Source: /home/coolhand/datasets/language-data/glottolog_languoid.csv
Saturn profiled 23,740 rows across 16 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/glottolog_languoid.csv",
"--findings", "language-data-glottolog_languoid.json",
"--llm", "anthropic:claude-opus-4-7",
])This dataset is a Glottolog languoid catalog with 23,740 rows and 16 columns describing languages, dialects, and families along with geographic and endangerment metadata. The `level` field splits the records into three classes — dialect (10,920), language (8,481), and family (4,339) — making it the natural primary lens. Endangerment `status` is dominated by 'safe' (~79.9%), but the remaining categories flag thousands of vulnerable to extinct languages worth investigating. Geography is concentrated: `country_ids` is led by PG (874), ID (695), and NG (480), and `family_id` is heavily skewed toward atla1278 (4,663) and aust1307 (3,850). Note that `iso639P3code`, `latitude`, and `longitude` are ~66% null, so spatial analysis will only cover about a third of rows.
citing: level · status · country_ids · family_id · latitude · longitude · iso639P3code · child_dialect_count · bookkeeping
saturn.schema() · 16 columns
| column | kind | n | null% | unique | alerts |
|---|---|---|---|---|---|
| id | text | 23,740 | 0.0% | 23,740 | near_unique one_word short_text |
| family_id | categorical | 23,740 | 1.8% | 287 | |
| parent_id | text | 23,740 | 1.8% | 7,338 | one_word short_text duplicates |
| name | text | 23,740 | 0.0% | 23,740 | near_unique one_word |
| bookkeeping | categorical | 23,740 | 0.0% | 2 | imbalance |
| level | categorical | 23,740 | 0.0% | 3 | |
| status | categorical | 23,740 | 0.0% | 6 | |
| latitude | numeric | 23,740 | 66.5% | 7,798 | null_rate |
| longitude | numeric | 23,740 | 66.5% | 7,757 | null_rate |
| iso639P3code | text | 23,740 | 66.4% | 7,968 | near_unique one_word null_rate short_text |
| description | unknown | 23,740 | 0.0% | — | skipped |
| markup_description | unknown | 23,740 | 0.0% | — | skipped |
| child_family_count | numeric | 23,740 | 0.0% | 88 | high_skew outliers |
| child_language_count | numeric | 23,740 | 0.0% | 126 | high_skew outliers |
| child_dialect_count | numeric | 23,740 | 0.0% | 164 | high_skew outliers |
| country_ids | categorical | 23,740 | 64.2% | 680 | null_rate |
| value | count | share |
|---|---|---|
| dialect | 10920 | 46.0% |
| language | 8481 | 35.7% |
| family | 4339 | 18.3% |
| value | count | share |
|---|---|---|
| safe | 18965 | 79.9% |
| definitely endangered | 1814 | 7.6% |
| vulnerable | 1194 | 5.0% |
| extinct | 889 | 3.7% |
| critically endangered | 465 | 2.0% |
| severely endangered | 413 | 1.7% |
| value | count | share |
|---|---|---|
| atla1278 | 4663 | 19.6% |
| aust1307 | 3850 | 16.2% |
| indo1319 | 2201 | 9.3% |
| sino1245 | 1666 | 7.0% |
| afro1255 | 1259 | 5.3% |
| nucl1709 | 762 | 3.2% |
| pama1250 | 598 | 2.5% |
| aust1305 | 503 | 2.1% |
| book1242 | 399 | 1.7% |
| otom1299 | 338 | 1.4% |
| mand1469 | 303 | 1.3% |
| sign1238 | 259 | 1.1% |
| drav1251 | 255 | 1.1% |
| cent2225 | 251 | 1.1% |
| turk1311 | 229 | 1.0% |
| taik1256 | 223 | 0.9% |
| nilo1247 | 201 | 0.8% |
| ural1272 | 185 | 0.8% |
| japo1237 | 179 | 0.8% |
| tupi1275 | 157 | 0.7% |
| value | count | share |
|---|---|---|
| PG | 874 | 3.7% |
| ID | 695 | 2.9% |
| NG | 480 | 2.0% |
| AU | 432 | 1.8% |
| IN | 356 | 1.5% |
| MX | 297 | 1.3% |
