Jupyter

Information

Jupyter is an open-source, web-based interactive computing environment. Notebooks (.ipynb files) combine live code, narrative text (Markdown), equations (LaTeX), and visualizations in a single document. Originally created as IPython Notebook, Jupyter now supports over 40 programming language kernels — including Python, R, and Julia.

JupyterLab is the modern, IDE-style successor to the classic Notebook interface, providing a tabbed workspace with a file browser, terminal, text editor, and notebook viewer side by side.

Voilà converts Jupyter notebooks into standalone interactive web dashboards without exposing source code.

Installation

pip install jupyterlab notebook voila

Rocky Linux / Fedora (via Python venv)

mkdir jupyter-probe
cd jupyter-probe
python3 -m venv .venv
source .venv/bin/activate
pip install jupyterlab notebook voila
jupyter-lab

Using the smi helper scripts:

smi-download-package python313
smi-install-package python313
export PATH=/opt/python-3.13.7/bin:${PATH}
python3 --version
smi-create-venv
smi-venv-command pip install jupyterlab notebook voila
smi-venv-command jupyter-lab
smi-venv-command pip freeze > requirements.txt

Debian / Ubuntu

sudo apt install python3-jupyter-core jupyter-notebook
# Or via pip in a venv (preferred for the latest version)
pip install jupyterlab

Start Jupyter

jupyter-lab

Open the browser at http://localhost:8888/.

Matplotlib and NumPy

Preparations

Installation with additionals

pip install jupyterlab notebook voila numpy pandas matplotlib seaborn tensorflow pyarrow

Script preparations

import os
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from tensorflow.keras import layers
import datetime
import timeit
import itertools
import pyarrow as pa

Version check

print("TensorFlow:", tf.__version__)
print("NumPy:", np.__version__)
print("pandas:", pd.__version__)
print("pyarrow:", pa.__version__)

Plots

fig, ax = plt.subplots()
ax.plot([1, 2, 3, 4], [1, 4, 2, 3])

x = np.linspace(0, 2, 100)
fig, ax = plt.subplots(figsize=(5, 2.7), layout='constrained')
ax.plot(x, x, label='linear')
ax.plot(x, x ** 2, label='quadratic')
ax.plot(x, x ** 3, label='cubic')
ax.set_xlabel('x label')
ax.set_ylabel('y label')
ax.set_title("Simple Plot")
ax.legend()

Return multiple values from a function

def return_multiple():
    return 1, 2, 3

return_all = return_multiple()
print(f'{return_all=}')

a, b, c = return_multiple()
print(f'{a=}')
print(f'{b=}')
print(f'{c=}')

a, *b = return_multiple()
print(f'{a=}')
print(f'{b=}')

See also