Overview

Dataset statistics

Number of variables15
Number of observations133
Missing cells5
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.5 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
Author용산구
URLhttps://data.seoul.go.kr/dataList/OA-11218/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 신청일자 and 2 other fieldsHigh correlation
지정번호 is highly overall correlated with 급수시설구분High correlation
신청일자 is highly overall correlated with 지정년도 and 2 other fieldsHigh correlation
지정일자 is highly overall correlated with 지정년도 and 2 other fieldsHigh correlation
영업장면적(㎡) is highly overall correlated with 급수시설구분High correlation
소재지전화번호 has 5 (3.8%) missing valuesMissing
허가(신고)번호 has unique valuesUnique

Reproduction

Analysis started2024-05-11 06:00:13.407026
Analysis finished2024-05-11 06:00:18.670171
Duration5.26 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
3020000
133 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3020000
2nd row3020000
3rd row3020000
4th row3020000
5th row3020000

Common Values

ValueCountFrequency (%)
3020000 133
100.0%

Length

2024-05-11T15:00:18.781785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:00:18.928718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3020000 133
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.7368
Minimum2001
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T15:00:19.060643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2001
Q12010
median2017
Q32020
95-th percentile2022.4
Maximum2023
Range22
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.7654366
Coefficient of variation (CV)0.0033579753
Kurtosis-0.47151499
Mean2014.7368
Median Absolute Deviation (MAD)3
Skewness-0.85489726
Sum267960
Variance45.771132
MonotonicityDecreasing
2024-05-11T15:00:19.236523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2018 19
14.3%
2001 14
10.5%
2020 12
9.0%
2016 11
8.3%
2015 11
8.3%
2022 11
8.3%
2019 9
 
6.8%
2023 7
 
5.3%
2021 7
 
5.3%
2007 6
 
4.5%
Other values (10) 26
19.5%
ValueCountFrequency (%)
2001 14
10.5%
2003 1
 
0.8%
2005 5
 
3.8%
2006 1
 
0.8%
2007 6
4.5%
2008 2
 
1.5%
2009 1
 
0.8%
2010 5
 
3.8%
2011 2
 
1.5%
2012 3
 
2.3%
ValueCountFrequency (%)
2023 7
 
5.3%
2022 11
8.3%
2021 7
 
5.3%
2020 12
9.0%
2019 9
6.8%
2018 19
14.3%
2017 3
 
2.3%
2016 11
8.3%
2015 11
8.3%
2014 3
 
2.3%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.203008
Minimum1
Maximum171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T15:00:19.413532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q314
95-th percentile136.6
Maximum171
Range170
Interquartile range (IQR)10

Descriptive statistics

Standard deviation43.651644
Coefficient of variation (CV)1.7320014
Kurtosis3.8528561
Mean25.203008
Median Absolute Deviation (MAD)4
Skewness2.2859163
Sum3352
Variance1905.4661
MonotonicityNot monotonic
2024-05-11T15:00:19.631500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
7 11
 
8.3%
6 10
 
7.5%
4 10
 
7.5%
5 9
 
6.8%
8 9
 
6.8%
1 8
 
6.0%
2 8
 
6.0%
3 8
 
6.0%
12 7
 
5.3%
13 6
 
4.5%
Other values (33) 47
35.3%
ValueCountFrequency (%)
1 8
6.0%
2 8
6.0%
3 8
6.0%
4 10
7.5%
5 9
6.8%
6 10
7.5%
7 11
8.3%
8 9
6.8%
9 2
 
1.5%
10 6
4.5%
ValueCountFrequency (%)
171 1
0.8%
170 1
0.8%
169 1
0.8%
161 1
0.8%
160 1
0.8%
149 1
0.8%
139 1
0.8%
135 1
0.8%
132 1
0.8%
130 1
0.8%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20153581
Minimum20060630
Maximum20230831
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T15:00:19.861583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060630
5-th percentile20060630
Q120100618
median20170831
Q320200831
95-th percentile20220824
Maximum20230831
Range170201
Interquartile range (IQR)100213

Descriptive statistics

Standard deviation56983.129
Coefficient of variation (CV)0.0028274444
Kurtosis-1.1157687
Mean20153581
Median Absolute Deviation (MAD)30000
Skewness-0.52074961
Sum2.6804263 × 109
Variance3.247077 × 109
MonotonicityNot monotonic
2024-05-11T15:00:20.042500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
20060630 21
15.8%
20180830 19
14.3%
20200831 12
9.0%
20220819 11
 
8.3%
20160831 11
 
8.3%
20151012 9
 
6.8%
20210929 7
 
5.3%
20230831 6
 
4.5%
20070718 5
 
3.8%
20100618 4
 
3.0%
Other values (19) 28
21.1%
ValueCountFrequency (%)
20060630 21
15.8%
20070718 5
 
