CLN: moments/test_moments_rolling.py for apply

pandas/tests/window/moments/test_moments_rolling.py

@@ -1,5 +1,4 @@
 import copy
-import warnings
 
 import numpy as np
 from numpy.random import randn
@@ -856,24 +855,6 @@ def test_rolling_quantile_param():
         ser.rolling(3).quantile("foo")
 
 
-def test_rolling_apply(raw, series, frame):
-    # suppress warnings about empty slices, as we are deliberately testing
-    # with a 0-length Series
-
-    def f(x):
-        with warnings.catch_warnings():
-            warnings.filterwarnings(
-                "ignore",
-                message=".*(empty slice|0 for slice).*",
-                category=RuntimeWarning,
-            )
-            return x[np.isfinite(x)].mean()
-
-    _check_moment_func(
-        np.mean, name="apply", func=f, raw=raw, series=series, frame=frame
-    )
-
-
 def test_rolling_std(raw, series, frame):
     _check_moment_func(
         lambda x: np.std(x, ddof=1), name="std", raw=raw, series=series, frame=frame

pandas/tests/window/moments/test_moments_rolling_apply.py

@@ -0,0 +1,151 @@
+import warnings
+
+import numpy as np
+import pytest
+
+from pandas import DataFrame, Series, concat, isna, notna
+import pandas._testing as tm
+
+import pandas.tseries.offsets as offsets
+
+
+def f(x):
+    # suppress warnings about empty slices, as we are deliberately testing
+    # with a 0-length Series
+    with warnings.catch_warnings():
+        warnings.filterwarnings(
+            "ignore",
+            message=".*(empty slice|0 for slice).*",
+            category=RuntimeWarning,
+        )
+        return x[np.isfinite(x)].mean()
+
+
+def test_series(raw, series):
+    result = series.rolling(50).apply(f, raw=raw)
+    assert isinstance(result, Series)
+    tm.assert_almost_equal(result.iloc[-1], np.mean(series[-50:]))
+
+
+def test_frame(raw, frame):
+    result = frame.rolling(50).apply(f, raw=raw)
+    assert isinstance(result, DataFrame)
+    tm.assert_series_equal(
+        result.iloc[-1, :],
+        frame.iloc[-50:, :].apply(np.mean, axis=0, raw=raw),
+        check_names=False,
+    )
+
+
+def test_time_rule_series(raw, series):
+    win = 25
+    minp = 10
+    ser = series[::2].resample("B").mean()
+    series_result = ser.rolling(window=win, min_periods=minp).apply(f, raw=raw)
+    last_date = series_result.index[-1]
+    prev_date = last_date - 24 * offsets.BDay()
+
+    trunc_series = series[::2].truncate(prev_date, last_date)
+    tm.assert_almost_equal(series_result[-1], np.mean(trunc_series))
+
+
+def test_time_rule_frame(raw, frame):
+    win = 25
+    minp = 10
+    frm = frame[::2].resample("B").mean()
+    frame_result = frm.rolling(window=win, min_periods=minp).apply(f, raw=raw)
+    last_date = frame_result.index[-1]
+    prev_date = last_date - 24 * offsets.BDay()
+
+    trunc_frame = frame[::2].truncate(prev_date, last_date)
+    tm.assert_series_equal(
+        frame_result.xs(last_date),
+        trunc_frame.apply(np.mean, raw=raw),
+        check_names=False,
+    )
+
+
+def test_nans(raw):
+    obj = Series(np.random.randn(50))
+    obj[:10] = np.NaN
+    obj[-10:] = np.NaN
+
+    result = obj.rolling(50, min_periods=30).apply(f, raw=raw)
+    tm.assert_almost_equal(result.iloc[-1], np.mean(obj[10:-10]))
+
+    # min_periods is working correctly
+    result = obj.rolling(20, min_periods=15).apply(f, raw=raw)
+    assert isna(result.iloc[23])
+    assert not isna(result.iloc[24])
+
+    assert not isna(result.iloc[-6])
+    assert isna(result.iloc[-5])
+
+    obj2 = Series(np.random.randn(20))
+    result = obj2.rolling(10, min_periods=5).apply(f, raw=raw)
+    assert isna(result.iloc[3])
+    assert notna(result.iloc[4])
+
+    result0 = obj.rolling(20, min_periods=0).apply(f, raw=raw)
+    result1 = obj.rolling(20, min_periods=1).apply(f, raw=raw)
+    tm.assert_almost_equal(result0, result1)
+
+
+@pytest.mark.parametrize("minp", [0, 99, 100])
+def test_min_periods(raw, series, minp):
+    result = series.rolling(len(series) + 1, min_periods=minp).apply(f, raw=raw)
+    expected = series.rolling(len(series), min_periods=minp).apply(f, raw=raw)
+    nan_mask = isna(result)
+    tm.assert_series_equal(nan_mask, isna(expected))
+
+    nan_mask = ~nan_mask
+    tm.assert_almost_equal(result[nan_mask], expected[nan_mask])
+
+
+def test_center(raw):
+    obj = Series(np.random.randn(50))
+    obj[:10] = np.NaN
+    obj[-10:] = np.NaN
+
+    result = obj.rolling(20, min_periods=15, center=True).apply(f, raw=raw)
+    expected = (
+        concat([obj, Series([np.NaN] * 9)])
+        .rolling(20, min_periods=15)
+        .apply(f, raw=raw)[9:]
+        .reset_index(drop=True)
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_center_reindex_series(raw, series):
+    # shifter index
+    s = [f"x{x:d}" for x in range(12)]
+    minp = 10
+
+    series_xp = (
+        series.reindex(list(series.index) + s)
+        .rolling(window=25, min_periods=minp)
+        .apply(f, raw=raw)
+        .shift(-12)
+        .reindex(series.index)
+    )
+    series_rs = series.rolling(window=25, min_periods=minp, center=True).apply(
+        f, raw=raw
+    )
+    tm.assert_series_equal(series_xp, series_rs)
+
+
+def test_center_reindex_frame(raw, frame):
+    # shifter index
+    s = [f"x{x:d}" for x in range(12)]
+    minp = 10
+
+    frame_xp = (
+        frame.reindex(list(frame.index) + s)
+        .rolling(window=25, min_periods=minp)
+        .apply(f, raw=raw)
+        .shift(-12)
+        .reindex(frame.index)
+    )
+    frame_rs = frame.rolling(window=25, min_periods=minp, center=True).apply(f, raw=raw)
+    tm.assert_frame_equal(frame_xp, frame_rs)