audit-labs/tutorials

Learn how to perform data analysis, scripting, automation, and more using reproducible Jupyter Notebooks.

Table of contents

• Project overview
• Who this is for
• What's included
• Getting started
  • Prerequisites
  • Quick start — using Binder (no local setup)
  • Run locally (recommended)
  • Run in Google Colab
  • Run with Docker
  • Run headless / export notebooks
• Best practices for notebooks
• Contributing
• License & Code of Conduct
• Contact / Support

Project overview

This repository contains interactive tutorials and example notebooks designed to teach practical skills in data analysis, scripting, automation, and related topics using Jupyter Notebooks. Each notebook demonstrates concepts through hands-on examples so you can follow along and adapt the patterns to your own projects.

Who this is for

• Data analysts and engineers learning reproducible workflows.
• Developers who want to prototype automation or analysis in notebooks.
• Students and instructors seeking ready-made examples for teaching.

What's included

• A collection of Jupyter Notebook tutorials (look in the repository root or notebooks/ folder for .ipynb files).
• Guidance and examples that demonstrate common patterns for data ingestion, transformation, visualization, and basic automation.

(If your repo has a specific folder layout or important notebooks, consider adding a short list here with links to the most important notebooks.)

Getting started

Prerequisites

• Python 3.10+ (3.10 recommended)
• Git (to clone the repo)
• JupyterLab or Jupyter Notebook (for local development)

Optional:

• Conda (recommended for reproducible environments)
• Docker (for containerized runs)

This repository is licensed under the GNU General Public License v3.0 (GPL-3.0). See the included LICENSE file for details.

Quick start — using Binder (no local setup)

To run the notebooks in your browser with no local install, use Binder:

• Launch Binder: https://mybinder.org/v2/gh/audit-labs/tutorials/HEAD

Binder will respect environment.yml or requirements.txt if present; this repository includes an environment.yml to produce a reproducible environment.

Run locally (recommended)

1. Clone the repository

   git clone https://github.com/audit-labs/tutorials.git
   cd tutorials
   

2. Create and activate an environment

   Using conda (recommended):

   conda env create -f environment.yml
   conda activate audit-tutorials
   

   Or with pip and virtualenv:

   python -m venv venv
   source venv/bin/activate   # macOS / Linux
   venv\Scripts\activate      # Windows
   pip install --upgrade pip
   pip install -r requirements.txt
   

3. Install JupyterLab (if not already)

   pip install jupyterlab
   jupyter lab
   

   Or run the classic notebook server:

   jupyter notebook
   

4. Open the notebooks in the browser and follow the cells.

Run in Google Colab

To open a notebook in Colab:

• Navigate to the notebook file on GitHub, then use "Open in Colab" or open via: https://colab.research.google.com/github/audit-labs/tutorials/blob/HEAD/path/to/notebook.ipynb
• Colab will run in the cloud; you may need to pip-install extra dependencies at the top of the notebook using !pip install ....

Run with Docker

You can run the notebooks inside a Docker container using Jupyter's base images:

docker run -p 8888:8888 -v "$(pwd)":/home/jovyan/work jupyter/base-notebook:latest

Then open http://localhost:8888 and navigate to work/.

Run headless / export notebooks

To execute notebooks and export them programmatically:

pip install nbconvert nbclient
jupyter nbconvert --to html --execute path/to/notebook.ipynb

This is useful for CI pipelines and automated report generation.

Best practices for notebooks

• Keep notebooks focused: one concept or analysis per notebook.
• Include a short README or top-level markdown cell describing purpose and inputs.
• Avoid long-running data downloads inside notebooks—prefer referencing local sample data or scripts.
• Use version control: commit notebooks regularly. Consider tools like nbstripout or nbdime to manage diffs.
• Parametrize notebooks for reproducibility (e.g., use papermill for parameterized runs).

Contributing

See CONTRIBUTING.md for contribution guidelines.

License & Code of Conduct

This project is licensed under the GNU General Public License v3.0 (GPL-3.0). See LICENSE for details.

Please review CODEOFCONDUCT.md for expected behavior when contributing.

Contact / Support

For questions or help, open an issue in this repository or contact the maintainers listed in the repository settings.