Develop Reference

Circular imports hell

Python is extremely elegant language. Well, except... except imports. I still can't get it work the way it seems natural to me.
I have a class MyObjectA which is in file mypackage/myobjecta.py. This object uses some utility functions which are in mypackage/utils.py. So in my first lines in myobjecta.py I write:
from mypackage.utils import util_func1, util_func2
But some of the utility functions create and return new instances of MyObjectA. So I need to write in utils.py:
from mypackage.myobjecta import MyObjectA
Well, no I can't. This is a circular import and Python will refuse to do that.
There are many question here regarding this issue, but none seems to give satisfactory answer. From what I can read in all the answers:
Reorganize your modules, you are doing it wrong! But I do not know
how better to organize my modules even in such a simple case as I
presented.
Try just import ... rather than from ... import ...
(personally I hate to write and potentially refactor all the full
name qualifiers; I love to see what exactly I am importing into
module from the outside world). Would that help? I am not sure,
still there are circular imports.
Do hacks like import something in the inner scope of a function body just one line before you use something from other module.
I am still hoping there is solution number 4) which would be Pythonic in the sense of being functional and elegant and simple and working. Or is there not?
Note: I am primarily a C++ programmer, the example above is so much easily solved by including corresponding headers that I can't believe it is not possible in Python.

There is nothing hackish about importing something in a function body, it's an absolutely valid pattern:
def some_function():
import logging
do_some_logging()
Usually ImportErrors are only raised because of the way import() evaluates top level statements of the entire file when called.
In case you do not have a logic circular dependency...
, nothing is impossible in python...
There is a way around it if you positively want your imports on top:
From David Beazleys excellent talk Modules and Packages: Live and Let Die! - PyCon 2015, 1:54:00, here is a way to deal with circular imports in python:
try:
from images.serializers import SimplifiedImageSerializer
except ImportError:
import sys
SimplifiedImageSerializer = sys.modules[__package__ + '.SimplifiedImageSerializer']
This tries to import SimplifiedImageSerializer and if ImportError is raised (due to a circular import error or the it not existing) it will pull it from the importcache.
PS: You have to read this entire post in David Beazley's voice.

Don't import mypackage.utils to your main module, it already exists in mypackage.myobjecta. Once you import mypackage.myobjecta the code from that module is being executed and you don't need to import anything to your current module, because mypackage.myobjecta is already complete.

What you want isn't possible. There's no way for Python to know in which order it needs to execute the top-level code in order to do what you ask.
Assume you import utils first. Python will begin by evaluating the first statement, from mypackage.myobjecta import MyObjectA, which requires executing the top level of the myobjecta module. Python must then execute from mypackage.utils import util_func1, util_func2, but it can't do that until it resolves the myobjecta import.
Instead of recursing infinitely, Python resolves this situation by allowing the innermost import to complete without finishing. Thus, the utils import completes without executing the rest of the file, and your import statement fails because util_func1 doesn't exist yet.
The reason import myobjecta works is that it allows the symbols to be resolved later, after the body of every module has executed. Personally, I've run into a lot of confusion even with this kind of circular import, and so I don't recommend using them at all.
If you really want to use a circular import anyway, and you want them to be "from" imports, I think the only way it can reliably work is this: Define all symbols used by another module before importing from that module. In this case, your definitions for util_func1 and util_func2 must be before your from mypackage.myobjecta import MyObjectA statement in utils, and the definition of MyObjectA must be before from mypackage.utils import util_func1, util_func2 in myobjecta.
Compiled languages like C# can handle situations like this because the top level is a collection of definitions, not instructions. They don't have to create every class and every function in the order given. They can work things out in whatever order is required to avoid any cycles. (C++ does it by duplicating information in prototypes, which I personally feel is a rather hacky solution, but that's also not how Python works.)
The advantage of a system like Python is that it's highly dynamic. Yes you can define a class or a function differently based on something you only know at runtime. Or modify a class after it's been created. Or try to import dependencies and go without them if they're not available. If you don't feel these things are worth the inconvenience of adhering to a strict dependency tree, that's totally reasonable, and maybe you'd be better served by a compiled language.

