Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells2723
Missing cells (%)3.4%
Duplicate rows293
Duplicate rows (%)2.9%
Total size in memory732.4 KiB
Average record size in memory75.0 B

Variable types

Text3
Categorical2
Numeric3

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 293 (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 1730 (17.3%) missing valuesMissing
조사물량 has 993 (9.9%) missing valuesMissing
재배면적 is highly skewed (γ1 = 57.69677845)Skewed
조사물량 is highly skewed (γ1 = 33.28474963)Skewed

Reproduction

Analysis started2023-12-11 03:01:46.547293
Analysis finished2023-12-11 03:01:48.348372
Duration1.8 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목명
Text

Distinct252
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T12:01:48.549595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length17
Mean length3.0624
Min length1

Characters and Unicode

Total characters30624
Distinct characters268
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 row기타(감귤)
2nd row상추
3rd row당근
4th row파프리카
5th row방울토마토
ValueCountFrequency (%)
딸기 719
 
7.1%
사과 594
 
5.9%
조생귤 379
 
3.7%
포도 367
 
3.6%
배 321
 
3.2%
단감 285
 
2.8%
양파 262
 
2.6%
풋고추 262
 
2.6%
멥쌀(일반 257
 
2.5%
메현미 219
 
2.2%
Other values (250) 6456
63.8%
2023-12-11T12:01:49.023126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
추 1269
 
4.1%
) 1055
 
3.4%
( 1055
 
3.4%
기 1009
 
3.3%
고 953
 
3.1%
리 828
 
2.7%
딸 743
 
2.4%
마 741
 
2.4%
토 673
 
2.2%
파 643
 
2.1%
Other values (258) 21655
70.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28354
92.6%
Close Punctuation 1055
 
3.4%
Open Punctuation 1055
 
3.4%
Space Separator 121
 
0.4%
Decimal Number 33
 
0.1%
Math Symbol 5
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
추 1269
 
4.5%
기 1009
 
3.6%
고 953
 
3.4%
리 828
 
2.9%
딸 743
 
2.6%
마 741
 
2.6%
토 673
 
2.4%
파 643
 
2.3%
배 635
 
2.2%
사 624
 
2.2%
Other values (247) 20236
71.4%
Decimal Number
ValueCountFrequency (%)
4 7
21.2%
6 7
21.2%
1 5
15.2%
2 5
15.2%
5 5
15.2%
3 4
12.1%
Close Punctuation
ValueCountFrequency (%)
) 1055
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1055
100.0%
Space Separator
ValueCountFrequency (%)
121
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 28354
92.6%
Common 2270
 
7.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
추 1269
 
4.5%
기 1009
 
3.6%
고 953
 
3.4%
리 828
 
2.9%
딸 743
 
2.6%
마 741
 
2.6%
토 673
 
2.4%
파 643
 
2.3%
배 635
 
2.2%
사 624
 
2.2%
Other values (247) 20236
71.4%
Common
ValueCountFrequency (%)
) 1055
46.5%
( 1055
46.5%
121
 
5.3%
4 7
 
0.3%
6 7
 
0.3%
1 5
 
0.2%
~ 5
 
0.2%
2 5
 
0.2%
5 5
 
0.2%
3 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 28354
92.6%
ASCII 2270
 
7.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
추 1269
 
4.5%
기 1009
 
3.6%
고 953
 
3.4%
리 828
 
2.9%
딸 743
 
2.6%
마 741
 
2.6%
토 673
 
2.4%
파 643
 
2.3%
배 635
 
2.2%
사 624
 
2.2%
Other values (247) 20236
71.4%
ASCII
ValueCountFrequency (%)
) 1055
46.5%
( 1055
46.5%
121
 
5.3%
4 7
 
0.3%
6 7
 
0.3%
1 5
 
0.2%
~ 5
 
0.2%
2 5
 
0.2%
5 5
 
0.2%
3 4
 
0.2%

수거단계
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
생산
8276 
유통/판매
1724 

Length

Max length5
Median length2
Mean length2.5172
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
생산 8276
82.8%
유통/판매 1724
 
