ENH: Rolling window with step size (pandas-devGH-15354)

pandas/_libs/window/indexers.pyi

@@ -8,5 +8,6 @@ def calculate_variable_window_bounds(
     min_periods,
     center: bool,
     closed: str | None,
+    step: int | None,
     index: np.ndarray,  # const int64_t[:]
-) -> tuple[npt.NDArray[np.int64], npt.NDArray[np.int64]]: ...
+) -> tuple[npt.NDArray[np.int64], npt.NDArray[np.int64], npt.NDArray[np.int64]]: ...

pandas/_libs/window/indexers.pyx

@@ -16,6 +16,7 @@ def calculate_variable_window_bounds(
     object min_periods,  # unused but here to match get_window_bounds signature
     bint center,
     str closed,
+    int64_t step,
     const int64_t[:] index
 ):
     """
@@ -38,17 +39,20 @@ def calculate_variable_window_bounds(
     closed : str
         string of side of the window that should be closed
 
+    step : int64
+        Spacing between windows
+
     index : ndarray[int64]
         time series index to roll over
 
     Returns
     -------
-    (ndarray[int64], ndarray[int64])
+    (ndarray[int64], ndarray[int64], ndarray[int64])
     """
     cdef:
         bint left_closed = False
         bint right_closed = False
-        ndarray[int64_t, ndim=1] start, end
+        ndarray[int64_t, ndim=1] start, end, ref
         int64_t start_bound, end_bound, index_growth_sign = 1
         Py_ssize_t i, j
 
@@ -143,4 +147,6 @@ def calculate_variable_window_bounds(
             # right endpoint is open
             if not right_closed and not center:
                 end[i] -= 1
-    return start, end
+    ref = (None if step is None or step == 1
+           else np.arange(0, num_values, step, dtype='int64'))
+    return start[::step], end[::step], ref

pandas/core/generic.py

@@ -11263,6 +11263,7 @@ def rolling(
         on: str | None = None,
         axis: Axis = 0,
         closed: str | None = None,
+        step: int | None = None,
         method: str = "single",
     ):
         axis = self._get_axis_number(axis)
@@ -11277,6 +11278,7 @@ def rolling(
                 on=on,
                 axis=axis,
                 closed=closed,
+                step=step,
                 method=method,
             )
 
@@ -11289,6 +11291,7 @@ def rolling(
             on=on,
             axis=axis,
             closed=closed,
+            step=step,
             method=method,
         )
 

pandas/core/indexers/objects.py

@@ -27,13 +27,18 @@
     center passed from the top level rolling API
 closed : str, default None
     closed passed from the top level rolling API
+step : int, default None
+    step passed from the top level rolling API
 win_type : str, default None
     win_type passed from the top level rolling API
 
 Returns
 -------
-A tuple of ndarray[int64]s, indicating the boundaries of each
-window
+A tuple of ndarray[int64]s:
+    start : array of start boundaries
+    end : array of end boundaries
+    ref : array of window reference locations, or None indicating all
+          must be None if step is None or 1
 """
 
 
@@ -55,14 +60,25 @@ def __init__(
         for key, value in kwargs.items():
             setattr(self, key, value)
 
+    def _get_default_ref(self, num_values: int = 0, step: int | None = None):
+        """
+        Returns the default window reference locations.
+        """
+        return (
+            None
+            if step is None or step == 1
+            else np.arange(0, num_values, step, dtype="int64")
+        )
+
     @Appender(get_window_bounds_doc)
     def get_window_bounds(
         self,
         num_values: int = 0,
         min_periods: int | None = None,
         center: bool | None = None,
         closed: str | None = None,
-    ) -> tuple[np.ndarray, np.ndarray]:
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray | None]:
 
         raise NotImplementedError
 
@@ -77,14 +93,15 @@ def get_window_bounds(
         min_periods: int | None = None,
         center: bool | None = None,
         closed: str | None = None,
-    ) -> tuple[np.ndarray, np.ndarray]:
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray | None]:
 
         if center:
             offset = (self.window_size - 1) // 2
         else:
             offset = 0
 
-        end = np.arange(1 + offset, num_values + 1 + offset, dtype="int64")
+        end = np.arange(1 + offset, num_values + 1 + offset, step, dtype="int64")
         start = end - self.window_size
         if closed in ["left", "both"]:
             start -= 1
@@ -94,7 +111,8 @@ def get_window_bounds(
         end = np.clip(end, 0, num_values)
         start = np.clip(start, 0, num_values)
 
