本文实例讲述了Python实现的重启关机程序的方法,对Python程序设计有一定的参考价值。具体方法如下:
实例代码如下:
#!/usr/bin/python #coding=utf-8 import time from os import system runing = True while runing: input = raw_input('关机(s)OR重启(r)?(q退出)') input = input.lower() if input == 'q' or input =='quit': runing = False print '程序退出' break seconds = int(raw_input('请输入暂停时间(单位:秒):')) time.sleep(seconds) print '暂停时间:', seconds runing = False if input == 's': print '关机ing' system('halt') elif input == 'r': print '重启ing' system('reboot') else: print '程序错误重新输入' runing = True print '程序结束~~~!'
该实例在linux下测试通过,windows的话需要判断执行命令。

The reasons why Python scripts cannot run on Unix systems include: 1) Insufficient permissions, using chmod xyour_script.py to grant execution permissions; 2) Shebang line is incorrect or missing, you should use #!/usr/bin/envpython; 3) The environment variables are not set properly, and you can print os.environ debugging; 4) Using the wrong Python version, you can specify the version on the Shebang line or the command line; 5) Dependency problems, using virtual environment to isolate dependencies; 6) Syntax errors, using python-mpy_compileyour_script.py to detect.

Using Python arrays is more suitable for processing large amounts of numerical data than lists. 1) Arrays save more memory, 2) Arrays are faster to operate by numerical values, 3) Arrays force type consistency, 4) Arrays are compatible with C arrays, but are not as flexible and convenient as lists.

Listsare Better ForeflexibilityandMixdatatatypes, Whilearraysares Superior Sumerical Computation Sand Larged Datasets.1) Unselable List Xibility, MixedDatatypes, andfrequent elementchanges.2) Usarray's sensory -sensical operations, Largedatasets, AndwhenMemoryEfficiency

NumPymanagesmemoryforlargearraysefficientlyusingviews,copies,andmemory-mappedfiles.1)Viewsallowslicingwithoutcopying,directlymodifyingtheoriginalarray.2)Copiescanbecreatedwiththecopy()methodforpreservingdata.3)Memory-mappedfileshandlemassivedatasetsb

ListsinPythondonotrequireimportingamodule,whilearraysfromthearraymoduledoneedanimport.1)Listsarebuilt-in,versatile,andcanholdmixeddatatypes.2)Arraysaremorememory-efficientfornumericdatabutlessflexible,requiringallelementstobeofthesametype.

Pythonlistscanstoreanydatatype,arraymodulearraysstoreonetype,andNumPyarraysarefornumericalcomputations.1)Listsareversatilebutlessmemory-efficient.2)Arraymodulearraysarememory-efficientforhomogeneousdata.3)NumPyarraysareoptimizedforperformanceinscient

WhenyouattempttostoreavalueofthewrongdatatypeinaPythonarray,you'llencounteraTypeError.Thisisduetothearraymodule'sstricttypeenforcement,whichrequiresallelementstobeofthesametypeasspecifiedbythetypecode.Forperformancereasons,arraysaremoreefficientthanl

Pythonlistsarepartofthestandardlibrary,whilearraysarenot.Listsarebuilt-in,versatile,andusedforstoringcollections,whereasarraysareprovidedbythearraymoduleandlesscommonlyusedduetolimitedfunctionality.


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