Building a Multi-Step Wizard App with Kivy in Python

Kivy Tutorial: Build Dynamic Forms with Custom Validation In the world of application development, user input is a cornerstone. Whether you're building a simple contact form, a complex registration system, or an order processing interface, the way users interact with your application through forms is critical to its success. In this tutorial, we'll dive deep into building dynamic forms with custom validation in Kivy, a powerful Python framework for rapid application development....

Appium Python: A Complete Tutorial for Mobile App Automation ✅ What you'll learn from this tutorial: ✅ Why use Appium with Python ✅ Getting started with Appium, Python, Android Studio(Emulators) ✅ Writing the first test ✅ Running the test on Android Emulatpr locally ✅ How to run Appium Python tests on the TestMu AI's Real Device Cloud? Read the full article here: https://lnkd.in/dVWJ69d7 #Appium #Python #MobileTesting #TestAutomation #Tutorial

𝗧𝗵𝗲 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗢𝗳 𝗢𝗯𝗷𝗲𝗰𝘁-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 When you start learning Python, you often write everything in one file. This works for small programs, but as your project grows, the code becomes hard to maintain and messy. You can organize your code using Object-Oriented Programming (OOP). OOP helps you group related logic into classes. This makes your code cleaner and easier to understand. Here are the benefits of OOP: - Cleaner code structure - Easier to maintain and debug A clean Python project should have a simple structure: - main.py: program entry point - user.py: handles user data - post_manager.py: handles posts - utils.py: helper functions Each file has one clear responsibility. This makes your project look more professional. You can create a User class to store user information. You can also create a PostManager class to manage posts. To get started, create the following files: - user.py - post_manager.py - utils.py - main.py In main.py, you can connect everything together. You can run your project using python main.py. Refactoring your script into an OOP project helps you write cleaner code and structure your projects better. It also helps you think like a software engineer. Start simple and keep improving your structure. Your Python projects will feel more professional. Source: https://lnkd.in/gSMRF9hY

“CLAUDE.md Playbook: Principles for Simple, Scalable, and AI-Guided Python Development” As I started learning Claude code, first thing is to create and optimize CLAUDE.md files - i.e Set up context files that claude automatically pulls into every conversation containing project -specific information , commands and guidelines. CLAUDE.md provides guidance to claude code when working with python code in the repository . Core Development Philosphy 1. Keep it simple and stupid.- simplicity should be a key goal in design and choose straigtforward solutions over complex. whenever possible,simple solutions are easier to understand , maintain and debug. 2. YAGNI ( you aren't gonna need it ) - Avoid building functionality on speculation - implement features only when they are needed, not when you anticipate they might be useful in future. 3. Design priciples - Dependency inversion : high level modules should not depend on low level modules. both should depend on abstraction. - Open/closed principle : software entities should be open for extension but closed for modification. - single responsibility : Each function , class and module should have one clear purpose. -Fail Fast : check for potential errors early and raise exceptions immediately when issues occur. 4. code structure and modularity - file and function limits : never create a file longer than 500 lines of code - function should be under 50 lines with single , clear responsibility. -classes should be under 100 lines and represent single concept or entity -organize code into clearly separated modules grouped by features or responsibilities -line should be Max 100 characters - use VENU Linux ( virtual environment ) whenever executing python commands. -Project Architecture : Follow strict vertical slice architecture with test living next tot code they test.

Most Python developers learned packaging the same way: Install Python → create a virtual environment → install dependencies with pip. The core of this workflow has always been pip, the package installer that pulls libraries from the Python Package Index (PyPI). pip does its job well, but it was never designed to manage the broader concerns of a Python project. Things like Python version management, environment isolation, dependency locking, and reproducible setups have traditionally been handled by a collection of separate tools layered around it. Over time, this led to a fairly fragmented developer experience. Setting up a project often meant juggling multiple utilities and expecting every developer (or user) to configure them correctly before running even a simple script. Recently I’ve been exploring uv, a newer tool that approaches this problem from a different angle. Instead of focusing purely on package installation, uv acts as a broader project and environment manager. It can automatically handle Python versions, create isolated environments, resolve dependencies, and run scripts—all from a single interface, and significantly faster than the traditional stack. The interesting part isn’t that uv replaces pip entirely, but that it collapses several layers of the traditional Python tooling ecosystem into something much simpler to work with. I wrote a short article breaking down how pip fits into the traditional workflow and where uv changes the model. If you work with Python or manage Python environments across teams, it might be a useful read. https://lnkd.in/g2s3wpEN This post is part of my Tech101 series, where I explore fundamental developer tools and concepts. If you found this useful, follow along for future posts. I'm curious how others are approaching this. Are you sticking with the classic pip + virtualenv setup, or starting to experiment with tools like uv? #python #softwaredevelopment #latest #softwareengineer #bestpractices #tech101

