Overview

Dataset statistics

Number of variables8
Number of observations414
Missing cells317
Missing cells (%)9.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.8 KiB
Average record size in memory66.3 B

Variable types

Categorical2
Text3
Numeric2
DateTime1

Dataset

Description서산시에 영업허가된 공중 위생업소(이발소. 미용실, 세탁소, 목욕탕, 사우나, 네일아트, 피부관리샵)정보로 업종명, 업소명, 업소소재지, 소재지에 대한 정보를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=445&beforeMenuCd=DOM_000000201001001000&publicdatapk=15000677

Alerts

데이터기준일 has constant value ""Constant
의자수 is highly overall correlated with 침대수High correlation
침대수 is highly overall correlated with 의자수 and 2 other fieldsHigh correlation
업종명 is highly overall correlated with 침대수 and 1 other fieldsHigh correlation
업태명 is highly overall correlated with 침대수 and 1 other fieldsHigh correlation
업종명 is highly imbalanced (61.4%)Imbalance
업태명 is highly imbalanced (63.2%)Imbalance
소재지전화 has 69 (16.7%) missing valuesMissing
의자수 has 35 (8.5%) missing valuesMissing
침대수 has 213 (51.4%) missing valuesMissing
의자수 has 83 (20.0%) zerosZeros
침대수 has 148 (35.7%) zerosZeros

Reproduction

Analysis started2024-01-09 21:32:10.893475
Analysis finished2024-01-09 21:32:11.750759
Duration0.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
미용업(일반)
319 
미용업(피부)
54 
미용업
 
27
미용업(종합)
 
7
미용업(손톱ㆍ발톱)
 
4
Other values (3)
 
3

Length

Max length19
Median length7
Mean length6.8478261
Min length3

Unique

Unique3 ?
Unique (%)0.7%

Sample

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

Common Values

ValueCountFrequency (%)
미용업(일반) 319
77.1%
미용업(피부) 54
 
13.0%
미용업 27
 
6.5%
미용업(종합) 7
 
1.7%
미용업(손톱ㆍ발톱) 4
 
1.0%
미용업(일반), 미용업(피부) 1
 
0.2%
미용업(일반), 미용업(손톱ㆍ발톱) 1
 
0.2%
미용업(피부), 미용업(손톱ㆍ발톱) 1
 
0.2%

Length

2024-01-10T06:32:11.809981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:32:11.907516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미용업(일반 321
77.0%
미용업(피부 56
 
13.4%
미용업 27
 
6.5%
미용업(종합 7
 
1.7%
미용업(손톱ㆍ발톱 6
 
1.4%

업소명
Text

Distinct412
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2024-01-10T06:32:12.164621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length5.7463768
Min length2

Characters and Unicode

Total characters2379
Distinct characters346
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

Unique410 ?
Unique (%)99.0%

Sample

1st row이브미용실
2nd row벧엘미용실
3rd row해화미용실
4th row리안헤어 중앙점
5th row터미널미용실
ValueCountFrequency (%)
헤어샵 9
 
1.9%
미용실 8
 
1.6%
헤어 6
 
1.2%
서산점 3
 
0.6%
피부관리실 3
 
0.6%
네일 3
 
0.6%
스킨케어 2
 
0.4%
스킨앤바디 2
 
0.4%
주현미용실 2
 
0.4%
헤어갤러리 2
 
0.4%
Other values (442) 445
91.8%
2024-01-10T06:32:12.607805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 217
 
9.1%
헤 205
 
8.6%
미 132
 
5.5%
실 107
 
4.5%
용 97
 
4.1%
71
 
3.0%
스 62
 
2.6%
이 56
 
2.4%
리 56
 
2.4%
샵 48
 
2.0%
Other values (336) 1328
55.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2263
95.1%
Space Separator 71
 
