venv

venv/lib/python3.12/site-packages/pandas/tests/series/accessors/test_cat_accessor.py

@@ -0,0 +1,258 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Categorical,
+    DataFrame,
+    Index,
+    Series,
+    Timestamp,
+    date_range,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core.arrays.categorical import CategoricalAccessor
+from pandas.core.indexes.accessors import Properties
+
+
+class TestCatAccessor:
+    @pytest.mark.parametrize(
+        "method",
+        [
+            lambda x: x.cat.set_categories([1, 2, 3]),
+            lambda x: x.cat.reorder_categories([2, 3, 1], ordered=True),
+            lambda x: x.cat.rename_categories([1, 2, 3]),
+            lambda x: x.cat.remove_unused_categories(),
+            lambda x: x.cat.remove_categories([2]),
+            lambda x: x.cat.add_categories([4]),
+            lambda x: x.cat.as_ordered(),
+            lambda x: x.cat.as_unordered(),
+        ],
+    )
+    def test_getname_categorical_accessor(self, method):
+        # GH#17509
+        ser = Series([1, 2, 3], name="A").astype("category")
+        expected = "A"
+        result = method(ser).name
+        assert result == expected
+
+    def test_cat_accessor(self):
+        ser = Series(Categorical(["a", "b", np.nan, "a"]))
+        tm.assert_index_equal(ser.cat.categories, Index(["a", "b"]))
+        assert not ser.cat.ordered, False
+
+        exp = Categorical(["a", "b", np.nan, "a"], categories=["b", "a"])
+
+        res = ser.cat.set_categories(["b", "a"])
+        tm.assert_categorical_equal(res.values, exp)
+
+        ser[:] = "a"
+        ser = ser.cat.remove_unused_categories()
+        tm.assert_index_equal(ser.cat.categories, Index(["a"]))
+
+    def test_cat_accessor_api(self):
+        # GH#9322
+
+        assert Series.cat is CategoricalAccessor
+        ser = Series(list("aabbcde")).astype("category")
+        assert isinstance(ser.cat, CategoricalAccessor)
+
+        invalid = Series([1])
+        with pytest.raises(AttributeError, match="only use .cat accessor"):
+            invalid.cat
+        assert not hasattr(invalid, "cat")
+
+    def test_cat_accessor_no_new_attributes(self):
+        # https://github.com/pandas-dev/pandas/issues/10673
+        cat = Series(list("aabbcde")).astype("category")
+        with pytest.raises(AttributeError, match="You cannot add any new attribute"):
+            cat.cat.xlabel = "a"
+
+    def test_categorical_delegations(self):
+        # invalid accessor
+        msg = r"Can only use \.cat accessor with a 'category' dtype"
+        with pytest.raises(AttributeError, match=msg):
+            Series([1, 2, 3]).cat
+        with pytest.raises(AttributeError, match=msg):
+            Series([1, 2, 3]).cat()
+        with pytest.raises(AttributeError, match=msg):
+            Series(["a", "b", "c"]).cat
+        with pytest.raises(AttributeError, match=msg):
+            Series(np.arange(5.0)).cat
+        with pytest.raises(AttributeError, match=msg):
+            Series([Timestamp("20130101")]).cat
+
+        # Series should delegate calls to '.categories', '.codes', '.ordered'
+        # and the methods '.set_categories()' 'drop_unused_categories()' to the
+        # categorical
+        ser = Series(Categorical(["a", "b", "c", "a"], ordered=True))
+        exp_categories = Index(["a", "b", "c"])
+        tm.assert_index_equal(ser.cat.categories, exp_categories)
+        ser = ser.cat.rename_categories([1, 2, 3])
+        exp_categories = Index([1, 2, 3])
+        tm.assert_index_equal(ser.cat.categories, exp_categories)
+
+        exp_codes = Series([0, 1, 2, 0], dtype="int8")
+        tm.assert_series_equal(ser.cat.codes, exp_codes)
+
+        assert ser.cat.ordered
+        ser = ser.cat.as_unordered()
+        assert not ser.cat.ordered
+
+        ser = ser.cat.as_ordered()
+        assert ser.cat.ordered
+
+        # reorder
+        ser = Series(Categorical(["a", "b", "c", "a"], ordered=True))
+        exp_categories = Index(["c", "b", "a"])
+        exp_values = np.array(["a", "b", "c", "a"], dtype=np.object_)
+        ser = ser.cat.set_categories(["c", "b", "a"])
+        tm.assert_index_equal(ser.cat.categories, exp_categories)
+        tm.assert_numpy_array_equal(ser.values.__array__(), exp_values)
+        tm.assert_numpy_array_equal(ser.__array__(), exp_values)
+
+        # remove unused categories
+        ser = Series(Categorical(["a", "b", "b", "a"], categories=["a", "b", "c"]))
+        exp_categories = Index(["a", "b"])
+        exp_values = np.array(["a", "b", "b", "a"], dtype=np.object_)
+        ser = ser.cat.remove_unused_categories()
+        tm.assert_index_equal(ser.cat.categories, exp_categories)
+        tm.assert_numpy_array_equal(ser.values.__array__(), exp_values)
+        tm.assert_numpy_array_equal(ser.__array__(), exp_values)
+
+        # This method is likely to be confused, so test that it raises an error
+        # on wrong inputs:
+        msg = "'Series' object has no attribute 'set_categories'"
+        with pytest.raises(AttributeError, match=msg):
+            ser.set_categories([4, 3, 2, 1])
+
+        # right: ser.cat.set_categories([4,3,2,1])
+
+        # GH#18862 (let Series.cat.rename_categories take callables)
+        ser = Series(Categorical(["a", "b", "c", "a"], ordered=True))
+        result = ser.cat.rename_categories(lambda x: x.upper())
+        expected = Series(
+            Categorical(["A", "B", "C", "A"], categories=["A", "B", "C"], ordered=True)
+        )
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "idx",
+        [
+            date_range("1/1/2015", periods=5),
+            date_range("1/1/2015", periods=5, tz="MET"),
+            period_range("1/1/2015", freq="D", periods=5),
+            timedelta_range("1 days", "10 days"),
+        ],
+    )
+    def test_dt_accessor_api_for_categorical(self, idx):
+        # https://github.com/pandas-dev/pandas/issues/10661
+
+        ser = Series(idx)
+        cat = ser.astype("category")
+
+        # only testing field (like .day)
+        # and bool (is_month_start)
+        attr_names = type(ser._values)._datetimelike_ops
+
+        assert isinstance(cat.dt, Properties)
+
+        special_func_defs = [
+            ("strftime", ("%Y-%m-%d",), {}),
+            ("round", ("D",), {}),
+            ("floor", ("D",), {}),
+            ("ceil", ("D",), {}),
+            ("asfreq", ("D",), {}),
+            ("as_unit", ("s"), {}),
+        ]
+        if idx.dtype == "M8[ns]":
+            # exclude dt64tz since that is already localized and would raise
+            tup = ("tz_localize", ("UTC",), {})
+            special_func_defs.append(tup)
+        elif idx.dtype.kind == "M":
+            # exclude dt64 since that is not localized so would raise
+            tup = ("tz_convert", ("EST",), {})
+            special_func_defs.append(tup)
+
+        _special_func_names = [f[0] for f in special_func_defs]
+
+        _ignore_names = ["components", "tz_localize", "tz_convert"]
+
+        func_names = [
+            fname
+            for fname in dir(ser.dt)
+            if not (
+                fname.startswith("_")
+                or fname in attr_names
+                or fname in _special_func_names
+                or fname in _ignore_names
+            )
+        ]
+
+        func_defs = [(fname, (), {}) for fname in func_names]
+        func_defs.extend(
+            f_def for f_def in special_func_defs if f_def[0] in dir(ser.dt)
+        )
+
+        for func, args, kwargs in func_defs:
+            warn_cls = []
+            if func == "to_period" and getattr(idx, "tz", None) is not None:
+                # dropping TZ
+                warn_cls.append(UserWarning)
+            if func == "to_pydatetime":
+                # deprecated to return Index[object]
+                warn_cls.append(FutureWarning)
+            if warn_cls:
+                warn_cls = tuple(warn_cls)
+            else:
+                warn_cls = None
+            with tm.assert_produces_warning(warn_cls):
+                res = getattr(cat.dt, func)(*args, **kwargs)
+                exp = getattr(ser.dt, func)(*args, **kwargs)
+
+            tm.assert_equal(res, exp)
+
+        for attr in attr_names:
+            res = getattr(cat.dt, attr)
+            exp = getattr(ser.dt, attr)
+
+            tm.assert_equal(res, exp)
+
+    def test_dt_accessor_api_for_categorical_invalid(self):
+        invalid = Series([1, 2, 3]).astype("category")
+        msg = "Can only use .dt accessor with datetimelike"
+
+        with pytest.raises(AttributeError, match=msg):
+            invalid.dt
+        assert not hasattr(invalid, "str")
+
+    def test_set_categories_setitem(self):
+        # GH#43334
+
+        df = DataFrame({"Survived": [1, 0, 1], "Sex": [0, 1, 1]}, dtype="category")
+
+        df["Survived"] = df["Survived"].cat.rename_categories(["No", "Yes"])
+        df["Sex"] = df["Sex"].cat.rename_categories(["female", "male"])
+
+        # values should not be coerced to NaN
+        assert list(df["Sex"]) == ["female", "male", "male"]
+        assert list(df["Survived"]) == ["Yes", "No", "Yes"]
+
+        df["Sex"] = Categorical(df["Sex"], categories=["female", "male"], ordered=False)
+        df["Survived"] = Categorical(
+            df["Survived"], categories=["No", "Yes"], ordered=False
+        )
+
+        # values should not be coerced to NaN
+        assert list(df["Sex"]) == ["female", "male", "male"]
+        assert list(df["Survived"]) == ["Yes", "No", "Yes"]
+
+    def test_categorical_of_booleans_is_boolean(self):
+        # https://github.com/pandas-dev/pandas/issues/46313
+        df = DataFrame(
+            {"int_cat": [1, 2, 3], "bool_cat": [True, False, False]}, dtype="category"
+        )
+        value = df["bool_cat"].cat.categories.dtype
+        expected = np.dtype(np.bool_)
+        assert value is expected

venv/lib/python3.12/site-packages/pandas/tests/series/accessors/test_dt_accessor.py

@@ -0,0 +1,843 @@
+import calendar
+from datetime import (
+    date,
+    datetime,
+    time,
+)
+import locale
+import unicodedata
+
+import numpy as np
+import pytest
+import pytz
+
+from pandas._libs.tslibs.timezones import maybe_get_tz
+from pandas.errors import SettingWithCopyError
+
+from pandas.core.dtypes.common import (
+    is_integer_dtype,
+    is_list_like,
+)
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    Period,
+    PeriodIndex,
+    Series,
+    TimedeltaIndex,
+    date_range,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core.arrays import (
+    DatetimeArray,
+    PeriodArray,
+    TimedeltaArray,
+)
+
+ok_for_period = PeriodArray._datetimelike_ops
+ok_for_period_methods = ["strftime", "to_timestamp", "asfreq"]
+ok_for_dt = DatetimeArray._datetimelike_ops
+ok_for_dt_methods = [
+    "to_period",
+    "to_pydatetime",
+    "tz_localize",
+    "tz_convert",
+    "normalize",
+    "strftime",
+    "round",
+    "floor",
+    "ceil",
+    "day_name",
+    "month_name",
+    "isocalendar",
+    "as_unit",
+]
+ok_for_td = TimedeltaArray._datetimelike_ops
+ok_for_td_methods = [
+    "components",
+    "to_pytimedelta",
+    "total_seconds",
+    "round",
+    "floor",
+    "ceil",
+    "as_unit",
+]
+
+
+def get_dir(ser):
+    # check limited display api
+    results = [r for r in ser.dt.__dir__() if not r.startswith("_")]
+    return sorted(set(results))
+
+
+class TestSeriesDatetimeValues:
+    def _compare(self, ser, name):
+        # GH 7207, 11128
+        # test .dt namespace accessor
+
+        def get_expected(ser, prop):
+            result = getattr(Index(ser._values), prop)
+            if isinstance(result, np.ndarray):
+                if is_integer_dtype(result):
+                    result = result.astype("int64")
+            elif not is_list_like(result) or isinstance(result, DataFrame):
+                return result
+            return Series(result, index=ser.index, name=ser.name)
+
+        left = getattr(ser.dt, name)
+        right = get_expected(ser, name)
+        if not (is_list_like(left) and is_list_like(right)):
+            assert left == right
+        elif isinstance(left, DataFrame):
+            tm.assert_frame_equal(left, right)
+        else:
+            tm.assert_series_equal(left, right)
+
+    @pytest.mark.parametrize("freq", ["D", "s", "ms"])
+    def test_dt_namespace_accessor_datetime64(self, freq):
+        # GH#7207, GH#11128
+        # test .dt namespace accessor
+
+        # datetimeindex
+        dti = date_range("20130101", periods=5, freq=freq)
+        ser = Series(dti, name="xxx")
+
+        for prop in ok_for_dt:
+            # we test freq below
+            if prop != "freq":
+                self._compare(ser, prop)
+
+        for prop in ok_for_dt_methods:
+            getattr(ser.dt, prop)
+
+        msg = "The behavior of DatetimeProperties.to_pydatetime is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = ser.dt.to_pydatetime()
+        assert isinstance(result, np.ndarray)
+        assert result.dtype == object
+
+        result = ser.dt.tz_localize("US/Eastern")
+        exp_values = DatetimeIndex(ser.values).tz_localize("US/Eastern")
+        expected = Series(exp_values, index=ser.index, name="xxx")
+        tm.assert_series_equal(result, expected)
+
+        tz_result = result.dt.tz
+        assert str(tz_result) == "US/Eastern"
+        freq_result = ser.dt.freq
+        assert freq_result == DatetimeIndex(ser.values, freq="infer").freq
+
+        # let's localize, then convert
+        result = ser.dt.tz_localize("UTC").dt.tz_convert("US/Eastern")
+        exp_values = (
+            DatetimeIndex(ser.values).tz_localize("UTC").tz_convert("US/Eastern")
+        )
+        expected = Series(exp_values, index=ser.index, name="xxx")
+        tm.assert_series_equal(result, expected)
+
+    def test_dt_namespace_accessor_datetime64tz(self):
+        # GH#7207, GH#11128
+        # test .dt namespace accessor
+
+        # datetimeindex with tz
+        dti = date_range("20130101", periods=5, tz="US/Eastern")
+        ser = Series(dti, name="xxx")
+        for prop in ok_for_dt:
+            # we test freq below
+            if prop != "freq":
+                self._compare(ser, prop)
+
+        for prop in ok_for_dt_methods:
+            getattr(ser.dt, prop)
+
+        msg = "The behavior of DatetimeProperties.to_pydatetime is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = ser.dt.to_pydatetime()
+        assert isinstance(result, np.ndarray)
+        assert result.dtype == object
+
+        result = ser.dt.tz_convert("CET")
+        expected = Series(ser._values.tz_convert("CET"), index=ser.index, name="xxx")
+        tm.assert_series_equal(result, expected)
+
+        tz_result = result.dt.tz
+        assert str(tz_result) == "CET"
+        freq_result = ser.dt.freq
+        assert freq_result == DatetimeIndex(ser.values, freq="infer").freq
+
+    def test_dt_namespace_accessor_timedelta(self):
+        # GH#7207, GH#11128
+        # test .dt namespace accessor
+
+        # timedelta index
+        cases = [
+            Series(
+                timedelta_range("1 day", periods=5), index=list("abcde"), name="xxx"
+            ),
+            Series(timedelta_range("1 day 01:23:45", periods=5, freq="s"), name="xxx"),
+            Series(
+                timedelta_range("2 days 01:23:45.012345", periods=5, freq="ms"),
+                name="xxx",
+            ),
+        ]
+        for ser in cases:
+            for prop in ok_for_td:
+                # we test freq below
+                if prop != "freq":
+                    self._compare(ser, prop)
+
+            for prop in ok_for_td_methods:
+                getattr(ser.dt, prop)
+
+            result = ser.dt.components
+            assert isinstance(result, DataFrame)
+            tm.assert_index_equal(result.index, ser.index)
+
+            result = ser.dt.to_pytimedelta()
+            assert isinstance(result, np.ndarray)
+            assert result.dtype == object
+
+            result = ser.dt.total_seconds()
+            assert isinstance(result, Series)
+            assert result.dtype == "float64"
+
+            freq_result = ser.dt.freq
+            assert freq_result == TimedeltaIndex(ser.values, freq="infer").freq
+
+    def test_dt_namespace_accessor_period(self):
+        # GH#7207, GH#11128
+        # test .dt namespace accessor
+
+        # periodindex
+        pi = period_range("20130101", periods=5, freq="D")
+        ser = Series(pi, name="xxx")
+
+        for prop in ok_for_period:
+            # we test freq below
+            if prop != "freq":
+                self._compare(ser, prop)
+
+        for prop in ok_for_period_methods:
+            getattr(ser.dt, prop)
+
+        freq_result = ser.dt.freq
+        assert freq_result == PeriodIndex(ser.values).freq
+
+    def test_dt_namespace_accessor_index_and_values(self):
+        # both
+        index = date_range("20130101", periods=3, freq="D")
+        dti = date_range("20140204", periods=3, freq="s")
+        ser = Series(dti, index=index, name="xxx")
+        exp = Series(
+            np.array([2014, 2014, 2014], dtype="int32"), index=index, name="xxx"
+        )
+        tm.assert_series_equal(ser.dt.year, exp)
+
+        exp = Series(np.array([2, 2, 2], dtype="int32"), index=index, name="xxx")
+        tm.assert_series_equal(ser.dt.month, exp)
+
+        exp = Series(np.array([0, 1, 2], dtype="int32"), index=index, name="xxx")
+        tm.assert_series_equal(ser.dt.second, exp)
+
+        exp = Series([ser.iloc[0]] * 3, index=index, name="xxx")
+        tm.assert_series_equal(ser.dt.normalize(), exp)
+
+    def test_dt_accessor_limited_display_api(self):
+        # tznaive
+        ser = Series(date_range("20130101", periods=5, freq="D"), name="xxx")
+        results = get_dir(ser)
+        tm.assert_almost_equal(results, sorted(set(ok_for_dt + ok_for_dt_methods)))
+
+        # tzaware
+        ser = Series(date_range("2015-01-01", "2016-01-01", freq="min"), name="xxx")
+        ser = ser.dt.tz_localize("UTC").dt.tz_convert("America/Chicago")
+        results = get_dir(ser)
+        tm.assert_almost_equal(results, sorted(set(ok_for_dt + ok_for_dt_methods)))
+
+        # Period
+        idx = period_range("20130101", periods=5, freq="D", name="xxx").astype(object)
+        with tm.assert_produces_warning(FutureWarning, match="Dtype inference"):
+            ser = Series(idx)
+        results = get_dir(ser)
+        tm.assert_almost_equal(
+            results, sorted(set(ok_for_period + ok_for_period_methods))
+        )
+
+    def test_dt_accessor_ambiguous_freq_conversions(self):
+        # GH#11295
+        # ambiguous time error on the conversions
+        ser = Series(date_range("2015-01-01", "2016-01-01", freq="min"), name="xxx")
+        ser = ser.dt.tz_localize("UTC").dt.tz_convert("America/Chicago")
+
+        exp_values = date_range(
+            "2015-01-01", "2016-01-01", freq="min", tz="UTC"
+        ).tz_convert("America/Chicago")
+        # freq not preserved by tz_localize above
+        exp_values = exp_values._with_freq(None)
+        expected = Series(exp_values, name="xxx")
+        tm.assert_series_equal(ser, expected)
+
+    def test_dt_accessor_not_writeable(self, using_copy_on_write, warn_copy_on_write):
+        # no setting allowed
+        ser = Series(date_range("20130101", periods=5, freq="D"), name="xxx")
+        with pytest.raises(ValueError, match="modifications"):
+            ser.dt.hour = 5
+
+        # trying to set a copy
+        msg = "modifications to a property of a datetimelike.+not supported"
+        with pd.option_context("chained_assignment", "raise"):
+            if using_copy_on_write:
+                with tm.raises_chained_assignment_error():
+                    ser.dt.hour[0] = 5
+            elif warn_copy_on_write:
+                with tm.assert_produces_warning(
+                    FutureWarning, match="ChainedAssignmentError"
+                ):
+                    ser.dt.hour[0] = 5
+            else:
+                with pytest.raises(SettingWithCopyError, match=msg):
+                    ser.dt.hour[0] = 5
+
+    @pytest.mark.parametrize(
+        "method, dates",
+        [
+            ["round", ["2012-01-02", "2012-01-02", "2012-01-01"]],
+            ["floor", ["2012-01-01", "2012-01-01", "2012-01-01"]],
+            ["ceil", ["2012-01-02", "2012-01-02", "2012-01-02"]],
+        ],
+    )
+    def test_dt_round(self, method, dates):
+        # round
+        ser = Series(
+            pd.to_datetime(
+                ["2012-01-01 13:00:00", "2012-01-01 12:01:00", "2012-01-01 08:00:00"]
+            ),
+            name="xxx",
+        )
+        result = getattr(ser.dt, method)("D")
+        expected = Series(pd.to_datetime(dates), name="xxx")
+        tm.assert_series_equal(result, expected)
+
+    def test_dt_round_tz(self):
+        ser = Series(
+            pd.to_datetime(
+                ["2012-01-01 13:00:00", "2012-01-01 12:01:00", "2012-01-01 08:00:00"]
+            ),
+            name="xxx",
+        )
+        result = ser.dt.tz_localize("UTC").dt.tz_convert("US/Eastern").dt.round("D")
+
+        exp_values = pd.to_datetime(
+            ["2012-01-01", "2012-01-01", "2012-01-01"]
+        ).tz_localize("US/Eastern")
+        expected = Series(exp_values, name="xxx")
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("method", ["ceil", "round", "floor"])
+    def test_dt_round_tz_ambiguous(self, method):
+        # GH 18946 round near "fall back" DST
+        df1 = DataFrame(
+            [
+                pd.to_datetime("2017-10-29 02:00:00+02:00", utc=True),
+                pd.to_datetime("2017-10-29 02:00:00+01:00", utc=True),
+                pd.to_datetime("2017-10-29 03:00:00+01:00", utc=True),
+            ],
+            columns=["date"],
+        )
+        df1["date"] = df1["date"].dt.tz_convert("Europe/Madrid")
+        # infer
+        result = getattr(df1.date.dt, method)("h", ambiguous="infer")
+        expected = df1["date"]
+        tm.assert_series_equal(result, expected)
+
+        # bool-array
+        result = getattr(df1.date.dt, method)("h", ambiguous=[True, False, False])
+        tm.assert_series_equal(result, expected)
+
+        # NaT
+        result = getattr(df1.date.dt, method)("h", ambiguous="NaT")
+        expected = df1["date"].copy()
+        expected.iloc[0:2] = pd.NaT
+        tm.assert_series_equal(result, expected)
+
+        # raise
+        with tm.external_error_raised(pytz.AmbiguousTimeError):
+            getattr(df1.date.dt, method)("h", ambiguous="raise")
+
+    @pytest.mark.parametrize(
+        "method, ts_str, freq",
+        [
+            ["ceil", "2018-03-11 01:59:00-0600", "5min"],
+            ["round", "2018-03-11 01:59:00-0600", "5min"],
+            ["floor", "2018-03-11 03:01:00-0500", "2h"],
+        ],
+    )
+    def test_dt_round_tz_nonexistent(self, method, ts_str, freq):
+        # GH 23324 round near "spring forward" DST
+        ser = Series([pd.Timestamp(ts_str, tz="America/Chicago")])
+        result = getattr(ser.dt, method)(freq, nonexistent="shift_forward")
+        expected = Series([pd.Timestamp("2018-03-11 03:00:00", tz="America/Chicago")])
+        tm.assert_series_equal(result, expected)
+
+        result = getattr(ser.dt, method)(freq, nonexistent="NaT")
+        expected = Series([pd.NaT]).dt.tz_localize(result.dt.tz)
+        tm.assert_series_equal(result, expected)
+
+        with pytest.raises(pytz.NonExistentTimeError, match="2018-03-11 02:00:00"):
+            getattr(ser.dt, method)(freq, nonexistent="raise")
+
+    @pytest.mark.parametrize("freq", ["ns", "us", "1000us"])
+    def test_dt_round_nonnano_higher_resolution_no_op(self, freq):
+        # GH 52761
+        ser = Series(
+            ["2020-05-31 08:00:00", "2000-12-31 04:00:05", "1800-03-14 07:30:20"],
+            dtype="datetime64[ms]",
+        )
+        expected = ser.copy()
+        result = ser.dt.round(freq)
+        tm.assert_series_equal(result, expected)
+
+        assert not np.shares_memory(ser.array._ndarray, result.array._ndarray)
+
+    def test_dt_namespace_accessor_categorical(self):
+        # GH 19468
+        dti = DatetimeIndex(["20171111", "20181212"]).repeat(2)
+        ser = Series(pd.Categorical(dti), name="foo")
+        result = ser.dt.year
+        expected = Series([2017, 2017, 2018, 2018], dtype="int32", name="foo")
+        tm.assert_series_equal(result, expected)
+
+    def test_dt_tz_localize_categorical(self, tz_aware_fixture):
+        # GH 27952
+        tz = tz_aware_fixture
+        datetimes = Series(
+            ["2019-01-01", "2019-01-01", "2019-01-02"], dtype="datetime64[ns]"
+        )
+        categorical = datetimes.astype("category")
+        result = categorical.dt.tz_localize(tz)
+        expected = datetimes.dt.tz_localize(tz)
+        tm.assert_series_equal(result, expected)
+
+    def test_dt_tz_convert_categorical(self, tz_aware_fixture):
+        # GH 27952
+        tz = tz_aware_fixture
+        datetimes = Series(
