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@@ -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)
diff --git a/venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_delitem.py b/venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_delitem.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d1082c3d040b95063d10c7160154b62dc1c84f6
--- /dev/null
+++ b/venv/lib/python3.10/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)
diff --git a/venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_get.py b/venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_get.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f3711ad919036a779c5357e3e6ae921bb795458
--- /dev/null
+++ b/venv/lib/python3.10/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
diff --git a/venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_getitem.py b/venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_getitem.py
new file mode 100644
index 0000000000000000000000000000000000000000..596a225c288b890461d4ea5d03bef6f6eb1bc04b
--- /dev/null
+++ b/venv/lib/python3.10/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
diff --git a/venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_indexing.py b/venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_indexing.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4992b758af74610286c45a386ccbf469a9a0b9d
--- /dev/null
+++ b/venv/lib/python3.10/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)
diff --git a/venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_mask.py b/venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_mask.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c21cd0d5ca648dcf0f1ac412dd232221c031c6f
--- /dev/null
+++ b/venv/lib/python3.10/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))
diff --git a/venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_set_value.py b/venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_set_value.py
new file mode 100644
index 0000000000000000000000000000000000000000..cbe1a8bf296c8106c2cfa3ad519b0a7d44401493
--- /dev/null
+++ b/venv/lib/python3.10/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
diff --git a/venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_setitem.py b/venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_setitem.py
new file mode 100644
index 0000000000000000000000000000000000000000..23137f0975fb18425d137e84e7d04bf39950c8f1
--- /dev/null
+++ b/venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_setitem.py
@@ -0,0 +1,1847 @@
+from datetime import (
+    date,
+    datetime,
+)
+from decimal import Decimal
+
+import numpy as np
+import pytest
+
+from pandas.compat.numpy import np_version_gte1p24
+from pandas.errors import IndexingError
+
+from pandas.core.dtypes.common import is_list_like
+
+from pandas import (
+    NA,
+    Categorical,
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    Interval,
+    IntervalIndex,
+    MultiIndex,
+    NaT,
+    Period,
+    Series,
+    Timedelta,
+    Timestamp,
+    array,
+    concat,
+    date_range,
+    interval_range,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+from pandas.tseries.offsets import BDay
+
+
+class TestSetitemDT64Values:
+    def test_setitem_none_nan(self):
+        series = Series(date_range("1/1/2000", periods=10))
+        series[3] = None
+        assert series[3] is NaT
+
+        series[3:5] = None
+        assert series[4] is NaT
+
+        series[5] = np.nan
+        assert series[5] is NaT
+
+        series[5:7] = np.nan
+        assert series[6] is NaT
+
+    def test_setitem_multiindex_empty_slice(self):
+        # https://github.com/pandas-dev/pandas/issues/35878
+        idx = MultiIndex.from_tuples([("a", 1), ("b", 2)])
+        result = Series([1, 2], index=idx)
+        expected = result.copy()
+        result.loc[[]] = 0
+        tm.assert_series_equal(result, expected)
+
+    def test_setitem_with_string_index(self):
+        # GH#23451
+        # Set object dtype to avoid upcast when setting date.today()
+        ser = Series([1, 2, 3], index=["Date", "b", "other"], dtype=object)
+        ser["Date"] = date.today()
+        assert ser.Date == date.today()
+        assert ser["Date"] == date.today()
+
+    def test_setitem_tuple_with_datetimetz_values(self):
+        # GH#20441
+        arr = date_range("2017", periods=4, tz="US/Eastern")
+        index = [(0, 1), (0, 2), (0, 3), (0, 4)]
+        result = Series(arr, index=index)
+        expected = result.copy()
+        result[(0, 1)] = np.nan
+        expected.iloc[0] = np.nan
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("tz", ["US/Eastern", "UTC", "Asia/Tokyo"])
+    def test_setitem_with_tz(self, tz, indexer_sli):
+        orig = Series(date_range("2016-01-01", freq="h", periods=3, tz=tz))
+        assert orig.dtype == f"datetime64[ns, {tz}]"
+
+        exp = Series(
+            [
+                Timestamp("2016-01-01 00:00", tz=tz),
+                Timestamp("2011-01-01 00:00", tz=tz),
+                Timestamp("2016-01-01 02:00", tz=tz),
+            ],
+            dtype=orig.dtype,
+        )
+
+        # scalar
+        ser = orig.copy()
+        indexer_sli(ser)[1] = Timestamp("2011-01-01", tz=tz)
+        tm.assert_series_equal(ser, exp)
+
+        # vector
+        vals = Series(
+            [Timestamp("2011-01-01", tz=tz), Timestamp("2012-01-01", tz=tz)],
+            index=[1, 2],
+            dtype=orig.dtype,
+        )
+        assert vals.dtype == f"datetime64[ns, {tz}]"
+
+        exp = Series(
+            [
+                Timestamp("2016-01-01 00:00", tz=tz),
+                Timestamp("2011-01-01 00:00", tz=tz),
+                Timestamp("2012-01-01 00:00", tz=tz),
+            ],
+            dtype=orig.dtype,
+        )
+
+        ser = orig.copy()
+        indexer_sli(ser)[[1, 2]] = vals
+        tm.assert_series_equal(ser, exp)
+
+    def test_setitem_with_tz_dst(self, indexer_sli):
+        # GH#14146 trouble setting values near DST boundary
+        tz = "US/Eastern"
+        orig = Series(date_range("2016-11-06", freq="h", periods=3, tz=tz))
+        assert orig.dtype == f"datetime64[ns, {tz}]"
+
+        exp = Series(
+            [
+                Timestamp("2016-11-06 00:00-04:00", tz=tz),
+                Timestamp("2011-01-01 00:00-05:00", tz=tz),
+                Timestamp("2016-11-06 01:00-05:00", tz=tz),
+            ],
+            dtype=orig.dtype,
+        )
+
+        # scalar
+        ser = orig.copy()
+        indexer_sli(ser)[1] = Timestamp("2011-01-01", tz=tz)
+        tm.assert_series_equal(ser, exp)
+
+        # vector
+        vals = Series(
+            [Timestamp("2011-01-01", tz=tz), Timestamp("2012-01-01", tz=tz)],
+            index=[1, 2],
+            dtype=orig.dtype,
+        )
+        assert vals.dtype == f"datetime64[ns, {tz}]"
+
+        exp = Series(
+            [
+                Timestamp("2016-11-06 00:00", tz=tz),
+                Timestamp("2011-01-01 00:00", tz=tz),
+                Timestamp("2012-01-01 00:00", tz=tz),
+            ],
+            dtype=orig.dtype,
+        )
+
+        ser = orig.copy()
+        indexer_sli(ser)[[1, 2]] = vals
+        tm.assert_series_equal(ser, exp)
+
+    def test_object_series_setitem_dt64array_exact_match(self):
+        # make sure the dt64 isn't cast by numpy to integers
+        # https://github.com/numpy/numpy/issues/12550
+
+        ser = Series({"X": np.nan}, dtype=object)
+
+        indexer = [True]
+
+        # "exact_match" -> size of array being set matches size of ser
+        value = np.array([4], dtype="M8[ns]")
+
+        ser.iloc[indexer] = value
+
+        expected = Series([value[0]], index=["X"], dtype=object)
+        assert all(isinstance(x, np.datetime64) for x in expected.values)
+
+        tm.assert_series_equal(ser, expected)
+
+
+class TestSetitemScalarIndexer:
+    def test_setitem_negative_out_of_bounds(self):
+        ser = Series(["a"] * 10, index=["a"] * 10)
+
+        # string index falls back to positional
+        msg = "index -11|-1 is out of bounds for axis 0 with size 10"
+        warn_msg = "Series.__setitem__ treating keys as positions is deprecated"
+        with pytest.raises(IndexError, match=msg):
+            with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+                ser[-11] = "foo"
+
+    @pytest.mark.parametrize("indexer", [tm.loc, tm.at])
+    @pytest.mark.parametrize("ser_index", [0, 1])
+    def test_setitem_series_object_dtype(self, indexer, ser_index):
+        # GH#38303
+        ser = Series([0, 0], dtype="object")
+        idxr = indexer(ser)
+        idxr[0] = Series([42], index=[ser_index])
+        expected = Series([Series([42], index=[ser_index]), 0], dtype="object")
+        tm.assert_series_equal(ser, expected)
+
+    @pytest.mark.parametrize("index, exp_value", [(0, 42), (1, np.nan)])
+    def test_setitem_series(self, index, exp_value):
+        # GH#38303
+        ser = Series([0, 0])
+        ser.loc[0] = Series([42], index=[index])
+        expected = Series([exp_value, 0])
+        tm.assert_series_equal(ser, expected)
+
+
+class TestSetitemSlices:
+    def test_setitem_slice_float_raises(self, datetime_series):
+        msg = (
+            "cannot do slice indexing on DatetimeIndex with these indexers "
+            r"\[{key}\] of type float"
+        )
+        with pytest.raises(TypeError, match=msg.format(key=r"4\.0")):
+            datetime_series[4.0:10.0] = 0
+
