pandas.Index.argmax#

Index.argmax(axis=None,skipna=True,*args,**kwargs)[source]#

Return int position of the largest value in the Series.

If the maximum is achieved in multiple locations,the first row position is returned.

Parameters:

axis{None}

Unused. Parameter needed for compatibility with DataFrame.

skipnabool, default True

Exclude NA/null values when showing the result.

*args, **kwargs

Additional arguments and keywords for compatibility with NumPy.

Returns:

int

Row position of the maximum value.

See also

Series.argmax

Return position of the maximum value.

Series.argmin

Return position of the minimum value.

numpy.ndarray.argmax

Equivalent method for numpy arrays.

Series.idxmax

Return index label of the maximum values.

Series.idxmin

Return index label of the minimum values.

Examples

Consider dataset containing cereal calories

>>>s=pd.Series({'Corn Flakes':100.0,'Almond Delight':110.0,...'Cinnamon Toast Crunch':120.0,'Cocoa Puff':110.0})>>>sCorn Flakes              100.0Almond Delight           110.0Cinnamon Toast Crunch    120.0Cocoa Puff               110.0dtype: float64

>>>s.argmax()2>>>s.argmin()0

The maximum cereal calories is the third element andthe minimum cereal calories is the first element,since series is zero-indexed.