Bug groupby idxmin

doc/source/whatsnew/v0.24.2.rst

@@ -86,6 +86,7 @@ Bug Fixes
 
 - Bug in :meth:`pandas.core.groupby.GroupBy.transform` where applying a function to a timezone aware column would return a timezone naive result (:issue:`24198`)
 - Bug in :func:`DataFrame.join` when joining on a timezone aware :class:`DatetimeIndex` (:issue:`23931`)
+- Bug in groupby.idxmin() that returns DateTime values for DataTime columns (:issue:`25444`)
 -
 
 **Visualization**

pandas/core/groupby/base.py

@@ -89,7 +89,8 @@ def _gotitem(self, key, ndim, subset=None):
 cython_transforms = frozenset(['cumprod', 'cumsum', 'shift',
                                'cummin', 'cummax'])
 
-cython_cast_blacklist = frozenset(['rank', 'count', 'size'])
+cython_cast_blacklist = frozenset(['rank', 'count', 'size', 'idxmin',
+                                   'idxmax'])
 
 
 def whitelist_method_generator(base, klass, whitelist):

pandas/core/groupby/generic.py

@@ -254,8 +254,13 @@ def _aggregate_item_by_item(self, func, *args, **kwargs):
                 data = obj[item]
                 colg = SeriesGroupBy(data, selection=item,
                                      grouper=self.grouper)
-                result[item] = self._try_cast(
-                    colg.aggregate(func, *args, **kwargs), data)
+
+                cast = self._transform_should_cast(func)
+
+                result[item] = colg.aggregate(func, *args, **kwargs)
+                if cast:
+                    result[item] = self._try_cast(result[item], data)
+
             except ValueError:
                 cannot_agg.append(item)
                 continue

pandas/tests/groupby/test_function.py

@@ -400,6 +400,25 @@ def test_groupby_non_arithmetic_agg_int_like_precision(i):
         assert res.iloc[0].b == data["expected"]
 
 
+@pytest.mark.parametrize("func, values", [
+    ("idxmin", {'c_int': [0, 2], 'c_float': [1, 3], 'c_date': [1, 2]}),
+    ("idxmax", {'c_int': [1, 3], 'c_float': [0, 2], 'c_date': [0, 3]})
+])
+def test_idxmin_idxmax_returns_int_types(func, values):
+    # GH 25444
+    df = pd.DataFrame({'name': ['A', 'A', 'B', 'B'],
+                       'c_int': [1, 2, 3, 4],
+                       'c_float': [4.02, 3.03, 2.04, 1.05],
+                       'c_date': ['2019', '2018', '2016', '2017']})
+    df['c_date'] = pd.to_datetime(df['c_date'])
+
+    result = getattr(df.groupby('name'), func)()
+
+    expected = pd.DataFrame(values, index=Index(['A', 'B'], name="name"))
+
+    tm.assert_frame_equal(result, expected)
+
+
 def test_fill_consistency():
 
     # GH9221