$ infer --source regional-sales.csv
The shape of the data picks the chart
Every chart below is chosen by a rule that reads the columns —
their type, how many distinct values they have, whether they look like
dates — and picks a bar, pie, line, scatter, histogram or heatmap
accordingly. Switch datasets,
try a suggested view, or paste your own table: the same rule runs every
time.
[1] regional-sales.csv
[2] site-traffic.csv
[3] plan-survey.csv
[4] support-load.csv
[5] paste your own
chart
paste csv (first row = headers)
load example
analyze ▶
Use Tab to move between marks and Enter to read a value.
Select a mark for its exact value.
The chart choice is a fixed rule, not a model. A column is
typed as a date, a number, or a category by inspecting every value in it; a
category paired with a number becomes a bar — or a pie, when there are
five or fewer categories and the number is a whole-number total whose shares
mean something. A date paired with a number becomes a line, two numbers
become a scatter, one number on its own becomes a histogram, and two
categories with a number become a heatmap. Nothing here was trained on
anything, and the same rule decides the chart whether the data is one of the
four samples or something you paste yourself.
The suggested views are a script, not a conversation. Each
one preselects the columns to look at — it does not understand the
question, and there is no chat behind it. What is real is that the chart
form is re-derived from scratch every time the columns change.
The four built-in tables are illustrative sample data, not a live feed or
a real company’s numbers. Anything you paste is parsed in this page and
never leaves it — there is no upload, and nothing is kept after reload.