Introduction

DataPyn is a professional IDE designed for data analysts and engineers. It combines SQL and Python in a single unified environment, allowing you to query databases, manipulate data with Pandas, and visualize results - all without switching tools.

With native GitHub Copilot integration, you can chat with AI to generate queries, create visualizations, and automate your data workflow using natural language.

Installation

Windows (Recommended)

Download the MSI installer from the GitHub releases page. No Python installation required.

# Or install from source:
git clone https://github.com/natharuc/datapyn.git
cd datapyn
.\scripts\install.bat
.\scripts\run.bat

Manual Installation

For Linux/macOS or custom setups:

python -m venv .venv
# Windows:
.venv\Scripts\activate
# Linux/macOS:
source .venv/bin/activate

pip install -r requirements.txt
python source/main.py

First Connection

To start querying data, you need to set up a database connection.

1. Click the database icon in the left sidebar or press Ctrl+Shift+C
2. Click "New Connection" and fill in your database details
3. Click "Test Connection" to verify the settings
4. Save and double-click the connection to connect

Once connected, you can start writing SQL queries in any SQL block.

Code Blocks

DataPyn organizes code into blocks. Each block can be SQL or Python, and blocks execute independently or in sequence.

Adding Blocks

• Click + SQL or + Python button
• Use Ctrl+Shift+S for SQL, Ctrl+Shift+P for Python
• Click the language selector to switch an existing block's type

Block Naming

You can name SQL blocks (e.g., "customers"). The result DataFrame will use that name instead of the default "df". This allows you to reference specific query results by name in Python blocks.

-- Block named "customers"
SELECT * FROM customers WHERE active = 1

# In Python, reference by name:
print(customers.head())

Database Support

DataPyn connects to the most popular database systems:

Code Execution

Execute code using keyboard shortcuts or toolbar buttons:

Cross-Syntax Execution

SQL results are automatically available in Python as DataFrames:

-- SQL Block
SELECT * FROM sales WHERE year = 2024

# Python Block - df contains SQL result
total = df['amount'].sum()
print(f"Total: ${total:,.2f}")

Visualization

Create charts directly from your data using Matplotlib:

import matplotlib.pyplot as plt

# df is your SQL result
df.plot(kind='bar', x='category', y='revenue')
plt.title('Revenue by Category')
plt.show()

Charts render inline in the results panel. You can also use seaborn, plotly, or any other visualization library.

Import & Export

Drag and Drop Import

Drag files directly into the editor. DataPyn generates import code automatically:

• .csv - pd.read_csv()
• .xlsx - pd.read_excel()
• .json - pd.read_json()
• .dpconn - Connection file auto-import

Export Options

Export your DataFrames or entire workflow:

• Excel/CSV/JSON - Right-click results table
• Python Script - Menu > Export > Python Script (standalone)
• Workspace - Save as .dpw file with all tabs and connections

GitHub Copilot Integration

DataPyn features deep integration with GitHub Copilot. The AI assistant has full access to your session context - it can see your code, understand your database schema, and take actions like creating blocks, executing queries, and generating visualizations.

Authentication

To use Copilot, you need a GitHub account with Copilot access. On first use, DataPyn will open a browser window for GitHub authentication. Once authorized, you can chat with Copilot directly in the sidebar panel.

Available Models

• GPT-4o - Best for complex analysis
• GPT-4o Mini - Faster, cost-effective
• Claude 3.5 Sonnet - Great for code generation
• o3-mini - Reasoning model

Available Copilot Tools

Copilot has access to specialized tools that let it interact with DataPyn. You don't need to memorize these - just ask naturally and Copilot will use the right tools.

Useful Prompts

Here are some example prompts to help you get started with Copilot:

Keyboard Shortcuts

Execution

Editing

Tabs & Sessions

Blocks

Frequently Asked Questions

How do I connect to SQL Server with Windows Authentication?

In the connection dialog, select "Windows Auth" as the authentication type. DataPyn will use your current Windows credentials.

Can I use multiple database connections in the same document?

Yes! Each SQL block can have its own connection. Click the connection selector in the block header to choose a different database.

Where are my credentials stored?

Database passwords are encrypted using AES-256 and stored locally in your user profile. They never leave your machine.

How do I access GitHub Copilot?

Click the Copilot icon in the sidebar or press Ctrl+Shift+I. You'll need to authenticate with your GitHub account on first use.

Can I export my work as a Python script?

Yes! Go to Menu > Export > Python Script. DataPyn generates a standalone .py file with all your SQL queries embedded as strings and your Python code ready to run.