How Python Manages Memory and Garbage Collection

✨ The Python Story – Episode 8: The Global Interpreter Lock (GIL) ✨ If Episode 7 was about why Python is called “slow,” today we dive into one of the biggest reasons behind that reputation — and one of Python’s most misunderstood features: the Global Interpreter Lock, or simply, the GIL. 🧠 When Guido van Rossum created Python, his goal was to make a simple, safe, and readable language. But there was one tricky problem — memory management. Python uses reference counting to track how many variables point to an object in memory. When no references remain, that memory is freed. It’s clean and elegant — but not thread-safe. If two threads update those counts at the same time, chaos can occur — corrupted memory or random crashes. Guido’s fix? A simple yet powerful idea — the Global Interpreter Lock. 💡 What exactly is the GIL? The GIL is a mutex — a lock that allows only one thread to execute Python bytecode at a time in a process. Even if your computer has 8 cores, only one runs Python code at once; others wait. That sounds limiting — and it is, for CPU-heavy work — but it also made Python safe, stable, and easy to extend with C libraries. For Guido, simplicity and reliability mattered more than theoretical speed. ⚙️ So does that mean Python can’t do multi-threading? Not really. Threads in Python still handle I/O-bound tasks well. When one thread waits for input/output — reading files, APIs, databases — the GIL is released so another thread can run. That’s why frameworks like Flask, FastAPI, and Scrapy manage thousands of concurrent operations. But for CPU-bound tasks — image processing, ML, or crunching data — the GIL becomes a bottleneck. 🚀 Workarounds Developers learned to work around the GIL: 🔹 Use multiprocessing to run multiple processes instead of threads. 🔹 Offload heavy work to NumPy, Cython, or C extensions. 🔹 Use asyncio for efficient concurrency. Despite its limits, these techniques made Python incredibly versatile — from automation scripts to large-scale systems. 🧩 The road to “No GIL” Recently, Python’s community has been working toward a GIL-free future. Python 3.13 introduced an experimental “no-GIL” build, a huge leap for true multi-threaded Python. And in Python 3.14, it gets practical — you can now disable the GIL at runtime for the new free-threaded build! 🎉 python -X gil=0 your_script.py # or PYTHON_GIL=0 The same lock that once symbolized simplicity may soon be unlocked in the name of progress. 🔓 📌 Next Sunday – Episode 9: Memory & Garbage Collection in Python 🧹 How Python keeps programs clean behind the scenes — reference counting, garbage collection, and what really happens when you call del. ⚡ Fun Fact: Run a multi-threaded CPU task in Python and watch — only one core hits 100%, while the rest relax. 😅 #python #ThePythonStory #StoryOfPython #programming #developers #PythonInternals

✨ The Python Story – Episode 7: Why People Call Python “Slow” ✨ Python is loved for its simplicity — but often teased for one thing: “Python is slow.” You’ve probably heard it, maybe even said it. But have you ever wondered why people say that? Let’s go back to the beginning. ⏳ When Guido van Rossum designed Python in 1989, he had a clear vision — a language that made coding easy to write, easy to read, and fun to use. He wasn’t chasing speed — he was chasing simplicity. “The time of a developer is more important than the time of a machine.” – Guido van Rossum Back then, computers were getting faster every year. Guido believed the future would belong to languages that valued human efficiency over machine efficiency. So Python made a few conscious trade-offs — the very reasons people call it “slow” today. 💡 1️⃣ Python is interpreted, not compiled Python doesn’t go straight to machine code like C or C++. It first compiles to bytecode (.pyc) and then executes line by line in a virtual machine. That flexibility and portability cost time — like having a translator between you and the computer. 💡 2️⃣ Python is dynamically typed No need to declare types — Python figures it out as it runs. It’s great for productivity but adds runtime overhead. Dynamic typing = freedom for you, extra work for the machine. 💡 3️⃣ The Global Interpreter Lock (GIL) To keep memory safe, only one thread can run Python bytecode at a time — even on multi-core CPUs. (We’ll explore this fully in Episode 8!) 💡 4️⃣ Readability over raw optimization Lists, dictionaries, and exceptions make Python beautiful and expressive — but all that elegance adds processing under the hood. 🚀 And here’s the truth: Python’s “slowness” rarely matters. For most real-world work — web apps, AI, scripting, automation — the bottleneck isn’t Python’s speed; it’s the network, database, or human time. And when performance does matter, Python calls faster languages like C, C++, or Rust using libraries such as NumPy or TensorFlow. That’s Python’s magic: It may not be the fastest to run, but it’s the fastest to build with. ⚡ 📌 Next Sunday – Episode 8: The Global Interpreter Lock (GIL) We’ll uncover what the GIL really is, why Guido introduced it, and how it affects Python on multi-core systems. ⚡ Fun Fact: Python compiles your code into portable bytecode — saved in the __pycache__ folder every time you run a program! #python #ThePythonStory #StoryOfPython #programming #developers #PythonInternals

