How to migrate to Python from Java: 2 common situations

Midterm: The Mid-Term was a project-based assignment that tested every Python concept we've covered so far. It wasn't an easy test, but because the complexity and nature of the problems were similar to our rigorous weekly homework, I was able to successfully apply my foundational knowledge to complete it. It was incredibly validating to see my built-up Python skills hold up under pressure and confirm that all the hard work is paying off! Week 7 Recap: This week marked a major shift as I started learning Java. The contrast with Python is immediate: Java is a compiled language, which feels distinctly different from Python's interpreted environment. The biggest change is that Java is statically typed, meaning I have to explicitly declare the data type for every single variable. This requires more attention than Python's dynamic typing, but I see how this structure enforces discipline and leads to more robust, readable code. I covered basic Java syntax (like loops) and focused on its mandatory class-based structure, where the core OOP concepts are strictly applied. I’m also starting to understand why programmers say that once you learn one language, the rest follow quickly. My pace of picking up Java feels much faster than when I first started Python, and it’s an exciting challenge to step outside the comfort of Python and begin mastering this powerful new paradigm!

If you're a Java developer and your management tells you to write AI agents in Python, show them this comparison between Embabel and the popular LangGraph Python agent framework. Point out that writing in Java with Embabel: - Will give you frictionless access to existing domain models and infrastructure - Will avoid error prone magic strings throughout your code - Will give you a much more elegant API - Will facilitate established best practices like unit testing - Will avoid the need to stand up a new stack It's a no brainer. If you have zero enterprise applications in Python, that remains the right number. https://lnkd.in/gdXM6F26 #java Matt Asay #spring #springboot

Saw Rod Johnson at #JFall a few days ago presenting his new framework Embabel (em-BAY-bel). Built on the concept of #GOAP, you define goals and actions, and the framework figures out the workflow, but in a #JVM world. So strongly typed, easier to test and of course, #Spring integration 💪🏼 “With Embabel we don’t need to specify the workflow in code. Embabel plans an execution path by analyzing our code” Definitely worth keeping an eye on for future ai powered projects

I have been doing DSA in python for very long time and python is known for its simplicity, easy-to-understand and beginner friendly syntax while other languages like java, c++ are very verbose in terms of lots of things from defining the hashmaps, hashsets, or working with maps of maps and list of lists etc. and also you write less code which is a nice thing when you're under time pressure. Beneath its elegant syntax lies a potential challenge - performance bottleneck, people who have been doing LeetCode,CodeForces,CodeChef etc they know in lots of problems where the constraints are very high 1 <= n <= 10^5 1 <= nums[i] <= 10^9 a bruteforce approach often results in TLE(Time Limit Exceeded) while the same logic might get passed in C++, after spending lots of time in python I’ve learned various techniques on the way that we can tweak and twist to achieve some kind of efficiency and optimization especially in context of DSA. So I’ll be sharing posts highlighting such optimization techniques, practical insights, and performance hacks to write more efficient Python solutions.

Python vs Java – What Really Happens When You Run Them 👇 Ever run a Python script or a Java program and wondered what’s happening behind the scenes? Let’s break it down simply 👇 𝗣𝘆𝘁𝗵𝗼𝗻 (𝗖𝗣𝘆𝘁𝗵𝗼𝗻 𝗥𝘂𝗻𝘁𝗶𝗺𝗲) ◾ Your `.py` file is first compiled into bytecode automatically in memory. ◾ Python sometimes saves this as a `.pyc` file, so the next run is faster. ◾ The Import System loads all required modules and dependencies. ◾ Finally, the Python Virtual Machine (PVM) interprets the bytecode line by line — flexible and beginner-friendly, but a bit slower. 𝗝𝗮𝘃𝗮 (𝗝𝗩𝗠 𝗥𝘂𝗻𝘁𝗶𝗺𝗲) ◾ Your `.java` file is compiled into `.class` bytecode using `javac`. ◾ The Class Loader loads the bytecode into the Java Virtual Machine (JVM). ◾ The JVM verifies, interprets, and optimizes the code for execution. ◾ The Just-In-Time (JIT) Compiler converts frequently used code (hot paths) into native machine code, making Java faster over time. 𝗥𝗲𝘀𝘂𝗹𝘁 ◾ Python prioritizes simplicity and flexibility. ◾ Java focuses on speed and runtime optimization. __________ ♻️ Reshare if you found this helpful. ✅Follow Jafar Muzeyin for daily tips 📺 Watch more on my YouTube channel: [https://lnkd.in/eBmq2kuY] Reference -> ByteByteGo

