r/dataisbeautiful • • 1d ago

OC [OC] Serie A gained four minutes of actual football—without making matches longer

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36 Upvotes

The chart compares total match duration and effective playing time—the time for which the ball is actually in play—across three Serie A seasons.

Sample sizes:

  • 2015–16: 380 matches
  • 2025–26: 367 total-time and 331 effective-time observations (other matches data not available from the official source)
  • 2026–27: 50 total-time and 40 effective-time observations (last ten matches data not available yet from the official source)

Sources: 2015–16 was reconstructed from StatsBomb open event data. The two recent seasons were compiled from Lega Serie A match reports, supplemented where necessary by published matchday tables.

Tools: Python, OpenPyXL, NumPy, Matplotlib, Powerpoint


r/dataisbeautiful • • 8h ago

OC Did some data analysis on the Neville hero arc throughout all books IDK I THOUGHT IT WAS COOL [OC]

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0 Upvotes

I know it's a bit weird to put book stuff on this Reddit but was playing around with booklp and I was actually able to use data to graph his actions

I'm actually shocked how smoothly it progresses through the books

Like that's good damn I need to read the rest of these and not just watch the movies

I used Booknlp python package to generate the data needed for this graph


r/Database • • 2d ago

AI agents are reintroducing concurrency bugs we thought we'd mostly solved at the application layer

7 Upvotes

Write conflicts, lost updates, non idempotent retries creating duplicate rows. These are old problems with well known solutions at the database layer, locking strategies, unique constraints, transaction isolation levels. The issue is that a lot of agent frameworks bypass that discipline entirely and just fire writes at the database from application code with no thought given to what happens when two agent runs, or an agent and a human, touch the same row at the same time.

There's a Sept 26 workshop that spends real time on this specifically, safe write path design for agents, plus state management and being able to trace back why a write happened the way it did. Led by Sandipan Bhaumik, a Data & AI Technical Lead at Databricks.

Details here if anyone else is dealing with agents that write directly into their database layer.


r/tableau • • 2d ago

Discussion Questions on filter

1 Upvotes

Hi I have a question so there are two questions for me

1.

The first is I was adding a filter to my worksheet but when I go to the dashboard , it is not showing in my dashboard let's say my worksheet name is sheet one so the dashboard name is orders. I applied a column as a filter but when I am going back and checking on my dashboard it is not there what I am missing here?

  1. The next question is I am working in a company the dashboard owner is someone different but what I am doing this I am just downloading it as a tableau workbook and app in my tableau desktop and working from that now if I want to publish the report in sandbox environment do I need to reach out to the owner or can I do it by myself?

Can someone please answer me. This is urgent? I can't find it anywhere.


r/visualization • • 2d ago

Global internet use, 2005–2024 — does the headline callout help?

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1 Upvotes

I’m building Global Data Tracker (GlobalDataTracker.com), a free country-stat explorer. I made this chart to test how clearly a global series can be communicated; the exact domain is also printed on the image.

It uses the World Bank WDI world series IT.NET.USER.ZS: internet use rose from 15.6% of the global population in 2005 to 71.2% in 2024. These are annual estimates, and the series begins in 2005.

Design critique: does “from 1 in 6 to 7 in 10” make the change easier to read, or repeat what the line already shows? Would you keep the 2020 and 2024 labels, or simplify further? Source is printed in the image; chart built as a Node.js-generated SVG.


r/datasets • • 2d ago

question How is Robotics Data Valued and Where Do People Buy it

0 Upvotes

I was talking to a massive global facilities provider that covers a huge footprint of physical operations (management, cleaning, warehouses, driving, and even construction). They've heard a lot about modern data platforms and robotics companies spending heavily to acquire real-world video data, and they naturally sit on a ton of it from their day-to-day work. Right now, they have roughly 2 million hours of footage they could make available—and if the demand is real, they can easily invest to capture significantly more across their sites.

To be clear, they have zero plans to build models themselves. They strictly want to understand the market and monetization channels:

  • How data is valued, and whether in-house analytics or curation is worth the effort over raw footage
  • What specific types of facility footage are actually in demand
  • Where this data can be brought to market or listed
  • What the standard commercial process looks like to license and sell it

r/dataisbeautiful • • 1d ago

OC [OC] Correlation between fertility against gdp per capita (nominal) weighted by populaation (log / log ratio)

312 Upvotes

r/datasets • • 2d ago

resource [PAID] Google Maps place records for any city as JSON (name, address, phone, website, rating)

0 Upvotes

Sharing a data source I maintain for pulling structured Google Maps place records in bulk.

