Learning Pydantic- Advanced Data Validation In Python

Learning Pydantic: Advanced Data Validation In Python, available at $19.99, has an average rating of 4.95, with 102 lectures, based on 24 reviews, and has 398 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone interested in learning about Pydantic It is particularly useful for Anyone interested in learning about Pydantic.

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Summary

Title: Learning Pydantic: Advanced Data Validation In Python

Price: $19.99

Average Rating: 4.95

Number of Lectures: 102

Number of Published Lectures: 102

Number of Curriculum Items: 102

Number of Published Curriculum Objects: 102

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Anyone interested in learning about Pydantic

Target Audiences

Anyone interested in learning about Pydantic

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!

Course Curriculum

Chapter 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

Chapter 2: Type Hinting Foundations

Lecture 1: Date And Time Types

Lecture 2: Lists And Nested Lists

Lecture 3: Dictionaries And Typed Key-Values

Lecture 4: Sets And Tuples

Lecture 5: Unions

Chapter 3: Factories, Enums, And Other Props

Lecture 1: Optional, Any And Defaults

Lecture 2: UUIDs And Default Factories

Lecture 3: Immutable Attributes

Lecture 4: Additional Properties

Lecture 5: Enumerations

Lecture 6: For Better Performance: Literals

Chapter 4: Custom Validators

Lecture 1: Customizing Field Validators

Lecture 2: Model-Level Validators

Lecture 3: Extra: A Closer Look At Error Objects

Chapter 5: Model Serialization And Deserialization

Lecture 1: Instance Serialization To Dict And JSON

Lecture 2: Field Exclusions

Lecture 3: JSON Schema

Lecture 4: Deserialization

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

Lecture 1: Course Resource Part 2

Lecture 2: Overview

Lecture 3: Creating A Virtual Environment

Lecture 4: Our First Dependencies

Lecture 5: Application Directory Structure

Lecture 6: API Hello World

Lecture 7: Defining Our First Poll Model

Lecture 8: Polls Create With Placeholders

Lecture 9: Polls In The Request Body

Lecture 10: Defining The Choice Data Model

Lecture 11: Splitting Into Read And Write Models

Lecture 12: Poll vs PollCreate

Lecture 13: Polls Should Have Between 2 and 5 Choices

Lecture 14: poll_create With Incrementing Choice Labels

Lecture 15: Creating Polls Through The API

Lecture 16: Refactoring To HTTPExceptions

Lecture 17: Conceptual Introduction To Redis: Our Key-Value Store

Lecture 18: Setting Up A Redis Instance

Lecture 19: Connecting, Saving, And Retrieving Data From Redis

Lecture 20: Refactoring Connection Parameters To Environment Variables

Lecture 21: Defining utils.py

Lecture 22: Integrating save_poll With POST /polls/create

Lecture 23: Defining And Integrating GET Poll

Lecture 24: Modular Re-organization With API Routers

Lecture 25: Application Metadata

Lecture 26: Faster Iteration With Visual HTTP Clients

Lecture 27: Voting Pydantic Data Models

Lecture 28: The Votes API Router

Lecture 29: Get Choice ID From Label

Lecture 30: Creating And Returning Vote Instances

Lecture 31: Storing And Retrieving Votes In Redis Hashsets

Lecture 32: Integrating Vote Saving With The Routes

Lecture 33: Double Voting Should Not Be Allowed

Lecture 34: Voting On Expired Polls Should Not Be Allowed

Lecture 35: Other Voting Validations

Lecture 36: Optimizing Get get_choice_id_by_label()

Lecture 37: Dependency Injecting Common Validations

Lecture 38: Get All Polls

Lecture 39: Batching Requests With .mget()

Lecture 40: Parameterizing Get Polls For Poll Status

Lecture 41: Tracking Vote Counts With Hash Increment By

Lecture 42: Displaying Vote Tallies

Lecture 43: Defining The Poll Results Pydantic Data Models

Lecture 44: Returning PollResults

Lecture 45: Deleting Poll Data

Lecture 46: Extra: Custom Exception Handlers

Lecture 47: Deployment Checklist

Lecture 48: Requirements.txt And Build Configuration

Lecture 49: Git Repository And .gitignore

Lecture 50: Pushing To GitHub

Lecture 51: Deployment

Chapter 7: Appendix A – Python Programming Crash Course

Lecture 1: Section Resources

Lecture 2: Data Types

Lecture 3: Variables

Lecture 4: Arithmetic And Augmented Assignment Operators

Lecture 5: Ints And Floats

Lecture 6: Booleans And Comparison Operators

Lecture 7: Strings

Lecture 8: Methods

Lecture 9: Containers I – Lists

Lecture 10: Lists vs. Strings

Lecture 11: List Methods And Functions

Lecture 12: Containers II: Tuples

Lecture 13: Containers III: Sets

Lecture 14: Containers IV: Dictionaries

Lecture 15: Dictionary Keys And Values

Lecture 16: Membership Operators

Lecture 17: Controlling Flow: if, else, And elif

Lecture 18: Truth Value Of Non-booleans

Lecture 19: For Loops

Instructors

Andy Bek
Software Consultant

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5 stars: 22 votes

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