Overview

Dataset statistics

Number of variables10
Number of observations2287
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory185.5 KiB
Average record size in memory83.1 B

Variable types

Numeric3
Text4
DateTime1
Categorical2

Dataset

Description침몰선박의 정보를 체계적으로 관리하고 해양오염사고 유발 가능성에 대한 평가를 위한 침몰선박 현황 데이터로 침몰선박명 , 선종 , 총톤수 , 보고기관 등의 정보를 제공함
Author해양환경공단
URLhttps://www.data.go.kr/data/15004149/fileData.do

Alerts

위험물질량 is highly overall correlated with 총위해도High correlation
총위해도 is highly overall correlated with 위험물질량High correlation
선종 is highly imbalanced (66.9%)Imbalance
번호 has unique valuesUnique
위험물질량 has 977 (42.7%) zerosZeros
총위해도 has 946 (41.4%) zerosZeros

Reproduction

Analysis started2023-12-12 16:46:49.195276
Analysis finished2023-12-12 16:46:50.798899
Duration1.6 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct2287
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1144
Minimum1
Maximum2287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.2 KiB
2023-12-13T01:46:50.870528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile115.3
Q1572.5
median1144
Q31715.5
95-th percentile2172.7
Maximum2287
Range2286
Interquartile range (IQR)1143

Descriptive statistics

Standard deviation660.34435
Coefficient of variation (CV)0.57722409
Kurtosis-1.2
Mean1144
Median Absolute Deviation (MAD)572
Skewness0
Sum2616328
Variance436054.67
MonotonicityStrictly increasing
2023-12-13T01:46:51.349535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
1529 1
 
< 0.1%
1523 1
 
< 0.1%
1524 1
 
< 0.1%
1525 1
 
< 0.1%
1526 1
 
< 0.1%
1527 1
 
< 0.1%
1528 1
 
< 0.1%
1530 1
 
< 0.1%
1521 1
 
< 0.1%
Other values (2277) 2277
99.6%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
2287 1
< 0.1%
2286 1
< 0.1%
2285 1
< 0.1%
2284 1
< 0.1%
2283 1
< 0.1%
2282 1
< 0.1%
2281 1
< 0.1%
2280 1
< 0.1%
2279 1
< 0.1%
2278 1
< 0.1%

침몰선박명
Text

Distinct2171
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Memory size18.0 KiB
2023-12-13T01:46:51.665624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length5.5220813
Min length1

Characters and Unicode

Total characters12629
Distinct characters344
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2084 ?
Unique (%)91.1%

Sample

1st row101경남호
2nd row101고려호
3rd row101공진호
4th row101근양호
5th row101대성호
ValueCountFrequency (%)
무명 8
 
0.3%
광덕호 4
 
0.2%
대양호 4
 
0.2%
승진호 4
 
0.2%
대성호 4
 
0.2%
해성호 4
 
0.2%
일광호 3
 
0.1%
해덕호 3
 
0.1%
창영호 3
 
0.1%
광명호 3
 
0.1%
Other values (2184) 2270
98.3%
2023-12-13T01:46:52.202229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
호 2199
17.4%
1 818
 
6.5%
0 736
 
5.8%
9 635
 
5.0%
2 631
 
5.0%
( 493
 
3.9%
) 493
 
3.9%
제 464
 
3.7%
성 433
 
3.4%
3 299
 
2.4%
Other values (334) 5428
43.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7404
58.6%
Decimal Number 3985
31.6%
Open Punctuation 493
 
3.9%
Close Punctuation 493
 
3.9%
Uppercase Letter 197
 
1.6%
Space Separator 33
 
0.3%
Dash Punctuation 14
 
0.1%
Lowercase Letter 8
 
0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 2199
29.7%
제 464
 
6.3%
성 433
 
5.8%
진 258
 
3.5%
영 252
 
3.4%
대 198
 
2.7%
양 180
 
2.4%
광 162
 
2.2%
해 151
 
2.0%
동 142
 
1.9%
Other values (287) 2965
40.0%
Uppercase Letter
ValueCountFrequency (%)
N 28
14.2%
A 21
 