| CN | 271 | 1.1% |
| BR | 263 | 1.1% |
| US | 247 | 1.0% |
| CM | 196 | 0.8% |
| PH | 177 | 0.7% |
| CD | 156 | 0.7% |
| VU | 118 | 0.5% |
| SD | 99 | 0.4% |
| PE | 97 | 0.4% |
| TZ | 93 | 0.4% |
| MY | 90 | 0.4% |
| TD | 88 | 0.4% |
| RU | 83 | 0.3% |
| CO | 82 | 0.3% |
| bin | count |
|---|---|
| -55.27 – -52.06 | 5 |
| -52.06 – -48.85 | 1 |
| -48.85 – -45.64 | 1 |
| -45.64 – -42.43 | 4 |
| -42.43 – -39.22 | 7 |
| -39.22 – -36.01 | 16 |
| -36.01 – -32.8 | 29 |
| -32.8 – -29.59 | 26 |
| -29.59 – -26.38 | 48 |
| -26.38 – -23.17 | 78 |
| -23.17 – -19.96 | 125 |
| -19.96 – -16.75 | 141 |
| -16.75 – -13.54 | 281 |
| -13.54 – -10.33 | 256 |
| -10.33 – -7.121 | 495 |
| -7.121 – -3.911 | 788 |
| -3.911 – -0.7005 | 681 |
| -0.7005 – 2.51 | 379 |
| 2.51 – 5.72 | 469 |
| 5.72 – 8.93 | 664 |
| 8.93 – 12.14 | 710 |
| 12.14 – 15.35 | 303 |
| 15.35 – 18.56 | 387 |
| 18.56 – 21.77 | 233 |
| 21.77 – 24.98 | 318 |
| 24.98 – 28.19 | 373 |
| 28.19 – 31.4 | 167 |
| 31.4 – 34.61 | 144 |
| 34.61 – 37.82 | 179 |
| 37.82 – 41.03 | 113 |
| 41.03 – 44.24 | 138 |
| 44.24 – 47.45 | 79 |
| 47.45 – 50.66 | 78 |
| 50.66 – 53.87 | 76 |
| 53.87 – 57.08 | 46 |
| 57.08 – 60.29 | 21 |
| 60.29 – 63.5 | 41 |
| 63.5 – 66.71 | 23 |
| 66.71 – 69.93 | 14 |
| 69.93 – 73.14 | 6 |
| column | kind | null % |
|---|---|---|
| id | text | 0.0% |
| family_id | categorical | 1.8% |
| parent_id | text | 1.8% |
| name | text | 0.0% |
| bookkeeping | categorical | 0.0% |
| level | categorical | 0.0% |
| status | categorical | 0.0% |
| latitude | numeric | 66.5% |
| longitude | numeric | 66.5% |
| iso639P3code | text | 66.4% |
| description | unknown | 0.0% |
| markup_description | unknown | 0.0% |
| child_family_count | numeric | 0.0% |
| child_language_count | numeric | 0.0% |
| child_dialect_count | numeric | 0.0% |
| country_ids | categorical | 64.2% |
| latitude | longitude | child_family_count | child_language_count | child_dialect_count | |
|---|---|---|---|---|---|
| latitude | +1.00 | -0.31 | +0.03 | +0.01 | +0.06 |
| longitude | -0.31 | +1.00 | -0.03 | -0.04 | -0.05 |
| child_family_count | +0.03 | -0.03 | +1.00 | +0.96 | +0.74 |
| child_language_count | +0.01 | -0.04 | +0.96 | +1.00 | +0.69 |
| child_dialect_count | +0.06 | -0.05 | +0.74 | +0.69 | +1.00 |
Fixed 8-character single-token codes (e.g., 'melk1240', 'yang1299'), unique across all 23,740 rows with no nulls or duplicates. The pattern of four letters followed by four digits is consistent with Glottolog-style language identifiers, making this a primary key rather than analyzable text.
Treatment: Use as the row key for joins; exclude from modelling features.
saturn.columns["id"].stats
| stat | value |
|---|---|
| n | 23,740 |
| nulls | 0 (0.0%) |
| unique | 23,740 |
| 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 | 0 |
| duplicate_rate | 0 |
| vocab_size | 20,000 |
| readability_flesch_mean | 86.11 |
| 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 |
|---|---|
| 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 | 23740 |
| 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 |
Looks like a Glottolog-style language family identifier (e.g. 'atla1278' for Atlantic-Congo, 'aust1307' for Austronesian) tagging each of the 23,740 rows. The distribution is heavily skewed: the top family alone covers 20.0% of rows and the top 10 of 287 families dominate, yielding an entropy ratio of 0.598. Null rate is low at 1.81%.
