Beyond Jupyter: Better Bash Notebooks

You searched “how to run bash in Jupyter Notebook” because you want a notebook experience for shell commands. But Jupyter was built for Python data science, not bash. If you still want to use Jupyter, check out our guide on how to run bash in Jupyter Notebook.

But there are better options.

Why People Want Bash Notebooks

The notebook paradigm is compelling:

• Literate execution: Mix documentation with runnable code
• Cell-by-cell running: Execute steps independently
• Visible output: See results inline
• Shareable format: Send procedures to teammates

These benefits apply to bash just as much as Python. The question is: what’s the best tool?

The Problem with Jupyter for Bash

When you run bash in Jupyter Notebook, you’re fighting the tool:

Python-Centric Architecture

Jupyter’s architecture assumes Python:

• Kernels are Python processes
• Cell execution goes through Python
• Bash runs as subprocess calls

This adds latency and complexity.

Heavy Infrastructure

Running a bash command requires:

1. Jupyter server running
2. Browser open
3. Kernel connected
4. Network round-trips

For a simple kubectl get pods, that’s massive overhead.

No SSH Support

Jupyter doesn’t understand SSH. Every remote execution requires workarounds:

# Awkward
!ssh server "command"

# Doesn't work
!ssh -t server "interactive-command"

JSON File Format

Jupyter notebooks are JSON:

{
  "cells": [
    {
      "cell_type": "code",
      "source": ["!kubectl get pods"],
      "outputs": [...]
    }
  ]
}

This means:

• Noisy git diffs
• Can’t edit in normal text editors
• Doesn’t render on GitHub without viewer
• Outputs bloat file size

Alternative 1: Bash Kernel for Jupyter

If you want to stay in Jupyter, install the bash kernel:

pip install bash_kernel
python -m bash_kernel.install

Now notebooks can be pure bash:

# Cell 1
kubectl get pods

# Cell 2
kubectl describe pod api-xyz

Pros

• Native bash experience
• Environment persists between cells
• Familiar Jupyter interface

Cons

• Still requires Jupyter infrastructure
• Still JSON format
• Still no SSH support
• Can’t mix languages in one notebook

Alternative 2: Observable for Shell

Observable notebooks support shell execution:

shell`kubectl get pods -n production`

Pros

• Web-based, shareable
• Good visualization capabilities
• JavaScript interactivity

Cons

• Requires internet connection
• Learning curve
• Not designed for ops workflows

Alternative 3: Org-Mode (Emacs)

Emacs Org-mode is the original literate programming environment:

* Check Pod Status

#+begin_src sh
kubectl get pods -n production
#+end_src

#+RESULTS:
| NAME                  | READY | STATUS  |
| api-7d4f8b6c9-x2k4j  | 1/1   | Running |

Pros

• Powerful and flexible
• Plain text format
• Works offline
• SSH integration via TRAMP

Cons

• Steep learning curve
• Requires Emacs
• Not everyone can read/edit org files

Alternative 4: R Markdown / Quarto

R Markdown supports bash chunks:

# Deployment Runbook

Check current status:

```{bash}
kubectl get pods -n production
```

Restart deployment:

```{bash}
kubectl rollout restart deployment/api -n production
```

Pros

• Plain text (mostly)
• Good rendering
• Supports multiple languages

Cons

• R ecosystem overhead
• Rendering requires processing
• Not designed for ops use cases

Alternative 5: Executable Markdown

The most natural approach: Markdown files where code blocks execute directly.

# Restart API Service

Check pod status:

​```bash
kubectl get pods -n production -l app=api
​```

Trigger restart:

​```bash
kubectl rollout restart deployment/api -n production
​```

Watch progress:

​```bash
kubectl rollout status deployment/api -n production
​```

With the right tool, each code block becomes a runnable cell.

Pros

• Pure Markdown—edits anywhere
• Git-friendly diffs
• No special infrastructure
• Natural documentation format

Cons

• Requires tooling to execute

What the Ideal Bash Notebook Looks Like

Based on why people search “how to run bash in Jupyter Notebook,” the ideal solution should:

Use Plain Text

Markdown, not JSON. Editable everywhere, clean diffs, renders on GitHub.

Run Without Servers

No kernel servers, no browser requirements. Run in terminal or optionally in browser.

Support SSH Natively

Remote execution should be first-class:

​```bash @production-server
kubectl get pods
​```

Execute Cell-by-Cell

Run one step, see output, decide next action. Not all-or-nothing like scripts.

Store in Git

Version control with normal git workflows. Review changes in PRs.

Work During Incidents

Fast to open, fast to run, no waiting for servers to start.

Comparison Table

Feature	Jupyter + Bash	Bash Kernel	Org-Mode	Executable Markdown
Plain text format	❌	❌	✅	✅
No server required	❌	❌	✅	✅
SSH support	❌	❌	✅	✅
Git-friendly	❌	❌	✅	✅
Low learning curve	✅	✅	❌	✅
Works in browser	✅	✅	❌	✅
Works in terminal	❌	❌	✅	✅

The Stew Approach

Stew takes the executable Markdown approach:

1. Write in Markdown — Standard format, works everywhere
2. Execute anywhere — Terminal, browser, over SSH
3. Step-by-step — Run cells independently, see output
4. Git-native — Plain text, clean diffs, normal workflows

If you’re searching how to run bash in Jupyter Notebook because you want notebook-style execution for ops commands, Stew is built exactly for that.

Join the waitlist and try bash notebooks done right.