Source: /home/coolhand/datasets/language-data/wals_languages.csv
Saturn profiled 3,573 rows across 17 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/datasets/language-data/wals_languages.csv",
"--findings", "language-data-wals_languages.json",
"--llm", "anthropic:claude-opus-4-7",
])This dataset catalogs 3,573 world languages (WALS) across 17 columns combining identifiers (ISO codes, Glottocode), classifications (Family, Genus, Subfamily), geography (Latitude, Longitude, Macroarea, Country_ID), and sampling flags. The Family and Macroarea distributions are the most informative starting point: Niger-Congo and Austronesian dominate at 324 languages each, and Eurasia (659) and Africa (606) lead the macroareas out of just six categories. Note that roughly a quarter of rows (null_rate ~0.255) are missing geographic and family fields in lockstep, suggesting a shared set of unclassified entries worth investigating. The Samples_100 and Samples_200 flags are highly imbalanced (only 100 and 200 'True' values respectively), reflecting curated WALS sub-samples. Subfamily is sparsely populated (74.5% null) so treat it as supplementary rather than primary.
citing: Family.top_values · Macroarea.top_values · Genus.top_values · Country_ID.top_values · Samples_100.stats · Samples_200.stats · Latitude.stats · Longitude.stats · Subfamily.null_rate
saturn.schema() · 17 columns
| column | kind | n | null% | unique | alerts |
|---|---|---|---|---|---|
| ID | text | 3,573 | 0.0% | 3,573 | near_unique one_word short_text |
| Name | text | 3,573 | 0.0% | 3,198 | one_word short_text |
| Macroarea | categorical | 3,573 | 25.5% | 6 | null_rate |
| Latitude | numeric | 3,573 | 25.5% | 887 | null_rate |
| Longitude | numeric | 3,573 | 25.5% | 1,360 | null_rate |
| Glottocode | text | 3,573 | 26.0% | 2,502 | one_word null_rate short_text |
| ISO639P3code | text | 3,573 | 26.8% | 2,442 | one_word null_rate short_text |
| Family | categorical | 3,573 | 25.5% | 254 | null_rate |
| Subfamily | categorical | 3,573 | 74.5% | 32 | null_rate |
| Genus | categorical | 3,573 | 25.5% | 625 | null_rate |
| GenusIcon | categorical | 3,573 | 82.5% | 613 | long_tail null_rate |
| ISO_codes | text | 3,573 | 26.1% | 2,468 | one_word null_rate short_text |
| Samples_100 | categorical | 3,573 | 25.5% | 2 | null_rate imbalance |
| Samples_200 | categorical | 3,573 | 25.5% | 2 | null_rate |
| Country_ID | categorical | 3,573 | 25.7% | 337 | null_rate |
| Source | text | 3,573 | 30.1% | 2,373 | one_word null_rate |
| Parent_ID | categorical | 3,573 | 7.1% | 911 | long_tail |
| value | count | share |
|---|---|---|
| Niger-Congo | 324 | 9.1% |
| Austronesian | 324 | 9.1% |
| Indo-European | 176 | 4.9% |
| Sino-Tibetan | 146 | 4.1% |
| Afro-Asiatic | 145 | 4.1% |
| Pama-Nyungan | 121 | 3.4% |
| Trans-New Guinea | 98 | 2.7% |
| other | 72 | 2.0% |
| Altaic | 65 | 1.8% |
| Oto-Manguean | 56 | 1.6% |
| Austro-Asiatic | 48 | 1.3% |
| Eastern Sudanic | 47 | 1.3% |
| Uto-Aztecan | 44 | 1.2% |
| Algic | 31 | 0.9% |
| Mayan | 30 | 0.8% |
| Arawakan | 29 | 0.8% |
| Nakh-Daghestanian | 28 | 0.8% |
| Mande | 28 | 0.8% |
| Uralic | 27 | 0.8% |
| Hokan | 26 | 0.7% |
| value | count | share |
|---|---|---|
| Eurasia | 659 | 18.4% |
| Africa | 606 | 17.0% |
| Papunesia | 560 | 15.7% |
| North America | 396 | 11.1% |
| South America | 258 | 7.2% |
| Australia | 183 | 5.1% |
| value | count | share |
|---|---|---|
| PG | 214 | 6.0% |
| AU | 185 | 5.2% |
| US | 177 | 5.0% |
| ID | 177 | 5.0% |
| IN | 120 | 3.4% |
| MX | 120 | 3.4% |
| RU | 89 | 2.5% |
| NG | 66 | 1.8% |
| BR | 66 | 1.8% |
| CN | 54 | 1.5% |
| CD | 49 | 1.4% |
| CM | 46 | 1.3% |
| CA | 45 | 1.3% |
| CO | 39 | 1.1% |
| ET | 36 | 1.0% |
| PH | 36 | 1.0% |
| PE | 35 | 1.0% |
| NP | 32 | 0.9% |
| TZ | 28 | 0.8% |
| VU | 28 | 0.8% |
| bin | count |
|---|---|
| -55 – -51.84 | 2 |
| -51.84 – -48.69 | 1 |
| -48.69 – -45.53 | 1 |
