Overview

Dataset statistics

Number of variables4
Number of observations370
Missing cells143
Missing cells (%)9.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.7 KiB
Average record size in memory32.4 B

Variable types

Categorical1
Text3

Dataset

Description부산광역시_서구_이미용업현황_20220412
Author부산광역시 서구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15055155

Alerts

소재지전화 has 143 (38.6%) missing valuesMissing

Reproduction

Analysis started2023-12-10 16:32:39.888762
Analysis finished2023-12-10 16:32:40.364347
Duration0.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct12
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
미용업
143 
일반미용업
113 
이용업
46 
피부미용업
32 
네일미용업
18 
Other values (7)
18 

Length

Max length19
Median length3
Mean length4.1891892
Min length3

Unique

Unique4 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 143
38.6%
일반미용업 113
30.5%
이용업 46
 
12.4%
피부미용업 32
 
8.6%
네일미용업 18
 
4.9%
종합미용업 8
 
2.2%
일반미용업, 네일미용업 3
 
0.8%
피부미용업, 네일미용업 3
 
0.8%
일반미용업, 피부미용업 1
 
0.3%
화장ㆍ분장 미용업 1
 
0.3%
Other values (2) 2
 
0.5%

Length

2023-12-11T01:32:40.450078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 145
38.0%
일반미용업 118
30.9%
이용업 46
 
12.0%
피부미용업 37
 
9.7%
네일미용업 26
 
6.8%
종합미용업 8
 
2.1%
화장ㆍ분장 2
 
0.5%

업소명
Text

Distinct366
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2023-12-11T01:32:40.890798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length5.0945946
Min length1

Characters and Unicode

Total characters1885
Distinct characters347
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique362 ?
Unique (%)97.8%

Sample

1st row부곡이용원
2nd row대신중학교구내
3rd row신안
4th row울산
5th row덕원
ValueCountFrequency (%)
헤어 18
 
3.9%
네일 7
 
1.5%
미용실 6
 
1.3%
에스테틱 4
 
0.9%
뷰티 4
 
0.9%
hair 3
 
0.6%
헤어샵 3
 
0.6%
대신점 3
 
0.6%
정 3
 
0.6%
모아 2
 
0.4%
Other values (404) 412
88.6%
2023-12-11T01:32:41.543192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 139
 
7.4%
헤 134
 
7.1%
111
 
5.9%
미 47
 
2.5%
리 38
 
2.0%
이 37
 
2.0%
스 37
 
2.0%
일 31
 
1.6%
샵 31
 
1.6%
용 29
 
1.5%
Other values (337) 1251
66.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1604
85.1%
Space Separator 111
 
5.9%
Lowercase Letter 64
 
3.4%
Uppercase Letter 55
 
2.9%
Other Punctuation 15
 
0.8%
Close Punctuation 12
 
0.6%
Open Punctuation 12
 
0.6%
Decimal Number 11
 
0.6%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 139
 
8.7%
헤 134
 
8.4%
미 47
 
2.9%
리 38
 
2.4%
이 37
 
2.3%
스 37
 
2.3%
일 31
 
1.9%
샵 31
 
1.9%
용 29
 
1.8%
네 26
 
1.6%
Other values (289) 1055
65.8%
Lowercase Letter
ValueCountFrequency (%)
i 9
14.1%
a 7
10.9%
e 7
10.9%
r 6
9.4%
h 5
7.8%
o 5
7.8%
n 4
 
6.2%
t 4
 
6.2%
u 3
 
4.7%
d 3
 
4.7%
Other values (8) 11
17.2%
Uppercase Letter
ValueCountFrequency (%)
J 8
14.5%
H 5
9.1%
A 5
9.1%
O 5
9.1%
E 5
9.1%
N 4
 
7.3%
C 3
 
5.5%
I 3
 
5.5%
L 3
 
5.5%
M 3
 
5.5%
Other values (7) 11
20.0%
Other Punctuation
ValueCountFrequency (%)
# 6
40.0%
. 3
20.0%
& 2
 
13.3%
, 2
 
13.3%
' 1
 
6.7%
: 1
 
6.7%
Decimal Number
ValueCountFrequency (%)
1 6
54.5%
7 3
27.3%
3 2
 
18.2%
Space Separator
ValueCountFrequency (%)
111
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1604
85.1%
Common 162
 
8.6%
Latin 119
 
6.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 139
 
8.7%
헤 134
 
8.4%
미 47
 
2.9%
리 38
 
2.4%
이 37
 
2.3%
스 37
 
2.3%
일 31
 
1.9%
샵 31
 
1.9%
용 29
 
1.8%
네 26
 
1.6%
Other values (289) 1055
65.8%
Latin
ValueCountFrequency (%)
i 9
 
7.6%
J 8
 
6.7%
a 7
 
5.9%
e 7
 
5.9%
r 6
 
5.0%
H 5
 
4.2%
A 5
 
4.2%
h 5
 
4.2%
O 5
 
4.2%
E 5
 
4.2%
Other values (25) 57
47.9%
Common
ValueCountFrequency (%)
111
68.5%
) 12
 
7.4%
( 12
 
7.4%
# 6
 
3.7%
1 6
 
3.7%
7 3
 
1.9%
. 3
 
1.9%
& 2
 
1.2%
3 2
 
1.2%
, 2
 
1.2%
Other values (3) 3
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1604
85.1%
ASCII 281
 