3.8%
20070904 1
 
0.8%
20080627 1
 
0.8%
20080808 1
 
0.8%
20090410 1
 
0.8%
20100219 1
 
0.8%
20100618 4
 
3.0%
20110707 3
 
2.3%
20120910 1
 
0.8%
ValueCountFrequency (%)
20230831 6
4.5%
20220831 1
 
0.8%
20220819 11
8.3%
20210929 7
5.3%
20200831 12
9.0%
20190829 2
 
1.5%
20190828 1
 
0.8%
20190826 2
 
1.5%
20190823 1
 
0.8%
20190821 1
 
0.8%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20148326
Minimum20010630
Maximum20231011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T15:00:20.209201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010630
5-th percentile20010630
Q120100721
median20171013
Q320201013
95-th percentile20224957
Maximum20231011
Range220381
Interquartile range (IQR)100292

Descriptive statistics

Standard deviation67783.532
Coefficient of variation (CV)0.0033642264
Kurtosis-0.46896333
Mean20148326
Median Absolute Deviation (MAD)30000
Skewness-0.85817704
Sum2.6797274 × 109
Variance4.5946072 × 109
MonotonicityDecreasing
2024-05-11T15:00:20.383122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
20181012 19
14.3%
20010630 14
10.5%
20201013 12
9.0%
20161219 11
8.3%
20151211 11
8.3%
20220921 11
8.3%
20191011 9
 
6.8%
20231011 7
 
5.3%
20211022 7
 
5.3%
20070718 5
 
3.8%
Other values (12) 27
20.3%
ValueCountFrequency (%)
20010630 14
10.5%
20030731 1
 
0.8%
20050725 5
 
3.8%
20060630 1
 
0.8%
20070718 5
 
3.8%
20071012 1
 
0.8%
20080829 2
 
1.5%
20090225 1
 
0.8%
20100428 1
 
0.8%
20100721 4
 
3.0%
ValueCountFrequency (%)
20231011 7
 
5.3%
20220921 11
8.3%
20211022 7
 
5.3%
20201013 12
9.0%
20191011 9
6.8%
20181012 19
14.3%
20171013 3
 
2.3%
20161219 11
8.3%
20151211 11
8.3%
20141201 3
 
2.3%

업소명
Text

Distinct131
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-05-11T15:00:20.679848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length16
Mean length5.6541353
Min length2

Characters and Unicode

Total characters752
Distinct characters266
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

Unique129 ?
Unique (%)97.0%

Sample

1st row풍성집
2nd row이춘복참치
3rd row금천문
4th row섬집
5th row큰집닭한마리
ValueCountFrequency (%)
열정갈비 2
 
1.4%
섬집 2
 
1.4%
아이오유(i.o.u 1
 
0.7%
마산아구탕 1
 
0.7%
이촌삼계탕 1
 
0.7%
더함 1
 
0.7%
동천홍 1
 
0.7%
다채 1
 
0.7%
봉추찜닭 1
 
0.7%
숙대점 1
 
0.7%
Other values (136) 136
91.9%
2024-05-11T15:00:21.310507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 27
 
3.6%
점 17
 
2.3%
15
 
2.0%
집 14
 
1.9%
산 12
 
1.6%
비 12
 
1.6%
( 12
 
1.6%
) 12
 
1.6%
한 11
 
1.5%
대 11
 
1.5%
Other values (256) 609
81.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 685
91.1%
Space Separator 15
 
2.0%
Uppercase Letter 13
 
1.7%
Open Punctuation 12
 
1.6%
Close Punctuation 12
 
1.6%
Decimal Number 7
 
0.9%
Lowercase Letter 5
 
0.7%
Other Punctuation 3
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 27
 
3.9%
점 17
 
2.5%
집 14
 
2.0%
산 12
 
1.8%
비 12
 
1.8%
한 11
 
1.6%
대 11
 
1.6%
갈 10
 
1.5%
국 10
 
1.5%
마 10
 
1.5%
Other values (234) 551
80.4%
Uppercase Letter
ValueCountFrequency (%)
B 3
23.1%
I 3
23.1%
U 2
15.4%
D 2
15.4%
S 1
 
7.7%
A 1
 
7.7%
O 1
 
7.7%
Decimal Number
ValueCountFrequency (%)
0 3
42.9%
1 1
 
14.3%
9 1
 
14.3%
8 1
 
14.3%
5 1
 
14.3%
Lowercase Letter
ValueCountFrequency (%)
o 1
20.0%
l 1
20.0%
s 1
20.0%
a 1
20.0%
m 1
20.0%
Other Punctuation
ValueCountFrequency (%)
. 2
66.7%
& 1
33.3%
Space Separator
ValueCountFrequency (%)
15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 684
91.0%
Common 49
 
