pandas.DataFrame.aggregate

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

Aggregate using one or more operations over the specified axis.

New in version 0.20.0.

Parameters:

func : function, str, list or dict

Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to 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.

axis : {0 or ‘index’, 1 or ‘columns’}, default 0

If 0 or ‘index’: apply function to each column. If 1 or ‘columns’: apply function to each row.

*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

The aggregation operations are always performed over an axis, either the

index (default) or the column axis. This behavior is different from

`numpy` aggregation functions (`mean`, `median`, `prod`, `sum`, `std`,

`var`), where the default is to compute the aggregation of the flattened

array, e.g., ``numpy.mean(arr_2d)`` as opposed to ``numpy.mean(arr_2d,

axis=0)``.

`agg` is an alias for `aggregate`. Use the alias.

See also

DataFrame.apply

Perform any type of operations.

DataFrame.transform

Perform transformation type operations.

pandas.core.groupby.GroupBy

Perform operations over groups.

pandas.core.resample.Resampler

Perform operations over resampled bins.

pandas.core.window.Rolling

Perform operations over rolling window.

pandas.core.window.Expanding

Perform operations over expanding window.

pandas.core.window.EWM

Perform operation over exponential weighted window.

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([[1, 2, 3],
...                    [4, 5, 6],
...                    [7, 8, 9],
...                    [np.nan, np.nan, np.nan]],
...                   columns=['A', 'B', 'C'])

Aggregate these functions over the rows.

>>> df.agg(['sum', 'min'])
        A     B     C
sum  12.0  15.0  18.0
min   1.0   2.0   3.0

Different aggregations per column.

>>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
        A    B
max   NaN  8.0
min   1.0  2.0
sum  12.0  NaN

Aggregate over the columns.

>>> df.agg("mean", axis="columns")
0    2.0
1    5.0
2    8.0
3    NaN
dtype: float64