pandas.core.window.Rolling.aggregate¶
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Rolling.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
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.rolling(3).sum() A B C 0 NaN NaN NaN 1 NaN NaN NaN 2 -2.655105 0.637799 -2.135068 3 -0.971785 -0.600366 -3.280224 4 -0.214334 -1.294599 -3.227500 5 1.514216 2.028250 -2.989060 6 1.074618 5.709767 -2.322600 7 2.718061 3.850718 0.256446 8 -0.289082 2.454418 1.416871 9 0.212668 0.403198 -0.093924
>>> df.rolling(3).agg({'A':'sum', 'B':'min'}) A B 0 NaN NaN 1 NaN NaN 2 -2.655105 -0.165272 3 -0.971785 -1.340923 4 -0.214334 -1.340923 5 1.514216 -1.340923 6 1.074618 0.211596 7 2.718061 -1.647453 8 -0.289082 -1.647453 9 0.212668 -1.647453