Overview

Dataset statistics

Number of variables8
Number of observations33
Missing cells4
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory69.9 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,짜장면,3500
Author인천광역시 부평구
URLhttps://www.data.go.kr/data/3078705/fileData.do

Alerts

시군구 has constant value ""Constant
연락처 has 4 (12.1%) missing valuesMissing
연번 has unique valuesUnique
업소명 has unique valuesUnique
주소 has unique valuesUnique

Reproduction

Analysis started2024-03-14 19:12:17.139001
Analysis finished2024-03-14 19:12:18.888932
Duration1.75 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size425.0 B
2024-03-15T04:12:18.999511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.6
Q19
median17
Q325
95-th percentile31.4
Maximum33
Range32
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.6695398
Coefficient of variation (CV)0.56879646
Kurtosis-1.2
Mean17
Median Absolute Deviation (MAD)8
Skewness0
Sum561
Variance93.5
MonotonicityStrictly increasing
2024-03-15T04:12:19.227312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1 1
 
3.0%
26 1
 
3.0%
20 1
 
3.0%
21 1
 
3.0%
22 1
 
3.0%
23 1
 
3.0%
24 1
 
3.0%
25 1
 
3.0%
27 1
 
3.0%
2 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
1 1
3.0%
2 1
3.0%
3 1
3.0%
4 1
3.0%
5 1
3.0%
6 1
3.0%
7 1
3.0%
8 1
3.0%
9 1
3.0%
10 1
3.0%
ValueCountFrequency (%)
33 1
3.0%
32 1
3.0%
31 1
3.0%
30 1
3.0%
29 1
3.0%
28 1
3.0%
27 1
3.0%
26 1
3.0%
25 1
3.0%
24 1
3.0%

업종
Categorical

Distinct7
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Memory size392.0 B
한식
14 
이미용업
13 
양식
2 
세탁업
 
1
중식
 
1
Other values (2)
2 

Length

Max length6
Median length2
Mean length3
Min length2

Unique

Unique4 ?
Unique (%)12.1%

Sample

1st row양식
2nd row이미용업
3rd row한식
4th row이미용업
5th row한식

Common Values

ValueCountFrequency (%)
한식 14
42.4%
이미용업 13
39.4%
양식 2
 
6.1%
세탁업 1
 
3.0%
중식 1
 
3.0%
기타서비스업 1
 
3.0%
기타양식 1
 
3.0%

Length

2024-03-15T04:12:19.684704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T04:12:20.058514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 14
42.4%
이미용업 13
39.4%
양식 2
 
6.1%
세탁업 1
 
3.0%
중식 1
 
3.0%
기타서비스업 1
 
3.0%
기타양식 1
 
3.0%

업소명
Text

UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size392.0 B
2024-03-15T04:12:21.434468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.2121212
Min length3

Characters and Unicode

Total characters172
Distinct characters103
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

Unique33 ?
Unique (%)100.0%

Sample

1st row갈산냉면家돈까스
2nd row미장제이케이
3rd row타박네
4th row동양이용원
5th row면사무소
ValueCountFrequency (%)
갈산냉면家돈까스 1
 
2.9%
레인보우 1
 
2.9%
머리사랑미용실 1
 
2.9%
진수성찬 1
 
2.9%
짬뽕집 1
 
2.9%
낙원당구장 1
 
2.9%
짱구머리 1
 
2.9%
컷맨컷보이 1
 
2.9%
임계동커피집 1
 
2.9%
미장제이케이 1
 
2.9%
Other values (24) 24
70.6%
2024-03-15T04:12:23.369548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 6
 
3.5%
구 5
 
2.9%
용 5
 
2.9%
동 4
 
2.3%
어 4
 
2.3%
머 4
 
2.3%
부 4
 
2.3%
원 4
 
2.3%
미 4
 
2.3%
장 4
 
2.3%
Other values (93) 128
74.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 171
99.4%
Space Separator 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 6
 
