Cheatsheet: Jupyter Lab

What is Jupyter Lab?

Jupyter Lab is an upgraded interface for Jupyter Notebooks — a tool widely used in research, analytics, education, and early-stage software development.

Each file in Jupyter Lab is called a Notebook, and has the extension .ipynb — which stands for IPython Notebook, a name from its earlier days.

Notebooks are made up of cells — individual blocks that can contain either:

• Code cells (for Python and other supported languages)
• Markdown cells (for headings, formatted text, bullet points, etc.)

Jupyter Lab runs inside your browser but executes code locally or on a server kernel in the background.

Running Code in Jupyter Lab

The most important feature of Jupyter Lab is the ability to run code directly inside the notebook. This is done using code cells.

To run a code cell:

• Click inside the cell
• Press Shift + Return (or Shift + Enter)

This will execute the code and display the output just below the cell.

Example

print("Hello, Jupyter Lab!")

Progress Bar Example

You can use libraries like tqdm to visualize progress in a loop:

import time
from tqdm import tqdm

spams = ["spam"] * 1000

for spam in tqdm(spams):
    time.sleep(0.01)

Tip: If tqdm is not installed, run !pip install tqdm in a code cell to install it.

Writing Markdown

Besides running Python code, Jupyter notebooks let you add formatted text using Markdown cells. This is helpful for notes, explanations, and headings in between code blocks.

How to Use

• Select a cell
• Click the dropdown at the top (usually shows "Code")
• Choose Markdown
• Type your text and press Shift + Return to render

Common Markdown Examples

# Heading 1
## Heading 2
**Bold text**
*Italic text*
- List item 1
- List item 2

Using Markdown from Python

You can also display Markdown output from Python using IPython.display.Markdown:

from IPython.display import Markdown, display
display(Markdown("**Bold** and *italic* text"))

Tip: Markdown is not just for comments — use it to explain logic, show instructions, or format project documentation directly inside your notebook.

Navigating the Interface

Jupyter Lab has a multi-panel interface, designed to feel more like an IDE. Here's how to get around:

Main Components

• File Browser (Left Sidebar): Navigate folders, open notebooks, create new files.
• Launcher: Start new notebooks, terminals, markdown files, or consoles.
• Notebook Panel: Where your code and text cells live.
• Tabs: Jupyter Lab supports multiple open notebooks or consoles at once, just like a code editor.

Working with Files

• Right-click in the file browser to create, rename, delete, or duplicate files
• Click and drag to rearrange tabs
• Use Ctrl/Command + S to save changes

Handy Shortcuts & Tips

Jupyter Lab supports useful keyboard shortcuts that speed up navigation and editing. Here are the ones you'll use most often:

Basic Cell Actions

• Shift + Return — Run current cell
• Control + Return — Run cell, stay in cell
• Esc + A — Add cell above
• Esc + B — Add cell below
• Esc + D D — Delete selected cell
• Esc + M — Convert cell to Markdown
• Esc + Y — Convert cell to Code

Editing Tips

• Double-click a Markdown cell to edit it
• Press Tab for autocomplete and suggestions
• Use ? after a function to open inline help:
  python Copy

  len?

Magic Commands

Jupyter supports "magic" commands that begin with % or %%. These are great for quick utilities:

• %time — Time a single line of code
• %%timeit — Run a cell multiple times and show average time
• %ls — List files in current directory (like Linux ls)
• %matplotlib inline — Display matplotlib plots directly in notebook

Summary & Next Steps

Jupyter Lab is one of the best environments for writing and experimenting with Python code. Whether you're analyzing data, learning programming, or testing logic, it helps you write code and see results instantly.

You now know how to:

• Run Python code in interactive cells
• Write formatted notes using Markdown
• Navigate the Jupyter interface and file system
• Use time-saving shortcuts and magic commands

If you want to explore more advanced features, visit the official Jupyter Lab documentation. But even without that, you're now ready to start using it effectively.

Happy coding!