Discover exactly how Polars and DuckDB can change your data processing process.
Are you still making use of pandas for information evaluation? You could be leaving 90 % of your performance on the table.
Data processing pipes are frequently the traffic jam in machine learning workflows. While pandas has actually been the go-to library for Python data scientists for many years, 2 effective alternatives are revolutionizing how we deal with information: Polars and DuckDB This powerful combination provides blazing-fast data handling with marginal code adjustments.
Why Your Current Information Pipeline Is Most Likely Also Sluggish
If you collaborate with datasets bigger than a few gigabytes, you’ve likely experienced the discomfort:
- Waiting minutes for simple group-by procedures
- Watching your memory usage balloon during joins
- Writing intricate code to filter and manipulate information
- Handling complicated workflows with multiple intermediate steps
I recently encountered these difficulties when dealing with a 50 GB dataset of ecommerce deals. A basic …