Drawing Empirical Cumulative Distribution Function(ECDF) using seaborn

Overview:

A Cumulative Distribution Function(CDF) returns the probabilities of a range of outcomes for a random variable either discrete or continuous.
When the Cumulative Distribution Function describes probabilities of sample outcomes drawn from a population, it is called the Empirical Cumulative Distribution Function(ECDF).
ECDF answers the following questions:
- What is the probability that outcome of a random variable(sample value from the population) is less than or equal to a certain value? The answer is to sum the probabilities of each individual outcome.
- The Empirical Cumulative Probability Distribution function P(X) is given by
  
  P(X)=p(x1)+p(x2)+⋯p(xn)
  
  where x1, x2, xn-1 ≤ xn.
  
  X – name of the random variable
  
  x – value of the random variable or outcome from the sample
- In an ECDF plot, the Y axis denotes the probabilities. It also denotes the percentile values of the sample. For example, 0.2 in the Y axis refers to the 20th percentile of the probabilities. Correlating with the X axis one can get the individual probability of a certain outcome, as well as the cumulative probability of the outcome being less than or equal to that value.
The function ecdfplot() of the seaborn library draws the ECDF plot for a given sample data.

# Example Python program that plots the
# Empirical Cumulative Distribution Function(ECDF)
# for a sample of values drawn from a population

import seaborn as sns
import matplotlib.pyplot as plt

# Restaurants and their Sample wait times
d1 = {"Restaurant1":[2, 5, 10, 12, 15, 20, 35],
"Restaurant2":[2, 8, 10, 19, 25, 30, 36]};

# Draw the ECDF plot (a.k.a, Step Plot)
sns.ecdfplot(data=d1);

plt.xlabel('Wait Time');
plt.title("ECDF - for the sample wait times");
plt.show();

Empirical Cumulative Distribution Function(ECDF) plot drawn using seaborn