Overview

Dataset statistics

Number of variables5
Number of observations335
Missing cells119
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.5 KiB
Average record size in memory41.4 B

Variable types

Categorical1
Text3
Numeric1

Dataset

Description해당 정보는 부산광역시 영도구 소재 미용업 현황에 대한 데이터로 업종, 업소명, 소재지, 전화번호 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15112900/fileData.do

Alerts

업종명 is highly imbalanced (62.3%)Imbalance
소재지전화 has 119 (35.5%) missing valuesMissing

Reproduction

Analysis started2023-12-12 13:27:33.930569
Analysis finished2023-12-12 13:27:34.639315
Duration0.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

IMBALANCE 

Distinct13
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
일반미용업
254 
네일미용업
29 
피부미용업
28 
종합미용업
 
7
피부미용업, 네일미용업
 
4
Other values (8)
 
13

Length

Max length23
Median length5
Mean length5.5283582
Min length5

Unique

Unique4 ?
Unique (%)1.2%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 254
75.8%
네일미용업 29
 
8.7%
피부미용업 28
 
8.4%
종합미용업 7
 
2.1%
피부미용업, 네일미용업 4
 
1.2%
네일미용업, 화장ㆍ분장 미용업 3
 
0.9%
일반미용업, 네일미용업 2
 
0.6%
화장ㆍ분장 미용업 2
 
0.6%
일반미용업, 네일미용업, 화장ㆍ분장 미용업 2
 
0.6%
일반미용업, 화장ㆍ분장 미용업 1
 
0.3%
Other values (3) 3
 
0.9%

Length

2023-12-12T22:27:34.715539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 260
71.2%
네일미용업 41
 
11.2%
피부미용업 35
 
9.6%
화장ㆍ분장 11
 
3.0%
미용업 11
 
3.0%
종합미용업 7
 
1.9%

업소명
Text

Distinct330
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2023-12-12T22:27:35.011437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length21
Mean length5.238806
Min length1

Characters and Unicode

Total characters1755
Distinct characters337
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique325 ?
Unique (%)97.0%

Sample

1st row순평
2nd row스타 헤어
3rd row부여
4th row평화
5th row미즈헤어샵
ValueCountFrequency (%)
헤어 8
 
2.0%
네일 4
 
1.0%
헤어샵 3
 
0.7%
미용실 3
 
0.7%
영도점 3
 
0.7%
the 3
 
0.7%
에스테틱 3
 
0.7%
원 2
 
0.5%
헤어살롱 2
 
0.5%
태후사랑 2
 
0.5%
Other values (364) 376
91.9%
2023-12-12T22:27:35.517033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 130
 
7.4%
헤 125
 
7.1%
75
 
4.3%
미 59
 
3.4%
스 37
 
2.1%
리 35
 
2.0%
일 33
 
1.9%
이 31
 
1.8%
네 30
 
1.7%
용 27
 
1.5%
Other values (327) 1173
66.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1472
83.9%
Uppercase Letter 76
 
4.3%
Space Separator 75
 
4.3%
Lowercase Letter 66
 
3.8%
Other Punctuation 20
 
1.1%
Close Punctuation 18
 
1.0%
Open Punctuation 18
 
1.0%
Decimal Number 6
 
0.3%
Connector Punctuation 2
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 130
 
8.8%
헤 125
 
8.5%
미 59
 
4.0%
스 37
 
2.5%
리 35
 
2.4%
일 33
 
2.2%
이 31
 
2.1%
네 30
 
2.0%
용 27
 
1.8%
샵 27
 
1.8%
Other values (274) 938
63.7%
Uppercase Letter
ValueCountFrequency (%)
E 10
13.2%
M 9
11.8%
A 8
10.5%
S 6
7.9%
H 6
7.9%
N 6
7.9%
R 6
7.9%
I 6
7.9%
T 4
 
5.3%
J 3
 
3.9%
Other values (9) 12
15.8%
Lowercase Letter
ValueCountFrequency (%)
a 11
16.7%
r 9
13.6%
i 8
12.1%
e 6
9.1%
h 6
9.1%
t 4
 
6.1%
l 4
 
6.1%
o 4
 
6.1%
s 3
 
4.5%
p 2
 
3.0%
Other values (7) 9
13.6%
Other Punctuation
ValueCountFrequency (%)
. 7
35.0%
# 6
30.0%
' 2
 
