pandas.core.groupby.SeriesGroupBy.idxmax

SeriesGroupBy.idxmax(skipna=True)

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:

Series

Indexes of maxima in each group.

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]