Installation & Setup

# Install via pip
pip install notebook
pip install jupyterlab

# Install via conda
conda install notebook
conda install jupyterlab

# Launch Jupyter Notebook
jupyter notebook
jupyter notebook --port 8888

# Launch JupyterLab
jupyter lab

# List running servers
jupyter notebook list

# Stop server
jupyter notebook stop 8888

# Configuration
jupyter notebook --generate-config

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Notebook Interface – Key Areas

• Kernel – the computation engine (Python, R, Julia, etc.)
• Cell – individual unit of code, text, or markdown.
• Toolbar – run, stop, save, insert cells, etc.
• Command Palette – search and execute commands.
• File Browser – manage notebooks, files, and directories.

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Cell Types

Cell Type	Purpose	Shortcut
Code	Executable code (Python, etc.)	y
Markdown	Formatted text (headings, lists, LaTeX)	m
Raw	Text with no formatting	r
Heading	Legacy; use Markdown # instead	1-6 (deprecated)

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Keyboard Shortcuts (Command Mode)

# Navigation
Up/Down      # move between cells
a            # insert cell above
b            # insert cell below
j/k          # move selection down/up
Shift+Up     # select multiple cells above
Shift+Down   # select multiple cells below

# Cell Operations
y            # change to Code cell
m            # change to Markdown cell
r            # change to Raw cell
d,d          # delete selected cell
z            # undo cell deletion
c            # copy cell
x            # cut cell
v            # paste cell below
Shift+v      # paste cell above
o            # toggle output scroll
Shift+o      # toggle output
l            # toggle line numbers
s            # save notebook
i            # interrupt kernel
0,0          # restart kernel (press twice)

# Run Cells
Enter        # enter edit mode
Ctrl+Enter   # run cell
Shift+Enter  # run cell and select below
Alt+Enter    # run cell and insert below

# Help
h            # show keyboard shortcuts
Shift+Tab    # show docstring (in edit mode)
?            # object help (in code cell)

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Magic Commands (IPython)

Magics are special commands that control the behavior of the notebook.

Line Magics (prefixed with %)

# Run external files
%run script.py
%run script.py arg1 arg2

# Time execution
%time
%timeit -n 100 -r 5

# System commands
%ls
%pwd
%cd /path/to/dir

# Environment variables
%env
%env MY_VAR=value

# Debugging
%debug          # enter debugger after exception
%pdb            # auto-debug on error

# List / load extensions
%load_ext autoreload
%autoreload 2   # auto-reload modules before execution

# Display
%matplotlib inline  # show plots inline
%matplotlib notebook  # interactive plots
%config InlineBackend.figure_format = 'retina'

Cell Magics (prefixed with %%)

# Run different language
%%bash
echo "Hello from bash"
ls -la

%%html
HTML in notebook

%%javascript
console.log('JS in notebook');

%%latex
\int_0^\infty e^{-x} dx = 1

# Time entire cell
%%time
import time
time.sleep(2)

# Write to file
%%writefile output.txt
This is written to file

# Capture output
%%capture
print('This is captured')

# Interactive widgets
%%widget
# ...

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Markdown in Notebooks

Basic Syntax

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

**Bold text**
*Italic text*
`inline code`

- bullet list
- item 2

1. numbered list
2. item 2

[Link text](url)
![alt text](image.png)

> blockquote

---

Horizontal rule

LaTeX Math (inline)

$E = mc^2$
$\sum_{i=1}^n i = \frac{n(n+1)}{2}$

# Block equations
$$
\int_0^\infty e^{-x} dx = 1
$$

$$
\begin{bmatrix}
1 & 2 \\
3 & 4
\end{bmatrix}
$$

Tables

| Header 1 | Header 2 |
|----------|----------|
| cell 1   | cell 2   |
| cell 3   | cell 4   |

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Kernels

Jupyter supports multiple language kernels.

# List available kernels
jupyter kernelspec list

# Install additional kernels
pip install ipykernel
python -m ipykernel install --user --name myenv --display-name "Python (myenv)"

pip install r-irkernel
pip install bash_kernel
python -m bash_kernel.install

# Change kernel in notebook
Kernel → Change Kernel → select

# Remove kernel
jupyter kernelspec remove myenv

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Widgets (ipywidgets)

Interactive UI components for data exploration.

