I’m working with a large dataset (~10 million rows and 50 columns) in pandas and experiencing significant performance issues during data manipulation and analysis. The operations include filtering, merging, and aggregating the data, and they are currently taking too long to execute.

I’ve read about several optimization techniques, but I’m unsure which ones are most effective and applicable to my case. Here are a few specifics about my workflow:

I primarily use pandas for data cleaning, transformation, and analysis.
My operations include multiple groupby and apply functions.
I’m running the analysis on a machine with 16GB RAM.

Could the community share best practices for optimizing pandas performance on large datasets?

1.Memory management techniques.
2.Efficient ways to perform groupby and apply.
3.Alternatives to pandas for handling large datasets.
4.Any tips for parallel processing or utilizing multiple cores effectively.

I primarily use pandas for data cleaning, transformation, and analysis.
My operations include multiple groupby and apply functions.
I’m running the analysis on a machine with 16GB RAM.