Overview

Dataset statistics

Number of variables15
Number of observations67
Missing cells7
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.4 KiB
Average record size in memory128.0 B

Variable types

Categorical4
Numeric5
Text6

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,주된음식,영업장면적(㎡),행정동명,급수시설구분,소재지전화번호
Author강동구
URLhttps://data.seoul.go.kr/dataList/OA-10679/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
업태명 is highly overall correlated with 급수시설구분High correlation
행정동명 is highly overall correlated with 급수시설구분High correlation
급수시설구분 is highly overall correlated with 지정년도 and 6 other fieldsHigh correlation
지정년도 is highly overall correlated with 신청일자 and 2 other fieldsHigh correlation
지정번호 is highly overall correlated with 급수시설구분High correlation
신청일자 is highly overall correlated with 지정년도 and 2 other fieldsHigh correlation
지정일자 is highly overall correlated with 지정년도 and 2 other fieldsHigh correlation
영업장면적(㎡) is highly overall correlated with 급수시설구분High correlation
소재지전화번호 has 7 (10.4%) missing valuesMissing
업소명 has unique valuesUnique
허가(신고)번호 has unique valuesUnique

Reproduction

Analysis started2024-05-11 08:12:28.724804
Analysis finished2024-05-11 08:12:40.795418
Duration12.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
3240000
67 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3240000
2nd row3240000
3rd row3240000
4th row3240000
5th row3240000

Common Values

ValueCountFrequency (%)
3240000 67
100.0%

Length

2024-05-11T08:12:41.035521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:12:41.456646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3240000 67
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.1642
Minimum2008
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-05-11T08:12:41.746437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2008
Q12009
median2013
Q32015
95-th percentile2016
Maximum2017
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.9521282
Coefficient of variation (CV)0.0014671408
Kurtosis-1.4184257
Mean2012.1642
Median Absolute Deviation (MAD)2
Skewness-0.22011056
Sum134815
Variance8.7150611
MonotonicityNot monotonic
2024-05-11T08:12:42.077939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2008 15
22.4%
2015 13
19.4%
2014 8
11.9%
2016 7
10.4%
2011 7
10.4%
2012 5
 
7.5%
2013 5
 
7.5%
2010 3
 
4.5%
2009 3
 
4.5%
2017 1
 
1.5%
ValueCountFrequency (%)
2008 15
22.4%
2009 3
 
4.5%
2010 3
 
4.5%
2011 7
10.4%
2012 5
 
7.5%
2013 5
 
7.5%
2014 8
11.9%
2015 13
19.4%
2016 7
10.4%
2017 1
 
1.5%
ValueCountFrequency (%)
2017 1
 
1.5%
2016 7
10.4%
2015 13
19.4%
2014 8
11.9%
2013 5
 
7.5%
2012 5
 
7.5%
2011 7
10.4%
2010 3
 
4.5%
2009 3
 
4.5%
2008 15
22.4%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean131.26866
Minimum8
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-05-11T08:12:42.544687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile28.5
Q185.5
median113
Q3124.5
95-th percentile160.4
Maximum2017
Range2009
Interquartile range (IQR)39

Descriptive statistics

Standard deviation236.87355
Coefficient of variation (CV)1.8044943
Kurtosis63.515244
Mean131.26866
Median Absolute Deviation (MAD)14
Skewness7.8650433
Sum8795
Variance56109.078
MonotonicityNot monotonic
2024-05-11T08:12:43.115360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122 3
 
4.5%
113 3
 
4.5%
112 3
 
4.5%
120 2
 
3.0%
129 2
 
3.0%
118 2
 
3.0%
121 2
 
3.0%
125 2
 
3.0%
108 2
 
3.0%
104 2
 
3.0%
Other values (42) 44
65.7%
ValueCountFrequency (%)
8 1
1.5%
11 1
1.5%
15 1
1.5%
27 1
1.5%
32 1
1.5%
35 1
1.5%
39 1
1.5%
44 1
1.5%
45 1
1.5%
46 1
1.5%
ValueCountFrequency (%)
2017 1
1.5%
178 1
1.5%
176 1
1.5%
167 1
1.5%
145 1
1.5%
143 1
1.5%
139 1
1.5%
132 2
3.0%
131 1
1.5%
130 1
1.5%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20116425
Minimum20030526
Maximum20171027
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-05-11T08:12:43.575028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20030526
5-th percentile20070604
Q120075560
median20121022
Q320151111
95-th percentile20161111
Maximum20171027
Range140501
Interquartile range (IQR)75551

