Use pandas is_integer_dtype

Author: miker985

pandas/core/arrays/categorical.py

@@ -27,6 +27,7 @@
     is_timedelta64_dtype,
     is_categorical,
     is_categorical_dtype,
+    is_integer_dtype,
     is_list_like, is_sequence,
     is_scalar, is_iterator,
     is_dict_like)
@@ -629,10 +630,11 @@ def from_codes(cls, codes, categories, ordered=False):
             categorical. If not given, the resulting categorical will be
             unordered.
         """
-        if isna(codes).any():
-            raise ValueError("nan is not a valid code. Use -1")
+        codes = np.asarray(codes)
+        if not is_integer_dtype(codes):
+            raise ValueError("codes need to be array-like integers")
         try:
-            codes = coerce_indexer_dtype(np.asarray(codes), categories)
+            codes = coerce_indexer_dtype(codes, categories)
         except (ValueError, TypeError):
             raise ValueError(
                 "codes need to be convertible to an arrays of integers")

pandas/tests/arrays/categorical/test_constructors.py

@@ -472,8 +472,7 @@ def test_from_codes_with_nan_code(self):
         # GH21767
         codes = [1, 2, np.nan]
         categories = ['a', 'b', 'c']
-        with pytest.raises(ValueError,
-                           match='nan is not a valid code. Use -1'):
+        with pytest.raises(ValueError):
             Categorical.from_codes(codes, categories)
 
     @pytest.mark.parametrize('dtype', [None, 'category'])