Python Probabilities 🐍

Library for accurate statistical calculations using Python.

• Binomial Distributions
  • Binomial Probability Distribution
  • Binomial Cumulative Distribution
  • Inverse Binomial Cumulative Distribution

Binomial Distributions

Binomial Probability Distribution

If a random variable X has the binomial distribution B(n, p), then its probability mass function can be calculated via Bpd(r, n, p).
(where r is the number of successes, n is the number of trials, and p is the probability of success)

The code below would be to calculate P(X=7) for the binomial distribution X~B(11, 0.33).

>>> from python_probabilities import Bpd
>>> Bpd(7, 11, 0.33)
0.0283407102416171981610

Binomial Cumulative Distribution

For the binomial distribution X~B(n, p), the cumulative probability function can be calculated via Bcd(r, n, p).
(where r is the number of successes, n is the number of trials, and p is the probability of success)

The code below would be to calculate P(X≤7) for the binomial distribution X~B(11, 0.33).

>>> from python_probabilities import Bcd
>>> Bcd(7, 11, 0.33)
0.9917567634324003237640

"A cumulative probability function for a random variable X tells you the sum of all the individual probabilities up to and including the given value of x in the calculation for P(X < x)".

Inverse Binomial Cumulative Distribution

Given the probability for a cumulative probability function, the value for r (number of successes) can be calculated via InvB(x, n, p).
(where x is the probability, n is the number of trials, and p is the probability of success)

>>> from python_probabilities import *
>>> InvB(0.9917567634, 11, 0.33)
7
>>> Bcd(7, 11, 0.33)
0.9917567634324003237640