Jupyter is the notebook environment data scientists reach for when they want to write code, see the output and keep their notes in one place. This workspace starts with the Python kernel ready, so you can open the notebook and run a cell straight away. No conda setup, no local install.
The Jupyter Python kernel comes installed. Open main.ipynb, pick the Python environment and run a cell.
The full cloud editor with the built-in notebook UI, an integrated terminal and extensions, all in your browser.
Clone, commit and push your notebooks from the terminal, or connect a GitHub repo whenever you want.
Boots in about a minute on a 2 vCPU machine with the kernel already in place.
Jupyter is an open-source notebook environment for interactive computing. A notebook mixes runnable code cells with text, equations and charts, so you can explore data step by step and keep the explanation right next to the result. It's a staple in data science, research and teaching, and Python is by far its most common language.
This workspace runs Jupyter through the VS Code notebook editor. You get the same cell-by-cell workflow you'd expect, with the Python kernel already installed so there's nothing to configure before you start writing code.
The starter is a single notebook, main.ipynb, with one cell that prints "Hello, World!". It's there to show the run flow rather than to do anything fancy, so you can clear it out and start your own analysis.
Because it runs on the VS Code notebook editor, you get cell output inline, including tables and plots, plus a variable view and an integrated terminal for installing packages or moving files around.
Open any .ipynb file, click Select Kernel, choose Python Environments and pick the version you want. From there, run a single cell with Run Cell or the whole notebook with Run All. Output appears directly under each cell.
Need a library like pandas, NumPy or Matplotlib? Install it from the terminal with pip and import it in your next cell. The notebook interface opens in a browser preview, so everything stays in the tab.
Data exploration and cleaning, quick charts and visualizations, model prototyping, or teaching material where the code and the explanation live side by side. Anything that suits an iterative, cell-at-a-time workflow fits here.
Python. The workspace ships with the Jupyter Python kernel installed. If you'd rather work in R, there's a separate Jupyter R template.
Open a .ipynb file, choose Select Kernel and pick a Python environment, then use Run Cell for a single cell or Run All to run the whole notebook. Output shows up under each cell.
Yes. Open the integrated terminal and install with pip, then import the package in a cell. Anything on PyPI works.
Your files live in the workspace and you can commit them with Git. Push to a GitHub repo from the terminal to keep a permanent copy.
No. The kernel and tooling are already in the container, so you skip the local conda or virtualenv setup entirely and start running cells right away.
This template runs on a paid plan. Templates that are free are marked as such on the Studio page. You can upgrade from the pricing page to launch a Jupyter workspace.
