Overview

Dataset statistics

Number of variables6
Number of observations101
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.0 KiB
Average record size in memory50.3 B

Variable types

Text5
Numeric1

Dataset

Description지리적표시관리 인증, 심사 등의 업무 관리(등록번호, 등록명칭, 등록일자, 대상지역, 생산계획량, 구성현황 등)
Author국립농산물품질관리원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220204000000001691

Alerts

등록번호 has unique valuesUnique
등록명칭 has unique valuesUnique

Reproduction

Analysis started2024-03-23 07:27:43.722069
Analysis finished2024-03-23 07:27:46.478361
Duration2.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

등록번호
Text

UNIQUE 

Distinct101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
2024-03-23T07:27:46.884458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.029703
Min length3

Characters and Unicode

Total characters407
Distinct characters12
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

Unique101 ?
Unique (%)100.0%

Sample

1st row제1호
2nd row제2호
3rd row제3호
4th row제5호
5th row제6호
ValueCountFrequency (%)
제1호 1
 
1.0%
제62호 1
 
1.0%
제84호 1
 
1.0%
제83호 1
 
1.0%
제82호 1
 
1.0%
제81호 1
 
1.0%
제80호 1
 
1.0%
제79호 1
 
1.0%
제78호 1
 
1.0%
제77호 1
 
1.0%
Other values (91) 91
90.1%
2024-03-23T07:27:47.915020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 101
24.8%
호 101
24.8%
1 32
 
7.9%
9 21
 
5.2%
6 21
 
5.2%
7 21
 
5.2%
8 21
 
5.2%
0 21
 
5.2%
2 19
 
4.7%
3 18
 
4.4%
Other values (2) 31
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 205
50.4%
Other Letter 202
49.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 32
15.6%
9 21
10.2%
6 21
10.2%
7 21
10.2%
8 21
10.2%
0 21
10.2%
2 19
9.3%
3 18
8.8%
5 17
8.3%
4 14
6.8%
Other Letter
ValueCountFrequency (%)
제 101
50.0%
호 101
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 205
50.4%
Hangul 202
49.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 32
15.6%
9 21
10.2%
6 21
10.2%
7 21
10.2%
8 21
10.2%
0 21
10.2%
2 19
9.3%
3 18
8.8%
5 17
8.3%
4 14
6.8%
Hangul
ValueCountFrequency (%)
제 101
50.0%
호 101
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 205
50.4%
Hangul 202
49.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 101
50.0%
호 101
50.0%
ASCII
ValueCountFrequency (%)
1 32
15.6%
9 21
10.2%
6 21
10.2%
7 21
10.2%
8 21
10.2%
0 21
10.2%
2 19
9.3%
3 18
8.8%
5 17
8.3%
4 14
6.8%

등록명칭
Text

UNIQUE 

Distinct101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
2024-03-23T07:27:48.605031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length4.8316832
Min length3

Characters and Unicode

Total characters488
Distinct characters158
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

Unique101 ?
Unique (%)100.0%

Sample

1st row보성녹차
2nd row하동녹차
3rd row고창복분자주
4th row영양고춧가루
5th row의성마늘
ValueCountFrequency (%)
보성녹차 1
 
1.0%
의령망개떡 1
 
1.0%
진도검정쌀 1
 
1.0%
한우 1
 
1.0%
고흥 1
 
1.0%
창녕마늘 1
 
1.0%
나주배 1
 
1.0%
영광한우 1
 
1.0%
김포쌀 1
 
1.0%
인제콩 1
 
1.0%
Other values (95) 95
90.5%
2024-03-23T07:27:49.665994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
고 30
 
6.1%
산 15
 
3.1%
쌀 12
 
2.5%
영 12
 
2.5%
주 12
 
2.5%
삼 11
 
2.3%
수 11
 
2.3%
한 11
 
2.3%
려 10
 
2.0%
천 9
 
1.8%
Other values (148) 355
72.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 480
98.4%
Space Separator 4
 
0.8%
Other Punctuation 2
 
0.4%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
고 30
 
6.2%
산 15
 
3.1%
쌀 12
 
2.5%
영 12
 
2.5%
주 12
 
2.5%
삼 11
 
2.3%
수 11
 
2.3%
한 11
 
2.3%
려 10
 
2.1%
천 9
 
1.9%
Other values (144) 347
72.3%
Space Separator
ValueCountFrequency (%)
4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 480
98.4%
Common 8
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
고 30
 