# Install
pip install ipywidgets
jupyter nbextension enable --py widgetsnbextension

# Basic widgets
import ipywidgets as widgets
from IPython.display import display

# Slider
slider = widgets.IntSlider(
    value=5, min=0, max=10, step=1,
    description='Slider:'
)
display(slider)

# Text input
text = widgets.Text(description='Name:')
display(text)

# Dropdown
dropdown = widgets.Dropdown(
    options=['Option 1', 'Option 2', 'Option 3'],
    description='Select:'
)
display(dropdown)

# Interact decorator
from ipywidgets import interact

@interact(x=(0, 10))
def f(x):
    return x**2

# Layout
button = widgets.Button(description='Click me')
output = widgets.Output()

def on_button_click(b):
    with output:
        print('Button clicked!')

button.on_click(on_button_click)
display(button, output)

---

File Operations

# Read CSV
import pandas as pd
df = pd.read_csv('data.csv')

# Load image
from IPython.display import Image
Image('image.png')

# Display HTML
from IPython.display import HTML
HTML('
Hello
')

# Display video
from IPython.display import Video
Video('video.mp4')

# Display audio
from IPython.display import Audio
Audio('audio.wav')

---

Exporting Notebooks

# From UI
File → Download as → (.ipynb, .html, .pdf, .py, .md, .rst, etc.)

# Command line
jupyter nbconvert --to html notebook.ipynb
jupyter nbconvert --to pdf notebook.ipynb
jupyter nbconvert --to python notebook.ipynb
jupyter nbconvert --to markdown notebook.ipynb

# With execute
jupyter nbconvert --execute --to html notebook.ipynb

# With output
jupyter nbconvert --to notebook --execute --output output.ipynb notebook.ipynb

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JupyterLab vs Notebook

Feature	Jupyter Notebook	JupyterLab
Interface	Single-document	Multi-tab, IDE-like
Extensions	Limited	Rich extension ecosystem
File browser	Yes	Yes, integrated
Terminal	Yes	Yes, integrated
Debugger	Basic	Advanced (with xeus-python)
Drag and drop	No	Yes
Command palette	Limited	Full (Ctrl+Shift+C)

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Useful Extensions

For Jupyter Notebook

• nbextensions – collection of notebook extensions.
• jupyter_contrib_nbextensions – install with pip install jupyter_contrib_nbextensions.

For JupyterLab

jupyter labextension install @jupyterlab/toc  # Table of Contents
jupyter labextension install @jupyterlab/git
jupyter labextension install @jupyterlab/debugger
jupyter labextension install @jupyterlab/datagrid

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Display and Visualisation

# Matplotlib (inline)
%matplotlib inline
import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4])
plt.show()

# Plotly (interactive)
import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x='sepal_width', y='sepal_length')
fig.show()

# Pandas table styling
df.style.background_gradient(cmap='coolwarm')

# Rich output
from IPython.display import display, HTML
display(HTML('
Styled output
'))

# Progress bar
from tqdm import tqdm
for i in tqdm(range(100)):
    pass

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Best Practices

• Use Markdown cells – document your analysis with headings and explanations.
• Split code into logical cells – each cell should do one thing.
• Use magic commands for timing – %timeit and %%time.
• Keep notebook linear – avoid running cells out of order.
• Restart kernel regularly – ensure reproducibility.
• Use __name__ == "__main__" – when converting to Python scripts.
• Version control notebooks – be careful with output cells; use nbstripout to strip outputs.
• Use environment variables – for configuration paths.
• Use logging – not print, for better debugging.
• Export to scripts – for production-ready code.
• Use %load_ext autoreload – auto-reload modules during development.

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Common Troubleshooting

• Kernel not starting? – check Python environment, install ipykernel.
• Plots not showing? – use %matplotlib inline.
• Widgets not working? – install ipywidgets and enable extensions.
• Out of memory? – restart kernel, clear outputs, use gc.collect().
• Slow execution? – use %timeit to profile, vectorise operations.
• Can't save? – check file permissions; use Save and Checkpoint.