ENH: add numeric_only to Dataframe.cum* methods

doc/source/whatsnew/v2.1.0.rst

@@ -113,6 +113,7 @@ Other enhancements
 - Let :meth:`DataFrame.to_feather` accept a non-default :class:`Index` and non-string column names (:issue:`51787`)
 - Performance improvement in :func:`read_csv` (:issue:`52632`) with ``engine="c"``
 - :meth:`Categorical.from_codes` has gotten a ``validate`` parameter (:issue:`50975`)
+- :meth:`DataFrame.cum*` methods now have a ``numeric_only`` parameter (:issue:`53072`)
 - :meth:`DataFrame.unstack` gained the ``sort`` keyword to dictate whether the resulting :class:`MultiIndex` levels are sorted (:issue:`15105`)
 - :meth:`DataFrameGroupby.agg` and :meth:`DataFrameGroupby.transform` now support grouping by multiple keys when the index is not a :class:`MultiIndex` for ``engine="numba"`` (:issue:`53486`)
 - :meth:`Series.explode` now supports pyarrow-backed list types (:issue:`53602`)

pandas/core/frame.py

@@ -11102,20 +11102,52 @@ def kurt(
     product = prod
 
     @doc(make_doc("cummin", ndim=2))
-    def cummin(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs):
-        return NDFrame.cummin(self, axis, skipna, *args, **kwargs)
+    def cummin(
+        self,
+        axis: Axis | None = None,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        *args,
+        **kwargs,
+    ):
+        data = self._get_numeric_data() if numeric_only else self
+        return NDFrame.cummin(data, axis, skipna, *args, **kwargs)
 
     @doc(make_doc("cummax", ndim=2))
-    def cummax(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs):
-        return NDFrame.cummax(self, axis, skipna, *args, **kwargs)
+    def cummax(
+        self,
+        axis: Axis | None = None,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        *args,
+        **kwargs,
+    ):
+        data = self._get_numeric_data() if numeric_only else self
+        return NDFrame.cummax(data, axis, skipna, *args, **kwargs)
 
     @doc(make_doc("cumsum", ndim=2))
-    def cumsum(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs):
-        return NDFrame.cumsum(self, axis, skipna, *args, **kwargs)
+    def cumsum(
+        self,
+        axis: Axis | None = None,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        *args,
+        **kwargs,
+    ):
+        data = self._get_numeric_data() if numeric_only else self
+        return NDFrame.cumsum(data, axis, skipna, *args, **kwargs)
 
     @doc(make_doc("cumprod", 2))
-    def cumprod(self, axis: Axis | None = None, skipna: bool = True, *args, **kwargs):
-        return NDFrame.cumprod(self, axis, skipna, *args, **kwargs)
+    def cumprod(
+        self,
+        axis: Axis | None = None,
+        skipna: bool = True,
+        numeric_only: bool = False,
+        *args,
+        **kwargs,
+    ):
+        data = self._get_numeric_data() if numeric_only else self
+        return NDFrame.cumprod(data, axis, skipna, *args, **kwargs)
 
     def nunique(self, axis: Axis = 0, dropna: bool = True) -> Series:
         """

pandas/core/generic.py

@@ -12402,6 +12402,8 @@ def last_valid_index(self) -> Hashable | None:
 skipna : bool, default True
     Exclude NA/null values. If an entire row/column is NA, the result
     will be NA.
+numeric_only : bool, default False
+    Include only float, int, boolean columns.
 *args, **kwargs
     Additional keywords have no effect but might be accepted for
     compatibility with NumPy.

pandas/tests/frame/test_cumulative.py

@@ -12,6 +12,7 @@
 from pandas import (
     DataFrame,
     Series,
+    Timestamp,
 )
 import pandas._testing as tm
 
@@ -79,3 +80,25 @@ def test_cumsum_preserve_dtypes(self):
             }
         )
         tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("method", ["cumsum", "cumprod", "cummin", "cummax"])
+    def test_numeric_only_flag(self, method):
+        df = DataFrame(
+            {
+                "int": [1, 2, 3],
+                "bool": [True, False, False],
+                "string": ["a", "b", "c"],
+                "float": [1.0, 3.5, 4.0],
+                "datetime": [
+                    Timestamp(2018, 1, 1),
+                    Timestamp(2019, 1, 1),
+                    Timestamp(2020, 1, 1),
+                ],
+            }
+        )
+        df_numeric_only = df.drop(["string", "datetime"], axis=1)
+
+        for axis in [0, 1]:
+            result = getattr(df, method)(axis=axis, numeric_only=True)
+            expected = getattr(df_numeric_only, method)(axis)
+            tm.assert_frame_equal(result, expected)

pandas/tests/groupby/test_api.py

@@ -182,9 +182,8 @@ def test_frame_consistency(groupby_func):
         exclude_expected = {"downcast", "inplace", "axis"}
     elif groupby_func in ("cummax", "cummin"):
         exclude_expected = {"skipna", "args"}
-        exclude_result = {"numeric_only"}
     elif groupby_func in ("cumprod", "cumsum"):
-        exclude_expected = {"skipna"}
+        exclude_expected = {"skipna", "numeric_only"}
     elif groupby_func in ("pct_change",):
         exclude_expected = {"kwargs"}
         exclude_result = {"axis"}

pandas/tests/groupby/test_function.py

@@ -560,7 +560,7 @@ def test_axis1_numeric_only(request, groupby_func, numeric_only):
         kwargs["numeric_only"] = numeric_only
 
     # Functions without numeric_only and axis args
-    no_args = ("cumprod", "cumsum", "diff", "fillna", "pct_change", "rank", "shift")
+    no_args = ("diff", "fillna", "pct_change", "rank", "shift")
     # Functions with axis args
     has_axis = (
         "cumprod",
@@ -579,9 +579,8 @@ def test_axis1_numeric_only(request, groupby_func, numeric_only):
     if numeric_only is not None and groupby_func in no_args:
         msg = "got an unexpected keyword argument 'numeric_only'"
         if groupby_func in ["cumprod", "cumsum"]:
-            with pytest.raises(TypeError, match=msg):
-                with tm.assert_produces_warning(FutureWarning, match=warn_msg):
-                    method(*args, **kwargs)
+            with tm.assert_produces_warning(FutureWarning, match=warn_msg):
+                method(*args, **kwargs)
         else:
             with pytest.raises(TypeError, match=msg):
                 method(*args, **kwargs)