Core Python Programming (2nd Edition)

[id(x) for x in hubby]
[9919616, 11826320]
>>> [id(x) for x in wifey]
[9919616, 11826320]
AFTER:
>>> [id(x) for x in hubby]
[12092832, 11826320]
>>> [id(x) for x in wifey]
[12191712, 11826320]
If the intention was to create a joint account for the couple, then we have a great solution, but if we
want separate accounts, we need to change something. In order to obtain a full or deep copy of the
objectcreating a new container but containing references to completely new copies (references) of the
element in the original objectwe need to use the copy.deepcopy() function. Let us redo the entire
example but using deep copies instead:
>>> person = ['name', ['savings', 100.00]]
>>> hubby = person
>>> import copy
>>> wifey = copy.deepcopy(person)
>>> [id(x) for x in person, hubby, wifey]
[12242056, 12242056, 12224232]
>>> hubby[0] = 'joe'
>>> wifey[0] = 'jane'
>>> hubby, wifey
(['joe', ['savings', 100.0]], ['jane', ['savings', 100.0]])
>>> hubby[1][1] = 50.00
>>> hubby, wifey
(['joe', ['savings', 50.0]], ['jane', ['savings', 100.0]])
Now it is just the way we want it. For kickers, let us confirm that all four objects are different:
>>> [id(x) for x in hubby]
[12191712, 11826280]
>>> [id(x) for x in wifey]
[12114080, 12224792]
There are a few more caveats to object copying. The first is that non-container types (i.e., numbers,
strings, and other "atomic" objects like code, type, and xrange objects) are not copied. Shallow copies of
sequences are all done using complete slices. Finally, deep copies of tuples are not made if they contain
only atomic objects. If we changed the banking information to a tuple, we would get only a shallow copy
even though we asked for a deep copy:
>>> person = ['name', ('savings', 100.00)]
>>> newPerson = copy.deepcopy(person)
>>> [id(x) for x in person, newPerson]
[12225352, 12226112]
>>> [id(x) for x in person]