Python Learning

Decorators in Python

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Important Python Function and their Purpose

What is Walrus Operator (:=) in Python?

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Python Data Structures: Quick Visual Guide 🐍 🔹 Lists: Ordered, mutable, created with [ ] → Access/modify via index: myList[0], myList[-1] → Methods: .append(), .sort(), .pop() → Mixed types allowed → Loop: for item in myList: 🔹 Tuples: Immutable, ordered → (1, 2, 3) 🔹 Sets: Unordered, unique elements 🔹 Dictionaries: Key-value pairs, fast lookups 🔹 Arrays: Mainly for numeric data (array/NumPy) 🔑 Key Points: ✅ Indexing: 0 to len-1 (forward), -1 backward ✅ Assignment myList[i] = x modifies in place ✅ Lists are the most versatile & commonly used This is the perfect cheat sheet for beginners and for quick revision!

🔥 Python vs SQL: Who Cleans Data Better? 🧹

Decorators Are Not Magic. They’re Callbacks in Disguise You’ve used @lru_cache to speed up a slow function, and it worked... until your app started eating RAM because the cache never forgot anything. from functools import lru_cache @lru_cache def fib(n): return fib(n-1) + fib(n-2) # ← Cache grows forever! 👉Here’s what’s really happening: A decorator is just a function that wraps another function. When you write @lru_cache, Python replaces your fib with a new version that remembers every answer it’s ever given. Cool😄 until n goes from 1 to 100,000. ✅ Fix it like a pro: from functools import lru_cache @lru_cache(maxsize=128) # Only keep last 128 results def fib(n): if n > 1000: return manual_calc(n) # Skip cache for huge inputs return fib(n-1) + fib(n-2) Now the cache stays small, predictable, and safe. 📌Bonus: Write your own @timerdecorator in 5 lines. no more time.time() spam.

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await Is Not Optional in Async 💻 You’re racing 10 API calls with asyncio… and it still takes 10 seconds. Sound familiar? async def fetch(): return requests.get(url).json() # ← Blocks the entire event loop ✅ Fix: await every I/O. Swap requests for httpx (same API, truly async). import httpx async def fetch(): async with httpx.AsyncClient() as client: r = await client.get(url) return r.json() ▶️ Now 10 calls = 1 second.

Data Cleaning in Python

Python Roadmap