In practice, what are the main uses for the new "yield from" syntax in Python 3.3?

Question

I'm having a hard time wrapping my brain around PEP 380.

1. What are the situations where "yield from" is useful?
2. What is the classic use case?
3. Why is it compared to micro-threads?

[ update ]

Now I understand the cause of my difficulties. I've 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.

Thanks for the great answers, but special thanks to agf and his comment linking to David Beazley presentations. David rocks.

Answer 1

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.

What 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.

Reading data from a generator using yield from

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.

Sending data to a generator (coroutine) using yield from - Part 1

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 writer()?

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!

Sending data to a generator yield from - Part 2 - Exception handling

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 (writer)

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

This works.

# Result
>>  0
>>  1
>>  2
***
>>  4

But so does this!

def writer_wrapper(coro):
    yield from coro

The 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.

References:

1. PEP 380 - Syntax for delegating to a sub-generator (Ewing) [v3.3, 2009-02-13]
2. PEP 342 - Coroutines via Enhanced Generators (GvR, Eby) [v2.5, 2005-05-10]