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。

I think one of the reasons that del has its own syntax is that replacing it with a function might be hard in certain cases given it operates on the binding or variable and not the value it references. Thus if a function version of del were to be created a context would need to be passed in. del foo would need to become globals().remove('foo') or locals().remove('foo') which gets messy and less readable. Still I say getting rid of del would be good given its seemingly rare use. But removing language features/flaws can be painful. Maybe python 4 will remove it :)

我发现del有用的一个地方是清除for循环中的无关变量:

for x in some_list:
  do(x)
del x

现在你可以确定，如果你在for循环之外使用x，它将是没有定义的。

"del"命令对于控制数组中的数据非常有用，例如:

elements = ["A", "B", "C", "D"]
# Remove first element.
del elements[:1]
print(elements)

输出:

[' b '， ' c '， ' d ']

Del删除变量及其所指向的对象的绑定。

>>> a = ['a', 'b', 'c']
>>> b = a
>>> del a
>>> b
['a', 'b', 'c']
>>> a
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'a' is not defined

我能想到的一个简单的用例是，如果你已经使用内置函数名作为变量，并且你想在它已经被你的变量名“覆盖”之后使用该函数。

t = ('a', "letter")
value, type = t
print(value, type)
del type
print(type(value))

输出:

a letter
<class 'str'>

只是另一种想法。

当在Django这样的框架中调试http应用程序时，调用堆栈中充满了以前使用过的无用和混乱的变量，特别是当它是一个非常长的列表时，这对开发人员来说是非常痛苦的。因此，在这一点上，名称空间控制可能是有用的。