saturn

/home/coolhand/html/datavis/data_trove/data/wild/disasters/airplane_crashes.csv 5,268 rows sample n=5,268 seed 42 2026-05-01T18:06:16+00:00

Overview

Source/home/coolhand/html/datavis/data_trove/data/wild/disasters/airplane_crashes.csv
Total rows5,268
Profiled sample5,268
Columns13
Generated2026-05-01T18:06:16+00:00

Insights opt-in

Model-generated narrative. These are opinions, not facts — the stats below are what saturn measured. Generated by: anthropic:claude-opus-4-7.

Errors during insight pass (14)
  • dataset:__global__:anthropic:claude-opus-4-7: BadRequestError — Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.'}, 'request_id': 'req_011CacHxYGjsqx97AxeRTMRS'}
  • column:Date:anthropic:claude-opus-4-7: BadRequestError — Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.'}, 'request_id': 'req_011CacHxYin6pDJ5x6chVx2y'}
  • column:Time:anthropic:claude-opus-4-7: BadRequestError — Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.'}, 'request_id': 'req_011CacHxZJ12D9RY5eVPEK56'}
  • column:Location:anthropic:claude-opus-4-7: BadRequestError — Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.'}, 'request_id': 'req_011CacHxZj3waTG1cmJyTwZw'}
  • column:Operator:anthropic:claude-opus-4-7: BadRequestError — Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.'}, 'request_id': 'req_011CacHxaGnrhbp83hLWJezd'}
  • column:Flight #:anthropic:claude-opus-4-7: BadRequestError — Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.'}, 'request_id': 'req_011CacHxaq2agSjrwoqQyFbA'}
  • column:Route:anthropic:claude-opus-4-7: BadRequestError — Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.'}, 'request_id': 'req_011CacHxbMXjUSVHHc8EG3Zm'}
  • column:Type:anthropic:claude-opus-4-7: BadRequestError — Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.'}, 'request_id': 'req_011CacHxc3wue5WjnK5j5HoH'}
  • column:Registration:anthropic:claude-opus-4-7: BadRequestError — Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.'}, 'request_id': 'req_011CacHxcgu1gAdJQ9i2meZU'}
  • column:cn/In:anthropic:claude-opus-4-7: BadRequestError — Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.'}, 'request_id': 'req_011CacHxdCehCp8zCeFQDSBB'}
  • column:Aboard:anthropic:claude-opus-4-7: BadRequestError — Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.'}, 'request_id': 'req_011CacHxdfBqvsFvRp2c2x9W'}
  • column:Fatalities:anthropic:claude-opus-4-7: BadRequestError — Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.'}, 'request_id': 'req_011CacHxerNMUmd4n3LQZbiR'}
  • column:Ground:anthropic:claude-opus-4-7: BadRequestError — Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.'}, 'request_id': 'req_011CacHxffyhMEymYHjx7nam'}
  • column:Summary:anthropic:claude-opus-4-7: BadRequestError — Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.'}, 'request_id': 'req_011CacHxg7mgau6UxZGA7Yna'}

Numeric correlation

Languages detected

Per-string language detection across text columns (sampled).

Date text

100.0% rows are a single word 100.0% rows are all-caps 95th-percentile length under 20 chars
rows5,268
null0 (0.0%)
unique4,753
len_min10
len_max10
len_mean10.000
len_median10.000
len_p9510.000
word_mean1.000
word_median1.000
n_empty0
n_duplicates515
duplicate_rate0.098
vocab_size4,753
readability_flesch_mean121.220
emoji_rate0.000
url_rate0.000
one_word_rate1.000
allcaps_rate1.000
boilerplate_rate0.000
Sample values (first 10)
  1. 09/03/1915
  2. 07/02/1990
  3. 12/05/1997
  4. 01/14/1995
  5. 03/09/1968
  6. 03/07/2006
  7. 12/31/1968
  8. 03/17/2000
  9. 04/23/1995
  10. 06/29/1929

Time text

99.9% rows are a single word 99.7% rows are all-caps 42.1% null 95th-percentile length under 20 chars 67.0% duplicate strings
rows5,268
null2,219 (42.1%)
unique1,005
len_min4
len_max7
len_mean5.003
len_median5.000
len_p955.000
word_mean1.001
word_median1.000
n_empty0
n_duplicates2,044
duplicate_rate0.670
vocab_size1,004
readability_flesch_mean121.215
emoji_rate0.000
url_rate0.000
one_word_rate0.999
allcaps_rate0.997
boilerplate_rate0.000
Sample values (first 10)
  1. 10:30
  2. 22:00
  3. 18:21
  4. 09:40
  5. 12:45
  6. 05:08
  7. 07:34
  8. 09:32
  9. 17:54
  10. 16:35

