REGR: DataFrame reduction with min_count

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

@@ -9775,7 +9775,6 @@ def _reduce(
         **kwds,
     ):
 
-        min_count = kwds.get("min_count", 0)
         assert filter_type is None or filter_type == "bool", filter_type
         out_dtype = "bool" if filter_type == "bool" else None
 
@@ -9824,7 +9823,7 @@ def _get_data() -> DataFrame:
                 data = self._get_bool_data()
             return data
 
-        if (numeric_only is not None or axis == 0) and min_count == 0:
+        if numeric_only is not None or axis == 0:
             # For numeric_only non-None and axis non-None, we know
             #  which blocks to use and no try/except is needed.
             #  For numeric_only=None only the case with axis==0 and no object

pandas/core/internals/blocks.py

@@ -393,7 +393,7 @@ def reduce(self, func, ignore_failures: bool = False) -> list[Block]:
                 return []
             raise
 
-        if np.ndim(result) == 0:
+        if self.values.ndim == 1:
             # TODO(EA2D): special case not needed with 2D EAs
             res_values = np.array([[result]])
         else:

pandas/core/nanops.py

@@ -245,8 +245,7 @@ def _maybe_get_mask(
     """
     if mask is None:
         if is_bool_dtype(values.dtype) or is_integer_dtype(values.dtype):
-            # Boolean data cannot contain nulls, so signal via mask being None
-            return None
+            return np.broadcast_to(False, values.shape)
 
         if skipna or needs_i8_conversion(values.dtype):
             mask = isna(values)

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
@@ -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)