Overview

Dataset statistics

Number of variables12
Number of observations789
Missing cells33
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory77.2 KiB
Average record size in memory100.2 B

Variable types

Numeric4
Text5
Categorical1
DateTime2

Dataset

Description국립농산물품질관리원에서 관리하는 농산물이력추적등록단체 정보서비스(등록번호, 등록기관, 단계구분, 생산자단체, 대표자명, 유효기간, 대표품목, 주소, 등록농가수, 등록필지, 재배면적 등)
Author국립농산물품질관리원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220204000000001681

Alerts

등록번호 is highly overall correlated with 등록농가수High correlation
등록농가수 is highly overall correlated with 등록번호High correlation
등록필지 is highly overall correlated with 재배면적High correlation
재배면적 is highly overall correlated with 등록필지High correlation
대표품목 has 31 (3.9%) missing valuesMissing
등록번호 has unique valuesUnique
등록필지 has 227 (28.8%) zerosZeros
재배면적 has 227 (28.8%) zerosZeros

Reproduction

Analysis started2024-04-19 07:07:59.372298
Analysis finished2024-04-19 07:08:02.049127
Duration2.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

등록번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct789
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8546.4689
Minimum14
Maximum11902
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-04-19T16:08:02.117904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile1852.4
Q16311
median10200
Q311484
95-th percentile11862.6
Maximum11902
Range11888
Interquartile range (IQR)5173

Descriptive statistics

Standard deviation3469.2102
Coefficient of variation (CV)0.40592322
Kurtosis-0.54611141
Mean8546.4689
Median Absolute Deviation (MAD)1604
Skewness-0.88569433
Sum6743164
Variance12035420
MonotonicityStrictly increasing
2024-04-19T16:08:02.256271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 1
 
0.1%
10953 1
 
0.1%
10959 1
 
0.1%
10960 1
 
0.1%
10963 1
 
0.1%
10981 1
 
0.1%
10983 1
 
0.1%
10989 1
 
0.1%
10992 1
 
0.1%
11011 1
 
0.1%
Other values (779) 779
98.7%
ValueCountFrequency (%)
14 1
0.1%
19 1
0.1%
112 1
0.1%
121 1
0.1%
152 1
0.1%
278 1
0.1%
304 1
0.1%
420 1
0.1%
471 1
0.1%
609 1
0.1%
ValueCountFrequency (%)
11902 1
0.1%
11901 1
0.1%
11900 1
0.1%
11899 1
0.1%
11898 1
0.1%
11897 1
0.1%
11896 1
0.1%
11895 1
0.1%
11894 1
0.1%
11893 1
0.1%

등록기관
Text

Distinct94
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
2024-04-19T16:08:02.508674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length10
Mean length10.841572
Min length10

Characters and Unicode

Total characters8554
Distinct characters104
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)2.3%

Sample

1st row경북지원 문경사무소
2nd row전남지원 나주사무소
3rd row전남지원 강진ㆍ완도사무소
4th row전남지원 강진ㆍ완도사무소
5th row경남지원 진주사무소
ValueCountFrequency (%)
전남지원 159
 
10.1%
경남지원 158
 
10.0%
경기지원 105
 
6.7%
품질관리과 86
 
5.4%
제주지원 84
 
5.3%
경북지원 83
 
5.3%
충남지원 83
 
5.3%
강원지원 48
 
3.0%
전북지원 41
 
2.6%
서귀포사무소 41
 
2.6%
Other values (87) 690
43.7%
2024-04-19T16:08:02.884945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 883
 
10.3%
지 789
 
9.2%
789
 
9.2%
사 717
 
8.4%
무 714
 
8.3%
소 703
 
8.2%
남 438
 
5.1%
경 378
 
4.4%
ㆍ 204
 
2.4%
전 200
 
2.3%
Other values (94) 2739
32.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7765
90.8%
Space Separator 789
 
9.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 883
 
11.4%
지 789
 
10.2%
사 717
 
9.2%
무 714
 
9.2%
소 703
 
9.1%
남 438
 
5.6%
경 378
 
4.9%
ㆍ 204
 
2.6%
전 200
 
2.6%
주 164
 
2.1%
Other values (93) 2575
33.2%
Space Separator
ValueCountFrequency (%)
789
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7765
90.8%
Common 789
 