+            ["2019-01-01", "2019-01-01", "2019-01-02"], dtype="datetime64[ns, MET]"
+        )
+        categorical = datetimes.astype("category")
+        result = categorical.dt.tz_convert(tz)
+        expected = datetimes.dt.tz_convert(tz)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("accessor", ["year", "month", "day"])
+    def test_dt_other_accessors_categorical(self, accessor):
+        # GH 27952
+        datetimes = Series(
+            ["2018-01-01", "2018-01-01", "2019-01-02"], dtype="datetime64[ns]"
+        )
+        categorical = datetimes.astype("category")
+        result = getattr(categorical.dt, accessor)
+        expected = getattr(datetimes.dt, accessor)
+        tm.assert_series_equal(result, expected)
+
+    def test_dt_accessor_no_new_attributes(self):
+        # https://github.com/pandas-dev/pandas/issues/10673
+        ser = Series(date_range("20130101", periods=5, freq="D"))
+        with pytest.raises(AttributeError, match="You cannot add any new attribute"):
+            ser.dt.xlabel = "a"
+
+    # error: Unsupported operand types for + ("List[None]" and "List[str]")
+    @pytest.mark.parametrize(
+        "time_locale", [None] + tm.get_locales()  # type: ignore[operator]
+    )
+    def test_dt_accessor_datetime_name_accessors(self, time_locale):
+        # Test Monday -> Sunday and January -> December, in that sequence
+        if time_locale is None:
+            # If the time_locale is None, day-name and month_name should
+            # return the english attributes
+            expected_days = [
+                "Monday",
+                "Tuesday",
+                "Wednesday",
+                "Thursday",
+                "Friday",
+                "Saturday",
+                "Sunday",
+            ]
+            expected_months = [
+                "January",
+                "February",
+                "March",
+                "April",
+                "May",
+                "June",
+                "July",
+                "August",
+                "September",
+                "October",
+                "November",
+                "December",
+            ]
+        else:
+            with tm.set_locale(time_locale, locale.LC_TIME):
+                expected_days = calendar.day_name[:]
+                expected_months = calendar.month_name[1:]
+
+        ser = Series(date_range(freq="D", start=datetime(1998, 1, 1), periods=365))
+        english_days = [
+            "Monday",
+            "Tuesday",
+            "Wednesday",
+            "Thursday",
+            "Friday",
+            "Saturday",
+            "Sunday",
+        ]
+        for day, name, eng_name in zip(range(4, 11), expected_days, english_days):
+            name = name.capitalize()
+            assert ser.dt.day_name(locale=time_locale)[day] == name
+            assert ser.dt.day_name(locale=None)[day] == eng_name
+        ser = pd.concat([ser, Series([pd.NaT])])
+        assert np.isnan(ser.dt.day_name(locale=time_locale).iloc[-1])
+
+        ser = Series(date_range(freq="ME", start="2012", end="2013"))
+        result = ser.dt.month_name(locale=time_locale)
+        expected = Series([month.capitalize() for month in expected_months])
+
+        # work around https://github.com/pandas-dev/pandas/issues/22342
+        result = result.str.normalize("NFD")
+        expected = expected.str.normalize("NFD")
+
+        tm.assert_series_equal(result, expected)
+
+        for s_date, expected in zip(ser, expected_months):
+            result = s_date.month_name(locale=time_locale)
+            expected = expected.capitalize()
+
+            result = unicodedata.normalize("NFD", result)
+            expected = unicodedata.normalize("NFD", expected)
+
+            assert result == expected
+
+        ser = pd.concat([ser, Series([pd.NaT])])
+        assert np.isnan(ser.dt.month_name(locale=time_locale).iloc[-1])
+
+    def test_strftime(self):
+        # GH 10086
+        ser = Series(date_range("20130101", periods=5))
+        result = ser.dt.strftime("%Y/%m/%d")
+        expected = Series(
+            ["2013/01/01", "2013/01/02", "2013/01/03", "2013/01/04", "2013/01/05"]
+        )
+        tm.assert_series_equal(result, expected)
+
+        ser = Series(date_range("2015-02-03 11:22:33.4567", periods=5))
+        result = ser.dt.strftime("%Y/%m/%d %H-%M-%S")
+        expected = Series(
+            [
+                "2015/02/03 11-22-33",
+                "2015/02/04 11-22-33",
+                "2015/02/05 11-22-33",
+                "2015/02/06 11-22-33",
+                "2015/02/07 11-22-33",
+            ]
+        )
+        tm.assert_series_equal(result, expected)
+
+        ser = Series(period_range("20130101", periods=5))
+        result = ser.dt.strftime("%Y/%m/%d")
+        expected = Series(
+            ["2013/01/01", "2013/01/02", "2013/01/03", "2013/01/04", "2013/01/05"]
+        )
+        tm.assert_series_equal(result, expected)
+
+        ser = Series(period_range("2015-02-03 11:22:33.4567", periods=5, freq="s"))
+        result = ser.dt.strftime("%Y/%m/%d %H-%M-%S")
+        expected = Series(
+            [
+                "2015/02/03 11-22-33",
+                "2015/02/03 11-22-34",
+                "2015/02/03 11-22-35",
+                "2015/02/03 11-22-36",
+                "2015/02/03 11-22-37",
+            ]
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_strftime_dt64_days(self):
+        ser = Series(date_range("20130101", periods=5))
+        ser.iloc[0] = pd.NaT
+        result = ser.dt.strftime("%Y/%m/%d")
+        expected = Series(
+            [np.nan, "2013/01/02", "2013/01/03", "2013/01/04", "2013/01/05"]
+        )
+        tm.assert_series_equal(result, expected)
+
+        datetime_index = date_range("20150301", periods=5)
+        result = datetime_index.strftime("%Y/%m/%d")
+
+        expected = Index(
+            ["2015/03/01", "2015/03/02", "2015/03/03", "2015/03/04", "2015/03/05"],
+            dtype=np.object_,
+        )
+        # dtype may be S10 or U10 depending on python version
+        tm.assert_index_equal(result, expected)
+
+    def test_strftime_period_days(self, using_infer_string):
+        period_index = period_range("20150301", periods=5)
+        result = period_index.strftime("%Y/%m/%d")
+        expected = Index(
+            ["2015/03/01", "2015/03/02", "2015/03/03", "2015/03/04", "2015/03/05"],
+            dtype="=U10",
+        )
+        if using_infer_string:
+            expected = expected.astype("string[pyarrow_numpy]")
+        tm.assert_index_equal(result, expected)
+
+    def test_strftime_dt64_microsecond_resolution(self):
+        ser = Series([datetime(2013, 1, 1, 2, 32, 59), datetime(2013, 1, 2, 14, 32, 1)])
+        result = ser.dt.strftime("%Y-%m-%d %H:%M:%S")
+        expected = Series(["2013-01-01 02:32:59", "2013-01-02 14:32:01"])
+        tm.assert_series_equal(result, expected)
+
+    def test_strftime_period_hours(self):
+        ser = Series(period_range("20130101", periods=4, freq="h"))
+        result = ser.dt.strftime("%Y/%m/%d %H:%M:%S")
+        expected = Series(
+            [
+                "2013/01/01 00:00:00",
+                "2013/01/01 01:00:00",
+                "2013/01/01 02:00:00",
+                "2013/01/01 03:00:00",
+            ]
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_strftime_period_minutes(self):
+        ser = Series(period_range("20130101", periods=4, freq="ms"))
+        result = ser.dt.strftime("%Y/%m/%d %H:%M:%S.%l")
+        expected = Series(
+            [
+                "2013/01/01 00:00:00.000",
+                "2013/01/01 00:00:00.001",
+                "2013/01/01 00:00:00.002",
+                "2013/01/01 00:00:00.003",
+            ]
+        )
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "data",
+        [
+            DatetimeIndex(["2019-01-01", pd.NaT]),
+            PeriodIndex(["2019-01-01", pd.NaT], dtype="period[D]"),
+        ],
+    )
+    def test_strftime_nat(self, data):
+        # GH 29578
+        ser = Series(data)
+        result = ser.dt.strftime("%Y-%m-%d")
+        expected = Series(["2019-01-01", np.nan])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "data", [DatetimeIndex([pd.NaT]), PeriodIndex([pd.NaT], dtype="period[D]")]
+    )
+    def test_strftime_all_nat(self, data):
+        # https://github.com/pandas-dev/pandas/issues/45858
+        ser = Series(data)
+        with tm.assert_produces_warning(None):
+            result = ser.dt.strftime("%Y-%m-%d")
+        expected = Series([np.nan], dtype=object)
+        tm.assert_series_equal(result, expected)
+
+    def test_valid_dt_with_missing_values(self):
+        # GH 8689
+        ser = Series(date_range("20130101", periods=5, freq="D"))
+        ser.iloc[2] = pd.NaT
+
+        for attr in ["microsecond", "nanosecond", "second", "minute", "hour", "day"]:
+            expected = getattr(ser.dt, attr).copy()
+            expected.iloc[2] = np.nan
+            result = getattr(ser.dt, attr)
+            tm.assert_series_equal(result, expected)
+
+        result = ser.dt.date
+        expected = Series(
+            [
+                date(2013, 1, 1),
+                date(2013, 1, 2),
+                pd.NaT,
+                date(2013, 1, 4),
+                date(2013, 1, 5),
+            ],
+            dtype="object",
+        )
+        tm.assert_series_equal(result, expected)
+
+        result = ser.dt.time
+        expected = Series([time(0), time(0), pd.NaT, time(0), time(0)], dtype="object")
+        tm.assert_series_equal(result, expected)
+
+    def test_dt_accessor_api(self):
+        # GH 9322
+        from pandas.core.indexes.accessors import (
+            CombinedDatetimelikeProperties,
+            DatetimeProperties,
+        )
+
+        assert Series.dt is CombinedDatetimelikeProperties
+
+        ser = Series(date_range("2000-01-01", periods=3))
+        assert isinstance(ser.dt, DatetimeProperties)
+
+    @pytest.mark.parametrize(
+        "ser",
+        [
+            Series(np.arange(5)),
+            Series(list("abcde")),
+            Series(np.random.default_rng(2).standard_normal(5)),
+        ],
+    )
+    def test_dt_accessor_invalid(self, ser):
+        # GH#9322 check that series with incorrect dtypes don't have attr
+        with pytest.raises(AttributeError, match="only use .dt accessor"):
+            ser.dt
+        assert not hasattr(ser, "dt")
+
+    def test_dt_accessor_updates_on_inplace(self):
+        ser = Series(date_range("2018-01-01", periods=10))
+        ser[2] = None
+        return_value = ser.fillna(pd.Timestamp("2018-01-01"), inplace=True)
+        assert return_value is None
+        result = ser.dt.date
+        assert result[0] == result[2]
+
+    def test_date_tz(self):
+        # GH11757
+        rng = DatetimeIndex(
+            ["2014-04-04 23:56", "2014-07-18 21:24", "2015-11-22 22:14"],
+            tz="US/Eastern",
+        )
+        ser = Series(rng)
+        expected = Series([date(2014, 4, 4), date(2014, 7, 18), date(2015, 11, 22)])
+        tm.assert_series_equal(ser.dt.date, expected)
+        tm.assert_series_equal(ser.apply(lambda x: x.date()), expected)
+
+    def test_dt_timetz_accessor(self, tz_naive_fixture):
+        # GH21358
+        tz = maybe_get_tz(tz_naive_fixture)
+
+        dtindex = DatetimeIndex(
+            ["2014-04-04 23:56", "2014-07-18 21:24", "2015-11-22 22:14"], tz=tz
+        )
+        ser = Series(dtindex)
+        expected = Series(
+            [time(23, 56, tzinfo=tz), time(21, 24, tzinfo=tz), time(22, 14, tzinfo=tz)]
+        )
+        result = ser.dt.timetz
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "input_series, expected_output",
+        [
+            [["2020-01-01"], [[2020, 1, 3]]],
+            [[pd.NaT], [[np.nan, np.nan, np.nan]]],
+            [["2019-12-31", "2019-12-29"], [[2020, 1, 2], [2019, 52, 7]]],
+            [["2010-01-01", pd.NaT], [[2009, 53, 5], [np.nan, np.nan, np.nan]]],
+            # see GH#36032
+            [["2016-01-08", "2016-01-04"], [[2016, 1, 5], [2016, 1, 1]]],
+            [["2016-01-07", "2016-01-01"], [[2016, 1, 4], [2015, 53, 5]]],
+        ],
+    )
+    def test_isocalendar(self, input_series, expected_output):
+        result = pd.to_datetime(Series(input_series)).dt.isocalendar()
+        expected_frame = DataFrame(
+            expected_output, columns=["year", "week", "day"], dtype="UInt32"
+        )
+        tm.assert_frame_equal(result, expected_frame)
+
+    def test_hour_index(self):
+        dt_series = Series(
+            date_range(start="2021-01-01", periods=5, freq="h"),
+            index=[2, 6, 7, 8, 11],
+            dtype="category",
+        )
+        result = dt_series.dt.hour
+        expected = Series(
+            [0, 1, 2, 3, 4],
+            dtype="int32",
+            index=[2, 6, 7, 8, 11],
+        )
+        tm.assert_series_equal(result, expected)
+
+
+class TestSeriesPeriodValuesDtAccessor:
+    @pytest.mark.parametrize(
+        "input_vals",
+        [
+            [Period("2016-01", freq="M"), Period("2016-02", freq="M")],
+            [Period("2016-01-01", freq="D"), Period("2016-01-02", freq="D")],
+            [
+                Period("2016-01-01 00:00:00", freq="h"),
+                Period("2016-01-01 01:00:00", freq="h"),
+            ],
+            [
+                Period("2016-01-01 00:00:00", freq="M"),
+                Period("2016-01-01 00:01:00", freq="M"),
+            ],
+            [
+                Period("2016-01-01 00:00:00", freq="s"),
+                Period("2016-01-01 00:00:01", freq="s"),
+            ],
+        ],
+    )
+    def test_end_time_timevalues(self, input_vals):
+        # GH#17157
+        # Check that the time part of the Period is adjusted by end_time
+        # when using the dt accessor on a Series
+        input_vals = PeriodArray._from_sequence(np.asarray(input_vals))
+
+        ser = Series(input_vals)
+        result = ser.dt.end_time
+        expected = ser.apply(lambda x: x.end_time)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("input_vals", [("2001"), ("NaT")])
+    def test_to_period(self, input_vals):
+        # GH#21205
+        expected = Series([input_vals], dtype="Period[D]")
+        result = Series([input_vals], dtype="datetime64[ns]").dt.to_period("D")
+        tm.assert_series_equal(result, expected)
+
+
+def test_normalize_pre_epoch_dates():
+    # GH: 36294
+    ser = pd.to_datetime(Series(["1969-01-01 09:00:00", "2016-01-01 09:00:00"]))
+    result = ser.dt.normalize()
+    expected = pd.to_datetime(Series(["1969-01-01", "2016-01-01"]))
+    tm.assert_series_equal(result, expected)
+
+
+def test_day_attribute_non_nano_beyond_int32():
+    # GH 52386
+    data = np.array(
+        [
+            136457654736252,
+            134736784364431,
+            245345345545332,
+            223432411,
+            2343241,
+            3634548734,
+            23234,
+        ],
+        dtype="timedelta64[s]",
+    )
+    ser = Series(data)
+    result = ser.dt.days
+    expected = Series([1579371003, 1559453522, 2839645203, 2586, 27, 42066, 0])
+    tm.assert_series_equal(result, expected)

