Overview

Dataset statistics

Number of variables38
Number of observations10000
Missing cells93843
Missing cells (%)24.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 MiB
Average record size in memory331.0 B

Variable types

Numeric13
Text7
Categorical7
DateTime6
Unsupported5

Dataset

Description해양수산부_EEZ_일본어선한국EEZ신청기본에 대한 정보로 EEZ신청번호,어선영문명,어선일문명,일본어선번호,어업종류코드,국가코드,선적항영문명,선적항일문명,선박총톤수,선박마력,최대속도,최대승무원수,호출부호명,선창종류코드,선창수량,선창용량,부속선척수,허가신청일자,허가유무,최대허용어획량,출력단위구분,신청구분,신청한글내용,신청일문내용,기준년도,EEZ허가번호,일련번호,어선종류내역,허가일자,허가종료일자,최초생성시점,최종변경시점,재교부여부,재교부일자,재교부사유한글내역,재교부사유일문내역,재교부이전EEZ허가번호,데이터기준일자 제공합니다.
URLhttps://www.data.go.kr/data/15115863/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
국가코드 is highly imbalanced (94.8%)Imbalance
허가유무 is highly imbalanced (75.0%)Imbalance
출력단위구분 is highly imbalanced (74.2%)Imbalance
신청한글내용 is highly imbalanced (96.8%)Imbalance
신청일문내용 is highly imbalanced (96.8%)Imbalance
어선종류내역 is highly imbalanced (98.7%)Imbalance
선창종류코드 has 4592 (45.9%) missing valuesMissing
선창수량 has 1470 (14.7%) missing valuesMissing
선창용량 has 1472 (14.7%) missing valuesMissing
부속선척수 has 7184 (71.8%) missing valuesMissing
최대허용어획량 has 6628 (66.3%) missing valuesMissing
배타적경제수역(EEZ)허가번호 has 4892 (48.9%) missing valuesMissing
일련번호 has 6626 (66.3%) missing valuesMissing
허가일자 has 588 (5.9%) missing valuesMissing
허가종료일자 has 9896 (99.0%) missing valuesMissing
재교부여부 has 10000 (100.0%) missing valuesMissing
재교부일자 has 10000 (100.0%) missing valuesMissing
재교부사유한글내역 has 10000 (100.0%) missing valuesMissing
재교부사유일문내역 has 10000 (100.0%) missing valuesMissing
재교부이전배타적경제수역(EEZ)허가번호 has 10000 (100.0%) missing valuesMissing
선창용량 is highly skewed (γ1 = 65.19580295)Skewed
배타적경제수역(EEZ)신청번호 has unique valuesUnique
재교부여부 is an unsupported type, check if it needs cleaning or further analysisUnsupported
재교부일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
재교부사유한글내역 is an unsupported type, check if it needs cleaning or further analysisUnsupported
재교부사유일문내역 is an unsupported type, check if it needs cleaning or further analysisUnsupported
재교부이전배타적경제수역(EEZ)허가번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported
선창수량 has 3133 (31.3%) zerosZeros
선창용량 has 3133 (31.3%) zerosZeros
부속선척수 has 1546 (15.5%) zerosZeros
최대허용어획량 has 3148 (31.5%) zerosZeros

Reproduction

Analysis started2023-12-12 17:06:47.715491
Analysis finished2023-12-12 17:06:49.259086
Duration1.54 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

배타적경제수역(EEZ)신청번호
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0053813 × 108
Minimum2 × 108
Maximum2.0150044 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:06:49.333394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2 × 108
5-th percentile2.000005 × 108
Q12.002003 × 108
median2.0050011 × 108
Q32.0080086 × 108
95-th percentile2.0130028 × 108
Maximum2.0150044 × 108
Range1500438
Interquartile range (IQR)600554.5

Descriptive statistics

Standard deviation427792.21
Coefficient of variation (CV)0.0021332214
Kurtosis-0.65997523
Mean2.0053813 × 108
Median Absolute Deviation (MAD)300101
Skewness0.60361196
Sum2.0053813 × 1012
Variance1.8300618 × 1011
MonotonicityNot monotonic
2023-12-13T02:06:49.492275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200100281 1
 
< 0.1%
200800169 1
 
< 0.1%
201000928 1
 
< 0.1%
200401221 1
 
< 0.1%
200301192 1
 
< 0.1%
201000106 1
 
< 0.1%
200000192 1
 
< 0.1%
200200885 1
 
< 0.1%
200201070 1
 
< 0.1%
200001159 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
200000001 1
< 0.1%
200000002 1
< 0.1%
200000003 1
< 0.1%
200000004 1
< 0.1%
200000005 1
< 0.1%
200000006 1
< 0.1%
200000007 1
< 0.1%
200000008 1
< 0.1%
200000009 1
< 0.1%
200000010 1
< 0.1%
ValueCountFrequency (%)
201500439 1
< 0.1%
201500438 1
< 0.1%
201500437 1
< 0.1%
201500436 1
< 0.1%
201500435 1
< 0.1%
201500434 1
< 0.1%
201500433 1
< 0.1%
201500432 1
< 0.1%
201500431 1
< 0.1%
201500430 1
< 0.1%
Distinct1874
Distinct (%)18.8%
Missing16
Missing (%)0.2%
Memory size156.2 KiB
2023-12-13T02:06:49.771333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length25
Mean length13.115986
Min length3

Characters and Unicode

Total characters130950
Distinct characters51
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

Unique367 ?
Unique (%)3.7%

Sample

1st rowOKITSUMARU
2nd rowNO.1 TAKESIO MARU
3rd rowTAIYOU MARU
4th rowDAI 5 KEIUNMARU
5th rowKAISYUUMARU
ValueCountFrequency (%)
maru 1019
 
6.4%
dai 673
 
4.2%
no.2 312
 
1.9%
no.8 311
 
1.9%
no.1 254
 
1.6%
no.5 205
 
1.3%
no.18 198
 
1.2%
no.3 180
 
1.1%
ebisumaru 175
 
1.1%
shotoku-maru 155
 
1.0%
Other values (1123) 12530
78.3%
2023-12-13T02:06:50.183717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 19254
14.7%
A 15825
12.1%
R 11524
 
8.8%
M 11296
 
8.6%
O 9736
 
7.4%
I 8768
 
6.7%
7018
 
5.4%
N 5970
 
4.6%
K 5468
 
4.2%
S 4712
 
3.6%
Other values (41) 31379
24.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 110971
84.7%
Space Separator 7022
 
5.4%
Decimal Number 6710
 
5.1%
Other Punctuation 3247
 
2.5%
Dash Punctuation 1769
 
1.4%
Lowercase Letter 1231
 
0.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 19254
17.4%
A 15825
14.3%
R 11524
10.4%
M 11296
10.2%
O 9736
8.8%
I 8768
7.9%
N 5970
 
5.4%
K 5468
 
4.9%
S 4712
 
4.2%
Y 4359
 
3.9%
Other values (13) 14059
12.7%
Decimal Number
ValueCountFrequency (%)
1 1704
25.4%
8 1425
21.2%
2 1094
16.3%
3 877
13.1%
5 749
11.2%
7 398
 
5.9%
6 354
 
5.3%
0 102
 
1.5%
4 6
 
0.1%
9 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
o 1143
92.9%
u 31
 
2.5%
a 18
 
1.5%
r 14
 
1.1%
k 12
 
1.0%
i 4
 
0.3%
y 3
 
0.2%
s 2
 
0.2%
h 2
 
0.2%
n 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 3244
99.9%
& 1
 
< 0.1%
# 1
 
< 0.1%
; 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
7018
99.9%
  4
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 1763
99.7%
6
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 112202
85.7%
Common 18748
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 19254
17.2%
A 15825
14.1%
R 11524
10.3%
M 11296
10.1%
O 9736
8.7%
I 8768
7.8%
N 5970
 
5.3%
K 5468
 
4.9%
S 4712
 
4.2%
Y 4359
 
3.9%
Other values (23) 15290
13.6%
Common
ValueCountFrequency (%)
7018
37.4%
. 3244
17.3%
- 1763
 
