Udacity Data Analyst Degree - Project II
In this project, I will explore data related to bike share systems for three major cities in the United States—Chicago, New York City, and Washington. Using Python, I will write a code import the data and answer interesting questions about it by computing descriptive statistics. I will also write a script that takes in raw input to create an interactive experience in the terminal to present these statistics.
To complete this project, i'll require the following softwares:
- Python
- A text editor, like Sublime or Atom
- A terminal application
Randomly selected data for the first six months of 2017 are provided for all three cities. All three of the data files contain the same core six (6) columns:
Start Time (e.g., 2017-01-01 00:07:57) End Time (e.g., 2017-01-01 00:20:53) Trip Duration (in seconds - e.g., 776) Start Station (e.g., Broadway & Barry Ave) End Station (e.g., Sedgwick St & North Ave) User Type (Subscriber or Customer) The Chicago and New York City files also have the following two columns: Gender Birth Year
You will learn about bike share use in Chicago, New York City, and Washington by computing a variety of descriptive statistics. In this project, you'll write code to provide the following information:
most common month most common day of week most common hour of day
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)
total travel time average travel time
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)
- chicago.csv
- new_york_city.csv
- washington.csv