FastAPI Testing with Pytest: A Comprehensive Guide

FastAPI with Pytest: Effective API Testing for Beginners In the world of web development, building APIs is just the beginning. Ensuring that your API functions as expected, handles various scenarios gracefully, and remains robust against changes is paramount. This is where testing comes in. Testing is not just a good practice; it's a necessity for creating reliable and maintainable APIs. In this tutorial, we'll dive deep into testing FastAPI applications using Pytest, a powerful and versatile testing framework for Python....

FastAPI with pytest-asyncio: Testing Asynchronous APIs In the rapidly evolving world of web development, building high-performance APIs is crucial. FastAPI, with its asynchronous capabilities and modern Python syntax, has emerged as a popular choice for developers. However, writing robust and reliable APIs requires rigorous testing. This is where pytest-asyncio comes in, providing a seamless way to test asynchronous FastAPI applications. Why Test Asynchronous APIs? Asynchronous programming allows your API to handle multiple requests concurrently, leading to improved performance and responsiveness....

Developed a Custom Authentication System using Django and Python that replaces the default user model with a fully customized implementation. The system uses email based authentication instead of usernames and includes secure user registration, login, logout, and protected dashboard access. Key features include: 1.Custom user model built with Django authentication system 2. Secure registration and login functionality 3.Access control for authenticated and guest users 4.Form validation and user-friendly UI for account creation 5.Dashboard access restricted to logged in users This project helped strengthen my understanding of backend development, authentication workflows, and Django.

Building reliable connections between Python backends (FastAPI/Django) and React frontends requires careful engineering. Here’s a streamlined breakdown of the challenges and solutions: The Challenges: Race Conditions & Memory Leaks Race Conditions: When multiple API calls overlap, the UI might display stale data from an earlier request that finished last. This creates a confusing and inconsistent user experience. Memory Leaks: If an API call completes after a React component has unmounted, the component may still try to update its state. This can degrade application performance and stability. Python Backend Solutions (FastAPI/Django) Custom Exceptions & Handlers: Avoid generic errors. Define specific exception classes for different conditions (e.g., UserNotFoundError). Use global exception handlers to catch these, log details server-side, and send structured, user-friendly JSON responses back to the client. Structured Error Responses: Consistency is crucial. Ensure your backend always returns a predictable error structure, including: A machine-readable error code (e.g., ERR_AUTH_FAILED). A clear message for the user. Optional details for troubleshooting. React Frontend Solutions Controlled Fetching with useEffect & Axios: Leverage the useEffect hook in combination with Axios to create a structured data flow for asynchronous requests. Explicit State Management: Utilize distinct state variables (e.g., loading, data, error) to provide immediate visual feedback to the user and gracefully handle all request outcomes. This prevents UI issues arising from incomplete data. Cleanup Functions with AbortControllers: Prevent Memory Leaks: Implement cleanup functions within useEffect using AbortController. This ensures that pending API requests are cancelled if the component unmounts or the effect re-runs, preventing state updates on unmounted components. 💡 Key Takeaway Predictable and resilient data flow is essential for production-ready applications. By prioritizing robust error handling from backend to frontend and implementing controlled data fetching with proper cleanup, you create a more stable, user-friendly, and maintainable full-stack application. Mastering these patterns is a significant step towards engineering high-quality software. #Python #FastAPI #ReactJS #WebDevelopment #FullStack #SoftwareEngineering #LearningInPublic

Added to AGENTS.MD in a frontend + Django Python API project. Simple Codex instructions like creating a feature or writing PR comments were consuming ~30% of context memory per call. These notes brought that down to roughly 10–17%. here it goes : To reduce context size and memory usage, follow these rules when analyzing the repository: ### Step 1 — Diff First 1. Inspect the **git diff** before performing any repository exploration. 2. List the files modified in the PR. ### Step 2 — Targeted Analysis 3. Only open files that appear in the diff. 4. Do NOT explore the repository before analyzing the diff. 5. Do NOT read the entire repository. ### Step 3 — Limited Context Expansion 6. If additional context is required, use **repository search first**. 7. Only open additional files if strictly necessary to understand the change. 8. Any file opened that is not part of the diff must be **explicitly justified**. ### Step 4 — Exploration Limits 9. Maximum automatic file reads: **5 files**. 10. Prefer reading **small sections of files** instead of entire files. ### Step 5 — PR Review Output 11. After minimal exploration, generate the PR review comment. 12. The review must be written in a **human-like format using Markdown**. 13. Respect task scope. If the task is backend-only, do not inspect frontend code and vice versa.

