"Free Python Pitfalls Book & Solutions"

Hey LinkedIn fam! 👋 Let’s talk Python – it’s no secret that Python is a powerhouse in the coding world, especially as we head into 2025. But while most devs are familiar with staples like NumPy, Pandas, and Flask, there are some seriously underrated libraries that don’t get the spotlight they deserve. Mastering these can truly set you apart and boost your productivity. Here are 5 Python libraries every developer should have on their radar this year: 1. Rich – Amp up your terminal output with pretty formatting, tables, and even progress bars. It makes debugging and logs way more readable. 2. Typer – Building CLI apps? Typer makes it so simple and intuitive to create clean command-line interfaces with minimal code. 3. Pydantic – Perfect for data validation, it helps keep your data models neat and error-free, especially with APIs and configuration files. 4. Poetry – The future of dependency management. Forget about messy requirements.txt files—Poetry streamlines package management and publishing. 5. HTTPX – A modern alternative to Requests, it supports async programming, which is crucial for high-performance apps. Why are these libraries flying under the radar? Maybe because they’re newer or niche. But learning them now can give you a huge edge in building cleaner, faster, and more reliable Python apps. What’s been your experience with underrated tools lately? Any hidden gems you swear by? Let’s share and learn! #Python #SoftwareDevelopment #CodingTips #DeveloperTools #PythonLibraries #TechTrends2025 #Programming #DevCommunity

🚀 Starting Your Python Journey: The Smart Way! 🐍 Python isn’t just a programming language — it’s a gateway to automation, data analysis, AI, and so much more. If you’ve been thinking about learning to code, Python is the best place to start! Here’s a quick roadmap to kickstart your journey 👇 💻 Step 1: Set Up Your Environment Install Python from [python.org](https://www.python.org) and make sure to check “Add Python to PATH.” 🧠 Step 2: Pick Your Editor Use VS Code,PyCharm, or Jupyter Notebook for smooth coding and debugging. 📦 Step 3: Learn the Basics Start simple — print statements, loops, functions, and data types like lists, tuples, and dictionaries. 💡 Step 4: Explore Real-World Use Cases From automation script to data visualizations** with libraries like `pandas`, `numpy`, and `matplotlib`, Python powers it all. 🔥 Pro Tip:Create small projects — a calculator, a to-do app, or a data analysis mini-project. They help you learn faster than theory alone. Python is not just for developers — it’s for anyone who wants to think smarter and work faster. Are you ready to start your Python journey? 🐍💪 Follow Supriya Darisa For More Content #Python #Programming #Learning #DataScience #Coding #BeginnersGuide

🚀 Starting Your Python Journey: The Smart Way! 🐍 Python isn’t just a programming language — it’s a gateway to automation, data analysis, AI, and so much more. If you’ve been thinking about learning to code, Python is the best place to start! Here’s a quick roadmap to kickstart your journey 👇 💻 Step 1: Set Up Your Environment Install Python from [python.org](https://www.python.org) and make sure to check “Add Python to PATH.” 🧠 Step 2: Pick Your Editor Use VS Code,PyCharm, or Jupyter Notebook for smooth coding and debugging. 📦 Step 3: Learn the Basics Start simple — print statements, loops, functions, and data types like lists, tuples, and dictionaries. 💡 Step 4: Explore Real-World Use Cases From automation script to data visualizations** with libraries like `pandas`, `numpy`, and `matplotlib`, Python powers it all. 🔥 Pro Tip:Create small projects — a calculator, a to-do app, or a data analysis mini-project. They help you learn faster than theory alone. Python is not just for developers — it’s for anyone who wants to think smarter and work faster. Are you ready to start your Python journey? 🐍💪 Follow Harshitha Shapuram For More Content #Python #Programming #Learning #DataScience #Coding #BeginnersGuide

Hello there Just solved a fun Python challenge using sets on HackerRank! The task was to compute the average height of distinct plants in a greenhouse. This problem was a great reminder of how powerful sets are in Python — they automatically remove duplicates and make calculations like this super clean and efficient. Here’s the key concept: ``` def average(array): distinct = set(array) return round(sum(distinct) / len(distinct), 3) ``` For example: Input - 161 182 161 154 176 170 167 171 170 174 Output - 169.375 You can check out the full solution on my GitHub: https://lnkd.in/dgsj_mpP In this repo, I’ve implemented solutions in Python for a variety of challenge types: list manipulations, nested lists, sets, dictionaries, and more. These exercises not only reinforce fundamentals but also help me prepare for real-world coding scenarios and technical interviews. Highlights: 1.Focused on clean, readable Python 3 code. 2.Included interactive prompts so the code can run in IDLE or the terminal. 3.Tackled duplicate elimination (with sets), dictionary lookups, list sorting and more. #Python #HackerRank #Programming #Coding #LearningJourney #ProblemSolving

Following community recommendations, I continued exploring Python package development tools and their R equivalents! 🐍📦 After sharing my experience as an R developer diving into Python package development, I received valuable feedback from community members who suggested tools to address the frustrations I initially encountered. I've just published a follow-up article where I explore these recommendations: ✨ Module reloading solutions: IPython's %autoreload extension and Marimo (my favorite discovery!) — bringing back that pkgload::load_all() feeling 📚 Documentation tools: quartodoc as a {pkgdown}-like solution for generating beautiful documentation websites ✅ Code quality checks: pre-commit and ruff to replace the "dreaded" devtools::check() While some workflows still feel less automated than in R (especially documentation), I've essentially found all the features I appreciate in R within Python. Read more: https://lnkd.in/eusvWCdr A big thank you to Joseph Barbier, James Azam, PhD, Uriah Finkel, Arthur Bréant and Antoine Languillaume for their feedback on the first article! 🙏 #Python #RStats #PackageDevelopment #DataScience

