Overview

Dataset statistics

Number of variables5
Number of observations34
Missing cells22
Missing cells (%)12.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory44.9 B

Variable types

Categorical1
Text3
Numeric1

Dataset

Description부산광역시서구_공중위생업소(네일)_20230324
Author부산광역시 서구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15112943

Alerts

소재지전화 has 22 (64.7%) missing valuesMissing
업소명 has unique valuesUnique
영업소 주소(도로명) has unique valuesUnique

Reproduction

Analysis started2023-12-10 17:09:43.614230
Analysis finished2023-12-10 17:09:44.460876
Duration0.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct6
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size404.0 B
네일미용업
18 
종합미용업
8 
일반미용업, 네일미용업
3 
피부미용업, 네일미용업
3 
네일미용업, 화장ㆍ분장 미용업
 
1

Length

Max length19
Median length5
Mean length6.9705882
Min length5

Unique

Unique2 ?
Unique (%)5.9%

Sample

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

Common Values

ValueCountFrequency (%)
네일미용업 18
52.9%
종합미용업 8
23.5%
일반미용업, 네일미용업 3
 
8.8%
피부미용업, 네일미용업 3
 
8.8%
네일미용업, 화장ㆍ분장 미용업 1
 
2.9%
일반미용업, 피부미용업, 네일미용업 1
 
2.9%

Length

2023-12-11T02:09:44.576899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:09:44.777075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
네일미용업 26
59.1%
종합미용업 8
 
18.2%
일반미용업 4
 
9.1%
피부미용업 4
 
9.1%
화장ㆍ분장 1
 
2.3%
미용업 1
 
2.3%

업소명
Text

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-11T02:09:45.043731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length9
Mean length6
Min length3

Characters and Unicode

Total characters204
Distinct characters101
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

Unique34 ?
Unique (%)100.0%

Sample

1st row엠제이헤어
2nd row디아망 네일
3rd row톡톡튀는뷰티
4th row김진경뷰티
5th row원 헤어샵
ValueCountFrequency (%)
네일 7
 
13.2%
엠제이헤어 1
 
1.9%
네일하자오늘 1
 
1.9%
달라 1
 
1.9%
티나다 1
 
1.9%
뷰티 1
 
1.9%
에스 1
 
1.9%
스킨앤바디 1
 
1.9%
eye 1
 
1.9%
you 1
 
1.9%
Other values (37) 37
69.8%
2023-12-11T02:09:45.593712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
네 22
 
10.8%
일 22
 
10.8%
20
 
9.8%
티 4
 
2.0%
헤 4
 
2.0%
어 4
 
2.0%
e 4
 
2.0%
A 3
 
1.5%
) 3
 
1.5%
( 3
 
1.5%
Other values (91) 115
56.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 147
72.1%
Space Separator 20
 
9.8%
Uppercase Letter 16
 
7.8%
Lowercase Letter 11
 
5.4%
Other Punctuation 4
 
2.0%
Close Punctuation 3
 
1.5%
Open Punctuation 3
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
네 22
 
15.0%
일 22
 
15.0%
티 4
 
2.7%
헤 4
 
2.7%
어 4
 
2.7%
뷰 3
 
2.0%
이 3
 
2.0%
스 2
 
1.4%
즈 2
 
1.4%
리 2
 
1.4%
Other values (69) 79
53.7%
Uppercase Letter
ValueCountFrequency (%)
A 3
18.8%
M 2
12.5%
N 2
12.5%
I 2
12.5%
L 2
12.5%
O 1
 
6.2%
C 1
 
6.2%
Y 1
 
6.2%
E 1
 
6.2%
D 1
 
6.2%
Lowercase Letter
ValueCountFrequency (%)
e 4
36.4%
y 2
18.2%
o 1
 
9.1%
u 1
 
9.1%
h 1
 
9.1%
t 1
 
9.1%
w 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
, 2
50.0%
Space Separator
ValueCountFrequency (%)
20
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 147
72.1%
Common 30
 
