Boost Python Performance with 3.13 and Polars

Python Learning Series 🐍 PYTHON MEMORY MANAGEMENT 📌 TOPIC 1: What is Memory Management? Memory management is the process of allocating and deallocating memory for data in a program. It's crucial for application performance. In Python, this is handled automatically by the Python Memory Manager (PMM), which is part of the Python Virtual Machine (PVM). 💼 Interview Q: "What is memory management, and who handles it in Python?" ✅ Answer: Memory management is about controlling how memory is used—when to grab it and when to free it. In Python, we don't do this manually. Python has its own built-in Python Memory Manager (PMM) inside the Python Virtual Machine (PVM) that handles all of this automatically behind the scenes. 📌 TOPIC 2: Types of Memory Allocation 🔹 Static Allocation → Happens at compile time, uses the STACK. Python does NOT use this. 🔹 Dynamic Allocation → Happens at runtime, uses the HEAP. This is what Python uses. 💼 Interview Q: "What are the types of memory allocation? Which one does Python use?" ✅ Answer: There are two types—static (at compile time, using the stack) and dynamic (at runtime, using the heap). Python exclusively uses dynamic memory allocation. Every time you create a variable or object in Python, memory is allocated at runtime on the heap. ━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📌 TOPIC 3: Stack vs Heap Memory 🔹 Stack: - Sequential (ordered) memory - Stores function calls & local variables - Fast but limited 🔹 Heap: - Random order memory - Stores all Python objects & data structures - Larger and flexible 💼 Interview Q: "What is the difference between stack and heap memory in Python?" ✅ Answer: The stack is used for function calls and local variables—it's sequential and fast. When a function is called, it gets a slot on the stack; when it returns, that slot is freed. The heap is where all actual Python objects live—integers, lists, classes, everything. It's randomly ordered but much more flexible. In Python, variables don't store values directly; they store references (like arrows) pointing to objects sitting in the heap. Example: x = 10 Here, 'x' is stored in the variable area, and the integer object '10' lives in the heap. 'x' just holds the memory address of that object. 📌 TOPIC 4: Python Memory Areas & Execution Model Python's memory is divided into 5 areas: 1️⃣ Code Area – Stores the entire program/code 2️⃣ Variable Area – Stores variable names with references to heap objects 3️⃣ Heap Area – Stores all Python objects dynamically 4️⃣ Free Area – Unused memory, available to stack/heap as needed 5️⃣ Stack Area – Stores functions and their local variables 🔑 Key point: Python's heap is PRIVATE—only the Python interpreter can access it directly. Both the stack and the heap grow/shrink dynamically, borrowing from or returning memory to the free area. Each object in the heap has a unique memory address, which you can check using: 👉 id(variable_name)

If you’re a Python developer who writes more than the occasional small script, you already know the truth: threads in Python are one of the longest-running frustrations in the language. But now, Python has finally decided it’s time to grow up. The most recent Python release introduces one of the boldest and most far-reaching changes the language has ever seen: official support for Free Threading. 𝐏𝐲𝐭𝐡𝐨𝐧 𝐚𝐧𝐝 𝐓𝐡𝐫𝐞𝐚𝐝𝐬: 𝐓𝐡𝐢𝐫𝐭𝐲 𝐘𝐞𝐚𝐫𝐬 𝐨𝐟 𝐒𝐨𝐥𝐢𝐭𝐮𝐝𝐞 Python is no longer a young language. Created nearly 35 years ago by Guido Van-Rossum, and with Python 3 released back in 2008, it has proven itself across countless domains and applications. Yet in one important area, the language has long carried an implementation that is hard to justify in a mature ecosystem: thread support. CPython, the most common implementation, never truly got along with threads.   Proper thread support is difficult. It demands careful handling of race conditions, deadlocks, shared state, and memory management. When all of this is embedded in a dynamic interpreter with pervasive object sharing and a garbage collector, the challenge becomes far greater than in a tightly bounded application. 𝐋𝐨𝐨𝐤𝐬 𝐋𝐢𝐤𝐞 𝐚 𝐓𝐡𝐫𝐞𝐚𝐝. 𝐅𝐞𝐞𝐥𝐬 𝐋𝐢𝐤𝐞 𝐚 𝐓𝐡𝐫𝐞𝐚𝐝. 𝐁𝐮𝐭 𝐈𝐬𝐧’𝐭 𝐚 𝐓𝐡𝐫𝐞𝐚𝐝. From an early stage, Python’s solution was awkward but effective: allow concurrency, forbid true parallelism. Multiple threads can exist and run concurrently, but not in parallel. Even on multi-core systems, only one thread executes Python bytecode at any given moment. This is enforced by the GIL (the Global Interpreter Lock) which ensures that only a single thread runs Python code at a time. So why have threads at all? For I/O-bound workloads, they still help: while one thread waits for I/O, another can run. Some computation-heavy libraries achieve true parallelism through specialized native code. But in general, anyone wanting Python to scale across CPU cores has had to rely on multiple processes. 𝐅𝐫𝐞𝐞-𝐓𝐡𝐫𝐞𝐚𝐝𝐢𝐧𝐠 𝐏𝐲𝐭𝐡𝐨𝐧: 𝐓𝐡𝐢𝐬 𝐈𝐬 𝐇𝐨𝐰 𝐈𝐭 𝐒𝐭𝐚𝐫𝐭𝐬 In recent years, a serious effort has been underway to free Python from the GIL. The previous release introduced this capability experimentally. Python 3.14 takes the most decisive step yet: for the first time, Python officially supports running without the GIL. This is not the default. Running without the GIL requires explicit configuration. Not out of conservatism, but because of the magnitude of the change and the costs it carries, like performance penalties and breaking existing code. For these reasons, the Python steering committee has not set a target date for making GIL-less execution the default. Over the coming years, the mechanism will be tested in real systems, its impact better understood, and performance overhead reduced. When will that happen? We’ll have to wait for the decision of the (former) benevolent dictator and his council.

