pandas.Series.max¶

Series.max(axis=_NoDefault.no_default, skipna=True, level=None, numeric_only=None, **kwargs)[source]¶

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.
skipnabool, default True: Exclude NA/null values when computing the result.
levelint or level name, default None: If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar.

Deprecated since version 1.3.0: The level keyword is deprecated. Use groupby instead.
numeric_onlybool, default None: Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

Deprecated since version 1.5.0: Specifying numeric_only=None is deprecated. The default value will be False in a future version of pandas.
**kwargs: Additional keyword arguments to be passed to the function.

Returns:

scalar or Series (if level specified)

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

pandas.Series.mask

pandas.Series.mean