Скачать или смотреть How to Validate Dates in Pydantic While Allowing Empty Strings

Learn how to create a Pydantic model that accepts empty strings and valid dates in AWS Lambda. Follow our guide for effective date validation!
---
This video is based on the question https://stackoverflow.com/q/72464624/ asked by the user 'Noah13' ( https://stackoverflow.com/u/8525484/ ) and on the answer https://stackoverflow.com/a/72468527/ provided by the user 'fchancel' ( https://stackoverflow.com/u/12930958/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Python validate date using pydantic to accept empty string

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
How to Validate Dates in Pydantic While Allowing Empty Strings

When working with data validation in Python, especially using libraries like Pydantic, you might encounter scenarios where a field must accept valid data types or, in some cases, an empty string. In this guide, we’ll discuss a common problem faced by developers: ensuring that a date field can accept either a valid date or an empty string. This is particularly relevant for those using AWS Lambda, where data validation is crucial.

The Problem

Suppose you’re using Pydantic to define a data model for an employee, where one of the fields is a date. You may want the flexibility of allowing either a valid date or an empty string. However, when you try to pass an empty string to your date field, you might encounter a validation error like the following:

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

This error arises because the model is expecting a valid datetime object. Let’s dive deeper into how to create a solution for this issue.

Understanding the Solution

Step 1: Identify the Issue

The error indicates that the input for the date field is being interpreted as a string rather than as the expected datetime. Pydantic raises this error because it doesn’t recognize an empty string as a valid datetime object.

Step 2: Creating a Custom Validator

To resolve this problem, we can modify the model to include a custom validator. This will allow us to define the rules for acceptable input directly. Here’s how you can set up your Pydantic model:

Example Code

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

Step 3: Explanation of the Code

Union of Types: In the model definition, we specify that the date field can either be a datetime object or a string. This allows it to accept values flexibly.

Custom Validator Function: The date_validator function checks whether the input is valid:

If the input is neither a valid datetime nor an empty string, it raises a ValueError.

If it's valid, the function simply returns the value.

Using the Validator: The validator decorator binds the date_validator function to the date field, ensuring the validation logic is executed when instances of EmployeeInput are created.

Step 4: Validation in Action

Now, when you instantiate the EmployeeInput with a valid date, it should validate correctly. Similarly, passing an empty string will no longer raise an error:

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

Conclusion

By implementing a custom validator, we have successfully created a Pydantic model that accepts both valid dates and empty strings for the date field. This approach enhances the flexibility of your data validation requirements and ensures that errors are handled gracefully. Now, you can work confidently within AWS Lambda without worrying about date input errors!

Feel free to reach out if you have any questions about the implementation or need further assistance with your Pydantic models. Happy coding!