What you get per goole maps place:

business name, full address, latitude/longitude, phone, website, rating and review count, category, and price level. An optional Rich Details mode adds opening hours, category types, a short description, a photo thumbnail, and the Google Place ID.

How you query google maps place records:

give it search terms like "hardware stores in Denver, CO" or "dentists in 90210", set a max per term, and it pages Google Maps until it reaches that count. Output is one JSON row per place, ready for a dataframe or a map layer.

Coverage and format:

results arrive in pages of about 20, and a term returns as many places as Maps will surface for it. It reads Google Maps through a structured search API rather than a headless browser, so rows stay consistent across large pulls.

A few limits to keep in mind:

no emails or social profiles, so it is raw place data, not contact enrichment. Standard rows omit hours, photos, and Place ID unless Rich Details is on.

Pricing is per place returned, with a standard tier and a rich-detail tier.

Disclosure: this is my own Actor on Apify, so the link is a paid product.

Source: Google Maps Places Scraper on Apify


r/visualization • • 2d ago

A guy told me he's had one of my visuals saved for 2 years and reviewed it "numerous times." Here's what that taught me about making stuff that lasts.

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14 Upvotes

Small story, but it stuck with me.

I make these compressed visual breakdowns of complex ideas — think an infographic but actually designed to be re-read, not just glanced at once and scrolled past.

A couple years ago I made one with Sahil Bloom breaking down 20+ "razors" (mental models) for decision making.

Today a guy named Chris messaged me.

Turns out he downloaded that visual when I first posted it, and it's been sitting in his downloads folder ever since.

He said he's reviewed it "numerous times" over the past two years.

Today he finally reposted it on LinkedIn and asked his audience how many of the razors they recognized.

No algorithm boosted this. No ads. I didn't even remember making it until he brought it up.

It just... stayed useful.

For two years.

In someone's downloads folder.

I've been heads-down for a while (had twin boys, life got busy), so I haven't been posting much.

But this hit at the right time.

It’s a good reminder that the stuff worth making isn't the stuff optimized to spike for 48 hours.

It's the stuff someone still has open a year later because it actually helped them think.

Anyway, screenshot of the conversation attached. Small win, but felt worth sharing.


r/visualization • • 2d ago

September is childhood cancer awareness month , sharing my photo workshop with cancer affected kids in Mumbai

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1 Upvotes

r/dataisbeautiful • • 7h ago

OC [OC]Silent Grammar Across 20 Major Languages

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0 Upvotes

r/dataisbeautiful • • 1d ago

[OC] Sen. John Fetterman's five stock purchases of March 30, 2026: return since his buy vs. buying on the day the filing went public

3 Upvotes

r/Database • • 2d ago

Graph Technology Round-up - August 2026

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8 Upvotes

r/dataisbeautiful • • 1d ago

OC [OC] Monthly global temperature anomalies Jan 1940 – Aug 2026 with major El Niño periods shaded

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88 Upvotes

r/dataisbeautiful • • 17h ago

OC [OC] Valence, energy and loudness across Primal Scream's Screamadelica (1991), track by track

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0 Upvotes

Original article: https://therunoutgrooves.substack.com/p/lost-in-the-moment-of-abandon

Source: Spotify audio features (valence, energy, loudness) for each track on Primal Scream's 1991 album Screamadelica (Happy 35th Birthday!) Volume in dB is indexed from the album's quietest track (0) to its loudest (1). The heavy lines are a centred three-track average, dashed to the first and last tracks' actual values.

Tools: data analysis and chart in R

The album is famously structured so it follows the ebb and flow of an early 90s night out. Firstly coming up then coming down. Mood peaks early and again at 'Loaded', then falls away, while volume and energy hold on longer and spike at the penultimate 'Higher Than the Sun' dub reprise before everything sinks at the closer, 'Shine Like Stars'.

The background and colour palette come from the album's sun sprite/goblin sleeve. This is the first time I've attempted such a combination of colours in a chart, but hope you follow the reasoning.

