Overview

Dataset statistics

Number of variables6
Number of observations1735
Missing cells855
Missing cells (%)8.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory84.8 KiB
Average record size in memory50.1 B

Variable types

Numeric2
Categorical1
Text3

Dataset

Description부평구 미용업 현황에 대한 데이터로 업종명 ,신고일자, 업소명, 도로명 주소, 지번 주소, 소재지전화,영업자시작일의 항목을 제공합니다.
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=3045128&srcSe=7661IVAWM27C61E190

Alerts

연번 is highly overall correlated with 업종명High correlation
업종명 is highly overall correlated with 연번High correlation
소재지전화 has 847 (48.8%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-18 03:52:13.840787
Analysis finished2024-03-18 03:52:16.185003
Duration2.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1735
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean868
Minimum1
Maximum1735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2024-03-18T12:52:16.278444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile87.7
Q1434.5
median868
Q31301.5
95-th percentile1648.3
Maximum1735
Range1734
Interquartile range (IQR)867

Descriptive statistics

Standard deviation500.99568
Coefficient of variation (CV)0.57718396
Kurtosis-1.2
Mean868
Median Absolute Deviation (MAD)434
Skewness0
Sum1505980
Variance250996.67
MonotonicityStrictly increasing
2024-03-18T12:52:16.475113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1154 1
 
0.1%
1165 1
 
0.1%
1164 1
 
0.1%
1163 1
 
0.1%
1162 1
 
0.1%
1161 1
 
0.1%
1160 1
 
0.1%
1159 1
 
0.1%
1158 1
 
0.1%
Other values (1725) 1725
99.4%
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 (%)
1735 1
0.1%
1734 1
0.1%
1733 1
0.1%
1732 1
0.1%
1731 1
0.1%
1730 1
0.1%
1729 1
0.1%
1728 1
0.1%
1727 1
0.1%
1726 1
0.1%

업종명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size13.7 KiB
일반미용업
721 
미용업
341 
피부미용업
195 
네일미용업
179 
네일미용업, 화장ㆍ분장 미용업
 
69
Other values (11)
230 

Length

Max length23
Median length5
Mean length6.140634
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 721
41.6%
미용업 341
19.7%
피부미용업 195
 
11.2%
네일미용업 179
 
10.3%
네일미용업, 화장ㆍ분장 미용업 69
 
4.0%
화장ㆍ분장 미용업 62
 
3.6%
피부미용업, 네일미용업 36
 
2.1%
피부미용업, 네일미용업, 화장ㆍ분장 미용업 36
 
2.1%
종합미용업 29
 
1.7%
피부미용업, 화장ㆍ분장 미용업 27
 
1.6%
Other values (6) 40
 
2.3%

Length

2024-03-18T12:52:16.715369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 761
34.4%
미용업 560
25.4%
네일미용업 336
15.2%
피부미용업 304
 
13.8%
화장ㆍ분장 219
 
9.9%
종합미용업 29
 
1.3%

업소명
Text

Distinct1650
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size13.7 KiB
2024-03-18T12:52:17.001828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length27
Mean length6.4322767
Min length1

Characters and Unicode

Total characters11160
Distinct characters631
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1592 ?
Unique (%)91.8%

Sample

1st row손미용실
2nd row헤어 가
3rd row청실헤어데코
4th row새한헤어콜렉션
5th row삼거리미장원
ValueCountFrequency (%)
헤어 24
 
1.1%
hair 21
 
1.0%
nail 18
 
0.8%
네일 18
 
0.8%
에스테틱 10
 
0.5%
리안헤어 9
 
0.4%
부평점 9
 
0.4%
살롱 8
 
0.4%
뷰티 8
 
0.4%
salon 6
 
0.3%
Other values (1826) 2000
93.9%
2024-03-18T12:52:17.402875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 730
 
6.5%
헤 705
 
6.3%
396
 
3.5%
미 286
 
2.6%
일 265
 
2.4%
네 265
 
2.4%
이 262
 
2.3%
리 248
 
2.2%
) 236
 
2.1%
( 236
 
2.1%
Other values (621) 7531
67.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8771
78.6%
Lowercase Letter 717
 
6.4%
Uppercase Letter 597
 
5.3%
Space Separator 396
 
3.5%
Close Punctuation 236
 
2.1%
Open Punctuation 236
 
2.1%
Other Punctuation 123
 
1.1%
Decimal Number 70
 
0.6%
Dash Punctuation 8
 
0.1%
Connector Punctuation 4
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 730
 
