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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I recently had the need to take a couple pages out of a PDF and save it to a new PDF. This is a fairly simple task but every time I do it, it takes some time to figure out the right command line parameters to make it work. In addition, my co-workers wanted similar functionality and since they are not comfortable on the command line, I wanted to build a small graphical front end for this task.

One solution is to use Gooey which is a really good option that I cover in my prior article. However, I wanted to try out another library and decided to give appJar a try. This article will walk through an example of using appJar to create a GUI that allows a user to select a PDF, strip out one or more pages and save it to a new file. This approach is simple, useful and shows how to integrate a GUI into other python applications you create.

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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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It is difficult to write a python script that does not have some interaction with the file system. The activity could be as simple as reading a data file into a pandas DataFrame or as complex as parsing thousands of files in a deeply nested directory structure. Python’s standard library has several helpful functions for these tasks - including the pathlib module.

The pathlib module was first included in python 3.4 and has been enhanced in each of the subsequent releases. Pathlib is an object oriented interface to the filesystem and provides a more intuitive method to interact with the filesystem in a platform agnostic and pythonic manner.

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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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