Traktor/myenv/Lib/site-packages/pandas/tests/window/test_expanding.py

import numpy as np
import pytest

from pandas import (
    DataFrame,
    DatetimeIndex,
    Index,
    MultiIndex,
    Series,
    isna,
    notna,
)
import pandas._testing as tm


def test_doc_string():
    df = DataFrame({"B": [0, 1, 2, np.nan, 4]})
    df
    df.expanding(2).sum()


def test_constructor(frame_or_series):
    # GH 12669

    c = frame_or_series(range(5)).expanding

    # valid
    c(min_periods=1)


@pytest.mark.parametrize("w", [2.0, "foo", np.array([2])])
def test_constructor_invalid(frame_or_series, w):
    # not valid

    c = frame_or_series(range(5)).expanding
    msg = "min_periods must be an integer"
    with pytest.raises(ValueError, match=msg):
        c(min_periods=w)


@pytest.mark.parametrize(
    "expander",
    [
        1,
        pytest.param(
            "ls",
            marks=pytest.mark.xfail(
                reason="GH#16425 expanding with offset not supported"
            ),
        ),
    ],
)
def test_empty_df_expanding(expander):
    # GH 15819 Verifies that datetime and integer expanding windows can be
    # applied to empty DataFrames

    expected = DataFrame()
    result = DataFrame().expanding(expander).sum()
    tm.assert_frame_equal(result, expected)

    # Verifies that datetime and integer expanding windows can be applied
    # to empty DataFrames with datetime index
    expected = DataFrame(index=DatetimeIndex([]))
    result = DataFrame(index=DatetimeIndex([])).expanding(expander).sum()
    tm.assert_frame_equal(result, expected)


def test_missing_minp_zero():
    # https://github.com/pandas-dev/pandas/pull/18921
    # minp=0
    x = Series([np.nan])
    result = x.expanding(min_periods=0).sum()
    expected = Series([0.0])
    tm.assert_series_equal(result, expected)

    # minp=1
    result = x.expanding(min_periods=1).sum()
    expected = Series([np.nan])
    tm.assert_series_equal(result, expected)


def test_expanding_axis(axis_frame):
    # see gh-23372.
    df = DataFrame(np.ones((10, 20)))
    axis = df._get_axis_number(axis_frame)

    if axis == 0:
        msg = "The 'axis' keyword in DataFrame.expanding is deprecated"
        expected = DataFrame(
            {i: [np.nan] * 2 + [float(j) for j in range(3, 11)] for i in range(20)}
        )
    else:
        # axis == 1
        msg = "Support for axis=1 in DataFrame.expanding is deprecated"
        expected = DataFrame([[np.nan] * 2 + [float(i) for i in range(3, 21)]] * 10)

    with tm.assert_produces_warning(FutureWarning, match=msg):
        result = df.expanding(3, axis=axis_frame).sum()
    tm.assert_frame_equal(result, expected)


def test_expanding_count_with_min_periods(frame_or_series):
    # GH 26996
    result = frame_or_series(range(5)).expanding(min_periods=3).count()
    expected = frame_or_series([np.nan, np.nan, 3.0, 4.0, 5.0])
    tm.assert_equal(result, expected)


def test_expanding_count_default_min_periods_with_null_values(frame_or_series):
    # GH 26996
    values = [1, 2, 3, np.nan, 4, 5, 6]
    expected_counts = [1.0, 2.0, 3.0, 3.0, 4.0, 5.0, 6.0]

    result = frame_or_series(values).expanding().count()
    expected = frame_or_series(expected_counts)
    tm.assert_equal(result, expected)


def test_expanding_count_with_min_periods_exceeding_series_length(frame_or_series):
    # GH 25857
    result = frame_or_series(range(5)).expanding(min_periods=6).count()
    expected = frame_or_series([np.nan, np.nan, np.nan, np.nan, np.nan])
    tm.assert_equal(result, expected)


