Overview

Dataset statistics

Number of variables8
Number of observations55
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory67.4 B

Variable types

Numeric1
Categorical5
Text2

Dataset

Description광주광역시 뷰티우수업소 현황 정보입니다.□ 목적 : 뷰티업소의 대표성 및 자긍심 고취를 통하여 뷰티관광 콘텐츠와 연계하여 뷰티문화 허브 조성□ 선정방법 : 공모 후 신청업소에 대한 평가를 통해 선정 * 공고 ⇒ 업소 신청 ⇒ 1차 평가(구청주관) ⇒ 2차 평가(시청주관)□ 신청자격 : 광주광역시에 소재한 뷰티업소□ 뷰티 우수업소 지원 : 지정서 교부 및 표지판 부착, 홍보 및 홍보물 등 제공 등
Author광주광역시
URLhttps://www.data.go.kr/data/15121537/fileData.do

Alerts

연번 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 연번High correlation
대한민국미용명장여부 is highly imbalanced (86.9%)Imbalance
광주시미용명장여부 is highly imbalanced (77.5%)Imbalance
백년가게여부 is highly imbalanced (77.5%)Imbalance
연번 has unique valuesUnique
주소 has unique valuesUnique

Reproduction

Analysis started2024-04-17 13:23:00.307561
Analysis finished2024-04-17 13:23:00.921657
Duration0.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28
Minimum1
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2024-04-17T22:23:00.987915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.7
Q114.5
median28
Q341.5
95-th percentile52.3
Maximum55
Range54
Interquartile range (IQR)27

Descriptive statistics

Standard deviation16.02082
Coefficient of variation (CV)0.57217214
Kurtosis-1.2
Mean28
Median Absolute Deviation (MAD)14
Skewness0
Sum1540
Variance256.66667
MonotonicityStrictly increasing
2024-04-17T22:23:01.113926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.8%
2 1
 
1.8%
31 1
 
1.8%
32 1
 
1.8%
33 1
 
1.8%
34 1
 
1.8%
35 1
 
1.8%
36 1
 
1.8%
37 1
 
1.8%
38 1
 
1.8%
Other values (45) 45
81.8%
ValueCountFrequency (%)
1 1
1.8%
2 1
1.8%
3 1
1.8%
4 1
1.8%
5 1
1.8%
6 1
1.8%
7 1
1.8%
8 1
1.8%
9 1
1.8%
10 1
1.8%
ValueCountFrequency (%)
55 1
1.8%
54 1
1.8%
53 1
1.8%
52 1
1.8%
51 1
1.8%
50 1
1.8%
49 1
1.8%
48 1
1.8%
47 1
1.8%
46 1
1.8%

구분
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Memory size572.0 B
동구
25 
서구
9 
남구
7 
북구
7 
광산구
6 

Length

Max length3
Median length2
Mean length2.1272727
Min length2

Unique

Unique1 ?
Unique (%)1.8%

Sample

1st row동구
2nd row동구
3rd row동구
4th row동구
5th row동구

Common Values

ValueCountFrequency (%)
동구 25
45.5%
서구 9
 
16.4%
남구 7
 
12.7%
북구 7
 
12.7%
광산구 6
 
10.9%
동구 1
 
1.8%

Length

2024-04-17T22:23:01.230495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T22:23:01.328064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동구 26
47.3%
서구 9
 
16.4%
남구 7
 
12.7%
북구 7
 
12.7%
광산구 6
 
10.9%

자치구
Categorical

Distinct5
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size572.0 B
헤어
38 
피부
7 
메이크업
5 
네일
4 
일반
 
1

Length

Max length4
Median length2
Mean length2.1818182
Min length2

Unique

Unique1 ?
Unique (%)1.8%

Sample

1st row헤어
2nd row헤어
3rd row헤어
4th row헤어
5th row헤어

Common Values

ValueCountFrequency (%)
헤어 38
69.1%
피부 7
 
12.7%
메이크업 5
 
9.1%
네일 4
 
7.3%
일반 1
 
1.8%

Length

2024-04-17T22:23:01.441154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T22:23:01.553521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
헤어 38
69.1%
피부 7
 
12.7%
메이크업 5
 
9.1%
네일 4
 
7.3%
일반 1
 
1.8%

업체명
Text

Distinct54
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size572.0 B
2024-04-17T22:23:01.752592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length6.9818182
Min length3

