Adding a new column to existing DataFrame in Pandas in Python

In this tutorial, we are going to learn how to add a new column to an existing DataFrame in pandas. There are several methods to accomplish this task, each with its own advantages depending on your specific needs.

Using Direct Assignment

The simplest way to add a new column is by using direct assignment with a list. This method assigns the new column data to the DataFrame like a dictionary element ?

Example

# importing pandas
import pandas as pd

# creating a DataFrame
data = {
    'Name': ['Hafeez', 'Aslan', 'Kareem'],
    'Age': [19, 18, 15],
    'Profession': ['Pythoneer', 'Programmer', 'Student']
}
dataframe = pd.DataFrame(data)
print('-----------Before adding a new column----------')
print(dataframe)
print('\n')

# creating a list for new column
places = ['Nellore', 'Mumbai', 'Andhra']

# adding the list to the dataframe as column
dataframe['Places'] = places
print('---------------After adding a new column------------')
print(dataframe)

Output

-----------Before adding a new column----------
     Name  Age Profession
0  Hafeez   19  Pythoneer
1   Aslan   18 Programmer
2  Kareem   15    Student

---------------After adding a new column------------
     Name  Age Profession   Places
0  Hafeez   19  Pythoneer  Nellore
1   Aslan   18 Programmer   Mumbai
2  Kareem   15    Student   Andhra

Using DataFrame.insert()

The insert() method allows you to add a new column at a specific position in the DataFrame. This is useful when you need to control the column order ?

Syntax

DataFrame.insert(loc, column, value)

Where loc is the insertion index, column is the column name, and value is the column data.

Example

# importing pandas
import pandas as pd

# creating a DataFrame
data = {
    'Name': ['Hafeez', 'Aslan', 'Kareem'],
    'Age': [19, 18, 15],
    'Profession': ['Pythoneer', 'Programmer', 'Student']
}
dataframe = pd.DataFrame(data)
print('-----------Before adding a new column----------')
print(dataframe)
print('\n')

# creating a list for new column
places = ['Nellore', 'Mumbai', 'Andhra']

# inserting the column at index 2 (between Age and Profession)
dataframe.insert(2, 'Places', places)
print('---------------After adding a new column------------')
print(dataframe)

Output

-----------Before adding a new column----------
     Name  Age Profession
0  Hafeez   19  Pythoneer
1   Aslan   18 Programmer
2  Kareem   15    Student

---------------After adding a new column------------
     Name  Age   Places Profession
0  Hafeez   19  Nellore  Pythoneer
1   Aslan   18   Mumbai Programmer
2  Kareem   15   Andhra    Student

Using DataFrame.assign()

The assign() method creates a new DataFrame with the additional column, leaving the original DataFrame unchanged. This method is useful for functional programming approaches ?

Example

# importing pandas
import pandas as pd

# creating a DataFrame
data = {
    'Name': ['Hafeez', 'Aslan', 'Kareem'],
    'Age': [19, 18, 15],
    'Profession': ['Pythoneer', 'Programmer', 'Student']
}
dataframe = pd.DataFrame(data)
print('-----------Before adding a new column----------')
print(dataframe)
print('\n')

# creating a list for new column
places = ['Nellore', 'Mumbai', 'Andhra']

# adding the list to the dataframe as column using assign()
new_dataframe = dataframe.assign(Places=places)
print('---------------After adding a new column------------')
print(new_dataframe)

Output

-----------Before adding a new column----------
     Name  Age Profession
0  Hafeez   19  Pythoneer
1   Aslan   18 Programmer
2  Kareem   15    Student

---------------After adding a new column------------
     Name  Age Profession   Places
0  Hafeez   19  Pythoneer  Nellore
1   Aslan   18 Programmer   Mumbai
2  Kareem   15    Student   Andhra

Comparison of Methods

Conclusion

Use direct assignment for simple column additions, insert() when you need to control column position, and assign() for immutable operations. All methods require that the new column data length matches the DataFrame's row count.