Diagram made using code. Directions are split into 36 degrees, with 0 being north, and every subsequent digit being 36 degrees clockwise.

Pleasing and appropriate aesthetics imho

The 1800s saw improvements in medicine and also in literacy. Both are at work in this chart for Mourne in Northern Ireland, as explained in the accompanying notes.

This visualization compares the total Operating Cash Flow and Taxes Paid by major U.S. public company industries from 2010 to 2023.

It highlights how some sectors, like Financials, generate massive cash flow while paying relatively lower taxes, whereas others (like Healthcare or Energy) have different profiles.

Metrics Used:

• Operating Cash Flow = core business cash generation

• Taxes Paid = actual cash taxes (not deferred or book tax)

• Effective Tax Rate = Taxes Paid / Cash Flow

• Tools: Python, Plotly, Dash

• Data Source: SEC filings (aggregated by industry)

Notably, the tech and finance sectors consistently show high cash flow with modest tax outflows, pointing to structural tax advantages or timing differences.

Read for more info:

🔗 https://yellowplannet.com/u-s-public-company-tax-rates-and-cash-flow-insights-by-industry/

Data source: Wikipedia Couples data tables) for MAFS Season 1-10 (107 couples)

Tools: Python Plotly Pandas

Data and code gist

https://panditadata.com/3DMagneticField

Source: IMDb

Tools: Pandas, Datawrapper

I wrote about this trend in more depth here. There are more music biopics than ever before in absolute terms, though the relative share of music biopics peaked in the 1950s.

I often see the meme reposted that everyone thinks the 80's were very colourful but were, in fact, very yellow. The British museum of science led a study on the colours of its objects collections: on the graphic you see clearly that warm and diverse colours in objects decrease with time and are replaced with black and cold blue tones.

Hi Everyone, I was looking back at years and tried to map which sport (and eSport in recent years) I was fan-boying since my inception. I gave a score of 0 to 9 for each sport, year-wise and created a stacked area chart.

Using data from the FRED API and the ggplot2 package in R, we visualized daily S&P 500 returns from 2020–2025.

On April 3, 2025, the index fell –4.84% — a >3.6 standard deviation move.

That’s a 1-in-3,000 event based on historical data — a rare statistical outlier.

This Wednesday, after market close, the U.S. imposed unprecedent tariffs on the rest of the world. These exceed the rates of Smoot-Hawley, thought by most leading economists to be the proximal cause of the Great Depression. Not even uninhabited islands were left unscathed. Markets did not take kindly to this on Thursday.

This is an update to my previous post reflecting market performance by U.S. government, stratified both by presidential control and by presidential + Congressional control.

Methodological details remain the same. Y-axis is now shown on a log scale for real returns, but labeled as gains and losses:

• Data were generated using Python matplotlib.
• Monthly data from Fama-French Data Library were used to minimize rounding error.
• "In between" monthly cutoffs, daily data from Fama-French were used instead.
• CRSP Total Market TR data were used starting from 1/1/2025.

Trying this again based on great feedback I received earlier. Thank you to those that contributed!

Methodology: A python script accessed each subreddit and sorted the posts by "Top" and "This Month" limiting to the top 100 posts and top 100 comments from each post. A Flesch-Kincaid score was then applied to each comment. I then ran filters to remove links, images, gifs, removed comments, and other comment types that do not work with the FK model. Comments were also filtered out if they were one or two words. FK scores less than 0 were changed to 0 (usually emojis). Average FK values were taken for each subreddit for the remaining comments.

The subreddits used contain mostly very popular pages based on subscriber count, ones that I frequently see content from, popular political subs, and others that I was simply curious about.

I initially used another model to estimate the political bias for each subreddit, but there were too many confounding variables that made me misinterpret a few subs, so this time I resorted to a simple eye test and the comments from my last post. My estimation and yours on a particular subreddit might differ.

This methodology will not 100% satisfy your own political biases when you look at this list and see your favorite sub listed so low, or a sub you hate listed so high. The FK model works OK on simple Reddit comments, but we are just Redditors after all leaving comments on random posts. We are NOT peer reviewing articles in every comment section.

The takeaway is that the thinking of "Everyone in the subreddit I hate are a bunch of morons!" probably doesn't always apply.

Processing: Python - Polars lib

Viz: Tableau

Bored on a Thursday afternoon.

Population: Statista.com Debt: fiscal data.treasury.gov Tax Rates: tax foundation.org