+        with pytest.raises(TypeError, match=msg.format(key=r"4\.5")):
+            datetime_series[4.5:10.0] = 0
+
+    def test_setitem_slice(self):
+        ser = Series(range(10), index=list(range(10)))
+        ser[-12:] = 0
+        assert (ser == 0).all()
+
+        ser[:-12] = 5
+        assert (ser == 0).all()
+
+    def test_setitem_slice_integers(self):
+        ser = Series(
+            np.random.default_rng(2).standard_normal(8),
+            index=[2, 4, 6, 8, 10, 12, 14, 16],
+        )
+
+        ser[:4] = 0
+        assert (ser[:4] == 0).all()
+        assert not (ser[4:] == 0).any()
+
+    def test_setitem_slicestep(self):
+        # caught this bug when writing tests
+        series = Series(
+            np.arange(20, dtype=np.float64), index=np.arange(20, dtype=np.int64)
+        )
+
+        series[::2] = 0
+        assert (series[::2] == 0).all()
+
+    def test_setitem_multiindex_slice(self, indexer_sli):
+        # GH 8856
+        mi = MultiIndex.from_product(([0, 1], list("abcde")))
+        result = Series(np.arange(10, dtype=np.int64), mi)
+        indexer_sli(result)[::4] = 100
+        expected = Series([100, 1, 2, 3, 100, 5, 6, 7, 100, 9], mi)
+        tm.assert_series_equal(result, expected)
+
+
+class TestSetitemBooleanMask:
+    def test_setitem_mask_cast(self):
+        # GH#2746
+        # need to upcast
+        ser = Series([1, 2], index=[1, 2], dtype="int64")
+        ser[[True, False]] = Series([0], index=[1], dtype="int64")
+        expected = Series([0, 2], index=[1, 2], dtype="int64")
+
+        tm.assert_series_equal(ser, expected)
+
+    def test_setitem_mask_align_and_promote(self):
+        # GH#8387: test that changing types does not break alignment
+        ts = Series(
+            np.random.default_rng(2).standard_normal(100), index=np.arange(100, 0, -1)
+        ).round(5)
+        mask = ts > 0
+        left = ts.copy()
+        right = ts[mask].copy().map(str)
+        with tm.assert_produces_warning(
+            FutureWarning, match="item of incompatible dtype"
+        ):
+            left[mask] = right
+        expected = ts.map(lambda t: str(t) if t > 0 else t)
+        tm.assert_series_equal(left, expected)
+
+    def test_setitem_mask_promote_strs(self):
+        ser = Series([0, 1, 2, 0])
+        mask = ser > 0
+        ser2 = ser[mask].map(str)
+        with tm.assert_produces_warning(
+            FutureWarning, match="item of incompatible dtype"
+        ):
+            ser[mask] = ser2
+
+        expected = Series([0, "1", "2", 0])
+        tm.assert_series_equal(ser, expected)
+
+    def test_setitem_mask_promote(self):
+        ser = Series([0, "foo", "bar", 0])
+        mask = Series([False, True, True, False])
+        ser2 = ser[mask]
+        ser[mask] = ser2
+
+        expected = Series([0, "foo", "bar", 0])
+        tm.assert_series_equal(ser, expected)
+
+    def test_setitem_boolean(self, string_series):
+        mask = string_series > string_series.median()
+
+        # similar indexed series
+        result = string_series.copy()
+        result[mask] = string_series * 2
+        expected = string_series * 2
+        tm.assert_series_equal(result[mask], expected[mask])
+
+        # needs alignment
+        result = string_series.copy()
+        result[mask] = (string_series * 2)[0:5]
+        expected = (string_series * 2)[0:5].reindex_like(string_series)
+        expected[-mask] = string_series[mask]
+        tm.assert_series_equal(result[mask], expected[mask])
+
+    def test_setitem_boolean_corner(self, datetime_series):
+        ts = datetime_series
+        mask_shifted = ts.shift(1, freq=BDay()) > ts.median()
+
+        msg = (
+            r"Unalignable boolean Series provided as indexer \(index of "
+            r"the boolean Series and of the indexed object do not match"
+        )
+        with pytest.raises(IndexingError, match=msg):
+            ts[mask_shifted] = 1
+
+        with pytest.raises(IndexingError, match=msg):
+            ts.loc[mask_shifted] = 1
+
+    def test_setitem_boolean_different_order(self, string_series):
+        ordered = string_series.sort_values()
+
+        copy = string_series.copy()
+        copy[ordered > 0] = 0
+
+        expected = string_series.copy()
+        expected[expected > 0] = 0
+
+        tm.assert_series_equal(copy, expected)
+
+    @pytest.mark.parametrize("func", [list, np.array, Series])
+    def test_setitem_boolean_python_list(self, func):
+        # GH19406
+        ser = Series([None, "b", None])
+        mask = func([True, False, True])
+        ser[mask] = ["a", "c"]
+        expected = Series(["a", "b", "c"])
+        tm.assert_series_equal(ser, expected)
+
+    def test_setitem_boolean_nullable_int_types(self, any_numeric_ea_dtype):
+        # GH: 26468
+        ser = Series([5, 6, 7, 8], dtype=any_numeric_ea_dtype)
+        ser[ser > 6] = Series(range(4), dtype=any_numeric_ea_dtype)
+        expected = Series([5, 6, 2, 3], dtype=any_numeric_ea_dtype)
+        tm.assert_series_equal(ser, expected)
+
+        ser = Series([5, 6, 7, 8], dtype=any_numeric_ea_dtype)
+        ser.loc[ser > 6] = Series(range(4), dtype=any_numeric_ea_dtype)
+        tm.assert_series_equal(ser, expected)
+
+        ser = Series([5, 6, 7, 8], dtype=any_numeric_ea_dtype)
+        loc_ser = Series(range(4), dtype=any_numeric_ea_dtype)
+        ser.loc[ser > 6] = loc_ser.loc[loc_ser > 1]
+        tm.assert_series_equal(ser, expected)
+
+    def test_setitem_with_bool_mask_and_values_matching_n_trues_in_length(self):
+        # GH#30567
+        ser = Series([None] * 10)
+        mask = [False] * 3 + [True] * 5 + [False] * 2
+        ser[mask] = range(5)
+        result = ser
+        expected = Series([None] * 3 + list(range(5)) + [None] * 2, dtype=object)
+        tm.assert_series_equal(result, expected)
+
+    def test_setitem_nan_with_bool(self):
+        # GH 13034
+        result = Series([True, False, True])
+        with tm.assert_produces_warning(
+            FutureWarning, match="item of incompatible dtype"
+        ):
+            result[0] = np.nan
+        expected = Series([np.nan, False, True], dtype=object)
+        tm.assert_series_equal(result, expected)
+
+    def test_setitem_mask_smallint_upcast(self):
+        orig = Series([1, 2, 3], dtype="int8")
+        alt = np.array([999, 1000, 1001], dtype=np.int64)
+
+        mask = np.array([True, False, True])
+
+        ser = orig.copy()
+        with tm.assert_produces_warning(
+            FutureWarning, match="item of incompatible dtype"
+        ):
+            ser[mask] = Series(alt)
+        expected = Series([999, 2, 1001])
+        tm.assert_series_equal(ser, expected)
+
+        ser2 = orig.copy()
+        with tm.assert_produces_warning(
+            FutureWarning, match="item of incompatible dtype"
+        ):
+            ser2.mask(mask, alt, inplace=True)
+        tm.assert_series_equal(ser2, expected)
+
+        ser3 = orig.copy()
+        res = ser3.where(~mask, Series(alt))
+        tm.assert_series_equal(res, expected)
+
+    def test_setitem_mask_smallint_no_upcast(self):
+        # like test_setitem_mask_smallint_upcast, but while we can't hold 'alt',
+        #  we *can* hold alt[mask] without casting
+        orig = Series([1, 2, 3], dtype="uint8")
+        alt = Series([245, 1000, 246], dtype=np.int64)
+
+        mask = np.array([True, False, True])
+
+        ser = orig.copy()
+        ser[mask] = alt
+        expected = Series([245, 2, 246], dtype="uint8")
+        tm.assert_series_equal(ser, expected)
+
+        ser2 = orig.copy()
+        ser2.mask(mask, alt, inplace=True)
+        tm.assert_series_equal(ser2, expected)
+
+        # TODO: ser.where(~mask, alt) unnecessarily upcasts to int64
+        ser3 = orig.copy()
+        res = ser3.where(~mask, alt)
+        tm.assert_series_equal(res, expected, check_dtype=False)
+
+
+class TestSetitemViewCopySemantics:
+    def test_setitem_invalidates_datetime_index_freq(self, using_copy_on_write):
+        # GH#24096 altering a datetime64tz Series inplace invalidates the
+        #  `freq` attribute on the underlying DatetimeIndex
+
+        dti = date_range("20130101", periods=3, tz="US/Eastern")
+        ts = dti[1]
+        ser = Series(dti)
+        assert ser._values is not dti
+        if using_copy_on_write:
+            assert ser._values._ndarray.base is dti._data._ndarray.base
+        else:
+            assert ser._values._ndarray.base is not dti._data._ndarray.base
+        assert dti.freq == "D"
+        ser.iloc[1] = NaT
+        assert ser._values.freq is None
+
+        # check that the DatetimeIndex was not altered in place
+        assert ser._values is not dti
+        assert ser._values._ndarray.base is not dti._data._ndarray.base
+        assert dti[1] == ts
+        assert dti.freq == "D"
+
+    def test_dt64tz_setitem_does_not_mutate_dti(self, using_copy_on_write):
+        # GH#21907, GH#24096
+        dti = date_range("2016-01-01", periods=10, tz="US/Pacific")
+        ts = dti[0]
+        ser = Series(dti)
+        assert ser._values is not dti
+        if using_copy_on_write:
+            assert ser._values._ndarray.base is dti._data._ndarray.base
+            assert ser._mgr.arrays[0]._ndarray.base is dti._data._ndarray.base
+        else:
+            assert ser._values._ndarray.base is not dti._data._ndarray.base
+            assert ser._mgr.arrays[0]._ndarray.base is not dti._data._ndarray.base
+