9.4%
1 1704
 
9.1%
8 1425
 
7.6%
2 1094
 
5.8%
3 877
 
4.7%
5 749
 
4.0%
7 398
 
2.1%
6 354
 
1.9%
Other values (8) 122
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130940
> 99.9%
None 10
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 19254
14.7%
A 15825
12.1%
R 11524
 
8.8%
M 11296
 
8.6%
O 9736
 
7.4%
I 8768
 
6.7%
7018
 
5.4%
N 5970
 
4.6%
K 5468
 
4.2%
S 4712
 
3.6%
Other values (39) 31369
24.0%
None
ValueCountFrequency (%)
6
60.0%
  4
40.0%
Distinct1684
Distinct (%)16.9%
Missing28
Missing (%)0.3%
Memory size156.2 KiB
2023-12-13T02:06:50.461715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length5.0037104
Min length2

Characters and Unicode

Total characters49897
Distinct characters434
Distinct categories7 ?
Distinct scripts5 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique276 ?
Unique (%)2.8%

Sample

1st row沖津丸
2nd row第 1 剛汐丸
3rd row大洋丸
4th row第五惠運丸
5th row海州丸
ValueCountFrequency (%)
3343
 
19.3%
8 289
 
1.7%
2 257
 
1.5%
1 243
 
1.4%
昭德丸 227
 
1.3%
18 223
 
1.3%
3 208
 
1.2%
5 193
 
1.1%
海幸丸 186
 
1.1%
源福丸 132
 
0.8%
Other values (1158) 11986
69.3%
2023-12-13T02:06:50.945504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9853
19.7%
7726
 
15.5%
4402
 
8.8%
1 1258
 
2.5%
1208
 
2.4%
8 1111
 
2.2%
984
 
2.0%
878
 
1.8%
2 869
 
1.7%
780
 
1.6%
Other values (424) 20828
41.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 36689
73.5%
Space Separator 7737
 
15.5%
Decimal Number 5220
 
10.5%
Other Punctuation 111
 
0.2%
Close Punctuation 69
 
0.1%
Open Punctuation 67
 
0.1%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
9853
26.9%
4402
 
12.0%
1208
 
3.3%
984
 
2.7%
878
 
2.4%
780
 
2.1%
491
 
1.3%
471
 
1.3%
468
 
1.3%
448
 
1.2%
Other values (404) 16706
45.5%
Decimal Number
ValueCountFrequency (%)
1 1258
24.1%
8 1111
21.3%
2 869
16.6%
3 722
13.8%
5 559
10.7%
7 327
 
6.3%
6 266
 
5.1%
0 58
 
1.1%
4 41
 
0.8%
6
 
0.1%
Other Punctuation
ValueCountFrequency (%)
; 37
33.3%
& 37
33.3%
# 37
33.3%
Space Separator
ValueCountFrequency (%)
7726
99.9%
  11
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 68
98.6%
1
 
1.4%
Open Punctuation
ValueCountFrequency (%)
( 67
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 35427
71.0%
Common 13208
 
26.5%
Hiragana 1216
 
2.4%
Katakana 24
 
< 0.1%
Hangul 22
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
9853
27.8%
4402
 
12.4%
1208
 
3.4%
984
 
2.8%
878
 
2.5%
780
 
2.2%
491
 
1.4%
471
 
1.3%
468
 
1.3%
448
 
1.3%
Other values (341) 15444
43.6%
Hiragana
ValueCountFrequency (%)
117
 
9.6%
108
 
8.9%
101
 
8.3%
99
 
8.1%
96
 
7.9%
57
 
4.7%
57
 
4.7%
53
 
4.4%
50
 
4.1%
42
 
3.5%
Other values (31) 436
35.9%
Common
ValueCountFrequency (%)
7726
58.5%
1 1258
 
9.5%
8 1111
 
8.4%
2 869
 
6.6%
3 722
 
5.5%
5 559
 
4.2%
7 327
 
2.5%
6 266
 
2.0%
) 68
 
0.5%
( 67
 
0.5%
Other values (10) 235
 
1.8%
Hangul
ValueCountFrequency (%)
3
13.6%
2
 
9.1%
2
 
9.1%
2
 
9.1%
2
 
9.1%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
Other values (6) 6
27.3%
Katakana
ValueCountFrequency (%)
4
16.7%
4
16.7%
4
16.7%
4
16.7%
4
16.7%
4
16.7%

Most occurring blocks

ValueCountFrequency (%)
CJK 35145
70.4%
ASCII 13190
 
26.4%
Hiragana 1216
 
2.4%
CJK Compat Ideographs 282
 
0.6%
Katakana 24
 
< 0.1%
Hangul 22
 
< 0.1%
None 18
 
< 0.1%

Most frequent character per block

CJK
ValueCountFrequency (%)
9853
28.0%
4402
 
12.5%
1208
 
3.4%
984
 
2.8%
878
 
2.5%
780
 
2.2%
491
 
1.4%
471
 
1.3%
468
 
1.3%
448
 
1.3%
Other values (334) 15162
43.1%
ASCII
ValueCountFrequency (%)
7726
58.6%
1 1258
 
9.5%
8 1111
 
8.4%
2 869
 
6.6%
3 722
 
5.5%
5 559
 
4.2%
7 327
 
2.5%
6 266
 
2.0%
) 68
 
0.5%
( 67
 
0.5%
Other values (7) 217
 
1.6%
Hiragana
ValueCountFrequency (%)
117
 
9.6%
108
 
8.9%
101
 
8.3%
99
 
8.1%
96
 
7.9%
57
 
4.7%
57
 
4.7%
53
 
4.4%
50
 
4.1%
42
 
3.5%
Other values (31) 436
35.9%
CJK Compat Ideographs
ValueCountFrequency (%)
99
35.1%
89
31.6%
73
25.9%
9
 
3.2%
6
 
2.1%
3
 
1.1%
3
 
1.1%
None
ValueCountFrequency (%)
  11
61.1%
6
33.3%
1
 
5.6%
Katakana
ValueCountFrequency (%)
4
16.7%
4
16.7%
4
16.7%
4
16.7%
4
16.7%
4
16.7%
Hangul
ValueCountFrequency (%)
3
13.6%
2
 
9.1%
2
 
9.1%
2
 
9.1%
2
 
9.1%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
Other values (6) 6
27.3%
Distinct1697
Distinct (%)17.0%
Missing28
Missing (%)0.3%
Memory size156.2 KiB
2023-12-13T02:06:51.241143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length8.5690935
Min length4

Characters and Unicode

Total characters85451
Distinct characters33
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique239 ?
Unique (%)2.4%

Sample

1st rowFO2-5852
2nd rowNS3-86988
3rd rowWK2-3849
4th rowSN2-2587
5th rowNS2-13835
ValueCountFrequency (%)
sa2-1895 29
 
0.3%
sa2-1751 26
 
0.3%
ns1-991 20
 
0.2%
ns2-23040 20
 
0.2%
ns2-23228 20
 
0.2%
ns1-1025 19
 
0.2%
ns1-1024 19
 
0.2%
ns1-1096 18
 
0.2%
ns1-1104 18
 
0.2%
ns2-13751 18
 
0.2%
Other values (1610) 9769
97.9%
2023-12-13T02:06:51.699706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 10281
12.0%
- 9906
11.6%
1 8680
10.2%
3 7206
 
8.4%
S 6478
 
7.6%
N 5969
 
7.0%
0 5325
 
6.2%
5 4773
 
5.6%
8 4296
 
5.0%
6 4282
 
5.0%
Other values (23) 18255
21.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 55260
64.7%
Uppercase Letter 19812
 
23.2%
Dash Punctuation 9906
 
11.6%
Space Separator 467
 
0.5%
Other Letter 3
 
< 0.1%
Lowercase Letter 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 6478
32.7%
N 5969
30.1%
T 964
 
