克隆列表似乎起着别名的作用，即使已明确声明为一个克隆

Question

I am having some trouble with the following script. It should make 3 copies of the following list so that they can be modified independently. However, it seems to be creating 3 clones of the same list, and when you modify one you modify them all. Here is the function:

def calculateProportions(strategies,proportions):
   import itertools
   combinations = []
   columns = list(itertools.product(strategies,repeat=3))
   for i in range(0,len(columns)):
      columns[i] = list(columns[i])
   for n in range(0,len(strategies)):
      combinations.append(columns[:])
   combinations[0][0][0] = "THIS SHOULD ONLY BE IN ONE PLACE"
   print combinations 
strategies = [[0,0],[0,50],[50,50]]
calculateProportions(strategies,[])

Notice how, when you run this, you see the string "THIS SHOULD BE IN ONE PLACE" 3 times (position [0][0][0],[1][0][0], and [2][0][0], not once. This appears to be because the lists are aliased together rather than cloned. However I explicitly cloned it.

I have spent the last hour banging my head into the table on this. Your suggested solutions are much appreciated!

Answer 1

You're only performing a shallow copy when you clone columns , ie the list is cloned but its items are not, so the same item references are used in both combinations and columns .

You can use the copy.deepcopy() function to perform a deep copy of the object:

def calculateProportions(strategies,proportions):
    import copy
    import itertools
    combinations = []
    columns = list(itertools.product(strategies, repeat=3))
    for i in range(0, len(columns)):
        columns[i] = list(columns[i])
    for n in range(0, len(strategies)):
        combinations.append(copy.deepcopy(columns))

Or, more simply, a list comprehension :

def calculateProportions(strategies,proportions):
    import itertools
    combinations = []
    columns = list(itertools.product(strategies, repeat=3))
    for i in range(0, len(columns)):
        columns[i] = list(columns[i])
    for n in range(0, len(strategies)):
        combinations.append([item[:] for item in columns])

Answer 2

>>> import copy
>>> help(copy)

In the very first lines, you can see functions copy and deepcopy . These correspond to shallow and deep copying. For details, refer to http://en.wikipedia.org/wiki/Object_copy

Answer 3

Getting a copy of a list like list[:] does not create copies of the elements contained in the list (ie it is a flat copy, not a deep copy). The following example code illustrates this:

>>> n1 = [1, 2]
>>> n2 = [3, 4]
>>> l1 = [n1, n2]
>>> l2 = l1[:]
>>> l2[0] = "was n1" # change in l1 only
>>> l1
[[1, 2], [3, 4]]
>>> l2
['was n1', [3, 4]]
>>> l2[1][0] = "was 3 in n2" # affects both l1 and l2
>>> l1
[[1, 2], ['was 3 in n2', 4]]
>>> l2
['was n1', ['was 3 in n2', 4]]

As suggested by ulidtko, the copy module might help in your case.

Answer 4

when you write

l = alist[:]

you're doing a shallow copy. that is to say that the list is different, but the two lists are pointing to the same objects. so if you modify one element of a list, the element in the other list will be modified too.

you need to make a deep copy, ie. copying the list and all the object in the list.

import copy
copy.deepcopy()

Answer 5

Instead of trying to fix up the deep copies, I would just create the desired data with nested list comprehensions. This also avoids the ugly manual "accumulation" of the final data.

def calculateProportions(strategies, proportions):
  import itertools
  combinations = [
    [list(x) for x in itertools.product(strategies, repeat = 3)]
    for strategy in strategies
  ]
  combinations[0][0][0] = "THIS SHOULD ONLY BE IN ONE PLACE"
  print combinations