How to write better Python code: 7 simple habits

𝘼𝙗𝙨𝙤𝙡𝙪𝙩𝙚𝙡𝙮, 𝙄’𝙢 𝙙𝙚𝙩𝙚𝙧𝙢𝙞𝙣𝙚𝙙 𝙩𝙤 𝙝𝙖𝙧𝙣𝙚𝙨𝙨 𝙩𝙝𝙞𝙨 𝙢𝙞𝙙𝙩𝙚𝙧𝙢 𝙗𝙧𝙚𝙖𝙠 𝙩𝙤 𝙧𝙚𝙜𝙖𝙞𝙣 𝙢𝙮 𝙧𝙝𝙮𝙩𝙝𝙢, 𝙣𝙤 𝙨𝙠𝙞𝙥𝙥𝙚𝙙 𝙙𝙖𝙮𝙨, 𝙣𝙤 𝙚𝙭𝙘𝙪𝙨𝙚𝙨. 𝙅𝙪𝙨𝙩 𝙘𝙤𝙣𝙨𝙞𝙨𝙩𝙚𝙣𝙩 𝙘𝙤𝙙𝙞𝙣𝙜 𝙖𝙣𝙙 𝙨𝙩𝙚𝙖𝙙𝙮 𝙜𝙧𝙤𝙬𝙩𝙝🚀💻 Namespaces, Scope & A Number Guessing Game That Tested My Logic 🧠🐍 Day 12 of #100DaysOfCode Journey, though the I have not been following up on the streak. 𝘽𝙪𝙩, 𝙞𝙩 𝙬𝙖𝙨 𝙖 𝙥𝙧𝙤𝙙𝙪𝙘𝙩𝙞𝙫𝙚 𝙖𝙣𝙙 𝙞𝙣𝙨𝙞𝙜𝙝𝙩𝙛𝙪𝙡 𝙙𝙖𝙮🌟 I hunt through into Namespaces and Scope, one of the most fascinating concepts in Python. I learnt how Python determines where each variable “lives” and how it can be accessed throughout a program. Differentiating between Local and Global Scope: local variables exist only inside functions, while global variables are available throughout the program. One interesting discovery?🤔 Python doesn’t have block scope, that means variables declared inside conditionals or loops can still be accessible outside of them, unlike in many other programming languages. The 11th coding challenge, Prime Number Checker, helped reinforce logic and condition handling. Each exercise builds up my understanding and confidence bit by bit 💪. Next came the discussion on modifying global variables, a practice that’s often cautioned against but sometimes necessary. For example, in the final project of the day: the Number Guessing Game 🎲, modifying a global variable helps track the player’s attempts and control game flow efficiently. We also explored Python constants and how they relate to global scope. Both are defined at the program’s top level, but constants are meant to remain fixed, while globals can change depending on program needs. It’s amazing how something as simple as scope can make or break your logic⚙️ After tackling a few scope quizzes and revisiting the instructor’s walkthrough, I polished my version of the Number Guessing Game. Seeing it run smoothly was so satisfying! 😎 #100DaysOfCode #Python #CodingJourney #LearningInPublic #NumberGuessingGame #TechGrowth

Agents show >70% repository-level issue resolution. I still see comments saying coding agents can’t solve real problems. “They don’t understand the codebase.” “They just autocomplete.” “They need step-by-step guidance.” Well… Here’s a benchmark across 12 open-source Python repositories on GitHub. Agents are given only the issue description — no extra context, and they fix it. When this benchmark was published, GPT-4o solved 33.2% of issues. Today, the best agents solve over 70%. We’re only at the beginning. PS Useful links are in the first comment.

CS50 Python Progress Update – Week 5 🧪 (Unit Tests) Continuing my journey through Harvard’s CS50’s Introduction to Programming with Python, I’ve now completed Week 5: Unit Tests — a crucial week focused on writing reliable, predictable, and well-tested code. This week strengthened my understanding of test-driven development (TDD), edge-case handling, and ensuring software correctness using Python’s pytest framework. Here are some of the assignments I worked on: 👓 test_twttr.py Implemented test cases that evaluate vowel-removal logic from Week 2, correctness across multiple edge cases. 👉https://lnkd.in/dqGZ4rn8 🎯 test_plates.py Wrote robust tests to validate license plate rules including formatting, length, and starting conditions using structured unit test cases. 👉 https://lnkd.in/dPa-ncVK 🧮 test_fuel.py Created tests for fraction-to-percentage conversion and fuel gauge logic, targeting invalid inputs and boundary conditions. 👉 https://lnkd.in/d_F-KDYh 📌 Commit marking Week 5 progress: 👉https://lnkd.in/dA2TJ3i4 This week taught me how to think like a quality-focused engineer, anticipating errors before they happen and ensuring reliability across all scenarios. Unit testing is a core industry skill — and this week made my Python foundations even stronger. 🚀 #CS50 #Python #UnitTests #TDD #SoftwareEngineering #LearningJourney #GitHub #Coding

