pandas.Series.agg — pandas 0.24.0rc1 documentation (original) (raw)

Series. agg(func, axis=0, *args, **kwargs)[source]¶

Aggregate using one or more operations over the specified axis.

New in version 0.20.0.

Parameters:	func : function, str, list or dict Function to use for aggregating the data. If a function, must either work when passed a Series or when passed to Series.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. axis : {0 or ‘index’} Parameter needed for compatibility with DataFrame. 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

Parameters:

func : function, str, list or dict Function to use for aggregating the data. If a function, must either work when passed a Series or when passed to Series.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. axis : {0 or ‘index’} Parameter needed for compatibility with DataFrame. *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

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]) s 0 1 1 2 2 3 3 4 dtype: int64

s.agg(['min', 'max']) min 1 max 4 dtype: int64