How can I improve the performance of Dask DataFrames?
To improve Dask DataFrame performance, consider optimizing your computations, using efficient data types, and leveraging parallelism.
What are some specific optimizations I can make?
You can optimize by using persist()
to keep data in memory, adjusting the number of partitions, and using map_partitions
for custom functions.
How do I determine the right number of partitions?
A good starting point is to have 1 partition per CPU core. You can also experiment with different numbers to find the best performance.
Are there any tools to monitor Dask performance?
Yes, you can use the Dask dashboard, which provides real-time insights into task progress and resource usage.
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