python - How to implement about calculating weighted average if it's without for loop -

this code following:

students = [{'name': 'tom',              'subjects': {'math': 50,                           'english': 100,                           'science': 72}},             {'name': 'alex',              'subjects': {'math': 100,                           'english': 90,                           'science': 95}}]  weighting_coefficient = {'math': 2,                          'english': 10,                          'science': 8}  total = sum(weighting_coefficient.values())  index, student in enumerate(students):     subjects = student['subjects']     weighting_score = 0     subject, score in subjects.items():         weighting_score += weighting_coefficient[subject] * score     students[index]['weighted_average'] = float(weighting_score)/total  print students 

the result:

[{'name': 'tom',   'subjects': {'english': 100, 'math': 50, 'science': 72},   'weighted_average': 83.8},  {'name': 'alex',   'subjects': {'english': 90, 'math': 100, 'science': 95},   'weighted_average': 93.0}] 

i sure complete calculation, available implement codes if don't use foor-loop it?

thanks @kevin guan great tip (helped me advance own "python career")

using list comprehension, suggested:

students = [{'name': 'tom',          'subjects': {'math': 50,                       'english': 100,                       'science': 72}},         {'name': 'alex',          'subjects': {'math': 100,                       'english': 90,                       'science': 95}}]  weighting_coefficient = {'math': 2,                      'english': 10,                      'science': 8}  total = sum(weighting_coefficient.values())  student in students:     student['weighted_average'] = float( sum( [student['subjects'][subj] * weighting_coefficient[subj] subj in student['subjects'].keys() ] ) ) / total  print students 

the code looks messy @ first, create more variables hold key information (and make weighting_coefficients shorter).

i've tested both original data set , 1 (not included here results matched using both op's method , method).