Pythonistas frown upon importing from a function. Pythonistas usually frown upon global variables. Yet, I saw both and don't think the projects that used them were any worse than others done by some strict Pythhonistas. The feature does exist, not going into a long argument over its utility.
There's an alternative to the problem of importing from a function: when you import from the top of a file (or the bottom, really), this import will take some time (some small time, but some time), but Python will cache the entire file and if another file needs the same import, Python can retrieve the module quickly without importing. Whereas, if you import from a function, things get complicated: Python will have to process the import line each time you call the function, which might, in a tiny way, slow your program down.
A solution to this is to cache the module independently. Okay, this uses imports inside function bodies AND global variables. Wow!
_MODULEA = None
def util1():
if _MODULEA is None:
from mymodule import modulea as _MODULEA
obj = _MODULEA.ClassYouWant
return obj
I saw this strategy adopted with a project using a flat API. Whether you like it or not (and I'm not sure about that myself), it works and is fast, because the import line is executed only once (when the function first executes). Still, I would recommend restructuring: problems with circular imports show a problem in structure, usually, and this is always worth fixing. I do agree, though, it would be nice if Python provided more useful errors when this kind of situation happens.

Are there possible negative consequences to using import inline?

I'm working on a Python program that needs to be able to run even if some of the libraries it needs for some features are missing. (EDIT: I wrote a little code to implement the best suggested solution and it's here, with a doctest here.)
I solved this by putting the import statements for such libraries inline to the functions that use them, rather than at the top of the Python file. That means that you can load the file perfectly well even if you don't have the library, though of course you'll throw an ImportError if you try to call one of the functions.
This has worked so well that I find myself sometimes doing this for standard library modules as well - but now I'm wondering if I'm incurring some hidden cost by doing so?
Baseline code:
import numpy
def foo():
return numpy.array([])
def bar():
return numpy.array([1, 2, 3])
Code with inline imports:
def foo():
import numpy
return numpy.array([])
def bar():
import numpy
return numpy.array([1, 2, 3])
EDIT:
I agree completely about not inlining standard library code - obviously bad.
I now think that the guarded import is the correct solution.
In particular, I did some timing tests on the calls, and while the time difference probably isn't significant for most applications, it is appreciable (fine line, I know!)
In the trivial case
import numpy
def f():
return numpy
takes about 180ms on my machine for 100,000 repetitions but
def f():
import numpy
return numpy
takes about 870ms.
Very rough takeaway is that this costs as much as four trivial function calls - noticeable but not significant in most cases. Still, best to avoid if it costs you nothing to do so.
In experimenting, I also realized another downside to inline imports - it's that these imports go off at an unpredictable time, when the function is called. In my application, which has real-time elements, this is unacceptable.

There's no appreciable performance hit, but it makes your code messy. If you decide to add a new import or have to change an old one, you have to change it everywhere instead of just in one place.
Also, you should be sure this is documented. Some users may be irritated if the library appears to import correctly but then fails much later when a specific function is called. In addition, although there's no overall performance hit, there can be a performance "reshuffling" which causes slowdowns in unexpected places. The first time you call a function that imports numpy, it will have to do the import, and this will take time. Users may also find this undesirable and want all the slow imports to be done up front.
You can easily get a similar effect with all-at-the-top imports:
try:
import numpy
except ImportError:
warnings.warn("Numpy not available, some functions may not work!")
Later attempts to use functions that try to access numpy will now fail with a NameError. By using a warning (or just a printed/logged message), you also provide advance notice that some things will not work, instead of just suddenly failing later.

You're not following PEP 8 by doing this. In the case of standard library imports, you're doing so without good reason, which is doubly bad and enough for some people to shun your code (or at least politely nag that you shouldn't do it).
Of course, PEP 8 doesn't say that for no reason at all. In this case, there's an even better reason than personal preference and uniformity: If you put all imports at the top, one can very easily find the dependencies of a module. This becomes more of a hassle if the imports are spread all over the file. Moreover, now practically every call to your library can raise ImportError, which is rather unfortunate: The usual workflow is to import everything, and if it can be imported, it's assumed to work (this is a useful manual test when setting up a virtualenv). Not-quite-well-written code might start doing things like I/O, call your function in between (not expecting an ImportError), and then be surprised by the error and fail to clean up properly.
There is also a slight overhead, in that a few extra instructions will be executed each time a function containing an import is called. However, this overhead is rather small for most purposes, and it won't import the module twice (or thrice, or umpteen times). Of course, it also violates DRY.
When confronted with this problem, I and other people have opted to put the import at the top of the file anyway, surrounded with try: ... except ImportError:. Then you can assign a dummy value, emit a warning, log something, or do whatever else makes sense in your case. You might even import a replacement module (e.g. when supporting old Python versions that don't have certain modules) or a stub module you supply yourself.

