![]() The font choice this time is Open Sans Condensed, a Google Font. I particularly like the sequential palettes, which follow a clean gradient between white and another color (or between two or three colors), although I ignore some of the lighter colors as they may not be visible against white backgrounds. I strongly recommend using the ColorBrewer palettes, helpfully provided for this use case with the paletteable Python library by Matt Davis. Let’s remake those word clouds.Īt this point I should mention appropriate color palettes for word clouds since the rainbows of the stereotypical word clouds can be distracting. ![]() One of the earliest word clouds I made was for the Yelp reviews dataset from the Yelp Dataset Challenge to compare and contrast verbiage between 1-star reviews and 5-star reviews. In this example, I will be using the DIN Condensed font, a font native to OS X and a font you’ve likely seen in media advertisements and website logos. For word clouds, I prefer to use condensed font families, as they can allow for more information to be displayed in the word cloud. Let’s use the word counts of titles of submissions to the /r/dataisbeautiful subreddit on Reddit which have scored at least 100 points (using the Reddit data dump located on BigQuery).Īdditionally, we can improve on the design of the default word cloud output by forcing all-caps text and by changing the text font. Why not make a word cloud which looks like a line chart? ![]() Reddit Data and Thematic Iconsįont Awesome has icons for charts, which logically appeals to me as a data person. Now every Font Awesome icon can be used as a word cloud mask! And the icons can be exported at any size: for this post, I render the word clouds at 2048x2048px, larger than most desktop screens! After hacking the Python scripts included with the package which were used to create the default word clouds, I managed to create a few interesting examples. ![]()
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