@pytest.mark.parametrize(
    "df,expected,min_periods",
    [
        (
            DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}),
            [
                ({"A": [1], "B": [4]}, [0]),
                ({"A": [1, 2], "B": [4, 5]}, [0, 1]),
                ({"A": [1, 2, 3], "B": [4, 5, 6]}, [0, 1, 2]),
            ],
            3,
        ),
        (
            DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}),
            [
                ({"A": [1], "B": [4]}, [0]),
                ({"A": [1, 2], "B": [4, 5]}, [0, 1]),
                ({"A": [1, 2, 3], "B": [4, 5, 6]}, [0, 1, 2]),
            ],
            2,
        ),
        (
            DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}),
            [
                ({"A": [1], "B": [4]}, [0]),
                ({"A": [1, 2], "B": [4, 5]}, [0, 1]),
                ({"A": [1, 2, 3], "B": [4, 5, 6]}, [0, 1, 2]),
            ],
            1,
        ),
        (DataFrame({"A": [1], "B": [4]}), [], 2),
        (DataFrame(), [({}, [])], 1),
        (
            DataFrame({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}),
            [
                ({"A": [1.0], "B": [np.nan]}, [0]),
                ({"A": [1, np.nan], "B": [np.nan, 5]}, [0, 1]),
                ({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}, [0, 1, 2]),
            ],
            3,
        ),
        (
            DataFrame({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}),
            [
                ({"A": [1.0], "B": [np.nan]}, [0]),
                ({"A": [1, np.nan], "B": [np.nan, 5]}, [0, 1]),
                ({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}, [0, 1, 2]),
            ],
            2,
        ),
        (
            DataFrame({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}),
            [
                ({"A": [1.0], "B": [np.nan]}, [0]),
                ({"A": [1, np.nan], "B": [np.nan, 5]}, [0, 1]),
                ({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}, [0, 1, 2]),
            ],
            1,
        ),
    ],
)
def test_iter_expanding_dataframe(df, expected, min_periods):
    # GH 11704
    expected = [DataFrame(values, index=index) for (values, index) in expected]

    for expected, actual in zip(expected, df.expanding(min_periods)):
        tm.assert_frame_equal(actual, expected)


@pytest.mark.parametrize(
    "ser,expected,min_periods",
    [
        (Series([1, 2, 3]), [([1], [0]), ([1, 2], [0, 1]), ([1, 2, 3], [0, 1, 2])], 3),
        (Series([1, 2, 3]), [([1], [0]), ([1, 2], [0, 1]), ([1, 2, 3], [0, 1, 2])], 2),
        (Series([1, 2, 3]), [([1], [0]), ([1, 2], [0, 1]), ([1, 2, 3], [0, 1, 2])], 1),
        (Series([1, 2]), [([1], [0]), ([1, 2], [0, 1])], 2),
        (Series([np.nan, 2]), [([np.nan], [0]), ([np.nan, 2], [0, 1])], 2),
        (Series([], dtype="int64"), [], 2),
    ],
)
def test_iter_expanding_series(ser, expected, min_periods):
    # GH 11704
    expected = [Series(values, index=index) for (values, index) in expected]

    for expected, actual in zip(expected, ser.expanding(min_periods)):
        tm.assert_series_equal(actual, expected)


def test_center_invalid():
    # GH 20647
    df = DataFrame()
    with pytest.raises(TypeError, match=".* got an unexpected keyword"):
        df.expanding(center=True)


def test_expanding_sem(frame_or_series):
    # GH: 26476
    obj = frame_or_series([0, 1, 2])
    result = obj.expanding().sem()
    if isinstance(result, DataFrame):
        result = Series(result[0].values)
    expected = Series([np.nan] + [0.707107] * 2)
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("method", ["skew", "kurt"])
def test_expanding_skew_kurt_numerical_stability(method):
    # GH: 6929
    s = Series(np.random.default_rng(2).random(10))
    expected = getattr(s.expanding(3), method)()
    s = s + 5000
    result = getattr(s.expanding(3), method)()
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("window", [1, 3, 10, 20])
@pytest.mark.parametrize("method", ["min", "max", "average"])
@pytest.mark.parametrize("pct", [True, False])
@pytest.mark.parametrize("ascending", [True, False])
@pytest.mark.parametrize("test_data", ["default", "duplicates", "nans"])
def test_rank(window, method, pct, ascending, test_data):
    length = 20
    if test_data == "default":
        ser = Series(data=np.random.default_rng(2).random(length))
    elif test_data == "duplicates":
        ser = Series(data=np.random.default_rng(2).choice(3, length))
    elif test_data == "nans":
        ser = Series(
            data=np.random.default_rng(2).choice(
                [1.0, 0.25, 0.75, np.nan, np.inf, -np.inf], length
            )
        )

    expected = ser.expanding(window).apply(
        lambda x: x.rank(method=method, pct=pct, ascending=ascending).iloc[-1]
    )
    result = ser.expanding(window).rank(method=method, pct=pct, ascending=ascending)

    tm.assert_series_equal(result, expected)