Day 47 — Dockerizing a Python Application Today’s task focused on containerizing a Python application and deploying it using Docker. The goal was to create a reproducible, lightweight environment where the app and its dependencies could run seamlessly. Here’s what I did: ++ Created a Dockerfile that defines how the Python app should be built and executed. ++ Installed dependencies from a requirements.txt file. ++ Exposed the application port (6000) and set the CMD instruction to run the app with server.py. ++ Built a custom image and deployed it as a container, mapping host port 8097 to container port 6000. Dockerfile used: FROM python:3.9-slim WORKDIR /app COPY src/ /app/ RUN pip install --no-cache-dir -r requirements.txt EXPOSE 6000 CMD ["python", "server.py"] Each instruction in the Dockerfile plays a key role — from defining the base image to installing dependencies and launching the app. Once built and run, the Python application became accessible on the host system via port 8097. For those looking to explore Python Docker images and adapt them to their own projects, the official Docker Hub repository is a great starting point: 🔗 https://lnkd.in/d96wsukF Tomorrow we'll start looking into Kubernetes and begin docker orchestration😇

✅ 3.14.0 is a stable feature release of the Python programming🛑🛑 🌎 Python 3.14.0 is a stable feature release of the Python programming language, part of the ongoing development process that introduces new features, optimizations, and improvements over previous versions ✅ 🌎 🌎 Here are some key details about this release: ✅ Key Features and Changes 🌎 🌎New Syntax Features:✅ Enhanced pattern matching capabilities. New syntax for defining type hints more succinctly. 🌎 🌎Standard Library Improvements:✅ Updates to existing modules (e.g., asyncio, collections, math). Introduction of new modules or classes that improve functionality. 🌎 🌎Performance Enhancements:✅ Optimizations aimed at improving the speed of common operations and data structures. Reduced memory usage in certain contexts. 🌎 🌎Deprecations and Removals:✅ Certain outdated functions or modules may be deprecated or removed to streamline the language. 🌎 🌎Improved Error Messages:✅ More informative error messages to help developers debug issues more effectively. 🌎 🌎Compatibility:✅ Ongoing commitment to backward compatibility while introducing new features. 🌎 🌎Installation and Compatibility:✅ Python 3.14.0 can be installed from the official Python website or through package managers. It is compatible with various operating systems, including Windows, macOS, and Linux. 🌎 🌎Documentation and Resources:✅ The official Python documentation provides detailed information about new features, changes, and usage. Community forums, tutorials, and courses are available for those looking to learn or transition to this version. 🌎 🌎Conclusion:✅ Python 3.14.0 represents a significant step forward in the evolution of Python, continuing its tradition of being a versatile and powerful programming language. Developers are encouraged to explore the new features and update their projects accordingly ✅👍

Learning Python: Writing Clean, Reusable Logic with Functions Coming from years of working in Java, one thing I immediately appreciate about Python is how quickly you can translate intent into code. Functions in Python feel natural: they let you keep scripts modular without the boilerplate we usually have in Java. Defining and reusing logic is as straightforward as it gets. Example: def check_server(name): print(f"Checking status of {name}") for server in ["server1", "server2", "server3"]: check_server(server) In just a few lines, you define a function, pass arguments dynamically, and keep the code readable. No type declarations (it has its cons!!!!), no verbosity — just clear logic. Even though Python and Java differ in syntax, the principles remain the same: Keep functions small and meaningful. Reuse logic instead of repeating it. Write code that others can read and maintain easily. This simplicity is what makes Python ideal for automation and scripting. You focus more on what the code does rather than how much structure it needs.

Python vs Java: The battle of modern programming giants. Python and Java rank among the two languages with the largest global influence on technology and programming. While they are both very powerful languages with support for objects they have their unique perspectives and advantages that attract different kinds of programmers. #java #python #medium #writing #blog #srini #article #post https://lnkd.in/gkUEPr-5