6.5%
Latin 18
 
2.4%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 27
 
3.9%
점 17
 
2.5%
집 14
 
2.0%
산 12
 
1.8%
비 12
 
1.8%
한 11
 
1.6%
대 11
 
1.6%
갈 10
 
1.5%
국 10
 
1.5%
마 10
 
1.5%
Other values (233) 550
80.4%
Latin
ValueCountFrequency (%)
B 3
16.7%
I 3
16.7%
U 2
11.1%
D 2
11.1%
S 1
 
5.6%
A 1
 
5.6%
o 1
 
5.6%
l 1
 
5.6%
s 1
 
5.6%
a 1
 
5.6%
Other values (2) 2
11.1%
Common
ValueCountFrequency (%)
15
30.6%
( 12
24.5%
) 12
24.5%
0 3
 
6.1%
. 2
 
4.1%
& 1
 
2.0%
1 1
 
2.0%
9 1
 
2.0%
8 1
 
2.0%
5 1
 
2.0%
Han
ValueCountFrequency (%)
新 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 684
91.0%
ASCII 67
 
8.9%
CJK 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 27
 
3.9%
점 17
 
2.5%
집 14
 
2.0%
산 12
 
1.8%
비 12
 
1.8%
한 11
 
1.6%
대 11
 
1.6%
갈 10
 
1.5%
국 10
 
1.5%
마 10
 
1.5%
Other values (233) 550
80.4%
ASCII
ValueCountFrequency (%)
15
22.4%
( 12
17.9%
) 12
17.9%
B 3
 
4.5%
0 3
 
4.5%
I 3
 
4.5%
U 2
 
3.0%
. 2
 
3.0%
D 2
 
3.0%
& 1
 
1.5%
Other values (12) 12
17.9%
CJK
ValueCountFrequency (%)
新 1
100.0%

소재지도로명
Text

Distinct130
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-05-11T15:00:21.673012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length44
Mean length34.300752
Min length22

Characters and Unicode

Total characters4562
Distinct characters118
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

Unique129 ?
Unique (%)97.0%

Sample

1st row서울특별시 용산구 한강대로62길 38, (한강로1가,(지상1층))
2nd row서울특별시 용산구 한강대로 266-2, (남영동)
3rd row서울특별시 용산구 한강대로 268, (남영동,(지상1층))
4th row서울특별시 용산구 한강대로14길 18, (한강로3가,(지상1층))
5th row서울특별시 용산구 원효로 259-1, 1층 (원효로1가)
ValueCountFrequency (%)
서울특별시 133
 
17.2%
용산구 133
 
17.2%
1층 27
 
3.5%
한강대로 17
 
2.2%
지상1층 12
 
1.6%
후암로 10
 
1.3%
한강로3가 10
 
1.3%
한남동 9
 
1.2%
한강로2가 8
 
1.0%
남영동 8
 
1.0%
Other values (261) 405
52.5%
2024-05-11T15:00:22.287753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
639
 
14.0%
1 231
 
5.1%
, 228
 
5.0%
) 187
 
4.1%
( 187
 
4.1%
로 169
 
3.7%
산 144
 
3.2%
서 143
 
3.1%
용 143
 
3.1%
울 136
 
3.0%
Other values (108) 2355
51.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2520
55.2%
Decimal Number 753
 
16.5%
Space Separator 639
 
14.0%
Other Punctuation 230
 
5.0%
Close Punctuation 187
 
4.1%
Open Punctuation 187
 
4.1%
Dash Punctuation 39
 
0.9%
Uppercase Letter 4
 
0.1%
Math Symbol 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 169
 
6.7%
산 144
 
5.7%
서 143
 
5.7%
용 143
 
5.7%
울 136
 
5.4%
시 134
 
5.3%
특 133
 
5.3%
별 133
 
5.3%
구 133
 
5.3%
동 118
 
4.7%
Other values (90) 1134
45.0%
Decimal Number
ValueCountFrequency (%)
1 231
30.7%
2 124
16.5%
4 72
 
9.6%
3 60
 
8.0%
0 57
 
7.6%
6 48
 
6.4%
5 45
 
6.0%
8 42
 
5.6%
7 42
 
5.6%
9 32
 
4.2%
Other Punctuation
ValueCountFrequency (%)
, 228
99.1%
. 2
 
0.9%
Space Separator
ValueCountFrequency (%)
639
100.0%
Close Punctuation
ValueCountFrequency (%)
) 187
100.0%
Open Punctuation
ValueCountFrequency (%)
( 187
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 39
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2520
55.2%
Common 2038
44.7%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 169
 
6.7%
산 144
 
5.7%
서 143
 
5.7%
용 143
 
5.7%
울 136
 
5.4%
시 134
 
5.3%
특 133
 
5.3%
별 133
 
5.3%
구 133
 
5.3%
동 118
 
4.7%
Other values (90) 1134
45.0%
Common
ValueCountFrequency (%)
639
31.4%
1 231
 
11.3%
, 228
 
11.2%
) 187
 
9.2%
( 187
 
9.2%
2 124
 
6.1%
4 72
 
3.5%
3 60
 
2.9%
0 57
 
2.8%
6 48
 
2.4%
Other values (7) 205
 
10.1%
Latin
ValueCountFrequency (%)
B 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2520
55.2%
ASCII 2042
44.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
639
31.3%
1 231
 
11.3%
, 228
 
11.2%
) 187
 
9.2%
( 187
 
9.2%
2 124
 
6.1%
4 72
 
3.5%
3 60
 
2.9%
0 57
 
2.8%
6 48
 
2.4%
Other values (8) 209
 
10.2%
Hangul
ValueCountFrequency (%)
로 169
 
6.7%
산 144
 
5.7%
서 143
 
5.7%
용 143
 
5.7%
울 136
 
5.4%
시 134
 
5.3%
특 133
 
5.3%
별 133
 
5.3%
구 133
 
5.3%
동 118
 
4.7%
Other values (90) 1134
45.0%

소재지지번
Text

Distinct130
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-05-11T15:00:22.580333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length39
Mean length30.879699
Min length23

Characters and Unicode

Total characters4107
Distinct characters95
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

Unique129 ?
Unique (%)97.0%

Sample

1st row서울특별시 용산구 한강로1가 231번지 21호 (지상1층)
2nd row서울특별시 용산구 남영동 85번지 1호 (지상1층)
3rd row서울특별시 용산구 남영동 84번지 8호 (지상1층)
4th row서울특별시 용산구 한강로3가 65번지 162호 (지상1층)
5th row서울특별시 용산구 원효로1가 39번지 10호
ValueCountFrequency (%)
서울특별시 133
 
17.3%
용산구 133
 
17.3%
지상1층 42
 
5.5%
동자동 18
 
2.3%
1호 15
 
1.9%
한남동 14
 
1.8%
남영동 14
 
1.8%
1층 12
 
1.6%
한강로3가 12
 
1.6%
이태원동 11
 
1.4%
Other values (208) 366
47.5%
2024-05-11T15:00:23.092842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
933
22.7%
지 214
 
5.2%
1 211
 
5.1%
용 143
 
3.5%
산 141
 
3.4%
서 138
 
3.4%
울 136
 
3.3%
시 134
 
3.3%
구 133
 
3.2%
별 133
 
3.2%
Other values (85) 1791
43.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2311
56.3%
Space Separator 933
22.7%
Decimal Number 703
 
17.1%
Open Punctuation 63
 
1.5%
Close Punctuation 63
 
1.5%
Other Punctuation 26
 
0.6%
Uppercase Letter 5
 
0.1%
Dash Punctuation 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 214
 
9.3%
용 143
 
6.2%
산 141
 
6.1%
서 138
 
6.0%
울 136
 
5.9%
시 134
 
5.8%
구 133
 
5.8%
별 133
 
5.8%
번 133
 
5.8%
특 133
 
5.8%
Other values (67) 873
37.8%
Decimal Number
ValueCountFrequency (%)
1 211
30.0%
2 129
18.3%
3 82
 
11.7%
4 56
 
8.0%
0 44
 
6.3%
5 44
 
6.3%
6 42
 
6.0%
9 34
 
4.8%
8 31
 
4.4%
7 30
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 22
84.6%
. 4
 
15.4%
Space Separator
ValueCountFrequency (%)
933
100.0%
Open Punctuation
ValueCountFrequency (%)
( 63
100.0%
Close Punctuation
ValueCountFrequency (%)
) 63
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2311
56.3%
Common 1791
43.6%
Latin 5
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 214
 
9.3%
용 143
 
6.2%
산 141
 
6.1%
서 138
 
6.0%
울 136
 
5.9%
시 134
 
5.8%
구 133
 
5.8%
별 133
 
5.8%
번 133
 
5.8%
특 133
 
5.8%
Other values (67) 873
37.8%
Common
ValueCountFrequency (%)
933
52.1%
1 211
 
11.8%
2 129
 
7.2%
3 82
 
4.6%
( 63
 
3.5%
) 63
 
3.5%
4 56
 
3.1%
0 44
 
2.5%
5 44
 
2.5%
6 42
 
2.3%
Other values (7) 124
 
6.9%
Latin
ValueCountFrequency (%)
B 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2311
56.3%
ASCII 1796
43.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
933
51.9%
1 211
 
