BUG: resample Grouper in a list grouping on a column with NaT throws an error

[nmichaud]

Code Sample, a copy-pastable example if possible

df = pd.DataFrame({'completed':pd.date_range('1/1/2011', periods=365, freq='D')})

# In the real case i have nulls in my data
df.iloc[-1] = pd.NaT

# This works
df.groupby(pd.Grouper(key='completed', freq='AS'))

# This doesn't and throws an error
df.groupby([pd.Grouper(key='completed', freq='AS')])

Problem description

Second form throws the following error:

/anaconda/lib/python2.7/site-packages/pandas/indexes/base.pyc in _assert_take_fillable(self, values, indices, allow_fill, fill_value, na_value)
   1537                 taken[mask] = na_value
   1538         else:
-> 1539             taken = values.take(indices)
   1540         return taken
   1541

IndexError: index 364 is out of bounds for size 364

Expected Output

A proper grouper

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 2.7.12.final.0 python-bits: 64 OS: Darwin OS-release: 14.5.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: None.None

pandas: 0.19.1
nose: 1.3.7
pip: 8.1.2
setuptools: 0.6
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None

[nmichaud]

If there are no pd.NaT's in the column then both forms work

[jreback]

so this works fine as the index. (this formulation is functionally equivalent).

In [10]: df.set_index('completed',drop=False).resample('AS').first()
Out[10]: 
            completed
completed            
2011-01-01 2011-01-01

# and here
In [11]: df.resample('AS', on='completed').first()
Out[11]: 
            completed
completed            
2011-01-01 2011-01-01

[jreback]

pull-requests welcome

[nmichaud]

It's functionally equivalent, but my original use case is:

grouper = df.groupby([pd.Grouper(key='completed', freq='AS'), 
                      pd.Grouper(key='completed', freq='QS'),
                      pd.Grouper(key='completed', freq='MS')])

# Some agg with grouper
grouper.agg('sum')

[discort]

@jreback

I couldn't reproduce the original issue. Both variants work fine.

[jreback]

can u do a PR with a test for this?