Generating data using np.random.normal()

At times, it is necessary to generate data according to a particular distribution when implementing various machine learning models. For example, we've previously shown how to generate random uniform data using np.random.uniform(). In this post, we’ll examine how to use the NumPy’s random.normal() function, which will generate n values from a normal distribution, given a particular mean and standard deviation.

Check out the code examples in the canvas below, or keep reading for a step-by-step walkthrough.

Basic Syntax: np.random.normal(loc, scale, size)

NOTE: since NumPy version 1.17.0, it has been recommended to first instantiate a Generator, such as default_rng(), and then call the numpy.random.normal() function on the Generator object. In this case, we've set the random seed for the Generator so that the results are reproducible.

The normal() function takes three arguments:

• loc: the desired mean of the data
• scale: the desired standard deviation of the data
• size: the number of values to generate

If we use seaborn's histplot() function to create histograms of the distribution of our two samples, we'll see that they are roughly normal, as expected.

Example 1: np.normal(loc = 0, scale = 1, size = 500)

# Sample 1: mean = 0, std devation = 1, 500 numbers
mu1, sigma1 = 0, 1
sample1 = rng.normal(loc = mu1, scale = sigma1, size = 500)
print("Sample 1:")
print(sample1)

Output:

Sample 1:
[ 0.34558419  0.82161814  0.33043708 ... -2.13583105  0.23237325
  0.02812631]

Example 2: np.normal(loc = -5, scale = 3, size = 500)

# Sample 2: mean = -1, std devation = 3, 500 numbers
mu2, sigma2 = -5, 3
sample2 = rng.normal(loc = mu2, scale = sigma2, size = 500)
print("Sample 2:")
print(sample2)

Output:

Sample 2:
[-9.11102074  1.52679377 -9.16223969 ... -5.22859825 -6.191401
 -4.17513103]

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