14.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 139
 
8.7%
헤 134
 
8.4%
미 47
 
2.9%
리 38
 
2.4%
이 37
 
2.3%
스 37
 
2.3%
일 31
 
1.9%
샵 31
 
1.9%
용 29
 
1.8%
네 26
 
1.6%
Other values (289) 1055
65.8%
ASCII
ValueCountFrequency (%)
111
39.5%
) 12
 
4.3%
( 12
 
4.3%
i 9
 
3.2%
J 8
 
2.8%
a 7
 
2.5%
e 7
 
2.5%
# 6
 
2.1%
1 6
 
2.1%
r 6
 
2.1%
Other values (38) 97
34.5%

영업소 주소(도로명)
Text

Distinct361
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2023-12-11T01:32:41.911993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length47
Mean length28.756757
Min length17

Characters and Unicode

Total characters10640
Distinct characters150
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

Unique352 ?
Unique (%)95.1%

Sample

1st row부산광역시 서구 해돋이로 256-2 (아미동2가)
2nd row부산광역시 서구 대신로109번길 10 (서대신동3가)
3rd row부산광역시 서구 아미로38번길 3 (아미동2가)
4th row부산광역시 서구 망양로33번길 24 (서대신동3가)
5th row부산광역시 서구 천해로 37 (암남동)
ValueCountFrequency (%)
부산광역시 370
 
18.1%
서구 370
 
18.1%
1층 60
 
2.9%
서대신동3가 47
 
2.3%
남부민동 45
 
2.2%
동대신동3가 40
 
2.0%
서대신동2가 29
 
1.4%
2층 28
 
1.4%
구덕로 27
 
1.3%
서대신동1가 25
 
1.2%
Other values (391) 999
49.0%
2023-12-11T01:32:42.518935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1670
 
15.7%
동 481
 
4.5%
1 477
 
4.5%
서 473
 
4.4%
부 467
 
4.4%
구 453
 
4.3%
시 383
 
3.6%
산 373
 
3.5%
광 372
 
3.5%
역 370
 
3.5%
Other values (140) 5121
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6178
58.1%
Decimal Number 1811
 
17.0%
Space Separator 1670
 
15.7%
Open Punctuation 368
 
3.5%
Close Punctuation 368
 
3.5%
Other Punctuation 159
 
1.5%
Dash Punctuation 83
 
0.8%
Uppercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 481
 
7.8%
서 473
 
7.7%
부 467
 
7.6%
구 453
 
7.3%
시 383
 
6.2%
산 373
 
6.0%
광 372
 
6.0%
역 370
 
6.0%
로 356
 
5.8%
대 335
 
5.4%
Other values (124) 2115
34.2%
Decimal Number
ValueCountFrequency (%)
1 477
26.3%
2 349
19.3%
3 296
16.3%
0 120
 
6.6%
5 117
 
6.5%
4 110
 
6.1%
8 100
 
5.5%
6 88
 
4.9%
7 88
 
4.9%
9 66
 
3.6%
Space Separator
ValueCountFrequency (%)
1670
100.0%
Open Punctuation
ValueCountFrequency (%)
( 368
100.0%
Close Punctuation
ValueCountFrequency (%)
) 368
100.0%
Other Punctuation
ValueCountFrequency (%)
, 159
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 83
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6178
58.1%
Common 4459
41.9%
Latin 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 481
 
7.8%
서 473
 
7.7%
부 467
 
7.6%
구 453
 
7.3%
시 383
 
6.2%
산 373
 
6.0%
광 372
 
6.0%
역 370
 
6.0%
로 356
 
5.8%
대 335
 
5.4%
Other values (124) 2115
34.2%
Common
ValueCountFrequency (%)
1670
37.5%
1 477
 
10.7%
( 368
 
8.3%
) 368
 
8.3%
2 349
 
7.8%
3 296
 
6.6%
, 159
 
3.6%
0 120
 
2.7%
5 117
 
2.6%
4 110
 
2.5%
Other values (5) 425
 
9.5%
Latin
ValueCountFrequency (%)
B 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6178
58.1%
ASCII 4462
41.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1670
37.4%
1 477
 
