Overview

Dataset statistics

Number of variables8
Number of observations47
Missing cells9
Missing cells (%)2.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory68.8 B

Variable types

Categorical2
Text4
Numeric2

Dataset

Description서울특별시 광진구의 맛집멋집 업태, 업소명, 주소, 전화번호, 위경도 등에 대한 정보( 또는 데이터)를 제공합니다.
Author서울특별시 광진구
URLhttps://www.data.go.kr/data/15052408/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
업태 is highly imbalanced (58.4%)Imbalance
주취급음식 has 6 (12.8%) missing valuesMissing
전화번호 has 3 (6.4%) missing valuesMissing
업소명 has unique valuesUnique
소재지(도로명) has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:54:42.636975
Analysis finished2023-12-12 09:54:43.767937
Duration1.13 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업태
Categorical

IMBALANCE 

Distinct5
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size508.0 B
한식
39 
일식
 
3
중국식
 
3
뷔페식
 
1
경양식
 
1

Length

Max length3
Median length2
Mean length2.106383
Min length2

Unique

Unique2 ?
Unique (%)4.3%

Sample

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

Common Values

ValueCountFrequency (%)
한식 39
83.0%
일식 3
 
6.4%
중국식 3
 
6.4%
뷔페식 1
 
2.1%
경양식 1
 
2.1%

Length

2023-12-12T18:54:43.833344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:54:43.949195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 39
83.0%
일식 3
 
6.4%
중국식 3
 
6.4%
뷔페식 1
 
2.1%
경양식 1
 
2.1%

업소명
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-12T18:54:44.192065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length10
Mean length5.2978723
Min length2

Characters and Unicode

Total characters249
Distinct characters141
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)100.0%

Sample

1st row(주)남원추어탕
2nd row(주)장군갈비
3rd row가마솥도가니탕 장수옥
4th row거보주
5th row건국갈비
ValueCountFrequency (%)
주)남원추어탕 1
 
1.9%
하이난 1
 
1.9%
옹기생삼겹살 1
 
1.9%
유일설렁탕 1
 
1.9%
중일가든 1
 
1.9%
지담정 1
 
1.9%
찜집 1
 
1.9%
채선당 1
 
1.9%
천년의섬 1
 
1.9%
명품 1
 
1.9%
Other values (43) 43
81.1%
2023-12-12T18:54:44.670732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
탕 7
 
2.8%
6
 
2.4%
어 6
 
2.4%
가 6
 
2.4%
장 5
 
2.0%
원 5
 
2.0%
비 5
 
2.0%
이 5
 
2.0%
주 4
 
1.6%
대 4
 
1.6%
Other values (131) 196
78.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 232
93.2%
Space Separator 6
 
2.4%
Uppercase Letter 4
 
1.6%
Open Punctuation 3
 
1.2%
Close Punctuation 3
 
1.2%
Other Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 7
 
3.0%
어 6
 
2.6%
가 6
 
2.6%
장 5
 
2.2%
원 5
 
2.2%
비 5
 
2.2%
이 5
 
2.2%
주 4
 
1.7%
대 4
 
1.7%
점 3
 
1.3%
Other values (123) 182
78.4%
Uppercase Letter
ValueCountFrequency (%)
S 1
25.0%
P 1
25.0%
I 1
25.0%
V 1
25.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 232
93.2%
Common 13
 
5.2%
Latin 4
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 7
 
3.0%
어 6
 
2.6%
가 6
 
2.6%
장 5
 
2.2%
원 5
 
2.2%
비 5
 
2.2%
이 5
 
2.2%
주 4
 
1.7%
대 4
 
1.7%
점 3
 
1.3%
Other values (123) 182
78.4%
Common
ValueCountFrequency (%)
6
46.2%
( 3
23.1%
) 3
23.1%
& 1
 
7.7%
Latin
ValueCountFrequency (%)
S 1
25.0%
P 1
25.0%
I 1
25.0%
V 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 232
93.2%
ASCII 17
 
6.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
탕 7
 
3.0%
어 6
 
2.6%
가 6
 
2.6%
장 5
 
2.2%
원 5
 
2.2%
비 5
 
2.2%
이 5
 
2.2%
주 4
 
1.7%
대 4
 
1.7%
점 3
 
1.3%
Other values (123) 182
78.4%
ASCII
ValueCountFrequency (%)
6
35.3%
( 3
17.6%
) 3
17.6%
& 1
 
5.9%
S 1
 
5.9%
P 1
 
5.9%
I 1
 
5.9%
V 1
 
5.9%

소재지(도로명)
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-12T18:54:44.968372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length34
Mean length25.93617
Min length13

Characters and Unicode

Total characters1219
Distinct characters66
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

Unique47 ?
Unique (%)100.0%

Sample

1st row서울특별시 광진구 동일로 114 (화양동)
2nd row서울특별시 광진구 용마산로 11 (중곡2동)
3rd row서울특별시 광진구 용마산로 124 (중곡4동)
4th row서울특별시 광진구 아차산로51길 24 (구의1동)
5th row서울특별시 광진구 광나루로22가길18(화양동)
ValueCountFrequency (%)
서울특별시 47
20.2%
광진구 46
19.7%
화양동 6
 
2.6%
구의3동 4
 
1.7%
광장동 4
 
1.7%
자양4동 4
 
1.7%
광나루로 4
 
1.7%
군자동 4
 
1.7%
아차산로 4
 
1.7%
능동로 4
 
1.7%
Other values (91) 106
45.5%
2023-12-12T18:54:45.439226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
186
 
15.3%
광 59
 
4.8%
동 58
 
4.8%
구 57
 
4.7%
서 47
 
3.9%
울 47
 
3.9%
시 47
 
3.9%
별 47
 
3.9%
특 47
 
3.9%
진 46
 
3.8%
Other values (56) 578
47.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 723
59.3%
Decimal Number 205
 
16.8%
Space Separator 186
 
15.3%
Close Punctuation 44
 
3.6%
Open Punctuation 44
 
3.6%
Other Punctuation 11
 
0.9%
Dash Punctuation 6
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 59
 
8.2%
동 58
 
8.0%
구 57
 
7.9%
서 47
 
6.5%
울 47
 
6.5%
시 47
 
6.5%
별 47
 
6.5%
특 47
 
6.5%
진 46
 
6.4%
로 44
 
6.1%
Other values (41) 224
31.0%
Decimal Number
ValueCountFrequency (%)
1 41
20.0%
2 31
15.1%
4 30
14.6%
3 26
12.7%
6 18
8.8%
7 15
 
7.3%
8 13
 
6.3%
5 12
 
5.9%
9 10
 
4.9%
0 9
 
4.4%
Space Separator
ValueCountFrequency (%)
186
100.0%
Close Punctuation
ValueCountFrequency (%)
) 44
100.0%
Open Punctuation
ValueCountFrequency (%)
( 44
100.0%
Other Punctuation
ValueCountFrequency (%)
, 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 723
59.3%
Common 496
40.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 59
 
8.2%
동 58
 
8.0%
구 57
 
7.9%
서 47
 
6.5%
울 47
 
6.5%
시 47
 
6.5%
별 47
 
6.5%
특 47
 
6.5%
진 46
 
6.4%
로 44
 
6.1%
Other values (41) 224
31.0%
Common
ValueCountFrequency (%)
186
37.5%
) 44
 
8.9%
( 44
 
8.9%
1 41
 
8.3%
2 31
 
6.2%
4 30
 
6.0%
3 26
 
5.2%
6 18
 
3.6%
7 15
 
3.0%
8 13
 
2.6%
Other values (5) 48
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 723
59.3%
ASCII 496
40.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
186
37.5%
) 44
 
8.9%
( 44
 
8.9%
1 41
 
8.3%
2 31
 
6.2%
4 30
 
6.0%
3 26
 
5.2%
6 18
 
3.6%
7 15
 
3.0%
8 13
 
2.6%
Other values (5) 48
 
9.7%
Hangul
ValueCountFrequency (%)
광 59
 
8.2%
동 58
 
8.0%
구 57
 
7.9%
서 47
 
6.5%
울 47
 
6.5%
시 47
 
6.5%
별 47
 
6.5%
특 47
 
6.5%
진 46
 
6.4%
로 44
 
6.1%
Other values (41) 224
31.0%

주취급음식
Text

MISSING 

Distinct35
Distinct (%)85.4%
Missing6
Missing (%)12.8%
Memory size508.0 B
2023-12-12T18:54:45.670921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length3
Mean length3.7073171
Min length2

