645 lines
16 KiB
Python
645 lines
16 KiB
Python
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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 DataFrame, Series, date_range
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import pandas._testing as tm
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def test_centered_axis_validation():
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# ok
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Series(np.ones(10)).rolling(window=3, center=True, axis=0).mean()
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# bad axis
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msg = "No axis named 1 for object type Series"
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with pytest.raises(ValueError, match=msg):
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Series(np.ones(10)).rolling(window=3, center=True, axis=1).mean()
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# ok ok
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DataFrame(np.ones((10, 10))).rolling(window=3, center=True, axis=0).mean()
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DataFrame(np.ones((10, 10))).rolling(window=3, center=True, axis=1).mean()
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# bad axis
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msg = "No axis named 2 for object type DataFrame"
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with pytest.raises(ValueError, match=msg):
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(DataFrame(np.ones((10, 10))).rolling(window=3, center=True, axis=2).mean())
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@td.skip_if_no_scipy
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def test_cmov_mean():
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# GH 8238
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vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81, 13.49, 16.68, 9.48, 10.63, 14.48])
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result = Series(vals).rolling(5, center=True).mean()
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expected_values = [
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np.nan,
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np.nan,
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9.962,
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11.27,
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11.564,
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12.516,
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12.818,
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12.952,
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np.nan,
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np.nan,
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]
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expected = Series(expected_values)
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tm.assert_series_equal(expected, result)
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@td.skip_if_no_scipy
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def test_cmov_window():
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# GH 8238
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vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81, 13.49, 16.68, 9.48, 10.63, 14.48])
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result = Series(vals).rolling(5, win_type="boxcar", center=True).mean()
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expected_values = [
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np.nan,
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np.nan,
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9.962,
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11.27,
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11.564,
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12.516,
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12.818,
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12.952,
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np.nan,
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np.nan,
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]
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expected = Series(expected_values)
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tm.assert_series_equal(expected, result)
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@td.skip_if_no_scipy
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def test_cmov_window_corner():
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# GH 8238
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# all nan
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vals = Series([np.nan] * 10)
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result = vals.rolling(5, center=True, win_type="boxcar").mean()
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assert np.isnan(result).all()
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# empty
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vals = Series([], dtype=object)
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result = vals.rolling(5, center=True, win_type="boxcar").mean()
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assert len(result) == 0
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# shorter than window
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vals = Series(np.random.randn(5))
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result = vals.rolling(10, win_type="boxcar").mean()
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assert np.isnan(result).all()
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assert len(result) == 5
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@td.skip_if_no_scipy
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@pytest.mark.parametrize(
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"f,xp",
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[
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(
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"mean",
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[
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[np.nan, np.nan],
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[np.nan, np.nan],
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[9.252, 9.392],
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[8.644, 9.906],
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[8.87, 10.208],