10.7%
( 368
 
8.2%
) 368
 
8.2%
2 349
 
7.8%
3 296
 
6.6%
, 159
 
3.6%
0 120
 
2.7%
5 117
 
2.6%
4 110
 
2.5%
Other values (6) 428
 
9.6%
Hangul
ValueCountFrequency (%)
동 481
 
7.8%
서 473
 
7.7%
부 467
 
7.6%
구 453
 
7.3%
시 383
 
6.2%
산 373
 
6.0%
광 372
 
6.0%
역 370
 
6.0%
로 356
 
5.8%
대 335
 
5.4%
Other values (124) 2115
34.2%

소재지전화
Text

MISSING 

Distinct227
Distinct (%)100.0%
Missing143
Missing (%)38.6%
Memory size3.0 KiB
2023-12-11T01:32:43.293442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length13.969163
Min length10

Characters and Unicode

Total characters3171
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

Unique227 ?
Unique (%)100.0%

Sample

1st row 051- 247-2115
2nd row 051- 253-3776
3rd row 051- 248-1731
4th row 051- 247-2885
5th row051 -243 -5448
ValueCountFrequency (%)
051 219
41.5%
242 12
 
2.3%
244 11
 
2.1%
254 8
 
1.5%
255 6
 
1.1%
231 5
 
0.9%
253 4
 
0.8%
243 4
 
0.8%
241 4
 
0.8%
245 3
 
0.6%
Other values (245) 252
47.7%
2023-12-11T01:32:43.893024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 454
14.3%
448
14.1%
5 429
13.5%
2 350
11.0%
0 340
10.7%
1 340
10.7%
4 255
8.0%
3 138
 
4.4%
7 137
 
4.3%
8 100
 
3.2%
Other values (2) 180
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2269
71.6%
Dash Punctuation 454
 
14.3%
Space Separator 448
 
14.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 429
18.9%
2 350
15.4%
0 340
15.0%
1 340
15.0%
4 255
11.2%
3 138
 
6.1%
7 137
 
6.0%
8 100
 
4.4%
6 94
 
4.1%
9 86
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 454
100.0%
Space Separator
ValueCountFrequency (%)
448
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3171
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 454
14.3%
448
14.1%
5 429
13.5%
2 350
11.0%
0 340
10.7%
1 340
10.7%
4 255
8.0%
3 138
 
4.4%
7 137
 
4.3%
8 100
 
3.2%
Other values (2) 180
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3171
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 454
14.3%
448
14.1%
5 429
13.5%
2 350
11.0%
0 340
10.7%
1 340
10.7%
4 255
8.0%
3 138
 
4.4%
7 137
 
4.3%
8 100
 
3.2%
Other values (2) 180
 
5.7%

Missing values

2023-12-11T01:32:40.218222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:32:40.323797image/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
0이용업부곡이용원부산광역시 서구 해돋이로 256-2 (아미동2가)051- 247-2115
1이용업대신중학교구내부산광역시 서구 대신로109번길 10 (서대신동3가)<NA>
2이용업신안부산광역시 서구 아미로38번길 3 (아미동2가)<NA>
3이용업울산부산광역시 서구 망양로33번길 24 (서대신동3가)051- 253-3776
4이용업덕원부산광역시 서구 천해로 37 (암남동)051- 248-1731
5이용업동양부산광역시 서구 대신로 20 (서대신동3가)051- 247-2885
6이용업금성부산광역시 서구 까치고개로160번길 31 (아미동2가)<NA>
7이용업황태자부산광역시 서구 충무대로 286-1 (충무동1가)051 -243 -5448
8이용업신흥부산광역시 서구 천마로 134 (남부민동)051- 243-6049
9이용업옥광부산광역시 서구 암남로 8 (암남동)051- 243-3520
업종명업소명영업소 주소(도로명)소재지전화
360일반미용업, 피부미용업린뷰티부산광역시 서구 대영로85번길 30, 1층 (동대신동2가)<NA>
361일반미용업, 네일미용업we네일부산광역시 서구 망양로33번길 27, 상가1동 112호 (서대신동3가, 구덕자유아파트)<NA>
362일반미용업, 네일미용업네일하자오늘부산광역시 서구 구덕로339번길 39 (서대신동3가)<NA>
363일반미용업, 네일미용업킹즈퀸 헤어부산광역시 서구 꽃마을로 43-1 (서대신동3가)051 -253 -8723
364피부미용업, 네일미용업eye you부산광역시 서구 대영로 71 (동대신동2가)<NA>
365피부미용업, 네일미용업에스 스킨앤바디부산광역시 서구 대영로73번길 102, 103호 (동대신동3가, 이십일베스트빌라13차)051 -244 -3634
366피부미용업, 네일미용업티나다 뷰티부산광역시 서구 동대로19번길 32, 상가동 108호 (동대신동3가, 브라운스톤 하이포레)<NA>
367화장ㆍ분장 미용업눈썹살롱부산광역시 서구 충무대로 237, 1-2층 (남부민동)<NA>
368네일미용업, 화장ㆍ분장 미용업달라 네일부산광역시 서구 대영로 32, 1층 (서대신동1가)<NA>
369일반미용업, 피부미용업, 네일미용업빛네일부산광역시 서구 대영로85번길 75, 1층 (동대신동3가)<NA>