Overview

Dataset statistics

Number of variables6
Number of observations78
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory49.7 B

Variable types

Text4
Categorical2

Dataset

Description우수한 우리 식품의 계승발전을 위해 지정한 식품제조, 가공, 조리 등의 분야의 식품명인 현황정보
Author농림축산식품부
URLhttps://www.data.go.kr/data/3076487/fileData.do

Alerts

비 고 has constant value ""Constant
지정번호 has unique valuesUnique
성 명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 04:34:08.227090
Analysis finished2023-12-12 04:34:08.917326
Duration0.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지정번호
Text

UNIQUE 

Distinct78
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size756.0 B
2023-12-12T13:34:09.147724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.974359
Min length3

Characters and Unicode

Total characters310
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

Unique78 ?
Unique (%)100.0%

Sample

1st row제1호
2nd row제2호
3rd row제6호
4th row제7호
5th row제9호
ValueCountFrequency (%)
제1호 1
 
1.3%
제58호 1
 
1.3%
제65호 1
 
1.3%
제64호 1
 
1.3%
제63호 1
 
1.3%
제62호 1
 
1.3%
제61호 1
 
1.3%
제60호 1
 
1.3%
제71호 1
 
1.3%
제57호 1
 
1.3%
Other values (68) 68
87.2%
2023-12-12T13:34:09.605930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 78
25.2%
호 78
25.2%
6 19
 
6.1%
4 18
 
5.8%
7 18
 
5.8%
2 18
 
5.8%
1 17
 
5.5%
3 16
 
5.2%
5 15
 
4.8%
8 14
 
4.5%
Other values (4) 19
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 158
51.0%
Decimal Number 150
48.4%
Dash Punctuation 2
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 19
12.7%
4 18
12.0%
7 18
12.0%
2 18
12.0%
1 17
11.3%
3 16
10.7%
5 15
10.0%
8 14
9.3%
9 8
5.3%
0 7
 
4.7%
Other Letter
ValueCountFrequency (%)
제 78
49.4%
호 78
49.4%
가 2
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 158
51.0%
Common 152
49.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 19
12.5%
4 18
11.8%
7 18
11.8%
2 18
11.8%
1 17
11.2%
3 16
10.5%
5 15
9.9%
8 14
9.2%
9 8
5.3%
0 7
 
4.6%
Hangul
ValueCountFrequency (%)
제 78
49.4%
호 78
49.4%
가 2
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 158
51.0%
ASCII 152
49.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 78
49.4%
호 78
49.4%
가 2
 
1.3%
ASCII
ValueCountFrequency (%)
6 19
12.5%
4 18
11.8%
7 18
11.8%
2 18
11.8%
1 17
11.2%
3 16
10.5%
5 15
9.9%
8 14
9.2%
9 8
5.3%
0 7
 
4.6%

성 명
Text

UNIQUE 

Distinct78
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size756.0 B
2023-12-12T13:34:09.914094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters234
Distinct characters96
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

Unique78 ?
Unique (%)100.0%

Sample

1st row조영귀
2nd row김창수
3rd row박재서
4th row이기춘
5th row조정형
ValueCountFrequency (%)
조영귀 1
 
1.3%
이인자 1
 
1.3%
백정자 1
 
1.3%
강순옥 1
 
1.3%
김영근 1
 
1.3%
서분례 1
 
1.3%
김견식 1
 
1.3%
안복자 1
 
1.3%
정영석 1
 
1.3%
강순의 1
 
1.3%
Other values (68) 68
87.2%
2023-12-12T13:34:10.404627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 15
 
6.4%
순 11
 
4.7%
자 11
 
4.7%
숙 9
 
3.8%
정 8
 
3.4%
임 8
 
3.4%
영 7
 
3.0%
수 6
 
2.6%
이 6
 
2.6%
옥 6
 
2.6%
Other values (86) 147
62.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 234
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 15
 
6.4%
순 11
 
4.7%
자 11
 
4.7%
숙 9
 
3.8%
정 8
 
3.4%
임 8
 
3.4%
영 7
 
3.0%
수 6
 
2.6%
이 6
 
2.6%
옥 6
 
2.6%
Other values (86) 147
62.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 234
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 15
 
6.4%
순 11
 
4.7%
자 11
 
4.7%
숙 9
 
3.8%
정 8
 
3.4%
임 8
 
3.4%
영 7
 
3.0%
수 6
 
2.6%
이 6
 
2.6%
옥 6
 
2.6%
Other values (86) 147
62.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 234
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 15
 
