Overview

Dataset statistics

Number of variables15
Number of observations316
Missing cells130
Missing cells (%)2.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.0 KiB
Average record size in memory126.4 B

Variable types

Categorical4
Numeric5
Text6

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,주된음식,영업장면적(㎡),행정동명,급수시설구분,소재지전화번호
Author강남구
URLhttps://data.seoul.go.kr/dataList/OA-11295/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
급수시설구분 is highly overall correlated with 지정년도 and 6 other fieldsHigh correlation
행정동명 is highly overall correlated with 급수시설구분High 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
주된음식 has 11 (3.5%) missing valuesMissing
소재지전화번호 has 116 (36.7%) missing valuesMissing
허가(신고)번호 has unique valuesUnique

Reproduction

Analysis started2024-05-11 05:26:46.140851
Analysis finished2024-05-11 05:26:53.199790
Duration7.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
3220000
316 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3220000
2nd row3220000
3rd row3220000
4th row3220000
5th row3220000

Common Values

ValueCountFrequency (%)
3220000 316
100.0%

Length

2024-05-11T14:26:53.275803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:26:53.663677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3220000 316
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.2627
Minimum2010
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-05-11T14:26:53.753808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2010
Q12010
median2016
Q32021
95-th percentile2023
Maximum2023
Range13
Interquartile range (IQR)11

Descriptive statistics

Standard deviation4.6466234
Coefficient of variation (CV)0.0023045725
Kurtosis-1.4060929
Mean2016.2627
Median Absolute Deviation (MAD)5
Skewness-0.074767674
Sum637139
Variance21.591109
MonotonicityNot monotonic
2024-05-11T14:26:53.886805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2010 80
25.3%
2016 65
20.6%
2021 64
20.3%
2014 27
 
8.5%
2023 26
 
8.2%
2022 18
 
5.7%
2019 15
 
4.7%
2017 7
 
2.2%
2013 5
 
1.6%
2012 3
 
0.9%
Other values (2) 6
 
1.9%
ValueCountFrequency (%)
2010 80
25.3%
2011 3
 
0.9%
2012 3
 
0.9%
2013 5
 
1.6%
2014 27
 
8.5%
2015 3
 
0.9%
2016 65
20.6%
2017 7
 
2.2%
2019 15
 
4.7%
2021 64
20.3%
ValueCountFrequency (%)
2023 26
 
8.2%
2022 18
 
5.7%
2021 64
20.3%
2019 15
 
4.7%
2017 7
 
2.2%
2016 65
20.6%
2015 3
 
0.9%
2014 27
8.5%
2013 5
 
1.6%
2012 3
 
0.9%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct202
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.08544
Minimum2
Maximum475
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-05-11T14:26:54.040562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q127.75
median110.5
Q3298
95-th percentile366.5
Maximum475
Range473
Interquartile range (IQR)270.25

Descriptive statistics

Standard deviation131.03365
Coefficient of variation (CV)0.85595109
Kurtosis-1.0815088
Mean153.08544
Median Absolute Deviation (MAD)92.5
Skewness0.52128509
Sum48375
Variance17169.818
MonotonicityNot monotonic
2024-05-11T14:26:54.236629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 7
 
2.2%
58 7
 
2.2%
25 7
 
2.2%
18 6
 
1.9%
61 6
 
1.9%
53 5
 
1.6%
11 5
 
1.6%
14 5
 
1.6%
16 4
 
1.3%
22 4
 
1.3%
Other values (192) 260
82.3%
ValueCountFrequency (%)
2 2
 
0.6%
3 3
0.9%
4 4
1.3%
5 3
0.9%
6 3
0.9%
7 4
1.3%
8 2
 
0.6%
10 1
 
0.3%
11 5
1.6%
12 1
 
0.3%
ValueCountFrequency (%)
475 1
0.3%
470 1
0.3%
466 1
0.3%
463 1
0.3%
461 1
0.3%
456 1
0.3%
454 1
0.3%
380 1
0.3%
378 1
0.3%
376 1
0.3%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20159734
Minimum20080630
Maximum20231010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-05-11T14:26:54.390238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20080630
5-th percentile20100210
Q120100730
median20161125
Q320201001
95-th percentile20231010
Maximum20231010
Range150380
Interquartile range (IQR)100271

