Overview

Dataset statistics

Number of variables19
Number of observations1423
Missing cells2690
Missing cells (%)9.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory222.5 KiB
Average record size in memory160.1 B

Variable types

DateTime1
Categorical8
Numeric8
Text2

Dataset

Description해상에서 발생한 모든 선박관련 해양사고 중 해양경찰청에 신고.접수된 사고의 상세데이터(위치, 발생유형, 원인 등) 제공
Author해양경찰청
URLhttps://www.data.go.kr/data/15066678/fileData.do

Alerts

선종2 is highly overall correlated with 구조인원 and 3 other fieldsHigh correlation
선종3 is highly overall correlated with 구조인원 and 10 other fieldsHigh correlation
구조인원 is highly overall correlated with 톤수2 and 3 other fieldsHigh correlation
부상인원 is highly overall correlated with 선종3High correlation
사망인원 is highly overall correlated with 선종2 and 1 other fieldsHigh correlation
실종인원 is highly overall correlated with 선종3High correlation
톤수1 is highly overall correlated with 톤수3 and 1 other fieldsHigh correlation
톤수2 is highly overall correlated with 구조인원 and 2 other fieldsHigh correlation
톤수3 is highly overall correlated with 구조인원 and 5 other fieldsHigh correlation
관할해경서 is highly overall correlated with 톤수3 and 1 other fieldsHigh correlation
발생해역 is highly overall correlated with 선종3High correlation
발생유형 is highly overall correlated with 선종3High correlation
발생원인 is highly overall correlated with 톤수3High correlation
선종1 is highly overall correlated with 선종3High correlation
기상특보 is highly imbalanced (66.9%)Imbalance
선종2 is highly imbalanced (73.9%)Imbalance
선종3 is highly imbalanced (95.9%)Imbalance
톤수2 has 1233 (86.6%) missing valuesMissing
톤수3 has 1409 (99.0%) missing valuesMissing
구조인원 is highly skewed (γ1 = 26.55204428)Skewed
사망인원 is highly skewed (γ1 = 35.8318805)Skewed
톤수1 is highly skewed (γ1 = 29.88045815)Skewed
구조인원 has 169 (11.9%) zerosZeros
부상인원 has 1378 (96.8%) zerosZeros
사망인원 has 1388 (97.5%) zerosZeros
실종인원 has 1392 (97.8%) zerosZeros

Reproduction

Analysis started2023-12-12 05:37:33.037149
Analysis finished2023-12-12 05:37:41.928734
Duration8.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct1412
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
Minimum2010-01-01 00:00:00
Maximum2010-12-30 17:15:00
2023-12-12T14:37:42.004379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:42.512163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

관할해경서
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
통영
249 
군산
140 
부산
123 
태안
121 
여수
120 
Other values (9)
670 

Length

Max length3
Median length2
Mean length2.0646521
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row포항
2nd row포항
3rd row군산
4th row여수
5th row부산

Common Values

ValueCountFrequency (%)
통영 249
17.5%
군산 140
9.8%
부산 123
8.6%
태안 121
8.5%
여수 120
8.4%
포항 114
8.0%
인천 97
 
6.8%
서귀포 92
 
6.5%
제주 82
 
5.8%
목포 80
 
5.6%
Other values (4) 205
14.4%

Length

2023-12-12T14:37:42.783423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
통영 249
17.5%
군산 140
9.8%
부산 123
8.6%
태안 121
8.5%
여수 120
8.4%
포항 114
8.0%
인천 97
 
6.8%
서귀포 92
 
6.5%
제주 82
 
5.8%
목포 80
 
5.6%
Other values (4) 205
14.4%

발생해역
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
영해
701 
항계내
410 
영해-EEZ
144 
협수로
110 
공해
 
31
Other values (2)
 
27

Length

Max length11
Median length2
Mean length2.921293
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영해-EEZ
2nd row영해-EEZ
3rd row영해
4th row항계내
5th row항계내

Common Values

ValueCountFrequency (%)
영해 701
49.3%
항계내 410
28.8%
영해-EEZ 144
 
10.1%
협수로 110
 
7.7%
공해 31
 
2.2%
EEZ 30마일 이내 23
 
1.6%
외국해역 4
 
0.3%

Length

2023-12-12T14:37:42.968853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:37:43.159118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영해 701
47.7%
항계내 410
27.9%
영해-eez 144
 
