Mastering Modules and Packages in Python

🚀 Built a PDF Text Extractor using Python & Streamlit! I often needed a quick way to extract text from PDFs without heavy software. So, I built one myself. 📄 Upload any PDF, and it instantly extracts all the text from every page — clean and simple. ⚙️ The main challenge was handling multi-page PDFs accurately across different formats using PyPDF2. 🛠️ Tech Stack: •Python 3.11.9 • Streamlit • PyPDF2 🔗 GitHub: https://lnkd.in/gvFFf2yA Would love your feedback and suggestions! 🙌 #Python #Streamlit #OpenSource #PythonDeveloper

Quick demo of my Automated Certificate Generator (Episode 10) This tool takes away the stress of manually creating certificates, whether for one person or hundreds.... Just enter the details or upload a file, and Python handles the rest. Fast, simple, and repeatable. Built with Python, Streamlit, Pandas, and Pillow. What do you think? Is this something you would use? Try it out 👇 https://lnkd.in/dQrAqgm9

Topic 10/100 🚀 🧠 Topic 10 — Partial Functions What if you could pre-fill some arguments of a function and reuse it later? 🤯 👉 What is it? Partial functions allow you to fix a few arguments of a function and generate a new function with fewer parameters. 👉 Use Case: Used in real-world applications for: Pre-configuring functions Simplifying repeated function calls Building reusable utilities 👉 Why it’s Helpful: Reduces repetition Makes code cleaner Improves readability 💻 Example: from functools import partial def multiply(x, y): return x * y double = partial(multiply, 2) print(double(5)) # Output: 10 🧠 What’s happening here? We fixed the value of x = 2, creating a new function (double) that only needs one argument. ⚡ Pro Tip: Use partial functions when you find yourself passing the same arguments repeatedly. 💬 Follow this series for more Topics #Python #BackendDevelopment #100TopicOfCode #SoftwareEngineering #LearnInPublic

A lot of developers try to make their code “clean” by following a checklist. Small classes. Short functions. Everything abstracted behind services and containers. The result often looks tidy, but changing the behaviour of the program is still hard. The real logic is scattered across too many layers. In today’s video, I refactor a small Python program and show why optimizing for smallness often leads to worse designs. What actually matters is cohesion: grouping things that change together and making the workflow of the program easy to understand. 👉 Watch the full video here: https://lnkd.in/g2txBqi6. #python #softwaredesign #cleancode #refactoring #arjancodes

Today’s Python lesson felt like the moment code started moving on its own. 🐍 Day 10 of my #30DaysOfPython journey was all about loops, and this topic made one thing very clear: repetition is not boring in programming — it is powerful. Loops help us repeat tasks without writing the same code again and again. Today I explored: 1. while loop — repeats as long as a condition stays true 2. for loop — runs through each item in a collection 3. else with loops — runs only when the loop ends normally 4. break — exits the loop early 5. continue — skips the current step and moves to the next one 6. range() — generates numbers with start, end, and step 7. pass — a placeholder when we do not want to execute anything yet 8. nested for loops What stood out to me today was how loops make Python feel more efficient and more alive. Instead of doing one thing at a time manually, you let the program handle repetition with logic. One more day, one more topic, one more step toward writing code that does more with less. Which loop concept made the most sense to you first: for, while, break, or continue? Github Link - https://lnkd.in/gbzDtx6f #Python #LearnPython #CodingJourney #30DaysOfPython #Programming #DeveloperJourney

We've published the full Advanced Controls Crash Course on our YouTube channel, to go with our updated worksheets (now with Python codes!) downloadable from our super "How to use Advanced Controls in PTC Mathcad Prime" blog! Watch: https://lnkd.in/g8tA3Anj | Read & Download: https://lnkd.in/g5sNNztq

Nobody Cares How You Built It 🐍 Spent days on an analysis. Custom functions. Optimized queries. Clean, modular code. Every edge case handled. Then dashboard is presented. The stakeholder looked at the output for 30 seconds and asked one question: "So, what does this mean for next quarter?" Not a single question about the method. Not a word about the code. They didn't care how it’s built. They never do. What they care about is the answer. The implication. What happens next. 👉 The analysis is not the deliverable. The decision it enables is. 👉 Lead with the answer. Save the method for when someone asks. #DataAnalytics #Python #AnalyticsThinking #LearningInPublic

Day 6/100 – #100DaysOfCode Solved the problem “Remove Element”, focusing on in-place array manipulation. Problem Summary: Remove all occurrences of a given value from an array without using extra space, and return the count of remaining elements. Approach: Used a two-pointer technique to efficiently overwrite unwanted elements while iterating through the array. Complexity: • Time: O(n) • Space: O(1) Key Insight: Efficient problem solving is not always about removing data — sometimes it's about rearranging it smartly in-place. Consistent progress > perfection. On to Day 7 #100DaysOfCode #Day6 #DSA #Python #LeetCode #ProblemSolving #CodingJourney

🚀 New release: 𝗯𝗲𝘁𝘁𝗲𝗿-𝗿𝗲𝘀𝘂𝗹𝘁-𝗽𝘆 𝘃𝟭.𝟭.𝟬 Inspired by the better-result library in the JS ecosystem, I built better-result-py to bring a Rust-style Result type to Python. With v1.1.0, I introduced the 𝙾𝚔 and 𝙴𝚛𝚛 classes to make control flow clearer and more explicit: ✅ Success → 𝚛𝚎𝚝𝚞𝚛𝚗 𝙾𝚔(𝚃) ❌ Error → 𝚛𝚎𝚝𝚞𝚛𝚗 𝙴𝚛𝚛(𝚖𝚎𝚜𝚜𝚊𝚐𝚎) This helps with control flow if you're trying to achieve a more monadic-style error handling. 📦 Install: uv add better-result-py 👩💻 Repo: https://lnkd.in/dQ_4R46N

Managing Python environments shouldn’t be a nightmare. But for many — it still is. Here’s how to keep things clean, fast, and production-ready in 2026: 🛠️ Use uv or poetry to manage environments & dependencies. Faster, safer, and simpler than old-school pip+venv. ⚡ Speed matters – use polars, numpy, and numba to vectorize heavy loops. Even small tweaks can give 10× performance wins. 🧼 Lint + Format + Type-check = non-negotiable Ruff for linting Black for formatting Pyright or Pyrefly for fast type-checks 💡 Bonus tip: Use Typer to build CLIs in minutes. So clean, it feels like magic. 💬 What’s one Python setup rule you wish you knew earlier? #Python #CodeQuality #Productivity #DevTools #DataEngineering