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[6.81, 8.588],
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[7.792, 8.644],
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[9.05, 7.824],
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[np.nan, np.nan],
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[np.nan, np.nan],
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],
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),
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(
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"std",
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[
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[np.nan, np.nan],
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[np.nan, np.nan],
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[3.789706, 4.068313],
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[3.429232, 3.237411],
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[3.589269, 3.220810],
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[3.405195, 2.380655],
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[3.281839, 2.369869],
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[3.676846, 1.801799],
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[np.nan, np.nan],
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[np.nan, np.nan],
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],
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),
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(
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"var",
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[
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[np.nan, np.nan],
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[np.nan, np.nan],
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[14.36187, 16.55117],
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[11.75963, 10.48083],
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[12.88285, 10.37362],
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[11.59535, 5.66752],
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[10.77047, 5.61628],
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[13.51920, 3.24648],
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[np.nan, np.nan],
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[np.nan, np.nan],
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],
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),
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(
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"sum",
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[
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[np.nan, np.nan],
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[np.nan, np.nan],
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[46.26, 46.96],
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[43.22, 49.53],
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[44.35, 51.04],
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[34.05, 42.94],
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[38.96, 43.22],
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[45.25, 39.12],
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[np.nan, np.nan],
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[np.nan, np.nan],
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],
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),
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],
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)
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def test_cmov_window_frame(f, xp):
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# Gh 8238
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df = DataFrame(
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np.array(
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[
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[12.18, 3.64],
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[10.18, 9.16],
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[13.24, 14.61],
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[4.51, 8.11],
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[6.15, 11.44],
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[9.14, 6.21],
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[11.31, 10.67],
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[2.94, 6.51],
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[9.42, 8.39],
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[12.44, 7.34],
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]
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)
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)
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xp = DataFrame(np.array(xp))
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roll = df.rolling(5, win_type="boxcar", center=True)
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rs = getattr(roll, f)()
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tm.assert_frame_equal(xp, rs)
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@td.skip_if_no_scipy
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def test_cmov_window_na_min_periods():
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# min_periods
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vals = Series(np.random.randn(10))
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vals[4] = np.nan
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vals[8] = np.nan
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xp = vals.rolling(5, min_periods=4, center=True).mean()
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rs = vals.rolling(5, win_type="boxcar", min_periods=4, center=True).mean()
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tm.assert_series_equal(xp, rs)
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@td.skip_if_no_scipy
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def test_cmov_window_regular(win_types):
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# GH 8238
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vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81, 13.49, 16.68, 9.48, 10.63, 14.48])
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xps = {
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"hamming": [
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np.nan,
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np.nan,
