Bicycle Algorithm

import requests
import lxml.html
import time
import numpy
import unicodedata
import pylab
import collections

timestore = numpy.array([[0]])
locastore = numpy.array([['dummy location']])

for year in range(12,14):
    if year == 12:
        mstart = 2
        mlimit = 13
    if year == 13:
        mlimit = 10
    ystring = str(year)
    for month in range(mstart,mlimit,1):
        if month == 2:
            dlimit = 28
        if month == 1 or month == 3 or month == 5 or month == 7 or month == 8 or month == 10 or month == 12:
            dlimit = 32
        if month == 4 or month == 6 or month == 9 or month == 7:
            dlimit = 31
        for day in range(1,dlimit):
            if month < 10:
                mstring = '0'+ str(month)
            if month >= 10:
                mstring = str(month)
            if day < 10:
                dstring = '0'+str(day)
            if day >= 10:
                dstring = str(day)
            req = requests.get('http://www.safety.ncsu.edu/newblotter.asp?NOTDTE='+mstring+'%2F'+dstring+'%2F'+ystring+'')
            root = lxml.html.fromstring(req.text)
            no_records = 'I am sorry'
            if no_records in req.text:
                print ('NO RECORDS')
            else:
                interest_words = ['bicycle']
                seen_with = 'stolen'

                # Find last table
                tables = root.cssselect('table')
                table = tables[-1]

                rows = table.cssselect('tr')
                # Exclude table header
                rows = rows[1:]

                for row in rows:
                    cells = row.cssselect('td')
                    date_time = cells[1].text_content().strip()
                    narrative = cells[5].text_content().strip()
                    location = cells[4].text_content().strip()
                    for word in interest_words:
                        if word in narrative and seen_with in narrative:
                            #Storing date and time
                            date_time = unicodedata.normalize('NFKD', date_time).encode('ascii','ignore')
                            if date_time.find(':') == 1:
                                times = time.strptime(date_time,"%I:%M  %p")
                            hour, minute = times[3:5]
                            minute = minute/60.
                            hour += minute
                            storearray = numpy.array([[hour]])
                            timestore = numpy.concatenate((timestore,storearray))

                            #Storing location
                            location = unicodedata.normalize('NFKD', location).encode('ascii','ignore')
                            locaarray = numpy.array([[location]])
                            locastore = numpy.concatenate((locastore,locaarray))


pylab.figure(1)
pylab.hist(timestore,bins=24, range=(0,24), normed=True, histtype='stepfilled')
pylab.xlim(0,24)
pylab.xticks(numpy.arange(0,24,4))
pylab.title('Incidinces of Bike Theft from 02/01/12 to 09/31/13')

pylab.figure(2)
counter = collections.Counter(locastore.ravel())
ks = numpy.array([[]])
vs = numpy.array([[]])
for k, v in sorted(counter.iteritems(), key=lambda x:x[::-1]):
    ks = numpy.concatenate([[k]])
    vs = numpy.concatenate([[v]])

pylab.show()