Time for action – manipulating array shapes

We already learned about the reshape() function. Another recurring task is flattening of arrays. When we flatten multidimensional NumPy arrays, the result is a one-dimensional array with the same data.

1. Ravel: Accomplish this with the ravel() function:

   In: b
   Out:
   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]]])
   In: b.ravel()
   Out:
   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])
   

2. Flatten: The appropriately named function, flatten() does the same as ravel(), but flatten() always allocates new memory whereas ravel() might return a view of the array. A view is a way to share an array, but you need to be careful with views because modifying the view affects the underlying array, and therefore this impacts other views. An array copy is safer; however, it uses more memory:

   In: b...