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diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/__init__.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/__init__.py
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diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/__init__.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..bcb0d30f405e2079a1ff21d838f52cd5f659fd93
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/__init__.py
@@ -0,0 +1,7 @@
+"""
+Test files dedicated to individual (stand-alone) Series methods
+
+Ideally these files/tests should correspond 1-to-1 with tests.frame.methods
+
+These may also present opportunities for sharing/de-duplicating test code.
+"""
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diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_add_prefix_suffix.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_add_prefix_suffix.py
new file mode 100644
index 0000000000000000000000000000000000000000..289a56b98b7e123fb1c1edf5cc9ff41369d51122
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_add_prefix_suffix.py
@@ -0,0 +1,41 @@
+import pytest
+
+from pandas import Index
+import pandas._testing as tm
+
+
+def test_add_prefix_suffix(string_series):
+    with_prefix = string_series.add_prefix("foo#")
+    expected = Index([f"foo#{c}" for c in string_series.index])
+    tm.assert_index_equal(with_prefix.index, expected)
+
+    with_suffix = string_series.add_suffix("#foo")
+    expected = Index([f"{c}#foo" for c in string_series.index])
+    tm.assert_index_equal(with_suffix.index, expected)
+
+    with_pct_prefix = string_series.add_prefix("%")
+    expected = Index([f"%{c}" for c in string_series.index])
+    tm.assert_index_equal(with_pct_prefix.index, expected)
+
+    with_pct_suffix = string_series.add_suffix("%")
+    expected = Index([f"{c}%" for c in string_series.index])
+    tm.assert_index_equal(with_pct_suffix.index, expected)
+
+
+def test_add_prefix_suffix_axis(string_series):
+    # GH 47819
+    with_prefix = string_series.add_prefix("foo#", axis=0)
+    expected = Index([f"foo#{c}" for c in string_series.index])
+    tm.assert_index_equal(with_prefix.index, expected)
+
+    with_pct_suffix = string_series.add_suffix("#foo", axis=0)
+    expected = Index([f"{c}#foo" for c in string_series.index])
+    tm.assert_index_equal(with_pct_suffix.index, expected)
+
+
+def test_add_prefix_suffix_invalid_axis(string_series):
+    with pytest.raises(ValueError, match="No axis named 1 for object type Series"):
+        string_series.add_prefix("foo#", axis=1)
+
+    with pytest.raises(ValueError, match="No axis named 1 for object type Series"):
+        string_series.add_suffix("foo#", axis=1)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_align.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_align.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb60cd2e5bcf33c259ed8c5f8506eb11384d2d79
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_align.py
@@ -0,0 +1,249 @@
+from datetime import timezone
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Series,
+    date_range,
+    period_range,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize(
+    "first_slice,second_slice",
+    [
+        [[2, None], [None, -5]],
+        [[None, 0], [None, -5]],
+        [[None, -5], [None, 0]],
+        [[None, 0], [None, 0]],
+    ],
+)
+@pytest.mark.parametrize("fill", [None, -1])
+def test_align(datetime_series, first_slice, second_slice, join_type, fill):
+    a = datetime_series[slice(*first_slice)]
+    b = datetime_series[slice(*second_slice)]
+
+    aa, ab = a.align(b, join=join_type, fill_value=fill)
+
+    join_index = a.index.join(b.index, how=join_type)
+    if fill is not None:
+        diff_a = aa.index.difference(join_index)
+        diff_b = ab.index.difference(join_index)
+        if len(diff_a) > 0:
+            assert (aa.reindex(diff_a) == fill).all()
+        if len(diff_b) > 0:
+            assert (ab.reindex(diff_b) == fill).all()
+
+    ea = a.reindex(join_index)
+    eb = b.reindex(join_index)
+
+    if fill is not None:
+        ea = ea.fillna(fill)
+        eb = eb.fillna(fill)
+
+    tm.assert_series_equal(aa, ea)
+    tm.assert_series_equal(ab, eb)
+    assert aa.name == "ts"
+    assert ea.name == "ts"
+    assert ab.name == "ts"
+    assert eb.name == "ts"
+
+
+@pytest.mark.parametrize(
+    "first_slice,second_slice",
+    [
+        [[2, None], [None, -5]],
+        [[None, 0], [None, -5]],
+        [[None, -5], [None, 0]],
+        [[None, 0], [None, 0]],
+    ],
+)
+@pytest.mark.parametrize("method", ["pad", "bfill"])
+@pytest.mark.parametrize("limit", [None, 1])
+def test_align_fill_method(
+    datetime_series, first_slice, second_slice, join_type, method, limit
+):
+    a = datetime_series[slice(*first_slice)]
+    b = datetime_series[slice(*second_slice)]
+
+    msg = (
+        "The 'method', 'limit', and 'fill_axis' keywords in Series.align "
+        "are deprecated"
+    )
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        aa, ab = a.align(b, join=join_type, method=method, limit=limit)
+
+    join_index = a.index.join(b.index, how=join_type)
+    ea = a.reindex(join_index)
+    eb = b.reindex(join_index)
+
+    msg2 = "Series.fillna with 'method' is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg2):
+        ea = ea.fillna(method=method, limit=limit)
+        eb = eb.fillna(method=method, limit=limit)
+
+    tm.assert_series_equal(aa, ea)
+    tm.assert_series_equal(ab, eb)
+
+
+def test_align_nocopy(datetime_series, using_copy_on_write):
+    b = datetime_series[:5].copy()
+
+    # do copy
+    a = datetime_series.copy()
+    ra, _ = a.align(b, join="left")
+    ra[:5] = 5
+    assert not (a[:5] == 5).any()
+
+    # do not copy
+    a = datetime_series.copy()
+    ra, _ = a.align(b, join="left", copy=False)
+    ra[:5] = 5
+    if using_copy_on_write:
+        assert not (a[:5] == 5).any()
+    else:
+        assert (a[:5] == 5).all()
+
+    # do copy
+    a = datetime_series.copy()
+    b = datetime_series[:5].copy()
+    _, rb = a.align(b, join="right")
+    rb[:3] = 5
+    assert not (b[:3] == 5).any()
+
+    # do not copy
+    a = datetime_series.copy()
+    b = datetime_series[:5].copy()
+    _, rb = a.align(b, join="right", copy=False)
+    rb[:2] = 5
+    if using_copy_on_write:
+        assert not (b[:2] == 5).any()
+    else:
+        assert (b[:2] == 5).all()
+
+
+def test_align_same_index(datetime_series, using_copy_on_write):
+    a, b = datetime_series.align(datetime_series, copy=False)
+    if not using_copy_on_write:
+        assert a.index is datetime_series.index
+        assert b.index is datetime_series.index
+    else:
+        assert a.index.is_(datetime_series.index)
+        assert b.index.is_(datetime_series.index)
+
+    a, b = datetime_series.align(datetime_series, copy=True)
+    assert a.index is not datetime_series.index
+    assert b.index is not datetime_series.index
+    assert a.index.is_(datetime_series.index)
+    assert b.index.is_(datetime_series.index)
+
+
+def test_align_multiindex():
+    # GH 10665
+
+    midx = pd.MultiIndex.from_product(
+        [range(2), range(3), range(2)], names=("a", "b", "c")
+    )
+    idx = pd.Index(range(2), name="b")
+    s1 = Series(np.arange(12, dtype="int64"), index=midx)
+    s2 = Series(np.arange(2, dtype="int64"), index=idx)
+
+    # these must be the same results (but flipped)
+    res1l, res1r = s1.align(s2, join="left")
+    res2l, res2r = s2.align(s1, join="right")
+
+    expl = s1
+    tm.assert_series_equal(expl, res1l)
+    tm.assert_series_equal(expl, res2r)
+    expr = Series([0, 0, 1, 1, np.nan, np.nan] * 2, index=midx)
+    tm.assert_series_equal(expr, res1r)
+    tm.assert_series_equal(expr, res2l)
+
+    res1l, res1r = s1.align(s2, join="right")
+    res2l, res2r = s2.align(s1, join="left")
+
+    exp_idx = pd.MultiIndex.from_product(
+        [range(2), range(2), range(2)], names=("a", "b", "c")
+    )
+    expl = Series([0, 1, 2, 3, 6, 7, 8, 9], index=exp_idx)
+    tm.assert_series_equal(expl, res1l)
+    tm.assert_series_equal(expl, res2r)
+    expr = Series([0, 0, 1, 1] * 2, index=exp_idx)
+    tm.assert_series_equal(expr, res1r)
+    tm.assert_series_equal(expr, res2l)
+
+
+@pytest.mark.parametrize("method", ["backfill", "bfill", "pad", "ffill", None])
+def test_align_with_dataframe_method(method):
+    # GH31788
+    ser = Series(range(3), index=range(3))
+    df = pd.DataFrame(0.0, index=range(3), columns=range(3))
+
+    msg = (
+        "The 'method', 'limit', and 'fill_axis' keywords in Series.align "
+        "are deprecated"
+    )
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result_ser, result_df = ser.align(df, method=method)
+    tm.assert_series_equal(result_ser, ser)
+    tm.assert_frame_equal(result_df, df)
+
+
+def test_align_dt64tzindex_mismatched_tzs():
+    idx1 = date_range("2001", periods=5, freq="h", tz="US/Eastern")
+    ser = Series(np.random.default_rng(2).standard_normal(len(idx1)), index=idx1)
+    ser_central = ser.tz_convert("US/Central")
+    # different timezones convert to UTC
+
+    new1, new2 = ser.align(ser_central)
+    assert new1.index.tz is timezone.utc
+    assert new2.index.tz is timezone.utc
+
+
+def test_align_periodindex(join_type):
+    rng = period_range("1/1/2000", "1/1/2010", freq="Y")
+    ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng)
+
+    # TODO: assert something?
+    ts.align(ts[::2], join=join_type)
+
+
+def test_align_left_fewer_levels():
+    # GH#45224
+    left = Series([2], index=pd.MultiIndex.from_tuples([(1, 3)], names=["a", "c"]))
+    right = Series(
+        [1], index=pd.MultiIndex.from_tuples([(1, 2, 3)], names=["a", "b", "c"])
+    )
+    result_left, result_right = left.align(right)
+
+    expected_right = Series(
+        [1], index=pd.MultiIndex.from_tuples([(1, 3, 2)], names=["a", "c", "b"])
+    )
+    expected_left = Series(
+        [2], index=pd.MultiIndex.from_tuples([(1, 3, 2)], names=["a", "c", "b"])
+    )
+    tm.assert_series_equal(result_left, expected_left)
+    tm.assert_series_equal(result_right, expected_right)
+
+
+def test_align_left_different_named_levels():
+    # GH#45224
+    left = Series(
+        [2], index=pd.MultiIndex.from_tuples([(1, 4, 3)], names=["a", "d", "c"])
+    )
+    right = Series(
+        [1], index=pd.MultiIndex.from_tuples([(1, 2, 3)], names=["a", "b", "c"])
+    )
+    result_left, result_right = left.align(right)
+
+    expected_left = Series(
+        [2], index=pd.MultiIndex.from_tuples([(1, 4, 3, 2)], names=["a", "d", "c", "b"])
+    )
+    expected_right = Series(
+        [1], index=pd.MultiIndex.from_tuples([(1, 4, 3, 2)], names=["a", "d", "c", "b"])
+    )
+    tm.assert_series_equal(result_left, expected_left)
+    tm.assert_series_equal(result_right, expected_right)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_argsort.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_argsort.py
new file mode 100644
index 0000000000000000000000000000000000000000..432c0eceee01107cdcd23056a39e0ef4bd55545b
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_argsort.py
@@ -0,0 +1,84 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Series,
+    Timestamp,
+    isna,
+)
+import pandas._testing as tm
+
+
+class TestSeriesArgsort:
+    def test_argsort_axis(self):
+        # GH#54257
+        ser = Series(range(3))
+
+        msg = "No axis named 2 for object type Series"
+        with pytest.raises(ValueError, match=msg):
+            ser.argsort(axis=2)
+
+    def test_argsort_numpy(self, datetime_series):
+        ser = datetime_series
+
+        res = np.argsort(ser).values
+        expected = np.argsort(np.array(ser))
+        tm.assert_numpy_array_equal(res, expected)
+
+        # with missing values
+        ts = ser.copy()
+        ts[::2] = np.nan
+
+        msg = "The behavior of Series.argsort in the presence of NA values"
+        with tm.assert_produces_warning(
+            FutureWarning, match=msg, check_stacklevel=False
+        ):
+            result = np.argsort(ts)[1::2]
+        expected = np.argsort(np.array(ts.dropna()))
+
+        tm.assert_numpy_array_equal(result.values, expected)
+
+    def test_argsort(self, datetime_series):
+        argsorted = datetime_series.argsort()
+        assert issubclass(argsorted.dtype.type, np.integer)
+
+    def test_argsort_dt64(self, unit):
+        # GH#2967 (introduced bug in 0.11-dev I think)
+        ser = Series(
+            [Timestamp(f"201301{i:02d}") for i in range(1, 6)], dtype=f"M8[{unit}]"
+        )
+        assert ser.dtype == f"datetime64[{unit}]"
+        shifted = ser.shift(-1)
+        assert shifted.dtype == f"datetime64[{unit}]"
+        assert isna(shifted[4])
+
+        result = ser.argsort()
+        expected = Series(range(5), dtype=np.intp)
+        tm.assert_series_equal(result, expected)
+
+        msg = "The behavior of Series.argsort in the presence of NA values"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = shifted.argsort()
+        expected = Series(list(range(4)) + [-1], dtype=np.intp)
+        tm.assert_series_equal(result, expected)
+
+    def test_argsort_stable(self):
+        ser = Series(np.random.default_rng(2).integers(0, 100, size=10000))
+        mindexer = ser.argsort(kind="mergesort")
+        qindexer = ser.argsort()
+
+        mexpected = np.argsort(ser.values, kind="mergesort")
+        qexpected = np.argsort(ser.values, kind="quicksort")
+
+        tm.assert_series_equal(mindexer.astype(np.intp), Series(mexpected))
+        tm.assert_series_equal(qindexer.astype(np.intp), Series(qexpected))
+        msg = (
+            r"ndarray Expected type <class 'numpy\.ndarray'>, "
+            r"found <class 'pandas\.core\.series\.Series'> instead"
+        )
+        with pytest.raises(AssertionError, match=msg):
+            tm.assert_numpy_array_equal(qindexer, mindexer)
+
+    def test_argsort_preserve_name(self, datetime_series):
+        result = datetime_series.argsort()
+        assert result.name == datetime_series.name
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_asof.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_asof.py
new file mode 100644
index 0000000000000000000000000000000000000000..2acc2921e5efc089b1dd4ed7aa9a6cebc1a3d0fe
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_asof.py
@@ -0,0 +1,205 @@
+import numpy as np
+import pytest
+
+from pandas._libs.tslibs import IncompatibleFrequency
+
+from pandas import (
+    DatetimeIndex,
+    PeriodIndex,
+    Series,
+    Timestamp,
+    date_range,
+    isna,
+    notna,
+    offsets,
+    period_range,
+)
+import pandas._testing as tm
+
+
+class TestSeriesAsof:
+    def test_asof_nanosecond_index_access(self):
+        ts = Timestamp("20130101").as_unit("ns")._value
+        dti = DatetimeIndex([ts + 50 + i for i in range(100)])
+        ser = Series(np.random.default_rng(2).standard_normal(100), index=dti)
+
+        first_value = ser.asof(ser.index[0])
+
+        # GH#46903 previously incorrectly was "day"
+        assert dti.resolution == "nanosecond"
+
+        # this used to not work bc parsing was done by dateutil that didn't
+        #  handle nanoseconds
+        assert first_value == ser["2013-01-01 00:00:00.000000050"]
+
+        expected_ts = np.datetime64("2013-01-01 00:00:00.000000050", "ns")
+        assert first_value == ser[Timestamp(expected_ts)]
+
+    def test_basic(self):
+        # array or list or dates
+        N = 50
+        rng = date_range("1/1/1990", periods=N, freq="53s")
+        ts = Series(np.random.default_rng(2).standard_normal(N), index=rng)
+        ts.iloc[15:30] = np.nan
+        dates = date_range("1/1/1990", periods=N * 3, freq="25s")
+
+        result = ts.asof(dates)
+        assert notna(result).all()
+        lb = ts.index[14]
+        ub = ts.index[30]
+
+        result = ts.asof(list(dates))
+        assert notna(result).all()
+        lb = ts.index[14]
+        ub = ts.index[30]
+
+        mask = (result.index >= lb) & (result.index < ub)
+        rs = result[mask]
+        assert (rs == ts[lb]).all()
+
+        val = result[result.index[result.index >= ub][0]]
+        assert ts[ub] == val
+
+    def test_scalar(self):
+        N = 30
+        rng = date_range("1/1/1990", periods=N, freq="53s")
+        # Explicit cast to float avoid implicit cast when setting nan
+        ts = Series(np.arange(N), index=rng, dtype="float")
+        ts.iloc[5:10] = np.nan
+        ts.iloc[15:20] = np.nan
+
+        val1 = ts.asof(ts.index[7])
+        val2 = ts.asof(ts.index[19])
+
+        assert val1 == ts.iloc[4]
+        assert val2 == ts.iloc[14]
+
+        # accepts strings
+        val1 = ts.asof(str(ts.index[7]))
+        assert val1 == ts.iloc[4]
+
+        # in there
+        result = ts.asof(ts.index[3])
+        assert result == ts.iloc[3]
+
+        # no as of value
+        d = ts.index[0] - offsets.BDay()
+        assert np.isnan(ts.asof(d))
+
+    def test_with_nan(self):
+        # basic asof test
+        rng = date_range("1/1/2000", "1/2/2000", freq="4h")
+        s = Series(np.arange(len(rng)), index=rng)
+        r = s.resample("2h").mean()
+
+        result = r.asof(r.index)
+        expected = Series(
+            [0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6.0],
+            index=date_range("1/1/2000", "1/2/2000", freq="2h"),
+        )
+        tm.assert_series_equal(result, expected)
+
+        r.iloc[3:5] = np.nan
+        result = r.asof(r.index)
+        expected = Series(
+            [0, 0, 1, 1, 1, 1, 3, 3, 4, 4, 5, 5, 6.0],
+            index=date_range("1/1/2000", "1/2/2000", freq="2h"),
+        )
+        tm.assert_series_equal(result, expected)
+
+        r.iloc[-3:] = np.nan
+        result = r.asof(r.index)
+        expected = Series(
+            [0, 0, 1, 1, 1, 1, 3, 3, 4, 4, 4, 4, 4.0],
+            index=date_range("1/1/2000", "1/2/2000", freq="2h"),
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_periodindex(self):
+        # array or list or dates
+        N = 50
+        rng = period_range("1/1/1990", periods=N, freq="h")
+        ts = Series(np.random.default_rng(2).standard_normal(N), index=rng)
+        ts.iloc[15:30] = np.nan
+        dates = date_range("1/1/1990", periods=N * 3, freq="37min")
+
+        result = ts.asof(dates)
+        assert notna(result).all()
+        lb = ts.index[14]
+        ub = ts.index[30]
+
+        result = ts.asof(list(dates))
+        assert notna(result).all()
+        lb = ts.index[14]
+        ub = ts.index[30]
+
+        pix = PeriodIndex(result.index.values, freq="h")
+        mask = (pix >= lb) & (pix < ub)
+        rs = result[mask]
+        assert (rs == ts[lb]).all()
+
+        ts.iloc[5:10] = np.nan
+        ts.iloc[15:20] = np.nan
+
+        val1 = ts.asof(ts.index[7])
+        val2 = ts.asof(ts.index[19])
+
+        assert val1 == ts.iloc[4]
+        assert val2 == ts.iloc[14]
+
+        # accepts strings
+        val1 = ts.asof(str(ts.index[7]))
+        assert val1 == ts.iloc[4]
+
+        # in there
+        assert ts.asof(ts.index[3]) == ts.iloc[3]
+
+        # no as of value
+        d = ts.index[0].to_timestamp() - offsets.BDay()
+        assert isna(ts.asof(d))
+
+        # Mismatched freq
+        msg = "Input has different freq"
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            ts.asof(rng.asfreq("D"))
+
+    def test_errors(self):
+        s = Series(
+            [1, 2, 3],
+            index=[Timestamp("20130101"), Timestamp("20130103"), Timestamp("20130102")],
+        )
+
+        # non-monotonic
+        assert not s.index.is_monotonic_increasing
+        with pytest.raises(ValueError, match="requires a sorted index"):
+            s.asof(s.index[0])
+
+        # subset with Series
+        N = 10
+        rng = date_range("1/1/1990", periods=N, freq="53s")
+        s = Series(np.random.default_rng(2).standard_normal(N), index=rng)
+        with pytest.raises(ValueError, match="not valid for Series"):
+            s.asof(s.index[0], subset="foo")
+
+    def test_all_nans(self):
+        # GH 15713
+        # series is all nans
+
+        # testing non-default indexes
+        N = 50
+        rng = date_range("1/1/1990", periods=N, freq="53s")
+
+        dates = date_range("1/1/1990", periods=N * 3, freq="25s")
+        result = Series(np.nan, index=rng).asof(dates)
+        expected = Series(np.nan, index=dates)
+        tm.assert_series_equal(result, expected)
+
+        # testing scalar input
+        date = date_range("1/1/1990", periods=N * 3, freq="25s")[0]
+        result = Series(np.nan, index=rng).asof(date)
+        assert isna(result)
+
+        # test name is propagated
+        result = Series(np.nan, index=[1, 2, 3, 4], name="test").asof([4, 5])
+        expected = Series(np.nan, index=[4, 5], name="test")
+        tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_astype.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_astype.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c8028e74ee5518ec97c1e571c9cb04fa0045c85
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_astype.py
@@ -0,0 +1,683 @@
+from datetime import (
+    datetime,
+    timedelta,
+)
+from importlib import reload
+import string
+import sys
+
+import numpy as np
+import pytest
+
+from pandas._libs.tslibs import iNaT
+import pandas.util._test_decorators as td
+
+from pandas import (
+    NA,
+    Categorical,
+    CategoricalDtype,
+    DatetimeTZDtype,
+    Index,
+    Interval,
+    NaT,
+    Series,
+    Timedelta,
+    Timestamp,
+    cut,
+    date_range,
+    to_datetime,
+)
+import pandas._testing as tm
+
+
+def rand_str(nchars: int) -> str:
+    """
+    Generate one random byte string.
+    """
+    RANDS_CHARS = np.array(
+        list(string.ascii_letters + string.digits), dtype=(np.str_, 1)
+    )
+    return "".join(np.random.default_rng(2).choice(RANDS_CHARS, nchars))
+
+
+class TestAstypeAPI:
+    def test_astype_unitless_dt64_raises(self):
+        # GH#47844
+        ser = Series(["1970-01-01", "1970-01-01", "1970-01-01"], dtype="datetime64[ns]")
+        df = ser.to_frame()
+
+        msg = "Casting to unit-less dtype 'datetime64' is not supported"
+        with pytest.raises(TypeError, match=msg):
+            ser.astype(np.datetime64)
+        with pytest.raises(TypeError, match=msg):
+            df.astype(np.datetime64)
+        with pytest.raises(TypeError, match=msg):
+            ser.astype("datetime64")
+        with pytest.raises(TypeError, match=msg):
+            df.astype("datetime64")
+
+    def test_arg_for_errors_in_astype(self):
+        # see GH#14878
+        ser = Series([1, 2, 3])
+
+        msg = (
+            r"Expected value of kwarg 'errors' to be one of \['raise', "
+            r"'ignore'\]\. Supplied value is 'False'"
+        )
+        with pytest.raises(ValueError, match=msg):
+            ser.astype(np.float64, errors=False)
+
+        ser.astype(np.int8, errors="raise")
+
+    @pytest.mark.parametrize("dtype_class", [dict, Series])
+    def test_astype_dict_like(self, dtype_class):
+        # see GH#7271
+        ser = Series(range(0, 10, 2), name="abc")
+
+        dt1 = dtype_class({"abc": str})
+        result = ser.astype(dt1)
+        expected = Series(["0", "2", "4", "6", "8"], name="abc", dtype=object)
+        tm.assert_series_equal(result, expected)
+
+        dt2 = dtype_class({"abc": "float64"})
+        result = ser.astype(dt2)
+        expected = Series([0.0, 2.0, 4.0, 6.0, 8.0], dtype="float64", name="abc")
+        tm.assert_series_equal(result, expected)
+
+        dt3 = dtype_class({"abc": str, "def": str})
+        msg = (
+            "Only the Series name can be used for the key in Series dtype "
+            r"mappings\."
+        )
+        with pytest.raises(KeyError, match=msg):
+            ser.astype(dt3)
+
+        dt4 = dtype_class({0: str})
+        with pytest.raises(KeyError, match=msg):
+            ser.astype(dt4)
+
+        # GH#16717
+        # if dtypes provided is empty, it should error
+        if dtype_class is Series:
+            dt5 = dtype_class({}, dtype=object)
+        else:
+            dt5 = dtype_class({})
+
+        with pytest.raises(KeyError, match=msg):
+            ser.astype(dt5)
+
+
+class TestAstype:
+    @pytest.mark.parametrize("tz", [None, "UTC", "US/Pacific"])
+    def test_astype_object_to_dt64_non_nano(self, tz):
+        # GH#55756, GH#54620
+        ts = Timestamp("2999-01-01")
+        dtype = "M8[us]"
+        if tz is not None:
+            dtype = f"M8[us, {tz}]"
+        vals = [ts, "2999-01-02 03:04:05.678910", 2500]
+        ser = Series(vals, dtype=object)
+        result = ser.astype(dtype)
+
+        # The 2500 is interpreted as microseconds, consistent with what
+        #  we would get if we created DatetimeIndexes from vals[:2] and vals[2:]
+        #  and concated the results.
+        pointwise = [
+            vals[0].tz_localize(tz),
+            Timestamp(vals[1], tz=tz),
+            to_datetime(vals[2], unit="us", utc=True).tz_convert(tz),
+        ]
+        exp_vals = [x.as_unit("us").asm8 for x in pointwise]
+        exp_arr = np.array(exp_vals, dtype="M8[us]")
+        expected = Series(exp_arr, dtype="M8[us]")
+        if tz is not None:
+            expected = expected.dt.tz_localize("UTC").dt.tz_convert(tz)
+        tm.assert_series_equal(result, expected)
+
+    def test_astype_mixed_object_to_dt64tz(self):
+        # pre-2.0 this raised ValueError bc of tz mismatch
+        # xref GH#32581
+        ts = Timestamp("2016-01-04 05:06:07", tz="US/Pacific")
+        ts2 = ts.tz_convert("Asia/Tokyo")
+
+        ser = Series([ts, ts2], dtype=object)
+        res = ser.astype("datetime64[ns, Europe/Brussels]")
+        expected = Series(
+            [ts.tz_convert("Europe/Brussels"), ts2.tz_convert("Europe/Brussels")],
+            dtype="datetime64[ns, Europe/Brussels]",
+        )
+        tm.assert_series_equal(res, expected)
+
+    @pytest.mark.parametrize("dtype", np.typecodes["All"])
+    def test_astype_empty_constructor_equality(self, dtype):
+        # see GH#15524
+
+        if dtype not in (
+            "S",
+            "V",  # poor support (if any) currently
+            "M",
+            "m",  # Generic timestamps raise a ValueError. Already tested.
+        ):
+            init_empty = Series([], dtype=dtype)
+            as_type_empty = Series([]).astype(dtype)
+            tm.assert_series_equal(init_empty, as_type_empty)
+
+    @pytest.mark.parametrize("dtype", [str, np.str_])
+    @pytest.mark.parametrize(
+        "series",
+        [
+            Series([string.digits * 10, rand_str(63), rand_str(64), rand_str(1000)]),
+            Series([string.digits * 10, rand_str(63), rand_str(64), np.nan, 1.0]),
+        ],
+    )
+    def test_astype_str_map(self, dtype, series, using_infer_string):
+        # see GH#4405
+        result = series.astype(dtype)
+        expected = series.map(str)
+        if using_infer_string:
+            expected = expected.astype(object)
+        tm.assert_series_equal(result, expected)
+
+    def test_astype_float_to_period(self):
+        result = Series([np.nan]).astype("period[D]")
+        expected = Series([NaT], dtype="period[D]")
+        tm.assert_series_equal(result, expected)
+
+    def test_astype_no_pandas_dtype(self):
+        # https://github.com/pandas-dev/pandas/pull/24866
+        ser = Series([1, 2], dtype="int64")
+        # Don't have NumpyEADtype in the public API, so we use `.array.dtype`,
+        # which is a NumpyEADtype.
+        result = ser.astype(ser.array.dtype)
+        tm.assert_series_equal(result, ser)
+
+    @pytest.mark.parametrize("dtype", [np.datetime64, np.timedelta64])
+    def test_astype_generic_timestamp_no_frequency(self, dtype, request):
+        # see GH#15524, GH#15987
+        data = [1]
+        ser = Series(data)
+
+        if np.dtype(dtype).name not in ["timedelta64", "datetime64"]:
+            mark = pytest.mark.xfail(reason="GH#33890 Is assigned ns unit")
+            request.applymarker(mark)
+
+        msg = (
+            rf"The '{dtype.__name__}' dtype has no unit\. "
+            rf"Please pass in '{dtype.__name__}\[ns\]' instead."
+        )
+        with pytest.raises(ValueError, match=msg):
+            ser.astype(dtype)
+
+    def test_astype_dt64_to_str(self):
+        # GH#10442 : testing astype(str) is correct for Series/DatetimeIndex
+        dti = date_range("2012-01-01", periods=3)
+        result = Series(dti).astype(str)
+        expected = Series(["2012-01-01", "2012-01-02", "2012-01-03"], dtype=object)
+        tm.assert_series_equal(result, expected)
+
+    def test_astype_dt64tz_to_str(self):
+        # GH#10442 : testing astype(str) is correct for Series/DatetimeIndex
+        dti_tz = date_range("2012-01-01", periods=3, tz="US/Eastern")
+        result = Series(dti_tz).astype(str)
+        expected = Series(
+            [
+                "2012-01-01 00:00:00-05:00",
+                "2012-01-02 00:00:00-05:00",
+                "2012-01-03 00:00:00-05:00",
+            ],
+            dtype=object,
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_astype_datetime(self, unit):
+        ser = Series(iNaT, dtype=f"M8[{unit}]", index=range(5))
+
+        ser = ser.astype("O")
+        assert ser.dtype == np.object_
+
+        ser = Series([datetime(2001, 1, 2, 0, 0)])
+
+        ser = ser.astype("O")
+        assert ser.dtype == np.object_
+
+        ser = Series(
+            [datetime(2001, 1, 2, 0, 0) for i in range(3)], dtype=f"M8[{unit}]"
+        )
+
+        ser[1] = np.nan
+        assert ser.dtype == f"M8[{unit}]"
+
+        ser = ser.astype("O")
+        assert ser.dtype == np.object_
+
+    def test_astype_datetime64tz(self):
+        ser = Series(date_range("20130101", periods=3, tz="US/Eastern"))
+
+        # astype
+        result = ser.astype(object)
+        expected = Series(ser.astype(object), dtype=object)
+        tm.assert_series_equal(result, expected)
+
+        result = Series(ser.values).dt.tz_localize("UTC").dt.tz_convert(ser.dt.tz)
+        tm.assert_series_equal(result, ser)
+
+        # astype - object, preserves on construction
+        result = Series(ser.astype(object))
+        expected = ser.astype(object)
+        tm.assert_series_equal(result, expected)
+
+        # astype - datetime64[ns, tz]
+        msg = "Cannot use .astype to convert from timezone-naive"
+        with pytest.raises(TypeError, match=msg):
+            # dt64->dt64tz astype deprecated
+            Series(ser.values).astype("datetime64[ns, US/Eastern]")
+
+        with pytest.raises(TypeError, match=msg):
+            # dt64->dt64tz astype deprecated
+            Series(ser.values).astype(ser.dtype)
+
+        result = ser.astype("datetime64[ns, CET]")
+        expected = Series(date_range("20130101 06:00:00", periods=3, tz="CET"))
+        tm.assert_series_equal(result, expected)
+
+    def test_astype_str_cast_dt64(self):
+        # see GH#9757
+        ts = Series([Timestamp("2010-01-04 00:00:00")])
+        res = ts.astype(str)
+
+        expected = Series(["2010-01-04"], dtype=object)
+        tm.assert_series_equal(res, expected)
+
+        ts = Series([Timestamp("2010-01-04 00:00:00", tz="US/Eastern")])
+        res = ts.astype(str)
+
+        expected = Series(["2010-01-04 00:00:00-05:00"], dtype=object)
+        tm.assert_series_equal(res, expected)
+
+    def test_astype_str_cast_td64(self):
+        # see GH#9757
+
+        td = Series([Timedelta(1, unit="d")])
+        ser = td.astype(str)
+
+        expected = Series(["1 days"], dtype=object)
+        tm.assert_series_equal(ser, expected)
+
+    def test_dt64_series_astype_object(self):
+        dt64ser = Series(date_range("20130101", periods=3))
+        result = dt64ser.astype(object)
+        assert isinstance(result.iloc[0], datetime)
+        assert result.dtype == np.object_
+
+    def test_td64_series_astype_object(self):
+        tdser = Series(["59 Days", "59 Days", "NaT"], dtype="timedelta64[ns]")
+        result = tdser.astype(object)
+        assert isinstance(result.iloc[0], timedelta)
+        assert result.dtype == np.object_
+
+    @pytest.mark.parametrize(
+        "data, dtype",
+        [
+            (["x", "y", "z"], "string[python]"),
+            pytest.param(
+                ["x", "y", "z"],
+                "string[pyarrow]",
+                marks=td.skip_if_no("pyarrow"),
+            ),
+            (["x", "y", "z"], "category"),
+            (3 * [Timestamp("2020-01-01", tz="UTC")], None),
+            (3 * [Interval(0, 1)], None),
+        ],
+    )
+    @pytest.mark.parametrize("errors", ["raise", "ignore"])
+    def test_astype_ignores_errors_for_extension_dtypes(self, data, dtype, errors):
+        # https://github.com/pandas-dev/pandas/issues/35471
+        ser = Series(data, dtype=dtype)
+        if errors == "ignore":
+            expected = ser
+            result = ser.astype(float, errors="ignore")
+            tm.assert_series_equal(result, expected)
+        else:
+            msg = "(Cannot cast)|(could not convert)"
+            with pytest.raises((ValueError, TypeError), match=msg):
+                ser.astype(float, errors=errors)
+
+    @pytest.mark.parametrize("dtype", [np.float16, np.float32, np.float64])
+    def test_astype_from_float_to_str(self, dtype):
+        # https://github.com/pandas-dev/pandas/issues/36451
+        ser = Series([0.1], dtype=dtype)
+        result = ser.astype(str)
+        expected = Series(["0.1"], dtype=object)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "value, string_value",
+        [
+            (None, "None"),
+            (np.nan, "nan"),
+            (NA, "<NA>"),
+        ],
+    )
+    def test_astype_to_str_preserves_na(self, value, string_value):
+        # https://github.com/pandas-dev/pandas/issues/36904
+        ser = Series(["a", "b", value], dtype=object)
+        result = ser.astype(str)
+        expected = Series(["a", "b", string_value], dtype=object)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", ["float32", "float64", "int64", "int32"])
+    def test_astype(self, dtype):
+        ser = Series(np.random.default_rng(2).standard_normal(5), name="foo")
+        as_typed = ser.astype(dtype)
+
+        assert as_typed.dtype == dtype
+        assert as_typed.name == ser.name
+
+    @pytest.mark.parametrize("value", [np.nan, np.inf])
+    @pytest.mark.parametrize("dtype", [np.int32, np.int64])
+    def test_astype_cast_nan_inf_int(self, dtype, value):
+        # gh-14265: check NaN and inf raise error when converting to int
+        msg = "Cannot convert non-finite values \\(NA or inf\\) to integer"
+        ser = Series([value])
+
+        with pytest.raises(ValueError, match=msg):
+            ser.astype(dtype)
+
+    @pytest.mark.parametrize("dtype", [int, np.int8, np.int64])
+    def test_astype_cast_object_int_fail(self, dtype):
+        arr = Series(["car", "house", "tree", "1"])
+        msg = r"invalid literal for int\(\) with base 10: 'car'"
+        with pytest.raises(ValueError, match=msg):
+            arr.astype(dtype)
+
+    def test_astype_float_to_uint_negatives_raise(
+        self, float_numpy_dtype, any_unsigned_int_numpy_dtype
+    ):
+        # GH#45151 We don't cast negative numbers to nonsense values
+        # TODO: same for EA float/uint dtypes, signed integers?
+        arr = np.arange(5).astype(float_numpy_dtype) - 3  # includes negatives
+        ser = Series(arr)
+
+        msg = "Cannot losslessly cast from .* to .*"
+        with pytest.raises(ValueError, match=msg):
+            ser.astype(any_unsigned_int_numpy_dtype)
+
+        with pytest.raises(ValueError, match=msg):
+            ser.to_frame().astype(any_unsigned_int_numpy_dtype)
+
+        with pytest.raises(ValueError, match=msg):
+            # We currently catch and re-raise in Index.astype
+            Index(ser).astype(any_unsigned_int_numpy_dtype)
+
+        with pytest.raises(ValueError, match=msg):
+            ser.array.astype(any_unsigned_int_numpy_dtype)
+
+    def test_astype_cast_object_int(self):
+        arr = Series(["1", "2", "3", "4"], dtype=object)
+        result = arr.astype(int)
+
+        tm.assert_series_equal(result, Series(np.arange(1, 5)))
+
+    def test_astype_unicode(self, using_infer_string):
+        # see GH#7758: A bit of magic is required to set
+        # default encoding to utf-8
+        digits = string.digits
+        test_series = [
+            Series([digits * 10, rand_str(63), rand_str(64), rand_str(1000)]),
+            Series(["データーサイエンス、お前はもう死んでいる"]),
+        ]
+
+        former_encoding = None
+
+        if sys.getdefaultencoding() == "utf-8":
+            # GH#45326 as of 2.0 Series.astype matches Index.astype by handling
+            #  bytes with obj.decode() instead of str(obj)
+            item = "野菜食べないとやばい"
+            ser = Series([item.encode()])
+            result = ser.astype(np.str_)
+            expected = Series([item], dtype=object)
+            tm.assert_series_equal(result, expected)
+
+        for ser in test_series:
+            res = ser.astype(np.str_)
+            expec = ser.map(str)
+            if using_infer_string:
+                expec = expec.astype(object)
+            tm.assert_series_equal(res, expec)
+
+        # Restore the former encoding
+        if former_encoding is not None and former_encoding != "utf-8":
+            reload(sys)
+            sys.setdefaultencoding(former_encoding)
+
+    def test_astype_bytes(self):
+        # GH#39474
+        result = Series(["foo", "bar", "baz"]).astype(bytes)
+        assert result.dtypes == np.dtype("S3")
+
+    def test_astype_nan_to_bool(self):
+        # GH#43018
+        ser = Series(np.nan, dtype="object")
+        result = ser.astype("bool")
+        expected = Series(True, dtype="bool")
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "dtype",
+        tm.ALL_INT_EA_DTYPES + tm.FLOAT_EA_DTYPES,
+    )
+    def test_astype_ea_to_datetimetzdtype(self, dtype):
+        # GH37553
+        ser = Series([4, 0, 9], dtype=dtype)
+        result = ser.astype(DatetimeTZDtype(tz="US/Pacific"))
+
+        expected = Series(
+            {
+                0: Timestamp("1969-12-31 16:00:00.000000004-08:00", tz="US/Pacific"),
+                1: Timestamp("1969-12-31 16:00:00.000000000-08:00", tz="US/Pacific"),
+                2: Timestamp("1969-12-31 16:00:00.000000009-08:00", tz="US/Pacific"),
+            }
+        )
+
+        tm.assert_series_equal(result, expected)
+
+    def test_astype_retain_attrs(self, any_numpy_dtype):
+        # GH#44414
+        ser = Series([0, 1, 2, 3])
+        ser.attrs["Location"] = "Michigan"
+
+        result = ser.astype(any_numpy_dtype).attrs
+        expected = ser.attrs
+
+        tm.assert_dict_equal(expected, result)
+
+
+class TestAstypeString:
+    @pytest.mark.parametrize(
+        "data, dtype",
+        [
+            ([True, NA], "boolean"),
+            (["A", NA], "category"),
+            (["2020-10-10", "2020-10-10"], "datetime64[ns]"),
+            (["2020-10-10", "2020-10-10", NaT], "datetime64[ns]"),
+            (
+                ["2012-01-01 00:00:00-05:00", NaT],
+                "datetime64[ns, US/Eastern]",
+            ),
+            ([1, None], "UInt16"),
+            (["1/1/2021", "2/1/2021"], "period[M]"),
+            (["1/1/2021", "2/1/2021", NaT], "period[M]"),
+            (["1 Day", "59 Days", NaT], "timedelta64[ns]"),
+            # currently no way to parse IntervalArray from a list of strings
+        ],
+    )
+    def test_astype_string_to_extension_dtype_roundtrip(
+        self, data, dtype, request, nullable_string_dtype
+    ):
+        if dtype == "boolean":
+            mark = pytest.mark.xfail(
+                reason="TODO StringArray.astype() with missing values #GH40566"
+            )
+            request.applymarker(mark)
+        # GH-40351
+        ser = Series(data, dtype=dtype)
+
+        # Note: just passing .astype(dtype) fails for dtype="category"
+        #  with bc ser.dtype.categories will be object dtype whereas
+        #  result.dtype.categories will have string dtype
+        result = ser.astype(nullable_string_dtype).astype(ser.dtype)
+        tm.assert_series_equal(result, ser)
+
+
+class TestAstypeCategorical:
+    def test_astype_categorical_to_other(self):
+        cat = Categorical([f"{i} - {i + 499}" for i in range(0, 10000, 500)])
+        ser = Series(np.random.default_rng(2).integers(0, 10000, 100)).sort_values()
+        ser = cut(ser, range(0, 10500, 500), right=False, labels=cat)
+
+        expected = ser
+        tm.assert_series_equal(ser.astype("category"), expected)
+        tm.assert_series_equal(ser.astype(CategoricalDtype()), expected)
+        msg = r"Cannot cast object|string dtype to float64"
+        with pytest.raises(ValueError, match=msg):
+            ser.astype("float64")
+
+        cat = Series(Categorical(["a", "b", "b", "a", "a", "c", "c", "c"]))
+        exp = Series(["a", "b", "b", "a", "a", "c", "c", "c"], dtype=object)
+        tm.assert_series_equal(cat.astype("str"), exp)
+        s2 = Series(Categorical(["1", "2", "3", "4"]))
+        exp2 = Series([1, 2, 3, 4]).astype("int")
+        tm.assert_series_equal(s2.astype("int"), exp2)
+
+        # object don't sort correctly, so just compare that we have the same
+        # values
+        def cmp(a, b):
+            tm.assert_almost_equal(np.sort(np.unique(a)), np.sort(np.unique(b)))
+
+        expected = Series(np.array(ser.values), name="value_group")
+        cmp(ser.astype("object"), expected)
+        cmp(ser.astype(np.object_), expected)
+
+        # array conversion
+        tm.assert_almost_equal(np.array(ser), np.array(ser.values))
+
+        tm.assert_series_equal(ser.astype("category"), ser)
+        tm.assert_series_equal(ser.astype(CategoricalDtype()), ser)
+
+        roundtrip_expected = ser.cat.set_categories(
+            ser.cat.categories.sort_values()
+        ).cat.remove_unused_categories()
+        result = ser.astype("object").astype("category")
+        tm.assert_series_equal(result, roundtrip_expected)
+        result = ser.astype("object").astype(CategoricalDtype())
+        tm.assert_series_equal(result, roundtrip_expected)
+
+    def test_astype_categorical_invalid_conversions(self):
+        # invalid conversion (these are NOT a dtype)
+        cat = Categorical([f"{i} - {i + 499}" for i in range(0, 10000, 500)])
+        ser = Series(np.random.default_rng(2).integers(0, 10000, 100)).sort_values()
+        ser = cut(ser, range(0, 10500, 500), right=False, labels=cat)
+
+        msg = (
+            "dtype '<class 'pandas.core.arrays.categorical.Categorical'>' "
+            "not understood"
+        )
+        with pytest.raises(TypeError, match=msg):
+            ser.astype(Categorical)
+        with pytest.raises(TypeError, match=msg):
+            ser.astype("object").astype(Categorical)
+
+    def test_astype_categoricaldtype(self):
+        ser = Series(["a", "b", "a"])
+        result = ser.astype(CategoricalDtype(["a", "b"], ordered=True))
+        expected = Series(Categorical(["a", "b", "a"], ordered=True))
+        tm.assert_series_equal(result, expected)
+
+        result = ser.astype(CategoricalDtype(["a", "b"], ordered=False))
+        expected = Series(Categorical(["a", "b", "a"], ordered=False))
+        tm.assert_series_equal(result, expected)
+
+        result = ser.astype(CategoricalDtype(["a", "b", "c"], ordered=False))
+        expected = Series(
+            Categorical(["a", "b", "a"], categories=["a", "b", "c"], ordered=False)
+        )
+        tm.assert_series_equal(result, expected)
+        tm.assert_index_equal(result.cat.categories, Index(["a", "b", "c"]))
+
+    @pytest.mark.parametrize("name", [None, "foo"])
+    @pytest.mark.parametrize("dtype_ordered", [True, False])
+    @pytest.mark.parametrize("series_ordered", [True, False])
+    def test_astype_categorical_to_categorical(
+        self, name, dtype_ordered, series_ordered
+    ):
+        # GH#10696, GH#18593
+        s_data = list("abcaacbab")
+        s_dtype = CategoricalDtype(list("bac"), ordered=series_ordered)
+        ser = Series(s_data, dtype=s_dtype, name=name)
+
+        # unspecified categories
+        dtype = CategoricalDtype(ordered=dtype_ordered)
+        result = ser.astype(dtype)
+        exp_dtype = CategoricalDtype(s_dtype.categories, dtype_ordered)
+        expected = Series(s_data, name=name, dtype=exp_dtype)
+        tm.assert_series_equal(result, expected)
+
+        # different categories
+        dtype = CategoricalDtype(list("adc"), dtype_ordered)
+        result = ser.astype(dtype)
+        expected = Series(s_data, name=name, dtype=dtype)
+        tm.assert_series_equal(result, expected)
+
+        if dtype_ordered is False:
+            # not specifying ordered, so only test once
+            expected = ser
+            result = ser.astype("category")
+            tm.assert_series_equal(result, expected)
+
+    def test_astype_bool_missing_to_categorical(self):
+        # GH-19182
+        ser = Series([True, False, np.nan])
+        assert ser.dtypes == np.object_
+
+        result = ser.astype(CategoricalDtype(categories=[True, False]))
+        expected = Series(Categorical([True, False, np.nan], categories=[True, False]))
+        tm.assert_series_equal(result, expected)
+
+    def test_astype_categories_raises(self):
+        # deprecated GH#17636, removed in GH#27141
+        ser = Series(["a", "b", "a"])
+        with pytest.raises(TypeError, match="got an unexpected"):
+            ser.astype("category", categories=["a", "b"], ordered=True)
+
+    @pytest.mark.parametrize("items", [["a", "b", "c", "a"], [1, 2, 3, 1]])
+    def test_astype_from_categorical(self, items):
+        ser = Series(items)
+        exp = Series(Categorical(items))
+        res = ser.astype("category")
+        tm.assert_series_equal(res, exp)
+
+    def test_astype_from_categorical_with_keywords(self):
+        # with keywords
+        lst = ["a", "b", "c", "a"]
+        ser = Series(lst)
+        exp = Series(Categorical(lst, ordered=True))
+        res = ser.astype(CategoricalDtype(None, ordered=True))
+        tm.assert_series_equal(res, exp)
+
+        exp = Series(Categorical(lst, categories=list("abcdef"), ordered=True))
+        res = ser.astype(CategoricalDtype(list("abcdef"), ordered=True))
+        tm.assert_series_equal(res, exp)
+
+    def test_astype_timedelta64_with_np_nan(self):
+        # GH45798
+        result = Series([Timedelta(1), np.nan], dtype="timedelta64[ns]")
+        expected = Series([Timedelta(1), NaT], dtype="timedelta64[ns]")
+        tm.assert_series_equal(result, expected)
+
+    @td.skip_if_no("pyarrow")
+    def test_astype_int_na_string(self):
+        # GH#57418
+        ser = Series([12, NA], dtype="Int64[pyarrow]")
+        result = ser.astype("string[pyarrow]")
+        expected = Series(["12", NA], dtype="string[pyarrow]")
+        tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_autocorr.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_autocorr.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1d768cf02f37baceb01b9ef39b91e419cef1e0f
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_autocorr.py
@@ -0,0 +1,30 @@
+import numpy as np
+
+
+class TestAutoCorr:
+    def test_autocorr(self, datetime_series):
+        # Just run the function
+        corr1 = datetime_series.autocorr()
+
+        # Now run it with the lag parameter
+        corr2 = datetime_series.autocorr(lag=1)
+
+        # corr() with lag needs Series of at least length 2
+        if len(datetime_series) <= 2:
+            assert np.isnan(corr1)
+            assert np.isnan(corr2)
+        else:
+            assert corr1 == corr2
+
+        # Choose a random lag between 1 and length of Series - 2
+        # and compare the result with the Series corr() function
+        n = 1 + np.random.default_rng(2).integers(max(1, len(datetime_series) - 2))
+        corr1 = datetime_series.corr(datetime_series.shift(n))
+        corr2 = datetime_series.autocorr(lag=n)
+
+        # corr() with lag needs Series of at least length 2
+        if len(datetime_series) <= 2:
+            assert np.isnan(corr1)
+            assert np.isnan(corr2)
+        else:
+            assert corr1 == corr2
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_between.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_between.py
new file mode 100644
index 0000000000000000000000000000000000000000..3913419038876938c9538ef435a7eb02c37f62a6
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_between.py
@@ -0,0 +1,75 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Series,
+    bdate_range,
+    date_range,
+    period_range,
+)
+import pandas._testing as tm
+
+
+class TestBetween:
+    def test_between(self):
+        series = Series(date_range("1/1/2000", periods=10))
+        left, right = series[[2, 7]]
+
+        result = series.between(left, right)
+        expected = (series >= left) & (series <= right)
+        tm.assert_series_equal(result, expected)
+
+    def test_between_datetime_object_dtype(self):
+        ser = Series(bdate_range("1/1/2000", periods=20), dtype=object)
+        ser[::2] = np.nan
+
+        result = ser[ser.between(ser[3], ser[17])]
+        expected = ser[3:18].dropna()
+        tm.assert_series_equal(result, expected)
+
+        result = ser[ser.between(ser[3], ser[17], inclusive="neither")]
+        expected = ser[5:16].dropna()
+        tm.assert_series_equal(result, expected)
+
+    def test_between_period_values(self):
+        ser = Series(period_range("2000-01-01", periods=10, freq="D"))
+        left, right = ser[[2, 7]]
+        result = ser.between(left, right)
+        expected = (ser >= left) & (ser <= right)
+        tm.assert_series_equal(result, expected)
+
+    def test_between_inclusive_string(self):
+        # GH 40628
+        series = Series(date_range("1/1/2000", periods=10))
+        left, right = series[[2, 7]]
+
+        result = series.between(left, right, inclusive="both")
+        expected = (series >= left) & (series <= right)
+        tm.assert_series_equal(result, expected)
+
+        result = series.between(left, right, inclusive="left")
+        expected = (series >= left) & (series < right)
+        tm.assert_series_equal(result, expected)
+
+        result = series.between(left, right, inclusive="right")
+        expected = (series > left) & (series <= right)
+        tm.assert_series_equal(result, expected)
+
+        result = series.between(left, right, inclusive="neither")
+        expected = (series > left) & (series < right)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("inclusive", ["yes", True, False])
+    def test_between_error_args(self, inclusive):
+        # GH 40628
+        series = Series(date_range("1/1/2000", periods=10))
+        left, right = series[[2, 7]]
+
+        value_error_msg = (
+            "Inclusive has to be either string of 'both',"
+            "'left', 'right', or 'neither'."
+        )
+
+        with pytest.raises(ValueError, match=value_error_msg):
+            series = Series(date_range("1/1/2000", periods=10))
+            series.between(left, right, inclusive=inclusive)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_case_when.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_case_when.py
new file mode 100644
index 0000000000000000000000000000000000000000..7cb60a11644a357811136c991143554c4477d485
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_case_when.py
@@ -0,0 +1,148 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    Series,
+    array as pd_array,
+    date_range,
+)
+import pandas._testing as tm
+
+
+@pytest.fixture
+def df():
+    """
+    base dataframe for testing
+    """
+    return DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+
+
+def test_case_when_caselist_is_not_a_list(df):
+    """
+    Raise ValueError if caselist is not a list.
+    """
+    msg = "The caselist argument should be a list; "
+    msg += "instead got.+"
+    with pytest.raises(TypeError, match=msg):  # GH39154
+        df["a"].case_when(caselist=())
+
+
+def test_case_when_no_caselist(df):
+    """
+    Raise ValueError if no caselist is provided.
+    """
+    msg = "provide at least one boolean condition, "
+    msg += "with a corresponding replacement."
+    with pytest.raises(ValueError, match=msg):  # GH39154
+        df["a"].case_when([])
+
+
+def test_case_when_odd_caselist(df):
+    """
+    Raise ValueError if no of caselist is odd.
+    """
+    msg = "Argument 0 must have length 2; "
+    msg += "a condition and replacement; instead got length 3."
+
+    with pytest.raises(ValueError, match=msg):
+        df["a"].case_when([(df["a"].eq(1), 1, df.a.gt(1))])
+
+
+def test_case_when_raise_error_from_mask(df):
+    """
+    Raise Error from within Series.mask
+    """
+    msg = "Failed to apply condition0 and replacement0."
+    with pytest.raises(ValueError, match=msg):
+        df["a"].case_when([(df["a"].eq(1), [1, 2])])
+
+
+def test_case_when_single_condition(df):
+    """
+    Test output on a single condition.
+    """
+    result = Series([np.nan, np.nan, np.nan]).case_when([(df.a.eq(1), 1)])
+    expected = Series([1, np.nan, np.nan])
+    tm.assert_series_equal(result, expected)
+
+
+def test_case_when_multiple_conditions(df):
+    """
+    Test output when booleans are derived from a computation
+    """
+    result = Series([np.nan, np.nan, np.nan]).case_when(
+        [(df.a.eq(1), 1), (Series([False, True, False]), 2)]
+    )
+    expected = Series([1, 2, np.nan])
+    tm.assert_series_equal(result, expected)
+
+
+def test_case_when_multiple_conditions_replacement_list(df):
+    """
+    Test output when replacement is a list
+    """
+    result = Series([np.nan, np.nan, np.nan]).case_when(
+        [([True, False, False], 1), (df["a"].gt(1) & df["b"].eq(5), [1, 2, 3])]
+    )
+    expected = Series([1, 2, np.nan])
+    tm.assert_series_equal(result, expected)
+
+
+def test_case_when_multiple_conditions_replacement_extension_dtype(df):
+    """
+    Test output when replacement has an extension dtype
+    """
+    result = Series([np.nan, np.nan, np.nan]).case_when(
+        [
+            ([True, False, False], 1),
+            (df["a"].gt(1) & df["b"].eq(5), pd_array([1, 2, 3], dtype="Int64")),
+        ],
+    )
+    expected = Series([1, 2, np.nan], dtype="Float64")
+    tm.assert_series_equal(result, expected)
+
+
+def test_case_when_multiple_conditions_replacement_series(df):
+    """
+    Test output when replacement is a Series
+    """
+    result = Series([np.nan, np.nan, np.nan]).case_when(
+        [
+            (np.array([True, False, False]), 1),
+            (df["a"].gt(1) & df["b"].eq(5), Series([1, 2, 3])),
+        ],
+    )
+    expected = Series([1, 2, np.nan])
+    tm.assert_series_equal(result, expected)
+
+
+def test_case_when_non_range_index():
+    """
+    Test output if index is not RangeIndex
+    """
+    rng = np.random.default_rng(seed=123)
+    dates = date_range("1/1/2000", periods=8)
+    df = DataFrame(
+        rng.standard_normal(size=(8, 4)), index=dates, columns=["A", "B", "C", "D"]
+    )
+    result = Series(5, index=df.index, name="A").case_when([(df.A.gt(0), df.B)])
+    expected = df.A.mask(df.A.gt(0), df.B).where(df.A.gt(0), 5)
+    tm.assert_series_equal(result, expected)
+
+
+def test_case_when_callable():
+    """
+    Test output on a callable
+    """
+    # https://numpy.org/doc/stable/reference/generated/numpy.piecewise.html
+    x = np.linspace(-2.5, 2.5, 6)
+    ser = Series(x)
+    result = ser.case_when(
+        caselist=[
+            (lambda df: df < 0, lambda df: -df),
+            (lambda df: df >= 0, lambda df: df),
+        ]
+    )
+    expected = np.piecewise(x, [x < 0, x >= 0], [lambda x: -x, lambda x: x])
+    tm.assert_series_equal(result, Series(expected))
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_combine.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_combine.py
new file mode 100644
index 0000000000000000000000000000000000000000..75d47e3daa10339f4c4cc7b35c52f24bbb20277a
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_combine.py
@@ -0,0 +1,17 @@
+from pandas import Series
+import pandas._testing as tm
+
+
+class TestCombine:
+    def test_combine_scalar(self):
+        # GH#21248
+        # Note - combine() with another Series is tested elsewhere because
+        # it is used when testing operators
+        ser = Series([i * 10 for i in range(5)])
+        result = ser.combine(3, lambda x, y: x + y)
+        expected = Series([i * 10 + 3 for i in range(5)])
+        tm.assert_series_equal(result, expected)
+
+        result = ser.combine(22, lambda x, y: min(x, y))
+        expected = Series([min(i * 10, 22) for i in range(5)])
+        tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_compare.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_compare.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe2016a245ec7c1373c72f40c7b6e7d899cf4f96
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_compare.py
@@ -0,0 +1,141 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize("align_axis", [0, 1, "index", "columns"])
+def test_compare_axis(align_axis):
+    # GH#30429
+    s1 = pd.Series(["a", "b", "c"])
+    s2 = pd.Series(["x", "b", "z"])
+
+    result = s1.compare(s2, align_axis=align_axis)
+
+    if align_axis in (1, "columns"):
+        indices = pd.Index([0, 2])
+        columns = pd.Index(["self", "other"])
+        expected = pd.DataFrame(
+            [["a", "x"], ["c", "z"]], index=indices, columns=columns
+        )
+        tm.assert_frame_equal(result, expected)
+    else:
+        indices = pd.MultiIndex.from_product([[0, 2], ["self", "other"]])
+        expected = pd.Series(["a", "x", "c", "z"], index=indices)
+        tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "keep_shape, keep_equal",
+    [
+        (True, False),
+        (False, True),
+        (True, True),
+        # False, False case is already covered in test_compare_axis
+    ],
+)
+def test_compare_various_formats(keep_shape, keep_equal):
+    s1 = pd.Series(["a", "b", "c"])
+    s2 = pd.Series(["x", "b", "z"])
+
+    result = s1.compare(s2, keep_shape=keep_shape, keep_equal=keep_equal)
+
+    if keep_shape:
+        indices = pd.Index([0, 1, 2])
+        columns = pd.Index(["self", "other"])
+        if keep_equal:
+            expected = pd.DataFrame(
+                [["a", "x"], ["b", "b"], ["c", "z"]], index=indices, columns=columns
+            )
+        else:
+            expected = pd.DataFrame(
+                [["a", "x"], [np.nan, np.nan], ["c", "z"]],
+                index=indices,
+                columns=columns,
+            )
+    else:
+        indices = pd.Index([0, 2])
+        columns = pd.Index(["self", "other"])
+        expected = pd.DataFrame(
+            [["a", "x"], ["c", "z"]], index=indices, columns=columns
+        )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_compare_with_equal_nulls():
+    # We want to make sure two NaNs are considered the same
+    # and dropped where applicable
+    s1 = pd.Series(["a", "b", np.nan])
+    s2 = pd.Series(["x", "b", np.nan])
+
+    result = s1.compare(s2)
+    expected = pd.DataFrame([["a", "x"]], columns=["self", "other"])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_compare_with_non_equal_nulls():
+    # We want to make sure the relevant NaNs do not get dropped
+    s1 = pd.Series(["a", "b", "c"])
+    s2 = pd.Series(["x", "b", np.nan])
+
+    result = s1.compare(s2, align_axis=0)
+
+    indices = pd.MultiIndex.from_product([[0, 2], ["self", "other"]])
+    expected = pd.Series(["a", "x", "c", np.nan], index=indices)
+    tm.assert_series_equal(result, expected)
+
+
+def test_compare_multi_index():
+    index = pd.MultiIndex.from_arrays([[0, 0, 1], [0, 1, 2]])
+    s1 = pd.Series(["a", "b", "c"], index=index)
+    s2 = pd.Series(["x", "b", "z"], index=index)
+
+    result = s1.compare(s2, align_axis=0)
+
+    indices = pd.MultiIndex.from_arrays(
+        [[0, 0, 1, 1], [0, 0, 2, 2], ["self", "other", "self", "other"]]
+    )
+    expected = pd.Series(["a", "x", "c", "z"], index=indices)
+    tm.assert_series_equal(result, expected)
+
+
+def test_compare_unaligned_objects():
+    # test Series with different indices
+    msg = "Can only compare identically-labeled Series objects"
+    with pytest.raises(ValueError, match=msg):
+        ser1 = pd.Series([1, 2, 3], index=["a", "b", "c"])
+        ser2 = pd.Series([1, 2, 3], index=["a", "b", "d"])
+        ser1.compare(ser2)
+
+    # test Series with different lengths
+    msg = "Can only compare identically-labeled Series objects"
+    with pytest.raises(ValueError, match=msg):
+        ser1 = pd.Series([1, 2, 3])
+        ser2 = pd.Series([1, 2, 3, 4])
+        ser1.compare(ser2)
+
+
+def test_compare_datetime64_and_string():
+    # Issue https://github.com/pandas-dev/pandas/issues/45506
+    # Catch OverflowError when comparing datetime64 and string
+    data = [
+        {"a": "2015-07-01", "b": "08335394550"},
+        {"a": "2015-07-02", "b": "+49 (0) 0345 300033"},
+        {"a": "2015-07-03", "b": "+49(0)2598 04457"},
+        {"a": "2015-07-04", "b": "0741470003"},
+        {"a": "2015-07-05", "b": "04181 83668"},
+    ]
+    dtypes = {"a": "datetime64[ns]", "b": "string"}
+    df = pd.DataFrame(data=data).astype(dtypes)
+
+    result_eq1 = df["a"].eq(df["b"])
+    result_eq2 = df["a"] == df["b"]
+    result_neq = df["a"] != df["b"]
+
+    expected_eq = pd.Series([False] * 5)  # For .eq and ==
+    expected_neq = pd.Series([True] * 5)  # For !=
+
+    tm.assert_series_equal(result_eq1, expected_eq)
+    tm.assert_series_equal(result_eq2, expected_eq)
+    tm.assert_series_equal(result_neq, expected_neq)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_convert_dtypes.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_convert_dtypes.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0a920ba02cadeb21a7c0cc93f615cb1bc49dcbb
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_convert_dtypes.py
@@ -0,0 +1,306 @@
+from itertools import product
+
+import numpy as np
+import pytest
+
+from pandas._libs import lib
+
+import pandas as pd
+import pandas._testing as tm
+
+# Each test case consists of a tuple with the data and dtype to create the
+# test Series, the default dtype for the expected result (which is valid
+# for most cases), and the specific cases where the result deviates from
+# this default. Those overrides are defined as a dict with (keyword, val) as
+# dictionary key. In case of multiple items, the last override takes precedence.
