Overview

Dataset statistics

Number of variables7
Number of observations843
Missing cells349
Missing cells (%)5.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory47.9 KiB
Average record size in memory58.2 B

Variable types

Numeric2
Categorical2
Text3

Dataset

Description대전광역시 동구 미용업소 현황으로 2023.4.17. 기준 자료입니다. 업종명, 업소명, 주소, 면적, 전화, 업태명 등을 포함하고 있습니다.
URLhttps://www.data.go.kr/data/15086326/fileData.do

Alerts

연번 is highly overall correlated with 업종명 and 1 other fieldsHigh correlation
업종명 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
업태명 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
소재지전화 has 342 (40.6%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 08:06:05.301426
Analysis finished2023-12-12 08:06:06.758829
Duration1.46 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct843
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean422
Minimum1
Maximum843
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2023-12-12T17:06:06.883685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile43.1
Q1211.5
median422
Q3632.5
95-th percentile800.9
Maximum843
Range842
Interquartile range (IQR)421

Descriptive statistics

Standard deviation243.49743
Coefficient of variation (CV)0.57700814
Kurtosis-1.2
Mean422
Median Absolute Deviation (MAD)211
Skewness0
Sum355746
Variance59291
MonotonicityStrictly increasing
2023-12-12T17:06:07.117397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
581 1
 
0.1%
557 1
 
0.1%
558 1
 
0.1%
559 1
 
0.1%
560 1
 
0.1%
561 1
 
0.1%
562 1
 
0.1%
563 1
 
0.1%
564 1
 
0.1%
Other values (833) 833
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 (%)
843 1
0.1%
842 1
0.1%
841 1
0.1%
840 1
0.1%
839 1
0.1%
838 1
0.1%
837 1
0.1%
836 1
0.1%
835 1
0.1%
834 1
0.1%

업종명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
일반미용업
527 
이용업
99 
피부미용업
68 
종합미용업
56 
네일미용업
 
45
Other values (10)
 
48

Length

Max length23
Median length5
Mean length5.3345196
Min length3

Unique

Unique2 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 527
62.5%
이용업 99
 
11.7%
피부미용업 68
 
8.1%
종합미용업 56
 
6.6%
네일미용업 45
 
5.3%
일반미용업, 화장ㆍ분장 미용업 11
 
1.3%
피부미용업, 네일미용업 8
 
0.9%
네일미용업, 화장ㆍ분장 미용업 8
 
0.9%
일반미용업, 네일미용업 5
 
0.6%
화장ㆍ분장 미용업 5
 
0.6%
Other values (5) 11
 
1.3%

Length

2023-12-12T17:06:07.356482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 549
59.3%
이용업 99
 
10.7%
피부미용업 81
 
8.7%
네일미용업 74
 
8.0%
종합미용업 56
 
6.0%
미용업 34
 
3.7%
화장ㆍ분장 33
 
3.6%

업소명
Text

Distinct813
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
2023-12-12T17:06:07.694044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length28
Mean length5.8635824
Min length1

Characters and Unicode

Total characters4943
Distinct characters489
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

Unique787 ?
Unique (%)93.4%

Sample

1st row살롱더디엠(SALON THE DM)
2nd row현미헤어샵
3rd row모던
4th row나비미용실
5th row신화미용실
ValueCountFrequency (%)
헤어 10
 
1.0%
hair 9
 
0.9%
헤어샵 5
 
0.5%
미용실 5
 
0.5%
네일 5
 
0.5%
서울이용원 4
 
0.4%
이용원 4
 
0.4%
수미용실 3
 
0.3%
미장원 3
 
0.3%
수정미용실 3
 
0.3%
Other values (888) 925
94.8%
2023-12-12T17:06:08.791548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 356
 
7.2%
헤 334
 
6.8%
용 200
 
4.0%
미 172
 
3.5%
이 164
 
3.3%
133
 
2.7%
실 120
 
2.4%
원 93
 
1.9%
리 90
 
1.8%
스 88
 
1.8%
Other values (479) 3193
64.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4223
85.4%
Lowercase Letter 217
 
