Overview

Dataset statistics

Number of variables15
Number of observations131
Missing cells8
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.2 KiB
Average record size in memory127.0 B

Variable types

Categorical4
Numeric5
Text6

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,주된음식,영업장면적(㎡),행정동명,급수시설구분,소재지전화번호
Author광진구
URLhttps://data.seoul.go.kr/dataList/OA-9909/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
행정동명 is highly overall correlated with 급수시설구분High correlation
급수시설구분 is highly overall correlated with 지정년도 and 6 other fieldsHigh correlation
업태명 is highly overall correlated with 급수시설구분High correlation
지정년도 is highly overall correlated with 지정번호 and 3 other fieldsHigh correlation
지정번호 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
신청일자 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
지정일자 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
영업장면적(㎡) is highly overall correlated with 급수시설구분High correlation
소재지전화번호 has 7 (5.3%) missing valuesMissing
소재지도로명 has unique valuesUnique
소재지지번 has unique valuesUnique
허가(신고)번호 has unique valuesUnique

Reproduction

Analysis started2024-05-10 23:44:43.757685
Analysis finished2024-05-10 23:44:56.518739
Duration12.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
3040000
131 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3040000
2nd row3040000
3rd row3040000
4th row3040000
5th row3040000

Common Values

ValueCountFrequency (%)
3040000 131
100.0%

Length

2024-05-10T23:44:56.753620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:44:57.127909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3040000 131
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.8931
Minimum1998
Maximum2029
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-10T23:44:57.593066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1998
5-th percentile1999
Q12006
median2012
Q32018
95-th percentile2023
Maximum2029
Range31
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.5296868
Coefficient of variation (CV)0.0037425879
Kurtosis-0.98166364
Mean2011.8931
Median Absolute Deviation (MAD)6
Skewness-0.12246554
Sum263558
Variance56.696183
MonotonicityNot monotonic
2024-05-10T23:44:58.042266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
2008 11
 
8.4%
2023 9
 
6.9%
2005 8
 
6.1%
1999 8
 
6.1%
2018 7
 
5.3%
2016 7
 
5.3%
2012 7
 
5.3%
2011 6
 
4.6%
2013 6
 
4.6%
2017 6
 
4.6%
Other values (17) 56
42.7%
ValueCountFrequency (%)
1998 3
 
2.3%
1999 8
6.1%
2000 1
 
0.8%
2001 3
 
2.3%
2002 1
 
0.8%
2003 5
3.8%
2004 1
 
0.8%
2005 8
6.1%
2006 6
4.6%
2007 4
3.1%
ValueCountFrequency (%)
2029 1
 
0.8%
2023 9
6.9%
2022 6
4.6%
2021 4
3.1%
2020 5
3.8%
2019 5
3.8%
2018 7
5.3%
2017 6
4.6%
2016 7
5.3%
2015 5
3.8%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3086.5573
Minimum1
Maximum5659
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-10T23:44:58.676532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median5167
Q35549.5
95-th percentile5647
Maximum5659
Range5658
Interquartile range (IQR)5544.5

Descriptive statistics

Standard deviation2718.5239
Coefficient of variation (CV)0.88076251
Kurtosis-1.953869
Mean3086.5573
Median Absolute Deviation (MAD)478
Skewness-0.25695532
Sum404339
Variance7390372.3
MonotonicityNot monotonic
2024-05-10T23:44:59.278164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 10
 
7.6%
2 8
 
6.1%
7 6
 
4.6%
4 6
 
4.6%
6 5
 
3.8%
3 5
 
3.8%
5 5
 
3.8%
9 3
 
2.3%
8 3
 
2.3%
10 2
 
1.5%
Other values (77) 78
59.5%
ValueCountFrequency (%)
1 10
7.6%
2 8
6.1%
3 5
3.8%
4 6
4.6%
5 5
3.8%
6 5
3.8%
7 6
4.6%
8 3
 
2.3%
9 3
 
2.3%
10 2
 
1.5%
ValueCountFrequency (%)
5659 1
0.8%
5658 1
0.8%
5654 1
0.8%
5653 1
0.8%
5652 1
0.8%
5650 1
0.8%
5648 1
0.8%
5646 1
0.8%
5645 1
0.8%
5644 1
0.8%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20119344
Minimum19980220
Maximum20230930
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-10T23:44:59.878378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19980220
5-th percentile19990730
Q120060714
median20121102
Q320180867
95-th percentile20230801
Maximum20230930
Range250710
Interquartile range (IQR)120153.5

Descriptive statistics

Standard deviation74489.043
Coefficient of variation (CV)0.0037023594
Kurtosis-1.0707748
Mean20119344
Median Absolute Deviation (MAD)60079
Skewness-0.17866366
Sum2.6356341 × 109
Variance5.5486176 × 109
MonotonicityNot monotonic
2024-05-10T23:45:00.694815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
20230801 9
 