Characters and Unicode

Total characters152
Distinct characters72
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

Unique31 ?
Unique (%)75.6%

Sample

1st row추어탕
2nd row갈비탕
3rd row갈비
4th row돼지갈비, 삼겹살
5th row돼지갈비
ValueCountFrequency (%)
설렁탕 3
 
7.1%
갈비 3
 
7.1%
돼지갈비 2
 
4.8%
삼겹살 2
 
4.8%
자장면 2
 
4.8%
추어탕 2
 
4.8%
비빔국수 1
 
2.4%
한정식 1
 
2.4%
생고기 1
 
2.4%
꽃등심 1
 
2.4%
Other values (24) 24
57.1%
2023-12-12T18:54:46.027952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
비 9
 
5.9%
탕 7
 
4.6%
갈 7
 
4.6%
생 4
 
2.6%
어 4
 
2.6%
, 4
 
2.6%
지 3
 
2.0%
회 3
 
2.0%
찜 3
 
2.0%
리 3
 
2.0%
Other values (62) 105
69.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 147
96.7%
Other Punctuation 4
 
2.6%
Space Separator 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
비 9
 
6.1%
탕 7
 
4.8%
갈 7
 
4.8%
생 4
 
2.7%
어 4
 
2.7%
지 3
 
2.0%
회 3
 
2.0%
찜 3
 
2.0%
리 3
 
2.0%
렁 3
 
2.0%
Other values (60) 101
68.7%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 147
96.7%
Common 5
 
3.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
비 9
 
6.1%
탕 7
 
4.8%
갈 7
 
4.8%
생 4
 
2.7%
어 4
 
2.7%
지 3
 
2.0%
회 3
 
2.0%
찜 3
 
2.0%
리 3
 
2.0%
렁 3
 
2.0%
Other values (60) 101
68.7%
Common
ValueCountFrequency (%)
, 4
80.0%
1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 147
96.7%
ASCII 5
 
3.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
비 9
 
6.1%
탕 7
 
4.8%
갈 7
 
4.8%
생 4
 
2.7%
어 4
 
2.7%
지 3
 
2.0%
회 3
 
2.0%
찜 3
 
2.0%
리 3
 
2.0%
렁 3
 
2.0%
Other values (60) 101
68.7%
ASCII
ValueCountFrequency (%)
, 4
80.0%
1
 
20.0%

전화번호
Text

MISSING 

Distinct44
Distinct (%)100.0%
Missing3
Missing (%)6.4%
Memory size508.0 B
2023-12-12T18:54:46.335539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length11.113636
Min length11

Characters and Unicode

Total characters489
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

Unique44 ?
Unique (%)100.0%

Sample

1st row02-498-8649
2nd row02-457-3241
3rd row02-444-6888
4th row02-456-2266
5th row02-2201-0018
ValueCountFrequency (%)
02-457-3241 1
 
2.3%
02-444-6888 1
 
2.3%
02-446-4005 1
 
2.3%
02-469-0072 1
 
2.3%
02-447-3492 1
 
2.3%
02-452-5860 1
 
2.3%
02-447-4111 1
 
2.3%
02-2205-3777 1
 
2.3%
02-446-1149 1
 
2.3%
02-452-5200 1
 
2.3%
Other values (34) 34
77.3%
2023-12-12T18:54:46.901425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 88
18.0%
0 76
15.5%
4 76
15.5%
2 73
14.9%
6 36
7.4%
5 31
 
6.3%
7 23
 
4.7%
9 23
 
4.7%
8 22
 
4.5%
1 21
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 401
82.0%
Dash Punctuation 88
 
18.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 76
19.0%
4 76
19.0%
2 73
18.2%
6 36
9.0%
5 31
7.7%
7 23
 
5.7%
9 23
 
5.7%
8 22
 
5.5%
1 21
 
5.2%
3 20
 
5.0%
Dash Punctuation
ValueCountFrequency (%)
- 88
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 489
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 88
18.0%
0 76
15.5%
4 76
15.5%
2 73
14.9%
6 36
7.4%
5 31
 
