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Pydantic A field expected to be a list received a different type.
Understanding Pydantic
Pydantic is a data validation and settings management library for Python, leveraging Python's type annotations. It is designed to provide a simple yet powerful way to validate and parse data, ensuring that your application receives the correct data types. Pydantic is widely used in FastAPI and other Python applications to enforce data integrity and type safety.
Recognizing the Symptom
When working with Pydantic, you might encounter the error code type_error.list. This error typically manifests when a field in your data model is expected to be a list, but the input data provides a different type. The error message might look something like this:
pydantic.error_wrappers.ValidationError: 1 validation error for ModelName
field_name
value is not a valid list (type=type_error.list)
Exploring the Issue
The type_error.list error occurs when Pydantic's data validation process identifies a mismatch between the expected data type and the actual data type provided. In this case, a field defined as a list in your Pydantic model is receiving a non-list value, such as a string, integer, or dictionary.
Common Causes
- Incorrect data type in the input JSON or dictionary.
- Misconfigured Pydantic model where the field is not properly defined as a list.
- Data transformation or parsing errors before validation.
Steps to Fix the Issue
To resolve the type_error.list error, follow these steps:
1. Verify the Input Data
Ensure that the data being passed to the Pydantic model is in the correct format. If the model expects a list, make sure the input data provides a list. For example:
{
"field_name": ["item1", "item2", "item3"]
}
If the input data is coming from an external source, such as an API or user input, validate the data before passing it to the model.
2. Check the Pydantic Model Definition
Review the Pydantic model to ensure that the field is correctly defined as a list. For example:
from pydantic import BaseModel
from typing import List
class ModelName(BaseModel):
field_name: List[str]
Ensure that the field uses the List type from the typing module.
3. Use Pydantic's Built-in Validators
Pydantic provides built-in validators that can be used to enforce data types. Consider using these validators to ensure that the input data is correctly formatted before validation. More information can be found in the Pydantic Validators Documentation.
Conclusion
By following the steps outlined above, you can effectively resolve the type_error.list error in Pydantic. Ensuring that your data matches the expected types in your models is crucial for maintaining data integrity and preventing runtime errors. For more information on Pydantic and its features, visit the official Pydantic documentation.
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