Overview

Dataset statistics

Number of variables8
Number of observations35
Missing cells5
Missing cells (%)1.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory69.8 B

Variable types

Numeric2
Categorical2
Text4

Dataset

Description부평구 업종별 착한가격업소 현황(연번,업종,업소명,시군구,주소,연락처,품목,가격)예) 1,음식업(중식),북경관(북경중화요리),부평구,인천광역시 부평구 주부토로262번길 25,032-502-8259,자장면,3500
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=3078705&srcSe=7661IVAWM27C61E190

Alerts

시군구 has constant value ""Constant
연락처 has 5 (14.3%) missing valuesMissing
연번 has unique valuesUnique
업소명 has unique valuesUnique
주소 has unique valuesUnique

Reproduction

Analysis started2024-03-13 06:17:16.433097
Analysis finished2024-03-13 06:17:17.393811
Duration0.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18
Minimum1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2024-03-13T15:17:17.457237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.7
Q19.5
median18
Q326.5
95-th percentile33.3
Maximum35
Range34
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.246951
Coefficient of variation (CV)0.56927504
Kurtosis-1.2
Mean18
Median Absolute Deviation (MAD)9
Skewness0
Sum630
Variance105
MonotonicityStrictly increasing
2024-03-13T15:17:17.615678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1 1
 
2.9%
2 1
 
2.9%
21 1
 
2.9%
22 1
 
2.9%
23 1
 
2.9%
24 1
 
2.9%
25 1
 
2.9%
26 1
 
2.9%
27 1
 
2.9%
28 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
1 1
2.9%
2 1
2.9%
3 1
2.9%
4 1
2.9%
5 1
2.9%
6 1
2.9%
7 1
2.9%
8 1
2.9%
9 1
2.9%
10 1
2.9%
ValueCountFrequency (%)
35 1
2.9%
34 1
2.9%
33 1
2.9%
32 1
2.9%
31 1
2.9%
30 1
2.9%
29 1
2.9%
28 1
2.9%
27 1
2.9%
26 1
2.9%

업종
Categorical

Distinct8
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
한식
16 
미용업
10 
이용업
3 
양식
2 
기타요식업
 
1
Other values (3)
3 

Length

Max length6
Median length2
Mean length2.6
Min length2

Unique

Unique4 ?
Unique (%)11.4%

Sample

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

Common Values

ValueCountFrequency (%)
한식 16
45.7%
미용업 10
28.6%
이용업 3
 
8.6%
양식 2
 
5.7%
기타요식업 1
 
2.9%
기타비요식업 1
 
2.9%
세탁업 1
 
2.9%
중식 1
 
2.9%

Length

2024-03-13T15:17:17.847052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T15:17:17.981993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 16
45.7%
미용업 10
28.6%
이용업 3
 
8.6%
양식 2
 
5.7%
기타요식업 1
 
2.9%
기타비요식업 1
 
2.9%
세탁업 1
 
2.9%
중식 1
 
2.9%

업소명
Text

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size412.0 B
2024-03-13T15:17:18.227888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.2571429
Min length3

Characters and Unicode

Total characters184
Distinct characters107
Distinct categories2 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)100.0%

Sample

1st row김현정헤어샵
2nd row명가설렁탕
3rd row청솔미용실
4th row미가헤어포인트
5th row부부밥상
ValueCountFrequency (%)
김현정헤어샵 1
 
2.8%
컷맨컷보이 1
 
2.8%
촌장서서갈비 1
 
2.8%
진수성찬 1
 
2.8%
송이네잔치국수 1
 
2.8%
머리하는날 1
 
2.8%
아구오구 1
 
2.8%
서가면옥 1
 
2.8%
포동포동 1
 
2.8%
부평수제칼국수 1
 
2.8%
Other values (26) 26
72.2%
2024-03-13T15:17:18.594249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 6
 
