🌟 PlotSense: AI-Powered Data Visualization Assistant

📌 Overview

PlotSense is an AI-powered assistant that helps data professionals and analysts make smarter, faster, and more explainable data visualizations. Whether you're exploring a new dataset or building dashboards, PlotSense simplifies the process with:

• ✅ Smart Visualization Suggestions - Recommends the best plots based on your data structure and relationships.
• 📊 Visualization Plot - Generates suggested plot with ease.
• 🧠 Natural Language Explanations – Automatically explains charts in plain English.
• 🔗 Seamless Integration – Works out of the box with pandas, matplotlib, and seaborn.

Let AI supercharge your EDA (Exploratory Data Analysis).

📚 Documentation

• 📖 Technical Roadmap - Future features and development plans
• 🏗️ Architecture & Methodology - System design and technical implementation

💬 Community

Join our community to get help, share ideas, and connect with other PlotSense users:

• 💭 Discord Server - Chat with the community and get real-time support

⚡ Quickstart

🔧 Install the package

Using pip:

pip install plotsense

Using uv (recommended for faster installation):

uv pip install plotsense

🧠 Import PlotSense:

import plotsense as ps
from plotsense import recommender, plotgen, explainer

🔐 Authenticate with Groq API:

Get your free API key from Groq Cloud https://console.groq.com/home

import os
# Set GROQ_API_KEY environment variable
os.environ['GROQ_API_KEY'] = 'your-api-key-here'

#or

# Set API key (one-time setup)
ps.set_api_key("your-api-key-here")

🚀 Core Features

🎯 1. AI-Recommended Visualizations

Let PlotSense analyze your data and suggest optimal charts.

import pandas as pd
# Load your dataset (e.g., pandas DataFrame)
df = pd.read_csv("data.csv")

# Get AI-recommended visualizations
suggestions = recommender(df) # default number of suggestions is 5
print(suggestions)

📊 Sample Output:

🎛️ Want more suggestions?

suggestions = recommender(df, n=10)  

📈 2. One-Click Plot Generation

Generate recommended charts instantly using .iloc

plot1 = plotgen(df, suggestions.iloc[0]) # This will plot a bar chart with variables 'survived', 'pclass'
plot2 = plotgen(df, suggestions.iloc[1]) # This will plot a bar chart with variables 'survived', 'sex'
plot3 = plotgen(df, suggestions.iloc[2]) # This will plot a histogram with variable 'age'

or Generate recommended charts instantly using three argurments

plot1 = plotgen(df, 0, suggestions) # This will plot a bar chart with variables 'survived', 'pclass'
plot2 = plotgen(df, 1, suggestions) # This will plot a bar chart with variables 'survived', 'sex'
plot3 = plotgen(df, 2, suggestions) # This will plot a histogram with variable 'age'

🎛️ Want more control?

plot1 = plotgen(df, suggestions.iloc[0], x='pclass', y='survived')

Supported Plots

• scatter
• bar
• barh
• histogram
• boxplot
• violinplot
• pie
• hexbin

🧾 3. AI-Powered Plot Explanation

Turn your visualizations into stories with natural language insights:

explanation = explainer(plot1)

print(explanation)

⚙️ Advanced Options

• Custom Prompts: You can provide your own prompt to guide the explanation

explanation = explainer(
    fig,
    prompt="Explain the key trends in this sales data visualization"
)

• Multiple Refinement Iterations: Increase the number of refinement cycles for more polished explanations:

explanation = explainer(fig, max_iterations=3)  # Default is 2

🔄 Combined Workflow: Suggest → Plot → Explain

suggestions = recommender(df)
plot = plotgen(df, suggestions.iloc[0])
insight = explainer(plot)

🧪 Testing

PlotSenseAI has comprehensive test coverage for both Python and frontend code.

