📘 Overview

DataGuard brings dataset analysis directly into Visual Studio Code.

Open a supported dataset and instantly access an interactive workspace for inspection, visualization, cleaning, and optional AI-assisted insights — without leaving your editor.

All core processing runs locally.

AI capabilities remain optional and disabled until configured.

✨ Features

Dataset Analysis

• Automatic dataset profiling
• Dataset overview and metadata
• Row and column inspection
• Column type detection
• Missing value analysis
• Duplicate discovery
• Statistical summaries

Interactive Visualizations

• Dataset composition
• Numerical distributions
• Missing value breakdown
• Column exploration
• Interactive charts

Data Cleaning

Perform cleaning directly inside VS Code:

• Remove duplicates
• Fill missing values
• Convert data types
• Column operations
• Safe save workflow

Changes remain local.

Optional AI Insights

Configure an AI provider to generate:

• Dataset summaries
• Cleaning suggestions
• Pattern discovery
• High-level observations

Compatible with configurable AI providers:

• OpenAI
• Anthropic
• Google Gemini
• Groq
• Cohere

AI providers are optional and require user configuration.

🧩 Supported Formats

📸 Screenshots

Profile Dataset

Explore Dashboard

Clean Data

AI Insights (Optional)

🎥 Demonstration

Watch the demo on YouTube:

🗂 Dataset Attribution

Screenshots, demonstrations, and promotional materials shown in this repository may include examples generated using the googleplaystore.csv dataset.

Dataset source:

• L. Gupta, "Google Play Store Apps," Feb 2019. [Online]. Available: Kaggle

Usage purpose:

• Product demonstration
• Dashboard showcase
• Visualization examples
• Documentation screenshots

DataGuard is not affiliated with, endorsed by, or associated with the dataset maintainers.

The extension itself is dataset-agnostic and supports analysis of user-provided datasets in supported formats.

🚀 Quick Start

1. Install DataGuard
2. Open a supported dataset
3. DataGuard activates automatically
4. Explore visualizations and statistics
5. Apply cleaning operations
6. Save changes

⚙️ Requirements

DataGuard automatically detects required Python dependencies.

If manual installation is needed:

pip install pandas numpy

If Python path detection does not work:

Open Command Palette → DataGuard: Set Python Path

🔄 Workflow

Dataset
↓
Profile
↓
Visualize
↓
Clean
↓
Save

🔒 Privacy

DataGuard processes datasets locally.

AI features require explicit configuration.

No data leaves your machine unless an AI provider is enabled.

📈 Performance

Performance depends on:

• Dataset size
• Available memory
• Python environment

Designed to support analysis across datasets of varying sizes.

🔗 Resources

• Documentation
• Releases
• Security
• Issues