Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells4019
Missing cells (%)5.0%
Duplicate rows430
Duplicate rows (%)4.3%
Total size in memory722.7 KiB
Average record size in memory74.0 B

Variable types

Text3
Categorical2
Numeric2
DateTime1

Dataset

Description국립농산물품질관리원에서 관리하는 생산, 유통단계에서의 농산물 잔류농약 분석결과 누계(품목, 수거단계, 재배양식, 생산 지역, 재배면적, 조사물량, 등록일자, 분석결과)
Author국립농산물품질관리원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20170912000000000791

Alerts

Dataset has 430 (4.3%) 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 재배면적 and 1 other fieldsHigh correlation
재배양식 is highly overall correlated with 수거단계High correlation
재배면적 has 2303 (23.0%) missing valuesMissing
조사물량 has 1716 (17.2%) missing valuesMissing
재배면적 is highly skewed (γ1 = 78.79109442)Skewed
조사물량 is highly skewed (γ1 = 23.49558781)Skewed

Reproduction

Analysis started2023-12-11 03:01:55.280663
Analysis finished2023-12-11 03:01:56.793141
Duration1.51 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목명
Text

Distinct256
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T12:01:57.033426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length17
Mean length3.1067
Min length1

Characters and Unicode

Total characters31067
Distinct characters274
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)0.5%

Sample

1st row고구마
2nd row양배추
3rd row방울토마토
4th row쌀
5th row산마늘(명이나물)
ValueCountFrequency (%)
딸기 641
 
6.3%
양파 523
 
5.1%
상추 409
 
4.0%
오이 365
 
3.6%
토마토 352
 
3.5%
마늘 351
 
3.4%
감자 315
 
3.1%
방울토마토 272
 
2.7%
조생귤 271
 
2.7%
풋고추 265
 
2.6%
Other values (256) 6414
63.0%
2023-12-11T12:01:57.479071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
추 1458
 
4.7%
마 1251
 
4.0%
토 1250
 
4.0%
파 1119
 
3.6%
리 974
 
3.1%
기 855
 
2.8%
고 838
 
2.7%
양 808
 
2.6%
) 743
 
2.4%
( 743
 
2.4%
Other values (264) 21028
67.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29371
94.5%
Close Punctuation 743
 
2.4%
Open Punctuation 743
 
2.4%
Space Separator 178
 
0.6%
Decimal Number 26
 
0.1%
Math Symbol 5
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
추 1458
 
5.0%
마 1251
 
4.3%
토 1250
 
4.3%
파 1119
 
3.8%
리 974
 
3.3%
기 855
 
2.9%
고 838
 
2.9%
양 808
 
2.8%
딸 661
 
2.3%
박 655
 
2.2%
Other values (253) 19502
66.4%
Decimal Number
ValueCountFrequency (%)
5 6
23.1%
1 5
19.2%
2 5
19.2%
4 4
15.4%
3 3
11.5%
6 3
11.5%
Close Punctuation
ValueCountFrequency (%)
) 743
100.0%
Open Punctuation
ValueCountFrequency (%)
( 743
100.0%
Space Separator
ValueCountFrequency (%)
178
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29371
94.5%
Common 1696
 
5.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
추 1458
 
5.0%
마 1251
 
4.3%
토 1250
 
4.3%
파 1119
 
3.8%
리 974
 
3.3%
기 855
 
2.9%
고 838
 
2.9%
양 808
 
2.8%
딸 661
 
2.3%
박 655
 
2.2%
Other values (253) 19502
66.4%
Common
ValueCountFrequency (%)
) 743
43.8%
( 743
43.8%
178
 
10.5%
5 6
 
0.4%
1 5
 
0.3%
2 5
 
0.3%
~ 5
 
0.3%
4 4
 
0.2%
3 3
 
0.2%
6 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29371
94.5%
ASCII 1696
 
5.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
추 1458
 
5.0%
마 1251
 
4.3%
토 1250
 
4.3%
파 1119
 
3.8%
리 974
 
3.3%
기 855
 
2.9%
고 838
 
2.9%
양 808
 
2.8%
딸 661
 
2.3%
박 655
 
2.2%
Other values (253) 19502
66.4%
ASCII
ValueCountFrequency (%)
) 743
43.8%
( 743
43.8%
178
 
10.5%
5 6
 
0.4%
1 5
 
0.3%
2 5
 
0.3%
~ 5
 
0.3%
4 4
 
0.2%
3 3
 
0.2%
6 3
 
0.2%

수거단계
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
생산
7703 
유통/판매
2297 

Length

Max length5
Median length2
Mean length2.6891
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
생산 7703
77.0%
유통/판매 2297
 
23.0%

Length

2023-12-11T12:01:57.655744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:01:57.775965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생산 7703
77.0%
유통/판매 2297
 
23.0%

재배양식
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반
4744 
친환경(인증) 무농약
2578 
친환경(인증) 유기
1503 
GAP(인증)
969 
친환경(인증)
 
100
Other values (5)
 
106

Length

Max length16
Median length14
Mean length6.7425
Min length3

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row일반
2nd row친환경(인증) 무농약
3rd rowGAP(인증)
4th row일반
5th row친환경(인증) 무농약

Common Values

ValueCountFrequency (%)
일반 4744
47.4%
친환경(인증) 무농약 2578
25.8%
친환경(인증) 유기 1503
 
15.0%
GAP(인증) 969
 
9.7%
친환경(인증) 100
 
1.0%
친환경(인증) 취급자 73
 
0.7%
친환경(인증) 유기가공품 24
 
0.2%
친환경(인증) 무농약원료가공품 7
 
0.1%
친환경(인증) 유기축산 1
 
< 0.1%
친환경(인증) 무항생제축산 1
 
< 0.1%

Length

2023-12-11T12:01:57.884688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:01:58.024663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 4744
33.4%
친환경(인증 4287
30.2%
무농약 2578
18.2%
유기 1503
 
10.6%
gap(인증 969
 
6.8%
취급자 73
 
0.5%
유기가공품 24
 
0.2%
무농약원료가공품 7
 
< 0.1%
유기축산 1
 
< 0.1%
무항생제축산 1
 
< 0.1%

주소
Text

Distinct190
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T12:01:58.490937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length11.3317
Min length4

Characters and Unicode

Total characters113317
Distinct characters127
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

Unique13 ?
Unique (%)0.1%

Sample

1st row부산광역시 기장군
2nd row경상남도 밀양시
3rd row인천광역시 강화군
4th row전라남도 목포시
5th row경상북도 영양군
ValueCountFrequency (%)
경상남도 1771
 
8.9%
전라남도 1518
 
7.6%
제주특별자치도 1188
 
5.9%
경기도 1115
 
5.6%
경상북도 1013
 
5.1%
충청남도 841
 
4.2%
제주시 610
 
3.1%
전라북도 582
 
2.9%
서귀포시 578
 
2.9%
충청북도 561
 
2.8%
Other values (187) 10217
51.1%
2023-12-11T12:01:59.150485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40000
35.3%
도 9265
 
8.2%
시 6034
 
5.3%
남 4509
 
4.0%
군 4374
 
3.9%
경 4050
 
3.6%
주 3510
 
3.1%
상 2900
 
2.6%
북 2269
 
2.0%
전 2171
 
1.9%
Other values (117) 34235
30.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 73317
64.7%
Space Separator 40000
35.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 9265
 
12.6%
시 6034
 
8.2%
남 4509
 
6.2%
군 4374
 
6.0%
경 4050
 
5.5%
주 3510
 
4.8%
상 2900
 
4.0%
북 2269
 
3.1%
전 2171
 
3.0%
라 2100
 
2.9%
Other values (116) 32135
43.8%
Space Separator
ValueCountFrequency (%)
40000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 73317
64.7%
Common 40000
35.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 9265
 
