Source: /home/coolhand/html/datavis/data_trove/data/wild/ghost_sightings.csv
Saturn profiled 10,992 rows across 12 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/wild/ghost_sightings.csv",
"--findings", "wild-ghost_sightings.json",
"--llm", "anthropic:claude-opus-4-7",
])This dataset catalogs 10,992 reported ghost sightings across the United States, with each record describing a location (city, state, latitude/longitude) and a free-text description of the sighting. Every record is in the United States (country has only 1 unique value), and California, Texas, and Pennsylvania lead the state counts — California alone holds about 9.7% of all sightings. The location column is rich with thematic words like 'school', 'cemetery', 'high', and 'house', hinting at the kinds of places people report hauntings. Worth a closer look: the geographic skew toward a few populous states, and the description field which averages ~70 words per entry and is nearly all unique — a good candidate for text mining. Note that latitude/longitude have ~11.5% nulls and a handful of outliers (including a min latitude of -45 that falls outside the US).
citing: row_count · column_count · country.top_rate · state.top_values · state.entropy_ratio · location.top_words · description.word_mean · description.n_unique · latitude.null_rate · latitude.min · longitude.n_outliers
saturn.schema() · 12 columns
| column | kind | n | null% | unique | alerts |
|---|---|---|---|---|---|
| city | text | 10,992 | 0.0% | 4,385 | one_word short_text duplicates |
| country | categorical | 10,992 | 0.0% | 1 | imbalance |
| description | text | 10,992 | 0.0% | 10,987 | near_unique |
| location | text | 10,992 | 0.0% | 9,903 | |
| state | categorical | 10,992 | 0.0% | 51 | |
| state_abbrev | categorical | 10,992 | 0.0% | 51 | |
| longitude | numeric | 10,992 | 11.5% | 8,774 | |
| latitude | numeric | 10,992 | 11.5% | 8,775 | |
| city_longitude | numeric | 10,992 | 0.3% | 5,222 | |
| city_latitude | numeric | 10,992 | 0.3% | 5,310 | |
| location_2 | text | 10,992 | 11.5% | 8,776 | allcaps |
| city_location | text | 10,992 | 0.3% | 5,311 | allcaps duplicates |
| value | count | share |
|---|---|---|
| California | 1070 | 9.7% |
| Texas | 696 | 6.3% |
| Pennsylvania | 649 | 5.9% |
| Michigan | 529 | 4.8% |
| Ohio | 477 | 4.3% |
| New York | 459 | 4.2% |
| Illinois | 395 | 3.6% |
| Kentucky | 370 | 3.4% |
| Indiana | 351 | 3.2% |
| Massachusetts | 342 | 3.1% |
| Florida | 328 | 3.0% |
| Missouri | 314 | 2.9% |
| Georgia | 289 | 2.6% |
| Wisconsin | 274 | 2.5% |
| Alabama | 224 | 2.0% |
| Tennessee | 221 | 2.0% |
| Washington | 218 | 2.0% |
| Oklahoma | 211 | 1.9% |
| North Carolina | 211 | 1.9% |
| New Jersey | 194 | 1.8% |
| chars | count |
|---|---|
| 2 – 27 | 9633 |
| 27 – 53 | 1280 |
| 53 – 78 | 57 |
| 78 – 103 | 7 |
| 103 – 129 | 0 |
| 129 – 154 | 0 |
| 154 – 179 | 0 |
| 179 – 205 | 2 |
| 205 – 230 | 2 |
| 230 – 256 | 0 |
| 256 – 281 | 2 |
| 281 – 306 | 0 |
| 306 – 332 | 0 |
| 332 – 357 | 1 |
| 357 – 382 | 0 |
| 382 – 408 | 1 |
| 408 – 433 | 0 |
| 433 – 458 | 1 |
| 458 – 484 | 0 |
| 484 – 509 | 1 |
| 509 – 534 | 0 |
| 534 – 560 | 0 |
| 560 – 585 | 0 |
| 585 – 610 | 0 |
| 610 – 636 | 0 |
| 636 – 661 | 0 |
| 661 – 686 | 0 |
| 686 – 712 | 0 |
| 712 – 737 | 0 |
| 737 – 762 | 0 |
| 762 – 788 | 0 |
| 788 – 813 | 0 |
| 813 – 839 | 0 |
| 839 – 864 | 0 |
| 864 – 889 | 0 |
| 889 – 915 | 0 |
| 915 – 940 | 1 |
| 940 – 965 | 0 |
| 965 – 991 | 0 |
| 991 – 1016 | 1 |
| bin | count |
|---|---|
| -45.02 – -42.23 | 1 |
| -42.23 – -39.43 | 0 |
| -39.43 – -36.63 | 0 |
| -36.63 – -33.83 | 1 |
| -33.83 – -31.03 | 0 |
| -31.03 – -28.24 | 0 |
| -28.24 – -25.44 | 0 |
