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317 lines
9.2 KiB
317 lines
9.2 KiB
import warnings
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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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Index,
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MultiIndex,
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Series,
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Timestamp,
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concat,
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date_range,
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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 f(x):
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# suppress warnings about empty slices, as we are deliberately testing
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# with a 0-length Series
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with warnings.catch_warnings():
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warnings.filterwarnings(
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"ignore",
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message=".*(empty slice|0 for slice).*",
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category=RuntimeWarning,
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)
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return x[np.isfinite(x)].mean()
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@pytest.mark.parametrize("bad_raw", [None, 1, 0])
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def test_rolling_apply_invalid_raw(bad_raw):
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with pytest.raises(ValueError, match="raw parameter must be `True` or `False`"):
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Series(range(3)).rolling(1).apply(len, raw=bad_raw)
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def test_rolling_apply_out_of_bounds(engine_and_raw):
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# gh-1850
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engine, raw = engine_and_raw
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vals = Series([1, 2, 3, 4])
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result = vals.rolling(10).apply(np.sum, engine=engine, raw=raw)
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assert result.isna().all()
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result = vals.rolling(10, min_periods=1).apply(np.sum, engine=engine, raw=raw)
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expected = Series([1, 3, 6, 10], dtype=float)
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tm.assert_almost_equal(result, expected)
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@pytest.mark.parametrize("window", [2, "2s"])
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def test_rolling_apply_with_pandas_objects(window):
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# 5071
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df = DataFrame(
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{"A": np.random.randn(5), "B": np.random.randint(0, 10, size=5)},
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index=date_range("20130101", periods=5, freq="s"),
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)
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# we have an equal spaced timeseries index
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# so simulate removing the first period
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def f(x):
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if x.index[0] == df.index[0]:
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return np.nan
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return x.iloc[-1]
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result = df.rolling(window).apply(f, raw=False)
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expected = df.iloc[2:].reindex_like(df)
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tm.assert_frame_equal(result, expected)
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with tm.external_error_raised(AttributeError):
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df.rolling(window).apply(f, raw=True)
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def test_rolling_apply(engine_and_raw):
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engine, raw = engine_and_raw
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expected = Series([], dtype="float64")
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result = expected.rolling(10).apply(lambda x: x.mean(), engine=engine, raw=raw)
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tm.assert_series_equal(result, expected)
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# gh-8080
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s = Series([None, None, None])
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result = s.rolling(2, min_periods=0).apply(lambda x: len(x), engine=engine, raw=raw)
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expected = Series([1.0, 2.0, 2.0])
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tm.assert_series_equal(result, expected)
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result = s.rolling(2, min_periods=0).apply(len, engine=engine, raw=raw)
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tm.assert_series_equal(result, expected)
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def test_all_apply(engine_and_raw):
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engine, raw = engine_and_raw
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df = (
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DataFrame(
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{"A": date_range("20130101", periods=5, freq="s"), "B": range(5)}
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).set_index("A")
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* 2
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)
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er = df.rolling(window=1)
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r = df.rolling(window="1s")
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result = r.apply(lambda x: 1, engine=engine, raw=raw)
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expected = er.apply(lambda x: 1, engine=engine, raw=raw)
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tm.assert_frame_equal(result, expected)
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def test_ragged_apply(engine_and_raw):
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engine, raw = engine_and_raw
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df = DataFrame({"B": range(5)})
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df.index = [
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Timestamp("20130101 09:00:00"),
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Timestamp("20130101 09:00:02"),
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Timestamp("20130101 09:00:03"),
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Timestamp("20130101 09:00:05"),
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Timestamp("20130101 09:00:06"),
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]
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f = lambda x: 1
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result = df.rolling(window="1s", min_periods=1).apply(f, engine=engine, raw=raw)
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expected = df.copy()
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expected["B"] = 1.0
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tm.assert_frame_equal(result, expected)
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result = df.rolling(window="2s", min_periods=1).apply(f, engine=engine, raw=raw)
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expected = df.copy()
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expected["B"] = 1.0
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tm.assert_frame_equal(result, expected)
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result = df.rolling(window="5s", min_periods=1).apply(f, engine=engine, raw=raw)
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expected = df.copy()
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expected["B"] = 1.0
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tm.assert_frame_equal(result, expected)
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def test_invalid_engine():
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with pytest.raises(ValueError, match="engine must be either 'numba' or 'cython'"):
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Series(range(1)).rolling(1).apply(lambda x: x, engine="foo")
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def test_invalid_engine_kwargs_cython():