🐍 New Blog: Master Python Dictionaries in 5 Minutes Just published a comprehensive guide on Python dictionaries - one of the most powerful data structures every developer should master. In this post, you'll learn: ✅ How dictionaries work and why they're lightning-fast (O(1) lookup!) ✅ Creating, accessing, and modifying dictionaries ✅ Real-world use cases: caching, counting, data grouping ✅ Best practices and common pitfalls to avoid ✅ Advanced techniques with nested dictionaries Whether you're a beginner or looking to level up your Python skills, this guide has something for you. Read the full article here: https://lnkd.in/dbEzvWBD #Python #Programming #SoftwareDevelopment #DataStructures #LearnToCode #PythonProgramming #TechTips Innomatics Research Labs

I recently wrote a technical article titled “How Python Uses Data Structures Behind the Scenes: Lists, Tuples, Sets, and Dictionaries.” In this article, I explore what happens under the hood when we use Python’s most common data structures. While lists, tuples, sets, and dictionaries are easy to use, their internal implementations play a major role in performance and memory efficiency. 🔍 What the article covers: • How Python’s object and memory model works • Why lists are implemented as dynamic arrays • How tuple immutability improves speed and memory usage • Why sets and dictionaries provide O(1) average lookup time • Practical tips for choosing the right data structure for optimized code 📌 Why this matters: Understanding internal behavior helps write cleaner, faster, and more scalable Python code—especially useful in software development, AI/ML, and data engineering. #Python #DataStructures #SoftwareEngineering #ComputerScience #AI #MachineLearning #Programming #InnomaticsResearchLabs #TechBlog

Ever wondered why your Python script slows down when your data grows? 🐍 I used to think of Lists and Dictionaries as just simple "containers," but digging into how Python handles memory "under the hood" changed my perspective on writing efficient code. In my latest blog post, I break down: 🔹 The "Moving Day" problem: How Lists actually grow in memory. 🔹 The Library GPS: Why Dictionaries are so much faster than Lists. 🔹 Why Tuples are the lightweight "speedsters" of Python. If you're a student or developer looking to move from just "making it work" to "making it smart," this one is for you. #Python #Coding #DataStructures #SoftwareEngineering #LearningJourney

I recently wrote a techechnical article ⚙️ The article focuses on how Python implements these core data structures internally and how understanding this helps in writing efficient and optimized code. It was a great learning experience while strengthening my Python fundamentals and understanding the "why" behind performance. Key Highlights: The $O(1)$ search efficiency of Dictionaries and Sets via Hashing. Memory management: Lists vs. Tuples. How internal mechanics impact time complexity and scalability. #Python #DataStructures #ComputerScience #Programming #LearningJourney #InnomaticsResearchLabs

First released in 1991, Python is an open-source interpreted programming language with a focus on code readability. The main philosophy of python is to allow developers to write logical and clear code for their projects. Python has emerged as the top programming language along with Java and C++. This growth in Python’s popularity can be attributed to the rise of Artificial Intelligence (AI) and Machine Learning (ML). https://lnkd.in/dmiAQr6Y

This is my first medium article that I have published https://lnkd.in/gdtvfxKc https://lnkd.in/gx2QkqcQ Python dictionaries store data using a key–value structure, making them perfect for connecting related information like name → phone number or student → marks. Unlike lists, dictionaries allow fast and direct access to data using meaningful keys instead of index positions. Real-world systems such as phone books, student records, login systems, and product management rely heavily on dictionary logic. Dictionaries are flexible — we can easily add, update, or remove data without disturbing the rest of the structure. Mastering dictionaries is essential for building structured, efficient, and real-world Python applications. Hashtags #Python #PythonProgramming #CodingForBeginners #LearnToCode Innomatics Research Labs