+        assert ser._mgr.arrays[0] is not dti
+
+        ser[::3] = NaT
+        assert ser[0] is NaT
+        assert dti[0] == ts
+
+
+class TestSetitemCallable:
+    def test_setitem_callable_key(self):
+        # GH#12533
+        ser = Series([1, 2, 3, 4], index=list("ABCD"))
+        ser[lambda x: "A"] = -1
+
+        expected = Series([-1, 2, 3, 4], index=list("ABCD"))
+        tm.assert_series_equal(ser, expected)
+
+    def test_setitem_callable_other(self):
+        # GH#13299
+        inc = lambda x: x + 1
+
+        # set object dtype to avoid upcast when setting inc
+        ser = Series([1, 2, -1, 4], dtype=object)
+        ser[ser < 0] = inc
+
+        expected = Series([1, 2, inc, 4])
+        tm.assert_series_equal(ser, expected)
+
+
+class TestSetitemWithExpansion:
+    def test_setitem_empty_series(self):
+        # GH#10193
+        key = Timestamp("2012-01-01")
+        series = Series(dtype=object)
+        series[key] = 47
+        expected = Series(47, [key])
+        tm.assert_series_equal(series, expected)
+
+    def test_setitem_empty_series_datetimeindex_preserves_freq(self):
+        # GH#33573 our index should retain its freq
+        dti = DatetimeIndex([], freq="D", dtype="M8[ns]")
+        series = Series([], index=dti, dtype=object)
+        key = Timestamp("2012-01-01")
+        series[key] = 47
+        expected = Series(47, DatetimeIndex([key], freq="D").as_unit("ns"))
+        tm.assert_series_equal(series, expected)
+        assert series.index.freq == expected.index.freq
+
+    def test_setitem_empty_series_timestamp_preserves_dtype(self):
+        # GH 21881
+        timestamp = Timestamp(1412526600000000000)
+        series = Series([timestamp], index=["timestamp"], dtype=object)
+        expected = series["timestamp"]
+
+        series = Series([], dtype=object)
+        series["anything"] = 300.0
+        series["timestamp"] = timestamp
+        result = series["timestamp"]
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "td",
+        [
+            Timedelta("9 days"),
+            Timedelta("9 days").to_timedelta64(),
+            Timedelta("9 days").to_pytimedelta(),
+        ],
+    )
+    def test_append_timedelta_does_not_cast(self, td, using_infer_string, request):
+        # GH#22717 inserting a Timedelta should _not_ cast to int64
+        if using_infer_string and not isinstance(td, Timedelta):
+            # TODO: GH#56010
+            request.applymarker(pytest.mark.xfail(reason="inferred as string"))
+
+        expected = Series(["x", td], index=[0, "td"], dtype=object)
+
+        ser = Series(["x"])
+        ser["td"] = td
+        tm.assert_series_equal(ser, expected)
+        assert isinstance(ser["td"], Timedelta)
+
+        ser = Series(["x"])
+        ser.loc["td"] = Timedelta("9 days")
+        tm.assert_series_equal(ser, expected)
+        assert isinstance(ser["td"], Timedelta)
+
+    def test_setitem_with_expansion_type_promotion(self):
+        # GH#12599
+        ser = Series(dtype=object)
+        ser["a"] = Timestamp("2016-01-01")
+        ser["b"] = 3.0
+        ser["c"] = "foo"
+        expected = Series([Timestamp("2016-01-01"), 3.0, "foo"], index=["a", "b", "c"])
+        tm.assert_series_equal(ser, expected)
+
+    def test_setitem_not_contained(self, string_series):
+        # set item that's not contained
+        ser = string_series.copy()
+        assert "foobar" not in ser.index
+        ser["foobar"] = 1
+
+        app = Series([1], index=["foobar"], name="series")
+        expected = concat([string_series, app])
+        tm.assert_series_equal(ser, expected)
+
+    def test_setitem_keep_precision(self, any_numeric_ea_dtype):
+        # GH#32346
+        ser = Series([1, 2], dtype=any_numeric_ea_dtype)
+        ser[2] = 10
+        expected = Series([1, 2, 10], dtype=any_numeric_ea_dtype)
+        tm.assert_series_equal(ser, expected)
+
+    @pytest.mark.parametrize(
+        "na, target_na, dtype, target_dtype, indexer, warn",
+        [
+            (NA, NA, "Int64", "Int64", 1, None),
+            (NA, NA, "Int64", "Int64", 2, None),
+            (NA, np.nan, "int64", "float64", 1, None),
+            (NA, np.nan, "int64", "float64", 2, None),
+            (NaT, NaT, "int64", "object", 1, FutureWarning),
+            (NaT, NaT, "int64", "object", 2, None),
+            (np.nan, NA, "Int64", "Int64", 1, None),
+            (np.nan, NA, "Int64", "Int64", 2, None),
+            (np.nan, NA, "Float64", "Float64", 1, None),
+            (np.nan, NA, "Float64", "Float64", 2, None),
+            (np.nan, np.nan, "int64", "float64", 1, None),
+            (np.nan, np.nan, "int64", "float64", 2, None),
+        ],
+    )
+    def test_setitem_enlarge_with_na(
+        self, na, target_na, dtype, target_dtype, indexer, warn
+    ):
+        # GH#32346
+        ser = Series([1, 2], dtype=dtype)
+        with tm.assert_produces_warning(warn, match="incompatible dtype"):
+            ser[indexer] = na
+        expected_values = [1, target_na] if indexer == 1 else [1, 2, target_na]
+        expected = Series(expected_values, dtype=target_dtype)
+        tm.assert_series_equal(ser, expected)
+
+    def test_setitem_enlargement_object_none(self, nulls_fixture, using_infer_string):
+        # GH#48665
+        ser = Series(["a", "b"])
+        ser[3] = nulls_fixture
+        dtype = (
+            "string[pyarrow_numpy]"
+            if using_infer_string and not isinstance(nulls_fixture, Decimal)
+            else object
+        )
+        expected = Series(["a", "b", nulls_fixture], index=[0, 1, 3], dtype=dtype)
+        tm.assert_series_equal(ser, expected)
+        if using_infer_string:
+            ser[3] is np.nan
+        else:
+            assert ser[3] is nulls_fixture
+
+
+def test_setitem_scalar_into_readonly_backing_data():
+    # GH#14359: test that you cannot mutate a read only buffer
+
+    array = np.zeros(5)
+    array.flags.writeable = False  # make the array immutable
+    series = Series(array, copy=False)
+
+    for n in series.index:
+        msg = "assignment destination is read-only"
+        with pytest.raises(ValueError, match=msg):
+            series[n] = 1
+
+        assert array[n] == 0
+
+
+def test_setitem_slice_into_readonly_backing_data():
+    # GH#14359: test that you cannot mutate a read only buffer
+
+    array = np.zeros(5)
+    array.flags.writeable = False  # make the array immutable
+    series = Series(array, copy=False)
+
+    msg = "assignment destination is read-only"
+    with pytest.raises(ValueError, match=msg):
+        series[1:3] = 1
+
+    assert not array.any()
+
+
+def test_setitem_categorical_assigning_ops():
+    orig = Series(Categorical(["b", "b"], categories=["a", "b"]))
+    ser = orig.copy()
+    ser[:] = "a"
+    exp = Series(Categorical(["a", "a"], categories=["a", "b"]))
+    tm.assert_series_equal(ser, exp)
+
+    ser = orig.copy()
+    ser[1] = "a"
+    exp = Series(Categorical(["b", "a"], categories=["a", "b"]))
+    tm.assert_series_equal(ser, exp)
+
+    ser = orig.copy()
+    ser[ser.index > 0] = "a"
+    exp = Series(Categorical(["b", "a"], categories=["a", "b"]))
+    tm.assert_series_equal(ser, exp)
+
+    ser = orig.copy()
+    ser[[False, True]] = "a"
+    exp = Series(Categorical(["b", "a"], categories=["a", "b"]))
+    tm.assert_series_equal(ser, exp)
+
+    ser = orig.copy()
+    ser.index = ["x", "y"]
+    ser["y"] = "a"
+    exp = Series(Categorical(["b", "a"], categories=["a", "b"]), index=["x", "y"])
+    tm.assert_series_equal(ser, exp)
+
+
+def test_setitem_nan_into_categorical():
+    # ensure that one can set something to np.nan
+    ser = Series(Categorical([1, 2, 3]))
+    exp = Series(Categorical([1, np.nan, 3], categories=[1, 2, 3]))
+    ser[1] = np.nan
+    tm.assert_series_equal(ser, exp)
+
+
+class TestSetitemCasting:
+    @pytest.mark.parametrize("unique", [True, False])
+    @pytest.mark.parametrize("val", [3, 3.0, "3"], ids=type)
+    def test_setitem_non_bool_into_bool(self, val, indexer_sli, unique):
+        # dont cast these 3-like values to bool
+        ser = Series([True, False])
+        if not unique:
+            ser.index = [1, 1]
+
+        with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+            indexer_sli(ser)[1] = val
+        assert type(ser.iloc[1]) == type(val)
+
+        expected = Series([True, val], dtype=object, index=ser.index)
+        if not unique and indexer_sli is not tm.iloc:
+            expected = Series([val, val], dtype=object, index=[1, 1])
+        tm.assert_series_equal(ser, expected)
+
+    def test_setitem_boolean_array_into_npbool(self):
+        # GH#45462
+        ser = Series([True, False, True])
+        values = ser._values
+        arr = array([True, False, None])
+
+        ser[:2] = arr[:2]  # no NAs -> can set inplace
+        assert ser._values is values
+
+        with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+            ser[1:] = arr[1:]  # has an NA -> cast to boolean dtype
+        expected = Series(arr)
+        tm.assert_series_equal(ser, expected)
+
+
+class SetitemCastingEquivalents:
+    """
+    Check each of several methods that _should_ be equivalent to `obj[key] = val`
+
+    We assume that
+        - obj.index is the default Index(range(len(obj)))
+        - the setitem does not expand the obj
+    """
+
+    @pytest.fixture
+    def is_inplace(self, obj, expected):
+        """
+        Whether we expect the setting to be in-place or not.
