Prior to beginning tutoring sessions, I ask new students to fill out a brief
selfassessment where they rate their understanding of various Python concepts. Some
topics ("control flow with if/else" or "defining and using functions") are
understood by a majority of students before ever beginning tutoring. There are a
handful of topics, however, that almost all students report having no
knowledge or very limited understanding of. Of these, "generators
and the yield
keyword" is one of the biggest culprits. I'm guessing this is the case for most
novice Python programmers.
Many report having difficulty understanding generators
and the yield
keyword even after making a concerted effort to teach themselves the topic.
I want to change that. In this post, I'll explain what the yield
keyword does, why it's useful, and how to use it.
What is a Python Generator (Textbook Definition)
A Python generator is a function which returns a generator iterator (just an object we can iterate over) by calling yield
. yield
may be called with a value, in which case that value is treated as the "generated" value. The next time next()
is called on the generator iterator (i.e. in the next step in a for
loop, for example),Rthe generator resumes execution from where it called yield
, not from the beginning of the function. All of the state, like the values of local variables, is recovered and the generator contiues to execute until the next call to yield
.
If this doesn't make any sense to you, don't worry. I wanted to get the textbook definition out of the way so I can explain to you what all that nonsense actually means.
Note: In recent years, generators have grown more powerful as features have been added through PEPs. In my next post, I'll explore the true power of yield
with respect to coroutines, cooperative multitasking and asynchronous I/O (especially their use in the "tulip" prototype implementation GvR has been working on). Before we get there, however, we need a solid understanding of how the yield
keyword and generators
work.
Coroutines and Subroutines
When we call a normal Python function, execution starts at function's first line
and continues until a return
statement, exception
, or the end of the
function (which is seen as an implicit return None
) is encountered.
Once a function returns control to its caller, that's it. Any work done by the
function and stored in local variables is lost. A new call to the function
creates everything from scratch.
This is all very standard when discussing functions (more generally referred to as subroutines) in computer programming. There are times, though, when it's beneficial to have the ability to create a "function" which, instead of simply returning a single value, is able to yield a series of values. To do so, such a function would need to be able to "save its work," so to speak.
I said, "yield a series of values" because our hypothetical function
doesn't "return" in the normal sense. return
implies that the function
is returning control of execution to the point where the function was called.
"Yield," however, implies that the transfer of control is temporary and voluntary,
and our function expects to regain it in the future.
In Python, "functions" with these capabilities are called generators
, and
they're incredibly useful. generators
(and the yield
statement) were initially introduced to give
programmers a more straightforward way to write code responsible for producing a series of
values. Previously, creating something like a random number generator required
a class or module that both generated values and kept track of state between calls.
With the introduction of generators
, this became much simpler.
To better understand the problem generators
solve, let's take a look at an
example. Throughout the example, keep in mind the core problem being solved:
generating a series of values.
Note: Outside of Python, all but the simplest generators
would be referred to as coroutines
. I'll use the latter term later in the post. The important thing to remember is, in Python, everything described here as a coroutine
is still a generator
. Python formally defines the term generator
; coroutine
is used in discussion but has no formal definition in the language.
Example: Fun With Prime Numbers
Suppose our boss asks us to write a function that takes a list
of int
s and
returns some Iterable containing the elements which are prime^{1} numbers.
Remember, an Iterable is just an object capable of returning its members one at a time.
"Simple," we say, and we write the following:
def get_primes(input_list): result_list = list() for element in input_list: if is_prime(element): result_list.append() return result_list # or better yet... def get_primes(input_list): return (element for element in input_list if is_prime(element)) # not germane to the example, but here's a possible implementation of # is_prime... def is_prime(number): if number > 1: if number == 2: return True if number % 2 == 0: return False for current in range(3, int(math.sqrt(number) + 1), 2): if number % current == 0: return False return True return False
Either get_primes
implementation above fulfills the requirements, so we tell our
boss we're done. She reports our function works and is exactly what she wanted.
Dealing With Infinite Sequences
Well, not quite exactly. A few days later, our boss comes back and tells
us she's run into a small problem: she wants to use our get_primes
function on a
very large list of numbers. In fact, the list is so large that merely creating
it would consume all of the system's memory. To work around this, she wants to be
able to call get_primes
with a start
value and get all the primes
larger than start
(perhaps she's solving Project Euler problem 10).
Once we think about this new requirement, it becomes clear that it requires
more than a simple change to get_primes
. Clearly, we can't return a
list of all the prime numbers from start
to infinity (operating on infinite sequences, though, has a wide range of useful applications).
The chances of solving this problem using a normal function seem bleak.
Before we give up, let's determine the core obstacle preventing us from writing a function that satisfies our boss's new requirements. Thinking about it, we arrive at the following: functions only get one chance to return results, and thus must return all results at once. It seems pointless to make such an obvious statement; "functions just work that way," we think. The real value lies in asking, "but what if they didn't?"