Treatment: left-join on this id to a language-family reference table; consider grouping the long tail before modelling.
saturn.columns["family_id"].stats
| stat | value |
|---|---|
| n | 23,740 |
| nulls | 429 (1.8%) |
| unique | 287 |
| top_value | atla1278 |
| top_rate | 0.2 |
| cardinality | 287 |
| entropy | 4.886 |
| entropy_ratio | 0.5984 |
family_id.
| value | count | share |
|---|---|---|
| atla1278 | 4663 | 19.6% |
| aust1307 | 3850 | 16.2% |
| indo1319 | 2201 | 9.3% |
| sino1245 | 1666 | 7.0% |
| afro1255 | 1259 | 5.3% |
| nucl1709 | 762 | 3.2% |
| pama1250 | 598 | 2.5% |
| aust1305 | 503 | 2.1% |
| book1242 | 399 | 1.7% |
| otom1299 | 338 | 1.4% |
| mand1469 | 303 | 1.3% |
| sign1238 | 259 | 1.1% |
| drav1251 | 255 | 1.1% |
| cent2225 | 251 | 1.1% |
| turk1311 | 229 | 1.0% |
| taik1256 | 223 | 0.9% |
| nilo1247 | 201 | 0.8% |
| ural1272 | 185 | 0.8% |
| japo1237 | 179 | 0.8% |
| tupi1275 | 157 | 0.7% |
Fixed-width 8-character single-token codes (e.g. 'book1242', 'uncl1493') with len_min=len_max=8 and one_word_rate=1.0 — these look like Glottolog-style language/family identifiers used as a parent reference. With 7338 unique values across 23740 rows and a 68.5% duplicate_rate, many children share parents; 'book1242' alone accounts for 399 rows. Null rate is low at 1.81%.
Treatment: left-join on this id to a parent/language lookup table.
saturn.columns["parent_id"].stats
| stat | value |
|---|---|
| n | 23,740 |
| nulls | 429 (1.8%) |
| unique | 7,338 |
| 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 | 15,973 |
| duplicate_rate | 0.6852 |
| vocab_size | 7,189 |
| readability_flesch_mean | 91.19 |
| 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: short_text | 95th-percentile length under 20 chars |
| alert: duplicates | 68.5% duplicate strings |
parent_id.
| 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 | 23311 |
| 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 |
The `name` column holds 23,740 fully unique short labels (null_rate 0.0, n_unique equals n), with a mean length of 9.95 characters and 69.5% being a single word. Top tokens like `nuclear`, `central`, `western`, `eastern`, `northern`, `southern`, and `language` suggest these are entity/topic names rather than person names. Vocabulary is broad (17,915 distinct words across only ~1.4 words per row), and there are no duplicates, URLs, or emoji.
Treatment: Treat as a unique label key; drop from modelling features or hash/embed if semantic content is needed.
saturn.columns["name"].stats
| stat | value |
|---|---|
| n | 23,740 |
| nulls | 0 (0.0%) |
| unique | 23,740 |
| len_min | 1 |
| len_max | 58 |
| len_mean | 9.95 |
| len_median | 8 |
| len_p95 | 22 |
| word_mean | 1.398 |
| word_median | 1 |
| n_empty | 0 |
| n_duplicates | 0 |
| duplicate_rate | 0 |
| vocab_size | 17,915 |
| readability_flesch_mean | 42.62 |
| emoji_rate | 0 |
| url_rate | 0 |
| one_word_rate | 0.6953 |
| allcaps_rate | 0.0001685 |
| boilerplate_rate | 0 |
| alert: near_unique | 100.0% of rows are unique strings |
| alert: one_word | 69.5% rows are a single word |
name.
