Data Interview Qs - Parking space probabilities

Parking space probabilities
Data Analysis Python Pandas External Dataset Data Manipulation Data Visualization Statistics Probability Mass Function

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Suppose you're analyzing the utilization of a small parking lot you invested in. Customers enter the automated lot, receive a ticket, and select one of 20 spots at random (e.g. they are not assigned). Upon leaving, customers pay in relation to their time in the lot.

You have collected the following dataset, which shows spot utilization for a month. Each # in the dataset corresponds to a spot # (1-20) and the # of times it appears in a row corresponds to the frequency of how many customers parked in that spot.

Using all of this information, write code to visualize the Probability Mass Function (PMF) of your customers' spot selections. Your resultant chart should show each spot # (1-20) along with the probability of that spot being chosen based on your dataset. You can ignore seasonality and assume this month represents a standard month of parking at your lot.

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