How with keyword saves you from bugs in Python

Ever feel like your Python code just refuses to cooperate when things get heavy? Imagine you’ve crafted the perfect string matching algorithm. It works fine with small data, but when you scale up, everything slows to a crawl. Frustrating, right? That’s when I remembered an old trick: offloading critical parts to C. Turns out, a little C integration can make your Python programs run up to 150 times faster. I’ve tried subprocess calls, ctypes, and even full C extensions, and the speed gains are staggering. It’s like giving your code a turbo boost without abandoning Python’s simplicity. The key takeaway? 🧩 Use subprocess for quick wins—compile C code to executable and call from Python. 🚀 Load your C functions directly into Python with ctypes for seamless speed. 🎯 For the fastest performance, write Python C extensions to get native-like speed. When your code hits a wall, consider whether a C shortcut can get you across faster. Are we just coding in Python, or starting to think like data scientists with a performance edge? What’s your favorite way to speed up Python? #DataScience #PythonTips #Performance #CodingHacks

Master Python Faster — with this Ultimate Cheatsheet! Whether you’re just starting out or brushing up your skills, this Python Cheatsheet covers all the essentials — syntax, data types, loops, functions, and more — in one easy reference. No fluff. Just the core concepts you actually use in real projects. 💡 Perfect for learners, data analysts, and developers who want to save time and code smarter. 📄 Grab your copy and keep it handy — your future self will thank you! Follow Rajat Jain for more! Follow me on Instagram https://lnkd.in/gApHetW8

🚀 10 Python Libraries You’ve Never Heard Of — But Will Change Your Life! 🐍✨ Most developers use Pandas, NumPy & Requests… but Python hides some insanely powerful gems that can 10x your productivity. From Rich (beautiful terminals) to Polars (super-fast DataFrames) and MoviePy (video editing in Python!) — these libraries will upgrade your entire workflow. If you want to level up your Python skills in 2025, this is your sign. 💡🔥 Medium - https://lnkd.in/dz5xy_KW Google Blogs - https://lnkd.in/dDVujqN3 Personal Site - https://lnkd.in/dF6xifBW Medium - https://lnkd.in/dz5xy_KW #Python #Developers #Coding #Programming #PythonLibraries #Productivity #TechCommunity #DataScience #Automation #MachineLearning #SoftwareEngineering

Every great coder starts with one thing — strong fundamentals. Before building complex projects or learning advanced topics, you need to truly understand how code thinks. Start with the essentials: 🔹 Variables — how your code stores data 🔹 Operators — how logic works behind the scenes 🔹 Strings — how Python handles text 🔹 Control Flow — how decisions are made in your program I’ve created a complete Python playlist (till Control Flow) that walks you through these step-by-step — simple, practical, and beginner-friendly. If you’re just starting your Python journey, this is where you build your foundation. 💪 🎥 Watch the full playlist here → https://lnkd.in/g4gbKJEf 👉 Like to show your support. 🔁 Repost to share with your network. 👥 Follow Shilpa Das to get such more like this. #Python #ProgrammingBasics #ControlFlow #PythonTutorial #DataEngineering #Topmate

We all love writing documentation 📚, and keeping it up-do-date, don't we 😅?! Well, I have a quick tip to make your life a little easier at least: In Python, __doc__ is a special attribute that holds the docstring of an object. A docstring is a string that provides documentation for a module, class, function, or method, basically a brief description of what the object does. 💡 If you add a docstring to your Python file that defines your Dag, you can re-use it in the doc_md parameter of your Dag by passing __doc__ to it. 🧑💻 When you click on "Dag Docs" within the Dag view, it shows the rendered Markdown documentation right in the Airflow UI. With this approach, you have the documentation next to the code. Your fellow data engineers directly see the purpose of a Dag when opening the Python file, BUT ALSO, it's shown to any Airflow users directly in the UI, without having to maintain separate documentation versions and keeping it very visible next to the implementation, so you don't forget to update it. 🎯 One source of truth = less headache #DataEngineering #Python #Airflow #Documentation

