Oxford Sun Hours | Python Fiddle

import pprint

sun_hours = [
[43.8, 60.5, 190.2, 144.7, 240.9, 210.3, 219.7, 176.3, 199.1, 109.2, 78.7, 67.0],
[49.9, 54.3, 109.7, 102.0, 134.5, 211.2, 174.1, 207.5, 108.2, 113.5, 68.7, 23.3],
[63.7, 72.0, 142.3, 93.5, 150.1, 158.7, 127.9, 135.5, 92.3, 102.5, 62.4, 38.5],
[51.0, 57.9, 133.4, 110.9, 112.4, 199.3, 124.0, 178.3, 102.1, 100.7, 55.7, 58.0],
[69.5, 94.3, 187.6, 152.5, 170.2, 226.9, 237.6, 242.7, 177.3, 101.3, 53.9, 59.0],
[65.9, 96.6, 122.5, 124.9, 216.3, 192.7, 269.3, 184.9, 149.1, 81.5, 48.7, 31.3],
[48.1, 62.0, 121.5, 127.3, 188.5, 196.3, 274.3, 199.9, 144.7, 102.6, 65.4, 48.9],
[43.4, 89.2, 71.4, 133.2, 179.5, 166.2, 119.2, 184.7, 79.3, 103.1, 48.9, 62.3],
[50.9, 66.6, 99.7, 103.1, 185.0, 181.3, 140.1, 202.3, 143.0, 79.1, 65.9, 41.2],
[41.2, 66.9, 172.3, 180.9, 144.9, 190.6, 133.5, 151.3, 110.9, 118.1, 70.0, 52.4],
[46.4, 104.9, 86.2, 171.7, 184.9, 227.9, 139.7, 153.7, 147.0, 94.3, 41.1, 46.0],
[83.1, 22.8, 128.3, 118.1, 215.4, 273.4, 165.1, 199.5, 179.5, 95.5, 76.8, 46.5],
[41.7, 67.9, 118.7, 106.9, 141.9, 210.3, 227.5, 163.7, 123.7, 120.2, 47.1, 46.9],
[45.1, 53.9, 69.4, 202.5, 209.4, 234.0, 150.1, 132.7, 124.5, 84.6, 57.8, 51.0],
[54.7, 79.3, 132.9, 166.6, 244.1, 192.9, 196.7, 178.3, 142.5, 84.9, 72.3, 49.5],
[41.2, 62.4, 142.7, 147.0, 235.6, 170.3, 97.5, 185.2, 143.8, 102.0, 49.3, 64.1],
[51.5, 65.7, 152.6, 209.1, 156.1, 182.4, 159.0, 144.8, 64.9, 111.7, 31.0, 46.6],
[49.9, 78.7, 107.2, 203.3, 162.9, 149.8, 197.6, 134.8, 98.5, 79.3, 42.9, 74.7],
[59.5, 26.3, 70.9, 150.5, 147.3, 185.9, 144.5, 274.9, 159.9, 107.3, 75.4, 37.9],
[45.7, 92.9, 160.2, 205.2, 237.1, 124.2, 174.7, 133.7, 146.4, 93.7, 68.6, 65.4],
[51.0, 115.1, 112.5, 182.5, 233.3, 242.1, 262.5, 210.3, 151.1, 125.0, 76.2, 65.4],
[40.6, 67.5, 138.8, 163.7, 174.1, 244.5, 174.0, 171.1, 112.7, 96.6, 56.9, 55.3],
[48.9, 58.6, 92.6, 200.4, 152.1, 251.9, 216.7, 174.7, 110.8, 105.6, 75.1, 69.8],
[94.1, 96.7, 105.0, 178.2, 207.0, 217.6, 194.0, 180.5, 140.3, 105.0, 72.1, 77.7],
[42.5, 75.9, 140.7, 183.3, 223.0, 139.7, 203.4, 237.4, 151.7, 84.1, 54.4, 28.4],
[75.7, 79.7, 107.9, 202.4, 145.9, 157.1, 157.1, 123.5, 168.8, 94.5, 60.1, 54.5],
[40.1, 86.3, 161.4, 173.7, 217.5, 155.3, 268.3, 188.0, 153.1, 119.7, 71.5, 47.3],
