问题:查找和替换列表中的字符串值

我得到了这个清单:

words = ['how', 'much', 'is[br]', 'the', 'fish[br]', 'no', 'really']

我想[br]用一些与之相似的奇异值代替,<br />从而得到一个新的清单:

words = ['how', 'much', 'is<br />', 'the', 'fish<br />', 'no', 'really']

I got this list:

words = ['how', 'much', 'is[br]', 'the', 'fish[br]', 'no', 'really']

What I would like is to replace [br] with some fantastic value similar to &lt;br /&gt; and thus getting a new list:

words = ['how', 'much', 'is<br />', 'the', 'fish<br />', 'no', 'really']

回答 0

words = [w.replace('[br]', '<br />') for w in words]

这些称为列表推导

words = [w.replace('[br]', '<br />') for w in words]

These are called List Comprehensions.


回答 1

您可以使用,例如:

words = [word.replace('[br]','<br />') for word in words]

You can use, for example:

words = [word.replace('[br]','<br />') for word in words]

回答 2

除了列表理解之外,您还可以尝试地图

>>> map(lambda x: str.replace(x, "[br]", "<br/>"), words)
['how', 'much', 'is<br/>', 'the', 'fish<br/>', 'no', 'really']

Beside list comprehension, you can try map

>>> map(lambda x: str.replace(x, "[br]", "<br/>"), words)
['how', 'much', 'is<br/>', 'the', 'fish<br/>', 'no', 'really']

回答 3

如果您想知道不同方法的性能,请参考以下时间安排:

In [1]: words = [str(i) for i in range(10000)]

In [2]: %timeit replaced = [w.replace('1', '<1>') for w in words]
100 loops, best of 3: 2.98 ms per loop

In [3]: %timeit replaced = map(lambda x: str.replace(x, '1', '<1>'), words)
100 loops, best of 3: 5.09 ms per loop

In [4]: %timeit replaced = map(lambda x: x.replace('1', '<1>'), words)
100 loops, best of 3: 4.39 ms per loop

In [5]: import re

In [6]: r = re.compile('1')

In [7]: %timeit replaced = [r.sub('<1>', w) for w in words]
100 loops, best of 3: 6.15 ms per loop

如您所见,对于这种简单的模式,可接受的列表理解是最快的,但请查看以下内容:

In [8]: %timeit replaced = [w.replace('1', '<1>').replace('324', '<324>').replace('567', '<567>') for w in words]
100 loops, best of 3: 8.25 ms per loop

In [9]: r = re.compile('(1|324|567)')

In [10]: %timeit replaced = [r.sub('<\1>', w) for w in words]
100 loops, best of 3: 7.87 ms per loop

这表明对于更复杂的替换,预编译的reg-exp(如中的9-10)可以更快。这实际上取决于您的问题和reg-exp的最短部分。

In case you’re wondering about the performance of the different approaches, here are some timings:

In [1]: words = [str(i) for i in range(10000)]

In [2]: %timeit replaced = [w.replace('1', '<1>') for w in words]
100 loops, best of 3: 2.98 ms per loop

In [3]: %timeit replaced = map(lambda x: str.replace(x, '1', '<1>'), words)
100 loops, best of 3: 5.09 ms per loop

In [4]: %timeit replaced = map(lambda x: x.replace('1', '<1>'), words)
100 loops, best of 3: 4.39 ms per loop

In [5]: import re

In [6]: r = re.compile('1')

In [7]: %timeit replaced = [r.sub('<1>', w) for w in words]
100 loops, best of 3: 6.15 ms per loop

as you can see for such simple patterns the accepted list comprehension is the fastest, but look at the following:

In [8]: %timeit replaced = [w.replace('1', '<1>').replace('324', '<324>').replace('567', '<567>') for w in words]
100 loops, best of 3: 8.25 ms per loop

In [9]: r = re.compile('(1|324|567)')

In [10]: %timeit replaced = [r.sub('<\1>', w) for w in words]
100 loops, best of 3: 7.87 ms per loop

This shows that for more complicated substitutions a pre-compiled reg-exp (as in 9-10) can be (much) faster. It really depends on your problem and the shortest part of the reg-exp.


回答 4

一个for循环的示例(我更喜欢列表理解)。

a, b = '[br]', '<br />'
for i, v in enumerate(words):
    if a in v:
        words[i] = v.replace(a, b)
print(words)
# ['how', 'much', 'is<br/>', 'the', 'fish<br/>', 'no', 'really']

An example with for loop (I prefer List Comprehensions).

a, b = '[br]', '<br />'
for i, v in enumerate(words):
    if a in v:
        words[i] = v.replace(a, b)
print(words)
# ['how', 'much', 'is<br/>', 'the', 'fish<br/>', 'no', 'really']

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