Mako Code

Your personal Polars sketch pad and analytics workflow

What is Mako Code?

Mako Code is an open source Independent Analytics Environment (IAE) that is built for data people who love to code. It is a workflow designed for fast ad hoc analysis with features to let you productize your work. So you can get to answers quickly while building a foundation for future data products.

Mako Code is not a vibe coding tool, and doesn't come with an LLM. But it does come with an opinion.

• The Monaco Code Editor
• Python built in functions
• Polars only for dataframe manipulation
• Direct link to Polars docs and AI chat
• Bokeh for data viz
• Parquets as the local file type
• Apache Arrow for data serialization
• Pydantic data models
• Ruff for linting
• Fast API backend
• Svelte JS front end

However, it is easy to customize as needed. The goals is to provide a concise workspace that is light in dependencies and easy to install.

Key Features

• 🚀 Interactive code editor with syntax highlighting and multiple language support
• 📊 Real-time code execution environment
• 💾 Efficient data management system for importing and analyzing datasets
• 🎨 Beautiful split-pane interface with adjustable views
• 📁 Smart file management with multiple tabs
• 📈 Dataset visualization and analysis tools
• ⌨️ Comprehensive keyboard shortcuts for productivity
• 📝 SQL query support with dataset integration
• 📊 Interactive Bokeh plotting functionality

Code Editor

Dataset Preview

Tech Stack

Frontend

• SvelteKit
• Monaco Editor (VS Code's editor)
• TypeScript
• Bokeh for interactive visualizations

Backend

• FastAPI
• Polars for data processing
• Ruff for code linting
• Python standard library

Local Development Setup

Prerequisites:

• Python 3.11+
• Node.js 20+
• uv package manager (for Python dependencies)

Launch development environment:

To launch Mako have Docker Desktop installed then use Docker Composer

1. Use docker compose

# build the image
docker-compose build

# run the application
docker-compose up

2. Use Make (Alternative Method for Mac only)

# Setup both frontend and backend dependencies
make setup

# Run the application (both frontend and backend)
make run

# To run frontend and backend separately:
make frontend  # Runs frontend server
make backend   # Runs backend server

# To clean up dependencies and build artifacts
make clean

The backend API documentation will be available at http://localhost:8001/api/docs

Getting Started

Once you have Mako running, here's how to start analyzing data:

1. Import data using ⌘/Ctrl + Shift + I
2. Open the data management sidebar with ⌘/Ctrl + D
3. Find your dataset in the list
4. Click the dataset name to view details including:
   • Column names and types
   • Sample data preview
   • Documentation notes
5. Click the menu (⋯) next to the dataset and select "Analyze"
6. This opens a new tab with pre-populated Polars code to start your analysis

The interface provides:

• Left sidebar: Saved functions, Polars API docs link, Mako docs and keyboard shortcuts
• Right sidebar: Data management (toggle with ⌘/Ctrl + D)
• Editor tabs: Code, visualization and documentation views

Key shortcuts:

• ⌘/Ctrl + Enter: Run current file
• ⌘/Ctrl + Shift + P: New Polars file
• ⌘/Ctrl + Shift + L: New SQL file
• ⌘/Ctrl + Shift + B: New Bokeh visualization
• ⌘/Ctrl + S: Save Context File
• ⌘/Ctrl + E: Export your current tab script

Contributing

We welcome contributions! Please feel free to submit a Pull Request. I will make sure any accepted PRs are added to the next release and you are credited in the changelog.