Overview

Dataset statistics

Number of variables15
Number of observations130
Missing cells7
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.1 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
Author중구
URLhttps://data.seoul.go.kr/dataList/OA-10217/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
업태명 is highly imbalanced (51.6%)Imbalance
주된음식 has 2 (1.5%) missing valuesMissing
소재지전화번호 has 5 (3.8%) missing valuesMissing
허가(신고)번호 has unique valuesUnique

Reproduction

Analysis started2024-05-11 06:48:00.860085
Analysis finished2024-05-11 06:48:06.004313
Duration5.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
3010000
130 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3010000
2nd row3010000
3rd row3010000
4th row3010000
5th row3010000

Common Values

ValueCountFrequency (%)
3010000 130
100.0%

Length

2024-05-11T15:48:06.143107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:48:06.313510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3010000 130
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.6
Minimum2001
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T15:48:06.485496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2001
Q12006
median2012
Q32016
95-th percentile2023
Maximum2023
Range22
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.785301
Coefficient of variation (CV)0.0033730866
Kurtosis-0.95556188
Mean2011.6
Median Absolute Deviation (MAD)5.5
Skewness-0.050420281
Sum261508
Variance46.04031
MonotonicityDecreasing
2024-05-11T15:48:06.704202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2001 18
13.8%
2012 15
11.5%
2014 11
 
8.5%
2023 9
 
6.9%
2013 8
 
6.2%
2011 8
 
6.2%
2019 8
 
6.2%
2006 6
 
4.6%
2015 5
 
3.8%
2020 5
 
3.8%
Other values (13) 37
28.5%
ValueCountFrequency (%)
2001 18
13.8%
2002 2
 
1.5%
2003 4
 
3.1%
2004 2
 
1.5%
2005 2
 
1.5%
2006 6
 
4.6%
2007 4
 
3.1%
2008 4
 
3.1%
2009 2
 
1.5%
2010 5
 
3.8%
ValueCountFrequency (%)
2023 9
6.9%
2022 3
 
2.3%
2021 3
 
2.3%
2020 5
3.8%
2019 8
6.2%
2018 3
 
2.3%
2017 1
 
0.8%
2016 2
 
1.5%
2015 5
3.8%
2014 11
8.5%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)62.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.38462
Minimum1
Maximum442
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T15:48:06.928531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.45
Q17.25
median26
Q3259
95-th percentile414.75
Maximum442
Range441
Interquartile range (IQR)251.75

Descriptive statistics

Standard deviation157.26433
Coefficient of variation (CV)1.264339
Kurtosis-0.87564039
Mean124.38462
Median Absolute Deviation (MAD)23
Skewness0.92284892
Sum16170
Variance24732.068
MonotonicityNot monotonic
2024-05-11T15:48:07.160086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7
 
5.4%
3 6
 
4.6%
2 6
 
4.6%
10 4
 
3.1%
9 4
 
3.1%
5 4
 
3.1%
4 4
 
3.1%
17 3
 
2.3%
26 3
 
2.3%
19 3
 
2.3%
Other values (71) 86
66.2%
ValueCountFrequency (%)
1 7
5.4%
2 6
4.6%
3 6
4.6%
4 4
3.1%
5 4
3.1%
6 3
2.3%
7 3
2.3%
8 2
 
1.5%
9 4
3.1%
10 4
3.1%
ValueCountFrequency (%)
442 1
0.8%
441 1
0.8%
436 1
0.8%
428 1
0.8%
424 1
0.8%
419 1
0.8%
417 1
0.8%
412 1
0.8%
411 1
0.8%
402 1
0.8%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20116989
Minimum20010501
Maximum20231214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T15:48:07.395863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010501
5-th percentile20010501
Q120060908
median20121203
Q320161214
95-th percentile20231214
Maximum20231214
Range220713
Interquartile range (IQR)100306.25

Descriptive statistics

Standard deviation67966.717
Coefficient of variation (CV)0.003378573
Kurtosis-0.94969102
Mean20116989
Median Absolute Deviation (MAD)54894
Skewness-0.05966521
Sum2.6152086 × 109
Variance4.6194746 × 109
MonotonicityNot monotonic
2024-05-11T15:48:07.636822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
20010501 18
 
13.8%
20231214 9
 
6.9%
20141031 8
 
6.2%
20120514 7
 
5.4%
20130718 6
 
4.6%
20130717 6
 
4.6%
20101001 5
 
3.8%
20111222 4
 
3.1%
20110425 4
 
3.1%
20030930 4
 
3.1%
Other values (34) 59
45.4%
ValueCountFrequency (%)
20010501 18
13.8%
20020401 2
 
