sqlshell 0.2.1

A powerful SQL shell with GUI interface for data analysis

SQLShell

A powerful SQL shell with GUI interface for data analysis

🚀 Key Features

• Interactive SQL Interface - Rich syntax highlighting for enhanced query writing
• Context-Aware Suggestions - Intelligent SQL autocompletion based on query context and schema
• DuckDB Integration - Powerful analytical queries powered by DuckDB
• Multi-Format Support - Import and query Excel (.xlsx, .xls), CSV, and Parquet files effortlessly
• Modern UI - Clean, tabular results display with intuitive controls
• Table Preview - Quick view of imported data tables
• Test Data Generation - Built-in sample data for testing and learning
• Multiple Views - Support for multiple concurrent table views
• Productivity Tools - Streamlined workflow with keyboard shortcuts (e.g., Ctrl+Enter for query execution)

📦 Installation

Using pip (Recommended)

pip install sqlshell

Linux Setup with Virtual Environment

# 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

Development Installation

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

🎯 Getting Started

1. Launch the Application

   sqls
   

   If the sqls command doesn't work (e.g., "access denied" on Windows), you can use this alternative:

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

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

5. Test Data

   • Load sample test data using the "Test" button for quick experimentation

6. Using Context-Aware Suggestions

   • Press Ctrl+Space to manually trigger suggestions
   • Suggestions appear automatically as you type
   • Context-specific suggestions based on your query position:
     • After SELECT: columns and functions
     • After FROM/JOIN: tables with join conditions
     • After WHERE: columns with appropriate operators
     • Inside functions: relevant column suggestions

📝 Query Examples

Basic Join Operation

SELECT *
FROM sample_sales_data cd
INNER JOIN product_catalog pc ON pc.productid = cd.productid
LIMIT 3;

Multi-Statement Queries

-- 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
• The context-aware suggestions learn from your query patterns
• Type table_name. to see all columns for a specific table
• After JOIN keyword, the system suggests relevant tables and join conditions

📊 Column Profiler

The Column Profiler provides quick statistical insights into your table columns:

Using the Column Profiler

1. Access the Profiler

   • Right-click on any table in the schema browser
   • Select "Profile Table" from the context menu

2. View Column Statistics

   • Instantly see key metrics for each column:
     • Data type
     • Non-null count and percentage
     • Unique values count
     • Mean, median, min, and max values (for numeric columns)
     • Most frequent values and their counts
     • Distribution visualization

3. Benefits

   • Quickly understand data distribution
   • Identify outliers and data quality issues
   • Make informed decisions about query conditions
   • Assess column cardinality for join operations

The Column Profiler is an invaluable tool for exploratory data analysis, helping you gain insights before writing complex queries.

📋 Requirements

• Python 3.8 or higher
• Dependencies (automatically installed):
  • PyQt6 ≥ 6.4.0
  • DuckDB ≥ 0.9.0
  • Pandas ≥ 2.0.0
  • NumPy ≥ 1.24.0
  • openpyxl ≥ 3.1.0 (Excel support)
  • pyarrow ≥ 14.0.1 (Parquet support)
  • fastparquet ≥ 2023.10.1 (Alternative parquet engine)
  • xlrd ≥ 2.0.1 (Support for older .xls files)

📄 License

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