random walks and more about data vizualization but why do there appear to be far fewer circle markers than triangle or markersbecause many of the ewdrunk' walks ended up at the same place this is not surprisinggiven the small number of possible endpoints ( for the ewdrunk also the circle markers seem to be fairly uniformly spaced across the -axiswhich is consistent with the relatively high coefficient of variation that we noticed earlier it is still not obviousat least to uswhy the colddrunk manageson averageto get so much further from the origin than the other kinds of drunks perhaps it' time to look not at the average endpoint of many walksbut at the path followed by single walk the code in figure produces the plot on the right def tracewalk(drunkkindsnumsteps)stylechoice styleiterator((' +'' ^''mo') field(for dclass in drunkkindsd dclass( adddrunk(dlocation( )locs [for in range(numsteps) movedrunk(dlocs append( getloc( )xvals [yvals [for in locsxvals append( getx()yvals append( gety()curstyle stylechoice nextstyle(pylab plot(xvalsyvalscurstylelabel dclass __name__pylab title('spots visited on walk (str(numstepssteps)'pylab xlabel('steps east/west of origin'pylab ylabel('steps north/south of origin'pylab legend(loc 'best'tracewalk((usualdrunkcolddrunkewdrunk) figure tracing walks since the walk is steps long and the ewdrunk' walk visits fewer than different locationsit' clear that he is spending lot of time retracing his steps the same kind of observation holds for the usualdrunk in contrastwhile the colddrunk is not exactly making beeline for floridahe is managing to spend relatively less time visiting places he has already been