8.3%
헤 705
 
8.0%
미 286
 
3.3%
일 265
 
3.0%
네 265
 
3.0%
이 262
 
3.0%
리 248
 
2.8%
스 214
 
2.4%
실 146
 
1.7%
용 142
 
1.6%
Other values (542) 5508
62.8%
Uppercase Letter
ValueCountFrequency (%)
A 66
 
11.1%
N 52
 
8.7%
H 46
 
7.7%
I 42
 
7.0%
S 39
 
6.5%
L 37
 
6.2%
B 35
 
5.9%
E 35
 
5.9%
O 28
 
4.7%
J 27
 
4.5%
Other values (16) 190
31.8%
Lowercase Letter
ValueCountFrequency (%)
a 98
13.7%
i 92
12.8%
n 65
9.1%
e 62
8.6%
o 56
 
7.8%
l 55
 
7.7%
r 41
 
5.7%
s 39
 
5.4%
h 35
 
4.9%
y 28
 
3.9%
Other values (15) 146
20.4%
Other Punctuation
ValueCountFrequency (%)
, 35
28.5%
& 29
23.6%
# 18
14.6%
' 15
12.2%
. 13
 
10.6%
: 4
 
3.3%
· 4
 
3.3%
＆ 2
 
1.6%
! 1
 
0.8%
? 1
 
0.8%
Decimal Number
ValueCountFrequency (%)
2 14
20.0%
1 11
15.7%
0 11
15.7%
3 8
11.4%
6 6
8.6%
9 5
 
7.1%
8 5
 
7.1%
7 4
 
5.7%
4 3
 
4.3%
5 3
 
4.3%
Space Separator
ValueCountFrequency (%)
396
100.0%
Close Punctuation
ValueCountFrequency (%)
) 236
100.0%
Open Punctuation
ValueCountFrequency (%)
( 236
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 1
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8761
78.5%
Latin 1314
 
11.8%
Common 1075
 
9.6%
Han 10
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 730
 
8.3%
헤 705
 
8.0%
미 286
 
3.3%
일 265
 
3.0%
네 265
 
3.0%
이 262
 
3.0%
리 248
 
2.8%
스 214
 
2.4%
실 146
 
1.7%
용 142
 
1.6%
Other values (538) 5498
62.8%
Latin
ValueCountFrequency (%)
a 98
 
7.5%
i 92
 
7.0%
A 66
 
5.0%
n 65
 
4.9%
e 62
 
4.7%
o 56
 
4.3%
l 55
 
4.2%
N 52
 
4.0%
H 46
 
3.5%
I 42
 
3.2%
Other values (41) 680
51.8%
Common
ValueCountFrequency (%)
396
36.8%
) 236
22.0%
( 236
22.0%
, 35
 
3.3%
& 29
 
2.7%
# 18
 
1.7%
' 15
 
1.4%
2 14
 
1.3%
. 13
 
1.2%
1 11
 
1.0%
Other values (18) 72
 
6.7%
Han
ValueCountFrequency (%)
美 7
70.0%
色 1
 
10.0%
秀 1
 
10.0%
林 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8761
78.5%
ASCII 2382
 
21.3%
CJK 10
 
0.1%
None 7
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 730
 
8.3%
헤 705
 
8.0%
미 286
 
3.3%
일 265
 
3.0%
네 265
 
3.0%
이 262
 
3.0%
리 248
 
2.8%
스 214
 
2.4%
실 146
 
1.7%
용 142
 
1.6%
Other values (538) 5498
62.8%
ASCII
ValueCountFrequency (%)
396
 
16.6%
) 236
 
9.9%
( 236
 
9.9%
a 98
 
4.1%
i 92
 
3.9%
A 66
 
2.8%
n 65
 
2.7%
e 62
 
2.6%
o 56
 
2.4%
l 55
 
2.3%
Other values (66) 1020
42.8%
CJK
ValueCountFrequency (%)
美 7
70.0%
色 1
 
10.0%
秀 1
 
10.0%
林 1
 
10.0%
None
ValueCountFrequency (%)
· 4
57.1%
＆ 2
28.6%
´ 1
 
14.3%

영업소 주소(도로명)
Text

Distinct1698
Distinct (%)98.0%
Missing2
Missing (%)0.1%
Memory size13.7 KiB
2024-03-18T12:52:17.654881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length69
Median length52
Mean length35.526832
Min length21