venv/lib/python3.12/site-packages/pandas/tests/series/accessors/test_list_accessor.py

@@ -0,0 +1,129 @@
+import re
+
+import pytest
+
+from pandas import (
+    ArrowDtype,
+    Series,
+)
+import pandas._testing as tm
+
+pa = pytest.importorskip("pyarrow")
+
+from pandas.compat import pa_version_under11p0
+
+
+@pytest.mark.parametrize(
+    "list_dtype",
+    (
+        pa.list_(pa.int64()),
+        pa.list_(pa.int64(), list_size=3),
+        pa.large_list(pa.int64()),
+    ),
+)
+def test_list_getitem(list_dtype):
+    ser = Series(
+        [[1, 2, 3], [4, None, 5], None],
+        dtype=ArrowDtype(list_dtype),
+    )
+    actual = ser.list[1]
+    expected = Series([2, None, None], dtype="int64[pyarrow]")
+    tm.assert_series_equal(actual, expected)
+
+
+def test_list_getitem_slice():
+    ser = Series(
+        [[1, 2, 3], [4, None, 5], None],
+        dtype=ArrowDtype(pa.list_(pa.int64())),
+    )
+    if pa_version_under11p0:
+        with pytest.raises(
+            NotImplementedError, match="List slice not supported by pyarrow "
+        ):
+            ser.list[1:None:None]
+    else:
+        actual = ser.list[1:None:None]
+        expected = Series(
+            [[2, 3], [None, 5], None], dtype=ArrowDtype(pa.list_(pa.int64()))
+        )
+        tm.assert_series_equal(actual, expected)
+
+
+def test_list_len():
+    ser = Series(
+        [[1, 2, 3], [4, None], None],
+        dtype=ArrowDtype(pa.list_(pa.int64())),
+    )
+    actual = ser.list.len()
+    expected = Series([3, 2, None], dtype=ArrowDtype(pa.int32()))
+    tm.assert_series_equal(actual, expected)
+
+
+def test_list_flatten():
+    ser = Series(
+        [[1, 2, 3], [4, None], None],
+        dtype=ArrowDtype(pa.list_(pa.int64())),
+    )
+    actual = ser.list.flatten()
+    expected = Series([1, 2, 3, 4, None], dtype=ArrowDtype(pa.int64()))
+    tm.assert_series_equal(actual, expected)
+
+
+def test_list_getitem_slice_invalid():
+    ser = Series(
+        [[1, 2, 3], [4, None, 5], None],
+        dtype=ArrowDtype(pa.list_(pa.int64())),
+    )
+    if pa_version_under11p0:
+        with pytest.raises(
+            NotImplementedError, match="List slice not supported by pyarrow "
+        ):
+            ser.list[1:None:0]
+    else:
+        with pytest.raises(pa.lib.ArrowInvalid, match=re.escape("`step` must be >= 1")):
+            ser.list[1:None:0]
+
+
+def test_list_accessor_non_list_dtype():
+    ser = Series(
+        [1, 2, 4],
+        dtype=ArrowDtype(pa.int64()),
+    )
+    with pytest.raises(
+        AttributeError,
+        match=re.escape(
+            "Can only use the '.list' accessor with 'list[pyarrow]' dtype, "
+            "not int64[pyarrow]."
+        ),
+    ):
+        ser.list[1:None:0]
+
+
+@pytest.mark.parametrize(
+    "list_dtype",
+    (
+        pa.list_(pa.int64()),
+        pa.list_(pa.int64(), list_size=3),
+        pa.large_list(pa.int64()),
+    ),
+)
+def test_list_getitem_invalid_index(list_dtype):
+    ser = Series(
+        [[1, 2, 3], [4, None, 5], None],
+        dtype=ArrowDtype(list_dtype),
+    )
+    with pytest.raises(pa.lib.ArrowInvalid, match="Index -1 is out of bounds"):
+        ser.list[-1]
+    with pytest.raises(pa.lib.ArrowInvalid, match="Index 5 is out of bounds"):
+        ser.list[5]
+    with pytest.raises(ValueError, match="key must be an int or slice, got str"):
+        ser.list["abc"]
+
+
+def test_list_accessor_not_iterable():
+    ser = Series(
+        [[1, 2, 3], [4, None], None],
+        dtype=ArrowDtype(pa.list_(pa.int64())),
+    )
+    with pytest.raises(TypeError, match="'ListAccessor' object is not iterable"):
+        iter(ser.list)

venv/lib/python3.12/site-packages/pandas/tests/series/accessors/test_sparse_accessor.py

@@ -0,0 +1,9 @@
+from pandas import Series
+
+
+class TestSparseAccessor:
+    def test_sparse_accessor_updates_on_inplace(self):
+        ser = Series([1, 1, 2, 3], dtype="Sparse[int]")
+        return_value = ser.drop([0, 1], inplace=True)
+        assert return_value is None
+        assert ser.sparse.density == 1.0

venv/lib/python3.12/site-packages/pandas/tests/series/accessors/test_str_accessor.py

@@ -0,0 +1,25 @@
+import pytest
+
+from pandas import Series
+import pandas._testing as tm
+
+
+class TestStrAccessor:
+    def test_str_attribute(self):
+        # GH#9068
+        methods = ["strip", "rstrip", "lstrip"]
+        ser = Series([" jack", "jill ", " jesse ", "frank"])
+        for method in methods:
+            expected = Series([getattr(str, method)(x) for x in ser.values])
+            tm.assert_series_equal(getattr(Series.str, method)(ser.str), expected)
+
+        # str accessor only valid with string values
+        ser = Series(range(5))
+        with pytest.raises(AttributeError, match="only use .str accessor"):
+            ser.str.repeat(2)
+
+    def test_str_accessor_updates_on_inplace(self):
+        ser = Series(list("abc"))
+        return_value = ser.drop([0], inplace=True)
+        assert return_value is None
+        assert len(ser.str.lower()) == 2

venv/lib/python3.12/site-packages/pandas/tests/series/accessors/test_struct_accessor.py

@@ -0,0 +1,196 @@
+import re
+
+import pytest
+
+from pandas.compat.pyarrow import (
+    pa_version_under11p0,
+    pa_version_under13p0,
+)
+
+from pandas import (
+    ArrowDtype,
+    DataFrame,
+    Index,
+    Series,
+)
+import pandas._testing as tm
+
+pa = pytest.importorskip("pyarrow")
+pc = pytest.importorskip("pyarrow.compute")
+
+
+def test_struct_accessor_dtypes():
+    ser = Series(
+        [],
+        dtype=ArrowDtype(
+            pa.struct(
+                [
+                    ("int_col", pa.int64()),
+                    ("string_col", pa.string()),
+                    (
+                        "struct_col",
+                        pa.struct(
+                            [
+                                ("int_col", pa.int64()),
+                                ("float_col", pa.float64()),
+                            ]
+                        ),
+                    ),
+                ]
+            )
+        ),
+    )
+    actual = ser.struct.dtypes
+    expected = Series(
+        [
+            ArrowDtype(pa.int64()),
+            ArrowDtype(pa.string()),
+            ArrowDtype(
+                pa.struct(
+                    [
+                        ("int_col", pa.int64()),
+                        ("float_col", pa.float64()),
+                    ]
+                )
+            ),
+        ],
+        index=Index(["int_col", "string_col", "struct_col"]),
+    )
+    tm.assert_series_equal(actual, expected)
+
+
+@pytest.mark.skipif(pa_version_under13p0, reason="pyarrow>=13.0.0 required")
+def test_struct_accessor_field():
+    index = Index([-100, 42, 123])
+    ser = Series(
+        [
+            {"rice": 1.0, "maize": -1, "wheat": "a"},
+            {"rice": 2.0, "maize": 0, "wheat": "b"},
+            {"rice": 3.0, "maize": 1, "wheat": "c"},
+        ],
+        dtype=ArrowDtype(
+            pa.struct(
+                [
+                    ("rice", pa.float64()),
+                    ("maize", pa.int64()),
+                    ("wheat", pa.string()),
+                ]
+            )
+        ),
+        index=index,
+    )
+    by_name = ser.struct.field("maize")
+    by_name_expected = Series(
+        [-1, 0, 1],
+        dtype=ArrowDtype(pa.int64()),
+        index=index,
+        name="maize",
+    )
+    tm.assert_series_equal(by_name, by_name_expected)
+
+    by_index = ser.struct.field(2)
+    by_index_expected = Series(
+        ["a", "b", "c"],
+        dtype=ArrowDtype(pa.string()),
+        index=index,
+        name="wheat",
+    )
+    tm.assert_series_equal(by_index, by_index_expected)
+
+
+def test_struct_accessor_field_with_invalid_name_or_index():
+    ser = Series([], dtype=ArrowDtype(pa.struct([("field", pa.int64())])))
+
+    with pytest.raises(ValueError, match="name_or_index must be an int, str,"):
+        ser.struct.field(1.1)
+
+
+@pytest.mark.skipif(pa_version_under11p0, reason="pyarrow>=11.0.0 required")
+def test_struct_accessor_explode():
+    index = Index([-100, 42, 123])
+    ser = Series(
+        [
+            {"painted": 1, "snapping": {"sea": "green"}},
+            {"painted": 2, "snapping": {"sea": "leatherback"}},
+            {"painted": 3, "snapping": {"sea": "hawksbill"}},
+        ],
+        dtype=ArrowDtype(
+            pa.struct(
+                [
+                    ("painted", pa.int64()),
+                    ("snapping", pa.struct([("sea", pa.string())])),
+                ]
+            )
+        ),
+        index=index,
+    )
+    actual = ser.struct.explode()
+    expected = DataFrame(
+        {
+            "painted": Series([1, 2, 3], index=index, dtype=ArrowDtype(pa.int64())),
+            "snapping": Series(
+                [{"sea": "green"}, {"sea": "leatherback"}, {"sea": "hawksbill"}],
+                index=index,
+                dtype=ArrowDtype(pa.struct([("sea", pa.string())])),
+            ),
+        },
+    )
+    tm.assert_frame_equal(actual, expected)
+
+
+@pytest.mark.parametrize(
+    "invalid",
+    [
+        pytest.param(Series([1, 2, 3], dtype="int64"), id="int64"),
+        pytest.param(
+            Series(["a", "b", "c"], dtype="string[pyarrow]"), id="string-pyarrow"
+        ),
+    ],
+)
+def test_struct_accessor_api_for_invalid(invalid):
+    with pytest.raises(
+        AttributeError,
+        match=re.escape(
+            "Can only use the '.struct' accessor with 'struct[pyarrow]' dtype, "
+            f"not {invalid.dtype}."
+        ),
+    ):
+        invalid.struct
+
+
+@pytest.mark.parametrize(
+    ["indices", "name"],
+    [
+        (0, "int_col"),
+        ([1, 2], "str_col"),
+        (pc.field("int_col"), "int_col"),
+        ("int_col", "int_col"),
+        (b"string_col", b"string_col"),
+        ([b"string_col"], "string_col"),
+    ],
+)
+@pytest.mark.skipif(pa_version_under13p0, reason="pyarrow>=13.0.0 required")
+def test_struct_accessor_field_expanded(indices, name):
+    arrow_type = pa.struct(
+        [
+            ("int_col", pa.int64()),
+            (
+                "struct_col",
+                pa.struct(
+                    [
+                        ("int_col", pa.int64()),
+                        ("float_col", pa.float64()),
+                        ("str_col", pa.string()),
+                    ]
+                ),
+            ),
+            (b"string_col", pa.string()),
+        ]
+    )
+
+    data = pa.array([], type=arrow_type)
+    ser = Series(data, dtype=ArrowDtype(arrow_type))
+    expected = pc.struct_field(data, indices)
+    result = ser.struct.field(indices)
+    tm.assert_equal(result.array._pa_array.combine_chunks(), expected)
+    assert result.name == name

