Define a data model using Pydantic, including attributes, types, default values, type annotations, and data model inheritance

In order to define the data model using Pydantic, we need to do the following preparatory work: 1. Install Python: Ensure that Python has been installed and can be accessed from the official website（ https://www.python.org/downloads/ ）Download and install the latest version of Python. 2. Create a virtual environment (optional): It is recommended to use a virtual environment in the project to isolate project dependencies. You can use the 'venv' module to create a virtual environment. Run the following command to create a virtual environment: python3 -m venv myenv 3. Activate virtual environment: Run the following command to activate the virtual environment: - Windows: myenv\Scripts\activate - macOS/Linux: source myenv/bin/activate 4. Install Pydantic: Run the following command in a virtual environment to install Pydantic: pip install pydantic Now that we have completed the preparation work, we can create our data model. In Pydantic, we use the 'BaseModel' class to define the data model. Suppose we want to define a data model representing users, including attributes' name ',' age 'and' email '. The types are' str ',' int 'and' str ', respectively. The default value is an empty string, and the type comment is a string. In addition, we also want to create a derived class representing administrator users, including an additional attribute 'role' of type 'str' with a default value of 'admin'. python from pydantic import BaseModel class User(BaseModel): name: str = "" age: int = 0 email: str = "" class Admin(User): role: str = "admin" In the above code, we defined a data model named 'User' that inherits from the 'BaseModel' class` The User 'model has three attributes:' name ',' age 'and' email '. The specified types are' str ',' int 'and' str 'respectively. The default value is an empty string. We also defined a data model called 'Admin', which inherits from the 'User' model` The Admin model has added an additional attribute 'role' of type 'str' with a default value of 'admin'. Here is a complete example of using these data models: python from pydantic import BaseModel class User(BaseModel): name: str = "" age: int = 0 email: str = "" class Admin(User): role: str = "admin" #Create User Object user = User(name="Alice", age=25, email="alice@example.com") print(user.json()) #Create Admin object admin = Admin(name="Bob", age=30, email="bob@example.com", role="superadmin") print(admin.json()) Output results: plaintext {"name": "Alice", "age": 25, "email": "alice@example.com"} {"name": "Bob", "age": 30, "email": "bob@example.com", "role": "superadmin"} In the above code, we created a 'User' object named 'user' and used the 'json()' method to convert it into a JSON string for printing. Then, we created an 'Admin' object named 'admin' and used the 'json()' method to convert it into a JSON string for printing. Note that the 'admin' object has an additional 'role' attribute than the 'user' object. Summary: In this article, we first completed the preparation work for defining a data model using Pydantic, including setting up the environment and installing Pydantic. Then, we defined a simple user data model using the 'BaseModel' class and created 'User' and 'Admin' objects as examples. By using Pydantic, we can easily define and use data models, including attributes, types, default values, and type annotations.