The key to dealing with API authentication is to understand and use the authentication method correctly. 1. APIKey is the simplest authentication method, usually placed in the request header or URL parameters; 2. BasicAuth uses username and password for Base64 encoding transmission, which is suitable for internal systems; 3. OAuth2 needs to obtain the token first through client_id and client_secret, and then bring the BearerToken in the request header; 4. In order to deal with the token expiration, the token management class can be encapsulated and automatically refreshed the token; in short, selecting the appropriate method according to the document and safely storing the key information is the key.

To test the API, you need to use Python's Requests library. The steps are to install the library, send requests, verify responses, set timeouts and retry. First, install the library through pipinstallrequests; then use requests.get() or requests.post() and other methods to send GET or POST requests; then check response.status_code and response.json() to ensure that the return result is in compliance with expectations; finally, add timeout parameters to set the timeout time, and combine the retrying library to achieve automatic retry to enhance stability.

The way to access nested JSON objects in Python is to first clarify the structure and then index layer by layer. First, confirm the hierarchical relationship of JSON, such as a dictionary nested dictionary or list; then use dictionary keys and list index to access layer by layer, such as data "details"["zip"] to obtain zip encoding, data "details"[0] to obtain the first hobby; to avoid KeyError and IndexError, the default value can be set by the .get() method, or the encapsulation function safe_get can be used to achieve secure access; for complex structures, recursively search or use third-party libraries such as jmespath to handle.

Asynchronous programming is made easier in Python with async and await keywords. It allows writing non-blocking code to handle multiple tasks concurrently, especially for I/O-intensive operations. asyncdef defines a coroutine that can be paused and restored, while await is used to wait for the task to complete without blocking the entire program. Running asynchronous code requires an event loop. It is recommended to start with asyncio.run(). Asyncio.gather() is available when executing multiple coroutines concurrently. Common patterns include obtaining multiple URL data at the same time, reading and writing files, and processing of network services. Notes include: Use libraries that support asynchronously, such as aiohttp; CPU-intensive tasks are not suitable for asynchronous; avoid mixed

In Python, variables defined inside a function are local variables and are only valid within the function; externally defined are global variables that can be read anywhere. 1. Local variables are destroyed as the function is executed; 2. The function can access global variables but cannot be modified directly, so the global keyword is required; 3. If you want to modify outer function variables in nested functions, you need to use the nonlocal keyword; 4. Variables with the same name do not affect each other in different scopes; 5. Global must be declared when modifying global variables, otherwise UnboundLocalError error will be raised. Understanding these rules helps avoid bugs and write more reliable functions.

To create modern and efficient APIs using Python, FastAPI is recommended; it is based on standard Python type prompts and can automatically generate documents, with excellent performance. After installing FastAPI and ASGI server uvicorn, you can write interface code. By defining routes, writing processing functions, and returning data, APIs can be quickly built. FastAPI supports a variety of HTTP methods and provides automatically generated SwaggerUI and ReDoc documentation systems. URL parameters can be captured through path definition, while query parameters can be implemented by setting default values ??for function parameters. The rational use of Pydantic models can help improve development efficiency and accuracy.

Add timeout control to Python's for loop. 1. You can record the start time with the time module, and judge whether it is timed out in each iteration and use break to jump out of the loop; 2. For polling class tasks, you can use the while loop to match time judgment, and add sleep to avoid CPU fullness; 3. Advanced methods can consider threading or signal to achieve more precise control, but the complexity is high, and it is not recommended for beginners to choose; summary key points: manual time judgment is the basic solution, while is more suitable for time-limited waiting class tasks, sleep is indispensable, and advanced methods are suitable for specific scenarios.

How to efficiently handle large JSON files in Python? 1. Use the ijson library to stream and avoid memory overflow through item-by-item parsing; 2. If it is in JSONLines format, you can read it line by line and process it with json.loads(); 3. Or split the large file into small pieces and then process it separately. These methods effectively solve the memory limitation problem and are suitable for different scenarios.