Python | Pandas Series.replace()

Last Updated : 14 Oct, 2025

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='None')

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.

Python 
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(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.

Python 
# 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.

Python 
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(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.

Python 
# replace the old ones in the list with the new values result = sr.replace(to_replace = ['New York', 'Rio'], value = ['London', 'Brisbane']) 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.

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