Dive into the world of parallelism in Python by understanding the differences between multiprocessing and threading and when to use each approach.

Multiprocessing vs Threading - Python Parallelism Explained

<h1>Introduction</h1>
<p>Parallelism is an essential concept in programming, as it allows multiple tasks to be executed simultaneously, improving the performance of your code. In Python, there are two main ways to achieve parallelism: multiprocessing and threading. In this article, we will explore the key differences between these two methods and when to use each approach in your Python applications.</p>
<h2>Table of Contents</h2>
<ul>
<li><a href="#what-is-parallelism">What is Parallelism?</a></li>
<li><a href="#multiprocessing">Multiprocessing</a>
<ul>
<li><a href="#advantages-of-multiprocessing">Advantages</a></li>
<li><a href="#disadvantages-of-multiprocessing">Disadvantages</a></li>
</ul>
</li>
<li><a href="#threading">Threading</a>
<ul>
<li><a href="#advantages-of-threading">Advantages</a></li>
<li><a href="#disadvantages-of-threading">Disadvantages</a></li>
</ul>
</li>
<li><a href="#when-to-use-multiprocessing-or-threading-in-python">When to Use Multiprocessing or Threading in Python</a></li>
<li><a href="#conclusion">Conclusion</a></li>
</ul>
<h2>What is Parallelism?</h2>
<p>In the context of programming, parallelism refers to the execution of multiple tasks or processes concurrently. It can be achieved at both the hardware level (</p>
