Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells2536
Missing cells (%)3.2%
Duplicate rows295
Duplicate rows (%)2.9%
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 295 (2.9%) duplicate rowsDuplicates
재배면적 is highly overall correlated with 조사물량High correlation
조사물량 is highly overall correlated with 재배면적High correlation
수거단계 is highly overall correlated with 재배양식High correlation
재배양식 is highly overall correlated with 수거단계High correlation
재배면적 has 1736 (17.4%) missing valuesMissing
조사물량 has 800 (8.0%) missing valuesMissing
조사물량 is highly skewed (γ1 = 83.25800005)Skewed

Reproduction

Analysis started2023-12-11 03:01:27.730199
Analysis finished2023-12-11 03:01:29.387106
Duration1.66 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목명
Text

Distinct268
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T12:01:29.817099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length17
Mean length3.054
Min length1

Characters and Unicode

Total characters30540
Distinct characters287
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

Unique56 ?
Unique (%)0.6%

Sample

1st row배
2nd row애호박(인큐애호박,진주애호박)
3rd row토마토
4th row갓
5th row양파
ValueCountFrequency (%)
사과 510
 
5.1%
딸기 506
 
5.1%
양파 375
 
3.7%
배 356
 
3.6%
포도 336
 
3.4%
쌀 313
 
3.1%
감자 310
 
3.1%
상추 306
 
3.1%
고구마 302
 
3.0%
오이 298
 
3.0%
Other values (259) 6389
63.9%
2023-12-11T12:01:30.395475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
추 1325
 
4.3%
마 1124
 
3.7%
고 1051
 
3.4%
토 1015
 
3.3%
파 856
 
2.8%
) 796
 
2.6%
( 796
 
2.6%
리 786
 
2.6%
박 753
 
2.5%
호 678
 
2.2%
Other values (277) 21360
69.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28678
93.9%
Close Punctuation 796
 
2.6%
Open Punctuation 796
 
2.6%
Other Punctuation 172
 
0.6%
Decimal Number 90
 
0.3%
Math Symbol 7
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
추 1325
 
4.6%
마 1124
 
3.9%
고 1051
 
3.7%
토 1015
 
3.5%
파 856
 
3.0%
리 786
 
2.7%
박 753
 
2.6%
호 678
 
2.4%
배 656
 
2.3%
기 603
 
2.1%
Other values (266) 19831
69.2%
Decimal Number
ValueCountFrequency (%)
6 25
27.8%
5 24
26.7%
4 21
23.3%
2 7
 
7.8%
1 7
 
7.8%
3 6
 
6.7%
Close Punctuation
ValueCountFrequency (%)
) 796
100.0%
Open Punctuation
ValueCountFrequency (%)
( 796
100.0%
Other Punctuation
ValueCountFrequency (%)
, 172
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 28678
93.9%
Common 1862
 
6.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
추 1325
 
4.6%
마 1124
 
3.9%
고 1051
 
3.7%
토 1015
 
3.5%
파 856
 
3.0%
리 786
 
2.7%
박 753
 
2.6%
호 678
 
2.4%
배 656
 
2.3%
기 603
 
2.1%
Other values (266) 19831
69.2%
Common
ValueCountFrequency (%)
) 796
42.7%
( 796
42.7%
, 172
 
9.2%
6 25
 
1.3%
5 24
 
1.3%
4 21
 
1.1%
2 7
 
0.4%
~ 7
 
0.4%
1 7
 
0.4%
3 6
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 28678
93.9%
ASCII 1862
 
6.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
추 1325
 
4.6%
마 1124
 
3.9%
고 1051
 
3.7%
토 1015
 
3.5%
파 856
 
3.0%
리 786
 
2.7%
박 753
 
2.6%
호 678
 
2.4%
배 656
 
2.3%
기 603
 
2.1%
Other values (266) 19831
69.2%
ASCII
ValueCountFrequency (%)
) 796
42.7%
( 796
42.7%
, 172
 
9.2%
6 25
 
1.3%
5 24
 
1.3%
4 21
 
1.1%
2 7
 
0.4%
~ 7
 
0.4%
1 7
 
0.4%
3 6
 
0.3%

수거단계
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
생산
8262 
유통/판매
1738 

Length

Max length5
Median length2
Mean length2.5214
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
생산 8262
82.6%
유통/판매 1738
 
17.4%

Length

2023-12-11T12:01:30.540517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:01:30.908586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생산 8262
82.6%
유통/판매 1738
 
17.4%

재배양식
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반
5959 
친환경(인증) 무농약
1581 
GAP(인증)
1514 
친환경(인증) 유기
824 
친환경(인증) 취급자
 
87
Other values (4)
 
35

Length

Max length16
Median length3
Mean length5.702
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반 5959
59.6%
친환경(인증) 무농약 1581
 