3.5%
구 5
 
2.9%
용 5
 
2.9%
동 4
 
2.3%
어 4
 
2.3%
머 4
 
2.3%
부 4
 
2.3%
원 4
 
2.3%
미 4
 
2.3%
장 4
 
2.3%
Other values (92) 127
74.3%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 170
98.8%
Han 1
 
0.6%
Common 1
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 6
 
3.5%
구 5
 
2.9%
용 5
 
2.9%
동 4
 
2.4%
어 4
 
2.4%
머 4
 
2.4%
부 4
 
2.4%
원 4
 
2.4%
미 4
 
2.4%
장 4
 
2.4%
Other values (91) 126
74.1%
Han
ValueCountFrequency (%)
家 1
100.0%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 170
98.8%
CJK 1
 
0.6%
ASCII 1
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 6
 
3.5%
구 5
 
2.9%
용 5
 
2.9%
동 4
 
2.4%
어 4
 
2.4%
머 4
 
2.4%
부 4
 
2.4%
원 4
 
2.4%
미 4
 
2.4%
장 4
 
2.4%
Other values (91) 126
74.1%
CJK
ValueCountFrequency (%)
家 1
100.0%
ASCII
ValueCountFrequency (%)
1
100.0%

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size392.0 B
부평구
33 

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 33
100.0%

Length

2024-03-15T04:12:23.804483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T04:12:24.216589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부평구 33
100.0%

주소
Text

UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size392.0 B
2024-03-15T04:12:25.246620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length34
Mean length28.333333
Min length22

Characters and Unicode

Total characters935
Distinct characters75
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

Unique33 ?
Unique (%)100.0%

Sample

1st row인천광역시 부평구 주부토로 308, 1층 (갈산동)
2nd row인천광역시 부평구 열우물로50번길 83, 1층 (십정동)
3rd row인천광역시 부평구 길주로602번길 3(부평동)
4th row인천광역시 부평구 안남로417번길 13, 1층 일부(청천동)
5th row인천광역시 부평구 부흥로303번길 31, 1층(부평동)
ValueCountFrequency (%)
인천광역시 33
20.6%
부평구 33
20.6%
1층 5
 
3.1%
1층(부평동 2
 
1.2%
부평동 2
 
1.2%
남부역로 2
 
1.2%
주부토로66번길 2
 
1.2%
경인로 2
 
1.2%
32 2
 
1.2%
13(부평동 2
 
1.2%
Other values (75) 75
46.9%
2024-03-15T04:12:26.901931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
127
 
13.6%
부 71
 
7.6%
평 54
 
5.8%
1 41
 
4.4%
동 36
 
3.9%
인 35
 
3.7%
역 35
 
3.7%
천 34
 
3.6%
광 34
 
3.6%
시 33
 
3.5%
Other values (65) 435
46.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 563
60.2%
Decimal Number 157
 
16.8%
Space Separator 127
 
13.6%
Open Punctuation 33
 
3.5%
Close Punctuation 33
 
3.5%
Other Punctuation 19
 
2.0%
Dash Punctuation 2
 
0.2%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 71
12.6%
평 54
 
9.6%
동 36
 
6.4%
인 35
 
6.2%
역 35
 
6.2%
천 34
 
6.0%
광 34
 
6.0%
시 33
 
5.9%
로 33
 
5.9%
구 33
 
5.9%
Other values (49) 165
29.3%
Decimal Number
ValueCountFrequency (%)
1 41
26.1%
3 26
16.6%
6 19
12.1%
0 18
11.5%
2 16
 
10.2%
4 12
 
7.6%
5 8
 
5.1%
8 6
 
3.8%
9 6
 
3.8%
7 5
 
3.2%
Space Separator
ValueCountFrequency (%)
127
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Other Punctuation
ValueCountFrequency (%)
, 19
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
C 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 563
60.2%
Common 371
39.7%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 71
12.6%
평 54
 
