diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/indexing/test_datetime.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/indexing/test_datetime.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc1c80eb4dec6e1b81114e8de181fc8f6b77d489
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/indexing/test_datetime.py
@@ -0,0 +1,499 @@
+"""
+Also test support for datetime64[ns] in Series / DataFrame
+"""
+from datetime import (
+    datetime,
+    timedelta,
+)
+import re
+
+from dateutil.tz import (
+    gettz,
+    tzutc,
+)
+import numpy as np
+import pytest
+import pytz
+
+from pandas._libs import index as libindex
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Series,
+    Timestamp,
+    date_range,
+    period_range,
+)
+import pandas._testing as tm
+
+
+def test_fancy_getitem():
+    dti = date_range(
+        freq="WOM-1FRI", start=datetime(2005, 1, 1), end=datetime(2010, 1, 1)
+    )
+
+    s = Series(np.arange(len(dti)), index=dti)
+
+    msg = "Series.__getitem__ treating keys as positions is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        assert s[48] == 48
+    assert s["1/2/2009"] == 48
+    assert s["2009-1-2"] == 48
+    assert s[datetime(2009, 1, 2)] == 48
+    assert s[Timestamp(datetime(2009, 1, 2))] == 48
+    with pytest.raises(KeyError, match=r"^'2009-1-3'$"):
+        s["2009-1-3"]
+    tm.assert_series_equal(
+        s["3/6/2009":"2009-06-05"], s[datetime(2009, 3, 6) : datetime(2009, 6, 5)]
+    )
+
+
+def test_fancy_setitem():
+    dti = date_range(
+        freq="WOM-1FRI", start=datetime(2005, 1, 1), end=datetime(2010, 1, 1)
+    )
+
+    s = Series(np.arange(len(dti)), index=dti)
+
+    msg = "Series.__setitem__ treating keys as positions is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        s[48] = -1
+    assert s.iloc[48] == -1
+    s["1/2/2009"] = -2
+    assert s.iloc[48] == -2
+    s["1/2/2009":"2009-06-05"] = -3
+    assert (s[48:54] == -3).all()
+
+
+@pytest.mark.parametrize("tz_source", ["pytz", "dateutil"])
+def test_getitem_setitem_datetime_tz(tz_source):
+    if tz_source == "pytz":
+        tzget = pytz.timezone
+    else:
+        # handle special case for utc in dateutil
+        tzget = lambda x: tzutc() if x == "UTC" else gettz(x)
+
+    N = 50
+    # testing with timezone, GH #2785
+    rng = date_range("1/1/1990", periods=N, freq="h", tz=tzget("US/Eastern"))
+    ts = Series(np.random.default_rng(2).standard_normal(N), index=rng)
+
+    # also test Timestamp tz handling, GH #2789
+    result = ts.copy()
+    result["1990-01-01 09:00:00+00:00"] = 0
+    result["1990-01-01 09:00:00+00:00"] = ts.iloc[4]
+    tm.assert_series_equal(result, ts)
+
+    result = ts.copy()
+    result["1990-01-01 03:00:00-06:00"] = 0
+    result["1990-01-01 03:00:00-06:00"] = ts.iloc[4]
+    tm.assert_series_equal(result, ts)
+
+    # repeat with datetimes
+    result = ts.copy()
+    result[datetime(1990, 1, 1, 9, tzinfo=tzget("UTC"))] = 0
+    result[datetime(1990, 1, 1, 9, tzinfo=tzget("UTC"))] = ts.iloc[4]
+    tm.assert_series_equal(result, ts)
+
+    result = ts.copy()
+    dt = Timestamp(1990, 1, 1, 3).tz_localize(tzget("US/Central"))
+    dt = dt.to_pydatetime()
+    result[dt] = 0
+    result[dt] = ts.iloc[4]
+    tm.assert_series_equal(result, ts)
+
+
+def test_getitem_setitem_datetimeindex():
+    N = 50
+    # testing with timezone, GH #2785
+    rng = date_range("1/1/1990", periods=N, freq="h", tz="US/Eastern")
+    ts = Series(np.random.default_rng(2).standard_normal(N), index=rng)
+
+    result = ts["1990-01-01 04:00:00"]
+    expected = ts.iloc[4]
+    assert result == expected
+
+    result = ts.copy()
+    result["1990-01-01 04:00:00"] = 0
+    result["1990-01-01 04:00:00"] = ts.iloc[4]
+    tm.assert_series_equal(result, ts)
+
+    result = ts["1990-01-01 04:00:00":"1990-01-01 07:00:00"]
+    expected = ts[4:8]
+    tm.assert_series_equal(result, expected)
+
+    result = ts.copy()
+    result["1990-01-01 04:00:00":"1990-01-01 07:00:00"] = 0
+    result["1990-01-01 04:00:00":"1990-01-01 07:00:00"] = ts[4:8]
+    tm.assert_series_equal(result, ts)
+
+    lb = "1990-01-01 04:00:00"
+    rb = "1990-01-01 07:00:00"
+    # GH#18435 strings get a pass from tzawareness compat
+    result = ts[(ts.index >= lb) & (ts.index <= rb)]
+    expected = ts[4:8]
+    tm.assert_series_equal(result, expected)
+
+    lb = "1990-01-01 04:00:00-0500"
+    rb = "1990-01-01 07:00:00-0500"
+    result = ts[(ts.index >= lb) & (ts.index <= rb)]
+    expected = ts[4:8]
+    tm.assert_series_equal(result, expected)
+
+    # But we do not give datetimes a pass on tzawareness compat
+    msg = "Cannot compare tz-naive and tz-aware datetime-like objects"
+    naive = datetime(1990, 1, 1, 4)
+    for key in [naive, Timestamp(naive), np.datetime64(naive, "ns")]:
+        with pytest.raises(KeyError, match=re.escape(repr(key))):
+            # GH#36148 as of 2.0 we require tzawareness-compat
+            ts[key]
+
+    result = ts.copy()
+    # GH#36148 as of 2.0 we do not ignore tzawareness mismatch in indexing,
+    #  so setting it as a new key casts to object rather than matching
+    #  rng[4]
+    result[naive] = ts.iloc[4]
+    assert result.index.dtype == object
+    tm.assert_index_equal(result.index[:-1], rng.astype(object))
+    assert result.index[-1] == naive
+
+    msg = "Cannot compare tz-naive and tz-aware datetime-like objects"
+    with pytest.raises(TypeError, match=msg):
+        # GH#36148 require tzawareness compat as of 2.0
+        ts[naive : datetime(1990, 1, 1, 7)]
+
+    result = ts.copy()
+    with pytest.raises(TypeError, match=msg):
+        # GH#36148 require tzawareness compat as of 2.0
+        result[naive : datetime(1990, 1, 1, 7)] = 0
+    with pytest.raises(TypeError, match=msg):
+        # GH#36148 require tzawareness compat as of 2.0
+        result[naive : datetime(1990, 1, 1, 7)] = 99
+    # the __setitems__ here failed, so result should still match ts
+    tm.assert_series_equal(result, ts)
+
+    lb = naive
+    rb = datetime(1990, 1, 1, 7)
+    msg = r"Invalid comparison between dtype=datetime64\[ns, US/Eastern\] and datetime"
+    with pytest.raises(TypeError, match=msg):
+        # tznaive vs tzaware comparison is invalid
+        # see GH#18376, GH#18162
+        ts[(ts.index >= lb) & (ts.index <= rb)]
+
+    lb = Timestamp(naive).tz_localize(rng.tzinfo)
+    rb = Timestamp(datetime(1990, 1, 1, 7)).tz_localize(rng.tzinfo)
+    result = ts[(ts.index >= lb) & (ts.index <= rb)]
+    expected = ts[4:8]
+    tm.assert_series_equal(result, expected)
+
+    result = ts[ts.index[4]]
+    expected = ts.iloc[4]
+    assert result == expected
+
+    result = ts[ts.index[4:8]]
+    expected = ts[4:8]
+    tm.assert_series_equal(result, expected)
+
+    result = ts.copy()
+    result[ts.index[4:8]] = 0
+    result.iloc[4:8] = ts.iloc[4:8]
+    tm.assert_series_equal(result, ts)
+
+    # also test partial date slicing
+    result = ts["1990-01-02"]
+    expected = ts[24:48]
+    tm.assert_series_equal(result, expected)
+
+    result = ts.copy()
+    result["1990-01-02"] = 0
+    result["1990-01-02"] = ts[24:48]
+    tm.assert_series_equal(result, ts)
+
+
+def test_getitem_setitem_periodindex():
+    N = 50
+    rng = period_range("1/1/1990", periods=N, freq="h")
+    ts = Series(np.random.default_rng(2).standard_normal(N), index=rng)
+
+    result = ts["1990-01-01 04"]
+    expected = ts.iloc[4]
+    assert result == expected
+
+    result = ts.copy()
+    result["1990-01-01 04"] = 0
+    result["1990-01-01 04"] = ts.iloc[4]
+    tm.assert_series_equal(result, ts)
+
+    result = ts["1990-01-01 04":"1990-01-01 07"]
+    expected = ts[4:8]
+    tm.assert_series_equal(result, expected)
+
+    result = ts.copy()
+    result["1990-01-01 04":"1990-01-01 07"] = 0
+    result["1990-01-01 04":"1990-01-01 07"] = ts[4:8]
+    tm.assert_series_equal(result, ts)
+
+    lb = "1990-01-01 04"
+    rb = "1990-01-01 07"
+    result = ts[(ts.index >= lb) & (ts.index <= rb)]
+    expected = ts[4:8]
+    tm.assert_series_equal(result, expected)
+
+    # GH 2782
+    result = ts[ts.index[4]]
+    expected = ts.iloc[4]
+    assert result == expected
+
+    result = ts[ts.index[4:8]]
+    expected = ts[4:8]
+    tm.assert_series_equal(result, expected)
+
+    result = ts.copy()
+    result[ts.index[4:8]] = 0
+    result.iloc[4:8] = ts.iloc[4:8]
+    tm.assert_series_equal(result, ts)
+
+
+def test_datetime_indexing():
+    index = date_range("1/1/2000", "1/7/2000")
+    index = index.repeat(3)
+
+    s = Series(len(index), index=index)
+    stamp = Timestamp("1/8/2000")
+
+    with pytest.raises(KeyError, match=re.escape(repr(stamp))):
+        s[stamp]
+    s[stamp] = 0
+    assert s[stamp] == 0
+
+    # not monotonic
+    s = Series(len(index), index=index)
+    s = s[::-1]
+
+    with pytest.raises(KeyError, match=re.escape(repr(stamp))):
+        s[stamp]
+    s[stamp] = 0
+    assert s[stamp] == 0
+
+
+# test duplicates in time series
+
+
+def test_indexing_with_duplicate_datetimeindex(
+    rand_series_with_duplicate_datetimeindex,
+):
+    ts = rand_series_with_duplicate_datetimeindex
+
+    uniques = ts.index.unique()
+    for date in uniques:
+        result = ts[date]
+
+        mask = ts.index == date
+        total = (ts.index == date).sum()
+        expected = ts[mask]
+        if total > 1:
+            tm.assert_series_equal(result, expected)
+        else:
+            tm.assert_almost_equal(result, expected.iloc[0])
+
+        cp = ts.copy()
+        cp[date] = 0
+        expected = Series(np.where(mask, 0, ts), index=ts.index)
+        tm.assert_series_equal(cp, expected)
+
+    key = datetime(2000, 1, 6)
+    with pytest.raises(KeyError, match=re.escape(repr(key))):
+        ts[key]
+
+    # new index
+    ts[datetime(2000, 1, 6)] = 0
+    assert ts[datetime(2000, 1, 6)] == 0
+
+
+def test_loc_getitem_over_size_cutoff(monkeypatch):
+    # #1821
+
+    monkeypatch.setattr(libindex, "_SIZE_CUTOFF", 1000)
+
+    # create large list of non periodic datetime
+    dates = []
+    sec = timedelta(seconds=1)
+    half_sec = timedelta(microseconds=500000)
+    d = datetime(2011, 12, 5, 20, 30)
+    n = 1100
+    for i in range(n):
+        dates.append(d)
+        dates.append(d + sec)
+        dates.append(d + sec + half_sec)
+        dates.append(d + sec + sec + half_sec)
+        d += 3 * sec
+
+    # duplicate some values in the list
+    duplicate_positions = np.random.default_rng(2).integers(0, len(dates) - 1, 20)
+    for p in duplicate_positions:
+        dates[p + 1] = dates[p]
+
+    df = DataFrame(
+        np.random.default_rng(2).standard_normal((len(dates), 4)),
+        index=dates,
+        columns=list("ABCD"),
+    )
+
+    pos = n * 3
+    timestamp = df.index[pos]
+    assert timestamp in df.index
+
+    # it works!
+    df.loc[timestamp]
+    assert len(df.loc[[timestamp]]) > 0
+
+
+def test_indexing_over_size_cutoff_period_index(monkeypatch):
+    # GH 27136
+
+    monkeypatch.setattr(libindex, "_SIZE_CUTOFF", 1000)
+
+    n = 1100
+    idx = period_range("1/1/2000", freq="min", periods=n)
+    assert idx._engine.over_size_threshold
+
+    s = Series(np.random.default_rng(2).standard_normal(len(idx)), index=idx)
+
+    pos = n - 1
+    timestamp = idx[pos]
+    assert timestamp in s.index
+
+    # it works!
+    s[timestamp]
+    assert len(s.loc[[timestamp]]) > 0
+
+
+def test_indexing_unordered():
+    # GH 2437
+    rng = date_range(start="2011-01-01", end="2011-01-15")
+    ts = Series(np.random.default_rng(2).random(len(rng)), index=rng)
+    ts2 = pd.concat([ts[0:4], ts[-4:], ts[4:-4]])
+
+    for t in ts.index:
+        expected = ts[t]
+        result = ts2[t]
+        assert expected == result
+
+    # GH 3448 (ranges)
+    def compare(slobj):
+        result = ts2[slobj].copy()
+        result = result.sort_index()
+        expected = ts[slobj]
+        expected.index = expected.index._with_freq(None)
+        tm.assert_series_equal(result, expected)
+
+    for key in [
+        slice("2011-01-01", "2011-01-15"),
+        slice("2010-12-30", "2011-01-15"),
+        slice("2011-01-01", "2011-01-16"),
+        # partial ranges
+        slice("2011-01-01", "2011-01-6"),
+        slice("2011-01-06", "2011-01-8"),
+        slice("2011-01-06", "2011-01-12"),
+    ]:
+        with pytest.raises(
+            KeyError, match="Value based partial slicing on non-monotonic"
+        ):
+            compare(key)
+
+    # single values
+    result = ts2["2011"].sort_index()
+    expected = ts["2011"]
+    expected.index = expected.index._with_freq(None)
+    tm.assert_series_equal(result, expected)
+
+
+def test_indexing_unordered2():
+    # diff freq
+    rng = date_range(datetime(2005, 1, 1), periods=20, freq="ME")
+    ts = Series(np.arange(len(rng)), index=rng)
+    ts = ts.take(np.random.default_rng(2).permutation(20))
+
+    result = ts["2005"]
+    for t in result.index:
+        assert t.year == 2005
+
+
+def test_indexing():
+    idx = date_range("2001-1-1", periods=20, freq="ME")
+    ts = Series(np.random.default_rng(2).random(len(idx)), index=idx)
+
+    # getting
+
+    # GH 3070, make sure semantics work on Series/Frame
+    result = ts["2001"]
+    tm.assert_series_equal(result, ts.iloc[:12])
+
+    df = DataFrame({"A": ts.copy()})
+
+    # GH#36179 pre-2.0 df["2001"] operated as slicing on rows. in 2.0 it behaves
+    #  like any other key, so raises
+    with pytest.raises(KeyError, match="2001"):
+        df["2001"]
+
+    # setting
+    ts = Series(np.random.default_rng(2).random(len(idx)), index=idx)
+    expected = ts.copy()
+    expected.iloc[:12] = 1
+    ts["2001"] = 1
+    tm.assert_series_equal(ts, expected)
+
+    expected = df.copy()
+    expected.iloc[:12, 0] = 1
+    df.loc["2001", "A"] = 1
+    tm.assert_frame_equal(df, expected)
+
+
+def test_getitem_str_month_with_datetimeindex():
+    # GH3546 (not including times on the last day)
+    idx = date_range(start="2013-05-31 00:00", end="2013-05-31 23:00", freq="h")
+    ts = Series(range(len(idx)), index=idx)
+    expected = ts["2013-05"]
+    tm.assert_series_equal(expected, ts)
+
+    idx = date_range(start="2013-05-31 00:00", end="2013-05-31 23:59", freq="s")
+    ts = Series(range(len(idx)), index=idx)
+    expected = ts["2013-05"]
+    tm.assert_series_equal(expected, ts)
+
+
+def test_getitem_str_year_with_datetimeindex():
+    idx = [
+        Timestamp("2013-05-31 00:00"),
+        Timestamp(datetime(2013, 5, 31, 23, 59, 59, 999999)),
+    ]
+    ts = Series(range(len(idx)), index=idx)
+    expected = ts["2013"]
+    tm.assert_series_equal(expected, ts)
+
+
+def test_getitem_str_second_with_datetimeindex():
+    # GH14826, indexing with a seconds resolution string / datetime object
+    df = DataFrame(
+        np.random.default_rng(2).random((5, 5)),
+        columns=["open", "high", "low", "close", "volume"],
+        index=date_range("2012-01-02 18:01:00", periods=5, tz="US/Central", freq="s"),
+    )
+
+    # this is a single date, so will raise
+    with pytest.raises(KeyError, match=r"^'2012-01-02 18:01:02'$"):
+        df["2012-01-02 18:01:02"]
+
+    msg = r"Timestamp\('2012-01-02 18:01:02-0600', tz='US/Central'\)"
+    with pytest.raises(KeyError, match=msg):
+        df[df.index[2]]
+
+
+def test_compare_datetime_with_all_none():
+    # GH#54870
+    ser = Series(["2020-01-01", "2020-01-02"], dtype="datetime64[ns]")
+    ser2 = Series([None, None])
+    result = ser > ser2
+    expected = Series([False, False])
+    tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/indexing/test_delitem.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/indexing/test_delitem.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d1082c3d040b95063d10c7160154b62dc1c84f6
--- /dev/null
+++ b/env-llmeval/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/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/indexing/test_get.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/indexing/test_get.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f3711ad919036a779c5357e3e6ae921bb795458
--- /dev/null
+++ b/env-llmeval/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/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/indexing/test_getitem.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/indexing/test_getitem.py
new file mode 100644
index 0000000000000000000000000000000000000000..596a225c288b890461d4ea5d03bef6f6eb1bc04b
--- /dev/null
+++ b/env-llmeval/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/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/indexing/test_take.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/indexing/test_take.py
new file mode 100644
index 0000000000000000000000000000000000000000..43fbae89089665fb2bf589d35db1b3f3041dd018
--- /dev/null
+++ b/env-llmeval/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/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/indexing/test_xs.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/indexing/test_xs.py
new file mode 100644
index 0000000000000000000000000000000000000000..a67f3ec708f24a97dab544bb5cf859dc54659215
--- /dev/null
+++ b/env-llmeval/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/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_clip.py
@@ -0,0 +1,146 @@
+from datetime import datetime
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Series,
+    Timestamp,
+    isna,
+    notna,
+)
+import pandas._testing as tm
+
+
+class TestSeriesClip:
+    def test_clip(self, datetime_series):
+        val = datetime_series.median()
+
+        assert datetime_series.clip(lower=val).min() == val
+        assert datetime_series.clip(upper=val).max() == val
+
+        result = datetime_series.clip(-0.5, 0.5)
+        expected = np.clip(datetime_series, -0.5, 0.5)
+        tm.assert_series_equal(result, expected)
+        assert isinstance(expected, Series)
+
+    def test_clip_types_and_nulls(self):
+        sers = [
+            Series([np.nan, 1.0, 2.0, 3.0]),
+            Series([None, "a", "b", "c"]),
+            Series(pd.to_datetime([np.nan, 1, 2, 3], unit="D")),
+        ]
+
+        for s in sers:
+            thresh = s[2]
+            lower = s.clip(lower=thresh)
+            upper = s.clip(upper=thresh)
+            assert lower[notna(lower)].min() == thresh
+            assert upper[notna(upper)].max() == thresh
+            assert list(isna(s)) == list(isna(lower))
+            assert list(isna(s)) == list(isna(upper))
+
+    def test_series_clipping_with_na_values(self, any_numeric_ea_dtype, nulls_fixture):
+        # Ensure that clipping method can handle NA values with out failing
+        # GH#40581
+
+        if nulls_fixture is pd.NaT:
+            # constructor will raise, see
+            #  test_constructor_mismatched_null_nullable_dtype
+            pytest.skip("See test_constructor_mismatched_null_nullable_dtype")
+
+        ser = Series([nulls_fixture, 1.0, 3.0], dtype=any_numeric_ea_dtype)
+        s_clipped_upper = ser.clip(upper=2.0)
+        s_clipped_lower = ser.clip(lower=2.0)
+
+        expected_upper = Series([nulls_fixture, 1.0, 2.0], dtype=any_numeric_ea_dtype)
+        expected_lower = Series([nulls_fixture, 2.0, 3.0], dtype=any_numeric_ea_dtype)
+
+        tm.assert_series_equal(s_clipped_upper, expected_upper)
+        tm.assert_series_equal(s_clipped_lower, expected_lower)
+
+    def test_clip_with_na_args(self):
+        """Should process np.nan argument as None"""
+        # GH#17276
+        s = Series([1, 2, 3])
+
+        tm.assert_series_equal(s.clip(np.nan), Series([1, 2, 3]))
+        tm.assert_series_equal(s.clip(upper=np.nan, lower=np.nan), Series([1, 2, 3]))
+
+        # GH#19992
+        msg = "Downcasting behavior in Series and DataFrame methods 'where'"
+        # TODO: avoid this warning here?  seems like we should never be upcasting
+        #  in the first place?