🐍 *Important Python Built-in Functions Every Developer Should Know* Great Python developers don’t just rely on libraries — they master built-in functions. *Why built-ins matter:* ✔ Faster (implemented in C) ✔ Cleaner & readable code ✔ Useful in interviews and real projects *Key categories* : 🔹 *Type Conversion* : int(), float(), str(), bool(), list(), tuple(), set(), dict() 🔹 *Iteration* : range(), enumerate(), zip(), map(), filter(), iter(), next() 🔹 *Math & Aggregation* : sum(), min(), max(), abs(), round(), pow(), all(), any() 🔹 *Daily Utilities* : len(), sorted(), reversed(), print(), type(), help(), id() 🔹 *File Handling* : open() 🔹 *Use Carefully* : eval(), exec() 💡 *Tip* : Before writing custom code or importing a library, check if a built-in function already solves it. Small tools → cleaner and more efficient Python code. 🚀

Yesterday, I said I will work on a roadmap to help me become a better Python Developer. Today, I created the desktop application that will help me accomplish that. The idea is really simple but is really easy to use. First, I have the Task Board on the left and the Calendar on the right. When someone writes in the Task Board and hits 'Enter', the text is stored as a pill. The settings at the top ribbon allowed me to set the colour of the pill, the intensity of the task or the XP of the task which will eventually assist in calculating the progress of the user. The pills themselves can be dragged and dropped into any day in the calendar to allow for easy sorting and resorting. However, supposing you wrote everything you needed in the correct order in the Task Board, you could automatically arrange them in the calendar (as depicted in the video) in ascending or descending order. You could also automatically arrange them according to your preferred days. That is, if you wanted to work only on Tuesdays and Wednesdays alone, then you could select that in the calendar and the tasks will be auto-arranged to fill only Tuesdays and Wednesdays. Finally, I added a little widget that pops up at boot time and 50 minutes after. The widget shows you your learning streak, reminds you of the last task you were required to do and reminds you how far behind you are from the last task. It also shows you your overall progress based off the XP assigned to the task. Then the cherry on top is the timer that you click immediately you want to start working on your project.

🚀 pywho — a debugging painkiller for Python developers 💡 What is pywho? A zero-dependency Python CLI that explains your environment, traces imports, and detects module shadowing. No guessing. No scattered checks. Just clear answers. ⚠️ Pain point: Debugging Python issues usually means checking: • Interpreter • Virtualenv • sys.path • pip • Import resolution 👉 All separately → slow, repetitive, and perfect for “works on my machine” problems 📊 Existing tools: • Python built-in site/path inspection • pip debug • Manual import checks 👉 Useful individually, but each shows only part of the picture 🛠️ What pywho does: One CLI that gives you: ✅ Interpreter details ✅ Virtualenv detection ✅ Import tracing ✅ Import resolution insights ✅ Module shadow scanning ✅ JSON output for CI/sharing ➡️ One place, not five ➡️ Zero dependency ➡️ Cross-platform ➡️ Built for real debugging workflows 👨💻 For all Python developers 🔗 GitHub: https://lnkd.in/dMvz9PYM 🔗 PyPI: https://lnkd.in/dM72_8rs 🔗 Docs: https://lnkd.in/dCvUBAeu 💬 What’s the most confusing Python environment issue you’ve debugged? ♻️ Resharing to support the Python community #Python #PythonDeveloper #PythonDev #PyPI #PythonTools #DebuggingTools #DeveloperTools #DevTools #CLItools #CommandLine #SoftwareEngineering #BackendDevelopment #DevOps #OpenSource #OpenSourceProject #Programming #CodingLife #BuildInPublic #TechInnovation #ProductivityTools #Automation #CI_CD #TestingTools #PythonTips #CodeQuality #SoftwareDevelopment #DevelopersLife #TechCommunity #GitHubProjects

🐍📰 The Django Template Language: Tags and Filters Django templates use their own mini-language that's inspired by Python. This tutorial covers Django template tags and filters, explaining how to compile and use templates. #python