BUG: PandasArray[uint].factorize

pandas/core/arrays/_mixins.py

@@ -190,6 +190,9 @@ def _from_factorized(cls, values, original):
     def _values_for_argsort(self) -> np.ndarray:
         return self._ndarray
 
+    def _values_for_factorize(self):
+        return self._ndarray, self._internal_fill_value
+
     # Signature of "argmin" incompatible with supertype "ExtensionArray"
     def argmin(self, axis: int = 0, skipna: bool = True):  # type: ignore[override]
         # override base class by adding axis keyword

pandas/core/arrays/categorical.py

@@ -2298,9 +2298,6 @@ def unique(self):
         unique_codes = unique1d(self.codes)
         return self._from_backing_data(unique_codes)
 
-    def _values_for_factorize(self):
-        return self._ndarray, -1
-
     def _cast_quantile_result(self, res_values: np.ndarray) -> np.ndarray:
         # make sure we have correct itemsize for resulting codes
         res_values = coerce_indexer_dtype(res_values, self.dtype.categories)

pandas/core/arrays/datetimelike.py

@@ -549,9 +549,6 @@ def copy(self: DatetimeLikeArrayT, order="C") -> DatetimeLikeArrayT:
         new_obj._freq = self.freq
         return new_obj
 
-    def _values_for_factorize(self):
-        return self._ndarray, self._internal_fill_value
-
     # ------------------------------------------------------------------
     # Validation Methods
     # TODO: try to de-duplicate these, ensure identical behavior

pandas/core/arrays/numpy_.py

@@ -189,8 +189,12 @@ def _validate_scalar(self, fill_value):
             fill_value = self.dtype.na_value
         return fill_value
 
-    def _values_for_factorize(self) -> tuple[np.ndarray, int]:
-        return self._ndarray, -1
+    def _values_for_factorize(self) -> tuple[np.ndarray, float | None]:
+        if self.dtype.kind in ["i", "u", "b"]:
+            fv = None
+        else:
+            fv = np.nan
+        return self._ndarray, fv
 
     # ------------------------------------------------------------------------
     # Reductions

pandas/tests/arrays/numpy_/test_numpy.py

@@ -309,3 +309,16 @@ def test_quantile_empty(dtype):
     result = arr._quantile(idx, interpolation="linear")
     expected = PandasArray(np.array([np.nan, np.nan]))
     tm.assert_extension_array_equal(result, expected)
+
+
+def test_factorize_unsigned():
+    # don't raise when calling factorize on unsigned int PandasArray
+    arr = np.array([1, 2, 3], dtype=np.uint64)
+    obj = PandasArray(arr)
+
+    res_codes, res_unique = obj.factorize()
+    exp_codes, exp_unique = pd.factorize(arr)
+
+    tm.assert_numpy_array_equal(res_codes, exp_codes)
+
+    tm.assert_extension_array_equal(res_unique, PandasArray(exp_unique))