Specific Fields Python3 - python-3.x

Question

I try to select specific fields from my Qdata.txt file and use field[2] to calculate average for every years separate. My code give only total average.
data file looks like: (1. day of year: 101 and last: 1231)
Date 3700300 6701500
20000101 21.00 223.00
20000102 20.00 218.00
. .
20001231 7.40 104.00
20010101 6.70 104.00
. .
20130101 8.37 111.63
. .
20131231 45.00 120.98
import sys
td=open("Qdata.txt","r") # open file Qdata
total=0
count=0
row1=True
for row in td :
if (row1) :
row1=False # row1 is for topic
else:
fields=row.split()
try:
total=total+float(fields[2])
count=count+1
# Errors.
except IndexError:
continue
except ValueError:
print("File is incorrect.")
sys.exit()
print("Average in 2000 was: ",total/count)

Answer 1

You could use itertools.groupby using the first four characters as the key for grouping.
with open("data.txt") as f:
next(f) # skip first line
groups = itertools.groupby(f, key=lambda s: s[:4])
for k, g in groups:
print(k, [s.split() for s in g])
This gives you the entries grouped by year, for further processing.
Output for your example data:
2000 [['20000101', '21.00', '223.00'], ['20000102', '20.00', '218.00'], ['20001231', '7.40', '104.00']]
2001 [['20010101', '6.70', '104.00']]
2013 [['20130101', '8.37', '111.63'], ['20131231', '45.00', '120.98']]

Answer 2

You could create a dict (or even a defaultdict) for total and count instead:
import sys
from collections import defaultdict
td=open("Qdata.txt","r") # open file Qdata
total=defaultdict(float)
count=defaultdict(int)
row1=True
for row in td :
if (row1) :
row1=False # row1 is for topic
else:
fields=row.split()
try:
year = int(fields[0][:4])
total[year] += float(fields[2])
count[year] += 1
# Errors.
except IndexError:
continue
except ValueError:
print("File is incorrect.")
sys.exit()
print("Average in 2000 was: ",total[2000]/count[2000])

Answer 3

Every year separate? You have to divide your input into groups, something like this might be what you want:
from collections import defaultdict
row1 = True
year_sums = defaultdict(list)
for row in td:
if row1:
row1 = False
continue
fields = row.split()
year = fields[0][:4]
year_sums[year].append(float(fields[2]))
for year in year_sums:
avarage = sum(year_sums[year])/count(year_sums[year])
print("Avarage in {} was: {}".format(year, avarage)
That is just some example code, I don't know if it works for sure, but should give you an idea what you can do. year_sums is a defaultdict containing lists of values grouped by years. You can then use it for other statistics if you want.