BUG: support casting from bool array to EA Integer dtype

doc/source/whatsnew/v0.25.0.rst

@@ -298,8 +298,9 @@ Numeric
 - Bug in :meth:`Series.divmod` and :meth:`Series.rdivmod` which would raise an (incorrect) ``ValueError`` rather than return a pair of :class:`Series` objects as result (:issue:`25557`)
 - Raises a helpful exception when a non-numeric index is sent to :meth:`interpolate` with methods which require numeric index. (:issue:`21662`)
 - Bug in :meth:`~pandas.eval` when comparing floats with scalar operators, for example: ``x < -0.1`` (:issue:`25928`)
+- Fixed bug where casting all-boolean array to integer extension array failed (:issue:`25211`)
+-
 -
-
 
 Conversion
 ^^^^^^^^^^

pandas/core/arrays/integer.py

@@ -187,6 +187,9 @@ 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) or dtype is None):
+        values = np.array(values, dtype=int, copy=copy)
+
     elif not (is_integer_dtype(values) or is_float_dtype(values)):
         raise TypeError("{} cannot be converted to an IntegerDtype".format(
             values.dtype))

pandas/core/series.py

@@ -17,6 +17,7 @@
 from pandas.util._decorators import Appender, Substitution, deprecate
 from pandas.util._validators import validate_bool_kwarg
 
+from pandas.core.dtypes.cast import infer_dtype_from
 from pandas.core.dtypes.common import (
     _is_unorderable_exception, ensure_platform_int, is_bool,
     is_categorical_dtype, is_datetime64_dtype, is_datetimelike, is_dict_like,
@@ -2638,15 +2639,19 @@ def combine(self, other, func, fill_value=None):
         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.
+            non_na_values = [v for v in new_values if notna(v)]
+            if non_na_values:
+                new_dtype, _ = infer_dtype_from(non_na_values, pandas_dtype=True)
+            else:
+                new_dtype = self.dtype
             try:
-                new_values = self._values._from_sequence(new_values)
+                new_values = self._values._from_sequence(new_values, dtype=new_dtype)
             except Exception:
                 # https://github.com/pandas-dev/pandas/issues/22850
                 # pandas has no control over what 3rd-party ExtensionArrays
                 # do in _values_from_sequence. We still want ops to work
                 # though, so we catch any regular Exception.
                 pass
-
         return self._constructor(new_values, index=new_index, name=new_name)
 
     def combine_first(self, other):

pandas/tests/arrays/test_integer.py

@@ -614,6 +614,21 @@ def test_to_integer_array_float():
     assert result.dtype == Int64Dtype()
 
 
+@pytest.mark.parametrize(
+    'bool_values, int_values, target_dtype, expected_dtype',
+    [([False, True], [0, 1], Int64Dtype(), Int64Dtype()),
+     ([False, True], [0, 1], 'Int64', Int64Dtype()),
+     ([False, True, np.nan], [0, 1, np.nan], Int64Dtype(), Int64Dtype()),
+     ([False, True], [0, 1], None, Int64Dtype()),
+     ([False, True, np.nan], [0, 1, np.nan], None, Int64Dtype())])
+def test_to_integer_array_bool(bool_values, int_values, target_dtype,
+                               expected_dtype):
+    result = integer_array(bool_values, dtype=target_dtype)
+    assert result.dtype == expected_dtype
+    expected = integer_array(int_values, dtype=target_dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+
 @pytest.mark.parametrize(
     'values, to_dtype, result_dtype',
     [

pandas/tests/extension/base/methods.py

@@ -163,13 +163,15 @@ def test_combine_add(self, data_repeated):
             expected = pd.Series(
                 orig_data1._from_sequence([a + b for (a, b) in
                                            zip(list(orig_data1),
-                                               list(orig_data2))]))
+                                               list(orig_data2))],
+                                          dtype=s1.dtype))
         self.assert_series_equal(result, expected)
 
         val = s1.iloc[0]
         result = s1.combine(val, lambda x1, x2: x1 + x2)
         expected = pd.Series(
-            orig_data1._from_sequence([a + val for a in list(orig_data1)]))
+            orig_data1._from_sequence([a + val for a in list(orig_data1)],
+                                      dtype=s1.dtype))
         self.assert_series_equal(result, expected)
 
     def test_combine_first(self, data):