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. If the entire Series is NA, or if skipna=False and there is an NA value, this method will raise a ValueError.
*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(
...     [100.0, 110.0, 120.0, 110.0],
...     index=[
...         "Corn Flakes",
...         "Almond Delight",
...         "Cinnamon Toast Crunch",
...         "Cocoa Puff",
...     ],
... )
>>> s
Corn Flakes              100.0
Almond Delight           110.0
Cinnamon Toast Crunch    120.0
Cocoa Puff               110.0
dtype: float64

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

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