| -25.44 – -22.64 | 0 |
| -22.64 – -19.84 | 0 |
| -19.84 – -17.04 | 0 |
| -17.04 – -14.25 | 0 |
| -14.25 – -11.45 | 0 |
| -11.45 – -8.651 | 0 |
| -8.651 – -5.853 | 0 |
| -5.853 – -3.055 | 0 |
| -3.055 – -0.2572 | 0 |
| -0.2572 – 2.541 | 0 |
| 2.541 – 5.339 | 0 |
| 5.339 – 8.137 | 0 |
| 8.137 – 10.93 | 0 |
| 10.93 – 13.73 | 1 |
| 13.73 – 16.53 | 0 |
| 16.53 – 19.33 | 0 |
| 19.33 – 22.13 | 91 |
| 22.13 – 24.92 | 5 |
| 24.92 – 27.72 | 176 |
| 27.72 – 30.52 | 509 |
| 30.52 – 33.32 | 672 |
| 33.32 – 36.12 | 1609 |
| 36.12 – 38.91 | 1506 |
| 38.91 – 41.71 | 2549 |
| 41.71 – 44.51 | 1854 |
| 44.51 – 47.31 | 561 |
| 47.31 – 50.11 | 164 |
| 50.11 – 52.9 | 2 |
| 52.9 – 55.7 | 2 |
| 55.7 – 58.5 | 0 |
| 58.5 – 61.3 | 14 |
| 61.3 – 64.09 | 4 |
| 64.09 – 66.89 | 10 |
| bin | count |
|---|---|
| -164.7 – -156.4 | 86 |
| -156.4 – -148.1 | 22 |
| -148.1 – -139.7 | 10 |
| -139.7 – -131.4 | 3 |
| -131.4 – -123 | 65 |
| -123 – -114.7 | 1371 |
| -114.7 – -106.4 | 436 |
| -106.4 – -98.04 | 590 |
| -98.04 – -89.7 | 1570 |
| -89.7 – -81.37 | 2857 |
| -81.37 – -73.03 | 2086 |
| -73.03 – -64.7 | 625 |
| -64.7 – -56.36 | 0 |
| -56.36 – -48.03 | 0 |
| -48.03 – -39.69 | 0 |
| -39.69 – -31.35 | 0 |
| -31.35 – -23.02 | 0 |
| -23.02 – -14.68 | 0 |
| -14.68 – -6.348 | 0 |
| -6.348 – 1.987 | 2 |
| 1.987 – 10.32 | 0 |
| 10.32 – 18.66 | 0 |
| 18.66 – 26.99 | 0 |
| 26.99 – 35.33 | 0 |
| 35.33 – 43.66 | 1 |
| 43.66 – 52 | 0 |
| 52 – 60.34 | 0 |
| 60.34 – 68.67 | 0 |
| 68.67 – 77.01 | 1 |
| 77.01 – 85.34 | 3 |
| 85.34 – 93.68 | 0 |
| 93.68 – 102 | 0 |
| 102 – 110.3 | 0 |
| 110.3 – 118.7 | 0 |
| 118.7 – 127 | 0 |
| 127 – 135.4 | 0 |
| 135.4 – 143.7 | 2 |
| 143.7 – 152 | 0 |
| 152 – 160.4 | 0 |
| 160.4 – 168.7 | 1 |
| chars | count |
|---|---|
| 2 – 134 | 1623 |
| 134 – 266 | 3145 |
| 266 – 398 | 2473 |
| 398 – 530 | 1528 |
| 530 – 662 | 810 |
| 662 – 794 | 509 |
| 794 – 926 | 303 |
| 926 – 1058 | 195 |
| 1058 – 1190 | 124 |
| 1190 – 1322 | 71 |
| 1322 – 1453 | 63 |
| 1453 – 1585 | 37 |
| 1585 – 1717 | 27 |
| 1717 – 1849 | 20 |
| 1849 – 1981 | 15 |
| 1981 – 2113 | 12 |
| 2113 – 2245 | 10 |
| 2245 – 2377 | 8 |
| 2377 – 2509 | 6 |
| 2509 – 2641 | 2 |
| 2641 – 2773 | 1 |
| 2773 – 2905 | 2 |
| 2905 – 3037 | 1 |
| 3037 – 3169 | 2 |
| 3169 – 3301 | 0 |
| 3301 – 3433 | 0 |
| 3433 – 3565 | 2 |
| 3565 – 3697 | 0 |
| 3697 – 3829 | 1 |
| 3829 – 3960 | 0 |
| 3960 – 4092 | 0 |
| 4092 – 4224 | 0 |
| 4224 – 4356 | 0 |
| 4356 – 4488 | 0 |
| 4488 – 4620 | 0 |
| 4620 – 4752 | 0 |
| 4752 – 4884 | 1 |
| 4884 – 5016 | 0 |
| 5016 – 5148 | 0 |
| 5148 – 5280 | 1 |
| column | kind | null % |
|---|---|---|
| city | text | 0.0% |
| country | categorical | 0.0% |
| description | text | 0.0% |
| location | text | 0.0% |
| state | categorical | 0.0% |
| state_abbrev | categorical | 0.0% |
| longitude | numeric | 11.5% |
| latitude | numeric | 11.5% |
| city_longitude | numeric | 0.3% |
| city_latitude | numeric | 0.3% |
| location_2 | text | 11.5% |
| city_location | text | 0.3% |
| longitude | latitude | city_longitude | city_latitude | |
|---|---|---|---|---|
| longitude | +1.00 | +0.27 | +0.48 | +0.18 |
| latitude | +0.27 | +1.00 | +0.16 | +0.53 |
| city_longitude | +0.48 | +0.16 | +1.00 | +0.30 |
| city_latitude | +0.18 | +0.53 | +0.30 | +1.00 |
Short place-name strings (mean 9 characters, 73% one-word) with familiar US city names like Los Angeles, San Antonio, and Houston dominating the top values. Heavy duplication (60%, 6604 rows) is expected for a city field and 4385 uniques suggests broad geographic coverage. Top word frequencies ('city', 'county', 'san', 'st.', 'new', 'fort') confirm conventional US toponymy with no emoji, URLs, or boilerplate noise.
