random.uniform(): R runif() Equivalent Function in Python

Renesh Bedre    2 minute read

In R, the runif() function generates random numbers from a uniform distribution.

In Python, numpy.random.uniform() (from NumPy package) function is equivalent to runif() fuction from R for generating random numbers from a uniform distribution.

In a uniform distribution, every value within the given range is equally likely to occur. The uniform distribution is mostly used in the generation of random numbers.

The general syntax of numpy.random.uniform() looks like this:

# import package
import numpy as np

# generate random numbers
np.random.uniform(low = 0, high = 1, size = 10)

Where,

Parameter Description
low minimum value for the random numbers (default is 0)
high maximum value for the random numbers (default is 1)
size number of random numbers to generate

The following examples explains how to use the numpy.random.uniform() function to generate random numbers in Python.

Example 1: Generate random numbers between 0 and 1

The following example shows how to generate 5 random numbers between 0 and 1 using runif() equivalent in Python.

By default, the numpy.random.uniform() function generates random numbers between 0 to 1 from a uniform distribution.

If you run the numpy.random.uniform() function multiple times, you may see different results every time. To get reproducible random results every time, you should use numpy.random.seed() function.

# import package
import numpy as np

# set random seed for reproducible results
np.random.seed(2)

# generate 5 random values between 0 to 1
np.random.uniform(size = 5)

# output
array([0.4359949 , 0.02592623, 0.54966248, 0.43532239, 0.4203678 ])

You can see that the 5 random values between 0 and 1 are generated from the uniform distribution.

Example 2: Generate random numbers between 1 and 50

The following example shows how to generate 10 random numbers between 1 and 50 from uniform distribution using numpy.random.uniform() function.

# import package
import numpy as np

# set random seed for reproducible results
np.random.seed(2)

# generate 10 random values between 1 to 50
np.random.uniform(low = 1, high = 50, size = 10)

# output
array([22.3637502 ,  2.27038536, 27.93346142, 22.33079724, 21.5980223 ,
       17.18640623, 11.02778307, 31.34427735, 15.68307901, 14.07453648])

You can see that the 10 random values between 1 and 50 are generated from the uniform distribution.

Example 3: Generate histogram from uniform distribution

You can also numpy.random.uniform() function to generate random numbers and plot a histogram of uniform distribution (rectangular distribution).

The following example generates 1000 random numbers and plots a histogram,

# import package
import numpy as np
import matplotlib.pyplot as plt

# generate 1000 random integer values between 1 to 10 
ran_num = np.random.uniform(low = 1, high = 10, size = 1000)

# Create a histogram to visualize uniform distribution
sns.histplot(data = ran_num)
plt.show()

uniform distribution histogram python

The above histogram visualizes the distribution of 1000 random numbers generated using the numpy.random.uniform() function.

Related: How to Use runif in R (Practical Examples)

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