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4.7 Counting Lines in a File

Credit: Luther Blissett

4.7.1 Problem

You need to compute the number of lines in a file.

4.7.2 Solution

The simplest approach, for reasonably sized files, is to read the file as a list of lines so that the count of lines is the length of the list. If the file's path is in a string bound to the thefilepath variable, that's just: 

count = len(open(thefilepath).readlines(  ))

For a truly huge file, this may be very slow or even fail to work. If you have to worry about humongous files, a loop using the xreadlines method always works: 

count = 0
for line in open(thefilepath).xreadlines(  ): count += 1

Here's a slightly tricky alternative, if the line terminator is '\n' (or has '\n' as a substring, as happens on Windows): 

count = 0
thefile = open(thefilepath, 'rb')
while 1:
    buffer = thefile.read(8192*1024)
    if not buffer: break
    count += buffer.count('\n')
thefile.close(  )

Without the 'rb' argument to open, this will work anywhere, but performance may suffer greatly on Windows or Macintosh platforms.

4.7.3 Discussion

If you have an external program that counts a file's lines, such as wc -l on Unix-like platforms, you can of course choose to use that (e.g., via os.popen( )). However, it's generally simpler, faster, and more portable to do the line-counting in your program. You can rely on almost all text files having a reasonable size, so that reading the whole file into memory at once is feasible. For all such normal files, the len of the result of readlines gives you the count of lines in the simplest way.

If the file is larger than available memory (say, a few hundred of megabytes on a typical PC today), the simplest solution can become slow, as the operating system struggles to fit the file's contents into virtual memory. It may even fail, when swap space is exhausted and virtual memory can't help any more. On a typical PC, with 256 MB of RAM and virtually unlimited disk space, you should still expect serious problems when you try to read into memory files of, say, 1 or 2 GB, depending on your operating system (some operating systems are much more fragile than others in handling virtual-memory issues under such overstressed load conditions). In this case, the xreadlines method of file objects, introduced in Python 2.1, is generally a good way to process text files line by line. In Python 2.2, you can do even better, in terms of both clarity and speed, by looping directly on the file object: 

for line in open(thefilepath): count += 1

However, xreadlines does not return a sequence, and neither does a loop directly on the file object, so you can't just use len in these cases to get the number of lines. Rather, you have to loop and count line by line, as shown in the solution.

Counting line-terminator characters while reading the file by bytes, in reasonably sized chunks, is the key idea in the third approach. It's probably the least immediately intuitive, and it's not perfectly cross-platform, but you might hope that it's fastest (for example, by analogy with Recipe 8.2 in the Perl Cookbook).

However, remember that, in most cases, performance doesn't really matter all that much. When it does matter, the time sink might not be what your intuition tells you it is, so you should never trust your intuition in this matter�instead, always benchmark and measure. For example, I took a typical Unix syslog file of middling size, a bit over 18 MB of text in 230,000 lines: 

[situ@tioni nuc]$ wc nuc
 231581 2312730 18508908 nuc

and I set up the following benchmark framework script, bench.py: 

import time

def timeo(fun, n=10):
    start = time.clock(  )
    for i in range(n): fun(  )
    stend = time.clock(  )
    thetime = stend-start
    return fun._ _name_ _, thetime

import os

def linecount_wc(  ):
    return int(os.popen('wc -l nuc').read().split(  )[0])

def linecount_1(  ):
    return len(open('nuc').readlines(  ))

def linecount_2(  ):
    count = 0
    for line in open('nuc').xreadlines(  ): count += 1
    return count

def linecount_3(  ):
    count = 0
    thefile = open('nuc')
    while 1:
        buffer = thefile.read(65536)
        if not buffer: break
        count += buffer.count('\n')
    return count

for f in linecount_wc, linecount_1, linecount_2, linecount_3:
    print f._ _name_ _, f(  )

for f in linecount_1, linecount_2, linecount_3:
    print "%s: %.2f"%timeo(f)

First, I print the line counts obtained by all methods, thus ensuring that there is no anomaly or error (counting tasks are notoriously prone to off-by-one errors). Then, I run each alternative 10 times, under the control of the timing function timeo, and look at the results. Here they are: 

[situ@tioni nuc]$ python -O bench.py
linecount_wc 231581
linecount_1 231581
linecount_2 231581
linecount_3 231581
linecount_1: 4.84
linecount_2: 4.54
linecount_3: 5.02

As you can see, the performance differences hardly matter: a difference of 10% or so in one auxiliary task is something that your users will never even notice. However, the fastest approach (for my particular circumstances, a cheap but very recent PC running a popular Linux distribution, as well as this specific benchmark) is the humble loop-on-every-line technique, while the slowest one is the ambitious technique that counts line terminators by chunks. In practice, unless I had to worry about files of many hundreds of megabytes, I'd always use the simplest approach (i.e., the first one presented in this recipe).

4.7.4 See Also

The Library Reference section on file objects and the time module; Perl Cookbook Recipe 8.2.

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