pyspark.pandas.Series.drop

Series.drop(labels: Union[Any, Tuple[Any, …], List[Union[Any, Tuple[Any, …]]], None] = None, index: Union[Any, Tuple[Any, …], List[Union[Any, Tuple[Any, …]]], None] = None, level: Optional[int] = None) → pyspark.pandas.series.Series

Return Series with specified index labels removed.

Remove elements of a Series based on specifying the index labels. When using a multi-index, labels on different levels can be removed by specifying the level.

Parameters

labelssingle label or list-like

Index labels to drop.

indexNone

Redundant for application on Series, but index can be used instead of labels.

levelint or level name, optional

For MultiIndex, level for which the labels will be removed.

Returns

Series

Series with specified index labels removed.

See also

Series.dropna

Examples

>>> s = ps.Series(data=np.arange(3), index=['A', 'B', 'C'])
>>> s
A    0
B    1
C    2
dtype: int64

Drop single label A

>>> s.drop('A')
B    1
C    2
dtype: int64

Drop labels B and C

>>> s.drop(labels=['B', 'C'])
A    0
dtype: int64

With ‘index’ rather than ‘labels’ returns exactly same result.

>>> s.drop(index='A')
B    1
C    2
dtype: int64

>>> s.drop(index=['B', 'C'])
A    0
dtype: int64

Also support for MultiIndex

>>> midx = pd.MultiIndex([['lama', 'cow', 'falcon'],
...                       ['speed', 'weight', 'length']],
...                      [[0, 0, 0, 1, 1, 1, 2, 2, 2],
...                       [0, 1, 2, 0, 1, 2, 0, 1, 2]])
>>> s = ps.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3],
...               index=midx)
>>> s
lama    speed      45.0
        weight    200.0
        length      1.2
cow     speed      30.0
        weight    250.0
        length      1.5
falcon  speed     320.0
        weight      1.0
        length      0.3
dtype: float64

>>> s.drop(labels='weight', level=1)
lama    speed      45.0
        length      1.2
cow     speed      30.0
        length      1.5
falcon  speed     320.0
        length      0.3
dtype: float64

>>> s.drop(('lama', 'weight'))
lama    speed      45.0
        length      1.2
cow     speed      30.0
        weight    250.0
        length      1.5
falcon  speed     320.0
        weight      1.0
        length      0.3
dtype: float64

>>> s.drop([('lama', 'speed'), ('falcon', 'weight')])
lama    weight    200.0
        length      1.2
cow     speed      30.0
        weight    250.0
        length      1.5
falcon  speed     320.0
        length      0.3
dtype: float64

pyspark.pandas.Series.align pyspark.pandas.Series.droplevel