Prevent Dictionary KeyError with Python's .get() Method

We recently open-sourced tinyfix, a minimal FIX protocol library for Python: https://lnkd.in/eaHHXywv The goal of tinyfix is simple: provide a small API for working directly with FIX messages, without the heavy abstractions that most FIX engines introduce. It is designed primarily for: building FIX tooling such as dropcopy clients or automations, prototyping FIX clients or servers. #ElectronicTrading #fixprotocol #FinTech

⚡ Today I learned about Ruff the modern, ultra-fast Python linter and formatter that’s redefining code quality. As developers, maintaining clean, consistent, and error-free code is essential. But using multiple tools for linting, formatting, and import management can slow down workflows. Ruff solves this by combining everything into one powerful tool. 🛠 What I explored: Using Ruff, I learned how to: - Detects syntax errors and code quality issues instantly - Automatically fix unused imports and common mistakes - Format Python code consistently - Replace multiple tools like flake8, isort, and autoflake - Integrate Ruff into real development workflows ⚡ Why it’s powerful: Ruff is extremely useful for: - Improving code quality automatically - Saving time with ultra-fast linting - Maintaining clean and production-ready codebases - Standardizing code across teams - Boosting developer productivity 💡 My key insight: Once you start using Ruff, you realize how much manual effort traditional linting required, Ruff automates code quality so you can focus on building, not fixing. #Python #Ruff #SoftwareEngineering #CodeQuality #BackendDevelopment #WebDevelopment #DeveloperTools #Programming #Developers

🚀 Unlocking Smarter Testing Workflows for Embedded Software! Proud to share this insightful article by my colleague Romain Andrieux on how Scade One models can be tested using Python and modern testing frameworks like pytest. 👇 🔗 https://lnkd.in/eFS3NAKg In this piece, they walk through how to leverage PyScadeOne, the Python bridge to Scade One, to integrate models into the Python ecosystem — enabling: ✔️ Exporting Scade One models as Python-callable functions ✔️ Writing and running automated tests with pytest ✔️ Using Python tools like NumPy, SciPy, and Jupyter Notebooks for deeper analysis ✔️ Bringing models into modern CI/CD pipelines This approach truly bridges model-based design with flexible, scalable testing workflows. 👏 A great read for anyone working with model-based development and automated testing! #modelbaseddevelopment #python #testing #pytest #embeddedsoftware #Ansys #ScadeOne

Python Project Manager is not a trivial task, unfortunately, and there are a plethora of tools to use for managing your dependencies in your python projects. I like to keep things simple, and use the tools that are released with python itself (mainly, pip). However, I spotted a repeated pattern when working with python projects. So, I created yapping, a thin wrapper for automating repetitive task on my day-to-day. You can read more on my blog about yapping: https://lnkd.in/ekMRpE3a #Python #Packaging

Day 15 of My Python Full-Stack Journey 🐍 | Nested If Statements When I first looked at nested if statements, I thought — why would anyone put an if inside another if? Then I tried to build real logic. And it all made sense. Nested if statements are essentially decision trees in code. The outer condition acts as a gatekeeper, and only when it passes do you dive deeper into more specific conditions. It mimics how we actually think as humans. A simple example that clicked for me: python age = 20 has_id = True if age >= 18: if has_id: print("Access granted") else: print("ID required") else: print("You must be 18 or older") The outer if checks age. The inner if checks for ID. Neither alone tells the full story — but together, they handle the real world scenario perfectly. What I learned today: Nesting adds precision, but it also adds complexity. The deeper you nest, the harder it becomes to read and debug. That's why experienced developers often refactor deeply nested logic using logical operators (and / or) or early returns to keep code clean. It's Day 15 and I'm already seeing how programming forces you to think in layers — just like real-life decision making. The journey continues. 💻 #Python #FullStack #100DaysOfCode #PythonJourney #Day15 #CodingJourney #Beginners #Programming #NestedIf #LinkedInLearning

