BUG: arithmetic ops not propagating mask when combining mask arrays and numpy

doc/source/whatsnew/v2.0.0.rst

@@ -804,6 +804,7 @@ Timezones
 Numeric
 ^^^^^^^
 - Bug in :meth:`DataFrame.add` cannot apply ufunc when inputs contain mixed DataFrame type and Series type (:issue:`39853`)
+- Bug in arithmetic operations on :class:`Series` not propagating mask when combining masked dtypes and numpy dtypes (:issue:`45810`, :issue:`42630`)
 - Bug in DataFrame reduction methods (e.g. :meth:`DataFrame.sum`) with object dtype, ``axis=1`` and ``numeric_only=False`` would not be coerced to float (:issue:`49551`)
 - Bug in :meth:`DataFrame.sem` and :meth:`Series.sem` where an erroneous ``TypeError`` would always raise when using data backed by an :class:`ArrowDtype` (:issue:`49759`)
 

pandas/core/arrays/masked.py

@@ -609,9 +609,13 @@ def _propagate_mask(
             if other is libmissing.NA:
                 # GH#45421 don't alter inplace
                 mask = mask | True
+            elif is_list_like(other) and len(other) == len(mask):
+                mask = mask | isna(other)
         else:
             mask = self._mask | mask
-        return mask
+        # Incompatible return value type (got "Optional[ndarray[Any, dtype[bool_]]]",
+        # expected "ndarray[Any, dtype[bool_]]")
+        return mask  # type: ignore[return-value]
 
     def _arith_method(self, other, op):
         op_name = op.__name__

pandas/tests/series/test_arithmetic.py

@@ -307,6 +307,25 @@ def test_arithmetic_with_duplicate_index(self):
         expected = Series(Timedelta("9 hours"), index=[2, 2, 3, 3, 4])
         tm.assert_series_equal(result, expected)
 
+    def test_masked_and_non_masked_propagate_na(self):
+        # GH#45810
+        ser1 = Series([0, np.nan], dtype="float")
+        ser2 = Series([0, 1], dtype="Int64")
+        result = ser1 * ser2
+        expected = Series([0, pd.NA], dtype="Float64")
+        tm.assert_series_equal(result, expected)
+
+    def test_mask_div_propagate_na_for_non_na_dtype(self):
+        # GH#42630
+        ser1 = Series([15, pd.NA, 5, 4], dtype="Int64")
+        ser2 = Series([15, 5, np.nan, 4])
+        result = ser1 / ser2
+        expected = Series([1.0, pd.NA, pd.NA, 1.0], dtype="Float64")
+        tm.assert_series_equal(result, expected)
+
+        result = ser2 / ser1
+        tm.assert_series_equal(result, expected)
+
 
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