Arrays and Matrix in Python vs Matlab

Question

Is there any more concise syntax to stack array/matrix? In MatLab, you can simply do [x, y] to stack horizontally, and [x; y] to stack vertically, and it can be easily chained, such as [x, x; y, y]; while in python, it seems to be more tedious, see below:

import numpy as np
x = np.array([[1, 1, 1], [1, 2, 3]])
y = x*10
np.vstack((x, y))

array([[ 1,  1,  1],
       [ 1,  2,  3],
       [10, 10, 10],
       [10, 20, 30]])

np.hstack((x, y))

array([[ 1,  1,  1, 10, 10, 10],
       [ 1,  2,  3, 10, 20, 30]])

np.vstack((np.hstack((x, x)), np.hstack((y, y))))

array([[ 1,  1,  1,  1,  1,  1],
       [ 1,  2,  3,  1,  2,  3],
       [10, 10, 10, 10, 10, 10],
       [10, 20, 30, 10, 20, 30]])

I don't think that this is possible as there is no python syntax for something like this — User that hates AI
– User that hates AI, Commented Jan 28, 2018 at 7:51

hpaulj · Accepted Answer · 2018-01-29 04:12:38Z

MATLAB has its own interpreter, so it can interpret the ; etc to suit its needs. numpyuses the Python interpreter, so can't use or reuse basic syntactic characters like [],;. So the basic array constructor wraps a nested list of lists (takes a list as argument):

np.array([[1,2,3], [4,5,6]])

But that nesting can be carried to any depth, np.array([]), np.array([[[[['foo']]]]]), because arrays can have 0,1, 2 etc dimensions.

MATLAB initially only had 2d matrices, and still can't have 1 or 0d.

In MATLAB that matrix is the basic object (cell and struct came later). In Python lists are the basic object (with tuples and dicts close behind).

np.matrix takes a string argument that imitates the MATLAB syntax. np.matrix('1 2; 3 4'). But np.matrix like the original MATLAB is fixed at 2d.

https://docs.scipy.org/doc/numpy/reference/arrays.classes.html#matrix-objects

https://docs.scipy.org/doc/numpy/reference/generated/numpy.bmat.html#numpy.bmat

But seriously, who makes real, useful matrices with the 1, 2; 3, 4 syntax? Those are toys. I prefer to use np.arange(12).reshape(3,4) if I need a simple example.

numpy has added a np.stack which gives more ways of joining arrays into new constructs. And a np.block:

https://docs.scipy.org/doc/numpy/reference/generated/numpy.block.html#numpy.block

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