Showcase Your Jupyter Skills with a GitHub README Badge

Use this markdown in your README:

![Jupyter](https://img.shields.io/badge/Jupyter-F37626?style=for-the-badge&logo=jupyter&logoColor=white)

The #F37626 is Jupyter's official orange. The jupyter logo identifier renders the Jupyter circle logo from Simple Icons. This orange badge is strongly associated with data science and research computing environments.

Most data scientists and ML engineers use Jupyter — it is the default tool rather than a differentiator. Upgrade your Jupyter signal by adding context about how you use it: exploratory data analysis and visualization, machine learning experiments with model comparison, research documentation that mixes code, equations, and prose, or production notebook pipelines using Papermill or Ploomber.

Your GitHub repositories can demonstrate Jupyter expertise through well-documented notebooks with clear narrative, properly cleaned-up output cells, reusable utility functions, and visualizations that tell a data story. A notebook that is readable as a document — not just runnable code — shows communication skills that data teams value highly.

Jupyter appears at the center of the data science stack:

• Data analysis stack: Jupyter + Python + Pandas + NumPy + Matplotlib/Seaborn
• ML research stack: Jupyter + Python + TensorFlow/PyTorch + Scikit-learn + NumPy
• Data engineering exploration: Jupyter + Python + SQL + PySpark + Pandas
• Scientific computing: Jupyter + Python + SciPy + NumPy + Matplotlib

In the context of a data science profile, consider grouping Jupyter with Python and the core libraries rather than with web frameworks. A dedicated 'Data Science Stack' badge row showing Jupyter + Python + Pandas + NumPy + Scikit-learn immediately communicates your analytical focus to recruiters and data team leads.

Jupyter notebook files (.ipynb) are detected as 'Jupyter Notebook' by GitHub's linguist and will often dominate your top languages percentage if you have many notebooks in public repositories. This is accurate — if you are a data scientist, your primary work product is notebooks. However, if you want to show that you also write production Python code, use the hide=jupyter notebook parameter to exclude notebooks from your language card.

Pinning specific Jupyter notebooks on your profile (GitHub allows pinning individual files, not just repos) is less effective than pinning the repository. Instead, create a portfolio-style repository with a curated collection of notebooks, each with a clear README explaining the analysis and findings.