14.7%
Latin 27
 
13.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
네 22
 
15.0%
일 22
 
15.0%
티 4
 
2.7%
헤 4
 
2.7%
어 4
 
2.7%
뷰 3
 
2.0%
이 3
 
2.0%
스 2
 
1.4%
즈 2
 
1.4%
리 2
 
1.4%
Other values (69) 79
53.7%
Latin
ValueCountFrequency (%)
e 4
14.8%
A 3
11.1%
y 2
 
7.4%
M 2
 
7.4%
N 2
 
7.4%
I 2
 
7.4%
L 2
 
7.4%
o 1
 
3.7%
u 1
 
3.7%
O 1
 
3.7%
Other values (7) 7
25.9%
Common
ValueCountFrequency (%)
20
66.7%
) 3
 
10.0%
( 3
 
10.0%
. 2
 
6.7%
, 2
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 147
72.1%
ASCII 57
 
27.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
네 22
 
15.0%
일 22
 
15.0%
티 4
 
2.7%
헤 4
 
2.7%
어 4
 
2.7%
뷰 3
 
2.0%
이 3
 
2.0%
스 2
 
1.4%
즈 2
 
1.4%
리 2
 
1.4%
Other values (69) 79
53.7%
ASCII
ValueCountFrequency (%)
20
35.1%
e 4
 
7.0%
A 3
 
5.3%
) 3
 
5.3%
( 3
 
5.3%
. 2
 
3.5%
y 2
 
3.5%
M 2
 
3.5%
, 2
 
3.5%
N 2
 
3.5%
Other values (12) 14
24.6%

영업소 주소(도로명)
Text

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-11T02:09:45.981340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length40
Mean length32.5
Min length23

Characters and Unicode

Total characters1105
Distinct characters94
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

Unique34 ?
Unique (%)100.0%

Sample

1st row부산광역시 서구 보동길311번길 12, 1층 (동대신동2가)
2nd row부산광역시 서구 구덕로 302 (동대신동2가)
3rd row부산광역시 서구 보수대로 15, 212호 (토성동1가, 봄여름가을겨울)
4th row부산광역시 서구 대영로 23, 2층 (서대신동2가)
5th row부산광역시 서구 구덕로280번길 26, 1층 (동대신동1가)
ValueCountFrequency (%)
부산광역시 34
 
16.3%
서구 34
 
16.3%
1층 11
 
5.3%
서대신동3가 6
 
2.9%
2층 5
 
2.4%
동대신동3가 5
 
2.4%
구덕로 5
 
2.4%
서대신동1가 5
 
2.4%
충무동1가 4
 
1.9%
대영로 4
 
1.9%
Other values (81) 96
45.9%
2023-12-11T02:09:46.501555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
175
 
15.8%
1 64
 
5.8%
동 48
 
4.3%
서 47
 
4.3%
구 44
 
4.0%
3 41
 
3.7%
대 39
 
3.5%
부 38
 
3.4%
시 36
 
3.3%
( 34
 
3.1%
Other values (84) 539
48.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 627
56.7%
Decimal Number 196
 
17.7%
Space Separator 175
 
15.8%
Open Punctuation 34
 
3.1%
Close Punctuation 34
 
3.1%
Other Punctuation 32
 
2.9%
Dash Punctuation 7
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 48
 
7.7%
서 47
 
7.5%
구 44
 
7.0%
대 39
 
6.2%
부 38
 
6.1%
시 36
 
5.7%
광 34
 
5.4%
역 34
 
5.4%
산 34
 
5.4%
가 34
 
5.4%
Other values (69) 239
38.1%
Decimal Number
ValueCountFrequency (%)
1 64
32.7%
3 41
20.9%
2 32
16.3%
0 18
 
9.2%
5 11
 
5.6%
7 10
 
5.1%
8 6
 
3.1%
6 6
 
3.1%
9 4
 
2.0%
4 4
 
2.0%
Space Separator
ValueCountFrequency (%)
175
100.0%
Open Punctuation
ValueCountFrequency (%)
( 34
100.0%
Close Punctuation
ValueCountFrequency (%)
) 34
100.0%
Other Punctuation
ValueCountFrequency (%)
, 32
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 627
56.7%
Common 478
43.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 48
 