venv/lib/python3.12/site-packages/pandas/tests/series/indexing/test_datetime.py

@@ -0,0 +1,499 @@
+"""
+Also test support for datetime64[ns] in Series / DataFrame
+"""
+from datetime import (
+    datetime,
+    timedelta,
+)
+import re
+
+from dateutil.tz import (
+    gettz,
+    tzutc,
+)
+import numpy as np
+import pytest
+import pytz
+
+from pandas._libs import index as libindex
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Series,
+    Timestamp,
+    date_range,
+    period_range,
+)
+import pandas._testing as tm
+
+
+def test_fancy_getitem():
+    dti = date_range(
+        freq="WOM-1FRI", start=datetime(2005, 1, 1), end=datetime(2010, 1, 1)
+    )
+
+    s = Series(np.arange(len(dti)), index=dti)
+
+    msg = "Series.__getitem__ treating keys as positions is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        assert s[48] == 48
+    assert s["1/2/2009"] == 48
+    assert s["2009-1-2"] == 48
+    assert s[datetime(2009, 1, 2)] == 48
+    assert s[Timestamp(datetime(2009, 1, 2))] == 48
+    with pytest.raises(KeyError, match=r"^'2009-1-3'$"):
+        s["2009-1-3"]
+    tm.assert_series_equal(
+        s["3/6/2009":"2009-06-05"], s[datetime(2009, 3, 6) : datetime(2009, 6, 5)]
+    )
+
+
+def test_fancy_setitem():
+    dti = date_range(
+        freq="WOM-1FRI", start=datetime(2005, 1, 1), end=datetime(2010, 1, 1)
+    )
+
+    s = Series(np.arange(len(dti)), index=dti)
+
+    msg = "Series.__setitem__ treating keys as positions is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        s[48] = -1
+    assert s.iloc[48] == -1
+    s["1/2/2009"] = -2
+    assert s.iloc[48] == -2
+    s["1/2/2009":"2009-06-05"] = -3
+    assert (s[48:54] == -3).all()
+
+
+@pytest.mark.parametrize("tz_source", ["pytz", "dateutil"])
+def test_getitem_setitem_datetime_tz(tz_source):
+    if tz_source == "pytz":
+        tzget = pytz.timezone
+    else:
+        # handle special case for utc in dateutil
+        tzget = lambda x: tzutc() if x == "UTC" else gettz(x)
+
+    N = 50
+    # testing with timezone, GH #2785
+    rng = date_range("1/1/1990", periods=N, freq="h", tz=tzget("US/Eastern"))
+    ts = Series(np.random.default_rng(2).standard_normal(N), index=rng)
+
+    # also test Timestamp tz handling, GH #2789
+    result = ts.copy()
+    result["1990-01-01 09:00:00+00:00"] = 0
+    result["1990-01-01 09:00:00+00:00"] = ts.iloc[4]
+    tm.assert_series_equal(result, ts)
+
+    result = ts.copy()
+    result["1990-01-01 03:00:00-06:00"] = 0
+    result["1990-01-01 03:00:00-06:00"] = ts.iloc[4]
+    tm.assert_series_equal(result, ts)
+
+    # repeat with datetimes
+    result = ts.copy()
+    result[datetime(1990, 1, 1, 9, tzinfo=tzget("UTC"))] = 0
+    result[datetime(1990, 1, 1, 9, tzinfo=tzget("UTC"))] = ts.iloc[4]
+    tm.assert_series_equal(result, ts)
+
+    result = ts.copy()
+    dt = Timestamp(1990, 1, 1, 3).tz_localize(tzget("US/Central"))
+    dt = dt.to_pydatetime()
+    result[dt] = 0
+    result[dt] = ts.iloc[4]
+    tm.assert_series_equal(result, ts)
+
+
+def test_getitem_setitem_datetimeindex():
+    N = 50
+    # testing with timezone, GH #2785
+    rng = date_range("1/1/1990", periods=N, freq="h", tz="US/Eastern")
+    ts = Series(np.random.default_rng(2).standard_normal(N), index=rng)
+
+    result = ts["1990-01-01 04:00:00"]
+    expected = ts.iloc[4]
+    assert result == expected
+
+    result = ts.copy()
+    result["1990-01-01 04:00:00"] = 0
+    result["1990-01-01 04:00:00"] = ts.iloc[4]
+    tm.assert_series_equal(result, ts)
+
+    result = ts["1990-01-01 04:00:00":"1990-01-01 07:00:00"]
+    expected = ts[4:8]
+    tm.assert_series_equal(result, expected)
+
+    result = ts.copy()
+    result["1990-01-01 04:00:00":"1990-01-01 07:00:00"] = 0
+    result["1990-01-01 04:00:00":"1990-01-01 07:00:00"] = ts[4:8]
+    tm.assert_series_equal(result, ts)
+
+    lb = "1990-01-01 04:00:00"
+    rb = "1990-01-01 07:00:00"
+    # GH#18435 strings get a pass from tzawareness compat
+    result = ts[(ts.index >= lb) & (ts.index <= rb)]
+    expected = ts[4:8]
+    tm.assert_series_equal(result, expected)
+
+    lb = "1990-01-01 04:00:00-0500"
+    rb = "1990-01-01 07:00:00-0500"
+    result = ts[(ts.index >= lb) & (ts.index <= rb)]
+    expected = ts[4:8]
+    tm.assert_series_equal(result, expected)
+
+    # But we do not give datetimes a pass on tzawareness compat
+    msg = "Cannot compare tz-naive and tz-aware datetime-like objects"
+    naive = datetime(1990, 1, 1, 4)
+    for key in [naive, Timestamp(naive), np.datetime64(naive, "ns")]:
+        with pytest.raises(KeyError, match=re.escape(repr(key))):
+            # GH#36148 as of 2.0 we require tzawareness-compat
+            ts[key]
+
+    result = ts.copy()
+    # GH#36148 as of 2.0 we do not ignore tzawareness mismatch in indexing,
+    #  so setting it as a new key casts to object rather than matching
+    #  rng[4]
+    result[naive] = ts.iloc[4]
+    assert result.index.dtype == object
+    tm.assert_index_equal(result.index[:-1], rng.astype(object))
+    assert result.index[-1] == naive
+
+    msg = "Cannot compare tz-naive and tz-aware datetime-like objects"
+    with pytest.raises(TypeError, match=msg):
+        # GH#36148 require tzawareness compat as of 2.0
+        ts[naive : datetime(1990, 1, 1, 7)]
+
+    result = ts.copy()
+    with pytest.raises(TypeError, match=msg):
+        # GH#36148 require tzawareness compat as of 2.0
+        result[naive : datetime(1990, 1, 1, 7)] = 0
+    with pytest.raises(TypeError, match=msg):
+        # GH#36148 require tzawareness compat as of 2.0
+        result[naive : datetime(1990, 1, 1, 7)] = 99
+    # the __setitems__ here failed, so result should still match ts
+    tm.assert_series_equal(result, ts)
+
+    lb = naive
+    rb = datetime(1990, 1, 1, 7)
+    msg = r"Invalid comparison between dtype=datetime64\[ns, US/Eastern\] and datetime"
+    with pytest.raises(TypeError, match=msg):
+        # tznaive vs tzaware comparison is invalid
+        # see GH#18376, GH#18162
+        ts[(ts.index >= lb) & (ts.index <= rb)]
+
+    lb = Timestamp(naive).tz_localize(rng.tzinfo)
+    rb = Timestamp(datetime(1990, 1, 1, 7)).tz_localize(rng.tzinfo)
+    result = ts[(ts.index >= lb) & (ts.index <= rb)]
+    expected = ts[4:8]
+    tm.assert_series_equal(result, expected)
+
+    result = ts[ts.index[4]]
+    expected = ts.iloc[4]
+    assert result == expected
+
+    result = ts[ts.index[4:8]]
+    expected = ts[4:8]
+    tm.assert_series_equal(result, expected)
+
+    result = ts.copy()
+    result[ts.index[4:8]] = 0
+    result.iloc[4:8] = ts.iloc[4:8]
+    tm.assert_series_equal(result, ts)
+
+    # also test partial date slicing
+    result = ts["1990-01-02"]
+    expected = ts[24:48]
+    tm.assert_series_equal(result, expected)
+
+    result = ts.copy()
+    result["1990-01-02"] = 0
+    result["1990-01-02"] = ts[24:48]
+    tm.assert_series_equal(result, ts)
+
+
+def test_getitem_setitem_periodindex():
+    N = 50
+    rng = period_range("1/1/1990", periods=N, freq="h")
+    ts = Series(np.random.default_rng(2).standard_normal(N), index=rng)
+
+    result = ts["1990-01-01 04"]
+    expected = ts.iloc[4]
+    assert result == expected
+
+    result = ts.copy()
+    result["1990-01-01 04"] = 0
+    result["1990-01-01 04"] = ts.iloc[4]
+    tm.assert_series_equal(result, ts)
+
+    result = ts["1990-01-01 04":"1990-01-01 07"]
+    expected = ts[4:8]
+    tm.assert_series_equal(result, expected)
+
+    result = ts.copy()
+    result["1990-01-01 04":"1990-01-01 07"] = 0
+    result["1990-01-01 04":"1990-01-01 07"] = ts[4:8]
+    tm.assert_series_equal(result, ts)
+
+    lb = "1990-01-01 04"
+    rb = "1990-01-01 07"
+    result = ts[(ts.index >= lb) & (ts.index <= rb)]
+    expected = ts[4:8]
+    tm.assert_series_equal(result, expected)
+
+    # GH 2782
+    result = ts[ts.index[4]]
+    expected = ts.iloc[4]
+    assert result == expected
+
+    result = ts[ts.index[4:8]]
+    expected = ts[4:8]
+    tm.assert_series_equal(result, expected)
+
+    result = ts.copy()
+    result[ts.index[4:8]] = 0
+    result.iloc[4:8] = ts.iloc[4:8]
+    tm.assert_series_equal(result, ts)
+
+
+def test_datetime_indexing():
+    index = date_range("1/1/2000", "1/7/2000")
+    index = index.repeat(3)
+
+    s = Series(len(index), index=index)
+    stamp = Timestamp("1/8/2000")
+
+    with pytest.raises(KeyError, match=re.escape(repr(stamp))):
+        s[stamp]
+    s[stamp] = 0
+    assert s[stamp] == 0
+
+    # not monotonic
+    s = Series(len(index), index=index)
+    s = s[::-1]
+
+    with pytest.raises(KeyError, match=re.escape(repr(stamp))):
+        s[stamp]
+    s[stamp] = 0
+    assert s[stamp] == 0
+
+
+# test duplicates in time series
+
+
+def test_indexing_with_duplicate_datetimeindex(
+    rand_series_with_duplicate_datetimeindex,
+):
+    ts = rand_series_with_duplicate_datetimeindex
+
+    uniques = ts.index.unique()
+    for date in uniques:
+        result = ts[date]
+
+        mask = ts.index == date
+        total = (ts.index == date).sum()
+        expected = ts[mask]
+        if total > 1:
+            tm.assert_series_equal(result, expected)
+        else:
+            tm.assert_almost_equal(result, expected.iloc[0])
+
+        cp = ts.copy()
+        cp[date] = 0
+        expected = Series(np.where(mask, 0, ts), index=ts.index)
+        tm.assert_series_equal(cp, expected)
+
+    key = datetime(2000, 1, 6)
+    with pytest.raises(KeyError, match=re.escape(repr(key))):
+        ts[key]
+
+    # new index
+    ts[datetime(2000, 1, 6)] = 0
+    assert ts[datetime(2000, 1, 6)] == 0
+
+
+def test_loc_getitem_over_size_cutoff(monkeypatch):
+    # #1821
+
+    monkeypatch.setattr(libindex, "_SIZE_CUTOFF", 1000)
+
+    # create large list of non periodic datetime
+    dates = []
+    sec = timedelta(seconds=1)
+    half_sec = timedelta(microseconds=500000)
+    d = datetime(2011, 12, 5, 20, 30)
+    n = 1100
+    for i in range(n):
+        dates.append(d)
+        dates.append(d + sec)
+        dates.append(d + sec + half_sec)
+        dates.append(d + sec + sec + half_sec)
+        d += 3 * sec
+
+    # duplicate some values in the list
+    duplicate_positions = np.random.default_rng(2).integers(0, len(dates) - 1, 20)
+    for p in duplicate_positions:
+        dates[p + 1] = dates[p]
+
+    df = DataFrame(
+        np.random.default_rng(2).standard_normal((len(dates), 4)),
+        index=dates,
+        columns=list("ABCD"),
+    )
+
+    pos = n * 3
+    timestamp = df.index[pos]
+    assert timestamp in df.index
+
+    # it works!
+    df.loc[timestamp]
+    assert len(df.loc[[timestamp]]) > 0
+
+
+def test_indexing_over_size_cutoff_period_index(monkeypatch):
+    # GH 27136
+
+    monkeypatch.setattr(libindex, "_SIZE_CUTOFF", 1000)
+
+    n = 1100
+    idx = period_range("1/1/2000", freq="min", periods=n)
+    assert idx._engine.over_size_threshold
+
+    s = Series(np.random.default_rng(2).standard_normal(len(idx)), index=idx)
+
+    pos = n - 1
+    timestamp = idx[pos]
+    assert timestamp in s.index
+
+    # it works!
+    s[timestamp]
+    assert len(s.loc[[timestamp]]) > 0
+
+
+def test_indexing_unordered():
+    # GH 2437
+    rng = date_range(start="2011-01-01", end="2011-01-15")
+    ts = Series(np.random.default_rng(2).random(len(rng)), index=rng)
+    ts2 = pd.concat([ts[0:4], ts[-4:], ts[4:-4]])
+
+    for t in ts.index:
+        expected = ts[t]
+        result = ts2[t]
+        assert expected == result
+
+    # GH 3448 (ranges)
+    def compare(slobj):
+        result = ts2[slobj].copy()
+        result = result.sort_index()
+        expected = ts[slobj]
+        expected.index = expected.index._with_freq(None)
+        tm.assert_series_equal(result, expected)
+
+    for key in [
+        slice("2011-01-01", "2011-01-15"),
+        slice("2010-12-30", "2011-01-15"),
+        slice("2011-01-01", "2011-01-16"),
+        # partial ranges
+        slice("2011-01-01", "2011-01-6"),
+        slice("2011-01-06", "2011-01-8"),
+        slice("2011-01-06", "2011-01-12"),
+    ]:
+        with pytest.raises(
+            KeyError, match="Value based partial slicing on non-monotonic"
+        ):
+            compare(key)
+
+    # single values
+    result = ts2["2011"].sort_index()
+    expected = ts["2011"]
+    expected.index = expected.index._with_freq(None)
+    tm.assert_series_equal(result, expected)
+
+
+def test_indexing_unordered2():
+    # diff freq
+    rng = date_range(datetime(2005, 1, 1), periods=20, freq="ME")
+    ts = Series(np.arange(len(rng)), index=rng)
+    ts = ts.take(np.random.default_rng(2).permutation(20))
+
+    result = ts["2005"]
+    for t in result.index:
+        assert t.year == 2005
+
+
+def test_indexing():
+    idx = date_range("2001-1-1", periods=20, freq="ME")
+    ts = Series(np.random.default_rng(2).random(len(idx)), index=idx)
+
+    # getting
+
+    # GH 3070, make sure semantics work on Series/Frame
+    result = ts["2001"]
+    tm.assert_series_equal(result, ts.iloc[:12])
+
+    df = DataFrame({"A": ts.copy()})
+
+    # GH#36179 pre-2.0 df["2001"] operated as slicing on rows. in 2.0 it behaves
+    #  like any other key, so raises
+    with pytest.raises(KeyError, match="2001"):
+        df["2001"]
+
+    # setting
+    ts = Series(np.random.default_rng(2).random(len(idx)), index=idx)
+    expected = ts.copy()
+    expected.iloc[:12] = 1
+    ts["2001"] = 1
+    tm.assert_series_equal(ts, expected)
+
+    expected = df.copy()
+    expected.iloc[:12, 0] = 1
+    df.loc["2001", "A"] = 1
+    tm.assert_frame_equal(df, expected)
+
+
+def test_getitem_str_month_with_datetimeindex():
+    # GH3546 (not including times on the last day)
+    idx = date_range(start="2013-05-31 00:00", end="2013-05-31 23:00", freq="h")
+    ts = Series(range(len(idx)), index=idx)
+    expected = ts["2013-05"]
+    tm.assert_series_equal(expected, ts)
+
+    idx = date_range(start="2013-05-31 00:00", end="2013-05-31 23:59", freq="s")
+    ts = Series(range(len(idx)), index=idx)
+    expected = ts["2013-05"]
+    tm.assert_series_equal(expected, ts)
+
+
+def test_getitem_str_year_with_datetimeindex():
+    idx = [
+        Timestamp("2013-05-31 00:00"),
+        Timestamp(datetime(2013, 5, 31, 23, 59, 59, 999999)),
+    ]
+    ts = Series(range(len(idx)), index=idx)
+    expected = ts["2013"]
+    tm.assert_series_equal(expected, ts)
+
+
+def test_getitem_str_second_with_datetimeindex():
+    # GH14826, indexing with a seconds resolution string / datetime object
+    df = DataFrame(
+        np.random.default_rng(2).random((5, 5)),
+        columns=["open", "high", "low", "close", "volume"],
+        index=date_range("2012-01-02 18:01:00", periods=5, tz="US/Central", freq="s"),
+    )
+
+    # this is a single date, so will raise
+    with pytest.raises(KeyError, match=r"^'2012-01-02 18:01:02'$"):
+        df["2012-01-02 18:01:02"]
+
+    msg = r"Timestamp\('2012-01-02 18:01:02-0600', tz='US/Central'\)"
+    with pytest.raises(KeyError, match=msg):
+        df[df.index[2]]
+
+
+def test_compare_datetime_with_all_none():
+    # GH#54870
+    ser = Series(["2020-01-01", "2020-01-02"], dtype="datetime64[ns]")
+    ser2 = Series([None, None])
+    result = ser > ser2
+    expected = Series([False, False])
+    tm.assert_series_equal(result, expected)

venv/lib/python3.12/site-packages/pandas/tests/series/indexing/test_delitem.py

@@ -0,0 +1,70 @@
+import pytest
+
+from pandas import (
+    Index,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestSeriesDelItem:
+    def test_delitem(self):
+        # GH#5542
+        # should delete the item inplace
+        s = Series(range(5))
+        del s[0]
+
+        expected = Series(range(1, 5), index=range(1, 5))
+        tm.assert_series_equal(s, expected)
+
+        del s[1]
+        expected = Series(range(2, 5), index=range(2, 5))
+        tm.assert_series_equal(s, expected)
+
+        # only 1 left, del, add, del
+        s = Series(1)
+        del s[0]
+        tm.assert_series_equal(s, Series(dtype="int64", index=Index([], dtype="int64")))
+        s[0] = 1
+        tm.assert_series_equal(s, Series(1))
+        del s[0]
+        tm.assert_series_equal(s, Series(dtype="int64", index=Index([], dtype="int64")))
+
+    def test_delitem_object_index(self, using_infer_string):
+        # Index(dtype=object)
+        dtype = "string[pyarrow_numpy]" if using_infer_string else object
+        s = Series(1, index=Index(["a"], dtype=dtype))
+        del s["a"]
+        tm.assert_series_equal(s, Series(dtype="int64", index=Index([], dtype=dtype)))
+        s["a"] = 1
+        tm.assert_series_equal(s, Series(1, index=Index(["a"], dtype=dtype)))
+        del s["a"]
+        tm.assert_series_equal(s, Series(dtype="int64", index=Index([], dtype=dtype)))
+
+    def test_delitem_missing_key(self):
+        # empty
+        s = Series(dtype=object)
+
+        with pytest.raises(KeyError, match=r"^0$"):
+            del s[0]
+
+    def test_delitem_extension_dtype(self):
+        # GH#40386
+        # DatetimeTZDtype
+        dti = date_range("2016-01-01", periods=3, tz="US/Pacific")
+        ser = Series(dti)
+
+        expected = ser[[0, 2]]
+        del ser[1]
+        assert ser.dtype == dti.dtype
+        tm.assert_series_equal(ser, expected)
+
+        # PeriodDtype
+        pi = dti.tz_localize(None).to_period("D")
+        ser = Series(pi)
+
+        expected = ser[:2]
+        del ser[2]
+        assert ser.dtype == pi.dtype
+        tm.assert_series_equal(ser, expected)