| chars | count |
|---|---|
| 1 – 2 | 54 |
| 2 – 4 | 496 |
| 4 – 5 | 4648 |
| 5 – 7 | 3145 |
| 7 – 8 | 4510 |
| 8 – 10 | 1373 |
| 10 – 11 | 1069 |
| 11 – 12 | 1997 |
| 12 – 14 | 1023 |
| 14 – 15 | 1768 |
| 15 – 17 | 644 |
| 17 – 18 | 848 |
| 18 – 20 | 340 |
| 20 – 21 | 281 |
| 21 – 22 | 518 |
| 22 – 24 | 224 |
| 24 – 25 | 298 |
| 25 – 27 | 101 |
| 27 – 28 | 140 |
| 28 – 30 | 46 |
| 30 – 31 | 44 |
| 31 – 32 | 69 |
| 32 – 34 | 24 |
| 34 – 35 | 34 |
| 35 – 37 | 12 |
| 37 – 38 | 9 |
| 38 – 39 | 4 |
| 39 – 41 | 2 |
| 41 – 42 | 3 |
| 42 – 44 | 3 |
| 44 – 45 | 5 |
| 45 – 47 | 1 |
| 47 – 48 | 2 |
| 48 – 49 | 1 |
| 49 – 51 | 0 |
| 51 – 52 | 2 |
| 52 – 54 | 0 |
| 54 – 55 | 0 |
| 55 – 57 | 0 |
| 57 – 58 | 2 |
Binary boolean flag indicating whether a record involves bookkeeping, with only two values across 23740 rows and no nulls. The distribution is severely imbalanced: 'False' covers 98.3% (23341 rows) versus only 399 'True' cases, yielding an entropy ratio of just 0.12.
Treatment: Encode as boolean and apply class-imbalance handling (e.g., stratification or reweighting) if used as a target.
saturn.columns["bookkeeping"].stats
| stat | value |
|---|---|
| n | 23,740 |
| nulls | 0 (0.0%) |
| unique | 2 |
| top_value | False |
| top_rate | 0.9832 |
| cardinality | 2 |
| entropy | 0.1231 |
| entropy_ratio | 0.1231 |
| alert: imbalance | top value is 98.3% of rows |
bookkeeping.
| value | count | share |
|---|---|---|
| False | 23341 | 98.3% |
| True | 399 | 1.7% |
This is a categorical taxonomy tag with exactly 3 levels (dialect, language, family) and no nulls across 23,740 rows. The distribution is well-spread (entropy_ratio 0.94), with 'dialect' leading at 46.0%, followed by 'language' (8,481) and 'family' (4,339). Looks like a linguistic classification level rather than a free-form attribute.
Treatment: one-hot encode for modelling or use directly as a stratification key.
saturn.columns["level"].stats
| stat | value |
|---|---|
| n | 23,740 |
| nulls | 0 (0.0%) |
| unique | 3 |
| top_value | dialect |
| top_rate | 0.46 |
| cardinality | 3 |
| entropy | 1.494 |
| entropy_ratio | 0.9426 |
level.
| value | count | share |
|---|---|---|
| dialect | 10920 | 46.0% |
| language | 8481 | 35.7% |
| family | 4339 | 18.3% |
This is a categorical status column with 6 levels matching UNESCO-style language endangerment categories (safe, definitely endangered, vulnerable, extinct, critically endangered, severely endangered). The distribution is heavily imbalanced: 'safe' accounts for 79.9% of 23,740 rows, while the rarest level 'severely endangered' has only 413 records. Entropy ratio is 0.44, confirming low diversity despite 6 classes.
Treatment: Treat as ordinal target; stratify or rebalance before classification given the 80/20 dominance of 'safe'.
saturn.columns["status"].stats
| stat | value |
|---|---|
| n | 23,740 |
| nulls | 0 (0.0%) |
| unique | 6 |
| top_value | safe |
| top_rate | 0.7989 |
| cardinality | 6 |
| entropy | 1.15 |
| entropy_ratio | 0.4447 |
status.
| value | count | share |
|---|---|---|
| safe | 18965 | 79.9% |
| definitely endangered | 1814 | 7.6% |
| vulnerable | 1194 | 5.0% |
| extinct | 889 | 3.7% |
| critically endangered | 465 | 2.0% |
| severely endangered | 413 | 1.7% |
Geographic latitude coordinate spanning -55.27 to 73.14, consistent with valid Earth latitudes. Two-thirds of rows are null (null_rate 0.6654), which severely limits coverage. The distribution is mildly right-skewed (0.54) with median 6.31 and IQR ~24.5, suggesting a bias toward northern-hemisphere but tropical-leaning observations.
