BUG Fix for Add numeric_only to function signature of DataFrameGroupBy.cumprod and `DataFrameGroupBy.cumsum

doc/source/whatsnew/v3.0.0.rst

@@ -610,6 +610,7 @@ Groupby/resample/rolling
 - Bug in :meth:`DataFrameGroupBy.agg` that raises ``AttributeError`` when there is dictionary input and duplicated columns, instead of returning a DataFrame with the aggregation of all duplicate columns. (:issue:`55041`)
 - Bug in :meth:`DataFrameGroupBy.apply` that was returning a completely empty DataFrame when all return values of ``func`` were ``None`` instead of returning an empty DataFrame with the original columns and dtypes. (:issue:`57775`)
 - Bug in :meth:`DataFrameGroupBy.apply` with ``as_index=False`` that was returning :class:`MultiIndex` instead of returning :class:`Index`. (:issue:`58291`)
+- Bug in :meth:`DataFrameGroupBy.cumsum` and :meth:`DataFrameGroupBy.cumprod` where ``numeric_only`` parameter was passed indirectly through kwargs instead of passing directly. (:issue:`58811`)
 - Bug in :meth:`DataFrameGroupBy.cumsum` where it did not return the correct dtype when the label contained ``None``. (:issue:`58811`)
 - Bug in :meth:`DataFrameGroupby.transform` and :meth:`SeriesGroupby.transform` with a reducer and ``observed=False`` that coerces dtype to float when there are unobserved categories. (:issue:`55326`)
 - Bug in :meth:`Rolling.apply` where the applied function could be called on fewer than ``min_period`` periods if ``method="table"``. (:issue:`58868`)

pandas/core/groupby/groupby.py

@@ -4681,12 +4681,14 @@ def rank(
     @final
     @Substitution(name="groupby")
     @Substitution(see_also=_common_see_also)
-    def cumprod(self, *args, **kwargs) -> NDFrameT:
+    def cumprod(self, numeric_only: bool = False, *args, **kwargs) -> NDFrameT:
         """
         Cumulative product for each group.
 
         Parameters
         ----------
+        numeric_only : bool
+            Optional argument with default as False.
         *args : tuple
             Positional arguments to be passed to `func`.
         **kwargs : dict
@@ -4734,18 +4736,20 @@ def cumprod(self, *args, **kwargs) -> NDFrameT:
         horse  16  10
         bull    6   9
         """
-        nv.validate_groupby_func("cumprod", args, kwargs, ["numeric_only", "skipna"])
-        return self._cython_transform("cumprod", **kwargs)
+        nv.validate_groupby_func("cumprod", args, kwargs, ["skipna"])
+        return self._cython_transform("cumprod", numeric_only, **kwargs)
 
     @final
     @Substitution(name="groupby")
     @Substitution(see_also=_common_see_also)
-    def cumsum(self, *args, **kwargs) -> NDFrameT:
+    def cumsum(self, numeric_only: bool = False, *args, **kwargs) -> NDFrameT:
         """
         Cumulative sum for each group.
 
         Parameters
         ----------
+        numeric_only : bool
+            Optional argument with default as False.
         *args : tuple
             Positional arguments to be passed to `func`.
         **kwargs : dict
@@ -4793,8 +4797,8 @@ def cumsum(self, *args, **kwargs) -> NDFrameT:
         gorilla  10   7
         lion      6   9
         """
-        nv.validate_groupby_func("cumsum", args, kwargs, ["numeric_only", "skipna"])
-        return self._cython_transform("cumsum", **kwargs)
+        nv.validate_groupby_func("cumsum", args, kwargs, ["skipna"])
+        return self._cython_transform("cumsum", numeric_only, **kwargs)
 
     @final
     @Substitution(name="groupby")

pandas/tests/groupby/test_api.py

@@ -185,7 +185,7 @@ def test_frame_consistency(groupby_func):
     elif groupby_func in ("cummax", "cummin"):
         exclude_expected = {"axis", "skipna", "args"}
     elif groupby_func in ("cumprod", "cumsum"):
-        exclude_expected = {"axis", "skipna", "numeric_only"}
+        exclude_expected = {"axis", "skipna"}
     elif groupby_func in ("pct_change",):
         exclude_expected = {"kwargs"}
     elif groupby_func in ("rank",):
@@ -245,6 +245,7 @@ def test_series_consistency(request, groupby_func):
         exclude_result = {"numeric_only"}
     elif groupby_func in ("cumprod", "cumsum"):
         exclude_expected = {"skipna"}
+        exclude_result = {"numeric_only"}
     elif groupby_func in ("pct_change",):
         exclude_expected = {"kwargs"}
     elif groupby_func in ("rank",):