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