pyspark.pandas.Series.pop#
- Series.pop(item)[source]#
Return item and drop from series.
- Parameters
- itemlabel
Label of index to be popped.
- Returns
- Value that is popped from series.
Examples
>>> s = ps.Series(data=np.arange(3), index=['A', 'B', 'C']) >>> s A 0 B 1 C 2 dtype: int64
>>> s.pop('A') 0
>>> s B 1 C 2 dtype: int64
>>> s = ps.Series(data=np.arange(3), index=['A', 'A', 'C']) >>> s A 0 A 1 C 2 dtype: int64
>>> s.pop('A') A 0 A 1 dtype: int64
>>> s C 2 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.pop('lama') speed 45.0 weight 200.0 length 1.2 dtype: float64
>>> s cow speed 30.0 weight 250.0 length 1.5 falcon speed 320.0 weight 1.0 length 0.3 dtype: float64
Also support for MultiIndex with several indexes.
>>> midx = pd.MultiIndex([['a', 'b', 'c'], ... ['lama', 'cow', 'falcon'], ... ['speed', 'weight', 'length']], ... [[0, 0, 0, 0, 0, 0, 1, 1, 1], ... [0, 0, 0, 1, 1, 1, 2, 2, 2], ... [0, 1, 2, 0, 1, 2, 0, 0, 2]] ... ) >>> s = ps.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3], ... index=midx) >>> s a lama speed 45.0 weight 200.0 length 1.2 cow speed 30.0 weight 250.0 length 1.5 b falcon speed 320.0 speed 1.0 length 0.3 dtype: float64
>>> s.pop(('a', 'lama')) speed 45.0 weight 200.0 length 1.2 dtype: float64
>>> s a cow speed 30.0 weight 250.0 length 1.5 b falcon speed 320.0 speed 1.0 length 0.3 dtype: float64
>>> s.pop(('b', 'falcon', 'speed')) (b, falcon, speed) 320.0 (b, falcon, speed) 1.0 dtype: float64