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, columns: Union[Any, Tuple[Any, …], List[Union[Any, Tuple[Any, …]]], None] = None, level: Optional[int] = None, inplace: bool = False) → 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.
indexsingle label or list-like: Redundant for application on Series, but index can be used instead of labels.
columnssingle label or list-like: No change is made to the Series; use ‘index’ or ‘labels’ instead.
levelint or level name, optional: For MultiIndex, level for which the labels will be removed.
inplace: bool, default False: If True, do operation inplace and return None

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

With ‘columns’, no change is made to the Series.

>>> s.drop(columns=['A'])
A    0
B    1
C    2
dtype: int64

With ‘inplace=True’, do operation inplace and return None.

>>> s.drop(index=['B', 'C'], inplace=True)
>>> s
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