10.0%
, 2
 
10.0%
& 2
 
10.0%
: 1
 
5.0%
Decimal Number
ValueCountFrequency (%)
5 2
33.3%
0 1
16.7%
7 1
16.7%
1 1
16.7%
3 1
16.7%
Space Separator
ValueCountFrequency (%)
75
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1471
83.8%
Latin 142
 
8.1%
Common 141
 
8.0%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 130
 
8.8%
헤 125
 
8.5%
미 59
 
4.0%
스 37
 
2.5%
리 35
 
2.4%
일 33
 
2.2%
이 31
 
2.1%
네 30
 
2.0%
용 27
 
1.8%
샵 27
 
1.8%
Other values (273) 937
63.7%
Latin
ValueCountFrequency (%)
a 11
 
7.7%
E 10
 
7.0%
M 9
 
6.3%
r 9
 
6.3%
A 8
 
5.6%
i 8
 
5.6%
S 6
 
4.2%
H 6
 
4.2%
e 6
 
4.2%
h 6
 
4.2%
Other values (26) 63
44.4%
Common
ValueCountFrequency (%)
75
53.2%
) 18
 
12.8%
( 18
 
12.8%
. 7
 
5.0%
# 6
 
4.3%
' 2
 
1.4%
5 2
 
1.4%
, 2
 
1.4%
& 2
 
1.4%
_ 2
 
1.4%
Other values (7) 7
 
5.0%
Han
ValueCountFrequency (%)
美 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1471
83.8%
ASCII 283
 
16.1%
CJK 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 130
 
8.8%
헤 125
 
8.5%
미 59
 
4.0%
스 37
 
2.5%
리 35
 
2.4%
일 33
 
2.2%
이 31
 
2.1%
네 30
 
2.0%
용 27
 
1.8%
샵 27
 
1.8%
Other values (273) 937
63.7%
ASCII
ValueCountFrequency (%)
75
26.5%
) 18
 
6.4%
( 18
 
6.4%
a 11
 
3.9%
E 10
 
3.5%
M 9
 
3.2%
r 9
 
3.2%
A 8
 
2.8%
i 8
 
2.8%
. 7
 
2.5%
Other values (43) 110
38.9%
CJK
ValueCountFrequency (%)
美 1
100.0%

영업소 주소(도로명)
Text

Distinct327
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2023-12-12T22:27:35.842609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length43
Mean length29.149254
Min length21

Characters and Unicode

Total characters9765
Distinct characters180
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

Unique319 ?
Unique (%)95.2%

Sample

1st row부산광역시 영도구 대평로 19 (대평동1가)
2nd row부산광역시 영도구 태종로83번길 33 (봉래동1가)
3rd row부산광역시 영도구 사택길 212 (봉래동5가)
4th row부산광역시 영도구 한결길 25 (봉래동3가)
5th row부산광역시 영도구 꿈나무길 295 (신선동3가)
ValueCountFrequency (%)
부산광역시 335
 
17.2%
영도구 335
 
17.2%
동삼동 89
 
4.6%
1층 79
 
4.0%
청학동 58
 
3.0%
태종로 43
 
2.2%
2층 35
 
1.8%
동삼로 24
 
1.2%
영선동2가 24
 
1.2%
봉래동3가 22
 
1.1%
Other values (398) 908
46.5%
2023-12-12T22:27:36.349697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1618
 
16.6%
동 508
 
5.2%
영 484
 
5.0%
1 370
 
3.8%
산 353
 
3.6%
도 349
 
3.6%
시 342
 
3.5%
부 341
 
3.5%
구 337
 
3.5%
광 336
 
3.4%
Other values (170) 4727
48.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5765
59.0%
Space Separator 1618
 
16.6%
Decimal Number 1456
 
14.9%
Close Punctuation 335
 
3.4%
Open Punctuation 335
 
3.4%
Other Punctuation 214
 
2.2%
Dash Punctuation 40
 
0.4%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 508
 
8.8%
영 484
 
8.4%
산 353
 
6.1%
도 349
 
6.1%
시 342
 
5.9%
부 341
 
5.9%
구 337
 
5.8%
광 336
 
5.8%
역 335
 
5.8%
로 266
 
4.6%
Other values (154) 2114
36.7%
Decimal Number
ValueCountFrequency (%)
1 370
25.4%
2 242
16.6%
3 215
14.8%
0 119
 