Not really.
The import will only happen once, but it might happen at an unexpected time (for the user), namely the first time the function is called that does the import.
Also, it's a matter of readability - if you follow the convention to do the imports at the top, every reader of your code knows immediately what its dependencies are. That clarity may get lost when the import is happening on line 284 of your module...

No, there should be no downsides or hidden costs when doing this. Modules are cached and only executed once, even if you import them multiple times. The import then just (re)sets the local reference to the module.

Does importing a module (but not using it) decrease performance in Python?

I'm running a website using Django, and I import ipdb at the beginning of almost all of my scripts to make debugging easier. However, most of the time I never use the functions from the module (only when I'm debugging).
Just wondering, will this decrease my performance? It's just that when I want to create a breakpoint I prefer to write:
ipdb.set_trace()
as opposed to:
import ipdb; ipdb.set_trace()
But I've seen the second example done in several places, which makes me wonder if it's more efficient...
I just don't know how importing python modules relates to efficiency (assuming you're not using the module methods within your script).

As #wRAR mentioned, Loading a module may imply executing any amounts of code which can take any amount of time. On the other hand, the module will only be loaded once and any subsequent attempt to import will find the module present in os.sys.modules and reference to that.
In a Django environment in debuging mode, modules are removed from Django's AppCache and actually re-imported only when they are changed, which you will probably not do with ipdb, so in your case it should not be an issue.
However, in cases it would be an issue, there are some ways around it. Suppose you have a custom module that you use to load anyway, you can add a function to it that imports ipdb only when you require it:
# much used module: mymodule
def set_trace():
import ipdb
ipdb.set_trace()
in the module you want to use ipdb.set_trace:
import mymodule
mymodule.set_trace()
or, on top of your module, use the cross-module __debug__ variable:
if __debug__:
from ipdp import set_trace
else:
def set_trace(): return

Short answer: Not usually
Long answer:
It will take time to load the module. This may be noticeable if you are loading python off a network drive or other slow source. But if running directly off a hard drive you'll never notice.
As #wRar points out, importing a module can execute any amount of code. You can have whatever code you want executed at module startup. However, most modules avoid executing unreasonable amounts of code during startup. So that itself probably isn't a huge cause.
However, importing very large modules especially those that also result in importing a large number of c modules will take time.
So importing will take time, but only once per module imported. If you import modules at the top of your modules (as opposed to in functions) it only applies to startup time anyways. Basically, you aren't going to get much optimisation mileage out of avoiding importing modules.

Importing a module but not using it decreases (the system) performance:
It takes time to import the module
Imported modules use up memory
While the first point makes your program slower to start, the second point might make ALL your programs slower, depending on the total amount of memory you have on your system.

Python - optimize by not importing at module level?

In a framework such as Django, I'd imagine that if a user lands on a page (running a view function called "some_page"), and you have 8 imports at the top of module that are irrelevant to that view, you're wasting cycles on those imports. My questions are:
Is it a large enough amount of resources to make an impact on a high-traffic website?
Is it such a bad practice to import inside of a function for this purpose that it should be avoided at said impact?
Note: This could be considered premature optimization, but I'm not interested in that argument. Let's assume, for the sake of practical theory, that this is a completed site with loads of traffic, needing to be optimized in every way possible, and the application code, as well as DB have been fully optimized by 50 PhD database admins and developers, and these imports are the only thing left.

No, don't do this. In a normal python execution environment on the web (mod_wsgi, gunicorn, etc.) when your process starts those imports will be executed, and then all subsequent requests will not re-execute the script. If you put the imports inside the functions they'll have to be processed every time the function is called.