9.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 883
 
11.4%
지 789
 
10.2%
사 717
 
9.2%
무 714
 
9.2%
소 703
 
9.1%
남 438
 
5.6%
경 378
 
4.9%
ㆍ 204
 
2.6%
전 200
 
2.6%
주 164
 
2.1%
Other values (93) 2575
33.2%
Common
ValueCountFrequency (%)
789
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7561
88.4%
ASCII 789
 
9.2%
Compat Jamo 204
 
2.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 883
 
11.7%
지 789
 
10.4%
사 717
 
9.5%
무 714
 
9.4%
소 703
 
9.3%
남 438
 
5.8%
경 378
 
5.0%
전 200
 
2.6%
주 164
 
2.2%
북 152
 
2.0%
Other values (92) 2423
32.0%
ASCII
ValueCountFrequency (%)
789
100.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 204
100.0%

단계구분
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
생산
560 
유통
170 
판매
59 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
생산 560
71.0%
유통 170
 
21.5%
판매 59
 
7.5%

Length

2024-04-19T16:08:03.020714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T16:08:03.112313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생산 560
71.0%
유통 170
 
21.5%
판매 59
 
7.5%

생산자단체명
Text

Distinct731
Distinct (%)92.9%
Missing2
Missing (%)0.3%
Memory size6.3 KiB
2024-04-19T16:08:03.331509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length19
Mean length7.3748412
Min length2

Characters and Unicode

Total characters5804
Distinct characters398
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique682 ?
Unique (%)86.7%

Sample

1st row문경농협경제사업본부
2nd row호남버섯영농조합법인
3rd row아트팜영농조합법인
4th row꾸메땅영농조합
5th row이제웅
ValueCountFrequency (%)
농업회사법인 16
 
1.8%
주식회사 7
 
0.8%
개인 5
 
0.6%
동철원농업협동조합 4
 
0.5%
작목반 4
 
0.5%
하나로마트 4
 
0.5%
영농조합법인 4
 
0.5%
주)조이팜 3
 
0.3%
배병남 3
 
0.3%
김화농협apc 2
 
0.2%
Other values (767) 816
94.0%
2024-04-19T16:08:03.691551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
농 345
 
5.9%
조 172
 
3.0%
합 172
 
3.0%
인 159
 
2.7%
협 155
 
2.7%
영 147
 
2.5%
목 137
 
2.4%
작 135
 
2.3%
법 133
 
2.3%
반 123
 
2.1%
Other values (388) 4126
71.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5554
95.7%
Space Separator 82
 
1.4%
Uppercase Letter 75
 
1.3%
Open Punctuation 36
 
0.6%
Close Punctuation 36
 
0.6%
Decimal Number 12
 
0.2%
Lowercase Letter 7
 
0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
농 345
 
6.2%
조 172
 
3.1%
합 172
 
3.1%
인 159
 
2.9%
협 155
 
2.8%
영 147
 
2.6%
목 137
 
2.5%
작 135
 
2.4%
법 133
 
2.4%
반 123
 
2.2%
Other values (364) 3876
69.8%
Uppercase Letter
ValueCountFrequency (%)
P 24
32.0%
A 18
24.0%
G 15
20.0%
C 9
 
12.0%
R 5
 
6.7%
K 1
 
1.3%
T 1
 
1.3%
M 1
 
1.3%
E 1
 
1.3%
Decimal Number
ValueCountFrequency (%)
2 5
41.7%
1 3
25.0%
3 2
 
16.7%
7 1
 
8.3%
5 1
 
8.3%
Lowercase Letter
ValueCountFrequency (%)
p 2
28.6%
e 2
28.6%
k 1
14.3%
o 1
14.3%
r 1
14.3%
Other Punctuation
ValueCountFrequency (%)
. 1
50.0%
& 1
50.0%
Space Separator
ValueCountFrequency (%)
82
100.0%
Open Punctuation
ValueCountFrequency (%)
( 36
100.0%
Close Punctuation
ValueCountFrequency (%)
) 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5554
95.7%
Common 168
 