4.4%
Uppercase Letter 189
 
3.8%
Space Separator 133
 
2.7%
Open Punctuation 56
 
1.1%
Close Punctuation 56
 
1.1%
Other Punctuation 33
 
0.7%
Decimal Number 33
 
0.7%
Dash Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 356
 
8.4%
헤 334
 
7.9%
용 200
 
4.7%
미 172
 
4.1%
이 164
 
3.9%
실 120
 
2.8%
원 93
 
2.2%
리 90
 
2.1%
스 88
 
2.1%
샵 71
 
1.7%
Other values (414) 2535
60.0%
Uppercase Letter
ValueCountFrequency (%)
A 25
13.2%
H 19
 
10.1%
I 15
 
7.9%
S 15
 
7.9%
R 14
 
7.4%
L 13
 
6.9%
N 12
 
6.3%
B 10
 
5.3%
T 9
 
4.8%
E 9
 
4.8%
Other values (13) 48
25.4%
Lowercase Letter
ValueCountFrequency (%)
a 32
14.7%
i 24
11.1%
o 23
10.6%
n 19
8.8%
r 17
7.8%
e 16
 
7.4%
l 14
 
6.5%
s 13
 
6.0%
y 11
 
5.1%
t 8
 
3.7%
Other values (11) 40
18.4%
Other Punctuation
ValueCountFrequency (%)
& 10
30.3%
. 7
21.2%
# 7
21.2%
, 4
 
12.1%
; 1
 
3.0%
% 1
 
3.0%
· 1
 
3.0%
' 1
 
3.0%
: 1
 
3.0%
Decimal Number
ValueCountFrequency (%)
1 7
21.2%
2 7
21.2%
5 4
12.1%
6 4
12.1%
3 4
12.1%
0 3
9.1%
8 2
 
6.1%
7 2
 
6.1%
Space Separator
ValueCountFrequency (%)
133
100.0%
Open Punctuation
ValueCountFrequency (%)
( 56
100.0%
Close Punctuation
ValueCountFrequency (%)
) 56
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4222
85.4%
Latin 406
 
8.2%
Common 314
 
6.4%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 356
 
8.4%
헤 334
 
7.9%
용 200
 
4.7%
미 172
 
4.1%
이 164
 
3.9%
실 120
 
2.8%
원 93
 
2.2%
리 90
 
2.1%
스 88
 
2.1%
샵 71
 
1.7%
Other values (413) 2534
60.0%
Latin
ValueCountFrequency (%)
a 32
 
7.9%
A 25
 
6.2%
i 24
 
5.9%
o 23
 
5.7%
n 19
 
4.7%
H 19
 
4.7%
r 17
 
4.2%
e 16
 
3.9%
I 15
 
3.7%
S 15
 
3.7%
Other values (34) 201
49.5%
Common
ValueCountFrequency (%)
133
42.4%
( 56
17.8%
) 56
17.8%
& 10
 
3.2%
1 7
 
2.2%
2 7
 
2.2%
. 7
 
2.2%
# 7
 
2.2%
5 4
 
1.3%
, 4
 
1.3%
Other values (11) 23
 
7.3%
Han
ValueCountFrequency (%)
旻 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4222
85.4%
ASCII 719
 
14.5%
CJK 1
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 356
 
8.4%
헤 334
 
7.9%
용 200
 
4.7%
미 172
 
4.1%
이 164
 
3.9%
실 120
 
2.8%
원 93
 
2.2%
리 90
 
2.1%
스 88
 
2.1%
샵 71
 
1.7%
Other values (413) 2534
60.0%
ASCII
ValueCountFrequency (%)
133
18.5%
( 56
 
7.8%
) 56
 
7.8%
a 32
 
4.5%
A 25
 
3.5%
i 24
 
3.3%
o 23
 
3.2%
n 19
 
2.6%
H 19
 
2.6%
r 17
 
2.4%
Other values (54) 315
43.8%
CJK
ValueCountFrequency (%)
旻 1
100.0%
None
ValueCountFrequency (%)
· 1
100.0%

영업소 주소(도로명)
Text

Distinct827
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
2023-12-12T17:06:09.314506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length48
Mean length29.023725
Min length16

Characters and Unicode

Total characters24467
Distinct characters216
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique811 ?
Unique (%)96.2%

