From 7fe59506c901f09981344a54dc819330a4a17bb9 Mon Sep 17 00:00:00 2001
From: Richard Shadrach <rhshadrach@gmail.com>
Date: Sun, 21 Feb 2021 07:32:59 -0500
Subject: [PATCH] CLN: Move the rest of Series.apply into apply

---
 pandas/core/apply.py  | 30 ++++++++++++++++++++++++++++++
 pandas/core/series.py | 23 +----------------------
 2 files changed, 31 insertions(+), 22 deletions(-)

diff --git a/pandas/core/apply.py b/pandas/core/apply.py
index b41c432dff172..b0f8534f2da54 100644
--- a/pandas/core/apply.py
+++ b/pandas/core/apply.py
@@ -845,6 +845,36 @@ def apply(self) -> FrameOrSeriesUnion:
 
         return self.apply_standard()
 
+    def agg(self):
+        result = super().agg()
+        if result is None:
+            f = self.f
+            args = self.args
+            kwargs = self.kwargs
+
+            # string, list-like, and dict-like are entirely handled in super
+            assert callable(f)
+
+            # we can be called from an inner function which
+            # passes this meta-data
+            kwargs.pop("_axis", None)
+            kwargs.pop("_level", None)
+
+            # try a regular apply, this evaluates lambdas
+            # row-by-row; however if the lambda is expected a Series
+            # expression, e.g.: lambda x: x-x.quantile(0.25)
+            # this will fail, so we can try a vectorized evaluation
+
+            # we cannot FIRST try the vectorized evaluation, because
+            # then .agg and .apply would have different semantics if the
+            # operation is actually defined on the Series, e.g. str
+            try:
+                result = self.obj.apply(f, *args, **kwargs)
+            except (ValueError, AttributeError, TypeError):
+                result = f(self.obj, *args, **kwargs)
+
+        return result
+
     def apply_empty_result(self) -> Series:
         obj = self.obj
         return obj._constructor(dtype=obj.dtype, index=obj.index).__finalize__(
diff --git a/pandas/core/series.py b/pandas/core/series.py
index cbb66918a661b..f3fbecd748e6d 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -4003,26 +4003,6 @@ def aggregate(self, func=None, axis=0, *args, **kwargs):
 
         op = series_apply(self, func, args=args, kwargs=kwargs)
         result = op.agg()
-        if result is None:
-
-            # we can be called from an inner function which
-            # passes this meta-data
-            kwargs.pop("_axis", None)
-            kwargs.pop("_level", None)
-
-            # try a regular apply, this evaluates lambdas
-            # row-by-row; however if the lambda is expected a Series
-            # expression, e.g.: lambda x: x-x.quantile(0.25)
-            # this will fail, so we can try a vectorized evaluation
-
-            # we cannot FIRST try the vectorized evaluation, because
-            # then .agg and .apply would have different semantics if the
-            # operation is actually defined on the Series, e.g. str
-            try:
-                result = self.apply(func, *args, **kwargs)
-            except (ValueError, AttributeError, TypeError):
-                result = func(self, *args, **kwargs)
-
         return result
 
     agg = aggregate
@@ -4146,8 +4126,7 @@ def apply(
         Helsinki    2.484907
         dtype: float64
         """
-        op = series_apply(self, func, convert_dtype, args, kwargs)
-        return op.apply()
+        return series_apply(self, func, convert_dtype, args, kwargs).apply()
 
     def _reduce(
         self,