2.9%
Latin 82
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
농 345
 
6.2%
조 172
 
3.1%
합 172
 
3.1%
인 159
 
2.9%
협 155
 
2.8%
영 147
 
2.6%
목 137
 
2.5%
작 135
 
2.4%
법 133
 
2.4%
반 123
 
2.2%
Other values (364) 3876
69.8%
Latin
ValueCountFrequency (%)
P 24
29.3%
A 18
22.0%
G 15
18.3%
C 9
 
11.0%
R 5
 
6.1%
p 2
 
2.4%
e 2
 
2.4%
K 1
 
1.2%
T 1
 
1.2%
M 1
 
1.2%
Other values (4) 4
 
4.9%
Common
ValueCountFrequency (%)
82
48.8%
( 36
21.4%
) 36
21.4%
2 5
 
3.0%
1 3
 
1.8%
3 2
 
1.2%
. 1
 
0.6%
7 1
 
0.6%
& 1
 
0.6%
5 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5554
95.7%
ASCII 250
 
4.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
농 345
 
6.2%
조 172
 
3.1%
합 172
 
3.1%
인 159
 
2.9%
협 155
 
2.8%
영 147
 
2.6%
목 137
 
2.5%
작 135
 
2.4%
법 133
 
2.4%
반 123
 
2.2%
Other values (364) 3876
69.8%
ASCII
ValueCountFrequency (%)
82
32.8%
( 36
14.4%
) 36
14.4%
P 24
 
9.6%
A 18
 
7.2%
G 15
 
6.0%
C 9
 
3.6%
R 5
 
2.0%
2 5
 
2.0%
1 3
 
1.2%
Other values (14) 17
 
6.8%

대표자명
Text

Distinct701
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
2024-04-19T16:08:04.012135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.9987326
Min length2

Characters and Unicode

Total characters2366
Distinct characters187
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

Unique625 ?
Unique (%)79.2%

Sample

1st row김종호
2nd row김재창
3rd row명동주
4th row김양현
5th row이제웅
ValueCountFrequency (%)
이태식 5
 
0.6%
엄충국 4
 
0.5%
김영남 3
 
0.4%
배병남 3
 
0.4%
이부권 3
 
0.4%
김종운 3
 
0.4%
김군진 3
 
0.4%
임문수 3
 
0.4%
오근선 3
 
0.4%
전병순 2
 
0.3%
Other values (691) 757
95.9%
2024-04-19T16:08:04.472789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 149
 
6.3%
이 132
 
5.6%
영 72
 
3.0%
정 63
 
2.7%
수 54
 
2.3%
박 52
 
2.2%
호 48
 
2.0%
성 45
 
1.9%
기 45
 
1.9%
종 43
 
1.8%
Other values (177) 1663
70.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2364
99.9%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 149
 
6.3%
이 132
 
5.6%
영 72
 
3.0%
정 63
 
2.7%
수 54
 
2.3%
박 52
 
2.2%
호 48
 
2.0%
성 45
 
1.9%
기 45
 
1.9%
종 43
 
1.8%
Other values (175) 1661
70.3%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2364
99.9%
Common 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 149
 
6.3%
이 132
 
5.6%
영 72
 
3.0%
정 63
 
2.7%
수 54
 
2.3%
박 52
 
2.2%
호 48
 
2.0%
성 45
 
1.9%
기 45
 
1.9%
종 43
 
1.8%
Other values (175) 1661
70.3%
Common
ValueCountFrequency (%)
( 1
50.0%
) 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2364
99.9%
ASCII 2
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 149
 
6.3%
이 132
 
5.6%
영 72
 
3.0%
정 63
 
2.7%
수 54
 
2.3%
박 52
 
2.2%
호 48
 
2.0%
성 45
 
1.9%
기 45
 
1.9%
종 43
 
1.8%
Other values (175) 1661
70.3%
ASCII
ValueCountFrequency (%)
( 1
50.0%
) 1
50.0%

유효기간 시작일
Date

Distinct435
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
Minimum2013-11-11 00:00:00
Maximum2018-09-29 00:00:00
2024-04-19T16:08:04.604635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T16:08:04.737956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

유효기간 종료일
Date

Distinct420
Distinct (%)53.2%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
Minimum2018-10-01 00:00:00
Maximum2021-09-28 00:00:00
2024-04-19T16:08:04.876763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T16:08:05.308980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

