🚀 NASA Near-Earth Object (NEO) Tracker

A Python CLI tool that retrieves, analyzes, and visualizes Near-Earth Object (NEO) data using NASA's NeoWs API.

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🌌 About

NASA NEO Tracker is a command-line Python project that uses NASA's Near Earth Object Web Service (NeoWs) API to retrieve asteroid data for a selected date range.

The project started as a simple API-based asteroid tracker and evolved into a data-analysis and visualization project using Pandas and Matplotlib.

Current Version

Version 3.0 — Data Visualization with Matplotlib

✨ Features

🔭 Search for Near-Earth Objects using a custom date range
🪨 Display asteroid names and estimated diameters
⚠️ Identify potentially hazardous asteroids
📏 Find the largest asteroid in the selected date range
📐 Find the smallest asteroid in the selected date range
🌍 Find the closest asteroid based on miss distance
📅 Display the closest approach date
📊 Calculate average asteroid diameter
⚠️ Calculate the percentage of potentially hazardous asteroids
🏆 Display the top 5 largest asteroids
📆 Group potentially hazardous asteroids by date
🔢 Sort asteroid data by estimated diameter
✅ Validate date formats and date ranges
🛡️ Handle API request errors
💻 Simple command-line interface
🌌 ASCII art splash screen
✨ Improved CLI formatting and presentation
📊 Visualize the top 5 largest asteroids using a bar chart
📈 Generate graphical representations of asteroid data
🖥️ Access visualizations through the CLI menu

🛠️ Technologies Used

Python
NASA NeoWs API
Requests
Pandas
Matplotlib
python-dotenv
Git & GitHub

Pandas is used to organize the API response into a DataFrame and perform operations such as filtering, sorting, grouping, and calculating statistics.

Matplotlib is used to visualize asteroid data through charts and graphs.

📋 Requirements

Before running the project, make sure you have:

Python 3.x
A NASA API key
Internet connection

The required Python packages are listed in requirements.txt.

📦 Installation

1. Clone the repository

git clone https://github.com/TheCreatorJJ/Nasa-asteroid-tracker.git

2. Enter the project directory

cd Nasa-asteroid-tracker

3. Install dependencies

pip install -r requirements.txt

🔑 API Key Setup

This project uses a NASA API key to access the NeoWs API.

Create a file named .env in the project folder:

NASA_API_KEY=your_nasa_api_key_here

Your project structure should look similar to:

Nasa-asteroid-tracker/
│
├── main.py
├── art.py
├── README.md
├── requirements.txt
├── .gitignore
└── .env

🚀 Usage

Run the program with:

python main.py

The program will display the NASA NEO Tracker menu:

======================================
           NASA NEO TRACKER
 Near-Earth Object (NEO) Data Analyzer
======================================

1. Search asteroids
2. Show top 5 Largest
3. Exit

Search Asteroids

Select option:

Enter a start date:

Enter the start date (YYYY-MM-DD):

Then enter an end date:

Enter the end date (YYYY-MM-DD):

Example:

Enter the start date (YYYY-MM-DD): 2026-01-01
Enter the end date (YYYY-MM-DD): 2026-01-07

The program retrieves Near-Earth Object data from NASA and analyzes the results using Pandas.

View Top 5 Largest

After searching for asteroid data, select:

The program displays a Matplotlib bar chart showing the top 5 largest asteroids from the searched date range.

ℹ️ Asteroid data must be loaded using option 1 before the visualization can be displayed.

📊 Data Analysis

Version 2 introduced Pandas to make the project more than just an API data viewer.

The program currently performs analysis such as:

Largest Asteroid

Finds the asteroid with the largest estimated maximum diameter.

Smallest Asteroid

Finds the asteroid with the smallest estimated maximum diameter.

Closest Approach

Finds the asteroid with the smallest recorded miss distance.

Average Diameter

Calculates the average estimated maximum diameter of the retrieved asteroids.

Hazardous Asteroids

Counts the number of potentially hazardous asteroids and calculates their percentage of the total results.

Top 5 Largest

Sorts the asteroid DataFrame by diameter and displays the five largest asteroids.

Hazardous Asteroids by Date

Groups potentially hazardous asteroids by their close-approach date.

📈 Data Visualization

Version 3.0 introduced Matplotlib for visualizing the analyzed asteroid data.

The current visualization allows the user to view the top 5 largest asteroids in a bar chart.

The chart displays:

Asteroid names
Estimated maximum diameter
Formatted data labels
Rotated x-axis labels for improved readability

Visualization Menu

The visualization feature is accessed through the CLI.

Users must first search for asteroid data before viewing the available visualization.