3.3%
머 5
 
2.7%
이 5
 
2.7%
국 5
 
2.7%
부 5
 
2.7%
용 5
 
2.7%
리 5
 
2.7%
구 5
 
2.7%
원 4
 
2.2%
동 4
 
2.2%
Other values (97) 135
73.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 183
99.5%
Space Separator 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 6
 
3.3%
머 5
 
2.7%
이 5
 
2.7%
국 5
 
2.7%
부 5
 
2.7%
용 5
 
2.7%
리 5
 
2.7%
구 5
 
2.7%
원 4
 
2.2%
동 4
 
2.2%
Other values (96) 134
73.2%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 182
98.9%
Han 1
 
0.5%
Common 1
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 6
 
3.3%
머 5
 
2.7%
이 5
 
2.7%
국 5
 
2.7%
부 5
 
2.7%
용 5
 
2.7%
리 5
 
2.7%
구 5
 
2.7%
원 4
 
2.2%
동 4
 
2.2%
Other values (95) 133
73.1%
Han
ValueCountFrequency (%)
家 1
100.0%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 182
98.9%
CJK 1
 
0.5%
ASCII 1
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 6
 
3.3%
머 5
 
2.7%
이 5
 
2.7%
국 5
 
2.7%
부 5
 
2.7%
용 5
 
2.7%
리 5
 
2.7%
구 5
 
2.7%
원 4
 
2.2%
동 4
 
2.2%
Other values (95) 133
73.1%
CJK
ValueCountFrequency (%)
家 1
100.0%
ASCII
ValueCountFrequency (%)
1
100.0%

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
부평구
35 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부평구 35
100.0%

Length

2024-03-13T15:17:18.733623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T15:17:18.868701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부평구 35
100.0%

주소
Text

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size412.0 B
2024-03-13T15:17:19.121862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length35
Mean length28.428571
Min length22

Characters and Unicode

Total characters995
Distinct characters72
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

Unique35 ?
Unique (%)100.0%

Sample

1st row인천광역시 부평구 수변로9번길 4, 1호(부개동)
2nd row인천광역시 부평구 주부토로66번길 14, 1층 102호(부평동)
3rd row인천광역시 부평구 경인로 966-12(부평동)
4th row인천광역시 부평구 배곶남로9번길 40(십정동)
5th row인천광역시 부평구 경원대로1363번길 5(부평동)
ValueCountFrequency (%)
인천광역시 35
20.8%
부평구 35
20.8%
1층 4
 
2.4%
13(부평동 3
 
1.8%
주부토로66번길 2
 
1.2%
경인로 2
 
1.2%
32 2
 
1.2%
수변로9번길 2
 
1.2%
1층(부평동 2
 
1.2%
14(부평동 1
 
0.6%
Other values (80) 80
47.6%
2024-03-13T15:17:19.582807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
133
 
13.4%
부 78
 
7.8%
평 60
 
6.0%
1 44
 
4.4%
동 39
 
3.9%
인 37
 
3.7%
역 37
 
3.7%
광 36
 
3.6%
천 36
 
3.6%
로 35
 
3.5%
Other values (62) 460
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 599
60.2%
Decimal Number 170
 
17.1%
Space Separator 133
 
13.4%
Close Punctuation 35
 
3.5%
Open Punctuation 35
 
3.5%
Other Punctuation 19
 
1.9%
Dash Punctuation 3
 
0.3%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 78
13.0%
평 60
 
10.0%
동 39
 
6.5%
인 37
 
6.2%
역 37
 
6.2%
광 36
 
6.0%
천 36
 
6.0%
로 35
 
5.8%
시 35
 
5.8%
구 35
 
5.8%
Other values (46) 171
28.5%
Decimal Number
ValueCountFrequency (%)
1 44
25.9%
3 30
17.6%
6 22
12.9%
2 19
11.2%
0 17
 
