Overview

Dataset statistics

Number of variables7
Number of observations624
Missing cells246
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.9 KiB
Average record size in memory57.2 B

Variable types

Numeric1
Categorical2
Text3
DateTime1

Dataset

Description공중이 이용하는 영업의 위생관리등에 관한 사항을 규정함으로써 위생수준을 향상시켜 국민의 건강증진에 기여합니다.미용업소의 업종명, 업소명, 업소소재지 등의 정보를 제공합니다.
Author대전광역시 대덕구
URLhttps://www.data.go.kr/data/15040046/fileData.do

Alerts

데이터기준일 has constant value ""Constant
업종명 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
기타유의사항 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 is highly overall correlated with 업종명 and 1 other fieldsHigh correlation
업종명 is highly imbalanced (55.7%)Imbalance
소재지전화 has 246 (39.4%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-04-13 12:08:01.340046
Analysis finished2024-04-13 12:08:04.617308
Duration3.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct624
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean312.5
Minimum1
Maximum624
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-04-13T21:08:04.755106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile32.15
Q1156.75
median312.5
Q3468.25
95-th percentile592.85
Maximum624
Range623
Interquartile range (IQR)311.5

Descriptive statistics

Standard deviation180.27756
Coefficient of variation (CV)0.5768882
Kurtosis-1.2
Mean312.5
Median Absolute Deviation (MAD)156
Skewness0
Sum195000
Variance32500
MonotonicityStrictly increasing
2024-04-13T21:08:05.010021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
421 1
 
0.2%
414 1
 
0.2%
415 1
 
0.2%
416 1
 
0.2%
417 1
 
0.2%
418 1
 
0.2%
419 1
 
0.2%
420 1
 
0.2%
422 1
 
0.2%
Other values (614) 614
98.4%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
624 1
0.2%
623 1
0.2%
622 1
0.2%
621 1
0.2%
620 1
0.2%
619 1
0.2%
618 1
0.2%
617 1
0.2%
616 1
0.2%
615 1
0.2%

업종명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct14
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
일반미용업
423 
피부미용업
78 
네일미용업
49 
종합미용업
 
41
피부미용업, 네일미용업
 
7
Other values (9)
 
26

Length

Max length23
Median length5
Mean length5.4839744
Min length3

Unique

Unique3 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 423
67.8%
피부미용업 78
 
12.5%
네일미용업 49
 
7.9%
종합미용업 41
 
6.6%
피부미용업, 네일미용업 7
 
1.1%
화장ㆍ분장 미용업 6
 
1.0%
피부미용업, 화장ㆍ분장 미용업 6
 
1.0%
일반미용업, 피부미용업 3
 
0.5%
일반미용업, 네일미용업, 화장ㆍ분장 미용업 3
 
0.5%
피부미용업, 네일미용업, 화장ㆍ분장 미용업 3
 
0.5%
Other values (4) 5
 
0.8%

Length

2024-04-13T21:08:05.277354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 432
63.9%
피부미용업 97
 
14.3%
네일미용업 65
 
9.6%
종합미용업 41
 
6.1%
미용업 21
 
3.1%
화장ㆍ분장 20
 
3.0%

업소명
Text

Distinct601
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
2024-04-13T21:08:06.073090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length5.4455128
Min length2

Characters and Unicode

Total characters3398
Distinct characters410
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique581 ?
Unique (%)93.1%

Sample

1st row정다운헤어
2nd row제일미용실
3rd row대전미용실
4th row락희미용실
5th row명동미용실
ValueCountFrequency (%)
헤어스토리 4
 
0.6%
더 3
 
0.5%
송촌점 3
 
0.5%
미용실 3
 
0.5%
미소헤어 3
 
0.5%
머리만들기 2
 
0.3%
경미용실 2
 
0.3%
뷰티 2
 
0.3%
예쁜머리 2
 
0.3%
미장원 2
 
0.3%
Other values (615) 633
96.1%
2024-04-13T21:08:07.119885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 282
 
8.3%
헤 268
 
7.9%
미 182
 
5.4%
용 137
 
4.0%
실 136
 
4.0%
스 88
 
2.6%
리 80
 
2.4%
이 76
 
2.2%
샵 65
 
1.9%
아 57
 
1.7%
Other values (400) 2027
59.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3284
96.6%
Space Separator 35
 
1.0%
Uppercase Letter 24
 
0.7%
Decimal Number 17
 
0.5%
Open Punctuation 9
 
0.3%
Close Punctuation 9
 
0.3%
Lowercase Letter 9
 
0.3%
Other Punctuation 9
 
0.3%
Math Symbol 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 282
 
8.6%
헤 268
 
8.2%
미 182
 
5.5%
용 137
 
4.2%
실 136
 
4.1%
스 88
 
2.7%
리 80
 
2.4%
이 76
 
2.3%
샵 65
 
2.0%
아 57
 
1.7%
Other values (362) 1913
58.3%
Uppercase Letter
ValueCountFrequency (%)
E 5
20.8%
R 3
12.5%
H 2
 