9.8%
협수로 110
 
7.5%
공해 31
 
2.1%
eez 23
 
1.6%
30마일 23
 
1.6%
이내 23
 
1.6%
외국해역 4
 
0.3%

발생유형
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
기관고장
453 
침수
208 
충돌
177 
좌초
122 
표류
113 
Other values (6)
350 

Length

Max length5
Median length2
Mean length2.9121574
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기관고장
2nd row충돌
3rd row충돌
4th row화재
5th row전복

Common Values

ValueCountFrequency (%)
기관고장 453
31.8%
침수 208
14.6%
충돌 177
 
12.4%
좌초 122
 
8.6%
표류 113
 
7.9%
추진기장애 112
 
7.9%
화재 84
 
5.9%
전복 53
 
3.7%
기타 48
 
3.4%
타기고장 28
 
2.0%

Length

2023-12-12T14:37:43.329732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기관고장 453
31.8%
침수 208
14.6%
충돌 177
 
12.4%
좌초 122
 
8.6%
표류 113
 
7.9%
추진기장애 112
 
7.9%
화재 84
 
5.9%
전복 53
 
3.7%
기타 48
 
3.4%
타기고장 28
 
2.0%

발생원인
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
정비불량
539 
운항부주의
461 
기타
193 
관리소홀
103 
기상악화
60 
Other values (4)
67 

Length

Max length7
Median length4
Mean length4.1243851
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row정비불량
2nd row운항부주의
3rd row운항부주의
4th row기타
5th row기상악화

Common Values

ValueCountFrequency (%)
정비불량 539
37.9%
운항부주의 461
32.4%
기타 193
 
13.6%
관리소홀 103
 
7.2%
기상악화 60
 
4.2%
화기취급부주의 34
 
2.4%
연료고갈 19
 
1.3%
재질불량 9
 
0.6%
적재불량 5
 
0.4%

Length

2023-12-12T14:37:43.489867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:37:43.638664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정비불량 539
37.9%
운항부주의 461
32.4%
기타 193
 
13.6%
관리소홀 103
 
7.2%
기상악화 60
 
4.2%
화기취급부주의 34
 
2.4%
연료고갈 19
 
1.3%
재질불량 9
 
0.6%
적재불량 5
 
0.4%

기상특보
Categorical

IMBALANCE 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
양호
1174 
풍랑주의보
 
95
황천5급
 
69
저시정
 
38
황천4급
 
33
Other values (4)
 
14

Length

Max length5
Median length2
Mean length2.3942375
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row풍랑주의보
2nd row풍랑주의보
3rd row황천4급
4th row양호
5th row풍랑주의보

Common Values

ValueCountFrequency (%)
양호 1174
82.5%
풍랑주의보 95
 
6.7%
황천5급 69
 
4.8%
저시정 38
 
2.7%
황천4급 33
 
2.3%
태풍주의보 6
 
0.4%
황천3급 5
 
0.4%
풍랑경보 2
 
0.1%
태풍경보 1
 
0.1%

Length

2023-12-12T14:37:43.848775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:37:44.009744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
양호 1174
82.5%
풍랑주의보 95
 
6.7%
황천5급 69
 
4.8%
저시정 38
 
2.7%
황천4급 33
 
2.3%
태풍주의보 6
 
0.4%
황천3급 5
 
0.4%
풍랑경보 2
 
0.1%
태풍경보 1
 
0.1%

사고선박수
Real number (ℝ)

Distinct6
Distinct (%)0.4%
Missing8
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean1.1498233
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-12-12T14:37:44.146087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.42735118
Coefficient of variation (CV)0.37166683
Kurtosis39.840335
Mean1.1498233
Median Absolute Deviation (MAD)0
Skewness4.6384892
Sum1627
Variance0.18262904
MonotonicityNot monotonic
2023-12-12T14:37:44.280777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 1225
86.1%
2 176
 
12.4%
3 11
 
0.8%
7 1
 
0.1%
4 1
 
0.1%
6 1
 
0.1%
(Missing) 8
 
0.6%
ValueCountFrequency (%)
1 1225
86.1%
2 176
 
12.4%
3 11
 
0.8%
4 1
 
0.1%
6 1
 
0.1%
7 1
 
0.1%
ValueCountFrequency (%)
7 1
 
0.1%
6 1
 
0.1%
4 1
 
0.1%
3 11
 
0.8%
2 176
 
12.4%
1 1225
86.1%

구조인원
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct49
Distinct (%)3.5%
Missing8
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean6.9568905
Minimum0
Maximum976
Zeros169
Zeros (%)11.9%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-12-12T14:37:44.444648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile19
Maximum976
Range976
Interquartile range (IQR)6