10.7%
I 16
 
8.1%
S 13
 
6.6%
E 13
 
6.6%
T 11
 
5.6%
O 11
 
5.6%
P 11
 
5.6%
G 10
 
5.1%
H 10
 
5.1%
Other values (14) 53
26.9%
Decimal Number
ValueCountFrequency (%)
1 818
20.5%
0 736
18.5%
9 635
15.9%
2 631
15.8%
3 299
 
7.5%
7 254
 
6.4%
5 198
 
5.0%
8 186
 
4.7%
6 148
 
3.7%
4 80
 
2.0%
Lowercase Letter
ValueCountFrequency (%)
i 2
25.0%
o 1
12.5%
z 1
12.5%
g 1
12.5%
n 1
12.5%
h 1
12.5%
s 1
12.5%
Other Punctuation
ValueCountFrequency (%)
. 1
50.0%
! 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 493
100.0%
Close Punctuation
ValueCountFrequency (%)
) 493
100.0%
Space Separator
ValueCountFrequency (%)
33
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7404
58.6%
Common 5020
39.7%
Latin 205
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 2199
29.7%
제 464
 
6.3%
성 433
 
5.8%
진 258
 
3.5%
영 252
 
3.4%
대 198
 
2.7%
양 180
 
2.4%
광 162
 
2.2%
해 151
 
2.0%
동 142
 
1.9%
Other values (287) 2965
40.0%
Latin
ValueCountFrequency (%)
N 28
13.7%
A 21
 
10.2%
I 16
 
7.8%
S 13
 
6.3%
E 13
 
6.3%
T 11
 
5.4%
O 11
 
5.4%
P 11
 
5.4%
G 10
 
4.9%
H 10
 
4.9%
Other values (21) 61
29.8%
Common
ValueCountFrequency (%)
1 818
16.3%
0 736
14.7%
9 635
12.6%
2 631
12.6%
( 493
9.8%
) 493
9.8%
3 299
 
6.0%
7 254
 
5.1%
5 198
 
3.9%
8 186
 
3.7%
Other values (6) 277
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7404
58.6%
ASCII 5225
41.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
호 2199
29.7%
제 464
 
6.3%
성 433
 
5.8%
진 258
 
3.5%
영 252
 
3.4%
대 198
 
2.7%
양 180
 
2.4%
광 162
 
2.2%
해 151
 
2.0%
동 142
 
1.9%
Other values (287) 2965
40.0%
ASCII
ValueCountFrequency (%)
1 818
15.7%
0 736
14.1%
9 635
12.2%
2 631
12.1%
( 493
9.4%
) 493
9.4%
3 299
 
5.7%
7 254
 
4.9%
5 198
 
3.8%
8 186
 
3.6%
Other values (37) 482
9.2%

침몰일자
Date

Distinct1953
Distinct (%)85.4%
Missing0
Missing (%)0.0%
Memory size18.0 KiB
Minimum1983-03-17 00:00:00
Maximum2023-06-08 00:00:00
2023-12-13T01:46:52.349598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:46:52.513230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

선종
Categorical

IMBALANCE 

Distinct12
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size18.0 KiB
어선
1881 
일반화물선
 
82
미상
 
75
기타
 
73
부선
 
62
Other values (7)
 
114

Length

Max length5
Median length2
Mean length2.1442938
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
어선 1881
82.2%
일반화물선 82
 
3.6%
미상 75
 
3.3%
기타 73
 
3.2%
부선 62
 
2.7%
예인선 50
 
2.2%
예선 30
 
1.3%
기타선 13
 
0.6%
유조선 10
 
0.4%
작업선 6
 
0.3%
Other values (2) 5
 
0.2%

Length

2023-12-13T01:46:52.648795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
어선 1881
82.2%
일반화물선 82
 