def test_expanding_corr(series):
    A = series.dropna()
    B = (A + np.random.default_rng(2).standard_normal(len(A)))[:-5]

    result = A.expanding().corr(B)

    rolling_result = A.rolling(window=len(A), min_periods=1).corr(B)

    tm.assert_almost_equal(rolling_result, result)


def test_expanding_count(series):
    result = series.expanding(min_periods=0).count()
    tm.assert_almost_equal(
        result, series.rolling(window=len(series), min_periods=0).count()
    )


def test_expanding_quantile(series):
    result = series.expanding().quantile(0.5)

    rolling_result = series.rolling(window=len(series), min_periods=1).quantile(0.5)

    tm.assert_almost_equal(result, rolling_result)


def test_expanding_cov(series):
    A = series
    B = (A + np.random.default_rng(2).standard_normal(len(A)))[:-5]

    result = A.expanding().cov(B)

    rolling_result = A.rolling(window=len(A), min_periods=1).cov(B)

    tm.assert_almost_equal(rolling_result, result)


def test_expanding_cov_pairwise(frame):
    result = frame.expanding().cov()

    rolling_result = frame.rolling(window=len(frame), min_periods=1).cov()

    tm.assert_frame_equal(result, rolling_result)


def test_expanding_corr_pairwise(frame):
    result = frame.expanding().corr()

    rolling_result = frame.rolling(window=len(frame), min_periods=1).corr()
    tm.assert_frame_equal(result, rolling_result)


@pytest.mark.parametrize(
    "func,static_comp",
    [
        ("sum", np.sum),
        ("mean", lambda x: np.mean(x, axis=0)),
        ("max", lambda x: np.max(x, axis=0)),
        ("min", lambda x: np.min(x, axis=0)),
    ],
    ids=["sum", "mean", "max", "min"],
)
def test_expanding_func(func, static_comp, frame_or_series):
    data = frame_or_series(np.array(list(range(10)) + [np.nan] * 10))

    msg = "The 'axis' keyword in (Series|DataFrame).expanding is deprecated"
    with tm.assert_produces_warning(FutureWarning, match=msg):
        obj = data.expanding(min_periods=1, axis=0)
    result = getattr(obj, func)()
    assert isinstance(result, frame_or_series)

    msg = "The behavior of DataFrame.sum with axis=None is deprecated"
    warn = None
    if frame_or_series is DataFrame and static_comp is np.sum:
        warn = FutureWarning
    with tm.assert_produces_warning(warn, match=msg, check_stacklevel=False):
        expected = static_comp(data[:11])
    if frame_or_series is Series:
        tm.assert_almost_equal(result[10], expected)
    else:
        tm.assert_series_equal(result.iloc[10], expected, check_names=False)


@pytest.mark.parametrize(
    "func,static_comp",
    [("sum", np.sum), ("mean", np.mean), ("max", np.max), ("min", np.min)],
    ids=["sum", "mean", "max", "min"],
)
def test_expanding_min_periods(func, static_comp):
    ser = Series(np.random.default_rng(2).standard_normal(50))

    msg = "The 'axis' keyword in Series.expanding is deprecated"
    with tm.assert_produces_warning(FutureWarning, match=msg):
        result = getattr(ser.expanding(min_periods=30, axis=0), func)()
    assert result[:29].isna().all()
    tm.assert_almost_equal(result.iloc[-1], static_comp(ser[:50]))

    # min_periods is working correctly
    with tm.assert_produces_warning(FutureWarning, match=msg):
        result = getattr(ser.expanding(min_periods=15, axis=0), func)()
    assert isna(result.iloc[13])
    assert notna(result.iloc[14])

    ser2 = Series(np.random.default_rng(2).standard_normal(20))
    with tm.assert_produces_warning(FutureWarning, match=msg):
        result = getattr(ser2.expanding(min_periods=5, axis=0), func)()
    assert isna(result[3])
    assert notna(result[4])