🖥️ Example Output

Example results from a previous search:

================================
        ASTEROID SUMMARY
================================

Total Asteroids: 36
Potentially Hazardous: 7
Hazardous Percentage: 19.44%

Largest Asteroid: 418265 (2008 EA32)
Diameter: 2,992.54 meters

Smallest Asteroid: (2018 TV5)
Diameter: 7.48 meters

Closest Asteroid: (2014 AF16)
Miss Distance: 3,071,593.48 km
Approach Date: 2026-01-04

Average Diameter: 285.73 meters
================================

Top 5 Largest Asteroids

================================
      TOP 5 LARGEST ASTEROIDS
================================

1. 418265 (2008 EA32) - 2,992.54 meters
2. 26663 (2000 XK47) - 1,412.67 meters
3. 620103 (2018 LC3) - 1,042.41 meters
4. (2018 LC3) - 1,032.86 meters
5. 367390 (2008 MB5) - 924.78 meters

================================

Hazardous Asteroids by Dates

================================
       HAZARDOUS BY DATE
================================

2026-01-01: 3
2026-01-02: 1
2026-01-04: 1
2026-01-05: 1
2026-01-07: 1

Note: The values above are example results. Results will vary depending on the selected date range and the data returned by NASA's API.

🧠 What I Learned

This project is part of my Python learning journey.

While building it, I practiced:

Working with APIs
Making HTTP requests with requests
Using environment variables
Working with JSON data
Working with dictionaries and lists
Using loops
Using conditional statements
Creating functions
Working with dates using datetime
Handling errors with try and except
Building a CLI application
Creating Pandas DataFrames
Filtering DataFrames
Sorting DataFrame data
Finding maximum and minimum values
Calculating averages
Grouping and counting data
Iterating through DataFrame rows
Formatting numerical output
Creating data visualizations
Using Matplotlib
Creating bar charts
Formatting chart labels
Improving chart readability
Using Git
Using GitHub
Managing project dependencies

🗂️ Project Structure

Nasa-asteroid-tracker/
│
├── main.py              # Main application
├── art.py               # ASCII art
├── README.md            # Project documentation
├── requirements.txt     # Python dependencies
├── .gitignore           # Files excluded from Git
└── .env                 # Private NASA API key

.env is intentionally excluded from GitHub.

📈 Project Versions

Version 1 — NASA API Tracker

The first version focused on learning how to work with an external API.

NASA NeoWs API integration
Custom date-range search
Basic asteroid information
Hazardous asteroid identification
Largest asteroid detection
Closest asteroid detection
Close-approach date
Date validation
API error handling
Basic Command Line Interface

Version 2 — Data Analysis & CLI Improvements

The second version expanded the project into a small data-analysis application.

Added Pandas
Added DataFrame-based analysis
Added smallest asteroid detection
Added average diameter calculation
Added hazardous asteroid percentage
Added top 5 largest asteroid ranking
Added hazardous asteroids grouped by date
Added sorting and filtering
Improved numerical formatting
Improved CLI layout
Added ASCII art
Improved project documentation

Version 3.0 — Data Visualization

The third version introduced data visualization using Matplotlib.

Added Matplotlib
Added Top 5 largest asteroid bar chart
Added formatted chart data labels
Added rotated x-axis labels for readability
Added visualization option to the CLI
Added Matplotlib to project dependencies
Improved graphical presentation of analyzed data
Visualizes the top 5 largest asteroids
Displays asteroid names and estimated diameters

🔮 Future Improvements

Possible improvements for future versions:

🖥️ Add a Pandas-based CLI data viewer
📊 Allow users to select different visualization types
📈 Add histogram visualizations
📉 Add scatter plot visualizations
📅 Add line charts for asteroid activity by date
📊 Add an option to display all visualizations
📁 Add CSV export
🔎 Add more detailed asteroid filtering
📊 Add additional statistics
🖥️ Further improve the CLI
🌐 Eventually build a web-based dashboard
🚀 Expand the project for NASA Space Apps Challenge

🌎 Data Source

Asteroid data is provided by NASA's Near Earth Object Web Service (NeoWs) API.

NASA API:

https://api.nasa.gov/

📚 Project Purpose

This project was created as a hands-on Python learning project.

The goal is to gradually develop the project while learning new programming and data-analysis concepts rather than building everything at once.

V1 → API Integration

V2 → Pandas & Data Analysis

V3 → Data Visualization with Matplotlib

V4 → Data Exploration & Advanced CLI

Future → More advanced data science

👨‍💻 Author

Created by TheCreatorJJ as a Python and Data Science learning project.

⭐ If you find the project interesting, feel free to explore the code and follow its development.

Name		Name	Last commit message	Last commit date
.gitignore		.gitignore
README.md		README.md
art.py		art.py
main.py		main.py
requirements.txt		requirements.txt

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