Overview

Dataset statistics

Number of variables21
Number of observations317
Missing cells343
Missing cells (%)5.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory54.6 KiB
Average record size in memory176.4 B

Variable types

Categorical8
Text6
Numeric6
Boolean1

Dataset

Description해양수산부_EEZ_일본어선한국EEZ허가기본정보로 허가년도,EEZ허가번호,어선영문명,어선일문명,일본어선번호,어업종류코드,국가코드,선적항영문명,선적항일문명,선박총톤수,선박마력,최대속도,최대승무원수,호출부호명,선창종류코드,선창수량,선창용량,부속선척수,허가일자,한국공사명,EEZ신청번호,최대허용어획량,출력단위구분,허가종료사유내역,허가종료일자,허가증번호,허가유무,이전EEZ허가번호,일련번호,신청구분,신청한글내용,신청일문내용,어선종류내역,최초생성시점,최종변경시점,데이터기준일자 제공합니다.
Author공공데이터포털
URLhttps://www.data.go.kr/data/15115864/fileData.do

Alerts

허가년도 has constant value ""Constant
데이터기준일자 has constant value ""Constant
부속선척수 is highly imbalanced (96.9%)Imbalance
허가유무 is highly imbalanced (67.3%)Imbalance
신청한글내용 is highly imbalanced (75.1%)Imbalance
이전배타적경제수역(EEZ)허가번호 has 313 (98.7%) missing valuesMissing
일련번호 has 27 (8.5%) missing valuesMissing
배타적경제수역(EEZ)허가번호 has unique valuesUnique
배타적경제수역(EEZ)신청번호 has unique valuesUnique
허가증번호 has unique valuesUnique

Reproduction

Analysis started2024-04-20 22:51:43.261581
Analysis finished2024-04-20 22:51:43.705853
Duration0.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

허가년도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2015
317 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2015 317
100.0%

Length

2024-04-21T07:51:43.815791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T07:51:43.977058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2015 317
100.0%

배타적경제수역(EEZ)허가번호
Text

UNIQUE 

Distinct317
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-04-21T07:51:45.158076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters2536
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique317 ?
Unique (%)100.0%

Sample

1st rowJ01-0015
2nd rowJ01-0024
3rd rowJ01-0025
4th rowJ01-0026
5th rowJ01-0035
ValueCountFrequency (%)
j01-0015 1
 
0.3%
j06-0340 1
 
0.3%
j06-0320 1
 
0.3%
j06-0318 1
 
0.3%
j06-0051 1
 
0.3%
j06-0018 1
 
0.3%
j06-0016 1
 
0.3%
j05-h001 1
 
0.3%
j05-e001 1
 
0.3%
j05-a001 1
 
0.3%
Other values (307) 307
96.8%
2024-04-21T07:51:46.587238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 833
32.8%
J 317
 
12.5%
- 317
 
12.5%
1 234
 
9.2%
5 169
 
6.7%
3 111
 
4.4%
4 108
 
4.3%
6 100
 
3.9%
7 87
 
3.4%
2 85
 
3.4%
Other values (7) 175
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1829
72.1%
Uppercase Letter 390
 
15.4%
Dash Punctuation 317
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 833
45.5%
1 234
 
12.8%
5 169
 
9.2%
3 111
 
6.1%
4 108
 
5.9%
6 100
 
5.5%
7 87
 
4.8%
2 85
 
4.6%
9 55
 
3.0%
8 47
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
J 317
81.3%
H 40
 
10.3%
E 17
 
4.4%
C 9
 
2.3%
B 4
 
1.0%
A 3
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 317
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2146
84.6%
Latin 390
 
15.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 833
38.8%
- 317
 
14.8%
1 234
 
10.9%
5 169
 
7.9%
3 111
 
5.2%
4 108
 
5.0%
6 100
 
4.7%
7 87
 
4.1%
2 85
 
4.0%
9 55
 
2.6%
Latin
ValueCountFrequency (%)
J 317
81.3%
H 40
 
10.3%
E 17
 
4.4%
C 9
 
2.3%
B 4
 
1.0%
A 3
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 833
32.8%
J 317
 
12.5%
- 317
 
12.5%
1 234
 
9.2%
5 169
 
6.7%
3 111
 
4.4%
4 108
 
4.3%
6 100
 
3.9%
7 87
 
3.4%
2 85
 
3.4%
Other values (7) 175
 
6.9%

어선영문명
Text

Distinct295
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-04-21T07:51:47.657928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length19
Mean length14.599369
Min length4

Characters and Unicode

Total characters4628
Distinct characters35
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

Unique279 ?
Unique (%)88.0%

Sample

1st rowNo.18 KIYO-MARU
2nd rowNo.18 SHOTOKU-MARU
3rd rowNo.21 SHOTOKU-MARU
4th rowNo.31 SHOTOKU-MARU
5th rowNo.8 KOYO-MARU
ValueCountFrequency (%)
maru 40
 
