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@@ -191,3 +191,5 @@ llmeval-env/lib/python3.10/site-packages/nvidia/nccl/lib/libnccl.so.2 filter=lfs
 llmeval-env/lib/python3.10/site-packages/nvidia/cusparse/lib/libcusparse.so.12 filter=lfs diff=lfs merge=lfs -text
 llmeval-env/lib/python3.10/site-packages/numexpr/interpreter.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
 llmeval-env/lib/python3.10/site-packages/nvidia/cufft/lib/libcufft.so.11 filter=lfs diff=lfs merge=lfs -text
+llmeval-env/lib/python3.10/site-packages/pandas/_libs/interval.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+llmeval-env/lib/python3.10/site-packages/pandas/_libs/join.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text

llmeval-env/lib/python3.10/site-packages/pandas/_libs/interval.cpython-310-x86_64-linux-gnu.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7e13e757cc911f2818ebea4f5cfaf5197639154cf160d8be4d0e0c27451997f4
+size 1532904

llmeval-env/lib/python3.10/site-packages/pandas/_libs/join.cpython-310-x86_64-linux-gnu.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:97df0241d7470fafc2785aaef2c2c4216803f45190f2c617a9042fecfc04678b
+size 1409928

llmeval-env/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)

llmeval-env/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)

llmeval-env/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

llmeval-env/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)

llmeval-env/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))

llmeval-env/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)

llmeval-env/lib/python3.10/site-packages/pandas/tests/series/methods/test_combine_first.py

@@ -0,0 +1,149 @@
+from datetime import datetime
+
+import numpy as np
+
+import pandas as pd
+from pandas import (
+    Period,
+    Series,
+    date_range,
+    period_range,
+    to_datetime,
+)
+import pandas._testing as tm
+
+
+class TestCombineFirst:
+    def test_combine_first_period_datetime(self):
+        # GH#3367
+        didx = date_range(start="1950-01-31", end="1950-07-31", freq="ME")
+        pidx = period_range(start=Period("1950-1"), end=Period("1950-7"), freq="M")
+        # check to be consistent with DatetimeIndex
+        for idx in [didx, pidx]:
+            a = Series([1, np.nan, np.nan, 4, 5, np.nan, 7], index=idx)
+            b = Series([9, 9, 9, 9, 9, 9, 9], index=idx)
+
+            result = a.combine_first(b)
+            expected = Series([1, 9, 9, 4, 5, 9, 7], index=idx, dtype=np.float64)
+            tm.assert_series_equal(result, expected)
+
+    def test_combine_first_name(self, datetime_series):
+        result = datetime_series.combine_first(datetime_series[:5])
+        assert result.name == datetime_series.name
+
+    def test_combine_first(self):
+        values = np.arange(20, dtype=np.float64)
+        series = Series(values, index=np.arange(20, dtype=np.int64))
+
+        series_copy = series * 2
+        series_copy[::2] = np.nan
+
+        # nothing used from the input
+        combined = series.combine_first(series_copy)
+
+        tm.assert_series_equal(combined, series)
+
+        # Holes filled from input
+        combined = series_copy.combine_first(series)
+        assert np.isfinite(combined).all()
+
+        tm.assert_series_equal(combined[::2], series[::2])
+        tm.assert_series_equal(combined[1::2], series_copy[1::2])
+
+        # mixed types
+        index = pd.Index([str(i) for i in range(20)])
+        floats = Series(np.random.default_rng(2).standard_normal(20), index=index)
+        strings = Series([str(i) for i in range(10)], index=index[::2], dtype=object)
+
+        combined = strings.combine_first(floats)
+
+        tm.assert_series_equal(strings, combined.loc[index[::2]])
+        tm.assert_series_equal(floats[1::2].astype(object), combined.loc[index[1::2]])
+
+        # corner case
+        ser = Series([1.0, 2, 3], index=[0, 1, 2])
+        empty = Series([], index=[], dtype=object)
+        msg = "The behavior of array concatenation with empty entries is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = ser.combine_first(empty)
+        ser.index = ser.index.astype("O")
+        tm.assert_series_equal(ser, result)
+
+    def test_combine_first_dt64(self, unit):
+        s0 = to_datetime(Series(["2010", np.nan])).dt.as_unit(unit)
+        s1 = to_datetime(Series([np.nan, "2011"])).dt.as_unit(unit)
+        rs = s0.combine_first(s1)
+        xp = to_datetime(Series(["2010", "2011"])).dt.as_unit(unit)
+        tm.assert_series_equal(rs, xp)
+
+        s0 = to_datetime(Series(["2010", np.nan])).dt.as_unit(unit)
+        s1 = Series([np.nan, "2011"])
+        rs = s0.combine_first(s1)
+
+        xp = Series([datetime(2010, 1, 1), "2011"], dtype="datetime64[ns]")
+
+        tm.assert_series_equal(rs, xp)
+
+    def test_combine_first_dt_tz_values(self, tz_naive_fixture):
+        ser1 = Series(
+            pd.DatetimeIndex(["20150101", "20150102", "20150103"], tz=tz_naive_fixture),
+            name="ser1",
+        )
+        ser2 = Series(
+            pd.DatetimeIndex(["20160514", "20160515", "20160516"], tz=tz_naive_fixture),
+            index=[2, 3, 4],
+            name="ser2",
+        )
+        result = ser1.combine_first(ser2)
+        exp_vals = pd.DatetimeIndex(
+            ["20150101", "20150102", "20150103", "20160515", "20160516"],
+            tz=tz_naive_fixture,
+        )
+        exp = Series(exp_vals, name="ser1")
+        tm.assert_series_equal(exp, result)
+
+    def test_combine_first_timezone_series_with_empty_series(self):
+        # GH 41800
+        time_index = date_range(
+            datetime(2021, 1, 1, 1),
+            datetime(2021, 1, 1, 10),
+            freq="h",
+            tz="Europe/Rome",
+        )
+        s1 = Series(range(10), index=time_index)
+        s2 = Series(index=time_index)
+        msg = "The behavior of array concatenation with empty entries is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = s1.combine_first(s2)
+        tm.assert_series_equal(result, s1)
+
+    def test_combine_first_preserves_dtype(self):
+        # GH51764
+        s1 = Series([1666880195890293744, 1666880195890293837])
+        s2 = Series([1, 2, 3])
+        result = s1.combine_first(s2)
+        expected = Series([1666880195890293744, 1666880195890293837, 3])
+        tm.assert_series_equal(result, expected)
+
+    def test_combine_mixed_timezone(self):
+        # GH 26283
+        uniform_tz = Series({pd.Timestamp("2019-05-01", tz="UTC"): 1.0})
+        multi_tz = Series(
+            {
+                pd.Timestamp("2019-05-01 01:00:00+0100", tz="Europe/London"): 2.0,
+                pd.Timestamp("2019-05-02", tz="UTC"): 3.0,
+            }
+        )
+
+        result = uniform_tz.combine_first(multi_tz)
+        expected = Series(
+            [1.0, 3.0],
+            index=pd.Index(
+                [
+                    pd.Timestamp("2019-05-01 00:00:00+00:00", tz="UTC"),
+                    pd.Timestamp("2019-05-02 00:00:00+00:00", tz="UTC"),
+                ],
+                dtype="object",
+            ),
+        )
+        tm.assert_series_equal(result, expected)

