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data trove airplane crashes fatalities 1908 2009

saturn notebook · generated 2026-06-21 Report Notebook

Overview

Source: /home/coolhand/html/datavis/data_trove/data/wild/disasters/airplane_crashes.csv

Saturn profiled 5,268 rows across 13 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).

[2]:
!pip install saturn-dissect
import subprocess
subprocess.run([
    "saturn", "analyze", "/home/coolhand/html/datavis/data_trove/data/wild/disasters/airplane_crashes.csv",
    "--findings", "data-trove-airplane-crashes-fatalities-1908-2009.json",
    "--llm", "anthropic:default",
])

Summary confidence: high

This dataset catalogues 5,268 aviation accidents spanning roughly a century, recording details such as date, operator, aircraft type, location, passengers aboard, fatalities, and ground casualties. Two numeric columns stand out immediately: Fatalities (mean 20, max 583) and Aboard (mean 28, max 644) are both highly right-skewed with significant outliers, suggesting a small number of catastrophic mass-casualty events dominate the tail. The Operator column reveals that Aeroflot (179 incidents) and U.S. military branches collectively account for a large share of recorded crashes, worth examining for era-specific clustering. Ground fatalities are near-zero in 95% of cases but spike dramatically in rare events (max 2,750), likely reflecting high-profile urban crashes.

citing: Fatalities.stats.mean · Fatalities.stats.max · Fatalities.stats.skew · Aboard.stats.mean · Aboard.stats.max · Aboard.stats.skew · Ground.stats.median · Ground.stats.max · Ground.stats.zero_rate · Operator.top_values · Type.top_values · row_count

Out[4]:

saturn.schema() · 13 columns

column kind n null% unique alerts
Date text 5,268 0.0% 4,753 one_word allcaps short_text
Time text 5,268 42.1% 1,005 one_word allcaps null_rate short_text duplicates
Location text 5,268 0.4% 4,303
Operator text 5,268 0.3% 2,476 multilingual duplicates
Flight # categorical 5,268 79.7% 724 long_tail null_rate
Route text 5,268 32.4% 3,244 multilingual null_rate
Type text 5,268 0.5% 2,446 duplicates
Registration text 5,268 6.4% 4,905 near_unique one_word allcaps short_text
cn/In text 5,268 23.3% 3,707 one_word allcaps null_rate short_text
Aboard numeric 5,268 0.4% 239 high_skew outliers
Fatalities numeric 5,268 0.2% 191 high_skew outliers
Ground numeric 5,268 0.4% 50 high_skew
Summary text 5,268 7.4% 4,673 near_unique
Fig 1.
Fatalities · Expect a sharp right skew — the vast majority of crashes kill fewer than 25 people, but rare outliers reach 583; look for the long tail.
Show data table
bincount
0 – 14.573314
14.57 – 29.15980
29.15 – 43.72343
43.72 – 58.3215
58.3 – 72.8896
72.88 – 87.4590
87.45 – 10251
102 – 116.642
116.6 – 131.239
131.2 – 145.819
145.8 – 160.318
160.3 – 174.99
174.9 – 189.511
189.5 – 2043
204 – 218.62
218.6 – 233.26
233.2 – 247.82
247.8 – 262.35
262.3 – 276.94
276.9 – 291.51
291.5 – 306.11
306.1 – 320.60
320.6 – 335.21
335.2 – 349.82
349.8 – 364.40
364.4 – 378.90
378.9 – 393.50
393.5 – 408.10
408.1 – 422.70
422.7 – 437.20
437.2 – 451.80
451.8 – 466.40
466.4 – 4810
481 – 495.50
495.5 – 510.10
510.1 – 524.71
524.7 – 539.30
539.3 – 553.90
553.9 – 568.40
568.4 – 5831
Fig 2.
Aboard · Distribution of passengers aboard mirrors fatalities' skew, with most flights carrying under 30 people and a handful of jumbo-jet disasters pushing past 600.
Show data table
bincount
0 – 16.12978
16.1 – 32.21055
32.2 – 48.3430
48.3 – 64.4230
64.4 – 80.5129
80.5 – 96.6105
96.6 – 112.775
112.7 – 128.856
128.8 – 144.946
144.9 – 16135
161 – 177.127
177.1 – 193.216
193.2 – 209.38
209.3 – 225.47
225.4 – 241.59
241.5 – 257.64
257.6 – 273.79
273.7 – 289.83
289.8 – 305.99
305.9 – 3223
322 – 338.12
338.1 – 354.23
354.2 – 370.31
370.3 – 386.41
386.4 – 402.52
402.5 – 418.60
418.6 – 434.70
434.7 – 450.80
450.8 – 466.90
466.9 – 4830
483 – 499.10
499.1 – 515.20
515.2 – 531.32
531.3 – 547.40
547.4 – 563.50
563.5 – 579.60
579.6 – 595.70
595.7 – 611.80
611.8 – 627.90
627.9 – 6441
Fig 3.
Operator · Aeroflot and U.S. military operators lead crash counts by a wide margin — check whether this reflects era bias or operational volume.
Show data table
charscount
3 – 596
5 – 6233
6 – 8140
8 – 9462
9 – 11169
11 – 12184
12 – 14128
14 – 15395
15 – 17270
17 – 18447
18 – 20407
20 – 22143
22 – 23340
23 – 25205
25 – 26542
26 – 28166
28 – 29229
29 – 31102
31 – 32194
32 – 3454
34 – 36127
36 – 3762
37 – 3935
39 – 4030
40 – 4213
42 – 4325
43 – 4511
45 – 465
46 – 487
48 – 507
50 – 517
51 – 530
53 – 549
54 – 561
56 – 573
57 – 590
59 – 601
60 – 620
62 – 630
63 – 651
Fig 4.
Type · Douglas DC-3 appears 334 times, far ahead of any other aircraft type, reflecting its dominance in mid-20th-century aviation.
Show data table
charscount
4 – 56
5 – 65
6 – 76
7 – 819
8 – 832
8 – 957
9 – 10178
10 – 11255
11 – 12685
12 – 130
13 – 14522
14 – 15331
15 – 16441
16 – 17369
17 – 18208
18 – 18158
18 – 19154
19 – 20166
20 – 21154
21 – 220
22 – 23109
23 – 24120
24 – 25158
25 – 26188
26 – 26174
26 – 27107
27 – 2873
28 – 2985
29 – 3039
30 – 310
31 – 3266
32 – 3358
33 – 3455
34 – 3543
35 – 3616
36 – 3625
36 – 3716
37 – 389
38 – 3921
39 – 40133
Fig 5.
Ground · Ground casualties are zero in over 95% of incidents, but extreme outliers exist — spot the rare catastrophic urban crash events.
Show data table
bincount
0 – 68.755235
68.75 – 137.58
137.5 – 206.20
206.2 – 2751
275 – 343.80
343.8 – 412.50
412.5 – 481.20
481.2 – 5500
550 – 618.80
618.8 – 687.50
687.5 – 756.20
756.2 – 8250
825 – 893.80
893.8 – 962.50
962.5 – 10310
1031 – 11000
1100 – 11690
1169 – 12380
1238 – 13060
1306 – 13750
1375 – 14440
1444 – 15120
1512 – 15810
1581 – 16500
1650 – 17190
1719 – 17880
1788 – 18560
1856 – 19250
1925 – 19940
1994 – 20620
2062 – 21310
2131 – 22000
2200 – 22690
2269 – 23380
2338 – 24060
2406 – 24750
2475 – 25440
2544 – 26120
2612 – 26810
2681 – 27502
Fig 6.
Per-column null rate across the corpus. Columns are ordered by input position.
Show data table
columnkindnull %
Datetext0.0%
Timetext42.1%
Locationtext0.4%
Operatortext0.3%
Flight #categorical79.7%
Routetext32.4%
Typetext0.5%
Registrationtext6.4%
cn/Intext23.3%
Aboardnumeric0.4%
Fatalitiesnumeric0.2%
Groundnumeric0.4%
Summarytext7.4%
Fig 7.
Language mix across all text columns (per-string detection, sampled).
Show data table
langcountshare
en590774.7%
es4615.8%
it3664.6%
de2903.7%
fr2473.1%
pt1552.0%
id931.2%
nl730.9%
sv510.6%
ca390.5%
pl310.4%
ru270.3%
no220.3%
sl200.3%
tr180.2%
ceb140.2%
hr140.2%
cs110.1%
eo70.1%
uk60.1%
hu60.1%
fi60.1%
ms60.1%
ro60.1%
da50.1%
bs30.0%
vi30.0%
sh30.0%
et30.0%
gl20.0%
lt20.0%
la20.0%
eu10.0%
ku10.0%
te10.0%
gd10.0%
ja10.0%
Fig 8.
Pearson correlation across numeric columns (sampled, bounded).
Show data table
AboardFatalitiesGround
Aboard+1.00+0.04+0.06
Fatalities+0.04+1.00+0.05
Ground+0.06+0.05+1.00

Date text timestamp

This column contains dates stored as text strings in MM/DD/YYYY format, with every value exactly 10 characters long and zero nulls across 5,268 rows. The duplicate rate of ~9.8% (515 duplicates across only 4,753 unique values) is notable — multiple records share the same date, with the most frequent dates appearing up to 4 times, including historically significant dates like 09/11/2001 and 06/06/1944, suggesting the dataset may track events tied to recurring or landmark dates. The 'allcaps' alert is a false positive from the date format containing no letters.

