Learning Pydantic: Advanced Data Validation In Python from Udemy

Welcome to the best resource online for learning modern Pydantic, a data validation library that has taken the python community by storm.

Pydantic is was first released in 2018 and has since become one of the most popular python libraries. It is nowadays downloaded more than 130 million times a month, and is used by some of the largest organizations out there, from the tech giants like Google, Amazon, Apple, Meta, and Netflix, to large conglomerates in various other industries, such as Starbucks, JPMorgan Chase. Oh, and yes, even NASA.

There's a good reason for this. Pydantic is a powerful library that elegantly solves a very common problem in software development: data validation.

Pydantic's speed, simple declarative syntax, and extensibility make it an indispensable utility in modern python development.

And in this course, you will learn everything you need to know to get started with Pydantic, from the very basics of defining data models, to more advanced topics such as fields with factory defaults, creating custom model-validators, data serialization, and much more.

The first part of the course will be purely about pydantic, where we explore it in isolation. You will learn:

how to define data models with pydantic
how to compose more complex models from simpler ones via inheritance
the foundations of type hinting in python, including enumerations, literals, and other advanced types-
how to use pydantic's powerful validation system
how to serialize and deserialize data
how to extract models to schemas
how to validate data against pydantic models

Then in the second part of the course we will turn our attention to the Capstone Project, where we will use pydantic to develop and deploy a python web API that allows users to create and vote on polls. This app will use Redis as our durable key-value data store, and will be deployed to production as a serverless function.

The Capstone will be developed step by step, in a series of about 30 skill challenges, where you will be asked to incrementally implement small features. This will give you the opportunity to practice what you've learned in the first part of the course, and to:

get a practical feel for how Pydantic is used in real-world applications
learn about modern API development with python
understand what Redis is and how it can be used as a durable data store
learn about virtual environments and dependency management in python
practice using git and github
learn the basics of serverless computing by deploying the API as a serverless function

The course will use the latest version of Pydantic, which leverages the power of Rust to achieve blazing fast performance.

Also, if you're new to python or haven't used the used the language in a while, there's a full-featured python crash course included as an extra appendix which will get you up to speed in no time.

I'm very excited to share this with you, and I look forward to seeing you in the course.

What's inside

Learning objectives

Gain an in-depth understanding of what pydantic is and how it is used
Practice defining pydantic data models using modern type hints, custom validations, and fine-tuned configuration
Learn how to define complex, interdependent, and nested data models with pydantic

Serialize model instances into json and deserialize incoming data
Practice using pydantic in the context of building and deploying a real-world python web api
Master relevant concepts in modern python application development, like dependency management and version control

Gain an in-depth understanding of what pydantic is and how it is used
Practice defining pydantic data models using modern type hints, custom validations, and fine-tuned configuration
Learn how to define complex, interdependent, and nested data models with pydantic
Serialize model instances into json and deserialize incoming data
Practice using pydantic in the context of building and deploying a real-world python web api
Master relevant concepts in modern python application development, like dependency management and version control