8.2%
4 118
 
8.1%
5 101
 
6.9%
6 88
 
6.0%
8 83
 
5.7%
7 62
 
4.3%
9 58
 
4.0%
Space Separator
ValueCountFrequency (%)
1618
100.0%
Close Punctuation
ValueCountFrequency (%)
) 335
100.0%
Open Punctuation
ValueCountFrequency (%)
( 335
100.0%
Other Punctuation
ValueCountFrequency (%)
, 214
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5765
59.0%
Common 3998
40.9%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 508
 
8.8%
영 484
 
8.4%
산 353
 
6.1%
도 349
 
6.1%
시 342
 
5.9%
부 341
 
5.9%
구 337
 
5.8%
광 336
 
5.8%
역 335
 
5.8%
로 266
 
4.6%
Other values (154) 2114
36.7%
Common
ValueCountFrequency (%)
1618
40.5%
1 370
 
9.3%
) 335
 
8.4%
( 335
 
8.4%
2 242
 
6.1%
3 215
 
5.4%
, 214
 
5.4%
0 119
 
3.0%
4 118
 
3.0%
5 101
 
2.5%
Other values (5) 331
 
8.3%
Latin
ValueCountFrequency (%)
B 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5765
59.0%
ASCII 4000
41.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1618
40.5%
1 370
 
9.2%
) 335
 
8.4%
( 335
 
8.4%
2 242
 
6.0%
3 215
 
5.4%
, 214
 
5.3%
0 119
 
3.0%
4 118
 
2.9%
5 101
 
2.5%
Other values (6) 333
 
8.3%
Hangul
ValueCountFrequency (%)
동 508
 
8.8%
영 484
 
8.4%
산 353
 
6.1%
도 349
 
6.1%
시 342
 
5.9%
부 341
 
5.9%
구 337
 
5.8%
광 336
 
5.8%
역 335
 
5.8%
로 266
 
4.6%
Other values (154) 2114
36.7%

우편번호(도로명)
Real number (ℝ)

Distinct95
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49063.325
Minimum49003
Maximum49128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-12T22:27:36.523016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49003
5-th percentile49007.4
Q149036
median49060
Q349089
95-th percentile49117.3
Maximum49128
Range125
Interquartile range (IQR)53

Descriptive statistics

Standard deviation33.829775
Coefficient of variation (CV)0.00068951248
Kurtosis-0.99465308
Mean49063.325
Median Absolute Deviation (MAD)28
Skewness-0.022680758
Sum16436214
Variance1144.4537
MonotonicityNot monotonic
2023-12-12T22:27:36.699285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49056 21
 
6.3%
49052 12
 
3.6%
49079 11
 
3.3%
49005 11
 
3.3%
49098 9
 
2.7%
49089 8
 
2.4%
49036 8
 
2.4%
49055 7
 
2.1%
49088 7
 
2.1%
49060 7
 
2.1%
Other values (85) 234
69.9%
ValueCountFrequency (%)
49003 1
 
0.3%
49004 1
 
0.3%
49005 11
3.3%
49006 4
 
1.2%
49008 1
 
0.3%
49009 4
 
1.2%
49010 4
 
1.2%
49012 1
 
0.3%
49014 2
 
0.6%
49015 3
 
0.9%
ValueCountFrequency (%)
49128 2
0.6%
49127 1
 
0.3%
49126 2
0.6%
49124 1
 
0.3%
49123 1
 
0.3%
49122 1
 
0.3%
49121 3
0.9%
49120 1
 
0.3%
49119 1
 
0.3%
49118 4
1.2%

소재지전화
Text

MISSING 

Distinct213
Distinct (%)98.6%
Missing119
Missing (%)35.5%
Memory size2.7 KiB
2023-12-12T22:27:37.027199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.037037
Min length12

Characters and Unicode

Total characters2600
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

Unique210 ?
Unique (%)97.2%

Sample

1st row051-415-3081
2nd row051-416-3287
3rd row051-416-6630
4th row051-417-8204
5th row051-416-0489
ValueCountFrequency (%)
070-4300-2795 2
 
0.9%
051-418-6562 2
 
0.9%
051-416-6215 2
 
0.9%
051-418-8020 1
 
0.5%
051-417-8046 1
 
0.5%
051-414-9110 1
 
0.5%
051-404-5552 1
 
0.5%
070-8902-2568 1
 
0.5%
051-403-4809 1
 
0.5%
051-416-0612 1
 
0.5%
Other values (203) 203
94.0%
2023-12-12T22:27:37.535026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 432
16.6%
1 431
16.6%
0 393
15.1%
5 345
13.3%
4 291
11.2%
3 142
 
