Introduction to Python memory usage (in-depth copy) and python

Introduction to Python memory usage (in-depth copy) and python

This article focuses on the usage of Python memory (deep copy). The details are as follows.

A shallow copy is a copy of the reference (only copying the parent object)

Deep copy is the copy of object Resources.

>>> a=[1,2,3,'a','b']>>> b=a>>> b[1, 2, 3, 'a', 'b']>>> a[1, 2, 3, 'a', 'b']>>> id(a)3021737547592>>> id(b)3021737547592>>> a.append('c')>>> a[1, 2, 3, 'a', 'b', 'c']>>> b[1, 2, 3, 'a', 'b', 'c']>>> b.append(4)>>> b[1, 2, 3, 'a', 'b', 'c', 4]>>> a[1, 2, 3, 'a', 'b', 'c', 4]

From the above operation, we can see that after assigning a to B, the addresses of a and B are the same. No matter which one changes, the other will change and remain the same.

>>> Import copy >>> a = [1, 2, 3, ['A', 'B ', 'C'] >>> B = a >>> c = copy. copy (a) >>> B [1, 2, 3, ['A', 'B', 'C'] >>> c [1, 2, 3, ['A', 'B', 'C'] >>>> id (a) 3021737548104 >>> id (B) 3021737548104 >>>> id (c) 3021737494536 # the address of the Child copy parent object is different >>>. append ('D') >>> a [1, 2, 3, ['A', 'B', 'C'], 'D']> B [1, 2, 3, ['A', 'B', 'C'], 'D']> c [1, 2, 3, ['A', 'B', 'C'] # the address of a and c is different. Therefore, a is changed, c does not change >>> id (a [0]) 1686357680 >>> id (c [0]) 1686357680 >>> id (a [3]) 3021737547528 >>> id (c [3]) 3021737547528 # the space occupied by the entire parent object is different, but the space occupied by the same inner data is the same >>> a [3]. append ('D') >>> a [1, 2, 3, ['A', 'B', 'C', 'D'], 'D ']> c [1, 2, 3, ['A',' B ', 'C ', 'D'] # because the data in the inner layer occupies the same space, a changes and c changes accordingly.

The above isShortest copy: The addresses of the entire parent object are different. The addresses of the inner layer data are the same. The operations on the inner layer data change together. When the operation object is a parent object, the copy object does not change.

>>> A [1, 2, 3, ['A', 'B', 'C', 'D'], 'D'] >>> d = copy. deepcopy (a) >>> d [1, 2, 3, ['A', 'B', 'C', 'D'], 'D'] >>>> id (a) 3021737548104 >>>> id (d) 3021737547656 # the address of the deep copy parent object is different >>>. append ('E') >>> a [1, 2, 3, ['A', 'B', 'C', 'D'], 'D ', 'E'] >>> d [1, 2, 3, ['A', 'B', 'C', 'D'], 'D'] # the address of a and d is different, so a changes, d does not change >>> id (a [0]) 1686357680 >>> id (d [0]) 1686357680 >>> id (a [3]) 3021737547528 >>> id (d [3]) 3021737493256 # the address of the inner layer data is different >>> a [3]. append ('x') >>> a [1, 2, 3, ['A', 'B', 'C', 'D', 'x'], 'D', 'E'] >>> d [1, 2, 3, ['A', 'B', 'C', 'D'], 'D']

The above isDeep copy.

Differences:

The shortest copy address is the same as the inner data address of the original object;
Deep copy is completely independent from the original object.

Summary

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