6.2%
산 15
 
3.1%
쌀 12
 
2.5%
영 12
 
2.5%
주 12
 
2.5%
삼 11
 
2.3%
수 11
 
2.3%
한 11
 
2.3%
려 10
 
2.1%
천 9
 
1.9%
Other values (144) 347
72.3%
Common
ValueCountFrequency (%)
4
50.0%
, 2
25.0%
) 1
 
12.5%
( 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 480
98.4%
ASCII 8
 
1.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
고 30
 
6.2%
산 15
 
3.1%
쌀 12
 
2.5%
영 12
 
2.5%
주 12
 
2.5%
삼 11
 
2.3%
수 11
 
2.3%
한 11
 
2.3%
려 10
 
2.1%
천 9
 
1.9%
Other values (144) 347
72.3%
ASCII
ValueCountFrequency (%)
4
50.0%
, 2
25.0%
) 1
 
12.5%
( 1
 
12.5%

등록일자
Real number (ℝ)

Distinct72
Distinct (%)71.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20105549
Minimum20020125
Maximum20210913
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-23T07:27:49.952330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20020125
5-th percentile20050825
Q120070702
median20100325
Q320131210
95-th percentile20200226
Maximum20210913
Range190788
Interquartile range (IQR)60508

Descriptive statistics

Standard deviation45011.735
Coefficient of variation (CV)0.0022387718
Kurtosis-0.18672432
Mean20105549
Median Absolute Deviation (MAD)30196
Skewness0.68719387
Sum2.0306604 × 109
Variance2.0260563 × 109
MonotonicityNot monotonic
2024-03-23T07:27:50.249418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20100325 4
 
4.0%
20131210 4
 
4.0%
20070827 3
 
3.0%
20061207 3
 
3.0%
20101108 3
 
3.0%
20150330 3
 
3.0%
20120305 2
 
2.0%
20061229 2
 
2.0%
20110504 2
 
2.0%
20080616 2
 
2.0%
Other values (62) 73
72.3%
ValueCountFrequency (%)
20020125 1
1.0%
20030502 1
1.0%
20040115 1
1.0%
20050305 1
1.0%
20050718 1
1.0%
20050825 1
1.0%
20051014 1
1.0%
20051201 1
1.0%
20051226 1
1.0%
20060508 1
1.0%
ValueCountFrequency (%)
20210913 1
1.0%
20210622 1
1.0%
20210408 1
1.0%
20210305 1
1.0%
20201119 1
1.0%
20200226 1
1.0%
20190917 2
2.0%
20180528 1
1.0%
20180319 1
1.0%
20170511 1
1.0%

대상지역
Text

Distinct72
Distinct (%)71.3%
Missing0
Missing (%)0.0%
Memory size940.0 B
2024-03-23T07:27:50.787107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length69
Median length17
Mean length15.871287
Min length2

Characters and Unicode

Total characters1603
Distinct characters106
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

Unique56 ?
Unique (%)55.4%

Sample

1st row행정구역상 전라남도 보성군 일원
2nd row행정구역상 경상남도 하동군 일원
3rd row행정구역상 전라북도 고창군 일원
4th row행정구역상 경상북도 영양군 일원
5th row행정구역상 경상북도 의성군 일원
ValueCountFrequency (%)
일원 87
22.6%
행정구역상 77
20.0%
전라남도 27
 
7.0%
강원도 14
 
3.6%
경상북도 9
 
2.3%
국내 8
 
2.1%
경상남도 8
 
2.1%
전라북도 7
 
1.8%
충청남도 7
 
1.8%
충청북도 6
 
1.6%
Other values (101) 135
35.1%
2024-03-23T07:27:51.678783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
284
17.7%
원 102
 
6.4%
상 96
 
6.0%
도 93
 
5.8%
일 87
 
5.4%
역 84
 
5.2%
정 80
 
5.0%
구 79
 
4.9%
행 77
 
4.8%
군 65
 
4.1%
Other values (96) 556
34.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1297
80.9%
Space Separator 284
 
17.7%
Other Punctuation 16
 
1.0%
Decimal Number 3
 
0.2%
Open Punctuation 1
 
0.1%
Lowercase Letter 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 102
 
7.9%
상 96
 
7.4%
도 93
 
7.2%
일 87
 
6.7%
역 84
 
6.5%
정 80
 
6.2%
구 79
 
6.1%
행 77
 
5.9%
군 65
 
5.0%
남 48
 
3.7%
Other values (89) 486
37.5%
Decimal Number
ValueCountFrequency (%)
0 2
66.7%
4 1
33.3%
Space Separator
ValueCountFrequency (%)
284
100.0%
Other Punctuation
ValueCountFrequency (%)
, 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1297
80.9%
Common 305
 
