534 lines
18 KiB
Python
534 lines
18 KiB
Python
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from datetime import datetime
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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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DatetimeIndex,
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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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@pytest.mark.parametrize(
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"compare_func, roll_func, kwargs",
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[
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[np.mean, "mean", {}],
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[np.nansum, "sum", {}],
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pytest.param(
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lambda x: np.isfinite(x).astype(float).sum(),
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"count",
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{},
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marks=pytest.mark.filterwarnings("ignore:min_periods:FutureWarning"),
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),
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[np.median, "median", {}],
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[np.min, "min", {}],
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[np.max, "max", {}],
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[lambda x: np.std(x, ddof=1), "std", {}],
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[lambda x: np.std(x, ddof=0), "std", {"ddof": 0}],
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[lambda x: np.var(x, ddof=1), "var", {}],
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[lambda x: np.var(x, ddof=0), "var", {"ddof": 0}],
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],
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)
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def test_series(series, compare_func, roll_func, kwargs, step):
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result = getattr(series.rolling(50, step=step), roll_func)(**kwargs)
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assert isinstance(result, Series)
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end = range(0, len(series), step or 1)[-1] + 1
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tm.assert_almost_equal(result.iloc[-1], compare_func(series[end - 50 : end]))
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@pytest.mark.parametrize(
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"compare_func, roll_func, kwargs",
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[
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[np.mean, "mean", {}],
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[np.nansum, "sum", {}],
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pytest.param(
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lambda x: np.isfinite(x).astype(float).sum(),
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"count",
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{},
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marks=pytest.mark.filterwarnings("ignore:min_periods:FutureWarning"),
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),
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[np.median, "median", {}],
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[np.min, "min", {}],
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[np.max, "max", {}],
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[lambda x: np.std(x, ddof=1), "std", {}],
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[lambda x: np.std(x, ddof=0), "std", {"ddof": 0}],
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[lambda x: np.var(x, ddof=1), "var", {}],
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[lambda x: np.var(x, ddof=0), "var", {"ddof": 0}],
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],
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)
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def test_frame(raw, frame, compare_func, roll_func, kwargs, step):
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result = getattr(frame.rolling(50, step=step), roll_func)(**kwargs)
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assert isinstance(result, DataFrame)
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end = range(0, len(frame), step or 1)[-1] + 1
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tm.assert_series_equal(
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result.iloc[-1, :],
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frame.iloc[end - 50 : end, :].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(
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"compare_func, roll_func, kwargs, minp",
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[
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[np.mean, "mean", {}, 10],
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[np.nansum, "sum", {}, 10],
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[lambda x: np.isfinite(x).astype(float).sum(), "count", {}, 0],
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[np.median, "median", {}, 10],
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[np.min, "min", {}, 10],
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[np.max, "max", {}, 10],