1.5%
20030930 4
 
3.1%
20040614 2
 
1.5%
20050608 2
 
1.5%
20060801 3
 
2.3%
20060804 1
 
0.8%
20060808 1
 
0.8%
20061207 1
 
0.8%
20071025 3
 
2.3%
ValueCountFrequency (%)
20231214 9
6.9%
20221104 1
 
0.8%
20220704 1
 
0.8%
20220428 1
 
0.8%
20211007 1
 
0.8%
20210908 1
 
0.8%
20210907 1
 
0.8%
20201020 1
 
0.8%
20200907 2
 
1.5%
20200828 2
 
1.5%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20116975
Minimum20010813
Maximum20231214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T15:48:07.864357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010813
5-th percentile20010813
Q120061222
median20121221
Q320161214
95-th percentile20231214
Maximum20231214
Range220401
Interquartile range (IQR)99992

Descriptive statistics

Standard deviation67929.682
Coefficient of variation (CV)0.0033767345
Kurtosis-0.95142549
Mean20116975
Median Absolute Deviation (MAD)54997.5
Skewness-0.049622575
Sum2.6152067 × 109
Variance4.6144416 × 109
MonotonicityDecreasing
2024-05-11T15:48:08.110584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
20010813 18
 
13.8%
20231214 9
 
6.9%
20141113 8
 
6.2%
20121221 8
 
6.2%
20190905 8
 
6.2%
20130715 8
 
6.2%
20120529 7
 
5.4%
20061222 6
 
4.6%
20201104 5
 
3.8%
20101112 5
 
3.8%
Other values (18) 48
36.9%
ValueCountFrequency (%)
20010813 18
13.8%
20020417 2
 
1.5%
20031013 4
 
3.1%
20040807 2
 
1.5%
20050907 2
 
1.5%
20061222 6
 
4.6%
20071218 4
 
3.1%
20080711 1
 
0.8%
20081231 3
 
2.3%
20091216 2
 
1.5%
ValueCountFrequency (%)
20231214 9
6.9%
20221227 3
 
2.3%
20211103 3
 
2.3%
20201104 5
3.8%
20190905 8
6.2%
20181213 3
 
2.3%
20171215 1
 
0.8%
20161214 2
 
1.5%
20151109 4
3.1%
20150626 1
 
0.8%

업소명
Text

Distinct129
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-05-11T15:48:08.644328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length5.7230769
Min length2

Characters and Unicode

Total characters744
Distinct characters246
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique128 ?
Unique (%)98.5%

Sample

1st row남영동 양문
2nd row파티n프렌즈
3rd row제주본가
4th row을지로 전주옥
5th row본스치킨 동대문점
ValueCountFrequency (%)
왕비집 3
 
1.8%
삼계탕 3
 
1.8%
냉면 2
 
1.2%
서울 2
 
1.2%
떡볶이 1
 
0.6%
아이러브 1
 
0.6%
참맛감자탕 1
 
0.6%
명동부대찌개 1
 
0.6%
황기족발 1
 
0.6%
브이아이피 1
 
0.6%
Other values (151) 151
90.4%
2024-05-11T15:48:09.506211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37
 
5.0%
동 28
 
3.8%
점 17
 
2.3%
집 14
 
1.9%
탕 12
 
1.6%
이 11
 
1.5%
명 11
 
1.5%
( 10
 
1.3%
회 10
 
1.3%
옥 10
 
1.3%
Other values (236) 584
78.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 664
89.2%
Space Separator 37
 
5.0%
Uppercase Letter 14
 
1.9%
Open Punctuation 10
 
1.3%
Close Punctuation 10
 
1.3%
Other Punctuation 4
 
0.5%
Decimal Number 3
 
0.4%
Lowercase Letter 1
 
0.1%
Letter Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 28
 
4.2%
점 17
 
2.6%
집 14
 
2.1%
탕 12
 
1.8%
이 11
 
1.7%
명 11
 
1.7%
회 10
 
1.5%
옥 10
 
1.5%
리 9
 
1.4%
주 9
 
1.4%
Other values (215) 533
80.3%
Uppercase Letter
ValueCountFrequency (%)
R 2
14.3%
B 2
14.3%
N 2
14.3%
A 2
14.3%
G 1
7.1%
E 1
7.1%
D 1
7.1%
U 1
7.1%
C 1
7.1%
H 1
7.1%
Other Punctuation
ValueCountFrequency (%)
& 2
50.0%
, 1
25.0%
. 1
25.0%
Decimal Number
ValueCountFrequency (%)
1 1
33.3%
4 1
33.3%
5 1
33.3%
Space Separator
ValueCountFrequency (%)
37
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Lowercase Letter
ValueCountFrequency (%)
n 1
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 661
88.8%
Common 64
 