llmeval-env/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)

llmeval-env/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)

llmeval-env/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

llmeval-env/lib/python3.10/site-packages/pandas/tests/series/methods/test_cov_corr.py

@@ -0,0 +1,185 @@
+import math
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Series,
+    date_range,
+    isna,
+)
+import pandas._testing as tm
+
+
+class TestSeriesCov:
+    def test_cov(self, datetime_series):
+        # full overlap
+        tm.assert_almost_equal(
+            datetime_series.cov(datetime_series), datetime_series.std() ** 2
+        )
+
+        # partial overlap
+        tm.assert_almost_equal(
+            datetime_series[:15].cov(datetime_series[5:]),
+            datetime_series[5:15].std() ** 2,
+        )
+
+        # No overlap
+        assert np.isnan(datetime_series[::2].cov(datetime_series[1::2]))
+
+        # all NA
+        cp = datetime_series[:10].copy()
+        cp[:] = np.nan
+        assert isna(cp.cov(cp))
+
+        # min_periods
+        assert isna(datetime_series[:15].cov(datetime_series[5:], min_periods=12))
+
+        ts1 = datetime_series[:15].reindex(datetime_series.index)
+        ts2 = datetime_series[5:].reindex(datetime_series.index)
+        assert isna(ts1.cov(ts2, min_periods=12))
+
+    @pytest.mark.parametrize("test_ddof", [None, 0, 1, 2, 3])
+    @pytest.mark.parametrize("dtype", ["float64", "Float64"])
+    def test_cov_ddof(self, test_ddof, dtype):
+        # GH#34611
+        np_array1 = np.random.default_rng(2).random(10)
+        np_array2 = np.random.default_rng(2).random(10)
+
+        s1 = Series(np_array1, dtype=dtype)
+        s2 = Series(np_array2, dtype=dtype)
+
+        result = s1.cov(s2, ddof=test_ddof)
+        expected = np.cov(np_array1, np_array2, ddof=test_ddof)[0][1]
+        assert math.isclose(expected, result)
+
+
+class TestSeriesCorr:
+    @pytest.mark.parametrize("dtype", ["float64", "Float64"])
+    def test_corr(self, datetime_series, dtype):
+        stats = pytest.importorskip("scipy.stats")
+
+        datetime_series = datetime_series.astype(dtype)
+
+        # full overlap
+        tm.assert_almost_equal(datetime_series.corr(datetime_series), 1)
+
+        # partial overlap
+        tm.assert_almost_equal(datetime_series[:15].corr(datetime_series[5:]), 1)
+
+        assert isna(datetime_series[:15].corr(datetime_series[5:], min_periods=12))
+
+        ts1 = datetime_series[:15].reindex(datetime_series.index)
+        ts2 = datetime_series[5:].reindex(datetime_series.index)
+        assert isna(ts1.corr(ts2, min_periods=12))
+
+        # No overlap
+        assert np.isnan(datetime_series[::2].corr(datetime_series[1::2]))
+
+        # all NA
+        cp = datetime_series[:10].copy()
+        cp[:] = np.nan
+        assert isna(cp.corr(cp))
+
+        A = Series(
+            np.arange(10, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            name="ts",
+        )
+        B = A.copy()
+        result = A.corr(B)
+        expected, _ = stats.pearsonr(A, B)
+        tm.assert_almost_equal(result, expected)
+
+    def test_corr_rank(self):
+        stats = pytest.importorskip("scipy.stats")
+
+        # kendall and spearman
+        A = Series(
+            np.arange(10, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            name="ts",
+        )
+        B = A.copy()
+        A[-5:] = A[:5].copy()
+        result = A.corr(B, method="kendall")
+        expected = stats.kendalltau(A, B)[0]
+        tm.assert_almost_equal(result, expected)
+
+        result = A.corr(B, method="spearman")
+        expected = stats.spearmanr(A, B)[0]
+        tm.assert_almost_equal(result, expected)
+
+        # results from R
+        A = Series(
+            [
+                -0.89926396,
+                0.94209606,
+                -1.03289164,
+                -0.95445587,
+                0.76910310,
+                -0.06430576,
+                -2.09704447,
+                0.40660407,
+                -0.89926396,
+                0.94209606,
+            ]
+        )
+        B = Series(
+            [
+                -1.01270225,
+                -0.62210117,
+                -1.56895827,
+                0.59592943,
+                -0.01680292,
+                1.17258718,
+                -1.06009347,
+                -0.10222060,
+                -0.89076239,
+                0.89372375,
+            ]
+        )
+        kexp = 0.4319297
+        sexp = 0.5853767
+        tm.assert_almost_equal(A.corr(B, method="kendall"), kexp)
+        tm.assert_almost_equal(A.corr(B, method="spearman"), sexp)
+
+    def test_corr_invalid_method(self):
+        # GH PR #22298
+        s1 = Series(np.random.default_rng(2).standard_normal(10))
+        s2 = Series(np.random.default_rng(2).standard_normal(10))
+        msg = "method must be either 'pearson', 'spearman', 'kendall', or a callable, "
+        with pytest.raises(ValueError, match=msg):
+            s1.corr(s2, method="____")
+
+    def test_corr_callable_method(self, datetime_series):
+        # simple correlation example
+        # returns 1 if exact equality, 0 otherwise
+        my_corr = lambda a, b: 1.0 if (a == b).all() else 0.0
+
+        # simple example
+        s1 = Series([1, 2, 3, 4, 5])
+        s2 = Series([5, 4, 3, 2, 1])
+        expected = 0
+        tm.assert_almost_equal(s1.corr(s2, method=my_corr), expected)
+
+        # full overlap
+        tm.assert_almost_equal(
+            datetime_series.corr(datetime_series, method=my_corr), 1.0
+        )
+
+        # partial overlap
+        tm.assert_almost_equal(
+            datetime_series[:15].corr(datetime_series[5:], method=my_corr), 1.0
+        )
+
+        # No overlap
+        assert np.isnan(
+            datetime_series[::2].corr(datetime_series[1::2], method=my_corr)
+        )
+
+        # dataframe example
+        df = pd.DataFrame([s1, s2])
+        expected = pd.DataFrame([{0: 1.0, 1: 0}, {0: 0, 1: 1.0}])
+        tm.assert_almost_equal(df.transpose().corr(method=my_corr), expected)

