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Learning NumPy Array

You're reading from  Learning NumPy Array

Product type Book
Published in Jun 2014
Publisher
ISBN-13 9781783983902
Pages 164 pages
Edition 1st Edition
Languages
Author (1):
Ivan Idris Ivan Idris
Profile icon Ivan Idris

Table of Contents (14) Chapters

Learning NumPy Array
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started with NumPy 2. NumPy Basics 3. Basic Data Analysis with NumPy 4. Simple Predictive Analytics with NumPy 5. Signal Processing Techniques 6. Profiling, Debugging, and Testing 7. The Scientific Python Ecosystem Index

Creating a multidimensional array


Now that we know how to create a vector, we are ready to create a multidimensional NumPy array. After we create the matrix, we will again want to display its shape (see the arrayattributes.py file in the Chapter02 folder of this book's code bundle), as shown in the following code snippets:

  • To create a multidimensional array, see the following code:

    In: m = array([arange(2), arange(2)])
    In: m
    Out:
    array([[0, 1],[0, 1]])
  • To display the array shape, see the following lines of code:

    In: m.shape
    Out: (2, 2)

We created a 2 x 2 array with the arange() function. Without any warning, the array() function appeared on the stage.

The array() function creates an array from an object that you give to it. The object needs to be array-like, for instance, a Python list. In the preceding example, we passed in a list of arrays. The object is the only required argument of the array() function. NumPy functions tend to have a lot of optional arguments with predefined defaults.

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