Descriptive statistics

Standard deviation44838.558
Coefficient of variation (CV)0.0022241641
Kurtosis-1.2471423
Mean20159734
Median Absolute Deviation (MAD)39876
Skewness0.0067986242
Sum6.370476 × 109
Variance2.0104962 × 109
MonotonicityNot monotonic
2024-05-11T14:26:54.547366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
20100210 70
22.2%
20161125 63
19.9%
20141006 26
 
8.2%
20231010 26
 
8.2%
20201001 22
 
7.0%
20221104 18
 
5.7%
20211001 18
 
5.7%
20181001 15
 
4.7%
20191128 10
 
3.2%
20100730 9
 
2.8%
Other values (19) 39
12.3%
ValueCountFrequency (%)
20080630 1
 
0.3%
20100210 70
22.2%
20100730 9
 
2.8%
20101128 1
 
0.3%
20110701 1
 
0.3%
20110816 2
 
0.6%
20111230 1
 
0.3%
20120706 3
 
0.9%
20130710 6
 
1.9%
20130906 1
 
0.3%
ValueCountFrequency (%)
20231010 26
8.2%
20221104 18
5.7%
20211116 2
 
0.6%
20211001 18
5.7%
20201001 22
7.0%
20200101 1
 
0.3%
20191128 10
 
3.2%
20191001 1
 
0.3%
20190930 4
 
1.3%
20181001 15
4.7%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20163508
Minimum20100210
Maximum20231123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-05-11T14:26:54.719877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20100210
5-th percentile20100210
Q120100915
median20161125
Q320211116
95-th percentile20231123
Maximum20231123
Range130913
Interquartile range (IQR)110201

Descriptive statistics

Standard deviation46760.407
Coefficient of variation (CV)0.0023190611
Kurtosis-1.4028076
Mean20163508
Median Absolute Deviation (MAD)49991
Skewness-0.08366645
Sum6.3716684 × 109
Variance2.1865357 × 109
MonotonicityNot monotonic
2024-05-11T14:26:54.862657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
20100210 71
22.5%
20161125 64
20.3%
20211116 64
20.3%
20141006 27
 
8.5%
20231123 26
 
8.2%
20221104 18
 
5.7%
20190930 11
 
3.5%
20100915 9
 
2.8%
20171124 7
 
2.2%
20130710 5
 
1.6%
Other values (6) 14
 
4.4%
ValueCountFrequency (%)
20100210 71
22.5%
20100915 9
 
2.8%
20110816 2
 
0.6%
20111230 1
 
0.3%
20120706 3
 
0.9%
20130710 5
 
1.6%
20141006 27
 
8.5%
20151106 3
 
0.9%
20161124 1
 
0.3%
20161125 64
20.3%
ValueCountFrequency (%)
20231123 26
8.2%
20221104 18
 
5.7%
20211116 64
20.3%
20191128 4
 
1.3%
20190930 11
 
3.5%
20171124 7
 
2.2%
20161125 64
20.3%
20161124 1
 
0.3%
20151106 3
 
0.9%
20141006 27
8.5%

업소명
Text

Distinct314
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-05-11T14:26:55.237643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length18
Mean length6.0506329
Min length2

Characters and Unicode

Total characters1912
Distinct characters380
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique312 ?
Unique (%)98.7%

Sample

1st row이도곰탕
2nd row솥내음 스타필드 코엑스몰점
3rd row스시히로바
4th row서백자 간장게장
5th row주)봉산집
ValueCountFrequency (%)
역삼점 4
 
1.0%
코엑스점 3
 
0.7%
주식회사 3
 
0.7%
압구정점 3
 
0.7%
본가 2
 
0.5%
압구정본점 2
 
0.5%
아야진생태찌개 2
 
0.5%
임고집 2
 
0.5%
하영호신촌설렁탕 2
 
0.5%
신의주 2
 
0.5%
Other values (379) 387
93.9%
2024-05-11T14:26:55.821682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
96
 
5.0%
점 58
 
3.0%
) 33
 
1.7%
주 33
 
1.7%
( 32
 
1.7%
가 31
 
1.6%
대 29
 
1.5%
이 28
 
1.5%
집 26
 
1.4%
스 26
 
1.4%
Other values (370) 1520
79.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1724
90.2%
Space Separator 96
 