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            res = s.clip(lower=[0, 4, np.nan])
+        tm.assert_series_equal(res, Series([1, 4, 3]))
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            res = s.clip(upper=[1, np.nan, 1])
+        tm.assert_series_equal(res, Series([1, 2, 1]))
+
+        # GH#40420
+        s = Series([1, 2, 3])
+        result = s.clip(0, [np.nan, np.nan, np.nan])
+        tm.assert_series_equal(s, result)
+
+    def test_clip_against_series(self):
+        # GH#6966
+
+        s = Series([1.0, 1.0, 4.0])
+
+        lower = Series([1.0, 2.0, 3.0])
+        upper = Series([1.5, 2.5, 3.5])
+
+        tm.assert_series_equal(s.clip(lower, upper), Series([1.0, 2.0, 3.5]))
+        tm.assert_series_equal(s.clip(1.5, upper), Series([1.5, 1.5, 3.5]))
+
+    @pytest.mark.parametrize("inplace", [True, False])
+    @pytest.mark.parametrize("upper", [[1, 2, 3], np.asarray([1, 2, 3])])
+    def test_clip_against_list_like(self, inplace, upper):
+        # GH#15390
+        original = Series([5, 6, 7])
+        result = original.clip(upper=upper, inplace=inplace)
+        expected = Series([1, 2, 3])
+
+        if inplace:
+            result = original
+        tm.assert_series_equal(result, expected, check_exact=True)
+
+    def test_clip_with_datetimes(self):
+        # GH#11838
+        # naive and tz-aware datetimes
+
+        t = Timestamp("2015-12-01 09:30:30")
+        s = Series([Timestamp("2015-12-01 09:30:00"), Timestamp("2015-12-01 09:31:00")])
+        result = s.clip(upper=t)
+        expected = Series(
+            [Timestamp("2015-12-01 09:30:00"), Timestamp("2015-12-01 09:30:30")]
+        )
+        tm.assert_series_equal(result, expected)
+
+        t = Timestamp("2015-12-01 09:30:30", tz="US/Eastern")
+        s = Series(
+            [
+                Timestamp("2015-12-01 09:30:00", tz="US/Eastern"),
+                Timestamp("2015-12-01 09:31:00", tz="US/Eastern"),
+            ]
+        )
+        result = s.clip(upper=t)
+        expected = Series(
+            [
+                Timestamp("2015-12-01 09:30:00", tz="US/Eastern"),
+                Timestamp("2015-12-01 09:30:30", tz="US/Eastern"),
+            ]
+        )
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", [object, "M8[us]"])
+    def test_clip_with_timestamps_and_oob_datetimes(self, dtype):
+        # GH-42794
+        ser = Series([datetime(1, 1, 1), datetime(9999, 9, 9)], dtype=dtype)
+
+        result = ser.clip(lower=Timestamp.min, upper=Timestamp.max)
+        expected = Series([Timestamp.min, Timestamp.max], dtype=dtype)
+
+        tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_combine_first.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_combine_first.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1ec8afda33a9740806cc993474780aaf37d435e
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_combine_first.py
@@ -0,0 +1,149 @@
+from datetime import datetime
+
+import numpy as np
+
+import pandas as pd
+from pandas import (
+    Period,
+    Series,
+    date_range,
+    period_range,
+    to_datetime,
+)
+import pandas._testing as tm
+
+
+class TestCombineFirst:
+    def test_combine_first_period_datetime(self):
+        # GH#3367
+        didx = date_range(start="1950-01-31", end="1950-07-31", freq="ME")
+        pidx = period_range(start=Period("1950-1"), end=Period("1950-7"), freq="M")
+        # check to be consistent with DatetimeIndex
+        for idx in [didx, pidx]:
+            a = Series([1, np.nan, np.nan, 4, 5, np.nan, 7], index=idx)
+            b = Series([9, 9, 9, 9, 9, 9, 9], index=idx)
+
+            result = a.combine_first(b)
+            expected = Series([1, 9, 9, 4, 5, 9, 7], index=idx, dtype=np.float64)
+            tm.assert_series_equal(result, expected)
+
+    def test_combine_first_name(self, datetime_series):
+        result = datetime_series.combine_first(datetime_series[:5])
+        assert result.name == datetime_series.name
+
+    def test_combine_first(self):
+        values = np.arange(20, dtype=np.float64)
+        series = Series(values, index=np.arange(20, dtype=np.int64))
+
+        series_copy = series * 2
+        series_copy[::2] = np.nan
+
+        # nothing used from the input
+        combined = series.combine_first(series_copy)
+
+        tm.assert_series_equal(combined, series)
+
+        # Holes filled from input
+        combined = series_copy.combine_first(series)
+        assert np.isfinite(combined).all()
+
+        tm.assert_series_equal(combined[::2], series[::2])
+        tm.assert_series_equal(combined[1::2], series_copy[1::2])
+
+        # mixed types
+        index = pd.Index([str(i) for i in range(20)])
+        floats = Series(np.random.default_rng(2).standard_normal(20), index=index)
+        strings = Series([str(i) for i in range(10)], index=index[::2], dtype=object)
+
+        combined = strings.combine_first(floats)
+
+        tm.assert_series_equal(strings, combined.loc[index[::2]])
+        tm.assert_series_equal(floats[1::2].astype(object), combined.loc[index[1::2]])
+
+        # corner case
+        ser = Series([1.0, 2, 3], index=[0, 1, 2])
+        empty = Series([], index=[], dtype=object)
+        msg = "The behavior of array concatenation with empty entries is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = ser.combine_first(empty)
+        ser.index = ser.index.astype("O")
+        tm.assert_series_equal(ser, result)
+
+    def test_combine_first_dt64(self, unit):
+        s0 = to_datetime(Series(["2010", np.nan])).dt.as_unit(unit)
+        s1 = to_datetime(Series([np.nan, "2011"])).dt.as_unit(unit)
+        rs = s0.combine_first(s1)
+        xp = to_datetime(Series(["2010", "2011"])).dt.as_unit(unit)
+        tm.assert_series_equal(rs, xp)
+
+        s0 = to_datetime(Series(["2010", np.nan])).dt.as_unit(unit)
+        s1 = Series([np.nan, "2011"])
+        rs = s0.combine_first(s1)
+
+        xp = Series([datetime(2010, 1, 1), "2011"], dtype="datetime64[ns]")
+
+        tm.assert_series_equal(rs, xp)
+
+    def test_combine_first_dt_tz_values(self, tz_naive_fixture):
+        ser1 = Series(
+            pd.DatetimeIndex(["20150101", "20150102", "20150103"], tz=tz_naive_fixture),
+            name="ser1",
+        )
+        ser2 = Series(
+            pd.DatetimeIndex(["20160514", "20160515", "20160516"], tz=tz_naive_fixture),
+            index=[2, 3, 4],
+            name="ser2",
+        )
+        result = ser1.combine_first(ser2)
+        exp_vals = pd.DatetimeIndex(
+            ["20150101", "20150102", "20150103", "20160515", "20160516"],
+            tz=tz_naive_fixture,
+        )
+        exp = Series(exp_vals, name="ser1")
+        tm.assert_series_equal(exp, result)
+
+    def test_combine_first_timezone_series_with_empty_series(self):
+        # GH 41800
+        time_index = date_range(
+            datetime(2021, 1, 1, 1),
+            datetime(2021, 1, 1, 10),
+            freq="h",
+            tz="Europe/Rome",
+        )
+        s1 = Series(range(10), index=time_index)
+        s2 = Series(index=time_index)
+        msg = "The behavior of array concatenation with empty entries is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = s1.combine_first(s2)
+        tm.assert_series_equal(result, s1)
+
+    def test_combine_first_preserves_dtype(self):
+        # GH51764
+        s1 = Series([1666880195890293744, 1666880195890293837])
+        s2 = Series([1, 2, 3])
+        result = s1.combine_first(s2)
+        expected = Series([1666880195890293744, 1666880195890293837, 3])
+        tm.assert_series_equal(result, expected)
+
+    def test_combine_mixed_timezone(self):
+        # GH 26283
+        uniform_tz = Series({pd.Timestamp("2019-05-01", tz="UTC"): 1.0})
+        multi_tz = Series(
+            {
+                pd.Timestamp("2019-05-01 01:00:00+0100", tz="Europe/London"): 2.0,
+                pd.Timestamp("2019-05-02", tz="UTC"): 3.0,
+            }
+        )
+
+        result = uniform_tz.combine_first(multi_tz)
+        expected = Series(
+            [1.0, 3.0],
+            index=pd.Index(
+                [
+                    pd.Timestamp("2019-05-01 00:00:00+00:00", tz="UTC"),
+                    pd.Timestamp("2019-05-02 00:00:00+00:00", tz="UTC"),
+                ],
+                dtype="object",
+            ),
+        )
+        tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_cov_corr.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_cov_corr.py
new file mode 100644
index 0000000000000000000000000000000000000000..a369145b4e884d740af39b236edbf2ce6e088cd0
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_cov_corr.py
@@ -0,0 +1,185 @@
+import math
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Series,
+    date_range,
+    isna,
+)
+import pandas._testing as tm
+
+
+class TestSeriesCov:
+    def test_cov(self, datetime_series):
+        # full overlap
+        tm.assert_almost_equal(
+            datetime_series.cov(datetime_series), datetime_series.std() ** 2
+        )
+
+        # partial overlap
+        tm.assert_almost_equal(
+            datetime_series[:15].cov(datetime_series[5:]),
+            datetime_series[5:15].std() ** 2,
+        )
+
+        # No overlap
+        assert np.isnan(datetime_series[::2].cov(datetime_series[1::2]))
+
+        # all NA
+        cp = datetime_series[:10].copy()
+        cp[:] = np.nan
+        assert isna(cp.cov(cp))
+
+        # min_periods
+        assert isna(datetime_series[:15].cov(datetime_series[5:], min_periods=12))
+
+        ts1 = datetime_series[:15].reindex(datetime_series.index)
+        ts2 = datetime_series[5:].reindex(datetime_series.index)
+        assert isna(ts1.cov(ts2, min_periods=12))
+
+    @pytest.mark.parametrize("test_ddof", [None, 0, 1, 2, 3])
+    @pytest.mark.parametrize("dtype", ["float64", "Float64"])
+    def test_cov_ddof(self, test_ddof, dtype):
+        # GH#34611
+        np_array1 = np.random.default_rng(2).random(10)
+        np_array2 = np.random.default_rng(2).random(10)
+
+        s1 = Series(np_array1, dtype=dtype)
+        s2 = Series(np_array2, dtype=dtype)
+
+        result = s1.cov(s2, ddof=test_ddof)
+        expected = np.cov(np_array1, np_array2, ddof=test_ddof)[0][1]
+        assert math.isclose(expected, result)
+
+
+class TestSeriesCorr:
+    @pytest.mark.parametrize("dtype", ["float64", "Float64"])
+    def test_corr(self, datetime_series, dtype):
+        stats = pytest.importorskip("scipy.stats")
+
+        datetime_series = datetime_series.astype(dtype)
+
+        # full overlap
+        tm.assert_almost_equal(datetime_series.corr(datetime_series), 1)
+
+        # partial overlap
+        tm.assert_almost_equal(datetime_series[:15].corr(datetime_series[5:]), 1)
+
+        assert isna(datetime_series[:15].corr(datetime_series[5:], min_periods=12))
+
+        ts1 = datetime_series[:15].reindex(datetime_series.index)
+        ts2 = datetime_series[5:].reindex(datetime_series.index)
+        assert isna(ts1.corr(ts2, min_periods=12))
+
+        # No overlap
+        assert np.isnan(datetime_series[::2].corr(datetime_series[1::2]))
+
+        # all NA
+        cp = datetime_series[:10].copy()
+        cp[:] = np.nan
+        assert isna(cp.corr(cp))
+
+        A = Series(
+            np.arange(10, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            name="ts",
+        )
+        B = A.copy()
+        result = A.corr(B)
+        expected, _ = stats.pearsonr(A, B)
+        tm.assert_almost_equal(result, expected)
+
+    def test_corr_rank(self):
+        stats = pytest.importorskip("scipy.stats")
+
+        # kendall and spearman
+        A = Series(
+            np.arange(10, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            name="ts",
+        )
+        B = A.copy()
+        A[-5:] = A[:5].copy()
+        result = A.corr(B, method="kendall")
+        expected = stats.kendalltau(A, B)[0]
+        tm.assert_almost_equal(result, expected)
+
+        result = A.corr(B, method="spearman")
+        expected = stats.spearmanr(A, B)[0]
+        tm.assert_almost_equal(result, expected)
+
+        # results from R
+        A = Series(
+            [
+                -0.89926396,
+                0.94209606,
+                -1.03289164,
+                -0.95445587,
+                0.76910310,
+                -0.06430576,
+                -2.09704447,
+                0.40660407,
+                -0.89926396,
+                0.94209606,
+            ]
+        )
+        B = Series(
+            [
+                -1.01270225,
+                -0.62210117,
+                -1.56895827,
+                0.59592943,
+                -0.01680292,
+                1.17258718,
+                -1.06009347,
+                -0.10222060,
+                -0.89076239,
+                0.89372375,
+            ]
+        )
+        kexp = 0.4319297
+        sexp = 0.5853767
+        tm.assert_almost_equal(A.corr(B, method="kendall"), kexp)
+        tm.assert_almost_equal(A.corr(B, method="spearman"), sexp)
+
+    def test_corr_invalid_method(self):
+        # GH PR #22298
+        s1 = Series(np.random.default_rng(2).standard_normal(10))
+        s2 = Series(np.random.default_rng(2).standard_normal(10))
+        msg = "method must be either 'pearson', 'spearman', 'kendall', or a callable, "
+        with pytest.raises(ValueError, match=msg):
+            s1.corr(s2, method="____")
+
+    def test_corr_callable_method(self, datetime_series):
+        # simple correlation example
+        # returns 1 if exact equality, 0 otherwise
+        my_corr = lambda a, b: 1.0 if (a == b).all() else 0.0
+
+        # simple example
+        s1 = Series([1, 2, 3, 4, 5])
+        s2 = Series([5, 4, 3, 2, 1])
+        expected = 0
+        tm.assert_almost_equal(s1.corr(s2, method=my_corr), expected)
+
+        # full overlap
+        tm.assert_almost_equal(
+            datetime_series.corr(datetime_series, method=my_corr), 1.0
+        )
+
+        # partial overlap
+        tm.assert_almost_equal(
+            datetime_series[:15].corr(datetime_series[5:], method=my_corr), 1.0
+        )
+
+        # No overlap
+        assert np.isnan(
+            datetime_series[::2].corr(datetime_series[1::2], method=my_corr)
+        )
+
+        # dataframe example
+        df = pd.DataFrame([s1, s2])
+        expected = pd.DataFrame([{0: 1.0, 1: 0}, {0: 0, 1: 1.0}])
+        tm.assert_almost_equal(df.transpose().corr(method=my_corr), expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_drop.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_drop.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d9a469915cfb718aba9020b82105c66d93b429f
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_drop.py
@@ -0,0 +1,99 @@
+import pytest
+
+from pandas import (
+    Index,
+    Series,
+)
+import pandas._testing as tm
+from pandas.api.types import is_bool_dtype
+
+
+@pytest.mark.parametrize(
+    "data, index, drop_labels, axis, expected_data, expected_index",
+    [
+        # Unique Index
+        ([1, 2], ["one", "two"], ["two"], 0, [1], ["one"]),
+        ([1, 2], ["one", "two"], ["two"], "rows", [1], ["one"]),
+        ([1, 1, 2], ["one", "two", "one"], ["two"], 0, [1, 2], ["one", "one"]),
+        # GH 5248 Non-Unique Index
+        ([1, 1, 2], ["one", "two", "one"], "two", 0, [1, 2], ["one", "one"]),
+        ([1, 1, 2], ["one", "two", "one"], ["one"], 0, [1], ["two"]),
+        ([1, 1, 2], ["one", "two", "one"], "one", 0, [1], ["two"]),
+    ],
+)
+def test_drop_unique_and_non_unique_index(
+    data, index, axis, drop_labels, expected_data, expected_index
+):
+    ser = Series(data=data, index=index)
+    result = ser.drop(drop_labels, axis=axis)
+    expected = Series(data=expected_data, index=expected_index)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data, index, drop_labels, axis, error_type, error_desc",
+    [
+        # single string/tuple-like
+        (range(3), list("abc"), "bc", 0, KeyError, "not found in axis"),
+        # bad axis
+        (range(3), list("abc"), ("a",), 0, KeyError, "not found in axis"),
+        (range(3), list("abc"), "one", "columns", ValueError, "No axis named columns"),
+    ],
+)
+def test_drop_exception_raised(data, index, drop_labels, axis, error_type, error_desc):
+    ser = Series(data, index=index)
+    with pytest.raises(error_type, match=error_desc):
+        ser.drop(drop_labels, axis=axis)
+
+
+def test_drop_with_ignore_errors():
+    # errors='ignore'
+    ser = Series(range(3), index=list("abc"))
+    result = ser.drop("bc", errors="ignore")
+    tm.assert_series_equal(result, ser)
+    result = ser.drop(["a", "d"], errors="ignore")
+    expected = ser.iloc[1:]
+    tm.assert_series_equal(result, expected)
+
+    # GH 8522
+    ser = Series([2, 3], index=[True, False])
+    assert is_bool_dtype(ser.index)
+    assert ser.index.dtype == bool
+    result = ser.drop(True)
+    expected = Series([3], index=[False])
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("index", [[1, 2, 3], [1, 1, 3]])
+@pytest.mark.parametrize("drop_labels", [[], [1], [3]])
+def test_drop_empty_list(index, drop_labels):
+    # GH 21494
+    expected_index = [i for i in index if i not in drop_labels]
+    series = Series(index=index, dtype=object).drop(drop_labels)
+    expected = Series(index=expected_index, dtype=object)
+    tm.assert_series_equal(series, expected)
+
+
+@pytest.mark.parametrize(
+    "data, index, drop_labels",
+    [
+        (None, [1, 2, 3], [1, 4]),
+        (None, [1, 2, 2], [1, 4]),
+        ([2, 3], [0, 1], [False, True]),
+    ],
+)
+def test_drop_non_empty_list(data, index, drop_labels):
+    # GH 21494 and GH 16877
+    dtype = object if data is None else None
+    ser = Series(data=data, index=index, dtype=dtype)
+    with pytest.raises(KeyError, match="not found in axis"):
+        ser.drop(drop_labels)
+
+
+def test_drop_index_ea_dtype(any_numeric_ea_dtype):
+    # GH#45860
+    df = Series(100, index=Index([1, 2, 2], dtype=any_numeric_ea_dtype))
+    idx = Index([df.index[1]])
+    result = df.drop(idx)
+    expected = Series(100, index=Index([1], dtype=any_numeric_ea_dtype))
+    tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_duplicated.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_duplicated.py
new file mode 100644
index 0000000000000000000000000000000000000000..e177b5275d855fffbede91280d9ee7fb61ece2cd
--- /dev/null
+++ b/env-llmeval/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/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_equals.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_equals.py
new file mode 100644
index 0000000000000000000000000000000000000000..875ffdd3fe8514edb4c313c8f558330c787cef08
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_equals.py
@@ -0,0 +1,145 @@
+from contextlib import nullcontext
+import copy
+
+import numpy as np
+import pytest
+
+from pandas._libs.missing import is_matching_na
+from pandas.compat.numpy import np_version_gte1p25
+
+from pandas.core.dtypes.common import is_float
+
+from pandas import (
+    Index,
+    MultiIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize(
+    "arr, idx",
+    [
+        ([1, 2, 3, 4], [0, 2, 1, 3]),
+        ([1, np.nan, 3, np.nan], [0, 2, 1, 3]),
+        (
+            [1, np.nan, 3, np.nan],
+            MultiIndex.from_tuples([(0, "a"), (1, "b"), (2, "c"), (3, "c")]),
+        ),
+    ],
+)
+def test_equals(arr, idx):
+    s1 = Series(arr, index=idx)
+    s2 = s1.copy()
+    assert s1.equals(s2)
+
+    s1[1] = 9
+    assert not s1.equals(s2)
+
+
+@pytest.mark.parametrize(
+    "val", [1, 1.1, 1 + 1j, True, "abc", [1, 2], (1, 2), {1, 2}, {"a": 1}, None]
+)
+def test_equals_list_array(val):
+    # GH20676 Verify equals operator for list of Numpy arrays
+    arr = np.array([1, 2])
+    s1 = Series([arr, arr])
+    s2 = s1.copy()
+    assert s1.equals(s2)
+
+    s1[1] = val
+
+    cm = (
+        tm.assert_produces_warning(FutureWarning, check_stacklevel=False)
+        if isinstance(val, str) and not np_version_gte1p25
+        else nullcontext()
+    )
+    with cm:
+        assert not s1.equals(s2)
+
+
+def test_equals_false_negative():
+    # GH8437 Verify false negative behavior of equals function for dtype object
+    arr = [False, np.nan]
+    s1 = Series(arr)
+    s2 = s1.copy()
+    s3 = Series(index=range(2), dtype=object)
+    s4 = s3.copy()
+    s5 = s3.copy()
+    s6 = s3.copy()
+
+    s3[:-1] = s4[:-1] = s5[0] = s6[0] = False
+    assert s1.equals(s1)
+    assert s1.equals(s2)
+    assert s1.equals(s3)
+    assert s1.equals(s4)
+    assert s1.equals(s5)
+    assert s5.equals(s6)
+
+
+def test_equals_matching_nas():
+    # matching but not identical NAs
+    left = Series([np.datetime64("NaT")], dtype=object)
+    right = Series([np.datetime64("NaT")], dtype=object)
+    assert left.equals(right)
+    with tm.assert_produces_warning(FutureWarning, match="Dtype inference"):
+        assert Index(left).equals(Index(right))
+    assert left.array.equals(right.array)
+
+    left = Series([np.timedelta64("NaT")], dtype=object)
+    right = Series([np.timedelta64("NaT")], dtype=object)
+    assert left.equals(right)
+    with tm.assert_produces_warning(FutureWarning, match="Dtype inference"):
+        assert Index(left).equals(Index(right))
+    assert left.array.equals(right.array)
+
+    left = Series([np.float64("NaN")], dtype=object)
+    right = Series([np.float64("NaN")], dtype=object)
+    assert left.equals(right)
+    assert Index(left, dtype=left.dtype).equals(Index(right, dtype=right.dtype))
+    assert left.array.equals(right.array)
+
+
+def test_equals_mismatched_nas(nulls_fixture, nulls_fixture2):
+    # GH#39650
+    left = nulls_fixture
+    right = nulls_fixture2
+    if hasattr(right, "copy"):
+        right = right.copy()
+    else:
+        right = copy.copy(right)
+
+    ser = Series([left], dtype=object)
+    ser2 = Series([right], dtype=object)
+
+    if is_matching_na(left, right):
+        assert ser.equals(ser2)
+    elif (left is None and is_float(right)) or (right is None and is_float(left)):
+        assert ser.equals(ser2)
+    else:
+        assert not ser.equals(ser2)
+
+
+def test_equals_none_vs_nan():
+    # GH#39650
+    ser = Series([1, None], dtype=object)
+    ser2 = Series([1, np.nan], dtype=object)
+
+    assert ser.equals(ser2)
+    assert Index(ser, dtype=ser.dtype).equals(Index(ser2, dtype=ser2.dtype))
+    assert ser.array.equals(ser2.array)
+
+
+def test_equals_None_vs_float():
+    # GH#44190
+    left = Series([-np.inf, np.nan, -1.0, 0.0, 1.0, 10 / 3, np.inf], dtype=object)
+    right = Series([None] * len(left))
+
+    # these series were found to be equal due to a bug, check that they are correctly
+    # found to not equal
+    assert not left.equals(right)
+    assert not right.equals(left)
+    assert not left.to_frame().equals(right.to_frame())
+    assert not right.to_frame().equals(left.to_frame())
+    assert not Index(left, dtype="object").equals(Index(right, dtype="object"))
+    assert not Index(right, dtype="object").equals(Index(left, dtype="object"))
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_get_numeric_data.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_get_numeric_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..8325cc884ebcba069c18f457371cfa061893cf81
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_get_numeric_data.py
@@ -0,0 +1,38 @@
+from pandas import (
+    Index,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestGetNumericData:
+    def test_get_numeric_data_preserve_dtype(
+        self, using_copy_on_write, warn_copy_on_write
+    ):
+        # get the numeric data
+        obj = Series([1, 2, 3])
+        result = obj._get_numeric_data()
+        tm.assert_series_equal(result, obj)
+
+        # returned object is a shallow copy
+        with tm.assert_cow_warning(warn_copy_on_write):
+            result.iloc[0] = 0
+        if using_copy_on_write:
+            assert obj.iloc[0] == 1
+        else:
+            assert obj.iloc[0] == 0
+
+        obj = Series([1, "2", 3.0])
+        result = obj._get_numeric_data()
+        expected = Series([], dtype=object, index=Index([], dtype=object))
+        tm.assert_series_equal(result, expected)
+
+        obj = Series([True, False, True])
+        result = obj._get_numeric_data()
+        tm.assert_series_equal(result, obj)
+
+        obj = Series(date_range("20130101", periods=3))
+        result = obj._get_numeric_data()
+        expected = Series([], dtype="M8[ns]", index=Index([], dtype=object))
+        tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_head_tail.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_head_tail.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9f8d85eda350e0627790626d52b7703d23ead02
--- /dev/null
+++ b/env-llmeval/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/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_interpolate.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_interpolate.py
new file mode 100644
index 0000000000000000000000000000000000000000..d854f0b7877595fba5ac0050a281aa3708240b0e
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_interpolate.py
@@ -0,0 +1,868 @@
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    Index,
+    MultiIndex,
+    Series,
+    date_range,
+    isna,
+)
+import pandas._testing as tm
+
+
+@pytest.fixture(
+    params=[
+        "linear",
+        "index",
+        "values",
+        "nearest",
+        "slinear",
+        "zero",
+        "quadratic",
+        "cubic",
+        "barycentric",
+        "krogh",
+        "polynomial",
+        "spline",
+        "piecewise_polynomial",
+        "from_derivatives",
+        "pchip",
+        "akima",
+        "cubicspline",
+    ]
+)
+def nontemporal_method(request):
+    """Fixture that returns an (method name, required kwargs) pair.
