Overview

Dataset statistics

Number of variables4
Number of observations28
Missing cells20
Missing cells (%)17.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 KiB
Average record size in memory36.7 B

Variable types

Categorical1
Text3

Dataset

Description부산광역시 서구 관내에 영업신고 된 공중위생업소 중 네일미용업 현황입니다.업소명, 주소, 소재지전화번호 등을 제공합니다.
Author부산광역시 서구
URLhttps://www.data.go.kr/data/15112943/fileData.do

Alerts

소재지전화 has 20 (71.4%) missing valuesMissing
업소명 has unique valuesUnique
영업소 주소(도로명) has unique valuesUnique

Reproduction

Analysis started2024-04-21 02:15:52.654309
Analysis finished2024-04-21 02:15:54.522158
Duration1.87 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct4
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size356.0 B
네일미용업
19 
피부미용업, 네일미용업
5 
일반미용업, 네일미용업
3 
일반미용업, 피부미용업, 네일미용업
 
1

Length

Max length19
Median length5
Mean length7.5
Min length5

Unique

Unique1 ?
Unique (%)3.6%

Sample

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

Common Values

ValueCountFrequency (%)
네일미용업 19
67.9%
피부미용업, 네일미용업 5
 
17.9%
일반미용업, 네일미용업 3
 
10.7%
일반미용업, 피부미용업, 네일미용업 1
 
3.6%

Length

2024-04-21T11:15:54.594589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:15:54.705559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
네일미용업 28
73.7%
피부미용업 6
 
15.8%
일반미용업 4
 
10.5%

업소명
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-04-21T11:15:54.873691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length9
Mean length6
Min length3

Characters and Unicode

Total characters168
Distinct characters80
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st row민 네일
2nd row세린네일
3rd row로즈마리
4th rowY.M 네일샵
5th row은희네일
ValueCountFrequency (%)
네일 5
 
11.6%
그녀의 1
 
2.3%
러브민네일 1
 
2.3%
르모이네일 1
 
2.3%
썸네일 1
 
2.3%
네일비 1
 
2.3%
여린네일 1
 
2.3%
we네일 1
 
2.3%
네일하자오늘 1
 
2.3%
킹즈퀸 1
 
2.3%
Other values (29) 29
67.4%
2024-04-21T11:15:55.233546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
네 22
 
13.1%
일 22
 
13.1%
16
 
9.5%
e 4
 
2.4%
( 3
 
1.8%
티 3
 
1.8%
A 3
 
1.8%
) 3
 
1.8%
스 3
 
1.8%
L 2
 
1.2%
Other values (70) 87
51.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 115
68.5%
Space Separator 16
 
9.5%
Uppercase Letter 16
 
9.5%
Lowercase Letter 11
 
6.5%
Other Punctuation 4
 
2.4%
Open Punctuation 3
 
1.8%
Close Punctuation 3
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
네 22
19.1%
일 22
19.1%
티 3
 
2.6%
스 3
 
2.6%
민 2
 
1.7%
뷰 2
 
1.7%
이 2
 
1.7%
안 2
 
1.7%
자 2
 
1.7%
에 2
 
1.7%
Other values (48) 53
46.1%
Uppercase Letter
ValueCountFrequency (%)
A 3
18.8%
L 2
12.5%
I 2
12.5%
N 2
12.5%
M 2
12.5%
C 1
 
6.2%
D 1
 
6.2%
Y 1
 
6.2%
E 1
 
6.2%
O 1
 
6.2%
Lowercase Letter
ValueCountFrequency (%)
e 4
36.4%
y 2
18.2%
u 1
 
9.1%
w 1
 
9.1%
o 1
 
9.1%
t 1
 
9.1%
h 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
, 2
50.0%
. 2
50.0%
Space Separator
ValueCountFrequency (%)
16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 115
68.5%
Latin 27
 
16.1%
Common 26
 
15.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
네 22
19.1%
일 22
19.1%
티 3
 
2.6%
스 3
 
2.6%
민 2
 
1.7%
뷰 2
 
1.7%
이 2
 
1.7%
안 2
 
1.7%
자 2
 
1.7%
에 2
 
1.7%
Other values (48) 53
46.1%
Latin
ValueCountFrequency (%)
e 4
14.8%
A 3
11.1%
L 2
 
7.4%
y 2
 
7.4%
I 2
 
7.4%
N 2
 
7.4%
M 2
 
7.4%
u 1
 
3.7%
w 1
 
3.7%
o 1
 
3.7%
Other values (7) 7
25.9%
Common
ValueCountFrequency (%)
16
61.5%
( 3
 
11.5%
) 3
 
11.5%
, 2
 
7.7%
. 2
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 115
68.5%
ASCII 53
31.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
네 22
19.1%
일 22
19.1%
티 3
 