5.0%
Close Punctuation 33
 
1.7%
Open Punctuation 32
 
1.7%
Decimal Number 8
 
0.4%
Uppercase Letter 8
 
0.4%
Lowercase Letter 6
 
0.3%
Other Punctuation 5
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 58
 
3.4%
주 33
 
1.9%
가 31
 
1.8%
대 29
 
1.7%
이 28
 
1.6%
집 26
 
1.5%
스 26
 
1.5%
삼 24
 
1.4%
정 24
 
1.4%
수 23
 
1.3%
Other values (346) 1422
82.5%
Uppercase Letter
ValueCountFrequency (%)
A 1
12.5%
L 1
12.5%
H 1
12.5%
N 1
12.5%
U 1
12.5%
G 1
12.5%
C 1
12.5%
F 1
12.5%
Decimal Number
ValueCountFrequency (%)
1 3
37.5%
4 2
25.0%
2 1
 
12.5%
0 1
 
12.5%
9 1
 
12.5%
Lowercase Letter
ValueCountFrequency (%)
i 2
33.3%
m 1
16.7%
t 1
16.7%
e 1
16.7%
d 1
16.7%
Other Punctuation
ValueCountFrequency (%)
＆ 2
40.0%
& 2
40.0%
？ 1
20.0%
Space Separator
ValueCountFrequency (%)
96
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1718
89.9%
Common 174
 
9.1%
Latin 14
 
0.7%
Han 6
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 58
 
3.4%
주 33
 
1.9%
가 31
 
1.8%
대 29
 
1.7%
이 28
 
1.6%
집 26
 
1.5%
스 26
 
1.5%
삼 24
 
1.4%
정 24
 
1.4%
수 23
 
1.3%
Other values (340) 1416
82.4%
Latin
ValueCountFrequency (%)
i 2
14.3%
A 1
 
7.1%
L 1
 
7.1%
m 1
 
7.1%
t 1
 
7.1%
e 1
 
7.1%
d 1
 
7.1%
H 1
 
7.1%
N 1
 
7.1%
U 1
 
7.1%
Other values (3) 3
21.4%
Common
ValueCountFrequency (%)
96
55.2%
) 33
 
19.0%
( 32
 
18.4%
1 3
 
1.7%
＆ 2
 
1.1%
& 2
 
1.1%
4 2
 
1.1%
2 1
 
0.6%
0 1
 
0.6%
9 1
 
0.6%
Han
ValueCountFrequency (%)
州 1
16.7%
緝 1
16.7%
亭 1
16.7%
濟 1
16.7%
鷗 1
16.7%
狎 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1718
89.9%
ASCII 185
 
9.7%
CJK 6
 
0.3%
None 3
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
96
51.9%
) 33
 
17.8%
( 32
 
17.3%
1 3
 
1.6%
i 2
 
1.1%
& 2
 
1.1%
4 2
 
1.1%
2 1
 
0.5%
A 1
 
0.5%
0 1
 
0.5%
Other values (12) 12
 
6.5%
Hangul
ValueCountFrequency (%)
점 58
 
3.4%
주 33
 
1.9%
가 31
 
1.8%
대 29
 
1.7%
이 28
 
1.6%
집 26
 
1.5%
스 26
 
1.5%
삼 24
 
1.4%
정 24
 
1.4%
수 23
 
1.3%
Other values (340) 1416
82.4%
None
ValueCountFrequency (%)
＆ 2
66.7%
？ 1
33.3%
CJK
ValueCountFrequency (%)
州 1
16.7%
緝 1
16.7%
亭 1
16.7%
濟 1
16.7%
鷗 1
16.7%
狎 1
16.7%

소재지도로명
Text

Distinct313
Distinct (%)100.0%
Missing3
Missing (%)0.9%
Memory size2.6 KiB
2024-05-11T14:26:56.133881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length62
Median length50
Mean length33.578275
Min length24

Characters and Unicode

Total characters10510
Distinct characters201
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

