Practical Python Data Wrangling and Data Quality

Chapter 10. Presenting Your Data

After all the effort we’ve put into accessing, assessing, cleaning, transforming, augmenting, and analyzing our data, we’ve finally reached the point where we’re ready to start thinking about communicating what we’ve learned to others. Whether it’s for a formal presentation to colleagues or a social media post for friends and followers, sharing the insights we’ve generated through our data wrangling work is an opportunity for our work to have impact beyond ourselves.

Like every other part of our data wrangling process, effectively and accurately conveying our insights involves applying only a few hard-and-fast rules but a whole lot of judgment. This is certainly true of written communications, but perhaps even more so when it comes to the aspect of data communication that often gets the most attention: visualization.

As we touched on in “Visualization for Data Analysis”, creating visualizations to effectively share our data insights with others requires a different focus and approach than we had when building the visualizations for generating those insights in the first place. For example, unless you’re trying to reach a pretty specialized audience (say, through an academic publication), it’s deeply unlikely that a histogram will find its way into your visualization vocabulary when it’s time to share your findings with the world. At the same time, it is extremely likely that you will end up using some form of bar or column chart¹ to share your ...