[50.3, 78.9, 149.7, 158.7, 246.6, 145.0, 168.0, 161.4, 94.3, 116.5, 77.9, 18.2],
[50.8, 83.1, 110.2, 168.0, 205.6, 297.1, 157.9, 170.5, 102.6, 92.9, 76.4, 62.3],
[54.6, 55.4, 110.7, 145.2, 196.0, 145.7, 188.1, 119.6, 118.0, 93.7, 51.8, 29.5],
[85.8, 65.5, 102.0, 153.8, 228.0, 226.3, 272.7, 245.6, 213.9, 144.2, 70.6, 45.0],
[37.8, 82.3, 78.0, 164.9, 182.3, 274.9, 129.7, 147.1, 122.8, 60.9, 73.4, 54.5],
[43.6, 65.1, 173.2, 86.9, 225.2, 231.2, 196.5, 185.7, 135.8, 118.2, 63.4, 76.5],
[70.0, 70.6, 126.3, 143.3, 177.5, 280.3, 137.3, 154.5, 142.3, 108.8, 32.7, 72.6],
[58.9, 66.4, 85.8, 119.1, 193.4, 199.4, 188.2, 142.6, 129.7, 78.8, 60.4, 49.8],
[37.2, 57.3, 65.9, 128.5, 190.8, 156.1, 214.7, 217.7, 210.4, 134.5, 55.0, 51.1],
[83.7, 31.1, 137.7, 141.6, 179.6, 188.7, 122.8, 181.2, 122.9, 109.9, 77.4, 71.9],
[42.5, 41.5, 121.5, 81.5, 234.9, 199.0, 149.7, 188.6, 168.0, 90.4, 61.0, 41.7],
[64.2, 88.2, 174.6, 130.8, 184.2, 232.0, 234.4, 167.1, 116.5, 95.1, 69.2, 70.6],
[50.0, 54.0, 148.5, 184.5, 155.0, 206.6, 136.2, 124.0, 114.9, 66.5, 47.9, 35.9],
[40.0, 78.1, 70.5, 221.3, 161.9, 276.9, 243.8, 157.5, 97.4, 112.0, 84.6, 35.6],
[34.5, 115.9, 120.8, 132.7, 224.8, 270.9, 192.4, 185.6, 157.3, 106.2, 64.7, 43.8],
[42.1, 69.5, 106.0, 122.9, 228.9, 143.5, 259.3, 134.2, 166.5, 135.2, 102.0, 29.8],
[41.8, 27.3, 144.0, 117.6, 141.9, 150.4, 168.7, 160.9, 129.1, 91.6, 80.6, 47.6],
[38.8, 74.1, 150.7, 167.7, 168.0, 249.5, 171.1, 192.0, 153.9, 95.1, 89.1, 62.9],
[56.3, 58.3, 101.7, 142.1, 191.4, 206.2, 187.8, 198.7, 146.5, 105.4, 52.9, 58.8],
[44.7, 57.8, 72.7, 131.4, 159.1, 301.0, 242.4, 218.6, 147.0, 120.7, 85.1, 34.4],
[70.3, 42.6, 107.8, 148.7, 172.0, 261.4, 254.2, 257.4, 118.2, 43.6, 54.1, 58.6],
[35.4, 74.4, 87.2, 157.9, 217.5, 123.2, 193.6, 123.4, 101.8, 107.3, 102.4, 45.2],
[53.6, 58.9, 128.1, 113.6, 202.8, 171.7, 146.4, 157.4, 159.3, 87.5, 77.3, 34.3],
[72.0, 67.3, 92.9, 126.4, 190.9, 166.6, 192.2, 167.4, 171.0, 117.0, 70.0, 59.7],
[84.0, 58.8, 86.7, 165.4, 228.7, 186.7, 168.9, 169.0, 136.4, 111.8, 61.4, 64.4],
[50.9, 75.3, 57.8, 110.4, 98.3, 122.8, 129.0, 199.8, 157.3, 101.9, 43.7, 57.5],
[55.0, 33.8, 144.7, 164.4, 187.2, 148.4, 151.4, 159.8, 141.0, 66.3, 68.5, 60.4],
