Web application security needs to be paid attention to. Common vulnerabilities on Python websites include XSS, SQL injection, CSRF and file upload risks. For XSS, the template engine should be used to automatically escape, filter rich text HTML and set CSP policies; to prevent SQL injection, parameterized query or ORM framework, and verify user input; to prevent CSRF, CSRFTToken mechanism must be enabled and sensitive operations must be confirmed twice; file upload vulnerabilities must be used to restrict types, rename files, and prohibit execution permissions. Following the norms and using mature tools can effectively reduce risks, and safety needs continuous attention and testing.

Python's unittest and pytest are two widely used testing frameworks that simplify the writing, organizing and running of automated tests. 1. Both support automatic discovery of test cases and provide a clear test structure: unittest defines tests by inheriting the TestCase class and starting with test\_; pytest is more concise, just need a function starting with test\_. 2. They all have built-in assertion support: unittest provides assertEqual, assertTrue and other methods, while pytest uses an enhanced assert statement to automatically display the failure details. 3. All have mechanisms for handling test preparation and cleaning: un

setup.py is a traditional Python packaged configuration file used to define project metadata, dependencies and construction processes, and is written as a Python script using setuptools; pyproject.toml is a modern standard configuration file that is introduced in PEP518 to standardize system requirements in TOML format and improve security. 1. setup.py supports project name, version, package list, dependencies (install_requires) and command line entry points (entrypoints), but there are risks due to the execution of arbitrary code; 2. pyproject.toml declares through fields such as [build-system] and [project]

Python's default parameters are only initialized once when defined. If mutable objects (such as lists or dictionaries) are used as default parameters, unexpected behavior may be caused. For example, when using an empty list as the default parameter, multiple calls to the function will reuse the same list instead of generating a new list each time. Problems caused by this behavior include: 1. Unexpected sharing of data between function calls; 2. The results of subsequent calls are affected by previous calls, increasing the difficulty of debugging; 3. It causes logical errors and is difficult to detect; 4. It is easy to confuse both novice and experienced developers. To avoid problems, the best practice is to set the default value to None and create a new object inside the function, such as using my_list=None instead of my_list=[] and initially in the function

Deploying Python applications to production environments requires attention to stability, security and maintenance. First, use Gunicorn or uWSGI to replace the development server to support concurrent processing; second, cooperate with Nginx as a reverse proxy to improve performance; third, configure the number of processes according to the number of CPU cores to optimize resources; fourth, use a virtual environment to isolate dependencies and freeze versions to ensure consistency; fifth, enable detailed logs, integrate monitoring systems, and set up alarm mechanisms to facilitate operation and maintenance; sixth, avoid root permissions to run applications, close debugging information, and configure HTTPS to ensure security; finally, automatic deployment is achieved through CI/CD tools to reduce human errors.

Ellipsis (...) in Python has three main uses: 1. As a placeholder for code stubs or unfinished logic, such as temporarily leaving a blank structure in functions or classes; 2. Indicate all leading dimensions in multi-dimensional array slices (such as NumPy), simplifying access to high-dimensional data; 3. In Python 3.9 type prompts to represent mutable or unspecified parameters, such as in combination with typing.Concatenate. These uses correspond to structural reservations, simplified high-dimensional data slicing and complex type declarations in the early stages of development. Although they are not commonly used, they are very practical in specific scenarios.

Python works well with other languages ??and systems in microservice architecture, the key is how each service runs independently and communicates effectively. 1. Using standard APIs and communication protocols (such as HTTP, REST, gRPC), Python builds APIs through frameworks such as Flask and FastAPI, and uses requests or httpx to call other language services; 2. Using message brokers (such as Kafka, RabbitMQ, Redis) to realize asynchronous communication, Python services can publish messages for other language consumers to process, improving system decoupling, scalability and fault tolerance; 3. Expand or embed other language runtimes (such as Jython) through C/C to achieve implementation

PythonisidealfordataanalysisduetoNumPyandPandas.1)NumPyexcelsatnumericalcomputationswithfast,multi-dimensionalarraysandvectorizedoperationslikenp.sqrt().2)PandashandlesstructureddatawithSeriesandDataFrames,supportingtaskslikeloading,cleaning,filterin