Our SQL cells can now display interactive summary charts as part of the results table!

We kept finding ourselves looking for good datasets to use to demonstrate, teach and discuss working with data. In our search we realized we'll never find the One True Dataset, so we started developing this collection of curated datasets.

If you're used to working in Python and creating charts using Matplotlib, this notebook will walk you through related concepts in Observable Plot and how to conceptualize making charts with Plot.

We designed Observable Plot’s API to be succinct so that when you want to visualize data, you can do it quickly and without struggling to remember Plot’s API. This notebook showcases Plot’s “shorthand” syntax

Plot Cheatsheets is an interactive overview of the foundational concepts of the Observable Plot library including: Marks, Scales, Transforms, Layout & Colors.

This year, we would like to recognize some members of our community who have gone above and beyond to be engaged and supportive community members as we continue on the journey of making data accessible to everyone.

What makes the Observable community of data collaborators so unique? Check out our State of Dataviz research report to find out!

Data Wrangler provides an interface to guide code composition using the Arquero package, a robust data wrangling library based on the grammar of data manipulation.

Today, we are launching the first set of Observable Templates. Now, you can create expressive and interactive data visualizations, charts and dashboards, without starting from a blank slate. And, you can use these templates without expertise in dataviz!

Our User Research templates help you quickly visualize product usage data. Learn how to quickly explore and analyze survey responses with the Survey Report Builder template.

A semi-guided tutorial for data exploration with Observable Plot.

We are thrilled to announce Observable Plot, a new open-source JavaScript library for exploratory data visualization.