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An in-depth look at ways to customize (and perhaps improve) Django's admin app.
A step-by-step guide on how to set up a Python environment using VSCode and Docker. It explains why you’d use these tools at all, and walks you through what you need to get them going.
Using PyStack’s “forbidden magic” to debug deadlocks, segmentation faults, crashes and other difficult bugs in Python
This guide provides valuable insights and practical tips for new and experienced developers to leverage async programming in Django for non-blocking operations, improved scalability, and enhanced responsiveness.
There are several modules in Python that are directly callable from the command line, including the ability to gzip and pretty print JSON. This article introduces you to what is available and how Simon discovered them.
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 your code even more.
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