# pandas.DataFrame.agg¶

Aggregate using one or more operations over the specified axis.

New in version 0.20.0.

- param 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.

- param 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.

- return
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.

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.

Warning

This feature is currently unsupported by Intel Scalable Dataframe Compiler