from datetime import (
    date,
    datetime,
)

import numpy as np
import pytest

from pandas.core.dtypes.common import is_list_like

from pandas import (
    Categorical,
    DataFrame,
    DatetimeIndex,
    Index,
    Interval,
    IntervalIndex,
    MultiIndex,
    NaT,
    Series,
    Timedelta,
    Timestamp,
    concat,
    date_range,
    period_range,
)
import pandas._testing as tm
from pandas.core.indexing import IndexingError

from pandas.tseries.offsets import BDay


class TestSetitemDT64Values:
    def test_setitem_none_nan(self):
        series = Series(date_range("1/1/2000", periods=10))
        series[3] = None
        assert series[3] is NaT

        series[3:5] = None
        assert series[4] is NaT

        series[5] = np.nan
        assert series[5] is NaT

        series[5:7] = np.nan
        assert series[6] is NaT

    def test_setitem_multiindex_empty_slice(self):
        # https://github.com/pandas-dev/pandas/issues/35878
        idx = MultiIndex.from_tuples([("a", 1), ("b", 2)])
        result = Series([1, 2], index=idx)
        expected = result.copy()
        result.loc[[]] = 0
        tm.assert_series_equal(result, expected)

    def test_setitem_with_string_index(self):
        # GH#23451
        ser = Series([1, 2, 3], index=["Date", "b", "other"])
        ser["Date"] = date.today()
        assert ser.Date == date.today()
        assert ser["Date"] == date.today()

    def test_setitem_tuple_with_datetimetz_values(self):
        # GH#20441
        arr = date_range("2017", periods=4, tz="US/Eastern")
        index = [(0, 1), (0, 2), (0, 3), (0, 4)]
        result = Series(arr, index=index)
        expected = result.copy()
        result[(0, 1)] = np.nan
        expected.iloc[0] = np.nan
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize("tz", ["US/Eastern", "UTC", "Asia/Tokyo"])
    def test_setitem_with_tz(self, tz, indexer_sli):
        orig = Series(date_range("2016-01-01", freq="H", periods=3, tz=tz))
        assert orig.dtype == f"datetime64[ns, {tz}]"

        exp = Series(
            [
                Timestamp("2016-01-01 00:00", tz=tz),
                Timestamp("2011-01-01 00:00", tz=tz),
                Timestamp("2016-01-01 02:00", tz=tz),
            ]
        )

        # scalar
        ser = orig.copy()
        indexer_sli(ser)[1] = Timestamp("2011-01-01", tz=tz)
        tm.assert_series_equal(ser, exp)

        # vector
        vals = Series(
            [Timestamp("2011-01-01", tz=tz), Timestamp("2012-01-01", tz=tz)],
            index=[1, 2],
        )
        assert vals.dtype == f"datetime64[ns, {tz}]"

        exp = Series(
            [
                Timestamp("2016-01-01 00:00", tz=tz),
                Timestamp("2011-01-01 00:00", tz=tz),
                Timestamp("2012-01-01 00:00", tz=tz),
            ]
        )

        ser = orig.copy()
        indexer_sli(ser)[[1, 2]] = vals
        tm.assert_series_equal(ser, exp)

    def test_setitem_with_tz_dst(self, indexer_sli):
        # GH#14146 trouble setting values near DST boundary
        tz = "US/Eastern"
        orig = Series(date_range("2016-11-06", freq="H", periods=3, tz=tz))
        assert orig.dtype == f"datetime64[ns, {tz}]"

        exp = Series(
            [
                Timestamp("2016-11-06 00:00-04:00", tz=tz),
                Timestamp("2011-01-01 00:00-05:00", tz=tz),
                Timestamp("2016-11-06 01:00-05:00", tz=tz),
            ]
        )

        # scalar
        ser = orig.copy()
        indexer_sli(ser)[1] = Timestamp("2011-01-01", tz=tz)
        tm.assert_series_equal(ser, exp)

        # vector
        vals = Series(
            [Timestamp("2011-01-01", tz=tz), Timestamp("2012-01-01", tz=tz)],
            index=[1, 2],
        )
        assert vals.dtype == f"datetime64[ns, {tz}]"

        exp = Series(
            [
                Timestamp("2016-11-06 00:00", tz=tz),
                Timestamp("2011-01-01 00:00", tz=tz),
                Timestamp("2012-01-01 00:00", tz=tz),
            ]
        )

        ser = orig.copy()
        indexer_sli(ser)[[1, 2]] = vals
        tm.assert_series_equal(ser, exp)

    def test_object_series_setitem_dt64array_exact_match(self):
        # make sure the dt64 isn't cast by numpy to integers
        # https://github.com/numpy/numpy/issues/12550

        ser = Series({"X": np.nan}, dtype=object)

        indexer = [True]

        # "exact_match" -> size of array being set matches size of ser
        value = np.array([4], dtype="M8[ns]")

        ser.iloc[indexer] = value

        expected = Series([value[0]], index=["X"], dtype=object)
        assert all(isinstance(x, np.datetime64) for x in expected.values)

        tm.assert_series_equal(ser, expected)


class TestSetitemScalarIndexer:
    def test_setitem_negative_out_of_bounds(self):
        ser = Series(tm.rands_array(5, 10), index=tm.rands_array(10, 10))

        msg = "index -11 is out of bounds for axis 0 with size 10"
        with pytest.raises(IndexError, match=msg):
            ser[-11] = "foo"

    @pytest.mark.parametrize("indexer", [tm.loc, tm.at])
    @pytest.mark.parametrize("ser_index", [0, 1])
    def test_setitem_series_object_dtype(self, indexer, ser_index):
        # GH#38303
        ser = Series([0, 0], dtype="object")
        idxr = indexer(ser)
        idxr[0] = Series([42], index=[ser_index])
        expected = Series([Series([42], index=[ser_index]), 0], dtype="object")
        tm.assert_series_equal(ser, expected)

