from functools import partial
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import numpy as np
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import pytest
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import pandas.util._test_decorators as td
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from pandas import (
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DataFrame,
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Series,
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concat,
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isna,
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notna,
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)
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import pandas._testing as tm
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from pandas.tseries import offsets
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@td.skip_if_no_scipy
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@pytest.mark.parametrize("sp_func, roll_func", [["kurtosis", "kurt"], ["skew", "skew"]])
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def test_series(series, sp_func, roll_func):
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import scipy.stats
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compare_func = partial(getattr(scipy.stats, sp_func), bias=False)
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result = getattr(series.rolling(50), roll_func)()
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assert isinstance(result, Series)
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tm.assert_almost_equal(result.iloc[-1], compare_func(series[-50:]))
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@td.skip_if_no_scipy
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@pytest.mark.parametrize("sp_func, roll_func", [["kurtosis", "kurt"], ["skew", "skew"]])
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def test_frame(raw, frame, sp_func, roll_func):
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import scipy.stats
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compare_func = partial(getattr(scipy.stats, sp_func), bias=False)
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result = getattr(frame.rolling(50), roll_func)()
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assert isinstance(result, DataFrame)
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tm.assert_series_equal(
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result.iloc[-1, :],
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frame.iloc[-50:, :].apply(compare_func, axis=0, raw=raw),
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check_names=False,
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)
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@td.skip_if_no_scipy
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@pytest.mark.parametrize("sp_func, roll_func", [["kurtosis", "kurt"], ["skew", "skew"]])
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def test_time_rule_series(series, sp_func, roll_func):
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import scipy.stats
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compare_func = partial(getattr(scipy.stats, sp_func), bias=False)
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win = 25
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ser = series[::2].resample("B").mean()
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series_result = getattr(ser.rolling(window=win, min_periods=10), roll_func)()
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last_date = series_result.index[-1]
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prev_date = last_date - 24 * offsets.BDay()
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trunc_series = series[::2].truncate(prev_date, last_date)
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tm.assert_almost_equal(series_result[-1], compare_func(trunc_series))
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@td.skip_if_no_scipy
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@pytest.mark.parametrize("sp_func, roll_func", [["kurtosis", "kurt"], ["skew", "skew"]])
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def test_time_rule_frame(raw, frame, sp_func, roll_func):
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import scipy.stats
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compare_func = partial(getattr(scipy.stats, sp_func), bias=False)
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win = 25
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frm = frame[::2].resample("B").mean()
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frame_result = getattr(frm.rolling(window=win, min_periods=10), roll_func)()
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last_date = frame_result.index[-1]
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prev_date = last_date - 24 * offsets.BDay()
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trunc_frame = frame[::2].truncate(prev_date, last_date)
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tm.assert_series_equal(
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frame_result.xs(last_date),
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trunc_frame.apply(compare_func, raw=raw),
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check_names=False,
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)
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@td.skip_if_no_scipy
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@pytest.mark.parametrize("sp_func, roll_func", [["kurtosis", "kurt"], ["skew", "skew"]])
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def test_nans(sp_func, roll_func):
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import scipy.stats
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compare_func = partial(getattr(scipy.stats, sp_func), bias=False)
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obj = Series(np.random.randn(50))
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obj[:10] = np.NaN
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obj[-10:] = np.NaN
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result = getattr(obj.rolling(50, min_periods=30), roll_func)()
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tm.assert_almost_equal(result.iloc[-1], compare_func(obj[10:-10]))
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# min_periods is working correctly
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result = getattr(obj.rolling(20, min_periods=15), roll_func)()
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assert isna(result.iloc[23])
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assert not isna(result.iloc[24])
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assert not isna(result.iloc[-6])
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assert isna(result.iloc[-5])
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obj2 = Series(np.random.randn(20))
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result = getattr(obj2.rolling(10, min_periods=5), roll_func)()
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assert isna(result.iloc[3])
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assert notna(result.iloc[4])
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result0 = getattr(obj.rolling(20, min_periods=0), roll_func)()
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result1 = getattr(obj.rolling(20, min_periods=1), roll_func)()
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tm.assert_almost_equal(result0, result1)
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@pytest.mark.parametrize("minp", [0, 99, 100])
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@pytest.mark.parametrize("roll_func", ["kurt", "skew"])
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def test_min_periods(series, minp, roll_func, step):
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result = getattr(
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series.rolling(len(series) + 1, min_periods=minp, step=step), roll_func
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)()
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expected = getattr(