8.6%
Latin 16
 
2.2%
Han 3
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 28
 
4.2%
점 17
 
2.6%
집 14
 
2.1%
탕 12
 
1.8%
이 11
 
1.7%
명 11
 
1.7%
회 10
 
1.5%
옥 10
 
1.5%
리 9
 
1.4%
주 9
 
1.4%
Other values (212) 530
80.2%
Latin
ValueCountFrequency (%)
R 2
12.5%
B 2
12.5%
N 2
12.5%
A 2
12.5%
G 1
6.2%
E 1
6.2%
D 1
6.2%
U 1
6.2%
n 1
6.2%
C 1
6.2%
Other values (2) 2
12.5%
Common
ValueCountFrequency (%)
37
57.8%
( 10
 
15.6%
) 10
 
15.6%
& 2
 
3.1%
, 1
 
1.6%
. 1
 
1.6%
1 1
 
1.6%
4 1
 
1.6%
5 1
 
1.6%
Han
ValueCountFrequency (%)
禮 1
33.3%
品 1
33.3%
一 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 661
88.8%
ASCII 79
 
10.6%
CJK 2
 
0.3%
Number Forms 1
 
0.1%
CJK Compat Ideographs 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37
46.8%
( 10
 
12.7%
) 10
 
12.7%
R 2
 
2.5%
B 2
 
2.5%
N 2
 
2.5%
A 2
 
2.5%
& 2
 
2.5%
G 1
 
1.3%
E 1
 
1.3%
Other values (10) 10
 
12.7%
Hangul
ValueCountFrequency (%)
동 28
 
4.2%
점 17
 
2.6%
집 14
 
2.1%
탕 12
 
1.8%
이 11
 
1.7%
명 11
 
1.7%
회 10
 
1.5%
옥 10
 
1.5%
리 9
 
1.4%
주 9
 
1.4%
Other values (212) 530
80.2%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
禮 1
100.0%
CJK
ValueCountFrequency (%)
品 1
50.0%
一 1
50.0%

소재지도로명
Text

Distinct129
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-05-11T15:48:09.937593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length42
Mean length31.023077
Min length22

Characters and Unicode

Total characters4033
Distinct characters145
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique128 ?
Unique (%)98.5%

Sample

1st row서울특별시 중구 수표로 43, (저동2가)
2nd row서울특별시 중구 다산로46길 17, (흥인동, 지하1층(101호,103호~149호,151호~153호))
3rd row서울특별시 중구 을지로1길 8, 지상1,2층 (을지로1가)
4th row서울특별시 중구 을지로 101, 1층 (을지로2가)
5th row서울특별시 중구 마른내로 169, (광희동1가, 1층)
ValueCountFrequency (%)
서울특별시 130
 
17.6%
중구 130
 
17.6%
1층 21
 
2.8%
2층 12
 
1.6%
지하1층 11
 
1.5%
신당동 8
 
1.1%
마른내로 6
 
0.8%
서소문동 6
 
0.8%
명동1가 5
 
0.7%
명동8나길 5
 
0.7%
Other values (277) 405
54.8%
2024-05-11T15:48:10.596138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
609
 
15.1%
1 225
 
5.6%
, 215
 
5.3%
) 159
 
3.9%
( 159
 
3.9%
서 149
 
3.7%
로 144
 
3.6%
2 141
 
3.5%
중 134
 
3.3%
시 132
 
3.3%
Other values (135) 1966
48.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2157
53.5%
Decimal Number 687
 
17.0%
Space Separator 609
 
15.1%
Other Punctuation 215
 
5.3%
Close Punctuation 159
 
3.9%
Open Punctuation 159
 
3.9%
Dash Punctuation 29
 
0.7%
Uppercase Letter 9
 
0.2%
Math Symbol 7
 
0.2%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 149
 
6.9%
로 144
 
6.7%
중 134
 
6.2%
시 132
 
6.1%
구 131
 
6.1%
울 131
 
6.1%
특 130
 
6.0%
별 130
 
6.0%
동 121
 
5.6%
층 97
 
4.5%
Other values (111) 858
39.8%
Decimal Number
ValueCountFrequency (%)
1 225
32.8%
2 141
20.5%
3 69
 
10.0%
0 49
 
7.1%
4 45
 
6.6%
6 37
 
5.4%
5 34
 
4.9%
8 33
 
4.8%
9 28
 
4.1%
7 26
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
B 4
44.4%
I 1
 
11.1%
J 1
 
11.1%
K 1
 
11.1%
G 1
 
11.1%
Y 1
 
11.1%
Lowercase Letter
ValueCountFrequency (%)
i 1
50.0%
z 1
50.0%
Space Separator
ValueCountFrequency (%)
609
100.0%
Other Punctuation
ValueCountFrequency (%)
, 215
100.0%
Close Punctuation
ValueCountFrequency (%)
) 159
100.0%
Open Punctuation
ValueCountFrequency (%)
( 159
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2157
53.5%
Common 1865
46.2%
Latin 11
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 149
 