    @pytest.mark.parametrize("index, exp_value", [(0, 42), (1, np.nan)])
    def test_setitem_series(self, index, exp_value):
        # GH#38303
        ser = Series([0, 0])
        ser.loc[0] = Series([42], index=[index])
        expected = Series([exp_value, 0])
        tm.assert_series_equal(ser, expected)


class TestSetitemSlices:
    def test_setitem_slice_float_raises(self, datetime_series):
        msg = (
            "cannot do slice indexing on DatetimeIndex with these indexers "
            r"\[{key}\] of type float"
        )
        with pytest.raises(TypeError, match=msg.format(key=r"4\.0")):
            datetime_series[4.0:10.0] = 0

        with pytest.raises(TypeError, match=msg.format(key=r"4\.5")):
            datetime_series[4.5:10.0] = 0

    def test_setitem_slice(self):
        ser = Series(range(10), index=list(range(10)))
        ser[-12:] = 0
        assert (ser == 0).all()

        ser[:-12] = 5
        assert (ser == 0).all()

    def test_setitem_slice_integers(self):
        ser = Series(np.random.randn(8), index=[2, 4, 6, 8, 10, 12, 14, 16])

        ser[:4] = 0
        assert (ser[:4] == 0).all()
        assert not (ser[4:] == 0).any()

    def test_setitem_slicestep(self):
        # caught this bug when writing tests
        series = Series(tm.makeIntIndex(20).astype(float), index=tm.makeIntIndex(20))

        series[::2] = 0
        assert (series[::2] == 0).all()

    def test_setitem_multiindex_slice(self, indexer_sli):
        # GH 8856
        mi = MultiIndex.from_product(([0, 1], list("abcde")))
        result = Series(np.arange(10, dtype=np.int64), mi)
        indexer_sli(result)[::4] = 100
        expected = Series([100, 1, 2, 3, 100, 5, 6, 7, 100, 9], mi)
        tm.assert_series_equal(result, expected)


class TestSetitemBooleanMask:
    def test_setitem_mask_cast(self):
        # GH#2746
        # need to upcast
        ser = Series([1, 2], index=[1, 2], dtype="int64")
        ser[[True, False]] = Series([0], index=[1], dtype="int64")
        expected = Series([0, 2], index=[1, 2], dtype="int64")

        tm.assert_series_equal(ser, expected)

    def test_setitem_mask_align_and_promote(self):
        # GH#8387: test that changing types does not break alignment
        ts = Series(np.random.randn(100), index=np.arange(100, 0, -1)).round(5)
        mask = ts > 0
        left = ts.copy()
        right = ts[mask].copy().map(str)
        left[mask] = right
        expected = ts.map(lambda t: str(t) if t > 0 else t)
        tm.assert_series_equal(left, expected)

    def test_setitem_mask_promote_strs(self):
        ser = Series([0, 1, 2, 0])
        mask = ser > 0
        ser2 = ser[mask].map(str)
        ser[mask] = ser2

        expected = Series([0, "1", "2", 0])
        tm.assert_series_equal(ser, expected)

    def test_setitem_mask_promote(self):
        ser = Series([0, "foo", "bar", 0])
        mask = Series([False, True, True, False])
        ser2 = ser[mask]
        ser[mask] = ser2

        expected = Series([0, "foo", "bar", 0])
        tm.assert_series_equal(ser, expected)

    def test_setitem_boolean(self, string_series):
        mask = string_series > string_series.median()

        # similar indexed series
        result = string_series.copy()
        result[mask] = string_series * 2
        expected = string_series * 2
        tm.assert_series_equal(result[mask], expected[mask])

        # needs alignment
        result = string_series.copy()
        result[mask] = (string_series * 2)[0:5]
        expected = (string_series * 2)[0:5].reindex_like(string_series)
        expected[-mask] = string_series[mask]
        tm.assert_series_equal(result[mask], expected[mask])

    def test_setitem_boolean_corner(self, datetime_series):
        ts = datetime_series
        mask_shifted = ts.shift(1, freq=BDay()) > ts.median()

        msg = (
            r"Unalignable boolean Series provided as indexer \(index of "
            r"the boolean Series and of the indexed object do not match"
        )
        with pytest.raises(IndexingError, match=msg):
            ts[mask_shifted] = 1

        with pytest.raises(IndexingError, match=msg):
            ts.loc[mask_shifted] = 1

    def test_setitem_boolean_different_order(self, string_series):
        ordered = string_series.sort_values()

        copy = string_series.copy()
        copy[ordered > 0] = 0

        expected = string_series.copy()
        expected[expected > 0] = 0

        tm.assert_series_equal(copy, expected)

    @pytest.mark.parametrize("func", [list, np.array, Series])
    def test_setitem_boolean_python_list(self, func):
        # GH19406
        ser = Series([None, "b", None])
        mask = func([True, False, True])
        ser[mask] = ["a", "c"]
        expected = Series(["a", "b", "c"])
        tm.assert_series_equal(ser, expected)

    def test_setitem_boolean_nullable_int_types(self, any_numeric_ea_dtype):
        # GH: 26468
        ser = Series([5, 6, 7, 8], dtype=any_numeric_ea_dtype)
        ser[ser > 6] = Series(range(4), dtype=any_numeric_ea_dtype)
        expected = Series([5, 6, 2, 3], dtype=any_numeric_ea_dtype)
        tm.assert_series_equal(ser, expected)

        ser = Series([5, 6, 7, 8], dtype=any_numeric_ea_dtype)
        ser.loc[ser > 6] = Series(range(4), dtype=any_numeric_ea_dtype)
        tm.assert_series_equal(ser, expected)

        ser = Series([5, 6, 7, 8], dtype=any_numeric_ea_dtype)
        loc_ser = Series(range(4), dtype=any_numeric_ea_dtype)
        ser.loc[ser > 6] = loc_ser.loc[loc_ser > 1]
        tm.assert_series_equal(ser, expected)

    def test_setitem_with_bool_mask_and_values_matching_n_trues_in_length(self):
        # GH#30567
        ser = Series([None] * 10)
        mask = [False] * 3 + [True] * 5 + [False] * 2
        ser[mask] = range(5)
        result = ser
        expected = Series([None] * 3 + list(range(5)) + [None] * 2).astype("object")
        tm.assert_series_equal(result, expected)

    def test_setitem_nan_with_bool(self):
        # GH 13034
        result = Series([True, False, True])
        result[0] = np.nan
        expected = Series([np.nan, False, True], dtype=object)
        tm.assert_series_equal(result, expected)


class TestSetitemViewCopySemantics:
    def test_setitem_invalidates_datetime_index_freq(self):
        # GH#24096 altering a datetime64tz Series inplace invalidates the
        #  `freq` attribute on the underlying DatetimeIndex

        dti = date_range("20130101", periods=3, tz="US/Eastern")
        ts = dti[1]
        ser = Series(dti)
        assert ser._values is not dti
        assert ser._values._data.base is not dti._data._data.base
        assert dti.freq == "D"
        ser.iloc[1] = NaT
        assert ser._values.freq is None