Treatment: Impute or filter the 66% nulls before any spatial modelling; pair with longitude for geo-features.
saturn.columns["latitude"].stats
| stat | value |
|---|---|
| n | 23,740 |
| nulls | 15,797 (66.5%) |
| unique | 7,798 |
| min | -55.27 |
| max | 73.14 |
| mean | 8.17 |
| median | 6.306 |
| std | 18.96 |
| q1 | -5.137 |
| q3 | 19.34 |
| iqr | 24.47 |
| skew | 0.5403 |
| kurtosis | 0.3006 |
| n_outliers | 129 |
| outlier_rate | 0.01624 |
| zero_rate | 0 |
| alert: null_rate | 66.5% null |
latitude. Vertical dash marks the median.
| bin | count |
|---|---|
| -55.27 – -52.06 | 5 |
| -52.06 – -48.85 | 1 |
| -48.85 – -45.64 | 1 |
| -45.64 – -42.43 | 4 |
| -42.43 – -39.22 | 7 |
| -39.22 – -36.01 | 16 |
| -36.01 – -32.8 | 29 |
| -32.8 – -29.59 | 26 |
| -29.59 – -26.38 | 48 |
| -26.38 – -23.17 | 78 |
| -23.17 – -19.96 | 125 |
| -19.96 – -16.75 | 141 |
| -16.75 – -13.54 | 281 |
| -13.54 – -10.33 | 256 |
| -10.33 – -7.121 | 495 |
| -7.121 – -3.911 | 788 |
| -3.911 – -0.7005 | 681 |
| -0.7005 – 2.51 | 379 |
| 2.51 – 5.72 | 469 |
| 5.72 – 8.93 | 664 |
| 8.93 – 12.14 | 710 |
| 12.14 – 15.35 | 303 |
| 15.35 – 18.56 | 387 |
| 18.56 – 21.77 | 233 |
| 21.77 – 24.98 | 318 |
| 24.98 – 28.19 | 373 |
| 28.19 – 31.4 | 167 |
| 31.4 – 34.61 | 144 |
| 34.61 – 37.82 | 179 |
| 37.82 – 41.03 | 113 |
| 41.03 – 44.24 | 138 |
| 44.24 – 47.45 | 79 |
| 47.45 – 50.66 | 78 |
| 50.66 – 53.87 | 76 |
| 53.87 – 57.08 | 46 |
| 57.08 – 60.29 | 21 |
| 60.29 – 63.5 | 41 |
| 63.5 – 66.71 | 23 |
| 66.71 – 69.93 | 14 |
| 69.93 – 73.14 | 6 |
Geographic longitude in decimal degrees, with values spanning -178.785 to 179.306, consistent with the global WGS84 range. Two-thirds of rows are null (null_rate 0.6654), so coverage is the dominant concern; among populated rows the distribution is mildly left-skewed (-0.48) with a median of 47.72 suggesting an Eastern-Hemisphere bias. Only 13 outliers (0.16%) and no zeros, so the populated values themselves look clean.
Treatment: Pair with latitude for geospatial features; impute or filter the 66.5% missing before modelling.
saturn.columns["longitude"].stats
| stat | value |
|---|---|
| n | 23,740 |
| nulls | 15,797 (66.5%) |
| unique | 7,757 |
| min | -178.8 |
| max | 179.3 |
| mean | 51.27 |
| median | 47.72 |
| std | 81.14 |
| q1 | 7.235 |
| q3 | 124.1 |
| iqr | 116.9 |
| skew | -0.4832 |
| kurtosis | -0.7745 |
| n_outliers | 13 |
| outlier_rate | 0.001637 |
| zero_rate | 0 |
| alert: null_rate | 66.5% null |
longitude. Vertical dash marks the median.
| bin | count |
|---|---|
| -178.8 – -169.8 | 13 |
| -169.8 – -160.9 | 4 |
| -160.9 – -151.9 | 10 |
| -151.9 – -143 | 11 |
| -143 – -134 | 10 |
| -134 – -125.1 | 17 |
| -125.1 – -116.1 | 124 |
| -116.1 – -107.2 | 47 |
| -107.2 – -98.21 | 78 |
| -98.21 – -89.26 | 280 |
| -89.26 – -80.31 | 59 |
| -80.31 – -71.36 | 235 |
| -71.36 – -62.41 | 218 |
| -62.41 – -53.45 | 150 |
| -53.45 – -44.5 | 60 |
| -44.5 – -35.55 | 40 |
| -35.55 – -26.6 | 0 |
| -26.6 – -17.64 | 4 |
| -17.64 – -8.692 | 105 |
| -8.692 – 0.2605 | 275 |
| 0.2605 – 9.213 | 444 |
| 9.213 – 18.17 | 751 |
| 18.17 – 27.12 | 322 |
| 27.12 – 36.07 | 430 |
| 36.07 – 45.02 | 228 |
| 45.02 – 53.97 | 126 |
| 53.97 – 62.93 | 35 |
| 62.93 – 71.88 | 80 |
| 71.88 – 80.83 | 210 |
| 80.83 – 89.78 | 208 |
| 89.78 – 98.74 | 269 |
| 98.74 – 107.7 | 457 |
| 107.7 – 116.6 | 239 |
| 116.6 – 125.6 | 502 |
| 125.6 – 134.5 | 316 |
| 134.5 – 143.5 | 598 |
| 143.5 – 152.4 | 667 |
| 152.4 – 161.4 | 123 |
| 161.4 – 170.4 | 186 |
| 170.4 – 179.3 | 12 |
This column holds ISO 639-3 language codes: every non-null value is exactly 3 characters and one word (len_min=len_max=3, one_word_rate=1.0), with 7968 distinct codes across 23740 rows. Two-thirds are missing (null_rate=0.6644), and the cardinality is near-unique among populated rows, suggesting one code per language entry rather than a repeated categorical.