17.2%

Length

2023-12-11T12:01:49.199255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:01:49.326006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생산 8276
82.8%
유통/판매 1724
 
17.2%

재배양식
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반
4092 
GAP(인증)
2599 
친환경(인증) 무농약
1981 
친환경(인증) 유기
1105 
친환경(인증)
 
146
Other values (4)
 
77

Length

Max length16
Median length13
Mean length6.7975
Min length3

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반 4092
40.9%
GAP(인증) 2599
26.0%
친환경(인증) 무농약 1981
19.8%
친환경(인증) 유기 1105
 
11.1%
친환경(인증) 146
 
1.5%
친환경(인증) 취급자 51
 
0.5%
친환경(인증) 유기가공품 24
 
0.2%
친환경(인증) 비식용유기가공품 1
 
< 0.1%
이력추적(인증) 1
 
< 0.1%

Length

2023-12-11T12:01:49.461255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:01:49.618114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 4092
31.1%
친환경(인증 3308
25.1%
gap(인증 2599
19.7%
무농약 1981
15.1%
유기 1105
 
8.4%
취급자 51
 
0.4%
유기가공품 24
 
0.2%
비식용유기가공품 1
 
< 0.1%
이력추적(인증 1
 
< 0.1%

주소
Text

Distinct179
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T12:01:50.006389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length11.2784
Min length6

Characters and Unicode

Total characters112784
Distinct characters125
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

Unique15 ?
Unique (%)0.1%

Sample

1st row제주특별자치도 서귀포시
2nd row전라북도 익산시
3rd row제주특별자치도 서귀포시
4th row경상남도 창녕군
5th row경상남도 함안군
ValueCountFrequency (%)
경상남도 2845
 
14.2%
경상북도 2751
 
13.8%
제주특별자치도 933
 
4.7%
전라남도 779
 
3.9%
진주시 589
 
2.9%
경기도 566
 
2.8%
서귀포시 500
 
2.5%
충청남도 480
 
2.4%
강원도 451
 
2.3%
제주시 433
 
2.2%
Other values (178) 9673
48.4%
2023-12-11T12:01:50.543166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40000
35.5%
도 9806
 
8.7%
경 6498
 
5.8%
상 5960
 
5.3%
시 5490
 
4.9%
군 4830
 
4.3%
남 4313
 
3.8%
주 3543
 
3.1%
북 3476
 
3.1%
청 1511
 
1.3%
Other values (115) 27357
24.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 72776
64.5%
Space Separator 40000
35.5%
Math Symbol 6
 
< 0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 9806
 
13.5%
경 6498
 
8.9%
상 5960
 
8.2%
시 5490
 
7.5%
군 4830
 
6.6%
남 4313
 
5.9%
주 3543
 
4.9%
북 3476
 
4.8%
청 1511
 
2.1%
제 1484
 
2.0%
Other values (112) 25865
35.5%
Space Separator
ValueCountFrequency (%)
40000
100.0%
Math Symbol
ValueCountFrequency (%)
| 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 72776
64.5%
Common 40008
35.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 9806
 
13.5%
경 6498
 
8.9%
상 5960
 
8.2%
시 5490
 
7.5%
군 4830
 
6.6%
남 4313
 
5.9%
주 3543
 
4.9%
북 3476
 
4.8%
청 1511
 
2.1%
제 1484
 
2.0%
Other values (112) 25865
35.5%
Common
ValueCountFrequency (%)
40000
> 99.9%
| 6
 
< 0.1%
- 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 72776
64.5%
ASCII 40008
35.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40000
> 99.9%
| 6
 
< 0.1%
- 2
 
< 0.1%
Hangul
ValueCountFrequency (%)
도 9806
 
13.5%
경 6498
 
8.9%
상 5960
 
8.2%
시 5490
 
7.5%
군 4830
 
6.6%
남 4313
 
5.9%
주 3543
 
4.9%
북 3476
 
4.8%
청 1511
 
2.1%
제 1484
 
2.0%
Other values (112) 25865
35.5%

재배면적
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct3022
Distinct (%)36.5%
Missing1730
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean3143.825
Minimum15
Maximum1040666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:01:50.771593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile330
Q1660
median1488
Q33000
95-th percentile10000
Maximum1040666
Range1040651
Interquartile range (IQR)2340