4.9%
K 879
 
4.4%
G 801
 
4.0%
F 726
 
3.7%
O 718
 
3.6%
W 684
 
3.5%
Y 587
 
3.0%
A 541
 
2.7%
Other values (5) 1465
 
7.4%
Decimal Number
ValueCountFrequency (%)
2 10281
18.6%
1 8680
15.7%
3 7206
13.0%
0 5325
9.6%
5 4773
8.6%
8 4296
7.8%
6 4282
7.7%
7 3979
 
7.2%
9 3271
 
5.9%
4 3167
 
5.7%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Lowercase Letter
ValueCountFrequency (%)
f 1
50.0%
o 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 9906
100.0%
Space Separator
ValueCountFrequency (%)
467
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 65634
76.8%
Latin 19814
 
23.2%
Han 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 6478
32.7%
N 5969
30.1%
T 964
 
4.9%
K 879
 
4.4%
G 801
 
4.0%
F 726
 
3.7%
O 718
 
3.6%
W 684
 
3.5%
Y 587
 
3.0%
A 541
 
2.7%
Other values (7) 1467
 
7.4%
Common
ValueCountFrequency (%)
2 10281
15.7%
- 9906
15.1%
1 8680
13.2%
3 7206
11.0%
0 5325
8.1%
5 4773
7.3%
8 4296
6.5%
6 4282
6.5%
7 3979
 
6.1%
9 3271
 
5.0%
Other values (3) 3635
 
5.5%
Han
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 85448
> 99.9%
CJK 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 10281
12.0%
- 9906
11.6%
1 8680
10.2%
3 7206
 
8.4%
S 6478
 
7.6%
N 5969
 
7.0%
0 5325
 
6.2%
5 4773
 
5.6%
8 4296
 
5.0%
6 4282
 
5.0%
Other values (20) 18252
21.4%
CJK
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

어업종류코드
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing67
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean467109.63
Minimum100000
Maximum7000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:06:51.821205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100000
5-th percentile100000
Q1300000
median600000
Q3600000
95-th percentile700000
Maximum7000000
Range6900000
Interquartile range (IQR)300000

Descriptive statistics

Standard deviation242034.75
Coefficient of variation (CV)0.51815406
Kurtosis159.58446
Mean467109.63
Median Absolute Deviation (MAD)100000
Skewness5.7961767
Sum4.6398 × 109
Variance5.8580821 × 1010
MonotonicityNot monotonic
2023-12-13T02:06:51.946842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
600000 4382
43.8%
100000 1706
 
17.1%
500000 1281
 
12.8%
400000 1083
 
10.8%
300000 659
 
6.6%
700000 342
 
3.4%
200000 219
 
2.2%
1100000 127
 
1.3%
800000 82
 
0.8%
1300000 33
 
0.3%
Other values (3) 19
 
0.2%
(Missing) 67
 
0.7%
ValueCountFrequency (%)
100000 1706
 
17.1%
200000 219
 
2.2%
300000 659
 
6.6%
400000 1083
 
10.8%
500000 1281
 
12.8%
600000 4382
43.8%
700000 342
 
3.4%
800000 82
 
0.8%
1000000 15
 
0.1%
1100000 127
 
1.3%
ValueCountFrequency (%)
7000000 3
 
< 0.1%
1300000 33
 
0.3%
1200000 1
 
< 0.1%
1100000 127
 
1.3%
1000000 15
 
0.1%
800000 82
 
0.8%
700000 342
 
3.4%
600000 4382
43.8%
500000 1281
 
12.8%
400000 1083
 
10.8%

국가코드
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
JP
9886 
J
 
73
<NA>
 
31
KR
 
10

Length

Max length4
Median length2
Mean length1.9989
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
JP 9886
98.9%
J 73
 
0.7%
<NA> 31
 
0.3%
KR 10
 
0.1%

Length

2023-12-13T02:06:52.114054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:06:52.223440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
jp 9886
98.9%
j 73
 
0.7%
na 31
 
0.3%
kr 10
 
0.1%
Distinct376
Distinct (%)3.8%
Missing46
Missing (%)0.5%
Memory size156.2 KiB
2023-12-13T02:06:52.543350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length40
Mean length24.043902
Min length6

Characters and Unicode

Total characters239333
Distinct characters42
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)0.4%

Sample

1st rowFUKUOKAKEN MUNAKATAGUN GENKAIMACHI
2nd rowNAGASAKIKEN KAMITUSIMATYOU
3rd rowWAKAYAMA-KEN SUSAMICHOU
4th rowSIMANEKEN MATUESI KASIMATYOU
5th rowNAGASAKIKEN OZIKACHOU
ValueCountFrequency (%)
nagasakiken 3997
 
19.0%
ozikachou 838
 
4.0%
nagasakikenn 707
 
3.4%
fukuokaken 674
 
3.2%
nagasaki-ken 644
 
3.1%
wakayama-ken 569
 
2.7%
sagaken 497
 
2.4%
miyazakiken 460
 
2.2%
tsushimashi 445
 
2.1%
susamichou 364
 
1.7%
Other values (264) 11841
56.3%
2023-12-13T02:06:53.160959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 40873
17.1%
K 24656
10.3%
I 23554
9.8%
N 22375
9.3%
S 16608
 
6.9%
13505
 
5.6%
U 13190
 
5.5%
O 13032
 
5.4%
E 12399
 
5.2%
H 11440
 
4.8%
Other values (32) 47701
19.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 221354
92.5%
Space Separator 13585
 
5.7%
Dash Punctuation 3720
 
1.6%
Lowercase Letter 614
 
0.3%
Other Punctuation 50
 
< 0.1%
Other Letter 10
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 40873
18.5%
K 24656
11.1%
I 23554
10.6%
N 22375
10.1%
S 16608
7.5%
U 13190
 
6.0%
O 13032
 
5.9%
E 12399
 
5.6%
H 11440
 
5.2%
G 9856
 
4.5%
Other values (12) 33371
15.1%
Lowercase Letter
ValueCountFrequency (%)
a 144
23.5%
i 91
14.8%
n 81
13.2%
k 48
 
7.8%
s 42
 
6.8%
g 40
 
6.5%
h 38
 
6.2%
e 32
 
5.2%
m 24
 
3.9%
u 24
 
3.9%
Other values (5) 50
 
8.1%
Space Separator
ValueCountFrequency (%)
13505
99.4%
  80
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 3720
100.0%
Other Punctuation
ValueCountFrequency (%)
, 50
100.0%
Other Letter
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 221968
92.7%
Common 17355
 
7.3%
Hangul 10
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 40873
18.4%
K 24656
11.1%
I 23554
10.6%
N 22375
10.1%
S 16608
7.5%
U 13190
 
5.9%
O 13032
 
5.9%
E 12399
 
5.6%
H 11440
 
5.2%
G 9856
 
4.4%
Other values (27) 33985
15.3%
Common
ValueCountFrequency (%)
13505
77.8%
- 3720
 
21.4%
  80
 
0.5%
, 50
 
0.3%
Hangul
ValueCountFrequency (%)
10
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 239243
> 99.9%
None 80
 
< 0.1%
Compat Jamo 10
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 40873
17.1%
K 24656
10.3%
I 23554
9.8%
N 22375
9.4%
S 16608
 
6.9%
13505
 
5.6%
U 13190
 
5.5%
O 13032
 
5.4%
E 12399
 
5.2%
H 11440
 
4.8%
Other values (30) 47611
19.9%
None
ValueCountFrequency (%)
  80
100.0%
Compat Jamo
ValueCountFrequency (%)
10
100.0%
Distinct283
Distinct (%)2.8%
Missing47
Missing (%)0.5%
Memory size156.2 KiB
2023-12-13T02:06:53.643411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length7
Mean length7.7604742
Min length3

Characters and Unicode

Total characters77240
Distinct characters224
Distinct categories4 ?
Distinct scripts5 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)0.4%

Sample

1st row福岡縣 宗像郡 玄海町
2nd row長崎縣 上對馬町
3rd row和歌山縣 すさみ町
4th row島根縣 松江市 鹿島町
5th row長崎縣 小値賀町
ValueCountFrequency (%)
長崎縣 5126
24.9%
小値賀町 834
 
