Overview

Dataset statistics

Number of variables15
Number of observations107
Missing cells21
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.3 KiB
Average record size in memory127.2 B

Variable types

Categorical4
Numeric5
Text6

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,주된음식,영업장면적(㎡),행정동명,급수시설구분,소재지전화번호
Author동작구
URLhttps://data.seoul.go.kr/dataList/OA-10602/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
지정년도 is highly overall correlated with 신청일자 and 1 other fieldsHigh correlation
신청일자 is highly overall correlated with 지정년도 and 1 other fieldsHigh correlation
지정일자 is highly overall correlated with 지정년도 and 1 other fieldsHigh correlation
행정동명 is highly overall correlated with 급수시설구분High correlation
급수시설구분 is highly overall correlated with 행정동명High correlation
소재지전화번호 has 21 (19.6%) missing valuesMissing
허가(신고)번호 has unique valuesUnique

Reproduction

Analysis started2024-05-11 06:15:21.622230
Analysis finished2024-05-11 06:15:28.147866
Duration6.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size988.0 B
3190000
107 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3190000
2nd row3190000
3rd row3190000
4th row3190000
5th row3190000

Common Values

ValueCountFrequency (%)
3190000 107
100.0%

Length

2024-05-11T15:15:28.278955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:15:28.551932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3190000 107
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.1402
Minimum2001
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-11T15:15:28.840928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2004.3
Q12013
median2018
Q32021
95-th percentile2023
Maximum2023
Range22
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.03363
Coefficient of variation (CV)0.0029926639
Kurtosis-0.35178456
Mean2016.1402
Median Absolute Deviation (MAD)4
Skewness-0.79754152
Sum215727
Variance36.404691
MonotonicityNot monotonic
2024-05-11T15:15:29.778093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2021 25
23.4%
2023 13
12.1%
2013 13
12.1%
2019 9
 
8.4%
2018 5
 
4.7%
2017 4
 
3.7%
2012 4
 
3.7%
2011 3
 
2.8%
2007 3
 
2.8%
2016 3
 
2.8%
Other values (13) 25
23.4%
ValueCountFrequency (%)
2001 2
1.9%
2002 1
 
0.9%
2003 2
1.9%
2004 1
 
0.9%
2005 2
1.9%
2006 1
 
0.9%
2007 3
2.8%
2008 3
2.8%
2009 3
2.8%
2010 1
 
0.9%
ValueCountFrequency (%)
2023 13
12.1%
2022 3
 
2.8%
2021 25
23.4%
2020 2
 
1.9%
2019 9
 
8.4%
2018 5
 
4.7%
2017 4
 
3.7%
2016 3
 
2.8%
2015 2
 
1.9%
2014 2
 
1.9%

지정번호
Real number (ℝ)

Distinct33
Distinct (%)30.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.345794
Minimum1
Maximum912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-11T15:15:30.031834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q315.5
95-th percentile32.5
Maximum912
Range911
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation149.24844
Coefficient of variation (CV)4.2225232
Kurtosis32.028814
Mean35.345794
Median Absolute Deviation (MAD)5
Skewness5.7723573
Sum3782
Variance22275.096
MonotonicityNot monotonic
2024-05-11T15:15:30.296787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
2 10
 
9.3%
1 8
 
7.5%
4 7
 
6.5%
6 7
 
6.5%
3 7
 
6.5%
7 6
 
5.6%
11 5
 
4.7%
10 5
 
4.7%
9 5
 
4.7%
5 5
 
4.7%
Other values (23) 42
39.3%
ValueCountFrequency (%)
1 8
7.5%
2 10
9.3%
3 7
6.5%
4 7
6.5%
5 5
4.7%
6 7
6.5%
7 6
5.6%
8 5
4.7%
9 5
4.7%
10 5
4.7%
ValueCountFrequency (%)
912 1
0.9%
909 1
0.9%
905 1
0.9%
37 1
0.9%
35 1
0.9%
34 1
0.9%
29 1
0.9%
27 2
1.9%
26 1
0.9%
24 1
0.9%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20162380
Minimum20010301
Maximum20231013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-11T15:15:30.585807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010301
5-th percentile20043405
Q120131207
median20180927
Q320211015
95-th percentile20231013
Maximum20231013
Range220712
Interquartile range (IQR)79808

Descriptive statistics

Standard deviation60399.485
Coefficient of variation (CV)0.0029956526
Kurtosis-0.34661802
Mean20162380
Median Absolute Deviation (MAD)40087
Skewness-0.79985911
Sum2.1573747 × 109
Variance3.6480978 × 109
MonotonicityNot monotonic
2024-05-11T15:15:30.881201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
20211015 25
23.4%
20231013 13
 
