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Resampler.aggregate(arg, *args, **kwargs)[source]
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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 Notesagg is an alias for aggregate. Use it. Examples>>> 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
Resampler.aggregate()
 
          2025-01-10 15:47:30
            
          
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