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Python | Pandas Series.drop()

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  • Last Updated : 15 Feb, 2019
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Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.

Pandas Series.drop() function return Series with specified index labels removed. It remove elements of a Series based on specifying the index labels.

Syntax: Series.drop(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors=’raise’)

Parameter :
labels : Index labels to drop.
axis : Redundant for application on Series.
index, columns : Redundant for application on Series, but index can be used instead of labels.
level : For MultiIndex, level for which the labels will be removed.
inplace : If True, do operation inplace and return None.
errors : If ‘ignore’, suppress error and only existing labels are dropped.

Returns : dropped : pandas.Series

Example #1: Use Series.drop() function to drop the values corresponding to the passed index labels in the given series object.




# importing pandas as pd
import pandas as pd
  
# Creating the Series
sr = pd.Series([80, 25, 3, 25, 24, 6])
  
# Create the Index
index_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp']
  
# set the index
sr.index = index_
  
# Print the series
print(sr)

Output :

Now we will use Series.drop() function to drop the values corresponding to the passed index labels in the given series object.




# drop the passed labels
result = sr.drop(labels = ['Sprite', 'Dew']) 
  
# Print the result
print(result)

Output :


As we can see in the output, the Series.drop() function has successfully dropped the entries corresponding to the passed index labels.
 
Example #2 : Use Series.drop() function to drop the values corresponding to the passed index labels in the given series object.




# importing pandas as pd
import pandas as pd
  
# Creating the Series
sr = pd.Series([11, 11, 8, 18, 65, 18, 32, 10, 5, 32, 32])
  
# Create the Index
index_ = pd.date_range('2010-10-09', periods = 11, freq ='M')
  
# set the index
sr.index = index_
  
# Print the series
print(sr)

Output :

Now we will use Series.drop() function to drop the values corresponding to the passed index labels in the given series object.




# drop the passed labels
result = sr.drop(labels = [pd.Timestamp('2010-12-31'),
                           pd.Timestamp('2011-04-30'), pd.Timestamp('2011-08-31')])
  
# Print the result
print(result)

Output :


As we can see in the output, the Series.drop() function has successfully dropped the entries corresponding to the passed index labels.


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