+
+    This fixture does not include method 'time' as a parameterization; that
+    method requires a Series with a DatetimeIndex, and is generally tested
+    separately from these non-temporal methods.
+    """
+    method = request.param
+    kwargs = {"order": 1} if method in ("spline", "polynomial") else {}
+    return method, kwargs
+
+
+@pytest.fixture(
+    params=[
+        "linear",
+        "slinear",
+        "zero",
+        "quadratic",
+        "cubic",
+        "barycentric",
+        "krogh",
+        "polynomial",
+        "spline",
+        "piecewise_polynomial",
+        "from_derivatives",
+        "pchip",
+        "akima",
+        "cubicspline",
+    ]
+)
+def interp_methods_ind(request):
+    """Fixture that returns a (method name, required kwargs) pair to
+    be tested for various Index types.
+
+    This fixture does not include methods - 'time', 'index', 'nearest',
+    'values' as a parameterization
+    """
+    method = request.param
+    kwargs = {"order": 1} if method in ("spline", "polynomial") else {}
+    return method, kwargs
+
+
+class TestSeriesInterpolateData:
+    @pytest.mark.xfail(reason="EA.fillna does not handle 'linear' method")
+    def test_interpolate_period_values(self):
+        orig = Series(date_range("2012-01-01", periods=5))
+        ser = orig.copy()
+        ser[2] = pd.NaT
+
+        # period cast
+        ser_per = ser.dt.to_period("D")
+        res_per = ser_per.interpolate()
+        expected_per = orig.dt.to_period("D")
+        tm.assert_series_equal(res_per, expected_per)
+
+    def test_interpolate(self, datetime_series):
+        ts = Series(np.arange(len(datetime_series), dtype=float), datetime_series.index)
+
+        ts_copy = ts.copy()
+        ts_copy[5:10] = np.nan
+
+        linear_interp = ts_copy.interpolate(method="linear")
+        tm.assert_series_equal(linear_interp, ts)
+
+        ord_ts = Series(
+            [d.toordinal() for d in datetime_series.index], index=datetime_series.index
+        ).astype(float)
+
+        ord_ts_copy = ord_ts.copy()
+        ord_ts_copy[5:10] = np.nan
+
+        time_interp = ord_ts_copy.interpolate(method="time")
+        tm.assert_series_equal(time_interp, ord_ts)
+
+    def test_interpolate_time_raises_for_non_timeseries(self):
+        # When method='time' is used on a non-TimeSeries that contains a null
+        # value, a ValueError should be raised.
+        non_ts = Series([0, 1, 2, np.nan])
+        msg = "time-weighted interpolation only works on Series.* with a DatetimeIndex"
+        with pytest.raises(ValueError, match=msg):
+            non_ts.interpolate(method="time")
+
+    def test_interpolate_cubicspline(self):
+        pytest.importorskip("scipy")
+        ser = Series([10, 11, 12, 13])
+
+        expected = Series(
+            [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00],
+            index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
+        )
+        # interpolate at new_index
+        new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
+            float
+        )
+        result = ser.reindex(new_index).interpolate(method="cubicspline").loc[1:3]
+        tm.assert_series_equal(result, expected)
+
+    def test_interpolate_pchip(self):
+        pytest.importorskip("scipy")
+        ser = Series(np.sort(np.random.default_rng(2).uniform(size=100)))
+
+        # interpolate at new_index
+        new_index = ser.index.union(
+            Index([49.25, 49.5, 49.75, 50.25, 50.5, 50.75])
+        ).astype(float)
+        interp_s = ser.reindex(new_index).interpolate(method="pchip")
+        # does not blow up, GH5977
+        interp_s.loc[49:51]
+
+    def test_interpolate_akima(self):
+        pytest.importorskip("scipy")
+        ser = Series([10, 11, 12, 13])
+
+        # interpolate at new_index where `der` is zero
+        expected = Series(
+            [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00],
+            index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
+        )
+        new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
+            float
+        )
+        interp_s = ser.reindex(new_index).interpolate(method="akima")
+        tm.assert_series_equal(interp_s.loc[1:3], expected)
+
+        # interpolate at new_index where `der` is a non-zero int
+        expected = Series(
+            [11.0, 1.0, 1.0, 1.0, 12.0, 1.0, 1.0, 1.0, 13.0],
+            index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
+        )
+        new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
+            float
+        )
+        interp_s = ser.reindex(new_index).interpolate(method="akima", der=1)
+        tm.assert_series_equal(interp_s.loc[1:3], expected)
+
+    def test_interpolate_piecewise_polynomial(self):
+        pytest.importorskip("scipy")
+        ser = Series([10, 11, 12, 13])
+
+        expected = Series(
+            [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00],
+            index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
+        )
+        # interpolate at new_index
+        new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
+            float
+        )
+        interp_s = ser.reindex(new_index).interpolate(method="piecewise_polynomial")
+        tm.assert_series_equal(interp_s.loc[1:3], expected)
+
+    def test_interpolate_from_derivatives(self):
+        pytest.importorskip("scipy")
+        ser = Series([10, 11, 12, 13])
+
+        expected = Series(
+            [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00],
+            index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
+        )
+        # interpolate at new_index
+        new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
+            float
+        )
+        interp_s = ser.reindex(new_index).interpolate(method="from_derivatives")
+        tm.assert_series_equal(interp_s.loc[1:3], expected)
+
+    @pytest.mark.parametrize(
+        "kwargs",
+        [
+            {},
+            pytest.param(
+                {"method": "polynomial", "order": 1}, marks=td.skip_if_no("scipy")
+            ),
+        ],
+    )
+    def test_interpolate_corners(self, kwargs):
+        s = Series([np.nan, np.nan])
+        tm.assert_series_equal(s.interpolate(**kwargs), s)
+
+        s = Series([], dtype=object).interpolate()
+        tm.assert_series_equal(s.interpolate(**kwargs), s)
+
+    def test_interpolate_index_values(self):
+        s = Series(np.nan, index=np.sort(np.random.default_rng(2).random(30)))
+        s.loc[::3] = np.random.default_rng(2).standard_normal(10)
+
+        vals = s.index.values.astype(float)
+
+        result = s.interpolate(method="index")
+
+        expected = s.copy()
+        bad = isna(expected.values)
+        good = ~bad
+        expected = Series(
+            np.interp(vals[bad], vals[good], s.values[good]), index=s.index[bad]
+        )
+
+        tm.assert_series_equal(result[bad], expected)
+
+        # 'values' is synonymous with 'index' for the method kwarg
+        other_result = s.interpolate(method="values")
+
+        tm.assert_series_equal(other_result, result)
+        tm.assert_series_equal(other_result[bad], expected)
+
+    def test_interpolate_non_ts(self):
+        s = Series([1, 3, np.nan, np.nan, np.nan, 11])
+        msg = (
+            "time-weighted interpolation only works on Series or DataFrames "
+            "with a DatetimeIndex"
+        )
+        with pytest.raises(ValueError, match=msg):
+            s.interpolate(method="time")
+
+    @pytest.mark.parametrize(
+        "kwargs",
+        [
+            {},
+            pytest.param(
+                {"method": "polynomial", "order": 1}, marks=td.skip_if_no("scipy")
+            ),
+        ],
+    )
+    def test_nan_interpolate(self, kwargs):
+        s = Series([0, 1, np.nan, 3])
+        result = s.interpolate(**kwargs)
+        expected = Series([0.0, 1.0, 2.0, 3.0])
+        tm.assert_series_equal(result, expected)
+
+    def test_nan_irregular_index(self):
+        s = Series([1, 2, np.nan, 4], index=[1, 3, 5, 9])
+        result = s.interpolate()
+        expected = Series([1.0, 2.0, 3.0, 4.0], index=[1, 3, 5, 9])
+        tm.assert_series_equal(result, expected)
+
+    def test_nan_str_index(self):
+        s = Series([0, 1, 2, np.nan], index=list("abcd"))
+        result = s.interpolate()
+        expected = Series([0.0, 1.0, 2.0, 2.0], index=list("abcd"))
+        tm.assert_series_equal(result, expected)
+
+    def test_interp_quad(self):
+        pytest.importorskip("scipy")
+        sq = Series([1, 4, np.nan, 16], index=[1, 2, 3, 4])
+        result = sq.interpolate(method="quadratic")
+        expected = Series([1.0, 4.0, 9.0, 16.0], index=[1, 2, 3, 4])
+        tm.assert_series_equal(result, expected)
+
+    def test_interp_scipy_basic(self):
+        pytest.importorskip("scipy")
+        s = Series([1, 3, np.nan, 12, np.nan, 25])
+        # slinear
+        expected = Series([1.0, 3.0, 7.5, 12.0, 18.5, 25.0])
+        result = s.interpolate(method="slinear")
+        tm.assert_series_equal(result, expected)
+
+        msg = "The 'downcast' keyword in Series.interpolate is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = s.interpolate(method="slinear", downcast="infer")
+        tm.assert_series_equal(result, expected)
+        # nearest
+        expected = Series([1, 3, 3, 12, 12, 25])
+        result = s.interpolate(method="nearest")
+        tm.assert_series_equal(result, expected.astype("float"))
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = s.interpolate(method="nearest", downcast="infer")
+        tm.assert_series_equal(result, expected)
+        # zero
+        expected = Series([1, 3, 3, 12, 12, 25])
+        result = s.interpolate(method="zero")
+        tm.assert_series_equal(result, expected.astype("float"))
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = s.interpolate(method="zero", downcast="infer")
+        tm.assert_series_equal(result, expected)
+        # quadratic
+        # GH #15662.
+        expected = Series([1, 3.0, 6.823529, 12.0, 18.058824, 25.0])
+        result = s.interpolate(method="quadratic")
+        tm.assert_series_equal(result, expected)
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = s.interpolate(method="quadratic", downcast="infer")
+        tm.assert_series_equal(result, expected)
+        # cubic
+        expected = Series([1.0, 3.0, 6.8, 12.0, 18.2, 25.0])
+        result = s.interpolate(method="cubic")
+        tm.assert_series_equal(result, expected)
+
+    def test_interp_limit(self):
+        s = Series([1, 3, np.nan, np.nan, np.nan, 11])
+
+        expected = Series([1.0, 3.0, 5.0, 7.0, np.nan, 11.0])
+        result = s.interpolate(method="linear", limit=2)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("limit", [-1, 0])
+    def test_interpolate_invalid_nonpositive_limit(self, nontemporal_method, limit):
+        # GH 9217: make sure limit is greater than zero.
+        s = Series([1, 2, np.nan, 4])
+        method, kwargs = nontemporal_method
+        with pytest.raises(ValueError, match="Limit must be greater than 0"):
+            s.interpolate(limit=limit, method=method, **kwargs)
+
+    def test_interpolate_invalid_float_limit(self, nontemporal_method):
+        # GH 9217: make sure limit is an integer.
+        s = Series([1, 2, np.nan, 4])
+        method, kwargs = nontemporal_method
+        limit = 2.0
+        with pytest.raises(ValueError, match="Limit must be an integer"):
+            s.interpolate(limit=limit, method=method, **kwargs)
+
+    @pytest.mark.parametrize("invalid_method", [None, "nonexistent_method"])
+    def test_interp_invalid_method(self, invalid_method):
+        s = Series([1, 3, np.nan, 12, np.nan, 25])
+
+        msg = f"method must be one of.* Got '{invalid_method}' instead"
+        if invalid_method is None:
+            msg = "'method' should be a string, not None"
+        with pytest.raises(ValueError, match=msg):
+            s.interpolate(method=invalid_method)
+
+        # When an invalid method and invalid limit (such as -1) are
+        # provided, the error message reflects the invalid method.
+        with pytest.raises(ValueError, match=msg):
+            s.interpolate(method=invalid_method, limit=-1)
+
+    def test_interp_invalid_method_and_value(self):
+        # GH#36624
+        ser = Series([1, 3, np.nan, 12, np.nan, 25])
+
+        msg = "'fill_value' is not a valid keyword for Series.interpolate"
+        msg2 = "Series.interpolate with method=pad"
+        with pytest.raises(ValueError, match=msg):
+            with tm.assert_produces_warning(FutureWarning, match=msg2):
+                ser.interpolate(fill_value=3, method="pad")
+
+    def test_interp_limit_forward(self):
+        s = Series([1, 3, np.nan, np.nan, np.nan, 11])
+
+        # Provide 'forward' (the default) explicitly here.
+        expected = Series([1.0, 3.0, 5.0, 7.0, np.nan, 11.0])
+
+        result = s.interpolate(method="linear", limit=2, limit_direction="forward")
+        tm.assert_series_equal(result, expected)
+
+        result = s.interpolate(method="linear", limit=2, limit_direction="FORWARD")
+        tm.assert_series_equal(result, expected)
+
+    def test_interp_unlimited(self):
+        # these test are for issue #16282 default Limit=None is unlimited
+        s = Series([np.nan, 1.0, 3.0, np.nan, np.nan, np.nan, 11.0, np.nan])
+        expected = Series([1.0, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 11.0])
+        result = s.interpolate(method="linear", limit_direction="both")
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([np.nan, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 11.0])
+        result = s.interpolate(method="linear", limit_direction="forward")
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([1.0, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, np.nan])
+        result = s.interpolate(method="linear", limit_direction="backward")
+        tm.assert_series_equal(result, expected)
+
+    def test_interp_limit_bad_direction(self):
+        s = Series([1, 3, np.nan, np.nan, np.nan, 11])
+
+        msg = (
+            r"Invalid limit_direction: expecting one of \['forward', "
+            r"'backward', 'both'\], got 'abc'"
+        )
+        with pytest.raises(ValueError, match=msg):
+            s.interpolate(method="linear", limit=2, limit_direction="abc")
+
+        # raises an error even if no limit is specified.
+        with pytest.raises(ValueError, match=msg):
+            s.interpolate(method="linear", limit_direction="abc")
+
+    # limit_area introduced GH #16284
+    def test_interp_limit_area(self):
+        # These tests are for issue #9218 -- fill NaNs in both directions.
+        s = Series([np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan])
+
+        expected = Series([np.nan, np.nan, 3.0, 4.0, 5.0, 6.0, 7.0, np.nan, np.nan])
+        result = s.interpolate(method="linear", limit_area="inside")
+        tm.assert_series_equal(result, expected)
+
+        expected = Series(
+            [np.nan, np.nan, 3.0, 4.0, np.nan, np.nan, 7.0, np.nan, np.nan]
+        )
+        result = s.interpolate(method="linear", limit_area="inside", limit=1)
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([np.nan, np.nan, 3.0, 4.0, np.nan, 6.0, 7.0, np.nan, np.nan])
+        result = s.interpolate(
+            method="linear", limit_area="inside", limit_direction="both", limit=1
+        )
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, 7.0])
+        result = s.interpolate(method="linear", limit_area="outside")
+        tm.assert_series_equal(result, expected)
+
+        expected = Series(
+            [np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, np.nan]
+        )
+        result = s.interpolate(method="linear", limit_area="outside", limit=1)
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([np.nan, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, np.nan])
+        result = s.interpolate(
+            method="linear", limit_area="outside", limit_direction="both", limit=1
+        )
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([3.0, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, np.nan, np.nan])
+        result = s.interpolate(
+            method="linear", limit_area="outside", limit_direction="backward"
+        )
+        tm.assert_series_equal(result, expected)
+
+        # raises an error even if limit type is wrong.
+        msg = r"Invalid limit_area: expecting one of \['inside', 'outside'\], got abc"
+        with pytest.raises(ValueError, match=msg):
+            s.interpolate(method="linear", limit_area="abc")
+
+    @pytest.mark.parametrize(
+        "method, limit_direction, expected",
+        [
+            ("pad", "backward", "forward"),
+            ("ffill", "backward", "forward"),
+            ("backfill", "forward", "backward"),
+            ("bfill", "forward", "backward"),
+            ("pad", "both", "forward"),
+            ("ffill", "both", "forward"),
+            ("backfill", "both", "backward"),
+            ("bfill", "both", "backward"),
+        ],
+    )
+    def test_interp_limit_direction_raises(self, method, limit_direction, expected):
+        # https://github.com/pandas-dev/pandas/pull/34746
+        s = Series([1, 2, 3])
+
+        msg = f"`limit_direction` must be '{expected}' for method `{method}`"
+        msg2 = "Series.interpolate with method="
+        with pytest.raises(ValueError, match=msg):
+            with tm.assert_produces_warning(FutureWarning, match=msg2):
+                s.interpolate(method=method, limit_direction=limit_direction)
+
+    @pytest.mark.parametrize(
+        "data, expected_data, kwargs",
+        (
+            (
+                [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
+                [np.nan, np.nan, 3.0, 3.0, 3.0, 3.0, 7.0, np.nan, np.nan],
+                {"method": "pad", "limit_area": "inside"},
+            ),
+            (
+                [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
+                [np.nan, np.nan, 3.0, 3.0, np.nan, np.nan, 7.0, np.nan, np.nan],
+                {"method": "pad", "limit_area": "inside", "limit": 1},
+            ),
+            (
+                [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
+                [np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, 7.0],
+                {"method": "pad", "limit_area": "outside"},
+            ),
+            (
+                [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
+                [np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, np.nan],
+                {"method": "pad", "limit_area": "outside", "limit": 1},
+            ),
+            (
+                [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
+                [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
+                {"method": "pad", "limit_area": "outside", "limit": 1},
+            ),
+            (
+                range(5),
+                range(5),
+                {"method": "pad", "limit_area": "outside", "limit": 1},
+            ),
+        ),
+    )
+    def test_interp_limit_area_with_pad(self, data, expected_data, kwargs):
+        # GH26796
+
+        s = Series(data)
+        expected = Series(expected_data)
+        msg = "Series.interpolate with method=pad"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = s.interpolate(**kwargs)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "data, expected_data, kwargs",
+        (
+            (
+                [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
+                [np.nan, np.nan, 3.0, 7.0, 7.0, 7.0, 7.0, np.nan, np.nan],
+                {"method": "bfill", "limit_area": "inside"},
+            ),
+            (
+                [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
+                [np.nan, np.nan, 3.0, np.nan, np.nan, 7.0, 7.0, np.nan, np.nan],
+                {"method": "bfill", "limit_area": "inside", "limit": 1},
+            ),
+            (
+                [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
+                [3.0, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, np.nan, np.nan],
+                {"method": "bfill", "limit_area": "outside"},
+            ),
+            (
+                [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
+                [np.nan, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, np.nan, np.nan],
+                {"method": "bfill", "limit_area": "outside", "limit": 1},
+            ),
+        ),
+    )
+    def test_interp_limit_area_with_backfill(self, data, expected_data, kwargs):
+        # GH26796
+
+        s = Series(data)
+        expected = Series(expected_data)
+        msg = "Series.interpolate with method=bfill"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = s.interpolate(**kwargs)
+        tm.assert_series_equal(result, expected)
+
+    def test_interp_limit_direction(self):
+        # These tests are for issue #9218 -- fill NaNs in both directions.
