Detailed analysis of Python mutable objects and immutable objects

Everything in Python can be viewed as an object. Each object has its own id, type and value.

1. id: When an object is created, its id will not change. The id here is actually the address of the object in memory. You can use id() to view the object in memory. address.
2. type: Like id, once an object is created, its type cannot be changed. The type determines the operations that the object can support.
3. value: The value of the object
   

Whether an object is mutable or not depends on whether the value supports changes.

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Immutable objects

Common immutability Object (immutable objects):

1. Number: int, float, complex
2. string
3. tuple
4. frozenset: another type of set
   

Let us feel it through examples

Example

>>> x=1 >>> y=x >>> id(x)==id(y) True >>> id(1)==id(y) True

Here two int objects are created, and let x, y points to the same object, and then we try to modify x so that x 1

>>>x=x+1

At this time, is the id value of x still the same as the id of y and the id of 1?

>>> x=x+1 >>> id(x)==id(y) False >>> id(x)==id(1) False >>> id(1)==id(y) True

It is not difficult to see from the results that the object pointed to by x has changed, but 1 has not changed. This is an immutable object. For immutable objects, once created, they are not allowed to be changed.

Note: Immutability is not strictly equivalent to absolute immutability

>>> a=([1],2,3) >>> id(a) 4338862696 >>> id(a[0]) 4338949128 >>> a[0][0]=0 >>> id(a) 4338862696 >>> id(a[0]) 4338949128

It is easy to see from the above example that we changed the value of a[0], but it did not affect its id value. If an immutable container object contains a reference to a mutable object, when the value of the latter changes, the value of the former will also change; but the container is still an immutable object because its id value will not change.

Mutable objects

Common mutable objects:

1. list
2. dict
3. set
   

Similarly experience it through examples

Example

>>> c = [1,2,3] >>> b = c >>> id(b)==id(c) True

Create two list objects, and Let c, d point to the same object, and then we try to modify c

c[0]=4

At this time, check whether the id values of the two objects are consistent

>>> id(c)==id(b) True

After modification, c, d still points to The same object, so it is not difficult to conclude that for a variable object, modifying the value will not have any impact on its id

Function parameters

For mutable objects and immutable objects, they behave differently when the function is passed as a parameter

Example

eg0

def update_list(nums_list): print(f'2 id:{id(nums_list)}') nums_list += [10] print(f'3 id:{id(nums_list)}') number_list = [5, 6] print(f'1:{number_list}, id:{id(number_list)}') update_list(number_list) print(f'4:{number_list}, id:{id(number_list)}')

Look at the results

1:[5, 6], id:4457707912
2 id:4457707912
3 id:4457707912
4:[5, 6, 10], id:4457707912

Looking at another example

eg1

def add_num(number): print(f'2:{id(number)}') number += 1 print(f'3:{id(number)}') num = 1 print(f'1:{num}, id:{id(num)}') add_num(num) print(f'4:{num}, id:{id(num)}')

Take a look Result

1:1, id:4343768416
2 id:4343768416
3 id:4343768448
4:1, id:4343768416

In some other articles, eg0 above is called pass by reference, and eg1 is called pass by value. However, in the official documents, no content related to these two terms was found, or this method is not either of the above two.

The key point here is:For mutable objects, changes in the value in the function will cause the object to change; for immutable objects, changes in the value in the function will not cause any changes to the object. Influence

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