-        return start, end
+        ref = self._get_default_ref(num_values, step)
+        return start, end, ref
 
 
 class VariableWindowIndexer(BaseIndexer):
@@ -107,7 +125,8 @@ def get_window_bounds(
         min_periods: int | None = None,
         center: bool | None = None,
         closed: str | None = None,
-    ) -> tuple[np.ndarray, np.ndarray]:
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray | None]:
 
         # error: Argument 4 to "calculate_variable_window_bounds" has incompatible
         # type "Optional[bool]"; expected "bool"
@@ -119,6 +138,7 @@ def get_window_bounds(
             min_periods,
             center,  # type: ignore[arg-type]
             closed,
+            step if step is not None else 1,
             self.index_array,  # type: ignore[arg-type]
         )
 
@@ -145,11 +165,14 @@ def get_window_bounds(
         min_periods: int | None = None,
         center: bool | None = None,
         closed: str | None = None,
-    ) -> tuple[np.ndarray, np.ndarray]:
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray | None]:
 
         # if windows is variable, default is 'right', otherwise default is 'both'
         if closed is None:
             closed = "right" if self.index is not None else "both"
+        if step is None:
+            step = 1
 
         right_closed = closed in ["right", "both"]
         left_closed = closed in ["left", "both"]
@@ -202,7 +225,8 @@ def get_window_bounds(
             if not right_closed:
                 end[i] -= 1
 
-        return start, end
+        ref = self._get_default_ref(num_values, step)
+        return start[::step], end[::step], ref
 
 
 class ExpandingIndexer(BaseIndexer):
@@ -215,12 +239,15 @@ def get_window_bounds(
         min_periods: int | None = None,
         center: bool | None = None,
         closed: str | None = None,
-    ) -> tuple[np.ndarray, np.ndarray]:
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray | None]:
 
-        return (
-            np.zeros(num_values, dtype=np.int64),
-            np.arange(1, num_values + 1, dtype=np.int64),
-        )
+        if step is None:
+            step = 1
+        end = np.arange(1, num_values + 1, step, dtype=np.int64)
+        start = np.zeros(len(end), dtype=np.int64)
+        ref = self._get_default_ref(num_values, step)
+        return start, end, ref
 
 
 class FixedForwardWindowIndexer(BaseIndexer):
@@ -256,21 +283,25 @@ def get_window_bounds(
         min_periods: int | None = None,
         center: bool | None = None,
         closed: str | None = None,
-    ) -> tuple[np.ndarray, np.ndarray]:
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray | None]:
 
         if center:
             raise ValueError("Forward-looking windows can't have center=True")
         if closed is not None:
             raise ValueError(
                 "Forward-looking windows don't support setting the closed argument"
             )
+        if step is None:
+            step = 1
 
-        start = np.arange(num_values, dtype="int64")
+        start = np.arange(num_values, step=step, dtype="int64")
         end = start + self.window_size
         if self.window_size:
-            end[-self.window_size :] = num_values
+            end = np.clip(end, 0, num_values)
 
-        return start, end
+        ref = self._get_default_ref(num_values, step)
+        return start, end, ref
 
 
 class GroupbyIndexer(BaseIndexer):
@@ -319,12 +350,14 @@ def get_window_bounds(
         min_periods: int | None = None,
         center: bool | None = None,
         closed: str | None = None,
-    ) -> tuple[np.ndarray, np.ndarray]:
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray | None]:
         # 1) For each group, get the indices that belong to the group
         # 2) Use the indices to calculate the start & end bounds of the window
         # 3) Append the window bounds in group order
         start_arrays = []
         end_arrays = []
+        ref_arrays = []
         window_indices_start = 0
         for key, indices in self.groupby_indices.items():
             index_array: np.ndarray | None
@@ -338,11 +371,12 @@ def get_window_bounds(
                 window_size=self.window_size,
                 **self.indexer_kwargs,
             )
-            start, end = indexer.get_window_bounds(
-                len(indices), min_periods, center, closed
+            start, end, ref = indexer.get_window_bounds(
+                len(indices), min_periods, center, closed, step
             )
             start = start.astype(np.int64)
             end = end.astype(np.int64)
+            ref = None if ref is None else ref.astype(np.int64)
             assert len(start) == len(
                 end
             ), "these should be equal in length from get_window_bounds"
@@ -358,9 +392,13 @@ def get_window_bounds(
             )
             start_arrays.append(window_indices.take(ensure_platform_int(start)))
             end_arrays.append(window_indices.take(ensure_platform_int(end)))
+            ref_arrays.append(
+                None if ref is None else window_indices.take(ensure_platform_int(ref))
+            )
         start = np.concatenate(start_arrays)
         end = np.concatenate(end_arrays)
-        return start, end
+        ref = None if step is None or step == 1 else np.concatenate(ref_arrays)
+        return start, end, ref
 
