Pandas DataFrame: aggregate() function

Last update on August 19 2022 21:50:33 (UTC/GMT +8 hours)

DataFrame - aggregate() function

The aggregate() function is used to aggregate using one or more operations over the specified axis.

Syntax:

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

Parameters:

Name	Description	Type/Default Value	Required / Optional
func	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.	function, str, list or dict	Required
axis	If 0 or 'index': apply function to each column. If 1 or 'columns': apply function to each row.	{0 or 'index', 1 or 'columns'} Default Value: 0	Required
*args	Positional arguments to pass to func.		Required
**kwargs	Keyword arguments to pass to func.		Required

Returns: 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.

Example:

Download the Pandas DataFrame Notebooks from here.

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