Datasets / scikit-learn / UCI

Wine recognition

Chemical analysis of wines from three cultivars.

Ask a question of it Download CSV Jupyter notebook

178 rows14 columns scikit-learn / UCI

Worked examples

Each of these is a real run: the pipeline picked the columns, shaped the rows and chose the form. Open the trace to see what it decided and why.

Alcohol against colour intensity, by cultivar

Two chemical measures with the cultivar as colour: the clusters are the answer, not any single axis.

show alcohol against colour intensity by cultivar — 178 rows charted from 178, drawn as a scatter

Ask this yourself See the working

How it decided

Finding the data57 ms

Wine recognition: 178 rows, 14 columns. alcohol, color_intensity and cultivar.

Shaping the data2 ms

178 rows in, 178 out across 3 columns. Two chemical measures with the cultivar as colour: the clusters are the answer, not any single axis.

Choosing the chart8 ms

scatter. Two measures split by a category: a scatter, capped at three series.

Applying defaults1 ms

Palette, spacing, axis titles and legend placement applied. 0 issues found.

Measurements ranked by spread between cultivars

Each measurement is scored by how far the cultivar means spread relative to the overall mean, so measures on wildly different units can be ranked against each other.

which measurements differ most between cultivars? — 8 rows charted from 178, drawn as a hbar

Ask this yourself See the working

How it decided

Finding the data50 ms

Wine recognition: 178 rows, 14 columns. every numeric column, grouped by cultivar.

Shaping the data16 ms

178 rows in, 8 out across 2 columns. Each measurement is scored by how far the cultivar means spread relative to the overall mean, so measures on wildly different units can be ranked against each other.

Choosing the chart4 ms

hbar. A ranking of long-named things is a horizontal bar chart. The top eight are shown because the tail carries no signal.

Applying defaults1 ms

Palette, spacing, axis titles and legend placement applied. 0 issues found.

Schema

14 columns, typed as the pipeline sees them — which is how it knows what can go on a time axis and what can be summed.

Column	Type	Example
alcohol	number	14.23
malic_acid	number	1.71
ash	number	2.43
alcalinity_of_ash	number	15.6
magnesium	number	127.0
total_phenols	number	2.8
flavanoids	number	3.06
nonflavanoid_phenols	number	0.28
proanthocyanins	number	2.29
color_intensity	number	5.64
hue	number	1.04
od280/od315_of_diluted_wines	number	3.92
proline	number	1065.0
cultivar	text	class_0

First 8 rows

alcohol	malic_acid	ash	alcalinity_of_ash	magnesium	total_phenols	flavanoids	nonflavanoid_phenols	proanthocyanins	color_intensity	hue	od280/od315_of_diluted_wines	proline	cultivar
14.23	1.71	2.43	15.6	127.0	2.8	3.06	0.28	2.29	5.64	1.04	3.92	1065.0	class_0
13.2	1.78	2.14	11.2	100.0	2.65	2.76	0.26	1.28	4.38	1.05	3.4	1050.0	class_0
13.16	2.36	2.67	18.6	101.0	2.8	3.24	0.3	2.81	5.68	1.03	3.17	1185.0	class_0
14.37	1.95	2.5	16.8	113.0	3.85	3.49	0.24	2.18	7.8	0.86	3.45	1480.0	class_0
13.24	2.59	2.87	21.0	118.0	2.8	2.69	0.39	1.82	4.32	1.04	2.93	735.0	class_0
14.2	1.76	2.45	15.2	112.0	3.27	3.39	0.34	1.97	6.75	1.05	2.85	1450.0	class_0
14.39	1.87	2.45	14.6	96.0	2.5	2.52	0.3	1.98	5.25	1.02	3.58	1290.0	class_0
14.06	2.15	2.61	17.6	121.0	2.6	2.51	0.31	1.25	5.05	1.06	3.58	1295.0	class_0

Ask it something else

which measurements differ most between cultivars?show alcohol against colour intensity by cultivar

Start free