Treatment: Parse to datetime dtype (strptime MM/DD/YYYY) before any time-based analysis or feature engineering.

anthropic:default · confidence high
Out[14]:

saturn.columns["Date"].stats

statvalue
n5,268
nulls0 (0.0%)
unique4,753
len_min 10
len_max 10
len_mean 10
len_median 10
len_p95 10
word_mean 1
word_median 1
n_empty 0
n_duplicates 515
duplicate_rate 0.09776
vocab_size 4,753
readability_flesch_mean 121.2
emoji_rate 0
url_rate 0
one_word_rate 1
allcaps_rate 1
boilerplate_rate 0
alert: one_word100.0% rows are a single word
alert: allcaps100.0% rows are all-caps
alert: short_text95th-percentile length under 20 chars
Fig 9.
Character-length distribution for Date.
Show data table
charscount
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 105268
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100
10 – 100

Time text feature

This column contains clock times in HH:MM format (lengths 4–7 characters), almost certainly representing scheduled or recorded event times. Two signals warrant attention: the null rate is high at 42.12%, meaning nearly half of all 5,268 rows carry no time value, and the duplicate rate is 67.04% — expected for a time-of-day field with only 1,005 distinct values across non-null rows. The 'allcaps' alert is a false positive from saturn misclassifying colon-separated digit strings.

Treatment: Parse to datetime.time or extract hour/minute as numeric features; investigate the 42.12% null rate before deciding on imputation or exclusion strategy.

anthropic:default · confidence high
Out[17]:

saturn.columns["Time"].stats

statvalue
n5,268
nulls2,219 (42.1%)
unique1,005
len_min 4
len_max 7
len_mean 5.003
len_median 5
len_p95 5
word_mean 1.001
word_median 1
n_empty 0
n_duplicates 2,044
duplicate_rate 0.6704
vocab_size 1,004
readability_flesch_mean 121.2
emoji_rate 0
url_rate 0
one_word_rate 0.999
allcaps_rate 0.9974
boilerplate_rate 0
alert: one_word99.9% rows are a single word
alert: allcaps99.7% rows are all-caps
alert: null_rate42.1% null
alert: short_text95th-percentile length under 20 chars
alert: duplicates67.0% duplicate strings
Fig 10.
Character-length distribution for Time.
Show data table
charscount
4 – 47
4 – 40
4 – 40
4 – 40
4 – 40
4 – 40
4 – 50
5 – 50
5 – 50
5 – 50
5 – 50
5 – 50
5 – 50
5 – 53033
5 – 50
5 – 50
5 – 50
5 – 50
5 – 50
5 – 60
6 – 60
6 – 60
6 – 60
6 – 60
6 – 60
6 – 60
6 – 63
6 – 60
6 – 60
6 – 60
6 – 60
6 – 60
6 – 60
6 – 70
7 – 70
7 – 70
7 – 70
7 – 70
7 – 70
7 – 76

Location text feature

This column contains free-text geographic location descriptions, most commonly in 'City, Country/State' format (mean ~2.9 words, median length 19 characters), representing where individual events occurred. The high frequency of the word 'near' (1,272 occurrences out of 5,268 rows) indicates a substantial proportion of entries are approximate locations rather than precise place names, which could complicate geocoding. The duplicate rate of 18% (945 duplicates across 4,303 unique values) is expected for a location field but the long tail of near-unique entries (vocab size 4,541) suggests significant free-text variation in how locations are recorded.

