If you're camera shy, then you might be less than thrilled to be living in the modern era. Everyone has a camera on their phone, and you can expect to be in other people's photos, as well as get requests for selfies with them.
If you're walking down the street in a touristy area, then you might well be a part of someone's travel memories. You might even be a part of their social-media postings. And of course, while plenty of people take still photos, people are taking plenty of videos, meaning that you're an extra in many other people's travelogues.
But individuals are only part of the recent explosion in photography. Governments have also been getting into the act, with video cameras in all sorts of places, from busy streets to train stations. We're told that the cameras are there to keep us safe, and I'm sure that's true to some degree.
But as John Oliver described in Sunday's Last Week Tonight (https://www.youtube.com/watch?v=lnBPhelCdWE), there are a lot of troubling questions about how these technologies are being used. At the end of the show, Oliver pointed to the Electronic Frontier Foundation's "Atlas of Surveillance" site (https://www.atlasofsurveillance.org/), which is gathering information on the many types of surveillance being used by government agencies, and the companies that are supplying the technologies they're using.
As a data person, I immediately went to the site, and was delighted to discover that the database is downloadable. This week, we'll thus look at surveillance technology in the US – who is using it, and what they're using.
Data and six questions
The data, as I mentioned above, is from the EFF's Atlas of Surveillance site at https://www.atlasofsurveillance.org/ . Clicking on the "download this dataset" button gives you the link https://www.atlasofsurveillance.org/download.csv, which then downloads a CSV version of the database. The database appears to be getting updates on a regular basis, so your results might well differ from mine. I'm going to use the version from August 5th, 2026.
Paid subscribers, both to Bamboo Weekly and to my LernerPython+data membership program (https://LernerPython.com) get all of the questions and answers, as well as downloadable data files, downloadable versions of my notebooks, one-click access to my notebooks, and invitations to monthly office hours.
Learning goals for this week include cleaning data, working with dates and times, joins, pivot tables, and plotting with Plotly.
Here are my six questions for this week. I'll be back tomorrow with my solutions and explanations:
- Create a Pandas data frame based on the file. Any columns containing dates (all of which have the word "date" in them) should be turned into
datetimecolumns. (Do the best you can with the admittedly inconsistent date formats.) Remove any columns that contain only NaN values. - What cities and states have the most reported purchases of surveillance equipment over the years? For each of the top five, what agencies have been making purchases, and what did they buy? Are only police departments making these purchases?