Source: /home/coolhand/html/datavis/data_trove/data/quirky/wine_by_country.json
Saturn profiled 62 rows across 2 columns. The stats below are deterministic and machine-readable; the prose is a language-model interpretation of those stats (opt-in, added after the fact, never sees raw rows).
!pip install saturn-dissect
import subprocess
subprocess.run([
"saturn", "analyze", "/home/coolhand/html/datavis/data_trove/data/quirky/wine_by_country.json",
"--findings", "data-trove-wine-varieties-regions.json",
"--llm", "anthropic:default",
])This dataset lists wine production (or a related wine metric) aggregated by country, covering 62 countries each with an associated count. The count distribution is extremely skewed: the median is just 2, yet the mean is nearly 19 and the maximum reaches 476, with 10 flagged outliers — suggesting a small handful of countries dominate the wine landscape entirely. France tops the list and is worth examining alongside the other high-count outliers to understand which countries drive the bulk of the totals.
citing: mean · median · max · n_outliers · outlier_rate · skew · kurtosis · top_value · row_count
| bin | count |
|---|---|
| 1 – 68.86 | 59 |
| 68.86 – 136.7 | 2 |
| 136.7 – 204.6 | 0 |
| 204.6 – 272.4 | 0 |
| 272.4 – 340.3 | 0 |
| 340.3 – 408.1 | 0 |
| 408.1 – 476 | 1 |
| value | count | share |
|---|---|---|
| France | 1 | 1.6% |
| United Kingdom | 1 | 1.6% |
| Germany | 1 | 1.6% |
| Spain | 1 | 1.6% |
| Belgium | 1 | 1.6% |
| Italy | 1 | 1.6% |
| United States | 1 | 1.6% |
| Switzerland | 1 | 1.6% |
| Australia | 1 | 1.6% |
| Bolivia | 1 | 1.6% |
| Austria | 1 | 1.6% |
| Croatia | 1 | 1.6% |
| Canada | 1 | 1.6% |
| Portugal | 1 | 1.6% |
| Poland | 1 | 1.6% |
| Netherlands | 1 | 1.6% |
| Ireland | 1 | 1.6% |
| Romania | 1 | 1.6% |
| Argentina | 1 | 1.6% |
| Unknown | 1 | 1.6% |
| bin | count |
|---|---|
| 1 – 68.86 | 59 |
| 68.86 – 136.7 | 2 |
| 136.7 – 204.6 | 0 |
| 204.6 – 272.4 | 0 |
| 272.4 – 340.3 | 0 |
| 340.3 – 408.1 | 0 |
| 408.1 – 476 | 1 |
| column | kind | null % |
|---|---|---|
| name | categorical | 0.0% |
| count | numeric | 0.0% |
This column contains country names, with 62 unique values across 62 rows and a null rate of 0.0 — every row holds a distinct country. The entropy ratio of ~1.0 and top_rate of 0.016 confirm perfect cardinality: each country appears exactly once, making this effectively a unique identifier rather than a grouping variable. The 'long_tail' alert is technically triggered but is a non-issue here since all values are equally frequent.
Treatment: Use as a row label or join key; not useful as a categorical feature without additional rows per country.
saturn.columns["name"].stats
| stat | value |
|---|---|
| n | 62 |
| nulls | 0 (0.0%) |
| unique | 62 |
| top_value | France |
| top_rate | 0.01613 |
| cardinality | 62 |
| entropy | 5.954 |
| entropy_ratio | 1 |
| alert: long_tail | 62 singleton categories |
name.
| value | count | share |
|---|---|---|
| France | 1 | 1.6% |
| United Kingdom | 1 | 1.6% |
| Germany | 1 | 1.6% |
| Spain | 1 | 1.6% |
| Belgium | 1 | 1.6% |
| Italy | 1 | 1.6% |
| United States | 1 | 1.6% |
| Switzerland | 1 | 1.6% |
| Australia | 1 | 1.6% |
| Bolivia | 1 | 1.6% |
| Austria | 1 | 1.6% |
| Croatia | 1 | 1.6% |
| Canada | 1 | 1.6% |
| Portugal | 1 | 1.6% |
| Poland | 1 | 1.6% |
| Netherlands | 1 | 1.6% |
| Ireland | 1 | 1.6% |
| Romania | 1 | 1.6% |
| Argentina | 1 | 1.6% |
| Unknown | 1 | 1.6% |
This column appears to be a frequency or occurrence count, likely representing how many times an event or item appears in some grouping. The distribution is extremely right-skewed (skew=6.26, kurtosis=41.80): the median is just 2.0 while the mean is 18.94 and the maximum reaches 476.0, indicating a handful of dominant entries vastly outnumber the rest. With only 21 unique values across 62 rows and 10 outliers (16.1% of records), a small number of high-count observations are pulling the distribution heavily. The IQR of 7.25 (Q1=1.0, Q3=8.25) confirms that 75% of values sit at or below 8, making the max of 476 a stark anomaly.
Treatment: Log-transform (log1p) before modelling to reduce extreme skew; investigate the 10 outliers, particularly the max of 476, for data integrity.
saturn.columns["count"].stats
| stat | value |
|---|---|
| n | 62 |
| nulls | 0 (0.0%) |
| unique | 21 |
| min | 1 |
| max | 476 |
| mean | 18.94 |
| median | 2 |
| std | 63.53 |
| q1 | 1 |
| q3 | 8.25 |
| iqr | 7.25 |
| skew | 6.255 |
| kurtosis | 41.8 |
| n_outliers | 10 |
| outlier_rate | 0.1613 |
| zero_rate | 0 |
| alert: high_skew | skew=+6.26 |
| alert: outliers | 16.1% rows beyond 1.5 IQR |
count. Vertical dash marks the median.
| bin | count |
|---|---|
| 1 – 68.86 | 59 |
| 68.86 – 136.7 | 2 |
| 136.7 – 204.6 | 0 |
| 204.6 – 272.4 | 0 |
| 272.4 – 340.3 | 0 |
| 340.3 – 408.1 | 0 |
| 408.1 – 476 | 1 |
click to copy
@misc{saturn-data-trove-wine-varieties-regions-2026,
author = {Steuber, Luke},
title = {Saturn reading: data trove wine varieties regions},
year ={2026},
howpublished = {\url{https://dr.eamer.dev/saturn/view/data-trove-wine-varieties-regions}},
note = {Profiled with saturn-dissect v0.2.0, prompt saturn-insight-v2, model anthropic:default},
}
Steuber, L. (2026). Saturn reading: data trove wine varieties regions. Source: /home/coolhand/html/datavis/data_trove/data/quirky/wine_by_country.json. Profiled with saturn-dissect v0.2.0 (saturn-insight-v2, anthropic:default). Retrieved from https://dr.eamer.dev/saturn/view/data-trove-wine-varieties-regions