Data Cleaning in Python

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.

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👨‍🏫 The Zero to Mastery Python Cheat Sheet is a clean, colorful cheatsheet packed with practical code snippets for everyday tasks like loops, functions, and list comprehension.

🤩 It’s visually organized with clear sections and real examples, which makes it a favorite for beginners and intermediates who want to code faster and smarter.

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.

🔥 Python vs SQL: Who Cleans Data Better? 🧹

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

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📖 Python's Ayncio
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📖 Python Asyncio: The Complete Guide
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What is Walrus Operator (:=) in Python?

Important Python Function and their Purpose

Put your answers in the comment below🔽

Concise reference compiled from Stack Overflow Q&A covering syntax, OOP, modules, error handling, and advanced topics like decorators.

Decorators in Python

Put your answers in the comment below

Python Roadmap For AI/ML

Python For Data Science Cheatsheet: Part 1