pandas 0.16.2 groupby and transform does not properly work with datetime objects

[agolbin]

In the 0.16.2 version, transform returns original values, while in the 0.15.2 version it properly transforms the values into group counts. The problem seems to be with how pandas deal with datetime objects.

The only difference is the pandas version, and everything else is the same.

---

import pandas as pd
import datetime
df = pd.DataFrame([[pd.to_datetime(datetime.date(2015,8,10)), 'A', 10],
                   [pd.to_datetime(datetime.date(2015,8,10)), 'A', 20],
                   [pd.to_datetime(datetime.date(2015,8,11)), 'B', 30],
                   [pd.to_datetime(datetime.date(2015,8,10)), 'B', 40],
                  ], columns=['timestamp', 'name', 'value'])
grp = df.groupby(['timestamp', 'name'])['value']
grp.transform(lambda x: x.count())

---

0.15.2 version

0 2
1 2
2 1
3 1
Name: value, dtype: int64

---

0.16.2 version

0 10
1 20
2 30
3 40
Name: value, dtype: int64

---

Pandas versions

INSTALLED VERSIONS

commit: None
python: 3.3.5.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 62 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.16.2
nose: 1.3.4
Cython: 0.22
numpy: 1.9.1
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 2.4.1
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.1
pytz: 2014.9
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.2
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.6.6
lxml: 3.4.2
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: 2.5.5 (dt dec pq3 ext)

[jreback]

this is a dupe of #10114
and closed by #10124 in master and the forthcoming 0.17.0 release.

[jreback]

FYI, you should use .transform('count') as this is substantially faster

[agolbin]

Thanks!