+        s = Series([1, 3, np.nan, np.nan, np.nan, 11])
+
+        expected = Series([1.0, 3.0, np.nan, 7.0, 9.0, 11.0])
+        result = s.interpolate(method="linear", limit=2, limit_direction="backward")
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([1.0, 3.0, 5.0, np.nan, 9.0, 11.0])
+        result = s.interpolate(method="linear", limit=1, limit_direction="both")
+        tm.assert_series_equal(result, expected)
+
+        # Check that this works on a longer series of nans.
+        s = Series([1, 3, np.nan, np.nan, np.nan, 7, 9, np.nan, np.nan, 12, np.nan])
+
+        expected = Series([1.0, 3.0, 4.0, 5.0, 6.0, 7.0, 9.0, 10.0, 11.0, 12.0, 12.0])
+        result = s.interpolate(method="linear", limit=2, limit_direction="both")
+        tm.assert_series_equal(result, expected)
+
+        expected = Series(
+            [1.0, 3.0, 4.0, np.nan, 6.0, 7.0, 9.0, 10.0, 11.0, 12.0, 12.0]
+        )
+        result = s.interpolate(method="linear", limit=1, limit_direction="both")
+        tm.assert_series_equal(result, expected)
+
+    def test_interp_limit_to_ends(self):
+        # These test are for issue #10420 -- flow back to beginning.
+        s = Series([np.nan, np.nan, 5, 7, 9, np.nan])
+
+        expected = Series([5.0, 5.0, 5.0, 7.0, 9.0, np.nan])
+        result = s.interpolate(method="linear", limit=2, limit_direction="backward")
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([5.0, 5.0, 5.0, 7.0, 9.0, 9.0])
+        result = s.interpolate(method="linear", limit=2, limit_direction="both")
+        tm.assert_series_equal(result, expected)
+
+    def test_interp_limit_before_ends(self):
+        # These test are for issue #11115 -- limit ends properly.
+        s = Series([np.nan, np.nan, 5, 7, np.nan, np.nan])
+
+        expected = Series([np.nan, np.nan, 5.0, 7.0, 7.0, np.nan])
+        result = s.interpolate(method="linear", limit=1, limit_direction="forward")
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([np.nan, 5.0, 5.0, 7.0, np.nan, np.nan])
+        result = s.interpolate(method="linear", limit=1, limit_direction="backward")
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([np.nan, 5.0, 5.0, 7.0, 7.0, np.nan])
+        result = s.interpolate(method="linear", limit=1, limit_direction="both")
+        tm.assert_series_equal(result, expected)
+
+    def test_interp_all_good(self):
+        pytest.importorskip("scipy")
+        s = Series([1, 2, 3])
+        result = s.interpolate(method="polynomial", order=1)
+        tm.assert_series_equal(result, s)
+
+        # non-scipy
+        result = s.interpolate()
+        tm.assert_series_equal(result, s)
+
+    @pytest.mark.parametrize(
+        "check_scipy", [False, pytest.param(True, marks=td.skip_if_no("scipy"))]
+    )
+    def test_interp_multiIndex(self, check_scipy):
+        idx = MultiIndex.from_tuples([(0, "a"), (1, "b"), (2, "c")])
+        s = Series([1, 2, np.nan], index=idx)
+
+        expected = s.copy()
+        expected.loc[2] = 2
+        result = s.interpolate()
+        tm.assert_series_equal(result, expected)
+
+        msg = "Only `method=linear` interpolation is supported on MultiIndexes"
+        if check_scipy:
+            with pytest.raises(ValueError, match=msg):
+                s.interpolate(method="polynomial", order=1)
+
+    def test_interp_nonmono_raise(self):
+        pytest.importorskip("scipy")
+        s = Series([1, np.nan, 3], index=[0, 2, 1])
+        msg = "krogh interpolation requires that the index be monotonic"
+        with pytest.raises(ValueError, match=msg):
+            s.interpolate(method="krogh")
+
+    @pytest.mark.parametrize("method", ["nearest", "pad"])
+    def test_interp_datetime64(self, method, tz_naive_fixture):
+        pytest.importorskip("scipy")
+        df = Series(
+            [1, np.nan, 3], index=date_range("1/1/2000", periods=3, tz=tz_naive_fixture)
+        )
+        warn = None if method == "nearest" else FutureWarning
+        msg = "Series.interpolate with method=pad is deprecated"
+        with tm.assert_produces_warning(warn, match=msg):
+            result = df.interpolate(method=method)
+        if warn is not None:
+            # check the "use ffill instead" is equivalent
+            alt = df.ffill()
+            tm.assert_series_equal(result, alt)
+
+        expected = Series(
+            [1.0, 1.0, 3.0],
+            index=date_range("1/1/2000", periods=3, tz=tz_naive_fixture),
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_interp_pad_datetime64tz_values(self):
+        # GH#27628 missing.interpolate_2d should handle datetimetz values
+        dti = date_range("2015-04-05", periods=3, tz="US/Central")
+        ser = Series(dti)
+        ser[1] = pd.NaT
+
+        msg = "Series.interpolate with method=pad is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = ser.interpolate(method="pad")
+        # check the "use ffill instead" is equivalent
+        alt = ser.ffill()
+        tm.assert_series_equal(result, alt)
+
+        expected = Series(dti)
+        expected[1] = expected[0]
+        tm.assert_series_equal(result, expected)
+
+    def test_interp_limit_no_nans(self):
+        # GH 7173
+        s = Series([1.0, 2.0, 3.0])
+        result = s.interpolate(limit=1)
+        expected = s
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("method", ["polynomial", "spline"])
+    def test_no_order(self, method):
+        # see GH-10633, GH-24014
+        pytest.importorskip("scipy")
+        s = Series([0, 1, np.nan, 3])
+        msg = "You must specify the order of the spline or polynomial"
+        with pytest.raises(ValueError, match=msg):
+            s.interpolate(method=method)
+
+    @pytest.mark.parametrize("order", [-1, -1.0, 0, 0.0, np.nan])
+    def test_interpolate_spline_invalid_order(self, order):
+        pytest.importorskip("scipy")
+        s = Series([0, 1, np.nan, 3])
+        msg = "order needs to be specified and greater than 0"
+        with pytest.raises(ValueError, match=msg):
+            s.interpolate(method="spline", order=order)
+
+    def test_spline(self):
+        pytest.importorskip("scipy")
+        s = Series([1, 2, np.nan, 4, 5, np.nan, 7])
+        result = s.interpolate(method="spline", order=1)
+        expected = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0])
+        tm.assert_series_equal(result, expected)
+
+    def test_spline_extrapolate(self):
+        pytest.importorskip("scipy")
+        s = Series([1, 2, 3, 4, np.nan, 6, np.nan])
+        result3 = s.interpolate(method="spline", order=1, ext=3)
+        expected3 = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 6.0])
+        tm.assert_series_equal(result3, expected3)
+
+        result1 = s.interpolate(method="spline", order=1, ext=0)
+        expected1 = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0])
+        tm.assert_series_equal(result1, expected1)
+
+    def test_spline_smooth(self):
+        pytest.importorskip("scipy")
+        s = Series([1, 2, np.nan, 4, 5.1, np.nan, 7])
+        assert (
+            s.interpolate(method="spline", order=3, s=0)[5]
+            != s.interpolate(method="spline", order=3)[5]
+        )
+
+    def test_spline_interpolation(self):
+        # Explicit cast to float to avoid implicit cast when setting np.nan
+        pytest.importorskip("scipy")
+        s = Series(np.arange(10) ** 2, dtype="float")
+        s[np.random.default_rng(2).integers(0, 9, 3)] = np.nan
+        result1 = s.interpolate(method="spline", order=1)
+        expected1 = s.interpolate(method="spline", order=1)
+        tm.assert_series_equal(result1, expected1)
+
+    def test_interp_timedelta64(self):
+        # GH 6424
+        df = Series([1, np.nan, 3], index=pd.to_timedelta([1, 2, 3]))
+        result = df.interpolate(method="time")
+        expected = Series([1.0, 2.0, 3.0], index=pd.to_timedelta([1, 2, 3]))
+        tm.assert_series_equal(result, expected)
+
+        # test for non uniform spacing
+        df = Series([1, np.nan, 3], index=pd.to_timedelta([1, 2, 4]))
+        result = df.interpolate(method="time")
+        expected = Series([1.0, 1.666667, 3.0], index=pd.to_timedelta([1, 2, 4]))
+        tm.assert_series_equal(result, expected)
+
+    def test_series_interpolate_method_values(self):
+        # GH#1646
+        rng = date_range("1/1/2000", "1/20/2000", freq="D")
+        ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng)
+
+        ts[::2] = np.nan
+
+        result = ts.interpolate(method="values")
+        exp = ts.interpolate()
+        tm.assert_series_equal(result, exp)
+
+    def test_series_interpolate_intraday(self):
+        # #1698
+        index = date_range("1/1/2012", periods=4, freq="12D")
+        ts = Series([0, 12, 24, 36], index)
+        new_index = index.append(index + pd.DateOffset(days=1)).sort_values()
+
+        exp = ts.reindex(new_index).interpolate(method="time")
+
+        index = date_range("1/1/2012", periods=4, freq="12h")
+        ts = Series([0, 12, 24, 36], index)
+        new_index = index.append(index + pd.DateOffset(hours=1)).sort_values()
+        result = ts.reindex(new_index).interpolate(method="time")
+
+        tm.assert_numpy_array_equal(result.values, exp.values)
+
+    @pytest.mark.parametrize(
+        "ind",
+        [
+            ["a", "b", "c", "d"],
+            pd.period_range(start="2019-01-01", periods=4),
+            pd.interval_range(start=0, end=4),
+        ],
+    )
+    def test_interp_non_timedelta_index(self, interp_methods_ind, ind):
+        # gh 21662
+        df = pd.DataFrame([0, 1, np.nan, 3], index=ind)
+
+        method, kwargs = interp_methods_ind
+        if method == "pchip":
+            pytest.importorskip("scipy")
+
+        if method == "linear":
+            result = df[0].interpolate(**kwargs)
+            expected = Series([0.0, 1.0, 2.0, 3.0], name=0, index=ind)
+            tm.assert_series_equal(result, expected)
+        else:
+            expected_error = (
+                "Index column must be numeric or datetime type when "
+                f"using {method} method other than linear. "
+                "Try setting a numeric or datetime index column before "
+                "interpolating."
+            )
+            with pytest.raises(ValueError, match=expected_error):
+                df[0].interpolate(method=method, **kwargs)
+
+    def test_interpolate_timedelta_index(self, request, interp_methods_ind):
+        """
+        Tests for non numerical index types  - object, period, timedelta
+        Note that all methods except time, index, nearest and values
+        are tested here.
+        """
+        # gh 21662
+        pytest.importorskip("scipy")
+        ind = pd.timedelta_range(start=1, periods=4)
+        df = pd.DataFrame([0, 1, np.nan, 3], index=ind)
+
+        method, kwargs = interp_methods_ind
+
+        if method in {"cubic", "zero"}:
+            request.applymarker(
+                pytest.mark.xfail(
+                    reason=f"{method} interpolation is not supported for TimedeltaIndex"
+                )
+            )
+        result = df[0].interpolate(method=method, **kwargs)
+        expected = Series([0.0, 1.0, 2.0, 3.0], name=0, index=ind)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "ascending, expected_values",
+        [(True, [1, 2, 3, 9, 10]), (False, [10, 9, 3, 2, 1])],
+    )
+    def test_interpolate_unsorted_index(self, ascending, expected_values):
+        # GH 21037
+        ts = Series(data=[10, 9, np.nan, 2, 1], index=[10, 9, 3, 2, 1])
+        result = ts.sort_index(ascending=ascending).interpolate(method="index")
+        expected = Series(data=expected_values, index=expected_values, dtype=float)
+        tm.assert_series_equal(result, expected)
+
+    def test_interpolate_asfreq_raises(self):
+        ser = Series(["a", None, "b"], dtype=object)
+        msg2 = "Series.interpolate with object dtype"
+        msg = "Invalid fill method"
+        with pytest.raises(ValueError, match=msg):
+            with tm.assert_produces_warning(FutureWarning, match=msg2):
+                ser.interpolate(method="asfreq")
+
+    def test_interpolate_fill_value(self):
+        # GH#54920
+        pytest.importorskip("scipy")
+        ser = Series([np.nan, 0, 1, np.nan, 3, np.nan])
+        result = ser.interpolate(method="nearest", fill_value=0)
+        expected = Series([np.nan, 0, 1, 1, 3, 0])
+        tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_is_unique.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_is_unique.py
new file mode 100644
index 0000000000000000000000000000000000000000..edf3839c2cebb6f51d0bb21b06ea8a1c47dec0fe
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_is_unique.py
@@ -0,0 +1,40 @@
+import numpy as np
+import pytest
+
+from pandas import Series
+
+
+@pytest.mark.parametrize(
+    "data, expected",
+    [
+        (np.random.default_rng(2).integers(0, 10, size=1000), False),
+        (np.arange(1000), True),
+        ([], True),
+        ([np.nan], True),
+        (["foo", "bar", np.nan], True),
+        (["foo", "foo", np.nan], False),
+        (["foo", "bar", np.nan, np.nan], False),
+    ],
+)
+def test_is_unique(data, expected):
+    # GH#11946 / GH#25180
+    ser = Series(data)
+    assert ser.is_unique is expected
+
+
+def test_is_unique_class_ne(capsys):
+    # GH#20661
+    class Foo:
+        def __init__(self, val) -> None:
+            self._value = val
+
+        def __ne__(self, other):
+            raise Exception("NEQ not supported")
+
+    with capsys.disabled():
+        li = [Foo(i) for i in range(5)]
+        ser = Series(li, index=list(range(5)))
+
+    ser.is_unique
+    captured = capsys.readouterr()
+    assert len(captured.err) == 0
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_isin.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_isin.py
new file mode 100644
index 0000000000000000000000000000000000000000..f94f67b8cc40a2031cab0791ccaf2fbc207b5023
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_isin.py
@@ -0,0 +1,252 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.core import algorithms
+from pandas.core.arrays import PeriodArray
+
+
+class TestSeriesIsIn:
+    def test_isin(self):
+        s = Series(["A", "B", "C", "a", "B", "B", "A", "C"])
+
+        result = s.isin(["A", "C"])
+        expected = Series([True, False, True, False, False, False, True, True])
+        tm.assert_series_equal(result, expected)
+
+        # GH#16012
+        # This specific issue has to have a series over 1e6 in len, but the
+        # comparison array (in_list) must be large enough so that numpy doesn't
+        # do a manual masking trick that will avoid this issue altogether
+        s = Series(list("abcdefghijk" * 10**5))
+        # If numpy doesn't do the manual comparison/mask, these
+        # unorderable mixed types are what cause the exception in numpy
+        in_list = [-1, "a", "b", "G", "Y", "Z", "E", "K", "E", "S", "I", "R", "R"] * 6
+
+        assert s.isin(in_list).sum() == 200000
+
+    def test_isin_with_string_scalar(self):
+        # GH#4763
+        s = Series(["A", "B", "C", "a", "B", "B", "A", "C"])
+        msg = (
+            r"only list-like objects are allowed to be passed to isin\(\), "
+            r"you passed a `str`"
+        )
+        with pytest.raises(TypeError, match=msg):
+            s.isin("a")
+
+        s = Series(["aaa", "b", "c"])
+        with pytest.raises(TypeError, match=msg):
+            s.isin("aaa")
+
+    def test_isin_datetimelike_mismatched_reso(self):
+        expected = Series([True, True, False, False, False])
+
+        ser = Series(date_range("jan-01-2013", "jan-05-2013"))
+
+        # fails on dtype conversion in the first place
+        day_values = np.asarray(ser[0:2].values).astype("datetime64[D]")
+        result = ser.isin(day_values)
+        tm.assert_series_equal(result, expected)
+
+        dta = ser[:2]._values.astype("M8[s]")
+        result = ser.isin(dta)
+        tm.assert_series_equal(result, expected)
+
+    def test_isin_datetimelike_mismatched_reso_list(self):
+        expected = Series([True, True, False, False, False])
+
+        ser = Series(date_range("jan-01-2013", "jan-05-2013"))
+
+        dta = ser[:2]._values.astype("M8[s]")
+        result = ser.isin(list(dta))
+        tm.assert_series_equal(result, expected)
+
+    def test_isin_with_i8(self):
+        # GH#5021
+
+        expected = Series([True, True, False, False, False])
+        expected2 = Series([False, True, False, False, False])
+
+        # datetime64[ns]
+        s = Series(date_range("jan-01-2013", "jan-05-2013"))
+
+        result = s.isin(s[0:2])
+        tm.assert_series_equal(result, expected)
+
+        result = s.isin(s[0:2].values)
+        tm.assert_series_equal(result, expected)
+
+        result = s.isin([s[1]])
+        tm.assert_series_equal(result, expected2)
+
+        result = s.isin([np.datetime64(s[1])])
+        tm.assert_series_equal(result, expected2)
+
+        result = s.isin(set(s[0:2]))
+        tm.assert_series_equal(result, expected)
+
+        # timedelta64[ns]
+        s = Series(pd.to_timedelta(range(5), unit="d"))
+        result = s.isin(s[0:2])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("empty", [[], Series(dtype=object), np.array([])])
+    def test_isin_empty(self, empty):
+        # see GH#16991
+        s = Series(["a", "b"])
+        expected = Series([False, False])
+
+        result = s.isin(empty)
+        tm.assert_series_equal(expected, result)
+
+    def test_isin_read_only(self):
+        # https://github.com/pandas-dev/pandas/issues/37174
+        arr = np.array([1, 2, 3])
+        arr.setflags(write=False)
+        s = Series([1, 2, 3])
+        result = s.isin(arr)
+        expected = Series([True, True, True])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", [object, None])
+    def test_isin_dt64_values_vs_ints(self, dtype):
+        # GH#36621 dont cast integers to datetimes for isin
+        dti = date_range("2013-01-01", "2013-01-05")
+        ser = Series(dti)
+
+        comps = np.asarray([1356998400000000000], dtype=dtype)
+
+        res = dti.isin(comps)
+        expected = np.array([False] * len(dti), dtype=bool)
+        tm.assert_numpy_array_equal(res, expected)
+
+        res = ser.isin(comps)
+        tm.assert_series_equal(res, Series(expected))
+
+        res = pd.core.algorithms.isin(ser, comps)
+        tm.assert_numpy_array_equal(res, expected)
+
+    def test_isin_tzawareness_mismatch(self):
+        dti = date_range("2013-01-01", "2013-01-05")
+        ser = Series(dti)
+
+        other = dti.tz_localize("UTC")
+
+        res = dti.isin(other)
+        expected = np.array([False] * len(dti), dtype=bool)
+        tm.assert_numpy_array_equal(res, expected)
+
+        res = ser.isin(other)
+        tm.assert_series_equal(res, Series(expected))
+
+        res = pd.core.algorithms.isin(ser, other)
+        tm.assert_numpy_array_equal(res, expected)
+
+    def test_isin_period_freq_mismatch(self):
+        dti = date_range("2013-01-01", "2013-01-05")
+        pi = dti.to_period("M")
+        ser = Series(pi)
+
+        # We construct another PeriodIndex with the same i8 values
+        #  but different dtype
+        dtype = dti.to_period("Y").dtype
+        other = PeriodArray._simple_new(pi.asi8, dtype=dtype)
+
+        res = pi.isin(other)
+        expected = np.array([False] * len(pi), dtype=bool)
+        tm.assert_numpy_array_equal(res, expected)
+
+        res = ser.isin(other)
+        tm.assert_series_equal(res, Series(expected))
+
+        res = pd.core.algorithms.isin(ser, other)
+        tm.assert_numpy_array_equal(res, expected)
+
+    @pytest.mark.parametrize("values", [[-9.0, 0.0], [-9, 0]])
+    def test_isin_float_in_int_series(self, values):
+        # GH#19356 GH#21804
+        ser = Series(values)
+        result = ser.isin([-9, -0.5])
+        expected = Series([True, False])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", ["boolean", "Int64", "Float64"])
+    @pytest.mark.parametrize(
+        "data,values,expected",
+        [
+            ([0, 1, 0], [1], [False, True, False]),
+            ([0, 1, 0], [1, pd.NA], [False, True, False]),
+            ([0, pd.NA, 0], [1, 0], [True, False, True]),
+            ([0, 1, pd.NA], [1, pd.NA], [False, True, True]),
+            ([0, 1, pd.NA], [1, np.nan], [False, True, False]),
+            ([0, pd.NA, pd.NA], [np.nan, pd.NaT, None], [False, False, False]),
+        ],
+    )
+    def test_isin_masked_types(self, dtype, data, values, expected):
+        # GH#42405
+        ser = Series(data, dtype=dtype)
+
+        result = ser.isin(values)
+        expected = Series(expected, dtype="boolean")
+
+        tm.assert_series_equal(result, expected)
+
+
+def test_isin_large_series_mixed_dtypes_and_nan(monkeypatch):
+    # https://github.com/pandas-dev/pandas/issues/37094
+    # combination of object dtype for the values
+    # and > _MINIMUM_COMP_ARR_LEN elements
+    min_isin_comp = 5
+    ser = Series([1, 2, np.nan] * min_isin_comp)
