# Creating random data in numpy

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

### random.random

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)
```

### random.randint

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]
```

### random.choice

`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.

### random.standard_normal

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]]
```

Visit the PythonInformer Discussion Forum for numeric Python.

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

#### Popular tags

2d arrays abstract data type alignment and animation arc array arrays bezier curve built-in function callable object circle classes close closure cmyk colour comparison operator comprehension context context manager conversion creational pattern data types design pattern device space dictionary drawing duck typing efficiency else encryption enumerate fill filter font font style for loop function function composition function plot functools game development generativepy tutorial generator geometry gif gradient greyscale higher order function hsl html image image processing imagesurface immutable object index inner function input installing iter iterable iterator itertools l system lambda function len line linspace list list comprehension logical operator lru_cache magic method mandelbrot mandelbrot set map monad mutability named parameter numeric python numpy object open operator optional parameter or partial application path polygon positional parameter print pure function pycairo radial gradient range recipes rectangle recursion reduce rgb rotation scaling sector segment sequence singleton slice slicing sound spirograph sprite square str stream string stroke subpath symmetric encryption template text text metrics tinkerbell fractal transform translation transparency tuple turtle unpacking user space vectorisation webserver website while loop zip