| -45.53 – -42.38 | 2 |
| -42.38 – -39.22 | 3 |
| -39.22 – -36.06 | 5 |
| -36.06 – -32.91 | 9 |
| -32.91 – -29.75 | 18 |
| -29.75 – -26.59 | 24 |
| -26.59 – -23.44 | 29 |
| -23.44 – -20.28 | 47 |
| -20.28 – -17.12 | 64 |
| -17.12 – -13.97 | 85 |
| -13.97 – -10.81 | 86 |
| -10.81 – -7.656 | 129 |
| -7.656 – -4.5 | 187 |
| -4.5 – -1.344 | 190 |
| -1.344 – 1.812 | 130 |
| 1.812 – 4.969 | 119 |
| 4.969 – 8.125 | 196 |
| 8.125 – 11.28 | 161 |
| 11.28 – 14.44 | 104 |
| 14.44 – 17.59 | 145 |
| 17.59 – 20.75 | 82 |
| 20.75 – 23.91 | 63 |
| 23.91 – 27.06 | 74 |
| 27.06 – 30.22 | 84 |
| 30.22 – 33.38 | 66 |
| 33.38 – 36.53 | 84 |
| 36.53 – 39.69 | 67 |
| 39.69 – 42.84 | 86 |
| 42.84 – 46 | 62 |
| 46 – 49.16 | 74 |
| 49.16 – 52.31 | 52 |
| 52.31 – 55.47 | 44 |
| 55.47 – 58.62 | 18 |
| 58.62 – 61.78 | 20 |
| 61.78 – 64.94 | 23 |
| 64.94 – 68.09 | 19 |
| 68.09 – 71.25 | 7 |
| value | count | share |
|---|---|---|
| False | 2462 | 68.9% |
| True | 200 | 5.6% |
| column | kind | null % |
|---|---|---|
| ID | text | 0.0% |
| Name | text | 0.0% |
| Macroarea | categorical | 25.5% |
| Latitude | numeric | 25.5% |
| Longitude | numeric | 25.5% |
| Glottocode | text | 26.0% |
| ISO639P3code | text | 26.8% |
| Family | categorical | 25.5% |
| Subfamily | categorical | 74.5% |
| Genus | categorical | 25.5% |
| GenusIcon | categorical | 82.5% |
| ISO_codes | text | 26.1% |
| Samples_100 | categorical | 25.5% |
| Samples_200 | categorical | 25.5% |
| Country_ID | categorical | 25.7% |
| Source | text | 30.1% |
| Parent_ID | categorical | 7.1% |
| Latitude | Longitude | |
|---|---|---|
| Latitude | +1.00 | -0.38 |
| Longitude | -0.38 | +1.00 |
This is an identifier column: every one of the 3573 rows holds a unique single-token string with no nulls or duplicates. Values are short (median length 3, max 36) and the vocabulary equals the row count (3573), confirming one-to-one uniqueness. Top tokens like 'aab', 'aar', 'aba' suggest short alphabetic codes rather than numeric keys.
Treatment: drop from modelling; retain only as a join key.
saturn.columns["ID"].stats
| stat | value |
|---|---|
| n | 3,573 |
| nulls | 0 (0.0%) |
| unique | 3,573 |
| len_min | 2 |
| len_max | 36 |
| len_mean | 5.982 |
| len_median | 3 |
| len_p95 | 17 |
| word_mean | 1 |
| word_median | 1 |
| n_empty | 0 |
| n_duplicates | 0 |
| duplicate_rate | 0 |
| vocab_size | 3,573 |
| readability_flesch_mean | 61.58 |
| emoji_rate | 0 |
| url_rate | 0 |
| one_word_rate | 1 |
| allcaps_rate | 0 |
| boilerplate_rate | 0 |
| alert: near_unique | 100.0% of rows are unique strings |
| alert: one_word | 100.0% rows are a single word |
| alert: short_text | 95th-percentile length under 20 chars |
ID.
| chars | count |
|---|---|
| 2 – 3 | 11 |
| 3 – 4 | 2651 |
| 4 – 5 | 0 |
| 5 – 5 | 0 |
| 5 – 6 | 0 |
| 6 – 7 | 0 |
| 7 – 8 | 0 |
| 8 – 9 | 2 |
| 9 – 10 | 17 |
| 10 – 10 | 53 |
| 10 – 11 | 89 |
| 11 – 12 | 137 |
| 12 – 13 | 122 |
| 13 – 14 | 0 |
| 14 – 15 | 110 |
| 15 – 16 | 90 |
| 16 – 16 | 67 |
| 16 – 17 | 52 |
| 17 – 18 | 48 |
| 18 – 19 | 0 |
| 19 – 20 | 21 |
| 20 – 21 | 22 |
| 21 – 22 | 20 |
| 22 – 22 | 17 |
| 22 – 23 | 10 |
| 23 – 24 | 11 |
| 24 – 25 | 0 |
| 25 – 26 | 7 |
| 26 – 27 | 3 |
| 27 – 28 | 0 |
| 28 – 28 | 2 |
| 28 – 29 | 3 |
| 29 – 30 | 2 |
| 30 – 31 | 0 |
| 31 – 32 | 1 |
| 32 – 33 | 2 |
| 33 – 33 | 2 |
| 33 – 34 | 0 |
| 34 – 35 | 0 |
| 35 – 36 | 1 |
This column holds short proper-noun labels — almost certainly language or ethnonym names, given top values like 'Basque', 'Ainu', 'Beothuk' and frequent words 'sign', 'language', 'arabic', 'german'. Entries are terse (mean 8.7 chars, 80% one-word) but not unique: 375 duplicates (10.5%) and only 3,198 distinct names across 3,573 rows, with several names appearing exactly 3 times — suggesting the dataset repeats each language across multiple records or variants (e.g. '(northern)', '(southern)'). No nulls, no URLs, no emoji.