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[lambda x: np.std(x, ddof=1), "std", {}, 10],
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[lambda x: np.std(x, ddof=0), "std", {"ddof": 0}, 10],
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[lambda x: np.var(x, ddof=1), "var", {}, 10],
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[lambda x: np.var(x, ddof=0), "var", {"ddof": 0}, 10],
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],
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)
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def test_time_rule_series(series, compare_func, roll_func, kwargs, minp):
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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=minp), roll_func)(
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**kwargs
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)
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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(
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"compare_func, roll_func, kwargs, minp",
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[
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[np.mean, "mean", {}, 10],
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[np.nansum, "sum", {}, 10],
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[lambda x: np.isfinite(x).astype(float).sum(), "count", {}, 0],
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[np.median, "median", {}, 10],
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[np.min, "min", {}, 10],
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[np.max, "max", {}, 10],
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[lambda x: np.std(x, ddof=1), "std", {}, 10],
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[lambda x: np.std(x, ddof=0), "std", {"ddof": 0}, 10],
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[lambda x: np.var(x, ddof=1), "var", {}, 10],
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[lambda x: np.var(x, ddof=0), "var", {"ddof": 0}, 10],
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],
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)
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def test_time_rule_frame(raw, frame, compare_func, roll_func, kwargs, minp):
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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=minp), roll_func)(
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**kwargs
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)
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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(
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"compare_func, roll_func, kwargs",
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[
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[np.mean, "mean", {}],
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[np.nansum, "sum", {}],
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[np.median, "median", {}],
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[np.min, "min", {}],
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[np.max, "max", {}],
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[lambda x: np.std(x, ddof=1), "std", {}],
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[lambda x: np.std(x, ddof=0), "std", {"ddof": 0}],
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[lambda x: np.var(x, ddof=1), "var", {}],
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[lambda x: np.var(x, ddof=0), "var", {"ddof": 0}],
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],
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)
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def test_nans(compare_func, roll_func, kwargs):
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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)(**kwargs)
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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)(**kwargs)
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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)(**kwargs)
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assert isna(result.iloc[3])
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assert notna(result.iloc[4])
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if roll_func != "sum":
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result0 = getattr(obj.rolling(20, min_periods=0), roll_func)(**kwargs)
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result1 = getattr(obj.rolling(20, min_periods=1), roll_func)(**kwargs)
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tm.assert_almost_equal(result0, result1)
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def test_nans_count():
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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).count()
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tm.assert_almost_equal(
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result.iloc[-1], np.isfinite(obj[10:-10]).astype(float).sum()
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)
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@pytest.mark.parametrize(
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"roll_func, kwargs",
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[
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["mean", {}],
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["sum", {}],