Sample

1st row대전광역시 동구 은어송로 36, 수성프라자 2층 202호 (가오동)
2nd row대전광역시 동구 계족로362번길 6 (성남동)
3rd row대전광역시 동구 중앙로193번길 31 (중동)
4th row대전광역시 동구 중앙로193번길 11 (중동)
5th row대전광역시 동구 대전로 828 (정동)
ValueCountFrequency (%)
대전광역시 843
 
16.9%
동구 843
 
16.9%
1층 272
 
5.4%
가양동 146
 
2.9%
용전동 97
 
1.9%
용운동 81
 
1.6%
가오동 75
 
1.5%
2층 72
 
1.4%
자양동 66
 
1.3%
대동 51
 
1.0%
Other values (805) 2454
49.1%
2023-12-12T17:06:09.962959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4157
 
17.0%
동 1973
 
8.1%
대 1164
 
4.8%
1 1160
 
4.7%
전 1124
 
4.6%
구 865
 
3.5%
시 856
 
3.5%
) 849
 
3.5%
( 849
 
3.5%
역 845
 
3.5%
Other values (206) 10625
43.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13441
54.9%
Decimal Number 4365
 
17.8%
Space Separator 4157
 
17.0%
Close Punctuation 849
 
3.5%
Open Punctuation 849
 
3.5%
Other Punctuation 631
 
2.6%
Dash Punctuation 161
 
0.7%
Uppercase Letter 11
 
< 0.1%
Lowercase Letter 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1973
14.7%
대 1164
 
8.7%
전 1124
 
8.4%
구 865
 
6.4%
시 856
 
6.4%
역 845
 
6.3%
광 845
 
6.3%
로 811
 
6.0%
길 454
 
3.4%
번 422
 
3.1%
Other values (182) 4082
30.4%
Decimal Number
ValueCountFrequency (%)
1 1160
26.6%
2 587
13.4%
3 417
 
9.6%
0 410
 
9.4%
4 376
 
8.6%
5 345
 
7.9%
6 344
 
7.9%
7 271
 
6.2%
8 255
 
5.8%
9 200
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
L 2
18.2%
B 2
18.2%
H 2
18.2%
E 2
18.2%
W 2
18.2%
A 1
9.1%
Other Punctuation
ValueCountFrequency (%)
, 614
97.3%
@ 17
 
2.7%
Space Separator
ValueCountFrequency (%)
4157
100.0%
Close Punctuation
ValueCountFrequency (%)
) 849
100.0%
Open Punctuation
ValueCountFrequency (%)
( 849
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 161
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13441
54.9%
Common 11013
45.0%
Latin 13
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1973
14.7%
대 1164
 
8.7%
전 1124
 
8.4%
구 865
 
6.4%
시 856
 
6.4%
역 845
 
6.3%
광 845
 
6.3%
로 811
 
6.0%
길 454
 
3.4%
번 422
 
3.1%
Other values (182) 4082
30.4%
Common
ValueCountFrequency (%)
4157
37.7%
1 1160
 
10.5%
) 849
 
7.7%
( 849
 
7.7%
, 614
 
5.6%
2 587
 
5.3%
3 417
 
3.8%
0 410
 
3.7%
4 376
 
3.4%
5 345
 
3.1%
Other values (7) 1249
 
11.3%
Latin
ValueCountFrequency (%)
L 2
15.4%
B 2
15.4%
H 2
15.4%
E 2
15.4%
W 2
15.4%
e 2
15.4%
A 1
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13441
54.9%
ASCII 11026
45.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4157
37.7%
1 1160
 
10.5%
) 849
 
7.7%
( 849
 
7.7%
, 614
 
5.6%
2 587
 
5.3%
3 417
 
3.8%
0 410
 
3.7%
4 376
 
3.4%
5 345
 
3.1%
Other values (14) 1262
 
11.4%
Hangul
ValueCountFrequency (%)
동 1973
14.7%
대 1164
 
8.7%
전 1124
 
8.4%
구 865
 
6.4%
시 856
 
6.4%
역 845
 
6.3%
광 845
 
6.3%
로 811
 
6.0%
길 454
 
3.4%
번 422
 
3.1%
Other values (182) 4082
30.4%

영업장면적
Real number (ℝ)

