Backport PR #31939: BUG: Fix construction of Categorical from pd.NA (#32200)

Co-authored-by: Daniel Saxton <2658661+dsaxton@users.noreply.github.com>

Author: meeseeksmachine

doc/source/whatsnew/v1.0.2.rst

@@ -65,6 +65,7 @@ Bug fixes
 **Categorical**
 
 - Fixed bug where :meth:`Categorical.from_codes` improperly raised a ``ValueError`` when passed nullable integer codes. (:issue:`31779`)
+- Fixed bug where :meth:`Categorical` constructor would raise a ``TypeError`` when given a numpy array containing ``pd.NA``. (:issue:`31927`)
 - Bug in :class:`Categorical` that would ignore or crash when calling :meth:`Series.replace` with a list-like ``to_replace`` (:issue:`31720`)
 
 **I/O**
@@ -86,4 +87,4 @@ Bug fixes
 Contributors
 ~~~~~~~~~~~~
 
-.. contributors:: v1.0.1..v1.0.2|HEAD
+.. contributors:: v1.0.1..v1.0.2|HEAD

pandas/_libs/hashtable_class_helper.pxi.in

@@ -10,6 +10,7 @@ WARNING: DO NOT edit .pxi FILE directly, .pxi is generated from .pxi.in
 # ----------------------------------------------------------------------
 
 from pandas._libs.tslibs.util cimport get_c_string
+from pandas._libs.missing cimport C_NA
 
 {{py:
 
@@ -1032,8 +1033,12 @@ cdef class PyObjectHashTable(HashTable):
             val = values[i]
             hash(val)
 
-            if ignore_na and ((val != val or val is None)
-                              or (use_na_value and val == na_value)):
+            if ignore_na and (
+                (val is C_NA)
+                or (val != val)
+                or (val is None)
+                or (use_na_value and val == na_value)
+            ):
                 # if missing values do not count as unique values (i.e. if
                 # ignore_na is True), skip the hashtable entry for them, and
                 # replace the corresponding label with na_sentinel

pandas/tests/arrays/categorical/test_constructors.py

@@ -458,6 +458,18 @@ def test_constructor_with_categorical_categories(self):
         result = Categorical(["a", "b"], categories=CategoricalIndex(["a", "b", "c"]))
         tm.assert_categorical_equal(result, expected)
 
+    @pytest.mark.parametrize("klass", [lambda x: np.array(x, dtype=object), list])
+    def test_construction_with_null(self, klass, nulls_fixture):
+        # https://github.com/pandas-dev/pandas/issues/31927
+        values = klass(["a", nulls_fixture, "b"])
+        result = Categorical(values)
+
+        dtype = CategoricalDtype(["a", "b"])
+        codes = [0, -1, 1]
+        expected = Categorical.from_codes(codes=codes, dtype=dtype)
+
+        tm.assert_categorical_equal(result, expected)
+
     def test_from_codes(self):
 
         # too few categories