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This article mainly introduces the "brief introduction of python descriptors". In daily operation, I believe many people have doubts about the brief introduction of python descriptors. The editor consulted all kinds of materials and sorted out simple and easy-to-use operation methods. I hope it will be helpful to answer the doubts of "brief introduction of python descriptors"! Next, please follow the editor to study!

What is a descriptor?

The python descriptor is an object property of a "binding behavior", and in the descriptor protocol, it can override access to the property through methods. These methods are get (), set (), and _ _ delete__ (). If any of these methods are defined in an object, then the object is a descriptor.

In other words: an object that contains one of the following three methods is a descriptor

Three methods (protocols):

Get (self, instance, owner): triggers when a property is called

Set (self, instance, value): triggers when a property is assigned a value

Delete (self, instance): triggers when an attribute is deleted with del

What is self instance, owner?

Self is the current descriptor (object) itself, similar to the C++this pointer, instance is the class object entity to which the descriptor belongs, and owner is the class in which the descriptor is located (who owns these things is, of course, the highest class)

Class Desc (object):

Def _ _ get__ (self, instance, owner):

Print ("_ _ get__...")

Print ("self:\ t\ t", self)

Print ("instance:\ t", instance)

Print ("owner:\ t", owner)

Print ('='* 40, "\ n")

Def _ _ set__ (self, instance, value):

Print ('_ _ set__...')

Print ("self:\ t\ t", self)

Print ("instance:\ t", instance)

Print ("value:\ t", value)

Print ('='* 40, "\ n")

Class TestDesc (object):

X = Desc ()

# the following is the test code

T = TestDesc ()

T.x

# the following is the output information:

_ _ get__...

Self:

Instance:

Owner:

= =

@ property disguises a function call as an access to a property

Class Foo:

@ property

Def attr (self):

Print ('getting attr')

Return 'attr value'

Def bar (self): pass

Foo = Foo ()

X = property (get, set, del)

Container type protocol:

If you want the custom container to be immutable: you only need to define the magic of len () getitem ()

If you want the customized container to be changeable, you also need to define _ _ setitem__ delitem _ ().

Class CountList:

Def _ _ init__ (self, * args):

Self.values = [x for x in args] # list derivation

Self.count = {} .fromkeys (range (len (self.values)), 0) # set up a dictionary to record the number of visits

# dictionary; 0-len (self.value)

Def _ len__ (self):

Return len (self.values)

# get the value, not the number of visits

# gwtitem, as long as access is bound to trigger, this is magic

Def _ getitem__ (self,key):

Self.count [key] + = 1

Return self.values [key]

Python iterator: iter next

String = 'hello, python'

It = iter (string)

Next (it)

Next (it)

...

Throw an exception when the iter iterator is false

It = iter ("hello,world")

While True:

Try:

Each = next (it)

Print (each)

Except:

Break

The magic method of iterator:

Iter ()

Next ()

Class Iter:

Def _ _ init__ (self, n = 100):

Self.a = 0

Self.b = 1

Self.n = n

Def _ iter__ (self):

Return self

Def _ next__ (self):

Self.a, self.b = self.b, self.a + self.b

If self.a > self.n: # stop iteration

Raise StopIteration

Return self.a

Generators: functions of yeild statements

The generator is also an iterator: the yield statement returns

Random entry: Hu Jinli, no rule, hey

Collaborative program

A collaborative program is an independent function call that can be run, which can be paused or suspended and continue to execute from where the program leaves when needed.

A generator is also an iterator, so it can be used with next (iterator object), just like an iterator.

Watch carefully: yield returns after the first call of the generator generator, and then does not start at the beginning of the function, but continues to call from the place where it left

Def generator (): http://www.xadsyyfk.com/ of Zhengzhou Gynecology Hospital

A = 0

B = 1

Print ("start iteration generator")

While True:

A, b, b

Yield a

For each in generator ():

If (each > 100):

Break

Print (each,end ='') # do not wrap lines, replace line breaks with spaces

The generator derivation can be used as an argument to a function without ()

Eg: 0 +... + 9

Originally: sum ((i for i range (10))

Harmony off () sum sum (i for i in range (10))

List derivation [i for i in range (10)]

Dictionary derivation {i for i in range (100)}

There is no tuple deduction Oh, it becomes the generator generator deduction

Module

Containers: encapsulation of data

Functions: encapsulation of statements

Classes: encapsulation of methods and properties

Module: it is the program.

Import module name:

Find1:

Import: import + module name (as t)

Call: module name (t). + function

Name = = main

If name = = main function: let python identify whether the program is running in the main program or in the module: that is, let python identify whether it is a private program or a public program

That is, if name = = main is the main program? If so, what do you do? if not, what do you do?

Search path

The process of importing python module has a process of path search.

Sys.path.append first ("module path")

Re-import module

Package package

The first step is to create a folder storage module. The name of the file is the name of the bag.

The second step is to create a _ _ init__.py module file in the folder. The content can be empty.

The third step is to put the relevant modules in the folder (package)

Import: package name. Module name

At this point, the study of "A brief introduction to python descriptors" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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