What is the purpose of deepcopy's second parameter, memo?

user_234322 0
Viewed 9839
from copy import* 
a=[1,2,3,4]
c={'a':'aaa'}
print c
#{'a': 'aaa'}
b=deepcopy(a,c)
print b

print c
# print {'a': 'aaa', 10310992: 3, 10310980: 4, 10311016: 1, 11588784: [1, 2, 3, 4, [1, 2, 3, 4]], 11566456: [1, 2, 3, 4], 10311004: 2}

why c print that

Please try to use the code, rather than text, because my English is not very good, thank you

in django.utils.tree.py

def __deepcopy__(self, memodict):
        """
        Utility method used by copy.deepcopy().
        """
        obj = Node(connector=self.connector, negated=self.negated)
        obj.__class__ = self.__class__
        obj.children = deepcopy(self.children, memodict)
        obj.subtree_parents = deepcopy(self.subtree_parents, memodict)
        return obj



import copy
memo = {}
x1 = range(5)
x2=range(6,9)
x3=[2,3,4,11]
y1 = copy.deepcopy(x1, memo)
y2=copy.deepcopy(x2, memo)
y3=copy.deepcopy(x3,memo)
print memo
print id(y1),id(y2),id(y3)
y1[0]='www'
print y1,y2,y3
print memo

print :

{10310992: 3, 10310980: 4, 10311016: 1, 11588784: [0, 1, 2, 3, 4, [0, 1, 2, 3, 4]], 10311028: 0, 11566456: [0, 1, 2, 3, 4], 10311004: 2}
{11572448: [6, 7, 8], 10310992: 3, 10310980: 4, 10311016: 1, 11572368: [2, 3, 4, 11], 10310956: 6, 10310896: 11, 10310944: 7, 11588784: [0, 1, 2, 3, 4, [0, 1, 2, 3, 4], 6, 7, 8, [6, 7, 8], 11, [2, 3, 4, 11]], 10311028: 0, 11566456: [0, 1, 2, 3, 4], 10310932: 8, 10311004: 2}
11572408 11581280 11580960
['www', 1, 2, 3, 4] [6, 7, 8] [2, 3, 4, 11]
{11572448: [6, 7, 8], 10310992: 3, 10310980: 4, 10311016: 1, 11572368: [2, 3, 4, 11], 10310956: 6, 10310896: 11, 10310944: 7, 11588784: [0, 1, 2, 3, 4, [0, 1, 2, 3, 4], 6, 7, 8, [6, 7, 8], 11, [2, 3, 4, 11]], 10311028: 0, 11566456: ['www', 1, 2, 3, 4], 10310932: 8, 10311004: 2}
python
4 Answers

It's the memo dict, where id-to-object correspondence is kept to reconstruct complex object graphs perfectly. Hard to "use the code", but, let's try:

>>> import copy
>>> memo = {}
>>> x = range(5)
>>> y = copy.deepcopy(x, memo)
>>> memo
{399680: [0, 1, 2, 3, 4], 16790896: 3, 16790884: 4, 16790920: 1,
 438608: [0, 1, 2, 3, 4, [0, 1, 2, 3, 4]], 16790932: 0, 16790908: 2}
>>>

and

>>> id(x)
399680
>>> for j in x: print j, id(j)
... 
0 16790932
1 16790920
2 16790908
3 16790896
4 16790884

so as you see the IDs are exactly right. Also:

>>> for k, v in memo.items(): print k, id(v)
... 
399680 435264
16790896 16790896
16790884 16790884
16790920 16790920
438608 435464
16790932 16790932
16790908 16790908

you see the identity for the (immutable) integers.

So here's a graph:

>>> z = [x, x]
>>> t = copy.deepcopy(z, memo)
>>> print id(t[0]), id(t[1]), id(y)
435264 435264 435264

so you see all the subcopies are the same objects as y (since we reused the memo).

user_958100

You can read more by checking the Python online documentation:

http://docs.python.org/library/copy.html

The deepcopy() function is recursive, and it will work its way down through a deeply nested object. It uses a dictionary to detect objects it has seen before, to detect an infinite loop. You should just ignore this dictionary.

class A(object):
    def __init__(self, *args):
        self.lst = args

class B(object):
    def __init__(self):
        self.x = self

def my_deepcopy(arg):
    try:
        obj = type(arg)()  # get new, empty instance of type arg
        for key in arg.__dict__:
            obj.__dict__[key] = my_deepcopy(arg.__dict__[key])
        return obj
    except AttributeError:
        return type(arg)(arg)  # return new instance of a simple type such as str

a = A(1, 2, 3)
b = B()
b.x is b  # evaluates to True
c = my_deepcopy(a)  # works fine
c = my_deepcopy(b)  # stack overflow, recurses forever

from copy import deepcopy
c = deepcopy(b)  # this works because of the second, hidden, dict argument

Just ignore the second, hidden, dict argument. Do not try to use it.

user_1669490
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