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
简单的方法:
1)
>>> f = len(open("myfile.txt").readlines())
>>> f
430
>>> f = open("myfile.txt").read().count('\n')
>>> f
430
>>>
num_lines = len(list(open('myfile.txt')))
其他回答
这是我用纯python发现的最快的东西。 你可以通过设置buffer来使用任意大小的内存,不过在我的电脑上2**16似乎是一个最佳位置。
from functools import partial
buffer=2**16
with open(myfile) as f:
print sum(x.count('\n') for x in iter(partial(f.read,buffer), ''))
我在这里找到了答案为什么在c++中从stdin读取行要比Python慢得多?稍微调整了一下。这是一个非常好的阅读来理解如何快速计数行,尽管wc -l仍然比其他任何方法快75%。
这个怎么样?
import fileinput
import sys
counter=0
for line in fileinput.input([sys.argv[1]]):
counter+=1
fileinput.close()
print counter
在perfplot分析之后,必须推荐缓冲读取解决方案
def buf_count_newlines_gen(fname):
def _make_gen(reader):
while True:
b = reader(2 ** 16)
if not b: break
yield b
with open(fname, "rb") as f:
count = sum(buf.count(b"\n") for buf in _make_gen(f.raw.read))
return count
它速度快,内存效率高。大多数其他解决方案大约要慢20倍。
代码重现情节:
import mmap
import subprocess
from functools import partial
import perfplot
def setup(n):
fname = "t.txt"
with open(fname, "w") as f:
for i in range(n):
f.write(str(i) + "\n")
return fname
def for_enumerate(fname):
i = 0
with open(fname) as f:
for i, _ in enumerate(f):
pass
return i + 1
def sum1(fname):
return sum(1 for _ in open(fname))
def mmap_count(fname):
with open(fname, "r+") as f:
buf = mmap.mmap(f.fileno(), 0)
lines = 0
while buf.readline():
lines += 1
return lines
def for_open(fname):
lines = 0
for _ in open(fname):
lines += 1
return lines
def buf_count_newlines(fname):
lines = 0
buf_size = 2 ** 16
with open(fname) as f:
buf = f.read(buf_size)
while buf:
lines += buf.count("\n")
buf = f.read(buf_size)
return lines
def buf_count_newlines_gen(fname):
def _make_gen(reader):
b = reader(2 ** 16)
while b:
yield b
b = reader(2 ** 16)
with open(fname, "rb") as f:
count = sum(buf.count(b"\n") for buf in _make_gen(f.raw.read))
return count
def wc_l(fname):
return int(subprocess.check_output(["wc", "-l", fname]).split()[0])
def sum_partial(fname):
with open(fname) as f:
count = sum(x.count("\n") for x in iter(partial(f.read, 2 ** 16), ""))
return count
def read_count(fname):
return open(fname).read().count("\n")
b = perfplot.bench(
setup=setup,
kernels=[
for_enumerate,
sum1,
mmap_count,
for_open,
wc_l,
buf_count_newlines,
buf_count_newlines_gen,
sum_partial,
read_count,
],
n_range=[2 ** k for k in range(27)],
xlabel="num lines",
)
b.save("out.png")
b.show()
打开一个文件的结果是一个迭代器,它可以转换为一个序列,它有一个长度:
with open(filename) as f:
return len(list(f))
这比显式循环更简洁,并避免了枚举。
这是我用的,看起来很干净:
import subprocess
def count_file_lines(file_path):
"""
Counts the number of lines in a file using wc utility.
:param file_path: path to file
:return: int, no of lines
"""
num = subprocess.check_output(['wc', '-l', file_path])
num = num.split(' ')
return int(num[0])
更新:这比使用纯python略快,但以内存使用为代价。子进程在执行您的命令时将派生一个与父进程具有相同内存占用的新进程。