pandas.DataFrame.aggregate

DataFrame.aggregate(func, axis=0, *args, **kwargs)

Aggregate using callable, string, dict, or list of string/callables

New in version 0.20.0.

Parameters:

func : callable, string, dictionary, or list of string/callables

Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply. For a DataFrame, can pass a dict, if the keys are DataFrame column names.

Accepted Combinations are:

• string function name
• function
• list of functions
• dict of column names -> functions (or list of functions)

Returns:

aggregated : DataFrame

See also

pandas.DataFrame.apply, pandas.DataFrame.transform, pandas.DataFrame.groupby.aggregate, pandas.DataFrame.resample.aggregate, pandas.DataFrame.rolling.aggregate

Notes

Numpy functions mean/median/prod/sum/std/var are special cased so the default behavior is applying the function along axis=0 (e.g., np.mean(arr_2d, axis=0)) as opposed to mimicking the default Numpy behavior (e.g., np.mean(arr_2d)).

agg is an alias for aggregate. Use it.

Examples

>>> df = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'],
...                   index=pd.date_range('1/1/2000', periods=10))
>>> df.iloc[3:7] = np.nan

Aggregate these functions across all columns

>>> df.agg(['sum', 'min'])
            A         B         C
sum -0.182253 -0.614014 -2.909534
min -1.916563 -1.460076 -1.568297

Different aggregations per column

>>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
            A         B
max       NaN  1.514318
min -1.916563 -1.460076
sum -0.182253       NaN