Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells838
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory810.5 KiB
Average record size in memory83.0 B

Variable types

Numeric3
Categorical4
Text2

Dataset

Description국립농산물품질관리원에서 관리하는 원산지표시 시도별 위반품목 및 위반물량 현황 정보(처분년월, 업무구분, 시도명, 위반품목, 위반유형, 위반건수, 위반물량)
Author국립농산물품질관리원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220204000000001684

Alerts

JOB_SE_NM is highly overall correlated with JOB_SE_CODEHigh correlation
JOB_SE_CODE is highly overall correlated with JOB_SE_NMHigh correlation
JOB_SE_CODE is highly imbalanced (80.1%)Imbalance
JOB_SE_NM is highly imbalanced (80.1%)Imbalance
DSPS_YM has 433 (4.3%) missing valuesMissing
VIOLT_VOLM has 405 (4.0%) missing valuesMissing
VIOLT_CO is highly skewed (γ1 = 27.09378052)Skewed
VIOLT_VOLM is highly skewed (γ1 = 97.30301362)Skewed

Reproduction

Analysis started2024-03-23 07:47:38.805471
Analysis finished2024-03-23 07:47:43.578180
Duration4.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

DSPS_YM
Real number (ℝ)

MISSING 

Distinct268
Distinct (%)2.8%
Missing433
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean201106.31
Minimum199802
Maximum202112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T07:47:43.794950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum199802
5-th percentile200108
Q1200611
median201105
Q3201602
95-th percentile202010
Maximum202112
Range2310
Interquartile range (IQR)991

Descriptive statistics

Standard deviation587.78271
Coefficient of variation (CV)0.0029227462
Kurtosis-0.97780588
Mean201106.31
Median Absolute Deviation (MAD)496
Skewness-0.081743668
Sum1.9239841 × 109
Variance345488.52
MonotonicityNot monotonic
2024-03-23T07:47:44.292206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200909 117
 
1.2%
201002 92
 
0.9%
201302 87
 
0.9%
201201 79
 
0.8%
201101 78
 
0.8%
201301 77
 
0.8%
201005 74
 
0.7%
200502 70
 
0.7%
202001 70
 
0.7%
201209 69
 
0.7%
Other values (258) 8754
87.5%
(Missing) 433
 
4.3%
ValueCountFrequency (%)
199802 1
 
< 0.1%
199804 1
 
< 0.1%
199908 1
 
< 0.1%
199912 1
 
< 0.1%
200001 30
0.3%
200002 32
0.3%
200003 26
0.3%
200004 24
0.2%
200005 20
0.2%
200006 34
0.3%
ValueCountFrequency (%)
202112 14
 
0.1%
202111 19
 
0.2%
202110 21
0.2%
202109 37
0.4%
202108 30
0.3%
202107 37
0.4%
202106 42
0.4%
202105 49
0.5%
202104 38
0.4%
202103 45
0.4%

JOB_SE_CODE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
ORI
9173 
GRA
 
478
BEF
 
311
GIN
 
21
WRE
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowORI
2nd rowBEF
3rd rowORI
4th rowORI
5th rowORI

Common Values

ValueCountFrequency (%)
ORI 9173
91.7%
GRA 478
 
4.8%
BEF 311
 
3.1%
GIN 21
 
0.2%
WRE 12
 
0.1%
GMO 5
 
0.1%

Length

2024-03-23T07:47:44.701089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T07:47:45.153045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ori 9173
91.7%
gra 478
 
4.8%
bef 311
 
3.1%
gin 21
 
0.2%
wre 12
 
0.1%
gmo 5
 
< 0.1%

JOB_SE_NM
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
원산지단속
9173 
양곡표시
 
478
축산물이력
 
311
미검사품
 
21
재사용화환
 
12

Length

Max length5
Median length5
Mean length4.9491
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row원산지단속
2nd row축산물이력
3rd row원산지단속
4th row원산지단속
5th row원산지단속

