Pydantic V2- Essentials

Pydantic V2: Essentials, available at $19.99, has an average rating of 4.89, with 85 lectures, based on 255 reviews, and has 2432 subscribers.

You will learn about Create Advanced Pydantic V2 Models Custom Validators and Serializers Leverage Annotated Types with Pydantic Aliases, Properties and Computed Fields Pydantic applications, including validating Python function arguments This course is ideal for individuals who are This course is for experienced Python developers who want to learn the essential parts of Pydantic in depth. or Course covers the latest version of Pydantic – V2.x (not V1.x) It is particularly useful for This course is for experienced Python developers who want to learn the essential parts of Pydantic in depth. or Course covers the latest version of Pydantic – V2.x (not V1.x).

Enroll now: Pydantic V2: Essentials

Summary

Title: Pydantic V2: Essentials

Price: $19.99

Average Rating: 4.89

Number of Lectures: 85

Number of Published Lectures: 85

Number of Curriculum Items: 85

Number of Published Curriculum Objects: 85

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Create Advanced Pydantic V2 Models

Custom Validators and Serializers

Leverage Annotated Types with Pydantic

Aliases, Properties and Computed Fields

Pydantic applications, including validating Python function arguments

Who Should Attend

This course is for experienced Python developers who want to learn the essential parts of Pydantic in depth.

Course covers the latest version of Pydantic – V2.x (not V1.x)

Target Audiences

This course is for experienced Python developers who want to learn the essential parts of Pydantic in depth.

Course covers the latest version of Pydantic – V2.x (not V1.x)

This is an advanced level course on using the Pydantic V2 library. This course is not for beginners!

I have worked with Pydantic (starting with v1) for many years, and use that experience to bring you a course that focuses on the essential parts of Pydantic you will need to know to use it professionally,  effectively and to leverage it’s full potential.

Pydantic provides a very flexible framework for modeling, validating and parsing data in Python.

Although Pydantic is often associated with frameworks such FastAPI, it has far broader applications well beyond just REST API development. From modeling and validating data in databases (like Redis, DynamoDB, Clickhouse), queues (like SQS, ElasticMQ, RabbitMQ), and even CSV files,  to even providing argument validation for your custom Python functions!

Pydantic is a very flexible, fast-to-develop, and easy-to-understand data modeling framework that belongs in every serious Python developer’s toolkit.

Anytime you have a Python project that contains a fair amount of data validation and modeling into Python classes, Pydantic can be leveraged very effectively.

You can think of Pydantic as somewhat similar to Python’s dataclasses, but with an advanced and flexible data validation layer, as well as the easy ability to deserialize (load) and serialize (output) these Python/Pydantic classes into plain dictionaries and JSON. Just like dataclasses, Pydantic uses Python’s type hinting capabilities to define data models, but then adds in validation and serialization/deserialization capabilities, which are all fully customizable.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Goals and Prerequisites

Lecture 2: Course Curriculum Overview

Lecture 3: Using the Companion GitHub Repository

Chapter 2: Basics

Lecture 1: Introduction

Lecture 2: Creating a Pydantic Model

Lecture 3: Deserialization

Lecture 4: Serialization

Lecture 5: Type Coercion

Lecture 6: Required vs Optional Fields

Lecture 7: Nullable Fields

Lecture 8: Combining Nullable and Optional

Lecture 9: Inspecting Fields

Lecture 10: JSON Schema Generation

Lecture 11: Project

Lecture 12: Project Solution

Chapter 3: Model Configuration

Lecture 1: Introduction

Lecture 2: Handling Extra Fields

Lecture 3: Strict vs Lax Type Coercion

Lecture 4: Validating Default Values

Lecture 5: Validating Assignments

Lecture 6: Mutability

Lecture 7: Coercing Numbers to Strings

Lecture 8: Standardizing Strings

Lecture 9: Handling Python Enums

Lecture 10: Project

Lecture 11: Project Solution

Chapter 4: Field Aliasing, Serialization and Deserialization

Lecture 1: Introduction

Lecture 2: Field Aliases and Default Values

Lecture 3: Alias Generator Functions

Lecture 4: Deserializing by Field Name or Alias

Lecture 5: Serialization Aliases

Lecture 6: Validation Aliases

Lecture 7: Custom Serializers

Lecture 8: Project

Lecture 9: Project Solution

Chapter 5: Specialized Pydantic Types

Lecture 1: Introduction

Lecture 2: PositiveInt

Lecture 3: Constrained Lists

Lecture 4: UUID

Lecture 5: Date Related Types

Lecture 6: Network Types

Lecture 7: Project

Lecture 8: Project Solution

Chapter 6: Additional Field Features

Lecture 1: Introduction

Lecture 2: Numerical Constraints

Lecture 3: String Constraints

Lecture 4: Default Factories

Lecture 5: Additional Field Configurations

Lecture 6: Project

Lecture 7: Project Solution

Chapter 7: Annotated Types

Lecture 1: Introduction

Lecture 2: Pydantic and Annotated Types

Lecture 3: Annotated Types and Type Variables

Lecture 4: String Constraints

Lecture 5: Project

Lecture 6: Project Solution

Chapter 8: Custom Validators

Lecture 1: Introduction

Lecture 2: After Validators

Lecture 3: Before Validators

Lecture 4: Combining Before and After Validators

Lecture 5: Custom Validators using Annotated Types

Lecture 6: Dependent Field Validations

Lecture 7: Project

Lecture 8: Project Solution

Chapter 9: Properties and Computed Fields

Lecture 1: Introduction

Lecture 2: Properties

Lecture 3: Computed Fields

Lecture 4: Project

Lecture 5: Project Solution

Chapter 10: Custom Serializers using Annotated Types

Lecture 1: Introduction

Lecture 2: Custom Serializers with Annotated Types

Lecture 3: Project

Lecture 4: Project Solution

Chapter 11: Complex Models

Lecture 1: Introduction

Lecture 2: Model Composition

Lecture 3: Model Inheritance

Lecture 4: Project

Lecture 5: Project Solution

Lecture 6: Final Project Solution Recap

Chapter 12: Pydantic Applications

Lecture 1: Introduction

Lecture 2: Consuming a REST API

Lecture 3: Ingesting a CSV File

Lecture 4: Validating Function Arguments

Lecture 5: Model Code Generators

Chapter 13: Conclusion

Lecture 1: Conclusion

Instructors

Dr. Fred Baptiste
Software Engineer and Mathematician

Rating Distribution

1 stars: 1 votes

2 stars: 0 votes

3 stars: 2 votes

4 stars: 36 votes

5 stars: 216 votes

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