Creating new columns in Pandas

Introduction

We create new columns to add extra information or calculations to our data table. This helps us analyze data better.

You want to calculate a new value from existing columns, like total price from quantity and price per item.

You need to label data based on conditions, like marking students as pass or fail based on scores.

You want to add a constant value to all rows, like a new column showing the data source.

You want to combine text from two columns into one, like full names from first and last names.

Syntax

Pandas

df['new_column'] = expression_using_existing_columns_or_values

Use square brackets with the new column name as a string to create or replace a column.

The expression can be a calculation, a constant, or a function applied to existing columns.

Examples

Creates a new column 'total' by multiplying 'quantity' and 'price' columns.

Pandas

df['total'] = df['quantity'] * df['price']

Adds a 'status' column with 'pass' or 'fail' based on the 'score' column.

Pandas

df['status'] = ['pass' if x >= 50 else 'fail' for x in df['score']]

Adds a new column 'constant' with the same value 100 for all rows.

Pandas

df['constant'] = 100

Combines 'first_name' and 'last_name' columns into a new 'full_name' column.

Pandas

df['full_name'] = df['first_name'] + ' ' + df['last_name']

Sample Program

This code creates a DataFrame with some sample data. Then it adds four new columns:

• total: product of quantity and price
• status: pass/fail based on score
• constant: a fixed value 100
• full_name: combined first and last names

Finally, it prints the updated DataFrame.

Pandas

import pandas as pd

data = {'quantity': [2, 5, 3], 'price': [10, 7, 12], 'score': [45, 82, 67], 'first_name': ['Anna', 'Bob', 'Cara'], 'last_name': ['Smith', 'Jones', 'Lee']}
df = pd.DataFrame(data)

df['total'] = df['quantity'] * df['price']
df['status'] = ['pass' if x >= 50 else 'fail' for x in df['score']]
df['constant'] = 100
df['full_name'] = df['first_name'] + ' ' + df['last_name']

print(df)

Important Notes

When creating new columns with conditions, list comprehensions or apply() can be used.

Assigning a constant value adds the same value to all rows.

Make sure the new column name does not conflict with existing columns unless you want to replace them.

Summary

New columns add extra information or calculations to your data.

Use df['new_column'] = ... to create or update columns.

You can use calculations, conditions, constants, or combine text to make new columns.