Pydantic Error Handling

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It allows you to define a model or schema for your data, which can then be used to validate incoming data against that schema. This is great because it helps prevent errors before they even happen!

But what happens when an error does occur? That’s where Pydantic’s error handling comes in. And let me tell you, it’s not for the faint of heart (or stomach).

First up, we have the classic `ValueError`. This is a catch-all error that can be used to handle any type of invalid data. For example:

# Here is the context before the script:
# en!

# But what happens when an error does occur? That's where Pydantic's error handling comes in. And let me tell you, it's not for the faint of heart (or stomach).

# First up, we have the classic `ValueError`. This is a catch-all error that can be used to handle any type of invalid data. For example:

# Here is the script:
# Importing the necessary module from Pydantic
from pydantic import BaseModel

# Creating a class called MyModel that inherits from the BaseModel class
class MyModel(BaseModel):
    # Defining two attributes, name and age, with their respective data types
    name: str
    age: int
    
    # Defining a constructor method that takes in any number of arguments and keyword arguments
    def __init__(self, *args, **kwargs):
        # Calling the constructor method of the parent class and passing in the keyword arguments
        super().__init__(**kwargs)
        
        # This is where we handle our errors!
        # Checking if the age attribute is less than 18
        if self.age < 18:
            # Raising a ValueError with a message if the condition is met
            raise ValueError("You must be at least 18 years old to use this service.")

In this example, we’re using Pydantic’s `BaseModel` class as a starting point for defining our model. We then override the constructor and add some custom error handling logic. If the age is less than 18, we raise a `ValueError`.

But what if you want to handle errors in a more specific way? That’s where Pydantic’s built-in error wrappers come in handy! These allow you to wrap your own custom error classes around Pydantic’s standard errors. For example:

# Here is the context before the script:
# Error handling logic. If the age is less than 18, we raise a `ValueError`.

# But what if you want to handle errors in a more specific way? That's where Pydantic's built-in error wrappers come in handy! These allow you to wrap your own custom error classes around Pydantic's standard errors. For example:

# Here is the script:
# Importing necessary modules
from pydantic import BaseModel, ValidationError
from my_custom_errors import MyCustomError

# Defining a model with a required attribute "name"
class MyModel(BaseModel):
    name: str
    
    # Defining a custom __init__ method to handle errors
    def __init__(self, *args, **kwargs):
        # Calling the parent class's __init__ method
        super().__init__(**kwargs)
        
        # This is where we handle our errors!
        try:
            # Calling a custom method to validate the age
            self.validate_for_age()
        # Catching any validation errors
        except ValidationError as e:
            # Checking if the error message contains "age"
            if "age" in str(e):
                # Raising a custom error with a more specific message
                raise MyCustomError("Invalid age provided.") from e

In this example, we’re using Pydantic’s `BaseModel` class again. But instead of raising a generic `ValueError`, we’re wrapping the error in our own custom error class called `MyCustomError`. This allows us to provide more specific information about what went wrong and how to fix it.

Pydantic also provides some handy decorators that can be used to handle errors at a higher level. For example:

# Importing necessary modules
from pydantic import BaseModel, ValidationError
from my_custom_errors import MyCustomError

# Creating a class for our model
class MyModel(BaseModel):
    name: str # Declaring a variable "name" of type string
    
    # Using a decorator to validate the "age" variable
    @validator("age")
    def validate_for_age(cls, v):
        if v < 18: # Checking if the value of "age" is less than 18
            raise ValueError("You must be at least 18 years old to use this service.") # Raising a ValueError if the condition is not met
        
    # Configuring the error handling for our model
    class Config:
        error_wrap = MyCustomError # Wrapping any errors in our custom error class "MyCustomError" for more specific information

# End of script.

In this example, we’re using Pydantic’s `@validator` decorator to add some custom validation logic for the age field. If the age is less than 18, a generic `ValueError` will be raised. But by setting the `error_wrap` attribute on our model’s configuration class (`Config`) to our own custom error class, we can wrap that error in something more specific and informative!

And there you have it Pydantic error handling made easy(ish)! After all, who doesn’t love a good laugh while debugging?

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