Here are some of the latest and greatest Plotly plots! Check out our Tumblr to see this same post with the interactive plots embedded, and to find more awesome content.

## Be a Hero! Surveil Your Office’s Bathrooms!

This plot comes from DIY Trying (“Attach sensors to all the things!”). They attached sensors to their bathroom doors, and sent those signals to www.bathroomstatus.com so people in the office would be able to tell when either bathroom was occupied without having to check. Then, they added the plotly Python API to their code to plot the bathroom data.

The Discovery Digital Network office has only two bathrooms for 54 people, so the website gets a lot of use. The code also keeps track of how long the bathroom door has been closed, so users can investigate any false positives. Check out the how-to video!

## North Carolina’s Unusually Cool August

It was an usually cool August in the Raleigh-Durham area, and meteorologist Nate Johnson (nsj) made the plot to prove it. The highs are well below the August average for the area since 1945. The lows are right on-target in the 60's, though, so as Johnson tweeted, “not too many complaints about that yet. ☺”:

## Ace AP Bio

The plot shows photosynthesis as related to intensity of light, in an alternative format to a standard bar graph. We remade it from the AP Bio Quantitative Skills Guide, 2012:

Figure 2 shows a variation of a bar graph that only plotted a point for each mean, but there are still standard error bars around each mean. […] The points imply a function between the two variables. […] Had a line been drawn between the points, this would have been a line graph. However, since data were not taken at all light intensities, a line is not appropriate. With more advanced mathematical techniques, one could draw a line of best fit to describe the relationship.

For extra credit, we applied an “advanced mathematical technique” and used the Plotly fit line tool.

## Math Education: Singapore is Above the Curve, the US is Below

This bubble chart by Sineof1 shows the correlation between a country’s level of development and the quality of its math education, as measured by Programme for International Student Assessment (PISA) scores:

The bubbles are sized by a country’s 20:20 ratio, which is “the ratio of the income of the richest 20% to that of the poorest 20%.” We can see that a large income disparity, evidenced by a high 20:20 ratio, also tends to correlate with both low PISA scores and development levels.

## A Colorful Crime Rates Bubble Chart

This matplotlib-based plot comes from a tutorial inspired by a Glowing Python post, which was in turn inspired by a FlowingData R tutorial. From Glowing Python:

It shows the number of burglaries versus the number of murders per 100,000 population. Every bubble is a state of America, the size of the bubbles represents the population of the state and the color is the number of larcenies.

Check out the notebook tutorial!

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## I Love Charts

#### Charts, cartoons, visual humor and neat stuff.

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