Treatment: Normalize 'near X' / 'off X' patterns, then geocode or extract country/region via NLP before modelling.

anthropic:default · confidence high
Out[20]:

saturn.columns["Location"].stats

statvalue
n5,268
nulls20 (0.4%)
unique4,303
len_min 5
len_max 60
len_mean 20.38
len_median 19
len_p95 31
word_mean 2.866
word_median 3
n_empty 0
n_duplicates 945
duplicate_rate 0.1801
vocab_size 4,541
readability_flesch_mean 24.03
emoji_rate 0
url_rate 0
one_word_rate 0.01124
allcaps_rate 0
boilerplate_rate 0
Fig 11.
Character-length distribution for Location.
Show data table
charscount
5 – 621
6 – 88
8 – 922
9 – 1016
10 – 1261
12 – 13326
13 – 15280
15 – 16289
16 – 17753
17 – 19413
19 – 20828
20 – 22383
22 – 23313
23 – 24479
24 – 26189
26 – 27170
27 – 28244
28 – 3085
30 – 31114
31 – 3236
32 – 3428
34 – 3552
35 – 3725
37 – 3823
38 – 3926
39 – 4111
41 – 4218
42 – 443
44 – 4513
45 – 463
46 – 481
48 – 495
49 – 502
50 – 521
52 – 533
53 – 541
54 – 560
56 – 570
57 – 591
59 – 602

Operator text label

This column contains the name of the airline or military branch operating an aircraft involved in an incident, making it a categorical label field. With 2,476 unique values across 5,268 rows, the duplicate rate of 52.8% is expected for a label of this type — operators recur across multiple incidents. The multilingual alert is a natural artifact of international airline names (German, French, Italian, Spanish, Russian operators all present), not a data quality issue per se, though analysts should be aware that variant spellings of the same operator may inflate cardinality. Top values (Aeroflot at 179, U.S. Air Force at 176) reveal a mix of commercial and military operators.

Treatment: Normalize operator name variants, then encode as categorical (target-encode or embed) for modelling; consider grouping military sub-branches under a single 'Military' category.

anthropic:default · confidence high
Out[23]:

saturn.columns["Operator"].stats

statvalue
n5,268
nulls18 (0.3%)
unique2,476
len_min 3
len_max 65
len_mean 19.49
len_median 19
len_p95 35
word_mean 3.047
word_median 3
n_empty 0
n_duplicates 2,774
duplicate_rate 0.5284
vocab_size 2,370
readability_flesch_mean 19.61
emoji_rate 0
url_rate 0
one_word_rate 0.1651
allcaps_rate 0.03733
boilerplate_rate 0
alert: multilingual31 languages detected in sample
alert: duplicates52.8% duplicate strings
Fig 12.
Character-length distribution for Operator.
Show data table
charscount
3 – 596
5 – 6233
6 – 8140
8 – 9462
9 – 11169
11 – 12184
12 – 14128
14 – 15395
15 – 17270
17 – 18447
18 – 20407
20 – 22143
22 – 23340
23 – 25205
25 – 26542
26 – 28166
28 – 29229
29 – 31102
31 – 32194
32 – 3454
34 – 36127
36 – 3762
37 – 3935
39 – 4030
40 – 4213
42 – 4325
43 – 4511
45 – 465
46 – 487
48 – 507
50 – 517
51 – 530
53 – 549
54 – 561
56 – 573
57 – 590
59 – 601
60 – 620
62 – 630
63 – 651

Flight # categorical label

This column represents a flight number identifier, likely recording the flight designation for each row in the dataset. Two major issues stand out: 79.71% of values are null, making the column largely unpopulated, and the most frequent non-null value is a placeholder dash ('-') appearing 67 times, suggesting systematic missing-data encoding. With 724 unique values across only 1,073 non-null rows and an entropy ratio of 0.953, the distribution is near-uniform with a pronounced long tail — no single flight number dominates meaningfully beyond the placeholder.

Treatment: Treat '-' as null, impute or drop rows depending on whether flight number is required; with 79.71% nulls, consider dropping this column unless the analysis specifically targets flight-level granularity.

anthropic:default · confidence high
Out[26]:

saturn.columns["Flight #"].stats

statvalue
n5,268
nulls4,199 (79.7%)
unique724
top_value -
top_rate 0.06268
cardinality 724
entropy 9.058
entropy_ratio 0.9534
alert: long_tail543 singleton categories
alert: null_rate79.7% null
Fig 13.
Top values for Flight #.
Show data table
valuecountshare
-671.3%
1100.2%
470.1%
660.1%
2160.1%
10160.1%
90160.1%
750.1%
20150.1%
70150.1%
70650.1%
70350.1%
240.1%
20340.1%
30440.1%
60140.1%
51440.1%
1140.1%
21740.1%
11440.1%

Route text label

This column represents aviation route descriptions, capturing both origin-destination pairs (e.g., 'Saigon - Paris', 'Bogota - Barranquilla') and flight purpose labels (e.g., 'Training', 'Sightseeing', 'Test flight'). The null rate of 32.38% is a significant concern, meaning roughly one-third of records lack route information. The multilingual alert is expected given the international nature of routes — English dominates at 2,567 detections but Spanish (237), Portuguese (100), German (88), and French (64) are well-represented, reflecting global aviation data. The high n_unique count (3,244 of 5,268 non-null values) with a duplicate rate of 8.93% (318 duplicates) confirms this is a descriptive label field with many distinct routes but some recurring purpose/training entries.

