PERF: avoid creating many Series in apply_standard

pandas/core/apply.py

@@ -4,16 +4,13 @@
 
 import numpy as np
 
+from pandas._config import option_context
+
 from pandas._libs import reduction as libreduction
 from pandas._typing import Axis
 from pandas.util._decorators import cache_readonly
 
-from pandas.core.dtypes.common import (
-    is_dict_like,
-    is_extension_array_dtype,
-    is_list_like,
-    is_sequence,
-)
+from pandas.core.dtypes.common import is_dict_like, is_list_like, is_sequence
 from pandas.core.dtypes.generic import ABCSeries
 
 from pandas.core.construction import create_series_with_explicit_dtype
@@ -266,53 +263,6 @@ def apply_standard(self):
         # partial result that may be returned from reduction
         partial_result = None
 
-        # try to reduce first (by default)
-        # this only matters if the reduction in values is of different dtype
-        # e.g. if we want to apply to a SparseFrame, then can't directly reduce
-
-        # we cannot reduce using non-numpy dtypes,
-        # as demonstrated in gh-12244
-        if (
-            self.result_type in ["reduce", None]
-            and not self.dtypes.apply(is_extension_array_dtype).any()
-            # Disallow dtypes where setting _index_data will break
-            #  ExtensionArray values, see GH#31182
-            and not self.dtypes.apply(lambda x: x.kind in ["m", "M"]).any()
-            # Disallow complex_internals since libreduction shortcut raises a TypeError
-            and not self.agg_axis._has_complex_internals
-        ):
-
-            values = self.values
-            index = self.obj._get_axis(self.axis)
-            labels = self.agg_axis
-            empty_arr = np.empty(len(index), dtype=values.dtype)
-
-            # Preserve subclass for e.g. test_subclassed_apply
-            dummy = self.obj._constructor_sliced(
-                empty_arr, index=index, dtype=values.dtype
-            )
-
-            try:
-                result, reduction_success = libreduction.compute_reduction(
-                    values, self.f, axis=self.axis, dummy=dummy, labels=labels
-                )
-            except TypeError:
-                # e.g. test_apply_ignore_failures we just ignore
-                if not self.ignore_failures:
-                    raise
-            except ZeroDivisionError:
-                # reached via numexpr; fall back to python implementation
-                pass
-            else:
-                if reduction_success:
-                    return self.obj._constructor_sliced(result, index=labels)
-
-                # no exceptions - however reduction was unsuccessful,
-                # use the computed function result for first element
-                partial_result = result[0]
-                if isinstance(partial_result, ABCSeries):
-                    partial_result = partial_result.infer_objects()
-
         # compute the result using the series generator,
         # use the result computed while trying to reduce if available.
         results, res_index = self.apply_series_generator(partial_result)
@@ -350,7 +300,14 @@ def apply_series_generator(self, partial_result=None) -> Tuple[ResType, "Index"]
         else:
             for i, v in series_gen_enumeration:
 
-                results[i] = self.f(v)
+                with option_context("mode.chained_assignment", None):
+                    # ignore SettingWithCopy here in case the user mutates
+                    results[i] = self.f(v)
+
+                if isinstance(results[i], ABCSeries):
+                    # If we have a view on v, we need to make a copy because
+                    #  series_generator will swap out the underlying data
+                    results[i] = results[i].copy(deep=False)
 
         return results, res_index
 
@@ -361,7 +318,6 @@ def wrap_results(
 
         # see if we can infer the results
         if len(results) > 0 and 0 in results and is_sequence(results[0]):
-
             return self.wrap_results_for_axis(results, res_index)
 
         # dict of scalars
@@ -401,9 +357,30 @@ def result_columns(self) -> "Index":
 
     def wrap_results_for_axis(
         self, results: ResType, res_index: "Index"
-    ) -> "DataFrame":
+    ) -> Union["Series", "DataFrame"]:
         """ return the results for the rows """
-        result = self.obj._constructor(data=results)
+
+        if self.result_type == "reduce":
+            # e.g. test_apply_dict GH#8735
+            return self.obj._constructor_sliced(results)
+        elif self.result_type is None and all(
+            isinstance(x, dict) for x in results.values()
+        ):
+            # Our operation was a to_dict op e.g.
+            #  test_apply_dict GH#8735, test_apply_reduce_rows_to_dict GH#25196
+            return self.obj._constructor_sliced(results)
+
+        try:
+            result = self.obj._constructor(data=results)
+        except ValueError as err:
+            if "arrays must all be same length" in str(err):
+                # e.g. result = [[2, 3], [1.5], ['foo', 'bar']]
+                #  see test_agg_listlike_result GH#29587
+                res = self.obj._constructor_sliced(results)
+                res.index = res_index
+                return res
+            else:
+                raise
 
         if not isinstance(results[0], ABCSeries):
             if len(result.index) == len(self.res_columns):
@@ -424,11 +401,19 @@ def apply_broadcast(self, target: "DataFrame") -> "DataFrame":
 
     @property
     def series_generator(self):
-        constructor = self.obj._constructor_sliced
-        return (
-            constructor(arr, index=self.columns, name=name)
-            for i, (arr, name) in enumerate(zip(self.values, self.index))
-        )
+        values = self.values
+        assert len(values) > 0
+
+        # We create one Series object, and will swap out the data inside
+        #  of it.  Kids: don't do this at home.
+        ser = self.obj._ixs(0, axis=0)
+        mgr = ser._mgr
+        blk = mgr.blocks[0]
+
+        for (arr, name) in zip(values, self.index):
+            blk.values = arr
+            ser.name = name
+            yield ser
 
     @property
     def result_index(self) -> "Index":
@@ -450,9 +435,7 @@ def wrap_results_for_axis(
 
         # we have a non-series and don't want inference
         elif not isinstance(results[0], ABCSeries):
-            from pandas import Series
-
-            result = Series(results)
+            result = self.obj._constructor_sliced(results)
             result.index = res_index
 
         # we may want to infer results