 
 class ExponentialMovingWindowIndexer(BaseIndexer):
@@ -373,6 +411,11 @@ def get_window_bounds(
         min_periods: int | None = None,
         center: bool | None = None,
         closed: str | None = None,
-    ) -> tuple[np.ndarray, np.ndarray]:
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray | None]:
 
-        return np.array([0], dtype=np.int64), np.array([num_values], dtype=np.int64)
+        return (
+            np.array([0], dtype=np.int64),
+            np.array([num_values], dtype=np.int64),
+            None,
+        )

pandas/core/window/common.py

@@ -22,7 +22,9 @@ def flex_binary_moment(arg1, arg2, f, pairwise=False):
         from pandas import DataFrame
 
         def dataframe_from_int_dict(data, frame_template):
-            result = DataFrame(data, index=frame_template.index)
+            result = DataFrame(
+                data, index=None if len(data) > 0 else frame_template.index
+            )
             if len(result.columns) > 0:
                 result.columns = frame_template.columns[result.columns]
             return result
@@ -42,13 +44,16 @@ def dataframe_from_int_dict(data, frame_template):
                         raise ValueError("'arg2' columns are not unique")
                     X, Y = arg1.align(arg2, join="outer")
                     X, Y = prep_binary(X, Y)
+                    result_index = X.index
                     res_columns = arg1.columns.union(arg2.columns)
                     for col in res_columns:
                         if col in X and col in Y:
                             results[col] = f(X[col], Y[col])
-                    return DataFrame(results, index=X.index, columns=res_columns)
+                            result_index = results[col].index
+                    return DataFrame(results, index=result_index, columns=res_columns)
             elif pairwise is True:
                 results = defaultdict(dict)
+                result_index = arg1.index.union(arg2.index)
                 for i in range(len(arg1.columns)):
                     for j in range(len(arg2.columns)):
                         if j < i and arg2 is arg1:
@@ -58,10 +63,10 @@ def dataframe_from_int_dict(data, frame_template):
                             results[i][j] = f(
                                 *prep_binary(arg1.iloc[:, i], arg2.iloc[:, j])
                             )
+                            result_index = results[i][j].index
 
                 from pandas import concat
 
-                result_index = arg1.index.union(arg2.index)
                 if len(result_index):
 
                     # construct result frame

pandas/core/window/ewm.py

@@ -418,7 +418,7 @@ def __init__(
             )
 
     def _check_window_bounds(
-        self, start: np.ndarray, end: np.ndarray, num_vals: int
+        self, start: np.ndarray, end: np.ndarray, ref: np.ndarray, num_vals: int
     ) -> None:
         # emw algorithms are iterative with each point
         # ExponentialMovingWindowIndexer "bounds" are the entire window
@@ -732,11 +732,12 @@ def cov_func(x, y):
                 if self.min_periods is not None
                 else window_indexer.window_size
             )
-            start, end = window_indexer.get_window_bounds(
+            start, end, ref = window_indexer.get_window_bounds(
                 num_values=len(x_array),
                 min_periods=min_periods,
                 center=self.center,
                 closed=self.closed,
+                step=self.step,
             )
             result = window_aggregations.ewmcov(
                 x_array,
@@ -798,11 +799,12 @@ def cov_func(x, y):
                 if self.min_periods is not None
                 else window_indexer.window_size
             )
-            start, end = window_indexer.get_window_bounds(
+            start, end, ref = window_indexer.get_window_bounds(
                 num_values=len(x_array),
                 min_periods=min_periods,
                 center=self.center,
                 closed=self.closed,
+                step=self.step,
             )
 
             def _cov(X, Y):

pandas/core/window/numba_.py

@@ -234,8 +234,8 @@ def roll_table(
         minimum_periods: int,
         *args: Any,
     ):
-        result = np.empty(values.shape)
-        min_periods_mask = np.empty(values.shape)
+        result = np.empty((len(begin), values.shape[1]))
+        min_periods_mask = np.empty(result.shape)
         for i in numba.prange(len(result)):
             start = begin[i]
             stop = end[i]