Python计算大文件行数方法及性能比较

690阅读 0评论2021-11-18 wwm
分类:Python/Ruby

https://www.cnblogs.com/jhao/p/13488867.html

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
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