17th September 2021

Explore Us Bikshare Data

Description

In this project, I will explore data related to bike share systems for three major cities Chicago, New York City, and Washington in the Us. 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.

The Datasets

The datasets contains randomly selected data for the first six months of 2017 for all three cities. The data files for all three cities contain the same core six columns:

• Start Time
• End Time
• Trip Duration
• Start Station
• End Station
• User Type

The Chicago and New York City files however contains two extra columns:

• Gender
• Birth Year

Statistics Computed

1. Popular times of travel:

   • 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:

3. Trip duration:

   • total travel time
   • average travel time

4. User info:

   • counts of each user type
   • counts of each gender (only available for New York City and Chicago)
   • earliest, most recent, most common year of birth (only available for New York City and Chicago)

Softwares needed

To complete this project, I used the following softwares:

• Python
• A text editor, I used Atom
• A terminal application

Builtin library used

• datetime
• Pandas
• Numpy

Interactive Environment

This project explores Bikeshare data from 3 differents cities. It uses data from csv files to compute statistics from those 3 cities, also takes user inputs to create an interactive user experience.

Files used

• chicago.csv
• new_york_city.csv
• washington.csv