19.0%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 102
 
7.9%
상 96
 
7.4%
도 93
 
7.2%
일 87
 
6.7%
역 84
 
6.5%
정 80
 
6.2%
구 79
 
6.1%
행 77
 
5.9%
군 65
 
5.0%
남 48
 
3.7%
Other values (89) 486
37.5%
Common
ValueCountFrequency (%)
284
93.1%
, 16
 
5.2%
0 2
 
0.7%
( 1
 
0.3%
4 1
 
0.3%
) 1
 
0.3%
Latin
ValueCountFrequency (%)
m 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1297
80.9%
ASCII 306
 
19.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
284
92.8%
, 16
 
5.2%
0 2
 
0.7%
( 1
 
0.3%
4 1
 
0.3%
m 1
 
0.3%
) 1
 
0.3%
Hangul
ValueCountFrequency (%)
원 102
 
7.9%
상 96
 
7.4%
도 93
 
7.2%
일 87
 
6.7%
역 84
 
6.5%
정 80
 
6.2%
구 79
 
6.1%
행 77
 
5.9%
군 65
 
5.0%
남 48
 
3.7%
Other values (89) 486
37.5%

생산계획량
Text

Distinct100
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
2024-03-23T07:27:52.242255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.7722772
Min length1

Characters and Unicode

Total characters482
Distinct characters12
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

Unique99 ?
Unique (%)98.0%

Sample

1st row10톤
2nd row6톤
3rd row2톤
4th row395톤
5th row10340톤
ValueCountFrequency (%)
2378톤 2
 
2.0%
21톤 1
 
1.0%
55.1톤 1
 
1.0%
7081톤 1
 
1.0%
1240톤 1
 
1.0%
31524톤 1
 
1.0%
7070톤 1
 
1.0%
503톤 1
 
1.0%
21370톤 1
 
1.0%
1356톤 1
 
1.0%
Other values (90) 90
89.1%
2024-03-23T07:27:53.298833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
톤 101
21.0%
1 56
11.6%
0 53
11.0%
2 44
9.1%
3 40
 
8.3%
5 33
 
6.8%
6 33
 
6.8%
4 31
 
6.4%
8 30
 
6.2%
9 29
 
6.0%
Other values (2) 32
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 377
78.2%
Other Letter 101
 
21.0%
Other Punctuation 4
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 56
14.9%
0 53
14.1%
2 44
11.7%
3 40
10.6%
5 33
8.8%
6 33
8.8%
4 31
8.2%
8 30
8.0%
9 29
7.7%
7 28
7.4%
Other Letter
ValueCountFrequency (%)
톤 101
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 381
79.0%
Hangul 101
 
21.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 56
14.7%
0 53
13.9%
2 44
11.5%
3 40
10.5%
5 33
8.7%
6 33
8.7%
4 31
8.1%
8 30
7.9%
9 29
7.6%
7 28
7.3%
Hangul
ValueCountFrequency (%)
톤 101
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 381
79.0%
Hangul 101
 
21.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
톤 101
100.0%
ASCII
ValueCountFrequency (%)
1 56
14.7%
0 53
13.9%
2 44
11.5%
3 40
10.5%
5 33
8.7%
6 33
8.7%
4 31
8.1%
8 30
7.9%
9 29
7.6%
7 28
7.3%

구성현황
Text

Distinct91
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Memory size940.0 B
2024-03-23T07:27:53.736934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length17
Mean length12.653465
Min length7

Characters and Unicode

Total characters1278
Distinct characters189
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

Unique86 ?
Unique (%)85.1%

Sample

1st row영농조합법인 보성녹차연합회
2nd row하동차영농조합법인
3rd row고창으뜸복분자주 영농조합법인
4th row영양고추영농조합법인
5th row의성마늘생산자단체협의회영농조합법인
ValueCountFrequency (%)
사단법인 13
 
10.1%
영농조합법인 11
 
8.5%
사)고려인삼연합회 7
 
5.4%
영월고추영농조합법인 2
 
1.6%
영광고추산업연합회 2
 
1.6%
동고령농업협동조합 2
 
1.6%
안성시농특산물생산자연합회 2
 
1.6%
나주배지리적표시영농조합법인 1
 
0.8%
보성녹차연합회 1
 
0.8%
사)의령망개떡협의회 1
 
0.8%
Other values (87) 87
67.4%
2024-03-23T07:27:54.575694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
합 89
 
