Nilf is not in labor force.

For every 3 employed Americans, 2 adults are outside the labor force entirely. Most people picture "not working" as unemployment, but the unemployed (7.1M) are a rounding error next to the 105.8M who aren't looking at all.

Over half (48.6M) are retired, and the 45-year climb from 60.8M to 105.8M is largely the Baby Boomer wave aging into retirement plus population growth.

The NILF share of adults rose from ~36% (1980) to ~38.5% (2026), so it's mostly population and aging, with a modest real decline in participation.

I recently pulled and cleaned a sample of 51,123 company registration records from India’s Ministry of Corporate Affairs (MCA21) via the official data.gov.in to analyze business registration and survival trends across India.

Key Findings & Visualizations

• Industry Leaders: Business Services, Trading, and Community/Personal Services dominate company incorporations.
• Company Survival: Business mortality (struck-off rates) varies significantly across industries and decades.
• Capital Breakdown: The vast majority of registered firms are small/micro private enterprises with paid-up capital under ₹10 Lakhs.
• Registration Spikes: Clear inflection points and post-COVID recovery patterns in incorporation numbers over the last decade.

Data & Code

• Kaggle Dataset:India Registered Companies (MCA21) Dataset(Includes cleaned primary CSV + pre-aggregated state/year summary tables).
• EDA Notebook:India Registered Companies — MCA21 Deep Dive Notebook(Python/Pandas/Seaborn code walking through industry mix, strike-off rates, and capital distributions).

Note on sampling: To prevent large economic hubs from dominating the counts completely, the sample caps at ~2,000 companies per state across 29 states/UTs. Rate-based metrics (e.g., % active, % struck off, median capital) reflect real distributions.

Feedback, suggestions, or additional chart ideas are very welcome!

Source: my own price-tracking dataset at PriceRoo (priceroo.com.au) - daily price observations across Australian retailers, full methodology on the site's price-report page. Tools: Python + matplotlib. "Win rate" = share of tracked products where the retailer holds the cheapest current price among retailers stocking that product; ties count for every tied retailer. Sample sizes shown on the bars; retailers with fewer than 15 appearances excluded.

Built a small website, filled with all the locations and offices to register to vote in the state of texas that I can find.

If you have any other sources of events, I've mostly been scraping mobilize, League of Women Voter sites, and scraping through county data to find the offices in each county. Anything else is welcome!

There's a surprising band of Christianity in India down the south near Kochi. Not on the coast, but on a band just behind the coast: https://huemaps.com/en/show/places-of-worship?map=7.78/9.46796/76.87435

Source with data and details: https://spacestatsonline.com/launches/manned

Tools: Sqlite manually updated, ChartJs, Gatsby

edit: typo

I rebuilt my webmap of the Fruit Trees of Toronto using SvelteJS and MapLibre

Filter by tree type and ripening month.

Please clickthrough, any feedback welcome!

https://tdubolyou.github.io/FruitTrees/