Overview

Dataset statistics

Number of variables7
Number of observations807
Missing cells158
Missing cells (%)2.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory46.6 KiB
Average record size in memory59.2 B

Variable types

Numeric3
Categorical2
Text2

Dataset

Description강원도 강릉시 미용업소에 대한 데이터로 업종명, 업소명, 업소소재지도로명주소, 지번주소, 위도, 경도 등의 항목을 제공합니다.
Author강원도 강릉시
URLhttps://www.data.go.kr/data/3043765/fileData.do

Alerts

기준일 has constant value ""Constant
연번 is highly overall correlated with 업종명High correlation
업종명 is highly overall correlated with 연번High correlation
위도 has 79 (9.8%) missing valuesMissing
경도 has 79 (9.8%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 19:45:25.107672
Analysis finished2023-12-12 19:45:27.073745
Duration1.97 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct807
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean404
Minimum1
Maximum807
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2023-12-13T04:45:27.147617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile41.3
Q1202.5
median404
Q3605.5
95-th percentile766.7
Maximum807
Range806
Interquartile range (IQR)403

Descriptive statistics

Standard deviation233.10513
Coefficient of variation (CV)0.57699289
Kurtosis-1.2
Mean404
Median Absolute Deviation (MAD)202
Skewness0
Sum326028
Variance54338
MonotonicityStrictly increasing
2023-12-13T04:45:27.289299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
543 1
 
0.1%
533 1
 
0.1%
534 1
 
0.1%
535 1
 
0.1%
536 1
 
0.1%
537 1
 
0.1%
538 1
 
0.1%
539 1
 
0.1%
540 1
 
0.1%
Other values (797) 797
98.8%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
807 1
0.1%
806 1
0.1%
805 1
0.1%
804 1
0.1%
803 1
0.1%
802 1
0.1%
801 1
0.1%
800 1
0.1%
799 1
0.1%
798 1
0.1%

업종명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
일반미용업
278 
미용업
270 
피부미용업
118 
종합미용업
44 
네일미용업
41 
Other values (9)
56 

Length

Max length23
Median length5
Mean length4.9863693
Min length3

Unique

Unique2 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 278
34.4%
미용업 270
33.5%
피부미용업 118
14.6%
종합미용업 44
 
5.5%
네일미용업 41
 
5.1%
화장ㆍ분장 미용업 13
 
1.6%
네일미용업, 화장ㆍ분장 미용업 11
 
1.4%
일반미용업, 화장ㆍ분장 미용업 8
 
1.0%
피부미용업, 네일미용업 7
 
0.9%
일반미용업, 네일미용업, 화장ㆍ분장 미용업 7
 
0.9%
Other values (4) 10
 
1.2%

Length

2023-12-13T04:45:27.447152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 313
34.7%
일반미용업 299
33.2%
피부미용업 130
14.4%
네일미용업 72
 
8.0%
종합미용업 44
 
4.9%
화장ㆍ분장 43
 
4.8%

업소명
Text

Distinct805
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
2023-12-13T04:45:27.735727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22
Mean length5.8934325
Min length1

Characters and Unicode

Total characters4756
Distinct characters482
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique803 ?
Unique (%)99.5%

Sample

1st row국제미용실
2nd row은파미용실
3rd row현대미용실
4th row선아미용실
5th row성미용실
ValueCountFrequency (%)
헤어 17
 
1.8%
네일 7
 
0.7%
nail 6
 
0.6%
hair 4
 
0.4%
살롱 4
 
0.4%
앤 4
 
0.4%
헤어샵 4
 
0.4%
더 4
 
0.4%
태후사랑 4
 
0.4%
스킨케어 3
 
0.3%
Other values (863) 884
93.9%
2023-12-13T04:45:28.443714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 382
 
8.0%
헤 353
 
7.4%
미 176
 
3.7%
실 134
 
2.8%
134
 
2.8%
리 132
 
2.8%
용 129
 
2.7%
스 124
 
2.6%
이 101
 
2.1%
아 77
 
1.6%
Other values (472) 3014
63.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4221
88.8%
Lowercase Letter 150
 
