I'm trying to flatten a nested generator of generators but I'm getting an unexpected result:
>>> g = ((3*i + j for j in range(3)) for i in range(3))
>>> list(itertools.chain(*g))
[6, 7, 8, 6, 7, 8, 6, 7, 8]
I expected the result to look like this:
[0, 1, 2, 3, 4, 5, 6, 7, 8]
I think I'm getting the unexpected result because the inner generators are not being evaluated until the outer generator has already been iterated over, setting i
to 2. I can hack together a solution by forcing evaluation of the inner generators by using a list comprehension instead of a generator expression:
>>> g = ([3*i + j for j in range(3)] for i in range(3))
>>> list(itertools.chain(*g))
[0, 1, 2, 3, 4, 5, 6, 7, 8]
Ideally, I would like a solution that's completely lazy and doesn't force evaluation of the inner nested elements until they're used.
Is there a way to flatten nested generator expressions of arbitrary depth (maybe using something other than itertools.chain
)?
Edit:
No, my question is not a duplicate of Variable Scope In Generators In Classes. I honestly can't tell how these two questions are related at all. Maybe the moderator could explain why he thinks this is a duplicate.
Also, both answers to my question are correct in that they can be used to write a function that flattens nested generators correctly.
def flattened1(iterable):
iter1, iter2 = itertools.tee(iterable)
if isinstance(next(iter1), collections.Iterable):
return flattened1(x for y in iter2 for x in y)
else:
return iter2
def flattened2(iterable):
iter1, iter2 = itertools.tee(iterable)
if isinstance(next(iter1), collections.Iterable):
return flattened2(itertools.chain.from_iterable(iter2))
else:
return iter2
As far as I can tell with timeit
, they both perform identically.
>>> timeit(test1, setup1, number=1000000)
18.173431718023494
>>> timeit(test2, setup2, number=1000000)
17.854709611972794
I'm not sure which one is better from a style standpoint either, since x for y in iter2 for x in y
is a bit of a brain twister, but arguably more elegant than itertools.chain.from_iterable(iter2)
. Input is appreciated.
Regrettably, I was only able to mark one of the two equally good answers correct.
Instead of using chain(*g)
, you can use chain.from_iterable
:
>>> g = ((3*i + j for j in range(3)) for i in range(3))
>>> list(itertools.chain(*g))
[6, 7, 8, 6, 7, 8, 6, 7, 8]
>>> g = ((3*i + j for j in range(3)) for i in range(3))
>>> list(itertools.chain.from_iterable(g))
[0, 1, 2, 3, 4, 5, 6, 7, 8]
Guess you already have your answer, but here's another perspective.
The problem is that when each inner generator is created, the value-generating expression is closed over the outer variable i
so even when the first inner generator starts generating values, it's using the "current" value of i
. This will have value i=2
if the outer generator has been fully consumed (and that's exactly the case right after the argument in the chain(*g)
call is evaluated, before chain
is actually called).
The following devious trick will work around the problem:
g = ((3*i1 + j for i1 in [i] for j in range(3)) for i in range(3))
Note that these inner generators aren't closed over i
because the for
clauses are evaluated at generator creation time so the singleton list [i]
is evaluated and its value "frozen" in the face of further changes to the value of i
.
This approach has the advantage over the from_iterable
answer that it's a little more general if you want to use it outside a chain.from_iterable
call -- it will always produce the "correct" inner generators, whether the outer generator is partially or fully consumed before the inner generators are used. For example, in the following code:
g = ((3*i1 + j for i1 in [i] for j in range(3)) for i in range(3))
g1 = next(g)
g2 = next(g)
g3 = next(g)
you can insert the lines:
list(g1)
list(g2)
list(g3)
in any order at any point after the respective inner generator has been defined, and you'll get the correct results.
How about this:
[x for y in g for x in y]
Which yields:
[0, 1, 2, 3, 4, 5, 6, 7, 8]
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