BUG: fix groupby.aggregate resulting dtype coercion, xref pandas-dev#11444, pandas-dev#13046

make sure .size includes the name of the grouped

Author: jreback

doc/source/whatsnew/v0.20.0.txt

@@ -632,12 +632,12 @@ Bug Fixes
 
 - Bug in ``DataFrame.to_stata()`` and ``StataWriter`` which produces incorrectly formatted files to be produced for some locales (:issue:`13856`)
 - Bug in ``pd.concat()`` in which concatting with an empty dataframe with ``join='inner'`` was being improperly handled (:issue:`15328`)
-- Bug in ``groupby.agg()`` incorrectly localizing timezone on ``datetime`` (:issue:`15426`, :issue:`10668`)
+- Bug in ``groupby.agg()`` incorrectly localizing timezone on ``datetime`` (:issue:`15426`, :issue:`10668`, :issue:`13046`)
 
 
 
 - Bug in ``.read_csv()`` with ``parse_dates`` when multiline headers are specified (:issue:`15376`)
-- Bug in ``groupby.transform()`` that would coerce the resultant dtypes back to the original (:issue:`10972`)
+- Bug in ``groupby.transform()`` that would coerce the resultant dtypes back to the original (:issue:`10972`, :issue:`11444`)
 
 - Bug in ``DataFrame.hist`` where ``plt.tight_layout`` caused an ``AttributeError``  (use ``matplotlib >= 0.2.0``) (:issue:`9351`)
 - Bug in ``DataFrame.boxplot`` where ``fontsize`` was not applied to the tick labels on both axes (:issue:`15108`)

pandas/core/groupby.py

@@ -767,19 +767,23 @@ def _index_with_as_index(self, b):
         new.names = gp.names + original.names
         return new
 
-    def _try_cast(self, result, obj):
+    def _try_cast(self, result, obj, numeric_only=False):
         """
         try to cast the result to our obj original type,
         we may have roundtripped thru object in the mean-time
 
+        if numeric_only is True, then only try to cast numerics
+        and not datetimelikes
+
         """
         if obj.ndim > 1:
             dtype = obj.values.dtype
         else:
             dtype = obj.dtype
 
         if not is_scalar(result):
-            result = _possibly_downcast_to_dtype(result, dtype)
+            if numeric_only and is_numeric_dtype(dtype) or not numeric_only:
+                result = _possibly_downcast_to_dtype(result, dtype)
 
         return result
 
@@ -830,7 +834,7 @@ def _python_agg_general(self, func, *args, **kwargs):
         for name, obj in self._iterate_slices():
             try:
                 result, counts = self.grouper.agg_series(obj, f)
-                output[name] = self._try_cast(result, obj)
+                output[name] = self._try_cast(result, obj, numeric_only=True)
             except TypeError:
                 continue
 
@@ -1117,7 +1121,11 @@ def sem(self, ddof=1):
     @Appender(_doc_template)
     def size(self):
         """Compute group sizes"""
-        return self.grouper.size()
+        result = self.grouper.size()
+
+        if isinstance(self.obj, Series):
+            result.name = getattr(self, 'name', None)
+        return result
 
     sum = _groupby_function('sum', 'add', np.sum)
     prod = _groupby_function('prod', 'prod', np.prod)
@@ -1689,7 +1697,9 @@ def size(self):
         ids, _, ngroup = self.group_info
         ids = _ensure_platform_int(ids)
         out = np.bincount(ids[ids != -1], minlength=ngroup or None)
-        return Series(out, index=self.result_index, dtype='int64')
+        return Series(out,
+                      index=self.result_index,
+                      dtype='int64')
 
     @cache_readonly
     def _max_groupsize(self):
@@ -2908,7 +2918,8 @@ def transform(self, func, *args, **kwargs):
         result = concat(results).sort_index()
 
         # we will only try to coerce the result type if
-        # we have a numeric dtype
+        # we have a numeric dtype, as these are *always* udfs
+        # the cython take a different path (and casting)
         dtype = self._selected_obj.dtype
         if is_numeric_dtype(dtype):
             result = _possibly_downcast_to_dtype(result, dtype)

pandas/tests/groupby/test_aggregate.py

@@ -154,6 +154,29 @@ def test_agg_dict_parameter_cast_result_dtypes(self):
         assert_series_equal(grouped.time.last(), exp['time'])
         assert_series_equal(grouped.time.agg('last'), exp['time'])
 
