mohamwd-najm / exploring-bikeshare-us-system-data Goto Github PK

Overview

In this project, I used of Python to explore data related to bike share systems for three major cities in the United States—Chicago, New York City, and Washington. I wrote code to import the data and answer interesting questions about it by computing descriptive statistics. I also wrote a script that takes in raw input to create an interactive experience in the terminal to present these statistics.

The Datasets 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

Statistics Computed

The purpose of this project is to analyze bike share use in Chicago, New York City, and Washington by computing a variety of descriptive statistics:

#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)

The Files

To answer these questions using Python, I need to write a Python script using the three city dataset files :

chicago.csv new_york_city.csv washington.csv