Treatment: Treat as a categorical key; deduplicate or join on a normalized form before aggregating.
saturn.columns["Name"].stats
| stat | value |
|---|---|
| n | 3,573 |
| nulls | 0 (0.0%) |
| unique | 3,198 |
| len_min | 2 |
| len_max | 46 |
| len_mean | 8.705 |
| len_median | 7 |
| len_p95 | 19 |
| word_mean | 1.258 |
| word_median | 1 |
| n_empty | 0 |
| n_duplicates | 375 |
| duplicate_rate | 0.105 |
| vocab_size | 3,383 |
| readability_flesch_mean | 48.16 |
| emoji_rate | 0 |
| url_rate | 0 |
| one_word_rate | 0.8002 |
| allcaps_rate | 0 |
| boilerplate_rate | 0 |
| alert: one_word | 80.0% rows are a single word |
| alert: short_text | 95th-percentile length under 20 chars |
Name.
| chars | count |
|---|---|
| 2 – 3 | 112 |
| 3 – 4 | 361 |
| 4 – 5 | 528 |
| 5 – 6 | 581 |
| 6 – 8 | 502 |
| 8 – 9 | 305 |
| 9 – 10 | 198 |
| 10 – 11 | 152 |
| 11 – 12 | 84 |
| 12 – 13 | 88 |
| 13 – 14 | 131 |
| 14 – 15 | 89 |
| 15 – 16 | 76 |
| 16 – 17 | 77 |
| 17 – 18 | 66 |
| 18 – 20 | 46 |
| 20 – 21 | 34 |
| 21 – 22 | 31 |
| 22 – 23 | 21 |
| 23 – 24 | 19 |
| 24 – 25 | 21 |
| 25 – 26 | 8 |
| 26 – 27 | 9 |
| 27 – 28 | 9 |
| 28 – 30 | 5 |
| 30 – 31 | 2 |
| 31 – 32 | 3 |
| 32 – 33 | 4 |
| 33 – 34 | 2 |
| 34 – 35 | 3 |
| 35 – 36 | 2 |
| 36 – 37 | 1 |
| 37 – 38 | 1 |
| 38 – 39 | 0 |
| 39 – 40 | 0 |
| 40 – 42 | 0 |
| 42 – 43 | 0 |
| 43 – 44 | 0 |
| 44 – 45 | 0 |
| 45 – 46 | 2 |
Macroarea is a coarse geographic grouping with 6 categories spanning Eurasia, Africa, Papunesia, North America, South America, and Australia — consistent with WALS/Glottolog-style language area labels. Distribution is relatively even (entropy ratio 0.95, top value Eurasia at 24.8%), so no single region dominates. Note the 25.5% null rate, which is substantial and flagged.
Treatment: One-hot encode and add an explicit 'missing' category to preserve the 25.5% nulls.
saturn.columns["Macroarea"].stats
| stat | value |
|---|---|
| n | 3,573 |
| nulls | 911 (25.5%) |
| unique | 6 |
| top_value | Eurasia |
| top_rate | 0.2476 |
| cardinality | 6 |
| entropy | 2.459 |
| entropy_ratio | 0.9511 |
| alert: null_rate | 25.5% null |
Macroarea.
| value | count | share |
|---|---|---|
| Eurasia | 659 | 18.4% |
| Africa | 606 | 17.0% |
| Papunesia | 560 | 15.7% |
| North America | 396 | 11.1% |
| South America | 258 | 7.2% |
| Australia | 183 | 5.1% |
Geographic latitude in degrees, ranging from -55.0 to 71.25 with a median of 8.29 and IQR of 33.0, consistent with a worldwide point distribution. The 25.5% null rate is notable and flagged, while skew (0.36) and kurtosis (-0.50) indicate a fairly symmetric, slightly flat spread with only one outlier.
Treatment: Impute or filter the 25.5% missing values, and pair with longitude for any geospatial modelling.
saturn.columns["Latitude"].stats
| stat | value |
|---|---|
| n | 3,573 |
| nulls | 911 (25.5%) |
| unique | 887 |
| min | -55 |
| max | 71.25 |
| mean | 11.88 |
| median | 8.292 |
| std | 22.72 |
| q1 | -5 |
| q3 | 28 |
| iqr | 33 |
| skew | 0.3562 |
| kurtosis | -0.5023 |
| n_outliers | 1 |
| outlier_rate | 0.0003757 |
| zero_rate | 0.002254 |
| alert: null_rate | 25.5% null |
Latitude. Vertical dash marks the median.