10.0%
4 14
 
8.2%
7 7
 
4.1%
5 6
 
3.5%
9 6
 
3.5%
8 5
 
2.9%
Space Separator
ValueCountFrequency (%)
133
100.0%
Close Punctuation
ValueCountFrequency (%)
) 35
100.0%
Open Punctuation
ValueCountFrequency (%)
( 35
100.0%
Other Punctuation
ValueCountFrequency (%)
, 19
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Uppercase Letter
ValueCountFrequency (%)
C 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 599
60.2%
Common 395
39.7%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 78
13.0%
평 60
 
10.0%
동 39
 
6.5%
인 37
 
6.2%
역 37
 
6.2%
광 36
 
6.0%
천 36
 
6.0%
로 35
 
5.8%
시 35
 
5.8%
구 35
 
5.8%
Other values (46) 171
28.5%
Common
ValueCountFrequency (%)
133
33.7%
1 44
 
11.1%
) 35
 
8.9%
( 35
 
8.9%
3 30
 
7.6%
6 22
 
5.6%
2 19
 
4.8%
, 19
 
4.8%
0 17
 
4.3%
4 14
 
3.5%
Other values (5) 27
 
6.8%
Latin
ValueCountFrequency (%)
C 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 599
60.2%
ASCII 396
39.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
133
33.6%
1 44
 
11.1%
) 35
 
8.8%
( 35
 
8.8%
3 30
 
7.6%
6 22
 
5.6%
2 19
 
4.8%
, 19
 
4.8%
0 17
 
4.3%
4 14
 
3.5%
Other values (6) 28
 
7.1%
Hangul
ValueCountFrequency (%)
부 78
13.0%
평 60
 
10.0%
동 39
 
6.5%
인 37
 
6.2%
역 37
 
6.2%
광 36
 
6.0%
천 36
 
6.0%
로 35
 
5.8%
시 35
 
5.8%
구 35
 
5.8%
Other values (46) 171
28.5%

연락처
Text

MISSING 

Distinct30
Distinct (%)100.0%
Missing5
Missing (%)14.3%
Memory size412.0 B
2024-03-13T15:17:19.834804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.033333
Min length12

Characters and Unicode

Total characters361
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

Unique30 ?
Unique (%)100.0%

Sample

1st row032-526-4666
2nd row032-330-8277
3rd row032-502-2179
4th row032-425-4454
5th row032-513-1694
ValueCountFrequency (%)
032-512-5036 1
 
3.3%
032-502-2179 1
 
3.3%
032-514-7767 1
 
3.3%
032-519-6595 1
 
3.3%
032-515-6660 1
 
3.3%
032-518-1053 1
 
3.3%
032-522-8252 1
 
3.3%
032-519-3383 1
 
3.3%
032-455-0292 1
 
3.3%
032-522-6669 1
 
3.3%
Other values (20) 20
66.7%
2024-03-13T15:17:20.220635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 60
16.6%
3 55
15.2%
2 54
15.0%
0 52
14.4%
5 41
11.4%
6 22
 
6.1%
1 19
 
5.3%
4 16
 
4.4%
9 15
 
4.2%
8 14
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 301
83.4%
Dash Punctuation 60
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 55
18.3%
2 54
17.9%
0 52
17.3%
5 41
13.6%
6 22
 
7.3%
1 19
 
6.3%
4 16
 
5.3%
9 15
 
5.0%
8 14
 
4.7%
7 13
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 60
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 361
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 60
16.6%
3 55
15.2%
2 54
15.0%
0 52
14.4%
5 41
11.4%
6 22
 
6.1%
1 19
 
5.3%
4 16
 
4.4%
9 15
 
4.2%
8 14
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 361
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 60
16.6%
3 55
15.2%
2 54
15.0%
0 52
14.4%
5 41
11.4%
6 22
 
6.1%
1 19
 
5.3%
4 16
 
4.4%
9 15
 
4.2%
8 14
 
3.9%

품목
Text

Distinct23
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Memory size412.0 B
2024-03-13T15:17:20.416178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length7
Mean length3.2857143
Min length2