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["median", {}],
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["min", {}],
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["max", {}],
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["std", {}],
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["std", {"ddof": 0}],
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["var", {}],
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["var", {"ddof": 0}],
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],
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)
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@pytest.mark.parametrize("minp", [0, 99, 100])
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def test_min_periods(series, minp, roll_func, kwargs, 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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)(**kwargs)
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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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)(**kwargs)
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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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def test_min_periods_count(series, step):
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result = series.rolling(len(series) + 1, min_periods=0, step=step).count()
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expected = series.rolling(len(series), min_periods=0, step=step).count()
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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(
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"roll_func, kwargs, minp",
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[
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["mean", {}, 15],
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["sum", {}, 15],
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["count", {}, 0],
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["median", {}, 15],
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["min", {}, 15],
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["max", {}, 15],
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["std", {}, 15],
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["std", {"ddof": 0}, 15],
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["var", {}, 15],
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["var", {"ddof": 0}, 15],
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],
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)
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def test_center(roll_func, kwargs, minp):
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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, min_periods=minp, center=True), roll_func)(
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**kwargs
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)
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expected = (
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getattr(
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concat([obj, Series([np.NaN] * 9)]).rolling(20, min_periods=minp), roll_func
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)(**kwargs)
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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(
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"roll_func, kwargs, minp, fill_value",
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[
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["mean", {}, 10, None],
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["sum", {}, 10, None],
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["count", {}, 0, 0],
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["median", {}, 10, None],
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["min", {}, 10, None],
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["max", {}, 10, None],
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["std", {}, 10, None],
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["std", {"ddof": 0}, 10, None],
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["var", {}, 10, None],
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["var", {"ddof": 0}, 10, None],
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],
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)
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def test_center_reindex_series(series, roll_func, kwargs, minp, fill_value):
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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, min_periods=minp),
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roll_func,
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)(**kwargs)
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.shift(-12)
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.reindex(series.index)
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)
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series_rs = getattr(
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series.rolling(window=25, min_periods=minp, center=True), roll_func
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)(**kwargs)
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if fill_value is not None:
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series_xp = series_xp.fillna(fill_value)
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tm.assert_series_equal(series_xp, series_rs)
