BUG: Fix concat series loss of timezone

doc/source/whatsnew/v0.24.0.rst

@@ -1545,6 +1545,7 @@ Reshaping
 - Bug in :meth:`DataFrame.append` with a :class:`Series` with a dateutil timezone would raise a ``TypeError`` (:issue:`23682`)
 - Bug in ``Series`` construction when passing no data and ``dtype=str`` (:issue:`22477`)
 - Bug in :func:`cut` with ``bins`` as an overlapping ``IntervalIndex`` where multiple bins were returned per item instead of raising a ``ValueError`` (:issue:`23980`)
+- Bug in :func:`pandas.concat` when joining series datetimetz with series category would lose timezone (:issue:`23816`)
 - Bug in :meth:`DataFrame.join` when joining on partial MultiIndex would drop names (:issue:`20452`).
 
 .. _whatsnew_0240.bug_fixes.sparse:

pandas/core/dtypes/concat.py

@@ -191,15 +191,6 @@ def _concat_categorical(to_concat, axis=0):
         A single array, preserving the combined dtypes
     """
 
-    def _concat_asobject(to_concat):
-        to_concat = [x.get_values() if is_categorical_dtype(x.dtype)
-                     else np.asarray(x).ravel() for x in to_concat]
-        res = _concat_compat(to_concat)
-        if axis == 1:
-            return res.reshape(1, len(res))
-        else:
-            return res
-
     # we could have object blocks and categoricals here
     # if we only have a single categoricals then combine everything
     # else its a non-compat categorical
@@ -214,7 +205,14 @@ def _concat_asobject(to_concat):
         if all(first.is_dtype_equal(other) for other in to_concat[1:]):
             return union_categoricals(categoricals)
 
-    return _concat_asobject(to_concat)
+    # extract the categoricals & coerce to object if needed
+    to_concat = [x.get_values() if is_categorical_dtype(x.dtype)
+                 else np.asarray(x).ravel() if not is_datetime64tz_dtype(x) 
+                 else np.asarray(x.astype(object)) for x in to_concat]
+    result = _concat_compat(to_concat)
+    if axis == 1:
+        result = result.reshape(1, len(result))
+    return result
 
 
 def union_categoricals(to_union, sort_categories=False, ignore_order=False):

pandas/tests/reshape/test_concat.py

@@ -2552,3 +2552,16 @@ def test_concat_series_name_npscalar_tuple(s1name, s2name):
     result = pd.concat([s1, s2])
     expected = pd.Series({'a': 1, 'b': 2, 'c': 5, 'd': 6})
     tm.assert_series_equal(result, expected)
+
+
+def test_concat_categorical_tz():
+    # GH-23816
+    a = pd.Series(pd.date_range('2017-01-01', periods=2, tz='US/Pacific'))
+    b = pd.Series(['a', 'b'], dtype='category')
+    result = pd.concat([a, b], ignore_index=True)
+    expected = pd.Series([
+        pd.Timestamp('2017-01-01', tz="US/Pacific"),
+        pd.Timestamp('2017-01-02', tz="US/Pacific"),
+        'a', 'b'
+    ])
+    tm.assert_series_equal(result, expected)