


Implementation method of directory structure for Python to extract Linux kernel source code
Today I used Python to extract the directory tree structure of the Linux kernel source code. I have never written a script program. I actually spent 2 hours on how to enumerate all the files and folders in a given directory, os. Walk can enumerate, but os.walk only gives directory names and file names, but no absolute paths. This can be achieved by using os.path.listdir, and then creating the directory. Since when the directory exists, a creation failure error will be prompted, so I first wanted to delete all directories and then create them again, but I found that there was still a problem. It is best to use Determine if the directory does not exist before creating it. If it exists, do not create it. Paste the code:
# @This script can be used to iterate the given directory,and create the # empty directory structure without file in it,e.g,I want to have you directory # as the linux kernel source, but i don't want the files, then this script comes. # @This script is running under python 3.1 # @author:zhangchao # @Time:2011年7月25日18:43:26 ########################################################################### import os import re #listmydirs is created to recursivly list all the entrys in the specified path. #In fact, we have os.walk to handle this problem # #level:目录的层数,不要也可以,主要是为了显示目录在那一层 #srcpath:内核源代码所在的路路径 #destpath:将要生成的内核源代码的目录结构所在路径 # def createkerneldirs(level,srcpath,destpath): for entrys in os.listdir(srcpath): #学习listdir函数的用法 tmpsrcpath=srcpath+os.sep+entrys tmpdestpath = tmpsrcpath.replace(srcpath,destpath)#将源路径中的E:\linux-2.6替换为E:\tmp,学习字符串替换函数的用法 print('in level:'+str(level)) print(tmpsrcpath) print(tmpdestpath) if os.path.isdir(tmpsrcpath): listmydirs(level+1,tmpsrcpath,tmpdestpath) if os.path.exists(tmpdestpath)==False: #如果文件不存在才创建文件 os.makedirs(tmpdestpath) if __name__=='__main__': #将E:\linux-2.6的内核源代码目录结构拷贝到E:\tmp目录下 createkerneldirs(1,r'E:\linux-2.6',r'E:\tmp')
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Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.


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