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What are Jupyter Notebooks?

Jupyter notebooks are interactive documents that combine code, visualizations, and narrative text. They’re the standard tool for data science, research, and exploratory analysis. In Alph, notebooks are first-class citizens with enhanced features:
  • AI-powered code generation
  • Real-time collaboration
  • Cloud execution
  • GitHub synchronization
  • Semantic search and discovery

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Creating a Notebook

1

Navigate to Notebooks

From your organization dashboard, click Notebooks in the sidebar
2

Create new notebook

Click the New Notebook button and provide:
  • Title: A descriptive name
  • Visibility: Private or Public
  • Tags (optional): For organization and discovery
3

Start editing

Your new notebook opens in the editor with an empty code cell ready to go

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The Notebook Editor

Alph’s notebook editor provides a powerful, modern interface for working with Jupyter notebooks.

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Key Components

  • Cell Editor
  • AI Panel
  • Toolbar
  • Cell Outputs
CodeMirror-powered editing with:
  • Syntax highlighting for Python, R, Julia, and more
  • Intelligent autocomplete
  • Code formatting and linting
  • Vim mode support (enable in settings)
  • Multi-cursor editing
Cell types:
  • Code cells: Execute Python/R/Julia code
  • Markdown cells: Rich text, images, LaTeX math

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Working with Cells

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Adding Cells

Add new cells in several ways:
  1. Click the + button between cells
  2. Use keyboard shortcuts: A (above) or B (below) in command mode
  3. AI generation: Click + AI to generate cells with AI

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Cell Modes

Cells have two modes:

Edit Mode

Active when you’re typing in a cell
  • Green border indicator
  • Cursor visible in cell
  • Type to edit content
  • Press Esc to exit

Command Mode

Active for cell-level operations
  • Blue border indicator
  • Navigate with arrow keys
  • Execute shortcuts (run, delete, etc.)
  • Press Enter to edit

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Executing Cells

Run code cells to see results:
  • Single cell: Click ▶️ or press Shift + Enter
  • All cells: Click Run All in toolbar
  • Selected cells: Select multiple and click Run Selected
Code execution requires a connected kernel. See Notebook Execution for details.

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Organizing Cells

Keep notebooks organized:
  • Reorder: Drag cells using the handle on the left
  • Cut/Copy/Paste: Use toolbar buttons or shortcuts
  • Merge: Select multiple cells and merge
  • Split: Split cell at cursor position

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Markdown Support

Markdown cells support rich formatting:

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Text Formatting

**Bold text**
*Italic text*
`inline code`
[Links](https://example.com)

# Heading 1
## Heading 2
### Heading 3

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Lists and Tables

- Bullet list
- Another item

1. Numbered list
2. Second item

| Column 1 | Column 2 |
|----------|----------|
| Data     | More data|

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LaTeX Math

Inline math: $E = mc^2$ Block math: 
$$
\int_{a}^{b} f(x) dx = F(b) - F(a)
$$

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Code Blocks

```python
def hello():
    print("Hello, world!")
```

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Notebook Metadata

Each notebook stores metadata:
  • Title and description
  • Author and creation date
  • Tags for categorization
  • Visibility settings
  • Kernel information
  • View count and engagement
Edit metadata from the notebook settings panel.

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Keyboard Shortcuts

Master these shortcuts for efficient editing:

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Command Mode

ShortcutAction
EnterEnter edit mode
AInsert cell above
BInsert cell below
D DDelete cell
MChange to markdown
YChange to code
Shift + ↑/↓Select multiple cells

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Edit Mode

ShortcutAction
EscEnter command mode
Shift + EnterRun cell and select below
Ctrl/Cmd + EnterRun cell
Alt + EnterRun cell and insert below
TabCode completion
Press ? in the notebook editor to see the full list of keyboard shortcuts.

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Autosaving

Alph automatically saves your notebook:
  • Every 2 minutes while editing
  • After cell execution
  • When you navigate away
Manual save: Click the save button or press Ctrl/Cmd + S

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Best Practices

Add markdown cells to explain your thought process, document assumptions, and describe results. This helps both human readers and AI assistants understand your work.
Structure notebooks with clear sections:
  1. Introduction/objectives
  2. Data loading and preparation
  3. Analysis and modeling
  4. Results and conclusions
One logical operation per cell makes notebooks easier to understand and debug. Avoid giant cells that do many things.
Ensure your notebook runs from top to bottom without errors. This makes it reproducible and shareable.
Tag notebooks with relevant keywords (e.g., “machine-learning”, “data-viz”, “tutorial”) to improve discoverability.

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Next Steps

AI Assistance

Supercharge your workflow with AI-powered features

Execution

Learn about kernels, projects, and running code

Publishing

Share your notebooks with teammates or the public

GitHub Integration

Import and sync notebooks from GitHub