+        """
+        return expected.dtype == obj.dtype
+
+    def check_indexer(self, obj, key, expected, val, indexer, is_inplace):
+        orig = obj
+        obj = obj.copy()
+        arr = obj._values
+
+        indexer(obj)[key] = val
+        tm.assert_series_equal(obj, expected)
+
+        self._check_inplace(is_inplace, orig, arr, obj)
+
+    def _check_inplace(self, is_inplace, orig, arr, obj):
+        if is_inplace is None:
+            # We are not (yet) checking whether setting is inplace or not
+            pass
+        elif is_inplace:
+            if arr.dtype.kind in ["m", "M"]:
+                # We may not have the same DTA/TDA, but will have the same
+                #  underlying data
+                assert arr._ndarray is obj._values._ndarray
+            else:
+                assert obj._values is arr
+        else:
+            # otherwise original array should be unchanged
+            tm.assert_equal(arr, orig._values)
+
+    def test_int_key(self, obj, key, expected, warn, val, indexer_sli, is_inplace):
+        if not isinstance(key, int):
+            pytest.skip("Not relevant for int key")
+
+        with tm.assert_produces_warning(warn, match="incompatible dtype"):
+            self.check_indexer(obj, key, expected, val, indexer_sli, is_inplace)
+
+        if indexer_sli is tm.loc:
+            with tm.assert_produces_warning(warn, match="incompatible dtype"):
+                self.check_indexer(obj, key, expected, val, tm.at, is_inplace)
+        elif indexer_sli is tm.iloc:
+            with tm.assert_produces_warning(warn, match="incompatible dtype"):
+                self.check_indexer(obj, key, expected, val, tm.iat, is_inplace)
+
+        rng = range(key, key + 1)
+        with tm.assert_produces_warning(warn, match="incompatible dtype"):
+            self.check_indexer(obj, rng, expected, val, indexer_sli, is_inplace)
+
+        if indexer_sli is not tm.loc:
+            # Note: no .loc because that handles slice edges differently
+            slc = slice(key, key + 1)
+            with tm.assert_produces_warning(warn, match="incompatible dtype"):
+                self.check_indexer(obj, slc, expected, val, indexer_sli, is_inplace)
+
+        ilkey = [key]
+        with tm.assert_produces_warning(warn, match="incompatible dtype"):
+            self.check_indexer(obj, ilkey, expected, val, indexer_sli, is_inplace)
+
+        indkey = np.array(ilkey)
+        with tm.assert_produces_warning(warn, match="incompatible dtype"):
+            self.check_indexer(obj, indkey, expected, val, indexer_sli, is_inplace)
+
+        genkey = (x for x in [key])
+        with tm.assert_produces_warning(warn, match="incompatible dtype"):
+            self.check_indexer(obj, genkey, expected, val, indexer_sli, is_inplace)
+
+    def test_slice_key(self, obj, key, expected, warn, val, indexer_sli, is_inplace):
+        if not isinstance(key, slice):
+            pytest.skip("Not relevant for slice key")
+
+        if indexer_sli is not tm.loc:
+            # Note: no .loc because that handles slice edges differently
+            with tm.assert_produces_warning(warn, match="incompatible dtype"):
+                self.check_indexer(obj, key, expected, val, indexer_sli, is_inplace)
+
+        ilkey = list(range(len(obj)))[key]
+        with tm.assert_produces_warning(warn, match="incompatible dtype"):
+            self.check_indexer(obj, ilkey, expected, val, indexer_sli, is_inplace)
+
+        indkey = np.array(ilkey)
+        with tm.assert_produces_warning(warn, match="incompatible dtype"):
+            self.check_indexer(obj, indkey, expected, val, indexer_sli, is_inplace)
+
+        genkey = (x for x in indkey)
+        with tm.assert_produces_warning(warn, match="incompatible dtype"):
+            self.check_indexer(obj, genkey, expected, val, indexer_sli, is_inplace)
+
+    def test_mask_key(self, obj, key, expected, warn, val, indexer_sli):
+        # setitem with boolean mask
+        mask = np.zeros(obj.shape, dtype=bool)
+        mask[key] = True
+
+        obj = obj.copy()
+
+        if is_list_like(val) and len(val) < mask.sum():
+            msg = "boolean index did not match indexed array along dimension"
+            with pytest.raises(IndexError, match=msg):
+                indexer_sli(obj)[mask] = val
+            return
+
+        with tm.assert_produces_warning(warn, match="incompatible dtype"):
+            indexer_sli(obj)[mask] = val
+        tm.assert_series_equal(obj, expected)
+
+    def test_series_where(self, obj, key, expected, warn, val, is_inplace):
+        mask = np.zeros(obj.shape, dtype=bool)
+        mask[key] = True
+
+        if is_list_like(val) and len(val) < len(obj):
+            # Series.where is not valid here
+            msg = "operands could not be broadcast together with shapes"
+            with pytest.raises(ValueError, match=msg):
+                obj.where(~mask, val)
+            return
+
+        orig = obj
+        obj = obj.copy()
+        arr = obj._values
+
+        res = obj.where(~mask, val)
+
+        if val is NA and res.dtype == object:
+            expected = expected.fillna(NA)
+        elif val is None and res.dtype == object:
+            assert expected.dtype == object
+            expected = expected.copy()
+            expected[expected.isna()] = None
+        tm.assert_series_equal(res, expected)
+
+        self._check_inplace(is_inplace, orig, arr, obj)
+
+    def test_index_where(self, obj, key, expected, warn, val, using_infer_string):
+        mask = np.zeros(obj.shape, dtype=bool)
+        mask[key] = True
+
+        if using_infer_string and obj.dtype == object:
+            with pytest.raises(TypeError, match="Scalar must"):
+                Index(obj).where(~mask, val)
+        else:
+            res = Index(obj).where(~mask, val)
+            expected_idx = Index(expected, dtype=expected.dtype)
+            tm.assert_index_equal(res, expected_idx)
+
+    def test_index_putmask(self, obj, key, expected, warn, val, using_infer_string):
+        mask = np.zeros(obj.shape, dtype=bool)
+        mask[key] = True
+
+        if using_infer_string and obj.dtype == object:
+            with pytest.raises(TypeError, match="Scalar must"):
+                Index(obj).putmask(mask, val)
+        else:
+            res = Index(obj).putmask(mask, val)
+            tm.assert_index_equal(res, Index(expected, dtype=expected.dtype))
+
+
+@pytest.mark.parametrize(
+    "obj,expected,key,warn",
+    [
+        pytest.param(
+            # GH#45568 setting a valid NA value into IntervalDtype[int] should
+            #  cast to IntervalDtype[float]
+            Series(interval_range(1, 5)),
+            Series(
+                [Interval(1, 2), np.nan, Interval(3, 4), Interval(4, 5)],
+                dtype="interval[float64]",
+            ),
+            1,
+            FutureWarning,
+            id="interval_int_na_value",
+        ),
+        pytest.param(
+            # these induce dtype changes
+            Series([2, 3, 4, 5, 6, 7, 8, 9, 10]),
+            Series([np.nan, 3, np.nan, 5, np.nan, 7, np.nan, 9, np.nan]),
+            slice(None, None, 2),
+            None,
+            id="int_series_slice_key_step",
+        ),
+        pytest.param(
+            Series([True, True, False, False]),
+            Series([np.nan, True, np.nan, False], dtype=object),
+            slice(None, None, 2),
+            FutureWarning,
+            id="bool_series_slice_key_step",
+        ),
+        pytest.param(
+            # these induce dtype changes
+            Series(np.arange(10)),
+            Series([np.nan, np.nan, np.nan, np.nan, np.nan, 5, 6, 7, 8, 9]),
+            slice(None, 5),
+            None,
+            id="int_series_slice_key",
+        ),
+        pytest.param(
+            # changes dtype GH#4463
+            Series([1, 2, 3]),
+            Series([np.nan, 2, 3]),
+            0,
+            None,
+            id="int_series_int_key",
+        ),
+        pytest.param(
+            # changes dtype GH#4463
+            Series([False]),
+            Series([np.nan], dtype=object),
+            # TODO: maybe go to float64 since we are changing the _whole_ Series?
+            0,
+            FutureWarning,
+            id="bool_series_int_key_change_all",
+        ),
+        pytest.param(
+            # changes dtype GH#4463
+            Series([False, True]),
+            Series([np.nan, True], dtype=object),
+            0,
+            FutureWarning,
+            id="bool_series_int_key",
+        ),
+    ],
+)
+class TestSetitemCastingEquivalents(SetitemCastingEquivalents):
+    @pytest.fixture(params=[np.nan, np.float64("NaN"), None, NA])
+    def val(self, request):
+        """
+        NA values that should generally be valid_na for *all* dtypes.
+
+        Include both python float NaN and np.float64; only np.float64 has a
+        `dtype` attribute.
+        """
+        return request.param
+
+
+class TestSetitemTimedelta64IntoNumeric(SetitemCastingEquivalents):
+    # timedelta64 should not be treated as integers when setting into
+    #  numeric Series
+
+    @pytest.fixture
+    def val(self):
+        td = np.timedelta64(4, "ns")
+        return td
+        # TODO: could also try np.full((1,), td)
+
+    @pytest.fixture(params=[complex, int, float])
+    def dtype(self, request):
+        return request.param
+
+    @pytest.fixture
+    def obj(self, dtype):
+        arr = np.arange(5).astype(dtype)
+        ser = Series(arr)
+        return ser
+
+    @pytest.fixture
+    def expected(self, dtype):
+        arr = np.arange(5).astype(dtype)
+        ser = Series(arr)
+        ser = ser.astype(object)
+        ser.iloc[0] = np.timedelta64(4, "ns")
+        return ser
+
+    @pytest.fixture
+    def key(self):
+        return 0
+
+    @pytest.fixture
+    def warn(self):
+        return FutureWarning
+
+
+class TestSetitemDT64IntoInt(SetitemCastingEquivalents):
+    # GH#39619 dont cast dt64 to int when doing this setitem
+
+    @pytest.fixture(params=["M8[ns]", "m8[ns]"])
+    def dtype(self, request):
+        return request.param
+
+    @pytest.fixture
+    def scalar(self, dtype):
+        val = np.datetime64("2021-01-18 13:25:00", "ns")
+        if dtype == "m8[ns]":
+            val = val - val
+        return val
+
+    @pytest.fixture
+    def expected(self, scalar):
+        expected = Series([scalar, scalar, 3], dtype=object)
+        assert isinstance(expected[0], type(scalar))
+        return expected
+
+    @pytest.fixture
+    def obj(self):
+        return Series([1, 2, 3])
+
+    @pytest.fixture
+    def key(self):
+        return slice(None, -1)
+
+    @pytest.fixture(params=[None, list, np.array])
+    def val(self, scalar, request):
+        box = request.param
+        if box is None:
+            return scalar
+        return box([scalar, scalar])
+
+    @pytest.fixture
+    def warn(self):
+        return FutureWarning
+
+
+class TestSetitemNAPeriodDtype(SetitemCastingEquivalents):
+    # Setting compatible NA values into Series with PeriodDtype
+
+    @pytest.fixture
+    def expected(self, key):
+        exp = Series(period_range("2000-01-01", periods=10, freq="D"))
+        exp._values.view("i8")[key] = NaT._value
+        assert exp[key] is NaT or all(x is NaT for x in exp[key])
+        return exp
+
+    @pytest.fixture
+    def obj(self):
+        return Series(period_range("2000-01-01", periods=10, freq="D"))
+
+    @pytest.fixture(params=[3, slice(3, 5)])
+    def key(self, request):
+        return request.param
+
+    @pytest.fixture(params=[None, np.nan])
+    def val(self, request):
+        return request.param
+
+    @pytest.fixture
+    def warn(self):
+        return None
+
+
+class TestSetitemNADatetimeLikeDtype(SetitemCastingEquivalents):
+    # some nat-like values should be cast to datetime64/timedelta64 when
+    #  inserting into a datetime64/timedelta64 series.  Others should coerce
+    #  to object and retain their dtypes.
