2020-05-11
This project uses Python to explore data related to bike share systems for three major cities in the United States—Chicago, New York City, and Washington.
It imports raw data to create an interactive experience in the terminal to answer interesting questions about the data by computing descriptive statistics.
By computing a variety of descriptive statistics, this project provide the following information:
1. Popular times of travel
- most common month
- most common day of week
- most common hour of day
2. Popular stations and trip
- most common start station
- most common end station
- 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)
In this project, I used data provided by Motivate, a bike share system provider for many major cities in the United States, to uncover bike share usage patterns.
The data files used are:
- chicago.csv
- new_york_city.csv
- washington.csv
- Python 3, NumPy, and pandas
- Atom
- Terminal on Mac
- This project is an assignment of Udacity Programming for Data Science with Python.
- I was inspired by repo create-your-own-adventure for README writing.
- I checked Markdown quick reference cheat sheet for markdown reference.