PEP 8规定:

导入总是放在文件的顶部,就在任何模块注释和文档字符串之后,在模块全局变量和常量之前。

然而,如果我导入的类/方法/函数只在很少的情况下使用,那么在需要时进行导入肯定会更有效吗?

这不是:

class SomeClass(object):

    def not_often_called(self)
        from datetime import datetime
        self.datetime = datetime.now()

比这更有效率?

from datetime import datetime

class SomeClass(object):

    def not_often_called(self)
        self.datetime = datetime.now()

当前回答

我很惊讶没有看到重复负载检查的实际成本数字,尽管有很多很好的解释。

如果你在顶部导入,不管发生什么,你都要加载命中。这非常小,但通常是毫秒级,而不是纳秒级。

If you import within a function(s), then you only take the hit for loading if and when one of those functions is first called. As many have pointed out, if that doesn't happen at all, you save the load time. But if the function(s) get called a lot, you take a repeated though much smaller hit (for checking that it has been loaded; not for actually re-loading). On the other hand, as @aaronasterling pointed out you also save a little because importing within a function lets the function use slightly-faster local variable lookups to identify the name later (http://stackoverflow.com/questions/477096/python-import-coding-style/4789963#4789963).

下面是一个简单测试的结果,该测试从函数内部导入了一些内容。报告的时间(在2.3 GHz Intel Core i7上的Python 2.7.14中)如下所示(第2个调用比后面的调用多似乎是一致的,尽管我不知道为什么)。

 0 foo:   14429.0924 µs
 1 foo:      63.8962 µs
 2 foo:      10.0136 µs
 3 foo:       7.1526 µs
 4 foo:       7.8678 µs
 0 bar:       9.0599 µs
 1 bar:       6.9141 µs
 2 bar:       7.1526 µs
 3 bar:       7.8678 µs
 4 bar:       7.1526 µs

代码:

from __future__ import print_function
from time import time

def foo():
    import collections
    import re
    import string
    import math
    import subprocess
    return

def bar():
    import collections
    import re
    import string
    import math
    import subprocess
    return

t0 = time()
for i in xrange(5):
    foo()
    t1 = time()
    print("    %2d foo: %12.4f \xC2\xB5s" % (i, (t1-t0)*1E6))
    t0 = t1
for i in xrange(5):
    bar()
    t1 = time()
    print("    %2d bar: %12.4f \xC2\xB5s" % (i, (t1-t0)*1E6))
    t0 = t1

其他回答

下面是一个示例,其中所有导入都位于最顶部(这是我唯一一次需要这样做)。我希望能够在Un*x和Windows上终止子进程。

import os
# ...
try:
    kill = os.kill  # will raise AttributeError on Windows
    from signal import SIGTERM
    def terminate(process):
        kill(process.pid, SIGTERM)
except (AttributeError, ImportError):
    try:
        from win32api import TerminateProcess  # use win32api if available
        def terminate(process):
            TerminateProcess(int(process._handle), -1)
    except ImportError:
        def terminate(process):
            raise NotImplementedError  # define a dummy function

(回顾:约翰·米利金所说。)

我采用了将所有导入放在使用它们的函数中,而不是放在模块的顶部的做法。

这样做的好处是能够更可靠地进行重构。当我将一个函数从一个模块移动到另一个模块时,我知道该函数将继续工作,并且保留所有遗留的测试。如果我将导入放在模块的顶部,当我移动一个函数时,我发现我最终要花费大量时间来完成新模块的导入并使其最小化。重构IDE可能会让这一点变得无关紧要。

正如在其他地方提到的那样,有一个速度惩罚。我在我的应用程序中测量了这一点,发现它对我的目的来说是微不足道的。

不需要搜索(例如grep)就能看到所有模块依赖关系也是很好的。然而,我关心模块依赖关系的原因通常是因为我正在安装、重构或移动由多个文件组成的整个系统,而不仅仅是单个模块。在这种情况下,我无论如何都要执行全局搜索,以确保具有系统级依赖关系。因此,我还没有找到全局导入来帮助我在实践中理解一个系统。

我通常把sys的导入放在if __name__=='__main__'检查中,然后将参数(如sys.argv[1:])传递给main()函数。这允许我在sys未被导入的上下文中使用main。

I do not aspire to provide complete answer, because others have already done this very well. I just want to mention one use case when I find especially useful to import modules inside functions. My application uses python packages and modules stored in certain location as plugins. During application startup, the application walks through all the modules in the location and imports them, then it looks inside the modules and if it finds some mounting points for the plugins (in my case it is a subclass of a certain base class having a unique ID) it registers them. The number of plugins is large (now dozens, but maybe hundreds in the future) and each of them is used quite rarely. Having imports of third party libraries at the top of my plugin modules was a bit penalty during application startup. Especially some thirdparty libraries are heavy to import (e.g. import of plotly even tries to connect to internet and download something which was adding about one second to startup). By optimizing imports (calling them only in the functions where they are used) in the plugins I managed to shrink the startup from 10 seconds to some 2 seconds. That is a big difference for my users.

所以我的答案是否定的,不要总是把导入放在模块的顶部。

Curt提出了一个很好的观点:第二个版本更清晰,并且会在加载时失败,而不是在加载后失败,而且出乎意料。

通常我不担心加载模块的效率,因为它(a)非常快,(b)大多数只发生在启动时。

如果你不得不在意想不到的时候加载重量级模块,使用__import__函数动态加载它们可能更有意义,并确保捕获ImportError异常,并以合理的方式处理它们。

除了已经给出的优秀答案之外,值得注意的是导入的位置不仅仅是风格的问题。有时,模块具有需要首先导入或初始化的隐式依赖项,而顶层导入可能会导致违反所需的执行顺序。

这个问题经常出现在Apache Spark的Python API中,在导入任何pyspark包或模块之前,你需要初始化SparkContext。最好将pyspark导入放在保证SparkContext可用的范围内。