Clean Python Code with Pydantic Field Aliases

PART 1 of building my python final! In my previous post, I mentioned that we focused on structure before coding the python project- heres a more detailed breakdown of how it went! (also a small guide if you wanna try it yourself!) We designed the system using a three-layer architecture: • GUI Layer • Service Layer • Data Access Layer This ensured that the interface, logic, and database were all separated. At first, it felt slower than just coding everything together. But as features increased, this structure kept the code clean, made debugging easier and allowed independent changes. Next, we needed to define how different users would interact with it. In the upcoming posts, I'll elaborate more and you can join me on the journey!! DAY 10 #SystemDesign #CleanCode #BackendDevelopment

Killpy Launches Unified CLI for Python Environment Cleanup 📌 Killpy revolutionizes Python dev cleanup by unifying environment deletion across 11 package managers - from Conda to Pipenv - in one CLI. It smartly targets obsolete artifacts while sparing active setups, slashing disk bloat with zero-risk automation. Perfect for devs drowning in 10–40GB of stale virtual environments. 🔗 Read more: https://lnkd.in/d4n_iGqk #Killpy #Python #Cli #Virtualenvironment #Packagemanager

Log 2/100: Telnetting into the Switch Python Library: telnetlib Today, I wrote a script to Telnet into my lab switch, but it kept crashing. After some troubleshooting, I found the exact issue: Python is just too fast. The script kept pasting the username before the switch had even generated the login prompt. The Fix: I added an extra line to the script (read_until) to force Python to wait and read the output until it actually saw the word "login:", and then asked it to paste the username. I applied the same logic to the Password prompt as well. Suddenly, the script started working flawlessly. The Takeaway: Writing the code is the easy part. Anticipating timing issues and making sure the script actually works without breaking in production is the tough part! The scirpts executes the show version command on the switch. Git Hub Repo: https://lnkd.in/gFkfNyWm Git hub Profile: https://lnkd.in/gjJJJQeT #Python #NetworkAutomation #NetDevOps #NetworkEngineering #100DaysOfCode #ArubaCX #CodingJourney

YAML is a data serialization format that's designed to be easy to read and write. And if you've worked with config files, Docker Compose, Kubernetes, or CI/CD pipelines, you've likely used it. In this guide, Bala teaches you how to work with YAML in Python with code examples. https://lnkd.in/gkuXgZEX

Manual data extraction from websites does not scale. Web scraping automates text collection for competitive analysis, price monitoring, and content aggregation. Eugenijus Denisov, Lead Backend Developer at IPRoyal, covers Python libraries, selectors, best practices, and how to handle dynamic content reliably: https://lnkd.in/dd2mTeBe

Today’s Python topic felt like the point where code stops being one-time work and starts becoming reusable. 🐍 Day 11 of my #30DaysOfPython journey was all about the basics of function, and this one was a big reminder that good code is not just about writing more — it is about writing smarter. A function is a reusable block of code designed to do a specific task, and in Python, we define it using the def keyword. Today I explored: 1. How functions are created and called 2. How return sends values back from a function and return None when nothing is returned 3. Passing parameters and arguments 4. Passing arguments using key-value style 5. Default parameters 6. Arbitrary arguments with *args 7. Arbitrary named arguments with **kwargs What stood out to me today was how functions make code feel organized, reusable, and much easier to scale. Instead of repeating the same logic again and again, you write it once and use it wherever needed. One more day, one more topic, one more step toward writing code that is cleaner, smarter, and actually built to last. Github Link - https://lnkd.in/gUhhaW_y #Python #LearnPython #CodingJourney #30DaysOfPython #Programming #DeveloperJourney

One thing that significantly improved my Python code quality: Static analysis is not optional at scale. For a long time, I relied on code reviews to catch issues. Eventually, I realized something: 👉 Humans are bad at consistently spotting patterns. 👉 Tools are not. That’s where static analysis changed everything. Without running the code, these tools analyze your source and detect: bugs code smells complexity issues type inconsistencies All before production The combination that worked best for me: Ruff → fast linting and code quality Replaces multiple tools (flake8, isort, etc.) and runs extremely fast Mypy → type checking Uses type hints to catch bugs before runtime, bringing discipline to Python’s dynamic nature Radon → complexity analysis Measures cyclomatic complexity and highlights functions that are hard to maintain. #Python #StaticAnalysis #BackendEngineering #Django #CleanCode #SoftwareEngineering #DevOps

Today’s Python lesson felt like the moment code stopped being just instructions and started feeling reusable, flexible, and kind of smart. 🐍 Day 14 of my #30DaysOfPython journey was all about higher order functions, and this one made Python feel a lot more powerful. In Python, functions are first-class citizens, which means they can: 1. take other functions as parameters 2. return functions as results 3. be modified 4. be assigned to variables Today I also explored: 1. Functions as parameters 2. Functions as return values 3. Closures — where an inner function can use the outer function’s scope 4. Decorators — a clean way to add extra behavior without changing the original function 5. Built-in higher order functions like: i. map() → transforms items ii. filter() → keeps only matching items iii. reduce() → combines items into one value What stood out to me today was how Python lets functions do more than one job. They are not just blocks of code anymore — they can actually shape how other code behaves. One more day, one more topic, one more step toward thinking in cleaner, more reusable logic. Which one feels the most interesting to you right now: map(), filter(), reduce(), or decorators? Github Link - https://lnkd.in/gc-mj8Qi #Python #LearnPython #CodingJourney #30DaysOfPython #Programming #DeveloperJourney

Built a small multi-agent coding system over the weekend. Instead of asking one LLM to write perfect code on the first try, three agents take turns: - Architect breaks down the task - Coder implements it in Python - Tester runs it in a REPL and files a bug report if something's wrong The loop repeats until the code passes. Nothing fancy, just a cleaner way to think about LLM-generated code. GitHub: https://lnkd.in/dMq3mwSn #AI #LLM #MultiAgent #LangChain #Python #OpenSource

Today’s Python lesson was a quiet reminder that time is one of the most useful things code can help us handle. 🐍 Day 16 of my #30DaysOfPython journey was all about date and time. Python’s date and time module helps us work with: 1. current date and time 2. formatted date strings 3. converting strings into datetime objects 4. time objects 5. time differences and time spans A few things I explored today: 1. dir() and help() to check what a module offers 2. datetime.now() for current date, time, and timestamp 3. strftime() for formatting dates and time 4. strptime() for converting string dates into datetime objects 5. date() to get only day, month, and year 6. subtraction to find the difference between two time points 7. timedelta() to work with time intervals What stood out to me today was how Python does not just store time — it helps you shape it, compare it, and format it in ways that actually make sense for real projects. One more day, one more topic, one more layer of Python making everyday things easier to manage. Github Link - https://lnkd.in/gMy-QseU #Python #LearnPython #CodingJourney #30DaysOfPython #Programming #DeveloperJourney