PROSAGA码农传奇-ECN-从列表中的元组有效地连接两个字符串？

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    使用列表推导，对于两个元素，我将使用元组解包和连接：
  
   <pre>
 <code>
 myanswer = [s1 + s2 for s1, s2 in mylist]

</code>
 </pre>
  
    另一个选择是使用a
    <a href="https://docs.python.org/3/reference/lexical_analysis.html#f-strings" rel="nofollow noreferrer">
      格式化字符串文字
    </A>
    ：
  
   <pre>
 <code>
 myanswer = [f"{s1}{s2}" for s1, s2 in mylist]

</code>
 </pre>
  
    两者都相当快：
  
   <pre>
 <code>
 >>> from random import choice
>>> from string import ascii_letters
>>> from timeit import Timer
>>> testdata = [(choice(ascii_letters), choice(ascii_letters)) for _ in range(10000)]
>>> count, total = Timer('[f"{s1}{s2}" for s1, s2 in mylist]', 'from __main__ import testdata as mylist').autorange()
>>> print(f"List comp with f-string, 10k elements: {total / count * 1000000:7.2f} microseconds")
List comp with f-string, 10k elements: 1249.37 microseconds
>>> count, total = Timer('[s1 + s2 for s1, s2 in mylist]', 'from __main__ import testdata as mylist').autorange()
>>> print(f"List comp with concatenation, 10k elements: {total / count * 1000000:6.2f} microseconds")
List comp with concatenation, 10k elements: 1061.89 microseconds

</code>
 </pre>
  
    连接在这里胜出。
  
  
    列表理解消除了查找列表对象及其的需要
     <code>
 .append()
 </code>
     每次在循环中的方法，请参阅
    <a href="https://stackoverflow.com/questions/16341775/what-is-the-advantage-of-a-list-comprehension-over-a-for-loop/16341841#16341841">
      列表理解优于for循环的优势是什么？
    </A>
  
  
    Python 3.6中引入的格式化字符串文字，很容易用插值元素组成字符串的最快方法（即使它们
    <a href =“https://stackoverflow.com/questions/37365311/why-are-literal-formatted-strings-so-slow-in-python-3-6-alpha-now-fixed-in-3-6 “>
      并没有这样开始
    </A>
    ）。
  
  
    我也尝试过[
     <code>
 itertools.starmap()
 </code>
    ] [带]
     <code>
 operator.add()
 </code>
    ]和[
     <code>
 str.join()
 </code>
    ]，但这似乎没有竞争力：
  
   <pre>
 <code>
 >>> count, total = Timer('list(starmap(add, mylist))', 'from __main__ import testdata as mylist; from itertools import starmap; from operator import add').autorange()
>>> print(f"itertools.starmap and operator.add, 10k elements: {total / count * 1000000:6.2f} microseconds")
itertools.starmap and operator.add, 10k elements: 1275.02 microseconds
>>> count, total = Timer('list(starmap(str.join, mylist))', 'from __main__ import testdata as mylist; from itertools import starmap').autorange()
>>> print(f"itertools.starmap and str.join, 10k elements: {total / count * 1000000:6.2f} microseconds")
itertools.starmap and str.join, 10k elements: 1564.79 microseconds

</code>
 </pre>
  
    它确实随着更多元素而改进;百万元素，
     <code>
 map(starmap(add, largelist))
 </code>
     以小幅度获胜（133ms与140ms相比，列表理解与连接）。
  
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