in the previous examplemultiplying the nlog component and comparing it to notice how for very low values of nthe time to completet is actually lower for an algorithm that runs in time howeverfor values above about the log function begins to dominateflattening the output until at the comparatively moderate size the performance is more than twice than that of an algorithm running in time notice also that the disappearance of the constant factor is irrelevant at high values of the code used to generate these graphs is as followsimport matplotlib pyplot as plt import math =list(range( , ) =[] =[] plt plot( [ for in xplt plot( [( * )math log( for in ]plt show(you will need to install the matplotlib libraryif it is not installed alreadyfor this to work details can be found at the following addressi encourage you to experiment with this list comprehension expression used to generate the plots for exampleadding the following plot statementplt plot( [( * )math log( for in ]