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)