pyspark.pandas.Series.idxmax¶
-
Series.
idxmax
(skipna: bool = True) → Union[Tuple, Any][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/null values. If the entire Series is NA, the result will be NA.
- Returns
- Index
Label of the maximum value.
- Raises
- ValueError
If the Series is empty.
See also
Series.idxmin
Return index label of the first occurrence of minimum of values.
Examples
>>> s = ps.Series(data=[1, None, 4, 3, 5], ... index=['A', 'B', 'C', 'D', 'E']) >>> s A 1.0 B NaN C 4.0 D 3.0 E 5.0 dtype: float64
>>> s.idxmax() 'E'
If skipna is False and there is an NA value in the data, the function returns
nan
.>>> s.idxmax(skipna=False) nan
In case of multi-index, you get a tuple:
>>> index = pd.MultiIndex.from_arrays([ ... ['a', 'a', 'b', 'b'], ['c', 'd', 'e', 'f']], names=('first', 'second')) >>> s = ps.Series(data=[1, None, 4, 5], index=index) >>> s first second a c 1.0 d NaN b e 4.0 f 5.0 dtype: float64
>>> s.idxmax() ('b', 'f')
If multiple values equal the maximum, the first row label with that value is returned.
>>> s = ps.Series([1, 100, 1, 100, 1, 100], index=[10, 3, 5, 2, 1, 8]) >>> s 10 1 3 100 5 1 2 100 1 1 8 100 dtype: int64
>>> s.idxmax() 3