Descriptive statistics

Standard deviation36132.046
Coefficient of variation (CV)0.0017961464
Kurtosis-1.0777884
Mean20116425
Median Absolute Deviation (MAD)30089
Skewness-0.42730447
Sum1.3478005 × 109
Variance1.3055247 × 109
MonotonicityNot monotonic
2024-05-11T08:12:43.981303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
20070604 14
20.9%
20151111 12
17.9%
20141105 8
11.9%
20161111 6
9.0%
20111004 6
9.0%
20121022 5
 
7.5%
20100510 3
 
4.5%
20090508 3
 
4.5%
20131202 3
 
4.5%
20171027 1
 
1.5%
Other values (6) 6
9.0%
ValueCountFrequency (%)
20030526 1
 
1.5%
20050502 1
 
1.5%
20050504 1
 
1.5%
20070604 14
20.9%
20080516 1
 
1.5%
20090508 3
 
4.5%
20100510 3
 
4.5%
20111004 6
9.0%
20121022 5
 
7.5%
20131113 1
 
1.5%
ValueCountFrequency (%)
20171027 1
 
1.5%
20161111 6
9.0%
20151222 1
 
1.5%
20151111 12
17.9%
20141105 8
11.9%
20131202 3
 
4.5%
20131113 1
 
1.5%
20121022 5
7.5%
20111004 6
9.0%
20100510 3
 
4.5%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20122639
Minimum20080630
Maximum20171027
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-05-11T08:12:44.444382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20080630
5-th percentile20080630
Q120090630
median20131202
Q320151222
95-th percentile20161223
Maximum20171027
Range90397
Interquartile range (IQR)60592

Descriptive statistics

Standard deviation29762.697
Coefficient of variation (CV)0.0014790653
Kurtosis-1.4210615
Mean20122639
Median Absolute Deviation (MAD)20171
Skewness-0.22506958
Sum1.3482168 × 109
Variance8.8581815 × 108
MonotonicityNot monotonic
2024-05-11T08:12:44.910921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
20080630 15
22.4%
20151222 13
19.4%
20141212 8
11.9%
20161223 7
10.4%
20111031 7
10.4%
20121031 5
 
7.5%
20131202 5
 
7.5%
20100630 3
 
4.5%
20090630 3
 
4.5%
20171027 1
 
1.5%
ValueCountFrequency (%)
20080630 15
22.4%
20090630 3
 
4.5%
20100630 3
 
4.5%
20111031 7
10.4%
20121031 5
 
7.5%
20131202 5
 
7.5%
20141212 8
11.9%
20151222 13
19.4%
20161223 7
10.4%
20171027 1
 
1.5%
ValueCountFrequency (%)
20171027 1
 
1.5%
20161223 7
10.4%
20151222 13
19.4%
20141212 8
11.9%
20131202 5
 
7.5%
20121031 5
 
7.5%
20111031 7
10.4%
20100630 3
 
4.5%
20090630 3
 
4.5%
20080630 15
22.4%

업소명
Text

UNIQUE 

Distinct67
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size668.0 B
2024-05-11T08:12:45.705120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length11
Mean length6.4179104
Min length2

Characters and Unicode

Total characters430
Distinct characters205
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67 ?
Unique (%)100.0%

Sample

1st row더 파크뷰
2nd row길조
3rd row지호한방삼계탕
4th row옛날불고기
5th row더두툼생고기
ValueCountFrequency (%)
더 1
 
1.1%
파크뷰 1
 
1.1%
강동점 1
 
1.1%
가인채 1
 
1.1%
길동회직판장 1
 
1.1%
나눔터 1
 
1.1%
강동kd부대찌게 1
 
1.1%
3호점 1
 
1.1%
등갈비달인 1
 
1.1%
작은깡통 1
 
1.1%
Other values (78) 78
88.6%
2024-05-11T08:12:46.973725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21
 
4.9%
점 14
 
3.3%
동 8
 
1.9%
탕 8
 
1.9%
수 7
 
1.6%
부 7
 
1.6%
강 7
 
1.6%
장 7
 
1.6%
대 6
 
1.4%
국 6
 
1.4%
Other values (195) 339
78.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 398
92.6%
Space Separator 21
 
4.9%
Open Punctuation 3
 
0.7%
Close Punctuation 3
 
0.7%
Other Punctuation 2
 
0.5%
Uppercase Letter 2
 
0.5%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 14
 
3.5%
동 8
 
2.0%
탕 8
 
2.0%
수 7
 
1.8%
부 7
 
1.8%
강 7
 
1.8%
장 7
 
1.8%
대 6
 
1.5%
국 6
 
1.5%
어 6
 
1.5%
Other values (187) 322
80.9%
Other Punctuation
ValueCountFrequency (%)
, 1
50.0%
& 1
50.0%
Uppercase Letter
ValueCountFrequency (%)
K 1
50.0%
D 1
50.0%
Space Separator
ValueCountFrequency (%)
21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 398
92.6%
Common 30
 