6.8%
shotoku-maru 27
 
4.6%
dai 25
 
4.3%
genpuku-maru 14
 
2.4%
kaiko-maru 13
 
2.2%
no.1 9
 
1.5%
daiei-maru 9
 
1.5%
tenoh-maru 9
 
1.5%
no.5 8
 
1.4%
no.2 8
 
1.4%
Other values (242) 424
72.4%
2024-04-21T07:51:49.022333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 575
12.4%
A 524
 
11.3%
M 341
 
7.4%
R 339
 
7.3%
298
 
6.4%
O 277
 
6.0%
I 274
 
5.9%
N 255
 
5.5%
K 194
 
4.2%
. 159
 
3.4%
Other values (25) 1392
30.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3545
76.6%
Decimal Number 343
 
7.4%
Space Separator 298
 
6.4%
Other Punctuation 159
 
3.4%
Dash Punctuation 145
 
3.1%
Lowercase Letter 138
 
3.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 575
16.2%
A 524
14.8%
M 341
9.6%
R 339
9.6%
O 277
7.8%
I 274
7.7%
N 255
7.2%
K 194
 
5.5%
S 148
 
4.2%
E 120
 
3.4%
Other values (12) 498
14.0%
Decimal Number
ValueCountFrequency (%)
1 91
26.5%
8 64
18.7%
2 53
15.5%
3 47
13.7%
5 38
11.1%
7 24
 
7.0%
6 24
 
7.0%
0 2
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 143
98.6%
－ 2
 
1.4%
Space Separator
ValueCountFrequency (%)
298
100.0%
Other Punctuation
ValueCountFrequency (%)
. 159
100.0%
Lowercase Letter
ValueCountFrequency (%)
o 138
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3683
79.6%
Common 945
 
20.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 575
15.6%
A 524
14.2%
M 341
9.3%
R 339
9.2%
O 277
7.5%
I 274
7.4%
N 255
6.9%
K 194
 
5.3%
S 148
 
4.0%
o 138
 
3.7%
Other values (13) 618
16.8%
Common
ValueCountFrequency (%)
298
31.5%
. 159
16.8%
- 143
15.1%
1 91
 
9.6%
8 64
 
6.8%
2 53
 
5.6%
3 47
 
5.0%
5 38
 
4.0%
7 24
 
2.5%
6 24
 
2.5%
Other values (2) 4
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4626
> 99.9%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 575
12.4%
A 524
 
11.3%
M 341
 
7.4%
R 339
 
7.3%
298
 
6.4%
O 277
 
6.0%
I 274
 
5.9%
N 255
 
5.5%
K 194
 
4.2%
. 159
 
3.4%
Other values (24) 1390
30.0%
None
ValueCountFrequency (%)
－ 2
100.0%

일본어선번호
Text

Distinct306
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-04-21T07:51:50.167737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length8.2586751
Min length7

Characters and Unicode

Total characters2618
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique297 ?
Unique (%)93.7%

Sample

1st rowNS1-1015
2nd rowNS1-1039
3rd rowNS1-1104
4th rowNS1-1104
5th rowTT1-158
ValueCountFrequency (%)
ns2-10338 3
 
0.9%
ns2-10361 3
 
0.9%
ns1-1104 3
 
0.9%
ns2-10333 2
 
0.6%
tt2-1786 2
 
0.6%
ns1-1081 2
 
0.6%
ns2-10313 2
 
0.6%
sa2-1895 2
 
0.6%
sa2-1958 2
 
0.6%
wk2-5096 1
 
0.3%
Other values (295) 295
93.1%
2024-04-21T07:51:51.570778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 368
14.1%
2 323
12.3%
- 317
12.1%
3 192
 
7.3%
S 176
 
6.7%
0 175
 
6.7%
N 162
 
6.2%
5 142
 
5.4%
6 116
 
4.4%
8 93
 
3.6%
Other values (17) 554
21.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1655
63.2%
Uppercase Letter 633
 
24.2%
Dash Punctuation 317
 
12.1%
Space Separator 13
 
0.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 176
27.8%
N 162
25.6%
T 57
 
9.0%
F 33
 
5.2%
O 32
 
5.1%
K 25
 
3.9%
G 23
 
3.6%
M 21
 
3.3%
Z 20
 
3.2%
W 19
 
3.0%
Other values (5) 65
 
10.3%
Decimal Number
ValueCountFrequency (%)
1 368
22.2%
2 323
19.5%
3 192
11.6%
0 175
10.6%
5 142
 
8.6%
6 116
 
7.0%
8 93
 
5.6%
4 89
 
5.4%
7 87
 
5.3%
9 70
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 317
100.0%
Space Separator
ValueCountFrequency (%)
13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1985
75.8%
Latin 633
 
24.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 176
27.8%
N 162
25.6%
T 57
 
9.0%
F 33
 
5.2%
O 32
 
5.1%
K 25
 
3.9%
G 23
 
3.6%
M 21
 
3.3%
Z 20
 
3.2%
W 19
 
3.0%
Other values (5) 65
 
10.3%
Common
ValueCountFrequency (%)
1 368
18.5%
2 323
16.3%
- 317
16.0%
3 192
9.7%
0 175
8.8%
5 142
 
