Visualizing the results

Finally, the juicy bits. In this section, we're going to visualize some results. From a data science perspective, I'm not very interested in going deep into analysis, especially because the data is completely random, but still, this code will get you started with graphs and other features.

Something I learned in my life, and this may come as a surprise to you, is that—looks also count, so it's very important that when you present your results, you do your best to make them pretty.

First, we tell pandas to render graphs in the cell output frame, which is convenient. We do it with the following:

#24%matplotlib inline

Then, we proceed with some styling:

#25import matplotlib.pyplot as pltplt.style.use(['classic', 'ggplot']) ...

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