Python Design Patterns by Suresh Kumar Srivastava

🐍📰 In this tutorial, we will explore the JupyterLab authoring environment and its benefits to the Jupyter Notebook. You will discover its various tools and how they can be utilized together to improve your notebook experience. #python

🐍📺 In this video course, you'll learn how to manipulate ZIP files using Python's zipfile module from the standard library. Through hands-on examples, you'll learn how to read, write, compress, and extract files from your ZIP files quickly #python

🐍📰 Python Classes: The Power of Object-Oriented Programming Learn how to define and use Python classes to implement object-oriented programming. Dive into attributes, methods, inheritance, and more https://lnkd.in/gBSBbw7i

🚀 Day 12: Exploring Advanced Python Concepts As I continue my Python journey, I’ve started diving into concepts that make code more powerful, efficient, and professional. 👉 Welcome to Advanced Python. These concepts help write cleaner, smarter, and more optimized code. 🔹 Key Advanced Concepts: ✔ List Comprehension A concise way to create lists ✔ Lambda Functions Small anonymous functions for quick operations ✔ Decorators Modify the behavior of functions without changing their code ✔ Generators Efficient way to handle large data using yield ✔ Iterators Objects used to iterate over data step by step 💡 Example: List Comprehension nums = [x for x in range(5)] Lambda Function square = lambda x: x * x 📌 Why it matters? Advanced Python concepts: ✔ Improve performance ✔ Reduce code complexity ✔ Make your code more elegant and readable These are the concepts that separate beginner developers from professionals. 💡 Clean code is not just written it is designed. 📈 Step by step, moving toward expert-level programming. #Python #AdvancedPython #Programming #Developers #Coding #BackendDevelopment #LearningJourney #Django

If you are not leveraging asynchronous programming, your program is likely wasting most of its time waiting for external I/O-bound operations like network requests or database calls rather than actually processing data or handling user requirements. In other words, your program is literally wasting time doing nothing rather than switching to another task. Asynchronous programming solves this problem by ensuring the program isn’t blocked by I/O-bound tasks, allowing it to switch to other operations instead of staying idle. In this guide, we will break down how to implement this effectively in your Python projects. By the end of this tutorial, you’ll understand: 1. What “asynchronous” actually means and why it is the key to handling waiting I/O tasks. 2. How to choose between asyncio, threads, and subprocesses. 3. The fundamentals of coroutines, including writing your first async code, identifying the “sequential async trap,” and understanding how the event loop runs tasks concurrently. 4. How to use Tasks, an abstraction above coroutines that allows us to schedule and manage concurrent execution. 5. The future, the third type of awaitable in Python, and how it represents an eventual result.

📺🐍 Using Loguru to Simplify Python Logging Learn how to use Loguru for simpler Python logging, from zero-config setup and custom formats to file rotation, retention, and adding context https://lnkd.in/gabD5QTq

🐍📰 Object-Oriented Programming (OOP) in Python 3 In this article you’ll learn the fundamentals of object-oriented programming (OOP) in Python and how to work with classes, objects, and constructors. #python

The latest Qt for Python 6.11 release focuses on Qt Bridges, simplifying Python-Qt integration, and continues alignment with Qt/C++ updates to keep Qt for Python up to date with upstream improvements. See what is new and what is still in progress in the release blog:

The latest Qt for Python 6.11 release focuses on Qt Bridges, simplifying Python-Qt integration, and continues alignment with Qt/C++ updates to keep Qt for Python up to date with upstream improvements. See what is new and what is still in progress in the release blog: