedelta_range,
    to_datetime,
)
import pandas._testing as tm
from pandas.api.types import CategoricalDtype
import pandas.core.reshape.tile as tmod


def test_simple():
    data = np.ones(5, dtype="int64")
    result = cut(data, 4, labels=False)

    expected = np.array([1, 1, 1, 1, 1])
    tm.assert_numpy_array_equal(result, expected, check_dtype=False)


@pytest.mark.parametrize("func", [list, np.array])
def test_bins(func):
    data = func([0.2, 1.4, 2.5, 6.2, 9.7, 2.1])
    result, bins = cut(data, 3, retbins=True)

    intervals = IntervalIndex.from_breaks(bins.round(3))
    intervals = intervals.take([0, 0, 0, 1, 2, 0])
    expected = Categorical(intervals, ordered=True)

    tm.assert_categorical_equal(result, expected)
    tm.assert_almost_equal(bins, np.array([0.1905, 3.36666667, 6.53333333, 9.7]))


def test_right():
    data = np.array([0.2, 1.4, 2.5, 6.2, 9.7, 2.1, 2.575])
    result, bins = cut(data, 4, right=True, retbins=True)

    intervals = IntervalIndex.from_breaks(bins.round(3))
    expected = Categorical(intervals, ordered=True)
    expected = expected.take([0, 0, 0, 2, 3, 0, 0])

    tm.assert_categorical_equal(result, expected)
    tm.assert_almost_equal(bins, np.array([0.1905, 2.575, 4.95, 7.325, 9.7]))


def test_no_right():
    data = np.array([0.2, 1.4, 2.5, 6.2, 9.7, 2.1, 2.575])
    result, bins = cut(data, 4, right=False, retbins=True)

    intervals = IntervalIndex.from_breaks(bins.round(3), closed="left")
    intervals = intervals.take([0, 0, 0, 2, 3, 0, 1])
    expected = Categorical(intervals, ordered=True)

    tm.assert_categorical_equal(result, expected)
    tm.assert_almost_equal(bins, np.array([0.2, 2.575, 4.95, 7.325, 9.7095]))


def test_bins_from_interval_index():
    c = cut(range(5), 3)
    expected = c
    result = cut(range(5), bins=expected.categories)
    tm.assert_categorical_equal(result, expected)

    expected = Categorical.from_codes(
        np.append(c.codes, -1), categories=c.categories, ordered=True
    )
    result = cut(range(6), bins=expected.categories)
    tm.assert_categorical_equal(result, expected)


def test_bins_from_interval_index_doc_example():
    # Make sure we preserve the bins.
    ages = np.array([10, 15, 13, 12, 23, 25, 28, 59, 60])
    c = cut(ages, bins=[0, 18, 35, 70])
    expected = IntervalIndex.from_tuples([(0, 18), (18, 35), (35, 70)])
    tm.assert_index_equal(c.categories, expected)

    result = cut([25, 20, 50], bins=c.categories)
    tm.assert_index_equal(result.categories, expected)
    tm.assert_numpy_array_equal(result.codes, np.array([1, 1, 2], dtype="int8"))


def test_bins_not_overlapping_from_interval_index():
    # see gh-23980
    msg = "Overlapping IntervalIndex is not accepted"
    ii = IntervalIndex.from_tuples([(0, 10), (2, 12), (4, 14)])

    with pytest.raises(ValueError, match=msg):
        cut([5, 6], bins=ii)


def test_bins_not_monotonic():
    msg = "bins must increase monotonically"
    data = [0.2, 1.4, 2.5, 6.2, 9.7, 2.1]

    with pytest.raises(ValueError, match=msg):
        cut(data, [0.1, 1.5, 1, 10])


