Design Python APIs for Clarity and Conciseness

As a long-time Java engineer, I continue to be impressed by how much Python has evolved. What once felt like a simple scripting language has grown into a remarkably capable ecosystem: C-backed libraries like NumPy, performance-oriented tooling in Rust, native coroutine support with async and await, and multiple concurrency models for very different workloads. One thing I find especially interesting is Python’s concurrency toolbox. Choosing the right model usually comes down to one question: What is your code actually waiting on? If your program is mostly waiting on the network, a database, or disk, you are likely dealing with an I/O-bound problem. In that case, asyncio can be a strong fit when the surrounding stack is async-native. If your program spends most of its time computing, parsing, or transforming data, you are likely dealing with a CPU-bound problem. In standard CPython, threads usually do not speed up pure Python CPU work because of the GIL. For that, multiprocessing is often the better fit. A few practical rules I keep in mind: • asyncio for high-concurrency I/O with async-native libraries • threads for blocking libraries or simpler concurrency • multiprocessing for CPU-heavy pure Python workloads • threads with native libraries when heavy work runs in C or Rust A good example is PyArrow and PyIceberg. PyArrow’s Parquet reader supports multi-threaded reads. That means you can get parallelism without rewriting everything around asyncio, because the heavy work happens in native code rather than Python bytecode. PyIceberg builds on this ecosystem. From the Python caller’s point of view, the workflow is still synchronous, while file access and data processing can benefit from native parallelism underneath. The key lesson for me: Not every high-performance I/O workflow in Python needs asyncio. If the underlying engine is native and already parallelizes efficiently, threads can be the right tool. If the stack is async-native, asyncio becomes much more compelling. A simple mental model: Async-native I/O → asyncio Native libraries parallelizing outside Python → threads CPU-heavy pure Python → multiprocessing Not sure → profile first That mindset is often more useful than memorizing any specific framework. #Python #Java #Concurrency #AsyncIO #Threading #Multiprocessing #Performance #SoftwareEngineering #DataEngineering

Python vs Java – Choosing the Right Tool for the Job This visual highlights a quick comparison between two of the most popular programming languages: Python and Java. 💡 Key Differences: Typing: Python is dynamically typed, while Java is statically typed Code Length: Python is concise and readable; Java is more structured but verbose Frameworks: Python (Django, Flask) vs Java (Spring, Hibernate) Learning Curve: Python is beginner-friendly; Java requires more setup and understanding Industry Use: Both are widely used by top companies for scalable applications 🚀 Final Thought: There’s no “better” language — it depends on your goal. Choose Python for speed, simplicity, AI, and automation Choose Java for large-scale, enterprise-level applications

After years of Java, I finally tried Python.  Honestly? I didn't expect to enjoy it this much. No semicolons. No curly braces. No type declarations. Just... clean, readable code that almost reads like English. As a Java developer, some things caught me off guard: → Returning multiple values without creating a class → List comprehensions replacing 5 lines with 1 → Decorators that actually execute code (unlike Java annotations) → Context managers that feel conversational I wrote about my first impressions — the good, the surprising, and where I still trust Java more. If you're a Java developer curious about Python, this one's for you. #Python #Java #SoftwareDevelopment #Programming #LearningInPublic

Switching from Python to Java: Coming from a Python-heavy background, working with Java has been a real shift in perspective. In Python, a lot is taken care of for you through powerful high-level abstractions. You can move quickly, write less code, and focus on solving problems. But Java? It makes you slow down in a good way. You start paying attention to details you might have overlooked before: type definitions, structure, and the mechanics behind what your code is actually doing. It demands more explicitness, more discipline, and a deeper level of understanding. And that’s the beauty of it. Different languages, different strengths, but stepping outside your comfort zone is where real growth happens. https://lnkd.in/deNbabM5 #Java #Python #SoftwareEngineering #CodingJourney #LearningToCode

🐍📰 13 Project Ideas for Intermediate Python Developers In this article, you'll learn how you can get started on 13 Python project ideas that are just right for intermediate Python developers. #python

I taught several of my coworkers a crash-course on python/powershell and procedural/OO[1] code the other day, and it went well. The crash-course was the most basics of basics: In a turing-complete language[2], you're almost certainly working with state. That state can be constant or variable. It's all binary under the hood, but the binary is understood contextually by its type: int, str, float, bool, etc. Programs are generally accomplished with sequence, selection, and looping. Structured programming having syntax which supports those semantics explicitly, i.e, functions and for/while loops. High-level language dealing not with the machine and often not even directly with memory. We deal with indices based on the number of values. 0 is a value, and the 0th index of a collection maps to a value. I spent a good deal of time explaining that length and index are not synonymous and why. The face-rake of off-by-one errors is always tines-up, and it's very easy to step on it if you don't know it exists. In about an hour and a half-ish, I managed to scratch the surface. Enough to tell someone what they're looking at and encourage them to use learning resources. [1]: I actually really dislike the way most people teach OO code, and I think its owing to C++ and Java. Deeply nested inheritance everywhere, and owing to java in particular, the inability for functions to exist without a chaperone. Like, yes, inheritance is a feature, but really an object is just a data structure bundled and treated as one unit with the means of interacting with that data. Simple as [2]: HTML is a declarative language, which I argue is still a programming language in that it is for telling a computer with rigorous rules what to do.

Several years ago I came accross the Python paradox by Paul Graham. He argues that lisp and Python programmers are among the ones who trutly care about programming 🐍… https://lnkd.in/eJjqEtPA

⚡ Why Java Still Wins in 2026 (Performance Reality) Is Python really “too slow” for modern systems? We’ve all heard: • Python is easier • Python has better libraries • Developer speed > machine speed That’s true… until you hit production scale. 💥 At scale: • Latency becomes visible • Infrastructure costs increase • Concurrency becomes a real bottleneck This is where Java still has a strong edge. 🧠 JVM optimizations (like JIT compilation) allow Java to handle high-load systems far more efficiently — sometimes dramatically so, depending on the workload. That said — Python is still the right choice in many scenarios (especially AI, data, and rapid prototyping). The real question isn’t “Which is better?” 👉 It’s “When should you use which?” I break this down in detail here: https://lnkd.in/dVjP3x4S Curious — what are you using in production today? #Java #Python #SoftwareEngineering #Backend #SystemDesign

🐍📈 Become a Python Web Developer This learning path will provide you with the foundational skills you need to become a Python web developer. You’ll cover the most popular Python web development frameworks & working with databases. #python #learnpython

In Java, private means private. In Python, it means: “I trust you not to look.” I was exploring encapsulation and discovered something interesting. In Java, access is enforced. In Python, it’s… negotiated. A double underscore (__attr) doesn’t truly hide anything. It just renames it. Which means: You *can* still access it — if you know how. That realization changed how I think about class design. Java protects the code. Python trusts the developer. Two different philosophies. Which one do you prefer? 👇 Curious to hear your perspective #Python #Java #OOP #SoftwareEngineering #LearningInPublic