6.9%
로 144
 
6.7%
중 134
 
6.2%
시 132
 
6.1%
구 131
 
6.1%
울 131
 
6.1%
특 130
 
6.0%
별 130
 
6.0%
동 121
 
5.6%
층 97
 
4.5%
Other values (111) 858
39.8%
Common
ValueCountFrequency (%)
609
32.7%
1 225
 
12.1%
, 215
 
11.5%
) 159
 
8.5%
( 159
 
8.5%
2 141
 
7.6%
3 69
 
3.7%
0 49
 
2.6%
4 45
 
2.4%
6 37
 
2.0%
Other values (6) 157
 
8.4%
Latin
ValueCountFrequency (%)
B 4
36.4%
I 1
 
9.1%
J 1
 
9.1%
K 1
 
9.1%
G 1
 
9.1%
Y 1
 
9.1%
i 1
 
9.1%
z 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2157
53.5%
ASCII 1876
46.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
609
32.5%
1 225
 
12.0%
, 215
 
11.5%
) 159
 
8.5%
( 159
 
8.5%
2 141
 
7.5%
3 69
 
3.7%
0 49
 
2.6%
4 45
 
2.4%
6 37
 
2.0%
Other values (14) 168
 
9.0%
Hangul
ValueCountFrequency (%)
서 149
 
6.9%
로 144
 
6.7%
중 134
 
6.2%
시 132
 
6.1%
구 131
 
6.1%
울 131
 
6.1%
특 130
 
6.0%
별 130
 
6.0%
동 121
 
5.6%
층 97
 
4.5%
Other values (111) 858
39.8%

소재지지번
Text

Distinct129
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-05-11T15:48:11.175648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length43
Mean length28.9
Min length22

Characters and Unicode

Total characters3757
Distinct characters125
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique128 ?
Unique (%)98.5%

Sample

1st row서울특별시 중구 저동2가 76번지 6호
2nd row서울특별시 중구 흥인동 13번지 1호 지하1층(101호,103호~149호,151호~153호)
3rd row서울특별시 중구 을지로1가 47번지 3호
4th row서울특별시 중구 을지로2가 101번지 11호
5th row서울특별시 중구 광희동1가 194번지 2호 1층
ValueCountFrequency (%)
서울특별시 130
 
17.4%
중구 130
 
17.4%
1호 22
 
3.0%
1층 21
 
2.8%
2호 14
 
1.9%
지하1층 14
 
1.9%
신당동 11
 
1.5%
2층 11
 
1.5%
서소문동 10
 
1.3%
3호 8
 
1.1%
Other values (220) 374
50.2%
2024-05-11T15:48:11.887653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
926
24.6%
1 229
 
6.1%
지 186
 
5.0%
서 141
 
3.8%
중 133
 
3.5%
시 131
 
3.5%
울 131
 
3.5%
번 131
 
3.5%
특 130
 
3.5%
별 130
 
3.5%
Other values (115) 1489
39.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2020
53.8%
Space Separator 926
24.6%
Decimal Number 704
 
18.7%
Close Punctuation 33
 
0.9%
Open Punctuation 33
 
0.9%
Other Punctuation 24
 
0.6%
Uppercase Letter 8
 
0.2%
Math Symbol 6
 
0.2%
Lowercase Letter 2
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 186
 
9.2%
서 141
 
7.0%
중 133
 
6.6%
시 131
 
6.5%
울 131
 
6.5%
번 131
 
6.5%
특 130
 
6.4%
별 130
 
6.4%
구 130
 
6.4%
호 130
 
6.4%
Other values (90) 647
32.0%
Decimal Number
ValueCountFrequency (%)
1 229
32.5%
2 127
18.0%
3 76
 
10.8%
0 59
 
8.4%
5 51
 
7.2%
6 39
 
5.5%
4 35
 
5.0%
8 33
 
4.7%
7 29
 
4.1%
9 26
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
B 3
37.5%
J 1
 
12.5%
K 1
 
12.5%
I 1
 
12.5%
Y 1
 
12.5%
G 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
, 23
95.8%
. 1
 
4.2%
Lowercase Letter
ValueCountFrequency (%)
i 1
50.0%
z 1
50.0%
Space Separator
ValueCountFrequency (%)
926
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2020
53.8%
Common 1727
46.0%
Latin 10
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 186
 
