Why is my Python script so slow when processing large CSV files with Pandas?

All answers to this question.

The issue you're hitting is that Pandas loads the entire dataframe into RAM by default, which is a recipe for disaster with a 5GB file on an 8GB machine. You need to use the chunksize parameter in read_csv. This returns an iterable object, allowing you to process the data in smaller, manageable bites (e.g., 100,000 rows at a time). Also, check your data types! Converting "object" columns to "category" and using float32 instead of float64 can reduce your memory footprint by up to 70%. If you need even more speed, look into Polars—it's written in Rust and is significantly faster than Pandas for these types of heavy-duty IO operations.

   Answered 2025-03-16 by Brenda Collins

Have you considered using a database like SQLite as an intermediate step instead of keeping everything in memory? Sometimes it's better to let a SQL engine handle the heavy filtering before you bring the "clean" data into Python. What specific transformations are you performing on these rows once they are loaded?

   Answered 2025-03-18 by Jeffrey Vance

• Jeffrey, I'm doing a lot of complex string manipulation and regex filtering which I found hard to do in standard SQL. However, your point about the SQL engine is well-taken. I’ve started looking into DuckDB as an alternative because it integrates directly with Python and is optimized for analytical queries on large files. It seems to handle the memory pressure much better than Pandas does when running these aggregate functions on the fly.

     Commented 2025-03-20 by Heather Miller

Use Dask! It’s designed specifically to parallelize Pandas operations across multiple CPU cores and handles out-of-core computation, meaning it won't crash your RAM.

   Answered 2025-03-22 by Gregory Scott

• Dask is a lifesaver. I agree with Gregory; it’s basically the industry standard for when your data outgrows your local memory but you don't want to move to a Spark cluster yet.

     Commented 2025-03-24 by Brenda Collins