Python-浅拷贝、深拷贝

输出结果

case1():
# 44713512 <=====> [45433248, 2053241056, 15717896]
# 44713512 <=====> [45433248, 2053241056, 15717896]
# True
# 44713512 <=====> [45433248, 2053241056, 15717896, 15187264]
# 44713512 <=====> [45433248, 2053241056, 15717896, 15187264]
# True
# 44713512 <=====> [45433408, 2053241056, 15717896, 15187264]
# 44713512 <=====> [45433408, 2053241056, 15717896, 15187264]
# True
# 44713512 <=====> [45433408, 2053241056, 15717896, 15187264]
# 44713512 <=====> [45433408, 2053241056, 15717896, 15187264]
# True
# 44854952 <=====> [45433472, 2053241056, 44855016]
# 44713512 <=====> [45433408, 2053241056, 15717896, 15187264]
# False

case2():
# 23424584 <=====> [60113312, 2053241056, 23424616]
# 23424520 <=====> [60113312, 2053241056, 23424616]
# False
# 23424584 <=====> [60113312, 2053241056, 23424616, 22723904]
# 23424520 <=====> [60113312, 2053241056, 23424616]
# False
# 23424584 <=====> [60113312, 2053241056, 23424616, 22723904]
# 23424520 <=====> [60113472, 2053241056, 23424616]
# False
# 23424584 <=====> [60113312, 2053241056, 23424616, 22723904]
# 23424520 <=====> [60113472, 2053241056, 23424616]
# False
# ['huashen', 20, ['student', 'male', '1.72'], 'next']
#  ['yang', 20, ['student', 'male', '1.72']]
# 23424328 <=====> [60113536, 2053241056, 23424488]
# 23424520 <=====> [60113472, 2053241056, 23424616]
# False

case3():
# 13463112 <=====> [13648288, 2053241056, 13463144]
# 13463048 <=====> [13648288, 2053241056, 13463016]
# False
# 13463112 <=====> [13648288, 2053241056, 13463144, 12565824]
# 13463048 <=====> [13648288, 2053241056, 13463016]
# False
# 13463112 <=====> [13648288, 2053241056, 13463144, 12565824]
# 13463048 <=====> [13648448, 2053241056, 13463016]
# False
# 13463112 <=====> [13648288, 2053241056, 13463144, 12565824]
# 13463048 <=====> [13648448, 2053241056, 13463016]
# False
# ['huashen', 20, ['student', 'male'], 'next']
#  ['yang', 20, ['student', 'male', '1.72']]
# 13462888 <=====> [13648512, 2053241056, 13462920]
# 13463048 <=====> [13648448, 2053241056, 13463016]
# False

# 深拷贝和浅拷贝

# 复制的深度，引用地址——》值
# 复制的越深，说明两个对象之间的关联性就越小
# 赋值操作，会改变引用地址
# 数字、元组、字符串的改变，因为是不可变对象，所以引用地址改变l
# 列表、字典、集合是可变对象，对其操作（非赋值）可能会改变元素的地址（元素可能为可变对象）但不会改变最外层的引用
# 使用id(obj)查看内存地址

# 浅复制、深复制都不是简单的拿到引用地址，而是获取值，但是浅复制在1一层，而深复制在第五层

# 切片、copy.copy()、list、set　　可以实现浅拷贝
# copy.deepcopy()　　实现深拷贝



def showId(x,y):
    print(id(x), '<=====>', [id(ele) for ele in x])
    print(id(y), '<=====>', [id(ele) for ele in y])
    if x is y:
        return print(True)
    return print(False)


def case1():
    # 1、正常情况下
    info = ['huashen', 20, ['student','male']]
    info1 = info
    showId(info,info1)

    # 发生改动
    info.append('next')
    showId(info,info1)

    info1[0] = 'yang'
    showId(info,info1)

    info1[2].append('1.72')
    showId(info,info1)

    info = ['xxx', 20, ['student','male']]
    showId(info,info1)

    # 正常的 = 赋值，是将两个变量的引用复制了
    # 所以可见，任意一边改动，只要不是重新赋值，都不会改变引用内存地址，两边的值都相同
    ...


def case2():
    # 2、浅复制，使用copy
    import copy
    info = ['huashen', 20, ['student','male']]
    info1 = copy.copy(info)
    showId(info, info1)

    # 发生改动
    info.append('next')
    showId(info, info1)

    info1[0] = 'yang'
    showId(info, info1)

    info1[2].append('1.72')
    showId(info, info1)
    print(info, '\n', info1) # 为列表的元素的改动，同步了

    info = ['xxx', 20, ['student','male']]
    showId(info, info1)

    # 浅复制的时候，两个对象的引用地址发生变化，但是指向的内存地址的内容还是一样的
    ...


def case3():
    # 2、浅复制，使用copy
    import copy
    info = ['huashen', 20, ['student', 'male']]
    info1 = copy.deepcopy(info)
    showId(info, info1)   # 在复制的时候，列表的引用地址就发生变化了
    
    # 发生改动
    info.append('next')
    showId(info, info1)
    
    info1[0] = 'yang'
    showId(info, info1)
    
    info1[2].append('1.72')
    showId(info, info1)
    print(info, '\n', info1)  # 为列表的元素的改动，不发生关联
    
    info = ['xxx', 20, ['student', 'male']]
    showId(info, info1)
    
    # 深复制的时候，虽然值相同，列表的引用地址，嵌套列表的引用地址都发生了变化，
    # 深复制时，获取的时复制对象的值，而内存地址（引用）重新分配
    ...