Web app for analyzing bike share data for the 2018 Capital One SWE Summit Challenge

Deployed App on Heroku: https://mbs-analysis.herokuapp.com/

1. Data Visuals: Display or graph 3 metrics or trends from the data set that are interesting to you.
   1. Metric 1: Average Bike Rentals and Returns Per Hour in a Day (See Graph 1)
   2. Metric 2: Average Trip Duration by Passholder Type (See Graph 2)
   3. Metric 3: Bike Efficiency - Number of Trips and Total Trip Duration by Bike ID (See Graph 3)
2. Which start/stop stations are most popular? (See Graph 4)
3. What is the average distance traveled? (See Graph 5)
4. How many riders include bike sharing as a regular part of their commute? (See Graph 6)
5. (Bonus 1) How does ridership change with seasons? (See Graph 7)
6. (Bonus 2) Is there a net change of bikes over the course of a day? If so, when and where should bikes be transported in order to make sure bikes match travel patterns? (See Graph 8a, 8b)
7. (Bonus 3) What is the breakdown of Trip Route Category-Passholder type combinations? What might make a particular combination more popular? (See Graph 9)

1. analysis.py: Data Analysis using pandas
   1. Clean raw CSV
   2. Analyze using dataframes and dictionaries
   3. Write analysis results to CSV files stored in the /data folder (these CSV files are rendered in app.py)
   4. Note: Executing python analysis.py will generate all the CSV files needed to display the results in app.py
2. app.py: Web App created using Dash (Python framework built on Flask, React.js, and Plotly.js)
   1. Render CSV files from analysis.py as graphs
   2. Graphs have toggle option to show/hide bars
   3. Hover over bars to reveal their numerical values
   4. Analysis of each graph is located below the graph

• I assumed that each trip designates a unique rider and that "regular user" indicates a user who bought a plan (Monthly Pass or Flex Pass).
• Additionally, I initially cleaned the dataset before analysis, removing rows with any NaN values.
• Furthermore, to calculate distance traveled by round trips, I assumed the round trip average speed could be estimated by the average speed of all one-way trips originating from the given station. If the given station did not have an average speed from one-way trips, I used the average speed of all one-way trips from any station.