6.9%
20080430 8
 
6.1%
19990730 8
 
6.1%
20161021 7
 
5.3%
20121102 7
 
5.3%
20130730 6
 
4.6%
20051111 6
 
4.6%
20170915 6
 
4.6%
20111201 6
 
4.6%
20200901 5
 
3.8%
Other values (32) 63
48.1%
ValueCountFrequency (%)
19980220 3
 
2.3%
19990730 8
6.1%
20001107 1
 
0.8%
20010517 1
 
0.8%
20011218 2
 
1.5%
20021010 1
 
0.8%
20030630 2
 
1.5%
20031203 3
 
2.3%
20040512 1
 
0.8%
20050705 2
 
1.5%
ValueCountFrequency (%)
20230930 1
 
0.8%
20230801 9
6.9%
20221020 2
 
1.5%
20221019 2
 
1.5%
20221017 2
 
1.5%
20211015 4
3.1%
20200901 5
3.8%
20190910 5
3.8%
20180928 1
 
0.8%
20180903 2
 
1.5%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)25.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20119892
Minimum19980220
Maximum20290930
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-10T23:45:01.305120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19980220
5-th percentile19990730
Q120060819
median20121231
Q320181023
95-th percentile20230930
Maximum20290930
Range310710
Interquartile range (IQR)120204

Descriptive statistics

Standard deviation75398.033
Coefficient of variation (CV)0.0037474373
Kurtosis-0.9806684
Mean20119892
Median Absolute Deviation (MAD)60103
Skewness-0.12479324
Sum2.6357058 × 109
Variance5.6848634 × 109
MonotonicityNot monotonic
2024-05-10T23:45:01.940593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
20230930 9
 
6.9%
19990730 8
 
6.1%
20080620 8
 
6.1%
20121231 7
 
5.3%
20181023 7
 
5.3%
20161021 7
 
5.3%
20171018 6
 
4.6%
20221207 6
 
4.6%
20130912 6
 
4.6%
20111201 6
 
4.6%
Other values (23) 61
46.6%
ValueCountFrequency (%)
19980220 3
 
2.3%
19990730 8
6.1%
20001107 1
 
0.8%
20010517 1
 
0.8%
20011218 2
 
1.5%
20021010 1
 
0.8%
20030630 2
 
1.5%
20031203 3
 
2.3%
20040512 1
 
0.8%
20050705 2
 
1.5%
ValueCountFrequency (%)
20290930 1
 
0.8%
20230930 9
6.9%
20221207 6
4.6%
20211129 4
3.1%
20201111 5
3.8%
20191029 5
3.8%
20181023 7
5.3%
20171018 6
4.6%
20161021 7
5.3%
20151013 5
3.8%

업소명
Text

Distinct129
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-05-10T23:45:02.680792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length18
Mean length6.1526718
Min length2

Characters and Unicode

Total characters806
Distinct characters256
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique127 ?
Unique (%)96.9%

Sample

1st row명가네남원추어탕
2nd row부림정
3rd row대원칼국수
4th row유성집
5th row포몬스(건대점)
ValueCountFrequency (%)
군자점 3
 
2.0%
부림정 2
 
1.4%
고향반점 2
 
1.4%
고기싸롱중곡점 1
 
0.7%
함흥본가면옥 1
 
0.7%
코바코 1
 
0.7%
다옴순대국감자탕 1
 
0.7%
태평양수산회세꼬시 1
 
0.7%
팀스쿡 1
 
0.7%
하이난 1
 
0.7%
Other values (133) 133
90.5%
2024-05-10T23:45:04.097497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 32
 
4.0%
16
 
2.0%
가 15
 
1.9%
대 15
 
1.9%
탕 13
 
1.6%
원 12
 
1.5%
국 11
 
1.4%
치 11
 
1.4%
의 11
 
1.4%
한 10
 
1.2%
Other values (246) 660
81.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 756
93.8%
Space Separator 16
 
2.0%
Open Punctuation 9
 
1.1%
Close Punctuation 9
 
1.1%
Lowercase Letter 9
 
1.1%
Other Punctuation 3
 
0.4%
Uppercase Letter 2
 
0.2%
Decimal Number 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 32
 
4.2%
가 15
 
2.0%
대 15
 
2.0%
탕 13
 
1.7%
원 12
 
1.6%
국 11
 
1.5%
치 11
 
1.5%
의 11
 
1.5%
한 10
 
1.3%
자 10
 
1.3%
Other values (233) 616
81.5%
Lowercase Letter
ValueCountFrequency (%)
u 2
22.2%
l 2
22.2%
o 2
22.2%
h 1
11.1%
d 1
11.1%
e 1
11.1%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%
Space Separator
ValueCountFrequency (%)
16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Other Punctuation
ValueCountFrequency (%)
& 3
100.0%
Uppercase Letter
ValueCountFrequency (%)
S 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 754
93.5%
Common 39
 
