Python Probability Point Function
import numpy as np from scipy.stats import norm import matplotlib.pyplot as plt np.random.seed(17) mean = 0 std_dev = 1 size = 1000 data = np.random.normal(loc=mean, scale=std_dev, size=size) Example 1
lowest 20%
ppf_20 = norm.ppf(0.2, loc=data.mean(), scale=data.std()) print(ppf_20) 
lowest 70%
ppf_70 = norm.ppf(0.7, loc=data.mean(), scale=data.std()) print(ppf_70) 
Example 2 Find the data points that are between 25 and 50%
ppf_25 = norm.ppf(0.25, loc=data.mean(), scale=data.std()) ppf_50 = norm.ppf(0.50, loc=data.mean(), scale=data.std()) print(ppf_25) 
print(ppf_50) 
Example 3 Find the top 20% data points
ppf_top_20 = norm.ppf((1-0.2), loc=data.mean(), scale=data.std()) ppf_top_20 
Example 4 Numpy Percentiles
Cover Quartile, Decile, Percentile in the next video
ppf_75 = np.percentile(data, 75) Example 5 Graph Matplotlib
seaborn doesnt have a PPF Option
sorted_data = np.sort(data) cumulative_probabilities = np.linspace(0, 1, len(sorted_data), endpoint=False) plt.plot(cumulative_probabilities, sorted_data, marker='o', linestyle='none', label='PPF') plt.title('PPF of Normally Distributed Data') plt.xlabel('Cumulative Probability') plt.ylabel('Data Values') plt.legend() plt.grid(True) plt.show() 
