What are the main differences between Pandas and Dask for data analysis?
Pandas is great for in-memory data manipulation, while Dask is designed for larger-than-memory datasets and parallel computing.
Can you give me examples of when to use each library?
Use Pandas for small to medium datasets where performance is not an issue. Use Dask when working with large datasets that don't fit in memory.
What about performance? How do they compare?
Pandas is faster for smaller datasets, but Dask scales better with larger datasets due to its parallel processing capabilities.
Are there any other libraries I should consider?
Yes, consider Vaex for out-of-core DataFrames or Modin for a drop-in replacement for Pandas that uses Dask or Ray.
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