@pytest.mark.parametrize(
    "x, bins, expected",
    [
        (
            date_range("2017-12-31", periods=3),
            [Timestamp.min, Timestamp("2018-01-01"), Timestamp.max],
            IntervalIndex.from_tuples(
                [
                    (Timestamp.min, Timestamp("2018-01-01")),
                    (Timestamp("2018-01-01"), Timestamp.max),
                ]
            ),
        ),
        (
            [-1, 0, 1],
            np.array(
                [np.iinfo(np.int64).min, 0, np.iinfo(np.int64).max], dtype="int64"
            ),
            IntervalIndex.from_tuples(
                [(np.iinfo(np.int64).min, 0), (0, np.iinfo(np.int64).max)]
            ),
        ),
        (
            [
                np.timedelta64(-1, "ns"),
                np.timedelta64(0, "ns"),
                np.timedelta64(1, "ns"),
            ],
            np.array(
                [
                    np.timedelta64(-np.iinfo(np.int64).max, "ns"),
                    np.timedelta64(0, "ns"),
                    np.timedelta64(np.iinfo(np.int64).max, "ns"),
                ]
            ),
            IntervalIndex.from_tuples(
                [
                    (
                        np.timedelta64(-np.iinfo(np.int64).max, "ns"),
                        np.timedelta64(0, "ns"),
                    ),
                    (
                        np.timedelta64(0, "ns"),
                        np.timedelta64(np.iinfo(np.int64).max, "ns"),
                    ),
                ]
            ),
        ),
    ],
)
def test_bins_monotonic_not_overflowing(x, bins, expected):
    # GH 26045
    result = cut(x, bins)
    tm.assert_index_equal(result.categories, expected)


def test_wrong_num_labels():
    msg = "Bin labels must be one fewer than the number of bin edges"
    data = [0.2, 1.4, 2.5, 6.2, 9.7, 2.1]

    with pytest.raises(ValueError, match=msg):
        cut(data, [0, 1, 10], labels=["foo", "bar", "baz"])


@pytest.mark.parametrize(
    "x,bins,msg",
    [
        ([], 2, "Cannot cut empty array"),
        ([1, 2, 3], 0.5, "`bins` should be a positive integer"),
    ],
)
def test_cut_corner(x, bins, msg):
    with pytest.raises(ValueError, match=msg):
        cut(x, bins)


@pytest.mark.parametrize("arg", [2, np.eye(2), DataFrame(np.eye(2))])
@pytest.mark.parametrize("cut_func", [cut, qcut])
def test_cut_not_1d_arg(arg, cut_func):
    msg = "Input array must be 1 dimensional"
    with pytest.raises(ValueError, match=msg):
        cut_func(arg, 2)


@pytest.mark.parametrize(
    "data",
    [
        [0, 1, 2, 3, 4, np.inf],
        [-np.inf, 0, 1, 2, 3, 4],
        [-np.inf, 0, 1, 2, 3, 4, np.inf],
    ],
)
def test_int_bins_with_inf(data):
    # GH 24314
    msg = "cannot specify integer `bins` when input data contains infinity"
    with pytest.raises(ValueError, match=msg):
        cut(data, bins=3)


def test_cut_out_of_range_more():
    # see gh-1511
    name = "x"

    ser = Series([0, -1, 0, 1, -3], name=name)
    ind = cut(ser, [0, 1], labels=False)

    exp = Series([np.nan, np.nan, np.nan, 0, np.nan], name=name)
    tm.assert_series_equal(ind, exp)


@pytest.mark.parametrize(
    "right,breaks,closed",
    [
        (True, [-1e-3, 0.25, 0.5, 0.75, 1], "right"),
        (False, [0, 0.25, 0.5, 0.75, 1 + 1e-3], "left"),
    ],
)
def test_labels(right, breaks, closed):
    arr = np.tile(np.arange(0, 1.01, 0.1), 4)

    result, bins = cut(arr, 4, retbins=True, right=right)
    ex_levels = IntervalIndex.from_breaks(breaks, closed=closed)
    tm.assert_index_equal(result.categories, ex_levels)


def test_cut_pass_series_name_to_factor():
    name = "foo"
    ser = Series(np.random.default_rng(2).standard_normal(100), name=name)

    factor = cut(ser, 4)
    assert factor.name == name


def test_label_precision():
    arr = np.arange(0, 0.73, 0.01)
    result = cut(arr, 4, precision=2)

    ex_levels = IntervalIndex.from_breaks([-0.00072, 0.18, 0.36, 0.54, 0.72])
    tm.assert_index_equal(result.categories, ex_levels)


