List Comprehension in Python

🚀 Excited to announce the Alpha release of py-nabla! As developers, we’ve always faced a gap: we think in terms of mathematical formulas, but we write in terms of nested code. Translating complex LaTeX into SymPy or NumPy has always been a tedious, error-prone process. That ends today. I’ve just released py-nabla, a production-grade Python library designed to be the "LaTeX-first" bridge between mathematical thought and programmatic execution. 🔹 Why py-nabla? Instead of wrestling with syntax, you simply pass raw LaTeX strings. Our "Unbreakable" Earley Parser handles the ambiguity, while the engine intelligently bridges the gap between symbolic manipulation and high-performance numeric analysis. 🔹 Key Technical Highlights: ✅ Earley Parser Integration: Handles complex nested structures and mathematical ambiguities gracefully. ✅ Robustness by Design: Stress-tested against deep nesting, calculus operations, and complex limits. ✅ Developer Experience (DX): Featuring visual error pointers (^--) and deep decision logging for total transparency. ✅ Hybrid Engine: Seamlessly vectorizes symbolic math for NumPy-level performance. This isn't just a project; it’s an attempt to teach Python to speak math fluently. 🌐 GitHub: https://lnkd.in/duV5Z8Ph 📦 PyPI: pip install py-nabla I’m looking for contributors and early adopters to help expand our grammar and push the boundaries of scientific computing in Python. Let’s build the future of mathematics, one equation at a time. 🚀 #Python #OpenSource #Mathematics #DataScience #Calculus #ScientificComputing #Nabla #PyPI #DevRel

💡 A Simple Recursive Way to Check Power of Two Today I revisited a basic but interesting problem: Check if a number is a power of two. Instead of jumping straight to bit manipulation, I tried solving it using recursion — and it turned out to be a neat approach. 🔁 Idea: A number is a power of 2 if: It keeps dividing cleanly by 2 And eventually becomes 1 ⚙️ Approach: If n == 1 → ✅ True If n <= 0 or n is odd → ❌ False Otherwise → recursively check n // 2 ✨ What I liked about this: Very intuitive Mirrors the mathematical definition Easy to understand and implement 📊 Complexity: Time: O(log n) Space: O(log n) due to recursion stack 🧠 Takeaway: Sometimes, going back to the mathematical intuition behind a problem leads to the simplest solution. Of course, there’s also a more optimized bit manipulation trick: 👉 n & (n - 1) == 0 But recursion helps in building strong fundamentals. python code https://lnkd.in/giwyBUiQ How would you approach this — recursion or bit manipulation? 👇 #Algorithms #Recursion #Python #CodingInterview #ProblemSolving #LeetCode Rajan Arora

🚀 Writing loops in Python for array operations? You’re doing it the hard way ❌ Let’s fix that with NumPy Broadcasting 👇 --- 📌 What is Broadcasting? It allows NumPy to perform operations on arrays of different shapes WITHOUT writing loops 🤯 --- 👀 Example: import numpy as np arr = np.array([1, 2, 3]) result = arr + 10 print(result) ✅ Output: [11 12 13] 👉 NumPy automatically "broadcasts" 10 to match array shape! --- 🔥 Now the real power: arr1 = np.array([[1,2,3], [4,5,6]]) arr2 = np.array([10,20,30]) result = arr1 + arr2 print(result) ✅ Output: [[11 22 33] [14 25 36]] 💡 Smaller array is stretched across rows automatically! --- 📌 Broadcasting Rules (Simple Version): ✔ Shapes must be compatible ✔ Matching from right to left ✔ Dimensions should be equal OR 1 --- 🔥 Why it matters? ✅ No loops → Faster code ✅ Cleaner syntax ✅ Used heavily in ML & Data Science --- ⚠️ Common Mistake: np.array([1,2,3]) + np.array([1,2]) ❌ Error: Shapes not compatible Follow for daily coding clarity 🚀 #Python #NumPy #DataScience #MachineLearning #Coding #Programming #LearnToCode #CodingBlockHisar #Hisar