7.2%
6 116
 
5.8%
8 93
 
4.7%
4 89
 
4.5%
7 87
 
4.4%
Other values (2) 83
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2618
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 368
14.1%
2 323
12.3%
- 317
12.1%
3 192
 
7.3%
S 176
 
6.7%
0 175
 
6.7%
N 162
 
6.2%
5 142
 
5.4%
6 116
 
4.4%
8 93
 
3.6%
Other values (17) 554
21.2%

선적항영문명
Categorical

Distinct48
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
NAGASAKI-KEN NAGASAKI-SHI
42 
NAGASAKIKEN OZIKACHOU
30 
FUKUOKAKEN MUNAKATASHI
29 
NAGASAKI-KEN HIRADO-SHI
24 
EHIME-KEN MINAMIUWA-GUN AINAN-CHO
 
15
Other values (43)
177 

Length

Max length48
Median length34
Mean length24.77918
Min length16

Unique

Unique15 ?
Unique (%)4.7%

Sample

1st rowFUKUOKA-KEN FUKUOKA-SHI
2nd rowNAGASAKI-KEN NAGASAKI-SHI
3rd rowNAGASAKI-KEN NAGASAKI-SHI
4th rowNAGASAKI-KEN NAGASAKI-SHI
5th rowTOTTORI-KEN SAKAIMINATO-SHI

Common Values

ValueCountFrequency (%)
NAGASAKI-KEN NAGASAKI-SHI 42
 
13.2%
NAGASAKIKEN OZIKACHOU 30
 
9.5%
FUKUOKAKEN MUNAKATASHI 29
 
9.1%
NAGASAKI-KEN HIRADO-SHI 24
 
7.6%
EHIME-KEN MINAMIUWA-GUN AINAN-CHO 15
 
4.7%
SAGAKEN KARATUSHI 15
 
4.7%
TOTTORI-KEN SAKAIMINATO-SHI 13
 
4.1%
MIYAZAKIKEN NICHINANSHI NANGOUCHOU 12
 
3.8%
YAMAGUCHI-KEN SHIMONOSEKI-SHI 12
 
3.8%
NAGASAKIKEN OZIKACHOU 11
 
3.5%
Other values (38) 114
36.0%

Length

2024-04-21T07:51:52.035434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nagasaki-ken 83
 
12.2%
nagasakiken 51
 
7.5%
nagasaki-shi 49
 
7.2%
ozikachou 41
 
6.0%
fukuokaken 31
 
4.6%
munakatashi 30
 
4.4%
hirado-shi 24
 
3.5%
miyazakiken 20
 
2.9%
nichinanshi 20
 
2.9%
wakayama-ken 19
 
2.8%
Other values (58) 313
46.0%

선박총톤수
Real number (ℝ)

Distinct101
Distinct (%)31.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.112461
Minimum4.5
Maximum692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-21T07:51:52.260579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile4.9
Q112
median75
Q3119
95-th percentile328.4
Maximum692
Range687.5
Interquartile range (IQR)107

Descriptive statistics

Standard deviation108.93064
Coefficient of variation (CV)1.1574519
Kurtosis2.9322957
Mean94.112461
Median Absolute Deviation (MAD)60
Skewness1.6409705
Sum29833.65
Variance11865.885
MonotonicityNot monotonic
2024-04-21T07:51:52.510669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85.0 43
 
13.6%
19.0 35
 
11.0%
75.0 18
 
5.7%
135.0 16
 
5.0%
4.9 16
 
5.0%
7.3 8
 
2.5%
6.6 7
 
2.2%
18.0 7
 
2.2%
8.5 6
 
1.9%
7.9 6
 
1.9%
Other values (91) 155
48.9%
ValueCountFrequency (%)
4.5 1
 
0.3%
4.8 3
 
0.9%
4.85 1
 
0.3%
4.9 16
5.0%
5.0 1
 
0.3%
5.3 1
 
0.3%
5.5 3
 
0.9%
6.0 5
 
1.6%
6.1 3
 
0.9%
6.2 3
 
0.9%
ValueCountFrequency (%)
692.0 1
 
0.3%
441.0 1
 
0.3%
396.0 1
 
0.3%
359.0 1
 
0.3%
343.0 1
 
0.3%
340.0 2
0.6%
339.0 3
0.9%
338.0 3
0.9%
335.0 1
 
0.3%
334.0 1
 
0.3%

선박마력
Real number (ℝ)

Distinct59
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean536.82965
Minimum70
Maximum2574
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-21T07:51:52.767089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile90
Q1260
median500
Q3669
95-th percentile1000
Maximum2574
Range2504
Interquartile range (IQR)409

Descriptive statistics

Standard deviation418.47644
Coefficient of variation (CV)0.77953303
Kurtosis7.378883
Mean536.82965
Median Absolute Deviation (MAD)212
Skewness2.3023776
Sum170175
Variance175122.53
MonotonicityNot monotonic
2024-04-21T07:51:53.026456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500 47
14.8%
736 27
 