venv/lib/python3.12/site-packages/pandas/tests/series/indexing/test_get.py

@@ -0,0 +1,238 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DatetimeIndex,
+    Index,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+
+
+def test_get():
+    # GH 6383
+    s = Series(
+        np.array(
+            [
+                43,
+                48,
+                60,
+                48,
+                50,
+                51,
+                50,
+                45,
+                57,
+                48,
+                56,
+                45,
+                51,
+                39,
+                55,
+                43,
+                54,
+                52,
+                51,
+                54,
+            ]
+        )
+    )
+
+    result = s.get(25, 0)
+    expected = 0
+    assert result == expected
+
+    s = Series(
+        np.array(
+            [
+                43,
+                48,
+                60,
+                48,
+                50,
+                51,
+                50,
+                45,
+                57,
+                48,
+                56,
+                45,
+                51,
+                39,
+                55,
+                43,
+                54,
+                52,
+                51,
+                54,
+            ]
+        ),
+        index=Index(
+            [
+                25.0,
+                36.0,
+                49.0,
+                64.0,
+                81.0,
+                100.0,
+                121.0,
+                144.0,
+                169.0,
+                196.0,
+                1225.0,
+                1296.0,
+                1369.0,
+                1444.0,
+                1521.0,
+                1600.0,
+                1681.0,
+                1764.0,
+                1849.0,
+                1936.0,
+            ],
+            dtype=np.float64,
+        ),
+    )
+
+    result = s.get(25, 0)
+    expected = 43
+    assert result == expected
+
+    # GH 7407
+    # with a boolean accessor
+    df = pd.DataFrame({"i": [0] * 3, "b": [False] * 3})
+    vc = df.i.value_counts()
+    result = vc.get(99, default="Missing")
+    assert result == "Missing"
+
+    vc = df.b.value_counts()
+    result = vc.get(False, default="Missing")
+    assert result == 3
+
+    result = vc.get(True, default="Missing")
+    assert result == "Missing"
+
+
+def test_get_nan(float_numpy_dtype):
+    # GH 8569
+    s = Index(range(10), dtype=float_numpy_dtype).to_series()
+    assert s.get(np.nan) is None
+    assert s.get(np.nan, default="Missing") == "Missing"
+
+
+def test_get_nan_multiple(float_numpy_dtype):
+    # GH 8569
+    # ensure that fixing "test_get_nan" above hasn't broken get
+    # with multiple elements
+    s = Index(range(10), dtype=float_numpy_dtype).to_series()
+
+    idx = [2, 30]
+    assert s.get(idx) is None
+
+    idx = [2, np.nan]
+    assert s.get(idx) is None
+
+    # GH 17295 - all missing keys
+    idx = [20, 30]
+    assert s.get(idx) is None
+
+    idx = [np.nan, np.nan]
+    assert s.get(idx) is None
+
+
+def test_get_with_default():
+    # GH#7725
+    d0 = ["a", "b", "c", "d"]
+    d1 = np.arange(4, dtype="int64")
+
+    for data, index in ((d0, d1), (d1, d0)):
+        s = Series(data, index=index)
+        for i, d in zip(index, data):
+            assert s.get(i) == d
+            assert s.get(i, d) == d
+            assert s.get(i, "z") == d
+
+            assert s.get("e", "z") == "z"
+            assert s.get("e", "e") == "e"
+
+            msg = "Series.__getitem__ treating keys as positions is deprecated"
+            warn = None
+            if index is d0:
+                warn = FutureWarning
+            with tm.assert_produces_warning(warn, match=msg):
+                assert s.get(10, "z") == "z"
+                assert s.get(10, 10) == 10
+
+
+@pytest.mark.parametrize(
+    "arr",
+    [
+        np.random.default_rng(2).standard_normal(10),
+        DatetimeIndex(date_range("2020-01-01", periods=10), name="a").tz_localize(
+            tz="US/Eastern"
+        ),
+    ],
+)
+def test_get_with_ea(arr):
+    # GH#21260
+    ser = Series(arr, index=[2 * i for i in range(len(arr))])
+    assert ser.get(4) == ser.iloc[2]
+
+    result = ser.get([4, 6])
+    expected = ser.iloc[[2, 3]]
+    tm.assert_series_equal(result, expected)
+
+    result = ser.get(slice(2))
+    expected = ser.iloc[[0, 1]]
+    tm.assert_series_equal(result, expected)
+
+    assert ser.get(-1) is None
+    assert ser.get(ser.index.max() + 1) is None
+
+    ser = Series(arr[:6], index=list("abcdef"))
+    assert ser.get("c") == ser.iloc[2]
+
+    result = ser.get(slice("b", "d"))
+    expected = ser.iloc[[1, 2, 3]]
+    tm.assert_series_equal(result, expected)
+
+    result = ser.get("Z")
+    assert result is None
+
+    msg = "Series.__getitem__ treating keys as positions is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        assert ser.get(4) == ser.iloc[4]
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        assert ser.get(-1) == ser.iloc[-1]
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        assert ser.get(len(ser)) is None
+
+    # GH#21257
+    ser = Series(arr)
+    ser2 = ser[::2]
+    assert ser2.get(1) is None
+
+
+def test_getitem_get(string_series, object_series):
+    msg = "Series.__getitem__ treating keys as positions is deprecated"
+
+    for obj in [string_series, object_series]:
+        idx = obj.index[5]
+
+        assert obj[idx] == obj.get(idx)
+        assert obj[idx] == obj.iloc[5]
+
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        assert string_series.get(-1) == string_series.get(string_series.index[-1])
+    assert string_series.iloc[5] == string_series.get(string_series.index[5])
+
+
+def test_get_none():
+    # GH#5652
+    s1 = Series(dtype=object)
+    s2 = Series(dtype=object, index=list("abc"))
+    for s in [s1, s2]:
+        result = s.get(None)
+        assert result is None

venv/lib/python3.12/site-packages/pandas/tests/series/indexing/test_getitem.py

@@ -0,0 +1,735 @@
+"""
+Series.__getitem__ test classes are organized by the type of key passed.
+"""
+from datetime import (
+    date,
+    datetime,
+    time,
+)
+
+import numpy as np
+import pytest
+
+from pandas._libs.tslibs import (
+    conversion,
+    timezones,
+)
+
+from pandas.core.dtypes.common import is_scalar
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    Series,
+    Timestamp,
+    date_range,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core.indexing import IndexingError
+
+from pandas.tseries.offsets import BDay
+
+
+class TestSeriesGetitemScalars:
+    def test_getitem_object_index_float_string(self):
+        # GH#17286
+        ser = Series([1] * 4, index=Index(["a", "b", "c", 1.0]))
+        assert ser["a"] == 1
+        assert ser[1.0] == 1
+
+    def test_getitem_float_keys_tuple_values(self):
+        # see GH#13509
+
+        # unique Index
+        ser = Series([(1, 1), (2, 2), (3, 3)], index=[0.0, 0.1, 0.2], name="foo")
+        result = ser[0.0]
+        assert result == (1, 1)
+
+        # non-unique Index
+        expected = Series([(1, 1), (2, 2)], index=[0.0, 0.0], name="foo")
+        ser = Series([(1, 1), (2, 2), (3, 3)], index=[0.0, 0.0, 0.2], name="foo")
+
+        result = ser[0.0]
+        tm.assert_series_equal(result, expected)
+
+    def test_getitem_unrecognized_scalar(self):
+        # GH#32684 a scalar key that is not recognized by lib.is_scalar
+
+        # a series that might be produced via `frame.dtypes`
+        ser = Series([1, 2], index=[np.dtype("O"), np.dtype("i8")])
+
+        key = ser.index[1]
+
+        result = ser[key]
+        assert result == 2
+
+    def test_getitem_negative_out_of_bounds(self):
+        ser = Series(["a"] * 10, index=["a"] * 10)
+
+        msg = "index -11 is out of bounds for axis 0 with size 10|index out of bounds"
+        warn_msg = "Series.__getitem__ treating keys as positions is deprecated"
+        with pytest.raises(IndexError, match=msg):
+            with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+                ser[-11]
+
+    def test_getitem_out_of_bounds_indexerror(self, datetime_series):
+        # don't segfault, GH#495
+        msg = r"index \d+ is out of bounds for axis 0 with size \d+"
+        warn_msg = "Series.__getitem__ treating keys as positions is deprecated"
+        with pytest.raises(IndexError, match=msg):
+            with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+                datetime_series[len(datetime_series)]
+
+    def test_getitem_out_of_bounds_empty_rangeindex_keyerror(self):
+        # GH#917
+        # With a RangeIndex, an int key gives a KeyError
+        ser = Series([], dtype=object)
+        with pytest.raises(KeyError, match="-1"):
+            ser[-1]
+
+    def test_getitem_keyerror_with_integer_index(self, any_int_numpy_dtype):
+        dtype = any_int_numpy_dtype
+        ser = Series(
+            np.random.default_rng(2).standard_normal(6),
+            index=Index([0, 0, 1, 1, 2, 2], dtype=dtype),
+        )
+
+        with pytest.raises(KeyError, match=r"^5$"):
+            ser[5]
+
+        with pytest.raises(KeyError, match=r"^'c'$"):
+            ser["c"]
+
+        # not monotonic
+        ser = Series(
+            np.random.default_rng(2).standard_normal(6), index=[2, 2, 0, 0, 1, 1]
+        )
+
+        with pytest.raises(KeyError, match=r"^5$"):
+            ser[5]
+
+        with pytest.raises(KeyError, match=r"^'c'$"):
+            ser["c"]
+
+    def test_getitem_int64(self, datetime_series):
+        idx = np.int64(5)
+        msg = "Series.__getitem__ treating keys as positions is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            res = datetime_series[idx]
+        assert res == datetime_series.iloc[5]
+
+    def test_getitem_full_range(self):
+        # github.com/pandas-dev/pandas/commit/4f433773141d2eb384325714a2776bcc5b2e20f7
+        ser = Series(range(5), index=list(range(5)))
+        result = ser[list(range(5))]
+        tm.assert_series_equal(result, ser)
+
+    # ------------------------------------------------------------------
+    # Series with DatetimeIndex
+
+    @pytest.mark.parametrize("tzstr", ["Europe/Berlin", "dateutil/Europe/Berlin"])
+    def test_getitem_pydatetime_tz(self, tzstr):
+        tz = timezones.maybe_get_tz(tzstr)
+
+        index = date_range(
+            start="2012-12-24 16:00", end="2012-12-24 18:00", freq="h", tz=tzstr
+        )
+        ts = Series(index=index, data=index.hour)
+        time_pandas = Timestamp("2012-12-24 17:00", tz=tzstr)
+
+        dt = datetime(2012, 12, 24, 17, 0)
+        time_datetime = conversion.localize_pydatetime(dt, tz)
+        assert ts[time_pandas] == ts[time_datetime]
+
+    @pytest.mark.parametrize("tz", ["US/Eastern", "dateutil/US/Eastern"])
+    def test_string_index_alias_tz_aware(self, tz):
+        rng = date_range("1/1/2000", periods=10, tz=tz)
+        ser = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng)
+
+        result = ser["1/3/2000"]
+        tm.assert_almost_equal(result, ser.iloc[2])
+
+    def test_getitem_time_object(self):
+        rng = date_range("1/1/2000", "1/5/2000", freq="5min")
+        ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng)
+
+        mask = (rng.hour == 9) & (rng.minute == 30)
+        result = ts[time(9, 30)]
+        expected = ts[mask]
+        result.index = result.index._with_freq(None)
+        tm.assert_series_equal(result, expected)
+
+    # ------------------------------------------------------------------
+    # Series with CategoricalIndex
+
+    def test_getitem_scalar_categorical_index(self):
+        cats = Categorical([Timestamp("12-31-1999"), Timestamp("12-31-2000")])
+
+        ser = Series([1, 2], index=cats)
+
+        expected = ser.iloc[0]
+        result = ser[cats[0]]
+        assert result == expected
+
+    def test_getitem_numeric_categorical_listlike_matches_scalar(self):
+        # GH#15470
+        ser = Series(["a", "b", "c"], index=pd.CategoricalIndex([2, 1, 0]))
+
+        # 0 is treated as a label
+        assert ser[0] == "c"
+
+        # the listlike analogue should also be treated as labels
+        res = ser[[0]]
+        expected = ser.iloc[-1:]
+        tm.assert_series_equal(res, expected)
+
+        res2 = ser[[0, 1, 2]]
+        tm.assert_series_equal(res2, ser.iloc[::-1])
+
+    def test_getitem_integer_categorical_not_positional(self):
+        # GH#14865
+        ser = Series(["a", "b", "c"], index=Index([1, 2, 3], dtype="category"))
+        assert ser.get(3) == "c"
+        assert ser[3] == "c"
+
+    def test_getitem_str_with_timedeltaindex(self):
+        rng = timedelta_range("1 day 10:11:12", freq="h", periods=500)
+        ser = Series(np.arange(len(rng)), index=rng)
+
+        key = "6 days, 23:11:12"
+        indexer = rng.get_loc(key)
+        assert indexer == 133
+
+        result = ser[key]
+        assert result == ser.iloc[133]
+
+        msg = r"^Timedelta\('50 days 00:00:00'\)$"
+        with pytest.raises(KeyError, match=msg):
+            rng.get_loc("50 days")
+        with pytest.raises(KeyError, match=msg):
+            ser["50 days"]
+
+    def test_getitem_bool_index_positional(self):
+        # GH#48653
+        ser = Series({True: 1, False: 0})
+        msg = "Series.__getitem__ treating keys as positions is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = ser[0]
+        assert result == 1
+
+
+class TestSeriesGetitemSlices:
+    def test_getitem_partial_str_slice_with_datetimeindex(self):
+        # GH#34860
+        arr = date_range("1/1/2008", "1/1/2009")
+        ser = arr.to_series()
+        result = ser["2008"]
+
+        rng = date_range(start="2008-01-01", end="2008-12-31")
+        expected = Series(rng, index=rng)
+
+        tm.assert_series_equal(result, expected)
+
+    def test_getitem_slice_strings_with_datetimeindex(self):
+        idx = DatetimeIndex(
+            ["1/1/2000", "1/2/2000", "1/2/2000", "1/3/2000", "1/4/2000"]
+        )
+
+        ts = Series(np.random.default_rng(2).standard_normal(len(idx)), index=idx)
+
+        result = ts["1/2/2000":]
+        expected = ts[1:]
+        tm.assert_series_equal(result, expected)
+
+        result = ts["1/2/2000":"1/3/2000"]
+        expected = ts[1:4]
+        tm.assert_series_equal(result, expected)
+
+    def test_getitem_partial_str_slice_with_timedeltaindex(self):
+        rng = timedelta_range("1 day 10:11:12", freq="h", periods=500)
+        ser = Series(np.arange(len(rng)), index=rng)
+
+        result = ser["5 day":"6 day"]
+        expected = ser.iloc[86:134]
+        tm.assert_series_equal(result, expected)
+
+        result = ser["5 day":]
+        expected = ser.iloc[86:]
+        tm.assert_series_equal(result, expected)
+
+        result = ser[:"6 day"]
+        expected = ser.iloc[:134]
+        tm.assert_series_equal(result, expected)
+
+    def test_getitem_partial_str_slice_high_reso_with_timedeltaindex(self):
+        # higher reso
+        rng = timedelta_range("1 day 10:11:12", freq="us", periods=2000)
+        ser = Series(np.arange(len(rng)), index=rng)
+
+        result = ser["1 day 10:11:12":]
+        expected = ser.iloc[0:]
+        tm.assert_series_equal(result, expected)
+
+        result = ser["1 day 10:11:12.001":]
+        expected = ser.iloc[1000:]
+        tm.assert_series_equal(result, expected)
+
+        result = ser["1 days, 10:11:12.001001"]
+        assert result == ser.iloc[1001]
+
+    def test_getitem_slice_2d(self, datetime_series):
+        # GH#30588 multi-dimensional indexing deprecated
+        with pytest.raises(ValueError, match="Multi-dimensional indexing"):
+            datetime_series[:, np.newaxis]
+
+    def test_getitem_median_slice_bug(self):
+        index = date_range("20090415", "20090519", freq="2B")
+        ser = Series(np.random.default_rng(2).standard_normal(13), index=index)
+
+        indexer = [slice(6, 7, None)]
+        msg = "Indexing with a single-item list"
+        with pytest.raises(ValueError, match=msg):
+            # GH#31299
+            ser[indexer]
+        # but we're OK with a single-element tuple
+        result = ser[(indexer[0],)]
+        expected = ser[indexer[0]]
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "slc, positions",
+        [
+            [slice(date(2018, 1, 1), None), [0, 1, 2]],
+            [slice(date(2019, 1, 2), None), [2]],
+            [slice(date(2020, 1, 1), None), []],
+            [slice(None, date(2020, 1, 1)), [0, 1, 2]],
+            [slice(None, date(2019, 1, 1)), [0]],
+        ],
+    )
+    def test_getitem_slice_date(self, slc, positions):
+        # https://github.com/pandas-dev/pandas/issues/31501
+        ser = Series(
+            [0, 1, 2],
+            DatetimeIndex(["2019-01-01", "2019-01-01T06:00:00", "2019-01-02"]),
+        )
+        result = ser[slc]
+        expected = ser.take(positions)
+        tm.assert_series_equal(result, expected)
+
+    def test_getitem_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]
+
+        with pytest.raises(TypeError, match=msg.format(key=r"4\.5")):
+            datetime_series[4.5:10.0]
+
+    def test_getitem_slice_bug(self):
+        ser = Series(range(10), index=list(range(10)))
+        result = ser[-12:]
+        tm.assert_series_equal(result, ser)
+
+        result = ser[-7:]
+        tm.assert_series_equal(result, ser[3:])
+
+        result = ser[:-12]
+        tm.assert_series_equal(result, ser[:0])
+
+    def test_getitem_slice_integers(self):
+        ser = Series(
+            np.random.default_rng(2).standard_normal(8),
+            index=[2, 4, 6, 8, 10, 12, 14, 16],
+        )
+
+        result = ser[:4]
+        expected = Series(ser.values[:4], index=[2, 4, 6, 8])
+        tm.assert_series_equal(result, expected)
+
+
+class TestSeriesGetitemListLike:
+    @pytest.mark.parametrize("box", [list, np.array, Index, Series])
+    def test_getitem_no_matches(self, box):
+        # GH#33462 we expect the same behavior for list/ndarray/Index/Series
+        ser = Series(["A", "B"])
+
+        key = Series(["C"], dtype=object)
+        key = box(key)
+
+        msg = (
+            r"None of \[Index\(\['C'\], dtype='object|string'\)\] are in the \[index\]"
+        )
+        with pytest.raises(KeyError, match=msg):
+            ser[key]
+
+    def test_getitem_intlist_intindex_periodvalues(self):
+        ser = Series(period_range("2000-01-01", periods=10, freq="D"))
+
+        result = ser[[2, 4]]
+        exp = Series(
+            [pd.Period("2000-01-03", freq="D"), pd.Period("2000-01-05", freq="D")],
+            index=[2, 4],
+            dtype="Period[D]",
+        )
+        tm.assert_series_equal(result, exp)
+        assert result.dtype == "Period[D]"
+
+    @pytest.mark.parametrize("box", [list, np.array, Index])
+    def test_getitem_intlist_intervalindex_non_int(self, box):
+        # GH#33404 fall back to positional since ints are unambiguous
+        dti = date_range("2000-01-03", periods=3)._with_freq(None)
+        ii = pd.IntervalIndex.from_breaks(dti)
+        ser = Series(range(len(ii)), index=ii)
+
+        expected = ser.iloc[:1]
+        key = box([0])
+        msg = "Series.__getitem__ treating keys as positions is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = ser[key]
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("box", [list, np.array, Index])
+    @pytest.mark.parametrize("dtype", [np.int64, np.float64, np.uint64])
+    def test_getitem_intlist_multiindex_numeric_level(self, dtype, box):
+        # GH#33404 do _not_ fall back to positional since ints are ambiguous
+        idx = Index(range(4)).astype(dtype)
+        dti = date_range("2000-01-03", periods=3)
+        mi = pd.MultiIndex.from_product([idx, dti])
+        ser = Series(range(len(mi))[::-1], index=mi)
+
+        key = box([5])
+        with pytest.raises(KeyError, match="5"):
+            ser[key]
+
+    def test_getitem_uint_array_key(self, any_unsigned_int_numpy_dtype):
+        # GH #37218
+        ser = Series([1, 2, 3])
+        key = np.array([4], dtype=any_unsigned_int_numpy_dtype)
+
+        with pytest.raises(KeyError, match="4"):
+            ser[key]
+        with pytest.raises(KeyError, match="4"):
+            ser.loc[key]
+
+
+class TestGetitemBooleanMask:
+    def test_getitem_boolean(self, string_series):
+        ser = string_series
+        mask = ser > ser.median()
+
+        # passing list is OK
+        result = ser[list(mask)]
+        expected = ser[mask]
+        tm.assert_series_equal(result, expected)
+        tm.assert_index_equal(result.index, ser.index[mask])
+
+    def test_getitem_boolean_empty(self):
+        ser = Series([], dtype=np.int64)
+        ser.index.name = "index_name"
+        ser = ser[ser.isna()]
+        assert ser.index.name == "index_name"
+        assert ser.dtype == np.int64
+
+        # GH#5877
+        # indexing with empty series
+        ser = Series(["A", "B"], dtype=object)
+        expected = Series(dtype=object, index=Index([], dtype="int64"))
+        result = ser[Series([], dtype=object)]
+        tm.assert_series_equal(result, expected)
+
+        # invalid because of the boolean indexer
+        # that's empty or not-aligned
+        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):
+            ser[Series([], dtype=bool)]
+
+        with pytest.raises(IndexingError, match=msg):
+            ser[Series([True], dtype=bool)]
+
+    def test_getitem_boolean_object(self, string_series):
+        # using column from DataFrame
+
+        ser = string_series
+        mask = ser > ser.median()
+        omask = mask.astype(object)
+
+        # getitem
+        result = ser[omask]
+        expected = ser[mask]
+        tm.assert_series_equal(result, expected)
+
+        # setitem
+        s2 = ser.copy()
+        cop = ser.copy()
+        cop[omask] = 5
+        s2[mask] = 5
+        tm.assert_series_equal(cop, s2)
+
+        # nans raise exception
+        omask[5:10] = np.nan
+        msg = "Cannot mask with non-boolean array containing NA / NaN values"
+        with pytest.raises(ValueError, match=msg):
+            ser[omask]
+        with pytest.raises(ValueError, match=msg):
+            ser[omask] = 5
+
+    def test_getitem_boolean_dt64_copies(self):
+        # GH#36210
+        dti = date_range("2016-01-01", periods=4, tz="US/Pacific")
+        key = np.array([True, True, False, False])
+
+        ser = Series(dti._data)
+
+        res = ser[key]
+        assert res._values._ndarray.base is None
+
+        # compare with numeric case for reference
+        ser2 = Series(range(4))
+        res2 = ser2[key]
+        assert res2._values.base is None
+
+    def test_getitem_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]
+
+        with pytest.raises(IndexingError, match=msg):
+            ts.loc[mask_shifted]
+
+    def test_getitem_boolean_different_order(self, string_series):
+        ordered = string_series.sort_values()
+
+        sel = string_series[ordered > 0]
+        exp = string_series[string_series > 0]
+        tm.assert_series_equal(sel, exp)
+
+    def test_getitem_boolean_contiguous_preserve_freq(self):
+        rng = date_range("1/1/2000", "3/1/2000", freq="B")
+
+        mask = np.zeros(len(rng), dtype=bool)
+        mask[10:20] = True
+
+        masked = rng[mask]
+        expected = rng[10:20]
+        assert expected.freq == rng.freq
+        tm.assert_index_equal(masked, expected)
+
+        mask[22] = True
+        masked = rng[mask]
+        assert masked.freq is None
+
+
+class TestGetitemCallable:
+    def test_getitem_callable(self):
+        # GH#12533
+        ser = Series(4, index=list("ABCD"))
+        result = ser[lambda x: "A"]
+        assert result == ser.loc["A"]
+
+        result = ser[lambda x: ["A", "B"]]
+        expected = ser.loc[["A", "B"]]
+        tm.assert_series_equal(result, expected)
+
+        result = ser[lambda x: [True, False, True, True]]
+        expected = ser.iloc[[0, 2, 3]]
+        tm.assert_series_equal(result, expected)
+
+
+def test_getitem_generator(string_series):
+    gen = (x > 0 for x in string_series)
+    result = string_series[gen]
+    result2 = string_series[iter(string_series > 0)]
+    expected = string_series[string_series > 0]
+    tm.assert_series_equal(result, expected)
+    tm.assert_series_equal(result2, expected)
+
+
+@pytest.mark.parametrize(
+    "series",
+    [
+        Series([0, 1]),
+        Series(date_range("2012-01-01", periods=2)),
+        Series(date_range("2012-01-01", periods=2, tz="CET")),
+    ],
+)
+def test_getitem_ndim_deprecated(series):
+    with pytest.raises(ValueError, match="Multi-dimensional indexing"):
+        series[:, None]
+
+
+def test_getitem_multilevel_scalar_slice_not_implemented(
+    multiindex_year_month_day_dataframe_random_data,
+):
+    # not implementing this for now
+    df = multiindex_year_month_day_dataframe_random_data
+    ser = df["A"]
+
+    msg = r"\(2000, slice\(3, 4, None\)\)"
+    with pytest.raises(TypeError, match=msg):
+        ser[2000, 3:4]
+
+
+def test_getitem_dataframe_raises():
+    rng = list(range(10))
+    ser = Series(10, index=rng)
+    df = DataFrame(rng, index=rng)
+    msg = (
+        "Indexing a Series with DataFrame is not supported, "
+        "use the appropriate DataFrame column"
+    )
+    with pytest.raises(TypeError, match=msg):
+        ser[df > 5]
+
+
+def test_getitem_assignment_series_alignment():
+    # https://github.com/pandas-dev/pandas/issues/37427
+    # with getitem, when assigning with a Series, it is not first aligned
+    ser = Series(range(10))
+    idx = np.array([2, 4, 9])
+    ser[idx] = Series([10, 11, 12])
+    expected = Series([0, 1, 10, 3, 11, 5, 6, 7, 8, 12])
+    tm.assert_series_equal(ser, expected)
+
+
+def test_getitem_duplicate_index_mistyped_key_raises_keyerror():
+    # GH#29189 float_index.get_loc(None) should raise KeyError, not TypeError
+    ser = Series([2, 5, 6, 8], index=[2.0, 4.0, 4.0, 5.0])
+    with pytest.raises(KeyError, match="None"):
+        ser[None]
+
+    with pytest.raises(KeyError, match="None"):
+        ser.index.get_loc(None)
+
+    with pytest.raises(KeyError, match="None"):
+        ser.index._engine.get_loc(None)
+
+
+def test_getitem_1tuple_slice_without_multiindex():
+    ser = Series(range(5))
+    key = (slice(3),)
+
+    result = ser[key]
+    expected = ser[key[0]]
+    tm.assert_series_equal(result, expected)
+
+
+def test_getitem_preserve_name(datetime_series):
+    result = datetime_series[datetime_series > 0]
+    assert result.name == datetime_series.name
+
+    msg = "Series.__getitem__ treating keys as positions is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = datetime_series[[0, 2, 4]]
+    assert result.name == datetime_series.name
+
+    result = datetime_series[5:10]
+    assert result.name == datetime_series.name
+
+
+def test_getitem_with_integer_labels():
+    # integer indexes, be careful
+    ser = Series(
+        np.random.default_rng(2).standard_normal(10), index=list(range(0, 20, 2))
+    )
+    inds = [0, 2, 5, 7, 8]
+    arr_inds = np.array([0, 2, 5, 7, 8])
+    with pytest.raises(KeyError, match="not in index"):
+        ser[inds]
+
+    with pytest.raises(KeyError, match="not in index"):
+        ser[arr_inds]
+
+
+def test_getitem_missing(datetime_series):
+    # missing
+    d = datetime_series.index[0] - BDay()
+    msg = r"Timestamp\('1999-12-31 00:00:00'\)"
+    with pytest.raises(KeyError, match=msg):
+        datetime_series[d]
+
+
+def test_getitem_fancy(string_series, object_series):
+    msg = "Series.__getitem__ treating keys as positions is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        slice1 = string_series[[1, 2, 3]]
+        slice2 = object_series[[1, 2, 3]]
+    assert string_series.index[2] == slice1.index[1]
+    assert object_series.index[2] == slice2.index[1]
+    assert string_series.iloc[2] == slice1.iloc[1]
+    assert object_series.iloc[2] == slice2.iloc[1]
+
+
+def test_getitem_box_float64(datetime_series):
+    msg = "Series.__getitem__ treating keys as positions is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        value = datetime_series[5]
+    assert isinstance(value, np.float64)
+
+
+def test_getitem_unordered_dup():
+    obj = Series(range(5), index=["c", "a", "a", "b", "b"])
+    assert is_scalar(obj["c"])
+    assert obj["c"] == 0
+
+
+def test_getitem_dups():
+    ser = Series(range(5), index=["A", "A", "B", "C", "C"], dtype=np.int64)
+    expected = Series([3, 4], index=["C", "C"], dtype=np.int64)
+    result = ser["C"]
+    tm.assert_series_equal(result, expected)
+
+
+def test_getitem_categorical_str():
+    # GH#31765
+    ser = Series(range(5), index=Categorical(["a", "b", "c", "a", "b"]))
+    result = ser["a"]
+    expected = ser.iloc[[0, 3]]
+    tm.assert_series_equal(result, expected)
+
+
+def test_slice_can_reorder_not_uniquely_indexed():
+    ser = Series(1, index=["a", "a", "b", "b", "c"])
+    ser[::-1]  # it works!
+
+
+@pytest.mark.parametrize("index_vals", ["aabcd", "aadcb"])
+def test_duplicated_index_getitem_positional_indexer(index_vals):
+    # GH 11747
+    s = Series(range(5), index=list(index_vals))
+
+    msg = "Series.__getitem__ treating keys as positions is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = s[3]
+    assert result == 3
+
+
+class TestGetitemDeprecatedIndexers:
+    @pytest.mark.parametrize("key", [{1}, {1: 1}])
+    def test_getitem_dict_and_set_deprecated(self, key):
+        # GH#42825 enforced in 2.0
+        ser = Series([1, 2, 3])
+        with pytest.raises(TypeError, match="as an indexer is not supported"):
+            ser[key]
+
+    @pytest.mark.parametrize("key", [{1}, {1: 1}])
+    def test_setitem_dict_and_set_disallowed(self, key):
+        # GH#42825 enforced in 2.0
+        ser = Series([1, 2, 3])
+        with pytest.raises(TypeError, match="as an indexer is not supported"):
+            ser[key] = 1

