Renaming columns in Data Analysis Python

Introduction

Renaming columns helps make data easier to understand and work with. It gives clear names that describe the data better.

When column names are unclear or too long.

When you want to standardize column names across different datasets.

When preparing data for reports or presentations.

When fixing typos or inconsistent naming in columns.

When merging datasets that have different column names for the same data.

Syntax

Data Analysis Python

df.rename(columns={'old_name': 'new_name', ...}, inplace=False)

Use a dictionary to map old column names to new ones.

Set inplace=True to change the original DataFrame directly.

Examples

Rename the column 'Age' to 'age_years'. This returns a new DataFrame.

Data Analysis Python

df.rename(columns={'Age': 'age_years'})

Rename multiple columns and update the original DataFrame.

Data Analysis Python

df.rename(columns={'Name': 'Full Name', 'Salary': 'Monthly Salary'}, inplace=True)

Create a new DataFrame with renamed columns without changing the original.

Data Analysis Python

new_df = df.rename(columns={'Old': 'New'})

Sample Program

This code creates a simple table with names, ages, and salaries. Then it renames 'Name' to 'Full Name' and 'Salary' to 'Monthly Salary'. It shows the original and renamed tables.

Data Analysis Python

import pandas as pd

# Create a sample DataFrame
data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30], 'Salary': [50000, 60000]}
df = pd.DataFrame(data)

print('Original DataFrame:')
print(df)

# Rename columns
renamed_df = df.rename(columns={'Name': 'Full Name', 'Salary': 'Monthly Salary'})

print('\nDataFrame after renaming columns:')
print(renamed_df)

Important Notes

If you forget inplace=True, the original DataFrame stays the same.

You can rename one or many columns at once using the dictionary.

Renaming does not change the data, only the column labels.

Summary

Renaming columns makes data easier to read and use.

Use df.rename(columns={...}) with a dictionary of old and new names.

Remember to use inplace=True if you want to change the original DataFrame.