Data visualization in Jupyter, including charts, maps, 3D rendering, and interactive views.
A colleague sends you a SQLite file, a Parquet dataset, or an Avro snapshot. You need to explore the content, but you’re not sure what’s inside or even how to open it. You shouldn’t have to write code, craft SQL queries, or recall the syntax for read_parquet in a Python library.
Jupyter notebooks are the de facto standard for interactive computing and data analysis, combining code, prose, and visualizations in a single document.
Welcome to the first part of a series of articles showing you how to create a dashboard in Jupyter and deploy it as a standalone web app. Importantly, you won’t need to rewrite your app in a different framework for deployment. We will use a pure Python solution with no JavaScript or CSS required.
Bringing the Power of OpenLayers to Jupyter.
Today, we are proud to announce that the ipydatagrid open source project has been incorporated into Project Jupyter as part of the Jupyter Widgets subproject.
ITables, or Interactive Tables, is a MIT-licensed Python package that renders Python DataFrames using the DataTables JavaScript library. ITables 2.0, that I have just released, adds support for the DataTables Extensions. In this post we review the functionalities brought by this release.
A tutorial that demonstrates a zero-server, interactive 3D rendering notebook and walks through the quick and easy configuration that can be customized to your needs.
If there is one thing that recent events tell us, it is that genomics is a large source of data, and that its manipulation and understanding allow for the quick development of new drugs and… vaccines.
In this post, we present the first public release of XVega, a C++ library for producing Vega-Lite charts.
Scientific visualization in the Jupyter notebook
Use Jupyter and 3D Slicer kernel to implement biomedical data processing workflows in a notebook.
The Jupyter widgets ecosystem offers a broad variety of data visualization tools for exploratory analysis in the notebook. However, we lack a good story for exploratory graph visualization.
This report is long overdue! From June 3rd to June 6th 2019, thirty-five developers from the Jupyter community met in Paris for a four-day workshop on dashboarding with Project Jupyter.
As you may already know, the Jupyter Notebook and JupyterLab are Browser-based applications. Browsers are incredibly powerful, they allow you to swap rich and interactive graphical interfaces containing buttons, sliders, maps, 2D and 3D plots and even video games in your webpages!
As Jupyter grew in popularity, a broad ecosystem of visualization packages based on Jupyter widgets has been developed, bringing even more interactivity to the Jupyter world.
Project Jupyter is a huge hit in data science, but it has not yet found widespread adoption in robotics. Today, we are releasing the first version of jupyter-ros, a collection of Jupyter interactive widgets inspired by Qt and RViz, to bring their features to the Jupyter ecosystem.
With the success of the notebook file format as a medium for communicating scientific results, more than an interactive development environment, Jupyter is turning into an interactive scientific authoring environment.
