Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory105.0 B

Variable types

DateTime1
Categorical5
Text5
Numeric1

Dataset

Description선박검역결과 중 입항화물 정보 (선박국적, 선박종류, 검역구분, 오염구분, 출발국가, 선사대리점, 주요적재화물, 대종품목, 총적재량)
Author질병관리청
URLhttps://www.data.go.kr/data/3074707/fileData.do

Alerts

오염_비오염 is highly imbalanced (51.6%)Imbalance
총적재량 has 7705 (77.0%) zerosZeros

Reproduction

Analysis started2023-12-12 12:21:09.473610
Analysis finished2023-12-12 12:21:11.303721
Duration1.83 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

검역일시
Date

Distinct9604
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-01-01 03:21:00
Maximum2022-12-31 23:30:00
2023-12-12T21:21:11.392796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:11.558750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

검역소
Categorical

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
국립부산검역소
3578 
국립인천검역소
1370 
국립평택검역소
1209 
국립여수검역소
1203 
국립울산검역소
1020 
Other values (6)
1620 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row국립부산검역소
2nd row국립울산검역소
3rd row국립마산검역소
4th row국립여수검역소
5th row국립부산검역소

Common Values

ValueCountFrequency (%)
국립부산검역소 3578
35.8%
국립인천검역소 1370
 
13.7%
국립평택검역소 1209
 
12.1%
국립여수검역소 1203
 
12.0%
국립울산검역소 1020
 
10.2%
국립마산검역소 582
 
5.8%
국립포항검역소 380
 
3.8%
국립군산검역소 340
 
3.4%
국립동해검역소 242
 
2.4%
국립목포검역소 73
 
0.7%

Length

2023-12-12T21:21:11.704402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
국립부산검역소 3578
35.8%
국립인천검역소 1370
 
13.7%
국립평택검역소 1209
 
12.1%
국립여수검역소 1203
 
12.0%
국립울산검역소 1020
 
10.2%
국립마산검역소 582
 
5.8%
국립포항검역소 380
 
3.8%
국립군산검역소 340
 
3.4%
국립동해검역소 242
 
2.4%
국립목포검역소 73
 
0.7%

선박명
Text

Distinct4344
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T21:21:12.366553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length19
Mean length11.3678
Min length2

Characters and Unicode

Total characters113678
Distinct characters63
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

Unique2680 ?
Unique (%)26.8%

Sample

1st rowTY INCHEON
2nd rowKEOYOUNG DREAM 3
3rd rowTOLEDO CARRIER
4th rowMP MR TANKER 2
5th rowSUNNY MAPLE
ValueCountFrequency (%)
xin 265
 
1.3%
hai 255
 
1.2%
sunny 226
 
1.1%
star 208
 
1.0%
ocean 164
 
0.8%
new 158
 
0.8%
msc 156
 
0.8%
maersk 155
 
0.8%
sitc 136
 
0.7%
maru 136
 
0.7%
Other values (3460) 18669
90.9%
2023-12-12T21:21:12.955340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 12190
 
10.7%
10532
 
9.3%
N 10461
 
9.2%
E 8547
 
7.5%
I 8303
 
7.3%
O 6655
 
5.9%
R 6086
 
5.4%
S 5951
 
5.2%
G 4623
 
4.1%
U 4011
 
3.5%
Other values (53) 36319
31.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 100717
88.6%
Space Separator 10532
 
9.3%
Decimal Number 1970
 
1.7%
Other Punctuation 290
 
0.3%
Dash Punctuation 118
 
0.1%
Other Letter 51
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 12190
12.1%
N 10461
 
10.4%
E 8547
 
8.5%
I 8303
 
8.2%
O 6655
 
6.6%
R 6086
 
6.0%
S 5951
 
5.9%
G 4623
 
4.6%
U 4011
 
4.0%
L 3828
 
3.8%
Other values (16) 30062
29.8%
Other Letter
ValueCountFrequency (%)
호 7
13.7%
어 5
 