9.2%
서 141
 
7.0%
중 133
 
6.6%
시 131
 
6.5%
울 131
 
6.5%
번 131
 
6.5%
특 130
 
6.4%
별 130
 
6.4%
구 130
 
6.4%
호 130
 
6.4%
Other values (90) 647
32.0%
Common
ValueCountFrequency (%)
926
53.6%
1 229
 
13.3%
2 127
 
7.4%
3 76
 
4.4%
0 59
 
3.4%
5 51
 
3.0%
6 39
 
2.3%
4 35
 
2.0%
) 33
 
1.9%
( 33
 
1.9%
Other values (7) 119
 
6.9%
Latin
ValueCountFrequency (%)
B 3
30.0%
J 1
 
10.0%
K 1
 
10.0%
I 1
 
10.0%
Y 1
 
10.0%
G 1
 
10.0%
i 1
 
10.0%
z 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2020
53.8%
ASCII 1737
46.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
926
53.3%
1 229
 
13.2%
2 127
 
7.3%
3 76
 
4.4%
0 59
 
3.4%
5 51
 
2.9%
6 39
 
2.2%
4 35
 
2.0%
) 33
 
1.9%
( 33
 
1.9%
Other values (15) 129
 
7.4%
Hangul
ValueCountFrequency (%)
지 186
 
9.2%
서 141
 
7.0%
중 133
 
6.6%
시 131
 
6.5%
울 131
 
6.5%
번 131
 
6.5%
특 130
 
6.4%
별 130
 
6.4%
구 130
 
6.4%
호 130
 
6.4%
Other values (90) 647
32.0%

허가(신고)번호
Text

UNIQUE 

Distinct130
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-05-11T15:48:12.193111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2860
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

Unique130 ?
Unique (%)100.0%

Sample

1st row3010000-101-2001-13564
2nd row3010000-101-2015-00332
3rd row3010000-101-2023-00529
4th row3010000-101-2023-00267
5th row3010000-101-2017-00468
ValueCountFrequency (%)
3010000-101-2001-13564 1
 
0.8%
3010000-101-2004-00330 1
 
0.8%
3010000-101-1997-08141 1
 
0.8%
3010000-101-1993-04999 1
 
0.8%
3010000-101-2003-00147 1
 
0.8%
3010000-101-2002-00313 1
 
0.8%
3010000-101-2002-00049 1
 
0.8%
3010000-101-2007-00117 1
 
0.8%
3010000-101-2006-00224 1
 
0.8%
3010000-101-2001-14202 1
 
0.8%
Other values (120) 120
92.3%
2024-05-11T15:48:12.638072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1135
39.7%
1 593
20.7%
- 390
 
13.6%
3 195
 
6.8%
9 140
 
4.9%
2 130
 
4.5%
8 68
 
2.4%
4 61
 
2.1%
6 54
 
1.9%
7 53
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2470
86.4%
Dash Punctuation 390
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1135
46.0%
1 593
24.0%
3 195
 
7.9%
9 140
 
5.7%
2 130
 
5.3%
8 68
 
2.8%
4 61
 
2.5%
6 54
 
2.2%
7 53
 
2.1%
5 41
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 390
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2860
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1135
39.7%
1 593
20.7%
- 390
 
13.6%
3 195
 
6.8%
9 140
 
4.9%
2 130
 
4.5%
8 68
 
2.4%
4 61
 
2.1%
6 54
 
1.9%
7 53
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2860
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1135
39.7%
1 593
20.7%
- 390
 
13.6%
3 195
 
6.8%
9 140
 
4.9%
2 130
 
4.5%
8 68
 
2.4%
4 61
 
2.1%
6 54
 
1.9%
7 53
 
1.9%

업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
한식
91 
일식
14 
경양식
11 
중국식
 
6
호프/통닭
 
3
Other values (5)
 
5

Length

Max length8
Median length2
Mean length2.2846154
Min length2

Unique

Unique5 ?
Unique (%)3.8%

Sample

1st row호프/통닭
2nd row뷔페식
3rd row한식
4th row한식
5th row호프/통닭

Common Values

ValueCountFrequency (%)
한식 91
70.0%
일식 14
 
10.8%
경양식 11
 
8.5%
중국식 6
 
4.6%
호프/통닭 3
 
2.3%
뷔페식 1
 
0.8%
통닭(치킨) 1
 
0.8%
식육(숯불구이) 1
 
0.8%
기타 1
 
0.8%
분식 1
 
0.8%

Length

2024-05-11T15:48:12.853812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:48:13.088114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 91
70.0%
일식 14
 
10.8%
경양식 11
 
8.5%
중국식 6
 
4.6%
호프/통닭 3
 
2.3%
뷔페식 1
 
0.8%
통닭(치킨 1
 
0.8%
식육(숯불구이 1
 
0.8%
기타 1
 
0.8%
분식 1
 
0.8%

주된음식
Text

MISSING 

Distinct85
Distinct (%)66.4%
Missing2
Missing (%)1.5%
Memory size1.1 KiB
2024-05-11T15:48:13.530713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length10
Mean length3.625
Min length1

Characters and Unicode

Total characters464
Distinct characters123
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

