Overview

Dataset statistics

Number of variables5
Number of observations81
Missing cells71
Missing cells (%)17.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory41.6 B

Variable types

Text5

Dataset

Description농림축산식품에서 지정한 대한민국 전통식품명인에 대한 현황으로 지정번호, 성명, 지정품목, 지정일, 소재지에 대한 정보를 제공합니다.
Author농림축산식품부
URLhttps://www.data.go.kr/data/15106664/fileData.do

Alerts

품목 has 71 (87.7%) missing valuesMissing
지정번호 has unique valuesUnique
명인 has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:35:02.209876
Analysis finished2023-12-12 22:35:02.734808
Duration0.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목
Text

MISSING 

Distinct10
Distinct (%)100.0%
Missing71
Missing (%)87.7%
Memory size780.0 B
2023-12-13T07:35:02.814699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length6
Mean length7
Min length6

Characters and Unicode

Total characters70
Distinct characters30
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

Unique10 ?
Unique (%)100.0%

Sample

1st row주류 (25)
2nd row김치 (5)
3rd row차류 (6)
4th row육류 (갈비) (4)
5th row장류 (13)
ValueCountFrequency (%)
6 2
 
9.1%
주류 1
 
4.5%
떡 1
 
4.5%
기타 1
 
4.5%
3 1
 
4.5%
식초 1
 
4.5%
2 1
 
4.5%
인삼 1
 
4.5%
엿류 1
 
4.5%
10 1
 
4.5%
Other values (11) 11
50.0%
2023-12-13T07:35:03.054125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12
17.1%
( 11
15.7%
) 11
15.7%
류 5
 
7.1%
2 2
 
2.9%
5 2
 
2.9%
3 2
 
2.9%
1 2
 
2.9%
6 2
 
2.9%
주 1
 
1.4%
Other values (20) 20
28.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 23
32.9%
Decimal Number 13
18.6%
Space Separator 12
17.1%
Open Punctuation 11
15.7%
Close Punctuation 11
15.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
류 5
21.7%
주 1
 
4.3%
인 1
 
4.3%
엿 1
 
4.3%
초 1
 
4.3%
삼 1
 
4.3%
식 1
 
4.3%
과 1
 
4.3%
기 1
 
4.3%
타 1
 
4.3%
Other values (9) 9
39.1%
Decimal Number
ValueCountFrequency (%)
2 2
15.4%
5 2
15.4%
3 2
15.4%
1 2
15.4%
6 2
15.4%
0 1
7.7%
4 1
7.7%
7 1
7.7%
Space Separator
ValueCountFrequency (%)
12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47
67.1%
Hangul 23
32.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
류 5
21.7%
주 1
 
4.3%
인 1
 
4.3%
엿 1
 
4.3%
초 1
 
4.3%
삼 1
 
4.3%
식 1
 
4.3%
과 1
 
4.3%
기 1
 
4.3%
타 1
 
4.3%
Other values (9) 9
39.1%
Common
ValueCountFrequency (%)
12
25.5%
( 11
23.4%
) 11
23.4%
2 2
 
4.3%
5 2
 
4.3%
3 2
 
4.3%
1 2
 
4.3%
6 2
 
4.3%
0 1
 
2.1%
4 1
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47
67.1%
Hangul 23
32.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12
25.5%
( 11
23.4%
) 11
23.4%
2 2
 
4.3%
5 2
 
4.3%
3 2
 
4.3%
1 2
 
4.3%
6 2
 
4.3%
0 1
 
2.1%
4 1
 
2.1%
Hangul
ValueCountFrequency (%)
류 5
21.7%
주 1
 
4.3%
인 1
 
4.3%
엿 1
 
4.3%
초 1
 
4.3%
삼 1
 
4.3%
식 1
 
4.3%
과 1
 
4.3%
기 1
 
4.3%
타 1
 
4.3%
Other values (9) 9
39.1%

지정번호
Text

UNIQUE 

Distinct81
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size780.0 B
2023-12-13T07:35:03.313869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.0246914
Min length3

Characters and Unicode

Total characters326
Distinct characters14
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

Unique81 ?
Unique (%)100.0%

Sample

1st row제1호
2nd row제2호
3rd row제4-가호
4th row제6호
5th row제7호
ValueCountFrequency (%)
제1호 1
 
1.2%
제36-가호 1
 
1.2%
제53호 1
 
1.2%
제52호 1
 
1.2%
제46호 1
 
1.2%
제42호 1
 
1.2%
제33호 1
 
1.2%
제26호 1
 
1.2%
제23호 1
 
1.2%
제78호 1
 
1.2%
Other values (71) 71
87.7%
2023-12-13T07:35:03.699443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 81
24.8%
호 81
24.8%
2 19
 
