Overview

Dataset statistics

Number of variables9
Number of observations49
Missing cells2
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory75.7 B

Variable types

DateTime1
Categorical4
Text3
Numeric1

Dataset

DescriptionSample
Author에이치더블유
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_000327

Alerts

ORPLC_NTN_NM is highly overall correlated with SOF and 2 other fieldsHigh correlation
XPORT_NTN_NM is highly overall correlated with SOF and 2 other fieldsHigh correlation
RN is highly overall correlated with SOF and 1 other fieldsHigh correlation
SOF is highly overall correlated with RN and 3 other fieldsHigh correlation
PRDCT_NM is highly overall correlated with RN and 3 other fieldsHigh correlation
OVSEA_MF_CMPNY_ADDR has 2 (4.1%) missing valuesMissing
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:47:38.190268
Analysis finished2023-12-10 14:47:38.939504
Duration0.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

IM_YMD
Date

Distinct3
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2021-08-10 00:00:00
Maximum2021-08-31 00:00:00
2023-12-10T23:47:38.975492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:39.057221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

SOF
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
오징어
24 
명태
13 
갈치
9 
고등어
3 

Length

Max length3
Median length3
Mean length2.5510204
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
오징어 24
49.0%
명태 13
26.5%
갈치 9
 
18.4%
고등어 3
 
6.1%

Length

2023-12-10T23:47:39.162376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:47:39.245383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
오징어 24
49.0%
명태 13
26.5%
갈치 9
 
18.4%
고등어 3
 
6.1%

PRDCT_NM
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Memory size524.0 B
갈치(냉동)
7 
오징어(냉동,동체)
6 
오징어(냉동,동체,자숙)
6 
명태(냉동)
5 
오징어(냉동,다리)
4 
Other values (9)
21 

Length

Max length13
Median length12
Mean length9.1020408
Min length6

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row오징어(냉동,다리)
2nd row오징어(냉동,다리)
3rd row오징어(냉동,동체)
4th row오징어(냉동,동체)
5th row오징어(냉동,동체)

Common Values

ValueCountFrequency (%)
갈치(냉동) 7
14.3%
오징어(냉동,동체) 6
12.2%
오징어(냉동,동체,자숙) 6
12.2%
명태(냉동) 5
10.2%
오징어(냉동,다리) 4
8.2%
오징어(냉동,지느러미) 4
8.2%
오징어(냉동) 4
8.2%
고등어(냉동,필렛(F)) 2
 
4.1%
명태(건조,껍질) 2
 
4.1%
명태(냉동,필렛(F)) 2
 
4.1%
Other values (4) 7
14.3%

Length

2023-12-10T23:47:39.350320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
갈치(냉동 7
14.3%
오징어(냉동,동체 6
12.2%
오징어(냉동,동체,자숙 6
12.2%
명태(냉동 5
10.2%
오징어(냉동,다리 4
8.2%
오징어(냉동,지느러미 4
8.2%
오징어(냉동 4
8.2%
고등어(냉동,필렛(f 2
 
4.1%
명태(건조,껍질 2
 
4.1%
명태(냉동,필렛(f 2
 
4.1%
Other values (4) 7
14.3%

ORPLC_NTN_NM
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Memory size524.0 B
페루
18 
러시아
11 
세네갈
4 
일본
4 
칠레
3 
Other values (7)
9 

Length

Max length9
Median length2
Mean length2.5714286
Min length2

Unique

Unique5 ?
Unique (%)10.2%

Sample

1st row칠레
2nd row페루
3rd row칠레
4th row페루
5th row페루

Common Values

ValueCountFrequency (%)
페루 18
36.7%
러시아 11
22.4%
세네갈 4
 
8.2%
일본 4
 
8.2%
칠레 3
 
6.1%
노르웨이 2
 
4.1%
중국 2
 
4.1%
인도 1
 
2.0%
오만 1
 
2.0%
대만 1
 
2.0%
Other values (2) 2
 
4.1%

Length

2023-12-10T23:47:39.470052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
페루 18
36.0%
러시아 11
22.0%
세네갈 4
 