Treatment: Treat as a categorical/geographic feature; consider geocoding or grouping rare values before encoding.
saturn.columns["city"].stats
| stat | value |
|---|---|
| n | 10,992 |
| nulls | 3 (0.0%) |
| unique | 4,385 |
| len_min | 3 |
| len_max | 49 |
| len_mean | 9.043 |
| len_median | 9 |
| len_p95 | 14 |
| word_mean | 1.291 |
| word_median | 1 |
| n_empty | 0 |
| n_duplicates | 6,604 |
| duplicate_rate | 0.601 |
| vocab_size | 3,988 |
| readability_flesch_mean | 20.49 |
| emoji_rate | 0 |
| url_rate | 9.1e-05 |
| one_word_rate | 0.7323 |
| allcaps_rate | 0.000455 |
| boilerplate_rate | 0 |
| alert: one_word | 73.2% rows are a single word |
| alert: short_text | 95th-percentile length under 20 chars |
| alert: duplicates | 60.1% duplicate strings |
city.
| chars | count |
|---|---|
| 3 – 4 | 239 |
| 4 – 5 | 615 |
| 5 – 6 | 1387 |
| 6 – 8 | 1495 |
| 8 – 9 | 1431 |
| 9 – 10 | 1548 |
| 10 – 11 | 2391 |
| 11 – 12 | 630 |
| 12 – 13 | 455 |
| 13 – 14 | 318 |
| 14 – 16 | 145 |
| 16 – 17 | 137 |
| 17 – 18 | 63 |
| 18 – 19 | 49 |
| 19 – 20 | 32 |
| 20 – 21 | 11 |
| 21 – 23 | 7 |
| 23 – 24 | 5 |
| 24 – 25 | 5 |
| 25 – 26 | 5 |
| 26 – 27 | 10 |
| 27 – 28 | 0 |
| 28 – 29 | 6 |
| 29 – 31 | 1 |
| 31 – 32 | 1 |
| 32 – 33 | 0 |
| 33 – 34 | 1 |
| 34 – 35 | 0 |
| 35 – 36 | 0 |
| 36 – 38 | 0 |
| 38 – 39 | 0 |
| 39 – 40 | 0 |
| 40 – 41 | 0 |
| 41 – 42 | 1 |
| 42 – 43 | 0 |
| 43 – 44 | 0 |
| 44 – 46 | 0 |
| 46 – 47 | 0 |
| 47 – 48 | 0 |
| 48 – 49 | 1 |
This column records country, but every one of the 10,992 rows is "United States" — cardinality is 1 and top_rate is 1.0. It carries zero information (entropy 0.0) and cannot discriminate between records.
Treatment: Drop; constant column with no signal.
saturn.columns["country"].stats
| stat | value |
|---|---|
| n | 10,992 |
| nulls | 0 (0.0%) |
| unique | 1 |
| top_value | United States |
| top_rate | 1 |
| cardinality | 1 |
| entropy | 0 |
| entropy_ratio | 0 |
| alert: imbalance | top value is 100.0% of rows |
country.
| value | count | share |
|---|---|---|
| United States | 10992 | 100.0% |
Free-text descriptions, averaging 70 words (median 55) and reaching up to 5,280 characters, with a Flesch readability of ~69.7 indicating fairly accessible prose. Nearly every row is unique (10,987 of 10,992) with only 5 duplicates and a vocabulary of 33,001 tokens, so this reads as bespoke long-form copy rather than templated text. Boilerplate (0.6%), URLs (0.5%), all-caps (0.06%) and emoji (0%) are all negligible, and the top words are common English stopwords — consistent with natural English narrative.