    # min_periods=0
    with tm.assert_produces_warning(FutureWarning, match=msg):
        result0 = getattr(ser.expanding(min_periods=0, axis=0), func)()
    with tm.assert_produces_warning(FutureWarning, match=msg):
        result1 = getattr(ser.expanding(min_periods=1, axis=0), func)()
    tm.assert_almost_equal(result0, result1)

    with tm.assert_produces_warning(FutureWarning, match=msg):
        result = getattr(ser.expanding(min_periods=1, axis=0), func)()
    tm.assert_almost_equal(result.iloc[-1], static_comp(ser[:50]))


def test_expanding_apply(engine_and_raw, frame_or_series):
    engine, raw = engine_and_raw
    data = frame_or_series(np.array(list(range(10)) + [np.nan] * 10))
    result = data.expanding(min_periods=1).apply(
        lambda x: x.mean(), raw=raw, engine=engine
    )
    assert isinstance(result, frame_or_series)

    if frame_or_series is Series:
        tm.assert_almost_equal(result[9], np.mean(data[:11], axis=0))
    else:
        tm.assert_series_equal(
            result.iloc[9], np.mean(data[:11], axis=0), check_names=False
        )


def test_expanding_min_periods_apply(engine_and_raw):
    engine, raw = engine_and_raw
    ser = Series(np.random.default_rng(2).standard_normal(50))

    result = ser.expanding(min_periods=30).apply(
        lambda x: x.mean(), raw=raw, engine=engine
    )
    assert result[:29].isna().all()
    tm.assert_almost_equal(result.iloc[-1], np.mean(ser[:50]))

    # min_periods is working correctly
    result = ser.expanding(min_periods=15).apply(
        lambda x: x.mean(), raw=raw, engine=engine
    )
    assert isna(result.iloc[13])
    assert notna(result.iloc[14])

    ser2 = Series(np.random.default_rng(2).standard_normal(20))
    result = ser2.expanding(min_periods=5).apply(
        lambda x: x.mean(), raw=raw, engine=engine
    )
    assert isna(result[3])
    assert notna(result[4])

    # min_periods=0
    result0 = ser.expanding(min_periods=0).apply(
        lambda x: x.mean(), raw=raw, engine=engine
    )
    result1 = ser.expanding(min_periods=1).apply(
        lambda x: x.mean(), raw=raw, engine=engine
    )
    tm.assert_almost_equal(result0, result1)

    result = ser.expanding(min_periods=1).apply(
        lambda x: x.mean(), raw=raw, engine=engine
    )
    tm.assert_almost_equal(result.iloc[-1], np.mean(ser[:50]))


@pytest.mark.parametrize(
    "f",
    [
        lambda x: (x.expanding(min_periods=5).cov(x, pairwise=True)),
        lambda x: (x.expanding(min_periods=5).corr(x, pairwise=True)),
    ],
)
def test_moment_functions_zero_length_pairwise(f):
    df1 = DataFrame()
    df2 = DataFrame(columns=Index(["a"], name="foo"), index=Index([], name="bar"))
    df2["a"] = df2["a"].astype("float64")

    df1_expected = DataFrame(index=MultiIndex.from_product([df1.index, df1.columns]))
    df2_expected = DataFrame(
        index=MultiIndex.from_product([df2.index, df2.columns], names=["bar", "foo"]),
        columns=Index(["a"], name="foo"),
        dtype="float64",
    )

    df1_result = f(df1)
    tm.assert_frame_equal(df1_result, df1_expected)

    df2_result = f(df2)
    tm.assert_frame_equal(df2_result, df2_expected)


@pytest.mark.parametrize(
    "f",
    [
        lambda x: x.expanding().count(),
        lambda x: x.expanding(min_periods=5).cov(x, pairwise=False),
        lambda x: x.expanding(min_periods=5).corr(x, pairwise=False),
        lambda x: x.expanding(min_periods=5).max(),
        lambda x: x.expanding(min_periods=5).min(),
        lambda x: x.expanding(min_periods=5).sum(),
        lambda x: x.expanding(min_periods=5).mean(),
        lambda x: x.expanding(min_periods=5).std(),
        lambda x: x.expanding(min_periods=5).var(),
        lambda x: x.expanding(min_periods=5).skew(),
        lambda x: x.expanding(min_periods=5).kurt(),
        lambda x: x.expanding(min_periods=5).quantile(0.5),
        lambda x: x.expanding(min_periods=5).median(),
        lambda x: x.expanding(min_periods=5).apply(sum, raw=False),
        lambda x: x.expanding(min_periods=5).apply(sum, raw=True),
    ],
)
def test_moment_functions_zero_length(f):
    # GH 8056
    s = Series(dtype=np.float64)
    s_expected = s
    df1 = DataFrame()
    df1_expected = df1
    df2 = DataFrame(columns=["a"])
    df2["a"] = df2["a"].astype("float64")
    df2_expected = df2

    s_result = f(s)
    tm.assert_series_equal(s_result, s_expected)

    df1_result = f(df1)
    tm.assert_frame_equal(df1_result, df1_expected)

