How Python Manages Memory

Today I learned something that genuinely changed how I look at data processing in Python — Generators. Instead of creating and storing an entire list in memory, generators produce items one at a time, only when needed. And that simple idea makes them incredibly efficient. Here’s a simple example that explains the difference: What surprised me is this: yield doesn’t return all results at once. It pauses the function, remembers its state, and continues from where it left off the next time it’s called. This makes generators perfect for: 🔹 Large datasets 🔹 Streaming data 🔹 Memory-efficient pipelines 🔹 Infinite sequences Instead of thinking in terms of “lists”, generators helped me start thinking in terms of flows — generating data only when the program actually needs it. Learning Python is slowly shifting from “how to write code” to “how to write efficient code.” 👉 This code prints the square of every number from 0 to 4, but it does NOT create any list in memory. It only generates the next value when needed. #Python #LearningInPublic #Generators #DeveloperJourney #ProgrammingConcepts #Efficiency #100DaysOfCode

👨💻 Day 36 of my Python learning journey Today I learned about Method Overloading in Python — even though Python doesn’t support traditional overloading like some other languages, it still allows us to achieve similar behavior using flexible techniques. 🔍 What I learned: ✅ Python allows default parameters to simulate method overloading. ✅ We can also use *args and **kwargs to accept variable numbers of arguments. ✅ Method overloading helps create functions that behave differently based on how many inputs they receive. ✅ This improves code flexibility and makes function usage more convenient. 💡 Real-World Example: Think of a calculator app — the same “add” button can add 2 numbers or even 3 numbers depending on the input. That’s the idea behind overloading: same function name, different ways of working. ⚙️ Key Takeaways: Python uses dynamic typing & default arguments to simulate overloading. It keeps code clean, readable, and reusable. Perfect for functions that need multiple behaviors. 🚀 Learning Insight: Method overloading shows how Python gives us freedom and flexibility without being overly strict like other languages. #Python #Day36 #MethodOverloading #OOP #LearningPython #AI #ML #CodingJourney #LinkedInLearning #100DaysOfCode #TechWithSuhit

Python isn’t just about print() statements or loops 🧠🐍 — it’s full of deep inner concepts that make it powerful 💪. Things like mutable and immutable objects decide how data changes in memory 💾, while the LEGB rule explains how Python searches for variables 🔍. Generators and iterators help save memory when working with big data ⚙️, and decorators let us add new features to a function without touching its core logic 🎨. Understanding args and kwargs makes our code super flexible 🧩, and knowing the difference between shallow copy and deep copy saves us from tricky bugs 🪲. Even small things like how Python handles memory management 🧮 or how is and == work differently show how beautifully designed this language really is 💫🔥 #python #internal working of Python ⚙️💭

🚀 Python 3.14- Biggest Step Forward is here Python 3.14 arrives, and with it, the long-standing Global Interpreter Lock (GIL) finally disappears. For years, the GIL has quietly kept Python single-threaded. Even if you had 8 CPU cores, only one could run Python code at a time. That’s why multithreaded Python often didn’t run faster We worked around it using multiprocessing, native extensions, or async code (common in LLMS) Now we won’t have to. With Python 3.14, true parallelism in pure Python becomes real. ✅ Multiple threads can run at once across all CPU cores ✅ Async and threads can work together smoothly ✅ Heavy workloads like data prep, ML pipelines, and simulations can scale naturally In my next post, I’ll share real performance comparisons between Python 3.13 and 3.14, thread scaling, CPU utilization, and runtime differences. Follow me if you’d like to see how the no-GIL build performs in practice

The number of libraries you can use for Operations Research and Optimization in Python is not limited to Pyomo and Pulp, and there are more than you might expect. If you want to read the 5th article in my series, you can take a look. Python Optimization Libraries for Operations Research — Part 5 https://lnkd.in/dVAEznnQ #optimization #datascience #operationsresearch #operationalresearch #python #industrialengineering

Python MarkItDown: Convert Documents Into LLM-Ready Markdown Get started with Python MarkItDown to turn PDFs, Office files, images, and URLs into clean, LLM-ready Markdown in seconds. https://lnkd.in/eUbCyEiF

Python MarkItDown: Convert Documents Into LLM-Ready Markdown Get started with Python MarkItDown to turn PDFs, Office files, images, and URLs into clean, LLM-ready Markdown in seconds. https://lnkd.in/eUbCyEiF

🚀 Exciting News for Python Enthusiasts! Python 3.14 is the latest stable release of Python, launched on October 7, 2025. It brings practical features for production: template strings, deferred annotations, a relaxed GIL option for better multithreading, subinterpreters, and an experimental JIT. Together these make Python faster and safer for backend and AI workloads. This is a big step toward ending the Global Interpreter Lock. Before, threads were limited to a single CPU core. Now you can run threads across multiple cores and make much better use of available hardware. Keep in mind the default CPython build still has a GIL, so choose free threaded builds or subinterpreters when you need true parallelism and double check library and GPU compatibility first. I will be testing 3.14 on our services to measure multicore gains and to see if the JIT helps inference speed. 🔗 Curious about the details? Check out my Medium post! #Python314 #Backend #AI #Multithreading #NoGIL #Python

🚀 Set in Python - A Set in Python is a collection data type that is unordered, unindexed, and contains unique elements. It is mainly used when you want to store non-duplicate items and perform mathematical set operations like union, intersection, and difference. 🧩 Key Features: ▪️ Unordered: Elements have no defined order. ▪️ Mutable: You can add or remove items after creation. ▪️ No duplicates: Automatically removes repeated elements. ▪️ Supports set operations like union(), intersection(), difference(), etc. 💡 When to Use: 🔸 You need unique values. 🔸 You want to perform fast membership testing. 🔸 You need set-based operations (like finding common elements). #Python #PythonLearning #PythonBasics #DataStructures #Coding #LearnPython #SetInPython