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Bite-Sized Python News & Updates |
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Credly Badges for Pybites Learning Paths 🎉 |
Hey Pythonista,
We're excited to be rolling out a new feature on our platform: the pytest, OOP and data analysis learning paths are now rewarded with prestigious Credly badges (example) which you can add to your LinkedIn profile to demonstrate your hard-earned real-world Python skills. 🐍 😍
If you've yet to give the Pybites Platform a try, this is a great reason to get into it.
The job market is tough, and competition within companies is high. Sometimes the right person for the job or that single promotion slot is selected by the smallest of margins, so anything you can do to give yourself the edge is worth it.
Click the link to be taken to the platform learning paths page and start working towards your badges!
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| Start earning your Credly badges 💪
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Have you embraced uv yet? |
In Michael Aydinbas' new in-depth case study article he addresses common developer pains:
- How exactly should project folders be organised?
- Should tests be inside or outside the package directory?
- Does the package itself belong at the root level or in a special src directory?
- And how do you properly import and test package functionality from scripts or external test files?
uv is the clear way forward and you'll see how the tool stays out of your way and makes you more productive as a developer. 🚀
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| Learn how to manage a project with uv
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Write more DRY pytest code |
Want to test a function or class with multiple inputs/outputs? Use `pytest.mark.parametrize` 🐍 😍
It keeps your code DRY ("don't repeat yourself"). Check out a quick example below 👇
Without `parametrize`, you'd end up writing 3 separate functions, duplicating logic. This decorator = clean + maintainable tests. 🚀
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| Learn more about parametrize
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Generators for the win 🏃 💡 |
They are easy to integrate and can really optimize your code.
Josh Engroff shows you how in his in-depth article: Optimizing Python: Understanding Generator Mechanics, Expressions, and Efficiency
Especially for larger datasets, this is where traditional approaches may be memory intensive.
Unlike standard functions that compute and return all values at once, generators produce values on demand through the yield statement, enabling efficient memory usage and creating new possibilities for data processing workflows.
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| Dive into generators
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What’s a Python developer’s favourite game? |
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Snake
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Keep pushing forward on your coding journey with Pybites as your partner. We're here to help you succeed every step of the way. Check out our products here.
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