9.6%
동 36
 
6.4%
인 35
 
6.2%
역 35
 
6.2%
천 34
 
6.0%
광 34
 
6.0%
시 33
 
5.9%
로 33
 
5.9%
구 33
 
5.9%
Other values (49) 165
29.3%
Common
ValueCountFrequency (%)
127
34.2%
1 41
 
11.1%
( 33
 
8.9%
) 33
 
8.9%
3 26
 
7.0%
, 19
 
5.1%
6 19
 
5.1%
0 18
 
4.9%
2 16
 
4.3%
4 12
 
3.2%
Other values (5) 27
 
7.3%
Latin
ValueCountFrequency (%)
C 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 563
60.2%
ASCII 372
39.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
127
34.1%
1 41
 
11.0%
( 33
 
8.9%
) 33
 
8.9%
3 26
 
7.0%
, 19
 
5.1%
6 19
 
5.1%
0 18
 
4.8%
2 16
 
4.3%
4 12
 
3.2%
Other values (6) 28
 
7.5%
Hangul
ValueCountFrequency (%)
부 71
12.6%
평 54
 
9.6%
동 36
 
6.4%
인 35
 
6.2%
역 35
 
6.2%
천 34
 
6.0%
광 34
 
6.0%
시 33
 
5.9%
로 33
 
5.9%
구 33
 
5.9%
Other values (49) 165
29.3%

연락처
Text

MISSING 

Distinct29
Distinct (%)100.0%
Missing4
Missing (%)12.1%
Memory size392.0 B
2024-03-15T04:12:27.749821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.034483
Min length12

Characters and Unicode

Total characters349
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

Unique29 ?
Unique (%)100.0%

Sample

1st row032-513-7982
2nd row032-519-6595
3rd row032-519-3383
4th row032-515-6660
5th row032-518-1053
ValueCountFrequency (%)
032-513-7982 1
 
3.4%
032-528-3232 1
 
3.4%
032-330-8277 1
 
3.4%
032-502-2179 1
 
3.4%
032-425-4454 1
 
3.4%
032-513-1694 1
 
3.4%
032-505-8664 1
 
3.4%
032-505-9133 1
 
3.4%
032-506-4759 1
 
3.4%
032-330-4030 1
 
3.4%
Other values (19) 19
65.5%
2024-03-15T04:12:28.776280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 58
16.6%
3 53
15.2%
2 52
14.9%
0 50
14.3%
5 39
11.2%
6 22
 
6.3%
1 18
 
5.2%
9 15
 
4.3%
4 15
 
4.3%
8 14
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 291
83.4%
Dash Punctuation 58
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 53
18.2%
2 52
17.9%
0 50
17.2%
5 39
13.4%
6 22
7.6%
1 18
 
6.2%
9 15
 
5.2%
4 15
 
5.2%
8 14
 
4.8%
7 13
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 349
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 58
16.6%
3 53
15.2%
2 52
14.9%
0 50
14.3%
5 39
11.2%
6 22
 
6.3%
1 18
 
5.2%
9 15
 
4.3%
4 15
 
4.3%
8 14
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 349
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 58
16.6%
3 53
15.2%
2 52
14.9%
0 50
14.3%
5 39
11.2%
6 22
 
6.3%
1 18
 
5.2%
9 15
 
4.3%
4 15
 
4.3%
8 14
 
4.0%

주요품목
Text

Distinct28
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Memory size392.0 B
2024-03-15T04:12:29.414077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length4.3333333
Min length2

Characters and Unicode

Total characters143
Distinct characters80
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)78.8%

Sample

1st row매콤까스
2nd row남성커트
3rd row가정식백반
4th row커트
5th row멸치국수
ValueCountFrequency (%)
커트 6
 
