Seaborn is one of the go-to tools for statistical data visualization in python. It has been actively developed since 2012 and in July 2018, the author released version 0.9. This version of Seaborn has several new plotting features, API changes and documentation updates which combine to enhance an already great library. This article will walk through a few of the highlights and show how to use the new scatter and line plot functions for quickly creating very useful visualizations of data.
In my last article, I presented a flowchart that can be useful for those trying to select the appropriate python library for a visualization task. Based on some comments from that article, I decided to use Bokeh to create waterfall charts and bullet graphs. The rest of this article shows how to use Bokeh to create these unique and useful visualizations.
This brief article introduces a flowchart that shows how to select a python visualization tool for the job at hand. The criteria for choosing the tools is weighted more towards the “common” tools out there that have been in use for several years. There may be some debate about some of the recommendations but I believe this should be helpful for someone that is new to the python visualization landscape and trying to make a decision about where to invest their time to learn how to use one of these libraries.
Jake VanderPlas covered this topic in his PyCon 2017 talk and the landscape has probably gotten even more confusing in the year since this talk was presented.
Jake is also one of the creators of Altair (discussed in this post) and is back with another plotting library called pdvega. This library leverages some of the concepts introduced in Altair but seeks to tackle a smaller subset of visualization problems. This article will go through a couple examples of using pdvega and compare it to the basic capabilities present in pandas today.
Over on Kaggle, there is an interesting data set of over 130K wine reviews that have been scraped and pulled together into a single file. I thought this data set would be really useful for showing how to build an interactive visualization using Bokeh. This article will walk through how to build a Bokeh application that has good examples of many of its features. The app itself is really helpful and I had a lot of fun exploring this data set using the visuals. Additionally, this application shows the power of Bokeh and it should give you some ideas as to how you could use it in your own projects. Let’s get started by exploring the “rich, smokey flavors with a hint of oak, tea and maple” that are embedded in this data set.