Location text

rows5,268
null20 (0.4%)
unique4,303
len_min5
len_max60
len_mean20.380
len_median19.000
len_p9531.000
word_mean2.866
word_median3.000
n_empty0
n_duplicates945
duplicate_rate0.180
vocab_size4,541
readability_flesch_mean24.031
emoji_rate0.000
url_rate0.000
one_word_rate0.011
allcaps_rate0.000
boilerplate_rate0.000
Sample values (first 10)
  1. Off Cuxhaven, Germany
  2. Near Port Morseby, New Guinea
  3. Little Grand Rapids, Canada
  4. Kathmandu, Nepal
  5. St. Louis, Missouri
  6. Labiano, Spain
  7. Near Bradford, Pennsylvania
  8. Ennadai Lake, Canada
  9. Near Palaly AFB, Sri Lanka
  10. Lake Constance, Switzerland

Operator text

31 languages detected in sample 52.8% duplicate strings
rows5,268
null18 (0.3%)
unique2,476
len_min3
len_max65
len_mean19.494
len_median19.000
len_p9535.000
word_mean3.047
word_median3.000
n_empty0
n_duplicates2,774
duplicate_rate0.528
vocab_size2,370
readability_flesch_mean19.611
emoji_rate0.000
url_rate0.000
one_word_rate0.165
allcaps_rate0.037
boilerplate_rate0.000
Sample values (first 10)
  1. Military - German Navy
  2. Eagle Air
  3. Military - Russian Air Force
  4. Air Taxi - Wolfe Air Aviation Ltd.
  5. Military - Russian Air Force
  6. TriCoastal Air
  7. China Air Lines
  8. Aeroperlas
  9. Bristow Helicopters
  10. Deutsche Lufthansa

Flight # categorical

543 singleton categories 79.7% null
rows5,268
null4,199 (79.7%)
unique724
top_value-
top_rate0.063
cardinality724
entropy9.058
entropy_ratio0.953
Top values (rank 1–20)
  1. - — 67
  2. 1 — 10
  3. 4 — 7
  4. 6 — 6
  5. 21 — 6
  6. 101 — 6
  7. 901 — 6
  8. 7 — 5
  9. 201 — 5
  10. 701 — 5
  11. 706 — 5
  12. 703 — 5
  13. 2 — 4
  14. 203 — 4
  15. 304 — 4
  16. 601 — 4
  17. 514 — 4
  18. 11 — 4
  19. 217 — 4
  20. 114 — 4

Route text

31 languages detected in sample 32.4% null
rows5,268
null1,706 (32.4%)
unique3,244
len_min4
len_max59
len_mean22.088
len_median20.000
len_p9537.000
word_mean4.065
word_median4.000
n_empty0
n_duplicates318
duplicate_rate0.089
vocab_size3,647
readability_flesch_mean27.155
emoji_rate0.000
url_rate0.000
one_word_rate0.041
allcaps_rate2.81e-04
boilerplate_rate0.000
Sample values (first 10)
  1. Lympne, England - Rotterdam, The Netherlands
  2. Isfahan - Terhan
  3. Mexico City - Reynosa - Matamoros
  4. Anchorage, AK - Hoholitna River, AK
  5. Panchkhal - Tribuvan
  6. Kongolo - Goma
  7. Honolulu - Lihue
  8. Jomsom - Pokhara
  9. Jaffna - Colombo
  10. El Paso, TX - Pueblo, CO

Type text

53.3% duplicate strings
rows5,268
null27 (0.5%)
unique2,446
len_min4
len_max40
len_mean18.326
len_median16.000
len_p9534.000
word_mean2.718
word_median2.000
n_empty0
n_duplicates2,795
duplicate_rate0.533
vocab_size2,534
readability_flesch_mean69.259
emoji_rate0.000
url_rate0.000
one_word_rate7.44e-03
allcaps_rate9.54e-03
boilerplate_rate0.000
Sample values (first 10)
  1. Zeppelin L-10 (airship)
  2. Beech King Air B90
  3. Swearingen SA-226T Metro II
  4. de Havilland Canada DHC-6 Twin Otter 300
  5. Bell UH-1H / Bell UH-1H (helicopter)
  6. Swearingen SA.226TC Metro II
  7. Handley Page Dart Herald 201
  8. de Havilland Canada DHC-6 Twin Otter 300
  9. Hawker Siddeley HS-748-357/2B SCD
  10. Lockheed Vega

