如果您的大型文本文件是严格结构良好的(意味着每一行都有相同的长度l)，您可以使用第n行

with open(file) as f:
    f.seek(n*l)
    line = f.readline() 
    last_pos = f.tell()

这只适用于具有相同长度的文件!

如果您的大型文本文件是严格结构良好的(意味着每一行都有相同的长度l)，您可以使用第n行

with open(file) as f:
    f.seek(n*l)
    line = f.readline() 
    last_pos = f.tell()

这只适用于具有相同长度的文件!

读取文件非常快。读取一个100MB的文件需要不到0.1秒(请参阅我的文章用Python读写文件)。因此，你应该完整地阅读它，然后处理单行。

大多数回答这里做的不是错，而是风格不好。打开文件应该总是用with，因为它可以确保文件再次关闭。

所以你应该这样做:

with open("path/to/file.txt") as f:
    lines = f.readlines()
print(lines[26])  # or whatever you want to do with this line
print(lines[30])  # or whatever you want to do with this line

巨大的文件

如果你有一个巨大的文件，内存消耗是一个问题，你可以逐行处理它:

with open("path/to/file.txt") as f:
    for i, line in enumerate(f):
        pass  # process line i

从某一行读出:

n = 4   # for reading from 5th line
with open("write.txt",'r') as t:
     for i,line in enumerate(t):
         if i >= n:             # i == n-1 for nth line
            print(line)

不要使用阅读线!

我的解决方案是:

with open(filename) as f:
    specify = [26, 30]
    results = list(
        map(lambda line: line[1],
            filter(lambda line: line[0] in specify,
                   enumerate(f))
            )
    )

对6.5G文件进行如下测试:

import time

filename = 'a.txt'
start = time.time()
with open(filename, 'w') as f:
    for i in range(10_000_000):
        f.write(f'{str(i)*100}\n')       
end1 = time.time()

with open(filename) as f:
    specify = [26, 30]
    results = list(
        map(lambda line: line[1],
            filter(lambda line: line[0] in specify,
                   enumerate(f))
            )
    )
end2 = time.time()
print(f'write time: {end1-start}')
print(f'read time: {end2-end1}')
# write time: 14.38945460319519
# read time: 8.380386352539062

打印第3行，

line_number = 3

with open(filename,"r") as file:
current_line = 1
for line in file:
    if current_line == line_number:
        print(file.readline())
        break
    current_line += 1

原作者:Frank Hofmann