    df2_result = f(df2)
    tm.assert_frame_equal(df2_result, df2_expected)


def test_expanding_apply_empty_series(engine_and_raw):
    engine, raw = engine_and_raw
    ser = Series([], dtype=np.float64)
    tm.assert_series_equal(
        ser, ser.expanding().apply(lambda x: x.mean(), raw=raw, engine=engine)
    )


def test_expanding_apply_min_periods_0(engine_and_raw):
    # GH 8080
    engine, raw = engine_and_raw
    s = Series([None, None, None])
    result = s.expanding(min_periods=0).apply(lambda x: len(x), raw=raw, engine=engine)
    expected = Series([1.0, 2.0, 3.0])
    tm.assert_series_equal(result, expected)


def test_expanding_cov_diff_index():
    # GH 7512
    s1 = Series([1, 2, 3], index=[0, 1, 2])
    s2 = Series([1, 3], index=[0, 2])
    result = s1.expanding().cov(s2)
    expected = Series([None, None, 2.0])
    tm.assert_series_equal(result, expected)

    s2a = Series([1, None, 3], index=[0, 1, 2])
    result = s1.expanding().cov(s2a)
    tm.assert_series_equal(result, expected)

    s1 = Series([7, 8, 10], index=[0, 1, 3])
    s2 = Series([7, 9, 10], index=[0, 2, 3])
    result = s1.expanding().cov(s2)
    expected = Series([None, None, None, 4.5])
    tm.assert_series_equal(result, expected)


def test_expanding_corr_diff_index():
    # GH 7512
    s1 = Series([1, 2, 3], index=[0, 1, 2])
    s2 = Series([1, 3], index=[0, 2])
    result = s1.expanding().corr(s2)
    expected = Series([None, None, 1.0])
    tm.assert_series_equal(result, expected)

    s2a = Series([1, None, 3], index=[0, 1, 2])
    result = s1.expanding().corr(s2a)
    tm.assert_series_equal(result, expected)

    s1 = Series([7, 8, 10], index=[0, 1, 3])
    s2 = Series([7, 9, 10], index=[0, 2, 3])
    result = s1.expanding().corr(s2)
    expected = Series([None, None, None, 1.0])
    tm.assert_series_equal(result, expected)


def test_expanding_cov_pairwise_diff_length():
    # GH 7512
    df1 = DataFrame([[1, 5], [3, 2], [3, 9]], columns=Index(["A", "B"], name="foo"))
    df1a = DataFrame(
        [[1, 5], [3, 9]], index=[0, 2], columns=Index(["A", "B"], name="foo")
    )
    df2 = DataFrame(
        [[5, 6], [None, None], [2, 1]], columns=Index(["X", "Y"], name="foo")
    )
    df2a = DataFrame(
        [[5, 6], [2, 1]], index=[0, 2], columns=Index(["X", "Y"], name="foo")
    )
    # TODO: xref gh-15826
    # .loc is not preserving the names
    result1 = df1.expanding().cov(df2, pairwise=True).loc[2]
    result2 = df1.expanding().cov(df2a, pairwise=True).loc[2]
    result3 = df1a.expanding().cov(df2, pairwise=True).loc[2]
    result4 = df1a.expanding().cov(df2a, pairwise=True).loc[2]
    expected = DataFrame(
        [[-3.0, -6.0], [-5.0, -10.0]],
        columns=Index(["A", "B"], name="foo"),
        index=Index(["X", "Y"], name="foo"),
    )
    tm.assert_frame_equal(result1, expected)
    tm.assert_frame_equal(result2, expected)
    tm.assert_frame_equal(result3, expected)
    tm.assert_frame_equal(result4, expected)


def test_expanding_corr_pairwise_diff_length():
    # GH 7512
    df1 = DataFrame(
        [[1, 2], [3, 2], [3, 4]], columns=["A", "B"], index=Index(range(3), name="bar")
    )
    df1a = DataFrame(
        [[1, 2], [3, 4]], index=Index([0, 2], name="bar"), columns=["A", "B"]
    )
    df2 = DataFrame(
        [[5, 6], [None, None], [2, 1]],
        columns=["X", "Y"],
        index=Index(range(3), name="bar"),
    )
    df2a = DataFrame(
        [[5, 6], [2, 1]], index=Index([0, 2], name="bar"), columns=["X", "Y"]
    )
    result1 = df1.expanding().corr(df2, pairwise=True).loc[2]
    result2 = df1.expanding().corr(df2a, pairwise=True).loc[2]
    result3 = df1a.expanding().corr(df2, pairwise=True).loc[2]
    result4 = df1a.expanding().corr(df2a, pairwise=True).loc[2]
    expected = DataFrame(
        [[-1.0, -1.0], [-1.0, -1.0]], columns=["A", "B"], index=Index(["X", "Y"])
    )
    tm.assert_frame_equal(result1, expected)
    tm.assert_frame_equal(result2, expected)
    tm.assert_frame_equal(result3, expected)
    tm.assert_frame_equal(result4, expected)