        # check that the DatetimeIndex was not altered in place
        assert ser._values is not dti
        assert ser._values._data.base is not dti._data._data.base
        assert dti[1] == ts
        assert dti.freq == "D"

    def test_dt64tz_setitem_does_not_mutate_dti(self):
        # GH#21907, GH#24096
        dti = date_range("2016-01-01", periods=10, tz="US/Pacific")
        ts = dti[0]
        ser = Series(dti)
        assert ser._values is not dti
        assert ser._values._data.base is not dti._data._data.base
        assert ser._mgr.arrays[0] is not dti
        assert ser._mgr.arrays[0]._data.base is not dti._data._data.base

        ser[::3] = NaT
        assert ser[0] is NaT
        assert dti[0] == ts


class TestSetitemCallable:
    def test_setitem_callable_key(self):
        # GH#12533
        ser = Series([1, 2, 3, 4], index=list("ABCD"))
        ser[lambda x: "A"] = -1

        expected = Series([-1, 2, 3, 4], index=list("ABCD"))
        tm.assert_series_equal(ser, expected)

    def test_setitem_callable_other(self):
        # GH#13299
        inc = lambda x: x + 1

        ser = Series([1, 2, -1, 4])
        ser[ser < 0] = inc

        expected = Series([1, 2, inc, 4])
        tm.assert_series_equal(ser, expected)


class TestSetitemWithExpansion:
    def test_setitem_empty_series(self):
        # GH#10193
        key = Timestamp("2012-01-01")
        series = Series(dtype=object)
        series[key] = 47
        expected = Series(47, [key])
        tm.assert_series_equal(series, expected)

    def test_setitem_empty_series_datetimeindex_preserves_freq(self):
        # GH#33573 our index should retain its freq
        series = Series([], DatetimeIndex([], freq="D"), dtype=object)
        key = Timestamp("2012-01-01")
        series[key] = 47
        expected = Series(47, DatetimeIndex([key], freq="D"))
        tm.assert_series_equal(series, expected)
        assert series.index.freq == expected.index.freq

    def test_setitem_empty_series_timestamp_preserves_dtype(self):
        # GH 21881
        timestamp = Timestamp(1412526600000000000)
        series = Series([timestamp], index=["timestamp"], dtype=object)
        expected = series["timestamp"]

        series = Series([], dtype=object)
        series["anything"] = 300.0
        series["timestamp"] = timestamp
        result = series["timestamp"]
        assert result == expected

    @pytest.mark.parametrize(
        "td",
        [
            Timedelta("9 days"),
            Timedelta("9 days").to_timedelta64(),
            Timedelta("9 days").to_pytimedelta(),
        ],
    )
    def test_append_timedelta_does_not_cast(self, td):
        # GH#22717 inserting a Timedelta should _not_ cast to int64
        expected = Series(["x", td], index=[0, "td"], dtype=object)

        ser = Series(["x"])
        ser["td"] = td
        tm.assert_series_equal(ser, expected)
        assert isinstance(ser["td"], Timedelta)

        ser = Series(["x"])
        ser.loc["td"] = Timedelta("9 days")
        tm.assert_series_equal(ser, expected)
        assert isinstance(ser["td"], Timedelta)

    def test_setitem_with_expansion_type_promotion(self):
        # GH#12599
        ser = Series(dtype=object)
        ser["a"] = Timestamp("2016-01-01")
        ser["b"] = 3.0
        ser["c"] = "foo"
        expected = Series([Timestamp("2016-01-01"), 3.0, "foo"], index=["a", "b", "c"])
        tm.assert_series_equal(ser, expected)

    def test_setitem_not_contained(self, string_series):
        # set item that's not contained
        ser = string_series.copy()
        assert "foobar" not in ser.index
        ser["foobar"] = 1

        app = Series([1], index=["foobar"], name="series")
        expected = concat([string_series, app])
        tm.assert_series_equal(ser, expected)


def test_setitem_scalar_into_readonly_backing_data():
    # GH#14359: test that you cannot mutate a read only buffer

    array = np.zeros(5)
    array.flags.writeable = False  # make the array immutable
    series = Series(array)

    for n in range(len(series)):
        msg = "assignment destination is read-only"
        with pytest.raises(ValueError, match=msg):
            series[n] = 1

        assert array[n] == 0


def test_setitem_slice_into_readonly_backing_data():
    # GH#14359: test that you cannot mutate a read only buffer

    array = np.zeros(5)
    array.flags.writeable = False  # make the array immutable
    series = Series(array)

    msg = "assignment destination is read-only"
    with pytest.raises(ValueError, match=msg):
        series[1:3] = 1

    assert not array.any()


def test_setitem_categorical_assigning_ops():
    orig = Series(Categorical(["b", "b"], categories=["a", "b"]))
    ser = orig.copy()
    ser[:] = "a"
    exp = Series(Categorical(["a", "a"], categories=["a", "b"]))
    tm.assert_series_equal(ser, exp)

    ser = orig.copy()
    ser[1] = "a"
    exp = Series(Categorical(["b", "a"], categories=["a", "b"]))
    tm.assert_series_equal(ser, exp)

    ser = orig.copy()
    ser[ser.index > 0] = "a"
    exp = Series(Categorical(["b", "a"], categories=["a", "b"]))
    tm.assert_series_equal(ser, exp)

    ser = orig.copy()
    ser[[False, True]] = "a"
    exp = Series(Categorical(["b", "a"], categories=["a", "b"]))
    tm.assert_series_equal(ser, exp)

    ser = orig.copy()
    ser.index = ["x", "y"]
    ser["y"] = "a"
    exp = Series(Categorical(["b", "a"], categories=["a", "b"]), index=["x", "y"])
    tm.assert_series_equal(ser, exp)


def test_setitem_nan_into_categorical():
    # ensure that one can set something to np.nan
    ser = Series(Categorical([1, 2, 3]))
    exp = Series(Categorical([1, np.nan, 3], categories=[1, 2, 3]))
    ser[1] = np.nan
    tm.assert_series_equal(ser, exp)


class TestSetitemCasting:
    @pytest.mark.parametrize("unique", [True, False])
    @pytest.mark.parametrize("val", [3, 3.0, "3"], ids=type)
    def test_setitem_non_bool_into_bool(self, val, indexer_sli, unique):
        # dont cast these 3-like values to bool
        ser = Series([True, False])
        if not unique:
            ser.index = [1, 1]

        indexer_sli(ser)[1] = val
        assert type(ser.iloc[1]) == type(val)

        expected = Series([True, val], dtype=object, index=ser.index)
        if not unique and indexer_sli is not tm.iloc:
            expected = Series([val, val], dtype=object, index=[1, 1])
        tm.assert_series_equal(ser, expected)


class SetitemCastingEquivalents:
    """
    Check each of several methods that _should_ be equivalent to `obj[key] = val`