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@pytest.mark.parametrize(
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"roll_func, kwargs, minp, fill_value",
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[
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["mean", {}, 10, None],
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["sum", {}, 10, None],
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["count", {}, 0, 0],
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["median", {}, 10, None],
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["min", {}, 10, None],
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["max", {}, 10, None],
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["std", {}, 10, None],
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["std", {"ddof": 0}, 10, None],
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["var", {}, 10, None],
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["var", {"ddof": 0}, 10, None],
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],
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)
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def test_center_reindex_frame(frame, roll_func, kwargs, minp, fill_value):
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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, min_periods=minp),
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roll_func,
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)(**kwargs)
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.shift(-12)
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.reindex(frame.index)
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)
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frame_rs = getattr(
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frame.rolling(window=25, min_periods=minp, center=True), roll_func
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)(**kwargs)
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if fill_value is not None:
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frame_xp = frame_xp.fillna(fill_value)
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tm.assert_frame_equal(frame_xp, frame_rs)
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@pytest.mark.parametrize(
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"f",
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[
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lambda x: x.rolling(window=10, min_periods=5).cov(x, pairwise=False),
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lambda x: x.rolling(window=10, min_periods=5).corr(x, pairwise=False),
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lambda x: x.rolling(window=10, min_periods=5).max(),
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lambda x: x.rolling(window=10, min_periods=5).min(),
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lambda x: x.rolling(window=10, min_periods=5).sum(),
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lambda x: x.rolling(window=10, min_periods=5).mean(),
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lambda x: x.rolling(window=10, min_periods=5).std(),
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lambda x: x.rolling(window=10, min_periods=5).var(),
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lambda x: x.rolling(window=10, min_periods=5).skew(),
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lambda x: x.rolling(window=10, min_periods=5).kurt(),
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lambda x: x.rolling(window=10, min_periods=5).quantile(quantile=0.5),
|
||
|
|
lambda x: x.rolling(window=10, min_periods=5).median(),
|
||
|
|
lambda x: x.rolling(window=10, min_periods=5).apply(sum, raw=False),
|
||
|
|
lambda x: x.rolling(window=10, min_periods=5).apply(sum, raw=True),
|
||
|
|
pytest.param(
|
||
|
|
lambda x: x.rolling(win_type="boxcar", window=10, min_periods=5).mean(),
|
||
|
|
marks=td.skip_if_no_scipy,
|
||
|
|
),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_rolling_functions_window_non_shrinkage(f):
|
||
|
|
# GH 7764
|
||
|
|
s = Series(range(4))
|
||
|
|
s_expected = Series(np.nan, index=s.index)
|
||
|
|
df = DataFrame([[1, 5], [3, 2], [3, 9], [-1, 0]], columns=["A", "B"])
|
||
|
|
df_expected = DataFrame(np.nan, index=df.index, columns=df.columns)
|
||
|
|
|
||
|
|
s_result = f(s)
|
||
|
|
tm.assert_series_equal(s_result, s_expected)
|
||
|
|
|
||
|
|
df_result = f(df)
|
||
|
|
tm.assert_frame_equal(df_result, df_expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_rolling_max_gh6297(step):
|
||
|
|
"""Replicate result expected in GH #6297"""
|
||
|
|
indices = [datetime(1975, 1, i) for i in range(1, 6)]
|
||
|
|
# So that we can have 2 datapoints on one of the days
|
||
|
|
indices.append(datetime(1975, 1, 3, 6, 0))
|
||
|
|
series = Series(range(1, 7), index=indices)
|
||
|
|
# Use floats instead of ints as values
|
||
|
|
series = series.map(lambda x: float(x))
|
||
|
|
# Sort chronologically
|
||
|
|
series = series.sort_index()
|
||
|
|
|
||
|
|
expected = Series(
|
||
|
|
[1.0, 2.0, 6.0, 4.0, 5.0],
|
||
|
|
index=DatetimeIndex([datetime(1975, 1, i, 0) for i in range(1, 6)], freq="D"),
|
||
|
|
)[::step]
|
||
|
|
x = series.resample("D").max().rolling(window=1, step=step).max()
|
||
|
|
tm.assert_series_equal(expected, x)
|
||
|
|
|
||
|
|
|
||
|
|
def test_rolling_max_resample(step):
|
||
|
|
|
||
|
|
indices = [datetime(1975, 1, i) for i in range(1, 6)]
|
||
|
|
