Mastering List Comprehensions in Python

Day 3 — A lesson learned from a mistake 🚀 Today I had two placement assessments. One was general coding, and the other was supposed to be Python based (perfect match for my role 😎). During the Python test, I confidently wrote my code… and boom 💥 syntax errors. Again. And again. And again. At that point, I was like, “Is my code wrong or is the universe testing me today?” 😅 I kept debugging, rewriting, and stressing as time kept ticking ⏳ Then came the plot twist 🤯 The issue was not my code. The platform itself was the problem. Even though I chose Python, the environment was actually SQL based. So basically… I was speaking Python in a room that only understood SQL 😂 Instead of panicking, I switched gears. I wrote a basic SQL query, created a table, printed values, and managed to pass at least one test case. Not perfect, but definitely a smart recovery 💡 “Do not cling to a mistake just because you spent a lot of time making it.” What I learned today 👇 ✔ Always check the environment before coding ✔ Adapt fast when things don’t make sense ✔ Time management matters more than ego What I’ll do next time 🔧 • Test a small sample code first • Set a limit for debugging before switching approach • Stay flexible instead of forcing one solution “Adaptability under pressure is stronger than perfect execution.” Today was not about perfect code. It was about thinking smart under pressure 🧠⚡ #WinYourLinkedIn #ClearThinking #MaturityUnderPressure #StrategyImprovement

“If you’re always using 𝒔𝒆𝒍𝒇 in Python… you’re probably doing it wrong.” ⚠️ For the longest time, every method I wrote looked like this: def some_function(self):   ... Didn’t matter what it did, I just slapped 𝒔𝒆𝒍𝒇 on it. Until one code review changed everything. 👀 My senior asked: “Why is this an instance method?” I didn’t have an answer. That’s when I realized: 👉 I wasn’t designing… I was just coding. 💡The simple rule that changed how I think: 🔹 𝐈𝐧𝐬𝐭𝐚𝐧𝐜𝐞 𝐌𝐞𝐭𝐡𝐨𝐝 (𝐬𝐞𝐥𝐟) Use when your logic depends on object data class User:   def greet(self):     return f"Hi, {self.name}" ✔ Tied to a specific object ✔ Reads/modifies instance state 🔹 𝐂𝐥𝐚𝐬𝐬 𝐌𝐞𝐭𝐡𝐨𝐝 (𝐜𝐥𝐬) Use when logic belongs to the class as a whole class User:    count = 0   @classmethod   def get_count(cls):     return cls.count ✔ Shared across all objects ✔ Great for factory methods 🔹 𝐒𝐭𝐚𝐭𝐢𝐜 𝐌𝐞𝐭𝐡𝐨𝐝 (𝐧𝐨 𝐬𝐞𝐥𝐟, 𝐧𝐨 𝐜𝐥𝐬) Use when logic is just related, not dependent class MathUtils:   @staticmethod   def add(a, b):     return a + b ✔ Pure function ✔ Just grouped inside class for clarity 🔥 The shift that made me better: Before: “I need a method → add self” After: “What does this method actually depend on?” ⚡ Rule of thumb: Needs object data? → Instance method Needs class state? → Class method Needs neither? → Static method That one small distinction… 👉 Improved my code quality 👉 Made my design cleaner 👉 And made code reviews way easier 😄 Most devs know these concepts. Few actually apply them correctly. #Python #SoftwareEngineering #BackendDevelopment #Coding #TechGrowth #Developers #CleanCode #Programming