    We assume that
        - obj.index is the default Index(range(len(obj)))
        - the setitem does not expand the obj
    """

    @pytest.fixture
    def is_inplace(self):
        """
        Indicate that we are not (yet) checking whether or not setting is inplace.
        """
        return None

    def check_indexer(self, obj, key, expected, val, indexer, is_inplace):
        orig = obj
        obj = obj.copy()
        arr = obj._values

        indexer(obj)[key] = val
        tm.assert_series_equal(obj, expected)

        self._check_inplace(is_inplace, orig, arr, obj)

    def _check_inplace(self, is_inplace, orig, arr, obj):
        if is_inplace is None:
            # We are not (yet) checking whether setting is inplace or not
            pass
        elif is_inplace:
            if arr.dtype.kind in ["m", "M"]:
                # We may not have the same DTA/TDA, but will have the same
                #  underlying data
                assert arr._ndarray is obj._values._ndarray
            else:
                assert obj._values is arr
        else:
            # otherwise original array should be unchanged
            tm.assert_equal(arr, orig._values)

    def test_int_key(self, obj, key, expected, val, indexer_sli, is_inplace):
        if not isinstance(key, int):
            return

        self.check_indexer(obj, key, expected, val, indexer_sli, is_inplace)

        if indexer_sli is tm.loc:
            self.check_indexer(obj, key, expected, val, tm.at, is_inplace)
        elif indexer_sli is tm.iloc:
            self.check_indexer(obj, key, expected, val, tm.iat, is_inplace)

        rng = range(key, key + 1)
        self.check_indexer(obj, rng, expected, val, indexer_sli, is_inplace)

        if indexer_sli is not tm.loc:
            # Note: no .loc because that handles slice edges differently
            slc = slice(key, key + 1)
            self.check_indexer(obj, slc, expected, val, indexer_sli, is_inplace)

        ilkey = [key]
        self.check_indexer(obj, ilkey, expected, val, indexer_sli, is_inplace)

        indkey = np.array(ilkey)
        self.check_indexer(obj, indkey, expected, val, indexer_sli, is_inplace)

        genkey = (x for x in [key])
        self.check_indexer(obj, genkey, expected, val, indexer_sli, is_inplace)

    def test_slice_key(self, obj, key, expected, val, indexer_sli, is_inplace):
        if not isinstance(key, slice):
            return

        if indexer_sli is not tm.loc:
            # Note: no .loc because that handles slice edges differently
            self.check_indexer(obj, key, expected, val, indexer_sli, is_inplace)

        ilkey = list(range(len(obj)))[key]
        self.check_indexer(obj, ilkey, expected, val, indexer_sli, is_inplace)

        indkey = np.array(ilkey)
        self.check_indexer(obj, indkey, expected, val, indexer_sli, is_inplace)

        genkey = (x for x in indkey)
        self.check_indexer(obj, genkey, expected, val, indexer_sli, is_inplace)

    def test_mask_key(self, obj, key, expected, val, indexer_sli):
        # setitem with boolean mask
        mask = np.zeros(obj.shape, dtype=bool)
        mask[key] = True

        obj = obj.copy()

        if is_list_like(val) and len(val) < mask.sum():
            msg = "boolean index did not match indexed array along dimension"
            with pytest.raises(IndexError, match=msg):
                indexer_sli(obj)[mask] = val
            return

        indexer_sli(obj)[mask] = val
        tm.assert_series_equal(obj, expected)

    def test_series_where(self, obj, key, expected, val, is_inplace):
        mask = np.zeros(obj.shape, dtype=bool)
        mask[key] = True

        if is_list_like(val) and len(val) < len(obj):
            # Series.where is not valid here
            msg = "operands could not be broadcast together with shapes"
            with pytest.raises(ValueError, match=msg):
                obj.where(~mask, val)
            return

        orig = obj
        obj = obj.copy()
        arr = obj._values

        res = obj.where(~mask, val)
        tm.assert_series_equal(res, expected)

        self._check_inplace(is_inplace, orig, arr, obj)

    def test_index_where(self, obj, key, expected, val, request):
        if obj.dtype == bool:
            # TODO(GH#45061): Should become unreachable
            pytest.skip("test not applicable for this dtype")

        mask = np.zeros(obj.shape, dtype=bool)
        mask[key] = True

        res = Index(obj).where(~mask, val)
        tm.assert_index_equal(res, Index(expected))

    def test_index_putmask(self, obj, key, expected, val):
        if obj.dtype == bool:
            # TODO(GH#45061): Should become unreachable
            pytest.skip("test not applicable for this dtype")

        mask = np.zeros(obj.shape, dtype=bool)
        mask[key] = True

        res = Index(obj).putmask(mask, val)
        tm.assert_index_equal(res, Index(expected))


@pytest.mark.parametrize(
    "obj,expected,key",
    [
        pytest.param(
            # these induce dtype changes
            Series([2, 3, 4, 5, 6, 7, 8, 9, 10]),
            Series([np.nan, 3, np.nan, 5, np.nan, 7, np.nan, 9, np.nan]),
            slice(None, None, 2),
            id="int_series_slice_key_step",
        ),
        pytest.param(
            Series([True, True, False, False]),
            Series([np.nan, True, np.nan, False], dtype=object),
            slice(None, None, 2),
            id="bool_series_slice_key_step",
        ),
        pytest.param(
            # these induce dtype changes
            Series(np.arange(10)),
            Series([np.nan, np.nan, np.nan, np.nan, np.nan, 5, 6, 7, 8, 9]),
            slice(None, 5),
            id="int_series_slice_key",
        ),
        pytest.param(
            # changes dtype GH#4463
            Series([1, 2, 3]),
            Series([np.nan, 2, 3]),
            0,
            id="int_series_int_key",
        ),
        pytest.param(
            # changes dtype GH#4463
            Series([False]),
            Series([np.nan], dtype=object),
            # TODO: maybe go to float64 since we are changing the _whole_ Series?
            0,
            id="bool_series_int_key_change_all",
        ),
        pytest.param(
            # changes dtype GH#4463
            Series([False, True]),
            Series([np.nan, True], dtype=object),
            0,
            id="bool_series_int_key",
        ),
    ],
)
class TestSetitemCastingEquivalents(SetitemCastingEquivalents):
    @pytest.fixture(params=[np.nan, np.float64("NaN")])
    def val(self, request):
        """
        One python float NaN, one np.float64.  Only np.float64 has a `dtype`
        attribute.
        """
        return request.param


class TestSetitemTimedelta64IntoNumeric(SetitemCastingEquivalents):
    # timedelta64 should not be treated as integers when setting into
    #  numeric Series