8.0%
일본 4
 
8.0%
칠레 3
 
6.0%
노르웨이 2
 
4.0%
중국 2
 
4.0%
인도 1
 
2.0%
오만 1
 
2.0%
대만 1
 
2.0%
Other values (3) 3
 
6.0%

XPORT_NTN_NM
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
페루
18 
중국
9 
러시아
6 
세네갈
4 
일본
4 
Other values (6)
8 

Length

Max length9
Median length2
Mean length2.3877551
Min length2

Unique

Unique5 ?
Unique (%)10.2%

Sample

1st row칠레
2nd row페루
3rd row칠레
4th row페루
5th row페루

Common Values

ValueCountFrequency (%)
페루 18
36.7%
중국 9
18.4%
러시아 6
 
12.2%
세네갈 4
 
8.2%
일본 4
 
8.2%
칠레 3
 
6.1%
인도 1
 
2.0%
오만 1
 
2.0%
대만 1
 
2.0%
뉴질랜드 1
 
2.0%

Length

2023-12-10T23:47:39.585817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
페루 18
36.0%
중국 9
18.0%
러시아 6
 
12.0%
세네갈 4
 
8.0%
일본 4
 
8.0%
칠레 3
 
6.0%
인도 1
 
2.0%
오만 1
 
2.0%
대만 1
 
2.0%
뉴질랜드 1
 
2.0%
Other values (2) 2
 
4.0%

OVSEA_MF_CMPNY
Text

Distinct35
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:47:39.827502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length37
Mean length27.938776
Min length15

Characters and Unicode

Total characters1369
Distinct characters53
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)57.1%

Sample

1st rowSEA STAR EXPORTADORA DE PRODUCTS DEL MAR LTDA
2nd rowCORPESMAR S.A.C
3rd rowSEA STAR EXPORTADORA DE PRODUCTS DEL MAR LTDA
4th rowTHAXU EXPORT S.A.C
5th rowTHAXU EXPORT S.A.C
ValueCountFrequency (%)
s.a.c 12
 
5.8%
del 8
 
3.9%
mar 7
 
3.4%
co 6
 
2.9%
ltd 6
 
2.9%
export 5
 
2.4%
products 4
 
1.9%
farm 4
 
1.9%
sea 4
 
1.9%
collective 4
 
1.9%
Other values (95) 147
71.0%
2023-12-10T23:47:40.444435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
159
 
11.6%
A 116
 
8.5%
O 101
 
7.4%
E 85
 
6.2%
R 81
 
5.9%
S 79
 
5.8%
C 67
 
4.9%
. 64
 
4.7%
T 62
 
4.5%
D 61
 
4.5%
Other values (43) 494
36.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1014
74.1%
Space Separator 159
 
11.6%
Lowercase Letter 110
 
8.0%
Other Punctuation 77
 
5.6%
Open Punctuation 3
 
0.2%
Close Punctuation 3
 
0.2%
Decimal Number 3
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 116
11.4%
O 101
 
10.0%
E 85
 
8.4%
R 81
 
8.0%
S 79
 
7.8%
C 67
 
6.6%
T 62
 
6.1%
D 61
 
6.0%
I 56
 
5.5%
L 48
 
4.7%
Other values (16) 258
25.4%
Lowercase Letter
ValueCountFrequency (%)
e 17
15.5%
i 12
10.9%
r 9
 
8.2%
l 9
 
8.2%
n 8
 
7.3%
y 8
 
7.3%
o 6
 
5.5%
s 6
 
5.5%
a 6
 
5.5%
b 5
 
4.5%
Other values (8) 24
21.8%
Other Punctuation
ValueCountFrequency (%)
. 64
83.1%
, 11
 
14.3%
" 2
 
2.6%
Decimal Number
ValueCountFrequency (%)
1 1
33.3%
5 1
33.3%
3 1
33.3%
Space Separator
ValueCountFrequency (%)
159
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1124
82.1%
Common 245
 
