use initial instead + fix test for non-infer mode

Author: jorisvandenbossche

pandas/_libs/groupby.pyi

@@ -67,7 +67,7 @@ def group_sum(
     result_mask: np.ndarray | None = ...,
     min_count: int = ...,
     is_datetimelike: bool = ...,
-    is_string: bool = ...,
+    initial: object = ...,
     skipna: bool = ...,
 ) -> None: ...
 def group_prod(

pandas/_libs/groupby.pyx

@@ -707,7 +707,7 @@ def group_sum(
     uint8_t[:, ::1] result_mask=None,
     Py_ssize_t min_count=0,
     bint is_datetimelike=False,
-    bint is_string=False,
+    object initial=0,
     bint skipna=True,
 ) -> None:
     """
@@ -726,13 +726,15 @@ def group_sum(
         raise ValueError("len(index) != len(labels)")
 
     nobs = np.zeros((<object>out).shape, dtype=np.int64)
-    # the below is equivalent to `np.zeros_like(out)` but faster
-    sumx = np.zeros((<object>out).shape, dtype=(<object>out).base.dtype)
-    compensation = np.zeros((<object>out).shape, dtype=(<object>out).base.dtype)
-
-    if is_string:
-        # for strings start with empty string instead of 0 as `initial` value
-        sumx = np.full((<object>out).shape, "", dtype=object)
+    if initial == 0:
+        # the below is equivalent to `np.zeros_like(out)` but faster
+        sumx = np.zeros((<object>out).shape, dtype=(<object>out).base.dtype)
+        compensation = np.zeros((<object>out).shape, dtype=(<object>out).base.dtype)
+    else:
+        # in practice this path is only taken for strings to use empty string as initial
+        assert sum_t is object
+        sumx = np.full((<object>out).shape, initial, dtype=object)
+        # object code path does not use `compensation`
 
     N, K = (<object>values).shape
     if uses_mask:

pandas/core/arrays/base.py

@@ -2608,7 +2608,7 @@ def _groupby_op(
         kind = WrappedCythonOp.get_kind_from_how(how)
         op = WrappedCythonOp(how=how, kind=kind, has_dropped_na=has_dropped_na)
 
-        is_string = False
+        initial = 0
         # GH#43682
         if isinstance(self.dtype, StringDtype):
             # StringArray
@@ -2633,7 +2633,7 @@ def _groupby_op(
 
             arr = self
             if op.how == "sum":
-                is_string = True
+                initial = ""
                 # https://github.com/pandas-dev/pandas/issues/60229
                 # All NA should result in the empty string.
                 assert "skipna" in kwargs
@@ -2651,7 +2651,7 @@ def _groupby_op(
             ngroups=ngroups,
             comp_ids=ids,
             mask=None,
-            is_string=is_string,
+            initial=initial,
             **kwargs,
         )
 

pandas/core/groupby/ops.py

@@ -12,6 +12,7 @@
 import functools
 from typing import (
     TYPE_CHECKING,
+    Any,
     Generic,
     final,
 )
@@ -319,7 +320,7 @@ def _cython_op_ndim_compat(
         comp_ids: np.ndarray,
         mask: npt.NDArray[np.bool_] | None = None,
         result_mask: npt.NDArray[np.bool_] | None = None,
-        is_string: bool = False,
+        initial: Any = 0,
         **kwargs,
     ) -> np.ndarray:
         if values.ndim == 1:
@@ -336,7 +337,7 @@ def _cython_op_ndim_compat(
                 comp_ids=comp_ids,
                 mask=mask,
                 result_mask=result_mask,
-                is_string=is_string,
+                initial=initial,
                 **kwargs,
             )
             if res.shape[0] == 1:
@@ -352,7 +353,7 @@ def _cython_op_ndim_compat(
             comp_ids=comp_ids,
             mask=mask,
             result_mask=result_mask,
-            is_string=is_string,
+            initial=initial,
             **kwargs,
         )
 
@@ -366,7 +367,7 @@ def _call_cython_op(
         comp_ids: np.ndarray,
         mask: npt.NDArray[np.bool_] | None,
         result_mask: npt.NDArray[np.bool_] | None,
-        is_string: bool = False,
+        initial: Any = 0,
         **kwargs,
     ) -> np.ndarray:  # np.ndarray[ndim=2]
         orig_values = values
@@ -427,7 +428,7 @@ def _call_cython_op(
                 if self.how == "sum":
                     # pass in through kwargs only for sum (other functions don't have
                     # the keyword)
-                    kwargs["is_string"] = is_string
+                    kwargs["initial"] = initial
                 func(
                     out=result,
                     counts=counts,

pandas/tests/groupby/test_categorical.py

@@ -325,7 +325,7 @@ def test_apply(ordered):
     tm.assert_series_equal(result, expected)
 
 
-def test_observed(observed):
+def test_observed(observed, using_infer_string):
     # multiple groupers, don't re-expand the output space
     # of the grouper
     # gh-14942 (implement)
@@ -360,7 +360,10 @@ def test_observed(observed):
     result = gb.sum()
     if not observed:
         expected = cartesian_product_for_groupers(
-            expected, [cat1, cat2], list("AB"), fill_value={"values": 0, "C": ""}
+            expected,
+            [cat1, cat2],
+            list("AB"),
+            fill_value={"values": 0, "C": ""} if using_infer_string else 0,
         )
 
     tm.assert_frame_equal(result, expected)