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

When there are no valid values for a group. Then can happen if:

There is an unobserved group and observed=False.

All values for a group are NA.

Some values for a group are NA and skipna=False.

Changed in version 3.0.0: Previously if all values for a group are NA or some values for a group are NA and skipna=False, this method would return NA. Now it raises instead.

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年01月01日", "2023年01月15日", "2023年02月01日", "2023年02月15日"]
...  ),
... )
>>> ser
2023年01月01日 1
2023年01月15日 2
2023年02月01日 3
2023年02月15日 4
dtype: int64

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

pandas.core.groupby.SeriesGroupBy.idxmax#

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