I’m having a hard time wrapping my brain around PEP 380.
yield fromis useful?
So far I have used generators, but never really used coroutines (introduced by PEP-342). Despite some similarities, generators and coroutines are basically two different concepts. Understanding coroutines (not only generators) is the key to understanding the new syntax.
IMHO coroutines are the most obscure Python feature, most books make it look useless and uninteresting.
What are the situations where “yield from” is useful?
Every situation where you have a loop like this:
for x in subgenerator: yield x
As the PEP describes, this is a rather naive attempt at using the subgenerator, it’s missing several aspects, especially the proper handling of the
.close() mechanisms introduced by PEP 342. To do this properly, rather complicated code is necessary.
What is the classic use case?
Consider that you want to extract information from a recursive data structure. Let’s say we want to get all leaf nodes in a tree:
def traverse_tree(node): if not node.children: yield node for child in node.children: yield from traverse_tree(child)
Even more important is the fact that until the
yield from, there was no simple method of refactoring the generator code. Suppose you have a (senseless) generator like this:
def get_list_values(lst): for item in lst: yield int(item) for item in lst: yield str(item) for item in lst: yield float(item)
Now you decide to factor out these loops into separate generators. Without
yield from, this is ugly, up to the point where you will think twice whether you actually want to do it. With
yield from, it’s actually nice to look at:
def get_list_values(lst): for sub in [get_list_values_as_int, get_list_values_as_str, get_list_values_as_float]: yield from sub(lst)
Why is it compared to micro-threads?
I think what this section in the PEP is talking about is that every generator does have its own isolated execution context. Together with the fact that execution is switched between the generator-iterator and the caller using
__next__(), respectively, this is similar to threads, where the operating system switches the executing thread from time to time, along with the execution context (stack, registers, …).
The effect of this is also comparable: Both the generator-iterator and the caller progress in their execution state at the same time, their executions are interleaved. For example, if the generator does some kind of computation and the caller prints out the results, you’ll see the results as soon as they’re available. This is a form of concurrency.
That analogy isn’t anything specific to
yield from, though – it’s rather a general property of generators in Python.
Wherever you invoke a generator from within a generator you need a “pump” to re-
yield the values:
for v in inner_generator: yield v. As the PEP points out there are subtle complexities to this which most people ignore. Non-local flow-control like
throw() is one example given in the PEP. The new syntax
yield from inner_generator is used wherever you would have written the explicit
for loop before. It’s not merely syntactic sugar, though: It handles all of the corner cases that are ignored by the
for loop. Being “sugary” encourages people to use it and thus get the right behaviors.
This message in the discussion thread talks about these complexities:
With the additional generator features introduced by PEP 342, that is no
longer the case: as described in Greg’s PEP, simple iteration doesn’t
support send() and throw() correctly. The gymnastics needed to support
send() and throw() actually aren’t that complex when you break them
down, but they aren’t trivial either.
I can’t speak to a comparison with micro-threads, other than to observe that generators are a type of paralellism. You can consider the suspended generator to be a thread which sends values via
yield to a consumer thread. The actual implementation may be nothing like this (and the actual implementation is obviously of great interest to the Python developers) but this does not concern the users.
yield from syntax does not add any additional capability to the language in terms of threading, it just makes it easier to use existing features correctly. Or more precisely it makes it easier for a novice consumer of a complex inner generator written by an expert to pass through that generator without breaking any of its complex features.
yield from basically chains iterators in a efficient way:
# chain from itertools: def chain(*iters): for it in iters: for item in it: yield item # with the new keyword def chain(*iters): for it in iters: yield from it
As you can see it removes one pure Python loop. That’s pretty much all it does, but chaining iterators is a pretty common pattern in Python.
Threads are basically a feature that allow you to jump out of functions at completely random points and jump back into the state of another function. The thread supervisor does this very often, so the program appears to run all these functions at the same time. The problem is that the points are random, so you need to use locking to prevent the supervisor from stopping the function at a problematic point.
Generators are pretty similar to threads in this sense: They allow you to specify specific points (whenever they
yield) where you can jump in and out. When used this way, generators are called coroutines.
Let’s get one thing out of the way first. The explanation that
yield from g is equivalent to
for v in g: yield v does not even begin to do justice to what
yield from is all about. Because, let’s face it, if all
yield from does is expand the
for loop, then it does not warrant adding
yield from to the language and preclude a whole bunch of new features from being implemented in Python 2.x.
yield from does is it establishes a transparent bidirectional connection between the caller and the sub-generator:
The connection is “transparent” in the sense that it will propagate everything correctly too, not just the elements being generated (e.g. exceptions are propagated).
The connection is “bidirectional” in the sense that data can be both sent from and to a generator.
(If we were talking about TCP,
yield from g might mean “now temporarily disconnect my client’s socket and reconnect it to this other server socket”.)