+
+
+@pytest.fixture(
+    params=[
+        (
+            # data
+            [1, 2, 3],
+            # original dtype
+            np.dtype("int32"),
+            # default expected dtype
+            "Int32",
+            # exceptions on expected dtype
+            {("convert_integer", False): np.dtype("int32")},
+        ),
+        (
+            [1, 2, 3],
+            np.dtype("int64"),
+            "Int64",
+            {("convert_integer", False): np.dtype("int64")},
+        ),
+        (
+            ["x", "y", "z"],
+            np.dtype("O"),
+            pd.StringDtype(),
+            {("convert_string", False): np.dtype("O")},
+        ),
+        (
+            [True, False, np.nan],
+            np.dtype("O"),
+            pd.BooleanDtype(),
+            {("convert_boolean", False): np.dtype("O")},
+        ),
+        (
+            ["h", "i", np.nan],
+            np.dtype("O"),
+            pd.StringDtype(),
+            {("convert_string", False): np.dtype("O")},
+        ),
+        (  # GH32117
+            ["h", "i", 1],
+            np.dtype("O"),
+            np.dtype("O"),
+            {},
+        ),
+        (
+            [10, np.nan, 20],
+            np.dtype("float"),
+            "Int64",
+            {
+                ("convert_integer", False, "convert_floating", True): "Float64",
+                ("convert_integer", False, "convert_floating", False): np.dtype(
+                    "float"
+                ),
+            },
+        ),
+        (
+            [np.nan, 100.5, 200],
+            np.dtype("float"),
+            "Float64",
+            {("convert_floating", False): np.dtype("float")},
+        ),
+        (
+            [3, 4, 5],
+            "Int8",
+            "Int8",
+            {},
+        ),
+        (
+            [[1, 2], [3, 4], [5]],
+            None,
+            np.dtype("O"),
+            {},
+        ),
+        (
+            [4, 5, 6],
+            np.dtype("uint32"),
+            "UInt32",
+            {("convert_integer", False): np.dtype("uint32")},
+        ),
+        (
+            [-10, 12, 13],
+            np.dtype("i1"),
+            "Int8",
+            {("convert_integer", False): np.dtype("i1")},
+        ),
+        (
+            [1.2, 1.3],
+            np.dtype("float32"),
+            "Float32",
+            {("convert_floating", False): np.dtype("float32")},
+        ),
+        (
+            [1, 2.0],
+            object,
+            "Int64",
+            {
+                ("convert_integer", False): "Float64",
+                ("convert_integer", False, "convert_floating", False): np.dtype(
+                    "float"
+                ),
+                ("infer_objects", False): np.dtype("object"),
+            },
+        ),
+        (
+            [1, 2.5],
+            object,
+            "Float64",
+            {
+                ("convert_floating", False): np.dtype("float"),
+                ("infer_objects", False): np.dtype("object"),
+            },
+        ),
+        (["a", "b"], pd.CategoricalDtype(), pd.CategoricalDtype(), {}),
+        (
+            pd.to_datetime(["2020-01-14 10:00", "2020-01-15 11:11"]).as_unit("s"),
+            pd.DatetimeTZDtype(tz="UTC"),
+            pd.DatetimeTZDtype(tz="UTC"),
+            {},
+        ),
+        (
+            pd.to_datetime(["2020-01-14 10:00", "2020-01-15 11:11"]).as_unit("ms"),
+            pd.DatetimeTZDtype(tz="UTC"),
+            pd.DatetimeTZDtype(tz="UTC"),
+            {},
+        ),
+        (
+            pd.to_datetime(["2020-01-14 10:00", "2020-01-15 11:11"]).as_unit("us"),
+            pd.DatetimeTZDtype(tz="UTC"),
+            pd.DatetimeTZDtype(tz="UTC"),
+            {},
+        ),
+        (
+            pd.to_datetime(["2020-01-14 10:00", "2020-01-15 11:11"]).as_unit("ns"),
+            pd.DatetimeTZDtype(tz="UTC"),
+            pd.DatetimeTZDtype(tz="UTC"),
+            {},
+        ),
+        (
+            pd.to_datetime(["2020-01-14 10:00", "2020-01-15 11:11"]).as_unit("ns"),
+            "datetime64[ns]",
+            np.dtype("datetime64[ns]"),
+            {},
+        ),
+        (
+            pd.to_datetime(["2020-01-14 10:00", "2020-01-15 11:11"]).as_unit("ns"),
+            object,
+            np.dtype("datetime64[ns]"),
+            {("infer_objects", False): np.dtype("object")},
+        ),
+        (
+            pd.period_range("1/1/2011", freq="M", periods=3),
+            None,
+            pd.PeriodDtype("M"),
+            {},
+        ),
+        (
+            pd.arrays.IntervalArray([pd.Interval(0, 1), pd.Interval(1, 5)]),
+            None,
+            pd.IntervalDtype("int64", "right"),
+            {},
+        ),
+    ]
+)
+def test_cases(request):
+    return request.param
+
+
+class TestSeriesConvertDtypes:
+    @pytest.mark.parametrize("params", product(*[(True, False)] * 5))
+    def test_convert_dtypes(
+        self,
+        test_cases,
+        params,
+        using_infer_string,
+    ):
+        data, maindtype, expected_default, expected_other = test_cases
+        if (
+            hasattr(data, "dtype")
+            and lib.is_np_dtype(data.dtype, "M")
+            and isinstance(maindtype, pd.DatetimeTZDtype)
+        ):
+            # this astype is deprecated in favor of tz_localize
+            msg = "Cannot use .astype to convert from timezone-naive dtype"
+            with pytest.raises(TypeError, match=msg):
+                pd.Series(data, dtype=maindtype)
+            return
+
+        if maindtype is not None:
+            series = pd.Series(data, dtype=maindtype)
+        else:
+            series = pd.Series(data)
+
+        result = series.convert_dtypes(*params)
+
+        param_names = [
+            "infer_objects",
+            "convert_string",
+            "convert_integer",
+            "convert_boolean",
+            "convert_floating",
+        ]
+        params_dict = dict(zip(param_names, params))
+
+        expected_dtype = expected_default
+        for spec, dtype in expected_other.items():
+            if all(params_dict[key] is val for key, val in zip(spec[::2], spec[1::2])):
+                expected_dtype = dtype
+        if (
+            using_infer_string
+            and expected_default == "string"
+            and expected_dtype == object
+            and params[0]
+            and not params[1]
+        ):
+            # If we would convert with convert strings then infer_objects converts
+            # with the option
+            expected_dtype = "string[pyarrow_numpy]"
+
+        expected = pd.Series(data, dtype=expected_dtype)
+        tm.assert_series_equal(result, expected)
+
+        # Test that it is a copy
+        copy = series.copy(deep=True)
+
+        if result.notna().sum() > 0 and result.dtype in ["interval[int64, right]"]:
+            with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+                result[result.notna()] = np.nan
+        else:
+            result[result.notna()] = np.nan
+
+        # Make sure original not changed
+        tm.assert_series_equal(series, copy)
+
+    def test_convert_string_dtype(self, nullable_string_dtype):
+        # https://github.com/pandas-dev/pandas/issues/31731 -> converting columns
+        # that are already string dtype
+        df = pd.DataFrame(
+            {"A": ["a", "b", pd.NA], "B": ["ä", "ö", "ü"]}, dtype=nullable_string_dtype
+        )
+        result = df.convert_dtypes()
+        tm.assert_frame_equal(df, result)
+
+    def test_convert_bool_dtype(self):
+        # GH32287
+        df = pd.DataFrame({"A": pd.array([True])})
+        tm.assert_frame_equal(df, df.convert_dtypes())
+
+    def test_convert_byte_string_dtype(self):
+        # GH-43183
+        byte_str = b"binary-string"
+
+        df = pd.DataFrame(data={"A": byte_str}, index=[0])
+        result = df.convert_dtypes()
+        expected = df
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "infer_objects, dtype", [(True, "Int64"), (False, "object")]
+    )
+    def test_convert_dtype_object_with_na(self, infer_objects, dtype):
+        # GH#48791
+        ser = pd.Series([1, pd.NA])
+        result = ser.convert_dtypes(infer_objects=infer_objects)
+        expected = pd.Series([1, pd.NA], dtype=dtype)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "infer_objects, dtype", [(True, "Float64"), (False, "object")]
+    )
+    def test_convert_dtype_object_with_na_float(self, infer_objects, dtype):
+        # GH#48791
+        ser = pd.Series([1.5, pd.NA])
+        result = ser.convert_dtypes(infer_objects=infer_objects)
+        expected = pd.Series([1.5, pd.NA], dtype=dtype)
+        tm.assert_series_equal(result, expected)
+
+    def test_convert_dtypes_pyarrow_to_np_nullable(self):
+        # GH 53648
+        pytest.importorskip("pyarrow")
+        ser = pd.Series(range(2), dtype="int32[pyarrow]")
+        result = ser.convert_dtypes(dtype_backend="numpy_nullable")
+        expected = pd.Series(range(2), dtype="Int32")
+        tm.assert_series_equal(result, expected)
+
+    def test_convert_dtypes_pyarrow_null(self):
+        # GH#55346
+        pa = pytest.importorskip("pyarrow")
+        ser = pd.Series([None, None])
+        result = ser.convert_dtypes(dtype_backend="pyarrow")
+        expected = pd.Series([None, None], dtype=pd.ArrowDtype(pa.null()))
+        tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_copy.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_copy.py
new file mode 100644
index 0000000000000000000000000000000000000000..23dbe85075916dbb901afdcf8267c8877db3b3f8
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_copy.py
@@ -0,0 +1,91 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Series,
+    Timestamp,
+)
+import pandas._testing as tm
+
+
+class TestCopy:
+    @pytest.mark.parametrize("deep", ["default", None, False, True])
+    def test_copy(self, deep, using_copy_on_write, warn_copy_on_write):
+        ser = Series(np.arange(10), dtype="float64")
+
+        # default deep is True
+        if deep == "default":
+            ser2 = ser.copy()
+        else:
+            ser2 = ser.copy(deep=deep)
+
+        if using_copy_on_write:
+            # INFO(CoW) a shallow copy doesn't yet copy the data
+            # but parent will not be modified (CoW)
+            if deep is None or deep is False:
+                assert np.may_share_memory(ser.values, ser2.values)
+            else:
+                assert not np.may_share_memory(ser.values, ser2.values)
+
+        with tm.assert_cow_warning(warn_copy_on_write and deep is False):
+            ser2[::2] = np.nan
+
+        if deep is not False or using_copy_on_write:
+            # Did not modify original Series
+            assert np.isnan(ser2[0])
+            assert not np.isnan(ser[0])
+        else:
+            # we DID modify the original Series
+            assert np.isnan(ser2[0])
+            assert np.isnan(ser[0])
+
+    @pytest.mark.filterwarnings("ignore:Setting a value on a view:FutureWarning")
+    @pytest.mark.parametrize("deep", ["default", None, False, True])
+    def test_copy_tzaware(self, deep, using_copy_on_write):
+        # GH#11794
+        # copy of tz-aware
+        expected = Series([Timestamp("2012/01/01", tz="UTC")])
+        expected2 = Series([Timestamp("1999/01/01", tz="UTC")])
+
+        ser = Series([Timestamp("2012/01/01", tz="UTC")])
+
+        if deep == "default":
+            ser2 = ser.copy()
+        else:
+            ser2 = ser.copy(deep=deep)
+
+        if using_copy_on_write:
+            # INFO(CoW) a shallow copy doesn't yet copy the data
+            # but parent will not be modified (CoW)
+            if deep is None or deep is False:
+                assert np.may_share_memory(ser.values, ser2.values)
+            else:
+                assert not np.may_share_memory(ser.values, ser2.values)
+
+        ser2[0] = Timestamp("1999/01/01", tz="UTC")
+
+        # default deep is True
+        if deep is not False or using_copy_on_write:
+            # Did not modify original Series
+            tm.assert_series_equal(ser2, expected2)
+            tm.assert_series_equal(ser, expected)
+        else:
+            # we DID modify the original Series
+            tm.assert_series_equal(ser2, expected2)
+            tm.assert_series_equal(ser, expected2)
+
+    def test_copy_name(self, datetime_series):
+        result = datetime_series.copy()
+        assert result.name == datetime_series.name
+
+    def test_copy_index_name_checking(self, datetime_series):
+        # don't want to be able to modify the index stored elsewhere after
+        # making a copy
+
+        datetime_series.index.name = None
+        assert datetime_series.index.name is None
+        assert datetime_series is datetime_series
+
+        cp = datetime_series.copy()
+        cp.index.name = "foo"
+        assert datetime_series.index.name is None
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_count.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_count.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ba163f347198a5533c67fdeffeb4012a804066f
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_count.py
@@ -0,0 +1,34 @@
+import numpy as np
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestSeriesCount:
+    def test_count(self, datetime_series):
+        assert datetime_series.count() == len(datetime_series)
+
+        datetime_series[::2] = np.nan
+
+        assert datetime_series.count() == np.isfinite(datetime_series).sum()
+
+    def test_count_inf_as_na(self):
+        # GH#29478
+        ser = Series([pd.Timestamp("1990/1/1")])
+        msg = "use_inf_as_na option is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            with pd.option_context("use_inf_as_na", True):
+                assert ser.count() == 1
+
+    def test_count_categorical(self):
+        ser = Series(
+            Categorical(
+                [np.nan, 1, 2, np.nan], categories=[5, 4, 3, 2, 1], ordered=True
+            )
+        )
+        result = ser.count()
+        assert result == 2
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_describe.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_describe.py
new file mode 100644
index 0000000000000000000000000000000000000000..79ec11feb530817e735cf1d45cd7985839cd4d05
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_describe.py
@@ -0,0 +1,203 @@
+import numpy as np
+import pytest
+
+from pandas.compat.numpy import np_version_gte1p25
+
+from pandas.core.dtypes.common import (
+    is_complex_dtype,
+    is_extension_array_dtype,
+)
+
+from pandas import (
+    NA,
+    Period,
+    Series,
+    Timedelta,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestSeriesDescribe:
+    def test_describe_ints(self):
+        ser = Series([0, 1, 2, 3, 4], name="int_data")
+        result = ser.describe()
+        expected = Series(
+            [5, 2, ser.std(), 0, 1, 2, 3, 4],
+            name="int_data",
+            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_describe_bools(self):
+        ser = Series([True, True, False, False, False], name="bool_data")
+        result = ser.describe()
+        expected = Series(
+            [5, 2, False, 3], name="bool_data", index=["count", "unique", "top", "freq"]
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_describe_strs(self):
+        ser = Series(["a", "a", "b", "c", "d"], name="str_data")
+        result = ser.describe()
+        expected = Series(
+            [5, 4, "a", 2], name="str_data", index=["count", "unique", "top", "freq"]
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_describe_timedelta64(self):
+        ser = Series(
+            [
+                Timedelta("1 days"),
+                Timedelta("2 days"),
+                Timedelta("3 days"),
+                Timedelta("4 days"),
+                Timedelta("5 days"),
+            ],
+            name="timedelta_data",
+        )
+        result = ser.describe()
+        expected = Series(
+            [5, ser[2], ser.std(), ser[0], ser[1], ser[2], ser[3], ser[4]],
+            name="timedelta_data",
+            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_describe_period(self):
+        ser = Series(
+            [Period("2020-01", "M"), Period("2020-01", "M"), Period("2019-12", "M")],
+            name="period_data",
+        )
+        result = ser.describe()
+        expected = Series(
+            [3, 2, ser[0], 2],
+            name="period_data",
+            index=["count", "unique", "top", "freq"],
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_describe_empty_object(self):
+        # https://github.com/pandas-dev/pandas/issues/27183
+        s = Series([None, None], dtype=object)
+        result = s.describe()
+        expected = Series(
+            [0, 0, np.nan, np.nan],
+            dtype=object,
+            index=["count", "unique", "top", "freq"],
+        )
+        tm.assert_series_equal(result, expected)
+
+        result = s[:0].describe()
+        tm.assert_series_equal(result, expected)
+        # ensure NaN, not None
+        assert np.isnan(result.iloc[2])
+        assert np.isnan(result.iloc[3])
+
+    def test_describe_with_tz(self, tz_naive_fixture):
+        # GH 21332
+        tz = tz_naive_fixture
+        name = str(tz_naive_fixture)
+        start = Timestamp(2018, 1, 1)
+        end = Timestamp(2018, 1, 5)
+        s = Series(date_range(start, end, tz=tz), name=name)
+        result = s.describe()
+        expected = Series(
+            [
+                5,
+                Timestamp(2018, 1, 3).tz_localize(tz),
+                start.tz_localize(tz),
+                s[1],
+                s[2],
+                s[3],
+                end.tz_localize(tz),
+            ],
+            name=name,
+            index=["count", "mean", "min", "25%", "50%", "75%", "max"],
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_describe_with_tz_numeric(self):
+        name = tz = "CET"
+        start = Timestamp(2018, 1, 1)
+        end = Timestamp(2018, 1, 5)
+        s = Series(date_range(start, end, tz=tz), name=name)
+
+        result = s.describe()
+
+        expected = Series(
+            [
+                5,
+                Timestamp("2018-01-03 00:00:00", tz=tz),
+                Timestamp("2018-01-01 00:00:00", tz=tz),
+                Timestamp("2018-01-02 00:00:00", tz=tz),
+                Timestamp("2018-01-03 00:00:00", tz=tz),
+                Timestamp("2018-01-04 00:00:00", tz=tz),
+                Timestamp("2018-01-05 00:00:00", tz=tz),
+            ],
+            name=name,
+            index=["count", "mean", "min", "25%", "50%", "75%", "max"],
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_datetime_is_numeric_includes_datetime(self):
+        s = Series(date_range("2012", periods=3))
+        result = s.describe()
+        expected = Series(
+            [
+                3,
+                Timestamp("2012-01-02"),
+                Timestamp("2012-01-01"),
+                Timestamp("2012-01-01T12:00:00"),
+                Timestamp("2012-01-02"),
+                Timestamp("2012-01-02T12:00:00"),
+                Timestamp("2012-01-03"),
+            ],
+            index=["count", "mean", "min", "25%", "50%", "75%", "max"],
+        )
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.filterwarnings("ignore:Casting complex values to real discards")
+    def test_numeric_result_dtype(self, any_numeric_dtype):
+        # GH#48340 - describe should always return float on non-complex numeric input
+        if is_extension_array_dtype(any_numeric_dtype):
+            dtype = "Float64"
+        else:
+            dtype = "complex128" if is_complex_dtype(any_numeric_dtype) else None
+
+        ser = Series([0, 1], dtype=any_numeric_dtype)
+        if dtype == "complex128" and np_version_gte1p25:
+            with pytest.raises(
+                TypeError, match=r"^a must be an array of real numbers$"
+            ):
+                ser.describe()
+            return
+        result = ser.describe()
+        expected = Series(
+            [
+                2.0,
+                0.5,
+                ser.std(),
+                0,
+                0.25,
+                0.5,
+                0.75,
+                1.0,
+            ],
+            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
+            dtype=dtype,
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_describe_one_element_ea(self):
+        # GH#52515
+        ser = Series([0.0], dtype="Float64")
+        with tm.assert_produces_warning(None):
+            result = ser.describe()
+        expected = Series(
+            [1, 0, NA, 0, 0, 0, 0, 0],
+            dtype="Float64",
+            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
+        )
+        tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_diff.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_diff.py
new file mode 100644
index 0000000000000000000000000000000000000000..18de81a927c3a7697ee67290c8a6c73336de5643
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_diff.py
@@ -0,0 +1,88 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Series,
+    TimedeltaIndex,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestSeriesDiff:
+    def test_diff_np(self):
+        # TODO(__array_function__): could make np.diff return a Series
+        #  matching ser.diff()
+
+        ser = Series(np.arange(5))
+
+        res = np.diff(ser)
+        expected = np.array([1, 1, 1, 1])
+        tm.assert_numpy_array_equal(res, expected)
+
+    def test_diff_int(self):
+        # int dtype
+        a = 10000000000000000
+        b = a + 1
+        ser = Series([a, b])
+
+        result = ser.diff()
+        assert result[1] == 1
+
+    def test_diff_tz(self):
+        # Combined datetime diff, normal diff and boolean diff test
+        ts = Series(
+            np.arange(10, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            name="ts",
+        )
+        ts.diff()
+
+        # neg n
+        result = ts.diff(-1)
+        expected = ts - ts.shift(-1)
+        tm.assert_series_equal(result, expected)
+
+        # 0
+        result = ts.diff(0)
+        expected = ts - ts
+        tm.assert_series_equal(result, expected)
+
+    def test_diff_dt64(self):
+        # datetime diff (GH#3100)
+        ser = Series(date_range("20130102", periods=5))
+        result = ser.diff()
+        expected = ser - ser.shift(1)
+        tm.assert_series_equal(result, expected)
+
+        # timedelta diff
+        result = result - result.shift(1)  # previous result
+        expected = expected.diff()  # previously expected
+        tm.assert_series_equal(result, expected)
+
+    def test_diff_dt64tz(self):
+        # with tz
+        ser = Series(
+            date_range("2000-01-01 09:00:00", periods=5, tz="US/Eastern"), name="foo"
+        )
+        result = ser.diff()
+        expected = Series(TimedeltaIndex(["NaT"] + ["1 days"] * 4), name="foo")
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "input,output,diff",
+        [([False, True, True, False, False], [np.nan, True, False, True, False], 1)],
+    )
+    def test_diff_bool(self, input, output, diff):
+        # boolean series (test for fixing #17294)
+        ser = Series(input)
+        result = ser.diff()
+        expected = Series(output)
+        tm.assert_series_equal(result, expected)
+
+    def test_diff_object_dtype(self):
+        # object series
+        ser = Series([False, True, 5.0, np.nan, True, False])
+        result = ser.diff()
+        expected = ser - ser.shift(1)
+        tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_drop_duplicates.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_drop_duplicates.py
new file mode 100644
index 0000000000000000000000000000000000000000..10b2e98586365929e4ff05df0d93660d55cf8850
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_drop_duplicates.py
@@ -0,0 +1,267 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    Series,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize(
+    "keep, expected",
+    [
+        ("first", Series([False, False, False, False, True, True, False])),
+        ("last", Series([False, True, True, False, False, False, False])),
+        (False, Series([False, True, True, False, True, True, False])),
+    ],
+)
+def test_drop_duplicates(any_numpy_dtype, keep, expected):
+    tc = Series([1, 0, 3, 5, 3, 0, 4], dtype=np.dtype(any_numpy_dtype))
+
+    if tc.dtype == "bool":
+        pytest.skip("tested separately in test_drop_duplicates_bool")
+
+    tm.assert_series_equal(tc.duplicated(keep=keep), expected)
+    tm.assert_series_equal(tc.drop_duplicates(keep=keep), tc[~expected])
+    sc = tc.copy()
+    return_value = sc.drop_duplicates(keep=keep, inplace=True)
+    assert return_value is None
+    tm.assert_series_equal(sc, tc[~expected])
+
+
+@pytest.mark.parametrize(
+    "keep, expected",
+    [
+        ("first", Series([False, False, True, True])),
+        ("last", Series([True, True, False, False])),
+        (False, Series([True, True, True, True])),
+    ],
+)
+def test_drop_duplicates_bool(keep, expected):
+    tc = Series([True, False, True, False])
+
+    tm.assert_series_equal(tc.duplicated(keep=keep), expected)
+    tm.assert_series_equal(tc.drop_duplicates(keep=keep), tc[~expected])
+    sc = tc.copy()
+    return_value = sc.drop_duplicates(keep=keep, inplace=True)
+    tm.assert_series_equal(sc, tc[~expected])
+    assert return_value is None
+
+
+@pytest.mark.parametrize("values", [[], list(range(5))])
+def test_drop_duplicates_no_duplicates(any_numpy_dtype, keep, values):
+    tc = Series(values, dtype=np.dtype(any_numpy_dtype))
+    expected = Series([False] * len(tc), dtype="bool")
+
+    if tc.dtype == "bool":
+        # 0 -> False and 1-> True
+        # any other value would be duplicated
+        tc = tc[:2]
+        expected = expected[:2]
+
+    tm.assert_series_equal(tc.duplicated(keep=keep), expected)
+
+    result_dropped = tc.drop_duplicates(keep=keep)
+    tm.assert_series_equal(result_dropped, tc)
+
+    # validate shallow copy
+    assert result_dropped is not tc
+
+
+class TestSeriesDropDuplicates:
+    @pytest.fixture(
+        params=["int_", "uint", "float64", "str_", "timedelta64[h]", "datetime64[D]"]
+    )
+    def dtype(self, request):
+        return request.param
+
+    @pytest.fixture
+    def cat_series_unused_category(self, dtype, ordered):
+        # Test case 1
+        cat_array = np.array([1, 2, 3, 4, 5], dtype=np.dtype(dtype))
+
+        input1 = np.array([1, 2, 3, 3], dtype=np.dtype(dtype))
+        cat = Categorical(input1, categories=cat_array, ordered=ordered)
+        tc1 = Series(cat)
+        return tc1
+
+    def test_drop_duplicates_categorical_non_bool(self, cat_series_unused_category):
+        tc1 = cat_series_unused_category
+
+        expected = Series([False, False, False, True])
+
+        result = tc1.duplicated()
+        tm.assert_series_equal(result, expected)
+
+        result = tc1.drop_duplicates()
+        tm.assert_series_equal(result, tc1[~expected])
+
+        sc = tc1.copy()
+        return_value = sc.drop_duplicates(inplace=True)
+        assert return_value is None
+        tm.assert_series_equal(sc, tc1[~expected])
+
+    def test_drop_duplicates_categorical_non_bool_keeplast(
+        self, cat_series_unused_category
+    ):
+        tc1 = cat_series_unused_category
+
+        expected = Series([False, False, True, False])
+
+        result = tc1.duplicated(keep="last")
+        tm.assert_series_equal(result, expected)
+
+        result = tc1.drop_duplicates(keep="last")
+        tm.assert_series_equal(result, tc1[~expected])
+
+        sc = tc1.copy()
+        return_value = sc.drop_duplicates(keep="last", inplace=True)
+        assert return_value is None
+        tm.assert_series_equal(sc, tc1[~expected])
+
+    def test_drop_duplicates_categorical_non_bool_keepfalse(
+        self, cat_series_unused_category
+    ):
+        tc1 = cat_series_unused_category
+
+        expected = Series([False, False, True, True])
+
+        result = tc1.duplicated(keep=False)
+        tm.assert_series_equal(result, expected)
+
+        result = tc1.drop_duplicates(keep=False)
+        tm.assert_series_equal(result, tc1[~expected])
+
+        sc = tc1.copy()
+        return_value = sc.drop_duplicates(keep=False, inplace=True)
+        assert return_value is None
+        tm.assert_series_equal(sc, tc1[~expected])
+
+    @pytest.fixture
+    def cat_series(self, dtype, ordered):
+        # no unused categories, unlike cat_series_unused_category
+        cat_array = np.array([1, 2, 3, 4, 5], dtype=np.dtype(dtype))
+
+        input2 = np.array([1, 2, 3, 5, 3, 2, 4], dtype=np.dtype(dtype))
+        cat = Categorical(input2, categories=cat_array, ordered=ordered)
+        tc2 = Series(cat)
+        return tc2
+
+    def test_drop_duplicates_categorical_non_bool2(self, cat_series):
+        tc2 = cat_series
+
+        expected = Series([False, False, False, False, True, True, False])
+
+        result = tc2.duplicated()
+        tm.assert_series_equal(result, expected)
+
+        result = tc2.drop_duplicates()
+        tm.assert_series_equal(result, tc2[~expected])
+
+        sc = tc2.copy()
+        return_value = sc.drop_duplicates(inplace=True)
+        assert return_value is None
+        tm.assert_series_equal(sc, tc2[~expected])
+
+    def test_drop_duplicates_categorical_non_bool2_keeplast(self, cat_series):
+        tc2 = cat_series
+
+        expected = Series([False, True, True, False, False, False, False])
+
+        result = tc2.duplicated(keep="last")
+        tm.assert_series_equal(result, expected)
+
+        result = tc2.drop_duplicates(keep="last")
+        tm.assert_series_equal(result, tc2[~expected])
+
+        sc = tc2.copy()
+        return_value = sc.drop_duplicates(keep="last", inplace=True)
+        assert return_value is None
+        tm.assert_series_equal(sc, tc2[~expected])
+
+    def test_drop_duplicates_categorical_non_bool2_keepfalse(self, cat_series):
+        tc2 = cat_series
+
+        expected = Series([False, True, True, False, True, True, False])
+
+        result = tc2.duplicated(keep=False)
+        tm.assert_series_equal(result, expected)
+
+        result = tc2.drop_duplicates(keep=False)
+        tm.assert_series_equal(result, tc2[~expected])
+
+        sc = tc2.copy()
+        return_value = sc.drop_duplicates(keep=False, inplace=True)
+        assert return_value is None
+        tm.assert_series_equal(sc, tc2[~expected])
+
+    def test_drop_duplicates_categorical_bool(self, ordered):
+        tc = Series(
+            Categorical(
+                [True, False, True, False], categories=[True, False], ordered=ordered
+            )
+        )
+
+        expected = Series([False, False, True, True])
+        tm.assert_series_equal(tc.duplicated(), expected)
+        tm.assert_series_equal(tc.drop_duplicates(), tc[~expected])
+        sc = tc.copy()
+        return_value = sc.drop_duplicates(inplace=True)
+        assert return_value is None
+        tm.assert_series_equal(sc, tc[~expected])
+
+        expected = Series([True, True, False, False])
+        tm.assert_series_equal(tc.duplicated(keep="last"), expected)
+        tm.assert_series_equal(tc.drop_duplicates(keep="last"), tc[~expected])
+        sc = tc.copy()
+        return_value = sc.drop_duplicates(keep="last", inplace=True)
+        assert return_value is None
+        tm.assert_series_equal(sc, tc[~expected])
+
+        expected = Series([True, True, True, True])
+        tm.assert_series_equal(tc.duplicated(keep=False), expected)
+        tm.assert_series_equal(tc.drop_duplicates(keep=False), tc[~expected])
+        sc = tc.copy()
+        return_value = sc.drop_duplicates(keep=False, inplace=True)
+        assert return_value is None
+        tm.assert_series_equal(sc, tc[~expected])
+
+    def test_drop_duplicates_categorical_bool_na(self, nulls_fixture):
+        # GH#44351
+        ser = Series(
+            Categorical(
+                [True, False, True, False, nulls_fixture],
+                categories=[True, False],
+                ordered=True,
+            )
+        )
+        result = ser.drop_duplicates()
+        expected = Series(
+            Categorical([True, False, np.nan], categories=[True, False], ordered=True),
+            index=[0, 1, 4],
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_drop_duplicates_ignore_index(self):
+        # GH#48304
+        ser = Series([1, 2, 2, 3])
+        result = ser.drop_duplicates(ignore_index=True)
+        expected = Series([1, 2, 3])
+        tm.assert_series_equal(result, expected)
+
+    def test_duplicated_arrow_dtype(self):
+        pytest.importorskip("pyarrow")
+        ser = Series([True, False, None, False], dtype="bool[pyarrow]")
+        result = ser.drop_duplicates()
+        expected = Series([True, False, None], dtype="bool[pyarrow]")
+        tm.assert_series_equal(result, expected)
+
+    def test_drop_duplicates_arrow_strings(self):
+        # GH#54904
+        pa = pytest.importorskip("pyarrow")
+        ser = Series(["a", "a"], dtype=pd.ArrowDtype(pa.string()))
+        result = ser.drop_duplicates()
+        expecetd = Series(["a"], dtype=pd.ArrowDtype(pa.string()))
+        tm.assert_series_equal(result, expecetd)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_dropna.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_dropna.py
new file mode 100644
index 0000000000000000000000000000000000000000..d03fcac24003e99b724a2e7ac43b66d3d9b51bcf
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_dropna.py
@@ -0,0 +1,117 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DatetimeIndex,
+    IntervalIndex,
+    NaT,
+    Period,
+    Series,
+    Timestamp,
+)
+import pandas._testing as tm
+
+
+class TestDropna:
+    def test_dropna_empty(self):
+        ser = Series([], dtype=object)
+
+        assert len(ser.dropna()) == 0
+        return_value = ser.dropna(inplace=True)
+        assert return_value is None
+        assert len(ser) == 0
+
+        # invalid axis
+        msg = "No axis named 1 for object type Series"
+        with pytest.raises(ValueError, match=msg):
+            ser.dropna(axis=1)
+
+    def test_dropna_preserve_name(self, datetime_series):
+        datetime_series[:5] = np.nan
+        result = datetime_series.dropna()
+        assert result.name == datetime_series.name
+        name = datetime_series.name
+        ts = datetime_series.copy()
+        return_value = ts.dropna(inplace=True)
+        assert return_value is None
+        assert ts.name == name
+
+    def test_dropna_no_nan(self):
+        for ser in [
+            Series([1, 2, 3], name="x"),
+            Series([False, True, False], name="x"),
+        ]:
+            result = ser.dropna()
+            tm.assert_series_equal(result, ser)
+            assert result is not ser
+
+            s2 = ser.copy()
+            return_value = s2.dropna(inplace=True)
+            assert return_value is None
+            tm.assert_series_equal(s2, ser)
+
+    def test_dropna_intervals(self):
+        ser = Series(
+            [np.nan, 1, 2, 3],
+            IntervalIndex.from_arrays([np.nan, 0, 1, 2], [np.nan, 1, 2, 3]),
+        )
+
+        result = ser.dropna()
+        expected = ser.iloc[1:]
+        tm.assert_series_equal(result, expected)
+
+    def test_dropna_period_dtype(self):
+        # GH#13737
+        ser = Series([Period("2011-01", freq="M"), Period("NaT", freq="M")])
+        result = ser.dropna()
+        expected = Series([Period("2011-01", freq="M")])
+
+        tm.assert_series_equal(result, expected)
+
+    def test_datetime64_tz_dropna(self, unit):
+        # DatetimeLikeBlock
+        ser = Series(
+            [
+                Timestamp("2011-01-01 10:00"),
+                NaT,
+                Timestamp("2011-01-03 10:00"),
+                NaT,
+            ],
+            dtype=f"M8[{unit}]",
+        )
+        result = ser.dropna()
+        expected = Series(
+            [Timestamp("2011-01-01 10:00"), Timestamp("2011-01-03 10:00")],
+            index=[0, 2],
+            dtype=f"M8[{unit}]",
+        )
+        tm.assert_series_equal(result, expected)
+
+        # DatetimeTZBlock
+        idx = DatetimeIndex(
+            ["2011-01-01 10:00", NaT, "2011-01-03 10:00", NaT], tz="Asia/Tokyo"
+        ).as_unit(unit)
+        ser = Series(idx)
+        assert ser.dtype == f"datetime64[{unit}, Asia/Tokyo]"
+        result = ser.dropna()
+        expected = Series(
+            [
+                Timestamp("2011-01-01 10:00", tz="Asia/Tokyo"),
+                Timestamp("2011-01-03 10:00", tz="Asia/Tokyo"),
+            ],
+            index=[0, 2],
+            dtype=f"datetime64[{unit}, Asia/Tokyo]",
+        )
+        assert result.dtype == f"datetime64[{unit}, Asia/Tokyo]"
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("val", [1, 1.5])
+    def test_dropna_ignore_index(self, val):
+        # GH#31725
+        ser = Series([1, 2, val], index=[3, 2, 1])
+        result = ser.dropna(ignore_index=True)
+        expected = Series([1, 2, val])
+        tm.assert_series_equal(result, expected)
+
+        ser.dropna(ignore_index=True, inplace=True)
+        tm.assert_series_equal(ser, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_dtypes.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_dtypes.py
new file mode 100644
index 0000000000000000000000000000000000000000..82260bc2a65b9f7e387532b380b9ce013d29cbb7
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_dtypes.py
@@ -0,0 +1,7 @@
+import numpy as np
+
+
+class TestSeriesDtypes:
+    def test_dtype(self, datetime_series):
+        assert datetime_series.dtype == np.dtype("float64")
+        assert datetime_series.dtypes == np.dtype("float64")
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_explode.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_explode.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a0188585ef30c12f7222a054714957df4ed2ff2
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_explode.py
@@ -0,0 +1,175 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+def test_basic():
+    s = pd.Series([[0, 1, 2], np.nan, [], (3, 4)], index=list("abcd"), name="foo")
+    result = s.explode()
+    expected = pd.Series(
+        [0, 1, 2, np.nan, np.nan, 3, 4], index=list("aaabcdd"), dtype=object, name="foo"
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_mixed_type():
+    s = pd.Series(
+        [[0, 1, 2], np.nan, None, np.array([]), pd.Series(["a", "b"])], name="foo"
+    )
+    result = s.explode()
+    expected = pd.Series(
+        [0, 1, 2, np.nan, None, np.nan, "a", "b"],
+        index=[0, 0, 0, 1, 2, 3, 4, 4],
+        dtype=object,
+        name="foo",
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_empty():
+    s = pd.Series(dtype=object)
+    result = s.explode()
+    expected = s.copy()
+    tm.assert_series_equal(result, expected)
+
+
+def test_nested_lists():
+    s = pd.Series([[[1, 2, 3]], [1, 2], 1])
+    result = s.explode()
+    expected = pd.Series([[1, 2, 3], 1, 2, 1], index=[0, 1, 1, 2])
+    tm.assert_series_equal(result, expected)
+
+
+def test_multi_index():
+    s = pd.Series(
+        [[0, 1, 2], np.nan, [], (3, 4)],
+        name="foo",
+        index=pd.MultiIndex.from_product([list("ab"), range(2)], names=["foo", "bar"]),
+    )
+    result = s.explode()
+    index = pd.MultiIndex.from_tuples(
+        [("a", 0), ("a", 0), ("a", 0), ("a", 1), ("b", 0), ("b", 1), ("b", 1)],
+        names=["foo", "bar"],
+    )
+    expected = pd.Series(
+        [0, 1, 2, np.nan, np.nan, 3, 4], index=index, dtype=object, name="foo"
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_large():
+    s = pd.Series([range(256)]).explode()
+    result = s.explode()
+    tm.assert_series_equal(result, s)
+
+
+def test_invert_array():
+    df = pd.DataFrame({"a": pd.date_range("20190101", periods=3, tz="UTC")})
+
+    listify = df.apply(lambda x: x.array, axis=1)
+    result = listify.explode()
+    tm.assert_series_equal(result, df["a"].rename())
+
+
+@pytest.mark.parametrize(
+    "s", [pd.Series([1, 2, 3]), pd.Series(pd.date_range("2019", periods=3, tz="UTC"))]
+)
+def test_non_object_dtype(s):
+    result = s.explode()
+    tm.assert_series_equal(result, s)
+
+
+def test_typical_usecase():
+    df = pd.DataFrame(
+        [{"var1": "a,b,c", "var2": 1}, {"var1": "d,e,f", "var2": 2}],
+        columns=["var1", "var2"],
+    )
+    exploded = df.var1.str.split(",").explode()
+    result = df[["var2"]].join(exploded)
+    expected = pd.DataFrame(
+        {"var2": [1, 1, 1, 2, 2, 2], "var1": list("abcdef")},
+        columns=["var2", "var1"],
+        index=[0, 0, 0, 1, 1, 1],
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_nested_EA():
+    # a nested EA array
+    s = pd.Series(
+        [
+            pd.date_range("20170101", periods=3, tz="UTC"),
+            pd.date_range("20170104", periods=3, tz="UTC"),
+        ]
+    )
+    result = s.explode()
+    expected = pd.Series(
+        pd.date_range("20170101", periods=6, tz="UTC"), index=[0, 0, 0, 1, 1, 1]
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_duplicate_index():
+    # GH 28005
+    s = pd.Series([[1, 2], [3, 4]], index=[0, 0])
+    result = s.explode()
+    expected = pd.Series([1, 2, 3, 4], index=[0, 0, 0, 0], dtype=object)
+    tm.assert_series_equal(result, expected)
+
+
+def test_ignore_index():
+    # GH 34932
+    s = pd.Series([[1, 2], [3, 4]])
+    result = s.explode(ignore_index=True)
+    expected = pd.Series([1, 2, 3, 4], index=[0, 1, 2, 3], dtype=object)
+    tm.assert_series_equal(result, expected)
+
+
+def test_explode_sets():
+    # https://github.com/pandas-dev/pandas/issues/35614
+    s = pd.Series([{"a", "b", "c"}], index=[1])
+    result = s.explode().sort_values()
+    expected = pd.Series(["a", "b", "c"], index=[1, 1, 1])
+    tm.assert_series_equal(result, expected)
+
+
+def test_explode_scalars_can_ignore_index():
+    # https://github.com/pandas-dev/pandas/issues/40487
+    s = pd.Series([1, 2, 3], index=["a", "b", "c"])
+    result = s.explode(ignore_index=True)
+    expected = pd.Series([1, 2, 3])
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("ignore_index", [True, False])
+def test_explode_pyarrow_list_type(ignore_index):
+    # GH 53602
+    pa = pytest.importorskip("pyarrow")
+
+    data = [
+        [None, None],
+        [1],
+        [],
+        [2, 3],
+        None,
+    ]
+    ser = pd.Series(data, dtype=pd.ArrowDtype(pa.list_(pa.int64())))
+    result = ser.explode(ignore_index=ignore_index)
+    expected = pd.Series(
+        data=[None, None, 1, None, 2, 3, None],
+        index=None if ignore_index else [0, 0, 1, 2, 3, 3, 4],
+        dtype=pd.ArrowDtype(pa.int64()),
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("ignore_index", [True, False])
+def test_explode_pyarrow_non_list_type(ignore_index):
+    pa = pytest.importorskip("pyarrow")
+    data = [1, 2, 3]
+    ser = pd.Series(data, dtype=pd.ArrowDtype(pa.int64()))
+    result = ser.explode(ignore_index=ignore_index)
+    expected = pd.Series([1, 2, 3], dtype="int64[pyarrow]", index=[0, 1, 2])
+    tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_fillna.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_fillna.py
new file mode 100644
index 0000000000000000000000000000000000000000..293259661cd9a107eb4ad8e33c0b73dfb4010a14
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_fillna.py
@@ -0,0 +1,1155 @@
+from datetime import (
+    datetime,
+    timedelta,
+    timezone,
+)
+
+import numpy as np
+import pytest
+import pytz
+
+from pandas import (
+    Categorical,
+    DataFrame,
+    DatetimeIndex,
+    NaT,
+    Period,
+    Series,
+    Timedelta,
+    Timestamp,
+    date_range,
+    isna,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core.arrays import period_array
+
+
+@pytest.mark.filterwarnings(
+    "ignore:(Series|DataFrame).fillna with 'method' is deprecated:FutureWarning"
+)
+class TestSeriesFillNA:
+    def test_fillna_nat(self):
+        series = Series([0, 1, 2, NaT._value], dtype="M8[ns]")
+
+        filled = series.fillna(method="pad")
+        filled2 = series.fillna(value=series.values[2])
+
+        expected = series.copy()
+        expected.iloc[3] = expected.iloc[2]
+
+        tm.assert_series_equal(filled, expected)
+        tm.assert_series_equal(filled2, expected)
+
+        df = DataFrame({"A": series})
+        filled = df.fillna(method="pad")
+        filled2 = df.fillna(value=series.values[2])
+        expected = DataFrame({"A": expected})
+        tm.assert_frame_equal(filled, expected)
+        tm.assert_frame_equal(filled2, expected)
+
+        series = Series([NaT._value, 0, 1, 2], dtype="M8[ns]")
+
+        filled = series.fillna(method="bfill")
+        filled2 = series.fillna(value=series[1])
+
+        expected = series.copy()
+        expected[0] = expected[1]
+
+        tm.assert_series_equal(filled, expected)
+        tm.assert_series_equal(filled2, expected)
+
+        df = DataFrame({"A": series})
+        filled = df.fillna(method="bfill")
+        filled2 = df.fillna(value=series[1])
+        expected = DataFrame({"A": expected})
+        tm.assert_frame_equal(filled, expected)
+        tm.assert_frame_equal(filled2, expected)
+
+    def test_fillna_value_or_method(self, datetime_series):
+        msg = "Cannot specify both 'value' and 'method'"
+        with pytest.raises(ValueError, match=msg):
+            datetime_series.fillna(value=0, method="ffill")
+
+    def test_fillna(self):
+        ts = Series(
+            [0.0, 1.0, 2.0, 3.0, 4.0], index=date_range("2020-01-01", periods=5)
+        )
+
+        tm.assert_series_equal(ts, ts.fillna(method="ffill"))
+
+        ts.iloc[2] = np.nan
+
+        exp = Series([0.0, 1.0, 1.0, 3.0, 4.0], index=ts.index)
+        tm.assert_series_equal(ts.fillna(method="ffill"), exp)
+
+        exp = Series([0.0, 1.0, 3.0, 3.0, 4.0], index=ts.index)
+        tm.assert_series_equal(ts.fillna(method="backfill"), exp)
+
+        exp = Series([0.0, 1.0, 5.0, 3.0, 4.0], index=ts.index)
+        tm.assert_series_equal(ts.fillna(value=5), exp)
+
+        msg = "Must specify a fill 'value' or 'method'"
+        with pytest.raises(ValueError, match=msg):
+            ts.fillna()
+
+    def test_fillna_nonscalar(self):
+        # GH#5703
+        s1 = Series([np.nan])
+        s2 = Series([1])
+        result = s1.fillna(s2)
+        expected = Series([1.0])
+        tm.assert_series_equal(result, expected)
+        result = s1.fillna({})
+        tm.assert_series_equal(result, s1)
+        result = s1.fillna(Series((), dtype=object))
+        tm.assert_series_equal(result, s1)
+        result = s2.fillna(s1)
+        tm.assert_series_equal(result, s2)
+        result = s1.fillna({0: 1})
+        tm.assert_series_equal(result, expected)
+        result = s1.fillna({1: 1})
+        tm.assert_series_equal(result, Series([np.nan]))
+        result = s1.fillna({0: 1, 1: 1})
+        tm.assert_series_equal(result, expected)
+        result = s1.fillna(Series({0: 1, 1: 1}))
+        tm.assert_series_equal(result, expected)
+        result = s1.fillna(Series({0: 1, 1: 1}, index=[4, 5]))
+        tm.assert_series_equal(result, s1)
+
+    def test_fillna_aligns(self):
+        s1 = Series([0, 1, 2], list("abc"))
+        s2 = Series([0, np.nan, 2], list("bac"))
+        result = s2.fillna(s1)
+        expected = Series([0, 0, 2.0], list("bac"))
+        tm.assert_series_equal(result, expected)
+
+    def test_fillna_limit(self):
+        ser = Series(np.nan, index=[0, 1, 2])
+        result = ser.fillna(999, limit=1)
+        expected = Series([999, np.nan, np.nan], index=[0, 1, 2])
+        tm.assert_series_equal(result, expected)
+
+        result = ser.fillna(999, limit=2)
+        expected = Series([999, 999, np.nan], index=[0, 1, 2])
+        tm.assert_series_equal(result, expected)
+
+    def test_fillna_dont_cast_strings(self):
+        # GH#9043
+        # make sure a string representation of int/float values can be filled
+        # correctly without raising errors or being converted
+        vals = ["0", "1.5", "-0.3"]
+        for val in vals:
+            ser = Series([0, 1, np.nan, np.nan, 4], dtype="float64")
+            result = ser.fillna(val)
+            expected = Series([0, 1, val, val, 4], dtype="object")
+            tm.assert_series_equal(result, expected)
+
+    def test_fillna_consistency(self):
+        # GH#16402
+        # fillna with a tz aware to a tz-naive, should result in object
+
+        ser = Series([Timestamp("20130101"), NaT])
+
+        result = ser.fillna(Timestamp("20130101", tz="US/Eastern"))
+        expected = Series(
+            [Timestamp("20130101"), Timestamp("2013-01-01", tz="US/Eastern")],
+            dtype="object",
+        )
+        tm.assert_series_equal(result, expected)
+
+        result = ser.where([True, False], Timestamp("20130101", tz="US/Eastern"))
+        tm.assert_series_equal(result, expected)
+
+        result = ser.where([True, False], Timestamp("20130101", tz="US/Eastern"))
+        tm.assert_series_equal(result, expected)
+
+        # with a non-datetime
+        result = ser.fillna("foo")
+        expected = Series([Timestamp("20130101"), "foo"])
+        tm.assert_series_equal(result, expected)
+
+        # assignment
+        ser2 = ser.copy()
+        with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+            ser2[1] = "foo"
+        tm.assert_series_equal(ser2, expected)
+
+    def test_fillna_downcast(self):
+        # GH#15277
+        # infer int64 from float64
+        ser = Series([1.0, np.nan])
+        msg = "The 'downcast' keyword in fillna is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = ser.fillna(0, downcast="infer")
+        expected = Series([1, 0])
+        tm.assert_series_equal(result, expected)
+
+        # infer int64 from float64 when fillna value is a dict
+        ser = Series([1.0, np.nan])
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = ser.fillna({1: 0}, downcast="infer")
+        expected = Series([1, 0])
+        tm.assert_series_equal(result, expected)
+
+    def test_fillna_downcast_infer_objects_to_numeric(self):
+        # GH#44241 if we have object-dtype, 'downcast="infer"' should
+        #  _actually_ infer
+
+        arr = np.arange(5).astype(object)
+        arr[3] = np.nan
+
+        ser = Series(arr)
+
+        msg = "The 'downcast' keyword in fillna is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            res = ser.fillna(3, downcast="infer")
+        expected = Series(np.arange(5), dtype=np.int64)
+        tm.assert_series_equal(res, expected)
+
+        msg = "The 'downcast' keyword in ffill is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            res = ser.ffill(downcast="infer")
+        expected = Series([0, 1, 2, 2, 4], dtype=np.int64)
+        tm.assert_series_equal(res, expected)
+
+        msg = "The 'downcast' keyword in bfill is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            res = ser.bfill(downcast="infer")
+        expected = Series([0, 1, 2, 4, 4], dtype=np.int64)
+        tm.assert_series_equal(res, expected)
+
+        # with a non-round float present, we will downcast to float64
+        ser[2] = 2.5
+
+        expected = Series([0, 1, 2.5, 3, 4], dtype=np.float64)
+        msg = "The 'downcast' keyword in fillna is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            res = ser.fillna(3, downcast="infer")
+        tm.assert_series_equal(res, expected)
+
+        msg = "The 'downcast' keyword in ffill is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            res = ser.ffill(downcast="infer")
+        expected = Series([0, 1, 2.5, 2.5, 4], dtype=np.float64)
+        tm.assert_series_equal(res, expected)
+
+        msg = "The 'downcast' keyword in bfill is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            res = ser.bfill(downcast="infer")
+        expected = Series([0, 1, 2.5, 4, 4], dtype=np.float64)
+        tm.assert_series_equal(res, expected)
+
+    def test_timedelta_fillna(self, frame_or_series, unit):
+        # GH#3371
+        ser = Series(
+            [
+                Timestamp("20130101"),
+                Timestamp("20130101"),
+                Timestamp("20130102"),
+                Timestamp("20130103 9:01:01"),
+            ],
+            dtype=f"M8[{unit}]",
+        )
+        td = ser.diff()
+        obj = frame_or_series(td).copy()
+
+        # reg fillna
+        result = obj.fillna(Timedelta(seconds=0))
+        expected = Series(
+            [
+                timedelta(0),
+                timedelta(0),
+                timedelta(1),
+                timedelta(days=1, seconds=9 * 3600 + 60 + 1),
+            ],
+            dtype=f"m8[{unit}]",
+        )
+        expected = frame_or_series(expected)
+        tm.assert_equal(result, expected)
+
+        # GH#45746 pre-1.? ints were interpreted as seconds.  then that was
+        #  deprecated and changed to raise. In 2.0 it casts to common dtype,
+        #  consistent with every other dtype's behavior
+        res = obj.fillna(1)
+        expected = obj.astype(object).fillna(1)
+        tm.assert_equal(res, expected)
+
+        result = obj.fillna(Timedelta(seconds=1))
+        expected = Series(
+            [
+                timedelta(seconds=1),
+                timedelta(0),
+                timedelta(1),
+                timedelta(days=1, seconds=9 * 3600 + 60 + 1),
+            ],
+            dtype=f"m8[{unit}]",
+        )
+        expected = frame_or_series(expected)
+        tm.assert_equal(result, expected)
+
+        result = obj.fillna(timedelta(days=1, seconds=1))
+        expected = Series(
+            [
+                timedelta(days=1, seconds=1),
+                timedelta(0),
+                timedelta(1),
+                timedelta(days=1, seconds=9 * 3600 + 60 + 1),
+            ],
+            dtype=f"m8[{unit}]",
+        )
+        expected = frame_or_series(expected)
+        tm.assert_equal(result, expected)
+
+        result = obj.fillna(np.timedelta64(10**9))
+        expected = Series(
+            [
+                timedelta(seconds=1),
+                timedelta(0),
+                timedelta(1),
+                timedelta(days=1, seconds=9 * 3600 + 60 + 1),
+            ],
+            dtype=f"m8[{unit}]",
+        )
+        expected = frame_or_series(expected)
+        tm.assert_equal(result, expected)
+
+        result = obj.fillna(NaT)
+        expected = Series(
+            [
+                NaT,
+                timedelta(0),
+                timedelta(1),
+                timedelta(days=1, seconds=9 * 3600 + 60 + 1),
+            ],
+            dtype=f"m8[{unit}]",
+        )
+        expected = frame_or_series(expected)
+        tm.assert_equal(result, expected)
+
+        # ffill
+        td[2] = np.nan
+        obj = frame_or_series(td).copy()
+        result = obj.ffill()
+        expected = td.fillna(Timedelta(seconds=0))
+        expected[0] = np.nan
+        expected = frame_or_series(expected)
+
+        tm.assert_equal(result, expected)
+
+        # bfill
+        td[2] = np.nan
+        obj = frame_or_series(td)
+        result = obj.bfill()
+        expected = td.fillna(Timedelta(seconds=0))
+        expected[2] = timedelta(days=1, seconds=9 * 3600 + 60 + 1)
+        expected = frame_or_series(expected)
+        tm.assert_equal(result, expected)
+
+    def test_datetime64_fillna(self):
+        ser = Series(
+            [
+                Timestamp("20130101"),
+                Timestamp("20130101"),
+                Timestamp("20130102"),
+                Timestamp("20130103 9:01:01"),
+            ]
+        )
+        ser[2] = np.nan
+
+        # ffill
+        result = ser.ffill()
+        expected = Series(
+            [
+                Timestamp("20130101"),
+                Timestamp("20130101"),
+                Timestamp("20130101"),
+                Timestamp("20130103 9:01:01"),
+            ]
+        )
+        tm.assert_series_equal(result, expected)
+
+        # bfill
+        result = ser.bfill()
+        expected = Series(
+            [
+                Timestamp("20130101"),
+                Timestamp("20130101"),
+                Timestamp("20130103 9:01:01"),
+                Timestamp("20130103 9:01:01"),
+            ]
+        )
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "scalar",
+        [
+            False,
+            pytest.param(
+                True,
+                marks=pytest.mark.xfail(
+                    reason="GH#56410 scalar case not yet addressed"
+                ),
+            ),
+        ],
+    )
+    @pytest.mark.parametrize("tz", [None, "UTC"])
+    def test_datetime64_fillna_mismatched_reso_no_rounding(self, tz, scalar):
+        # GH#56410
+        dti = date_range("2016-01-01", periods=3, unit="s", tz=tz)
+        item = Timestamp("2016-02-03 04:05:06.789", tz=tz)
+        vec = date_range(item, periods=3, unit="ms")
+
+        exp_dtype = "M8[ms]" if tz is None else "M8[ms, UTC]"
+        expected = Series([item, dti[1], dti[2]], dtype=exp_dtype)
+
+        ser = Series(dti)
+        ser[0] = NaT
+        ser2 = ser.copy()
+
+        res = ser.fillna(item)
+        res2 = ser2.fillna(Series(vec))
+
+        if scalar:
+            tm.assert_series_equal(res, expected)
+        else:
+            tm.assert_series_equal(res2, expected)
+
+    @pytest.mark.parametrize(
+        "scalar",
+        [
+            False,
+            pytest.param(
+                True,
+                marks=pytest.mark.xfail(
+                    reason="GH#56410 scalar case not yet addressed"
+                ),
+            ),
+        ],
+    )
+    def test_timedelta64_fillna_mismatched_reso_no_rounding(self, scalar):
+        # GH#56410
+        tdi = date_range("2016-01-01", periods=3, unit="s") - Timestamp("1970-01-01")
+        item = Timestamp("2016-02-03 04:05:06.789") - Timestamp("1970-01-01")
+        vec = timedelta_range(item, periods=3, unit="ms")
+
+        expected = Series([item, tdi[1], tdi[2]], dtype="m8[ms]")
+
+        ser = Series(tdi)
+        ser[0] = NaT
+        ser2 = ser.copy()
+
+        res = ser.fillna(item)
+        res2 = ser2.fillna(Series(vec))
+
+        if scalar:
+            tm.assert_series_equal(res, expected)
+        else:
+            tm.assert_series_equal(res2, expected)
+
+    def test_datetime64_fillna_backfill(self):
+        # GH#6587
+        # make sure that we are treating as integer when filling
+        ser = Series([NaT, NaT, "2013-08-05 15:30:00.000001"], dtype="M8[ns]")
+
+        expected = Series(
+            [
+                "2013-08-05 15:30:00.000001",
+                "2013-08-05 15:30:00.000001",
+                "2013-08-05 15:30:00.000001",
+            ],
+            dtype="M8[ns]",
+        )
+        result = ser.fillna(method="backfill")
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("tz", ["US/Eastern", "Asia/Tokyo"])
+    def test_datetime64_tz_fillna(self, tz, unit):
+        # DatetimeLikeBlock
+        ser = Series(
+            [
+                Timestamp("2011-01-01 10:00"),
+                NaT,
+                Timestamp("2011-01-03 10:00"),
+                NaT,
+            ],
+            dtype=f"M8[{unit}]",
+        )
+        null_loc = Series([False, True, False, True])
+
+        result = ser.fillna(Timestamp("2011-01-02 10:00"))
+        expected = Series(
+            [
+                Timestamp("2011-01-01 10:00"),
+                Timestamp("2011-01-02 10:00"),
+                Timestamp("2011-01-03 10:00"),
+                Timestamp("2011-01-02 10:00"),
+            ],
+            dtype=f"M8[{unit}]",
+        )
+        tm.assert_series_equal(expected, result)
+        # check s is not changed
+        tm.assert_series_equal(isna(ser), null_loc)
+
+        result = ser.fillna(Timestamp("2011-01-02 10:00", tz=tz))
+        expected = Series(
+            [
+                Timestamp("2011-01-01 10:00"),
+                Timestamp("2011-01-02 10:00", tz=tz),
+                Timestamp("2011-01-03 10:00"),
+                Timestamp("2011-01-02 10:00", tz=tz),
+            ]
+        )
+        tm.assert_series_equal(expected, result)
+        tm.assert_series_equal(isna(ser), null_loc)
+
+        result = ser.fillna("AAA")
+        expected = Series(
+            [
+                Timestamp("2011-01-01 10:00"),
+                "AAA",
+                Timestamp("2011-01-03 10:00"),
+                "AAA",
+            ],
+            dtype=object,
+        )
+        tm.assert_series_equal(expected, result)
+        tm.assert_series_equal(isna(ser), null_loc)
+
+        result = ser.fillna(
+            {
+                1: Timestamp("2011-01-02 10:00", tz=tz),
+                3: Timestamp("2011-01-04 10:00"),
+            }
+        )
+        expected = Series(
+            [
+                Timestamp("2011-01-01 10:00"),
+                Timestamp("2011-01-02 10:00", tz=tz),
+                Timestamp("2011-01-03 10:00"),
+                Timestamp("2011-01-04 10:00"),
+            ]
+        )
+        tm.assert_series_equal(expected, result)
+        tm.assert_series_equal(isna(ser), null_loc)
+
+        result = ser.fillna(
+            {1: Timestamp("2011-01-02 10:00"), 3: Timestamp("2011-01-04 10:00")}
+        )
+        expected = Series(
+            [
+                Timestamp("2011-01-01 10:00"),
+                Timestamp("2011-01-02 10:00"),
+                Timestamp("2011-01-03 10:00"),
+                Timestamp("2011-01-04 10:00"),
+            ],
+            dtype=f"M8[{unit}]",
+        )
+        tm.assert_series_equal(expected, result)
+        tm.assert_series_equal(isna(ser), null_loc)
+
+        # DatetimeTZBlock
+        idx = DatetimeIndex(
+            ["2011-01-01 10:00", NaT, "2011-01-03 10:00", NaT], tz=tz
+        ).as_unit(unit)
+        ser = Series(idx)
+        assert ser.dtype == f"datetime64[{unit}, {tz}]"
+        tm.assert_series_equal(isna(ser), null_loc)
+
+        result = ser.fillna(Timestamp("2011-01-02 10:00"))
+        expected = Series(
+            [
+                Timestamp("2011-01-01 10:00", tz=tz),
+                Timestamp("2011-01-02 10:00"),
+                Timestamp("2011-01-03 10:00", tz=tz),
+                Timestamp("2011-01-02 10:00"),
+            ]
+        )
+        tm.assert_series_equal(expected, result)
+        tm.assert_series_equal(isna(ser), null_loc)
+
+        result = ser.fillna(Timestamp("2011-01-02 10:00", tz=tz))
+        idx = DatetimeIndex(
+            [
+                "2011-01-01 10:00",
+                "2011-01-02 10:00",
+                "2011-01-03 10:00",
+                "2011-01-02 10:00",
+            ],
+            tz=tz,
+        ).as_unit(unit)
+        expected = Series(idx)
+        tm.assert_series_equal(expected, result)
+        tm.assert_series_equal(isna(ser), null_loc)
+
+        result = ser.fillna(Timestamp("2011-01-02 10:00", tz=tz).to_pydatetime())
+        idx = DatetimeIndex(
+            [
+                "2011-01-01 10:00",
+                "2011-01-02 10:00",
+                "2011-01-03 10:00",
+                "2011-01-02 10:00",
+            ],
+            tz=tz,
+        ).as_unit(unit)
+        expected = Series(idx)
+        tm.assert_series_equal(expected, result)
+        tm.assert_series_equal(isna(ser), null_loc)
+
+        result = ser.fillna("AAA")
+        expected = Series(
+            [
+                Timestamp("2011-01-01 10:00", tz=tz),
+                "AAA",
+                Timestamp("2011-01-03 10:00", tz=tz),
+                "AAA",
+            ],
+            dtype=object,
+        )
+        tm.assert_series_equal(expected, result)
+        tm.assert_series_equal(isna(ser), null_loc)
+
+        result = ser.fillna(
+            {
+                1: Timestamp("2011-01-02 10:00", tz=tz),
+                3: Timestamp("2011-01-04 10:00"),
+            }
+        )
+        expected = Series(
+            [
+                Timestamp("2011-01-01 10:00", tz=tz),
+                Timestamp("2011-01-02 10:00", tz=tz),
+                Timestamp("2011-01-03 10:00", tz=tz),
+                Timestamp("2011-01-04 10:00"),
+            ]
+        )
+        tm.assert_series_equal(expected, result)
+        tm.assert_series_equal(isna(ser), null_loc)
+
+        result = ser.fillna(
+            {
+                1: Timestamp("2011-01-02 10:00", tz=tz),
+                3: Timestamp("2011-01-04 10:00", tz=tz),
+            }
+        )
+        expected = Series(
+            [
+                Timestamp("2011-01-01 10:00", tz=tz),
+                Timestamp("2011-01-02 10:00", tz=tz),
+                Timestamp("2011-01-03 10:00", tz=tz),
+                Timestamp("2011-01-04 10:00", tz=tz),
+            ]
+        ).dt.as_unit(unit)
+        tm.assert_series_equal(expected, result)
+        tm.assert_series_equal(isna(ser), null_loc)
+
+        # filling with a naive/other zone, coerce to object
+        result = ser.fillna(Timestamp("20130101"))
+        expected = Series(
+            [
+                Timestamp("2011-01-01 10:00", tz=tz),
+                Timestamp("2013-01-01"),
+                Timestamp("2011-01-03 10:00", tz=tz),
+                Timestamp("2013-01-01"),
+            ]
+        )
+        tm.assert_series_equal(expected, result)
+        tm.assert_series_equal(isna(ser), null_loc)
+
+        # pre-2.0 fillna with mixed tzs would cast to object, in 2.0
+        #  it retains dtype.
