目录[-]
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 |