7.7%
서 47
 
7.5%
구 44
 
7.0%
대 39
 
6.2%
부 38
 
6.1%
시 36
 
5.7%
광 34
 
5.4%
역 34
 
5.4%
산 34
 
5.4%
가 34
 
5.4%
Other values (69) 239
38.1%
Common
ValueCountFrequency (%)
175
36.6%
1 64
 
13.4%
3 41
 
8.6%
( 34
 
7.1%
) 34
 
7.1%
2 32
 
6.7%
, 32
 
6.7%
0 18
 
3.8%
5 11
 
2.3%
7 10
 
2.1%
Other values (5) 27
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 627
56.7%
ASCII 478
43.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
175
36.6%
1 64
 
13.4%
3 41
 
8.6%
( 34
 
7.1%
) 34
 
7.1%
2 32
 
6.7%
, 32
 
6.7%
0 18
 
3.8%
5 11
 
2.3%
7 10
 
2.1%
Other values (5) 27
 
5.6%
Hangul
ValueCountFrequency (%)
동 48
 
7.7%
서 47
 
7.5%
구 44
 
7.0%
대 39
 
6.2%
부 38
 
6.1%
시 36
 
5.7%
광 34
 
5.4%
역 34
 
5.4%
산 34
 
5.4%
가 34
 
5.4%
Other values (69) 239
38.1%

우편번호(도로명)
Real number (ℝ)

Distinct19
Distinct (%)55.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49229.353
Minimum49201
Maximum49269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-11T02:09:46.636324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49201
5-th percentile49207
Q149212.75
median49226.5
Q349247
95-th percentile49256.35
Maximum49269
Range68
Interquartile range (IQR)34.25

Descriptive statistics

Standard deviation18.222335
Coefficient of variation (CV)0.00037015182
Kurtosis-0.93586196
Mean49229.353
Median Absolute Deviation (MAD)15
Skewness0.45786187
Sum1673798
Variance332.05348
MonotonicityNot monotonic
2023-12-11T02:09:46.753840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
49228 4
 
11.8%
49253 3
 
8.8%
49225 2
 
5.9%
49247 2
 
5.9%
49234 2
 
5.9%
49210 2
 
5.9%
49211 2
 
5.9%
49212 2
 
5.9%
49217 2
 
5.9%
49252 2
 
5.9%
Other values (9) 11
32.4%
ValueCountFrequency (%)
49201 1
2.9%
49207 2
5.9%
49210 2
5.9%
49211 2
5.9%
49212 2
5.9%
49215 1
2.9%
49217 2
5.9%
49221 1
2.9%
49223 2
5.9%
49225 2
5.9%
ValueCountFrequency (%)
49269 1
 
2.9%
49257 1
 
2.9%
49256 1
 
2.9%
49253 3
8.8%
49252 2
5.9%
49247 2
5.9%
49234 2
5.9%
49232 1
 
2.9%
49228 4
11.8%
49225 2
5.9%

소재지전화
Text

MISSING 

Distinct12
Distinct (%)100.0%
Missing22
Missing (%)64.7%
Memory size404.0 B
2023-12-11T02:09:46.891905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters168
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

Unique12 ?
Unique (%)100.0%

Sample

1st row 051-416 -2699
2nd row051 -244 -3998
3rd row051 -247 -3497
4th row 051- 988-0606
5th row051 -255 -3833
ValueCountFrequency (%)
051 10
30.3%
244 3
 
9.1%
051-416 1
 
3.0%
7782-6012 1
 
3.0%
8723 1
 
3.0%
253 1
 
3.0%
7331 1
 
3.0%
911 1
 
3.0%
2822 1
 
3.0%
242 1
 
3.0%
Other values (12) 12
36.4%
2023-12-11T02:09:47.153682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 24
14.3%
23
13.7%
0 17
10.1%
2 17
10.1%
1 16
9.5%
5 15
8.9%
4 12
7.1%
3 11
6.5%
8 10
6.0%
7 10
6.0%
Other values (2) 13
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
72.0%
Dash Punctuation 24
 
14.3%
Space Separator 23
 
13.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17
14.0%
2 17
14.0%
1 16
13.2%
5 15
12.4%
4 12
9.9%
3 11
9.1%
8 10
8.3%
7 10
8.3%
9 7
5.8%
6 6
 