When I started coding, my code was honestly a mess. Random variable names. No structure. And debugging used to take forever. Over time, I realized that writing good code isn’t about writing complex code. It’s about building good habits. Here are 5 simple things that helped me improve my code a lot: 1. 𝐒𝐭𝐚𝐫𝐭 𝐬𝐦𝐚𝐥𝐥 Instead of trying to build big projects immediately, start with small programs. Break problems into smaller parts and test your code often. 2. 𝐔𝐬𝐞 𝐦𝐞𝐚𝐧𝐢𝐧𝐠𝐟𝐮𝐥 𝐯𝐚𝐫𝐢𝐚𝐛𝐥𝐞 𝐧𝐚𝐦𝐞𝐬 Avoid names like x, temp, or data1. Names like total_price or calculate_tax() make the code much easier to read. 3. 𝐖𝐫𝐢𝐭𝐞 𝐮𝐬𝐞𝐟𝐮𝐥 𝐜𝐨𝐦𝐦𝐞𝐧𝐭𝐬 Comments should explain why something is done, not just what the code is doing. Bad comment: # 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞 𝐢 𝐛𝐲 1 Better comment: # 𝐭𝐫𝐚𝐜𝐤 𝐧𝐮𝐦𝐛𝐞𝐫 𝐨𝐟 𝐥𝐨𝐠𝐢𝐧 𝐚𝐭𝐭𝐞𝐦𝐩𝐭𝐬 4. 𝐊𝐞𝐞𝐩 𝐭𝐡𝐞 𝐜𝐨𝐝𝐞 𝐜𝐥𝐞𝐚𝐧 Good formatting makes a big difference. Proper spacing, consistent style, and readable structure make debugging much easier. 5. 𝐃𝐨𝐧’𝐭 𝐫𝐞𝐩𝐞𝐚𝐭 𝐭𝐡𝐞 𝐬𝐚𝐦𝐞 𝐜𝐨𝐝𝐞 If you are copying and pasting the same logic again and again, it’s a sign that you should create a function instead. One more thing that helped me a lot: 𝐑𝐞𝐚𝐝𝐢𝐧𝐠 𝐨𝐭𝐡𝐞𝐫 𝐩𝐞𝐨𝐩𝐥𝐞’𝐬 𝐜𝐨𝐝𝐞. You learn many small tricks that way. Curious to know — What is one habit that helped you improve your coding skills? Share your thoughts in the comments 👇 If you found this helpful, 👍 Like ♻ Reshare ➕ Follow me for more posts on Data Analytics, SQL, Python, and AI. #DataAnalytics #Python #CodingTips #Programming #LearningToCode #Learning

𝗦𝘁𝗼𝗽 𝘄𝗿𝗶𝘁𝗶𝗻𝗴 𝗰𝗼𝗱𝗲 𝘁𝗵𝗮𝘁 𝗼𝗻𝗹𝘆 𝘄𝗼𝗿𝗸𝘀 𝘄𝗵𝗲𝗻 𝘁𝗵𝗶𝗻𝗴𝘀 𝗮𝗿𝗲 𝗽𝗲𝗿𝗳𝗲𝗰𝘁. 🛑 In my latest deep dive into Python, I shifted my focus from logic to resilience. It’s one thing to build a script that works in a "perfect world," but the real world is messy. Users type text into number fields. Files go missing. Servers go down. If your code isn't prepared for the "tire burst," it’s going to crash. 💥 In my latest Python Essentials series, I explored how to move beyond basic scripts by mastering Exceptions and Custom Error Handling. 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀:  • 𝗧𝗵𝗲 𝟯 𝗣𝗶𝗹𝗹𝗮𝗿𝘀 𝗼𝗳 𝗘𝗿𝗿𝗼𝗿𝘀: Understanding the difference between a Syntax Error, a Runtime Exception, and the "silent" Logical Error.  • 𝗧𝗵𝗲 𝗦𝗮𝗳𝗲𝘁𝘆 𝗡𝗲𝘁: How to use try-except-else-finally blocks to catch unpredictable issues before they crash your program.  • 𝗘𝗻𝗳𝗼𝗿𝗰𝗶𝗻𝗴 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗥𝘂𝗹𝗲𝘀: Why I create Custom Exceptions (like InsufficientBalanceError) to protect my "Rules" rather than just relying on technical failures.  • 𝗧𝗵𝗲 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗘𝗱𝗴𝗲: Why return is stronger than break, and how 4-space indentation is the key to clean, surviving code. 𝗧𝗵𝗲 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗠𝗶𝗻𝗱𝘀𝗲𝘁 𝗦𝗵𝗶𝗳𝘁: I stopped asking, "Where should I use try?" and started asking, "Where can something go wrong?" That shift is the difference between a fragile script and a professional tool. Are you still writing code for a perfect world, or have you started building for the real one? 𝗖𝗵𝗲𝗰𝗸 𝗼𝘂𝘁 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻 𝗵𝗲𝗿𝗲: 🔗 https://lnkd.in/dZ6KwFVx #Python #Programming #CleanCode #ErrorHandling #SoftwareEngineering #PythonEssentials #Automation #CodingTips #VAULT

