Create a New Column in Pandas DataFrame based on the Existing Columns

In this article, we’ll explore different ways to create a new column in a Pandas DataFrame based on existing columns. This is a common task in data analysis when you need to transform or categorize your data.

Sample DataFrame:

Output

Date Event Cost

0 10/2/2011 Music 10000

1 11/2/2011 Poetry 5000

2 12/2/2011 Theatre 15000

3 13/2/2011 Comedy 2000

1. Using the apply() Function

apply() function allows us to apply a custom function to each row or column. Here, we create a new column Discounted_Price by applying a 10% discount on the Cost column.

Output

  Date Event Cost Discounted_Price
0 10/2/2011 Music 10000 9000.0
1 11/2/2011 Poetry 5000 4500.0
2 12/2/2011 Theatre 15000 13500.0
3 13/2/2011 Comedy 2000 1800.0

Explanation:

• apply(): applies a function row by row (axis=1).
• lambda row: anonymous function for each row.
• row.Cost: gets value from Cost column.
• row.Cost * 0.1: calculates 10% discount.
• row.Cost - (row.Cost * 0.1): subtracts discount.

2. Element-wise Operation on Columns

Another simpler approach to create a new column is to perform an element-wise operation on an existing column. Here, we will directly apply the discount calculation to the Cost column.

Output

Date Event Cost Discounted_Price
0 10/2/2011 Music 10000 9000.0
1 11/2/2011 Poetry 5000 4500.0
2 12/2/2011 Theatre 15000 13500.0
3 13/2/2011 Comedy 2000 1800.0

Explanation:

• df['Cost']: selects the Cost column.
• 0.1 * df['Cost']: calculates 10% of each cost (the discount).
• df['Cost'] - (0.1 * df['Cost']): subtracts the discount from the original cost.
• df['Discounted_Price'] = ... creates a new column called Discounted_Price with the discounted values.

3. Using map() Function

map() function is useful when you want to map one set of values to another. In this example, we’ll create a new column called salary_stats based on the salary column by using a mapping function.

Output

Date Event Cost Cost_Category
0 10/2/2011 Music 10000 Medium
1 11/2/2011 Poetry 5000 Medium
2 12/2/2011 Theatre 15000 High
3 13/2/2011 Comedy 2000 Low

Explanation:

• df['Cost']: selects the Cost column.
• .map(cost_category): applies the cost_category function to each value in the Cost column.
• df['Cost_Category'] = ... stores the mapped results in a new column called Cost_Category.

Read More:

• Creating a Pandas dataframe column based on a given condition
• How to Replace Values in Column Based on Condition in Pandas?
• Change String To Date In Pandas Dataframe