DEPR: min_periods=None behavior for Rolling.count

doc/source/whatsnew/v1.2.0.rst

@@ -265,6 +265,7 @@ Deprecations
 - Deprecated indexing :class:`DataFrame` rows with datetime-like strings ``df[string]``, use ``df.loc[string]`` instead (:issue:`36179`)
 - Deprecated casting an object-dtype index of ``datetime`` objects to :class:`DatetimeIndex` in the :class:`Series` constructor (:issue:`23598`)
 - Deprecated :meth:`Index.is_all_dates` (:issue:`27744`)
+- :meth:`Rolling.count` with ``min_periods=None`` will default to the size of the window in a future version (:issue:`31302`)
 
 .. ---------------------------------------------------------------------------
 
@@ -404,6 +405,7 @@ Groupby/resample/rolling
 - Bug in :meth:`DataFrame.groupby` does not always maintain column index name for ``any``, ``all``, ``bfill``, ``ffill``, ``shift`` (:issue:`29764`)
 - Bug in :meth:`DataFrameGroupBy.apply` raising error with ``np.nan`` group(s) when ``dropna=False`` (:issue:`35889`)
 - Bug in :meth:`Rolling.sum()` returned wrong values when dtypes where mixed between float and integer and axis was equal to one (:issue:`20649`, :issue:`35596`)
+- Bug in :meth:`Rolling.count` returned ``np.nan`` with :class:`pandas.api.indexers.FixedForwardWindowIndexer` as window, ``min_periods=0`` and only missing values in window (:issue:`35579`)
 
 Reshaping
 ^^^^^^^^^

pandas/_libs/window/aggregations.pyx

@@ -89,62 +89,6 @@ cdef bint is_monotonic_start_end_bounds(
 # Physical description: 366 p.
 #               Series: Prentice-Hall Series in Automatic Computation
 
-# ----------------------------------------------------------------------
-# Rolling count
-# this is only an impl for index not None, IOW, freq aware
-
-
-def roll_count(
-    ndarray[float64_t] values,
-    ndarray[int64_t] start,
-    ndarray[int64_t] end,
-    int64_t minp,
-):
-    cdef:
-        float64_t val, count_x = 0.0
-        int64_t s, e, nobs, N = len(values)
-        Py_ssize_t i, j
-        ndarray[float64_t] output
-
-    output = np.empty(N, dtype=float)
-
-    with nogil:
-
-        for i in range(0, N):
-            s = start[i]
-            e = end[i]
-
-            if i == 0:
-
-                # setup
-                count_x = 0.0
-                for j in range(s, e):
-                    val = values[j]
-                    if notnan(val):
-                        count_x += 1.0
-
-            else:
-
-                # calculate deletes
-                for j in range(start[i - 1], s):
-                    val = values[j]
-                    if notnan(val):
-                        count_x -= 1.0
-
-                # calculate adds
-                for j in range(end[i - 1], e):
-                    val = values[j]
-                    if notnan(val):
-                        count_x += 1.0
-
-            if count_x >= minp:
-                output[i] = count_x
-            else:
-                output[i] = NaN
-
-    return output
-
-
 # ----------------------------------------------------------------------
 # Rolling sum
 

pandas/core/window/rolling.py

@@ -17,6 +17,7 @@
     Type,
     Union,
 )
+import warnings
 
 import numpy as np
 
@@ -469,31 +470,39 @@ def _get_window_indexer(self, window: int) -> BaseIndexer:
             return VariableWindowIndexer(index_array=self._on.asi8, window_size=window)
         return FixedWindowIndexer(window_size=window)
 
-    def _apply_series(self, homogeneous_func: Callable[..., ArrayLike]) -> "Series":
+    def _apply_series(
+        self, homogeneous_func: Callable[..., ArrayLike], name: Optional[str] = None
+    ) -> "Series":
         """
         Series version of _apply_blockwise
         """
         obj = self._create_data(self._selected_obj)
 
         try:
-            values = self._prep_values(obj.values)
+            # GH 12541: Special case for count where we support date-like types
+            input = obj.values if name != "count" else notna(obj.values).astype(int)
+            values = self._prep_values(input)
         except (TypeError, NotImplementedError) as err:
             raise DataError("No numeric types to aggregate") from err
 
         result = homogeneous_func(values)
         return obj._constructor(result, index=obj.index, name=obj.name)
 
     def _apply_blockwise(
-        self, homogeneous_func: Callable[..., ArrayLike]
+        self, homogeneous_func: Callable[..., ArrayLike], name: Optional[str] = None
     ) -> FrameOrSeriesUnion:
         """
         Apply the given function to the DataFrame broken down into homogeneous
         sub-frames.
         """
         if self._selected_obj.ndim == 1:
-            return self._apply_series(homogeneous_func)
+            return self._apply_series(homogeneous_func, name)
 
         obj = self._create_data(self._selected_obj)
+        if name == "count":
+            # GH 12541: Special case for count where we support date-like types
+            obj = notna(obj).astype(int)
+            obj._mgr = obj._mgr.consolidate()
         mgr = obj._mgr
 
