The final release of Python 3.12 is scheduled for October 2023, which is growing closer. In the meantime, you can download and install its preview version to get a sneak peek at the upcoming features. One of the biggest changes announced is support for the Linux perf profiler, which is a powerful performance profiling tool. To fully benefit from using the perf profiler in Python 3.12, you should have a fairly good understanding of how the underlying hardware and the […]

With many businesses turning to Python’s powerful data science ecosystem to analyze their data, understanding how to avoid introducing bias into datasets is absolutely vital. If you’ve studied some statistics, then you’re probably familiar with terms like reporting bias, selection bias, and sampling bias. There’s another type of bias that plays an important role when you’re dealing with numeric data: rounding bias. Understanding how rounding works in Python can help you avoid biasing your dataset. This is an important skill. […]

If you’re doing numeric calculations, NumPy is a lot faster than than plain Python—but sometimes that’s not enough. What should you do when your NumPy-based code is too slow? Your first thought might be parallelism, but that should probably be the last thing you consider. There are many speedups you can do before parallelism becomes helpful, from algorithmic improvements to working around NumPy’s architectural limitations. Let’s see why NumPy can be slow, and then some solutions to help speed up […]

In Python, range() is most commonly used in for loops to iterate over a known range of numbers. But that’s not all it can do! There are other interesting uses of range(). For example, you can pick out elements of a range or check whether a given number belongs to a range of numbers. The latter operation is known as a membership test. Python’s range() objects support most of the operations that you’ve come to expect from lists. For example, […]