BTW, if you are not sure what sending data to a generator even means, you need to drop everything and read about coroutines first—they’re very useful (contrast them with subroutines), but unfortunately lesser-known in Python. Dave Beazley’s Curious Course on Coroutines is an excellent start. Read slides 24-33 for a quick primer.
def reader(): """A generator that fakes a read from a file, socket, etc.""" for i in range(4): yield '<< %s' % i def reader_wrapper(g): # Manually iterate over data produced by reader for v in g: yield v wrap = reader_wrapper(reader()) for i in wrap: print(i) # Result << 0 << 1 << 2 << 3
Instead of manually iterating over
reader(), we can just
yield from it.
def reader_wrapper(g): yield from g
That works, and we eliminated one line of code. And probably the intent is a little bit clearer (or not). But nothing life changing.
Now let’s do something more interesting. Let’s create a coroutine called
writer that accepts data sent to it and writes to a socket, fd, etc.
def writer(): """A coroutine that writes data *sent* to it to fd, socket, etc.""" while True: w = (yield) print('>> ', w)
Now the question is, how should the wrapper function handle sending data to the writer, so that any data that is sent to the wrapper is transparently sent to the
def writer_wrapper(coro): # TBD pass w = writer() wrap = writer_wrapper(w) wrap.send(None) # "prime" the coroutine for i in range(4): wrap.send(i) # Expected result >> 0 >> 1 >> 2 >> 3
The wrapper needs to accept the data that is sent to it (obviously) and should also handle the
StopIteration when the for loop is exhausted. Evidently just doing
for x in coro: yield x won’t do. Here is a version that works.
def writer_wrapper(coro): coro.send(None) # prime the coro while True: try: x = (yield) # Capture the value that's sent coro.send(x) # and pass it to the writer except StopIteration: pass
Or, we could do this.
def writer_wrapper(coro): yield from coro
That saves 6 lines of code, make it much much more readable and it just works. Magic!
Let’s make it more complicated. What if our writer needs to handle exceptions? Let’s say the
writer handles a
SpamException and it prints
*** if it encounters one.
class SpamException(Exception): pass def writer(): while True: try: w = (yield) except SpamException: print('***') else: print('>> ', w)
What if we don’t change
writer_wrapper? Does it work? Let’s try
# writer_wrapper same as above w = writer() wrap = writer_wrapper(w) wrap.send(None) # "prime" the coroutine for i in [0, 1, 2, 'spam', 4]: if i == 'spam': wrap.throw(SpamException) else: wrap.send(i) # Expected Result >> 0 >> 1 >> 2 *** >> 4 # Actual Result >> 0 >> 1 >> 2 Traceback (most recent call last): ... redacted ... File ... in writer_wrapper x = (yield) __main__.SpamException
Um, it’s not working because
x = (yield) just raises the exception and everything comes to a crashing halt. Let’s make it work, but manually handling exceptions and sending them or throwing them into the sub-generator (
def writer_wrapper(coro): """Works. Manually catches exceptions and throws them""" coro.send(None) # prime the coro while True: try: try: x = (yield) except Exception as e: # This catches the SpamException coro.throw(e) else: coro.send(x) except StopIteration: pass
# Result >> 0 >> 1 >> 2 *** >> 4
But so does this!
def writer_wrapper(coro): yield from coro
yield from transparently handles sending the values or throwing values into the sub-generator.
This still does not cover all the corner cases though. What happens if the outer generator is closed? What about the case when the sub-generator returns a value (yes, in Python 3.3+, generators can return values), how should the return value be propagated? That
yield from transparently handles all the corner cases is really impressive.
yield from just magically works and handles all those cases.
I personally feel
yield from is a poor keyword choice because it does not make the two-way nature apparent. There were other keywords proposed (like
delegate but were rejected because adding a new keyword to the language is much more difficult than combining existing ones.
In summary, it’s best to think of
yield from as a
transparent two way channel between the caller and the sub-generator.
A short example will help you understand one of
yield from‘s use case: get value from another generator
def flatten(sequence): """flatten a multi level list or something >>> list(flatten([1, , 3])) [1, 2, 3] >>> list(flatten([1, , [3, ]])) [1, 2, 3, 4] """ for element in sequence: if hasattr(element, '__iter__'): yield from flatten(element) else: yield element print(list(flatten([1, , [3, ]])))
yield from is used by the generator-based coroutine.
For Asyncio, if there’s no need to support an older Python version (i.e. >3.5),
await is the recommended syntax to define a coroutine. Thus
yield from is no longer needed in a coroutine.
But in general outside of asyncio,
yield from <sub-generator> has still some other usage in iterating the sub-generator as mentioned in the earlier answer.
This code defines a function
fixed_sum_digits returning a generator enumerating all six digits numbers such that the sum of digits is 20.
def iter_fun(sum, deepness, myString, Total): if deepness == 0: if sum == Total: yield myString else: for i in range(min(10, Total - sum + 1)): yield from iter_fun(sum + i,deepness - 1,myString + str(i),Total) def fixed_sum_digits(digits, Tot): return iter_fun(0,digits,"",Tot)
Try to write it without
yield from. If you find an effective way to do it let me know.
I think that for cases like this one: visiting trees,
yield from makes the code simpler and cleaner.
yield from provides tail recursion for iterator functions.
yield will yields single value into collection.
yield from will yields collection into collection and make it flatten.
Check this example:
def yieldOnly(): yield "A" yield "B" yield "C" def yieldFrom(): for i in [1, 2, 3]: yield from yieldOnly() test = yieldFrom() for i in test: print(i)
In console you will see:
A B C A B C A B C