Characters and Unicode

Total characters61568
Distinct characters349
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1668 ?
Unique (%)96.2%

Sample

1st row인천광역시 부평구 부평대로71번길 14 (부평동)
2nd row인천광역시 부평구 마장로 18, 1층 103호 (십정동, 이레하이니스)
3rd row인천광역시 부평구 안남로222번길 30, 나동 2층 11호, 12호 (산곡동, 경남아파트)
4th row인천광역시 부평구 경인로901번길 9 (부평동,성원파크타운 102호)
5th row인천광역시 부평구 마장로 41 (십정동)
ValueCountFrequency (%)
인천광역시 1733
 
14.2%
부평구 1733
 
14.2%
부평동 745
 
6.1%
1층 558
 
4.6%
일부호 357
 
2.9%
2층 257
 
2.1%
부개동 162
 
1.3%
삼산동 156
 
1.3%
산곡동 150
 
1.2%
십정동 141
 
1.2%
Other values (1780) 6177
50.8%
2024-03-18T12:52:18.118705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10440
 
17.0%
부 3830
 
6.2%
평 2990
 
4.9%
1 2824
 
4.6%
동 2169
 
3.5%
천 1917
 
3.1%
인 1854
 
3.0%
, 1844
 
3.0%
역 1837
 
3.0%
광 1832
 
3.0%
Other values (339) 30031
48.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 35363
57.4%
Space Separator 10440
 
17.0%
Decimal Number 9831
 
16.0%
Other Punctuation 1864
 
3.0%
Open Punctuation 1785
 
2.9%
Close Punctuation 1784
 
2.9%
Dash Punctuation 276
 
0.4%
Uppercase Letter 213
 
0.3%
Math Symbol 4
 
< 0.1%
Lowercase Letter 4
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 3830
 
10.8%
평 2990
 
8.5%
동 2169
 
6.1%
천 1917
 
5.4%
인 1854
 
5.2%
역 1837
 
5.2%
광 1832
 
5.2%
시 1804
 
5.1%
로 1803
 
5.1%
구 1750
 
4.9%
Other values (290) 13577
38.4%
Uppercase Letter
ValueCountFrequency (%)
A 33
15.5%
E 22
10.3%
U 17
 
8.0%
C 17
 
8.0%
B 17
 
8.0%
R 16
 
7.5%
N 10
 
4.7%
T 10
 
4.7%
I 10
 
4.7%
S 10
 
4.7%
Other values (11) 51
23.9%
Decimal Number
ValueCountFrequency (%)
1 2824
28.7%
2 1613
16.4%
0 1058
 
10.8%
3 976
 
9.9%
4 824
 
8.4%
6 661
 
6.7%
5 610
 
6.2%
7 468
 
4.8%
8 408
 
4.2%
9 389
 
4.0%
Other Punctuation
ValueCountFrequency (%)
, 1844
98.9%
@ 13
 
0.7%
. 4
 
0.2%
＆ 1
 
0.1%
· 1
 
0.1%
& 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
i 1
25.0%
t 1
25.0%
y 1
25.0%
e 1
25.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 2
66.7%
Ⅰ 1
33.3%
Space Separator
ValueCountFrequency (%)
10440
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1785
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1784
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 276
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 35363
57.4%
Common 25985
42.2%
Latin 220
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 3830
 
10.8%
평 2990
 
8.5%
동 2169
 
6.1%
천 1917
 
5.4%
인 1854
 
5.2%
역 1837
 
5.2%
광 1832
 
5.2%
시 1804
 
5.1%
로 1803
 
5.1%
구 1750
 
4.9%
Other values (290) 13577
38.4%
Latin
ValueCountFrequency (%)
A 33
15.0%
E 22
 
10.0%
U 17
 
7.7%
C 17
 
7.7%
B 17
 
7.7%
R 16
 
7.3%
N 10
 
4.5%
T 10
 
4.5%
I 10
 
4.5%
S 10
 
4.5%
Other values (17) 58
26.4%
Common
ValueCountFrequency (%)
10440
40.2%
1 2824
 
