Two comments,
> ...
> self.item3 = float(foo[c]); c+=1
> self.item4 = float(foo[c]); c+=1
> self.item5 = float(foo[c]); c+=1
> self.item6 = float(foo[c]); c+=1
> ...
this here (and your code in general) is mind boggling and not in a
good way,
as for you original question, I don't think that reading in files of
the size you mention can cause any substantial problems, I think the
problem is somewhere else,
you can run the code below to see that the read times are unaffected
by the order of processing
----------
import timeit
# make a big file
NUM= 10**5
fp = open('bigfile.txt', 'wt')
longline = ' ABC '* 60 + '\n'
for count in xrange( NUM ):
fp.write( longline )
fp.close()
setup1 = """
def readLines():
data = []
for line in file('bigfile.txt'):
data.append( line )
return data
"""
stmt1 = """
data = readLines()
"""
stmt2 = """
data = readLines()
data = readLines()
"""
stmt3 = """
data = file('bigfile.txt').readlines()
"""
def run( setup, stmt, N=5 ):
t = timeit.Timer(stmt=stmt, setup=setup)
msec = 1000 * t.timeit(number=N)/N
print "%f msec/pass" % msec
if __name__ == '__main__':
for stmt in (stmt1, stmt2, stmt3):
run(setup=setup1, stmt=stmt)
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