7.0%
Latin 2
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 14
 
3.5%
동 8
 
2.0%
탕 8
 
2.0%
수 7
 
1.8%
부 7
 
1.8%
강 7
 
1.8%
장 7
 
1.8%
대 6
 
1.5%
국 6
 
1.5%
어 6
 
1.5%
Other values (187) 322
80.9%
Common
ValueCountFrequency (%)
21
70.0%
( 3
 
10.0%
) 3
 
10.0%
, 1
 
3.3%
& 1
 
3.3%
3 1
 
3.3%
Latin
ValueCountFrequency (%)
K 1
50.0%
D 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 398
92.6%
ASCII 32
 
7.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21
65.6%
( 3
 
9.4%
) 3
 
9.4%
, 1
 
3.1%
& 1
 
3.1%
3 1
 
3.1%
K 1
 
3.1%
D 1
 
3.1%
Hangul
ValueCountFrequency (%)
점 14
 
3.5%
동 8
 
2.0%
탕 8
 
2.0%
수 7
 
1.8%
부 7
 
1.8%
강 7
 
1.8%
장 7
 
1.8%
대 6
 
1.5%
국 6
 
1.5%
어 6
 
1.5%
Other values (187) 322
80.9%

소재지도로명
Text

Distinct66
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
2024-05-11T08:12:47.629282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length39
Mean length28.955224
Min length23

Characters and Unicode

Total characters1940
Distinct characters92
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

Unique65 ?
Unique (%)97.0%

Sample

1st row서울특별시 강동구 천호대로 1102, (성내동)
2nd row서울특별시 강동구 천호대로 1173, (길동)
3rd row서울특별시 강동구 올림픽로 813, (암사동)
4th row서울특별시 강동구 상암로12길 4, (천호동)
5th row서울특별시 강동구 구천면로 666, (상일동,(1층))
ValueCountFrequency (%)
서울특별시 67
18.6%
강동구 67
18.6%
성내동 13
 
3.6%
길동 11
 
3.0%
명일동 9
 
2.5%
천호대로 8
 
2.2%
암사동 7
 
1.9%
동남로75길 5
 
1.4%
32 5
 
1.4%
올림픽로 5
 
1.4%
Other values (125) 164
45.4%
2024-05-11T08:12:48.860215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
294
 
15.2%
동 151
 
7.8%
, 90
 
4.6%
1 76
 
3.9%
) 75
 
3.9%
( 75
 
3.9%
강 75
 
3.9%
구 73
 
3.8%
울 67
 
3.5%
서 67
 
3.5%
Other values (82) 897
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1092
56.3%
Decimal Number 302
 
15.6%
Space Separator 294
 
15.2%
Other Punctuation 90
 
4.6%
Close Punctuation 75
 
3.9%
Open Punctuation 75
 
3.9%
Dash Punctuation 9
 
0.5%
Lowercase Letter 2
 
0.1%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 151
13.8%
강 75
 
6.9%
구 73
 
6.7%
울 67
 
6.1%
서 67
 
6.1%
특 67
 
6.1%
시 67
 
6.1%
별 67
 
6.1%
로 64
 
5.9%
길 46
 
4.2%
Other values (64) 348
31.9%
Decimal Number
ValueCountFrequency (%)
1 76
25.2%
2 45
14.9%
3 42
13.9%
5 29
 
9.6%
7 23
 
7.6%
6 19
 
6.3%
4 18
 
6.0%
0 18
 
6.0%
8 16
 
5.3%
9 16
 
5.3%
Lowercase Letter
ValueCountFrequency (%)
y 1
50.0%
x 1
50.0%
Space Separator
ValueCountFrequency (%)
294
100.0%
Other Punctuation
ValueCountFrequency (%)
, 90
100.0%
Close Punctuation
ValueCountFrequency (%)
) 75
100.0%
Open Punctuation
ValueCountFrequency (%)
( 75
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1092
56.3%
Common 845
43.6%
Latin 3
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 151
13.8%
강 75
 
