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Learning IPython for Interactive Computing and Data Visualization, Second Edition

You're reading from   Learning IPython for Interactive Computing and Data Visualization, Second Edition Get started with Python for data analysis and numerical computing in the Jupyter notebook

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Product type Paperback
Published in Oct 2015
Publisher
ISBN-13 9781783986989
Length 200 pages
Edition 1st Edition
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Author (1):
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Cyrille Rossant Cyrille Rossant
Author Profile Icon Cyrille Rossant
Cyrille Rossant
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Creating and loading arrays

In this section, we will see how to create and load NumPy arrays.

Creating arrays

First, there are several NumPy functions for creating common types of arrays. For example, np.zeros(shape) creates an array containing only zeros. The shape argument is a tuple giving the size of every axis. Hence, np.zeros((3, 4)) creates an array of size (3, 4) (note the double parentheses, because we pass a tuple to the function).

Here are some further examples:

In [1]: import numpy as np
        print("ones", np.ones(5))
        print("arange", np.arange(5))
        print("linspace", np.linspace(0., 1., 5))
        print("random", np.random.uniform(size=3))
        print("custom", np.array([2, 3, 5]))
Out[1]: ones [ 1.  1.  1.  1.  1.]
        arange [0 1 2 3 4]
        linspace [ 0.    0.25  0.5   0.75  1.  ]
        random [ 0.68361911  0.33585308  0.70733934]
        custom [2 3 5]

The np.arange() and np.linspace() functions create...

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