pandas.core.window.Expanding.aggregate

Expanding.aggregate(self, 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:

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.expanding.aggregate

DataFrame.rolling.aggregate

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

>>> 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