Descriptive statistics

Standard deviation29.680457
Coefficient of variation (CV)4.2663396
Kurtosis825.48694
Mean6.9568905
Median Absolute Deviation (MAD)2
Skewness26.552044
Sum9844
Variance880.92954
MonotonicityNot monotonic
2023-12-12T14:37:44.661662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2 234
16.4%
1 206
14.5%
0 169
11.9%
3 130
9.1%
4 114
8.0%
5 92
 
6.5%
7 64
 
4.5%
6 60
 
4.2%
8 54
 
3.8%
9 46
 
3.2%
Other values (39) 246
17.3%
ValueCountFrequency (%)
0 169
11.9%
1 206
14.5%
2 234
16.4%
3 130
9.1%
4 114
8.0%
5 92
 
6.5%
6 60
 
4.2%
7 64
 
4.5%
8 54
 
3.8%
9 46
 
3.2%
ValueCountFrequency (%)
976 1
0.1%
388 1
0.1%
212 1
0.1%
140 1
0.1%
100 1
0.1%
98 2
0.1%
79 1
0.1%
74 1
0.1%
66 1
0.1%
58 1
0.1%

부상인원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.6%
Missing8
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.055123675
Minimum0
Maximum14
Zeros1378
Zeros (%)96.8%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-12-12T14:37:44.788282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.52092044
Coefficient of variation (CV)9.450031
Kurtosis406.76949
Mean0.055123675
Median Absolute Deviation (MAD)0
Skewness17.807625
Sum78
Variance0.2713581
MonotonicityNot monotonic
2023-12-12T14:37:44.927633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 1378
96.8%
1 24
 
1.7%
2 6
 
0.4%
3 2
 
0.1%
7 1
 
0.1%
5 1
 
0.1%
4 1
 
0.1%
14 1
 
0.1%
6 1
 
0.1%
(Missing) 8
 
0.6%
ValueCountFrequency (%)
0 1378
96.8%
1 24
 
1.7%
2 6
 
0.4%
3 2
 
0.1%
4 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
7 1
 
0.1%
14 1
 
0.1%
ValueCountFrequency (%)
14 1
 
0.1%
7 1
 
0.1%
6 1
 
0.1%
5 1
 
0.1%
4 1
 
0.1%
3 2
 
0.1%
2 6
 
0.4%
1 24
 
1.7%
0 1378
96.8%

사망인원
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct6
Distinct (%)0.4%
Missing8
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.060070671
Minimum0
Maximum46
Zeros1388
Zeros (%)97.5%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-12-12T14:37:45.110663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum46
Range46
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2427709
Coefficient of variation (CV)20.68848
Kurtosis1323.2683
Mean0.060070671
Median Absolute Deviation (MAD)0
Skewness35.831881
Sum85
Variance1.5444795
MonotonicityNot monotonic
2023-12-12T14:37:45.267474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1388
97.5%
1 16
 
1.1%
2 8
 
0.6%
4 1
 
0.1%
46 1
 
0.1%
3 1
 
0.1%
(Missing) 8
 
0.6%
ValueCountFrequency (%)
0 1388
97.5%
1 16
 
1.1%
2 8
 
0.6%
3 1
 
0.1%
4 1
 
0.1%
46 1
 
0.1%
ValueCountFrequency (%)
46 1
 
0.1%
4 1
 
0.1%
3 1
 
0.1%
2 8
 
0.6%
1 16
 
1.1%
0 1388
97.5%

실종인원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.6%
Missing8
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.048056537
Minimum0
Maximum9
Zeros1392
Zeros (%)97.8%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-12-12T14:37:45.378625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.48799749
Coefficient of variation (CV)10.154654
Kurtosis182.60448
Mean0.048056537
Median Absolute Deviation (MAD)0
Skewness12.878714
Sum68
Variance0.23814155
MonotonicityNot monotonic
2023-12-12T14:37:45.491766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 1392
97.8%
1 11
 
