BUG: to_csv casting datetimes in categorical to int

doc/source/whatsnew/v1.4.0.rst

@@ -607,6 +607,7 @@ Categorical
 ^^^^^^^^^^^
 - Bug in setting dtype-incompatible values into a :class:`Categorical` (or ``Series`` or ``DataFrame`` backed by ``Categorical``) raising ``ValueError`` instead of ``TypeError`` (:issue:`41919`)
 - Bug in :meth:`Categorical.searchsorted` when passing a dtype-incompatible value raising ``KeyError`` instead of ``TypeError`` (:issue:`41919`)
+- Bug in :meth:`Categorical.astype` casting datetimes to int for dtype ``object`` (:issue:`44930`)
 - Bug in :meth:`Series.where` with ``CategoricalDtype`` when passing a dtype-incompatible value raising ``ValueError`` instead of ``TypeError`` (:issue:`41919`)
 - Bug in :meth:`Categorical.fillna` when passing a dtype-incompatible value raising ``ValueError`` instead of ``TypeError`` (:issue:`41919`)
 - Bug in :meth:`Categorical.fillna` with a tuple-like category raising ``ValueError`` instead of ``TypeError`` when filling with a non-category tuple (:issue:`41919`)
@@ -753,6 +754,7 @@ I/O
 - Bug in :class:`ExcelWriter`, where ``engine_kwargs`` were not passed through to all engines (:issue:`43442`)
 - Bug in :func:`read_csv` raising ``ValueError`` when ``parse_dates`` was used with ``MultiIndex`` columns (:issue:`8991`)
 - Bug in :func:`read_csv` not raising an ``ValueError`` when ``\n`` was specified as ``delimiter`` or ``sep`` which conflicts with ``lineterminator`` (:issue:`43528`)
+- Bug in :func:`to_csv` converting datetimes in categorical :class:`Series` to integers (:issue:`40754`)
 - Bug in :func:`read_csv` converting columns to numeric after date parsing failed (:issue:`11019`)
 - Bug in :func:`read_csv` not replacing ``NaN`` values with ``np.nan`` before attempting date conversion (:issue:`26203`)
 - Bug in :func:`read_csv` raising ``AttributeError`` when attempting to read a .csv file and infer index column dtype from an nullable integer type (:issue:`44079`)

pandas/core/arrays/categorical.py

@@ -108,6 +108,7 @@
 )
 import pandas.core.common as com
 from pandas.core.construction import (
+    ensure_wrapped_if_datetimelike,
     extract_array,
     sanitize_array,
 )
@@ -538,8 +539,13 @@ def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
 
         else:
             # GH8628 (PERF): astype category codes instead of astyping array
-            try:
+            if is_datetime64_dtype(self.categories):
+                new_cats = ensure_wrapped_if_datetimelike(self.categories._values)
+                # new_cats = values._format_native_types()
+            else:
                 new_cats = np.asarray(self.categories)
+
+            try:
                 new_cats = new_cats.astype(dtype=dtype, copy=copy)
                 fill_value = lib.item_from_zerodim(np.array(np.nan).astype(dtype))
             except (

pandas/core/internals/blocks.py

@@ -46,6 +46,7 @@
     soft_convert_objects,
 )
 from pandas.core.dtypes.common import (
+    ensure_platform_int,
     is_1d_only_ea_dtype,
     is_1d_only_ea_obj,
     is_dtype_equal,
@@ -88,6 +89,7 @@
     replace_regex,
     should_use_regex,
 )
+from pandas.core.array_algos.take import take_nd
 from pandas.core.array_algos.transforms import shift
 from pandas.core.arrays import (
     Categorical,
@@ -2105,6 +2107,14 @@ def to_native_types(
     **kwargs,
 ) -> np.ndarray:
     """convert to our native types format"""
+    if isinstance(values, Categorical):
+        # GH#40754 Convert categorical datetimes to datetime array
+        values = take_nd(
+            values.categories._values,
+            ensure_platform_int(values._codes),
+            fill_value=na_rep,
+        )
+
     values = ensure_wrapped_if_datetimelike(values)
 
     if isinstance(values, (DatetimeArray, TimedeltaArray)):

pandas/tests/arrays/categorical/test_dtypes.py

@@ -7,8 +7,10 @@
     Categorical,
     CategoricalIndex,
     Index,
+    NaT,
     Series,
     Timestamp,
+    to_datetime,
 )
 import pandas._testing as tm
 
@@ -176,6 +178,13 @@ def test_astype_category(self, dtype_ordered, cat_ordered):
             expected = cat
             tm.assert_categorical_equal(result, expected)
 
+    def test_astype_object_datetime_categories(self):
+        # GH#40754
+        cat = Categorical(to_datetime(["2021-03-27", NaT]))
+        result = cat.astype(object)
+        expected = np.array([Timestamp("2021-03-27 00:00:00"), np.nan], dtype="object")
+        tm.assert_numpy_array_equal(result, expected)
+
     def test_iter_python_types(self):
         # GH-19909
         cat = Categorical([1, 2])

pandas/tests/io/formats/test_to_csv.py

@@ -282,6 +282,21 @@ def test_to_csv_date_format(self):
         df_sec_grouped = df_sec.groupby([pd.Grouper(key="A", freq="1h"), "B"])
         assert df_sec_grouped.mean().to_csv(date_format="%Y-%m-%d") == expected_ymd_sec
 
+    def test_to_csv_date_format_in_categorical(self):
+        # GH#40754
+        ser = pd.Series(pd.to_datetime(["2021-03-27", pd.NaT], format="%Y-%m-%d"))
+        ser = ser.astype("category")
+        expected = tm.convert_rows_list_to_csv_str(["0", "2021-03-27", '""'])
+        assert ser.to_csv(index=False) == expected
+
+        ser = pd.Series(
+            pd.date_range(
+                start="2021-03-27", freq="D", periods=1, tz="Europe/Berlin"
+            ).append(pd.DatetimeIndex([pd.NaT]))
+        )
+        ser = ser.astype("category")
+        assert ser.to_csv(index=False, date_format="%Y-%m-%d") == expected
+
     def test_to_csv_multi_index(self):
         # see gh-6618
         df = DataFrame([1], columns=pd.MultiIndex.from_arrays([[1], [2]]))

pandas/tests/series/indexing/test_where.py

@@ -1,8 +1,6 @@
 import numpy as np
 import pytest
 
-import pandas.util._test_decorators as td
-
 from pandas.core.dtypes.common import is_integer
 
 import pandas as pd
@@ -444,11 +442,6 @@ def test_where_datetimelike_categorical(request, tz_naive_fixture, using_array_m
     # GH#37682
     tz = tz_naive_fixture
 
-    if using_array_manager and tz is None:
-        # TODO(ArrayManager) DataFrame.values not yet correctly returning datetime array
-        # for categorical with datetime categories
-        td.mark_array_manager_not_yet_implemented(request)
-
     dr = date_range("2001-01-01", periods=3, tz=tz)._with_freq(None)
     lvals = pd.DatetimeIndex([dr[0], dr[1], pd.NaT])
     rvals = pd.Categorical([dr[0], pd.NaT, dr[2]])