11.7%
2 129
 
7.2%
3 82
 
4.6%
( 63
 
3.5%
) 63
 
3.5%
4 56
 
3.1%
0 44
 
2.4%
5 44
 
2.4%
6 42
 
2.3%
Other values (8) 129
 
7.2%
Hangul
ValueCountFrequency (%)
지 214
 
9.3%
용 143
 
6.2%
산 141
 
6.1%
서 138
 
6.0%
울 136
 
5.9%
시 134
 
5.8%
구 133
 
5.8%
별 133
 
5.8%
번 133
 
5.8%
특 133
 
5.8%
Other values (67) 873
37.8%

허가(신고)번호
Text

UNIQUE 

Distinct133
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-05-11T15:00:23.425457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2926
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

Unique133 ?
Unique (%)100.0%

Sample

1st row3020000-101-1986-07037
2nd row3020000-101-2008-00164
3rd row3020000-101-2000-07490
4th row3020000-101-2011-00087
5th row3020000-101-2012-00126
ValueCountFrequency (%)
3020000-101-1986-07037 1
 
0.8%
3020000-101-2008-00152 1
 
0.8%
3020000-101-1995-05661 1
 
0.8%
3020000-101-1997-07546 1
 
0.8%
3020000-101-2010-00282 1
 
0.8%
3020000-101-2011-00264 1
 
0.8%
3020000-101-2011-00164 1
 
0.8%
3020000-101-1986-06645 1
 
0.8%
3020000-101-2002-00082 1
 
0.8%
3020000-101-1987-06167 1
 
0.8%
Other values (123) 123
92.5%
2024-05-11T15:00:23.872218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1228
42.0%
1 438
 
15.0%
- 399
 
13.6%
2 300
 
10.3%
3 174
 
5.9%
9 106
 
3.6%
4 62
 
2.1%
8 59
 
2.0%
6 57
 
1.9%
7 52
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2527
86.4%
Dash Punctuation 399
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1228
48.6%
1 438
 
17.3%
2 300
 
11.9%
3 174
 
6.9%
9 106
 
4.2%
4 62
 
2.5%
8 59
 
2.3%
6 57
 
2.3%
7 52
 
2.1%
5 51
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 399
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2926
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1228
42.0%
1 438
 
15.0%
- 399
 
13.6%
2 300
 
10.3%
3 174
 
5.9%
9 106
 
3.6%
4 62
 
2.1%
8 59
 
2.0%
6 57
 
1.9%
7 52
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2926
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1228
42.0%
1 438
 
15.0%
- 399
 
13.6%
2 300
 
10.3%
3 174
 
5.9%
9 106
 
3.6%
4 62
 
2.1%
8 59
 
2.0%
6 57
 
1.9%
7 52
 
1.8%

업태명
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
한식
91 
중국식
12 
일식
11 
경양식
11 
기타
 
3
Other values (5)
 
5

Length

Max length5
Median length2
Mean length2.2180451
Min length2

Unique

Unique5 ?
Unique (%)3.8%

Sample

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

Common Values

ValueCountFrequency (%)
한식 91
68.4%
중국식 12
 
9.0%
일식 11
 
8.3%
경양식 11
 
8.3%
기타 3
 
2.3%
까페 1
 
0.8%
복어취급 1
 
0.8%
냉면집 1
 
0.8%
호프/통닭 1
 
0.8%
분식 1
 
0.8%

Length

2024-05-11T15:00:24.085691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:00:24.280512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 91
68.4%
중국식 12
 
9.0%
일식 11
 
8.3%
경양식 11
 
8.3%
기타 3
 
2.3%
까페 1
 
0.8%
복어취급 1
 
0.8%
냉면집 1
 
0.8%
호프/통닭 1
 
0.8%
분식 1
 
0.8%

주된음식
Text

Distinct107
Distinct (%)80.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-05-11T15:00:24.681868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length4.3157895
Min length2

Characters and Unicode

Total characters574
Distinct characters166
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

Unique92 ?
Unique (%)69.2%

Sample

1st row차돌박이
2nd row참치스페셜
3rd row오향족발
4th row간장게장, 참게꽃게매운탕
5th row닭한마리
ValueCountFrequency (%)
한정식 6
 
4.4%
돼지갈비 4
 
2.9%
삼겹살 3
 
2.2%
짬뽕 3
 
2.2%
갈비탕 3
 
2.2%
냉면 3
 
2.2%
중식코스 3
 
2.2%
참치 2
 
1.5%
돈까스 2
 
1.5%
스테이크 2
 
1.5%
Other values (99) 105
77.2%
2024-05-11T15:00:25.183949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
비 20
 
