pandas.Series.aggregate#

Series.aggregate(func=None, axis=0, *args, **kwargs)[source]#

Aggregate using one or more operations over the specified axis.

Parameters
funcfunction, str, list or dict

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

Parameter needed for compatibility with DataFrame.

*args

Positional arguments to pass to func.

**kwargs

Keyword arguments to pass to func.

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.

See also

Series.apply

Invoke function on a Series.

Series.transform

Transform function producing a Series with like indexes.

Notes

agg is an alias for aggregate. Use the alias.

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See Mutating with User Defined Function (UDF) methods for more details.

A passed user-defined-function will be passed a Series for evaluation.

Examples

>>> s = pd.Series([1, 2, 3, 4])
>>> s
0    1
1    2
2    3
3    4
dtype: int64

>>> s.agg('min')
1

>>> s.agg(['min', 'max'])
min   1
max   4
dtype: int64

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