4.1%
和歌山縣 640
 
3.1%
福岡縣 638
 
3.1%
長崎市 551
 
2.7%
佐賀縣 499
 
2.4%
對馬市 449
 
2.2%
宮崎縣 446
 
2.2%
境港市 391
 
1.9%
鳥取縣 376
 
1.8%
Other values (204) 10602
51.6%
2023-12-13T02:06:54.177378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11385
 
14.7%
9664
 
12.5%
6498
 
8.4%
6146
 
8.0%
5489
 
7.1%
4577
 
5.9%
1403
 
1.8%
1385
 
1.8%
1145
 
1.5%
1059
 
1.4%
Other values (214) 28489
36.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 65761
85.1%
Space Separator 11439
 
14.8%
Decimal Number 25
 
< 0.1%
Other Punctuation 15
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
9664
 
14.7%
6498
 
9.9%
6146
 
9.3%
5489
 
8.3%
4577
 
7.0%
1403
 
2.1%
1385
 
2.1%
1145
 
1.7%
1059
 
1.6%
1012
 
1.5%
Other values (204) 27383
41.6%
Decimal Number
ValueCountFrequency (%)
7 5
20.0%
6 5
20.0%
4 5
20.0%
0 5
20.0%
3 5
20.0%
Other Punctuation
ValueCountFrequency (%)
; 5
33.3%
# 5
33.3%
& 5
33.3%
Space Separator
ValueCountFrequency (%)
11385
99.5%
  54
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Han 64462
83.5%
Common 11479
 
14.9%
Hiragana 1160
 
1.5%
Katakana 91
 
0.1%
Hangul 48
 
0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
9664
 
15.0%
6498
 
10.1%
6146
 
9.5%
5489
 
8.5%
4577
 
7.1%
1403
 
2.2%
1385
 
2.1%
1145
 
1.8%
1059
 
1.6%
1012
 
1.6%
Other values (183) 26084
40.5%
Hangul
ValueCountFrequency (%)
9
18.8%
9
18.8%
9
18.8%
8
16.7%
6
12.5%
2
 
4.2%
1
 
2.1%
1
 
2.1%
1
 
2.1%
1
 
2.1%
Common
ValueCountFrequency (%)
11385
99.2%
  54
 
0.5%
7 5
 
< 0.1%
; 5
 
< 0.1%
6 5
 
< 0.1%
4 5
 
< 0.1%
# 5
 
< 0.1%
& 5
 
< 0.1%
0 5
 
< 0.1%
3 5
 
< 0.1%
Hiragana
ValueCountFrequency (%)
383
33.0%
364
31.4%
364
31.4%
19
 
1.6%
19
 
1.6%
8
 
0.7%
1
 
0.1%
1
 
0.1%
1
 
0.1%
Katakana
ValueCountFrequency (%)
91
100.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 64332
83.3%
ASCII 11425
 
14.8%
Hiragana 1160
 
1.5%
CJK Compat Ideographs 130
 
0.2%
Katakana 91
 
0.1%
None 54
 
0.1%
Hangul 48
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11385
99.6%
7 5
 
< 0.1%
; 5
 
< 0.1%
6 5
 
< 0.1%
4 5
 
< 0.1%
# 5
 
< 0.1%
& 5
 
< 0.1%
0 5
 
< 0.1%
3 5
 
< 0.1%
CJK
ValueCountFrequency (%)
9664
 
15.0%
6498
 
10.1%
6146
 
9.6%
5489
 
8.5%
4577
 
7.1%
1403
 
2.2%
1385
 
2.2%
1145
 
1.8%
1059
 
1.6%
1012
 
1.6%
Other values (178) 25954
40.3%
Hiragana
ValueCountFrequency (%)
383
33.0%
364
31.4%
364
31.4%
19
 
1.6%
19
 
1.6%
8
 
0.7%
1
 
0.1%
1
 
0.1%
1
 
0.1%
Katakana
ValueCountFrequency (%)
91
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
75
57.7%
37
28.5%
11
 
8.5%
4
 
3.1%
3
 
2.3%
None
ValueCountFrequency (%)
  54
100.0%
Hangul
ValueCountFrequency (%)
9
18.8%
9
18.8%
9
18.8%
8
16.7%
6
12.5%
2
 
4.2%
1
 
2.1%
1
 
2.1%
1
 
2.1%
1
 
2.1%

선박총톤수
Real number (ℝ)

Distinct323
Distinct (%)3.2%
Missing26
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean49.641808
Minimum0
Maximum2630
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:06:54.369124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.6
Q14.9
median11.3
Q375
95-th percentile255
Maximum2630
Range2630
Interquartile range (IQR)70.1

Descriptive statistics

Standard deviation93.703269
Coefficient of variation (CV)1.8875878
Kurtosis180.70325
Mean49.641808
Median Absolute Deviation (MAD)6.7
Skewness8.4400894
Sum495127.39
Variance8780.3026
MonotonicityNot monotonic
2023-12-13T02:06:54.570040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.9 1427
 
14.3%
19.0 1048
 
10.5%
85.0 479
 
4.8%
75.0 465
 
4.7%
4.8 411
 
4.1%
135.0 402
 
4.0%
7.3 331
 
3.3%
6.6 300
 
3.0%
7.9 245
 
2.5%
8.5 184
 
1.8%
Other values (313) 4682
46.8%
ValueCountFrequency (%)
0.0 2
 
< 0.1%
2.9 7
 
0.1%
3.0 1
 
< 0.1%
3.4 4
 
< 0.1%
3.6 3
 
< 0.1%
3.8 27
 
0.3%
3.9 13
 
0.1%
4.0 87
0.9%
4.1 26
 
0.3%
4.2 63
0.6%
ValueCountFrequency (%)
2630.0 3
 
< 0.1%
892.0 5
 
0.1%
692.0 14
0.1%
509.0 1
 
< 0.1%
499.76 7
0.1%
499.0 9
0.1%
441.0 3
 
< 0.1%
396.0 7
0.1%
359.0 1
 
< 0.1%
346.0 8
0.1%

선박마력
Real number (ℝ)

Distinct111
Distinct (%)1.1%
Missing62
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean280.30358
Minimum0
Maximum7000
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:06:54.753365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile70
Q190
median160
Q3440
95-th percentile736
Maximum7000
Range7000
Interquartile range (IQR)350

Descriptive statistics

Standard deviation294.64803
Coefficient of variation (CV)1.0511747
Kurtosis89.883688
Mean280.30358
Median Absolute Deviation (MAD)70
Skewness5.5797023
Sum2785657
Variance86817.461
MonotonicityNot monotonic
2023-12-13T02:06:54.957382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 2353
23.5%
160 748
 
7.5%
120 741
 
7.4%
190 649
 
6.5%
500 581
 
5.8%
70 576
 
5.8%
640 537
 
5.4%
80 531
 
5.3%
440 296
 
3.0%
380 277
 
2.8%
Other values (101) 2649
26.5%
ValueCountFrequency (%)
0 2
 
< 0.1%
40 4
 
< 0.1%
45 3
 
< 0.1%
50 30
 
0.3%
60 24
 
0.2%
65 6
 
0.1%
70 576
 
5.8%
80 531
 
5.3%
85 2
 
< 0.1%
90 2353
23.5%
ValueCountFrequency (%)
7000 3
 
< 0.1%
2574 7
0.1%
2500 3
 
< 0.1%
2352 1
 
< 0.1%
2206 15
0.1%
2059 10
0.1%
1885 14
0.1%
1839 6
 
0.1%
1618 5
 
0.1%
1471 15
0.1%

최대속도
Real number (ℝ)

Distinct97
Distinct (%)1.0%
Missing62
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean18.918345
Minimum0
Maximum38
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:06:55.136200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q113
median16
Q325
95-th percentile30
Maximum38
Range38
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.8403102
Coefficient of variation (CV)0.36157023
Kurtosis-1.1359814
Mean18.918345
Median Absolute Deviation (MAD)5
Skewness0.3937849
Sum188010.51
Variance46.789844
MonotonicityNot monotonic
2023-12-13T02:06:55.299998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.0 1083
 