llmeval-env/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)

llmeval-env/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)

llmeval-env/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)

llmeval-env/lib/python3.10/site-packages/pandas/tests/series/methods/test_duplicated.py

@@ -0,0 +1,77 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    NA,
+    Categorical,
+    Series,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize(
+    "keep, expected",
+    [
+        ("first", Series([False, False, True, False, True], name="name")),
+        ("last", Series([True, True, False, False, False], name="name")),
+        (False, Series([True, True, True, False, True], name="name")),
+    ],
+)
+def test_duplicated_keep(keep, expected):
+    ser = Series(["a", "b", "b", "c", "a"], name="name")
+
+    result = ser.duplicated(keep=keep)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "keep, expected",
+    [
+        ("first", Series([False, False, True, False, True])),
+        ("last", Series([True, True, False, False, False])),
+        (False, Series([True, True, True, False, True])),
+    ],
+)
+def test_duplicated_nan_none(keep, expected):
+    ser = Series([np.nan, 3, 3, None, np.nan], dtype=object)
+
+    result = ser.duplicated(keep=keep)
+    tm.assert_series_equal(result, expected)
+
+
+def test_duplicated_categorical_bool_na(nulls_fixture):
+    # GH#44351
+    ser = Series(
+        Categorical(
+            [True, False, True, False, nulls_fixture],
+            categories=[True, False],
+            ordered=True,
+        )
+    )
+    result = ser.duplicated()
+    expected = Series([False, False, True, True, False])
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "keep, vals",
+    [
+        ("last", [True, True, False]),
+        ("first", [False, True, True]),
+        (False, [True, True, True]),
+    ],
+)
+def test_duplicated_mask(keep, vals):
+    # GH#48150
+    ser = Series([1, 2, NA, NA, NA], dtype="Int64")
+    result = ser.duplicated(keep=keep)
+    expected = Series([False, False] + vals)
+    tm.assert_series_equal(result, expected)
+
+
+def test_duplicated_mask_no_duplicated_na(keep):
+    # GH#48150
+    ser = Series([1, 2, NA], dtype="Int64")
+    result = ser.duplicated(keep=keep)
+    expected = Series([False, False, False])
+    tm.assert_series_equal(result, expected)

llmeval-env/lib/python3.10/site-packages/pandas/tests/series/methods/test_head_tail.py

@@ -0,0 +1,8 @@
+import pandas._testing as tm
+
+
+def test_head_tail(string_series):
+    tm.assert_series_equal(string_series.head(), string_series[:5])
+    tm.assert_series_equal(string_series.head(0), string_series[0:0])
+    tm.assert_series_equal(string_series.tail(), string_series[-5:])
+    tm.assert_series_equal(string_series.tail(0), string_series[0:0])

llmeval-env/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

llmeval-env/lib/python3.10/site-packages/pandas/tests/series/methods/test_interpolate.py

