Normally distribute occurence or counts
Here is one way to do it:

from collections import Counter

from numpy.random import binomial

# Generate mock sales data by randomly sampling if a salesperson makes a sale  
p = 0.5 # Control "flatness" of distribution
n_records = 14
salesperson_id = binomial(n=100, p=p, size=n_records).tolist() # Binomial is a reasonable approximation for a discrete normal distribution
count_per_salesperson = Counter(salesperson_id)
id, count = zip(*count_per_salesperson.items())