Distinct607
Distinct (%)72.6%
Missing7
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean37.898852
Minimum0
Maximum299.69
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2023-12-12T17:06:10.174468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.1125
Q121.4925
median29.625
Q343
95-th percentile98.485
Maximum299.69
Range299.69
Interquartile range (IQR)21.5075

Descriptive statistics

Standard deviation30.152017
Coefficient of variation (CV)0.79559185
Kurtosis15.804991
Mean37.898852
Median Absolute Deviation (MAD)9.71
Skewness3.2869823
Sum31683.44
Variance909.14416
MonotonicityNot monotonic
2023-12-12T17:06:10.386083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.0 22
 
2.6%
23.1 10
 
1.2%
29.7 9
 
1.1%
13.2 9
 
1.1%
36.0 9
 
1.1%
19.8 8
 
0.9%
30.0 7
 
0.8%
23.86 6
 
0.7%
26.4 6
 
0.7%
20.0 6
 
0.7%
Other values (597) 744
88.3%
(Missing) 7
 
0.8%
ValueCountFrequency (%)
0.0 2
0.2%
4.0 1
0.1%
5.0 2
0.2%
5.61 1
0.1%
6.55 1
0.1%
6.6 1
0.1%
7.0 1
0.1%
8.25 1
0.1%
8.3 1
0.1%
8.52 1
0.1%
ValueCountFrequency (%)
299.69 1
0.1%
254.19 1
0.1%
213.44 1
0.1%
201.95 1
0.1%
200.2 1
0.1%
194.01 1
0.1%
167.6 1
0.1%
166.44 1
0.1%
156.75 1
0.1%
151.36 1
0.1%

소재지전화
Text

MISSING 

Distinct500
Distinct (%)99.8%
Missing342
Missing (%)40.6%
Memory size6.7 KiB
2023-12-12T17:06:10.714927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length14
Mean length14.363273
Min length12

Characters and Unicode

Total characters7196
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

Unique499 ?
Unique (%)99.6%

Sample

1st row 042- 622-5588
2nd row 042- 253-4105
3rd row 042- 256-2040
4th row 042- 627-0323
5th row 042- 283-9633
ValueCountFrequency (%)
042 485
48.8%
070 5
 
0.5%
282-4270 2
 
0.2%
635-5053 2
 
0.2%
628-4040 2
 
0.2%
7789-1070 1
 
0.1%
636-5039 1
 
0.1%
271-6766 1
 
0.1%
285-1009 1
 
0.1%
272-0545 1
 
0.1%
Other values (493) 493
49.6%
2023-12-12T17:06:11.251643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 1174
16.3%
1167
16.2%
- 1002
13.9%
0 765
10.6%
4 746
10.4%
6 506
7.0%
3 455
 
6.3%
7 345
 
4.8%
8 305
 
4.2%
5 275
 
3.8%
Other values (2) 456
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5027
69.9%
Space Separator 1167
 
16.2%
Dash Punctuation 1002
 
13.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1174
23.4%
0 765
15.2%
4 746
14.8%
6 506
10.1%
3 455
 
9.1%
7 345
 
6.9%
8 305
 
6.1%
5 275
 
5.5%
1 262
 
5.2%
9 194
 
3.9%
Space Separator
ValueCountFrequency (%)
1167
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1002
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7196
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1174
16.3%
1167
16.2%
- 1002
13.9%
0 765
10.6%
4 746
10.4%
6 506
7.0%
3 455
 
6.3%
7 345
 
4.8%
8 305
 
4.2%
5 275
 
3.8%
Other values (2) 456
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7196
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1174
16.3%
1167
16.2%
- 1002
13.9%
0 765
10.6%
4 746
10.4%
6 506
7.0%
3 455
 
6.3%
7 345
 
4.8%
8 305
 
4.2%
5 275
 
3.8%
Other values (2) 456
 
6.3%

업태명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
일반미용업
570 
일반이용업
97 
네일아트업
77 
피부미용업
76 
기타
 
