Jesper Dramsch’s Post

💡 Generated Subsets with Duplicates Using Recursion — But There’s Room to Improve Today I worked on the Subsets II problem: generate all possible subsets from an array that may contain duplicates. Example: [1,2,2] Valid output: [], [1], [2], [1,2], [2,2], [1,2,2] ⚙️ My Approach: Recursive Include / Exclude I used classic backtracking logic: For each element: Exclude it from current subset Include it in current subset To handle duplicates: First sort the array Generate all subsets Remove duplicate subsets at the end using set() ✨ Why I liked this approach: Very intuitive recursion pattern Easy to understand Great for learning include/exclude decisions Python code : https://lnkd.in/gXcYNZa9 📊 Complexity: Time: O(2^n) Space: O(n) recursion stack (excluding output) 🧠 But here’s the real question: 👉 Can you give the best optimized solution? Instead of generating duplicates first and removing later, how would you skip duplicates during recursion itself? Would love to learn cleaner approaches from the community 👇 #Recursion #Backtracking #Algorithms #Python #CodingInterview #LeetCode #ProblemSolving Rajan Arora

Most “slow APIs” in Python aren’t CPU-bound. They’re blocking the event loop without realizing it. Classic FastAPI mistake: @app.get("/users") async def get_users(): users = db.fetch_all() # blocking call return users Looks async. Isn’t. Result: * event loop stalls * requests queue up * latency spikes under load Fix → respect async boundaries @app.get("/users") async def get_users(): users = await db.fetch_all() return users Or offload properly: from asyncio import to_thread users = await to_thread(sync_db_call) Advanced production pattern: * separate sync + async layers clearly * use connection pools (asyncpg, aiomysql) * never mix blocking ORM calls inside async routes Hidden issue: One blocking call can freeze thousands of concurrent requests. Build-in-public lesson: Async isn’t about syntax. It’s about protecting the event loop at all costs. AI can convert code to async— but only experience catches where it’s still secretly blocking. #Python #BackendEngineering #FastAPI #Scalability #SystemDesign

📅 Day 17/30 — House Recommendation System (Python + ML + Streamlit) 🏡🤖 🔹 Project Overview: Built a House Recommendation System that helps users find properties based on their specific requirements using Machine Learning. Designed an interactive system with Streamlit to take user inputs and return relevant house suggestions. 🔹 Tools Used: Python | Machine Learning | Streamlit 🔹 Key Features: • Personalized house recommendations based on user preferences 🏡 • User input-driven filtering (budget, location, features) 🎯 • Real-time property suggestions ⚡ • Data preprocessing and feature engineering 🔧 • Efficient recommendation logic using similarity techniques 🤖 🔹 What I Learned: • Building practical recommendation systems using ML • Handling user input and mapping it to meaningful outputs • Feature engineering for improving recommendations • Creating interactive applications with Streamlit • Applying ML to solve real-world user problems 🔗 GitHub Repository: https://lnkd.in/dH79ives #Python #MachineLearning #Streamlit #RecommendationSystem #DataScience #MLProjects #TechProjects #30DaysOfCode 🚀

50% of Python Pandas users do this: df[df['customer_age'] > 50][['cust_id', 'cust_age', 'address']] Instead this : df.loc[df['customer_age'] > 50, ['cust_id', 'cust_age', 'address']] So Which one is better? While Both yield same results Most people stop at: “just use loc, it’s cleaner.” The REAL difference is 1. One indexing operation 2. Row and column selection in a single step 3. No intermediate DataFrame creation 4. Direct reference to the original dataset If your transformation has business meaning, don’t let it be split across implicit steps. Make it explicit. Make it atomic. That’s what .loc really enforces. . . . . . . . . #Python #Pandas #DataEngineering #DataScience #CodeNewbie

I built my first AI Smart Study Assistant using Python and Streamlit. It’s a simple project where I tried to understand how AI apps actually work with a clean user interface. ✨ What it can do: 📄 Summarize text (demo mode) 🧠 Explain topics in simple words ❓ Generate quiz questions 🎨 Simple and interactive web UI 🛠️ Tech used: Python, Streamlit While building this, I understood how user input flows into logic and how AI-based applications are structured. Right now this is a demo version, but I designed it in a way that it can be upgraded later with real AI models and a chat interface. Next step for me is to improve this project further and keep building more AI-based applications. Would love feedback or suggestions 🙌#AI #Python #Streamlit #MachineLearning #LearningByDoing #ArtificialIntelligence #TechJourney #WomenInTech #DataScience Microsoft Google OpenAI https://lnkd.in/eA-xwtqG

