The MarkItDown library lets you quickly turn PDFs, Office files, images, HTML, audio, and URLs into LLM-ready Markdown. In this tutorial, you’ll compare MarkItDown with Pandoc, run it from the command line, use it in Python code, and integrate conversions into AI-powered workflows. By the end of this tutorial, you’ll understand that: You can install MarkItDown with pip using the [all] specifier to pull in optional dependencies. The CLI’s results can be saved to a file using the -o or […]

Have you ever wanted to create an app with an appealing interface that works in the command line? Welcome to Textual, a Python toolkit and framework for creating beautiful, functional text-based user interface (TUI) applications. The Textual library provides a powerful and flexible framework for building TUIs. It offers a variety of features that allow you to create interactive and engaging console applications. In this video course, you’ll learn how to create, style, and enhance Textual apps with layouts, events, […]

Interactive Quiz ⋅ 10 QuestionsBy Martin Breuss Share In this quiz, you’ll test your understanding of the FastAPI example project that can shuffle lists, pick random items, and generate random numbers. By working through this quiz, you’ll revisit how path parameters and type hints enable automatic validation, how request bodies model data, how async endpoints improve performance, and how CORS allows safe cross-origin requests. To go deeper, read A Close Look at a FastAPI Example Application. You can also review […]

Now that you have a working FastAPI application, it’s time to add some functionality to your API. You’ll add endpoints that demonstrate different FastAPI features while creating a randomizer API. Each endpoint you create will showcase a different aspect of FastAPI, from handling dynamic URLs to processing complex request data. Declare Path and Query Parameters With Types FastAPI’s path parameters allow your API to handle dynamic URLs. For your randomizer API, you’ll create an endpoint that generates random numbers within […]

Concurrency is the act of having your computer do multiple things at the same time. If you’ve heard a lot of talk about asyncio being added to Python but are curious how it compares to other concurrency methods or are wondering what concurrency is and how it might speed up your program, you’ve come to the right place. In this course, you’ll learn the following: How I/O bound programs are effected by latency Which concurrent programming patterns to use What […]

Interactive Quiz ⋅ 8 QuestionsBy Martin Breuss Share You’ll revisit how Python handles parameters and arguments—from default values and their order to flexible patterns like *args and **kwargs. You’ll also see when a simple Boolean flag can make your function calls clearer and more expressive. In this quiz, you’ll test your understanding of how mutable default argument values can lead to unexpected behavior. You’ll also practice unpacking sequences and mappings in function calls and formatting output with flags. For a […]

Interactive Quiz ⋅ 8 QuestionsBy Leodanis Pozo Ramos Share How well do you know the different areas where Python shines? In this quiz, you’ll revisit web apps and APIs, GUI apps, CLI tools, machine learning, and more. You’ll also check what Python isn’t suited for and which alternatives work better. Get ready to explore the wide scope of what you can do with Python. The quiz contains 8 questions and there is no time limit. You’ll get 1 point for […]

Interactive Quiz ⋅ 10 QuestionsBy Ian Eyre Share In this quiz, you’ll test your understanding of the Polars vs pandas: What’s the Difference? tutorial. Most of the answers are somewhere in the tutorial, but you may need to use some of the links to clarify some of the questions. The quiz contains 10 questions and there is no time limit. You’ll get 1 point for each correct answer. At the end of the quiz, you’ll receive a total score. The […]

Polars and pandas both provide DataFrame-based data analysis in Python, but they differ in syntax, performance, and features. In this tutorial on Polars vs pandas, you’ll compare their method chaining styles, run timed performance tests, explore LazyFrame optimizations in Polars, convert data between the two libraries, and create plots with their built-in tools. You’ll also examine scenarios where each library’s strengths make it the better choice. By the end of this tutorial, you’ll understand that: Polars expressions and contexts let […]