Yes, it is a bad practice to import at the function level. By using smarter imports at the top of the module, you create a one time, small cost. However, if you place an import in a function you will suffer the cost of the import each time that function is run. So, rather than import in the function, just import at the top of the module.
A few things you can do to clean up and improve your imports:
Don't use wild imports e.g. from x import *
Where possible, just use a normal import e.g. import x
Try to split your code up into smaller modules that can be called separately, so that fewer imports are made
Also, placing imports at the top of the module is a matter of style. There's a reason why PEP 8 says that modules need to be imported at the top. It's far more readable and maintainable that way.
Finally, some imports at function level will cause compatibility issues in the future, as from x import * is not valid Python 3.x at function level.

1) The answer is no. Django/Python is not like PHP. Your whole module will not be reinterpreted with each pageview like happens with PHP includes. The module will be in memory and each page view will make a simple function call to your view.
2) Yes, it will be a counter-optimization to make imports at the view level.

No. Same reason as other answers.
Yes. Same reason as other answes.
BTW, you can also do import lazily.
For example, Importing toolkit can "import" a module in top level code, but the module is not actually loaded until one of the attributes is accessed.

Sometimes following boiler-plate makes sense:
foo = None
def foorify():
global foo
if not foo: from xxx import foo
foo.bar()
This makes sense when foorification is conditional on something that rarely changes, e.g. one server foorifies while another never does or if you don't want or cannot safely import foo during application startup or most tests.

Should import statements always be at the top of a module?

PEP 8 states:
Imports are always put at the top of the file, just after any module comments and docstrings, and before module globals and constants.
However if the class/method/function that I am importing is only used in rare cases, surely it is more efficient to do the import when it is needed?
Isn't this:
class SomeClass(object):
def not_often_called(self)
from datetime import datetime
self.datetime = datetime.now()
more efficient than this?
from datetime import datetime
class SomeClass(object):
def not_often_called(self)
self.datetime = datetime.now()

Module importing is quite fast, but not instant. This means that:
Putting the imports at the top of the module is fine, because it's a trivial cost that's only paid once.
Putting the imports within a function will cause calls to that function to take longer.
So if you care about efficiency, put the imports at the top. Only move them into a function if your profiling shows that would help (you did profile to see where best to improve performance, right??)
The best reasons I've seen to perform lazy imports are:
Optional library support. If your code has multiple paths that use different libraries, don't break if an optional library is not installed.
In the __init__.py of a plugin, which might be imported but not actually used. Examples are Bazaar plugins, which use bzrlib's lazy-loading framework.

Putting the import statement inside of a function can prevent circular dependencies.
For example, if you have 2 modules, X.py and Y.py, and they both need to import each other, this will cause a circular dependency when you import one of the modules causing an infinite loop. If you move the import statement in one of the modules then it won't try to import the other module till the function is called, and that module will already be imported, so no infinite loop. Read here for more - effbot.org/zone/import-confusion.htm

I have adopted the practice of putting all imports in the functions that use them, rather than at the top of the module.
The benefit I get is the ability to refactor more reliably. When I move a function from one module to another, I know that the function will continue to work with all of its legacy of testing intact. If I have my imports at the top of the module, when I move a function, I find that I end up spending a lot of time getting the new module's imports complete and minimal. A refactoring IDE might make this irrelevant.
There is a speed penalty as mentioned elsewhere. I have measured this in my application and found it to be insignificant for my purposes.
It is also nice to be able to see all module dependencies up front without resorting to search (e.g. grep). However, the reason I care about module dependencies is generally because I'm installing, refactoring, or moving an entire system comprising multiple files, not just a single module. In that case, I'm going to perform a global search anyway to make sure I have the system-level dependencies. So I have not found global imports to aid my understanding of a system in practice.
I usually put the import of sys inside the if __name__=='__main__' check and then pass arguments (like sys.argv[1:]) to a main() function. This allows me to use main in a context where sys has not been imported.

Most of the time this would be useful for clarity and sensible to do but it's not always the case. Below are a couple of examples of circumstances where module imports might live elsewhere.
Firstly, you could have a module with a unit test of the form:
if __name__ == '__main__':
import foo
aa = foo.xyz() # initiate something for the test
Secondly, you might have a requirement to conditionally import some different module at runtime.
if [condition]:
import foo as plugin_api
else:
import bar as plugin_api
xx = plugin_api.Plugin()
[...]
There are probably other situations where you might place imports in other parts in the code.