6.9%
구 73
 
6.7%
울 67
 
6.1%
서 67
 
6.1%
특 67
 
6.1%
시 67
 
6.1%
별 67
 
6.1%
로 64
 
5.9%
길 46
 
4.2%
Other values (64) 348
31.9%
Common
ValueCountFrequency (%)
294
34.8%
, 90
 
10.7%
1 76
 
9.0%
) 75
 
8.9%
( 75
 
8.9%
2 45
 
5.3%
3 42
 
5.0%
5 29
 
3.4%
7 23
 
2.7%
6 19
 
2.2%
Other values (5) 77
 
9.1%
Latin
ValueCountFrequency (%)
y 1
33.3%
x 1
33.3%
B 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1092
56.3%
ASCII 848
43.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
294
34.7%
, 90
 
10.6%
1 76
 
9.0%
) 75
 
8.8%
( 75
 
8.8%
2 45
 
5.3%
3 42
 
5.0%
5 29
 
3.4%
7 23
 
2.7%
6 19
 
2.2%
Other values (8) 80
 
9.4%
Hangul
ValueCountFrequency (%)
동 151
13.8%
강 75
 
6.9%
구 73
 
6.7%
울 67
 
6.1%
서 67
 
6.1%
특 67
 
6.1%
시 67
 
6.1%
별 67
 
6.1%
로 64
 
5.9%
길 46
 
4.2%
Other values (64) 348
31.9%

소재지지번
Text

Distinct64
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
2024-05-11T08:12:49.822893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length38
Mean length27.313433
Min length22

Characters and Unicode

Total characters1830
Distinct characters79
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

Unique61 ?
Unique (%)91.0%

Sample

1st row서울특별시 강동구 성내동 199번지 10호
2nd row서울특별시 강동구 길동 236번지 8호
3rd row서울특별시 강동구 암사동 504번지 21호
4th row서울특별시 강동구 천호동 287번지 4호
5th row서울특별시 강동구 상일동 224번지 (1층)
ValueCountFrequency (%)
서울특별시 67
18.5%
강동구 67
18.5%
성내동 18
 
5.0%
명일동 13
 
3.6%
길동 12
 
3.3%
1호 10
 
2.8%
둔촌동 7
 
1.9%
1층 7
 
1.9%
암사동 7
 
1.9%
천호동 6
 
1.7%
Other values (104) 149
41.0%
2024-05-11T08:12:51.089759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
470
25.7%
동 135
 
7.4%
강 72
 
3.9%
호 70
 
3.8%
구 68
 
3.7%
서 67
 
3.7%
번 67
 
3.7%
특 67
 
3.7%
별 67
 
3.7%
시 67
 
3.7%
Other values (69) 680
37.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1007
55.0%
Space Separator 470
25.7%
Decimal Number 322
 
17.6%
Open Punctuation 9
 
0.5%
Close Punctuation 9
 
0.5%
Dash Punctuation 8
 
0.4%
Other Punctuation 3
 
0.2%
Lowercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 135
13.4%
강 72
 
7.1%
호 70
 
7.0%
구 68
 
6.8%
서 67
 
6.7%
번 67
 
6.7%
특 67
 
6.7%
별 67
 
6.7%
시 67
 
6.7%
울 67
 
6.7%
Other values (52) 260
25.8%
Decimal Number
ValueCountFrequency (%)
1 62
19.3%
4 51
15.8%
2 42
13.0%
3 32
9.9%
5 28
8.7%
0 25
7.8%
7 21
 
6.5%
8 21
 
6.5%
6 21
 
6.5%
9 19
 
5.9%
Lowercase Letter
ValueCountFrequency (%)
x 1
50.0%
y 1
50.0%
Space Separator
ValueCountFrequency (%)
470
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1007
55.0%
Common 821
44.9%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 135
13.4%
강 72
 
7.1%
호 70
 
7.0%
구 68
 
6.8%
서 67
 
6.7%
번 67
 
6.7%
특 67
 
6.7%
별 67
 
6.7%
시 67
 
6.7%
울 67
 
6.7%
Other values (52) 260
25.8%
Common
ValueCountFrequency (%)
470
57.2%
1 62
 
7.6%
4 51
 
6.2%
2 42
 
5.1%
3 32
 
3.9%
5 28
 
3.4%
0 25
 
3.0%
7 21
 
2.6%
8 21
 
2.6%
6 21
 
2.6%
Other values (5) 48
 
5.8%
Latin
ValueCountFrequency (%)
x 1
50.0%
y 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1007
55.0%
ASCII 823
45.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
470
57.1%
1 62
 