9.8%
대 5
 
9.8%
제 4
 
7.8%
요 4
 
7.8%
성 3
 
5.9%
진 3
 
5.9%
해 3
 
5.9%
화 2
 
3.9%
금 2
 
3.9%
Other values (13) 13
25.5%
Decimal Number
ValueCountFrequency (%)
1 413
21.0%
8 274
13.9%
2 246
12.5%
5 226
11.5%
6 190
9.6%
7 174
8.8%
0 168
8.5%
3 146
 
7.4%
9 125
 
6.3%
4 8
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 244
84.1%
/ 46
 
15.9%
Space Separator
ValueCountFrequency (%)
10532
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 118
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 100717
88.6%
Common 12910
 
11.4%
Hangul 51
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 12190
12.1%
N 10461
 
10.4%
E 8547
 
8.5%
I 8303
 
8.2%
O 6655
 
6.6%
R 6086
 
6.0%
S 5951
 
5.9%
G 4623
 
4.6%
U 4011
 
4.0%
L 3828
 
3.8%
Other values (16) 30062
29.8%
Hangul
ValueCountFrequency (%)
호 7
13.7%
어 5
 
9.8%
대 5
 
9.8%
제 4
 
7.8%
요 4
 
7.8%
성 3
 
5.9%
진 3
 
5.9%
해 3
 
5.9%
화 2
 
3.9%
금 2
 
3.9%
Other values (13) 13
25.5%
Common
ValueCountFrequency (%)
10532
81.6%
1 413
 
3.2%
8 274
 
2.1%
2 246
 
1.9%
. 244
 
1.9%
5 226
 
1.8%
6 190
 
1.5%
7 174
 
1.3%
0 168
 
1.3%
3 146
 
1.1%
Other values (4) 297
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113627
> 99.9%
Hangul 51
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 12190
 
10.7%
10532
 
9.3%
N 10461
 
9.2%
E 8547
 
7.5%
I 8303
 
7.3%
O 6655
 
5.9%
R 6086
 
5.4%
S 5951
 
5.2%
G 4623
 
4.1%
U 4011
 
3.5%
Other values (30) 36268
31.9%
Hangul
ValueCountFrequency (%)
호 7
13.7%
어 5
 
9.8%
대 5
 
9.8%
제 4
 
7.8%
요 4
 
7.8%
성 3
 
5.9%
진 3
 
5.9%
해 3
 
5.9%
화 2
 
3.9%
금 2
 
3.9%
Other values (13) 13
25.5%

선박국적
Text

Distinct62
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T21:21:13.209440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length7
Mean length3.0047
Min length2

Characters and Unicode

Total characters30047
Distinct characters113
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st row한국
2nd row한국
3rd row마샬군도
4th row싱가폴
5th row한국
ValueCountFrequency (%)
파나마 2267
22.5%
한국 1951
19.4%
마샬군도 849
 
8.4%
라이베리아 705
 
7.0%
벨리즈 684
 
6.8%
홍콩 636
 
6.3%
중국 496
 
4.9%
싱가폴 459
 
4.6%
일본 270
 
2.7%
러시아연방 229
 
2.3%
Other values (56) 1520
15.1%
2023-12-12T21:21:13.597074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
마 3395
 
11.3%
국 2551
 
8.5%
나 2278
 
7.6%
파 2267
 
7.5%
한 1951
 
6.5%
리 1634
 
5.4%
아 1026
 
3.4%
라 925
 
3.1%
이 919
 
3.1%
도 897
 
3.0%
Other values (103) 12204
40.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29981
99.8%
Space Separator 66
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
마 3395
 
11.3%
국 2551
 
8.5%
나 2278
 
7.6%
파 2267
 
7.6%
한 1951
 
6.5%
리 1634
 
5.5%
아 1026
 
3.4%
라 925
 
3.1%
이 919
 
3.1%
도 897
 
3.0%
Other values (102) 12138
40.5%
Space Separator
ValueCountFrequency (%)
66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29981
99.8%
Common 66
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
마 3395
 