17.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 116
 
10.3%
O 101
 
9.0%
E 85
 
7.6%
R 81
 
7.2%
S 79
 
7.0%
C 67
 
6.0%
T 62
 
5.5%
D 61
 
5.4%
I 56
 
5.0%
L 48
 
4.3%
Other values (34) 368
32.7%
Common
ValueCountFrequency (%)
159
64.9%
. 64
26.1%
, 11
 
4.5%
( 3
 
1.2%
) 3
 
1.2%
" 2
 
0.8%
1 1
 
0.4%
5 1
 
0.4%
3 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1369
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
159
 
11.6%
A 116
 
8.5%
O 101
 
7.4%
E 85
 
6.2%
R 81
 
5.9%
S 79
 
5.8%
C 67
 
4.9%
. 64
 
4.7%
T 62
 
4.5%
D 61
 
4.5%
Other values (43) 494
36.1%

OVSEA_MF_CMPNY_ADDR
Text

MISSING 

Distinct33
Distinct (%)70.2%
Missing2
Missing (%)4.1%
Memory size524.0 B
2023-12-10T23:47:40.697694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length101
Median length80
Mean length58.87234
Min length18

Characters and Unicode

Total characters2767
Distinct characters71
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)55.3%

Sample

1st rowAlto Penuelas Sitio 66 Barrio Industrial COQUIMBO?, ElquiChile
2nd rowMZA.CLOTE.13A.H JOSE OLAYA-PIURA-PIURA-PERU
3rd rowAlto Penuelas Sitio 66 Barrio Industrial COQUIMBO?, ElquiChile
4th rowMZ R.LT 08 URB MIRAFLORES I ETAPA (FRENTE DE PARROQUIA SAGRADO CORAZON)CASTILLA PIURA PERU
5th rowMZ R.LT 08 URB MIRAFLORES I ETAPA (FRENTE DE PARROQUIA SAGRADO CORAZON)CASTILLA PIURA PERU
ValueCountFrequency (%)
de 11
 
3.1%
piura 7
 
2.0%
china 6
 
1.7%
str 5
 
1.4%
yantai 4
 
1.1%
peru 4
 
1.1%
urb 4
 
1.1%
mz 4
 
1.1%
industrial 4
 
1.1%
city 4
 
1.1%
Other values (199) 303
85.1%
2023-12-10T23:47:41.062668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
312
 
11.3%
A 202
 
7.3%
I 139
 
5.0%
E 125
 
4.5%
O 117
 
4.2%
R 111
 
4.0%
N 89
 
3.2%
T 88
 
3.2%
L 83
 
3.0%
U 78
 
2.8%
Other values (61) 1423
51.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1570
56.7%
Lowercase Letter 576
 
20.8%
Space Separator 312
 
11.3%
Other Punctuation 134
 
4.8%
Decimal Number 115
 
4.2%
Dash Punctuation 45
 
1.6%
Open Punctuation 7
 
0.3%
Close Punctuation 7
 
0.3%
Letter Number 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 202
12.9%
I 139
 
8.9%
E 125
 
8.0%
O 117
 
7.5%
R 111
 
7.1%
N 89
 
5.7%
T 88
 
5.6%
L 83
 
5.3%
U 78
 
5.0%
P 75
 
4.8%
Other values (16) 463
29.5%
Lowercase Letter
ValueCountFrequency (%)
a 69
12.0%
i 55
 
9.5%
o 51
 
8.9%
t 43
 
7.5%
n 39
 
6.8%
s 39
 
6.8%
e 36
 
6.2%
r 30
 
5.2%
l 29
 
5.0%
h 29
 
5.0%
Other values (15) 156
27.1%
Decimal Number
ValueCountFrequency (%)
1 30
26.1%
3 17
14.8%
4 16
13.9%
0 14
12.2%
2 10
 