@pytest.mark.parametrize("labels", [None, False])
def test_na_handling(labels):
    arr = np.arange(0, 0.75, 0.01)
    arr[::3] = np.nan

    result = cut(arr, 4, labels=labels)
    result = np.asarray(result)

    expected = np.where(isna(arr), np.nan, result)
    tm.assert_almost_equal(result, expected)


def test_inf_handling():
    data = np.arange(6)
    data_ser = Series(data, dtype="int64")

    bins = [-np.inf, 2, 4, np.inf]
    result = cut(data, bins)
    result_ser = cut(data_ser, bins)

    ex_uniques = IntervalIndex.from_breaks(bins)
    tm.assert_index_equal(result.categories, ex_uniques)

    assert result[5] == Interval(4, np.inf)
    assert result[0] == Interval(-np.inf, 2)
    assert result_ser[5] == Interval(4, np.inf)
    assert result_ser[0] == Interval(-np.inf, 2)


def test_cut_out_of_bounds():
    arr = np.random.default_rng(2).standard_normal(100)
    result = cut(arr, [-1, 0, 1])

    mask = isna(result)
    ex_mask = (arr < -1) | (arr > 1)
    tm.assert_numpy_array_equal(mask, ex_mask)


@pytest.mark.parametrize(
    "get_labels,get_expected",
    [
        (
            lambda labels: labels,
            lambda labels: Categorical(
                ["Medium"] + 4 * ["Small"] + ["Medium", "Large"],
                categories=labels,
                ordered=True,
            ),
        ),
        (
            lambda labels: Categorical.from_codes([0, 1, 2], labels),
            lambda labels: Categorical.from_codes([1] + 4 * [0] + [1, 2], labels),
        ),
    ],
)
def test_cut_pass_labels(get_labels, get_expected):
    bins = [0, 25, 50, 100]
    arr = [50, 5, 10, 15, 20, 30, 70]
    labels = ["Small", "Medium", "Large"]

    result = cut(arr, bins, labels=get_labels(labels))
    tm.assert_categorical_equal(result, get_expected(labels))


def test_cut_pass_labels_compat():
    # see gh-16459
    arr = [50, 5, 10, 15, 20, 30, 70]
    labels = ["Good", "Medium", "Bad"]

    result = cut(arr, 3, labels=labels)
    exp = cut(arr, 3, labels=Categorical(labels, categories=labels, ordered=True))
    tm.assert_categorical_equal(result, exp)


@pytest.mark.parametrize("x", [np.arange(11.0), np.arange(11.0) / 1e10])
def test_round_frac_just_works(x):
    # It works.
    cut(x, 2)


@pytest.mark.parametrize(
    "val,precision,expected",
    [
        (-117.9998, 3, -118),
        (117.9998, 3, 118),
        (117.9998, 2, 118),
        (0.000123456, 2, 0.00012),
    ],
)
def test_round_frac(val, precision, expected):
    # see gh-1979
    result = tmod._round_frac(val, precision=precision)
    assert result == expected


def test_cut_return_intervals():
    ser = Series([0, 1, 2, 3, 4, 5, 6, 7, 8])
    result = cut(ser, 3)

    exp_bins = np.linspace(0, 8, num=4).round(3)
    exp_bins[0] -= 0.008

    expected = Series(
        IntervalIndex.from_breaks(exp_bins, closed="right").take(
            [0, 0, 0, 1, 1, 1, 2, 2, 2]
        )
    ).astype(CategoricalDtype(ordered=True))
    tm.assert_series_equal(result, expected)


def test_series_ret_bins():
    # see gh-8589
    ser = Series(np.arange(4))
    result, bins = cut(ser, 2, retbins=True)