+        result = ser.fillna(Timestamp("20130101", tz="US/Pacific"))
+        expected = Series(
+            [
+                Timestamp("2011-01-01 10:00", tz=tz),
+                Timestamp("2013-01-01", tz="US/Pacific").tz_convert(tz),
+                Timestamp("2011-01-03 10:00", tz=tz),
+                Timestamp("2013-01-01", tz="US/Pacific").tz_convert(tz),
+            ]
+        ).dt.as_unit(unit)
+        tm.assert_series_equal(expected, result)
+        tm.assert_series_equal(isna(ser), null_loc)
+
+    def test_fillna_dt64tz_with_method(self):
+        # with timezone
+        # GH#15855
+        ser = Series([Timestamp("2012-11-11 00:00:00+01:00"), NaT])
+        exp = Series(
+            [
+                Timestamp("2012-11-11 00:00:00+01:00"),
+                Timestamp("2012-11-11 00:00:00+01:00"),
+            ]
+        )
+        tm.assert_series_equal(ser.fillna(method="pad"), exp)
+
+        ser = Series([NaT, Timestamp("2012-11-11 00:00:00+01:00")])
+        exp = Series(
+            [
+                Timestamp("2012-11-11 00:00:00+01:00"),
+                Timestamp("2012-11-11 00:00:00+01:00"),
+            ]
+        )
+        tm.assert_series_equal(ser.fillna(method="bfill"), exp)
+
+    def test_fillna_pytimedelta(self):
+        # GH#8209
+        ser = Series([np.nan, Timedelta("1 days")], index=["A", "B"])
+
+        result = ser.fillna(timedelta(1))
+        expected = Series(Timedelta("1 days"), index=["A", "B"])
+        tm.assert_series_equal(result, expected)
+
+    def test_fillna_period(self):
+        # GH#13737
+        ser = Series([Period("2011-01", freq="M"), Period("NaT", freq="M")])
+
+        res = ser.fillna(Period("2012-01", freq="M"))
+        exp = Series([Period("2011-01", freq="M"), Period("2012-01", freq="M")])
+        tm.assert_series_equal(res, exp)
+        assert res.dtype == "Period[M]"
+
+    def test_fillna_dt64_timestamp(self, frame_or_series):
+        ser = Series(
+            [
+                Timestamp("20130101"),
+                Timestamp("20130101"),
+                Timestamp("20130102"),
+                Timestamp("20130103 9:01:01"),
+            ]
+        )
+        ser[2] = np.nan
+        obj = frame_or_series(ser)
+
+        # reg fillna
+        result = obj.fillna(Timestamp("20130104"))
+        expected = Series(
+            [
+                Timestamp("20130101"),
+                Timestamp("20130101"),
+                Timestamp("20130104"),
+                Timestamp("20130103 9:01:01"),
+            ]
+        )
+        expected = frame_or_series(expected)
+        tm.assert_equal(result, expected)
+
+        result = obj.fillna(NaT)
+        expected = obj
+        tm.assert_equal(result, expected)
+
+    def test_fillna_dt64_non_nao(self):
+        # GH#27419
+        ser = Series([Timestamp("2010-01-01"), NaT, Timestamp("2000-01-01")])
+        val = np.datetime64("1975-04-05", "ms")
+
+        result = ser.fillna(val)
+        expected = Series(
+            [Timestamp("2010-01-01"), Timestamp("1975-04-05"), Timestamp("2000-01-01")]
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_fillna_numeric_inplace(self):
+        x = Series([np.nan, 1.0, np.nan, 3.0, np.nan], ["z", "a", "b", "c", "d"])
+        y = x.copy()
+
+        return_value = y.fillna(value=0, inplace=True)
+        assert return_value is None
+
+        expected = x.fillna(value=0)
+        tm.assert_series_equal(y, expected)
+
+    # ---------------------------------------------------------------
+    # CategoricalDtype
+
+    @pytest.mark.parametrize(
+        "fill_value, expected_output",
+        [
+            ("a", ["a", "a", "b", "a", "a"]),
+            ({1: "a", 3: "b", 4: "b"}, ["a", "a", "b", "b", "b"]),
+            ({1: "a"}, ["a", "a", "b", np.nan, np.nan]),
+            ({1: "a", 3: "b"}, ["a", "a", "b", "b", np.nan]),
+            (Series("a"), ["a", np.nan, "b", np.nan, np.nan]),
+            (Series("a", index=[1]), ["a", "a", "b", np.nan, np.nan]),
+            (Series({1: "a", 3: "b"}), ["a", "a", "b", "b", np.nan]),
+            (Series(["a", "b"], index=[3, 4]), ["a", np.nan, "b", "a", "b"]),
+        ],
+    )
+    def test_fillna_categorical(self, fill_value, expected_output):
+        # GH#17033
+        # Test fillna for a Categorical series
+        data = ["a", np.nan, "b", np.nan, np.nan]
+        ser = Series(Categorical(data, categories=["a", "b"]))
+        exp = Series(Categorical(expected_output, categories=["a", "b"]))
+        result = ser.fillna(fill_value)
+        tm.assert_series_equal(result, exp)
+
+    @pytest.mark.parametrize(
+        "fill_value, expected_output",
+        [
+            (Series(["a", "b", "c", "d", "e"]), ["a", "b", "b", "d", "e"]),
+            (Series(["b", "d", "a", "d", "a"]), ["a", "d", "b", "d", "a"]),
+            (
+                Series(
+                    Categorical(
+                        ["b", "d", "a", "d", "a"], categories=["b", "c", "d", "e", "a"]
+                    )
+                ),
+                ["a", "d", "b", "d", "a"],
+            ),
+        ],
+    )
+    def test_fillna_categorical_with_new_categories(self, fill_value, expected_output):
+        # GH#26215
+        data = ["a", np.nan, "b", np.nan, np.nan]
+        ser = Series(Categorical(data, categories=["a", "b", "c", "d", "e"]))
+        exp = Series(Categorical(expected_output, categories=["a", "b", "c", "d", "e"]))
+        result = ser.fillna(fill_value)
+        tm.assert_series_equal(result, exp)
+
+    def test_fillna_categorical_raises(self):
+        data = ["a", np.nan, "b", np.nan, np.nan]
+        ser = Series(Categorical(data, categories=["a", "b"]))
+        cat = ser._values
+
+        msg = "Cannot setitem on a Categorical with a new category"
+        with pytest.raises(TypeError, match=msg):
+            ser.fillna("d")
+
+        msg2 = "Length of 'value' does not match."
+        with pytest.raises(ValueError, match=msg2):
+            cat.fillna(Series("d"))
+
+        with pytest.raises(TypeError, match=msg):
+            ser.fillna({1: "d", 3: "a"})
+
+        msg = '"value" parameter must be a scalar or dict, but you passed a "list"'
+        with pytest.raises(TypeError, match=msg):
+            ser.fillna(["a", "b"])
+
+        msg = '"value" parameter must be a scalar or dict, but you passed a "tuple"'
+        with pytest.raises(TypeError, match=msg):
+            ser.fillna(("a", "b"))
+
+        msg = (
+            '"value" parameter must be a scalar, dict '
+            'or Series, but you passed a "DataFrame"'
+        )
+        with pytest.raises(TypeError, match=msg):
+            ser.fillna(DataFrame({1: ["a"], 3: ["b"]}))
+
+    @pytest.mark.parametrize("dtype", [float, "float32", "float64"])
+    @pytest.mark.parametrize("fill_type", tm.ALL_REAL_NUMPY_DTYPES)
+    @pytest.mark.parametrize("scalar", [True, False])
+    def test_fillna_float_casting(self, dtype, fill_type, scalar):
+        # GH-43424
+        ser = Series([np.nan, 1.2], dtype=dtype)
+        fill_values = Series([2, 2], dtype=fill_type)
+        if scalar:
+            fill_values = fill_values.dtype.type(2)
+
+        result = ser.fillna(fill_values)
+        expected = Series([2.0, 1.2], dtype=dtype)
+        tm.assert_series_equal(result, expected)
+
+        ser = Series([np.nan, 1.2], dtype=dtype)
+        mask = ser.isna().to_numpy()
+        ser[mask] = fill_values
+        tm.assert_series_equal(ser, expected)
+
+        ser = Series([np.nan, 1.2], dtype=dtype)
+        ser.mask(mask, fill_values, inplace=True)
+        tm.assert_series_equal(ser, expected)
+
+        ser = Series([np.nan, 1.2], dtype=dtype)
+        res = ser.where(~mask, fill_values)
+        tm.assert_series_equal(res, expected)
+
+    def test_fillna_f32_upcast_with_dict(self):
+        # GH-43424
+        ser = Series([np.nan, 1.2], dtype=np.float32)
+        result = ser.fillna({0: 1})
+        expected = Series([1.0, 1.2], dtype=np.float32)
+        tm.assert_series_equal(result, expected)
+
+    # ---------------------------------------------------------------
+    # Invalid Usages
+
+    def test_fillna_invalid_method(self, datetime_series):
+        try:
+            datetime_series.fillna(method="ffil")
+        except ValueError as inst:
+            assert "ffil" in str(inst)
+
+    def test_fillna_listlike_invalid(self):
+        ser = Series(np.random.default_rng(2).integers(-100, 100, 50))
+        msg = '"value" parameter must be a scalar or dict, but you passed a "list"'
+        with pytest.raises(TypeError, match=msg):
+            ser.fillna([1, 2])
+
+        msg = '"value" parameter must be a scalar or dict, but you passed a "tuple"'
+        with pytest.raises(TypeError, match=msg):
+            ser.fillna((1, 2))
+
+    def test_fillna_method_and_limit_invalid(self):
+        # related GH#9217, make sure limit is an int and greater than 0
+        ser = Series([1, 2, 3, None])
+        msg = "|".join(
+            [
+                r"Cannot specify both 'value' and 'method'\.",
+                "Limit must be greater than 0",
+                "Limit must be an integer",
+            ]
+        )
+        for limit in [-1, 0, 1.0, 2.0]:
+            for method in ["backfill", "bfill", "pad", "ffill", None]:
+                with pytest.raises(ValueError, match=msg):
+                    ser.fillna(1, limit=limit, method=method)
+
+    def test_fillna_datetime64_with_timezone_tzinfo(self):
+        # https://github.com/pandas-dev/pandas/issues/38851
+        # different tzinfos representing UTC treated as equal
+        ser = Series(date_range("2020", periods=3, tz="UTC"))
+        expected = ser.copy()
+        ser[1] = NaT
+        result = ser.fillna(datetime(2020, 1, 2, tzinfo=timezone.utc))
+        tm.assert_series_equal(result, expected)
+
+        # pre-2.0 we cast to object with mixed tzs, in 2.0 we retain dtype
+        ts = Timestamp("2000-01-01", tz="US/Pacific")
+        ser2 = Series(ser._values.tz_convert("dateutil/US/Pacific"))
+        assert ser2.dtype.kind == "M"
+        result = ser2.fillna(ts)
+        expected = Series(
+            [ser2[0], ts.tz_convert(ser2.dtype.tz), ser2[2]],
+            dtype=ser2.dtype,
+        )
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "input, input_fillna, expected_data, expected_categories",
+        [
+            (["A", "B", None, "A"], "B", ["A", "B", "B", "A"], ["A", "B"]),
+            (["A", "B", np.nan, "A"], "B", ["A", "B", "B", "A"], ["A", "B"]),
+        ],
+    )
+    def test_fillna_categorical_accept_same_type(
+        self, input, input_fillna, expected_data, expected_categories
+    ):
+        # GH32414
+        cat = Categorical(input)
+        ser = Series(cat).fillna(input_fillna)
+        filled = cat.fillna(ser)
+        result = cat.fillna(filled)
+        expected = Categorical(expected_data, categories=expected_categories)
+        tm.assert_categorical_equal(result, expected)
+
+
+@pytest.mark.filterwarnings(
+    "ignore:Series.fillna with 'method' is deprecated:FutureWarning"
+)
+class TestFillnaPad:
+    def test_fillna_bug(self):
+        ser = Series([np.nan, 1.0, np.nan, 3.0, np.nan], ["z", "a", "b", "c", "d"])
+        filled = ser.fillna(method="ffill")
+        expected = Series([np.nan, 1.0, 1.0, 3.0, 3.0], ser.index)
+        tm.assert_series_equal(filled, expected)
+
+        filled = ser.fillna(method="bfill")
+        expected = Series([1.0, 1.0, 3.0, 3.0, np.nan], ser.index)
+        tm.assert_series_equal(filled, expected)
+
+    def test_ffill(self):
+        ts = Series(
+            [0.0, 1.0, 2.0, 3.0, 4.0], index=date_range("2020-01-01", periods=5)
+        )
+        ts.iloc[2] = np.nan
+        tm.assert_series_equal(ts.ffill(), ts.fillna(method="ffill"))
+
+    def test_ffill_mixed_dtypes_without_missing_data(self):
+        # GH#14956
+        series = Series([datetime(2015, 1, 1, tzinfo=pytz.utc), 1])
+        result = series.ffill()
+        tm.assert_series_equal(series, result)
+
+    def test_bfill(self):
+        ts = Series(
+            [0.0, 1.0, 2.0, 3.0, 4.0], index=date_range("2020-01-01", periods=5)
+        )
+        ts.iloc[2] = np.nan
+        tm.assert_series_equal(ts.bfill(), ts.fillna(method="bfill"))
+
+    def test_pad_nan(self):
+        x = Series(
+            [np.nan, 1.0, np.nan, 3.0, np.nan], ["z", "a", "b", "c", "d"], dtype=float
+        )
+
+        return_value = x.fillna(method="pad", inplace=True)
+        assert return_value is None
+
+        expected = Series(
+            [np.nan, 1.0, 1.0, 3.0, 3.0], ["z", "a", "b", "c", "d"], dtype=float
+        )
+        tm.assert_series_equal(x[1:], expected[1:])
+        assert np.isnan(x.iloc[0]), np.isnan(expected.iloc[0])
+
+    def test_series_fillna_limit(self):
+        index = np.arange(10)
+        s = Series(np.random.default_rng(2).standard_normal(10), index=index)
+
+        result = s[:2].reindex(index)
+        result = result.fillna(method="pad", limit=5)
+
+        expected = s[:2].reindex(index).fillna(method="pad")
+        expected[-3:] = np.nan
+        tm.assert_series_equal(result, expected)
+
+        result = s[-2:].reindex(index)
+        result = result.fillna(method="bfill", limit=5)
+
+        expected = s[-2:].reindex(index).fillna(method="backfill")
+        expected[:3] = np.nan
+        tm.assert_series_equal(result, expected)
+
+    def test_series_pad_backfill_limit(self):
+        index = np.arange(10)
+        s = Series(np.random.default_rng(2).standard_normal(10), index=index)
+
+        result = s[:2].reindex(index, method="pad", limit=5)
+
+        expected = s[:2].reindex(index).fillna(method="pad")
+        expected[-3:] = np.nan
+        tm.assert_series_equal(result, expected)
+
+        result = s[-2:].reindex(index, method="backfill", limit=5)
+
+        expected = s[-2:].reindex(index).fillna(method="backfill")
+        expected[:3] = np.nan
+        tm.assert_series_equal(result, expected)
+
+    def test_fillna_int(self):
+        ser = Series(np.random.default_rng(2).integers(-100, 100, 50))
+        return_value = ser.fillna(method="ffill", inplace=True)
+        assert return_value is None
+        tm.assert_series_equal(ser.fillna(method="ffill", inplace=False), ser)
+
+    def test_datetime64tz_fillna_round_issue(self):
+        # GH#14872
+
+        data = Series(
+            [NaT, NaT, datetime(2016, 12, 12, 22, 24, 6, 100001, tzinfo=pytz.utc)]
+        )
+
+        filled = data.bfill()
+
+        expected = Series(
+            [
+                datetime(2016, 12, 12, 22, 24, 6, 100001, tzinfo=pytz.utc),
+                datetime(2016, 12, 12, 22, 24, 6, 100001, tzinfo=pytz.utc),
+                datetime(2016, 12, 12, 22, 24, 6, 100001, tzinfo=pytz.utc),
+            ]
+        )
+
+        tm.assert_series_equal(filled, expected)
+
+    def test_fillna_parr(self):
+        # GH-24537
+        dti = date_range(
+            Timestamp.max - Timedelta(nanoseconds=10), periods=5, freq="ns"
+        )
+        ser = Series(dti.to_period("ns"))
+        ser[2] = NaT
+        arr = period_array(
+            [
+                Timestamp("2262-04-11 23:47:16.854775797"),
+                Timestamp("2262-04-11 23:47:16.854775798"),
+                Timestamp("2262-04-11 23:47:16.854775798"),
+                Timestamp("2262-04-11 23:47:16.854775800"),
+                Timestamp("2262-04-11 23:47:16.854775801"),
+            ],
+            freq="ns",
+        )
+        expected = Series(arr)
+
+        filled = ser.ffill()
+
+        tm.assert_series_equal(filled, expected)
+
+    @pytest.mark.parametrize("func", ["pad", "backfill"])
+    def test_pad_backfill_deprecated(self, func):
+        # GH#33396
+        ser = Series([1, 2, 3])
+        with tm.assert_produces_warning(FutureWarning):
+            getattr(ser, func)()
+
+
+@pytest.mark.parametrize(
+    "data, expected_data, method, 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],
+            "ffill",
+            {"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],
+            "ffill",
+            {"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],
+            "ffill",
+            {"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],
+            "ffill",
+            {"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],
+            "ffill",
+            {"limit_area": "outside", "limit": 1},
+        ),
+        (
+            range(5),
+            range(5),
+            "ffill",
+            {"limit_area": "outside", "limit": 1},
+        ),
+        (
+            [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],
+            "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],
+            "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],
+            "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],
+            "bfill",
+            {"limit_area": "outside", "limit": 1},
+        ),
+    ),
+)
+def test_ffill_bfill_limit_area(data, expected_data, method, kwargs):
+    # GH#56492
+    s = Series(data)
+    expected = Series(expected_data)
+    result = getattr(s, method)(**kwargs)
+    tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_infer_objects.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_infer_objects.py
new file mode 100644
index 0000000000000000000000000000000000000000..29abac6b3780ec500ef0c635785c9624e4264934
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_infer_objects.py
@@ -0,0 +1,56 @@
+import numpy as np
+
+from pandas import (
+    Series,
+    interval_range,
+)
+import pandas._testing as tm
+
+
+class TestInferObjects:
+    def test_copy(self, index_or_series):
+        # GH#50096
+        # case where we don't need to do inference because it is already non-object
+        obj = index_or_series(np.array([1, 2, 3], dtype="int64"))
+
+        result = obj.infer_objects(copy=False)
+        assert tm.shares_memory(result, obj)
+
+        # case where we try to do inference but can't do better than object
+        obj2 = index_or_series(np.array(["foo", 2], dtype=object))
+        result2 = obj2.infer_objects(copy=False)
+        assert tm.shares_memory(result2, obj2)
+
+    def test_infer_objects_series(self, index_or_series):
+        # GH#11221
+        actual = index_or_series(np.array([1, 2, 3], dtype="O")).infer_objects()
+        expected = index_or_series([1, 2, 3])
+        tm.assert_equal(actual, expected)
+
+        actual = index_or_series(np.array([1, 2, 3, None], dtype="O")).infer_objects()
+        expected = index_or_series([1.0, 2.0, 3.0, np.nan])
+        tm.assert_equal(actual, expected)
+
+        # only soft conversions, unconvertible pass thru unchanged
+
+        obj = index_or_series(np.array([1, 2, 3, None, "a"], dtype="O"))
+        actual = obj.infer_objects()
+        expected = index_or_series([1, 2, 3, None, "a"], dtype=object)
+
+        assert actual.dtype == "object"
+        tm.assert_equal(actual, expected)
+
+    def test_infer_objects_interval(self, index_or_series):
+        # GH#50090
+        ii = interval_range(1, 10)
+        obj = index_or_series(ii)
+
+        result = obj.astype(object).infer_objects()
+        tm.assert_equal(result, obj)
+
+    def test_infer_objects_bytes(self):
+        # GH#49650
+        ser = Series([b"a"], dtype="bytes")
+        expected = ser.copy()
+        result = ser.infer_objects()
+        tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_info.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_info.py
new file mode 100644
index 0000000000000000000000000000000000000000..29dd704f6efa97804d4d18ceceb0e160fde6948c
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_info.py
@@ -0,0 +1,181 @@
+from io import StringIO
+from string import ascii_uppercase
+import textwrap
+
+import numpy as np
+import pytest
+
+from pandas.compat import PYPY
+
+from pandas import (
+    CategoricalIndex,
+    MultiIndex,
+    Series,
+    date_range,
+)
+
+
+def test_info_categorical_column_just_works():
+    n = 2500
+    data = np.array(list("abcdefghij")).take(
+        np.random.default_rng(2).integers(0, 10, size=n, dtype=int)
+    )
+    s = Series(data).astype("category")
+    s.isna()
+    buf = StringIO()
+    s.info(buf=buf)
+
+    s2 = s[s == "d"]
+    buf = StringIO()
+    s2.info(buf=buf)
+
+
+def test_info_categorical():
+    # GH14298
+    idx = CategoricalIndex(["a", "b"])
+    s = Series(np.zeros(2), index=idx)
+    buf = StringIO()
+    s.info(buf=buf)
+
+
+@pytest.mark.parametrize("verbose", [True, False])
+def test_info_series(lexsorted_two_level_string_multiindex, verbose):
+    index = lexsorted_two_level_string_multiindex
+    ser = Series(range(len(index)), index=index, name="sth")
+    buf = StringIO()
+    ser.info(verbose=verbose, buf=buf)
+    result = buf.getvalue()
+
+    expected = textwrap.dedent(
+        """\
+        <class 'pandas.core.series.Series'>
+        MultiIndex: 10 entries, ('foo', 'one') to ('qux', 'three')
+        """
+    )
+    if verbose:
+        expected += textwrap.dedent(
+            """\
+            Series name: sth
+            Non-Null Count  Dtype
+            --------------  -----
+            10 non-null     int64
+            """
+        )
+    expected += textwrap.dedent(
+        f"""\
+        dtypes: int64(1)
+        memory usage: {ser.memory_usage()}.0+ bytes
+        """
+    )
+    assert result == expected
+
+
+def test_info_memory():
+    s = Series([1, 2], dtype="i8")
+    buf = StringIO()
+    s.info(buf=buf)
+    result = buf.getvalue()
+    memory_bytes = float(s.memory_usage())
+    expected = textwrap.dedent(
+        f"""\
+    <class 'pandas.core.series.Series'>
+    RangeIndex: 2 entries, 0 to 1
+    Series name: None
+    Non-Null Count  Dtype
+    --------------  -----
+    2 non-null      int64
+    dtypes: int64(1)
+    memory usage: {memory_bytes} bytes
+    """
+    )
+    assert result == expected
+
+
+def test_info_wide():
+    s = Series(np.random.default_rng(2).standard_normal(101))
+    msg = "Argument `max_cols` can only be passed in DataFrame.info, not Series.info"
+    with pytest.raises(ValueError, match=msg):
+        s.info(max_cols=1)
+
+
+def test_info_shows_dtypes():
+    dtypes = [
+        "int64",
+        "float64",
+        "datetime64[ns]",
+        "timedelta64[ns]",
+        "complex128",
+        "object",
+        "bool",
+    ]
+    n = 10
+    for dtype in dtypes:
+        s = Series(np.random.default_rng(2).integers(2, size=n).astype(dtype))
+        buf = StringIO()
+        s.info(buf=buf)
+        res = buf.getvalue()
+        name = f"{n:d} non-null     {dtype}"
+        assert name in res
+
+
+@pytest.mark.xfail(PYPY, reason="on PyPy deep=True doesn't change result")
+def test_info_memory_usage_deep_not_pypy():
+    s_with_object_index = Series({"a": [1]}, index=["foo"])
+    assert s_with_object_index.memory_usage(
+        index=True, deep=True
+    ) > s_with_object_index.memory_usage(index=True)
+
+    s_object = Series({"a": ["a"]})
+    assert s_object.memory_usage(deep=True) > s_object.memory_usage()
+
+
+@pytest.mark.xfail(not PYPY, reason="on PyPy deep=True does not change result")
+def test_info_memory_usage_deep_pypy():
+    s_with_object_index = Series({"a": [1]}, index=["foo"])
+    assert s_with_object_index.memory_usage(
+        index=True, deep=True
+    ) == s_with_object_index.memory_usage(index=True)
+
+    s_object = Series({"a": ["a"]})
+    assert s_object.memory_usage(deep=True) == s_object.memory_usage()
+
+
+@pytest.mark.parametrize(
+    "series, plus",
+    [
+        (Series(1, index=[1, 2, 3]), False),
+        (Series(1, index=list("ABC")), True),
+        (Series(1, index=MultiIndex.from_product([range(3), range(3)])), False),
+        (
+            Series(1, index=MultiIndex.from_product([range(3), ["foo", "bar"]])),
+            True,
+        ),
+    ],
+)
+def test_info_memory_usage_qualified(series, plus):
+    buf = StringIO()
+    series.info(buf=buf)
+    if plus:
+        assert "+" in buf.getvalue()
+    else:
+        assert "+" not in buf.getvalue()
+
+
+def test_info_memory_usage_bug_on_multiindex():
+    # GH 14308
+    # memory usage introspection should not materialize .values
+    N = 100
+    M = len(ascii_uppercase)
+    index = MultiIndex.from_product(
+        [list(ascii_uppercase), date_range("20160101", periods=N)],
+        names=["id", "date"],
+    )
+    s = Series(np.random.default_rng(2).standard_normal(N * M), index=index)
+
+    unstacked = s.unstack("id")
+    assert s.values.nbytes == unstacked.values.nbytes
+    assert s.memory_usage(deep=True) > unstacked.memory_usage(deep=True).sum()
+
+    # high upper bound
+    diff = unstacked.memory_usage(deep=True).sum() - s.memory_usage(deep=True)
+    assert diff < 2000
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_is_monotonic.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_is_monotonic.py
new file mode 100644
index 0000000000000000000000000000000000000000..073ec4172aff6b041d29011bc3151f84f3cbeb19
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_is_monotonic.py
@@ -0,0 +1,26 @@
+import numpy as np
+
+from pandas import (
+    Series,
+    date_range,
+)
+
+
+class TestIsMonotonic:
+    def test_is_monotonic_numeric(self):
+        ser = Series(np.random.default_rng(2).integers(0, 10, size=1000))
+        assert not ser.is_monotonic_increasing
+        ser = Series(np.arange(1000))
+        assert ser.is_monotonic_increasing is True
+        assert ser.is_monotonic_increasing is True
+        ser = Series(np.arange(1000, 0, -1))
+        assert ser.is_monotonic_decreasing is True
+
+    def test_is_monotonic_dt64(self):
+        ser = Series(date_range("20130101", periods=10))
+        assert ser.is_monotonic_increasing is True
+        assert ser.is_monotonic_increasing is True
+
+        ser = Series(list(reversed(ser)))
+        assert ser.is_monotonic_increasing is False
+        assert ser.is_monotonic_decreasing is True
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_isna.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_isna.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e324aa86a052246a074950082e272fee7e505e3
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_isna.py
@@ -0,0 +1,35 @@
+"""
+We also test Series.notna in this file.
+"""
+import numpy as np
+
+from pandas import (
+    Period,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestIsna:
+    def test_isna_period_dtype(self):
+        # GH#13737
+        ser = Series([Period("2011-01", freq="M"), Period("NaT", freq="M")])
+
+        expected = Series([False, True])
+
+        result = ser.isna()
+        tm.assert_series_equal(result, expected)
+
+        result = ser.notna()
+        tm.assert_series_equal(result, ~expected)
+
+    def test_isna(self):
+        ser = Series([0, 5.4, 3, np.nan, -0.001])
+        expected = Series([False, False, False, True, False])
+        tm.assert_series_equal(ser.isna(), expected)
+        tm.assert_series_equal(ser.notna(), ~expected)
+
+        ser = Series(["hi", "", np.nan])
+        expected = Series([False, False, True])
+        tm.assert_series_equal(ser.isna(), expected)
+        tm.assert_series_equal(ser.notna(), ~expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_item.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_item.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e8c33619d564ef87d51416748b8fdc9058e5a41
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_item.py
@@ -0,0 +1,59 @@
+"""
+Series.item method, mainly testing that we get python scalars as opposed to
+numpy scalars.
+"""
+import pytest
+
+from pandas import (
+    Series,
+    Timedelta,
+    Timestamp,
+    date_range,
+)
+
+
+class TestItem:
+    def test_item(self):
+        # We are testing that we get python scalars as opposed to numpy scalars
+        ser = Series([1])
+        result = ser.item()
+        assert result == 1
+        assert result == ser.iloc[0]
+        assert isinstance(result, int)  # i.e. not np.int64
+
+        ser = Series([0.5], index=[3])
+        result = ser.item()
+        assert isinstance(result, float)
+        assert result == 0.5
+
+        ser = Series([1, 2])
+        msg = "can only convert an array of size 1"
+        with pytest.raises(ValueError, match=msg):
+            ser.item()
+
+        dti = date_range("2016-01-01", periods=2)
+        with pytest.raises(ValueError, match=msg):
+            dti.item()
+        with pytest.raises(ValueError, match=msg):
+            Series(dti).item()
+
+        val = dti[:1].item()
+        assert isinstance(val, Timestamp)
+        val = Series(dti)[:1].item()
+        assert isinstance(val, Timestamp)
+
+        tdi = dti - dti
+        with pytest.raises(ValueError, match=msg):
+            tdi.item()
+        with pytest.raises(ValueError, match=msg):
+            Series(tdi).item()
+
+        val = tdi[:1].item()
+        assert isinstance(val, Timedelta)
+        val = Series(tdi)[:1].item()
+        assert isinstance(val, Timedelta)
+
+        # Case where ser[0] would not work
+        ser = Series(dti, index=[5, 6])
+        val = ser.iloc[:1].item()
+        assert val == dti[0]
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_nlargest.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_nlargest.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8de1cd89e3974f2dff0d775f35655b8caa02a08
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_nlargest.py
@@ -0,0 +1,248 @@
+"""
+Note: for naming purposes, most tests are title with as e.g. "test_nlargest_foo"
+but are implicitly also testing nsmallest_foo.
+"""
+from itertools import product
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import Series
+import pandas._testing as tm
+
+main_dtypes = [
+    "datetime",
+    "datetimetz",
+    "timedelta",
+    "int8",
+    "int16",
+    "int32",
+    "int64",
+    "float32",
+    "float64",
+    "uint8",
+    "uint16",
+    "uint32",
+    "uint64",
+]
+
+
+@pytest.fixture
+def s_main_dtypes():
+    """
+    A DataFrame with many dtypes
+
+    * datetime
+    * datetimetz
+    * timedelta
+    * [u]int{8,16,32,64}
+    * float{32,64}
+
+    The columns are the name of the dtype.
+    """
+    df = pd.DataFrame(
+        {
+            "datetime": pd.to_datetime(["2003", "2002", "2001", "2002", "2005"]),
+            "datetimetz": pd.to_datetime(
+                ["2003", "2002", "2001", "2002", "2005"]
+            ).tz_localize("US/Eastern"),
+            "timedelta": pd.to_timedelta(["3d", "2d", "1d", "2d", "5d"]),
+        }
+    )
+
+    for dtype in [
+        "int8",
+        "int16",
+        "int32",
+        "int64",
+        "float32",
+        "float64",
+        "uint8",
+        "uint16",
+        "uint32",
+        "uint64",
+    ]:
+        df[dtype] = Series([3, 2, 1, 2, 5], dtype=dtype)
+
+    return df
+
+
+@pytest.fixture(params=main_dtypes)
+def s_main_dtypes_split(request, s_main_dtypes):
+    """Each series in s_main_dtypes."""
+    return s_main_dtypes[request.param]
+
+
+def assert_check_nselect_boundary(vals, dtype, method):
+    # helper function for 'test_boundary_{dtype}' tests
+    ser = Series(vals, dtype=dtype)
+    result = getattr(ser, method)(3)
+    expected_idxr = [0, 1, 2] if method == "nsmallest" else [3, 2, 1]
+    expected = ser.loc[expected_idxr]
+    tm.assert_series_equal(result, expected)
+
+
+class TestSeriesNLargestNSmallest:
+    @pytest.mark.parametrize(
+        "r",
+        [
+            Series([3.0, 2, 1, 2, "5"], dtype="object"),
+            Series([3.0, 2, 1, 2, 5], dtype="object"),
+            # not supported on some archs
+            # Series([3., 2, 1, 2, 5], dtype='complex256'),
+            Series([3.0, 2, 1, 2, 5], dtype="complex128"),
+            Series(list("abcde")),
+            Series(list("abcde"), dtype="category"),
+        ],
+    )
+    def test_nlargest_error(self, r):
+        dt = r.dtype
+        msg = f"Cannot use method 'n(largest|smallest)' with dtype {dt}"
+        args = 2, len(r), 0, -1
+        methods = r.nlargest, r.nsmallest
+        for method, arg in product(methods, args):
+            with pytest.raises(TypeError, match=msg):
+                method(arg)
+
+    def test_nsmallest_nlargest(self, s_main_dtypes_split):
+        # float, int, datetime64 (use i8), timedelts64 (same),
+        # object that are numbers, object that are strings
+        ser = s_main_dtypes_split
+
+        tm.assert_series_equal(ser.nsmallest(2), ser.iloc[[2, 1]])
+        tm.assert_series_equal(ser.nsmallest(2, keep="last"), ser.iloc[[2, 3]])
+
+        empty = ser.iloc[0:0]
+        tm.assert_series_equal(ser.nsmallest(0), empty)
+        tm.assert_series_equal(ser.nsmallest(-1), empty)
+        tm.assert_series_equal(ser.nlargest(0), empty)
+        tm.assert_series_equal(ser.nlargest(-1), empty)
+
+        tm.assert_series_equal(ser.nsmallest(len(ser)), ser.sort_values())
+        tm.assert_series_equal(ser.nsmallest(len(ser) + 1), ser.sort_values())
+        tm.assert_series_equal(ser.nlargest(len(ser)), ser.iloc[[4, 0, 1, 3, 2]])
+        tm.assert_series_equal(ser.nlargest(len(ser) + 1), ser.iloc[[4, 0, 1, 3, 2]])
+
+    def test_nlargest_misc(self):
+        ser = Series([3.0, np.nan, 1, 2, 5])
+        result = ser.nlargest()
+        expected = ser.iloc[[4, 0, 3, 2, 1]]
+        tm.assert_series_equal(result, expected)
+        result = ser.nsmallest()
+        expected = ser.iloc[[2, 3, 0, 4, 1]]
+        tm.assert_series_equal(result, expected)
+
+        msg = 'keep must be either "first", "last"'
+        with pytest.raises(ValueError, match=msg):
+            ser.nsmallest(keep="invalid")
+        with pytest.raises(ValueError, match=msg):
+            ser.nlargest(keep="invalid")
+
+        # GH#15297
+        ser = Series([1] * 5, index=[1, 2, 3, 4, 5])
+        expected_first = Series([1] * 3, index=[1, 2, 3])
+        expected_last = Series([1] * 3, index=[5, 4, 3])
+
+        result = ser.nsmallest(3)
+        tm.assert_series_equal(result, expected_first)
+
+        result = ser.nsmallest(3, keep="last")
+        tm.assert_series_equal(result, expected_last)
+
+        result = ser.nlargest(3)
+        tm.assert_series_equal(result, expected_first)
+
+        result = ser.nlargest(3, keep="last")
+        tm.assert_series_equal(result, expected_last)
+
+    @pytest.mark.parametrize("n", range(1, 5))
+    def test_nlargest_n(self, n):
+        # GH 13412
+        ser = Series([1, 4, 3, 2], index=[0, 0, 1, 1])
+        result = ser.nlargest(n)
+        expected = ser.sort_values(ascending=False).head(n)
+        tm.assert_series_equal(result, expected)
+
+        result = ser.nsmallest(n)
+        expected = ser.sort_values().head(n)
+        tm.assert_series_equal(result, expected)
+
+    def test_nlargest_boundary_integer(self, nselect_method, any_int_numpy_dtype):
+        # GH#21426
+        dtype_info = np.iinfo(any_int_numpy_dtype)
+        min_val, max_val = dtype_info.min, dtype_info.max
+        vals = [min_val, min_val + 1, max_val - 1, max_val]
+        assert_check_nselect_boundary(vals, any_int_numpy_dtype, nselect_method)
+
+    def test_nlargest_boundary_float(self, nselect_method, float_numpy_dtype):
+        # GH#21426
+        dtype_info = np.finfo(float_numpy_dtype)
+        min_val, max_val = dtype_info.min, dtype_info.max
+        min_2nd, max_2nd = np.nextafter([min_val, max_val], 0, dtype=float_numpy_dtype)
+        vals = [min_val, min_2nd, max_2nd, max_val]
+        assert_check_nselect_boundary(vals, float_numpy_dtype, nselect_method)
+
+    @pytest.mark.parametrize("dtype", ["datetime64[ns]", "timedelta64[ns]"])
+    def test_nlargest_boundary_datetimelike(self, nselect_method, dtype):
+        # GH#21426
+        # use int64 bounds and +1 to min_val since true minimum is NaT
+        # (include min_val/NaT at end to maintain same expected_idxr)
+        dtype_info = np.iinfo("int64")
+        min_val, max_val = dtype_info.min, dtype_info.max
+        vals = [min_val + 1, min_val + 2, max_val - 1, max_val, min_val]
+        assert_check_nselect_boundary(vals, dtype, nselect_method)
+
+    def test_nlargest_duplicate_keep_all_ties(self):
+        # see GH#16818
+        ser = Series([10, 9, 8, 7, 7, 7, 7, 6])
+        result = ser.nlargest(4, keep="all")
+        expected = Series([10, 9, 8, 7, 7, 7, 7])
+        tm.assert_series_equal(result, expected)
+
+        result = ser.nsmallest(2, keep="all")
+        expected = Series([6, 7, 7, 7, 7], index=[7, 3, 4, 5, 6])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "data,expected", [([True, False], [True]), ([True, False, True, True], [True])]
+    )
+    def test_nlargest_boolean(self, data, expected):
+        # GH#26154 : ensure True > False
+        ser = Series(data)
+        result = ser.nlargest(1)
+        expected = Series(expected)
+        tm.assert_series_equal(result, expected)
+
+    def test_nlargest_nullable(self, any_numeric_ea_dtype):
+        # GH#42816
+        dtype = any_numeric_ea_dtype
+        if dtype.startswith("UInt"):
+            # Can't cast from negative float to uint on some platforms
+            arr = np.random.default_rng(2).integers(1, 10, 10)
+        else:
+            arr = np.random.default_rng(2).standard_normal(10)
+        arr = arr.astype(dtype.lower(), copy=False)
+
+        ser = Series(arr.copy(), dtype=dtype)
+        ser[1] = pd.NA
+        result = ser.nlargest(5)
+
+        expected = (
+            Series(np.delete(arr, 1), index=ser.index.delete(1))
+            .nlargest(5)
+            .astype(dtype)
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_nsmallest_nan_when_keep_is_all(self):
+        # GH#46589
+        s = Series([1, 2, 3, 3, 3, None])
+        result = s.nsmallest(3, keep="all")
+        expected = Series([1.0, 2.0, 3.0, 3.0, 3.0])
+        tm.assert_series_equal(result, expected)
+
+        s = Series([1, 2, None, None, None])
+        result = s.nsmallest(3, keep="all")
+        expected = Series([1, 2, None, None, None])
+        tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_nunique.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_nunique.py
new file mode 100644
index 0000000000000000000000000000000000000000..826132eb28162603d03635add59c3ea3da569256
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_nunique.py
@@ -0,0 +1,24 @@
+import numpy as np
+
+from pandas import (
+    Categorical,
+    Series,
+)
+
+
+def test_nunique():
+    # basics.rst doc example
+    series = Series(np.random.default_rng(2).standard_normal(500))
+    series[20:500] = np.nan
+    series[10:20] = 5000
+    result = series.nunique()
+    assert result == 11
+
+
+def test_nunique_categorical():
+    # GH#18051
+    ser = Series(Categorical([]))
+    assert ser.nunique() == 0
+
+    ser = Series(Categorical([np.nan]))
+    assert ser.nunique() == 0
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_pct_change.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_pct_change.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c80e711c36846e565014c1d1c001ae2ba3cf929
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_pct_change.py
@@ -0,0 +1,128 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestSeriesPctChange:
+    def test_pct_change(self, datetime_series):
+        msg = (
+            "The 'fill_method' keyword being not None and the 'limit' keyword in "
+            "Series.pct_change are deprecated"
+        )
+
+        rs = datetime_series.pct_change(fill_method=None)
+        tm.assert_series_equal(rs, datetime_series / datetime_series.shift(1) - 1)
+
+        rs = datetime_series.pct_change(2)
+        filled = datetime_series.ffill()
+        tm.assert_series_equal(rs, filled / filled.shift(2) - 1)
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            rs = datetime_series.pct_change(fill_method="bfill", limit=1)
+        filled = datetime_series.bfill(limit=1)
+        tm.assert_series_equal(rs, filled / filled.shift(1) - 1)
+
+        rs = datetime_series.pct_change(freq="5D")
+        filled = datetime_series.ffill()
+        tm.assert_series_equal(
+            rs, (filled / filled.shift(freq="5D") - 1).reindex_like(filled)
+        )
+
+    def test_pct_change_with_duplicate_axis(self):
+        # GH#28664
+        common_idx = date_range("2019-11-14", periods=5, freq="D")
+        result = Series(range(5), common_idx).pct_change(freq="B")
+
+        # the reason that the expected should be like this is documented at PR 28681
+        expected = Series([np.nan, np.inf, np.nan, np.nan, 3.0], common_idx)
+
+        tm.assert_series_equal(result, expected)
+
+    def test_pct_change_shift_over_nas(self):
+        s = Series([1.0, 1.5, np.nan, 2.5, 3.0])
+
+        msg = "The default fill_method='pad' in Series.pct_change is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            chg = s.pct_change()
+
+        expected = Series([np.nan, 0.5, 0.0, 2.5 / 1.5 - 1, 0.2])
+        tm.assert_series_equal(chg, expected)
+
+    @pytest.mark.parametrize(
+        "freq, periods, fill_method, limit",
+        [
+            ("5B", 5, None, None),
+            ("3B", 3, None, None),
+            ("3B", 3, "bfill", None),
+            ("7B", 7, "pad", 1),
+            ("7B", 7, "bfill", 3),
+            ("14B", 14, None, None),
+        ],
+    )
+    def test_pct_change_periods_freq(
+        self, freq, periods, fill_method, limit, datetime_series
+    ):
+        msg = (
+            "The 'fill_method' keyword being not None and the 'limit' keyword in "
+            "Series.pct_change are deprecated"
+        )
+
+        # GH#7292
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            rs_freq = datetime_series.pct_change(
+                freq=freq, fill_method=fill_method, limit=limit
+            )
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            rs_periods = datetime_series.pct_change(
+                periods, fill_method=fill_method, limit=limit
+            )
+        tm.assert_series_equal(rs_freq, rs_periods)
+
+        empty_ts = Series(index=datetime_series.index, dtype=object)
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            rs_freq = empty_ts.pct_change(
+                freq=freq, fill_method=fill_method, limit=limit
+            )
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            rs_periods = empty_ts.pct_change(
+                periods, fill_method=fill_method, limit=limit
+            )
+        tm.assert_series_equal(rs_freq, rs_periods)
+
+
+@pytest.mark.parametrize("fill_method", ["pad", "ffill", None])
+def test_pct_change_with_duplicated_indices(fill_method):
+    # GH30463
+    s = Series([np.nan, 1, 2, 3, 9, 18], index=["a", "b"] * 3)
+
+    warn = None if fill_method is None else FutureWarning
+    msg = (
+        "The 'fill_method' keyword being not None and the 'limit' keyword in "
+        "Series.pct_change are deprecated"
+    )
+    with tm.assert_produces_warning(warn, match=msg):
+        result = s.pct_change(fill_method=fill_method)
+
+    expected = Series([np.nan, np.nan, 1.0, 0.5, 2.0, 1.0], index=["a", "b"] * 3)
+    tm.assert_series_equal(result, expected)
+
+
+def test_pct_change_no_warning_na_beginning():
+    # GH#54981
+    ser = Series([None, None, 1, 2, 3])
+    result = ser.pct_change()
+    expected = Series([np.nan, np.nan, np.nan, 1, 0.5])
+    tm.assert_series_equal(result, expected)
+
+
+def test_pct_change_empty():
+    # GH 57056
+    ser = Series([], dtype="float64")
+    expected = ser.copy()
+    result = ser.pct_change(periods=0)
+    tm.assert_series_equal(expected, result)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_pop.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_pop.py
new file mode 100644
index 0000000000000000000000000000000000000000..7453f98ab3735e924dd7601622d23b4bafdd2176
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_pop.py
@@ -0,0 +1,13 @@
+from pandas import Series
+import pandas._testing as tm
+
+
+def test_pop():
+    # GH#6600
+    ser = Series([0, 4, 0], index=["A", "B", "C"], name=4)
+
+    result = ser.pop("B")
+    assert result == 4
+
+    expected = Series([0, 0], index=["A", "C"], name=4)
+    tm.assert_series_equal(ser, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_quantile.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_quantile.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa0563271d7df7cb6fdb3f2f7ad807057313f4c2
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_quantile.py
@@ -0,0 +1,247 @@
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.common import is_integer
+
+import pandas as pd
+from pandas import (
+    Index,
+    Series,
+)
+import pandas._testing as tm
+from pandas.core.indexes.datetimes import Timestamp
+
+
+class TestSeriesQuantile:
+    def test_quantile(self, datetime_series):
+        q = datetime_series.quantile(0.1)
+        assert q == np.percentile(datetime_series.dropna(), 10)
+
+        q = datetime_series.quantile(0.9)
+        assert q == np.percentile(datetime_series.dropna(), 90)
+
+        # object dtype
+        q = Series(datetime_series, dtype=object).quantile(0.9)
+        assert q == np.percentile(datetime_series.dropna(), 90)
+
+        # datetime64[ns] dtype
+        dts = datetime_series.index.to_series()
+        q = dts.quantile(0.2)
+        assert q == Timestamp("2000-01-10 19:12:00")
+
+        # timedelta64[ns] dtype
+        tds = dts.diff()
+        q = tds.quantile(0.25)
+        assert q == pd.to_timedelta("24:00:00")
+
+        # GH7661
+        result = Series([np.timedelta64("NaT")]).sum()
+        assert result == pd.Timedelta(0)
+
+        msg = "percentiles should all be in the interval \\[0, 1\\]"
+        for invalid in [-1, 2, [0.5, -1], [0.5, 2]]:
+            with pytest.raises(ValueError, match=msg):
+                datetime_series.quantile(invalid)
+
+        s = Series(np.random.default_rng(2).standard_normal(100))
+        percentile_array = [-0.5, 0.25, 1.5]
+        with pytest.raises(ValueError, match=msg):
+            s.quantile(percentile_array)
+
+    def test_quantile_multi(self, datetime_series, unit):
+        datetime_series.index = datetime_series.index.as_unit(unit)
+        qs = [0.1, 0.9]
+        result = datetime_series.quantile(qs)
+        expected = Series(
+            [
+                np.percentile(datetime_series.dropna(), 10),
+                np.percentile(datetime_series.dropna(), 90),
+            ],
+            index=qs,
+            name=datetime_series.name,
+        )
+        tm.assert_series_equal(result, expected)
+
+        dts = datetime_series.index.to_series()
+        dts.name = "xxx"
+        result = dts.quantile((0.2, 0.2))
+        expected = Series(
+            [Timestamp("2000-01-10 19:12:00"), Timestamp("2000-01-10 19:12:00")],
+            index=[0.2, 0.2],
+            name="xxx",
+            dtype=f"M8[{unit}]",
+        )
+        tm.assert_series_equal(result, expected)
+
+        result = datetime_series.quantile([])
+        expected = Series(
+            [], name=datetime_series.name, index=Index([], dtype=float), dtype="float64"
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_quantile_interpolation(self, datetime_series):
+        # see gh-10174
+
+        # interpolation = linear (default case)
+        q = datetime_series.quantile(0.1, interpolation="linear")
+        assert q == np.percentile(datetime_series.dropna(), 10)
+        q1 = datetime_series.quantile(0.1)
+        assert q1 == np.percentile(datetime_series.dropna(), 10)
+
+        # test with and without interpolation keyword
+        assert q == q1
+
+    def test_quantile_interpolation_dtype(self):
+        # GH #10174
+
+        # interpolation = linear (default case)
+        q = Series([1, 3, 4]).quantile(0.5, interpolation="lower")
+        assert q == np.percentile(np.array([1, 3, 4]), 50)
+        assert is_integer(q)
+
+        q = Series([1, 3, 4]).quantile(0.5, interpolation="higher")
+        assert q == np.percentile(np.array([1, 3, 4]), 50)
+        assert is_integer(q)
+
+    def test_quantile_nan(self):
+        # GH 13098
+        ser = Series([1, 2, 3, 4, np.nan])
+        result = ser.quantile(0.5)
+        expected = 2.5
+        assert result == expected
+
+        # all nan/empty
+        s1 = Series([], dtype=object)
+        cases = [s1, Series([np.nan, np.nan])]
+
+        for ser in cases:
+            res = ser.quantile(0.5)
+            assert np.isnan(res)
+
+            res = ser.quantile([0.5])
+            tm.assert_series_equal(res, Series([np.nan], index=[0.5]))
+
+            res = ser.quantile([0.2, 0.3])
+            tm.assert_series_equal(res, Series([np.nan, np.nan], index=[0.2, 0.3]))
+
+    @pytest.mark.parametrize(
+        "case",
+        [
+            [
+                Timestamp("2011-01-01"),
+                Timestamp("2011-01-02"),
+                Timestamp("2011-01-03"),
+            ],
+            [
+                Timestamp("2011-01-01", tz="US/Eastern"),
+                Timestamp("2011-01-02", tz="US/Eastern"),
+                Timestamp("2011-01-03", tz="US/Eastern"),
+            ],
+            [pd.Timedelta("1 days"), pd.Timedelta("2 days"), pd.Timedelta("3 days")],
+            # NaT
+            [
+                Timestamp("2011-01-01"),
+                Timestamp("2011-01-02"),
+                Timestamp("2011-01-03"),
+                pd.NaT,
+            ],
+            [
+                Timestamp("2011-01-01", tz="US/Eastern"),
+                Timestamp("2011-01-02", tz="US/Eastern"),
+                Timestamp("2011-01-03", tz="US/Eastern"),
+                pd.NaT,
+            ],
+            [
+                pd.Timedelta("1 days"),
+                pd.Timedelta("2 days"),
+                pd.Timedelta("3 days"),
+                pd.NaT,
+            ],
+        ],
+    )
+    def test_quantile_box(self, case):
+        ser = Series(case, name="XXX")
+        res = ser.quantile(0.5)
+        assert res == case[1]
+
+        res = ser.quantile([0.5])
+        exp = Series([case[1]], index=[0.5], name="XXX")
+        tm.assert_series_equal(res, exp)
+
+    def test_datetime_timedelta_quantiles(self):
+        # covers #9694
+        assert pd.isna(Series([], dtype="M8[ns]").quantile(0.5))
+        assert pd.isna(Series([], dtype="m8[ns]").quantile(0.5))
+
+    def test_quantile_nat(self):
+        res = Series([pd.NaT, pd.NaT]).quantile(0.5)
+        assert res is pd.NaT
+
+        res = Series([pd.NaT, pd.NaT]).quantile([0.5])
+        tm.assert_series_equal(res, Series([pd.NaT], index=[0.5]))
+
+    @pytest.mark.parametrize(
+        "values, dtype",
+        [([0, 0, 0, 1, 2, 3], "Sparse[int]"), ([0.0, None, 1.0, 2.0], "Sparse[float]")],
+    )
+    def test_quantile_sparse(self, values, dtype):
+        ser = Series(values, dtype=dtype)
+        result = ser.quantile([0.5])
+        expected = Series(np.asarray(ser)).quantile([0.5]).astype("Sparse[float]")
+        tm.assert_series_equal(result, expected)
+
+    def test_quantile_empty_float64(self):
+        # floats
+        ser = Series([], dtype="float64")
+
+        res = ser.quantile(0.5)
+        assert np.isnan(res)
+
+        res = ser.quantile([0.5])
+        exp = Series([np.nan], index=[0.5])
+        tm.assert_series_equal(res, exp)
+
+    def test_quantile_empty_int64(self):
+        # int
+        ser = Series([], dtype="int64")
+
+        res = ser.quantile(0.5)
+        assert np.isnan(res)
+
+        res = ser.quantile([0.5])
+        exp = Series([np.nan], index=[0.5])
+        tm.assert_series_equal(res, exp)
+
+    def test_quantile_empty_dt64(self):
+        # datetime
+        ser = Series([], dtype="datetime64[ns]")
+
+        res = ser.quantile(0.5)
+        assert res is pd.NaT
+
+        res = ser.quantile([0.5])
+        exp = Series([pd.NaT], index=[0.5], dtype=ser.dtype)
+        tm.assert_series_equal(res, exp)
+
+    @pytest.mark.parametrize("dtype", [int, float, "Int64"])
+    def test_quantile_dtypes(self, dtype):
+        result = Series([1, 2, 3], dtype=dtype).quantile(np.arange(0, 1, 0.25))
+        expected = Series(np.arange(1, 3, 0.5), index=np.arange(0, 1, 0.25))
+        if dtype == "Int64":
+            expected = expected.astype("Float64")
+        tm.assert_series_equal(result, expected)
+
+    def test_quantile_all_na(self, any_int_ea_dtype):
+        # GH#50681
+        ser = Series([pd.NA, pd.NA], dtype=any_int_ea_dtype)
+        with tm.assert_produces_warning(None):
+            result = ser.quantile([0.1, 0.5])
+        expected = Series([pd.NA, pd.NA], dtype=any_int_ea_dtype, index=[0.1, 0.5])
+        tm.assert_series_equal(result, expected)
+
+    def test_quantile_dtype_size(self, any_int_ea_dtype):
+        # GH#50681
+        ser = Series([pd.NA, pd.NA, 1], dtype=any_int_ea_dtype)
+        result = ser.quantile([0.1, 0.5])
+        expected = Series([1, 1], dtype=any_int_ea_dtype, index=[0.1, 0.5])
+        tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_rank.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_rank.py
new file mode 100644
index 0000000000000000000000000000000000000000..24cf97c05c0a810bac00a8843b21d0ee88a1c00d
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_rank.py
@@ -0,0 +1,519 @@
+from itertools import chain
+import operator
+
+import numpy as np
+import pytest
+
+from pandas._libs.algos import (
+    Infinity,
+    NegInfinity,
+)
+import pandas.util._test_decorators as td
+
+from pandas import (
+    NA,
+    NaT,
+    Series,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.api.types import CategoricalDtype
+
+
+@pytest.fixture
+def ser():
+    return Series([1, 3, 4, 2, np.nan, 2, 1, 5, np.nan, 3])
+
+
+@pytest.fixture(
+    params=[
+        ["average", np.array([1.5, 5.5, 7.0, 3.5, np.nan, 3.5, 1.5, 8.0, np.nan, 5.5])],
+        ["min", np.array([1, 5, 7, 3, np.nan, 3, 1, 8, np.nan, 5])],
+        ["max", np.array([2, 6, 7, 4, np.nan, 4, 2, 8, np.nan, 6])],
+        ["first", np.array([1, 5, 7, 3, np.nan, 4, 2, 8, np.nan, 6])],
+        ["dense", np.array([1, 3, 4, 2, np.nan, 2, 1, 5, np.nan, 3])],
+    ]
+)
+def results(request):
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        "object",
+        "float64",
+        "int64",
+        "Float64",
+        "Int64",
+        pytest.param("float64[pyarrow]", marks=td.skip_if_no("pyarrow")),
+        pytest.param("int64[pyarrow]", marks=td.skip_if_no("pyarrow")),
+    ]
+)
+def dtype(request):
+    return request.param
+
+
+class TestSeriesRank:
+    def test_rank(self, datetime_series):
+        sp_stats = pytest.importorskip("scipy.stats")
+
+        datetime_series[::2] = np.nan
+        datetime_series[:10:3] = 4.0
+
+        ranks = datetime_series.rank()
+        oranks = datetime_series.astype("O").rank()
+
+        tm.assert_series_equal(ranks, oranks)
+
+        mask = np.isnan(datetime_series)
+        filled = datetime_series.fillna(np.inf)
+
+        # rankdata returns a ndarray
+        exp = Series(sp_stats.rankdata(filled), index=filled.index, name="ts")
+        exp[mask] = np.nan
+
+        tm.assert_series_equal(ranks, exp)
+
+        iseries = Series(np.arange(5).repeat(2))
+
+        iranks = iseries.rank()
+        exp = iseries.astype(float).rank()
+        tm.assert_series_equal(iranks, exp)
+        iseries = Series(np.arange(5)) + 1.0
+        exp = iseries / 5.0
+        iranks = iseries.rank(pct=True)
+
+        tm.assert_series_equal(iranks, exp)
+
+        iseries = Series(np.repeat(1, 100))
+        exp = Series(np.repeat(0.505, 100))
+        iranks = iseries.rank(pct=True)
+        tm.assert_series_equal(iranks, exp)
+
+        # Explicit cast to float to avoid implicit cast when setting nan
+        iseries = iseries.astype("float")
+        iseries[1] = np.nan
+        exp = Series(np.repeat(50.0 / 99.0, 100))
+        exp[1] = np.nan
+        iranks = iseries.rank(pct=True)
+        tm.assert_series_equal(iranks, exp)
+
+        iseries = Series(np.arange(5)) + 1.0
+        iseries[4] = np.nan
+        exp = iseries / 4.0
+        iranks = iseries.rank(pct=True)
+        tm.assert_series_equal(iranks, exp)
+
+        iseries = Series(np.repeat(np.nan, 100))
+        exp = iseries.copy()
+        iranks = iseries.rank(pct=True)
+        tm.assert_series_equal(iranks, exp)
+
+        # Explicit cast to float to avoid implicit cast when setting nan
+        iseries = Series(np.arange(5), dtype="float") + 1
+        iseries[4] = np.nan
+        exp = iseries / 4.0
+        iranks = iseries.rank(pct=True)
+        tm.assert_series_equal(iranks, exp)
+
+        rng = date_range("1/1/1990", periods=5)
+        # Explicit cast to float to avoid implicit cast when setting nan
+        iseries = Series(np.arange(5), rng, dtype="float") + 1
+        iseries.iloc[4] = np.nan
+        exp = iseries / 4.0
+        iranks = iseries.rank(pct=True)
+        tm.assert_series_equal(iranks, exp)
+
+        iseries = Series([1e-50, 1e-100, 1e-20, 1e-2, 1e-20 + 1e-30, 1e-1])
+        exp = Series([2, 1, 3, 5, 4, 6.0])
+        iranks = iseries.rank()
+        tm.assert_series_equal(iranks, exp)
+
+        # GH 5968
+        iseries = Series(["3 day", "1 day 10m", "-2 day", NaT], dtype="m8[ns]")
+        exp = Series([3, 2, 1, np.nan])
+        iranks = iseries.rank()
+        tm.assert_series_equal(iranks, exp)
+
+        values = np.array(
+            [-50, -1, -1e-20, -1e-25, -1e-50, 0, 1e-40, 1e-20, 1e-10, 2, 40],
+            dtype="float64",
+        )
+        random_order = np.random.default_rng(2).permutation(len(values))
+        iseries = Series(values[random_order])
+        exp = Series(random_order + 1.0, dtype="float64")
+        iranks = iseries.rank()
+        tm.assert_series_equal(iranks, exp)
+
+    def test_rank_categorical(self):
+        # GH issue #15420 rank incorrectly orders ordered categories
+
+        # Test ascending/descending ranking for ordered categoricals
+        exp = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
+        exp_desc = Series([6.0, 5.0, 4.0, 3.0, 2.0, 1.0])
+        ordered = Series(
+            ["first", "second", "third", "fourth", "fifth", "sixth"]
+        ).astype(
+            CategoricalDtype(
+                categories=["first", "second", "third", "fourth", "fifth", "sixth"],
+                ordered=True,
+            )
+        )
+        tm.assert_series_equal(ordered.rank(), exp)
+        tm.assert_series_equal(ordered.rank(ascending=False), exp_desc)
+
+        # Unordered categoricals should be ranked as objects
+        unordered = Series(
+            ["first", "second", "third", "fourth", "fifth", "sixth"]
+        ).astype(
+            CategoricalDtype(
+                categories=["first", "second", "third", "fourth", "fifth", "sixth"],
+                ordered=False,
+            )
+        )
+        exp_unordered = Series([2.0, 4.0, 6.0, 3.0, 1.0, 5.0])
+        res = unordered.rank()
+        tm.assert_series_equal(res, exp_unordered)
+
+        unordered1 = Series([1, 2, 3, 4, 5, 6]).astype(
+            CategoricalDtype([1, 2, 3, 4, 5, 6], False)
+        )
+        exp_unordered1 = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
+        res1 = unordered1.rank()
+        tm.assert_series_equal(res1, exp_unordered1)
+
+        # Test na_option for rank data
+        na_ser = Series(
+            ["first", "second", "third", "fourth", "fifth", "sixth", np.nan]
+        ).astype(
+            CategoricalDtype(
+                ["first", "second", "third", "fourth", "fifth", "sixth", "seventh"],
+                True,
+            )
+        )
+
+        exp_top = Series([2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 1.0])
+        exp_bot = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0])
+        exp_keep = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, np.nan])
+
+        tm.assert_series_equal(na_ser.rank(na_option="top"), exp_top)
+        tm.assert_series_equal(na_ser.rank(na_option="bottom"), exp_bot)
+        tm.assert_series_equal(na_ser.rank(na_option="keep"), exp_keep)
+
+        # Test na_option for rank data with ascending False
+        exp_top = Series([7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0])
+        exp_bot = Series([6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 7.0])
+        exp_keep = Series([6.0, 5.0, 4.0, 3.0, 2.0, 1.0, np.nan])
+
+        tm.assert_series_equal(na_ser.rank(na_option="top", ascending=False), exp_top)
+        tm.assert_series_equal(
+            na_ser.rank(na_option="bottom", ascending=False), exp_bot
+        )
+        tm.assert_series_equal(na_ser.rank(na_option="keep", ascending=False), exp_keep)
+
+        # Test invalid values for na_option
+        msg = "na_option must be one of 'keep', 'top', or 'bottom'"
+
+        with pytest.raises(ValueError, match=msg):
+            na_ser.rank(na_option="bad", ascending=False)
+
+        # invalid type
+        with pytest.raises(ValueError, match=msg):
+            na_ser.rank(na_option=True, ascending=False)
+
+        # Test with pct=True
+        na_ser = Series(["first", "second", "third", "fourth", np.nan]).astype(
+            CategoricalDtype(["first", "second", "third", "fourth"], True)
+        )
+        exp_top = Series([0.4, 0.6, 0.8, 1.0, 0.2])
+        exp_bot = Series([0.2, 0.4, 0.6, 0.8, 1.0])
+        exp_keep = Series([0.25, 0.5, 0.75, 1.0, np.nan])
+
+        tm.assert_series_equal(na_ser.rank(na_option="top", pct=True), exp_top)
+        tm.assert_series_equal(na_ser.rank(na_option="bottom", pct=True), exp_bot)
