Getting to Know Pydantic

00:00 Python’s Pydantic Library One of Python’s main attractions is that it’s a dynamically typed language. Dynamic typing means that variable types are determined at runtime, unlike statically typed languages where they’re explicitly declared at compile time.

00:16 While dynamic typing is great for rapid development and ease of use, you often need more robust type checking and data validation for real-world applications.

00:25 This is where Python’s Pydantic Library has you covered.

00:30 Pydantic is a popular and widely used data validation library for Python. In this section of the course, you’ll get an overview of Pydantic and a preview of the library’s powerful features.

00:40 You’ll also learn how to install Pydantic along with the additional dependencies you’ll need for this course.

00:47 Pydantic is a powerful library that leverages type hints to help you easily validate and serialize your data schemas. This makes your code more robust, readable, concise, and easier to debug.

01:00 Pydantic also integrates well with many popular static typing tools and IDEs, which allows you to catch some schema issues before running your code. Some of Pydantic’s distinguishing features include: customization.

01:14 There’s almost no limit to the kinds of data you can validate with Pydantic, from primitive Python types to highly nested data structures, Pydantic lets you validate and serialize nearly any Python object. Flexibility.

01:28 Pydantic gives you control over how strict or lax you want to be when validating your data. In some cases, you might want to coerce incoming data to the correct type.

01:37 For example, you could accept data that’s intended to be a float, but is received as an integer. In other cases, you might want to strictly enforce the data types you’re receiving.

01:47 Pydantic lets you do either. Serialization. You can serialize and deserialize Pydantic objects as dictionaries and JSON strings. This means you can seamlessly convert your Pydantic objects to and from JSON.

02:03 This capability has led to self-documenting APIs and integration with just about any tool that supports JSON schemas performance. Thanks to its core validation logic, written in Rust, Pydantic is exceptionally fast.

02:18 This performance advantage gives you swift and reliable data processing, especially in high throughput applications such as REST APIs that need to scale to a large number of requests. Ecosystem and industry adoption.

02:32 Pydantic is a dependency of many popular Python libraries, such as FastAPI, LangChain, and Polars. It’s also used by most of the largest tech companies and throughout many other industries.

02:43 This is a testament to Pydantic’s community support, reliability, and resilience.

02:50 These are just a few key features that make Pydantic an attractive data validation library, and you’ll get to see these in action throughout this course. But first, you’ll need to install it.

03:01 Pydantic is available on the Python Package Index, and you can install it with pip. You’ll need to open a terminal or command-line prompt, create a new virtual environment and activate it, and then install Pydantic first.

03:13 You’ll see this being done on macOS, and these commands will work on Linux as well.

03:33 And here are the commands you’ll need to do the same on Windows Terminal. Note that only the activation line is different.

03:52 This will install the latest version of Pydantic from the Python Package Index onto your machine.

03:58 To verify that the installation was successful, open up a Python REPL and import Pydantic.

04:05 If the import runs without error, then you’ve successfully installed Pydantic and you now have the core of Pydantic installed on your system.

04:14 You can install optional dependencies with Pydantic as well. In this course, you’ll be working with email validation and you can include these dependencies in your install.

04:31 Pydantic has a separate package for settings management, which you’ll also be covering in this course, and you can install it with a command seen on screen.

04:46 With that, you’ve installed all the dependencies you’ll need and you are ready to start exploring Pydantic. So that’s what you’ll be doing next, covering models which are Pydantic’s primary way of defining data schemas.