5 Best Ways to Convert a Python Dict to a Pydantic BaseModel

💡 Problem Formulation: Converting a dictionary to a Pydantic BaseModel is a common task in modern Python development, particularly when dealing with data validation and serialization for API development. The input is a Python dict with key-value pairs, and the desired output is an instance of a Pydantic BaseModel that validates the dict data according to the model’s schema.

Method 1: Using BaseModel’s parse_obj method

This method involves utilizing the BaseModel.parse_obj() class method which is provided by Pydantic. This method allows for direct conversion by parsing the dictionary as the object to be validated according to the BaseModel’s schema.

♥️ Info: Are you AI curious but you still have to create real impactful projects? Join our official AI builder club on Skool (only $5): SHIP! - One Project Per Month

Here’s an example:

from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int

user_dict = {'name': 'John', 'age': 30}
user = User.parse_obj(user_dict)
print(user)

Output:

name='John' age=30

This code snippet defines a simple Pydantic BaseModel with fields name and age, then creates a dictionary that matches the schema of this model. By calling User.parse_obj(user_dict), we’re converting the dictionary into a validated User instance.

Method 2: Using the BaseModel constructor directly

Another straightforward method is to pass the dictionary directly to the BaseModel’s constructor. The Pydantic BaseModel is designed to accept keyword arguments that correspond to the model’s field names, making a dictionary a suitable argument when unpacked.

Here’s an example:

from pydantic import BaseModel

class Product(BaseModel):
    id: int
    name: str
    price: float

product_dict = {'id': 1, 'name': 'Keyboard', 'price': 49.99}
product = Product(**product_dict)
print(product)

Output:

id=1 name='Keyboard' price=49.99

In this example, a Product BaseModel is defined and a dictionary matching the BaseModel schema is used to create an instance by unpacking it with **product_dict as an argument to the constructor.

Method 3: Using the BaseModel’s construct method

For cases where you need to create a BaseModel instance without running validation, which can be useful for performance critical sections, you can use the construct method. It’s important to note that this skips validation and should only be used with trusted data.

Here’s an example:

from pydantic import BaseModel

class Item(BaseModel):
    id: int
    name: str

item_data = {'id': '2', 'name': 'Cup'}  # Intentionally using incorrect type for 'id'
item = Item.construct(**item_data)
print(item)

Output:

id='2' name='Cup'

The construct method creates a new instance of the BaseModel without validation, allowing incorrect data type for ‘id’ to slip through. This method can be useful but must be used with caution.

Method 4: Creating a BaseModel from a dict using custom validation

Sometimes, your data requires custom pre-processing before fitting into a BaseModel. In such scenarios, you can design custom validation logic to process the dictionary before creating the BaseModel instance.

Here’s an example:

from pydantic import BaseModel, validator

class Order(BaseModel):
    id: int
    status: str

    @validator('status')
    def validate_status(cls, v):
        assert v in {'placed', 'shipped', 'delivered'}, 'status must be a valid option'
        return v

order_data = {'id': 123, 'status': 'placed'}
order = Order(**order_data)
print(order)

Output:

id=123 status='placed'

This example defines a validator within the Order BaseModel, ensuring that the status field has an acceptable value before creating an Order instance with the provided dictionary.

Bonus One-Liner Method 5: Using a data parsing utility function

For a quick one-liner solution, you can write or use a utility function that automates the conversion by passing the dictionary to the BaseModel constructor internally. This is useful for keeping codebases clean and to adhere to DRY principles.

Here’s an example:

from pydantic import BaseModel

def to_base_model(Model, data):
    return Model(**data)

class Device(BaseModel):
    id: int
    type: str

device_data = {'id': 456, 'type': 'Sensor'}
device = to_base_model(Device, device_data)
print(device)

Output:

id=456 type='Sensor'

A utility function to_base_model is defined, taking a BaseModel and a dictionary to return an instance. This abstracts away the constructor call, making the data conversion to BaseModel instances one-liner throughout your code.

Summary/Discussion

Method 1: Parsing with parse_obj. It’s straightforward and ensures data validation, but it may be slower than other methods due to the validation process.

Method 2: Direct constructor unpacking. It’s simple and elegant; however, it does not provide additional custom pre-validation like method 4.

Method 3: Using construct. This method is fast because it skips validation, but caution is necessary as it can introduce bugs if used with untrusted data.

Method 4: Custom validation. Custom validation provides flexibility and robustness but requires additional boilerplate code.

Method 5: Utility Function. The one-liner utility function promotes code reuse and simplicity at the cost of potentially obscuring what’s happening under the hood for beginners.