3.6%
미상 75
 
3.3%
기타 73
 
3.2%
부선 62
 
2.7%
예인선 50
 
2.2%
예선 30
 
1.3%
기타선 13
 
0.6%
유조선 10
 
0.4%
작업선 6
 
0.3%
Other values (2) 5
 
0.2%

사고유형
Categorical

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size18.0 KiB
전복
983 
미상
972 
충돌
127 
침수
108 
좌초
 
42
Other values (4)
 
55

Length

Max length4
Median length2
Mean length2.0139921
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row미상
2nd row미상
3rd row미상
4th row전복
5th row전복

Common Values

ValueCountFrequency (%)
전복 983
43.0%
미상 972
42.5%
충돌 127
 
5.6%
침수 108
 
4.7%
좌초 42
 
1.8%
화재 32
 
1.4%
기상악화 16
 
0.7%
폭발 4
 
0.2%
절단 3
 
0.1%

Length

2023-12-13T01:46:52.790562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:46:52.905782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전복 983
43.0%
미상 972
42.5%
충돌 127
 
5.6%
침수 108
 
4.7%
좌초 42
 
1.8%
화재 32
 
1.4%
기상악화 16
 
0.7%
폭발 4
 
0.2%
절단 3
 
0.1%

수심
Text

Distinct412
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size18.0 KiB
2023-12-13T01:46:53.247337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length1.8696983
Min length1

Characters and Unicode

Total characters4276
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique211 ?
Unique (%)9.2%

Sample

1st row0
2nd row0
3rd row0
4th row22.9
5th row80
ValueCountFrequency (%)
0 952
41.6%
육지 48
 
2.1%
2 33
 
1.4%
1 33
 
1.4%
100 31
 
1.4%
3 31
 
1.4%
50 30
 
1.3%
10 29
 
1.3%
30 23
 
1.0%
60 20
 
0.9%
Other values (402) 1057
46.2%
2023-12-13T01:46:53.890618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1406
32.9%
1 527
 
12.3%
. 362
 
8.5%
2 327
 
7.6%
3 279
 
6.5%
5 265
 
6.2%
4 250
 
5.8%
7 194
 
4.5%
6 193
 
4.5%
9 193
 
4.5%
Other values (5) 280
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3814
89.2%
Other Punctuation 362
 
8.5%
Other Letter 100
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1406
36.9%
1 527
 
13.8%
2 327
 
8.6%
3 279
 
7.3%
5 265
 
6.9%
4 250
 
6.6%
7 194
 
5.1%
6 193
 
5.1%
9 193
 
5.1%
8 180
 
4.7%
Other Letter
ValueCountFrequency (%)
육 48
48.0%
지 48
48.0%
미 2
 
2.0%
상 2
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 362
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4176
97.7%
Hangul 100
 
2.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1406
33.7%
1 527
 
12.6%
. 362
 
8.7%
2 327
 
7.8%
3 279
 
6.7%
5 265
 
6.3%
4 250
 
6.0%
7 194
 
4.6%
6 193
 
4.6%
9 193
 
4.6%
Hangul
ValueCountFrequency (%)
육 48
48.0%
지 48
48.0%
미 2
 
2.0%
상 2
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4176
97.7%
Hangul 100
 
2.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1406
33.7%
1 527
 
12.6%
. 362
 
8.7%
2 327
 
7.8%
3 279
 
6.7%
5 265
 
6.3%
4 250
 
6.0%
7 194
 
4.6%
6 193
 
4.6%
9 193
 
4.6%
Hangul
ValueCountFrequency (%)
육 48
48.0%
지 48
48.0%
미 2
 
2.0%
상 2
 
2.0%

위험물질량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct692
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8564443
Minimum0
Maximum380
Zeros977
Zeros (%)42.7%
Negative0
Negative (%)0.0%
Memory size20.2 KiB
2023-12-13T01:46:54.073534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5325
Q33.46835
95-th percentile24.128
Maximum380
Range380
Interquartile range (IQR)3.46835

