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.
Enroll now: Learning Pydantic: Advanced Data Validation In Python
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:
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how to define data models with pydantic
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how to compose more complex models from simpler ones via inheritance
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the foundations of type hinting in python, including enumerations, literals, and other advanced types-
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how to use pydantic’s powerful validation system
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how to serialize and deserialize data
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how to extract models to schemas
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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:
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get a practical feel for how Pydantic is used in real-world applications
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learn about modern API development with python
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understand what Redis is and how it can be used as a durable data store
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learn about virtual environments and dependency management in python
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practice using git and github
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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
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Andy Bek
Software Consultant
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- 4 stars: 2 votes
- 5 stars: 22 votes
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