How to declare a 2 dimensional array with different row lengths using np.array?

Question

For example, I want a 2 row matrix, with a first row of length 1, and second row of length 2. I could do,

list1 = np.array([1])    

list2 = np.array([2,3])    

matrix = []    

matrix.append(list1)    

matrix.append(list2)    

matrix = np.array(matrix)

I wonder if I could declare a matrix of this shape directly in the beginning of a program without going through the above procedure?

Your matrix is a list of lists with unequal lengths which is not a matrix (at least from my point of view). Matrices consist of rows with equal lengths. Therefore I doubt that one can declare this matrix of this shape directly. What do you want to achieve with this shape? What are your further steps? — plonser
– plonser, Commented Apr 14, 2015 at 12:48
Numpy does not support ragged arrays (or matrices). See this recent question stackoverflow.com/q/29517392/553404 — YXD
– YXD, Commented Apr 14, 2015 at 13:07

miradulo · Accepted Answer · 2015-04-16 20:14:20Z

2

A matrix is by definition a rectangular array of numbers. NumPy does not support arrays that do not have a rectangular shape. Currently, what your code produces is an array, containing a list (matrix), containing two more arrays.

array([array([1]), array([2, 3])], dtype=object)

I don't really see what the purpose of this shape could be, and would advise you simply use nested lists for whatever you are doing with this shape. Should you have found some use for this structure with NumPy however, you can produce it much more idiomatically like this:

>>> np.array([list1,list2])   
array([array([1]), array([2, 3])], dtype=object)

answered Apr 16, 2015 at 20:14

miradulo

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