    @pytest.fixture
    def val(self):
        td = np.timedelta64(4, "ns")
        return td
        # TODO: could also try np.full((1,), td)

    @pytest.fixture(params=[complex, int, float])
    def dtype(self, request):
        return request.param

    @pytest.fixture
    def obj(self, dtype):
        arr = np.arange(5).astype(dtype)
        ser = Series(arr)
        return ser

    @pytest.fixture
    def expected(self, dtype):
        arr = np.arange(5).astype(dtype)
        ser = Series(arr)
        ser = ser.astype(object)
        ser.values[0] = np.timedelta64(4, "ns")
        return ser

    @pytest.fixture
    def key(self):
        return 0

    @pytest.fixture
    def is_inplace(self):
        """
        Indicate we do _not_ expect the setting to be done inplace.
        """
        return False


class TestSetitemDT64IntoInt(SetitemCastingEquivalents):
    # GH#39619 dont cast dt64 to int when doing this setitem

    @pytest.fixture(params=["M8[ns]", "m8[ns]"])
    def dtype(self, request):
        return request.param

    @pytest.fixture
    def scalar(self, dtype):
        val = np.datetime64("2021-01-18 13:25:00", "ns")
        if dtype == "m8[ns]":
            val = val - val
        return val

    @pytest.fixture
    def expected(self, scalar):
        expected = Series([scalar, scalar, 3], dtype=object)
        assert isinstance(expected[0], type(scalar))
        return expected

    @pytest.fixture
    def obj(self):
        return Series([1, 2, 3])

    @pytest.fixture
    def key(self):
        return slice(None, -1)

    @pytest.fixture(params=[None, list, np.array])
    def val(self, scalar, request):
        box = request.param
        if box is None:
            return scalar
        return box([scalar, scalar])

    @pytest.fixture
    def is_inplace(self):
        return False


class TestSetitemNAPeriodDtype(SetitemCastingEquivalents):
    # Setting compatible NA values into Series with PeriodDtype

    @pytest.fixture
    def expected(self, key):
        exp = Series(period_range("2000-01-01", periods=10, freq="D"))
        exp._values.view("i8")[key] = NaT.value
        assert exp[key] is NaT or all(x is NaT for x in exp[key])
        return exp

    @pytest.fixture
    def obj(self):
        return Series(period_range("2000-01-01", periods=10, freq="D"))

    @pytest.fixture(params=[3, slice(3, 5)])
    def key(self, request):
        return request.param

    @pytest.fixture(params=[None, np.nan])
    def val(self, request):
        return request.param

    @pytest.fixture
    def is_inplace(self):
        return True


class TestSetitemNADatetimeLikeDtype(SetitemCastingEquivalents):
    # some nat-like values should be cast to datetime64/timedelta64 when
    #  inserting into a datetime64/timedelta64 series.  Others should coerce
    #  to object and retain their dtypes.
    # GH#18586 for td64 and boolean mask case

    @pytest.fixture(
        params=["m8[ns]", "M8[ns]", "datetime64[ns, UTC]", "datetime64[ns, US/Central]"]
    )
    def dtype(self, request):
        return request.param

    @pytest.fixture
    def obj(self, dtype):
        i8vals = date_range("2016-01-01", periods=3).asi8
        idx = Index(i8vals, dtype=dtype)
        assert idx.dtype == dtype
        return Series(idx)

    @pytest.fixture(
        params=[
            None,
            np.nan,
            NaT,
            np.timedelta64("NaT", "ns"),
            np.datetime64("NaT", "ns"),
        ]
    )
    def val(self, request):
        return request.param

    @pytest.fixture
    def is_inplace(self, val, obj):
        # td64   -> cast to object iff val is datetime64("NaT")
        # dt64   -> cast to object iff val is timedelta64("NaT")
        # dt64tz -> cast to object with anything _but_ NaT
        return val is NaT or val is None or val is np.nan or obj.dtype == val.dtype

    @pytest.fixture
    def expected(self, obj, val, is_inplace):
        dtype = obj.dtype if is_inplace else object
        expected = Series([val] + list(obj[1:]), dtype=dtype)
        return expected

    @pytest.fixture
    def key(self):
        return 0


class TestSetitemMismatchedTZCastsToObject(SetitemCastingEquivalents):
    # GH#24024
    @pytest.fixture
    def obj(self):
        return Series(date_range("2000", periods=2, tz="US/Central"))

    @pytest.fixture
    def val(self):
        return Timestamp("2000", tz="US/Eastern")

    @pytest.fixture
    def key(self):
        return 0

    @pytest.fixture
    def expected(self):
        expected = Series(
            [
                Timestamp("2000-01-01 00:00:00-05:00", tz="US/Eastern"),
                Timestamp("2000-01-02 00:00:00-06:00", tz="US/Central"),
            ],
            dtype=object,
        )
        return expected

    @pytest.fixture(autouse=True)
    def assert_warns(self, request):
        # check that we issue a FutureWarning about timezone-matching
        if request.function.__name__ == "test_slice_key":
            key = request.getfixturevalue("key")
            if not isinstance(key, slice):
                # The test is a no-op, so no warning will be issued
                yield
            return
        with tm.assert_produces_warning(FutureWarning, match="mismatched timezone"):
            yield


@pytest.mark.parametrize(
    "obj,expected",
    [
        # For numeric series, we should coerce to NaN.
        (Series([1, 2, 3]), Series([np.nan, 2, 3])),
        (Series([1.0, 2.0, 3.0]), Series([np.nan, 2.0, 3.0])),
        # For datetime series, we should coerce to NaT.
        (
            Series([datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)]),
            Series([NaT, datetime(2000, 1, 2), datetime(2000, 1, 3)]),
        ),
        # For objects, we should preserve the None value.
        (Series(["foo", "bar", "baz"]), Series([None, "bar", "baz"])),
    ],
)
class TestSeriesNoneCoercion(SetitemCastingEquivalents):
    @pytest.fixture
    def key(self):
        return 0

