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pandas.Series.argmax

Series.argmax(axis=0, skipna=True, *args, **kwargs)[source]
Deprecated since version 0.21.0:
The current behaviour of ‘Series.argmax’ is deprecated, use ‘idxmax’
instead. The behavior of ‘argmax’ will be corrected to return the positional maximum in the future. For now, use ‘series.values.argmax’ or ‘np.argmax(np.array(values))’ to get the position of the maximum row.

Return the row label of the maximum value.

If multiple values equal the maximum, the first row label with that value is returned.

Parameters:

skipna : boolean, default True

Exclude NA/null values. If the entire Series is NA, the result will be NA.

axis : int, default 0

For compatibility with DataFrame.idxmax. Redundant for application on Series.

*args, **kwargs

Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns:
idxmax : Index of maximum of values.
Raises:

ValueError

If the Series is empty.

See also

numpy.argmax
Return indices of the maximum values along the given axis.
DataFrame.idxmax
Return index of first occurrence of maximum over requested axis.
Series.idxmin
Return index label of the first occurrence of minimum of values.

Notes

This method is the Series version of ndarray.argmax. This method returns the label of the maximum, while ndarray.argmax returns the position. To get the position, use series.values.argmax().

Examples

>>> s = pd.Series(data=[1, None, 4, 3, 4],
...               index=['A', 'B', 'C', 'D', 'E'])
>>> s
A    1.0
B    NaN
C    4.0
D    3.0
E    4.0
dtype: float64
>>> s.idxmax()
'C'

If skipna is False and there is an NA value in the data, the function returns nan.

>>> s.idxmax(skipna=False)
nan
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