I can’t really think of any reason why python needs the del keyword (and most languages seem to not have a similar keyword). For instance, rather than deleting a variable, one could just assign None to it. And when deleting from a dictionary, a del method could be added.

Is there any reason to keep del in python, or is it a vestige of Python’s pre-garbage collection days?

Firstly, you can del other things besides local variables

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

Both of which should be clearly useful. Secondly, using del on a local variable makes the intent clearer. Compare:

del foo

to

foo = None

I know in the case of del foo that the intent is to remove the variable from scope. It’s not clear that foo = None is doing that. If somebody just assigned foo = None I might think it was dead code. But I instantly know what somebody who codes del foo was trying to do.

There’s this part of what del does (from the Python Language Reference):

Deletion of a name removes the binding of that name from the local or global namespace

Assigning None to a name does not remove the binding of the name from the namespace.

(I suppose there could be some debate about whether removing a name binding is actually useful, but that’s another question.)

One place I’ve found del useful is cleaning up extraneous variables in for loops:

for x in some_list:
  do(x)
del x

Now you can be sure that x will be undefined if you use it outside the for loop.

Deleting a variable is different than setting it to None

Deleting variable names with del is probably something used rarely, but it is something that could not trivially be achieved without a keyword. If you can create a variable name by writing a=1, it is nice that you can theoretically undo this by deleting a.

It can make debugging easier in some cases as trying to access a deleted variable will raise an NameError.

You can delete class instance attributes

Python lets you write something like:

class A(object):
    def set_a(self, a):
        self.a=a
a=A()
a.set_a(3)
if hasattr(a, "a"):
    print("Hallo")

If you choose to dynamically add attributes to a class instance, you certainly want to be able to undo it by writing

del a.a

There is a specific example of when you should use del (there may be others, but I know about this one off hand) when you are using sys.exc_info() to inspect an exception. This function returns a tuple, the type of exception that was raised, the message, and a traceback.

The first two values are usually sufficient to diagnose an error and act on it, but the third contains the entire call stack between where the exception was raised and where the the exception is caught. In particular, if you do something like

try:
    do_evil()
except:
    exc_type, exc_value, tb = sys.exc_info()
    if something(exc_value):
        raise

the traceback, tb ends up in the locals of the call stack, creating a circular reference that cannot be garbage collected. Thus, it is important to do:

try:
    do_evil()
except:
    exc_type, exc_value, tb = sys.exc_info()
    del tb
    if something(exc_value):
        raise

to break the circular reference. In many cases where you would want to call sys.exc_info(), like with metaclass magic, the traceback is useful, so you have to make sure that you clean it up before you can possibly leave the exception handler. If you don’t need the traceback, you should delete it immediately, or just do:

exc_type, exc_value = sys.exc_info()[:2]

To avoid it all together.

Just another thinking.

When debugging http applications in framework like Django, the call stack full of useless and messed up variables previously used, especially when it’s a very long list, could be very painful for developers. so, at this point, namespace controlling could be useful.

Using “del” explicitly is also better practice than assigning a variable to None. If you attempt to del a variable that doesn’t exist, you’ll get a runtime error but if you attempt to set a variable that doesn’t exist to None, Python will silently set a new variable to None, leaving the variable you wanted deleted where it was. So del will help you catch your mistakes earlier

To add a few points to above answers: del x

Definition of x indicates r -> o (a reference r pointing to an object o) but del x changes r rather than o. It is an operation on the reference (pointer) to object rather than the object associated with x. Distinguishing between r and o is key here.

• It removes it from locals().
• Removes it from globals() if x belongs there.
• Removes it from the stack frame (removes the reference physically from it, but the object itself resides in object pool and not in the stack frame).
• Removes it from the current scope. It is very useful to limit the span of definition of a local variable, which otherwise can cause problems.
• It is more about declaration of the name rather than definition of content.
• It affects where x belongs to, not where x points to. The only physical change in memory is this. For example if x is in a dictionary or list, it (as a reference) is removed from there(and not necessarily from the object pool). In this example, the dictionary it belongs is the stack frame (locals()), which overlaps with globals().

Force closing a file after using numpy.load:

A niche usage perhaps but I found it useful when using numpy.load to read a file. Every once in a while I would update the file and need to copy a file with the same name to the directory.

I used del to release the file and allow me to copy in the new file.

Note I want to avoid the with context manager as I was playing around with plots on the command line and didn’t want to be pressing tab a lot!

See this question.

del is often seen in __init__.py files. Any global variable that is defined in an __init__.py file is automatically “exported” (it will be included in a from module import *). One way to avoid this is to define __all__, but this can get messy and not everyone uses it.

For example, if you had code in __init__.py like

import sys
if sys.version_info < (3,):
    print("Python 2 not supported")

Then your module would export the sys name. You should instead write

import sys
if sys.version_info < (3,):
    print("Python 2 not supported")

del sys

As an example of what del can be used for, I find it useful i situations like this:

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'

These two functions can be in different packages/modules and the programmer doesn’t need to know what default value argument c in f actually have. So by using kwargs in combination with del you can say “I want the default value on c” by setting it to None (or in this case also leave it).

You could do the same thing with something like:

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

    return f(**kwargs)

However I find the previous example more DRY and elegant.

When is del useful in python?

You can use it to remove a single element of an array instead of the slice syntax x[i:i+1]=[]. This may be useful if for example you are in os.walk and wish to delete an element in the directory. I would not consider a keyword useful for this though, since one could just make a [].remove(index) method (the .remove method is actually search-and-remove-first-instance-of-value).

I’ve found del to be useful for pseudo-manual memory management when handling large data with Numpy. For example:

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()

This can be the difference between bringing a script to a grinding halt as the system swaps like mad when the Python GC can’t keep up, and it running perfectly smooth below a loose memory threshold that leaves plenty of headroom to use the machine to browse and code while it’s working.

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 :)

I would like to elaborate on the accepted answer to highlight the nuance between setting a variable to None versus removing it with del:

Given the variable foo = 'bar', and the following function definition:

def test_var(var):
    if var:
        print('variable tested true')
    else:
        print('variable tested false')

Once initially declared, test_var(foo) yields variable tested true as expected.

Now try:

foo = None
test_var(foo)

which yields variable tested false.

Contrast this behavior with:

del foo
test_var(foo)

which now raises NameError: name 'foo' is not defined.

Yet another niche usage: In pyroot with ROOT5 or ROOT6, “del” may be useful to remove a python object that referred to a no-longer existing C++ object. This allows the dynamic lookup of pyroot to find an identically-named C++ object and bind it to the python name. So you can have a scenario such as:

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

Hopefully, this niche will be closed with ROOT7’s saner object management.

The “del” command is very useful for controlling data in an array, for example:

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

Output:

[‘B’, ‘C’, ‘D’]

Once I had to use:

del serial
serial = None

because using only:

serial = None

didn’t release the serial port fast enough to immediately open it again. From that lesson I learned that del really meant: “GC this NOW! and wait until it’s done” and that is really useful in a lot of situations. Of course, you may have a system.gc.del_this_and_wait_balbalbalba(obj).

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?

Every object in python has an identifier, Type, reference count associated with it, when we use del the reference count is reduced, when the reference count becomes zero it is a potential candidate for getting garbage collected. This differentiates the del when compared to setting an identifier to None. In later case it simply means the object is just left out wild( until we are out of scope in which case the count is reduced) and simply now the identifier point to some other object(memory location).