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Create visualizations for NYC Bike share program investors using Tableau. Import csv data to pandas dataframe, clean data, export to csv for Tableau import

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bikesharing

Purpose

OverView

A bike sharing program has been proposed for Des Moines. The purpose of the analysis is to create visualizations for potential investors utilizing data from NYC bike sharing program.

Results

Story Board

The visualizations can viewed on the Tableu website here: Story Board

Images with Explanations

Checkout Times by Gender

  • A significant portion of the riders are male, with females and others being less than half of the riders.

Checkout Times for Users

  • Checkout time by users indicates that most of the rides are 20 minutes to 30 minutes long, and none of them went over an hour.

Peak August Hours

  • This graph shows that most of the rides occur between the hours of 8am to 9pm everyday.

Top Starting and Ending Locations

  • These graphs reshows that most rides are generally short becuase all the top starting locations are near the top stopping locations. People do not like to ride for more than 30 minutes.

Trips by Gender (Weekday per Hour)

  • This is a heatmap by weekday per hour that has been split between the three genders. Darker colors indicate heavier usage. Once again we see that males ride more than females and other at almost every our of the day available.

Trips by Weekday per Hour

  • This is another heatmap that shows a clear pattern during the weekdays for which hours are most popular among riders. On the weekend it is fairly even usage, but during the week the most popular hours are 6-9am and 5pm-7pm.

User Trips by Gender by Weekday

  • The final graph represents that subscribers to the bikesharing program ride much more than the customer counterparts. Customers are one time buyers and subscribers pay a annual fee.

Conclusion

Summary

Overall, the bike sharing program is successful with over 2 million riders during the month of August. This information should be helpful for potential investors. The visualizations allow us to see usage by gender, weekday, and length of time.

Future Analysis

One additional chart showing a full years spectrum of riders to visualize the different seasons and how it effects the rider populations. A second chart that would be helpful would be to see the profit margins to see if bike repair is a substantial cost for running this buisness.

Source

201908-citibike-tripdata.csv

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Create visualizations for NYC Bike share program investors using Tableau. Import csv data to pandas dataframe, clean data, export to csv for Tableau import

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