如果你想要一些明显(不需要在手册中查找深奥的东西)不需要导入就可以工作的东西，请尝试/except，并且可以在相当大范围的Python 2上工作。X版本(2.2至2.6):

def headn(file_name, n):
    """Like *x head -N command"""
    result = []
    nlines = 0
    assert n >= 1
    for line in open(file_name):
        result.append(line)
        nlines += 1
        if nlines >= n:
            break
    return result

if __name__ == "__main__":
    import sys
    rval = headn(sys.argv[1], int(sys.argv[2]))
    print rval
    print len(rval)

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)

我所做的就是用熊猫形来称呼N行。我认为性能不是最好的，但是举个例子，如果N=1000:

import pandas as pd
yourfile = pd.read_csv('path/to/your/file.csv',nrows=1000)

这对我很有效

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

从Python 2.6开始，您可以利用IO基类中更复杂的函数。所以上面评分最高的答案可以改写为:

    with open("datafile") as myfile:
       head = myfile.readlines(N)
    print head

(你不必担心你的文件少于N行，因为没有StopIteration异常抛出。)

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