is a data visualization researcher
based in Montreal, Canada.
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!
I gave a talk at the Data Science, Design and Technology Montreal meetup which was a lot of fun, especially when other members of the community presented the apps that they'd created with Dash!
I recently did a guest talk at the Arup Montreal office regarding the differences between Software Product Organizations and Professional Services Organizations.
Data visualization uses algorithms to create images from data so humans can understand and respond to that data more effectively. Artificial intelligence development is the quest for algorithms that can “understand” and respond to data the same was as a human can – or better. It might be tempting to think that the relationship between the two is that to the extent that AI development succeeds, datavis will become irrelevant. After all, will we need a speedometer to visualize how fast a car is going when it’s driving itself? Perhaps in some distant future, it might be the case that we delegate so much to AI systems that we lose the desire to understand the world for ourselves, but we are far from that dystopia today. As it stands, despite the name, AI development is still very much a human endeavour and AI developers make heavy use of data visualization, and on the other hand, AI techniques have the potential to transform how data visualization is done.