Treatment: Impute or flag nulls (32.38% missing); split into 'purpose' vs 'OD-pair' subtypes using presence of '-' delimiter before encoding or embedding.

anthropic:default · confidence high
Out[29]:

saturn.columns["Route"].stats

statvalue
n5,268
nulls1,706 (32.4%)
unique3,244
len_min 4
len_max 59
len_mean 22.09
len_median 20
len_p95 37
word_mean 4.065
word_median 4
n_empty 0
n_duplicates 318
duplicate_rate 0.08928
vocab_size 3,647
readability_flesch_mean 27.15
emoji_rate 0
url_rate 0
one_word_rate 0.04099
allcaps_rate 0.0002807
boilerplate_rate 0
alert: multilingual31 languages detected in sample
alert: null_rate32.4% null
Fig 14.
Character-length distribution for Route.
Show data table
charscount
4 – 58
5 – 74
7 – 893
8 – 106
10 – 115
11 – 12100
12 – 1499
14 – 15155
15 – 16452
16 – 18247
18 – 19443
19 – 20170
20 – 22179
22 – 23286
23 – 25155
25 – 26135
26 – 27245
27 – 2994
29 – 30213
30 – 3271
32 – 3349
33 – 3474
34 – 3639
36 – 3740
37 – 3851
38 – 4020
40 – 4127
41 – 4212
42 – 4410
44 – 4519
45 – 476
47 – 484
48 – 4917
49 – 519
51 – 526
52 – 543
54 – 554
55 – 568
56 – 582
58 – 592

Type text label

This column captures aircraft model designations (e.g., 'Douglas DC-3', 'de Havilland Canada DHC-6 Twin Otter 300'), making it an aircraft type label in what appears to be an aviation incident or accident dataset. The duplicate rate of 53.3% (2,795 of 5,268 rows) is expected for a categorical-like field where many incidents share the same aircraft type, with 'Douglas DC-3' alone appearing 334 times. There are 2,446 unique values against a vocabulary of 2,534 words, indicating many near-unique variant spellings or sub-model suffixes (e.g., 'Douglas C-47', 'Douglas C-47A', 'Douglas C-47B' are counted separately), which is the key analyst surprise. Null rate is negligible at 0.51%.

Treatment: Normalize variant spellings and sub-model suffixes into canonical families before grouping or encoding; consider a manufacturer + model hierarchy for feature engineering.

anthropic:default · confidence high
Out[32]:

saturn.columns["Type"].stats

statvalue
n5,268
nulls27 (0.5%)
unique2,446
len_min 4
len_max 40
len_mean 18.33
len_median 16
len_p95 34
word_mean 2.718
word_median 2
n_empty 0
n_duplicates 2,795
duplicate_rate 0.5333
vocab_size 2,534
readability_flesch_mean 69.26
emoji_rate 0
url_rate 0
one_word_rate 0.007441
allcaps_rate 0.00954
boilerplate_rate 0
alert: duplicates53.3% duplicate strings
Fig 15.
Character-length distribution for Type.
Show data table
charscount
4 – 56
5 – 65
6 – 76
7 – 819
8 – 832
8 – 957
9 – 10178
10 – 11255
11 – 12685
12 – 130
13 – 14522
14 – 15331
15 – 16441
16 – 17369
17 – 18208
18 – 18158
18 – 19154
19 – 20166
20 – 21154
21 – 220
22 – 23109
23 – 24120
24 – 25158
25 – 26188
26 – 26174
26 – 27107
27 – 2873
28 – 2985
29 – 3039
30 – 310
31 – 3266
32 – 3358
33 – 3455
34 – 3543
35 – 3616
36 – 3625
36 – 3716
37 – 389
38 – 3921
39 – 40133

Registration text identifier

This column contains vehicle or aircraft registration codes — short, almost entirely uppercase alphanumeric identifiers (allcaps_rate 99.2%, median length 6 characters) consistent with licence plates or tail numbers. With 4905 unique values out of 5268 rows and only 28 duplicates, it behaves as a near-unique identifier, though the 6.36% null rate and occasional slash-containing entries (top word '/' appears 36 times) suggest some composite or malformed registrations worth inspecting. The presence of tokens like 'HK-' (a Colombian aviation prefix) and 'NC10809' hints at international aircraft tail numbers rather than road vehicle plates.

