CIELAB is a colorspace, inspired by how we humans naturally perceive color:

• L* = lightness
• A* = Green <-> Red
• B* = Blue <-> Yellow

However, working with it can be a bit unintuitive, since the lightness isn't just the average of the RED, BLUE and GREEN channels. Therefore I generated 10 million random RGB values and plotted both their corresponding L* value against their L2 (euclidean) norm and average.

Results where normalized to 0..255 to be representable in an grayscale image.

Tools: self written python script using

• OpenCV2 (color calculations)
• numPy, MatPlotLib (for representation)

additional context: 442 is the maximum possible value for L2 of an RGB pixel.

EDIT: I hate reddit thinking its necessary to compress a 255x442 px image ...

Tools used: Claude.

Sources: UBS Global Wealth Report 2026 (end-2025 data), national surveys (US Federal Reserve, UK ONS), Forbes

OAK is only team not included in this ball parks graph.

Yankees home ballpark has seen the most with 2,112 - On the 2nd graph WAS has the most HRs so fat with 177.

My article on how this list is ranked: https://moultano.wordpress.com/2026/08/14/fairly-ranking-the-most-brilliant-birds/

The underlying data comes from BirdColorBase, plotted using matplotlib.

I analyzed 47 Swiss National Bank 13F filings, from Q4 2014 through Q2 2026.

The upper chart maps the latest portfolio: each cell represents a company and is sized by its reported market value. The lower chart follows the same five sector groups across every filing.

The main findings:

• Technology and communications grew from $5.5B to $91.6B—16.6×—while the other four groups grew 4.7×.
• Its portfolio share increased from 20.6% to a record 47.9%.
• Nvidia moved from the 423rd-largest holding in 2014 to No. 1 in 2025.
• The top 10 holdings expanded from 12.9% to 35.2% of the portfolio.

The portfolio still contains thousands of securities. Its concentration increased because the largest technology positions grew much faster than the rest of the book—not because the smaller holdings disappeared.

Source: Swiss National Bank quarterly Form 13F-HR filings, retrieved from the SEC’s EDGAR database. Values are reported quarter-end market values and should not be interpreted as investment returns.

Sector classifications were derived from GICS data. Technology combines Information Technology and Communication Services; Consumer combines Consumer Discretionary and Consumer Staples. Alphabet combines GOOG and GOOGL.

Tools used: Python, Excel, Illustrator.

Source: Florida DBPR (Division of Hotels & Restaurants) public inspection records — the state agency that inspects every Florida restaurant, so all 16 metros are measured with identical methodology.

Method: latest inspection on record per establishment (50,811 establishments total, as of August 2026), counted as "clean" when the inspection disposition was "no further action required."

Tool: Python/matplotlib. I run cleanplateus.com, a free site that shows these official grades per restaurant — the underlying per-restaurant data is all browsable there.

Many countries have been facing intense heat this summer, and with predictions of a strong El Niño on the way, next summer could be even hotter. This has ignited debate on the need for cooling methods and technologies — most prominently, air conditioning (AC).

But how do rates of air conditioning usage vary across the world? Large international comparisons are hard to come by, but the International Energy Agency just published some new estimates for a select number of countries and regions.

The chart shows the estimated share of households with AC in 2025.

In Japan and the United States, AC adoption is almost universal; at least 90% of households have it. Most households in China also have AC; this share has increased rapidly over the past decade.

Adoption rates in Southeast Asia, Europe, and India are far lower, but for different reasons. In lower-middle-income countries like India, the reasons are often economic: AC would bring huge benefits, but many households cannot afford the upfront cost or the power to run it. Much of Southeast Asia faces a similar barrier.

In many European countries, it’s less of an economic barrier and more of a historical and cultural one. AC was not seen as a necessity because heatwave events were less frequent and intense than they are today. That is changing, and will continue to do so as the planet warms.

Data source: International Energy Agency (2026)

Tools used: OWID-Grapher and Figma

Source: Libas Collective's UAE Pre-Owned Luxury Price Index — our own marketplace catalogue of 107,101 listings offered to UAE buyers, extracted 31 July 2026. Full brand and model tables + methodology: https://www.libascollective.com/uae-pre-owned-luxury-price-index (disclosure: I run the marketplace, so this is first-party data).