🚀 Day 11 of #90DaysOfCode — Debugging Deep Dive (Python Learning Day) Today wasn’t a project-building day — and that’s completely okay. Instead, I dedicated my time to something equally important: learning and practicing debugging in Python 🔍🐍 Sometimes, stepping back from building and focusing on understanding errors makes us better developers in the long run. Today’s session helped me sharpen my problem-solving approach and get more comfortable with breaking down issues systematically. 🛠️ What I learned today: 🔎 How to read and understand Python error messages 🧵 Tracing code execution step-by-step 🐞 Using print-based debugging to check variable flow 🧰 Introduction to Python’s built-in pdb debugger 🎯 Identifying logic errors vs. syntax errors 🧹 Improving debugging habits for cleaner, more predictable code 💡 How structured debugging makes future projects faster & smoother Even without a finished project, today was a powerful reminder: > Good developers write code. Great developers debug it. This journey continues — one day, one lesson, one improvement at a time 💪 #Python #Programming #Day11 #90DaysOfCode #CodingJourney #LearnInPublic #Debugging #PythonLearning #Developer #SoftwareEngineering #CodeNewbie

🚀 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

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

Today, I tackled three classic problems using Python’s itertools module: 1. Cartesian Product – Computed all possible pairs from two lists, equivalent to nested loops, and printed them in a clean, space-separated format. 2. Permutations – Generated all size-k permutations of a string in lexicographic order, practicing both string manipulation and combinatorial logic. 3. Combinations – Produced all combinations of a string from size 1 up to k, also sorted lexicographically, highlighting the power of itertools.combinations. All solutions are user-friendly, run directly in IDLE, and demonstrate effective use of Python’s standard library for problem-solving. These exercises reinforced core Python skills, including iteration, sorting, and handling tuples, and they’re great practice for interviews or coding challenges. You can find the solutions to these challenges on my GitHub at https://lnkd.in/dgsj_mpP. And of course you are free to clone any repository that interests you and make a contribution that improves the code base. #Python #Coding #ProblemSolving #Itertools #Programming #DataStructures #Algorithms

We’ve Started a #LearnPython YouTube Series! We recently created a new Python Learning Series on YouTube — beginner-friendly, practical, and step-by-step. you can checkout it here : https://lnkd.in/gNvwXADK And the latest episode we uploaded focuses on something every developer must know: 🐍 Installing Python + Setting Up VS Code For this particular video refer this : https://lnkd.in/g4bvdH3D Here’s what we covered: 1️⃣ Installing Python • Open your browser → search python.org • Go to Downloads • Download the recommended Python installer for your OS • Important: Check “Add Python to PATH” • Complete the installation • Verify using: python --version in your terminal If it prints the version — Python is ready! 2️⃣ Installing & Setting Up VS Code • Visit code.visualstudio.com • Download VS Code for your operating system • Install with default settings • Create a folder for your Python files • Create a file named hello.py • Write your first line: print("Hello, Gyani's!") • Run it in the terminal → and your first program executes 🎉 🎯 What’s coming next? In the upcoming episodes, we’ll learn: ✔️ Syntax ✔️ Variables ✔️ Data Types ✔️ Operators ✔️ Conditions ✔️ Loops … and more — all in an easy, practical way. Let’s keep growing together! 🚀 #Python #VSCode #LearningSeries #YouTube #Programming #Gyanvah #TechCommunity #Developers #CodingBeginners

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

Hello there Just solved a fun Python challenge using sets on HackerRank! The task was to compute the average height of distinct plants in a greenhouse. This problem was a great reminder of how powerful sets are in Python — they automatically remove duplicates and make calculations like this super clean and efficient. Here’s the key concept: ``` def average(array): distinct = set(array) return round(sum(distinct) / len(distinct), 3) ``` For example: Input - 161 182 161 154 176 170 167 171 170 174 Output - 169.375 You can check out the full solution on my GitHub: https://lnkd.in/dgsj_mpP In this repo, I’ve implemented solutions in Python for a variety of challenge types: list manipulations, nested lists, sets, dictionaries, and more. These exercises not only reinforce fundamentals but also help me prepare for real-world coding scenarios and technical interviews. Highlights: 1.Focused on clean, readable Python 3 code. 2.Included interactive prompts so the code can run in IDLE or the terminal. 3.Tackled duplicate elimination (with sets), dictionary lookups, list sorting and more. #Python #HackerRank #Programming #Coding #LearningJourney #ProblemSolving