| bin | count |
|---|---|
| -55 – -51.84 | 2 |
| -51.84 – -48.69 | 1 |
| -48.69 – -45.53 | 1 |
| -45.53 – -42.38 | 2 |
| -42.38 – -39.22 | 3 |
| -39.22 – -36.06 | 5 |
| -36.06 – -32.91 | 9 |
| -32.91 – -29.75 | 18 |
| -29.75 – -26.59 | 24 |
| -26.59 – -23.44 | 29 |
| -23.44 – -20.28 | 47 |
| -20.28 – -17.12 | 64 |
| -17.12 – -13.97 | 85 |
| -13.97 – -10.81 | 86 |
| -10.81 – -7.656 | 129 |
| -7.656 – -4.5 | 187 |
| -4.5 – -1.344 | 190 |
| -1.344 – 1.812 | 130 |
| 1.812 – 4.969 | 119 |
| 4.969 – 8.125 | 196 |
| 8.125 – 11.28 | 161 |
| 11.28 – 14.44 | 104 |
| 14.44 – 17.59 | 145 |
| 17.59 – 20.75 | 82 |
| 20.75 – 23.91 | 63 |
| 23.91 – 27.06 | 74 |
| 27.06 – 30.22 | 84 |
| 30.22 – 33.38 | 66 |
| 33.38 – 36.53 | 84 |
| 36.53 – 39.69 | 67 |
| 39.69 – 42.84 | 86 |
| 42.84 – 46 | 62 |
| 46 – 49.16 | 74 |
| 49.16 – 52.31 | 52 |
| 52.31 – 55.47 | 44 |
| 55.47 – 58.62 | 18 |
| 58.62 – 61.78 | 20 |
| 61.78 – 64.94 | 23 |
| 64.94 – 68.09 | 19 |
| 68.09 – 71.25 | 7 |
Geographic longitude in degrees, spanning the full globe from -178.17 to 179.17 with a near-zero skew (-0.33) and flat kurtosis (-1.05), consistent with a worldwide point distribution. The 25.5% null rate is the main concern, and despite 3573 rows only 1360 unique values appear, suggesting repeated locations or rounded coordinates. No outliers flagged, as expected for a bounded angular measure.
Treatment: Pair with Latitude for geospatial features; impute or drop the 25.5% missing before modelling.
saturn.columns["Longitude"].stats
| stat | value |
|---|---|
| n | 3,573 |
| nulls | 911 (25.5%) |
| unique | 1,360 |
| min | -178.2 |
| max | 179.2 |
| mean | 35.17 |
| median | 34.79 |
| std | 89.35 |
| q1 | -45.75 |
| q3 | 121 |
| iqr | 166.8 |
| skew | -0.3259 |
| kurtosis | -1.047 |
| n_outliers | 0 |
| outlier_rate | 0 |
| zero_rate | 0.001503 |
| alert: null_rate | 25.5% null |
Longitude. Vertical dash marks the median.
| bin | count |
|---|---|
| -178.2 – -169.2 | 13 |
| -169.2 – -160.3 | 5 |
| -160.3 – -151.4 | 7 |
| -151.4 – -142.4 | 8 |
| -142.4 – -133.5 | 4 |
| -133.5 – -124.6 | 15 |
| -124.6 – -115.6 | 96 |
| -115.6 – -106.7 | 36 |
| -106.7 – -97.77 | 57 |
| -97.77 – -88.83 | 107 |
| -88.83 – -79.9 | 33 |
| -79.9 – -70.97 | 114 |
| -70.97 – -62.03 | 95 |
| -62.03 – -53.1 | 55 |
| -53.1 – -44.17 | 22 |
| -44.17 – -35.23 | 7 |
| -35.23 – -26.3 | 0 |
| -26.3 – -17.37 | 1 |
| -17.37 – -8.433 | 42 |
| -8.433 – 0.5 | 109 |
| 0.5 – 9.433 | 108 |
| 9.433 – 18.37 | 170 |
| 18.37 – 27.3 | 107 |
| 27.3 – 36.23 | 159 |
| 36.23 – 45.17 | 94 |
| 45.17 – 54.1 | 62 |
| 54.1 – 63.03 | 17 |
| 63.03 – 71.97 | 20 |
| 71.97 – 80.9 | 67 |
| 80.9 – 89.83 | 73 |
| 89.83 – 98.77 | 102 |
| 98.77 – 107.7 | 83 |
| 107.7 – 116.6 | 56 |
| 116.6 – 125.6 | 129 |
| 125.6 – 134.5 | 112 |
| 134.5 – 143.4 | 190 |
| 143.4 – 152.4 | 177 |
| 152.4 – 161.3 | 48 |
| 161.3 – 170.2 | 51 |
| 170.2 – 179.2 | 11 |
This column holds Glottocodes — fixed 8-character language identifiers from the Glottolog catalogue (e.g. 'basq1248', 'stan1295'), with every value a single token of length exactly 8. About 26% of rows are null and 2502 distinct codes cover 3573 records, with a 5.4% duplicate rate; the most repeated code 'basq1248' appears 11 times, suggesting multiple records can share a language.
