How do Pandas and Dask compare in terms of memory usage?

How do Pandas and Dask compare in terms of memory usage?

Pandas is great for smaller datasets that fit into memory, while Dask is designed for larger datasets that exceed memory limits.

Can you explain how Dask handles larger datasets?

Dask breaks datasets into smaller chunks and processes them in parallel, allowing it to handle data that doesn't fit in memory.

Are there any performance trade-offs when using Dask instead of Pandas?

Yes, Dask can introduce some overhead due to its parallel processing and task scheduling, which might slow down operations on smaller datasets.

What are some scenarios where I should prefer Dask over Pandas?

If you're working with large datasets, need parallel processing, or require out-of-core computation, Dask is the better choice.