Exploring US Bike Share System Data

Description

This project is part of the Udacity data analysis nanodegree.

In this project, I am exploring data related to bike share systems for three major cities in the United States; Chicago, New York City, and Washington. After taking input from the user, this script answers interesting questions about the data by computing descriptive statistics using pandas library.

Requirements

Language: Python 3.7 or above
Libraries : pandas

Usage

Run the commands below from terminal after navigating to the project directory.

python bikeshare.py

Script Flow

User Input

The script prompts the user to choose one of the three aforementioned cities. Afterwards, the user is asked to choose the filters based on which the statistics are computed.

Available filters:

• Month: filter by a specific month only
• Day: filter by a specific day of the week only
• All: no filters

The user is then prompted to choose the month, day or both based on the filter choice.

Presenting Statistics

Frequent Times of Travel:

1. Most common month
2. Most common day of the week
3. Most frequent start hour

Station statistics:

1. Most common start station
2. Most common end station
3. Most common combination of start and end stations

Trip duration statistics:

1. Total trip duration
2. Average trip duration

User statistics:

1. Subscribers vs. customers distribution
2. Gender distribution
3. Earliest year of birth, most recent year of birth and most common year of birth

Displaying raw data

The user is prompted if he/she wishes to view individual raw trip data. If the user inputs "yes", the data of 5 trips will be presented in raw format.

The same prompt is repeated until the user inputs "no". The user is finally prompted if he/she wishes to restart the exploration.