How to replace shortcut keys in pycharm

The following steps are required to develop RESTful API using Flask: 1. Design a clear routing structure, use plural nouns and HTTP methods to control operations, and define paths through @app.route(); 2. Process requests and responses, use request to obtain data, jsonify returns standardized format, and accurately return status code; 3. Use Blueprint to organize modular code to improve maintainability; 4. Add global error handling and logging to facilitate troubleshooting. The above methods can help build a RESTfulAPI with clear structure, standardized and stable structure.

To build a high-performance, scalable API, it is a wise decision to choose FastAPI, but it also needs to focus on architecture design and development habits. 1. Use dependency injection management logic rationally, encapsulate common operations such as database connections and authentication checks into dependencies, improve the degree of modularity, and facilitate maintenance and testing; 2. Use asynchronously to improve concurrency performance, and release event loops through asyncdef and await in I/O-intensive tasks, but pay attention to the support of asynchronous libraries and are not suitable for CPU-intensive tasks; 3. Use hierarchical design and routing grouping, tiering routes, services, and models, and use APIRouter grouping management to make the project structure clear and easy for collaborative development and post-maintenance.

Use Python's logging module to record logs requires attention to configuration, module distinction, level control and performance optimization. 1. Set the basic configuration, such as the log level is INFO or DEBUG, and the output to the file and format can be specified; 2. Assign independent loggers to different modules through getLogger (__name__) to facilitate trace sources; 3. Use the log level reasonably, DEBUG is used for debugging, INFO represents normal process, and WARNING and above records abnormal situations; 4. Avoid frequent splicing of strings, and parameter replacement should be used to reduce unnecessary performance overhead.

Python can be used for edge computing, reducing latency, saving bandwidth and improving real-time by processing data near data sources. 1. The advantages of using Python for edge computing include high development efficiency, strong ecosystem, and good cross-platform compatibility. Although its performance is not as good as C/C, it performs well in lightweight tasks; 2. Application scenarios cover image recognition, sensor data analysis and local data filtering; 3. Deployment methods include the use of lightweight frameworks, model conversion, Docker containers and MicroPython; 4. Recommended tools include RaspberryPi, JetsonNano, CoralDevBoard and related libraries and learning resources.

Python is widely used in data processing, analytics and machine learning on Databricks. 1. Notebook supports writing Python code directly, with a good interactive experience, and you can use %run to introduce other Notebooks; 2. It is recommended to use SparkDataFrame for distributed processing, Pandas is only suitable for stand-alone data; 3. SparkAPI can be called through PySpark, supporting filtering, aggregation and other operations, and combining SQL query to improve efficiency; 4. Library management recommends installing packages at the cluster level, and %pipinstall can also be temporarily installed, but you need to pay attention to version conflicts and compatibility issues. Rational use of cache and resource allocation can further improve performance.

To start using PyQt for desktop applications, first install PyQt5 or PyQt6, then create the app and window and start the main loop. Then, the interface can be built through built-in controls such as QLabel, QLineEdit, QPushButton, etc., and the controls can be arranged using layout managers such as QHBoxLayout, QVBoxLayout, QGridLayout, etc. Finally, interactive logic is implemented through event binding, such as button clicks, input verification, etc., so that the interface is more practical.

Python is suitable for asynchronous I/O operations, especially in network requests, file systems, or time-consuming API scenarios. 1. Use asyncdef to define coroutine functions, use await to call other coroutines internally, and start the event loop through asyncio.run; 2. Select libraries such as aiohttp and aiomysql designed for asynchronous avoid blocking; 3. Use Semaphore to limit the number of concurrent tasks to prevent resource exhaustion; 4. Combining timeout control, exception handling and log monitoring to improve stability and maintainability.

There are five ways to add comments in Python, namely single-line comments, multi-line comments, in-line comments, code blocks commented in three quotes, and temporary comments out code. 1. Single-line comments are used to use #, which is suitable for explaining single-line code or blocking debugging content; 2. Multi-line comments are used in three quotes or """, which are often used for function or module descriptions; 3. In-line comments are added # after the same line of code to briefly explain the content of the current line; 4. Wrap multiple lines of code in three quotes to realize batch annotation, which is commonly used during debugging; 5. Comments should be cleaned regularly to keep the project clean. Comments should be concise and clear, which helps improve the readability and maintenance of the code.