3.0%
Close Punctuation 11
 
0.5%
Open Punctuation 11
 
0.5%
Lowercase Letter 8
 
0.3%
Uppercase Letter 7
 
0.3%
Other Punctuation 5
 
0.2%
Decimal Number 2
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 217
 
9.6%
헤 205
 
9.1%
미 132
 
5.8%
실 107
 
4.7%
용 97
 
4.3%
스 62
 
2.7%
이 56
 
2.5%
리 56
 
2.5%
샵 48
 
2.1%
아 34
 
1.5%
Other values (315) 1249
55.2%
Lowercase Letter
ValueCountFrequency (%)
i 2
25.0%
h 1
12.5%
l 1
12.5%
v 1
12.5%
e 1
12.5%
a 1
12.5%
r 1
12.5%
Uppercase Letter
ValueCountFrequency (%)
S 1
14.3%
E 1
14.3%
Z 1
14.3%
J 1
14.3%
P 1
14.3%
O 1
14.3%
Y 1
14.3%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
1 1
50.0%
Space Separator
ValueCountFrequency (%)
71
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Other Punctuation
ValueCountFrequency (%)
? 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2263
95.1%
Common 101
 
4.2%
Latin 15
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 217
 
9.6%
헤 205
 
9.1%
미 132
 
5.8%
실 107
 
4.7%
용 97
 
4.3%
스 62
 
2.7%
이 56
 
2.5%
리 56
 
2.5%
샵 48
 
2.1%
아 34
 
1.5%
Other values (315) 1249
55.2%
Latin
ValueCountFrequency (%)
i 2
13.3%
S 1
 
6.7%
E 1
 
6.7%
Z 1
 
6.7%
J 1
 
6.7%
P 1
 
6.7%
h 1
 
6.7%
O 1
 
6.7%
l 1
 
6.7%
v 1
 
6.7%
Other values (4) 4
26.7%
Common
ValueCountFrequency (%)
71
70.3%
) 11
 
10.9%
( 11
 
10.9%
? 5
 
5.0%
2 1
 
1.0%
- 1
 
1.0%
1 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2263
95.1%
ASCII 116
 
4.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 217
 
9.6%
헤 205
 
9.1%
미 132
 
5.8%
실 107
 
4.7%
용 97
 
4.3%
스 62
 
2.7%
이 56
 
2.5%
리 56
 
2.5%
샵 48
 
2.1%
아 34
 
1.5%
Other values (315) 1249
55.2%
ASCII
ValueCountFrequency (%)
71
61.2%
) 11
 
9.5%
( 11
 
9.5%
? 5
 
4.3%
i 2
 
1.7%
2 1
 
0.9%
S 1
 
0.9%
E 1
 
0.9%
Z 1
 
0.9%
J 1
 
0.9%
Other values (11) 11
 
9.5%

업소소재지(도로명)
Text

Distinct404
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2024-01-10T06:32:12.900304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length45
Mean length27.876812
Min length19

Characters and Unicode

Total characters11541
Distinct characters173
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

Unique395 ?
Unique (%)95.4%

Sample

1st row충청남도 서산시 시장4길 7 (동문동,-14)
2nd row충청남도 서산시 고북면 고북1로 306
3rd row충청남도 서산시 대산읍 충의로 1908, 1층
4th row충청남도 서산시 번화1로 32 (동문동)
5th row충청남도 서산시 안견로 190 (동문동)
ValueCountFrequency (%)
충청남도 414
 
16.7%
서산시 414
 
16.7%
1층 168
 
6.8%
동문동 155
 
6.2%
읍내동 57
 
2.3%
2층 48
 
1.9%
예천동 33
 
1.3%
석림동 31
 
1.2%
상가동 30
 
1.2%
고운로 28
 
1.1%
Other values (475) 1104
44.5%
2024-01-10T06:32:13.271337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2115
18.3%
동 610
 
5.3%
1 577
 
5.0%
시 464
 
4.0%
서 453
 
3.9%
산 451
 
3.9%
남 449
 
3.9%
충 428
 
3.7%
도 419
 
3.6%
청 415
 
3.6%
Other values (163) 5160
44.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6366
55.2%
Space Separator 2115
 
18.3%
Decimal Number 1843
 
16.0%
Close Punctuation 396
 
3.4%
Open Punctuation 396
 
3.4%
Other Punctuation 331
 
2.9%
Dash Punctuation 88
 
0.8%
Uppercase Letter 6
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 610
 
