As you remember, with the `.pmf()` function, we can calculate the probability over a range using the addition rule. Look at the example:
**Example 1/2:** We know that per day the expected value of users is 100. Calculate the probability that 110 users will visit the app. 
This distribution is discrete, so to calculate the probability of getting the exact number of customers, we can use the `.pmf()` function with two parameters: the first is our desored number of events, and the second is lambda.

**Python realization:**

We will use `.pmf()` function for the Poisson distribution using `stats.poisson.pmf()`.

import scipy.stats as stats
probability = stats.poisson.pmf(110, 100)
print("The probability is", probability * 100, "%")  

**Example 2/2:**

The expected value of sunny days per month is `15`. Calculate the probability that the number of sunny days will equal `16`, `17`, `18`, or `19`.

**Python realization:**

import scipy.stats as stats

prob_1 = stats.poisson.pmf(16, 15)
prob_2 = stats.poisson.pmf(17, 15)
prob_3 = stats.poisson.pmf(18, 15)
prob_4 = stats.poisson.pmf(19, 15)

probability = prob_1 + prob_2 + prob_3 + prob_4

print("The probability is", probability * 100, "%")  

Poisson Distribution 2/3

Poisson Distribution 2/3