Arrays have a fixed size. You can't append to them. If you know the upper bounds of the array, you can preallocate the array with
np.empty((10, 10, 10))
For a 10x10x10 matrix. You can then keep 3 indices x,y,z to track the actual size you have. Eg, add a new element with:
matrix[x,y,z] = newElement
x += 1
Then when you're done, you can extract the submatrix with
finalMatrix = matrix[:x,:y,:z]
Arrays have a fixed size. You can't append to them. If you know the upper bounds of the array, you can preallocate the array with
np.empty((10, 10, 10))
For a 10x10x10 matrix. You can then keep 3 indices x,y,z to track the actual size you have. Eg, add a new element with:
matrix[x,y,z] = newElement
x += 1
Then when you're done, you can extract the submatrix with
finalMatrix = matrix[:x,:y,:z]
You can use np.empty((x,y,z))
You can find a good explanation in this answer: How to create 3 dimensions matrix in numpy , like matlab a(:,:,:)
I'd suggest using np.full_like to choose the fill-value directly...
x = np.full_like((3, 1), None, dtype=object)
... of course the dtype you chose kind of defines what you mean by "empty"
I am guessing that by empty, you mean an array filled with zeros.
Use np.zeros() to create an array with zeros. np.empty() just allocates the array, so the numbers in there are garbage. It is provided as a way to even reduce the cost of setting the values to zero. But it is generally safer to use np.zeros().
That is the wrong mental model for using NumPy efficiently. NumPy arrays are stored in contiguous blocks of memory. To append rows or columns to an existing array, the entire array needs to be copied to a new block of memory, creating gaps for the new elements to be stored. This is very inefficient if done repeatedly.
Instead of appending rows, allocate a suitably sized array, and then assign to it row-by-row:
>>> import numpy as np
>>> a = np.zeros(shape=(3, 2))
>>> a
array([[ 0., 0.],
[ 0., 0.],
[ 0., 0.]])
>>> a[0] = [1, 2]
>>> a[1] = [3, 4]
>>> a[2] = [5, 6]
>>> a
array([[ 1., 2.],
[ 3., 4.],
[ 5., 6.]])
A NumPy array is a very different data structure from a list and is designed to be used in different ways. Your use of hstack is potentially very inefficient... every time you call it, all the data in the existing array is copied into a new one. (The append function will have the same issue.) If you want to build up your matrix one column at a time, you might be best off to keep it in a list until it is finished, and only then convert it into an array.
e.g.
mylist = []
for item in data:
mylist.append(item)
mat = numpy.array(mylist)
item can be a list, an array or any iterable, as long
as each item has the same number of elements.
In this particular case (data is some iterable holding the matrix columns) you can simply use
mat = numpy.array(data)
(Also note that using list as a variable name is probably not good practice since it masks the built-in type by that name, which can lead to bugs.)
EDIT:
If for some reason you really do want to create an empty array, you can just use numpy.array([]), but this is rarely useful!