Treatment: Use as a near-unique entity key; cleanse slash-delimited entries and nulls before joining or deduplicating on this field.

anthropic:default · confidence high
Out[35]:

saturn.columns["Registration"].stats

statvalue
n5,268
nulls335 (6.4%)
unique4,905
len_min 1
len_max 15
len_mean 6.394
len_median 6
len_p95 10
word_mean 1.018
word_median 1
n_empty 0
n_duplicates 28
duplicate_rate 0.005676
vocab_size 4,948
readability_flesch_mean 103
emoji_rate 0
url_rate 0
one_word_rate 0.9899
allcaps_rate 0.9919
boilerplate_rate 0
alert: near_unique99.4% of rows are unique strings
alert: one_word99.0% rows are a single word
alert: allcaps99.2% rows are all-caps
alert: short_text95th-percentile length under 20 chars
Fig 16.
Character-length distribution for Registration.
Show data table
charscount
1 – 11
1 – 20
2 – 236
2 – 20
2 – 30
3 – 364
3 – 30
3 – 40
4 – 469
4 – 40
4 – 50
5 – 5398
5 – 60
6 – 60
6 – 63228
6 – 70
7 – 70
7 – 7512
7 – 80
8 – 80
8 – 8267
8 – 90
9 – 942
9 – 90
9 – 100
10 – 10206
10 – 100
10 – 110
11 – 1110
11 – 120
12 – 120
12 – 1212
12 – 130
13 – 130
13 – 1341
13 – 140
14 – 140
14 – 148
14 – 150
15 – 1539

cn/In text label

This column ('cn/In') appears to be a short coded identifier or reference field — likely a chemical notation, index number, or abbreviated category code — given its near-universal single-word (98.4%), all-caps (96.6%) character and very short values (median length 5, max 20). The top word '/' appearing 49 times suggests some values are compound codes using slash-delimited notation (e.g., 'CN/IN' style references), while most top values are pure numeric strings ('178', '19', '229', etc.). Two signals warrant attention: the null rate is high at 23.3%, and despite 3,707 unique values across 5,268 rows, there are 333 duplicates, indicating this is not a strict unique identifier.

Treatment: Investigate nulls (23.3% missing) before use; treat as categorical label or join key after resolving slash-delimited compound values.

anthropic:default · confidence medium
Out[38]:

saturn.columns["cn/In"].stats

statvalue
n5,268
nulls1,228 (23.3%)
unique3,707
len_min 1
len_max 20
len_mean 5.645
len_median 5
len_p95 10
word_mean 1.026
word_median 1
n_empty 0
n_duplicates 333
duplicate_rate 0.08243
vocab_size 3,739
readability_flesch_mean 121.2
emoji_rate 0
url_rate 0
one_word_rate 0.9842
allcaps_rate 0.9663
boilerplate_rate 0
alert: one_word98.4% rows are a single word
alert: allcaps96.6% rows are all-caps
alert: null_rate23.3% null
alert: short_text95th-percentile length under 20 chars
Fig 17.
Character-length distribution for cn/In.
Show data table
charscount
1 – 123
1 – 20
2 – 2113
2 – 30
3 – 3604
3 – 40
4 – 4866
4 – 50
5 – 5895
5 – 60
6 – 6268
6 – 70
7 – 7269
7 – 80
8 – 8281
8 – 90
9 – 9457
9 – 100
10 – 10125
10 – 100
10 – 110
11 – 1192
11 – 120
12 – 1214
12 – 130
13 – 139
13 – 140
14 – 142
14 – 150
15 – 155
15 – 160
16 – 164
16 – 170
17 – 175
17 – 180
18 – 182
18 – 190
19 – 192
19 – 200
20 – 204

Aboard numeric feature

This column records the number of people aboard a vehicle (likely an aircraft or ship) at the time of an incident. The distribution is severely right-skewed (skew=4.25, kurtosis=28.41): the median is only 13 passengers while the mean is 27.6, and the max reaches 644 — consistent with a few large commercial aircraft disasters pulling the tail far right. Roughly 10% of rows (529) are flagged as outliers, and the IQR spans just 5–30, meaning the vast majority of incidents involve small craft.