8.3%
S 2
 
8.3%
A 2
 
8.3%
C 2
 
8.3%
P 1
 
4.2%
L 1
 
4.2%
T 1
 
4.2%
Y 1
 
4.2%
Other values (4) 4
16.7%
Decimal Number
ValueCountFrequency (%)
3 4
23.5%
0 3
17.6%
4 2
11.8%
8 2
11.8%
1 2
11.8%
2 2
11.8%
5 1
 
5.9%
6 1
 
5.9%
Lowercase Letter
ValueCountFrequency (%)
e 4
44.4%
v 1
 
11.1%
c 1
 
11.1%
o 1
 
11.1%
r 1
 
11.1%
l 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
# 3
33.3%
, 2
22.2%
. 2
22.2%
& 1
 
11.1%
' 1
 
11.1%
Math Symbol
ValueCountFrequency (%)
< 1
50.0%
> 1
50.0%
Space Separator
ValueCountFrequency (%)
35
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3282
96.6%
Common 81
 
2.4%
Latin 33
 
1.0%
Han 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 282
 
8.6%
헤 268
 
8.2%
미 182
 
5.5%
용 137
 
4.2%
실 136
 
4.1%
스 88
 
2.7%
리 80
 
2.4%
이 76
 
2.3%
샵 65
 
2.0%
아 57
 
1.7%
Other values (361) 1911
58.2%
Latin
ValueCountFrequency (%)
E 5
15.2%
e 4
 
12.1%
R 3
 
9.1%
H 2
 
6.1%
S 2
 
6.1%
A 2
 
6.1%
C 2
 
6.1%
P 1
 
3.0%
L 1
 
3.0%
v 1
 
3.0%
Other values (10) 10
30.3%
Common
ValueCountFrequency (%)
35
43.2%
( 9
 
11.1%
) 9
 
11.1%
3 4
 
4.9%
# 3
 
3.7%
0 3
 
3.7%
, 2
 
2.5%
. 2
 
2.5%
4 2
 
2.5%
8 2
 
2.5%
Other values (8) 10
 
12.3%
Han
ValueCountFrequency (%)
美 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3281
96.6%
ASCII 114
 
3.4%
CJK 2
 
0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 282
 
8.6%
헤 268
 
8.2%
미 182
 
5.5%
용 137
 
4.2%
실 136
 
4.1%
스 88
 
2.7%
리 80
 
2.4%
이 76
 
2.3%
샵 65
 
2.0%
아 57
 
1.7%
Other values (360) 1910
58.2%
ASCII
ValueCountFrequency (%)
35
30.7%
( 9
 
7.9%
) 9
 
7.9%
E 5
 
4.4%
e 4
 
3.5%
3 4
 
3.5%
# 3
 
2.6%
R 3
 
2.6%
0 3
 
2.6%
, 2
 
1.8%
Other values (28) 37
32.5%
CJK
ValueCountFrequency (%)
美 2
100.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%

업소소재지(도로명)
Text

Distinct606
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
2024-04-13T21:08:07.988680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length49
Mean length31.939103
Min length21

Characters and Unicode

Total characters19930
Distinct characters179
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

Unique588 ?
Unique (%)94.2%

Sample

1st row대전광역시 대덕구 중리남로8번길 102, 1층 (중리동)
2nd row대전광역시 대덕구 아리랑로113번길 57 (읍내동)
3rd row대전광역시 대덕구 대덕대로1605번길 13 (석봉동)
4th row대전광역시 대덕구 동심1길 7 (오정동)
5th row대전광역시 대덕구 신탄진로 829-1 (신탄진동)
ValueCountFrequency (%)
대전광역시 624
 
16.3%
대덕구 624
 
16.3%
1층 272
 
7.1%
중리동 116
 
3.0%
송촌동 107
 
2.8%
법동 63
 
1.6%
비래동 59
 
1.5%
석봉동 59
 
1.5%
신탄진동 58
 
1.5%
2층 46
 
1.2%
Other values (612) 1796
47.0%
2024-04-13T21:08:09.243392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3201
 
16.1%
대 1448
 
7.3%
1 1058
 
5.3%
동 872
 
4.4%
덕 739
 
3.7%
전 648
 
3.3%
) 631
 
3.2%
( 631
 
3.2%
시 624
 
3.1%
역 624
 
3.1%
Other values (169) 9454
47.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11460
57.5%
Decimal Number 3378
 