7.5%
4 51
 
6.2%
2 42
 
5.1%
3 32
 
3.9%
5 28
 
3.4%
0 25
 
3.0%
7 21
 
2.6%
8 21
 
2.6%
6 21
 
2.6%
Other values (7) 50
 
6.1%
Hangul
ValueCountFrequency (%)
동 135
13.4%
강 72
 
7.1%
호 70
 
7.0%
구 68
 
6.8%
서 67
 
6.7%
번 67
 
6.7%
특 67
 
6.7%
별 67
 
6.7%
시 67
 
6.7%
울 67
 
6.7%
Other values (52) 260
25.8%

허가(신고)번호
Text

UNIQUE 

Distinct67
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size668.0 B
2024-05-11T08:12:51.745186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters1474
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

Unique67 ?
Unique (%)100.0%

Sample

1st row3240000-101-1998-00966
2nd row3240000-101-1989-10414
3rd row3240000-101-2007-00281
4th row3240000-101-2000-12035
5th row3240000-101-2010-00050
ValueCountFrequency (%)
3240000-101-1998-00966 1
 
1.5%
3240000-101-2002-13263 1
 
1.5%
3240000-101-1995-07328 1
 
1.5%
3240000-101-2008-00121 1
 
1.5%
3240000-101-2004-00441 1
 
1.5%
3240000-101-2015-00118 1
 
1.5%
3240000-101-2004-00189 1
 
1.5%
3240000-101-1998-09841 1
 
1.5%
3240000-101-1983-02903 1
 
1.5%
3240000-101-2004-00581 1
 
1.5%
Other values (57) 57
85.1%
2024-05-11T08:12:52.964843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 541
36.7%
1 223
15.1%
- 201
 
13.6%
2 155
 
10.5%
3 104
 
7.1%
4 101
 
6.9%
9 61
 
4.1%
8 30
 
2.0%
6 20
 
1.4%
7 20
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1273
86.4%
Dash Punctuation 201
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 541
42.5%
1 223
17.5%
2 155
 
12.2%
3 104
 
8.2%
4 101
 
7.9%
9 61
 
4.8%
8 30
 
2.4%
6 20
 
1.6%
7 20
 
1.6%
5 18
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1474
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 541
36.7%
1 223
15.1%
- 201
 
13.6%
2 155
 
10.5%
3 104
 
7.1%
4 101
 
6.9%
9 61
 
4.1%
8 30
 
2.0%
6 20
 
1.4%
7 20
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1474
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 541
36.7%
1 223
15.1%
- 201
 
13.6%
2 155
 
10.5%
3 104
 
7.1%
4 101
 
6.9%
9 61
 
4.1%
8 30
 
2.0%
6 20
 
1.4%
7 20
 
1.4%

업태명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Memory size668.0 B
한식
49 
일식
 
4
뷔페식
 
3
중국식
 
3
분식
 
3
Other values (3)
5 

Length

Max length8
Median length2
Mean length2.1791045
Min length2

Unique

Unique2 ?
Unique (%)3.0%

Sample

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

Common Values

ValueCountFrequency (%)
한식 49
73.1%
일식 4
 
6.0%
뷔페식 3
 
4.5%
중국식 3
 
4.5%
분식 3
 
4.5%
회집 3
 
4.5%
식육(숯불구이) 1
 
1.5%
까페 1
 
1.5%

Length

2024-05-11T08:12:53.793704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:12:54.347708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 49
73.1%
일식 4
 
6.0%
뷔페식 3
 
4.5%
중국식 3
 
4.5%
분식 3
 
4.5%
회집 3
 
4.5%
식육(숯불구이 1
 
1.5%
까페 1
 
1.5%

주된음식
Text

Distinct50
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Memory size668.0 B
2024-05-11T08:12:55.195559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length10
Mean length4.2089552
Min length1

Characters and Unicode

Total characters282
Distinct characters96
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)61.2%

Sample

1st row부페
2nd row해물탕, 찜
3rd row삼계탕
4th row불고기
5th row삼겹살
ValueCountFrequency (%)
추어탕 4
 
5.3%
샤브샤브 4
 
5.3%
삼계탕 3
 
4.0%
칼국수 3
 
4.0%
삼겹살 3
 
4.0%
부대찌개 3
 
4.0%
부페 2
 
2.7%
소고기 2
 
2.7%
초밥 2
 
2.7%
회 2
 
2.7%
Other values (47) 47
62.7%
2024-05-11T08:12:56.268873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 16
 
5.7%
샤 12
 
4.3%
브 12
 
4.3%
고 11
 
3.9%
탕 10
 
3.5%
기 10
 
3.5%
삼 9
 
3.2%
8
 
2.8%
회 7
 
2.5%
국 6
 
2.1%
Other values (86) 181
64.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 258
91.5%
Other Punctuation 16
 