Treatment: Tokenize and embed before modelling; do not use as a categorical key.
saturn.columns["description"].stats
| stat | value |
|---|---|
| n | 10,992 |
| nulls | 0 (0.0%) |
| unique | 10,987 |
| len_min | 2 |
| len_max | 5,280 |
| len_mean | 380 |
| len_median | 297 |
| len_p95 | 954 |
| word_mean | 70.33 |
| word_median | 55 |
| n_empty | 0 |
| n_duplicates | 5 |
| duplicate_rate | 0.0004549 |
| vocab_size | 33,001 |
| readability_flesch_mean | 69.67 |
| emoji_rate | 0 |
| url_rate | 0.005095 |
| one_word_rate | 0.000182 |
| allcaps_rate | 0.0006368 |
| boilerplate_rate | 0.006459 |
| alert: near_unique | 100.0% of rows are unique strings |
description.
| chars | count |
|---|---|
| 2 – 134 | 1623 |
| 134 – 266 | 3145 |
| 266 – 398 | 2473 |
| 398 – 530 | 1528 |
| 530 – 662 | 810 |
| 662 – 794 | 509 |
| 794 – 926 | 303 |
| 926 – 1058 | 195 |
| 1058 – 1190 | 124 |
| 1190 – 1322 | 71 |
| 1322 – 1453 | 63 |
| 1453 – 1585 | 37 |
| 1585 – 1717 | 27 |
| 1717 – 1849 | 20 |
| 1849 – 1981 | 15 |
| 1981 – 2113 | 12 |
| 2113 – 2245 | 10 |
| 2245 – 2377 | 8 |
| 2377 – 2509 | 6 |
| 2509 – 2641 | 2 |
| 2641 – 2773 | 1 |
| 2773 – 2905 | 2 |
| 2905 – 3037 | 1 |
| 3037 – 3169 | 2 |
| 3169 – 3301 | 0 |
| 3301 – 3433 | 0 |
| 3433 – 3565 | 2 |
| 3565 – 3697 | 0 |
| 3697 – 3829 | 1 |
| 3829 – 3960 | 0 |
| 3960 – 4092 | 0 |
| 4092 – 4224 | 0 |
| 4224 – 4356 | 0 |
| 4356 – 4488 | 0 |
| 4488 – 4620 | 0 |
| 4620 – 4752 | 0 |
| 4752 – 4884 | 1 |
| 4884 – 5016 | 0 |
| 5016 – 5148 | 0 |
| 5148 – 5280 | 1 |
Short free-text place names averaging 2.98 words and 19.3 characters, with values like 'Prince Georges county', 'Cemetery', 'Cry Baby Bridge', and 'Wal-Mart' — likely the named site of a story or sighting (probably ghost/folklore lore given top entries). Vocabulary is dominated by 'school', 'cemetery', 'high', 'university', 'house', 'road', suggesting a strong tilt toward institutional and roadside locations. High cardinality (9903 unique of 10992) coexists with a 9.9% duplicate rate and one value ('Prince Georges county') hit 18 times, so most entries are unique strings but a long tail of repeated landmarks exists.
Treatment: Normalise casing/whitespace and geocode or entity-link to extract structured place features before modelling.
saturn.columns["location"].stats
| stat | value |
|---|---|
| n | 10,992 |
| nulls | 3 (0.0%) |
| unique | 9,903 |
| len_min | 2 |
| len_max | 1,016 |
| len_mean | 19.3 |
| len_median | 17 |
| len_p95 | 34 |
| word_mean | 2.976 |
| word_median | 3 |
| n_empty | 0 |
| n_duplicates | 1,086 |
| duplicate_rate | 0.09883 |
| vocab_size | 8,232 |
| readability_flesch_mean | 45.79 |
| emoji_rate | 0 |
| url_rate | 0 |
| one_word_rate | 0.06334 |
| allcaps_rate | 0.003367 |
| boilerplate_rate | 0 |
location.
| chars | count |
|---|---|
| 2 – 27 | 9633 |
| 27 – 53 | 1280 |
| 53 – 78 | 57 |
| 78 – 103 | 7 |
| 103 – 129 | 0 |
| 129 – 154 | 0 |
| 154 – 179 | 0 |
| 179 – 205 | 2 |
| 205 – 230 | 2 |
| 230 – 256 | 0 |
| 256 – 281 | 2 |
| 281 – 306 | 0 |
| 306 – 332 | 0 |
| 332 – 357 | 1 |
| 357 – 382 | 0 |
| 382 – 408 | 1 |
| 408 – 433 | 0 |
| 433 – 458 | 1 |
| 458 – 484 | 0 |
| 484 – 509 | 1 |
| 509 – 534 | 0 |
| 534 – 560 | 0 |
| 560 – 585 | 0 |
| 585 – 610 | 0 |
| 610 – 636 | 0 |
| 636 – 661 | 0 |
| 661 – 686 | 0 |
| 686 – 712 | 0 |
| 712 – 737 | 0 |
| 737 – 762 | 0 |
| 762 – 788 | 0 |
| 788 – 813 | 0 |
| 813 – 839 | 0 |
| 839 – 864 | 0 |
| 864 – 889 | 0 |
| 889 – 915 | 0 |
| 915 – 940 | 1 |
| 940 – 965 | 0 |
| 965 – 991 | 0 |
| 991 – 1016 | 1 |
This is a US state field with 51 distinct values (likely the 50 states plus DC) across 10,992 rows and no nulls. Distribution is broad with high entropy ratio (0.916); California leads at 9.73% (1,070 rows), followed by Texas (696) and Pennsylvania (649), roughly tracking population except Pennsylvania ranking unusually high above Florida (which doesn't appear in the top 10).