    expected = Series(
        IntervalIndex.from_breaks([-0.003, 1.5, 3], closed="right").repeat(2)
    ).astype(CategoricalDtype(ordered=True))
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize(
    "kwargs,msg",
    [
        ({"duplicates": "drop"}, None),
        ({}, "Bin edges must be unique"),
        ({"duplicates": "raise"}, "Bin edges must be unique"),
        ({"duplicates": "foo"}, "invalid value for 'duplicates' parameter"),
    ],
)
def test_cut_duplicates_bin(kwargs, msg):
    # see gh-20947
    bins = [0, 2, 4, 6, 10, 10]
    values = Series(np.array([1, 3, 5, 7, 9]), index=["a", "b", "c", "d", "e"])

    if msg is not None:
        with pytest.raises(ValueError, match=msg):
            cut(values, bins, **kwargs)
    else:
        result = cut(values, bins, **kwargs)
        expected = cut(values, pd.unique(np.asarray(bins)))
        tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("data", [9.0, -9.0, 0.0])
@pytest.mark.parametrize("length", [1, 2])
def test_single_bin(data, length):
    # see gh-14652, gh-15428
    ser = Series([data] * length)
    result = cut(ser, 1, labels=False)

    expected = Series([0] * length, dtype=np.intp)
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize(
    "array_1_writeable,array_2_writeable", [(True, True), (True, False), (False, False)]
)
def test_cut_read_only(array_1_writeable, array_2_writeable):
    # issue 18773
    array_1 = np.arange(0, 100, 10)
    array_1.flags.writeable = array_1_writeable

    array_2 = np.arange(0, 100, 10)
    array_2.flags.writeable = array_2_writeable

    hundred_elements = np.arange(100)
    tm.assert_categorical_equal(
        cut(hundred_elements, array_1), cut(hundred_elements, array_2)
    )


@pytest.mark.parametrize(
    "conv",
    [
        lambda v: Timestamp(v),
        lambda v: to_datetime(v),
        lambda v: np.datetime64(v),
        lambda v: Timestamp(v).to_pydatetime(),
    ],
)
def test_datetime_bin(conv):
    data = [np.datetime64("2012-12-13"), np.datetime64("2012-12-15")]
    bin_data = ["2012-12-12", "2012-12-14", "2012-12-16"]

    expected = Series(
        IntervalIndex(
            [
                Interval(Timestamp(bin_data[0]), Timestamp(bin_data[1])),
                Interval(Timestamp(bin_data[1]), Timestamp(bin_data[2])),
            ]
        )
    ).astype(CategoricalDtype(ordered=True))

    bins = [conv(v) for v in bin_data]
    result = Series(cut(data, bins=bins))
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("box", [Series, Index, np.array, list])
def test_datetime_cut(unit, box):
    # see gh-14714
    #
    # Testing time data when it comes in various collection types.
    data = to_datetime(["2013-01-01", "2013-01-02", "2013-01-03"]).astype(f"M8[{unit}]")
    data = box(data)
    result, _ = cut(data, 3, retbins=True)

    if box is list:
        # We don't (yet) do inference on these, so get nanos
        unit = "ns"

    if unit == "s":
        # See https://github.com/pandas-dev/pandas/pull/56101#discussion_r1405325425
        # for why we round to 8 seconds instead of 7
        left = DatetimeIndex(
            ["2012-12-31 23:57:08", "2013-01-01 16:00:00", "2013-01-02 08:00:00"],
            dtype=f"M8[{unit}]",
        )
    else:
        left = DatetimeIndex(
            [
                "2012-12-31 23:57:07.200000",
                "2013-01-01 16:00:00",
                "2013-01-02 08:00:00",
            ],
            dtype=f"M8[{unit}]",
        )
    right = DatetimeIndex(
        ["2013-01-01 16:00:00", "2013-01-02 08:00:00", "2013-01-03 00:00:00"],
        dtype=f"M8[{unit}]",
    )

    exp_intervals = IntervalIndex.from_arrays(left, right)
    expected = Series(exp_intervals).astype(CategoricalDtype(ordered=True))
    tm.assert_series_equal(Series(result), expected)


