DOC/TST: Update the parquet (pyarrow >= 0.15) docs and tests regarding Categorical support

doc/source/user_guide/io.rst

@@ -4710,7 +4710,8 @@ Several caveats.
   indexes. This extra column can cause problems for non-Pandas consumers that are not expecting it. You can
   force including or omitting indexes with the ``index`` argument, regardless of the underlying engine.
 * Index level names, if specified, must be strings.
-* Categorical dtypes can be serialized to parquet, but will de-serialize as ``object`` dtype.
+* In the ``pyarrow`` engine, categorical dtypes for non-string types can be serialized to parquet, but will de-serialize as their primitive dtype.
+* The ``pyarrow`` engine preserves the ``ordered`` flag of categorical dtypes with string types. ``fastparquet`` does not preserve the ``ordered`` flag.
 * Non supported types include ``Period`` and actual Python object types. These will raise a helpful error message
   on an attempt at serialization.
 
@@ -4734,7 +4735,8 @@ See the documentation for `pyarrow <https://arrow.apache.org/docs/python/>`__ an
                       'd': np.arange(4.0, 7.0, dtype='float64'),
                       'e': [True, False, True],
                       'f': pd.date_range('20130101', periods=3),
-                      'g': pd.date_range('20130101', periods=3, tz='US/Eastern')})
+                      'g': pd.date_range('20130101', periods=3, tz='US/Eastern'),
+                      'h': pd.Categorical(list('abc'))})
 
    df
    df.dtypes

doc/source/whatsnew/v1.0.0.rst

@@ -174,6 +174,7 @@ Categorical
 - Added test to assert the :func:`fillna` raises the correct ValueError message when the value isn't a value from categories (:issue:`13628`)
 - Bug in :meth:`Categorical.astype` where ``NaN`` values were handled incorrectly when casting to int (:issue:`28406`)
 - :meth:`Categorical.searchsorted` and :meth:`CategoricalIndex.searchsorted` now work on unordered categoricals also (:issue:`21667`)
+- Added test to assert roundtripping to parquet with :func:`DataFrame.to_parquet` or :func:`read_parquet` will preserve Categorical dtypes for string types (:issue:`27955`)
 -
 
 

pandas/tests/io/test_parquet.py

@@ -167,6 +167,7 @@ def compare(repeat):
             df.to_parquet(path, **write_kwargs)
             with catch_warnings(record=True):
                 actual = read_parquet(path, **read_kwargs)
+
             tm.assert_frame_equal(expected, actual, check_names=check_names)
 
     if path is None:
@@ -461,11 +462,26 @@ def test_unsupported(self, pa):
     def test_categorical(self, pa):
 
         # supported in >= 0.7.0
-        df = pd.DataFrame({"a": pd.Categorical(list("abc"))})
+        df = pd.DataFrame()
+        df["a"] = pd.Categorical(list("abcdef"))
 
-        # de-serialized as object
-        expected = df.assign(a=df.a.astype(object))
-        check_round_trip(df, pa, expected=expected)
+        # test for null, out-of-order values, and unobserved category
+        df["b"] = pd.Categorical(
+            ["bar", "foo", "foo", "bar", None, "bar"],
+            dtype=pd.CategoricalDtype(["foo", "bar", "baz"]),
+        )
+
+        # test for ordered flag
+        df["c"] = pd.Categorical(
+            ["a", "b", "c", "a", "c", "b"], categories=["b", "c", "d"], ordered=True
+        )
+
+        if LooseVersion(pyarrow.__version__) >= LooseVersion("0.15.0"):
+            check_round_trip(df, pa)
+        else:
+            # de-serialized as object for pyarrow < 0.15
+            expected = df.astype(object)
+            check_round_trip(df, pa, expected=expected)
 
     def test_s3_roundtrip(self, df_compat, s3_resource, pa):
         # GH #19134