Descriptive statistics

Standard deviation15.597415
Coefficient of variation (CV)3.2116944
Kurtosis251.83235
Mean4.8564443
Median Absolute Deviation (MAD)0.5325
Skewness13.035609
Sum11106.688
Variance243.27934
MonotonicityNot monotonic
2023-12-13T01:46:54.267785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 977
42.7%
2.81515 30
 
1.3%
0.1 25
 
1.1%
3.46835 24
 
1.0%
10.295 23
 
1.0%
0.2 17
 
0.7%
13.845 12
 
0.5%
0.355 12
 
0.5%
3.55 12
 
0.5%
5.325 11
 
0.5%
Other values (682) 1144
50.0%
ValueCountFrequency (%)
0.0 977
42.7%
0.02 1
 
< 0.1%
0.03 1
 
< 0.1%
0.05 1
 
< 0.1%
0.06 1
 
< 0.1%
0.1 25
 
1.1%
0.1065 1
 
< 0.1%
0.1136 2
 
0.1%
0.13135 1
 
< 0.1%
0.142 1
 
< 0.1%
ValueCountFrequency (%)
380.0 1
< 0.1%
320.0 1
< 0.1%
227.0 1
< 0.1%
202.0 1
< 0.1%
126.0 1
< 0.1%
112.6 1
< 0.1%
95.78 1
< 0.1%
95.2 1
< 0.1%
84.95 1
< 0.1%
78.8 1
< 0.1%

총위해도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.352864
Minimum0
Maximum61
Zeros946
Zeros (%)41.4%
Negative0
Negative (%)0.0%
Memory size20.2 KiB
2023-12-13T01:46:54.475348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median26
Q337
95-th percentile45
Maximum61
Range61
Interquartile range (IQR)37

Descriptive statistics

Standard deviation18.115778
Coefficient of variation (CV)0.89008497
Kurtosis-1.620812
Mean20.352864
Median Absolute Deviation (MAD)16.5
Skewness-0.02941802
Sum46547
Variance328.18143
MonotonicityNot monotonic
2023-12-13T01:46:54.683472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 946
41.4%
37.0 118
 
5.2%
39.0 80
 
3.5%
36.0 69
 
3.0%
35.0 57
 
2.5%
32.0 54
 
2.4%
33.0 54
 
2.4%
38.0 51
 
2.2%
30.0 50
 
2.2%
34.0 47
 
2.1%
Other values (70) 761
33.3%
ValueCountFrequency (%)
0.0 946
41.4%
15.0 1
 
< 0.1%
17.0 3
 
0.1%
17.5 1
 
< 0.1%
18.0 13
 
0.6%
19.0 11
 
0.5%
19.5 3
 
0.1%
20.0 6
 
0.3%
20.5 2
 
0.1%
21.0 25
 
1.1%
ValueCountFrequency (%)
61.0 1
 
< 0.1%
60.5 1
 
< 0.1%
60.0 1
 
< 0.1%
59.0 2
 
0.1%
56.0 5
0.2%
55.0 1
 
< 0.1%
54.0 4
0.2%
53.5 2
 
0.1%
53.0 2
 
0.1%
52.0 5
0.2%

위도
Text

Distinct1394
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Memory size18.0 KiB
2023-12-13T01:46:55.037834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length13
Mean length6.5579362
Min length1

Characters and Unicode

Total characters14998
Distinct characters20
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1129 ?
Unique (%)49.4%

Sample

1st row37-14-0
2nd row33-34-0
3rd row32-47-0
4th row35-34-48
5th row36-6-15
ValueCountFrequency (%)
0 376
 