5.8%
7 19
 
5.8%
1 18
 
5.5%
4 18
 
5.5%
8 18
 
5.5%
6 17
 
5.2%
3 15
 
4.6%
5 14
 
4.3%
Other values (4) 26
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 166
50.9%
Decimal Number 156
47.9%
Dash Punctuation 4
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 19
12.2%
7 19
12.2%
1 18
11.5%
4 18
11.5%
8 18
11.5%
6 17
10.9%
3 15
9.6%
5 14
9.0%
9 10
6.4%
0 8
5.1%
Other Letter
ValueCountFrequency (%)
제 81
48.8%
호 81
48.8%
가 4
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 166
50.9%
Common 160
49.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2 19
11.9%
7 19
11.9%
1 18
11.2%
4 18
11.2%
8 18
11.2%
6 17
10.6%
3 15
9.4%
5 14
8.8%
9 10
6.2%
0 8
5.0%
Hangul
ValueCountFrequency (%)
제 81
48.8%
호 81
48.8%
가 4
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 166
50.9%
ASCII 160
49.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 81
48.8%
호 81
48.8%
가 4
 
2.4%
ASCII
ValueCountFrequency (%)
2 19
11.9%
7 19
11.9%
1 18
11.2%
4 18
11.2%
8 18
11.2%
6 17
10.6%
3 15
9.4%
5 14
8.8%
9 10
6.2%
0 8
5.0%

명인
Text

UNIQUE 

Distinct81
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size780.0 B
2023-12-13T07:35:03.991470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters243
Distinct characters98
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)100.0%

Sample

1st row조영귀
2nd row김창수
3rd row이성우
4th row박재서
5th row이기춘
ValueCountFrequency (%)
조영귀 1
 
1.2%
조종현 1
 
1.2%
김영숙 1
 
1.2%
이연순 1
 
1.2%
김현의 1
 
1.2%
김왕자 1
 
1.2%
박순애 1
 
1.2%
김규흔 1
 
1.2%
최봉석 1
 
1.2%
조정숙 1
 
1.2%
Other values (71) 71
87.7%
2023-12-13T07:35:04.465919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 15
 
6.2%
순 12
 
4.9%
숙 10
 
4.1%
자 10
 
4.1%
임 7
 
2.9%
정 7
 
2.9%
영 7
 
2.9%
박 7
 
2.9%
수 6
 
2.5%
이 6
 
2.5%
Other values (88) 156
64.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 243
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 15
 
6.2%
순 12
 
4.9%
숙 10
 
4.1%
자 10
 
4.1%
임 7
 
2.9%
정 7
 
2.9%
영 7
 
2.9%
박 7
 
2.9%
수 6
 
2.5%
이 6
 
2.5%
Other values (88) 156
64.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 243
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 15
 
6.2%
순 12
 
4.9%
숙 10
 
4.1%
자 10
 
4.1%
임 7
 
2.9%
정 7
 
2.9%
영 7
 
2.9%
박 7
 
2.9%
수 6
 
2.5%
이 6
 
2.5%
Other values (88) 156
64.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 243
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 15
 
6.2%
순 12
 
4.9%
숙 10
 
4.1%
자 10
 
4.1%
임 7
 
2.9%
정 7
 
2.9%
영 7
 
2.9%
박 7
 
2.9%
수 6
 
2.5%
이 6
 
2.5%
Other values (88) 156
64.2%

보유기능
Text

Distinct75
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Memory size780.0 B
2023-12-13T07:35:04.780753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length8.6049383
Min length7

Characters and Unicode

Total characters697
Distinct characters136
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

Unique70 ?
Unique (%)86.4%

Sample

1st row주류(송화백일주)
2nd row주류(금산인삼주)
3rd row주류(계룡백일주)
4th row주류(안동소주)
5th row주류(문배주)
ValueCountFrequency (%)
식품(가리구이 3
 
3.6%
식품(유과 2
 
2.4%
주류(안동소주 2
 
2.4%
식품(순창고추장 2
 
2.4%
식품(쌀엿 2
 
2.4%
식품(죽염홍된장 1
 
1.2%
식품(천리장 1
 
1.2%
식품(대맥장 1
 
1.2%
식품(갈골산자 1
 
1.2%
식품(승검초단자 1
 
1.2%
Other values (68) 68
81.0%
2023-12-13T07:35:05.355250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 81
 
11.6%
) 81
 
11.6%
60
 
8.6%
식 60
 
8.6%
품 57
 
8.2%
주 49
 
7.0%
류 24
 
3.4%
장 13
 
1.9%
리 10
 
1.4%
과 8
 
1.1%
Other values (126) 254
36.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 470
67.4%
Open Punctuation 81
 
11.6%
Close Punctuation 81
 
11.6%
Space Separator 60
 
8.6%
Other Punctuation 5
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
식 60
 
12.8%
품 57
 
12.1%
주 49
 
10.4%
류 24
 
5.1%
장 13
 
2.8%
리 10
 
2.1%
과 8
 
1.7%
차 8
 
1.7%
고 7
 
1.5%
소 6
 
1.3%
Other values (122) 228
48.5%
Open Punctuation
ValueCountFrequency (%)
( 81
100.0%
Close Punctuation
ValueCountFrequency (%)
) 81
100.0%
Space Separator
ValueCountFrequency (%)
60
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 470
67.4%
Common 227
32.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
식 60
 