10.8%
16.0 907
 
9.1%
11.0 849
 
8.5%
13.0 765
 
7.6%
12.0 738
 
7.4%
30.0 735
 
7.3%
14.0 519
 
5.2%
20.0 470
 
4.7%
28.0 443
 
4.4%
10.0 423
 
4.2%
Other values (87) 3006
30.1%
ValueCountFrequency (%)
0.0 2
 
< 0.1%
2.0 1
 
< 0.1%
5.0 18
 
0.2%
8.0 5
 
0.1%
9.0 11
 
0.1%
10.0 423
4.2%
10.5 1
 
< 0.1%
10.7 2
 
< 0.1%
11.0 849
8.5%
11.49 9
 
0.1%
ValueCountFrequency (%)
38.0 3
 
< 0.1%
37.0 12
 
0.1%
36.0 22
 
0.2%
35.0 37
 
0.4%
34.0 28
 
0.3%
33.0 41
 
0.4%
32.0 69
 
0.7%
31.0 8
 
0.1%
30.0 735
7.3%
29.0 91
 
0.9%

최대승무원수
Real number (ℝ)

Distinct33
Distinct (%)0.3%
Missing62
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean6.8351781
Minimum0
Maximum37
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:06:55.443928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q310
95-th percentile23
Maximum37
Range37
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.7919085
Coefficient of variation (CV)0.99366957
Kurtosis1.8418916
Mean6.8351781
Median Absolute Deviation (MAD)3
Skewness1.5273158
Sum67928
Variance46.130021
MonotonicityNot monotonic
2023-12-13T02:06:55.594906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
2 2029
20.3%
1 1787
17.9%
3 931
9.3%
8 652
 
6.5%
6 533
 
5.3%
12 509
 
5.1%
4 464
 
4.6%
5 381
 
3.8%
11 340
 
3.4%
14 340
 
3.4%
Other values (23) 1972
19.7%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 1787
17.9%
2 2029
20.3%
3 931
9.3%
4 464
 
4.6%
5 381
 
3.8%
6 533
 
5.3%
7 214
 
2.1%
8 652
 
6.5%
9 222
 
2.2%
ValueCountFrequency (%)
37 1
 
< 0.1%
35 2
 
< 0.1%
33 5
 
0.1%
30 67
0.7%
29 20
 
0.2%
28 69
0.7%
27 24
 
0.2%
26 31
 
0.3%
25 161
1.6%
24 112
1.1%
Distinct1836
Distinct (%)18.4%
Missing39
Missing (%)0.4%
Memory size156.2 KiB
2023-12-13T02:06:55.972930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length29
Mean length13.606666
Min length1

Characters and Unicode

Total characters135536
Distinct characters45
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique330 ?
Unique (%)3.3%

Sample

1st rowOKITSUMARU
2nd rowTAKESIO MARU
3rd rowNAGANO TAIYOU MARU
4th rowDAI5 KEIUNMARU
5th rowMAEDA KAISYUUMARU
ValueCountFrequency (%)
maru 809
 
4.9%
dai 800
 
4.9%
makiyama 182
 
1.1%
konpiramaru 115
 
0.7%
daihachi 114
 
0.7%
ebisumaru 101
 
0.6%
8 98
 
0.6%
nakamura 93
 
0.6%
akebonomaru 91
 
0.6%
koueimaru 90
 
0.5%
Other values (1621) 13994
84.9%
2023-12-13T02:06:56.466352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 19250
14.2%
U 17573
13.0%
M 12082
 
8.9%
I 11646
 
8.6%
R 9837
 
7.3%
O 7804
 
5.8%
6960
 
5.1%
K 5765
 
4.3%
S 5140
 
3.8%
Y 4511
 
3.3%
Other values (35) 34968
25.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 118291
87.3%
Decimal Number 9597
 
7.1%
Space Separator 6960
 
5.1%
Dash Punctuation 484
 
0.4%
Other Punctuation 130
 
0.1%
Lowercase Letter 54
 
< 0.1%
Other Letter 20
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 19250
16.3%
U 17573
14.9%
M 12082
10.2%
I 11646
9.8%
R 9837
8.3%
O 7804
 
6.6%
K 5765
 
4.9%
S 5140
 
4.3%
Y 4511
 
3.8%
H 4159
 
3.5%
Other values (16) 20524
17.4%
Decimal Number
ValueCountFrequency (%)
5 2135
22.2%
8 1129
11.8%
6 1092
11.4%
3 977
10.2%
1 866
9.0%
2 823
 
8.6%
4 718
 
7.5%
0 652
 
6.8%
9 640
 
6.7%
7 565
 
5.9%
Other Letter
ValueCountFrequency (%)
16
80.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Space Separator
ValueCountFrequency (%)
6960
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 484
100.0%
Other Punctuation
ValueCountFrequency (%)
. 130
100.0%
Lowercase Letter
ValueCountFrequency (%)
o 54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 118345
87.3%
Common 17171
 
12.7%
Han 20
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 19250
16.3%
U 17573
14.8%
M 12082
10.2%
I 11646
9.8%
R 9837
8.3%
O 7804
 
6.6%
K 5765
 
4.9%
S 5140
 
4.3%
Y 4511
 
3.8%
H 4159
 
3.5%
Other values (17) 20578
17.4%
Common
ValueCountFrequency (%)
6960
40.5%
5 2135
 
12.4%
8 1129
 
6.6%
6 1092
 
6.4%
3 977
 
5.7%
1 866
 
5.0%
2 823
 
4.8%
4 718
 
4.2%
0 652
 
3.8%
9 640
 
3.7%
Other values (3) 1179
 
6.9%
Han
ValueCountFrequency (%)
16
80.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 135516
> 99.9%
CJK 20
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 19250
14.2%
U 17573
13.0%
M 12082
 
8.9%
I 11646
 
8.6%
R 9837
 
7.3%
O 7804
 
5.8%
6960
 
5.1%
K 5765
 
4.3%
S 5140
 
3.8%
Y 4511
 
3.3%
Other values (30) 34948
25.8%
CJK
ValueCountFrequency (%)
16
80.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%

선창종류코드
Real number (ℝ)

MISSING 

Distinct29
Distinct (%)0.5%
Missing4592
Missing (%)45.9%
Infinite0
Infinite (%)0.0%
Mean3.2152084 × 108
Minimum0
Maximum2.0003 × 1010
Zeros15
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:06:56.617088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median6
Q32.000001 × 108
95-th percentile2.030405 × 108
Maximum2.0003 × 1010
Range2.0003 × 1010
Interquartile range (IQR)2.000001 × 108

Descriptive statistics

Standard deviation1.5814537 × 109
Coefficient of variation (CV)4.9186662
Kurtosis41.607594
Mean3.2152084 × 108
Median Absolute Deviation (MAD)4
Skewness6.4258313
Sum1.7387847 × 1012
Variance2.5009958 × 1018
MonotonicityNot monotonic
2023-12-13T02:06:56.769637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 1678
 
16.8%
6 1420
 
14.2%
200000099 546
 
5.5%
203040000 366
 
3.7%
200000000 294
 
2.9%
203040500 264
 
2.6%
203040099 259
 
2.6%
203040599 120
 
1.2%
10203040500 108
 
1.1%
4 84
 
0.8%
Other values (19) 269
 
2.7%
(Missing) 4592
45.9%
ValueCountFrequency (%)
0 15
 
0.1%
1 4
 
< 0.1%
2 1678
16.8%
3 4
 
< 0.1%
4 84
 
0.8%
5 12
 
0.1%
6 1420
14.2%
9 2
 
< 0.1%
105 1
 
< 0.1%
40500 58
 
0.6%
ValueCountFrequency (%)
20003000500 2
 
< 0.1%
10203040500 108
 
1.1%
10200000000 14
 
0.1%
10000000099 5
 
0.1%
902030405 1
 
< 0.1%
203040599 120
 
1.2%
203040500 264
2.6%
203040099 259
2.6%
203040000 366
3.7%
203000500 24
 
0.2%

선창수량
Real number (ℝ)

