Circular imports are one of the most common, annoying, and avoidable problems in Python development. They crush productivity, break your app, and signal bad architecture. This guide delivers: By the end, you’ll never Google “circular import fix” again. Table of Contents What Are Circular Imports? Circular imports occur when two or more modules rely on…

AsyncIO is deceptively simple—until it isn’t. You’re probably here because you hit one of these maddening errors: RuntimeError: This event loop is already runningRuntimeError: Event loop is closedTask was destroyed but it is pending! This isn’t another surface-level tutorial. This is the in-depth guide on how to debug Python AsyncIO, specifically event loop-related issues. If…

Struggling with a Python app that keeps eating up memory? Learn how to find and fix Python memory leaks using tracemalloc, Python’s built-in memory tracking tool. This step-by-step guide covers how to diagnose leaks, analyze memory usage, and optimize your code for better performance.

Struggling with Python’s dreaded ModuleNotFoundError? This fast debugging guide covers exactly why it happens and how to fix it—whether it’s an install issue, broken environment, or import path problem. Learn step-by-step fixes and clean up your Python workflow.

Struggling with Python import errors? Learn how to fix ImportError and ModuleNotFoundError with this complete troubleshooting guide. Check installed modules, verify environments, inspect sys.path, and resolve naming conflicts to keep your Python projects running smoothly.

Python and JavaScript are not the same, and yet, I keep seeing Python code that reeks of JavaScript habits. Stop it. Just stop. If you write Python like it’s JavaScript, you’re making life harder for everyone—including yourself. JavaScript is designed for asynchronous, event-driven environments, while Python prioritizes readability, simplicity, and explicitness. The patterns that work…

List comprehensions are one of Python’s most powerful features, making code cleaner, faster, and more efficient—but they’re often misused. In this guide, we break down best practices, advanced use cases, and common mistakes that can impact performance and readability. Learn when to use list comprehensions, when to avoid them, and how to optimize them for…

Bugs slowing you down? Debugging Python doesn’t have to be frustrating. This guide covers seven powerful Python debugging techniques, including interactive debugging with pdb, smarter logging, handling exceptions properly, debugging failing tests with pytest, and tracking memory leaks with tracemalloc. Learn how to fix bugs faster, improve error messages, and streamline your development workflow like…

Is your Python code running slower than it should? Whether you’re working with large datasets, backend APIs, or CPU-heavy computations, optimizing your code can drastically improve performance. In this guide, we break down seven essential Python performance optimization tips, including profiling, multiprocessing, caching, and memory-efficient techniques. Learn how to speed up execution times, reduce memory…