Pandas DataFrame assign() Method – Create new Columns in DataFrame

The assign() method in Pandas is used to create or modify columns in a DataFrame while preserving the original DataFrame. It returns a new DataFrame with the specified modifications. This method allows adding single or multiple columns, performing calculations using existing columns and applying functions dynamically. If a column name already exists, assign() updates its values.

Example:

import pandas as pd

a = pd.DataFrame({'A': [1, 2, 3]})
print(a)

# add a new column
b = a.assign(B=[4,5,6])
print(b)

Output:

add a new column

Explanation: This code first creates a Pandas DataFrame a with a single column ‘A’ containing values [1, 2, 3]. It then uses the assign() method to add a new column ‘B’ with values [4, 5, 6], resulting in a modified DataFrame b while keeping the original DataFrame unchanged.

Syntax

DataFrame.assign(**kwargs)

Parameters:

• kwargs: Column names as keyword arguments.
• If the values provided are callable (i.e., functions), they will be computed on the DataFrame.
• If the values are not callable (e.g., Series, scalar, or array), they are directly assigned.

Returns: A new DataFrame with additional or modified columns while keeping the original DataFrame unchanged.

For the link to the CSV file Used in the Code, click here

Examples of using assign()

Let’s explore some practical examples demonstrating how to use the assign() method.

Example 1: Assigning a New Column with Salary Increment

import pandas as pd

# Reading data from CSV file
df = pd.read_csv("nba.csv")

# Displaying the first 10 rows
df.head(10)

# create a new column
df_new = df.assign(Revised_Salary=lambda x: x['Salary'] + x['Salary'] * 0.10)

df_new.head(10)

Output

Assigning a new column

Explanation: This code displays the first 10 rows using head(10). After that, a new DataFrame (df_new) is created using the assign() method, where a new column “Revised_Salary” is added. This column is calculated by increasing each value in the “Salary” column by 10%. Finally, the first 10 rows of the updated DataFrame are displayed using head(10).

Example 2: Assigning Multiple Columns at Once

We can add multiple new columns simultaneously using the assign() method. In this example:

• The New_Team column is created by appending ‘_GO’ to each team name.
• The Revised_Salary column is calculated by increasing the original Salary column by 10%.

import pandas as pd  

# Reading data from CSV file  
df = pd.read_csv("nba.csv")  

# Creating multiple new columns  
df_new = df.assign(
    New_Team=lambda x: x['Team'] + '_GO',  
    Revised_Salary=lambda x: x['Salary'] + x['Salary'] * 0.10  
)  

df_new.head(10)

Output

Assigning Multiple Columns at Once

Explanation: This code creates a new DataFrame (df_new) by adding two new columns using the assign() method. The “New_Team” column is generated by appending “_GO” to each value in the “Team” column. The “Revised_Salary” column is computed by increasing each value in the “Salary” column by 10%. Finally, the first 10 rows of the updated DataFrame are displayed using head(10).