    @pytest.fixture
    def val(self):
        return None

    @pytest.fixture
    def is_inplace(self, obj):
        # This is specific to the 4 cases currently implemented for this class.
        return obj.dtype.kind != "i"


class TestSetitemFloatIntervalWithIntIntervalValues(SetitemCastingEquivalents):
    # GH#44201 Cast to shared IntervalDtype rather than object

    def test_setitem_example(self):
        # Just a case here to make obvious what this test class is aimed at
        idx = IntervalIndex.from_breaks(range(4))
        obj = Series(idx)
        val = Interval(0.5, 1.5)

        obj[0] = val
        assert obj.dtype == "Interval[float64, right]"

    @pytest.fixture
    def obj(self):
        idx = IntervalIndex.from_breaks(range(4))
        return Series(idx)

    @pytest.fixture
    def val(self):
        return Interval(0.5, 1.5)

    @pytest.fixture
    def key(self):
        return 0

    @pytest.fixture
    def expected(self, obj, val):
        data = [val] + list(obj[1:])
        idx = IntervalIndex(data, dtype="Interval[float64]")
        return Series(idx)


class TestSetitemRangeIntoIntegerSeries(SetitemCastingEquivalents):
    # GH#44261 Setting a range with sufficiently-small integers into
    #  small-itemsize integer dtypes should not need to upcast

    @pytest.fixture
    def obj(self, any_int_numpy_dtype):
        dtype = np.dtype(any_int_numpy_dtype)
        ser = Series(range(5), dtype=dtype)
        return ser

    @pytest.fixture
    def val(self):
        return range(2, 4)

    @pytest.fixture
    def key(self):
        return slice(0, 2)

    @pytest.fixture
    def expected(self, any_int_numpy_dtype):
        dtype = np.dtype(any_int_numpy_dtype)
        exp = Series([2, 3, 2, 3, 4], dtype=dtype)
        return exp

    @pytest.fixture
    def inplace(self):
        return True


@pytest.mark.parametrize(
    "val",
    [
        np.array([2.0, 3.0]),
        np.array([2.5, 3.5]),
        np.array([2 ** 65, 2 ** 65 + 1], dtype=np.float64),  # all ints, but can't cast
    ],
)
class TestSetitemFloatNDarrayIntoIntegerSeries(SetitemCastingEquivalents):
    @pytest.fixture
    def obj(self):
        return Series(range(5), dtype=np.int64)

    @pytest.fixture
    def key(self):
        return slice(0, 2)

    @pytest.fixture
    def inplace(self, val):
        # NB: this condition is based on currently-harcoded "val" cases
        return val[0] == 2

    @pytest.fixture
    def expected(self, val, inplace):
        if inplace:
            dtype = np.int64
        else:
            dtype = np.float64
        res_values = np.array(range(5), dtype=dtype)
        res_values[:2] = val
        return Series(res_values)


@pytest.mark.parametrize("val", [512, np.int16(512)])
class TestSetitemIntoIntegerSeriesNeedsUpcast(SetitemCastingEquivalents):
    @pytest.fixture
    def obj(self):
        return Series([1, 2, 3], dtype=np.int8)

    @pytest.fixture
    def key(self):
        return 1

    @pytest.fixture
    def inplace(self):
        return False

    @pytest.fixture
    def expected(self):
        return Series([1, 512, 3], dtype=np.int16)

    def test_int_key(self, obj, key, expected, val, indexer_sli, is_inplace, request):
        if not isinstance(val, np.int16):
            mark = pytest.mark.xfail
            request.node.add_marker(mark)
        super().test_int_key(obj, key, expected, val, indexer_sli, is_inplace)

    def test_mask_key(self, obj, key, expected, val, indexer_sli, request):
        if not isinstance(val, np.int16):
            mark = pytest.mark.xfail
            request.node.add_marker(mark)
        super().test_mask_key(obj, key, expected, val, indexer_sli)


@pytest.mark.parametrize("val", [2 ** 33 + 1.0, 2 ** 33 + 1.1, 2 ** 62])
class TestSmallIntegerSetitemUpcast(SetitemCastingEquivalents):
    # https://github.com/pandas-dev/pandas/issues/39584#issuecomment-941212124
    @pytest.fixture
    def obj(self):
        return Series([1, 2, 3], dtype="i4")

    @pytest.fixture
    def key(self):
        return 0

    @pytest.fixture
    def inplace(self):
        return False

    @pytest.fixture
    def expected(self, val):
        if val == 2 ** 62:
            return Series([val, 2, 3], dtype="i8")
        elif val == 2 ** 33 + 1.1:
            return Series([val, 2, 3], dtype="f8")
        else:
            return Series([val, 2, 3], dtype="i8")

    def test_series_where(self, obj, key, expected, val, is_inplace, request):
        if isinstance(val, float) and val % 1 == 0:
            mark = pytest.mark.xfail
            request.node.add_marker(mark)
        super().test_series_where(obj, key, expected, val, is_inplace)

    def test_int_key(self, obj, key, expected, val, indexer_sli, is_inplace, request):
        if val % 1 == 0:
            mark = pytest.mark.xfail
            request.node.add_marker(mark)
        super().test_int_key(obj, key, expected, val, indexer_sli, is_inplace)

    def test_mask_key(self, obj, key, expected, val, indexer_sli, request):
        if val % 1 == 0:
            mark = pytest.mark.xfail
            request.node.add_marker(mark)
        super().test_mask_key(obj, key, expected, val, indexer_sli)


def test_20643():
    # closed by GH#45121
    orig = Series([0, 1, 2], index=["a", "b", "c"])

    expected = Series([0, 2.7, 2], index=["a", "b", "c"])

    ser = orig.copy()
    ser.at["b"] = 2.7
    tm.assert_series_equal(ser, expected)

    ser = orig.copy()
    ser.loc["b"] = 2.7
    tm.assert_series_equal(ser, expected)

    ser = orig.copy()
    ser["b"] = 2.7
    tm.assert_series_equal(ser, expected)

    ser = orig.copy()
    ser.iat[1] = 2.7
    tm.assert_series_equal(ser, expected)