Treatment: Treat as a language-code key; left-join to an ISO 639-3 reference table and handle the 66% nulls explicitly.
saturn.columns["iso639P3code"].stats
| stat | value |
|---|---|
| n | 23,740 |
| nulls | 15,772 (66.4%) |
| unique | 7,968 |
| 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 | 0 |
| duplicate_rate | 0 |
| vocab_size | 7,968 |
| readability_flesch_mean | 119.5 |
| 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: null_rate | 66.4% 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 | 7968 |
| 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 |
This column is named 'description' but saturn skipped profiling, so no kind, uniqueness, or value statistics are available. Only the row count (23740) and a null rate of 0.0 are reported. Without further stats, the content and structure cannot be characterized.
Treatment: Re-profile or sample manually before deciding; if textual, tokenize and embed before modelling.
saturn.columns["description"].stats
| stat | value |
|---|---|
| n | 23,740 |
| nulls | 0 (0.0%) |
| unique | — |
| alert: skipped | no profiler for kind=unknown |
This column was skipped by the profiler, so no statistics, uniqueness count, or value samples are available beyond a row count of 23,740 and a null rate of 0.0. The name suggests it holds markup or descriptive text (likely HTML or formatted product/item descriptions), but that is inferred from the label alone, not from evidence. No distributional signal can be assessed here.
Treatment: Re-run the profiler with text handling enabled, then tokenize and embed before modelling.
saturn.columns["markup_description"].stats
| stat | value |
|---|---|
| n | 23,740 |
| nulls | 0 (0.0%) |
| unique | — |
| alert: skipped | no profiler for kind=unknown |
A numeric count of child families per record, with 23740 rows and only 88 distinct values. It is overwhelmingly zero (zero_rate 0.9082) so q1, median, and q3 are all 0 and IQR is 0, yet a long tail pushes max to 859 with mean 0.879 and std 13.20. Skew of 44.40 and kurtosis of 2352.94 confirm an extreme heavy-tailed distribution, and 2179 rows (9.18%) flag as outliers.
Treatment: Binarize (zero vs non-zero) or apply log1p before modelling to tame the extreme skew.
saturn.columns["child_family_count"].stats
| stat | value |
|---|---|
| n | 23,740 |
| nulls | 0 (0.0%) |
| unique | 88 |
| min | 0 |
| max | 859 |
| mean | 0.8792 |
| median | 0 |
| std | 13.2 |
| q1 | 0 |
| q3 | 0 |
| iqr | 0 |
| skew | 44.4 |
| kurtosis | 2353 |
| n_outliers | 2,179 |
| outlier_rate | 0.09179 |
| zero_rate | 0.9082 |
| alert: high_skew | skew=+44.40 |
| alert: outliers | 9.2% rows beyond 1.5 IQR |
child_family_count. Vertical dash marks the median.
