Interactive data visualizations¶
Jupyter Notebook has support for many kinds of interactive outputs, including the ipywidgets ecosystem as well as many interactive visualization libraries. These are supported in Jupyter Book, with the right configuration. This page has a few common examples.
First off, we’ll download a little bit of data and show its structure:
import plotly.express as px data = px.data.iris() data.head()
Interactive outputs will work under the assumption that the outputs they produce have
self-contained HTML that works without requiring any external dependencies to load.
Altair installation instructions
to get set up with Altair. Below is some example output.
import altair as alt alt.Chart(data=data).mark_point().encode( x="sepal_width", y="sepal_length", color="species", size='sepal_length' )
Plotly is another interactive plotting library that provides a high-level API for visualization. See the Plotly JupyterLab documentation to get started with Plotly in the notebook.
Below is some example output.
For these plots to show, it may be necessary to load
require.js, in your
sphinx: config: html_js_files: - https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js
import plotly.io as pio import plotly.express as px import plotly.offline as py df = px.data.iris() fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", size="sepal_length") fig