This project is an analysis of New York Citi Bike data, using data visualization tools to explore the viability of a bike-sharing business in Des Moines.

• Data Source: Citi Bike Data
• Software: Python 3.7.7, Anaconda Navigator 1.9.12, Conda 4.8.4, Jupyter Notebook 6.0.3, Tableau Public 2020.3.2

Link to dashboard

• There were over 2.3 million rides for the month of August 2019.
• 81% of the users were subscribers. 65% of the users were confirmed males and 25% were confirmed females.
• Younger users tend to use the service for longer rides.
• Top ride starting locations are in the most touristic and busy areas, as we see here in Manhattan.

• Highest activity hours are from 5:00 PM to 7:00 PM and require the most resources mobilized.
• The activity from 2:00 AM to 5:00 AM is low so this would be the window for bike maintenance.
• Bikes are mostly checked out for 4 to 6 hours.
• Male users take approximately 3 times more rides than the female users.

• Most weekday rides are around 7:00 AM to 9 AM and 5:00 PM to 7:00 PM.
• Weekend rides are highest from 10:00 AM to 7:00 PM.
• Those rides are mostly taken by male users.

The data shows high activity of the bike sharing service in New York during the month of August 2019.
The far majority of the rides were occuring at a busy frequency within Manhattan, and taken by "He/Him" riders during morning and evening rush hours. This implies that Citi Bike services are used as a solid alternative to public transportation by commuting workers.
Additional analysis would be beneficial by :

• gathering and comparing data from a 12 month span to determine trends across the year.
• gathering and intergrating weather data to our analysis, and compare weather to rides.