Jupyter Notebooks.
No Servers Required.
A full notebook experience running entirely in the browser — NumPy, Pandas, Matplotlib, Plotly, Panel, interactive terminal, and inline plots. Every learner gets their own kernel instantly.
Built on Pyodide (Python compiled to WebAssembly). No JupyterHub. No containers. No IT tickets. Notebooks live directly inside the curriculum alongside reading, exercises, and AI tutoring.
Notebooks Live Inside the Textbook
Code cells, rich narrative, terminal output, and inline plots — all on a single page alongside the curriculum. No separate app, no context-switching.
Three-Dimensional Plotting in Matplotlib
Three-dimensional plots are enabled by importing the mplot3d toolkit, included with the main Matplotlib installation:
import numpy as np
import matplotlib.pyplot as plt
>>>
A three-dimensional axes can be created by passing the keyword projection='3d' to the normal axes creation routines:
theta = np.linspace(-0.9 * np.pi, 0.8 * np.pi, 40)
r, theta = np.meshgrid(r, theta)
X, Y, Z = r * np.sin(theta), r * np.cos(theta), f(X, Y)
ax = plt.axes(projection='3d')
ax.plot_surface(X, Y, Z, rstride=1, cstride=1,
cmap='viridis', edgecolor='none')
A surface plot is like a wireframe, but each face is a filled polygon. Adding a colormap aids perception of the surface topology.
Teach. Don't Troubleshoot Infrastructure.
Eliminate everything between your curriculum and your learners. No servers to provision, no environments to configure, no submission pipelines to wrangle.
Zero Infrastructure
No JupyterHub, no Docker containers, no cloud compute to manage. The Python kernel runs in each learner's browser natively. A class of 500 costs the same as a class of 5.
See Every Learner's Work
Select any learner from the roster and instantly see their exact notebook state — code edits, outputs, execution state. No collecting .ipynb files, no re-running to see output.
Auto-Grading Built In
Attach unit tests to individual cells. Learners see pass/fail inline. Results flow into the gradebook automatically — no nbgrader setup required.
Progress Analytics
Track which cells each learner has executed, how many attempts they made, and time spent. Identify struggling learners before they fall behind.
Part of the Curriculum
Notebooks aren't standalone files — they're embedded inline with reading, videos, multiple-choice questions, and other activities on a single page.
Secure by Default
Learner code runs in a secure browser sandbox — no access to the host OS, no server to compromise, no risk of one learner affecting another's environment.
A Real Notebook Experience.
Nothing to Install.
Learners open their textbook, run code cells, see plots inline, interact with a terminal — all from a Chromebook, tablet, or school lab PC.
Real Terminal Output, Streaming Live
Every code cell includes a built-in terminal that streams stdout and stderr in real time. Learners can use Python's input() function and respond interactively.
- ✓ Streaming output appears character-by-character as code runs
- ✓ Interactive prompts let learners type responses directly
- ✓ Long-running code can be interrupted instantly with the Stop button
- ✓ Terminal is resizable and clearable per-cell
name = input(f"Student {'{'}i+1{'}'}: ")
print(f"Hello, {'{'}name{'}'}!")
Hello, Alice!
>>> Student 2: _
See Exactly What Changed
Learners toggle "View Changes" to see a line-by-line diff of their edits against the original starter code. Teachers see the same diff when reviewing learner work.
- ✓ One-click reset reverts any cell to the instructor's original content
- ✓ Edits are saved automatically — no manual save-and-submit workflow
- ✓ Multi-file cells let learners organize code across tabs
See Any Learner's Work Instantly
Select a learner from the class roster and see their exact notebook state — every code edit, every output, every execution. No collecting files, no re-running notebooks.
- ✓ Live progress tracking — see who has completed which cells
- ✓ Built-in grading with rubric support
- ✓ Flag curriculum issues with one-click bug reports
- ✓ Role-aware UI: learners see editing, teachers see grading controls
Adjusted Gross receipts in millions of dollars. Use np.round to retain only two decimal places.
# millions = top.select(0).with_columns(...)
# millions
Select a learner from the dropdown to view their work.
Load Data from Google Drive
Learners load CSV, JSON, and Sheets files directly from Google Drive into Pandas — with a built-in OAuth picker. No manual download-upload cycle.
- ✓ Built-in Google Drive file picker with OAuth authentication
- ✓ Load private Sheets and CSVs directly into DataFrames
- ✓ Virtual file system pre-loaded with curriculum data files
- ✓ Learners can write output files that persist for the session
# Securely load CSV and Spreadsheet from Google Drive
df = pd.read_csv("https://drive.google.com/file/d/1.../view")
df.head()
| name | score | grade | |
|---|---|---|---|
| 0 | Alice | 92 | A |
| 1 | Brian | 78 | B+ |
| 2 | Carmen | 85 | A- |
| 3 | David | 64 | C |
Build AI-Powered Apps Inside the Notebook
Learners go beyond static analysis — they build live, interactive UIs with Panel. Chatbots, dashboards, form-driven tools — all rendered inline, no deployment needed.
- ✓ Panel widgets render live inside the notebook output cell
- ✓ Build chatbots, dashboards, and interactive tools in a few lines
- ✓ Connect to OpenAI or any LLM API for conversational AI projects
- ✓ Fully built Intro to Generative AI course ready to assign — chatbots, prompt engineering, and RAG
reply = get_completion(msg.value)
log.object += f"<b>You:</b> {'{'}msg.value{'}'}\n<b>Bot:</b> {'{'}reply{'}'}\n\n"
app = pn.Column("## OrderBot",
pn.Row(msg := pn.widgets.TextInput(), pn.widgets.Button(name="Send", on_click=on_send)),
log := pn.pane.Markdown(""))
- Margherita — classic cheese and tomato
- Pepperoni — pepperoni and cheese
- Veggie Delight — peppers, onions, olives
- BBQ Chicken — BBQ sauce, chicken, onions
Would you like to order one of these?
Full Textbooks, Pre-Loaded and Ready to Go
Assign a notebook and learners are coding within minutes — no setup, no file distribution.
Have a Jupyter-based textbook of your own? Alps can onboard any Jupyter book as an interactive course. Get in touch.
Purpose-Built for Teaching
See how Alps Notebooks compare to traditional Jupyter infrastructure.
| Feature | Alps Notebooks | JupyterHub / Colab |
|---|---|---|
| Setup required | None — open browser and code | Server provisioning or cloud account |
| Infrastructure cost | $0 per learner kernel | Scales with learner count |
| Kernel startup | ~2 seconds (WebAssembly) | 10–60 seconds (container spin-up) |
| Device support | Any browser — Chromebooks, tablets | Browser; some limits on mobile |
| Curriculum integration | Inline with reading & exercises | Standalone .ipynb files |
| Learner work visibility | One-click view of any learner's state | Collect .ipynb files and re-run |
| Auto-grading | Per-cell unit tests, built-in gradebook | Requires nbgrader or external tool |
| Progress analytics | Execution tracking, attempt counts | No built-in analytics |
| Code diff view | Built-in diff against starter code | Requires Git or nbdime |
| Security model | Secure Browser Sandbox — no OS access | Docker isolation required |
| NumPy, Pandas, Matplotlib | ||
| ML frameworks (TensorFlow, PyTorch) | Focused on intro/data science | |
| Shell / terminal access | Focused learning environment |
Ready to Simplify Your Data Science Class?
Teach data science with Alps Notebooks, without the infrastructure headaches.