5.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%
Space Separator
ValueCountFrequency (%)
23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 168
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 24
14.3%
23
13.7%
0 17
10.1%
2 17
10.1%
1 16
9.5%
5 15
8.9%
4 12
7.1%
3 11
6.5%
8 10
6.0%
7 10
6.0%
Other values (2) 13
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 24
14.3%
23
13.7%
0 17
10.1%
2 17
10.1%
1 16
9.5%
5 15
8.9%
4 12
7.1%
3 11
6.5%
8 10
6.0%
7 10
6.0%
Other values (2) 13
7.7%

Interactions

2023-12-11T02:09:44.019580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:09:47.268219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업소명영업소 주소(도로명)우편번호(도로명)소재지전화
업종명1.0001.0001.0000.0001.000
업소명1.0001.0001.0001.0001.000
영업소 주소(도로명)1.0001.0001.0001.0001.000
우편번호(도로명)0.0001.0001.0001.0001.000
소재지전화1.0001.0001.0001.0001.000
2023-12-11T02:09:47.391274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호(도로명)업종명
우편번호(도로명)1.0000.000
업종명0.0001.000

Missing values

2023-12-11T02:09:44.250575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:09:44.411688image/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)우편번호(도로명)소재지전화
0종합미용업엠제이헤어부산광역시 서구 보동길311번길 12, 1층 (동대신동2가)49215051-416 -2699
1종합미용업디아망 네일부산광역시 서구 구덕로 302 (동대신동2가)49217051 -244 -3998
2종합미용업톡톡튀는뷰티부산광역시 서구 보수대로 15, 212호 (토성동1가, 봄여름가을겨울)49247<NA>
3종합미용업김진경뷰티부산광역시 서구 대영로 23, 2층 (서대신동2가)49234<NA>
4종합미용업원 헤어샵부산광역시 서구 구덕로280번길 26, 1층 (동대신동1가)49221051 -247 -3497
5종합미용업태후사랑 대신점부산광역시 서구 대신로 8, 2층 201호 (서대신동3가)49210<NA>
6종합미용업마녀살롱부산광역시 서구 대영로30번길 10, 1층 (서대신동1가)49228051- 988-0606
7종합미용업명혜란헤어부산광역시 서구 충무시장길 6, 101호 (충무동1가, 헤리츠아파트3차)49253<NA>
8네일미용업민 네일부산광역시 서구 구덕로334번길 8 (동대신동3가)49211051 -255 -3833
9네일미용업세린네일부산광역시 서구 대영로 36, 2층 (서대신동1가)49228<NA>
업종명업소명영업소 주소(도로명)우편번호(도로명)소재지전화
24네일미용업썸네일부산광역시 서구 대영로30번길 20, 1층 (서대신동1가)49228<NA>
25네일미용업네일비부산광역시 서구 대영로45번길 20, 1층 (서대신동2가)49234<NA>
26일반미용업, 네일미용업we네일부산광역시 서구 망양로33번길 27, 상가1동 112호 (서대신동3가, 구덕자유아파트)49210<NA>
27일반미용업, 네일미용업네일하자오늘부산광역시 서구 구덕로339번길 39 (서대신동3가)49225<NA>
28일반미용업, 네일미용업킹즈퀸 헤어부산광역시 서구 꽃마을로 43-1 (서대신동3가)49207051 -253 -8723
29피부미용업, 네일미용업eye you부산광역시 서구 대영로 71 (동대신동2가)49217<NA>
30피부미용업, 네일미용업에스 스킨앤바디부산광역시 서구 대영로73번길 102, 103호 (동대신동3가, 이십일베스트빌라13차)49212051 -244 -3634
31피부미용업, 네일미용업티나다 뷰티부산광역시 서구 동대로19번길 32, 상가동 108호 (동대신동3가, 브라운스톤 하이포레)49201<NA>
32네일미용업, 화장ㆍ분장 미용업달라 네일부산광역시 서구 대영로 32, 1층 (서대신동1가)49228<NA>
33일반미용업, 피부미용업, 네일미용업빛네일부산광역시 서구 대영로85번길 75, 1층 (동대신동3가)49212<NA>