@@ -0,0 +1,868 @@
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    Index,
+    MultiIndex,
+    Series,
+    date_range,
+    isna,
+)
+import pandas._testing as tm
+
+
+@pytest.fixture(
+    params=[
+        "linear",
+        "index",
+        "values",
+        "nearest",
+        "slinear",
+        "zero",
+        "quadratic",
+        "cubic",
+        "barycentric",
+        "krogh",
+        "polynomial",
+        "spline",
+        "piecewise_polynomial",
+        "from_derivatives",
+        "pchip",
+        "akima",
+        "cubicspline",
+    ]
+)
+def nontemporal_method(request):
+    """Fixture that returns an (method name, required kwargs) pair.
+
+    This fixture does not include method 'time' as a parameterization; that
+    method requires a Series with a DatetimeIndex, and is generally tested
+    separately from these non-temporal methods.
+    """
+    method = request.param
+    kwargs = {"order": 1} if method in ("spline", "polynomial") else {}
+    return method, kwargs
+
+
+@pytest.fixture(
+    params=[
+        "linear",
+        "slinear",
+        "zero",
+        "quadratic",
+        "cubic",
+        "barycentric",
+        "krogh",
+        "polynomial",
+        "spline",
+        "piecewise_polynomial",
+        "from_derivatives",
+        "pchip",
+        "akima",
+        "cubicspline",
+    ]
+)
+def interp_methods_ind(request):
+    """Fixture that returns a (method name, required kwargs) pair to
+    be tested for various Index types.
+
+    This fixture does not include methods - 'time', 'index', 'nearest',
+    'values' as a parameterization
+    """
+    method = request.param
+    kwargs = {"order": 1} if method in ("spline", "polynomial") else {}
+    return method, kwargs
+
+
+class TestSeriesInterpolateData:
+    @pytest.mark.xfail(reason="EA.fillna does not handle 'linear' method")
+    def test_interpolate_period_values(self):
+        orig = Series(date_range("2012-01-01", periods=5))
+        ser = orig.copy()
+        ser[2] = pd.NaT
+
+        # period cast
+        ser_per = ser.dt.to_period("D")
+        res_per = ser_per.interpolate()
+        expected_per = orig.dt.to_period("D")
+        tm.assert_series_equal(res_per, expected_per)
+
+    def test_interpolate(self, datetime_series):
+        ts = Series(np.arange(len(datetime_series), dtype=float), datetime_series.index)
+
+        ts_copy = ts.copy()
+        ts_copy[5:10] = np.nan
+
+        linear_interp = ts_copy.interpolate(method="linear")
+        tm.assert_series_equal(linear_interp, ts)
+
+        ord_ts = Series(
+            [d.toordinal() for d in datetime_series.index], index=datetime_series.index
+        ).astype(float)
+
+        ord_ts_copy = ord_ts.copy()
+        ord_ts_copy[5:10] = np.nan
+
+        time_interp = ord_ts_copy.interpolate(method="time")
+        tm.assert_series_equal(time_interp, ord_ts)
+
+    def test_interpolate_time_raises_for_non_timeseries(self):
+        # When method='time' is used on a non-TimeSeries that contains a null
+        # value, a ValueError should be raised.
+        non_ts = Series([0, 1, 2, np.nan])
+        msg = "time-weighted interpolation only works on Series.* with a DatetimeIndex"
+        with pytest.raises(ValueError, match=msg):
+            non_ts.interpolate(method="time")
+
+    def test_interpolate_cubicspline(self):
+        pytest.importorskip("scipy")
+        ser = Series([10, 11, 12, 13])
+
+        expected = Series(
+            [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00],
+            index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
+        )
+        # interpolate at new_index
+        new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
+            float
+        )
+        result = ser.reindex(new_index).interpolate(method="cubicspline").loc[1:3]
+        tm.assert_series_equal(result, expected)
+
+    def test_interpolate_pchip(self):
+        pytest.importorskip("scipy")
+        ser = Series(np.sort(np.random.default_rng(2).uniform(size=100)))
+
+        # interpolate at new_index
+        new_index = ser.index.union(
+            Index([49.25, 49.5, 49.75, 50.25, 50.5, 50.75])
+        ).astype(float)
+        interp_s = ser.reindex(new_index).interpolate(method="pchip")
+        # does not blow up, GH5977
+        interp_s.loc[49:51]
+
+    def test_interpolate_akima(self):
+        pytest.importorskip("scipy")
+        ser = Series([10, 11, 12, 13])
+
+        # interpolate at new_index where `der` is zero
+        expected = Series(
+            [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00],
+            index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
+        )
+        new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
+            float
+        )
+        interp_s = ser.reindex(new_index).interpolate(method="akima")
+        tm.assert_series_equal(interp_s.loc[1:3], expected)
+
+        # interpolate at new_index where `der` is a non-zero int
+        expected = Series(
+            [11.0, 1.0, 1.0, 1.0, 12.0, 1.0, 1.0, 1.0, 13.0],
+            index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
+        )
+        new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
+            float
+        )
+        interp_s = ser.reindex(new_index).interpolate(method="akima", der=1)
+        tm.assert_series_equal(interp_s.loc[1:3], expected)
+
+    def test_interpolate_piecewise_polynomial(self):
+        pytest.importorskip("scipy")
+        ser = Series([10, 11, 12, 13])
+
+        expected = Series(
+            [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00],
+            index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
+        )
+        # interpolate at new_index
+        new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
+            float
+        )
+        interp_s = ser.reindex(new_index).interpolate(method="piecewise_polynomial")
+        tm.assert_series_equal(interp_s.loc[1:3], expected)
+
+    def test_interpolate_from_derivatives(self):
+        pytest.importorskip("scipy")
+        ser = Series([10, 11, 12, 13])
+
+        expected = Series(
+            [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00],
+            index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
+        )
+        # interpolate at new_index
+        new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
+            float
+        )
+        interp_s = ser.reindex(new_index).interpolate(method="from_derivatives")
+        tm.assert_series_equal(interp_s.loc[1:3], expected)
+
+    @pytest.mark.parametrize(
+        "kwargs",
+        [
+            {},
+            pytest.param(
+                {"method": "polynomial", "order": 1}, marks=td.skip_if_no("scipy")
+            ),
+        ],
+    )
