How does numpy reshape works?

Question

I have data in a numpy array:

a = np.arange(100)
a = a.reshape((20,5))

When I type

a[:10]

it returns

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]])

Now i decided to reshape the array into 3d array.

b = a.reshape((5,4,5))

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]]])

How do I slice b to that I obtain the values like a[:10]? I tried

b[:10,0,:5]
array([[ 0,  1,  2,  3,  4],
       [10, 11, 12, 13, 14],
       [20, 21, 22, 23, 24],
       [30, 31, 32, 33, 34],
       [40, 41, 42, 43, 44],
       [50, 51, 52, 53, 54],
       [60, 61, 62, 63, 64],
       [70, 71, 72, 73, 74],
       [80, 81, 82, 83, 84],
       [90, 91, 92, 93, 94]])

But its not correct. Thank you in advance!

Answer 1

When you use b = a.reshape((5,4,5)) you just create a different view on the same data used by the array a. (ie changes to the elements of a will appear in b). reshape() does not copy data in this case, so it is a very fast operation. Slicing b and slicing a accesses the same memory, so there shouldn't be any need for a different syntax for the b array (just use a[:10]). If you have created a copy of the data, perhaps with np.resize(), and discarded a, just reshape b: b.reshape((20,5))[:10].