重庆网站建设开发公司,大学生网站建设例题答案,网站免费制作教程,wordpress wp play浅拷贝的时候#xff0c;修改原来的对象#xff0c;深拷贝的对象不会发生改变。 对象的赋值 对象的赋值实际上是对象之间的引用#xff1a;当创建一个对象#xff0c;然后将这个对象赋值给另外一个变量的时候#xff0c;python并没有拷贝这个对象#xff0c;而只是拷贝了…浅拷贝的时候修改原来的对象深拷贝的对象不会发生改变。 对象的赋值 对象的赋值实际上是对象之间的引用当创建一个对象然后将这个对象赋值给另外一个变量的时候python并没有拷贝这个对象而只是拷贝了这个对象的引用。
aList [kel,abc,123]
print(aList, id(aList))
bList aListbList.append(add)print(aList, id(aList))
print(bList, id(bList))
([kel, abc, 123], 139637569314688)
([kel, abc, 123, add], 139637569314688)
([kel, abc, 123, add], 139637569314688)
同样 numpy 下的数据结构与数据类型的转换np.array vs. np.asarray
np.array() 是深拷贝np.asarray() 是浅拷贝
两者主要的区别在于array默认复制一份对象asarray不会执行这一动作。
def asarray(a, dtypeNone, orderNone):return array(a, dtype, copyFalse, orderorder)
示例一
import numpy as np arr1np.ones((3,3))
arr2np.array(arr1)
arr3np.asarray(arr1)
print(arr2 is arr1)
print(arr3 is arr1)
print(arr1:,arr1, id(arr1))
print(arr2:,arr2, id(arr2))
print(arr3:,arr3, id(arr3))
False
True
(arr1:, array([[ 1., 1., 1.],[ 1., 1., 1.],[ 1., 1., 1.]]), 139637569303856)
(arr2:, array([[ 1., 1., 1.],[ 1., 1., 1.],[ 1., 1., 1.]]), 139637569303776)
(arr3:, array([[ 1., 1., 1.],[ 1., 1., 1.],[ 1., 1., 1.]]), 139637569303856)
示例二
import numpy as np arr1np.ones((3,3))
arr2np.array(arr1)
arr3np.asarray(arr1)
arr1[1]2
print(arr1:,arr1, id(arr1))
print(arr2:,arr2, id(arr2))
print(arr3:,arr3, id(arr3))
(arr1:, array([[ 1., 1., 1.],[ 2., 2., 2.],[ 1., 1., 1.]]), 139637569303296)
(arr2:, array([[ 1., 1., 1.],[ 1., 1., 1.],[ 1., 1., 1.]]), 139637569303376)
(arr3:, array([[ 1., 1., 1.],[ 2., 2., 2.],[ 1., 1., 1.]]), 139637569303296) 对象的复制 当你想修改一个对象而且让原始的对象不受影响的时候那么就需要使用到对象的复制对象的复制可以通过三种方法实现
a、 使用切片操作进行拷贝--slice operation
b、 使用工厂函数进行拷贝list/dir/set--factoryfunction
c、 copy.copy--use copymodule
在复制的时候使用的是浅拷贝复制了对象但是对象中的元素依然使用引用。
person [name,[savings,100.00]]
hubby person[:] #切片操作
wifey list(person) #使用工厂函数[id(x) for x in person,hubby,wifey]
print(The person is:, person, id(person))
print(The hubby is:, hubby, id(hubby))
print(The wifey is:, wifey, id(wifey))
(The person is:, [name, [savings, 100.0]], 139637569566984)
(The hubby is:, [name, [savings, 100.0]], 139637569544848)
(The wifey is:, [name, [savings, 100.0]], 139637569405656)
print(The person inside is:, [id(x) for x in person])
print(The hubby inside is:, [id(x) for x in hubby])
print(The wifey inside is:, [id(x) for x in wifey])
(The person inside is:, [139639860076144, 139637569544344])
(The hubby inside is:, [139639860076144, 139637569544344])
(The wifey inside is:, [139639860076144, 139637569544344]) hubby[0] kel
wifey[0] jane
hubby[1][1] 50.0
print(The person is:, person, id(person))
print(The hubby is:, hubby, id(hubby))
print(The wifey is:, wifey, id(wifey))
(The person is:, [name, [savings, 50.0]], 139637570044992)
(The hubby is:, [kel, [savings, 50.0]], 139637569460344)
