还有一个小众用途: 在带有ROOT5或ROOT6的pyroot中，"del"可以用于删除引用不再存在的c++对象的python对象。这允许pyroot的动态查找找到同名的c++对象，并将其绑定到python名称。所以你可以有这样一个场景:

import ROOT as R
input_file = R.TFile('inputs/___my_file_name___.root')
tree = input_file.Get('r')
tree.Draw('hy>>hh(10,0,5)')
R.gPad.Close()
R.hy # shows that hy is still available. It can even be redrawn at this stage.
tree.Draw('hy>>hh(3,0,3)') # overwrites the C++ object in ROOT's namespace
R.hy # shows that R.hy is None, since the C++ object it pointed to is gone
del R.hy
R.hy # now finds the new C++ object

希望ROOT7的更健全的对象管理能够填补这个空缺。

首先，你可以删除除局部变量之外的其他东西

del list_item[4]
del dictionary["alpha"]

这两者显然都是有用的。其次，在局部变量上使用del使意图更加明确。比较:

del foo

to

foo = None

我知道在del foo的情况下，目的是从作用域中删除变量。foo = None是否这样做还不清楚。如果有人只是赋值foo = None，我可能会认为这是死代码。但我立刻就知道，编码del foo的人想要做什么。

del is the equivalent of "unset" in many languages and as a cross reference point moving from another language to python.. people tend to look for commands that do the same thing that they used to do in their first language... also setting a var to "" or none doesn't really remove the var from scope..it just empties its value the name of the var itself would still be stored in memory...why?!? in a memory intensive script..keeping trash behind its just a no no and anyways...every language out there has some form of an "unset/delete" var function..why not python?

作为del可以用来做什么的例子，我发现它在这样的情况下很有用:

def f(a, b, c=3):
    return '{} {} {}'.format(a, b, c)

def g(**kwargs):
    if 'c' in kwargs and kwargs['c'] is None:
        del kwargs['c']

    return f(**kwargs)

# g(a=1, b=2, c=None) === '1 2 3'
# g(a=1, b=2) === '1 2 3'
# g(a=1, b=2, c=4) === '1 2 4'

这两个函数可以在不同的包/模块中，程序员不需要知道f中的参数c实际上有什么默认值。因此，通过将kwargs与del结合使用，您可以将其设置为None(或者在这种情况下也可以保留它)，从而说“I want the default value on c”。

你也可以这样做:

def g(a, b, c=None):
    kwargs = {'a': a,
              'b': b}
    if c is not None:
        kwargs['c'] = c

    return f(**kwargs)

然而，我发现前面的例子更加DRY和优雅。

显式使用"del"也是比将变量赋值为None更好的实践。如果你试图删除一个不存在的变量，你会得到一个运行时错误，但如果你试图将一个不存在的变量设置为None, Python会无声地将一个新变量设置为None，让你想要删除的变量留在原来的位置。所以del会帮助你尽早发现错误

我发现在使用Numpy处理大数据时，del对于伪手动内存管理非常有用。例如:

for image_name in large_image_set:
    large_image = io.imread(image_name)
    height, width, depth = large_image.shape
    large_mask = np.all(large_image == <some_condition>)
    # Clear memory, make space
    del large_image; gc.collect()

    large_processed_image = np.zeros((height, width, depth))
    large_processed_image[large_mask] = (new_value)
    io.imsave("processed_image.png", large_processed_image)

    # Clear memory, make space
    del large_mask, large_processed_image; gc.collect()

当Python GC无法跟上时，系统会疯狂地切换，这可能会导致脚本停止，而它在宽松的内存阈值下运行得非常流畅，从而在机器工作时留下了足够的空间来使用机器浏览和编码。