Descriptive statistics

Standard deviation13487.368
Coefficient of variation (CV)4.290114
Kurtosis4281.8371
Mean3143.825
Median Absolute Deviation (MAD)958
Skewness57.696778
Sum25999433
Variance1.8190909 × 108
MonotonicityNot monotonic
2023-12-11T12:01:51.257314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
330 541
 
5.4%
660 511
 
5.1%
1000 410
 
4.1%
2000 161
 
1.6%
500 141
 
1.4%
1500 109
 
1.1%
100 104
 
1.0%
600 98
 
1.0%
3000 95
 
0.9%
400 68
 
0.7%
Other values (3012) 6032
60.3%
(Missing) 1730
 
17.3%
ValueCountFrequency (%)
15 2
< 0.1%
30 2
< 0.1%
50 2
< 0.1%
52 2
< 0.1%
60 2
< 0.1%
63 1
< 0.1%
66 2
< 0.1%
68 1
< 0.1%
76 1
< 0.1%
77 2
< 0.1%
ValueCountFrequency (%)
1040666 1
< 0.1%
326400 1
< 0.1%
148143 1
< 0.1%
141074 1
< 0.1%
138742 1
< 0.1%
111077 1
< 0.1%
107631 1
< 0.1%
100385 1
< 0.1%
90000 1
< 0.1%
88568 1
< 0.1%

조사물량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1026
Distinct (%)11.4%
Missing993
Missing (%)9.9%
Infinite0
Infinite (%)0.0%
Mean6071.7676
Minimum0
Maximum2400000
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:01:51.452095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q1100
median695
Q33000
95-th percentile20000
Maximum2400000
Range2400000
Interquartile range (IQR)2900

Descriptive statistics

Standard deviation43031.122
Coefficient of variation (CV)7.0870831
Kurtosis1537.2175
Mean6071.7676
Median Absolute Deviation (MAD)684
Skewness33.28475
Sum54688411
Variance1.8516774 × 109
MonotonicityNot monotonic
2023-12-11T12:01:51.609820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000.0 514
 
5.1%
100.0 447
 
4.5%
10.0 435
 
4.3%
500.0 424
 
4.2%
1.0 389
 
3.9%
300.0 376
 
3.8%
200.0 329
 
3.3%
50.0 310
 
3.1%
2000.0 303
 
3.0%
3000.0 240
 
2.4%
Other values (1016) 5240
52.4%
(Missing) 993
 
9.9%
ValueCountFrequency (%)
0.0 4
 
< 0.1%
0.2 1
 
< 0.1%
0.48 1
 
< 0.1%
0.5 2
 
< 0.1%
0.6 2
 
< 0.1%
0.8 1
 
< 0.1%
1.0 389
3.9%
1.2 1
 
< 0.1%
1.35 1
 
< 0.1%
1.4 1
 
< 0.1%
ValueCountFrequency (%)
2400000.0 1
< 0.1%
1875000.0 1
< 0.1%
1000000.0 1
< 0.1%
840000.0 1
< 0.1%
768000.0 1
< 0.1%
636000.0 1
< 0.1%
573300.0 1
< 0.1%
546000.0 1
< 0.1%
480000.0 1
< 0.1%
462000.0 1
< 0.1%

등록일자
Real number (ℝ)

Distinct301
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20201693
Minimum20200102
Maximum20210226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:01:51.796403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200102
5-th percentile20200409
Q120200629
median20200925
Q320201109
95-th percentile20210204
Maximum20210226
Range10124
Interquartile range (IQR)480

Descriptive statistics

Standard deviation2689.2179
Coefficient of variation (CV)0.00013311844
Kurtosis6.0225798
Mean20201693
Median Absolute Deviation (MAD)198
Skewness2.8126678
Sum2.0201693 × 1011
Variance7231892.7
MonotonicityNot monotonic
2023-12-11T12:01:51.972483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20201109 170
 
