Why A[0] and A[0:1] have different sizes?

[Antonio]

So I have this:

candidates = 
array([[1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0,
        0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0],
       [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,
        0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1],
       [1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,
        0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0]])

And I do not understand why this one:

candidates[0] = 
array([1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0,
       0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0]

candidates[0].shape = (34,)

is different from this one:

candidates[0:1] = 
array([[1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0,
        0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0]])

candidates[0:1].shape = (1, 34)

So basically, why candidates[0] is not exactly similar to candidates[0:1]? Because the later is supposed to represent the first element only!

[killerrex]

I imagine array is numpy.array. The recommended way to use numpy is:

import numpy as np
candidates = np.array(
    [[1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0,
      0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0],
     [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,
      0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1],
     [1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,
      0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0]])

About why is different, candidates is a (3, 34) matrix, so candidates[0] selects the 1st row and numpy returns it as a single vector matrix (1 dimension matrix)
But candidates[0:1] selects many rows (in this case just 1) and return a sub-matrix of your original matrix, so that is the reason the dimensions are (1, 34).
To see it better, think on what you expect as result of candidates[0:2]. It shall be a matrix of (2, 34) with the first 2 rows no?.

[Antonio]

I imagine array is numpy.array. The recommended way to use numpy is:

import numpy as np
candidates = np.array(
    [[1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0,
      0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0],
     [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,
      0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1],
     [1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,
      0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0]])

About why is different, candidates is a (3, 34) matrix, so candidates[0] selects the 1st row and numpy returns it as a single vector matrix (1 dimension matrix)
But candidates[0:1] selects many rows (in this case just 1) and return a sub-matrix of your original matrix, so that is the reason the dimensions are (1, 34).
To see it better, think on what you expect as result of candidates[0:2]. It shall be a matrix of (2, 34) with the first 2 rows no?.

Thank you!