userlist.py

import math

lsst = []
lsst.append({'id':'40','name':'Joe Bloggs','posts':'4'})
lsst.append({'id':'567','name':'Jenny Smith','posts':'3'})
lsst.append({'id':'3','name':'Frank Jones','posts':'54'})
lsst.append({'id':'46','name':'Samantha Wills','posts':'0'})
lsst.append({'id':'6789','name':'Ahmed Joseph Naran','posts':'15'})

def orderList(lst):
    orderedList = []
    for elem in lst:
        split_name = elem['name'].split(' ')
        last_name = split_name.pop()
        first_name = split_name.pop()
        if elem['posts'] != '0':
            orderedList.append({'first_name':first_name, 'last_name':last_name,'posts':elem['posts']})
    orderedList = sorted(orderedList, key=lambda k: int(k['posts'])) 
    orderedList.reverse()
    return orderedList

def generateMathFxns(lst):
    total_posts = 0
    average_divisor = 0
    #when dividing to find average, will total users include those who didnt post? 
    #decision : no
    lst = orderList(lst)
    list_length = len(lst)
    maximum = int(lst[0]['posts'])
    minimum = int(lst[list_length-1]['posts'])
    
    if list_length % 2 == 0:
        median_index_1 = (list_length / 2) - 1
        median_index_2 = median_index_1 + 1
        median = float((float(lst[median_index_1]['posts']) + float(lst[median_index_2]['posts'])) / 2)
    else:
        median_index = (list_length + 1) / 2
        median = int(lst[median_index]['posts'])
    
    for elem in lst:
        total_posts = total_posts + int(elem['posts'])
        average_divisor = average_divisor + 1
        
    average = float(float(total_posts) / float(average_divisor))
    
    total_variance = 0
    
    for elem in lst:
        variance_val = float(elem['posts']) - average
        variance_val = math.pow(variance_val, 2)
        total_variance = total_variance + variance_val
    
    standard_dev = float(math.sqrt(total_variance))
    
    print "Average: %f" % average
    print "Median: %f" % median
    print "Minimum: %d" % minimum
    print "Maximum: %d" % maximum
    print "Standard deviation: %f" % standard_dev
    
generateMathFxns(lsst)