(The wifey is:, [jane, [savings, 50.0]], 139637569406160)
print(The person inside is:, [id(x) for x in person])
print(The hubby inside is:, [id(x) for x in hubby])
print(The wifey inside is:, [id(x) for x in wifey])
(The person inside is:, [139639860076144, 139637569810016])
(The hubby inside is:, [139637569356104, 139637569810016])
(The wifey inside is:, [139637569378272, 139637569810016])
在使用浅拷贝的时候发现引用的id都是相同的但是字符串的id却发生了变化是因为在python中字符串是不可变的从而在每次进行修改的时候都是新建一个对象从而引用发生了变化。 copy模块 浅拷贝和深拷贝的操作都可以在copy模块中找到其实copy模块中只有两个函数可用copy进行浅拷贝操作而deepcopy进行深拷贝操作 #1
import copy
aList [1,kel,[1,2,3]]
print(The aList is:, aList, id(aList))shadeList copy.copy(aList)
print(The shadeList is:, shadeList, id(shadeList))deepList copy.deepcopy(aList)
print(The deepList is:, deepList, id(deepList))aList[2].append(kel)print(The aList is:, aList, id(aList))print(The shadeList is:, shadeList, id(shadeList))print(The deepList is:, deepList, id(deepList))
(The aList is:, [1, kel, [1, 2, 3]], 139639722291712)
(The shadeList is:, [1, kel, [1, 2, 3]], 139639722170344)
(The deepList is:, [1, kel, [1, 2, 3]], 139637569586096)
(The aList is:, [1, kel, [1, 2, 3, kel]], 139639722291712)
(The shadeList is:, [1, kel, [1, 2, 3, kel]], 139639722170344)
(The deepList is:, [1, kel, [1, 2, 3]], 139637569586096) #2
import copy
aList [1,kel,[1,2,3]]
print(The aList is:, aList, id(aList))shadeList copy.copy(aList)
print(The shadeList is:, shadeList, id(shadeList))deepList copy.deepcopy(aList)
print(The deepList is:, deepList, id(deepList))shadeList[2].append(kel)print(The aList is:, aList, id(aList))print(The shadeList is:, shadeList, id(shadeList))print(The deepList is:, deepList, id(deepList))
(The aList is:, [1, kel, [1, 2, 3]], 139637569846448)
(The shadeList is:, [1, kel, [1, 2, 3]], 139637569406520)
(The deepList is:, [1, kel, [1, 2, 3]], 139637569407240)
(The aList is:, [1, kel, [1, 2, 3, kel]], 139637569846448)
(The shadeList is:, [1, kel, [1, 2, 3, kel]], 139637569406520)
(The deepList is:, [1, kel, [1, 2, 3]], 139637569407240) #3
import copy
aList [1,kel,[1,2,3]]
print(The aList is:, aList, id(aList))shadeList copy.copy(aList)
print(The shadeList is:, shadeList, id(shadeList))deepList copy.deepcopy(aList)
print(The deepList is:, deepList, id(deepList))deepList[2].append(kel)
print(The deepList is:, deepList, id(deepList))print(The aList is:, aList, id(aList))print(The shadeList is:, shadeList, id(shadeList))
(The aList is:, [1, kel, [1, 2, 3]], 139637569460776)
(The shadeList is:, [1, kel, [1, 2, 3]], 139637569461496)
(The deepList is:, [1, kel, [1, 2, 3]], 139637569585592)
(The deepList is:, [1, kel, [1, 2, 3, kel]], 139637569585592)
(The aList is:, [1, kel, [1, 2, 3]], 139637569460776)
(The shadeList is:, [1, kel, [1, 2, 3]], 139637569461496) 参考文献 numpy 下的数据结构与数据类型的转换np.array vs. np.asarray
numpy中array和asarray的区别
python中的深拷贝与浅拷贝
