Expanding.
aggregate
Aggregate using one or more operations over the specified axis.
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']
[np.sum, 'mean']
dict of axis labels -> functions, function names or list of such.
Positional arguments to pass to func.
Keyword arguments to pass to func.
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