Project name: Bikeshare

author

thuong

Project Overview

Over the past decade, bicycle-sharing systems have been growing in number and popularity in cities across the world. Bicycle-sharing systems allow users to rent bicycles on a very short-term basis for a price. This allows people to borrow a bike from point A and return it at point B, though they can also return it to the same location if they'd like to just go for a ride. Regardless, each bike can serve several users per day.

Thanks to the rise in information technologies, it is easy for a user of the system to access a dock within the system to unlock or return bicycles. These technologies also provide a wealth of data that can be used to explore how these bike-sharing systems are used.

In this project, you will use data provided by Motivate(opens in a new tab), a bike share system provider for many major cities in the United States, to uncover bike share usage patterns. You will compare the system usage between three large cities: Chicago, New York City, and Washington, DC.

Params

1. Popular times of travel (i.e., occurs most often in the start time)

   • most common month
   • most common day of week
   • most common hour of day

2. Popular stations and trip

   • most common start station-1
   • most common end station-2
   • most common trip from start to end (i.e., most frequent combination of start station and end station)

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, most common year of birth (only available for NYC and Chicago)

Prequisites

1. You should have Python 3, NumPy, and pandas installed using Anaconda
2. A text editor, like Sublime(opens in a new tab) or Atom(opens in a new tab).
3. A terminal application (Terminal on Mac and Linux or Git Bash on Windows).

Files

1. chicago.csv
2. new_york_city.csv
3. washington.csv

date: 24.06.03