How to Use multiprocessing.Pool().starmap() for Iterable Returns in Python

Learn how to effectively use `multiprocessing.Pool().starmap()` to return iterables while constructing dataframes in Python.
---

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: How to get multiprocessing.Pool().starmap() to return iterable

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Mastering multiprocessing.Pool().starmap() for Iterable Returns in Python

When working with large datasets in Python, the speed at which you can process data can be critical. For many developers, the need for efficiency leads them to use the multiprocessing module. This post addresses a common problem: How do you get multiprocessing.Pool().starmap() to return an iterable when building data from multiple inputs and outputs? Let's break down the solution step-by-step.

The Problem: Using starmap with Nested Loops

Typically, if you have two nested for loops that call a function like this:

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

This is straightforward, but what if you want to speed up this process with parallel processing? You might be tempted to refactor it using multiprocessing.Pool() along with starmap to handle multiple inputs concurrently.

The challenge arises because, in starmap, the inputs i and j are not explicitly available after the call. You need to ensure both the input pairs (i,j) and the output return value from your function (k) are preserved for constructing your dataframe.

The Solution: Organizing Your Code with starmap

Here's how you can properly structure your code to maintain access to the inputs while leveraging the power of parallel processing with starmap.

Step 1: Define Your Function

First, define the function that will process the inputs:

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

Step 2: Generate Input Pairs Correctly

The structure of your input generation for starmap is crucial. Instead of nested loops where the outer loop iterates over j, you want to switch the order:

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

Step 3: Return Results in Order

The starmap will now return results in the same order they were submitted, which makes it easier for further processing.

Step 4: Chunk the Results

You can chunk the results based on y, the number of columns you expect in your dataframe:

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

Step 5: Putting It All Together

Finally, integrate everything into your main block. This will help capture the values of i, j, and their corresponding outputs while constructing your final list or dataframe:

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

Conclusion

By using multiprocessing.Pool() combined with starmap, you can significantly reduce processing time when building complex data structures. The key is effectively managing the input-output pairs such that you don’t lose track of the original indices. The method detailed here allows you to retain both your input values and the results from your function, making it easier to populate your dataframe efficiently.

In summary, the restructured approach to utilizing starmap helps keep your code organized and boosts performance when handling larger datasets. Happy coding!