         def hfunc(bvalues: ArrayLike) -> ArrayLike:
@@ -606,7 +615,7 @@ def calc(x):
 
             return result
 
-        return self._apply_blockwise(homogeneous_func)
+        return self._apply_blockwise(homogeneous_func, name)
 
     def aggregate(self, func, *args, **kwargs):
         result, how = self._aggregate(func, *args, **kwargs)
@@ -1265,33 +1274,8 @@ class RollingAndExpandingMixin(BaseWindow):
     )
 
     def count(self):
-        # GH 32865. Using count with custom BaseIndexer subclass
-        # implementations shouldn't end up here
-        assert not isinstance(self.window, BaseIndexer)
-
-        obj = self._create_data(self._selected_obj)
-
-        def hfunc(values: np.ndarray) -> np.ndarray:
-            result = notna(values)
-            result = result.astype(int)
-            frame = type(obj)(result.T)
-            result = self._constructor(
-                frame,
-                window=self._get_window(),
-                min_periods=self.min_periods or 0,
-                center=self.center,
-                axis=self.axis,
-                closed=self.closed,
-            ).sum()
-            return result.values.T
-
-        new_mgr = obj._mgr.apply(hfunc)
-        out = obj._constructor(new_mgr)
-        if obj.ndim == 1:
-            out.name = obj.name
-        else:
-            self._insert_on_column(out, obj)
-        return out
+        window_func = self._get_cython_func_type("roll_sum")
+        return self._apply(window_func, center=self.center, name="count")
 
     _shared_docs["apply"] = dedent(
         r"""
@@ -2050,14 +2034,16 @@ def aggregate(self, func, *args, **kwargs):
     @Substitution(name="rolling")
     @Appender(_shared_docs["count"])
     def count(self):
-
-        # different impl for freq counting
-        # GH 32865. Use a custom count function implementation
-        # when using a BaseIndexer subclass as a window
-        if self.is_freq_type or isinstance(self.window, BaseIndexer):
-            window_func = self._get_roll_func("roll_count")
-            return self._apply(window_func, center=self.center, name="count")
-
+        if self.min_periods is None:
+            warnings.warn(
+                (
+                    "min_periods=None will default to the size of window "
+                    "consistent with other methods in a future version. "
+                    "Specify min_periods=0 instead."
+                ),
+                FutureWarning,
+            )
+            self.min_periods = 0
         return super().count()
 
     @Substitution(name="rolling")

pandas/tests/window/moments/test_moments_consistency_rolling.py

@@ -452,7 +452,7 @@ def test_moment_functions_zero_length():
     df2_expected = df2
 
     functions = [
-        lambda x: x.rolling(window=10).count(),
+        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(),

pandas/tests/window/moments/test_moments_rolling_functions.py

@@ -12,7 +12,12 @@
     [
         [np.mean, "mean", {}],
         [np.nansum, "sum", {}],
-        [lambda x: np.isfinite(x).astype(float).sum(), "count", {}],
+        pytest.param(
+            lambda x: np.isfinite(x).astype(float).sum(),
+            "count",
+            {},
+            marks=pytest.mark.filterwarnings("ignore:min_periods:FutureWarning"),
+        ),
         [np.median, "median", {}],
         [np.min, "min", {}],
         [np.max, "max", {}],
@@ -33,7 +38,12 @@ def test_series(series, compare_func, roll_func, kwargs):
     [
         [np.mean, "mean", {}],
         [np.nansum, "sum", {}],
-        [lambda x: np.isfinite(x).astype(float).sum(), "count", {}],
+        pytest.param(
+            lambda x: np.isfinite(x).astype(float).sum(),
+            "count",
+            {},
+            marks=pytest.mark.filterwarnings("ignore:min_periods:FutureWarning"),
+        ),
         [np.median, "median", {}],
         [np.min, "min", {}],
         [np.max, "max", {}],

pandas/tests/window/test_base_indexer.py

@@ -138,6 +138,7 @@ def get_window_bounds(self, num_values, min_periods, center, closed):
         ),
     ],
 )
+@pytest.mark.filterwarnings("ignore:min_periods:FutureWarning")
 def test_rolling_forward_window(constructor, func, np_func, expected, np_kwargs):
     # GH 32865
     values = np.arange(10.0)
@@ -253,3 +254,12 @@ def test_non_fixed_variable_window_indexer(closed, expected_data):
     result = df.rolling(indexer, closed=closed).sum()
     expected = DataFrame(expected_data, index=index)
     tm.assert_frame_equal(result, expected)
+
+
+def test_fixed_forward_indexer_count():
+    # GH: 35579
+    df = DataFrame({"b": [None, None, None, 7]})
+    indexer = FixedForwardWindowIndexer(window_size=2)
+    result = df.rolling(window=indexer, min_periods=0).count()
+    expected = DataFrame({"b": [0.0, 0.0, 1.0, 1.0]})
+    tm.assert_frame_equal(result, expected)