Python doesn’t have to be a "trial by fire." 🐍🔥 Most beginners get stuck in the "Tutorial Hell" loop because they try to memorize everything at once. The secret? You don't need to know every library. You just need to master the 5 Pillars. I’ve put together this "Absolute Beginner’s Cheat Sheet" to help you cut through the noise and focus on what actually moves the needle: 1️⃣ Data Types & Variables: The building blocks. If you don't know your Strings from your Integers, your code won't know how to "think." 2️⃣ Storing Data: Lists and Dictionaries are your digital filing cabinets. Organization is 90% of the battle. 3️⃣ Flow Control: This is the "brain." If/Else statements and Loops turn static code into a dynamic program. (Remember: Indentation is your best friend!) 4️⃣ Essentials: Functions allow you to write code once and use it forever. Efficiency is the name of the game. 5️⃣ Handling Errors: Real developers don't write perfect code; they write code that knows how to handle mistakes. Whether you’re looking to break into Data Science, Web Dev, or just automate a boring spreadsheet,this is your starting line. 📌 Save this post for the next time your code throws a tantrum. #Python #Coding #DataScience #CareerChange #ProgrammingTips #LearnToCode #SoftwareEngineering #TechCommunity #PythonBeginner A

The gap between an "average" Python developer and a "great" one often comes down to habits, not just IQ Writing code that works is easy. Writing code that is maintainable, efficient, and scalable is hard. I’ve reviewed countless pull requests, and the best developers consistently do things differently. They move beyond syntax and focus on craftsmanship. Based on the visual below, here is the framework for leveling up your Python game: 🐍 1. Stop Reinventing the Wheel: The Standard Library is gold. Master collections, itertools, and functools before writing your own complex logic. 📖 2. Readability is a Feature: Code is read 10x more than it’s written. Adhering to PEP 8 isn't pedantic; it's professional courtesy to your future self and your team. ✅ 3. "It Works on My Machine" is Not a Strategy: Great devs embrace testing early. pytest isn't an afterthought; it's a safety net. 🧠 4. Data Structure Intuition: Knowing exactly when to use a set versus a list versus a dictionary is crucial for performance. 🌐 5. Respect Dependency Isolation: Never pollute your global environment. Use venv or conda for every single project. No exceptions. 🤝 6. The Ecosystem Moves Fast: The best aren't just coding; they are learning, mentoring, and contributing back to the open-source community. 🤖 7. Automate the Boring Stuff: Don't argue about formatting in PRs. Let tooling like black, flake8, and CI/CD pipelines handle the repetitive tasks. Call to Action: Be honest—which of these 7 habits is the hardest for you to stick to consistently? Let me know in the comments! 👇 Hashtags: #Python #SoftwareEngineering #CleanCode #BestPractices #DeveloperLife #ProgrammingTips #CareerTwinAI

I still think one of the best ways to understand Claude Code is to build a tiny version of it yourself. Not because it’s hard, but because it’s surprisingly easy. About a year ago, I came across a post about building a coding agent in 94 lines of Ruby. I’d never written Ruby, so I had Claude translate it to Python. An hour later, I had a rough CLI agent that could do five things: - take input in the terminal - find files - read files - edit files - run bash commands That was it. And it was already useful. It didn’t need a huge framework or some giant architecture diagram. A model, a small toolset, and a loop were enough to get something working that could do real work. Since then, the space has moved fast. Back then, I had to implement the now-standard "plan → implement → review" flow myself, because at that time that mostly lived inside dedicated SWE-bench solvers. Claude Code now has many more features, but the core idea is still the same, and it’s still surprisingly effective. --- A decent coding agent will be able to create a prototype from this product description: ``` # Build a CLI Coding Agent in Python An interactive terminal-based coding agent powered by the Anthropic API (Claude Opus 4.6 by default). ## How It Works - Users type instructions directly in the CLI - The agent responds with readable terminal output - It operates within the current working directory ## Tools (4) 1. **List Files** — Recursively list the contents of the working directory 2. **Read File** — Read a file's contents into the agent's context 3. **Edit File** — Replace a target string in a file with new content 4. **Run Bash** — Execute any bash command (requires user confirmation before running) ``` If you try it, you’ll probably be surprised by two things: - how little you need to get started, - and how much less mysterious these tools feel once you’ve built one yourself.