3.2%
Space Separator 134
 
2.8%
Uppercase Letter 115
 
2.4%
Open Punctuation 40
 
0.8%
Close Punctuation 40
 
0.8%
Other Punctuation 26
 
0.5%
Decimal Number 26
 
0.5%
Dash Punctuation 3
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 382
 
9.0%
헤 353
 
8.4%
미 176
 
4.2%
실 134
 
3.2%
리 132
 
3.1%
용 129
 
3.1%
스 124
 
2.9%
이 101
 
2.4%
아 77
 
1.8%
샵 69
 
1.6%
Other values (410) 2544
60.3%
Lowercase Letter
ValueCountFrequency (%)
a 21
14.0%
i 20
13.3%
e 20
13.3%
o 13
8.7%
n 12
8.0%
r 10
 
6.7%
l 10
 
6.7%
h 8
 
5.3%
s 6
 
4.0%
t 4
 
2.7%
Other values (12) 26
17.3%
Uppercase Letter
ValueCountFrequency (%)
S 12
10.4%
N 12
10.4%
A 10
 
8.7%
H 10
 
8.7%
I 8
 
7.0%
O 8
 
7.0%
L 7
 
6.1%
M 7
 
6.1%
T 6
 
5.2%
J 6
 
5.2%
Other values (12) 29
25.2%
Decimal Number
ValueCountFrequency (%)
0 8
30.8%
1 5
19.2%
2 4
15.4%
3 3
 
11.5%
5 3
 
11.5%
6 2
 
7.7%
4 1
 
3.8%
Other Punctuation
ValueCountFrequency (%)
& 10
38.5%
' 5
19.2%
# 4
 
15.4%
, 4
 
15.4%
. 2
 
7.7%
? 1
 
3.8%
Space Separator
ValueCountFrequency (%)
134
100.0%
Open Punctuation
ValueCountFrequency (%)
( 40
100.0%
Close Punctuation
ValueCountFrequency (%)
) 40
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Math Symbol
ValueCountFrequency (%)
= 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4219
88.7%
Common 270
 
5.7%
Latin 265
 
5.6%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 382
 
9.1%
헤 353
 
8.4%
미 176
 
4.2%
실 134
 
3.2%
리 132
 
3.1%
용 129
 
3.1%
스 124
 
2.9%
이 101
 
2.4%
아 77
 
1.8%
샵 69
 
1.6%
Other values (409) 2542
60.3%
Latin
ValueCountFrequency (%)
a 21
 
7.9%
i 20
 
7.5%
e 20
 
7.5%
o 13
 
4.9%
S 12
 
4.5%
n 12
 
4.5%
N 12
 
4.5%
r 10
 
3.8%
A 10
 
3.8%
H 10
 
3.8%
Other values (34) 125
47.2%
Common
ValueCountFrequency (%)
134
49.6%
( 40
 
14.8%
) 40
 
14.8%
& 10
 
3.7%
0 8
 
3.0%
' 5
 
1.9%
1 5
 
1.9%
# 4
 
1.5%
2 4
 
1.5%
, 4
 
1.5%
Other values (8) 16
 
5.9%
Han
ValueCountFrequency (%)
美 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4219
88.7%
ASCII 535
 
11.2%
CJK 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 382
 
9.1%
헤 353
 
8.4%
미 176
 
4.2%
실 134
 
3.2%
리 132
 
3.1%
용 129
 
3.1%
스 124
 
2.9%
이 101
 
2.4%
아 77
 
1.8%
샵 69
 
1.6%
Other values (409) 2542
60.3%
ASCII
ValueCountFrequency (%)
134
25.0%
( 40
 
7.5%
) 40
 
7.5%
a 21
 
3.9%
i 20
 
3.7%
e 20
 
3.7%
o 13
 
2.4%
S 12
 
2.2%
n 12
 
2.2%
N 12
 
2.2%
Other values (52) 211
39.4%
CJK
ValueCountFrequency (%)
美 2
100.0%

업소소재지(도로명)
Text

Distinct782
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
2023-12-13T04:45:28.747945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length44
Mean length25.111524
Min length17