+    # GH#18586 for td64 and boolean mask case
+
+    @pytest.fixture(
+        params=["m8[ns]", "M8[ns]", "datetime64[ns, UTC]", "datetime64[ns, US/Central]"]
+    )
+    def dtype(self, request):
+        return request.param
+
+    @pytest.fixture
+    def obj(self, dtype):
+        i8vals = date_range("2016-01-01", periods=3).asi8
+        idx = Index(i8vals, dtype=dtype)
+        assert idx.dtype == dtype
+        return Series(idx)
+
+    @pytest.fixture(
+        params=[
+            None,
+            np.nan,
+            NaT,
+            np.timedelta64("NaT", "ns"),
+            np.datetime64("NaT", "ns"),
+        ]
+    )
+    def val(self, request):
+        return request.param
+
+    @pytest.fixture
+    def is_inplace(self, val, obj):
+        # td64   -> cast to object iff val is datetime64("NaT")
+        # dt64   -> cast to object iff val is timedelta64("NaT")
+        # dt64tz -> cast to object with anything _but_ NaT
+        return val is NaT or val is None or val is np.nan or obj.dtype == val.dtype
+
+    @pytest.fixture
+    def expected(self, obj, val, is_inplace):
+        dtype = obj.dtype if is_inplace else object
+        expected = Series([val] + list(obj[1:]), dtype=dtype)
+        return expected
+
+    @pytest.fixture
+    def key(self):
+        return 0
+
+    @pytest.fixture
+    def warn(self, is_inplace):
+        return None if is_inplace else FutureWarning
+
+
+class TestSetitemMismatchedTZCastsToObject(SetitemCastingEquivalents):
+    # GH#24024
+    @pytest.fixture
+    def obj(self):
+        return Series(date_range("2000", periods=2, tz="US/Central"))
+
+    @pytest.fixture
+    def val(self):
+        return Timestamp("2000", tz="US/Eastern")
+
+    @pytest.fixture
+    def key(self):
+        return 0
+
+    @pytest.fixture
+    def expected(self, obj, val):
+        # pre-2.0 this would cast to object, in 2.0 we cast the val to
+        #  the target tz
+        expected = Series(
+            [
+                val.tz_convert("US/Central"),
+                Timestamp("2000-01-02 00:00:00-06:00", tz="US/Central"),
+            ],
+            dtype=obj.dtype,
+        )
+        return expected
+
+    @pytest.fixture
+    def warn(self):
+        return None
+
+
+@pytest.mark.parametrize(
+    "obj,expected,warn",
+    [
+        # For numeric series, we should coerce to NaN.
+        (Series([1, 2, 3]), Series([np.nan, 2, 3]), None),
+        (Series([1.0, 2.0, 3.0]), Series([np.nan, 2.0, 3.0]), None),
+        # For datetime series, we should coerce to NaT.
+        (
+            Series([datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)]),
+            Series([NaT, datetime(2000, 1, 2), datetime(2000, 1, 3)]),
+            None,
+        ),
+        # For objects, we should preserve the None value.
+        (Series(["foo", "bar", "baz"]), Series([None, "bar", "baz"]), None),
+    ],
+)
+class TestSeriesNoneCoercion(SetitemCastingEquivalents):
+    @pytest.fixture
+    def key(self):
+        return 0
+
+    @pytest.fixture
+    def val(self):
+        return None
+
+
+class TestSetitemFloatIntervalWithIntIntervalValues(SetitemCastingEquivalents):
+    # GH#44201 Cast to shared IntervalDtype rather than object
+
+    def test_setitem_example(self):
+        # Just a case here to make obvious what this test class is aimed at
+        idx = IntervalIndex.from_breaks(range(4))
+        obj = Series(idx)
+        val = Interval(0.5, 1.5)
+
+        with tm.assert_produces_warning(
+            FutureWarning, match="Setting an item of incompatible dtype"
+        ):
+            obj[0] = val
+        assert obj.dtype == "Interval[float64, right]"
+
+    @pytest.fixture
+    def obj(self):
+        idx = IntervalIndex.from_breaks(range(4))
+        return Series(idx)
+
+    @pytest.fixture
+    def val(self):
+        return Interval(0.5, 1.5)
+
+    @pytest.fixture
+    def key(self):
+        return 0
+
+    @pytest.fixture
+    def expected(self, obj, val):
+        data = [val] + list(obj[1:])
+        idx = IntervalIndex(data, dtype="Interval[float64]")
+        return Series(idx)
+
+    @pytest.fixture
+    def warn(self):
+        return FutureWarning
+
+
+class TestSetitemRangeIntoIntegerSeries(SetitemCastingEquivalents):
+    # GH#44261 Setting a range with sufficiently-small integers into
+    #  small-itemsize integer dtypes should not need to upcast
+
+    @pytest.fixture
+    def obj(self, any_int_numpy_dtype):
+        dtype = np.dtype(any_int_numpy_dtype)
+        ser = Series(range(5), dtype=dtype)
+        return ser
+
+    @pytest.fixture
+    def val(self):
+        return range(2, 4)
+
+    @pytest.fixture
+    def key(self):
+        return slice(0, 2)
+
+    @pytest.fixture
+    def expected(self, any_int_numpy_dtype):
+        dtype = np.dtype(any_int_numpy_dtype)
+        exp = Series([2, 3, 2, 3, 4], dtype=dtype)
+        return exp
+
+    @pytest.fixture
+    def warn(self):
+        return None
+
+
+@pytest.mark.parametrize(
+    "val, warn",
+    [
+        (np.array([2.0, 3.0]), None),
+        (np.array([2.5, 3.5]), FutureWarning),
+        (
+            np.array([2**65, 2**65 + 1], dtype=np.float64),
+            FutureWarning,
+        ),  # all ints, but can't cast
+    ],
+)
+class TestSetitemFloatNDarrayIntoIntegerSeries(SetitemCastingEquivalents):
+    @pytest.fixture
+    def obj(self):
+        return Series(range(5), dtype=np.int64)
+
+    @pytest.fixture
+    def key(self):
+        return slice(0, 2)
+
+    @pytest.fixture
+    def expected(self, val):
+        if val[0] == 2:
+            # NB: this condition is based on currently-hardcoded "val" cases
+            dtype = np.int64
+        else:
+            dtype = np.float64
+        res_values = np.array(range(5), dtype=dtype)
+        res_values[:2] = val
+        return Series(res_values)
+
+
+@pytest.mark.parametrize("val", [512, np.int16(512)])
+class TestSetitemIntoIntegerSeriesNeedsUpcast(SetitemCastingEquivalents):
+    @pytest.fixture
+    def obj(self):
+        return Series([1, 2, 3], dtype=np.int8)
+
+    @pytest.fixture
+    def key(self):
+        return 1
+
+    @pytest.fixture
+    def expected(self):
+        return Series([1, 512, 3], dtype=np.int16)
+
+    @pytest.fixture
+    def warn(self):
+        return FutureWarning
+
+
+@pytest.mark.parametrize("val", [2**33 + 1.0, 2**33 + 1.1, 2**62])
+class TestSmallIntegerSetitemUpcast(SetitemCastingEquivalents):
+    # https://github.com/pandas-dev/pandas/issues/39584#issuecomment-941212124
+    @pytest.fixture
+    def obj(self):
+        return Series([1, 2, 3], dtype="i4")
+
+    @pytest.fixture
+    def key(self):
+        return 0
+
+    @pytest.fixture
+    def expected(self, val):
+        if val % 1 != 0:
+            dtype = "f8"
+        else:
+            dtype = "i8"
+        return Series([val, 2, 3], dtype=dtype)
+
+    @pytest.fixture
+    def warn(self):
+        return FutureWarning
+
+
+class CoercionTest(SetitemCastingEquivalents):
+    # Tests ported from tests.indexing.test_coercion
+
+    @pytest.fixture
+    def key(self):
+        return 1
+
+    @pytest.fixture
+    def expected(self, obj, key, val, exp_dtype):
+        vals = list(obj)
+        vals[key] = val
+        return Series(vals, dtype=exp_dtype)
+
+
+@pytest.mark.parametrize(
+    "val,exp_dtype,warn",
+    [(np.int32(1), np.int8, None), (np.int16(2**9), np.int16, FutureWarning)],
+)
+class TestCoercionInt8(CoercionTest):
+    # previously test_setitem_series_int8 in tests.indexing.test_coercion
+    @pytest.fixture
+    def obj(self):
+        return Series([1, 2, 3, 4], dtype=np.int8)
+
+
+@pytest.mark.parametrize("val", [1, 1.1, 1 + 1j, True])
+@pytest.mark.parametrize("exp_dtype", [object])
+class TestCoercionObject(CoercionTest):
+    # previously test_setitem_series_object in tests.indexing.test_coercion
+    @pytest.fixture
+    def obj(self):
+        return Series(["a", "b", "c", "d"], dtype=object)
+
+    @pytest.fixture
+    def warn(self):
+        return None
+
+
+@pytest.mark.parametrize(
+    "val,exp_dtype,warn",
+    [
+        (1, np.complex128, None),
+        (1.1, np.complex128, None),
+        (1 + 1j, np.complex128, None),
+        (True, object, FutureWarning),
+    ],
+)
+class TestCoercionComplex(CoercionTest):
+    # previously test_setitem_series_complex128 in tests.indexing.test_coercion
+    @pytest.fixture
+    def obj(self):
+        return Series([1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j])
+
+
+@pytest.mark.parametrize(
+    "val,exp_dtype,warn",
+    [
+        (1, object, FutureWarning),
+        ("3", object, FutureWarning),
+        (3, object, FutureWarning),
+        (1.1, object, FutureWarning),
+        (1 + 1j, object, FutureWarning),
+        (True, bool, None),
+    ],
+)
+class TestCoercionBool(CoercionTest):
+    # previously test_setitem_series_bool in tests.indexing.test_coercion
+    @pytest.fixture
+    def obj(self):
+        return Series([True, False, True, False], dtype=bool)
+
+
+@pytest.mark.parametrize(
+    "val,exp_dtype,warn",
+    [
+        (1, np.int64, None),
+        (1.1, np.float64, FutureWarning),
+        (1 + 1j, np.complex128, FutureWarning),
+        (True, object, FutureWarning),
+    ],
+)
+class TestCoercionInt64(CoercionTest):
+    # previously test_setitem_series_int64 in tests.indexing.test_coercion
+    @pytest.fixture
+    def obj(self):
+        return Series([1, 2, 3, 4])
+
+
+@pytest.mark.parametrize(
+    "val,exp_dtype,warn",
+    [
+        (1, np.float64, None),
+        (1.1, np.float64, None),
+        (1 + 1j, np.complex128, FutureWarning),
+        (True, object, FutureWarning),
+    ],
+)
+class TestCoercionFloat64(CoercionTest):
+    # previously test_setitem_series_float64 in tests.indexing.test_coercion
+    @pytest.fixture
+    def obj(self):
+        return Series([1.1, 2.2, 3.3, 4.4])
+
+
+@pytest.mark.parametrize(
+    "val,exp_dtype,warn",
+    [
+        (1, np.float32, None),
+        pytest.param(
+            1.1,
+            np.float32,
+            None,
+            marks=pytest.mark.xfail(
+                (
+                    not np_version_gte1p24
+                    or (np_version_gte1p24 and np._get_promotion_state() != "weak")
+                ),
+                reason="np.float32(1.1) ends up as 1.100000023841858, so "
+                "np_can_hold_element raises and we cast to float64",
+            ),
+        ),
+        (1 + 1j, np.complex128, FutureWarning),
+        (True, object, FutureWarning),
+        (np.uint8(2), np.float32, None),
+        (np.uint32(2), np.float32, None),
+        # float32 cannot hold np.iinfo(np.uint32).max exactly
+        # (closest it can hold is 4294967300.0 which off by 5.0), so
+        # we cast to float64
+        (np.uint32(np.iinfo(np.uint32).max), np.float64, FutureWarning),
+        (np.uint64(2), np.float32, None),
+        (np.int64(2), np.float32, None),
+    ],
+)
+class TestCoercionFloat32(CoercionTest):
+    @pytest.fixture
+    def obj(self):
+        return Series([1.1, 2.2, 3.3, 4.4], dtype=np.float32)
+
+    def test_slice_key(self, obj, key, expected, warn, val, indexer_sli, is_inplace):
+        super().test_slice_key(obj, key, expected, warn, val, indexer_sli, is_inplace)
+
+        if isinstance(val, float):
+            # the xfail would xpass bc test_slice_key short-circuits
+            raise AssertionError("xfail not relevant for this test.")
