Debug Your Infrastructure
Get Instant Solutions for Kubernetes, Databases, Docker and more
Pydantic type_error.bool
Understanding Pydantic
Pydantic is a data validation and settings management library for Python, leveraging Python's type annotations. It is designed to provide a robust way to validate and parse data, ensuring that the data conforms to the expected types and constraints. Pydantic is widely used in applications where data integrity is crucial, such as web applications, data processing pipelines, and configuration management.
Identifying the Symptom
When using Pydantic, you might encounter the error code type_error.bool. This error typically manifests when a field in your data model, expected to be a boolean, receives a value of a different type. This can cause your application to fail to parse the data correctly, leading to unexpected behavior or crashes.
Example of the Error
Consider the following Pydantic model:
from pydantic import BaseModel
class Config(BaseModel):
is_active: bool
config = Config(is_active="yes") # This will raise a type_error.bool
In this example, the is_active field is expected to be a boolean, but a string is provided instead, resulting in a type_error.bool.
Explaining the Issue
The type_error.bool error occurs when Pydantic attempts to coerce a value into a boolean but fails because the value is not of a compatible type. Pydantic expects the input to be either True, False, 1, or 0, or any other value that can be directly interpreted as a boolean.
Common Causes
- Providing a string instead of a boolean value.
- Using numbers other than
1or0that cannot be interpreted as boolean. - Passing
nullorNonewhere a boolean is expected.
Steps to Fix the Issue
To resolve the type_error.bool, ensure that the data being passed to the Pydantic model is of the correct type. Here are some steps you can follow:
Step 1: Validate Input Data
Before passing data to the Pydantic model, validate that the boolean fields are indeed boolean. You can use Python's built-in bool() function to convert values:
def convert_to_bool(value):
if isinstance(value, str):
return value.lower() in ('true', '1', 't', 'y', 'yes')
return bool(value)
is_active_value = convert_to_bool("yes")
config = Config(is_active=is_active_value)
Step 2: Use Pydantic Validators
Pydantic allows you to define custom validators to enforce data constraints. You can use this feature to ensure that fields are correctly typed:
from pydantic import BaseModel, validator
class Config(BaseModel):
is_active: bool
@validator('is_active', pre=True, always=True)
def check_boolean(cls, v):
if isinstance(v, str):
return v.lower() in ('true', '1', 't', 'y', 'yes')
return bool(v)
Step 3: Update Data Sources
If the data originates from an external source, such as a database or API, ensure that the data is correctly formatted before it reaches your application. This might involve updating queries or API calls to return boolean values.
Additional Resources
For more information on Pydantic and its features, you can refer to the official Pydantic documentation. Additionally, the Python documentation on the bool function provides insights into how Python handles boolean conversions.
Master
Pydantic type_error.bool
debugging in Minutes
— Grab the Ultimate Cheatsheet
(Perfect for DevOps & SREs)
Pydantic type_error.bool
Cheatsheet
(Perfect for DevOps & SREs)
MORE ISSUES
Backed by
