pandas.Series.max#

Series.max(axis=0,skipna=True,numeric_only=False,**kwargs)[source]#

Return the maximum of the values over the requested axis.

If you want theindex of the maximum, useidxmax. This is the equivalent of thenumpy.ndarray methodargmax.

Parameters:

axis{index (0)}

Axis for the function to be applied on.ForSeries this parameter is unused and defaults to 0.

For DataFrames, specifyingaxis=None will apply the aggregationacross both axes.

Added in version 2.0.0.

skipnabool, default True

Exclude NA/null values when computing the result.

numeric_onlybool, 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)>>>sblooded  animalwarm     dog       4         falcon    2cold     fish      0         spider    8Name: legs, dtype: int64

>>>s.max()8