Back to writing
- Table of contents
- What are they
- Writing one
- Using one
- Minor notes
- More examples
- Yielding individual items from batches
- Further reading
What are they (§)
Generators are a type of iterable that create their contents on the fly. Unlike a list, whose entire contents are available before beginning any loops or manipulations, generators don't know how many items they will produce or when they will stop. They can even go on forever!
Writing one (§)
Writing a generator looks like writing a function, but instead of
return, you use
yield. The object which is yielded is what you'll get when you do a loop over the generator. This one lets you count to a billion:
def billion(): x = 0 while x < 1000000000: yield x x += 1
I purposely used a
while loop instead of
for x in range() to show the extra work.
Note that, unlike a
return statement, you can include more code after a
yield statement. Also notice that generators keep track of their internal state — the
billion generator has an
x that it increments every time you loop over it. You can imagine the code pausing after the
yield line, and resuming when you come back for the next cycle. Try this with some extra print statements to help visualize.
Generators can also take arguments. Here's a generator that counts to a custom amount:
def count_to(y): x = 0 while x < y: yield x x += 1
Using one (§)
Although generators look like functions when you're writing them, they feel more like classes when using them. Remember that generators don't calculate their contents until they are actually used in a loop, so simply doing:
numbers = count_to(100)
does not create a list of 100 numbers. It creates a new instance of the generator that is ready to be iterated over, like this:
numbers = count_to(100) for number in numbers: print(number)
for number in count_to(100): print(number)
This should remind you of:
for number in range(100): print(number)
range class behaves a lot like a generator (but not exactly).
Generators are excellent for cases where using a list is infeasible or unnecessary. In order to loop over a list, you have to generate the entire thing first. If you're only going to use each item once, storing the entire list can be a big memory waste when a generator could take its place. With a generator, the items are created, used, and thrown away, so memory can be recycled.
Since generators can go on forever, they're great for abstracting out ugly
while loops, so you can get down to business faster.
To get a single item from a generator without looping, use
Generators pause and resume a lot, but they still flow like normal functions. As long as there is no endless
while loop inside, they'll come to an end at some point. When a generator is all finished, it will raise a
StopIteration exception every time you try to do
next() on it. Luckily,
for loops will detect this automatically and stop themselves.
>>> g = count_to(10) >>> l = list(g) >>> next(g) Traceback (most recent call last): File "<stdin>", line 1, in <module> StopIteration
Earlier, I said that generators use
yield instead of
return, but in fact you can include a return statement too. If it is encountered, it will raise a
StopIteration, and the generator will not resume even if there is more code.
>>> def generator(): ... yield 1 ... return 2 ... yield 3 ... >>> >>> g = generator() >>> next(g) 1 >>> next(g) Traceback (most recent call last): File "<stdin>", line 1, in <module> StopIteration: 2 >>> next(g) Traceback (most recent call last): File "<stdin>", line 1, in <module> StopIteration >>>
Notice that the 2 became part of the StopIteration that was raised at that line.
Minor notes (§)
- You cannot access the items of a generator by index, because only one item exists at a time. Once you do a loop over a generator, those items are gone forever unless you kept them somewhere else.
More examples (§)
Yielding individual items from batches (§)
Suppose you're getting data from an imaginary website which sends you items in groups of 100. You want to let the user loop over the items without having to worry about the groups.
def item_generator(url): page = 0 while True: # get_items is a pretend method that collects the 100 items from that page batch = get_items(url, page=page) if len(batch) == 0: # for this imaginary website, the batch will be empty when that page # doesn't have any items on it. break for item in batch: # by yielding individual items, the user can just do a for loop # over this generator and get them all one by one. yield item page += 1 # When the while loop breaks, we reach the end of the function body, # concluding the generator.
Now the user can just do this:
comments = item_generator('http://example.com/user/voussoir/comments') for comment in comments: print(comment.body)
Further reading (§)
Stack Overflow - What are the main uses for
yield from? — If you like recursive functions, how about recursive generators?
Stack Overflow - Python generator
send function purpose? — This quickly dives out of "quick tips" territory.
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