5.7%
Space Separator 8
 
2.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
샤 12
 
4.7%
브 12
 
4.7%
고 11
 
4.3%
탕 10
 
3.9%
기 10
 
3.9%
삼 9
 
3.5%
회 7
 
2.7%
국 6
 
2.3%
수 6
 
2.3%
겹 6
 
2.3%
Other values (84) 169
65.5%
Other Punctuation
ValueCountFrequency (%)
, 16
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 258
91.5%
Common 24
 
8.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
샤 12
 
4.7%
브 12
 
4.7%
고 11
 
4.3%
탕 10
 
3.9%
기 10
 
3.9%
삼 9
 
3.5%
회 7
 
2.7%
국 6
 
2.3%
수 6
 
2.3%
겹 6
 
2.3%
Other values (84) 169
65.5%
Common
ValueCountFrequency (%)
, 16
66.7%
8
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 258
91.5%
ASCII 24
 
8.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 16
66.7%
8
33.3%
Hangul
ValueCountFrequency (%)
샤 12
 
4.7%
브 12
 
4.7%
고 11
 
4.3%
탕 10
 
3.9%
기 10
 
3.9%
삼 9
 
3.5%
회 7
 
2.7%
국 6
 
2.3%
수 6
 
2.3%
겹 6
 
2.3%
Other values (84) 169
65.5%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178.64179
Minimum25.7
Maximum1504.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-05-11T08:12:56.707214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25.7
5-th percentile49.78
Q182.5
median132
Q3192.76
95-th percentile360.74
Maximum1504.4
Range1478.7
Interquartile range (IQR)110.26

Descriptive statistics

Standard deviation221.30944
Coefficient of variation (CV)1.2388447
Kurtosis24.731538
Mean178.64179
Median Absolute Deviation (MAD)49.5
Skewness4.7085456
Sum11969
Variance48977.867
MonotonicityNot monotonic
2024-05-11T08:12:57.495468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132.0 4
 
6.0%
82.5 3
 
4.5%
133.0 2
 
3.0%
72.6 2
 
3.0%
1504.4 1
 
1.5%
251.0 1
 
1.5%
148.2 1
 
1.5%
198.0 1
 
1.5%
1160.97 1
 
1.5%
80.24 1
 
1.5%
Other values (50) 50
74.6%
ValueCountFrequency (%)
25.7 1
1.5%
28.12 1
1.5%
42.0 1
1.5%
48.4 1
1.5%
53.0 1
1.5%
54.32 1
1.5%
56.25 1
1.5%
59.4 1
1.5%
66.83 1
1.5%
67.0 1
1.5%
ValueCountFrequency (%)
1504.4 1
1.5%
1160.97 1
1.5%
396.0 1
1.5%
377.9 1
1.5%
320.7 1
1.5%
288.2 1
1.5%
286.0 1
1.5%
270.26 1
1.5%
263.5 1
1.5%
254.07 1
1.5%

행정동명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Memory size668.0 B
길동
12 
명일제2동
10 
성내제2동
8 
둔촌제2동
7 
성내제1동
6 
Other values (8)
24 

Length

Max length5
Median length5
Mean length4.4029851
Min length2

Unique

Unique1 ?
Unique (%)1.5%

Sample

1st row성내제2동
2nd row길동
3rd row암사제2동
4th row천호제2동
5th row상일제1동

Common Values

ValueCountFrequency (%)
길동 12
17.9%
명일제2동 10
14.9%
성내제2동 8
11.9%
둔촌제2동 7
10.4%
성내제1동 6
9.0%
암사제2동 5
7.5%
천호제2동 5
7.5%
성내제3동 4
 
6.0%
명일제1동 3
 
4.5%
상일제1동 2
 
3.0%
Other values (3) 5
7.5%

Length

2024-05-11T08:12:57.952157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
길동 12
17.9%
명일제2동 10
14.9%
성내제2동 8
11.9%
둔촌제2동 7
10.4%
성내제1동 6
9.0%
암사제2동 5
7.5%
천호제2동 5
7.5%
성내제3동 4
 
6.0%
명일제1동 3
 
4.5%
상일제1동 2
 
3.0%
Other values (3) 5
7.5%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size668.0 B
<NA>
42 
상수도전용
25 

Length

Max length5
Median length4
Mean length4.3731343
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row상수도전용
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 42
62.7%
상수도전용 25
37.3%

Length

2024-05-11T08:12:58.353271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:12:58.680198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 42
62.7%
상수도전용 25
37.3%

소재지전화번호
Text

MISSING 

Distinct60
Distinct (%)100.0%
Missing7
Missing (%)10.4%
Memory size668.0 B
2024-05-11T08:12:59.166246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.583333
Min length8

