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, i will use data provided by Motivate, a bike share system provider for many major cities in the United States, to uncover bike share usage patterns. I will compare the system usage between three large cities: Chicago, New York City, and Washington, DC.
In this project, I will explore data related to bike share systems using Python, for three major cities in the United States—Chicago, New York City, and Washington. I will write code to 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, the following software requirements apply:
- Install Python 3, NumPy, and pandas using Anaconda
- A text editor, like Sublime or Atom.
- A terminal application (Terminal on Mac and Linux or Cygwin on Windows).
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
The original files are much larger and messier, and you don't need to download them, but they can be accessed here if you'd like to see them (Chicago, New York City, Washington). These files had more columns and they differed in format in many cases. Some data wrangling has been performed to condense these files to the above core six columns.
The code output will 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)
The bikeshare.py file is set up as a script that takes in raw input to create an interactive experience in the terminal that answers questions about the dataset. The experience is interactive because depending on a user's input, There are four questions that will change the answers:
- Would you like to see data for Chicago, New York, or Washington?
- Would you like to filter the data by month, day, or not at all?
- (If they chose month) Which month - January, February, March, April, May, or June?
- (If they chose day) Which day - Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday?
The answers to the questions above will determine the city and timeframe on which data analysis will be done. After filtering the dataset, users will see the statistical result of the data, and choose to start again or exit.
The script also ask the user whether they would like want to see the raw data. If the user answers 'yes,' then the script should print 5 rows of the data at a time, then ask the user if they would like to see 5 more rows of the data. The script continue prompting and printing the next 5 rows at a time until the user chooses 'no,' they do not want any more raw data to be displayed.