Creating random data in numpy

Martin McBride, 2019-09-16
Tags arrays random
Categories numpy

This article is part of a series on numpy. If you find this article useful you might like our Numpy Recipes e-book.

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))

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


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))

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)


[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)


[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))


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

Visit the PythonInformer Discussion Forum for numeric Python.

Tag cloud

2d arrays abstract data type alignment and array arrays bezier curve built-in function close closure colour comparison operator comprehension context conversion data types design pattern device space dictionary duck typing efficiency encryption enumerate filter font font style for loop function function plot functools generator gif gradient html image processing imagesurface immutable object index input installing iter iterator itertools lambda function len linspace list list comprehension logical operator lru_cache mandelbrot map monad mutability named parameter numeric python numpy object open operator optional parameter or path positional parameter print pure function radial gradient range recursion reduce rotation scaling sequence slice slicing sound spirograph str stream string subpath symmetric encryption template text text metrics transform translation transparency tuple unpacking user space vectorisation webserver website while loop zip

Copyright (c) Axlesoft Ltd 2020