


Tutorial on how to use the platform module to obtain system information in Python
Operating system related
- system(): operating system type (see example)
- version(): operating system version
- release(): operating system release number, for example, win 7 returns 7, and NT, 2.2.0, etc.
- platform(aliased=0, terse=0): operating system information string, combined with system()+win32_ver()[:3]
- win32_ver(release='', version='', csd='', ptype=''): win system related information
- linux_distribution(distname='', version='', id='', supported_dists=('SuSE', 'debiaare', 'yellowdog', 'gentoo', 'UnitedLinux', 'turbolinux'), full_distribution_name= 1): Linux system related information
- dist(distname='', version='', id='', supported_dists=('SuSE', 'debian', 'fedora', 'redhat', 'centos', 'mandrake', 'mandriva' , 'rocks', 'slackware', 'yellowdog', 'gentoo', 'UnitedLinux', 'turbolinux')): Try to obtain Linux OS release version information. Return (distname, version, id). dist means release version .
- mac_ver(release='', versioninfo=('', '', ''), machine=''): mac version
- java_ver(release='', vendor='', vminfo=('', '', ''), osinfo=('', '', '')): java version
- libc_ver(executable=r'c:Python27python.exe', lib='', version='', chunksize=2048): libc version, Linux related.
The returned tuple of the above corresponding version query corresponds to its formal parameters.
platform.system() 'Linux' # python 3.3.2+ 64 bits on debian jessie 64 bits 'Windows' # python 3.3.2 32 bits on windows 8.1 64 bits 'Windows' # python 3.3.2 64 bits on windows 8.1 64 bits 'Darwin' # python 3.4.1 64 bits on mac os x 10.9.4 'Java' platform.version() '#1 SMP Debian 3.10.11-1 (2013-09-10)' # python 3.3.2+ 64 bits on debian jessie 64 bits '6.2.9200' # python 3.3.2 32 bits on windows 8.1 64 bits '6.2.9200' # python 3.3.2 64 bits on windows 8.1 64 bits 'Darwin Kernel Version 13.3.0: Tue Jun 3 21:27:35 PDT 2014; root:xnu-2422.110.17~1/RELEASE_X86_64' # python 3.4.1 64 bits on mac os x 10.9.4 platform() 'Windows-7-6.1.7601-SP1' win32_ver() ('7', '6.1.7601', 'SP1', u'Multiprocessor Free') platform.dist() ('debian', 'jessie/sid', '') # python 3.3.2+ 64 bits on debian jessie 64 bits
System Information
- uname(): Returns tuple, system, node, release, version, machine, processor.
- architecture(executable=r'c:Python27python.exe', bits='', linkage=''): System architecture
- machine(): CPU platform, AMD, x86? (see example)
- node(): node name (machine name, such as Hom-T400)
- processor() : CPU information
- system_alias(system, release, version): Returns the corresponding tuple.. Useless.
- platform.architecture()
('64bit', 'ELF') # python 3.3.2+ 64 bits on debian jessie 64 bits ('32bit', 'WindowsPE') # python 2.7.2 32 bits on windows 7 64 bits ('64bit', 'WindowsPE') # python 3.3.2 64 bits on wndows 8.1 64 bits ('64bit', '') # python 3.4.1 64 bits on mac os x 10.9.4 platform.machine() 'x86_64' # python 3.3.2+ 64 bits on debian jessie 64 bits 'AMD64' # python 3.3.2 32 bits on windows 8.1 64 bits 'AMD64' # python 3.3.2 64 bits on windows 8.1 64 bits 'x86_64' # python 3.4.1 64 bits on mac os x 10.9.4 platform.node() 'Hom-T400' platform.processor() 'Intel64 Family 6 Model 23 Stepping 10, GenuineIntel' platform.uname() ('Windows', 'Hom-T400', '7', '6.1.7601', 'AMD64', 'Intel64 Family 6 Model 23 Stepping 10, GenuineIntel') uname_result(system='Linux', node='work', release='3.10-3-amd64', version='#1 SMP Debian 3.10.11-1 (2013-09-10)', machine='x86_64', processor='') # python 3.3.2+ 64 bits on debian jessie 64 bits uname_result(system='Windows', node='work-xxx', release='8', version='6.2.9200', machine='AMD64', processor='Intel64 Family 6 Model 58 Stepping 9,GenuineIntel') # python 3.3.2 32 bits on windows 8.1 64 bits uname_result(system='Darwin', node='mba', release='13.3.0', version='Darwin Kernel Version 13.3.0: Tue Jun 3 21:27:35 PDT 2014; root:xnu-2422.110.17~1/RELEASE_X86_64', machine='x86_64', processor='i386') # python 3.4.1 64 bits on mac os x 10.9.4
Python related
- python_version(): py version number
- python_branch(): python branch (subversion information), usually empty.
- python_build(): python compilation number (default) and date.
- python_compiler(): py compiler information
- python_implementation(): python installation implementation method, such as CPython, Jython, Pypy, IronPython (.net), etc.
- python_revision(): python type revision information, usually empty.
- python_version_tuple(): tuple divided by python version number.
- popen(cmd, mode='r', bufsize=None): portable popen() interface, executes various commands.
- python_verison()
'3.3.2+' # python 3.3.2+ 64 bits on debian jessie 64 bits '3.3.3' # python 3.3.2 32 bits on windows 8.1 64 bits python_version_tuple() ('2', '7', '2') python_build() ('default', 'Jun 12 2011 15:08:59') python_compiler() 'MSC v.1500 32 bit (Intel)' pl.python_implementation() 'CPython'
Get username:
>>> import getpass >>> getpass.getuser() 'root'
Get environment variables:
>>> import os >>> import pwd >>> os.environ['LANG'] 'en_US.UTF-8' >>> print os.getenv('LANG') en_US.UTF-8 >>> print os.getenv('PWD') /root >>> print os.getenv('HOME') /root >>> print os.getenv('USER') root >>> print os.getenv('HOSTNAME') localhost.localdomain >>> print os.getenv('SHELL') /bin/bash >>> pwd.getpwuid(os.getuid()) pwd.struct_passwd(pw_name='root', pw_passwd='x', pw_uid=0, pw_gid=0, pw_gecos='root', pw_dir='/root', pw_shell='/bin/bash') >>> pwd.getpwuid(os.getuid())[0] #获得用户名 'root' >>> pwd.getpwuid(os.getuid())[5] #获得家目录 '/root' >>> pwd.getpwuid(os.getuid())[6] #获得shell '/bin/bash'
There is also os.environ.get, which will return all environment variables as a dictionary

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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