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Python has its pros and cons, but it's nonetheless used extensively. For example, Python is frequently used in data crunching tasks even when there are more appropriate languages to choose from. Why? Well, Python is relatively easy to learn. Someone with a science background can pick up Python much more quickly than, say, C. However, Python's inherent approachability also creates a couple of problems. Whenever Python is updated, it means a big refactoring workload, which often gets dealt with poorly – or not at all. That leads to poor performance and security vulnerabilities. But maybe there is a better way: a tool to keep your Python tasks running smoothly and securely day in, day out. Let's take a look. It's slow, but it does the job Python isn't the fastest language around, but despite its comparative disadvantages, you'll often see it used for intensive data crunching operations. Think machine learning, computer vision, or even pure math in high-perform

Progress powers technology forward. But progress also has a cost: by adding new capabilities and features, the developer community is constantly adjusting the building blocks. That includes the fundamental languages used to code technology solutions. When the building blocks change, the code behind the technology solution must change too. It's a challenging and time-consuming exercise that drains resources. But what if there's an alternative? The problem: reading code someone else wrote Let's take a step back and take a look at one of the fundamental challenges in development: editing someone else's code. Editing code you just wrote, or wrote a couple of weeks ago, is just fine. But editing your own code written years ago – never mind someone else's code - that's a different story. In-house code style rules can help but there are always odd naming conventions for variables and functions, or unusual choices for algorithms. Arguably, a programmer's abilit