Generators and Parallelism in Python: Boost Your Code’s Efficiency

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When working with large datasets or long-running tasks in Python, parallel processing can save you time and improve performance. Generators can work well with parallelism to process data efficiently. They allow you to stream data lazily while leveraging parallel execution to handle tasks faster.

In this article, we’ll explore how you can use Python’s generators alongside parallel processing. We’ll look at a library that helps you achieve parallelism easily and provide code examples to illustrate the concepts.

Why Use Parallelism?

Parallel processing allows you to execute multiple tasks at the same time using multiple CPU cores. This can lead to significant improvements in performance, especially when dealing with large amounts of data or compute-intensive tasks.

Using Generators with Parallelism

To combine generators with parallelism, you can use Python’s concurrent.futures module. It provides an easy way to run tasks in parallel using threads or processes.

Example 1: Parallel Data Processing with Generators

Here’s a simple example of how you can use generators with the concurrent.futures module to process data…