Python | Pandas Series.replace()

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.

# 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.

# 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.

# 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.

# 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.