Overview

Dataset statistics

Number of variables7
Number of observations94
Missing cells94
Missing cells (%)14.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.5 KiB
Average record size in memory59.4 B

Variable types

Numeric1
Text4
Categorical1
Unsupported1

Dataset

Description전라북도_대한민국식품명인지정현황_20200414
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=204416

Alerts

구분 is highly overall correlated with 지정일High correlation
지정일 is highly overall correlated with 구분High correlation
비 고 has 94 (100.0%) missing valuesMissing
구분 has unique valuesUnique
지정번호 has unique valuesUnique
성 명 has unique valuesUnique
비 고 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 00:45:29.774719
Analysis finished2024-03-14 00:45:30.538692
Duration0.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct94
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.5
Minimum1
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size978.0 B
2024-03-14T09:45:30.608056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.65
Q124.25
median47.5
Q370.75
95-th percentile89.35
Maximum94
Range93
Interquartile range (IQR)46.5

Descriptive statistics

Standard deviation27.279418
Coefficient of variation (CV)0.57430354
Kurtosis-1.2
Mean47.5
Median Absolute Deviation (MAD)23.5
Skewness0
Sum4465
Variance744.16667
MonotonicityStrictly increasing
2024-03-14T09:45:30.727264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.1%
61 1
 
1.1%
70 1
 
1.1%
69 1
 
1.1%
68 1
 
1.1%
67 1
 
1.1%
66 1
 
1.1%
65 1
 
1.1%
64 1
 
1.1%
63 1
 
1.1%
Other values (84) 84
89.4%
ValueCountFrequency (%)
1 1
1.1%
2 1
1.1%
3 1
1.1%
4 1
1.1%
5 1
1.1%
6 1
1.1%
7 1
1.1%
8 1
1.1%
9 1
1.1%
10 1
1.1%
ValueCountFrequency (%)
94 1
1.1%
93 1
1.1%
92 1
1.1%
91 1
1.1%
90 1
1.1%
89 1
1.1%
88 1
1.1%
87 1
1.1%
86 1
1.1%
85 1
1.1%

지정번호
Text

UNIQUE 

Distinct94
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size884.0 B
2024-03-14T09:45:30.962285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.0638298
Min length3

Characters and Unicode

Total characters382
Distinct characters17
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

Unique94 ?
Unique (%)100.0%

Sample

1st row제1호
2nd row제2호
3rd row제3호
4th row제4호
5th row제5호
ValueCountFrequency (%)
수산 6
 
6.0%
제6호 2
 
2.0%
제2호 2
 
2.0%
제3호 2
 
2.0%
제1호 2
 
2.0%
제4호 2
 
2.0%
제5호 2
 
2.0%
제7호 1
 
1.0%
제67호 1
 
1.0%
제65호 1
 
1.0%
Other values (79) 79
79.0%
2024-03-14T09:45:31.369091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 94
24.6%
호 94
24.6%
3 21
 
5.5%
4 21
 
5.5%
6 21
 
5.5%
2 20
 
5.2%
5 20
 
5.2%
1 20
 
5.2%
7 18
 
4.7%
8 15
 
3.9%
Other values (7) 38
9.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 202
52.9%
Decimal Number 172
45.0%
Space Separator 6
 
1.6%
Dash Punctuation 2
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 21
12.2%
4 21
12.2%
6 21
12.2%
2 20
11.6%
5 20
11.6%
1 20
11.6%
7 18
10.5%
8 15
8.7%
0 8
 
4.7%
9 8
 
4.7%
Other Letter
ValueCountFrequency (%)
제 94
46.5%
호 94
46.5%
수 6
 
3.0%
산 6
 
3.0%
가 2
 
1.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 202
52.9%
Common 180
47.1%

Most frequent character per script

Common
ValueCountFrequency (%)
3 21
11.7%
4 21
11.7%
6 21
11.7%
2 20
11.1%
5 20
11.1%
1 20
11.1%
7 18
10.0%
8 15
8.3%
0 8
 
4.4%
9 8
 
4.4%
Other values (2) 8
 
4.4%
Hangul
ValueCountFrequency (%)
제 94
46.5%
호 94
46.5%
수 6
 
3.0%
산 6
 
3.0%
가 2
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 202
52.9%
ASCII 180
47.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 94
46.5%
호 94
46.5%
수 6
 
3.0%
산 6
 
3.0%
가 2
 
1.0%
ASCII
ValueCountFrequency (%)
3 21
11.7%
4 21
11.7%
6 21
11.7%
2 20
11.1%
5 20
11.1%
1 20
11.1%
7 18
10.0%
8 15
8.3%
0 8
 
4.4%
9 8
 
4.4%
Other values (2) 8
 
4.4%

성 명
Text

UNIQUE 

Distinct94
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size884.0 B
2024-03-14T09:45:31.613510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters282
Distinct characters101
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

Unique94 ?
Unique (%)100.0%

Sample

1st row조영귀
2nd row김창수
3rd row이한영
4th row지복남
5th row이기양
ValueCountFrequency (%)
조영귀 1
 
1.1%
이인자 1
 
1.1%
정승환 1
 
1.1%
윤미월 1
 
1.1%
백정자 1
 
1.1%
강순옥 1
 
1.1%
김영근 1
 
1.1%
서분례 1
 
1.1%
김견식 1
 
1.1%
안복자 1
 
1.1%
Other values (84) 84
89.4%
2024-03-14T09:45:31.968922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 20
 
7.1%
자 13
 
4.6%
순 11
 
3.9%
정 10
 
3.5%
영 10
 
3.5%
숙 9
 
3.2%
이 9
 
3.2%
옥 8
 
2.8%
임 8
 
2.8%
수 6
 
2.1%
Other values (91) 178
63.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 282
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 20
 
7.1%
자 13
 
4.6%
순 11
 
3.9%
정 10
 
3.5%
영 10
 
3.5%
숙 9
 
3.2%
이 9
 
3.2%
옥 8
 
2.8%
임 8
 
2.8%
수 6
 
2.1%
Other values (91) 178
63.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 282
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 20
 
7.1%
자 13
 
4.6%
순 11
 
3.9%
정 10
 
3.5%
영 10
 
3.5%
숙 9
 
3.2%
이 9
 
3.2%
옥 8
 
2.8%
임 8
 
2.8%
수 6
 
2.1%
Other values (91) 178
63.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 282
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 20
 
7.1%
자 13
 
4.6%
순 11
 
3.9%
정 10
 
3.5%
영 10
 
3.5%
숙 9
 
3.2%
이 9
 
3.2%
옥 8
 
2.8%
임 8
 
2.8%
수 6
 
2.1%
Other values (91) 178
63.1%

보유기능
Text

Distinct84
Distinct (%)89.4%
Missing0
Missing (%)0.0%
Memory size884.0 B
2024-03-14T09:45:32.151198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length8.0212766
Min length6

Characters and Unicode

Total characters754
Distinct characters145
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

Unique74 ?
Unique (%)78.7%

Sample

1st row주류(송화백일주)
2nd row주류(금산인삼주)
3rd row주류(옥선주)
4th row주류(계룡백일주)
5th row주류(감홍로주)
ValueCountFrequency (%)
식품(유과 2
 
2.0%
주류(김천과하주 2
 
2.0%
말차 2
 
2.0%
식품(황차 2
 
2.0%
장류(순창고추장 2
 
2.0%
엿류(쌀엿 2
 
2.0%
식품(포기김치 2
 
2.0%
주류(계룡백일주 2
 
2.0%
주류(옥선주 2
 
2.0%
식품(죽염 2
 
2.0%
Other values (79) 80
80.0%
2024-03-14T09:45:32.697985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 94
 
12.5%
) 94
 
12.5%
주 60
 
8.0%
식 50
 
6.6%
류 49
 
6.5%
품 45
 
6.0%
장 23
 
3.1%
차 10
 
1.3%
엿 10
 
1.3%
과 10
 
1.3%
Other values (135) 309
41.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 552
73.2%
Open Punctuation 94
 
