Overview

Dataset statistics

Number of variables6
Number of observations66
Missing cells19
Missing cells (%)4.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory51.0 B

Variable types

Numeric1
Categorical2
Text3

Dataset

Description식생활교육기관의 명칭, 주소, 연락처 등의 정보
Author농림축산식품부
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20150108000000000415

Alerts

지정번호 is highly overall correlated with 지정일High correlation
지정일 is highly overall correlated with 지정번호High correlation
유선번호1 has 19 (28.8%) missing valuesMissing
지정번호 has unique valuesUnique
기관명 has unique valuesUnique
주소 has unique valuesUnique

Reproduction

Analysis started2023-12-11 03:35:23.367878
Analysis finished2023-12-11 03:35:24.112091
Duration0.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지정번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct66
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.5
Minimum1
Maximum66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size726.0 B
2023-12-11T12:35:24.197820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.25
Q117.25
median33.5
Q349.75
95-th percentile62.75
Maximum66
Range65
Interquartile range (IQR)32.5

Descriptive statistics

Standard deviation19.196354
Coefficient of variation (CV)0.57302549
Kurtosis-1.2
Mean33.5
Median Absolute Deviation (MAD)16.5
Skewness0
Sum2211
Variance368.5
MonotonicityStrictly increasing
2023-12-11T12:35:24.381211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.5%
51 1
 
1.5%
37 1
 
1.5%
38 1
 
1.5%
39 1
 
1.5%
40 1
 
1.5%
41 1
 
1.5%
42 1
 
1.5%
43 1
 
1.5%
44 1
 
1.5%
Other values (56) 56
84.8%
ValueCountFrequency (%)
1 1
1.5%
2 1
1.5%
3 1
1.5%
4 1
1.5%
5 1
1.5%
6 1
1.5%
7 1
1.5%
8 1
1.5%
9 1
1.5%
10 1
1.5%
ValueCountFrequency (%)
66 1
1.5%
65 1
1.5%
64 1
1.5%
63 1
1.5%
62 1
1.5%
61 1
1.5%
60 1
1.5%
59 1
1.5%
58 1
1.5%
57 1
1.5%

지역
Categorical

Distinct16
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Memory size660.0 B
서울
14 
전북
8 
경기
7 
경북
5 
부산
4 
Other values (11)
28 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique2 ?
Unique (%)3.0%

Sample

1st row서울
2nd row부산
3rd row광주
4th row서울
5th row충남

Common Values

ValueCountFrequency (%)
서울 14
21.2%
전북 8
12.1%
경기 7
10.6%
경북 5
 
7.6%
부산 4
 
6.1%
충남 4
 
6.1%
인천 3
 
4.5%
충북 3
 
4.5%
경남 3
 
4.5%
강원 3
 
4.5%
Other values (6) 12
18.2%

Length

2023-12-11T12:35:24.542630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 14
21.2%
전북 8
12.1%
경기 7
10.6%
경북 5
 
7.6%
부산 4
 
6.1%
충남 4
 
6.1%
인천 3
 
4.5%
충북 3
 
4.5%
경남 3
 
4.5%
강원 3
 
4.5%
Other values (6) 12
18.2%

지정일
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Memory size660.0 B
2013-11-19
23 
2010-08-10
11 
2012-11-07
3 
2010-11-29
3 
2014-12-31
3 
Other values (16)
23 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique10 ?
Unique (%)15.2%

Sample

1st row2010-08-10
2nd row2010-08-10
3rd row2010-08-10
4th row2010-08-10
5th row2010-08-10

Common Values

ValueCountFrequency (%)
2013-11-19 23
34.8%
2010-08-10 11
16.7%
2012-11-07 3
 
4.5%
2010-11-29 3
 
4.5%
2014-12-31 3
 
4.5%
2011-04-08 3
 
4.5%
2015-07-28 2
 
3.0%
2020-07-10 2
 
3.0%
2021-06-04 2
 
3.0%
2010-12-24 2
 
3.0%
Other values (11) 12
18.2%

Length

2023-12-11T12:35:24.687132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2013-11-19 23
34.8%
2010-08-10 11
16.7%
2012-11-07 3
 
4.5%
2010-11-29 3
 
4.5%
2014-12-31 3
 
4.5%
2011-04-08 3
 
4.5%
2021-06-04 2
 
3.0%
2010-12-24 2
 
3.0%
2011-01-31 2
 
3.0%
2020-07-10 2
 
3.0%
Other values (11) 12
18.2%

기관명
Text

UNIQUE 

Distinct66
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size660.0 B
2023-12-11T12:35:24.984205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length20
Mean length11.439394
Min length4

Characters and Unicode

Total characters755
Distinct characters140
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)100.0%

Sample

1st row숙명여자대학교 한국음식연구원
2nd row부산교육대학교
3rd row광주교육대학교
4th row서울교육대학교 교육연수원
5th row공주교육대학교
ValueCountFrequency (%)
소비자생활협동조합 3
 
