The yield statement suspends function’s execution and sends a value back to caller, but retains enough state to enable function to resume where it is left off. When resumed, the function continues execution immediately after the last yield run. This allows its code to produce a series of values over time, rather them computing them at once and sending them back like a list.
We answer the following questions:
- What are generators?
- What is yield?
- Difference between return and yield
- Where is yield used?
To understand what yield does, you must understand what generators are. And before generators come iterables.
Iterables When you create a list, you can read its items one by one, and it’s called iteration: >>> mylist = [1, 2, 3] >>> for i in mylist: ... print(i) 1 2 3
Mylist is an iterable. When you use a list comprehension, you create a list, and so an iterable:
>>> mylist = [x*x for x in range(3)] >>> for i in mylist: ... print(i) 0 1 4
Everything you can use “for… in…” on is an iterable: lists, strings, files… These iterables are handy because you can read them as much as you wish, but you store all the values in memory and it’s not always what you want when you have a lot of values.
What are Generators?
Generators are iterators, but you can only iterate over them once. It’s because they do not store all the values in memory, they generate the values on the fly:
>>> mygenerator = (x*x for x in range(3)) >>> for i in mygenerator: ... print(i) 0 1 4
It is just the same except you used () instead of . BUT, you can not perform for i in mygenerator a second time since generators can only be used once: they calculate 0, then forget about it and calculate 1, and end calculating 4, one by one.
What is Yield?
Yield is a keyword that is used like return, except the function will return a generator.
>>> def createGenerator(): ... mylist = range(3) ... for i in mylist: ... yield i*i ... >>> mygenerator = createGenerator() # create a generator >>> print(mygenerator) # mygenerator is an object! <generator object createGenerator at 0xb7555c34> >>> for i in mygenerator: ... print(i) 0 1 4
To master yield, you must understand that when you call the function, the code you have written in the function body does not run. The function only returns the generator object, this is a bit tricky 🙂
Then, your code will be run each time the for uses the generator.
Now the hard part:
The first time the for calls the generator object created from your function, it will run the code in your function from the beginning until it hits yield, then it’ll return the first value of the loop. Then, each other call will run the loop you have written in the function one more time, and return the next value, until there is no value to return.
The generator is considered empty once the function runs but does not hit yield anymore. It can be because the loop had come to an end, or because you do not satisfy a “if/else” anymore.
Let’s see with an example:
# A Simple Python program to demonstrate working # of yield # A generator function that yields 1 for first time, # 2 second time and 3 third time def simpleGeneratorFun(): yield 1 yield 2 yield 3 # Driver code to check above generator function for value in simpleGeneratorFun(): print(value)
1 2 3
Difference between return and yield
Return sends a specified value back to its caller whereas Yield can produce a sequence of values. We should use yield when we want to iterate over a sequence, but don’t want to store the entire sequence in memory.
Where is yield used?
Yield are used in Python generators.
A generator function is defined like a normal function, but whenever it needs to generate a value, it does so with the yield keyword rather than return. If the body of a def contains yield, the function automatically becomes a generator function.
# A Python program to generate squares from 1 # to 100 using yield and therefore generator # An infinite generator function that prints # next square number. It starts with 1 def nextSquare(): i = 1; # An Infinite loop to generate squares while True: yield i*i i += 1 # Next execution resumes # from this point # Driver code to test above generator # function for num in nextSquare(): if num > 100: break print(num)
1 4 9 16 25 36 49 64 81 100
Points to Remember about yield
- generators are used to generate a series of values.
- yield is like the return of generator functions.
- The only other thing yield does is save the "state" of a generator function.
- A generator is just a special type of iterator.
This is a companion discussion topic for the original entry at http://iq.opengenus.org/yield-python/