Imagine what we could do if get_primes
could simply return the next value
instead of all the values at once. It wouldn't need to create
a list at all. No list, no memory issues. Since our boss told
us she's just iterating over the results, she wouldn't know
the difference.
Unfortunately, this doesn't seem possible. Even if we had a
magical function that allowed us to iterate from n
to infinity
, we'd
get stuck after returning the first value:
def get_primes(start): for element in magical_infinite_range(start): if is_prime(element): return element
Imagine get_primes
is called like so:
def solve_number_10(): # She *is* working on Project Euler #10, I knew it! total = 2 for next_prime in get_primes(3): if next_prime < 2000000: total += next_prime else: print(total) return
Clearly, in get_primes
, we would immediately hit the case where number = 3
and return at line 4.
Instead of return
, we need a way to generate a value and, when asked for
the next one, pick up where we left off.
Functions, though, can't do this. When they return
, they're
done for good. Even if we could guarantee a function would be called again, we
have no way of saying, "OK, now, instead of starting at the first line like
we normally do, start up where we left off at line 4." Functions have a single entry
point
: the first line.
Enter the Generator
This sort of problem is so common that a new construct was added to Python
to solve it: the generator
. A generator
"generates" values. Creating
generators
was made as straightforward as possible through the concept
of generator functions
, introduced simultaneously.
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
(even if it
also contains a return
statement). There's nothing else we need to do to create one.
generator functions
create generator iterators
. That's the last time
you'll see the term generator iterator
, though, since they're almost
always referred to as "generators
". Just remember that a generator
is a special type of iterator
. To be considered an iterator
, generators
must define a few methods, one of which is __next__()
.
To get the next value from a generator
, we use the same builtin function as
for iterators
: next()
.
This point bears repeating: to get the next value from a generator
, we use the same builtin function as for iterators
: next()
.
(next()
takes care of calling the generator's __next__()
method). Since a
generator
is a type of iterator
, it can be used in a for
loop.
So whenever next()
is called on a generator
, the generator
is responsible
for passing back a value to whomever called next()
. It does so by calling yield
along with the value to be passed back (e.g. yield 7
). The easiest way to remember
what yield
does is to think of it as return
(plus a little magic) for generator functions
.**
Again, this bears repeating: yield
is just return
(plus a little magic) for generator functions
.
Here's a simple generator function
:
>>> def simple_generator_function(): >>> yield 1 >>> yield 2 >>> yield 3
And here are two simple ways to use it:
>>> for value in simple_generator_function(): >>> print(value) 1 2 3 >>> our_generator = simple_generator_function() >>> next(our_generator) 1 >>> next(our_generator) 2 >>> next(our_generator) 3
Magic?
What's the magic part? Glad you asked! When a generator function
calls yield
,
the "state" of the generator function
is frozen; the values of all variables are saved
and the next line of code to be executed is recorded until next()
is called
again. Once it is, the generator function
simply resumes where it left off.
If next()
is never called again, the state recorded during the yield
call
is (eventually) discarded.
Let's rewrite get_primes
as a generator function
. Notice that we no longer need
the magical_infinite_range
function. Using a simple while
loop, we can
create our own infinite sequence:
def get_primes(number): while True: if is_prime(number): yield number number += 1
If a generator function
calls return
or reaches the end its definition, a
StopIteration
exception is raised. This signals to whoever was calling next()
that the generator
is exhausted (this is normal iterator
behavior). It is also
the reason the while True:
loop is present in get_primes
.
If it weren't, the first time next()
was called we would check
if the number is prime and possibly yield it. If next()
were
called again, we would uselessly add 1
to number
and hit the end of the
generator function
(causing StopIteration
to be raised). Once a generator has been
exhausted, calling next()
on it will result in an error, so you can only consume all
the values of a generator
once. The following will not work:
>>> our_generator = simple_generator_function() >>> for value in our_generator: >>> print(value) >>> # our_generator has been exhausted... >>> print(next(our_generator)) Traceback (most recent call last): File "<ipythoninput137e48a609051a>", line 1, in <module> next(our_generator) StopIteration >>> # however, we can always create a new generator >>> # by calling the generator function again... >>> new_generator = simple_generator_function() >>> print(next(new_generator)) # perfectly valid 1
Thus, the while
loop is there to make sure we never reach the end of
get_primes
. It allows us to generate a value for as long as next()
is called
on the generator. This is a common idiom when dealing with infinite series (and
generators
in general).
Visualizing the flow
Let's go back to the code that was calling get_primes
: solve_number_10
.
def solve_number_10(): # She *is* working on Project Euler #10, I knew it! total = 2 for next_prime in get_primes(3): if next_prime < 2000000: total += next_prime else: print(total) return
It's helpful to visualize how the first few elements are created when we call
get_primes
in solve_number_10
's for
loop. When the for
loop requests
the first value from get_primes
, we enter get_primes
as we would in a normal
function.
 We enter the
while
loop on line 3  The
if
condition holds (3
is prime)  We yield the value
3
and control tosolve_number_10
.