Common Values

ValueCountFrequency (%)
원산지단속 9173
91.7%
양곡표시 478
 
4.8%
축산물이력 311
 
3.1%
미검사품 21
 
0.2%
재사용화환 12
 
0.1%
GMO 5
 
0.1%

Length

2024-03-23T07:47:45.594319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T07:47:45.941794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
원산지단속 9173
91.7%
양곡표시 478
 
4.8%
축산물이력 311
 
3.1%
미검사품 21
 
0.2%
재사용화환 12
 
0.1%
gmo 5
 
< 0.1%

CTY_DO_NM
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경기도
1138 
서울특별시
855 
경상북도
847 
전라북도
801 
전라남도
800 
Other values (12)
5559 

Length

Max length7
Median length5
Mean length4.2285
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상북도
2nd row서울특별시
3rd row경상남도
4th row충청남도
5th row강원도

Common Values

ValueCountFrequency (%)
경기도 1138
11.4%
서울특별시 855
 
8.6%
경상북도 847
 
8.5%
전라북도 801
 
8.0%
전라남도 800
 
8.0%
강원도 758
 
7.6%
경상남도 722
 
7.2%
충청남도 677
 
6.8%
충청북도 666
 
6.7%
광주광역시 561
 
5.6%
Other values (7) 2175
21.8%

Length

2024-03-23T07:47:46.340329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 1138
11.4%
서울특별시 855
 
8.6%
경상북도 847
 
8.5%
전라북도 801
 
8.0%
전라남도 800
 
8.0%
강원도 758
 
7.6%
경상남도 722
 
7.2%
충청남도 677
 
6.8%
충청북도 666
 
6.7%
광주광역시 561
 
5.6%
Other values (7) 2175
21.8%

VIOLT_PRDLST
Text

Distinct702
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-23T07:47:47.071829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length3.703
Min length1

Characters and Unicode

Total characters37030
Distinct characters369
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

Unique265 ?
Unique (%)2.6%

Sample

1st row삼겹살
2nd row쇠고기(한우)
3rd row돼지고기
4th row배추김치
5th row무순(일반)
ValueCountFrequency (%)
돼지고기 775
 
7.6%
쇠고기 544
 
5.4%
배추김치 499
 
4.9%
쌀 337
 
3.3%
쇠고기(한우 324
 
3.2%
닭고기 279
 
2.7%
고추가루 210
 
2.1%
멥쌀 201
 
2.0%
기타 176
 
1.7%
두부류 171
 
1.7%
Other values (693) 6652
65.4%
2024-03-23T07:47:48.051780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 3114
 
8.4%
고 2989
 
8.1%
( 1342
 
3.6%
) 1342
 
3.6%
지 1234
 
3.3%
쇠 1087
 
2.9%
추 1036
 
2.8%
돼 975
 
2.6%
류 735
 
2.0%
쌀 731
 
2.0%
Other values (359) 22445
60.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 34172
92.3%
Open Punctuation 1342
 
3.6%
Close Punctuation 1342
 
3.6%
Space Separator 168
 
0.5%
Decimal Number 4
 
< 0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 3114
 
9.1%
고 2989
 
8.7%
지 1234
 
3.6%
쇠 1087
 
3.2%
추 1036
 
3.0%
돼 975
 
2.9%
류 735
 
2.2%
쌀 731
 
2.1%
반 684
 
2.0%
일 682
 
2.0%
Other values (352) 20905
61.2%
Decimal Number
ValueCountFrequency (%)
4 2
50.0%
6 1
25.0%
1 1
25.0%
Open Punctuation
ValueCountFrequency (%)
( 1342
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1342
100.0%
Space Separator
ValueCountFrequency (%)
168
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 34172
92.3%
Common 2858
 
7.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 3114
 
9.1%
고 2989
 
8.7%
지 1234
 
3.6%
쇠 1087
 
3.2%
추 1036
 
3.0%
돼 975
 
2.9%
류 735
 
2.2%
쌀 731
 
2.1%
반 684
 
2.0%
일 682
 
2.0%
Other values (352) 20905
61.2%
Common
ValueCountFrequency (%)
( 1342
47.0%
) 1342
47.0%
168
 
