Numpy Random.rand() - Generate Random Data using Numpy

Random. rand() function can help you generate random data in a very simple way. In this article, we will learn how to generate random values using the random.rand() function. So let’s learn about it.

What Is the random.rand() Function?

The random.rand() function is used to return randomly generated values in a given shape. The function returns an array that has the shape as specified and fills the array with random values which are normally distributed in the range [0,1].

Syntax of the random.rand() Function

numpy.random.rand(d0, d1, …, dn)

Parameter	Description
d0,d1,…..,dn	The dimensions of the array.

For example:

import numpy as np

x = np.random.rand()
print(x)

Output:

0.2967574962954477

You can also incorporate the seed() function into the random.rand () function to generate output that will remain constant with every run.

import numpy as np

np.random.seed(0)

x = np.random.rand()
print(x)

Output:

0.5488135039273248

Let’s see how we can generate 1-D and 2-D arrays with the help of the Numpy random.rand() function.

1-D Array with np.random.rand() function

The following code will return a 1-D array of the specified shape.

import numpy as np

np.random.seed(0)

x = np.random.rand(6)
print(x)

Output:

[0.5488135  0.71518937 0.60276338 0.54488318 0.4236548  0.64589411]

If you want to generate more range of numbers, use the following code.

import numpy as np

np.random.seed(0)

x = np.random.rand(6)*10
print(x)

Output:

[5.48813504 7.15189366 6.02763376 5.44883183 4.23654799 6.45894113]

2-D array with the np.random.rand () function

The following code will generate a 2-D array.

import numpy as np

np.random.seed(0)

x = np.random.rand(2,3)
print(x)

Output:

[[0.5488135  0.71518937 0.60276338]
 [0.54488318 0.4236548  0.64589411]]

Multi-dimensional array with np.random.rand() function

The following code will generate arrays of higher dimensions.

import numpy as np

np.random.seed(0)

x = np.random.rand(2,4,2,4)

print(x)

Output:

[[[[0.5488135  0.71518937 0.60276338 0.54488318]
   [0.4236548  0.64589411 0.43758721 0.891773  ]]

  [[0.96366276 0.38344152 0.79172504 0.52889492]
   [0.56804456 0.92559664 0.07103606 0.0871293 ]]

  [[0.0202184  0.83261985 0.77815675 0.87001215]
   [0.97861834 0.79915856 0.46147936 0.78052918]]

  [[0.11827443 0.63992102 0.14335329 0.94466892]
   [0.52184832 0.41466194 0.26455561 0.77423369]]]


 [[[0.45615033 0.56843395 0.0187898  0.6176355 ]
   [0.61209572 0.616934   0.94374808 0.6818203 ]]

  [[0.3595079  0.43703195 0.6976312  0.06022547]
   [0.66676672 0.67063787 0.21038256 0.1289263 ]]

  [[0.31542835 0.36371077 0.57019677 0.43860151]
   [0.98837384 0.10204481 0.20887676 0.16130952]]

  [[0.65310833 0.2532916  0.46631077 0.24442559]
   [0.15896958 0.11037514 0.65632959 0.13818295]]]]

Conclusion

In this article, you learned how to generate arrays of one dimension, two-dimension, and also higher dimension using the np.random.rand() function.Hope you found this article helpful.