12.1%
20131223 5
 
4.7%
20131217 3
 
2.8%
20110602 3
 
2.8%
20120925 3
 
2.8%
20160930 3
 
2.8%
20190930 3
 
2.8%
20090609 3
 
2.8%
20221014 3
 
2.8%
Other values (32) 43
40.2%
ValueCountFrequency (%)
20010301 1
0.9%
20011201 1
0.9%
20020326 1
0.9%
20030925 1
0.9%
20031231 1
0.9%
20040309 1
0.9%
20050630 1
0.9%
20051104 1
0.9%
20060801 1
0.9%
20070629 1
0.9%
ValueCountFrequency (%)
20231013 13
12.1%
20221014 3
 
2.8%
20211015 25
23.4%
20201102 1
 
0.9%
20201029 1
 
0.9%
20191210 2
 
1.9%
20190930 3
 
2.8%
20190926 2
 
1.9%
20190925 2
 
1.9%
20180927 2
 
1.9%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20162500
Minimum20010330
Maximum20231123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-11T15:15:31.141098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010330
5-th percentile20043471
Q120131213
median20181112
Q320211129
95-th percentile20231123
Maximum20231123
Range220793
Interquartile range (IQR)79916

Descriptive statistics

Standard deviation60422.308
Coefficient of variation (CV)0.0029967666
Kurtosis-0.34463896
Mean20162500
Median Absolute Deviation (MAD)40006
Skewness-0.8010871
Sum2.1573875 × 109
Variance3.6508554 × 109
MonotonicityNot monotonic
2024-05-11T15:15:31.405811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
20211129 25
23.4%
20131213 13
12.1%
20231123 13
12.1%
20191209 8
 
7.5%
20181112 5
 
4.7%
20171116 4
 
3.7%
20121217 4
 
3.7%
20161108 3
 
2.8%
20090618 3
 
2.8%
20111130 3
 
2.8%
Other values (19) 26
24.3%
ValueCountFrequency (%)
20010330 1
0.9%
20011228 1
0.9%
20020328 1
0.9%
20031009 1
0.9%
20031231 1
0.9%
20040403 1
0.9%
20050630 1
0.9%
20051121 1
0.9%
20060830 1
0.9%
20070629 1
0.9%
ValueCountFrequency (%)
20231123 13
12.1%
20221118 3
 
2.8%
20211129 25
23.4%
20201210 2
 
1.9%
20191210 1
 
0.9%
20191209 8
 
7.5%
20181112 5
 
4.7%
20171116 4
 
3.7%
20161108 3
 
2.8%
20151210 2
 
1.9%

업소명
Text

Distinct104
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size988.0 B
2024-05-11T15:15:31.807292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length6.5794393
Min length2

Characters and Unicode

Total characters704
Distinct characters231
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

Unique101 ?
Unique (%)94.4%

Sample

1st row김종구참치박사
2nd row구름산추어탕
3rd row어촌
4th row대방회집
5th row이수샤브샤브
ValueCountFrequency (%)
노량진점 3
 
2.1%
구름산추어탕 2
 
1.4%
이수역점 2
 
1.4%
대방점 2
 
1.4%
보라매점 2
 
1.4%
주식회사 2
 
1.4%
중앙대점 2
 
1.4%
신대방삼거리역점 2
 
1.4%
숯불구이 2
 
1.4%
다독이네 2
 
1.4%
Other values (115) 119
85.0%
2024-05-11T15:15:32.454855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
33
 
4.7%
점 20
 
2.8%
대 15
 
2.1%
이 14
 
2.0%
마 14
 
2.0%
방 11
 
1.6%
식 11
 
1.6%
집 9
 
1.3%
진 9
 
1.3%
정 9
 
1.3%
Other values (221) 559
79.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 626
88.9%
Space Separator 33
 
4.7%
Lowercase Letter 14
 
2.0%
Decimal Number 10
 
1.4%
Uppercase Letter 7
 
1.0%
Open Punctuation 6
 
0.9%
Close Punctuation 6
 
0.9%
Other Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 20
 
3.2%
대 15
 
2.4%
이 14
 
2.2%
마 14
 
2.2%
방 11
 
1.8%
식 11
 
1.8%
집 9
 
1.4%
진 9
 
1.4%
정 9
 
1.4%
구 9
 
1.4%
Other values (198) 505
80.7%
Lowercase Letter
ValueCountFrequency (%)
l 2
14.3%
r 2
14.3%
o 2
14.3%
s 2
14.3%
a 2
14.3%
d 1
7.1%
i 1
7.1%
t 1
7.1%
u 1
7.1%
Uppercase Letter
ValueCountFrequency (%)
D 2
28.6%
I 1
14.3%
A 1
14.3%
H 1
14.3%
C 1
14.3%
G 1
14.3%
Decimal Number
ValueCountFrequency (%)
2 3
30.0%
3 3
30.0%
9 2
20.0%
0 2
20.0%
Space Separator
ValueCountFrequency (%)
33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Other Punctuation
ValueCountFrequency (%)
& 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 626
88.9%
Common 57
 
8.1%
Latin 21
 
3.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 20
 
3.2%
대 15
 
2.4%
이 14
 
2.2%
마 14
 
2.2%
방 11
 
1.8%
식 11
 
1.8%
집 9
 
1.4%
진 9
 
1.4%
정 9
 
1.4%
구 9
 
1.4%
Other values (198) 505
80.7%
Latin
ValueCountFrequency (%)
l 2
 
9.5%
r 2
 
9.5%
o 2
 
9.5%
s 2
 
9.5%
D 2
 
9.5%
a 2
 
9.5%
d 1
 
4.8%
I 1
 
4.8%
A 1
 
4.8%
H 1
 
4.8%
Other values (5) 5
23.8%
Common
ValueCountFrequency (%)
33
57.9%
( 6
 
10.5%
) 6
 
10.5%
2 3
 
5.3%
3 3
 
5.3%
& 2
 
3.5%
9 2
 
3.5%
0 2
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 626
88.9%
ASCII 78
 
11.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
33
42.3%
( 6
 
7.7%
) 6
 
7.7%
2 3
 
3.8%
3 3
 
3.8%
l 2
 
2.6%
& 2
 
2.6%
r 2
 
2.6%
o 2
 
2.6%
s 2
 
2.6%
Other values (13) 17
21.8%
Hangul
ValueCountFrequency (%)
점 20
 
3.2%
대 15
 
2.4%
이 14
 
2.2%
마 14
 
2.2%
방 11
 
1.8%
식 11
 
1.8%
집 9
 
1.4%
진 9
 
1.4%
정 9
 
1.4%
구 9
 
1.4%
Other values (198) 505
80.7%

소재지도로명
Text

Distinct106
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size988.0 B
2024-05-11T15:15:33.080830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length40
Mean length30.186916
Min length22

Characters and Unicode

Total characters3230
Distinct characters127
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

Unique105 ?
Unique (%)98.1%

Sample

1st row서울특별시 동작구 동작대로 25-1, (사당동, 1층)
2nd row서울특별시 동작구 성대로1길 22, 지상1층 (상도동)
3rd row서울특별시 동작구 상도로 122, (상도동, 1층)
4th row서울특별시 동작구 여의대방로 134-1, (대방동)
5th row서울특별시 동작구 동작대로29길 8, 2층 (사당동)
ValueCountFrequency (%)
서울특별시 107
 
17.2%
동작구 107
 
17.2%
1층 28
 
4.5%
사당동 27
 
4.3%
상도동 20
 
3.2%
노량진동 20
 
3.2%
대방동 15
 
2.4%
2층 9
 
1.4%
흑석동 8
 
1.3%
상도로 8
 
1.3%
Other values (185) 272
43.8%
2024-05-11T15:15:34.098272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
514
 
15.9%
동 236
 
7.3%
1 152
 
4.7%
, 133
 
4.1%
작 124
 
3.8%
서 111
 
3.4%
) 110
 
3.4%
( 110
 
3.4%
시 108
 
3.3%
울 107
 
3.3%
Other values (117) 1525
47.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1866
57.8%
Space Separator 514
 
15.9%
Decimal Number 476
 
14.7%
Other Punctuation 133
 
4.1%
Close Punctuation 110
 
3.4%
Open Punctuation 110
 
3.4%
Dash Punctuation 17
 
0.5%
Uppercase Letter 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 236
 
12.6%
작 124
 
6.6%
서 111
 
5.9%
시 108
 
5.8%
울 107
 
5.7%
구 107
 
5.7%
별 107
 
5.7%
특 107
 
5.7%
로 105
 
5.6%
길 64
 
3.4%
Other values (100) 690
37.0%
Decimal Number
ValueCountFrequency (%)
1 152
31.9%
2 93
19.5%
0 36
 
7.6%
3 36
 
7.6%
6 34
 
7.1%
5 33
 
6.9%
4 31
 
6.5%
7 31
 
6.5%
8 22
 
4.6%
9 8
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
B 3
75.0%
A 1
 
25.0%
Space Separator
ValueCountFrequency (%)
514
100.0%
Other Punctuation
ValueCountFrequency (%)
, 133
100.0%
Close Punctuation
ValueCountFrequency (%)
) 110
100.0%
Open Punctuation
ValueCountFrequency (%)
( 110
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1866
57.8%
Common 1360
42.1%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 236
 
