pandas.core.resample.Resampler.aggregate¶
-
Resampler.
aggregate
(func, *args, **kwargs)[source]¶ Aggregate using one or more operations over the specified axis.
Parameters: - func : function, 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: - DataFrame, Series or scalar
if DataFrame.agg is called with a single function, returns a Series if DataFrame.agg is called with several functions, returns a DataFrame if Series.agg is called with single function, returns a scalar if Series.agg is called with several functions, returns a Series
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 = 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, 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