15.8%
GAP(인증) 1514
 
15.1%
친환경(인증) 유기 824
 
8.2%
친환경(인증) 취급자 87
 
0.9%
친환경(인증) 유기가공품 25
 
0.2%
친환경(인증) 무농약원료가공품 5
 
0.1%
지리적표시 4
 
< 0.1%
친환경(인증) 무항생제축산 1
 
< 0.1%

Length

2023-12-11T12:01:31.037086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:01:31.171008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 5959
47.6%
친환경(인증 2523
20.1%
무농약 1581
 
12.6%
gap(인증 1514
 
12.1%
유기 824
 
6.6%
취급자 87
 
0.7%
유기가공품 25
 
0.2%
무농약원료가공품 5
 
< 0.1%
지리적표시 4
 
< 0.1%
무항생제축산 1
 
< 0.1%

주소
Text

Distinct209
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T12:01:31.596243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length11.0565
Min length6

Characters and Unicode

Total characters110565
Distinct characters132
Distinct categories4 ?
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 (%)0.3%

Sample

1st row충청남도 천안시
2nd row강원도 화천군
3rd row전라북도 장수군
4th row전라북도 정읍시
5th row경상북도 예천군
ValueCountFrequency (%)
경상북도 1623
 
8.1%
경상남도 1499
 
7.5%
전라남도 1416
 
7.1%
충청남도 1241
 
6.2%
전라북도 970
 
4.9%
경기도 806
 
4.0%
충청북도 738
 
3.7%
강원도 734
 
3.7%
제주특별자치도 415
 
2.1%
진주시 259
 
1.3%
Other values (190) 10298
51.5%
2023-12-11T12:01:32.151616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40000
36.2%
도 9654
 
8.7%
시 5385
 
4.9%
군 4873
 
4.4%
남 4529
 
4.1%
경 4161
 
3.8%
북 3400
 
3.1%
상 3274
 
3.0%
전 2490
 
2.3%
주 2436
 
2.2%
Other values (122) 30363
27.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 70561
63.8%
Space Separator 40000
36.2%
Math Symbol 3
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 9654
 
13.7%
시 5385
 
7.6%
군 4873
 
6.9%
남 4529
 
6.4%
경 4161
 
5.9%
북 3400
 
4.8%
상 3274
 
4.6%
전 2490
 
3.5%
주 2436
 
3.5%
청 2402
 
3.4%
Other values (119) 27957
39.6%
Space Separator
ValueCountFrequency (%)
40000
100.0%
Math Symbol
ValueCountFrequency (%)
| 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 70561
63.8%
Common 40004
36.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 9654
 
13.7%
시 5385
 
7.6%
군 4873
 
6.9%
남 4529
 
6.4%
경 4161
 
5.9%
북 3400
 
4.8%
상 3274
 
4.6%
전 2490
 
3.5%
주 2436
 
3.5%
청 2402
 
3.4%
Other values (119) 27957
39.6%
Common
ValueCountFrequency (%)
40000
> 99.9%
| 3
 
< 0.1%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 70561
63.8%
ASCII 40004
36.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40000
> 99.9%
| 3
 
< 0.1%
- 1
 
< 0.1%
Hangul
ValueCountFrequency (%)
도 9654
 
13.7%
시 5385
 
7.6%
군 4873
 
6.9%
남 4529
 
6.4%
경 4161
 
5.9%
북 3400
 
4.8%
상 3274
 
4.6%
전 2490
 
3.5%
주 2436
 
3.5%
청 2402
 
3.4%
Other values (119) 27957
39.6%

재배면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2072
Distinct (%)25.1%
Missing1736
Missing (%)17.4%
Infinite0
Infinite (%)0.0%
Mean1853.1811
Minimum15
Maximum75000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:01:32.327112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile200
Q1400
median970
Q32076
95-th percentile5930.2
Maximum75000
Range74985
Interquartile range (IQR)1676

Descriptive statistics

Standard deviation3258.6021
Coefficient of variation (CV)1.758383
Kurtosis95.713935
Mean1853.1811
Median Absolute Deviation (MAD)640
Skewness7.5560554
Sum15314689
Variance10618488
MonotonicityNot monotonic
2023-12-11T12:01:32.536444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
330.0 1017
 
