---
title: "Jupyter Notebooks"
id: "1760"
type: "page"
slug: "jupyter"
published_at: "2026-09-20T21:16:35+00:00"
modified_at: "2026-09-21T01:36:28+00:00"
url: "https://xedant.com/agents/analytics/docs/jupyter"
markdown_url: "https://xedant.com/agents/analytics/docs/jupyter.md"
excerpt: "The NotebookContent block embeds a full Jupyter Notebook right into a report — next to…"
---

# Jupyter Notebooks

[https://xedant.com/agents/analytics/docs/jupyter.md](https://xedant.com/agents/analytics/docs/jupyter.md)

The NotebookContent block embeds a full Jupyter Notebook right into a report — next to the charts and tables. Complex statistics, forecasts and ML models live in the same place as the business report.

## Execution

- the notebook runs through papermill + ipykernel;
- the result is displayed next to the report’s other blocks;
- the execution timeout is 10 minutes;
- the block shows the status and the time of the last save.

## The automatic environment

- Python 3 is already built into the Docker image — nothing to install on the host;
- the service creates the virtualenv itself on the notebook’s first run;
- dependencies are installed from `data/requirements.txt` — the file is seeded on first launch, and you can extend it with your own libraries.

## Built-in libraries

- **pandas** and **NumPy** — data processing;
- **Matplotlib** and **Seaborn** — visualization;
- **SQLAlchemy** — access to databases.

## Templates and storage

- notebook templates: `basic`, `data-analysis`, `machine-learning`;
- two storage options: the notebook’s JSON embedded in the block, or an external `.ipynb` file;
- the block displays a cell counter.

## Access to data

Through SQLAlchemy the notebook connects to your databases, and Lakehouse files (Parquet, SQLite, Markdown) are read directly from the data folder. Details in the [Lakehouse](/agents/analytics/docs/lakehouse)
 section.

[← Back to the documentation index](/agents/analytics/docs)