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.]])
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!
arrays - How do I get an empty list of any size in Python? - Stack Overflow
python - How to create an empty array and append it? - Stack Overflow
[Python] How to make empty array?
How can create empty array in python
How do I declare an empty array of size n in Python?
Is a Python list the same as an array?
How do I check if an array is empty?
If by "array" you actually mean a Python list, you can use
Use with AIa = [0] * 10
or
Use with AIa = [None] * 10
You can't do exactly what you want in Python (if I read you correctly). You need to put values in for each element of the list (or as you called it, array).
But, try this:
Use with AIa = [0 for x in range(N)] # N = size of list you want
a[i] = 5 # as long as i < N, you're okay
For lists of other types, use something besides 0. None is often a good choice as well.
I suspect you are trying to replicate this working list code:
In [56]: x = []
In [57]: x.append([1,2])
In [58]: x
Out[58]: [[1, 2]]
In [59]: np.array(x)
Out[59]: array([[1, 2]])
But with arrays:
In [53]: x = np.empty((2,2),int)
In [54]: x
Out[54]:
array([[73096208, 10273248],
[ 2, -1]])
Despite the name, the np.empty array is NOT a close of the empty list. It has 4 elements, the shape that you specified.
In [55]: np.append(x, np.array([1,2]), axis=0)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-55-64dd8e7900e3> in <module>
----> 1 np.append(x, np.array([1,2]), axis=0)
<__array_function__ internals> in append(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/numpy/lib/function_base.py in append(arr, values, axis)
4691 values = ravel(values)
4692 axis = arr.ndim-1
-> 4693 return concatenate((arr, values), axis=axis)
4694
4695
<__array_function__ internals> in concatenate(*args, **kwargs)
ValueError: all the input arrays must have same number of dimensions, but the array at index 0 has 2 dimension(s) and the array at index 1 has 1 dimension(s)
Note that np.append has passed the task on to np.concatenate. With the axis parameter, that's all this append does. It is NOT a list append clone.
np.concatenate demands consistency in the dimensions of its inputs. One is (2,2), the other (2,). Mismatched dimensions.
np.append is a dangerous function, and not that useful even when used correctly. np.concatenate (and the various stack) functions are useful. But you need to pay attention to shapes. And don't use them iteratively. List append is more efficient for that.
When you got this error, did you look up the np.append, np.empty (and np.concatenate) functions? Read and understand the docs? In the long run SO questions aren't a substitute for reading the documentation.
You can create empty list by []. In order to add new item use append. For add other list use extend.
x = [1, 2, 3]
x.append(4)
x.extend([5, 6])
print(x)
# [1, 2, 3, 4, 5, 6]