groupby, TimeGrouper error

[jgbarah]

The following code seems to raise an error, since the result object does not make sense (well, at least to me):

In [61]: import datetime

In [62]: import pandas as pd

In [63]: df = pd.DataFrame.from_records ( [[datetime.datetime(2014,9,10),1234,"start"],
  [datetime.datetime(2013,10,10),1234,"start"]], columns = ["date", "change", "event"] )

In [64]: df
Out[64]: 
        date  change  event
0 2014-09-10    1234  start
1 2013-10-10    1234  start

In [65]: ts = df.set_index('date')

In [66]: ts
Out[66]: 
            change  event
date                     
2014-09-10    1234  start
2013-10-10    1234  start

In [67]: byperiod = ts.groupby([pd.TimeGrouper(freq="M"), "event"], as_index=False)

In [68]: byperiod.groups
Out[68]: 
{<pandas.tseries.resample.TimeGrouper at 0xab6bcaec>: [Timestamp('2014-09-10 00:00:00')],
 'event': [Timestamp('2013-10-10 00:00:00')]}

I would expect, for Out[68], two groups, one for each (date, event) pair.

Am I wring, or this is a bug?

[jreback]

show pd.show_versions()

[jgbarah]

In [13]: pd.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 32
OS: Linux
OS-release: 3.14-2-686-pae
machine: i686
processor: 
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.14.1
nose: 1.3.4
Cython: None
numpy: 1.9.0
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.3.0
sphinx: 1.2.3
patsy: 0.3.0
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.7
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.9
apiclient: None
rpy2: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
'''

[jgbarah]

BTW, with three elements in the dataframe, it seems to work:

In [8]: df = pd.DataFrame.from_records ( [[datetime.datetime(2014,9,10),1234,"start"], [datetime.datetime(2014,10,10),1238,"start"], [datetime.datetime(2014,12,10),1564,"start"]], columns = ["date", "change", "event"] )

In [9]: ts = df.set_index('date')

In [10]: byperiod = ts.groupby([pd.TimeGrouper(freq="M"), "event"], as_index=False)

In [11]: byperiod.groups
Out[11]: 
{(Timestamp('2014-09-30 00:00:00'),
  'start'): [Timestamp('2014-09-10 00:00:00')],
 (Timestamp('2014-10-31 00:00:00'),
  'start'): [Timestamp('2014-10-10 00:00:00')],
 (Timestamp('2014-12-31 00:00:00'),
  'start'): [Timestamp('2014-12-10 00:00:00')]}
'''

[jreback]

the prob with 2 elements is that the frequency is not defined for the timeseries

so maybe not behaving properly

will mark as a bug
feel free to have a look !

[jgbarah]

Thanks! I'm not familiar with the internals of pandas, but I'll try.

[victorpoluceno]

hey @jreback I found that check on the code that drives that behavior: https://github.com/pydata/pandas/blob/master/pandas/core/groupby.py#L2077

The match_axis_length will be True because size of groups and keys are equal, thus this code will be executed:

if (not any_callable and not all_in_columns
        and not any_arraylike and match_axis_length
            and level is None):
    keys = [com._asarray_tuplesafe(keys)]

The thing is even if I remove match_axis_length from the above condition, all others checks will evaluate True on my case, and then keys = [com._asarray_tuplesafe(keys)] will execute anyway.

Do you have any ideas on how to relax/remove the match_axis_length and still keep the other cases?

[jreback]

I think a way to make this work is to relax the freqency inference engine to only require 2 dates (though this may break lots of other things, not sure).

In [10]: pd.DatetimeIndex(df['date'])
Out[10]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2014-09-10, 2013-10-10]
Length: 2, Freq: None, Timezone: None

[alep]

@victorpoluceno did you figure out a solution?