Why Python is called "slow" and why it matters

✨ 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 9: Memory & Garbage Collection in Python ✨ The Episode 8 revealed the mystery of the GIL, today we go deeper — into the place where every program lives: memory. Behind Python’s friendly syntax lies a system quietly cleaning up after your code… even when you forget to. This is how Python manages memory — how it knows when something is no longer needed, how it frees space, and how it keeps your programs running smoothly. 💡 Python’s Secret Cleaner: Reference Counting From its earliest days, Python used a simple idea: every object keeps a count of how many variables reference it. If that count drops to zero, Python frees the memory immediately. This makes memory management intuitive — you don’t manually free anything like in C. Python simply handles it. But reference counting has a flaw. 🔁 The Problem of Cycles If two objects reference each other, their counts never reach zero. They become “immortal junk” — unreachable, but never freed. Python needed a solution. 🧠 Enter: The Garbage Collector To handle cycles, Python added a generational garbage collector — a system that occasionally scans for groups of objects that reference each other but are no longer useful. It organizes objects into: • Young objects • Middle-aged objects • Old objects Most objects “die young,” so Python checks those generations more often. Older objects are scanned less frequently. This keeps GC efficient without heavy overhead. 🧩 Why Python Memory Feels “Safe” Python protects developers from: • Dangling pointers • Double frees • Memory corruption • Manual allocator mistakes Programs rarely crash due to memory bugs. This safety is part of why Python is both beginner-friendly and powerful for experts. 🔄 But There’s a Cost This convenience comes with trade-offs: • Reference counting adds overhead • Garbage collection introduces occasional pauses • Cycles require extra scanning • Python objects use more memory than raw machine types And yet — Python frees developers to focus on ideas, not memory addresses. Exactly what Guido wanted. 🧹 The Future of Memory in Python With the push toward a no-GIL future in Python 3.13 and 3.14, memory management is evolving again. Reference counting is being redesigned for thread safety, GC is being optimized, and Python’s internals are being modernized for multi-core systems. But through it all, Python keeps its promise: “Let me handle the complexity. You focus on the idea.” 📌 Next Sunday – Episode 10: Dynamic Typing vs Type Hints Why Python chose dynamic typing, how type hints entered decades later, and how both coexist today. ⚡ Fun Fact: Python’s garbage collector doesn’t clean everything — immutable objects like small integers and strings are often cached for reuse! #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

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

🔍 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!

map(), filter(), sort() with Lambda Functions In Python! ⚙️ ✨I explored how map, filter, and sort work with lambda functions that results in 2-3 code lines, sped up coding, and code redeability 👉The best part about lambda functions is that they can easily shrink 4-5 lines of code into just 2-3, crazy right? 🤓Also, lambda functions are interesting in a way that they collaborate seamlessly with map, filter, and sort to provide you with same results that a big chunk of code would ‼️Important: My python functions repo has been updated for Lambda Functions with map, filter, and sort HAPPY CODING! 😉 ---------------------------- ☺️ Here are Python (Beginner to Intermediate) GitHub Repos for you: 📁Python Variables: https://lnkd.in/e9rjz-_D 📁Python Operators: https://lnkd.in/e6hzgHSn 📁Python Conditionals: https://lnkd.in/egQNGZBF 📁Python Loops: https://lnkd.in/eezUg_-y 📁Python Functions: https://lnkd.in/eKdU6nex 📁Python Lists & Tuples: https://lnkd.in/eZ8KiQNs 📁Python Dictionaries & Sets: https://lnkd.in/eDmgj7pc 📁Python OOP: https://lnkd.in/eJFupCiK ------------------------- ⚡ Follow my learning journey: 📎 GitHub: https://lnkd.in/ehu8wX85 🔗GitLab: https://lnkd.in/eiiQP2gw 💬 Feedback: I’d love your thoughts and tips! 🤝 Collab: If you’re also exploring Python, DM me! Let’s grow together! -------------------------- 📞Book A Call With Me: https://lnkd.in/e23BtnR9 -------------------------- #lambdafunctions #pythonprogramming #pythonforbeginners #lambdafunctionsinpython #pythonlanguage

Python Tuple Packing and Unpacking 🐍 In Python, tuples are more than just immutable lists. They are powerful tools that make your code cleaner, more readable, and incredibly Pythonic. And the concepts of tuple packing and unpacking are at the heart of writing elegant Python code. 🔹 Tuple Packing: Packing means grouping multiple values into a single tuple variable. Python makes this seamless: my_tuple = 1, 2, 3, 4, 5 👉Values are packed into a tuple 👉This allows you to store multiple values in a single variable, return multiple values from a function, or pass collections around without extra boilerplate. 🔹 Tuple Unpacking: 👉Unpacking is the reverse: extracting tuple elements into individual variables in a single, readable line. a, b, c = (10, 20, 30) print(a, b, c) 👉Output: 10 20 30 💡 Why Tuple Packing & Unpacking Matters? 1. Makes code more readable than indexing elements manually. 2. Enables returning multiple values from functions effortlessly. 3. Works beautifully with loops, function arguments, and nested data structures. ✨ Pro Tip: Tuple unpacking is especially powerful when swapping variables without a temporary placeholder: x, y = y, x No extra line, no temp variable—just clean, Pythonic magic. -------------------------- 🤓 Check Out More About Tuple Packing and Unpacking in my Python Lists Repo down below! -------------------------- ☺️ Here are Python (Beginner to Intermediate) GitHub Repos for you: 📁Python Variables: https://lnkd.in/e9rjz-_D 📁Python Operators: https://lnkd.in/e6hzgHSn 📁Python Conditionals: https://lnkd.in/egQNGZBF 📁Python Loops: https://lnkd.in/eezUg_-y 📁Python Functions: https://lnkd.in/eKdU6nex 📁Python Lists: https://lnkd.in/eZ8KiQNs ------------------------- ⚡ Follow my learning journey: 📎 GitHub: https://lnkd.in/ehu8wX85 🔗GitLab: https://lnkd.in/eiiQP2gw 💬 Feedback: I’d love your thoughts and tips! 🤝 Collab: If you’re also exploring Python, DM me! Let’s grow together! -------------------------- 📞Book A Call With Me: https://lnkd.in/e23BtnR9 -------------------------- #pythontuples #tuplepackingandunpacking #pythonforbeginners #pythonlanguage #pythonfordatascience

🐍 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

🚀 Python Just Got 20x Faster with Mojo Integration For years, developers accepted a trade-off: Python gives us simplicity, but C/C++ deliver raw speed. That line just blurred. A recent test using Mojo, the new superset of Python, produced jaw-dropping results: 🔥 Mandelbrot Set Calculations • 20x faster than pure Python • 5x faster than NumPy • Zero compromise on readability ⚡ Sigmoid Function Processing • 12x faster than Python • 2x faster than NumPy • Same Python-like syntax we already love But here's the twist: NumPy still wins at some tasks. In numerical integration, its vectorized operations outperformed Mojo. This isn't a replacement-it's a new weapon in the toolkit. 💡 The secret sauce? Mojo lets you write high-performance code without leaving the Python ecosystem. Offload the heavy lifting to Mojo, keep the rest in Python, and you get the best of both worlds. 👉 Pro tip: Minimize Python↔Mojo crossings and stick to native Mojo types for peak performance. With 20x more speed at your fingertips, the question isn't if you'll use Mojo-it's what you'll build with it. #Python #Mojo #Programming #TechInnovation #Developers 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/d6uc43xX