    ser = orig.copy()
    ser.iloc[1] = 2.7
    tm.assert_series_equal(ser, expected)

    orig_df = orig.to_frame("A")
    expected_df = expected.to_frame("A")

    df = orig_df.copy()
    df.at["b", "A"] = 2.7
    tm.assert_frame_equal(df, expected_df)

    df = orig_df.copy()
    df.loc["b", "A"] = 2.7
    tm.assert_frame_equal(df, expected_df)

    df = orig_df.copy()
    df.iloc[1, 0] = 2.7
    tm.assert_frame_equal(df, expected_df)

    df = orig_df.copy()
    df.iat[1, 0] = 2.7
    tm.assert_frame_equal(df, expected_df)


def test_20643_comment():
    # https://github.com/pandas-dev/pandas/issues/20643#issuecomment-431244590
    # fixed sometime prior to GH#45121
    orig = Series([0, 1, 2], index=["a", "b", "c"])
    expected = Series([np.nan, 1, 2], index=["a", "b", "c"])

    ser = orig.copy()
    ser.iat[0] = None
    tm.assert_series_equal(ser, expected)

    ser = orig.copy()
    ser.iloc[0] = None
    tm.assert_series_equal(ser, expected)


def test_15413():
    # fixed by GH#45121
    ser = Series([1, 2, 3])

    ser[ser == 2] += 0.5
    expected = Series([1, 2.5, 3])
    tm.assert_series_equal(ser, expected)

    ser = Series([1, 2, 3])
    ser[1] += 0.5
    tm.assert_series_equal(ser, expected)

    ser = Series([1, 2, 3])
    ser.loc[1] += 0.5
    tm.assert_series_equal(ser, expected)

    ser = Series([1, 2, 3])
    ser.iloc[1] += 0.5
    tm.assert_series_equal(ser, expected)

    ser = Series([1, 2, 3])
    ser.iat[1] += 0.5
    tm.assert_series_equal(ser, expected)

    ser = Series([1, 2, 3])
    ser.at[1] += 0.5
    tm.assert_series_equal(ser, expected)


def test_37477():
    # fixed by GH#45121
    orig = DataFrame({"A": [1, 2, 3], "B": [3, 4, 5]})
    expected = DataFrame({"A": [1, 2, 3], "B": [3, 1.2, 5]})

    df = orig.copy()
    df.at[1, "B"] = 1.2
    tm.assert_frame_equal(df, expected)

    df = orig.copy()
    df.loc[1, "B"] = 1.2
    tm.assert_frame_equal(df, expected)

    df = orig.copy()
    df.iat[1, 1] = 1.2
    tm.assert_frame_equal(df, expected)

    df = orig.copy()
    df.iloc[1, 1] = 1.2
    tm.assert_frame_equal(df, expected)


def test_32878_int_itemsize():
    # Fixed by GH#45121
    arr = np.arange(5).astype("i4")
    ser = Series(arr)
    val = np.int64(np.iinfo(np.int64).max)
    ser[0] = val
    expected = Series([val, 1, 2, 3, 4], dtype=np.int64)
    tm.assert_series_equal(ser, expected)


def test_26395(indexer_al):
    # .at case fixed by GH#45121 (best guess)
    df = DataFrame(index=["A", "B", "C"])
    df["D"] = 0

    indexer_al(df)["C", "D"] = 2
    expected = DataFrame({"D": [0, 0, 2]}, index=["A", "B", "C"], dtype=np.int64)
    tm.assert_frame_equal(df, expected)

    indexer_al(df)["C", "D"] = 44.5
    expected = DataFrame({"D": [0, 0, 44.5]}, index=["A", "B", "C"], dtype=np.float64)
    tm.assert_frame_equal(df, expected)

    indexer_al(df)["C", "D"] = "hello"
    expected = DataFrame({"D": [0, 0, "hello"]}, index=["A", "B", "C"], dtype=object)
    tm.assert_frame_equal(df, expected)


def test_37692(indexer_al):
    # GH#37692
    ser = Series([1, 2, 3], index=["a", "b", "c"])
    indexer_al(ser)["b"] = "test"
    expected = Series([1, "test", 3], index=["a", "b", "c"], dtype=object)
    tm.assert_series_equal(ser, expected)


def test_setitem_bool_int_float_consistency(indexer_sli):
    # GH#21513
    # bool-with-int and bool-with-float both upcast to object
    #  int-with-float and float-with-int are both non-casting so long
    #  as the setitem can be done losslessly
    for dtype in [np.float64, np.int64]:
        ser = Series(0, index=range(3), dtype=dtype)
        indexer_sli(ser)[0] = True
        assert ser.dtype == object

        ser = Series(0, index=range(3), dtype=bool)
        ser[0] = dtype(1)
        assert ser.dtype == object

    # 1.0 can be held losslessly, so no casting
    ser = Series(0, index=range(3), dtype=np.int64)
    indexer_sli(ser)[0] = np.float64(1.0)
    assert ser.dtype == np.int64

    # 1 can be held losslessly, so no casting
    ser = Series(0, index=range(3), dtype=np.float64)
    indexer_sli(ser)[0] = np.int64(1)


def test_6942(indexer_al):
    # check that the .at __setitem__ after setting "Live" actually sets the data
    start = Timestamp("2014-04-01")
    t1 = Timestamp("2014-04-23 12:42:38.883082")
    t2 = Timestamp("2014-04-24 01:33:30.040039")

    dti = date_range(start, periods=1)
    orig = DataFrame(index=dti, columns=["timenow", "Live"])

    df = orig.copy()
    indexer_al(df)[start, "timenow"] = t1

    df["Live"] = True

    df.at[start, "timenow"] = t2
    assert df.iloc[0, 0] == t2


@pytest.mark.xfail(reason="Doesn't catch when numpy raises.")
def test_45070():
    ser = Series([1, 2, 3], index=["a", "b", "c"])

    ser[0] = "X"
    expected = Series(["X", 2, 3], index=["a", "b", "c"], dtype=object)
    tm.assert_series_equal(ser, expected)


@pytest.mark.xfail(reason="unwanted upcast")
def test_15231():
    df = DataFrame([[1, 2], [3, 4]], columns=["a", "b"])
    df.loc[2] = Series({"a": 5, "b": 6})
    assert (df.dtypes == np.int64).all()

    df.loc[3] = Series({"a": 7})