8.7%
8 9
 
7.8%
6 9
 
7.8%
5 5
 
4.3%
7 3
 
2.6%
9 2
 
1.7%
Other Punctuation
ValueCountFrequency (%)
, 69
51.5%
. 60
44.8%
? 3
 
2.2%
/ 1
 
0.7%
' 1
 
0.7%
Space Separator
ValueCountFrequency (%)
312
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2147
77.6%
Common 620
 
22.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 202
 
9.4%
I 139
 
6.5%
E 125
 
5.8%
O 117
 
5.4%
R 111
 
5.2%
N 89
 
4.1%
T 88
 
4.1%
L 83
 
3.9%
U 78
 
3.6%
P 75
 
3.5%
Other values (42) 1040
48.4%
Common
ValueCountFrequency (%)
312
50.3%
, 69
 
11.1%
. 60
 
9.7%
- 45
 
7.3%
1 30
 
4.8%
3 17
 
2.7%
4 16
 
2.6%
0 14
 
2.3%
2 10
 
1.6%
8 9
 
1.5%
Other values (9) 38
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2766
> 99.9%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
312
 
11.3%
A 202
 
7.3%
I 139
 
5.0%
E 125
 
4.5%
O 117
 
4.2%
R 111
 
4.0%
N 89
 
3.2%
T 88
 
3.2%
L 83
 
3.0%
U 78
 
2.8%
Other values (60) 1422
51.4%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

CMPNY_CD
Text

Distinct35
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:47:41.235470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters294
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)53.1%

Sample

1st row4229C0
2nd row2012C0
3rd row4229C0
4th row5051C0
5th row5051C0
ValueCountFrequency (%)
4245c0 4
 
8.2%
2012c0 4
 
8.2%
4229c0 3
 
6.1%
2022c0 2
 
4.1%
1029i0 2
 
4.1%
5028c0 2
 
4.1%
4101c0 2
 
4.1%
4062c0 2
 
4.1%
5051c0 2
 
4.1%
4187c0 1
 
2.0%
Other values (25) 25
51.0%
2023-12-10T23:47:41.549079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 91
31.0%
2 40
13.6%
C 39
13.3%
4 34
 
11.6%
1 22
 
7.5%
5 20
 
6.8%
I 10
 
3.4%
3 9
 
3.1%
7 9
 
3.1%
6 8
 
2.7%
Other values (2) 12
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 245
83.3%
Uppercase Letter 49
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 91
37.1%
2 40
16.3%
4 34
 
13.9%
1 22
 
9.0%
5 20
 
8.2%
3 9
 
3.7%
7 9
 
3.7%
6 8
 
3.3%
8 7
 
2.9%
9 5
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
C 39
79.6%
I 10
 
20.4%

Most occurring scripts

ValueCountFrequency (%)
Common 245
83.3%
Latin 49
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 91
37.1%
2 40
16.3%
4 34
 
13.9%
1 22
 
9.0%
5 20
 
8.2%
3 9
 
3.7%
7 9
 
3.7%
6 8
 
3.3%
8 7
 
2.9%
9 5
 
2.0%
Latin
ValueCountFrequency (%)
C 39
79.6%
I 10
 
20.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 294
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 91
31.0%
2 40
13.6%
C 39
13.3%
4 34
 
11.6%
1 22
 
7.5%
5 20
 
6.8%
I 10
 
3.4%
3 9
 
3.1%
7 9
 
3.1%
6 8
 
2.7%
Other values (2) 12
 
4.1%

RN
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26
Minimum2
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:47:41.669726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.4
Q114
median26
Q338
95-th percentile47.6
Maximum50
Range48
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.28869
Coefficient of variation (CV)0.54956501
Kurtosis-1.2
Mean26
Median Absolute Deviation (MAD)12
Skewness0
Sum1274
Variance204.16667
MonotonicityStrictly increasing
2023-12-10T23:47:41.794513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2 1
 