4.8%
Latin 11
 
1.4%
Han 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 32
 
4.2%
가 15
 
2.0%
대 15
 
2.0%
탕 13
 
1.7%
원 12
 
1.6%
국 11
 
1.5%
치 11
 
1.5%
의 11
 
1.5%
한 10
 
1.3%
자 10
 
1.3%
Other values (231) 614
81.4%
Latin
ValueCountFrequency (%)
u 2
18.2%
S 2
18.2%
l 2
18.2%
o 2
18.2%
h 1
9.1%
d 1
9.1%
e 1
9.1%
Common
ValueCountFrequency (%)
16
41.0%
( 9
23.1%
) 9
23.1%
& 3
 
7.7%
1 1
 
2.6%
2 1
 
2.6%
Han
ValueCountFrequency (%)
宴 1
50.0%
禧 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 754
93.5%
ASCII 50
 
6.2%
CJK 2
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 32
 
4.2%
가 15
 
2.0%
대 15
 
2.0%
탕 13
 
1.7%
원 12
 
1.6%
국 11
 
1.5%
치 11
 
1.5%
의 11
 
1.5%
한 10
 
1.3%
자 10
 
1.3%
Other values (231) 614
81.4%
ASCII
ValueCountFrequency (%)
16
32.0%
( 9
18.0%
) 9
18.0%
& 3
 
6.0%
u 2
 
4.0%
S 2
 
4.0%
l 2
 
4.0%
o 2
 
4.0%
h 1
 
2.0%
d 1
 
2.0%
Other values (3) 3
 
6.0%
CJK
ValueCountFrequency (%)
宴 1
50.0%
禧 1
50.0%

소재지도로명
Text

UNIQUE 

Distinct131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-05-10T23:45:04.646982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length42
Mean length28.969466
Min length23

Characters and Unicode

Total characters3795
Distinct characters113
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

Unique131 ?
Unique (%)100.0%

Sample

1st row서울특별시 광진구 능동로37길 11, 1층 (중곡동)
2nd row서울특별시 광진구 뚝섬로 476-7, (자양동)
3rd row서울특별시 광진구 자양로18길 56, 거송빌딩 2층 (구의동)
4th row서울특별시 광진구 천호대로137길 9, 지하2층 (구의동)
5th row서울특별시 광진구 아차산로 208, (자양동,상록빌딩 1층)
ValueCountFrequency (%)
서울특별시 131
18.0%
광진구 131
18.0%
자양동 28
 
3.9%
1층 25
 
3.4%
구의동 23
 
3.2%
중곡동 20
 
2.8%
화양동 18
 
2.5%
광장동 12
 
1.7%
아차산로 12
 
1.7%
능동로 11
 
1.5%
Other values (204) 316
43.5%
2024-05-10T23:45:05.829014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
596
 
15.7%
동 168
 
4.4%
구 163
 
4.3%
, 163
 
4.3%
광 158
 
4.2%
1 150
 
4.0%
) 139
 
3.7%
( 139
 
3.7%
로 132
 
3.5%
진 132
 
3.5%
Other values (103) 1855
48.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2166
57.1%
Space Separator 596
 
15.7%
Decimal Number 574
 
15.1%
Other Punctuation 164
 
4.3%
Close Punctuation 139
 
3.7%
Open Punctuation 139
 
3.7%
Dash Punctuation 11
 
0.3%
Uppercase Letter 4
 
0.1%
Math Symbol 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 168
 
7.8%
구 163
 
7.5%
광 158
 
7.3%
로 132
 
6.1%
진 132
 
6.1%
시 132
 
6.1%
울 131
 
6.0%
별 131
 
6.0%
특 131
 
6.0%
서 131
 
6.0%
Other values (84) 757
34.9%
Decimal Number
ValueCountFrequency (%)
1 150
26.1%
2 66
11.5%
3 62
10.8%
6 52
 
9.1%
4 46
 
8.0%
5 44
 
7.7%
7 43
 
7.5%
8 42
 
7.3%
0 41
 
7.1%
9 28
 
4.9%
Other Punctuation
ValueCountFrequency (%)
, 163
99.4%
/ 1
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
B 3
75.0%
D 1
 
25.0%
Space Separator
ValueCountFrequency (%)
596
100.0%
Close Punctuation
ValueCountFrequency (%)
) 139
100.0%
Open Punctuation
ValueCountFrequency (%)
( 139
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2166
57.1%
Common 1625
42.8%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 168
 