Treatment: Log-transform (log1p) before regression or modelling to reduce skew; retain outliers as they represent real large-scale events.

anthropic:default · confidence high
Out[41]:

saturn.columns["Aboard"].stats

statvalue
n5,268
nulls22 (0.4%)
unique239
min 0
max 644
mean 27.55
median 13
std 43.08
q1 5
q3 30
iqr 25
skew 4.247
kurtosis 28.41
n_outliers 529
outlier_rate 0.1008
zero_rate 0.0003812
alert: high_skewskew=+4.25
alert: outliers10.1% rows beyond 1.5 IQR
Fig 18.
Distribution of Aboard. Vertical dash marks the median.
Show data table
bincount
0 – 16.12978
16.1 – 32.21055
32.2 – 48.3430
48.3 – 64.4230
64.4 – 80.5129
80.5 – 96.6105
96.6 – 112.775
112.7 – 128.856
128.8 – 144.946
144.9 – 16135
161 – 177.127
177.1 – 193.216
193.2 – 209.38
209.3 – 225.47
225.4 – 241.59
241.5 – 257.64
257.6 – 273.79
273.7 – 289.83
289.8 – 305.99
305.9 – 3223
322 – 338.12
338.1 – 354.23
354.2 – 370.31
370.3 – 386.41
386.4 – 402.52
402.5 – 418.60
418.6 – 434.70
434.7 – 450.80
450.8 – 466.90
466.9 – 4830
483 – 499.10
499.1 – 515.20
515.2 – 531.32
531.3 – 547.40
547.4 – 563.50
563.5 – 579.60
579.6 – 595.70
595.7 – 611.80
611.8 – 627.90
627.9 – 6441

Fatalities numeric numeric_target

This column records the number of fatalities per incident (likely aviation accidents, conflicts, or similar events). The distribution is extremely right-skewed (skew = 4.95, kurtosis = 42.79): the median is only 9 fatalities while the mean is 20.07 and the maximum reaches 583, indicating a long tail of mass-casualty events. 444 rows (8.4%) are flagged as outliers, and the IQR of 20 against a std of 33.2 confirms that most incidents are low-fatality but a meaningful minority are catastrophic.

Treatment: Log-transform (log1p) before regression or modelling to reduce skew; retain outliers as they represent real high-severity events.

anthropic:default · confidence high
Out[44]:

saturn.columns["Fatalities"].stats

statvalue
n5,268
nulls12 (0.2%)
unique191
min 0
max 583
mean 20.07
median 9
std 33.2
q1 3
q3 23
iqr 20
skew 4.948
kurtosis 42.79
n_outliers 444
outlier_rate 0.08447
zero_rate 0.01104
alert: high_skewskew=+4.95
alert: outliers8.4% rows beyond 1.5 IQR
Fig 19.
Distribution of Fatalities. Vertical dash marks the median.
Show data table
bincount
0 – 14.573314
14.57 – 29.15980
29.15 – 43.72343
43.72 – 58.3215
58.3 – 72.8896
72.88 – 87.4590
87.45 – 10251
102 – 116.642
116.6 – 131.239
131.2 – 145.819
145.8 – 160.318
160.3 – 174.99
174.9 – 189.511
189.5 – 2043
204 – 218.62
218.6 – 233.26
233.2 – 247.82
247.8 – 262.35
262.3 – 276.94
276.9 – 291.51
291.5 – 306.11
306.1 – 320.60
320.6 – 335.21
335.2 – 349.82
349.8 – 364.40
364.4 – 378.90
378.9 – 393.50
393.5 – 408.10
408.1 – 422.70
422.7 – 437.20
437.2 – 451.80
451.8 – 466.40
466.4 – 4810
481 – 495.50
495.5 – 510.10
510.1 – 524.71
524.7 – 539.30
539.3 – 553.90
553.9 – 568.40
568.4 – 5831

Ground numeric feature

This column likely represents a ground elevation, ground clearance, or grounding-related measurement (possibly in feet or meters) associated with physical infrastructure or flight/equipment records. The distribution is extreme: 95.8% of values are exactly zero, yet the maximum reaches 2750.0 with a skew of 50.34 and kurtosis of 2558.60, indicating a tiny fraction of records carry very large non-zero values. Only 50 unique values exist across 5,268 rows, and 219 observations (4.17%) are flagged as outliers — the near-zero IQR (Q1=Q3=0) confirms the overwhelming concentration at zero.

