Python uses Python to validate and parse complex data structures, including nested JSON, XML, YAML, and more

Environmental construction and preparation work: 1. Install Python: The first step is to install Python. You can download the latest version of Python from the Python official website and install it. 2. Install Pydantic library: Execute the 'pip install pydantic' command from the command line to install the Pydantic library. Dependent class libraries: 1. Pydantic: Pydantic is a Python library used for data validation and parsing. It provides a decorator class and validator function that can be used to define structured data models. When validating and parsing complex data structures, Pydantic can be used to define the data model, and then the model can be used for validation and parsing. Implement a complete example: The following is an example of using Pydantic to validate and parse complex data structures, demonstrated using nested JSON data. Firstly, we need to define a Pydantic data model to describe the data structure to be validated and parsed. Suppose we have a nested JSON data that contains some user information and a list of users' friends. We can use Pydantic to define a corresponding data model, as follows: python from pydantic import BaseModel from typing import List class Friend(BaseModel): name: str age: int class User(BaseModel): id: int name: str age: int friends: List[Friend] In the above code, we defined two data models: 'Friend' and 'User'` Friend 'represents the user's friend, which includes two attributes: name and age` User 'represents a user, which includes four attributes: user ID, name, age, and friend list. Note that the 'friends' attribute of' User 'is of type' List [Friend] ', representing a list containing multiple' Friend 'objects. Next, we can use the defined data model to validate and parse the data. Assuming we have the following nested JSON data: json { "id": 123, "name": "Alice", "age": 25, "friends": [ { "name": "Bob", "age": 30 }, { "name": "Charlie", "age": 28 } ] } We can use Pydantic to verify whether the data conforms to the defined data model and parse the data as the corresponding object. The following is the complete Python code to complete validation and parsing: python from pydantic import BaseModel from typing import List class Friend(BaseModel): name: str age: int class User(BaseModel): id: int name: str age: int friends: List[Friend] data = { "id": 123, "name": "Alice", "age": 25, "friends": [ { "name": "Bob", "age": 30 }, { "name": "Charlie", "age": 28 } ] } user = User(**data) print(user) Run the above code and the output result is: id=123 name='Alice' age=25 friends=[Friend(name='Bob', age=30), Friend(name='Charlie', age=28)] In the above code, we use 'User (* * data)' to parse nested JSON data into a 'User' object. If the data does not match the defined data model, Pydantic will throw the corresponding validation exception. Summary: Pydantic is a very convenient Python library for validating and parsing complex data structures. Before using Pydantic, we need to define a corresponding data model to describe the data structure. Then, we can use the data model to validate and parse the data. By using Pydantic, we can easily handle complex data structures such as nested JSON, XML, YAML, etc.