Function annotations are a feature used in Python to add metadata, which can improve code readability and maintenance. It does not force type checking, but provides type prompts or other information for parameters and return values. Its uses include: 1. Improve code readability and enable developers to clarify the expected input and output of functions; 2. Use it in conjunction with static type checking tools (such as mypy and pyright); 3. Used by frameworks (such as FastAPI) to generate documents or verify requests. Annotations do not affect the operation of the program. For example, name:str and ->str in defgreet(name:str)->str are only additional information, and the actual parameter transmission can still be of other types. Use suggestions include keeping the annotations concise and combining types and types

The key to using Python to call WebAPI to obtain data is to master the basic processes and common tools. 1. Using requests to initiate HTTP requests is the most direct way. Use the get method to obtain the response and use json() to parse the data; 2. For APIs that need authentication, you can add tokens or keys through headers; 3. You need to check the response status code, it is recommended to use response.raise_for_status() to automatically handle exceptions; 4. Facing the paging interface, you can request different pages in turn and add delays to avoid frequency limitations; 5. When processing the returned JSON data, you need to extract information according to the structure, and complex data can be converted to Data

C STL improves code efficiency through containers, algorithms and iterators. 1. The container includes vector (dynamic array, suitable for tail insertion and deletion), list (bidirectional linked list, suitable for frequent intermediate insertion and deletion), map and set (based on red and black trees, automatic sorting and searching fast). When choosing, consider the use scenario and time complexity; 2. Algorithms such as sort(), find(), copy(), etc. operate the data range through iterators to improve universality and security. When using it, pay attention to whether the original data is modified and the iterator's validity; 3. Function objects and lambda expressions can be used for custom operations. lambdas are suitable for simple logic, and function objects are suitable for multiplexing or complex logic. At the same time, pay attention to capturing the list to avoid dangling references. Palm

ThemoveassignmentoperatorinC isaspecialmemberfunctionthatefficientlytransfersresourcesfromatemporaryobjecttoanexistingone.ItisdefinedasMyClass&operator=(MyClass&&other)noexcept;,takinganon-constrvaluereferencetoallowmodificationofthesour

The key scenario where virtual destructors must be used is when you want to safely delete derived class objects through base class pointers. 1. If the destructor of the base class is not a virtual function, deleting the derived class object through the base class pointer will lead to undefined behavior, and only calling the base class destructor and ignoring the derived class destructor logic may cause resource leakage; 2. The solution is to declare the base class destructor as virtual to ensure that the derived class destructor is called correctly; 3. The performance impact of the virtual destructor is usually negligible, but if the class is not inherited or does not manage the life cycle through pointers, there is no need for a virtual destructor.

Updating a JSON file requires three steps: reading, modifying, and writing. 1. Use json.load() to read the file into a Python data structure; 2. Access the modified value through keys such as data['age']=31 or nested modification; 3. Use json.dump(data,f) to save the changes back to the file and it is recommended to add indent to beautify the output. Before operation, you should confirm that the file exists and backups should be made if necessary. Remote data must be processed in conjunction with the requests module.

Object slice refers to the phenomenon that only part of the base class data is copied when assigning or passing a derived class object to a base class object, resulting in the loss of new members of the derived class. 1. Object slices occur in containers that directly assign values, pass parameters by value, or store polymorphic objects in storage base classes; 2. The consequences include data loss, abnormal behavior and difficult to debug; 3. Avoiding methods include passing polymorphic objects using pointers or references, or using smart pointers to manage the object life cycle.

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