7.8%
구 163
 
7.5%
광 158
 
7.3%
로 132
 
6.1%
진 132
 
6.1%
시 132
 
6.1%
울 131
 
6.0%
별 131
 
6.0%
특 131
 
6.0%
서 131
 
6.0%
Other values (84) 757
34.9%
Common
ValueCountFrequency (%)
596
36.7%
, 163
 
10.0%
1 150
 
9.2%
) 139
 
8.6%
( 139
 
8.6%
2 66
 
4.1%
3 62
 
3.8%
6 52
 
3.2%
4 46
 
2.8%
5 44
 
2.7%
Other values (7) 168
 
10.3%
Latin
ValueCountFrequency (%)
B 3
75.0%
D 1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2166
57.1%
ASCII 1629
42.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
596
36.6%
, 163
 
10.0%
1 150
 
9.2%
) 139
 
8.5%
( 139
 
8.5%
2 66
 
4.1%
3 62
 
3.8%
6 52
 
3.2%
4 46
 
2.8%
5 44
 
2.7%
Other values (9) 172
 
10.6%
Hangul
ValueCountFrequency (%)
동 168
 
7.8%
구 163
 
7.5%
광 158
 
7.3%
로 132
 
6.1%
진 132
 
6.1%
시 132
 
6.1%
울 131
 
6.0%
별 131
 
6.0%
특 131
 
6.0%
서 131
 
6.0%
Other values (84) 757
34.9%

소재지지번
Text

UNIQUE 

Distinct131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-05-10T23:45:06.498357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length40
Mean length27.320611
Min length22

Characters and Unicode

Total characters3579
Distinct characters97
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

Unique131 ?
Unique (%)100.0%

Sample

1st row서울특별시 광진구 중곡동 649번지 13호 1층
2nd row서울특별시 광진구 자양동 52번지 2호
3rd row서울특별시 광진구 구의동 243번지 6호 거송빌딩
4th row서울특별시 광진구 구의동 57번지 30호 지하2층
5th row서울특별시 광진구 자양동 8번지 3호 상록빌딩 1층
ValueCountFrequency (%)
서울특별시 131
18.4%
광진구 131
18.4%
자양동 33
 
4.6%
구의동 30
 
4.2%
1층 22
 
3.1%
중곡동 20
 
2.8%
화양동 20
 
2.8%
광장동 13
 
1.8%
군자동 12
 
1.7%
1호 12
 
1.7%
Other values (179) 288
40.4%
2024-05-10T23:45:07.448023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
921
25.7%
구 161
 
4.5%
1 152
 
4.2%
광 145
 
4.1%
지 139
 
3.9%
시 133
 
3.7%
진 132
 
3.7%
동 132
 
3.7%
서 131
 
3.7%
번 131
 
3.7%
Other values (87) 1402
39.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1997
55.8%
Space Separator 921
25.7%
Decimal Number 621
 
17.4%
Other Punctuation 12
 
0.3%
Close Punctuation 9
 
0.3%
Open Punctuation 9
 
0.3%
Dash Punctuation 6
 
0.2%
Uppercase Letter 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 161
 
8.1%
광 145
 
7.3%
지 139
 
7.0%
시 133
 
6.7%
진 132
 
6.6%
동 132
 
6.6%
서 131
 
6.6%
번 131
 
6.6%
특 131
 
6.6%
별 131
 
6.6%
Other values (69) 631
31.6%
Decimal Number
ValueCountFrequency (%)
1 152
24.5%
2 103
16.6%
3 63
10.1%
5 60
 
9.7%
4 60
 
9.7%
6 45
 
7.2%
7 39
 
6.3%
0 37
 
6.0%
9 33
 
5.3%
8 29
 
4.7%
Other Punctuation
ValueCountFrequency (%)
, 11
91.7%
/ 1
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
B 3
75.0%
D 1
 
25.0%
Space Separator
ValueCountFrequency (%)
921
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1997
55.8%
Common 1578
44.1%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 161
 
8.1%
광 145
 
7.3%
지 139
 
7.0%
시 133
 
6.7%
진 132
 
6.6%
동 132
 
6.6%
서 131
 
6.6%
번 131
 
6.6%
특 131
 
6.6%
별 131
 
6.6%
Other values (69) 631
31.6%
Common
ValueCountFrequency (%)
921
58.4%
1 152
 
9.6%
2 103
 
6.5%
3 63
 
4.0%
5 60
 
3.8%
4 60
 
3.8%
6 45
 
2.9%
7 39
 
2.5%
0 37
 
2.3%
9 33
 
2.1%
Other values (6) 65
 
4.1%
Latin
ValueCountFrequency (%)
B 3
75.0%
D 1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1997
55.8%
ASCII 1582
44.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
921
58.2%
1 152
 