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8.71384,
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9.56348,
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12.38009,
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14.03687,
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13.8567,
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11.81473,
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np.nan,
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np.nan,
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],
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"triang": [
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np.nan,
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np.nan,
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9.28667,
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10.34667,
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12.00556,
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13.33889,
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13.38,
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12.33667,
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np.nan,
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np.nan,
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],
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"barthann": [
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np.nan,
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np.nan,
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8.4425,
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9.1925,
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12.5575,
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14.3675,
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14.0825,
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11.5675,
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np.nan,
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np.nan,
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],
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"bohman": [
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np.nan,
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np.nan,
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7.61599,
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9.1764,
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12.83559,
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14.17267,
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14.65923,
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11.10401,
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np.nan,
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np.nan,
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],
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"blackmanharris": [
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np.nan,
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np.nan,
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6.97691,
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9.16438,
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13.05052,
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14.02156,
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15.10512,
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10.74574,
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np.nan,
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np.nan,
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],
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"nuttall": [
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np.nan,
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np.nan,
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7.04618,
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9.16786,
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13.02671,
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14.03559,
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15.05657,
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10.78514,
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np.nan,
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np.nan,
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],
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"blackman": [
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np.nan,
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np.nan,
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7.73345,
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9.17869,
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12.79607,
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14.20036,
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14.57726,
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11.16988,
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np.nan,
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np.nan,
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],
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"bartlett": [
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np.nan,
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np.nan,
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8.4425,
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9.1925,
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12.5575,
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14.3675,
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14.0825,
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11.5675,
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np.nan,
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np.nan,
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],
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}
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xp = Series(xps[win_types])
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rs = Series(vals).rolling(5, win_type=win_types, center=True).mean()
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tm.assert_series_equal(xp, rs)
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@td.skip_if_no_scipy
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def test_cmov_window_regular_linear_range(win_types):
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# GH 8238
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vals = np.array(range(10), dtype=float)
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xp = vals.copy()
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xp[:2] = np.nan