✨ The Python Story – Episode 6: Why Python Became Loved Anyway ❤️ ✨ By 2020, the long journey from Guido van Rossum’s Christmas hobby to a worldwide movement had finally come full circle. Python 3 was stable. The 2 vs 3 debates had faded. And something beautiful had happened — developers didn’t just use Python anymore… they loved it. 💖 But why? After all, Python isn’t the fastest language. It isn’t the newest, the flashiest, or the most rigidly typed. So what made it special? 💡 Because Python was built for humans. From the start, Guido’s goal wasn’t machine efficiency — it was developer happiness. He believed code should read like English, not like algebra. Every indentation, every clear keyword, every “there should be one obvious way to do it” made Python feel friendly. Beginners could learn it in days. Experts could express ideas in minutes. And that simplicity turned into power. 🔹 Educators used it to teach the next generation. 🔹 Researchers used it to explore data and AI. 🔹 Startups used it to build quickly and dream big. 🔹 Big companies used it to automate the world. Python quietly became the universal translator between creativity and code. 🌍 Community over complexity While many languages revolved around companies or committees, Python revolved around people. Mailing lists became meetups. Meetups became conferences. The humble “import this” Zen became a shared philosophy. Python wasn’t just a tool — it was a culture of kindness, readability, and sharing. 🚀 And that’s why it won. Even with its slowness jokes, indentation memes, and version wars — Python kept growing because it stayed human-centric. It evolved without losing its heart. Today, from AI labs to classrooms, from NASA scripts to web startups, Python speaks one language — clarity. 💭 But even with all its success, Python isn’t perfect. It’s often called “slow.” Some say its dynamic typing can be risky. Others debate its threading model or memory behavior. Yet every one of these “drawbacks” has a reason — a story behind it. And next season, we’ll explore exactly why Python works the way it does. Stay tuned. ⚙️ 📌 Next Sunday – Season 2 Begins Episode 7: Why People Call Python “Slow” We’ll peek under the hood and uncover how Python really works — its interpreter, bytecode, and the trade-offs that make it both loved and misunderstood. ⚡ Fun Fact: Guido once said, “Code is read much more often than it is written.” That single belief shaped everything Python stands for. #python #ThePythonStory #StoryOfPython #programming #developers

🔍 Python's Hidden Gem: Short-Circuit Evaluation with Operand Return Ever wondered why Python's "or" and "and" operators are more powerful than they seem? Unlike Java or C++, they don't just return True/False – they return the actual values! Short-circuit evaluation means that logical operators like and and or stop evaluating as soon as the result of the expression is known. In Python, these operators don’t just return True or False; they actually return the operand that determined the result — that’s what is meant by operand return. Python's logical operators ("or" and "and") return one of the actual operands, not necessarily True or False. The "or" Operator - - Returns the first truthy value it encounters - If all values are false, returns the last value The "and" Operator in Python - - Returns the first false value it encounters - If all values are truthy, returns the last value ``` # OR examples print(None or [1, 2, 3])   # [1, 2, 3] - returns first truthy print("hello" or "world")   # "hello" - returns first truthy print(False or 0 or [] or {}) # {} - all falsy, returns last print(42 or False)       # 42 - returns first truthy # AND examples   print([1, 2] and "text")    # "text" - all truthy, returns last print("text" and None)     # None - returns first falsy print(5 and 10 and 20)     # 20 - all truthy, returns last print(0 and [1, 2, 3])     # 0 - returns first falsy # Check if user exists AND has valid credentials OR is admin user = get_user(username) access_granted = (user and user.password == password) or is_admin # Returns: user object if password matches, False if not, or True if admin ``` False Values in Python (the complete list) : False None 0 (integer zero) 0.0 (float zero) 0j (complex zero) "" (empty string) [ ] (empty list) ( ) (empty tuple) { } (empty dict) set( ) (empty set) range(0) (empty range) Truth Values in Python (everything else!) : True Any non-zero number: 1, -1, 3.14, 2+3j Any non-empty string: "hello", "0", " " (even a space!) Any non-empty collection: [1, 2], (1,), {"a": 1} Any object or instance (by default) Functions, classes, modules ⚡ Why This Matters: - Reduces code complexity - Eliminates nested if-else pyramids - Makes default value handling elegant - Improves code readability dramatically #Python #CleanCode #CodingTips #SoftwareDevelopment