Treatment: Left-join on this code to a Glottolog reference table; impute or flag the 26% nulls separately.
saturn.columns["Glottocode"].stats
| stat | value |
|---|---|
| n | 3,573 |
| nulls | 928 (26.0%) |
| unique | 2,502 |
| len_min | 8 |
| len_max | 8 |
| len_mean | 8 |
| len_median | 8 |
| len_p95 | 8 |
| word_mean | 1 |
| word_median | 1 |
| n_empty | 0 |
| n_duplicates | 143 |
| duplicate_rate | 0.05406 |
| vocab_size | 2,502 |
| readability_flesch_mean | 92.88 |
| emoji_rate | 0 |
| url_rate | 0 |
| one_word_rate | 1 |
| allcaps_rate | 0 |
| boilerplate_rate | 0 |
| alert: one_word | 100.0% rows are a single word |
| alert: null_rate | 26.0% null |
| alert: short_text | 95th-percentile length under 20 chars |
Glottocode.
| chars | count |
|---|---|
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 2645 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
| 8 – 8 | 0 |
This column holds ISO 639-3 language codes — every non-null value is exactly 3 characters and a single word (len_mean 3.0, one_word_rate 1.0), with familiar codes like 'eus' (Basque), 'deu' (German), and 'gsw' (Swiss German) at the top. Coverage is incomplete: 26.84% of rows are null, and across 3573 rows there are 2442 unique codes with a 6.58% duplicate rate. Nothing in the evidence indicates which entity each code is tagging.
Treatment: Treat as a categorical join key to an ISO 639-3 reference table; impute or filter the 26.84% nulls before use.
saturn.columns["ISO639P3code"].stats
| stat | value |
|---|---|
| n | 3,573 |
| nulls | 959 (26.8%) |
| unique | 2,442 |
| len_min | 3 |
| len_max | 3 |
| len_mean | 3 |
| len_median | 3 |
| len_p95 | 3 |
| word_mean | 1 |
| word_median | 1 |
| n_empty | 0 |
| n_duplicates | 172 |
| duplicate_rate | 0.0658 |
| vocab_size | 2,442 |
| readability_flesch_mean | 119.5 |
| emoji_rate | 0 |
| url_rate | 0 |
| one_word_rate | 1 |
| allcaps_rate | 0 |
| boilerplate_rate | 0 |
| alert: one_word | 100.0% rows are a single word |
| alert: null_rate | 26.8% null |
| alert: short_text | 95th-percentile length under 20 chars |
ISO639P3code.
| chars | count |
|---|---|
| 2 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 2614 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 4 | 0 |
Categorical label assigning each of 3,573 rows to one of 254 language families, headed by Niger-Congo and Austronesian (tied at 324 rows, 12.2% each). The long tail is heavy — entropy ratio 0.705 indicates the distribution is fairly spread across families rather than dominated by a few — and 25.5% of rows are null, which is a substantial gap for what looks like a taxonomic feature.
Treatment: Impute or add an explicit 'unknown' category for the 25.5% nulls, then group rare families before encoding.
saturn.columns["Family"].stats
| stat | value |
|---|---|
| n | 3,573 |
| nulls | 911 (25.5%) |
| unique | 254 |
| top_value | Niger-Congo |
| top_rate | 0.1217 |
| cardinality | 254 |
| entropy | 5.631 |
| entropy_ratio | 0.7049 |
| alert: null_rate | 25.5% null |
Family.
| value | count | share |
|---|---|---|
| Niger-Congo | 324 | 9.1% |
| Austronesian | 324 | 9.1% |
| Indo-European | 176 | 4.9% |
| Sino-Tibetan | 146 | 4.1% |
| Afro-Asiatic | 145 | 4.1% |
| Pama-Nyungan | 121 | 3.4% |
| Trans-New Guinea | 98 | 2.7% |
| other | 72 | 2.0% |
| Altaic | 65 | 1.8% |
| Oto-Manguean | 56 | 1.6% |
| Austro-Asiatic | 48 | 1.3% |
| Eastern Sudanic | 47 | 1.3% |
| Uto-Aztecan | 44 | 1.2% |
| Algic | 31 | 0.9% |
| Mayan | 30 | 0.8% |
| Arawakan | 29 | 0.8% |
| Nakh-Daghestanian | 28 | 0.8% |
| Mande | 28 | 0.8% |
| Uralic | 27 | 0.8% |
| Hokan | 26 | 0.7% |
This column records the linguistic subfamily classification of entries, drawn from a controlled vocabulary of 32 values such as Benue-Congo, Eastern Malayo-Polynesian, and Tibeto-Burman. Coverage is the main concern: 74.5% of rows are null, leaving only ~910 labelled records, with Benue-Congo accounting for 21.95% of those. Among populated rows the distribution is reasonably diverse (entropy ratio 0.77), so the signal is informative where present but sparse overall.
Treatment: Treat as a sparse categorical: impute an explicit 'unknown' level before encoding, since 74.5% are null.
saturn.columns["Subfamily"].stats
| stat | value |
|---|---|
| n | 3,573 |
| nulls | 2,662 (74.5%) |
| unique | 32 |
| top_value | Benue-Congo |
| top_rate | 0.2195 |
| cardinality | 32 |
| entropy | 3.856 |
| entropy_ratio | 0.7712 |
| alert: null_rate | 74.5% null |
Subfamily.
| value | count | share |
|---|---|---|
| Benue-Congo | 200 | 5.6% |
| Eastern Malayo-Polynesian | 159 | 4.5% |
| Tibeto-Burman | 139 | 3.9% |
| Chadic | 47 | 1.3% |
| Mon-Khmer | 38 | 1.1% |
| Adamawa-Ubangi | 30 | 0.8% |
| Gur | 27 | 0.8% |
| Daghestanian | 25 | 0.7% |
| Cushitic | 24 | 0.7% |
| Finno-Ugric | 21 | 0.6% |
| Kwa | 20 | 0.6% |
| North-Central Atlantic | 20 | 0.6% |
| Nilotic | 19 | 0.5% |
| Mixtecan | 18 | 0.5% |
| Omotic | 15 | 0.4% |
| Kainantu-Goroka | 14 | 0.4% |
| Madang | 13 | 0.4% |
| Awyu-Ok | 10 | 0.3% |
| Surmic | 10 | 0.3% |
| Je | 9 | 0.3% |
Genus is a linguistic genus label (subfamily-level grouping of languages), with values like Oceanic, Bantu, Indic, and Semitic. It is highly diverse — 625 distinct genera across 3573 rows with entropy ratio 0.86 and the top value Oceanic covering only 5.6% — and 25.5% of rows are null, which is the flagged concern.
