Think of a treemap as a pie chart, but with hierarchy — every slice has its own pie-chart breakdown inside it, and so on, all the way down. Nested rectangles stand in for slices, each sized in proportion to a numeric value, so the whole distribution — and the relative weight of every part at every level — is visible at a glance. A hat tip to GrandPerspective, the classic macOS disk-usage viewer whose dense, label-free mosaic inspired gp-treemap's visual style.
Below: interactive sample visualizations built with
gp-treemap.
Each entry is a self-contained HTML file — bundle and dataset
are inlined, so they render from any URL (or file://)
with no server.
Features you'll find in every view below:
∞ to see every leaf. The budget re-applies from the
current zoom root, so you can drill in and still get a labelled,
uncluttered view at each level.
The gp-treemap repository looking at itself — the working tree plus
.git and node_modules. Cells are sized by
bytes on disk and colored by file extension; hover for exact sizes,
click to select, scroll-wheel to focus an ancestor, double-click to
zoom. To scan any directory on your own machine and get a
self-contained HTML like this one:
npx -p @imbue-ai/gp-treemap gpdu ~/path/to/dir
The entries below use the path / tabular treemap
(gp-treemap-table), which builds the hierarchy on the fly
from a flat table of rows. The Path control in the header lists
the columns used to group the data, from outermost to innermost:
Drag the chips to reorder them and the nesting re-computes immediately:
with Fuel first (as above) you get one cell per fuel with
continents inside; drag Continent to the front instead
and the same rows re-tile as one cell per continent with fuels inside.
Same data, totally different story — and no re-export, just a drag.
Click the × on a chip to drop that column from the
hierarchy entirely, or the + to add another.
Ember's Yearly Electricity Data
(2023, TWh by country and fuel). Built with
tools/table-treemap.js from tabular JSONL: use the Size,
Color and Path controls in the header to re-group on the fly — drag the
Path chips to reorder the hierarchy. Colored by fuel, Catppuccin
Mocha theme.
Reproduce it from the pre-filtered JSONL in this repo:
curl -LO https://github.com/imbue-ai/gp-treemap/raw/main/samples/data/table/energy-2023.jsonl
npx -p @imbue-ai/gp-treemap gp-treemap-table \
--size=Generation_TWh --color=Fuel \
--path=Fuel,Continent,Country \
--theme=catppuccin \
energy-2023.jsonl
Every California city employee with reported wages in 2024 — about
334,000 rows from the state's
Government Compensation in California raw export, cell size =
TotalWages. Grouped by
department → county → city → position → _row, but
with depth = 1 at first so only the top-level
Department bucket is drawn — a cleaner first read of where the
money goes. Police and Fire dominate; Parks & Rec, Public
Works and Transportation come next. Click the + in the
component toolbar to reveal another level, or double-click any
cell to zoom in — the depth budget re-applies from the new root,
so you can drill in without losing your orientation.
Trivia: DepartmentOrSubdivision is a free-text field
with no normalization, so each spelling is a separate top-level
cell. Police alone shows up as Police,
Police Department, Police Services Agency,
Police Administration, and Police Field Services.
Fire is Fire, Fire Department,
Fire-Rescue, Fire Operations,
Fire Suppression, and
Fire Suppression & Rescue. Parks & Rec is
Recreation And Parks,
Recreation and Park Commission,
Parks and Recreation, Parks & Recreation,
and Parks & Recreation Administration. And
Airports and Airport Commission are two
different cells too.
Same file as above, but with the depth budget lifted so every one of the 334k rows gets its own pixel-sliver cell inside its position / city / county / department grouping. Cell size is the employee's total wages, so the layout itself reveals pay distribution: zoom into Police or a big city and you can see a handful of disproportionately large cells (the top earners) alongside a vast mosaic of small, similarly-sized cells (the bulk of staff at comparable pay).
(See the intro above for the Police / Fire / Parks & Rec spelling carnival.)
To reproduce this plot from scratch, grab the raw GCC zip and pipe
its CSV into gp-treemap-table:
curl -LO https://github.com/imbue-ai/gp-treemap/raw/main/samples/data/table/ca-raw/2024_City.zip
unzip 2024_City.zip
npx -p @imbue-ai/gp-treemap gp-treemap-table \
--size=TotalWages --color=DepartmentOrSubdivision \
--path=DepartmentOrSubdivision,EmployerCounty,EmployerName,Position,_row \
--theme=one-dark \
2024_City.csvPublic-record compensation for every University of California employee paid at least $1,000 in 2024 (~334k rows). Each top-level cell is one of the 11 campuses, sized by total wages and colored by campus; zoom into one to see its positions, and zoom again to see individual employees. UCLA and UCSF (both home to big medical centers) dominate; the Office of the President is the smallest.
Trivia: the state's raw export lists one of the campuses as
"Univeristy (sic) of California - Santa Cruz". Hi, Santa Cruz!
Also curious: ~10,000 employees, including some of UCLA's top
earners at $3–4M, are filed under
Blank Assistant 1/2/3.
That's an actual
UC clerical/administrative title series ("Class B – Clerical
and Allied Services"), so the low-end Blank Assistants are
clerical workers as advertised — but the million-dollar ones at
UCLA (a big one is pre-focused) are clearly not clerical staff.
We don't know what they really are; the public export doesn't
say.
Same dataset as above, regrouped the other way: each top-level
cell is now one job title, and inside that cell you see how that
title's payroll divides among the 11 campuses. Where the previous
view let you compare "what does a campus do?", this one lets you
compare "who pays more for the same title?" — e.g. clinical
nurses at UCSF vs. UCLA vs. UCSD, or the mutual-fund-like
Professor series versus the Athletics coaches hiding inside
Blank Assistant.
Gross fiscal-year-to-date outlays as of 2024-09-30, from the US
Treasury's
Monthly Treasury Statement. Path: Category → Agency → Bureau,
colored categorically by top-level category with the Viridis
palette. Click into a cell to zoom; change Color to
YoY_Change_Pct to see which areas grew or shrank.
Same outlays data, now colored quantitatively with a diverging Cool–Warm palette: blue = spent less than FY2023, red = spent more. A crisp example of how swapping Color between a categorical and a numeric column transforms the story the treemap tells.
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