# [Numpy-discussion] Array concatenation performance

Skipper Seabold jsseabold@gmail....
Thu Jul 15 10:15:13 CDT 2010

```On Thu, Jul 15, 2010 at 11:05 AM, John Porter <jporter@cambridgesys.com> wrote:
> You're right - I screwed up the timing for the one that works...
> It does seem to be faster.
>
> I've always just built arrays using nx.array([]) in the past though
> and was surprised
> that it performs so badly.
>
>
> On Thu, Jul 15, 2010 at 2:41 PM, Skipper Seabold <jsseabold@gmail.com> wrote:
>> On Thu, Jul 15, 2010 at 5:54 AM, John Porter <jporter@cambridgesys.com> wrote:
>>> Has anyone got any advice about array creation. I've been using numpy
>>> for a long time and have just noticed something unexpected about array
>>> concatenation.
>>>
>>> It seems that using numpy.array([a,b,c]) is around 20 times slower
>>> than creating an empty array and adding the individual elements.
>>>
>>> Other things that don't work well either:
>>>    numpy.concatenate([a,b,c]).reshape(3,-1)
>>>    numpy.concatenate([[a],[b],[c]))
>>>
>>> Is there a better way to efficiently create the array ?
>>>
>>
>> What was your timing for concatenate?  It wins for me given the shape of a.
>>
>> In [1]: import numpy as np
>>
>> In [2]: a = np.arange(1000*1000)
>>
>> In [3]: timeit b0 = np.array([a,a,a])
>> 1 loops, best of 3: 216 ms per loop
>>
>> In [4]: timeit b1 = np.empty(((3,)+a.shape)); b1[0]=a;b1[1]=a;b1[2]=a
>> 100 loops, best of 3: 19.3 ms per loop
>>
>> In [5]: timeit b2 = np.c_[a,a,a].T
>> 10 loops, best of 3: 30.5 ms per loop
>>
>> In [6]: timeit b3 = np.concatenate([a,a,a]).reshape(3,-1)
>> 100 loops, best of 3: 9.33 ms per loop
>>

One more.

In [26]: timeit b4 = np.vstack((a,a,a))
100 loops, best of 3: 9.46 ms per loop

Skipper
```