Unique62 ?
Unique (%)48.4%

Sample

1st row소갈비
2nd row뷔페
3rd row삼겹살
4th row불갈비찜
5th row튀김닭
ValueCountFrequency (%)
삼겹살 8
 
5.8%
냉면 6
 
4.4%
삼계탕 6
 
4.4%
등심 5
 
3.6%
한식 4
 
2.9%
추어탕 3
 
2.2%
칼국수 3
 
2.2%
자장면 3
 
2.2%
한정식 3
 
2.2%
베트남쌀국수 2
 
1.5%
Other values (78) 94
68.6%
2024-05-11T15:48:14.053351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
탕 20
 
4.3%
, 18
 
3.9%
삼 17
 
3.7%
기 13
 
2.8%
면 13
 
2.8%
식 12
 
2.6%
한 12
 
2.6%
리 10
 
2.2%
비 10
 
2.2%
갈 10
 
2.2%
Other values (113) 329
70.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 437
94.2%
Other Punctuation 18
 
3.9%
Space Separator 9
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 20
 
4.6%
삼 17
 
3.9%
기 13
 
3.0%
면 13
 
3.0%
식 12
 
2.7%
한 12
 
2.7%
리 10
 
2.3%
비 10
 
2.3%
갈 10
 
2.3%
고 10
 
2.3%
Other values (111) 310
70.9%
Other Punctuation
ValueCountFrequency (%)
, 18
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 437
94.2%
Common 27
 
5.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 20
 
4.6%
삼 17
 
3.9%
기 13
 
3.0%
면 13
 
3.0%
식 12
 
2.7%
한 12
 
2.7%
리 10
 
2.3%
비 10
 
2.3%
갈 10
 
2.3%
고 10
 
2.3%
Other values (111) 310
70.9%
Common
ValueCountFrequency (%)
, 18
66.7%
9
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 437
94.2%
ASCII 27
 
5.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
탕 20
 
4.6%
삼 17
 
3.9%
기 13
 
3.0%
면 13
 
3.0%
식 12
 
2.7%
한 12
 
2.7%
리 10
 
2.3%
비 10
 
2.3%
갈 10
 
2.3%
고 10
 
2.3%
Other values (111) 310
70.9%
ASCII
ValueCountFrequency (%)
, 18
66.7%
9
33.3%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct129
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean209.73769
Minimum35.78
Maximum2118.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T15:48:14.580583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.78
5-th percentile49.241
Q189.38
median155.655
Q3219.395
95-th percentile550.3775
Maximum2118.02
Range2082.24
Interquartile range (IQR)130.015

Descriptive statistics

Standard deviation238.04125
Coefficient of variation (CV)1.1349474
Kurtosis33.874624
Mean209.73769
Median Absolute Deviation (MAD)65.975
Skewness4.9627695
Sum27265.9
Variance56663.639
MonotonicityNot monotonic
2024-05-11T15:48:14.760621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
175.21 2
 
1.5%
74.31 1
 
0.8%
261.62 1
 
0.8%
85.49 1
 
0.8%
349.16 1
 
0.8%
1221.54 1
 
0.8%
111.78 1
 
0.8%
108.12 1
 
0.8%
153.16 1
 
0.8%
350.49 1
 
0.8%
Other values (119) 119
91.5%
ValueCountFrequency (%)
35.78 1
0.8%
37.2 1
0.8%
39.67 1
0.8%
41.46 1
0.8%
43.04 1
0.8%
44.0 1
0.8%
48.17 1
0.8%
50.55 1
0.8%
52.46 1
0.8%
56.76 1
0.8%
ValueCountFrequency (%)
2118.02 1
0.8%
1221.54 1
0.8%
789.43 1
0.8%
694.2 1
0.8%
630.6 1
0.8%
588.66 1
0.8%
553.55 1
0.8%
546.5 1
0.8%
531.75 1
0.8%
514.63 1
0.8%

행정동명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
명동
40 
소공동
33 
회현동
10 
을지로동
9 
필동
8 
Other values (10)
30 

Length

Max length5
Median length3
Mean length2.7153846
Min length2

Unique

Unique3 ?
Unique (%)2.3%

Sample

1st row을지로동
2nd row신당동
3rd row명동
4th row명동
5th row광희동

Common Values

ValueCountFrequency (%)
명동 40
30.8%
소공동 33
25.4%
회현동 10
 
7.7%
을지로동 9
 
6.9%
필동 8
 
6.2%
광희동 7
 
5.4%
신당동 6
 
4.6%
장충동 5
 
3.8%
다산동 3
 
2.3%
중림동 2
 
1.5%
Other values (5) 7
 
5.4%

Length

2024-05-11T15:48:14.975845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
명동 40
30.8%
소공동 33
25.4%
회현동 10
 