+    def test_interpolate_corners(self, kwargs):
+        s = Series([np.nan, np.nan])
+        tm.assert_series_equal(s.interpolate(**kwargs), s)
+
+        s = Series([], dtype=object).interpolate()
+        tm.assert_series_equal(s.interpolate(**kwargs), s)
+
+    def test_interpolate_index_values(self):
+        s = Series(np.nan, index=np.sort(np.random.default_rng(2).random(30)))
+        s.loc[::3] = np.random.default_rng(2).standard_normal(10)
+
+        vals = s.index.values.astype(float)
+
+        result = s.interpolate(method="index")
+
+        expected = s.copy()
+        bad = isna(expected.values)
+        good = ~bad
+        expected = Series(
+            np.interp(vals[bad], vals[good], s.values[good]), index=s.index[bad]
+        )
+
+        tm.assert_series_equal(result[bad], expected)
+
+        # 'values' is synonymous with 'index' for the method kwarg
+        other_result = s.interpolate(method="values")
+
+        tm.assert_series_equal(other_result, result)
+        tm.assert_series_equal(other_result[bad], expected)
+
+    def test_interpolate_non_ts(self):
+        s = Series([1, 3, np.nan, np.nan, np.nan, 11])
+        msg = (
+            "time-weighted interpolation only works on Series or DataFrames "
+            "with a DatetimeIndex"
+        )
+        with pytest.raises(ValueError, match=msg):
+            s.interpolate(method="time")
+
+    @pytest.mark.parametrize(
+        "kwargs",
+        [
+            {},
+            pytest.param(
+                {"method": "polynomial", "order": 1}, marks=td.skip_if_no("scipy")
+            ),
+        ],
+    )
+    def test_nan_interpolate(self, kwargs):
+        s = Series([0, 1, np.nan, 3])
+        result = s.interpolate(**kwargs)
+        expected = Series([0.0, 1.0, 2.0, 3.0])
+        tm.assert_series_equal(result, expected)
+
+    def test_nan_irregular_index(self):
+        s = Series([1, 2, np.nan, 4], index=[1, 3, 5, 9])
+        result = s.interpolate()
+        expected = Series([1.0, 2.0, 3.0, 4.0], index=[1, 3, 5, 9])
+        tm.assert_series_equal(result, expected)
+
+    def test_nan_str_index(self):
+        s = Series([0, 1, 2, np.nan], index=list("abcd"))
+        result = s.interpolate()
+        expected = Series([0.0, 1.0, 2.0, 2.0], index=list("abcd"))
+        tm.assert_series_equal(result, expected)
+
+    def test_interp_quad(self):
+        pytest.importorskip("scipy")
+        sq = Series([1, 4, np.nan, 16], index=[1, 2, 3, 4])
+        result = sq.interpolate(method="quadratic")
+        expected = Series([1.0, 4.0, 9.0, 16.0], index=[1, 2, 3, 4])
+        tm.assert_series_equal(result, expected)
+
+    def test_interp_scipy_basic(self):
+        pytest.importorskip("scipy")
+        s = Series([1, 3, np.nan, 12, np.nan, 25])
+        # slinear
+        expected = Series([1.0, 3.0, 7.5, 12.0, 18.5, 25.0])
+        result = s.interpolate(method="slinear")
+        tm.assert_series_equal(result, expected)
+
+        msg = "The 'downcast' keyword in Series.interpolate is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = s.interpolate(method="slinear", downcast="infer")
+        tm.assert_series_equal(result, expected)
+        # nearest
+        expected = Series([1, 3, 3, 12, 12, 25])
+        result = s.interpolate(method="nearest")
+        tm.assert_series_equal(result, expected.astype("float"))
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = s.interpolate(method="nearest", downcast="infer")
+        tm.assert_series_equal(result, expected)
+        # zero
+        expected = Series([1, 3, 3, 12, 12, 25])
+        result = s.interpolate(method="zero")
+        tm.assert_series_equal(result, expected.astype("float"))
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = s.interpolate(method="zero", downcast="infer")
+        tm.assert_series_equal(result, expected)
+        # quadratic
+        # GH #15662.
+        expected = Series([1, 3.0, 6.823529, 12.0, 18.058824, 25.0])
+        result = s.interpolate(method="quadratic")
+        tm.assert_series_equal(result, expected)
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = s.interpolate(method="quadratic", downcast="infer")
+        tm.assert_series_equal(result, expected)
+        # cubic
+        expected = Series([1.0, 3.0, 6.8, 12.0, 18.2, 25.0])
+        result = s.interpolate(method="cubic")
+        tm.assert_series_equal(result, expected)
+
+    def test_interp_limit(self):
+        s = Series([1, 3, np.nan, np.nan, np.nan, 11])
+
+        expected = Series([1.0, 3.0, 5.0, 7.0, np.nan, 11.0])
+        result = s.interpolate(method="linear", limit=2)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("limit", [-1, 0])
+    def test_interpolate_invalid_nonpositive_limit(self, nontemporal_method, limit):
+        # GH 9217: make sure limit is greater than zero.
+        s = Series([1, 2, np.nan, 4])
+        method, kwargs = nontemporal_method
+        with pytest.raises(ValueError, match="Limit must be greater than 0"):
+            s.interpolate(limit=limit, method=method, **kwargs)
+
+    def test_interpolate_invalid_float_limit(self, nontemporal_method):
+        # GH 9217: make sure limit is an integer.
+        s = Series([1, 2, np.nan, 4])
+        method, kwargs = nontemporal_method
+        limit = 2.0
+        with pytest.raises(ValueError, match="Limit must be an integer"):
+            s.interpolate(limit=limit, method=method, **kwargs)
+
+    @pytest.mark.parametrize("invalid_method", [None, "nonexistent_method"])
+    def test_interp_invalid_method(self, invalid_method):
+        s = Series([1, 3, np.nan, 12, np.nan, 25])
+
+        msg = f"method must be one of.* Got '{invalid_method}' instead"
+        if invalid_method is None:
+            msg = "'method' should be a string, not None"
+        with pytest.raises(ValueError, match=msg):
+            s.interpolate(method=invalid_method)
+
+        # When an invalid method and invalid limit (such as -1) are
+        # provided, the error message reflects the invalid method.
+        with pytest.raises(ValueError, match=msg):
+            s.interpolate(method=invalid_method, limit=-1)
+
+    def test_interp_invalid_method_and_value(self):
+        # GH#36624
+        ser = Series([1, 3, np.nan, 12, np.nan, 25])
+
+        msg = "'fill_value' is not a valid keyword for Series.interpolate"
+        msg2 = "Series.interpolate with method=pad"
+        with pytest.raises(ValueError, match=msg):
+            with tm.assert_produces_warning(FutureWarning, match=msg2):
+                ser.interpolate(fill_value=3, method="pad")
+
+    def test_interp_limit_forward(self):
+        s = Series([1, 3, np.nan, np.nan, np.nan, 11])
+
+        # Provide 'forward' (the default) explicitly here.
+        expected = Series([1.0, 3.0, 5.0, 7.0, np.nan, 11.0])
+
+        result = s.interpolate(method="linear", limit=2, limit_direction="forward")
