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

Last Updated : 29 Sep, 2025

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:

Python 
import pandas as pd

df = pd.DataFrame({'Date':['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'],
                    'Event':['Music', 'Poetry', 'Theatre', 'Comedy'],
                    'Cost':[10000, 5000, 15000, 2000]})

print(df)

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.

Python 
df['Discounted_Price'] = df.apply(lambda row: row.Cost - 
                                  (row.Cost * 0.1), axis = 1)

print(df)

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.

Python 
import pandas as pd

df['Discounted_Price'] = df['Cost'] - (0.1 * df['Cost'])

print(df)

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.

Python 
def cost_category(value):
    if value < 5000:
        return "Low"
    elif 5000 <= value < 12000:
        return "Medium"
    else:
        return "High"

# Create a new column using map()
df['Cost_Category'] = df['Cost'].map(cost_category)

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.

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