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Poisson.pmf

As explained in Statistics Every Programmer Needs

poisson.pmf is a SciPy statistical function that returns probability mass function values for the Poisson distribution.

poisson.pmf returns the probability of observing exactly k events when a Poisson process has rate parameter lam. It provides a direct SciPy implementation of the Poisson probability mass function. section 3.1.4

Calculate an event-count probability

Import poisson from scipy.stats, then call poisson.pmf(k, lam). With lam = 2 and k = 4, the probability is approximately 9.02%. With lam = 10 and k = 8, it is approximately 11.26%. These values match the calculation from the Poisson PMF formula, exp(-lam) × lam**k / k!. section 3.1.4

How Statistics Every Programmer Needs covers it

How deep it goes
Defines the PMF, gives its formula, and demonstrates equivalent arithmetic and SciPy implementations without deeper validation.
Examples
Uses rates and event counts of 2 and 4, then 10 and 8, producing probabilities near 9% and 11%.
What it assumes
Assumes basic probability and Python arithmetic while introducing the Poisson PMF and its rate and count parameters.
What you can do after
Apply it via SciPy to compute and interpret exact Poisson event-count probabilities.
How it connects
Connects Poisson probability mass functions with rate parameters, independent events, and empirical event counts.
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This explainer was generated by Manning's knowledge graph from the text of Statistics Every Programmer Needs. The section links point to the parts of the book it draws on.