Need help for the print() function with a better order

[380162267qq at gmail.com]

在 2016年10月2日星期日 UTC-4下午1:05:58，Peter Pearson写道：
> On Sat, 1 Oct 2016 18:12:29 -0700 (PDT), 380162267qq at gmail.com wrote:
> > I am trying to print a simple decision tree for my homework.
> > The answer must keep in this format:
> >
> > Top 7,4,0.95
> > career gain = 100
> > 	1.Management 2, 3, 0.9709505944546686
> > 	2.Service 5, 1, 0.6500224216483541
> > location gain = 100
> > 	1.Oregon 4, 1, 0.7219280948873623
> > 	2.California 3, 3, 1.0
> > edu_level gain = 100
> > 	1.High School 5, 1, 0.6500224216483541
> > 	2.College 2, 3, 0.9709505944546686
> > years_exp gain = 100
> > 	1.Less than 3 3, 1, 0.8112781244591328
> > 	2.3 to 10 2, 1, 0.9182958340544896
> > 	3.More than 10 2, 2, 1.0
> >
> > Here is my code:
> >     features={'edu_level':['High School',
>                              'College'],
>                 'career':    ['Management',
>                               'Service'],
>                 'years_exp':['Less than 3',
>                              '3 to 10',
>                              'More than 10'],
>                 'location':['Oregon',
>                             'California']}
> >
> >     print('Top 7,4,0.95')
> >     for key in features:
> >         print('{} gain = {}'.format(key,100))
> >         attributes_list=features[key]
> >         kargs={}
> >         for i in range(len(attributes_list)):
> >             kargs[key]=attributes_list[i]
> >             low=table.count('Low',**kargs)
> >             high=table.count('High',**kargs)
> >             print('\t{}.{} {}, {}, {}'.format(
>                     i+1,attributes_list[i],low,high,entropy(low,high)))
> >
> > I set all the gain as 100 now.But actually the gain must calculate
> > with the data below.  For example, the career gain need the data of
> > 'Management' and 'Service'.  I don't know how to do.  or Anyone can
> > provide me a better logic?
> 
> I interpret your question as meaning that the value that you 
> print after "gain =" should depend on features[key].  To do that,
> you'll need to insert a line resembling
>           gain = gain_from_features(features[key])
> before the print statement.  You'll have to write the gain_from_features
> function, and provide it with the numbers from which it will
> compute the gain.
> 
> As a stylistic suggestion, note that Python allows you to break
> your "features=" line into a more readable format, as I have done
> above.
> 
> Another stylistic suggestions:
>   for key, attributes_list in features.iteritems():
> 
> -- 
> To email me, substitute nowhere->runbox, invalid->com.

Thank you for your suggestion.I was so tired last night printing the structure of decision tree crushed me.
I am not familiar with python and now I will try to modify my code.