5.9%
. 2
 
0.1%
4 2
 
0.1%
6 1
 
< 0.1%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 34172
92.3%
ASCII 2858
 
7.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 3114
 
9.1%
고 2989
 
8.7%
지 1234
 
3.6%
쇠 1087
 
3.2%
추 1036
 
3.0%
돼 975
 
2.9%
류 735
 
2.2%
쌀 731
 
2.1%
반 684
 
2.0%
일 682
 
2.0%
Other values (352) 20905
61.2%
ASCII
ValueCountFrequency (%)
( 1342
47.0%
) 1342
47.0%
168
 
5.9%
. 2
 
0.1%
4 2
 
0.1%
6 1
 
< 0.1%
1 1
 
< 0.1%

VIOLT_TY
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
거짓표시
5711 
미표시
4265 
영수증미비치
 
23
시정명령 위반
 
1

Length

Max length7
Median length4
Mean length3.5784
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row거짓표시
2nd row미표시
3rd row거짓표시
4th row미표시
5th row거짓표시

Common Values

ValueCountFrequency (%)
거짓표시 5711
57.1%
미표시 4265
42.6%
영수증미비치 23
 
0.2%
시정명령 위반 1
 
< 0.1%

Length

2024-03-23T07:47:48.497130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T07:47:48.865580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
거짓표시 5711
57.1%
미표시 4265
42.6%
영수증미비치 23
 
0.2%
시정명령 1
 
< 0.1%
위반 1
 
< 0.1%

VIOLT_CO
Real number (ℝ)

SKEWED 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0896
Minimum1
Maximum263
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T07:47:49.217620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile6
Maximum263
Range262
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.4804441
Coefficient of variation (CV)2.1441635
Kurtosis1281.6197
Mean2.0896
Median Absolute Deviation (MAD)0
Skewness27.093781
Sum20896
Variance20.074379
MonotonicityNot monotonic
2024-03-23T07:47:49.518144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1 6776
67.8%
2 1483
 
14.8%
3 639
 
6.4%
4 323
 
3.2%
5 176
 
1.8%
6 130
 
1.3%
7 100
 
1.0%
8 91
 
0.9%
9 61
 
0.6%
10 40
 
0.4%
Other values (37) 181
 
1.8%
ValueCountFrequency (%)
1 6776
67.8%
2 1483
 
14.8%
3 639
 
6.4%
4 323
 
3.2%
5 176
 
1.8%
6 130
 
1.3%
7 100
 
1.0%
8 91
 
0.9%
9 61
 
0.6%
10 40
 
0.4%
ValueCountFrequency (%)
263 1
< 0.1%
114 1
< 0.1%
113 1
< 0.1%
96 1
< 0.1%
77 1
< 0.1%
72 1
< 0.1%
70 1
< 0.1%
66 1
< 0.1%
62 1
< 0.1%
61 1
< 0.1%

VIOLT_VOLM
Real number (ℝ)

MISSING  SKEWED 

Distinct3150
Distinct (%)32.8%
Missing405
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean11973.982
Minimum0
Maximum69000000
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T07:47:49.831481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median30
Q3320
95-th percentile10000
Maximum69000000
Range69000000
Interquartile range (IQR)314

Descriptive statistics

Standard deviation705948.66
Coefficient of variation (CV)58.956881
Kurtosis9509.0702
Mean11973.982
Median Absolute Deviation (MAD)28.2
Skewness97.303014
Sum1.1489036 × 108
Variance4.983635 × 1011
MonotonicityNot monotonic
2024-03-23T07:47:50.273116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.0 343
 