[54.9, 74.0, 89.5, 150.5, 126.8, 180.3, 257.5, 214.5, 92.4, 119.7, 44.3, 62.9],
[86.1, 66.0, 48.8, 236.8, 143.4, 244.3, 249.4, 199.8, 99.6, 88.6, 53.8, 57.6],
[51.1, 78.0, 112.4, 138.3, 178.3, 165.0, 216.0, 164.9, 143.3, 100.8, 86.1, 44.2],
[76.4, 71.2, 127.3, 139.6, 205.6, 222.7, 201.2, 147.0, 171.6, 119.7, 77.9, 64.8],
[68.8, 67.8, 111.6, 158.7, 168.7, 129.3, 179.4, 158.2, 132.3, 109.5, 43.9, 42.9],
[47.7, 103.7, 85.2, 132.0, 178.1, 142.3, 138.8, 178.8, 136.9, 120.0, 91.4, 46.7],
[68.2, 107.0, 100.9, 133.9, 300.8, 244.4, 280.4, 269.5, 141.6, 90.8, 104.1, 26.5],
[58.3, 95.7, 144.1, 234.4, 285.0, 121.1, 268.5, 236.6, 164.7, 124.4, 83.7, 58.8],
[66.9, 60.0, 87.2, 156.6, 142.9, 150.0, 217.3, 241.4, 165.4, 79.4, 57.4, 58.4],
[46.8, 67.3, 73.3, 139.2, 262.7, 212.2, 164.0, 173.6, 120.2, 101.3, 61.5, 47.2],
[38.0, 54.7, 135.0, 111.9, 196.7, 231.4, 190.0, 238.3, 107.7, 120.0, 76.3, 55.0],
[87.0, 77.6, 127.6, 177.7, 162.1, 254.9, 248.3, 191.8, 113.1, 137.5, 46.7, 68.2],
[61.8, 74.9, 198.7, 190.1, 233.5, 194.4, 247.6, 285.1, 135.3, 139.9, 78.1, 40.9],
[29.3, 103.4, 76.4, 148.3, 185.7, 290.7, 256.6, 211.6, 125.3, 130.8, 101.0, 55.5],
[51.4, 64.2, 150.2, 189.9, 261.4, 137.1, 231.7, 172.0, 169.7, 153.8, 47.1, 55.7],
[64.0, 113.0, 77.5, 105.8, 199.8, 114.0, 157.0, 225.0, 133.8, 94.5, 66.3, 38.1],
[51.1, 81.3, 97.4, 147.6, 153.6, 202.1, 235.4, 159.2, 155.2, 144.8, 81.1, 60.9],
[82.1, 104.9, 112.6, 143.4, 189.8, 164.6, 161.2, 209.4, 126.1, 83.9, 69.2, 51.9],
[83.3, 85.0, 74.1, 148.2, 198.3, 226.8, 206.1, 184.1, 123.0, 100.9, 86.9, 79.2],
[44.4, 80.5, 101.1, 210.0, 177.5, 163.3, 178.8, 166.2, 167.1, 104.8, 52.3, 41.3],
[87.7, 94.4, 154.8, 169.8, 191.2, 213.6, 192.0, 228.4, 175.3, 134.8, 78.9, 53.6],
[62.7, 79.1, 101.5, 150.3, 195.5, 223.6, 169.5, 194.1, 174.4, 102.4, 52.4, 58.3],
[65.4, 66.3, 79.3, 136.3, 226.4, 177.6, 192.0, 235.7, 155.4, 92.0, 88.0, 55.7],
[54.9, 73.1, 95.5, 152.5, 165.7, 246.2, 303.7, 167.2, 156.5, 109.0, 101.2, 42.7],
[79.8, 67.6, 165.4, 210.7, 165.5, 149.0, 195.1, 209.2, 142.6, 102.5, 86.9, 57.2],
[62.4, 124.1, 115.2, 161.2, 173.2, 223.8, 198.5, 141.8, 113.5, 132.2, 67.0, 73.5],
[69.3, 64.5, 161.4, 168.4, 226.1, 203.3, 212.3, 190.6, 163.7, 109.7, 73.5, 61.5],
]