2.9%
아이쿱 2
 
1.9%
사범대학 2
 
1.9%
교육연수원 2
 
1.9%
산학협력단 2
 
1.9%
대한영양사협회 2
 
1.9%
농업기술센터 2
 
1.9%
식생활교육센터 2
 
1.9%
식생활교육기관 2
 
1.9%
숙명여자대학교 1
 
1.0%
Other values (83) 83
80.6%
2023-12-11T12:35:25.790583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 60
 
7.9%
학 42
 
5.6%
대 38
 
5.0%
38
 
5.0%
육 29
 
3.8%
생 28
 
3.7%
활 25
 
3.3%
식 25
 
3.3%
원 17
 
2.3%
협 15
 
2.0%
Other values (130) 438
58.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 702
93.0%
Space Separator 38
 
5.0%
Uppercase Letter 8
 
1.1%
Close Punctuation 3
 
0.4%
Open Punctuation 3
 
0.4%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 60
 
8.5%
학 42
 
6.0%
대 38
 
5.4%
육 29
 
4.1%
생 28
 
4.0%
활 25
 
3.6%
식 25
 
3.6%
원 17
 
2.4%
협 15
 
2.1%
연 15
 
2.1%
Other values (120) 408
58.1%
Uppercase Letter
ValueCountFrequency (%)
O 2
25.0%
C 2
25.0%
P 1
12.5%
Y 1
12.5%
W 1
12.5%
A 1
12.5%
Space Separator
ValueCountFrequency (%)
38
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
i 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 702
93.0%
Common 44
 
5.8%
Latin 9
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 60
 
8.5%
학 42
 
6.0%
대 38
 
5.4%
육 29
 
4.1%
생 28
 
4.0%
활 25
 
3.6%
식 25
 
3.6%
원 17
 
2.4%
협 15
 
2.1%
연 15
 
2.1%
Other values (120) 408
58.1%
Latin
ValueCountFrequency (%)
O 2
22.2%
C 2
22.2%
i 1
11.1%
P 1
11.1%
Y 1
11.1%
W 1
11.1%
A 1
11.1%
Common
ValueCountFrequency (%)
38
86.4%
) 3
 
6.8%
( 3
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 702
93.0%
ASCII 53
 
7.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 60
 
8.5%
학 42
 
6.0%
대 38
 
5.4%
육 29
 
4.1%
생 28
 
4.0%
활 25
 
3.6%
식 25
 
3.6%
원 17
 
2.4%
협 15
 
2.1%
연 15
 
2.1%
Other values (120) 408
58.1%
ASCII
ValueCountFrequency (%)
38
71.7%
) 3
 
5.7%
( 3
 
5.7%
O 2
 
3.8%
C 2
 
3.8%
i 1
 
1.9%
P 1
 
1.9%
Y 1
 
1.9%
W 1
 
1.9%
A 1
 
1.9%

유선번호1
Text

MISSING 

Distinct47
Distinct (%)100.0%
Missing19
Missing (%)28.8%
Memory size660.0 B
2023-12-11T12:35:26.086515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.93617
Min length11

Characters and Unicode

Total characters561
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)100.0%

Sample

1st row051-500-7285
2nd row062-520-4190
3rd row041-850-1373
4th row053-620-1395
5th row032-540-1280
ValueCountFrequency (%)
051-500-7285 1
 
2.1%
054-855-0127 1
 
2.1%
02-3290-5383 1
 
2.1%
051-343-2220 1
 
2.1%
053-564-9090 1
 
2.1%
032-516-2212 1
 
2.1%
031-426-6423 1
 
2.1%
031-870-3510 1
 
2.1%
031-272-9572 1
 
2.1%
031-577-5267 1
 
2.1%
Other values (37) 37
78.7%
2023-12-11T12:35:26.526924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 94
16.8%
0 85
15.2%
2 62
11.1%
3 57
10.2%
5 54
9.6%
1 48
8.6%
6 42
7.5%
4 40
7.1%
8 34
 
6.1%
7 27
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 467
83.2%
Dash Punctuation 94
 
16.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 85
18.2%
2 62
13.3%
3 57
12.2%
5 54
11.6%
1 48
10.3%
6 42
9.0%
4 40
8.6%
8 34
 
7.3%
7 27
 
5.8%
9 18
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 94
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 561
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 94
16.8%
0 85
15.2%
2 62
11.1%
3 57
10.2%
5 54
9.6%
1 48
8.6%
6 42
7.5%
4 40
7.1%
8 34
 
6.1%
7 27
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 561
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 94
16.8%
0 85
15.2%
2 62
11.1%
3 57
10.2%
5 54
9.6%
1 48
8.6%
6 42
7.5%
4 40
7.1%
8 34
 
6.1%
7 27
 
4.8%

주소
Text

UNIQUE 

Distinct66
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size660.0 B
2023-12-11T12:35:26.913308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length34
Mean length28.151515
Min length16

Characters and Unicode

Total characters1858
Distinct characters219
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

Unique66 ?
Unique (%)100.0%

Sample

1st row서울특별시 용산구 청파로 47길 100
2nd row부산광역시 연제구 교대로 24(거제동)
3rd row광주광역시 북구 필문대로 55(풍향동)
4th row서울특별시 서초구 서초중앙로 96 서울교육대학교 생활과학교육과
5th row충청남도 공주시 웅진로 27(봉활동)
ValueCountFrequency (%)
서울특별시 13
 
3.3%
경기도 7
 
1.8%
전라북도 7
 
1.8%
부산광역시 4
 
1.0%
4층 4
 
1.0%
대학로 4
 
1.0%
충청남도 4
 
1.0%
북구 4
 
1.0%
경상북도 4
 
1.0%
전라남도 3
 
0.8%
Other values (303) 336
86.2%
2023-12-11T12:35:27.590545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
328
 
17.7%
로 62
 
3.3%
시 59
 
3.2%
1 55
 
3.0%
구 43
 
2.3%
대 43
 
2.3%
도 40
 
2.2%
2 39
 
2.1%
학 35
 
1.9%
동 34
 
1.8%
Other values (209) 1120
60.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1198
64.5%
Space Separator 328
 
17.7%
Decimal Number 270
 
14.5%
Close Punctuation 19
 
1.0%
Open Punctuation 19
 
1.0%
Dash Punctuation 11
 
0.6%
Other Punctuation 7
 
0.4%
Uppercase Letter 6
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 62
 
5.2%
시 59
 
4.9%
구 43
 
3.6%
대 43
 
3.6%
도 40
 
3.3%
학 35
 
2.9%
동 34
 
2.8%
서 29
 
2.4%
교 26
 
2.2%
주 25
 
2.1%
Other values (188) 802
66.9%
Decimal Number
ValueCountFrequency (%)
1 55
20.4%
2 39
14.4%
5 29
10.7%
0 28
10.4%
4 24
8.9%
3 22
 
8.1%
9 21
 
7.8%
7 20
 
7.4%
6 18
 
6.7%
8 14
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
K 1
16.7%
A 1
16.7%
C 1
16.7%
W 1
16.7%
Y 1
16.7%
T 1
16.7%
Space Separator
ValueCountFrequency (%)
328
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1198
64.5%
Common 654
35.2%
Latin 6
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 62
 
5.2%
시 59
 
4.9%
구 43
 
3.6%
대 43
 
3.6%
도 40
 
3.3%
학 35
 
2.9%
동 34
 
2.8%
서 29
 
2.4%
교 26
 
2.2%
주 25
 
2.1%
Other values (188) 802
66.9%
Common
ValueCountFrequency (%)
328
50.2%
1 55
 
8.4%
2 39
 
6.0%
5 29
 
4.4%
0 28
 
4.3%
4 24
 
3.7%
3 22
 
3.4%
9 21
 
3.2%
7 20
 
3.1%
) 19
 
2.9%
Other values (5) 69
 
10.6%
Latin
ValueCountFrequency (%)
K 1
16.7%
A 1
16.7%
C 1
16.7%
W 1
16.7%
Y 1
16.7%
T 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1198
64.5%
ASCII 660
35.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
328
49.7%
1 55
 
8.3%
2 39
 
5.9%
5 29
 
4.4%
0 28
 
4.2%
4 24
 
3.6%
3 22
 
3.3%
9 21
 
3.2%
7 20
 
3.0%
) 19
 
2.9%
Other values (11) 75
 
11.4%
Hangul
ValueCountFrequency (%)
로 62
 
5.2%
시 59
 
4.9%
구 43
 
3.6%
대 43
 
3.6%
도 40
 
3.3%
학 35
 
2.9%
동 34
 
2.8%
서 29
 
2.4%
교 26
 
2.2%
주 25
 
2.1%
Other values (188) 802
66.9%

Interactions

2023-12-11T12:35:23.790965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:35:27.740433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정번호지역지정일기관명유선번호1주소
지정번호1.0000.6230.9071.0001.0001.000
지역0.6231.0000.4631.0001.0001.000
지정일0.9070.4631.0001.0001.0001.000
기관명1.0001.0001.0001.0001.0001.000
유선번호11.0001.0001.0001.0001.0001.000
주소1.0001.0001.0001.0001.0001.000
2023-12-11T12:35:27.858679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정일지역
지정일1.0000.131
지역0.1311.000
2023-12-11T12:35:27.965232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정번호지역지정일
지정번호1.0000.2830.585
지역0.2831.0000.131
지정일0.5850.1311.000

Missing values

2023-12-11T12:35:23.922651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:35:24.060363image/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

지정번호지역지정일기관명유선번호1주소
01서울2010-08-10숙명여자대학교 한국음식연구원<NA>서울특별시 용산구 청파로 47길 100
12부산2010-08-10부산교육대학교051-500-7285부산광역시 연제구 교대로 24(거제동)
23광주2010-08-10광주교육대학교062-520-4190광주광역시 북구 필문대로 55(풍향동)
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