6.4%
순 11
 
4.7%
자 11
 
4.7%
숙 9
 
3.8%
정 8
 
3.4%
임 8
 
3.4%
영 7
 
3.0%
수 6
 
2.6%
이 6
 
2.6%
옥 6
 
2.6%
Other values (86) 147
62.8%

보유기능
Text

Distinct76
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size756.0 B
2023-12-12T13:34:10.752552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length7.8717949
Min length6

Characters and Unicode

Total characters614
Distinct characters134
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

Unique74 ?
Unique (%)94.9%

Sample

1st row주류(송화백일주)
2nd row주류(금산인삼주)
3rd row주류(안동소주)
4th row주류(문배주)
5th row주류(전주이강주)
ValueCountFrequency (%)
식품(유과 3
 
3.8%
엿류(쌀엿 2
 
2.5%
식품(해물섞박지 1
 
1.3%
주류(송화백일주 1
 
1.3%
식품(백김치 1
 
1.3%
식품(즙장 1
 
1.3%
식품(순창고추장 1
 
1.3%
식품(도토리묵 1
 
1.3%
식품(청국장 1
 
1.3%
주류(병영소주 1
 
1.3%
Other values (66) 66
83.5%
2023-12-12T13:34:11.198089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 78
 
12.7%
) 78
 
12.7%
식 49
 
8.0%
주 47
 
7.7%
품 45
 
7.3%
류 32
 
5.2%
장 15
 
2.4%
엿 9
 
1.5%
차 8
 
1.3%
과 8
 
1.3%
Other values (124) 245
39.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 453
73.8%
Open Punctuation 78
 
12.7%
Close Punctuation 78
 
12.7%
Other Punctuation 4
 
0.7%
Space Separator 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
식 49
 
10.8%
주 47
 
10.4%
품 45
 
9.9%
류 32
 
7.1%
장 15
 
3.3%
엿 9
 
2.0%
차 8
 
1.8%
과 8
 
1.8%
초 7
 
1.5%
김 6
 
1.3%
Other values (120) 227
50.1%
Open Punctuation
ValueCountFrequency (%)
( 78
100.0%
Close Punctuation
ValueCountFrequency (%)
) 78
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 453
73.8%
Common 161
 
26.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
식 49
 
10.8%
주 47
 
10.4%
품 45
 
9.9%
류 32
 
7.1%
장 15
 
3.3%
엿 9
 
2.0%
차 8
 
1.8%
과 8
 
1.8%
초 7
 
1.5%
김 6
 
1.3%
Other values (120) 227
50.1%
Common
ValueCountFrequency (%)
( 78
48.4%
) 78
48.4%
, 4
 
2.5%
1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 453
73.8%
ASCII 161
 
26.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 78
48.4%
) 78
48.4%
, 4
 
2.5%
1
 
0.6%
Hangul
ValueCountFrequency (%)
식 49
 
10.8%
주 47
 
10.4%
품 45
 
9.9%
류 32
 
7.1%
장 15
 
3.3%
엿 9
 
2.0%
차 8
 
1.8%
과 8
 
1.8%
초 7
 
1.5%
김 6
 
1.3%
Other values (120) 227
50.1%

지정일
Categorical

Distinct27
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Memory size756.0 B
‘18. 11. 30
9 
‘16. 12. 08
7 
‘15. 09. 23
7 
‘13. 12. 03
7 
‘12. 10. 09
6 
Other values (22)
42 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique9 ?
Unique (%)11.5%

Sample

1st row’94. 08. 06
2nd row’94. 08. 06
3rd row’95. 07. 15
4th row’95. 07. 15
5th row’96. 04. 04

Common Values

ValueCountFrequency (%)
‘18. 11. 30 9
 
11.5%
‘16. 12. 08 7
 
9.0%
‘15. 09. 23 7
 
9.0%
‘13. 12. 03 7
 
9.0%
‘12. 10. 09 6
 
7.7%
‘14. 12. 23 5
 
6.4%
’96. 04. 04 4
 
5.1%
‘19. 12. 05 3
 
3.8%
‘10. 01. 04 3
 
3.8%
’97. 12. 15 2
 
2.6%
Other values (17) 25
32.1%

Length

2023-12-12T13:34:11.379469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12 30
 
12.8%
04 16
 
6.8%
09 16
 
6.8%
08 12
 
5.1%
23 12
 
5.1%
03 10
 
4.3%
05 10
 
4.3%
‘18 9
 
3.8%
30 9
 
3.8%
11 9
 
3.8%
Other values (29) 101
43.2%

소재지
Text

Distinct55
Distinct (%)70.5%
Missing0
Missing (%)0.0%
Memory size756.0 B
2023-12-12T13:34:11.647883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0512821
Min length5