3.4%
1.0 305
 
3.0%
2.0 263
 
2.6%
3.0 246
 
2.5%
5.0 234
 
2.3%
20.0 215
 
2.1%
4.0 212
 
2.1%
6.0 175
 
1.8%
8.0 130
 
1.3%
40.0 115
 
1.1%
Other values (3140) 7357
73.6%
(Missing) 405
 
4.0%
ValueCountFrequency (%)
0.0 6
0.1%
0.01 1
 
< 0.1%
0.02 1
 
< 0.1%
0.046 1
 
< 0.1%
0.05 1
 
< 0.1%
0.081 1
 
< 0.1%
0.088 1
 
< 0.1%
0.1 10
0.1%
0.12 1
 
< 0.1%
0.15 3
 
< 0.1%
ValueCountFrequency (%)
69000000.0 1
< 0.1%
2207677.0 1
< 0.1%
1674769.0 1
< 0.1%
1380483.0 1
< 0.1%
1253930.0 1
< 0.1%
986298.5 1
< 0.1%
955070.0 1
< 0.1%
915376.0 1
< 0.1%
874180.0 1
< 0.1%
794015.0 1
< 0.1%

VIOLT_PRDLST_CODE
Text

Distinct765
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-23T07:47:51.093229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters60000
Distinct characters23
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

Unique305 ?
Unique (%)3.0%

Sample

1st row440227
2nd row430600
3rd row4304EG
4th row910403
5th row103901
ValueCountFrequency (%)
4304eg 775
 
7.8%
440100 544
 
5.4%
910403 499
 
5.0%
010300 334
 
3.3%
430600 324
 
3.2%
910801 210
 
2.1%
010313 201
 
2.0%
9107eb 167
 
1.7%
9107dh 162
 
1.6%
440300 151
 
1.5%
Other values (755) 6633
66.3%
2024-03-23T07:47:52.294957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 21367
35.6%
1 9496
15.8%
4 7306
 
12.2%
3 4696
 
7.8%
9 4045
 
6.7%
2 2427
 
4.0%
7 1865
 
3.1%
6 1846
 
3.1%
E 1436
 
2.4%
5 1337
 
2.2%
Other values (13) 4179
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 55288
92.1%
Uppercase Letter 4712
 
7.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 1436
30.5%
G 1026
21.8%
H 325
 
6.9%
J 324
 
6.9%
A 324
 
6.9%
B 306
 
6.5%
D 264
 
5.6%
F 231
 
4.9%
I 215
 
4.6%
C 172
 
3.7%
Other values (3) 89
 
1.9%
Decimal Number
ValueCountFrequency (%)
0 21367
38.6%
1 9496
17.2%
4 7306
 
13.2%
3 4696
 
8.5%
9 4045
 
7.3%
2 2427
 
4.4%
7 1865
 
3.4%
6 1846
 
3.3%
5 1337
 
2.4%
8 903
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 55288
92.1%
Latin 4712
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1436
30.5%
G 1026
21.8%
H 325
 
6.9%
J 324
 
6.9%
A 324
 
6.9%
B 306
 
6.5%
D 264
 
5.6%
F 231
 
4.9%
I 215
 
4.6%
C 172
 
3.7%
Other values (3) 89
 
1.9%
Common
ValueCountFrequency (%)
0 21367
38.6%
1 9496
17.2%
4 7306
 
13.2%
3 4696
 
8.5%
9 4045
 
7.3%
2 2427
 
4.4%
7 1865
 
3.4%
6 1846
 
3.3%
5 1337
 
2.4%
8 903
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21367
35.6%
1 9496
15.8%
4 7306
 
