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

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

Python's list, dictionary and collection derivation improves code readability and writing efficiency through concise syntax. They are suitable for simplifying iteration and conversion operations, such as replacing multi-line loops with single-line code to implement element transformation or filtering. 1. List comprehensions such as [x2forxinrange(10)] can directly generate square sequences; 2. Dictionary comprehensions such as {x:x2forxinrange(5)} clearly express key-value mapping; 3. Conditional filtering such as [xforxinnumbersifx%2==0] makes the filtering logic more intuitive; 4. Complex conditions can also be embedded, such as combining multi-condition filtering or ternary expressions; but excessive nesting or side-effect operations should be avoided to avoid reducing maintainability. The rational use of derivation can reduce

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

To implement a custom iterator, you need to define the __iter__ and __next__ methods in the class. ① The __iter__ method returns the iterator object itself, usually self, to be compatible with iterative environments such as for loops; ② The __next__ method controls the value of each iteration, returns the next element in the sequence, and when there are no more items, StopIteration exception should be thrown; ③ The status must be tracked correctly and the termination conditions must be set to avoid infinite loops; ④ Complex logic such as file line filtering, and pay attention to resource cleaning and memory management; ⑤ For simple logic, you can consider using the generator function yield instead, but you need to choose a suitable method based on the specific scenario.

Dynamic programming (DP) optimizes the solution process by breaking down complex problems into simpler subproblems and storing their results to avoid repeated calculations. There are two main methods: 1. Top-down (memorization): recursively decompose the problem and use cache to store intermediate results; 2. Bottom-up (table): Iteratively build solutions from the basic situation. Suitable for scenarios where maximum/minimum values, optimal solutions or overlapping subproblems are required, such as Fibonacci sequences, backpacking problems, etc. In Python, it can be implemented through decorators or arrays, and attention should be paid to identifying recursive relationships, defining the benchmark situation, and optimizing the complexity of space.

Future trends in Python include performance optimization, stronger type prompts, the rise of alternative runtimes, and the continued growth of the AI/ML field. First, CPython continues to optimize, improving performance through faster startup time, function call optimization and proposed integer operations; second, type prompts are deeply integrated into languages ??and toolchains to enhance code security and development experience; third, alternative runtimes such as PyScript and Nuitka provide new functions and performance advantages; finally, the fields of AI and data science continue to expand, and emerging libraries promote more efficient development and integration. These trends indicate that Python is constantly adapting to technological changes and maintaining its leading position.