python - convert itertools array into numpy array -

i'm creating array:

a=itertools.combinations(range(6),2) 

and have manipulate array numpy, like:

a.reshape(.. 

if dimensions is high, command list(a) slow.

how can "convert" itertools array numpy array?

update 1: i've tried solution of hpaulj, in specific situation little bit slower, idea?

start=time.clock()  a=it.combinations(range(495),3) a=np.array(list(a)) print  stop=time.clock() print stop-start start=time.clock()  a=np.fromiter(it.chain(*it.combinations(range(495),3)),dtype=int).reshape (-1,3) print  stop=time.clock() print stop-start 

results:

[[  0   1   2]  [  0   1   3]  [  0   1   4]  ...,   [491 492 494]  [491 493 494]  [492 493 494]] 10.323822 [[  0   1   2]  [  0   1   3]  [  0   1   4]  ...,   [491 492 494]  [491 493 494]  [492 493 494]] 12.289898 

i'm reopening because dislike linked answer. accepted answer suggests using

np.array(list(a))  # producing (15,2) array 

but op aparently has tried list(a), , found slow.

another answer suggests using np.fromiter. buried in comments note fromiter requires 1d array.

in [102]: a=itertools.combinations(range(6),2) in [103]: np.fromiter(a,dtype=int) --------------------------------------------------------------------------- valueerror                                traceback (most recent call last) <ipython-input-103-29db40e69c08> in <module>() ----> 1 np.fromiter(a,dtype=int)  valueerror: setting array element sequence. 

so using fromiter itertools requires somehow flattening iterator.

a quick set of timings suggests list isn't slow step. it's converting list array slow:

in [104]: timeit itertools.combinations(range(6),2) 1000000 loops, best of 3: 1.1 µs per loop in [105]: timeit list(itertools.combinations(range(6),2)) 100000 loops, best of 3: 3.1 µs per loop in [106]: timeit np.array(list(itertools.combinations(range(6),2))) 100000 loops, best of 3: 14.7 µs per loop 

i think fastest way use fromiter flatten combinations idiomatic use of itertools.chain:

in [112]: timeit np.fromiter(itertools.chain(*itertools.combinations(range(6),2)),dtype=int)    .reshape(-1,2) 100000 loops, best of 3: 12.1 µs per loop 

not of time savings, @ least on small size. (fromiter takes count, shaves off µs. larger case, range(60), fromiter takes half time of array.

---

a quick search on [numpy] itertools turns number of suggestions of pure numpy ways of generating combinations. itertools fast, generating pure python structures, converting arrays slow step.

---

a picky point question.

a generator, not array. list(a) produce nested list, can described loosely array. isn't np.array, , not have reshape method.