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series.rolling(len(series), min_periods=minp, step=step), roll_func
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)()
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nan_mask = isna(result)
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tm.assert_series_equal(nan_mask, isna(expected))
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nan_mask = ~nan_mask
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tm.assert_almost_equal(result[nan_mask], expected[nan_mask])
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@pytest.mark.parametrize("roll_func", ["kurt", "skew"])
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def test_center(roll_func):
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obj = Series(np.random.randn(50))
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obj[:10] = np.NaN
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obj[-10:] = np.NaN
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result = getattr(obj.rolling(20, center=True), roll_func)()
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expected = (
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getattr(concat([obj, Series([np.NaN] * 9)]).rolling(20), roll_func)()
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.iloc[9:]
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.reset_index(drop=True)
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)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("roll_func", ["kurt", "skew"])
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def test_center_reindex_series(series, roll_func):
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# shifter index
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s = [f"x{x:d}" for x in range(12)]
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series_xp = (
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getattr(
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series.reindex(list(series.index) + s).rolling(window=25),
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roll_func,
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)()
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.shift(-12)
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.reindex(series.index)
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)
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series_rs = getattr(series.rolling(window=25, center=True), roll_func)()
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tm.assert_series_equal(series_xp, series_rs)
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@pytest.mark.slow
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@pytest.mark.parametrize("roll_func", ["kurt", "skew"])
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def test_center_reindex_frame(frame, roll_func):
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# shifter index
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s = [f"x{x:d}" for x in range(12)]
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frame_xp = (
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getattr(
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frame.reindex(list(frame.index) + s).rolling(window=25),
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roll_func,
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)()
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.shift(-12)
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.reindex(frame.index)
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)
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frame_rs = getattr(frame.rolling(window=25, center=True), roll_func)()
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tm.assert_frame_equal(frame_xp, frame_rs)
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def test_rolling_skew_edge_cases(step):
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expected = Series([np.NaN] * 4 + [0.0])[::step]
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# yields all NaN (0 variance)
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d = Series([1] * 5)
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x = d.rolling(window=5, step=step).skew()
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# index 4 should be 0 as it contains 5 same obs
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tm.assert_series_equal(expected, x)
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expected = Series([np.NaN] * 5)[::step]
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# yields all NaN (window too small)
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d = Series(np.random.randn(5))
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x = d.rolling(window=2, step=step).skew()
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tm.assert_series_equal(expected, x)
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# yields [NaN, NaN, NaN, 0.177994, 1.548824]
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d = Series([-1.50837035, -0.1297039, 0.19501095, 1.73508164, 0.41941401])
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expected = Series([np.NaN, np.NaN, np.NaN, 0.177994, 1.548824])[::step]
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x = d.rolling(window=4, step=step).skew()
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tm.assert_series_equal(expected, x)
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def test_rolling_kurt_edge_cases(step):
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expected = Series([np.NaN] * 4 + [-3.0])[::step]
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# yields all NaN (0 variance)
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d = Series([1] * 5)
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x = d.rolling(window=5, step=step).kurt()
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tm.assert_series_equal(expected, x)
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# yields all NaN (window too small)
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expected = Series([np.NaN] * 5)[::step]
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d = Series(np.random.randn(5))
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x = d.rolling(window=3, step=step).kurt()
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tm.assert_series_equal(expected, x)
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# yields [NaN, NaN, NaN, 1.224307, 2.671499]
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d = Series([-1.50837035, -0.1297039, 0.19501095, 1.73508164, 0.41941401])
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expected = Series([np.NaN, np.NaN, np.NaN, 1.224307, 2.671499])[::step]
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x = d.rolling(window=4, step=step).kurt()
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tm.assert_series_equal(expected, x)
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def test_rolling_skew_eq_value_fperr(step):
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# #18804 all rolling skew for all equal values should return Nan
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# #46717 update: all equal values should return 0 instead of NaN
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a = Series([1.1] * 15).rolling(window=10, step=step).skew()
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assert (a[a.index >= 9] == 0).all()
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assert a[a.index < 9].isna().all()
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def test_rolling_kurt_eq_value_fperr(step):
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# #18804 all rolling kurt for all equal values should return Nan
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# #46717 update: all equal values should return -3 instead of NaN
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a = Series([1.1] * 15).rolling(window=10, step=step).kurt()
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assert (a[a.index >= 9] == -3).all()
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assert a[a.index < 9].isna().all()
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