Registration text

99.4% of rows are unique strings 99.0% rows are a single word 99.2% rows are all-caps 95th-percentile length under 20 chars
rows5,268
null335 (6.4%)
unique4,905
len_min1
len_max15
len_mean6.394
len_median6.000
len_p9510.000
word_mean1.018
word_median1.000
n_empty0
n_duplicates28
duplicate_rate5.68e-03
vocab_size4,948
readability_flesch_mean103.026
emoji_rate0.000
url_rate0.000
one_word_rate0.990
allcaps_rate0.992
boilerplate_rate0.000
Sample values (first 10)
  1. 77
  2. FAC-1150
  3. HP-986PS
  4. 4R-HVA
  5. PP-SAD
  6. P4-AOD
  7. PI-C1131
  8. LV-ZSR
  9. RA-65617
  10. P-BALSA

cn/In text

98.4% rows are a single word 96.6% rows are all-caps 23.3% null 95th-percentile length under 20 chars
rows5,268
null1,228 (23.3%)
unique3,707
len_min1
len_max20
len_mean5.645
len_median5.000
len_p9510.000
word_mean1.026
word_median1.000
n_empty0
n_duplicates333
duplicate_rate0.082
vocab_size3,739
readability_flesch_mean121.205
emoji_rate0.000
url_rate0.000
one_word_rate0.984
allcaps_rate0.966
boilerplate_rate0.000
Sample values (first 10)
  1. HP-25
  2. 24805/1878
  3. 12
  4. 10670
  5. 20436/788
  6. 742
  7. 3817
  8. 45108
  9. 31-033B
  10. 1957

Aboard numeric

skew=+4.25 10.1% rows beyond 1.5 IQR
rows5,268
null22 (0.4%)
unique239
min0.000
max644.000
mean27.555
median13.000
std43.077
q15.000
q330.000
iqr25.000
skew4.247
kurtosis28.414
n_outliers529
outlier_rate0.101
zero_rate3.81e-04

Fatalities numeric

skew=+4.95 8.4% rows beyond 1.5 IQR
rows5,268
null12 (0.2%)
unique191
min0.000
max583.000
mean20.068
median9.000
std33.200
q13.000
q323.000
iqr20.000
skew4.948
kurtosis42.791
n_outliers444
outlier_rate0.084
zero_rate0.011

Ground numeric

skew=+50.34
rows5,268
null22 (0.4%)
unique50
min0.000
max2,750
mean1.609
median0.000
std53.988
q10.000
q30.000
iqr0.000
skew50.336
kurtosis2,559
n_outliers219
outlier_rate0.042
zero_rate0.958

Summary text

95.8% of rows are unique strings
rows5,268
null390 (7.4%)
unique4,673
len_min6
len_max1,954
len_mean200.736
len_median136.000
len_p95584.000
word_mean33.240
word_median23.000
n_empty0
n_duplicates205
duplicate_rate0.042
vocab_size12,513
readability_flesch_mean61.678
emoji_rate0.000
url_rate0.000
one_word_rate4.10e-04
allcaps_rate0.000
boilerplate_rate0.000
Sample values (first 10)
  1. Crashed into trees while attempting to land after being shot down by British and French aircraft.
  2. Flew into a box canyon and crashed at an elevation of 4,000 ft. VFR flight by the pilot into instrument meteorological conditions, and the pilot's failure to maintain sufficient altitude and/or clearance from mountainous terrain. Factors related to the accident were: the adverse…
  3. Midair collision. The Beechcraft was on a flight from Lyon to Lorient, approaching Lorient, when it requested permission to fly over the ocean liner Norway. While circling the Norway, it collided with the Cessna. One killed aboard the Cessna, 14 aboard the Beechcraft. Failure of…
  4. The aircraft crashed into a 8,000 ft. mountain in the Sierra Grande range while climbing en route from Comodoro Rivadavia to Cordoba in heavy rain and strong turbulence. The passengers included military personnel and their dependents.
  5. The helicopter collided with trees after experiencing engine failure. Pilot overshot two suitable landing areas.
  6. The jetliner crashed into the Black Sea and broke up in driving rain and low visibility after making a second attempt to land. The plane disappeared from radar screens just under four miles from shore and crashed after making a turn and heading toward Adler airport for a landing.…
  7. Due to heavy traffic, the flight was diverted from the planned route. The aircraft failed to follow the assigned airway and crashed into a cloud obscured Montseny Mountain while on approach. The deviation from the assigned airway may have been caused by malfunctioning equipment. …
  8. The aircraft crashed into the Persian Gulf and exploded in flames while attempting to land at Bahrain International Airport. The crew decided to perform a missed approach after it was determined the aircraft was coming in too high and fast. Instructions were given for a 180 degre…
  9. Diverted from Madang to Bagasin, overran the runway and crashed.
  10. Crashed into a radio antenna tower and tore off a wing in dense fog.