🐍 Python isn’t hard — you just haven’t learned it the right way yet. Everyone says “learn Python,” but few explain how to build a solid foundation. If you want to grow from beginner ➜ advanced, here’s your blueprint 🔥 🚀 Master These 10 Python Concepts First: 1️⃣ Variables & Data Types → the building blocks. 2️⃣ Functions → reusable, clean, and modular code. 3️⃣ Libraries & Modules → stop reinventing the wheel. 4️⃣ Classes & Objects → think OOP, not just code. 5️⃣ Error Handling → your code shouldn’t crash. 6️⃣ Iterators & Generators → memory-efficient loops. 7️⃣ Map, Filter, Reduce → cleaner functional code. 8️⃣ Decorators → modify behavior without rewriting. 9️⃣ Regex → string superpowers. 🔟 Serialization (JSON) → move data between systems. 💡 Pro tip: You don’t need 100 tutorials. You need 20 concepts done deeply — and one real project to connect them all. #Python #Programming #DataEngineering #LearningInPublic #CareerGrowth #CodeNewbie

🐍 Python isn’t hard — you just haven’t learned it the right way yet. Everyone says “learn Python,” but few explain how to build a solid foundation. If you want to grow from beginner ➜ advanced, here’s your blueprint 🔥 🚀 Master These 10 Python Concepts First: 1️⃣ Variables & Data Types → the building blocks. 2️⃣ Functions → reusable, clean, and modular code. 3️⃣ Libraries & Modules → stop reinventing the wheel. 4️⃣ Classes & Objects → think OOP, not just code. 5️⃣ Error Handling → your code shouldn’t crash. 6️⃣ Iterators & Generators → memory-efficient loops. 7️⃣ Map, Filter, Reduce → cleaner functional code. 8️⃣ Decorators → modify behavior without rewriting. 9️⃣ Regex → string superpowers. 🔟 Serialization (JSON) → move data between systems. 💡 Pro tip: You don’t need 100 tutorials. You need 20 concepts done deeply — and one real project to connect them all. #Python #Programming #DataEngineering #LearningInPublic #CareerGrowth #CodeNewbie

🐍 Python isn’t hard — you just haven’t learned it the right way yet. Everyone says “learn Python,” but few explain how to build a solid foundation. If you want to grow from beginner ➜ advanced, here’s your blueprint 🔥 🚀 Master These 10 Python Concepts First: 1️⃣ Variables & Data Types → the building blocks. 2️⃣ Functions → reusable, clean, and modular code. 3️⃣ Libraries & Modules → stop reinventing the wheel. 4️⃣ Classes & Objects → think OOP, not just code. 5️⃣ Error Handling → your code shouldn’t crash. 6️⃣ Iterators & Generators → memory-efficient loops. 7️⃣ Map, Filter, Reduce → cleaner functional code. 8️⃣ Decorators → modify behavior without rewriting. 9️⃣ Regex → string superpowers. 🔟 Serialization (JSON) → move data between systems. 💡 Pro tip: You don’t need 100 tutorials. You need 20 concepts done deeply — and one real project to connect them all. #Python #Programming #DataEngineering #LearningInPublic #CareerGrowth #CodeNewbie

🚀 Day 44 of #100DaysOfCode 💡 Today’s project: “Top 10 Stack Overflow Posts” I built a Python script that fetches and displays the Top 10 highest-voted questions from Stack Overflow using the Stack Exchange API. You can even filter results by topic — like Python, JavaScript, or C++ — to instantly explore what developers are discussing the most. 🧠 Key Features: ✅ Fetches top 10 questions by votes ✅ Shows title, score, tags, and link ✅ Optional tag-based filtering 🧰 Tech Stack: Python + Stack Exchange API GitHub Link : https://lnkd.in/gXSdDDJ6 This project gave me a deeper understanding of how public APIs work and how to extract structured data for real-world use! 🌐

🚀 Day 2 of My Python Problem Solving Challenge 💻🐍 Consistency is the key to growth, and I’m excited to share my Day 2 progress in my ongoing Python Problem Solving Journey! Today, I focused on strengthening my fundamentals with problems based on arrays and strings, which really helped me think logically and write cleaner code. Here’s what I solved 👇 ✅ Sum of all elements in an array ✅ Finding the largest and second largest numbers ✅ Checking whether an array is sorted ✅ Reversing an array ✅ Removing falsy values from a list ✅ Removing duplicates and finding unique elements ✅ Printing uppercase letters first and in reverse order Each problem helped me understand loops, conditionals, and list operations more deeply — building a stronger foundation one day at a time 💪 You can check out all my solutions on GitHub here 🔗 👉 https://lnkd.in/grq42QrB #Day2 #Python #ProblemSolving #CodingChallenge #LearnEveryday #DeveloperJourney #MERNStack #PythonProgramming #CodingConsistency #GitHub #10000coders