PYTHON PERFORMANCE ROADMAP 🐍
Your Python code works.
But does it scale?
Don't optimize code you haven't measured.
Find the bottleneck first.
Then optimize it.
Save this roadmap. 🔖
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@property isn't just fancy syntax. 🐍
It lets you keep an attribute-like interface while adding validation or logic behind it.
Looks like data. Behaves like logic.
Save this Python concept. 🔖
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Python has a lot of “magic” that isn't really magic. 🐍
Descriptors are one of the concepts behind controlled attribute access.
__get__()
__set__()
__delete__()
Understand those, and things like @property start making much more sense.
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Python doesn’t only understand True and False. 👀
It also checks whether a value is truthy or falsy.
bool([]) # False
bool([0]) # True
bool("") # False
bool("0") # True
The interesting part?
"0" is True, but 0 is False. 🤯
Do you know why?
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Python closures are one of those concepts that suddenly make a lot of Python behaviour click. 🐍
The outer function finishes.
Yet the inner function can still access the enclosed variable.
Function + remembered state = closure.
Save this one. 🔖
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Python has a tiny behavior that catches people off guard. 🐍💀
append() modifies the list in place it doesn't return the updated list.
Would you have predicted None? 👇
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Implementation of a Python 🐍 function that is both sync and async depending on a parameter and with type annotation/hints 🚀
Do you know a better way to do it 🤓? Comment it!
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The real skill isn't catching errors.
It's knowing what should happen after the error.
Save this Python roadmap for later. 🔖
What’s one Python exception concept that confused you at first? 👇
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Two lists.
Need to combine them?
Don't reach for indexes immediately.
→ What zip() does
→ Pairing multiple iterables
→ zip() with lists
Small function.
Surprisingly useful.
Save this for your Python toolkit.
Where do you use zip() most? 👇
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🐍 Python Tip
✅ List of Collables in Python
User Defined Functions
Generators
Classes
Instance Methods
Class Instance (__call()__)
build in functions
build in methids
Check Code 👇
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We’re back for another #PythonTips Tuesday! 🐍
This week, we dig deeper into LLM file names — specifically the B that stands for billions of parameters that act like tiny adjustable knobs in a neural network.
Together these parameters shape how a model learns language. 💪