I just built a fully working RAG (Retrieval-Augmented Generation) system from scratch. 🚀. Here's what it does and how: 𝗪𝗵𝗮𝘁 𝗶𝘁 𝗱𝗼𝗲𝘀: Upload any PDF → ask questions → get answers with cited sources 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: 1. PDF is loaded and split into 500-character chunks 2. Each chunk is converted into embeddings (vectors) 3. Stored in ChromaDB — a vector database 4. At query time, the top 3 relevant chunks are retrieved 5. Chunks + question are sent to a local LLM 6. Answer returned with source citations 𝗡𝗼 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻: The LLM is explicitly instructed to answer only from the retrieved context. If the answer isn't in the document, it says so — no fabrication. 𝗧𝗲𝗰𝗵 𝘀𝘁𝗮𝗰𝗸: Python · LangChain · ChromaDB · Sentence Transformers · Ollama · Streamlit Runs fully locally — zero API costs. GitHub: https://lnkd.in/drfgqa-B #AIEngineering #RAG #Python #LLM #MachineLearning #BuildingInPublic

Master the Pythonic Way: DSA Basics Ready to level up your Data Structures and Algorithms (DSA) game? Doing DSA in Python isn’t just about getting the right output; it’s about writing clean, efficient, and Pythonic code. 🚀 If you’re still using 5 lines of code for a simple filter or a basic if-else block, it’s time for an upgrade. Today, I’m diving into two essential tools that make your algorithms sleeker: List Comprehensions and Ternary Operators. 1. List Comprehensions: The One-Liner Powerhouse Why write a for loop when you can generate a list in a single, readable line? It’s faster and keeps your workspace clutter-free. 2. Ternary Operators: Logic at a Glance When your algorithm needs a quick decision, ternary operators (conditional expressions) are your best friend. They are perfect for assigning values based on a condition without breaking the flow. The Syntax: value_if_true if condition else value_if_false 💡 Why this matters for DSA: Readability: Interviewers love code that is easy to follow. Efficiency: List comprehensions are often slightly faster than manual append() calls. Focus: It allows you to focus on the logic of the algorithm rather than the boilerplate of the syntax. What’s your favorite Python trick for competitive programming? Let’s discuss in the comments! 👇 #Python #DataStructures #Algorithms #Coding #SoftwareEngineering #Pythonic #ProgrammingTips

🚀 Python Series – Day 19: Polymorphism (One Name, Many Forms!) Yesterday, we learned Inheritance 🔁 Today, let’s understand another powerful OOP concept — 👉 Polymorphism 🧠 What is Polymorphism? 👉 The word Polymorphism means: 📌 Poly = Many 📌 Morph = Forms So, One method / function behaves differently in different situations 🔹 Real-Life Example Think of the word Run 🏃 Human runs 🚗 Car runs 💻 Software runs 👉 Same word run, different meanings. That is Polymorphism 🔥 💻 Example 1: Same Method, Different Classes class Dog: def sound(self): print("Dog barks") class Cat: def sound(self): print("Cat meows") for animal in (Dog(), Cat()): animal.sound() Output: Dog barks Cat meows 🔹 Example 2: Built-in Polymorphism print(len("Python")) print(len([1,2,3,4])) Output: 6 4 👉 Same len() function works for string and list. 🎯 Why Polymorphism is Important? ✔️ Cleaner code ✔️ Flexible programs ✔️ Easy to extend features ✔️ Used in real-world software development Pro Tip 👉 Write generic code that works with many object types. 🔥 One-Line Summary 👉 Polymorphism = Same method name, different behavior 📌 Tomorrow: Encapsulation (Protect Your Data Like a Pro!) Follow me to master Python step-by-step 🚀 #Python #Coding #Programming #OOP #DataScience #LearnPython #100DaysOfCode #Tech #MustaqeemSiddiqui