Characters and Unicode

Total characters394
Distinct characters62
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

Unique40 ?
Unique (%)51.3%

Sample

1st row전북 전주
2nd row충남 금산
3rd row경북 안동
4th row경기 김포
5th row전북 전주
ValueCountFrequency (%)
전남 17
 
11.0%
경기 12
 
7.7%
전북 10
 
6.5%
경남 8
 
5.2%
충남 8
 
5.2%
경북 8
 
5.2%
담양 6
 
3.9%
하동 4
 
2.6%
강원 4
 
2.6%
전주 3
 
1.9%
Other values (56) 75
48.4%
2023-12-12T13:34:12.038615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
77
19.5%
남 37
 
9.4%
전 30
 
7.6%
경 29
 
7.4%
북 21
 
5.3%
주 18
 
4.6%
기 13
 
3.3%
충 12
 
3.0%
양 12
 
3.0%
천 12
 
3.0%
Other values (52) 133
33.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 317
80.5%
Space Separator 77
 
19.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 37
 
11.7%
전 30
 
9.5%
경 29
 
9.1%
북 21
 
6.6%
주 18
 
5.7%
기 13
 
4.1%
충 12
 
3.8%
양 12
 
3.8%
천 12
 
3.8%
강 8
 
2.5%
Other values (51) 125
39.4%
Space Separator
ValueCountFrequency (%)
77
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 317
80.5%
Common 77
 
19.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 37
 
11.7%
전 30
 
9.5%
경 29
 
9.1%
북 21
 
6.6%
주 18
 
5.7%
기 13
 
4.1%
충 12
 
3.8%
양 12
 
3.8%
천 12
 
3.8%
강 8
 
2.5%
Other values (51) 125
39.4%
Common
ValueCountFrequency (%)
77
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 317
80.5%
ASCII 77
 
19.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
77
100.0%
Hangul
ValueCountFrequency (%)
남 37
 
11.7%
전 30
 
9.5%
경 29
 
9.1%
북 21
 
6.6%
주 18
 
5.7%
기 13
 
4.1%
충 12
 
3.8%
양 12
 
3.8%
천 12
 
3.8%
강 8
 
2.5%
Other values (51) 125
39.4%

비 고
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size756.0 B
-
78 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 78
100.0%

Length

2023-12-12T13:34:12.177639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:34:12.276577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
78
100.0%

Correlations

2023-12-12T13:34:12.335942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정번호성 명보유기능지정일소재지
지정번호1.0001.0001.0001.0001.000
성 명1.0001.0001.0001.0001.000
보유기능1.0001.0001.0001.0000.987
지정일1.0001.0001.0001.0000.000
소재지1.0001.0000.9870.0001.000

Missing values

2023-12-12T13:34:08.779562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:34:08.881325image/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

지정번호성 명보유기능지정일소재지비 고
0제1호조영귀주류(송화백일주)’94. 08. 06전북 전주-
1제2호김창수주류(금산인삼주)’94. 08. 06충남 금산-
2제6호박재서주류(안동소주)’95. 07. 15경북 안동-
3제7호이기춘주류(문배주)’95. 07. 15경기 김포-
4제9호조정형주류(전주이강주)’96. 04. 04전북 전주-
5제10호유민자주류(옥로주)’96. 04. 04경기 용인-
6제11호임영순주류(구기자주)’96. 04. 04충남 청양-
7제12호최옥근주류(계명주)’96. 04. 04경기남양주-
8제13호남상란주류(가야곡왕주)’97. 12. 15충남 논산-
9제14호홍쌍리식품(매실농축액)’97. 12. 15전남 광양-
지정번호성 명보유기능지정일소재지비 고
68제78호조정숙장류(된장)‘18. 11. 30충북 청주-
69제79호김용세주류(연잎주)‘18. 11. 30충남 당진-
70제80호원이숙엿류(쌀엿)‘18. 11. 30전북 임실-
71제81호구경숙떡류(기정떡)‘18. 11. 30전남 화순-
72제82호박규완육류(가리구이)‘18. 11. 30전남 담양-
73제83호최송자엿류(쌀엿)‘18. 11. 30경북 울진-
74제84호김희숙주류(고소리술)‘18. 11. 30제주 서귀포-
75제85호김순옥엿류(찹쌀조이당조청)‘19. 12. 05전남 순천-
76제86호임경만식초류(보리식초)‘19. 12. 05경북 영천-
77제36-가호조종현장류(순창고추장)‘19. 12. 05전북 순창-