@pytest.mark.parametrize("box", [list, np.array, Index, Series])
def test_datetime_tz_cut_mismatched_tzawareness(box):
    # GH#54964
    bins = box(
        [
            Timestamp("2013-01-01 04:57:07.200000"),
            Timestamp("2013-01-01 21:00:00"),
            Timestamp("2013-01-02 13:00:00"),
            Timestamp("2013-01-03 05:00:00"),
        ]
    )
    ser = Series(date_range("20130101", periods=3, tz="US/Eastern"))

    msg = "Cannot use timezone-naive bins with timezone-aware values"
    with pytest.raises(ValueError, match=msg):
        cut(ser, bins)


@pytest.mark.parametrize(
    "bins",
    [
        3,
        [
            Timestamp("2013-01-01 04:57:07.200000", tz="UTC").tz_convert("US/Eastern"),
            Timestamp("2013-01-01 21:00:00", tz="UTC").tz_convert("US/Eastern"),
            Timestamp("2013-01-02 13:00:00", tz="UTC").tz_convert("US/Eastern"),
            Timestamp("2013-01-03 05:00:00", tz="UTC").tz_convert("US/Eastern"),
        ],
    ],
)
@pytest.mark.parametrize("box", [list, np.array, Index, Series])
def test_datetime_tz_cut(bins, box):
    # see gh-19872
    tz = "US/Eastern"
    ser = Series(date_range("20130101", periods=3, tz=tz))

    if not isinstance(bins, int):
        bins = box(bins)

    result = cut(ser, bins)
    expected = Series(
        IntervalIndex(
            [
                Interval(
                    Timestamp("2012-12-31 23:57:07.200000", tz=tz),
                    Timestamp("2013-01-01 16:00:00", tz=tz),
                ),
                Interval(
                    Timestamp("2013-01-01 16:00:00", tz=tz),
                    Timestamp("2013-01-02 08:00:00", tz=tz),
                ),
                Interval(
                    Timestamp("2013-01-02 08:00:00", tz=tz),
                    Timestamp("2013-01-03 00:00:00", tz=tz),
                ),
            ]
        )
    ).astype(CategoricalDtype(ordered=True))
    tm.assert_series_equal(result, expected)


def test_datetime_nan_error():
    msg = "bins must be of datetime64 dtype"

    with pytest.raises(ValueError, match=msg):
        cut(date_range("20130101", periods=3), bins=[0, 2, 4])


def test_datetime_nan_mask():
    result = cut(
        date_range("20130102", periods=5), bins=date_range("20130101", periods=2)
    )

    mask = result.categories.isna()
    tm.assert_numpy_array_equal(mask, np.array([False]))

    mask = result.isna()
    tm.assert_numpy_array_equal(mask, np.array([False, True, True, True, True]))


@pytest.mark.parametrize("tz", [None, "UTC", "US/Pacific"])
def test_datetime_cut_roundtrip(tz, unit):
    # see gh-19891
    ser = Series(date_range("20180101", periods=3, tz=tz, unit=unit))
    result, result_bins = cut(ser, 2, retbins=True)

    expected = cut(ser, result_bins)
    tm.assert_series_equal(result, expected)

    if unit == "s":
        # TODO: constructing DatetimeIndex with dtype="M8[s]" without truncating
        #  the first entry here raises in array_to_datetime. Should truncate
        #  instead of raising?
        # See https://github.com/pandas-dev/pandas/pull/56101#discussion_r1405325425
        # for why we round to 8 seconds instead of 7
        expected_bins = DatetimeIndex(
            ["2017-12-31 23:57:08", "2018-01-02 00:00:00", "2018-01-03 00:00:00"],
            dtype=f"M8[{unit}]",
        )
    else:
        expected_bins = DatetimeIndex(
            [
                "2017-12-31 23:57:07.200000",
                "2018-01-02 00:00:00",
                "2018-01-03 00:00:00",
            ],
            dtype=f"M8[{unit}]",
        )
    expected_bins = expected_bins.tz_localize(tz)
    tm.assert_index_equal(result_bins, expected_bins)