+        tm.assert_series_equal(na_ser.rank(na_option="keep", pct=True), exp_keep)
+
+    def test_rank_signature(self):
+        s = Series([0, 1])
+        s.rank(method="average")
+        msg = "No axis named average for object type Series"
+        with pytest.raises(ValueError, match=msg):
+            s.rank("average")
+
+    @pytest.mark.parametrize("dtype", [None, object])
+    def test_rank_tie_methods(self, ser, results, dtype):
+        method, exp = results
+        ser = ser if dtype is None else ser.astype(dtype)
+        result = ser.rank(method=method)
+        tm.assert_series_equal(result, Series(exp))
+
+    @pytest.mark.parametrize("ascending", [True, False])
+    @pytest.mark.parametrize("method", ["average", "min", "max", "first", "dense"])
+    @pytest.mark.parametrize("na_option", ["top", "bottom", "keep"])
+    @pytest.mark.parametrize(
+        "dtype, na_value, pos_inf, neg_inf",
+        [
+            ("object", None, Infinity(), NegInfinity()),
+            ("float64", np.nan, np.inf, -np.inf),
+            ("Float64", NA, np.inf, -np.inf),
+            pytest.param(
+                "float64[pyarrow]",
+                NA,
+                np.inf,
+                -np.inf,
+                marks=td.skip_if_no("pyarrow"),
+            ),
+        ],
+    )
+    def test_rank_tie_methods_on_infs_nans(
+        self, method, na_option, ascending, dtype, na_value, pos_inf, neg_inf
+    ):
+        pytest.importorskip("scipy")
+        if dtype == "float64[pyarrow]":
+            if method == "average":
+                exp_dtype = "float64[pyarrow]"
+            else:
+                exp_dtype = "uint64[pyarrow]"
+        else:
+            exp_dtype = "float64"
+
+        chunk = 3
+        in_arr = [neg_inf] * chunk + [na_value] * chunk + [pos_inf] * chunk
+        iseries = Series(in_arr, dtype=dtype)
+        exp_ranks = {
+            "average": ([2, 2, 2], [5, 5, 5], [8, 8, 8]),
+            "min": ([1, 1, 1], [4, 4, 4], [7, 7, 7]),
+            "max": ([3, 3, 3], [6, 6, 6], [9, 9, 9]),
+            "first": ([1, 2, 3], [4, 5, 6], [7, 8, 9]),
+            "dense": ([1, 1, 1], [2, 2, 2], [3, 3, 3]),
+        }
+        ranks = exp_ranks[method]
+        if na_option == "top":
+            order = [ranks[1], ranks[0], ranks[2]]
+        elif na_option == "bottom":
+            order = [ranks[0], ranks[2], ranks[1]]
+        else:
+            order = [ranks[0], [np.nan] * chunk, ranks[1]]
+        expected = order if ascending else order[::-1]
+        expected = list(chain.from_iterable(expected))
+        result = iseries.rank(method=method, na_option=na_option, ascending=ascending)
+        tm.assert_series_equal(result, Series(expected, dtype=exp_dtype))
+
+    def test_rank_desc_mix_nans_infs(self):
+        # GH 19538
+        # check descending ranking when mix nans and infs
+        iseries = Series([1, np.nan, np.inf, -np.inf, 25])
+        result = iseries.rank(ascending=False)
+        exp = Series([3, np.nan, 1, 4, 2], dtype="float64")
+        tm.assert_series_equal(result, exp)
+
+    @pytest.mark.parametrize("method", ["average", "min", "max", "first", "dense"])
+    @pytest.mark.parametrize(
+        "op, value",
+        [
+            [operator.add, 0],
+            [operator.add, 1e6],
+            [operator.mul, 1e-6],
+        ],
+    )
+    def test_rank_methods_series(self, method, op, value):
+        sp_stats = pytest.importorskip("scipy.stats")
+
+        xs = np.random.default_rng(2).standard_normal(9)
+        xs = np.concatenate([xs[i:] for i in range(0, 9, 2)])  # add duplicates
+        np.random.default_rng(2).shuffle(xs)
+
+        index = [chr(ord("a") + i) for i in range(len(xs))]
+        vals = op(xs, value)
+        ts = Series(vals, index=index)
+        result = ts.rank(method=method)
+        sprank = sp_stats.rankdata(vals, method if method != "first" else "ordinal")
+        expected = Series(sprank, index=index).astype("float64")
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "ser, exp",
+        [
+            ([1], [1]),
+            ([2], [1]),
+            ([0], [1]),
+            ([2, 2], [1, 1]),
+            ([1, 2, 3], [1, 2, 3]),
+            ([4, 2, 1], [3, 2, 1]),
+            ([1, 1, 5, 5, 3], [1, 1, 3, 3, 2]),
+            ([-5, -4, -3, -2, -1], [1, 2, 3, 4, 5]),
+        ],
+    )
+    def test_rank_dense_method(self, dtype, ser, exp):
+        s = Series(ser).astype(dtype)
+        result = s.rank(method="dense")
+        expected = Series(exp).astype(result.dtype)
+        tm.assert_series_equal(result, expected)
+
+    def test_rank_descending(self, ser, results, dtype):
+        method, _ = results
+        if "i" in dtype:
+            s = ser.dropna()
+        else:
+            s = ser.astype(dtype)
+
+        res = s.rank(ascending=False)
+        expected = (s.max() - s).rank()
+        tm.assert_series_equal(res, expected)
+
+        expected = (s.max() - s).rank(method=method)
+        res2 = s.rank(method=method, ascending=False)
+        tm.assert_series_equal(res2, expected)
+
+    def test_rank_int(self, ser, results):
+        method, exp = results
+        s = ser.dropna().astype("i8")
+
+        result = s.rank(method=method)
+        expected = Series(exp).dropna()
+        expected.index = result.index
+        tm.assert_series_equal(result, expected)
+
+    def test_rank_object_bug(self):
+        # GH 13445
+
+        # smoke tests
+        Series([np.nan] * 32).astype(object).rank(ascending=True)
+        Series([np.nan] * 32).astype(object).rank(ascending=False)
+
+    def test_rank_modify_inplace(self):
+        # GH 18521
+        # Check rank does not mutate series
+        s = Series([Timestamp("2017-01-05 10:20:27.569000"), NaT])
+        expected = s.copy()
+
+        s.rank()
+        result = s
+        tm.assert_series_equal(result, expected)
+
+    def test_rank_ea_small_values(self):
+        # GH#52471
+        ser = Series(
+            [5.4954145e29, -9.791984e-21, 9.3715776e-26, NA, 1.8790257e-28],
+            dtype="Float64",
+        )
+        result = ser.rank(method="min")
+        expected = Series([4, 1, 3, np.nan, 2])
+        tm.assert_series_equal(result, expected)
+
+
+# GH15630, pct should be on 100% basis when method='dense'
+
+
+@pytest.mark.parametrize(
+    "ser, exp",
+    [
+        ([1], [1.0]),
+        ([1, 2], [1.0 / 2, 2.0 / 2]),
+        ([2, 2], [1.0, 1.0]),
+        ([1, 2, 3], [1.0 / 3, 2.0 / 3, 3.0 / 3]),
+        ([1, 2, 2], [1.0 / 2, 2.0 / 2, 2.0 / 2]),
+        ([4, 2, 1], [3.0 / 3, 2.0 / 3, 1.0 / 3]),
+        ([1, 1, 5, 5, 3], [1.0 / 3, 1.0 / 3, 3.0 / 3, 3.0 / 3, 2.0 / 3]),
+        ([1, 1, 3, 3, 5, 5], [1.0 / 3, 1.0 / 3, 2.0 / 3, 2.0 / 3, 3.0 / 3, 3.0 / 3]),
+        ([-5, -4, -3, -2, -1], [1.0 / 5, 2.0 / 5, 3.0 / 5, 4.0 / 5, 5.0 / 5]),
+    ],
+)
+def test_rank_dense_pct(dtype, ser, exp):
+    s = Series(ser).astype(dtype)
+    result = s.rank(method="dense", pct=True)
+    expected = Series(exp).astype(result.dtype)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "ser, exp",
+    [
+        ([1], [1.0]),
+        ([1, 2], [1.0 / 2, 2.0 / 2]),
+        ([2, 2], [1.0 / 2, 1.0 / 2]),
+        ([1, 2, 3], [1.0 / 3, 2.0 / 3, 3.0 / 3]),
+        ([1, 2, 2], [1.0 / 3, 2.0 / 3, 2.0 / 3]),
+        ([4, 2, 1], [3.0 / 3, 2.0 / 3, 1.0 / 3]),
+        ([1, 1, 5, 5, 3], [1.0 / 5, 1.0 / 5, 4.0 / 5, 4.0 / 5, 3.0 / 5]),
+        ([1, 1, 3, 3, 5, 5], [1.0 / 6, 1.0 / 6, 3.0 / 6, 3.0 / 6, 5.0 / 6, 5.0 / 6]),
+        ([-5, -4, -3, -2, -1], [1.0 / 5, 2.0 / 5, 3.0 / 5, 4.0 / 5, 5.0 / 5]),
+    ],
+)
+def test_rank_min_pct(dtype, ser, exp):
+    s = Series(ser).astype(dtype)
+    result = s.rank(method="min", pct=True)
+    expected = Series(exp).astype(result.dtype)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "ser, exp",
+    [
+        ([1], [1.0]),
+        ([1, 2], [1.0 / 2, 2.0 / 2]),
+        ([2, 2], [1.0, 1.0]),
+        ([1, 2, 3], [1.0 / 3, 2.0 / 3, 3.0 / 3]),
+        ([1, 2, 2], [1.0 / 3, 3.0 / 3, 3.0 / 3]),
+        ([4, 2, 1], [3.0 / 3, 2.0 / 3, 1.0 / 3]),
+        ([1, 1, 5, 5, 3], [2.0 / 5, 2.0 / 5, 5.0 / 5, 5.0 / 5, 3.0 / 5]),
+        ([1, 1, 3, 3, 5, 5], [2.0 / 6, 2.0 / 6, 4.0 / 6, 4.0 / 6, 6.0 / 6, 6.0 / 6]),
+        ([-5, -4, -3, -2, -1], [1.0 / 5, 2.0 / 5, 3.0 / 5, 4.0 / 5, 5.0 / 5]),
+    ],
+)
+def test_rank_max_pct(dtype, ser, exp):
+    s = Series(ser).astype(dtype)
+    result = s.rank(method="max", pct=True)
+    expected = Series(exp).astype(result.dtype)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "ser, exp",
+    [
+        ([1], [1.0]),
+        ([1, 2], [1.0 / 2, 2.0 / 2]),
+        ([2, 2], [1.5 / 2, 1.5 / 2]),
+        ([1, 2, 3], [1.0 / 3, 2.0 / 3, 3.0 / 3]),
+        ([1, 2, 2], [1.0 / 3, 2.5 / 3, 2.5 / 3]),
+        ([4, 2, 1], [3.0 / 3, 2.0 / 3, 1.0 / 3]),
+        ([1, 1, 5, 5, 3], [1.5 / 5, 1.5 / 5, 4.5 / 5, 4.5 / 5, 3.0 / 5]),
+        ([1, 1, 3, 3, 5, 5], [1.5 / 6, 1.5 / 6, 3.5 / 6, 3.5 / 6, 5.5 / 6, 5.5 / 6]),
+        ([-5, -4, -3, -2, -1], [1.0 / 5, 2.0 / 5, 3.0 / 5, 4.0 / 5, 5.0 / 5]),
+    ],
+)
+def test_rank_average_pct(dtype, ser, exp):
+    s = Series(ser).astype(dtype)
+    result = s.rank(method="average", pct=True)
+    expected = Series(exp).astype(result.dtype)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "ser, exp",
+    [
+        ([1], [1.0]),
+        ([1, 2], [1.0 / 2, 2.0 / 2]),
+        ([2, 2], [1.0 / 2, 2.0 / 2.0]),
+        ([1, 2, 3], [1.0 / 3, 2.0 / 3, 3.0 / 3]),
+        ([1, 2, 2], [1.0 / 3, 2.0 / 3, 3.0 / 3]),
+        ([4, 2, 1], [3.0 / 3, 2.0 / 3, 1.0 / 3]),
+        ([1, 1, 5, 5, 3], [1.0 / 5, 2.0 / 5, 4.0 / 5, 5.0 / 5, 3.0 / 5]),
+        ([1, 1, 3, 3, 5, 5], [1.0 / 6, 2.0 / 6, 3.0 / 6, 4.0 / 6, 5.0 / 6, 6.0 / 6]),
+        ([-5, -4, -3, -2, -1], [1.0 / 5, 2.0 / 5, 3.0 / 5, 4.0 / 5, 5.0 / 5]),
+    ],
+)
+def test_rank_first_pct(dtype, ser, exp):
+    s = Series(ser).astype(dtype)
+    result = s.rank(method="first", pct=True)
+    expected = Series(exp).astype(result.dtype)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.single_cpu
+def test_pct_max_many_rows():
+    # GH 18271
+    s = Series(np.arange(2**24 + 1))
+    result = s.rank(pct=True).max()
+    assert result == 1
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_reindex_like.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_reindex_like.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f24c778feb1b4556587773f711e21521efc537c
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_reindex_like.py
@@ -0,0 +1,41 @@
+from datetime import datetime
+
+import numpy as np
+
+from pandas import Series
+import pandas._testing as tm
+
+
+def test_reindex_like(datetime_series):
+    other = datetime_series[::2]
+    tm.assert_series_equal(
+        datetime_series.reindex(other.index), datetime_series.reindex_like(other)
+    )
+
+    # GH#7179
+    day1 = datetime(2013, 3, 5)
+    day2 = datetime(2013, 5, 5)
+    day3 = datetime(2014, 3, 5)
+
+    series1 = Series([5, None, None], [day1, day2, day3])
+    series2 = Series([None, None], [day1, day3])
+
+    result = series1.reindex_like(series2, method="pad")
+    expected = Series([5, np.nan], index=[day1, day3])
+    tm.assert_series_equal(result, expected)
+
+
+def test_reindex_like_nearest():
+    ser = Series(np.arange(10, dtype="int64"))
+
+    target = [0.1, 0.9, 1.5, 2.0]
+    other = ser.reindex(target, method="nearest")
+    expected = Series(np.around(target).astype("int64"), target)
+
+    result = ser.reindex_like(other, method="nearest")
+    tm.assert_series_equal(expected, result)
+
+    result = ser.reindex_like(other, method="nearest", tolerance=1)
+    tm.assert_series_equal(expected, result)
+    result = ser.reindex_like(other, method="nearest", tolerance=[1, 2, 3, 4])
+    tm.assert_series_equal(expected, result)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_rename_axis.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_rename_axis.py
new file mode 100644
index 0000000000000000000000000000000000000000..58c095d697ede213be5c730e305bf1789810ac4a
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_rename_axis.py
@@ -0,0 +1,47 @@
+import pytest
+
+from pandas import (
+    Index,
+    MultiIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestSeriesRenameAxis:
+    def test_rename_axis_mapper(self):
+        # GH 19978
+        mi = MultiIndex.from_product([["a", "b", "c"], [1, 2]], names=["ll", "nn"])
+        ser = Series(list(range(len(mi))), index=mi)
+
+        result = ser.rename_axis(index={"ll": "foo"})
+        assert result.index.names == ["foo", "nn"]
+
+        result = ser.rename_axis(index=str.upper, axis=0)
+        assert result.index.names == ["LL", "NN"]
+
+        result = ser.rename_axis(index=["foo", "goo"])
+        assert result.index.names == ["foo", "goo"]
+
+        with pytest.raises(TypeError, match="unexpected"):
+            ser.rename_axis(columns="wrong")
+
+    def test_rename_axis_inplace(self, datetime_series):
+        # GH 15704
+        expected = datetime_series.rename_axis("foo")
+        result = datetime_series
+        no_return = result.rename_axis("foo", inplace=True)
+
+        assert no_return is None
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("kwargs", [{"mapper": None}, {"index": None}, {}])
+    def test_rename_axis_none(self, kwargs):
+        # GH 25034
+        index = Index(list("abc"), name="foo")
+        ser = Series([1, 2, 3], index=index)
+
+        result = ser.rename_axis(**kwargs)
+        expected_index = index.rename(None) if kwargs else index
+        expected = Series([1, 2, 3], index=expected_index)
+        tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_replace.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_replace.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0f4e233ba5eba4de29b320bb7592c6b2671ebcd
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_replace.py
@@ -0,0 +1,813 @@
+import re
+
+import numpy as np
+import pytest
+
+from pandas._config import using_pyarrow_string_dtype
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import IntervalArray
+
+
+class TestSeriesReplace:
+    def test_replace_explicit_none(self):
+        # GH#36984 if the user explicitly passes value=None, give it to them
+        ser = pd.Series([0, 0, ""], dtype=object)
+        result = ser.replace("", None)
+        expected = pd.Series([0, 0, None], dtype=object)
+        tm.assert_series_equal(result, expected)
+
+        # Cast column 2 to object to avoid implicit cast when setting entry to ""
+        df = pd.DataFrame(np.zeros((3, 3))).astype({2: object})
+        df.iloc[2, 2] = ""
+        result = df.replace("", None)
+        expected = pd.DataFrame(
+            {
+                0: np.zeros(3),
+                1: np.zeros(3),
+                2: np.array([0.0, 0.0, None], dtype=object),
+            }
+        )
+        assert expected.iloc[2, 2] is None
+        tm.assert_frame_equal(result, expected)
+
+        # GH#19998 same thing with object dtype
+        ser = pd.Series([10, 20, 30, "a", "a", "b", "a"])
+        result = ser.replace("a", None)
+        expected = pd.Series([10, 20, 30, None, None, "b", None])
+        assert expected.iloc[-1] is None
+        tm.assert_series_equal(result, expected)
+
+    def test_replace_noop_doesnt_downcast(self):
+        # GH#44498
+        ser = pd.Series([None, None, pd.Timestamp("2021-12-16 17:31")], dtype=object)
+        res = ser.replace({np.nan: None})  # should be a no-op
+        tm.assert_series_equal(res, ser)
+        assert res.dtype == object
+
+        # same thing but different calling convention
+        res = ser.replace(np.nan, None)
+        tm.assert_series_equal(res, ser)
+        assert res.dtype == object
+
+    def test_replace(self):
+        N = 50
+        ser = pd.Series(np.random.default_rng(2).standard_normal(N))
+        ser[0:4] = np.nan
+        ser[6:10] = 0
+
+        # replace list with a single value
+        return_value = ser.replace([np.nan], -1, inplace=True)
+        assert return_value is None
+
+        exp = ser.fillna(-1)
+        tm.assert_series_equal(ser, exp)
+
+        rs = ser.replace(0.0, np.nan)
+        ser[ser == 0.0] = np.nan
+        tm.assert_series_equal(rs, ser)
+
+        ser = pd.Series(
+            np.fabs(np.random.default_rng(2).standard_normal(N)),
+            pd.date_range("2020-01-01", periods=N),
+            dtype=object,
+        )
+        ser[:5] = np.nan
+        ser[6:10] = "foo"
+        ser[20:30] = "bar"
+
+        # replace list with a single value
+        msg = "Downcasting behavior in `replace`"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            rs = ser.replace([np.nan, "foo", "bar"], -1)
+
+        assert (rs[:5] == -1).all()
+        assert (rs[6:10] == -1).all()
+        assert (rs[20:30] == -1).all()
+        assert (pd.isna(ser[:5])).all()
+
+        # replace with different values
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            rs = ser.replace({np.nan: -1, "foo": -2, "bar": -3})
+
+        assert (rs[:5] == -1).all()
+        assert (rs[6:10] == -2).all()
+        assert (rs[20:30] == -3).all()
+        assert (pd.isna(ser[:5])).all()
+
+        # replace with different values with 2 lists
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            rs2 = ser.replace([np.nan, "foo", "bar"], [-1, -2, -3])
+        tm.assert_series_equal(rs, rs2)
+
+        # replace inplace
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            return_value = ser.replace([np.nan, "foo", "bar"], -1, inplace=True)
+        assert return_value is None
+
+        assert (ser[:5] == -1).all()
+        assert (ser[6:10] == -1).all()
+        assert (ser[20:30] == -1).all()
+
+    def test_replace_nan_with_inf(self):
+        ser = pd.Series([np.nan, 0, np.inf])
+        tm.assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0))
+
+        ser = pd.Series([np.nan, 0, "foo", "bar", np.inf, None, pd.NaT])
+        tm.assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0))
+        filled = ser.copy()
+        filled[4] = 0
+        tm.assert_series_equal(ser.replace(np.inf, 0), filled)
+
+    def test_replace_listlike_value_listlike_target(self, datetime_series):
+        ser = pd.Series(datetime_series.index)
+        tm.assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0))
+
+        # malformed
+        msg = r"Replacement lists must match in length\. Expecting 3 got 2"
+        with pytest.raises(ValueError, match=msg):
+            ser.replace([1, 2, 3], [np.nan, 0])
+
+        # ser is dt64 so can't hold 1 or 2, so this replace is a no-op
+        result = ser.replace([1, 2], [np.nan, 0])
+        tm.assert_series_equal(result, ser)
+
+        ser = pd.Series([0, 1, 2, 3, 4])
+        result = ser.replace([0, 1, 2, 3, 4], [4, 3, 2, 1, 0])
+        tm.assert_series_equal(result, pd.Series([4, 3, 2, 1, 0]))
+
+    def test_replace_gh5319(self):
+        # API change from 0.12?
+        # GH 5319
+        ser = pd.Series([0, np.nan, 2, 3, 4])
+        expected = ser.ffill()
+        msg = (
+            "Series.replace without 'value' and with non-dict-like "
+            "'to_replace' is deprecated"
+        )
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = ser.replace([np.nan])
+        tm.assert_series_equal(result, expected)
+
+        ser = pd.Series([0, np.nan, 2, 3, 4])
+        expected = ser.ffill()
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = ser.replace(np.nan)
+        tm.assert_series_equal(result, expected)
+
+    def test_replace_datetime64(self):
+        # GH 5797
+        ser = pd.Series(pd.date_range("20130101", periods=5))
+        expected = ser.copy()
+        expected.loc[2] = pd.Timestamp("20120101")
+        result = ser.replace({pd.Timestamp("20130103"): pd.Timestamp("20120101")})
+        tm.assert_series_equal(result, expected)
+        result = ser.replace(pd.Timestamp("20130103"), pd.Timestamp("20120101"))
+        tm.assert_series_equal(result, expected)
+
+    def test_replace_nat_with_tz(self):
+        # GH 11792: Test with replacing NaT in a list with tz data
+        ts = pd.Timestamp("2015/01/01", tz="UTC")
+        s = pd.Series([pd.NaT, pd.Timestamp("2015/01/01", tz="UTC")])
+        result = s.replace([np.nan, pd.NaT], pd.Timestamp.min)
+        expected = pd.Series([pd.Timestamp.min, ts], dtype=object)
+        tm.assert_series_equal(expected, result)
+
+    def test_replace_timedelta_td64(self):
+        tdi = pd.timedelta_range(0, periods=5)
+        ser = pd.Series(tdi)
+
+        # Using a single dict argument means we go through replace_list
+        result = ser.replace({ser[1]: ser[3]})
+
+        expected = pd.Series([ser[0], ser[3], ser[2], ser[3], ser[4]])
+        tm.assert_series_equal(result, expected)
+
+    def test_replace_with_single_list(self):
+        ser = pd.Series([0, 1, 2, 3, 4])
+        msg2 = (
+            "Series.replace without 'value' and with non-dict-like "
+            "'to_replace' is deprecated"
+        )
+        with tm.assert_produces_warning(FutureWarning, match=msg2):
+            result = ser.replace([1, 2, 3])
+        tm.assert_series_equal(result, pd.Series([0, 0, 0, 0, 4]))
+
+        s = ser.copy()
+        with tm.assert_produces_warning(FutureWarning, match=msg2):
+            return_value = s.replace([1, 2, 3], inplace=True)
+        assert return_value is None
+        tm.assert_series_equal(s, pd.Series([0, 0, 0, 0, 4]))
+
+        # make sure things don't get corrupted when fillna call fails
+        s = ser.copy()
+        msg = (
+            r"Invalid fill method\. Expecting pad \(ffill\) or backfill "
+            r"\(bfill\)\. Got crash_cymbal"
+        )
+        msg3 = "The 'method' keyword in Series.replace is deprecated"
+        with pytest.raises(ValueError, match=msg):
+            with tm.assert_produces_warning(FutureWarning, match=msg3):
+                return_value = s.replace([1, 2, 3], inplace=True, method="crash_cymbal")
+            assert return_value is None
+        tm.assert_series_equal(s, ser)
+
+    def test_replace_mixed_types(self):
+        ser = pd.Series(np.arange(5), dtype="int64")
+
+        def check_replace(to_rep, val, expected):
+            sc = ser.copy()
+            result = ser.replace(to_rep, val)
+            return_value = sc.replace(to_rep, val, inplace=True)
+            assert return_value is None
+            tm.assert_series_equal(expected, result)
+            tm.assert_series_equal(expected, sc)
+
+        # 3.0 can still be held in our int64 series, so we do not upcast GH#44940
+        tr, v = [3], [3.0]
+        check_replace(tr, v, ser)
+        # Note this matches what we get with the scalars 3 and 3.0
+        check_replace(tr[0], v[0], ser)
+
+        # MUST upcast to float
+        e = pd.Series([0, 1, 2, 3.5, 4])
+        tr, v = [3], [3.5]
+        check_replace(tr, v, e)
+
+        # casts to object
+        e = pd.Series([0, 1, 2, 3.5, "a"])
+        tr, v = [3, 4], [3.5, "a"]
+        check_replace(tr, v, e)
+
+        # again casts to object
+        e = pd.Series([0, 1, 2, 3.5, pd.Timestamp("20130101")])
+        tr, v = [3, 4], [3.5, pd.Timestamp("20130101")]
+        check_replace(tr, v, e)
+
+        # casts to object
+        e = pd.Series([0, 1, 2, 3.5, True], dtype="object")
+        tr, v = [3, 4], [3.5, True]
+        check_replace(tr, v, e)
+
+        # test an object with dates + floats + integers + strings
+        dr = pd.Series(pd.date_range("1/1/2001", "1/10/2001", freq="D"))
+        result = dr.astype(object).replace([dr[0], dr[1], dr[2]], [1.0, 2, "a"])
+        expected = pd.Series([1.0, 2, "a"] + dr[3:].tolist(), dtype=object)
+        tm.assert_series_equal(result, expected)
+
+    def test_replace_bool_with_string_no_op(self):
+        s = pd.Series([True, False, True])
+        result = s.replace("fun", "in-the-sun")
+        tm.assert_series_equal(s, result)
+
+    def test_replace_bool_with_string(self):
+        # nonexistent elements
+        s = pd.Series([True, False, True])
+        result = s.replace(True, "2u")
+        expected = pd.Series(["2u", False, "2u"])
+        tm.assert_series_equal(expected, result)
+
+    def test_replace_bool_with_bool(self):
+        s = pd.Series([True, False, True])
+        result = s.replace(True, False)
+        expected = pd.Series([False] * len(s))
+        tm.assert_series_equal(expected, result)
+
+    def test_replace_with_dict_with_bool_keys(self):
+        s = pd.Series([True, False, True])
+        result = s.replace({"asdf": "asdb", True: "yes"})
+        expected = pd.Series(["yes", False, "yes"])
+        tm.assert_series_equal(result, expected)
+
+    def test_replace_Int_with_na(self, any_int_ea_dtype):
+        # GH 38267
+        result = pd.Series([0, None], dtype=any_int_ea_dtype).replace(0, pd.NA)
+        expected = pd.Series([pd.NA, pd.NA], dtype=any_int_ea_dtype)
+        tm.assert_series_equal(result, expected)
+        result = pd.Series([0, 1], dtype=any_int_ea_dtype).replace(0, pd.NA)
+        result.replace(1, pd.NA, inplace=True)
+        tm.assert_series_equal(result, expected)
+
+    def test_replace2(self):
+        N = 50
+        ser = pd.Series(
+            np.fabs(np.random.default_rng(2).standard_normal(N)),
+            pd.date_range("2020-01-01", periods=N),
+            dtype=object,
+        )
+        ser[:5] = np.nan
+        ser[6:10] = "foo"
+        ser[20:30] = "bar"
+
+        # replace list with a single value
+        msg = "Downcasting behavior in `replace`"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            rs = ser.replace([np.nan, "foo", "bar"], -1)
+
+        assert (rs[:5] == -1).all()
+        assert (rs[6:10] == -1).all()
+        assert (rs[20:30] == -1).all()
+        assert (pd.isna(ser[:5])).all()
+
+        # replace with different values
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            rs = ser.replace({np.nan: -1, "foo": -2, "bar": -3})
+
+        assert (rs[:5] == -1).all()
+        assert (rs[6:10] == -2).all()
+        assert (rs[20:30] == -3).all()
+        assert (pd.isna(ser[:5])).all()
+
+        # replace with different values with 2 lists
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            rs2 = ser.replace([np.nan, "foo", "bar"], [-1, -2, -3])
+        tm.assert_series_equal(rs, rs2)
+
+        # replace inplace
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            return_value = ser.replace([np.nan, "foo", "bar"], -1, inplace=True)
+        assert return_value is None
+        assert (ser[:5] == -1).all()
+        assert (ser[6:10] == -1).all()
+        assert (ser[20:30] == -1).all()
+
+    @pytest.mark.parametrize("inplace", [True, False])
+    def test_replace_cascade(self, inplace):
+        # Test that replaced values are not replaced again
+        # GH #50778
+        ser = pd.Series([1, 2, 3])
+        expected = pd.Series([2, 3, 4])
+
+        res = ser.replace([1, 2, 3], [2, 3, 4], inplace=inplace)
+        if inplace:
+            tm.assert_series_equal(ser, expected)
+        else:
+            tm.assert_series_equal(res, expected)
+
+    def test_replace_with_dictlike_and_string_dtype(self, nullable_string_dtype):
+        # GH 32621, GH#44940
+        ser = pd.Series(["one", "two", np.nan], dtype=nullable_string_dtype)
+        expected = pd.Series(["1", "2", np.nan], dtype=nullable_string_dtype)
+        result = ser.replace({"one": "1", "two": "2"})
+        tm.assert_series_equal(expected, result)
+
+    def test_replace_with_empty_dictlike(self):
+        # GH 15289
+        s = pd.Series(list("abcd"))
+        tm.assert_series_equal(s, s.replace({}))
+
+        empty_series = pd.Series([])
+        tm.assert_series_equal(s, s.replace(empty_series))
+
+    def test_replace_string_with_number(self):
+        # GH 15743
+        s = pd.Series([1, 2, 3])
+        result = s.replace("2", np.nan)
+        expected = pd.Series([1, 2, 3])
+        tm.assert_series_equal(expected, result)
+
+    def test_replace_replacer_equals_replacement(self):
+        # GH 20656
+        # make sure all replacers are matching against original values
+        s = pd.Series(["a", "b"])
+        expected = pd.Series(["b", "a"])
+        result = s.replace({"a": "b", "b": "a"})
+        tm.assert_series_equal(expected, result)
+
+    def test_replace_unicode_with_number(self):
+        # GH 15743
+        s = pd.Series([1, 2, 3])
+        result = s.replace("2", np.nan)
+        expected = pd.Series([1, 2, 3])
+        tm.assert_series_equal(expected, result)
+
+    def test_replace_mixed_types_with_string(self):
+        # Testing mixed
+        s = pd.Series([1, 2, 3, "4", 4, 5])
+        msg = "Downcasting behavior in `replace`"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = s.replace([2, "4"], np.nan)
+        expected = pd.Series([1, np.nan, 3, np.nan, 4, 5])
+        tm.assert_series_equal(expected, result)
+
+    @pytest.mark.xfail(using_pyarrow_string_dtype(), reason="can't fill 0 in string")
+    @pytest.mark.parametrize(
+        "categorical, numeric",
+        [
+            (pd.Categorical(["A"], categories=["A", "B"]), [1]),
+            (pd.Categorical(["A", "B"], categories=["A", "B"]), [1, 2]),
+        ],
+    )
+    def test_replace_categorical(self, categorical, numeric):
+        # GH 24971, GH#23305
+        ser = pd.Series(categorical)
+        msg = "Downcasting behavior in `replace`"
+        msg = "with CategoricalDtype is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = ser.replace({"A": 1, "B": 2})
+        expected = pd.Series(numeric).astype("category")
+        if 2 not in expected.cat.categories:
+            # i.e. categories should be [1, 2] even if there are no "B"s present
+            # GH#44940
+            expected = expected.cat.add_categories(2)
+        tm.assert_series_equal(expected, result)
+
+    @pytest.mark.parametrize(
+        "data, data_exp", [(["a", "b", "c"], ["b", "b", "c"]), (["a"], ["b"])]
+    )
+    def test_replace_categorical_inplace(self, data, data_exp):
+        # GH 53358
+        result = pd.Series(data, dtype="category")
+        msg = "with CategoricalDtype is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result.replace(to_replace="a", value="b", inplace=True)
+        expected = pd.Series(data_exp, dtype="category")
+        tm.assert_series_equal(result, expected)
+
+    def test_replace_categorical_single(self):
+        # GH 26988
+        dti = pd.date_range("2016-01-01", periods=3, tz="US/Pacific")
+        s = pd.Series(dti)
+        c = s.astype("category")
+
+        expected = c.copy()
+        expected = expected.cat.add_categories("foo")
+        expected[2] = "foo"
+        expected = expected.cat.remove_unused_categories()
+        assert c[2] != "foo"
+
+        msg = "with CategoricalDtype is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = c.replace(c[2], "foo")
+        tm.assert_series_equal(expected, result)
+        assert c[2] != "foo"  # ensure non-inplace call does not alter original
+
+        msg = "with CategoricalDtype is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            return_value = c.replace(c[2], "foo", inplace=True)
+        assert return_value is None
+        tm.assert_series_equal(expected, c)
+
+        first_value = c[0]
+        msg = "with CategoricalDtype is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            return_value = c.replace(c[1], c[0], inplace=True)
+        assert return_value is None
+        assert c[0] == c[1] == first_value  # test replacing with existing value
+
+    def test_replace_with_no_overflowerror(self):
+        # GH 25616
+        # casts to object without Exception from OverflowError
+        s = pd.Series([0, 1, 2, 3, 4])
+        result = s.replace([3], ["100000000000000000000"])
+        expected = pd.Series([0, 1, 2, "100000000000000000000", 4])
+        tm.assert_series_equal(result, expected)
+
+        s = pd.Series([0, "100000000000000000000", "100000000000000000001"])
+        result = s.replace(["100000000000000000000"], [1])
+        expected = pd.Series([0, 1, "100000000000000000001"])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "ser, to_replace, exp",
+        [
+            ([1, 2, 3], {1: 2, 2: 3, 3: 4}, [2, 3, 4]),
+            (["1", "2", "3"], {"1": "2", "2": "3", "3": "4"}, ["2", "3", "4"]),
+        ],
+    )
+    def test_replace_commutative(self, ser, to_replace, exp):
+        # GH 16051
+        # DataFrame.replace() overwrites when values are non-numeric
+
+        series = pd.Series(ser)
+
+        expected = pd.Series(exp)
+        result = series.replace(to_replace)
+
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "ser, exp", [([1, 2, 3], [1, True, 3]), (["x", 2, 3], ["x", True, 3])]
+    )
+    def test_replace_no_cast(self, ser, exp):
+        # GH 9113
+        # BUG: replace int64 dtype with bool coerces to int64
+
+        series = pd.Series(ser)
+        result = series.replace(2, True)
+        expected = pd.Series(exp)
+
+        tm.assert_series_equal(result, expected)
+
+    def test_replace_invalid_to_replace(self):
+        # GH 18634
+        # API: replace() should raise an exception if invalid argument is given
+        series = pd.Series(["a", "b", "c "])
+        msg = (
+            r"Expecting 'to_replace' to be either a scalar, array-like, "
+            r"dict or None, got invalid type.*"
+        )
+        msg2 = (
+            "Series.replace without 'value' and with non-dict-like "
+            "'to_replace' is deprecated"
+        )
+        with pytest.raises(TypeError, match=msg):
+            with tm.assert_produces_warning(FutureWarning, match=msg2):
+                series.replace(lambda x: x.strip())
+
+    @pytest.mark.parametrize("frame", [False, True])
+    def test_replace_nonbool_regex(self, frame):
+        obj = pd.Series(["a", "b", "c "])
+        if frame:
+            obj = obj.to_frame()
+
+        msg = "'to_replace' must be 'None' if 'regex' is not a bool"
+        with pytest.raises(ValueError, match=msg):
+            obj.replace(to_replace=["a"], regex="foo")
+
+    @pytest.mark.parametrize("frame", [False, True])
+    def test_replace_empty_copy(self, frame):
+        obj = pd.Series([], dtype=np.float64)
+        if frame:
+            obj = obj.to_frame()
+
+        res = obj.replace(4, 5, inplace=True)
+        assert res is None
+
+        res = obj.replace(4, 5, inplace=False)
+        tm.assert_equal(res, obj)
+        assert res is not obj
+
+    def test_replace_only_one_dictlike_arg(self, fixed_now_ts):
+        # GH#33340
+
+        ser = pd.Series([1, 2, "A", fixed_now_ts, True])
+        to_replace = {0: 1, 2: "A"}
+        value = "foo"
+        msg = "Series.replace cannot use dict-like to_replace and non-None value"
+        with pytest.raises(ValueError, match=msg):
+            ser.replace(to_replace, value)
+
+        to_replace = 1
+        value = {0: "foo", 2: "bar"}
+        msg = "Series.replace cannot use dict-value and non-None to_replace"
+        with pytest.raises(ValueError, match=msg):
+            ser.replace(to_replace, value)
+
+    def test_replace_extension_other(self, frame_or_series):
+        # https://github.com/pandas-dev/pandas/issues/34530
+        obj = frame_or_series(pd.array([1, 2, 3], dtype="Int64"))
+        result = obj.replace("", "")  # no exception
+        # should not have changed dtype
+        tm.assert_equal(obj, result)
+
+    def _check_replace_with_method(self, ser: pd.Series):
+        df = ser.to_frame()
+
+        msg1 = "The 'method' keyword in Series.replace is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg1):
+            res = ser.replace(ser[1], method="pad")
+        expected = pd.Series([ser[0], ser[0]] + list(ser[2:]), dtype=ser.dtype)
+        tm.assert_series_equal(res, expected)
+
+        msg2 = "The 'method' keyword in DataFrame.replace is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg2):
+            res_df = df.replace(ser[1], method="pad")
+        tm.assert_frame_equal(res_df, expected.to_frame())
+
+        ser2 = ser.copy()
+        with tm.assert_produces_warning(FutureWarning, match=msg1):
+            res2 = ser2.replace(ser[1], method="pad", inplace=True)
+        assert res2 is None
+        tm.assert_series_equal(ser2, expected)
+
+        with tm.assert_produces_warning(FutureWarning, match=msg2):
+            res_df2 = df.replace(ser[1], method="pad", inplace=True)
+        assert res_df2 is None
+        tm.assert_frame_equal(df, expected.to_frame())
+
+    def test_replace_ea_dtype_with_method(self, any_numeric_ea_dtype):
+        arr = pd.array([1, 2, pd.NA, 4], dtype=any_numeric_ea_dtype)
+        ser = pd.Series(arr)
+
+        self._check_replace_with_method(ser)
+
+    @pytest.mark.parametrize("as_categorical", [True, False])
+    def test_replace_interval_with_method(self, as_categorical):
+        # in particular interval that can't hold NA
+
+        idx = pd.IntervalIndex.from_breaks(range(4))
+        ser = pd.Series(idx)
+        if as_categorical:
+            ser = ser.astype("category")
+
+        self._check_replace_with_method(ser)
+
+    @pytest.mark.parametrize("as_period", [True, False])
+    @pytest.mark.parametrize("as_categorical", [True, False])
+    def test_replace_datetimelike_with_method(self, as_period, as_categorical):
+        idx = pd.date_range("2016-01-01", periods=5, tz="US/Pacific")
+        if as_period:
+            idx = idx.tz_localize(None).to_period("D")
+
+        ser = pd.Series(idx)
+        ser.iloc[-2] = pd.NaT
+        if as_categorical:
+            ser = ser.astype("category")
+
+        self._check_replace_with_method(ser)
+
+    def test_replace_with_compiled_regex(self):
+        # https://github.com/pandas-dev/pandas/issues/35680
+        s = pd.Series(["a", "b", "c"])
+        regex = re.compile("^a$")
+        result = s.replace({regex: "z"}, regex=True)
+        expected = pd.Series(["z", "b", "c"])
+        tm.assert_series_equal(result, expected)
+
+    def test_pandas_replace_na(self):
+        # GH#43344
+        ser = pd.Series(["AA", "BB", "CC", "DD", "EE", "", pd.NA], dtype="string")
+        regex_mapping = {
+            "AA": "CC",
+            "BB": "CC",
+            "EE": "CC",
+            "CC": "CC-REPL",
+        }
+        result = ser.replace(regex_mapping, regex=True)
+        exp = pd.Series(["CC", "CC", "CC-REPL", "DD", "CC", "", pd.NA], dtype="string")
+        tm.assert_series_equal(result, exp)
+
+    @pytest.mark.parametrize(
+        "dtype, input_data, to_replace, expected_data",
+        [
+            ("bool", [True, False], {True: False}, [False, False]),
+            ("int64", [1, 2], {1: 10, 2: 20}, [10, 20]),
+            ("Int64", [1, 2], {1: 10, 2: 20}, [10, 20]),
+            ("float64", [1.1, 2.2], {1.1: 10.1, 2.2: 20.5}, [10.1, 20.5]),
+            ("Float64", [1.1, 2.2], {1.1: 10.1, 2.2: 20.5}, [10.1, 20.5]),
+            ("string", ["one", "two"], {"one": "1", "two": "2"}, ["1", "2"]),
+            (
+                pd.IntervalDtype("int64"),
+                IntervalArray([pd.Interval(1, 2), pd.Interval(2, 3)]),
+                {pd.Interval(1, 2): pd.Interval(10, 20)},
+                IntervalArray([pd.Interval(10, 20), pd.Interval(2, 3)]),
+            ),
+            (
+                pd.IntervalDtype("float64"),
+                IntervalArray([pd.Interval(1.0, 2.7), pd.Interval(2.8, 3.1)]),
+                {pd.Interval(1.0, 2.7): pd.Interval(10.6, 20.8)},
+                IntervalArray([pd.Interval(10.6, 20.8), pd.Interval(2.8, 3.1)]),
+            ),
+            (
+                pd.PeriodDtype("M"),
+                [pd.Period("2020-05", freq="M")],
+                {pd.Period("2020-05", freq="M"): pd.Period("2020-06", freq="M")},
+                [pd.Period("2020-06", freq="M")],
+            ),
+        ],
+    )
+    def test_replace_dtype(self, dtype, input_data, to_replace, expected_data):
+        # GH#33484
+        ser = pd.Series(input_data, dtype=dtype)
+        result = ser.replace(to_replace)
+        expected = pd.Series(expected_data, dtype=dtype)
+        tm.assert_series_equal(result, expected)
+
+    def test_replace_string_dtype(self):
+        # GH#40732, GH#44940
+        ser = pd.Series(["one", "two", np.nan], dtype="string")
+        res = ser.replace({"one": "1", "two": "2"})
+        expected = pd.Series(["1", "2", np.nan], dtype="string")
+        tm.assert_series_equal(res, expected)
+
+        # GH#31644
+        ser2 = pd.Series(["A", np.nan], dtype="string")
+        res2 = ser2.replace("A", "B")
+        expected2 = pd.Series(["B", np.nan], dtype="string")
+        tm.assert_series_equal(res2, expected2)
+
+        ser3 = pd.Series(["A", "B"], dtype="string")
+        res3 = ser3.replace("A", pd.NA)
+        expected3 = pd.Series([pd.NA, "B"], dtype="string")
+        tm.assert_series_equal(res3, expected3)
+
+    def test_replace_string_dtype_list_to_replace(self):
+        # GH#41215, GH#44940
+        ser = pd.Series(["abc", "def"], dtype="string")
+        res = ser.replace(["abc", "any other string"], "xyz")
+        expected = pd.Series(["xyz", "def"], dtype="string")
+        tm.assert_series_equal(res, expected)
+
+    def test_replace_string_dtype_regex(self):
+        # GH#31644
+        ser = pd.Series(["A", "B"], dtype="string")
+        res = ser.replace(r".", "C", regex=True)
+        expected = pd.Series(["C", "C"], dtype="string")
+        tm.assert_series_equal(res, expected)
+
+    def test_replace_nullable_numeric(self):
+        # GH#40732, GH#44940
+
+        floats = pd.Series([1.0, 2.0, 3.999, 4.4], dtype=pd.Float64Dtype())
+        assert floats.replace({1.0: 9}).dtype == floats.dtype
+        assert floats.replace(1.0, 9).dtype == floats.dtype
+        assert floats.replace({1.0: 9.0}).dtype == floats.dtype
+        assert floats.replace(1.0, 9.0).dtype == floats.dtype
+
+        res = floats.replace(to_replace=[1.0, 2.0], value=[9.0, 10.0])
+        assert res.dtype == floats.dtype
+
+        ints = pd.Series([1, 2, 3, 4], dtype=pd.Int64Dtype())
+        assert ints.replace({1: 9}).dtype == ints.dtype
+        assert ints.replace(1, 9).dtype == ints.dtype
+        assert ints.replace({1: 9.0}).dtype == ints.dtype
+        assert ints.replace(1, 9.0).dtype == ints.dtype
+
+        # nullable (for now) raises instead of casting
+        with pytest.raises(TypeError, match="Invalid value"):
+            ints.replace({1: 9.5})
+        with pytest.raises(TypeError, match="Invalid value"):
+            ints.replace(1, 9.5)
+
+    @pytest.mark.xfail(using_pyarrow_string_dtype(), reason="can't fill 1 in string")
+    @pytest.mark.parametrize("regex", [False, True])
+    def test_replace_regex_dtype_series(self, regex):
+        # GH-48644
+        series = pd.Series(["0"])
+        expected = pd.Series([1])
+        msg = "Downcasting behavior in `replace`"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = series.replace(to_replace="0", value=1, regex=regex)
+        tm.assert_series_equal(result, expected)
+
+    def test_replace_different_int_types(self, any_int_numpy_dtype):
+        # GH#45311
+        labs = pd.Series([1, 1, 1, 0, 0, 2, 2, 2], dtype=any_int_numpy_dtype)
+
+        maps = pd.Series([0, 2, 1], dtype=any_int_numpy_dtype)
+        map_dict = dict(zip(maps.values, maps.index))
+
+        result = labs.replace(map_dict)
+        expected = labs.replace({0: 0, 2: 1, 1: 2})
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("val", [2, np.nan, 2.0])
+    def test_replace_value_none_dtype_numeric(self, val):
+        # GH#48231
+        ser = pd.Series([1, val])
+        result = ser.replace(val, None)
+        expected = pd.Series([1, None], dtype=object)
+        tm.assert_series_equal(result, expected)
+
+    def test_replace_change_dtype_series(self, using_infer_string):
+        # GH#25797
+        df = pd.DataFrame.from_dict({"Test": ["0.5", True, "0.6"]})
+        warn = FutureWarning if using_infer_string else None
+        with tm.assert_produces_warning(warn, match="Downcasting"):
+            df["Test"] = df["Test"].replace([True], [np.nan])
+        expected = pd.DataFrame.from_dict({"Test": ["0.5", np.nan, "0.6"]})
+        tm.assert_frame_equal(df, expected)
+
+        df = pd.DataFrame.from_dict({"Test": ["0.5", None, "0.6"]})
+        df["Test"] = df["Test"].replace([None], [np.nan])
+        tm.assert_frame_equal(df, expected)
+
+        df = pd.DataFrame.from_dict({"Test": ["0.5", None, "0.6"]})
+        df["Test"] = df["Test"].fillna(np.nan)
+        tm.assert_frame_equal(df, expected)
+
+    @pytest.mark.parametrize("dtype", ["object", "Int64"])
+    def test_replace_na_in_obj_column(self, dtype):
+        # GH#47480
+        ser = pd.Series([0, 1, pd.NA], dtype=dtype)
+        expected = pd.Series([0, 2, pd.NA], dtype=dtype)
+        result = ser.replace(to_replace=1, value=2)
+        tm.assert_series_equal(result, expected)
+
+        ser.replace(to_replace=1, value=2, inplace=True)
+        tm.assert_series_equal(ser, expected)
+
+    @pytest.mark.parametrize("val", [0, 0.5])
+    def test_replace_numeric_column_with_na(self, val):
+        # GH#50758
+        ser = pd.Series([val, 1])
+        expected = pd.Series([val, pd.NA])
+        result = ser.replace(to_replace=1, value=pd.NA)
+        tm.assert_series_equal(result, expected)
+
+        ser.replace(to_replace=1, value=pd.NA, inplace=True)
+        tm.assert_series_equal(ser, expected)
+
+    def test_replace_ea_float_with_bool(self):
+        # GH#55398
+        ser = pd.Series([0.0], dtype="Float64")
+        expected = ser.copy()
+        result = ser.replace(False, 1.0)
+        tm.assert_series_equal(result, expected)
+
+        ser = pd.Series([False], dtype="boolean")
+        expected = ser.copy()
+        result = ser.replace(0.0, True)
+        tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_reset_index.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_reset_index.py
new file mode 100644
index 0000000000000000000000000000000000000000..48e2608a1032a0664f7b6b3cb2c354e7f7c531f6
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_reset_index.py
@@ -0,0 +1,225 @@
+from datetime import datetime
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    RangeIndex,
+    Series,
+    date_range,
+    option_context,
+)
+import pandas._testing as tm
+
+
+class TestResetIndex:
+    def test_reset_index_dti_round_trip(self):
+        dti = date_range(start="1/1/2001", end="6/1/2001", freq="D")._with_freq(None)
+        d1 = DataFrame({"v": np.random.default_rng(2).random(len(dti))}, index=dti)
+        d2 = d1.reset_index()
+        assert d2.dtypes.iloc[0] == np.dtype("M8[ns]")
+        d3 = d2.set_index("index")
+        tm.assert_frame_equal(d1, d3, check_names=False)
+
+        # GH#2329
+        stamp = datetime(2012, 11, 22)
+        df = DataFrame([[stamp, 12.1]], columns=["Date", "Value"])
+        df = df.set_index("Date")
+
+        assert df.index[0] == stamp
+        assert df.reset_index()["Date"].iloc[0] == stamp
+
+    def test_reset_index(self):
+        df = DataFrame(
+            1.1 * np.arange(120).reshape((30, 4)),
+            columns=Index(list("ABCD"), dtype=object),
+            index=Index([f"i-{i}" for i in range(30)], dtype=object),
+        )[:5]
+        ser = df.stack(future_stack=True)
+        ser.index.names = ["hash", "category"]
+
+        ser.name = "value"
+        df = ser.reset_index()
+        assert "value" in df
+
+        df = ser.reset_index(name="value2")
+        assert "value2" in df
+
+        # check inplace
+        s = ser.reset_index(drop=True)
+        s2 = ser
+        return_value = s2.reset_index(drop=True, inplace=True)
+        assert return_value is None
+        tm.assert_series_equal(s, s2)
+
+        # level
+        index = MultiIndex(
+            levels=[["bar"], ["one", "two", "three"], [0, 1]],
+            codes=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]],
+        )
+        s = Series(np.random.default_rng(2).standard_normal(6), index=index)
+        rs = s.reset_index(level=1)
+        assert len(rs.columns) == 2
+
+        rs = s.reset_index(level=[0, 2], drop=True)
+        tm.assert_index_equal(rs.index, Index(index.get_level_values(1)))
+        assert isinstance(rs, Series)
+
+    def test_reset_index_name(self):
+        s = Series([1, 2, 3], index=Index(range(3), name="x"))
+        assert s.reset_index().index.name is None
+        assert s.reset_index(drop=True).index.name is None
+
+    def test_reset_index_level(self):
+        df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["A", "B", "C"])
+
+        for levels in ["A", "B"], [0, 1]:
+            # With MultiIndex
+            s = df.set_index(["A", "B"])["C"]
+
+            result = s.reset_index(level=levels[0])
+            tm.assert_frame_equal(result, df.set_index("B"))
+
+            result = s.reset_index(level=levels[:1])
+            tm.assert_frame_equal(result, df.set_index("B"))
+
+            result = s.reset_index(level=levels)
+            tm.assert_frame_equal(result, df)
+
+            result = df.set_index(["A", "B"]).reset_index(level=levels, drop=True)
+            tm.assert_frame_equal(result, df[["C"]])
+
+            with pytest.raises(KeyError, match="Level E "):
+                s.reset_index(level=["A", "E"])
+
+            # With single-level Index
+            s = df.set_index("A")["B"]
+
+            result = s.reset_index(level=levels[0])
+            tm.assert_frame_equal(result, df[["A", "B"]])
+
+            result = s.reset_index(level=levels[:1])
+            tm.assert_frame_equal(result, df[["A", "B"]])
+
+            result = s.reset_index(level=levels[0], drop=True)
+            tm.assert_series_equal(result, df["B"])
+
+            with pytest.raises(IndexError, match="Too many levels"):
+                s.reset_index(level=[0, 1, 2])
+
+        # Check that .reset_index([],drop=True) doesn't fail
+        result = Series(range(4)).reset_index([], drop=True)
+        expected = Series(range(4))
+        tm.assert_series_equal(result, expected)
+
+    def test_reset_index_range(self):
+        # GH 12071
+        s = Series(range(2), name="A", dtype="int64")
+        series_result = s.reset_index()
+        assert isinstance(series_result.index, RangeIndex)
+        series_expected = DataFrame(
+            [[0, 0], [1, 1]], columns=["index", "A"], index=RangeIndex(stop=2)
+        )
+        tm.assert_frame_equal(series_result, series_expected)
+
+    def test_reset_index_drop_errors(self):
+        #  GH 20925
+
+        # KeyError raised for series index when passed level name is missing
+        s = Series(range(4))
+        with pytest.raises(KeyError, match="does not match index name"):
+            s.reset_index("wrong", drop=True)
+        with pytest.raises(KeyError, match="does not match index name"):
+            s.reset_index("wrong")
+
+        # KeyError raised for series when level to be dropped is missing
+        s = Series(range(4), index=MultiIndex.from_product([[1, 2]] * 2))
+        with pytest.raises(KeyError, match="not found"):
+            s.reset_index("wrong", drop=True)
+
+    def test_reset_index_with_drop(self):
+        arrays = [
+            ["bar", "bar", "baz", "baz", "qux", "qux", "foo", "foo"],
+            ["one", "two", "one", "two", "one", "two", "one", "two"],
+        ]
+        tuples = zip(*arrays)
+        index = MultiIndex.from_tuples(tuples)
+        data = np.random.default_rng(2).standard_normal(8)
+        ser = Series(data, index=index)
+        ser.iloc[3] = np.nan
+
+        deleveled = ser.reset_index()
+        assert isinstance(deleveled, DataFrame)
+        assert len(deleveled.columns) == len(ser.index.levels) + 1
+        assert deleveled.index.name == ser.index.name
+
+        deleveled = ser.reset_index(drop=True)
+        assert isinstance(deleveled, Series)
+        assert deleveled.index.name == ser.index.name
+
+    def test_reset_index_inplace_and_drop_ignore_name(self):
+        # GH#44575
+        ser = Series(range(2), name="old")
+        ser.reset_index(name="new", drop=True, inplace=True)
+        expected = Series(range(2), name="old")
+        tm.assert_series_equal(ser, expected)
+
+    def test_reset_index_drop_infer_string(self):
+        # GH#56160
+        pytest.importorskip("pyarrow")
+        ser = Series(["a", "b", "c"], dtype=object)
+        with option_context("future.infer_string", True):
+            result = ser.reset_index(drop=True)
+        tm.assert_series_equal(result, ser)
+
+
+@pytest.mark.parametrize(
+    "array, dtype",
+    [
+        (["a", "b"], object),
+        (
+            pd.period_range("12-1-2000", periods=2, freq="Q-DEC"),
+            pd.PeriodDtype(freq="Q-DEC"),
+        ),
+    ],
+)
+def test_reset_index_dtypes_on_empty_series_with_multiindex(
+    array, dtype, using_infer_string
+):
+    # GH 19602 - Preserve dtype on empty Series with MultiIndex
+    idx = MultiIndex.from_product([[0, 1], [0.5, 1.0], array])
+    result = Series(dtype=object, index=idx)[:0].reset_index().dtypes
+    exp = "string" if using_infer_string else object
+    expected = Series(
+        {
+            "level_0": np.int64,
+            "level_1": np.float64,
+            "level_2": exp if dtype == object else dtype,
+            0: object,
+        }
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "names, expected_names",
+    [
+        (["A", "A"], ["A", "A"]),
+        (["level_1", None], ["level_1", "level_1"]),
+    ],
+)
+@pytest.mark.parametrize("allow_duplicates", [False, True])
+def test_column_name_duplicates(names, expected_names, allow_duplicates):
+    # GH#44755 reset_index with duplicate column labels
+    s = Series([1], index=MultiIndex.from_arrays([[1], [1]], names=names))
+    if allow_duplicates:
+        result = s.reset_index(allow_duplicates=True)
+        expected = DataFrame([[1, 1, 1]], columns=expected_names + [0])
+        tm.assert_frame_equal(result, expected)
+    else:
+        with pytest.raises(ValueError, match="cannot insert"):
+            s.reset_index()
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_round.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_round.py
new file mode 100644
index 0000000000000000000000000000000000000000..c330b7a7dfbbba7f68d5da6d038e6f85f9eedcb4
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_round.py
@@ -0,0 +1,74 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import Series
+import pandas._testing as tm
+
+
+class TestSeriesRound:
+    def test_round(self, datetime_series):
+        datetime_series.index.name = "index_name"
+        result = datetime_series.round(2)
+        expected = Series(
+            np.round(datetime_series.values, 2), index=datetime_series.index, name="ts"
+        )
+        tm.assert_series_equal(result, expected)
+        assert result.name == datetime_series.name
+
+    def test_round_numpy(self, any_float_dtype):
+        # See GH#12600
+        ser = Series([1.53, 1.36, 0.06], dtype=any_float_dtype)
+        out = np.round(ser, decimals=0)
+        expected = Series([2.0, 1.0, 0.0], dtype=any_float_dtype)
+        tm.assert_series_equal(out, expected)
+
+        msg = "the 'out' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            np.round(ser, decimals=0, out=ser)
+
+    def test_round_numpy_with_nan(self, any_float_dtype):
+        # See GH#14197
+        ser = Series([1.53, np.nan, 0.06], dtype=any_float_dtype)
+        with tm.assert_produces_warning(None):
+            result = ser.round()
+        expected = Series([2.0, np.nan, 0.0], dtype=any_float_dtype)
+        tm.assert_series_equal(result, expected)
+
+    def test_round_builtin(self, any_float_dtype):
+        ser = Series(
+            [1.123, 2.123, 3.123],
+            index=range(3),
+            dtype=any_float_dtype,
+        )
+        result = round(ser)
+        expected_rounded0 = Series(
+            [1.0, 2.0, 3.0], index=range(3), dtype=any_float_dtype
+        )
+        tm.assert_series_equal(result, expected_rounded0)
+
+        decimals = 2
+        expected_rounded = Series(
+            [1.12, 2.12, 3.12], index=range(3), dtype=any_float_dtype
+        )
+        result = round(ser, decimals)
+        tm.assert_series_equal(result, expected_rounded)
+
+    @pytest.mark.parametrize("method", ["round", "floor", "ceil"])
+    @pytest.mark.parametrize("freq", ["s", "5s", "min", "5min", "h", "5h"])
+    def test_round_nat(self, method, freq, unit):
+        # GH14940, GH#56158
+        ser = Series([pd.NaT], dtype=f"M8[{unit}]")
+        expected = Series(pd.NaT, dtype=f"M8[{unit}]")
+        round_method = getattr(ser.dt, method)
+        result = round_method(freq)
+        tm.assert_series_equal(result, expected)
+
+    def test_round_ea_boolean(self):
+        # GH#55936
+        ser = Series([True, False], dtype="boolean")
+        expected = ser.copy()
+        result = ser.round(2)
+        tm.assert_series_equal(result, expected)
+        result.iloc[0] = False
+        tm.assert_series_equal(ser, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_searchsorted.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_searchsorted.py
new file mode 100644
index 0000000000000000000000000000000000000000..239496052b99b42df262262a9ac89b71c93e0a26
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_searchsorted.py
@@ -0,0 +1,77 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Series,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.api.types import is_scalar
+
+
+class TestSeriesSearchSorted:
+    def test_searchsorted(self):
+        ser = Series([1, 2, 3])
+
+        result = ser.searchsorted(1, side="left")
+        assert is_scalar(result)
+        assert result == 0
+
+        result = ser.searchsorted(1, side="right")
+        assert is_scalar(result)
+        assert result == 1
+
+    def test_searchsorted_numeric_dtypes_scalar(self):
+        ser = Series([1, 2, 90, 1000, 3e9])
+        res = ser.searchsorted(30)
+        assert is_scalar(res)
+        assert res == 2
+
+        res = ser.searchsorted([30])
+        exp = np.array([2], dtype=np.intp)
+        tm.assert_numpy_array_equal(res, exp)
+
+    def test_searchsorted_numeric_dtypes_vector(self):
+        ser = Series([1, 2, 90, 1000, 3e9])
+        res = ser.searchsorted([91, 2e6])
+        exp = np.array([3, 4], dtype=np.intp)
+        tm.assert_numpy_array_equal(res, exp)
+
+    def test_searchsorted_datetime64_scalar(self):
+        ser = Series(date_range("20120101", periods=10, freq="2D"))
+        val = Timestamp("20120102")
+        res = ser.searchsorted(val)
+        assert is_scalar(res)
+        assert res == 1
+
+    def test_searchsorted_datetime64_scalar_mixed_timezones(self):
+        # GH 30086
+        ser = Series(date_range("20120101", periods=10, freq="2D", tz="UTC"))
+        val = Timestamp("20120102", tz="America/New_York")
+        res = ser.searchsorted(val)
+        assert is_scalar(res)
+        assert res == 1
+
+    def test_searchsorted_datetime64_list(self):
+        ser = Series(date_range("20120101", periods=10, freq="2D"))
+        vals = [Timestamp("20120102"), Timestamp("20120104")]
+        res = ser.searchsorted(vals)
+        exp = np.array([1, 2], dtype=np.intp)
+        tm.assert_numpy_array_equal(res, exp)
+
+    def test_searchsorted_sorter(self):
+        # GH8490
+        ser = Series([3, 1, 2])
+        res = ser.searchsorted([0, 3], sorter=np.argsort(ser))
+        exp = np.array([0, 2], dtype=np.intp)
+        tm.assert_numpy_array_equal(res, exp)
+
+    def test_searchsorted_dataframe_fail(self):
+        # GH#49620
+        ser = Series([1, 2, 3, 4, 5])
+        vals = pd.DataFrame([[1, 2], [3, 4]])
+        msg = "Value must be 1-D array-like or scalar, DataFrame is not supported"
+        with pytest.raises(ValueError, match=msg):
+            ser.searchsorted(vals)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_set_name.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_set_name.py
new file mode 100644
index 0000000000000000000000000000000000000000..cbc8ebde7a8ab79fabe94781123844c856c9c78b
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_set_name.py
@@ -0,0 +1,21 @@
+from datetime import datetime
+
+from pandas import Series
+
+
+class TestSetName:
+    def test_set_name(self):
+        ser = Series([1, 2, 3])
+        ser2 = ser._set_name("foo")
+        assert ser2.name == "foo"
+        assert ser.name is None
+        assert ser is not ser2
+
+    def test_set_name_attribute(self):
+        ser = Series([1, 2, 3])
+        ser2 = Series([1, 2, 3], name="bar")
+        for name in [7, 7.0, "name", datetime(2001, 1, 1), (1,), "\u05D0"]:
+            ser.name = name
+            assert ser.name == name
+            ser2.name = name
+            assert ser2.name == name
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_size.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_size.py
new file mode 100644
index 0000000000000000000000000000000000000000..20a454996fa4488501d6f623ad3afc6fa38e5634
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_size.py
@@ -0,0 +1,22 @@
+import pytest
+
+from pandas import Series
+
+
+@pytest.mark.parametrize(
+    "data, index, expected",
+    [
+        ([1, 2, 3], None, 3),
+        ({"a": 1, "b": 2, "c": 3}, None, 3),
+        ([1, 2, 3], ["x", "y", "z"], 3),
+        ([1, 2, 3, 4, 5], ["x", "y", "z", "w", "n"], 5),
+        ([1, 2, 3], None, 3),
+        ([1, 2, 3], ["x", "y", "z"], 3),