Then, back in solve_number_10
:
 The value
3
is passed back to thefor
loop  The
for
loop assignsnext_prime
to this value next_prime
is added tototal
 The
for
loop requests the next element fromget_primes
This time, though, instead of entering get_primes
back
at the top, we resume at line 5
, where we left off.
def get_primes(number): while True: if is_prime(number): yield number number += 1 # <<<<<<<<<<
Most importantly, number
still has the same value it did when we called yield
(i.e. 3
). Remember, yield
both passes a value to whoever called next()
,
and saves the "state" of the generator function
. Clearly, then, number
is incremented
to 4
, we hit the top of the while
loop, and keep incrementing number
until we hit
the next prime number (5
). Again we yield
the value of number
to the for
loop
in solve_number_10
. This cycle continues until the for
loop stops (at the first prime
greater than 2,000,000
).
Moar Power
In PEP 342, support was added for passing values into generators.
PEP 342 gave generator
s the power to yield a value (as before), receive a
value, or both yield a value and receive a (possibly different) value in a
single statement.
To illustrate how values are sent to a generator
, let's return to our
prime number example. This time, instead of simply printing
every prime number greater than number
, we'll find the smallest prime
number greater than successive powers of a number (i.e. for 10, we want
the smallest prime greater than 10, then 100, then 1000, etc.).
We start in the same way as get_primes
:
def print_successive_primes(iterations, base=10): # like normal functions, a generator function # can be assigned to a variable prime_generator = get_primes(base) # missing code... for power in range(iterations): # missing code... def get_primes(number): while True: if is_prime(number): # ... what goes here?
The next line of get_primes
takes a bit of explanation. While yield number
would yield the
value of number
, a statement of the form other = yield foo
means, "yield foo
and,
when a value is sent to me, set other
to that value." You can "send" values to
a generator using the generator's send
method.
def get_primes(number): while True: if is_prime(number): number = yield number number += 1
In this way, we can set number
to a different value each time the generator
yield
s. We can now fill in the missing code in print_successive_primes
:
def print_successive_primes(iterations, base=10): prime_generator = get_primes(base) prime_generator.send(None) for power in range(iterations): print(prime_generator.send(base ** power))
Two things to note here: First, we're printing the result of generator.send
,
which is possible because send
both sends a value to the generator and
returns the value yielded by the generator (mirroring how yield
works from
within the generator function
).
Second, notice the prime_generator.send(None)
line. When you're using send to "start" a generator
(that is, execute the code from the first line of the generator function up to
the first yield
statement), you must send None
. This makes sense, since by definition
the generator hasn't gotten to the first yield
statement yet, so if we sent a
real value there would be nothing to "receive" it. Once the generator is started, we
can send values as we do above.
Roundup
In the second half of this series, we'll discuss the various ways in which
generators
have been enhanced and the power they gained as a result. yield
has
become one of the most powerful keywords in Python. Now that we've built a solid
understanding of how yield
works, we have the knowledge necessary
to understand some of the more "mindbending" things that yield
can be used for.
Believe it or not, we've barely scratched the surface of the power of yield
.
For example, while send
does work as described above, it's almost never
used when generating simple sequences like our example. Below, I've pasted
a small demonstration of one common way send
is used. I'll not say any more
about it as figuring out how and why it works will be a good warmup for part
two.
import random def get_data(): """Return 3 random integers between 0 and 9""" return random.sample(range(10), 3) def consume(): """Displays a running average across lists of integers sent to it""" running_sum = 0 data_items_seen = 0 while True: data = yield data_items_seen += len(data) running_sum += sum(data) print('The running average is {}'.format(running_sum / float(data_items_seen))) def produce(consumer): """Produces a set of values and forwards them to the predefined consumer function""" while True: data = get_data() print('Produced {}'.format(data)) consumer.send(data) yield if __name__ == '__main__': consumer = consume() consumer.send(None) producer = produce(consumer) for _ in range(10): print('Producing...') next(producer)
Remember...
There are a few key ideas I hope you take away from this discussion:
generators
are used to generate a series of valuesyield
is like thereturn
ofgenerator functions
 The only other thing
yield
does is save the "state" of agenerator function
 A
generator
is just a special type ofiterator
 Like
iterators
, we can get the next value from agenerator
usingnext()
for
gets values by callingnext()
implicitly
I hope this post was helpful. If you had never heard
of generators
, I hope you now understand what they are,
why they're useful, and how to use them. If you were somewhat familiar with
generators
, I hope any confusion is now cleared up.
As always, if any section is unclear (or, more importantly, contains errors), by all means let me know. You can leave a comment below, email me at jeff@jeffknupp.com, or hit me up on Twitter @jeffknupp.

Quick refresher: a prime number is a positive integer greater than 1 that has no divisors other than 1 and itself. 3 is prime because there are no numbers that evenly divide it other than 1 and 3 itself. ↩