+    with monkeypatch.context() as m:
+        m.setattr(algorithms, "_MINIMUM_COMP_ARR_LEN", min_isin_comp)
+        result = ser.isin({"foo", "bar"})
+    expected = Series([False] * 3 * min_isin_comp)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "array,expected",
+    [
+        (
+            [0, 1j, 1j, 1, 1 + 1j, 1 + 2j, 1 + 1j],
+            Series([False, True, True, False, True, True, True], dtype=bool),
+        )
+    ],
+)
+def test_isin_complex_numbers(array, expected):
+    # GH 17927
+    result = Series(array).isin([1j, 1 + 1j, 1 + 2j])
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data,is_in",
+    [([1, [2]], [1]), (["simple str", [{"values": 3}]], ["simple str"])],
+)
+def test_isin_filtering_with_mixed_object_types(data, is_in):
+    # GH 20883
+
+    ser = Series(data)
+    result = ser.isin(is_in)
+    expected = Series([True, False])
+
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("data", [[1, 2, 3], [1.0, 2.0, 3.0]])
+@pytest.mark.parametrize("isin", [[1, 2], [1.0, 2.0]])
+def test_isin_filtering_on_iterable(data, isin):
+    # GH 50234
+
+    ser = Series(data)
+    result = ser.isin(i for i in isin)
+    expected_result = Series([True, True, False])
+
+    tm.assert_series_equal(result, expected_result)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_map.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_map.py
new file mode 100644
index 0000000000000000000000000000000000000000..251d4063008b9636a315a7c8b35de6cf45d1dee4
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_map.py
@@ -0,0 +1,609 @@
+from collections import (
+    Counter,
+    defaultdict,
+)
+from decimal import Decimal
+import math
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    bdate_range,
+    date_range,
+    isna,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+def test_series_map_box_timedelta():
+    # GH#11349
+    ser = Series(timedelta_range("1 day 1 s", periods=5, freq="h"))
+
+    def f(x):
+        return x.total_seconds()
+
+    ser.map(f)
+
+
+def test_map_callable(datetime_series):
+    with np.errstate(all="ignore"):
+        tm.assert_series_equal(datetime_series.map(np.sqrt), np.sqrt(datetime_series))
+
+    # map function element-wise
+    tm.assert_series_equal(datetime_series.map(math.exp), np.exp(datetime_series))
+
+    # empty series
+    s = Series(dtype=object, name="foo", index=Index([], name="bar"))
+    rs = s.map(lambda x: x)
+    tm.assert_series_equal(s, rs)
+
+    # check all metadata (GH 9322)
+    assert s is not rs
+    assert s.index is rs.index
+    assert s.dtype == rs.dtype
+    assert s.name == rs.name
+
+    # index but no data
+    s = Series(index=[1, 2, 3], dtype=np.float64)
+    rs = s.map(lambda x: x)
+    tm.assert_series_equal(s, rs)
+
+
+def test_map_same_length_inference_bug():
+    s = Series([1, 2])
+
+    def f(x):
+        return (x, x + 1)
+
+    s = Series([1, 2, 3])
+    result = s.map(f)
+    expected = Series([(1, 2), (2, 3), (3, 4)])
+    tm.assert_series_equal(result, expected)
+
+    s = Series(["foo,bar"])
+    result = s.map(lambda x: x.split(","))
+    expected = Series([("foo", "bar")])
+    tm.assert_series_equal(result, expected)
+
+
+def test_series_map_box_timestamps():
+    # GH#2689, GH#2627
+    ser = Series(date_range("1/1/2000", periods=3))
+
+    def func(x):
+        return (x.hour, x.day, x.month)
+
+    result = ser.map(func)
+    expected = Series([(0, 1, 1), (0, 2, 1), (0, 3, 1)])
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_series_stringdtype(any_string_dtype, using_infer_string):
+    # map test on StringDType, GH#40823
+    ser1 = Series(
+        data=["cat", "dog", "rabbit"],
+        index=["id1", "id2", "id3"],
+        dtype=any_string_dtype,
+    )
+    ser2 = Series(["id3", "id2", "id1", "id7000"], dtype=any_string_dtype)
+    result = ser2.map(ser1)
+
+    item = pd.NA
+    if ser2.dtype == object:
+        item = np.nan
+
+    expected = Series(data=["rabbit", "dog", "cat", item], dtype=any_string_dtype)
+    if using_infer_string and any_string_dtype == "object":
+        expected = expected.astype("string[pyarrow_numpy]")
+
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data, expected_dtype",
+    [(["1-1", "1-1", np.nan], "category"), (["1-1", "1-2", np.nan], object)],
+)
+def test_map_categorical_with_nan_values(data, expected_dtype, using_infer_string):
+    # GH 20714 bug fixed in: GH 24275
+    def func(val):
+        return val.split("-")[0]
+
+    s = Series(data, dtype="category")
+
+    result = s.map(func, na_action="ignore")
+    if using_infer_string and expected_dtype == object:
+        expected_dtype = "string[pyarrow_numpy]"
+    expected = Series(["1", "1", np.nan], dtype=expected_dtype)
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_empty_integer_series():
+    # GH52384
+    s = Series([], dtype=int)
+    result = s.map(lambda x: x)
+    tm.assert_series_equal(result, s)
+
+
+def test_map_empty_integer_series_with_datetime_index():
+    # GH 21245
+    s = Series([], index=date_range(start="2018-01-01", periods=0), dtype=int)
+    result = s.map(lambda x: x)
+    tm.assert_series_equal(result, s)
+
+
+@pytest.mark.parametrize("func", [str, lambda x: str(x)])
+def test_map_simple_str_callables_same_as_astype(
+    string_series, func, using_infer_string
+):
+    # test that we are evaluating row-by-row first
+    # before vectorized evaluation
+    result = string_series.map(func)
+    expected = string_series.astype(
+        str if not using_infer_string else "string[pyarrow_numpy]"
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_list_raises(string_series):
+    with pytest.raises(TypeError, match="'list' object is not callable"):
+        string_series.map([lambda x: x])
+
+
+def test_map():
+    data = {
+        "A": [0.0, 1.0, 2.0, 3.0, 4.0],
+        "B": [0.0, 1.0, 0.0, 1.0, 0.0],
+        "C": ["foo1", "foo2", "foo3", "foo4", "foo5"],
+        "D": bdate_range("1/1/2009", periods=5),
+    }
+
+    source = Series(data["B"], index=data["C"])
+    target = Series(data["C"][:4], index=data["D"][:4])
+
+    merged = target.map(source)
+
+    for k, v in merged.items():
+        assert v == source[target[k]]
+
+    # input could be a dict
+    merged = target.map(source.to_dict())
+
+    for k, v in merged.items():
+        assert v == source[target[k]]
+
+
+def test_map_datetime(datetime_series):
+    # function
+    result = datetime_series.map(lambda x: x * 2)
+    tm.assert_series_equal(result, datetime_series * 2)
+
+
+def test_map_category():
+    # GH 10324
+    a = Series([1, 2, 3, 4])
+    b = Series(["even", "odd", "even", "odd"], dtype="category")
+    c = Series(["even", "odd", "even", "odd"])
+
+    exp = Series(["odd", "even", "odd", np.nan], dtype="category")
+    tm.assert_series_equal(a.map(b), exp)
+    exp = Series(["odd", "even", "odd", np.nan])
+    tm.assert_series_equal(a.map(c), exp)
+
+
+def test_map_category_numeric():
+    a = Series(["a", "b", "c", "d"])
+    b = Series([1, 2, 3, 4], index=pd.CategoricalIndex(["b", "c", "d", "e"]))
+    c = Series([1, 2, 3, 4], index=Index(["b", "c", "d", "e"]))
+
+    exp = Series([np.nan, 1, 2, 3])
+    tm.assert_series_equal(a.map(b), exp)
+    exp = Series([np.nan, 1, 2, 3])
+    tm.assert_series_equal(a.map(c), exp)
+
+
+def test_map_category_string():
+    a = Series(["a", "b", "c", "d"])
+    b = Series(
+        ["B", "C", "D", "E"],
+        dtype="category",
+        index=pd.CategoricalIndex(["b", "c", "d", "e"]),
+    )
+    c = Series(["B", "C", "D", "E"], index=Index(["b", "c", "d", "e"]))
+
+    exp = Series(
+        pd.Categorical([np.nan, "B", "C", "D"], categories=["B", "C", "D", "E"])
+    )
+    tm.assert_series_equal(a.map(b), exp)
+    exp = Series([np.nan, "B", "C", "D"])
+    tm.assert_series_equal(a.map(c), exp)
+
+
+def test_map_empty(request, index):
+    if isinstance(index, MultiIndex):
+        request.applymarker(
+            pytest.mark.xfail(
+                reason="Initializing a Series from a MultiIndex is not supported"
+            )
+        )
+
+    s = Series(index)
+    result = s.map({})
+
+    expected = Series(np.nan, index=s.index)
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_compat():
+    # related GH 8024
+    s = Series([True, True, False], index=[1, 2, 3])
+    result = s.map({True: "foo", False: "bar"})
+    expected = Series(["foo", "foo", "bar"], index=[1, 2, 3])
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_int():
+    left = Series({"a": 1.0, "b": 2.0, "c": 3.0, "d": 4})
+    right = Series({1: 11, 2: 22, 3: 33})
+
+    assert left.dtype == np.float64
+    assert issubclass(right.dtype.type, np.integer)
+
+    merged = left.map(right)
+    assert merged.dtype == np.float64
+    assert isna(merged["d"])
+    assert not isna(merged["c"])
+
+
+def test_map_type_inference():
+    s = Series(range(3))
+    s2 = s.map(lambda x: np.where(x == 0, 0, 1))
+    assert issubclass(s2.dtype.type, np.integer)
+
+
+def test_map_decimal(string_series):
+    result = string_series.map(lambda x: Decimal(str(x)))
+    assert result.dtype == np.object_
+    assert isinstance(result.iloc[0], Decimal)
+
+
+def test_map_na_exclusion():
+    s = Series([1.5, np.nan, 3, np.nan, 5])
+
+    result = s.map(lambda x: x * 2, na_action="ignore")
+    exp = s * 2
+    tm.assert_series_equal(result, exp)
+
+
+def test_map_dict_with_tuple_keys():
+    """
+    Due to new MultiIndex-ing behaviour in v0.14.0,
+    dicts with tuple keys passed to map were being
+    converted to a multi-index, preventing tuple values
+    from being mapped properly.
+    """
+    # GH 18496
+    df = DataFrame({"a": [(1,), (2,), (3, 4), (5, 6)]})
+    label_mappings = {(1,): "A", (2,): "B", (3, 4): "A", (5, 6): "B"}
+
+    df["labels"] = df["a"].map(label_mappings)
+    df["expected_labels"] = Series(["A", "B", "A", "B"], index=df.index)
+    # All labels should be filled now
+    tm.assert_series_equal(df["labels"], df["expected_labels"], check_names=False)
+
+
+def test_map_counter():
+    s = Series(["a", "b", "c"], index=[1, 2, 3])
+    counter = Counter()
+    counter["b"] = 5
+    counter["c"] += 1
+    result = s.map(counter)
+    expected = Series([0, 5, 1], index=[1, 2, 3])
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_defaultdict():
+    s = Series([1, 2, 3], index=["a", "b", "c"])
+    default_dict = defaultdict(lambda: "blank")
+    default_dict[1] = "stuff"
+    result = s.map(default_dict)
+    expected = Series(["stuff", "blank", "blank"], index=["a", "b", "c"])
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_dict_na_key():
+    # https://github.com/pandas-dev/pandas/issues/17648
+    # Checks that np.nan key is appropriately mapped
+    s = Series([1, 2, np.nan])
+    expected = Series(["a", "b", "c"])
+    result = s.map({1: "a", 2: "b", np.nan: "c"})
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("na_action", [None, "ignore"])
+def test_map_defaultdict_na_key(na_action):
+    # GH 48813
+    s = Series([1, 2, np.nan])
+    default_map = defaultdict(lambda: "missing", {1: "a", 2: "b", np.nan: "c"})
+    result = s.map(default_map, na_action=na_action)
+    expected = Series({0: "a", 1: "b", 2: "c" if na_action is None else np.nan})
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("na_action", [None, "ignore"])
+def test_map_defaultdict_missing_key(na_action):
+    # GH 48813
+    s = Series([1, 2, np.nan])
+    default_map = defaultdict(lambda: "missing", {1: "a", 2: "b", 3: "c"})
+    result = s.map(default_map, na_action=na_action)
+    expected = Series({0: "a", 1: "b", 2: "missing" if na_action is None else np.nan})
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("na_action", [None, "ignore"])
+def test_map_defaultdict_unmutated(na_action):
+    # GH 48813
+    s = Series([1, 2, np.nan])
+    default_map = defaultdict(lambda: "missing", {1: "a", 2: "b", np.nan: "c"})
+    expected_default_map = default_map.copy()
+    s.map(default_map, na_action=na_action)
+    assert default_map == expected_default_map
+
+
+@pytest.mark.parametrize("arg_func", [dict, Series])
+def test_map_dict_ignore_na(arg_func):
+    # GH#47527
+    mapping = arg_func({1: 10, np.nan: 42})
+    ser = Series([1, np.nan, 2])
+    result = ser.map(mapping, na_action="ignore")
+    expected = Series([10, np.nan, np.nan])
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_defaultdict_ignore_na():
+    # GH#47527
+    mapping = defaultdict(int, {1: 10, np.nan: 42})
+    ser = Series([1, np.nan, 2])
+    result = ser.map(mapping)
+    expected = Series([10, 42, 0])
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "na_action, expected",
+    [(None, Series([10.0, 42.0, np.nan])), ("ignore", Series([10, np.nan, np.nan]))],
+)
+def test_map_categorical_na_ignore(na_action, expected):
+    # GH#47527
+    values = pd.Categorical([1, np.nan, 2], categories=[10, 1, 2])
+    ser = Series(values)
+    result = ser.map({1: 10, np.nan: 42}, na_action=na_action)
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_dict_subclass_with_missing():
+    """
+    Test Series.map with a dictionary subclass that defines __missing__,
+    i.e. sets a default value (GH #15999).
+    """
+
+    class DictWithMissing(dict):
+        def __missing__(self, key):
+            return "missing"
+
+    s = Series([1, 2, 3])
+    dictionary = DictWithMissing({3: "three"})
+    result = s.map(dictionary)
+    expected = Series(["missing", "missing", "three"])
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_dict_subclass_without_missing():
+    class DictWithoutMissing(dict):
+        pass
+
+    s = Series([1, 2, 3])
+    dictionary = DictWithoutMissing({3: "three"})
+    result = s.map(dictionary)
+    expected = Series([np.nan, np.nan, "three"])
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_abc_mapping(non_dict_mapping_subclass):
+    # https://github.com/pandas-dev/pandas/issues/29733
+    # Check collections.abc.Mapping support as mapper for Series.map
+    s = Series([1, 2, 3])
+    not_a_dictionary = non_dict_mapping_subclass({3: "three"})
+    result = s.map(not_a_dictionary)
+    expected = Series([np.nan, np.nan, "three"])
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_abc_mapping_with_missing(non_dict_mapping_subclass):
+    # https://github.com/pandas-dev/pandas/issues/29733
+    # Check collections.abc.Mapping support as mapper for Series.map
+    class NonDictMappingWithMissing(non_dict_mapping_subclass):
+        def __missing__(self, key):
+            return "missing"
+
+    s = Series([1, 2, 3])
+    not_a_dictionary = NonDictMappingWithMissing({3: "three"})
+    result = s.map(not_a_dictionary)
+    # __missing__ is a dict concept, not a Mapping concept,
+    # so it should not change the result!
+    expected = Series([np.nan, np.nan, "three"])
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_box_dt64(unit):
+    vals = [pd.Timestamp("2011-01-01"), pd.Timestamp("2011-01-02")]
+    ser = Series(vals).dt.as_unit(unit)
+    assert ser.dtype == f"datetime64[{unit}]"
+    # boxed value must be Timestamp instance
+    res = ser.map(lambda x: f"{type(x).__name__}_{x.day}_{x.tz}")
+    exp = Series(["Timestamp_1_None", "Timestamp_2_None"])
+    tm.assert_series_equal(res, exp)
+
+
+def test_map_box_dt64tz(unit):
+    vals = [
+        pd.Timestamp("2011-01-01", tz="US/Eastern"),
+        pd.Timestamp("2011-01-02", tz="US/Eastern"),
+    ]
+    ser = Series(vals).dt.as_unit(unit)
+    assert ser.dtype == f"datetime64[{unit}, US/Eastern]"
+    res = ser.map(lambda x: f"{type(x).__name__}_{x.day}_{x.tz}")
+    exp = Series(["Timestamp_1_US/Eastern", "Timestamp_2_US/Eastern"])
+    tm.assert_series_equal(res, exp)
+
+
+def test_map_box_td64(unit):
+    # timedelta
+    vals = [pd.Timedelta("1 days"), pd.Timedelta("2 days")]
+    ser = Series(vals).dt.as_unit(unit)
+    assert ser.dtype == f"timedelta64[{unit}]"
+    res = ser.map(lambda x: f"{type(x).__name__}_{x.days}")
+    exp = Series(["Timedelta_1", "Timedelta_2"])
+    tm.assert_series_equal(res, exp)
+
+
+def test_map_box_period():
+    # period
+    vals = [pd.Period("2011-01-01", freq="M"), pd.Period("2011-01-02", freq="M")]
+    ser = Series(vals)
+    assert ser.dtype == "Period[M]"
+    res = ser.map(lambda x: f"{type(x).__name__}_{x.freqstr}")
+    exp = Series(["Period_M", "Period_M"])
+    tm.assert_series_equal(res, exp)
+
+
+@pytest.mark.parametrize("na_action", [None, "ignore"])
+def test_map_categorical(na_action, using_infer_string):
+    values = pd.Categorical(list("ABBABCD"), categories=list("DCBA"), ordered=True)
+    s = Series(values, name="XX", index=list("abcdefg"))
+
+    result = s.map(lambda x: x.lower(), na_action=na_action)
+    exp_values = pd.Categorical(list("abbabcd"), categories=list("dcba"), ordered=True)
+    exp = Series(exp_values, name="XX", index=list("abcdefg"))
+    tm.assert_series_equal(result, exp)
+    tm.assert_categorical_equal(result.values, exp_values)
+
+    result = s.map(lambda x: "A", na_action=na_action)
+    exp = Series(["A"] * 7, name="XX", index=list("abcdefg"))
+    tm.assert_series_equal(result, exp)
+    assert result.dtype == object if not using_infer_string else "string"
+
+
+@pytest.mark.parametrize(
+    "na_action, expected",
+    (
+        [None, Series(["A", "B", "nan"], name="XX")],
+        [
+            "ignore",
+            Series(
+                ["A", "B", np.nan],
+                name="XX",
+                dtype=pd.CategoricalDtype(list("DCBA"), True),
+            ),
+        ],
+    ),
+)
+def test_map_categorical_na_action(na_action, expected):
+    dtype = pd.CategoricalDtype(list("DCBA"), ordered=True)
+    values = pd.Categorical(list("AB") + [np.nan], dtype=dtype)
+    s = Series(values, name="XX")
+    result = s.map(str, na_action=na_action)
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_datetimetz():
+    values = date_range("2011-01-01", "2011-01-02", freq="h").tz_localize("Asia/Tokyo")
+    s = Series(values, name="XX")
+
+    # keep tz
+    result = s.map(lambda x: x + pd.offsets.Day())
+    exp_values = date_range("2011-01-02", "2011-01-03", freq="h").tz_localize(
+        "Asia/Tokyo"
+    )
+    exp = Series(exp_values, name="XX")
+    tm.assert_series_equal(result, exp)
+
+    result = s.map(lambda x: x.hour)
+    exp = Series(list(range(24)) + [0], name="XX", dtype=np.int64)
+    tm.assert_series_equal(result, exp)
+
+    # not vectorized
+    def f(x):
+        if not isinstance(x, pd.Timestamp):
+            raise ValueError
+        return str(x.tz)
+
+    result = s.map(f)
+    exp = Series(["Asia/Tokyo"] * 25, name="XX")
+    tm.assert_series_equal(result, exp)
+
+
+@pytest.mark.parametrize(
+    "vals,mapping,exp",
+    [
+        (list("abc"), {np.nan: "not NaN"}, [np.nan] * 3 + ["not NaN"]),
+        (list("abc"), {"a": "a letter"}, ["a letter"] + [np.nan] * 3),
+        (list(range(3)), {0: 42}, [42] + [np.nan] * 3),
+    ],
+)
+def test_map_missing_mixed(vals, mapping, exp, using_infer_string):
+    # GH20495
+    s = Series(vals + [np.nan])
+    result = s.map(mapping)
+    exp = Series(exp)
+    if using_infer_string and mapping == {np.nan: "not NaN"}:
+        exp.iloc[-1] = np.nan
+    tm.assert_series_equal(result, exp)
+
+
+def test_map_scalar_on_date_time_index_aware_series():
+    # GH 25959
+    # Calling map on a localized time series should not cause an error
+    series = Series(
+        np.arange(10, dtype=np.float64),
+        index=date_range("2020-01-01", periods=10, tz="UTC"),
+        name="ts",
+    )
+    result = Series(series.index).map(lambda x: 1)
+    tm.assert_series_equal(result, Series(np.ones(len(series)), dtype="int64"))
+
+
+def test_map_float_to_string_precision():
+    # GH 13228
+    ser = Series(1 / 3)
+    result = ser.map(lambda val: str(val)).to_dict()
+    expected = {0: "0.3333333333333333"}
+    assert result == expected
+
+
+def test_map_to_timedelta():
+    list_of_valid_strings = ["00:00:01", "00:00:02"]
+    a = pd.to_timedelta(list_of_valid_strings)
+    b = Series(list_of_valid_strings).map(pd.to_timedelta)
+    tm.assert_series_equal(Series(a), b)
+
+    list_of_strings = ["00:00:01", np.nan, pd.NaT, pd.NaT]
+
+    a = pd.to_timedelta(list_of_strings)
+    ser = Series(list_of_strings)
+    b = ser.map(pd.to_timedelta)
+    tm.assert_series_equal(Series(a), b)
+
+
+def test_map_type():
+    # GH 46719
+    s = Series([3, "string", float], index=["a", "b", "c"])
+    result = s.map(type)
+    expected = Series([int, str, type], index=["a", "b", "c"])
+    tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_matmul.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_matmul.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ca3ad3f7031e20b8d6cec36caadbcefef8c4196
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_matmul.py
@@ -0,0 +1,82 @@
+import operator
+
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestMatmul:
+    def test_matmul(self):
+        # matmul test is for GH#10259
+        a = Series(