10.2%
660.0 532
 
5.3%
1000.0 508
 
5.1%
600.0 206
 
2.1%
500.0 195
 
1.9%
2000.0 188
 
1.9%
165.0 162
 
1.6%
400.0 154
 
1.5%
3000.0 124
 
1.2%
350.0 118
 
1.2%
Other values (2062) 5060
50.6%
(Missing) 1736
 
17.4%
ValueCountFrequency (%)
15.0 1
 
< 0.1%
16.0 1
 
< 0.1%
20.0 6
 
0.1%
24.0 1
 
< 0.1%
30.0 15
0.1%
33.0 10
0.1%
40.0 1
 
< 0.1%
50.0 24
0.2%
60.0 2
 
< 0.1%
66.0 9
 
0.1%
ValueCountFrequency (%)
75000.0 1
< 0.1%
63000.0 1
< 0.1%
59131.0 1
< 0.1%
49485.0 1
< 0.1%
48000.0 1
< 0.1%
47300.0 1
< 0.1%
38000.0 1
< 0.1%
35207.0 1
< 0.1%
35000.0 1
< 0.1%
34650.0 1
< 0.1%

조사물량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct422
Distinct (%)4.6%
Missing800
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean3346.3843
Minimum0
Maximum7420000
Zeros15
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:01:32.721580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q150
median200
Q31000
95-th percentile7005
Maximum7420000
Range7420000
Interquartile range (IQR)950

Descriptive statistics

Standard deviation81482.189
Coefficient of variation (CV)24.349322
Kurtosis7484.3219
Mean3346.3843
Median Absolute Deviation (MAD)192
Skewness83.258
Sum30786736
Variance6.6393472 × 109
MonotonicityNot monotonic
2023-12-11T12:01:32.880327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 924
 
9.2%
50.0 622
 
6.2%
1000.0 526
 
5.3%
200.0 513
 
5.1%
500.0 441
 
4.4%
300.0 402
 
4.0%
30.0 363
 
3.6%
2000.0 345
 
3.5%
10.0 313
 
3.1%
20.0 286
 
2.9%
Other values (412) 4465
44.6%
(Missing) 800
 
8.0%
ValueCountFrequency (%)
0.0 15
 
0.1%
0.5 3
 
< 0.1%
0.6 1
 
< 0.1%
0.7 1
 
< 0.1%
0.8 1
 
< 0.1%
1.0 216
2.2%
1.2 13
 
0.1%
1.35 1
 
< 0.1%
1.47 1
 
< 0.1%
1.5 20
 
0.2%
ValueCountFrequency (%)
7420000.0 1
< 0.1%
1600000.0 1
< 0.1%
1100000.0 1
< 0.1%
600000.0 2
< 0.1%
578916.0 1
< 0.1%
410000.0 1
< 0.1%
374000.0 1
< 0.1%
350000.0 1
< 0.1%
240000.0 2
< 0.1%
210000.0 2
< 0.1%

등록일자
Date

Distinct367
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-01-03 00:00:00
Maximum2023-06-15 00:00:00
2023-12-11T12:01:33.072269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:01:33.258910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

분석결과
Text

Distinct129
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T12:01:33.584396image/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

Unique106 ?
Unique (%)1.1%

Sample

1st row적합
2nd row적합
3rd row적합
4th row적합
5th row적합
ValueCountFrequency (%)
적합 9843
95.4%
부적합 153
 
1.5%
출하연기 153
 
1.5%
및 4
 
< 0.1%
생산 4
 
< 0.1%
단계 4
 
< 0.1%
재조사 4
 
< 0.1%
2022/08/06 4
 
< 0.1%
부적합(회수폐기 4
 
< 0.1%
2022/09/14 3
 
< 0.1%
Other values (125) 146
 
1.4%
2023-12-11T12:01:34.095964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
적 10000
43.5%
합 10000
43.5%
2 487
 
2.1%
0 357
 
1.6%
322
 
1.4%
/ 306
 
1.3%
( 157
 
0.7%
기 157
 
0.7%
부 157
 
0.7%
) 157
 
0.7%
Other values (22) 883
 
3.8%

Most occurring categories

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

Most frequent character per category

Other Letter
ValueCountFrequency (%)
적 10000
48.0%
합 10000
48.0%
기 157
 
0.8%
부 157
 
0.8%
연 153
 
0.7%
하 153
 
0.7%
출 153
 
0.7%
조 4
 
< 0.1%
재 4
 
< 0.1%
계 4
 
< 0.1%
Other values (8) 32
 
0.2%
Decimal Number
ValueCountFrequency (%)
2 487
39.8%
0 357
29.2%
1 114
 
9.3%
3 74
 
6.0%
4 41
 
3.3%
5 40
 
3.3%
6 36
 
2.9%
8 30
 
2.5%
7 27
 
2.2%
9 18
 
1.5%
Space Separator
ValueCountFrequency (%)
322
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 306
100.0%
Open Punctuation
ValueCountFrequency (%)
( 157
100.0%
Close 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%
기 157
 