MISSING  ZEROS 

Distinct35
Distinct (%)0.4%
Missing1470
Missing (%)14.7%
Infinite0
Infinite (%)0.0%
Mean8.291442
Minimum0
Maximum46
Zeros3133
Zeros (%)31.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:06:56.896311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q315
95-th percentile19
Maximum46
Range46
Interquartile range (IQR)15

Descriptive statistics

Standard deviation7.585998
Coefficient of variation (CV)0.91491902
Kurtosis-0.65363552
Mean8.291442
Median Absolute Deviation (MAD)8
Skewness0.3706949
Sum70726
Variance57.547365
MonotonicityNot monotonic
2023-12-13T02:06:57.021848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 3133
31.3%
15 525
 
5.2%
16 470
 
4.7%
17 408
 
4.1%
14 405
 
4.0%
12 368
 
3.7%
18 364
 
3.6%
11 296
 
3.0%
7 277
 
2.8%
9 264
 
2.6%
Other values (25) 2020
20.2%
(Missing) 1470
14.7%
ValueCountFrequency (%)
0 3133
31.3%
1 4
 
< 0.1%
2 7
 
0.1%
3 34
 
0.3%
4 197
 
2.0%
5 211
 
2.1%
6 231
 
2.3%
7 277
 
2.8%
8 250
 
2.5%
9 264
 
2.6%
ValueCountFrequency (%)
46 5
 
0.1%
35 12
0.1%
33 2
 
< 0.1%
32 20
0.2%
31 7
 
0.1%
30 3
 
< 0.1%
29 13
0.1%
27 9
0.1%
26 4
 
< 0.1%
25 15
0.1%

선창용량
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct1319
Distinct (%)15.5%
Missing1472
Missing (%)14.7%
Infinite0
Infinite (%)0.0%
Mean35.815696
Minimum0
Maximum71590
Zeros3133
Zeros (%)31.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:06:57.180229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10.9
Q320.5
95-th percentile71.71
Maximum71590
Range71590
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation1096.4969
Coefficient of variation (CV)30.614984
Kurtosis4253.4709
Mean35.815696
Median Absolute Deviation (MAD)10.9
Skewness65.195803
Sum305436.25
Variance1202305.5
MonotonicityNot monotonic
2023-12-13T02:06:57.368509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3133
31.3%
13.0 100
 
1.0%
14.0 98
 
1.0%
12.0 87
 
0.9%
10.0 81
 
0.8%
11.0 76
 
0.8%
15.0 66
 
0.7%
17.0 62
 
0.6%
16.0 61
 
0.6%
9.0 49
 
0.5%
Other values (1309) 4715
47.1%
(Missing) 1472
 
14.7%
ValueCountFrequency (%)
0.0 3133
31.3%
0.5 6
 
0.1%
1.0 1
 
< 0.1%
1.092 1
 
< 0.1%
1.423 3
 
< 0.1%
1.43 1
 
< 0.1%
1.6 7
 
0.1%
1.64 2
 
< 0.1%
1.884 1
 
< 0.1%
1.89 6
 
0.1%
ValueCountFrequency (%)
71590.0 2
< 0.1%
947.7 2
< 0.1%
334.07 4
< 0.1%
332.3 3
< 0.1%
326.05 2
< 0.1%
325.18 4
< 0.1%
281.0 1
 
< 0.1%
268.01 1
 
< 0.1%
258.75 4
< 0.1%
257.78 4
< 0.1%

부속선척수
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)0.2%
Missing7184
Missing (%)71.8%
Infinite0
Infinite (%)0.0%
Mean2.028054
Minimum0
Maximum20
Zeros1546
Zeros (%)15.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:06:57.499246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile5
Maximum20
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.304277
Coefficient of variation (CV)1.136201
Kurtosis-0.56120348
Mean2.028054
Median Absolute Deviation (MAD)0
Skewness0.44724866
Sum5711
Variance5.3096923
MonotonicityNot monotonic
2023-12-13T02:06:57.900040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1546
 
15.5%
5 707
 
7.1%
4 511
 
5.1%
3 22
 
0.2%
2 17
 
0.2%
1 12
 
0.1%
20 1
 
< 0.1%
(Missing) 7184
71.8%
ValueCountFrequency (%)
0 1546
15.5%
1 12
 
0.1%
2 17
 
0.2%
3 22
 
0.2%
4 511
 
5.1%
5 707
7.1%
20 1
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
5 707
7.1%
4 511
 
5.1%
3 22
 
0.2%
2 17
 
0.2%
1 12
 
0.1%
0 1546
15.5%
Distinct200
Distinct (%)2.0%
Missing12
Missing (%)0.1%
Memory size156.2 KiB
Minimum2000-01-26 00:00:00
Maximum2016-05-03 00:00:00
2023-12-13T02:06:58.025827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:06:58.150878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

허가유무
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Y
9315 
0
 
605
N
 
80

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Y 9315
93.2%
0 605
 
6.0%
N 80
 
0.8%

Length

2023-12-13T02:06:58.263029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:06:58.354804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
y 9315
93.2%
0 605
 
6.0%
n 80
 
0.8%

최대허용어획량
Real number (ℝ)

MISSING  ZEROS 

Distinct30
Distinct (%)0.9%
Missing6628
Missing (%)66.3%
Infinite0
Infinite (%)0.0%
Mean125.35342
Minimum0
Maximum3246.048
Zeros3148
Zeros (%)31.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:06:58.445183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile230.817
Maximum3246.048
Range3246.048
Interquartile range (IQR)0

Descriptive statistics

Standard deviation552.36739
Coefficient of variation (CV)4.4064803
Kurtosis18.158016
Mean125.35342
Median Absolute Deviation (MAD)0
Skewness4.4193533
Sum422691.75
Variance305109.74
MonotonicityNot monotonic
2023-12-13T02:06:58.575429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.0 3148
31.5%
2882.175 37
 
0.4%
2270.964 35
 
0.4%
2331.752 29
 
0.3%
2717.63 25
 
0.2%
3246.048 19
 
0.2%
204.949 8
 
0.1%
136.887 8
 
0.1%
1135.481 6
 
0.1%
100.728 6
 
0.1%
Other values (20) 51
 
0.5%
(Missing) 6628
66.3%
ValueCountFrequency (%)
0.0 3148
31.5%
57.44 3
 
< 0.1%
68.448 1
 
< 0.1%
69.076 1
 
< 0.1%
75.961 5
 
0.1%
76.75 4
 
< 0.1%
94.936 1
 
< 0.1%
100.728 6
 
0.1%
102.474 2
 
< 0.1%
113.445 3
 
< 0.1%
ValueCountFrequency (%)
3246.048 19
0.2%
2882.175 37
0.4%
2717.63 25
0.2%
2620.35 1
 
< 0.1%
2560.412 1
 
< 0.1%
2331.752 29
0.3%
2270.964 35
0.4%
2199.992 1
 
< 0.1%
1623.025 2
 
< 0.1%
1441.087 3
 
< 0.1%

출력단위구분
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
A
8881 
B
 
853
D
 
260
<NA>
 
4
C
 
2

Length

Max length4
Median length1
Mean length1.0012
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
A 8881
88.8%
B 853
 
8.5%
D 260
 
2.6%
<NA> 4
 
< 0.1%
C 2
 
< 0.1%

Length

2023-12-13T02:06:58.706971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:06:58.831907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 8881
88.8%
b 853
 
8.5%
d 260
 
2.6%
na 4
 
< 0.1%
c 2
 
< 0.1%

신청구분
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
4892 
A
3828 
B
1154 
C
 
116
D
 
10

Length

Max length4
Median length1
Mean length2.4676
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 4892
48.9%
A 3828
38.3%
B 1154
 
11.5%
C 116
 
1.2%
D 10
 
0.1%

Length

2023-12-13T02:06:58.971895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:06:59.143355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 4892
48.9%
a 3828
38.3%
b 1154
 