Syllabus

Pydantic In A Nutshell

Course Resource Part 1

Introduction To Pydantic

Our First Pydantic Model

Coercion And Strict Types

More Types And Constraints

Type Hinting Foundations

Date And Time Types

Lists And Nested Lists

Dictionaries And Typed Key-Values

Sets And Tuples

Unions

Factories, Enums, And Other Props

Optional, Any And Defaults

UUIDs And Default Factories

Immutable Attributes

Additional Properties

Enumerations

For Better Performance: Literals

Custom Validators

Customizing Field Validators

Model-Level Validators

Extra: A Closer Look At Error Objects

Model Serialization And Deserialization

Instance Serialization To Dict And JSON

Field Exclusions

JSON Schema

Deserialization

Capstone Project: Building A Modern Python API With Pydantic, FastAPI And Redis

Course Resource Part 2

Overview

Creating A Virtual Environment

Our First Dependencies

Application Directory Structure

API Hello World

Defining Our First Poll Model

Polls Create With Placeholders

Polls In The Request Body

Defining The Choice Data Model

Splitting Into Read And Write Models

Poll vs PollCreate

Polls Should Have Between 2 and 5 Choices

poll_create With Incrementing Choice Labels

Creating Polls Through The API

Refactoring To HTTPExceptions

Conceptual Introduction To Redis: Our Key-Value Store

Setting Up A Redis Instance

Connecting, Saving, And Retrieving Data From Redis

Refactoring Connection Parameters To Environment Variables

Defining utils.py

Integrating save_poll With POST /polls/create

Defining And Integrating GET Poll

Modular Re-organization With API Routers

Application Metadata

Faster Iteration With Visual HTTP Clients

Voting Pydantic Data Models

The Votes API Router

Get Choice ID From Label

Creating And Returning Vote Instances

Storing And Retrieving Votes In Redis Hashsets

Integrating Vote Saving With The Routes

Double Voting Should Not Be Allowed

Voting On Expired Polls Should Not Be Allowed

Other Voting Validations

Optimizing Get get_choice_id_by_label()

Dependency Injecting Common Validations

Get All Polls

Batching Requests With .mget()

Parameterizing Get Polls For Poll Status

Tracking Vote Counts With Hash Increment By

Displaying Vote Tallies

Defining The Poll Results Pydantic Data Models

Returning PollResults

Deleting Poll Data

Extra: Custom Exception Handlers

Deployment Checklist

Requirements.txt And Build Configuration

Git Repository And .gitignore

Pushing To GitHub

Deployment

Appendix A - Python Programming Crash Course

Section Resources

Data Types

Variables

Arithmetic And Augmented Assignment Operators

Ints And Floats

Booleans And Comparison Operators

Strings

Methods

Containers I - Lists

Lists vs. Strings

List Methods And Functions

Containers II: Tuples

Containers III: Sets

Containers IV: Dictionaries

Dictionary Keys And Values

Membership Operators

Controlling Flow: if, else, And elif

Truth Value Of Non-booleans

For Loops

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Learning Pydantic: Advanced Data Validation In Python with these activities:

Review Python Type Hinting

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Solidify your understanding of Python's type hinting system, which is fundamental to defining Pydantic models effectively.

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Read the official Python documentation on typing.
Practice using type hints in simple Python functions.
Experiment with different type hints like List, Dict, and Union.

Review 'Fluent Python'

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Gain a deeper understanding of Python's underlying mechanisms, which will enhance your ability to use Pydantic effectively.

View Fluent Python on Amazon

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Read the chapters on data structures and object-oriented programming.
Pay close attention to the sections on type hints and data validation.
Try to relate the concepts in the book to Pydantic's features.

Implement Custom Validators

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Sharpen your skills in creating custom validators, a crucial aspect of Pydantic for enforcing complex data constraints.

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Define a Pydantic model with several fields.
Implement custom validators for each field using different validation techniques.
Test your validators with various inputs to ensure they work correctly.

Four other activities

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Write a Blog Post on Pydantic

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Solidify your understanding of Pydantic by explaining its features and benefits to others in a blog post.

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Choose a specific aspect of Pydantic to focus on.
Research the topic thoroughly and gather relevant examples.
Write a clear and concise blog post explaining the concept.
Publish your blog post on a platform like Medium or your personal website.

Review 'Effective Python'

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Improve your overall Python coding skills, which will enhance your ability to use Pydantic effectively and write robust applications.

View Effective Python on Amazon

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Read the book and take notes on the key recommendations.
Apply the recommendations to your Pydantic projects.
Refactor existing code to follow the best practices outlined in the book.

Build a Data Validation API

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Apply your Pydantic knowledge to build a practical API that validates incoming data, reinforcing your understanding of real-world applications.

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Design the API endpoints and data models.
Implement the API using a framework like FastAPI or Flask.
Use Pydantic to validate the incoming data against your models.
Test the API thoroughly with various inputs.

Contribute to Pydantic

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Deepen your expertise by contributing to the Pydantic open-source project, gaining insights into its inner workings and best practices.

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Explore the Pydantic GitHub repository and identify areas for contribution.
Read the contribution guidelines and set up your development environment.
Contribute code, documentation, or bug fixes to the project.
Participate in code reviews and discussions with other contributors.

Career center

Learners who complete Learning Pydantic: Advanced Data Validation In Python will develop knowledge and skills that may be useful to these careers:

Learning Pydantic

Advanced Data Validation In Python

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