Introduction to scipy.stats.expon() | Python

scipy.stats.expon() is a Python function provided by the SciPy library, which is used for working with the exponential distribution. The exponential distribution describes the probability of time intervals between events occurring at a constant rate, such as the time it takes for an event to occur in a Poisson process.

The scipy.stats.expon() function allows programmers to access various statistical properties and probability functions associated with the exponential distribution. It provides methods to calculate the probability density function (PDF), cumulative distribution function (CDF), quantiles, moments, and other statistical measures.

Syntax

The syntax to use scipy.stats.expon() is as follows:

scipy.stats.expon([loc, scale])

The optional parameters loc and scale specify the location and scale parameters of the distribution, respectively. By default, loc is set to 0 and scale is set to 1.

Example

Here's an example to demonstrate the usage of scipy.stats.expon():

import scipy.stats as stats

# Create an exponential distribution object
expon_dist = stats.expon()

# Calculate the probability density function (PDF)
pdf_value = expon_dist.pdf(x)

# Calculate the cumulative distribution function (CDF)
cdf_value = expon_dist.cdf(x)

# Generate a random sample from the distribution
random_sample = expon_dist.rvs(size=100)

# Calculate the mean, variance, and standard deviation of the distribution
mean_value = expon_dist.mean()
variance_value = expon_dist.var()
std_deviation = expon_dist.std()

# Calculate the quantile for a given probability
quantile_value = expon_dist.ppf(probability)

Additional Resources

• SciPy Documentation on scipy.stats.expon()
• Exponential Distribution (Wikipedia)

In conclusion, the scipy.stats.expon() function provides programmers with a convenient way to work with the exponential distribution in Python. Its various methods allow for easy calculation of probability functions and statistical measures associated with this distribution.