Characters and Unicode

Total characters384
Distinct characters161
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

Unique53 ?
Unique (%)96.4%

Sample

1st row한울이미용실
2nd row이철헤어커커광주롯데백화점점
3rd row단청헤어 스튜디오
4th row세븐인치
5th row마루한 헤어
ValueCountFrequency (%)
리안헤어 2
 
3.0%
이철헤어커커광주롯데백화점점 1
 
1.5%
모이찌 1
 
1.5%
가이아헤어 1
 
1.5%
더아름헤어 1
 
1.5%
선숙현뷰티밸리 1
 
1.5%
문앤문 1
 
1.5%
헤어스토리 1
 
1.5%
박승철헤어스튜디오 1
 
1.5%
웨디아 1
 
1.5%
Other values (55) 55
83.3%
2024-04-17T22:23:02.092868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
헤 28
 
7.3%
어 27
 
7.0%
스 14
 
3.6%
이 13
 
3.4%
리 12
 
3.1%
11
 
2.9%
주 6
 
1.6%
점 6
 
1.6%
롱 5
 
1.3%
살 5
 
1.3%
Other values (151) 257
66.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 326
84.9%
Uppercase Letter 24
 
6.2%
Space Separator 11
 
2.9%
Lowercase Letter 10
 
2.6%
Open Punctuation 4
 
1.0%
Close Punctuation 4
 
1.0%
Other Punctuation 3
 
0.8%
Decimal Number 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
헤 28
 
8.6%
어 27
 
8.3%
스 14
 
4.3%
이 13
 
4.0%
리 12
 
3.7%
주 6
 
1.8%
점 6
 
1.8%
롱 5
 
1.5%
살 5
 
1.5%
가 5
 
1.5%
Other values (127) 205
62.9%
Uppercase Letter
ValueCountFrequency (%)
A 5
20.8%
R 5
20.8%
N 3
12.5%
H 2
 
8.3%
I 2
 
8.3%
U 2
 
8.3%
S 1
 
4.2%
J 1
 
4.2%
O 1
 
4.2%
B 1
 
4.2%
Lowercase Letter
ValueCountFrequency (%)
r 3
30.0%
i 2
20.0%
a 2
20.0%
h 1
 
10.0%
o 1
 
10.0%
m 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
& 2
66.7%
: 1
33.3%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
1 1
50.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 326
84.9%
Latin 34
 
8.9%
Common 24
 
6.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
헤 28
 
8.6%
어 27
 
8.3%
스 14
 
4.3%
이 13
 
4.0%
리 12
 
3.7%
주 6
 
1.8%
점 6
 
1.8%
롱 5
 
1.5%
살 5
 
1.5%
가 5
 
1.5%
Other values (127) 205
62.9%
Latin
ValueCountFrequency (%)
A 5
14.7%
R 5
14.7%
r 3
8.8%
N 3
8.8%
i 2
 
5.9%
a 2
 
5.9%
H 2
 
5.9%
I 2
 
5.9%
U 2
 
5.9%
S 1
 
2.9%
Other values (7) 7
20.6%
Common
ValueCountFrequency (%)
11
45.8%
( 4
 
16.7%
) 4
 
16.7%
& 2
 
8.3%
2 1
 
4.2%
1 1
 
4.2%
: 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 326
84.9%
ASCII 58
 
15.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
헤 28
 
8.6%
어 27
 
8.3%
스 14
 
4.3%
이 13
 
4.0%
리 12
 
3.7%
주 6
 
1.8%
점 6
 
1.8%
롱 5
 
1.5%
살 5
 
1.5%
가 5
 
1.5%
Other values (127) 205
62.9%
ASCII
ValueCountFrequency (%)
11
19.0%
A 5
 
8.6%
R 5
 
8.6%
( 4
 
6.9%
) 4
 
6.9%
r 3
 
5.2%
N 3
 
5.2%
i 2
 
3.4%
a 2
 
3.4%
& 2
 
3.4%
Other values (14) 17
29.3%

주소
Text

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size572.0 B
2024-04-17T22:23:02.376002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length32
Mean length26.145455
Min length14

Characters and Unicode

Total characters1438
Distinct characters118
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)100.0%