Treatment: One-hot or target-encode for modelling; consider grouping low-frequency states.
saturn.columns["state"].stats
| stat | value |
|---|---|
| n | 10,992 |
| nulls | 0 (0.0%) |
| unique | 51 |
| top_value | California |
| top_rate | 0.09734 |
| cardinality | 51 |
| entropy | 5.194 |
| entropy_ratio | 0.9157 |
state.
| value | count | share |
|---|---|---|
| California | 1070 | 9.7% |
| Texas | 696 | 6.3% |
| Pennsylvania | 649 | 5.9% |
| Michigan | 529 | 4.8% |
| Ohio | 477 | 4.3% |
| New York | 459 | 4.2% |
| Illinois | 395 | 3.6% |
| Kentucky | 370 | 3.4% |
| Indiana | 351 | 3.2% |
| Massachusetts | 342 | 3.1% |
| Florida | 328 | 3.0% |
| Missouri | 314 | 2.9% |
| Georgia | 289 | 2.6% |
| Wisconsin | 274 | 2.5% |
| Alabama | 224 | 2.0% |
| Tennessee | 221 | 2.0% |
| Washington | 218 | 2.0% |
| Oklahoma | 211 | 1.9% |
| North Carolina | 211 | 1.9% |
| New Jersey | 194 | 1.8% |
This is a US state abbreviation field with 51 distinct values (50 states plus presumably DC) and no nulls across 10,992 rows. Distribution is broadly spread (entropy ratio 0.916) with CA leading at 9.7% (1,070 rows), followed by TX (696) and PA (649), consistent with population-weighted geographic data.
Treatment: one-hot or target-encode for modelling; safe to use as-is for grouping or joins.
saturn.columns["state_abbrev"].stats
| stat | value |
|---|---|
| n | 10,992 |
| nulls | 0 (0.0%) |
| unique | 51 |
| top_value | CA |
| top_rate | 0.09734 |
| cardinality | 51 |
| entropy | 5.194 |
| entropy_ratio | 0.9157 |
state_abbrev.
| value | count | share |
|---|---|---|
| CA | 1070 | 9.7% |
| TX | 696 | 6.3% |
| PA | 649 | 5.9% |
| MI | 529 | 4.8% |
| OH | 477 | 4.3% |
| NY | 459 | 4.2% |
| IL | 395 | 3.6% |
| KY | 370 | 3.4% |
| IN | 351 | 3.2% |
| MA | 342 | 3.1% |
| FL | 328 | 3.0% |
| MO | 314 | 2.9% |
| GA | 289 | 2.6% |
| WI | 274 | 2.5% |
| AL | 224 | 2.0% |
| TN | 221 | 2.0% |
| WA | 218 | 2.0% |
| OK | 211 | 1.9% |
| NC | 211 | 1.9% |
| NJ | 194 | 1.8% |
Geographic longitude coordinates, with 8774 unique values across 10992 rows and an 11.47% null rate. Values span -164.72 to 168.70 but cluster tightly between Q1 -99.12 and Q3 -80.30, indicating most records sit in North America despite the global range. Kurtosis of 15.2 confirms heavy tails from the 131 outliers (1.35%) reaching across the Pacific.
Treatment: Pair with latitude for geospatial features; impute or filter the 11.47% nulls before mapping.
saturn.columns["longitude"].stats
| stat | value |
|---|---|
| n | 10,992 |
| nulls | 1,261 (11.5%) |
| unique | 8,774 |
| min | -164.7 |
| max | 168.7 |
| mean | -92 |
| median | -87.23 |
| std | 17.69 |
| q1 | -99.12 |
| q3 | -80.3 |
| iqr | 18.82 |
| skew | 0.2512 |
| kurtosis | 15.2 |
| n_outliers | 131 |
| outlier_rate | 0.01346 |
| zero_rate | 0 |
longitude. Vertical dash marks the median.
| bin | count |
|---|---|
| -164.7 – -156.4 | 86 |
| -156.4 – -148.1 | 22 |
| -148.1 – -139.7 | 10 |
| -139.7 – -131.4 | 3 |
| -131.4 – -123 | 65 |
| -123 – -114.7 | 1371 |
| -114.7 – -106.4 | 436 |
| -106.4 – -98.04 | 590 |
| -98.04 – -89.7 | 1570 |
| -89.7 – -81.37 | 2857 |
| -81.37 – -73.03 | 2086 |
| -73.03 – -64.7 | 625 |
| -64.7 – -56.36 | 0 |
| -56.36 – -48.03 | 0 |
| -48.03 – -39.69 | 0 |
| -39.69 – -31.35 | 0 |
| -31.35 – -23.02 | 0 |
| -23.02 – -14.68 | 0 |
| -14.68 – -6.348 | 0 |
| -6.348 – 1.987 | 2 |
| 1.987 – 10.32 | 0 |
| 10.32 – 18.66 | 0 |
| 18.66 – 26.99 | 0 |
| 26.99 – 35.33 | 0 |
| 35.33 – 43.66 | 1 |
| 43.66 – 52 | 0 |
| 52 – 60.34 | 0 |
| 60.34 – 68.67 | 0 |
| 68.67 – 77.01 | 1 |
| 77.01 – 85.34 | 3 |
| 85.34 – 93.68 | 0 |
| 93.68 – 102 | 0 |
| 102 – 110.3 | 0 |
| 110.3 – 118.7 | 0 |
| 118.7 – 127 | 0 |
| 127 – 135.4 | 0 |
| 135.4 – 143.7 | 2 |
| 143.7 – 152 | 0 |
| 152 – 160.4 | 0 |
| 160.4 – 168.7 | 1 |
Geographic latitude in decimal degrees, ranging from -45.02 to 66.89 with a median of 39.28 and IQR of 7.20, suggesting most observations cluster in the northern mid-latitudes. The distribution is left-skewed (-0.97) with heavy tails (kurtosis 11.38) and 124 outliers (1.27%), consistent with a few southern-hemisphere points dragging the tail. Note the 11.47% null rate, which will need handling before any spatial analysis.
