Date created

1/10/2023

BikeShare

This script enable users to analyze bike-sharing data and gain statistical insights by interactively exploring rental information in various cities.

Description

This bikeshare script is a Python program that provides statistical analysis and insights based on a dataset of bike-sharing systems. It allows users to interactively explore and analyze data related to bike rentals in different cities. When executed, the bikeshare.py script prompts the user to input certain parameters, such as the city, month, and day of the week, to filter the data for analysis. The supported cities typically include Chicago, New York City, and Washington, D.C.

Once the user provides the necessary inputs, the script reads the corresponding dataset file and performs various calculations and computations to generate statistical information and insights.

Key Features

Some of the key features and analyses provided by bikeshare.py include:

1. Popular travel times: The script determines the most common times of day, month, and day of the week for bike rentals.

2. Popular stations and routes: It identifies the most frequently used start and end stations, as well as the most common routes taken by riders.

3. Trip duration: The script calculates the total travel time, average trip duration, and other statistics related to the duration of bike rides.

4. User information: It provides insights into the types of users, such as subscribers or customers, and their corresponding counts.

5. User interaction: The script allows users to view raw data upon request, displaying individual trip records based on the applied filters.

6. References This display the author name and references used through-out the writing of this script.

Software

• Anaconda: python, numpy, pandas
• Pycharm

Files used

• chicago.csv
• ney_york_city.csv
• washington.csv

Credits

I would like to express my sincere gratitude and appreciation to the entire Udacity team, for empowering me with the knowledge and skills needed to excel in the field of data science. I am truly grateful for the opportunity to be a part of this remarkable learning community. You can click the links below for more infomation.

1. Udacity

2. W3Schools

3. Stack Overflow

4. Slack