Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells4913
Missing cells (%)6.1%
Duplicate rows193
Duplicate rows (%)1.9%
Total size in memory732.4 KiB
Average record size in memory75.0 B

Variable types

Text2
Categorical3
Numeric3

Dataset

Description생산 또는 유통 중인 농산물에 대해 시군, 생산자(판매자), 작물별로 중금속 여부를 분석한 결과
Author국립농산물품질관리원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220204000000001677

Alerts

Dataset has 193 (1.9%) duplicate rowsDuplicates
재배면적 is highly overall correlated with 조사물량 and 1 other fieldsHigh correlation
조사물량 is highly overall correlated with 재배면적High correlation
수거단계 is highly overall correlated with 재배면적High correlation
수거단계 is highly imbalanced (55.1%)Imbalance
재배양식 is highly imbalanced (88.6%)Imbalance
분석결과 is highly imbalanced (94.4%)Imbalance
재배면적 has 2970 (29.7%) missing valuesMissing
조사물량 has 1943 (19.4%) missing valuesMissing
조사물량 is highly skewed (γ1 = 50.71796958)Skewed

Reproduction

Analysis started2023-12-22 22:18:00.150119
Analysis finished2023-12-22 22:18:10.072887
Duration9.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목명
Text

Distinct348
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-22T22:18:10.568995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length28
Mean length4.7154
Min length1

Characters and Unicode

Total characters47154
Distinct characters323
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

Unique101 ?
Unique (%)1.0%

Sample

1st row홍고추(붉은고추)
2nd row홍고추(붉은고추)
3rd row멥쌀(일반)
4th row시금치
5th row현미
ValueCountFrequency (%)
멥쌀(일반 2352
23.5%
현미 1826
18.2%
홍고추(붉은고추 292
 
2.9%
수미(슈페리어 227
 
2.3%
밤고구마 202
 
2.0%
풋고추 183
 
1.8%
대파 183
 
1.8%
일반부추(조선부추 169
 
1.7%
찰옥수수(대학찰,먹찰 165
 
1.6%
시금치 162
 
1.6%
Other values (341) 4268
42.6%
2023-12-22T22:18:12.635831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 4405
 
9.3%
) 4405
 
9.3%
반 2860
 
6.1%
일 2849
 
6.0%
쌀 2414
 
5.1%
멥 2352
 
5.0%
미 2176
 
4.6%
현 1865
 
4.0%
추 1678
 
3.6%
고 1342
 
2.8%
Other values (313) 20808
44.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 37763
80.1%
Open Punctuation 4405
 
9.3%
Close Punctuation 4405
 
9.3%
Other Punctuation 478
 
1.0%
Decimal Number 65
 
0.1%
Space Separator 29
 
0.1%
Uppercase Letter 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
반 2860
 
7.6%
일 2849
 
7.5%
쌀 2414
 
6.4%
멥 2352
 
6.2%
미 2176
 
5.8%
현 1865
 
4.9%
추 1678
 
4.4%
고 1342
 
3.6%
수 1150
 
3.0%
마 670
 
1.8%
Other values (298) 18407
48.7%
Decimal Number
ValueCountFrequency (%)
1 16
24.6%
4 11
16.9%
5 10
15.4%
0 9
13.8%
7 7
10.8%
6 6
 
9.2%
3 6
 
9.2%
Uppercase Letter
ValueCountFrequency (%)
A 3
33.3%
M 3
33.3%
B 3
33.3%
Other Punctuation
ValueCountFrequency (%)
, 475
99.4%
. 3
 
0.6%
Open Punctuation
ValueCountFrequency (%)
( 4405
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4405
100.0%
Space Separator
ValueCountFrequency (%)
29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 37763
80.1%
Common 9382
 
19.9%
Latin 9
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
반 2860
 
7.6%
일 2849
 
7.5%
쌀 2414
 
6.4%
멥 2352
 
6.2%
미 2176
 
5.8%
현 1865
 
4.9%
추 1678
 
4.4%
고 1342
 
3.6%
수 1150
 
3.0%
마 670
 
1.8%
Other values (298) 18407
48.7%
Common
ValueCountFrequency (%)
( 4405
47.0%
) 4405
47.0%
, 475
 
5.1%
29
 
0.3%
1 16
 
0.2%
4 11
 
0.1%
5 10
 
0.1%
0 9
 
0.1%
7 7
 
0.1%
6 6
 
0.1%
Other values (2) 9
 
0.1%
Latin
ValueCountFrequency (%)
A 3
33.3%
M 3
33.3%
B 3
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 37763
80.1%
ASCII 9391
 
19.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 4405
46.9%
) 4405
46.9%
, 475
 
5.1%
29
 
0.3%
1 16
 
0.2%
4 11
 
0.1%
5 10
 
0.1%
0 9
 
0.1%
7 7
 
0.1%
6 6
 
0.1%
Other values (5) 18
 
0.2%
Hangul
ValueCountFrequency (%)
반 2860
 
7.6%
일 2849
 
7.5%
쌀 2414
 
6.4%
멥 2352
 
6.2%
미 2176
 
5.8%
현 1865
 
4.9%
추 1678
 
4.4%
고 1342
 
3.6%
수 1150
 
3.0%
마 670
 
1.8%
Other values (298) 18407
48.7%

수거단계
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
생산
7033 
유통/판매
2946 
출하
 
15
저장
 
6

Length

Max length5
Median length2
Mean length2.8838
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row생산
2nd row생산
3rd row유통/판매
4th row유통/판매
5th row생산

Common Values

ValueCountFrequency (%)
생산 7033
70.3%
유통/판매 2946
29.5%
출하 15
 
0.1%
저장 6
 
0.1%

Length

2023-12-22T22:18:13.420023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-22T22:18:13.976591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생산 7033
70.3%
유통/판매 2946
29.5%
출하 15
 
0.1%
저장 6
 
0.1%

재배양식
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반
9554 
친환경(인증) 무농약
 
267
직불제(쌀소득)
 
73
GAP(인증)
 
51
친환경(인증) 유기
 
30
Other values (3)
 
25

Length

Max length11
Median length3
Mean length3.323
Min length3

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반 9554
95.5%
친환경(인증) 무농약 267
 
2.7%
직불제(쌀소득) 73
 
0.7%
GAP(인증) 51
 
0.5%
친환경(인증) 유기 30
 
0.3%
친환경(인증) 저농약 23
 
0.2%
지리적표시 1
 
< 0.1%
친환경 저농약 1
 
< 0.1%

Length

2023-12-22T22:18:14.610633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-22T22:18:15.277174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 9554
92.6%
친환경(인증 320
 
3.1%
무농약 267
 
2.6%
직불제(쌀소득 73
 
0.7%
gap(인증 51
 
0.5%
유기 30
 
0.3%
저농약 24
 
0.2%
지리적표시 1
 
< 0.1%
친환경 1
 
< 0.1%

생산자 주소
Text

Distinct395
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-22T22:18:16.409620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length9.9359
Min length3

Characters and Unicode

Total characters99359
Distinct characters135
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)0.4%

Sample

1st row충청남도 청양군
2nd row충남 천안시
3rd row강원 양양군
4th row경기도 포천시
5th row충남 서천군
ValueCountFrequency (%)
경상북도 887
 
4.4%
경북 877
 
4.4%
충남 859
 
4.3%
충청남도 776
 
3.9%
전남 621
 
3.1%
경남 614
 
3.1%
강원도 594
 
3.0%
경상남도 580
 
2.9%
경기도 525
 
2.6%
강원 523
 
2.6%
Other values (223) 13142
65.7%
2023-12-22T22:18:18.800032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
39996
40.3%
군 5737
 
5.8%
도 4844
 
4.9%
시 4326
 
4.4%
남 4261
 
4.3%
경 4143
 
4.2%
북 3274
 
3.3%
충 2595
 
2.6%
청 1779
 
1.8%
전 1724
 
1.7%
Other values (125) 26680
26.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 59359
59.7%
Space Separator 39996
40.3%
Math Symbol 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 5737
 