Treatment: Pair with longitude for geospatial features; impute or drop the ~11% nulls before modelling.
saturn.columns["latitude"].stats
| stat | value |
|---|---|
| n | 10,992 |
| nulls | 1,261 (11.5%) |
| unique | 8,775 |
| min | -45.02 |
| max | 66.89 |
| mean | 38.34 |
| median | 39.28 |
| std | 5.259 |
| q1 | 34.68 |
| q3 | 41.87 |
| iqr | 7.197 |
| skew | -0.971 |
| kurtosis | 11.38 |
| n_outliers | 124 |
| outlier_rate | 0.01274 |
| zero_rate | 0 |
latitude. Vertical dash marks the median.
| bin | count |
|---|---|
| -45.02 – -42.23 | 1 |
| -42.23 – -39.43 | 0 |
| -39.43 – -36.63 | 0 |
| -36.63 – -33.83 | 1 |
| -33.83 – -31.03 | 0 |
| -31.03 – -28.24 | 0 |
| -28.24 – -25.44 | 0 |
| -25.44 – -22.64 | 0 |
| -22.64 – -19.84 | 0 |
| -19.84 – -17.04 | 0 |
| -17.04 – -14.25 | 0 |
| -14.25 – -11.45 | 0 |
| -11.45 – -8.651 | 0 |
| -8.651 – -5.853 | 0 |
| -5.853 – -3.055 | 0 |
| -3.055 – -0.2572 | 0 |
| -0.2572 – 2.541 | 0 |
| 2.541 – 5.339 | 0 |
| 5.339 – 8.137 | 0 |
| 8.137 – 10.93 | 0 |
| 10.93 – 13.73 | 1 |
| 13.73 – 16.53 | 0 |
| 16.53 – 19.33 | 0 |
| 19.33 – 22.13 | 91 |
| 22.13 – 24.92 | 5 |
| 24.92 – 27.72 | 176 |
| 27.72 – 30.52 | 509 |
| 30.52 – 33.32 | 672 |
| 33.32 – 36.12 | 1609 |
| 36.12 – 38.91 | 1506 |
| 38.91 – 41.71 | 2549 |
| 41.71 – 44.51 | 1854 |
| 44.51 – 47.31 | 561 |
| 47.31 – 50.11 | 164 |
| 50.11 – 52.9 | 2 |
| 52.9 – 55.7 | 2 |
| 55.7 – 58.5 | 0 |
| 58.5 – 61.3 | 14 |
| 61.3 – 64.09 | 4 |
| 64.09 – 66.89 | 10 |
Longitude of a city, with all 10992 values negative (min -164.72, max -67.84), placing every record in the Western Hemisphere and consistent with North American coverage. The distribution is left-skewed (skew -1.16) with median -87.09 pulled east of the mean -91.91, suggesting a concentration in the central/eastern US with a tail reaching toward Alaska or the Pacific. 5222 unique values across 10992 rows indicate many records share the same city, and 128 outliers (1.17%) likely correspond to far-western locations.
Treatment: Use as a geospatial feature alongside latitude; consider clustering or binning rather than treating as a plain scalar.
saturn.columns["city_longitude"].stats
| stat | value |
|---|---|
| n | 10,992 |
| nulls | 29 (0.3%) |
| unique | 5,222 |
| min | -164.7 |
| max | -67.84 |
| mean | -91.91 |
| median | -87.09 |
| std | 16.4 |
| q1 | -98.49 |
| q3 | -80.51 |
| iqr | 17.99 |
| skew | -1.155 |
| kurtosis | 1.328 |
| n_outliers | 128 |
| outlier_rate | 0.01168 |
| zero_rate | 0 |
city_longitude. Vertical dash marks the median.