9.6%
시 464
 
7.3%
서 453
 
7.1%
산 451
 
7.1%
남 449
 
7.1%
충 428
 
6.7%
도 419
 
6.6%
청 415
 
6.5%
로 347
 
5.5%
층 263
 
4.1%
Other values (144) 2067
32.5%
Decimal Number
ValueCountFrequency (%)
1 577
31.3%
2 321
17.4%
3 197
 
10.7%
4 139
 
7.5%
0 121
 
6.6%
6 113
 
6.1%
5 109
 
5.9%
9 96
 
5.2%
7 87
 
4.7%
8 83
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
B 3
50.0%
A 2
33.3%
C 1
 
16.7%
Other Punctuation
ValueCountFrequency (%)
, 327
98.8%
@ 4
 
1.2%
Space Separator
ValueCountFrequency (%)
2115
100.0%
Close Punctuation
ValueCountFrequency (%)
) 396
100.0%
Open Punctuation
ValueCountFrequency (%)
( 396
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 88
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6366
55.2%
Common 5169
44.8%
Latin 6
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 610
 
9.6%
시 464
 
7.3%
서 453
 
7.1%
산 451
 
7.1%
남 449
 
7.1%
충 428
 
6.7%
도 419
 
6.6%
청 415
 
6.5%
로 347
 
5.5%
층 263
 
4.1%
Other values (144) 2067
32.5%
Common
ValueCountFrequency (%)
2115
40.9%
1 577
 
11.2%
) 396
 
7.7%
( 396
 
7.7%
, 327
 
6.3%
2 321
 
6.2%
3 197
 
3.8%
4 139
 
2.7%
0 121
 
2.3%
6 113
 
2.2%
Other values (6) 467
 
9.0%
Latin
ValueCountFrequency (%)
B 3
50.0%
A 2
33.3%
C 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6366
55.2%
ASCII 5175
44.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2115
40.9%
1 577
 
11.1%
) 396
 
7.7%
( 396
 
7.7%
, 327
 
6.3%
2 321
 
6.2%
3 197
 
3.8%
4 139
 
2.7%
0 121
 
2.3%
6 113
 
2.2%
Other values (9) 473
 
9.1%
Hangul
ValueCountFrequency (%)
동 610
 
9.6%
시 464
 
7.3%
서 453
 
7.1%
산 451
 
7.1%
남 449
 
7.1%
충 428
 
6.7%
도 419
 
6.6%
청 415
 
6.5%
로 347
 
5.5%
층 263
 
4.1%
Other values (144) 2067
32.5%

소재지전화
Text

MISSING 

Distinct339
Distinct (%)98.3%
Missing69
Missing (%)16.7%
Memory size3.4 KiB
2024-01-10T06:32:13.476039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.014493
Min length8

Characters and Unicode

Total characters4145
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

Unique333 ?
Unique (%)96.5%

Sample

1st row041-665-5863
2nd row041-662-3078
3rd row041-681-7804
4th row041-668-5545
5th row041-667-1738
ValueCountFrequency (%)
041-665-8474 2
 
0.6%
041-663-3179 2
 
0.6%
041-669-5538 2
 
0.6%
041-664-1562 2
 
0.6%
041-662-0468 2
 
0.6%
041-665-0431 2
 
0.6%
041-669-5005 1
 
0.3%
041-669-3418 1
 
0.3%
041-668-2030 1
 
0.3%
041-669-9985 1
 
0.3%
Other values (329) 329
95.4%
2024-01-10T06:32:13.782167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 776
18.7%
- 689
16.6%
0 537
13.0%
4 507
12.2%
1 493
11.9%
8 225
 
5.4%
5 209
 
5.0%
7 207
 
5.0%
3 182
 
4.4%
9 162
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3456
83.4%
Dash Punctuation 689
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 776
22.5%
0 537
15.5%
4 507
14.7%
1 493
14.3%
8 225
 
