Pandas Series: drop() function

Last update on September 15 2022 12:55:37 (UTC/GMT +8 hours)

Remove series with specified index labels

The drop() function is used to get 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.

Syntax:

Series.drop(self, labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')

Parameters:

Name	Description	Type/Default Value	Required / Optional
labels	Index labels to drop.	single label or list-like	Required
axis	Redundant for application on Series.	0 Default Value: 0	Required
index, columns	Redundant for application on Series, but index can be used instead of labels.	Default Value: None	Required
level	For MultiIndex, level for which the labels will be removed.	int or level name	optional
inplace	If True, do operation inplace and return None.	bool Default Value: False	Required
errors	If ‘ignore’, suppress error and only existing labels are dropped.	{‘ignore’, ‘raise’} Default Value: ‘raise’	Required

Returns: Series
Series with specified index labels removed.

Raises: KeyError
If none of the labels are found in the index.

Example:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series(data=np.arange(3), index=['P', 'Q', 'R'])
s

Output:

P    0
Q    1
R    2
dtype: int32

Example - Drop labels Q en R:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series(data=np.arange(3), index=['P', 'Q', 'R'])
s.drop(labels=['Q', 'R'])

Output:

P    0
dtype: int32

Example - Drop 2nd level label in MultiIndex Series:

Python-Pandas Code:

import numpy as np
import pandas as pd
midx = pd.MultiIndex(levels=[['dog', 'cow', 'cat'],
                             ['speed', 'weight', 'length']],
                     codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
                            [0, 1, 2, 0, 1, 2, 0, 1, 2]])
s = pd.Series([50, 30, 1.6, 30, 250, 1.5, 40, 18, 1.1],
              index=midx)
s

Output:

dog  speed      50.0
     weight     30.0
     length      1.6
cow  speed      30.0
     weight    250.0
     length      1.5
cat  speed      40.0
     weight     18.0
     length      1.1
dtype: float64

Python-Pandas Code:

import numpy as np
import pandas as pd
midx = pd.MultiIndex(levels=[['dog', 'cow', 'cat'],
                             ['speed', 'weight', 'length']],
                     codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
                            [0, 1, 2, 0, 1, 2, 0, 1, 2]])
s = pd.Series([50, 30, 1.6, 30, 250, 1.5, 40, 18, 1.1],
              index=midx)
s.drop(labels='weight', level=1)

Output:

dog  speed     50.0
     length     1.6
cow  speed     30.0
     length     1.5
cat  speed     40.0
     length     1.1
dtype: float64

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