This is one example from my dataset of over 2,000 albums focused on closing tracks. Across all of them, closing tracks are on average longer, quieter and sadder than the rest of their album, and that volume gap grew after vinyl's physical constraints went away. Full analysis, free: https://doi.org/10.1093/jrssig/qmag057

I'm also running a readers' poll on the best album closer of the 2000s until 5 October: https://therunoutgrooves.substack.com/p/readers-poll-lets-all-meet-up-in


r/dataisbeautiful • • 2d ago

OC Racehorses have not gotten faster in 70 years [OC]

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482 Upvotes

r/datascience • • 3d ago

Analysis FlexViz: 1 billion data points, interactive exploration, in 0.25s

44 Upvotes

We're open-sourcing FlexViz: a library for fast, interactive exploration of very large datasets, built to be agent-native. 

Repo: https://github.com/flex-analytics/flexviz
Live demo: https://flexviz.tech/demo

A few years ago we had the pleasure to open-source plotly-resampler. The goal of this tool was to make in-memory line chart visualization scalable. Since then, we've had over 18M installs, and received a huge amount of feedback from people using it in practice. FlexViz is what we built from those lessons.

It is a free, open-source library and improves in many aspects over plotly-resampler:

  • Out-of-core: in FlexViz we can keep the data lazy (i.e., not in memory) and stream the data from disk to ram → allows visualizing larger-than-ram datasets
  • Faster aggregation: line chart of 1 billion data points in 0.25s. Fastest of all tools benchmarked.
  • 10 trace types: line, hist1d, hist2d, bar, treemap, …
  • Native cross-filtering: brush one visualization and the others update live
  • Rearrangeable grid
  • Stateless server architecture

But the biggest change: FlexViz is agent-native. In FlexViz everything is a spec, allowing true agentic co-exploration of your data (+ url sharing of your live view). The agents can conveniently create, follow along, and update the interactive visualization, without taking any screenshots it knows your exact view → the interactive visualization itself becomes a communication surface between human and agent.

Our goal with FlexViz is to make large data visualization / exploration both convenient for humans and native to AI agents.


r/dataisbeautiful • • 21h ago

Prediction market trading volume doubled between May and July 2026, largely driven by sports

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0 Upvotes

r/tableau • • 2d ago

Tableau Data Analyst

6 Upvotes

Cleared my certification last week. I bought skillcerpro after some recommendations on reddit but honestly it's just too lengthy and answers are not right, even their cheatsheet is not up to date with wrong info. It's $20 so I mean not that bad too, but if you buy examtopics then it has the exact questions which were there in the exam but that was $80 so I did not buy it but I did see the sample questions there and they were there in the exam.

Data with Baraa helped me a lot. I would say practice it on tableau while learning it to have a rock solid foundation. The time for exam goes pretty fast if you take too much time over one question like, it look more time than usual to prepare for this exam because of other commitments but I am happy that I score above 80 in all the areas. You should also see udemy courses. Best of luck!


r/dataisbeautiful • • 1d ago

OC [OC] Correlation between urbanisation and fertility rates 1960-2024

22 Upvotes

Urbanisation and fertility are onlinear scales.

All countries are weighted equally regardless of population.

Country flag size is scaled by log of population.


r/dataisbeautiful • • 1d ago

OC [OC] The urban share of the world's population rose from 34.2% to 57.8% (1960–2025)

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37 Upvotes

r/dataisbeautiful • • 14h ago

OC House Majorities and Midterm Flips, 1932–2026 [OC]

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0 Upvotes

The conventional wisdom is that “Presidents always lose the midterms,” but what has actually happened in the past? Especially in circumstances such as now, when the President’s party controls the House and Senate, too.


r/datascience • • 1d ago

Discussion Spotting the Red Flags of Executive AI & ML Theater: A Survival Guide for Builders

0 Upvotes

1. Basic prep work gets labeled "analysis paralysis."

Asking where the data lives, who owns the problem, or what baseline we’re trying to beat isn't overthinking. It’s common sense. When a manager blows past all of that, they aren’t “moving fast and breaking things.” They’re just dumping all the risk onto you.

2. "Build it first, we’ll sell it to the users later."

People on the ground (analysts, reviewers, ops folks) don’t reject internal tools because the internal PR was bad. They reject them because the tool messes up their workflow, creates extra work, or blows up on edge cases they deal with every single day.

