Error when fitting using nullable integer data with categorical transformer

rdt/transformers/categorical.py

@@ -59,12 +59,12 @@ def _order_categories(self, unique_data):
         nans = pd.isna(unique_data)
         if self.order_by == 'alphabetical':
             # pylint: disable=invalid-unary-operand-type
-            if any(map(lambda item: not isinstance(item, str), unique_data[~nans])):  # noqa: C417
+            if any(not isinstance(item, str) for item in unique_data[~nans]):
                 raise TransformerInputError(
                     "The data must be of type string if order_by is 'alphabetical'."
                 )
         elif self.order_by == 'numerical_value':
-            if not np.issubdtype(unique_data.dtype.type, np.number):
+            if any(not np.issubdtype(type(item), np.number) for item in unique_data[~nans]):
                 raise TransformerInputError(
                     "The data must be numerical if order_by is 'numerical_value'."
                 )

rdt/transformers/utils.py

@@ -176,14 +176,14 @@ def strings_from_regex(regex, max_repeat=16):
 
 
 def _fill_nan_with_none_series(data):
-    sentinel = object()
     dtype = data.dtype
     if isinstance(dtype, pd.CategoricalDtype):
+        sentinel = object()
         data = data.cat.add_categories([sentinel])
         data = data.fillna(sentinel).replace({sentinel: None})
         return pd.Series(pd.Categorical(data, categories=dtype.categories), index=data.index)
 
-    return data.fillna(sentinel).replace({sentinel: None})
+    return data.astype('object').where(~data.isna(), None)
 
 
 def fill_nan_with_none(data):

tests/unit/transformers/test_categorical.py

@@ -198,6 +198,24 @@ def test__fit(self):
         assert transformer.frequencies == expected_frequencies
         assert transformer.intervals == expected_intervals
 
+    def test_fit_with_nullable_integer_dtype(self):
+        """Test that the ``fit`` method works with nullable integer columns."""
+        # Setup
+        data = pd.DataFrame({'example': [1, 2, 3, None]}, dtype='Int64')
+        transformer = UniformEncoder()
+
+        # Run
+        transformer.fit(data=data, column='example')
+
+        # Assert
+        expected_frequencies = {
+            1: 0.25,
+            2: 0.25,
+            3: 0.25,
+            None: 0.25,
+        }
+        assert transformer.frequencies == expected_frequencies
+
     def test__set_fitted_parameters(self):
         """Test the ``_set_fitted_parameters`` method."""
         # Setup