9.6%
2 103
 
6.5%
3 63
 
4.0%
5 60
 
3.8%
4 60
 
3.8%
6 45
 
2.8%
7 39
 
2.5%
0 37
 
2.3%
9 33
 
2.1%
Other values (8) 69
 
4.4%
Hangul
ValueCountFrequency (%)
구 161
 
8.1%
광 145
 
7.3%
지 139
 
7.0%
시 133
 
6.7%
진 132
 
6.6%
동 132
 
6.6%
서 131
 
6.6%
번 131
 
6.6%
특 131
 
6.6%
별 131
 
6.6%
Other values (69) 631
31.6%

허가(신고)번호
Text

UNIQUE 

Distinct131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-05-10T23:45:07.989234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2882
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

Unique131 ?
Unique (%)100.0%

Sample

1st row3040000-101-2016-00001
2nd row3040000-101-1979-00456
3rd row3040000-101-1987-00710
4th row3040000-101-2015-00014
5th row3040000-101-2004-00280
ValueCountFrequency (%)
3040000-101-2016-00001 1
 
0.8%
3040000-101-1981-00796 1
 
0.8%
3040000-101-2006-00152 1
 
0.8%
3040000-101-1999-08290 1
 
0.8%
3040000-101-1998-06612 1
 
0.8%
3040000-101-1997-02022 1
 
0.8%
3040000-101-2002-00179 1
 
0.8%
3040000-101-2007-00069 1
 
0.8%
3040000-101-2004-00411 1
 
0.8%
3040000-101-2003-00322 1
 
0.8%
Other values (121) 121
92.4%
2024-05-10T23:45:08.830904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1187
41.2%
1 428
 
14.9%
- 393
 
13.6%
3 188
 
6.5%
4 173
 
6.0%
2 147
 
5.1%
9 143
 
5.0%
8 67
 
2.3%
6 59
 
2.0%
5 49
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2489
86.4%
Dash Punctuation 393
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1187
47.7%
1 428
 
17.2%
3 188
 
7.6%
4 173
 
7.0%
2 147
 
5.9%
9 143
 
5.7%
8 67
 
2.7%
6 59
 
2.4%
5 49
 
2.0%
7 48
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 393
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2882
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1187
41.2%
1 428
 
14.9%
- 393
 
13.6%
3 188
 
6.5%
4 173
 
6.0%
2 147
 
5.1%
9 143
 
5.0%
8 67
 
2.3%
6 59
 
2.0%
5 49
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2882
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1187
41.2%
1 428
 
14.9%
- 393
 
13.6%
3 188
 
6.5%
4 173
 
6.0%
2 147
 
5.1%
9 143
 
5.0%
8 67
 
2.3%
6 59
 
2.0%
5 49
 
1.7%

업태명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
한식
83 
중국식
20 
분식
 
6
일식
 
6
식육(숯불구이)
 
6
Other values (6)
10 

Length

Max length8
Median length2
Mean length2.5343511
Min length2

Unique

Unique3 ?
Unique (%)2.3%

Sample

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

Common Values

ValueCountFrequency (%)
한식 83
63.4%
중국식 20
 
15.3%
분식 6
 
4.6%
일식 6
 
4.6%
식육(숯불구이) 6
 
4.6%
경양식 3
 
2.3%
기타 2
 
1.5%
호프/통닭 2
 
1.5%
김밥(도시락) 1
 
0.8%
까페 1
 
0.8%

Length

2024-05-10T23:45:09.306364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 83
63.4%
중국식 20
 
15.3%
분식 6
 
4.6%
일식 6
 
4.6%
식육(숯불구이 6
 
4.6%
경양식 3
 
2.3%
기타 2
 
1.5%
호프/통닭 2
 
1.5%
김밥(도시락 1
 
0.8%
까페 1
 
0.8%

주된음식
Text

Distinct87
Distinct (%)66.9%
Missing1
Missing (%)0.8%
Memory size1.2 KiB
2024-05-10T23:45:09.866634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length3.5615385
Min length1

Characters and Unicode

Total characters463
Distinct characters124
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

Unique67 ?
Unique (%)51.5%

Sample

1st row추어탕
2nd row갈비
3rd row칼국수
4th row등심
5th row쌀국수
ValueCountFrequency (%)
갈비 7
 
5.0%
양꼬치 7
 
5.0%
자장면 6
 
4.3%
추어탕 5
 
3.6%
돼지갈비 5
 
3.6%
냉면 4
 
2.9%
부대찌개 3
 
2.1%
김밥 3
 
2.1%
칼국수 3
 
2.1%
초밥 3
 
2.1%
Other values (77) 94
67.1%
2024-05-10T23:45:10.968736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
갈 20
 