The first variant is indeed more efficient than the second when the function is called either zero or one times. With the second and subsequent invocations, however, the "import every call" approach is actually less efficient. See this link for a lazy-loading technique that combines the best of both approaches by doing a "lazy import".
But there are reasons other than efficiency why you might prefer one over the other. One approach is makes it much more clear to someone reading the code as to the dependencies that this module has. They also have very different failure characteristics -- the first will fail at load time if there's no "datetime" module while the second won't fail until the method is called.
Added Note: In IronPython, imports can be quite a bit more expensive than in CPython because the code is basically being compiled as it's being imported.

Curt makes a good point: the second version is clearer and will fail at load time rather than later, and unexpectedly.
Normally I don't worry about the efficiency of loading modules, since it's (a) pretty fast, and (b) mostly only happens at startup.
If you have to load heavyweight modules at unexpected times, it probably makes more sense to load them dynamically with the __import__ function, and be sure to catch ImportError exceptions, and handle them in a reasonable manner.

I wouldn't worry about the efficiency of loading the module up front too much. The memory taken up by the module won't be very big (assuming it's modular enough) and the startup cost will be negligible.
In most cases you want to load the modules at the top of the source file. For somebody reading your code, it makes it much easier to tell what function or object came from what module.
One good reason to import a module elsewhere in the code is if it's used in a debugging statement.
For example:
do_something_with_x(x)
I could debug this with:
from pprint import pprint
pprint(x)
do_something_with_x(x)
Of course, the other reason to import modules elsewhere in the code is if you need to dynamically import them. This is because you pretty much don't have any choice.
I wouldn't worry about the efficiency of loading the module up front too much. The memory taken up by the module won't be very big (assuming it's modular enough) and the startup cost will be negligible.

Here's an updated summary of the answers to this
and
related
questions.
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.

I was surprised not to see actual cost numbers for the repeated load-checks posted already, although there are many good explanations of what to expect.
If you import at the top, you take the load hit no matter what. That's pretty small, but commonly in the milliseconds, not nanoseconds.
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).
Here are the results of a simple test that imports a few things from inside a function. The times reported (in Python 2.7.14 on a 2.3 GHz Intel Core i7) are shown below (the 2nd call taking more than later calls seems consistent, though I don't know why).
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
The code:
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

It's a tradeoff, that only the programmer can decide to make.
Case 1 saves some memory and startup time by not importing the datetime module (and doing whatever initialization it might require) until needed. Note that doing the import 'only when called' also means doing it 'every time when called', so each call after the first one is still incurring the additional overhead of doing the import.
Case 2 save some execution time and latency by importing datetime beforehand so that not_often_called() will return more quickly when it is called, and also by not incurring the overhead of an import on every call.
Besides efficiency, it's easier to see module dependencies up front if the import statements are ... up front. Hiding them down in the code can make it more difficult to easily find what modules something depends on.
Personally I generally follow the PEP except for things like unit tests and such that I don't want always loaded because I know they aren't going to be used except for test code.

Here's an example where all the imports are at the very top (this is the only time I've needed to do this). I want to be able to terminate a subprocess on both Un*x and Windows.
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
(On review: what John Millikin said.)

This is like many other optimizations - you sacrifice some readability for speed. As John mentioned, if you've done your profiling homework and found this to be a significantly useful enough change and you need the extra speed, then go for it. It'd probably be good to put a note up with all the other imports:
from foo import bar
from baz import qux
# Note: datetime is imported in SomeClass below

Module initialization only occurs once - on the first import. If the module in question is from the standard library, then you will likely import it from other modules in your program as well. For a module as prevalent as datetime, it is also likely a dependency for a slew of other standard libraries. The import statement would cost very little then since the module intialization would have happened already. All it is doing at this point is binding the existing module object to the local scope.
Couple that information with the argument for readability and I would say that it is best to have the import statement at module scope.

