Python可以打印函数定义吗？

在JavaScript中，可以打印出函数的定义。有没有办法在Python中完成此任务？

（只是在交互模式下玩，所以我想在没有open（）的情况下读取模块。我只是很好奇。）

如果要导入功能，则可以使用inspect.getsource：

>>> import re
>>> import inspect
>>> print inspect.getsource(re.compile)
def compile(pattern, flags=0):
    "Compile a regular expression pattern, returning a pattern object."
    return _compile(pattern, flags)

这将在交互式提示中起作用，但显然仅适用于导入的对象（不适用于交互式提示中定义的对象）。当然，只有在Python可以找到源代码的情况下它才会起作用（因此不能在内置对象，C库，.pyc文件等上找到）

如果您使用的是iPython，则可以使用function_name?获得帮助，并且function_name??可以打印出源代码。

这是我想出的方法：

    import inspect as i
    import sys
    sys.stdout.write(i.getsource(MyFunction))

这样可以取出换行符并很好地打印功能

尽管我通常会认为这inspect是一个很好的答案，但我不同意您无法获得解释器中定义的对象的源代码。如果使用dill.source.getsourcefrom dill，即使它们是交互式定义的，也可以获取函数和lambda的来源。它也可以从咖喱中定义的绑定或未绑定类方法和函数中获取代码。但是，如果没有封闭对象的代码，则可能无法编译该代码。

>>> from dill.source import getsource
>>> 
>>> def add(x,y):
...   return x+y
... 
>>> squared = lambda x:x**2
>>> 
>>> print getsource(add)
def add(x,y):
  return x+y

>>> print getsource(squared)
squared = lambda x:x**2

>>> 
>>> class Foo(object):
...   def bar(self, x):
...     return x*x+x
... 
>>> f = Foo()
>>> 
>>> print getsource(f.bar)
def bar(self, x):
    return x*x+x

>>> 

用help(function)得到的功能说明。

您可以help() 在此处了解更多信息。

您可以使用__doc__关键字：

#print the class description
print string.__doc__
#print function description
print open.__doc__

您可以__doc__在函数中使用，以hog()函数为例：您可以看到这样的用法hog()：

from skimage.feature import hog

print hog.__doc__

输出将是：

Extract Histogram of Oriented Gradients (HOG) for a given image.
Compute a Histogram of Oriented Gradients (HOG) by

    1. (optional) global image normalisation
    2. computing the gradient image in x and y
    3. computing gradient histograms
    4. normalising across blocks
    5. flattening into a feature vector

Parameters
----------
image : (M, N) ndarray
    Input image (greyscale).
orientations : int
    Number of orientation bins.
pixels_per_cell : 2 tuple (int, int)
    Size (in pixels) of a cell.
cells_per_block  : 2 tuple (int,int)
    Number of cells in each block.
visualise : bool, optional
    Also return an image of the HOG.
transform_sqrt : bool, optional
    Apply power law compression to normalise the image before
    processing. DO NOT use this if the image contains negative
    values. Also see `notes` section below.
feature_vector : bool, optional
    Return the data as a feature vector by calling .ravel() on the result
    just before returning.
normalise : bool, deprecated
    The parameter is deprecated. Use `transform_sqrt` for power law
    compression. `normalise` has been deprecated.

Returns
-------
newarr : ndarray
    HOG for the image as a 1D (flattened) array.
hog_image : ndarray (if visualise=True)
    A visualisation of the HOG image.

References
----------
* http://en.wikipedia.org/wiki/Histogram_of_oriented_gradients

* Dalal, N and Triggs, B, Histograms of Oriented Gradients for
  Human Detection, IEEE Computer Society Conference on Computer
  Vision and Pattern Recognition 2005 San Diego, CA, USA

Notes
-----
Power law compression, also known as Gamma correction, is used to reduce
the effects of shadowing and illumination variations. The compression makes
the dark regions lighter. When the kwarg `transform_sqrt` is set to
``True``, the function computes the square root of each color channel
and then applies the hog algorithm to the image.