# So that we can have 3 datapoints on last day (4, 10, and 20)
|
||
|
|
indices.append(datetime(1975, 1, 5, 1))
|
||
|
|
indices.append(datetime(1975, 1, 5, 2))
|
||
|
|
series = Series(list(range(0, 5)) + [10, 20], index=indices)
|
||
|
|
# Use floats instead of ints as values
|
||
|
|
series = series.map(lambda x: float(x))
|
||
|
|
# Sort chronologically
|
||
|
|
series = series.sort_index()
|
||
|
|
|
||
|
|
# Default how should be max
|
||
|
|
expected = Series(
|
||
|
|
[0.0, 1.0, 2.0, 3.0, 20.0],
|
||
|
|
index=DatetimeIndex([datetime(1975, 1, i, 0) for i in range(1, 6)], freq="D"),
|
||
|
|
)[::step]
|
||
|
|
x = series.resample("D").max().rolling(window=1, step=step).max()
|
||
|
|
tm.assert_series_equal(expected, x)
|
||
|
|
|
||
|
|
# Now specify median (10.0)
|
||
|
|
expected = Series(
|
||
|
|
[0.0, 1.0, 2.0, 3.0, 10.0],
|
||
|
|
index=DatetimeIndex([datetime(1975, 1, i, 0) for i in range(1, 6)], freq="D"),
|
||
|
|
)[::step]
|
||
|
|
x = series.resample("D").median().rolling(window=1, step=step).max()
|
||
|
|
tm.assert_series_equal(expected, x)
|
||
|
|
|
||
|
|
# Now specify mean (4+10+20)/3
|
||
|
|
v = (4.0 + 10.0 + 20.0) / 3.0
|
||
|
|
expected = Series(
|
||
|
|
[0.0, 1.0, 2.0, 3.0, v],
|
||
|
|
index=DatetimeIndex([datetime(1975, 1, i, 0) for i in range(1, 6)], freq="D"),
|
||
|
|
)[::step]
|
||
|
|
x = series.resample("D").mean().rolling(window=1, step=step).max()
|
||
|
|
tm.assert_series_equal(expected, x)
|
||
|
|
|
||
|
|
|
||
|
|
def test_rolling_min_resample(step):
|
||
|
|
|
||
|
|
indices = [datetime(1975, 1, i) for i in range(1, 6)]
|
||
|
|
# So that we can have 3 datapoints on last day (4, 10, and 20)
|
||
|
|
indices.append(datetime(1975, 1, 5, 1))
|
||
|
|
indices.append(datetime(1975, 1, 5, 2))
|
||
|
|
series = Series(list(range(0, 5)) + [10, 20], index=indices)
|
||
|
|
# Use floats instead of ints as values
|
||
|
|
series = series.map(lambda x: float(x))
|
||
|
|
# Sort chronologically
|
||
|
|
series = series.sort_index()
|
||
|
|
|
||
|
|
# Default how should be min
|
||
|
|
expected = Series(
|
||
|
|
[0.0, 1.0, 2.0, 3.0, 4.0],
|
||
|
|
index=DatetimeIndex([datetime(1975, 1, i, 0) for i in range(1, 6)], freq="D"),
|
||
|
|
)[::step]
|
||
|
|
r = series.resample("D").min().rolling(window=1, step=step)
|
||
|
|
tm.assert_series_equal(expected, r.min())
|
||
|
|
|
||
|
|
|
||
|
|
def test_rolling_median_resample():
|
||
|
|
|
||
|
|
indices = [datetime(1975, 1, i) for i in range(1, 6)]
|
||
|
|
# So that we can have 3 datapoints on last day (4, 10, and 20)
|
||
|
|
indices.append(datetime(1975, 1, 5, 1))
|
||
|
|
indices.append(datetime(1975, 1, 5, 2))
|
||
|
|
series = Series(list(range(0, 5)) + [10, 20], index=indices)
|
||
|
|
# Use floats instead of ints as values
|
||
|
|
series = series.map(lambda x: float(x))
|
||
|
|
# Sort chronologically
|
||
|
|
series = series.sort_index()
|
||
|
|
|
||
|
|
# Default how should be median
|
||
|
|
expected = Series(
|
||
|
|
[0.0, 1.0, 2.0, 3.0, 10],
|
||
|
|
index=DatetimeIndex([datetime(1975, 1, i, 0) for i in range(1, 6)], freq="D"),
|
||
|
|
)
|
||
|
|
x = series.resample("D").median().rolling(window=1).median()
|
||
|
|
tm.assert_series_equal(expected, x)
|
||
|
|
|
||
|
|
|
||
|
|
def test_rolling_median_memory_error():
|
||
|
|
# GH11722
|
||
|
|
n = 20000
|
||
|
|
Series(np.random.randn(n)).rolling(window=2, center=False).median()
|
||
|
|
Series(np.random.randn(n)).rolling(window=2, center=False).median()
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"data_type",
|
||
|
|
[np.dtype(f"f{width}") for width in [4, 8]]
|
||
|
|
+ [np.dtype(f"{sign}{width}") for width in [1, 2, 4, 8] for sign in "ui"],
|
||
|
|
)
|
||
|
|
def test_rolling_min_max_numeric_types(data_type):
|
||
|
|
# GH12373
|
||
|
|
|
||
|
|
# Just testing that these don't throw exceptions and that
|
||
|
|
# the return type is float64. Other tests will cover quantitative
|
||
|
|
# correctness
|
||
|
|
result = DataFrame(np.arange(20, dtype=data_type)).rolling(window=5).max()
|
||
|
|
assert result.dtypes[0] == np.dtype("f8")
|
||
|
|
result = DataFrame(np.arange(20, dtype=data_type)).rolling(window=5).min()
|
||
|
|
assert result.dtypes[0] == np.dtype("f8")
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"f",
|
||
|
|
[
|
||
|
|
lambda x: x.rolling(window=10, min_periods=0).count(),
|
||
|
|
lambda x: x.rolling(window=10, min_periods=5).cov(x, pairwise=False),
|
||
|
|
lambda x: x.rolling(window=10, min_periods=5).corr(x, pairwise=False),
|
||
|
|
lambda x: x.rolling(window=10, min_periods=5).max(),
|
||
|
|
lambda x: x.rolling(window=10, min_periods=5).min(),
|
||
|
|
lambda x: x.rolling(window=10, min_periods=5).sum(),
|
||
|
|
lambda x: x.rolling(window=10, min_periods=5).mean(),
|
||
|
|
lambda x: x.rolling(window=10, min_periods=5).std(),
|
||
|
|
lambda x: x.rolling(window=10, min_periods=5).var(),
|
||
|
|
lambda x: x.rolling(window=10, min_periods=5).skew(),
|
||
|
|
lambda x: x.rolling(window=10, min_periods=5).kurt(),
|
||
|
|
lambda x: x.rolling(window=10, min_periods=5).quantile(0.5),
|
||
|
|
lambda x: x.rolling(window=10, min_periods=5).median(),
|
||
|
|
lambda x: x.rolling(window=10, min_periods=5).apply(sum, raw=False),
|
||
|
|
lambda x: x.rolling(window=10, min_periods=5).apply(sum, raw=True),
|
||
|
|
pytest.param(
|
||
|
|
lambda x: x.rolling(win_type="boxcar", window=10, min_periods=5).mean(),
|
||
|
|
marks=td.skip_if_no_scipy,
|
||
|
|
),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
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)
|