Practical Business Python

Taking care of business, one python script at a time


Mon 30 April 2018

Choosing a Python Visualization Tool

Posted by Chris Moffitt in articles   

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.

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Tue 20 February 2018

Intro to pdvega - Plotting for Pandas using Vega-Lite

Posted by Chris Moffitt in articles   

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.

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Tue 02 January 2018

Interactive Visualization of Australian Wine Ratings

Posted by Chris Moffitt in articles   

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.

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Mon 09 October 2017

Creating Interactive Visualizations with Plotly’s Dash Framework

Posted by Chris Moffitt in articles   

Python’s visualization landscape is quite complex with many available libraries for various types of data visualization. In previous articles, I have covered several approaches for visualizing data in python. These options are great for static data but oftentimes there is a need to create interactive visualizations to more easily explore data. Trying to cobble interactive charts together by hand is possible but certainly not desirable when deployment speed is critical. That’s where Dash comes in.

Dash is an open source framework created by the plotly team that leverages Flask, plotly.js and React.js to build custom data visualization apps. This article is a high level overview of how to get started with dash to build a simple, yet powerful interactive dashboard.

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