is a data visualization researcher
based in Montreal, Canada.
I’ve just published a personal project I’ve been thinking about doing for a few years now: revisiting figures from a 1967 book which has had a big influence on how I (and others!) think about data visualization, Jacques Bertin’s Semiology of Graphics.
I was recently interviewed on the IQT Podcast about Visualizing Data During a Pandemic, and how Plotly is contributing to COVID-19 response.
Plotly Express is the built-in high-level data visualization interface for Plotly.py, a leading interactive data visualization library for Python. With today’s release of Plotly.py 4.8, Plotly Express now gracefully operates on wide-form and mixed-form data – not just “tidy” long-form data. These new capabilities dramatically expand Plotly Express’ promise of ‘interactive data visualization in a single Python statement’, by removing the need to wrangle your data into a particular form before plotting.
Plotly Express is a new high-level Python visualization library: it’s a wrapper for Plotly.py that exposes a simple syntax for complex charts. Inspired by Seaborn and ggplot2, it was specifically designed to have a terse, consistent and easy-to-learn API: with just a single import, you can make richly interactive plots in just a single function call, including faceting, maps, animations, and trendlines. It comes with on-board datasets, color scales and themes, and just like Plotly.py, Plotly Express is totally free: with its permissive open-source MIT license, you can use it however you like (yes, even in commercial products!). Best of all, Plotly Express is fully compatible with the rest of Plotly ecosystem: use it in your Dash apps, export your figures to almost any file format using Orca, or edit them in a GUI with the JupyterLab Chart Editor!