+        tm.assert_series_equal(result, expected)
+
+        result = s.interpolate(method="linear", limit=2, limit_direction="FORWARD")
+        tm.assert_series_equal(result, expected)
+
+    def test_interp_unlimited(self):
+        # these test are for issue #16282 default Limit=None is unlimited
+        s = Series([np.nan, 1.0, 3.0, np.nan, np.nan, np.nan, 11.0, np.nan])
+        expected = Series([1.0, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 11.0])
+        result = s.interpolate(method="linear", limit_direction="both")
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([np.nan, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 11.0])
+        result = s.interpolate(method="linear", limit_direction="forward")
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([1.0, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, np.nan])
+        result = s.interpolate(method="linear", limit_direction="backward")
+        tm.assert_series_equal(result, expected)
+
+    def test_interp_limit_bad_direction(self):
+        s = Series([1, 3, np.nan, np.nan, np.nan, 11])
+
+        msg = (
+            r"Invalid limit_direction: expecting one of \['forward', "
+            r"'backward', 'both'\], got 'abc'"
+        )
+        with pytest.raises(ValueError, match=msg):
+            s.interpolate(method="linear", limit=2, limit_direction="abc")
+
+        # raises an error even if no limit is specified.
+        with pytest.raises(ValueError, match=msg):
+            s.interpolate(method="linear", limit_direction="abc")
+
+    # limit_area introduced GH #16284
+    def test_interp_limit_area(self):
+        # These tests are for issue #9218 -- fill NaNs in both directions.
+        s = Series([np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan])
+
+        expected = Series([np.nan, np.nan, 3.0, 4.0, 5.0, 6.0, 7.0, np.nan, np.nan])
+        result = s.interpolate(method="linear", limit_area="inside")
+        tm.assert_series_equal(result, expected)
+
+        expected = Series(
+            [np.nan, np.nan, 3.0, 4.0, np.nan, np.nan, 7.0, np.nan, np.nan]
+        )
+        result = s.interpolate(method="linear", limit_area="inside", limit=1)
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([np.nan, np.nan, 3.0, 4.0, np.nan, 6.0, 7.0, np.nan, np.nan])
+        result = s.interpolate(
+            method="linear", limit_area="inside", limit_direction="both", limit=1
+        )
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, 7.0])
+        result = s.interpolate(method="linear", limit_area="outside")
+        tm.assert_series_equal(result, expected)
+
+        expected = Series(
+            [np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, np.nan]
+        )
+        result = s.interpolate(method="linear", limit_area="outside", limit=1)
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([np.nan, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, np.nan])
+        result = s.interpolate(
+            method="linear", limit_area="outside", limit_direction="both", limit=1
+        )
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([3.0, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, np.nan, np.nan])
+        result = s.interpolate(
+            method="linear", limit_area="outside", limit_direction="backward"
+        )
+        tm.assert_series_equal(result, expected)
+
+        # raises an error even if limit type is wrong.
+        msg = r"Invalid limit_area: expecting one of \['inside', 'outside'\], got abc"
+        with pytest.raises(ValueError, match=msg):
+            s.interpolate(method="linear", limit_area="abc")
+
+    @pytest.mark.parametrize(
+        "method, limit_direction, expected",
+        [
+            ("pad", "backward", "forward"),
+            ("ffill", "backward", "forward"),
+            ("backfill", "forward", "backward"),
+            ("bfill", "forward", "backward"),
+            ("pad", "both", "forward"),
+            ("ffill", "both", "forward"),
+            ("backfill", "both", "backward"),
+            ("bfill", "both", "backward"),
+        ],
+    )
+    def test_interp_limit_direction_raises(self, method, limit_direction, expected):
+        # https://github.com/pandas-dev/pandas/pull/34746
+        s = Series([1, 2, 3])
+
+        msg = f"`limit_direction` must be '{expected}' for method `{method}`"
+        msg2 = "Series.interpolate with method="
+        with pytest.raises(ValueError, match=msg):
+            with tm.assert_produces_warning(FutureWarning, match=msg2):
+                s.interpolate(method=method, limit_direction=limit_direction)
+
+    @pytest.mark.parametrize(
+        "data, expected_data, kwargs",
+        (
+            (
+                [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
+                [np.nan, np.nan, 3.0, 3.0, 3.0, 3.0, 7.0, np.nan, np.nan],
+                {"method": "pad", "limit_area": "inside"},
+            ),
+            (
+                [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
+                [np.nan, np.nan, 3.0, 3.0, np.nan, np.nan, 7.0, np.nan, np.nan],
+                {"method": "pad", "limit_area": "inside", "limit": 1},
+            ),
+            (
+                [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
+                [np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, 7.0],
+                {"method": "pad", "limit_area": "outside"},
+            ),
+            (
+                [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
+                [np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, np.nan],
+                {"method": "pad", "limit_area": "outside", "limit": 1},
+            ),
+            (
+                [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
+                [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
+                {"method": "pad", "limit_area": "outside", "limit": 1},
+            ),
+            (
+                range(5),
+                range(5),
+                {"method": "pad", "limit_area": "outside", "limit": 1},
+            ),
+        ),
+    )
+    def test_interp_limit_area_with_pad(self, data, expected_data, kwargs):
+        # GH26796
+
+        s = Series(data)
+        expected = Series(expected_data)
+        msg = "Series.interpolate with method=pad"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = s.interpolate(**kwargs)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "data, expected_data, kwargs",
+        (
+            (
+                [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
+                [np.nan, np.nan, 3.0, 7.0, 7.0, 7.0, 7.0, np.nan, np.nan],
+                {"method": "bfill", "limit_area": "inside"},
+            ),
+            (