2.6%
스 3
 
2.6%
민 2
 
1.7%
뷰 2
 
1.7%
이 2
 
1.7%
안 2
 
1.7%
자 2
 
1.7%
에 2
 
1.7%
Other values (48) 53
46.1%
ASCII
ValueCountFrequency (%)
16
30.2%
e 4
 
7.5%
( 3
 
5.7%
A 3
 
5.7%
) 3
 
5.7%
L 2
 
3.8%
y 2
 
3.8%
I 2
 
3.8%
, 2
 
3.8%
N 2
 
3.8%
Other values (12) 14
26.4%

영업소 주소(도로명)
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-04-21T11:15:55.475884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length38
Mean length32.321429
Min length23

Characters and Unicode

Total characters905
Distinct characters87
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

Unique28 ?
Unique (%)100.0%

Sample

1st row부산광역시 서구 구덕로334번길 8 (동대신동3가)
2nd row부산광역시 서구 대영로 36, 2층 (서대신동1가)
3rd row부산광역시 서구 구덕로 116-1 (충무동1가)
4th row부산광역시 서구 천마로 197-1, 1층 (남부민동)
5th row부산광역시 서구 충무대로 24, 3층 306호 (암남동, 송도혜성주상복합빌딩)
ValueCountFrequency (%)
부산광역시 28
 
16.2%
서구 28
 
16.2%
1층 7
 
4.0%
2층 6
 
3.5%
서대신동3가 6
 
3.5%
동대신동3가 5
 
2.9%
구덕로 4
 
2.3%
충무대로 3
 
1.7%
서대신동1가 3
 
1.7%
충무동1가 3
 
1.7%
Other values (69) 80
46.2%
2024-04-21T11:15:55.841286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
145
 
16.0%
1 46
 
5.1%
서 38
 
4.2%
3 38
 
4.2%
동 37
 
4.1%
구 36
 
4.0%
부 33
 
3.6%
대 30
 
3.3%
시 29
 
3.2%
광 28
 
3.1%
Other values (77) 445
49.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 509
56.2%
Decimal Number 163
 
18.0%
Space Separator 145
 
16.0%
Close Punctuation 28
 
3.1%
Open Punctuation 28
 
3.1%
Other Punctuation 25
 
2.8%
Dash Punctuation 7
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 38
 
7.5%
동 37
 
7.3%
구 36
 
7.1%
부 33
 
6.5%
대 30
 
5.9%
시 29
 
5.7%
광 28
 
5.5%
역 28
 
5.5%
산 28
 
5.5%
로 27
 
5.3%
Other values (62) 195
38.3%
Decimal Number
ValueCountFrequency (%)
1 46
28.2%
3 38
23.3%
2 24
14.7%
0 15
 
9.2%
5 11
 
6.7%
7 10
 
6.1%
8 6
 
3.7%
4 5
 
3.1%
6 4
 
2.5%
9 4
 
2.5%
Space Separator
ValueCountFrequency (%)
145
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Other Punctuation
ValueCountFrequency (%)
, 25
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 509
56.2%
Common 396
43.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 38
 
7.5%
동 37
 
7.3%
구 36
 
7.1%
부 33
 
6.5%
대 30
 
5.9%
시 29
 
5.7%
광 28
 
5.5%
역 28
 
5.5%
산 28
 
5.5%
로 27
 
5.3%
Other values (62) 195
38.3%
Common
ValueCountFrequency (%)
145
36.6%
1 46
 
11.6%
3 38
 
9.6%
) 28
 
7.1%
( 28
 
7.1%
, 25
 
6.3%
2 24
 
6.1%
0 15
 
3.8%
5 11
 
2.8%
7 10
 
2.5%
Other values (5) 26
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 509
56.2%
ASCII 396
43.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
145
36.6%
1 46
 
11.6%
3 38
 
9.6%
) 28
 
7.1%
( 28
 
7.1%
, 25
 
6.3%
2 24
 
6.1%
0 15
 
3.8%
5 11
 
2.8%
7 10
 
2.5%
Other values (5) 26
 
6.6%
Hangul
ValueCountFrequency (%)
서 38
 
7.5%
동 37
 
7.3%
구 36
 
7.1%
부 33
 
6.5%
대 30
 
5.9%
시 29
 
5.7%
광 28
 
5.5%
역 28
 
5.5%
산 28
 
5.5%
로 27
 
5.3%
Other values (62) 195
38.3%

소재지전화
Text

MISSING 

Distinct8
Distinct (%)100.0%
Missing20
Missing (%)71.4%
Memory size356.0 B
2024-04-21T11:15:55.989501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters112
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)100.0%

