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Pandas DataFrame: aggregate() function

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:


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