Treatment: Treat as a high-cardinality categorical: target- or frequency-encode and explicitly model the 25.5% missing as its own category.
saturn.columns["Genus"].stats
| stat | value |
|---|---|
| n | 3,573 |
| nulls | 911 (25.5%) |
| unique | 625 |
| top_value | Oceanic |
| top_rate | 0.05597 |
| cardinality | 625 |
| entropy | 7.95 |
| entropy_ratio | 0.856 |
| alert: null_rate | 25.5% null |
Genus.
| value | count | share |
|---|---|---|
| Oceanic | 149 | 4.2% |
| Bantu | 141 | 3.9% |
| Indic | 50 | 1.4% |
| Western Pama-Nyungan | 49 | 1.4% |
| Semitic | 43 | 1.2% |
| Turkic | 41 | 1.1% |
| Sign Languages | 40 | 1.1% |
| Bodic | 40 | 1.1% |
| Germanic | 39 | 1.1% |
| Northern Pama-Nyungan | 33 | 0.9% |
| Creoles and Pidgins | 32 | 0.9% |
| Mayan | 30 | 0.8% |
| Algonquian | 29 | 0.8% |
| Central Malayo-Polynesian | 29 | 0.8% |
| Iranian | 26 | 0.7% |
| Romance | 24 | 0.7% |
| Biu-Mandara | 24 | 0.7% |
| Southeastern Pama-Nyungan | 23 | 0.6% |
| Dravidian | 23 | 0.6% |
| Malayo-Sumbawan | 22 | 0.6% |
GenusIcon is a high-cardinality categorical with 613 unique values across only 3573 rows, and 82.51% of those rows are null. Entropy ratio of 0.9988 and a top_rate of just 0.0032 mean the non-null values are nearly uniformly distributed, with the most frequent code 'c688033' appearing only twice. The hex-like tokens (e.g. 'c807D33') suggest icon identifiers or color/asset codes rather than a meaningful category.
Treatment: Drop or retain as a sparse asset reference; not useful as a modelling feature given near-unique values and 82.51% nulls.
saturn.columns["GenusIcon"].stats
| stat | value |
|---|---|
| n | 3,573 |
| nulls | 2,948 (82.5%) |
| unique | 613 |
| top_value | c688033 |
| top_rate | 0.0032 |
| cardinality | 613 |
| entropy | 9.249 |
| entropy_ratio | 0.9989 |
| alert: long_tail | 601 singleton categories |
| alert: null_rate | 82.5% null |
GenusIcon.
| value | count | share |
|---|---|---|
| c688033 | 2 | 0.1% |
| c803E33 | 2 | 0.1% |
| c804733 | 2 | 0.1% |
| c807D33 | 2 | 0.1% |
| c806233 | 2 | 0.1% |
| c805033 | 2 | 0.1% |
| c7A8033 | 2 | 0.1% |
| c805933 | 2 | 0.1% |
| c807433 | 2 | 0.1% |
| c806B33 | 2 | 0.1% |
| c718033 | 2 | 0.1% |
| c803533 | 2 | 0.1% |
| cCC8C51 | 1 | 0.0% |
| cCC6851 | 1 | 0.0% |
| cCC7E51 | 1 | 0.0% |
| c8FCC51 | 1 | 0.0% |
| cCC8051 | 1 | 0.0% |
| c528033 | 1 | 0.0% |
| cCC9F51 | 1 | 0.0% |
| cCCB551 | 1 | 0.0% |
Almost certainly ISO 639-3 language codes: 99% are single tokens, length is tightly clustered at 3 characters (min 3, max 7, p95 3), and top values like 'eus', 'deu', 'gsw', 'bod', 'roh' are recognisable three-letter language identifiers. Cardinality is high (2468 unique out of 3573) with a 26.1% null rate and 172 duplicates, so coverage is partial and no single code dominates (top value 'eus' appears just 12 times). The handful of length-7 entries is anomalous for a strict ISO 639-3 field and worth inspecting.