3.5%
갈 18
 
3.1%
식 17
 
3.0%
스 16
 
2.8%
국 15
 
2.6%
탕 14
 
2.4%
기 14
 
2.4%
이 14
 
2.4%
밥 11
 
1.9%
고 11
 
1.9%
Other values (156) 424
73.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 559
97.4%
Other Punctuation 10
 
1.7%
Space Separator 3
 
0.5%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
비 20
 
3.6%
갈 18
 
3.2%
식 17
 
3.0%
스 16
 
2.9%
국 15
 
2.7%
탕 14
 
2.5%
기 14
 
2.5%
이 14
 
2.5%
밥 11
 
2.0%
고 11
 
2.0%
Other values (152) 409
73.2%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 559
97.4%
Common 15
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
비 20
 
3.6%
갈 18
 
3.2%
식 17
 
3.0%
스 16
 
2.9%
국 15
 
2.7%
탕 14
 
2.5%
기 14
 
2.5%
이 14
 
2.5%
밥 11
 
2.0%
고 11
 
2.0%
Other values (152) 409
73.2%
Common
ValueCountFrequency (%)
, 10
66.7%
3
 
20.0%
( 1
 
6.7%
) 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 559
97.4%
ASCII 15
 
2.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
비 20
 
3.6%
갈 18
 
3.2%
식 17
 
3.0%
스 16
 
2.9%
국 15
 
2.7%
탕 14
 
2.5%
기 14
 
2.5%
이 14
 
2.5%
밥 11
 
2.0%
고 11
 
2.0%
Other values (152) 409
73.2%
ASCII
ValueCountFrequency (%)
, 10
66.7%
3
 
20.0%
( 1
 
6.7%
) 1
 
6.7%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct129
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.0612
Minimum21.5
Maximum1306.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T15:00:25.746149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21.5
5-th percentile47.634
Q173.2
median120.33
Q3177
95-th percentile379.364
Maximum1306.17
Range1284.67
Interquartile range (IQR)103.8

Descriptive statistics

Standard deviation149.77846
Coefficient of variation (CV)0.95974183
Kurtosis27.044974
Mean156.0612
Median Absolute Deviation (MAD)47.73
Skewness4.2849734
Sum20756.14
Variance22433.589
MonotonicityNot monotonic
2024-05-11T15:00:25.939700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.0 2
 
1.5%
165.0 2
 
1.5%
81.0 2
 
1.5%
177.0 2
 
1.5%
57.87 1
 
0.8%
431.24 1
 
0.8%
46.72 1
 
0.8%
72.73 1
 
0.8%
66.05 1
 
0.8%
231.0 1
 
0.8%
Other values (119) 119
89.5%
ValueCountFrequency (%)
21.5 1
0.8%
33.1 1
0.8%
42.17 1
0.8%
42.5 1
0.8%
42.84 1
0.8%
46.72 1
0.8%
46.86 1
0.8%
48.15 1
0.8%
49.06 1
0.8%
49.59 1
0.8%
ValueCountFrequency (%)
1306.17 1
0.8%
658.51 1
0.8%
601.0 1
0.8%
581.64 1
0.8%
431.24 1
0.8%
397.78 1
0.8%
380.0 1
0.8%
378.94 1
0.8%
377.26 1
0.8%
359.51 1
0.8%

행정동명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
남영동
37 
한강로동
29 
한남동
14 
이태원제1동
10 
원효로제1동
9 
Other values (10)
34 

Length

Max length6
Median length5
Mean length3.9473684
Min length3

Unique

Unique2 ?
Unique (%)1.5%

Sample

1st row한강로동
2nd row남영동
3rd row남영동
4th row한강로동
5th row원효로제1동

Common Values

ValueCountFrequency (%)
남영동 37
27.8%
한강로동 29
21.8%
한남동 14
 
10.5%
이태원제1동 10
 
7.5%
원효로제1동 9
 
6.8%
청파동 6
 
4.5%
이촌제1동 6
 
4.5%
원효로제2동 5
 
3.8%
서빙고동 4
 
3.0%
후암동 4
 
3.0%
Other values (5) 9
 
6.8%

Length

2024-05-11T15:00:26.147796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
남영동 37
27.8%
한강로동 29
21.8%
한남동 14
 
10.5%
이태원제1동 10
 
7.5%
원효로제1동 9
 
6.8%
청파동 6
 
4.5%
이촌제1동 6
 
4.5%
원효로제2동 5
 
3.8%
서빙고동 4
 
3.0%
후암동 4
 
3.0%
Other values (5) 9
 
6.8%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
상수도전용
80 
<NA>
53 

Length

Max length5
Median length5
Mean length4.6015038
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 80
60.2%
<NA> 53
39.8%

Length

2024-05-11T15:00:26.331196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:00:26.489978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 80
60.2%
na 53
39.8%

