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

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。

其他回答

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。

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。)

可读性

除了启动性能外,本地化import语句还需要考虑可读性。例如,在我目前的第一个python项目中使用python行号1283到1296:

listdata.append(['tk font version', font_version])
listdata.append(['Gtk version', str(Gtk.get_major_version())+"."+
                 str(Gtk.get_minor_version())+"."+
                 str(Gtk.get_micro_version())])

import xml.etree.ElementTree as ET

xmltree = ET.parse('/usr/share/gnome/gnome-version.xml')
xmlroot = xmltree.getroot()
result = []
for child in xmlroot:
    result.append(child.text)
listdata.append(['Gnome version', result[0]+"."+result[1]+"."+
                 result[2]+" "+result[3]])

如果import语句在文件的顶部,我将不得不向上滚动很长一段距离,或按Home键,以找出ET是什么。然后我将不得不返回到第1283行继续阅读代码。

实际上,即使import语句像许多人那样位于函数(或类)的顶部,也需要向上和向下分页。

很少会显示Gnome版本号,因此在文件顶部导入会引入不必要的启动延迟。

在函数中导入变量/局部作用域可以提高性能。这取决于函数中导入对象的使用情况。如果你多次循环并访问一个模块全局对象,将它导入为本地会有帮助。

test.py

X=10
Y=11
Z=12
def add(i):
  i = i + 10

runlocal.py

from test import add, X, Y, Z

    def callme():
      x=X
      y=Y
      z=Z
      ladd=add 
      for i  in range(100000000):
        ladd(i)
        x+y+z

    callme()

run.py

from test import add, X, Y, Z

def callme():
  for i in range(100000000):
    add(i)
    X+Y+Z

callme()

在Linux上的时间显示了一个小的增益

/usr/bin/time -f "\t%E real,\t%U user,\t%S sys" python run.py 
    0:17.80 real,   17.77 user, 0.01 sys
/tmp/test$ /usr/bin/time -f "\t%E real,\t%U user,\t%S sys" python runlocal.py 
    0:14.23 real,   14.22 user, 0.01 sys

真实的是挂钟。用户是程序中的时间。Sys是系统调用的时间。

https://docs.python.org/3.5/reference/executionmodel.html#resolution-of-names

我想提一下我的一个用例,与@John Millikin和@ v.k.提到的用例非常相似:

可选的进口

我使用Jupyter Notebook进行数据分析,我使用相同的IPython Notebook作为所有分析的模板。在某些情况下,我需要导入Tensorflow来做一些快速的模型运行,但有时我工作的地方,Tensorflow没有设置/导入很慢。在这些情况下,我将依赖于tensorflow的操作封装在一个helper函数中,在该函数中导入tensorflow,并将其绑定到一个按钮。

这样,我就可以“重新启动并运行全部”,而不必等待导入,也不必在导入失败时恢复其余的单元格。