173 lines
4.9 KiB
Python
173 lines
4.9 KiB
Python
from functools import partial
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import numpy as np
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import pytest
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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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import pandas.tseries.offsets as offsets
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def scoreatpercentile(a, per):
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values = np.sort(a, axis=0)
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idx = int(per / 1.0 * (values.shape[0] - 1))
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if idx == values.shape[0] - 1:
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retval = values[-1]
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else:
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qlow = idx / (values.shape[0] - 1)
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qhig = (idx + 1) / (values.shape[0] - 1)
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vlow = values[idx]
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vhig = values[idx + 1]
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retval = vlow + (vhig - vlow) * (per - qlow) / (qhig - qlow)
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return retval
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@pytest.mark.parametrize("q", [0.0, 0.1, 0.5, 0.9, 1.0])
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def test_series(series, q):
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compare_func = partial(scoreatpercentile, per=q)
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result = series.rolling(50).quantile(q)
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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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@pytest.mark.parametrize("q", [0.0, 0.1, 0.5, 0.9, 1.0])
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def test_frame(raw, frame, q):
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compare_func = partial(scoreatpercentile, per=q)
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result = frame.rolling(50).quantile(q)
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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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@pytest.mark.parametrize("q", [0.0, 0.1, 0.5, 0.9, 1.0])
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def test_time_rule_series(series, q):
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compare_func = partial(scoreatpercentile, per=q)
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win = 25
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ser = series[::2].resample("B").mean()
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series_result = ser.rolling(window=win, min_periods=10).quantile(q)
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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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@pytest.mark.parametrize("q", [0.0, 0.1, 0.5, 0.9, 1.0])
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def test_time_rule_frame(raw, frame, q):
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compare_func = partial(scoreatpercentile, per=q)
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win = 25
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frm = frame[::2].resample("B").mean()
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frame_result = frm.rolling(window=win, min_periods=10).quantile(q)
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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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@pytest.mark.parametrize("q", [0.0, 0.1, 0.5, 0.9, 1.0])
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def test_nans(q):
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compare_func = partial(scoreatpercentile, per=q)
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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 = obj.rolling(50, min_periods=30).quantile(q)
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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 = obj.rolling(20, min_periods=15).quantile(q)
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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 = obj2.rolling(10, min_periods=5).quantile(q)
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assert isna(result.iloc[3])
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assert notna(result.iloc[4])
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result0 = obj.rolling(20, min_periods=0).quantile(q)
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result1 = obj.rolling(20, min_periods=1).quantile(q)
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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("q", [0.0, 0.1, 0.5, 0.9, 1.0])
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def test_min_periods(series, minp, q):
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result = series.rolling(len(series) + 1, min_periods=minp).quantile(q)
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expected = series.rolling(len(series), min_periods=minp).quantile(q)
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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("q", [0.0, 0.1, 0.5, 0.9, 1.0])
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def test_center(q):
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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 = obj.rolling(20, center=True).quantile(q)
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expected = (
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concat([obj, Series([np.NaN] * 9)])
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.rolling(20)
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.quantile(q)[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("q", [0.0, 0.1, 0.5, 0.9, 1.0])
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def test_center_reindex_series(series, q):
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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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series.reindex(list(series.index) + s)
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.rolling(window=25)
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.quantile(q)
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.shift(-12)
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.reindex(series.index)
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)
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series_rs = series.rolling(window=25, center=True).quantile(q)
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tm.assert_series_equal(series_xp, series_rs)
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@pytest.mark.parametrize("q", [0.0, 0.1, 0.5, 0.9, 1.0])
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def test_center_reindex_frame(frame, q):
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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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frame.reindex(list(frame.index) + s)
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.rolling(window=25)
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.quantile(q)
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.shift(-12)
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.reindex(frame.index)
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)
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frame_rs = frame.rolling(window=25, center=True).quantile(q)
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tm.assert_frame_equal(frame_xp, frame_rs)
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