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Classe « Series »

Méthode pandas.Series.aggregate

Signature de la méthode aggregate

def aggregate(self, func=None, axis: 'Axis' = 0, *args, **kwargs) 

Description

help(Series.aggregate)

Aggregate using one or more operations over the specified axis.

Parameters
----------
func : function, 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'}
        Unused. 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

See Also
--------
Series.apply : Invoke function on a Series.
Series.transform : Transform function producing a Series with like indexes.

Notes
-----
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.

Functions that mutate the passed object can produce unexpected
behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
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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