Learning Pydantic: Advanced Data Validation In Python

Published 1/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.83 GB | Duration: 8h 27m

The Complete Guide To Pydantic Including A Full Capstone Project with FastAPI And Redis

What you’ll learn
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

Requirements
Some basic experience with python would help but is NOT required
A full-length introduction to Python is included as an optional Appendix
A general willingness to learn is the only prerequisite as all relevant concepts will be explained as and when used

Description
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 pydantichow to compose more complex models from simpler ones via inheritancethe foundations of type hinting in python, including enumerations, literals, and other advanced types- how to use pydantic’s powerful validation systemhow to serialize and deserialize data how to extract models to schemashow to validate data against pydantic modelsThen 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 storelearn about virtual environments and dependency management in pythonpractice using git and githublearn the basics of serverless computing by deploying the API as a serverless functionThe 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!

Overview
Section 1: Pydantic In A Nutshell

Lecture 1 Course Resource Part 1

Lecture 2 Introduction To Pydantic

Lecture 3 Our First Pydantic Model

Lecture 4 Coercion And Strict Types

Lecture 5 More Types And Constraints

Section 2: Type Hinting Foundations

Lecture 6 Date And Time Types

Lecture 7 Lists And Nested Lists

Lecture 8 Dictionaries And Typed Key-Values

Lecture 9 Sets And Tuples

Lecture 10 Unions

Section 3: Factories, Enums, And Other Props

Lecture 11 Optional, Any And Defaults

Lecture 12 UUIDs And Default Factories

Lecture 13 Immutable Attributes

Lecture 14 Additional Properties

Lecture 15 Enumerations

Lecture 16 For Better Performance: Literals

Section 4: Custom Validators

Lecture 17 Customizing Field Validators

Lecture 18 Model-Level Validators

Lecture 19 Extra: A Closer Look At Error Objects

Section 5: Model Serialization And Deserialization

Lecture 20 Instance Serialization To Dict And JSON

Lecture 21 Field Exclusions

Lecture 22 JSON Schema

Lecture 23 Deserialization

Section 6: Capstone Project: Building A Modern Python API With Pydantic, FastAPI And Redis

Lecture 24 Course Resource Part 2

Lecture 25 Overview

Lecture 26 Creating A Virtual Environment

Lecture 27 Our First Dependencies

Lecture 28 Application Directory Structure

Lecture 29 API Hello World

Lecture 30 Defining Our First Poll Model

Lecture 31 Polls Create With Placeholders

Lecture 32 Polls In The Request Body

Lecture 33 Defining The Choice Data Model

Lecture 34 Splitting Into Read And Write Models

Lecture 35 Poll vs PollCreate

Lecture 36 Polls Should Have Between 2 and 5 Choices

Lecture 37 poll_create With Incrementing Choice Labels

Lecture 38 Creating Polls Through The API

Lecture 39 Refactoring To HTTPExceptions

Lecture 40 Conceptual Introduction To Redis: Our Key-Value Store

Lecture 41 Setting Up A Redis Instance

Lecture 42 Connecting, Saving, And Retrieving Data From Redis

Lecture 43 Refactoring Connection Parameters To Environment Variables

Lecture 44 Defining utils.py

Lecture 45 Integrating save_poll With POST /polls/create

Lecture 46 Defining And Integrating GET Poll

Lecture 47 Modular Re-organization With API Routers

Lecture 48 Application Metadata

Lecture 49 Faster Iteration With Visual HTTP Clients

Lecture 50 Voting Pydantic Data Models

Lecture 51 The Votes API Router

Lecture 52 Get Choice ID From Label

Lecture 53 Creating And Returning Vote Instances

Lecture 54 Storing And Retrieving Votes In Redis Hashsets

Lecture 55 Integrating Vote Saving With The Routes

Lecture 56 Double Voting Should Not Be Allowed

Lecture 57 Voting On Expired Polls Should Not Be Allowed

Lecture 58 Other Voting Validations

Lecture 59 Optimizing Get get_choice_id_by_label()

Lecture 60 Dependency Injecting Common Validations

Lecture 61 Get All Polls

Lecture 62 Batching Requests With .mget()

Lecture 63 Parameterizing Get Polls For Poll Status

Lecture 64 Tracking Vote Counts With Hash Increment By

Lecture 65 Displaying Vote Tallies

Lecture 66 Defining The Poll Results Pydantic Data Models

Lecture 67 Returning PollResults

Lecture 68 Deleting Poll Data

Lecture 69 Extra: Custom Exception Handlers

Lecture 70 Deployment Checklist

Lecture 71 Requirements.txt And Build Configuration

Lecture 72 Git Repository And .gitignore

Lecture 73 Pushing To GitHub

Lecture 74 Deployment

Section 7: Appendix A – Python Programming Crash Course

Lecture 75 Section Resources

Lecture 76 Data Types

Lecture 77 Variables

Lecture 78 Arithmetic And Augmented Assignment Operators

Lecture 79 Ints And Floats

Lecture 80 Booleans And Comparison Operators

Lecture 81 Strings

Lecture 82 Methods

Lecture 83 Containers I – Lists

Lecture 84 Lists vs. Strings

Lecture 85 List Methods And Functions

Lecture 86 Containers II: Tuples

Lecture 87 Containers III: Sets

Lecture 88 Containers IV: Dictionaries

Lecture 89 Dictionary Keys And Values

Lecture 90 Membership Operators

Lecture 91 Controlling Flow: if, else, And elif

Lecture 92 Truth Value Of Non-booleans

Lecture 93 For Loops

Lecture 94 The range() Immutable Sequence

Lecture 95 While Loops

Lecture 96 Break And Continue

Lecture 97 Zipping Iterables

Lecture 98 List Comprehensions

Lecture 99 Defining Functions

Lecture 100 Function Arguments: Positional vs Keyword

Lecture 101 Lambdas

Lecture 102 Importing Modules

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