ABIDE Data Explorer

A comprehensive Streamlit application for exploring and analyzing ABIDE II (Autism Brain Imaging Data Exchange) Composite Phenotypic data.

Features

• 📋 Data Overview: View dataset statistics, missing values analysis, and basic information
• 🔧 Data Processing: Sort, filter, group, slice data, and convert data types
• 📊 Visualization: Create various plots including histograms, scatter plots, bar charts, box plots, and correlation heatmaps
• 💾 File Operations: Export data in multiple formats (CSV, JSON, Excel, Pickle) and perform file operations
• 🖥️ User-Friendly Interface: Intuitive Streamlit web interface with organized tabs

Installation

From PyPI (when published)

pip install abide-data-explorer

From Source

1. Clone or download the repository
2. Navigate to the project directory
3. Install the package:

pip install -e .

Usage

After installation, you can run the application from anywhere using:

abide-data-explorer

This will start the Streamlit server and open the application in your default web browser.

Using with Your Own Data

1. Launch the application
2. In the sidebar, select "Upload your own CSV"
3. Upload your CSV file
4. Explore your data using the various tabs and features

Using with Sample Data

The package includes sample ABIDE II data for demonstration purposes. Select "Use sample data" in the sidebar to get started immediately.

Requirements

• Python 3.7 or higher
• Streamlit 1.28.0 or higher
• Pandas 1.3.0 or higher
• NumPy 1.21.0 or higher
• Plotly 5.0.0 or higher
• Matplotlib 3.5.0 or higher
• OpenPyXL 3.0.0 or higher (for Excel export)

Data Format

The application works with CSV files. For ABIDE II data, ensure your CSV contains standard phenotypic columns such as:

• Combined_ID: Subject identifier
• Target: Classification target (0/1 for control/ASD)
• AGE_AT_SCAN: Age at scan time
• FIQ, VIQ, PIQ: IQ measures
• Other phenotypic measures

Features Overview

Data Overview Tab

• Dataset preview with configurable number of rows
• Basic statistics (rows, columns, memory usage)
• Comprehensive summary statistics
• Missing values analysis

Data Processing Tab

• Data Type Conversion: Convert columns between int, float, and string types
• Sorting: Sort data by any column in ascending or descending order
• Grouping: Group data and apply aggregation functions (mean, sum, count, std)
• Filtering: Filter data based on column values
• Slicing: Extract specific row ranges from the dataset

Visualization Tab

• Histogram: Distribution plots for numerical columns
• Scatter Plot: Relationship between two numerical variables with optional color coding
• Bar Chart: Categorical data visualization with automatic limiting for large categories
• Box Plot: Distribution analysis with optional grouping
• Correlation Heatmap: Correlation analysis for all numerical columns

File Operations Tab

• Export Data: Save processed data in various formats (CSV, JSON, Excel, Pickle)
• File Operations: Write binary files, append to text files, create XML output

Development

Project Structure

abide-data-explorer/
├── abide_data_explorer/
│   ├── __init__.py
│   ├── app.py           # Main Streamlit application
│   ├── cli.py           # Command-line interface
│   └── data/
│       └── sample_data.csv
├── setup.py
├── requirements.txt
├── README.md
└── MANIFEST.in

Building the Package

To build the package for distribution:

python setup.py sdist bdist_wheel

Installing in Development Mode

For development, install the package in editable mode:

pip install -e .

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For issues, questions, or contributions, please visit the project repository or contact the development team.

Acknowledgments

• ABIDE (Autism Brain Imaging Data Exchange) for providing the dataset structure
• Streamlit team for the excellent web app framework
• Plotly for interactive visualizations
• Pandas and NumPy teams for data manipulation tools