+
+
+@pytest.mark.parametrize(
+    "val,exp_dtype,warn",
+    [
+        (Timestamp("2012-01-01"), "datetime64[ns]", None),
+        (1, object, FutureWarning),
+        ("x", object, FutureWarning),
+    ],
+)
+class TestCoercionDatetime64(CoercionTest):
+    # previously test_setitem_series_datetime64 in tests.indexing.test_coercion
+
+    @pytest.fixture
+    def obj(self):
+        return Series(date_range("2011-01-01", freq="D", periods=4))
+
+    @pytest.fixture
+    def warn(self):
+        return None
+
+
+@pytest.mark.parametrize(
+    "val,exp_dtype,warn",
+    [
+        (Timestamp("2012-01-01", tz="US/Eastern"), "datetime64[ns, US/Eastern]", None),
+        # pre-2.0, a mis-matched tz would end up casting to object
+        (Timestamp("2012-01-01", tz="US/Pacific"), "datetime64[ns, US/Eastern]", None),
+        (Timestamp("2012-01-01"), object, FutureWarning),
+        (1, object, FutureWarning),
+    ],
+)
+class TestCoercionDatetime64TZ(CoercionTest):
+    # previously test_setitem_series_datetime64tz in tests.indexing.test_coercion
+    @pytest.fixture
+    def obj(self):
+        tz = "US/Eastern"
+        return Series(date_range("2011-01-01", freq="D", periods=4, tz=tz))
+
+    @pytest.fixture
+    def warn(self):
+        return None
+
+
+@pytest.mark.parametrize(
+    "val,exp_dtype,warn",
+    [
+        (Timedelta("12 day"), "timedelta64[ns]", None),
+        (1, object, FutureWarning),
+        ("x", object, FutureWarning),
+    ],
+)
+class TestCoercionTimedelta64(CoercionTest):
+    # previously test_setitem_series_timedelta64 in tests.indexing.test_coercion
+    @pytest.fixture
+    def obj(self):
+        return Series(timedelta_range("1 day", periods=4))
+
+    @pytest.fixture
+    def warn(self):
+        return None
+
+
+@pytest.mark.parametrize(
+    "val", ["foo", Period("2016", freq="Y"), Interval(1, 2, closed="both")]
+)
+@pytest.mark.parametrize("exp_dtype", [object])
+class TestPeriodIntervalCoercion(CoercionTest):
+    # GH#45768
+    @pytest.fixture(
+        params=[
+            period_range("2016-01-01", periods=3, freq="D"),
+            interval_range(1, 5),
+        ]
+    )
+    def obj(self, request):
+        return Series(request.param)
+
+    @pytest.fixture
+    def warn(self):
+        return FutureWarning
+
+
+def test_20643():
+    # closed by GH#45121
+    orig = Series([0, 1, 2], index=["a", "b", "c"])
+
+    expected = Series([0, 2.7, 2], index=["a", "b", "c"])
+
+    ser = orig.copy()
+    with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+        ser.at["b"] = 2.7
+    tm.assert_series_equal(ser, expected)
+
+    ser = orig.copy()
+    with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+        ser.loc["b"] = 2.7
+    tm.assert_series_equal(ser, expected)
+
+    ser = orig.copy()
+    with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+        ser["b"] = 2.7
+    tm.assert_series_equal(ser, expected)
+
+    ser = orig.copy()
+    with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+        ser.iat[1] = 2.7
+    tm.assert_series_equal(ser, expected)
+
+    ser = orig.copy()
+    with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+        ser.iloc[1] = 2.7
+    tm.assert_series_equal(ser, expected)
+
+    orig_df = orig.to_frame("A")
+    expected_df = expected.to_frame("A")
+
+    df = orig_df.copy()
+    with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+        df.at["b", "A"] = 2.7
+    tm.assert_frame_equal(df, expected_df)
+
+    df = orig_df.copy()
+    with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+        df.loc["b", "A"] = 2.7
+    tm.assert_frame_equal(df, expected_df)
+
+    df = orig_df.copy()
+    with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+        df.iloc[1, 0] = 2.7
+    tm.assert_frame_equal(df, expected_df)
+
+    df = orig_df.copy()
+    with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+        df.iat[1, 0] = 2.7
+    tm.assert_frame_equal(df, expected_df)
+
+
+def test_20643_comment():
+    # https://github.com/pandas-dev/pandas/issues/20643#issuecomment-431244590
+    # fixed sometime prior to GH#45121
+    orig = Series([0, 1, 2], index=["a", "b", "c"])
+    expected = Series([np.nan, 1, 2], index=["a", "b", "c"])
+
+    ser = orig.copy()
+    ser.iat[0] = None
+    tm.assert_series_equal(ser, expected)
+
+    ser = orig.copy()
+    ser.iloc[0] = None
+    tm.assert_series_equal(ser, expected)
+
+
+def test_15413():
+    # fixed by GH#45121
+    ser = Series([1, 2, 3])
+
+    with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+        ser[ser == 2] += 0.5
+    expected = Series([1, 2.5, 3])
+    tm.assert_series_equal(ser, expected)
+
+    ser = Series([1, 2, 3])
+    with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+        ser[1] += 0.5
+    tm.assert_series_equal(ser, expected)
+
+    ser = Series([1, 2, 3])
+    with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+        ser.loc[1] += 0.5
+    tm.assert_series_equal(ser, expected)
+
+    ser = Series([1, 2, 3])
+    with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+        ser.iloc[1] += 0.5
+    tm.assert_series_equal(ser, expected)
+
+    ser = Series([1, 2, 3])
+    with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+        ser.iat[1] += 0.5
+    tm.assert_series_equal(ser, expected)
+
+    ser = Series([1, 2, 3])
+    with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+        ser.at[1] += 0.5
+    tm.assert_series_equal(ser, expected)
+
+
+def test_32878_int_itemsize():
+    # Fixed by GH#45121
+    arr = np.arange(5).astype("i4")
+    ser = Series(arr)
+    val = np.int64(np.iinfo(np.int64).max)
+    with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+        ser[0] = val
+    expected = Series([val, 1, 2, 3, 4], dtype=np.int64)
+    tm.assert_series_equal(ser, expected)
+
+
+def test_32878_complex_itemsize():
+    arr = np.arange(5).astype("c8")
+    ser = Series(arr)
+    val = np.finfo(np.float64).max
+    val = val.astype("c16")
+
+    # GH#32878 used to coerce val to inf+0.000000e+00j
+    with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+        ser[0] = val
+    assert ser[0] == val
+    expected = Series([val, 1, 2, 3, 4], dtype="c16")
+    tm.assert_series_equal(ser, expected)
+
+
+def test_37692(indexer_al):
+    # GH#37692
+    ser = Series([1, 2, 3], index=["a", "b", "c"])
+    with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+        indexer_al(ser)["b"] = "test"
+    expected = Series([1, "test", 3], index=["a", "b", "c"], dtype=object)
+    tm.assert_series_equal(ser, expected)
+
+
+def test_setitem_bool_int_float_consistency(indexer_sli):
+    # GH#21513
+    # bool-with-int and bool-with-float both upcast to object
+    #  int-with-float and float-with-int are both non-casting so long
+    #  as the setitem can be done losslessly
+    for dtype in [np.float64, np.int64]:
+        ser = Series(0, index=range(3), dtype=dtype)
+        with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+            indexer_sli(ser)[0] = True
+        assert ser.dtype == object
+
+        ser = Series(0, index=range(3), dtype=bool)
+        with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+            ser[0] = dtype(1)
+        assert ser.dtype == object
+
+    # 1.0 can be held losslessly, so no casting
+    ser = Series(0, index=range(3), dtype=np.int64)
+    indexer_sli(ser)[0] = np.float64(1.0)
+    assert ser.dtype == np.int64
+
+    # 1 can be held losslessly, so no casting
+    ser = Series(0, index=range(3), dtype=np.float64)
+    indexer_sli(ser)[0] = np.int64(1)
+
+
+def test_setitem_positional_with_casting():
+    # GH#45070 case where in __setitem__ we get a KeyError, then when
+    #  we fallback we *also* get a ValueError if we try to set inplace.