Characters and Unicode

Total characters115
Distinct characters62
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)54.3%

Sample

1st row커트
2nd row설렁탕
3rd row커트
4th row커트
5th row간장게장+고등어구이
ValueCountFrequency (%)
커트 8
22.9%
칼국수 3
 
8.6%
남성커트 3
 
8.6%
백반 2
 
5.7%
순두부찌개 1
 
2.9%
커피 1
 
2.9%
여자커트 1
 
2.9%
가정식백반 1
 
2.9%
멸치국수 1
 
2.9%
삽겹살 1
 
2.9%
Other values (13) 13
37.1%
2024-03-13T15:17:20.716535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
커 13
 
11.3%
트 12
 
10.4%
국 5
 
4.3%
수 5
 
4.3%
반 3
 
2.6%
장 3
 
2.6%
치 3
 
2.6%
이 3
 
2.6%
백 3
 
2.6%
칼 3
 
2.6%
Other values (52) 62
53.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 111
96.5%
Decimal Number 2
 
1.7%
Math Symbol 1
 
0.9%
Other Punctuation 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
커 13
 
11.7%
트 12
 
10.8%
국 5
 
4.5%
수 5
 
4.5%
반 3
 
2.7%
장 3
 
2.7%
치 3
 
2.7%
이 3
 
2.7%
백 3
 
2.7%
칼 3
 
2.7%
Other values (48) 58
52.3%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 111
96.5%
Common 4
 
3.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
커 13
 
11.7%
트 12
 
10.8%
국 5
 
4.5%
수 5
 
4.5%
반 3
 
2.7%
장 3
 
2.7%
치 3
 
2.7%
이 3
 
2.7%
백 3
 
2.7%
칼 3
 
2.7%
Other values (48) 58
52.3%
Common
ValueCountFrequency (%)
+ 1
25.0%
1 1
25.0%
0 1
25.0%
, 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 111
96.5%
ASCII 4
 
3.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
커 13
 
11.7%
트 12
 
10.8%
국 5
 
4.5%
수 5
 
4.5%
반 3
 
2.7%
장 3
 
2.7%
치 3
 
2.7%
이 3
 
2.7%
백 3
 
2.7%
칼 3
 
2.7%
Other values (48) 58
52.3%
ASCII
ValueCountFrequency (%)
+ 1
25.0%
1 1
25.0%
0 1
25.0%
, 1
25.0%

가격(원)
Real number (ℝ)

Distinct15
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6725.7143
Minimum1000
Maximum20000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2024-03-13T15:17:20.832402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile3000
Q15000
median7000
Q38000
95-th percentile10000
Maximum20000
Range19000
Interquartile range (IQR)3000

Descriptive statistics

Standard deviation3171.2257
Coefficient of variation (CV)0.47150764
Kurtosis8.2606838
Mean6725.7143
Median Absolute Deviation (MAD)2000
Skewness2.0420122
Sum235400
Variance10056672
MonotonicityNot monotonic
2024-03-13T15:17:20.937439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
8000 6
17.1%
7000 5
14.3%
5000 5
14.3%
10000 4
11.4%
4500 3
8.6%
3000 2
 
