For a numpy array I have found that
x = numpy.array([]).reshape(0,4)
is fine and allows me to append (0,4) arrays to x without the array losing its structure (ie it dosnt just become a list of numbers). However, when I try
x = numpy.array([]).reshape(2,3)
it throws an error. Why is this?
This out put will explain what it mean to reshape an array...
np.array([2, 3, 4, 5, 6, 7]).reshape(2, 3)
Output -
array([[2, 3, 4],
[5, 6, 7]])
So reshaping just means reshaping an array. reshape(0, 4) means convert the current array into a format with 0 rows and 4 columns intuitively. But 0 rows means no elements means so it works as your array is empty. Similarly (2, 3) means 2 rows and 3 columns which is 6 elements...
reshape is not an 'append' function. It reshapes the array you give it to the dimensions you want.
np.array([]).reshape(0,4) works because you reshape a zero element array to a 0x4(=0 elements) array.
np.reshape([]).reshape(2,3) doesn't work because you're trying to reshape a zero element array to a 2x3(=6 elements) array.
To create an empty array use np.zeros((2,3)) instead.
And in case you're wondering, numpy arrays can't be appended to. You'll have to work around by casting it as a list, appending what you want and the converting back to a numpy array. Preferably, you only create a numpy array when you don't mean to append data later.
(2,3)arrays along what axis? Use sample arrays to demonstrate what you have in mind?numpy.array([]).reshape(2,3)throws error becausenumpy.array([])has zero elements, so you can't reshape that to(2,3)that expects total6elements..reshape(0,4)expects zero elements0*4 = 0, while in(2,3), it expects2*3 = 6elems.