Unique313 ?
Unique (%)100.0%

Sample

1st row서울특별시 강남구 논현로94길 29-5, 지상1층,지상2층 (역삼동)
2nd row서울특별시 강남구 영동대로 513, 코엑스 지하1층 O-107호 (삼성동)
3rd row서울특별시 강남구 삼성로 620, 블래스톤리조트 지상1층 (삼성동)
4th row서울특별시 강남구 삼성로 542, 지상2층 (삼성동, 석천빌딩)
5th row서울특별시 강남구 삼성로 564, 지상2층,지상3층 (삼성동)
ValueCountFrequency (%)
서울특별시 313
 
16.8%
강남구 313
 
16.8%
삼성동 51
 
2.7%
지상1층 46
 
2.5%
역삼동 45
 
2.4%
지하1층 43
 
2.3%
논현동 26
 
1.4%
대치동 26
 
1.4%
영동대로 25
 
1.3%
1층 23
 
1.2%
Other values (497) 948
51.0%
2024-05-11T14:26:56.608324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1546
 
14.7%
1 568
 
5.4%
, 537
 
5.1%
동 370
 
3.5%
남 346
 
3.3%
강 341
 
3.2%
구 326
 
3.1%
서 323
 
3.1%
울 316
 
3.0%
) 315
 
3.0%
Other values (191) 5522
52.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6017
57.3%
Decimal Number 1723
 
16.4%
Space Separator 1546
 
14.7%
Other Punctuation 537
 
5.1%
Close Punctuation 315
 
3.0%
Open Punctuation 315
 
3.0%
Uppercase Letter 31
 
0.3%
Dash Punctuation 24
 
0.2%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 370
 
6.1%
남 346
 
5.8%
강 341
 
5.7%
구 326
 
5.4%
서 323
 
5.4%
울 316
 
5.3%
별 314
 
5.2%
특 313
 
5.2%
시 313
 
5.2%
로 313
 
5.2%
Other values (163) 2742
45.6%
Uppercase Letter
ValueCountFrequency (%)
B 6
19.4%
J 4
12.9%
H 4
12.9%
Q 4
12.9%
O 3
9.7%
M 3
9.7%
S 2
 
6.5%
G 1
 
3.2%
L 1
 
3.2%
I 1
 
3.2%
Other values (2) 2
 
6.5%
Decimal Number
ValueCountFrequency (%)
1 568
33.0%
2 273
15.8%
3 160
 
9.3%
0 138
 
8.0%
5 130
 
7.5%
4 109
 
6.3%
6 107
 
6.2%
8 93
 
5.4%
7 85
 
4.9%
9 60
 
3.5%
Space Separator
ValueCountFrequency (%)
1546
100.0%
Other Punctuation
ValueCountFrequency (%)
, 537
100.0%
Close Punctuation
ValueCountFrequency (%)
) 315
100.0%
Open Punctuation
ValueCountFrequency (%)
( 315
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6017
57.3%
Common 4462
42.5%
Latin 31
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 370
 
6.1%
남 346
 
5.8%
강 341
 
5.7%
구 326
 
5.4%
서 323
 
5.4%
울 316
 
5.3%
별 314
 
5.2%
특 313
 
5.2%
시 313
 
5.2%
로 313
 
5.2%
Other values (163) 2742
45.6%
Common
ValueCountFrequency (%)
1546
34.6%
1 568
 
12.7%
, 537
 
12.0%
) 315
 
7.1%
( 315
 
7.1%
2 273
 
6.1%
3 160
 
3.6%
0 138
 
3.1%
5 130
 
2.9%
4 109
 
2.4%
Other values (6) 371
 
8.3%
Latin
ValueCountFrequency (%)
B 6
19.4%
J 4
12.9%
H 4
12.9%
Q 4
12.9%
O 3
9.7%
M 3
9.7%
S 2
 
6.5%
G 1
 
3.2%
L 1
 
3.2%
I 1
 
3.2%
Other values (2) 2
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6017
57.3%
ASCII 4493
42.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1546
34.4%
1 568
 
12.6%
, 537
 
12.0%
) 315
 
7.0%
( 315
 
7.0%
2 273
 
6.1%
3 160
 
3.6%
0 138
 
3.1%
5 130
 
2.9%
4 109
 
2.4%
Other values (18) 402
 
8.9%
Hangul
ValueCountFrequency (%)
동 370
 
6.1%
남 346
 
5.8%
강 341
 
5.7%
구 326
 
5.4%
서 323
 
5.4%
울 316
 
5.3%
별 314
 
5.2%
특 313
 
5.2%
시 313
 
5.2%
로 313
 
5.2%
Other values (163) 2742
45.6%

소재지지번
Text

Distinct303
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-05-11T14:26:56.923366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length43
Mean length28.705696
Min length22

Characters and Unicode

Total characters9071
Distinct characters174
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

Unique297 ?
Unique (%)94.0%

Sample

1st row서울특별시 강남구 역삼동 671번지 17호
2nd row서울특별시 강남구 삼성동 159번지 코엑스
3rd row서울특별시 강남구 삼성동 70번지 블래스톤리조트
4th row서울특별시 강남구 삼성동 151번지 4호 석천빌딩 2층
5th row서울특별시 강남구 삼성동 145번지 19호
ValueCountFrequency (%)
서울특별시 316
17.9%
강남구 316
17.9%
역삼동 73
 
4.1%
삼성동 69
 
3.9%
대치동 47
 
2.7%
지상1층 47
 
2.7%
논현동 35
 
2.0%
신사동 33
 
1.9%
159번지 30
 
1.7%
0호 23
 
1.3%
Other values (350) 772
43.8%
2024-05-11T14:26:57.407235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2213
24.4%
지 449
 
4.9%
1 446
 
4.9%
서 325
 
3.6%
강 320
 
3.5%
동 320
 
3.5%
남 319
 
3.5%
구 318
 
3.5%
별 317
 
3.5%
울 317
 
3.5%
Other values (164) 3727
41.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5200
57.3%
Space Separator 2213
24.4%
Decimal Number 1594
 
17.6%
Other Punctuation 32
 
0.4%
Uppercase Letter 15
 
0.2%
Dash Punctuation 12
 
0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 449
 
8.6%
서 325
 
6.2%
강 320
 
6.2%
동 320
 
6.2%
남 319
 
6.1%
구 318
 
6.1%
별 317
 
6.1%
울 317
 
6.1%
번 316
 
6.1%
특 316
 
6.1%
Other values (138) 1883
36.2%
Decimal Number
ValueCountFrequency (%)
1 446
28.0%
2 190
11.9%
5 145
 
9.1%
6 144
 
9.0%
9 140
 
8.8%
4 112
 
7.0%
7 108
 
6.8%
0 105
 
6.6%
8 105
 
6.6%
3 99
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
S 2
13.3%
Q 2
13.3%
B 2
13.3%
J 2
13.3%
H 2
13.3%
G 1
6.7%
O 1
6.7%
A 1
6.7%
M 1
6.7%
K 1
6.7%
Space Separator
ValueCountFrequency (%)
2213
100.0%
Other Punctuation
ValueCountFrequency (%)
, 32
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5200
57.3%
Common 3856
42.5%
Latin 15
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 449
 
8.6%
서 325
 
6.2%
강 320
 
6.2%
동 320
 
6.2%
남 319
 
6.1%
구 318
 
6.1%
별 317
 
6.1%
울 317
 
6.1%
번 316
 
6.1%
특 316
 
6.1%
Other values (138) 1883
36.2%
Common
ValueCountFrequency (%)
2213
57.4%
1 446
 
11.6%
2 190
 
4.9%
5 145
 
3.8%
6 144
 
3.7%
9 140
 
3.6%
4 112
 
2.9%
7 108
 
2.8%
0 105
 
2.7%
8 105
 
2.7%
Other values (6) 148
 
3.8%
Latin
ValueCountFrequency (%)
S 2
13.3%
Q 2
13.3%
B 2
13.3%
J 2
13.3%
H 2
13.3%
G 1
6.7%
O 1
6.7%
A 1
6.7%
M 1
6.7%
K 1
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5200
57.3%
ASCII 3871
42.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2213
57.2%
1 446
 
