How to get line count cheaply in Python?
I need to get a line count of a large file (hundreds of thousands of lines) in python. What is the most efficient way both memory- and time-wise?
At the moment I do:
def file_len(fname):
with open(fname) as f:
for i, l in enumerate(f):
pass
return i + 1
is it possible to do any better?
You can't get any better than that.
After all, any solution will have to read the entire file, figure out how many n
you have, and return that result.
Do you have a better way of doing that without reading the entire file? Not sure... The best solution will always be I/O-bound, best you can do is make sure you don't use unnecessary memory, but it looks like you have that covered.
一条线可能非常快:
num_lines = sum(1 for line in open('myfile.txt'))
I believe that a memory mapped file will be the fastest solution. I tried four functions: the function posted by the OP ( opcount
); a simple iteration over the lines in the file ( simplecount
); readline with a memory-mapped filed (mmap) ( mapcount
); and the buffer read solution offered by Mykola Kharechko ( bufcount
).
I ran each function five times, and calculated the average run-time for a 1.2 million-line text file.
Windows XP, Python 2.5, 2GB RAM, 2 GHz AMD processor
Here are my results:
mapcount : 0.465599966049
simplecount : 0.756399965286
bufcount : 0.546800041199
opcount : 0.718600034714
Edit : numbers for Python 2.6:
mapcount : 0.471799945831
simplecount : 0.634400033951
bufcount : 0.468800067902
opcount : 0.602999973297
So the buffer read strategy seems to be the fastest for Windows/Python 2.6
Here is the code:
from __future__ import with_statement
import time
import mmap
import random
from collections import defaultdict
def mapcount(filename):
f = open(filename, "r+")
buf = mmap.mmap(f.fileno(), 0)
lines = 0
readline = buf.readline
while readline():
lines += 1
return lines
def simplecount(filename):
lines = 0
for line in open(filename):
lines += 1
return lines
def bufcount(filename):
f = open(filename)
lines = 0
buf_size = 1024 * 1024
read_f = f.read # loop optimization
buf = read_f(buf_size)
while buf:
lines += buf.count('n')
buf = read_f(buf_size)
return lines
def opcount(fname):
with open(fname) as f:
for i, l in enumerate(f):
pass
return i + 1
counts = defaultdict(list)
for i in range(5):
for func in [mapcount, simplecount, bufcount, opcount]:
start_time = time.time()
assert func("big_file.txt") == 1209138
counts[func].append(time.time() - start_time)
for key, vals in counts.items():
print key.__name__, ":", sum(vals) / float(len(vals))
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