Making a Visual Argument in matplotlib
Learn to make effective, thoughtfully-designed data visualizations in Python with Matplotlib.
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Making a Visual Argument
Lesson 1 of 1
- 1All design choices impact how a viewer will understand a data visualization. Even the simplest visualizations have an argument, a thesis, or a central point — and the design choices we make (or ign…
- 2Charts and data visualizations help us to make sense of the world around us. They are most helpful when we can connect what we see on the page or screen to our everyday understanding of the world. …
- 3Not only can subplots help us fit graphs nicely onto a page, the arrangement of graphs in a subplot can also be used as a tool to make a clearer visual argument. * Side-by-side charts allow…
- 4We just talked about the advantages of displaying multiple plots next to each other. One common challenge with subplots–as we saw in the last exercise– is getting the graphs and their axes, label…
- 5We know how to set colors in matplotlib using the color parameter for each graph, but how do we use color to advance an argument? The right color choices will: * make the visualization easier to …
- 6Annotations are one of the most helpful tools for building a strong visual argument. While it may seem like “the data should speak for itself” or that a visualization should be obvious with no text…
- 7As we just saw, adding annotations is relatively simple: we can use plt.annotate(), and pass in parameters for text and (xy) position. To recap: plt.annotate(‘Look at this point!’, (15, 35)) pl…
- 8Finally, a crucial component to a strong visual argument is to help the audience understand why the visualization matters. Why is the takeaway from the graph relevant, interesting, or noteworth…
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