0.8%
2 3
 
0.2%
4 2
 
0.1%
7 2
 
0.1%
6 2
 
0.1%
3 1
 
0.1%
5 1
 
0.1%
9 1
 
0.1%
(Missing) 8
 
0.6%
ValueCountFrequency (%)
0 1392
97.8%
1 11
 
0.8%
2 3
 
0.2%
3 1
 
0.1%
4 2
 
0.1%
5 1
 
0.1%
6 2
 
0.1%
7 2
 
0.1%
9 1
 
0.1%
ValueCountFrequency (%)
9 1
 
0.1%
7 2
 
0.1%
6 2
 
0.1%
5 1
 
0.1%
4 2
 
0.1%
3 1
 
0.1%
2 3
 
0.2%
1 11
 
0.8%
0 1392
97.8%

위도
Text

Distinct1180
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
2023-12-12T14:37:45.887274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.9718904
Min length6

Characters and Unicode

Total characters11344
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1023 ?
Unique (%)71.9%

Sample

1st row36|30|00
2nd row36|14|50
3rd row36|03|00
4th row34|43|70
5th row35|01|69
ValueCountFrequency (%)
35|08|00 6
 
0.4%
37|17|00 6
 
0.4%
37|19|00 5
 
0.4%
35|09|00 5
 
0.4%
37|15|00 5
 
0.4%
35|05|00 5
 
0.4%
34|26|00 4
 
0.3%
33 4
 
0.3%
35|12|00 4
 
0.3%
35|28|00 4
 
0.3%
Other values (1172) 1380
96.6%
2023-12-12T14:37:46.482090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
| 2846
25.1%
3 2110
18.6%
0 1721
15.2%
5 1046
 
9.2%
4 992
 
8.7%
2 582
 
5.1%
1 526
 
4.6%
6 479
 
4.2%
7 445
 
3.9%
8 334
 
2.9%
Other values (2) 263
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8493
74.9%
Math Symbol 2846
 
25.1%
Space Separator 5
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2110
24.8%
0 1721
20.3%
5 1046
12.3%
4 992
11.7%
2 582
 
6.9%
1 526
 
6.2%
6 479
 
5.6%
7 445
 
5.2%
8 334
 
3.9%
9 258
 
3.0%
Math Symbol
ValueCountFrequency (%)
| 2846
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11344
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
| 2846
25.1%
3 2110
18.6%
0 1721
15.2%
5 1046
 
9.2%
4 992
 
8.7%
2 582
 
5.1%
1 526
 
4.6%
6 479
 
4.2%
7 445
 
3.9%
8 334
 
2.9%
Other values (2) 263
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
| 2846
25.1%
3 2110
18.6%
0 1721
15.2%
5 1046
 
9.2%
4 992
 
8.7%
2 582
 
5.1%
1 526
 
4.6%
6 479
 
4.2%
7 445
 
3.9%
8 334
 
2.9%
Other values (2) 263
 
2.3%

경도
Text

Distinct1152
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
2023-12-12T14:37:46.947167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.9683767
Min length7

Characters and Unicode

Total characters12762
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique984 ?
Unique (%)69.1%

Sample

1st row130|05|00
2nd row129|39|00
3rd row126|09|00
4th row127|44|50
5th row129|00|67
ValueCountFrequency (%)
126|22|00 7
 
0.5%
126|00|00 7
 
0.5%
126|39|00 6
 
0.4%
125|55|00 6
 
0.4%
126|21|00 6
 
0.4%
127|06|00 6
 
0.4%
126|55|30 5
 
0.4%
129|22|00 5
 
0.4%
126|25|00 4
 
0.3%
126|07|00 4
 
0.3%
Other values (1142) 1367
96.1%
2023-12-12T14:37:47.544805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
| 2846
22.3%
2 2062
16.2%
1 1974
15.5%
0 1701
13.3%
6 768
 
6.0%
3 675
 
5.3%
5 604
 
4.7%
4 578
 
4.5%
9 577
 
4.5%
8 550
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9916
77.7%
Math Symbol 2846
 
22.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2062
20.8%
1 1974
19.9%
0 1701
17.2%
6 768
 
7.7%
3 675
 
6.8%
5 604
 
6.1%
4 578
 
5.8%
9 577
 
5.8%
8 550
 
5.5%
7 427
 
4.3%
Math Symbol
ValueCountFrequency (%)
| 2846
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12762
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
| 2846
22.3%
2 2062
16.2%
1 1974
15.5%
0 1701
13.3%
6 768
 
6.0%
3 675
 
5.3%
5 604
 
4.7%
4 578
 
4.5%
9 577
 
4.5%
8 550
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12762
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
| 2846
22.3%
2 2062
16.2%
1 1974
15.5%
0 1701
13.3%
6 768
 