5.7%
6000 2
 
5.7%
6500 1
 
2.9%
1000 1
 
2.9%
5500 1
 
2.9%
Other values (5) 5
14.3%
ValueCountFrequency (%)
1000 1
 
2.9%
3000 2
 
5.7%
3500 1
 
2.9%
4000 1
 
2.9%
4500 3
8.6%
5000 5
14.3%
5500 1
 
2.9%
6000 2
 
5.7%
6500 1
 
2.9%
7000 5
14.3%
ValueCountFrequency (%)
20000 1
 
2.9%
10000 4
11.4%
8000 6
17.1%
7900 1
 
2.9%
7500 1
 
2.9%
7000 5
14.3%
6500 1
 
2.9%
6000 2
 
5.7%
5500 1
 
2.9%
5000 5
14.3%

Interactions

2024-03-13T15:17:17.001678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:17:16.789733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:17:17.096035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:17:16.912704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T15:17:21.055987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종업소명주소연락처품목가격(원)
연번1.0000.5751.0001.0001.0000.7280.637
업종0.5751.0001.0001.0001.0000.8950.262
업소명1.0001.0001.0001.0001.0001.0001.000
주소1.0001.0001.0001.0001.0001.0001.000
연락처1.0001.0001.0001.0001.0001.0001.000
품목0.7280.8951.0001.0001.0001.0000.843
가격(원)0.6370.2621.0001.0001.0000.8431.000
2024-03-13T15:17:21.190680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번가격(원)업종
연번1.0000.0040.276
가격(원)0.0041.0000.000
업종0.2760.0001.000

Missing values

2024-03-13T15:17:17.231014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T15:17:17.349256image/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

연번업종업소명시군구주소연락처품목가격(원)
01미용업김현정헤어샵부평구인천광역시 부평구 수변로9번길 4, 1호(부개동)032-526-4666커트10000
12한식명가설렁탕부평구인천광역시 부평구 주부토로66번길 14, 1층 102호(부평동)032-330-8277설렁탕7000
23미용업청솔미용실부평구인천광역시 부평구 경인로 966-12(부평동)032-502-2179커트7000
34미용업미가헤어포인트부평구인천광역시 부평구 배곶남로9번길 40(십정동)032-425-4454커트8000
45한식부부밥상부평구인천광역시 부평구 경원대로1363번길 5(부평동)032-513-1694간장게장+고등어구이6500
56한식홍두깨손칼국수부평구인천광역시 부평구 부흥로334번길 65(부평동)032-505-8664칼국수5000
67이용업부개이용원부평구인천광역시 부평구 수변로9번길 5(부개동)<NA>커트8000
78기타요식업임계동커피집부평구인천광역시 부평구 주부토로81번길 50, 래미안C상가 221호(부평동)032-505-9133커피3000
89미용업레인보우부평구인천광역시 부평구 남부역로20번길 16, 101호(부평동)032-506-4759이발8000
910미용업컷맨컷보이부평구인천광역시 부평구 부평문화로194번길 2, 1층(부개동)032-330-4030커트8000
연번업종업소명시군구주소연락처품목가격(원)
2526한식포동포동부평구인천광역시 부평구 안남로 261, 113호(산곡동, 전방프라자)032-330-3032김치찌개7000
2627한식어머니홍두깨칼국수부평구인천광역시 부평구 부흥로316번길 37, 1층 21호(부평동)032-522-6669칼국수3500
2728한식촌장서서갈비부평구인천광역시 부평구 부평대로165번길 10, 2층 203,204호(부평동)032-455-0292돼지갈비7900
2829이용업동양이용원부평구인천광역시 부평구 안남로417번길 13, 1층 일부(청천동)032-519-3383커트10000
2930한식태흥정육식당부평구인천광역시 부평구 주부토로146번길 32, 1층(갈산동)032-522-8252삽겹살8000
3031이용업중앙이용원부평구인천광역시 부평구 부흥로243번길 7, 106호(부평동)032-518-1053커트10000
3132한식면사무소부평구인천광역시 부평구 부흥로303번길 31, 1층(부평동)032-515-6660멸치국수4500
3233한식타박네부평구인천광역시 부평구 길주로602번길 3 (부평동)032-519-6595가정식백반8000
3334미용업착한머리부평구인천광역시 부평구 부흥로123번길 36, 상가동 204호 (산곡동)<NA>남성커트6000
3435양식갈산냉면家돈까스부평구인천광역시 부평구 주부토로 308, 1층 (갈산동)032-513-7982매콤까스7500