소재지전화번호
Text

MISSING 

Distinct128
Distinct (%)100.0%
Missing5
Missing (%)3.8%
Memory size1.2 KiB
2024-05-11T15:00:26.835092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.273438
Min length10

Characters and Unicode

Total characters1315
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

Unique128 ?
Unique (%)100.0%

Sample

1st row02 7952654
2nd row02 794 4558
3rd row02 7115556
4th row02 26358393
5th row02 7937766
ValueCountFrequency (%)
02 112
44.4%
793 2
 
0.8%
792 2
 
0.8%
7907555 1
 
0.4%
7073692 1
 
0.4%
7797781 1
 
0.4%
0872 1
 
0.4%
7929233 1
 
0.4%
7974443 1
 
0.4%
7932968 1
 
0.4%
Other values (129) 129
51.2%
2024-05-11T15:00:27.442402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 213
16.2%
7 201
15.3%
2 198
15.1%
138
10.5%
9 127
9.7%
5 89
6.8%
3 76
 
5.8%
4 73
 
5.6%
1 71
 
5.4%
8 68
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1177
89.5%
Space Separator 138
 
10.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 213
18.1%
7 201
17.1%
2 198
16.8%
9 127
10.8%
5 89
7.6%
3 76
 
6.5%
4 73
 
6.2%
1 71
 
6.0%
8 68
 
5.8%
6 61
 
5.2%
Space Separator
ValueCountFrequency (%)
138
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1315
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 213
16.2%
7 201
15.3%
2 198
15.1%
138
10.5%
9 127
9.7%
5 89
6.8%
3 76
 
5.8%
4 73
 
5.6%
1 71
 
5.4%
8 68
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1315
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 213
16.2%
7 201
15.3%
2 198
15.1%
138
10.5%
9 127
9.7%
5 89
6.8%
3 76
 
