以防万一

list = ['foo', 'fob', 'faz', 'funk']
string = 'bar'
for i in range(len(list)):
    list[i] += string
print(list)

list2 = ['%sbar' % (x,) for x in list]

不要用list作为名字;它隐藏内置类型。

以python的方式运行下面的实验:

[s + mystring for s in mylist]

似乎比明显使用for循环快35%:

i = 0
for s in mylist:
    mylist[i] = s+mystring
    i = i + 1

实验

import random
import string
import time

mystring = '/test/'

l = []
ref_list = []

for i in xrange( 10**6 ):
    ref_list.append( ''.join(random.choice(string.ascii_lowercase) for i in range(10)) )

for numOfElements in [5, 10, 15 ]:

    l = ref_list*numOfElements
    print 'Number of elements:', len(l)

    l1 = list( l )
    l2 = list( l )

    # Method A
    start_time = time.time()
    l2 = [s + mystring for s in l2]
    stop_time = time.time()
    dt1 = stop_time - start_time
    del l2
    #~ print "Method A: %s seconds" % (dt1)

    # Method B
    start_time = time.time()
    i = 0
    for s in l1:
        l1[i] = s+mystring
        i = i + 1
    stop_time = time.time()
    dt0 = stop_time - start_time
    del l1
    del l
    #~ print "Method B: %s seconds" % (dt0)

    print 'Method A is %.1f%% faster than Method B' % ((1 - dt1/dt0)*100)

结果

Number of elements: 5000000
Method A is 38.4% faster than Method B
Number of elements: 10000000
Method A is 33.8% faster than Method B
Number of elements: 15000000
Method A is 35.5% faster than Method B

这里有一个简单的答案，用熊猫。

import pandas as pd
list1 = ['foo', 'fob', 'faz', 'funk']
string = 'bar'

list2 = (pd.Series(list1) + string).tolist()
list2
# ['foobar', 'fobbar', 'fazbar', 'funkbar']

my_list = ['foo', 'fob', 'faz', 'funk']
string = 'bar'
my_new_list = [x + string for x in my_list]
print my_new_list

这将打印:

['foobar', 'fobbar', 'fazbar', 'funkbar']

以防万一

list = ['foo', 'fob', 'faz', 'funk']
string = 'bar'
for i in range(len(list)):
    list[i] += string
print(list)