venv/lib/python3.12/site-packages/pandas/tests/series/indexing/test_indexing.py

@@ -0,0 +1,518 @@
+""" test get/set & misc """
+from datetime import timedelta
+import re
+
+import numpy as np
+import pytest
+
+from pandas.errors import IndexingError
+
+from pandas import (
+    NA,
+    DataFrame,
+    Index,
+    IndexSlice,
+    MultiIndex,
+    NaT,
+    Series,
+    Timedelta,
+    Timestamp,
+    concat,
+    date_range,
+    isna,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+def test_basic_indexing():
+    s = Series(
+        np.random.default_rng(2).standard_normal(5), index=["a", "b", "a", "a", "b"]
+    )
+
+    warn_msg = "Series.__[sg]etitem__ treating keys as positions is deprecated"
+    msg = "index 5 is out of bounds for axis 0 with size 5"
+    with pytest.raises(IndexError, match=msg):
+        with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+            s[5]
+    with pytest.raises(IndexError, match=msg):
+        with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+            s[5] = 0
+
+    with pytest.raises(KeyError, match=r"^'c'$"):
+        s["c"]
+
+    s = s.sort_index()
+
+    with pytest.raises(IndexError, match=msg):
+        with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+            s[5]
+    msg = r"index 5 is out of bounds for axis (0|1) with size 5|^5$"
+    with pytest.raises(IndexError, match=msg):
+        with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+            s[5] = 0
+
+
+def test_getitem_numeric_should_not_fallback_to_positional(any_numeric_dtype):
+    # GH51053
+    dtype = any_numeric_dtype
+    idx = Index([1, 0, 1], dtype=dtype)
+    ser = Series(range(3), index=idx)
+    result = ser[1]
+    expected = Series([0, 2], index=Index([1, 1], dtype=dtype))
+    tm.assert_series_equal(result, expected, check_exact=True)
+
+
+def test_setitem_numeric_should_not_fallback_to_positional(any_numeric_dtype):
+    # GH51053
+    dtype = any_numeric_dtype
+    idx = Index([1, 0, 1], dtype=dtype)
+    ser = Series(range(3), index=idx)
+    ser[1] = 10
+    expected = Series([10, 1, 10], index=idx)
+    tm.assert_series_equal(ser, expected, check_exact=True)
+
+
+def test_basic_getitem_with_labels(datetime_series):
+    indices = datetime_series.index[[5, 10, 15]]
+
+    result = datetime_series[indices]
+    expected = datetime_series.reindex(indices)
+    tm.assert_series_equal(result, expected)
+
+    result = datetime_series[indices[0] : indices[2]]
+    expected = datetime_series.loc[indices[0] : indices[2]]
+    tm.assert_series_equal(result, expected)
+
+
+def test_basic_getitem_dt64tz_values():
+    # GH12089
+    # with tz for values
+    ser = Series(
+        date_range("2011-01-01", periods=3, tz="US/Eastern"), index=["a", "b", "c"]
+    )
+    expected = Timestamp("2011-01-01", tz="US/Eastern")
+    result = ser.loc["a"]
+    assert result == expected
+    result = ser.iloc[0]
+    assert result == expected
+    result = ser["a"]
+    assert result == expected
+
+
+def test_getitem_setitem_ellipsis(using_copy_on_write, warn_copy_on_write):
+    s = Series(np.random.default_rng(2).standard_normal(10))
+
+    result = s[...]
+    tm.assert_series_equal(result, s)
+
+    with tm.assert_cow_warning(warn_copy_on_write):
+        s[...] = 5
+    if not using_copy_on_write:
+        assert (result == 5).all()
+
+
+@pytest.mark.parametrize(
+    "result_1, duplicate_item, expected_1",
+    [
+        [
+            Series({1: 12, 2: [1, 2, 2, 3]}),
+            Series({1: 313}),
+            Series({1: 12}, dtype=object),
+        ],
+        [
+            Series({1: [1, 2, 3], 2: [1, 2, 2, 3]}),
+            Series({1: [1, 2, 3]}),
+            Series({1: [1, 2, 3]}),
+        ],
+    ],
+)
+def test_getitem_with_duplicates_indices(result_1, duplicate_item, expected_1):
+    # GH 17610
+    result = result_1._append(duplicate_item)
+    expected = expected_1._append(duplicate_item)
+    tm.assert_series_equal(result[1], expected)
+    assert result[2] == result_1[2]
+
+
+def test_getitem_setitem_integers():
+    # caused bug without test
+    s = Series([1, 2, 3], ["a", "b", "c"])
+
+    assert s.iloc[0] == s["a"]
+    s.iloc[0] = 5
+    tm.assert_almost_equal(s["a"], 5)
+
+
+def test_series_box_timestamp():
+    rng = date_range("20090415", "20090519", freq="B")
+    ser = Series(rng)
+    assert isinstance(ser[0], Timestamp)
+    assert isinstance(ser.at[1], Timestamp)
+    assert isinstance(ser.iat[2], Timestamp)
+    assert isinstance(ser.loc[3], Timestamp)
+    assert isinstance(ser.iloc[4], Timestamp)
+
+    ser = Series(rng, index=rng)
+    msg = "Series.__getitem__ treating keys as positions is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        assert isinstance(ser[0], Timestamp)
+    assert isinstance(ser.at[rng[1]], Timestamp)
+    assert isinstance(ser.iat[2], Timestamp)
+    assert isinstance(ser.loc[rng[3]], Timestamp)
+    assert isinstance(ser.iloc[4], Timestamp)
+
+
+def test_series_box_timedelta():
+    rng = timedelta_range("1 day 1 s", periods=5, freq="h")
+    ser = Series(rng)
+    assert isinstance(ser[0], Timedelta)
+    assert isinstance(ser.at[1], Timedelta)
+    assert isinstance(ser.iat[2], Timedelta)
+    assert isinstance(ser.loc[3], Timedelta)
+    assert isinstance(ser.iloc[4], Timedelta)
+
+
+def test_getitem_ambiguous_keyerror(indexer_sl):
+    ser = Series(range(10), index=list(range(0, 20, 2)))
+    with pytest.raises(KeyError, match=r"^1$"):
+        indexer_sl(ser)[1]
+
+
+def test_getitem_dups_with_missing(indexer_sl):
+    # breaks reindex, so need to use .loc internally
+    # GH 4246
+    ser = Series([1, 2, 3, 4], ["foo", "bar", "foo", "bah"])
+    with pytest.raises(KeyError, match=re.escape("['bam'] not in index")):
+        indexer_sl(ser)[["foo", "bar", "bah", "bam"]]
+
+
+def test_setitem_ambiguous_keyerror(indexer_sl):
+    s = Series(range(10), index=list(range(0, 20, 2)))
+
+    # equivalent of an append
+    s2 = s.copy()
+    indexer_sl(s2)[1] = 5
+    expected = concat([s, Series([5], index=[1])])
+    tm.assert_series_equal(s2, expected)
+
+
+def test_setitem(datetime_series):
+    datetime_series[datetime_series.index[5]] = np.nan
+    datetime_series.iloc[[1, 2, 17]] = np.nan
+    datetime_series.iloc[6] = np.nan
+    assert np.isnan(datetime_series.iloc[6])
+    assert np.isnan(datetime_series.iloc[2])
+    datetime_series[np.isnan(datetime_series)] = 5
+    assert not np.isnan(datetime_series.iloc[2])
+
+
+def test_setslice(datetime_series):
+    sl = datetime_series[5:20]
+    assert len(sl) == len(sl.index)
+    assert sl.index.is_unique is True
+
+
+def test_basic_getitem_setitem_corner(datetime_series):
+    # invalid tuples, e.g. td.ts[:, None] vs. td.ts[:, 2]
+    msg = "key of type tuple not found and not a MultiIndex"
+    with pytest.raises(KeyError, match=msg):
+        datetime_series[:, 2]
+    with pytest.raises(KeyError, match=msg):
+        datetime_series[:, 2] = 2
+
+    # weird lists. [slice(0, 5)] raises but not two slices
+    msg = "Indexing with a single-item list"
+    with pytest.raises(ValueError, match=msg):
+        # GH#31299
+        datetime_series[[slice(None, 5)]]
+
+    # but we're OK with a single-element tuple
+    result = datetime_series[(slice(None, 5),)]
+    expected = datetime_series[:5]
+    tm.assert_series_equal(result, expected)
+
+    # OK
+    msg = r"unhashable type(: 'slice')?"
+    with pytest.raises(TypeError, match=msg):
+        datetime_series[[5, [None, None]]]
+    with pytest.raises(TypeError, match=msg):
+        datetime_series[[5, [None, None]]] = 2
+
+
+def test_slice(string_series, object_series, using_copy_on_write, warn_copy_on_write):
+    original = string_series.copy()
+    numSlice = string_series[10:20]
+    numSliceEnd = string_series[-10:]
+    objSlice = object_series[10:20]
+
+    assert string_series.index[9] not in numSlice.index
+    assert object_series.index[9] not in objSlice.index
+
+    assert len(numSlice) == len(numSlice.index)
+    assert string_series[numSlice.index[0]] == numSlice[numSlice.index[0]]
+
+    assert numSlice.index[1] == string_series.index[11]
+    tm.assert_numpy_array_equal(np.array(numSliceEnd), np.array(string_series)[-10:])
+
+    # Test return view.
+    sl = string_series[10:20]
+    with tm.assert_cow_warning(warn_copy_on_write):
+        sl[:] = 0
+
+    if using_copy_on_write:
+        # Doesn't modify parent (CoW)
+        tm.assert_series_equal(string_series, original)
+    else:
+        assert (string_series[10:20] == 0).all()
+
+
+def test_timedelta_assignment():
+    # GH 8209
+    s = Series([], dtype=object)
+    s.loc["B"] = timedelta(1)
+    tm.assert_series_equal(s, Series(Timedelta("1 days"), index=["B"]))
+
+    s = s.reindex(s.index.insert(0, "A"))
+    tm.assert_series_equal(s, Series([np.nan, Timedelta("1 days")], index=["A", "B"]))
+
+    s.loc["A"] = timedelta(1)
+    expected = Series(Timedelta("1 days"), index=["A", "B"])
+    tm.assert_series_equal(s, expected)
+
+
+def test_underlying_data_conversion(using_copy_on_write):
+    # GH 4080
+    df = DataFrame({c: [1, 2, 3] for c in ["a", "b", "c"]})
+    return_value = df.set_index(["a", "b", "c"], inplace=True)
+    assert return_value is None
+    s = Series([1], index=[(2, 2, 2)])
+    df["val"] = 0
+    df_original = df.copy()
+    df
+
+    if using_copy_on_write:
+        with tm.raises_chained_assignment_error():
+            df["val"].update(s)
+        expected = df_original
+    else:
+        with tm.assert_produces_warning(FutureWarning, match="inplace method"):
+            df["val"].update(s)
+        expected = DataFrame(
+            {"a": [1, 2, 3], "b": [1, 2, 3], "c": [1, 2, 3], "val": [0, 1, 0]}
+        )
+        return_value = expected.set_index(["a", "b", "c"], inplace=True)
+        assert return_value is None
+    tm.assert_frame_equal(df, expected)
+
+
+def test_preserve_refs(datetime_series):
+    seq = datetime_series.iloc[[5, 10, 15]]
+    seq.iloc[1] = np.nan
+    assert not np.isnan(datetime_series.iloc[10])
+
+
+def test_multilevel_preserve_name(lexsorted_two_level_string_multiindex, indexer_sl):
+    index = lexsorted_two_level_string_multiindex
+    ser = Series(
+        np.random.default_rng(2).standard_normal(len(index)), index=index, name="sth"
+    )
+
+    result = indexer_sl(ser)["foo"]
+    assert result.name == ser.name
+
+
+# miscellaneous methods
+
+
+@pytest.mark.parametrize(
+    "index",
+    [
+        date_range("2014-01-01", periods=20, freq="MS"),
+        period_range("2014-01", periods=20, freq="M"),
+        timedelta_range("0", periods=20, freq="h"),
+    ],
+)
+def test_slice_with_negative_step(index):
+    keystr1 = str(index[9])
+    keystr2 = str(index[13])
+
+    ser = Series(np.arange(20), index)
+    SLC = IndexSlice
+
+    for key in [keystr1, index[9]]:
+        tm.assert_indexing_slices_equivalent(ser, SLC[key::-1], SLC[9::-1])
+        tm.assert_indexing_slices_equivalent(ser, SLC[:key:-1], SLC[:8:-1])
+
+        for key2 in [keystr2, index[13]]:
+            tm.assert_indexing_slices_equivalent(ser, SLC[key2:key:-1], SLC[13:8:-1])
+            tm.assert_indexing_slices_equivalent(ser, SLC[key:key2:-1], SLC[0:0:-1])
+
+
+def test_tuple_index():
+    # GH 35534 - Selecting values when a Series has an Index of tuples
+    s = Series([1, 2], index=[("a",), ("b",)])
+    assert s[("a",)] == 1
+    assert s[("b",)] == 2
+    s[("b",)] = 3
+    assert s[("b",)] == 3
+
+
+def test_frozenset_index():
+    # GH35747 - Selecting values when a Series has an Index of frozenset
+    idx0, idx1 = frozenset("a"), frozenset("b")
+    s = Series([1, 2], index=[idx0, idx1])
+    assert s[idx0] == 1
+    assert s[idx1] == 2
+    s[idx1] = 3
+    assert s[idx1] == 3
+
+
+def test_loc_setitem_all_false_indexer():
+    # GH#45778
+    ser = Series([1, 2], index=["a", "b"])
+    expected = ser.copy()
+    rhs = Series([6, 7], index=["a", "b"])
+    ser.loc[ser > 100] = rhs
+    tm.assert_series_equal(ser, expected)
+
+
+def test_loc_boolean_indexer_non_matching_index():
+    # GH#46551
+    ser = Series([1])
+    result = ser.loc[Series([NA, False], dtype="boolean")]
+    expected = Series([], dtype="int64")
+    tm.assert_series_equal(result, expected)
+
+
+def test_loc_boolean_indexer_miss_matching_index():
+    # GH#46551
+    ser = Series([1])
+    indexer = Series([NA, False], dtype="boolean", index=[1, 2])
+    with pytest.raises(IndexingError, match="Unalignable"):
+        ser.loc[indexer]
+
+
+def test_loc_setitem_nested_data_enlargement():
+    # GH#48614
+    df = DataFrame({"a": [1]})
+    ser = Series({"label": df})
+    ser.loc["new_label"] = df
+    expected = Series({"label": df, "new_label": df})
+    tm.assert_series_equal(ser, expected)
+
+
+def test_loc_ea_numeric_index_oob_slice_end():
+    # GH#50161
+    ser = Series(1, index=Index([0, 1, 2], dtype="Int64"))
+    result = ser.loc[2:3]
+    expected = Series(1, index=Index([2], dtype="Int64"))
+    tm.assert_series_equal(result, expected)
+
+
+def test_getitem_bool_int_key():
+    # GH#48653
+    ser = Series({True: 1, False: 0})
+    with pytest.raises(KeyError, match="0"):
+        ser.loc[0]
+
+
+@pytest.mark.parametrize("val", [{}, {"b": "x"}])
+@pytest.mark.parametrize("indexer", [[], [False, False], slice(0, -1), np.array([])])
+def test_setitem_empty_indexer(indexer, val):
+    # GH#45981
+    df = DataFrame({"a": [1, 2], **val})
+    expected = df.copy()
+    df.loc[indexer] = 1.5
+    tm.assert_frame_equal(df, expected)
+
+
+class TestDeprecatedIndexers:
+    @pytest.mark.parametrize("key", [{1}, {1: 1}])
+    def test_getitem_dict_and_set_deprecated(self, key):
+        # GH#42825 enforced in 2.0
+        ser = Series([1, 2])
+        with pytest.raises(TypeError, match="as an indexer is not supported"):
+            ser.loc[key]
+
+    @pytest.mark.parametrize("key", [{1}, {1: 1}, ({1}, 2), ({1: 1}, 2)])
+    def test_getitem_dict_and_set_deprecated_multiindex(self, key):
+        # GH#42825 enforced in 2.0
+        ser = Series([1, 2], index=MultiIndex.from_tuples([(1, 2), (3, 4)]))
+        with pytest.raises(TypeError, match="as an indexer is not supported"):
+            ser.loc[key]
+
+    @pytest.mark.parametrize("key", [{1}, {1: 1}])
+    def test_setitem_dict_and_set_disallowed(self, key):
+        # GH#42825 enforced in 2.0
+        ser = Series([1, 2])
+        with pytest.raises(TypeError, match="as an indexer is not supported"):
+            ser.loc[key] = 1
+
+    @pytest.mark.parametrize("key", [{1}, {1: 1}, ({1}, 2), ({1: 1}, 2)])
+    def test_setitem_dict_and_set_disallowed_multiindex(self, key):
+        # GH#42825 enforced in 2.0
+        ser = Series([1, 2], index=MultiIndex.from_tuples([(1, 2), (3, 4)]))
+        with pytest.raises(TypeError, match="as an indexer is not supported"):
+            ser.loc[key] = 1
+
+
+class TestSetitemValidation:
+    # This is adapted from pandas/tests/arrays/masked/test_indexing.py
+    # but checks for warnings instead of errors.
+    def _check_setitem_invalid(self, ser, invalid, indexer, warn):
+        msg = "Setting an item of incompatible dtype is deprecated"
+        msg = re.escape(msg)
+
+        orig_ser = ser.copy()
+
+        with tm.assert_produces_warning(warn, match=msg):
+            ser[indexer] = invalid
+            ser = orig_ser.copy()
+
+        with tm.assert_produces_warning(warn, match=msg):
+            ser.iloc[indexer] = invalid
+            ser = orig_ser.copy()
+
+        with tm.assert_produces_warning(warn, match=msg):
+            ser.loc[indexer] = invalid
+            ser = orig_ser.copy()
+
+        with tm.assert_produces_warning(warn, match=msg):
+            ser[:] = invalid
+
+    _invalid_scalars = [
+        1 + 2j,
+        "True",
+        "1",
+        "1.0",
+        NaT,
+        np.datetime64("NaT"),
+        np.timedelta64("NaT"),
+    ]
+    _indexers = [0, [0], slice(0, 1), [True, False, False], slice(None, None, None)]
+
+    @pytest.mark.parametrize(
+        "invalid", _invalid_scalars + [1, 1.0, np.int64(1), np.float64(1)]
+    )
+    @pytest.mark.parametrize("indexer", _indexers)
+    def test_setitem_validation_scalar_bool(self, invalid, indexer):
+        ser = Series([True, False, False], dtype="bool")
+        self._check_setitem_invalid(ser, invalid, indexer, FutureWarning)
+
+    @pytest.mark.parametrize("invalid", _invalid_scalars + [True, 1.5, np.float64(1.5)])
+    @pytest.mark.parametrize("indexer", _indexers)
+    def test_setitem_validation_scalar_int(self, invalid, any_int_numpy_dtype, indexer):
+        ser = Series([1, 2, 3], dtype=any_int_numpy_dtype)
+        if isna(invalid) and invalid is not NaT and not np.isnat(invalid):
+            warn = None
+        else:
+            warn = FutureWarning
+        self._check_setitem_invalid(ser, invalid, indexer, warn)
+
+    @pytest.mark.parametrize("invalid", _invalid_scalars + [True])
+    @pytest.mark.parametrize("indexer", _indexers)
+    def test_setitem_validation_scalar_float(self, invalid, float_numpy_dtype, indexer):
+        ser = Series([1, 2, None], dtype=float_numpy_dtype)
+        self._check_setitem_invalid(ser, invalid, indexer, FutureWarning)

venv/lib/python3.12/site-packages/pandas/tests/series/indexing/test_mask.py

@@ -0,0 +1,69 @@
+import numpy as np
+import pytest
+
+from pandas import Series
+import pandas._testing as tm
+
+
+def test_mask():
+    # compare with tested results in test_where
+    s = Series(np.random.default_rng(2).standard_normal(5))
+    cond = s > 0
+
+    rs = s.where(~cond, np.nan)
+    tm.assert_series_equal(rs, s.mask(cond))
+
+    rs = s.where(~cond)
+    rs2 = s.mask(cond)
+    tm.assert_series_equal(rs, rs2)
+
+    rs = s.where(~cond, -s)
+    rs2 = s.mask(cond, -s)
+    tm.assert_series_equal(rs, rs2)
+
+    cond = Series([True, False, False, True, False], index=s.index)
+    s2 = -(s.abs())
+    rs = s2.where(~cond[:3])
+    rs2 = s2.mask(cond[:3])
+    tm.assert_series_equal(rs, rs2)
+
+    rs = s2.where(~cond[:3], -s2)
+    rs2 = s2.mask(cond[:3], -s2)
+    tm.assert_series_equal(rs, rs2)
+
+    msg = "Array conditional must be same shape as self"
+    with pytest.raises(ValueError, match=msg):
+        s.mask(1)
+    with pytest.raises(ValueError, match=msg):
+        s.mask(cond[:3].values, -s)
+
+
+def test_mask_casts():
+    # dtype changes
+    ser = Series([1, 2, 3, 4])
+    result = ser.mask(ser > 2, np.nan)
+    expected = Series([1, 2, np.nan, np.nan])
+    tm.assert_series_equal(result, expected)
+
+
+def test_mask_casts2():
+    # see gh-21891
+    ser = Series([1, 2])
+    res = ser.mask([True, False])
+
+    exp = Series([np.nan, 2])
+    tm.assert_series_equal(res, exp)
+
+
+def test_mask_inplace():
+    s = Series(np.random.default_rng(2).standard_normal(5))
+    cond = s > 0
+
+    rs = s.copy()
+    rs.mask(cond, inplace=True)
+    tm.assert_series_equal(rs.dropna(), s[~cond])
+    tm.assert_series_equal(rs, s.mask(cond))
+
+    rs = s.copy()
+    rs.mask(cond, -s, inplace=True)
+    tm.assert_series_equal(rs, s.mask(cond, -s))

venv/lib/python3.12/site-packages/pandas/tests/series/indexing/test_set_value.py

@@ -0,0 +1,45 @@
+from datetime import datetime
+
+import numpy as np
+
+from pandas import (
+    DatetimeIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+def test_series_set_value():
+    # GH#1561
+
+    dates = [datetime(2001, 1, 1), datetime(2001, 1, 2)]
+    index = DatetimeIndex(dates)
+
+    s = Series(dtype=object)
+    s._set_value(dates[0], 1.0)
+    s._set_value(dates[1], np.nan)
+
+    expected = Series([1.0, np.nan], index=index)
+
+    tm.assert_series_equal(s, expected)
+
+
+def test_set_value_dt64(datetime_series):
+    idx = datetime_series.index[10]
+    res = datetime_series._set_value(idx, 0)
+    assert res is None
+    assert datetime_series[idx] == 0
+
+
+def test_set_value_str_index(string_series):
+    # equiv
+    ser = string_series.copy()
+    res = ser._set_value("foobar", 0)
+    assert res is None
+    assert ser.index[-1] == "foobar"
+    assert ser["foobar"] == 0
+
+    ser2 = string_series.copy()
+    ser2.loc["foobar"] = 0
+    assert ser2.index[-1] == "foobar"
+    assert ser2["foobar"] == 0

venv/lib/python3.12/site-packages/pandas/tests/series/indexing/test_take.py

@@ -0,0 +1,50 @@
+import pytest
+
+import pandas as pd
+from pandas import Series
+import pandas._testing as tm
+
+
+def test_take_validate_axis():
+    # GH#51022
+    ser = Series([-1, 5, 6, 2, 4])
+
+    msg = "No axis named foo for object type Series"
+    with pytest.raises(ValueError, match=msg):
+        ser.take([1, 2], axis="foo")
+
+
+def test_take():
+    ser = Series([-1, 5, 6, 2, 4])
+
+    actual = ser.take([1, 3, 4])
+    expected = Series([5, 2, 4], index=[1, 3, 4])
+    tm.assert_series_equal(actual, expected)
+
+    actual = ser.take([-1, 3, 4])
+    expected = Series([4, 2, 4], index=[4, 3, 4])
+    tm.assert_series_equal(actual, expected)
+
+    msg = "indices are out-of-bounds"
+    with pytest.raises(IndexError, match=msg):
+        ser.take([1, 10])
+    with pytest.raises(IndexError, match=msg):
+        ser.take([2, 5])
+
+
+def test_take_categorical():
+    # https://github.com/pandas-dev/pandas/issues/20664
+    ser = Series(pd.Categorical(["a", "b", "c"]))
+    result = ser.take([-2, -2, 0])
+    expected = Series(
+        pd.Categorical(["b", "b", "a"], categories=["a", "b", "c"]), index=[1, 1, 0]
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_take_slice_raises():
+    ser = Series([-1, 5, 6, 2, 4])
+
+    msg = "Series.take requires a sequence of integers, not slice"
+    with pytest.raises(TypeError, match=msg):
+        ser.take(slice(0, 3, 1))

