SciPy - Exponential Distribution

Exponential distribution is the probability distribution of the time between events in a Poisson point process, i.e., a process in which events occur continuously and independently at a constant average rate. It is a particular case of the gamma distribution. For example, customers arriving at a store, file requests on a server etc.

The probability density function (pdf) of exponential distribution is defined as:

Where, β is the scale parameter which is the inverse of the rate parameter λ = 1/β.

An exponential distribution has mean β and variance β².

The cumulative distribution function (cdf) evaluated at x, is the probability that the random variable (X) will take a value less than or equal to x. The cdf of exponential distribution is defined as:

The scipy.stats.expon contains all the methods required to generate and work with an exponential distribution. The most frequently methods are mentioned below:

Syntax

scipy.stats.expon.pdf(x, loc=0, scale=1)
scipy.stats.expon.cdf(x, loc=0, scale=1)
scipy.stats.expon.ppf(q, loc=0, scale=1)
scipy.stats.expon.rvs(loc=0, scale=1, size=1)

Parameters

expon.pdf()

The expon.pdf() function measures probability density function (pdf) of the distribution.

from scipy.stats import expon
import matplotlib.pyplot as plt
import numpy as np

#creating an array of values between
#-1 to 10 with a difference of 0.1
x = np.arange(-1, 10, 0.1)
   
y = expon.pdf(x, 0, 2)
   
plt.plot(x, y) 
plt.show()

The output of the above code will be:

expon.cdf()

The expon.cdf() function returns cumulative distribution function (cdf) of the distribution.

from scipy.stats import expon
import matplotlib.pyplot as plt
import numpy as np

#creating an array of values between
#-1 to 10 with a difference of 0.1
x = np.arange(-1, 10, 0.1)
   
y = expon.cdf(x, 0, 2)
   
plt.plot(x, y) 
plt.show()

The output of the above code will be:

expon.ppf()

The expon.ppf() function takes the probability value and returns cumulative value corresponding to probability value of the distribution.

from scipy.stats import expon
import matplotlib.pyplot as plt
import numpy as np

#creating an array of probability from
#0 to 1 with a difference of 0.01
x = np.arange(0, 1, 0.01)
   
y = expon.ppf(x, 0, 2)
   
plt.plot(x, y) 
plt.show()

The output of the above code will be:

expon.rvs()

The expon.ppf() function generates an array containing specified number of random numbers of the given exponential distribution. In the example below, a histogram is plotted to visualize the result.

from scipy.stats import expon
import matplotlib.pyplot as plt
import numpy as np

#fixing the seed for reproducibility
#of the result
np.random.seed(10)

#creating a vector containing 10000
#exponentially distributed random numbers
y = expon.rvs(0, 1, 10000)

#creating bin
bin = np.arange(-1,10,0.1)  

plt.hist(y, bins=bin, edgecolor='blue') 
plt.show()

The output of the above code will be: