Analyzing a locker storage system
Suppose you own a small storage locker facility where customers can select an open locker to store their items and are charged by the hour. You have collected the following data, which shows locker utitilization as well as profit for a month. Below are some clarifications on the data:
- Each # in the dataset corresponds to a locker # (1-12) and the # of times it appears in a row corresponds to the frequency of how many customers rented out that locker
- The 'locker_profit' field represents the profit you made from the customer storing their objects in that locker (you can assume the profit is random in the data and variable depending on what type of storage plan the customer chooses).
Using all of this information, write code to visualize the Probabiity Mass Function (PMF) of your customers' locker selections. Your resultant chart should show each spot # (1-12) along with the probability of that spot being chosen based on your dataset. Additonally, calculate the locker with the highest total profit and the highest profit per use.
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