关于python：使用”open()”vs”with open()”读取文件

File read using “open()” vs “with open()”

本问题已经有最佳答案，请猛点这里访问。

我知道有很多关于用python读取文件的文章和问题得到了解答。但我仍然想知道是什么让Python有多种方法来完成相同的任务。我只想知道，使用这两种方法对性能有什么影响？

使用with语句不是为了提高性能，我认为使用with语句不会带来任何性能上的收益或损失，只要您执行的清理活动与使用with语句自动执行的清理活动相同。

当使用带open功能的with语句时，不需要在文件末尾关闭文件，因为with会自动为您关闭文件。

另外，with语句不仅用于打开文件，还与上下文管理器结合使用。基本上，如果您有一个对象想要确保它在完成操作后被清除，或者发生某种错误，您可以将它定义为上下文管理器，并且with语句将在进入和退出With块时调用它的__enter__()和__exit__()方法。根据PEP 0343-

This PEP adds a new statement"with" to the Python language to make it possible to factor out standard uses of try/finally statements.

In this PEP, context managers provide __enter__() and __exit__() methods that are invoked on entry to and exit from the body of the with statement.

另外，使用with而不使用它的性能测试-

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In [14]: def foo():
....: f = open('a.txt','r')
....: for l in f:
....: pass
....: f.close()
....:

In [15]: def foo1():
....: with open('a.txt','r') as f:
....: for l in f:
....: pass
....:

In [17]: %timeit foo()
The slowest run took 41.91 times longer than the fastest. This could mean that an intermediate result is being cached
10000 loops, best of 3: 186 μs per loop

In [18]: %timeit foo1()
The slowest run took 206.14 times longer than the fastest. This could mean that an intermediate result is being cached
10000 loops, best of 3: 179 μs per loop

In [19]: %timeit foo()
The slowest run took 202.51 times longer than the fastest. This could mean that an intermediate result is being cached
10000 loops, best of 3: 180 μs per loop

In [20]: %timeit foo1()
10000 loops, best of 3: 193 μs per loop

In [21]: %timeit foo1()
10000 loops, best of 3: 194 μs per loop