In the R programming language, we can use the **runif()** function to generate a vector of random values that follow a uniform distribution with a specific minimum and maximum value.

For example, the following code shows how to use **runif()** to create a vector of 8 random values that follow a uniform distribution with a minimum value of 5 and maximum value of 10:

#make this example reproducible set.seed(1) #generate vector of 8 values that follow uniform distribution with min=5 and max=10 runif(n=8, min=5, max=10) [1] 6.327543 6.860619 7.864267 9.541039 6.008410 9.491948 9.723376 8.303989

The equivalent of the **runif()** function in Python is the **np.random.uniform()** function, which uses the following basic syntax:

**np.random.uniform(low=0, high=1, size=None)**

where:

**low**: Minimum value of the distribution**high**: Maximum value of the distribution**size**: Sample size

The following example shows how to use this function in practice.

**Example: Using the Equivalent of runif() in Python**

The following code shows how to use the **np.random.uniform()** function to generate an array of random values that follow a uniform distribution with a specific minimum and maximum value:

import numpy as np #make this example reproducible np.random.seed(1) #generate array of 8 values that follow uniform distribution with min=5 and max=10 np.random.uniform(low=5, high=10, size=8) array([7.08511002, 8.60162247, 5.00057187, 6.51166286, 5.73377945, 5.46169297, 5.93130106, 6.72780364])

The result is a NumPy array that contains 8 values generated from a uniform distribution with a minimum value of 5 and maximum value of 10.

You can also create a histogram using Matplotlib to visualize a normal distribution generated by the **np.random.uniform()** function:

import numpy as np import matplotlib.pyplot as plt #make this example reproducible np.random.seed(1) #generate array of 200 values that follow uniform distribution with min=5 and max=10 data = np.random.uniform(low=5, high=10, size=200) #create histogram to visualize distribution of values plt.hist(data, bins=30, edgecolor='black')

** **

The x-axis shows the values from the distribution and the y-axis shows the frequency of each value.

Notice that the x-axis ranges from 5 to 10 since these were the minimum and maximum values that we specified for the distribution.

**Note**: You can find the complete documentation for the **np.random.uniform()** function here.

**Additional Resources**

The following tutorials explain how to perform other common operations in pandas:

How to Create Pandas DataFrame with Random Data

How to Randomly Sample Rows in Pandas

How to Shuffle Rows in a Pandas DataFrame