This tutorial was built using Python 3.6
In this tutorial I’m aiming to help demystify this concept of generators within the Python programming language. We’ll look at what generators are and how we can utilize them within our python programs.
What Are Generators?
Generators are functions that can return multiple values at different times. These are iterators that we can subsequently iterate over in a variety of different ways.
Imagine we wanted to define a function that would return double the value of whatever we first passed into it. If we wanted to do this with normal python functions we would have to do something like this:
def my_func(x): return x*2
However this function would return the value of
x doubled only once. However
with generators we could make it double every time we called the
function on this particular generator. This is where the
yield keyword comes
into play. You can think of
yield in a similar way to how you would think of
return in a normal function. Except you can call
yield as many times as you
Let’s define a very simple generator that will double the value of whatever we pass into it a grand total of three times, so if we pass 2 into it, by the final call we should end up with a value of 16.
def my_generator(x): yield x*2 yield x*4 yield x*8
Complete Code Snippet
Let’s flesh this out into a full python script. In this we have our new
generator function with it’s 3
yield statements, below that we declare an
instance of our generator by calling
mygen = my_generator(2). We then print
next(mygen) value 3 different times to the console.
def my_generator(x): yield x*2 yield x*4 yield x*8 mygen = my_generator(2) print(next(mygen)) print(next(mygen)) print(next(mygen))
If we were then to run this code and call the
next() function 3 times on this
generator, you should see the value is doubled every time. The above code should
then print out the following in the console:
$ python3.6 generators.py 4 8 16
The StopIteration error is raised when a call to next() is made and there are no
subsequent values to be yielded from our generator function. If we added a 4th
print(next(mygen)) in the previous sample then you should see an error
like so print out:
$ python3.6 generators.py 4 8 16 Traceback (most recent call last): File "generators.py", line 12, in <module> print(next(mygen)) StopIteration FAIL
Let’s now improve upon our original generator function. We can implement an
iterator that continues to yield values
indefinitely. This will keep a reference of what value
x currently is and
after every yield it will double that value.
def my_generator(x): x = x while True: yield x*2 x = x*2 mygen = my_generator(2) print(next(mygen)) print(next(mygen)) print(next(mygen)) print(next(mygen))
We can then call the next() function as many times as we’d desire and we would
then see our original value of
x double with each subsequent call to our
next(mygen) function. Try change the value passed in to
experiment with the number of times you call
$ python3.6 generators.py 4 8 16 32
It’s important to note that the physical limitations still apply and I’m sure you’d start to hit some form of limit for the above program if you called next() indefinitely.
So, in this tutorial, we looked at generators in Python! We looked at how we can build our own Python generators and subsequently use them within our own Python applications for fame and fortune!
Hopefully you found this tutorial useful! If you did or require further assistance then please let me know in the comments section below or by tweeting me @Elliot_F.