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

[breamoreboy at gmail.com]

On Sunday, October 2, 2016 at 2:12:39 AM UTC+1, 38016... 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?

This code cannot run as neither count nor entropy are defined.

You can loop around the features like this:-

for key, attributes_list in features.items():

'iteritems' as suggested by Peter Pearson is Python 2 only.

You can loop around your attributes with:-

for attribute in attributes_list:

If you need the index in the loop use the enumerate function https://docs.python.org/3/library/functions.html#enumerate

Kindest regards.

Mark Lawrence.