yield and Generators
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转载: https://jeffknupp.com/blog/2013/04/07/improve-your-python-yield-and-generators-explained/
Prior to beginning tutoring sessions, I ask new students to fill out a brief self-assessment 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 orvery limited understanding of. Of these, "generators
and theyield
keyword" is one of the biggest culprits. I'm guessing this is the case formost 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 explainwhat the yield
keyword does, why it's useful, and how to use it.
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 ofyield
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 theyield
keyword and generators
work.
Coroutines and Subroutines
When we call a normal Python function, execution starts at function's first line and continues until areturn
statement, exception
, or the end of the function (which is seen as an implicitreturn 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 assubroutines) 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 thatthe 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 ofgenerators
, 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 ascoroutines
. I'll use the latter term later in the post. The important thing to remember is, in Python, everything described here as acoroutine
is still a generator
. Python formally defines the termgenerator
; 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
ofint
s and returns some Iterable containing the elements which are prime1 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 ourget_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 callget_primes
with a start
value and get all the primes larger thanstart
(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 toget_primes
. Clearly, we can't return a list of all the prime numbers fromstart
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 fromn
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: thegenerator
. 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 theyield
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 termgenerator iterator
, though, since they're almost always referred to as "generators
". Just remember that agenerator
is a special type of iterator
. To be considered aniterator
, generators
must define a few methods, one of which is__next__()
. To get the next value from a generator
, we use the same built-in function as foriterators
: next()
.
This point bears repeating: to get the next value from a generator
, we use the same built-in function as foriterators
: next()
.
(next()
takes care of calling the generator's __next__()
method). Since agenerator
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 asreturn
(plus a little magic) for generator functions
.**
Again, this bears repeating: yield
is just return
(plus a little magic) forgenerator 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)123>>> 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
callsyield
, 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 untilnext()
is called again. Once it is, the generator function
simply resumes where it left off. Ifnext()
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 themagical_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, aStopIteration
exception is raised. This signals to whoever was callingnext()
that the generator
is exhausted (this is normal iterator
behavior). It is also the reason the while True:
loop is present inget_primes
. If it weren't, the first time next()
was called we would check if the number is prime and possibly yield it. Ifnext()
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 agenerator
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 "<ipython-input-13-7e48a609051a>", 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 valid1
Thus, the while
loop is there to make sure we never reach the end ofget_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 (andgenerators
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 thefor
loop requests the first value from get_primes
, we enterget_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 line5
, 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 calledyield
(i.e. 3
). Remember, yield
both passes a value to whoever callednext()
, 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 weyield
the value of number
to the for
loop insolve_number_10
. This cycle continues until the for
loop stops (at the first prime greater than2,000,000
).
Moar Power
In PEP 342, support was added for passing valuesinto 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 thannumber
, 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 asget_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 formother = yield foo
means, "yield foo
and, when a value is sent to me, setother
to that value." You can "send" values to a generator using the generator'ssend
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 generatoryield
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 becausesend
both sends a value to the generator and returns the value yielded by the generator (mirroring howyield
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 firstyield
statement), you must send None
. This makes sense, since by definition the generator hasn't gotten to the firstyield
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.
Round-up
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 howyield
works, we have the knowledge necessary to understand some of the more "mind-bending" things thatyield
can be used for.
Believe it or not, we've barely scratched the surface of the power of yield
. For example, whilesend
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 waysend
is used. I'll not say any more about it as figuring out how and why it works will be a good warm-up for part two.
import randomdef 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 pre-defined consumer function""" while True: data = get_data() print('Produced {}'.format(data)) consumer.send(data) yieldif __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 withgenerators
, 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 atjeff@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. ↩
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