Resampler.apply()

Resampler.apply(arg, *args, **kwargs) [source]

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:

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

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

Notes

agg 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

Links:

http://pandas.pydata.org/pandas-docs/version/0.19.2/generated/pandas.tseries.resample.Resampler.apply.html

doc_Pandas

2025-01-10 15:47:30

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