+                [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
+                [np.nan, np.nan, 3.0, np.nan, np.nan, 7.0, 7.0, np.nan, np.nan],
+                {"method": "bfill", "limit_area": "inside", "limit": 1},
+            ),
+            (
+                [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
+                [3.0, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, np.nan, np.nan],
+                {"method": "bfill", "limit_area": "outside"},
+            ),
+            (
+                [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
+                [np.nan, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, np.nan, np.nan],
+                {"method": "bfill", "limit_area": "outside", "limit": 1},
+            ),
+        ),
+    )
+    def test_interp_limit_area_with_backfill(self, data, expected_data, kwargs):
+        # GH26796
+
+        s = Series(data)
+        expected = Series(expected_data)
+        msg = "Series.interpolate with method=bfill"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = s.interpolate(**kwargs)
+        tm.assert_series_equal(result, expected)
+
+    def test_interp_limit_direction(self):
+        # These tests are for issue #9218 -- fill NaNs in both directions.
+        s = Series([1, 3, np.nan, np.nan, np.nan, 11])
+
+        expected = Series([1.0, 3.0, np.nan, 7.0, 9.0, 11.0])
+        result = s.interpolate(method="linear", limit=2, limit_direction="backward")
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([1.0, 3.0, 5.0, np.nan, 9.0, 11.0])
+        result = s.interpolate(method="linear", limit=1, limit_direction="both")
+        tm.assert_series_equal(result, expected)
+
+        # Check that this works on a longer series of nans.
+        s = Series([1, 3, np.nan, np.nan, np.nan, 7, 9, np.nan, np.nan, 12, np.nan])
+
+        expected = Series([1.0, 3.0, 4.0, 5.0, 6.0, 7.0, 9.0, 10.0, 11.0, 12.0, 12.0])
+        result = s.interpolate(method="linear", limit=2, limit_direction="both")
+        tm.assert_series_equal(result, expected)
+
+        expected = Series(
+            [1.0, 3.0, 4.0, np.nan, 6.0, 7.0, 9.0, 10.0, 11.0, 12.0, 12.0]
+        )
+        result = s.interpolate(method="linear", limit=1, limit_direction="both")
+        tm.assert_series_equal(result, expected)
+
+    def test_interp_limit_to_ends(self):
+        # These test are for issue #10420 -- flow back to beginning.
+        s = Series([np.nan, np.nan, 5, 7, 9, np.nan])
+
+        expected = Series([5.0, 5.0, 5.0, 7.0, 9.0, np.nan])
+        result = s.interpolate(method="linear", limit=2, limit_direction="backward")
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([5.0, 5.0, 5.0, 7.0, 9.0, 9.0])
+        result = s.interpolate(method="linear", limit=2, limit_direction="both")
+        tm.assert_series_equal(result, expected)
+
+    def test_interp_limit_before_ends(self):
+        # These test are for issue #11115 -- limit ends properly.
+        s = Series([np.nan, np.nan, 5, 7, np.nan, np.nan])
+
+        expected = Series([np.nan, np.nan, 5.0, 7.0, 7.0, np.nan])
+        result = s.interpolate(method="linear", limit=1, limit_direction="forward")
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([np.nan, 5.0, 5.0, 7.0, np.nan, np.nan])
+        result = s.interpolate(method="linear", limit=1, limit_direction="backward")
+        tm.assert_series_equal(result, expected)
+
+        expected = Series([np.nan, 5.0, 5.0, 7.0, 7.0, np.nan])
+        result = s.interpolate(method="linear", limit=1, limit_direction="both")
+        tm.assert_series_equal(result, expected)
+
+    def test_interp_all_good(self):
+        pytest.importorskip("scipy")
+        s = Series([1, 2, 3])
+        result = s.interpolate(method="polynomial", order=1)
+        tm.assert_series_equal(result, s)
+
+        # non-scipy
+        result = s.interpolate()
+        tm.assert_series_equal(result, s)
+
+    @pytest.mark.parametrize(
+        "check_scipy", [False, pytest.param(True, marks=td.skip_if_no("scipy"))]
+    )
+    def test_interp_multiIndex(self, check_scipy):
+        idx = MultiIndex.from_tuples([(0, "a"), (1, "b"), (2, "c")])
+        s = Series([1, 2, np.nan], index=idx)
+
+        expected = s.copy()
+        expected.loc[2] = 2
+        result = s.interpolate()
+        tm.assert_series_equal(result, expected)
+
+        msg = "Only `method=linear` interpolation is supported on MultiIndexes"
+        if check_scipy:
+            with pytest.raises(ValueError, match=msg):
+                s.interpolate(method="polynomial", order=1)
+
+    def test_interp_nonmono_raise(self):
+        pytest.importorskip("scipy")
+        s = Series([1, np.nan, 3], index=[0, 2, 1])
+        msg = "krogh interpolation requires that the index be monotonic"
+        with pytest.raises(ValueError, match=msg):
+            s.interpolate(method="krogh")
+
+    @pytest.mark.parametrize("method", ["nearest", "pad"])
+    def test_interp_datetime64(self, method, tz_naive_fixture):
+        pytest.importorskip("scipy")
+        df = Series(
+            [1, np.nan, 3], index=date_range("1/1/2000", periods=3, tz=tz_naive_fixture)
+        )
+        warn = None if method == "nearest" else FutureWarning
+        msg = "Series.interpolate with method=pad is deprecated"
+        with tm.assert_produces_warning(warn, match=msg):
+            result = df.interpolate(method=method)
+        if warn is not None:
+            # check the "use ffill instead" is equivalent
+            alt = df.ffill()
+            tm.assert_series_equal(result, alt)
+
+        expected = Series(
+            [1.0, 1.0, 3.0],
+            index=date_range("1/1/2000", periods=3, tz=tz_naive_fixture),
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_interp_pad_datetime64tz_values(self):
+        # GH#27628 missing.interpolate_2d should handle datetimetz values
+        dti = date_range("2015-04-05", periods=3, tz="US/Central")
+        ser = Series(dti)
+        ser[1] = pd.NaT
+
+        msg = "Series.interpolate with method=pad is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = ser.interpolate(method="pad")
+        # check the "use ffill instead" is equivalent
+        alt = ser.ffill()
+        tm.assert_series_equal(result, alt)
+
+        expected = Series(dti)
+        expected[1] = expected[0]
+        tm.assert_series_equal(result, expected)
+
+    def test_interp_limit_no_nans(self):
+        # GH 7173
+        s = Series([1.0, 2.0, 3.0])
+        result = s.interpolate(limit=1)
+        expected = s
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("method", ["polynomial", "spline"])
+    def test_no_order(self, method):
+        # see GH-10633, GH-24014
+        pytest.importorskip("scipy")
+        s = Series([0, 1, np.nan, 3])
+        msg = "You must specify the order of the spline or polynomial"