16.9%
Space Separator 3201
 
16.1%
Close Punctuation 631
 
3.2%
Open Punctuation 631
 
3.2%
Other Punctuation 534
 
2.7%
Dash Punctuation 75
 
0.4%
Uppercase Letter 15
 
0.1%
Lowercase Letter 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 1448
 
12.6%
동 872
 
7.6%
덕 739
 
6.4%
전 648
 
5.7%
시 624
 
5.4%
역 624
 
5.4%
광 624
 
5.4%
구 624
 
5.4%
로 612
 
5.3%
길 436
 
3.8%
Other values (145) 4209
36.7%
Decimal Number
ValueCountFrequency (%)
1 1058
31.3%
2 439
13.0%
3 310
 
9.2%
4 296
 
8.8%
0 281
 
8.3%
5 225
 
6.7%
6 225
 
6.7%
8 206
 
6.1%
7 204
 
6.0%
9 134
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
A 6
40.0%
B 4
26.7%
C 1
 
6.7%
D 1
 
6.7%
G 1
 
6.7%
K 1
 
6.7%
T 1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
, 533
99.8%
& 1
 
0.2%
Space Separator
ValueCountFrequency (%)
3201
100.0%
Close Punctuation
ValueCountFrequency (%)
) 631
100.0%
Open Punctuation
ValueCountFrequency (%)
( 631
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 75
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11460
57.5%
Common 8450
42.4%
Latin 20
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 1448
 
12.6%
동 872
 
7.6%
덕 739
 
6.4%
전 648
 
5.7%
시 624
 
5.4%
역 624
 
5.4%
광 624
 
5.4%
구 624
 
5.4%
로 612
 
5.3%
길 436
 
3.8%
Other values (145) 4209
36.7%
Common
ValueCountFrequency (%)
3201
37.9%
1 1058
 
12.5%
) 631
 
7.5%
( 631
 
7.5%
, 533
 
6.3%
2 439
 
5.2%
3 310
 
3.7%
4 296
 
3.5%
0 281
 
3.3%
5 225
 
2.7%
Other values (6) 845
 
10.0%
Latin
ValueCountFrequency (%)
A 6
30.0%
e 5
25.0%
B 4
20.0%
C 1
 
5.0%
D 1
 
5.0%
G 1
 
5.0%
K 1
 
5.0%
T 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11460
57.5%
ASCII 8470
42.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3201
37.8%
1 1058
 
12.5%
) 631
 
7.4%
( 631
 
7.4%
, 533
 
6.3%
2 439
 
5.2%
3 310
 
3.7%
4 296
 
3.5%
0 281
 
3.3%
5 225
 
2.7%
Other values (14) 865
 
10.2%
Hangul
ValueCountFrequency (%)
대 1448
 
12.6%
동 872
 
7.6%
덕 739
 
6.4%
전 648
 
5.7%
시 624
 
5.4%
역 624
 
5.4%
광 624
 
5.4%
구 624
 
5.4%
로 612
 
5.3%
길 436
 
3.8%
Other values (145) 4209
36.7%

소재지전화
Text

MISSING 

Distinct377
Distinct (%)99.7%
Missing246
Missing (%)39.4%
Memory size5.0 KiB
2024-04-13T21:08:10.027298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.029101
Min length12

Characters and Unicode

Total characters4547
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

Unique376 ?
Unique (%)99.5%

Sample

1st row042-623-5896
2nd row042-932-0478
3rd row042-673-0883
4th row042-932-5489
5th row042-932-2184
ValueCountFrequency (%)
042-932-1035 2
 
0.5%
042-320-7702 1
 
0.3%
042-825-9219 1
 
0.3%
042-673-7994 1
 
0.3%
070-8846-9461 1
 
0.3%
042-639-6390 1
 
0.3%
042-631-5551 1
 
0.3%
042-934-1105 1
 
0.3%
042-621-3626 1
 
0.3%
042-471-2855 1
 
0.3%
Other values (367) 367
97.1%
2024-04-13T21:08:11.049466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 756
16.6%
2 737
16.2%
0 579
12.7%
4 546
12.0%
3 445
9.8%
6 432
9.5%
9 231
 
5.1%
1 215
 
4.7%
7 215
 
4.7%
5 197
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3791
83.4%
Dash Punctuation 756
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 737
19.4%
0 579
15.3%
4 546
14.4%
3 445
11.7%
6 432
11.4%
9 231
 
6.1%
1 215
 
5.7%
7 215
 
5.7%
5 197
 
5.2%
8 194
 
5.1%
Dash Punctuation
ValueCountFrequency (%)
- 756
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4547
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 756
16.6%
2 737
16.2%
0 579
12.7%
4 546
12.0%
3 445
9.8%
6 432
9.5%
9 231
 
5.1%
1 215
 
4.7%
7 215
 
4.7%
5 197
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4547
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 756
16.6%
2 737
16.2%
0 579
12.7%
4 546
12.0%
3 445
9.8%
6 432
9.5%
9 231
 