대표품목
Text

MISSING 

Distinct106
Distinct (%)14.0%
Missing31
Missing (%)3.9%
Memory size6.3 KiB
2024-04-19T16:08:05.541578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length2.2862797
Min length1

Characters and Unicode

Total characters1733
Distinct characters144
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)6.2%

Sample

1st row사과
2nd row팽이버섯
3rd row파프리카
4th row파프리카
5th row단감
ValueCountFrequency (%)
쌀 124
16.4%
사과 63
 
8.3%
배 49
 
6.5%
감귤 45
 
5.9%
딸기 40
 
5.3%
단감 37
 
4.9%
포도 35
 
4.6%
파프리카 33
 
4.4%
인삼 26
 
3.4%
복숭아 13
 
1.7%
Other values (95) 293
38.7%
2024-04-19T16:08:05.925930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
쌀 127
 
7.3%
감 109
 
6.3%
리 66
 
3.8%
과 66
 
3.8%
사 63
 
3.6%
배 59
 
3.4%
기 50
 
2.9%
귤 47
 
2.7%
딸 40
 
2.3%
단 38
 
2.2%
Other values (134) 1068
61.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1679
96.9%
Open Punctuation 26
 
1.5%
Close Punctuation 26
 
1.5%
Dash Punctuation 1
 
0.1%
Space Separator 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
쌀 127
 
7.6%
감 109
 
6.5%
리 66
 
3.9%
과 66
 
3.9%
사 63
 
3.8%
배 59
 
3.5%
기 50
 
3.0%
귤 47
 
2.8%
딸 40
 
2.4%
단 38
 
2.3%
Other values (130) 1014
60.4%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 26
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1679
96.9%
Common 54
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
쌀 127
 
7.6%
감 109
 
6.5%
리 66
 
3.9%
과 66
 
3.9%
사 63
 
3.8%
배 59
 
3.5%
기 50
 
3.0%
귤 47
 
2.8%
딸 40
 
2.4%
단 38
 
2.3%
Other values (130) 1014
60.4%
Common
ValueCountFrequency (%)
( 26
48.1%
) 26
48.1%
- 1
 
1.9%
1
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1679
96.9%
ASCII 54
 
3.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
쌀 127
 
7.6%
감 109
 
6.5%
리 66
 
3.9%
과 66
 
3.9%
사 63
 
3.8%
배 59
 
3.5%
기 50
 
3.0%
귤 47
 
2.8%
딸 40
 
2.4%
단 38
 
2.3%
Other values (130) 1014
60.4%
ASCII
ValueCountFrequency (%)
( 26
48.1%
) 26
48.1%
- 1
 
1.9%
1
 
1.9%

도로명주소
Text

Distinct721
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
2024-04-19T16:08:06.243506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length42
Mean length23.423321
Min length17

Characters and Unicode

Total characters18481
Distinct characters378
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique662 ?
Unique (%)83.9%

Sample

1st row경상북도 문경시 문경읍 주흘로83
2nd row전라남도 나주시 노안면 오정석정길15
3rd row전라남도 강진군 군동면 안풍길33
4th row전라남도 강진군 군동면 군장로349-80
5th row경상남도 진주시 집현면 냉정길519
ValueCountFrequency (%)
경상남도 155
 
4.5%
전라남도 153
 
4.5%
경기도 105
 
3.1%
제주특별자치도 84
 
2.5%
경상북도 83
 
2.4%
충청남도 77
 
2.3%
강원도 48
 
1.4%
제주시 43
 
1.3%
전라북도 41
 
1.2%
서귀포시 41
 
1.2%
Other values (1407) 2583
75.7%
2024-04-19T16:08:06.673920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2632
 
14.2%
도 824
 
4.5%
1 607
 
3.3%
남 517
 
2.8%
면 480
 
2.6%
길 470
 
2.5%
시 466
 
2.5%
로 449
 
2.4%
2 417
 
2.3%
경 399
 
2.2%
Other values (368) 11220
60.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12177
65.9%
Decimal Number 2930
 
