Python计算大文件行数方法及性能比较

如何使用Python快速高效地统计出大文件的总行数, 下面是一些实现方法和性能的比较。

• 1.readline读所有行
  使用readlines方法读取所有行:

def readline_count(file_name):
    return len(open(file_name).readlines())

• 2.依次读取每行
  依次读取文件每行内容进行计数:

def simple_count(file_name):
    lines = 0
    for _ in open(file_name):
        lines += 1
    return lines

• 3.sum计数
  使用sum函数计数:

def sum_count(file_name):
    return sum(1 for _ in open(file_name))

• 4.enumerate枚举计数:

def enumerate_count(file_name):
    with open(file_name) as f:
        for count, _ in enumerate(f, 1):
            pass
    return count

• 5.buff count
  每次读取固定大小,然后统计行数:

def buff_count(file_name):
    with open(file_name, 'rb') as f:
        count = 0
        buf_size = 1024 * 1024
        buf = f.read(buf_size)
        while buf:
            count += buf.count(b'\n')
            buf = f.read(buf_size)
        return count

• 6.wc count
  调用使用wc命令计算行:

def wc_count(file_name):
    import subprocess
    out = subprocess.getoutput("wc -l %s" % file_name)
    return int(out.split()[0])

• 7.partial count
  在buff_count基础上引入partial:

def partial_count(file_name):
    from functools import partial
    buffer = 1024 * 1024
    with open(file_name) as f:
        return sum(x.count('\n') for x in iter(partial(f.read, buffer), ''))

• 8.iter count
  在buff_count基础上引入itertools模块 :

def iter_count(file_name):
    from itertools import (takewhile, repeat)
    buffer = 1024 * 1024
    with open(file_name) as f:
        buf_gen = takewhile(lambda x: x, (f.read(buffer) for _ in repeat(None)))
        return sum(buf.count('\n') for buf in buf_gen)

下面是在我本机 4c8g python3.6的环境下,分别测试100m、500m、1g、10g大小文件运行的时间，单位秒：

方法	100M	500M	1G	10G
readline_count	0.25	1.82	3.27	45.04
simple_count	0.13	0.85	1.58	13.53
sum_count	0.15	0.77	1.59	14.07
enumerate_count	0.15	0.80	1.60	13.37
buff_count	0.13	0.62	1.18	10.21
wc_count	0.09	0.53	0.99	9.47
partial_count	0.12	0.55	1.11	8.92
iter_count	0.08	0.42	0.83	8.33