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

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

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.

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.

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.

Python's socket module is the basis of network programming, providing low-level network communication functions, suitable for building client and server applications. To set up a basic TCP server, you need to use socket.socket() to create objects, bind addresses and ports, call .listen() to listen for connections, and accept client connections through .accept(). To build a TCP client, you need to create a socket object and call .connect() to connect to the server, then use .sendall() to send data and .recv() to receive responses. To handle multiple clients, you can use 1. Threads: start a new thread every time you connect; 2. Asynchronous I/O: For example, the asyncio library can achieve non-blocking communication. Things to note

The core answer to Python list slicing is to master the [start:end:step] syntax and understand its behavior. 1. The basic format of list slicing is list[start:end:step], where start is the starting index (included), end is the end index (not included), and step is the step size; 2. Omit start by default start from 0, omit end by default to the end, omit step by default to 1; 3. Use my_list[:n] to get the first n items, and use my_list[-n:] to get the last n items; 4. Use step to skip elements, such as my_list[::2] to get even digits, and negative step values ??can invert the list; 5. Common misunderstandings include the end index not

Python's datetime module can meet basic date and time processing requirements. 1. You can get the current date and time through datetime.now(), or you can extract .date() and .time() respectively. 2. Can manually create specific date and time objects, such as datetime(year=2025, month=12, day=25, hour=18, minute=30). 3. Use .strftime() to output strings in format. Common codes include %Y, %m, %d, %H, %M, and %S; use strptime() to parse the string into a datetime object. 4. Use timedelta for date shipping