Issue converting list to NumPy array

Question

I have a list that consits of 2000 rows and 88200 columns:

testlist = list(split_audio_to_parts(audio, self.sample_rate, self.audio_index))

deb

Answer 1

You have a list of arrays. If each array in your list does not have the same length, your conversion will not be successful.

Here is a minimal example.

A = [np.array([1, 2]), np.array([4, 5, 6])]

A_2 = np.array(A)
# array([array([1, 2]), array([4, 5, 6])], dtype=object)

A_2.shape
# (2,)

If the lengths of your arrays are aligned, you will find no problem:

B = [np.array([1, 2, 3]), np.array([4, 5, 6])]

B_2 = np.array(B)
# array([[1, 2, 3],
#        [4, 5, 6]])

B_2.shape
# (2, 3)

---

To check the sizes of your arrays, you can use set:

array_sizes = set(map(len, A))

Answer 2

The problem is testlist is a list of different size arrays. For example checkout this code:

>>>import numpy as np
>>>import random 
>>>random.seed(3240324324)
>>> y=[np.array(list(range(random.randint(1,3)))) for _ in range(3)]
>>> y
[array([0, 1, 2]), array([0, 1, 2]), array([0])]
>>> np.array(y)
array([array([0, 1, 2]), array([0, 1, 2]), array([0])], dtype=object)
>>> np.array(y).shape
(3,)

and the array would be of object type instead of float. the only way for this to work is having same sized arrays.

If you really need to stuff these rows somehow into an array you can pad with zeros, for example:

>>> size = y[max(enumerate(y),key=lambda k:k[1].shape)[0]].shape[0]
>>> z=[np.append(x,np.zeros(size-x.shape[0])) for x in y]
>>> z
[array([ 0.,  1.,  2.]), array([0, 1, 2]), array([0, 0, 0])]
>>>np.array(z).shape
(3, 3)

but you would have to decide how you do this padding.