2017-01-28 21:46:23 +01:00

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---
layout: page
title: "Installation"
description: "Setup and first steps for Jupyter Notebooks and Home Assistant."
date: 2016-07-23 09:00
sidebar: true
comments: false
sharing: true
footer: true
---
To run Jupyter Notebooks locally, an installation of [Jupyter](http://jupyter.org/) is needed. Consider to run Jupyter in a [virtualenv](/getting-started/installation-virtualenv/).
```bash
$ pip3 install jupyter matplotlib
```
<p class='note warning'>
Certain notebooks hosted in the [Home Assistant notebooks repository](https://github.com/home-assistant/home-assistant-notebooks) require access to a running Home Assistant instance or parts of a Home Assistant installation. If you want to run those notebooks, install Home Assistant with `$ pip3 install homeassistant` as well.
</p>
Now you are able to start the application.
```bash
$ jupyter notebook
[I 17:22:18.081 NotebookApp] Writing notebook server cookie secret to /run/user/1000/jupyter/notebook_cookie_secret
[I 17:22:18.921 NotebookApp] Serving notebooks from local directory: /home/fabaff/home-assistant
[I 17:22:18.921 NotebookApp] 0 active kernels
[I 17:22:18.921 NotebookApp] The Jupyter Notebook is running at: http://localhost:8888/
[I 17:22:18.922 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
```
Open [http://localhost:8888/](http://localhost:8888/) in your browser. Press "New" -> "Python3" to open a new notebook.
<p class='img'>
<img src='{{site_root}}/images/screenshots/jupyter-new.png' />
</p>
You will get an empty notebook with one cell. Cells can contain code or text. To get the output of a cell you need to execute them with "Cell" -> "Run Cells" from the menu or by pressing the icon.
<p class='img'>
<img src='{{site_root}}/images/screenshots/jupyter-notebook.png' />
</p>
The downloadable version of this notebook is available in the [Home Assistant notebooks repository](https://github.com/home-assistant/home-assistant-notebooks/blob/master/first-notebook.ipynb).
As you can see is the workflow very similar to working directly with a Python shell. One advantage is that you can go back and forth as you please and save your work.