7.7%
을지로동 9
 
6.9%
필동 8
 
6.2%
광희동 7
 
5.4%
신당동 6
 
4.6%
장충동 5
 
3.8%
다산동 3
 
2.3%
중림동 2
 
1.5%
Other values (5) 7
 
5.4%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
상수도전용
93 
<NA>
37 

Length

Max length5
Median length5
Mean length4.7153846
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 (%)
상수도전용 93
71.5%
<NA> 37
 
28.5%

Length

2024-05-11T15:48:15.169318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:48:15.350827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 93
71.5%
na 37
 
28.5%

소재지전화번호
Text

MISSING 

Distinct125
Distinct (%)100.0%
Missing5
Missing (%)3.8%
Memory size1.1 KiB
2024-05-11T15:48:15.755190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.456
Min length7

Characters and Unicode

Total characters1307
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

Unique125 ?
Unique (%)100.0%

Sample

1st row0222631102
2nd row02 432 4400
3rd row0222791710
4th row02 22739599
5th row050714353221
ValueCountFrequency (%)
02 65
30.8%
777 3
 
1.4%
776 2
 
0.9%
752 2
 
0.9%
02778 1
 
0.5%
0222322362 1
 
0.5%
0222631743 1
 
0.5%
7549900 1
 
0.5%
6727 1
 
0.5%
7772254 1
 
0.5%
Other values (133) 133
63.0%
2024-05-11T15:48:16.272115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 275
21.0%
0 202
15.5%
7 168
12.9%
117
9.0%
3 106
 
8.1%
5 101
 
7.7%
8 77
 
5.9%
6 75
 
5.7%
9 71
 
5.4%
1 71
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1190
91.0%
Space Separator 117
 
9.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 275
23.1%
0 202
17.0%
7 168
14.1%
3 106
 
8.9%
5 101
 
8.5%
8 77
 
6.5%
6 75
 
6.3%
9 71
 
6.0%
1 71
 
6.0%
4 44
 
3.7%
Space Separator
ValueCountFrequency (%)
117
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1307
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 275
21.0%
0 202
15.5%
7 168
12.9%
117
9.0%
3 106
 
8.1%
5 101
 
7.7%
8 77
 
5.9%
6 75
 
5.7%
9 71
 
5.4%
1 71
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1307
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 275
21.0%
0 202
15.5%
7 168
12.9%
117
9.0%
3 106
 
