arrays - Python advanced slicing

Question

I am a little confused with Python's advanced slicing. I basically had a dictionary and with help from SO, I made it into an array.

   array1 = 
   ([[[36, 16],
    [48, 24],
    [12,  4],
    [12,  4]],

   [[48, 24],
    [64, 36],
    [16,  6],
    [16,  6]],

   [[12,  4],
    [16,  6],
    [ 4,  1],
    [ 4,  1]],

   [[12,  4],
    [16,  6],
    [ 4,  1],
    [ 4,  1]]])

To practice using matrix solver, the array was turned into a square matrix (4 x 4) using:

 array_matrix_sized = array[:, :, 0] 

I read that this means [number of indices, rows, columns]. I am a little clueless as to why [:,:,0] returns a 4 x 4 matrix. To try to help, I made an array that has a length 100, and I have been trying to turn it into a 10 x 10 matrix in a similar manner with no success. What throws me off is the number of rows is ":" and the number of columns is "0", if I read this concept correctly. For a 4 x 4 matrix, why isn't it array[:, 4, 4]? I am also assuming the : is because I am interested in all the values.

Thank you in advance for any help/advice. I do apologize if this is a simple question, but I really could use the clarification on how this works.

Still not quite understanding. If I have

 array2 = array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,
    13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,
    26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,
    39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,
    52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,
    65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
    78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
    91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,
   104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,
   117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,
   130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,
   143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,
   156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,
   169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
   182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,
   195, 196, 197, 198, 199])

To get it into a 10 X 10 matrix, I tried using array2[:,:,0] and get the error IndexError: too many indices for array. Isn't this similar to my first example?

Answer 1

I read that this means [number of indices, rows, columns]. [...] What throws me off is the number of rows is ":" and the number of columns is "0", if I read this concept correctly.

No. It means [which parts I want on dimension 1, which parts I want on dimension 2, which parts I want on dimension 3]. The indices are not how many rows/columns you want, they are which ones you want. And, as you said : means "all" in this context.

For a 4 x 4 matrix, why isn't it array[:, 4, 4]?

You don't specify the shape of the result. The shape of the result depends on the shape of the original array. Since your array is 4x4x2, getting one element on the last dimension gives you 4x4. If the array was 8x7x2, then [:, :, 0] would give you an 8x7 result.

So [:, :, 0] means "give me everything on the first two dimensions, but only the first item on the last dimension. This amounts to getting the first element of each "row" (or the first "column" as it appears in the display) which is why you get the result you get:

>>> array1[:, :, 0]
array([[36, 48, 12, 12],
       [48, 64, 16, 16],
       [12, 16,  4,  4],
       [12, 16,  4,  4]])

Likewise, doing [0, :, :] gives you the first "chunk":

>>> array1[0, :, :]
array([[36, 16],
       [48, 24],
       [12,  4],
       [12,  4]])

And doing [:, 0, :] gives you the first row of each chunk:

>>> x[:, 0, :]
array([[36, 16],
       [48, 24],
       [12,  4],
       [12,  4]])