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In the R programming language, we can use the **rnorm()** function to generate a vector of random values that follow a normal distribution with a specific mean and standard deviation.

For example, the following code shows how to use **rnorm()** to create a vector of 8 random values that follow a normal distribution with a mean of 5 and standard deviation of 2:

#make this example reproducible set.seed(1) #generate vector of 8 values that follow normal distribution with mean=5 and sd=2 rnorm(n=8, mean=5, sd=2) [1] 3.747092 5.367287 3.328743 8.190562 5.659016 3.359063 5.974858 6.476649

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

**np.random.normal(loc=0, scale=1, size=None)**

where:

**loc**: Mean of the distribution**scale**: Standard deviation of the distribution**size**: Sample size

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

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

The following code shows how to use the **np.random.normal()** function to generate an array of random values that follow a normal distribution with a specific mean and standard deviation.

import numpy as np #make this example reproducible np.random.seed(1) #generate array of 8 values that follow normal distribution with mean=5 and sd=2 np.random.normal(loc=5, scale=2, size=8) array([8.24869073, 3.77648717, 3.9436565 , 2.85406276, 6.73081526, 0.39692261, 8.48962353, 3.4775862 ])

The result is a NumPy array that contains 8 values generated from a normal distribution with a mean of 5 and a standard deviation of 2.

You can also create a histogram using Matplotlib to visualize a normal distribution generated by the **np.random.normal()** 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 normal distribution with mean=5 and sd=2 data = np.random.normal(loc=5, scale=2, size=200) #create histogram to visualize distribution of values plt.hist(data, bins=30, edgecolor='black')

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We can see that the distribution of values is roughly bell-shaped with a mean located at 5 and a standard deviation of 2.

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

**Additional Resources**

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

How to Calculate & Plot the Normal CDF in Python

How to Plot a Normal Distribution in Python

How to Test for Normality in Python