BUG: GroupBy.size doesnt attach index name properly if grouped by TimeGrouper

Author: sinhrks

doc/source/whatsnew/v0.16.1.txt

@@ -125,7 +125,7 @@ Bug Fixes
 - Bug in ``to_msgpack`` and ``read_msgpack`` zlib and blosc compression support (:issue:`9783`)
 - Bug in unequal comparisons between a ``Series`` of dtype `"category"` and a scalar (e.g. ``Series(Categorical(list("abc"), categories=list("cba"), ordered=True)) > "b"``, which wouldn't use the order of the categories but use the lexicographical order. (:issue:`9848`)
 
-
+- Bug ``GroupBy.size`` doesn't attach index name properly if grouped by ``TimeGrouper`` (:issue:`9925`)
 
 
 

pandas/core/groupby.py

@@ -1780,12 +1780,14 @@ def size(self):
         Compute group sizes
 
         """
-        base = Series(np.zeros(len(self.result_index), dtype=np.int64),
-                      index=self.result_index)
+        index = self.result_index
+        base = Series(np.zeros(len(index), dtype=np.int64), index=index)
         indices = self.indices
         for k, v in compat.iteritems(indices):
             indices[k] = len(v)
         bin_counts = Series(indices, dtype=np.int64)
+        # make bin_counts.index to have same name to preserve it
+        bin_counts.index.name = index.name
         result = base.add(bin_counts, fill_value=0)
         # addition with fill_value changes dtype to float64
         result = result.astype(np.int64)

pandas/tseries/tests/test_resample.py

@@ -875,47 +875,47 @@ def test_resmaple_dst_anchor(self):
         # 5172
         dti = DatetimeIndex([datetime(2012, 11, 4, 23)], tz='US/Eastern')
         df = DataFrame([5], index=dti)
-        assert_frame_equal(df.resample(rule='D', how='sum'), 
+        assert_frame_equal(df.resample(rule='D', how='sum'),
                            DataFrame([5], index=df.index.normalize()))
         df.resample(rule='MS', how='sum')
         assert_frame_equal(df.resample(rule='MS', how='sum'),
-                           DataFrame([5], index=DatetimeIndex([datetime(2012, 11, 1)], 
+                           DataFrame([5], index=DatetimeIndex([datetime(2012, 11, 1)],
                                                               tz='US/Eastern')))
 
         dti = date_range('2013-09-30', '2013-11-02', freq='30Min', tz='Europe/Paris')
         values = range(dti.size)
         df = DataFrame({"a": values, "b": values, "c": values}, index=dti, dtype='int64')
         how = {"a": "min", "b": "max", "c": "count"}
-        
+
         assert_frame_equal(df.resample("W-MON", how=how)[["a", "b", "c"]],
                            DataFrame({"a": [0, 48, 384, 720, 1056, 1394],
                                       "b": [47, 383, 719, 1055, 1393, 1586],
                                       "c": [48, 336, 336, 336, 338, 193]},
-                                     index=date_range('9/30/2013', '11/4/2013', 
+                                     index=date_range('9/30/2013', '11/4/2013',
                                                       freq='W-MON', tz='Europe/Paris')),
                            'W-MON Frequency')
 
         assert_frame_equal(df.resample("2W-MON", how=how)[["a", "b", "c"]],
                            DataFrame({"a": [0, 48, 720, 1394],
                                       "b": [47, 719, 1393, 1586],
                                       "c": [48, 672, 674, 193]},
-                                     index=date_range('9/30/2013', '11/11/2013', 
+                                     index=date_range('9/30/2013', '11/11/2013',
                                                       freq='2W-MON', tz='Europe/Paris')),
                            '2W-MON Frequency')
 
         assert_frame_equal(df.resample("MS", how=how)[["a", "b", "c"]],
                            DataFrame({"a": [0, 48, 1538],
                                       "b": [47, 1537, 1586],
                                       "c": [48, 1490, 49]},
-                                     index=date_range('9/1/2013', '11/1/2013', 
+                                     index=date_range('9/1/2013', '11/1/2013',
                                                       freq='MS', tz='Europe/Paris')),
                            'MS Frequency')
 
         assert_frame_equal(df.resample("2MS", how=how)[["a", "b", "c"]],
                            DataFrame({"a": [0, 1538],
                                       "b": [1537, 1586],
                                       "c": [1538, 49]},
-                                     index=date_range('9/1/2013', '11/1/2013', 
+                                     index=date_range('9/1/2013', '11/1/2013',
                                                       freq='2MS', tz='Europe/Paris')),
                            '2MS Frequency')
 
@@ -1553,6 +1553,8 @@ def test_aggregate_with_nat(self):
             expected.index = date_range(start='2013-01-01', freq='D', periods=5, name='key')
             dt_result = getattr(dt_grouped, func)()
             assert_series_equal(expected, dt_result)
+            # GH 9925
+            self.assertEqual(dt_result.index.name, 'key')
 
         # if NaT is included, 'var', 'std', 'mean', 'first','last' and 'nth' doesn't work yet