SQLShell

A fast SQL interface for analyzing data files ✨

Query CSV, Parquet, Excel files with SQL • DuckDB powered • No database setup required

📥 Download • 🚀 Install • 📖 Examples • 🤝 Contribute

What SQLShell Does

SQLShell is a desktop SQL interface specifically designed for analyzing data files. It's not a database client - instead, it lets you load CSV, Parquet, Excel, and other data files and query them with SQL using DuckDB's fast analytical engine.

🔥 Key Features

📁 File-Based Data Analysis

Important: SQLShell works with data files, not live databases. It's designed for:

• 📊 Data Files - CSV, Parquet, Excel, TSV, JSON files
• 🗃️ Local Analysis - Load files from your computer for SQL analysis
• ⚡ Fast Queries - DuckDB engine optimized for analytical workloads
• 🔍 Data Exploration - Search and filter capabilities across your datasets

Not supported: Live database connections (MySQL, PostgreSQL, etc.). Use dedicated database clients for those.

💫 What Makes SQLShell Useful

• 🏎️ DuckDB Powered - Fast analytical queries on data files
• 📊 Multiple File Formats - CSV, Parquet, Excel, Delta, TSV, JSON support
• 🎨 Clean Interface - Simple SQL editor with result display
• 🔍 Search Functionality - Find data across result sets quickly
• 🚀 Zero Database Setup - No server installation or configuration needed

🚀 Quick Install

📥 Download (Recommended)

Pre-built executables — no Python installation required:

👉 View all releases

🐍 Install via pip

Alternatively, install with pip if you have Python:

pip install sqlshell
sqls

That's it! 🎉 SQLShell opens and you can start loading data files.

# Create dedicated environment (recommended)
python3 -m venv ~/.venv/sqlshell
source ~/.venv/sqlshell/bin/activate
pip install sqlshell

# Add convenient alias
echo 'alias sqls="~/.venv/sqlshell/bin/sqls"' >> ~/.bashrc
source ~/.bashrc

python -c "import sqlshell; sqlshell.start()"

⚡ Getting Started

1. Launch: sqls
2. Load Data: Click "Load Files" to import your CSV, Parquet, or Excel files
3. Query: Write SQL queries against your loaded data
4. Execute: Hit Ctrl+Enter or F5 to run queries
5. Search: Press Ctrl+F to search through results

🔍 Search and Filter Features

⚡ Result Search with Ctrl+F

Once you have query results, use Ctrl+F to search across all columns:

• Cross-column search - Finds terms across all visible columns
• Case-insensitive - Flexible text matching
• Instant feedback - Filter results as you type
• Numeric support - Search numbers and dates

💪 Practical Use Cases

Workflow: Load file → Query data → Ctrl+F → Search → ESC to clear

🤖 Data Analysis Features

🔮 Text Encoding

Right-click text columns to create binary indicator columns for analysis:

-- Original data
SELECT category FROM products;
-- "Electronics", "Books", "Clothing"

-- After encoding
SELECT 
    category_Electronics,
    category_Books,
    category_Clothing
FROM products_encoded;

📊 Column Analysis

Right-click columns for quick statistical analysis and correlation insights.

🚀 Power User Features

⚡ F5/F9 Quick Execution

• F5 - Execute all SQL statements in sequence
• F9 - Execute only the current statement (where cursor is positioned)
• Useful for: Testing queries step by step

🧠 SQL Autocompletion

• Press Ctrl+Space for suggestions
• After SELECT: Available columns from loaded tables
• After FROM/JOIN: Loaded table names
• After WHERE: Column names with appropriate operators

📊 File Format Support

SQLShell can load and query:

• CSV/TSV - Comma and tab-separated files
• Parquet - Column-oriented format
• Excel - .xlsx and .xls files
• JSON - Structured JSON data
• Delta - Delta Lake format files

📝 Query Examples

Basic File Analysis

-- Load and explore your CSV data
SELECT * FROM my_data LIMIT 10;

-- Aggregate analysis
SELECT 
    category,
    AVG(price) as avg_price,
    COUNT(*) as count
FROM sales_data 
GROUP BY category
ORDER BY avg_price DESC;

Multi-File Analysis

-- Join data from multiple loaded files
SELECT 
    c.customer_name,
    SUM(o.order_total) as total_spent
FROM customers c
JOIN orders o ON c.customer_id = o.customer_id
GROUP BY c.customer_name
ORDER BY total_spent DESC
LIMIT 10;

🎯 Perfect For

📋 Requirements

• Python 3.8+
• Auto-installed dependencies: PyQt6, DuckDB, Pandas, NumPy

System Requirements: SQLShell is a desktop application that works on Windows, macOS, and Linux.

💡 Tips for Better Productivity

🚀 Typical Workflow

1. Load files (drag & drop or Load Files button)
2. Explore structure (SELECT * FROM table_name LIMIT 5)
3. Build analysis (use F9 to test statements)
4. Search results (Ctrl+F for specific data)
5. Export findings (copy results or save queries)

🔧 Advanced Features

🤝 Contributing

SQLShell is open source and welcomes contributions!

git clone https://github.com/oyvinrog/SQLShell.git
cd SQLShell
pip install -e .

Ways to contribute:

• 🐛 Report bugs and issues
• 💡 Suggest new features
• 📖 Improve documentation
• 🔧 Submit pull requests
• ⭐ Star the repo to show support

📄 License

MIT License - feel free to use SQLShell in your projects!

pip install sqlshell && sqls