+        ([1, 2, 3, 4], ["x", "y", "z", "w"], 4),
+    ],
+)
+def test_series(data, index, expected):
+    # GH#52897
+    ser = Series(data, index=index)
+    assert ser.size == expected
+    assert isinstance(ser.size, int)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_to_csv.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_to_csv.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c17013d621c7f78c1e8ae7e1346660aebe79b1e
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_to_csv.py
@@ -0,0 +1,182 @@
+from datetime import datetime
+from io import StringIO
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import Series
+import pandas._testing as tm
+
+from pandas.io.common import get_handle
+
+
+class TestSeriesToCSV:
+    def read_csv(self, path, **kwargs):
+        params = {"index_col": 0, "header": None}
+        params.update(**kwargs)
+
+        header = params.get("header")
+        out = pd.read_csv(path, **params).squeeze("columns")
+
+        if header is None:
+            out.name = out.index.name = None
+
+        return out
+
+    def test_from_csv(self, datetime_series, string_series):
+        # freq doesn't round-trip
+        datetime_series.index = datetime_series.index._with_freq(None)
+
+        with tm.ensure_clean() as path:
+            datetime_series.to_csv(path, header=False)
+            ts = self.read_csv(path, parse_dates=True)
+            tm.assert_series_equal(datetime_series, ts, check_names=False)
+
+            assert ts.name is None
+            assert ts.index.name is None
+
+            # see gh-10483
+            datetime_series.to_csv(path, header=True)
+            ts_h = self.read_csv(path, header=0)
+            assert ts_h.name == "ts"
+
+            string_series.to_csv(path, header=False)
+            series = self.read_csv(path)
+            tm.assert_series_equal(string_series, series, check_names=False)
+
+            assert series.name is None
+            assert series.index.name is None
+
+            string_series.to_csv(path, header=True)
+            series_h = self.read_csv(path, header=0)
+            assert series_h.name == "series"
+
+            with open(path, "w", encoding="utf-8") as outfile:
+                outfile.write("1998-01-01|1.0\n1999-01-01|2.0")
+
+            series = self.read_csv(path, sep="|", parse_dates=True)
+            check_series = Series(
+                {datetime(1998, 1, 1): 1.0, datetime(1999, 1, 1): 2.0}
+            )
+            tm.assert_series_equal(check_series, series)
+
+            series = self.read_csv(path, sep="|", parse_dates=False)
+            check_series = Series({"1998-01-01": 1.0, "1999-01-01": 2.0})
+            tm.assert_series_equal(check_series, series)
+
+    def test_to_csv(self, datetime_series):
+        with tm.ensure_clean() as path:
+            datetime_series.to_csv(path, header=False)
+
+            with open(path, newline=None, encoding="utf-8") as f:
+                lines = f.readlines()
+            assert lines[1] != "\n"
+
+            datetime_series.to_csv(path, index=False, header=False)
+            arr = np.loadtxt(path)
+            tm.assert_almost_equal(arr, datetime_series.values)
+
+    def test_to_csv_unicode_index(self):
+        buf = StringIO()
+        s = Series(["\u05d0", "d2"], index=["\u05d0", "\u05d1"])
+
+        s.to_csv(buf, encoding="UTF-8", header=False)
+        buf.seek(0)
+
+        s2 = self.read_csv(buf, index_col=0, encoding="UTF-8")
+        tm.assert_series_equal(s, s2)
+
+    def test_to_csv_float_format(self):
+        with tm.ensure_clean() as filename:
+            ser = Series([0.123456, 0.234567, 0.567567])
+            ser.to_csv(filename, float_format="%.2f", header=False)
+
+            rs = self.read_csv(filename)
+            xp = Series([0.12, 0.23, 0.57])
+            tm.assert_series_equal(rs, xp)
+
+    def test_to_csv_list_entries(self):
+        s = Series(["jack and jill", "jesse and frank"])
+
+        split = s.str.split(r"\s+and\s+")
+
+        buf = StringIO()
+        split.to_csv(buf, header=False)
+
+    def test_to_csv_path_is_none(self):
+        # GH 8215
+        # Series.to_csv() was returning None, inconsistent with
+        # DataFrame.to_csv() which returned string
+        s = Series([1, 2, 3])
+        csv_str = s.to_csv(path_or_buf=None, header=False)
+        assert isinstance(csv_str, str)
+
+    @pytest.mark.parametrize(
+        "s,encoding",
+        [
+            (
+                Series([0.123456, 0.234567, 0.567567], index=["A", "B", "C"], name="X"),
+                None,
+            ),
+            # GH 21241, 21118
+            (Series(["abc", "def", "ghi"], name="X"), "ascii"),
+            (Series(["123", "你好", "世界"], name="中文"), "gb2312"),
+            (
+                Series(["123", "Γειά σου", "Κόσμε"], name="Ελληνικά"),  # noqa: RUF001
+                "cp737",
+            ),
+        ],
+    )
+    def test_to_csv_compression(self, s, encoding, compression):
+        with tm.ensure_clean() as filename:
+            s.to_csv(filename, compression=compression, encoding=encoding, header=True)
+            # test the round trip - to_csv -> read_csv
+            result = pd.read_csv(
+                filename,
+                compression=compression,
+                encoding=encoding,
+                index_col=0,
+            ).squeeze("columns")
+            tm.assert_series_equal(s, result)
+
+            # test the round trip using file handle - to_csv -> read_csv
+            with get_handle(
+                filename, "w", compression=compression, encoding=encoding
+            ) as handles:
+                s.to_csv(handles.handle, encoding=encoding, header=True)
+
+            result = pd.read_csv(
+                filename,
+                compression=compression,
+                encoding=encoding,
+                index_col=0,
+            ).squeeze("columns")
+            tm.assert_series_equal(s, result)
+
+            # explicitly ensure file was compressed
+            with tm.decompress_file(filename, compression) as fh:
+                text = fh.read().decode(encoding or "utf8")
+                assert s.name in text
+
+            with tm.decompress_file(filename, compression) as fh:
+                tm.assert_series_equal(
+                    s,
+                    pd.read_csv(fh, index_col=0, encoding=encoding).squeeze("columns"),
+                )
+
+    def test_to_csv_interval_index(self, using_infer_string):
+        # GH 28210
+        s = Series(["foo", "bar", "baz"], index=pd.interval_range(0, 3))
+
+        with tm.ensure_clean("__tmp_to_csv_interval_index__.csv") as path:
+            s.to_csv(path, header=False)
+            result = self.read_csv(path, index_col=0)
+
+            # can't roundtrip intervalindex via read_csv so check string repr (GH 23595)
+            expected = s.copy()
+            if using_infer_string:
+                expected.index = expected.index.astype("string[pyarrow_numpy]")
+            else:
+                expected.index = expected.index.astype(str)
+            tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_to_dict.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_to_dict.py
new file mode 100644
index 0000000000000000000000000000000000000000..41c01f4537f23fdb90110685d2e774427916aee1
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_to_dict.py
@@ -0,0 +1,38 @@
+import collections
+
+import numpy as np
+import pytest
+
+from pandas import Series
+import pandas._testing as tm
+
+
+class TestSeriesToDict:
+    @pytest.mark.parametrize(
+        "mapping", (dict, collections.defaultdict(list), collections.OrderedDict)
+    )
+    def test_to_dict(self, mapping, datetime_series):
+        # GH#16122
+        result = Series(datetime_series.to_dict(into=mapping), name="ts")
+        expected = datetime_series.copy()
+        expected.index = expected.index._with_freq(None)
+        tm.assert_series_equal(result, expected)
+
+        from_method = Series(datetime_series.to_dict(into=collections.Counter))
+        from_constructor = Series(collections.Counter(datetime_series.items()))
+        tm.assert_series_equal(from_method, from_constructor)
+
+    @pytest.mark.parametrize(
+        "input",
+        (
+            {"a": np.int64(64), "b": 10},
+            {"a": np.int64(64), "b": 10, "c": "ABC"},
+            {"a": np.uint64(64), "b": 10, "c": "ABC"},
+        ),
+    )
+    def test_to_dict_return_types(self, input):
+        # GH25969
+
+        d = Series(input).to_dict()
+        assert isinstance(d["a"], int)
+        assert isinstance(d["b"], int)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_to_frame.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_to_frame.py
new file mode 100644
index 0000000000000000000000000000000000000000..0eadf696b34cc034d2be76ce5daba2cff679da74
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_to_frame.py
@@ -0,0 +1,63 @@
+import pytest
+
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestToFrame:
+    def test_to_frame_respects_name_none(self):
+        # GH#44212 if we explicitly pass name=None, then that should be respected,
+        #  not changed to 0
+        # GH-45448 this is first deprecated & enforced in 2.0
+        ser = Series(range(3))
+        result = ser.to_frame(None)
+
+        exp_index = Index([None], dtype=object)
+        tm.assert_index_equal(result.columns, exp_index)
+
+        result = ser.rename("foo").to_frame(None)
+        exp_index = Index([None], dtype=object)
+        tm.assert_index_equal(result.columns, exp_index)
+
+    def test_to_frame(self, datetime_series):
+        datetime_series.name = None
+        rs = datetime_series.to_frame()
+        xp = DataFrame(datetime_series.values, index=datetime_series.index)
+        tm.assert_frame_equal(rs, xp)
+
+        datetime_series.name = "testname"
+        rs = datetime_series.to_frame()
+        xp = DataFrame(
+            {"testname": datetime_series.values}, index=datetime_series.index
+        )
+        tm.assert_frame_equal(rs, xp)
+
+        rs = datetime_series.to_frame(name="testdifferent")
+        xp = DataFrame(
+            {"testdifferent": datetime_series.values}, index=datetime_series.index
+        )
+        tm.assert_frame_equal(rs, xp)
+
+    @pytest.mark.filterwarnings(
+        "ignore:Passing a BlockManager|Passing a SingleBlockManager:DeprecationWarning"
+    )
+    def test_to_frame_expanddim(self):
+        # GH#9762
+
+        class SubclassedSeries(Series):
+            @property
+            def _constructor_expanddim(self):
+                return SubclassedFrame
+
+        class SubclassedFrame(DataFrame):
+            pass
+
+        ser = SubclassedSeries([1, 2, 3], name="X")
+        result = ser.to_frame()
+        assert isinstance(result, SubclassedFrame)
+        expected = SubclassedFrame({"X": [1, 2, 3]})
+        tm.assert_frame_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_to_numpy.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_to_numpy.py
new file mode 100644
index 0000000000000000000000000000000000000000..4bc7631090761e720c61049f9b8fd2a7fadd89af
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_to_numpy.py
@@ -0,0 +1,49 @@
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+from pandas import (
+    NA,
+    Series,
+    Timedelta,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize("dtype", ["int64", "float64"])
+def test_to_numpy_na_value(dtype):
+    # GH#48951
+    ser = Series([1, 2, NA, 4])
+    result = ser.to_numpy(dtype=dtype, na_value=0)
+    expected = np.array([1, 2, 0, 4], dtype=dtype)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_to_numpy_cast_before_setting_na():
+    # GH#50600
+    ser = Series([1])
+    result = ser.to_numpy(dtype=np.float64, na_value=np.nan)
+    expected = np.array([1.0])
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@td.skip_if_no("pyarrow")
+def test_to_numpy_arrow_dtype_given():
+    # GH#57121
+    ser = Series([1, NA], dtype="int64[pyarrow]")
+    result = ser.to_numpy(dtype="float64")
+    expected = np.array([1.0, np.nan])
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_astype_ea_int_to_td_ts():
+    # GH#57093
+    ser = Series([1, None], dtype="Int64")
+    result = ser.astype("m8[ns]")
+    expected = Series([1, Timedelta("nat")], dtype="m8[ns]")
+    tm.assert_series_equal(result, expected)
+
+    result = ser.astype("M8[ns]")
+    expected = Series([1, Timedelta("nat")], dtype="M8[ns]")
+    tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_tolist.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_tolist.py
new file mode 100644
index 0000000000000000000000000000000000000000..4af473528e23850794139ac563cc04c6d3c54617
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_tolist.py
@@ -0,0 +1,36 @@
+import pytest
+
+import pandas.util._test_decorators as td
+
+from pandas import (
+    Interval,
+    Period,
+    Series,
+    Timedelta,
+    Timestamp,
+)
+
+
+@pytest.mark.parametrize(
+    "values, dtype, expected_dtype",
+    (
+        ([1], "int64", int),
+        ([1], "Int64", int),
+        ([1.0], "float64", float),
+        ([1.0], "Float64", float),
+        (["abc"], "object", str),
+        (["abc"], "string", str),
+        ([Interval(1, 3)], "interval", Interval),
+        ([Period("2000-01-01", "D")], "period[D]", Period),
+        ([Timedelta(days=1)], "timedelta64[ns]", Timedelta),
+        ([Timestamp("2000-01-01")], "datetime64[ns]", Timestamp),
+        pytest.param([1], "int64[pyarrow]", int, marks=td.skip_if_no("pyarrow")),
+        pytest.param([1.0], "float64[pyarrow]", float, marks=td.skip_if_no("pyarrow")),
+        pytest.param(["abc"], "string[pyarrow]", str, marks=td.skip_if_no("pyarrow")),
+    ),
+)
+def test_tolist_scalar_dtype(values, dtype, expected_dtype):
+    # GH49890
+    ser = Series(values, dtype=dtype)
+    result_dtype = type(ser.tolist()[0])
+    assert result_dtype == expected_dtype
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_truncate.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_truncate.py
new file mode 100644
index 0000000000000000000000000000000000000000..33eb5c10ae163862e342b1871669d64d74602e4e
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_truncate.py
@@ -0,0 +1,67 @@
+from datetime import datetime
+
+import pytest
+
+import pandas as pd
+from pandas import (
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestTruncate:
+    def test_truncate_datetimeindex_tz(self):
+        # GH 9243
+        idx = date_range("4/1/2005", "4/30/2005", freq="D", tz="US/Pacific")
+        s = Series(range(len(idx)), index=idx)
+        with pytest.raises(TypeError, match="Cannot compare tz-naive"):
+            # GH#36148 as of 2.0 we require tzawareness compat
+            s.truncate(datetime(2005, 4, 2), datetime(2005, 4, 4))
+
+        lb = idx[1]
+        ub = idx[3]
+        result = s.truncate(lb.to_pydatetime(), ub.to_pydatetime())
+        expected = Series([1, 2, 3], index=idx[1:4])
+        tm.assert_series_equal(result, expected)
+
+    def test_truncate_periodindex(self):
+        # GH 17717
+        idx1 = pd.PeriodIndex(
+            [pd.Period("2017-09-02"), pd.Period("2017-09-02"), pd.Period("2017-09-03")]
+        )
+        series1 = Series([1, 2, 3], index=idx1)
+        result1 = series1.truncate(after="2017-09-02")
+
+        expected_idx1 = pd.PeriodIndex(
+            [pd.Period("2017-09-02"), pd.Period("2017-09-02")]
+        )
+        tm.assert_series_equal(result1, Series([1, 2], index=expected_idx1))
+
+        idx2 = pd.PeriodIndex(
+            [pd.Period("2017-09-03"), pd.Period("2017-09-02"), pd.Period("2017-09-03")]
+        )
+        series2 = Series([1, 2, 3], index=idx2)
+        result2 = series2.sort_index().truncate(after="2017-09-02")
+
+        expected_idx2 = pd.PeriodIndex([pd.Period("2017-09-02")])
+        tm.assert_series_equal(result2, Series([2], index=expected_idx2))
+
+    def test_truncate_one_element_series(self):
+        # GH 35544
+        series = Series([0.1], index=pd.DatetimeIndex(["2020-08-04"]))
+        before = pd.Timestamp("2020-08-02")
+        after = pd.Timestamp("2020-08-04")
+
+        result = series.truncate(before=before, after=after)
+
+        # the input Series and the expected Series are the same
+        tm.assert_series_equal(result, series)
+
+    def test_truncate_index_only_one_unique_value(self):
+        # GH 42365
+        obj = Series(0, index=date_range("2021-06-30", "2021-06-30")).repeat(5)
+
+        truncated = obj.truncate("2021-06-28", "2021-07-01")
+
+        tm.assert_series_equal(truncated, obj)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_tz_localize.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_tz_localize.py
new file mode 100644
index 0000000000000000000000000000000000000000..45620a721f442ee038569cdd69c1341ac56fd858
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_tz_localize.py
@@ -0,0 +1,123 @@
+from datetime import timezone
+
+import pytest
+import pytz
+
+from pandas._libs.tslibs import timezones
+
+from pandas import (
+    DatetimeIndex,
+    NaT,
+    Series,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestTZLocalize:
+    def test_series_tz_localize_ambiguous_bool(self):
+        # make sure that we are correctly accepting bool values as ambiguous
+
+        # GH#14402
+        ts = Timestamp("2015-11-01 01:00:03")
+        expected0 = Timestamp("2015-11-01 01:00:03-0500", tz="US/Central")
+        expected1 = Timestamp("2015-11-01 01:00:03-0600", tz="US/Central")
+
+        ser = Series([ts])
+        expected0 = Series([expected0])
+        expected1 = Series([expected1])
+
+        with tm.external_error_raised(pytz.AmbiguousTimeError):
+            ser.dt.tz_localize("US/Central")
+
+        result = ser.dt.tz_localize("US/Central", ambiguous=True)
+        tm.assert_series_equal(result, expected0)
+
+        result = ser.dt.tz_localize("US/Central", ambiguous=[True])
+        tm.assert_series_equal(result, expected0)
+
+        result = ser.dt.tz_localize("US/Central", ambiguous=False)
+        tm.assert_series_equal(result, expected1)
+
+        result = ser.dt.tz_localize("US/Central", ambiguous=[False])
+        tm.assert_series_equal(result, expected1)
+
+    def test_series_tz_localize_matching_index(self):
+        # Matching the index of the result with that of the original series
+        # GH 43080
+        dt_series = Series(
+            date_range(start="2021-01-01T02:00:00", periods=5, freq="1D"),
+            index=[2, 6, 7, 8, 11],
+            dtype="category",
+        )
+        result = dt_series.dt.tz_localize("Europe/Berlin")
+        expected = Series(
+            date_range(
+                start="2021-01-01T02:00:00", periods=5, freq="1D", tz="Europe/Berlin"
+            ),
+            index=[2, 6, 7, 8, 11],
+        )
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "method, exp",
+        [
+            ["shift_forward", "2015-03-29 03:00:00"],
+            ["shift_backward", "2015-03-29 01:59:59.999999999"],
+            ["NaT", NaT],
+            ["raise", None],
+            ["foo", "invalid"],
+        ],
+    )
+    def test_tz_localize_nonexistent(self, warsaw, method, exp, unit):
+        # GH 8917
+        tz = warsaw
+        n = 60
+        dti = date_range(start="2015-03-29 02:00:00", periods=n, freq="min", unit=unit)
+        ser = Series(1, index=dti)
+        df = ser.to_frame()
+
+        if method == "raise":
+            with tm.external_error_raised(pytz.NonExistentTimeError):
+                dti.tz_localize(tz, nonexistent=method)
+            with tm.external_error_raised(pytz.NonExistentTimeError):
+                ser.tz_localize(tz, nonexistent=method)
+            with tm.external_error_raised(pytz.NonExistentTimeError):
+                df.tz_localize(tz, nonexistent=method)
+
+        elif exp == "invalid":
+            msg = (
+                "The nonexistent argument must be one of "
+                "'raise', 'NaT', 'shift_forward', 'shift_backward' "
+                "or a timedelta object"
+            )
+            with pytest.raises(ValueError, match=msg):
+                dti.tz_localize(tz, nonexistent=method)
+            with pytest.raises(ValueError, match=msg):
+                ser.tz_localize(tz, nonexistent=method)
+            with pytest.raises(ValueError, match=msg):
+                df.tz_localize(tz, nonexistent=method)
+
+        else:
+            result = ser.tz_localize(tz, nonexistent=method)
+            expected = Series(1, index=DatetimeIndex([exp] * n, tz=tz).as_unit(unit))
+            tm.assert_series_equal(result, expected)
+
+            result = df.tz_localize(tz, nonexistent=method)
+            expected = expected.to_frame()
+            tm.assert_frame_equal(result, expected)
+
+            res_index = dti.tz_localize(tz, nonexistent=method)
+            tm.assert_index_equal(res_index, expected.index)
+
+    @pytest.mark.parametrize("tzstr", ["US/Eastern", "dateutil/US/Eastern"])
+    def test_series_tz_localize_empty(self, tzstr):
+        # GH#2248
+        ser = Series(dtype=object)
+
+        ser2 = ser.tz_localize("utc")
+        assert ser2.index.tz == timezone.utc
+
+        ser2 = ser.tz_localize(tzstr)
+        timezones.tz_compare(ser2.index.tz, timezones.maybe_get_tz(tzstr))
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_unique.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_unique.py
new file mode 100644
index 0000000000000000000000000000000000000000..14e247332503253ede23e94fe1335e712a2675e7
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_unique.py
@@ -0,0 +1,76 @@
+import numpy as np
+
+from pandas import (
+    Categorical,
+    IntervalIndex,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestUnique:
+    def test_unique_uint64(self):
+        ser = Series([1, 2, 2**63, 2**63], dtype=np.uint64)
+        res = ser.unique()
+        exp = np.array([1, 2, 2**63], dtype=np.uint64)
+        tm.assert_numpy_array_equal(res, exp)
+
+    def test_unique_data_ownership(self):
+        # it works! GH#1807
+        Series(Series(["a", "c", "b"]).unique()).sort_values()
+
+    def test_unique(self):
+        # GH#714 also, dtype=float
+        ser = Series([1.2345] * 100)
+        ser[::2] = np.nan
+        result = ser.unique()
+        assert len(result) == 2
+
+        # explicit f4 dtype
+        ser = Series([1.2345] * 100, dtype="f4")
+        ser[::2] = np.nan
+        result = ser.unique()
+        assert len(result) == 2
+
+    def test_unique_nan_object_dtype(self):
+        # NAs in object arrays GH#714
+        ser = Series(["foo"] * 100, dtype="O")
+        ser[::2] = np.nan
+        result = ser.unique()
+        assert len(result) == 2
+
+    def test_unique_none(self):
+        # decision about None
+        ser = Series([1, 2, 3, None, None, None], dtype=object)
+        result = ser.unique()
+        expected = np.array([1, 2, 3, None], dtype=object)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_unique_categorical(self):
+        # GH#18051
+        cat = Categorical([])
+        ser = Series(cat)
+        result = ser.unique()
+        tm.assert_categorical_equal(result, cat)
+
+        cat = Categorical([np.nan])
+        ser = Series(cat)
+        result = ser.unique()
+        tm.assert_categorical_equal(result, cat)
+
+    def test_tz_unique(self):
+        # GH 46128
+        dti1 = date_range("2016-01-01", periods=3)
+        ii1 = IntervalIndex.from_breaks(dti1)
+        ser1 = Series(ii1)
+        uni1 = ser1.unique()
+        tm.assert_interval_array_equal(ser1.array, uni1)
+
+        dti2 = date_range("2016-01-01", periods=3, tz="US/Eastern")
+        ii2 = IntervalIndex.from_breaks(dti2)
+        ser2 = Series(ii2)
+        uni2 = ser2.unique()
+        tm.assert_interval_array_equal(ser2.array, uni2)
+
+        assert uni1.dtype != uni2.dtype
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_unstack.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_unstack.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c70e839c8e206c0b0e07a1046132666643329aa
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_unstack.py
@@ -0,0 +1,169 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+
+
+def test_unstack_preserves_object():
+    mi = MultiIndex.from_product([["bar", "foo"], ["one", "two"]])
+
+    ser = Series(np.arange(4.0), index=mi, dtype=object)
+
+    res1 = ser.unstack()
+    assert (res1.dtypes == object).all()
+
+    res2 = ser.unstack(level=0)
+    assert (res2.dtypes == object).all()
+
+
+def test_unstack():
+    index = MultiIndex(
+        levels=[["bar", "foo"], ["one", "three", "two"]],
+        codes=[[1, 1, 0, 0], [0, 1, 0, 2]],
+    )
+
+    s = Series(np.arange(4.0), index=index)
+    unstacked = s.unstack()
+
+    expected = DataFrame(
+        [[2.0, np.nan, 3.0], [0.0, 1.0, np.nan]],
+        index=["bar", "foo"],
+        columns=["one", "three", "two"],
+    )
+
+    tm.assert_frame_equal(unstacked, expected)
+
+    unstacked = s.unstack(level=0)
+    tm.assert_frame_equal(unstacked, expected.T)
+
+    index = MultiIndex(
+        levels=[["bar"], ["one", "two", "three"], [0, 1]],
+        codes=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]],
+    )
+    s = Series(np.random.default_rng(2).standard_normal(6), index=index)
+    exp_index = MultiIndex(
+        levels=[["one", "two", "three"], [0, 1]],
+        codes=[[0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]],
+    )
+    expected = DataFrame({"bar": s.values}, index=exp_index).sort_index(level=0)
+    unstacked = s.unstack(0).sort_index()
+    tm.assert_frame_equal(unstacked, expected)
+
+    # GH5873
+    idx = MultiIndex.from_arrays([[101, 102], [3.5, np.nan]])
+    ts = Series([1, 2], index=idx)
+    left = ts.unstack()
+    right = DataFrame(
+        [[np.nan, 1], [2, np.nan]], index=[101, 102], columns=[np.nan, 3.5]
+    )
+    tm.assert_frame_equal(left, right)
+
+    idx = MultiIndex.from_arrays(
+        [
+            ["cat", "cat", "cat", "dog", "dog"],
+            ["a", "a", "b", "a", "b"],
+            [1, 2, 1, 1, np.nan],
+        ]
+    )
+    ts = Series([1.0, 1.1, 1.2, 1.3, 1.4], index=idx)
+    right = DataFrame(
+        [[1.0, 1.3], [1.1, np.nan], [np.nan, 1.4], [1.2, np.nan]],
+        columns=["cat", "dog"],
+    )
+    tpls = [("a", 1), ("a", 2), ("b", np.nan), ("b", 1)]
+    right.index = MultiIndex.from_tuples(tpls)
+    tm.assert_frame_equal(ts.unstack(level=0), right)
+
+
+def test_unstack_tuplename_in_multiindex():
+    # GH 19966
+    idx = MultiIndex.from_product(
+        [["a", "b", "c"], [1, 2, 3]], names=[("A", "a"), ("B", "b")]
+    )
+    ser = Series(1, index=idx)
+    result = ser.unstack(("A", "a"))
+
+    expected = DataFrame(
+        [[1, 1, 1], [1, 1, 1], [1, 1, 1]],
+        columns=MultiIndex.from_tuples([("a",), ("b",), ("c",)], names=[("A", "a")]),
+        index=Index([1, 2, 3], name=("B", "b")),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "unstack_idx, expected_values, expected_index, expected_columns",
+    [
+        (
+            ("A", "a"),
+            [[1, 1], [1, 1], [1, 1], [1, 1]],
+            MultiIndex.from_tuples([(1, 3), (1, 4), (2, 3), (2, 4)], names=["B", "C"]),
+            MultiIndex.from_tuples([("a",), ("b",)], names=[("A", "a")]),
+        ),
+        (
+            (("A", "a"), "B"),
+            [[1, 1, 1, 1], [1, 1, 1, 1]],
+            Index([3, 4], name="C"),
+            MultiIndex.from_tuples(
+                [("a", 1), ("a", 2), ("b", 1), ("b", 2)], names=[("A", "a"), "B"]
+            ),
+        ),
+    ],
+)
+def test_unstack_mixed_type_name_in_multiindex(
+    unstack_idx, expected_values, expected_index, expected_columns
+):
+    # GH 19966
+    idx = MultiIndex.from_product(
+        [["a", "b"], [1, 2], [3, 4]], names=[("A", "a"), "B", "C"]
+    )
+    ser = Series(1, index=idx)
+    result = ser.unstack(unstack_idx)
+
+    expected = DataFrame(
+        expected_values, columns=expected_columns, index=expected_index
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_unstack_multi_index_categorical_values():
+    df = DataFrame(
+        np.random.default_rng(2).standard_normal((10, 4)),
+        columns=Index(list("ABCD"), dtype=object),
+        index=date_range("2000-01-01", periods=10, freq="B"),
+    )
+    mi = df.stack(future_stack=True).index.rename(["major", "minor"])
+    ser = Series(["foo"] * len(mi), index=mi, name="category", dtype="category")
+
+    result = ser.unstack()
+
+    dti = ser.index.levels[0]
+    c = pd.Categorical(["foo"] * len(dti))
+    expected = DataFrame(
+        {"A": c.copy(), "B": c.copy(), "C": c.copy(), "D": c.copy()},
+        columns=Index(list("ABCD"), name="minor"),
+        index=dti.rename("major"),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_unstack_mixed_level_names():
+    # GH#48763
+    arrays = [["a", "a"], [1, 2], ["red", "blue"]]
+    idx = MultiIndex.from_arrays(arrays, names=("x", 0, "y"))
+    ser = Series([1, 2], index=idx)
+    result = ser.unstack("x")
+    expected = DataFrame(
+        [[1], [2]],
+        columns=Index(["a"], name="x"),
+        index=MultiIndex.from_tuples([(1, "red"), (2, "blue")], names=[0, "y"]),
+    )
+    tm.assert_frame_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_values.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_values.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb1595e68264fbe5f07b014be4975657fa2fa8cf
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_values.py
@@ -0,0 +1,29 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    IntervalIndex,
+    Series,
+    period_range,
+)
+import pandas._testing as tm
+
+
+class TestValues:
+    @pytest.mark.parametrize(
+        "data",
+        [
+            period_range("2000", periods=4),
+            IntervalIndex.from_breaks([1, 2, 3, 4]),
+        ],
+    )
+    def test_values_object_extension_dtypes(self, data):
+        # https://github.com/pandas-dev/pandas/issues/23995
+        result = Series(data).values
+        expected = np.array(data.astype(object))
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_values(self, datetime_series):
+        tm.assert_almost_equal(
+            datetime_series.values, list(datetime_series), check_dtype=False
+        )
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_view.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_view.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e0ac372cd443cf9a468b469df1c22818f10aad2
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/methods/test_view.py
@@ -0,0 +1,61 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Index,
+    Series,
+    array,
+    date_range,
+)
+import pandas._testing as tm
+
+pytestmark = pytest.mark.filterwarnings(
+    "ignore:Series.view is deprecated and will be removed in a future version.:FutureWarning"  # noqa: E501
+)
+
+
+class TestView:
+    def test_view_i8_to_datetimelike(self):
+        dti = date_range("2000", periods=4, tz="US/Central")
+        ser = Series(dti.asi8)
+
+        result = ser.view(dti.dtype)
+        tm.assert_datetime_array_equal(result._values, dti._data._with_freq(None))
+
+        pi = dti.tz_localize(None).to_period("D")
+        ser = Series(pi.asi8)
+        result = ser.view(pi.dtype)
+        tm.assert_period_array_equal(result._values, pi._data)
+
+    def test_view_tz(self):
+        # GH#24024
+        ser = Series(date_range("2000", periods=4, tz="US/Central"))
+        result = ser.view("i8")
+        expected = Series(
+            [
+                946706400000000000,
+                946792800000000000,
+                946879200000000000,
+                946965600000000000,
+            ]
+        )
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "first", ["m8[ns]", "M8[ns]", "M8[ns, US/Central]", "period[D]"]
+    )
+    @pytest.mark.parametrize(
+        "second", ["m8[ns]", "M8[ns]", "M8[ns, US/Central]", "period[D]"]
+    )
+    @pytest.mark.parametrize("box", [Series, Index, array])
+    def test_view_between_datetimelike(self, first, second, box):
+        dti = date_range("2016-01-01", periods=3)
+
+        orig = box(dti)
+        obj = orig.view(first)
+        assert obj.dtype == first
+        tm.assert_numpy_array_equal(np.asarray(obj.view("i8")), dti.asi8)
+
+        res = obj.view(second)
+        assert res.dtype == second
+        tm.assert_numpy_array_equal(np.asarray(obj.view("i8")), dti.asi8)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_api.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_api.py
new file mode 100644
index 0000000000000000000000000000000000000000..29d6e2036476ef62ed1a7eea734400915d71f0ab
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_api.py
@@ -0,0 +1,300 @@
+import inspect
+import pydoc
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+    date_range,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+class TestSeriesMisc:
+    def test_tab_completion(self):
+        # GH 9910
+        s = Series(list("abcd"))
+        # Series of str values should have .str but not .dt/.cat in __dir__
+        assert "str" in dir(s)
+        assert "dt" not in dir(s)
+        assert "cat" not in dir(s)
+
+    def test_tab_completion_dt(self):
+        # similarly for .dt
+        s = Series(date_range("1/1/2015", periods=5))
+        assert "dt" in dir(s)
+        assert "str" not in dir(s)
+        assert "cat" not in dir(s)
+
+    def test_tab_completion_cat(self):
+        # Similarly for .cat, but with the twist that str and dt should be
+        # there if the categories are of that type first cat and str.
+        s = Series(list("abbcd"), dtype="category")
+        assert "cat" in dir(s)
+        assert "str" in dir(s)  # as it is a string categorical
+        assert "dt" not in dir(s)
+
+    def test_tab_completion_cat_str(self):
+        # similar to cat and str
+        s = Series(date_range("1/1/2015", periods=5)).astype("category")
+        assert "cat" in dir(s)
+        assert "str" not in dir(s)
+        assert "dt" in dir(s)  # as it is a datetime categorical
+
+    def test_tab_completion_with_categorical(self):
+        # test the tab completion display
+        ok_for_cat = [
+            "categories",
+            "codes",
+            "ordered",
+            "set_categories",
+            "add_categories",
+            "remove_categories",
+            "rename_categories",
+            "reorder_categories",
+            "remove_unused_categories",
+            "as_ordered",
+            "as_unordered",
+        ]
+
+        s = Series(list("aabbcde")).astype("category")
+        results = sorted({r for r in s.cat.__dir__() if not r.startswith("_")})
+        tm.assert_almost_equal(results, sorted(set(ok_for_cat)))
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            Index(list("ab") * 5, dtype="category"),
+            Index([str(i) for i in range(10)]),
+            Index(["foo", "bar", "baz"] * 2),
+            date_range("2020-01-01", periods=10),
+            period_range("2020-01-01", periods=10, freq="D"),
+            timedelta_range("1 day", periods=10),
+            Index(np.arange(10), dtype=np.uint64),
+            Index(np.arange(10), dtype=np.int64),
+            Index(np.arange(10), dtype=np.float64),
+            Index([True, False]),
+            Index([f"a{i}" for i in range(101)]),
+            pd.MultiIndex.from_tuples(zip("ABCD", "EFGH")),
+            pd.MultiIndex.from_tuples(zip([0, 1, 2, 3], "EFGH")),
+        ],
+    )
+    def test_index_tab_completion(self, index):
+        # dir contains string-like values of the Index.
+        s = Series(index=index, dtype=object)
+        dir_s = dir(s)
+        for i, x in enumerate(s.index.unique(level=0)):
+            if i < 100:
+                assert not isinstance(x, str) or not x.isidentifier() or x in dir_s
+            else:
+                assert x not in dir_s
+
+    @pytest.mark.parametrize("ser", [Series(dtype=object), Series([1])])
+    def test_not_hashable(self, ser):
+        msg = "unhashable type: 'Series'"
+        with pytest.raises(TypeError, match=msg):
+            hash(ser)
+
+    def test_contains(self, datetime_series):
+        tm.assert_contains_all(datetime_series.index, datetime_series)
+
+    def test_axis_alias(self):
+        s = Series([1, 2, np.nan])
+        tm.assert_series_equal(s.dropna(axis="rows"), s.dropna(axis="index"))
+        assert s.dropna().sum("rows") == 3
+        assert s._get_axis_number("rows") == 0
+        assert s._get_axis_name("rows") == "index"
+
+    def test_class_axis(self):
+        # https://github.com/pandas-dev/pandas/issues/18147
+        # no exception and no empty docstring
+        assert pydoc.getdoc(Series.index)
+
+    def test_ndarray_compat(self):
+        # test numpy compat with Series as sub-class of NDFrame
+        tsdf = DataFrame(
+            np.random.default_rng(2).standard_normal((1000, 3)),
+            columns=["A", "B", "C"],
+            index=date_range("1/1/2000", periods=1000),
+        )
+
+        def f(x):
+            return x[x.idxmax()]
+
+        result = tsdf.apply(f)
+        expected = tsdf.max()
+        tm.assert_series_equal(result, expected)
+
+    def test_ndarray_compat_like_func(self):
+        # using an ndarray like function
+        s = Series(np.random.default_rng(2).standard_normal(10))
+        result = Series(np.ones_like(s))
+        expected = Series(1, index=range(10), dtype="float64")
+        tm.assert_series_equal(result, expected)
+
+    def test_ndarray_compat_ravel(self):
+        # ravel
+        s = Series(np.random.default_rng(2).standard_normal(10))
+        with tm.assert_produces_warning(FutureWarning, match="ravel is deprecated"):
+            result = s.ravel(order="F")
+        tm.assert_almost_equal(result, s.values.ravel(order="F"))
+
+    def test_empty_method(self):
+        s_empty = Series(dtype=object)
+        assert s_empty.empty
+
+    @pytest.mark.parametrize("dtype", ["int64", object])
+    def test_empty_method_full_series(self, dtype):
+        full_series = Series(index=[1], dtype=dtype)
+        assert not full_series.empty
+
+    @pytest.mark.parametrize("dtype", [None, "Int64"])
+    def test_integer_series_size(self, dtype):
+        # GH 25580
+        s = Series(range(9), dtype=dtype)
+        assert s.size == 9
+
+    def test_attrs(self):
+        s = Series([0, 1], name="abc")
+        assert s.attrs == {}
+        s.attrs["version"] = 1
+        result = s + 1
+        assert result.attrs == {"version": 1}
+
+    def test_inspect_getmembers(self):
+        # GH38782
+        pytest.importorskip("jinja2")
+        ser = Series(dtype=object)
+        msg = "Series._data is deprecated"
+        with tm.assert_produces_warning(
+            DeprecationWarning, match=msg, check_stacklevel=False
+        ):
+            inspect.getmembers(ser)
+
+    def test_unknown_attribute(self):
+        # GH#9680
+        tdi = timedelta_range(start=0, periods=10, freq="1s")
+        ser = Series(np.random.default_rng(2).normal(size=10), index=tdi)
+        assert "foo" not in ser.__dict__
+        msg = "'Series' object has no attribute 'foo'"
+        with pytest.raises(AttributeError, match=msg):
+            ser.foo
+
+    @pytest.mark.parametrize("op", ["year", "day", "second", "weekday"])
+    def test_datetime_series_no_datelike_attrs(self, op, datetime_series):
+        # GH#7206
+        msg = f"'Series' object has no attribute '{op}'"
+        with pytest.raises(AttributeError, match=msg):
+            getattr(datetime_series, op)
+
+    def test_series_datetimelike_attribute_access(self):
+        # attribute access should still work!
+        ser = Series({"year": 2000, "month": 1, "day": 10})
+        assert ser.year == 2000
+        assert ser.month == 1
+        assert ser.day == 10
+
+    def test_series_datetimelike_attribute_access_invalid(self):
+        ser = Series({"year": 2000, "month": 1, "day": 10})
+        msg = "'Series' object has no attribute 'weekday'"
+        with pytest.raises(AttributeError, match=msg):
+            ser.weekday
+
+    @pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:FutureWarning")
+    @pytest.mark.parametrize(
+        "kernel, has_numeric_only",
+        [
+            ("skew", True),
+            ("var", True),
+            ("all", False),
+            ("prod", True),
+            ("any", False),
+            ("idxmin", False),
+            ("quantile", False),
+            ("idxmax", False),
+            ("min", True),
+            ("sem", True),
+            ("mean", True),
+            ("nunique", False),
+            ("max", True),
+            ("sum", True),
+            ("count", False),
+            ("median", True),
+            ("std", True),
+            ("backfill", False),
+            ("rank", True),
+            ("pct_change", False),
+            ("cummax", False),
+            ("shift", False),
+            ("diff", False),
+            ("cumsum", False),
+            ("cummin", False),
+            ("cumprod", False),
+            ("fillna", False),
+            ("ffill", False),
+            ("pad", False),
+            ("bfill", False),
+            ("sample", False),
+            ("tail", False),
+            ("take", False),
+            ("head", False),
+            ("cov", False),
+            ("corr", False),
+        ],
+    )
+    @pytest.mark.parametrize("dtype", [bool, int, float, object])
+    def test_numeric_only(self, kernel, has_numeric_only, dtype):
+        # GH#47500
+        ser = Series([0, 1, 1], dtype=dtype)
+        if kernel == "corrwith":
+            args = (ser,)
+        elif kernel == "corr":
+            args = (ser,)
+        elif kernel == "cov":
+            args = (ser,)
+        elif kernel == "nth":
+            args = (0,)
+        elif kernel == "fillna":
+            args = (True,)
+        elif kernel == "fillna":
+            args = ("ffill",)
+        elif kernel == "take":
+            args = ([0],)
+        elif kernel == "quantile":
+            args = (0.5,)
+        else:
+            args = ()
+        method = getattr(ser, kernel)
+        if not has_numeric_only:
+            msg = (
+                "(got an unexpected keyword argument 'numeric_only'"
+                "|too many arguments passed in)"
+            )
+            with pytest.raises(TypeError, match=msg):
+                method(*args, numeric_only=True)
+        elif dtype is object:
+            msg = f"Series.{kernel} does not allow numeric_only=True with non-numeric"
+            with pytest.raises(TypeError, match=msg):
+                method(*args, numeric_only=True)
+        else:
+            result = method(*args, numeric_only=True)
+            expected = method(*args, numeric_only=False)
+            if isinstance(expected, Series):
+                # transformer
+                tm.assert_series_equal(result, expected)
+            else:
+                # reducer
+                assert result == expected
+
+
+@pytest.mark.parametrize("converter", [int, float, complex])
+def test_float_int_deprecated(converter):
+    # GH 51101
+    with tm.assert_produces_warning(FutureWarning):
+        assert converter(Series([1])) == converter(1)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_arithmetic.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_arithmetic.py
new file mode 100644
index 0000000000000000000000000000000000000000..b40e2e99dae2eed2d1673797fcb9810bb412de0c
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_arithmetic.py
@@ -0,0 +1,984 @@
+from datetime import (
+    date,
+    timedelta,
+    timezone,
+)
+from decimal import Decimal
+import operator
+
+import numpy as np
+import pytest
+
+from pandas._libs import lib
+from pandas._libs.tslibs import IncompatibleFrequency
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DatetimeTZDtype,
+    Index,
+    Series,
+    Timedelta,
+    bdate_range,
+    date_range,
+    isna,
+)
+import pandas._testing as tm
+from pandas.core import ops
+from pandas.core.computation import expressions as expr
+from pandas.core.computation.check import NUMEXPR_INSTALLED
+
+
+@pytest.fixture(autouse=True, params=[0, 1000000], ids=["numexpr", "python"])
+def switch_numexpr_min_elements(request, monkeypatch):
+    with monkeypatch.context() as m:
+        m.setattr(expr, "_MIN_ELEMENTS", request.param)
+        yield
+
+
+def _permute(obj):
+    return obj.take(np.random.default_rng(2).permutation(len(obj)))
+
+
+class TestSeriesFlexArithmetic:
+    @pytest.mark.parametrize(
+        "ts",
+        [
+            (lambda x: x, lambda x: x * 2, False),
+            (lambda x: x, lambda x: x[::2], False),
+            (lambda x: x, lambda x: 5, True),
+            (
+                lambda x: Series(range(10), dtype=np.float64),
+                lambda x: Series(range(10), dtype=np.float64),
+                True,
+            ),
+        ],
+    )
+    @pytest.mark.parametrize(
+        "opname", ["add", "sub", "mul", "floordiv", "truediv", "pow"]
+    )
+    def test_flex_method_equivalence(self, opname, ts):
+        # check that Series.{opname} behaves like Series.__{opname}__,
+        tser = Series(
+            np.arange(20, dtype=np.float64),
+            index=date_range("2020-01-01", periods=20),
+            name="ts",
+        )
+
+        series = ts[0](tser)
+        other = ts[1](tser)
+        check_reverse = ts[2]
+
+        op = getattr(Series, opname)
+        alt = getattr(operator, opname)
+
+        result = op(series, other)
+        expected = alt(series, other)
+        tm.assert_almost_equal(result, expected)
+        if check_reverse:
+            rop = getattr(Series, "r" + opname)
+            result = rop(series, other)
+            expected = alt(other, series)
+            tm.assert_almost_equal(result, expected)
+
+    def test_flex_method_subclass_metadata_preservation(self, all_arithmetic_operators):
+        # GH 13208
+        class MySeries(Series):
+            _metadata = ["x"]
+
+            @property
+            def _constructor(self):
+                return MySeries
+
+        opname = all_arithmetic_operators
+        op = getattr(Series, opname)
+        m = MySeries([1, 2, 3], name="test")
+        m.x = 42
+        result = op(m, 1)
+        assert result.x == 42
+
+    def test_flex_add_scalar_fill_value(self):
+        # GH12723
+        ser = Series([0, 1, np.nan, 3, 4, 5])
+
+        exp = ser.fillna(0).add(2)
+        res = ser.add(2, fill_value=0)
+        tm.assert_series_equal(res, exp)
+
+    pairings = [(Series.div, operator.truediv, 1), (Series.rdiv, ops.rtruediv, 1)]
+    for op in ["add", "sub", "mul", "pow", "truediv", "floordiv"]:
+        fv = 0
+        lop = getattr(Series, op)
+        lequiv = getattr(operator, op)
+        rop = getattr(Series, "r" + op)
+        # bind op at definition time...
+        requiv = lambda x, y, op=op: getattr(operator, op)(y, x)
+        pairings.append((lop, lequiv, fv))
+        pairings.append((rop, requiv, fv))
+
+    @pytest.mark.parametrize("op, equiv_op, fv", pairings)
+    def test_operators_combine(self, op, equiv_op, fv):
+        def _check_fill(meth, op, a, b, fill_value=0):
+            exp_index = a.index.union(b.index)
+            a = a.reindex(exp_index)
+            b = b.reindex(exp_index)
+
+            amask = isna(a)
+            bmask = isna(b)
+
+            exp_values = []
+            for i in range(len(exp_index)):
+                with np.errstate(all="ignore"):
+                    if amask[i]:
+                        if bmask[i]:
+                            exp_values.append(np.nan)
+                            continue
+                        exp_values.append(op(fill_value, b[i]))
+                    elif bmask[i]:
+                        if amask[i]:
+                            exp_values.append(np.nan)
+                            continue
+                        exp_values.append(op(a[i], fill_value))
+                    else:
+                        exp_values.append(op(a[i], b[i]))
+
+            result = meth(a, b, fill_value=fill_value)
+            expected = Series(exp_values, exp_index)
+            tm.assert_series_equal(result, expected)
+
+        a = Series([np.nan, 1.0, 2.0, 3.0, np.nan], index=np.arange(5))
+        b = Series([np.nan, 1, np.nan, 3, np.nan, 4.0], index=np.arange(6))
+
+        result = op(a, b)
+        exp = equiv_op(a, b)
+        tm.assert_series_equal(result, exp)
+        _check_fill(op, equiv_op, a, b, fill_value=fv)
+        # should accept axis=0 or axis='rows'
+        op(a, b, axis=0)
+
+
+class TestSeriesArithmetic:
+    # Some of these may end up in tests/arithmetic, but are not yet sorted
+
+    def test_add_series_with_period_index(self):
+        rng = pd.period_range("1/1/2000", "1/1/2010", freq="Y")
+        ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng)
+
+        result = ts + ts[::2]
+        expected = ts + ts
+        expected.iloc[1::2] = np.nan
+        tm.assert_series_equal(result, expected)
+
+        result = ts + _permute(ts[::2])
+        tm.assert_series_equal(result, expected)
+
+        msg = "Input has different freq=D from Period\\(freq=Y-DEC\\)"
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            ts + ts.asfreq("D", how="end")
+
+    @pytest.mark.parametrize(
+        "target_add,input_value,expected_value",
+        [
+            ("!", ["hello", "world"], ["hello!", "world!"]),
+            ("m", ["hello", "world"], ["hellom", "worldm"]),
+        ],
+    )
+    def test_string_addition(self, target_add, input_value, expected_value):
+        # GH28658 - ensure adding 'm' does not raise an error
+        a = Series(input_value)
+
+        result = a + target_add
+        expected = Series(expected_value)
+        tm.assert_series_equal(result, expected)
+
+    def test_divmod(self):
+        # GH#25557
+        a = Series([1, 1, 1, np.nan], index=["a", "b", "c", "d"])
+        b = Series([2, np.nan, 1, np.nan], index=["a", "b", "d", "e"])
+
+        result = a.divmod(b)
+        expected = divmod(a, b)
+        tm.assert_series_equal(result[0], expected[0])
+        tm.assert_series_equal(result[1], expected[1])
+
+        result = a.rdivmod(b)
+        expected = divmod(b, a)
+        tm.assert_series_equal(result[0], expected[0])
+        tm.assert_series_equal(result[1], expected[1])
+
+    @pytest.mark.parametrize("index", [None, range(9)])
+    def test_series_integer_mod(self, index):
+        # GH#24396
+        s1 = Series(range(1, 10))
+        s2 = Series("foo", index=index)
+
+        msg = "not all arguments converted during string formatting|mod not"
+
+        with pytest.raises((TypeError, NotImplementedError), match=msg):
+            s2 % s1
+
+    def test_add_with_duplicate_index(self):
+        # GH14227
+        s1 = Series([1, 2], index=[1, 1])
+        s2 = Series([10, 10], index=[1, 2])
+        result = s1 + s2
+        expected = Series([11, 12, np.nan], index=[1, 1, 2])
+        tm.assert_series_equal(result, expected)
+
+    def test_add_na_handling(self):
+        ser = Series(
+            [Decimal("1.3"), Decimal("2.3")], index=[date(2012, 1, 1), date(2012, 1, 2)]
+        )
+
+        result = ser + ser.shift(1)
+        result2 = ser.shift(1) + ser
+        assert isna(result.iloc[0])
+        assert isna(result2.iloc[0])
+
+    def test_add_corner_cases(self, datetime_series):
+        empty = Series([], index=Index([]), dtype=np.float64)
+
+        result = datetime_series + empty
+        assert np.isnan(result).all()
+
+        result = empty + empty.copy()
+        assert len(result) == 0
+
+    def test_add_float_plus_int(self, datetime_series):
+        # float + int
+        int_ts = datetime_series.astype(int)[:-5]
+        added = datetime_series + int_ts
+        expected = Series(
+            datetime_series.values[:-5] + int_ts.values,
+            index=datetime_series.index[:-5],
+            name="ts",
+        )
+        tm.assert_series_equal(added[:-5], expected)
+
+    def test_mul_empty_int_corner_case(self):
+        s1 = Series([], [], dtype=np.int32)
+        s2 = Series({"x": 0.0})
+        tm.assert_series_equal(s1 * s2, Series([np.nan], index=["x"]))
+
+    def test_sub_datetimelike_align(self):
+        # GH#7500
+        # datetimelike ops need to align
+        dt = Series(date_range("2012-1-1", periods=3, freq="D"))
+        dt.iloc[2] = np.nan
+        dt2 = dt[::-1]
+
+        expected = Series([timedelta(0), timedelta(0), pd.NaT])
+        # name is reset
+        result = dt2 - dt
+        tm.assert_series_equal(result, expected)
+
+        expected = Series(expected, name=0)
+        result = (dt2.to_frame() - dt.to_frame())[0]
+        tm.assert_series_equal(result, expected)
+
+    def test_alignment_doesnt_change_tz(self):
+        # GH#33671
+        dti = date_range("2016-01-01", periods=10, tz="CET")
+        dti_utc = dti.tz_convert("UTC")
+        ser = Series(10, index=dti)
+        ser_utc = Series(10, index=dti_utc)
+
+        # we don't care about the result, just that original indexes are unchanged
+        ser * ser_utc
+
+        assert ser.index is dti
+        assert ser_utc.index is dti_utc
+
+    def test_alignment_categorical(self):
+        # GH13365
+        cat = Categorical(["3z53", "3z53", "LoJG", "LoJG", "LoJG", "N503"])
+        ser1 = Series(2, index=cat)
+        ser2 = Series(2, index=cat[:-1])
+        result = ser1 * ser2
+
+        exp_index = ["3z53"] * 4 + ["LoJG"] * 9 + ["N503"]
+        exp_index = pd.CategoricalIndex(exp_index, categories=cat.categories)
+        exp_values = [4.0] * 13 + [np.nan]
+        expected = Series(exp_values, exp_index)
+
+        tm.assert_series_equal(result, expected)
+
+    def test_arithmetic_with_duplicate_index(self):
+        # GH#8363
+        # integer ops with a non-unique index
+        index = [2, 2, 3, 3, 4]
+        ser = Series(np.arange(1, 6, dtype="int64"), index=index)
+        other = Series(np.arange(5, dtype="int64"), index=index)
+        result = ser - other
+        expected = Series(1, index=[2, 2, 3, 3, 4])
+        tm.assert_series_equal(result, expected)
+
+        # GH#8363
+        # datetime ops with a non-unique index
+        ser = Series(date_range("20130101 09:00:00", periods=5), index=index)
+        other = Series(date_range("20130101", periods=5), index=index)
+        result = ser - other
+        expected = Series(Timedelta("9 hours"), index=[2, 2, 3, 3, 4])
+        tm.assert_series_equal(result, expected)
+
+    def test_masked_and_non_masked_propagate_na(self):
+        # GH#45810
+        ser1 = Series([0, np.nan], dtype="float")
+        ser2 = Series([0, 1], dtype="Int64")
+        result = ser1 * ser2
+        expected = Series([0, pd.NA], dtype="Float64")
+        tm.assert_series_equal(result, expected)
+
+    def test_mask_div_propagate_na_for_non_na_dtype(self):
+        # GH#42630
+        ser1 = Series([15, pd.NA, 5, 4], dtype="Int64")
+        ser2 = Series([15, 5, np.nan, 4])
+        result = ser1 / ser2
+        expected = Series([1.0, pd.NA, pd.NA, 1.0], dtype="Float64")
+        tm.assert_series_equal(result, expected)
+
+        result = ser2 / ser1
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("val, dtype", [(3, "Int64"), (3.5, "Float64")])
+    def test_add_list_to_masked_array(self, val, dtype):
+        # GH#22962
+        ser = Series([1, None, 3], dtype="Int64")
+        result = ser + [1, None, val]
+        expected = Series([2, None, 3 + val], dtype=dtype)
+        tm.assert_series_equal(result, expected)
+
+        result = [1, None, val] + ser
+        tm.assert_series_equal(result, expected)
+
+    def test_add_list_to_masked_array_boolean(self, request):
+        # GH#22962
+        warning = (
+            UserWarning
+            if request.node.callspec.id == "numexpr" and NUMEXPR_INSTALLED
+            else None
+        )
+        ser = Series([True, None, False], dtype="boolean")
+        with tm.assert_produces_warning(warning):
+            result = ser + [True, None, True]
+        expected = Series([True, None, True], dtype="boolean")
+        tm.assert_series_equal(result, expected)
+
+        with tm.assert_produces_warning(warning):
+            result = [True, None, True] + ser
+        tm.assert_series_equal(result, expected)
+
+
+# ------------------------------------------------------------------
+# Comparisons
+
+
+class TestSeriesFlexComparison:
+    @pytest.mark.parametrize("axis", [0, None, "index"])
+    def test_comparison_flex_basic(self, axis, comparison_op):
+        left = Series(np.random.default_rng(2).standard_normal(10))
+        right = Series(np.random.default_rng(2).standard_normal(10))
+        result = getattr(left, comparison_op.__name__)(right, axis=axis)
+        expected = comparison_op(left, right)
+        tm.assert_series_equal(result, expected)
+
+    def test_comparison_bad_axis(self, comparison_op):
+        left = Series(np.random.default_rng(2).standard_normal(10))
+        right = Series(np.random.default_rng(2).standard_normal(10))
+
+        msg = "No axis named 1 for object type"
+        with pytest.raises(ValueError, match=msg):
+            getattr(left, comparison_op.__name__)(right, axis=1)
+
+    @pytest.mark.parametrize(
+        "values, op",
+        [
+            ([False, False, True, False], "eq"),
+            ([True, True, False, True], "ne"),
+            ([False, False, True, False], "le"),
+            ([False, False, False, False], "lt"),
+            ([False, True, True, False], "ge"),
+            ([False, True, False, False], "gt"),
+        ],
+    )
+    def test_comparison_flex_alignment(self, values, op):
+        left = Series([1, 3, 2], index=list("abc"))
+        right = Series([2, 2, 2], index=list("bcd"))
+        result = getattr(left, op)(right)
+        expected = Series(values, index=list("abcd"))
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "values, op, fill_value",
+        [
+            ([False, False, True, True], "eq", 2),
+            ([True, True, False, False], "ne", 2),
+            ([False, False, True, True], "le", 0),
+            ([False, False, False, True], "lt", 0),
+            ([True, True, True, False], "ge", 0),
+            ([True, True, False, False], "gt", 0),
+        ],
+    )
+    def test_comparison_flex_alignment_fill(self, values, op, fill_value):
+        left = Series([1, 3, 2], index=list("abc"))
+        right = Series([2, 2, 2], index=list("bcd"))
+        result = getattr(left, op)(right, fill_value=fill_value)
+        expected = Series(values, index=list("abcd"))
+        tm.assert_series_equal(result, expected)
+
+
+class TestSeriesComparison:
+    def test_comparison_different_length(self):
+        a = Series(["a", "b", "c"])
+        b = Series(["b", "a"])
+        msg = "only compare identically-labeled Series"
+        with pytest.raises(ValueError, match=msg):
+            a < b
+
+        a = Series([1, 2])
+        b = Series([2, 3, 4])
+        with pytest.raises(ValueError, match=msg):
+            a == b
+
+    @pytest.mark.parametrize("opname", ["eq", "ne", "gt", "lt", "ge", "le"])
+    def test_ser_flex_cmp_return_dtypes(self, opname):
+        # GH#15115
+        ser = Series([1, 3, 2], index=range(3))
+        const = 2
+        result = getattr(ser, opname)(const).dtypes
+        expected = np.dtype("bool")
+        assert result == expected
+
+    @pytest.mark.parametrize("opname", ["eq", "ne", "gt", "lt", "ge", "le"])
+    def test_ser_flex_cmp_return_dtypes_empty(self, opname):
+        # GH#15115 empty Series case
+        ser = Series([1, 3, 2], index=range(3))
+        empty = ser.iloc[:0]
+        const = 2
+        result = getattr(empty, opname)(const).dtypes
+        expected = np.dtype("bool")
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "names", [(None, None, None), ("foo", "bar", None), ("baz", "baz", "baz")]
+    )
+    def test_ser_cmp_result_names(self, names, comparison_op):
+        # datetime64 dtype
+        op = comparison_op
+        dti = date_range("1949-06-07 03:00:00", freq="h", periods=5, name=names[0])
+        ser = Series(dti).rename(names[1])
+        result = op(ser, dti)
+        assert result.name == names[2]
+
+        # datetime64tz dtype
+        dti = dti.tz_localize("US/Central")
+        dti = pd.DatetimeIndex(dti, freq="infer")  # freq not preserved by tz_localize
+        ser = Series(dti).rename(names[1])
+        result = op(ser, dti)
+        assert result.name == names[2]
+
+        # timedelta64 dtype
+        tdi = dti - dti.shift(1)
+        ser = Series(tdi).rename(names[1])
+        result = op(ser, tdi)
+        assert result.name == names[2]
+
+        # interval dtype
+        if op in [operator.eq, operator.ne]:
+            # interval dtype comparisons not yet implemented
+            ii = pd.interval_range(start=0, periods=5, name=names[0])
+            ser = Series(ii).rename(names[1])
+            result = op(ser, ii)
+            assert result.name == names[2]
+
+        # categorical
+        if op in [operator.eq, operator.ne]:
+            # categorical dtype comparisons raise for inequalities
+            cidx = tdi.astype("category")
+            ser = Series(cidx).rename(names[1])
+            result = op(ser, cidx)
+            assert result.name == names[2]
+
+    def test_comparisons(self, using_infer_string):
+        s = Series(["a", "b", "c"])
+        s2 = Series([False, True, False])
+
+        # it works!