11.3%
국 2551
 
8.5%
나 2278
 
7.6%
파 2267
 
7.6%
한 1951
 
6.5%
리 1634
 
5.5%
아 1026
 
3.4%
라 925
 
3.1%
이 919
 
3.1%
도 897
 
3.0%
Other values (102) 12138
40.5%
Common
ValueCountFrequency (%)
66
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29981
99.8%
ASCII 66
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
마 3395
 
11.3%
국 2551
 
8.5%
나 2278
 
7.6%
파 2267
 
7.6%
한 1951
 
6.5%
리 1634
 
5.5%
아 1026
 
3.4%
라 925
 
3.1%
이 919
 
3.1%
도 897
 
3.0%
Other values (102) 12138
40.5%
ASCII
ValueCountFrequency (%)
66
100.0%

선박종류
Categorical

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
풀컨테이너선
3467 
일반화물선
2208 
산물선(벌크선)
988 
석유제품운반선
499 
케미칼운반선
492 
Other values (26)
2346 

Length

Max length11
Median length9
Mean length5.8388
Min length3

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row풀컨테이너선
2nd row케미칼운반선
3rd row냉동냉장선
4th row석유제품운반선
5th row풀컨테이너선

Common Values

ValueCountFrequency (%)
풀컨테이너선 3467
34.7%
일반화물선 2208
22.1%
산물선(벌크선) 988
 
9.9%
석유제품운반선 499
 
5.0%
케미칼운반선 492
 
4.9%
화객선 414
 
4.1%
원양어선 302
 
3.0%
원유운반선 206
 
2.1%
자동차운반선 203
 
2.0%
LNG운반선 195
 
1.9%
Other values (21) 1026
 
10.3%

Length

2023-12-12T21:21:13.771269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
풀컨테이너선 3467
34.7%
일반화물선 2208
22.1%
산물선(벌크선 988
 
9.9%
석유제품운반선 499
 
5.0%
케미칼운반선 492
 
4.9%
화객선 414
 
4.1%
원양어선 302
 
3.0%
원유운반선 206
 
2.1%
자동차운반선 203
 
2.0%
lng운반선 195
 
1.9%
Other values (21) 1026
 
10.3%

검역구분
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
전자
7705 
승선
2295 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전자
2nd row전자
3rd row전자
4th row승선
5th row전자

Common Values

ValueCountFrequency (%)
전자 7705
77.0%
승선 2295
 
22.9%

Length

2023-12-12T21:21:13.923486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:21:14.043364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전자 7705
77.0%
승선 2295
 
22.9%

오염_비오염
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
오염
8953 
비오염
1047 

Length

Max length3
Median length2
Mean length2.1047
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
오염 8953
89.5%
비오염 1047
 
10.5%

Length

2023-12-12T21:21:14.177809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:21:14.289084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
오염 8953
89.5%
비오염 1047
 
10.5%

출발국가
Text

Distinct66
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T21:21:14.479023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length2
Mean length2.4478
Min length1

Characters and Unicode

Total characters24478
Distinct characters117
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)0.2%

Sample

1st row일본
2nd row일본
3rd row필리핀
4th row인도네시아
5th row일본
ValueCountFrequency (%)
중국 3694
36.9%
일본 3160
31.5%
러시아연방 613
 
6.1%
호주 292
 
2.9%
싱가폴 247
 
2.5%
대만 228
 
2.3%
미국 218
 
2.2%
인도네시아 199
 
2.0%
베트남 193
 
1.9%
파나마 140
 
1.4%
Other values (60) 1035
 
10.3%
2023-12-12T21:21:14.830913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
국 4051
16.5%
중 3694
15.1%
일 3163
12.9%
본 3160
12.9%
아 1088
 
4.4%
시 831
 
3.4%
러 613
 
2.5%
연 613
 
2.5%
방 613
 
2.5%
주 346
 
1.4%
Other values (107) 6306
25.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24454
99.9%
Space Separator 19
 
0.1%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 4051
16.6%
중 3694
15.1%
일 3163
12.9%
본 3160
12.9%
아 1088
 