15.9%
Space Separator 2632
 
14.2%
Dash Punctuation 291
 
1.6%
Close Punctuation 202
 
1.1%
Open Punctuation 202
 
1.1%
Other Punctuation 32
 
0.2%
Uppercase Letter 14
 
0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 824
 
6.8%
남 517
 
4.2%
면 480
 
3.9%
길 470
 
3.9%
시 466
 
3.8%
로 449
 
3.7%
경 399
 
3.3%
군 363
 
3.0%
동 307
 
2.5%
상 281
 
2.3%
Other values (339) 7621
62.6%
Decimal Number
ValueCountFrequency (%)
1 607
20.7%
2 417
14.2%
3 332
11.3%
4 278
9.5%
5 239
 
8.2%
6 235
 
8.0%
0 230
 
7.8%
7 213
 
7.3%
9 191
 
6.5%
8 188
 
6.4%
Uppercase Letter
ValueCountFrequency (%)
A 4
28.6%
C 2
14.3%
P 2
14.3%
B 2
14.3%
I 1
 
7.1%
L 1
 
7.1%
T 1
 
7.1%
H 1
 
7.1%
Other Punctuation
ValueCountFrequency (%)
, 16
50.0%
， 13
40.6%
/ 2
 
6.2%
. 1
 
3.1%
Close Punctuation
ValueCountFrequency (%)
) 201
99.5%
] 1
 
0.5%
Open Punctuation
ValueCountFrequency (%)
( 201
99.5%
[ 1
 
0.5%
Space Separator
ValueCountFrequency (%)
2632
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 291
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12178
65.9%
Common 6289
34.0%
Latin 14
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 824
 
6.8%
남 517
 
4.2%
면 480
 
3.9%
길 470
 
3.9%
시 466
 
3.8%
로 449
 
3.7%
경 399
 
3.3%
군 363
 
3.0%
동 307
 
2.5%
상 281
 
2.3%
Other values (340) 7622
62.6%
Common
ValueCountFrequency (%)
2632
41.9%
1 607
 
9.7%
2 417
 
6.6%
3 332
 
5.3%
- 291
 
4.6%
4 278
 
4.4%
5 239
 
3.8%
6 235
 
3.7%
0 230
 
3.7%
7 213
 
3.4%
Other values (10) 815
 
13.0%
Latin
ValueCountFrequency (%)
A 4
28.6%
C 2
14.3%
P 2
14.3%
B 2
14.3%
I 1
 
7.1%
L 1
 
7.1%
T 1
 
7.1%
H 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12177
65.9%
ASCII 6290
34.0%
None 14
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2632
41.8%
1 607
 
9.7%
2 417
 
6.6%
3 332
 
5.3%
- 291
 
4.6%
4 278
 
4.4%
5 239
 
3.8%
6 235
 
3.7%
0 230
 
3.7%
7 213
 
3.4%
Other values (17) 816
 
13.0%
Hangul
ValueCountFrequency (%)
도 824
 
6.8%
남 517
 
4.2%
면 480
 
3.9%
길 470
 
3.9%
시 466
 
3.8%
로 449
 
3.7%
경 399
 
3.3%
군 363
 
3.0%
동 307
 
2.5%
상 281
 
2.3%
Other values (339) 7621
62.6%
None
ValueCountFrequency (%)
， 13
92.9%
㈜ 1
 
7.1%

등록농가수
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2636248
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-04-19T16:08:06.792050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile12
Maximum20
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.5188337
Coefficient of variation (CV)0.82531503
Kurtosis3.996313
Mean4.2636248
Median Absolute Deviation (MAD)1
Skewness1.8820102
Sum3364
Variance12.38219
MonotonicityNot monotonic
2024-04-19T16:08:06.902086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2 192
24.3%
3 124
15.7%
1 122
15.5%
4 98
12.4%
5 55
 
7.0%
6 53
 
6.7%
8 35
 
4.4%
7 26
 
3.3%
9 18
 
2.3%
10 15
 
1.9%
Other values (9) 51
 
6.5%
ValueCountFrequency (%)
1 122
15.5%
2 192
24.3%
3 124
15.7%
4 98
12.4%
5 55
 
7.0%
6 53
 
6.7%
7 26
 
3.3%
8 35
 
4.4%
9 18
 
2.3%
10 15
 
1.9%
ValueCountFrequency (%)
20 4
 
0.5%
19 2
 
0.3%
17 7
0.9%
16 5
 
0.6%
15 2
 
0.3%
14 6
 
0.8%
13 6
 
0.8%
12 9
1.1%
11 10
1.3%
10 15
1.9%

등록필지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct169
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.068441
Minimum0
Maximum9669
Zeros227
Zeros (%)28.8%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-04-19T16:08:07.031328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q343
95-th percentile293.6
Maximum9669
Range9669
Interquartile range (IQR)43