𝐓𝐡𝐞 𝐆𝐡𝐨𝐬𝐭 𝐢𝐧 𝐭𝐡𝐞 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 Python is a memory safe language, right? After all, it's listed as memory‑safe language in the 2014 CISA recommendations. We trust Python's automatic GC (Garbage Collector) to tidy up, but sometimes, the GC is blinded. It’s not about poor coding (even though that can certainly be a factor); it’s about Python’s fundamental, hidden engine: 𝐑𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐂𝐨𝐮𝐧𝐭𝐢𝐧𝐠. This is the core mechanism that keeps track of every variable. If a reference count never hits zero, Python thinks the object is still needed. The code looks fine, but deep down, a forgotten pointer is hoarding gigabytes of memory. A reference cycle occurs when two or more objects refer to each other, forming a closed loop. For example, object A refers to B, and object B refers back to A. Even if no external code is referencing A or B anymore, their internal counter never drops to zero because they are holding each other hostage. Python's Garbage Collector (GC) runs periodically to break these cycles, but: - It is 𝐧𝐨𝐭 𝐠𝐮𝐚𝐫𝐚𝐧𝐭𝐞𝐞𝐝 to run immediately. - It creates a significant 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐬𝐩𝐢𝐤𝐞 when it does run. - It can be 𝐝𝐢𝐬𝐚𝐛𝐥𝐞𝐝 or 𝐝𝐞𝐥𝐚𝐲𝐞𝐝 by other code execution. If you are dealing with complex data structures, caches, or deeply nested class hierarchies, you are building the perfect hiding spot for a tenacious reference cycle. You need to be proactive, don't wait for the GC to save the day! 1. Use 𝘸𝘦𝘢𝘬𝘳𝘦𝘧: When designing data structures where objects need to point to each other (like parent-child relationships), use the built-in 𝘸𝘦𝘢𝘬𝘳𝘦𝘧 module. A weak reference does not increase an object's reference count, allowing the other object in the cycle to be collected properly. 2. Inspect with 𝘨𝘤: Use the 𝘨𝘤 module to investigate. 𝘨𝘤.𝘨𝘦𝘵_𝘰𝘣𝘫𝘦𝘤𝘵𝘴() can show you all managed objects, and 𝘨𝘤.𝘤𝘰𝘭𝘭𝘦𝘤𝘵() lets you manually trigger the cleanup process to see if it fixes your memory spike (revealing an existing cycle). 3. Profile with 𝘵𝘳𝘢𝘤𝘦𝘮𝘢𝘭𝘭𝘰𝘤: This module tracks memory allocation down to the specific line of code that allocated it. Run your service with 𝘵𝘳𝘢𝘤𝘦𝘮𝘢𝘭𝘭𝘰𝘤 enabled to pinpoint the exact function and data structure responsible for the leak. Not using these suggestions doesn't mean you're a bad developer, or that your code is lacking. Python's memory model is not the same as writing code, or even engineering scalable services . You have to be a real coding geek (like the ones in Kratos Core) to even be interested in this - but it could really make a difference!