1.7%
20201012 170
 
1.7%
20201026 154
 
1.5%
20201112 138
 
1.4%
20201006 125
 
1.2%
20201027 116
 
1.2%
20201019 116
 
1.2%
20201013 104
 
1.0%
20201104 103
 
1.0%
20201015 102
 
1.0%
Other values (291) 8702
87.0%
ValueCountFrequency (%)
20200102 8
 
0.1%
20200106 7
 
0.1%
20200107 5
 
0.1%
20200108 3
 
< 0.1%
20200109 4
 
< 0.1%
20200110 4
 
< 0.1%
20200113 8
 
0.1%
20200114 17
0.2%
20200115 23
0.2%
20200116 8
 
0.1%
ValueCountFrequency (%)
20210226 10
 
0.1%
20210225 53
0.5%
20210224 72
0.7%
20210223 73
0.7%
20210222 70
0.7%
20210219 24
 
0.2%
20210218 40
0.4%
20210217 55
0.5%
20210216 28
 
0.3%
20210215 28
 
0.3%

분석결과
Text

Distinct79
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T12:01:52.240776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length2
Mean length2.1729
Min length2

Characters and Unicode

Total characters21729
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

Unique68 ?
Unique (%)0.7%

Sample

1st row적합
2nd row적합
3rd row적합
4th row적합
5th row적합
ValueCountFrequency (%)
적합 9909
97.3%
부적합 88
 
0.9%
출하연기 88
 
0.9%
및 3
 
< 0.1%
생산 3
 
< 0.1%
단계 3
 
< 0.1%
재조사 3
 
< 0.1%
2021/03/05 3
 
< 0.1%
2020/12/19 3
 
< 0.1%
부적합(회수폐기 3
 
< 0.1%
Other values (75) 82
 
0.8%
2023-12-11T12:01:52.671912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
적 10000
46.0%
합 10000
46.0%
0 264
 
1.2%
2 235
 
1.1%
188
 
0.9%
/ 176
 
0.8%
1 92
 
0.4%
( 91
 
0.4%
) 91
 
0.4%
기 91
 
0.4%
Other values (22) 501
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20479
94.2%
Decimal Number 704
 
3.2%
Space Separator 188
 
0.9%
Other Punctuation 176
 
0.8%
Open Punctuation 91
 
0.4%
Close Punctuation 91
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
적 10000
48.8%
합 10000
48.8%
기 91
 
0.4%
부 91
 
0.4%
출 88
 
0.4%
하 88
 
0.4%
연 88
 
0.4%
조 3
 
< 0.1%
재 3
 
< 0.1%
계 3
 
< 0.1%
Other values (8) 24
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 264
37.5%
2 235
33.4%
1 92
 
13.1%
9 23
 
3.3%
5 19
 
2.7%
7 18
 
2.6%
3 15
 
2.1%
4 14
 
2.0%
6 13
 
1.8%
8 11
 
1.6%
Space Separator
ValueCountFrequency (%)
188
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 176
100.0%
Open Punctuation
ValueCountFrequency (%)
( 91
100.0%
Close Punctuation
ValueCountFrequency (%)
) 91
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20479
94.2%
Common 1250
 
5.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
적 10000
48.8%
합 10000
48.8%
기 91
 
0.4%
부 91
 
0.4%
출 88
 
0.4%
하 88
 
0.4%
연 88
 
0.4%
조 3
 
< 0.1%
재 3
 
< 0.1%
계 3
 
< 0.1%
Other values (8) 24
 
0.1%
Common
ValueCountFrequency (%)
0 264
21.1%
2 235
18.8%
188
15.0%
/ 176
14.1%
1 92
 
7.4%
( 91
 
7.3%
) 91
 
7.3%
9 23
 
1.8%
5 19
 
1.5%
7 18
 
1.4%
Other values (4) 53
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20479
94.2%
ASCII 1250
 
5.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
적 10000
48.8%
합 10000
48.8%
기 91
 
0.4%
부 91
 
0.4%
출 88
 
0.4%
하 88
 
0.4%
연 88
 
0.4%
조 3
 
< 0.1%
재 3
 
< 0.1%
계 3
 
< 0.1%
Other values (8) 24
 
0.1%
ASCII
ValueCountFrequency (%)
0 264
21.1%
2 235
18.8%
188
15.0%
/ 176
14.1%
1 92
 
