Python Full stack learning--day13 (iterator, generator)

An iterator

Python all objects

The object that can be used for looping is an iterative object.

Iterate objects: str, list, tuple, dict, set, range

Iterator: F1 file handle

Dir Prints all the action methods of the object:

s = ' python ' Print (dir (s))

Execution output:

[' __add__ ', ' __class__ ', ' __contains__ ', ' __delattr__ ', ' __dir__ ', ' __doc__ ', ' __eq__ ', ' __format__ ', ' __ge__ ', ' __ getattribute__ ', ' __getitem__ ', ' __getnewargs__ ', ' __gt__ ', ' __hash__ ', ' __init__ ', ' __iter__ ', ' __le__ ', ' __len__ ', ' _ _lt__ ', ' __mod__ ', ' __mul__ ', ' __ne__ ', ' __new__ ', ' __reduce__ ', ' __reduce_ex__ ', ' __repr__ ', ' __rmod__ ', ' __rmul__ ', ' _ _setattr__ ', ' __sizeof__ ', ' __str__ ', ' __subclasshook__ ', ' capitalize ', ' casefold ', ' center ', ' count ', ' encode ', ' EndsWith ', ' expandtabs ', ' find ', ' format ', ' Format_map ', ' index ', ' isalnum ', ' isalpha ', ' isdecimal ', ' isdigit ', ' Isidentifier ', ' islower ', ' isnumeric ', ' isprintable ', ' isspace ', ' istitle ', ' isupper ', ' join ', ' ljust ', ' lower ', ' Lstrip ' ', ' Maketrans ', ' partition ', ' replace ', ' rfind ', ' rindex ', ' rjust ', ' rpartition ', ' rsplit ', ' Rstrip ', ' Split ', ' Splitline S ', ' startswith ', ' strip ', ' swapcase ', ' title ', ' Translate ', ' upper ', ' Zfill ']

What is an iterative object: An object that contains an __iter__ method internally is called an iterative object, and an iterative object follows an iterative protocol.

How to judge two kinds of ways:

The first type:

s = ' python ' Print (' __iter__ ' in Dir (s))

　

Execution output:

True

The second type:

From collections import iterable  #此模块判断是否为可迭代对象l = [1,2,3,4]print (isinstance (l,iterable))

　

Execution output:

True

From collections Import Iterablel = [1, 2, 3, 4]print (Type (l)) print (Isinstance (l,list)

　　

Execution output:

<class ' list ' >
True

Type can only judge what kind of

Isinstance judgment is more extensive, not only to determine the type, but also to determine whether it is possible to iterate

Iterators

An iterative object can be transformed into an iterator: an object that can be iterated over. __ITER__ ()--and iterators

Iterators contain not only __iter__, but also __next__. follows the iterator protocol.

L1 = [1,2,3]L1_OJB = l1.__iter__ () #迭代器print (L1_OJB)

Execution output:

<list_iterator Object at 0x000001987d5eb668>

Indicates that it is a list iterator object

L1 = [1,2,3]l1_obj = l1.__iter__ () #迭代器print (' __iter__ ' in Dir (l1_obj)) #是否含有__iter__方法print (' __next__ ' in Dir (L1)     ) #是否含有__next__方法 here is the list of L1 that is given in Dir. Instead of L1_objprint (' __next__ ' in Dir (l1_obj))

　　

Execution output:

True
False
True

From the results, it can be seen that l1_obj is an object that contains both __iter__ and __next__, so it is an iterator

Iterators use __next__ to get a value

L1 = [1,2,3]l1_obj = l1.__iter__ ()  # iterator print (l1_obj.__next__ ()) #获取一个元素print (l1_obj.__next__ ()) Print (l1_obj.__ next__ ()) print (l1_obj.__next__ ())

　　

Execution Error:

1
Traceback (most recent):
2
3
File "e:/python_script/day13/iterator. Py", line 9, <module>
Print (l1_obj.__next__ ())
Stopiteration

If you take one more, you will get an error because the list has only 3 elements.

Use for loop mode

L1 = [1,2,3]L1_OJB = l1.__iter__ ()  #转换为迭代器for i in L1_OJB:    print (i)

Execution output:

1
2
3

The internal mechanism of the For loop is performed using the __next__ method. Why is there no error? It has an exception handling mechanism inside it.