+            np.random.default_rng(2).standard_normal(4), index=["p", "q", "r", "s"]
+        )
+        b = DataFrame(
+            np.random.default_rng(2).standard_normal((3, 4)),
+            index=["1", "2", "3"],
+            columns=["p", "q", "r", "s"],
+        ).T
+
+        # Series @ DataFrame -> Series
+        result = operator.matmul(a, b)
+        expected = Series(np.dot(a.values, b.values), index=["1", "2", "3"])
+        tm.assert_series_equal(result, expected)
+
+        # DataFrame @ Series -> Series
+        result = operator.matmul(b.T, a)
+        expected = Series(np.dot(b.T.values, a.T.values), index=["1", "2", "3"])
+        tm.assert_series_equal(result, expected)
+
+        # Series @ Series -> scalar
+        result = operator.matmul(a, a)
+        expected = np.dot(a.values, a.values)
+        tm.assert_almost_equal(result, expected)
+
+        # GH#21530
+        # vector (1D np.array) @ Series (__rmatmul__)
+        result = operator.matmul(a.values, a)
+        expected = np.dot(a.values, a.values)
+        tm.assert_almost_equal(result, expected)
+
+        # GH#21530
+        # vector (1D list) @ Series (__rmatmul__)
+        result = operator.matmul(a.values.tolist(), a)
+        expected = np.dot(a.values, a.values)
+        tm.assert_almost_equal(result, expected)
+
+        # GH#21530
+        # matrix (2D np.array) @ Series (__rmatmul__)
+        result = operator.matmul(b.T.values, a)
+        expected = np.dot(b.T.values, a.values)
+        tm.assert_almost_equal(result, expected)
+
+        # GH#21530
+        # matrix (2D nested lists) @ Series (__rmatmul__)
+        result = operator.matmul(b.T.values.tolist(), a)
+        expected = np.dot(b.T.values, a.values)
+        tm.assert_almost_equal(result, expected)
+
+        # mixed dtype DataFrame @ Series
+        a["p"] = int(a.p)
+        result = operator.matmul(b.T, a)
+        expected = Series(np.dot(b.T.values, a.T.values), index=["1", "2", "3"])
+        tm.assert_series_equal(result, expected)
+
+        # different dtypes DataFrame @ Series
+        a = a.astype(int)
+        result = operator.matmul(b.T, a)
+        expected = Series(np.dot(b.T.values, a.T.values), index=["1", "2", "3"])
+        tm.assert_series_equal(result, expected)
+
+        msg = r"Dot product shape mismatch, \(4,\) vs \(3,\)"
+        # exception raised is of type Exception
+        with pytest.raises(Exception, match=msg):
+            a.dot(a.values[:3])
+        msg = "matrices are not aligned"
+        with pytest.raises(ValueError, match=msg):
+            a.dot(b.T)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_reindex.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_reindex.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f0c8d751a92ae1e0683ef8d5a96ed9c172d6f0f
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_reindex.py
@@ -0,0 +1,448 @@
+import numpy as np
+import pytest
+
+from pandas._config import using_pyarrow_string_dtype
+
+import pandas.util._test_decorators as td
+
+from pandas import (
+    NA,
+    Categorical,
+    Float64Dtype,
+    Index,
+    MultiIndex,
+    NaT,
+    Period,
+    PeriodIndex,
+    RangeIndex,
+    Series,
+    Timedelta,
+    Timestamp,
+    date_range,
+    isna,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.xfail(
+    using_pyarrow_string_dtype(), reason="share memory doesn't work for arrow"
+)
+def test_reindex(datetime_series, string_series):
+    identity = string_series.reindex(string_series.index)
+
+    assert np.may_share_memory(string_series.index, identity.index)
+
+    assert identity.index.is_(string_series.index)
+    assert identity.index.identical(string_series.index)
+
+    subIndex = string_series.index[10:20]
+    subSeries = string_series.reindex(subIndex)
+
+    for idx, val in subSeries.items():
+        assert val == string_series[idx]
+
+    subIndex2 = datetime_series.index[10:20]
+    subTS = datetime_series.reindex(subIndex2)
+
+    for idx, val in subTS.items():
+        assert val == datetime_series[idx]
+    stuffSeries = datetime_series.reindex(subIndex)
+
+    assert np.isnan(stuffSeries).all()
+
+    # This is extremely important for the Cython code to not screw up
+    nonContigIndex = datetime_series.index[::2]
+    subNonContig = datetime_series.reindex(nonContigIndex)
+    for idx, val in subNonContig.items():
+        assert val == datetime_series[idx]
+
+    # return a copy the same index here
+    result = datetime_series.reindex()
+    assert result is not datetime_series
+
+
+def test_reindex_nan():
+    ts = Series([2, 3, 5, 7], index=[1, 4, np.nan, 8])
+
+    i, j = [np.nan, 1, np.nan, 8, 4, np.nan], [2, 0, 2, 3, 1, 2]
+    tm.assert_series_equal(ts.reindex(i), ts.iloc[j])
+
+    ts.index = ts.index.astype("object")
+
+    # reindex coerces index.dtype to float, loc/iloc doesn't
+    tm.assert_series_equal(ts.reindex(i), ts.iloc[j], check_index_type=False)
+
+
+def test_reindex_series_add_nat():
+    rng = date_range("1/1/2000 00:00:00", periods=10, freq="10s")
+    series = Series(rng)
+
+    result = series.reindex(range(15))
+    assert np.issubdtype(result.dtype, np.dtype("M8[ns]"))
+
+    mask = result.isna()
+    assert mask[-5:].all()
+    assert not mask[:-5].any()
+
+
+def test_reindex_with_datetimes():
+    rng = date_range("1/1/2000", periods=20)
+    ts = Series(np.random.default_rng(2).standard_normal(20), index=rng)
+
+    result = ts.reindex(list(ts.index[5:10]))
+    expected = ts[5:10]
+    expected.index = expected.index._with_freq(None)
+    tm.assert_series_equal(result, expected)
+
+    result = ts[list(ts.index[5:10])]
+    tm.assert_series_equal(result, expected)
+
+
+def test_reindex_corner(datetime_series):
+    # (don't forget to fix this) I think it's fixed
+    empty = Series(index=[])
+    empty.reindex(datetime_series.index, method="pad")  # it works
+
+    # corner case: pad empty series
+    reindexed = empty.reindex(datetime_series.index, method="pad")
+
+    # pass non-Index
+    reindexed = datetime_series.reindex(list(datetime_series.index))
+    datetime_series.index = datetime_series.index._with_freq(None)
+    tm.assert_series_equal(datetime_series, reindexed)
+
+    # bad fill method
+    ts = datetime_series[::2]
+    msg = (
+        r"Invalid fill method\. Expecting pad \(ffill\), backfill "
+        r"\(bfill\) or nearest\. Got foo"
+    )
+    with pytest.raises(ValueError, match=msg):
+        ts.reindex(datetime_series.index, method="foo")
+
+
+def test_reindex_pad():
+    s = Series(np.arange(10), dtype="int64")
+    s2 = s[::2]
+
+    reindexed = s2.reindex(s.index, method="pad")
+    reindexed2 = s2.reindex(s.index, method="ffill")
+    tm.assert_series_equal(reindexed, reindexed2)
+
+    expected = Series([0, 0, 2, 2, 4, 4, 6, 6, 8, 8])
+    tm.assert_series_equal(reindexed, expected)
+
+
+def test_reindex_pad2():
+    # GH4604
+    s = Series([1, 2, 3, 4, 5], index=["a", "b", "c", "d", "e"])
+    new_index = ["a", "g", "c", "f"]
+    expected = Series([1, 1, 3, 3], index=new_index)
+
+    # this changes dtype because the ffill happens after
+    result = s.reindex(new_index).ffill()
+    tm.assert_series_equal(result, expected.astype("float64"))
+
+    msg = "The 'downcast' keyword in ffill is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = s.reindex(new_index).ffill(downcast="infer")
+    tm.assert_series_equal(result, expected)
+
+    expected = Series([1, 5, 3, 5], index=new_index)
+    result = s.reindex(new_index, method="ffill")
+    tm.assert_series_equal(result, expected)
+
+
+def test_reindex_inference():
+    # inference of new dtype
+    s = Series([True, False, False, True], index=list("abcd"))
+    new_index = "agc"
+    msg = "Downcasting object dtype arrays on"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = s.reindex(list(new_index)).ffill()
+    expected = Series([True, True, False], index=list(new_index))
+    tm.assert_series_equal(result, expected)
+
+
+def test_reindex_downcasting():
+    # GH4618 shifted series downcasting
+    s = Series(False, index=range(5))
+    msg = "Downcasting object dtype arrays on"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = s.shift(1).bfill()
+    expected = Series(False, index=range(5))
+    tm.assert_series_equal(result, expected)
+
+
+def test_reindex_nearest():
+    s = Series(np.arange(10, dtype="int64"))
+    target = [0.1, 0.9, 1.5, 2.0]
+    result = s.reindex(target, method="nearest")
+    expected = Series(np.around(target).astype("int64"), target)
+    tm.assert_series_equal(expected, result)
+
+    result = s.reindex(target, method="nearest", tolerance=0.2)
+    expected = Series([0, 1, np.nan, 2], target)
+    tm.assert_series_equal(expected, result)
+
+    result = s.reindex(target, method="nearest", tolerance=[0.3, 0.01, 0.4, 3])
+    expected = Series([0, np.nan, np.nan, 2], target)
+    tm.assert_series_equal(expected, result)
+
+
+def test_reindex_int(datetime_series):
+    ts = datetime_series[::2]
+    int_ts = Series(np.zeros(len(ts), dtype=int), index=ts.index)
+
+    # this should work fine
+    reindexed_int = int_ts.reindex(datetime_series.index)
+
+    # if NaNs introduced
+    assert reindexed_int.dtype == np.float64
+
+    # NO NaNs introduced
+    reindexed_int = int_ts.reindex(int_ts.index[::2])
+    assert reindexed_int.dtype == np.dtype(int)
+
+
+def test_reindex_bool(datetime_series):
+    # A series other than float, int, string, or object
+    ts = datetime_series[::2]
+    bool_ts = Series(np.zeros(len(ts), dtype=bool), index=ts.index)
+
+    # this should work fine
+    reindexed_bool = bool_ts.reindex(datetime_series.index)
+
+    # if NaNs introduced
+    assert reindexed_bool.dtype == np.object_
+
+    # NO NaNs introduced
+    reindexed_bool = bool_ts.reindex(bool_ts.index[::2])
+    assert reindexed_bool.dtype == np.bool_
+
+
+def test_reindex_bool_pad(datetime_series):
+    # fail
+    ts = datetime_series[5:]
+    bool_ts = Series(np.zeros(len(ts), dtype=bool), index=ts.index)
+    filled_bool = bool_ts.reindex(datetime_series.index, method="pad")
+    assert isna(filled_bool[:5]).all()
+
+
+def test_reindex_categorical():
+    index = date_range("20000101", periods=3)
+
+    # reindexing to an invalid Categorical
+    s = Series(["a", "b", "c"], dtype="category")
+    result = s.reindex(index)
+    expected = Series(
+        Categorical(values=[np.nan, np.nan, np.nan], categories=["a", "b", "c"])
+    )
+    expected.index = index
+    tm.assert_series_equal(result, expected)
+
+    # partial reindexing
+    expected = Series(Categorical(values=["b", "c"], categories=["a", "b", "c"]))
+    expected.index = [1, 2]
+    result = s.reindex([1, 2])
+    tm.assert_series_equal(result, expected)
+
+    expected = Series(Categorical(values=["c", np.nan], categories=["a", "b", "c"]))
+    expected.index = [2, 3]
+    result = s.reindex([2, 3])
+    tm.assert_series_equal(result, expected)
+
+
+def test_reindex_astype_order_consistency():
+    # GH#17444
+    ser = Series([1, 2, 3], index=[2, 0, 1])
+    new_index = [0, 1, 2]
+    temp_dtype = "category"
+    new_dtype = str
+    result = ser.reindex(new_index).astype(temp_dtype).astype(new_dtype)
+    expected = ser.astype(temp_dtype).reindex(new_index).astype(new_dtype)
+    tm.assert_series_equal(result, expected)
+
+
+def test_reindex_fill_value():
+    # -----------------------------------------------------------
+    # floats
+    floats = Series([1.0, 2.0, 3.0])
+    result = floats.reindex([1, 2, 3])
+    expected = Series([2.0, 3.0, np.nan], index=[1, 2, 3])
+    tm.assert_series_equal(result, expected)
+
+    result = floats.reindex([1, 2, 3], fill_value=0)
+    expected = Series([2.0, 3.0, 0], index=[1, 2, 3])
+    tm.assert_series_equal(result, expected)
+
+    # -----------------------------------------------------------
+    # ints
+    ints = Series([1, 2, 3])
+
+    result = ints.reindex([1, 2, 3])
+    expected = Series([2.0, 3.0, np.nan], index=[1, 2, 3])
+    tm.assert_series_equal(result, expected)
+
+    # don't upcast
+    result = ints.reindex([1, 2, 3], fill_value=0)
+    expected = Series([2, 3, 0], index=[1, 2, 3])
+    assert issubclass(result.dtype.type, np.integer)
+    tm.assert_series_equal(result, expected)
+
+    # -----------------------------------------------------------
+    # objects
+    objects = Series([1, 2, 3], dtype=object)
+
+    result = objects.reindex([1, 2, 3])
+    expected = Series([2, 3, np.nan], index=[1, 2, 3], dtype=object)
+    tm.assert_series_equal(result, expected)
+
+    result = objects.reindex([1, 2, 3], fill_value="foo")
+    expected = Series([2, 3, "foo"], index=[1, 2, 3], dtype=object)
+    tm.assert_series_equal(result, expected)
+
+    # ------------------------------------------------------------
+    # bools
+    bools = Series([True, False, True])
+
+    result = bools.reindex([1, 2, 3])
+    expected = Series([False, True, np.nan], index=[1, 2, 3], dtype=object)
+    tm.assert_series_equal(result, expected)
+
+    result = bools.reindex([1, 2, 3], fill_value=False)
+    expected = Series([False, True, False], index=[1, 2, 3])
+    tm.assert_series_equal(result, expected)
+
+
+@td.skip_array_manager_not_yet_implemented
+@pytest.mark.parametrize("dtype", ["datetime64[ns]", "timedelta64[ns]"])
+@pytest.mark.parametrize("fill_value", ["string", 0, Timedelta(0)])
+def test_reindex_fill_value_datetimelike_upcast(dtype, fill_value, using_array_manager):
+    # https://github.com/pandas-dev/pandas/issues/42921
+    if dtype == "timedelta64[ns]" and fill_value == Timedelta(0):
+        # use the scalar that is not compatible with the dtype for this test
+        fill_value = Timestamp(0)
+
+    ser = Series([NaT], dtype=dtype)
+
+    result = ser.reindex([0, 1], fill_value=fill_value)
+    expected = Series([NaT, fill_value], index=[0, 1], dtype=object)
+    tm.assert_series_equal(result, expected)
+
+
+def test_reindex_datetimeindexes_tz_naive_and_aware():
+    # GH 8306
+    idx = date_range("20131101", tz="America/Chicago", periods=7)
+    newidx = date_range("20131103", periods=10, freq="h")
+    s = Series(range(7), index=idx)
+    msg = (
+        r"Cannot compare dtypes datetime64\[ns, America/Chicago\] "
+        r"and datetime64\[ns\]"
+    )
+    with pytest.raises(TypeError, match=msg):
+        s.reindex(newidx, method="ffill")
+
+
+def test_reindex_empty_series_tz_dtype():
+    # GH 20869
+    result = Series(dtype="datetime64[ns, UTC]").reindex([0, 1])
+    expected = Series([NaT] * 2, dtype="datetime64[ns, UTC]")
+    tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "p_values, o_values, values, expected_values",
+    [
+        (
+            [Period("2019Q1", "Q-DEC"), Period("2019Q2", "Q-DEC")],
+            [Period("2019Q1", "Q-DEC"), Period("2019Q2", "Q-DEC"), "All"],
+            [1.0, 1.0],
+            [1.0, 1.0, np.nan],
+        ),
+        (
+            [Period("2019Q1", "Q-DEC"), Period("2019Q2", "Q-DEC")],
+            [Period("2019Q1", "Q-DEC"), Period("2019Q2", "Q-DEC")],
+            [1.0, 1.0],
+            [1.0, 1.0],
+        ),
+    ],
+)
+def test_reindex_periodindex_with_object(p_values, o_values, values, expected_values):
+    # GH#28337
+    period_index = PeriodIndex(p_values)
+    object_index = Index(o_values)
+
+    ser = Series(values, index=period_index)
+    result = ser.reindex(object_index)
+    expected = Series(expected_values, index=object_index)
+    tm.assert_series_equal(result, expected)
+
+
+def test_reindex_too_many_args():
+    # GH 40980
+    ser = Series([1, 2])
+    msg = r"reindex\(\) takes from 1 to 2 positional arguments but 3 were given"
+    with pytest.raises(TypeError, match=msg):
+        ser.reindex([2, 3], False)
+
+
+def test_reindex_double_index():
+    # GH 40980
+    ser = Series([1, 2])
+    msg = r"reindex\(\) got multiple values for argument 'index'"
+    with pytest.raises(TypeError, match=msg):
+        ser.reindex([2, 3], index=[3, 4])
+
+
+def test_reindex_no_posargs():
+    # GH 40980
+    ser = Series([1, 2])
+    result = ser.reindex(index=[1, 0])
+    expected = Series([2, 1], index=[1, 0])
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("values", [[["a"], ["x"]], [[], []]])
+def test_reindex_empty_with_level(values):
+    # GH41170
+    ser = Series(
+        range(len(values[0])), index=MultiIndex.from_arrays(values), dtype="object"
+    )
+    result = ser.reindex(np.array(["b"]), level=0)
+    expected = Series(
+        index=MultiIndex(levels=[["b"], values[1]], codes=[[], []]), dtype="object"
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_reindex_missing_category():
+    # GH#18185
+    ser = Series([1, 2, 3, 1], dtype="category")
+    msg = r"Cannot setitem on a Categorical with a new category \(-1\)"
+    with pytest.raises(TypeError, match=msg):
+        ser.reindex([1, 2, 3, 4, 5], fill_value=-1)
+
+
+def test_reindexing_with_float64_NA_log():
+    # GH 47055
+    s = Series([1.0, NA], dtype=Float64Dtype())
+    s_reindex = s.reindex(range(3))
+    result = s_reindex.values._data
+    expected = np.array([1, np.nan, np.nan])
+    tm.assert_numpy_array_equal(result, expected)
+    with tm.assert_produces_warning(None):
+        result_log = np.log(s_reindex)
+        expected_log = Series([0, np.nan, np.nan], dtype=Float64Dtype())
+        tm.assert_series_equal(result_log, expected_log)
+
+
+@pytest.mark.parametrize("dtype", ["timedelta64", "datetime64"])
+def test_reindex_expand_nonnano_nat(dtype):
+    # GH 53497
+    ser = Series(np.array([1], dtype=f"{dtype}[s]"))
+    result = ser.reindex(RangeIndex(2))
+    expected = Series(
+        np.array([1, getattr(np, dtype)("nat", "s")], dtype=f"{dtype}[s]")
+    )
+    tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_rename.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_rename.py
new file mode 100644
index 0000000000000000000000000000000000000000..119654bd19b3fa1611499b9f5bbd4676cc372bc8
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_rename.py
@@ -0,0 +1,184 @@
+from datetime import datetime
+import re
+
+import numpy as np
+import pytest
+
+from pandas import (
+    Index,
+    MultiIndex,
+    Series,
+    array,
+)
+import pandas._testing as tm
+
+
+class TestRename:
+    def test_rename(self, datetime_series):
+        ts = datetime_series
+        renamer = lambda x: x.strftime("%Y%m%d")
+        renamed = ts.rename(renamer)
+        assert renamed.index[0] == renamer(ts.index[0])
+
+        # dict
+        rename_dict = dict(zip(ts.index, renamed.index))
+        renamed2 = ts.rename(rename_dict)
+        tm.assert_series_equal(renamed, renamed2)
+
+    def test_rename_partial_dict(self):
+        # partial dict
+        ser = Series(np.arange(4), index=["a", "b", "c", "d"], dtype="int64")
+        renamed = ser.rename({"b": "foo", "d": "bar"})
+        tm.assert_index_equal(renamed.index, Index(["a", "foo", "c", "bar"]))
+
+    def test_rename_retain_index_name(self):
+        # index with name
+        renamer = Series(
+            np.arange(4), index=Index(["a", "b", "c", "d"], name="name"), dtype="int64"
+        )
+        renamed = renamer.rename({})
+        assert renamed.index.name == renamer.index.name
+
+    def test_rename_by_series(self):
+        ser = Series(range(5), name="foo")
+        renamer = Series({1: 10, 2: 20})
+        result = ser.rename(renamer)
+        expected = Series(range(5), index=[0, 10, 20, 3, 4], name="foo")
+        tm.assert_series_equal(result, expected)
+
+    def test_rename_set_name(self, using_infer_string):
+        ser = Series(range(4), index=list("abcd"))
+        for name in ["foo", 123, 123.0, datetime(2001, 11, 11), ("foo",)]:
+            result = ser.rename(name)
+            assert result.name == name
+            if using_infer_string:
+                tm.assert_extension_array_equal(result.index.values, ser.index.values)
+            else:
+                tm.assert_numpy_array_equal(result.index.values, ser.index.values)
+            assert ser.name is None
+
+    def test_rename_set_name_inplace(self, using_infer_string):
+        ser = Series(range(3), index=list("abc"))
+        for name in ["foo", 123, 123.0, datetime(2001, 11, 11), ("foo",)]:
+            ser.rename(name, inplace=True)
+            assert ser.name == name
+            exp = np.array(["a", "b", "c"], dtype=np.object_)
+            if using_infer_string:
+                exp = array(exp, dtype="string[pyarrow_numpy]")
+                tm.assert_extension_array_equal(ser.index.values, exp)
+            else:
+                tm.assert_numpy_array_equal(ser.index.values, exp)
+
+    def test_rename_axis_supported(self):
+        # Supporting axis for compatibility, detailed in GH-18589
+        ser = Series(range(5))
+        ser.rename({}, axis=0)
+        ser.rename({}, axis="index")
+
+        with pytest.raises(ValueError, match="No axis named 5"):
+            ser.rename({}, axis=5)
+
+    def test_rename_inplace(self, datetime_series):
+        renamer = lambda x: x.strftime("%Y%m%d")
