Skip to main content

Understand the generators and yield keyword in python, on Python Hunter


Generators look like a function but there is huge difference b/w generators and function. Instead of return statements you will find only yield statements are used inside of the body of a generator , i.e. one or more yield statements. 

In short the main feature of generator is that the last execution statement is kept remembered by python interpreter and the next time when the same generator is called the it execute from the last yield location instead of starting the location from TOP. And the state of all local variable(s) are kept safe, that means the local variable(s) will not change their value(s) at next call.


 Follow Python Hunter on youtube   Follow Python Hunter on twitter Follow on tumbl ( at your own risk ) check profile on linkedIn 
Follow the example to understand the concept.

def gen():
    yield 1
    yield 2
    yield 3
    yield 4

f=gen()
print(next(f)) # will print 1
print(next(f)) # will print 2
print(next(f)) # will print 3

Let's take a real example...........
--------------------------------------------------CODE STARTS------------------------------------------
import time

def red_cars(arg0 = None):
    n = 0
    while True:
        if arg0 == "y":
            n = n + 1
            arg0 = yield n
        else:
            arg0 = yield n
            

car_color = red_cars()

#Python 2.x
#car_color.next()

#Python 3.x
car_color.__next__()


#Common method, works in both versions
# next(car_color)

stop = time.time() + 5 * 60
while time.time() < stop:
    answer = input('Did you spot a red car on the road? ("y" or "n"): ')
    print ('You have spotted ' + str(car_color.send(answer)) + ' cars so far!')
--------------------------------------------------CODE ENDS------------------------------------------    

Explanation:-
when car_color = red_cars() executes (without any param) it gets the generator object, 
then the car_color.__next__() initialize the generator and n is initalized to 0  and while loop executes and the else block executes because the "arg0" is not equal to "y"

The the console ask for input..
that input is send to generator with send(answer)..

now the real magic happens here.....
normally in method invocation the method execution start from top.

But when the yield keyword is used in any method then that method becomes generator.
and when that same method is invoked again the execution starts directly from the next statement of previous yield location (did not get it? don't worry the below screen shots will make it easy to understand).


when str(car_color.send(answer)) gets executed with input variable it calls the generator and gets the yielded value as returned value from generator.

Screen Shot:- 





    if you still don't get, feel free to email me with your queries at hitman47silent994c4@gmail.com.


Comments

  1. This was short and comprehensive example to understand the actual concept of generators with yield.
    Aman Salaria

    ReplyDelete

Post a Comment

Thanks in anticipation.

Popular posts from this blog

Understanding the usage of underscore( _ ) of Python for beginner. On Python Hunter

Introduction: Just like you, a newbie in python scripting language, me too was confused about lot of new things in python that are not valid or available in other languages like Java, .Net etc. Just like other things i had seen the use of '_' underscore in python, at beginning level that flabbergasted me for a while.      With some research and practice i have summarised the following usage of '_' underscore in python. Hope you will find it helpful at beginning level. First Usage : Hold the previous output value. When used in interpreter. 1 2 3 4 5 6 7 _ = input () # For example you typed '5' print (_) # This will print '5' print ( int ( _ ) * int ( _ ) ) # This will print '25' print ( int ( _ ) + 10 ) The above will print '15', because last input was "5" and in above   line of code is producing '25' as output but not being handl

XSLT apply import tag by pran sukh on python hunter blog

Modular Programming is good attitude of best programmer. We often need to keep our code in modular approach so that is would be easy to maintain  and update or remove dead code and also it ease the process of bug tracking. XML and XSL  processors provide freedom to import multiple imports to process the same  XML  document. In the following  XML  data we have data from collage where student's and teacher's data is given. But we want to process the same  XML  data with different XSL files. Here in this example we want to show the teacher data in red background color and student data in green background color. Data.xml 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 <?xml version = "1.0"?> <?xml-stylesheet type = "text/xsl" href = "RootXSLT.xsl"?> <data> <DataFor name = "Student" id = "001" > <firstname>

Understanding "with" keyword in python, on Python Hunter

J ust like anything in python, keyword "with" is introduced in python to make the things little easy. Imagine a situation where you have to manage the resources e.g opening file and closing them after the code is executed on file. To achieve this sort of task we have to write the code as follow:      Download   But if you do it often you could do this as follow to make the code reusable: Download But why do you need to do this when you know that you have to execute the only for once.  To answer this question  python-dev team finally came up with following approach: Download  Note:- Make sure you have "file.txt" and python code file in same dir. The "with"  keyword replaces the try finally block. "with" keyword executes the openFileClass() context manager and internally calls the __enter__(self) method, and whatever is being returned from __enter__(self) method is being stored in tar