+        with pytest.raises(ValueError, match=msg):
+            s.interpolate(method=method)
+
+    @pytest.mark.parametrize("order", [-1, -1.0, 0, 0.0, np.nan])
+    def test_interpolate_spline_invalid_order(self, order):
+        pytest.importorskip("scipy")
+        s = Series([0, 1, np.nan, 3])
+        msg = "order needs to be specified and greater than 0"
+        with pytest.raises(ValueError, match=msg):
+            s.interpolate(method="spline", order=order)
+
+    def test_spline(self):
+        pytest.importorskip("scipy")
+        s = Series([1, 2, np.nan, 4, 5, np.nan, 7])
+        result = s.interpolate(method="spline", order=1)
+        expected = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0])
+        tm.assert_series_equal(result, expected)
+
+    def test_spline_extrapolate(self):
+        pytest.importorskip("scipy")
+        s = Series([1, 2, 3, 4, np.nan, 6, np.nan])
+        result3 = s.interpolate(method="spline", order=1, ext=3)
+        expected3 = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 6.0])
+        tm.assert_series_equal(result3, expected3)
+
+        result1 = s.interpolate(method="spline", order=1, ext=0)
+        expected1 = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0])
+        tm.assert_series_equal(result1, expected1)
+
+    def test_spline_smooth(self):
+        pytest.importorskip("scipy")
+        s = Series([1, 2, np.nan, 4, 5.1, np.nan, 7])
+        assert (
+            s.interpolate(method="spline", order=3, s=0)[5]
+            != s.interpolate(method="spline", order=3)[5]
+        )
+
+    def test_spline_interpolation(self):
+        # Explicit cast to float to avoid implicit cast when setting np.nan
+        pytest.importorskip("scipy")
+        s = Series(np.arange(10) ** 2, dtype="float")
+        s[np.random.default_rng(2).integers(0, 9, 3)] = np.nan
+        result1 = s.interpolate(method="spline", order=1)
+        expected1 = s.interpolate(method="spline", order=1)
+        tm.assert_series_equal(result1, expected1)
+
+    def test_interp_timedelta64(self):
+        # GH 6424
+        df = Series([1, np.nan, 3], index=pd.to_timedelta([1, 2, 3]))
+        result = df.interpolate(method="time")
+        expected = Series([1.0, 2.0, 3.0], index=pd.to_timedelta([1, 2, 3]))
+        tm.assert_series_equal(result, expected)
+
+        # test for non uniform spacing
+        df = Series([1, np.nan, 3], index=pd.to_timedelta([1, 2, 4]))
+        result = df.interpolate(method="time")
+        expected = Series([1.0, 1.666667, 3.0], index=pd.to_timedelta([1, 2, 4]))
+        tm.assert_series_equal(result, expected)
+
+    def test_series_interpolate_method_values(self):
+        # GH#1646
+        rng = date_range("1/1/2000", "1/20/2000", freq="D")
+        ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng)
+
+        ts[::2] = np.nan
+
+        result = ts.interpolate(method="values")
+        exp = ts.interpolate()
+        tm.assert_series_equal(result, exp)
+
+    def test_series_interpolate_intraday(self):
+        # #1698
+        index = date_range("1/1/2012", periods=4, freq="12D")
+        ts = Series([0, 12, 24, 36], index)
+        new_index = index.append(index + pd.DateOffset(days=1)).sort_values()
+
+        exp = ts.reindex(new_index).interpolate(method="time")
+
+        index = date_range("1/1/2012", periods=4, freq="12h")
+        ts = Series([0, 12, 24, 36], index)
+        new_index = index.append(index + pd.DateOffset(hours=1)).sort_values()
+        result = ts.reindex(new_index).interpolate(method="time")
+
+        tm.assert_numpy_array_equal(result.values, exp.values)
+
+    @pytest.mark.parametrize(
+        "ind",
+        [
+            ["a", "b", "c", "d"],
+            pd.period_range(start="2019-01-01", periods=4),
+            pd.interval_range(start=0, end=4),
+        ],
+    )
+    def test_interp_non_timedelta_index(self, interp_methods_ind, ind):
+        # gh 21662
+        df = pd.DataFrame([0, 1, np.nan, 3], index=ind)
+
+        method, kwargs = interp_methods_ind
+        if method == "pchip":
+            pytest.importorskip("scipy")
+
+        if method == "linear":
+            result = df[0].interpolate(**kwargs)
+            expected = Series([0.0, 1.0, 2.0, 3.0], name=0, index=ind)
+            tm.assert_series_equal(result, expected)
+        else:
+            expected_error = (
+                "Index column must be numeric or datetime type when "
+                f"using {method} method other than linear. "
+                "Try setting a numeric or datetime index column before "
+                "interpolating."
+            )
+            with pytest.raises(ValueError, match=expected_error):
+                df[0].interpolate(method=method, **kwargs)
+
+    def test_interpolate_timedelta_index(self, request, interp_methods_ind):
+        """
+        Tests for non numerical index types  - object, period, timedelta
+        Note that all methods except time, index, nearest and values
+        are tested here.
+        """
+        # gh 21662
+        pytest.importorskip("scipy")
+        ind = pd.timedelta_range(start=1, periods=4)
+        df = pd.DataFrame([0, 1, np.nan, 3], index=ind)
+
+        method, kwargs = interp_methods_ind
+
+        if method in {"cubic", "zero"}:
+            request.applymarker(
+                pytest.mark.xfail(
+                    reason=f"{method} interpolation is not supported for TimedeltaIndex"
+                )
+            )
+        result = df[0].interpolate(method=method, **kwargs)
+        expected = Series([0.0, 1.0, 2.0, 3.0], name=0, index=ind)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "ascending, expected_values",
+        [(True, [1, 2, 3, 9, 10]), (False, [10, 9, 3, 2, 1])],
+    )
+    def test_interpolate_unsorted_index(self, ascending, expected_values):
+        # GH 21037
+        ts = Series(data=[10, 9, np.nan, 2, 1], index=[10, 9, 3, 2, 1])
+        result = ts.sort_index(ascending=ascending).interpolate(method="index")
+        expected = Series(data=expected_values, index=expected_values, dtype=float)
+        tm.assert_series_equal(result, expected)
+
+    def test_interpolate_asfreq_raises(self):
+        ser = Series(["a", None, "b"], dtype=object)
+        msg2 = "Series.interpolate with object dtype"
+        msg = "Invalid fill method"
+        with pytest.raises(ValueError, match=msg):
+            with tm.assert_produces_warning(FutureWarning, match=msg2):
+                ser.interpolate(method="asfreq")
+
+    def test_interpolate_fill_value(self):
+        # GH#54920
+        pytest.importorskip("scipy")
+        ser = Series([np.nan, 0, 1, np.nan, 3, np.nan])
+        result = ser.interpolate(method="nearest", fill_value=0)
+        expected = Series([np.nan, 0, 1, 1, 3, 0])
+        tm.assert_series_equal(result, expected)