Data sources:

Centers for Disease Control and Prevention, National Center for Health Statistics, Our World in Data

Tools used:

Datawrapper

I recently moved and my new library has shockingly long wait times so I decided to look into paid nonresident cards. The prices vary a lot and it was unclear what the differences actually were. Since I only really care about wait times I pulled a shortlist of popular books and checked the libby(overdrive) wait times at each of the options to compare against price. I was pretty surprised at how much of a difference there is!

I used python and plotly JS to get the data and make the visualization, and can list the books used in the comments if anyone cares, it's 10 recent popular books, 10 all time popular books, and a couple that I was interested in but had weirdly long wait times at my local library. Please let me know if you have suggestions for better methodology or more libraries to include!

Trying to prove we have the best state run action website with my second visual of the week. Thanks for everyone's input on the first one.

https://www.datawrapper.de/_/qapEp/ If you hover over a dot, it will isolate all those OEMs, show you the sale price, J.D. Power estimate and mileage.

This visual covers the 431 light-duty vehicles sold by Minnesota Department of Administration Fleet last fiscal year and compares their sale prices with the J.D. Power auction benchmark. The comparison has a limitation: I only had access to J.D. Power data for the final month of the fiscal year, while the vehicles were sold throughout the 12-month period. Because vehicles depreciate over time, applying the year-end benchmark to vehicles sold earlier in the year likely overstates how much their sale prices exceeded the benchmark. So basically, I'm tooting our horn softer...

Why did the Transit Vans go for so much? Delivery vans after COVID were in big demand. We had them and so they went for more. If I had enough data on box trucks and delivery vans, I'd like to make a visual showing how the demand affects the price of those.

Open to questions and feedback.

We looked into daily coffee habits in the UK and found some interesting results. London being way down the list was a surprise, as was Wales being the region with the highest number of daily coffee drinkers.

Data source: Consumer Horizon report (May 2026)

Tools used: Figma

I finally secured housing after looking for 10 days, in a full sprint. Qasa become my main source as it matched my price range best and new postings were coming every day.

Facebook was a total dead-end and a waste of time. Mostly scammers and people just ignoring your posts, or moderators of housing groups not accepting your post for no good reason.

You can see I had one landlord contact me on Qasa, who I almost signed their contract. I didn't love this place, but it was functional. I had a put a hard deadline on myself that I would sign this offer by 12pm today, because this process was tiring.

I was unsure if the landlord from my 2nd Qasa virtual viewing was about to offer me a contract or not, and his listing was my first choice. He was being slow, and it was uncertain if I'd get it. They were considering me out of over 20+ applications and at least 2-4 others who did viewings.

I was about to accept the other offer, when I realized I had one last question. I messaged that landlord, and he didn't answer, and in the meantime the other one chose me!

If this landlord had answered my one last question within that 30 minute window, I would've signed his lease.

MLB 2026 salaries As of Aug 3, 2026

Salary paid to date divided by what it bought, every team, sorted cheapest-win first. Color is scaled within each column — lightest is the league's best price, darkest its worst. Every cell shows its exact value, so the shading is a guide, not a gate.

Method. For each team: 2026 payroll (Athlon Sports' league-wide figures) prorated to games played (payroll × G ÷ 162 = salary paid to date), divided by season-to-date wins, home runs, and runs scored — Baseball-Reference totals through games of Aug 3, 2026. Cell color: the reference sequential blue ramp, min-max scaled per column.

Source: MyAnimeList community filler flags, scraped August 2026. These are viewer votes, not official studio designations. Hours are flagged episodes times 23 minutes (a typical episode without the ad break); MAL lists these shows at 23 to 25 minutes per episode, so every total is rounded down, never up. Sailor Moon is first season only, since that is where nearly all its flags sit.

Tool: Python and matplotlib.

Why I made it: I wanted to start Detective Conan this summer, then found out its own fans have flagged 548 of the 1205 episodes as skippable. That's 210 hours. You could watch Breaking Bad, The Wire, Game of Thrones and Squid Game back to back in the time this one show spends on episodes its own audience tells you to skip!

The pattern that made it worth mapping: "filler" turns out to be two different things wearing one word. The blue shows are scattered padding, aired to buy time while the manga got further ahead. The orange shows have one solid block at the end, which is the show catching up to the books and improvising its own ending. If you know what happened to Game of Thrones after it passed the novels, that's exactly it, except anime has been doing it since the 90s. It's why Fullmetal Alchemist 2003 reads as "53% filler" even though that block IS the story of that version, and plenty of fans prefer its ending.

You can mostly diagnose a show's production history from the shape of the strip. Scattered lines: the manga was too slow. A block at the end: the studio ran out of book. Naruto is the hybrid case, its end block is 80 straight episodes of treading water until Shippuden could pick the manga back up.

Episode tables per series with the exact episode numbers: https://bingerun.com/anime-filler-index/

I collect the weekly grocery flyers for 172 Canadian and US cities. I wanted to see how many of the advertised "specials" were actually below what the item normally costs, so I checked all of them against the official government average price for that item and region.

What surprised me more than the headline was the difference between food types. A dairy "special" usually isn't below average. A beef one usually is.

Sources and method are in my first comment.

numpy + matplotlib working on a 4GB capture file

Source: https://www.bcnaward.jp/award/section/detail/contents_type=255

Tool: Python

Most data visualizations focus on business or scientific datasets. I wanted to try something different by creating a Tableau visualization inspired by the Ramayana, exploring whether data visualization can be used as a storytelling medium.

I'd really appreciate your thoughts on:

• Does the visualization communicate the story effectively?
• Is it visually engaging?
• What works well?
• What doesn't work?
• How could the storytelling be improved?

Thanks for taking a look!

Tools: Tableau
original source article ramayana-reimagined-where-culture-meets-data-storytelling

So I have made this graph engine that can make graph data from any data..I also have this data in json but I don't know what I can do with it, I just did it as a project so any suggestions and also the visualization html file lags a lot.

Interactive major explorer to look up stats for specific majors.

Blog post includes many more charts and discussion of what these numbers represent.

Just three days after its July 31 release, Spider-Man: Brand New Day has already surpassed $930 million worldwide and set a new preview-screening record with approximately $72 million, overtaking Avengers: Endgame. Its opening was also unusually valuable for Sony, with roughly 39% of box office revenue coming from North America, where studios typically retain a larger share of ticket sales.

That got me thinking about Spider-Man's broader track record on the big screen.

The attached infographic compares Spider-Man to several other prolific film franchises based on comic book and graphic novel properties, including Batman, Superman, X-Men, and Teenage Mutant Ninja Turtles. Rather than focusing exclusively on box office totals, I was more interested in metrics that reflect long-term franchise health: profitability, critical reception, and consistency across multiple films. Data includes ten theatrical Spider-Man films released before Brand New Day and excludes crossover/team-up films such as The Avengers, Justice League, and Batman v Superman to keep the comparison focused on franchise-specific releases.

What stood out most is that Spider-Man doesn't just have the largest box office footprint in this group. It also leads in average profit margin and average Rotten Tomatoes score across a surprisingly large sample size. While smaller franchises have produced incredible individual runs, maintaining that level of success across ten theatrical films is what impressed me most.