If Python has such obvious, known limitations when it comes to writing performant, concurrent applications, then why do we continue to use it? The short answer is that it's a fantastic language to get work done in, and by work, I'm not necessarily talking about crunching through a computationally expensive task. It's an intuitive language, which is easy to pick up and understand for those who don't necessarily have a lot of programming experience.
The language has seen a huge adoption rate amongst data scientists and mathematicians working in incredibly interesting fields such as machine learning and quantitative analysis, who find it to be an incredibly useful tool in their arsenal.
In both the Python 2 and 3 ecosystems, you'll find a huge number of libraries that are designed specifically for these use cases, and by knowing about Python's limitations, we can effectively mitigate them, and produce software that is efficient and capable of doing exactly what is required of it.
So now that we understand what threads and processes are, as well as some of the limitations of Python, it's time to have a look at just how we can utilize multi-threading within our application in order to improve the speed of our programs.
