Resampler.aggregate(arg, *args, **kwargs) Apply aggregation function or functions to resampled groups, yielding most likely Series but in some cases DataFrame depending on the output of the aggregation function
| Parameters: |
func_or_funcs : function or list / dict of functions List/dict of functions will produce DataFrame with column names determined by the function names themselves (list) or the keys in the dict |
|---|---|
| Returns: |
Series or DataFrame |
See also
agg is an alias for aggregate. Use it.
>>> s = Series([1,2,3,4,5],
index=pd.date_range('20130101',
periods=5,freq='s'))
2013-01-01 00:00:00 1
2013-01-01 00:00:01 2
2013-01-01 00:00:02 3
2013-01-01 00:00:03 4
2013-01-01 00:00:04 5
Freq: S, dtype: int64
>>> r = s.resample('2s')
DatetimeIndexResampler [freq=<2 * Seconds>, axis=0, closed=left,
label=left, convention=start, base=0]
>>> r.agg(np.sum) 2013-01-01 00:00:00 3 2013-01-01 00:00:02 7 2013-01-01 00:00:04 5 Freq: 2S, dtype: int64
>>> r.agg(['sum','mean','max'])
sum mean max
2013-01-01 00:00:00 3 1.5 2
2013-01-01 00:00:02 7 3.5 4
2013-01-01 00:00:04 5 5.0 5
>>> r.agg({'result' : lambda x: x.mean() / x.std(),
'total' : np.sum})
total result
2013-01-01 00:00:00 3 2.121320
2013-01-01 00:00:02 7 4.949747
2013-01-01 00:00:04 5 NaN
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http://pandas.pydata.org/pandas-docs/version/0.18.1/generated/pandas.tseries.resample.Resampler.aggregate.html