2.0%
39 1
 
2.0%
29 1
 
2.0%
30 1
 
2.0%
31 1
 
2.0%
32 1
 
2.0%
33 1
 
2.0%
34 1
 
2.0%
35 1
 
2.0%
36 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
2 1
2.0%
3 1
2.0%
4 1
2.0%
5 1
2.0%
6 1
2.0%
7 1
2.0%
8 1
2.0%
9 1
2.0%
10 1
2.0%
11 1
2.0%
ValueCountFrequency (%)
50 1
2.0%
49 1
2.0%
48 1
2.0%
47 1
2.0%
46 1
2.0%
45 1
2.0%
44 1
2.0%
43 1
2.0%
42 1
2.0%
41 1
2.0%

Interactions

2023-12-10T23:47:38.683419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:47:41.880157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
IM_YMDSOFPRDCT_NMORPLC_NTN_NMXPORT_NTN_NMOVSEA_MF_CMPNYOVSEA_MF_CMPNY_ADDRCMPNY_CDRN
IM_YMD1.0000.6540.9010.9330.8000.9680.9890.9730.970
SOF0.6541.0001.0000.9980.8791.0001.0000.9930.867
PRDCT_NM0.9011.0001.0000.8740.8590.9850.9790.9790.898
ORPLC_NTN_NM0.9330.9980.8741.0000.9901.0001.0000.9850.705
XPORT_NTN_NM0.8000.8790.8590.9901.0001.0001.0000.9860.656
OVSEA_MF_CMPNY0.9681.0000.9851.0001.0001.0001.0000.9980.915
OVSEA_MF_CMPNY_ADDR0.9891.0000.9791.0001.0001.0001.0000.9980.913
CMPNY_CD0.9730.9930.9790.9850.9860.9980.9981.0000.930
RN0.9700.8670.8980.7050.6560.9150.9130.9301.000
2023-12-10T23:47:41.988478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PRDCT_NMSOFORPLC_NTN_NMXPORT_NTN_NM
PRDCT_NM1.0000.8820.5560.539
SOF0.8821.0000.8530.696
ORPLC_NTN_NM0.5560.8531.0000.937
XPORT_NTN_NM0.5390.6960.9371.000
2023-12-10T23:47:42.078325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RNSOFPRDCT_NMORPLC_NTN_NMXPORT_NTN_NM
RN1.0000.7060.6440.4000.320
SOF0.7061.0000.8820.8530.696
PRDCT_NM0.6440.8821.0000.5560.539
ORPLC_NTN_NM0.4000.8530.5561.0000.937
XPORT_NTN_NM0.3200.6960.5390.9371.000

Missing values

2023-12-10T23:47:38.780702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:47:38.893791image/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