Characters and Unicode

Total characters20265
Distinct characters194
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

Unique758 ?
Unique (%)93.9%

Sample

1st row강원도 강릉시 옥천로19번길 28 (옥천동)
2nd row강원도 강릉시 주문진읍 항구2길 12
3rd row강원도 강릉시 임영로 156-1 (임당동)
4th row강원도 강릉시 임영로157번길 24 (교동)
5th row강원도 강릉시 금성로 21 (성남동)
ValueCountFrequency (%)
강원도 807
 
18.1%
강릉시 807
 
18.1%
1층 175
 
3.9%
포남동 159
 
3.6%
교동 132
 
3.0%
주문진읍 61
 
1.4%
입암동 56
 
1.3%
성남동 55
 
1.2%
임당동 52
 
1.2%
2층 45
 
1.0%
Other values (726) 2108
47.3%
2023-12-13T04:45:29.221829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3650
18.0%
강 1780
 
8.8%
릉 857
 
4.2%
1 851
 
4.2%
시 849
 
4.2%
원 840
 
4.1%
도 810
 
4.0%
동 774
 
3.8%
( 732
 
3.6%
) 732
 
3.6%
Other values (184) 8390
41.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11362
56.1%
Space Separator 3650
 
18.0%
Decimal Number 3248
 
16.0%
Open Punctuation 732
 
3.6%
Close Punctuation 732
 
3.6%
Other Punctuation 358
 
1.8%
Dash Punctuation 177
 
0.9%
Uppercase Letter 5
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
강 1780
15.7%
릉 857
 
7.5%
시 849
 
7.5%
원 840
 
7.4%
도 810
 
7.1%
동 774
 
6.8%
로 583
 
5.1%
길 471
 
4.1%
남 282
 
2.5%
층 250
 
2.2%
Other values (164) 3866
34.0%
Decimal Number
ValueCountFrequency (%)
1 851
26.2%
2 611
18.8%
3 357
11.0%
4 254
 
7.8%
5 237
 
7.3%
6 224
 
6.9%
0 215
 
6.6%
7 188
 
5.8%
8 168
 
5.2%
9 143
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
A 2
40.0%
L 1
20.0%
H 1
20.0%
B 1
20.0%
Space Separator
ValueCountFrequency (%)
3650
100.0%
Open Punctuation
ValueCountFrequency (%)
( 732
100.0%
Close Punctuation
ValueCountFrequency (%)
) 732
100.0%
Other Punctuation
ValueCountFrequency (%)
, 358
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 177
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11362
56.1%
Common 8898
43.9%
Latin 5
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
강 1780
15.7%
릉 857
 
7.5%
시 849
 
7.5%
원 840
 
7.4%
도 810
 
7.1%
동 774
 
6.8%
로 583
 
5.1%
길 471
 
4.1%
남 282
 
2.5%
층 250
 
2.2%
Other values (164) 3866
34.0%
Common
ValueCountFrequency (%)
3650
41.0%
1 851
 
9.6%
( 732
 
8.2%
) 732
 
8.2%
2 611
 
6.9%
, 358
 
4.0%
3 357
 
4.0%
4 254
 
2.9%
5 237
 
2.7%
6 224
 
2.5%
Other values (6) 892
 
10.0%
Latin
ValueCountFrequency (%)
A 2
40.0%
L 1
20.0%
H 1
20.0%
B 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11362
56.1%
ASCII 8903
43.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3650
41.0%
1 851
 
