Accessing Parent Attributes in Pydantic Classes for Validation

Learn how to effectively inherit and validate attributes in Pydantic classes using practical examples and solutions.
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Accessing Parent Attributes in Pydantic Classes for Validation

If you're working with Pydantic, a popular data validation library for Python, you may encounter situations where you need to extend a parent class and validate its attributes. In this guide, we will dissect an example involving a Catalog class, a Collection class inheriting from it, and an extended version that adds additional attributes. We aim to answer the question of how to access parent attributes when performing validation as you extend your Pydantic models.

Understanding the Problem

In Pydantic, when you create classes that inherit from one another, it's common to face validation errors when trying to instantiate the child classes with data that might not meet the required schema. Consider the following scenario:

You've created a base Catalog class and a Collection class that extends it. When trying to manipulate these classes in a way that involves adding new attributes and ensuring they comply with the main structure, you encounter errors due to missing required fields. How do you effectively manage this and create a fully validated instance of your new class?

The Solution Explained

Step 1: Setup Your Base Classes

You start with the Catalog class, which specifies required fields like id and description, as well as optional ones like title. Here’s how it looks:

[[See Video to Reveal this Text or Code Snippet]]

Next, you introduce the Collection class that extends Catalog:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Create Your Extended Class

Now, you want to extend the Collection class by adding another field called field1. For enhanced validation, you’ll implement a validator to ensure that the type field matches 'Collection'.

Here's how you create the ExtendedCollection class:

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Initialize Instances Correctly

Now, initialization is key. You initially attempted to create an instance incorrectly by sending an object of type Collection. This led to errors because required attributes were missing. Instead, use the following correctly structured dictionary for your instance:

[[See Video to Reveal this Text or Code Snippet]]

Then you create an instance like this:

[[See Video to Reveal this Text or Code Snippet]]

Step 4: Review Validation Results

The validation works now because when you pass tst_col using the unpacking operator **, all required fields are available. Here’s what the final output will look like:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

By following these steps, you can clear up the confusion that arises when attempting to inherit attributes and perform validation in Pydantic classes. It’s essential to ensure that you provide all required fields either through optional default values or by properly formatting input dictionaries for your class instances.

This approach enhances both the usability and reliability of your Pydantic models when dealing with complex data structures.

With this understanding, you can confidently extend models while ensuring robust validation matches your application’s needs.

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