FastAPI for Beginners - Python Web Framework

Discover FastAPI—the modern, high-performance Python web framework. In this introduction, you’ll see how FastAPI makes building backends simple, fast, and production-ready. Learn what sets it apart from Django and Flask, and get a preview of the hands-on inventory project you’ll build step by step in this course.

Set up your FastAPI environment the right way and get coding without roadblocks. In this lecture, you’ll install Python, Node.js (optional for UI), and VS Code and configure a virtual environment with FastAPI and Uvicorn. Finish with a clean, reproducible setup ready to build and test backend APIs efficiently

Build your first FastAPI endpoint and see it live in minutes. In this lecture, you’ll create main.py, run Uvicorn, map a GET route, and understand localhost, ports, and core REST ideas. Finish with a clean, working backend that returns a response.

Build a /products endpoint that returns real product data. You’ll initialize FastAPI(), define a product model, seed an in-memory list, and wire a GET route to serve JSON. Result: a clean, working endpoint you can test in the browser along with Swagger or any HTTP client, with predictable structure and values.

Implement dynamic product lookups and creation with FastAPI. Build a GET /product/{id} endpoint that returns a matching item (or “not found”) and a POST /product that appends to an in-memory list. Test everything in Swagger /docs and understand route parameters and HTTP methods to deliver a dependable, testable API.

Implement robust updates and deletions with FastAPI. In this lecture, you’ll build PUT and DELETE endpoints on /product, accept an ID (and body for updates), replace or remove items in an in-memory list, and verify behavior in Swagger. Confidently manage records and complete CRUD for your product API.

Install SQLAlchemy and the PostgreSQL driver (psycopg2) and understand why ORMs matter. Configure your FastAPI project, set up PostgreSQL and pgAdmin, and see how models map to tables—so CRUD can move from an in-memory list to a real database with reliable, persistent data and cleaner code.

Configure a real database connection with SQLAlchemy. Create a database.py, build a create_engine, and wire a sessionmaker to PostgreSQL via a clean DB URL. Learn how sessions work and why ORMs replace raw SQL, so your FastAPI endpoints can query data reliably—without manual SQL plumbing.

Design your SQLAlchemy ORM model and generate real tables instantly. In this lecture, you’ll create a Base with declarative_base, define Product columns and types, and run Base.metadata.create_all(bind=engine) to build tables in PostgreSQL—giving your FastAPI app a clean schema ready for persistent data you can verify in pgAdmin.

Seed your database with real product data using SQLAlchemy. In this lecture, you’ll open a session, convert Pydantic models to ORM objects using model_dump() and ** unpacking, add rows, and commit(). You’ll also guard against duplicates with a table count check—ending with a clean, initialized PostgreSQL table ready for queries.

Implement database dependency injection with FastAPI. Create a reusable get_db() that yields a SQLAlchemy Session, ensures safe cleanup, and injects it into endpoints with Depends. Refactor /products and /product/{id} to query PostgreSQL directly, replacing in-memory lists. Cleaner, testable, production-style code with reliable connections and simpler queries.

Implement database-backed CRUD in FastAPI. In this lecture, you’ll add products with POST, update them with PUT, and delete them safely—using an SQLAlchemy Session injected via Depends. Map payloads to ORM models, call commit(), and verify results so your API persists real data with clean, production-style patterns.

Connect your React frontend to your FastAPI backend. In this lecture, you’ll align API paths (/products), run the React dev server, and configure CORS with allowed origin/methods to fix “Failed to fetch” errors. The UI reads and writes data to your API—so CRUD works end-to-end on localhost.