Treatment: Treat as a categorical code; validate against the ISO 639-3 list and investigate entries longer than 3 characters.
saturn.columns["ISO_codes"].stats
| stat | value |
|---|---|
| n | 3,573 |
| nulls | 933 (26.1%) |
| unique | 2,468 |
| len_min | 3 |
| len_max | 7 |
| len_mean | 3.039 |
| len_median | 3 |
| len_p95 | 3 |
| word_mean | 1.01 |
| word_median | 1 |
| n_empty | 0 |
| n_duplicates | 172 |
| duplicate_rate | 0.06515 |
| vocab_size | 2,486 |
| readability_flesch_mean | 117.4 |
| emoji_rate | 0 |
| url_rate | 0 |
| one_word_rate | 0.9902 |
| allcaps_rate | 0 |
| boilerplate_rate | 0 |
| alert: one_word | 99.0% rows are a single word |
| alert: null_rate | 26.1% null |
| alert: short_text | 95th-percentile length under 20 chars |
ISO_codes.
| chars | count |
|---|---|
| 3 – 3 | 2614 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 3 | 0 |
| 3 – 4 | 0 |
| 4 – 4 | 0 |
| 4 – 4 | 0 |
| 4 – 4 | 0 |
| 4 – 4 | 0 |
| 4 – 4 | 0 |
| 4 – 4 | 0 |
| 4 – 4 | 0 |
| 4 – 4 | 0 |
| 4 – 4 | 0 |
| 4 – 4 | 0 |
| 4 – 5 | 0 |
| 5 – 5 | 0 |
| 5 – 5 | 0 |
| 5 – 5 | 0 |
| 5 – 5 | 0 |
| 5 – 5 | 0 |
| 5 – 5 | 0 |
| 5 – 5 | 0 |
| 5 – 5 | 0 |
| 5 – 6 | 0 |
| 6 – 6 | 0 |
| 6 – 6 | 0 |
| 6 – 6 | 0 |
| 6 – 6 | 0 |
| 6 – 6 | 0 |
| 6 – 6 | 0 |
| 6 – 6 | 0 |
| 6 – 6 | 0 |
| 6 – 6 | 0 |
| 6 – 6 | 0 |
| 6 – 7 | 0 |
| 7 – 7 | 0 |
| 7 – 7 | 0 |
| 7 – 7 | 0 |
| 7 – 7 | 26 |
Boolean flag with only two values (False/True) where False dominates at 96.2% of non-null rows (2562 vs 100). The name 'Samples_100' plus the exact count of 100 True values suggests this marks a curated subset of 100 sampled records. A 25.5% null rate is notable and should be reconciled before use.
Treatment: Treat as a boolean subset indicator; impute or exclude nulls and avoid using as a model feature given severe imbalance.
saturn.columns["Samples_100"].stats
| stat | value |
|---|---|
| n | 3,573 |
| nulls | 911 (25.5%) |
| unique | 2 |
| top_value | False |
| top_rate | 0.9624 |
| cardinality | 2 |
| entropy | 0.231 |
| entropy_ratio | 0.231 |
| alert: null_rate | 25.5% null |
| alert: imbalance | top value is 96.2% of rows |
Samples_100.
| value | count | share |
|---|---|---|
| False | 2562 | 71.7% |
| True | 100 | 2.8% |
Binary True/False flag, almost certainly indicating membership in a 200-row sample (the name 'Samples_200' and the exact count of 200 'True' values support this). The column is heavily imbalanced — 'False' covers 92.5% of non-null rows — and 25.5% of values are null, which is unusual for a sampling indicator and worth investigating.
Treatment: Use as a boolean filter/split flag; reconcile the 25.5% nulls (treat as False or exclude) before relying on it.
saturn.columns["Samples_200"].stats
| stat | value |
|---|---|
| n | 3,573 |
| nulls | 911 (25.5%) |
| unique | 2 |
| top_value | False |
| top_rate | 0.9249 |
| cardinality | 2 |
| entropy | 0.3848 |
| entropy_ratio | 0.3848 |
| alert: null_rate | 25.5% null |
Samples_200.
| value | count | share |
|---|---|---|
| False | 2462 | 68.9% |
| True | 200 | 5.6% |
Two-letter country codes (PG, AU, US, ID, IN...) identifying the country associated with each record, with 337 distinct values across 3573 rows. The cardinality is suspiciously high since ISO 3166-1 alpha-2 only defines ~250 codes, hinting at non-standard or sub-region codes mixed in. Distribution is fairly flat (entropy ratio 0.752, top value PG only 8.06%) and 25.69% of rows are null.
Treatment: Validate codes against ISO 3166, impute or flag the 25.69% nulls, then left-join on this id.
saturn.columns["Country_ID"].stats
| stat | value |
|---|---|
| n | 3,573 |
| nulls | 918 (25.7%) |
| unique | 337 |
| top_value | PG |
| top_rate | 0.0806 |
| cardinality | 337 |
| entropy | 6.314 |
| entropy_ratio | 0.752 |
| alert: null_rate | 25.7% null |
Country_ID.
| value | count | share |
|---|---|---|
| PG | 214 | 6.0% |
| AU | 185 | 5.2% |
| US | 177 | 5.0% |
| ID | 177 | 5.0% |
| IN | 120 | 3.4% |
| MX | 120 | 3.4% |
| RU | 89 | 2.5% |
| NG | 66 | 1.8% |
| BR | 66 | 1.8% |
| CN | 54 | 1.5% |
| CD | 49 | 1.4% |
| CM | 46 | 1.3% |
| CA | 45 | 1.3% |
| CO | 39 | 1.1% |
| ET | 36 | 1.0% |
| PH | 36 | 1.0% |
| PE | 35 | 1.0% |
| NP | 32 | 0.9% |
| TZ | 28 | 0.8% |
| VU | 28 | 0.8% |
This column holds bibliographic citation tags (e.g., 'Huber-and-Reed-1992', 'Boelaars-1950'), almost certainly the source reference for each row in what appears to be a linguistic dataset. About 45% of values are a single token and the median length is 25 chars, consistent with compact Author-Year keys, but 30% of rows are null and 2,373 of 3,573 values are unique, with only 126 duplicates. Top citations like 'nichols-1992' and 'malherbe-and-rosenberg-1996' (113 occurrences each) dominate, suggesting a few reference works supply many entries.