+        # count
+        exp = pd.Series([2, 2, 2, 2],
+                        index=Index(list('ABCD'), name='class'),
+                        name='time')
+        assert_series_equal(grouped.time.agg(len), exp)
+        assert_series_equal(grouped.time.size(), exp)
+
+        exp = pd.Series([0, 1, 1, 2],
+                        index=Index(list('ABCD'), name='class'),
+                        name='time')
+        assert_series_equal(grouped.time.count(), exp)
+
+    def test_agg_cast_results_dtypes(self):
+        # similar to GH12821
+        # xref #11444
+        u = [datetime(2015, x + 1, 1) for x in range(12)]
+        v = list('aaabbbbbbccd')
+        df = pd.DataFrame({'X': v, 'Y': u})
+
+        result = df.groupby('X')['Y'].agg(len)
+        expected = df.groupby('X')['Y'].count()
+        assert_series_equal(result, expected)
+
     def test_agg_must_agg(self):
         grouped = self.df.groupby('A')['C']
         self.assertRaises(Exception, grouped.agg, lambda x: x.describe())

pandas/tests/groupby/test_transform.py

@@ -4,7 +4,8 @@
 import pandas as pd
 from pandas.util import testing as tm
 from pandas import Series, DataFrame, Timestamp, MultiIndex, concat, date_range
-from pandas.types.common import _ensure_platform_int
+from pandas.types.common import _ensure_platform_int, is_timedelta64_dtype
+from pandas.compat import StringIO
 from .common import MixIn, assert_fp_equal
 
 from pandas.util.testing import assert_frame_equal, assert_series_equal
@@ -227,6 +228,32 @@ def test_transform_datetime_to_numeric(self):
         expected = Series([0, 1], name='b')
         assert_series_equal(result, expected)
 
+    def test_transform_casting(self):
+        # 13046
+        data = """
+        idx     A         ID3              DATETIME
+        0   B-028  b76cd912ff "2014-10-08 13:43:27"
+        1   B-054  4a57ed0b02 "2014-10-08 14:26:19"
+        2   B-076  1a682034f8 "2014-10-08 14:29:01"
+        3   B-023  b76cd912ff "2014-10-08 18:39:34"
+        4   B-023  f88g8d7sds "2014-10-08 18:40:18"
+        5   B-033  b76cd912ff "2014-10-08 18:44:30"
+        6   B-032  b76cd912ff "2014-10-08 18:46:00"
+        7   B-037  b76cd912ff "2014-10-08 18:52:15"
+        8   B-046  db959faf02 "2014-10-08 18:59:59"
+        9   B-053  b76cd912ff "2014-10-08 19:17:48"
+        10  B-065  b76cd912ff "2014-10-08 19:21:38"
+        """
+        df = pd.read_csv(StringIO(data), sep='\s+',
+                         index_col=[0], parse_dates=['DATETIME'])
+
+        result = df.groupby('ID3')['DATETIME'].transform(lambda x: x.diff())
+        assert is_timedelta64_dtype(result.dtype)
+
+        result = df[['ID3', 'DATETIME']].groupby('ID3').transform(
+            lambda x: x.diff())
+        assert is_timedelta64_dtype(result.DATETIME.dtype)
+
     def test_transform_multiple(self):
         grouped = self.ts.groupby([lambda x: x.year, lambda x: x.month])
 

pandas/tests/tseries/test_resample.py

@@ -757,10 +757,8 @@ def test_resample_empty_series(self):
                         freq in ['M', 'D']):
                     # GH12871 - TODO: name should propagate, but currently
                     # doesn't on lower / same frequency with PeriodIndex
-                    assert_series_equal(result, expected, check_dtype=False,
-                                        check_names=False)
-                    # this assert will break when fixed
-                    self.assertTrue(result.name is None)
+                    assert_series_equal(result, expected, check_dtype=False)
+
                 else:
                     assert_series_equal(result, expected, check_dtype=False)