How to Use multiprocessing Pool.starmap with Multiple Arguments in Python

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Understanding the Problem

Breaking Down the Solutions

Common Errors

Before diving into solutions, it's crucial to understand the typical mistakes that can lead to these errors:

Progress Bar Issues: When using progress bars, the timing can become misleading. Using the progress bar during task submission won’t reflect task completion accurately.

Incorrect Pool Initialization: Instead of using process= you should be using processes= when initializing the pool.

These common pitfalls can be easily navigated with heightened awareness and adjustments in your coding strategy.

Recommended Approaches

Here are two suggested methods for using multiprocessing effectively with pandas DataFrames.

Using imap_unordered

This approach allows you to iterate over results as they come in without waiting for all tasks to finish. Here's an implementation:

[[See Video to Reveal this Text or Code Snippet]]

Using apply_async with Callbacks

In certain situations, you may find it beneficial to use apply_async with a callback function to handle real-time result updates. Here's how it's structured:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

Remember, proper initialization, understanding your data structure, and precise error handling are key to successful code execution in multiprocessing!