possible fix for integer_array with dtype=None

Author: vladserkoff

pandas/core/arrays/integer.py

@@ -187,7 +187,7 @@ def coerce_to_array(values, dtype, mask=None, copy=False):
             raise TypeError("{} cannot be converted to an IntegerDtype".format(
                 values.dtype))
 
-    elif is_bool_dtype(values) and is_integer_dtype(dtype):
+    elif is_bool_dtype(values) and (is_integer_dtype(dtype) or dtype is None):
         values = np.array(values, dtype=int, copy=copy)
 
     elif not (is_integer_dtype(values) or is_float_dtype(values)):

pandas/core/series.py

@@ -22,7 +22,7 @@
     _is_unorderable_exception, ensure_platform_int, is_bool,
     is_categorical_dtype, is_datetime64_dtype, is_datetimelike, is_dict_like,
     is_extension_array_dtype, is_extension_type, is_hashable, is_integer,
-    is_iterator, is_list_like, is_scalar, is_string_like, is_timedelta64_dtype)
+    is_iterator, is_list_like, is_scalar, is_sparse, is_string_like, is_timedelta64_dtype)
 from pandas.core.dtypes.generic import (
     ABCDataFrame, ABCDatetimeArray, ABCDatetimeIndex, ABCSeries,
     ABCSparseArray, ABCSparseSeries)
@@ -2644,6 +2644,8 @@ def combine(self, other, func, fill_value=None):
 
         if is_categorical_dtype(self.values):
             pass
+        elif is_bool(new_values[0]) and not is_sparse(self.values):
+            pass
         elif is_extension_array_dtype(self.values):
             # The function can return something of any type, so check
             # if the type is compatible with the calling EA.