11.5%
c 116
 
1.2%
d 10
 
0.1%

신청한글내용
Categorical

IMBALANCE 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9879 
신조업형태의 실증
 
81
츠시마난류 원류역에 있어서 부어 자원을 지속적으로 이용하기 위해 부어 치자어의 분포와 먹이환경 및 어장환경의 모니터링 조사를 실시한다
 
9
츠시마난류 원류역에서 부어자원을 지속적으로 이용하기 위해 부어치자어의 분포, 먹이환경, 해파리를 포함한 어장환경의 모니터링 조사실시
 
9
어획물운반, 어함, 어구, 유류 및 생활필수품보급등
 
5
Other values (8)
 
17

Length

Max length87
Median length4
Mean length4.2782
Min length4

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9879
98.8%
신조업형태의 실증 81
 
0.8%
츠시마난류 원류역에 있어서 부어 자원을 지속적으로 이용하기 위해 부어 치자어의 분포와 먹이환경 및 어장환경의 모니터링 조사를 실시한다 9
 
0.1%
츠시마난류 원류역에서 부어자원을 지속적으로 이용하기 위해 부어치자어의 분포, 먹이환경, 해파리를 포함한 어장환경의 모니터링 조사실시 9
 
0.1%
어획물운반, 어함, 어구, 유류 및 생활필수품보급등 5
 
0.1%
츠시마난류 원류역에 있어서 부어 자원을 지속적으로 이용하기 위해 부어 치자어의 분포와 먹이환경 및 어장환경의 모니터링 조사를 실시한다. 5
 
0.1%
어획물운반, 어함, 어구, 유류 및 생활필수품보급 등 4
 
< 0.1%
쓰시마난류의 원류역에 있어서의 해양환경과 플랑크톤의 분포ㆍ생산량을 조사해서, 지구온난화의 모니터링에 공헌한다. 2
 
< 0.1%
츠시마난류 원류역에 있어서 부어 자원을 지속적으로 이용하기 위해 부어 치자어의 분포와 먹이환경 및 대형 해파리를 포함한 어장환경의 모니터링 조사를 실시한다. 2
 
< 0.1%
쓰시마난류의 원류역에서의 어업자원을 지속적으로 이용하기 위한 해양환경과 저차생태계파악을 위한 모니터링 조사를 행한다. 1
 
< 0.1%
Other values (3) 3
 
< 0.1%

Length

2023-12-13T02:06:59.288165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9879
93.3%
실증 81
 
0.8%
신조업형태의 81
 
0.8%
부어 36
 
0.3%
모니터링 28
 
0.3%
지속적으로 28
 
0.3%
이용하기 28
 
0.3%
먹이환경 27
 
0.3%
어장환경의 27
 
0.3%
27
 
0.3%
Other values (52) 345
 
3.3%

신청일문내용
Categorical

IMBALANCE 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9879 
新操業形態の 實證
 
34
新操業形態の實證
 
22
新操業形態の 證
 
19
對馬暖流の源流域における浮魚資源を持續的に利用するために浮魚稚仔魚の分布と餌料環境および漁場環境のモニタリング調査を行う。
 
14
Other values (12)
 
32

Length

Max length70
Median length4
Mean length4.2403
Min length4

Unique

Unique6 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9879
98.8%
新操業形態の 實證 34
 
0.3%
新操業形態の實證 22
 
0.2%
新操業形態の 證 19
 
0.2%
對馬暖流の源流域における浮魚資源を持續的に利用するために浮魚稚仔魚の分布と餌料環境および漁場環境のモニタリング調査を行う。 14
 
0.1%
漁獲物運搬, 魚函, 漁具, 油類及び生活必需品補給 等 9
 
0.1%
對馬暖流の源流域における浮魚資源を持續的に利用するために浮魚稚仔魚の分布と餌料環境および大型クラゲを含 む漁場環境のモニタリング調査を行う 6
 
0.1%
新操業形態の證 5
 
0.1%
對馬暖流の源流域における浮魚資源を持續的に利用するために浮魚稚仔魚の分布と餌料環境およびクラゲを含 む漁場環境のモニタリング調査を行う 2
 
< 0.1%
對馬暖流の源流域における浮魚資源を持續的に利用するために浮魚稚仔魚の分布と餌料環境およびクラゲを含 む漁場環境のモニタリング調査を行う。 2
 
< 0.1%
Other values (7) 8
 
0.1%

Length

2023-12-13T02:06:59.432997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9879
97.8%
新操業形態の 53
 
0.5%
實證 34
 
0.3%
新操業形態の實證 22
 
0.2%
19
 
0.2%
對馬暖流の源流域における浮魚資源を持續的に利用するために浮魚稚仔魚の分布と餌料環境および漁場環境のモニタリング調査を行う。 14
 
0.1%
漁獲物運搬 9
 
0.1%
魚函 9
 
0.1%
漁具 9
 
0.1%
油類及び生活必需品補給 9
 
0.1%
Other values (14) 47
 
0.5%

기준년도
Real number (ℝ)

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.376
Minimum2000
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:06:59.573247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2000
Q12002
median2005
Q32008
95-th percentile2013
Maximum2015
Range15
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.2787913
Coefficient of variation (CV)0.0021336604
Kurtosis-0.65904739
Mean2005.376
Median Absolute Deviation (MAD)3
Skewness0.60423321
Sum20053760
Variance18.308055
MonotonicityNot monotonic
2023-12-13T02:06:59.719985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2000 1166
11.7%
2002 1149
11.5%
2001 1032
10.3%
2005 874
8.7%
2003 832
8.3%
2006 725
 
7.2%
2004 713
 
7.1%
2007 576
 
5.8%
2008 466
 
4.7%
2010 447
 
4.5%
Other values (5) 2020
20.2%
ValueCountFrequency (%)
2000 1166
11.7%
2001 1032
10.3%
2002 1149
11.5%
2003 832
8.3%
2004 713
7.1%
2005 874
8.7%
2006 725
7.2%
2007 576
5.8%
2008 466
 
4.7%
2009 436
 
4.4%
ValueCountFrequency (%)
2015 430
4.3%
2013 341
 
3.4%
2012 382
3.8%
2011 431
4.3%
2010 447
4.5%
2009 436
4.4%
2008 466
4.7%
2007 576
5.8%
2006 725
7.2%
2005 874
8.7%
Distinct1039
Distinct (%)20.3%
Missing4892
Missing (%)48.9%
Memory size156.2 KiB
2023-12-13T02:07:00.124445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.9992169
Min length3

Characters and Unicode

Total characters40860
Distinct characters19
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

Unique219 ?
Unique (%)4.3%

Sample

1st rowJ06-0388
2nd rowJ04-2003
3rd rowJ06-0419
4th rowJ06-0719
5th rowJ01-5105
ValueCountFrequency (%)
j06-0016 15
 
0.3%
j01-5064 15
 
0.3%
j05-0225 12
 
0.2%
j05-0192 12
 
0.2%
j01-0015 12
 
0.2%
j01-0035 12
 
0.2%
j05-0180 12
 
0.2%
j01-4001 12
 
0.2%
j01-0045 12
 
0.2%
j05-6001 12
 
0.2%
Other values (1028) 4982
97.5%
2023-12-13T02:07:00.654250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 13138
32.2%
J 5108
 
12.5%
- 5108
 
12.5%
1 3544
 
8.7%
5 2857
 
7.0%
6 2624
 
6.4%
3 1923
 
4.7%
4 1771
 
4.3%
2 1720
 
4.2%
7 1166
 
2.9%
Other values (9) 1901
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30424
74.5%
Uppercase Letter 5327
 
13.0%
Dash Punctuation 5108
 
12.5%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13138
43.2%
1 3544
 
11.6%
5 2857
 
9.4%
6 2624
 
8.6%
3 1923
 
6.3%
4 1771
 
5.8%
2 1720
 
5.7%
7 1166
 
3.8%
8 866
 
2.8%
9 815
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
J 5108
95.9%
H 73
 
1.4%
E 49
 
0.9%
A 35
 
0.7%
C 34
 
0.6%
B 26
 
0.5%
Z 2
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 5108
100.0%
Lowercase Letter
ValueCountFrequency (%)
h 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 35532
87.0%
Latin 5328
 
