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:
- 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.argmaxReturn indices of the maximum values along the given axis.
DataFrame.idxmaxReturn index of first occurrence of maximum over requested axis.
Series.idxminReturn 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]