我把这句话建立在fj的精彩回答上:

import re

def multiple_replacer(*key_values):
    replace_dict = dict(key_values)
    replacement_function = lambda match: replace_dict[match.group(0)]
    pattern = re.compile("|".join([re.escape(k) for k, v in key_values]), re.M)
    return lambda string: pattern.sub(replacement_function, string)

def multiple_replace(string, *key_values):
    return multiple_replacer(*key_values)(string)

一针用法:

>>> replacements = (u"café", u"tea"), (u"tea", u"café"), (u"like", u"love")
>>> print multiple_replace(u"Do you like café? No, I prefer tea.", *replacements)
Do you love tea? No, I prefer café.

注意，由于替换只在一次传递中完成，“café”会变成“tea”，但不会变回“café”。

如果你需要做相同的替换多次，你可以很容易地创建一个替换函数:

>>> my_escaper = multiple_replacer(('"','\\"'), ('\t', '\\t'))
>>> many_many_strings = (u'This text will be escaped by "my_escaper"',
                       u'Does this work?\tYes it does',
                       u'And can we span\nmultiple lines?\t"Yes\twe\tcan!"')
>>> for line in many_many_strings:
...     print my_escaper(line)
... 
This text will be escaped by \"my_escaper\"
Does this work?\tYes it does
And can we span
multiple lines?\t\"Yes\twe\tcan!\"

改进:

将代码转换为函数 增加了多线支持 修正了逃跑的错误 容易创建一个函数，用于特定的多个替换

享受吧!: -)

我在学校作业中也做过类似的练习。这就是我的解

dictionary = {1: ['hate', 'love'],
              2: ['salad', 'burger'],
              3: ['vegetables', 'pizza']}

def normalize(text):
    for i in dictionary:
        text = text.replace(dictionary[i][0], dictionary[i][1])
    return text

自己查看测试字符串上的结果

string_to_change = 'I hate salad and vegetables'
print(normalize(string_to_change))

这里有一个使用reduce的第一个解决方案的变体，如果你喜欢功能性的。：）

repls = {'hello' : 'goodbye', 'world' : 'earth'}
s = 'hello, world'
reduce(lambda a, kv: a.replace(*kv), repls.iteritems(), s)

马蒂诺的版本更好:

repls = ('hello', 'goodbye'), ('world', 'earth')
s = 'hello, world'
reduce(lambda a, kv: a.replace(*kv), repls, s)

下面是一个支持基本正则表达式替换的版本。主要的限制是表达式不能包含子组，并且可能存在一些边缘情况:

基于@bgusach和其他的代码

import re

class StringReplacer:

    def __init__(self, replacements, ignore_case=False):
        patterns = sorted(replacements, key=len, reverse=True)
        self.replacements = [replacements[k] for k in patterns]
        re_mode = re.IGNORECASE if ignore_case else 0
        self.pattern = re.compile('|'.join(("({})".format(p) for p in patterns)), re_mode)
        def tr(matcher):
            index = next((index for index,value in enumerate(matcher.groups()) if value), None)
            return self.replacements[index]
        self.tr = tr

    def __call__(self, string):
        return self.pattern.sub(self.tr, string)

测试

table = {
    "aaa"    : "[This is three a]",
    "b+"     : "[This is one or more b]",
    r"<\w+>" : "[This is a tag]"
}

replacer = StringReplacer(table, True)

sample1 = "whatever bb, aaa, <star> BBB <end>"

print(replacer(sample1))

# output: 
# whatever [This is one or more b], [This is three a], [This is a tag] [This is one or more b] [This is a tag]

诀窍是通过位置来识别匹配的组。它不是超级高效(O(n))，但它是有效的。

index = next((index for index,value in enumerate(matcher.groups()) if value), None)

替换是一次完成的。

或者简单说一下:

for line in to_read:
    read_buffer = line              
    stripped_buffer1 = read_buffer.replace("term1", " ")
    stripped_buffer2 = stripped_buffer1.replace("term2", " ")
    write_to_file = to_write.write(stripped_buffer2)

sentence='its some sentence with a something text'

def replaceAll(f,Array1,Array2):
    if len(Array1)==len(Array2):
        for x in range(len(Array1)):
            return f.replace(Array1[x],Array2[x])

newSentence=replaceAll(sentence,['a','sentence','something'],['another','sentence','something something'])

print(newSentence)