4.3%
탕 20
 
4.3%
비 20
 
4.3%
면 13
 
2.8%
치 12
 
2.6%
국 11
 
2.4%
10
 
2.2%
, 10
 
2.2%
지 10
 
2.2%
수 10
 
2.2%
Other values (114) 327
70.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 443
95.7%
Space Separator 10
 
2.2%
Other Punctuation 10
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
갈 20
 
4.5%
탕 20
 
4.5%
비 20
 
4.5%
면 13
 
2.9%
치 12
 
2.7%
국 11
 
2.5%
지 10
 
2.3%
수 10
 
2.3%
밥 9
 
2.0%
리 9
 
2.0%
Other values (112) 309
69.8%
Space Separator
ValueCountFrequency (%)
10
100.0%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 443
95.7%
Common 20
 
4.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
갈 20
 
4.5%
탕 20
 
4.5%
비 20
 
4.5%
면 13
 
2.9%
치 12
 
2.7%
국 11
 
2.5%
지 10
 
2.3%
수 10
 
2.3%
밥 9
 
2.0%
리 9
 
2.0%
Other values (112) 309
69.8%
Common
ValueCountFrequency (%)
10
50.0%
, 10
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 443
95.7%
ASCII 20
 
4.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
갈 20
 
4.5%
탕 20
 
4.5%
비 20
 
4.5%
면 13
 
2.9%
치 12
 
2.7%
국 11
 
2.5%
지 10
 
2.3%
수 10
 
2.3%
밥 9
 
2.0%
리 9
 
2.0%
Other values (112) 309
69.8%
ASCII
ValueCountFrequency (%)
10
50.0%
, 10
50.0%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct130
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.17084
Minimum17.88
Maximum605
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-10T23:45:11.391066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17.88
5-th percentile45.63
Q182
median104.37
Q3170.35
95-th percentile334.07
Maximum605
Range587.12
Interquartile range (IQR)88.35

Descriptive statistics

Standard deviation100.73578
Coefficient of variation (CV)0.72906687
Kurtosis7.4502939
Mean138.17084
Median Absolute Deviation (MAD)38.92
Skewness2.4177326
Sum18100.38
Variance10147.698
MonotonicityNot monotonic
2024-05-10T23:45:11.906621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82.0 2
 
1.5%
170.0 1
 
0.8%
116.86 1
 
0.8%
343.62 1
 
0.8%
99.04 1
 
0.8%
420.99 1
 
0.8%
259.92 1
 
0.8%
92.01 1
 
0.8%
149.52 1
 
0.8%
97.71 1
 
0.8%
Other values (120) 120
91.6%
ValueCountFrequency (%)
17.88 1
0.8%
29.25 1
0.8%
31.0 1
0.8%
37.5 1
0.8%
39.6 1
0.8%
44.2 1
0.8%
45.2 1
0.8%
46.06 1
0.8%
49.3 1
0.8%
50.77 1
0.8%
ValueCountFrequency (%)
605.0 1
0.8%
600.97 1
0.8%
524.88 1
0.8%
420.99 1
0.8%
362.08 1
0.8%
352.11 1
0.8%
343.62 1
0.8%
324.52 1
0.8%
322.7 1
0.8%
282.15 1
0.8%

행정동명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
화양동
20 
자양제4동
16 
구의제3동
16 
광장동
13 
군자동
12 
Other values (10)
54 

Length

Max length5
Median length5
Mean length4.2442748
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중곡제1동
2nd row자양제4동
3rd row구의제1동
4th row구의제2동
5th row자양제4동

Common Values

ValueCountFrequency (%)
화양동 20
15.3%
자양제4동 16
12.2%
구의제3동 16
12.2%
광장동 13
9.9%
군자동 12
9.2%
구의제1동 10
7.6%
자양제3동 8
 
6.1%
중곡제2동 7
 
5.3%
자양제1동 7
 
5.3%
중곡제1동 6
 
4.6%
Other values (5) 16
12.2%

Length

2024-05-10T23:45:12.305549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
화양동 20
15.3%
자양제4동 16
12.2%
구의제3동 16
12.2%
광장동 13
9.9%
군자동 12
9.2%
구의제1동 10
7.6%
자양제3동 8
 
6.1%
중곡제2동 7
 
5.3%
자양제1동 7
 
5.3%
중곡제1동 6
 
4.6%
Other values (5) 16
12.2%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
상수도전용
92 
<NA>
39 

Length

Max length5
Median length5
Mean length4.7022901
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 92
70.2%
<NA> 39
29.8%

