pandas.core.resample.Resampler.aggregate¶

Resampler.aggregate(func, *args, **kwargs)[source]¶

Aggregate using one or more operations over the specified axis.

Parameters

funcfunction, str, list or dict

Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply.

Accepted combinations are:

function
string function name
list of functions and/or function names, e.g. [np.sum, 'mean']
dict of axis labels -> functions, function names or list of such.

*args

Positional arguments to pass to func.

**kwargs

Keyword arguments to pass to func.

Returns

scalar, Series or DataFrame

The return can be:

scalar : when Series.agg is called with single function
Series : when DataFrame.agg is called with a single function
DataFrame : when DataFrame.agg is called with several functions

Return scalar, Series or DataFrame.

See also

DataFrame.groupby.aggregate: Aggregate using callable, string, dict, or list of string/callables.
DataFrame.resample.transform: Transforms the Series on each group based on the given function.
DataFrame.aggregate: Aggregate using one or more operations over the specified axis.

Notes

agg is an alias for aggregate. Use the alias.

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

Examples

>>> s = pd.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]

>>> 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

pandas.core.resample.Resampler.apply pandas.core.resample.Resampler.transform