Citi Bike Analysis

Project Overview

The client has asked to summarize various bike riding data to determine if a bike-sharing program is a worthwhile business pursuit in Des Moines. This includes:

• Show the length of time that bikes are checked out for all riders and genders
• Show the number of bike trips for all riders and genders for each hour of each day of the week
• Show the number of bike trips for each type of user and gender for each day of the week.

The client also wants all of this information displayed in a visual story via Tableau.

Methodology

• Tools/Programs/Languages used:

  • Jupyter Notebook
  • Pandas library
  • Tableau
  • Citi Bike Story

• I used the Pandas library to read the Citi Bike CSV and display it as a DataFrame. Then I changed the data type of the tripduration column so that the date and time would be displayed. I exported the new DataFrame as a CSV and loaded the new CSV in Tableau to create the story/visuals.

Citi Bike Analysis Results

1. Checkout per Users

• This graph shows that users who ride the bikes for about 3-9 minutes have the highest check-out rates.
• The longer the trip, the lower the number of users.

2. Checkout per Gender

• This graph shows the number of users by gender.
• The customer base seems to be predminately male.

3. Trips by Weekday

• This graph shows the number of trips throughout the week by the hour.
• Peak hours are from 7-9 AM and 5-7 PM.
• Most likely people commuting to or from work.

4. Trips by Gender

• This graph shows the number of trips throughout the week by the hour per gender.
• Peak hours are from 7-9 AM and 5-7 PM (normal working hours).
• Males use the bikes more frequently than females.

5. Trips by Gender by Weekday

• This graph shows the number of trips throughout the week by gender, and by user type.
• Males make up the bulk of the customer base, so they have the most subscriptions and are the most active.
• The most active days are Thursdays and Fridays.

6. Average Trip Duration

• This graph shows the average trip duration by birth year.
• The later the birth year, the longer the ride duration.

7. Top Starting Locations

• This graph shows the top starting locations.
• There's a noticeable difference between people who use the bikes in the Manhattan/Lower Manhattan areas vs. the rest of New York.