ipyvizzu - Build animated charts in Jupyter Notebook and similar environments with a simple Python syntax

Documentation · Examples · Code reference · Repository · Blog

ipyvizzu is an animated charting tool for Jupyter, Google Colab, Databricks, Kaggle and Deepnote notebooks among other platforms. ipyvizzu enables data scientists and analysts to utilize animation for storytelling with data using Python. It's built on the open-source JavaScript/C++ charting library Vizzu.

There is a new extension of ipyvizzu, ipyvizzu-story with which the animated charts can be presented right from the notebooks. Since ipyvizzu-story's syntax is a bit different to ipyvizzu's, we suggest you to start from the ipyvizzu-story repo if you're interested in using animated charts to present your findings live or to share your presentation as an HTML file.

Similarly to Vizzu, ipyvizzu utilizes a generic dataviz engine that generates many types of charts and seamlessly animates between them. It is designed for building animated data stories as it enables showing different perspectives of the data that the viewers can easily follow.

Main features:

• Designed with animation in focus;
• Defaults based on data visualization guidelines;
• Works with Pandas dataframe, while also JSON and inline data input is available;
• Auto scrolling feature to keep the actual chart in position while executing multiple cells.

pip install ipyvizzu

Visit Installation chapter for more options and details.

You can create the animation below with the following code snippet.

import pandas as pd
from ipyvizzu import Chart, Data, Config

df = pd.read_csv(
    "https://ipyvizzu.vizzuhq.com/latest/showcases/titanic/titanic.csv"
)
data = Data()
data.add_df(df)

chart = Chart(width="640px", height="360px")

chart.animate(data)

chart.animate(
    Config(
        {
            "x": "Count",
            "y": "Sex",
            "label": "Count",
            "title": "Passengers of the Titanic",
        }
    )
)
chart.animate(
    Config(
        {
            "x": ["Count", "Survived"],
            "label": ["Count", "Survived"],
            "color": "Survived",
        }
    )
)
chart.animate(Config({"x": "Count", "y": ["Sex", "Survived"]}))

Visit our Documentation site for more details and a step-by-step tutorial into ipyvizzu or check out our Example gallery.

ipyvizzu can be used in a wide variety of environments, visit Environments chapter for more details.

• Notebooks
  • Jupyter Notebook
  • Colab
  • Databricks
  • DataCamp
  • Deepnote
  • JupyterLab
  • JupyterLite
  • Kaggle
  • Noteable
• App platforms
  • Streamlit
  • Flask
  • Panel
  • Mercury
  • Voilà
• BI tools
  • Mode
• IDEs
  • PyCharm
  • VSCode Python

• ipyvizzu-story adds presentation controls to present data stories live or to share them as an interactive HTML file.

We welcome contributions to the project, visit our contributing guide for further info.

• Join our Slack if you have any questions or comments: vizzu-community.slack.com
• Drop us a line at hello@vizzuhq.com
• Follow us on Twitter: VizzuHQ

ipyvizzu collects aggregate usage statistics by default to follow the progress and overall trends of our library. This feature is optional, and users can choose to opt-out. However, we do not track, collect, or store any personal data or personally identifiable information. Please note that even when this feature is enabled, publishing anything made with ipyvizzu remains GDPR compatible. For more details, please visit Analytics chapter.

Copyright © 2022-2023 Vizzu Inc.

Released under the Apache 2.0 License.