What is Dask and how can I use it for parallel computing?

What is Dask and how can I use it for parallel computing?

Dask is a flexible library for parallel computing in Python. It allows you to scale your data processing tasks across multiple cores or even distributed systems.

Can you give me an example of how to use Dask with a DataFrame?

Sure! You can create a Dask DataFrame from a CSV file like this: import dask.dataframe as dd; df = dd.read_csv('data.csv'). Then you can perform operations like df.groupby('column').mean().

How does Dask handle large datasets that don't fit into memory?

Dask breaks large datasets into smaller chunks that fit into memory, processing them in parallel. This way, you can work with datasets larger than your RAM.

Are there any performance considerations I should be aware of?

Yes, while Dask is powerful, it's important to optimize your computations by minimizing data movement and using efficient algorithms. Profiling your tasks can also help identify bottlenecks.