progahmedfathi/explore-us-bikeshare-data

1st project in the egFWD scholarship for Data Analysis Nanodegree Program from Udacity.

Explore US Bikeshare Data

The 1st project in the egFWD scholarship for Data Analysis Nanodegree Program from Udacity.

These projects REVIEWED by Udacity reviewers (real persons) according to Project Rubric, and the projects MUST "meet the specifications" or "exceed specifications" in each category in order for my submission to pass.

About the project

In this project, I wrote Python code to import US bikeshare data and answered interesting questions about it by computing descriptive statistics. I also wrote a script that took in raw input to create an interactive experience in the terminal to present these statistics.

The Dataset

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. You will compare the system usage between three large cities: Chicago, New York City, and Washington DC.

You can find the dataset which I used on this link Dataset

About the Dataset

Data 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 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

Data for the first 10 rides in the new_york_city.csv file

Statistics Computed

I learned about bike share use in Chicago, New York City, and Washington by computing a variety of descriptive statistics. In this project, I wrote code to provide the following information:

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
   • 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)