def test_timedelta_cut_roundtrip():
    # see gh-19891
    ser = Series(timedelta_range("1day", periods=3))
    result, result_bins = cut(ser, 2, retbins=True)

    expected = cut(ser, result_bins)
    tm.assert_series_equal(result, expected)

    expected_bins = TimedeltaIndex(
        ["0 days 23:57:07.200000", "2 days 00:00:00", "3 days 00:00:00"]
    )
    tm.assert_index_equal(result_bins, expected_bins)


@pytest.mark.parametrize("bins", [6, 7])
@pytest.mark.parametrize(
    "box, compare",
    [
        (Series, tm.assert_series_equal),
        (np.array, tm.assert_categorical_equal),
        (list, tm.assert_equal),
    ],
)
def test_cut_bool_coercion_to_int(bins, box, compare):
    # issue 20303
    data_expected = box([0, 1, 1, 0, 1] * 10)
    data_result = box([False, True, True, False, True] * 10)
    expected = cut(data_expected, bins, duplicates="drop")
    result = cut(data_result, bins, duplicates="drop")
    compare(result, expected)


@pytest.mark.parametrize("labels", ["foo", 1, True])
def test_cut_incorrect_labels(labels):
    # GH 13318
    values = range(5)
    msg = "Bin labels must either be False, None or passed in as a list-like argument"
    with pytest.raises(ValueError, match=msg):
        cut(values, 4, labels=labels)


@pytest.mark.parametrize("bins", [3, [0, 5, 15]])
@pytest.mark.parametrize("right", [True, False])
@pytest.mark.parametrize("include_lowest", [True, False])
def test_cut_nullable_integer(bins, right, include_lowest):
    a = np.random.default_rng(2).integers(0, 10, size=50).astype(float)
    a[::2] = np.nan
    result = cut(
        pd.array(a, dtype="Int64"), bins, right=right, include_lowest=include_lowest
    )
    expected = cut(a, bins, right=right, include_lowest=include_lowest)
    tm.assert_categorical_equal(result, expected)


@pytest.mark.parametrize(
    "data, bins, labels, expected_codes, expected_labels",
    [
        ([15, 17, 19], [14, 16, 18, 20], ["A", "B", "A"], [0, 1, 0], ["A", "B"]),
        ([1, 3, 5], [0, 2, 4, 6, 8], [2, 0, 1, 2], [2, 0, 1], [0, 1, 2]),
    ],
)
def test_cut_non_unique_labels(data, bins, labels, expected_codes, expected_labels):
    # GH 33141
    result = cut(data, bins=bins, labels=labels, ordered=False)
    expected = Categorical.from_codes(
        expected_codes, categories=expected_labels, ordered=False
    )
    tm.assert_categorical_equal(result, expected)


@pytest.mark.parametrize(
    "data, bins, labels, expected_codes, expected_labels",
    [
        ([15, 17, 19], [14, 16, 18, 20], ["C", "B", "A"], [0, 1, 2], ["C", "B", "A"]),
        ([1, 3, 5], [0, 2, 4, 6, 8], [3, 0, 1, 2], [0, 1, 2], [3, 0, 1, 2]),
    ],
)
def test_cut_unordered_labels(data, bins, labels, expected_codes, expected_labels):
    # GH 33141
    result = cut(data, bins=bins, labels=labels, ordered=False)
    expected = Categorical.from_codes(
        expected_codes, categories=expected_labels, ordered=False
    )
    tm.assert_categorical_equal(result, expected)


def test_cut_unordered_with_missing_labels_raises_error():
    # GH 33141
    msg = "'labels' must be provided if 'ordered = False'"
    with pytest.raises(ValueError, match=msg):
        cut([0.5, 3], bins=[0, 1, 2], ordered=False)


