Python的标准库中的os模块包含普遍的操作系统功能。如果你希望你的程序能够与平台无关的话,这个模块是尤为重要的。即它允许一个程序在编写后不需要任何改动,也不会发生任何问题,就可以在Linux和Windows下运行。
下面列出了一些在os模块中比较有用的部分。它们中的大多数都简单明了。
os.sep可以取代操作系统特定的路径分隔符。windows下为 “\\”
os.name字符串指示你正在使用的平台。比如对于Windows,它是'nt',而对于Linux/Unix用户,它是'posix'。
os.getcwd()函数得到当前工作目录,即当前Python脚本工作的目录路径。
os.getenv()获取一个环境变量,如果没有返回none
os.putenv(key, value)设置一个环境变量值
os.listdir(path)返回指定目录下的所有文件和目录名。
os.remove(path)函数用来删除一个文件。
os.system(command)函数用来运行shell命令。
os.linesep字符串给出当前平台使用的行终止符。例如,Windows使用'\r\n',Linux使用'\n'而Mac使用'\r'。
os.path.split(p)函数返回一个路径的目录名和文件名。
os.path.isfile()和os.path.isdir()函数分别检验给出的路径是一个文件还是目录。
os.path.existe()函数用来检验给出的路径是否真地存在
os.curdir:返回当前目录('.')
os.chdir(dirname):改变工作目录到dirname
os.path.getsize(name):获得文件大小,如果name是目录返回0L
os.path.abspath(name):获得绝对路径
os.path.normpath(path):规范path字符串形式
os.path.splitext():分离文件名与扩展名
os.path.join(path,name):连接目录与文件名或目录
os.path.basename(path):返回文件名
os.path.dirname(path):返回文件路径

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.

Python's real-world applications include data analytics, web development, artificial intelligence and automation. 1) In data analysis, Python uses Pandas and Matplotlib to process and visualize data. 2) In web development, Django and Flask frameworks simplify the creation of web applications. 3) In the field of artificial intelligence, TensorFlow and PyTorch are used to build and train models. 4) In terms of automation, Python scripts can be used for tasks such as copying files.

Python is widely used in data science, web development and automation scripting fields. 1) In data science, Python simplifies data processing and analysis through libraries such as NumPy and Pandas. 2) In web development, the Django and Flask frameworks enable developers to quickly build applications. 3) In automated scripts, Python's simplicity and standard library make it ideal.

Python's flexibility is reflected in multi-paradigm support and dynamic type systems, while ease of use comes from a simple syntax and rich standard library. 1. Flexibility: Supports object-oriented, functional and procedural programming, and dynamic type systems improve development efficiency. 2. Ease of use: The grammar is close to natural language, the standard library covers a wide range of functions, and simplifies the development process.

Python is highly favored for its simplicity and power, suitable for all needs from beginners to advanced developers. Its versatility is reflected in: 1) Easy to learn and use, simple syntax; 2) Rich libraries and frameworks, such as NumPy, Pandas, etc.; 3) Cross-platform support, which can be run on a variety of operating systems; 4) Suitable for scripting and automation tasks to improve work efficiency.

Yes, learn Python in two hours a day. 1. Develop a reasonable study plan, 2. Select the right learning resources, 3. Consolidate the knowledge learned through practice. These steps can help you master Python in a short time.


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