12.6%
작 124
 
6.6%
서 111
 
5.9%
시 108
 
5.8%
울 107
 
5.7%
구 107
 
5.7%
별 107
 
5.7%
특 107
 
5.7%
로 105
 
5.6%
길 64
 
3.4%
Other values (100) 690
37.0%
Common
ValueCountFrequency (%)
514
37.8%
1 152
 
11.2%
, 133
 
9.8%
) 110
 
8.1%
( 110
 
8.1%
2 93
 
6.8%
0 36
 
2.6%
3 36
 
2.6%
6 34
 
2.5%
5 33
 
2.4%
Other values (5) 109
 
8.0%
Latin
ValueCountFrequency (%)
B 3
75.0%
A 1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1866
57.8%
ASCII 1364
42.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
514
37.7%
1 152
 
11.1%
, 133
 
9.8%
) 110
 
8.1%
( 110
 
8.1%
2 93
 
6.8%
0 36
 
2.6%
3 36
 
2.6%
6 34
 
2.5%
5 33
 
2.4%
Other values (7) 113
 
8.3%
Hangul
ValueCountFrequency (%)
동 236
 
12.6%
작 124
 
6.6%
서 111
 
5.9%
시 108
 
5.8%
울 107
 
5.7%
구 107
 
5.7%
별 107
 
5.7%
특 107
 
5.7%
로 105
 
5.6%
길 64
 
3.4%
Other values (100) 690
37.0%

소재지지번
Text

Distinct105
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size988.0 B
2024-05-11T15:15:34.643807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length41
Mean length27.626168
Min length23

Characters and Unicode

Total characters2956
Distinct characters108
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

Unique104 ?
Unique (%)97.2%

Sample

1st row서울특별시 동작구 사당동 1031번지 21호 1층
2nd row서울특별시 동작구 상도동 242번지 78호
3rd row서울특별시 동작구 상도동 355번지 8호 1층
4th row서울특별시 동작구 대방동 417번지 2호
5th row서울특별시 동작구 사당동 88번지 3호
ValueCountFrequency (%)
서울특별시 107
18.6%
동작구 107
18.6%
사당동 29
 
5.1%
노량진동 23
 
4.0%
상도동 20
 
3.5%
대방동 16
 
2.8%
1호 13
 
2.3%
1층 10
 
1.7%
흑석동 9
 
1.6%
6호 8
 
1.4%
Other values (158) 232
40.4%
2024-05-11T15:15:35.535135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
756
25.6%
동 219
 
7.4%
1 129
 
4.4%
지 112
 
3.8%
호 108
 
3.7%
작 108
 
3.7%
서 108
 
3.7%
시 107
 
3.6%
구 107
 
3.6%
울 107
 
3.6%
Other values (98) 1095
37.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1663
56.3%
Space Separator 756
25.6%
Decimal Number 527
 
17.8%
Open Punctuation 3
 
0.1%
Close Punctuation 3
 
0.1%
Uppercase Letter 2
 
0.1%
Dash Punctuation 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 219
13.2%
지 112
 
6.7%
호 108
 
6.5%
작 108
 
6.5%
서 108
 
6.5%
시 107
 
6.4%
구 107
 
6.4%
울 107
 
6.4%
특 107
 
6.4%
번 107
 
6.4%
Other values (81) 473
28.4%
Decimal Number
ValueCountFrequency (%)
1 129
24.5%
2 78
14.8%
3 70
13.3%
4 47
 
8.9%
0 46
 
8.7%
6 41
 
7.8%
7 34
 
6.5%
5 30
 
5.7%
8 26
 
4.9%
9 26
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
A 1
50.0%
B 1
50.0%
Space Separator
ValueCountFrequency (%)
756
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1663
56.3%
Common 1291
43.7%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 219
13.2%
지 112
 
6.7%
호 108
 
6.5%
작 108
 
6.5%
서 108
 
6.5%
시 107
 
6.4%
구 107
 
6.4%
울 107
 
6.4%
특 107
 
6.4%
번 107
 
6.4%
Other values (81) 473
28.4%
Common
ValueCountFrequency (%)
756
58.6%
1 129
 
10.0%
2 78
 
6.0%
3 70
 
5.4%
4 47
 
3.6%
0 46
 
3.6%
6 41
 
3.2%
7 34
 
2.6%
5 30
 
2.3%
8 26
 
2.0%
Other values (5) 34
 
2.6%
Latin
ValueCountFrequency (%)
A 1
50.0%
B 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1663
56.3%
ASCII 1293
43.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
756
58.5%
1 129
 
10.0%
2 78
 
6.0%
3 70
 
5.4%
4 47
 
3.6%
0 46
 
3.6%
6 41
 
3.2%
7 34
 
2.6%
5 30
 
2.3%
8 26
 
2.0%
Other values (7) 36
 
2.8%
Hangul
ValueCountFrequency (%)
동 219
13.2%
지 112
 
6.7%
호 108
 
6.5%
작 108
 
6.5%
서 108
 
6.5%
시 107
 
6.4%
구 107
 
6.4%
울 107
 
6.4%
특 107
 
6.4%
번 107
 
6.4%
Other values (81) 473
28.4%

허가(신고)번호
Text

UNIQUE 

Distinct107
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size988.0 B
2024-05-11T15:15:35.889815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2354
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