Treatment: Normalize casing and keep as a categorical provenance tag; impute or flag the 30% nulls rather than modelling the text.
saturn.columns["Source"].stats
| stat | value |
|---|---|
| n | 3,573 |
| nulls | 1,074 (30.1%) |
| unique | 2,373 |
| len_min | 7 |
| len_max | 452 |
| len_mean | 42.07 |
| len_median | 25 |
| len_p95 | 135 |
| word_mean | 2.854 |
| word_median | 2 |
| n_empty | 0 |
| n_duplicates | 126 |
| duplicate_rate | 0.05042 |
| vocab_size | 5,899 |
| readability_flesch_mean | 21.33 |
| emoji_rate | 0 |
| url_rate | 0 |
| one_word_rate | 0.4546 |
| allcaps_rate | 0 |
| boilerplate_rate | 0 |
| alert: one_word | 45.5% rows are a single word |
| alert: null_rate | 30.1% null |
Source.
| chars | count |
|---|---|
| 7 – 18 | 937 |
| 18 – 29 | 518 |
| 29 – 40 | 280 |
| 40 – 52 | 203 |
| 52 – 63 | 136 |
| 63 – 74 | 90 |
| 74 – 85 | 61 |
| 85 – 96 | 42 |
| 96 – 107 | 35 |
| 107 – 118 | 36 |
| 118 – 129 | 27 |
| 129 – 140 | 21 |
| 140 – 152 | 12 |
| 152 – 163 | 13 |
| 163 – 174 | 6 |
| 174 – 185 | 14 |
| 185 – 196 | 6 |
| 196 – 207 | 5 |
| 207 – 218 | 3 |
| 218 – 230 | 4 |
| 230 – 241 | 4 |
| 241 – 252 | 5 |
| 252 – 263 | 3 |
| 263 – 274 | 6 |
| 274 – 285 | 7 |
| 285 – 296 | 3 |
| 296 – 307 | 4 |
| 307 – 318 | 1 |
| 318 – 330 | 1 |
| 330 – 341 | 3 |
| 341 – 352 | 1 |
| 352 – 363 | 1 |
| 363 – 374 | 0 |
| 374 – 385 | 2 |
| 385 – 396 | 1 |
| 396 – 408 | 1 |
| 408 – 419 | 4 |
| 419 – 430 | 1 |
| 430 – 441 | 0 |
| 441 – 452 | 2 |
Parent_ID looks like a foreign key pointing to a linguistic genus (e.g. 'genus-oceanic', 'genus-bantu'), grouping the 3573 rows into 911 parent categories. The distribution is long-tailed but not dominated: the top value covers only 4.5% of rows and entropy is 8.55 (ratio 0.87), so most genera carry few members. About 7.1% of values are null, which will need a decision before any join or grouping.
Treatment: Left-join on this id to a genus lookup; impute or flag the 7.1% nulls before grouping.
saturn.columns["Parent_ID"].stats
| stat | value |
|---|---|
| n | 3,573 |
| nulls | 254 (7.1%) |
| unique | 911 |
| top_value | genus-oceanic |
| top_rate | 0.04489 |
| cardinality | 911 |
| entropy | 8.554 |
| entropy_ratio | 0.87 |
| alert: long_tail | 501 singleton categories |
Parent_ID.
| value | count | share |
|---|---|---|
| genus-oceanic | 149 | 4.2% |
| genus-bantu | 141 | 3.9% |
| genus-indic | 50 | 1.4% |
| genus-westernpamanyungan | 49 | 1.4% |
| genus-semitic | 43 | 1.2% |
| genus-turkic | 41 | 1.1% |
| genus-signlanguages | 40 | 1.1% |
| genus-bodic | 40 | 1.1% |
| genus-germanic | 39 | 1.1% |
| genus-northernpamanyungan | 33 | 0.9% |
| genus-creolesandpidgins | 32 | 0.9% |
| genus-mayan | 30 | 0.8% |
| family-austronesian | 30 | 0.8% |
| genus-algonquian | 29 | 0.8% |
| genus-centralmalayopolynesian | 29 | 0.8% |
| genus-iranian | 26 | 0.7% |
| family-transnewguinea | 25 | 0.7% |
| genus-romance | 24 | 0.7% |
| genus-biumandara | 24 | 0.7% |
| genus-southeasternpamanyungan | 23 | 0.6% |
click to copy
@misc{saturn-language-data-wals-languages-2026,
author = {Steuber, Luke},
title = {Saturn reading: language data wals languages},
year ={2026},
howpublished = {\url{https://dr.eamer.dev/saturn/view/language-data-wals_languages}},
note = {Profiled with saturn-dissect v0.2.0, prompt saturn-insight-v2, model anthropic:claude-opus-4-7},
}
Steuber, L. (2026). Saturn reading: language data wals languages. Source: /home/coolhand/datasets/language-data/wals_languages.csv. Profiled with saturn-dissect v0.2.0 (saturn-insight-v2, anthropic:claude-opus-4-7). Retrieved from https://dr.eamer.dev/saturn/view/language-data-wals_languages