Python Pandas rename() Simplified

The rename() function in Pandas is a versatile tool for renaming column or index labels in DataFrames. It’s essential for organizing data effectively.

What is rename()?

rename() allows you to rename specific columns or index labels in a DataFrame or Series for better clarity and usability.

Basic Syntax of rename()

DataFrame.rename(mapper=None, *, index=None, columns=None, axis=None, copy=True, inplace=False)

Parameters:

• index: Dictionary-like or function to rename index labels.
• columns: Dictionary-like or function to rename column labels.
• inplace: Modify the DataFrame directly if True. Default is False.

Renaming Columns in a DataFrame

Let’s start with renaming columns using a dictionary:

import pandas as pd

data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data)

# Rename columns
renamed_df = df.rename(columns={'Name': 'Full Name', 'Age': 'Years'})
print(renamed_df)

    Full Name  Years
0      Alice     25
1        Bob     30

Renaming Index Labels

You can also rename index labels using the index parameter:

# Rename index labels
renamed_index_df = df.rename(index={0: 'Row1', 1: 'Row2'})
print(renamed_index_df)

      Name  Age
Row1  Alice   25
Row2    Bob   30

Renaming Using Functions

Use a function to modify column or index labels:

# Use a function to rename columns
func_renamed_df = df.rename(columns=str.lower)
print(func_renamed_df)

      name  age
0    Alice   25
1      Bob   30

Using inplace=True

Modify the DataFrame in place without creating a new object:

# Rename columns in place
df.rename(columns={'Name': 'First Name'}, inplace=True)
print(df)

  First Name  Age
0      Alice   25
1        Bob   30

Applications of rename()

The rename() method is particularly useful for:

• Standardizing column names for consistent analysis.
• Preparing datasets for merging or concatenation.
• Improving readability and clarity of data.

Learn about merging datasets with our guide on Pandas merge().

Renaming Rows and Columns Simultaneously

Rename both rows and columns in a single call:

# Rename both rows and columns
renamed_df = df.rename(index={0: 'Row1'}, columns={'Age': 'Years'})
print(renamed_df)

      First Name  Years
Row1      Alice     25
1            Bob     30

Conclusion

The rename() function is a vital tool for data cleaning and organization in Pandas. It helps make datasets intuitive and easy to understand.

For advanced data handling, explore our guide on Pandas agg().