4.4%
시 831
 
3.4%
러 613
 
2.5%
연 613
 
2.5%
방 613
 
2.5%
주 346
 
1.4%
Other values (103) 6282
25.7%
Space Separator
ValueCountFrequency (%)
19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24454
99.9%
Common 24
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 4051
16.6%
중 3694
15.1%
일 3163
12.9%
본 3160
12.9%
아 1088
 
4.4%
시 831
 
3.4%
러 613
 
2.5%
연 613
 
2.5%
방 613
 
2.5%
주 346
 
1.4%
Other values (103) 6282
25.7%
Common
ValueCountFrequency (%)
19
79.2%
) 2
 
8.3%
( 2
 
8.3%
, 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24454
99.9%
ASCII 24
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
국 4051
16.6%
중 3694
15.1%
일 3163
12.9%
본 3160
12.9%
아 1088
 
4.4%
시 831
 
3.4%
러 613
 
2.5%
연 613
 
2.5%
방 613
 
2.5%
주 346
 
1.4%
Other values (103) 6282
25.7%
ASCII
ValueCountFrequency (%)
19
79.2%
) 2
 
8.3%
( 2
 
8.3%
, 1
 
4.2%

선사대리점
Text

Distinct345
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T21:21:15.095645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length26
Mean length8.4944
Min length3

Characters and Unicode

Total characters84944
Distinct characters257
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

Unique41 ?
Unique (%)0.4%

Sample

1st row태영상선(주)
2nd row그레이트해운(주)
3rd rowYEUNGHWA SHIPPING CO.
4th row(주)신성해운
5th row고려해운(주)
ValueCountFrequency (%)
co 563
 
4.3%
ltd 526
 
4.1%
주식회사 475
 
3.7%
고려해운(주 343
 
2.6%
장금상선(주 342
 
2.6%
korea 323
 
2.5%
미래쉬핑 274
 
2.1%
shipping 252
 
1.9%
cma 210
 
1.6%
cgm 210
 
1.6%
Other values (358) 9464
72.9%
2023-12-12T21:21:15.495285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 6547
 
7.7%
) 6304
 
7.4%
( 6288
 
7.4%
해 4662
 
5.5%
운 4582
 
5.4%
3508
 
4.1%
스 2061
 
2.4%
리 1373
 
1.6%
O 1315
 
1.5%
C 1233
 
1.5%
Other values (247) 47071
55.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 55948
65.9%
Uppercase Letter 12123
 
14.3%
Close Punctuation 6304
 
7.4%
Open Punctuation 6288
 
7.4%
Space Separator 3508
 
4.1%
Other Punctuation 771
 
0.9%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 6547
 
11.7%
해 4662
 
8.3%
운 4582
 
8.2%
스 2061
 
3.7%
리 1373
 
2.5%
선 1220
 
2.2%
아 1120
 
2.0%
양 1101
 
2.0%
코 1031
 
1.8%
에 963
 
1.7%
Other values (219) 31288
55.9%
Uppercase Letter
ValueCountFrequency (%)
O 1315
10.8%
C 1233
 
10.2%
A 1081
 
8.9%
P 848
 
7.0%
I 776
 
6.4%
G 773
 
6.4%
T 764
 
6.3%
L 742
 
6.1%
N 667
 
5.5%
E 652
 
5.4%
Other values (13) 3272
27.0%
Close Punctuation
ValueCountFrequency (%)
) 6304
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6288
100.0%
Space Separator
ValueCountFrequency (%)
3508
100.0%
Other Punctuation
ValueCountFrequency (%)
. 771
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 55948
65.9%
Common 16873
 
19.9%
Latin 12123
 
14.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 6547
 
11.7%
해 4662
 
8.3%
운 4582
 
8.2%
스 2061
 
3.7%
리 1373
 
2.5%
선 1220
 
2.2%
아 1120
 
2.0%
양 1101
 
2.0%
코 1031
 
1.8%
에 963
 
1.7%
Other values (219) 31288
55.9%
Latin
ValueCountFrequency (%)
O 1315
10.8%
C 1233
 
