A random variable whose values are not countable is called a continuous random variable. A continuous random variable is a random variable that can assume any value contained in one or more intervals. Because the number of values contained in any interval is infinite, the possible number of values that a continuous random variable can assume is also infinite (Mann 2012).
There exist many more continuous probability distributions than we may discuss here. However, be aware that the library Python scipy.stats has implemented a large variety of different discrete and continuous probability distributions (see here).
In genral, Python probability functions are accessible by calling different methods after the call for the probability distribution such as rvs, pmf, cdf and ppf. The rvs is the abbreviation for random variates, which generates random varibales, such as uniform.rvs(), for the uniform distribution or norm.rvs(), for the normal distribution. The pmf is the abbreviation for the probability mass function such as uniform.pmf() and norm.pmf(). The cdf is the abbreviation for the cumulative distribution function such as uniform.cdf() and norm.cdf(). The ppf is the abbreviation for the Percent point function, which generates the inverse of the cdf — percentiles, such as uniform.ppf() and norm.pff(). You may look up all applicable methods implement in scipy.stats here. Keep that in mind, when we further explore the capacities in Python.
Citation
The E-Learning project SOGA-Py was developed at the Department of Earth Sciences by Annette Rudolph, Joachim Krois and Kai Hartmann. You can reach us via mail by soga[at]zedat.fu-berlin.de.
Please cite as follow: Rudolph, A., Krois, J., Hartmann, K. (2023): Statistics and Geodata Analysis using Python (SOGA-Py). Department of Earth Sciences, Freie Universitaet Berlin.