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In Python, the equivalent of the R function rnorm() is the numpy.random.normal() function. This function takes in three parameters, the mean, size and standard deviation, and generates a normally distributed random sample that follows the given parameters. This sample can then be used for statistical analyses or visualizations.
In the R programming language, we can use the rnorm() function to generate a vector of random values that follow a 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')
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 .
The following tutorials explain how to perform other common operations in Python: