Understanding the Purpose of the __module__ Argument in pydantic.create_model

What will you learn?

  • Gain insight into the significance of the __module__ argument in pydantic.create_model.
  • Master the effective utilization of this argument in Pydantic models.

Introduction to the Problem and Solution

In Pydantic, a Python data validation library, creating models using pydantic.create_model may require specifying the module where the model is defined. This need arises particularly in projects with multiple modules or packages.

To address this issue, Pydantic offers an optional parameter called __module__. By setting this attribute during model creation, you can explicitly declare the module containing your Pydantic models.


from pydantic import create_model

# Define a simple example model without __module__
model_without_module = create_model('ExampleModel', foo=(str, ...))

# Define another example model with __module__
model_with_module = create_model('AnotherModel', bar=(int, ...), __module__='my_app.models')

# Print out both models' modules for demonstration

# Copyright PHD


When utilizing create_model to define Pydantic models, specifying the __module__ parameter aids in organizing code effectively by explicitly indicating the module housing that specific model. This practice proves vital in larger projects where clarity and maintainability are essential factors.

The value assigned to __module__ should be a string representing the full path of the module containing your model. Lack of an explicit module assignment results in Python assigning a default value based on its creation location.

Including this information within each Pydantic model definition enhances code readability and simplifies developer navigation through different project components.

    What happens if I don’t specify __module__ while creating a Pydantic model?

    If you omit specifying __module__, Python assigns a default value based on its creation location, which may not accurately reflect your project structure.

    Can I change or update the __module__ attribute after creating a Pydantic model?

    No, once set during creation via create_model(), you cannot directly modify or update the __module__. To make changes, you must recreate the entire model with appropriate attributes if required elsewhere.

    Is there any performance impact of using or not using…


    Understanding how to utilize the __module__ argument in Pydantic models is crucial for maintaining a well-organized and easily navigable codebase. By explicitly defining the module for each model, developers can enhance code readability and ensure clarity within their projects. Mastering this concept empowers you to efficiently manage complex Python projects involving multiple modules or packages.

    Leave a Comment