| bin | count |
|---|---|
| 0 – 21.48 | 23588 |
| 21.48 – 42.95 | 93 |
| 42.95 – 64.43 | 27 |
| 64.43 – 85.9 | 6 |
| 85.9 – 107.4 | 4 |
| 107.4 – 128.9 | 3 |
| 128.9 – 150.3 | 2 |
| 150.3 – 171.8 | 2 |
| 171.8 – 193.3 | 1 |
| 193.3 – 214.8 | 1 |
| 214.8 – 236.2 | 0 |
| 236.2 – 257.7 | 0 |
| 257.7 – 279.2 | 1 |
| 279.2 – 300.7 | 2 |
| 300.7 – 322.1 | 1 |
| 322.1 – 343.6 | 1 |
| 343.6 – 365.1 | 0 |
| 365.1 – 386.6 | 0 |
| 386.6 – 408 | 2 |
| 408 – 429.5 | 1 |
| 429.5 – 451 | 0 |
| 451 – 472.5 | 0 |
| 472.5 – 493.9 | 0 |
| 493.9 – 515.4 | 0 |
| 515.4 – 536.9 | 0 |
| 536.9 – 558.4 | 0 |
| 558.4 – 579.8 | 1 |
| 579.8 – 601.3 | 0 |
| 601.3 – 622.8 | 0 |
| 622.8 – 644.2 | 0 |
| 644.2 – 665.7 | 0 |
| 665.7 – 687.2 | 0 |
| 687.2 – 708.7 | 2 |
| 708.7 – 730.2 | 0 |
| 730.2 – 751.6 | 0 |
| 751.6 – 773.1 | 0 |
| 773.1 – 794.6 | 0 |
| 794.6 – 816.1 | 1 |
| 816.1 – 837.5 | 0 |
| 837.5 – 859 | 1 |
A numeric count of child languages per record, where 81.7% of rows are zero and Q1=median=Q3=0, so the typical entity has none. The distribution is extremely long-tailed (skew 41.86, kurtosis 2115) with a max of 1435 and 4339 outliers (18.3% outlier rate), suggesting a small set of hub-like records dominate.
Treatment: Binarize (has_children vs none) or log1p-transform before modelling given the heavy zero-inflation and skew.
saturn.columns["child_language_count"].stats
| stat | value |
|---|---|
| n | 23,740 |
| nulls | 0 (0.0%) |
| unique | 126 |
| min | 0 |
| max | 1,435 |
| mean | 1.996 |
| median | 0 |
| std | 23.41 |
| q1 | 0 |
| q3 | 0 |
| iqr | 0 |
| skew | 41.86 |
| kurtosis | 2115 |
| n_outliers | 4,339 |
| outlier_rate | 0.1828 |
| zero_rate | 0.8172 |
| alert: high_skew | skew=+41.86 |
| alert: outliers | 18.3% rows beyond 1.5 IQR |
child_language_count. Vertical dash marks the median.
| bin | count |
|---|---|
| 0 – 35.88 | 23547 |
| 35.88 – 71.75 | 121 |
| 71.75 – 107.6 | 37 |
| 107.6 – 143.5 | 6 |
| 143.5 – 179.4 | 3 |
| 179.4 – 215.2 | 4 |
| 215.2 – 251.1 | 3 |
| 251.1 – 287 | 2 |
| 287 – 322.9 | 2 |
| 322.9 – 358.8 | 0 |
| 358.8 – 394.6 | 1 |
| 394.6 – 430.5 | 1 |
| 430.5 – 466.4 | 0 |
| 466.4 – 502.2 | 1 |
| 502.2 – 538.1 | 1 |
| 538.1 – 574 | 2 |
| 574 – 609.9 | 1 |
| 609.9 – 645.8 | 0 |
| 645.8 – 681.6 | 0 |
| 681.6 – 717.5 | 1 |
| 717.5 – 753.4 | 2 |
| 753.4 – 789.2 | 0 |
| 789.2 – 825.1 | 0 |
| 825.1 – 861 | 0 |
| 861 – 896.9 | 0 |
| 896.9 – 932.8 | 0 |
| 932.8 – 968.6 | 0 |
| 968.6 – 1004 | 1 |
| 1004 – 1040 | 0 |
| 1040 – 1076 | 0 |
| 1076 – 1112 | 0 |
| 1112 – 1148 | 0 |
| 1148 – 1184 | 0 |
| 1184 – 1220 | 0 |
| 1220 – 1256 | 0 |
| 1256 – 1292 | 2 |
| 1292 – 1327 | 0 |
| 1327 – 1363 | 0 |
| 1363 – 1399 | 1 |
| 1399 – 1435 | 1 |
This is a count of child dialects per record, dominated by zeros (zero_rate 0.7442) with a median and Q3 of 0/1 yet a max of 2369. Skew of 42.22 and kurtosis of 2159 confirm an extreme long tail, and 17.99% of rows flag as outliers. The mean of 3.39 is pulled far above the median, so any aggregate using it will mislead.