3 Performance Mistakes Python Developers Make in Production Your code works locally. It passes tests. It even gets deployed. But in production? It slows down. Here are 3 common mistakes I keep seeing: 1. Using a List Instead of a Set for Lookups if x in my_list: Lists search one by one → O(n) If lookup is frequent, use: my_set = set(my_list) if x in my_set: Sets use hashing → O(1) average time Small change. Massive impact at scale. 2. Ignoring Time Complexity Nested loops feel harmless… Until data grows 100x. Quadratic logic in small datasets becomes a production bottleneck. If you don’t know the Big-O of your solution, you’re coding blind. 3. Ignoring Memory Usage Creating unnecessary copies: new_list = old_list[:] Loading huge datasets fully into memory instead of streaming. Using lists where generators would work. Performance isn’t just speed — it’s also memory efficiency. Real Engineering Insight: Production performance problems rarely come from “bad Python.” They come from weak algorithmic thinking. Code that works is beginner level. Code that scales is professional level. Which performance mistake did you learn the hard way? #Python #Performance #SoftwareEngineering #DSA #Programming #Developers #CleanCode

Day 14 — Modules and Packages: Organizing Real Projects As your code grows, one file is never enough. Professional developers don’t just write code. They organize it. Today you learned: • What modules are and how to create them • How to import specific functions or entire modules • The difference between import and from ... import • Why packages help structure larger applications • How reusable code saves time and reduces repetition This is where Python stops being small scripts and starts becoming real software. Modules and packages are essential for: • Scalable applications • Team collaboration • Clean project architecture • Production-ready systems If you understand this concept, you’re thinking beyond tutorials. Mini Challenge: Create a separate Python file with a function, then import and use it in another file. Post your approach in the comments. I’m sharing Python fundamentals — one practical concept per day. Built to help you move from beginner scripts to structured development. Next up: File Handling — reading and writing real data. Managing multi-file projects becomes much smoother in PyCharm by JetBrains, especially with navigation and project structure tools. Follow for the full Python series. Like • Save • Share with someone building their Python skills. #Python #LearnPython #PythonBeginners #Modules #Programming #CodingJourney #Developer #Tech #JetBrains #PyCharm

Discover the magical world of Python packages! 🐍✨ From NumPy to TensorFlow, these packages are like little wizards transforming your code dreams into reality. Organized, reusable, and easy to manage, Python packages are the secret sauce behind every successful software project. Forget package conflicts - embrace virtual environments with a wave of your wand (or maybe just a 'venv' command)! Imagine you're a wizard crafting spells: document your packages with Sphinx and follow the enchanted PEP 8 guidelines for maximum wizardry. Let's face it, Python packages are the superheroes of modern software development, making your coding adventures smoother and more exciting. 🦸♂️💻 So, wave your Python wand and dive into the world of packages! #Python #SoftwareDevelopment #PythonPackages #CodingMagic #VirtualEnvironments

Python makes it easy to write code. Docker makes it harder to lie about it. Your script works locally? Great. Now: • Does it run the same in a clean environment? • Are dependencies explicitly defined? • Is configuration separated from code? • Can someone else spin it up without asking you 5 questions? That’s where Docker changes your thinking. It forces discipline: – Explicit dependencies – Reproducible environments – Clear ports, volumes, networking – No “but it works on my machine” excuses Python gives you speed. Docker gives you reliability. Together, they turn experiments into deployable systems. At what point do you containerize your projects? #python #docker #softwareengineering

🚀 Starting Your Coding Journey? Begin with Python! If you’re just entering the tech world, Python is the perfect first step. Why? Because it’s: ✅ Simple & easy to read ✅ Beginner-friendly ✅ Super versatile (Web, Data, AI, Automation—you name it!) Here’s a roadmap to get started with Python 🐍👇 🔹 Step 1: Learn the Basics Variables & Data Types If/Else, Loops Functions 🔹 Step 2: Understand Data Structures Lists, Tuples, Dictionaries, Sets String Manipulation List Comprehensions 🔹 Step 3: Build Mini Projects Calculator App To-Do List Weather App (using APIs) 🔹 Step 4: Explore Real-World Applications Web Development (Flask/Django) Data Analysis (Pandas/Numpy) Automation (Selenium, Scripts) 🎯 Pro Tip: Don’t rush the process. Code daily. Break things. Learn by doing. 👉 Follow Kotha NandaKumari for more beginner-friendly tech content! #Python #CodingJourney #PythonForBeginners #LearnToCode #100DaysOfCode #ProgrammingTips3