month_names = 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',\
'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'

total_hours = [0]*12 # because 12 months in a year
for m in range(len(sun_hours[0])):
    for y in range(len(sun_hours)):
        total_hours[m] += sun_hours[y][m]

for i in range(len(total_hours)):
    total_hours[i] /= 81 # because 81 years spanned
    print '%s: %.1f' % (month_names[i], total_hours[i])
    
'''
Sample run:
Jan: 56.6
Feb: 72.7
Mar: 116.5
Apr: 153.2
May: 191.1
Jun: 198.5
Jul: 193.8
Aug: 184.3
Sep: 138.3
Oct: 104.6
Nov: 67.4
Dec: 52.4
'''

''' According to the means found in the preceding task, June has the
best weather because it has the greatest number of sun-hours, as
demonstrated Pythonically below'''

print '\n'+ month_names[total_hours.index(max(total_hours))]

'''
Sample run:
Jun
'''

for i in range(81):
    del sun_hours[i][1:11] # keeps months at index 0 and 
    # 11 of each year (Jan and dec)
jan_dec = sun_hours
'''
will need to create a list called Jan decade totals
which is filled with the total number of sun hours 
for all of the decade's Januarys.  this list will
have 8 items
'''
jan_total = [0]*8
dec_total = [0]*8
for i in range(8):
    for j in range(10):
        jan_total[i] += sun_hours[j*i + j + 1][0] # '1' is added because
        # we need to pair Dec 1929 with Jan 1930, for example
        dec_total[i] += sun_hours[j*i + j][1]
        
decades = ['1930-1939','1940-1949','1950-1959','1960-1969','1970-1979','1980-1989',\
           '1990-1999','2000-2009']
print '''\nAverage Number of Sun Hours Per Day in 
January and December by Decade (1930-2009)\n'''         
for i in range(len(jan_total)):
    jan_total[i] /= (10.*30) # because 10 years spanned with 30 days (assuming)
    # in each of those years' months
    dec_total[i] /= (10.*30) 
    print '%s:   JAN %5.1f,   DEC %5.1f,   JAN/DEC AVG %5.1f' % \
(decades[i], jan_total[i], dec_total[i], (jan_total[i]+dec_total[i])/2)

'''
Sample run:
1930-1939:   JAN   1.8,   DEC   1.6,   JAN/DEC AVG   1.7
1940-1949:   JAN   1.7,   DEC   1.6,   JAN/DEC AVG   1.7
1950-1959:   JAN   1.8,   DEC   1.6,   JAN/DEC AVG   1.7
1960-1969:   JAN   1.8,   DEC   1.9,   JAN/DEC AVG   1.8
1970-1979:   JAN   1.7,   DEC   1.7,   JAN/DEC AVG   1.7
1980-1989:   JAN   1.7,   DEC   1.6,   JAN/DEC AVG   1.7
1990-1999:   JAN   1.9,   DEC   1.7,   JAN/DEC AVG   1.8
2000-2009:   JAN   1.9,   DEC   1.7,   JAN/DEC AVG   1.8
'''

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