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Classe « Series »

Méthode pandas.Series.max

Signature de la méthode max

def max(self, axis: 'Axis | None' = 0, skipna: 'bool' = True, numeric_only: 'bool' = False, **kwargs) 

Description

help(Series.max)

Return the maximum of the values over the requested axis.

If you want the *index* of the maximum, use ``idxmax``. This is the equivalent of the ``numpy.ndarray`` method ``argmax``.

Parameters
----------
axis : {index (0)}
    Axis for the function to be applied on.
    For `Series` this parameter is unused and defaults to 0.

    For DataFrames, specifying ``axis=None`` will apply the aggregation
    across both axes.

    .. versionadded:: 2.0.0

skipna : bool, default True
    Exclude NA/null values when computing the result.
numeric_only : bool, default False
    Include only float, int, boolean columns. Not implemented for Series.

**kwargs
    Additional keyword arguments to be passed to the function.

Returns
-------
scalar or scalar

See Also
--------
Series.sum : Return the sum.
Series.min : Return the minimum.
Series.max : Return the maximum.
Series.idxmin : Return the index of the minimum.
Series.idxmax : Return the index of the maximum.
DataFrame.sum : Return the sum over the requested axis.
DataFrame.min : Return the minimum over the requested axis.
DataFrame.max : Return the maximum over the requested axis.
DataFrame.idxmin : Return the index of the minimum over the requested axis.
DataFrame.idxmax : Return the index of the maximum over the requested axis.

Examples
--------
>>> idx = pd.MultiIndex.from_arrays([
...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64

>>> s.max()
8

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