Treatment: Log1p-transform or bin (zero / one / many) before modelling.
saturn.columns["child_dialect_count"].stats
| stat | value |
|---|---|
| n | 23,740 |
| nulls | 0 (0.0%) |
| unique | 164 |
| min | 0 |
| max | 2,369 |
| mean | 3.389 |
| median | 0 |
| std | 36.8 |
| q1 | 0 |
| q3 | 1 |
| iqr | 1 |
| skew | 42.22 |
| kurtosis | 2159 |
| n_outliers | 4,272 |
| outlier_rate | 0.1799 |
| zero_rate | 0.7442 |
| alert: high_skew | skew=+42.22 |
| alert: outliers | 18.0% rows beyond 1.5 IQR |
child_dialect_count. Vertical dash marks the median.
| bin | count |
|---|---|
| 0 – 59.23 | 23575 |
| 59.23 – 118.5 | 99 |
| 118.5 – 177.7 | 24 |
| 177.7 – 236.9 | 18 |
| 236.9 – 296.1 | 4 |
| 296.1 – 355.4 | 2 |
| 355.4 – 414.6 | 0 |
| 414.6 – 473.8 | 1 |
| 473.8 – 533 | 1 |
| 533 – 592.2 | 1 |
| 592.2 – 651.5 | 1 |
| 651.5 – 710.7 | 2 |
| 710.7 – 769.9 | 0 |
| 769.9 – 829.1 | 1 |
| 829.1 – 888.4 | 0 |
| 888.4 – 947.6 | 3 |
| 947.6 – 1007 | 0 |
| 1007 – 1066 | 0 |
| 1066 – 1125 | 1 |
| 1125 – 1184 | 1 |
| 1184 – 1244 | 0 |
| 1244 – 1303 | 0 |
| 1303 – 1362 | 1 |
| 1362 – 1421 | 0 |
| 1421 – 1481 | 0 |
| 1481 – 1540 | 0 |
| 1540 – 1599 | 1 |
| 1599 – 1658 | 0 |
| 1658 – 1718 | 0 |
| 1718 – 1777 | 0 |
| 1777 – 1836 | 1 |
| 1836 – 1895 | 1 |
| 1895 – 1954 | 0 |
| 1954 – 2014 | 0 |
| 2014 – 2073 | 0 |
| 2073 – 2132 | 0 |
| 2132 – 2191 | 0 |
| 2191 – 2251 | 1 |
| 2251 – 2310 | 0 |
| 2310 – 2369 | 1 |
This column holds ISO-style country codes (PG, ID, NG, AU, IN…) as a categorical feature with 680 distinct values across 23,740 rows. Coverage is poor — 64.24% of rows are null — and the non-null distribution is broad (entropy 6.49, ratio 0.69) with Papua New Guinea leading at only 10.29%. The 680 distinct codes far exceed the ~250 real countries, suggesting multi-value concatenations or non-standard tokens behind the 'country_ids' plural.
Treatment: Split multi-code entries, normalise to ISO-3166, and impute or flag the 64% missing before one-hot or target encoding.
saturn.columns["country_ids"].stats
| stat | value |
|---|---|
| n | 23,740 |
| nulls | 15,250 (64.2%) |
| unique | 680 |
| top_value | PG |
| top_rate | 0.1029 |
| cardinality | 680 |
| entropy | 6.493 |
| entropy_ratio | 0.6901 |
| alert: null_rate | 64.2% null |
country_ids.
| value | count | share |
|---|---|---|
| PG | 874 | 3.7% |
| ID | 695 | 2.9% |
| NG | 480 | 2.0% |
| AU | 432 | 1.8% |
| IN | 356 | 1.5% |
| MX | 297 | 1.3% |
| CN | 271 | 1.1% |
| BR | 263 | 1.1% |
| US | 247 | 1.0% |
| CM | 196 | 0.8% |
| PH | 177 | 0.7% |
| CD | 156 | 0.7% |
| VU | 118 | 0.5% |
| SD | 99 | 0.4% |
| PE | 97 | 0.4% |
| TZ | 93 | 0.4% |
| MY | 90 | 0.4% |
| TD | 88 | 0.4% |
| RU | 83 | 0.3% |
| CO | 82 | 0.3% |
click to copy
@misc{saturn-language-data-glottolog-languoid-2026,
author = {Steuber, Luke},
title = {Saturn reading: language data glottolog languoid},
year ={2026},
howpublished = {\url{https://dr.eamer.dev/saturn/view/language-data-glottolog_languoid}},
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 glottolog languoid. Source: /home/coolhand/datasets/language-data/glottolog_languoid.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-glottolog_languoid