How python manages memory

Refactoring is not rewriting, but improving the code structure and readability without changing the function. Common reconstruction situations include too large functions or classes, many repetitive codes, fuzzy variable naming, and complex control processes. Refactoring should start with details, such as splitting large functions, extracting duplicate code, simplifying conditional judgment, and improving variable naming. Tools and testing are the key. Using pytest, black, isort, flake8, mypy and other tools to cooperate with unit testing can ensure that the changes are safe. Refactoring should be continuously optimized from a small way, rather than rewriting it all at once.

Python's memory management consists of automatic allocation and recycling mechanisms. When creating variables, memory will be allocated from the memory pool or system malloc according to the object size. Small objects preferentially use memory pools to improve efficiency. Memory recycling mainly relies on reference counting and garbage collector (gc module). Reference counting is zeroed and memory is released, while circular references are processed by garbage collector. To reduce memory usage, array, NumPy array, generator, and \_\_slots\_\_\_ can be used. The memory is not released immediately at the end of the del or function, which may be caused by garbage collection delay, external memory usage or object cache. You can use tracemalloc or memory\_profiler tools to analyze the memory situation.

Recursion is a method for function calls to solve problems in Python, and is suitable for scenarios such as factorial, Fibonacci sequence, nested list traversal and binary search. 1. Factorial is recursively calculated by n*factorial(n-1), and the basic situation is n==0 or 1, and the basic situation is n==0 or 1; 2. The Fibonacci sequence defines f(n)=f(n-1) f(n-2), and the basic situation is f(0)=0, f(1)=1, but the naive recursive efficiency is low, and it is recommended to use lru_cache to optimize; 3. When traversing the nested list, if the elements are lists, they will be processed recursively, otherwise they will be printed; 4. The binary search recursive version looks for the target value in an ordered array, and determines the recursive left and right intervals based on the comparison between the intermediate value and the target. The basic situation is low>hig

The core of custom loggingHandler is to inherit logging.Handler and implement the emit() method, which is suitable for scenarios such as sending logs to emails, writing to databases, or pushing remote servers. 1. The situations that need to be customized include: pushing logs to Slack or DingTalk, recording to database or API, processing by level, and adding additional information; 2. The implementation method is to inherit logging.Handler and rewriting emit(), where you write custom logic such as sending HTTP requests; 3. When using it, you need to pay attention to exception handling, formatting output, setting appropriate levels and formatters, and avoid duplicate output and propagation problems.

In Python, the difference between break and continue is that: 1.break is used to terminate the entire loop immediately, which is often used to exit the loop early or complete the search task; 2.continue only skips the current iteration and continues to execute the next loop, which is suitable for ignoring specific elements or filtering data. For example, use break after finding a match when searching a list, and skip invalid entries with continue when cleaning data. Although both control the cycle flow, their functions are completely different.

There are three ways to tile nested lists in Python: First, use list comprehension, the syntax is [itemforsublistinlist_of_listsforiteminsublist], which is suitable for two-dimensional lists; Second, use itertools.chain, which includes itertools.chain.from_iterable(list_of_lists) or itertools.chain(*list_of_lists), which has better performance; Third, when dealing with irregular nesting, judgment statements need to be added, for example, using isinstance(sublist, list) to distinguish lists from non-

Threading.Lock is needed to prevent race conditions for shared resources in multi-threading environments. 1. Create lock object lock=threading.Lock(); 2. Use withlock: Ensure the operation atomicity of shared variables; 3. Multiple threads accumulate 100,000 times for counters, and the final result is correct 500,000; 4. It is recommended to use the with statement to automatically manage the acquisition and release of locks; 5. Avoid nested acquisition of locks, and use threading.RLock() if necessary; 6. The scope of the lock should be as small as possible to improve performance; 7. Pay attention to avoid deadlocks due to inconsistent locking order.

Python is widely used in scientific computing because its mature libraries and tool chains can handle various tasks. Key points include: 1. Install core libraries such as NumPy (efficient arrays and mathematical functions), SciPy (advanced mathematical operations), Matplotlib (data visualization) and Pandas (table data processing), which can be installed through pip or conda; 2. Replace native lists with NumPy to improve performance, and support vectorized operations, broadcast mechanisms and linear algebra functions; 3. SciPy provides complex mathematical tools such as integral, optimization, and Fourier transform, such as using quad function to calculate definite integrals; 4. Matplotlib and its encapsulation library Seaborn are used for graph display, supporting style settings and professionalism