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This article mainly introduces how to improve the operating efficiency of Python, has a certain reference value, interested friends can refer to, I hope you can learn a lot after reading this article, the following let the editor take you to understand it.

Parallel programming of multi-process lines

For CPU-intensive programs, encapsulated classes such as multiprocessing's Process,Pool can be used to implement parallel computing in a multi-process way. However, because the communication cost in the process is relatively high, the efficiency of the program which needs a lot of data exchange between processes may not be greatly improved.

Second, multi-thread parallel programming

For IO-intensive programs, the multiprocessing.dummy module uses multiprocessing's interface to encapsulate threading, making multithreaded programming very easy. Distributed: the Managers class in multiprocessing provides a way to share data among different processes, based on which distributed programs can be developed. Different business scenarios can choose one or more combinations to optimize program performance.

Third, optimize the algorithm time

The time complexity of the algorithm has the greatest impact on the execution efficiency of the program. In Python, the time complexity can be optimized by selecting the appropriate data structure, for example, the time complexity of list and set to find an element is O (n) and O (1), respectively. Different scenarios have different optimization methods, generally speaking, there are general ideas such as divide and conquer, branch and boundary, greed, dynamic planning and so on.

IV. Optimization for the cycle

Each programming language emphasizes the need to optimize loops. When using Python, you can rely on a lot of techniques to make the loop run faster. However, one method that developers often miss is to avoid using point operations in a loop. The key to optimizing the loop is to reduce the amount of work that Python executes inside the loop, because Python's native interpreter really slows down execution in that case.

Fifth, function selection

Using xrange instead of range; using xrange during loops saves a lot of system memory because xrange () produces only one integer element per call in the sequence. Range (), on the other hand, will directly return the complete list of elements, which will have unnecessary overhead when used in the loop. Xrange no longer exists in python3, where range provides an iterator that can traverse a range of arbitrary lengths.

VI. Use performance analysis tools

In addition to the timeit module used in ipython above, there is also cProfile. The use of cProfile is also very simple: python-m cProfile filename.py,filename.py is the file name of the program to be run. You can see the number of times each function is called and the time it takes to run in the standard output, so as to find the performance bottleneck of the program, and then optimize it pertinently.

Thank you for reading this article carefully. I hope the article "how to improve the running efficiency of Python" shared by the editor will be helpful to everyone. At the same time, I also hope that you will support us and pay attention to the industry information channel. More related knowledge is waiting for you to learn!

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