How to replace a value in Pandas Column?

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

Table Of Contents

Introduction

Suppose we have a DataFrame,

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

We want to replace value ‘US’ with the string ‘United States’ in column ‘Location’ of the DataFrame. Like this,

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

Let’s see different ways to do that.

Replace values in DataFrame Column Using replace() method

Select the column and call replace() method on that column. Pass value to be replaced, and the replacement value as arguments in it. It will replace the value in a copy of the selected column. Then assign that modified column, back to the original column of DataFrame. Syntax is like this,

# Replace value 'to_be_replaced' with 'replacement_value States' in Column 'column_name'
df[column_name] = df[column_name].replace(to_be_replaced, replacement_value)

For example, to replace the value ‘US’ with ‘United States’ in Column ‘Location’ in the DataFrame, use the following syntax,

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

DataFrame contents will be like,

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

It will replace all the occurrences of ‘US’ with ‘Unites States’ in 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',   'India', 'Tech' ,  7),
            ('Shanky', 'India', 'PMO' ,   2),
            ('Shreya', 'India', '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 value 'US' with 'United States' in Column 'Location'
df['Location'] = df['Location'].replace('US', 'Unites States')

print(df)

Output

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


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

Replace values in DataFrame Column Using map() method

Select the DataFrame column, and call map() function on it with a mapping dictionary. This dictionary will contain the mapping of old and new values. It will replace all the columns values in dataframe which matches a key in the dictionary. The replacement value will be the corresponding value in dictionry. For column values, which dont exist in the dictionary as key, the replacement value will be NaN.

For example, to replace the value ‘US’ with ‘United States’ in Column ‘Location’ in the DataFrame, use the following syntax,

# Replace value 'US' with 'United States' in Column 'Location'
df['Location'] = df['Location'].map({'US' : 'United States'})

Output:

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

We passed a dictionary containing old value and new value, and passed that to the map() method of DataFrame column (Series). It will replace all occurrences of ‘US’ with ‘United States’. But all other values in column became NaN.

Let’s see the complete example,

import pandas as pd

# List of Tuples
employees= [('Mark',   'US',    'Tech',   5),
            ('Riti',   'India', 'Tech' ,  7),
            ('Shanky', 'India', 'PMO' ,   2),
            ('Shreya', 'India', '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 value 'US' with 'United States' in Column 'Location'
df['Location'] = df['Location'].map({'US' : 'United States'})

print(df)

Output

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


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

Summary

We learned about two different ways to replace values in a Pandas DataFrame column. Thanks.