Rename Columns in a Pandas DataFrame

June 01, 2019

There are two easy ways to rename one or more columns in a Pandas DataFrame. This post shows both methods with some example data.

Imports

import pandas as pd

Create Some Example Data

Say somebody sends you a file with three columns representing people’s name, age, and sex. The file is sent with these columns named a, b, and c. These aren’t useful column names; you’ll want to rename them to something more descriptive.

data = {"a": ["James", "Alice", "Phil", "James"],
		"b": [24, 28, 40, 24],
		"c": ["Male", "Female", "Male", "Male"]}
df = pd.DataFrame(data)
print(df)

Rename Columns Using the Rename Function

The most flexible way to rename columns is to use Pandas’ df.rename function. It can take as arguments either a dictionary of {old_name: new_name} pairs, or a function that you want to apply to all columns. An example of both:

# rename columns using a dictionary mapping old to new names
df_using_dict = df.rename(columns={'a': 'Name', 'b': 'Age', 'c': 'Sex'})
print(df_using_dict)

# make all column names uppercase using a function
df_using_function = df.rename(str.upper, axis='columns')
print(df_using_function)

Note that when applying a function to all column names instead of directly mapping them with a dictionary, you need to specify axis=columns so that Pandas knows to apply the function to column names rather than row indexes.

One nice trait about rename is that you can pick and choose which columns to apply it to. If you want to rename a small subset of columns, this is your easiest way of doing so.

Rename Columns Using df.columns

A second common way of renaming columns is by assigning a new list of values to df.columns.

# replace old column names with new ones by assigning a list of names to df.columns
df.columns = ['Name', 'Age', 'Sex']
print(df)

This approach is usually more labor-intensive because it requires you to specify the entire list of new column names, whereas the rename function lets you specify as few or as many columns as you wish.