Context Managers: The “Clean-Up Crew” of Python

Ever forget to close a file and wonder why your program is misbehaving?

Context managers prevent this headache.

When you use with, Python ensures that resources are properly acquired and released automatically. Think of it as hiring a clean-up crew: they take care of the dirty work while you focus on the important tasks.

with open('data.txt') as f:
    data = f.read()
# file is automatically closed here

You don’t have to remember to call f.close(). This small pattern prevents bugs, improves readability, and is a hallmark of professional Python code.

Python Roadmap

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.

DSA With Python Free Resources

Design and Analysis of Algorithms
🆓 Free Video Lectures
📒 Lecture Notes + Assignments with Solutions + Exams with their Answers
⏰ Duration: 40 hours
🏃‍♂️ Self Paced
📈 Difficulty: Advanced
👨‍🏫 Created by: MIT OpenCourseWare
🔗 Course Link

Data Structures and Algorithms in Python Full course
🆓 Free Online Course
⏰ Duration : ~13 hours
🏃‍♂️ Self Paced
📈 Difficulty: Beginner
👨‍🏫 Instructor: Aakash N S
🔗 Course Link

Data Structures & Algorithms in Python
🎬 Free Video Lectures
⏰ Duration: 1 hour
🏃‍♂️Self Paced
📈 Difficulty: Beginner
👨‍🏫 Created by: Simplilearn
🔗 Course Link

The Algorithms - Python
📚 500+ algorithms
🏃‍♂️ Self Paced
📈 Difficulty: All Levels
👨‍🏫 Created by: Community(Open-source)
🔗 Course Link

Data Structures and Algorithms
🆓 Free Video Series
⏰ Duration: 4 hours
🏃‍♂️ Self Paced
📈 Difficulty: Beginner
👨‍🏫 Created by: CS Dojo
🔗 Course Link

Python Data Structures
📚 Complete Course
🏃‍♂️Self Paced
📈Difficulty: Basic - Intermediate
👨‍🏫 Created by: prabhupant
🔗 Course Link

Reading Resources

📖 DSA with Python
📖 Problem Solving with Algorithms
📖 Algorithm Archive
📖 Python DSA

#DSA #Python
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @bigdataspecialist for more👈

👨‍🏫 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

This is the perfect cheat sheet for beginners and for quick revision!

Put your answers in the comment below🔽

FREE Courses On Python Asyncio

Advanced asyncio: Solving Real-World Production Problems
🆓 Free Video Course
⏰ Duration: 41 Min
🏃‍♂️ Self paced
📊 Difficulty: Advanced
👨‍🏫 Created by: PyVideo
🔗 Course Link

Async IO Basics
🆓 Free Online Course
⏰ Duration: ~22 minutes
🏃‍♂️ Self paced
📊 Difficulty: Beginner
👨‍🏫 Created by: Very Academy
🔗 Course Link

Asyncio in Python - Full Tutorial
🆓 Free Video Course
⏰ Duration: 25 Min
🏃‍♂️ Self paced
📊 Difficulty: Beginner
👨‍🏫 Created by: Tech with Tim
🔗 Course Link

Asyncio Basics - Asynchronous programming with coroutines
🆓 Step-by-step text + video
⏰ Duration: 25 Min
🏃‍♂️ Self paced
📊 Difficulty: Beginner - Intermediate
👨‍🏫Created by: Python Programming Tutorials
🔗 Course Link


Reading Materials

📖 Python's Ayncio
📖 Asyncio Tutorial for Beginners
📖 Python Asyncio: The Complete Guide
📖 Official Asyncio Docs
📖 Asyncio Learning Path


#python  #asyncio
➖➖➖➖➖➖➖➖➖➖
👉Join @bigdataspecialist for more👈

What is Walrus Operator (:=) in Python?

Important Python Function and their Purpose