0.8%
부 157
 
0.8%
연 153
 
0.7%
하 153
 
0.7%
출 153
 
0.7%
조 4
 
< 0.1%
재 4
 
< 0.1%
계 4
 
< 0.1%
Other values (8) 32
 
0.2%
Common
ValueCountFrequency (%)
2 487
22.5%
0 357
16.5%
322
14.9%
/ 306
14.1%
( 157
 
7.2%
) 157
 
7.2%
1 114
 
5.3%
3 74
 
3.4%
4 41
 
1.9%
5 40
 
1.8%
Other values (4) 111
 
5.1%

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%
기 157
 
0.8%
부 157
 
0.8%
연 153
 
0.7%
하 153
 
0.7%
출 153
 
0.7%
조 4
 
< 0.1%
재 4
 
< 0.1%
계 4
 
< 0.1%
Other values (8) 32
 
0.2%
ASCII
ValueCountFrequency (%)
2 487
22.5%
0 357
16.5%
322
14.9%
/ 306
14.1%
( 157
 
7.2%
) 157
 
7.2%
1 114
 
5.3%
3 74
 
3.4%
4 41
 
1.9%
5 40
 
1.8%
Other values (4) 111
 
5.1%

Interactions

2023-12-11T12:01:28.547881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:01:28.293537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:01:28.682746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:01:28.430418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:01:34.234446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수거단계재배양식재배면적조사물량
수거단계1.0000.5230.0000.000
재배양식0.5231.0000.1070.000
재배면적0.0000.1071.0000.000
조사물량0.0000.0000.0001.000
2023-12-11T12:01:34.349382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수거단계재배양식
수거단계1.0000.525
재배양식0.5251.000
2023-12-11T12:01:34.446426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재배면적조사물량수거단계재배양식
재배면적1.0000.5700.0000.048
조사물량0.5701.0000.0000.000
수거단계0.0000.0001.0000.525
재배양식0.0480.0000.5251.000

Missing values

2023-12-11T12:01:28.901298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:01:29.137595image/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.
2023-12-11T12:01:29.297261image/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
41235배유통/판매일반충청남도 천안시<NA>900.02022-10-25적합
15283애호박(인큐애호박,진주애호박)유통/판매친환경(인증) 무농약강원도 화천군<NA><NA>2022-08-04적합
48006토마토유통/판매GAP(인증)전라북도 장수군<NA>2.02022-11-24적합
32110갓생산일반전라북도 정읍시330.030.02022-11-02적합
26231양파생산일반경상북도 예천군1250.02000.02022-06-13적합
16243조생귤생산일반제주특별자치도 제주시1000.01000.02022-11-15적합
779브로코리생산일반제주특별자치도 제주시3000.02000.02023-01-11적합
48550피망생산일반강원도 평창군629.0400.02022-08-30적합
33435풋고추생산일반전라남도 강진군377.050.02022-08-18적합
48295콩(대두)생산친환경(인증) 유기강원도 정선군7000.0700.02022-11-22적합
품목명수거단계재배양식주소재배면적조사물량등록일자분석결과
9339아스파라거스생산일반강원도 양구군330.0100.02023-05-15적합
44656느타리버섯유통/판매친환경(인증) 무농약경기도 평택시<NA>2.02022-09-20적합
35258배생산일반전라남도 나주시6600.016200.02022-03-10적합
40609취나물생산일반충청남도 부여군165.070.02022-05-02적합
6145열무생산일반전라남도 나주시660.0100.02023-04-27적합
43406상추생산GAP(인증)세종특별자치시 금남면165.010.02022-10-24부적합 (2022/11/02 출하연기)
48912산마늘(명이나물)생산일반강원도 강릉시495.050.02022-04-08적합
46733상추생산일반대전광역시 유성구600.080.02022-03-11적합
42766표고버섯생산GAP(인증)충청남도 금산군200.020.02022-06-22적합
51162오이생산일반경기도 여주시660.0850.02022-04-27적합

Duplicate rows

Most frequently occurring

품목명수거단계재배양식주소재배면적조사물량등록일자분석결과# duplicates
71딸기생산일반경상남도 진주시660.030.02022-11-22적합6
33단감생산GAP(인증)경상남도 김해시1000.01000.02022-11-04적합5
92마늘유통/판매일반경상북도 영천시<NA><NA>2022-10-11적합5
111배생산일반경기도 남양주시1000.0800.02022-10-05적합5
138사과유통/판매GAP(인증)전라북도 무주군<NA><NA>2022-11-30적합5
212오이생산일반경기도 안성시660.0600.02023-05-03적합5
217오이유통/판매일반경상남도 창녕군<NA><NA>2022-10-19적합5
60딸기생산GAP(인증)경상남도 진주시660.030.02022-11-16적합4
143사과유통/판매일반경상북도 영주시<NA><NA>2022-11-03적합4
162수박생산일반경상북도 포항시330.0100.02022-06-28적합4