TestGeeks’ Post

Part 2 Python Indentation: The Silent Test Killer 🐍💀 A 100+ line scripts can fail not because of a bad API or a broken locator, but because of a single misplaced space. In Python, whitespace isn't just for "clean code"—it is the logic. One stray indent can turn a robust test suite into a "false pass" nightmare. Here are the 4 best practices we use to keep our automation bulletproof: 📏 1. Use 4 Spaces (The PEP 8 Way) Forget the "Tab vs. Space" war. PEP 8 is the industry standard. Pro-Tip: Configure your IDE (VS Code/PyCharm) to "Insert Spaces" when you hit the Tab key. This ensures 1 Level = Exactly 4 Spaces every time. 🔍 2. Render Whitespace Don’t guess—see your code. Turn on "Render Whitespace" in your editor settings. It turns invisible spaces into tiny dots, making it impossible to accidentally mix in a Tab (which leads to the dreaded TabError). 🤖 3. Automate with 'Black' Stop arguing about formatting in Pull Requests. We use Black, the "uncompromising" code formatter. It automatically re-formats your code on save, ensuring the entire team has identical indentation. 🛑 4. Watch Your Assertions! This is the most common QA mistake. Look at these two examples: ❌ The "False Pass": def test_all_users(users): for u in users: print(f"Checking {u.id}") assert u.active == True # 😱 ONLY runs for the very last user! ✅ The "Proper Nest": def test_all_users(users): for u in users: print(f"Checking {u.id}") assert u.active == True # ✨ Checks EVERY user in the loop. Final Lead Tip: If your automation report shows a "Pass" but your logs show only one item was checked, check your indentation first! What’s the most frustrating IndentationError you’ve ever had to debug? Let’s swap horror stories below! 👇 #Python #TestAutomation #CodingTips #QA #SoftwareTesting #CleanCode #ProgrammingBestPractices

Just finished building **ChatAction Desk** — a Python automation tool designed to turn busy business chats into clear action. The idea behind this project was simple: Business owners often receive many messages every day, and important tasks, follow-ups, deadlines, or order-related actions can easily get buried inside conversations. So I built a tool that detects action-required messages and converts them into structured items that can be reviewed, confirmed, tracked, and completed inside one clean interface. What this project demonstrates: • Python automation • workflow-focused product thinking • modern desktop UI • task detection and review flow • active and completed task tracking This was a strong exercise in building something practical, not just technical. #Python #Automation #DesktopApp #WorkflowAutomation #SoftwareDevelopment #PortfolioProject

🚀 I just built an API Test Automation project using Python! In this project, I implemented automated tests for a REST API using Python, Requests and Pytest. 🔹 Covered CRUD operations: GET (list & single user) POST (create user) PUT & PATCH (update user) DELETE (remove user) 🔹 Also included negative test scenarios This project helped me better understand how APIs work and how to validate responses without relying on UI. 🔗 GitHub project: https://lnkd.in/dFpYwxws #QA #AutomationTesting #Python #APITesting #Selenium #SoftwareTesting

Happy to share that my first Automation Engineering blog was published on LinkIT under INTECS - UoM. This article reflects my journey with Python and Selenium and what I learned from my first automation project.💻 Read here: https://lnkd.in/dyrmbtuD #Python #Selenium #Automation #INTECS #LinkIT

Is Python the "final boss" of test automation? It’s no accident that Python has become the go-to for QA. It’s not just about simple syntax; it’s about getting things done faster. Why it works: pytest is a powerhouse: Clean, modular, and has a plugin for almost everything. Readability: Tests are easier to write, easier to review, and—most importantly—easier to fix. Versatility: From hitting APIs to parsing logs or running AI-driven analysis, Python handles it all with less code. Speed: You spend less time fighting with boilerplate and more time actually testing. #TestAutomation #Python #SDET #QA #Coding

How I Combine Different Python Tools in Testing: In real projects, I’ve found that using a single approach is often not enough to ensure quality. Instead, I combine multiple techniques depending on the problem: • Requests (Python) → For validating API responses and backend logic • Selenium → For automating user flows and UI behavior • BeautifulSoup → For extracting and validating UI data against API responses For example: 👉 Validate data via API using Requests 👉 Capture the same data from UI using Selenium + BeautifulSoup 👉 Compare both to ensure consistency across layers This approach helps catch issues that are often missed when testing API or UI in isolation. It’s not just about automation - it’s about validating the system end-to-end with the right tools. How do you ensure data consistency between API and UI in your testing? #qaautomation #softwaretesting #apitesting #python #testautomation #webscraping

🚀 Turn any Python CLI script into a modern GUI – with zero extra dependencies. I just open‑sourced PyScript-to-GUI, a tool that instantly wraps your command‑line scripts into a clean, functional graphical interface. ⚡ No more boring terminals. Your users get a real window with dark mode, real‑time output, and interactive input dialogs – without writing a single line of GUI code. ✨ Key features: ✅ Zero external dependencies – uses only tkinter (built into Python) ✅ Smart input() handling – automatically converts prompts into pop‑up dialogs ✅ Live logging – all print() output appears in a scrollable terminal‑style area ✅ Multi‑threaded – the GUI never freezes, even during heavy tasks ✅ Hacker aesthetic – dark grey + lime green theme, ready to impress 🔧 Perfect for: Sharing your scripts with non‑technical colleagues Building quick internal tools with a professional look Teaching Python without scaring beginners with the terminal 🔗 GitHub repo: https://lnkd.in/dDpXCYSk 👨💻 Built by NULL200OK – because every script deserves a beautiful face. #Python #GUI #Tkinter #OpenSource #DeveloperTools #CLItoGUI #PyScriptToGUI #Coding

Every framework you have ever used is just design patterns written in production code. Day 06 of 30 -- Design Patterns in Python Advanced Python + Real Projects Series Django post_save is the Observer pattern. DRF renderer_classes is the Strategy pattern. logging.getLogger() is the Singleton pattern. @app.route is the Decorator pattern. Most developers use all of these every day without knowing the names. Today's Topic covers: Why patterns exist and the 3-category decision framework 6 patterns every Python backend developer must know Singleton with double-checked locking for thread safety Factory with self-registering decorator pattern Observer event bus with decorator-based subscriptions Strategy using typing.Protocol for structural subtyping Real scenario -- Factory + Strategy + Observer in one order pipeline 6 mistakes including pattern hunting and Observer without error isolation 5 best practices including why Python functions are strategies Key insight: Design patterns are not solutions you add to code. They are names for solutions already in your code. Phase 1 complete -- 6 days of Python internals done. #Python #DesignPatterns #SoftwareEngineering #BackendDevelopment #Django #FastAPI #100DaysOfCode #PythonDeveloper #TechContent #BuildInPublic #TechIndia #CleanCode #PythonProgramming #LinkedInCreator #LearnPython #PythonTutorial

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

Block Custom Components - Langflow 1.9 Feature Langflow allows custom components to execute Python code inside the runtime. The Block Custom Components setting disables this capability at the server level. When the environment variable is set: LANGFLOW_ALLOW_CUSTOM_COMPONENTS=false the system prevents: - creating new custom components - editing component code in the visual editor This restricts arbitrary code execution within the Langflow environment. When unset or set to true, custom components remain enabled and behave as before. In controlled environments, this can be combined with an allow-list by specifying trusted component paths, ensuring that only predefined components are available while blocking others. This feature is useful when running Langflow in shared or production environments where executing untrusted or generated code is not allowed. Available in Langflow. https://lnkd.in/diHc5mWn