Jupyter Notebook

Interactive, reproducible analysis in the browser.

Jupyter Notebook is taught here in 11 lessons, running from Notebook fundamentals through to Beyond local notebooks: Colab, Voila and Quarto. Each lesson takes one topic, shows the working code, and links onward to the next, so the course can be read straight through in order.

TrackPython & Data Science
Lessons11
LevelBeginner to intermediate
Reading timeabout 2 hours
PrerequisitesHelpful, but not required: Matplotlib

Lessons

  1. Notebook fundamentalsCells, kernels and execution order — how a notebook actually works and the habit that keeps results trustworthy.
  2. A notebook workflow that scalesProject layout, imports, reproducibility and handing a notebook to someone else without surprises.
  3. Installing JupyterLab and managing kernelsNotebook, JupyterLab and Notebook 7 compared, installing into a virtual environment, and why a kernel is not the same thing as an interpreter.
  4. Markdown, LaTeX and rich outputTurn a notebook into a readable document: headings, tables and maths in Markdown cells, plus images, HTML and audio objects from IPython.display.
  5. Magics: line, cell and shell commandsTime, capture, run and shell out from a notebook, and write a small magic of your own when the built-in ones run out.
  6. Widgets and interactive outputBuild sliders, dropdowns and live plots with ipywidgets, keep the update logic out of the widget handler, and know what stops working when you export.
  7. Visualisation inside notebooksInline backends, figure size and resolution, pandas and seaborn output, and interactive libraries that need a renderer.
  8. Notebooks vs scripts: jupytext, nbconvert and papermillKeep the analysis in a notebook and the review in a text file, export to any format, and run the same notebook once per parameter set.
  9. Version control, diffs and reproducible outputWhy notebook JSON produces unreadable commits, how to strip outputs automatically, and how to make a notebook that renders identically on another machine.
  10. Debugging and the hidden-state trapPost-mortem debugging, breakpoints, logging, and the techniques that expose a notebook whose results depend on cells you already deleted.
  11. Beyond local notebooks: Colab, Voila and QuartoHosted notebooks for zero setup, Voila for turning a notebook into a dashboard, Quarto for publishing, and the cases where a notebook is the wrong tool.

More in Python & Data Science

Python 3 NumPy pandas Matplotlib Flask FastAPI Django SciPy Pillow Python 2.x R Julia

FAQ

How long does the Jupyter Notebook course take?
It has 11 lessons, about 2 hours of reading. Expect roughly twice that if you type out and run every example.
Do I need prior experience for Jupyter Notebook?
Not strictly. It helps to have read Matplotlib first, because some lessons build on it, but every lesson explains its own assumptions.
What should I read after Jupyter Notebook?
Continue with Flask (12 lessons), the next course in Python & Data Science.