8.5%
90 27
 
8.5%
640 24
 
7.6%
850 22
 
6.9%
190 13
 
4.1%
120 13
 
4.1%
160 13
 
4.1%
440 12
 
3.8%
380 8
 
2.5%
Other values (49) 111
35.0%
ValueCountFrequency (%)
70 1
 
0.3%
80 6
 
1.9%
90 27
8.5%
100 2
 
0.6%
110 1
 
0.3%
120 13
4.1%
140 1
 
0.3%
150 1
 
0.3%
160 13
4.1%
190 13
4.1%
ValueCountFrequency (%)
2574 1
 
0.3%
2500 1
 
0.3%
2206 5
1.6%
2059 3
0.9%
1885 1
 
0.3%
1839 1
 
0.3%
1618 1
 
0.3%
1471 2
 
0.6%
1000 2
 
0.6%
950 1
 
0.3%

최대속도
Real number (ℝ)

Distinct61
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.352934
Minimum5
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-21T07:51:53.327251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile11
Q112.5
median14.2
Q317
95-th percentile28.2
Maximum34
Range29
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation5.6148188
Coefficient of variation (CV)0.34335238
Kurtosis0.42426077
Mean16.352934
Median Absolute Deviation (MAD)1.84
Skewness1.1805019
Sum5183.88
Variance31.52619
MonotonicityNot monotonic
2024-04-21T07:51:53.581612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.0 40
12.6%
16.0 40
12.6%
25.0 29
 
9.1%
12.0 27
 
8.5%
11.0 26
 
8.2%
14.0 24
 
7.6%
15.0 16
 
5.0%
30.0 12
 
3.8%
12.5 7
 
2.2%
10.0 7
 
2.2%
Other values (51) 89
28.1%
ValueCountFrequency (%)
5.0 2
 
0.6%
10.0 7
 
2.2%
11.0 26
8.2%
11.49 1
 
0.3%
11.71 2
 
0.6%
11.75 1
 
0.3%
11.77 1
 
0.3%
11.79 1
 
0.3%
11.89 2
 
0.6%
12.0 27
8.5%
ValueCountFrequency (%)
34.0 1
 
0.3%
32.0 2
 
0.6%
30.0 12
3.8%
29.0 1
 
0.3%
28.0 3
 
0.9%
27.0 3
 
0.9%
26.0 5
 
1.6%
25.0 29
9.1%
24.0 1
 
0.3%
23.0 5
 
1.6%

최대승무원수
Real number (ℝ)

Distinct28
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.555205
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-21T07:51:53.802656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q312
95-th percentile24
Maximum33
Range32
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.1175225
Coefficient of variation (CV)0.74488433
Kurtosis0.43174053
Mean9.555205
Median Absolute Deviation (MAD)4
Skewness0.96032207
Sum3029
Variance50.659126
MonotonicityNot monotonic
2024-04-21T07:51:54.017809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 44
13.9%
8 41
12.9%
12 28
 
8.8%
6 20
 
6.3%
9 17
 
5.4%
13 17
 
5.4%
10 17
 
5.4%
3 16
 
5.0%
2 15
 
4.7%
4 14
 
4.4%
Other values (18) 88
27.8%
ValueCountFrequency (%)
1 44
13.9%
2 15
 
4.7%
3 16
 
5.0%
4 14
 
4.4%
5 4
 
1.3%
6 20
6.3%
7 12
 
3.8%
8 41
12.9%
9 17
 
5.4%
10 17
 
5.4%
ValueCountFrequency (%)
33 1
 
0.3%
30 2
 
0.6%
29 1
 
0.3%
28 1
 
0.3%
27 1
 
0.3%
25 9
2.8%
24 9
2.8%
23 10
3.2%
22 5
1.6%
21 2
 
0.6%

호출부호명
Text

Distinct302
Distinct (%)96.2%
Missing3
Missing (%)0.9%
Memory size2.6 KiB
2024-04-21T07:51:55.206852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length27
Mean length11.754777
Min length1

Characters and Unicode

Total characters3691
Distinct characters37
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique293 ?
Unique (%)93.3%

Sample

1st rowJM5643
2nd rowJM5805
3rd rowJM6036
4th rowJM6036
5th rowJK4936
ValueCountFrequency (%)
maru 20
 
4.1%
dai 18
 
3.7%
jh 8
 
1.7%
jm 7
 
1.4%
akebonomaru 6
 
1.2%
tooyama 5
 
1.0%
yamaguchimaru 4
 
0.8%
ichi 4
 
0.8%
makiyama 4
 
0.8%
nakamura 4
 
0.8%
Other values (350) 403
83.4%
2024-04-21T07:51:56.653556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 453
 
12.3%
U 372
 
10.1%
M 316
 
8.6%
I 278
 
7.5%
R 198
 
5.4%
183
 
5.0%
J 174
 
4.7%
O 170
 
4.6%
5 128
 
3.5%
S 125
 
3.4%
Other values (27) 1294
35.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2890
78.3%
Decimal Number 614
 
16.6%
Space Separator 183
 
5.0%
Other Letter 2
 
0.1%
Lowercase Letter 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 453
15.7%
U 372
12.9%
M 316
10.9%
I 278
9.6%
R 198
 
