有几种方法可以达到相同的效果:

import sys
import inspect

def what_is_my_name():
    print(inspect.stack()[0][0].f_code.co_name)
    print(inspect.stack()[0][3])
    print(inspect.currentframe().f_code.co_name)
    print(sys._getframe().f_code.co_name)

注意inspect。堆栈调用比替代方法慢数千倍:

$ python -m timeit -s 'import inspect, sys' 'inspect.stack()[0][0].f_code.co_name'
1000 loops, best of 3: 499 usec per loop
$ python -m timeit -s 'import inspect, sys' 'inspect.stack()[0][3]'
1000 loops, best of 3: 497 usec per loop
$ python -m timeit -s 'import inspect, sys' 'inspect.currentframe().f_code.co_name'
10000000 loops, best of 3: 0.1 usec per loop
$ python -m timeit -s 'import inspect, sys' 'sys._getframe().f_code.co_name'
10000000 loops, best of 3: 0.135 usec per loop

2021年8月更新(原文章为Python2.7编写)

Python 3.9.1 (default, Dec 11 2020, 14:32:07)
[GCC 7.3.0] :: Anaconda, Inc. on linux

python -m timeit -s 'import inspect, sys' 'inspect.stack()[0][0].f_code.co_name'
500 loops, best of 5: 390 usec per loop
python -m timeit -s 'import inspect, sys' 'inspect.stack()[0][3]'
500 loops, best of 5: 398 usec per loop
python -m timeit -s 'import inspect, sys' 'inspect.currentframe().f_code.co_name'
2000000 loops, best of 5: 176 nsec per loop
python -m timeit -s 'import inspect, sys' 'sys._getframe().f_code.co_name'
5000000 loops, best of 5: 62.8 nsec per loop

我喜欢使用装饰器的想法，但我更喜欢避免触及函数参数。因此，我提供了另一种选择:

import functools

def withname(f):
    @functools.wraps(f)
    def wrapper(*args, **kwargs):
        global __name
        __saved_name = globals().get("__name")
        __name = f.__name__
        ret = f(*args, **kwargs)
        __name = __saved_name
        return ret
    return wrapper

@withname
def f():
    print(f"in f: __name=={__name}")
    g()
    print(f"back in f: __name=={__name}")

@withname
def g():
    print(f"in g: __name=={__name}")

由于__name是一个全局变量，所以在调用函数时需要保存和恢复__name。调用上面的f()会产生:

in f: __name==f
in g: __name==g
back in f: __name==f

不幸的是，如果我们不改变函数参数，就没有全局变量的替代品。引用一个不是在函数上下文中创建的变量，将生成寻找全局变量的代码:

>>> def f(): print(__function__)
>>> from dis import dis
>>> dis(f)
  1           0 LOAD_GLOBAL              0 (print)
              2 LOAD_GLOBAL              1 (__function__)
              4 CALL_FUNCTION            1
              6 POP_TOP
              8 LOAD_CONST               0 (None)
             10 RETURN_VALUE

有几种方法可以达到相同的效果:

import sys
import inspect

def what_is_my_name():
    print(inspect.stack()[0][0].f_code.co_name)
    print(inspect.stack()[0][3])
    print(inspect.currentframe().f_code.co_name)
    print(sys._getframe().f_code.co_name)

注意inspect。堆栈调用比替代方法慢数千倍:

$ python -m timeit -s 'import inspect, sys' 'inspect.stack()[0][0].f_code.co_name'
1000 loops, best of 3: 499 usec per loop
$ python -m timeit -s 'import inspect, sys' 'inspect.stack()[0][3]'
1000 loops, best of 3: 497 usec per loop
$ python -m timeit -s 'import inspect, sys' 'inspect.currentframe().f_code.co_name'
10000000 loops, best of 3: 0.1 usec per loop
$ python -m timeit -s 'import inspect, sys' 'sys._getframe().f_code.co_name'
10000000 loops, best of 3: 0.135 usec per loop

2021年8月更新(原文章为Python2.7编写)

Python 3.9.1 (default, Dec 11 2020, 14:32:07)
[GCC 7.3.0] :: Anaconda, Inc. on linux

python -m timeit -s 'import inspect, sys' 'inspect.stack()[0][0].f_code.co_name'
500 loops, best of 5: 390 usec per loop
python -m timeit -s 'import inspect, sys' 'inspect.stack()[0][3]'
500 loops, best of 5: 398 usec per loop
python -m timeit -s 'import inspect, sys' 'inspect.currentframe().f_code.co_name'
2000000 loops, best of 5: 176 nsec per loop
python -m timeit -s 'import inspect, sys' 'sys._getframe().f_code.co_name'
5000000 loops, best of 5: 62.8 nsec per loop

我想检查是最好的方法。例如:

import inspect
def bar():
    print("My name is", inspect.stack()[0][3])

你可以使用装饰器:

def my_function(name=None):
    return name

def get_function_name(function):
    return function(name=function.__name__)

>>> get_function_name(my_function)
'my_function'

你可以使用@Andreas Jung显示的方法获得它定义时的名称，但这可能不是调用函数时的名称:

import inspect

def Foo():
   print inspect.stack()[0][3]

Foo2 = Foo

>>> Foo()
Foo

>>> Foo2()
Foo

这种区别对你来说是否重要，我不能说。