𝗧𝘄𝗼 𝗪𝗮𝘆𝘀 𝘁𝗼 𝗦𝗲𝗿𝗶𝗮𝗹𝗶𝘇𝗲 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗗𝗷𝗮𝗻𝗴𝗼 — 𝗪𝗵𝗶𝗰𝗵 𝗜𝘀 𝗖𝗹𝗲𝗮𝗻𝗲𝗿? When you're just starting out with Django APIs, manually building your response dict feels natural.   You're in control. You know exactly what's going out. It works.   Then your response grows. Fields are added. Nested relationships appear. Validation logic creeps in. 𝗔𝗻𝗱 𝘀𝘂𝗱𝗱𝗲𝗻𝗹𝘆 𝘁𝗵𝗮𝘁 𝗺𝗮𝗻𝘂𝗮𝗹 𝗱𝗶𝗰𝘁 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗳𝗲𝗲𝗹 𝘀𝗼 𝘀𝗶𝗺𝗽𝗹𝗲 𝗮𝗻𝘆𝗺𝗼𝗿𝗲.   Look at the two approaches in the image. Same data being returned. Two very different amounts of code.   The manual approach gives you full control, useful for simple, one-off responses or when you need something very custom.   The serializer approach handles validation, nested data, and read/write logic out of the box and scales cleanly as your API grows.   What I've learned after building production APIs:   ➝ For simple, internal endpoints manual dicts are fine. Don't over-engineer. ➝ For public APIs or anything with validation, serializers will save you significant time. ➝ The real power of DRF serializers shows up when you need to handle POST and PUT, not just GET.   𝗧𝗵𝗲𝗿𝗲'𝘀 𝗻𝗼 𝘀𝗵𝗮𝗺𝗲 𝗶𝗻 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗺𝗮𝗻𝘂𝗮𝗹 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵. Most of us did. The key is knowing when to make the switch.   𝗪𝗵𝗲𝗻 𝗱𝗶𝗱 𝘆𝗼𝘂 𝗺𝗮𝗸𝗲 𝘁𝗵𝗲 𝗷𝘂𝗺𝗽 𝘁𝗼 𝘀𝗲𝗿𝗶𝗮𝗹𝗶𝘇𝗲𝗿𝘀 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝗳𝗶𝗻𝗮𝗹𝗹𝘆 𝗽𝘂𝘀𝗵𝗲𝗱 𝘆𝗼𝘂 𝘁𝗼 𝗱𝗼 𝗶𝘁? #Django #DRF #Python #BackendDevelopment #SoftwareEngineering #API

Nobody tells you this when you start coding. You can write code that works. And still have absolutely no idea what it is doing. I was that person. I wrote loops like I was placing furniture in a room blindfolded. Technically something landed somewhere. Practically, the couch was on the ceiling. The real shift happened when I stopped asking "does this run" and started asking "what is Python actually doing right now, line by line." Turns out there is an entire conversation happening inside your machine that nobody teaches you. Python checks every condition like a bouncer at a club. True gets in. False does not. Everything else is secretly converted into one of those two before the decision is made. A for loop is not magic. It is an assembly line. One item at a time. Same action. Repeat until the belt is empty. A while loop is a watchman who checks the gate every single second. The moment the answer becomes No, he locks up and goes home. Once you see the mechanics, something clicks. You stop guessing why your code broke. You already know. Because you understand the consequences of every line before you write it. Building logic is not about knowing syntax. Syntax you can Google in 10 seconds. Logic is about knowing what question your code is asking. And knowing what answer it expects back. Most people learn to type code. Very few learn to think in it. The ones who think in it are the ones who debug in 2 minutes while everyone else is on Stack Overflow for 2 hours. Learn the BTS. Not just the output. #Python #Coding #DataAnalytics #CareerGrowth #LearnToCode #100DaysOfCode #PythonDeveloper #TechCareers

I built my first Claude Code skill to learn how skills worked. That experiment became Python Mastery, and today I'm releasing it publicly for the first time. It's an opinionated, practitioner-grade Python reference covering the language from A to Z with the judgment of a senior engineer baked in. Not a cheat sheet. Twenty focused modules covering the stuff that actually trips people up in production: - When to use a class vs. a function, and why it matters - Safe refactoring without regressions - Debugging methodology, not just debugging syntax - FastAPI, SQLAlchemy, asyncio, Pydantic v2, pytest, Docker, and more - Architecture decisions, ADRs, tech debt and the architect mindset Built from real experience, not just the docs. See Readme on how to Install in one line, i tried to make it simple since its currently not nativly. Or grab the .skill file and upload it straight to Claude.ai. This is v1 and I want to make it better, so if you try it I'd love to hear what's missing, what's useful, or what could be sharper. Comments, GitHub issues, and DMs all welcome. Repo Link: https://lnkd.in/eM-cRG8A #Python #ClaudeCode