10.9%
, 1844
 
7.1%
( 1785
 
6.9%
) 1784
 
6.9%
2 1613
 
6.2%
0 1058
 
4.1%
3 976
 
3.8%
4 824
 
3.2%
6 661
 
2.5%
Other values (12) 2176
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 35363
57.4%
ASCII 26200
42.6%
Number Forms 3
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10440
39.8%
1 2824
 
10.8%
, 1844
 
7.0%
( 1785
 
6.8%
) 1784
 
6.8%
2 1613
 
6.2%
0 1058
 
4.0%
3 976
 
3.7%
4 824
 
3.1%
6 661
 
2.5%
Other values (35) 2391
 
9.1%
Hangul
ValueCountFrequency (%)
부 3830
 
10.8%
평 2990
 
8.5%
동 2169
 
6.1%
천 1917
 
5.4%
인 1854
 
5.2%
역 1837
 
5.2%
광 1832
 
5.2%
시 1804
 
5.1%
로 1803
 
5.1%
구 1750
 
4.9%
Other values (290) 13577
38.4%
Number Forms
ValueCountFrequency (%)
Ⅱ 2
66.7%
Ⅰ 1
33.3%
None
ValueCountFrequency (%)
＆ 1
50.0%
· 1
50.0%

우편번호(도로명)
Real number (ℝ)

Distinct137
Distinct (%)7.9%
Missing6
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean21379.129
Minimum21302
Maximum21458
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2024-03-18T12:52:18.269612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21302
5-th percentile21316
Q121351
median21388
Q321404
95-th percentile21442
Maximum21458
Range156
Interquartile range (IQR)53

Descriptive statistics

Standard deviation37.704306
Coefficient of variation (CV)0.0017636035
Kurtosis-0.7649882
Mean21379.129
Median Absolute Deviation (MAD)27
Skewness-0.12467307
Sum36964514
Variance1421.6147
MonotonicityNot monotonic
2024-03-18T12:52:18.442132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21404 172
 
9.9%
21389 64
 
3.7%
21344 57
 
3.3%
21405 46
 
2.7%
21394 40
 
2.3%
21318 39
 
2.2%
21360 36
 
2.1%
21393 32
 
1.8%
21351 30
 
1.7%
21377 29
 
1.7%
Other values (127) 1184
68.2%
ValueCountFrequency (%)
21302 6
 
0.3%
21303 1
 
0.1%
21304 11
0.6%
21305 7
 
0.4%
21307 2
 
0.1%
21308 1
 
0.1%
21309 4
 
0.2%
21310 7
 
0.4%
21311 6
 
0.3%
21312 24
1.4%
ValueCountFrequency (%)
21458 6
0.3%
21457 2
 
0.1%
21455 1
 
0.1%
21454 3
 
0.2%
21453 5
 
0.3%
21452 13
0.7%
21451 7
0.4%
21450 12
0.7%
21446 5
 
0.3%
21445 14
0.8%

소재지전화
Text

MISSING 

Distinct881
Distinct (%)99.2%
Missing847
Missing (%)48.8%
Memory size13.7 KiB
2024-03-18T12:52:18.709302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.045045
Min length2

Characters and Unicode

Total characters10696
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

Unique874 ?
Unique (%)98.4%

Sample

1st row032-502-7848
2nd row032-763-5020
3rd row032-502-4119
4th row032-522-2419
5th row032-522-0387
ValueCountFrequency (%)
070-7543-5181 2
 
0.2%
032-527-2527 2
 
0.2%
032-428-6476 2
 
0.2%
032-425-4454 2
 
0.2%
032-506-6131 2
 
0.2%
032-511-7943 2
 
0.2%
032-226-1262 2
 
0.2%
032-502-2436 1
 
0.1%
032-424-5823 1
 
0.1%
032-433-8900 1
 
0.1%
Other values (871) 871
98.1%
2024-03-18T12:52:19.141231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1776
16.6%
0 1665
15.6%
2 1607
15.0%
3 1440
13.5%
5 1154
10.8%
1 764
7.1%
7 555
 
5.2%
4 463
 
4.3%
6 452
 
4.2%
8 426
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8920
83.4%
Dash Punctuation 1776
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1665
18.7%
2 1607
18.0%
3 1440
16.1%
5 1154
12.9%
1 764
8.6%
7 555
 
6.2%
4 463
 
5.2%
6 452
 
5.1%
8 426
 
4.8%
9 394
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 1776
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10696
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1776
16.6%
0 1665
15.6%
2 1607
15.0%
3 1440
13.5%
5 1154
10.8%
1 764
7.1%
7 555
 
