haggag/pdsnd_github

GitHub project (Project 3) repository for PDSND

Bikeshare

Bikeshare is a simple script that analyzes the Bikeshare dataset and displays valuable statistics.

Dependencies

This project has the following requirements:

• Python 3.6 or higher
• Pandas
• Bikeshare dataset (see the dataset section)

Installation

Clone the GitHub repository and execute the main script:

$ git clone https://github.com/haggag/pdsnd_github.git
$ cd pdsnd_github
$ python ./bikeshare.py

Usage

The Bikeshare program works by executing the bikeshare.py script. It then interactively asks the user for the required filters. Finally, the statistics are computed and displayed on the terminal.

Statistics Computed

The following descriptive statistics are displayed:

1. Popular times of travel (i.e., occurs most often at the start time)
   • Most common month.
   • Most common day of the week.
   • Most common hour of the 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 and end stations).
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 birth year (only available for NYC and Chicago).

Dataset

This project analyzes data provided by Motivate, a bike share system provider for many major cities in the United States, to uncover bike share usage patterns.

The required dataset was created by randomly selecting data for the first six months of 2017 provided for Chicago, New York City, and Washington, DC. The data can be downloaded from Udacity as 3 CSV files. All three files contain the same core six 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

Credits

The following resources were consulted during the project implementation:

• Udacity Python lessons
• Pandas documentation

License

The Bikeshare program is distributed under the MIT license.