Sample

1st row051 -255 -3833
2nd row051 -244 -0727
3rd row070 -7782-6012
4th row051 -242 -2822
5th row051 -911 -7331
ValueCountFrequency (%)
051 7
30.4%
244 2
 
8.7%
255 1
 
4.3%
3833 1
 
4.3%
0727 1
 
4.3%
070 1
 
4.3%
7782-6012 1
 
4.3%
242 1
 
4.3%
2822 1
 
4.3%
911 1
 
4.3%
Other values (6) 6
26.1%
2024-04-21T11:15:56.234431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 16
14.3%
15
13.4%
2 13
11.6%
0 11
9.8%
1 11
9.8%
5 10
8.9%
3 9
8.0%
7 8
7.1%
4 7
6.2%
6 6
 
5.4%
Other values (2) 6
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 81
72.3%
Dash Punctuation 16
 
14.3%
Space Separator 15
 
13.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 13
16.0%
0 11
13.6%
1 11
13.6%
5 10
12.3%
3 9
11.1%
7 8
9.9%
4 7
8.6%
6 6
7.4%
8 4
 
4.9%
9 2
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Space Separator
ValueCountFrequency (%)
15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 112
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 16
14.3%
15
13.4%
2 13
11.6%
0 11
9.8%
1 11
9.8%
5 10
8.9%
3 9
8.0%
7 8
7.1%
4 7
6.2%
6 6
 
5.4%
Other values (2) 6
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 16
14.3%
15
13.4%
2 13
11.6%
0 11
9.8%
1 11
9.8%
5 10
8.9%
3 9
8.0%
7 8
7.1%
4 7
6.2%
6 6
 
5.4%
Other values (2) 6
 
5.4%

Correlations

2024-04-21T11:15:56.334499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업소명영업소 주소(도로명)소재지전화
업종명1.0001.0001.0001.000
업소명1.0001.0001.0001.000
영업소 주소(도로명)1.0001.0001.0001.000
소재지전화1.0001.0001.0001.000

Missing values

2024-04-21T11:15:54.353856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T11:15:54.485687image/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

업종명업소명영업소 주소(도로명)소재지전화
0네일미용업민 네일부산광역시 서구 구덕로334번길 8 (동대신동3가)051 -255 -3833
1네일미용업세린네일부산광역시 서구 대영로 36, 2층 (서대신동1가)<NA>
2네일미용업로즈마리부산광역시 서구 구덕로 116-1 (충무동1가)051 -244 -0727
3네일미용업Y.M 네일샵부산광역시 서구 천마로 197-1, 1층 (남부민동)070 -7782-6012
4네일미용업은희네일부산광역시 서구 충무대로 24, 3층 306호 (암남동, 송도혜성주상복합빌딩)<NA>
5네일미용업여우네일(토성점)부산광역시 서구 구덕로 125 (토성동5가)<NA>
6네일미용업양손네일부산광역시 서구 초장로 56 (토성동5가)<NA>
7네일미용업NAIL MADE (네일 메이드)부산광역시 서구 흑교로 105 (부용동1가)<NA>
8네일미용업네일리안부산광역시 서구 구덕로 117, 1층 (충무동1가)051 -242 -2822
9네일미용업네일은부산광역시 서구 대영로73번길 75, 101호 (동대신동3가, 한영리더스)<NA>
업종명업소명영업소 주소(도로명)소재지전화
18네일미용업여린네일부산광역시 서구 꽃마을로 48, 301동 2층 205호 (서대신동3가, 대신 더샵)<NA>
19일반미용업, 네일미용업we네일부산광역시 서구 망양로33번길 27, 상가1동 112호 (서대신동3가, 구덕자유아파트)<NA>
20일반미용업, 네일미용업네일하자오늘부산광역시 서구 구덕로339번길 39 (서대신동3가)<NA>
21일반미용업, 네일미용업킹즈퀸 헤어부산광역시 서구 꽃마을로 43-1 (서대신동3가)051 -253 -8723
22피부미용업, 네일미용업에스엠뷰티부산광역시 서구 까치고개로 193, 3층 (아미동2가)051 -466 -6679
23피부미용업, 네일미용업eye you부산광역시 서구 대영로 71 (동대신동2가)<NA>
24피부미용업, 네일미용업에스 스킨앤바디부산광역시 서구 대영로73번길 102, 103호 (동대신동3가, 이십일베스트빌라13차)051 -244 -3634
25피부미용업, 네일미용업티나다 뷰티부산광역시 서구 보수대로280번길 30, 2층 일부호 (동대신동3가)<NA>
26피부미용업, 네일미용업벨롱네일부산광역시 서구 충무대로 28, 2층 (암남동)<NA>
27일반미용업, 피부미용업, 네일미용업빛네일부산광역시 서구 대영로85번길 75, 1층 (동대신동3가)<NA>