+        expected = renamer(datetime_series.index[0])
+
+        datetime_series.rename(renamer, inplace=True)
+        assert datetime_series.index[0] == expected
+
+    def test_rename_with_custom_indexer(self):
+        # GH 27814
+        class MyIndexer:
+            pass
+
+        ix = MyIndexer()
+        ser = Series([1, 2, 3]).rename(ix)
+        assert ser.name is ix
+
+    def test_rename_with_custom_indexer_inplace(self):
+        # GH 27814
+        class MyIndexer:
+            pass
+
+        ix = MyIndexer()
+        ser = Series([1, 2, 3])
+        ser.rename(ix, inplace=True)
+        assert ser.name is ix
+
+    def test_rename_callable(self):
+        # GH 17407
+        ser = Series(range(1, 6), index=Index(range(2, 7), name="IntIndex"))
+        result = ser.rename(str)
+        expected = ser.rename(lambda i: str(i))
+        tm.assert_series_equal(result, expected)
+
+        assert result.name == expected.name
+
+    def test_rename_none(self):
+        # GH 40977
+        ser = Series([1, 2], name="foo")
+        result = ser.rename(None)
+        expected = Series([1, 2])
+        tm.assert_series_equal(result, expected)
+
+    def test_rename_series_with_multiindex(self):
+        # issue #43659
+        arrays = [
+            ["bar", "baz", "baz", "foo", "qux"],
+            ["one", "one", "two", "two", "one"],
+        ]
+
+        index = MultiIndex.from_arrays(arrays, names=["first", "second"])
+        ser = Series(np.ones(5), index=index)
+        result = ser.rename(index={"one": "yes"}, level="second", errors="raise")
+
+        arrays_expected = [
+            ["bar", "baz", "baz", "foo", "qux"],
+            ["yes", "yes", "two", "two", "yes"],
+        ]
+
+        index_expected = MultiIndex.from_arrays(
+            arrays_expected, names=["first", "second"]
+        )
+        series_expected = Series(np.ones(5), index=index_expected)
+
+        tm.assert_series_equal(result, series_expected)
+
+    def test_rename_series_with_multiindex_keeps_ea_dtypes(self):
+        # GH21055
+        arrays = [
+            Index([1, 2, 3], dtype="Int64").astype("category"),
+            Index([1, 2, 3], dtype="Int64"),
+        ]
+        mi = MultiIndex.from_arrays(arrays, names=["A", "B"])
+        ser = Series(1, index=mi)
+        result = ser.rename({1: 4}, level=1)
+
+        arrays_expected = [
+            Index([1, 2, 3], dtype="Int64").astype("category"),
+            Index([4, 2, 3], dtype="Int64"),
+        ]
+        mi_expected = MultiIndex.from_arrays(arrays_expected, names=["A", "B"])
+        expected = Series(1, index=mi_expected)
+
+        tm.assert_series_equal(result, expected)
+
+    def test_rename_error_arg(self):
+        # GH 46889
+        ser = Series(["foo", "bar"])
+        match = re.escape("[2] not found in axis")
+        with pytest.raises(KeyError, match=match):
+            ser.rename({2: 9}, errors="raise")
+
+    def test_rename_copy_false(self, using_copy_on_write, warn_copy_on_write):
+        # GH 46889
+        ser = Series(["foo", "bar"])
+        ser_orig = ser.copy()
+        shallow_copy = ser.rename({1: 9}, copy=False)
+        with tm.assert_cow_warning(warn_copy_on_write):
+            ser[0] = "foobar"
+        if using_copy_on_write:
+            assert ser_orig[0] == shallow_copy[0]
+            assert ser_orig[1] == shallow_copy[9]
+        else:
+            assert ser[0] == shallow_copy[0]
+            assert ser[1] == shallow_copy[9]
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_repeat.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_repeat.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ecc8052ff49c150444cf395b68e6163fb761775
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_repeat.py
@@ -0,0 +1,40 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    MultiIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestRepeat:
+    def test_repeat(self):
+        ser = Series(np.random.default_rng(2).standard_normal(3), index=["a", "b", "c"])
+
+        reps = ser.repeat(5)
+        exp = Series(ser.values.repeat(5), index=ser.index.values.repeat(5))
+        tm.assert_series_equal(reps, exp)
+
+        to_rep = [2, 3, 4]
+        reps = ser.repeat(to_rep)
+        exp = Series(ser.values.repeat(to_rep), index=ser.index.values.repeat(to_rep))
+        tm.assert_series_equal(reps, exp)
+
+    def test_numpy_repeat(self):
+        ser = Series(np.arange(3), name="x")
+        expected = Series(
+            ser.values.repeat(2), name="x", index=ser.index.values.repeat(2)
+        )
+        tm.assert_series_equal(np.repeat(ser, 2), expected)
+
+        msg = "the 'axis' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            np.repeat(ser, 2, axis=0)
+
+    def test_repeat_with_multiindex(self):
+        # GH#9361, fixed by  GH#7891
+        m_idx = MultiIndex.from_tuples([(1, 2), (3, 4), (5, 6), (7, 8)])
+        data = ["a", "b", "c", "d"]
+        m_df = Series(data, index=m_idx)
+        assert m_df.repeat(3).shape == (3 * len(data),)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_sort_index.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_sort_index.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6817aa179b7bd040e89468c960b2eb3f0259003
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_sort_index.py
@@ -0,0 +1,337 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DatetimeIndex,
+    IntervalIndex,
+    MultiIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+@pytest.fixture(params=["quicksort", "mergesort", "heapsort", "stable"])
+def sort_kind(request):
+    return request.param
+
+
+class TestSeriesSortIndex:
+    def test_sort_index_name(self, datetime_series):
+        result = datetime_series.sort_index(ascending=False)
+        assert result.name == datetime_series.name
+
+    def test_sort_index(self, datetime_series):
+        datetime_series.index = datetime_series.index._with_freq(None)
+
+        rindex = list(datetime_series.index)
+        np.random.default_rng(2).shuffle(rindex)
+
+        random_order = datetime_series.reindex(rindex)
+        sorted_series = random_order.sort_index()
+        tm.assert_series_equal(sorted_series, datetime_series)
+
+        # descending
+        sorted_series = random_order.sort_index(ascending=False)
+        tm.assert_series_equal(
+            sorted_series, datetime_series.reindex(datetime_series.index[::-1])
+        )
+
+        # compat on level
+        sorted_series = random_order.sort_index(level=0)
+        tm.assert_series_equal(sorted_series, datetime_series)
+
+        # compat on axis
+        sorted_series = random_order.sort_index(axis=0)
+        tm.assert_series_equal(sorted_series, datetime_series)
+
+        msg = "No axis named 1 for object type Series"
+        with pytest.raises(ValueError, match=msg):
+            random_order.sort_values(axis=1)
+
+        sorted_series = random_order.sort_index(level=0, axis=0)
+        tm.assert_series_equal(sorted_series, datetime_series)
+
+        with pytest.raises(ValueError, match=msg):
+            random_order.sort_index(level=0, axis=1)
+
+    def test_sort_index_inplace(self, datetime_series):
+        datetime_series.index = datetime_series.index._with_freq(None)
+
+        # For GH#11402
+        rindex = list(datetime_series.index)
+        np.random.default_rng(2).shuffle(rindex)
+
+        # descending
+        random_order = datetime_series.reindex(rindex)
+        result = random_order.sort_index(ascending=False, inplace=True)
+
+        assert result is None
+        expected = datetime_series.reindex(datetime_series.index[::-1])
+        expected.index = expected.index._with_freq(None)
+        tm.assert_series_equal(random_order, expected)
+
+        # ascending
+        random_order = datetime_series.reindex(rindex)
+        result = random_order.sort_index(ascending=True, inplace=True)
+
+        assert result is None
+        expected = datetime_series.copy()
+        expected.index = expected.index._with_freq(None)
+        tm.assert_series_equal(random_order, expected)
+
+    def test_sort_index_level(self):
+        mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list("ABC"))
+        s = Series([1, 2], mi)
+        backwards = s.iloc[[1, 0]]
+
+        res = s.sort_index(level="A")
+        tm.assert_series_equal(backwards, res)
+
+        res = s.sort_index(level=["A", "B"])
+        tm.assert_series_equal(backwards, res)
+
+        res = s.sort_index(level="A", sort_remaining=False)
+        tm.assert_series_equal(s, res)
+
+        res = s.sort_index(level=["A", "B"], sort_remaining=False)
+        tm.assert_series_equal(s, res)
+
+    @pytest.mark.parametrize("level", ["A", 0])  # GH#21052
+    def test_sort_index_multiindex(self, level):
+        mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list("ABC"))
+        s = Series([1, 2], mi)
+        backwards = s.iloc[[1, 0]]
+
+        # implicit sort_remaining=True
+        res = s.sort_index(level=level)
+        tm.assert_series_equal(backwards, res)
+
+        # GH#13496
+        # sort has no effect without remaining lvls
+        res = s.sort_index(level=level, sort_remaining=False)
+        tm.assert_series_equal(s, res)
+
+    def test_sort_index_kind(self, sort_kind):
+        # GH#14444 & GH#13589:  Add support for sort algo choosing
+        series = Series(index=[3, 2, 1, 4, 3], dtype=object)
+        expected_series = Series(index=[1, 2, 3, 3, 4], dtype=object)
+
+        index_sorted_series = series.sort_index(kind=sort_kind)
+        tm.assert_series_equal(expected_series, index_sorted_series)
+
+    def test_sort_index_na_position(self):
+        series = Series(index=[3, 2, 1, 4, 3, np.nan], dtype=object)
+        expected_series_first = Series(index=[np.nan, 1, 2, 3, 3, 4], dtype=object)
+
+        index_sorted_series = series.sort_index(na_position="first")
+        tm.assert_series_equal(expected_series_first, index_sorted_series)
+
+        expected_series_last = Series(index=[1, 2, 3, 3, 4, np.nan], dtype=object)
+
+        index_sorted_series = series.sort_index(na_position="last")
+        tm.assert_series_equal(expected_series_last, index_sorted_series)
+
+    def test_sort_index_intervals(self):
+        s = Series(
+            [np.nan, 1, 2, 3], IntervalIndex.from_arrays([0, 1, 2, 3], [1, 2, 3, 4])
+        )
+
+        result = s.sort_index()
+        expected = s
+        tm.assert_series_equal(result, expected)
+
+        result = s.sort_index(ascending=False)
+        expected = Series(
+            [3, 2, 1, np.nan], IntervalIndex.from_arrays([3, 2, 1, 0], [4, 3, 2, 1])
+        )
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("inplace", [True, False])
+    @pytest.mark.parametrize(
+        "original_list, sorted_list, ascending, ignore_index, output_index",
+        [
+            ([2, 3, 6, 1], [2, 3, 6, 1], True, True, [0, 1, 2, 3]),
+            ([2, 3, 6, 1], [2, 3, 6, 1], True, False, [0, 1, 2, 3]),
+            ([2, 3, 6, 1], [1, 6, 3, 2], False, True, [0, 1, 2, 3]),
+            ([2, 3, 6, 1], [1, 6, 3, 2], False, False, [3, 2, 1, 0]),
+        ],
+    )
+    def test_sort_index_ignore_index(
+        self, inplace, original_list, sorted_list, ascending, ignore_index, output_index
+    ):
+        # GH 30114
+        ser = Series(original_list)
+        expected = Series(sorted_list, index=output_index)
+        kwargs = {
+            "ascending": ascending,
+            "ignore_index": ignore_index,
+            "inplace": inplace,
+        }
+
+        if inplace:
+            result_ser = ser.copy()
+            result_ser.sort_index(**kwargs)
+        else:
+            result_ser = ser.sort_index(**kwargs)
+
+        tm.assert_series_equal(result_ser, expected)
+        tm.assert_series_equal(ser, Series(original_list))
+
+    def test_sort_index_ascending_list(self):
+        # GH#16934
+
+        # Set up a Series with a three level MultiIndex
+        arrays = [
+            ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
+            ["one", "two", "one", "two", "one", "two", "one", "two"],
+            [4, 3, 2, 1, 4, 3, 2, 1],
+        ]
+        tuples = zip(*arrays)
+        mi = MultiIndex.from_tuples(tuples, names=["first", "second", "third"])
+        ser = Series(range(8), index=mi)
+
+        # Sort with boolean ascending
+        result = ser.sort_index(level=["third", "first"], ascending=False)
+        expected = ser.iloc[[4, 0, 5, 1, 6, 2, 7, 3]]
+        tm.assert_series_equal(result, expected)
+
+        # Sort with list of boolean ascending
+        result = ser.sort_index(level=["third", "first"], ascending=[False, True])
+        expected = ser.iloc[[0, 4, 1, 5, 2, 6, 3, 7]]
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "ascending",
+        [
+            None,
+            (True, None),
+            (False, "True"),
+        ],
+    )
+    def test_sort_index_ascending_bad_value_raises(self, ascending):
+        ser = Series(range(10), index=[0, 3, 2, 1, 4, 5, 7, 6, 8, 9])
+        match = 'For argument "ascending" expected type bool'
+        with pytest.raises(ValueError, match=match):
+            ser.sort_index(ascending=ascending)
+
+
+class TestSeriesSortIndexKey:
+    def test_sort_index_multiindex_key(self):
+        mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list("ABC"))
+        s = Series([1, 2], mi)
+        backwards = s.iloc[[1, 0]]
+
+        result = s.sort_index(level="C", key=lambda x: -x)
+        tm.assert_series_equal(s, result)
+
+        result = s.sort_index(level="C", key=lambda x: x)  # nothing happens
+        tm.assert_series_equal(backwards, result)
+
+    def test_sort_index_multiindex_key_multi_level(self):
+        mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list("ABC"))
+        s = Series([1, 2], mi)
+        backwards = s.iloc[[1, 0]]
+
+        result = s.sort_index(level=["A", "C"], key=lambda x: -x)
+        tm.assert_series_equal(s, result)
+
+        result = s.sort_index(level=["A", "C"], key=lambda x: x)  # nothing happens
+        tm.assert_series_equal(backwards, result)
+
+    def test_sort_index_key(self):
+        series = Series(np.arange(6, dtype="int64"), index=list("aaBBca"))
+
+        result = series.sort_index()
+        expected = series.iloc[[2, 3, 0, 1, 5, 4]]
+        tm.assert_series_equal(result, expected)
+
+        result = series.sort_index(key=lambda x: x.str.lower())
+        expected = series.iloc[[0, 1, 5, 2, 3, 4]]
+        tm.assert_series_equal(result, expected)
+
+        result = series.sort_index(key=lambda x: x.str.lower(), ascending=False)
+        expected = series.iloc[[4, 2, 3, 0, 1, 5]]
+        tm.assert_series_equal(result, expected)
+
+    def test_sort_index_key_int(self):
+        series = Series(np.arange(6, dtype="int64"), index=np.arange(6, dtype="int64"))
+
+        result = series.sort_index()
+        tm.assert_series_equal(result, series)
+
+        result = series.sort_index(key=lambda x: -x)
+        expected = series.sort_index(ascending=False)
+        tm.assert_series_equal(result, expected)
+
+        result = series.sort_index(key=lambda x: 2 * x)
+        tm.assert_series_equal(result, series)
+
+    def test_sort_index_kind_key(self, sort_kind, sort_by_key):
+        # GH #14444 & #13589:  Add support for sort algo choosing
+        series = Series(index=[3, 2, 1, 4, 3], dtype=object)
+        expected_series = Series(index=[1, 2, 3, 3, 4], dtype=object)
+
+        index_sorted_series = series.sort_index(kind=sort_kind, key=sort_by_key)
+        tm.assert_series_equal(expected_series, index_sorted_series)
+
+    def test_sort_index_kind_neg_key(self, sort_kind):
+        # GH #14444 & #13589:  Add support for sort algo choosing
+        series = Series(index=[3, 2, 1, 4, 3], dtype=object)
+        expected_series = Series(index=[4, 3, 3, 2, 1], dtype=object)
+
+        index_sorted_series = series.sort_index(kind=sort_kind, key=lambda x: -x)
+        tm.assert_series_equal(expected_series, index_sorted_series)
+
+    def test_sort_index_na_position_key(self, sort_by_key):
+        series = Series(index=[3, 2, 1, 4, 3, np.nan], dtype=object)
+        expected_series_first = Series(index=[np.nan, 1, 2, 3, 3, 4], dtype=object)
+
+        index_sorted_series = series.sort_index(na_position="first", key=sort_by_key)
+        tm.assert_series_equal(expected_series_first, index_sorted_series)
+
+        expected_series_last = Series(index=[1, 2, 3, 3, 4, np.nan], dtype=object)
+
+        index_sorted_series = series.sort_index(na_position="last", key=sort_by_key)
+        tm.assert_series_equal(expected_series_last, index_sorted_series)
+
+    def test_changes_length_raises(self):
+        s = Series([1, 2, 3])
+        with pytest.raises(ValueError, match="change the shape"):
+            s.sort_index(key=lambda x: x[:1])
+
+    def test_sort_values_key_type(self):
+        s = Series([1, 2, 3], DatetimeIndex(["2008-10-24", "2008-11-23", "2007-12-22"]))
+
+        result = s.sort_index(key=lambda x: x.month)
+        expected = s.iloc[[0, 1, 2]]
+        tm.assert_series_equal(result, expected)
+
+        result = s.sort_index(key=lambda x: x.day)
+        expected = s.iloc[[2, 1, 0]]
+        tm.assert_series_equal(result, expected)
+
+        result = s.sort_index(key=lambda x: x.year)
+        expected = s.iloc[[2, 0, 1]]
+        tm.assert_series_equal(result, expected)
+
+        result = s.sort_index(key=lambda x: x.month_name())
+        expected = s.iloc[[2, 1, 0]]
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "ascending",
+        [
+            [True, False],
+            [False, True],
+        ],
+    )
+    def test_sort_index_multi_already_monotonic(self, ascending):
+        # GH 56049
+        mi = MultiIndex.from_product([[1, 2], [3, 4]])
+        ser = Series(range(len(mi)), index=mi)
+        result = ser.sort_index(ascending=ascending)
+        if ascending == [True, False]:
+            expected = ser.take([1, 0, 3, 2])
+        elif ascending == [False, True]:
+            expected = ser.take([2, 3, 0, 1])
+        tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_sort_values.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_sort_values.py
new file mode 100644
index 0000000000000000000000000000000000000000..4808272879071e8530c000daddbe7e9518deaefc
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_sort_values.py
@@ -0,0 +1,246 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Categorical,
+    DataFrame,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestSeriesSortValues:
+    def test_sort_values(self, datetime_series, using_copy_on_write):
+        # check indexes are reordered corresponding with the values
+        ser = Series([3, 2, 4, 1], ["A", "B", "C", "D"])
+        expected = Series([1, 2, 3, 4], ["D", "B", "A", "C"])
+        result = ser.sort_values()
+        tm.assert_series_equal(expected, result)
+
+        ts = datetime_series.copy()
+        ts[:5] = np.nan
+        vals = ts.values
+
+        result = ts.sort_values()
+        assert np.isnan(result[-5:]).all()
+        tm.assert_numpy_array_equal(result[:-5].values, np.sort(vals[5:]))
+
+        # na_position
+        result = ts.sort_values(na_position="first")
+        assert np.isnan(result[:5]).all()
+        tm.assert_numpy_array_equal(result[5:].values, np.sort(vals[5:]))
+
+        # something object-type
+        ser = Series(["A", "B"], [1, 2])
+        # no failure
+        ser.sort_values()
+
+        # ascending=False
+        ordered = ts.sort_values(ascending=False)
+        expected = np.sort(ts.dropna().values)[::-1]
+        tm.assert_almost_equal(expected, ordered.dropna().values)
+        ordered = ts.sort_values(ascending=False, na_position="first")
+        tm.assert_almost_equal(expected, ordered.dropna().values)
+
+        # ascending=[False] should behave the same as ascending=False
+        ordered = ts.sort_values(ascending=[False])
+        expected = ts.sort_values(ascending=False)
+        tm.assert_series_equal(expected, ordered)
+        ordered = ts.sort_values(ascending=[False], na_position="first")
+        expected = ts.sort_values(ascending=False, na_position="first")
+        tm.assert_series_equal(expected, ordered)
+
+        msg = 'For argument "ascending" expected type bool, received type NoneType.'
+        with pytest.raises(ValueError, match=msg):
+            ts.sort_values(ascending=None)
+        msg = r"Length of ascending \(0\) must be 1 for Series"
+        with pytest.raises(ValueError, match=msg):
+            ts.sort_values(ascending=[])
+        msg = r"Length of ascending \(3\) must be 1 for Series"
+        with pytest.raises(ValueError, match=msg):
+            ts.sort_values(ascending=[1, 2, 3])
+        msg = r"Length of ascending \(2\) must be 1 for Series"
+        with pytest.raises(ValueError, match=msg):
+            ts.sort_values(ascending=[False, False])
+        msg = 'For argument "ascending" expected type bool, received type str.'