6.0%
3 675
 
5.3%
5 604
 
4.7%
4 578
 
4.5%
9 577
 
4.5%
8 550
 
4.3%

선종1
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
어선
954 
기타
139 
낚시어선
 
91
예부선
 
65
모터보트
 
59
Other values (7)
115 

Length

Max length4
Median length2
Mean length2.3246662
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row어선
2nd row어선
3rd row어선
4th row어선
5th row어선

Common Values

ValueCountFrequency (%)
어선 954
67.0%
기타 139
 
9.8%
낚시어선 91
 
6.4%
예부선 65
 
4.6%
모터보트 59
 
4.1%
화물선 49
 
3.4%
요트 26
 
1.8%
유조선 15
 
1.1%
여객선 8
 
0.6%
<NA> 8
 
0.6%
Other values (2) 9
 
0.6%

Length

2023-12-12T14:37:47.718495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
어선 954
67.0%
기타 139
 
9.8%
낚시어선 91
 
6.4%
예부선 65
 
4.6%
모터보트 59
 
4.1%
화물선 49
 
3.4%
요트 26
 
1.8%
유조선 15
 
1.1%
여객선 8
 
0.6%
na 8
 
0.6%
Other values (2) 9
 
0.6%

톤수1
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct518
Distinct (%)36.6%
Missing8
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean418.06872
Minimum0
Maximum180000
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-12-12T14:37:47.911575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.82
Q12.6
median7.93
Q329
95-th percentile498
Maximum180000
Range180000
Interquartile range (IQR)26.4

Descriptive statistics

Standard deviation5213.0021
Coefficient of variation (CV)12.469247
Kurtosis1004.542
Mean418.06872
Median Absolute Deviation (MAD)6.39
Skewness29.880458
Sum591567.24
Variance27175391
MonotonicityNot monotonic
2023-12-12T14:37:48.108045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.77 95
 
6.7%
29.0 80
 
5.6%
7.93 60
 
4.2%
1.0 43
 
3.0%
3.0 27
 
1.9%
4.99 25
 
1.8%
2.0 19
 
1.3%
6.67 18
 
1.3%
7.31 15
 
1.1%
79.0 15
 
1.1%
Other values (508) 1018
71.5%
ValueCountFrequency (%)
0.0 2
0.1%
0.05 2
0.1%
0.1 2
0.1%
0.18 1
 
0.1%
0.2 1
 
0.1%
0.29 1
 
0.1%
0.3 4
0.3%
0.32 1
 
0.1%
0.34 1
 
0.1%
0.36 1
 
0.1%
ValueCountFrequency (%)
180000.0 1
0.1%
50905.0 1
0.1%
27779.0 1
0.1%
19817.0 1
0.1%
19534.0 1
0.1%
16761.0 1
0.1%
16472.0 1
0.1%
16137.0 1
0.1%
13058.0 1
0.1%
12961.0 1
0.1%

선종2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
<NA>
1233 
어선
 
105
화물선
 
31
예부선
 
23
기타
 
19
Other values (4)
 
12

Length

Max length4
Median length4
Mean length3.7800422
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row<NA>
2nd row어선
3rd row기타
4th row어선
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 1233
86.6%
어선 105
 
7.4%
화물선 31
 
2.2%
예부선 23
 
1.6%
기타 19
 
1.3%
유조선 7
 
0.5%
낚시어선 2
 
0.1%
요트 2
 
0.1%
모터보트 1
 
0.1%

Length

2023-12-12T14:37:48.285063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:37:48.457713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1233
86.6%
어선 105
 
7.4%
화물선 31
 
2.2%
예부선 23
 
1.6%
기타 19
 
1.3%
유조선 7
 
0.5%
낚시어선 2
 
0.1%
요트 2
 
0.1%
모터보트 1
 
0.1%

톤수2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct150
Distinct (%)78.9%
Missing1233
Missing (%)86.6%
Infinite0
Infinite (%)0.0%
Mean2782.7965
Minimum0
Maximum51320
Zeros4
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-12-12T14:37:48.653571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.917
Q14.23
median29.5
Q3470.5
95-th percentile21394.15
Maximum51320
Range51320
Interquartile range (IQR)466.27