Characters and Unicode

Total characters635
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

Unique60 ?
Unique (%)100.0%

Sample

1st row02 4777700
2nd row02 4769494
3rd row02 4413318
4th row02 4779236
5th row02 429 3774
ValueCountFrequency (%)
02 52
39.7%
429 4
 
3.1%
472 2
 
1.5%
428 2
 
1.5%
477 2
 
1.5%
441 2
 
1.5%
4164 1
 
0.8%
4830123 1
 
0.8%
7700 1
 
0.8%
4843782 1
 
0.8%
Other values (63) 63
48.1%
2024-05-11T08:13:00.192888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 101
15.9%
91
14.3%
4 88
13.9%
0 87
13.7%
8 58
9.1%
7 57
9.0%
3 40
 
6.3%
6 39
 
6.1%
9 29
 
4.6%
1 26
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 544
85.7%
Space Separator 91
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 101
18.6%
4 88
16.2%
0 87
16.0%
8 58
10.7%
7 57
10.5%
3 40
 
7.4%
6 39
 
7.2%
9 29
 
5.3%
1 26
 
4.8%
5 19
 
3.5%
Space Separator
ValueCountFrequency (%)
91
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 635
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 101
15.9%
91
14.3%
4 88
13.9%
0 87
13.7%
8 58
9.1%
7 57
9.0%
3 40
 
6.3%
6 39
 
6.1%
9 29
 
4.6%
1 26
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 635
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 101
15.9%
91
14.3%
4 88
13.9%
0 87
13.7%
8 58
9.1%
7 57
9.0%
3 40
 
6.3%
6 39
 
6.1%
9 29
 
4.6%
1 26
 
4.1%

Interactions

2024-05-11T08:12:38.042070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:31.689703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:33.555326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:35.107695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:36.595384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:38.359469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:32.258402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:33.810233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:35.398576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:36.848524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:38.703769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:32.564015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:34.144167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:35.720076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:37.170565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:38.981970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:32.877918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:34.491483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:35.994969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:37.479568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:39.244409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:33.192938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:34.824769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:36.317665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:12:37.764292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T08:13:00.522719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명소재지전화번호
지정년도1.0001.0000.9481.0001.0000.8310.7931.0000.0000.7510.0000.0001.000
지정번호1.0001.0000.0981.0001.0001.0001.0001.0000.0001.0000.0000.1111.000
신청일자0.9480.0981.0000.9481.0000.8830.7451.0000.0000.4740.1040.0001.000
지정일자1.0001.0000.9481.0001.0000.8310.7931.0000.0000.7510.0000.0001.000
업소명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명0.8311.0000.8830.8311.0001.0001.0001.0000.8920.9971.0001.0001.000
소재지지번0.7931.0000.7450.7931.0001.0001.0001.0000.8550.9960.0001.0001.000
허가(신고)번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
업태명0.0000.0000.0000.0001.0000.8920.8551.0001.0000.9670.5340.5191.000
주된음식0.7511.0000.4740.7511.0000.9970.9961.0000.9671.0000.0000.8831.000
영업장면적(㎡)0.0000.0000.1040.0001.0001.0000.0001.0000.5340.0001.0000.5301.000
행정동명0.0000.1110.0000.0001.0001.0001.0001.0000.5190.8830.5301.0001.000
소재지전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-05-11T08:13:00.843585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태명행정동명급수시설구분
업태명1.0000.2481.000
행정동명0.2481.0001.000
급수시설구분1.0001.0001.000
2024-05-11T08:13:01.023981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.000-0.0700.8831.000-0.2370.0000.0771.000
지정번호-0.0701.0000.029-0.0700.3020.0000.0781.000
신청일자0.8830.0291.0000.883-0.2480.0000.0001.000
지정일자1.000-0.0700.8831.000-0.2370.0000.0771.000
영업장면적(㎡)-0.2370.302-0.248-0.2371.0000.3540.2961.000
업태명0.0000.0000.0000.0000.3541.0000.2481.000
행정동명0.0770.0780.0000.0770.2960.2481.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-11T08:12:39.891379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T08:12:40.559786image/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

