REGR: fix DataFrame sum and prod with min_count and numeric_only

doc/source/whatsnew/v1.2.5.rst

@@ -15,6 +15,7 @@ including other versions of pandas.
 Fixed regressions
 ~~~~~~~~~~~~~~~~~
 - Regression in :func:`concat` between two :class:`DataFrames` where one has an :class:`Index` that is all-None and the other is :class:`DatetimeIndex` incorrectly raising (:issue:`40841`)
+- Fixed regression in :meth:`DataFrame.sum` and :meth:`DataFrame.prod` when ``min_count`` and ``numeric_only`` are both given (:issue:`41074`)
 - Regression in :func:`read_csv` when using ``memory_map=True`` with an non-UTF8 encoding (:issue:`40986`)
 -
 

pandas/core/frame.py

@@ -9856,36 +9856,42 @@ def _get_data() -> DataFrame:
 
             return out
 
-        assert numeric_only is None
-
         data = self
         values = data.values
 
-        try:
-            result = func(values)
+        if numeric_only is None:
 
-        except TypeError:
-            # e.g. in nanops trying to convert strs to float
+            try:
+                result = func(values)
 
-            data = _get_data()
-            labels = data._get_agg_axis(axis)
+            except TypeError:
+                # e.g. in nanops trying to convert strs to float
 
-            values = data.values
-            with np.errstate(all="ignore"):
-                result = func(values)
+                data = _get_data()
+                labels = data._get_agg_axis(axis)
 
-            # columns have been dropped GH#41480
-            arg_name = "numeric_only"
-            if name in ["all", "any"]:
-                arg_name = "bool_only"
-            warnings.warn(
-                "Dropping of nuisance columns in DataFrame reductions "
-                f"(with '{arg_name}=None') is deprecated; in a future "
-                "version this will raise TypeError.  Select only valid "
-                "columns before calling the reduction.",
-                FutureWarning,
-                stacklevel=5,
-            )
+                values = data.values
+                with np.errstate(all="ignore"):
+                    result = func(values)
+
+                # columns have been dropped GH#41480
+                arg_name = "numeric_only"
+                if name in ["all", "any"]:
+                    arg_name = "bool_only"
+                warnings.warn(
+                    "Dropping of nuisance columns in DataFrame reductions "
+                    f"(with '{arg_name}=None') is deprecated; in a future "
+                    "version this will raise TypeError.  Select only valid "
+                    "columns before calling the reduction.",
+                    FutureWarning,
+                    stacklevel=5,
+                )
+        else:
+            if numeric_only:
+                data = _get_data()
+                labels = data._get_agg_axis(axis)
+                values = data.values
+            result = func(values)
 
         if hasattr(result, "dtype"):
             if filter_type == "bool" and notna(result).all():

pandas/tests/frame/test_reductions.py

@@ -1,5 +1,6 @@
 from datetime import timedelta
 from decimal import Decimal
+import re
 
 from dateutil.tz import tzlocal
 import numpy as np
@@ -154,7 +155,7 @@ def assert_stat_op_api(opname, float_frame, float_string_frame, has_numeric_only
         DataFrame with columns of type float
     float_string_frame : DataFrame
         DataFrame with both float and string columns
-    has_numeric_only : bool, default False
+    has_numeric_only : bool, default True
         Whether the method "opname" has the kwarg "numeric_only"
     """
     # make sure works on mixed-type frame
@@ -811,35 +812,36 @@ def test_sum_corner(self):
         assert len(axis1) == 0
 
     @pytest.mark.parametrize("method, unit", [("sum", 0), ("prod", 1)])
-    def test_sum_prod_nanops(self, method, unit):
+    @pytest.mark.parametrize("numeric_only", [None, True, False])
+    def test_sum_prod_nanops(self, method, unit, numeric_only):
         idx = ["a", "b", "c"]
         df = DataFrame({"a": [unit, unit], "b": [unit, np.nan], "c": [np.nan, np.nan]})
         # The default
-        result = getattr(df, method)()
+        result = getattr(df, method)(numeric_only=numeric_only)
         expected = Series([unit, unit, unit], index=idx, dtype="float64")
         tm.assert_series_equal(result, expected)
 
         # min_count=1
-        result = getattr(df, method)(min_count=1)
+        result = getattr(df, method)(numeric_only=numeric_only, min_count=1)
         expected = Series([unit, unit, np.nan], index=idx)
         tm.assert_series_equal(result, expected)
 
         # min_count=0
-        result = getattr(df, method)(min_count=0)
+        result = getattr(df, method)(numeric_only=numeric_only, min_count=0)
         expected = Series([unit, unit, unit], index=idx, dtype="float64")
         tm.assert_series_equal(result, expected)
 
-        result = getattr(df.iloc[1:], method)(min_count=1)
+        result = getattr(df.iloc[1:], method)(numeric_only=numeric_only, min_count=1)
         expected = Series([unit, np.nan, np.nan], index=idx)
         tm.assert_series_equal(result, expected)
 
         # min_count > 1
         df = DataFrame({"A": [unit] * 10, "B": [unit] * 5 + [np.nan] * 5})
-        result = getattr(df, method)(min_count=5)
+        result = getattr(df, method)(numeric_only=numeric_only, min_count=5)
         expected = Series(result, index=["A", "B"])
         tm.assert_series_equal(result, expected)
 
-        result = getattr(df, method)(min_count=6)
+        result = getattr(df, method)(numeric_only=numeric_only, min_count=6)
         expected = Series(result, index=["A", "B"])
         tm.assert_series_equal(result, expected)
 
@@ -1685,7 +1687,7 @@ def test_minmax_extensionarray(method, numeric_only):
 
 
 @pytest.mark.parametrize("meth", ["max", "min", "sum", "mean", "median"])
-def test_groupy_regular_arithmetic_equivalent(meth):
+def test_groupby_regular_arithmetic_equivalent(meth):
     # GH#40660
     df = DataFrame(
         {"a": [pd.Timedelta(hours=6), pd.Timedelta(hours=7)], "b": [12.1, 13.3]}
@@ -1708,3 +1710,16 @@ def test_frame_mixed_numeric_object_with_timestamp(ts_value):
         result = df.sum()
     expected = Series([1, 1.1, "foo"], index=list("abc"))
     tm.assert_series_equal(result, expected)
+
+
+def test_prod_sum_min_count_mixed_object():
+    # https://github.com/pandas-dev/pandas/issues/41074
+    df = DataFrame([1, "a", True])
+
+    result = df.prod(axis=0, min_count=1, numeric_only=False)
+    expected = Series(["a"])
+    tm.assert_series_equal(result, expected)
+
+    msg = re.escape("unsupported operand type(s) for +: 'int' and 'str'")
+    with pytest.raises(TypeError, match=msg):
+        df.sum(axis=0, min_count=1, numeric_only=False)