13.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13138
37.0%
- 5108
 
14.4%
1 3544
 
10.0%
5 2857
 
8.0%
6 2624
 
7.4%
3 1923
 
5.4%
4 1771
 
5.0%
2 1720
 
4.8%
7 1166
 
3.3%
8 866
 
2.4%
Latin
ValueCountFrequency (%)
J 5108
95.9%
H 73
 
1.4%
E 49
 
0.9%
A 35
 
0.7%
C 34
 
0.6%
B 26
 
0.5%
Z 2
 
< 0.1%
h 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40860
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13138
32.2%
J 5108
 
12.5%
- 5108
 
12.5%
1 3544
 
8.7%
5 2857
 
7.0%
6 2624
 
6.4%
3 1923
 
4.7%
4 1771
 
4.3%
2 1720
 
4.2%
7 1166
 
2.9%
Other values (9) 1901
 
4.7%

일련번호
Real number (ℝ)

MISSING 

Distinct230
Distinct (%)6.8%
Missing6626
Missing (%)66.3%
Infinite0
Infinite (%)0.0%
Mean39.627149
Minimum1
Maximum232
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:07:00.823453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q111
median26
Q350
95-th percentile142
Maximum232
Range231
Interquartile range (IQR)39

Descriptive statistics

Standard deviation42.700707
Coefficient of variation (CV)1.0775619
Kurtosis4.1952183
Mean39.627149
Median Absolute Deviation (MAD)18
Skewness2.0212476
Sum133702
Variance1823.3504
MonotonicityNot monotonic
2023-12-13T02:07:00.988877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 111
 
1.1%
3 92
 
0.9%
2 88
 
0.9%
4 84
 
0.8%
5 81
 
0.8%
6 74
 
0.7%
7 72
 
0.7%
9 67
 
0.7%
8 66
 
0.7%
13 66
 
0.7%
Other values (220) 2573
 
25.7%
(Missing) 6626
66.3%
ValueCountFrequency (%)
1 111
1.1%
2 88
0.9%
3 92
0.9%
4 84
0.8%
5 81
0.8%
6 74
0.7%
7 72
0.7%
8 66
0.7%
9 67
0.7%
10 63
0.6%
ValueCountFrequency (%)
232 1
< 0.1%
231 1
< 0.1%
230 1
< 0.1%
229 1
< 0.1%
228 1
< 0.1%
227 1
< 0.1%
226 1
< 0.1%
225 1
< 0.1%
224 1
< 0.1%
223 1
< 0.1%

어선종류내역
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9979 
2
 
8
1
 
7
4
 
6

Length

Max length4
Median length4
Mean length3.9937
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9979
99.8%
2 8
 
0.1%
1 7
 
0.1%
4 6
 
0.1%

Length

2023-12-13T02:07:01.179036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:07:01.294931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9979
99.8%
2 8
 
0.1%
1 7
 
0.1%
4 6
 
0.1%

허가일자
Date

MISSING 

Distinct181
Distinct (%)1.9%
Missing588
Missing (%)5.9%
Memory size156.2 KiB
Minimum2000-01-26 00:00:00
Maximum2016-05-03 00:00:00
2023-12-13T02:07:01.437258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:01.597351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

허가종료일자
Date

MISSING 

Distinct21
Distinct (%)20.2%
Missing9896
Missing (%)99.0%
Memory size156.2 KiB
Minimum2005-06-27 00:00:00
Maximum2016-05-03 00:00:00
2023-12-13T02:07:01.748996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:01.872761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
Distinct193
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2000-01-26 00:00:00
Maximum2016-05-03 00:00:00
2023-12-13T02:07:02.061475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:02.233374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct333
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2000-01-26 00:00:00
Maximum2016-05-04 00:00:00
2023-12-13T02:07:02.394751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:02.533751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

재교부여부
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

재교부일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

재교부사유한글내역
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

재교부사유일문내역
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

재교부이전배타적경제수역(EEZ)허가번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2023-06-29 00:00:00
Maximum2023-06-29 00:00:00
2023-12-13T02:07:02.637367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:02.736466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Sample

배타적경제수역(EEZ)신청번호어선영문명어선일문명일본어선번호어업종류코드국가코드선적항영문명선적항일문명선박총톤수선박마력최대속도최대승무원수호출부호명선창종류코드선창수량선창용량부속선척수허가신청일자허가유무최대허용어획량출력단위구분신청구분신청한글내용신청일문내용기준년도배타적경제수역(EEZ)허가번호일련번호어선종류내역허가일자허가종료일자최초생성시점최종변경시점재교부여부재교부일자재교부사유한글내역재교부사유일문내역재교부이전배타적경제수역(EEZ)허가번호데이터기준일자
1447200100281OKITSUMARU沖津丸FO2-5852500000JPFUKUOKAKEN MUNAKATAGUN GENKAIMACHI福岡縣 宗像郡 玄海町19.7614016.06OKITSUMARU61444.4<NA>2001-03-07Y<NA>A<NA><NA><NA>2001<NA><NA><NA>2001-03-07<NA>2001-03-072001-03-07<NA><NA><NA><NA><NA>2023-06-29
3691200300341NO.1 TAKESIO MARU第 1 剛汐丸NS3-86988600000JPNAGASAKIKEN KAMITUSIMATYOU長崎縣 上對馬町4.69023.02TAKESIO MARU2000000991713.8<NA>2003-01-24Y<NA>A<NA><NA><NA>2003<NA><NA><NA>2003-02-12<NA>2003-01-242003-01-24<NA><NA><NA><NA><NA>2023-06-29
5383200500484TAIYOU MARU大洋丸WK2-3849600000JPWAKAYAMA-KEN SUSAMICHOU和歌山縣 すさみ町7.312024.014NAGANO TAIYOU MARU2030400001820.143<NA>2004-12-31Y<NA>AA<NA><NA>2005J06-0388<NA><NA>2005-04-04<NA>2004-12-312005-04-11<NA><NA><NA><NA><NA>2023-06-29
9049201200186DAI 5 KEIUNMARU第五惠運丸SN2-2587400000JPSIMANEKEN MATUESI KASIMATYOU島根縣 松江市 鹿島町19.019010.03DAI5 KEIUNMARU<NA>00.002012-07-04Y0.0AA<NA><NA>2012J04-200319<NA>2012-11-27<NA>2012-07-042012-11-27<NA><NA><NA><NA><NA>2023-06-29
6774200700338KAISYUUMARU海州丸NS2-13835600000JPNAGASAKIKEN OZIKACHOU長崎縣 小値賀町6.19025.01MAEDA KAISYUUMARU<NA>00.0<NA>2007-01-25Y0.0AA<NA><NA>2007J06-041990<NA>2007-01-25<NA>2007-01-252007-01-30<NA><NA><NA><NA><NA>2023-06-29
3660200300310EIHUKUMARU榮福丸SA2-1751400000JPSAGAKEN TINZEITYOU佐賀縣 鎭西町8.512023.03MAKIYAMA EIHUKUMARU2000000001119.85<NA>2003-01-24Y<NA>A<NA><NA><NA>2003<NA><NA><NA>2003-02-12<NA>2003-01-242003-01-24<NA><NA><NA><NA><NA>2023-06-29
3396200300046DAI10 SHINKOUMARU第 10 新幸丸FO2-5819500000JPFUKUOKAKEN MUNAKATAGUN GENKAIMACHI福岡縣 宗像郡 玄海町19.8619016.08DAI10 SHINKOUMARU2000000992038.72<NA>2003-01-22Y<NA>A<NA><NA><NA>2003<NA><NA><NA>2003-02-12<NA>2003-01-222003-02-02<NA><NA><NA><NA><NA>2023-06-29
1920200100754No.2 ASYOUMARU第 2 阿生丸NS3-504007600000JPNAGASAKIKEN OZIKACHOU長崎縣 小値賀町4.99025.01ITOU DAINI ASYOUMARU61712.18<NA>2001-01-27Y<NA>A<NA><NA><NA>2001<NA><NA><NA>2001-01-27<NA>2001-01-272001-01-27<NA><NA><NA><NA><NA>2023-06-29
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