5.5%
2 136
 
5.2%
7 132
 
5.1%
8 110
 
4.2%
6 103
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2168
83.4%
Dash Punctuation 432
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 431
19.9%
0 393
18.1%
5 345
15.9%
4 291
13.4%
3 142
 
6.5%
2 136
 
6.3%
7 132
 
6.1%
8 110
 
5.1%
6 103
 
4.8%
9 85
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 432
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 432
16.6%
1 431
16.6%
0 393
15.1%
5 345
13.3%
4 291
11.2%
3 142
 
5.5%
2 136
 
5.2%
7 132
 
5.1%
8 110
 
4.2%
6 103
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 432
16.6%
1 431
16.6%
0 393
15.1%
5 345
13.3%
4 291
11.2%
3 142
 
5.5%
2 136
 
5.2%
7 132
 
5.1%
8 110
 
4.2%
6 103
 
4.0%

Interactions

2023-12-12T22:27:34.317197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:27:37.659453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명우편번호(도로명)
업종명1.0000.000
우편번호(도로명)0.0001.000
2023-12-12T22:27:38.098624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호(도로명)업종명
우편번호(도로명)1.0000.000
업종명0.0001.000

Missing values

2023-12-12T22:27:34.465043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:27:34.593772image/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
0일반미용업순평부산광역시 영도구 대평로 19 (대평동1가)49043051-415-3081
1일반미용업스타 헤어부산광역시 영도구 태종로83번길 33 (봉래동1가)49034051-416-3287
2일반미용업부여부산광역시 영도구 사택길 212 (봉래동5가)49027051-416-6630
3일반미용업평화부산광역시 영도구 한결길 25 (봉래동3가)49059051-417-8204
4일반미용업미즈헤어샵부산광역시 영도구 꿈나무길 295 (신선동3가)49072051-416-0489
5일반미용업Mr.박부산광역시 영도구 청학북서길 40 (청학동)49024051-413-3496
6일반미용업서울부산광역시 영도구 청학로69번길 4 (청학동)49024051-413-2369
7일반미용업윤부산광역시 영도구 남항로25번길 12 (남항동1가)49054051-413-3388
8일반미용업지성부산광역시 영도구 청학동로 38 (청학동)49020051-417-1924
9일반미용업은성부산광역시 영도구 태종로 348 (청학동)49015051-417-2835
업종명업소명영업소 주소(도로명)우편번호(도로명)소재지전화
325화장ㆍ분장 미용업눈썹살롱부산광역시 영도구 남항로49번길 52, 1층 (영선동1가)49052<NA>
326일반미용업, 화장ㆍ분장 미용업이지헤어부산광역시 영도구 동삼북로 20, 108동 1층 104호 (동삼동)49098051-403-7405
327피부미용업, 화장ㆍ분장 미용업15.7도 뷰티부산광역시 영도구 웃서발로 86, 1층 (동삼동)49101<NA>
328네일미용업, 화장ㆍ분장 미용업홍샵부산광역시 영도구 태종로 358 (청학동)49015<NA>
329네일미용업, 화장ㆍ분장 미용업S THE NAIL(에스 더 네일)부산광역시 영도구 태종로 153 (봉래동3가)49006051-997-7919
330네일미용업, 화장ㆍ분장 미용업네일 혠부산광역시 영도구 태종로 308 (청학동)49023<NA>
331일반미용업, 피부미용업, 화장ㆍ분장 미용업EYE이뻐부산광역시 영도구 번영1길 10, 3층 (봉래동3가)49059<NA>
332일반미용업, 네일미용업, 화장ㆍ분장 미용업영도미용실 머리하기좋은날부산광역시 영도구 대교로 6, 1층 (봉래동3가)49005<NA>
333일반미용업, 네일미용업, 화장ㆍ분장 미용업머리해여부산광역시 영도구 남항서로 131, 1층 101호 (대교동1가, 영도 봄여름가을겨울)49042<NA>
334피부미용업, 네일미용업, 화장ㆍ분장 미용업미쁘다 브로우부산광역시 영도구 남항로 34, 2층 (남항동2가)49055<NA>