Just to complete Moe's answer and the original question:
When we have to deal with circular dependences we can do some "tricks". Assuming we're working with modules a.py and b.py that contain x() and b y(), respectively. Then:
We can move one of the from imports at the bottom of the module.
We can move one of the from imports inside the function or method that is actually requiring the import (this isn't always possible, as you may use it from several places).
We can change one of the two from imports to be an import that looks like: import a
So, to conclude. If you aren't dealing with circular dependencies and doing some kind of trick to avoid them, then it's better to put all your imports at the top because of the reasons already explained in other answers to this question. And please, when doing this "tricks" include a comment, it's always welcome! :)

In addition to the excellent answers already given, it's worth noting that the placement of imports is not merely a matter of style. Sometimes a module has implicit dependencies that need to be imported or initialized first, and a top-level import could lead to violations of the required order of execution.
This issue often comes up in Apache Spark's Python API, where you need to initialize the SparkContext before importing any pyspark packages or modules. It's best to place pyspark imports in a scope where the SparkContext is guaranteed to be available.

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.
So my answer is no, do not always put the imports at the top of your modules.

It's interesting that not a single answer mentioned parallel processing so far, where it might be REQUIRED that the imports are in the function, when the serialized function code is what is being pushed around to other cores, e.g. like in the case of ipyparallel.

Readability
In addition to startup performance, there is a readability argument to be made for localizing import statements. For example take python line numbers 1283 through 1296 in my current first python project:
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]])
If the import statement was at the top of file I would have to scroll up a long way, or press Home, to find out what ET was. Then I would have to navigate back to line 1283 to continue reading code.
Indeed even if the import statement was at the top of the function (or class) as many would place it, paging up and back down would be required.
Displaying the Gnome version number will rarely be done so the import at top of file introduces unnecessary startup lag.

There can be a performance gain by importing variables/local scoping inside of a function. This depends on the usage of the imported thing inside the function. If you are looping many times and accessing a module global object, importing it as local can help.
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()
A time on Linux shows a small gain
/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
real is wall clock. user is time in program. sys is time for system calls.
https://docs.python.org/3.5/reference/executionmodel.html#resolution-of-names

I would like to mention a usecase of mine, very similar to those mentioned by #John Millikin and #V.K.:
Optional Imports
I do data analysis with Jupyter Notebook, and I use the same IPython notebook as a template for all analyses. In some occasions, I need to import Tensorflow to do some quick model runs, but sometimes I work in places where tensorflow isn't set up / is slow to import. In those cases, I encapsulate my Tensorflow-dependent operations in a helper function, import tensorflow inside that function, and bind it to a button.
This way, I could do "restart-and-run-all" without having to wait for the import, or having to resume the rest of the cells when it fails.

While PEP encourages importing at the top of a module, it isn't an error to import at other levels. That indicates imports should be at the top, however there are exceptions.
It is a micro-optimization to load modules when they are used. Code that is sluggish importing can be optimized later if it makes a sizable difference.
Still, you might introduce flags to conditionally import at as near to the top as possible, allowing a user to use configuration to import the modules they need while still importing everything immediately.
Importing as soon as possible means the program will fail if any imports (or imports of imports) are missing or have syntax errors. If all imports occur at the top of all modules then python works in two steps. Compile. Run.
Built in modules work anywhere they are imported because they are well designed. Modules you write should be the same. Moving around your imports to the top or to their first use can help ensure there are no side effects and the code is injecting dependencies.
Whether you put imports at the top or not, your code should still work when the imports are at the top. So start by importing immediately then optimize as needed.

This is a fascinating discussion. Like many others I had never even considered this topic. I got cornered into having to have the imports in the functions because of wanting to use the Django ORM in one of my libraries. I was having to call django.setup() before importing my model classes and because this was at the top of the file it was being dragged into completely non-Django library code because of the IoC injector construction.
I kind of hacked around a bit and ended up putting the django.setup() in the singleton constructor and the relevant import at the top of each class method. Now this worked fine but made me uneasy because the imports weren't at the top and also I started worrying about the extra time hit of the imports. Then I came here and read with great interest everybody's take on this.
I have a long C++ background and now use Python/Cython. My take on this is that why not put the imports in the function unless it causes you a profiled bottleneck. It's only like declaring space for variables just before you need them. The trouble is I have thousands of lines of code with all the imports at the top! So I think I will do it from now on and change the odd file here and there when I'm passing through and have the time.