PERF: cache sortings in groupby.ops

pandas/core/groupby/groupby.py

@@ -1167,8 +1167,8 @@ def _wrap_applied_output(
     @final
     def _numba_prep(self, data: DataFrame):
         ids, _, ngroups = self.grouper.group_info
-        sorted_index = get_group_index_sorter(ids, ngroups)
-        sorted_ids = algorithms.take_nd(ids, sorted_index, allow_fill=False)
+        sorted_index = self.grouper._sort_idx
+        sorted_ids = self.grouper._sorted_ids
 
         sorted_data = data.take(sorted_index, axis=self.axis).to_numpy()
         if len(self.grouper.groupings) > 1:

pandas/core/groupby/ops.py

@@ -734,7 +734,14 @@ def _get_splitter(self, data: NDFrame, axis: AxisInt = 0) -> DataSplitter:
         Generator yielding subsetted objects
         """
         ids, _, ngroups = self.group_info
-        return _get_splitter(data, ids, ngroups, axis=axis)
+        return _get_splitter(
+            data,
+            ids,
+            ngroups,
+            sorted_ids=self._sorted_ids,
+            sort_idx=self._sort_idx,
+            axis=axis,
+        )
 
     @final
     @cache_readonly
@@ -747,45 +754,6 @@ def group_keys_seq(self):
             # provide "flattened" iterator for multi-group setting
             return get_flattened_list(ids, ngroups, self.levels, self.codes)
 
-    @final
-    def apply_groupwise(
-        self, f: Callable, data: DataFrame | Series, axis: AxisInt = 0
-    ) -> tuple[list, bool]:
-        mutated = False
-        splitter = self._get_splitter(data, axis=axis)
-        group_keys = self.group_keys_seq
-        result_values = []
-
-        # This calls DataSplitter.__iter__
-        zipped = zip(group_keys, splitter)
-
-        for key, group in zipped:
-            # Pinning name is needed for
-            #  test_group_apply_once_per_group,
-            #  test_inconsistent_return_type, test_set_group_name,
-            #  test_group_name_available_in_inference_pass,
-            #  test_groupby_multi_timezone
-            object.__setattr__(group, "name", key)
-
-            # group might be modified
-            group_axes = group.axes
-            res = f(group)
-            if not mutated and not _is_indexed_like(res, group_axes, axis):
-                mutated = True
-            result_values.append(res)
-        # getattr pattern for __name__ is needed for functools.partial objects
-        if len(group_keys) == 0 and getattr(f, "__name__", None) in [
-            "skew",
-            "sum",
-            "prod",
-        ]:
-            #  If group_keys is empty, then no function calls have been made,
-            #  so we will not have raised even if this is an invalid dtype.
-            #  So do one dummy call here to raise appropriate TypeError.
-            f(data.iloc[:0])
-
-        return result_values, mutated
-
     @cache_readonly
     def indices(self) -> dict[Hashable, npt.NDArray[np.intp]]:
         """dict {group name -> group indices}"""
@@ -1029,6 +997,61 @@ def _aggregate_series_pure_python(
 
         return result
 
+    @final
+    def apply_groupwise(
+        self, f: Callable, data: DataFrame | Series, axis: AxisInt = 0
+    ) -> tuple[list, bool]:
+        mutated = False
+        splitter = self._get_splitter(data, axis=axis)
+        group_keys = self.group_keys_seq
+        result_values = []
+
+        # This calls DataSplitter.__iter__
+        zipped = zip(group_keys, splitter)
+
+        for key, group in zipped:
+            # Pinning name is needed for
+            #  test_group_apply_once_per_group,
+            #  test_inconsistent_return_type, test_set_group_name,
+            #  test_group_name_available_in_inference_pass,
+            #  test_groupby_multi_timezone
+            object.__setattr__(group, "name", key)
+
+            # group might be modified
+            group_axes = group.axes
+            res = f(group)
+            if not mutated and not _is_indexed_like(res, group_axes, axis):
+                mutated = True
+            result_values.append(res)
+        # getattr pattern for __name__ is needed for functools.partial objects
+        if len(group_keys) == 0 and getattr(f, "__name__", None) in [
+            "skew",
+            "sum",
+            "prod",
+        ]:
+            #  If group_keys is empty, then no function calls have been made,
+            #  so we will not have raised even if this is an invalid dtype.
+            #  So do one dummy call here to raise appropriate TypeError.
+            f(data.iloc[:0])
+
+        return result_values, mutated
+
+    # ------------------------------------------------------------
+    # Methods for sorting subsets of our GroupBy's object
+
+    @final
+    @cache_readonly
+    def _sort_idx(self) -> npt.NDArray[np.intp]:
+        # Counting sort indexer
+        ids, _, ngroups = self.group_info
+        return get_group_index_sorter(ids, ngroups)
+
+    @final
+    @cache_readonly
+    def _sorted_ids(self) -> npt.NDArray[np.intp]:
+        ids, _, _ = self.group_info
+        return ids.take(self._sort_idx)
+
 
 class BinGrouper(BaseGrouper):
     """
@@ -1211,25 +1234,21 @@ def __init__(
         data: NDFrameT,
         labels: npt.NDArray[np.intp],
         ngroups: int,
+        *,
+        sort_idx: npt.NDArray[np.intp],
+        sorted_ids: npt.NDArray[np.intp],
         axis: AxisInt = 0,
     ) -> None:
         self.data = data
         self.labels = ensure_platform_int(labels)  # _should_ already be np.intp
         self.ngroups = ngroups
 
+        self._slabels = sorted_ids
+        self._sort_idx = sort_idx
+
         self.axis = axis
         assert isinstance(axis, int), axis
 
-    @cache_readonly
-    def _slabels(self) -> npt.NDArray[np.intp]:
-        # Sorted labels
-        return self.labels.take(self._sort_idx)
-
-    @cache_readonly
-    def _sort_idx(self) -> npt.NDArray[np.intp]:
-        # Counting sort indexer
-        return get_group_index_sorter(self.labels, self.ngroups)
-
     def __iter__(self) -> Iterator:
         sdata = self._sorted_data
 
@@ -1272,12 +1291,20 @@ def _chop(self, sdata: DataFrame, slice_obj: slice) -> DataFrame:
 
 
 def _get_splitter(
-    data: NDFrame, labels: np.ndarray, ngroups: int, axis: AxisInt = 0
+    data: NDFrame,
+    labels: npt.NDArray[np.intp],
+    ngroups: int,
+    *,
+    sort_idx: npt.NDArray[np.intp],
+    sorted_ids: npt.NDArray[np.intp],
+    axis: AxisInt = 0,
 ) -> DataSplitter:
     if isinstance(data, Series):
         klass: type[DataSplitter] = SeriesSplitter
     else:
         # i.e. DataFrame
         klass = FrameSplitter
 
-    return klass(data, labels, ngroups, axis)
+    return klass(
+        data, labels, ngroups, sort_idx=sort_idx, sorted_ids=sorted_ids, axis=axis
+    )