6.3%
7 23
 
4.7%
9 23
 
4.7%
8 22
 
4.5%
1 21
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 489
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 88
18.0%
0 76
15.5%
4 76
15.5%
2 73
14.9%
6 36
7.4%
5 31
 
6.3%
7 23
 
4.7%
9 23
 
4.7%
8 22
 
4.5%
1 21
 
4.3%

위도
Real number (ℝ)

Distinct46
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.546419
Minimum37.53166
Maximum37.567356
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-12T18:54:47.105477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.53166
5-th percentile37.535199
Q137.539898
median37.545213
Q337.551819
95-th percentile37.562608
Maximum37.567356
Range0.0356959
Interquartile range (IQR)0.0119214

Descriptive statistics

Standard deviation0.0085929685
Coefficient of variation (CV)0.00022886253
Kurtosis-0.26869051
Mean37.546419
Median Absolute Deviation (MAD)0.0063603
Skewness0.55759359
Sum1764.6817
Variance7.3839107 × 10-5
MonotonicityNot monotonic
2023-12-12T18:54:47.313465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
37.5466931 2
 
4.3%
37.5426714 1
 
2.1%
37.5477372 1
 
2.1%
37.5673562 1
 
2.1%
37.5474829 1
 
2.1%
37.5582095 1
 
2.1%
37.5331659 1
 
2.1%
37.5633567 1
 
2.1%
37.5382075 1
 
2.1%
37.5394861 1
 
2.1%
Other values (36) 36
76.6%
ValueCountFrequency (%)
37.5316603 1
2.1%
37.5331659 1
2.1%
37.5349372 1
2.1%
37.5358089 1
2.1%
37.5363359 1
2.1%
37.5363506 1
2.1%
37.5380016 1
2.1%
37.5382075 1
2.1%
37.538368 1
2.1%
37.5387443 1
2.1%
ValueCountFrequency (%)
37.5673562 1
2.1%
37.56468 1
2.1%
37.5633567 1
2.1%
37.5608602 1
2.1%
37.5582095 1
2.1%
37.557005 1
2.1%
37.5559236 1
2.1%
37.5558218 1
2.1%
37.5547181 1
2.1%
37.5545639 1
2.1%

경도
Real number (ℝ)

Distinct46
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.08369
Minimum127.06272
Maximum127.11097
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-12T18:54:47.531088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.06272
5-th percentile127.06561
Q1127.07599
median127.08289
Q3127.09285
95-th percentile127.10597
Maximum127.11097
Range0.0482533
Interquartile range (IQR)0.0168583

Descriptive statistics

Standard deviation0.012493156
Coefficient of variation (CV)9.8306524 × 10-5
Kurtosis-0.59936143
Mean127.08369
Median Absolute Deviation (MAD)0.009577
Skewness0.3177352
Sum5972.9334
Variance0.00015607894
MonotonicityNot monotonic
2023-12-12T18:54:47.699183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
127.1024213 2
 
4.3%
127.0646503 1
 
2.1%
127.1095417 1
 
2.1%
127.0864933 1
 
2.1%
127.1074896 1
 
2.1%
127.0792729 1
 
2.1%
127.0762135 1
 
2.1%
127.0872224 1
 
2.1%
127.0912914 1
 
2.1%
127.0924708 1
 
2.1%
Other values (36) 36
76.6%
ValueCountFrequency (%)
127.0627208 1
2.1%
127.0646503 1
2.1%
127.0652128 1
2.1%
127.0665517 1
2.1%
127.0667517 1
2.1%
127.0684009 1
2.1%
127.0692519 1
2.1%
127.0695705 1
2.1%
127.0699485 1
2.1%
127.0715491 1
2.1%
ValueCountFrequency (%)
127.1109741 1
2.1%
127.1095417 1
2.1%
127.1074896 1
2.1%
127.1024213 2
4.3%
127.0985331 1
2.1%
127.0968977 1
2.1%
127.0957088 1
2.1%
127.0943562 1
2.1%
127.0936838 1
2.1%
127.0936563 1
2.1%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size508.0 B
2021-01-29
47 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-01-29
2nd row2021-01-29
3rd row2021-01-29
4th row2021-01-29
5th row2021-01-29