There is a website you use regularly that has no Application Programming Interface (API). You want to automate your interactions with it — searching for people, managing your profile, sending messages. The traditional approach would be to spend days reverse-engineering the site’s Hypertext Markup Language (HTML) structure, writing a scraper, building a command-line interface (CLI), setting up tests, configuring Continuous Integration (CI), and deploying. What if you could do all of that in under ninety minutes, producing a well-tested, well-structured Python package with over two hundred tests and nearly five thousand lines of code? This essay describes exactly that process. It is not theoretical. Every claim here is verifiable from session logs, git history, and GitHub pull request records. The project is a Python CLI tool managed with uv (a fast Python package manager from Astral) for a website that has no public API. The tool uses authenticated Hypertext Transfer Protocol (HTTP) requests, scrapes HTML responses with BeautifulSoup, validates data with Pydantic models, and presents results through a Click command-line interface. It was built from a template project to a working tool with auth, search, and member-view commands in eighty-four minutes of wall-clock time. https://lnkd.in/giC9RzsP

Kivy Tutorial: Build a Multi-Step Wizard App in Python In the world of software development, user experience is paramount. One common pattern that significantly enhances user experience for complex processes is the multi-step wizard. Whether you're guiding users through a lengthy setup, a complex configuration, or a detailed data entry process, breaking it down into manageable steps makes the journey less daunting and more intuitive. In this tutorial, we'll dive deep into building a multi-step wizard application using Kivy, a powerful Python framework for creating cross-platform GUIs....

A dashboard is the soul of a business reflected in a system. A good dashboard should invite users to take action. Without that, the system becomes nothing more than CRUD operations with a beautiful graphical interface. A dashboard is a reflection of the organization’s KPIs (Key Performance Indicators). It transforms raw data into insights that guide decisions and drive results. Here are some dashboard screens from a recent project I worked on. Python + Django + DRF React + Js + TailwindCSS

FastAPI with Tortoise ORM: Building Asynchronous APIs with Databases In the world of web development, building fast, efficient, and scalable APIs is crucial. Asynchronous programming has emerged as a key technology to achieve this, allowing developers to handle multiple tasks concurrently without blocking the main thread. FastAPI, a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints, is perfectly suited for asynchronous development....

IDE vs. Code Editor – Understanding the Difference Python in 2026: IDE or Code Editor? The Choice Matters More Than You Think. Every Python developer starts with the same question: What do I write my code in? The answer used to be simple. Today, the landscape is more nuanced, and the choice between an IDE and a code editor defines your workflow. Let us clarify the difference. An Integrated Development Environment, or IDE, is a comprehensive toolkit. It bundles a text editor with a debugger, build automation tools, version control integration, and often a profiler. Everything comes in one package, ready to work together. PyCharm is the classic example. It understands your code deeply, points out errors as you type, and manages complex projects out of the box . A code editor is the lightweight alternative. Tools like Visual Studio Code or Sublime Text start simple. They are fast, flexible, and customizable. But they require plugins and extensions to gain IDE-like features. You build your own environment piece by piece . So which path is right for you? Choose a full IDE like PyCharm if you want everything configured from day one. It is ideal for large, organized projects where structure matters and you prefer a batteries-included approach . Choose a code editor like VS Code if you value speed and customization. You can add exactly what you need and nothing more. It scales from simple scripts to microservices, especially with its Python extension and Jupyter support . The right tool depends on your project and your preferences. There is no single correct answer. But understanding the trade-offs between a complete IDE and a modular editor will save you hours of frustration. What is your daily driver for Python development and why? #Python IDEs CodeEditors VS Code #PyCharm DeveloperTools Programming Productivity