1. Create Tableau sheets to show the following:
   • length of time bikes are checked out for all riders and genders
   • number of bike trips for all riders and genders for each hour of the day
   • number of bike trips for each type of user and gender for each day of the week
   • starting and ending locations based on frequency
   • checkout times by user and gender
2. Use the Tableau sheets to create dashboards to display the graphics
3. Use Tableau Stories to represent the story within the data

• Source of data: citibike.csv
• Software: Tableau Public 2021.4, Python 3.7.10, Conda 4.10.3, Jupyter Notebook 6.3.0, Visual Studio Code 1.60.2
• Please see the refactored code here.

- Overall we can see that Male subscribers use the bike-sharing service the most. Also the most used times are most likely when customers and subscribers are going to or leaving work (6:00am to 10:00am and 5:00pm to 8:00pm).

- The busiest locations are on the island of Manhattan with a few less frequently checked out in the surrounding areas.

- Checkout times are usually less than an hour with the most checkout time being at 5 minutes with 146,752 users.

You can see the interactive Tableau Story on Tableau Public here.

Looking at the data represented in Tableau we can see that the majority of users are males who have purchased subscriptions with Citi-Bike and are usually around 5 minutes in trip length. While looking at whether to bring a ride sharing service to Des Moines there are a couple further analysis that should be performed; the walkability of the main downtown area vs New York, overall population/building density in Des Moines and New York, and ownership of vehicles vs parking in Des Moines and New York. Looking at how Citi-Bike marketed could be a great resource on increasing the total checkout time as well and equaling out the gender demographics.

While bike-sharing is a great idea for the environment and health of individuals a big factor would be the usability within Des Moines itself.