17.1%
백반 2
 
5.7%
남자커트 2
 
5.7%
제육덮밥 1
 
2.9%
매콤까스 1
 
2.9%
원피스 1
 
2.9%
간장게장+고등어구이 1
 
2.9%
성인커트 1
 
2.9%
흑미칼만두 1
 
2.9%
아메리카노 1
 
2.9%
Other values (18) 18
51.4%
2024-03-15T04:12:30.528142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
커 12
 
8.4%
트 12
 
8.4%
반 4
 
2.8%
치 3
 
2.1%
이 3
 
2.1%
스 3
 
2.1%
성 3
 
2.1%
육 3
 
2.1%
수 3
 
2.1%
국 3
 
2.1%
Other values (70) 94
65.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 133
93.0%
Decimal Number 4
 
2.8%
Space Separator 2
 
1.4%
Close Punctuation 1
 
0.7%
Math Symbol 1
 
0.7%
Open Punctuation 1
 
0.7%
Other Punctuation 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
커 12
 
9.0%
트 12
 
9.0%
반 4
 
3.0%
치 3
 
2.3%
이 3
 
2.3%
스 3
 
2.3%
성 3
 
2.3%
육 3
 
2.3%
수 3
 
2.3%
국 3
 
2.3%
Other values (62) 84
63.2%
Decimal Number
ValueCountFrequency (%)
0 2
50.0%
1 1
25.0%
7 1
25.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 133
93.0%
Common 10
 
7.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
커 12
 
9.0%
트 12
 
9.0%
반 4
 
3.0%
치 3
 
2.3%
이 3
 
2.3%
스 3
 
2.3%
성 3
 
2.3%
육 3
 
2.3%
수 3
 
2.3%
국 3
 
2.3%
Other values (62) 84
63.2%
Common
ValueCountFrequency (%)
0 2
20.0%
2
20.0%
) 1
10.0%
1 1
10.0%
+ 1
10.0%
( 1
10.0%
, 1
10.0%
7 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 133
93.0%
ASCII 10
 
7.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
커 12
 
9.0%
트 12
 
9.0%
반 4
 
3.0%
치 3
 
2.3%
이 3
 
2.3%
스 3
 
2.3%
성 3
 
2.3%
육 3
 
2.3%
수 3
 
2.3%
국 3
 
2.3%
Other values (62) 84
63.2%
ASCII
ValueCountFrequency (%)
0 2
20.0%
2
20.0%
) 1
10.0%
1 1
10.0%
+ 1
10.0%
( 1
10.0%
, 1
10.0%
7 1
10.0%

가격(원)
Real number (ℝ)

Distinct14
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7851.5152
Minimum1000
Maximum20000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size425.0 B
2024-03-15T04:12:30.732557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile2140
Q17000
median8000
Q310000
95-th percentile12000
Maximum20000
Range19000
Interquartile range (IQR)3000

Descriptive statistics

Standard deviation3454.9893
Coefficient of variation (CV)0.44004109
Kurtosis4.0684449
Mean7851.5152
Median Absolute Deviation (MAD)2000
Skewness0.89956466
Sum259100
Variance11936951
MonotonicityNot monotonic
2024-03-15T04:12:31.103238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
8000 11
33.3%
10000 5
15.2%
7000 4
 
