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How can I create a copy of an object in Python?

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How do I make a copy of an existing object?

To create a copy of an existing object in the vault, right-click the object of your choice and select Make Copy from the context menu. This command creates an entirely new object using the metadata and contents of the source object.

What does Copy () do in Python?

Copy List Python: copy() Method. The Python copy() method creates a copy of an existing list. The copy() method is added to the end of a list object and so it does not accept any parameters. copy() returns a new list.

Can you copy an object?

In object-oriented programming, object copying is creating a copy of an existing object, a unit of data in object-oriented programming. The resulting object is called an object copy or simply copy of the original object. Copying is basic but has subtleties and can have significant overhead.

How do I copy an instance of a class in Python?

Yes, you can use copy. deepcopy . so just c2 = copy. deepcopy(c) then vars(c2) == {'a': 11, 'r': 2} and vars(c) == {'a': 11, 'r': 2} but the traceback your are reporting wouldn't be produced by the class definition you gave...


To get a fully independent copy of an object you can use the copy.deepcopy() function.

For more details about shallow and deep copying please refer to the other answers to this question and the nice explanation in this answer to a related question.


How can I create a copy of an object in Python?

So, if I change values of the fields of the new object, the old object should not be affected by that.

You mean a mutable object then.

In Python 3, lists get a copy method (in 2, you'd use a slice to make a copy):

>>> a_list = list('abc')
>>> a_copy_of_a_list = a_list.copy()
>>> a_copy_of_a_list is a_list
False
>>> a_copy_of_a_list == a_list
True

Shallow Copies

Shallow copies are just copies of the outermost container.

list.copy is a shallow copy:

>>> list_of_dict_of_set = [{'foo': set('abc')}]
>>> lodos_copy = list_of_dict_of_set.copy()
>>> lodos_copy[0]['foo'].pop()
'c'
>>> lodos_copy
[{'foo': {'b', 'a'}}]
>>> list_of_dict_of_set
[{'foo': {'b', 'a'}}]

You don't get a copy of the interior objects. They're the same object - so when they're mutated, the change shows up in both containers.

Deep copies

Deep copies are recursive copies of each interior object.

>>> lodos_deep_copy = copy.deepcopy(list_of_dict_of_set)
>>> lodos_deep_copy[0]['foo'].add('c')
>>> lodos_deep_copy
[{'foo': {'c', 'b', 'a'}}]
>>> list_of_dict_of_set
[{'foo': {'b', 'a'}}]

Changes are not reflected in the original, only in the copy.

Immutable objects

Immutable objects do not usually need to be copied. In fact, if you try to, Python will just give you the original object:

>>> a_tuple = tuple('abc')
>>> tuple_copy_attempt = a_tuple.copy()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'tuple' object has no attribute 'copy'

Tuples don't even have a copy method, so let's try it with a slice:

>>> tuple_copy_attempt = a_tuple[:]

But we see it's the same object:

>>> tuple_copy_attempt is a_tuple
True

Similarly for strings:

>>> s = 'abc'
>>> s0 = s[:]
>>> s == s0
True
>>> s is s0
True

and for frozensets, even though they have a copy method:

>>> a_frozenset = frozenset('abc')
>>> frozenset_copy_attempt = a_frozenset.copy()
>>> frozenset_copy_attempt is a_frozenset
True

When to copy immutable objects

Immutable objects should be copied if you need a mutable interior object copied.

>>> tuple_of_list = [],
>>> copy_of_tuple_of_list = tuple_of_list[:]
>>> copy_of_tuple_of_list[0].append('a')
>>> copy_of_tuple_of_list
(['a'],)
>>> tuple_of_list
(['a'],)
>>> deepcopy_of_tuple_of_list = copy.deepcopy(tuple_of_list)
>>> deepcopy_of_tuple_of_list[0].append('b')
>>> deepcopy_of_tuple_of_list
(['a', 'b'],)
>>> tuple_of_list
(['a'],)

As we can see, when the interior object of the copy is mutated, the original does not change.

Custom Objects

Custom objects usually store data in a __dict__ attribute or in __slots__ (a tuple-like memory structure.)

To make a copyable object, define __copy__ (for shallow copies) and/or __deepcopy__ (for deep copies).

from copy import copy, deepcopy

class Copyable:
    __slots__ = 'a', '__dict__'
    def __init__(self, a, b):
        self.a, self.b = a, b
    def __copy__(self):
        return type(self)(self.a, self.b)
    def __deepcopy__(self, memo): # memo is a dict of id's to copies
        id_self = id(self)        # memoization avoids unnecesary recursion
        _copy = memo.get(id_self)
        if _copy is None:
            _copy = type(self)(
                deepcopy(self.a, memo), 
                deepcopy(self.b, memo))
            memo[id_self] = _copy 
        return _copy

Note that deepcopy keeps a memoization dictionary of id(original) (or identity numbers) to copies. To enjoy good behavior with recursive data structures, make sure you haven't already made a copy, and if you have, return that.

So let's make an object:

>>> c1 = Copyable(1, [2])

And copy makes a shallow copy:

>>> c2 = copy(c1)
>>> c1 is c2
False
>>> c2.b.append(3)
>>> c1.b
[2, 3]

And deepcopy now makes a deep copy:

>>> c3 = deepcopy(c1)
>>> c3.b.append(4)
>>> c1.b
[2, 3]

Shallow copy with copy.copy()

#!/usr/bin/env python3

import copy

class C():
    def __init__(self):
        self.x = [1]
        self.y = [2]

# It copies.
c = C()
d = copy.copy(c)
d.x = [3]
assert c.x == [1]
assert d.x == [3]

# It's shallow.
c = C()
d = copy.copy(c)
d.x[0] = 3
assert c.x == [3]
assert d.x == [3]

Deep copy with copy.deepcopy()

#!/usr/bin/env python3
import copy
class C():
    def __init__(self):
        self.x = [1]
        self.y = [2]
c = C()
d = copy.deepcopy(c)
d.x[0] = 3
assert c.x == [1]
assert d.x == [3]

Documentation: https://docs.python.org/3/library/copy.html

Tested on Python 3.6.5.