Book Image

Learning NumPy Array

By : Ivan Idris
Book Image

Learning NumPy Array

By: Ivan Idris

Overview of this book

Table of Contents (14 chapters)
Learning NumPy Array
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Manipulating array shapes


Another recurring task is flattening of arrays. Flattening in this context means transforming a multidimensional array into a one-dimensional array. In this example, we will demonstrate a number of ways to manipulate array shapes starting with flattening:

  • ravel(): We can accomplish flattening with the ravel() function (see the shapemanipulation.py file in the Chapter02 folder of this book's code bundle), as shown in the following code:

    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])
  • flatten(): The appropriately-named function, flatten(), does the same as ravel(), but flatten() always allocates new memory, whereas ravel() might return a view of an array. This means that we can directly manipulate the array as follows:

    In: b.flatten()
    Out:
    array([ 0,  1,  2,  3,  4,  5...