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Editor to share with you how to quote classes in Python, I believe most people do not know much about it, so share this article for your reference, I hope you can learn a lot after reading this article, let's go to know it!

1. The concept of citation

A Reference is a pointer to an object

A reference is a pointer to a real object in memory, represented as a variable name or memory address

There is at least one reference for each object, and the id () function is used to obtain the reference

When passing parameters and assignments, Python passes references to the object instead of copying the object

Sample code:

List1 = [1,2,3,4] list2 = list1print (id (list1)) # 2044656837192print (id (list2)) # 204465683719 because list1 is the instantiation of the class, list2 refers to list1, and both are the most basic object classes referenced, so the results of the two are the same.

The handling of references by Python's internal mechanism

Immutable objects: the immutable interpreter maintains as few areas of memory as possible for the same value

Mutable objects: the mutable interpreter maintains different memory areas for each object

Sample code:

Text1 = "one bowl week" text2 = text1text3 = "one bowl week" text4 = "one bowl" text5 = "week" text6 = text4 + text5print (id (text1)) # 1616972638288print (id (text2)) # 1616972638288print (id (text3)) # 1616972638288print (id (text4) # 1616973621272print (id (text5) # 1616973578032print (id (text6)) # 16974246288

Because text1 and 2 are a referenced string, the memory address is the same; because the Python interpreter is likely to save memory space, when the values of immutable types are the same, Python will automatically reference an address space to save space, so the address space of text1/2/3 is the same. The Python interpreter does not optimize the address space of the calculated results. Even if the values of the two are the same, the Python interpreter will open up a new address space for the newly calculated results.

Sample code:

List1 = [] list2 = [] list3 = [] print (id (list1)) # 3204114440776print (id (list2)) # 3204114440840print (id (list3)) # 3204115873544

Each mutable object has its own independent address space and does not reuse the address space

There are generally four situations that lead to the citation being + 1.

Object is created

Object is referenced

Object is used as an argument to a function or method

Object is used as an element in a container

There are all four cases that lead to citation-1.

Object is deleted

The name of the object. Assign a new object.

Object leaves scope

The container in which the object is located is deleted

2. Copy of object

Copy is to copy an object as a new object. There is a "change" in memory space. The copy is divided into shallow copy and deep copy.

Shallow copy: only copy the top-level object, the default copy mode

Deep copy: how all objects are copied iteratively

Sample code (shallow copy 1)

List1 = ["Sweets", [1,2,3] list2 = list1.copy () # use copy method to copy list3 = list1 [:] # use slice copy list4 = list (list1) # copy for ch in [list1, list2, list3, list4]: for i in ch: print (I, id (I), "\ t", end= "") # none of the print list and id print (ch) Id (ch)) # print each list and id'- output results-- one bowl week 2905817180184 [1,2,3] 2905787490888 [one bowl week, [1,2,3]] 2905787490952 one bowl week 2905817180184 [1,2,3] 2905787490888 [one bowl week, [1,2,3]] 2905817092488 one bowl 2905817180184 [1,2,3] 29057490888 ['one bowl', [1,2,3]] 2905817180184 [1 2, 3] 2905787490888 [one Bowl week, [1,2,3]] 2905817771656

Shallow copy is only the memory space of the copied list, and the memory space of the elements in it will not be copied.

Sample code (shallow copy 2)

List1 = [one bowl week ", [1,2,3] list2 = list1.copy () # use copy method to copy list3 = list1 [:] # copy list4 = list (list1) using slices # copy list4 [1] .append (4) print (list1) print (list2) print (list3) print (list4)-- output the result-- [one bowl week, [1,2,3,4] [one bowl week] [1, 2, 3, 4]] ['one bowl week', [1, 2, 3, 4]] [1, 2, 3, 4]]

Here only data changes are made to the list4, but the contents of all the lists occur; this is because the contents referenced by each list are the same, so changing one or four will change.

Deep copy uses the deepcopy () method in the copy library to iteratively copy objects in the inner layer of the object, completely opening up new memory space to establish objects and various object elements in the lower layer of the object. Deep copy is only for variable categories, and immutable types are not allowed to create new objects.

Sample code

Import copy # Import library list1 = ["one bowl week", [1,2,3] list2 = copy.deepcopy (list1) # copy for ch in [list1, list2]: for i in ch: print (I, id (I), "\ t", end= "") # none of the items in the print list and id print (ch) using the deepcopy method of the copy library Id (ch)) # print each list and id'''--- output-one bowl week 2190823984184 [1,2,3] 2190853845832 [one bowl week, [1,2,3]] 2190853766728 one bowl week 2190823984184 [1,2,3] 2190853961544 [one bowl week, [1,2,3] 2190853961480bread

Because the "sweets" string is of immutable type, its address space will not change, and the rest of the address space will be changed.

2.1 references to instance methods

An instance method is also a reference, which is a reference to the object itself. when the method is referenced, the method (that is, the function) produces an object: the method object.

2.2 feature decorator for class

The @ property decorator can change a method to an externally visible "attribute", which is represented as a method inside the class and as an attribute on the outside.

Sample code

Class TestClass: def _ _ init__ (self, name): self.name = name @ property # convert the method to def age (self): return self.__age @ age.setter # assign to the attribute def age (self, value): if value

Value = 19 self.__age = valuett = TestClass ("one bowl of porridge") bb = TestClass ("one bowl of porridge") tt.age = 18bb.age =-19print (tt.age) # 18print (bb.age) # 193.The name modification of the class

Name modification (Name Mangling) is a convention for name conversion in a class. Python can complete some important functions through name modification. Underscore _ is used in Python for name modification, which can be divided into five cases.

_ name

Name_

_ _ name

_ _ name__

_

3.1 _ name modification at the beginning of a single underscore

The property or method at the beginning of a single underscore is a convention used within a class and is a convention specified by PEP8.

It's just an agreement, it can still be passed. Mode access

The difference in functionality is that properties or methods at the beginning of a single underscore are not imported when using from XX import *.

Sample code

Class TestClass: def _ init__ (self, name): self._name = name # agreed to use tt = TestClass ("one bowl week") print (tt._name) # one bowl week

Although the convention is used internally, it can still be accessed

3.2 _ name modification at the end of a single underscore

The attribute or method at the end of a single underscore is to avoid conflicts with reserved words or existing names, which is also stipulated by PEP8, which is only a convention and does not have any corresponding functions

3.3 _ _ name modifier at the beginning of a double underscore

The name of the property or method at the beginning of the double underscore will be modified by the interpreter to avoid naming conflicts, which is not a convention, but functional. _ nama will be modified to _ name to implement private properties and private methods. This is a kind of name modification of a class, which is indirectly treated as a private property or private method.

3.4name modification at the beginning and end of a double underscore in name__

The property or method at the beginning and end of the double underscore does not have any special function, the name cannot be modified, and some of the names are reserved properties or reserved methods.

3.5 single underline

Is the single underscore just an insignificant name with no special function?

4. The minimum empty class of Python

Function:

Class is a namespace, and the smallest empty class can be used as a namespace

The minimum empty class can assist in storage and use

Dynamically adding attributes is a feature of Python class

Sample code:

Class TestClass: passa = TestClassa.text = "one bowl week" print (a.text) # one bowl week # you can dynamically add attributes to store information. The above is all the content of this article "how to reference classes in Python". Thank you for reading! I believe we all have a certain understanding, hope to share the content to help you, if you want to learn more knowledge, welcome to follow the industry information channel!

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