10.2%
A 1081
 
8.9%
P 848
 
7.0%
I 776
 
6.4%
G 773
 
6.4%
T 764
 
6.3%
L 742
 
6.1%
N 667
 
5.5%
E 652
 
5.4%
Other values (13) 3272
27.0%
Common
ValueCountFrequency (%)
) 6304
37.4%
( 6288
37.3%
3508
20.8%
. 771
 
4.6%
- 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 55948
65.9%
ASCII 28996
34.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 6547
 
11.7%
해 4662
 
8.3%
운 4582
 
8.2%
스 2061
 
3.7%
리 1373
 
2.5%
선 1220
 
2.2%
아 1120
 
2.0%
양 1101
 
2.0%
코 1031
 
1.8%
에 963
 
1.7%
Other values (219) 31288
55.9%
ASCII
ValueCountFrequency (%)
) 6304
21.7%
( 6288
21.7%
3508
12.1%
O 1315
 
4.5%
C 1233
 
4.3%
A 1081
 
3.7%
P 848
 
2.9%
I 776
 
2.7%
G 773
 
2.7%
. 771
 
2.7%
Other values (18) 6099
21.0%

주요적재화물
Categorical

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
잡품
3164 
기타()
1844 
에너지
1296 
철강
969 
유기화합물
507 
Other values (21)
2220 

Length

Max length12
Median length2
Mean length2.9479
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row기타()
2nd row무기화합물
3rd row농산물
4th row유기화합물
5th row잡품

Common Values

ValueCountFrequency (%)
잡품 3164
31.6%
기타() 1844
18.4%
에너지 1296
13.0%
철강 969
 
9.7%
유기화합물 507
 
5.1%
수산물 480
 
4.8%
고철 353
 
3.5%
차량 205
 
2.1%
목재류/종이류 192
 
1.9%
사료 176
 
1.8%
Other values (16) 814
 
8.1%

Length

2023-12-12T21:21:15.645291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
잡품 3164
31.2%
기타 1844
18.2%
에너지 1296
12.8%
철강 969
 
9.5%
유기화합물 507
 
5.0%
수산물 480
 
4.7%
고철 353
 
3.5%
차량 205
 
2.0%
목재류/종이류 192
 
1.9%
사료 176
 
1.7%
Other values (18) 962
 
9.5%

대종품목
Text

Distinct51
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T21:21:15.827060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length2
Mean length3.3981
Min length1

Characters and Unicode

Total characters33981
Distinct characters113
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st row기타
2nd row무기화합물
3rd row채소
4th row유기화합물
5th row잡품
ValueCountFrequency (%)
잡품 2933
24.5%
에너지 1296
10.8%
연료 1296
10.8%
기타 1101
 
9.2%
철강제품 674
 
5.6%
유기화합물 507
 
4.2%
어류 480
 
4.0%
기타의비금속 405
 
3.4%
철강 348
 
2.9%
고철 296
 
2.5%
Other values (48) 2615
21.9%
2023-12-12T21:21:16.140159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
품 3795
 
11.2%
잡 2933
 
8.6%
기 2569
 
7.6%
1951
 
5.7%
료 1522
 
4.5%
타 1515
 
4.5%
지 1346
 
4.0%
연 1344
 
4.0%
철 1319
 
3.9%
에 1296
 
3.8%
Other values (103) 14391
42.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31428
92.5%
Space Separator 1951
 
5.7%
Other Punctuation 320
 
0.9%
Uppercase Letter 282
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
품 3795
 
12.1%
잡 2933
 
9.3%
기 2569
 
8.2%
료 1522
 
4.8%
타 1515
 
4.8%
지 1346
 
4.3%
연 1344
 
4.3%
철 1319
 
4.2%
에 1296
 
4.1%
너 1296
 
4.1%
Other values (92) 12493
39.8%
Uppercase Letter
ValueCountFrequency (%)
P 78
27.7%
C 47
16.7%
E 47
16.7%
R 39
13.8%
O 39
13.8%
N 8
 