+        with pytest.raises(ValueError, match=msg):
+            ts.sort_values(ascending="foobar")
+
+        # inplace=True
+        ts = datetime_series.copy()
+        return_value = ts.sort_values(ascending=False, inplace=True)
+        assert return_value is None
+        tm.assert_series_equal(ts, datetime_series.sort_values(ascending=False))
+        tm.assert_index_equal(
+            ts.index, datetime_series.sort_values(ascending=False).index
+        )
+
+        # GH#5856/5853
+        # Series.sort_values operating on a view
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 4)))
+        s = df.iloc[:, 0]
+
+        msg = (
+            "This Series is a view of some other array, to sort in-place "
+            "you must create a copy"
+        )
+        if using_copy_on_write:
+            s.sort_values(inplace=True)
+            tm.assert_series_equal(s, df.iloc[:, 0].sort_values())
+        else:
+            with pytest.raises(ValueError, match=msg):
+                s.sort_values(inplace=True)
+
+    def test_sort_values_categorical(self):
+        c = Categorical(["a", "b", "b", "a"], ordered=False)
+        cat = Series(c.copy())
+
+        # sort in the categories order
+        expected = Series(
+            Categorical(["a", "a", "b", "b"], ordered=False), index=[0, 3, 1, 2]
+        )
+        result = cat.sort_values()
+        tm.assert_series_equal(result, expected)
+
+        cat = Series(Categorical(["a", "c", "b", "d"], ordered=True))
+        res = cat.sort_values()
+        exp = np.array(["a", "b", "c", "d"], dtype=np.object_)
+        tm.assert_numpy_array_equal(res.__array__(), exp)
+
+        cat = Series(
+            Categorical(
+                ["a", "c", "b", "d"], categories=["a", "b", "c", "d"], ordered=True
+            )
+        )
+        res = cat.sort_values()
+        exp = np.array(["a", "b", "c", "d"], dtype=np.object_)
+        tm.assert_numpy_array_equal(res.__array__(), exp)
+
+        res = cat.sort_values(ascending=False)
+        exp = np.array(["d", "c", "b", "a"], dtype=np.object_)
+        tm.assert_numpy_array_equal(res.__array__(), exp)
+
+        raw_cat1 = Categorical(
+            ["a", "b", "c", "d"], categories=["a", "b", "c", "d"], ordered=False
+        )
+        raw_cat2 = Categorical(
+            ["a", "b", "c", "d"], categories=["d", "c", "b", "a"], ordered=True
+        )
+        s = ["a", "b", "c", "d"]
+        df = DataFrame(
+            {"unsort": raw_cat1, "sort": raw_cat2, "string": s, "values": [1, 2, 3, 4]}
+        )
+
+        # Cats must be sorted in a dataframe
+        res = df.sort_values(by=["string"], ascending=False)
+        exp = np.array(["d", "c", "b", "a"], dtype=np.object_)
+        tm.assert_numpy_array_equal(res["sort"].values.__array__(), exp)
+        assert res["sort"].dtype == "category"
+
+        res = df.sort_values(by=["sort"], ascending=False)
+        exp = df.sort_values(by=["string"], ascending=True)
+        tm.assert_series_equal(res["values"], exp["values"])
+        assert res["sort"].dtype == "category"
+        assert res["unsort"].dtype == "category"
+
+        # unordered cat, but we allow this
+        df.sort_values(by=["unsort"], ascending=False)
+
+        # multi-columns sort
+        # GH#7848
+        df = DataFrame(
+            {"id": [6, 5, 4, 3, 2, 1], "raw_grade": ["a", "b", "b", "a", "a", "e"]}
+        )
+        df["grade"] = Categorical(df["raw_grade"], ordered=True)
+        df["grade"] = df["grade"].cat.set_categories(["b", "e", "a"])
+
+        # sorts 'grade' according to the order of the categories
+        result = df.sort_values(by=["grade"])
+        expected = df.iloc[[1, 2, 5, 0, 3, 4]]
+        tm.assert_frame_equal(result, expected)
+
+        # multi
+        result = df.sort_values(by=["grade", "id"])
+        expected = df.iloc[[2, 1, 5, 4, 3, 0]]
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("inplace", [True, False])
+    @pytest.mark.parametrize(
+        "original_list, sorted_list, ignore_index, output_index",
+        [
+            ([2, 3, 6, 1], [6, 3, 2, 1], True, [0, 1, 2, 3]),
+            ([2, 3, 6, 1], [6, 3, 2, 1], False, [2, 1, 0, 3]),
+        ],
+    )
+    def test_sort_values_ignore_index(
+        self, inplace, original_list, sorted_list, ignore_index, output_index
+    ):
+        # GH 30114
+        ser = Series(original_list)
+        expected = Series(sorted_list, index=output_index)
+        kwargs = {"ignore_index": ignore_index, "inplace": inplace}
+
+        if inplace:
+            result_ser = ser.copy()
+            result_ser.sort_values(ascending=False, **kwargs)
+        else:
+            result_ser = ser.sort_values(ascending=False, **kwargs)
+
+        tm.assert_series_equal(result_ser, expected)
+        tm.assert_series_equal(ser, Series(original_list))
+
+    def test_mergesort_descending_stability(self):
+        # GH 28697
+        s = Series([1, 2, 1, 3], ["first", "b", "second", "c"])
+        result = s.sort_values(ascending=False, kind="mergesort")
+        expected = Series([3, 2, 1, 1], ["c", "b", "first", "second"])
+        tm.assert_series_equal(result, expected)
+
+    def test_sort_values_validate_ascending_for_value_error(self):
+        # GH41634
+        ser = Series([23, 7, 21])
+
+        msg = 'For argument "ascending" expected type bool, received type str.'
+        with pytest.raises(ValueError, match=msg):
+            ser.sort_values(ascending="False")
+
+    @pytest.mark.parametrize("ascending", [False, 0, 1, True])
+    def test_sort_values_validate_ascending_functional(self, ascending):
+        # GH41634
+        ser = Series([23, 7, 21])
+        expected = np.sort(ser.values)
+
+        sorted_ser = ser.sort_values(ascending=ascending)
+        if not ascending:
+            expected = expected[::-1]
+
+        result = sorted_ser.values
+        tm.assert_numpy_array_equal(result, expected)
+
+
+class TestSeriesSortingKey:
+    def test_sort_values_key(self):
+        series = Series(np.array(["Hello", "goodbye"]))
+
+        result = series.sort_values(axis=0)
+        expected = series
+        tm.assert_series_equal(result, expected)
+
+        result = series.sort_values(axis=0, key=lambda x: x.str.lower())
+        expected = series[::-1]
+        tm.assert_series_equal(result, expected)
+
+    def test_sort_values_key_nan(self):
+        series = Series(np.array([0, 5, np.nan, 3, 2, np.nan]))
+
+        result = series.sort_values(axis=0)
+        expected = series.iloc[[0, 4, 3, 1, 2, 5]]
+        tm.assert_series_equal(result, expected)
+
+        result = series.sort_values(axis=0, key=lambda x: x + 5)
+        expected = series.iloc[[0, 4, 3, 1, 2, 5]]
+        tm.assert_series_equal(result, expected)
+
+        result = series.sort_values(axis=0, key=lambda x: -x, ascending=False)
+        expected = series.iloc[[0, 4, 3, 1, 2, 5]]
+        tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_update.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_update.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f18ae6c138807f7bd84f4bd88125508703d86e8
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_update.py
@@ -0,0 +1,139 @@
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+from pandas import (
+    CategoricalDtype,
+    DataFrame,
+    NaT,
+    Series,
+    Timestamp,
+)
+import pandas._testing as tm
+
+
+class TestUpdate:
+    def test_update(self, using_copy_on_write):
+        s = Series([1.5, np.nan, 3.0, 4.0, np.nan])
+        s2 = Series([np.nan, 3.5, np.nan, 5.0])
+        s.update(s2)
+
+        expected = Series([1.5, 3.5, 3.0, 5.0, np.nan])
+        tm.assert_series_equal(s, expected)
+
+        # GH 3217
+        df = DataFrame([{"a": 1}, {"a": 3, "b": 2}])
+        df["c"] = np.nan
+        # Cast to object to avoid upcast when setting "foo"
+        df["c"] = df["c"].astype(object)
+        df_orig = df.copy()
+
+        if using_copy_on_write:
+            with tm.raises_chained_assignment_error():
+                df["c"].update(Series(["foo"], index=[0]))
+            expected = df_orig
+        else:
+            with tm.assert_produces_warning(FutureWarning, match="inplace method"):
+                df["c"].update(Series(["foo"], index=[0]))
+            expected = DataFrame(
+                [[1, np.nan, "foo"], [3, 2.0, np.nan]], columns=["a", "b", "c"]
+            )
+            expected["c"] = expected["c"].astype(object)
+        tm.assert_frame_equal(df, expected)
+
+    @pytest.mark.parametrize(
+        "other, dtype, expected, warn",
+        [
+            # other is int
+            ([61, 63], "int32", Series([10, 61, 12], dtype="int32"), None),
+            ([61, 63], "int64", Series([10, 61, 12]), None),
+            ([61, 63], float, Series([10.0, 61.0, 12.0]), None),
+            ([61, 63], object, Series([10, 61, 12], dtype=object), None),
+            # other is float, but can be cast to int
+            ([61.0, 63.0], "int32", Series([10, 61, 12], dtype="int32"), None),
+            ([61.0, 63.0], "int64", Series([10, 61, 12]), None),
+            ([61.0, 63.0], float, Series([10.0, 61.0, 12.0]), None),
+            ([61.0, 63.0], object, Series([10, 61.0, 12], dtype=object), None),
+            # others is float, cannot be cast to int
+            ([61.1, 63.1], "int32", Series([10.0, 61.1, 12.0]), FutureWarning),
+            ([61.1, 63.1], "int64", Series([10.0, 61.1, 12.0]), FutureWarning),
+            ([61.1, 63.1], float, Series([10.0, 61.1, 12.0]), None),
+            ([61.1, 63.1], object, Series([10, 61.1, 12], dtype=object), None),
+            # other is object, cannot be cast
+            ([(61,), (63,)], "int32", Series([10, (61,), 12]), FutureWarning),
+            ([(61,), (63,)], "int64", Series([10, (61,), 12]), FutureWarning),
+            ([(61,), (63,)], float, Series([10.0, (61,), 12.0]), FutureWarning),
+            ([(61,), (63,)], object, Series([10, (61,), 12]), None),
+        ],
+    )
+    def test_update_dtypes(self, other, dtype, expected, warn):
+        ser = Series([10, 11, 12], dtype=dtype)
+        other = Series(other, index=[1, 3])
+        with tm.assert_produces_warning(warn, match="item of incompatible dtype"):
+            ser.update(other)
+
+        tm.assert_series_equal(ser, expected)
+
+    @pytest.mark.parametrize(
+        "series, other, expected",
+        [
+            # update by key
+            (
+                Series({"a": 1, "b": 2, "c": 3, "d": 4}),
+                {"b": 5, "c": np.nan},
+                Series({"a": 1, "b": 5, "c": 3, "d": 4}),
+            ),
+            # update by position
+            (Series([1, 2, 3, 4]), [np.nan, 5, 1], Series([1, 5, 1, 4])),
+        ],
+    )
+    def test_update_from_non_series(self, series, other, expected):
+        # GH 33215
+        series.update(other)
+        tm.assert_series_equal(series, expected)
+
+    @pytest.mark.parametrize(
+        "data, other, expected, dtype",
+        [
+            (["a", None], [None, "b"], ["a", "b"], "string[python]"),
+            pytest.param(
+                ["a", None],
+                [None, "b"],
+                ["a", "b"],
+                "string[pyarrow]",
+                marks=td.skip_if_no("pyarrow"),
+            ),
+            ([1, None], [None, 2], [1, 2], "Int64"),
+            ([True, None], [None, False], [True, False], "boolean"),
+            (
+                ["a", None],
+                [None, "b"],
+                ["a", "b"],
+                CategoricalDtype(categories=["a", "b"]),
+            ),
+            (
+                [Timestamp(year=2020, month=1, day=1, tz="Europe/London"), NaT],
+                [NaT, Timestamp(year=2020, month=1, day=1, tz="Europe/London")],
+                [Timestamp(year=2020, month=1, day=1, tz="Europe/London")] * 2,
+                "datetime64[ns, Europe/London]",
+            ),
+        ],
+    )
+    def test_update_extension_array_series(self, data, other, expected, dtype):
+        result = Series(data, dtype=dtype)
+        other = Series(other, dtype=dtype)
+        expected = Series(expected, dtype=dtype)
+
+        result.update(other)
+        tm.assert_series_equal(result, expected)
+
+    def test_update_with_categorical_type(self):
+        # GH 25744
+        dtype = CategoricalDtype(["a", "b", "c", "d"])
+        s1 = Series(["a", "b", "c"], index=[1, 2, 3], dtype=dtype)
+        s2 = Series(["b", "a"], index=[1, 2], dtype=dtype)
+        s1.update(s2)
+        result = s1
+        expected = Series(["b", "a", "c"], index=[1, 2, 3], dtype=dtype)
+        tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_value_counts.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_value_counts.py
new file mode 100644
index 0000000000000000000000000000000000000000..859010d9c79c64fbac70f2f5eaa0033bddb4a0c2
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_value_counts.py
@@ -0,0 +1,271 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    CategoricalIndex,
+    Index,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestSeriesValueCounts:
+    def test_value_counts_datetime(self, unit):
+        # most dtypes are tested in tests/base
+        values = [
+            pd.Timestamp("2011-01-01 09:00"),
+            pd.Timestamp("2011-01-01 10:00"),
+            pd.Timestamp("2011-01-01 11:00"),
+            pd.Timestamp("2011-01-01 09:00"),
+            pd.Timestamp("2011-01-01 09:00"),
+            pd.Timestamp("2011-01-01 11:00"),
+        ]
+
+        exp_idx = pd.DatetimeIndex(
+            ["2011-01-01 09:00", "2011-01-01 11:00", "2011-01-01 10:00"],
+            name="xxx",
+        ).as_unit(unit)
+        exp = Series([3, 2, 1], index=exp_idx, name="count")
+
+        ser = Series(values, name="xxx").dt.as_unit(unit)
+        tm.assert_series_equal(ser.value_counts(), exp)
+        # check DatetimeIndex outputs the same result
+        idx = pd.DatetimeIndex(values, name="xxx").as_unit(unit)
+        tm.assert_series_equal(idx.value_counts(), exp)
+
+        # normalize
+        exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="proportion")
+        tm.assert_series_equal(ser.value_counts(normalize=True), exp)
+        tm.assert_series_equal(idx.value_counts(normalize=True), exp)
+
+    def test_value_counts_datetime_tz(self, unit):
+        values = [
+            pd.Timestamp("2011-01-01 09:00", tz="US/Eastern"),
+            pd.Timestamp("2011-01-01 10:00", tz="US/Eastern"),
+            pd.Timestamp("2011-01-01 11:00", tz="US/Eastern"),
+            pd.Timestamp("2011-01-01 09:00", tz="US/Eastern"),
+            pd.Timestamp("2011-01-01 09:00", tz="US/Eastern"),
+            pd.Timestamp("2011-01-01 11:00", tz="US/Eastern"),
+        ]
+
+        exp_idx = pd.DatetimeIndex(
+            ["2011-01-01 09:00", "2011-01-01 11:00", "2011-01-01 10:00"],
+            tz="US/Eastern",
+            name="xxx",
+        ).as_unit(unit)
+        exp = Series([3, 2, 1], index=exp_idx, name="count")
+
+        ser = Series(values, name="xxx").dt.as_unit(unit)
+        tm.assert_series_equal(ser.value_counts(), exp)
+        idx = pd.DatetimeIndex(values, name="xxx").as_unit(unit)
+        tm.assert_series_equal(idx.value_counts(), exp)
+
+        exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="proportion")
+        tm.assert_series_equal(ser.value_counts(normalize=True), exp)
+        tm.assert_series_equal(idx.value_counts(normalize=True), exp)
+
+    def test_value_counts_period(self):
+        values = [
+            pd.Period("2011-01", freq="M"),
+            pd.Period("2011-02", freq="M"),
+            pd.Period("2011-03", freq="M"),
+            pd.Period("2011-01", freq="M"),
+            pd.Period("2011-01", freq="M"),
+            pd.Period("2011-03", freq="M"),
+        ]
+
+        exp_idx = pd.PeriodIndex(
+            ["2011-01", "2011-03", "2011-02"], freq="M", name="xxx"
+        )
+        exp = Series([3, 2, 1], index=exp_idx, name="count")
+
+        ser = Series(values, name="xxx")
+        tm.assert_series_equal(ser.value_counts(), exp)
+        # check DatetimeIndex outputs the same result
+        idx = pd.PeriodIndex(values, name="xxx")
+        tm.assert_series_equal(idx.value_counts(), exp)
+
+        # normalize
+        exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="proportion")
+        tm.assert_series_equal(ser.value_counts(normalize=True), exp)
+        tm.assert_series_equal(idx.value_counts(normalize=True), exp)
+
+    def test_value_counts_categorical_ordered(self):
+        # most dtypes are tested in tests/base
+        values = Categorical([1, 2, 3, 1, 1, 3], ordered=True)
+
+        exp_idx = CategoricalIndex(
+            [1, 3, 2], categories=[1, 2, 3], ordered=True, name="xxx"
+        )
+        exp = Series([3, 2, 1], index=exp_idx, name="count")
+
+        ser = Series(values, name="xxx")
+        tm.assert_series_equal(ser.value_counts(), exp)
+        # check CategoricalIndex outputs the same result
+        idx = CategoricalIndex(values, name="xxx")
+        tm.assert_series_equal(idx.value_counts(), exp)
+
+        # normalize
+        exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="proportion")
+        tm.assert_series_equal(ser.value_counts(normalize=True), exp)
+        tm.assert_series_equal(idx.value_counts(normalize=True), exp)
+
+    def test_value_counts_categorical_not_ordered(self):
+        values = Categorical([1, 2, 3, 1, 1, 3], ordered=False)
+
+        exp_idx = CategoricalIndex(
+            [1, 3, 2], categories=[1, 2, 3], ordered=False, name="xxx"
+        )
+        exp = Series([3, 2, 1], index=exp_idx, name="count")
+
+        ser = Series(values, name="xxx")
+        tm.assert_series_equal(ser.value_counts(), exp)
+        # check CategoricalIndex outputs the same result
+        idx = CategoricalIndex(values, name="xxx")
+        tm.assert_series_equal(idx.value_counts(), exp)
+
+        # normalize
+        exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="proportion")
+        tm.assert_series_equal(ser.value_counts(normalize=True), exp)
+        tm.assert_series_equal(idx.value_counts(normalize=True), exp)
+
+    def test_value_counts_categorical(self):
+        # GH#12835
+        cats = Categorical(list("abcccb"), categories=list("cabd"))
+        ser = Series(cats, name="xxx")
+        res = ser.value_counts(sort=False)
+
+        exp_index = CategoricalIndex(
+            list("cabd"), categories=cats.categories, name="xxx"
+        )
+        exp = Series([3, 1, 2, 0], name="count", index=exp_index)
+        tm.assert_series_equal(res, exp)
+
+        res = ser.value_counts(sort=True)
+
+        exp_index = CategoricalIndex(
+            list("cbad"), categories=cats.categories, name="xxx"
+        )
+        exp = Series([3, 2, 1, 0], name="count", index=exp_index)
+        tm.assert_series_equal(res, exp)
+
+        # check object dtype handles the Series.name as the same
+        # (tested in tests/base)
+        ser = Series(["a", "b", "c", "c", "c", "b"], name="xxx")
+        res = ser.value_counts()
+        exp = Series([3, 2, 1], name="count", index=Index(["c", "b", "a"], name="xxx"))
+        tm.assert_series_equal(res, exp)
+
+    def test_value_counts_categorical_with_nan(self):
+        # see GH#9443
+
+        # sanity check
+        ser = Series(["a", "b", "a"], dtype="category")
+        exp = Series([2, 1], index=CategoricalIndex(["a", "b"]), name="count")
+
+        res = ser.value_counts(dropna=True)
+        tm.assert_series_equal(res, exp)
+
+        res = ser.value_counts(dropna=True)
+        tm.assert_series_equal(res, exp)
+
+        # same Series via two different constructions --> same behaviour
+        series = [
+            Series(["a", "b", None, "a", None, None], dtype="category"),
+            Series(
+                Categorical(["a", "b", None, "a", None, None], categories=["a", "b"])
+            ),
+        ]
+
+        for ser in series:
+            # None is a NaN value, so we exclude its count here
+            exp = Series([2, 1], index=CategoricalIndex(["a", "b"]), name="count")
+            res = ser.value_counts(dropna=True)
+            tm.assert_series_equal(res, exp)
+
+            # we don't exclude the count of None and sort by counts
+            exp = Series(
+                [3, 2, 1], index=CategoricalIndex([np.nan, "a", "b"]), name="count"
+            )
+            res = ser.value_counts(dropna=False)
+            tm.assert_series_equal(res, exp)
+
+            # When we aren't sorting by counts, and np.nan isn't a
+            # category, it should be last.
+            exp = Series(
+                [2, 1, 3], index=CategoricalIndex(["a", "b", np.nan]), name="count"
+            )
+            res = ser.value_counts(dropna=False, sort=False)
+            tm.assert_series_equal(res, exp)
+
+    @pytest.mark.parametrize(
+        "ser, dropna, exp",
+        [
+            (
+                Series([False, True, True, pd.NA]),
+                False,
+                Series([2, 1, 1], index=[True, False, pd.NA], name="count"),
+            ),
+            (
+                Series([False, True, True, pd.NA]),
+                True,
+                Series([2, 1], index=Index([True, False], dtype=object), name="count"),
+            ),
+            (
+                Series(range(3), index=[True, False, np.nan]).index,
+                False,
+                Series([1, 1, 1], index=[True, False, np.nan], name="count"),
+            ),
+        ],
+    )
+    def test_value_counts_bool_with_nan(self, ser, dropna, exp):
+        # GH32146
+        out = ser.value_counts(dropna=dropna)
+        tm.assert_series_equal(out, exp)
+
+    @pytest.mark.parametrize(
+        "input_array,expected",
+        [
+            (
+                [1 + 1j, 1 + 1j, 1, 3j, 3j, 3j],
+                Series(
+                    [3, 2, 1],
+                    index=Index([3j, 1 + 1j, 1], dtype=np.complex128),
+                    name="count",
+                ),
+            ),
+            (
+                np.array([1 + 1j, 1 + 1j, 1, 3j, 3j, 3j], dtype=np.complex64),
+                Series(
+                    [3, 2, 1],
+                    index=Index([3j, 1 + 1j, 1], dtype=np.complex64),
+                    name="count",
+                ),
+            ),
+        ],
+    )
+    def test_value_counts_complex_numbers(self, input_array, expected):
+        # GH 17927
+        result = Series(input_array).value_counts()
+        tm.assert_series_equal(result, expected)
+
+    def test_value_counts_masked(self):
+        # GH#54984
+        dtype = "Int64"
+        ser = Series([1, 2, None, 2, None, 3], dtype=dtype)
+        result = ser.value_counts(dropna=False)
+        expected = Series(
+            [2, 2, 1, 1],
+            index=Index([2, None, 1, 3], dtype=dtype),
+            dtype=dtype,
+            name="count",
+        )
+        tm.assert_series_equal(result, expected)
+
+        result = ser.value_counts(dropna=True)
+        expected = Series(
+            [2, 1, 1], index=Index([2, 1, 3], dtype=dtype), dtype=dtype, name="count"
+        )
+        tm.assert_series_equal(result, expected)