pandas.core.resample.Resampler.apply

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

Aggregate using one or more operations over the specified axis.

Parameters:

func : function, string, dictionary, or list of string/functions Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply. For a DataFrame, can pass a dict, if the keys are DataFrame column names. Accepted combinations are: string function name. function. list of functions. dict of column names -> functions (or list of functions). *args Positional arguments to pass to func. **kwargs Keyword arguments to pass to func.

Returns:

aggregated : DataFrame

See also

pandas.DataFrame.groupby.aggregate, pandas.DataFrame.resample.transform, pandas.DataFrame.aggregate

Notes

agg is an alias for aggregate. Use the alias.

A passed user-defined-function will be passed a Series for evaluation.

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