Shades cope

We should first introduce variable assignment mechanism

Shallow copy

Existing data

data = {
    "name":"alex",
    "age":18,
    "scores":{
        "语文":130,
        "数学":60,
        "英语":98,
    }
}
d2 = data
data["age"] = 20 
print(d2)

You said the value of d2's print, age 18 or 20?

　　{'name': 'alex', 'age': 20, 'scores': {'语文': 130, '数学': 60, '英语': 98}}

看一下id

　　　　>>> print ( 'Assignment', id (data), id (d2))

　　　　Assignment 4439292336 4439292336

　　Why is it 20? Because d2 = data corresponding to just get the data memory address, but the data in each k, v is a separate memory address.

　　d2, data will always share the data in dict, as the situation does not occur before the string a = 1, b = a, a = 2, b is equal to 1 still.

　　If I really want a complete copy of the data dict how to do it?

　　　　You can use shallow copy syntax

　　　　　　data = {
 　　　　　　   "name":"alex",
 　　　　 　　  "age":18,
  　　　　　　  "scores":{
      　　　   "语文":130,
        　　　 "数学":60,
       　　　　 "英语":98,
  　　　　 　　 }
　　　　　　}
　　　　d2 = data.copy()
　　　　data["age"] = 20
　　　　print(d2)
　　　　print(data)

　　Export

　　　　{'name': 'alex', 'age': 18, 'scores': {'语文': 130, '数学': 60, '英语': 98}}
　　　　{'name': 'alex', 'age': 20, 'scores': {'语文': 130, '数学': 60, '英语': 98}}

 id

　　　　>>> print('浅copy',id(data),id(d2))

　　　 Shallow copy 4440084720 4440087120

　　　　See id is the same, so the equivalent of 2 parts of independent data, but why this syntax is called a shallow copy of it? The change in the score you will know the value.

　　　　data = {
   　　　　 "name":"alex",
    　　　　"age":18,
   　　　　 "scores":{
      　　　　  "语文":130,
     　　　　   "数学":60,
     　　　　   "英语":98,
    　　　　}
　　　　}
　　　　d2 = data.copy()
　　　　data["age"] = 20
　　　　data["scores"]["数学"] = 77  
　　　　print(d2)
　　　　print(data)

　　　　Look output, it is amazing, the value of two Dict in age is independent, but dictionaries score a point value is seemingly shared

　　　　　　{'name': 'alex', 'age': 18, 'scores': {'语文': 130, '数学': 77, '英语': 98}}
　　　　　　{'name': 'alex', 'age': 20, 'scores': {'语文': 130, '数学': 77, '英语': 98}}

　　　　id

　　　　　　　　Print >>> (ID (Data [ "Scores"]), ID (D2 [ "Scores"])) # the second hierarchy data

　　　　　　　　4440080288 4440080288

　　Because the shallow copy will copy only the first layer of data dict, deeper still scores below the value of a share.

Deep copy

　　If you want to completely make the above two dict completely independent, no matter how many layers there are data. Then use a python tool bag of tools,

　　　　>>> import copy

　　　　>>> d2 = copy.deepcopy(data)

　　　　>>> print('深copy',id(data),id(d2))

　　　　　　Deep copy 4439292336 4440080288

　　　　>>> data["age"] = 20

　　　　>>> data["scores"]["数学"] = 77

　　　　>>> print(d2)

　　　　　　{ 'Name': 'alex', 'age': 18, 'scores': { 'Language': 130, 'mathematics': 60, 'English': 98}}

　　　　>>> print(data)

　　　　　　{ 'Name': 'alex', 'age': 20, 'scores': { 'Language': 130, 'mathematics': 77, 'English': 98}}

　　　　Print >>> (ID (Data [ "Age"]), ID (D2 [ "Age"])) # first layer data

　　　　　　4434787728 4434787664

　　　　Print >>> (ID (Data [ "Scores"]), ID (D2 [ "Scores"])) # the second hierarchy data

　　　　　　4440087120 4440896528

Shallow copy

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