    # df["a"] doesn't have any NaNs, should not have been cast
    exp_dtypes = Series([np.int64, np.float64], dtype=object, index=["a", "b"])
    tm.assert_series_equal(df.dtypes, exp_dtypes)


@pytest.mark.xfail(reason="Fails to upcast")
def test_32878_complex_itemsize():
    # TODO: when fixed, put adjacent to test_32878_int_itemsize
    arr = np.arange(5).astype("c8")
    ser = Series(arr)
    val = np.finfo(np.float64).max
    val = val.astype("c16")

    # GH#32878 used to coerce val to inf+0.000000e+00j
    ser[0] = val
    assert ser[0] == val
    expected = Series([val, 1, 2, 3, 4], dtype="c16")
    tm.assert_series_equal(ser, expected)


@pytest.mark.xfail(reason="Unnecessarily upcasts to float64")
def test_iloc_setitem_unnecesssary_float_upcasting():
    # GH#12255
    df = DataFrame(
        {
            0: np.array([1, 3], dtype=np.float32),
            1: np.array([2, 4], dtype=np.float32),
            2: ["a", "b"],
        }
    )
    orig = df.copy()

    values = df[0].values.reshape(2, 1)
    df.iloc[:, 0:1] = values

    tm.assert_frame_equal(df, orig)


@pytest.mark.xfail(reason="unwanted casting to dt64")
def test_12499():
    # TODO: OP in GH#12499 used np.datetim64("NaT") instead of pd.NaT,
    #  which has consequences for the expected df["two"] (though i think at
    #  the time it might not have because of a separate bug). See if it makes
    #  a difference which one we use here.
    ts = Timestamp("2016-03-01 03:13:22.98986", tz="UTC")

    data = [{"one": 0, "two": ts}]
    orig = DataFrame(data)
    df = orig.copy()
    df.loc[1] = [np.nan, NaT]

    expected = DataFrame(
        {"one": [0, np.nan], "two": Series([ts, NaT], dtype="datetime64[ns, UTC]")}
    )
    tm.assert_frame_equal(df, expected)

    data = [{"one": 0, "two": ts}]
    df = orig.copy()
    df.loc[1, :] = [np.nan, NaT]
    tm.assert_frame_equal(df, expected)


@pytest.mark.xfail(reason="Too many columns cast to float64")
def test_20476():
    mi = MultiIndex.from_product([["A", "B"], ["a", "b", "c"]])
    df = DataFrame(-1, index=range(3), columns=mi)
    filler = DataFrame([[1, 2, 3.0]] * 3, index=range(3), columns=["a", "b", "c"])
    df["A"] = filler

    expected = DataFrame(
        {
            0: [1, 1, 1],
            1: [2, 2, 2],
            2: [3.0, 3.0, 3.0],
            3: [-1, -1, -1],
            4: [-1, -1, -1],
            5: [-1, -1, -1],
        }
    )
    expected.columns = mi
    exp_dtypes = Series(
        [np.dtype(np.int64)] * 2 + [np.dtype(np.float64)] + [np.dtype(np.int64)] * 3,
        index=mi,
    )
    tm.assert_series_equal(df.dtypes, exp_dtypes)


def test_setitem_int_as_positional_fallback_deprecation():
    # GH#42215 deprecated falling back to positional on __setitem__ with an
    #  int not contained in the index
    ser = Series([1, 2, 3, 4], index=[1.1, 2.1, 3.0, 4.1])
    assert not ser.index._should_fallback_to_positional
    # assert not ser.index.astype(object)._should_fallback_to_positional

    with tm.assert_produces_warning(None):
        # 3.0 is in our index, so future behavior is unchanged
        ser[3] = 10
    expected = Series([1, 2, 10, 4], index=ser.index)
    tm.assert_series_equal(ser, expected)

    msg = "Treating integers as positional in Series.__setitem__"
    with tm.assert_produces_warning(FutureWarning, match=msg):
        with pytest.raises(IndexError, match="index 5 is out of bounds"):
            ser[5] = 5
    # Once the deprecation is enforced, we will have
    #  expected = Series([1, 2, 3, 4, 5], index=[1.1, 2.1, 3.0, 4.1, 5.0])

    ii = IntervalIndex.from_breaks(range(10))[::2]
    ser2 = Series(range(len(ii)), index=ii)
    expected2 = ser2.copy()
    expected2.iloc[-1] = 9
    with tm.assert_produces_warning(FutureWarning, match=msg):
        ser2[4] = 9
    tm.assert_series_equal(ser2, expected2)

    mi = MultiIndex.from_product([ser.index, ["A", "B"]])
    ser3 = Series(range(len(mi)), index=mi)
    expected3 = ser3.copy()
    expected3.iloc[4] = 99

    with tm.assert_produces_warning(FutureWarning, match=msg):
        ser3[4] = 99
    tm.assert_series_equal(ser3, expected3)


def test_setitem_with_bool_indexer():
    # GH#42530

    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
    result = df.pop("b")
    result[[True, False, False]] = 9
    expected = Series(data=[9, 5, 6], name="b")
    tm.assert_series_equal(result, expected)

    df.loc[[True, False, False], "a"] = 10
    expected = DataFrame({"a": [10, 2, 3]})
    tm.assert_frame_equal(df, expected)


@pytest.mark.parametrize("size", range(2, 6))
@pytest.mark.parametrize(
    "mask", [[True, False, False, False, False], [True, False], [False]]
)
@pytest.mark.parametrize(
    "item", [2.0, np.nan, np.finfo(float).max, np.finfo(float).min]
)
# Test numpy arrays, lists and tuples as the input to be
# broadcast
@pytest.mark.parametrize(
    "box", [lambda x: np.array([x]), lambda x: [x], lambda x: (x,)]
)
def test_setitem_bool_indexer_dont_broadcast_length1_values(size, mask, item, box):
    # GH#44265
    # see also tests.series.indexing.test_where.test_broadcast

    selection = np.resize(mask, size)

    data = np.arange(size, dtype=float)

    ser = Series(data)

    if selection.sum() != 1:
        msg = (
            "cannot set using a list-like indexer with a different "
            "length than the value"
        )
        with pytest.raises(ValueError, match=msg):
            # GH#44265
            ser[selection] = box(item)
    else:
        # In this corner case setting is equivalent to setting with the unboxed
        #  item
        ser[selection] = box(item)

        expected = Series(np.arange(size, dtype=float))
        expected[selection] = item
        tm.assert_series_equal(ser, expected)