2.8%
I 8
 
2.8%
K 8
 
2.8%
L 8
 
2.8%
Space Separator
ValueCountFrequency (%)
1951
100.0%
Other Punctuation
ValueCountFrequency (%)
. 320
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31428
92.5%
Common 2271
 
6.7%
Latin 282
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
품 3795
 
12.1%
잡 2933
 
9.3%
기 2569
 
8.2%
료 1522
 
4.8%
타 1515
 
4.8%
지 1346
 
4.3%
연 1344
 
4.3%
철 1319
 
4.2%
에 1296
 
4.1%
너 1296
 
4.1%
Other values (92) 12493
39.8%
Latin
ValueCountFrequency (%)
P 78
27.7%
C 47
16.7%
E 47
16.7%
R 39
13.8%
O 39
13.8%
N 8
 
2.8%
I 8
 
2.8%
K 8
 
2.8%
L 8
 
2.8%
Common
ValueCountFrequency (%)
1951
85.9%
. 320
 
14.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31428
92.5%
ASCII 2553
 
7.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
품 3795
 
12.1%
잡 2933
 
9.3%
기 2569
 
8.2%
료 1522
 
4.8%
타 1515
 
4.8%
지 1346
 
4.3%
연 1344
 
4.3%
철 1319
 
4.2%
에 1296
 
4.1%
너 1296
 
4.1%
Other values (92) 12493
39.8%
ASCII
ValueCountFrequency (%)
1951
76.4%
. 320
 
12.5%
P 78
 
3.1%
C 47
 
1.8%
E 47
 
1.8%
R 39
 
1.5%
O 39
 
1.5%
N 8
 
0.3%
I 8
 
0.3%
K 8
 
0.3%

총적재량
Real number (ℝ)

ZEROS 

Distinct1869
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4321.3225
Minimum0
Maximum289093
Zeros7705
Zeros (%)77.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:21:16.288123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile25305.55
Maximum289093
Range289093
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18486.36
Coefficient of variation (CV)4.2779404
Kurtosis90.184333
Mean4321.3225
Median Absolute Deviation (MAD)0
Skewness8.1507746
Sum43213225
Variance3.4174551 × 108
MonotonicityNot monotonic
2023-12-12T21:21:16.433627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7705
77.0%
500.0 86
 
0.9%
5000.0 25
 
0.2%
3000.0 24
 
0.2%
2200.0 22
 
0.2%
1.0 17
 
0.2%
8000.0 13
 
0.1%
2000.0 12
 
0.1%
50.0 7
 
0.1%
42000.0 6
 
0.1%
Other values (1859) 2083
 
20.8%
ValueCountFrequency (%)
0.0 7705
77.0%
1.0 17
 
0.2%
4.0 1
 
< 0.1%
7.0 2
 
< 0.1%
10.0 1
 
< 0.1%
11.0 1
 
< 0.1%
12.0 3
 
< 0.1%
13.0 3
 
< 0.1%
14.0 1
 
< 0.1%
16.0 2
 
< 0.1%
ValueCountFrequency (%)
289093.0 1
< 0.1%
283229.0 1
< 0.1%
279028.0 1
< 0.1%
277780.0 1
< 0.1%
275311.0 1
< 0.1%
274225.0 1
< 0.1%
274185.697 1
< 0.1%
270432.0 1
< 0.1%
268029.0 1
< 0.1%
267982.0 1
< 0.1%