12.6%
시 6034
 
8.2%
남 4509
 
6.2%
군 4374
 
6.0%
경 4050
 
5.5%
주 3510
 
4.8%
상 2900
 
4.0%
북 2269
 
3.1%
전 2171
 
3.0%
라 2100
 
2.9%
Other values (116) 32135
43.8%
Common
ValueCountFrequency (%)
40000
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 73317
64.7%
ASCII 40000
35.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40000
100.0%
Hangul
ValueCountFrequency (%)
도 9265
 
12.6%
시 6034
 
8.2%
남 4509
 
6.2%
군 4374
 
6.0%
경 4050
 
5.5%
주 3510
 
4.8%
상 2900
 
4.0%
북 2269
 
3.1%
전 2171
 
3.0%
라 2100
 
2.9%
Other values (116) 32135
43.8%

재배면적
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct2140
Distinct (%)27.8%
Missing2303
Missing (%)23.0%
Infinite0
Infinite (%)0.0%
Mean2555.7274
Minimum16
Maximum1706330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:01:59.331298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile200
Q1495
median1000
Q32368
95-th percentile8000
Maximum1706330
Range1706314
Interquartile range (IQR)1873

Descriptive statistics

Standard deviation20148.221
Coefficient of variation (CV)7.8835563
Kurtosis6647.2722
Mean2555.7274
Median Absolute Deviation (MAD)670
Skewness78.791094
Sum19671434
Variance4.059508 × 108
MonotonicityNot monotonic
2023-12-11T12:01:59.547074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
330.0 804
 
8.0%
660.0 545
 
5.5%
1000.0 489
 
4.9%
500.0 204
 
2.0%
600.0 184
 
1.8%
2000.0 150
 
1.5%
400.0 129
 
1.3%
300.0 127
 
1.3%
1500.0 116
 
1.2%
3000.0 96
 
1.0%
Other values (2130) 4853
48.5%
(Missing) 2303
23.0%
ValueCountFrequency (%)
16.0 1
 
< 0.1%
20.0 9
 
0.1%
25.0 1
 
< 0.1%
26.0 1
 
< 0.1%
29.0 1
 
< 0.1%
30.0 23
0.2%
33.0 5
 
0.1%
35.0 1
 
< 0.1%
40.0 2
 
< 0.1%
45.0 1
 
< 0.1%
ValueCountFrequency (%)
1706330.0 1
< 0.1%
165538.0 1
< 0.1%
111636.0 1
< 0.1%
90077.0 2
< 0.1%
88014.0 1
< 0.1%
79345.0 1
< 0.1%
68170.0 1
< 0.1%
67538.0 1
< 0.1%
66324.0 1
< 0.1%
60111.0 1
< 0.1%

조사물량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct573
Distinct (%)6.9%
Missing1716
Missing (%)17.2%
Infinite0
Infinite (%)0.0%
Mean4669.7887
Minimum0
Maximum1600000
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:01:59.786746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q160
median300
Q31700
95-th percentile15000
Maximum1600000
Range1600000
Interquartile range (IQR)1640

Descriptive statistics

Standard deviation32820.711
Coefficient of variation (CV)7.0283076
Kurtosis831.67667
Mean4669.7887
Median Absolute Deviation (MAD)290
Skewness23.495588
Sum38684530
Variance1.0771991 × 109
MonotonicityNot monotonic
2023-12-11T12:02:00.012035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 721
 
7.2%
1000.0 462
 
4.6%
50.0 437
 
4.4%
200.0 420
 
4.2%
500.0 361
 
3.6%
10.0 350
 
3.5%
300.0 346
 
3.5%
2000.0 284
 
2.8%
30.0 249
 
2.5%
20.0 228
 
2.3%
Other values (563) 4426
44.3%
(Missing) 1716
 
17.2%
ValueCountFrequency (%)
0.0 8
 
0.1%
0.2 1
 
< 0.1%
0.3 1
 
< 0.1%
0.5 5
 
0.1%
0.6 2
 
< 0.1%
0.8 3
 
< 0.1%
1.0 93
0.9%
1.2 4
 
< 0.1%
1.4 1
 
< 0.1%
1.5 22
 
0.2%
ValueCountFrequency (%)
1600000.0 1
< 0.1%
800000.0 1
< 0.1%
600000.0 2
< 0.1%
585987.0 1
< 0.1%
579000.0 1
< 0.1%
555000.0 1
< 0.1%
550000.0 1
< 0.1%
546000.0 2
< 0.1%
520000.0 1
< 0.1%
518400.0 1
< 0.1%

등록일자
Date

Distinct365
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-01-04 00:00:00
Maximum2022-06-10 00:00:00
2023-12-11T12:02:00.215315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:02:00.356994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

분석결과
Text

Distinct104
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T12:02:00.688658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length2
Mean length2.2983
Min length2

Characters and Unicode

Total characters22983
Distinct characters32
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

Unique66 ?
Unique (%)0.7%

Sample

1st row적합
2nd row적합
3rd row적합
4th row적합
5th row적합
ValueCountFrequency (%)
적합 9843
95.4%
출하연기 153
 
1.5%
부적합 153
 
1.5%
2022/04/16 6
 
0.1%
2022/05/27 5
 
< 0.1%
2022/04/30 4
 
< 0.1%
부적합(회수폐기 4
 
< 0.1%
및 4
 
< 0.1%
생산 4
 
< 0.1%
단계 4
 
< 0.1%
Other values (100) 142
 
1.4%
2023-12-11T12:02:01.216062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
적 10000
43.5%
합 10000
43.5%
2 520
 
2.3%
0 359
 
1.6%
322
 
1.4%
/ 306
 
1.3%
) 157
 
0.7%
부 157
 
0.7%
( 157
 
0.7%
기 157
 
0.7%
Other values (22) 848
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20817
90.6%
Decimal Number 1224
 
5.3%
Space Separator 322
 
1.4%
Other Punctuation 306
 
1.3%
Close Punctuation 157
 
0.7%
Open Punctuation 157
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
적 10000
48.0%
합 10000
48.0%
부 157
 
0.8%
기 157
 
0.8%
출 153
 
0.7%
하 153
 
0.7%
연 153
 
0.7%
회 4
 
< 0.1%
수 4
 
< 0.1%
폐 4
 
< 0.1%
Other values (8) 32
 
0.2%
Decimal Number
ValueCountFrequency (%)
2 520
42.5%
0 359
29.3%
1 104
 
8.5%
4 56
 
4.6%
5 52
 
4.2%
3 47
 
3.8%
6 45
 
3.7%
7 20
 
1.6%
9 11
 
0.9%
8 10
 
0.8%
Space Separator
ValueCountFrequency (%)
322
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 306
100.0%
Close Punctuation
ValueCountFrequency (%)
) 157
100.0%
Open Punctuation
ValueCountFrequency (%)
( 157
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20817
90.6%
Common 2166
 
9.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
적 10000
48.0%
합 10000
48.0%
부 157
 
0.8%
기 157
 
0.8%
출 153
 
0.7%
하 153
 
0.7%
연 153
 
0.7%
회 4
 
< 0.1%
수 4
 
< 0.1%
폐 4
 
< 0.1%
Other values (8) 32
 
0.2%
Common
ValueCountFrequency (%)
2 520
24.0%
0 359
16.6%
322
14.9%
/ 306
14.1%
) 157
 
7.2%
( 157
 
7.2%
1 104
 
4.8%
4 56
 
2.6%
5 52
 
2.4%
3 47
 
2.2%
Other values (4) 86
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20817
90.6%
ASCII 2166
 
9.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
적 10000
48.0%
합 10000
48.0%
부 157
 
0.8%
기 157
 
0.8%
출 153
 
0.7%
하 153
 
0.7%
연 153
 
0.7%
회 4
 
< 0.1%
수 4
 
< 0.1%
폐 4
 
< 0.1%
Other values (8) 32
 
0.2%
ASCII
ValueCountFrequency (%)
2 520
24.0%
0 359
16.6%
322
14.9%
/ 306
14.1%
) 157
 