+    ser = Series([1, 2, 3], index=["a", "b", "c"])
+
+    warn_msg = "Series.__setitem__ treating keys as positions is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+        ser[0] = "X"
+    expected = Series(["X", 2, 3], index=["a", "b", "c"], dtype=object)
+    tm.assert_series_equal(ser, expected)
+
+
+def test_setitem_positional_float_into_int_coerces():
+    # Case where we hit a KeyError and then trying to set in-place incorrectly
+    #  casts a float to an int
+    ser = Series([1, 2, 3], index=["a", "b", "c"])
+
+    warn_msg = "Series.__setitem__ treating keys as positions is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+        ser[0] = 1.5
+    expected = Series([1.5, 2, 3], index=["a", "b", "c"])
+    tm.assert_series_equal(ser, expected)
+
+
+def test_setitem_int_not_positional():
+    # GH#42215 deprecated falling back to positional on __setitem__ with an
+    #  int not contained in the index; enforced in 2.0
+    ser = Series([1, 2, 3, 4], index=[1.1, 2.1, 3.0, 4.1])
+    assert not ser.index._should_fallback_to_positional
+    # assert not ser.index.astype(object)._should_fallback_to_positional
+
+    # 3.0 is in our index, so post-enforcement behavior is unchanged
+    ser[3] = 10
+    expected = Series([1, 2, 10, 4], index=ser.index)
+    tm.assert_series_equal(ser, expected)
+
+    # pre-enforcement `ser[5] = 5` raised IndexError
+    ser[5] = 5
+    expected = Series([1, 2, 10, 4, 5], index=[1.1, 2.1, 3.0, 4.1, 5.0])
+    tm.assert_series_equal(ser, expected)
+
+    ii = IntervalIndex.from_breaks(range(10))[::2]
+    ser2 = Series(range(len(ii)), index=ii)
+    exp_index = ii.astype(object).append(Index([4]))
+    expected2 = Series([0, 1, 2, 3, 4, 9], index=exp_index)
+    # pre-enforcement `ser2[4] = 9` interpreted 4 as positional
+    ser2[4] = 9
+    tm.assert_series_equal(ser2, expected2)
+
+    mi = MultiIndex.from_product([ser.index, ["A", "B"]])
+    ser3 = Series(range(len(mi)), index=mi)
+    expected3 = ser3.copy()
+    expected3.loc[4] = 99
+    # pre-enforcement `ser3[4] = 99` interpreted 4 as positional
+    ser3[4] = 99
+    tm.assert_series_equal(ser3, expected3)
+
+
+def test_setitem_with_bool_indexer():
+    # GH#42530
+
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    result = df.pop("b").copy()
+    result[[True, False, False]] = 9
+    expected = Series(data=[9, 5, 6], name="b")
+    tm.assert_series_equal(result, expected)
+
+    df.loc[[True, False, False], "a"] = 10
+    expected = DataFrame({"a": [10, 2, 3]})
+    tm.assert_frame_equal(df, expected)
+
+
+@pytest.mark.parametrize("size", range(2, 6))
+@pytest.mark.parametrize(
+    "mask", [[True, False, False, False, False], [True, False], [False]]
+)
+@pytest.mark.parametrize(
+    "item", [2.0, np.nan, np.finfo(float).max, np.finfo(float).min]
+)
+# Test numpy arrays, lists and tuples as the input to be
+# broadcast
+@pytest.mark.parametrize(
+    "box", [lambda x: np.array([x]), lambda x: [x], lambda x: (x,)]
+)
+def test_setitem_bool_indexer_dont_broadcast_length1_values(size, mask, item, box):
+    # GH#44265
+    # see also tests.series.indexing.test_where.test_broadcast
+
+    selection = np.resize(mask, size)
+
+    data = np.arange(size, dtype=float)
+
+    ser = Series(data)
+
+    if selection.sum() != 1:
+        msg = (
+            "cannot set using a list-like indexer with a different "
+            "length than the value"
+        )
+        with pytest.raises(ValueError, match=msg):
+            # GH#44265
+            ser[selection] = box(item)
+    else:
+        # In this corner case setting is equivalent to setting with the unboxed
+        #  item
+        ser[selection] = box(item)
+
+        expected = Series(np.arange(size, dtype=float))
+        expected[selection] = item
+        tm.assert_series_equal(ser, expected)
+
+
+def test_setitem_empty_mask_dont_upcast_dt64():
+    dti = date_range("2016-01-01", periods=3)
+    ser = Series(dti)
+    orig = ser.copy()
+    mask = np.zeros(3, dtype=bool)
+
+    ser[mask] = "foo"
+    assert ser.dtype == dti.dtype  # no-op -> dont upcast
+    tm.assert_series_equal(ser, orig)
+
+    ser.mask(mask, "foo", inplace=True)
+    assert ser.dtype == dti.dtype  # no-op -> dont upcast
+    tm.assert_series_equal(ser, orig)
diff --git a/venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_take.py b/venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_take.py
new file mode 100644
index 0000000000000000000000000000000000000000..43fbae89089665fb2bf589d35db1b3f3041dd018
--- /dev/null
+++ b/venv/lib/python3.10/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))
diff --git a/venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_where.py b/venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_where.py
new file mode 100644
index 0000000000000000000000000000000000000000..c978481ca99886bb12d3d8b2d54eeed46de73532
--- /dev/null
+++ b/venv/lib/python3.10/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))
diff --git a/venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_xs.py b/venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_xs.py
new file mode 100644
index 0000000000000000000000000000000000000000..a67f3ec708f24a97dab544bb5cf859dc54659215
--- /dev/null
+++ b/venv/lib/python3.10/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)
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--- /dev/null
+++ b/venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_autocorr.py
@@ -0,0 +1,30 @@
+import numpy as np
+
+
+class TestAutoCorr:
+    def test_autocorr(self, datetime_series):
+        # Just run the function
+        corr1 = datetime_series.autocorr()
+
+        # Now run it with the lag parameter
+        corr2 = datetime_series.autocorr(lag=1)
+
+        # corr() with lag needs Series of at least length 2
+        if len(datetime_series) <= 2:
+            assert np.isnan(corr1)
+            assert np.isnan(corr2)
+        else:
+            assert corr1 == corr2
+
+        # Choose a random lag between 1 and length of Series - 2
+        # and compare the result with the Series corr() function
+        n = 1 + np.random.default_rng(2).integers(max(1, len(datetime_series) - 2))
+        corr1 = datetime_series.corr(datetime_series.shift(n))
+        corr2 = datetime_series.autocorr(lag=n)
+
+        # corr() with lag needs Series of at least length 2
+        if len(datetime_series) <= 2:
+            assert np.isnan(corr1)
+            assert np.isnan(corr2)
+        else:
+            assert corr1 == corr2
diff --git a/venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_between.py b/venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_between.py
new file mode 100644
index 0000000000000000000000000000000000000000..3913419038876938c9538ef435a7eb02c37f62a6
--- /dev/null
+++ b/venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_between.py
@@ -0,0 +1,75 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Series,
+    bdate_range,
+    date_range,
+    period_range,
+)
+import pandas._testing as tm
+
+
+class TestBetween:
+    def test_between(self):
+        series = Series(date_range("1/1/2000", periods=10))
+        left, right = series[[2, 7]]
+
+        result = series.between(left, right)
+        expected = (series >= left) & (series <= right)
+        tm.assert_series_equal(result, expected)
+
+    def test_between_datetime_object_dtype(self):
+        ser = Series(bdate_range("1/1/2000", periods=20), dtype=object)
+        ser[::2] = np.nan
+
+        result = ser[ser.between(ser[3], ser[17])]
+        expected = ser[3:18].dropna()
+        tm.assert_series_equal(result, expected)
+
+        result = ser[ser.between(ser[3], ser[17], inclusive="neither")]
+        expected = ser[5:16].dropna()
+        tm.assert_series_equal(result, expected)
+
+    def test_between_period_values(self):
+        ser = Series(period_range("2000-01-01", periods=10, freq="D"))
+        left, right = ser[[2, 7]]
+        result = ser.between(left, right)
+        expected = (ser >= left) & (ser <= right)
+        tm.assert_series_equal(result, expected)
+
+    def test_between_inclusive_string(self):
+        # GH 40628
+        series = Series(date_range("1/1/2000", periods=10))
+        left, right = series[[2, 7]]
+
+        result = series.between(left, right, inclusive="both")
+        expected = (series >= left) & (series <= right)
+        tm.assert_series_equal(result, expected)
+
+        result = series.between(left, right, inclusive="left")
+        expected = (series >= left) & (series < right)
+        tm.assert_series_equal(result, expected)
+
+        result = series.between(left, right, inclusive="right")
+        expected = (series > left) & (series <= right)
+        tm.assert_series_equal(result, expected)
+
+        result = series.between(left, right, inclusive="neither")
+        expected = (series > left) & (series < right)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("inclusive", ["yes", True, False])
+    def test_between_error_args(self, inclusive):
+        # GH 40628
+        series = Series(date_range("1/1/2000", periods=10))
+        left, right = series[[2, 7]]
+
+        value_error_msg = (
+            "Inclusive has to be either string of 'both',"
+            "'left', 'right', or 'neither'."
+        )
+
+        with pytest.raises(ValueError, match=value_error_msg):
+            series = Series(date_range("1/1/2000", periods=10))
+            series.between(left, right, inclusive=inclusive)
diff --git a/venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_duplicated.py b/venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_duplicated.py
new file mode 100644
index 0000000000000000000000000000000000000000..e177b5275d855fffbede91280d9ee7fb61ece2cd
--- /dev/null
+++ b/venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_duplicated.py
@@ -0,0 +1,77 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    NA,
+    Categorical,
+    Series,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize(
+    "keep, expected",
+    [
+        ("first", Series([False, False, True, False, True], name="name")),
+        ("last", Series([True, True, False, False, False], name="name")),
+        (False, Series([True, True, True, False, True], name="name")),
+    ],
+)
+def test_duplicated_keep(keep, expected):
+    ser = Series(["a", "b", "b", "c", "a"], name="name")
+
+    result = ser.duplicated(keep=keep)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "keep, expected",
+    [
+        ("first", Series([False, False, True, False, True])),
+        ("last", Series([True, True, False, False, False])),
+        (False, Series([True, True, True, False, True])),
+    ],
+)
+def test_duplicated_nan_none(keep, expected):
+    ser = Series([np.nan, 3, 3, None, np.nan], dtype=object)
+
+    result = ser.duplicated(keep=keep)
+    tm.assert_series_equal(result, expected)
+
+
+def test_duplicated_categorical_bool_na(nulls_fixture):
+    # GH#44351
+    ser = Series(
+        Categorical(
+            [True, False, True, False, nulls_fixture],
+            categories=[True, False],
+            ordered=True,
+        )
+    )
+    result = ser.duplicated()
+    expected = Series([False, False, True, True, False])
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "keep, vals",
+    [
+        ("last", [True, True, False]),
+        ("first", [False, True, True]),
+        (False, [True, True, True]),
+    ],
+)
+def test_duplicated_mask(keep, vals):
+    # GH#48150
+    ser = Series([1, 2, NA, NA, NA], dtype="Int64")
+    result = ser.duplicated(keep=keep)
+    expected = Series([False, False] + vals)
+    tm.assert_series_equal(result, expected)
+
+
+def test_duplicated_mask_no_duplicated_na(keep):
+    # GH#48150
+    ser = Series([1, 2, NA], dtype="Int64")
+    result = ser.duplicated(keep=keep)
+    expected = Series([False, False, False])
+    tm.assert_series_equal(result, expected)
diff --git a/venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_head_tail.py b/venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_head_tail.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9f8d85eda350e0627790626d52b7703d23ead02
--- /dev/null
+++ b/venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_head_tail.py
@@ -0,0 +1,8 @@
+import pandas._testing as tm
+
+
+def test_head_tail(string_series):
+    tm.assert_series_equal(string_series.head(), string_series[:5])
+    tm.assert_series_equal(string_series.head(0), string_series[0:0])
+    tm.assert_series_equal(string_series.tail(), string_series[-5:])
+    tm.assert_series_equal(string_series.tail(0), string_series[0:0])
diff --git a/venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_nlargest.py b/venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_nlargest.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8de1cd89e3974f2dff0d775f35655b8caa02a08
--- /dev/null
+++ b/venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_nlargest.py
@@ -0,0 +1,248 @@
+"""
+Note: for naming purposes, most tests are title with as e.g. "test_nlargest_foo"
+but are implicitly also testing nsmallest_foo.
