Replace multiple values in a Pandas Column

This tutorial will discuss about unique ways to replace multiple values in a pandas column.

Table Of Contents

Introduction

Suppose we have a DataFrame,

     Name Location    Team  Experience
0    Mark       US    Tech           5
1    Riti       UK    Tech           7
2  Shanky       UK     PMO           2
3  Shreya       UK  Design           2
4    Aadi       US    Tech          11
5     Sim       US    Tech           4

We want to replace multiple values in the DataFrame like,
* Replace ‘US’ with the string ‘United States’ in column ‘Location’.
* Replace ‘UK’ with the string ‘United Kingdom in column ‘Location’.

Like this,

     Name        Location    Team  Experience
0    Mark   Unites States    Tech           5
1    Riti  United Kingdom    Tech           7
2  Shanky  United Kingdom     PMO           2
3  Shreya  United Kingdom  Design           2
4    Aadi   Unites States    Tech          11
5     Sim   Unites States    Tech           4

Let’s see different ways to do that.

Method 1: Using replace() method

Select DataFrame column Location, and call replace() function on it. Pass following arguments to the replace() function,

• List containing values that need to be replaced. For example, ['US', 'UK']
• List containing the replacement strings. For example, [‘Unites States’, ‘United Kingdom’]

Syntax is,

# Replace 'US' with 'United States' in Column 'Location'
# Replace 'UK' with 'United Kingdom' in Column 'Location'
df['Location'] = df['Location'].replace(
                                ['US', 'UK'],
                                ['Unites States', 'United Kingdom'])

Output:

     Name        Location    Team  Experience
0    Mark   Unites States    Tech           5
1    Riti  United Kingdom    Tech           7
2  Shanky  United Kingdom     PMO           2
3  Shreya  United Kingdom  Design           2
4    Aadi   Unites States    Tech          11
5     Sim   Unites States    Tech           4

It replaced all the occurrences of US with Unites States, and UK with United Kingdom in the DataFrame column Location.

Let’s see the complete example,

import pandas as pd
import numpy as np

# List of Tuples
employees= [('Mark',   'US', 'Tech',   5),
            ('Riti',   'UK', 'Tech' ,  7),
            ('Shanky', 'UK', 'PMO' ,   2),
            ('Shreya', 'UK', 'Design', 2),
            ('Aadi',   'US', 'Tech',   11),
            ('Sim',    'US', 'Tech',   4)]

# Create a DataFrame object from list of tuples
df = pd.DataFrame(employees,
                  columns=['Name', 'Location', 'Team', 'Experience'])

print(df)

# Replace 'US' with 'United States' in Column 'Location'
# Replace 'UK' with 'United Kingdom' in Column 'Location'
df['Location'] = df['Location'].replace(
                                ['US', 'UK'],
                                ['Unites States', 'United Kingdom'])

print(df)

Output

     Name Location    Team  Experience
0    Mark       US    Tech           5
1    Riti       UK    Tech           7
2  Shanky       UK     PMO           2
3  Shreya       UK  Design           2
4    Aadi       US    Tech          11
5     Sim       US    Tech           4


     Name        Location    Team  Experience
0    Mark   Unites States    Tech           5
1    Riti  United Kingdom    Tech           7
2  Shanky  United Kingdom     PMO           2
3  Shreya  United Kingdom  Design           2
4    Aadi   Unites States    Tech          11
5     Sim   Unites States    Tech           4

Method 2: Using map() method

Create a dictionary and populate it with certain key-value pairs i.e.

• Values that need to be replaced in column, as keys in dictionary.
• Replacement Values as the values in dictionary.

Like,

replacementDict = { 'US' : 'Unites States',
                    'UK' : 'United Kingdom'}

Select DataFrame column Location, and call map() function on that column. Pass the above created dictionary in it, and assign the modified column back again to the DataFrame.

# Replace 'US' with 'United States' in Column 'Location'
# Replace 'UK' with 'United Kingdom' in Column 'Location'
df['Location'] = df['Location'].map(replacementDict)

Output:

     Name        Location    Team  Experience
0    Mark   Unites States    Tech           5
1    Riti  United Kingdom    Tech           7
2  Shanky  United Kingdom     PMO           2
3  Shreya  United Kingdom  Design           2
4    Aadi   Unites States    Tech          11
5     Sim   Unites States    Tech           4

It will replace all occurrences of US with ‘United States’ and all occurrences of UK with United Kingdom in Column Location of the DataFrame.

Let’s see the complete example,

import pandas as pd
import numpy as np

# List of Tuples
employees= [('Mark',   'US', 'Tech',   5),
            ('Riti',   'UK', 'Tech' ,  7),
            ('Shanky', 'UK', 'PMO' ,   2),
            ('Shreya', 'UK', 'Design', 2),
            ('Aadi',   'US', 'Tech',   11),
            ('Sim',    'US', 'Tech',   4)]

# Create a DataFrame object from list of tuples
df = pd.DataFrame(employees,
                  columns=['Name', 'Location', 'Team', 'Experience'])

print(df)

replacementDict = { 'US' : 'Unites States',
                    'UK' : 'United Kingdom'}

# Replace 'US' with 'United States' in Column 'Location'
# Replace 'UK' with 'United Kingdom' in Column 'Location'
df['Location'] = df['Location'].map(replacementDict)

print(df)

Output

     Name Location    Team  Experience
0    Mark       US    Tech           5
1    Riti       UK    Tech           7
2  Shanky       UK     PMO           2
3  Shreya       UK  Design           2
4    Aadi       US    Tech          11
5     Sim       US    Tech           4


     Name        Location    Team  Experience
0    Mark   Unites States    Tech           5
1    Riti  United Kingdom    Tech           7
2  Shanky  United Kingdom     PMO           2
3  Shreya  United Kingdom  Design           2
4    Aadi   Unites States    Tech          11
5     Sim   Unites States    Tech           4

Summary

We learned about different ways to replace multiple values in the DataFrame. Thanks.