9.6%
( 732
 
8.2%
) 732
 
8.2%
2 611
 
6.9%
, 358
 
4.0%
3 357
 
4.0%
4 254
 
2.9%
5 237
 
2.7%
6 224
 
2.5%
Other values (10) 897
 
10.1%
Hangul
ValueCountFrequency (%)
강 1780
15.7%
릉 857
 
7.5%
시 849
 
7.5%
원 840
 
7.4%
도 810
 
7.1%
동 774
 
6.8%
로 583
 
5.1%
길 471
 
4.1%
남 282
 
2.5%
층 250
 
2.2%
Other values (164) 3866
34.0%

위도
Real number (ℝ)

MISSING 

Distinct653
Distinct (%)89.7%
Missing79
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean37.770848
Minimum37.607405
Maximum37.896406
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2023-12-13T04:45:29.373108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.607405
5-th percentile37.744817
Q137.754113
median37.760136
Q337.770118
95-th percentile37.885967
Maximum37.896406
Range0.28900127
Interquartile range (IQR)0.016004865

Descriptive statistics

Standard deviation0.040857502
Coefficient of variation (CV)0.0010817206
Kurtosis5.3283904
Mean37.770848
Median Absolute Deviation (MAD)0.007318895
Skewness1.4161116
Sum27497.177
Variance0.0016693355
MonotonicityNot monotonic
2023-12-13T04:45:29.567647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.77179023 5
 
0.6%
37.76006393 5
 
0.6%
37.7631291 3
 
0.4%
37.75523994 3
 
0.4%
37.77077812 3
 
0.4%
37.75467062 3
 
0.4%
37.74376854 3
 
0.4%
37.75402512 3
 
0.4%
37.76625466 2
 
0.2%
37.77010612 2
 
0.2%
Other values (643) 696
86.2%
(Missing) 79
 
9.8%
ValueCountFrequency (%)
37.60740479 1
0.1%
37.60789333 1
0.1%
37.60829156 1
0.1%
37.60850399 1
0.1%
37.60868276 1
0.1%
37.60943538 1
0.1%
37.60990464 1
0.1%
37.71486737 1
0.1%
37.73788503 1
0.1%
37.73903292 1
0.1%
ValueCountFrequency (%)
37.89640606 1
0.1%
37.89582133 1
0.1%
37.89539857 1
0.1%
37.89524497 1
0.1%
37.8950035 1
0.1%
37.89492187 1
0.1%
37.89488642 1
0.1%
37.89478266 1
0.1%
37.89408142 1
0.1%
37.89385965 1
0.1%

경도
Real number (ℝ)

MISSING 

Distinct652
Distinct (%)89.6%
Missing79
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean128.89258
Minimum128.8243
Maximum129.03638
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2023-12-13T04:45:29.748276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.8243
5-th percentile128.82667
Q1128.88147
median128.89676
Q3128.90911
95-th percentile128.92001
Maximum129.03638
Range0.212077
Interquartile range (IQR)0.02763545

Descriptive statistics

Standard deviation0.028564726
Coefficient of variation (CV)0.00022161653
Kurtosis5.6843232
Mean128.89258
Median Absolute Deviation (MAD)0.0139097
Skewness0.22787064
Sum93833.797
Variance0.00081594357
MonotonicityNot monotonic
2023-12-13T04:45:29.892317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.9006618 5
 
0.6%
128.9143988 5
 
0.6%
128.8798566 3
 
0.4%
128.9206028 3
 
0.4%
128.8986238 3
 
0.4%
128.919939 3
 
0.4%
128.9125978 3
 
0.4%
128.8820893 3
 
0.4%
128.87486 2
 
0.2%
128.905151 2
 
0.2%
Other values (642) 696
86.2%
(Missing) 79
 
9.8%
ValueCountFrequency (%)
128.8243018 1
0.1%
128.8246371 1
0.1%
128.8246641 1
0.1%
128.8250345 1
0.1%
128.8250415 1
0.1%
128.8250429 1
0.1%
128.8251537 2
0.2%
128.8252578 1
0.1%
128.8253489 1
0.1%
128.8253617 1
0.1%
ValueCountFrequency (%)
129.0363788 1
0.1%
129.0357998 1
0.1%
129.0351452 1
0.1%
129.0346466 1
0.1%
129.0344279 1
0.1%
129.0343265 1
0.1%
129.0334028 1
0.1%
128.9394207 2
0.2%
128.9384675 1
0.1%
128.9369694 1
0.1%