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with pytest.raises(ValueError, match="cython engine does not accept engine_kwargs"):
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Series(range(1)).rolling(1).apply(
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lambda x: x, engine="cython", engine_kwargs={"nopython": False}
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)
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def test_invalid_raw_numba():
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with pytest.raises(
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ValueError, match="raw must be `True` when using the numba engine"
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):
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Series(range(1)).rolling(1).apply(lambda x: x, raw=False, engine="numba")
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@pytest.mark.parametrize("args_kwargs", [[None, {"par": 10}], [(10,), None]])
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def test_rolling_apply_args_kwargs(args_kwargs):
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# GH 33433
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def foo(x, par):
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return np.sum(x + par)
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df = DataFrame({"gr": [1, 1], "a": [1, 2]})
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idx = Index(["gr", "a"])
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expected = DataFrame([[11.0, 11.0], [11.0, 12.0]], columns=idx)
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result = df.rolling(1).apply(foo, args=args_kwargs[0], kwargs=args_kwargs[1])
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tm.assert_frame_equal(result, expected)
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midx = MultiIndex.from_tuples([(1, 0), (1, 1)], names=["gr", None])
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expected = Series([11.0, 12.0], index=midx, name="a")
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gb_rolling = df.groupby("gr")["a"].rolling(1)
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result = gb_rolling.apply(foo, args=args_kwargs[0], kwargs=args_kwargs[1])
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tm.assert_series_equal(result, expected)
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def test_nans(raw):
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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).apply(f, raw=raw)
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tm.assert_almost_equal(result.iloc[-1], np.mean(obj[10:-10]))
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# min_periods is working correctly
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result = obj.rolling(20, min_periods=15).apply(f, raw=raw)
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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).apply(f, raw=raw)
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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).apply(f, raw=raw)
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result1 = obj.rolling(20, min_periods=1).apply(f, raw=raw)
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tm.assert_almost_equal(result0, result1)
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def test_center(raw):
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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, min_periods=15, center=True).apply(f, raw=raw)
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expected = (
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concat([obj, Series([np.NaN] * 9)])
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.rolling(20, min_periods=15)
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.apply(f, raw=raw)[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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def test_series(raw, series):
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result = series.rolling(50).apply(f, raw=raw)
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assert isinstance(result, Series)
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tm.assert_almost_equal(result.iloc[-1], np.mean(series[-50:]))
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def test_frame(raw, frame):
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result = frame.rolling(50).apply(f, raw=raw)
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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(np.mean, axis=0, raw=raw),
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check_names=False,
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)
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def test_time_rule_series(raw, series):
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win = 25
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minp = 10
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ser = series[::2].resample("B").mean()
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series_result = ser.rolling(window=win, min_periods=minp).apply(f, raw=raw)
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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], np.mean(trunc_series))
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def test_time_rule_frame(raw, frame):
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win = 25
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minp = 10
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frm = frame[::2].resample("B").mean()
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frame_result = frm.rolling(window=win, min_periods=minp).apply(f, raw=raw)
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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(np.mean, raw=raw),
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check_names=False,
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)
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@pytest.mark.parametrize("minp", [0, 99, 100])
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def test_min_periods(raw, series, minp):
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result = series.rolling(len(series) + 1, min_periods=minp).apply(f, raw=raw)
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expected = series.rolling(len(series), min_periods=minp).apply(f, raw=raw)
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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_center_reindex_series(raw, series):
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# shifter index
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s = [f"x{x:d}" for x in range(12)]
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minp = 10
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series_xp = (
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series.reindex(list(series.index) + s)
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.rolling(window=25, min_periods=minp)
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.apply(f, raw=raw)
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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, min_periods=minp, center=True).apply(
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f, raw=raw
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)
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tm.assert_series_equal(series_xp, series_rs)
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def test_center_reindex_frame(raw, frame):
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# shifter index
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s = [f"x{x:d}" for x in range(12)]
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minp = 10
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frame_xp = (
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frame.reindex(list(frame.index) + s)
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.rolling(window=25, min_periods=minp)
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.apply(f, raw=raw)
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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, min_periods=minp, center=True).apply(f, raw=raw)
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tm.assert_frame_equal(frame_xp, frame_rs)
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def test_axis1(raw):
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# GH 45912
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df = DataFrame([1, 2])
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result = df.rolling(window=1, axis=1).apply(np.sum, raw=raw)
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expected = DataFrame([1.0, 2.0])
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tm.assert_frame_equal(result, expected)
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