Length

2024-05-10T23:45:12.649282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:45:12.971221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 92
70.2%
na 39
29.8%

소재지전화번호
Text

MISSING 

Distinct124
Distinct (%)100.0%
Missing7
Missing (%)5.3%
Memory size1.2 KiB
2024-05-10T23:45:13.707475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.016129
Min length9

Characters and Unicode

Total characters1242
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

Unique124 ?
Unique (%)100.0%

Sample

1st row02 4615977
2nd row02 4635455
3rd row02 4543112
4th row02 4546999
5th row02 4611721
ValueCountFrequency (%)
02 102
44.3%
4468167 1
 
0.4%
4983618 1
 
0.4%
4658989 1
 
0.4%
4447893 1
 
0.4%
4663618 1
 
0.4%
4571613 1
 
0.4%
4675211 1
 
0.4%
4621939 1
 
0.4%
4521610 1
 
0.4%
Other values (119) 119
51.7%
2024-05-10T23:45:15.077294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 199
16.0%
4 198
15.9%
2 196
15.8%
107
8.6%
6 93
7.5%
8 93
7.5%
5 83
6.7%
9 74
 
6.0%
7 74
 
6.0%
1 64
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1135
91.4%
Space Separator 107
 
8.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 199
17.5%
4 198
17.4%
2 196
17.3%
6 93
8.2%
8 93
8.2%
5 83
7.3%
9 74
 
6.5%
7 74
 
6.5%
1 64
 
5.6%
3 61
 
5.4%
Space Separator
ValueCountFrequency (%)
107
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1242
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 199
16.0%
4 198
15.9%
2 196
15.8%
107
8.6%
6 93
7.5%
8 93
7.5%
5 83
6.7%
9 74
 
6.0%
7 74
 
6.0%
1 64
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1242
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 199
16.0%
4 198
15.9%
2 196
15.8%
107
8.6%
6 93
7.5%
8 93
7.5%
5 83
6.7%
9 74
 
6.0%
7 74
 
6.0%
1 64
 
5.2%

Interactions

2024-05-10T23:44:53.057251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:45.754476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:47.699801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:49.494868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:51.042944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:53.446793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:46.039900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:48.098725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:49.800113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:51.462848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:53.785258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:46.392770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:48.464463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:50.215521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:51.854691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:54.095399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:46.871887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:48.783255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:50.462308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:52.181893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:54.373437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:47.347749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:49.238649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:50.741114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:52.562899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T23:45:15.442462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자업태명주된음식영업장면적(㎡)행정동명
지정년도1.0000.8260.9661.0000.1570.7310.0000.300
지정번호0.8261.0000.8270.8260.0000.8130.6070.565
신청일자0.9660.8271.0000.9660.0000.0000.0000.490
지정일자1.0000.8260.9661.0000.1570.7310.0000.300
업태명0.1570.0000.0000.1571.0000.9870.0000.573
주된음식0.7310.8130.0000.7310.9871.0000.7260.669
영업장면적(㎡)0.0000.6070.0000.0000.0000.7261.0000.000
행정동명0.3000.5650.4900.3000.5730.6690.0001.000
2024-05-10T23:45:15.932512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명급수시설구분업태명
행정동명1.0001.0000.256
급수시설구분1.0001.0001.000
업태명0.2561.0001.000
2024-05-10T23:45:16.207965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.000-0.5670.9991.000-0.1440.0480.0951.000
지정번호-0.5671.000-0.566-0.5660.0560.0000.2951.000
신청일자0.999-0.5661.0001.000-0.1480.0000.1931.000
지정일자1.000-0.5661.0001.000-0.1480.0480.0951.000
영업장면적(㎡)-0.1440.056-0.148-0.1481.0000.0000.0001.000
업태명0.0480.0000.0000.0480.0001.0000.2561.000
행정동명0.0950.2950.1930.0950.0000.2561.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-10T23:44:54.853601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T23:44:55.869787image/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.
2024-05-10T23:44:56.274792image/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

