Data at Depth

Data at Depth

Share this post

Data at Depth
Data at Depth
Optimizing Python Pandas for Large Datasets: 4 Practical Examples of Chunking

Optimizing Python Pandas for Large Datasets: 4 Practical Examples of Chunking

Breaking down large data sets for better memory management

John Loewen's avatar
John Loewen
Jun 12, 2023
∙ Paid

Share this post

Data at Depth
Data at Depth
Optimizing Python Pandas for Large Datasets: 4 Practical Examples of Chunking
Share

Breaking down large data sets for better memory management

Photo by Markus Spiske on Unsplash

Working with large datasets in Python is made possible with the Pandas library.

However, large datasets pose a challenge with memory management. To address this, we use a technique known as chunking.

Chunking involves reading data in smaller portions, or ‘chunks’, …

Keep reading with a 7-day free trial

Subscribe to Data at Depth to keep reading this post and get 7 days of free access to the full post archives.

Already a paid subscriber? Sign in
© 2025 John Loewen
Privacy ∙ Terms ∙ Collection notice
Start writingGet the app
Substack is the home for great culture

Share