5.8%
4 73
 
5.6%
1 71
 
5.4%
8 68
 
5.2%

Interactions

2024-05-11T15:00:17.441199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:00:14.431139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:00:15.384279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:00:16.023288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:00:16.723867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:00:17.587751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:00:14.533008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:00:15.503835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:00:16.152440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:00:16.839151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:00:17.727847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:00:14.936976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:00:15.633967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:00:16.324163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:00:16.984408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:00:17.862652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:00:15.098507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:00:15.798131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:00:16.471034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:00:17.124868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:00:18.000613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:00:15.242152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:00:15.909328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:00:16.599124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:00:17.271589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T15:00:27.603444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자업태명영업장면적(㎡)행정동명
지정년도1.0000.8400.9641.0000.0000.1790.636
지정번호0.8401.0000.7590.8400.0000.0000.627
신청일자0.9640.7591.0000.9670.0000.3750.619
지정일자1.0000.8400.9671.0000.0000.1990.630
업태명0.0000.0000.0000.0001.0000.0000.132
영업장면적(㎡)0.1790.0000.3750.1990.0001.0000.339
행정동명0.6360.6270.6190.6300.1320.3391.000
2024-05-11T15:00:27.785380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명급수시설구분업태명
행정동명1.0001.0000.038
급수시설구분1.0001.0001.000
업태명0.0381.0001.000
2024-05-11T15:00:27.949036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.000-0.4780.9981.000-0.2610.0000.2941.000
지정번호-0.4781.000-0.475-0.4780.1950.0000.3031.000
신청일자0.998-0.4751.0000.998-0.2690.0000.1941.000
지정일자1.000-0.4780.9981.000-0.2620.0000.2911.000
영업장면적(㎡)-0.2610.195-0.269-0.2621.0000.0000.1571.000
업태명0.0000.0000.0000.0000.0001.0000.0381.000
행정동명0.2940.3030.1940.2910.1570.0381.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-11T15:00:18.197440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T15:00:18.525726image/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
03020000202312023083120231011풍성집서울특별시 용산구 한강대로62길 38, (한강로1가,(지상1층))서울특별시 용산구 한강로1가 231번지 21호 (지상1층)3020000-101-1986-07037한식차돌박이33.1한강로동상수도전용02 7952654
13020000202372023083120231011이춘복참치서울특별시 용산구 한강대로 266-2, (남영동)서울특별시 용산구 남영동 85번지 1호 (지상1층)3020000-101-2008-00164일식참치스페셜126.0남영동<NA>02 794 4558
23020000202362023083120231011금천문서울특별시 용산구 한강대로 268, (남영동,(지상1층))서울특별시 용산구 남영동 84번지 8호 (지상1층)3020000-101-2000-07490한식오향족발73.03남영동상수도전용<NA>
33020000202322022083120231011섬집서울특별시 용산구 한강대로14길 18, (한강로3가,(지상1층))서울특별시 용산구 한강로3가 65번지 162호 (지상1층)3020000-101-2011-00087한식간장게장, 참게꽃게매운탕65.52한강로동<NA><NA>
43020000202332023083120231011큰집닭한마리서울특별시 용산구 원효로 259-1, 1층 (원효로1가)서울특별시 용산구 원효로1가 39번지 10호3020000-101-2012-00126기타닭한마리142.51원효로제1동<NA>02 7115556
53020000202342023083120231011산마루돌구이서울특별시 용산구 한강대로62다길 12, 지상1층 (한강로1가)서울특별시 용산구 한강로1가 211번지 1호 지상1층3020000-101-2015-00086까페산낙지돌구이101.73한강로동상수도전용02 26358393
63020000202352023083120231011남도미항 용산아이파크몰점서울특별시 용산구 한강대로23길 55, 용산역 4층 900-6호 (한강로3가)서울특별시 용산구 한강로3가 40번지 999호 용산역-900-63020000-101-2018-00360한식비빔밥, 찌게180.53한강로동상수도전용<NA>
73020000202262022081920220921피기 남영본점서울특별시 용산구 한강대로84길 12, 미성회관 1,2층 (남영동)서울특별시 용산구 남영동 60번지 3호 미성회관3020000-101-1988-06641한식갈비탕,꽃등심350.39남영동상수도전용02 7937766
83020000202232022081920220921장위동유성집서울특별시 용산구 한강대로 385, 지상1층 (동자동)서울특별시 용산구 동자동 43번지 59호 지상1층3020000-101-2014-00444한식등심(소고기)132.0남영동상수도전용02 7969292
93020000202272022081920220921백식당서울특별시 용산구 원효로58길 31, (신계동,(지상1층))서울특별시 용산구 신계동 25번지 10호 (지상1층)3020000-101-1996-05627한식육회,향정살,삼겹살42.5원효로제1동상수도전용02 7015656
시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
12330200002001122006063020010630대우정서울특별시 용산구 후암로57길 7, (동자동,(지상1,2층))서울특별시 용산구 동자동 10번지 4호 (지상1,2층)3020000-101-1981-06489한식소고기김치전골135.17남영동상수도전용02 7529685
12430200002001472006063020010630라쿠치나(주)세부유통서울특별시 용산구 회나무로44길 10, (이태원동)서울특별시 용산구 이태원동 258번지 7호 (지상1층.2층.3층)3020000-101-1989-01146경양식스테이크377.26이태원제1동상수도전용02 7946005
1253020000200182006063020010630은성서울특별시 용산구 한강대로84길 11-16, (남영동)서울특별시 용산구 남영동 40번지 4호3020000-101-1999-06589한식등심구이83.13남영동상수도전용02 7972855
126302000020011322006063020010630신머이베트남쌀국수 본점서울특별시 용산구 청파로45길 9, 지상1층 (청파동3가)서울특별시 용산구 청파동3가 24번지 44호 지상1층3020000-101-2005-00133경양식쌀국수97.69청파동<NA>02 7116175
12730200002001912006063020010630남산한정식서울특별시 용산구 회나무로44길 40, (이태원동,(지하1층))서울특별시 용산구 이태원동 243번지 42호 (지하1층)3020000-101-1998-01805한식불고기48.15이태원제1동상수도전용02 7979835
128302000020011182006063020010630신라갈비찜서울특별시 용산구 원효로 69, (원효로4가,(지상1층))서울특별시 용산구 원효로4가 127번지 2호 (지상1층)3020000-101-1993-06605한식매운갈비찜181.42원효로제2동상수도전용02 7191290
12930200002001282006063020010630봉피양(용산)서울특별시 용산구 한강대로40길 31, 지상1,2층 (한강로2가)서울특별시 용산구 한강로2가 77번지 1호 지상1,2층3020000-101-1996-05528한식한우생등심297.69한강로동상수도전용02795 6446
13030200002001392006063020010630열해서울특별시 용산구 이촌로 248, (이촌동,(한강맨션 21동 201호))서울특별시 용산구 이촌동 300번지 26호 (한강맨션 21동 201호)3020000-101-1991-00016일식모듬생선회91.06이촌제1동상수도전용02793 1188
131302000020011302006063020010630초원서울특별시 용산구 한강대로80길 7, (남영동)서울특별시 용산구 남영동 80번지 4호3020000-101-1997-03873한식흑돼지삼겹살177.0남영동상수도전용02 7954175
13230200002001162006063020010630강원정 삼계탕서울특별시 용산구 원효로89길 13-10, (원효로1가,(지상1층))서울특별시 용산구 원효로1가 48번지 7호 (지상1층)3020000-101-1987-07108한식삼계탕64.38원효로제1동상수도전용02 7199978