Descriptive statistics

Standard deviation8556.3492
Coefficient of variation (CV)3.0747304
Kurtosis15.722178
Mean2782.7965
Median Absolute Deviation (MAD)28.76
Skewness3.9395553
Sum528731.34
Variance73211111
MonotonicityNot monotonic
2023-12-12T14:37:48.819732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.93 10
 
0.7%
29.0 6
 
0.4%
9.77 5
 
0.4%
0.0 4
 
0.3%
2.0 3
 
0.2%
69.0 3
 
0.2%
5.0 2
 
0.1%
3.0 2
 
0.1%
17.0 2
 
0.1%
79.0 2
 
0.1%
Other values (140) 151
 
10.6%
(Missing) 1233
86.6%
ValueCountFrequency (%)
0.0 4
0.3%
0.26 1
 
0.1%
0.49 1
 
0.1%
0.5 1
 
0.1%
0.68 1
 
0.1%
0.8 1
 
0.1%
0.89 1
 
0.1%
0.95 1
 
0.1%
0.97 1
 
0.1%
0.98 1
 
0.1%
ValueCountFrequency (%)
51320.0 1
0.1%
47367.0 1
0.1%
45811.0 1
0.1%
38895.0 1
0.1%
36303.0 1
0.1%
35838.0 1
0.1%
29593.0 1
0.1%
25065.0 1
0.1%
23930.0 1
0.1%
22624.0 1
0.1%

선종3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
<NA>
1409 
어선
 
8
예부선
 
4
유조선
 
1
기타
 
1

Length

Max length4
Median length4
Mean length3.983837
Min length2

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row어선
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 1409
99.0%
어선 8
 
0.6%
예부선 4
 
0.3%
유조선 1
 
0.1%
기타 1
 
0.1%

Length

2023-12-12T14:37:49.036880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:37:49.293677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1409
99.0%
어선 8
 
0.6%
예부선 4
 
0.3%
유조선 1
 
0.1%
기타 1
 
0.1%

톤수3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)100.0%
Missing1409
Missing (%)99.0%
Infinite0
Infinite (%)0.0%
Mean466.19214
Minimum0.71
Maximum2242
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-12-12T14:37:49.526377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.71
5-th percentile0.827
Q13.685
median28
Q3642
95-th percentile2214.7
Maximum2242
Range2241.29
Interquartile range (IQR)638.315

Descriptive statistics

Standard deviation815.77906
Coefficient of variation (CV)1.7498773
Kurtosis1.5553507
Mean466.19214
Median Absolute Deviation (MAD)26.555
Skewness1.6815146
Sum6526.69
Variance665495.48
MonotonicityNot monotonic
2023-12-12T14:37:49.740301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
7.93 1
 
0.1%
2.27 1
 
0.1%
2200.0 1
 
0.1%
833.0 1
 
0.1%
26.0 1
 
0.1%
2242.0 1
 
0.1%
15.89 1
 
0.1%
1061.0 1
 
0.1%
36.0 1
 
0.1%
69.0 1
 
0.1%
Other values (4) 4
 
0.3%
(Missing) 1409
99.0%
ValueCountFrequency (%)
0.71 1
0.1%
0.89 1
0.1%
2.0 1
0.1%
2.27 1
0.1%
7.93 1
0.1%
15.89 1
0.1%
26.0 1
0.1%
30.0 1
0.1%
36.0 1
0.1%
69.0 1
0.1%
ValueCountFrequency (%)
2242.0 1
0.1%
2200.0 1
0.1%
1061.0 1
0.1%
833.0 1
0.1%
69.0 1
0.1%
36.0 1
0.1%
30.0 1
0.1%
26.0 1
0.1%
15.89 1
0.1%
7.93 1
0.1%