Unique107 ?
Unique (%)100.0%

Sample

1st row3190000-101-2013-00226
2nd row3190000-101-2016-00170
3rd row3190000-101-2013-00110
4th row3190000-101-2010-00059
5th row3190000-101-2017-00213
ValueCountFrequency (%)
3190000-101-2013-00226 1
 
0.9%
3190000-101-2011-00041 1
 
0.9%
3190000-101-1998-05743 1
 
0.9%
3190000-101-1996-07025 1
 
0.9%
3190000-101-1994-04416 1
 
0.9%
3190000-101-2019-00034 1
 
0.9%
3190000-101-2013-00235 1
 
0.9%
3190000-101-1999-07179 1
 
0.9%
3190000-101-2008-00179 1
 
0.9%
3190000-101-2016-00205 1
 
0.9%
Other values (97) 97
90.7%
2024-05-11T15:15:36.457291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 910
38.7%
1 442
18.8%
- 321
 
13.6%
9 192
 
8.2%
2 149
 
6.3%
3 145
 
6.2%
7 44
 
1.9%
6 41
 
1.7%
8 39
 
1.7%
5 36
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2033
86.4%
Dash Punctuation 321
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 910
44.8%
1 442
21.7%
9 192
 
9.4%
2 149
 
7.3%
3 145
 
7.1%
7 44
 
2.2%
6 41
 
2.0%
8 39
 
1.9%
5 36
 
1.8%
4 35
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 321
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 910
38.7%
1 442
18.8%
- 321
 
13.6%
9 192
 
8.2%
2 149
 
6.3%
3 145
 
6.2%
7 44
 
1.9%
6 41
 
1.7%
8 39
 
1.7%
5 36
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 910
38.7%
1 442
18.8%
- 321
 
13.6%
9 192
 
8.2%
2 149
 
6.3%
3 145
 
6.2%
7 44
 
1.9%
6 41
 
1.7%
8 39
 
1.7%
5 36
 
1.5%

업태명
Categorical

Distinct11
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size988.0 B
한식
67 
일식
9 
기타
 
6
경양식
 
6
중국식
 
5
Other values (6)
14 

Length

Max length10
Median length2
Mean length2.5233645
Min length2

Unique

Unique2 ?
Unique (%)1.9%

Sample

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

Common Values

ValueCountFrequency (%)
한식 67
62.6%
일식 9
 
8.4%
기타 6
 
5.6%
경양식 6
 
5.6%
중국식 5
 
4.7%
회집 4
 
3.7%
통닭(치킨) 3
 
2.8%
식육(숯불구이) 3
 
2.8%
호프/통닭 2
 
1.9%
정종/대포집/소주방 1
 
0.9%

Length

2024-05-11T15:15:36.765416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 67
62.6%
일식 9
 
8.4%
기타 6
 
5.6%
경양식 6
 
5.6%
중국식 5
 
4.7%
회집 4
 
3.7%
통닭(치킨 3
 
2.8%
식육(숯불구이 3
 
2.8%
호프/통닭 2
 
1.9%
정종/대포집/소주방 1
 
0.9%

주된음식
Text

Distinct70
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Memory size988.0 B
2024-05-11T15:15:37.158040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length3.4672897
Min length1

Characters and Unicode

Total characters371
Distinct characters109
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)44.9%

Sample

1st row회
2nd row추어탕
3rd row생선구이
4th row회
5th row샤브
ValueCountFrequency (%)
돼지갈비 9
 
8.3%
삼겹살 7
 
6.4%
회 4
 
3.7%
추어탕 3
 
2.8%
갈비탕 3
 
2.8%
초밥 3
 
2.8%
육개장 2
 
1.8%
모듬돈카츠 2
 
1.8%
갈비 2
 
1.8%
한정식 2
 
1.8%
Other values (60) 72
66.1%
2024-05-11T15:15:37.880068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
갈 18
 
4.9%
비 17
 
4.6%
탕 16
 
4.3%
삼 14
 
3.8%
돼 11
 
3.0%
겹 11
 
3.0%
지 11
 
3.0%
살 10
 
2.7%
국 9
 
2.4%
밥 8
 
2.2%
Other values (99) 246
66.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 357
96.2%
Other Punctuation 6
 