Descriptive statistics

Standard deviation494.50484
Coefficient of variation (CV)4.9915477
Kurtosis195.89066
Mean99.068441
Median Absolute Deviation (MAD)4
Skewness12.080954
Sum78165
Variance244535.04
MonotonicityNot monotonic
2024-04-19T16:08:07.163892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 227
28.8%
1 68
 
8.6%
3 41
 
5.2%
2 40
 
5.1%
5 31
 
3.9%
4 26
 
3.3%
6 22
 
2.8%
8 14
 
1.8%
10 11
 
1.4%
7 11
 
1.4%
Other values (159) 298
37.8%
ValueCountFrequency (%)
0 227
28.8%
1 68
 
8.6%
2 40
 
5.1%
3 41
 
5.2%
4 26
 
3.3%
5 31
 
3.9%
6 22
 
2.8%
7 11
 
1.4%
8 14
 
1.8%
9 10
 
1.3%
ValueCountFrequency (%)
9669 1
0.1%
4460 1
0.1%
4104 1
0.1%
3082 1
0.1%
2970 1
0.1%
2682 1
0.1%
2575 1
0.1%
1996 1
0.1%
1905 1
0.1%
1880 1
0.1%

재배면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct558
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean267420.82
Minimum0
Maximum18092009
Zeros227
Zeros (%)28.8%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-04-19T16:08:07.291136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11070
Q391912
95-th percentile870218.2
Maximum18092009
Range18092009
Interquartile range (IQR)91912

Descriptive statistics

Standard deviation1244987.4
Coefficient of variation (CV)4.6555364
Kurtosis93.796274
Mean267420.82
Median Absolute Deviation (MAD)11070
Skewness8.8221114
Sum2.1099503 × 108
Variance1.5499936 × 1012
MonotonicityNot monotonic
2024-04-19T16:08:07.445984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 227
28.8%
3000.0 2
 
0.3%
9500.0 2
 
0.3%
1320.0 2
 
0.3%
3300.0 2
 
0.3%
5176.0 2
 
0.3%
14967.0 1
 
0.1%
35193.0 1
 
0.1%
1049.0 1
 
0.1%
660.0 1
 
0.1%
Other values (548) 548
69.5%
ValueCountFrequency (%)
0.0 227
28.8%
100.0 1
 
0.1%
441.0 1
 
0.1%
495.0 1
 
0.1%
660.0 1
 
0.1%
698.0 1
 
0.1%
859.0 1
 
0.1%
964.0 1
 
0.1%
1049.0 1
 
0.1%
1147.0 1
 
0.1%
ValueCountFrequency (%)
18092009.0 1
0.1%
13213570.0 1
0.1%
12988036.0 1
0.1%
10017577.79 1
0.1%
8775116.0 1
0.1%
8757729.0 1
0.1%
7778717.0 1
0.1%
6217305.0 1
0.1%
5057169.47 1
0.1%
4948470.07 1
0.1%

Interactions

2024-04-19T16:08:01.293820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T16:08:00.129787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T16:08:00.558652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T16:08:00.931942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T16:08:01.382282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T16:08:00.220921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T16:08:00.655492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T16:08:01.014392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T16:08:01.492580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T16:08:00.326137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T16:08:00.750313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T16:08:01.116373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T16:08:01.588611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T16:08:00.417674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T16:08:00.834542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T16:08:01.195819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-19T16:08:07.560045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록번호등록기관단계구분등록농가수등록필지재배면적
등록번호1.0000.7470.3110.6790.2940.323
등록기관0.7471.0000.5140.7020.0000.000
단계구분0.3110.5141.0000.2180.0000.000
등록농가수0.6790.7020.2181.0000.3270.421
등록필지0.2940.0000.0000.3271.0000.954
재배면적0.3230.0000.0000.4210.9541.000
2024-04-19T16:08:07.663217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록번호등록농가수등록필지재배면적단계구분
등록번호1.000-0.619-0.064-0.0630.195
등록농가수-0.6191.0000.0570.0580.132
등록필지-0.0640.0571.0000.9710.000
재배면적-0.0630.0580.9711.0000.000
단계구분0.1950.1320.0000.0001.000