12.2%
3 4696
 
7.8%
9 4045
 
6.7%
2 2427
 
4.0%
7 1865
 
3.1%
6 1846
 
3.1%
E 1436
 
2.4%
5 1337
 
2.2%
Other values (13) 4179
 
7.0%

Interactions

2024-03-23T07:47:41.602975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:47:40.247467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:47:41.033666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:47:41.904175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:47:40.497575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:47:41.241494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:47:42.171607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:47:40.756516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:47:41.404451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T07:47:52.586092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DSPS_YMJOB_SE_CODEJOB_SE_NMCTY_DO_NMVIOLT_TYVIOLT_COVIOLT_VOLM
DSPS_YM1.0000.2200.2200.1500.3530.0390.000
JOB_SE_CODE0.2201.0001.0000.0550.2220.0000.000
JOB_SE_NM0.2201.0001.0000.0550.2220.0000.000
CTY_DO_NM0.1500.0550.0551.0000.0620.0030.000
VIOLT_TY0.3530.2220.2220.0621.0000.0220.000
VIOLT_CO0.0390.0000.0000.0030.0221.0000.000
VIOLT_VOLM0.0000.0000.0000.0000.0000.0001.000
2024-03-23T07:47:52.888822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
JOB_SE_NMCTY_DO_NMVIOLT_TYJOB_SE_CODE
JOB_SE_NM1.0000.0260.1451.000
CTY_DO_NM0.0261.0000.0350.026
VIOLT_TY0.1450.0351.0000.145
JOB_SE_CODE1.0000.0260.1451.000
2024-03-23T07:47:53.165253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DSPS_YMVIOLT_COVIOLT_VOLMJOB_SE_CODEJOB_SE_NMCTY_DO_NMVIOLT_TY
DSPS_YM1.000-0.006-0.0000.1170.1170.0590.218
VIOLT_CO-0.0061.0000.3020.0000.0000.0010.014
VIOLT_VOLM-0.0000.3021.0000.0000.0000.0000.000
JOB_SE_CODE0.1170.0000.0001.0001.0000.0260.145
JOB_SE_NM0.1170.0000.0001.0001.0000.0260.145
CTY_DO_NM0.0590.0010.0000.0260.0261.0000.035
VIOLT_TY0.2180.0140.0000.1450.1450.0351.000

Missing values

2024-03-23T07:47:42.558926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T07:47:43.081356image/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.
2024-03-23T07:47:43.434123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DSPS_YMJOB_SE_CODEJOB_SE_NMCTY_DO_NMVIOLT_PRDLSTVIOLT_TYVIOLT_COVIOLT_VOLMVIOLT_PRDLST_CODE
44195201007ORI원산지단속경상북도삼겹살거짓표시15.1440227
48068201310BEF축산물이력서울특별시쇠고기(한우)미표시325.0430600
50402201211ORI원산지단속경상남도돼지고기거짓표시396.04304EG
7064202103ORI원산지단속충청남도배추김치미표시4<NA>910403
42175201008ORI원산지단속강원도무순(일반)거짓표시10.2103901
21741200609ORI원산지단속경상북도돼지식육제품미표시124.04304EE
10378<NA>ORI원산지단속경상남도조미 기타미표시715571.0950044
58639201704ORI원산지단속서울특별시간장미표시1<NA>910817
39549201105GRA양곡표시서울특별시흑현미거짓표시166.4010605
3572202005ORI원산지단속서울특별시연근거짓표시14.0110500
DSPS_YMJOB_SE_CODEJOB_SE_NMCTY_DO_NMVIOLT_PRDLSTVIOLT_TYVIOLT_COVIOLT_VOLMVIOLT_PRDLST_CODE
48659201401ORI원산지단속광주광역시빵미표시119.59107AJ
61172201812ORI원산지단속충청남도단호박미표시111.0090205
15636200203ORI원산지단속경상북도오미자(일반)거짓표시13.0193001
2596202001ORI원산지단속충청남도배추김치(배추)거짓표시12750.0910463
12015200001ORI원산지단속충청북도육용 오리거짓표시112.0416230
12982200005ORI원산지단속서울특별시돼지고기거짓표시917187.114304EG
44018201101ORI원산지단속광주광역시새싹채소거짓표시11.15980401
56277201508ORI원산지단속제주특별자치도배추김치거짓표시21940.0910403
58596201802ORI원산지단속서울특별시돼지고기거짓표시52267.074304EG
1704201908ORI원산지단속세종특별자치시단호박미표시110.0090205