I built a tool that lets you ask questions about your codebase in plain English. 🧠 Like literally just type — "where is the FAISS vector store initialized?" — and it finds the exact file, function, and code for you. No more ctrl+F. No more digging through 20 files manually. It's called CodeMind. Getting started is super simple too — just paste your GitHub repo link and it'll clone it automatically, or upload a ZIP file if you prefer. That's it, you're ready to start asking questions. Here's how it works under the hood: → Loads your entire codebase → Breaks it into chunks and converts them into embeddings → Stores everything in a FAISS vector store → When you ask something, it pulls the most relevant code and sends it to Groq LLM for a proper answer Built with Python · LangChain · FAISS · Groq · Streamlit 🔗 Try it: https://lnkd.in/gYV8UfC8 🐙 GitHub: https://lnkd.in/gk3F5kZf Still a lot to improve but happy with how v1 turned out. Would love honest feedback from anyone into AI or dev tooling! 🙌 #RAG #LangChain #GenerativeAI #Python #OpenSource #BuildInPublic #AIEngineering

Hello dudes and dudettes!! 🚀 Day 11/150 — Cracked LeetCode 274: H-Index Today’s problem was less about coding… and more about thinking smart 😄 At first glance, it looks like just another array problem. But once I dug in, I realized it’s actually about measuring impact — not just numbers. 🔍 What’s the Problem About? You’re given an array where each number represents how many times a research paper was cited. Now the twist: 👉 You need to find the H-Index Which basically means: A researcher has an H-Index of h if they have h papers with at least h citations each. 🧠 The “Aha!” Moment Initially, it feels confusing… like, “Where do I even start?” But everything clicks when you: 👉 Sort the array in descending order Suddenly, the problem becomes super clean. Now it’s just about checking: “Do I have 1 paper with ≥1 citation?” “Do I have 2 papers with ≥2 citations?” “Do I have 3 papers with ≥3 citations?” …and so on. The moment this condition fails — boom, you stop. That’s your answer. 📊 Quick Example Input: [3, 0, 6, 1, 5] After sorting: [6, 5, 3, 1, 0] Now checking step-by-step: 1 paper → valid ✅ 2 papers → valid ✅ 3 papers → valid ✅ 4 papers → not valid ❌ 🎯 Final Answer: 3 💡 What Made This Problem Interesting? It’s not about checking everything — it’s about knowing when to stop A simple sort can completely change how you see the problem It teaches you to think in terms of thresholds and consistency, not just values 😎 My Takeaway This problem is a perfect reminder that: “Sometimes the smartest solution isn’t more work… it’s better perspective.” 🔥 One more step forward in the journey. Let’s keep building. #LeetCode #Algorithms #ProblemSolving #CodingJourney #100DaysOfCode #Python #LearningInPublic

🧠 Building consistency, one concept at a time. 📅 Day 6 of my Python Journey Today was all about strengthening core fundamentals and taking a step closer to writing structured, efficient code. 💡 What I worked on today: 🔁 While Loops Practiced control flow using while loops. Solved multiple logic-building problems like: Reversing a number. Checking palindrome numbers. Digit-based operations. ⚙️ Functions Learned how to break problems into reusable blocks. Practiced writing clean and modular code. 🧩 Types of Arguments Explored different ways to pass values into functions. Understood flexibility in function design. 📦 Started Data Structures in Python – Lists After functions, I moved into in-built data structures, starting with Lists. From the practice files today, I covered: ✔️ Basics of list creation and manipulation ✔️ Hands-on with in-built methods like: append(), insert(), extend() remove(), pop(), del index(), count() sort(), reverse(), copy(), clear() Also explored how nested lists can be used to represent 2D structures like matrices. 🚀 What’s next? Moving forward, I’ll be solving problem-based questions on lists to strengthen my understanding and logic. 📌 Key Insight: It’s not just about learning syntax… It’s about understanding how and where to use it effectively. Consistency is building. Clarity is improving. And that’s what matters. #Python #Day6 #CodingJourney #DataStructures #LearningInPublic #ProblemSolving #Developers #TechGrowth #SDE #Programming

Built my first skill -- Large Codebase Survival Guide AI coding agents (Claude Code, Cursor, Windsurf, etc.) are incredibly powerful — until they aren't. When working with large files or complex multi-step tasks, the agent's context window fills up and the session dies permanently (HTTP 503). No recovery. No /clear. All unsaved progress — gone. This guide was born from months of painful experience migrating a 15,000+ line Python application from SimpleGUI to WebView using Claude Code. Countless sessions were lost to 503 errors before a reliable methodology emerged. https://lnkd.in/gdn8rxjd 6 Principles: