Python Course Part 10: The Capstone Project
This is the one we've been building toward. Across nine posts you've collected every piece you need; now we assemble them into a single tool you'd actually keep around.
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This is the one we've been building toward. Across nine posts you've collected every piece you need; now we assemble them into a single tool you'd actually keep around.
There's a line every project eventually crosses — the one that separates a script (works on my machine, today, if I squint) from software (works reliably, survives refactors, can be changed without fear). Two habits get you across that line: type hints and automated tests.
Everything we've built so far has run on Python's standard library — the batteries that ship with the language. That library is huge and excellent. But the real gravity of Python is the other half million packages sitting on PyPI, one pip install away: HTTP clients, data frames, web frameworks, machine-learning toolkits, and the official SDKs we'll use in the finale.
Every program so far has kept its data loose: an IP in one variable, a status count in a dictionary, a server load somewhere else. That works until the things you're modelling start having both data and behaviour that belong together.
By now you can write working Python. This post is about writing Python that other Python developers nod at — code that's Pythonic.
GitHub is killing its Premium Request Units (PRU) system on June 1. Here’s how I managed to squeeze 90,000 lines of code out of Copilot's Agent Mode for absolute pennies before the party ended, and how the new token-based billing will change your development workflow.