9.7%
도 4844
 
8.2%
시 4326
 
7.3%
남 4261
 
7.2%
경 4143
 
7.0%
북 3274
 
5.5%
충 2595
 
4.4%
청 1779
 
3.0%
전 1724
 
2.9%
상 1594
 
2.7%
Other values (123) 25082
42.3%
Space Separator
ValueCountFrequency (%)
39996
100.0%
Math Symbol
ValueCountFrequency (%)
| 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 59359
59.7%
Common 40000
40.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 5737
 
9.7%
도 4844
 
8.2%
시 4326
 
7.3%
남 4261
 
7.2%
경 4143
 
7.0%
북 3274
 
5.5%
충 2595
 
4.4%
청 1779
 
3.0%
전 1724
 
2.9%
상 1594
 
2.7%
Other values (123) 25082
42.3%
Common
ValueCountFrequency (%)
39996
> 99.9%
| 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 59359
59.7%
ASCII 40000
40.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
39996
> 99.9%
| 4
 
< 0.1%
Hangul
ValueCountFrequency (%)
군 5737
 
9.7%
도 4844
 
8.2%
시 4326
 
7.3%
남 4261
 
7.2%
경 4143
 
7.0%
북 3274
 
5.5%
충 2595
 
4.4%
청 1779
 
3.0%
전 1724
 
2.9%
상 1594
 
2.7%
Other values (123) 25082
42.3%

재배면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2758
Distinct (%)39.2%
Missing2970
Missing (%)29.7%
Infinite0
Infinite (%)0.0%
Mean2015.9857
Minimum3.3
Maximum93000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-22T22:18:20.033283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.3
5-th percentile165
Q1660
median1451
Q32615.75
95-th percentile5120.1
Maximum93000
Range92996.7
Interquartile range (IQR)1955.75

Descriptive statistics

Standard deviation2721.762
Coefficient of variation (CV)1.35009
Kurtosis250.14992
Mean2015.9857
Median Absolute Deviation (MAD)901
Skewness10.968339
Sum14172379
Variance7407988.5
MonotonicityNot monotonic
2023-12-22T22:18:21.140930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
330.0 296
 
3.0%
1000.0 158
 
1.6%
660.0 118
 
1.2%
300.0 109
 
1.1%
100.0 107
 
1.1%
600.0 76
 
0.8%
500.0 73
 
0.7%
1500.0 71
 
0.7%
200.0 70
 
0.7%
165.0 66
 
0.7%
Other values (2748) 5886
58.9%
(Missing) 2970
29.7%
ValueCountFrequency (%)
3.3 1
 
< 0.1%
5.0 1
 
< 0.1%
6.0 1
 
< 0.1%
15.0 2
 
< 0.1%
17.0 1
 
< 0.1%
20.0 3
 
< 0.1%
26.0 3
 
< 0.1%
30.0 9
0.1%
33.0 8
0.1%
40.0 3
 
< 0.1%
ValueCountFrequency (%)
93000.0 1
< 0.1%
61000.0 1
< 0.1%
46000.0 1
< 0.1%
40000.0 2
< 0.1%
37000.0 1
< 0.1%
33000.0 2
< 0.1%
30000.0 2
< 0.1%
29700.0 1
< 0.1%
29588.0 1
< 0.1%
29000.0 1
< 0.1%

조사물량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1123
Distinct (%)13.9%
Missing1943
Missing (%)19.4%
Infinite0
Infinite (%)0.0%
Mean1583.0247
Minimum0
Maximum700485
Zeros41
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-22T22:18:22.028510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q1100
median510
Q31350
95-th percentile5000
Maximum700485
Range700485
Interquartile range (IQR)1250

Descriptive statistics

Standard deviation10135.125
Coefficient of variation (CV)6.4023795
Kurtosis3180.0621
Mean1583.0247
Median Absolute Deviation (MAD)480
Skewness50.71797
Sum12754430
Variance1.0272076 × 108
MonotonicityNot monotonic
2023-12-22T22:18:23.341446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 445
 
