jupyter — Interactive Notebooks#

What it is#

Jupyter notebooks (.ipynb files) let you mix executable Python cells, markdown, and rich outputs (plots, tables, HTML) in a single document. There are two interfaces:

• JupyterLab — modern IDE-like interface (recommended)
• Jupyter Notebook — classic single-document interface

Both run a local web server and open in your browser. Notebooks are widely used for data exploration, prototyping, and technical writing.

Install#

# JupyterLab (recommended)
pip install jupyterlab

# Classic Notebook interface
pip install notebook

# Both
pip install jupyterlab notebook

Launch#

jupyter lab           # opens JupyterLab at http://localhost:8888
jupyter notebook      # opens classic Notebook interface
jupyter lab --no-browser --port 8889   # headless / custom port

Output:

[I 2026-04-25 14:30:01.234 ServerApp] JupyterLab 4.2.0 is running at:
[I 2026-04-25 14:30:01.234 ServerApp] http://localhost:8888/lab?token=abc123...
[I 2026-04-25 14:30:01.234 ServerApp]  or http://127.0.0.1:8888/lab?token=abc123...
[I 2026-04-25 14:30:01.234 ServerApp] Use Control-C to stop this server...

Your browser opens automatically. If it doesn’t, copy the URL from the terminal output.

When / why to use it#

• Exploratory data analysis where you want to see output inline (plots, DataFrames).
• Prototyping an algorithm step by step before putting it in a .py module.
• Writing a technical document or report that mixes prose and code.
• Teaching or presenting code — cells can be run one at a time.

Common pitfalls#

[!WARNING] Execution order is not document order — cells can be run in any order. A notebook that runs top-to-bottom passes CI; one where you ran cells out of order may have hidden state. Always use Kernel → Restart & Run All before sharing to verify the notebook runs cleanly from scratch.

[!WARNING] Large notebooks in version control — .ipynb files contain cell outputs (images, dataframes) in JSON, making diffs huge. Use nbstripout to strip outputs before committing: pip install nbstripout && nbstripout --install.

[!TIP] Variables set in one cell persist for the rest of the session. If you delete or change a cell, the old value stays in memory until you restart the kernel.

Keyboard shortcuts#

These work in command mode (press Esc to enter, highlighted cell border turns blue):

In edit mode (green border): standard text editor shortcuts apply, plus Tab for autocomplete and Shift+Tab for docstring pop-up.

Magic commands#

Magic commands start with % (line magic) or %% (cell magic). They run before your Python code is evaluated.

# Time a single expression
%time sum(range(1_000_000))

Output:

CPU times: user 23.4 ms, sys: 0 ns, total: 23.4 ms
Wall time: 23.4 ms

# Benchmark over many runs
%%timeit
total = 0
for i in range(1_000_000):
    total += i

Output:

28.5 ms ± 312 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

%matplotlib inline    # render matplotlib plots inline in the notebook
%who                  # list all variables in scope
%whos                 # list variables with type and value
%env PATH             # show/set environment variable
%run script.py        # run a .py file and import its globals
%load utils.py        # load a file's contents into the current cell
%pwd                  # print working directory
%ls                   # list directory (like shell ls)

Richer example — inline plotting#

import numpy as np
import matplotlib.pyplot as plt

%matplotlib inline

x = np.linspace(0, 4 * np.pi, 500)
fig, ax = plt.subplots(figsize=(9, 3))
ax.plot(x, np.sin(x), label="sin(x)")
ax.plot(x, np.exp(-x / 5) * np.sin(x), label="damped sin(x)", linestyle="--")
ax.set_title("Waveforms")
ax.legend()
ax.grid(True, alpha=0.3)
plt.tight_layout()

Output:

[inline figure rendered in the notebook cell output — a waveform plot with two curves]

Export / convert notebooks#

# To HTML (share as a static page)
jupyter nbconvert --to html analysis.ipynb

# To Python script (strip all outputs and markdown, keep code cells)
jupyter nbconvert --to script analysis.ipynb

# To PDF (requires LaTeX)
jupyter nbconvert --to pdf analysis.ipynb

# To Markdown
jupyter nbconvert --to markdown analysis.ipynb

Output:

[NbConvertApp] Converting notebook analysis.ipynb to html
[NbConvertApp] Writing 312345 bytes to analysis.html

List and manage kernels#

jupyter kernelspec list             # list installed kernels
jupyter kernelspec remove myenv     # remove a kernel
pip install ipykernel               # install ipykernel to register a venv
python -m ipykernel install --user --name myenv --display-name "Python (myenv)"

Output of kernelspec list:

Available kernels:
  python3    /usr/local/share/jupyter/kernels/python3
  myenv      /home/user/.local/share/jupyter/kernels/myenv

[!TIP] Always install ipykernel inside the virtual environment you want to use, then register it with python -m ipykernel install --user --name <name>. This makes the venv selectable from the JupyterLab kernel picker.

Strip outputs before committing#

pip install nbstripout
nbstripout --install                # installs a git filter for this repo
# From now on: git diff and git add see clean notebooks (no outputs)