6.5%
5 209
 
6.0%
7 207
 
6.0%
3 182
 
5.3%
9 162
 
4.7%
2 158
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 689
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4145
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 776
18.7%
- 689
16.6%
0 537
13.0%
4 507
12.2%
1 493
11.9%
8 225
 
5.4%
5 209
 
5.0%
7 207
 
5.0%
3 182
 
4.4%
9 162
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4145
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 776
18.7%
- 689
16.6%
0 537
13.0%
4 507
12.2%
1 493
11.9%
8 225
 
5.4%
5 209
 
5.0%
7 207
 
5.0%
3 182
 
4.4%
9 162
 
3.9%

업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
일반미용업
342 
피부미용업
54 
네일아트업
 
13
기타
 
4
일반이용업
 
1

Length

Max length5
Median length5
Mean length4.9710145
Min length2

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 342
82.6%
피부미용업 54
 
13.0%
네일아트업 13
 
3.1%
기타 4
 
1.0%
일반이용업 1
 
0.2%

Length

2024-01-10T06:32:13.892353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:32:13.976381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 342
82.6%
피부미용업 54
 
13.0%
네일아트업 13
 
3.1%
기타 4
 
1.0%
일반이용업 1
 
0.2%

의자수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct12
Distinct (%)3.2%
Missing35
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean2.8258575
Minimum0
Maximum13
Zeros83
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2024-01-10T06:32:14.055715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile6
Maximum13
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0422175
Coefficient of variation (CV)0.72268948
Kurtosis2.5388604
Mean2.8258575
Median Absolute Deviation (MAD)1
Skewness0.84232468
Sum1071
Variance4.1706524
MonotonicityNot monotonic
2024-01-10T06:32:14.142612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 153
37.0%
0 83
20.0%
4 55
 
13.3%
2 39
 
9.4%
5 16
 
3.9%
6 15
 
3.6%
8 7
 
1.7%
7 5
 
1.2%
9 3
 
0.7%
13 1
 
0.2%
Other values (2) 2
 
0.5%
(Missing) 35
 
8.5%
ValueCountFrequency (%)
0 83
20.0%
1 1
 
0.2%
2 39
 
9.4%
3 153
37.0%
4 55
 
13.3%
5 16
 
3.9%
6 15
 
3.6%
7 5
 
1.2%
8 7
 
1.7%
9 3
 
0.7%
ValueCountFrequency (%)
13 1
 
0.2%
12 1
 
0.2%
9 3
 
0.7%
8 7
 
1.7%
7 5
 
1.2%
6 15
 
3.6%
5 16
 
3.9%
4 55
 
13.3%
3 153
37.0%
2 39
 
9.4%

침대수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)4.5%
Missing213
Missing (%)51.4%
Infinite0
Infinite (%)0.0%
Mean0.93532338
Minimum0
Maximum9
Zeros148
Zeros (%)35.7%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2024-01-10T06:32:14.225496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8250469
Coefficient of variation (CV)1.9512469
Kurtosis4.864055
Mean0.93532338
Median Absolute Deviation (MAD)0
Skewness2.2001871
Sum188
Variance3.330796
MonotonicityNot monotonic
2024-01-10T06:32:14.303354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 148
35.7%
3 20
 
