Finding Min and Max of list generated from CSV fil

I am trying to find the MIN and MAX values for each row of a CSV file and append them to the next position in the list, positions 5 and 6. I have managed to calculate the average, append this to the forth position and output this in highest to lowest however, I am struggling to work out how to find the MAX and MIN values of each row so I can do the same - highest to lowest. The original CSV is formatted: Fred,56,78,99 with each user on a new line.

Any help would be appreciate.

import csv
import operator

sample  = open("sampleData.txt", "r")

csv1 = csv.reader(sample, delimiter = ',')

sort = sorted(csv1,key=operator.itemgetter(0))

for i in range( 0, len(sort)):
    sort[i].append((int(sort[i][1]) + int(sort[i][2]) + int(sort[i][3])) / int(len(sort[i])-1))   
sort = list(reversed(sorted(sort,key=operator.itemgetter(4))))
for i in range( 0, len( sort ) ):
print(sort[i][0], round(sort[i][4]))

import csv

sample  = open("sampleData.txt", "r")
csv1 = csv.reader(sample, delimiter = ',')

sorted_list = []

for line in csv1:
    print '-- ORIG:', line
    tmp = sorted( [int(i) for i in line[1:]], reverse=True )  # eg: [99,78,56]
    stat_list = [round(sum(tmp)/float(len(tmp)), 2), min(tmp), max(tmp)]
    sorted_list.append( [line[0]] + tmp + stat_list )

for s in sorted_list: print '** NEW: ', s # has ['Fred',99,78,56,78.0,57,99]

You can use/modify the quick & dirty solution above. Note:

The result is a list of lists with digits converted to integers.
The float() is needed for computing average - just doing it on the denominator is enough for the whole result to be a float.
List comprehensions are great shortcuts and efficient for avoiding for loops.
The numpy module has builtin mean() function (among many others) that are useful and fast, especially for large arrays.