When Windows cannot detect a second monitor, first check whether the physical connection is normal, including power supply, cable plug-in and interface compatibility, and try to replace the cable or adapter; secondly, update or reinstall the graphics card driver through the Device Manager, and roll back the driver version if necessary; then manually click "Detection" in the display settings to identify the monitor to confirm whether it is correctly identified by the system; finally check whether the monitor input source is switched to the corresponding interface, and confirm whether the graphics card output port connected to the cable is correct. Following the above steps to check in turn, most dual-screen recognition problems can usually be solved.

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.

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.

If you want to know the computer configuration, you can view it through three methods: Task Manager, System Information and Third-party Software. 1. Press Ctrl Shift Esc to open the task manager and switch to the "Performance" page to quickly view basic information of CPU, memory, disk and GPU; 2. Enter msinfo32 through Win R to open the system information tool to obtain a complete list of software and hardware and software and support export; 3. Use third-party software such as CPU-Z, GPU-Z or HWiNFO to detect the complete configuration in one click and most of them can be used without installation.