Why Python Became a Developer Favorite

✨ 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 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

🚀 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

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

Just wrapped up Module 1 of AI Python for Beginners with Andrew Ng — and it’s been a great bridge between AI and code. For anyone learning AI or working with developers, this course shows how even a little Python can supercharge your understanding of how AI tools actually work. In my latest blog post, I cover what I learned, how I connected VS Code with ChatGPT, and yes — why I’m now a huge fan of Python’s f-strings. Read the full post here 👉 https://lnkd.in/er3EUFC8 #AI #Python #LearningInPublic #DeepLearningAI #AndrewNg #ChatGPT #AIAgentJourney

I would add this observation i posted on Mastodon: "Use any python library and you quickly find yourself buried under layers upon layers of encapsulated OOP inheritance crap, the tracebacks are order of magnitude worse than tidyverse NSE tracebacks before the rlang package (this is when the module author allows you to see traces). If you add to this the terrible docs you can understand the nihilist and p(y)edantic (pun intended) type annotations drive"

#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

🚀 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