16.4%
34-45-0 13
 
0.6%
34-15-0 11
 
0.5%
34-40-0 10
 
0.4%
37-30-0 8
 
0.3%
35-59-0 7
 
0.3%
35-0-0 7
 
0.3%
34-0-0 7
 
0.3%
33-42-0 7
 
0.3%
34-58-0 7
 
0.3%
Other values (1386) 1839
80.2%
2023-12-13T01:46:55.579614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 3817
25.5%
3 3066
20.4%
0 1891
12.6%
4 1423
 
9.5%
5 1200
 
8.0%
2 875
 
5.8%
1 741
 
4.9%
7 596
 
4.0%
6 567
 
3.8%
8 403
 
2.7%
Other values (10) 419
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11036
73.6%
Dash Punctuation 3817
 
25.5%
Other Punctuation 126
 
0.8%
Space Separator 12
 
0.1%
Other Letter 4
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Control 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 3066
27.8%
0 1891
17.1%
4 1423
12.9%
5 1200
 
10.9%
2 875
 
7.9%
1 741
 
6.7%
7 596
 
5.4%
6 567
 
5.1%
8 403
 
3.7%
9 274
 
2.5%
Other Letter
ValueCountFrequency (%)
선 2
50.0%
수 1
25.0%
미 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 124
98.4%
: 2
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 3817
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14994
> 99.9%
Hangul 4
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
- 3817
25.5%
3 3066
20.4%
0 1891
12.6%
4 1423
 
9.5%
5 1200
 
8.0%
2 875
 
5.8%
1 741
 
4.9%
7 596
 
4.0%
6 567
 
3.8%
8 403
 
2.7%
Other values (7) 415
 
2.8%
Hangul
ValueCountFrequency (%)
선 2
50.0%
수 1
25.0%
미 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14994
> 99.9%
Hangul 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 3817
25.5%
3 3066
20.4%
0 1891
12.6%
4 1423
 
9.5%
5 1200
 
8.0%
2 875
 
5.8%
1 741
 
4.9%
7 596
 
4.0%
6 567
 
3.8%
8 403
 
2.7%
Other values (7) 415
 
2.8%
Hangul
ValueCountFrequency (%)
선 2
50.0%
수 1
25.0%
미 1
25.0%

경도
Text

Distinct1391
Distinct (%)60.8%
Missing0
Missing (%)0.0%
Memory size18.0 KiB
2023-12-13T01:46:55.965587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length14
Mean length7.3463052
Min length1

Characters and Unicode

Total characters16801
Distinct characters18
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1110 ?
Unique (%)48.5%

Sample

1st row126-11-0
2nd row126-10-0
3rd row123-40-0
4th row126-7-30
5th row129-38-50
ValueCountFrequency (%)
0 376
 
16.4%
126-0-0 12
 
0.5%
126-35-0 10
 
0.4%
126-40-0 9
 
0.4%
126-14-0 8
 
0.3%
126-2-0 8
 
0.3%
126-22-0 8
 
0.3%
126-30-0 7
 
0.3%
126-8-0 6
 
0.3%
127-7-0 6
 
0.3%
Other values (1386) 1844
80.4%
2023-12-13T01:46:56.524198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 3812
22.7%
1 2665
15.9%
2 2573
15.3%
0 1893
11.3%
5 1132
 
6.7%
3 1021
 
6.1%
4 891
 
5.3%
6 871
 
5.2%
8 650
 
3.9%
9 610
 
3.6%
Other values (8) 683
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12847
76.5%
Dash Punctuation 3812
 
22.7%
Other Punctuation 130
 
0.8%
Space Separator 7
 
< 0.1%
Other Letter 4
 
< 0.1%
Control 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2665
20.7%
2 2573
20.0%
0 1893
14.7%
5 1132
8.8%
3 1021
 