12.1%
12000 2
 
6.1%
5000 2
 
6.1%
8500 1
 
3.0%
3000 1
 
3.0%
5500 1
 
3.0%
1000 1
 
3.0%
11000 1
 
3.0%
Other values (4) 4
 
12.1%
ValueCountFrequency (%)
1000 1
 
3.0%
1600 1
 
3.0%
2500 1
 
3.0%
3000 1
 
3.0%
5000 2
 
6.1%
5500 1
 
3.0%
6000 1
 
3.0%
7000 4
 
12.1%
8000 11
33.3%
8500 1
 
3.0%
ValueCountFrequency (%)
20000 1
 
3.0%
12000 2
 
6.1%
11000 1
 
3.0%
10000 5
15.2%
8500 1
 
3.0%
8000 11
33.3%
7000 4
 
12.1%
6000 1
 
3.0%
5500 1
 
3.0%
5000 2
 
6.1%

Interactions

2024-03-15T04:12:18.004848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:12:17.628200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:12:18.214065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:12:17.845963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T04:12:31.453853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종업소명주소연락처주요품목가격(원)
연번1.0000.2711.0001.0001.0000.8750.000
업종0.2711.0001.0001.0001.0001.0000.747
업소명1.0001.0001.0001.0001.0001.0001.000
주소1.0001.0001.0001.0001.0001.0001.000
연락처1.0001.0001.0001.0001.0001.0001.000
주요품목0.8751.0001.0001.0001.0001.0000.850
가격(원)0.0000.7471.0001.0001.0000.8501.000
2024-03-15T04:12:31.752620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번가격(원)업종
연번1.000-0.0620.015
가격(원)-0.0621.0000.478
업종0.0150.4781.000

Missing values

2024-03-15T04:12:18.565832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T04:12:18.800941image/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

연번업종업소명시군구주소연락처주요품목가격(원)
01양식갈산냉면家돈까스부평구인천광역시 부평구 주부토로 308, 1층 (갈산동)032-513-7982매콤까스8500
12이미용업미장제이케이부평구인천광역시 부평구 열우물로50번길 83, 1층 (십정동)<NA>남성커트8000
23한식타박네부평구인천광역시 부평구 길주로602번길 3(부평동)032-519-6595가정식백반8000
34이미용업동양이용원부평구인천광역시 부평구 안남로417번길 13, 1층 일부(청천동)032-519-3383커트12000
45한식면사무소부평구인천광역시 부평구 부흥로303번길 31, 1층(부평동)032-515-6660멸치국수5000
56이미용업중앙이용원부평구인천광역시 부평구 부흥로243번길 7, 106호(부평동)032-518-1053커트10000
67한식태흥정육식당부평구인천광역시 부평구 주부토로146번길 32, 1층(갈산동)032-522-8252돼지제육 백반8000
78한식촌장서서갈비부평구인천광역시 부평구 부평대로165번길 10, 2층 203,204호(부평동)032-505-0292불뚝배기8000
89한식어머니홍두깨칼국수부평구인천광역시 부평구 부흥로316번길 37, 1층 21호(부평동)032-522-6669잔치국수3000
910한식포동포동부평구인천광역시 부평구 안남로 261, 113호(산곡동,전방프라자)032-330-3032생고기김치찌개8000
연번업종업소명시군구주소연락처주요품목가격(원)
2324이미용업컷맨컷보이부평구인천광역시 부평구 부평문화로194번길 2, 1층(부개동)032-330-4030일반커트8000
2425이미용업레인보우부평구인천광역시 부평구 남부역로 20번길 16, 101호 (부평동)032-506-4759이발10000
2526기타양식임계동커피집부평구인천광역시 부평구 주부토로81번길 50, 래미안C상가 221호(부평동)032-505-9133아메리카노2500
2627한식홍두깨손칼국수부평구인천광역시 부평구 부흥로 334번길 65(부평동)032-505-8664흑미칼만두8000
2728이미용업부개이용원부평구인천광역시 부평구 수변로9번길5(부개동)<NA>성인커트10000
2829한식부부밥상부평구인천광역시 부평구 경원대로 1363번길 5(부평동)032-513-1694간장게장+고등어구이7000
2930이미용업미가헤어포인트부평구인천광역시 부평구 배곶남로9번길 40(십정동)032-425-4454커트8000
3031이미용업청솔미용실부평구인천광역시 부평구 경인로 966-12(부평동)032-502-2179남자커트7000
3132한식명가설렁탕부평구인천광역시 부평구 주부토로66번길 14, 1층 102호(부평동)032-330-8277떡만두국8000
3233이미용업김현정헤어샵부평구인천광역시 부평구 수변로9번길 5, 1호(부개동)032-526-4666남자커트10000