def test_cut_unordered_with_series_labels():
    # https://github.com/pandas-dev/pandas/issues/36603
    ser = Series([1, 2, 3, 4, 5])
    bins = Series([0, 2, 4, 6])
    labels = Series(["a", "b", "c"])
    result = cut(ser, bins=bins, labels=labels, ordered=False)
    expected = Series(["a", "a", "b", "b", "c"], dtype="category")
    tm.assert_series_equal(result, expected)


def test_cut_no_warnings():
    df = DataFrame({"value": np.random.default_rng(2).integers(0, 100, 20)})
    labels = [f"{i} - {i + 9}" for i in range(0, 100, 10)]
    with tm.assert_produces_warning(False):
        df["group"] = cut(df.value, range(0, 105, 10), right=False, labels=labels)


def test_cut_with_duplicated_index_lowest_included():
    # GH 42185
    expected = Series(
        [Interval(-0.001, 2, closed="right")] * 3
        + [Interval(2, 4, closed="right"), Interval(-0.001, 2, closed="right")],
        index=[0, 1, 2, 3, 0],
        dtype="category",
    ).cat.as_ordered()

    ser = Series([0, 1, 2, 3, 0], index=[0, 1, 2, 3, 0])
    result = cut(ser, bins=[0, 2, 4], include_lowest=True)
    tm.assert_series_equal(result, expected)


def test_cut_with_nonexact_categorical_indices():
    # GH 42424

    ser = Series(range(100))
    ser1 = cut(ser, 10).value_counts().head(5)
    ser2 = cut(ser, 10).value_counts().tail(5)
    result = DataFrame({"1": ser1, "2": ser2})

    index = pd.CategoricalIndex(
        [
            Interval(-0.099, 9.9, closed="right"),
            Interval(9.9, 19.8, closed="right"),
            Interval(19.8, 29.7, closed="right"),
            Interval(29.7, 39.6, closed="right"),
            Interval(39.6, 49.5, closed="right"),
            Interval(49.5, 59.4, closed="right"),
            Interval(59.4, 69.3, closed="right"),
            Interval(69.3, 79.2, closed="right"),
            Interval(79.2, 89.1, closed="right"),
            Interval(89.1, 99, closed="right"),
        ],
        ordered=True,
    )

    expected = DataFrame(
        {"1": [10] * 5 + [np.nan] * 5, "2": [np.nan] * 5 + [10] * 5}, index=index
    )

    tm.assert_frame_equal(expected, result)


def test_cut_with_timestamp_tuple_labels():
    # GH 40661
    labels = [(Timestamp(10),), (Timestamp(20),), (Timestamp(30),)]
    result = cut([2, 4, 6], bins=[1, 3, 5, 7], labels=labels)

    expected = Categorical.from_codes([0, 1, 2], labels, ordered=True)
    tm.assert_categorical_equal(result, expected)


def test_cut_bins_datetime_intervalindex():
    # https://github.com/pandas-dev/pandas/issues/46218
    bins = interval_range(Timestamp("2022-02-25"), Timestamp("2022-02-27"), freq="1D")
    # passing Series instead of list is important to trigger bug
    result = cut(Series([Timestamp("2022-02-26")]).astype("M8[ns]"), bins=bins)
    expected = Categorical.from_codes([0], bins, ordered=True)
    tm.assert_categorical_equal(result.array, expected)


def test_cut_with_nullable_int64():
    # GH 30787
    series = Series([0, 1, 2, 3, 4, pd.NA, 6, 7], dtype="Int64")
    bins = [0, 2, 4, 6, 8]
    intervals = IntervalIndex.from_breaks(bins)

    expected = Series(
        Categorical.from_codes([-1, 0, 0, 1, 1, -1, 2, 3], intervals, ordered=True)
    )

    result = cut(series, bins=bins)

    tm.assert_series_equal(result, expected)