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xp[-2:] = np.nan
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xp = Series(xp)
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rs = Series(vals).rolling(5, win_type=win_types, center=True).mean()
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tm.assert_series_equal(xp, rs)
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@td.skip_if_no_scipy
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def test_cmov_window_regular_missing_data(win_types):
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# GH 8238
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vals = np.array(
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[6.95, 15.21, 4.72, 9.12, 13.81, 13.49, 16.68, np.nan, 10.63, 14.48]
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)
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xps = {
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"bartlett": [
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np.nan,
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np.nan,
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9.70333,
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10.5225,
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8.4425,
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9.1925,
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12.5575,
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14.3675,
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15.61667,
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13.655,
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],
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"blackman": [
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np.nan,
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np.nan,
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9.04582,
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11.41536,
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7.73345,
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9.17869,
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12.79607,
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14.20036,
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15.8706,
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13.655,
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],
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"barthann": [
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np.nan,
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np.nan,
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9.70333,
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10.5225,
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8.4425,
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9.1925,
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12.5575,
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14.3675,
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15.61667,
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13.655,
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],
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"bohman": [
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np.nan,
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np.nan,
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8.9444,
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11.56327,
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7.61599,
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9.1764,
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12.83559,
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14.17267,
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15.90976,
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13.655,
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],
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"hamming": [
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np.nan,
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np.nan,
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9.59321,
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10.29694,
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8.71384,
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9.56348,
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12.38009,
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14.20565,
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15.24694,
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13.69758,
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],
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"nuttall": [
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np.nan,
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np.nan,
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8.47693,
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12.2821,
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7.04618,
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9.16786,
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13.02671,
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14.03673,
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16.08759,
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13.65553,
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],
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"triang": [
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np.nan,
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np.nan,
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9.33167,
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9.76125,
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9.28667,
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10.34667,
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12.00556,
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13.82125,
|
||
|
|
14.49429,
|
||
|
|
13.765,
|
||
|
|
],
|
||
|
|
"blackmanharris": [
|
||
|
|
np.nan,
|
||
|
|
np.nan,
|
||
|
|
8.42526,
|
||
|
|
12.36824,
|
||
|
|
6.97691,
|
||
|
|
9.16438,
|
||
|
|
13.05052,
|
||
|
|
14.02175,
|
||
|
|
16.1098,
|
||
|
|
13.65509,
|
||
|
|
],
|
||
|
|
}
|
||
|
|
|
||
|
|
xp = Series(xps[win_types])
|
||
|
|
rs = Series(vals).rolling(5, win_type=win_types, min_periods=3).mean()
|
||
|
|
tm.assert_series_equal(xp, rs)
|
||
|
|
|
||
|
|
|
||
|
|
@td.skip_if_no_scipy
|
||
|
|
def test_cmov_window_special(win_types_special):
|
||
|
|
# GH 8238
|
||
|
|
kwds = {
|
||
|
|
"kaiser": {"beta": 1.0},
