Normal distribution

Normal distributions occur frequently in real-life scenarios. A normal distribution is also known as a bell curve because of its characteristic shape. The probability density function models continuous distribution. The numpy.random subpackage offers lots of continuous distributions such as beta, gamma, logistic, exponential, multivariate normal, and normal distribution. The normal() functions find samples from Gaussian or normal distribution.

Let's write code for visualizing the normal distribution using the normal() function, as follows:

# Import required library
import numpy as np
import matplotlib.pyplot as plt

sample_size=225000

# Generate random values sample using normal distribution
sample = np.random.normal(size=sample_size)

# Create Histogram
n, bins, patch_list = plt.hist(sample, int(np.sqrt(sample_size)), density=True) 

# Set parameters
mu, sigma=0,1

x= bins
y= 1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (bins - mu)**2 / (2 * sigma**2) )

# Plot line plot(or bell...