NumPy : Beginner's Guide - Third Edition

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 ...