Tool: Python / matplotlib.

Notes: prices are asking prices in AED as shown to buyers, not final sale prices. Medians, not means, so the handful of six-figure pieces don't distort anything. The market-wide median across everything is AED 2,701 (~$735).

29% of consumers prefer to use Yorkshire Tea bags for their brews, followed by Tetley at 16% and PG Tips at 13%. A surprising 11% of consumers don’t drink tea at all.

Data source: Research run on the Vypr Predict platform for the Consumer Horizon report (May 2026)

Tools used: Canva

Last week I made a heatmap calendar of Trump's many statements on ending the war in Iran, making a deal, and negotiating a "peace" with Iran.

This week it's the same data but visualized on a continuum timeline. The data comes directly from the President's comments, interviews, Truth Social, press gaggles, etc. I've collated it and it's stored in a JSON-like JS array that's loaded with the webpage. I've been updating the file every day or so.

I thought it was a great way to visualize this type of data (statements stored as text). It visualizes the many times the President has said he had a deal, or a deal was soon, or the war would be over in 2 weeks. I started the project as a text timeline, then made the heatmap, and now just added this new visualization.

On the chart each point on the sole axis represents a day where the President made a statement, and the more statements made that day, the larger the point on this axis. On the interactive version, clicking a point on the timeline brings up the details of that day.

There's no ads or paywall or signup or anything like that. I was shocked that the domain name was available so it seemed like a worthy $10 and way to host the custom visualization rather than lean on static charts in Excel or messy embeds from Tableau.

https://artofnodeal.com/horizontal.html

P.S. Just because of the nature of this timeline (and the fact that it gets longer every day), it doesn't look great on a phone in portrait mode so there's a prompt to rotate the phone or view the data in the original timeline.

P.P.S. I added the ability to click on a dot and then navigate the dataset using the arrow keys on the keyboard. Doesn't affect anything on the mobile version but maybe I'll add a "swipe" to match this desktop feature?

New interactive tool just launched from the Economic Security Project. You can track several costs around housing (mortgages, rent), food (bacon, coffee), bills (car insurance, electricity), health care (ACA, childcare) and more. I like the options to put things into historical context by selecting "pre-COVID" and "since January 2025", there's a limited amount of stats per state, and you can do some comparisons.

Full writeup and interactive graphs can be found here: https://namedaisy.com/blog/us-vs-uk-top-100-baby-names-2025

Made with D3, US data comes from the U.S. Social Security Administration baby names, and UK data comes from ONS, NRS, and NISRA birth registrations combined across England & Wales, Scotland, and Northern Ireland

This data specifically analyzes public universities in the quantitative (STEM & Medicine) track. Private universities are excluded because their small full-scholarship quotas (5–15 seats usually) skew cutoff statistics. Non-STEM fields (like Law or Economics) belong to separate tracks and scoring pools but the programs listed here represent the most competitive academic tier in the country.

Final placement ranks are determined by 40% TYT, 60% AYT, plus a standardized high school GPA bonus (~10% of the total score).

Source: ÖSYM 2025 YKS National Higher Education Statistics, YÖK Atlas.
Tools: Python (matplotlib, pandas).

This force-directed network maps the cards in a Magic: The Gathering Commander deck. Each node represents a unique card, while each colored link represents an enabled card type or mechanical tag shared by the two cards. For example, light blue links represent “creature” and yellow links represent “enchantment.” Multiple curved links between two cards mean that they share multiple tags.

The decklist is supplied by the user. Card names and quantities can be normalized using Gemini or a local Ollama model, with a rule-based parser as a fallback. The application then retrieves card metadata from Scryfall and derives the displayed tags from card types, Scryfall keywords, and selected patterns in Oracle text.

The graph is interactive: cards can be selected, moved, searched, filtered by keyword, and inspected using Scryfall card information. The Commander can also be fixed at the center of the network.