如果您有一个非常大的文件，并假设您希望输出为numpy数组，则使用np。Genfromtxt将冻结您的计算机。以我的经验来看，这样好多了:

def load_big_file(fname,maxrows):
'''only works for well-formed text file of space-separated doubles'''

rows = []  # unknown number of lines, so use list

with open(fname) as f:
    j=0        
    for line in f:
        if j==maxrows:
            break
        else:
            line = [float(s) for s in line.split()]
            rows.append(np.array(line, dtype = np.double))
            j+=1
return np.vstack(rows)  # convert list of vectors to array

最直观的两种方法是:

逐行迭代文件，并在N行之后进行换行。 使用next()方法逐行迭代文件N次。(这本质上只是顶部答案的不同语法。)

代码如下:

# Method 1:
with open("fileName", "r") as f:
    counter = 0
    for line in f:
        print line
        counter += 1
        if counter == N: break

# Method 2:
with open("fileName", "r") as f:
    for i in xrange(N):
        line = f.next()
        print line

底线是，只要不使用readlines()或将整个文件枚举到内存中，您就有很多选择。

Python 3:

with open("datafile") as myfile:
    head = [next(myfile) for x in range(N)]
print(head)

Python 2:

with open("datafile") as myfile:
    head = [next(myfile) for x in xrange(N)]
print head

下面是另一种方法(Python 2和3都是):

from itertools import islice

with open("datafile") as myfile:
    head = list(islice(myfile, N))
print(head)

如果您有一个非常大的文件，并假设您希望输出为numpy数组，则使用np。Genfromtxt将冻结您的计算机。以我的经验来看，这样好多了:

def load_big_file(fname,maxrows):
'''only works for well-formed text file of space-separated doubles'''

rows = []  # unknown number of lines, so use list

with open(fname) as f:
    j=0        
    for line in f:
        if j==maxrows:
            break
        else:
            line = [float(s) for s in line.split()]
            rows.append(np.array(line, dtype = np.double))
            j+=1
return np.vstack(rows)  # convert list of vectors to array

这对我很有效

f = open("history_export.csv", "r")
line= 5
for x in range(line):
    a = f.readline()
    print(a)

#!/usr/bin/python

import subprocess

p = subprocess.Popen(["tail", "-n 3", "passlist"], stdout=subprocess.PIPE)

output, err = p.communicate()

print  output

这个方法对我很有效