1.6%
Open Punctuation 3
 
0.8%
Close Punctuation 3
 
0.8%
Space Separator 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
갈 18
 
5.0%
비 17
 
4.8%
탕 16
 
4.5%
삼 14
 
3.9%
돼 11
 
3.1%
겹 11
 
3.1%
지 11
 
3.1%
살 10
 
2.8%
국 9
 
2.5%
밥 8
 
2.2%
Other values (94) 232
65.0%
Other Punctuation
ValueCountFrequency (%)
? 3
50.0%
, 3
50.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 357
96.2%
Common 14
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
갈 18
 
5.0%
비 17
 
4.8%
탕 16
 
4.5%
삼 14
 
3.9%
돼 11
 
3.1%
겹 11
 
3.1%
지 11
 
3.1%
살 10
 
2.8%
국 9
 
2.5%
밥 8
 
2.2%
Other values (94) 232
65.0%
Common
ValueCountFrequency (%)
( 3
21.4%
) 3
21.4%
? 3
21.4%
, 3
21.4%
2
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 357
96.2%
ASCII 14
 
3.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
갈 18
 
5.0%
비 17
 
4.8%
탕 16
 
4.5%
삼 14
 
3.9%
돼 11
 
3.1%
겹 11
 
3.1%
지 11
 
3.1%
살 10
 
2.8%
국 9
 
2.5%
밥 8
 
2.2%
Other values (94) 232
65.0%
ASCII
ValueCountFrequency (%)
( 3
21.4%
) 3
21.4%
? 3
21.4%
, 3
21.4%
2
14.3%

영업장면적(㎡)
Real number (ℝ)

Distinct105
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148.54121
Minimum23.04
Maximum1049.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-11T15:15:38.163743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.04
5-th percentile30.69
Q166.03
median103.96
Q3165.035
95-th percentile475.135
Maximum1049.34
Range1026.3
Interquartile range (IQR)99.005

Descriptive statistics

Standard deviation155.39544
Coefficient of variation (CV)1.0461436
Kurtosis13.487169
Mean148.54121
Median Absolute Deviation (MAD)45
Skewness3.3198267
Sum15893.91
Variance24147.743
MonotonicityNot monotonic
2024-05-11T15:15:38.441605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105.0 2
 
1.9%
75.78 2
 
1.9%
160.8 1
 
0.9%
103.96 1
 
0.9%
164.77 1
 
0.9%
209.62 1
 
0.9%
59.5 1
 
0.9%
92.98 1
 
0.9%
86.45 1
 
0.9%
65.0 1
 
0.9%
Other values (95) 95
88.8%
ValueCountFrequency (%)
23.04 1
0.9%
23.94 1
0.9%
27.07 1
0.9%
28.0 1
0.9%
29.09 1
0.9%
29.7 1
0.9%
33.0 1
0.9%
34.8 1
0.9%
35.35 1
0.9%
36.8 1
0.9%
ValueCountFrequency (%)
1049.34 1
0.9%
755.32 1
0.9%
714.7 1
0.9%
556.44 1
0.9%
497.3 1
0.9%
496.0 1
0.9%
426.45 1
0.9%
374.76 1
0.9%
318.85 1
0.9%
316.0 1
0.9%

행정동명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Memory size988.0 B
대방동
16 
사당제1동
13 
노량진제1동
12 
노량진제2동
11 
상도제2동
10 
Other values (9)
45 

Length

Max length6
Median length5
Mean length4.7943925
Min length3

Unique

Unique2 ?
Unique (%)1.9%

Sample

1st row사당제1동
2nd row상도제4동
3rd row상도제4동
4th row대방동
5th row사당제2동

Common Values

ValueCountFrequency (%)
대방동 16
15.0%
사당제1동 13
12.1%
노량진제1동 12
11.2%
노량진제2동 11
10.3%
상도제2동 10
9.3%
흑석동 9
8.4%
사당제2동 8
7.5%
상도제1동 7
6.5%
사당제3동 6
 
5.6%
신대방제2동 5
 
4.7%
Other values (4) 10
9.3%

Length

2024-05-11T15:15:38.673709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대방동 16
15.0%
사당제1동 13
12.1%
노량진제1동 12
11.2%
노량진제2동 11
10.3%
상도제2동 10
9.3%
흑석동 9
8.4%
사당제2동 8
7.5%
상도제1동 7
6.5%
사당제3동 6
 
5.6%
신대방제2동 5
 
4.7%
Other values (4) 10
9.3%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size988.0 B
상수도전용
74 
<NA>
31 
상수도(음용)지하수(주방용)겸용
 
2

Length

Max length17
Median length5
Mean length4.9345794
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 74
69.2%
<NA> 31
29.0%
상수도(음용)지하수(주방용)겸용 2
 
1.9%

Length

2024-05-11T15:15:38.920578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:15:39.171722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 74
69.2%
na 31
29.0%
상수도(음용)지하수(주방용)겸용 2
 
