如何以最有效的内存和时间方式获取大文件的行数?
def file_len(filename):
with open(filename) as f:
for i, _ in enumerate(f):
pass
return i + 1
如何以最有效的内存和时间方式获取大文件的行数?
def file_len(filename):
with open(filename) as f:
for i, _ in enumerate(f):
pass
return i + 1
当前回答
我发现你可以。
f = open("data.txt")
linecout = len(f.readlines())
会给你答案吗
其他回答
这个呢
def file_len(fname):
counts = itertools.count()
with open(fname) as f:
for _ in f: counts.next()
return counts.next()
我相信内存映射文件将是最快的解决方案。我尝试了四个函数:由OP发布的函数(opcount);对文件中的行进行简单迭代(simplecount);带有内存映射字段(mmap)的Readline (mapcount);以及Mykola Kharechko (buffcount)提供的缓冲区读取解决方案。
我将每个函数运行五次,并计算出120万在线文本文件的平均运行时间。
Windows XP, Python 2.5, 2GB RAM, 2ghz AMD处理器
以下是我的结果:
mapcount : 0.465599966049
simplecount : 0.756399965286
bufcount : 0.546800041199
opcount : 0.718600034714
编辑:Python 2.6的数字:
mapcount : 0.471799945831
simplecount : 0.634400033951
bufcount : 0.468800067902
opcount : 0.602999973297
因此,对于Windows/Python 2.6,缓冲区读取策略似乎是最快的
代码如下:
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))
创建一个可执行脚本文件count.py:
#!/usr/bin/python
import sys
count = 0
for line in sys.stdin:
count+=1
然后将文件的内容导入python脚本:cat huge.txt | ./count.py。管道也适用于Powershell,因此您将最终计算行数。
对我来说,在Linux上它比简单的解决方案快30%:
count=1
with open('huge.txt') as f:
count+=1
一句话解决方案:
import os
os.system("wc -l filename")
我的代码片段:
>>> os.system('wc -l *.txt')
0 bar.txt
1000 command.txt
3 test_file.txt
1003 total
为了完成上述方法,我尝试了fileinput模块的一个变体:
import fileinput as fi
def filecount(fname):
for line in fi.input(fname):
pass
return fi.lineno()
并将一个60mil行文件传递给上述所有方法:
mapcount : 6.1331050396
simplecount : 4.588793993
opcount : 4.42918205261
filecount : 43.2780818939
bufcount : 0.170812129974
这让我有点惊讶,fileinput是如此糟糕,比所有其他方法都要糟糕得多…