7.2%
( 157
 
7.2%
1 104
 
4.8%
4 56
 
2.6%
5 52
 
2.4%
3 47
 
2.2%
Other values (4) 86
 
4.0%

Interactions

2023-12-11T12:01:56.189354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:01:55.952036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:01:56.299481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:01:56.074839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:02:01.621850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수거단계재배양식재배면적조사물량
수거단계1.0000.681NaN0.000
재배양식0.6811.0000.0000.290
재배면적NaN0.0001.0000.000
조사물량0.0000.2900.0001.000
2023-12-11T12:02:01.769428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수거단계재배양식
수거단계1.0000.530
재배양식0.5301.000
2023-12-11T12:02:01.874654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재배면적조사물량수거단계재배양식
재배면적1.0000.5831.0000.000
조사물량0.5831.0000.0000.157
수거단계1.0000.0001.0000.530
재배양식0.0000.1570.5301.000

Missing values

2023-12-11T12:01:56.456381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:01:56.623240image/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.
2023-12-11T12:01:56.731531image/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

품목명수거단계재배양식주소재배면적조사물량등록일자분석결과
16826고구마생산일반부산광역시 기장군3000.02000.02021-10-18적합
12971양배추유통/판매친환경(인증) 무농약경상남도 밀양시<NA><NA>2021-09-10적합
8405방울토마토생산GAP(인증)인천광역시 강화군429.0500.02022-05-30적합
5945쌀유통/판매일반전라남도 목포시<NA><NA>2022-04-11적합
2760산마늘(명이나물)생산친환경(인증) 무농약경상북도 영양군600.080.02022-04-06적합
5861양파생산일반전라남도 무안군1600.02000.02022-05-27적합
15864단감생산일반울산광역시 중구1706330.01100.02021-10-12적합
9246딸기생산GAP(인증)경기도 안성시4323.0100.02022-04-12적합
4808부추생산일반전라남도 장성군400.020.02022-04-19적합
6218참나물생산일반전라북도 완주군660.0200.02022-01-17적합
품목명수거단계재배양식주소재배면적조사물량등록일자분석결과
3826시금치생산일반경상북도 포항시890.01000.02022-03-22적합
9818쑥갓생산친환경(인증) 무농약서울특별시 강동구165.020.02022-03-25적합
5860양파생산일반전라남도 무안군800.01000.02022-05-27적합
11929배추생산친환경(인증) 유기전라남도 영광군10000.075200.02021-10-25적합
2926양파생산일반경상북도 고령군990.01500.02022-05-24적합
9828토마토유통/판매일반부산광역시 강서구<NA><NA>2022-02-14적합
2765사과생산일반경상북도 청송군10033.036000.02022-01-06적합
2493딸기생산일반경상남도 진주시660.01700.02022-03-03적합
12354기타(감귤)유통/판매친환경(인증) 무농약제주특별자치도 제주시<NA><NA>2021-12-03적합
13453알타리무유통/판매친환경(인증) 유기강원도 홍천군<NA><NA>2021-06-06적합

Duplicate rows

Most frequently occurring

품목명수거단계재배양식주소재배면적조사물량등록일자분석결과# duplicates
185배생산GAP(인증)울산광역시 울주군330.010.02021-07-26적합11
196벼생산친환경(인증) 무농약충청북도 진천군1000.02.02021-11-04적합10
304양파생산일반전라남도 영광군330.0100.02022-06-03적합8
320양파생산일반충청남도 당진시330.0300.02022-06-08적합8
69대파생산일반전라남도 영광군330.050.02022-03-23적합7
246세발나물생산친환경(인증) 무농약전라남도 해남군330.040.02022-05-03적합7
422풋고추유통/판매GAP(인증)경상남도 진주시<NA>100.02022-05-30적합7
180방울토마토생산일반충청남도 부여군660.0100.02022-05-03적합6
358참외생산일반경상북도 고령군660.0300.02022-05-16적합6
290양송이버섯유통/판매일반충청남도 부여군<NA>1.02022-05-24적합5