4.5%
1000.0 388
 
3.9%
200.0 275
 
2.8%
500.0 270
 
2.7%
300.0 257
 
2.6%
50.0 244
 
2.4%
2.0 214
 
2.1%
2000.0 200
 
2.0%
1500.0 186
 
1.9%
20.0 180
 
1.8%
Other values (1113) 5398
54.0%
(Missing) 1943
 
19.4%
ValueCountFrequency (%)
0.0 41
 
0.4%
0.1 20
 
0.2%
0.2 1
 
< 0.1%
0.5 16
 
0.2%
0.6 1
 
< 0.1%
0.8 1
 
< 0.1%
1.0 112
1.1%
1.4 1
 
< 0.1%
1.5 5
 
0.1%
1.6 1
 
< 0.1%
ValueCountFrequency (%)
700485.0 1
< 0.1%
420600.0 1
< 0.1%
120000.0 2
< 0.1%
110000.0 1
< 0.1%
100000.0 2
< 0.1%
95000.0 1
< 0.1%
79380.0 1
< 0.1%
72400.0 1
< 0.1%
66000.0 1
< 0.1%
60000.0 2
< 0.1%

등록일자
Real number (ℝ)

Distinct1776
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20134448
Minimum20061008
Maximum20200827
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-22T22:18:24.303463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20061008
5-th percentile20071006
Q120110822
median20131030
Q320161010
95-th percentile20190723
Maximum20200827
Range139819
Interquartile range (IQR)50188

Descriptive statistics

Standard deviation36104.434
Coefficient of variation (CV)0.0017931674
Kurtosis-0.92292414
Mean20134448
Median Absolute Deviation (MAD)29794.5
Skewness-0.1887696
Sum2.0134448 × 1011
Variance1.3035302 × 109
MonotonicityNot monotonic
2023-12-22T22:18:25.129316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20071004 84
 
0.8%
20091007 57
 
0.6%
20071015 56
 
0.6%
20071001 56
 
0.6%
20071009 55
 
0.5%
20081001 51
 
0.5%
20141007 49
 
0.5%
20141014 49
 
0.5%
20070928 48
 
0.5%
20071005 47
 
0.5%
Other values (1766) 9448
94.5%
ValueCountFrequency (%)
20061008 1
< 0.1%
20070712 1
< 0.1%
20070716 1
< 0.1%
20070718 1
< 0.1%
20070719 1
< 0.1%
20070720 1
< 0.1%
20070723 1
< 0.1%
20070724 2
< 0.1%
20070731 1
< 0.1%
20070801 1
< 0.1%
ValueCountFrequency (%)
20200827 2
< 0.1%
20200826 3
< 0.1%
20200824 1
 
< 0.1%
20200819 3
< 0.1%
20200811 1
 
< 0.1%
20200810 1
 
< 0.1%
20200806 2
< 0.1%
20200805 1
 
< 0.1%
20200804 1
 
< 0.1%
20200730 2
< 0.1%

분석결과
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
적합
9891 
부적합 (폐기)
 
107
부적합(회수폐기 및 생산 단계 재조사)
 
2

Length

Max length21
Median length2
Mean length2.068
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
적합 9891
98.9%
부적합 (폐기) 107
 
1.1%
부적합(회수폐기 및 생산 단계 재조사) 2
 
< 0.1%

Length

2023-12-22T22:18:25.887645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-22T22:18:26.408577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
적합 9891
97.8%
부적합 107
 
