Pandas Data Manipulate : INPLACE Attribute Usage

Beginner-Friendly Guide: inplace

If you're learning Pandas, you've probably seen the inplace parameter in methods like drop(), rename(), sort_values(), and fillna(). It looks harmless, but it changes everything about how the method works.

We'll use the same simple sample DataFrame throughout all examples so everything is easy to follow.

Our Sample DataFrame

First, create a simple DataFrame:

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 25],
    'City': ['New York', 'Los Angeles', 'Chicago']
}

df = pd.DataFrame(data)
print(df)

This will display:

	Name	Age	City
0	Alice	25	New York
1	Bob	30	Los Angeles
2	Charlie	25	Chicago

What does inplace actually do?

• inplace=False (default): Returns a new DataFrame with the changes. The original stays untouched.
• inplace=True: Modifies the original DataFrame directly and returns None.

Example 1: inplace=False (Recommended for beginners)

# Create a new DataFrame without the 'City' column
new_df = df.drop('City', axis=1)

print("Original df (unchanged):")
print(df)
print("\nNew df:")
print(new_df)

This will display:

Original df (unchanged):

	Name	Age	City
0	Alice	25	New York
1	Bob	30	Los Angeles
2	Charlie	25	Chicago

New df:

	Name	Age
0	Alice	25
1	Bob	30
2	Charlie	25

Example 2: inplace=True

# Modify the original DataFrame directly
df.drop('City', axis=1, inplace=True)

print(df)

This will display:

	Name	Age
0	Alice	25
1	Bob	30
2	Charlie	25

Warning: The original DataFrame is now permanently changed!

When to use which?

Use inplace=False when...	Use inplace=True when...
You want to keep the original data safe	You are certain you don't need the original
You are chaining multiple operations	You need tiny memory/performance gains
You're learning or experimenting	You're in optimized production code

Happy pandas coding ! 🐼