Backport PR #31794: BUG: Avoid casting Int to object in Categorical.from_codes (#31921)

Co-authored-by: Daniel Saxton <[email protected]>

Author: meeseeksmachine

doc/source/whatsnew/v1.0.2.rst

@@ -26,6 +26,10 @@ Fixed regressions
 Bug fixes
 ~~~~~~~~~
 
+**Categorical**
+
+- Fixed bug where :meth:`Categorical.from_codes` improperly raised a ``ValueError`` when passed nullable integer codes. (:issue:`31779`)
+
 **I/O**
 
 - Using ``pd.NA`` with :meth:`DataFrame.to_json` now correctly outputs a null value instead of an empty object (:issue:`31615`)

pandas/core/arrays/categorical.py

@@ -644,7 +644,13 @@ def from_codes(cls, codes, categories=None, ordered=None, dtype=None):
             )
             raise ValueError(msg)
 
-        codes = np.asarray(codes)  # #21767
+        if is_extension_array_dtype(codes) and is_integer_dtype(codes):
+            # Avoid the implicit conversion of Int to object
+            if isna(codes).any():
+                raise ValueError("codes cannot contain NA values")
+            codes = codes.to_numpy(dtype=np.int64)
+        else:
+            codes = np.asarray(codes)
         if len(codes) and not is_integer_dtype(codes):
             raise ValueError("codes need to be array-like integers")
 

pandas/tests/arrays/categorical/test_constructors.py

@@ -560,6 +560,23 @@ def test_from_codes_neither(self):
         with pytest.raises(ValueError, match=msg):
             Categorical.from_codes([0, 1])
 
+    def test_from_codes_with_nullable_int(self):
+        codes = pd.array([0, 1], dtype="Int64")
+        categories = ["a", "b"]
+
+        result = Categorical.from_codes(codes, categories=categories)
+        expected = Categorical.from_codes(codes.to_numpy(int), categories=categories)
+
+        tm.assert_categorical_equal(result, expected)
+
+    def test_from_codes_with_nullable_int_na_raises(self):
+        codes = pd.array([0, None], dtype="Int64")
+        categories = ["a", "b"]
+
+        msg = "codes cannot contain NA values"
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes(codes, categories=categories)
+
     @pytest.mark.parametrize("dtype", [None, "category"])
     def test_from_inferred_categories(self, dtype):
         cats = ["a", "b"]