Interactions

2023-12-12T21:21:10.835615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:21:16.525056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
검역소선박국적선박종류검역구분오염_비오염출발국가주요적재화물대종품목총적재량
검역소1.0000.5170.7820.2630.2170.5450.7150.7790.113
선박국적0.5171.0000.7980.3280.2940.7840.6900.7580.339
선박종류0.7820.7981.0000.2660.5200.7650.8780.9040.393
검역구분0.2630.3280.2661.0000.0890.2620.1910.2070.523
오염_비오염0.2170.2940.5200.0891.0000.9040.5040.5030.119
출발국가0.5450.7840.7650.2620.9041.0000.7460.7830.506
주요적재화물0.7150.6900.8780.1910.5040.7461.0000.9980.232
대종품목0.7790.7580.9040.2070.5030.7830.9981.0000.290
총적재량0.1130.3390.3930.5230.1190.5060.2320.2901.000
2023-12-12T21:21:16.647548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
검역소오염_비오염선박종류검역구분주요적재화물
검역소1.0000.2070.4000.2520.341
오염_비오염0.2071.0000.4450.0560.401
선박종류0.4000.4451.0000.2260.405
검역구분0.2520.0560.2261.0000.151
주요적재화물0.3410.4010.4050.1511.000
2023-12-12T21:21:16.756494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총적재량검역소선박종류검역구분오염_비오염주요적재화물
총적재량1.0000.0480.1490.4030.0910.086
검역소0.0481.0000.4000.2520.2070.341
선박종류0.1490.4001.0000.2260.4450.405
검역구분0.4030.2520.2261.0000.0560.151
오염_비오염0.0910.2070.4450.0561.0000.401
주요적재화물0.0860.3410.4050.1510.4011.000

Missing values

2023-12-12T21:21:10.986971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:21:11.207905image/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

검역일시검역소선박명선박국적선박종류검역구분오염_비오염출발국가선사대리점주요적재화물대종품목총적재량
363942022-12-16 20:03국립부산검역소TY INCHEON한국풀컨테이너선전자오염일본태영상선(주)기타()기타0.0
13372022-01-14 11:41국립울산검역소KEOYOUNG DREAM 3한국케미칼운반선전자오염일본그레이트해운(주)무기화합물무기화합물0.0
150862022-05-25 08:19국립마산검역소TOLEDO CARRIER마샬군도냉동냉장선전자오염필리핀YEUNGHWA SHIPPING CO.농산물채소0.0
86542022-03-25 11:00국립여수검역소MP MR TANKER 2싱가폴석유제품운반선승선오염인도네시아(주)신성해운유기화합물유기화합물34983.0
268702022-09-16 22:02국립부산검역소SUNNY MAPLE한국풀컨테이너선전자오염일본고려해운(주)잡품잡품0.0
335432022-11-19 18:25국립포항검역소PAMPERO몰타산물선(벌크선)승선오염호주신진해운(주)에너지연료 에너지90557.0
357052022-12-10 07:02국립부산검역소SKY PRIDE한국풀컨테이너선전자오염중국천경해운(주)잡품잡품0.0
224602022-08-03 08:45국립인천검역소JIN ZHU벨리즈일반화물선승선오염중국지엘해운 주식회사잡품잡품4931.0
274832022-09-24 09:30국립마산검역소KAN407중국기타선승선오염중국유)진해해운선박선박930.0
8662022-01-09 11:30국립목포검역소KEUM YANG PRIME한국일반화물선승선오염중국가사도 해운기타()플라스틱3516.0
검역일시검역소선박명선박국적선박종류검역구분오염_비오염출발국가선사대리점주요적재화물대종품목총적재량
325962022-11-11 10:00국립군산검역소FU CHENG중국일반화물선승선오염중국서교해운(주)사료사료2200.0
156362022-05-30 07:45국립부산검역소SHECAN한국풀컨테이너선전자오염중국남성해운잡품잡품0.0
34612022-02-04 09:50국립울산검역소DE SHENG HAI홍콩산물선(벌크선)전자비오염파나마신양상선금속동COPPER0.0
194442022-07-05 11:30국립울산검역소SUNRISE SAMBU한국케미칼운반선전자오염일본삼부해운(주)화학공업제품화학공업생산품0.0
109202022-04-16 09:18국립울산검역소ILSHIN POLARIS ROYAL한국일반화물선전자오염일본그레이트해운(주)철강철강제품0.0
242312022-08-20 09:12국립여수검역소MAERSK CHILKA홍콩풀컨테이너선전자오염중국루맥스해운(광양)잡품잡품0.0
140522022-05-15 17:00국립포항검역소FOISON OCEAN벨리즈일반화물선전자오염일본신진해운(주)고철고철0.0
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