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.replace()
function is used to replace values given in to_replace with value. The values of the Series are replaced with other values dynamically.
Syntax:
Series.replace(to_replace=None, value=None, inplace=False, limit=None, regex=False, method=’pad’)Parameters :
to_replace : How to find the values that will be replaced.
value : Value to replace any values matching to_replace with.
inplace : If True, in place.
limit : Maximum size gap to forward or backward fill.
regex : Whether to interpret to_replace and/or value as regular expressions
method : The method to use when for replacement, when to_replace is a scalar, list or tuple and value is None.Returns : Object after replacement.
Example #1: Use Series.replace()
function to replace some values from the given Series object.
Python3
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([ 10 , 25 , 3 , 11 , 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 :
Coca Cola 10
Sprite 25
Coke 3
Fanta 11
Dew 24
ThumbsUp 6
dtype: int64
Now we will use Series.replace()
function to replace the old values with the new ones.
Python3
# replace 3 by 1000 result = sr.replace(to_replace = 3 , value = 1000 ) # Print the result print (result) |
Output :
Coca Cola 10
Sprite 25
Coke 1000
Fanta 11
Dew 24
ThumbsUp 6
dtype: int64
As we can see in the output, the Series.replace()
function has successfully replaced the old value with the new one.
Example #2 : Use Series.replace()
function to replace some values from the given Series object.
Python3
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([ 'New York' , 'Chicago' , 'Toronto' , 'Lisbon' , 'Rio' ]) # Create the Index index_ = [ 'City 1' , 'City 2' , 'City 3' , 'City 4' , 'City 5' ] # set the index sr.index = index_ # Print the series print (sr) |
Output :
City 1 New York
City 2 Chicago
City 3 Toronto
City 4 Lisbon
City 5 Rio
dtype: object
Now we will use Series.replace()
function to replace the old values with the new ones using a list.
Python3
# replace the old ones in the list with # the new values result = sr.replace(to_replace = [ 'New York' , 'Rio' ], value = [ 'London' , 'Brisbane' ]) # Print the result print (result) |
Output :
City 1 London
City 2 Chicago
City 3 Toronto
City 4 Lisbon
City 5 Brisbane
dtype: object
As we can see in the output, the Series.replace()
function has successfully replaced the old value with the new one using the list.