这对我很有效

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

对于前5行，简单地做:

N=5
with open("data_file", "r") as file:
    for i in range(N):
       print file.next()

使用list(file_data)将CSV文件对象转换为列表

import csv;
with open('your_csv_file.csv') as file_obj:
    file_data = csv.reader(file_obj);
    file_list = list(file_data)
    for row in file_list[:4]:
        print(row)

基于gnibbler的投票结果(2009年11月20日0:27):这个类将head()和tail()方法添加到文件对象。

class File(file):
    def head(self, lines_2find=1):
        self.seek(0)                            #Rewind file
        return [self.next() for x in xrange(lines_2find)]

    def tail(self, lines_2find=1):  
        self.seek(0, 2)                         #go to end of file
        bytes_in_file = self.tell()             
        lines_found, total_bytes_scanned = 0, 0
        while (lines_2find+1 > lines_found and
               bytes_in_file > total_bytes_scanned): 
            byte_block = min(1024, bytes_in_file-total_bytes_scanned)
            self.seek(-(byte_block+total_bytes_scanned), 2)
            total_bytes_scanned += byte_block
            lines_found += self.read(1024).count('\n')
        self.seek(-total_bytes_scanned, 2)
        line_list = list(self.readlines())
        return line_list[-lines_2find:]

用法:

f = File('path/to/file', 'r')
f.head(3)
f.tail(3)

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

N = 10
with open("file.txt", "a") as file:  # the a opens it in append mode
    for i in range(N):
        line = next(file).strip()
        print(line)