Load and clean a CSV file using Python pandas directly inside a spreadsheet interface without switching tools.
Visualize data by writing Python plotting code in cells alongside the data it describes.
Run shell commands in a built-in Linux terminal while keeping results visible next to your spreadsheet.
Share an integrated data analysis environment without requiring anyone to install Python or configure environments locally.
Requires Docker to run the multi-component backend that bundles Go, Python, and Node.js together.
Grid studio is a web-based spreadsheet application that lets you write and run Python code directly inside the spreadsheet interface. Because it runs in a browser, you can access it from any machine without installing software on your laptop. The goal is to keep the full data workflow, from loading and cleaning files to manipulating and visualizing results, inside one interface rather than switching between a spreadsheet, a Python editor, and a terminal. The application works in two layers. A workspace manager handles creating, copying, and deleting workspaces and routes each user session to the right environment. Each workspace runs its own backend written in Go, which handles spreadsheet cell parsing and evaluation. Alongside the Go spreadsheet engine, each workspace also has a connected Python interpreter and a Node.js terminal session. This combination means you can use standard spreadsheet formulas for simple calculations, write Python scripts for data processing, and run arbitrary commands on the underlying Ubuntu Linux system, all without leaving the same interface. The aim is to give data work a single integrated view: standard spreadsheet formulas for quick calculations, Python scripting for more complex transformations, and a full Linux terminal for tasks that need command-line tools. Installation runs through Docker, which packages all the dependencies together: the Go runtime, Python 3 with data science libraries, and Node.js. You pull the Docker image and start the container to run it locally on a chosen port, then open the interface in a browser. More detailed setup instructions are available in the project wiki linked from the README.
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