Common Values

ValueCountFrequency (%)
2021-01-29 47
100.0%

Length

2023-12-12T18:54:47.832466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:54:47.928796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-01-29 47
100.0%

Interactions

2023-12-12T18:54:43.233991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:43.048368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:43.325267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:43.144628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:54:47.987521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태업소명소재지(도로명)주취급음식전화번호위도경도
업태1.0001.0001.0001.0001.0000.0000.534
업소명1.0001.0001.0001.0001.0001.0001.000
소재지(도로명)1.0001.0001.0001.0001.0001.0001.000
주취급음식1.0001.0001.0001.0001.0000.8030.902
전화번호1.0001.0001.0001.0001.0001.0001.000
위도0.0001.0001.0000.8031.0001.0000.737
경도0.5341.0001.0000.9021.0000.7371.000
2023-12-12T18:54:48.105569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도업태
위도1.0000.1430.000
경도0.1431.0000.225
업태0.0000.2251.000

Missing values

2023-12-12T18:54:43.443696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:54:43.601374image/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.
2023-12-12T18:54:43.715518image/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

업태업소명소재지(도로명)주취급음식전화번호위도경도데이터기준일자
0한식(주)남원추어탕서울특별시 광진구 동일로 114 (화양동)추어탕02-498-864937.542671127.064652021-01-29
1한식(주)장군갈비서울특별시 광진구 용마산로 11 (중곡2동)갈비탕02-457-324137.554718127.088122021-01-29
2한식가마솥도가니탕 장수옥서울특별시 광진구 용마산로 124 (중곡4동)갈비02-444-688837.56468127.0869442021-01-29
3한식거보주서울특별시 광진구 아차산로51길 24 (구의1동)돼지갈비, 삼겹살02-456-226637.538002127.0851622021-01-29
4한식건국갈비서울특별시 광진구 광나루로22가길18(화양동)돼지갈비02-2201-001837.545671127.0759072021-01-29
5한식광장동 가온서울특별시 광진구 아차산로78길 75 (광장동, 현대골든텔 106호)곰국수02-3436-710037.551573127.1109742021-01-29
6한식남원율천추어탕서울특별시 광진구 아차산로73길 48, 지하1층(광장동)<NA>02-549-306637.546693127.1024212021-01-29
7한식냉면전문점이대팔서울특별시 광진구 아차산로 238, 1층 (자양4동)<NA>02-499-045837.539935127.069572021-01-29
8한식대가식당서울특별시 광진구 광나루로 596 (구의3동)등심02-456-194437.541993127.0943562021-01-29
9한식대갈집서울특별시 광진구 능동로 49 (자양4동)갈비02-463-270237.535809127.0684012021-01-29
업태업소명소재지(도로명)주취급음식전화번호위도경도데이터기준일자
37한식함흥본가면옥서울특별시 광진구 광나루로 456 (화양동)냉면02-447-880637.545067127.0797572021-01-29
38한식황금어장서울특별시 광진구 광나루로 376 (화양동)생선회02-467-441437.547643127.0715492021-01-29
39한식장원닭한마리서울특별시 광진구 뚝섬로52길 8 (자양2동)닭한마리02-446-966237.53166127.0792922021-01-29
40한식민벅서울특별시 광진구 능동로13길 39(화양동)<NA><NA>37.543482127.0699482021-01-29
41한식주식회사 돕서울특별시 광진구 아차산로31길 9(화양동)<NA><NA>37.541444127.0692522021-01-29
42한식홍천화로숯불구이서울특별시 광진구 천호대로110길 27(능동)<NA><NA>37.555924127.079742021-01-29
43한식숨비소리서울특별시 광진구 화양동 112-16고등어회02-466-589237.547004127.0727992021-01-29
44한식콩이랑두부랑서울특별시 광진구 구의동 244-62순두부02-3425-380037.538368127.0903892021-01-29
45한식태평양수산회세꼬시서울특별시 군자로 183광어02-499-919237.557005127.0782572021-01-29
46한식한촌설렁탕군자점서울특별시 광진구 군자동 242번지 1층설렁탕02-468-420037.552881127.076862021-01-29