🚀 Day 26 — Understanding the Iterable Class in Python 🔹 What is an Iterable? An iterable is any object that can be looped over using a for loop. It can return an iterator using the built-in iter() function. Common examples: list, tuple, string, set, dictionary You can go through their elements one by one easily using a loop. 🔹 What is an Iterator? An iterator is an object that fetches elements one at a time from an iterable. It is created when you call iter() on an iterable. You use the next() function to get each item from the iterator. When all items are finished, Python raises a StopIteration exception to signal the end. 🔹 Relationship Between Iterable and Iterator All iterators are iterables, but not all iterables are iterators. 👉 Iterable → Can produce an iterator 👉 Iterator → Knows how to fetch the next item Think of it like this: Iterable is like a book you can read. Iterator is like a bookmark that remembers where you left off. 🔹 Making a Class Iterable To make your own class work in a for loop, you need to: 1️⃣ Define a method __iter__() that returns an iterator object. 2️⃣ The iterator must have a __next__() method that returns the next value. 3️⃣ When there are no more values, __next__() must raise StopIteration. That’s how Python knows how to iterate through your custom class objects. 🧠 Common Terms Iterable → Object that can return an iterator (supports iter()) Iterator → Object that returns elements one by one (supports next()) StopIteration → Signals the end of iteration iter() → Returns the iterator object next() → Returns the next element in the sequence 💡 Key Takeaways ✅ Iteration means accessing elements one by one. ✅ for loops work because they internally use iter() and next(). ✅ You can create your own iterable classes using __iter__() and __next__(). ✅ Once all elements are exhausted, iteration stops automatically. 📂 GitHub - https://lnkd.in/dGTqN_G9 🙏 Thank you, Saurabh Shukla Sir, for another beautifully explained concept! You make understanding Python’s hidden magic so simple and fun! 🐍💙 #100DaysLearningChallenge #Python #Iterable #Day26

#Day19 of #120DaysChallenge List Comprehensions in Python 💻 Every day in programming teaches me something new — and today, I learned one of the most elegant features of Python: List Comprehensions. When I first started learning Python, I often wrote long loops to create new lists. But today I discovered how a single line of code can replace multiple lines, making the program both cleaner and faster. What is a List Comprehension? A list comprehension is a concise way to create lists in Python. It combines a loop and an optional condition into one readable line. Syntax: new_list = [expression for item in iterable if condition] It may look confusing at first — but once you understand it, you’ll love how elegant it feels. Example 1: Creating a list of squares Without list comprehension: squares = [] for i in range(1, 6): squares.append(i**2) print(squares) Using list comprehension: squares = [i**2 for i in range(1, 6)] print(squares) Output: [1, 4, 9, 16, 25] So simple, right? The entire loop fits into one line! Example 2: Filtering even numbers You can also add conditions inside list comprehensions. even_numbers = [i for i in range(1, 11) if i % 2 == 0] print(even_numbers) Output: [2, 4, 6, 8, 10] Here, only the numbers that satisfy the condition i % 2 == 0 are included. Example 3: Using if–else inside Yes, we can even add if–else in the expression part! result = [i**2 if i % 2 == 0 else i*5 for i in range(10)] print(result) Output: [0, 5, 4, 15, 16, 25, 36, 35, 64, 45] Here: If the number is even → store its square. If it’s odd → multiply it by 5. 💡 Why I found it interesting: It makes code shorter and cleaner. Improves readability and performance. Encourages writing Pythonic code — something Python developers love. Learning list comprehensions made me realize how Python emphasizes clarity and simplicity. Codegnan Pooja Chinthakayala Day19 Challenge Done Pooja Chinthakayala Mam Small concepts like this bring a big difference in writing efficient code. #Python #LearningInPublic #FullStackDeveloper #CodeNewbie #ProgrammingJourney #100DaysOfCode #CodingCommunity #PythonProgramming #ListComprehension

🐍 Pythonic Idioms: A Core Driver of Python’s Popularity Python’s rise to dominance in the programming world isn’t just about its versatility—it’s about its philosophy. At the heart of this philosophy lies the concept of Pythonic idioms: elegant, readable, and concise coding patterns that make Python code feel natural and intuitive. 📜 A Brief History Python was created by Guido van Rossum in the late 1980s and released in 1991. From the beginning, it emphasized simplicity and readability. This ethos was later captured in the Zen of Python by Tim Peters—a collection of 19 guiding principles that define what it means to write “Pythonic” code. Some favorites: Beautiful is better than ugly Simple is better than complex Readability counts These principles laid the foundation for Pythonic idioms, which evolved as best practices within the community. 🎯 Why Pythonic Idioms Matter Pythonic idioms were introduced to: Promote clarity and readability Encourage consistency across codebases Improve performance and efficiency Reduce errors and complexity They serve as a shared language among Python developers, making collaboration smoother and code more maintainable. ✅ Benefits of Pythonic Idioms Readable & Maintainable: Easier to understand and debug. Concise: Express logic in fewer lines. Performant: Often faster than verbose alternatives. Community-Driven: Reinforced by PEP 8 and Python’s culture. 🧰 Examples of Pythonic Idioms Here are some idioms that every Python developer should know: # List Comprehension squares = [x**2 for x in range(10)] # Dictionary Comprehension expensive = {k: v for k, v in prices.items() if v > 0.4} # Enumerate for i, item in enumerate(['a', 'b', 'c']): print(i, item) # Zip for name, age in zip(names, ages): print(f"{name} is {age} years old") # Truthiness if items: print("List is not empty") # EAFP try: value = my_dict['key'] except KeyError: value = None # Context Manager with open('file.txt') as f: content = f.read() # Throwaway Variable for _, value in enumerate(data): process(value) # Safe Dictionary Access value = my_dict.get('key', default_value) # Conditional Expression status = "active" if is_enabled else "inactive" 📈 Impact on Python’s Popularity Research shows that Pythonic idioms: Boost developer productivity Improve code comprehension Drive adoption across industries Serve as a hallmark of expert-level Python They’re not just stylistic—they’re strategic. 🏁 Final Thoughts Pythonic idioms are a big reason why Python is loved by beginners and experts alike. They make code elegant, maintainable, and powerful. Whether you're just starting out or refining your craft, embracing Pythonic idioms will elevate your coding style and help you think more clearly about your solutions. 💬 What’s your favorite Pythonic idiom? 👇 Share it in the comments and let’s celebrate clean code! #Python #CleanCode #SoftwareEngineering #Pythonic #CodingTips #DeveloperExperience #ZenOfPython #ProgrammingPhilosophy