4.8%
2 15
 
3.6%
4 6
 
1.4%
7 4
 
1.0%
5 3
 
0.7%
9 2
 
0.5%
6 2
 
0.5%
1 1
 
0.2%
(Missing) 213
51.4%
ValueCountFrequency (%)
0 148
35.7%
1 1
 
0.2%
2 15
 
3.6%
3 20
 
4.8%
4 6
 
1.4%
5 3
 
0.7%
6 2
 
0.5%
7 4
 
1.0%
9 2
 
0.5%
ValueCountFrequency (%)
9 2
 
0.5%
7 4
 
1.0%
6 2
 
0.5%
5 3
 
0.7%
4 6
 
1.4%
3 20
 
4.8%
2 15
 
3.6%
1 1
 
0.2%
0 148
35.7%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
Minimum2016-05-16 00:00:00
Maximum2016-05-16 00:00:00
2024-01-10T06:32:14.374538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:32:14.439979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-10T06:32:11.396618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:32:11.264613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:32:11.458667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:32:11.335761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:32:14.493085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업태명의자수침대수
업종명1.0000.8210.6650.766
업태명0.8211.0000.5280.708
의자수0.6650.5281.0000.556
침대수0.7660.7080.5561.000
2024-01-10T06:32:14.569909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태명업종명
업태명1.0000.688
업종명0.6881.000
2024-01-10T06:32:14.636279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
의자수침대수업종명업태명
의자수1.000-0.7090.2770.358
침대수-0.7091.0000.5430.504
업종명0.2770.5431.0000.688
업태명0.3580.5040.6881.000

Missing values

2024-01-10T06:32:11.539063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:32:11.634438image/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.
2024-01-10T06:32:11.710184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

업종명업소명업소소재지(도로명)소재지전화업태명의자수침대수데이터기준일
0미용업이브미용실충청남도 서산시 시장4길 7 (동문동,-14)041-665-5863일반미용업3<NA>2016-05-16
1미용업벧엘미용실충청남도 서산시 고북면 고북1로 306041-662-3078일반미용업2<NA>2016-05-16
2미용업해화미용실충청남도 서산시 대산읍 충의로 1908, 1층041-681-7804일반미용업3<NA>2016-05-16
3미용업리안헤어 중앙점충청남도 서산시 번화1로 32 (동문동)041-668-5545일반미용업3<NA>2016-05-16
4미용업터미널미용실충청남도 서산시 안견로 190 (동문동)041-667-1738일반미용업8<NA>2016-05-16
5미용업봉쥬르미용실충청남도 서산시 안견로 149-12 (동문동)041-663-8280일반미용업2<NA>2016-05-16
6미용업뷰앤조이충청남도 서산시 호수공원8로 4-16 (예천동)041-666-1242일반미용업13<NA>2016-05-16
7미용업주현미용실충청남도 서산시 음암면 도당가금말길 2 ((1층))041-663-0709일반미용업0<NA>2016-05-16
8미용업동문미용실충청남도 서산시 대사동14길 2 (동문동)041-664-3626일반미용업0<NA>2016-05-16
9미용업헤어겔러리충청남도 서산시 부춘2로 22 (읍내동)041-666-2752일반미용업0<NA>2016-05-16
업종명업소명업소소재지(도로명)소재지전화업태명의자수침대수데이터기준일
404미용업(종합)벨라케어충청남도 서산시 호수공원12로 19, 1층 (예천동)041-667-3225기타432016-05-16
405미용업(종합)신디샵충청남도 서산시 호수공원9로 68, 1층 (예천동)041-663-1253기타422016-05-16
406미용업(종합)그녀는예쁘다충청남도 서산시 부춘2로 32, 1층 (읍내동)<NA>기타122016-05-16
407미용업(손톱ㆍ발톱)뷰티박스충청남도 서산시 대산읍 충의로 1942, 1층 103호 (대산종합시장)<NA>네일아트업202016-05-16
408미용업(손톱ㆍ발톱)모모네일충청남도 서산시 충의로 49, 나동 1층 (예천동)<NA>네일아트업402016-05-16
409미용업(손톱ㆍ발톱)하트네일충청남도 서산시 번화2로 33, 1층 (동문동)<NA>네일아트업202016-05-16
410미용업(손톱ㆍ발톱)포쉬네일 이마트서산점충청남도 서산시 서해로 지하 3685, 1층 (잠홍동)<NA>네일아트업802016-05-16
411미용업(일반), 미용업(피부)인아뷰티하우스충청남도 서산시 동헌로 65, 1층 (읍내동)041-920-6253기타212016-05-16
412미용업(일반), 미용업(손톱ㆍ발톱)래쉬랩스(서산점)충청남도 서산시 안견로 189, 2층 (동문동)041-667-1511일반미용업402016-05-16
413미용업(피부), 미용업(손톱ㆍ발톱)쥬네일충청남도 서산시 서령로 42, 상가동 1층 104호 (동문동, 보령훼미리아파트)<NA>네일아트업602016-05-16