🚲 US Bikeshare Analysis — Python Command-Line Application

An interactive command-line application that lets users explore US bikeshare trip data across three cities — Chicago, New York City, and Washington. Users filter data by city, month, and day of the week, and the program computes and displays a rich set of descriptive statistics in real time.

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📌 Project Overview

This project is built around a single Python script that loads city-specific CSV datasets, applies user-defined filters, and outputs key statistics about travel patterns, station usage, trip durations, and rider demographics. All inputs are validated interactively with clear error messages and retry prompts.

Built as part of the Udacity Programming for Data Science with Python Nanodegree.

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📁 Project Structure

├── Project.ipynb                # Main notebook / script
├── chicago.csv                  # Chicago bikeshare data
├── new_york_city.csv            # New York City bikeshare data
├── washington.csv               # Washington bikeshare data (no Gender/Birth Year)
└── README.md

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▶️ How to Run

Requirements: Python 3.x with pandas, numpy, and matplotlib

pip install pandas numpy matplotlib
jupyter notebook Project.ipynb

Or run as a standalone script if exported:

python bikeshare.py

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🖥️ Usage

The program guides the user through three sequential input prompts:

1. Choose a city:

- chicago
- new york city
- washington

2. Choose a month (or all):

- All, January, February, March, April, May, June

3. Choose a day of the week (or all):

- All, Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday

All inputs are case-insensitive — CHICAGO, chicago, and cHicAgo are all accepted. Invalid inputs trigger a descriptive error and re-prompt the user.

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📊 Statistics Computed

⏱️ Time Statistics (time_stats)

• Most common month of travel
• Most common day of the week
• Most common start hour

🚉 Station Statistics (station_stats)

• Most commonly used start station
• Most commonly used end station
• Most frequent start → end station combination

🕐 Trip Duration Statistics (trip_duration_stats)

• Total travel time — displayed in hours, minutes, and seconds
• Mean travel time — displayed in hours, minutes, and seconds

👤 User Statistics (user_stats)

• Count breakdown by user type (Subscriber / Customer) with bar chart
• Count breakdown by gender (Chicago & NYC only)
• Earliest, most recent, and most common birth year (Chicago & NYC only)

Washington's dataset does not include Gender or Birth Year columns — the program handles this gracefully with try/except blocks and informs the user.

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⚙️ Key Functions

Function	Description
get_filters()	Collects and validates city, month, and day inputs from the user
load_data(city, month, day)	Loads the appropriate CSV and applies the chosen filters
time_stats(df)	Computes most frequent travel times
station_stats(df)	Computes most popular stations and trip routes
trip_duration_stats(df)	Computes total and average trip duration
user_stats(df)	Computes user type, gender, and birth year stats
main()	Orchestrates the full pipeline with a restart loop

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🛠️ Tech Stack

Tool	Purpose
pandas	Data loading, filtering, and aggregation
numpy	Numerical operations
matplotlib	Bar chart for user type distribution
time	Execution time tracking per analysis step

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📝 Notes & Limitations

• Data is limited to January–June for all three cities
• Washington does not include Gender or Birth Year — stats for these fields are skipped with a user-friendly message
• The restart loop at the end of each session allows repeated queries without re-running the script
• Navigation direction for the arrow buttons is detected at page load — resizing mid-session won't affect scroll behavior

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👤 Author

Built as a submission for the Udacity Programming for Data Science with Python Nanodegree — Python Fundamentals & Data Exploration module.