IM_YMDSOFPRDCT_NMORPLC_NTN_NMXPORT_NTN_NMOVSEA_MF_CMPNYOVSEA_MF_CMPNY_ADDRCMPNY_CDRN
011-Aug-2021 00:00:00오징어오징어(냉동,다리)칠레칠레SEA STAR EXPORTADORA DE PRODUCTS DEL MAR LTDAAlto Penuelas Sitio 66 Barrio Industrial COQUIMBO?, ElquiChile4229C02
111-Aug-2021 00:00:00오징어오징어(냉동,다리)페루페루CORPESMAR S.A.CMZA.CLOTE.13A.H JOSE OLAYA-PIURA-PIURA-PERU2012C03
211-Aug-2021 00:00:00오징어오징어(냉동,동체)칠레칠레SEA STAR EXPORTADORA DE PRODUCTS DEL MAR LTDAAlto Penuelas Sitio 66 Barrio Industrial COQUIMBO?, ElquiChile4229C04
311-Aug-2021 00:00:00오징어오징어(냉동,동체)페루페루THAXU EXPORT S.A.CMZ R.LT 08 URB MIRAFLORES I ETAPA (FRENTE DE PARROQUIA SAGRADO CORAZON)CASTILLA PIURA PERU5051C05
411-Aug-2021 00:00:00오징어오징어(냉동,동체)페루페루THAXU EXPORT S.A.CMZ R.LT 08 URB MIRAFLORES I ETAPA (FRENTE DE PARROQUIA SAGRADO CORAZON)CASTILLA PIURA PERU5051C06
511-Aug-2021 00:00:00오징어오징어(냉동,동체)페루페루GUVERT EXPORT PERU S.A.C.CALLE H. CASTRO POZO 204 URB PIURA ESTAPA 4 MZ F1 LT.014062C07
611-Aug-2021 00:00:00오징어오징어(냉동,동체)페루페루THAXU EXPORT S.A.CMZ R.LT 08 URB MIRAFLORES I ETAPA (FRENTE DE PARROQUIA SAGRADO CORAZON)CASTILLA PIURA PERU4101C08
711-Aug-2021 00:00:00오징어오징어(냉동,동체)페루페루ALTAMAR FOODS PERU S.R.L.511 SE 5th Avenue, Suite 8 Fort Lauderdale, FL 33301 USA.4101C09
811-Aug-2021 00:00:00오징어오징어(냉동,동체,자숙)페루페루CORPESMAR S.A.CMZA.CLOTE.13A.H JOSE OLAYA-PIURA-PIURA-PERU2012C010
911-Aug-2021 00:00:00오징어오징어(냉동,동체,자숙)페루페루CORPESMAR S.A.CMZA.CLOTE.13A.H JOSE OLAYA-PIURA-PIURA-PERU2012C011
IM_YMDSOFPRDCT_NMORPLC_NTN_NMXPORT_NTN_NMOVSEA_MF_CMPNYOVSEA_MF_CMPNY_ADDRCMPNY_CDRN
3910-Aug-2021 00:00:00오징어오징어(냉동)대만대만COSMIC OCEAN CO.,LTD17f.-2no.6,Minquan 2nd Rd.,Qianzhen Dist.,Kaohsiun4187C041
4010-Aug-2021 00:00:00오징어오징어(냉동)중국중국DONGSHAN TAIHE FOOD CO., LTD.Tianwei Aquatic Product Comprehensive Market, Tongling Town, Dongshan, Fujian, China4062C042
4110-Aug-2021 00:00:00오징어오징어(냉동)뉴질랜드뉴질랜드SANFORD LIMITED22 JELLICOE STREET AUCKLAND, N.Z.5028C043
4210-Aug-2021 00:00:00오징어오징어(냉동,다리)페루페루CORPESMAR S.A.CMZA.CLOTE.13A.H JOSE OLAYA-PIURA-PIURA-PERU2012C044
4310-Aug-2021 00:00:00오징어오징어(냉동,다리)페루페루CORPORACION PESQUERA DEL MAR S.A.CMZA.C LOTE.13 A.H.JOSE OLAYA-PIURA-PERU3011I045
4431-Aug-2021 00:00:00갈치갈치(냉동)중국중국NINGDE HAIYANG FOOD CO., LTD.NO.3 FEIZHU ROAD LIUDE FEIZHU VILLAGE QIDU TOWN JIAOCHENG DISTRICT NINGDE CITY FUJIAN PROVINCE CHINA4074I046
4531-Aug-2021 00:00:00갈치갈치(냉동)세네갈세네갈SEOUL PECHE SUARLA COTE DE POSTE KAYAR THIES-SENEGAL DAKAR-SN4056C047
4631-Aug-2021 00:00:00갈치갈치(냉동)남아프리카 공화국남아프리카 공화국SEA HARVEST COOPERATION(PTY) LIMITED<NA>5022I048
4731-Aug-2021 00:00:00갈치갈치(냉장)일본일본SERIN CORPORATION5F,RICH SHOKUBUTSUEN BLDG,1-2,KITA4-JO,NISHI13-CHOME,CHUO-KU,SAPPORO,HOKKAIDO,JAPAN4110C049
4831-Aug-2021 00:00:00갈치갈치(냉장)일본일본SHOWA SUISAN K.K.4-10, Nishiki-cho, Japan7048C050