🚀 A classic Python “aha!” moment about environments Today I hit one of those “everything works… but also doesn’t” mysteries. My virtual environment had all the right packages: python-dotenv, langchain-core, langchain-openai. Running the project from the terminal? Perfect. ✅ Opening it in VS Code or Cursor? Endless “unresolved import” errors. 🤯 Here’s the twist - it had nothing to do with Python versions. The issue was that my IDE wasn’t using my project’s virtual environment for its background tasks like IntelliSense, linting, and autocomplete. There are two environments at play: 1. Runtime Python - what actually runs your code when you execute it in the terminal. 2. IDE Python Environment - what your editor uses (pylance) to analyze your code and provide smart hints. My terminal was running from ./env/bin/python, but the IDE was analyzing code with a different environment that didn’t include my installed packages. 💡 The fix: Press Cmd + Shift + P Search for Python: Select Interpreter Choose the one pointing to your project’s ./env/bin/python Instantly, all the “missing imports” disappeared. 🎯 Moral of the story: If your code runs perfectly in the terminal but your IDE claims it’s broken - check which Python environment your editor is actually using. #Python #VSCode #Cursor #LangChain #Debugging #DevTips #SoftwareEngineering

We’ve Started a #LearnPython YouTube Series! We recently created a new Python Learning Series on YouTube — beginner-friendly, practical, and step-by-step. you can checkout it here : https://lnkd.in/gNvwXADK And the latest episode we uploaded focuses on something every developer must know: 🐍 Installing Python + Setting Up VS Code For this particular video refer this : https://lnkd.in/g4bvdH3D Here’s what we covered: 1️⃣ Installing Python • Open your browser → search python.org • Go to Downloads • Download the recommended Python installer for your OS • Important: Check “Add Python to PATH” • Complete the installation • Verify using: python --version in your terminal If it prints the version — Python is ready! 2️⃣ Installing & Setting Up VS Code • Visit code.visualstudio.com • Download VS Code for your operating system • Install with default settings • Create a folder for your Python files • Create a file named hello.py • Write your first line: print("Hello, Gyani's!") • Run it in the terminal → and your first program executes 🎉 🎯 What’s coming next? In the upcoming episodes, we’ll learn: ✔️ Syntax ✔️ Variables ✔️ Data Types ✔️ Operators ✔️ Conditions ✔️ Loops … and more — all in an easy, practical way. Let’s keep growing together! 🚀 #Python #VSCode #LearningSeries #YouTube #Programming #Gyanvah #TechCommunity #Developers #CodingBeginners

**🐍 Choosing the Right Python Editor? Here's Your Guide!** Whether you're just starting your Python journey or you're a seasoned developer, the right code editor can make all the difference in your productivity and coding experience. **Here are some top choices:** ✅ **VSCode** – The versatile powerhouse loved by millions. Free, extensible, and packed with features for Python development. ✅ **PyCharm** – The ultimate Python IDE from JetBrains. Perfect for large projects with its intelligent code assistance and debugging tools. ✅ **Spyder** – Built specifically for data science and scientific computing. Great for those working with NumPy, pandas, and Matplotlib. ✅ **Vim** – For the keyboard warriors who never want to leave the terminal. Steep learning curve, but unmatched efficiency once mastered. ✅ **IDLE** – Python's built-in editor. Simple, lightweight, and perfect for beginners learning the basics. ✅ **PyDev** – Eclipse-based IDE that brings powerful features to Python development with strong debugging capabilities. **My take?** There's no one-size-fits-all answer. VSCode offers the best balance for most developers, while PyCharm shines for complex projects. Beginners might find IDLE or Spyder more approachable. **What's your go-to Python editor and why?** Drop your preferences in the comments! 👇 #Python #Programming #SoftwareDevelopment #Coding #TechTools #VSCode #PyCharm #DeveloperTools #LearnPython

I'd like to recommend a book that's been very helpful in the past year: Publishing Python Packages by Dane Hillard It's a concise guide to designing, building, publishing, and maintaining Python packages. For me the benefit was getting a guided tour on all the steps in the creation process. This process was something that I really struggled with when I tried it following only online tutorials (I sometimes was even unsure what questions to ask), whereas the book gave all the answers I needed. I also liked the rather actionable examples. In some cases, I also started feeling more comfortable with other tools: e.g. I now am way better with GitHub actions. It's true that some tools have changed since its publication (e.g. I managed to do everything I wanted with a pyproject.toml without inis, cfgs, or a setup py). To me it was a benefit, I engaged more with the core docs of the tools, looked at the relevant PEPs, and understood what and why changed. Overall, I think it's a valuable and perhaps underappreciated resource. #Python #Programming #DataScience

Today, I tackled three classic problems using Python’s itertools module: 1. Cartesian Product – Computed all possible pairs from two lists, equivalent to nested loops, and printed them in a clean, space-separated format. 2. Permutations – Generated all size-k permutations of a string in lexicographic order, practicing both string manipulation and combinatorial logic. 3. Combinations – Produced all combinations of a string from size 1 up to k, also sorted lexicographically, highlighting the power of itertools.combinations. All solutions are user-friendly, run directly in IDLE, and demonstrate effective use of Python’s standard library for problem-solving. These exercises reinforced core Python skills, including iteration, sorting, and handling tuples, and they’re great practice for interviews or coding challenges. You can find the solutions to these challenges on my GitHub at https://lnkd.in/dgsj_mpP. And of course you are free to clone any repository that interests you and make a contribution that improves the code base. #Python #Coding #ProblemSolving #Itertools #Programming #DataStructures #Algorithms