venv/lib/python3.12/site-packages/pandas/tests/series/indexing/test_where.py

@@ -0,0 +1,481 @@
+import numpy as np
+import pytest
+
+from pandas._config import using_pyarrow_string_dtype
+
+from pandas.core.dtypes.common import is_integer
+
+import pandas as pd
+from pandas import (
+    Series,
+    Timestamp,
+    date_range,
+    isna,
+)
+import pandas._testing as tm
+
+
+def test_where_unsafe_int(any_signed_int_numpy_dtype):
+    s = Series(np.arange(10), dtype=any_signed_int_numpy_dtype)
+    mask = s < 5
+
+    s[mask] = range(2, 7)
+    expected = Series(
+        list(range(2, 7)) + list(range(5, 10)),
+        dtype=any_signed_int_numpy_dtype,
+    )
+
+    tm.assert_series_equal(s, expected)
+
+
+def test_where_unsafe_float(float_numpy_dtype):
+    s = Series(np.arange(10), dtype=float_numpy_dtype)
+    mask = s < 5
+
+    s[mask] = range(2, 7)
+    data = list(range(2, 7)) + list(range(5, 10))
+    expected = Series(data, dtype=float_numpy_dtype)
+
+    tm.assert_series_equal(s, expected)
+
+
+@pytest.mark.parametrize(
+    "dtype,expected_dtype",
+    [
+        (np.int8, np.float64),
+        (np.int16, np.float64),
+        (np.int32, np.float64),
+        (np.int64, np.float64),
+        (np.float32, np.float32),
+        (np.float64, np.float64),
+    ],
+)
+def test_where_unsafe_upcast(dtype, expected_dtype):
+    # see gh-9743
+    s = Series(np.arange(10), dtype=dtype)
+    values = [2.5, 3.5, 4.5, 5.5, 6.5]
+    mask = s < 5
+    expected = Series(values + list(range(5, 10)), dtype=expected_dtype)
+    warn = (
+        None
+        if np.dtype(dtype).kind == np.dtype(expected_dtype).kind == "f"
+        else FutureWarning
+    )
+    with tm.assert_produces_warning(warn, match="incompatible dtype"):
+        s[mask] = values
+    tm.assert_series_equal(s, expected)
+
+
+def test_where_unsafe():
+    # see gh-9731
+    s = Series(np.arange(10), dtype="int64")
+    values = [2.5, 3.5, 4.5, 5.5]
+
+    mask = s > 5
+    expected = Series(list(range(6)) + values, dtype="float64")
+
+    with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+        s[mask] = values
+    tm.assert_series_equal(s, expected)
+
+    # see gh-3235
+    s = Series(np.arange(10), dtype="int64")
+    mask = s < 5
+    s[mask] = range(2, 7)
+    expected = Series(list(range(2, 7)) + list(range(5, 10)), dtype="int64")
+    tm.assert_series_equal(s, expected)
+    assert s.dtype == expected.dtype
+
+    s = Series(np.arange(10), dtype="int64")
+    mask = s > 5
+    s[mask] = [0] * 4
+    expected = Series([0, 1, 2, 3, 4, 5] + [0] * 4, dtype="int64")
+    tm.assert_series_equal(s, expected)
+
+    s = Series(np.arange(10))
+    mask = s > 5
+
+    msg = "cannot set using a list-like indexer with a different length than the value"
+    with pytest.raises(ValueError, match=msg):
+        s[mask] = [5, 4, 3, 2, 1]
+
+    with pytest.raises(ValueError, match=msg):
+        s[mask] = [0] * 5
+
+    # dtype changes
+    s = Series([1, 2, 3, 4])
+    result = s.where(s > 2, np.nan)
+    expected = Series([np.nan, np.nan, 3, 4])
+    tm.assert_series_equal(result, expected)
+
+    # GH 4667
+    # setting with None changes dtype
+    s = Series(range(10)).astype(float)
+    s[8] = None
+    result = s[8]
+    assert isna(result)
+
+    s = Series(range(10)).astype(float)
+    s[s > 8] = None
+    result = s[isna(s)]
+    expected = Series(np.nan, index=[9])
+    tm.assert_series_equal(result, expected)
+
+
+def test_where():
+    s = Series(np.random.default_rng(2).standard_normal(5))
+    cond = s > 0
+
+    rs = s.where(cond).dropna()
+    rs2 = s[cond]
+    tm.assert_series_equal(rs, rs2)
+
+    rs = s.where(cond, -s)
+    tm.assert_series_equal(rs, s.abs())
+
+    rs = s.where(cond)
+    assert s.shape == rs.shape
+    assert rs is not s
+
+    # test alignment
+    cond = Series([True, False, False, True, False], index=s.index)
+    s2 = -(s.abs())
+
+    expected = s2[cond].reindex(s2.index[:3]).reindex(s2.index)
+    rs = s2.where(cond[:3])
+    tm.assert_series_equal(rs, expected)
+
+    expected = s2.abs()
+    expected.iloc[0] = s2[0]
+    rs = s2.where(cond[:3], -s2)
+    tm.assert_series_equal(rs, expected)
+
+
+def test_where_error():
+    s = Series(np.random.default_rng(2).standard_normal(5))
+    cond = s > 0
+
+    msg = "Array conditional must be same shape as self"
+    with pytest.raises(ValueError, match=msg):
+        s.where(1)
+    with pytest.raises(ValueError, match=msg):
+        s.where(cond[:3].values, -s)
+
+    # GH 2745
+    s = Series([1, 2])
+    s[[True, False]] = [0, 1]
+    expected = Series([0, 2])
+    tm.assert_series_equal(s, expected)
+
+    # failures
+    msg = "cannot set using a list-like indexer with a different length than the value"
+    with pytest.raises(ValueError, match=msg):
+        s[[True, False]] = [0, 2, 3]
+
+    with pytest.raises(ValueError, match=msg):
+        s[[True, False]] = []
+
+
+@pytest.mark.parametrize("klass", [list, tuple, np.array, Series])
+def test_where_array_like(klass):
+    # see gh-15414
+    s = Series([1, 2, 3])
+    cond = [False, True, True]
+    expected = Series([np.nan, 2, 3])
+
+    result = s.where(klass(cond))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "cond",
+    [
+        [1, 0, 1],
+        Series([2, 5, 7]),
+        ["True", "False", "True"],
+        [Timestamp("2017-01-01"), pd.NaT, Timestamp("2017-01-02")],
+    ],
+)
+def test_where_invalid_input(cond):
+    # see gh-15414: only boolean arrays accepted
+    s = Series([1, 2, 3])
+    msg = "Boolean array expected for the condition"
+
+    with pytest.raises(ValueError, match=msg):
+        s.where(cond)
+
+    msg = "Array conditional must be same shape as self"
+    with pytest.raises(ValueError, match=msg):
+        s.where([True])
+
+
+def test_where_ndframe_align():
+    msg = "Array conditional must be same shape as self"
+    s = Series([1, 2, 3])
+
+    cond = [True]
+    with pytest.raises(ValueError, match=msg):
+        s.where(cond)
+
+    expected = Series([1, np.nan, np.nan])
+
+    out = s.where(Series(cond))
+    tm.assert_series_equal(out, expected)
+
+    cond = np.array([False, True, False, True])
+    with pytest.raises(ValueError, match=msg):
+        s.where(cond)
+
+    expected = Series([np.nan, 2, np.nan])
+
+    out = s.where(Series(cond))
+    tm.assert_series_equal(out, expected)
+
+
+@pytest.mark.xfail(using_pyarrow_string_dtype(), reason="can't set ints into string")
+def test_where_setitem_invalid():
+    # GH 2702
+    # make sure correct exceptions are raised on invalid list assignment
+
+    msg = (
+        lambda x: f"cannot set using a {x} indexer with a "
+        "different length than the value"
+    )
+    # slice
+    s = Series(list("abc"))
+
+    with pytest.raises(ValueError, match=msg("slice")):
+        s[0:3] = list(range(27))
+
+    s[0:3] = list(range(3))
+    expected = Series([0, 1, 2])
+    tm.assert_series_equal(s.astype(np.int64), expected)
+
+    # slice with step
+    s = Series(list("abcdef"))
+
+    with pytest.raises(ValueError, match=msg("slice")):
+        s[0:4:2] = list(range(27))
+
+    s = Series(list("abcdef"))
+    s[0:4:2] = list(range(2))
+    expected = Series([0, "b", 1, "d", "e", "f"])
+    tm.assert_series_equal(s, expected)
+
+    # neg slices
+    s = Series(list("abcdef"))
+
+    with pytest.raises(ValueError, match=msg("slice")):
+        s[:-1] = list(range(27))
+
+    s[-3:-1] = list(range(2))
+    expected = Series(["a", "b", "c", 0, 1, "f"])
+    tm.assert_series_equal(s, expected)
+
+    # list
+    s = Series(list("abc"))
+
+    with pytest.raises(ValueError, match=msg("list-like")):
+        s[[0, 1, 2]] = list(range(27))
+
+    s = Series(list("abc"))
+
+    with pytest.raises(ValueError, match=msg("list-like")):
+        s[[0, 1, 2]] = list(range(2))
+
+    # scalar
+    s = Series(list("abc"))
+    s[0] = list(range(10))
+    expected = Series([list(range(10)), "b", "c"])
+    tm.assert_series_equal(s, 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_broadcast(size, mask, item, box):
+    # GH#8801, GH#4195
+    selection = np.resize(mask, size)
+
+    data = np.arange(size, dtype=float)
+
+    # Construct the expected series by taking the source
+    # data or item based on the selection
+    expected = Series(
+        [item if use_item else data[i] for i, use_item in enumerate(selection)]
+    )
+
+    s = Series(data)
+
+    s[selection] = item
+    tm.assert_series_equal(s, expected)
+
+    s = Series(data)
+    result = s.where(~selection, box(item))
+    tm.assert_series_equal(result, expected)
+
+    s = Series(data)
+    result = s.mask(selection, box(item))
+    tm.assert_series_equal(result, expected)
+
+
+def test_where_inplace():
+    s = Series(np.random.default_rng(2).standard_normal(5))
+    cond = s > 0
+
+    rs = s.copy()
+
+    rs.where(cond, inplace=True)
+    tm.assert_series_equal(rs.dropna(), s[cond])
+    tm.assert_series_equal(rs, s.where(cond))
+
+    rs = s.copy()
+    rs.where(cond, -s, inplace=True)
+    tm.assert_series_equal(rs, s.where(cond, -s))
+
+
+def test_where_dups():
+    # GH 4550
+    # where crashes with dups in index
+    s1 = Series(list(range(3)))
+    s2 = Series(list(range(3)))
+    comb = pd.concat([s1, s2])
+    result = comb.where(comb < 2)
+    expected = Series([0, 1, np.nan, 0, 1, np.nan], index=[0, 1, 2, 0, 1, 2])
+    tm.assert_series_equal(result, expected)
+
+    # GH 4548
+    # inplace updating not working with dups
+    comb[comb < 1] = 5
+    expected = Series([5, 1, 2, 5, 1, 2], index=[0, 1, 2, 0, 1, 2])
+    tm.assert_series_equal(comb, expected)
+
+    comb[comb < 2] += 10
+    expected = Series([5, 11, 2, 5, 11, 2], index=[0, 1, 2, 0, 1, 2])
+    tm.assert_series_equal(comb, expected)
+
+
+def test_where_numeric_with_string():
+    # GH 9280
+    s = Series([1, 2, 3])
+    w = s.where(s > 1, "X")
+
+    assert not is_integer(w[0])
+    assert is_integer(w[1])
+    assert is_integer(w[2])
+    assert isinstance(w[0], str)
+    assert w.dtype == "object"
+
+    w = s.where(s > 1, ["X", "Y", "Z"])
+    assert not is_integer(w[0])
+    assert is_integer(w[1])
+    assert is_integer(w[2])
+    assert isinstance(w[0], str)
+    assert w.dtype == "object"
+
+    w = s.where(s > 1, np.array(["X", "Y", "Z"]))
+    assert not is_integer(w[0])
+    assert is_integer(w[1])
+    assert is_integer(w[2])
+    assert isinstance(w[0], str)
+    assert w.dtype == "object"
+
+
+@pytest.mark.parametrize("dtype", ["timedelta64[ns]", "datetime64[ns]"])
+def test_where_datetimelike_coerce(dtype):
+    ser = Series([1, 2], dtype=dtype)
+    expected = Series([10, 10])
+    mask = np.array([False, False])
+
+    msg = "Downcasting behavior in Series and DataFrame methods 'where'"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        rs = ser.where(mask, [10, 10])
+    tm.assert_series_equal(rs, expected)
+
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        rs = ser.where(mask, 10)
+    tm.assert_series_equal(rs, expected)
+
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        rs = ser.where(mask, 10.0)
+    tm.assert_series_equal(rs, expected)
+
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        rs = ser.where(mask, [10.0, 10.0])
+    tm.assert_series_equal(rs, expected)
+
+    rs = ser.where(mask, [10.0, np.nan])
+    expected = Series([10, np.nan], dtype="object")
+    tm.assert_series_equal(rs, expected)
+
+
+def test_where_datetimetz():
+    # GH 15701
+    timestamps = ["2016-12-31 12:00:04+00:00", "2016-12-31 12:00:04.010000+00:00"]
+    ser = Series([Timestamp(t) for t in timestamps], dtype="datetime64[ns, UTC]")
+    rs = ser.where(Series([False, True]))
+    expected = Series([pd.NaT, ser[1]], dtype="datetime64[ns, UTC]")
+    tm.assert_series_equal(rs, expected)
+
+
+def test_where_sparse():
+    # GH#17198 make sure we dont get an AttributeError for sp_index
+    ser = Series(pd.arrays.SparseArray([1, 2]))
+    result = ser.where(ser >= 2, 0)
+    expected = Series(pd.arrays.SparseArray([0, 2]))
+    tm.assert_series_equal(result, expected)
+
+
+def test_where_empty_series_and_empty_cond_having_non_bool_dtypes():
+    # https://github.com/pandas-dev/pandas/issues/34592
+    ser = Series([], dtype=float)
+    result = ser.where([])
+    tm.assert_series_equal(result, ser)
+
+
+def test_where_categorical(frame_or_series):
+    # https://github.com/pandas-dev/pandas/issues/18888
+    exp = frame_or_series(
+        pd.Categorical(["A", "A", "B", "B", np.nan], categories=["A", "B", "C"]),
+        dtype="category",
+    )
+    df = frame_or_series(["A", "A", "B", "B", "C"], dtype="category")
+    res = df.where(df != "C")
+    tm.assert_equal(exp, res)
+
+
+def test_where_datetimelike_categorical(tz_naive_fixture):
+    # GH#37682
+    tz = tz_naive_fixture
+
+    dr = date_range("2001-01-01", periods=3, tz=tz)._with_freq(None)
+    lvals = pd.DatetimeIndex([dr[0], dr[1], pd.NaT])
+    rvals = pd.Categorical([dr[0], pd.NaT, dr[2]])
+
+    mask = np.array([True, True, False])
+
+    # DatetimeIndex.where
+    res = lvals.where(mask, rvals)
+    tm.assert_index_equal(res, dr)
+
+    # DatetimeArray.where
+    res = lvals._data._where(mask, rvals)
+    tm.assert_datetime_array_equal(res, dr._data)
+
+    # Series.where
+    res = Series(lvals).where(mask, rvals)
+    tm.assert_series_equal(res, Series(dr))
+
+    # DataFrame.where
+    res = pd.DataFrame(lvals).where(mask[:, None], pd.DataFrame(rvals))
+
+    tm.assert_frame_equal(res, pd.DataFrame(dr))

venv/lib/python3.12/site-packages/pandas/tests/series/indexing/test_xs.py

@@ -0,0 +1,82 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    MultiIndex,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+
+
+def test_xs_datetimelike_wrapping():
+    # GH#31630 a case where we shouldn't wrap datetime64 in Timestamp
+    arr = date_range("2016-01-01", periods=3)._data._ndarray
+
+    ser = Series(arr, dtype=object)
+    for i in range(len(ser)):
+        ser.iloc[i] = arr[i]
+    assert ser.dtype == object
+    assert isinstance(ser[0], np.datetime64)
+
+    result = ser.xs(0)
+    assert isinstance(result, np.datetime64)
+
+
+class TestXSWithMultiIndex:
+    def test_xs_level_series(self, multiindex_dataframe_random_data):
+        df = multiindex_dataframe_random_data
+        ser = df["A"]
+        expected = ser[:, "two"]
+        result = df.xs("two", level=1)["A"]
+        tm.assert_series_equal(result, expected)
+
+    def test_series_getitem_multiindex_xs_by_label(self):
+        # GH#5684
+        idx = MultiIndex.from_tuples(
+            [("a", "one"), ("a", "two"), ("b", "one"), ("b", "two")]
+        )
+        ser = Series([1, 2, 3, 4], index=idx)
+        return_value = ser.index.set_names(["L1", "L2"], inplace=True)
+        assert return_value is None
+        expected = Series([1, 3], index=["a", "b"])
+        return_value = expected.index.set_names(["L1"], inplace=True)
+        assert return_value is None
+
+        result = ser.xs("one", level="L2")
+        tm.assert_series_equal(result, expected)
+
+    def test_series_getitem_multiindex_xs(self):
+        # GH#6258
+        dt = list(date_range("20130903", periods=3))
+        idx = MultiIndex.from_product([list("AB"), dt])
+        ser = Series([1, 3, 4, 1, 3, 4], index=idx)
+        expected = Series([1, 1], index=list("AB"))
+
+        result = ser.xs("20130903", level=1)
+        tm.assert_series_equal(result, expected)
+
+    def test_series_xs_droplevel_false(self):
+        # GH: 19056
+        mi = MultiIndex.from_tuples(
+            [("a", "x"), ("a", "y"), ("b", "x")], names=["level1", "level2"]
+        )
+        ser = Series([1, 1, 1], index=mi)
+        result = ser.xs("a", axis=0, drop_level=False)
+        expected = Series(
+            [1, 1],
+            index=MultiIndex.from_tuples(
+                [("a", "x"), ("a", "y")], names=["level1", "level2"]
+            ),
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_xs_key_as_list(self):
+        # GH#41760
+        mi = MultiIndex.from_tuples([("a", "x")], names=["level1", "level2"])
+        ser = Series([1], index=mi)
+        with pytest.raises(TypeError, match="list keys are not supported"):
+            ser.xs(["a", "x"], axis=0, drop_level=False)
+
+        with pytest.raises(TypeError, match="list keys are not supported"):
+            ser.xs(["a"], axis=0, drop_level=False)

venv/lib/python3.12/site-packages/pandas/tests/series/methods/__init__.py

@@ -0,0 +1,7 @@
+"""
+Test files dedicated to individual (stand-alone) Series methods
+
+Ideally these files/tests should correspond 1-to-1 with tests.frame.methods
+
+These may also present opportunities for sharing/de-duplicating test code.
+"""