+
−from pylab import *
+
−from scipy import *
+
−from scipy import stats
+
−
+
−scores = [[], [], [], [], []]
+
−ninety_percents = [{}, {}, {}, {}, {}]
+
−
+
−for record in open('sslc1.txt'):
+
−    record = record.strip()
+
−    fields = record.split(';')
+
−
+
−    region_code = fields[0].strip()
+
−   
+
−    for i, field in enumerate(fields[3:8]):
+
−        if region_code not in ninety_percents[i]:
+
−            ninety_percents[i][region_code] = 0
+
−        score_str = field.strip()
+
−        score = 0 if score_str == 'AA' else int(score_str)
+
−        scores[i].append(score)
+
−        if score > 90:
+
−            ninety_percents[i][region_code] += 1
+
−
+
−subj_total = []
+
−for subject in ninety_percents:
+
−    subj_total.append(sum(subject.values()))
+
−
+
−
+
−figure(1)
+
−pie(ninety_percents[3].values(), labels=ninety_percents[3].keys())
+
−title('Students scoring 90% and above in science by region')
+
−savefig('/tmp/science.png')
+
−
+
−figure(2)
+
−pie(subj_total, labels=['English', 'Hindi', 'Maths', 'Science', 'Social'])
+
−title('Students scoring more than 90% by subject(All regions combined).')
+
−savefig('/tmp/all_regions.png')
+
−
+
−math_scores = array(scores[2])
+
−# Mean score in Maths(All regions combined)
+
−print "Mean: ", mean(math_scores)
+
−
+
−# Median score in Maths(All regions combined)
+
−print "Median: ", median(math_scores)
+
−
+
−# Mode score in Maths(All regions combined)
+
−print "Mode: ", stats.mode(math_scores)
+
−
+
−# Standard deviation of scores in Maths(All regions combined)
+
−print "Standard Deviation: ", std(math_scores)
+
−