Are you a #data #scientist or #researcher wanting just enough Python to get started? The book "Learn Python Programming" (4th Edition) by Fabrizio Romano & Heinrich Kruger is a solid choice. It covers Python #fundamentals and practical #applications, including a chapter on #datascience. While it doesn’t deeply cover specific data-science libraries, it gives you a strong foundation in Python programming, which is essential for building robust and reproducible workflows. I first read this book a few years ago (the 1st edition from 2015) and found it very helpful for strengthening my #coding skills. I recently enjoyed the new edition, with some particularly well-done additions compared to the old edition. Some random highlights from the content: • I like the explanation of JSON Web Tokens (JWTs): it’s quite difficult to explain in just a few pages such a complex web-security artifact, but Romano and Krüger do a good job. • The chapter on testing theory is a strong addition and has been updated with newer and much more modern libraries like pytest. There’s even a basic explanation of TDD. • Finally, a book that talks about type hinting, an increasingly useful Python feature! In my experience, type hinting hasn’t always felt beneficial (especially when using mypy), but for large teams and wide codebases it’s something worth investing in. • The Data Science chapter is interesting for someone just learning to code for research or simple data-management tasks. It’s only an introduction though, and should be supplemented with other materials (e.g., advanced NumPy and pandas tutorials…). • There is a nice chapter on API development, with a solid introduction to CRUD operations using FastAPI and Pydantic. Good stuff! • A new chapter covers the basics of command-line interface (CLI) applications. I would probably extend it with a fuller example of using Typer (which is cited though). • The section of the book covering Python packaging is well done — discussing project definition, setup, build and distribution. I would still augment that with a modern tool (for example) like UV or Poetry. All these topics are very relevant for modern research and data-science tasks, where you often need to build reproducible pipelines, expose models via APIs, and package your code for distribution. Overall, I think the book’s style is very clear, simple, consistent and a good bet for a software-engineer’s personal library! #python #book #software #engineering #development

🌟 New Blog: Mastering Data Types in Python — The Foundation of Every Code! Every great Python project starts with one thing — understanding data types. In my latest blog, I break down this fundamental concept in a clear, simple, and practical way — perfect for beginners and aspiring data scientists. Here’s what you’ll learn 👇 💡 What are Data Types and why they matter in Python ⚙️ The key types — Integers, Floats, Strings, Lists, Tuples, Sets, and Dictionaries 🧠 How Python handles data behind the scenes 🚀 Real-world examples and interview insights If you’re just starting your Python or Data Science journey, this blog will help you build a strong foundation that makes advanced topics easier to understand later. I explored all of this in my first Medium blog: 👉 “Python and Its Data Types: Where Logic Meets Magic” https://lnkd.in/ghv5jvyX Big Thanks to Vishwanath Nyathani, Kanav Bansal, Raghu Ram Aduri, Supriya Seetharam, Naman Goswami, Harsha Mg for guiding me throughout this journey. #DataAnalytics #Python #DataTypes #LogicMeetsMagic #DataScience #Programming #StudentsWhoCode #MediumBlog #Learning https://lnkd.in/g28NQ4hC