5.2%
4 463
 
4.3%
6 452
 
4.2%
8 426
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1776
16.6%
0 1665
15.6%
2 1607
15.0%
3 1440
13.5%
5 1154
10.8%
1 764
7.1%
7 555
 
5.2%
4 463
 
4.3%
6 452
 
4.2%
8 426
 
4.0%

Interactions

2024-03-18T12:52:15.221869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:52:14.932144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:52:15.358519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:52:15.070274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T12:52:19.228830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명우편번호(도로명)
연번1.0000.9000.336
업종명0.9001.0000.252
우편번호(도로명)0.3360.2521.000
2024-03-18T12:52:19.316055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번우편번호(도로명)업종명
연번1.0000.0460.645
우편번호(도로명)0.0461.0000.100
업종명0.6450.1001.000

Missing values

2024-03-18T12:52:15.536665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T12:52:15.704848image/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.
2024-03-18T12:52:16.113666image/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)우편번호(도로명)소재지전화
01미용업손미용실인천광역시 부평구 부평대로71번길 14 (부평동)21388032-502-7848
12미용업헤어 가인천광역시 부평구 마장로 18, 1층 103호 (십정동, 이레하이니스)21413032-763-5020
23미용업청실헤어데코인천광역시 부평구 안남로222번길 30, 나동 2층 11호, 12호 (산곡동, 경남아파트)21377032-502-4119
34미용업새한헤어콜렉션인천광역시 부평구 경인로901번길 9 (부평동,성원파크타운 102호)21408032-522-2419
45미용업삼거리미장원인천광역시 부평구 마장로 41 (십정동)21433032-522-0387
56미용업정윤헤어인천광역시 부평구 길주로354번길 34 (산곡동)21369032-518-4596
67미용업손지미용실<NA><NA>032-502-4764
78미용업민철 헤어샵인천광역시 부평구 시장로 지하 42, 부평시장로터리지하상가 121,122호 (부평동)21392032-515-1701
89미용업보라미용실인천광역시 부평구 새갈로 6 (갈산동)21317032-519-9964
910미용업헤어리즈인천광역시 부평구 동수로 39 (부평동)21424032-515-3356
연번업종명업소명영업소 주소(도로명)우편번호(도로명)소재지전화
17251726피부미용업, 네일미용업, 화장ㆍ분장 미용업뉴야네일인천광역시 부평구 경원대로 1292, 1층 일부호 (부평동)21405<NA>
17261727피부미용업, 네일미용업, 화장ㆍ분장 미용업네일,또또인천광역시 부평구 길주로364번길 9, 111호 (산곡동, 부평 IPARK)21369<NA>
17271728피부미용업, 네일미용업, 화장ㆍ분장 미용업오,블레스(O,Bless)인천광역시 부평구 주부토로 49-1, 2층 일부호 (부평동)21359<NA>
17281729피부미용업, 네일미용업, 화장ㆍ분장 미용업수수네일인천광역시 부평구 부평대로 114, 402호 (부평동, 부평타워큐아파트)21358<NA>
17291730피부미용업, 네일미용업, 화장ㆍ분장 미용업미쁨에스테틱인천광역시 부평구 길주남로 19, 2층 (부평동)21354<NA>
17301731피부미용업, 네일미용업, 화장ㆍ분장 미용업지유네일(ZU NAIL)인천광역시 부평구 마장로 344, 2층 (산곡동)21313<NA>
17311732피부미용업, 네일미용업, 화장ㆍ분장 미용업홀리데이 네일인천광역시 부평구 광장로 16, 부평민자역사 지하2층 300,301호 (부평동)21404<NA>
17321733피부미용업, 네일미용업, 화장ㆍ분장 미용업란(RAN)토탈뷰티인천광역시 부평구 광장로 16, 부평민자역사 지하2층 217호 (부평동)21404<NA>
17331734피부미용업, 네일미용업, 화장ㆍ분장 미용업뷰티플라이인천광역시 부평구 동수북로 154-1, 태승빌딩 2층 201호 (부평동)21407<NA>
17341735피부미용업, 네일미용업, 화장ㆍ분장 미용업더예쁜뷰티,네일인천광역시 부평구 부흥로316번길 33, 미래씨티 1층 114호 (부평동)21391<NA>