Treatment: Treat as sparse indicator/feature; consider binarizing (zero vs. non-zero) or log1p-transforming the non-zero subset, and investigate whether the 2750.0 outliers are valid or data-entry errors.

anthropic:default · confidence medium
Out[47]:

saturn.columns["Ground"].stats

statvalue
n5,268
nulls22 (0.4%)
unique50
min 0
max 2,750
mean 1.609
median 0
std 53.99
q1 0
q3 0
iqr 0
skew 50.34
kurtosis 2559
n_outliers 219
outlier_rate 0.04175
zero_rate 0.9583
alert: high_skewskew=+50.34
Fig 20.
Distribution of Ground. Vertical dash marks the median.
Show data table
bincount
0 – 68.755235
68.75 – 137.58
137.5 – 206.20
206.2 – 2751
275 – 343.80
343.8 – 412.50
412.5 – 481.20
481.2 – 5500
550 – 618.80
618.8 – 687.50
687.5 – 756.20
756.2 – 8250
825 – 893.80
893.8 – 962.50
962.5 – 10310
1031 – 11000
1100 – 11690
1169 – 12380
1238 – 13060
1306 – 13750
1375 – 14440
1444 – 15120
1512 – 15810
1581 – 16500
1650 – 17190
1719 – 17880
1788 – 18560
1856 – 19250
1925 – 19940
1994 – 20620
2062 – 21310
2131 – 22000
2200 – 22690
2269 – 23380
2338 – 24060
2406 – 24750
2475 – 25440
2544 – 26120
2612 – 26810
2681 – 27502

Summary text free_text

This column contains free-text narrative summaries of aviation incidents or accidents, as evidenced by dominant domain terms 'crashed', 'into', and 'aircraft' appearing thousands of times across 5,268 records. Text length varies widely (min 6, median 136, max 1,954 characters), suggesting entries range from brief one-liners to detailed multi-sentence accounts. A duplicate rate of 4.2% (205 duplicates) is mildly surprising for free-text summaries and may indicate repeated incident templates or copy-paste entries. Flesch readability of 61.7 indicates moderate accessibility, consistent with factual incident reporting prose.

Treatment: Tokenize and embed (e.g., TF-IDF or sentence transformer) for modelling; deduplicate 205 exact-match rows before training.

anthropic:default · confidence high
Out[50]:

saturn.columns["Summary"].stats

statvalue
n5,268
nulls390 (7.4%)
unique4,673
len_min 6
len_max 1,954
len_mean 200.7
len_median 136
len_p95 584
word_mean 33.24
word_median 23
n_empty 0
n_duplicates 205
duplicate_rate 0.04203
vocab_size 12,513
readability_flesch_mean 61.68
emoji_rate 0
url_rate 0
one_word_rate 0.00041
allcaps_rate 0
boilerplate_rate 0
alert: near_unique95.8% of rows are unique strings
Fig 21.
Character-length distribution for Summary.
Show data table
charscount
6 – 55822
55 – 1031039
103 – 152800
152 – 201547
201 – 250364
250 – 298280
298 – 347231
347 – 396172
396 – 444123
444 – 493128
493 – 54286
542 – 59050
590 – 63957
639 – 68833
688 – 73637
736 – 78519
785 – 83415
834 – 88316
883 – 93111
931 – 98010
980 – 10295
1029 – 10771
1077 – 11266
1126 – 11753
1175 – 12244
1224 – 12723
1272 – 13212
1321 – 13701
1370 – 14181
1418 – 14673
1467 – 15161
1516 – 15642
1564 – 16131
1613 – 16624
1662 – 17100
1710 – 17590
1759 – 18080
1808 – 18570
1857 – 19050
1905 – 19541

How to cite

click to copy

BibTeX
@misc{saturn-data-trove-airplane-crashes-fatalities-1908-2009-2026,
  author       = {Steuber, Luke},
  title        = {Saturn reading: data trove airplane crashes fatalities 1908 2009},
  year         ={2026},
  howpublished = {\url{https://dr.eamer.dev/saturn/view/data-trove-airplane-crashes-fatalities-1908-2009}},
  note         = {Profiled with saturn-dissect v0.2.0, prompt saturn-insight-v2, model anthropic:default},
}
APA
Steuber, L. (2026). Saturn reading: data trove airplane crashes fatalities 1908 2009. Source: /home/coolhand/html/datavis/data_trove/data/wild/disasters/airplane_crashes.csv. Profiled with saturn-dissect v0.2.0 (saturn-insight-v2, anthropic:default). Retrieved from https://dr.eamer.dev/saturn/view/data-trove-airplane-crashes-fatalities-1908-2009