cross type arithmetic

pandas/core/arrays/integer.py

@@ -112,6 +112,7 @@ def coerce_to_array(values, dtype, mask=None, copy=False):
                                  'mixed-integer', 'mixed-integer-float']:
             raise TypeError("{} cannot be converted to an IntegerDtype".format(
                 values.dtype))
+
     elif not (is_integer_dtype(values) or is_float_dtype(values)):
         raise TypeError("{} cannot be converted to an IntegerDtype".format(
             values.dtype))
@@ -126,15 +127,19 @@ def coerce_to_array(values, dtype, mask=None, copy=False):
     if not mask.ndim == 1:
         raise TypeError("mask must be a 1D list-like")
 
+    # avoid float->int numpy conversion issues
+    if is_object_dtype(values):
+        mask |= isna(values)
+
+    # we copy as need to coerce here
     if mask.any():
-        # we copy as need to coerce here
         values = values.copy()
         values[mask] = 1
 
         values = values.astype(dtype.type)
-
     else:
         values = values.astype(dtype.type, copy=False)
+
     return values, mask
 
 

pandas/tests/extension/integer/test_integer.py

@@ -393,3 +393,22 @@ def test_to_integer_array(values, expected):
     # convert existing arrays to IntegerArrays
     result = to_integer_array(values)
     tm.assert_extension_array_equal(result, expected)
+
+
+def test_cross_type_arithmetic():
+
+    df = pd.DataFrame({'A': pd.Series([1, 2, np.nan], dtype='Int64'),
+                       'B': pd.Series([1, np.nan, 3], dtype='UInt8'),
+                       'C': [1, 2, 3]})
+
+    result = df.A + df.C
+    expected = pd.Series([2, 4, np.nan], dtype='Int64')
+    tm.assert_series_equal(result, expected)
+
+    result = (df.A + df.C) * 3 == 12
+    expected = pd.Series([False, True, False])
+    tm.assert_series_equal(result, expected)
+
+    result = df.A + df.B
+    expected = pd.Series([2, np.nan, np.nan], dtype='Int64')
+    tm.assert_series_equal(result, expected)