Metadata-Version: 2.4
Name: sqlshell
Version: 0.1.9
Summary: A powerful SQL shell with GUI interface for data analysis
Home-page: https://github.com/yourusername/sqlshell
Author: SQLShell Team
License: MIT
Project-URL: Homepage, https://github.com/oyvinrog/SQLShell
Keywords: sql,data analysis,gui,duckdb
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: pandas>=2.0.0
Requires-Dist: numpy>=1.24.0
Requires-Dist: PyQt6>=6.4.0
Requires-Dist: duckdb>=0.9.0
Requires-Dist: openpyxl>=3.1.0
Requires-Dist: pyarrow>=14.0.1
Requires-Dist: fastparquet>=2023.10.1
Requires-Dist: xlrd>=2.0.1
Dynamic: home-page
Dynamic: requires-python

# SQLShell

<div align="center">

<img src="sqlshell_logo.png" alt="SQLShell Logo" width="256" height="256">

**A modern SQL REPL interface for seamless querying of Excel, Parquet, and SQLite databases**

![SQLShell Interface](sqlshell_demo.png)

</div>

## 🚀 Key Features

- **Interactive SQL Interface** - Rich syntax highlighting for enhanced query writing
- **DuckDB Integration** - Built-in support for local DuckDB database (pool.db)
- **Multi-Format Support** - Import and query Excel (.xlsx, .xls), CSV, and Parquet files effortlessly
- **Modern UI** - Clean, tabular results display with intuitive controls
- **Productivity Tools** - Streamlined workflow with keyboard shortcuts (e.g., Ctrl+Enter for query execution)
- **Professional Design** - Human-readable interface with optimized graphics

## 📦 Installation

### Linux Setup with Virtual Environment

```bash
# Create and activate virtual environment
python3 -m venv ~/.venv/sqlshell
source ~/.venv/sqlshell/bin/activate

# Install SQLShell
pip install sqlshell

# Configure shell alias
echo 'alias sqls="~/.venv/sqlshell/bin/sqls"' >> ~/.bashrc  # or ~/.zshrc for Zsh
source ~/.bashrc  # or source ~/.zshrc
```

### Windows Quick Start
SQLShell is immediately available via the `sqls` command after installation:
```bash
pip install sqlshell
```

## 🎯 Getting Started

1. **Launch the Application**
   ```bash
   sqls
   ```

2. **Database Connection**
   - SQLShell automatically connects to a local DuckDB database named 'pool.db'

3. **Working with Data Files**
   - Click "Load Files" to select your Excel, CSV, or Parquet files
   - File contents are loaded as queryable SQL tables
   - Query using standard SQL syntax

4. **Query Execution**
   - Enter SQL in the editor
   - Execute using Ctrl+Enter or the "Execute" button
   - View results in the structured output panel

## 📝 Query Examples

### Basic Join Operation
```sql
SELECT *
FROM sample_sales_data cd
INNER JOIN product_catalog pc ON pc.productid = cd.productid
LIMIT 3;
```

### Multi-Statement Queries
```sql
-- Create a temporary view
CREATE OR REPLACE TEMPORARY VIEW test_v AS
SELECT *
FROM sample_sales_data cd
INNER JOIN product_catalog pc ON pc.productid = cd.productid;

-- Query the view
SELECT DISTINCT productid
FROM test_v;
```

## 💡 Pro Tips

- Use temporary views for complex query organization
- Leverage keyboard shortcuts for efficient workflow
- Explore the multi-format support for various data sources
- Create multiple tabs for parallel query development