Summarize:

Containing only the __iter__ method, it is the object that can iterate

that contains the __iter__ and __next__ methods is the iterator

2 Ways to Judge Iterators:

The first type:

L1 = [1,2,3]L1_OJB = l1.__iter__ () #转换为迭代器print (' __iter__ ' in Dir (L1_OJB))

The second type:

L1 = [1,2,3]l1_obj = l1.__iter__ () from collections Import Iterableprint (Isinstance (l1_obj,iterable))

Returns true, which means that it is yes

Benefits of Iterators:

1. Save memory space.

2. The inertia mechanism is met.

3. Can not be repeated value, irreversible.

Irreversible, indicating that the value has been taken, can not be taken again, he can only take the next.

for processing mechanism

L2 = [1,2,3,4,5,6,7,8]for i in L2:    print (i)

1. Convert an iterative object into an iterator

2. Internal use of the __next__ method to take the value

3. Using exception handling to deal with the error

The advantage of iterators is that they save memory space

Good programmers will consider memory optimizations, such as iterators

Use the while loop to specify that the list be traversed with the __next__ method

L2 = [1,2,3,4,5,6,7,8]l2_obj = l2.__iter__ ()  #1. Convert an iterative object to an iterator while True:    try:        i = l2_obj.__next__ ()  # Internally use the __next__ method to take the value        print (i)    except Exception:   #运用了异常去处理报错        break

Try inside the code, there is an error, will not prompt the red text.

Exception can receive all the error, indicating when the error, how to deal with, here directly using break out of the loop

Interview questions:

Use the while loop to traverse a finite object

You can use the above code directly.

Two, generator

Generator: The generator is essentially an iterator

L = [1,2,3]l.__iter__ ()

　　

#生成器的产生方式:

1. Builder function Constructs

2. Builder push-to-construct

3. Data type Conversion

123456

deffunc1():    print(111)    print(222)    print(333)    return666print(func1())

Execution output:

111
222
333
666

To replace a function with a generator

Def func1 ():    print (111)    Print (222)    Print (333)    yield 666g = func1 () print (g)

Execution output:

<generator Object func1 at 0x0000023c67c2c3b8>

First: As long as the function has yield then he is not a function, but a generator

Second: G is called the Generator object.

Iterators, using __next__ to take values

Def func1 ():    print (111)    Print (222)    Print (333)    yield 666g = func1 () print (g.__next__ ()) Print (g.__ Next__ ())

Execute error
A __netxt__ corresponds to a yield. And there's one more.

Case:

Create 10000 sets of garments

A manufacturer directly produces 10000 sets of

Def func1 (): For    I in range (1,10001):        print (' Selected costume%d '% i) func1 ()

Post-Execution output:

Selected Clothing 1

。。。。。。

。。。。。。

Selected clothing 10000

I'll give you 50 sets.

Def func1 (): For    I in range (1,10001):        yield ' selected costume%d set '% IG = func1 () for I in Range (1,51):   #我先生产 50 sets    p Rint (g.__next__ ())

　

Post-Execution output:

Selected Clothing 1

。。。。。。

。。。。。。

Selected Clothing 50

The ultimate boss is 200 sets.
First 50 sets, then 150 sets.

Def func1 (): For    I in range (1,10001):        yield ' selected costume%d set '% IG = func1 () #先给你生产50套, pending follow-up the aftermath is giving you 150 sets for I in range (1,51):    print (g.__next__ ()) for J in range:    print (g.__next__ ())

Post-Execution output:

Selected Clothing 1

。。。。。。

。。。。。。

Selected clothing 200

　

For a list, the For loop starts at 0.

For the builder, it is made by the pointer, __next__ once, and the pointer forwards once. It can't start from scratch.
Must be executed in sequence.

The difference between a generator and an iterator

Iterators: There are built-in methods

Builder: Developer Customization

Send

Def generator ():    print (123)    content = yield 1    print (' ===== ', content)    print (456)    print (' * * * ', RET)    Yieldg = Generator () ret = g.__next__ () print (' * * * ', ret) ret=g.send (' Hello ') print (' * * * ', ret)

　　

123
1
===== Hello
55W
1
None

The effect of send to get the next value is basically the same as next

Just pass a data to the position of the previous yield when you get the next value

Considerations for using Send:

The first time you use the generator, you get the next value with next.

The last yield cannot accept external values.

Next, like the Send function, is performed once

Send can assign a value to the previous yield.

Contents of Dictation:

1. What is an iterative object and what is an iterator

For:

An object that contains the __iter__ method internally is called an iterative object.

The internal must have an __iter__ method and an object of the __next__ method, called an iterator.

2. How can iterative objects be transformed into iterators

For:

Convert to Iterators: Iterate over objects. __ITER__ ()--and iterators

For example:

L1 = [1,2,3]l1_obj = l1.__iter__ ()

3. How iterators are evaluated

For

Iterators using the __next__ () method

4. What is a generator and how do I write a generator? How does the generator take the value?

For:

Generator, which generates a container. In Python, one side loops, one side of the computational mechanism, called the generator

Generator Example:

12	deffun1():    yield1

　　

The generator uses the __next__ () method to take a value, or a for loop

Python Full stack learning--day13 (iterator, generator)