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!

For creating an empty NumPy array without defining its shape you can do the following:

arr = np.array([])

The first one is preferred because you know you will be using this as a NumPy array. NumPy converts this to np.ndarray type afterward, without extra [] 'dimension'.

for adding new element to the array us can do:

arr = np.append(arr, 'new element')

Note that in the background for python there's no such thing as an array without defining its shape. as @hpaulj mentioned this also makes a one-rank array.

You can use the append function. For rows:

>>> from numpy import *
>>> a = array([10,20,30])
>>> append(a, [[1,2,3]], axis=0)
array([[10, 20, 30],      
       [1, 2, 3]])

For columns:

>>> append(a, [[15],[15]], axis=1)
array([[10, 20, 30, 15],      
       [1, 2, 3, 15]])

EDIT
Of course, as mentioned in other answers, unless you're doing some processing (ex. inversion) on the matrix/array EVERY time you append something to it, I would just create a list, append to it then convert it to an array.

Here is some workaround to make numpys look more like Lists

np_arr = np.array([])
np_arr = np.append(np_arr , 2)
np_arr = np.append(np_arr , 24)
print(np_arr)

OUTPUT: array([ 2., 24.])

Another simple way to create an empty array that can take array is:

import numpy as np
np.empty((2,3), dtype=object)

I think you want to handle most of the work with lists then use the result as a matrix. Maybe this is a way ;

ur_list = []
for col in columns:
    ur_list.append(list(col))

mat = np.matrix(ur_list)

I think you can create empty numpy array like:

>>> import numpy as np
>>> empty_array= np.zeros(0)
>>> empty_array
array([], dtype=float64)
>>> empty_array.shape
(0,)

This format is useful when you want to append numpy array in the loop.

Perhaps what you are looking for is something like this:

x=np.array(0)

In this way you can create an array without any element. It similar than:

x=[]

This way you will be able to append new elements to your array in advance.

The simplest way

Input:

import numpy as np
data = np.zeros((0, 0), dtype=float)   # (rows,cols)
data.shape

Output:
(0, 0)

Input:

for i in range(n_files):
     data = np.append(data, new_data, axis = 0)