12
Other values (2)
 
11

Length

Max length6
Median length5
Mean length4.9596679
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 570
67.6%
일반이용업 97
 
11.5%
네일아트업 77
 
9.1%
피부미용업 76
 
9.0%
기타 12
 
1.4%
메이크업업 9
 
1.1%
이용업 기타 2
 
0.2%

Length

2023-12-12T17:06:11.458841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:06:11.596937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 570
67.5%
일반이용업 97
 
11.5%
네일아트업 77
 
9.1%
피부미용업 76
 
9.0%
기타 14
 
1.7%
메이크업업 9
 
1.1%
이용업 2
 
0.2%

Interactions

2023-12-12T17:06:06.106281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:06:05.874697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:06:06.240925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:06:05.989459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:06:11.703510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명영업장면적업태명
연번1.0000.8790.2860.800
업종명0.8791.0000.1850.927
영업장면적0.2860.1851.0000.000
업태명0.8000.9270.0001.000
2023-12-12T17:06:11.795843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업태명
업종명1.0000.756
업태명0.7561.000
2023-12-12T17:06:11.889192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번영업장면적업종명업태명
연번1.0000.0980.5690.573
영업장면적0.0981.0000.0700.000
업종명0.5690.0701.0000.756
업태명0.5730.0000.7561.000

Missing values

2023-12-12T17:06:06.407100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:06:06.541112image/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.
2023-12-12T17:06:06.684705image/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

연번업종명업소명영업소 주소(도로명)영업장면적소재지전화업태명
01미용업살롱더디엠(SALON THE DM)대전광역시 동구 은어송로 36, 수성프라자 2층 202호 (가오동)117.42<NA>일반미용업
12일반미용업현미헤어샵대전광역시 동구 계족로362번길 6 (성남동)14.85042- 622-5588일반미용업
23일반미용업모던대전광역시 동구 중앙로193번길 31 (중동)11.61042- 253-4105일반미용업
34일반미용업나비미용실대전광역시 동구 중앙로193번길 11 (중동)<NA>042- 256-2040일반미용업
45일반미용업신화미용실대전광역시 동구 대전로 828 (정동)43.4042- 627-0323일반미용업
56일반미용업은성대전광역시 동구 새터2길 24 (신흥동)23.0042- 283-9633일반미용업
67일반미용업은혜대전광역시 동구 계족로358번길 57 (성남동)11.27042- 634-5495일반미용업
78일반미용업민대전광역시 동구 대전로791번길 44 (중동)51.37042- 252-3498일반미용업
89일반미용업민정대전광역시 동구 태전로 163 (삼성동)10.84042- 673-7695일반미용업
910일반미용업현대대전광역시 동구 대전로867번길 28, 1층 (삼성동)14.8042- 627-3628일반미용업
연번업종명업소명영업소 주소(도로명)영업장면적소재지전화업태명
833834이용업염색이야기대전광역시 동구 용운로 127 (용운동)25.2042- 286-8666일반이용업
834835이용업다옴 헤어컷(다옴 HAIR CUT)대전광역시 동구 백룡로57번길 159 (자양동)23.8<NA>일반이용업
835836이용업한남이용원대전광역시 동구 홍도로 10, 1층 (홍도동)30.78<NA>일반이용업
836837이용업다애이용원대전광역시 동구 대전로 832, 2층 (정동)36.0<NA>일반이용업
837838이용업가오목욕탕 이용원대전광역시 동구 대전로 461, 가오목욕탕내 (가오동)5.0<NA>일반이용업
838839이용업퍼스트이발관대전광역시 동구 용운로 6, 1층 (대동)33.0<NA>일반이용업
839840이용업BARBER BLEU(바버블루)대전광역시 동구 홍도로 36, 1층 (홍도동)61.3<NA>일반이용업
840841이용업니니헤어 맨즈대전광역시 동구 백룡로 8, 1층 (자양동)36.5<NA>일반이용업
841842이용업안전이용원대전광역시 동구 계족로 426, 안전빌딩 지하1층 (용전동)6.6<NA>일반이용업
842843이용업오땡큐 머리염색 가양점대전광역시 동구 흥룡로71번길 125, 1층 (가양동)25.65<NA>일반이용업