[python] How do I create an empty array/matrix in NumPy?

I can't figure out how to use an array or matrix in the way that I would normally use a list. I want to create an empty array (or matrix) and then add one column (or row) to it at a time.

At the moment the only way I can find to do this is like:

mat = None
for col in columns:
    if mat is None:
        mat = col
    else:
        mat = hstack((mat, col))

Whereas if it were a list, I'd do something like this:

list = []
for item in data:
    list.append(item)

Is there a way to use that kind of notation for NumPy arrays or matrices?

If you absolutely don't know the final size of the array, you can increment the size of the array like this:

my_arr = numpy.zeros((0,5))
for i in range(3):
    my_arr=numpy.concatenate( ( my_arr, numpy.ones((1,5)) ) )
print(my_arr)

[[ 1.  1.  1.  1.  1.]  [ 1.  1.  1.  1.  1.]  [ 1.  1.  1.  1.  1.]]

• Notice the 0 in the first line.
• numpy.append is another option. It calls numpy.concatenate.

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!

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.

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.

Depending on what you are using this for, you may need to specify the data type (see 'dtype').

For example, to create a 2D array of 8-bit values (suitable for use as a monochrome image):

myarray = numpy.empty(shape=(H,W),dtype='u1')

For an RGB image, include the number of color channels in the shape: shape=(H,W,3)

You may also want to consider zero-initializing with numpy.zeros instead of using numpy.empty. See the note here.

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)

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.])

You can apply it to build any kind of array, like zeros:

a = range(5)
a = [i*0 for i in a]
print a 
[0, 0, 0, 0, 0]

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.

I looked into this a lot because I needed to use a numpy.array as a set in one of my school projects and I needed to be initialized empty... I didn't found any relevant answer here on Stack Overflow, so I started doodling something.

# Initialize your variable as an empty list first
In [32]: x=[]
# and now cast it as a numpy ndarray
In [33]: x=np.array(x)

The result will be:

In [34]: x
Out[34]: array([], dtype=float64)

Therefore you can directly initialize an np array as follows:

In [36]: x= np.array([], dtype=np.float64)

I hope this helps.

For creating an empty NumPy array without defining its shape:

1. arr = np.array([])
   

   (this is preferred, because you know you will be using this as a NumPy array)

2.  arr = []   # and use it as NumPy array later by converting it
    arr = np.asarray(arr)
   

NumPy converts this to np.ndarray type afterward, without extra [] 'dimension'.

To create an empty multidimensional array in NumPy (e.g. a 2D array m*n to store your matrix), in case you don't know m how many rows you will append and don't care about the computational cost Stephen Simmons mentioned (namely re-buildinging the array at each append), you can squeeze to 0 the dimension to which you want to append to: X = np.empty(shape=[0, n]).

This way you can use for example (here m = 5 which we assume we didn't know when creating the empty matrix, and n = 2):

import numpy as np

n = 2
X = np.empty(shape=[0, n])

for i in range(5):
    for j  in range(2):
        X = np.append(X, [[i, j]], axis=0)

print X

which will give you:

[[ 0.  0.]
 [ 0.  1.]
 [ 1.  0.]
 [ 1.  1.]
 [ 2.  0.]
 [ 2.  1.]
 [ 3.  0.]
 [ 3.  1.]
 [ 4.  0.]
 [ 4.  1.]]