+        exp = Series([False, False, False])
+        if using_infer_string:
+            import pyarrow as pa
+
+            msg = "has no kernel"
+            # TODO(3.0) GH56008
+            with pytest.raises(pa.lib.ArrowNotImplementedError, match=msg):
+                s == s2
+            with tm.assert_produces_warning(
+                DeprecationWarning, match="comparison", check_stacklevel=False
+            ):
+                with pytest.raises(pa.lib.ArrowNotImplementedError, match=msg):
+                    s2 == s
+        else:
+            tm.assert_series_equal(s == s2, exp)
+            tm.assert_series_equal(s2 == s, exp)
+
+    # -----------------------------------------------------------------
+    # Categorical Dtype Comparisons
+
+    def test_categorical_comparisons(self):
+        # GH#8938
+        # allow equality comparisons
+        a = Series(list("abc"), dtype="category")
+        b = Series(list("abc"), dtype="object")
+        c = Series(["a", "b", "cc"], dtype="object")
+        d = Series(list("acb"), dtype="object")
+        e = Categorical(list("abc"))
+        f = Categorical(list("acb"))
+
+        # vs scalar
+        assert not (a == "a").all()
+        assert ((a != "a") == ~(a == "a")).all()
+
+        assert not ("a" == a).all()
+        assert (a == "a")[0]
+        assert ("a" == a)[0]
+        assert not ("a" != a)[0]
+
+        # vs list-like
+        assert (a == a).all()
+        assert not (a != a).all()
+
+        assert (a == list(a)).all()
+        assert (a == b).all()
+        assert (b == a).all()
+        assert ((~(a == b)) == (a != b)).all()
+        assert ((~(b == a)) == (b != a)).all()
+
+        assert not (a == c).all()
+        assert not (c == a).all()
+        assert not (a == d).all()
+        assert not (d == a).all()
+
+        # vs a cat-like
+        assert (a == e).all()
+        assert (e == a).all()
+        assert not (a == f).all()
+        assert not (f == a).all()
+
+        assert (~(a == e) == (a != e)).all()
+        assert (~(e == a) == (e != a)).all()
+        assert (~(a == f) == (a != f)).all()
+        assert (~(f == a) == (f != a)).all()
+
+        # non-equality is not comparable
+        msg = "can only compare equality or not"
+        with pytest.raises(TypeError, match=msg):
+            a < b
+        with pytest.raises(TypeError, match=msg):
+            b < a
+        with pytest.raises(TypeError, match=msg):
+            a > b
+        with pytest.raises(TypeError, match=msg):
+            b > a
+
+    def test_unequal_categorical_comparison_raises_type_error(self):
+        # unequal comparison should raise for unordered cats
+        cat = Series(Categorical(list("abc")))
+        msg = "can only compare equality or not"
+        with pytest.raises(TypeError, match=msg):
+            cat > "b"
+
+        cat = Series(Categorical(list("abc"), ordered=False))
+        with pytest.raises(TypeError, match=msg):
+            cat > "b"
+
+        # https://github.com/pandas-dev/pandas/issues/9836#issuecomment-92123057
+        # and following comparisons with scalars not in categories should raise
+        # for unequal comps, but not for equal/not equal
+        cat = Series(Categorical(list("abc"), ordered=True))
+
+        msg = "Invalid comparison between dtype=category and str"
+        with pytest.raises(TypeError, match=msg):
+            cat < "d"
+        with pytest.raises(TypeError, match=msg):
+            cat > "d"
+        with pytest.raises(TypeError, match=msg):
+            "d" < cat
+        with pytest.raises(TypeError, match=msg):
+            "d" > cat
+
+        tm.assert_series_equal(cat == "d", Series([False, False, False]))
+        tm.assert_series_equal(cat != "d", Series([True, True, True]))
+
+    # -----------------------------------------------------------------
+
+    def test_comparison_tuples(self):
+        # GH#11339
+        # comparisons vs tuple
+        s = Series([(1, 1), (1, 2)])
+
+        result = s == (1, 2)
+        expected = Series([False, True])
+        tm.assert_series_equal(result, expected)
+
+        result = s != (1, 2)
+        expected = Series([True, False])
+        tm.assert_series_equal(result, expected)
+
+        result = s == (0, 0)
+        expected = Series([False, False])
+        tm.assert_series_equal(result, expected)
+
+        result = s != (0, 0)
+        expected = Series([True, True])
+        tm.assert_series_equal(result, expected)
+
+        s = Series([(1, 1), (1, 1)])
+
+        result = s == (1, 1)
+        expected = Series([True, True])
+        tm.assert_series_equal(result, expected)
+
+        result = s != (1, 1)
+        expected = Series([False, False])
+        tm.assert_series_equal(result, expected)
+
+    def test_comparison_frozenset(self):
+        ser = Series([frozenset([1]), frozenset([1, 2])])
+
+        result = ser == frozenset([1])
+        expected = Series([True, False])
+        tm.assert_series_equal(result, expected)
+
+    def test_comparison_operators_with_nas(self, comparison_op):
+        ser = Series(bdate_range("1/1/2000", periods=10), dtype=object)
+        ser[::2] = np.nan
+
+        # test that comparisons work
+        val = ser[5]
+
+        result = comparison_op(ser, val)
+        expected = comparison_op(ser.dropna(), val).reindex(ser.index)
+
+        msg = "Downcasting object dtype arrays"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            if comparison_op is operator.ne:
+                expected = expected.fillna(True).astype(bool)
+            else:
+                expected = expected.fillna(False).astype(bool)
+
+        tm.assert_series_equal(result, expected)
+
+    def test_ne(self):
+        ts = Series([3, 4, 5, 6, 7], [3, 4, 5, 6, 7], dtype=float)
+        expected = np.array([True, True, False, True, True])
+        tm.assert_numpy_array_equal(ts.index != 5, expected)
+        tm.assert_numpy_array_equal(~(ts.index == 5), expected)
+
+    @pytest.mark.parametrize(
+        "left, right",
+        [
+            (
+                Series([1, 2, 3], index=list("ABC"), name="x"),
+                Series([2, 2, 2], index=list("ABD"), name="x"),
+            ),
+            (
+                Series([1, 2, 3], index=list("ABC"), name="x"),
+                Series([2, 2, 2, 2], index=list("ABCD"), name="x"),
+            ),
+        ],
+    )
+    def test_comp_ops_df_compat(self, left, right, frame_or_series):
+        # GH 1134
+        # GH 50083 to clarify that index and columns must be identically labeled
+        if frame_or_series is not Series:
+            msg = (
+                rf"Can only compare identically-labeled \(both index and columns\) "
+                f"{frame_or_series.__name__} objects"
+            )
+            left = left.to_frame()
+            right = right.to_frame()
+        else:
+            msg = (
+                f"Can only compare identically-labeled {frame_or_series.__name__} "
+                f"objects"
+            )
+
+        with pytest.raises(ValueError, match=msg):
+            left == right
+        with pytest.raises(ValueError, match=msg):
+            right == left
+
+        with pytest.raises(ValueError, match=msg):
+            left != right
+        with pytest.raises(ValueError, match=msg):
+            right != left
+
+        with pytest.raises(ValueError, match=msg):
+            left < right
+        with pytest.raises(ValueError, match=msg):
+            right < left
+
+    def test_compare_series_interval_keyword(self):
+        # GH#25338
+        ser = Series(["IntervalA", "IntervalB", "IntervalC"])
+        result = ser == "IntervalA"
+        expected = Series([True, False, False])
+        tm.assert_series_equal(result, expected)
+
+
+# ------------------------------------------------------------------
+# Unsorted
+#  These arithmetic tests were previously in other files, eventually
+#  should be parametrized and put into tests.arithmetic
+
+
+class TestTimeSeriesArithmetic:
+    def test_series_add_tz_mismatch_converts_to_utc(self):
+        rng = date_range("1/1/2011", periods=100, freq="h", tz="utc")
+
+        perm = np.random.default_rng(2).permutation(100)[:90]
+        ser1 = Series(
+            np.random.default_rng(2).standard_normal(90),
+            index=rng.take(perm).tz_convert("US/Eastern"),
+        )
+
+        perm = np.random.default_rng(2).permutation(100)[:90]
+        ser2 = Series(
+            np.random.default_rng(2).standard_normal(90),
+            index=rng.take(perm).tz_convert("Europe/Berlin"),
+        )
+
+        result = ser1 + ser2
+
+        uts1 = ser1.tz_convert("utc")
+        uts2 = ser2.tz_convert("utc")
+        expected = uts1 + uts2
+
+        # sort since input indexes are not equal
+        expected = expected.sort_index()
+
+        assert result.index.tz is timezone.utc
+        tm.assert_series_equal(result, expected)
+
+    def test_series_add_aware_naive_raises(self):
+        rng = date_range("1/1/2011", periods=10, freq="h")
+        ser = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng)
+
+        ser_utc = ser.tz_localize("utc")
+
+        msg = "Cannot join tz-naive with tz-aware DatetimeIndex"
+        with pytest.raises(Exception, match=msg):
+            ser + ser_utc
+
+        with pytest.raises(Exception, match=msg):
+            ser_utc + ser
+
+    # TODO: belongs in tests/arithmetic?
+    def test_datetime_understood(self, unit):
+        # Ensures it doesn't fail to create the right series
+        # reported in issue#16726
+        series = Series(date_range("2012-01-01", periods=3, unit=unit))
+        offset = pd.offsets.DateOffset(days=6)
+        result = series - offset
+        exp_dti = pd.to_datetime(["2011-12-26", "2011-12-27", "2011-12-28"]).as_unit(
+            unit
+        )
+        expected = Series(exp_dti)
+        tm.assert_series_equal(result, expected)
+
+    def test_align_date_objects_with_datetimeindex(self):
+        rng = date_range("1/1/2000", periods=20)
+        ts = Series(np.random.default_rng(2).standard_normal(20), index=rng)
+
+        ts_slice = ts[5:]
+        ts2 = ts_slice.copy()
+        ts2.index = [x.date() for x in ts2.index]
+
+        result = ts + ts2
+        result2 = ts2 + ts
+        expected = ts + ts[5:]
+        expected.index = expected.index._with_freq(None)
+        tm.assert_series_equal(result, expected)
+        tm.assert_series_equal(result2, expected)
+
+
+class TestNamePreservation:
+    @pytest.mark.parametrize("box", [list, tuple, np.array, Index, Series, pd.array])
+    @pytest.mark.parametrize("flex", [True, False])
+    def test_series_ops_name_retention(self, flex, box, names, all_binary_operators):
+        # GH#33930 consistent name-retention
+        op = all_binary_operators
+
+        left = Series(range(10), name=names[0])
+        right = Series(range(10), name=names[1])
+
+        name = op.__name__.strip("_")
+        is_logical = name in ["and", "rand", "xor", "rxor", "or", "ror"]
+
+        msg = (
+            r"Logical ops \(and, or, xor\) between Pandas objects and "
+            "dtype-less sequences"
+        )
+        warn = None
+        if box in [list, tuple] and is_logical:
+            warn = FutureWarning
+
+        right = box(right)
+        if flex:
+            if is_logical:
+                # Series doesn't have these as flex methods
+                return
+            result = getattr(left, name)(right)
+        else:
+            # GH#37374 logical ops behaving as set ops deprecated
+            with tm.assert_produces_warning(warn, match=msg):
+                result = op(left, right)
+
+        assert isinstance(result, Series)
+        if box in [Index, Series]:
+            assert result.name is names[2] or result.name == names[2]
+        else:
+            assert result.name is names[0] or result.name == names[0]
+
+    def test_binop_maybe_preserve_name(self, datetime_series):
+        # names match, preserve
+        result = datetime_series * datetime_series
+        assert result.name == datetime_series.name
+        result = datetime_series.mul(datetime_series)
+        assert result.name == datetime_series.name
+
+        result = datetime_series * datetime_series[:-2]
+        assert result.name == datetime_series.name
+
+        # names don't match, don't preserve
+        cp = datetime_series.copy()
+        cp.name = "something else"
+        result = datetime_series + cp
+        assert result.name is None
+        result = datetime_series.add(cp)
+        assert result.name is None
+
+        ops = ["add", "sub", "mul", "div", "truediv", "floordiv", "mod", "pow"]
+        ops = ops + ["r" + op for op in ops]
+        for op in ops:
+            # names match, preserve
+            ser = datetime_series.copy()
+            result = getattr(ser, op)(ser)
+            assert result.name == datetime_series.name
+
+            # names don't match, don't preserve
+            cp = datetime_series.copy()
+            cp.name = "changed"
+            result = getattr(ser, op)(cp)
+            assert result.name is None
+
+    def test_scalarop_preserve_name(self, datetime_series):
+        result = datetime_series * 2
+        assert result.name == datetime_series.name
+
+
+class TestInplaceOperations:
+    @pytest.mark.parametrize(
+        "dtype1, dtype2, dtype_expected, dtype_mul",
+        (
+            ("Int64", "Int64", "Int64", "Int64"),
+            ("float", "float", "float", "float"),
+            ("Int64", "float", "Float64", "Float64"),
+            ("Int64", "Float64", "Float64", "Float64"),
+        ),
+    )
+    def test_series_inplace_ops(self, dtype1, dtype2, dtype_expected, dtype_mul):
+        # GH 37910
+
+        ser1 = Series([1], dtype=dtype1)
+        ser2 = Series([2], dtype=dtype2)
+        ser1 += ser2
+        expected = Series([3], dtype=dtype_expected)
+        tm.assert_series_equal(ser1, expected)
+
+        ser1 -= ser2
+        expected = Series([1], dtype=dtype_expected)
+        tm.assert_series_equal(ser1, expected)
+
+        ser1 *= ser2
+        expected = Series([2], dtype=dtype_mul)
+        tm.assert_series_equal(ser1, expected)
+
+
+def test_none_comparison(request, series_with_simple_index):
+    series = series_with_simple_index
+
+    if len(series) < 1:
+        request.applymarker(
+            pytest.mark.xfail(reason="Test doesn't make sense on empty data")
+        )
+
+    # bug brought up by #1079
+    # changed from TypeError in 0.17.0
+    series.iloc[0] = np.nan
+
+    # noinspection PyComparisonWithNone
+    result = series == None  # noqa: E711
+    assert not result.iat[0]
+    assert not result.iat[1]
+
+    # noinspection PyComparisonWithNone
+    result = series != None  # noqa: E711
+    assert result.iat[0]
+    assert result.iat[1]
+
+    result = None == series  # noqa: E711
+    assert not result.iat[0]
+    assert not result.iat[1]
+
+    result = None != series  # noqa: E711
+    assert result.iat[0]
+    assert result.iat[1]
+
+    if lib.is_np_dtype(series.dtype, "M") or isinstance(series.dtype, DatetimeTZDtype):
+        # Following DatetimeIndex (and Timestamp) convention,
+        # inequality comparisons with Series[datetime64] raise
+        msg = "Invalid comparison"
+        with pytest.raises(TypeError, match=msg):
+            None > series
+        with pytest.raises(TypeError, match=msg):
+            series > None
+    else:
+        result = None > series
+        assert not result.iat[0]
+        assert not result.iat[1]
+
+        result = series < None
+        assert not result.iat[0]
+        assert not result.iat[1]
+
+
+def test_series_varied_multiindex_alignment():
+    # GH 20414
+    s1 = Series(
+        range(8),
+        index=pd.MultiIndex.from_product(
+            [list("ab"), list("xy"), [1, 2]], names=["ab", "xy", "num"]
+        ),
+    )
+    s2 = Series(
+        [1000 * i for i in range(1, 5)],
+        index=pd.MultiIndex.from_product([list("xy"), [1, 2]], names=["xy", "num"]),
+    )
+    result = s1.loc[pd.IndexSlice[["a"], :, :]] + s2
+    expected = Series(
+        [1000, 2001, 3002, 4003],
+        index=pd.MultiIndex.from_tuples(
+            [("a", "x", 1), ("a", "x", 2), ("a", "y", 1), ("a", "y", 2)],
+            names=["ab", "xy", "num"],
+        ),
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_rmod_consistent_large_series():
+    # GH 29602
+    result = Series([2] * 10001).rmod(-1)
+    expected = Series([1] * 10001)
+
+    tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_constructors.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_constructors.py
new file mode 100644
index 0000000000000000000000000000000000000000..387be8398e4b281421317c844269fe45a10eb6bc
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_constructors.py
@@ -0,0 +1,2290 @@
+from collections import OrderedDict
+from collections.abc import Iterator
+from datetime import (
+    datetime,
+    timedelta,
+)
+
+from dateutil.tz import tzoffset
+import numpy as np
+from numpy import ma
+import pytest
+
+from pandas._libs import (
+    iNaT,
+    lib,
+)
+from pandas.compat.numpy import np_version_gt2
+from pandas.errors import IntCastingNaNError
+import pandas.util._test_decorators as td
+
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    DatetimeIndex,
+    DatetimeTZDtype,
+    Index,
+    Interval,
+    IntervalIndex,
+    MultiIndex,
+    NaT,
+    Period,
+    RangeIndex,
+    Series,
+    Timestamp,
+    date_range,
+    isna,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core.arrays import (
+    IntegerArray,
+    IntervalArray,
+    period_array,
+)
+from pandas.core.internals.blocks import NumpyBlock
+
+
+class TestSeriesConstructors:
+    def test_from_ints_with_non_nano_dt64_dtype(self, index_or_series):
+        values = np.arange(10)
+
+        res = index_or_series(values, dtype="M8[s]")
+        expected = index_or_series(values.astype("M8[s]"))
+        tm.assert_equal(res, expected)
+
+        res = index_or_series(list(values), dtype="M8[s]")
+        tm.assert_equal(res, expected)
+
+    def test_from_na_value_and_interval_of_datetime_dtype(self):
+        # GH#41805
+        ser = Series([None], dtype="interval[datetime64[ns]]")
+        assert ser.isna().all()
+        assert ser.dtype == "interval[datetime64[ns], right]"
+
+    def test_infer_with_date_and_datetime(self):
+        # GH#49341 pre-2.0 we inferred datetime-and-date to datetime64, which
+        #  was inconsistent with Index behavior
+        ts = Timestamp(2016, 1, 1)
+        vals = [ts.to_pydatetime(), ts.date()]
+
+        ser = Series(vals)
+        expected = Series(vals, dtype=object)
+        tm.assert_series_equal(ser, expected)
+
+        idx = Index(vals)
+        expected = Index(vals, dtype=object)
+        tm.assert_index_equal(idx, expected)
+
+    def test_unparsable_strings_with_dt64_dtype(self):
+        # pre-2.0 these would be silently ignored and come back with object dtype
+        vals = ["aa"]
+        msg = "^Unknown datetime string format, unable to parse: aa, at position 0$"
+        with pytest.raises(ValueError, match=msg):
+            Series(vals, dtype="datetime64[ns]")
+
+        with pytest.raises(ValueError, match=msg):
+            Series(np.array(vals, dtype=object), dtype="datetime64[ns]")
+
+    @pytest.mark.parametrize(
+        "constructor",
+        [
+            # NOTE: some overlap with test_constructor_empty but that test does not
+            # test for None or an empty generator.
+            # test_constructor_pass_none tests None but only with the index also
+            # passed.
+            (lambda idx: Series(index=idx)),
+            (lambda idx: Series(None, index=idx)),
+            (lambda idx: Series({}, index=idx)),
+            (lambda idx: Series((), index=idx)),
+            (lambda idx: Series([], index=idx)),
+            (lambda idx: Series((_ for _ in []), index=idx)),
+            (lambda idx: Series(data=None, index=idx)),
+            (lambda idx: Series(data={}, index=idx)),
+            (lambda idx: Series(data=(), index=idx)),
+            (lambda idx: Series(data=[], index=idx)),
+            (lambda idx: Series(data=(_ for _ in []), index=idx)),
+        ],
+    )
+    @pytest.mark.parametrize("empty_index", [None, []])
+    def test_empty_constructor(self, constructor, empty_index):
+        # GH 49573 (addition of empty_index parameter)
+        expected = Series(index=empty_index)
+        result = constructor(empty_index)
+
+        assert result.dtype == object
+        assert len(result.index) == 0
+        tm.assert_series_equal(result, expected, check_index_type=True)
+
+    def test_invalid_dtype(self):
+        # GH15520
+        msg = "not understood"
+        invalid_list = [Timestamp, "Timestamp", list]
+        for dtype in invalid_list:
+            with pytest.raises(TypeError, match=msg):
+                Series([], name="time", dtype=dtype)
+
+    def test_invalid_compound_dtype(self):
+        # GH#13296
+        c_dtype = np.dtype([("a", "i8"), ("b", "f4")])
+        cdt_arr = np.array([(1, 0.4), (256, -13)], dtype=c_dtype)
+
+        with pytest.raises(ValueError, match="Use DataFrame instead"):
+            Series(cdt_arr, index=["A", "B"])
+
+    def test_scalar_conversion(self):
+        # Pass in scalar is disabled
+        scalar = Series(0.5)
+        assert not isinstance(scalar, float)
+
+    def test_scalar_extension_dtype(self, ea_scalar_and_dtype):
+        # GH 28401
+
+        ea_scalar, ea_dtype = ea_scalar_and_dtype
+
+        ser = Series(ea_scalar, index=range(3))
+        expected = Series([ea_scalar] * 3, dtype=ea_dtype)
+
+        assert ser.dtype == ea_dtype
+        tm.assert_series_equal(ser, expected)
+
+    def test_constructor(self, datetime_series, using_infer_string):
+        empty_series = Series()
+        assert datetime_series.index._is_all_dates
+
+        # Pass in Series
+        derived = Series(datetime_series)
+        assert derived.index._is_all_dates
+
+        tm.assert_index_equal(derived.index, datetime_series.index)
+        # Ensure new index is not created
+        assert id(datetime_series.index) == id(derived.index)
+
+        # Mixed type Series
+        mixed = Series(["hello", np.nan], index=[0, 1])
+        assert mixed.dtype == np.object_ if not using_infer_string else "string"
+        assert np.isnan(mixed[1])
+
+        assert not empty_series.index._is_all_dates
+        assert not Series().index._is_all_dates
+
+        # exception raised is of type ValueError GH35744
+        with pytest.raises(
+            ValueError,
+            match=r"Data must be 1-dimensional, got ndarray of shape \(3, 3\) instead",
+        ):
+            Series(np.random.default_rng(2).standard_normal((3, 3)), index=np.arange(3))
+
+        mixed.name = "Series"
+        rs = Series(mixed).name
+        xp = "Series"
+        assert rs == xp
+
+        # raise on MultiIndex GH4187
+        m = MultiIndex.from_arrays([[1, 2], [3, 4]])
+        msg = "initializing a Series from a MultiIndex is not supported"
+        with pytest.raises(NotImplementedError, match=msg):
+            Series(m)
+
+    def test_constructor_index_ndim_gt_1_raises(self):
+        # GH#18579
+        df = DataFrame([[1, 2], [3, 4], [5, 6]], index=[3, 6, 9])
+        with pytest.raises(ValueError, match="Index data must be 1-dimensional"):
+            Series([1, 3, 2], index=df)
+
+    @pytest.mark.parametrize("input_class", [list, dict, OrderedDict])
+    def test_constructor_empty(self, input_class, using_infer_string):
+        empty = Series()
+        empty2 = Series(input_class())
+
+        # these are Index() and RangeIndex() which don't compare type equal
+        # but are just .equals
+        tm.assert_series_equal(empty, empty2, check_index_type=False)
+
+        # With explicit dtype:
+        empty = Series(dtype="float64")
+        empty2 = Series(input_class(), dtype="float64")
+        tm.assert_series_equal(empty, empty2, check_index_type=False)
+
+        # GH 18515 : with dtype=category:
+        empty = Series(dtype="category")
+        empty2 = Series(input_class(), dtype="category")
+        tm.assert_series_equal(empty, empty2, check_index_type=False)
+
+        if input_class is not list:
+            # With index:
+            empty = Series(index=range(10))
+            empty2 = Series(input_class(), index=range(10))
+            tm.assert_series_equal(empty, empty2)
+
+            # With index and dtype float64:
+            empty = Series(np.nan, index=range(10))
+            empty2 = Series(input_class(), index=range(10), dtype="float64")
+            tm.assert_series_equal(empty, empty2)
+
+            # GH 19853 : with empty string, index and dtype str
+            empty = Series("", dtype=str, index=range(3))
+            if using_infer_string:
+                empty2 = Series("", index=range(3), dtype=object)
+            else:
+                empty2 = Series("", index=range(3))
+            tm.assert_series_equal(empty, empty2)
+
+    @pytest.mark.parametrize("input_arg", [np.nan, float("nan")])
+    def test_constructor_nan(self, input_arg):
+        empty = Series(dtype="float64", index=range(10))
+        empty2 = Series(input_arg, index=range(10))
+
+        tm.assert_series_equal(empty, empty2, check_index_type=False)
+
+    @pytest.mark.parametrize(
+        "dtype",
+        ["f8", "i8", "M8[ns]", "m8[ns]", "category", "object", "datetime64[ns, UTC]"],
+    )
+    @pytest.mark.parametrize("index", [None, Index([])])
+    def test_constructor_dtype_only(self, dtype, index):
+        # GH-20865
+        result = Series(dtype=dtype, index=index)
+        assert result.dtype == dtype
+        assert len(result) == 0
+
+    def test_constructor_no_data_index_order(self):
+        result = Series(index=["b", "a", "c"])
+        assert result.index.tolist() == ["b", "a", "c"]
+
+    def test_constructor_no_data_string_type(self):
+        # GH 22477
+        result = Series(index=[1], dtype=str)
+        assert np.isnan(result.iloc[0])
+
+    @pytest.mark.parametrize("item", ["entry", "ѐ", 13])
+    def test_constructor_string_element_string_type(self, item):
+        # GH 22477
+        result = Series(item, index=[1], dtype=str)
+        assert result.iloc[0] == str(item)
+
+    def test_constructor_dtype_str_na_values(self, string_dtype):
+        # https://github.com/pandas-dev/pandas/issues/21083
+        ser = Series(["x", None], dtype=string_dtype)
+        result = ser.isna()
+        expected = Series([False, True])
+        tm.assert_series_equal(result, expected)
+        assert ser.iloc[1] is None
+
+        ser = Series(["x", np.nan], dtype=string_dtype)
+        assert np.isnan(ser.iloc[1])
+
+    def test_constructor_series(self):
+        index1 = ["d", "b", "a", "c"]
+        index2 = sorted(index1)
+        s1 = Series([4, 7, -5, 3], index=index1)
+        s2 = Series(s1, index=index2)
+
+        tm.assert_series_equal(s2, s1.sort_index())
+
+    def test_constructor_iterable(self):
+        # GH 21987
+        class Iter:
+            def __iter__(self) -> Iterator:
+                yield from range(10)
+
+        expected = Series(list(range(10)), dtype="int64")
+        result = Series(Iter(), dtype="int64")
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_sequence(self):
+        # GH 21987
+        expected = Series(list(range(10)), dtype="int64")
+        result = Series(range(10), dtype="int64")
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_single_str(self):
+        # GH 21987
+        expected = Series(["abc"])
+        result = Series("abc")
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_list_like(self):
+        # make sure that we are coercing different
+        # list-likes to standard dtypes and not
+        # platform specific
+        expected = Series([1, 2, 3], dtype="int64")
+        for obj in [[1, 2, 3], (1, 2, 3), np.array([1, 2, 3], dtype="int64")]:
+            result = Series(obj, index=[0, 1, 2])
+            tm.assert_series_equal(result, expected)
+
+    def test_constructor_boolean_index(self):
+        # GH#18579
+        s1 = Series([1, 2, 3], index=[4, 5, 6])
+
+        index = s1 == 2
+        result = Series([1, 3, 2], index=index)
+        expected = Series([1, 3, 2], index=[False, True, False])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", ["bool", "int32", "int64", "float64"])
+    def test_constructor_index_dtype(self, dtype):
+        # GH 17088
+
+        s = Series(Index([0, 2, 4]), dtype=dtype)
+        assert s.dtype == dtype
+
+    @pytest.mark.parametrize(
+        "input_vals",
+        [
+            ([1, 2]),
+            (["1", "2"]),
+            (list(date_range("1/1/2011", periods=2, freq="h"))),
+            (list(date_range("1/1/2011", periods=2, freq="h", tz="US/Eastern"))),
+            ([Interval(left=0, right=5)]),
+        ],
+    )
+    def test_constructor_list_str(self, input_vals, string_dtype):
+        # GH 16605
+        # Ensure that data elements from a list are converted to strings
+        # when dtype is str, 'str', or 'U'
+        result = Series(input_vals, dtype=string_dtype)
+        expected = Series(input_vals).astype(string_dtype)
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_list_str_na(self, string_dtype):
+        result = Series([1.0, 2.0, np.nan], dtype=string_dtype)
+        expected = Series(["1.0", "2.0", np.nan], dtype=object)
+        tm.assert_series_equal(result, expected)
+        assert np.isnan(result[2])
+
+    def test_constructor_generator(self):
+        gen = (i for i in range(10))
+
+        result = Series(gen)
+        exp = Series(range(10))
+        tm.assert_series_equal(result, exp)
+
+        # same but with non-default index
+        gen = (i for i in range(10))
+        result = Series(gen, index=range(10, 20))
+        exp.index = range(10, 20)
+        tm.assert_series_equal(result, exp)
+
+    def test_constructor_map(self):
+        # GH8909
+        m = (x for x in range(10))
+
+        result = Series(m)
+        exp = Series(range(10))
+        tm.assert_series_equal(result, exp)
+
+        # same but with non-default index
+        m = (x for x in range(10))
+        result = Series(m, index=range(10, 20))
+        exp.index = range(10, 20)
+        tm.assert_series_equal(result, exp)
+
+    def test_constructor_categorical(self):
+        cat = Categorical([0, 1, 2, 0, 1, 2], ["a", "b", "c"])
+        res = Series(cat)
+        tm.assert_categorical_equal(res.values, cat)
+
+        # can cast to a new dtype
+        result = Series(Categorical([1, 2, 3]), dtype="int64")
+        expected = Series([1, 2, 3], dtype="int64")
+        tm.assert_series_equal(result, expected)
+
+    def test_construct_from_categorical_with_dtype(self):
+        # GH12574
+        ser = Series(Categorical([1, 2, 3]), dtype="category")
+        assert isinstance(ser.dtype, CategoricalDtype)
+
+    def test_construct_intlist_values_category_dtype(self):
+        ser = Series([1, 2, 3], dtype="category")
+        assert isinstance(ser.dtype, CategoricalDtype)
+
+    def test_constructor_categorical_with_coercion(self):
+        factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"])
+        # test basic creation / coercion of categoricals
+        s = Series(factor, name="A")
+        assert s.dtype == "category"
+        assert len(s) == len(factor)
+
+        # in a frame
+        df = DataFrame({"A": factor})
+        result = df["A"]
+        tm.assert_series_equal(result, s)
+        result = df.iloc[:, 0]
+        tm.assert_series_equal(result, s)
+        assert len(df) == len(factor)
+
+        df = DataFrame({"A": s})
+        result = df["A"]
+        tm.assert_series_equal(result, s)
+        assert len(df) == len(factor)
+
+        # multiples
+        df = DataFrame({"A": s, "B": s, "C": 1})
+        result1 = df["A"]
+        result2 = df["B"]
+        tm.assert_series_equal(result1, s)
+        tm.assert_series_equal(result2, s, check_names=False)
+        assert result2.name == "B"
+        assert len(df) == len(factor)
+
+    def test_constructor_categorical_with_coercion2(self):
+        # GH8623
+        x = DataFrame(
+            [[1, "John P. Doe"], [2, "Jane Dove"], [1, "John P. Doe"]],
+            columns=["person_id", "person_name"],
+        )
+        x["person_name"] = Categorical(x.person_name)  # doing this breaks transform
+
+        expected = x.iloc[0].person_name
+        result = x.person_name.iloc[0]
+        assert result == expected
+
+        result = x.person_name[0]
+        assert result == expected
+
+        result = x.person_name.loc[0]
+        assert result == expected
+
+    def test_constructor_series_to_categorical(self):
+        # see GH#16524: test conversion of Series to Categorical
+        series = Series(["a", "b", "c"])
+
+        result = Series(series, dtype="category")
+        expected = Series(["a", "b", "c"], dtype="category")
+
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_categorical_dtype(self):
+        result = Series(
+            ["a", "b"], dtype=CategoricalDtype(["a", "b", "c"], ordered=True)
+        )
+        assert isinstance(result.dtype, CategoricalDtype)
+        tm.assert_index_equal(result.cat.categories, Index(["a", "b", "c"]))
+        assert result.cat.ordered
+
+        result = Series(["a", "b"], dtype=CategoricalDtype(["b", "a"]))
+        assert isinstance(result.dtype, CategoricalDtype)
+        tm.assert_index_equal(result.cat.categories, Index(["b", "a"]))
+        assert result.cat.ordered is False
+
+        # GH 19565 - Check broadcasting of scalar with Categorical dtype
+        result = Series(
+            "a", index=[0, 1], dtype=CategoricalDtype(["a", "b"], ordered=True)
+        )
+        expected = Series(
+            ["a", "a"], index=[0, 1], dtype=CategoricalDtype(["a", "b"], ordered=True)
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_categorical_string(self):
+        # GH 26336: the string 'category' maintains existing CategoricalDtype
+        cdt = CategoricalDtype(categories=list("dabc"), ordered=True)
+        expected = Series(list("abcabc"), dtype=cdt)
+
+        # Series(Categorical, dtype='category') keeps existing dtype
+        cat = Categorical(list("abcabc"), dtype=cdt)
+        result = Series(cat, dtype="category")
+        tm.assert_series_equal(result, expected)
+
+        # Series(Series[Categorical], dtype='category') keeps existing dtype
+        result = Series(result, dtype="category")
+        tm.assert_series_equal(result, expected)
+
+    def test_categorical_sideeffects_free(self):
+        # Passing a categorical to a Series and then changing values in either
+        # the series or the categorical should not change the values in the
+        # other one, IF you specify copy!
+        cat = Categorical(["a", "b", "c", "a"])
+        s = Series(cat, copy=True)
+        assert s.cat is not cat
+        s = s.cat.rename_categories([1, 2, 3])
+        exp_s = np.array([1, 2, 3, 1], dtype=np.int64)
+        exp_cat = np.array(["a", "b", "c", "a"], dtype=np.object_)
+        tm.assert_numpy_array_equal(s.__array__(), exp_s)
+        tm.assert_numpy_array_equal(cat.__array__(), exp_cat)
+
+        # setting
+        s[0] = 2
+        exp_s2 = np.array([2, 2, 3, 1], dtype=np.int64)
+        tm.assert_numpy_array_equal(s.__array__(), exp_s2)
+        tm.assert_numpy_array_equal(cat.__array__(), exp_cat)
+
+        # however, copy is False by default
+        # so this WILL change values
+        cat = Categorical(["a", "b", "c", "a"])
+        s = Series(cat, copy=False)
+        assert s.values is cat
+        s = s.cat.rename_categories([1, 2, 3])
+        assert s.values is not cat
+        exp_s = np.array([1, 2, 3, 1], dtype=np.int64)
+        tm.assert_numpy_array_equal(s.__array__(), exp_s)
+
+        s[0] = 2
+        exp_s2 = np.array([2, 2, 3, 1], dtype=np.int64)
+        tm.assert_numpy_array_equal(s.__array__(), exp_s2)
+
+    def test_unordered_compare_equal(self):
+        left = Series(["a", "b", "c"], dtype=CategoricalDtype(["a", "b"]))
+        right = Series(Categorical(["a", "b", np.nan], categories=["a", "b"]))
+        tm.assert_series_equal(left, right)
+
+    def test_constructor_maskedarray(self):
+        data = ma.masked_all((3,), dtype=float)
+        result = Series(data)
+        expected = Series([np.nan, np.nan, np.nan])
+        tm.assert_series_equal(result, expected)
+
+        data[0] = 0.0
+        data[2] = 2.0
+        index = ["a", "b", "c"]
+        result = Series(data, index=index)
+        expected = Series([0.0, np.nan, 2.0], index=index)
+        tm.assert_series_equal(result, expected)
+
+        data[1] = 1.0
+        result = Series(data, index=index)
+        expected = Series([0.0, 1.0, 2.0], index=index)
+        tm.assert_series_equal(result, expected)
+
+        data = ma.masked_all((3,), dtype=int)
+        result = Series(data)
+        expected = Series([np.nan, np.nan, np.nan], dtype=float)
+        tm.assert_series_equal(result, expected)
+
+        data[0] = 0
+        data[2] = 2
+        index = ["a", "b", "c"]
+        result = Series(data, index=index)
+        expected = Series([0, np.nan, 2], index=index, dtype=float)
+        tm.assert_series_equal(result, expected)
+
+        data[1] = 1
+        result = Series(data, index=index)
+        expected = Series([0, 1, 2], index=index, dtype=int)
+        with pytest.raises(AssertionError, match="Series classes are different"):
+            # TODO should this be raising at all?
+            # https://github.com/pandas-dev/pandas/issues/56131
+            tm.assert_series_equal(result, expected)
+
+        data = ma.masked_all((3,), dtype=bool)
+        result = Series(data)
+        expected = Series([np.nan, np.nan, np.nan], dtype=object)
+        tm.assert_series_equal(result, expected)
+
+        data[0] = True
+        data[2] = False
+        index = ["a", "b", "c"]
+        result = Series(data, index=index)
+        expected = Series([True, np.nan, False], index=index, dtype=object)
+        tm.assert_series_equal(result, expected)
+
+        data[1] = True
+        result = Series(data, index=index)
+        expected = Series([True, True, False], index=index, dtype=bool)
+        with pytest.raises(AssertionError, match="Series classes are different"):
+            # TODO should this be raising at all?
+            # https://github.com/pandas-dev/pandas/issues/56131
+            tm.assert_series_equal(result, expected)
+
+        data = ma.masked_all((3,), dtype="M8[ns]")
+        result = Series(data)
+        expected = Series([iNaT, iNaT, iNaT], dtype="M8[ns]")
+        tm.assert_series_equal(result, expected)
+
+        data[0] = datetime(2001, 1, 1)
+        data[2] = datetime(2001, 1, 3)
+        index = ["a", "b", "c"]
+        result = Series(data, index=index)
+        expected = Series(
+            [datetime(2001, 1, 1), iNaT, datetime(2001, 1, 3)],
+            index=index,
+            dtype="M8[ns]",
+        )
+        tm.assert_series_equal(result, expected)
+
+        data[1] = datetime(2001, 1, 2)
+        result = Series(data, index=index)
+        expected = Series(
+            [datetime(2001, 1, 1), datetime(2001, 1, 2), datetime(2001, 1, 3)],
+            index=index,
+            dtype="M8[ns]",
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_maskedarray_hardened(self):
+        # Check numpy masked arrays with hard masks -- from GH24574
+        data = ma.masked_all((3,), dtype=float).harden_mask()
+        result = Series(data)
+        expected = Series([np.nan, np.nan, np.nan])
+        tm.assert_series_equal(result, expected)
+
+    def test_series_ctor_plus_datetimeindex(self, using_copy_on_write):
+        rng = date_range("20090415", "20090519", freq="B")
+        data = {k: 1 for k in rng}
+
+        result = Series(data, index=rng)
+        if using_copy_on_write:
+            assert result.index.is_(rng)
+        else:
+            assert result.index is rng
+
+    def test_constructor_default_index(self):
+        s = Series([0, 1, 2])
+        tm.assert_index_equal(s.index, Index(range(3)), exact=True)
+
+    @pytest.mark.parametrize(
+        "input",
+        [
+            [1, 2, 3],
+            (1, 2, 3),
+            list(range(3)),
+            Categorical(["a", "b", "a"]),
+            (i for i in range(3)),
+            (x for x in range(3)),
+        ],
+    )
+    def test_constructor_index_mismatch(self, input):
+        # GH 19342
+        # test that construction of a Series with an index of different length
+        # raises an error
+        msg = r"Length of values \(3\) does not match length of index \(4\)"
+        with pytest.raises(ValueError, match=msg):
+            Series(input, index=np.arange(4))
+
+    def test_constructor_numpy_scalar(self):
+        # GH 19342
+        # construction with a numpy scalar
+        # should not raise
+        result = Series(np.array(100), index=np.arange(4), dtype="int64")
+        expected = Series(100, index=np.arange(4), dtype="int64")
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_broadcast_list(self):
+        # GH 19342
+        # construction with single-element container and index
+        # should raise
+        msg = r"Length of values \(1\) does not match length of index \(3\)"
+        with pytest.raises(ValueError, match=msg):
+            Series(["foo"], index=["a", "b", "c"])
+
+    def test_constructor_corner(self):
+        df = DataFrame(range(5), index=date_range("2020-01-01", periods=5))
+        objs = [df, df]
+        s = Series(objs, index=[0, 1])
+        assert isinstance(s, Series)
+
+    def test_constructor_sanitize(self):
+        s = Series(np.array([1.0, 1.0, 8.0]), dtype="i8")
+        assert s.dtype == np.dtype("i8")
+
+        msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
+        with pytest.raises(IntCastingNaNError, match=msg):
+            Series(np.array([1.0, 1.0, np.nan]), copy=True, dtype="i8")
+
+    def test_constructor_copy(self):
+        # GH15125
+        # test dtype parameter has no side effects on copy=True
+        for data in [[1.0], np.array([1.0])]:
+            x = Series(data)
+            y = Series(x, copy=True, dtype=float)
+
+            # copy=True maintains original data in Series
+            tm.assert_series_equal(x, y)
+
+            # changes to origin of copy does not affect the copy
+            x[0] = 2.0
+            assert not x.equals(y)
+            assert x[0] == 2.0
+            assert y[0] == 1.0
+
+    @td.skip_array_manager_invalid_test  # TODO(ArrayManager) rewrite test
+    @pytest.mark.parametrize(
+        "index",
+        [
+            date_range("20170101", periods=3, tz="US/Eastern"),
+            date_range("20170101", periods=3),
+            timedelta_range("1 day", periods=3),
+            period_range("2012Q1", periods=3, freq="Q"),
+            Index(list("abc")),
+            Index([1, 2, 3]),
+            RangeIndex(0, 3),
+        ],
+        ids=lambda x: type(x).__name__,
+    )
+    def test_constructor_limit_copies(self, index):
+        # GH 17449
+        # limit copies of input
+        s = Series(index)
+
+        # we make 1 copy; this is just a smoke test here
+        assert s._mgr.blocks[0].values is not index
+
+    def test_constructor_shallow_copy(self):
+        # constructing a Series from Series with copy=False should still
+        # give a "shallow" copy (share data, not attributes)
+        # https://github.com/pandas-dev/pandas/issues/49523
+        s = Series([1, 2, 3])
+        s_orig = s.copy()
+        s2 = Series(s)
+        assert s2._mgr is not s._mgr
+        # Overwriting index of s2 doesn't change s
+        s2.index = ["a", "b", "c"]
+        tm.assert_series_equal(s, s_orig)
+
+    def test_constructor_pass_none(self):
+        s = Series(None, index=range(5))
+        assert s.dtype == np.float64
+
+        s = Series(None, index=range(5), dtype=object)
+        assert s.dtype == np.object_
+
+        # GH 7431
+        # inference on the index
+        s = Series(index=np.array([None]))
+        expected = Series(index=Index([None]))
+        tm.assert_series_equal(s, expected)
+
+    def test_constructor_pass_nan_nat(self):
+        # GH 13467
+        exp = Series([np.nan, np.nan], dtype=np.float64)
+        assert exp.dtype == np.float64
+        tm.assert_series_equal(Series([np.nan, np.nan]), exp)
+        tm.assert_series_equal(Series(np.array([np.nan, np.nan])), exp)
+
+        exp = Series([NaT, NaT])
+        assert exp.dtype == "datetime64[ns]"
+        tm.assert_series_equal(Series([NaT, NaT]), exp)
+        tm.assert_series_equal(Series(np.array([NaT, NaT])), exp)
+
+        tm.assert_series_equal(Series([NaT, np.nan]), exp)
+        tm.assert_series_equal(Series(np.array([NaT, np.nan])), exp)
+
+        tm.assert_series_equal(Series([np.nan, NaT]), exp)
+        tm.assert_series_equal(Series(np.array([np.nan, NaT])), exp)
+
+    def test_constructor_cast(self):
+        msg = "could not convert string to float"
+        with pytest.raises(ValueError, match=msg):
+            Series(["a", "b", "c"], dtype=float)
+
+    def test_constructor_signed_int_overflow_raises(self):
+        # GH#41734 disallow silent overflow, enforced in 2.0
+        if np_version_gt2:
+            msg = "The elements provided in the data cannot all be casted to the dtype"
+            err = OverflowError
+        else:
+            msg = "Values are too large to be losslessly converted"
+            err = ValueError
+        with pytest.raises(err, match=msg):
+            Series([1, 200, 923442], dtype="int8")
+
+        with pytest.raises(err, match=msg):
+            Series([1, 200, 923442], dtype="uint8")
+
+    @pytest.mark.parametrize(
+        "values",
+        [
+            np.array([1], dtype=np.uint16),
+            np.array([1], dtype=np.uint32),
+            np.array([1], dtype=np.uint64),
+            [np.uint16(1)],
+            [np.uint32(1)],
+            [np.uint64(1)],
+        ],
+    )
+    def test_constructor_numpy_uints(self, values):
+        # GH#47294
+        value = values[0]
+        result = Series(values)
+
+        assert result[0].dtype == value.dtype
+        assert result[0] == value
+
+    def test_constructor_unsigned_dtype_overflow(self, any_unsigned_int_numpy_dtype):
+        # see gh-15832
+        if np_version_gt2:
+            msg = (
+                f"The elements provided in the data cannot "
+                f"all be casted to the dtype {any_unsigned_int_numpy_dtype}"
+            )
+        else:
+            msg = "Trying to coerce negative values to unsigned integers"
+        with pytest.raises(OverflowError, match=msg):
+            Series([-1], dtype=any_unsigned_int_numpy_dtype)
+
+    def test_constructor_floating_data_int_dtype(self, frame_or_series):
+        # GH#40110
+        arr = np.random.default_rng(2).standard_normal(2)
+
+        # Long-standing behavior (for Series, new in 2.0 for DataFrame)
+        #  has been to ignore the dtype on these;
+        #  not clear if this is what we want long-term
+        # expected = frame_or_series(arr)
+
+        # GH#49599 as of 2.0 we raise instead of silently retaining float dtype
+        msg = "Trying to coerce float values to integer"
+        with pytest.raises(ValueError, match=msg):
+            frame_or_series(arr, dtype="i8")
+
+        with pytest.raises(ValueError, match=msg):
+            frame_or_series(list(arr), dtype="i8")
+
+        # pre-2.0, when we had NaNs, we silently ignored the integer dtype
+        arr[0] = np.nan
+        # expected = frame_or_series(arr)
+
+        msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
+        with pytest.raises(IntCastingNaNError, match=msg):
+            frame_or_series(arr, dtype="i8")
+
+        exc = IntCastingNaNError
+        if frame_or_series is Series:
+            # TODO: try to align these
+            exc = ValueError
+            msg = "cannot convert float NaN to integer"
+        with pytest.raises(exc, match=msg):
+            # same behavior if we pass list instead of the ndarray
+            frame_or_series(list(arr), dtype="i8")
+
+        # float array that can be losslessly cast to integers
+        arr = np.array([1.0, 2.0], dtype="float64")
+        expected = frame_or_series(arr.astype("i8"))
+
+        obj = frame_or_series(arr, dtype="i8")
+        tm.assert_equal(obj, expected)
+
+        obj = frame_or_series(list(arr), dtype="i8")
+        tm.assert_equal(obj, expected)
+
+    def test_constructor_coerce_float_fail(self, any_int_numpy_dtype):
+        # see gh-15832
+        # Updated: make sure we treat this list the same as we would treat
+        #  the equivalent ndarray
+        # GH#49599 pre-2.0 we silently retained float dtype, in 2.0 we raise
+        vals = [1, 2, 3.5]
+
+        msg = "Trying to coerce float values to integer"
+        with pytest.raises(ValueError, match=msg):
+            Series(vals, dtype=any_int_numpy_dtype)
+        with pytest.raises(ValueError, match=msg):
+            Series(np.array(vals), dtype=any_int_numpy_dtype)
+
+    def test_constructor_coerce_float_valid(self, float_numpy_dtype):
+        s = Series([1, 2, 3.5], dtype=float_numpy_dtype)
+        expected = Series([1, 2, 3.5]).astype(float_numpy_dtype)
+        tm.assert_series_equal(s, expected)
+
+    def test_constructor_invalid_coerce_ints_with_float_nan(self, any_int_numpy_dtype):
+        # GH 22585
+        # Updated: make sure we treat this list the same as we would treat the
+        # equivalent ndarray
+        vals = [1, 2, np.nan]
+        # pre-2.0 this would return with a float dtype, in 2.0 we raise
+
+        msg = "cannot convert float NaN to integer"
+        with pytest.raises(ValueError, match=msg):
+            Series(vals, dtype=any_int_numpy_dtype)
+        msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
+        with pytest.raises(IntCastingNaNError, match=msg):
+            Series(np.array(vals), dtype=any_int_numpy_dtype)
+
+    def test_constructor_dtype_no_cast(self, using_copy_on_write, warn_copy_on_write):
+        # see gh-1572
+        s = Series([1, 2, 3])
+        s2 = Series(s, dtype=np.int64)
+
+        warn = FutureWarning if warn_copy_on_write else None
+        with tm.assert_produces_warning(warn):
+            s2[1] = 5
+        if using_copy_on_write:
+            assert s[1] == 2
+        else:
+            assert s[1] == 5
+
+    def test_constructor_datelike_coercion(self):
+        # GH 9477
+        # incorrectly inferring on dateimelike looking when object dtype is
+        # specified
+        s = Series([Timestamp("20130101"), "NOV"], dtype=object)
+        assert s.iloc[0] == Timestamp("20130101")
+        assert s.iloc[1] == "NOV"
+        assert s.dtype == object
+
+    def test_constructor_datelike_coercion2(self):
+        # the dtype was being reset on the slicing and re-inferred to datetime
+        # even thought the blocks are mixed
+        belly = "216 3T19".split()
+        wing1 = "2T15 4H19".split()
+        wing2 = "416 4T20".split()
+        mat = pd.to_datetime("2016-01-22 2019-09-07".split())
+        df = DataFrame({"wing1": wing1, "wing2": wing2, "mat": mat}, index=belly)
+
+        result = df.loc["3T19"]
+        assert result.dtype == object
+        result = df.loc["216"]
+        assert result.dtype == object
+
+    def test_constructor_mixed_int_and_timestamp(self, frame_or_series):
+        # specifically Timestamp with nanos, not datetimes
+        objs = [Timestamp(9), 10, NaT._value]
+        result = frame_or_series(objs, dtype="M8[ns]")
+
+        expected = frame_or_series([Timestamp(9), Timestamp(10), NaT])
+        tm.assert_equal(result, expected)
+
+    def test_constructor_datetimes_with_nulls(self):
+        # gh-15869
+        for arr in [
+            np.array([None, None, None, None, datetime.now(), None]),
+            np.array([None, None, datetime.now(), None]),
+        ]:
+            result = Series(arr)
+            assert result.dtype == "M8[ns]"
+
+    def test_constructor_dtype_datetime64(self):
+        s = Series(iNaT, dtype="M8[ns]", index=range(5))
+        assert isna(s).all()
+
+        # in theory this should be all nulls, but since
+        # we are not specifying a dtype is ambiguous
+        s = Series(iNaT, index=range(5))
+        assert not isna(s).all()
+
+        s = Series(np.nan, dtype="M8[ns]", index=range(5))
+        assert isna(s).all()
+
+        s = Series([datetime(2001, 1, 2, 0, 0), iNaT], dtype="M8[ns]")
+        assert isna(s[1])
+        assert s.dtype == "M8[ns]"
+
+        s = Series([datetime(2001, 1, 2, 0, 0), np.nan], dtype="M8[ns]")
+        assert isna(s[1])
+        assert s.dtype == "M8[ns]"
+
+    def test_constructor_dtype_datetime64_10(self):
+        # GH3416
+        pydates = [datetime(2013, 1, 1), datetime(2013, 1, 2), datetime(2013, 1, 3)]
+        dates = [np.datetime64(x) for x in pydates]
+
+        ser = Series(dates)
+        assert ser.dtype == "M8[ns]"
+
+        ser.iloc[0] = np.nan
+        assert ser.dtype == "M8[ns]"
+
+        # GH3414 related
+        expected = Series(pydates, dtype="datetime64[ms]")
+
+        result = Series(Series(dates).astype(np.int64) / 1000000, dtype="M8[ms]")
+        tm.assert_series_equal(result, expected)
+
+        result = Series(dates, dtype="datetime64[ms]")
+        tm.assert_series_equal(result, expected)
+
+        expected = Series(
+            [NaT, datetime(2013, 1, 2), datetime(2013, 1, 3)], dtype="datetime64[ns]"
+        )
+        result = Series([np.nan] + dates[1:], dtype="datetime64[ns]")
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_dtype_datetime64_11(self):
+        pydates = [datetime(2013, 1, 1), datetime(2013, 1, 2), datetime(2013, 1, 3)]
+        dates = [np.datetime64(x) for x in pydates]
+
+        dts = Series(dates, dtype="datetime64[ns]")
+
+        # valid astype
+        dts.astype("int64")
+
+        # invalid casting
+        msg = r"Converting from datetime64\[ns\] to int32 is not supported"
+        with pytest.raises(TypeError, match=msg):
+            dts.astype("int32")
+
+        # ints are ok
+        # we test with np.int64 to get similar results on
+        # windows / 32-bit platforms
+        result = Series(dts, dtype=np.int64)
+        expected = Series(dts.astype(np.int64))
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_dtype_datetime64_9(self):
+        # invalid dates can be help as object
+        result = Series([datetime(2, 1, 1)])
+        assert result[0] == datetime(2, 1, 1, 0, 0)
+
+        result = Series([datetime(3000, 1, 1)])
+        assert result[0] == datetime(3000, 1, 1, 0, 0)
+
+    def test_constructor_dtype_datetime64_8(self):
+        # don't mix types
+        result = Series([Timestamp("20130101"), 1], index=["a", "b"])
+        assert result["a"] == Timestamp("20130101")
+        assert result["b"] == 1
+
+    def test_constructor_dtype_datetime64_7(self):
+        # GH6529
+        # coerce datetime64 non-ns properly
+        dates = date_range("01-Jan-2015", "01-Dec-2015", freq="ME")
+        values2 = dates.view(np.ndarray).astype("datetime64[ns]")
+        expected = Series(values2, index=dates)
+
+        for unit in ["s", "D", "ms", "us", "ns"]:
+            dtype = np.dtype(f"M8[{unit}]")
+            values1 = dates.view(np.ndarray).astype(dtype)
+            result = Series(values1, dates)
+            if unit == "D":
+                # for unit="D" we cast to nearest-supported reso, i.e. "s"
+                dtype = np.dtype("M8[s]")
+            assert result.dtype == dtype
+            tm.assert_series_equal(result, expected.astype(dtype))
+
+        # GH 13876
+        # coerce to non-ns to object properly
+        expected = Series(values2, index=dates, dtype=object)
+        for dtype in ["s", "D", "ms", "us", "ns"]:
+            values1 = dates.view(np.ndarray).astype(f"M8[{dtype}]")
+            result = Series(values1, index=dates, dtype=object)
+            tm.assert_series_equal(result, expected)
+
+        # leave datetime.date alone
+        dates2 = np.array([d.date() for d in dates.to_pydatetime()], dtype=object)
+        series1 = Series(dates2, dates)
+        tm.assert_numpy_array_equal(series1.values, dates2)
+        assert series1.dtype == object
+
+    def test_constructor_dtype_datetime64_6(self):
+        # as of 2.0, these no longer infer datetime64 based on the strings,
+        #  matching the Index behavior
+
+        ser = Series([None, NaT, "2013-08-05 15:30:00.000001"])
+        assert ser.dtype == object
+
+        ser = Series([np.nan, NaT, "2013-08-05 15:30:00.000001"])