Interactions

2023-12-12T14:37:40.596956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:34.583446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:35.424635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:36.548167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:37.505879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:38.370214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:39.155999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:39.889811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:40.683538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:34.694130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:35.531165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:36.673677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:37.609409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:38.463056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:39.259599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:39.969459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:40.750126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:34.793768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:35.855941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:36.789402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:37.724473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:38.549283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:39.381456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:40.057838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:40.828204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:34.907040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:35.950643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:36.949232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:37.851136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:38.661516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:39.489835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:40.146456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:40.921394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:35.014091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:36.058271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:37.082111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:37.952540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:38.750287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:39.573620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:40.290361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:41.022703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:35.118772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:36.169131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:37.198911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:38.064619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:38.852299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:39.664000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:40.378224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:41.115311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:35.221898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:36.291688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:37.307069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:38.186812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:38.937586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:39.757525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:40.451916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:41.205818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:35.321427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:36.417801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:37.426311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:38.288766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:39.062182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:39.827478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:37:40.526276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:37:49.928915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관할해경서발생해역발생유형발생원인기상특보사고선박수구조인원부상인원사망인원실종인원선종1톤수1선종2톤수2선종3톤수3
관할해경서1.0000.7210.3630.3520.3560.1450.0390.0000.0260.0000.3190.0000.3070.0000.7770.820
발생해역0.7211.0000.3790.2880.3300.0420.0000.0000.0000.0710.1840.1520.0000.0000.6670.000
발생유형0.3630.3791.0000.7080.1680.6110.2100.1090.0000.1920.4400.2040.3980.0000.6190.311
발생원인0.3520.2880.7081.0000.4500.4180.0190.0000.0000.1910.2550.0640.1410.0000.5030.778
기상특보0.3560.3300.1680.4501.0000.7060.0000.0000.0910.0000.1680.3620.0000.0000.8060.000
사고선박수0.1450.0420.6110.4180.7061.0000.0000.0600.0000.0000.2060.0000.0000.0000.0000.000
구조인원0.0390.0000.2100.0190.0000.0001.0000.0000.0000.0000.5740.392NaNNaNNaNNaN
부상인원0.0000.0000.1090.0000.0000.0600.0001.0000.0000.4000.0000.0000.2020.000NaNNaN
사망인원0.0260.0000.0000.0000.0910.0000.0000.0001.0000.0000.0000.000NaNNaNNaNNaN
실종인원0.0000.0710.1920.1910.0000.0000.0000.4000.0001.0000.0000.0000.0000.000NaNNaN
선종10.3190.1840.4400.2550.1680.2060.5740.0000.0000.0001.0000.3380.8190.0000.9900.480
톤수10.0000.1520.2040.0640.3620.0000.3920.0000.0000.0000.3381.0000.0000.000NaNNaN
선종20.3070.0000.3980.1410.0000.000NaN0.202NaN0.0000.8190.0001.0000.3361.0000.778
톤수20.0000.0000.0000.0000.0000.000NaN0.000NaN0.0000.0000.0000.3361.000NaNNaN
선종30.7770.6670.6190.5030.8060.000NaNNaNNaNNaN0.990NaN1.000NaN1.0000.752
톤수30.8200.0000.3110.7780.0000.000NaNNaNNaNNaN0.480NaN0.778NaN0.7521.000
2023-12-12T14:37:50.179410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
선종1발생해역발생원인발생유형관할해경서선종2선종3기상특보
선종11.0000.0910.1180.1430.1300.4060.8600.076
발생해역0.0911.0000.1560.1970.3530.0000.6550.180
발생원인0.1180.1561.0000.4190.1560.0740.3800.158
발생유형0.1430.1970.4191.0000.1500.1420.5000.076
관할해경서0.1300.3530.1560.1501.0000.1360.5340.158
선종20.4060.0000.0740.1420.1361.0000.9490.000
선종30.8600.6550.3800.5000.5340.9491.0000.427
기상특보0.0760.1800.1580.0760.1580.0000.4271.000
2023-12-12T14:37:50.366943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사고선박수구조인원부상인원사망인원실종인원톤수1톤수2톤수3관할해경서발생해역발생유형발생원인기상특보선종1선종2선종3
사고선박수1.0000.2680.1690.0500.0470.179-0.066-0.2480.0710.0250.3690.2220.4440.1070.0000.000
구조인원0.2681.0000.052-0.011-0.0050.4960.6180.6380.0200.0000.1160.0110.0000.3611.0001.000
부상인원0.1690.0521.0000.1400.0490.004-0.086-0.3780.0000.0000.0700.0240.0000.0000.0081.000
사망인원0.050-0.0110.1401.0000.3530.0740.107NaN0.0200.0000.0000.0000.0900.0001.0001.000
실종인원0.047-0.0050.0490.3531.0000.0920.130NaN0.0000.0370.0880.0620.0000.0000.0001.000
톤수10.1790.4960.0040.0740.0921.0000.4800.7930.0000.1050.1240.0410.2390.2100.0001.000
톤수2-0.0660.618-0.0860.1070.1300.4801.0000.9340.0000.0000.0000.0000.0000.0000.1661.000
톤수3-0.2480.638-0.378NaNNaN0.7930.9341.0000.5870.0000.1830.6830.0000.1440.6830.365
관할해경서0.0710.0200.0000.0200.0000.0000.0000.5871.0000.3530.1500.1560.1580.1300.1360.534
발생해역0.0250.0000.0000.0000.0370.1050.0000.0000.3531.0000.1970.1560.1800.0910.0000.655
발생유형0.3690.1160.0700.0000.0880.1240.0000.1830.1500.1971.0000.4190.0760.1430.1420.500
발생원인0.2220.0110.0240.0000.0620.0410.0000.6830.1560.1560.4191.0000.1580.1180.0740.380
기상특보0.4440.0000.0000.0900.0000.2390.0000.0000.1580.1800.0760.1581.0000.0760.0000.427
선종10.1070.3610.0000.0000.0000.2100.0000.1440.1300.0910.1430.1180.0761.0000.4060.860
선종20.0001.0000.0081.0000.0000.0000.1660.6830.1360.0000.1420.0740.0000.4061.0000.949
선종30.0001.0001.0001.0001.0001.0001.0000.3650.5340.6550.5000.3800.4270.8600.9491.000

