Extensions & Tools

Extensions & Tools

JupyterLab Extensions

Install via pip (modern approach):

# Table of Contents
pip install jupyterlab-toc

# Git integration
pip install jupyterlab-git

# Variable inspector
pip install lckr-jupyterlab-variableinspector

# Code formatter (Black/isort)
pip install jupyterlab-code-formatter black isort

# Spellchecker
pip install jupyterlab-spellchecker

# Execute Time — shows how long each cell took
pip install jupyterlab-execute-time

# Drawio diagrams
pip install jupyterlab-drawio

# System monitor (CPU/RAM in status bar)
pip install jupyter-resource-usage

Classic Notebook Extensions (nbextensions)

pip install jupyter_contrib_nbextensions
jupyter contrib nbextension install --user

# Enable specific extensions
jupyter nbextension enable toc2/main
jupyter nbextension enable collapsible_headings/main
jupyter nbextension enable execute_time/ExecuteTime
jupyter nbextension enable varInspector/main

Popular nbextensions:

• Table of Contents (toc2) — sidebar TOC from headings
• Collapsible Headings — fold sections
• ExecuteTime — show execution time per cell
• Variable Inspector — live variable browser
• Autopep8 — auto-format code cells
• Scratchpad — scratch cell that doesn’t affect notebook

JupyterHub

Multi-user Jupyter server for teams:

pip install jupyterhub
jupyterhub --generate-config
jupyterhub

Use cases:

• Team data science environments
• University courses
• Cloud deployments (AWS, GCP, Azure)

Voilà — Notebooks as Dashboards

Convert notebooks to interactive web apps without showing code:

pip install voila
voila my_notebook.ipynb
voila my_notebook.ipynb --port 8866

nbconvert & Automation

# Batch execute all notebooks in a directory
for nb in notebooks/*.ipynb; do
  jupyter nbconvert --to notebook --execute "$nb" --output "executed/$nb"
done

# Schedule with cron
0 6 * * * jupyter nbconvert --to notebook --execute /path/to/report.ipynb

Useful Libraries for Notebooks

# Rich tables and progress
from rich import print
from rich.table import Table

# Better DataFrames
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.float_format', '{:.2f}'.format)

# Profiling reports
import ydata_profiling  # formerly pandas-profiling
profile = ydata_profiling.ProfileReport(df)
profile.to_notebook_iframe()

# Interactive data exploration
import dtale
dtale.show(df)

# Sweetviz — EDA reports
import sweetviz as sv
report = sv.analyze(df)
report.show_notebook()

Remote Kernels & Cloud

# Connect to a remote kernel
jupyter notebook --no-browser --port=8888 --ip=0.0.0.0

# SSH tunnel from local machine
ssh -N -L 8888:localhost:8888 user@remote-server

# Then open http://localhost:8888 locally

Jupyter in VS Code

VS Code has native Jupyter support:

• Open .ipynb files directly
• Run cells with Shift+Enter
• Variable explorer built-in
• Debugger support
• GitHub Copilot integration in cells

nbformat — Programmatic Notebook Creation

import nbformat
from nbformat.v4 import new_notebook, new_code_cell, new_markdown_cell

nb = new_notebook()
nb.cells = [
    new_markdown_cell("# My Generated Notebook"),
    new_code_cell("import pandas as pd\ndf = pd.DataFrame({'a': [1,2,3]})"),
    new_code_cell("df.head()"),
]

with open('generated.ipynb', 'w') as f:
    nbformat.write(nb, f)