6.9%
J 174
 
6.0%
O 170
 
5.9%
S 125
 
4.3%
K 124
 
4.3%
N 116
 
4.0%
Other values (13) 564
19.5%
Decimal Number
ValueCountFrequency (%)
5 128
20.8%
6 88
14.3%
3 73
11.9%
8 60
9.8%
9 52
8.5%
2 48
 
7.8%
0 46
 
7.5%
4 42
 
6.8%
1 41
 
6.7%
7 36
 
5.9%
Space Separator
ValueCountFrequency (%)
183
100.0%
Other Letter
ValueCountFrequency (%)
無 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
o 1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2891
78.3%
Common 798
 
21.6%
Han 2
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 453
15.7%
U 372
12.9%
M 316
10.9%
I 278
9.6%
R 198
 
6.8%
J 174
 
6.0%
O 170
 
5.9%
S 125
 
4.3%
K 124
 
4.3%
N 116
 
4.0%
Other values (14) 565
19.5%
Common
ValueCountFrequency (%)
183
22.9%
5 128
16.0%
6 88
11.0%
3 73
 
9.1%
8 60
 
7.5%
9 52
 
6.5%
2 48
 
6.0%
0 46
 
5.8%
4 42
 
5.3%
1 41
 
5.1%
Other values (2) 37
 
4.6%
Han
ValueCountFrequency (%)
無 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3689
99.9%
CJK 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 453
 
12.3%
U 372
 
10.1%
M 316
 
8.6%
I 278
 
7.5%
R 198
 
5.4%
183
 
5.0%
J 174
 
4.7%
O 170
 
4.6%
5 128
 
3.5%
S 125
 
3.4%
Other values (26) 1292
35.0%
CJK
ValueCountFrequency (%)
無 2
100.0%

부속선척수
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
0
316 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 316
99.7%
1 1
 
0.3%

Length

2024-04-21T07:51:56.880182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T07:51:57.048844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 316
99.7%
1 1
 
0.3%

허가일자
Categorical

Distinct17
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2015-03-30
116 
2015-02-06
63 
2015-02-08
56 
2016-03-24
19 
2015-09-16
19 
Other values (12)
44 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique4 ?
Unique (%)1.3%

Sample

1st row2016-03-24
2nd row2016-03-24
3rd row2016-03-24
4th row2015-02-06
5th row2016-03-24

Common Values

ValueCountFrequency (%)
2015-03-30 116
36.6%
2015-02-06 63
19.9%
2015-02-08 56
17.7%
2016-03-24 19
 
6.0%
2015-09-16 19
 
6.0%
2015-02-11 15
 
4.7%
2015-02-23 11
 
3.5%
2015-06-17 3
 
0.9%
2015-06-29 3
 
0.9%
2016-03-23 2
 
0.6%
Other values (7) 10
 
3.2%

Length

2024-04-21T07:51:57.219712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2015-03-30 116
36.6%
2015-02-06 63
19.9%
2015-02-08 56
17.7%
2016-03-24 19
 
6.0%
2015-09-16 19
 
6.0%
2015-02-11 15
 
4.7%
2015-02-23 11
 
3.5%
2015-06-29 3
 
0.9%
2015-06-17 3
 
0.9%
2016-03-23 2
 
0.6%
Other values (7) 10
 
3.2%

배타적경제수역(EEZ)신청번호
Real number (ℝ)

UNIQUE 

Distinct317
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0150021 × 108
Minimum2.015 × 108
Maximum2.0150044 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-21T07:51:57.444338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.015 × 108
5-th percentile2.0150002 × 108
Q12.0150011 × 108
median2.0150021 × 108
Q32.0150033 × 108
95-th percentile2.0150042 × 108
Maximum2.0150044 × 108
Range435
Interquartile range (IQR)212

Descriptive statistics

Standard deviation124.91723
Coefficient of variation (CV)6.1993595 × 10-7
Kurtosis-1.1377249
Mean2.0150021 × 108
Median Absolute Deviation (MAD)103
Skewness0.098487635
Sum6.3875568 × 1010
Variance15604.314
MonotonicityNot monotonic
2024-04-21T07:51:57.689575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201500416 1
 
0.3%
201500348 1
 
0.3%
201500201 1
 
0.3%
201500199 1
 
0.3%
201500198 1
 
0.3%
201500196 1
 
0.3%
201500197 1
 
0.3%
201500349 1
 
0.3%
201500151 1
 
0.3%
201500347 1
 
0.3%
Other values (307) 307
96.8%
ValueCountFrequency (%)
201500004 1
0.3%
201500006 1
0.3%
201500007 1
0.3%
201500008 1
0.3%
201500009 1
0.3%
201500011 1
0.3%
201500014 1
0.3%
201500016 1
0.3%
201500017 1
0.3%
201500018 1
0.3%
ValueCountFrequency (%)
201500439 1
0.3%
201500437 1
0.3%
201500436 1
0.3%
201500435 1
0.3%
201500433 1
0.3%
201500432 1
0.3%
201500430 1
0.3%
201500429 1
0.3%
201500428 1
0.3%
201500426 1
0.3%