| bin | count |
|---|---|
| -164.7 – -162.3 | 2 |
| -162.3 – -159.9 | 0 |
| -159.9 – -157.5 | 83 |
| -157.5 – -155 | 14 |
| -155 – -152.6 | 0 |
| -152.6 – -150.2 | 3 |
| -150.2 – -147.8 | 13 |
| -147.8 – -145.3 | 5 |
| -145.3 – -142.9 | 4 |
| -142.9 – -140.5 | 0 |
| -140.5 – -138.1 | 0 |
| -138.1 – -135.7 | 0 |
| -135.7 – -133.2 | 2 |
| -133.2 – -130.8 | 2 |
| -130.8 – -128.4 | 0 |
| -128.4 – -126 | 0 |
| -126 – -123.5 | 33 |
| -123.5 – -121.1 | 529 |
| -121.1 – -118.7 | 249 |
| -118.7 – -116.3 | 661 |
| -116.3 – -113.9 | 83 |
| -113.9 – -111.4 | 245 |
| -111.4 – -109 | 92 |
| -109 – -106.6 | 96 |
| -106.6 – -104.2 | 260 |
| -104.2 – -101.7 | 93 |
| -101.7 – -99.33 | 159 |
| -99.33 – -96.91 | 530 |
| -96.91 – -94.48 | 545 |
| -94.48 – -92.06 | 492 |
| -92.06 – -89.64 | 447 |
| -89.64 – -87.22 | 792 |
| -87.22 – -84.79 | 877 |
| -84.79 – -82.37 | 1252 |
| -82.37 – -79.95 | 912 |
| -79.95 – -77.53 | 420 |
| -77.53 – -75.11 | 825 |
| -75.11 – -72.68 | 649 |
| -72.68 – -70.26 | 548 |
| -70.26 – -67.84 | 46 |
This column holds city latitudes in decimal degrees, ranging from 19.58 to 66.90 with a median of 39.28 and an IQR spanning 34.74 to 41.85. Values cluster firmly in the northern mid-latitudes (mean 38.38, std 5.07), with mild left skew (-0.32) and 128 outliers (1.17%) likely representing far-northern locales. Cardinality is moderate (5310 unique of 10992) and nulls are negligible (0.26%).
Treatment: Pair with longitude for geospatial features; avoid treating as a standalone scalar.
saturn.columns["city_latitude"].stats
| stat | value |
|---|---|
| n | 10,992 |
| nulls | 29 (0.3%) |
| unique | 5,310 |
| min | 19.58 |
| max | 66.9 |
| mean | 38.38 |
| median | 39.28 |
| std | 5.067 |
| q1 | 34.74 |
| q3 | 41.85 |
| iqr | 7.109 |
| skew | -0.325 |
| kurtosis | 1.478 |
| n_outliers | 128 |
| outlier_rate | 0.01168 |
| zero_rate | 0 |
city_latitude. Vertical dash marks the median.
| bin | count |
|---|---|
| 19.58 – 20.76 | 5 |
| 20.76 – 21.94 | 90 |
| 21.94 – 23.12 | 1 |
| 23.12 – 24.31 | 0 |
| 24.31 – 25.49 | 7 |
| 25.49 – 26.67 | 106 |
| 26.67 – 27.86 | 114 |
| 27.86 – 29.04 | 145 |
| 29.04 – 30.22 | 256 |
| 30.22 – 31.41 | 222 |
| 31.41 – 32.59 | 369 |
| 32.59 – 33.77 | 569 |
| 33.77 – 34.96 | 988 |
| 34.96 – 36.14 | 560 |
| 36.14 – 37.32 | 666 |
| 37.32 – 38.5 | 703 |
| 38.5 – 39.69 | 1004 |
| 39.69 – 40.87 | 1341 |
| 40.87 – 42.05 | 1276 |
| 42.05 – 43.24 | 1258 |
| 43.24 – 44.42 | 401 |
| 44.42 – 45.6 | 387 |
| 45.6 – 46.79 | 184 |
| 46.79 – 47.97 | 220 |
| 47.97 – 49.15 | 59 |
| 49.15 – 50.34 | 0 |
| 50.34 – 51.52 | 0 |
| 51.52 – 52.7 | 0 |
| 52.7 – 53.88 | 0 |
| 53.88 – 55.07 | 0 |
| 55.07 – 56.25 | 2 |
| 56.25 – 57.43 | 1 |
| 57.43 – 58.62 | 0 |
| 58.62 – 59.8 | 1 |
| 59.8 – 60.98 | 5 |
| 60.98 – 62.17 | 13 |
| 62.17 – 63.35 | 1 |
| 63.35 – 64.53 | 0 |
| 64.53 – 65.72 | 6 |
| 65.72 – 66.9 | 3 |
This column holds WKT geometry strings in the form `POINT(lon lat)`, with 8776 unique values across 10992 rows and an 11.47% null rate. Values are tightly bounded (length 22-43, always two 'words') and 9.81% are duplicates, with the most repeated coordinate appearing 20 times — suggesting clusters at recurring sites. The 'allcaps' alert is a false positive driven by the literal `POINT` prefix rather than natural language.