7.9%
4 891
 
6.9%
6 871
 
6.8%
8 650
 
5.1%
9 610
 
4.7%
7 541
 
4.2%
Other Letter
ValueCountFrequency (%)
선 2
50.0%
수 1
25.0%
미 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 128
98.5%
: 2
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 3812
100.0%
Space Separator
ValueCountFrequency (%)
7
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16797
> 99.9%
Hangul 4
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
- 3812
22.7%
1 2665
15.9%
2 2573
15.3%
0 1893
11.3%
5 1132
 
6.7%
3 1021
 
6.1%
4 891
 
5.3%
6 871
 
5.2%
8 650
 
3.9%
9 610
 
3.6%
Other values (5) 679
 
4.0%
Hangul
ValueCountFrequency (%)
선 2
50.0%
수 1
25.0%
미 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16797
> 99.9%
Hangul 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 3812
22.7%
1 2665
15.9%
2 2573
15.3%
0 1893
11.3%
5 1132
 
6.7%
3 1021
 
6.1%
4 891
 
5.3%
6 871
 
5.2%
8 650
 
3.9%
9 610
 
3.6%
Other values (5) 679
 
4.0%
Hangul
ValueCountFrequency (%)
선 2
50.0%
수 1
25.0%
미 1
25.0%

Interactions

2023-12-13T01:46:50.335569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:46:49.742910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:46:50.046812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:46:50.420997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:46:49.830409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:46:50.137561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:46:50.510084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:46:49.948674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:46:50.230964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:46:56.664141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호선종사고유형위험물질량총위해도
번호1.0000.1130.1280.1180.098
선종0.1131.0000.5350.4440.426
사고유형0.1280.5351.0000.3260.800
위험물질량0.1180.4440.3261.0000.504
총위해도0.0980.4260.8000.5041.000
2023-12-13T01:46:56.776390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사고유형선종
사고유형1.0000.260
선종0.2601.000
2023-12-13T01:46:56.875411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호위험물질량총위해도선종사고유형
번호1.0000.0110.0660.0470.058
위험물질량0.0111.0000.7860.2340.178
총위해도0.0660.7861.0000.1960.375
선종0.0470.2340.1961.0000.260
사고유형0.0580.1780.3750.2601.000

Missing values

2023-12-13T01:46:50.624164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:46:50.751246image/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.

Sample

번호침몰선박명침몰일자선종사고유형수심위험물질량총위해도위도경도
01101경남호2004-05-28예선미상00.00.037-14-0126-11-0
12101고려호1992-12-31어선미상00.00.033-34-0126-10-0
23101공진호2009-07-19어선미상00.00.032-47-0123-40-0
34101근양호1993-01-21어선전복22.934.8503537.035-34-48126-7-30
45101대성호2001-10-28어선전복8020.5932.036-6-15129-38-50
56101대양호1993-11-12어선충돌973.028.533-43-0125-20-0
67101봉진호1994-04-27어선미상00.00.033-45-0124-45-0
78101성원호2002-12-24어선침수7035.14529.036-11-48129-37-12
89101영신호2005-12-20예인선미상112.3636.535-29-0126-13-00
910101우일호2011-11-17어선충돌23.0741.533-25-0126-15-20
번호침몰선박명침몰일자선종사고유형수심위험물질량총위해도위도경도
22772278효명7호2019-11-24예인선침수312.033.037-03-17126-27-35
22782279효성호2020-07-20어선침수0.90.240.034-49-28127-46-05
22792280휘영호1988-07-08어선미상00.00.036-55-0125-40-0
22802281흥광호2000-02-03어선미상00.00.000
22812282흥국호1997-12-23어선미상00.00.037-16-0131-54-0
22822283흥룡호1984-09-25어선미상00.00.037-49-0129-23-0
22832284흥산호2001-04-11기타전복507.8134.034-48-8128-19-49
22842285흥신2호1996-06-19어선전복8.14.2647.035-59-9126-31-24
22852286흥신호1998-04-23어선미상00.00.000
22862287흥양호2007-07-29어선전복13331.0259523.037-21-22129-26-48