The code in this repository is very exploratory/developmental. It is in no way production ready. As the objective in this assignment was to generate effective visualizations and understand the data, I thought this would be the best way to achieve that goal. I have attempted to place comments throughout to annotate my work, but I would advise consulting the web report for methodology.
The entirety of the code is in the los-angeles-metro-bike-share-trip-data
folder. I describe each of the scripts briefly
daily_usage.py
: This gives the number of rides and can be configured to calculate these statistics over various timeframes.Bonus 1.ipynb
,Bonus 2.ipynb
,Bonus 3.ipynb
: Exploration for each of the bonus problems is in its own jupyter notebook, labeled appropriately.general_calculations.ipynb
: This file contains my preliminary data exploration and some work on arrival/departure counts and travel timesgeneral_stats.py
: This script calculates arrivals and departures accounting for round trips and other various factorsgraph_bikes.py
: This script graphs the bike mileagemake_weekday_breakdown.py
: This script graphs the ridership (absolute number and duration) by day of the weekRequired Questions.ipynb
: This jupyter notebook contains a lot of preliminary data work that I later visualized with more formal scripts.revenue_pie.py
: This script graphed the pie chart for revenue breakdownseasonal_grapher.py
: This script made the stacked bar graph for seasonal ridership