Jupyter Notebook

beginner

Interactive computing environment for data science.

Overview

Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Originally developed from IPython in 2014, Jupyter has become the de facto standard for interactive computing in data science, supporting over 40 programming languages including Python, R, Julia, and Scala. The name itself comes from the core languages it was designed to support: Julia, Python, and R. This Docker stack uses the official datascience-notebook image, which comes pre-configured with a complete scientific Python environment including pandas, scikit-learn, matplotlib, seaborn, NumPy, and SciPy. Unlike minimal Jupyter installations, this image also includes R and Julia kernels with their respective data science libraries, making it a comprehensive platform for multi-language data analysis. The stack provides a persistent work directory where notebooks and datasets are automatically saved between container restarts. This configuration is ideal for data scientists, researchers, and educators who need a consistent, portable environment for exploratory data analysis and machine learning experiments. Companies use this setup for onboarding new team members with identical toolsets, while individual researchers benefit from the ability to reproduce their work across different machines. The pre-installed libraries eliminate hours of environment setup, letting users focus immediately on data analysis rather than dependency management.

Key Features

Interactive notebooks with live code execution and rich output rendering including plots, tables, and HTML
Multi-kernel support with Python 3, R, and Julia environments pre-configured and ready to use
Complete data science stack including pandas, scikit-learn, matplotlib, seaborn, NumPy, SciPy, and statsmodels
JupyterLab interface with file browser, terminal access, and extension support for enhanced productivity
Notebook export capabilities to PDF, HTML, slides, and executable scripts for sharing and presentation
Built-in magic commands for profiling, debugging, and system integration within notebook cells
Variable inspector and debugger for interactive code exploration and troubleshooting
Git integration and terminal access for version control and command-line operations

Common Use Cases

1Exploratory data analysis and statistical modeling for business intelligence and research projects
2Machine learning model prototyping and hyperparameter tuning before production deployment
3Data science education and training with interactive tutorials and hands-on coding exercises
4Research reproducibility by sharing notebooks with embedded code, data, and analysis results
5Financial modeling and quantitative analysis with real-time data visualization and backtesting
6Bioinformatics and scientific computing with multi-language analysis pipelines
7Data cleaning and ETL pipeline development with immediate feedback and iterative refinement

Prerequisites

Minimum 2GB RAM recommended for data science workloads, 4GB+ for large datasets or multiple notebooks
Docker and Docker Compose installed with ability to pull images from Docker Hub
Port 8888 available on the host system for Jupyter web interface access
JUPYTER_TOKEN environment variable set for secure notebook access authentication
Basic familiarity with Python, R, or Julia programming for effective notebook usage
Understanding of Jupyter notebook concepts including cells, kernels, and markdown formatting

For development & testing. Review security settings, change default credentials, and test thoroughly before production use. See Terms

docker-compose.yml

1services: 
2  jupyter: 
3    image: jupyter/datascience-notebook:latest
4    container_name: jupyter
5    restart: unless-stopped
6    environment: 
7      JUPYTER_TOKEN: ${JUPYTER_TOKEN}
8    volumes: 
9      - jupyter_work:/home/jovyan/work
10    ports: 
11      - "8888:8888"
12
13volumes: 
14  jupyter_work:

.env Template

.env

1JUPYTER_TOKEN=changeme

Usage Notes

1Docs: https://jupyter.org/documentation
2Access at http://localhost:8888?token=JUPYTER_TOKEN
3Includes Python, R, Julia with scientific libraries pre-installed
4datascience-notebook has pandas, scikit-learn, matplotlib, seaborn
5Mount your notebooks at /home/jovyan/work
6Alternative images: scipy-notebook, tensorflow-notebook, pytorch-notebook

Quick Start

terminal

1# 1. Create the compose file
2cat > docker-compose.yml << 'EOF'
3services:
4  jupyter:
5    image: jupyter/datascience-notebook:latest
6    container_name: jupyter
7    restart: unless-stopped
8    environment:
9      JUPYTER_TOKEN: ${JUPYTER_TOKEN}
10    volumes:
11      - jupyter_work:/home/jovyan/work
12    ports:
13      - "8888:8888"
14
15volumes:
16  jupyter_work:
17EOF
18
19# 2. Create the .env file
20cat > .env << 'EOF'
21JUPYTER_TOKEN=changeme
22EOF
23
24# 3. Start the services
25docker compose up -d
26
27# 4. View logs
28docker compose logs -f

One-Liner

Run this command to download and set up the recipe in one step:

terminal

1curl -fsSL https://docker.recipes/api/recipes/jupyter-notebook/run | bash

Troubleshooting

Jupyter server not accessible: Verify JUPYTER_TOKEN environment variable is set and use the token in the URL http://localhost:8888?token=YOUR_TOKEN
Out of memory errors during data processing: Increase Docker memory limit to 4GB+ or use data chunking techniques in pandas
Notebooks not persisting after container restart: Ensure notebooks are saved in /home/jovyan/work directory which maps to the persistent volume
Kernel dies when importing large libraries: Restart the kernel and try importing libraries one at a time, or increase container memory allocation
Permission denied when accessing mounted directories: Check that mounted host directories have proper read/write permissions for UID 1000 (jovyan user)
Can't install additional packages: Use !pip install or !conda install within notebook cells, or create a custom Dockerfile extending the base image

Community Notes

Loading notes...

Download Recipe Kit

Get all files in a ready-to-deploy package

Includes docker-compose.yml, .env template, README, and license

Components

jupyter

Related Recipes

Ad Space

Shortcuts: C CopyF FavoriteD Download