시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
03040000201662016102120161021명가네남원추어탕서울특별시 광진구 능동로37길 11, 1층 (중곡동)서울특별시 광진구 중곡동 649번지 13호 1층3040000-101-2016-00001한식추어탕170.0중곡제1동<NA>02 4615977
13040000200855682008102420081201부림정서울특별시 광진구 뚝섬로 476-7, (자양동)서울특별시 광진구 자양동 52번지 2호3040000-101-1979-00456한식갈비112.1자양제4동상수도전용02 4635455
23040000200554492005111120051111대원칼국수서울특별시 광진구 자양로18길 56, 거송빌딩 2층 (구의동)서울특별시 광진구 구의동 243번지 6호 거송빌딩3040000-101-1987-00710분식칼국수132.44구의제1동상수도전용02 4543112
33040000201512015101320151013유성집서울특별시 광진구 천호대로137길 9, 지하2층 (구의동)서울특별시 광진구 구의동 57번지 30호 지하2층3040000-101-2015-00014한식등심162.33구의제2동<NA>02 4546999
43040000200554612005111120051111포몬스(건대점)서울특별시 광진구 아차산로 208, (자양동,상록빌딩 1층)서울특별시 광진구 자양동 8번지 3호 상록빌딩 1층3040000-101-2004-00280한식쌀국수161.13자양제4동상수도전용02 4611721
53040000200855592008043020080620(유)아웃백스테이크하우스코리아 건대스타시티점서울특별시 광진구 능동로 92, 롯데백화점 스타시티몰 2층 (자양동)서울특별시 광진구 자양동 227번지 342호 롯데백화점 스타시티몰2층3040000-101-2006-00139경양식스테이크605.0자양제3동상수도전용0222183021
63040000201256392012110220121231니뽕내뽕서울특별시 광진구 동일로20길 60, 1층 (자양동)서울특별시 광진구 자양동 10번지 45호 1층3040000-101-2011-00014중국식짬뽕82.0자양제4동<NA>02 4991828
73040000201732017091520171018미국가주우육면대왕&중화반점서울특별시 광진구 동일로18길 56, (자양동,(2층))서울특별시 광진구 자양동 11번지 5호 (2층)3040000-101-2006-00175중국식우육면89.38자양제4동상수도전용0264658999
83040000201256422012110220121231망향비빔국수서울특별시 광진구 천호대로 694, (구의동,(1층))서울특별시 광진구 구의동 73번지 5호 (1층)3040000-101-2008-00224한식비빔국수171.42구의제2동상수도전용02 4541357
93040000201156362011120120111201군자닭갈비서울특별시 광진구 능동로 315, 1층 (중곡동)서울특별시 광진구 중곡동 649번지 8호 1층3040000-101-2008-00251한식닭갈비64.48중곡제1동상수도전용0262459888
시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
1213040000202392023080120230930래빗홀버거컴퍼니서울특별시 광진구 광나루로 424, (화양동)서울특별시 광진구 화양동 490번지 2호3040000-101-1981-01263한식햄버거17.88화양동상수도전용02 4460424
1223040000200956002009091020091105원할머니보쌈서울특별시 광진구 용마산로 37, (중곡동)서울특별시 광진구 중곡동 123번지 6호3040000-101-1988-03919한식보쌈120.76중곡제2동상수도전용02 4460033
1233040000201256402012110220121231동해해물서울특별시 광진구 동일로26길 16, (화양동)서울특별시 광진구 화양동 37번지 1호3040000-101-2004-00291한식해물찜44.2화양동<NA>02 4695843
1243040000201256442012110220121231북창동 은이네서울특별시 광진구 천호대로 675, (구의동)서울특별시 광진구 구의동 54번지 3호3040000-101-2005-00187한식불고기냉면97.1구의제2동상수도전용02 4447292
1253040000200955912009091020091105광장동 가온서울특별시 광진구 아차산로78길 75, (광장동, 현대골든텔 106호)서울특별시 광진구 광장동 102번지3040000-101-2008-00119한식곰국수237.7광장동상수도전용0234367100
12630400002015122015101320151013연길왕꼬치서울특별시 광진구 동일로18길 86, (자양동)서울특별시 광진구 자양동 4번지 6호3040000-101-2014-00293중국식양꼬치, 탕수육195.77자양제4동<NA>02 4637741
1273040000201792017091520171018정성한줄서울특별시 광진구 아차산로 537-17, 1층 17호 (광장동)서울특별시 광진구 광장동 582번지 1층-173040000-101-2016-00313한식김밥45.2광장동<NA>0234378253
1283040000201772017091520171018송쉐프구의점서울특별시 광진구 아차산로 355, 타워더모스트광진아크로텔 2층 208호 (자양동)서울특별시 광진구 자양동 779번지 타워더모스트광진아크로텔3040000-101-2015-00110중국식짜장면193.37자양제1동<NA>02446 6000
1293040000202042020090120201111고기싸롱중곡점서울특별시 광진구 면목로 178, 1층 (중곡동)서울특별시 광진구 중곡동 198번지 11호 1층3040000-101-2019-00227한식갈비168.61중곡제3동<NA>02 4975400
1303040000202012020090120201111한촌설렁탕 군자점서울특별시 광진구 능동로 255, 1층 (군자동)서울특별시 광진구 군자동 242번지3040000-101-2019-00289한식설렁탕111.7군자동<NA>02 4684200