vectorize numpy append loop

Question

I am trying to vectorize the following loop in I am trying to append multiple arrays to an empty array.

# ff is a matrix of shape [100,1,96]
temp = np.array([]).reshape(0,96)
for kk in range(1,10,1):
   temp = np.append(tr,ff[kk],axis=0)
temp = temp.reshape(1,10,96)

Is it possible to vectorize the above loop using numpy? Any help is welcome!

I am trying to loop over elements of a matrix of dimension [100,1,96] which are of dimension [1,96] and appending to them to create [10,96] array — dissw.geek9
– dissw.geek9, Commented Jan 26, 2017 at 23:26
So the first 10 entries of the first dimension and all entries of all other dimensions? Shouldn't ff[:10,:,:] just give you the data you are looking for? — Nils Werner
– Nils Werner, Commented Jan 26, 2017 at 23:28
Regarding array append v. list append: stackoverflow.com/a/41793600/901925 — hpaulj
– hpaulj, Commented Jan 27, 2017 at 1:00

Nils Werner · Accepted Answer · 2017-01-26 23:34:45Z

2

You can use slicing to extract the data you need:

ff[:10,:,:]

That will yield an array of shape (10, 1, 96). To get rid of the empty dimension, you can run it through numpy.squeeze():

numpy.squeeze(ff[:10,:,:])

and get an array of shape (10, 96)

answered Jan 26, 2017 at 23:34

Nils Werner

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