Treatment: Parse into numeric longitude/latitude pair before any geospatial modelling.
saturn.columns["location_2"].stats
| stat | value |
|---|---|
| n | 10,992 |
| nulls | 1,261 (11.5%) |
| unique | 8,776 |
| len_min | 22 |
| len_max | 43 |
| len_mean | 30.81 |
| len_median | 29 |
| len_p95 | 36 |
| word_mean | 2 |
| word_median | 2 |
| n_empty | 0 |
| n_duplicates | 955 |
| duplicate_rate | 0.09814 |
| vocab_size | 17,549 |
| readability_flesch_mean | 120.2 |
| emoji_rate | 0 |
| url_rate | 0 |
| one_word_rate | 0 |
| allcaps_rate | 1 |
| boilerplate_rate | 0 |
| alert: allcaps | 100.0% rows are all-caps |
location_2.
| chars | count |
|---|---|
| 22 – 23 | 1 |
| 23 – 23 | 8 |
| 23 – 24 | 0 |
| 24 – 24 | 2 |
| 24 – 25 | 0 |
| 25 – 25 | 20 |
| 25 – 26 | 0 |
| 26 – 26 | 104 |
| 26 – 27 | 0 |
| 27 – 27 | 679 |
| 27 – 28 | 0 |
| 28 – 28 | 1007 |
| 28 – 29 | 0 |
| 29 – 29 | 3740 |
| 29 – 30 | 0 |
| 30 – 30 | 1567 |
| 30 – 31 | 0 |
| 31 – 31 | 0 |
| 31 – 32 | 0 |
| 32 – 32 | 1 |
| 32 – 33 | 13 |
| 33 – 34 | 0 |
| 34 – 34 | 54 |
| 34 – 35 | 0 |
| 35 – 35 | 453 |
| 35 – 36 | 0 |
| 36 – 36 | 1807 |
| 36 – 37 | 0 |
| 37 – 37 | 114 |
| 37 – 38 | 0 |
| 38 – 38 | 0 |
| 38 – 39 | 0 |
| 39 – 39 | 0 |
| 39 – 40 | 0 |
| 40 – 40 | 0 |
| 40 – 41 | 0 |
| 41 – 41 | 0 |
| 41 – 42 | 0 |
| 42 – 42 | 0 |
| 42 – 43 | 161 |
Despite the name, city_location stores WKT POINT(longitude latitude) geometry strings, not city names — every value matches that format (allcaps_rate 1.0, word_mean 2.0). Roughly half the rows are repeats (duplicate_rate 0.516, n_duplicates 5652) with the top coordinate (Los Angeles, 34.05/-118.24) appearing 61 times, suggesting a finite set of city centroids reused across records. Cardinality is still high (5311 unique of 10992) and nulls are negligible (0.26%).
Treatment: Parse the POINT() strings into numeric lat/long pairs before any geospatial modelling.
saturn.columns["city_location"].stats
| stat | value |
|---|---|
| n | 10,992 |
| nulls | 29 (0.3%) |
| unique | 5,311 |
| len_min | 22 |
| len_max | 43 |
| len_mean | 31.19 |
| len_median | 29 |
| len_p95 | 36 |
| word_mean | 2 |
| word_median | 2 |
| n_empty | 0 |
| n_duplicates | 5,652 |
| duplicate_rate | 0.5156 |
| vocab_size | 10,532 |
| readability_flesch_mean | 120.2 |
| emoji_rate | 0 |
| url_rate | 0 |
| one_word_rate | 0 |
| allcaps_rate | 1 |
| boilerplate_rate | 0 |
| alert: allcaps | 100.0% rows are all-caps |
| alert: duplicates | 51.6% duplicate strings |
city_location.
| chars | count |
|---|---|
| 22 – 23 | 2 |
| 23 – 23 | 0 |
| 23 – 24 | 0 |
| 24 – 24 | 1 |
| 24 – 25 | 0 |
| 25 – 25 | 2 |
| 25 – 26 | 0 |
| 26 – 26 | 45 |
| 26 – 27 | 0 |
| 27 – 27 | 158 |
| 27 – 28 | 0 |
| 28 – 28 | 890 |
| 28 – 29 | 0 |
| 29 – 29 | 4648 |
| 29 – 30 | 0 |
| 30 – 30 | 1988 |
| 30 – 31 | 0 |
| 31 – 31 | 0 |
| 31 – 32 | 0 |
| 32 – 32 | 1 |
| 32 – 33 | 1 |
| 33 – 34 | 0 |
| 34 – 34 | 21 |
| 34 – 35 | 0 |
| 35 – 35 | 238 |
| 35 – 36 | 0 |
| 36 – 36 | 2711 |
| 36 – 37 | 0 |
| 37 – 37 | 116 |
| 37 – 38 | 0 |
| 38 – 38 | 0 |
| 38 – 39 | 0 |
| 39 – 39 | 0 |
| 39 – 40 | 0 |
| 40 – 40 | 0 |
| 40 – 41 | 0 |
| 41 – 41 | 0 |
| 41 – 42 | 0 |
| 42 – 42 | 0 |
| 42 – 43 | 141 |
click to copy
@misc{saturn-wild-ghost-sightings-2026,
author = {Steuber, Luke},
title = {Saturn reading: wild ghost sightings},
year ={2026},
howpublished = {\url{https://dr.eamer.dev/saturn/view/wild-ghost_sightings}},
note = {Profiled with saturn-dissect v0.2.0, prompt saturn-insight-v2, model anthropic:claude-opus-4-7},
}
Steuber, L. (2026). Saturn reading: wild ghost sightings. Source: /home/coolhand/html/datavis/data_trove/data/wild/ghost_sightings.csv. Profiled with saturn-dissect v0.2.0 (saturn-insight-v2, anthropic:claude-opus-4-7). Retrieved from https://dr.eamer.dev/saturn/view/wild-ghost_sightings