기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
2020-10-20
807 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-10-20
2nd row2020-10-20
3rd row2020-10-20
4th row2020-10-20
5th row2020-10-20

Common Values

ValueCountFrequency (%)
2020-10-20 807
100.0%

Length

2023-12-13T04:45:30.028183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:45:30.144397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-10-20 807
100.0%

Interactions

2023-12-13T04:45:26.463205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:45:25.750624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:45:26.127657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:45:26.571950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:45:25.894342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:45:26.260760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:45:26.675021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:45:26.027508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:45:26.368569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:45:30.212027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명위도경도
연번1.0000.8290.0000.129
업종명0.8291.0000.0000.083
위도0.0000.0001.0000.807
경도0.1290.0830.8071.000
2023-12-13T04:45:30.306957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도업종명
연번1.000-0.052-0.0510.525
위도-0.0521.000-0.0460.000
경도-0.051-0.0461.0000.040
업종명0.5250.0000.0401.000

Missing values

2023-12-13T04:45:26.797179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:45:26.925906image/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.
2023-12-13T04:45:27.020823image/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

연번업종명업소명업소소재지(도로명)위도경도기준일
01미용업국제미용실강원도 강릉시 옥천로19번길 28 (옥천동)37.75606128.9007092020-10-20
12미용업은파미용실강원도 강릉시 주문진읍 항구2길 1237.894081128.8284142020-10-20
23미용업현대미용실강원도 강릉시 임영로 156-1 (임당동)37.755246128.8919612020-10-20
34미용업선아미용실강원도 강릉시 임영로157번길 24 (교동)37.756827128.8899952020-10-20
45미용업성미용실강원도 강릉시 금성로 21 (성남동)37.754025128.8986242020-10-20
56미용업명성미용실강원도 강릉시 경강로 2115 (임당동)37.755962128.8978162020-10-20
67미용업박계란미용실강원도 강릉시 신대학길 22-10 (금학동)37.752848128.8958662020-10-20
78미용업강남미용실강원도 강릉시 토성로 96 (홍제동)37.752454128.8878692020-10-20
89미용업제니정헤어필주문진점강원도 강릉시 주문진읍 주문로 39-237.887578128.8253622020-10-20
910미용업은미용실강원도 강릉시 남문길 7-1 (남문동)37.750846128.8937412020-10-20
연번업종명업소명업소소재지(도로명)위도경도기준일
797798일반미용업르그레이헤어강원도 강릉시 강릉대로106번길 16, 1층 (홍제동)<NA><NA>2020-10-20
798799네일미용업룩엣네일강원도 강릉시 하슬라로 147-1, 1층 101호 (교동)<NA><NA>2020-10-20
799800네일미용업끌리네일강원도 강릉시 범일로 684, 1층 (내곡동)<NA><NA>2020-10-20
800801네일미용업네일 세라강원도 강릉시 경강로 2120, 홈플러스 6층 629호 (옥천동)<NA><NA>2020-10-20
801802네일미용업, 화장ㆍ분장 미용업made J(메이드제이)강원도 강릉시 성덕포남로162번길 13, 1층 (포남동)<NA><NA>2020-10-20
802803네일미용업엄지공주 네일아트강원도 강릉시 경강로 2113, 1층 (임당동)<NA><NA>2020-10-20
803804일반미용업킴헤어강원도 강릉시 중앙시장4길 11-1, 1층 (성남동)<NA><NA>2020-10-20
804805일반미용업행복한 머리강원도 강릉시 화부산로 83, 1층 (교동)<NA><NA>2020-10-20
805806일반미용업태후사랑 임당점강원도 강릉시 경강로2115번길 1, 1층 (임당동)<NA><NA>2020-10-20
806807일반미용업모노헤어강원도 강릉시 금성로22번길 9, 1층 (금학동)<NA><NA>2020-10-20