Skip to content

How to Rename Columns in Pandas

Rajiv Chandra
Name
Rajiv Chandra

Updated on

Renaming columns is one of the most frequent tasks when cleaning or organizing data in Pandas. Clear and descriptive column names make your DataFrame more readable and drastically improve downstream analysis.

In this updated 2025 guide, you'll learn all major ways to rename columns in Pandas, with best practices, examples, and common pitfalls to avoid.

Want a faster way to explore and visualize your Pandas DataFrame—without writing any charts manually?

PyGWalker turns any DataFrame into a Tableau-style visual exploration UI. Try it here: https://github.com/Kanaries/pygwalker (opens in a new tab)

PyGWalker for Data visualization (opens in a new tab)

📌 What Does It Mean to Rename Columns in Pandas?

Column renaming simply means changing one or more column labels in your DataFrame.
This is useful when:

  • Column names are unclear or inconsistent
  • They contain spaces, special characters, or typos
  • You need standardized names for merging or modeling
  • You want human-friendly labels for presentation or visualization

Pandas provides multiple convenient ways to rename columns—each serving different needs.

🔧 How to Rename Columns in Pandas (All Methods)

Below are the most common and recommended approaches.

✅ 1. Rename a Single Column with rename()

This is the safest and most explicit method.

import pandas as pd
 
df = pd.DataFrame({
    'Name': ['John', 'Alex', 'Peter'],
    'Age': [25, 24, 28],
    'Gender': ['Male', 'Male', 'Male']
})
 
df = df.rename(columns={'Age': 'Years'})
print(df)

Output:

    Name  Years Gender
0   John     25   Male
1   Alex     24   Male
2  Peter     28   Male

✅ 2. Rename Multiple Columns with rename()

Just pass a larger dictionary.

df = df.rename(columns={
    'Age': 'Years',
    'Gender': 'Sex'
})

This approach avoids accidental column order issues and preserves the index.

✅ 3. Rename All Columns at Once Using df.columns = [...]

Useful when you know all new column names.

df.columns = ['ID', 'Years', 'Dept']

⚠️ Important: The list length must match the number of columns.

✅ 4. Rename Columns with set_axis() (Functional Style)

A method that returns a new DataFrame unless inplace=True.

df = df.set_axis(['A', 'Years', 'B'], axis=1)

While flexible, this method is less commonly used for simple renaming.

✅ 5. Rename Columns Dynamically with List Comprehension

Perfect for bulk formatting operations:

df.columns = [col.replace('_', ' ').title() for col in df.columns]

Great for automated cleanup:

  • Convert to lowercase
  • Remove spaces
  • Add prefixes or suffixes

Examples:

df.columns = [col.lower() for col in df.columns]
df.columns = [f"user_{col}" for col in df.columns]

✅ 6. Rename Columns by Index

Column index renaming is rarely needed but supported:

df = df.rename(columns={2: 'Dept'})

Useful when column names are unknown, duplicated, or dynamically generated.

🧠 Best Practices for Renaming Columns

✔ Prefer rename() for clarity

rename() is explicit and avoids surprises.

✔ Avoid modifying df.columns unless renaming all

Changing the entire list is fast—but unforgiving if counts mismatch.

✔ Use lowercase + underscore for clean data pipelines

df.columns = [c.lower().replace(' ', '_') for c in df.columns]

✔ Avoid inplace=True

Pandas maintains guidance that inplace=True offers no performance benefit.

⚡ Quick Cheatsheet

df.rename(columns={'old': 'new'})
df.columns = ['A', 'B', 'C']
df = df.set_axis(['A', 'B', 'C'], axis=1)
df.columns = [col.lower() for col in df.columns]
df.rename(columns={2: 'new_name'})

🏁 Conclusion

Renaming columns is a simple but essential part of preparing a DataFrame for analysis. Pandas provides multiple ways to do this—from explicit renaming with rename(), to bulk formatting via list comprehension.

The recommended approach is:

  • Use rename() when renaming specific columns
  • Use df.columns = [...] when renaming all columns
  • Use list comprehension for dynamic transformations
  • Use index-based renaming only when necessary

With these tools, you can keep your data clean, consistent, and easy to work with.

🔗 Related Tutorials

❓ Frequently Asked Questions

1. How do I rename a column in Pandas?

Use rename():

df.rename(columns={'old': 'new'})

2. How do I rename a column by index?

df.rename(columns={0: 'id'})

3. How do I rename all columns at once?

df.columns = ['A', 'B', 'C']