=========================
copy -- Duplicate objects
=========================
.. module:: copy
:synopsis: Provides functions for duplicating objects using shallow or deep copy semantics.
:Purpose: Provides functions for duplicating objects using shallow or deep copy semantics.
:Python Version: 1.4
The copy module includes 2 functions, copy() and deepcopy(), for duplicating
existing objects.
Shallow Copies
==============
The shallow copy created by copy() is a new container populated with
references to the contents of the original object. For example, a new list is
constructed and the elements of the original list are appended to it.
.. include:: copy_shallow.py
:literal:
:start-after: #end_pymotw_header
For a shallow copy, the MyClass instance is not duplicated so the reference in
the dup list is to the same object that is in the l list.
.. {{{cog
.. cog.out(run_script(cog.inFile, 'copy_shallow.py'))
.. }}}
::
$ python copy_shallow.py
l : [<__main__.MyClass instance at 0x10046dc68>]
dup: [<__main__.MyClass instance at 0x10046dc68>]
dup is l: False
dup == l: True
dup[0] is l[0]: True
dup[0] == l[0]: True
.. {{{end}}}
Deep Copies
===========
The deep copy created by deepcopy() is a new container populated with copies
of the contents of the original object. For example, a new list is constructed
and the elements of the original list are copied, then the copies are appended
to the new list.
By replacing the call to copy() with deepcopy(), the difference becomes
apparent.
::
dup = copy.deepcopy(l)
Notice that the first element of the list is no longer the same object
reference, but the two objects still evaluate as being equal.
.. {{{cog
.. cog.out(run_script(cog.inFile, 'copy_deep.py'))
.. }}}
::
$ python copy_deep.py
l : [<__main__.MyClass instance at 0x10046dc68>]
dup: [<__main__.MyClass instance at 0x10046dc20>]
dup is l: False
dup == l: True
dup[0] is l[0]: False
dup[0] == l[0]: True
.. {{{end}}}
Controlling Copy Behavior
=========================
It is possible to control how copies are made using the __copy__ and
__deepcopy__ hooks.
* __copy__() is called without any arguments and should return a shallow copy
of the object.
* __deepcopy__() is called with a memo dictionary, and should return a
deep copy of the object. Any member attributes that need to be
deep-copied should be passed to copy.deepcopy(), along with the memo
dictionary, to control for recursion (see below).
This example illustrates how the methods are called:
.. include:: copy_hooks.py
:literal:
:start-after: #end_pymotw_header
.. {{{cog
.. cog.out(run_script(cog.inFile, 'copy_hooks.py'))
.. }}}
::
$ python copy_hooks.py
__copy__()
__deepcopy__({})
.. {{{end}}}
Recursion in Deep Copy
======================
To avoid problems with duplicating recursive data structures, ``deepcopy()`` uses
a dictionary to track objects that have already been copied. This dictionary
is passed to the ``__deepcopy__()`` method so it can be used there as well.
This example shows how an interconnected data structure such as a Digraph
might assist with protecting against recursion by implementing a
``__deepcopy__()`` method. This particular example is just for illustration
purposes, since the default implementation of ``deepcopy()`` already handles the
recursion cases correctly.
.. include:: copy_recursion.py
:literal:
:start-after: #end_pymotw_header
First, some basic directed graph methods: A graph can be initialized with a
name and a list of existing nodes to which it is connected. The
addConnection() method is used to set up bi-directional connections. It is
also used by the deepcopy operator.
The ``__deepcopy__()`` method prints messages to show how it is called, and
manages the memo dictionary contents as needed. Instead of copying the
connection list wholesale, it creates a new list and appends copies of the
individual connections to it. That ensures that the memo dictionary is updated
as each new node is duplicated, and avoids recursion issues or extra copies of
nodes. As before, it returns the copied object when it is done.
Next we can set up a graph with a nodes *root*, *a*, and *b*. The
graph looks like:
.. digraph:: copy_example
"root";
"a" -> "root";
"b" -> "root";
"b" -> "a";
"root" -> "a";
"root" -> "b";
There are several cycles in the graph, but by handling the recursion
with the memo dictionary we can avoid having the traversal cause a
stack overflow error. When the *root* node is copied, we see:
.. {{{cog
.. cog.out(run_script(cog.inFile, 'copy_recursion.py'))
.. }}}
::
$ python copy_recursion.py
<Graph(root) id=4299647672>
{ }
COPYING TO <Graph(root) id=4299648032>
<Graph(a) id=4299647744>
{ <Graph(root) id=4299647672>: <Graph(root) id=4299648032>,
4298492288: ['root'],
4299554368: 'root'}
COPYING TO <Graph(a) id=4299648104>
<Graph(root) id=4299647672>
ALREADY COPIED TO <Graph(root) id=4299648032>
<Graph(b) id=4299647816>
{ <Graph(root) id=4299647672>: <Graph(root) id=4299648032>,
<Graph(a) id=4299647744>: <Graph(a) id=4299648104>,
4297827832: 'a',
4298492288: [ 'root',
'a',
<Graph(root) id=4299647672>,
<Graph(a) id=4299647744>],
4299554368: 'root',
4299647672: <Graph(root) id=4299648032>,
4299647744: <Graph(a) id=4299648104>}
COPYING TO <Graph(b) id=4299648680>
.. {{{end}}}
The second time the *root* node is encountered, while the *a* node is
being copied, ``__deepcopy__`` detects the recursion and re-uses the
existing value from the memo dictionary instead of creating a new
object.
.. seealso::
`copy <http://docs.python.org/library/copy.html>`_
The standard library documentation for this module.