7.4%
( 91
 
7.3%
) 91
 
7.3%
9 23
 
1.8%
5 19
 
1.5%
7 18
 
1.4%
Other values (4) 53
 
4.2%

Interactions

2023-12-11T12:01:47.754412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:01:47.188483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:01:47.463929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:01:47.840994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:01:47.272712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:01:47.562840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:01:47.947541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:01:47.369019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:01:47.659158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:01:52.820859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수거단계재배양식재배면적조사물량등록일자분석결과
수거단계1.0000.5620.0000.0000.0780.000
재배양식0.5621.0000.0040.1730.3920.000
재배면적0.0000.0041.0000.0780.0150.000
조사물량0.0000.1730.0781.0000.0380.000
등록일자0.0780.3920.0150.0381.0000.154
분석결과0.0000.0000.0000.0000.1541.000
2023-12-11T12:01:52.919957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수거단계재배양식
수거단계1.0000.565
재배양식0.5651.000
2023-12-11T12:01:53.012450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재배면적조사물량등록일자수거단계재배양식
재배면적1.0000.6270.0540.0000.003
조사물량0.6271.000-0.0030.0000.092
등록일자0.054-0.0031.0000.1300.188
수거단계0.0000.0000.1301.0000.565
재배양식0.0030.0920.1880.5651.000

Missing values

2023-12-11T12:01:48.079930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:01:48.200752image/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.
2023-12-11T12:01:48.300242image/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
4037기타(감귤)생산GAP(인증)제주특별자치도 서귀포시198010.020201103적합
949상추생산일반전라북도 익산시13050.020210129적합
9172당근생산일반제주특별자치도 서귀포시537510000.020200213적합
13943파프리카생산일반경상남도 창녕군30281000.020200512적합
361방울토마토생산GAP(인증)경상남도 함안군1003310000.020210222적합
9266대파생산일반제주특별자치도 제주시737915000.020201123적합
4603기타(콩)생산GAP(인증)인천광역시 강화군10002.020201110적합
1121멥쌀(일반)유통/판매친환경(인증) 취급자전라남도 보성군<NA><NA>20210225적합
10105배생산일반울산광역시 울주군694512000.020200730적합
13782풋고추생산친환경(인증) 무농약경상남도 밀양시1677150.020200220적합
품목명수거단계재배양식주소재배면적조사물량등록일자분석결과
9103참외유통/판매친환경(인증) 무농약경상북도 성주군<NA><NA>20200715적합
12098메현미생산일반경상남도 거제시15901000.020201008적합
5996딸기생산GAP(인증)전라남도 담양군19503600.020200504적합
301기타(보리)유통/판매친환경(인증) 취급자경상남도 함양군<NA>1.020210201적합
19261자두생산일반경상북도 의성군432500.020200616적합
12817두릅생산일반경상남도 거창군1000100.020200507적합
18304포도생산GAP(인증)경상북도 상주시100001000.020200910적합
13923마늘생산일반경상남도 창녕군1905200.020200601적합
14867풋고추생산GAP(인증)경상남도 진주시990200.020200518적합
7531피망유통/판매친환경(인증) 유기강원도 홍천군<NA>1.020201027적합

Duplicate rows

Most frequently occurring

품목명수거단계재배양식주소재배면적조사물량등록일자분석결과# duplicates
257콩(대두)생산GAP(인증)경상북도 안동시10010.020201104적합28
69딸기생산GAP(인증)경상남도 진주시66030.020201124적합14
68딸기생산GAP(인증)경상남도 진주시66030.020201123적합12
65딸기생산GAP(인증)경상남도 진주시66020.020201118적합11
74딸기생산GAP(인증)경상남도 진주시66050.020201202적합11
198사과생산GAP(인증)경상북도 칠곡군330300.020201112적합11
71딸기생산GAP(인증)경상남도 진주시66050.020201126적합10
139멥쌀(일반)생산GAP(인증)전라북도 부안군100100.020201021적합10
269파프리카생산일반경상남도 함안군2000200.020201013적합10
134메현미생산일반경상남도 함안군1000400.020201006적합9