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
这是一个只有程序员才能决定的权衡。
Case 1在需要时才导入datetime模块(并进行任何可能需要的初始化)，从而节省了一些内存和启动时间。请注意，“仅在被调用时”导入也意味着“每次被调用时”导入，因此第一次调用之后的每个调用仍然会产生执行导入的额外开销。
情况2通过提前导入datetime来节省一些执行时间和延迟，这样在调用not_often_called()时就会更快地返回，而且也不会在每次调用时都产生导入的开销。
除了效率，如果import语句是…前面。将它们隐藏在代码中会使查找某个组件所依赖的模块变得更加困难。
就我个人而言，我通常遵循PEP，除了单元测试之类的东西，我不希望总是加载这些东西，因为我知道除了测试代码之外，它们不会被使用。
其他回答
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。
为了完成老谋子的回答和最初的问题:
当我们不得不处理循环依赖关系时，我们可以做一些“技巧”。假设我们正在处理模块a.py和b.py，它们分别包含x()和b.y()。然后:
我们可以移动模块底部的from导入之一。 我们可以将其中一个from导入移动到实际需要导入的函数或方法中(这并不总是可行的，因为您可能从多个地方使用它)。 我们可以把其中一个import改成import，就像import a
总结一下。如果您没有处理循环依赖关系，也没有使用某种技巧来避免它们，那么最好将所有导入放在顶部，因为原因已经在这个问题的其他答案中解释过了。请在做这些“技巧”时附上评论，这总是受欢迎的!：）
以下是对这个问题的最新答案总结 而且 相关的 的问题。
PEP 8 recommends putting imports at the top. It's often more convenient to get ImportErrors when you first run your program rather than when your program first calls your function. Putting imports in the function scope can help avoid issues with circular imports. Putting imports in the function scope helps keep maintain a clean module namespace, so that it does not appear among tab-completion suggestions. Start-up time: imports in a function won't run until (if) that function is called. Might get significant with heavy-weight libraries. Even though import statements are super fast on subsequent runs, they still incur a speed penalty which can be significant if the function is trivial but frequently in use. Imports under the __name__ == "__main__" guard seem very reasonable. Refactoring might be easier if the imports are located in the function where they're used (facilitates moving it to another module). It can also be argued that this is good for readability. However, most would argue the contrary, i.e. Imports at the top enhance readability, since you can see all your dependencies at a glance. It seems unclear if dynamic or conditional imports favour one style over another.
这就像许多其他优化一样——你牺牲了一些可读性来换取速度。正如John提到的，如果您已经完成了分析作业，并且发现这是一个非常有用的更改，并且您需要额外的速度，那么就去做吧。最好把所有其他的导入都放在一起:
from foo import bar
from baz import qux
# Note: datetime is imported in SomeClass below
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
