Python: generator expression vs. yield

Using yield is nice if the expression is more complicated than just nested loops. Among other things you can return a special first or special last value. Consider:

def Generator(x):
  for i in xrange(x):
    yield(i)
  yield(None)

In this example, not really. But yield can be used for more complex constructs – for example it can accept values from the caller as well and modify the flow as a result. Read PEP 342 for more details (it’s an interesting technique worth knowing).

Anyway, the best advice is use whatever is clearer for your needs.

P.S. Here’s a simple coroutine example from Dave Beazley:

def grep(pattern):
    print "Looking for %s" % pattern
    while True:
        line = (yield)
        if pattern in line:
            print line,

# Example use
if __name__ == '__main__':
    g = grep("python")
    g.next()
    g.send("Yeah, but no, but yeah, but no")
    g.send("A series of tubes")
    g.send("python generators rock!")

When thinking about iterators, the itertools module:

… standardizes a core set of fast, memory efficient tools that are useful by themselves or in combination. Together, they form an “iterator algebra” making it possible to construct specialized tools succinctly and efficiently in pure Python.

For performance, consider itertools.product(*iterables[, repeat])

Cartesian product of input iterables.

Equivalent to nested for-loops in a generator expression. For example, product(A, B) returns the same as ((x,y) for x in A for y in B).

>>> import itertools
>>> def gen(x,y):
...     return itertools.product(xrange(x),xrange(y))
... 
>>> [t for t in gen(3,2)]
[(0, 0), (0, 1), (1, 0), (1, 1), (2, 0), (2, 1)]
>>> 

There is no difference for the kind of simple loops that you can fit into a generator expression. However yield can be used to create generators that do much more complex processing. Here is a simple example for generating the fibonacci sequence:

>>> def fibgen():
...    a = b = 1
...    while True:
...        yield a
...        a, b = b, a+b

>>> list(itertools.takewhile((lambda x: x<100), fibgen()))
[1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]

In usage, note a distinction between a generator object vs a generator function.

A generator object is use-once-only, in contrast to a generator function, which can be reused each time you call it again, because it returns a fresh generator object.

Generator expressions are in practice usually used “raw”, without wrapping them in a function, and they return a generator object.

E.g.:

def range_10_gen_func():
    x = 0
    while x < 10:
        yield x
        x = x + 1

print(list(range_10_gen_func()))
print(list(range_10_gen_func()))
print(list(range_10_gen_func()))

which outputs:

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

Compare with a slightly different usage:

range_10_gen = range_10_gen_func()
print(list(range_10_gen))
print(list(range_10_gen))
print(list(range_10_gen))

which outputs:

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
[]
[]

And compare with a generator expression:

range_10_gen_expr = (x for x in range(10))
print(list(range_10_gen_expr))
print(list(range_10_gen_expr))
print(list(range_10_gen_expr))

which also outputs:

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
[]
[]

There is a difference that could be important in some contexts that hasn’t been pointed out yet. Using yield prevents you from using return for something else than implicitly raising StopIteration (and coroutines related stuff).

This means this code is ill-formed (and feeding it to an interpreter will give you an AttributeError):

class Tea:

    """With a cloud of milk, please"""

    def __init__(self, temperature):
        self.temperature = temperature

def mary_poppins_purse(tea_time=False):
    """I would like to make one thing clear: I never explain anything."""
    if tea_time:
        return Tea(355)
    else:
        for item in ['lamp', 'mirror', 'coat rack', 'tape measure', 'ficus']:
            yield item

print(mary_poppins_purse(True).temperature)

On the other hand, this code works like a charm:

class Tea:

    """With a cloud of milk, please"""

    def __init__(self, temperature):
        self.temperature = temperature

def mary_poppins_purse(tea_time=False):
    """I would like to make one thing clear: I never explain anything."""
    if tea_time:
        return Tea(355)
    else:
        return (item for item in ['lamp', 'mirror', 'coat rack',
                                  'tape measure', 'ficus'])

print(mary_poppins_purse(True).temperature)

Yes there is a difference.

For the generator expression (x for var in expr), iter(expr) is called when the expression is created.

When using def and yield to create a generator, as in:

def my_generator():
    for var in expr:
        yield x

g = my_generator()

iter(expr) is not yet called. It will be called only when iterating on g (and might not be called at all).

Taking this iterator as an example:

from __future__ import print_function


class CountDown(object):
    def __init__(self, n):
        self.n = n

    def __iter__(self):
        print("ITER")
        return self

    def __next__(self):
        if self.n == 0:
            raise StopIteration()
        self.n -= 1
        return self.n

    next = __next__  # for python2

This code:

g1 = (i ** 2 for i in CountDown(3))  # immediately prints "ITER"
print("Go!")
for x in g1:
    print(x)

while:

def my_generator():
    for i in CountDown(3):
        yield i ** 2


g2 = my_generator()
print("Go!")
for x in g2:  # "ITER" is only printed here
    print(x)

Since most iterators do not do a lot of stuff in __iter__, it is easy to miss this behavior. A real world example would be Django’s QuerySet, which fetch data in __iter__ and data = (f(x) for x in qs) might take a lot of time, while def g(): for x in qs: yield f(x) followed by data=g() would return immediately.

For more info and the formal definition refer to PEP 289 — Generator Expressions.