I want to groupby timestamp (date) and access each group by timestamp, which looks not working properly. It looks like the group keys are strangely indexed with different formats.
df= pd.DataFrame({'DATE' : ['10-Oct-2013', '10-Oct-2013', '10-Oct-2013', '11-Oct-2013', '11-Oct-2013', '11-Oct-2013'],'VAL' : [1,2,3,4,5,6]})
>>> df
DATE VAL
0 10-Oct-2013 1
1 10-Oct-2013 2
2 10-Oct-2013 3
3 11-Oct-2013 4
4 11-Oct-2013 5
5 11-Oct-2013 6
dfg=df.groupby(df['DATE'].apply(lambda x: pd.to_datetime(x)))
>>> dfg.groups.keys()
[numpy.datetime64('NaT'), numpy.datetime64('2013-10-10T17:00:00.000000000-0700'), numpy.datetime64('2013-10-09T17:00:00.000000000-0700')]
for d in dfg.groups.keys():
try:
print d,dfg.get_group(d).describe()
except:
print 'err'
>>
NaT err
2013-10-10T17:00:00.000000000-0700 err
2013-10-09T17:00:00.000000000-0700 err
rng = pd.to_datetime(pd.date_range('10/10/2013', periods=3, freq='D'))
for d in rng:
try:
print d,dfg.get_group(d).describe()
except:
print 'err'
2013-10-10 00:00:00 err
2013-10-11 00:00:00 err
2013-10-12 00:00:00 err
Here's your frame
In [40]: df = pd.DataFrame({'DATE' : ['10-Oct-2013', '10-Oct-2013', '10-Oct-2013', '11-Oct-2013', '11-Oct-2013', '11-Oct-2013'],'VAL' : [1,2,3,4,5,6]})
Much faster to directly convert a date-like column
In [41]: df['DATE']= pd.to_datetime(df['DATE'])
In [42]: df.dtypes
Out[42]:
DATE datetime64[ns]
VAL int64
dtype: object
In [43]: df
Out[43]:
DATE VAL
0 2013-10-10 00:00:00 1
1 2013-10-10 00:00:00 2
2 2013-10-10 00:00:00 3
3 2013-10-11 00:00:00 4
4 2013-10-11 00:00:00 5
5 2013-10-11 00:00:00 6
This accomplishes what it loooks like you want
In [44]: df.groupby('DATE').describe()
Out[44]:
VAL
DATE
2013-10-10 count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
2013-10-11 count 3.0
mean 5.0
std 1.0
min 4.0
25% 4.5
50% 5.0
75% 5.5
max 6.0
If you REALLY want to get by a group individually
In [45]: g = df.groupby('DATE')
In [46]: key = g.groups.keys()[0]
In [47]: key
Out[47]: numpy.datetime64('2013-10-09T20:00:00.000000000-0400')
In [48]: g.get_group(key.astype('i8'))
Out[48]:
DATE VAL
0 2013-10-10 00:00:00 1
1 2013-10-10 00:00:00 2
2 2013-10-10 00:00:00 3
datetime64[ns] are stored internally as long integers, so that's how they need to be accessed You normally really have no reason to do this as you can just
df.groupby('DATE').apply(lambda x: .....)
or if you really want to iterate
for g, grp in df.groupby('DATE'):
......
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