11.5%
2 190
 
4.9%
5 145
 
3.7%
6 144
 
3.7%
9 140
 
3.6%
4 112
 
2.9%
7 108
 
2.8%
0 105
 
2.7%
8 105
 
2.7%
Other values (16) 163
 
4.2%
Hangul
ValueCountFrequency (%)
지 449
 
8.6%
서 325
 
6.2%
강 320
 
6.2%
동 320
 
6.2%
남 319
 
6.1%
구 318
 
6.1%
별 317
 
6.1%
울 317
 
6.1%
번 316
 
6.1%
특 316
 
6.1%
Other values (138) 1883
36.2%

허가(신고)번호
Text

UNIQUE 

Distinct316
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-05-11T14:26:57.710295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters6952
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

Unique316 ?
Unique (%)100.0%

Sample

1st row3220000-101-2001-24197
2nd row3220000-101-2020-00643
3rd row3220000-101-2002-00383
4th row3220000-101-2012-00416
5th row3220000-101-2011-01048
ValueCountFrequency (%)
3220000-101-2001-24197 1
 
0.3%
3220000-101-2009-00675 1
 
0.3%
3220000-101-2004-00804 1
 
0.3%
3220000-101-2004-01228 1
 
0.3%
3220000-101-1993-07112 1
 
0.3%
3220000-101-2004-00336 1
 
0.3%
3220000-101-2009-00674 1
 
0.3%
3220000-101-1993-00239 1
 
0.3%
3220000-101-2013-00626 1
 
0.3%
3220000-101-2011-00613 1
 
0.3%
Other values (306) 306
96.8%
2024-05-11T14:26:58.139862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2449
35.2%
1 1088
15.7%
2 1047
15.1%
- 948
 
13.6%
3 452
 
6.5%
9 290
 
4.2%
8 156
 
2.2%
4 149
 
2.1%
7 134
 
1.9%
6 123
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6004
86.4%
Dash Punctuation 948
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2449
40.8%
1 1088
18.1%
2 1047
17.4%
3 452
 
7.5%
9 290
 
4.8%
8 156
 
2.6%
4 149
 
2.5%
7 134
 
2.2%
6 123
 
2.0%
5 116
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 948
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2449
35.2%
1 1088
15.7%
2 1047
15.1%
- 948
 
13.6%
3 452
 
6.5%
9 290
 
4.2%
8 156
 
2.2%
4 149
 
2.1%
7 134
 
1.9%
6 123
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2449
35.2%
1 1088
15.7%
2 1047
15.1%
- 948
 
13.6%
3 452
 
6.5%
9 290
 
4.2%
8 156
 
2.2%
4 149
 
2.1%
7 134
 
1.9%
6 123
 
1.8%

업태명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
한식
208 
일식
35 
중국식
26 
경양식
21 
분식
 
15
Other values (6)
 
11

Length

Max length10
Median length2
Mean length2.2341772
Min length2

Unique

Unique3 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
한식 208
65.8%
일식 35
 
11.1%
중국식 26
 
8.2%
경양식 21
 
6.6%
분식 15
 
4.7%
기타 4
 
1.3%
뷔페식 2
 
0.6%
호프/통닭 2
 
0.6%
식육(숯불구이) 1
 
0.3%
패밀리레스트랑 1
 
0.3%

Length

2024-05-11T14:26:58.332392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 208
65.8%
일식 35
 
11.1%
중국식 26
 
8.2%
경양식 21
 
6.6%
분식 15
 
4.7%
기타 4
 
1.3%
뷔페식 2
 
0.6%
호프/통닭 2
 
0.6%
식육(숯불구이 1
 
0.3%
패밀리레스트랑 1
 
0.3%

주된음식
Text

MISSING 

Distinct195
Distinct (%)63.9%
Missing11
Missing (%)3.5%
Memory size2.6 KiB
2024-05-11T14:26:58.766168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length12
Mean length3.9409836
Min length1

Characters and Unicode

Total characters1202
Distinct characters196
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

Unique148 ?
Unique (%)48.5%

Sample

1st row곰탕
2nd row직화불백
3rd row초밥
4th row간장게장
5th row차돌박이
ValueCountFrequency (%)
삼겹살 10
 
2.8%
자장면 10
 
2.8%
회 10
 
2.8%
한정식 9
 
2.5%
구이 9
 
2.5%
설렁탕 8
 
2.3%
냉면 7
 
2.0%
소고기 7
 
2.0%
고기구이 6
 
1.7%
샤브샤브 6
 
1.7%
Other values (182) 272
76.8%
2024-05-11T14:26:59.550015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49
 