+"""
+from itertools import product
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import Series
+import pandas._testing as tm
+
+main_dtypes = [
+    "datetime",
+    "datetimetz",
+    "timedelta",
+    "int8",
+    "int16",
+    "int32",
+    "int64",
+    "float32",
+    "float64",
+    "uint8",
+    "uint16",
+    "uint32",
+    "uint64",
+]
+
+
+@pytest.fixture
+def s_main_dtypes():
+    """
+    A DataFrame with many dtypes
+
+    * datetime
+    * datetimetz
+    * timedelta
+    * [u]int{8,16,32,64}
+    * float{32,64}
+
+    The columns are the name of the dtype.
+    """
+    df = pd.DataFrame(
+        {
+            "datetime": pd.to_datetime(["2003", "2002", "2001", "2002", "2005"]),
+            "datetimetz": pd.to_datetime(
+                ["2003", "2002", "2001", "2002", "2005"]
+            ).tz_localize("US/Eastern"),
+            "timedelta": pd.to_timedelta(["3d", "2d", "1d", "2d", "5d"]),
+        }
+    )
+
+    for dtype in [
+        "int8",
+        "int16",
+        "int32",
+        "int64",
+        "float32",
+        "float64",
+        "uint8",
+        "uint16",
+        "uint32",
+        "uint64",
+    ]:
+        df[dtype] = Series([3, 2, 1, 2, 5], dtype=dtype)
+
+    return df
+
+
+@pytest.fixture(params=main_dtypes)
+def s_main_dtypes_split(request, s_main_dtypes):
+    """Each series in s_main_dtypes."""
+    return s_main_dtypes[request.param]
+
+
+def assert_check_nselect_boundary(vals, dtype, method):
+    # helper function for 'test_boundary_{dtype}' tests
+    ser = Series(vals, dtype=dtype)
+    result = getattr(ser, method)(3)
+    expected_idxr = [0, 1, 2] if method == "nsmallest" else [3, 2, 1]
+    expected = ser.loc[expected_idxr]
+    tm.assert_series_equal(result, expected)
+
+
+class TestSeriesNLargestNSmallest:
+    @pytest.mark.parametrize(
+        "r",
+        [
+            Series([3.0, 2, 1, 2, "5"], dtype="object"),
+            Series([3.0, 2, 1, 2, 5], dtype="object"),
+            # not supported on some archs
+            # Series([3., 2, 1, 2, 5], dtype='complex256'),
+            Series([3.0, 2, 1, 2, 5], dtype="complex128"),
+            Series(list("abcde")),
+            Series(list("abcde"), dtype="category"),
+        ],
+    )
+    def test_nlargest_error(self, r):
+        dt = r.dtype
+        msg = f"Cannot use method 'n(largest|smallest)' with dtype {dt}"
+        args = 2, len(r), 0, -1
+        methods = r.nlargest, r.nsmallest
+        for method, arg in product(methods, args):
+            with pytest.raises(TypeError, match=msg):
+                method(arg)
+
+    def test_nsmallest_nlargest(self, s_main_dtypes_split):
+        # float, int, datetime64 (use i8), timedelts64 (same),
+        # object that are numbers, object that are strings
+        ser = s_main_dtypes_split
+
+        tm.assert_series_equal(ser.nsmallest(2), ser.iloc[[2, 1]])
+        tm.assert_series_equal(ser.nsmallest(2, keep="last"), ser.iloc[[2, 3]])
+
+        empty = ser.iloc[0:0]
+        tm.assert_series_equal(ser.nsmallest(0), empty)
+        tm.assert_series_equal(ser.nsmallest(-1), empty)
+        tm.assert_series_equal(ser.nlargest(0), empty)
+        tm.assert_series_equal(ser.nlargest(-1), empty)
+
+        tm.assert_series_equal(ser.nsmallest(len(ser)), ser.sort_values())
+        tm.assert_series_equal(ser.nsmallest(len(ser) + 1), ser.sort_values())
+        tm.assert_series_equal(ser.nlargest(len(ser)), ser.iloc[[4, 0, 1, 3, 2]])
+        tm.assert_series_equal(ser.nlargest(len(ser) + 1), ser.iloc[[4, 0, 1, 3, 2]])
+
+    def test_nlargest_misc(self):
+        ser = Series([3.0, np.nan, 1, 2, 5])
+        result = ser.nlargest()
+        expected = ser.iloc[[4, 0, 3, 2, 1]]
+        tm.assert_series_equal(result, expected)
+        result = ser.nsmallest()
+        expected = ser.iloc[[2, 3, 0, 4, 1]]
+        tm.assert_series_equal(result, expected)
+
+        msg = 'keep must be either "first", "last"'
+        with pytest.raises(ValueError, match=msg):
+            ser.nsmallest(keep="invalid")
+        with pytest.raises(ValueError, match=msg):
+            ser.nlargest(keep="invalid")
+
+        # GH#15297
+        ser = Series([1] * 5, index=[1, 2, 3, 4, 5])
+        expected_first = Series([1] * 3, index=[1, 2, 3])
+        expected_last = Series([1] * 3, index=[5, 4, 3])
+
+        result = ser.nsmallest(3)
+        tm.assert_series_equal(result, expected_first)
+
+        result = ser.nsmallest(3, keep="last")
+        tm.assert_series_equal(result, expected_last)
+
+        result = ser.nlargest(3)
+        tm.assert_series_equal(result, expected_first)
+
+        result = ser.nlargest(3, keep="last")
+        tm.assert_series_equal(result, expected_last)
+
+    @pytest.mark.parametrize("n", range(1, 5))
+    def test_nlargest_n(self, n):
+        # GH 13412
+        ser = Series([1, 4, 3, 2], index=[0, 0, 1, 1])
+        result = ser.nlargest(n)
+        expected = ser.sort_values(ascending=False).head(n)
+        tm.assert_series_equal(result, expected)
+
+        result = ser.nsmallest(n)
+        expected = ser.sort_values().head(n)
+        tm.assert_series_equal(result, expected)
+
+    def test_nlargest_boundary_integer(self, nselect_method, any_int_numpy_dtype):
+        # GH#21426
+        dtype_info = np.iinfo(any_int_numpy_dtype)
+        min_val, max_val = dtype_info.min, dtype_info.max
+        vals = [min_val, min_val + 1, max_val - 1, max_val]
+        assert_check_nselect_boundary(vals, any_int_numpy_dtype, nselect_method)
+
+    def test_nlargest_boundary_float(self, nselect_method, float_numpy_dtype):
+        # GH#21426
+        dtype_info = np.finfo(float_numpy_dtype)
+        min_val, max_val = dtype_info.min, dtype_info.max
+        min_2nd, max_2nd = np.nextafter([min_val, max_val], 0, dtype=float_numpy_dtype)
+        vals = [min_val, min_2nd, max_2nd, max_val]
+        assert_check_nselect_boundary(vals, float_numpy_dtype, nselect_method)
+
+    @pytest.mark.parametrize("dtype", ["datetime64[ns]", "timedelta64[ns]"])
+    def test_nlargest_boundary_datetimelike(self, nselect_method, dtype):
+        # GH#21426
+        # use int64 bounds and +1 to min_val since true minimum is NaT
+        # (include min_val/NaT at end to maintain same expected_idxr)
+        dtype_info = np.iinfo("int64")
+        min_val, max_val = dtype_info.min, dtype_info.max
+        vals = [min_val + 1, min_val + 2, max_val - 1, max_val, min_val]
+        assert_check_nselect_boundary(vals, dtype, nselect_method)
+
+    def test_nlargest_duplicate_keep_all_ties(self):
+        # see GH#16818
+        ser = Series([10, 9, 8, 7, 7, 7, 7, 6])
+        result = ser.nlargest(4, keep="all")
+        expected = Series([10, 9, 8, 7, 7, 7, 7])
+        tm.assert_series_equal(result, expected)
+
+        result = ser.nsmallest(2, keep="all")
+        expected = Series([6, 7, 7, 7, 7], index=[7, 3, 4, 5, 6])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "data,expected", [([True, False], [True]), ([True, False, True, True], [True])]
+    )
+    def test_nlargest_boolean(self, data, expected):
+        # GH#26154 : ensure True > False
+        ser = Series(data)
+        result = ser.nlargest(1)
+        expected = Series(expected)
+        tm.assert_series_equal(result, expected)
+
+    def test_nlargest_nullable(self, any_numeric_ea_dtype):
+        # GH#42816
+        dtype = any_numeric_ea_dtype
+        if dtype.startswith("UInt"):
+            # Can't cast from negative float to uint on some platforms
+            arr = np.random.default_rng(2).integers(1, 10, 10)
+        else:
+            arr = np.random.default_rng(2).standard_normal(10)
+        arr = arr.astype(dtype.lower(), copy=False)
+
+        ser = Series(arr.copy(), dtype=dtype)
+        ser[1] = pd.NA
+        result = ser.nlargest(5)
+
+        expected = (
+            Series(np.delete(arr, 1), index=ser.index.delete(1))
+            .nlargest(5)
+            .astype(dtype)
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_nsmallest_nan_when_keep_is_all(self):
+        # GH#46589
+        s = Series([1, 2, 3, 3, 3, None])
+        result = s.nsmallest(3, keep="all")
+        expected = Series([1.0, 2.0, 3.0, 3.0, 3.0])
+        tm.assert_series_equal(result, expected)
+
+        s = Series([1, 2, None, None, None])
+        result = s.nsmallest(3, keep="all")
+        expected = Series([1, 2, None, None, None])
+        tm.assert_series_equal(result, expected)