시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
0324000020121202012102220121031더 파크뷰서울특별시 강동구 천호대로 1102, (성내동)서울특별시 강동구 성내동 199번지 10호3240000-101-1998-00966뷔페식부페1504.4성내제2동상수도전용02 4777700
1324000020121132012102220121031길조서울특별시 강동구 천호대로 1173, (길동)서울특별시 강동구 길동 236번지 8호3240000-101-1989-10414한식해물탕, 찜160.18길동<NA>02 4769494
2324000020121322012102220121031지호한방삼계탕서울특별시 강동구 올림픽로 813, (암사동)서울특별시 강동구 암사동 504번지 21호3240000-101-2007-00281한식삼계탕132.0암사제2동<NA>02 4413318
332400002016152005050420161223옛날불고기서울특별시 강동구 상암로12길 4, (천호동)서울특별시 강동구 천호동 287번지 4호3240000-101-2000-12035한식불고기247.0천호제2동<NA>02 4779236
4324000020141122014110520141212더두툼생고기서울특별시 강동구 구천면로 666, (상일동,(1층))서울특별시 강동구 상일동 224번지 (1층)3240000-101-2010-00050한식삼겹살108.81상일제1동<NA>02 429 3774
5324000020151192015111120151222예술갈비서울특별시 강동구 올림픽로 821, 1층 1호 (암사동)서울특별시 강동구 암사동 505번지 24호 1층-13240000-101-2007-00032식육(숯불구이)고기82.24암사제2동<NA>02 69528988
6324000020111172011100420111031놀부부대찌개 고덕역점서울특별시 강동구 동남로75길 13-21, (명일동)서울특별시 강동구 명일동 47번지 17호3240000-101-2007-00026한식부대찌개80.55명일제2동<NA>02 428 8882
7324000020111132011100420111031육대장 강동성심병원점서울특별시 강동구 성안로 158, (길동,(1층))서울특별시 강동구 길동 416번지 19호 (1층)3240000-101-2007-00437한식육개장, 한방보쌈195.52길동<NA>02 4736868
8324000020131042013111320131202차오차오(강일점)서울특별시 강동구 아리수로 423, 2층 201,202호 (강일동)서울특별시 강동구 강일동 679번지 1호 강일큐브-201,2023240000-101-2012-00364중국식중화요리133.0강일동<NA>02 429 8282
93240000201720172017102720171027돌판구이 또바기서울특별시 강동구 구천면로17길 32, 1층 (천호동)서울특별시 강동구 천호동 334번지 10호3240000-101-2017-00250한식고기류53.0천호제2동<NA>02 483 8285
시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
57324000020151232015111120151222아름다운 제주돈서울특별시 강동구 고덕로38길 39, (명일동)서울특별시 강동구 명일동 312번지 19호3240000-101-2013-00137한식돼지고기구이97.0명일제1동<NA>0263973745
58324000020141222014110520141212공릉동 멸치국수서울특별시 강동구 상암로12길 32, (천호동,보람아파트상가 101호)서울특별시 강동구 천호동 287번지 25호 보람아파트상가 101호3240000-101-2009-00429분식국수72.6천호제2동<NA>02 4885813
59324000020141042014110520141212토담골서울특별시 강동구 진황도로47길 78, (길동)서울특별시 강동구 길동 393번지 1호3240000-101-2003-13919한식보리밥82.5길동상수도전용02 4707933
60324000020081432007060420080630황토추어탕서울특별시 강동구 성안로 7-12, (성내동)서울특별시 강동구 성내동 447번지 6호3240000-101-2004-00252한식추어탕165.0성내제1동<NA>02473 6673
6132400002008682007060420080630고향집손칼국수서울특별시 강동구 동남로73길 23, 1층 (명일동)서울특별시 강동구 명일동 48번지 2호3240000-101-1998-06389분식칼국수88.74명일제2동상수도전용02 4291222
62324000020151322015111120151222웅골서울특별시 강동구 강동대로53길 18, 2층 (성내동)서울특별시 강동구 성내동 442번지 16호3240000-101-2015-00006한식샤브샤브214.53성내제3동<NA>02 4868861
63324000020151222015111120151222윌리엄커피서울특별시 강동구 풍성로 92, (성내동)서울특별시 강동구 성내동 111번지 6호3240000-101-2011-00164까페샌드위치,커피168.28성내제2동<NA>02 472 4164
64324000020151292015111120151222김선장횟집서울특별시 강동구 올림픽로98길 49, (암사동)서울특별시 강동구 암사동 495번지3240000-101-2014-00188일식회96.0암사제1동<NA>02 441 7706
65324000020121172012102220121031마실서울특별시 강동구 동남로73길 31, 2층 (명일동)서울특별시 강동구 명일동 48번지 1호 2층3240000-101-2012-00085한식한정식320.7명일제2동<NA>02 429 6999
66324000020101392010051020100630원할머니보쌈,족발&박가부대 암사점서울특별시 강동구 올림픽로 814, (암사동)서울특별시 강동구 암사동 463번지 1호3240000-101-2006-00188한식보쌈147.31암사제1동<NA>02 441 5372