12.8%
품 57
 
12.1%
주 49
 
10.4%
류 24
 
5.1%
장 13
 
2.8%
리 10
 
2.1%
과 8
 
1.7%
차 8
 
1.7%
고 7
 
1.5%
소 6
 
1.3%
Other values (122) 228
48.5%
Common
ValueCountFrequency (%)
( 81
35.7%
) 81
35.7%
60
26.4%
, 5
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 470
67.4%
ASCII 227
32.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 81
35.7%
) 81
35.7%
60
26.4%
, 5
 
2.2%
Hangul
ValueCountFrequency (%)
식 60
 
12.8%
품 57
 
12.1%
주 49
 
10.4%
류 24
 
5.1%
장 13
 
2.8%
리 10
 
2.1%
과 8
 
1.7%
차 8
 
1.7%
고 7
 
1.5%
소 6
 
1.3%
Other values (122) 228
48.5%

소재지
Text

Distinct53
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Memory size780.0 B
2023-12-13T07:35:05.696740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.9259259
Min length2

Characters and Unicode

Total characters399
Distinct characters61
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

Unique34 ?
Unique (%)42.0%

Sample

1st row전북 전주
2nd row충남 금산
3rd row충남 공주
4th row경북 안동
5th row경기 김포
ValueCountFrequency (%)
전남 16
 
10.3%
경기 15
 
9.6%
전북 10
 
6.4%
경북 9
 
5.8%
경남 8
 
5.1%
충남 8
 
5.1%
담양 6
 
3.8%
광주 5
 
3.2%
하동 4
 
2.6%
충북 4
 
2.6%
Other values (50) 71
45.5%
2023-12-13T07:35:06.176779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
75
18.8%
남 35
 
8.8%
경 32
 
8.0%
전 29
 
7.3%
북 23
 
5.8%
주 20
 
5.0%
기 15
 
3.8%
충 13
 
3.3%
천 13
 
3.3%
양 12
 
3.0%
Other values (51) 132
33.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 324
81.2%
Space Separator 75
 
18.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 35
 
10.8%
경 32
 
9.9%
전 29
 
9.0%
북 23
 
7.1%
주 20
 
6.2%
기 15
 
4.6%
충 13
 
4.0%
천 13
 
4.0%
양 12
 
3.7%
동 8
 
2.5%
Other values (50) 124
38.3%
Space Separator
ValueCountFrequency (%)
75
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 324
81.2%
Common 75
 
18.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 35
 
10.8%
경 32
 
9.9%
전 29
 
9.0%
북 23
 
7.1%
주 20
 
6.2%
기 15
 
4.6%
충 13
 
4.0%
천 13
 
4.0%
양 12
 
3.7%
동 8
 
2.5%
Other values (50) 124
38.3%
Common
ValueCountFrequency (%)
75
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 324
81.2%
ASCII 75
 
18.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
75
100.0%
Hangul
ValueCountFrequency (%)
남 35
 
10.8%
경 32
 
9.9%
전 29
 
9.0%
북 23
 
7.1%
주 20
 
6.2%
기 15
 
4.6%
충 13
 
4.0%
천 13
 
4.0%
양 12
 
3.7%
동 8
 
2.5%
Other values (50) 124
38.3%

Correlations

2023-12-13T07:35:06.308671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품목지정번호명인보유기능소재지
품목1.0001.0001.0001.0001.000
지정번호1.0001.0001.0001.0001.000
명인1.0001.0001.0001.0001.000
보유기능1.0001.0001.0001.0000.992
소재지1.0001.0001.0000.9921.000

Missing values

2023-12-13T07:35:02.618210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:35:02.700760image/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

품목지정번호명인보유기능소재지
0주류 (25)제1호조영귀주류(송화백일주)전북 전주
1<NA>제2호김창수주류(금산인삼주)충남 금산
2<NA>제4-가호이성우주류(계룡백일주)충남 공주
3<NA>제6호박재서주류(안동소주)경북 안동
4<NA>제7호이기춘주류(문배주)경기 김포
5<NA>제9호조정형주류(전주이강주)전북 전주
6<NA>제10호유민자주류(옥로주)경기 용인
7<NA>제11호임영순주류(구기자주)충남 청양
8<NA>제12호최옥근주류(계명주)경기 부천
9<NA>제13호남상란주류(가야곡왕주)충남 논산
품목지정번호명인보유기능소재지
71식초 (3)제41호임장옥식품(감식초)전북 정읍
72<NA>제73호현경태식품(흑초)경북 영천
73<NA>제86호임경만식품(보리식초)경북 영천
74기타 (7)제14호홍쌍리식품(매실농축액)전남 광양
75<NA>제25호오희숙식품(부각제조)경남 거창
76<NA>제39호김년임식품(전주비빔밥)전북 전주
77<NA>제63호김영근식품(도토리묵)충남 서천
78<NA>제72호임화자식품(쇠고기육포)전남 함평
79<NA>제77호문완기식품(식혜)경기 광주
80<NA>제90호고화순식품(고사리나물)경기 남양주