def test_expanding_apply_args_kwargs(engine_and_raw):
    def mean_w_arg(x, const):
        return np.mean(x) + const

    engine, raw = engine_and_raw

    df = DataFrame(np.random.default_rng(2).random((20, 3)))

    expected = df.expanding().apply(np.mean, engine=engine, raw=raw) + 20.0

    result = df.expanding().apply(mean_w_arg, engine=engine, raw=raw, args=(20,))
    tm.assert_frame_equal(result, expected)

    result = df.expanding().apply(mean_w_arg, raw=raw, kwargs={"const": 20})
    tm.assert_frame_equal(result, expected)


def test_numeric_only_frame(arithmetic_win_operators, numeric_only):
    # GH#46560
    kernel = arithmetic_win_operators
    df = DataFrame({"a": [1], "b": 2, "c": 3})
    df["c"] = df["c"].astype(object)
    expanding = df.expanding()
    op = getattr(expanding, kernel, None)
    if op is not None:
        result = op(numeric_only=numeric_only)

        columns = ["a", "b"] if numeric_only else ["a", "b", "c"]
        expected = df[columns].agg([kernel]).reset_index(drop=True).astype(float)
        assert list(expected.columns) == columns

        tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize("kernel", ["corr", "cov"])
@pytest.mark.parametrize("use_arg", [True, False])
def test_numeric_only_corr_cov_frame(kernel, numeric_only, use_arg):
    # GH#46560
    df = DataFrame({"a": [1, 2, 3], "b": 2, "c": 3})
    df["c"] = df["c"].astype(object)
    arg = (df,) if use_arg else ()
    expanding = df.expanding()
    op = getattr(expanding, kernel)
    result = op(*arg, numeric_only=numeric_only)

    # Compare result to op using float dtypes, dropping c when numeric_only is True
    columns = ["a", "b"] if numeric_only else ["a", "b", "c"]
    df2 = df[columns].astype(float)
    arg2 = (df2,) if use_arg else ()
    expanding2 = df2.expanding()
    op2 = getattr(expanding2, kernel)
    expected = op2(*arg2, numeric_only=numeric_only)

    tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize("dtype", [int, object])
def test_numeric_only_series(arithmetic_win_operators, numeric_only, dtype):
    # GH#46560
    kernel = arithmetic_win_operators
    ser = Series([1], dtype=dtype)
    expanding = ser.expanding()
    op = getattr(expanding, kernel)
    if numeric_only and dtype is object:
        msg = f"Expanding.{kernel} does not implement numeric_only"
        with pytest.raises(NotImplementedError, match=msg):
            op(numeric_only=numeric_only)
    else:
        result = op(numeric_only=numeric_only)
        expected = ser.agg([kernel]).reset_index(drop=True).astype(float)
        tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("kernel", ["corr", "cov"])
@pytest.mark.parametrize("use_arg", [True, False])
@pytest.mark.parametrize("dtype", [int, object])
def test_numeric_only_corr_cov_series(kernel, use_arg, numeric_only, dtype):
    # GH#46560
    ser = Series([1, 2, 3], dtype=dtype)
    arg = (ser,) if use_arg else ()
    expanding = ser.expanding()
    op = getattr(expanding, kernel)
    if numeric_only and dtype is object:
        msg = f"Expanding.{kernel} does not implement numeric_only"
        with pytest.raises(NotImplementedError, match=msg):
            op(*arg, numeric_only=numeric_only)
    else:
        result = op(*arg, numeric_only=numeric_only)

        ser2 = ser.astype(float)
        arg2 = (ser2,) if use_arg else ()
        expanding2 = ser2.expanding()
        op2 = getattr(expanding2, kernel)
        expected = op2(*arg2, numeric_only=numeric_only)
        tm.assert_series_equal(result, expected)


def test_keyword_quantile_deprecated():
    # GH #52550
    ser = Series([1, 2, 3, 4])
    with tm.assert_produces_warning(FutureWarning):
        ser.expanding().quantile(quantile=0.5)