허가증번호
Text

UNIQUE 

Distinct317
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-04-21T07:51:58.944006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters2536
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique317 ?
Unique (%)100.0%

Sample

1st rowJ01-0015
2nd rowJ01-0024
3rd rowJ01-0025
4th rowJ01-0026
5th rowJ01-0035
ValueCountFrequency (%)
j01-0015 1
 
0.3%
j06-0340 1
 
0.3%
j06-0320 1
 
0.3%
j06-0318 1
 
0.3%
j06-0051 1
 
0.3%
j06-0018 1
 
0.3%
j06-0016 1
 
0.3%
j05-h001 1
 
0.3%
j05-e001 1
 
0.3%
j05-a001 1
 
0.3%
Other values (307) 307
96.8%
2024-04-21T07:52:00.381785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 833
32.8%
J 317
 
12.5%
- 317
 
12.5%
1 234
 
9.2%
5 169
 
6.7%
3 111
 
4.4%
4 108
 
4.3%
6 100
 
3.9%
7 87
 
3.4%
2 85
 
3.4%
Other values (7) 175
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1829
72.1%
Uppercase Letter 390
 
15.4%
Dash Punctuation 317
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 833
45.5%
1 234
 
12.8%
5 169
 
9.2%
3 111
 
6.1%
4 108
 
5.9%
6 100
 
5.5%
7 87
 
4.8%
2 85
 
4.6%
9 55
 
3.0%
8 47
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
J 317
81.3%
H 40
 
10.3%
E 17
 
4.4%
C 9
 
2.3%
B 4
 
1.0%
A 3
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 317
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2146
84.6%
Latin 390
 
15.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 833
38.8%
- 317
 
14.8%
1 234
 
10.9%
5 169
 
7.9%
3 111
 
5.2%
4 108
 
5.0%
6 100
 
4.7%
7 87
 
4.1%
2 85
 
4.0%
9 55
 
2.6%
Latin
ValueCountFrequency (%)
J 317
81.3%
H 40
 
10.3%
E 17
 
4.4%
C 9
 
2.3%
B 4
 
1.0%
A 3
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 833
32.8%
J 317
 
12.5%
- 317
 
12.5%
1 234
 
9.2%
5 169
 
6.7%
3 111
 
4.4%
4 108
 
4.3%
6 100
 
3.9%
7 87
 
3.4%
2 85
 
3.4%
Other values (7) 175
 
6.9%

허가유무
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size445.0 B
True
298 
False
 
19
ValueCountFrequency (%)
True 298
94.0%
False 19
 
6.0%
2024-04-21T07:52:00.581725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

이전배타적경제수역(EEZ)허가번호
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing313
Missing (%)98.7%
Memory size2.6 KiB
2024-04-21T07:52:01.010507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters32
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st rowJ01-0025
2nd rowJ01-5081
3rd rowJ01-5082
4th rowJ01-5105
ValueCountFrequency (%)
j01-0025 1
25.0%
j01-5081 1
25.0%
j01-5082 1
25.0%
j01-5105 1
25.0%
2024-04-21T07:52:01.666100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9
28.1%
1 6
18.8%
5 5
15.6%
J 4
12.5%
- 4
12.5%
2 2
 
6.2%
8 2
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24
75.0%
Uppercase Letter 4
 
12.5%
Dash Punctuation 4
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9
37.5%
1 6
25.0%
5 5
20.8%
2 2
 
8.3%
8 2
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
J 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28
87.5%
Latin 4
 
12.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9
32.1%
1 6
21.4%
5 5
17.9%
- 4
14.3%
2 2
 
7.1%
8 2
 
7.1%
Latin
ValueCountFrequency (%)
J 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9
28.1%
1 6
18.8%
5 5
15.6%
J 4
12.5%
- 4
12.5%
2 2
 
6.2%
8 2
 
6.2%

일련번호
Real number (ℝ)

MISSING 

Distinct87
Distinct (%)30.0%
Missing27
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean26.137931
Minimum1
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-21T07:52:01.895070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median19
Q337
95-th percentile77.55
Maximum95
Range94
Interquartile range (IQR)29

Descriptive statistics

Standard deviation22.964429
Coefficient of variation (CV)0.87858633
Kurtosis0.68552181
Mean26.137931
Median Absolute Deviation (MAD)13
Skewness1.1870783
Sum7580
Variance527.36499
MonotonicityNot monotonic
2024-04-21T07:52:02.135455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 12
 
3.8%
3 10
 
3.2%
4 10
 
3.2%
7 9
 
2.8%
8 9
 
2.8%
5 8
 
2.5%
6 8
 
2.5%
2 8
 
2.5%
10 7
 
2.2%
16 7
 
2.2%
Other values (77) 202
63.7%
(Missing) 27
 
8.5%
ValueCountFrequency (%)
1 12
3.8%
2 8
2.5%
3 10
3.2%
4 10
3.2%
5 8
2.5%
6 8
2.5%
7 9
2.8%
8 9
2.8%
9 7
2.2%
10 7
2.2%
ValueCountFrequency (%)
95 1
0.3%
94 1
0.3%
93 1
0.3%
91 1
0.3%
89 1
0.3%
87 1
0.3%
86 1
0.3%
85 1
0.3%
84 1
0.3%
83 1
0.3%

