pyspark.pandas.Series.idxmax#

Series.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/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