Missing values

2023-12-12T14:37:41.343974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:37:41.565409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-12T14:37:41.787811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

발생일시관할해경서발생해역발생유형발생원인기상특보사고선박수구조인원부상인원사망인원실종인원위도경도선종1톤수1선종2톤수2선종3톤수3
02010-12-30 17:15포항영해-EEZ기관고장정비불량풍랑주의보11300036|30|00130|05|00어선67.0<NA><NA><NA><NA>
12010-12-30 06:34포항영해-EEZ충돌운항부주의풍랑주의보21300036|14|50129|39|00어선9.77어선15.0<NA><NA>
22010-12-29 14:57군산영해충돌운항부주의황천4급21400036|03|00126|09|00어선23.0기타69.0<NA><NA>
32010-12-29 01:50여수항계내화재기타양호3000034|43|70127|44|50어선6.6어선9.77어선7.93
42010-12-28 15:49부산항계내전복기상악화풍랑주의보1100035|01|69129|00|67어선1.06<NA><NA><NA><NA>
52010-12-28 14:00포항영해-EEZ추진기장애기타풍랑주의보11000036|22|00130|23|00어선92.28<NA><NA><NA><NA>
62010-12-27 15:26목포협수로기관고장정비불량양호1300034|07|10126|05|58어선1.88<NA><NA><NA><NA>
72010-12-27 09:32동해영해침수기상악화양호1100037|13|50129|22|20어선1.0<NA><NA><NA><NA>
82010-12-26 17:45제주항계내기타기상악화풍랑주의보197600033|31|80126|32|30여객선9645.0<NA><NA><NA><NA>
92010-12-26 09:15목포영해전복적재불량풍랑주의보11500034|04|00125|31|00화물선495.0<NA><NA><NA><NA>
발생일시관할해경서발생해역발생유형발생원인기상특보사고선박수구조인원부상인원사망인원실종인원위도경도선종1톤수1선종2톤수2선종3톤수3
14132010-01-02 18:38통영영해화재화기취급부주의양호3110034|49|04128|22|09기타0.89기타0.89기타0.89
14142010-01-02 15:46통영영해추진기장애운항부주의양호1300034|50|11128|18|42어선7.93<NA><NA><NA><NA>
14152010-01-02 12:10여수협수로추진기장애운항부주의양호1300034|35|55127|44|15낚시어선1.27<NA><NA><NA><NA>
14162010-01-02 11:10울산협수로표류기타양호1700035|28|37129|26|47어선16.0<NA><NA><NA><NA>
14172010-01-02 06:35서귀포항계내침수기타황천5급1000033|14|30126|12|45어선2.84<NA><NA><NA><NA>
14182010-01-02 00:15통영영해화재기타양호1300034|43|40128|41|50예부선106.21<NA><NA><NA><NA>
14192010-01-01 17:30통영영해좌초운항부주의양호1500034|49|15128|20|30낚시어선4.97<NA><NA><NA><NA>
14202010-01-01 09:50통영항계내침수관리소홀양호1000034|50|60128|26|11어선4.99<NA><NA><NA><NA>
14212010-01-01 09:40목포협수로기관고장정비불량양호2600034|55|32126|05|38어선9.77어선9.77<NA><NA>
14222010-01-01 00:00여수영해추진기장애운항부주의황천4급1600033|59|60127|29|30어선7.93<NA><NA><NA><NA>