신청한글내용
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
<NA>
294 
신조업형태의 실증
 
22
츠시마난류 원류역에서 부어자원을 지속적으로 이용하기 위해 부어치자어의 분포, 먹이환경, 해파리를 포함한 어장환경의 모니터링 조사실시
 
1

Length

Max length73
Median length4
Mean length4.5646688
Min length4

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 294
92.7%
신조업형태의 실증 22
 
6.9%
츠시마난류 원류역에서 부어자원을 지속적으로 이용하기 위해 부어치자어의 분포, 먹이환경, 해파리를 포함한 어장환경의 모니터링 조사실시 1
 
0.3%

Length

2024-04-21T07:52:02.365700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T07:52:02.546468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 294
83.5%
실증 22
 
6.2%
신조업형태의 22
 
6.2%
분포 1
 
0.3%
모니터링 1
 
0.3%
어장환경의 1
 
0.3%
포함한 1
 
0.3%
해파리를 1
 
0.3%
먹이환경 1
 
0.3%
위해 1
 
0.3%
Other values (7) 7
 
2.0%

최초생성시점
Categorical

Distinct15
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2015-03-30
116 
2015-02-06
64 
2015-02-08
56 
2015-09-16
19 
2016-03-23
17 
Other values (10)
45 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique3 ?
Unique (%)0.9%

Sample

1st row2016-03-23
2nd row2016-03-23
3rd row2016-03-23
4th row2015-02-06
5th row2016-03-23

Common Values

ValueCountFrequency (%)
2015-03-30 116
36.6%
2015-02-06 64
20.2%
2015-02-08 56
17.7%
2015-09-16 19
 
6.0%
2016-03-23 17
 
5.4%
2015-02-11 14
 
4.4%
2015-02-23 11
 
3.5%
2016-03-21 6
 
1.9%
2015-06-16 4
 
1.3%
2015-06-29 3
 
0.9%
Other values (5) 7
 
2.2%

Length

2024-04-21T07:52:02.739990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2015-03-30 116
36.6%
2015-02-06 64
20.2%
2015-02-08 56
17.7%
2015-09-16 19
 
6.0%
2016-03-23 17
 
5.4%
2015-02-11 14
 
4.4%
2015-02-23 11
 
3.5%
2016-03-21 6
 
1.9%
2015-06-16 4
 
1.3%
2015-06-29 3
 
0.9%
Other values (5) 7
 
2.2%

최종변경시점
Categorical

Distinct21
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2015-03-30
115 
2015-02-06
48 
2015-02-08
44 
2015-09-16
19 
2015-02-11
16 
Other values (16)
75 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique4 ?
Unique (%)1.3%

Sample

1st row2016-03-23
2nd row2016-03-23
3rd row2016-03-23
4th row2015-02-06
5th row2016-03-23

Common Values

ValueCountFrequency (%)
2015-03-30 115
36.3%
2015-02-06 48
15.1%
2015-02-08 44
 
13.9%
2015-09-16 19
 
6.0%
2015-02-11 16
 
5.0%
2016-03-23 15
 
4.7%
2015-02-22 14
 
4.4%
2016-03-24 12
 
3.8%
2016-03-21 6
 
1.9%
2015-02-23 6
 
1.9%
Other values (11) 22
 
6.9%

Length

2024-04-21T07:52:02.937504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2015-03-30 115
36.3%
2015-02-06 48
15.1%
2015-02-08 44
 
13.9%
2015-09-16 19
 
6.0%
2015-02-11 16
 
5.0%
2016-03-23 15
 
4.7%
2015-02-22 14
 
4.4%
2016-03-24 12
 
3.8%
2016-03-21 6
 
1.9%
2015-02-23 6
 
1.9%
Other values (11) 22
 
6.9%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-08-22
317 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-22
2nd row2023-08-22
3rd row2023-08-22
4th row2023-08-22
5th row2023-08-22

Common Values

ValueCountFrequency (%)
2023-08-22 317
100.0%

Length

2024-04-21T07:52:03.178265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T07:52:03.485967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-22 317
100.0%

