Jens Bruno Wittek’s Post

THE BEST WAY TO LEARN PYTHON ISN'T WATCHING VIDEOS. IT'S RUNNING THE CODE YOURSELF learn-python is a playground and cheatsheet in one repo every topic operators, data types, functions, classes, exceptions, file handling, standard libraries has real working scripts with code examples, inline comments, and assertions that show you the expected output right away you don't just read it. you run it, break it, change it, and test it https://lnkd.in/gZCT3EEK variables, loops, decorators, generators, lambda expressions, OOP all covered, all runnable

Doqtor officially supports Python now. I've been expanding the ecosystem so you can keep your Python docs in sync with your code automatically. It detects changes in .py functions and classes and updates your READMEs or docs so they stay honest. I also just added a Drift Analytics Dashboard. You can see your documentation health at a glance by tracking PRs, how much drift was detected, and which files are the top offenders. Data driven documentation management makes a huge difference. Check it out on GitHub: https://lnkd.in/g7pzX6Aw #OpenSource #Python #TypeScript #BuildInPublic #Documentation

Nobody teaches you this in Python tutorials. You learn variables. You learn functions. You learn classes. But scope? You learn scope the hard way. At 2am. With a bug you can't explain. Staring at code that looks perfectly fine. Here's what's actually happening: Python doesn't look for variables the way you think it does. It follows a very specific lookup order - Local → Enclosing → Global → Built-in - and if you don't know the rules, it will surprise you in the worst moments. I wrote a free guide to fix that gap: ✔ How Python actually resolves variable names ✔ Why closures behave the way they do ✔ The global and nonlocal keywords demystified ✔ Real examples of scope bugs - and how to squash them No fluff. No theory for the sake of theory. Just the stuff that makes you a sharper Python dev. 🎁 Free download: https://lnkd.in/dY8az6hc Drop a 🐍 in the comments if scope has burned you before. #Python #PythonDeveloper #LearnPython #Debugging #Scope #Variable

Better tools. Better code. Less stress. I shared 7 Python libraries that completely changed how I build automation projects. Check out the full article on my Medium account. Medium:@talhaulfat93

Today’s Python topic felt less like syntax and more like learning how code makes decisions. 🐍 Day 09 of my #30DaysOfPython journey was all about conditionals, and this one felt important because it is where Python starts reacting to situations instead of just following instructions. Conditionals help a program choose what to do based on whether something is true or false. Today I explored: 1. if — runs a block when a condition is true 2. else — runs when the condition is false 3. elif — used when there is more than one condition to check 4. shorthand if-else → code if condition else code 5. nested conditions → condition inside a condition 6. logical operators like and (both conditions needs to be true) & or (any one condition needs to be true) What stood out to me today was how much control conditionals give you. They are basically the part of Python that makes logic feel alive. One more day, one more topic, one more step toward writing code that can actually think through a situation. When you first learned conditionals, what was the trickiest part: if-else, elif, or nested conditions? Github Link - https://lnkd.in/g4_tYUDG #Python #LearnPython #CodingJourney #30DaysOfPython #Programming #DeveloperJourney

Today’s Python lesson was one of those “ohhh, so this is why programming feels so organized” moments. 🐍 Day 05 of my #30DaysOfPython journey was all about lists, and honestly, this topic felt like the first real step toward writing cleaner, more useful code. A list in Python is an ordered and mutable collection. It can hold different data types, and yes, it can even be empty. Here’s what I explored today: 1. Creating lists with [] and list() 2. Checking length with len() 3. Accessing items through indexing, slicing, and unpacking 4. Using in to check whether an item exists 5. Adding items with append() and insert() 6. Removing items with remove(), pop(), del, and clear() 7. Copying lists with copy() 8. Joining lists using + and extend() 9. Counting and locating items with count() and index() 10. Reversing with reverse() 11. Sorting with sort() and sorted() They are not just containers for values — they are one of the most practical ways to organize, update, combine, and manage data in Python. The fact that you can add, remove, slice, copy, reverse, and sort them makes them feel like a real data-handling tool rather than just a basic collection. Today reminded me that lists are one of the most useful structures in Python because they let you work with data in a very dynamic way. One more day, one more topic, one more layer of understanding. Github Link - https://lnkd.in/gUt9EfWs #Python #LearnPython #CodingJourney #30DaysOfPython #Programming #DeveloperJourney

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

Many Python I/O tutorials end at print() and open(). This one goes further. On PythonCodeCrack there's a full beginner tutorial on Python I/O that covers the ground many skip — not just how to use the tools, but why they work the way they do. What's inside: — stdin, stdout, and stderr: what they are, where they come from, and why Python didn't invent them — print() in full: sep, end, flush, and why flush=True doesn't mean your data is on disk — input() and why it always returns a string no matter what the user types — File modes r, w, a, and x — including why 'w' truncates before the first write, not during it — The three-layer CPython I/O stack (TextIOWrapper → BufferedWriter → FileIO) and how to inspect it live — PEP 393: why a single emoji in a 2 GB text file can force 4 bytes per character across the entire string — buffering=1 line-buffered mode for crash-safe log files — flush() vs os.fsync() — two entirely different operations that most tutorials treat as the same thing — Python 3.15 making UTF-8 the default on all platforms, and what that means for existing code — sys.__stdout__ vs sys.stdout, newline translation, file descriptors, and TOCTOU race conditions The tutorial includes interactive quizzes, spot-the-bug challenges, a code builder, predict-the-output exercises, a 15-question final exam, and a downloadable certificate of completion. https://lnkd.in/gbYPmYgv #Python #PythonProgramming #LearnPython #CodingEducation

Turning an n8n workflow into a Python script and running it on GitHub for free is easier than you might think. Here’s a simple way to do it, especially if you want to run scheduled workflows. First, download your n8n workflow as a JSON file. Next, upload that file to Claude and ask it to convert the workflow into a Python script. Once you have the script, create a new GitHub repository and add the Python file. Move any API keys or sensitive information into GitHub Secrets to keep them secure. Then, ask Claude to guide you through setting up a GitHub Actions workflow that will run your script on a schedule. That’s it. Now your workflow runs for free on GitHub, without needing to keep n8n running.

If your Python scripts are making 50 API calls, synchronous code spends most of its time waiting around doing absolutely nothing. Suresh Vina has written an intro to async Python using a simple analogy: boiling a kettle and making toast at the same time. Sync code does one, then the other. Async runs both concurrently. Same two tasks: 5 seconds sync, 3 seconds async. It’s not a big deal when making breakfast. But scale that concept across dozens of API calls and the difference adds up. The Infrahub Python SDK supports both sync and async natively. Switching between them is simple. Suresh walks through the core concepts, shows side-by-side code examples, and builds up to running the same operation across multiple sites concurrently. If async Python has been sitting on your "I should learn that someday" list, now’s your chance to *get up to speed*. (See what we did there? 😉) Link in comments 👇