10/17/2019

Explore US Bikeshare Data

This project uncovers bike share usage patterns using data provided by Motivate, a bike share system provider for many major cities in the United States. A variety of descriptive statistics are computed in order to learn about bike share usage in Chicago, New York City, and Washington DC during the time period of January through June 2017.

Questions answered are:

1. Popular Times of Travel

   • Most common month
   • Most common day of week
   • Most common hour of day

2. Popular Stations and Trips

   • Most common start station
   • Most common end station
   • Most common trip from start to end

3. Trip Duration

   • Total travel time
   • Average travel time

4. User info

   • Counts of each user type
   • Counts of each gender (only available for NYC and Chicago)
   • Earliest, most recent, and most common year of birth (only available for NYC and Chicago)

• bikeshare_2.py
• chicago.csv
• new_york_city.csv
• washington.csv

The resources used to complete this project were:

• How to handle Washington DC data that does not have gender or birth year information:

  • https://knowledge.udacity.com/questions/55524

• How to display DataFrame data for user review using .iloc:

  • https://stackoverflow.com/questions/31593201/how-are-iloc-ix-and-loc-different
  • https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.iloc.html
  • https://www.geeksforgeeks.org/python-extracting-rows-using-pandas-iloc/