Sample

1st row광주광역시 동구 동명로67번길 17, 1층 (지산동)
2nd row광주광역시 동구 독립로 268, 10층 (대인동)
3rd row광주광역시 동구 무등로 301, 1,2층 (계림동)
4th row광주광역시 동구 경양로 234, 108동 2층 424,425호 (계림동, 광주 그랜드센트럴)
5th row광주광역시 동구 중앙로160번길 15-3, 2층 (황금동)
ValueCountFrequency (%)
광주광역시 55
 
18.5%
동구 26
 
8.7%
1층 11
 
3.7%
2층 10
 
3.4%
서구 9
 
3.0%
북구 7
 
2.3%
남구 7
 
2.3%
광산구 5
 
1.7%
서석로 4
 
1.3%
수완동 4
 
1.3%
Other values (134) 160
53.7%
2024-04-17T22:23:02.795126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
243
 
16.9%
광 122
 
8.5%
1 69
 
4.8%
동 61
 
4.2%
로 61
 
4.2%
주 58
 
4.0%
구 56
 
3.9%
역 55
 
3.8%
시 55
 
3.8%
2 43
 
3.0%
Other values (108) 615
42.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 810
56.3%
Decimal Number 256
 
17.8%
Space Separator 243
 
16.9%
Other Punctuation 42
 
2.9%
Open Punctuation 37
 
2.6%
Close Punctuation 37
 
2.6%
Dash Punctuation 13
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 122
15.1%
동 61
 
7.5%
로 61
 
7.5%
주 58
 
7.2%
구 56
 
6.9%
역 55
 
6.8%
시 55
 
6.8%
층 38
 
4.7%
서 18
 
2.2%
번 14
 
1.7%
Other values (93) 272
33.6%
Decimal Number
ValueCountFrequency (%)
1 69
27.0%
2 43
16.8%
3 31
12.1%
0 21
 
8.2%
4 20
 
7.8%
9 15
 
5.9%
5 15
 
5.9%
8 15
 
5.9%
6 14
 
5.5%
7 13
 
5.1%
Space Separator
ValueCountFrequency (%)
243
100.0%
Other Punctuation
ValueCountFrequency (%)
, 42
100.0%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%
Close Punctuation
ValueCountFrequency (%)
) 37
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 810
56.3%
Common 628
43.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 122
15.1%
동 61
 
7.5%
로 61
 
7.5%
주 58
 
7.2%
구 56
 
6.9%
역 55
 
6.8%
시 55
 
6.8%
층 38
 
4.7%
서 18
 
2.2%
번 14
 
1.7%
Other values (93) 272
33.6%
Common
ValueCountFrequency (%)
243
38.7%
1 69
 
11.0%
2 43
 
6.8%
, 42
 
6.7%
( 37
 
5.9%
) 37
 
5.9%
3 31
 
4.9%
0 21
 
3.3%
4 20
 
3.2%
9 15
 
2.4%
Other values (5) 70
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 810
56.3%
ASCII 628
43.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
243
38.7%
1 69
 
11.0%
2 43
 
6.8%
, 42
 
6.7%
( 37
 
5.9%
) 37
 
5.9%
3 31
 
4.9%
0 21
 
3.3%
4 20
 
3.2%
9 15
 
2.4%
Other values (5) 70
 
11.1%
Hangul
ValueCountFrequency (%)
광 122
15.1%
동 61
 
7.5%
로 61
 
7.5%
주 58
 
7.2%
구 56
 
6.9%
역 55
 
6.8%
시 55
 
6.8%
층 38
 
4.7%
서 18
 
2.2%
번 14
 
1.7%
Other values (93) 272
33.6%

대한민국미용명장여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size572.0 B
X
54 
O
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.8%

Sample

1st rowO
2nd rowX
3rd rowX
4th rowX
5th rowX

Common Values

ValueCountFrequency (%)
X 54
98.2%
O 1
 
1.8%

Length

2024-04-17T22:23:02.916361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T22:23:02.999646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
x 54
98.2%
o 1
 
1.8%

광주시미용명장여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size572.0 B
X
53 
O
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowX
2nd rowX
3rd rowX
4th rowX
5th rowX

Common Values

ValueCountFrequency (%)
X 53
96.4%
O 2
 
3.6%

Length

2024-04-17T22:23:03.081450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T22:23:03.162140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
x 53
96.4%
o 2
 
3.6%

백년가게여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size572.0 B
X
53 
O
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowX
2nd rowX
3rd rowX
4th rowX
5th rowX

