使用csv。在我庞大的列表中，作者花了相当长的时间。我决定使用熊猫，它更快，更容易控制和理解:

 import pandas

 yourlist = [[...],...,[...]]
 pd = pandas.DataFrame(yourlist)
 pd.to_csv("mylist.csv")

好的部分是你可以改变一些东西来创建一个更好的csv文件:

 yourlist = [[...],...,[...]]
 columns = ["abcd","bcde","cdef"] #a csv with 3 columns
 index = [i[0] for i in yourlist] #first element of every list in yourlist
 not_index_list = [i[1:] for i in yourlist]
 pd = pandas.DataFrame(not_index_list, columns = columns, index = index)

 #Now you have a csv with columns and index:
 pd.to_csv("mylist.csv")

如果出于某种原因你想手动执行(不使用csv,pandas,numpy等模块):

with open('myfile.csv','w') as f:
    for sublist in mylist:
        for item in sublist:
            f.write(item + ',')
        f.write('\n')

当然，滚动您自己的版本可能容易出错且效率低下……这就是为什么会有专门的模块。但有时自己写可以帮助你理解它们是如何工作的，有时只是更简单。

Ambers的解决方案也适用于numpy数组:

from pylab import *
import csv

array_=arange(0,10,1)
list_=[array_,array_*2,array_*3]
with open("output.csv", "wb") as f:
    writer = csv.writer(f)
    writer.writerows(list_)

使用csv。在我庞大的列表中，作者花了相当长的时间。我决定使用熊猫，它更快，更容易控制和理解:

 import pandas

 yourlist = [[...],...,[...]]
 pd = pandas.DataFrame(yourlist)
 pd.to_csv("mylist.csv")

好的部分是你可以改变一些东西来创建一个更好的csv文件:

 yourlist = [[...],...,[...]]
 columns = ["abcd","bcde","cdef"] #a csv with 3 columns
 index = [i[0] for i in yourlist] #first element of every list in yourlist
 not_index_list = [i[1:] for i in yourlist]
 pd = pandas.DataFrame(not_index_list, columns = columns, index = index)

 #Now you have a csv with columns and index:
 pd.to_csv("mylist.csv")

我在csv中使用换行参数跟随示例时得到了一个错误消息。作家的功能。下面的代码适用于我。

 with open(strFileName, "w") as f:
    writer = csv.writer(f, delimiter=',',  quoting=csv.QUOTE_MINIMAL)
    writer.writerows(result)

import csv
with open(file_path, 'a') as outcsv:   
    #configure writer to write standard csv file
    writer = csv.writer(outcsv, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL, lineterminator='\n')
    writer.writerow(['number', 'text', 'number'])
    for item in list:
        #Write item to outcsv
        writer.writerow([item[0], item[1], item[2]])

官方文档:http://docs.python.org/2/library/csv.html