7.0%
인 80
 
6.3%
법 70
 
5.5%
영 66
 
5.2%
조 65
 
5.1%
농 62
 
4.9%
산 50
 
3.9%
회 49
 
3.8%
사 46
 
3.6%
자 31
 
2.4%
Other values (179) 670
52.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1208
94.5%
Space Separator 28
 
2.2%
Open Punctuation 21
 
1.6%
Close Punctuation 21
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
합 89
 
7.4%
인 80
 
6.6%
법 70
 
5.8%
영 66
 
5.5%
조 65
 
5.4%
농 62
 
5.1%
산 50
 
4.1%
회 49
 
4.1%
사 46
 
3.8%
자 31
 
2.6%
Other values (176) 600
49.7%
Space Separator
ValueCountFrequency (%)
28
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1208
94.5%
Common 70
 
5.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
합 89
 
7.4%
인 80
 
6.6%
법 70
 
5.8%
영 66
 
5.5%
조 65
 
5.4%
농 62
 
5.1%
산 50
 
4.1%
회 49
 
4.1%
사 46
 
3.8%
자 31
 
2.6%
Other values (176) 600
49.7%
Common
ValueCountFrequency (%)
28
40.0%
( 21
30.0%
) 21
30.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1208
94.5%
ASCII 70
 
5.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
합 89
 
7.4%
인 80
 
6.6%
법 70
 
5.8%
영 66
 
5.5%
조 65
 
5.4%
농 62
 
5.1%
산 50
 
4.1%
회 49
 
4.1%
사 46
 
3.8%
자 31
 
2.6%
Other values (176) 600
49.7%
ASCII
ValueCountFrequency (%)
28
40.0%
( 21
30.0%
) 21
30.0%

Interactions

2024-03-23T07:27:45.614068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T07:27:54.997785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록일자대상지역생산계획량구성현황
등록일자1.0000.8741.0000.902
대상지역0.8741.0000.9961.000
생산계획량1.0000.9961.0000.996
구성현황0.9021.0000.9961.000

Missing values

2024-03-23T07:27:45.999712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T07:27:46.347670image/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.

Sample

등록번호등록명칭등록일자대상지역생산계획량구성현황
0제1호보성녹차20020125행정구역상 전라남도 보성군 일원10톤영농조합법인 보성녹차연합회
1제2호하동녹차20030502행정구역상 경상남도 하동군 일원6톤하동차영농조합법인
2제3호고창복분자주20040115행정구역상 전라북도 고창군 일원2톤고창으뜸복분자주 영농조합법인
3제5호영양고춧가루20050305행정구역상 경상북도 영양군 일원395톤영양고추영농조합법인
4제6호의성마늘20050718행정구역상 경상북도 의성군 일원10340톤의성마늘생산자단체협의회영농조합법인
5제7호괴산고추20050825행정구역상 충청북도 괴산군 일원1482톤괴산고추영농조합법인
6제8호순창전통고추장20051014행정구역상 전라북도 순창군 일원60톤영농조합법인순창전통고추장연합회
7제9호괴산고춧가루20150708행정구역상 충청북도 괴산군 일원톤괴산군조합공동사업법인
8제10호성주참외20051201행정구역상 경상북도 성주군 일원65969톤성주참외생산자단체협의회영농조합법인
9제11호해남겨울배추20051226행정구역상 전라남도 해남군 일원68393톤해남겨울배추협의회영농조합법인
등록번호등록명칭등록일자대상지역생산계획량구성현황
91제101호안성한우20160212안성시 일원1069톤안성축산업협동조합
92제102호고려흑삼20160622국내19톤(사)고려인삼연합회
93제103호고려흑삼제품20160622국내13톤(사)고려인삼연합회
94제104호영광모싯잎송편20170511전라남도 영광군 일원1566톤(사)영광에서모싯잎떡을만드는사람들
95제105호기장쪽파20180319부산광역시 기장군2800톤기장쪽파영농조합법인
96제106호태안달래20180528충청남도 태안군264.76톤태안달래영농조합법인
97제107호부안오디20190917전라북도 부안군 일원1030톤부안뽕 영농조합법인
98제108호곡성토란20190917전라남도 곡성군 일원1700톤사단법인 곡성군토란생산자협의회
99제109호양구시래기20200226강원도 양구군 일원518톤양구시래기생산자연합회영농조합법인
100제110호서천한산소곡주20210913충남 서천군(한산면, 화양면, 기산면, 마산면)382800톤서천한산소곡주영농조합법인