1.9%

소재지전화번호
Text

MISSING 

Distinct86
Distinct (%)100.0%
Missing21
Missing (%)19.6%
Memory size988.0 B
2024-05-11T15:15:39.455070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.430233
Min length10

Characters and Unicode

Total characters897
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

Unique86 ?
Unique (%)100.0%

Sample

1st row02 523 9261
2nd row02 822 2719
3rd row02 822 3619
4th row02 8231949
5th row02 34762999
ValueCountFrequency (%)
02 65
37.4%
822 3
 
1.7%
823 3
 
1.7%
827 2
 
1.1%
532 2
 
1.1%
523 2
 
1.1%
02582 1
 
0.6%
32808761 1
 
0.6%
8359278 1
 
0.6%
6007 1
 
0.6%
Other values (93) 93
53.4%
2024-05-11T15:15:40.110566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 188
21.0%
0 133
14.8%
106
11.8%
8 95
10.6%
1 72
 
8.0%
3 67
 
7.5%
5 58
 
6.5%
7 49
 
5.5%
4 49
 
5.5%
9 46
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 791
88.2%
Space Separator 106
 
11.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 188
23.8%
0 133
16.8%
8 95
12.0%
1 72
 
9.1%
3 67
 
8.5%
5 58
 
7.3%
7 49
 
6.2%
4 49
 
6.2%
9 46
 
5.8%
6 34
 
4.3%
Space Separator
ValueCountFrequency (%)
106
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 897
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 188
21.0%
0 133
14.8%
106
11.8%
8 95
10.6%
1 72
 
8.0%
3 67
 
7.5%
5 58
 
6.5%
7 49
 
5.5%
4 49
 
5.5%
9 46
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 897
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 188
21.0%
0 133
14.8%
106
11.8%
8 95
10.6%
1 72
 
8.0%
3 67
 
7.5%
5 58
 
6.5%
7 49
 
5.5%
4 49
 
5.5%
9 46
 
5.1%

Interactions

2024-05-11T15:15:26.596889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:15:23.146981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:15:24.033664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:15:24.898577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:15:25.786973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:15:26.758513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:15:23.289071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:15:24.213625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:15:25.100569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:15:25.944283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:15:26.934194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:15:23.445857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:15:24.382674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:15:25.256691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:15:26.095422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:15:27.141356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:15:23.626031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:15:24.568531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:15:25.463182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:15:26.269629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:15:27.369353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:15:23.841290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:15:24.740551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:15:25.620709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:15:26.433101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T15:15:40.321728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
지정년도1.0000.8281.0001.0000.0000.0000.2950.2380.0001.000
지정번호0.8281.0000.8290.8290.0000.8530.0000.1550.0001.000
신청일자1.0000.8291.0001.0000.0000.0000.3260.1750.0001.000
지정일자1.0000.8291.0001.0000.0000.0000.3260.1750.0001.000
업태명0.0000.0000.0000.0001.0000.4820.5950.0000.0001.000
주된음식0.0000.8530.0000.0000.4821.0000.0000.8371.0001.000
영업장면적(㎡)0.2950.0000.3260.3260.5950.0001.0000.0000.0001.000
행정동명0.2380.1550.1750.1750.0000.8370.0001.0000.7251.000
급수시설구분0.0000.0000.0000.0000.0001.0000.0000.7251.0001.000
소재지전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-05-11T15:15:40.552047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명급수시설구분업태명
행정동명1.0000.6370.000
급수시설구분0.6371.0000.000
업태명0.0000.0001.000
2024-05-11T15:15:40.710611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.000-0.1800.9991.000-0.3820.0000.0750.000
지정번호-0.1801.000-0.168-0.178-0.0870.0000.1090.000
신청일자0.999-0.1681.0000.999-0.3840.0000.0460.000
지정일자1.000-0.1780.9991.000-0.3800.0000.0460.000
영업장면적(㎡)-0.382-0.087-0.384-0.3801.0000.3150.0000.000
업태명0.0000.0000.0000.0000.3151.0000.0000.000
행정동명0.0750.1090.0460.0460.0000.0001.0000.637
급수시설구분0.0000.0000.0000.0000.0000.0000.6371.000

