pandas.core.window.Expanding.aggregate — pandas 0.24.2 documentation (original) (raw)

Expanding. aggregate(arg, *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 Series/Dataframe or when passed to Series/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

Parameters:

func : function, str, list or dict Function to use for aggregating the data. If a function, must either work when passed a Series/Dataframe or when passed to Series/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

Notes

agg is an alias for aggregate. Use the alias.

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

Examples

df = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C']) df A B C 0 -2.385977 -0.102758 0.438822 1 -1.004295 0.905829 -0.954544 2 0.735167 -0.165272 -1.619346 3 -0.702657 -1.340923 -0.706334 4 -0.246845 0.211596 -0.901819 5 2.463718 3.157577 -1.380906 6 -1.142255 2.340594 -0.039875 7 1.396598 -1.647453 1.677227 8 -0.543425 1.761277 -0.220481 9 -0.640505 0.289374 -1.550670

df.ewm(alpha=0.5).mean() A B C 0 -2.385977 -0.102758 0.438822 1 -1.464856 0.569633 -0.490089 2 -0.207700 0.149687 -1.135379 3 -0.471677 -0.645305 -0.906555 4 -0.355635 -0.203033 -0.904111 5 1.076417 1.503943 -1.146293 6 -0.041654 1.925562 -0.588728 7 0.680292 0.132049 0.548693 8 0.067236 0.948257 0.163353 9 -0.286980 0.618493 -0.694496