12.5%
Close Punctuation 94
 
12.5%
Space Separator 7
 
0.9%
Other Punctuation 6
 
0.8%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 60
 
10.9%
식 50
 
9.1%
류 49
 
8.9%
품 45
 
8.2%
장 23
 
4.2%
차 10
 
1.8%
엿 10
 
1.8%
과 10
 
1.8%
초 9
 
1.6%
김 8
 
1.4%
Other values (130) 278
50.4%
Open Punctuation
ValueCountFrequency (%)
( 94
100.0%
Close Punctuation
ValueCountFrequency (%)
) 94
100.0%
Space Separator
ValueCountFrequency (%)
7
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 552
73.2%
Common 202
 
26.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 60
 
10.9%
식 50
 
9.1%
류 49
 
8.9%
품 45
 
8.2%
장 23
 
4.2%
차 10
 
1.8%
엿 10
 
1.8%
과 10
 
1.8%
초 9
 
1.6%
김 8
 
1.4%
Other values (130) 278
50.4%
Common
ValueCountFrequency (%)
( 94
46.5%
) 94
46.5%
7
 
3.5%
, 6
 
3.0%
- 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 552
73.2%
ASCII 202
 
26.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 94
46.5%
) 94
46.5%
7
 
3.5%
, 6
 
3.0%
- 1
 
0.5%
Hangul
ValueCountFrequency (%)
주 60
 
10.9%
식 50
 
9.1%
류 49
 
8.9%
품 45
 
8.2%
장 23
 
4.2%
차 10
 
1.8%
엿 10
 
1.8%
과 10
 
1.8%
초 9
 
1.6%
김 8
 
1.4%
Other values (130) 278
50.4%

지정일
Categorical

HIGH CORRELATION 

Distinct37
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Memory size884.0 B
`18. 11. 30.
9 
‘13. 12. 03
8 
‘15. 09. 23
8 
`16. 12. 8
7 
’12. 10. 09
6 
Other values (32)
56 

Length

Max length17
Median length11
Mean length11.478723
Min length10

Unique

Unique16 ?
Unique (%)17.0%

Sample

1st row’94. 08. 06
2nd row’94. 08. 06
3rd row’00. 8월 지정해제(사망)
4th row’09. 11월 지정해제(사망)
5th row’00. 10월 지정해제(사망)

Common Values

ValueCountFrequency (%)
`18. 11. 30. 9
 
9.6%
‘13. 12. 03 8
 
8.5%
‘15. 09. 23 8
 
8.5%
`16. 12. 8 7
 
7.4%
’12. 10. 09 6
 
6.4%
‘14. 12. 23 5
 
5.3%
’96. 04. 04 4
 
4.3%
’16. 12. 1 3
 
3.2%
`19. 12. 5. 3
 
3.2%
’10. 01. 04 3
 
3.2%
Other values (27) 38
40.4%

Length

2024-03-14T09:45:32.811617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12 34
 
12.1%
09 18
 
6.4%
04 16
 
5.7%
23 15
 
5.3%
18 11
 
3.9%
03 11
 
3.9%
11 10
 
3.5%
’12 10
 
3.5%
30 9
 
3.2%
‘13 8
 
2.8%
Other values (43) 140
49.6%

소재지
Text

Distinct61
Distinct (%)64.9%
Missing0
Missing (%)0.0%
Memory size884.0 B
2024-03-14T09:45:33.025550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0531915
Min length3

Characters and Unicode

Total characters475
Distinct characters66
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

Unique38 ?
Unique (%)40.4%

Sample

1st row전북 완주
2nd row충남 금산
3rd row강원 홍천
4th row충남 공주
5th row경기 파주
ValueCountFrequency (%)
전남 18
 
9.6%
경기 14
 
7.5%
전북 13
 
7.0%
충남 12
 
6.4%
경북 11
 
5.9%
경남 9
 
4.8%
담양 6
 
3.2%
강원 5
 
2.7%
광주 4
 
2.1%
하동 4
 
2.1%
Other values (62) 91
48.7%
2024-03-14T09:45:33.336754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
94
19.8%
남 43
 
9.1%
경 34
 
7.2%
전 33
 
6.9%
북 27
 
5.7%
주 24
 
5.1%
충 16
 
3.4%
기 14
 
2.9%
천 14
 
2.9%
양 13
 
2.7%
Other values (56) 163
34.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 381
80.2%
Space Separator 94
 
19.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 43
 
11.3%
경 34
 
8.9%
전 33
 
8.7%
북 27
 
7.1%
주 24
 
6.3%
충 16
 
4.2%
기 14
 
3.7%
천 14
 
3.7%
양 13
 
3.4%
동 10
 
2.6%
Other values (55) 153
40.2%
Space Separator
ValueCountFrequency (%)
94
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 381
80.2%
Common 94
 
19.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 43
 
11.3%
경 34
 
8.9%
전 33
 
8.7%
북 27
 
7.1%
주 24
 
6.3%
충 16
 
4.2%
기 14
 
3.7%
천 14
 
3.7%
양 13
 
3.4%
동 10
 
2.6%
Other values (55) 153
40.2%
Common
ValueCountFrequency (%)
94
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 381
80.2%
ASCII 94
 
19.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
94
100.0%
Hangul
ValueCountFrequency (%)
남 43
 
11.3%
경 34
 
8.9%
전 33
 
8.7%
북 27
 
7.1%
주 24
 
6.3%
충 16
 
4.2%
기 14
 
3.7%
천 14
 
3.7%
양 13
 
3.4%
동 10
 
2.6%
Other values (55) 153
40.2%

비 고
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing94
Missing (%)100.0%
Memory size978.0 B

Interactions

2024-03-14T09:45:30.319731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T09:45:33.436574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분지정번호성 명보유기능지정일소재지
구분1.0001.0001.0000.8930.9740.000
지정번호1.0001.0001.0001.0001.0001.000
성 명1.0001.0001.0001.0001.0001.000
보유기능0.8931.0001.0001.0000.6660.994
지정일0.9741.0001.0000.6661.0000.000
소재지0.0001.0001.0000.9940.0001.000
2024-03-14T09:45:33.524818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분지정일
구분1.0000.685
지정일0.6851.000

Missing values

2024-03-14T09:45:30.407550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T09:45:30.504200image/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

구분지정번호성 명보유기능지정일소재지비 고
01제1호조영귀주류(송화백일주)’94. 08. 06전북 완주<NA>
12제2호김창수주류(금산인삼주)’94. 08. 06충남 금산<NA>
23제3호이한영주류(옥선주)’00. 8월 지정해제(사망)강원 홍천<NA>
34제4호지복남주류(계룡백일주)’09. 11월 지정해제(사망)충남 공주<NA>
45제5호이기양주류(감홍로주)’00. 10월 지정해제(사망)경기 파주<NA>
56제6호박재서주류(안동소주)’95. 07. 15경북 안동<NA>
67제7호이기춘주류(문배주)’95. 07. 15경기 김포<NA>
78제8호송재성주류(김천과하주)’99. 6월 지정해제(사망)경북 김천<NA>
89제9호조정형주류(전주이강주)’96. 04. 04전북 전주<NA>
910제10호유민자주류(옥로주)’96. 04. 04경기 용인<NA>
구분지정번호성 명보유기능지정일소재지비 고
8485제84호김희숙주류(고소리술)`18. 11. 30.제주 서귀포<NA>
8586제36-가호조종현장류(순창고추장)`19. 12. 5.전북 순창<NA>
8687제85호김순옥엿류(찹쌀조이당조청)`19. 12. 5.전남 순천<NA>
8788제86호임경만식초류(보리식초)`19. 12. 5.경북 영천<NA>
8889수산 제1호김광자식품(어란)‘99. 11. 27제주도<NA>
8990수산 제2호이영자식품(제주옥돔)‘12. 05. 21전남 영암<NA>
9091수산 제3호정락현식품(죽염)‘15. 09. 23전북 부안<NA>
9192수산 제4호김윤세식품(죽염)’16. 12. 1경남 함양<NA>
9293수산 제5호김정배식품(새우젓)’16. 12. 1충남 아산<NA>
9394수산 제6호유명근식품(어리굴젓)’16. 12. 1충남 서산<NA>