Pydantic V2: Essentials

Focused View

Dr. Fred Baptiste

13:20:59

6 View

1. Introduction

1.1 web links.txt

1. Course Goals and Prerequisites.mp4

12:08

2.1 web links.txt

2. Course Curriculum Overview.mp4

09:48

3.1 web links.txt

3. Using the Companion GitHub Repository.mp4

04:14

2. Basics

1. Introduction.mp4

14:30

2. Creating a Pydantic Model.mp4

10:50

3. Deserialization.mp4

07:57

4. Serialization.mp4

08:21

5. Type Coercion.mp4

12:02

6. Required vs Optional Fields.mp4

12:57

7. Nullable Fields.mp4

10:32

8. Combining Nullable and Optional.mp4

04:56

9. Inspecting Fields.mp4

10:04

10. JSON Schema Generation.mp4

04:44

11. Project.mp4

04:05

12. Project Solution.mp4

03:01

3. Model Configuration

1. Introduction.mp4

02:06

2. Handling Extra Fields.mp4

12:20

3. Strict vs Lax Type Coercion.mp4

22:20

4. Validating Default Values.mp4

04:22

5. Validating Assignments.mp4

03:40

6. Mutability.mp4

05:42

7. Coercing Numbers to Strings.mp4

04:11

8. Standardizing Strings.mp4

06:31

9. Handling Python Enums.mp4

14:39

10. Project.mp4

01:29

11. Project Solution.mp4

01:35

4. Field Aliasing, Serialization and Deserialization

1. Introduction.mp4

17:39

2. Field Aliases and Default Values.mp4

13:32

3. Alias Generator Functions.mp4

16:13

4. Deserializing by Field Name or Alias.mp4

08:23

5. Serialization Aliases.mp4

06:07

6. Validation Aliases.mp4

22:12

7. Custom Serializers.mp4

32:14

8. Project.mp4

03:06

9. Project Solution.mp4

04:34

5. Specialized Pydantic Types

1. Introduction.mp4

02:35

2. PositiveInt.mp4

05:27

3. Constrained Lists.mp4

08:43

4. UUID.mp4

06:32

5. Date Related Types.mp4

11:24

6. Network Types.mp4

17:43

7. Project.mp4

01:54

8. Project Solution.mp4

02:18

6. Additional Field Features

1. Introduction.mp4

03:12

2. Numerical Constraints.mp4

06:18

3. String Constraints.mp4

13:22

4. Default Factories.mp4

16:46

5. Additional Field Configurations.mp4

16:20

6. Project.mp4

02:52

7. Project Solution.mp4

03:06

7. Annotated Types

1. Introduction.mp4

02:04

2. Pydantic and Annotated Types.mp4

08:49

3. Annotated Types and Type Variables.mp4

10:24

4. String Constraints.mp4

04:40

5. Project.mp4

02:39

6. Project Solution.mp4

05:54

8. Custom Validators

1. Introduction.mp4

18:20

2. After Validators.mp4

26:47

3. Before Validators.mp4

14:37

4. Combining Before and After Validators.mp4

09:17

5. Custom Validators using Annotated Types.mp4

24:33

6. Dependent Field Validations.mp4

15:11

7. Project.mp4

07:33

8. Project Solution.mp4

11:11

9. Properties and Computed Fields

1. Introduction.mp4

04:05

2. Properties.mp4

07:05

3. Computed Fields.mp4

11:04

4. Project.mp4

03:48

5. Project Solution.mp4

04:03

10. Custom Serializers using Annotated Types

1. Introduction.mp4

02:05

2. Custom Serializers with Annotated Types.mp4

07:05

3. Project.mp4

01:30

4. Project Solution.mp4

03:22

11. Complex Models

1. Introduction.mp4

07:12

2. Model Composition.mp4

21:48

3. Model Inheritance.mp4

16:00

4. Project.mp4

04:03

5. Project Solution.mp4

04:42

6. Final Project Solution Recap.mp4

02:48

12. Pydantic Applications

1. Introduction.mp4

02:24

2. Consuming a REST API.mp4

19:10

3. Ingesting a CSV File.mp4

12:31

4. Validating Function Arguments.mp4

17:20

5. Model Code Generators.mp4

29:54

13. Conclusion

1. Conclusion.mp4

05:25

Description

An in-depth guide to mastering Pydantic V2 for data modeling, parsing and validation

What You'll 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 is this for?

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)

What You Need to Know?

This is not a course for Python beginners - you must be proficient in Python

Known how to use the command line

Installing Python, virtual environments, pip installs

Solid understanding of Python concepts such as decorators, OOP, inheritance, composition

Python type hinting basics

More details

Description
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,Â 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,Â 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.
Who this course is for:
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)

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Hi,My name is Fred Baptiste.I am a professional software engineer with over 25 years of experience.I started my career with a PhD in mathematics and over the years have worked with a large variety of programming languages and technologies, including Python, Go, .Net (C# and VB), Java, C++, and JavaScript to name a few, databases including relational SQL databases such as MS SQL Server, Postgres and Snowflake, as well as No-SQL databases such as Clickhouse, DynamoDB, MongoDB, Couchbase and Neo4j, message queues such as RabbitMQ, ElasticMQ/SQS, and a variety of other technologies such as Airflow, Flink, DBT, Docker, Git, and many more.I have focused on Python based development for the last 10 years, mainly in REST API development, automation, and data engineering.I have spent a lot of time and effort learning and gaining an in-depth understanding of Python and writing idiomatic Python, and I'd like to share that with you. I also actively maintain my YouTube channel (mathbyteacademy) which provides a lot of additional in-depth Python development videos.I'm definitely a Python enthusiast for many reasons, and I'd like to share that passion with you too!

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