5.1%
1 215
 
4.7%
7 215
 
4.7%
5 197
 
4.3%

기타유의사항
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
<NA>
378 
데이터 미집계
246 

Length

Max length7
Median length4
Mean length5.1826923
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row데이터 미집계
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 378
60.6%
데이터 미집계 246
39.4%

Length

2024-04-13T21:08:11.293208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T21:08:11.484978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 378
43.4%
데이터 246
28.3%
미집계 246
28.3%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
Minimum2023-03-30 00:00:00
Maximum2023-03-30 00:00:00
2024-04-13T21:08:11.633163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:08:11.794174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-13T21:08:04.021212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-13T21:08:11.912244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명
연번1.0000.813
업종명0.8131.000
2024-04-13T21:08:12.051608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명기타유의사항
업종명1.0001.000
기타유의사항1.0001.000
2024-04-13T21:08:12.191319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명기타유의사항
연번1.0000.5001.000
업종명0.5001.0001.000
기타유의사항1.0001.0001.000

Missing values

2024-04-13T21:08:04.318141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-13T21:08:04.528273image/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

연번업종명업소명업소소재지(도로명)소재지전화기타유의사항데이터기준일
01미용업정다운헤어대전광역시 대덕구 중리남로8번길 102, 1층 (중리동)<NA>데이터 미집계2023-03-30
12일반미용업제일미용실대전광역시 대덕구 아리랑로113번길 57 (읍내동)042-623-5896<NA>2023-03-30
23일반미용업대전미용실대전광역시 대덕구 대덕대로1605번길 13 (석봉동)042-932-0478<NA>2023-03-30
34일반미용업락희미용실대전광역시 대덕구 동심1길 7 (오정동)042-673-0883<NA>2023-03-30
45일반미용업명동미용실대전광역시 대덕구 신탄진로 829-1 (신탄진동)042-932-5489<NA>2023-03-30
56일반미용업나비미용실대전광역시 대덕구 신탄진로810번길 6 (신탄진동)042-932-2184<NA>2023-03-30
67일반미용업정화미용실대전광역시 대덕구 신탄진로804번길 14 (신탄진동)042-932-3016<NA>2023-03-30
78일반미용업재희미용실대전광역시 대덕구 아리랑로113번길 44-9, 1층 (읍내동)042-622-2002<NA>2023-03-30
89일반미용업세나미용실대전광역시 대덕구 신탄진동로24번길 6, 1층 (신탄진동)042-933-2944<NA>2023-03-30
910일반미용업나비미용실대전광역시 대덕구 아리랑로125번길 9-20 (읍내동)042-623-0883<NA>2023-03-30
연번업종명업소명업소소재지(도로명)소재지전화기타유의사항데이터기준일
614615피부미용업, 화장ㆍ분장 미용업아미주대전광역시 대덕구 신탄진로844번길 38, 2층 (신탄진동)<NA>데이터 미집계2023-03-30
615616피부미용업, 화장ㆍ분장 미용업세안느뷰티대전광역시 대덕구 동춘당로15번길 46-7, 1층 (송촌동)<NA>데이터 미집계2023-03-30
616617피부미용업, 화장ㆍ분장 미용업뷰티샤인대전광역시 대덕구 홍도로 127, 1층 (중리동)<NA>데이터 미집계2023-03-30
617618네일미용업, 화장ㆍ분장 미용업뷰티쑤대전광역시 대덕구 비래동로32번길 12, 지하1층 (비래동)<NA>데이터 미집계2023-03-30
618619일반미용업, 네일미용업, 화장ㆍ분장 미용업빛초롬네일대전광역시 대덕구 계족산로5번안길 31, 1층 (법동)042-622-4124<NA>2023-03-30
619620일반미용업, 네일미용업, 화장ㆍ분장 미용업머리하는 천사대전광역시 대덕구 비래동로23번길 8, 1층 101호 (비래동, 우정빌라)<NA>데이터 미집계2023-03-30
620621일반미용업, 네일미용업, 화장ㆍ분장 미용업데이지헤어대전광역시 대덕구 계족로564번길 50, 1층 (중리동)<NA>데이터 미집계2023-03-30
621622피부미용업, 네일미용업, 화장ㆍ분장 미용업앨리스네일대전광역시 대덕구 동춘당로114번길 9, 송촌해피죤 3층 328호 (송촌동)<NA>데이터 미집계2023-03-30
622623피부미용업, 네일미용업, 화장ㆍ분장 미용업진네일#대전광역시 대덕구 비래동로15번길 2, B동 102호 (비래동)<NA>데이터 미집계2023-03-30
623624피부미용업, 네일미용업, 화장ㆍ분장 미용업힙티크대전광역시 대덕구 한밭대로1117번길 40, 1,2층 (중리동)<NA>데이터 미집계2023-03-30