Missing values

2024-05-11T15:15:27.615326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T15:15:28.000790image/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
03190000201722017092220171116김종구참치박사서울특별시 동작구 동작대로 25-1, (사당동, 1층)서울특별시 동작구 사당동 1031번지 21호 1층3190000-101-2013-00226일식회160.8사당제1동상수도전용02 523 9261
13190000201732017092220171116구름산추어탕서울특별시 동작구 성대로1길 22, 지상1층 (상도동)서울특별시 동작구 상도동 242번지 78호3190000-101-2016-00170한식추어탕136.71상도제4동상수도전용02 822 2719
231900002013232013122320131213어촌서울특별시 동작구 상도로 122, (상도동, 1층)서울특별시 동작구 상도동 355번지 8호 1층3190000-101-2013-00110한식생선구이168.7상도제4동상수도전용02 822 3619
331900002013292013122320131213대방회집서울특별시 동작구 여의대방로 134-1, (대방동)서울특별시 동작구 대방동 417번지 2호3190000-101-2010-00059일식회80.78대방동상수도전용02 8231949
431900002021222021101520211129이수샤브샤브서울특별시 동작구 동작대로29길 8, 2층 (사당동)서울특별시 동작구 사당동 88번지 3호3190000-101-2017-00213중국식샤브136.59사당제2동상수도전용<NA>
53190000201392013121720131213내고향나주곰탕서울특별시 동작구 사당로 183, (사당동)서울특별시 동작구 사당동 219번지 7호3190000-101-2008-00142한식곰탕107.1사당제3동상수도전용02 34762999
63190000201122011060220111130원할머니보쌈족발 이수역점서울특별시 동작구 동작대로27나길 22, (사당동)서울특별시 동작구 사당동 139번지 63호3190000-101-1993-00627한식보쌈121.09사당제2동상수도전용0234765252
73190000202152021101520211129가장맛있는족발보라매역점서울특별시 동작구 상도로 4, (대방동)서울특별시 동작구 대방동 400번지 5호3190000-101-2010-00055통닭(치킨)족발82.65대방동상수도전용02 823 3989
83190000202172021101520211129옛날그집서울특별시 동작구 장승배기로 121-2, 1층 (노량진동)서울특별시 동작구 노량진동 308번지 24호3190000-101-2014-00169기타삼겹살52.23노량진제2동상수도전용02 827 0678
931900002021122021101520211129마라농장서울특별시 동작구 장승배기로 138, 1층 (노량진동)서울특별시 동작구 노량진동 233번지 6호 부광장여관3190000-101-2019-00265중국식마라탕66.0노량진제1동<NA><NA>
시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
973190000202322023101320231123단아한정식서울특별시 동작구 동작대로27가길 44, (사당동,영지빌딩 2층)서울특별시 동작구 사당동 147번지 81호 영지빌딩 2층3190000-101-2009-00217한식한정식374.76사당제2동<NA>02532 3946
983190000202362023101320231123별미 냉삼서울특별시 동작구 동작대로7길 34, (사당동)서울특별시 동작구 사당동 1033번지 23호3190000-101-1999-06987한식냉동삼겹?김치찌개51.97사당제1동상수도전용02 5210177
993190000202342023101320231123카츠 디나인(카츠 D9)서울특별시 동작구 국사봉1길 12-3, 1층 (상도동)서울특별시 동작구 상도동 323번지 21호3190000-101-2022-00261일식모듬돈카츠75.78상도제3동<NA><NA>
1003190000202352023101320231123두루찌개3대서울특별시 동작구 노량진로8길 55, 1층 101호 (노량진동, 더 클래식 동작)서울특별시 동작구 노량진동 37번지 1호 더 클래식 동작3190000-101-1993-04275한식두루찌개46.8노량진제2동상수도전용02 8143444
1013190000201382013121720131213한마루가든서울특별시 동작구 여의대방로 250, 대림아파트(상가동) 4층 410호 (대방동)서울특별시 동작구 대방동 501번지 대림아파트(상가동)3190000-101-1994-00581한식삼겹살96.35대방동상수도전용02 8260119
1023190000201712017091820171116시골집서울특별시 동작구 양녕로26길 56, 2층 ,1층 나동 1호 (상도동)서울특별시 동작구 상도동 215번지 3호3190000-101-2008-00141한식제육불고기100.37상도제4동<NA>02 821 8889
1033190000202382023101320231123영흥정육식당서울특별시 동작구 만양로 85-1, (노량진동)서울특별시 동작구 노량진동 120번지3190000-101-2007-00162한식소한마리(구이류)23.94노량진제1동상수도전용02 813 1662
1043190000201982019092520191209신방통통 낙지서울특별시 동작구 상도로26길 12, (상도동)서울특별시 동작구 상도동 183번지 13호3190000-101-1991-00096한식추어탕105.0상도제2동상수도전용02 8216550
10531900002023122023101320231123원조부안집 신대방삼거리역점서울특별시 동작구 상도로12길 20, (대방동)서울특별시 동작구 대방동 337번지 5호3190000-101-1994-00200한식삼겹살55.0대방동상수도전용02 8242440
10631900002021212021101520211129육대장 푸줏간 이수역점서울특별시 동작구 동작대로31길 17, 새소망믿음기도원 1층 (사당동)서울특별시 동작구 사당동 89번지 6호 새소망믿음기도원3190000-101-2020-00193한식육개장89.95사당제2동<NA><NA>