pandas.core.groupby.SeriesGroupBy.idxmax#

SeriesGroupBy.idxmax(skipna=True)[source]#

Return the row label of the maximum value.

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

Parameters:

skipnabool, default True: Exclude NA values.

Returns:

Index: Label of the maximum value.

Raises:

ValueError: If the Series is empty or skipna=False and any value is NA.

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.

Examples

>>> ser = pd.Series(
...     [1, 2, 3, 4],
...     index=pd.DatetimeIndex(
...         ["2023-01-01", "2023-01-15", "2023-02-01", "2023-02-15"]
...     ),
... )
>>> ser
2023-01-01    1
2023-01-15    2
2023-02-01    3
2023-02-15    4
dtype: int64

>>> ser.groupby(["a", "a", "b", "b"]).idxmax()
a   2023-01-15
b   2023-02-15
dtype: datetime64[s]