+        assert ser.dtype == object
+
+        ser = Series([NaT, None, "2013-08-05 15:30:00.000001"])
+        assert ser.dtype == object
+
+        ser = Series([NaT, np.nan, "2013-08-05 15:30:00.000001"])
+        assert ser.dtype == object
+
+    def test_constructor_dtype_datetime64_5(self):
+        # tz-aware (UTC and other tz's)
+        # GH 8411
+        dr = date_range("20130101", periods=3)
+        assert Series(dr).iloc[0].tz is None
+        dr = date_range("20130101", periods=3, tz="UTC")
+        assert str(Series(dr).iloc[0].tz) == "UTC"
+        dr = date_range("20130101", periods=3, tz="US/Eastern")
+        assert str(Series(dr).iloc[0].tz) == "US/Eastern"
+
+    def test_constructor_dtype_datetime64_4(self):
+        # non-convertible
+        ser = Series([1479596223000, -1479590, NaT])
+        assert ser.dtype == "object"
+        assert ser[2] is NaT
+        assert "NaT" in str(ser)
+
+    def test_constructor_dtype_datetime64_3(self):
+        # if we passed a NaT it remains
+        ser = Series([datetime(2010, 1, 1), datetime(2, 1, 1), NaT])
+        assert ser.dtype == "object"
+        assert ser[2] is NaT
+        assert "NaT" in str(ser)
+
+    def test_constructor_dtype_datetime64_2(self):
+        # if we passed a nan it remains
+        ser = Series([datetime(2010, 1, 1), datetime(2, 1, 1), np.nan])
+        assert ser.dtype == "object"
+        assert ser[2] is np.nan
+        assert "NaN" in str(ser)
+
+    def test_constructor_with_datetime_tz(self):
+        # 8260
+        # support datetime64 with tz
+
+        dr = date_range("20130101", periods=3, tz="US/Eastern")
+        s = Series(dr)
+        assert s.dtype.name == "datetime64[ns, US/Eastern]"
+        assert s.dtype == "datetime64[ns, US/Eastern]"
+        assert isinstance(s.dtype, DatetimeTZDtype)
+        assert "datetime64[ns, US/Eastern]" in str(s)
+
+        # export
+        result = s.values
+        assert isinstance(result, np.ndarray)
+        assert result.dtype == "datetime64[ns]"
+
+        exp = DatetimeIndex(result)
+        exp = exp.tz_localize("UTC").tz_convert(tz=s.dt.tz)
+        tm.assert_index_equal(dr, exp)
+
+        # indexing
+        result = s.iloc[0]
+        assert result == Timestamp("2013-01-01 00:00:00-0500", tz="US/Eastern")
+        result = s[0]
+        assert result == Timestamp("2013-01-01 00:00:00-0500", tz="US/Eastern")
+
+        result = s[Series([True, True, False], index=s.index)]
+        tm.assert_series_equal(result, s[0:2])
+
+        result = s.iloc[0:1]
+        tm.assert_series_equal(result, Series(dr[0:1]))
+
+        # concat
+        result = pd.concat([s.iloc[0:1], s.iloc[1:]])
+        tm.assert_series_equal(result, s)
+
+        # short str
+        assert "datetime64[ns, US/Eastern]" in str(s)
+
+        # formatting with NaT
+        result = s.shift()
+        assert "datetime64[ns, US/Eastern]" in str(result)
+        assert "NaT" in str(result)
+
+        result = DatetimeIndex(s, freq="infer")
+        tm.assert_index_equal(result, dr)
+
+    def test_constructor_with_datetime_tz5(self):
+        # long str
+        ser = Series(date_range("20130101", periods=1000, tz="US/Eastern"))
+        assert "datetime64[ns, US/Eastern]" in str(ser)
+
+    def test_constructor_with_datetime_tz4(self):
+        # inference
+        ser = Series(
+            [
+                Timestamp("2013-01-01 13:00:00-0800", tz="US/Pacific"),
+                Timestamp("2013-01-02 14:00:00-0800", tz="US/Pacific"),
+            ]
+        )
+        assert ser.dtype == "datetime64[ns, US/Pacific]"
+        assert lib.infer_dtype(ser, skipna=True) == "datetime64"
+
+    def test_constructor_with_datetime_tz3(self):
+        ser = Series(
+            [
+                Timestamp("2013-01-01 13:00:00-0800", tz="US/Pacific"),
+                Timestamp("2013-01-02 14:00:00-0800", tz="US/Eastern"),
+            ]
+        )
+        assert ser.dtype == "object"
+        assert lib.infer_dtype(ser, skipna=True) == "datetime"
+
+    def test_constructor_with_datetime_tz2(self):
+        # with all NaT
+        ser = Series(NaT, index=[0, 1], dtype="datetime64[ns, US/Eastern]")
+        dti = DatetimeIndex(["NaT", "NaT"], tz="US/Eastern").as_unit("ns")
+        expected = Series(dti)
+        tm.assert_series_equal(ser, expected)
+
+    def test_constructor_no_partial_datetime_casting(self):
+        # GH#40111
+        vals = [
+            "nan",
+            Timestamp("1990-01-01"),
+            "2015-03-14T16:15:14.123-08:00",
+            "2019-03-04T21:56:32.620-07:00",
+            None,
+        ]
+        ser = Series(vals)
+        assert all(ser[i] is vals[i] for i in range(len(vals)))
+
+    @pytest.mark.parametrize("arr_dtype", [np.int64, np.float64])
+    @pytest.mark.parametrize("kind", ["M", "m"])
+    @pytest.mark.parametrize("unit", ["ns", "us", "ms", "s", "h", "m", "D"])
+    def test_construction_to_datetimelike_unit(self, arr_dtype, kind, unit):
+        # tests all units
+        # gh-19223
+        # TODO: GH#19223 was about .astype, doesn't belong here
+        dtype = f"{kind}8[{unit}]"
+        arr = np.array([1, 2, 3], dtype=arr_dtype)
+        ser = Series(arr)
+        result = ser.astype(dtype)
+
+        expected = Series(arr.astype(dtype))
+
+        if unit in ["ns", "us", "ms", "s"]:
+            assert result.dtype == dtype
+            assert expected.dtype == dtype
+        else:
+            # Otherwise we cast to nearest-supported unit, i.e. seconds
+            assert result.dtype == f"{kind}8[s]"
+            assert expected.dtype == f"{kind}8[s]"
+
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("arg", ["2013-01-01 00:00:00", NaT, np.nan, None])
+    def test_constructor_with_naive_string_and_datetimetz_dtype(self, arg):
+        # GH 17415: With naive string
+        result = Series([arg], dtype="datetime64[ns, CET]")
+        expected = Series(Timestamp(arg)).dt.tz_localize("CET")
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_datetime64_bigendian(self):
+        # GH#30976
+        ms = np.datetime64(1, "ms")
+        arr = np.array([np.datetime64(1, "ms")], dtype=">M8[ms]")
+
+        result = Series(arr)
+        expected = Series([Timestamp(ms)]).astype("M8[ms]")
+        assert expected.dtype == "M8[ms]"
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("interval_constructor", [IntervalIndex, IntervalArray])
+    def test_construction_interval(self, interval_constructor):
+        # construction from interval & array of intervals
+        intervals = interval_constructor.from_breaks(np.arange(3), closed="right")
+        result = Series(intervals)
+        assert result.dtype == "interval[int64, right]"
+        tm.assert_index_equal(Index(result.values), Index(intervals))
+
+    @pytest.mark.parametrize(
+        "data_constructor", [list, np.array], ids=["list", "ndarray[object]"]
+    )
+    def test_constructor_infer_interval(self, data_constructor):
+        # GH 23563: consistent closed results in interval dtype
+        data = [Interval(0, 1), Interval(0, 2), None]
+        result = Series(data_constructor(data))
+        expected = Series(IntervalArray(data))
+        assert result.dtype == "interval[float64, right]"
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "data_constructor", [list, np.array], ids=["list", "ndarray[object]"]
+    )
+    def test_constructor_interval_mixed_closed(self, data_constructor):
+        # GH 23563: mixed closed results in object dtype (not interval dtype)
+        data = [Interval(0, 1, closed="both"), Interval(0, 2, closed="neither")]
+        result = Series(data_constructor(data))
+        assert result.dtype == object
+        assert result.tolist() == data
+
+    def test_construction_consistency(self):
+        # make sure that we are not re-localizing upon construction
+        # GH 14928
+        ser = Series(date_range("20130101", periods=3, tz="US/Eastern"))
+
+        result = Series(ser, dtype=ser.dtype)
+        tm.assert_series_equal(result, ser)
+
+        result = Series(ser.dt.tz_convert("UTC"), dtype=ser.dtype)
+        tm.assert_series_equal(result, ser)
+
+        # Pre-2.0 dt64 values were treated as utc, which was inconsistent
+        #  with DatetimeIndex, which treats them as wall times, see GH#33401
+        result = Series(ser.values, dtype=ser.dtype)
+        expected = Series(ser.values).dt.tz_localize(ser.dtype.tz)
+        tm.assert_series_equal(result, expected)
+
+        with tm.assert_produces_warning(None):
+            # one suggested alternative to the deprecated (changed in 2.0) usage
+            middle = Series(ser.values).dt.tz_localize("UTC")
+            result = middle.dt.tz_convert(ser.dtype.tz)
+        tm.assert_series_equal(result, ser)
+
+        with tm.assert_produces_warning(None):
+            # the other suggested alternative to the deprecated usage
+            result = Series(ser.values.view("int64"), dtype=ser.dtype)
+        tm.assert_series_equal(result, ser)
+
+    @pytest.mark.parametrize(
+        "data_constructor", [list, np.array], ids=["list", "ndarray[object]"]
+    )
+    def test_constructor_infer_period(self, data_constructor):
+        data = [Period("2000", "D"), Period("2001", "D"), None]
+        result = Series(data_constructor(data))
+        expected = Series(period_array(data))
+        tm.assert_series_equal(result, expected)
+        assert result.dtype == "Period[D]"
+
+    @pytest.mark.xfail(reason="PeriodDtype Series not supported yet")
+    def test_construct_from_ints_including_iNaT_scalar_period_dtype(self):
+        series = Series([0, 1000, 2000, pd._libs.iNaT], dtype="period[D]")
+
+        val = series[3]
+        assert isna(val)
+
+        series[2] = val
+        assert isna(series[2])
+
+    def test_constructor_period_incompatible_frequency(self):
+        data = [Period("2000", "D"), Period("2001", "Y")]
+        result = Series(data)
+        assert result.dtype == object
+        assert result.tolist() == data
+
+    def test_constructor_periodindex(self):
+        # GH7932
+        # converting a PeriodIndex when put in a Series
+
+        pi = period_range("20130101", periods=5, freq="D")
+        s = Series(pi)
+        assert s.dtype == "Period[D]"
+        with tm.assert_produces_warning(FutureWarning, match="Dtype inference"):
+            expected = Series(pi.astype(object))
+        tm.assert_series_equal(s, expected)
+
+    def test_constructor_dict(self):
+        d = {"a": 0.0, "b": 1.0, "c": 2.0}
+
+        result = Series(d)
+        expected = Series(d, index=sorted(d.keys()))
+        tm.assert_series_equal(result, expected)
+
+        result = Series(d, index=["b", "c", "d", "a"])
+        expected = Series([1, 2, np.nan, 0], index=["b", "c", "d", "a"])
+        tm.assert_series_equal(result, expected)
+
+        pidx = period_range("2020-01-01", periods=10, freq="D")
+        d = {pidx[0]: 0, pidx[1]: 1}
+        result = Series(d, index=pidx)
+        expected = Series(np.nan, pidx, dtype=np.float64)
+        expected.iloc[0] = 0
+        expected.iloc[1] = 1
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_dict_list_value_explicit_dtype(self):
+        # GH 18625
+        d = {"a": [[2], [3], [4]]}
+        result = Series(d, index=["a"], dtype="object")
+        expected = Series(d, index=["a"])
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_dict_order(self):
+        # GH19018
+        # initialization ordering: by insertion order
+        d = {"b": 1, "a": 0, "c": 2}
+        result = Series(d)
+        expected = Series([1, 0, 2], index=list("bac"))
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_dict_extension(self, ea_scalar_and_dtype, request):
+        ea_scalar, ea_dtype = ea_scalar_and_dtype
+        if isinstance(ea_scalar, Timestamp):
+            mark = pytest.mark.xfail(
+                reason="Construction from dict goes through "
+                "maybe_convert_objects which casts to nano"
+            )
+            request.applymarker(mark)
+        d = {"a": ea_scalar}
+        result = Series(d, index=["a"])
+        expected = Series(ea_scalar, index=["a"], dtype=ea_dtype)
+
+        assert result.dtype == ea_dtype
+
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("value", [2, np.nan, None, float("nan")])
+    def test_constructor_dict_nan_key(self, value):
+        # GH 18480
+        d = {1: "a", value: "b", float("nan"): "c", 4: "d"}
+        result = Series(d).sort_values()
+        expected = Series(["a", "b", "c", "d"], index=[1, value, np.nan, 4])
+        tm.assert_series_equal(result, expected)
+
+        # MultiIndex:
+        d = {(1, 1): "a", (2, np.nan): "b", (3, value): "c"}
+        result = Series(d).sort_values()
+        expected = Series(
+            ["a", "b", "c"], index=Index([(1, 1), (2, np.nan), (3, value)])
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_dict_datetime64_index(self):
+        # GH 9456
+
+        dates_as_str = ["1984-02-19", "1988-11-06", "1989-12-03", "1990-03-15"]
+        values = [42544017.198965244, 1234565, 40512335.181958228, -1]
+
+        def create_data(constructor):
+            return dict(zip((constructor(x) for x in dates_as_str), values))
+
+        data_datetime64 = create_data(np.datetime64)
+        data_datetime = create_data(lambda x: datetime.strptime(x, "%Y-%m-%d"))
+        data_Timestamp = create_data(Timestamp)
+
+        expected = Series(values, (Timestamp(x) for x in dates_as_str))
+
+        result_datetime64 = Series(data_datetime64)
+        result_datetime = Series(data_datetime)
+        result_Timestamp = Series(data_Timestamp)
+
+        tm.assert_series_equal(result_datetime64, expected)
+        tm.assert_series_equal(result_datetime, expected)
+        tm.assert_series_equal(result_Timestamp, expected)
+
+    def test_constructor_dict_tuple_indexer(self):
+        # GH 12948
+        data = {(1, 1, None): -1.0}
+        result = Series(data)
+        expected = Series(
+            -1.0, index=MultiIndex(levels=[[1], [1], [np.nan]], codes=[[0], [0], [-1]])
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_mapping(self, non_dict_mapping_subclass):
+        # GH 29788
+        ndm = non_dict_mapping_subclass({3: "three"})
+        result = Series(ndm)
+        expected = Series(["three"], index=[3])
+
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_list_of_tuples(self):
+        data = [(1, 1), (2, 2), (2, 3)]
+        s = Series(data)
+        assert list(s) == data
+
+    def test_constructor_tuple_of_tuples(self):
+        data = ((1, 1), (2, 2), (2, 3))
+        s = Series(data)
+        assert tuple(s) == data
+
+    def test_constructor_dict_of_tuples(self):
+        data = {(1, 2): 3, (None, 5): 6}
+        result = Series(data).sort_values()
+        expected = Series([3, 6], index=MultiIndex.from_tuples([(1, 2), (None, 5)]))
+        tm.assert_series_equal(result, expected)
+
+    # https://github.com/pandas-dev/pandas/issues/22698
+    @pytest.mark.filterwarnings("ignore:elementwise comparison:FutureWarning")
+    def test_fromDict(self, using_infer_string):
+        data = {"a": 0, "b": 1, "c": 2, "d": 3}
+
+        series = Series(data)
+        tm.assert_is_sorted(series.index)
+
+        data = {"a": 0, "b": "1", "c": "2", "d": datetime.now()}
+        series = Series(data)
+        assert series.dtype == np.object_
+
+        data = {"a": 0, "b": "1", "c": "2", "d": "3"}
+        series = Series(data)
+        assert series.dtype == np.object_ if not using_infer_string else "string"
+
+        data = {"a": "0", "b": "1"}
+        series = Series(data, dtype=float)
+        assert series.dtype == np.float64
+
+    def test_fromValue(self, datetime_series, using_infer_string):
+        nans = Series(np.nan, index=datetime_series.index, dtype=np.float64)
+        assert nans.dtype == np.float64
+        assert len(nans) == len(datetime_series)
+
+        strings = Series("foo", index=datetime_series.index)
+        assert strings.dtype == np.object_ if not using_infer_string else "string"
+        assert len(strings) == len(datetime_series)
+
+        d = datetime.now()
+        dates = Series(d, index=datetime_series.index)
+        assert dates.dtype == "M8[us]"
+        assert len(dates) == len(datetime_series)
+
+        # GH12336
+        # Test construction of categorical series from value
+        categorical = Series(0, index=datetime_series.index, dtype="category")
+        expected = Series(0, index=datetime_series.index).astype("category")
+        assert categorical.dtype == "category"
+        assert len(categorical) == len(datetime_series)
+        tm.assert_series_equal(categorical, expected)
+
+    def test_constructor_dtype_timedelta64(self):
+        # basic
+        td = Series([timedelta(days=i) for i in range(3)])
+        assert td.dtype == "timedelta64[ns]"
+
+        td = Series([timedelta(days=1)])
+        assert td.dtype == "timedelta64[ns]"
+
+        td = Series([timedelta(days=1), timedelta(days=2), np.timedelta64(1, "s")])
+
+        assert td.dtype == "timedelta64[ns]"
+
+        # mixed with NaT
+        td = Series([timedelta(days=1), NaT], dtype="m8[ns]")
+        assert td.dtype == "timedelta64[ns]"
+
+        td = Series([timedelta(days=1), np.nan], dtype="m8[ns]")
+        assert td.dtype == "timedelta64[ns]"
+
+        td = Series([np.timedelta64(300000000), NaT], dtype="m8[ns]")
+        assert td.dtype == "timedelta64[ns]"
+
+        # improved inference
+        # GH5689
+        td = Series([np.timedelta64(300000000), NaT])
+        assert td.dtype == "timedelta64[ns]"
+
+        # because iNaT is int, not coerced to timedelta
+        td = Series([np.timedelta64(300000000), iNaT])
+        assert td.dtype == "object"
+
+        td = Series([np.timedelta64(300000000), np.nan])
+        assert td.dtype == "timedelta64[ns]"
+
+        td = Series([NaT, np.timedelta64(300000000)])
+        assert td.dtype == "timedelta64[ns]"
+
+        td = Series([np.timedelta64(1, "s")])
+        assert td.dtype == "timedelta64[ns]"
+
+        # valid astype
+        td.astype("int64")
+
+        # invalid casting
+        msg = r"Converting from timedelta64\[ns\] to int32 is not supported"
+        with pytest.raises(TypeError, match=msg):
+            td.astype("int32")
+
+        # this is an invalid casting
+        msg = "|".join(
+            [
+                "Could not convert object to NumPy timedelta",
+                "Could not convert 'foo' to NumPy timedelta",
+            ]
+        )
+        with pytest.raises(ValueError, match=msg):
+            Series([timedelta(days=1), "foo"], dtype="m8[ns]")
+
+        # leave as object here
+        td = Series([timedelta(days=i) for i in range(3)] + ["foo"])
+        assert td.dtype == "object"
+
+        # as of 2.0, these no longer infer timedelta64 based on the strings,
+        #  matching Index behavior
+        ser = Series([None, NaT, "1 Day"])
+        assert ser.dtype == object
+
+        ser = Series([np.nan, NaT, "1 Day"])
+        assert ser.dtype == object
+
+        ser = Series([NaT, None, "1 Day"])
+        assert ser.dtype == object
+
+        ser = Series([NaT, np.nan, "1 Day"])
+        assert ser.dtype == object
+
+    # GH 16406
+    def test_constructor_mixed_tz(self):
+        s = Series([Timestamp("20130101"), Timestamp("20130101", tz="US/Eastern")])
+        expected = Series(
+            [Timestamp("20130101"), Timestamp("20130101", tz="US/Eastern")],
+            dtype="object",
+        )
+        tm.assert_series_equal(s, expected)
+
+    def test_NaT_scalar(self):
+        series = Series([0, 1000, 2000, iNaT], dtype="M8[ns]")
+
+        val = series[3]
+        assert isna(val)
+
+        series[2] = val
+        assert isna(series[2])
+
+    def test_NaT_cast(self):
+        # GH10747
+        result = Series([np.nan]).astype("M8[ns]")
+        expected = Series([NaT], dtype="M8[ns]")
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_name_hashable(self):
+        for n in [777, 777.0, "name", datetime(2001, 11, 11), (1,), "\u05D0"]:
+            for data in [[1, 2, 3], np.ones(3), {"a": 0, "b": 1}]:
+                s = Series(data, name=n)
+                assert s.name == n
+
+    def test_constructor_name_unhashable(self):
+        msg = r"Series\.name must be a hashable type"
+        for n in [["name_list"], np.ones(2), {1: 2}]:
+            for data in [["name_list"], np.ones(2), {1: 2}]:
+                with pytest.raises(TypeError, match=msg):
+                    Series(data, name=n)
+
+    def test_auto_conversion(self):
+        series = Series(list(date_range("1/1/2000", periods=10)))
+        assert series.dtype == "M8[ns]"
+
+    def test_convert_non_ns(self):
+        # convert from a numpy array of non-ns timedelta64
+        arr = np.array([1, 2, 3], dtype="timedelta64[s]")
+        ser = Series(arr)
+        assert ser.dtype == arr.dtype
+
+        tdi = timedelta_range("00:00:01", periods=3, freq="s").as_unit("s")
+        expected = Series(tdi)
+        assert expected.dtype == arr.dtype
+        tm.assert_series_equal(ser, expected)
+
+        # convert from a numpy array of non-ns datetime64
+        arr = np.array(
+            ["2013-01-01", "2013-01-02", "2013-01-03"], dtype="datetime64[D]"
+        )
+        ser = Series(arr)
+        expected = Series(date_range("20130101", periods=3, freq="D"), dtype="M8[s]")
+        assert expected.dtype == "M8[s]"
+        tm.assert_series_equal(ser, expected)
+
+        arr = np.array(
+            ["2013-01-01 00:00:01", "2013-01-01 00:00:02", "2013-01-01 00:00:03"],
+            dtype="datetime64[s]",
+        )
+        ser = Series(arr)
+        expected = Series(
+            date_range("20130101 00:00:01", periods=3, freq="s"), dtype="M8[s]"
+        )
+        assert expected.dtype == "M8[s]"
+        tm.assert_series_equal(ser, expected)
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            date_range("1/1/2000", periods=10),
+            timedelta_range("1 day", periods=10),
+            period_range("2000-Q1", periods=10, freq="Q"),
+        ],
+        ids=lambda x: type(x).__name__,
+    )
+    def test_constructor_cant_cast_datetimelike(self, index):
+        # floats are not ok
+        # strip Index to convert PeriodIndex -> Period
+        # We don't care whether the error message says
+        # PeriodIndex or PeriodArray
+        msg = f"Cannot cast {type(index).__name__.rstrip('Index')}.*? to "
+
+        with pytest.raises(TypeError, match=msg):
+            Series(index, dtype=float)
+
+        # ints are ok
+        # we test with np.int64 to get similar results on
+        # windows / 32-bit platforms
+        result = Series(index, dtype=np.int64)
+        expected = Series(index.astype(np.int64))
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            date_range("1/1/2000", periods=10),
+            timedelta_range("1 day", periods=10),
+            period_range("2000-Q1", periods=10, freq="Q"),
+        ],
+        ids=lambda x: type(x).__name__,
+    )
+    def test_constructor_cast_object(self, index):
+        s = Series(index, dtype=object)
+        exp = Series(index).astype(object)
+        tm.assert_series_equal(s, exp)
+
+        s = Series(Index(index, dtype=object), dtype=object)
+        exp = Series(index).astype(object)
+        tm.assert_series_equal(s, exp)
+
+        s = Series(index.astype(object), dtype=object)
+        exp = Series(index).astype(object)
+        tm.assert_series_equal(s, exp)
+
+    @pytest.mark.parametrize("dtype", [np.datetime64, np.timedelta64])
+    def test_constructor_generic_timestamp_no_frequency(self, dtype, request):
+        # see gh-15524, gh-15987
+        msg = "dtype has no unit. Please pass in"
+
+        if np.dtype(dtype).name not in ["timedelta64", "datetime64"]:
+            mark = pytest.mark.xfail(reason="GH#33890 Is assigned ns unit")
+            request.applymarker(mark)
+
+        with pytest.raises(ValueError, match=msg):
+            Series([], dtype=dtype)
+
+    @pytest.mark.parametrize("unit", ["ps", "as", "fs", "Y", "M", "W", "D", "h", "m"])
+    @pytest.mark.parametrize("kind", ["m", "M"])
+    def test_constructor_generic_timestamp_bad_frequency(self, kind, unit):
+        # see gh-15524, gh-15987
+        # as of 2.0 we raise on any non-supported unit rather than silently
+        #  cast to nanos; previously we only raised for frequencies higher
+        #  than ns
+        dtype = f"{kind}8[{unit}]"
+
+        msg = "dtype=.* is not supported. Supported resolutions are"
+        with pytest.raises(TypeError, match=msg):
+            Series([], dtype=dtype)
+
+        with pytest.raises(TypeError, match=msg):
+            # pre-2.0 the DataFrame cast raised but the Series case did not
+            DataFrame([[0]], dtype=dtype)
+
+    @pytest.mark.parametrize("dtype", [None, "uint8", "category"])
+    def test_constructor_range_dtype(self, dtype):
+        # GH 16804
+        expected = Series([0, 1, 2, 3, 4], dtype=dtype or "int64")
+        result = Series(range(5), dtype=dtype)
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_range_overflows(self):
+        # GH#30173 range objects that overflow int64
+        rng = range(2**63, 2**63 + 4)
+        ser = Series(rng)
+        expected = Series(list(rng))
+        tm.assert_series_equal(ser, expected)
+        assert list(ser) == list(rng)
+        assert ser.dtype == np.uint64
+
+        rng2 = range(2**63 + 4, 2**63, -1)
+        ser2 = Series(rng2)
+        expected2 = Series(list(rng2))
+        tm.assert_series_equal(ser2, expected2)
+        assert list(ser2) == list(rng2)
+        assert ser2.dtype == np.uint64
+
+        rng3 = range(-(2**63), -(2**63) - 4, -1)
+        ser3 = Series(rng3)
+        expected3 = Series(list(rng3))
+        tm.assert_series_equal(ser3, expected3)
+        assert list(ser3) == list(rng3)
+        assert ser3.dtype == object
+
+        rng4 = range(2**73, 2**73 + 4)
+        ser4 = Series(rng4)
+        expected4 = Series(list(rng4))
+        tm.assert_series_equal(ser4, expected4)
+        assert list(ser4) == list(rng4)
+        assert ser4.dtype == object
+
+    def test_constructor_tz_mixed_data(self):
+        # GH 13051
+        dt_list = [
+            Timestamp("2016-05-01 02:03:37"),
+            Timestamp("2016-04-30 19:03:37-0700", tz="US/Pacific"),
+        ]
+        result = Series(dt_list)
+        expected = Series(dt_list, dtype=object)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("pydt", [True, False])
+    def test_constructor_data_aware_dtype_naive(self, tz_aware_fixture, pydt):
+        # GH#25843, GH#41555, GH#33401
+        tz = tz_aware_fixture
+        ts = Timestamp("2019", tz=tz)
+        if pydt:
+            ts = ts.to_pydatetime()
+
+        msg = (
+            "Cannot convert timezone-aware data to timezone-naive dtype. "
+            r"Use pd.Series\(values\).dt.tz_localize\(None\) instead."
+        )
+        with pytest.raises(ValueError, match=msg):
+            Series([ts], dtype="datetime64[ns]")
+
+        with pytest.raises(ValueError, match=msg):
+            Series(np.array([ts], dtype=object), dtype="datetime64[ns]")
+
+        with pytest.raises(ValueError, match=msg):
+            Series({0: ts}, dtype="datetime64[ns]")
+
+        msg = "Cannot unbox tzaware Timestamp to tznaive dtype"
+        with pytest.raises(TypeError, match=msg):
+            Series(ts, index=[0], dtype="datetime64[ns]")
+
+    def test_constructor_datetime64(self):
+        rng = date_range("1/1/2000 00:00:00", "1/1/2000 1:59:50", freq="10s")
+        dates = np.asarray(rng)
+
+        series = Series(dates)
+        assert np.issubdtype(series.dtype, np.dtype("M8[ns]"))
+
+    def test_constructor_datetimelike_scalar_to_string_dtype(
+        self, nullable_string_dtype
+    ):
+        # https://github.com/pandas-dev/pandas/pull/33846
+        result = Series("M", index=[1, 2, 3], dtype=nullable_string_dtype)
+        expected = Series(["M", "M", "M"], index=[1, 2, 3], dtype=nullable_string_dtype)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "values",
+        [
+            [np.datetime64("2012-01-01"), np.datetime64("2013-01-01")],
+            ["2012-01-01", "2013-01-01"],
+        ],
+    )
+    def test_constructor_sparse_datetime64(self, values):
+        # https://github.com/pandas-dev/pandas/issues/35762
+        dtype = pd.SparseDtype("datetime64[ns]")
+        result = Series(values, dtype=dtype)
+        arr = pd.arrays.SparseArray(values, dtype=dtype)
+        expected = Series(arr)
+        tm.assert_series_equal(result, expected)
+
+    def test_construction_from_ordered_collection(self):
+        # https://github.com/pandas-dev/pandas/issues/36044
+        result = Series({"a": 1, "b": 2}.keys())
+        expected = Series(["a", "b"])
+        tm.assert_series_equal(result, expected)
+
+        result = Series({"a": 1, "b": 2}.values())
+        expected = Series([1, 2])
+        tm.assert_series_equal(result, expected)
+
+    def test_construction_from_large_int_scalar_no_overflow(self):
+        # https://github.com/pandas-dev/pandas/issues/36291
+        n = 1_000_000_000_000_000_000_000
+        result = Series(n, index=[0])
+        expected = Series(n)
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_list_of_periods_infers_period_dtype(self):
+        series = Series(list(period_range("2000-01-01", periods=10, freq="D")))
+        assert series.dtype == "Period[D]"
+
+        series = Series(
+            [Period("2011-01-01", freq="D"), Period("2011-02-01", freq="D")]
+        )
+        assert series.dtype == "Period[D]"
+
+    def test_constructor_subclass_dict(self, dict_subclass):
+        data = dict_subclass((x, 10.0 * x) for x in range(10))
+        series = Series(data)
+        expected = Series(dict(data.items()))
+        tm.assert_series_equal(series, expected)
+
+    def test_constructor_ordereddict(self):
+        # GH3283
+        data = OrderedDict(
+            (f"col{i}", np.random.default_rng(2).random()) for i in range(12)
+        )
+
+        series = Series(data)
+        expected = Series(list(data.values()), list(data.keys()))
+        tm.assert_series_equal(series, expected)
+
+        # Test with subclass
+        class A(OrderedDict):
+            pass
+
+        series = Series(A(data))
+        tm.assert_series_equal(series, expected)
+
+    def test_constructor_dict_multiindex(self):
+        d = {("a", "a"): 0.0, ("b", "a"): 1.0, ("b", "c"): 2.0}
+        _d = sorted(d.items())
+        result = Series(d)
+        expected = Series(
+            [x[1] for x in _d], index=MultiIndex.from_tuples([x[0] for x in _d])
+        )
+        tm.assert_series_equal(result, expected)
+
+        d["z"] = 111.0
+        _d.insert(0, ("z", d["z"]))
+        result = Series(d)
+        expected = Series(
+            [x[1] for x in _d], index=Index([x[0] for x in _d], tupleize_cols=False)
+        )
+        result = result.reindex(index=expected.index)
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_dict_multiindex_reindex_flat(self):
+        # construction involves reindexing with a MultiIndex corner case
+        data = {("i", "i"): 0, ("i", "j"): 1, ("j", "i"): 2, "j": np.nan}
+        expected = Series(data)
+
+        result = Series(expected[:-1].to_dict(), index=expected.index)
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_dict_timedelta_index(self):
+        # GH #12169 : Resample category data with timedelta index
+        # construct Series from dict as data and TimedeltaIndex as index
+        # will result NaN in result Series data
+        expected = Series(
+            data=["A", "B", "C"], index=pd.to_timedelta([0, 10, 20], unit="s")
+        )
+
+        result = Series(
+            data={
+                pd.to_timedelta(0, unit="s"): "A",
+                pd.to_timedelta(10, unit="s"): "B",
+                pd.to_timedelta(20, unit="s"): "C",
+            },
+            index=pd.to_timedelta([0, 10, 20], unit="s"),
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_infer_index_tz(self):
+        values = [188.5, 328.25]
+        tzinfo = tzoffset(None, 7200)
+        index = [
+            datetime(2012, 5, 11, 11, tzinfo=tzinfo),
+            datetime(2012, 5, 11, 12, tzinfo=tzinfo),
+        ]
+        series = Series(data=values, index=index)
+
+        assert series.index.tz == tzinfo
+
+        # it works! GH#2443
+        repr(series.index[0])
+
+    def test_constructor_with_pandas_dtype(self):
+        # going through 2D->1D path
+        vals = [(1,), (2,), (3,)]
+        ser = Series(vals)
+        dtype = ser.array.dtype  # NumpyEADtype
+        ser2 = Series(vals, dtype=dtype)
+        tm.assert_series_equal(ser, ser2)
+
+    def test_constructor_int_dtype_missing_values(self):
+        # GH#43017
+        result = Series(index=[0], dtype="int64")
+        expected = Series(np.nan, index=[0], dtype="float64")
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_bool_dtype_missing_values(self):
+        # GH#43018
+        result = Series(index=[0], dtype="bool")
+        expected = Series(True, index=[0], dtype="bool")
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_int64_dtype(self, any_int_dtype):
+        # GH#44923
+        result = Series(["0", "1", "2"], dtype=any_int_dtype)
+        expected = Series([0, 1, 2], dtype=any_int_dtype)
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_raise_on_lossy_conversion_of_strings(self):
+        # GH#44923
+        if not np_version_gt2:
+            raises = pytest.raises(
+                ValueError, match="string values cannot be losslessly cast to int8"
+            )
+        else:
+            raises = pytest.raises(
+                OverflowError, match="The elements provided in the data"
+            )
+        with raises:
+            Series(["128"], dtype="int8")
+
+    def test_constructor_dtype_timedelta_alternative_construct(self):
+        # GH#35465
+        result = Series([1000000, 200000, 3000000], dtype="timedelta64[ns]")
+        expected = Series(pd.to_timedelta([1000000, 200000, 3000000], unit="ns"))
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.xfail(
+        reason="Not clear what the correct expected behavior should be with "
+        "integers now that we support non-nano. ATM (2022-10-08) we treat ints "
+        "as nanoseconds, then cast to the requested dtype. xref #48312"
+    )
+    def test_constructor_dtype_timedelta_ns_s(self):
+        # GH#35465
+        result = Series([1000000, 200000, 3000000], dtype="timedelta64[ns]")
+        expected = Series([1000000, 200000, 3000000], dtype="timedelta64[s]")
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.xfail(
+        reason="Not clear what the correct expected behavior should be with "
+        "integers now that we support non-nano. ATM (2022-10-08) we treat ints "
+        "as nanoseconds, then cast to the requested dtype. xref #48312"
+    )
+    def test_constructor_dtype_timedelta_ns_s_astype_int64(self):
+        # GH#35465
+        result = Series([1000000, 200000, 3000000], dtype="timedelta64[ns]").astype(
+            "int64"
+        )
+        expected = Series([1000000, 200000, 3000000], dtype="timedelta64[s]").astype(
+            "int64"
+        )
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:elementwise comparison failed:DeprecationWarning"
+    )
+    @pytest.mark.parametrize("func", [Series, DataFrame, Index, pd.array])
+    def test_constructor_mismatched_null_nullable_dtype(
+        self, func, any_numeric_ea_dtype
+    ):
+        # GH#44514
+        msg = "|".join(
+            [
+                "cannot safely cast non-equivalent object",
+                r"int\(\) argument must be a string, a bytes-like object "
+                "or a (real )?number",
+                r"Cannot cast array data from dtype\('O'\) to dtype\('float64'\) "
+                "according to the rule 'safe'",
+                "object cannot be converted to a FloatingDtype",
+                "'values' contains non-numeric NA",
+            ]
+        )
+
+        for null in tm.NP_NAT_OBJECTS + [NaT]:
+            with pytest.raises(TypeError, match=msg):
+                func([null, 1.0, 3.0], dtype=any_numeric_ea_dtype)
+
+    def test_series_constructor_ea_int_from_bool(self):
+        # GH#42137
+        result = Series([True, False, True, pd.NA], dtype="Int64")
+        expected = Series([1, 0, 1, pd.NA], dtype="Int64")
+        tm.assert_series_equal(result, expected)
+
+        result = Series([True, False, True], dtype="Int64")
+        expected = Series([1, 0, 1], dtype="Int64")
+        tm.assert_series_equal(result, expected)
+
+    def test_series_constructor_ea_int_from_string_bool(self):
+        # GH#42137
+        with pytest.raises(ValueError, match="invalid literal"):
+            Series(["True", "False", "True", pd.NA], dtype="Int64")
+
+    @pytest.mark.parametrize("val", [1, 1.0])
+    def test_series_constructor_overflow_uint_ea(self, val):
+        # GH#38798
+        max_val = np.iinfo(np.uint64).max - 1
+        result = Series([max_val, val], dtype="UInt64")
+        expected = Series(np.array([max_val, 1], dtype="uint64"), dtype="UInt64")
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("val", [1, 1.0])
+    def test_series_constructor_overflow_uint_ea_with_na(self, val):
+        # GH#38798
+        max_val = np.iinfo(np.uint64).max - 1
+        result = Series([max_val, val, pd.NA], dtype="UInt64")
+        expected = Series(
+            IntegerArray(
+                np.array([max_val, 1, 0], dtype="uint64"),
+                np.array([0, 0, 1], dtype=np.bool_),
+            )
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_series_constructor_overflow_uint_with_nan(self):
+        # GH#38798
+        max_val = np.iinfo(np.uint64).max - 1
+        result = Series([max_val, np.nan], dtype="UInt64")
+        expected = Series(
+            IntegerArray(
+                np.array([max_val, 1], dtype="uint64"),
+                np.array([0, 1], dtype=np.bool_),
+            )
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_series_constructor_ea_all_na(self):
+        # GH#38798
+        result = Series([np.nan, np.nan], dtype="UInt64")
+        expected = Series(
+            IntegerArray(
+                np.array([1, 1], dtype="uint64"),
+                np.array([1, 1], dtype=np.bool_),
+            )
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_series_from_index_dtype_equal_does_not_copy(self):
+        # GH#52008
+        idx = Index([1, 2, 3])
+        expected = idx.copy(deep=True)
+        ser = Series(idx, dtype="int64")
+        ser.iloc[0] = 100
+        tm.assert_index_equal(idx, expected)
+
+    def test_series_string_inference(self):
+        # GH#54430
+        pytest.importorskip("pyarrow")
+        dtype = "string[pyarrow_numpy]"
+        expected = Series(["a", "b"], dtype=dtype)
+        with pd.option_context("future.infer_string", True):
+            ser = Series(["a", "b"])
+        tm.assert_series_equal(ser, expected)
+
+        expected = Series(["a", 1], dtype="object")
+        with pd.option_context("future.infer_string", True):
+            ser = Series(["a", 1])
+        tm.assert_series_equal(ser, expected)
+
+    @pytest.mark.parametrize("na_value", [None, np.nan, pd.NA])
+    def test_series_string_with_na_inference(self, na_value):
+        # GH#54430
+        pytest.importorskip("pyarrow")
+        dtype = "string[pyarrow_numpy]"
+        expected = Series(["a", na_value], dtype=dtype)
+        with pd.option_context("future.infer_string", True):
+            ser = Series(["a", na_value])
+        tm.assert_series_equal(ser, expected)
+
+    def test_series_string_inference_scalar(self):
+        # GH#54430
+        pytest.importorskip("pyarrow")
+        expected = Series("a", index=[1], dtype="string[pyarrow_numpy]")
+        with pd.option_context("future.infer_string", True):
+            ser = Series("a", index=[1])
+        tm.assert_series_equal(ser, expected)
+
+    def test_series_string_inference_array_string_dtype(self):
+        # GH#54496
+        pytest.importorskip("pyarrow")
+        expected = Series(["a", "b"], dtype="string[pyarrow_numpy]")
+        with pd.option_context("future.infer_string", True):
+            ser = Series(np.array(["a", "b"]))
+        tm.assert_series_equal(ser, expected)
+
+    def test_series_string_inference_storage_definition(self):
+        # GH#54793
+        pytest.importorskip("pyarrow")
+        expected = Series(["a", "b"], dtype="string[pyarrow_numpy]")
+        with pd.option_context("future.infer_string", True):
+            result = Series(["a", "b"], dtype="string")
+        tm.assert_series_equal(result, expected)
+
+    def test_series_constructor_infer_string_scalar(self):
+        # GH#55537
+        with pd.option_context("future.infer_string", True):
+            ser = Series("a", index=[1, 2], dtype="string[python]")
+        expected = Series(["a", "a"], index=[1, 2], dtype="string[python]")
+        tm.assert_series_equal(ser, expected)
+        assert ser.dtype.storage == "python"
+
+    def test_series_string_inference_na_first(self):
+        # GH#55655
+        pytest.importorskip("pyarrow")
+        expected = Series([pd.NA, "b"], dtype="string[pyarrow_numpy]")
+        with pd.option_context("future.infer_string", True):
+            result = Series([pd.NA, "b"])
+        tm.assert_series_equal(result, expected)
+
+    def test_inference_on_pandas_objects(self):
+        # GH#56012
+        ser = Series([Timestamp("2019-12-31")], dtype=object)
+        with tm.assert_produces_warning(None):
+            # This doesn't do inference
+            result = Series(ser)
+        assert result.dtype == np.object_
+
+        idx = Index([Timestamp("2019-12-31")], dtype=object)
+
+        with tm.assert_produces_warning(FutureWarning, match="Dtype inference"):
+            result = Series(idx)
+        assert result.dtype != np.object_
+
+
+class TestSeriesConstructorIndexCoercion:
+    def test_series_constructor_datetimelike_index_coercion(self):
+        idx = date_range("2020-01-01", periods=5)
+        ser = Series(
+            np.random.default_rng(2).standard_normal(len(idx)), idx.astype(object)
+        )
+        # as of 2.0, we no longer silently cast the object-dtype index
+        #  to DatetimeIndex GH#39307, GH#23598
+        assert not isinstance(ser.index, DatetimeIndex)
+
+    @pytest.mark.parametrize("container", [None, np.array, Series, Index])
+    @pytest.mark.parametrize("data", [1.0, range(4)])
+    def test_series_constructor_infer_multiindex(self, container, data):
+        indexes = [["a", "a", "b", "b"], ["x", "y", "x", "y"]]
+        if container is not None:
+            indexes = [container(ind) for ind in indexes]
+
+        multi = Series(data, index=indexes)
+        assert isinstance(multi.index, MultiIndex)
+
+    # TODO: make this not cast to object in pandas 3.0
+    @pytest.mark.skipif(
+        not np_version_gt2, reason="StringDType only available in numpy 2 and above"
+    )
+    @pytest.mark.parametrize(
+        "data",
+        [
+            ["a", "b", "c"],
+            ["a", "b", np.nan],
+        ],
+    )
+    def test_np_string_array_object_cast(self, data):
+        from numpy.dtypes import StringDType
+
+        arr = np.array(data, dtype=StringDType())
+        res = Series(arr)
+        assert res.dtype == np.object_
+        assert (res == data).all()
+
+
+class TestSeriesConstructorInternals:
+    def test_constructor_no_pandas_array(self, using_array_manager):
+        ser = Series([1, 2, 3])
+        result = Series(ser.array)
+        tm.assert_series_equal(ser, result)
+        if not using_array_manager:
+            assert isinstance(result._mgr.blocks[0], NumpyBlock)
+            assert result._mgr.blocks[0].is_numeric
+
+    @td.skip_array_manager_invalid_test
+    def test_from_array(self):
+        result = Series(pd.array(["1h", "2h"], dtype="timedelta64[ns]"))
+        assert result._mgr.blocks[0].is_extension is False
+
+        result = Series(pd.array(["2015"], dtype="datetime64[ns]"))
+        assert result._mgr.blocks[0].is_extension is False
+
+    @td.skip_array_manager_invalid_test
+    def test_from_list_dtype(self):
+        result = Series(["1h", "2h"], dtype="timedelta64[ns]")
+        assert result._mgr.blocks[0].is_extension is False
+
+        result = Series(["2015"], dtype="datetime64[ns]")
+        assert result._mgr.blocks[0].is_extension is False
+
+
+def test_constructor(rand_series_with_duplicate_datetimeindex):
+    dups = rand_series_with_duplicate_datetimeindex
+    assert isinstance(dups, Series)
+    assert isinstance(dups.index, DatetimeIndex)
+
+
+@pytest.mark.parametrize(
+    "input_dict,expected",
+    [
+        ({0: 0}, np.array([[0]], dtype=np.int64)),
+        ({"a": "a"}, np.array([["a"]], dtype=object)),
+        ({1: 1}, np.array([[1]], dtype=np.int64)),
+    ],
+)
+def test_numpy_array(input_dict, expected):
+    result = np.array([Series(input_dict)])
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_index_ordered_dict_keys():
+    # GH 22077
+
+    param_index = OrderedDict(
+        [
+            ((("a", "b"), ("c", "d")), 1),
+            ((("a", None), ("c", "d")), 2),
+        ]
+    )
+    series = Series([1, 2], index=param_index.keys())
+    expected = Series(
+        [1, 2],
+        index=MultiIndex.from_tuples(
+            [(("a", "b"), ("c", "d")), (("a", None), ("c", "d"))]
+        ),
+    )
+    tm.assert_series_equal(series, expected)
+
+
+@pytest.mark.parametrize(
+    "input_list",
+    [
+        [1, complex("nan"), 2],
+        [1 + 1j, complex("nan"), 2 + 2j],
+    ],
+)
+def test_series_with_complex_nan(input_list):
+    # GH#53627
+    ser = Series(input_list)
+    result = Series(ser.array)
+    assert ser.dtype == "complex128"
+    tm.assert_series_equal(ser, result)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_cumulative.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_cumulative.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6f7b2a5e69e0a97e2f898c6a665372ba3ec2a6b
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_cumulative.py
@@ -0,0 +1,157 @@
+"""
+Tests for Series cumulative operations.
+
+See also
+--------
+tests.frame.test_cumulative
+"""
+
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+methods = {
+    "cumsum": np.cumsum,
+    "cumprod": np.cumprod,
+    "cummin": np.minimum.accumulate,
+    "cummax": np.maximum.accumulate,
+}
+
+
+class TestSeriesCumulativeOps:
+    @pytest.mark.parametrize("func", [np.cumsum, np.cumprod])
+    def test_datetime_series(self, datetime_series, func):
+        tm.assert_numpy_array_equal(
+            func(datetime_series).values,
+            func(np.array(datetime_series)),
+            check_dtype=True,
+        )
+
+        # with missing values
+        ts = datetime_series.copy()
+        ts[::2] = np.nan
+
+        result = func(ts)[1::2]
+        expected = func(np.array(ts.dropna()))
+
+        tm.assert_numpy_array_equal(result.values, expected, check_dtype=False)
+
+    @pytest.mark.parametrize("method", ["cummin", "cummax"])
+    def test_cummin_cummax(self, datetime_series, method):
+        ufunc = methods[method]
+
+        result = getattr(datetime_series, method)().values
+        expected = ufunc(np.array(datetime_series))
+
+        tm.assert_numpy_array_equal(result, expected)
+        ts = datetime_series.copy()
+        ts[::2] = np.nan
+        result = getattr(ts, method)()[1::2]
+        expected = ufunc(ts.dropna())
+
+        result.index = result.index._with_freq(None)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "ts",
+        [
+            pd.Timedelta(0),
+            pd.Timestamp("1999-12-31"),
+            pd.Timestamp("1999-12-31").tz_localize("US/Pacific"),
+        ],
+    )
+    @pytest.mark.parametrize(
+        "method, skipna, exp_tdi",
+        [
+            ["cummax", True, ["NaT", "2 days", "NaT", "2 days", "NaT", "3 days"]],
+            ["cummin", True, ["NaT", "2 days", "NaT", "1 days", "NaT", "1 days"]],
+            [
+                "cummax",
+                False,
+                ["NaT", "NaT", "NaT", "NaT", "NaT", "NaT"],
+            ],
+            [
+                "cummin",
+                False,
+                ["NaT", "NaT", "NaT", "NaT", "NaT", "NaT"],
+            ],
+        ],
+    )
+    def test_cummin_cummax_datetimelike(self, ts, method, skipna, exp_tdi):
+        # with ts==pd.Timedelta(0), we are testing td64; with naive Timestamp
+        #  we are testing datetime64[ns]; with Timestamp[US/Pacific]
+        #  we are testing dt64tz
+        tdi = pd.to_timedelta(["NaT", "2 days", "NaT", "1 days", "NaT", "3 days"])
+        ser = pd.Series(tdi + ts)
+
+        exp_tdi = pd.to_timedelta(exp_tdi)
+        expected = pd.Series(exp_tdi + ts)
+        result = getattr(ser, method)(skipna=skipna)
+        tm.assert_series_equal(expected, result)
+
+    @pytest.mark.parametrize(
+        "func, exp",
+        [
+            ("cummin", pd.Period("2012-1-1", freq="D")),
+            ("cummax", pd.Period("2012-1-2", freq="D")),
+        ],
+    )
+    def test_cummin_cummax_period(self, func, exp):
+        # GH#28385
+        ser = pd.Series(
+            [pd.Period("2012-1-1", freq="D"), pd.NaT, pd.Period("2012-1-2", freq="D")]
+        )
+        result = getattr(ser, func)(skipna=False)
+        expected = pd.Series([pd.Period("2012-1-1", freq="D"), pd.NaT, pd.NaT])
+        tm.assert_series_equal(result, expected)
+
+        result = getattr(ser, func)(skipna=True)
+        expected = pd.Series([pd.Period("2012-1-1", freq="D"), pd.NaT, exp])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "arg",
+        [
+            [False, False, False, True, True, False, False],
+            [False, False, False, False, False, False, False],
+        ],
+    )
+    @pytest.mark.parametrize(
+        "func", [lambda x: x, lambda x: ~x], ids=["identity", "inverse"]
+    )
+    @pytest.mark.parametrize("method", methods.keys())
+    def test_cummethods_bool(self, arg, func, method):
+        # GH#6270
+        # checking Series method vs the ufunc applied to the values
+
+        ser = func(pd.Series(arg))
+        ufunc = methods[method]
+
+        exp_vals = ufunc(ser.values)
+        expected = pd.Series(exp_vals)
+
+        result = getattr(ser, method)()
+
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "method, expected",
+        [
+            ["cumsum", pd.Series([0, 1, np.nan, 1], dtype=object)],
+            ["cumprod", pd.Series([False, 0, np.nan, 0])],
+            ["cummin", pd.Series([False, False, np.nan, False])],
+            ["cummax", pd.Series([False, True, np.nan, True])],
+        ],
+    )
+    def test_cummethods_bool_in_object_dtype(self, method, expected):
+        ser = pd.Series([False, True, np.nan, False])
+        result = getattr(ser, method)()
+        tm.assert_series_equal(result, expected)
+
+    def test_cumprod_timedelta(self):
+        # GH#48111
+        ser = pd.Series([pd.Timedelta(days=1), pd.Timedelta(days=3)])
+        with pytest.raises(TypeError, match="cumprod not supported for Timedelta"):
+            ser.cumprod()
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_formats.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_formats.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1c5018ea7961c5979b4f407f7096e81e2dda0c0
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_formats.py
@@ -0,0 +1,577 @@
+from datetime import (
+    datetime,
+    timedelta,
+)
+
+import numpy as np
+import pytest
+
+from pandas._config import using_pyarrow_string_dtype
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    Index,
+    Series,
+    date_range,
+    option_context,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+class TestSeriesRepr:
+    def test_multilevel_name_print_0(self):
+        # GH#55415 None does not get printed, but 0 does
+        # (matching DataFrame and flat index behavior)
+        mi = pd.MultiIndex.from_product([range(2, 3), range(3, 4)], names=[0, None])
+        ser = Series(1.5, index=mi)
+
+        res = repr(ser)
+        expected = "0   \n2  3    1.5\ndtype: float64"
+        assert res == expected
+
+    def test_multilevel_name_print(self, lexsorted_two_level_string_multiindex):
+        index = lexsorted_two_level_string_multiindex
+        ser = Series(range(len(index)), index=index, name="sth")
+        expected = [
+            "first  second",
+            "foo    one       0",
+            "       two       1",
+            "       three     2",
+            "bar    one       3",
+            "       two       4",
+            "baz    two       5",
+            "       three     6",
+            "qux    one       7",
+            "       two       8",
+            "       three     9",
+            "Name: sth, dtype: int64",
+        ]
+        expected = "\n".join(expected)
+        assert repr(ser) == expected
+
+    def test_small_name_printing(self):
+        # Test small Series.
+        s = Series([0, 1, 2])
+
+        s.name = "test"
+        assert "Name: test" in repr(s)
+
+        s.name = None
+        assert "Name:" not in repr(s)
+
+    def test_big_name_printing(self):
+        # Test big Series (diff code path).
+        s = Series(range(1000))
+
+        s.name = "test"
+        assert "Name: test" in repr(s)
+
+        s.name = None
+        assert "Name:" not in repr(s)
+
+    def test_empty_name_printing(self):
+        s = Series(index=date_range("20010101", "20020101"), name="test", dtype=object)
+        assert "Name: test" in repr(s)
+
+    @pytest.mark.parametrize("args", [(), (0, -1)])
+    def test_float_range(self, args):
+        str(
+            Series(
+                np.random.default_rng(2).standard_normal(1000),
+                index=np.arange(1000, *args),
+            )
+        )
+
+    def test_empty_object(self):
+        # empty
+        str(Series(dtype=object))
+
+    def test_string(self, string_series):
+        str(string_series)
+        str(string_series.astype(int))
+
+        # with NaNs
+        string_series[5:7] = np.nan
+        str(string_series)
+
+    def test_object(self, object_series):
+        str(object_series)
+
+    def test_datetime(self, datetime_series):
+        str(datetime_series)
+        # with Nones
+        ots = datetime_series.astype("O")
+        ots[::2] = None
+        repr(ots)
+
+    @pytest.mark.parametrize(
+        "name",
+        [
+            "",
+            1,
+            1.2,
+            "foo",
+            "\u03B1\u03B2\u03B3",
+            "loooooooooooooooooooooooooooooooooooooooooooooooooooong",
+            ("foo", "bar", "baz"),
+            (1, 2),
+            ("foo", 1, 2.3),
+            ("\u03B1", "\u03B2", "\u03B3"),
+            ("\u03B1", "bar"),
+        ],
+    )
+    def test_various_names(self, name, string_series):
+        # various names
+        string_series.name = name
+        repr(string_series)
+
+    def test_tuple_name(self):
+        biggie = Series(
+            np.random.default_rng(2).standard_normal(1000),
+            index=np.arange(1000),
+            name=("foo", "bar", "baz"),
+        )
+        repr(biggie)
+
+    @pytest.mark.parametrize("arg", [100, 1001])
+    def test_tidy_repr_name_0(self, arg):
+        # tidy repr
+        ser = Series(np.random.default_rng(2).standard_normal(arg), name=0)
+        rep_str = repr(ser)
+        assert "Name: 0" in rep_str
+
+    @pytest.mark.xfail(
+        using_pyarrow_string_dtype(), reason="TODO: investigate why this is failing"
+    )
+    def test_newline(self):
+        ser = Series(["a\n\r\tb"], name="a\n\r\td", index=["a\n\r\tf"])
+        assert "\t" not in repr(ser)
+        assert "\r" not in repr(ser)
+        assert "a\n" not in repr(ser)
+
+    @pytest.mark.parametrize(
+        "name, expected",
+        [
+            ["foo", "Series([], Name: foo, dtype: int64)"],
+            [None, "Series([], dtype: int64)"],
+        ],
+    )
+    def test_empty_int64(self, name, expected):
+        # with empty series (#4651)
+        s = Series([], dtype=np.int64, name=name)
+        assert repr(s) == expected
+
+    def test_repr_bool_fails(self, capsys):
+        s = Series(
+            [
+                DataFrame(np.random.default_rng(2).standard_normal((2, 2)))
+                for i in range(5)
+            ]
+        )
+
+        # It works (with no Cython exception barf)!
+        repr(s)
+
+        captured = capsys.readouterr()
+        assert captured.err == ""
+
+    def test_repr_name_iterable_indexable(self):
+        s = Series([1, 2, 3], name=np.int64(3))
+
+        # it works!