Quick Test Commands

Python Tests:

# Run all tests
pytest

# Run with coverage
pytest --cov=plotsense --cov-report=html

# Run fast tests only (skip slow tests)
pytest -m "not slow"

Frontend Tests:

cd web

# Run all tests
npm test

# Run with coverage
npm run test:coverage

# Run in watch mode
npm test -- --watch

Test Infrastructure

• Backend: pytest with 950+ lines of tests covering:

  • Unit tests for individual functions
  • Integration tests for component interaction
  • End-to-end workflow tests
  • Mock external API calls (Groq)

• Frontend: Vitest + React Testing Library

  • Component tests for UI elements
  • User interaction testing
  • Accessibility testing

• CI/CD: GitHub Actions runs tests automatically on every push and PR

• Pre-commit Hooks: Automated linting and quick tests before commits

For detailed testing documentation, see TESTING.md.

Setting Up Development Environment

Using uv (recommended):

# Install uv if not already installed
# Visit https://docs.astral.sh/uv/getting-started/installation/

# Sync all dependencies including dev extras
uv sync --all-extras

# Activate the virtual environment
# On Windows:
.venv\Scripts\activate
# On Unix or macOS:
source .venv/bin/activate

# Install frontend dependencies
cd web && npm install

# Install pre-commit hooks (optional but recommended)
uv run pre-commit install

Using pip (traditional):

# Install Python dependencies
pip install -e .
pip install pytest pytest-cov pytest-mock

# Install frontend dependencies
cd web && npm install

# Install pre-commit hooks (optional but recommended)
pip install pre-commit
pre-commit install

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Quick Start for Contributors

1. Fork and clone the repository

2. Set up development environment:

   Using uv (recommended):

   # Install uv: https://docs.astral.sh/uv/getting-started/installation/
   uv sync --all-extras
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   uv run pre-commit install

   Using pip:

   pip install -e .
   pip install pytest pytest-cov pytest-mock pre-commit
   pre-commit install

3. Create a feature branch: git checkout -b feature/your-feature

4. Make changes and add tests

5. Run tests: uv run pytest (or pytest) and cd web && npm test

6. Submit a Pull Request

Branching Strategy

• main → Production-ready version
• dev → Active development
• feature/<feature-name> → New features
• fix/<bug-name> → Bug fixes

💡 How to Help

• 🐞 Bug Reports → Open an issue
• 💡 Feature Requests → Request a feature
• ❓ Questions → Ask a question
• 🚀 Submit PRs → See CONTRIBUTING.md

📅 Roadmap

Upcoming features:

• More model integrations
• Automated insight highlighting
• Jupyter widget support
• Features/target analysis
• More supported plots
• PlotSense web interface
• PlotSense customised notebook template

📥 Install or Update

Using pip:

pip install --upgrade plotsense  # Get the latest features!

Using uv:

uv pip install --upgrade plotsense  # Get the latest features faster!

👥 Maintainers

PlotSense is maintained by a dedicated team of developers and data scientists:

Name	GitHub	Email
Christian Chimezie	@christianchimezie	chimeziechristiancc@gmail.com
Toluwaleke Ogidan	@T-leke	gbemilekeogidan@gmail.com
Onyekachukwu Ojumah	@ojumah20	Onyekaojumah22@gmail.com
Grace Farayola	@Itsmeright	gracefarayola@gmail.com
Amaka Iduwe	@Nwaamaka-Iduwe	nwaamaka_iduwe@yahoo.com
Nelson Ogbeide	@Nelsonchris1	Ogbeide331@gmail.com
Abayomi Olagunju	@jerryola1	https://abayomiolagunju.net/
Olamilekan Ajao	@olamilekanajao	olamilekan011@gmail.com

We welcome contributions from the community! See CONTRIBUTING.md for guidelines.

🛡 License

Apache License 2.0

🔐 API & Privacy Notes

• Your API key is securely held in memory for your current Python session.
• All requests are processed via Groq's API servers—no data is stored locally by PlotSense.
• Requires an internet connection for model-backed features.

Let your data speak—with clarity, power, and PlotSense. 📊✨

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