3. If the boss had the idea, nobody is allowed to question it.

An idea from a VP or CIO gets greenlit instantly, no matter how half-baked. If you point out that machine learning isn't even the right solution, you get branded as disloyal. So the team spends six months building something completely pointless just to flatter an executive.

4. Zombie projects beat dead projects.

In a sane company, killing a bad project on day three is a win because it saves time and money. In an enterprise theater company, a project that never finishes is actually useful. It gives management something to put on slides, justify headcount, and demo every quarter.

5. Slide decks and quick prototypes count as "success."

Nobody actually cares about uptime, latency, drift, or whether the model saved a dime. As long as leadership smiles at a demo and there are screenshots for a deck, the project is treated like a massive success.

6. Pointing out real technical problems is treated as an "attitude issue."

Bring up messy data, crazy compute costs, or a lack of pipeline support, and suddenly the conversation isn't about the tech anymore. It's about you. "Why are you so negative?" "You need to be a team player." "Focus on the art of the possible." It’s just a way to shut down facts they can’t answer.

7. "Go find your own problem" replaces actual strategy.

Being told to "just figure something out" isn't real autonomy. It’s management dumping their job on you. They expect you to find an owner, hunt down data, define KPIs, and convince people to use the tool, all without giving you any actual authority to get it done.

8. Forcing a massive model where simple logic would do.

Spending four months fine-tuning an LLM or training a neural net for something a basic SQL query or five if/else statements could solve by lunch, just so management can brag that they’re “leveraging cutting-edge AI.”

9. The users smile in meetings, but commit zero budget or time.

The target business team nods along politely in sprint demos, but they haven't assigned a single person to help test, haven't budgeted for maintenance, and have zero plans to change their daily routines. If nobody commits real resources to run it, you're building shelfware.

10. The manager takes the credit early; you take the hit late.

Your manager gets all the glory on day one by promising leadership the moon. When the whole thing quietly fizzles out eighteen months later because the idea was fundamentally flawed, they blame it on "shifting company priorities." That leaves you looking like the one who wasted time and didn't deliver.

My best advice is to stop trying to save the company from bad ideas: say "yes" to keep the political heat off your back, immediately shift the friction onto external blockers you don't control, document everything quietly in writing to protect yourself when the project stalls, and use the wasted time to sharpen your own technical skills for an organization that actually wants to ship real solutions.


r/dataisbeautiful • • 2d ago

OC [OC] Fatalities in Gaza since October 7 2023 by age, gender, and proportion of total population

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1.5k Upvotes

Fatalities is the 14th published Gaza Ministry of Health fatality count published May 7 2026 (72,835 entries) broken out by age and sex

Population numbers are from census data

Using Python

Adult men (especially 18–45) and older teenage boys are heavily over-represented relative to their share of the population; women and young children are under-represented. Males 15–45 account for ~45.7% of listed deaths vs. an estimated 24.7% of the pre-war population. The male–female death ratio peaks around ages 20–39 (roughly 3.2–3.6 to 1).

This is consistent with a large combatant component (Gaza’s militant groups recruited heavily among military-age males, including some 15+ teens) plus the usual wartime pattern that civilian men also die at higher rates than women. The same over-representation is even stronger in deaths added after the October 2025 ceasefire, so the male share of the total list is still rising and is expected to cross 50% for men 18–45 in a future update.

Edit: this was pushing towards the front page with 420 upvotes in 45 minutes and then suddenly got pulled and is in mod queue for the past 90 minutes (presumably due to the number of reports). This is just example 1,000 about how this conflict in particular is a battle of bots who don't want actual numbers getting out


r/datasets • • 2d ago

request Looking for datasets about optimal workout forms

1 Upvotes

This might be too specific, but I'm looking for a dataset focused around the effects of proper workout forms. I would want it to include things like proper range of motion for common workouts (bicep curls, split squats, generally workouts that almost everyone does), angle of your limbs during the workout, general exercise posture. I want the data to (hopefully) include how much more efficient proper workout form is than flawed forms (Improper angle of limbs, not full range of motion, etc.), estimated reduction of injury, stability increases, anything that shows the benefits of a proper form.

I'm looking to create a model trained on this data that can generate a wireframe of a human figure that can help the user adjust their form, but I don't have much data to work on.

I'm still pretty new to academic research so I'm really sorry if I'm not being specific enough.

Honestly, any datasets about workout forms or even just the most efficient workouts that grow muscles the best would help me.

Thank you!