🐍 Moving beyond basic Pandas… When datasets get bigger, how you write Pandas code starts to matter a lot. Here are a few techniques I’ve been learning to make analysis faster, cleaner, and more scalable: ✔ Vectorization instead of loops ✔ Using .loc[] and .iloc[] correctly ✔ Choosing apply() vs map() wisely ✔ Writing readable pipelines with method chaining ✔ Handling missing data before analysis Small improvements → Huge impact on real-world datasets 📊 Which Pandas technique improved your workflow the most? 👇 #Python #Pandas #DataAnalytics #LearningInPublic #AspiringDataAnalyst

Today's topic: recursion. A function that calls itself. Sounds simple, right? Here are two ways to add up a list of numbers: Without recursion — honest, reliable, easy to follow: python def suma(lista): suma = 0 for i in range(0, len(lista)): suma = suma + lista[i] return suma print(suma([6,3,4,2,10])) # 25 With recursion — elegant, almost poetic... and a little terrifying: python def suma(lista): if len(lista) == 1: return lista[0] else: return lista[0] + suma(lista[1:]) print(suma([6,3,4,2,10])) # 25 Same result. Two completely different roads to get there. The recursive version looks more "pro" — but if you forget to define when it stops, the function calls itself forever. Literally. Forever. 💀 So yes, it's getting challenging. And yes, recursion feels more elegant to write. But I'm not ready to fully trust something that could loop into oblivion if I blink wrong. Lesson of the day: simple is not the same as bad. And documenting the moments that confuse you? That's part of learning too. #Python #LearningToCode #DaysOfCode #PythonProgramming #CodingJourney #Recursion #BeginnerCoder #TechLearning #CodeNewbie #LinkedInLearning

A plain LLM call says: "The answer to 6 × 7 is 42." An agent calls multiply(6, 7), gets 42, then says: "The answer is 42." One returns text. The other runs code. We put together langgraph-zero-to-agent at Theseus AI Lab. Free, open source, 4 modules. Python basics is all you need to start. Module 1: you wire the graph by hand. Every node, every edge, every tool binding. Module 2: same agent, rebuilt with create_agent(). You see what abstraction actually buys you. Module 3: a coding assistant with a local shell. It writes Python files and runs them on your machine. Module 4: OpenAI Responses API. Web search, code interpreter, reasoning built in. 🔗 https://lnkd.in/dpgwyqq8 #LangGraph #AIAgents #Python #OpenSource

I used to write a lot of clumsy `if` statements just to group data. Checking if a key existed, then initializing a list, then appending. It felt clunky and repetitive. This simple Python trick lets you group any data points by category without boilerplate code, making your data prep for AI/ML much cleaner. It's perfect for aggregating model results by metric or sorting samples by class. 💡 What's your go-to Python trick for cleaning up data operations? #Python #PythonTips #MachineLearning #DataScience #Coding

Do you actually understand what Python is… or do you just know its definition?🐍 Most people say: “Python is a high-level, interpreted language created by Guido van Rossum in 1991.” That’s not understanding. That’s memorization. Python is not just a language. Python is a layer of abstraction. ⚙️ When early languages like C were designed, they stayed very close to the machine. 💻 You had to think about memory, pointers, and low-level details. That’s why C is fast—because it sits close to hardware. But here’s the trade-off: Closer to hardware → more control, more complexity Higher abstraction → less control, more productivity Python was built to move you away from the machine and toward problem-solving. Someone already did the hard work: Memory management? Handled. Complex system interactions? Hidden. Syntax complexity? Reduced. So instead of thinking: “How does the computer execute this?” You think: “What logic solves this problem?” 🚀 That’s why Python is widely used in: Machine Learning Web Development Automation Data Analysis Not because it’s the fastest — it’s not. But, because it allows you to build faster and think more clearly. Final point: 🎯 Python didn’t become popular by accident. It became popular because it removes friction between your idea and implementation. #python #pythonprogramming #learnpython #coding #programming #machinelearning #deeplearning #datascience #artificialintelligence #ai #ml #softwareengineering #systemdesign #computerscience #codinglife #programminglogic