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Python | Pandas Series.replace()

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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.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.



Last Updated : 24 Aug, 2023
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