Common Values

ValueCountFrequency (%)
X 53
96.4%
O 2
 
3.6%

Length

2024-04-17T22:23:03.246114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T22:23:03.328049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
x 53
96.4%
o 2
 
3.6%

Interactions

2024-04-17T22:23:00.691353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T22:23:03.389546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분자치구업체명주소대한민국미용명장여부광주시미용명장여부백년가게여부
연번1.0000.9120.4550.9331.0000.0000.1120.112
구분0.9121.0000.2090.9611.0000.0000.0000.000
자치구0.4550.2091.0001.0001.0000.0000.0000.000
업체명0.9330.9611.0001.0001.0001.0001.0001.000
주소1.0001.0001.0001.0001.0001.0001.0001.000
대한민국미용명장여부0.0000.0000.0001.0001.0001.0000.0000.000
광주시미용명장여부0.1120.0000.0001.0001.0000.0001.0000.000
백년가게여부0.1120.0000.0001.0001.0000.0000.0001.000
2024-04-17T22:23:03.495381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치구광주시미용명장여부대한민국미용명장여부백년가게여부구분
자치구1.0000.0000.0000.0000.135
광주시미용명장여부0.0001.0000.0000.0000.000
대한민국미용명장여부0.0000.0001.0000.0000.000
백년가게여부0.0000.0000.0001.0000.000
구분0.1350.0000.0000.0001.000
2024-04-17T22:23:03.593448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분자치구대한민국미용명장여부광주시미용명장여부백년가게여부
연번1.0000.7330.2210.0000.0570.057
구분0.7331.0000.1350.0000.0000.000
자치구0.2210.1351.0000.0000.0000.000
대한민국미용명장여부0.0000.0000.0001.0000.0000.000
광주시미용명장여부0.0570.0000.0000.0001.0000.000
백년가게여부0.0570.0000.0000.0000.0001.000

Missing values

2024-04-17T22:23:00.778942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T22:23:00.879507image/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

연번구분자치구업체명주소대한민국미용명장여부광주시미용명장여부백년가게여부
01동구헤어한울이미용실광주광역시 동구 동명로67번길 17, 1층 (지산동)OXX
12동구헤어이철헤어커커광주롯데백화점점광주광역시 동구 독립로 268, 10층 (대인동)XXX
23동구헤어단청헤어 스튜디오광주광역시 동구 무등로 301, 1,2층 (계림동)XXX
34동구헤어세븐인치광주광역시 동구 경양로 234, 108동 2층 424,425호 (계림동, 광주 그랜드센트럴)XXX
45동구헤어마루한 헤어광주광역시 동구 중앙로160번길 15-3, 2층 (황금동)XXX
56동구헤어인살롱광주광역시 동구 경양로 234, 광주 그랜드센트럴 119동 1층 807호XXX
67동구헤어제시뷰티광주광역시 동구 산수길39번길 30-1, 지하 1층 (계림동)XXX
78동구헤어제이주니헤어(J주니HAIR)광주광역시 동구 용산2로 3, 조은프라자타워 2층 203호, 204호 (용산동)XXX
89동구헤어이유헤어(2U헤어)광주광역시 동구 서석로 34, 2층 (광산동)XXX
910동구헤어소호 헤어샵광주광역시 동구 중앙로 162-1, 2층 (황금동)XXX
연번구분자치구업체명주소대한민국미용명장여부광주시미용명장여부백년가게여부
4546북구헤어유호뷰티클래스광주광역시 북구 서하로 372(문흥동)XXX
4647북구헤어이순미용실광주광역시 북구 용봉로 116(중흥동)XOX
4748북구헤어이은헤어센스광주광역시 북구 우치로 120-1(용봉동)XXO
4849북구헤어소정예헤어살롱감성광주광역시 북구 양일1로 7-9, 1층XXX
4950광산구헤어라움헤어트리광주광역시 광산구 임방울대로 364, 2층 (수완동)XXX
5051광산구헤어SARAhair광주광역시 광산구임방울대로 351, 6층 (수완동)XXX
5152광산구헤어비주헤어살롱광주광역시 광산구 첨단내촌로 53-9 (월계동)XXX
5253광산구피부김은영피부관리광주광역시 광산구 임방울대로 330, 8층 (수완동)XXX
5354광산구일반가이아헤어광주광역시 광산구 첨단강변로99번길 11 (쌍암동)XXX
5455광산구피부약손명가에스테틱광주광역시 광산구 장신로 133,402호 (수완동)XXX