Sample

허가년도배타적경제수역(EEZ)허가번호어선영문명일본어선번호선적항영문명선박총톤수선박마력최대속도최대승무원수호출부호명부속선척수허가일자배타적경제수역(EEZ)신청번호허가증번호허가유무이전배타적경제수역(EEZ)허가번호일련번호신청한글내용최초생성시점최종변경시점데이터기준일자
02015J01-0015No.18 KIYO-MARUNS1-1015FUKUOKA-KEN FUKUOKA-SHI135.064013.030JM564302016-03-24201500416J01-0015Y<NA>1<NA>2016-03-232016-03-232023-08-22
12015J01-0024No.18 SHOTOKU-MARUNS1-1039NAGASAKI-KEN NAGASAKI-SHI135.064013.028JM580502016-03-24201500417J01-0024Y<NA>2<NA>2016-03-232016-03-232023-08-22
22015J01-0025No.21 SHOTOKU-MARUNS1-1104NAGASAKI-KEN NAGASAKI-SHI135.064013.023JM603602016-03-24201500418J01-0025Y<NA>3<NA>2016-03-232016-03-232023-08-22
32015J01-0026No.31 SHOTOKU-MARUNS1-1104NAGASAKI-KEN NAGASAKI-SHI135.064013.023JM603602015-02-06201500004J01-0026Y<NA>4<NA>2015-02-062015-02-062023-08-22
42015J01-0035No.8 KOYO-MARUTT1-158TOTTORI-KEN SAKAIMINATO-SHI135.060013.025JK493602016-03-24201500419J01-0035Y<NA>5<NA>2016-03-232016-03-232023-08-22
52015J01-0036No.28 KOYO-MARUTT1-162TOTTORI-KEN SAKAIMINATO-SHI135.064014.023JE305602015-02-06201500006J01-0036Y<NA>6<NA>2015-02-062015-02-062023-08-22
62015J01-0040No.31 GENPUKU-MARUNS2-10451NAGASAKI-KEN HIRADO-SHI80.050012.025JM598902015-02-06201500007J01-0040Y<NA>7<NA>2015-02-062015-02-112023-08-22
72015J01-0042No.81 SHOTOKU-MARUNS2-10797NAGASAKI-KEN NAGASAKI-SHI80.050010.025JG538902015-02-06201500008J01-0042Y<NA>8<NA>2015-02-062015-02-112023-08-22
82015J01-0043No.1 EIKO-MARUNS2-10728NAGASAKI-KEN GOTO-SHI80.073614.025JM666802015-02-06201500009J01-0043N<NA>9<NA>2015-02-062015-10-262023-08-22
92015J01-0045No.58 TENOH-MARUEH2-8793EHIME-KEN MINAMIUWA-GUN AINAN-CHO80.050012.025JG536602015-07-21201500374J01-0045Y<NA>10<NA>2015-07-212015-07-212023-08-22
허가년도배타적경제수역(EEZ)허가번호어선영문명일본어선번호선적항영문명선박총톤수선박마력최대속도최대승무원수호출부호명부속선척수허가일자배타적경제수역(EEZ)신청번호허가증번호허가유무이전배타적경제수역(EEZ)허가번호일련번호신청한글내용최초생성시점최종변경시점데이터기준일자
3072015J70-E004YOKO-MARUNS1-1130Nagasaki-shi, Nagasaki, Japan692.0188514.61337JHB02016-05-03201500439J70-E004Y<NA>1츠시마난류 원류역에서 부어자원을 지속적으로 이용하기 위해 부어치자어의 분포, 먹이환경, 해파리를 포함한 어장환경의 모니터링 조사실시2016-05-032016-05-042023-08-22
3082015J70-H001No.11 GENPUKU-MARUNS1-1135NAGASAKI-KEN HIRADO-SHI199.0220616.027JD360402016-03-23201500435J70-H001Y<NA>14신조업형태의 실증2016-03-232016-03-232023-08-22
3092015J70-H002No.16 GENPUKU-MARUNS2-10426NAGASAKI-KEN HIRADO-SHI85.050014.09JM595302015-02-11201500171J70-H002Y<NA>15신조업형태의 실증2015-02-112015-02-222023-08-22
3102015J70-H003No.27 GENPUKU-MARUNS2-10333NAGASAKI-KEN HIRADO-SHI85.050013.59JM573902015-02-11201500172J70-H003N<NA>18신조업형태의 실증2015-02-112015-10-292023-08-22
3112015J70-H004No.61 GENPUKU-MARUNS1-1021NAGASAKI-KEN HIRADO-SHI270.064013.413JM565502015-02-11201500173J70-H004Y<NA>16신조업형태의 실증2015-02-112015-02-232023-08-22
3122015J70-H005No.73 GENPUKU-MARUNS1-1070NAGASAKI-KEN HIRADO-SHI340.085015.112JM595602015-02-11201500174J70-H005Y<NA>17신조업형태의 실증2015-02-112015-02-222023-08-22
3132015J70-H006No. 31 SHOTOKU-MARUNS1-1137NAGASAKI-KEN NAGASAKI-SHI199.0220615.9324JD381002016-03-23201500433J70-H006Y<NA><NA>신조업형태의 실증2016-05-032016-05-032023-08-22
3142015J70-H007No. 1 SHOTOKU-MARUNS1-1138NAGASAKI-KEN NAGASAKI-SHI155.0220616.99JD380702015-06-18201500363J70-H007Y<NA><NA>신조업형태의 실증2015-06-182015-06-182023-08-22
3152015J70-H008No. 7 SHOTOKU-MARUNS1-1081NAGASAKI-KEN NAGASAKI-SHI338.085012.08JM605902015-06-19201500367J70-H008Y<NA><NA>신조업형태의 실증2015-06-192015-06-192023-08-22
3162015J70-H009No.8SHOTOKU-MARUNS1-1139NAGASAKI-KEN NAGASAKI-SHI316.0220617.328JD380902015-06-18201500365J70-H009Y<NA><NA>신조업형태의 실증2015-06-182015-06-182023-08-22