+        repr(s)
+
+        s.name = ("\u05d0",) * 2
+        repr(s)
+
+    def test_repr_max_rows(self):
+        # GH 6863
+        with option_context("display.max_rows", None):
+            str(Series(range(1001)))  # should not raise exception
+
+    def test_unicode_string_with_unicode(self):
+        df = Series(["\u05d0"], name="\u05d1")
+        str(df)
+
+        ser = Series(["\u03c3"] * 10)
+        repr(ser)
+
+        ser2 = Series(["\u05d0"] * 1000)
+        ser2.name = "title1"
+        repr(ser2)
+
+    def test_str_to_bytes_raises(self):
+        # GH 26447
+        df = Series(["abc"], name="abc")
+        msg = "^'str' object cannot be interpreted as an integer$"
+        with pytest.raises(TypeError, match=msg):
+            bytes(df)
+
+    def test_timeseries_repr_object_dtype(self):
+        index = Index(
+            [datetime(2000, 1, 1) + timedelta(i) for i in range(1000)], dtype=object
+        )
+        ts = Series(np.random.default_rng(2).standard_normal(len(index)), index)
+        repr(ts)
+
+        ts = Series(
+            np.arange(20, dtype=np.float64), index=date_range("2020-01-01", periods=20)
+        )
+        assert repr(ts).splitlines()[-1].startswith("Freq:")
+
+        ts2 = ts.iloc[np.random.default_rng(2).integers(0, len(ts) - 1, 400)]
+        repr(ts2).splitlines()[-1]
+
+    def test_latex_repr(self):
+        pytest.importorskip("jinja2")  # uses Styler implementation
+        result = r"""\begin{tabular}{ll}
+\toprule
+ & 0 \\
+\midrule
+0 & $\alpha$ \\
+1 & b \\
+2 & c \\
+\bottomrule
+\end{tabular}
+"""
+        with option_context(
+            "styler.format.escape", None, "styler.render.repr", "latex"
+        ):
+            s = Series([r"$\alpha$", "b", "c"])
+            assert result == s._repr_latex_()
+
+        assert s._repr_latex_() is None
+
+    def test_index_repr_in_frame_with_nan(self):
+        # see gh-25061
+        i = Index([1, np.nan])
+        s = Series([1, 2], index=i)
+        exp = """1.0    1\nNaN    2\ndtype: int64"""
+
+        assert repr(s) == exp
+
+    def test_format_pre_1900_dates(self):
+        rng = date_range("1/1/1850", "1/1/1950", freq="YE-DEC")
+        msg = "DatetimeIndex.format is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            rng.format()
+        ts = Series(1, index=rng)
+        repr(ts)
+
+    def test_series_repr_nat(self):
+        series = Series([0, 1000, 2000, pd.NaT._value], dtype="M8[ns]")
+
+        result = repr(series)
+        expected = (
+            "0   1970-01-01 00:00:00.000000\n"
+            "1   1970-01-01 00:00:00.000001\n"
+            "2   1970-01-01 00:00:00.000002\n"
+            "3                          NaT\n"
+            "dtype: datetime64[ns]"
+        )
+        assert result == expected
+
+    def test_float_repr(self):
+        # GH#35603
+        # check float format when cast to object
+        ser = Series([1.0]).astype(object)
+        expected = "0    1.0\ndtype: object"
+        assert repr(ser) == expected
+
+    def test_different_null_objects(self):
+        # GH#45263
+        ser = Series([1, 2, 3, 4], [True, None, np.nan, pd.NaT])
+        result = repr(ser)
+        expected = "True    1\nNone    2\nNaN     3\nNaT     4\ndtype: int64"
+        assert result == expected
+
+
+class TestCategoricalRepr:
+    def test_categorical_repr_unicode(self):
+        # see gh-21002
+
+        class County:
+            name = "San Sebastián"
+            state = "PR"
+
+            def __repr__(self) -> str:
+                return self.name + ", " + self.state
+
+        cat = Categorical([County() for _ in range(61)])
+        idx = Index(cat)
+        ser = idx.to_series()
+
+        repr(ser)
+        str(ser)
+
+    def test_categorical_repr(self, using_infer_string):
+        a = Series(Categorical([1, 2, 3, 4]))
+        exp = (
+            "0    1\n1    2\n2    3\n3    4\n"
+            "dtype: category\nCategories (4, int64): [1, 2, 3, 4]"
+        )
+
+        assert exp == a.__str__()
+
+        a = Series(Categorical(["a", "b"] * 25))
+        if using_infer_string:
+            exp = (
+                "0     a\n1     b\n"
+                "     ..\n"
+                "48    a\n49    b\n"
+                "Length: 50, dtype: category\nCategories (2, string): [a, b]"
+            )
+        else:
+            exp = (
+                "0     a\n1     b\n"
+                "     ..\n"
+                "48    a\n49    b\n"
+                "Length: 50, dtype: category\nCategories (2, object): ['a', 'b']"
+            )
+        with option_context("display.max_rows", 5):
+            assert exp == repr(a)
+
+        levs = list("abcdefghijklmnopqrstuvwxyz")
+        a = Series(Categorical(["a", "b"], categories=levs, ordered=True))
+        if using_infer_string:
+            exp = (
+                "0    a\n1    b\n"
+                "dtype: category\n"
+                "Categories (26, string): [a < b < c < d ... w < x < y < z]"
+            )
+        else:
+            exp = (
+                "0    a\n1    b\n"
+                "dtype: category\n"
+                "Categories (26, object): ['a' < 'b' < 'c' < 'd' ... "
+                "'w' < 'x' < 'y' < 'z']"
+            )
+        assert exp == a.__str__()
+
+    def test_categorical_series_repr(self):
+        s = Series(Categorical([1, 2, 3]))
+        exp = """0    1
+1    2
+2    3
+dtype: category
+Categories (3, int64): [1, 2, 3]"""
+
+        assert repr(s) == exp
+
+        s = Series(Categorical(np.arange(10)))
+        exp = f"""0    0
+1    1
+2    2
+3    3
+4    4
+5    5
+6    6
+7    7
+8    8
+9    9
+dtype: category
+Categories (10, {np.dtype(int)}): [0, 1, 2, 3, ..., 6, 7, 8, 9]"""
+
+        assert repr(s) == exp
+
+    def test_categorical_series_repr_ordered(self):
+        s = Series(Categorical([1, 2, 3], ordered=True))
+        exp = """0    1
+1    2
+2    3
+dtype: category
+Categories (3, int64): [1 < 2 < 3]"""
+
+        assert repr(s) == exp
+
+        s = Series(Categorical(np.arange(10), ordered=True))
+        exp = f"""0    0
+1    1
+2    2
+3    3
+4    4
+5    5
+6    6
+7    7
+8    8
+9    9
+dtype: category
+Categories (10, {np.dtype(int)}): [0 < 1 < 2 < 3 ... 6 < 7 < 8 < 9]"""
+
+        assert repr(s) == exp
+
+    def test_categorical_series_repr_datetime(self):
+        idx = date_range("2011-01-01 09:00", freq="h", periods=5)
+        s = Series(Categorical(idx))
+        exp = """0   2011-01-01 09:00:00
+1   2011-01-01 10:00:00
+2   2011-01-01 11:00:00
+3   2011-01-01 12:00:00
+4   2011-01-01 13:00:00
+dtype: category
+Categories (5, datetime64[ns]): [2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00,
+                                 2011-01-01 12:00:00, 2011-01-01 13:00:00]"""  # noqa: E501
+
+        assert repr(s) == exp
+
+        idx = date_range("2011-01-01 09:00", freq="h", periods=5, tz="US/Eastern")
+        s = Series(Categorical(idx))
+        exp = """0   2011-01-01 09:00:00-05:00
+1   2011-01-01 10:00:00-05:00
+2   2011-01-01 11:00:00-05:00
+3   2011-01-01 12:00:00-05:00
+4   2011-01-01 13:00:00-05:00
+dtype: category
+Categories (5, datetime64[ns, US/Eastern]): [2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00,
+                                             2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00,
+                                             2011-01-01 13:00:00-05:00]"""  # noqa: E501
+
+        assert repr(s) == exp
+
+    def test_categorical_series_repr_datetime_ordered(self):
+        idx = date_range("2011-01-01 09:00", freq="h", periods=5)
+        s = Series(Categorical(idx, ordered=True))
+        exp = """0   2011-01-01 09:00:00
+1   2011-01-01 10:00:00
+2   2011-01-01 11:00:00
+3   2011-01-01 12:00:00
+4   2011-01-01 13:00:00
+dtype: category
+Categories (5, datetime64[ns]): [2011-01-01 09:00:00 < 2011-01-01 10:00:00 < 2011-01-01 11:00:00 <
+                                 2011-01-01 12:00:00 < 2011-01-01 13:00:00]"""  # noqa: E501
+
+        assert repr(s) == exp
+
+        idx = date_range("2011-01-01 09:00", freq="h", periods=5, tz="US/Eastern")
+        s = Series(Categorical(idx, ordered=True))
+        exp = """0   2011-01-01 09:00:00-05:00
+1   2011-01-01 10:00:00-05:00
+2   2011-01-01 11:00:00-05:00
+3   2011-01-01 12:00:00-05:00
+4   2011-01-01 13:00:00-05:00
+dtype: category
+Categories (5, datetime64[ns, US/Eastern]): [2011-01-01 09:00:00-05:00 < 2011-01-01 10:00:00-05:00 <
+                                             2011-01-01 11:00:00-05:00 < 2011-01-01 12:00:00-05:00 <
+                                             2011-01-01 13:00:00-05:00]"""  # noqa: E501
+
+        assert repr(s) == exp
+
+    def test_categorical_series_repr_period(self):
+        idx = period_range("2011-01-01 09:00", freq="h", periods=5)
+        s = Series(Categorical(idx))
+        exp = """0    2011-01-01 09:00
+1    2011-01-01 10:00
+2    2011-01-01 11:00
+3    2011-01-01 12:00
+4    2011-01-01 13:00
+dtype: category
+Categories (5, period[h]): [2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00,
+                            2011-01-01 13:00]"""  # noqa: E501
+
+        assert repr(s) == exp
+
+        idx = period_range("2011-01", freq="M", periods=5)
+        s = Series(Categorical(idx))
+        exp = """0    2011-01
+1    2011-02
+2    2011-03
+3    2011-04
+4    2011-05
+dtype: category
+Categories (5, period[M]): [2011-01, 2011-02, 2011-03, 2011-04, 2011-05]"""
+
+        assert repr(s) == exp
+
+    def test_categorical_series_repr_period_ordered(self):
+        idx = period_range("2011-01-01 09:00", freq="h", periods=5)
+        s = Series(Categorical(idx, ordered=True))
+        exp = """0    2011-01-01 09:00
+1    2011-01-01 10:00
+2    2011-01-01 11:00
+3    2011-01-01 12:00
+4    2011-01-01 13:00
+dtype: category
+Categories (5, period[h]): [2011-01-01 09:00 < 2011-01-01 10:00 < 2011-01-01 11:00 < 2011-01-01 12:00 <
+                            2011-01-01 13:00]"""  # noqa: E501
+
+        assert repr(s) == exp
+
+        idx = period_range("2011-01", freq="M", periods=5)
+        s = Series(Categorical(idx, ordered=True))
+        exp = """0    2011-01
+1    2011-02
+2    2011-03
+3    2011-04
+4    2011-05
+dtype: category
+Categories (5, period[M]): [2011-01 < 2011-02 < 2011-03 < 2011-04 < 2011-05]"""
+
+        assert repr(s) == exp
+
+    def test_categorical_series_repr_timedelta(self):
+        idx = timedelta_range("1 days", periods=5)
+        s = Series(Categorical(idx))
+        exp = """0   1 days
+1   2 days
+2   3 days
+3   4 days
+4   5 days
+dtype: category
+Categories (5, timedelta64[ns]): [1 days, 2 days, 3 days, 4 days, 5 days]"""
+
+        assert repr(s) == exp
+
+        idx = timedelta_range("1 hours", periods=10)
+        s = Series(Categorical(idx))
+        exp = """0   0 days 01:00:00
+1   1 days 01:00:00
+2   2 days 01:00:00
+3   3 days 01:00:00
+4   4 days 01:00:00
+5   5 days 01:00:00
+6   6 days 01:00:00
+7   7 days 01:00:00
+8   8 days 01:00:00
+9   9 days 01:00:00
+dtype: category
+Categories (10, timedelta64[ns]): [0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00,
+                                   3 days 01:00:00, ..., 6 days 01:00:00, 7 days 01:00:00,
+                                   8 days 01:00:00, 9 days 01:00:00]"""  # noqa: E501
+
+        assert repr(s) == exp
+
+    def test_categorical_series_repr_timedelta_ordered(self):
+        idx = timedelta_range("1 days", periods=5)
+        s = Series(Categorical(idx, ordered=True))
+        exp = """0   1 days
+1   2 days
+2   3 days
+3   4 days
+4   5 days
+dtype: category
+Categories (5, timedelta64[ns]): [1 days < 2 days < 3 days < 4 days < 5 days]"""
+
+        assert repr(s) == exp
+
+        idx = timedelta_range("1 hours", periods=10)
+        s = Series(Categorical(idx, ordered=True))
+        exp = """0   0 days 01:00:00
+1   1 days 01:00:00
+2   2 days 01:00:00
+3   3 days 01:00:00
+4   4 days 01:00:00
+5   5 days 01:00:00
+6   6 days 01:00:00
+7   7 days 01:00:00
+8   8 days 01:00:00
+9   9 days 01:00:00
+dtype: category
+Categories (10, timedelta64[ns]): [0 days 01:00:00 < 1 days 01:00:00 < 2 days 01:00:00 <
+                                   3 days 01:00:00 ... 6 days 01:00:00 < 7 days 01:00:00 <
+                                   8 days 01:00:00 < 9 days 01:00:00]"""  # noqa: E501
+
+        assert repr(s) == exp
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_iteration.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_iteration.py
new file mode 100644
index 0000000000000000000000000000000000000000..edc82455234bba0203d817417e7bf122c876bfff
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_iteration.py
@@ -0,0 +1,35 @@
+class TestIteration:
+    def test_keys(self, datetime_series):
+        assert datetime_series.keys() is datetime_series.index
+
+    def test_iter_datetimes(self, datetime_series):
+        for i, val in enumerate(datetime_series):
+            # pylint: disable-next=unnecessary-list-index-lookup
+            assert val == datetime_series.iloc[i]
+
+    def test_iter_strings(self, string_series):
+        for i, val in enumerate(string_series):
+            # pylint: disable-next=unnecessary-list-index-lookup
+            assert val == string_series.iloc[i]
+
+    def test_iteritems_datetimes(self, datetime_series):
+        for idx, val in datetime_series.items():
+            assert val == datetime_series[idx]
+
+    def test_iteritems_strings(self, string_series):
+        for idx, val in string_series.items():
+            assert val == string_series[idx]
+
+        # assert is lazy (generators don't define reverse, lists do)
+        assert not hasattr(string_series.items(), "reverse")
+
+    def test_items_datetimes(self, datetime_series):
+        for idx, val in datetime_series.items():
+            assert val == datetime_series[idx]
+
+    def test_items_strings(self, string_series):
+        for idx, val in string_series.items():
+            assert val == string_series[idx]
+
+        # assert is lazy (generators don't define reverse, lists do)
+        assert not hasattr(string_series.items(), "reverse")
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_logical_ops.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_logical_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9c94e871bd4bf39e0a380ca1bc3806dc33154c2
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_logical_ops.py
@@ -0,0 +1,548 @@
+from datetime import datetime
+import operator
+
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+    bdate_range,
+)
+import pandas._testing as tm
+from pandas.core import ops
+
+
+class TestSeriesLogicalOps:
+    @pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:FutureWarning")
+    @pytest.mark.parametrize("bool_op", [operator.and_, operator.or_, operator.xor])
+    def test_bool_operators_with_nas(self, bool_op):
+        # boolean &, |, ^ should work with object arrays and propagate NAs
+        ser = Series(bdate_range("1/1/2000", periods=10), dtype=object)
+        ser[::2] = np.nan
+
+        mask = ser.isna()
+        filled = ser.fillna(ser[0])
+
+        result = bool_op(ser < ser[9], ser > ser[3])
+
+        expected = bool_op(filled < filled[9], filled > filled[3])
+        expected[mask] = False
+        tm.assert_series_equal(result, expected)
+
+    def test_logical_operators_bool_dtype_with_empty(self):
+        # GH#9016: support bitwise op for integer types
+        index = list("bca")
+
+        s_tft = Series([True, False, True], index=index)
+        s_fff = Series([False, False, False], index=index)
+        s_empty = Series([], dtype=object)
+
+        res = s_tft & s_empty
+        expected = s_fff.sort_index()
+        tm.assert_series_equal(res, expected)
+
+        res = s_tft | s_empty
+        expected = s_tft.sort_index()
+        tm.assert_series_equal(res, expected)
+
+    def test_logical_operators_int_dtype_with_int_dtype(self):
+        # GH#9016: support bitwise op for integer types
+
+        s_0123 = Series(range(4), dtype="int64")
+        s_3333 = Series([3] * 4)
+        s_4444 = Series([4] * 4)
+
+        res = s_0123 & s_3333
+        expected = Series(range(4), dtype="int64")
+        tm.assert_series_equal(res, expected)
+
+        res = s_0123 | s_4444
+        expected = Series(range(4, 8), dtype="int64")
+        tm.assert_series_equal(res, expected)
+
+        s_1111 = Series([1] * 4, dtype="int8")
+        res = s_0123 & s_1111
+        expected = Series([0, 1, 0, 1], dtype="int64")
+        tm.assert_series_equal(res, expected)
+
+        res = s_0123.astype(np.int16) | s_1111.astype(np.int32)
+        expected = Series([1, 1, 3, 3], dtype="int32")
+        tm.assert_series_equal(res, expected)
+
+    def test_logical_operators_int_dtype_with_int_scalar(self):
+        # GH#9016: support bitwise op for integer types
+        s_0123 = Series(range(4), dtype="int64")
+
+        res = s_0123 & 0
+        expected = Series([0] * 4)
+        tm.assert_series_equal(res, expected)
+
+        res = s_0123 & 1
+        expected = Series([0, 1, 0, 1])
+        tm.assert_series_equal(res, expected)
+
+    def test_logical_operators_int_dtype_with_float(self):
+        # GH#9016: support bitwise op for integer types
+        s_0123 = Series(range(4), dtype="int64")
+
+        warn_msg = (
+            r"Logical ops \(and, or, xor\) between Pandas objects and "
+            "dtype-less sequences"
+        )
+
+        msg = "Cannot perform.+with a dtyped.+array and scalar of type"
+        with pytest.raises(TypeError, match=msg):
+            s_0123 & np.nan
+        with pytest.raises(TypeError, match=msg):
+            s_0123 & 3.14
+        msg = "unsupported operand type.+for &:"
+        with pytest.raises(TypeError, match=msg):
+            with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+                s_0123 & [0.1, 4, 3.14, 2]
+        with pytest.raises(TypeError, match=msg):
+            s_0123 & np.array([0.1, 4, 3.14, 2])
+        with pytest.raises(TypeError, match=msg):
+            s_0123 & Series([0.1, 4, -3.14, 2])
+
+    def test_logical_operators_int_dtype_with_str(self):
+        s_1111 = Series([1] * 4, dtype="int8")
+
+        warn_msg = (
+            r"Logical ops \(and, or, xor\) between Pandas objects and "
+            "dtype-less sequences"
+        )
+
+        msg = "Cannot perform 'and_' with a dtyped.+array and scalar of type"
+        with pytest.raises(TypeError, match=msg):
+            s_1111 & "a"
+        with pytest.raises(TypeError, match="unsupported operand.+for &"):
+            with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+                s_1111 & ["a", "b", "c", "d"]
+
+    def test_logical_operators_int_dtype_with_bool(self):
+        # GH#9016: support bitwise op for integer types
+        s_0123 = Series(range(4), dtype="int64")
+
+        expected = Series([False] * 4)
+
+        result = s_0123 & False
+        tm.assert_series_equal(result, expected)
+
+        warn_msg = (
+            r"Logical ops \(and, or, xor\) between Pandas objects and "
+            "dtype-less sequences"
+        )
+        with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+            result = s_0123 & [False]
+        tm.assert_series_equal(result, expected)
+
+        with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+            result = s_0123 & (False,)
+        tm.assert_series_equal(result, expected)
+
+        result = s_0123 ^ False
+        expected = Series([False, True, True, True])
+        tm.assert_series_equal(result, expected)
+
+    def test_logical_operators_int_dtype_with_object(self, using_infer_string):
+        # GH#9016: support bitwise op for integer types
+        s_0123 = Series(range(4), dtype="int64")
+
+        result = s_0123 & Series([False, np.nan, False, False])
+        expected = Series([False] * 4)
+        tm.assert_series_equal(result, expected)
+
+        s_abNd = Series(["a", "b", np.nan, "d"])
+        if using_infer_string:
+            import pyarrow as pa
+
+            with pytest.raises(pa.lib.ArrowNotImplementedError, match="has no kernel"):
+                s_0123 & s_abNd
+        else:
+            with pytest.raises(TypeError, match="unsupported.* 'int' and 'str'"):
+                s_0123 & s_abNd
+
+    def test_logical_operators_bool_dtype_with_int(self):
+        index = list("bca")
+
+        s_tft = Series([True, False, True], index=index)
+        s_fff = Series([False, False, False], index=index)
+
+        res = s_tft & 0
+        expected = s_fff
+        tm.assert_series_equal(res, expected)
+
+        res = s_tft & 1
+        expected = s_tft
+        tm.assert_series_equal(res, expected)
+
+    def test_logical_ops_bool_dtype_with_ndarray(self):
+        # make sure we operate on ndarray the same as Series
+        left = Series([True, True, True, False, True])
+        right = [True, False, None, True, np.nan]
+
+        msg = (
+            r"Logical ops \(and, or, xor\) between Pandas objects and "
+            "dtype-less sequences"
+        )
+
+        expected = Series([True, False, False, False, False])
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = left & right
+        tm.assert_series_equal(result, expected)
+        result = left & np.array(right)
+        tm.assert_series_equal(result, expected)
+        result = left & Index(right)
+        tm.assert_series_equal(result, expected)
+        result = left & Series(right)
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([True, True, True, True, True])
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = left | right
+        tm.assert_series_equal(result, expected)
+        result = left | np.array(right)
+        tm.assert_series_equal(result, expected)
+        result = left | Index(right)
+        tm.assert_series_equal(result, expected)
+        result = left | Series(right)
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([False, True, True, True, True])
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = left ^ right
+        tm.assert_series_equal(result, expected)
+        result = left ^ np.array(right)
+        tm.assert_series_equal(result, expected)
+        result = left ^ Index(right)
+        tm.assert_series_equal(result, expected)
+        result = left ^ Series(right)
+        tm.assert_series_equal(result, expected)
+
+    def test_logical_operators_int_dtype_with_bool_dtype_and_reindex(self):
+        # GH#9016: support bitwise op for integer types
+
+        index = list("bca")
+
+        s_tft = Series([True, False, True], index=index)
+        s_tft = Series([True, False, True], index=index)
+        s_tff = Series([True, False, False], index=index)
+
+        s_0123 = Series(range(4), dtype="int64")
+
+        # s_0123 will be all false now because of reindexing like s_tft
+        expected = Series([False] * 7, index=[0, 1, 2, 3, "a", "b", "c"])
+        with tm.assert_produces_warning(FutureWarning):
+            result = s_tft & s_0123
+        tm.assert_series_equal(result, expected)
+
+        # GH 52538: Deprecate casting to object type when reindex is needed;
+        # matches DataFrame behavior
+        expected = Series([False] * 7, index=[0, 1, 2, 3, "a", "b", "c"])
+        with tm.assert_produces_warning(FutureWarning):
+            result = s_0123 & s_tft
+        tm.assert_series_equal(result, expected)
+
+        s_a0b1c0 = Series([1], list("b"))
+
+        with tm.assert_produces_warning(FutureWarning):
+            res = s_tft & s_a0b1c0
+        expected = s_tff.reindex(list("abc"))
+        tm.assert_series_equal(res, expected)
+
+        with tm.assert_produces_warning(FutureWarning):
+            res = s_tft | s_a0b1c0
+        expected = s_tft.reindex(list("abc"))
+        tm.assert_series_equal(res, expected)
+
+    def test_scalar_na_logical_ops_corners(self):
+        s = Series([2, 3, 4, 5, 6, 7, 8, 9, 10])
+
+        msg = "Cannot perform.+with a dtyped.+array and scalar of type"
+        with pytest.raises(TypeError, match=msg):
+            s & datetime(2005, 1, 1)
+
+        s = Series([2, 3, 4, 5, 6, 7, 8, 9, datetime(2005, 1, 1)])
+        s[::2] = np.nan
+
+        expected = Series(True, index=s.index)
+        expected[::2] = False
+
+        msg = (
+            r"Logical ops \(and, or, xor\) between Pandas objects and "
+            "dtype-less sequences"
+        )
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = s & list(s)
+        tm.assert_series_equal(result, expected)
+
+    def test_scalar_na_logical_ops_corners_aligns(self):
+        s = Series([2, 3, 4, 5, 6, 7, 8, 9, datetime(2005, 1, 1)])
+        s[::2] = np.nan
+        d = DataFrame({"A": s})
+
+        expected = DataFrame(False, index=range(9), columns=["A"] + list(range(9)))
+
+        result = s & d
+        tm.assert_frame_equal(result, expected)
+
+        result = d & s
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("op", [operator.and_, operator.or_, operator.xor])
+    def test_logical_ops_with_index(self, op):
+        # GH#22092, GH#19792
+        ser = Series([True, True, False, False])
+        idx1 = Index([True, False, True, False])
+        idx2 = Index([1, 0, 1, 0])
+
+        expected = Series([op(ser[n], idx1[n]) for n in range(len(ser))])
+
+        result = op(ser, idx1)
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([op(ser[n], idx2[n]) for n in range(len(ser))], dtype=bool)
+
+        result = op(ser, idx2)
+        tm.assert_series_equal(result, expected)
+
+    def test_reversed_xor_with_index_returns_series(self):
+        # GH#22092, GH#19792 pre-2.0 these were aliased to setops
+        ser = Series([True, True, False, False])
+        idx1 = Index([True, False, True, False], dtype=bool)
+        idx2 = Index([1, 0, 1, 0])
+
+        expected = Series([False, True, True, False])
+        result = idx1 ^ ser
+        tm.assert_series_equal(result, expected)
+
+        result = idx2 ^ ser
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "op",
+        [
+            ops.rand_,
+            ops.ror_,
+        ],
+    )
+    def test_reversed_logical_op_with_index_returns_series(self, op):
+        # GH#22092, GH#19792
+        ser = Series([True, True, False, False])
+        idx1 = Index([True, False, True, False])
+        idx2 = Index([1, 0, 1, 0])
+
+        expected = Series(op(idx1.values, ser.values))
+        result = op(ser, idx1)
+        tm.assert_series_equal(result, expected)
+
+        expected = op(ser, Series(idx2))
+        result = op(ser, idx2)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "op, expected",
+        [
+            (ops.rand_, Series([False, False])),
+            (ops.ror_, Series([True, True])),
+            (ops.rxor, Series([True, True])),
+        ],
+    )
+    def test_reverse_ops_with_index(self, op, expected):
+        # https://github.com/pandas-dev/pandas/pull/23628
+        # multi-set Index ops are buggy, so let's avoid duplicates...
+        # GH#49503
+        ser = Series([True, False])
+        idx = Index([False, True])
+
+        result = op(ser, idx)
+        tm.assert_series_equal(result, expected)
+
+    def test_logical_ops_label_based(self, using_infer_string):
+        # GH#4947
+        # logical ops should be label based
+
+        a = Series([True, False, True], list("bca"))
+        b = Series([False, True, False], list("abc"))
+
+        expected = Series([False, True, False], list("abc"))
+        result = a & b
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([True, True, False], list("abc"))
+        result = a | b
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([True, False, False], list("abc"))
+        result = a ^ b
+        tm.assert_series_equal(result, expected)
+
+        # rhs is bigger
+        a = Series([True, False, True], list("bca"))
+        b = Series([False, True, False, True], list("abcd"))
+
+        expected = Series([False, True, False, False], list("abcd"))
+        result = a & b
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([True, True, False, False], list("abcd"))
+        result = a | b
+        tm.assert_series_equal(result, expected)
+
+        # filling
+
+        # vs empty
+        empty = Series([], dtype=object)
+
+        result = a & empty.copy()
+        expected = Series([False, False, False], list("abc"))
+        tm.assert_series_equal(result, expected)
+
+        result = a | empty.copy()
+        expected = Series([True, True, False], list("abc"))
+        tm.assert_series_equal(result, expected)
+
+        # vs non-matching
+        with tm.assert_produces_warning(FutureWarning):
+            result = a & Series([1], ["z"])
+        expected = Series([False, False, False, False], list("abcz"))
+        tm.assert_series_equal(result, expected)
+
+        with tm.assert_produces_warning(FutureWarning):
+            result = a | Series([1], ["z"])
+        expected = Series([True, True, False, False], list("abcz"))
+        tm.assert_series_equal(result, expected)
+
+        # identity
+        # we would like s[s|e] == s to hold for any e, whether empty or not
+        with tm.assert_produces_warning(FutureWarning):
+            for e in [
+                empty.copy(),
+                Series([1], ["z"]),
+                Series(np.nan, b.index),
+                Series(np.nan, a.index),
+            ]:
+                result = a[a | e]
+                tm.assert_series_equal(result, a[a])
+
+        for e in [Series(["z"])]:
+            warn = FutureWarning if using_infer_string else None
+            if using_infer_string:
+                import pyarrow as pa
+
+                with tm.assert_produces_warning(warn, match="Operation between non"):
+                    with pytest.raises(
+                        pa.lib.ArrowNotImplementedError, match="has no kernel"
+                    ):
+                        result = a[a | e]
+            else:
+                result = a[a | e]
+            tm.assert_series_equal(result, a[a])
+
+        # vs scalars
+        index = list("bca")
+        t = Series([True, False, True])
+
+        for v in [True, 1, 2]:
+            result = Series([True, False, True], index=index) | v
+            expected = Series([True, True, True], index=index)
+            tm.assert_series_equal(result, expected)
+
+        msg = "Cannot perform.+with a dtyped.+array and scalar of type"
+        for v in [np.nan, "foo"]:
+            with pytest.raises(TypeError, match=msg):
+                t | v
+
+        for v in [False, 0]:
+            result = Series([True, False, True], index=index) | v
+            expected = Series([True, False, True], index=index)
+            tm.assert_series_equal(result, expected)
+
+        for v in [True, 1]:
+            result = Series([True, False, True], index=index) & v
+            expected = Series([True, False, True], index=index)
+            tm.assert_series_equal(result, expected)
+
+        for v in [False, 0]:
+            result = Series([True, False, True], index=index) & v
+            expected = Series([False, False, False], index=index)
+            tm.assert_series_equal(result, expected)
+        msg = "Cannot perform.+with a dtyped.+array and scalar of type"
+        for v in [np.nan]:
+            with pytest.raises(TypeError, match=msg):
+                t & v
+
+    def test_logical_ops_df_compat(self):
+        # GH#1134
+        s1 = Series([True, False, True], index=list("ABC"), name="x")
+        s2 = Series([True, True, False], index=list("ABD"), name="x")
+
+        exp = Series([True, False, False, False], index=list("ABCD"), name="x")
+        tm.assert_series_equal(s1 & s2, exp)
+        tm.assert_series_equal(s2 & s1, exp)
+
+        # True | np.nan => True
+        exp_or1 = Series([True, True, True, False], index=list("ABCD"), name="x")
+        tm.assert_series_equal(s1 | s2, exp_or1)
+        # np.nan | True => np.nan, filled with False
+        exp_or = Series([True, True, False, False], index=list("ABCD"), name="x")
+        tm.assert_series_equal(s2 | s1, exp_or)
+
+        # DataFrame doesn't fill nan with False
+        tm.assert_frame_equal(s1.to_frame() & s2.to_frame(), exp.to_frame())
+        tm.assert_frame_equal(s2.to_frame() & s1.to_frame(), exp.to_frame())
+
+        exp = DataFrame({"x": [True, True, np.nan, np.nan]}, index=list("ABCD"))
+        tm.assert_frame_equal(s1.to_frame() | s2.to_frame(), exp_or1.to_frame())
+        tm.assert_frame_equal(s2.to_frame() | s1.to_frame(), exp_or.to_frame())
+
+        # different length
+        s3 = Series([True, False, True], index=list("ABC"), name="x")
+        s4 = Series([True, True, True, True], index=list("ABCD"), name="x")
+
+        exp = Series([True, False, True, False], index=list("ABCD"), name="x")
+        tm.assert_series_equal(s3 & s4, exp)
+        tm.assert_series_equal(s4 & s3, exp)
+
+        # np.nan | True => np.nan, filled with False
+        exp_or1 = Series([True, True, True, False], index=list("ABCD"), name="x")
+        tm.assert_series_equal(s3 | s4, exp_or1)
+        # True | np.nan => True
+        exp_or = Series([True, True, True, True], index=list("ABCD"), name="x")
+        tm.assert_series_equal(s4 | s3, exp_or)
+
+        tm.assert_frame_equal(s3.to_frame() & s4.to_frame(), exp.to_frame())
+        tm.assert_frame_equal(s4.to_frame() & s3.to_frame(), exp.to_frame())
+
+        tm.assert_frame_equal(s3.to_frame() | s4.to_frame(), exp_or1.to_frame())
+        tm.assert_frame_equal(s4.to_frame() | s3.to_frame(), exp_or.to_frame())
+
+    @pytest.mark.xfail(reason="Will pass once #52839 deprecation is enforced")
+    def test_int_dtype_different_index_not_bool(self):
+        # GH 52500
+        ser1 = Series([1, 2, 3], index=[10, 11, 23], name="a")
+        ser2 = Series([10, 20, 30], index=[11, 10, 23], name="a")
+        result = np.bitwise_xor(ser1, ser2)
+        expected = Series([21, 8, 29], index=[10, 11, 23], name="a")
+        tm.assert_series_equal(result, expected)
+
+        result = ser1 ^ ser2
+        tm.assert_series_equal(result, expected)
+
+    def test_pyarrow_numpy_string_invalid(self):
+        # GH#56008
+        pytest.importorskip("pyarrow")
+        ser = Series([False, True])
+        ser2 = Series(["a", "b"], dtype="string[pyarrow_numpy]")
+        result = ser == ser2
+        expected = Series(False, index=ser.index)
+        tm.assert_series_equal(result, expected)
+
+        result = ser != ser2
+        expected = Series(True, index=ser.index)
+        tm.assert_series_equal(result, expected)
+
+        with pytest.raises(TypeError, match="Invalid comparison"):
+            ser > ser2
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_missing.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_missing.py
new file mode 100644
index 0000000000000000000000000000000000000000..cafc69c4d0f20f42dc184366db0ac8933a512f54
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_missing.py
@@ -0,0 +1,105 @@
+from datetime import timedelta
+
+import numpy as np
+import pytest
+
+from pandas._libs import iNaT
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    Index,
+    NaT,
+    Series,
+    isna,
+)
+import pandas._testing as tm
+
+
+class TestSeriesMissingData:
+    def test_categorical_nan_handling(self):
+        # NaNs are represented as -1 in labels
+        s = Series(Categorical(["a", "b", np.nan, "a"]))
+        tm.assert_index_equal(s.cat.categories, Index(["a", "b"]))
+        tm.assert_numpy_array_equal(
+            s.values.codes, np.array([0, 1, -1, 0], dtype=np.int8)
+        )
+
+    def test_isna_for_inf(self):
+        s = Series(["a", np.inf, np.nan, pd.NA, 1.0])
+        msg = "use_inf_as_na option is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            with pd.option_context("mode.use_inf_as_na", True):
+                r = s.isna()
+                dr = s.dropna()
+        e = Series([False, True, True, True, False])
+        de = Series(["a", 1.0], index=[0, 4])
+        tm.assert_series_equal(r, e)
+        tm.assert_series_equal(dr, de)
+
+    def test_timedelta64_nan(self):
+        td = Series([timedelta(days=i) for i in range(10)])
+
+        # nan ops on timedeltas
+        td1 = td.copy()
+        td1[0] = np.nan
+        assert isna(td1[0])
+        assert td1[0]._value == iNaT
+        td1[0] = td[0]
+        assert not isna(td1[0])
+
+        # GH#16674 iNaT is treated as an integer when given by the user
+        with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
+            td1[1] = iNaT
+        assert not isna(td1[1])
+        assert td1.dtype == np.object_
+        assert td1[1] == iNaT
+        td1[1] = td[1]
+        assert not isna(td1[1])
+
+        td1[2] = NaT
+        assert isna(td1[2])
+        assert td1[2]._value == iNaT
+        td1[2] = td[2]
+        assert not isna(td1[2])
+
+        # boolean setting
+        # GH#2899 boolean setting
+        td3 = np.timedelta64(timedelta(days=3))
+        td7 = np.timedelta64(timedelta(days=7))
+        td[(td > td3) & (td < td7)] = np.nan
+        assert isna(td).sum() == 3
+
+    @pytest.mark.xfail(
+        reason="Chained inequality raises when trying to define 'selector'"
+    )
+    def test_logical_range_select(self, datetime_series):
+        # NumPy limitation =(
+        # https://github.com/pandas-dev/pandas/commit/9030dc021f07c76809848925cb34828f6c8484f3
+
+        selector = -0.5 <= datetime_series <= 0.5
+        expected = (datetime_series >= -0.5) & (datetime_series <= 0.5)
+        tm.assert_series_equal(selector, expected)
+
+    def test_valid(self, datetime_series):
+        ts = datetime_series.copy()
+        ts.index = ts.index._with_freq(None)
+        ts[::2] = np.nan
+
+        result = ts.dropna()
+        assert len(result) == ts.count()
+        tm.assert_series_equal(result, ts[1::2])
+        tm.assert_series_equal(result, ts[pd.notna(ts)])
+
+
+def test_hasnans_uncached_for_series():
+    # GH#19700
+    # set float64 dtype to avoid upcast when setting nan
+    idx = Index([0, 1], dtype="float64")
+    assert idx.hasnans is False
+    assert "hasnans" in idx._cache
+    ser = idx.to_series()
+    assert ser.hasnans is False
+    assert not hasattr(ser, "_cache")
+    ser.iloc[-1] = np.nan
+    assert ser.hasnans is True
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_npfuncs.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_npfuncs.py
new file mode 100644
index 0000000000000000000000000000000000000000..11a51c4700d5c1733e7136909d86d2ea7294dd8e
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_npfuncs.py
@@ -0,0 +1,46 @@
+"""
+Tests for np.foo applied to Series, not necessarily ufuncs.
+"""
+
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+from pandas import Series
+import pandas._testing as tm
+
+
+class TestPtp:
+    def test_ptp(self):
+        # GH#21614
+        N = 1000
+        arr = np.random.default_rng(2).standard_normal(N)
+        ser = Series(arr)
+        assert np.ptp(ser) == np.ptp(arr)
+
+
+def test_numpy_unique(datetime_series):
+    # it works!
+    np.unique(datetime_series)
+
+
+@pytest.mark.parametrize("index", [["a", "b", "c", "d", "e"], None])
+def test_numpy_argwhere(index):
+    # GH#35331
+
+    s = Series(range(5), index=index, dtype=np.int64)
+
+    result = np.argwhere(s > 2).astype(np.int64)
+    expected = np.array([[3], [4]], dtype=np.int64)
+
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@td.skip_if_no("pyarrow")
+def test_log_arrow_backed_missing_value():
+    # GH#56285
+    ser = Series([1, 2, None], dtype="float64[pyarrow]")
+    result = np.log(ser)
+    expected = np.log(Series([1, 2, None], dtype="float64"))
+    tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_reductions.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_reductions.py
new file mode 100644
index 0000000000000000000000000000000000000000..76353ab25fca6e5208f099b030aa308aeb8837c1
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_reductions.py
@@ -0,0 +1,218 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import Series
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize("operation, expected", [("min", "a"), ("max", "b")])
+def test_reductions_series_strings(operation, expected):
+    # GH#31746
+    ser = Series(["a", "b"], dtype="string")
+    res_operation_serie = getattr(ser, operation)()
+    assert res_operation_serie == expected
+
+
+@pytest.mark.parametrize("as_period", [True, False])
+def test_mode_extension_dtype(as_period):
+    # GH#41927 preserve dt64tz dtype
+    ser = Series([pd.Timestamp(1979, 4, n) for n in range(1, 5)])
+
+    if as_period:
+        ser = ser.dt.to_period("D")
+    else:
+        ser = ser.dt.tz_localize("US/Central")
+
+    res = ser.mode()
+    assert res.dtype == ser.dtype
+    tm.assert_series_equal(res, ser)
+
+
+def test_mode_nullable_dtype(any_numeric_ea_dtype):
+    # GH#55340
+    ser = Series([1, 3, 2, pd.NA, 3, 2, pd.NA], dtype=any_numeric_ea_dtype)
+    result = ser.mode(dropna=False)
+    expected = Series([2, 3, pd.NA], dtype=any_numeric_ea_dtype)
+    tm.assert_series_equal(result, expected)
+
+    result = ser.mode(dropna=True)
+    expected = Series([2, 3], dtype=any_numeric_ea_dtype)
+    tm.assert_series_equal(result, expected)
+
+    ser[-1] = pd.NA
+
+    result = ser.mode(dropna=True)
+    expected = Series([2, 3], dtype=any_numeric_ea_dtype)
+    tm.assert_series_equal(result, expected)
+
+    result = ser.mode(dropna=False)
+    expected = Series([pd.NA], dtype=any_numeric_ea_dtype)
+    tm.assert_series_equal(result, expected)
+
+
+def test_mode_infer_string():
+    # GH#56183
+    pytest.importorskip("pyarrow")
+    ser = Series(["a", "b"], dtype=object)
+    with pd.option_context("future.infer_string", True):
+        result = ser.mode()
+    expected = Series(["a", "b"], dtype=object)
+    tm.assert_series_equal(result, expected)
+
+
+def test_reductions_td64_with_nat():
+    # GH#8617
+    ser = Series([0, pd.NaT], dtype="m8[ns]")
+    exp = ser[0]
+    assert ser.median() == exp
+    assert ser.min() == exp
+    assert ser.max() == exp
+
+
+@pytest.mark.parametrize("skipna", [True, False])
+def test_td64_sum_empty(skipna):
+    # GH#37151
+    ser = Series([], dtype="timedelta64[ns]")
+
+    result = ser.sum(skipna=skipna)
+    assert isinstance(result, pd.Timedelta)
+    assert result == pd.Timedelta(0)
+
+
+def test_td64_summation_overflow():
+    # GH#9442
+    ser = Series(pd.date_range("20130101", periods=100000, freq="h"))
+    ser[0] += pd.Timedelta("1s 1ms")
+
+    # mean
+    result = (ser - ser.min()).mean()
+    expected = pd.Timedelta((pd.TimedeltaIndex(ser - ser.min()).asi8 / len(ser)).sum())
+
+    # the computation is converted to float so
+    # might be some loss of precision
+    assert np.allclose(result._value / 1000, expected._value / 1000)
+
+    # sum
+    msg = "overflow in timedelta operation"
+    with pytest.raises(ValueError, match=msg):
+        (ser - ser.min()).sum()
+
+    s1 = ser[0:10000]
+    with pytest.raises(ValueError, match=msg):
+        (s1 - s1.min()).sum()
+    s2 = ser[0:1000]
+    (s2 - s2.min()).sum()
+
+
+def test_prod_numpy16_bug():
+    ser = Series([1.0, 1.0, 1.0], index=range(3))
+    result = ser.prod()
+
+    assert not isinstance(result, Series)
+
+
+@pytest.mark.parametrize("func", [np.any, np.all])
+@pytest.mark.parametrize("kwargs", [{"keepdims": True}, {"out": object()}])
+def test_validate_any_all_out_keepdims_raises(kwargs, func):
+    ser = Series([1, 2])
+    param = next(iter(kwargs))
+    name = func.__name__
+
+    msg = (
+        f"the '{param}' parameter is not "
+        "supported in the pandas "
+        rf"implementation of {name}\(\)"
+    )
+    with pytest.raises(ValueError, match=msg):
+        func(ser, **kwargs)
+
+
+def test_validate_sum_initial():
+    ser = Series([1, 2])
+    msg = (
+        r"the 'initial' parameter is not "
+        r"supported in the pandas "
+        r"implementation of sum\(\)"
+    )
+    with pytest.raises(ValueError, match=msg):
+        np.sum(ser, initial=10)
+
+
+def test_validate_median_initial():
+    ser = Series([1, 2])
+    msg = (
+        r"the 'overwrite_input' parameter is not "
+        r"supported in the pandas "
+        r"implementation of median\(\)"
+    )
+    with pytest.raises(ValueError, match=msg):
+        # It seems like np.median doesn't dispatch, so we use the
+        # method instead of the ufunc.
+        ser.median(overwrite_input=True)
+
+
+def test_validate_stat_keepdims():
+    ser = Series([1, 2])
+    msg = (
+        r"the 'keepdims' parameter is not "
+        r"supported in the pandas "
+        r"implementation of sum\(\)"
+    )
+    with pytest.raises(ValueError, match=msg):
+        np.sum(ser, keepdims=True)
+
+
+def test_mean_with_convertible_string_raises(using_array_manager, using_infer_string):
+    # GH#44008
+    ser = Series(["1", "2"])
+    if using_infer_string:
+        msg = "does not support"
+        with pytest.raises(TypeError, match=msg):
+            ser.sum()
+    else:
+        assert ser.sum() == "12"
+    msg = "Could not convert string '12' to numeric|does not support"
+    with pytest.raises(TypeError, match=msg):
+        ser.mean()
+
+    df = ser.to_frame()
+    if not using_array_manager:
+        msg = r"Could not convert \['12'\] to numeric|does not support"
+    with pytest.raises(TypeError, match=msg):
+        df.mean()
+
+
+def test_mean_dont_convert_j_to_complex(using_array_manager):
+    # GH#36703
+    df = pd.DataFrame([{"db": "J", "numeric": 123}])
+    if using_array_manager:
+        msg = "Could not convert string 'J' to numeric"
+    else:
+        msg = r"Could not convert \['J'\] to numeric|does not support"
+    with pytest.raises(TypeError, match=msg):
+        df.mean()
+
+    with pytest.raises(TypeError, match=msg):
+        df.agg("mean")
+
+    msg = "Could not convert string 'J' to numeric|does not support"
+    with pytest.raises(TypeError, match=msg):
+        df["db"].mean()
+    msg = "Could not convert string 'J' to numeric|ufunc 'divide'"
+    with pytest.raises(TypeError, match=msg):
+        np.mean(df["db"].astype("string").array)
+
+
+def test_median_with_convertible_string_raises(using_array_manager):
+    # GH#34671 this _could_ return a string "2", but definitely not float 2.0
+    msg = r"Cannot convert \['1' '2' '3'\] to numeric|does not support"
+    ser = Series(["1", "2", "3"])
+    with pytest.raises(TypeError, match=msg):
+        ser.median()
+
+    if not using_array_manager:
+        msg = r"Cannot convert \[\['1' '2' '3'\]\] to numeric|does not support"
+    df = ser.to_frame()
+    with pytest.raises(TypeError, match=msg):
+        df.median()
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_subclass.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_subclass.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2d5afcf884b12b3007905061b7c503359e71a5d
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_subclass.py
@@ -0,0 +1,82 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+pytestmark = pytest.mark.filterwarnings(
+    "ignore:Passing a BlockManager|Passing a SingleBlockManager:DeprecationWarning"
+)
+
+
+class TestSeriesSubclassing:
+    @pytest.mark.parametrize(
+        "idx_method, indexer, exp_data, exp_idx",
+        [
+            ["loc", ["a", "b"], [1, 2], "ab"],
+            ["iloc", [2, 3], [3, 4], "cd"],
+        ],
+    )
+    def test_indexing_sliced(self, idx_method, indexer, exp_data, exp_idx):
+        s = tm.SubclassedSeries([1, 2, 3, 4], index=list("abcd"))
+        res = getattr(s, idx_method)[indexer]
+        exp = tm.SubclassedSeries(exp_data, index=list(exp_idx))
+        tm.assert_series_equal(res, exp)
+
+    def test_to_frame(self):
+        s = tm.SubclassedSeries([1, 2, 3, 4], index=list("abcd"), name="xxx")
+        res = s.to_frame()
+        exp = tm.SubclassedDataFrame({"xxx": [1, 2, 3, 4]}, index=list("abcd"))
+        tm.assert_frame_equal(res, exp)
+
+    def test_subclass_unstack(self):
+        # GH 15564
+        s = tm.SubclassedSeries([1, 2, 3, 4], index=[list("aabb"), list("xyxy")])
+
+        res = s.unstack()
+        exp = tm.SubclassedDataFrame({"x": [1, 3], "y": [2, 4]}, index=["a", "b"])
+
+        tm.assert_frame_equal(res, exp)
+
+    def test_subclass_empty_repr(self):
+        sub_series = tm.SubclassedSeries()
+        assert "SubclassedSeries" in repr(sub_series)
+
+    def test_asof(self):
+        N = 3
+        rng = pd.date_range("1/1/1990", periods=N, freq="53s")
+        s = tm.SubclassedSeries({"A": [np.nan, np.nan, np.nan]}, index=rng)
+
+        result = s.asof(rng[-2:])
+        assert isinstance(result, tm.SubclassedSeries)
+
+    def test_explode(self):
+        s = tm.SubclassedSeries([[1, 2, 3], "foo", [], [3, 4]])
+        result = s.explode()
+        assert isinstance(result, tm.SubclassedSeries)
+
+    def test_equals(self):
+        # https://github.com/pandas-dev/pandas/pull/34402
+        # allow subclass in both directions
+        s1 = pd.Series([1, 2, 3])
+        s2 = tm.SubclassedSeries([1, 2, 3])
+        assert s1.equals(s2)
+        assert s2.equals(s1)
+
+
+class SubclassedSeries(pd.Series):
+    @property
+    def _constructor(self):
+        def _new(*args, **kwargs):
+            # some constructor logic that accesses the Series' name
+            if self.name == "test":
+                return pd.Series(*args, **kwargs)
+            return SubclassedSeries(*args, **kwargs)
+
+        return _new
+
+
+def test_constructor_from_dict():
+    # https://github.com/pandas-dev/pandas/issues/52445
+    result = SubclassedSeries({"a": 1, "b": 2, "c": 3})
+    assert isinstance(result, SubclassedSeries)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_ufunc.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_ufunc.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d13ebf740eabb562fea10336a39edc36e431f5e
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_ufunc.py
@@ -0,0 +1,460 @@
+from collections import deque
+import re
+import string
+
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.arrays import SparseArray
+
+
+@pytest.fixture(params=[np.add, np.logaddexp])
+def ufunc(request):
+    # dunder op
+    return request.param
+
+
+@pytest.fixture(params=[True, False], ids=["sparse", "dense"])
+def sparse(request):
+    return request.param
+
+
+@pytest.fixture
+def arrays_for_binary_ufunc():
+    """
+    A pair of random, length-100 integer-dtype arrays, that are mostly 0.
+    """
+    a1 = np.random.default_rng(2).integers(0, 10, 100, dtype="int64")
+    a2 = np.random.default_rng(2).integers(0, 10, 100, dtype="int64")
+    a1[::3] = 0
+    a2[::4] = 0
+    return a1, a2
+
+
+@pytest.mark.parametrize("ufunc", [np.positive, np.floor, np.exp])
+def test_unary_ufunc(ufunc, sparse):
+    # Test that ufunc(pd.Series) == pd.Series(ufunc)
+    arr = np.random.default_rng(2).integers(0, 10, 10, dtype="int64")
+    arr[::2] = 0
+    if sparse:
+        arr = SparseArray(arr, dtype=pd.SparseDtype("int64", 0))
+
+    index = list(string.ascii_letters[:10])
+    name = "name"
+    series = pd.Series(arr, index=index, name=name)
+
+    result = ufunc(series)
+    expected = pd.Series(ufunc(arr), index=index, name=name)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("flip", [True, False], ids=["flipped", "straight"])
+def test_binary_ufunc_with_array(flip, sparse, ufunc, arrays_for_binary_ufunc):
+    # Test that ufunc(pd.Series(a), array) == pd.Series(ufunc(a, b))
+    a1, a2 = arrays_for_binary_ufunc
+    if sparse:
+        a1 = SparseArray(a1, dtype=pd.SparseDtype("int64", 0))
+        a2 = SparseArray(a2, dtype=pd.SparseDtype("int64", 0))
+
+    name = "name"  # op(pd.Series, array) preserves the name.
+    series = pd.Series(a1, name=name)
+    other = a2
+
+    array_args = (a1, a2)
+    series_args = (series, other)  # ufunc(series, array)
+
+    if flip:
+        array_args = reversed(array_args)
+        series_args = reversed(series_args)  # ufunc(array, series)
+
+    expected = pd.Series(ufunc(*array_args), name=name)
+    result = ufunc(*series_args)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("flip", [True, False], ids=["flipped", "straight"])
+def test_binary_ufunc_with_index(flip, sparse, ufunc, arrays_for_binary_ufunc):
+    # Test that
+    #   * func(pd.Series(a), pd.Series(b)) == pd.Series(ufunc(a, b))
+    #   * ufunc(Index, pd.Series) dispatches to pd.Series (returns a pd.Series)
+    a1, a2 = arrays_for_binary_ufunc
+    if sparse:
+        a1 = SparseArray(a1, dtype=pd.SparseDtype("int64", 0))
+        a2 = SparseArray(a2, dtype=pd.SparseDtype("int64", 0))
+
+    name = "name"  # op(pd.Series, array) preserves the name.
+    series = pd.Series(a1, name=name)
+
+    other = pd.Index(a2, name=name).astype("int64")
+
+    array_args = (a1, a2)
+    series_args = (series, other)  # ufunc(series, array)
+
+    if flip:
+        array_args = reversed(array_args)
+        series_args = reversed(series_args)  # ufunc(array, series)
+
+    expected = pd.Series(ufunc(*array_args), name=name)
+    result = ufunc(*series_args)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("shuffle", [True, False], ids=["unaligned", "aligned"])
+@pytest.mark.parametrize("flip", [True, False], ids=["flipped", "straight"])
+def test_binary_ufunc_with_series(
+    flip, shuffle, sparse, ufunc, arrays_for_binary_ufunc
+):
+    # Test that
+    #   * func(pd.Series(a), pd.Series(b)) == pd.Series(ufunc(a, b))
+    #   with alignment between the indices
+    a1, a2 = arrays_for_binary_ufunc
+    if sparse:
+        a1 = SparseArray(a1, dtype=pd.SparseDtype("int64", 0))
+        a2 = SparseArray(a2, dtype=pd.SparseDtype("int64", 0))
+
+    name = "name"  # op(pd.Series, array) preserves the name.
+    series = pd.Series(a1, name=name)
+    other = pd.Series(a2, name=name)
+
+    idx = np.random.default_rng(2).permutation(len(a1))
+
+    if shuffle:
+        other = other.take(idx)
+        if flip:
+            index = other.align(series)[0].index
+        else:
+            index = series.align(other)[0].index
+    else:
+        index = series.index
+
+    array_args = (a1, a2)
+    series_args = (series, other)  # ufunc(series, array)
+
+    if flip:
+        array_args = tuple(reversed(array_args))
+        series_args = tuple(reversed(series_args))  # ufunc(array, series)
+
+    expected = pd.Series(ufunc(*array_args), index=index, name=name)
+    result = ufunc(*series_args)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("flip", [True, False])
+def test_binary_ufunc_scalar(ufunc, sparse, flip, arrays_for_binary_ufunc):
+    # Test that
+    #   * ufunc(pd.Series, scalar) == pd.Series(ufunc(array, scalar))
+    #   * ufunc(pd.Series, scalar) == ufunc(scalar, pd.Series)
+    arr, _ = arrays_for_binary_ufunc
+    if sparse:
+        arr = SparseArray(arr)
+    other = 2
+    series = pd.Series(arr, name="name")
+
+    series_args = (series, other)
+    array_args = (arr, other)
+
+    if flip:
+        series_args = tuple(reversed(series_args))
+        array_args = tuple(reversed(array_args))
+
+    expected = pd.Series(ufunc(*array_args), name="name")
+    result = ufunc(*series_args)
+
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("ufunc", [np.divmod])  # TODO: np.modf, np.frexp
+@pytest.mark.parametrize("shuffle", [True, False])
+@pytest.mark.filterwarnings("ignore:divide by zero:RuntimeWarning")
+def test_multiple_output_binary_ufuncs(ufunc, sparse, shuffle, arrays_for_binary_ufunc):
+    # Test that
+    #  the same conditions from binary_ufunc_scalar apply to
+    #  ufuncs with multiple outputs.
+
+    a1, a2 = arrays_for_binary_ufunc
+    # work around https://github.com/pandas-dev/pandas/issues/26987
+    a1[a1 == 0] = 1
+    a2[a2 == 0] = 1
+
+    if sparse:
+        a1 = SparseArray(a1, dtype=pd.SparseDtype("int64", 0))
+        a2 = SparseArray(a2, dtype=pd.SparseDtype("int64", 0))
+
+    s1 = pd.Series(a1)
+    s2 = pd.Series(a2)
+
+    if shuffle:
+        # ensure we align before applying the ufunc
+        s2 = s2.sample(frac=1)
+
+    expected = ufunc(a1, a2)
+    assert isinstance(expected, tuple)
+
+    result = ufunc(s1, s2)
+    assert isinstance(result, tuple)
+    tm.assert_series_equal(result[0], pd.Series(expected[0]))
+    tm.assert_series_equal(result[1], pd.Series(expected[1]))
+
+
+def test_multiple_output_ufunc(sparse, arrays_for_binary_ufunc):
+    # Test that the same conditions from unary input apply to multi-output
+    # ufuncs
+    arr, _ = arrays_for_binary_ufunc
+
+    if sparse:
+        arr = SparseArray(arr)
+
+    series = pd.Series(arr, name="name")
+    result = np.modf(series)
+    expected = np.modf(arr)
+
+    assert isinstance(result, tuple)
+    assert isinstance(expected, tuple)
+
+    tm.assert_series_equal(result[0], pd.Series(expected[0], name="name"))
+    tm.assert_series_equal(result[1], pd.Series(expected[1], name="name"))
+
+
+def test_binary_ufunc_drops_series_name(ufunc, sparse, arrays_for_binary_ufunc):
+    # Drop the names when they differ.
+    a1, a2 = arrays_for_binary_ufunc
+    s1 = pd.Series(a1, name="a")
+    s2 = pd.Series(a2, name="b")
+
+    result = ufunc(s1, s2)
+    assert result.name is None
+
+
+def test_object_series_ok():
+    class Dummy:
+        def __init__(self, value) -> None:
+            self.value = value
+
+        def __add__(self, other):
+            return self.value + other.value
+
+    arr = np.array([Dummy(0), Dummy(1)])
+    ser = pd.Series(arr)
+    tm.assert_series_equal(np.add(ser, ser), pd.Series(np.add(ser, arr)))
+    tm.assert_series_equal(np.add(ser, Dummy(1)), pd.Series(np.add(ser, Dummy(1))))
+
+
+@pytest.fixture(
+    params=[
+        pd.array([1, 3, 2], dtype=np.int64),
+        pd.array([1, 3, 2], dtype="Int64"),
+        pd.array([1, 3, 2], dtype="Float32"),
+        pd.array([1, 10, 2], dtype="Sparse[int]"),
+        pd.to_datetime(["2000", "2010", "2001"]),
+        pd.to_datetime(["2000", "2010", "2001"]).tz_localize("CET"),
+        pd.to_datetime(["2000", "2010", "2001"]).to_period(freq="D"),
+        pd.to_timedelta(["1 Day", "3 Days", "2 Days"]),
+        pd.IntervalIndex([pd.Interval(0, 1), pd.Interval(2, 3), pd.Interval(1, 2)]),
+    ],
+    ids=lambda x: str(x.dtype),
+)
+def values_for_np_reduce(request):
+    # min/max tests assume that these are monotonic increasing
+    return request.param
+
+
+class TestNumpyReductions:
+    # TODO: cases with NAs, axis kwarg for DataFrame
+
+    def test_multiply(self, values_for_np_reduce, box_with_array, request):
+        box = box_with_array
+        values = values_for_np_reduce
+
+        with tm.assert_produces_warning(None):
+            obj = box(values)
+
+        if isinstance(values, pd.core.arrays.SparseArray):
+            mark = pytest.mark.xfail(reason="SparseArray has no 'prod'")
+            request.applymarker(mark)
+
+        if values.dtype.kind in "iuf":
+            result = np.multiply.reduce(obj)
+            if box is pd.DataFrame:
+                expected = obj.prod(numeric_only=False)
+                tm.assert_series_equal(result, expected)
+            elif box is pd.Index:
+                # Index has no 'prod'
+                expected = obj._values.prod()
+                assert result == expected
+            else:
+                expected = obj.prod()
+                assert result == expected
+        else:
+            msg = "|".join(
+                [
+                    "does not support reduction",
+                    "unsupported operand type",
+                    "ufunc 'multiply' cannot use operands",
+                ]
+            )
+            with pytest.raises(TypeError, match=msg):
+                np.multiply.reduce(obj)
+
+    def test_add(self, values_for_np_reduce, box_with_array):
+        box = box_with_array
+        values = values_for_np_reduce
+
+        with tm.assert_produces_warning(None):
+            obj = box(values)
+
+        if values.dtype.kind in "miuf":
+            result = np.add.reduce(obj)
+            if box is pd.DataFrame:
+                expected = obj.sum(numeric_only=False)
+                tm.assert_series_equal(result, expected)
+            elif box is pd.Index:
+                # Index has no 'sum'
+                expected = obj._values.sum()
+                assert result == expected
+            else:
+                expected = obj.sum()
+                assert result == expected
+        else:
+            msg = "|".join(
+                [
+                    "does not support reduction",
+                    "unsupported operand type",
+                    "ufunc 'add' cannot use operands",
+                ]
+            )
+            with pytest.raises(TypeError, match=msg):
+                np.add.reduce(obj)
+
+    def test_max(self, values_for_np_reduce, box_with_array):
+        box = box_with_array
+        values = values_for_np_reduce
+
+        same_type = True
+        if box is pd.Index and values.dtype.kind in ["i", "f"]:
+            # ATM Index casts to object, so we get python ints/floats
+            same_type = False
+
+        with tm.assert_produces_warning(None):
+            obj = box(values)
+
+        result = np.maximum.reduce(obj)
+        if box is pd.DataFrame:
+            # TODO: cases with axis kwarg
+            expected = obj.max(numeric_only=False)
+            tm.assert_series_equal(result, expected)
+        else:
+            expected = values[1]
+            assert result == expected
+            if same_type:
+                # check we have e.g. Timestamp instead of dt64
+                assert type(result) == type(expected)
+
+    def test_min(self, values_for_np_reduce, box_with_array):
+        box = box_with_array
+        values = values_for_np_reduce
+
+        same_type = True
+        if box is pd.Index and values.dtype.kind in ["i", "f"]:
+            # ATM Index casts to object, so we get python ints/floats
+            same_type = False
+
+        with tm.assert_produces_warning(None):
+            obj = box(values)
+
+        result = np.minimum.reduce(obj)
+        if box is pd.DataFrame:
+            expected = obj.min(numeric_only=False)
+            tm.assert_series_equal(result, expected)
+        else:
+            expected = values[0]
+            assert result == expected
+            if same_type:
+                # check we have e.g. Timestamp instead of dt64
+                assert type(result) == type(expected)
+
+
+@pytest.mark.parametrize("type_", [list, deque, tuple])
+def test_binary_ufunc_other_types(type_):
+    a = pd.Series([1, 2, 3], name="name")
+    b = type_([3, 4, 5])
+
+    result = np.add(a, b)
+    expected = pd.Series(np.add(a.to_numpy(), b), name="name")
+    tm.assert_series_equal(result, expected)
+
+
+def test_object_dtype_ok():
+    class Thing:
+        def __init__(self, value) -> None:
+            self.value = value
+
+        def __add__(self, other):
+            other = getattr(other, "value", other)
+            return type(self)(self.value + other)
+
+        def __eq__(self, other) -> bool:
+            return type(other) is Thing and self.value == other.value
+
+        def __repr__(self) -> str:
+            return f"Thing({self.value})"
+
+    s = pd.Series([Thing(1), Thing(2)])
+    result = np.add(s, Thing(1))
+    expected = pd.Series([Thing(2), Thing(3)])
+    tm.assert_series_equal(result, expected)
+
+
+def test_outer():
+    # https://github.com/pandas-dev/pandas/issues/27186
+    ser = pd.Series([1, 2, 3])
+    obj = np.array([1, 2, 3])
+
+    with pytest.raises(NotImplementedError, match=""):
+        np.subtract.outer(ser, obj)
+
+
+def test_np_matmul():
+    # GH26650
+    df1 = pd.DataFrame(data=[[-1, 1, 10]])
+    df2 = pd.DataFrame(data=[-1, 1, 10])
+    expected = pd.DataFrame(data=[102])
+
+    result = np.matmul(df1, df2)
+    tm.assert_frame_equal(expected, result)
+
+
+def test_array_ufuncs_for_many_arguments():
+    # GH39853
+    def add3(x, y, z):
+        return x + y + z
+
+    ufunc = np.frompyfunc(add3, 3, 1)
+    ser = pd.Series([1, 2])
+
+    result = ufunc(ser, ser, 1)
+    expected = pd.Series([3, 5], dtype=object)
+    tm.assert_series_equal(result, expected)
+
+    df = pd.DataFrame([[1, 2]])
+
+    msg = (
+        "Cannot apply ufunc <ufunc 'add3 (vectorized)'> "
+        "to mixed DataFrame and Series inputs."
+    )
+    with pytest.raises(NotImplementedError, match=re.escape(msg)):
+        ufunc(ser, ser, df)
+
+
+# TODO(CoW) see https://github.com/pandas-dev/pandas/pull/51082
+@td.skip_copy_on_write_not_yet_implemented
+def test_np_fix():
+    # np.fix is not a ufunc but is composed of several ufunc calls under the hood
+    # with `out` and `where` keywords
+    ser = pd.Series([-1.5, -0.5, 0.5, 1.5])
+    result = np.fix(ser)
+    expected = pd.Series([-1.0, -0.0, 0.0, 1.0])
+    tm.assert_series_equal(result, expected)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_unary.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_unary.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f153788e413c5e9198dc35867bc628823555dbf
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_unary.py
@@ -0,0 +1,50 @@
+import pytest
+
+from pandas import Series
+import pandas._testing as tm
+
+
+class TestSeriesUnaryOps:
+    # __neg__, __pos__, __invert__
+
+    def test_neg(self):
+        ser = Series(range(5), dtype="float64", name="series")
+        tm.assert_series_equal(-ser, -1 * ser)
+
+    def test_invert(self):
+        ser = Series(range(5), dtype="float64", name="series")
+        tm.assert_series_equal(-(ser < 0), ~(ser < 0))
+
+    @pytest.mark.parametrize(
+        "source, neg_target, abs_target",
+        [
+            ([1, 2, 3], [-1, -2, -3], [1, 2, 3]),
+            ([1, 2, None], [-1, -2, None], [1, 2, None]),
+        ],
+    )
+    def test_all_numeric_unary_operators(
+        self, any_numeric_ea_dtype, source, neg_target, abs_target
+    ):
+        # GH38794
+        dtype = any_numeric_ea_dtype
+        ser = Series(source, dtype=dtype)
+        neg_result, pos_result, abs_result = -ser, +ser, abs(ser)
+        if dtype.startswith("U"):
+            neg_target = -Series(source, dtype=dtype)
+        else:
+            neg_target = Series(neg_target, dtype=dtype)
+
+        abs_target = Series(abs_target, dtype=dtype)
+
+        tm.assert_series_equal(neg_result, neg_target)
+        tm.assert_series_equal(pos_result, ser)
+        tm.assert_series_equal(abs_result, abs_target)
+
+    @pytest.mark.parametrize("op", ["__neg__", "__abs__"])
+    def test_unary_float_op_mask(self, float_ea_dtype, op):
+        dtype = float_ea_dtype
+        ser = Series([1.1, 2.2, 3.3], dtype=dtype)
+        result = getattr(ser, op)()
+        target = result.copy(deep=True)
+        ser[0] = None
+        tm.assert_series_equal(result, target)
diff --git a/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_validate.py b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_validate.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c867f7582b7d3250bf5e009ffbf7545da404712
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/pandas/tests/series/test_validate.py
@@ -0,0 +1,26 @@
+import pytest
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        "reset_index",
+        "_set_name",
+        "sort_values",
+        "sort_index",
+        "rename",
+        "dropna",
+        "drop_duplicates",
+    ],
+)
+@pytest.mark.parametrize("inplace", [1, "True", [1, 2, 3], 5.0])
+def test_validate_bool_args(string_series, func, inplace):
+    """Tests for error handling related to data types of method arguments."""
+    msg = 'For argument "inplace" expected type bool'
+    kwargs = {"inplace": inplace}
+
+    if func == "_set_name":
+        kwargs["name"] = "hello"
+
+    with pytest.raises(ValueError, match=msg):
+        getattr(string_series, func)(**kwargs)