8.1%
5 101
 
7.7%
8 77
 
5.9%
6 75
 
5.7%
9 71
 
5.4%
1 71
 
5.4%

Interactions

2024-05-11T15:48:04.593923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:48:01.854576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:48:02.433959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:48:03.258625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:48:03.962096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:48:04.718772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:48:01.982365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:48:02.545301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:48:03.382181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:48:04.081410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:48:04.822012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:48:02.120302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:48:02.898408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:48:03.521930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:48:04.193567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:48:04.946716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:48:02.220198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:48:03.002719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:48:03.658285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:48:04.331660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:48:05.086070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:48:02.337336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:48:03.132599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:48:03.812405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:48:04.472810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T15:48:16.415394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자업태명주된음식영업장면적(㎡)행정동명
지정년도1.0000.7431.0001.0000.3890.7830.3760.354
지정번호0.7431.0000.7650.7650.2640.0000.3140.320
신청일자1.0000.7651.0001.0000.4000.7730.3630.333
지정일자1.0000.7651.0001.0000.4000.7730.3630.333
업태명0.3890.2640.4000.4001.0000.9330.6800.000
주된음식0.7830.0000.7730.7730.9331.0000.0000.876
영업장면적(㎡)0.3760.3140.3630.3630.6800.0001.0000.484
행정동명0.3540.3200.3330.3330.0000.8760.4841.000
2024-05-11T15:48:16.538984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명급수시설구분업태명
행정동명1.0001.0000.000
급수시설구분1.0001.0001.000
업태명0.0001.0001.000
2024-05-11T15:48:16.659975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.000-0.6080.9960.999-0.2450.1610.1201.000
지정번호-0.6081.000-0.605-0.6110.3100.0800.1181.000
신청일자0.996-0.6051.0000.998-0.2480.1630.1081.000
지정일자0.999-0.6110.9981.000-0.2420.1630.1081.000
영업장면적(㎡)-0.2450.310-0.248-0.2421.0000.4360.2381.000
업태명0.1610.0800.1630.1630.4361.0000.0001.000
행정동명0.1200.1180.1080.1080.2380.0001.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-11T15:48:05.317938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T15:48:05.695320image/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.
2024-05-11T15:48:05.903501image/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
03010000202312023121420231214남영동 양문서울특별시 중구 수표로 43, (저동2가)서울특별시 중구 저동2가 76번지 6호3010000-101-2001-13564호프/통닭소갈비74.31을지로동상수도전용0222631102
13010000202392023121420231214파티n프렌즈서울특별시 중구 다산로46길 17, (흥인동, 지하1층(101호,103호~149호,151호~153호))서울특별시 중구 흥인동 13번지 1호 지하1층(101호,103호~149호,151호~153호)3010000-101-2015-00332뷔페식뷔페2118.02신당동<NA>02 432 4400
23010000202382023121420231214제주본가서울특별시 중구 을지로1길 8, 지상1,2층 (을지로1가)서울특별시 중구 을지로1가 47번지 3호3010000-101-2023-00529한식삼겹살70.6명동<NA><NA>
33010000202372023121420231214을지로 전주옥서울특별시 중구 을지로 101, 1층 (을지로2가)서울특별시 중구 을지로2가 101번지 11호3010000-101-2023-00267한식불갈비찜160.0명동<NA>0222791710
43010000202362023121420231214본스치킨 동대문점서울특별시 중구 마른내로 169, (광희동1가, 1층)서울특별시 중구 광희동1가 194번지 2호 1층3010000-101-2017-00468호프/통닭튀김닭97.32광희동<NA>02 22739599
53010000202352023121420231214발라닭 신당점서울특별시 중구 퇴계로 411-34, 1층 (흥인동)서울특별시 중구 흥인동 14번지 26호3010000-101-2022-00318호프/통닭튀김닭132.0신당동<NA>050714353221
63010000202342023121420231214명운 참 숯불구이서울특별시 중구 청구로8길 19, (신당동, 1층)서울특별시 중구 신당동 304번지 564호 1층3010000-101-1999-12001한식돼지갈비41.46신당동상수도전용02 22336657
73010000202332023121420231214돌담길서울특별시 중구 서소문로9길 28, 지하1층 B123,B124호 (순화동, 덕수궁롯데캐슬)서울특별시 중구 순화동 217번지 덕수궁롯데캐슬 지하1층 B123,B1243010000-101-2018-00350한식삼겹살153.0소공동<NA><NA>
83010000202322023121420231214담비곱창서울특별시 중구 남대문로9길 40, (다동, YG타워 1층 106호)서울특별시 중구 다동 155번지 YG타워 1층 106호3010000-101-2014-00104통닭(치킨)곱창52.46명동<NA><NA>
93010000202212022042820221227김영희 아구찜&코다리 냉면서울특별시 중구 중림로 31, 지하1층 B01호 (중림동)서울특별시 중구 중림동 150번지 1호3010000-101-2021-00109한식아구찜, 코다리냉면105.84중림동<NA>02 60806677
시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
120301000020011552001050120010813미도리회집서울특별시 중구 퇴계로22길 7, (남산동3가)서울특별시 중구 남산동3가 13번지 20호3010000-101-1984-04798일식대구탕,초밥90.79명동상수도전용0207765560
121301000020012212001050120010813오장동흥남집서울특별시 중구 마른내로 114, (오장동, 지하1층~지상3층)서울특별시 중구 오장동 101번지 7호 지하1층~지상3층3010000-101-1968-11031한식물냉면514.63광희동상수도전용0222660735
12230100002001992001050120010813강서면옥서울특별시 중구 세종대로11길 35, (서소문동)서울특별시 중구 서소문동 120번지 15호3010000-101-1984-11348한식냉면187.07소공동상수도전용027521945
123301000020012372001050120010813오래복집서울특별시 중구 동호로 288, (장충동2가)서울특별시 중구 장충동2가 186번지 38호3010000-101-2000-13367한식복지리97.5장충동상수도전용0222669224
124301000020012042001050120010813문화옥서울특별시 중구 창경궁로 62-5, (주교동)서울특별시 중구 주교동 118번지 3호3010000-101-1999-09714한식설렁탕156.53을지로동상수도전용0222650322
125301000020011762001050120010813삼원서울특별시 중구 무교로 32, (무교동, 지하1층)서울특별시 중구 무교동 1번지 지하1층3010000-101-1993-05010일식대구탕164.6명동상수도전용02 7773680
12630100002001522001050120010813필동면옥서울특별시 중구 서애로 26, (필동3가)서울특별시 중구 필동3가 1번지 5호3010000-101-1992-09798한식냉면156.0필동상수도전용0222662611
127301000020012442001050120010813만다리서울특별시 중구 다산로22길 17, (신당동)서울특별시 중구 신당동 333번지 67호3010000-101-1993-11478중국식자장면76.5청구동상수도전용0222367615
128301000020011142001050120010813우래옥서울특별시 중구 창경궁로 62-29, (주교동)서울특별시 중구 주교동 118번지 1호3010000-101-1988-11496한식불고기789.43을지로동상수도전용0222650151
129301000020011492001050120010813동해일식서울특별시 중구 무교로 16, (무교동)서울특별시 중구 무교동 19번지 0호3010000-101-1998-04678일식대구탕198.18명동상수도전용02 7544161