1.1%
폐기 107
 
1.1%
부적합(회수폐기 2
 
< 0.1%
및 2
 
< 0.1%
생산 2
 
< 0.1%
단계 2
 
< 0.1%
재조사 2
 
< 0.1%

Interactions

2023-12-22T22:18:07.938188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:18:03.889699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:18:06.216647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:18:08.306898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:18:04.534203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:18:06.737770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:18:08.668473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:18:05.547825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-22T22:18:07.284213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-22T22:18:26.679716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수거단계재배양식재배면적조사물량등록일자분석결과
수거단계1.0000.187NaN0.0000.3590.050
재배양식0.1871.0000.2310.0000.2190.000
재배면적NaN0.2311.0000.0840.1040.000
조사물량0.0000.0000.0841.0000.0450.000
등록일자0.3590.2190.1040.0451.0000.074
분석결과0.0500.0000.0000.0000.0741.000
2023-12-22T22:18:27.177844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재배양식수거단계분석결과
재배양식1.0000.0850.000
수거단계0.0851.0000.047
분석결과0.0000.0471.000
2023-12-22T22:18:27.610121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재배면적조사물량등록일자수거단계재배양식분석결과
재배면적1.0000.716-0.0811.0000.1390.000
조사물량0.7161.000-0.0880.0000.0000.000
등록일자-0.081-0.0881.0000.2220.1060.045
수거단계1.0000.0000.2221.0000.0850.047
재배양식0.1390.0000.1060.0851.0000.000
분석결과0.0000.0000.0450.0470.0001.000

Missing values

2023-12-22T22:18:09.075722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-22T22:18:09.540107image/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.
2023-12-22T22:18:09.865093image/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

품목명수거단계재배양식생산자 주소재배면적조사물량등록일자분석결과
31896홍고추(붉은고추)생산일반충청남도 청양군178.050.020140717적합
37866홍고추(붉은고추)생산일반충남 천안시552.0400.020130805적합
40632멥쌀(일반)유통/판매일반강원 양양군<NA><NA>20130107적합
3149시금치유통/판매일반경기도 포천시<NA><NA>20190806적합
44233현미생산일반충남 서천군3967.01800.020120924적합
9796곤드레나물(고려엉겅퀴)생산일반강원도 정선군300.030.020180514적합
37932밤고구마유통/판매일반경북 안동시<NA>2.020130801적합
27653백태생산일반강원도 영월군3000.0160.020141030적합
15922새송이생산친환경(인증) 무농약경상북도 청도군2800.02000.020161107적합
18527현미생산일반충청남도 서산시1500.0750.020160830적합
품목명수거단계재배양식생산자 주소재배면적조사물량등록일자분석결과
57636멥쌀(일반)생산일반충북 진천군500.0850.020090928적합
6673단감생산일반전라남도 순천시965.0200.020181008적합
30497후지(부사,고을)생산일반경상북도 청송군2804.01500.020140922적합
51218생표고버섯생산일반경남 진주시900.050.020110809적합
16819멥쌀(일반)생산일반경기도 파주시621.0450.020161011적합
867미나리생산친환경(인증) 무농약경상남도 김해시180.0100.020191120적합
28735현미생산일반충청북도 영동군549.0100.020141013적합
6786멥쌀(일반)생산일반전라남도 담양군1250.0670.020181004적합
9091대파유통/판매일반경상북도 김천시<NA><NA>20180628적합
48423황태생산일반전남 보성군197.020.020111018적합

Duplicate rows

Most frequently occurring

품목명수거단계재배양식생산자 주소재배면적조사물량등록일자분석결과# duplicates
104신고생산친환경(인증) 저농약울산 울주군100.050.020121218적합7
131현미생산일반경남 창원시1950.01000.020121008적합7
50멥쌀(일반)생산일반경북 영덕군330.0300.020121004적합6
52멥쌀(일반)생산일반경상북도 경주시1000.0100.020141001적합6
61멥쌀(일반)유통/판매일반강원도 철원군<NA><NA>20171109적합6
23녹차유통/판매일반서울 서초구<NA><NA>20130628적합5
35멥쌀(일반)생산일반강원도 정선군300.0300.020191004적합5
48멥쌀(일반)생산일반경북 영덕군100.0100.020111006적합5
71멥쌀(일반)유통/판매일반전라북도 김제시<NA><NA>20150522적합5
123캠벨얼리(다크)유통/판매일반충남 보령시<NA><NA>20130830적합5