Missing values

2024-04-19T16:08:01.724806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-19T16:08:01.900340image/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.
2024-04-19T16:08:02.004052image/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

등록번호등록기관단계구분생산자단체명대표자명유효기간 시작일유효기간 종료일대표품목도로명주소등록농가수등록필지재배면적
014경북지원 문경사무소유통문경농협경제사업본부김종호2018-06-122021-06-11사과경상북도 문경시 문경읍 주흘로83500.0
119전남지원 나주사무소생산호남버섯영농조합법인김재창2018-03-292021-03-28팽이버섯전라남도 나주시 노안면 오정석정길158515560.0
2112전남지원 강진ㆍ완도사무소생산아트팜영농조합법인명동주2018-04-072021-04-06파프리카전라남도 강진군 군동면 안풍길3316616528.0
3121전남지원 강진ㆍ완도사무소생산꾸메땅영농조합김양현2018-04-072021-04-06파프리카전라남도 강진군 군동면 군장로349-8014527500.0
4152경남지원 진주사무소생산이제웅이제웅2018-04-142021-04-13단감경상남도 진주시 집현면 냉정길5194246464.0
5278전남지원 순천ㆍ광양사무소생산낙안배연구회안정호2018-04-242021-04-23배전라남도 순천시 낙안면 조정래길5026102223239.0
6304경기지원 화성ㆍ오산사무소유통수원지구원예농협산지유통센터이덕수2018-04-252021-04-24배경기도 화성시 팔탄면 노하길470번길25 (수원지구원예농협 산지유통센터)500.0
7420충남지원 품질관리과판매산내농업협동조합송경영2018-05-032021-05-02포도대전광역시 동구 산내로1324 (낭월동)800.0
8471경남지원 품질관리과생산김은균김은균2018-05-082021-05-07단감경상남도 창원시 의창구 북면 천주로603번길8-15619615.0
9609전북지원 남원사무소생산춘향골배공선출하회작목반안철섭2018-05-222021-05-21배전라북도 남원시 고산길61-51 (고죽동)5110277596.0
등록번호등록기관단계구분생산자단체명대표자명유효기간 시작일유효기간 종료일대표품목도로명주소등록농가수등록필지재배면적
77911893전남지원 해남ㆍ진도사무소유통오케이라이스센터박재현2018-07-242021-07-23쌀전라남도 해남군 옥천면 해남로621700.0
78011894전남지원 해남ㆍ진도사무소유통선진농협미곡처리장박상우2018-07-242021-07-23<NA>전라남도 진도군 고군면 오일시1길82100.0
78111895전남지원 해남ㆍ진도사무소생산서혁서혁2018-07-242021-07-23유자전라남도 진도군 군내면 공성구지길102-5135915.0
78211896충남지원 금산사무소유통금산농협농산물산지유통센터김일생2018-07-262021-07-25깻잎충청남도 금산군 남이면 비실길1541618886.0
78311897충북지원 영동사무소생산봄꽃가을풍경영농조합법인장시태2018-07-312021-07-30복숭아충청북도 영동군 양강면 남전2안길4-2112432401.0
78411898충남지원 금산사무소판매금산농협농산물산지유통센터김일생2018-08-092021-08-08깻잎충청남도 금산군 남이면 비실길15480114832.2
78511899경북지원 안동사무소생산농협양곡(주) 안동라이스센터이후자2018-08-132021-08-12쌀경상북도 안동시 풍산읍 미내길63114572984440.0
78611900경남지원 진주사무소생산강대동강대동2018-09-112021-09-10방울토마토경상남도 진주시 금산면 금산로1191914046.0
78711901강원지원 평창사무소생산조상현조상현2018-09-192021-09-18파프리카강원도 평창군 진부면 송정택지1길35 (석미아파트) 101동 601호157024.0
78811902경남지원 품질관리과생산김우중김우중2018-09-282021-09-27파프리카경상남도 창원시 마산합포구 진북면 학동로128-165144959.0