Martin McBride, 2019-09-16

Tags arrays, random

Categories numpy

In section Python libraries

In this section we will look at how to create numpy arrays initialised with random data.

There are various ways to create an array of random numbers in numpy.

If you read the numpy documentation, you will find that most of the random functions have several variants that do more or less the same thing. They might vary in minor ways - parameter order, whether the value range is inclusive or exclusive etc. The basic set described below should be enough to do everything you need, but if you prefer to use the other variants they will deliver the same results.

This will create an array of random numbers in the range 0.0 up to but not including 1.0. This means that the range can included anything from 0.0 up to the largest float that is less than 1 (eg something like 0.99999999...), but it will never actually include 1.0. In maths we sometimes write this as **[0.0, 1.0)**. The values are distributed uniformly, so every values is equally likely to occur.

r = np.random.random((3, 2)) print(r)

This creates a 3 by 2 array of random numbers, like this (of course you will get different numbers):

[[0.40704545 0.47734427] [0.76764629 0.37887717] [0.82443478 0.36409071]]

If you want to create random number over a different range, for example **[a, b)**, you can do it using vectorised operators like this:

r = (b - a)*np.random.random((3, 2)) + a print(r)

The `randint`

function creates an array of integers. In its simplest form it creates values in the range **[0, high)**, that is integers from 0 up to but not including `high`

:

r = np.random.randint(4, size=(3, 4)) print(r)

Notice that the `size`

is passed in as a named parameter, unfortunately it isn't just the first parameter like most numpy functions.

This code, with a value of 4, will create value in the range 0 to 3:

[[3 3 0 3] [3 1 3 0] [2 3 3 1]]

You can also pass in two values, `low`

and `high`

, resulting in numbers in the range **[low, high)**. For example to simulate a dice (output values 1 to 6 inclusive), you would use values 1 and 7:

r = np.random.randint(1, 7, size=10) print(r)

giving:

[1 3 3 5 4 1 2 1 6 4]

`choice`

picks values at random from a list (in this case the list is all prime numbers less than 20):

r = np.random.choice([2, 3, 5, 7, 11, 13, 17, 19], size=10) print(r)

giving:

[17 19 7 5 11 11 2 7 11 3]

There are other options (for example you can set different probabilities for each item in the list) but we won't cover that here.

This function creates values using the standard Normal distribution. The Normal distribution is the classic bell shaped curve, centred on zero.

r = np.random.standard_normal((3, 3)) print(r)

Giving:

[[-0.20059509 -1.70950313 0.1355992 ] [-0.84462048 1.27934375 1.30837433] [-1.34519813 -1.18474318 -0.83397725]]

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*If you found this article useful, you might be interested in the book NumPy Recipes, or other books, by the same author.*

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