|
||
|
|
"gaussian": {"std": 1.0},
|
||
|
|
"general_gaussian": {"p": 2.0, "sig": 2.0},
|
||
|
|
"exponential": {"tau": 10},
|
||
|
|
}
|
||
|
|
|
||
|
|
vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81, 13.49, 16.68, 9.48, 10.63, 14.48])
|
||
|
|
|
||
|
|
xps = {
|
||
|
|
"gaussian": [
|
||
|
|
np.nan,
|
||
|
|
np.nan,
|
||
|
|
8.97297,
|
||
|
|
9.76077,
|
||
|
|
12.24763,
|
||
|
|
13.89053,
|
||
|
|
13.65671,
|
||
|
|
12.01002,
|
||
|
|
np.nan,
|
||
|
|
np.nan,
|
||
|
|
],
|
||
|
|
"general_gaussian": [
|
||
|
|
np.nan,
|
||
|
|
np.nan,
|
||
|
|
9.85011,
|
||
|
|
10.71589,
|
||
|
|
11.73161,
|
||
|
|
13.08516,
|
||
|
|
12.95111,
|
||
|
|
12.74577,
|
||
|
|
np.nan,
|
||
|
|
np.nan,
|
||
|
|
],
|
||
|
|
"kaiser": [
|
||
|
|
np.nan,
|
||
|
|
np.nan,
|
||
|
|
9.86851,
|
||
|
|
11.02969,
|
||
|
|
11.65161,
|
||
|
|
12.75129,
|
||
|
|
12.90702,
|
||
|
|
12.83757,
|
||
|
|
np.nan,
|
||
|
|
np.nan,
|
||
|
|
],
|
||
|
|
"exponential": [
|
||
|
|
np.nan,
|
||
|
|
np.nan,
|
||
|
|
9.83364,
|
||
|
|
11.10472,
|
||
|
|
11.64551,
|
||
|
|
12.66138,
|
||
|
|
12.92379,
|
||
|
|
12.83770,
|
||
|
|
np.nan,
|
||
|
|
np.nan,
|
||
|
|
],
|
||
|
|
}
|
||
|
|
|
||
|
|
xp = Series(xps[win_types_special])
|
||
|
|
rs = (
|
||
|
|
Series(vals)
|
||
|
|
.rolling(5, win_type=win_types_special, center=True)
|
||
|
|
.mean(**kwds[win_types_special])
|
||
|
|
)
|
||
|
|
tm.assert_series_equal(xp, rs)
|
||
|
|
|
||
|
|
|
||
|
|
@td.skip_if_no_scipy
|
||
|
|
def test_cmov_window_special_linear_range(win_types_special):
|
||
|
|
# GH 8238
|
||
|
|
kwds = {
|
||
|
|
"kaiser": {"beta": 1.0},
|
||
|
|
"gaussian": {"std": 1.0},
|
||
|
|
"general_gaussian": {"p": 2.0, "sig": 2.0},
|
||
|
|
"slepian": {"width": 0.5},
|
||
|
|
"exponential": {"tau": 10},
|
||
|
|
}
|
||
|
|
|
||
|
|
vals = np.array(range(10), dtype=float)
|
||
|
|
xp = vals.copy()
|
||
|
|
xp[:2] = np.nan
|
||
|
|
xp[-2:] = np.nan
|
||
|
|
xp = Series(xp)
|
||
|
|
|
||
|
|
rs = (
|
||
|
|
Series(vals)
|
||
|
|
.rolling(5, win_type=win_types_special, center=True)
|
||
|
|
.mean(**kwds[win_types_special])
|
||
|
|
)
|
||
|
|
tm.assert_series_equal(xp, rs)
|
||
|
|
|
||
|
|
|
||
|
|
def test_rolling_min_min_periods():
|
||
|
|
a = Series([1, 2, 3, 4, 5])
|
||
|
|
result = a.rolling(window=100, min_periods=1).min()
|
||
|
|
expected = Series(np.ones(len(a)))
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
msg = "min_periods 5 must be <= window 3"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
Series([1, 2, 3]).rolling(window=3, min_periods=5).min()
|
||
|
|
|
||
|
|
|
||
|
|
def test_rolling_max_min_periods():
|
||
|
|
a = Series([1, 2, 3, 4, 5], dtype=np.float64)
|
||
|
|
b = a.rolling(window=100, min_periods=1).max()
|
||
|
|
tm.assert_almost_equal(a, b)
|
||
|
|
msg = "min_periods 5 must be <= window 3"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
Series([1, 2, 3]).rolling(window=3, min_periods=5).max()
|
||
|
|
|
||
|
|
|
||
|
|
def test_rolling_quantile_np_percentile():
|
||
|
|
# #9413: Tests that rolling window's quantile default behavior
|
||
|
|
# is analogous to Numpy's percentile
|
||
|
|
row = 10
|
||
|
|
col = 5
|
||
|
|
idx = date_range("20100101", periods=row, freq="B")
|
||
|
|
df = DataFrame(np.random.rand(row * col).reshape((row, -1)), index=idx)
|
||
|
|
|
||
|
|
df_quantile = df.quantile([0.25, 0.5, 0.75], axis=0)
|
||
|
|
np_percentile = np.percentile(df, [25, 50, 75], axis=0)
|
||
|
|
|
||
|
|
tm.assert_almost_equal(df_quantile.values, np.array(np_percentile))
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("quantile", [0.0, 0.1, 0.45, 0.5, 1])
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"interpolation", ["linear", "lower", "higher", "nearest", "midpoint"]
|
||
|
|
)
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"data",
|
||
|
|
[
|
||
|
|
[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0],
|
||
|
|
[8.0, 1.0, 3.0, 4.0, 5.0, 2.0, 6.0, 7.0],
|
||
|
|
[0.0, np.nan, 0.2, np.nan, 0.4],
|
||
|
|
[np.nan, np.nan, np.nan, np.nan],
|
||
|
|
[np.nan, 0.1, np.nan, 0.3, 0.4, 0.5],
|
||
|
|
[0.5],
|
||
|
|
[np.nan, 0.7, 0.6],
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_rolling_quantile_interpolation_options(quantile, interpolation, data):
|
||
|
|
# Tests that rolling window's quantile behavior is analogous to
|
||
|
|
# Series' quantile for each interpolation option
|
||
|
|
s = Series(data)
|
||
|
|
|
||
|
|
q1 = s.quantile(quantile, interpolation)
|
||
|
|
q2 = s.expanding(min_periods=1).quantile(quantile, interpolation).iloc[-1]
|
||
|
|
|
||
|
|
if np.isnan(q1):
|
||
|
|
assert np.isnan(q2)
|
||
|
|
else:
|
||
|
|
assert q1 == q2
|
||
|
|
|
||
|
|
|
||
|
|
def test_invalid_quantile_value():
|
||
|
|
data = np.arange(5)
|
||
|
|
s = Series(data)
|
||
|
|
|
||
|
|
msg = "Interpolation 'invalid' is not supported"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.rolling(len(data), min_periods=1).quantile(0.5, interpolation="invalid")
|
||
|
|
|
||
|
|
|
||
|
|
def test_rolling_quantile_param():
|
||
|
|
ser = Series([0.0, 0.1, 0.5, 0.9, 1.0])
|
||
|
|
msg = "quantile value -0.1 not in \\[0, 1\\]"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
ser.rolling(3).quantile(-0.1)
|
||
|
|
|
||
|
|
msg = "quantile value 10.0 not in \\[0, 1\\]"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
ser.rolling(3).quantile(10.0)
|
||
|
|
|
||
|
|
msg = "must be real number, not str"
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
ser.rolling(3).quantile("foo")
|
||
|
|
|
||
|
|
|
||
|
|
def test_rolling_std_1obs():
|
||
|
|
vals = Series([1.0, 2.0, 3.0, 4.0, 5.0])
|
||
|
|
|
||
|
|
result = vals.rolling(1, min_periods=1).std()
|
||
|
|
expected = Series([np.nan] * 5)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
result = vals.rolling(1, min_periods=1).std(ddof=0)
|
||
|
|
expected = Series([0.0] * 5)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
result = Series([np.nan, np.nan, 3, 4, 5]).rolling(3, min_periods=2).std()
|
||
|
|
assert np.isnan(result[2])
|
||
|
|
|
||
|
|
|
||
|
|
def test_rolling_std_neg_sqrt():
|
||
|
|
# unit test from Bottleneck
|
||
|
|
|
||
|
|
# Test move_nanstd for neg sqrt.
|
||
|
|
|
||
|
|
a = Series(
|
||
|
|
[
|
||
|
|
0.0011448196318903589,
|
||
|
|
0.00028718669878572767,
|
||
|
|
0.00028718669878572767,
|
||
|
|
0.00028718669878572767,
|
||
|
|
0.00028718669878572767,
|
||
|
|
]
|
||
|
|
)
|
||
|
|
b = a.rolling(window=3).std()
|
||
|
|
assert np.isfinite(b[2:]).all()
|
||
|
|
|
||
|
|
b = a.ewm(span=3).std()
|
||
|
|
assert np.isfinite(b[2:]).all()
|