4.1%
, 45
 
3.7%
이 34
 
2.8%
탕 31
 
2.6%
고 30
 
2.5%
기 30
 
2.5%
장 29
 
2.4%
구 29
 
2.4%
한 28
 
2.3%
식 28
 
2.3%
Other values (186) 869
72.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1106
92.0%
Space Separator 49
 
4.1%
Other Punctuation 45
 
3.7%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 34
 
3.1%
탕 31
 
2.8%
고 30
 
2.7%
기 30
 
2.7%
장 29
 
2.6%
구 29
 
2.6%
한 28
 
2.5%
식 28
 
2.5%
회 23
 
2.1%
비 22
 
2.0%
Other values (182) 822
74.3%
Space Separator
ValueCountFrequency (%)
49
100.0%
Other Punctuation
ValueCountFrequency (%)
, 45
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1106
92.0%
Common 96
 
8.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 34
 
3.1%
탕 31
 
2.8%
고 30
 
2.7%
기 30
 
2.7%
장 29
 
2.6%
구 29
 
2.6%
한 28
 
2.5%
식 28
 
2.5%
회 23
 
2.1%
비 22
 
2.0%
Other values (182) 822
74.3%
Common
ValueCountFrequency (%)
49
51.0%
, 45
46.9%
( 1
 
1.0%
) 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1106
92.0%
ASCII 96
 
8.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49
51.0%
, 45
46.9%
( 1
 
1.0%
) 1
 
1.0%
Hangul
ValueCountFrequency (%)
이 34
 
3.1%
탕 31
 
2.8%
고 30
 
2.7%
기 30
 
2.7%
장 29
 
2.6%
구 29
 
2.6%
한 28
 
2.5%
식 28
 
2.5%
회 23
 
2.1%
비 22
 
2.0%
Other values (182) 822
74.3%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct305
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean261.8893
Minimum30
Maximum2054.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-05-11T14:26:59.755476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile65.62
Q1111.575
median185.2
Q3329.44
95-th percentile691.4575
Maximum2054.77
Range2024.77
Interquartile range (IQR)217.865

Descriptive statistics

Standard deviation253.41832
Coefficient of variation (CV)0.96765435
Kurtosis14.977176
Mean261.8893
Median Absolute Deviation (MAD)89.145
Skewness3.2196647
Sum82757.02
Variance64220.847
MonotonicityNot monotonic
2024-05-11T14:26:59.950763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148.76 3
 
0.9%
140.0 3
 
0.9%
65.62 3
 
0.9%
190.0 2
 
0.6%
125.62 2
 
0.6%
141.0 2
 
0.6%
148.5 2
 
0.6%
69.75 2
 
0.6%
160.0 1
 
0.3%
374.33 1
 
0.3%
Other values (295) 295
93.4%
ValueCountFrequency (%)
30.0 1
0.3%
30.34 1
0.3%
35.28 1
0.3%
35.58 1
0.3%
36.1 1
0.3%
37.87 1
0.3%
39.2 1
0.3%
42.0 1
0.3%
42.34 1
0.3%
47.03 1
0.3%
ValueCountFrequency (%)
2054.77 1
0.3%
1729.1 1
0.3%
1683.0 1
0.3%
1218.34 1
0.3%
1205.02 1
0.3%
1145.52 1
0.3%
1115.0 1
0.3%
950.66 1
0.3%
857.93 1
0.3%
796.32 1
0.3%

행정동명
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
역삼1동
61 
삼성1동
61 
논현1동
27 
신사동
22 
대치1동
19 
Other values (16)
126 

Length

Max length4
Median length4
Mean length3.8449367
Min length3

Unique

Unique2 ?
Unique (%)0.6%

Sample

1st row역삼1동
2nd row삼성1동
3rd row삼성1동
4th row삼성1동
5th row삼성1동

Common Values

ValueCountFrequency (%)
역삼1동 61
19.3%
삼성1동 61
19.3%
논현1동 27
8.5%
신사동 22
 
7.0%
대치1동 19
 
6.0%
청담동 18
 
5.7%
대치2동 15
 
4.7%
대치4동 13
 
4.1%
압구정동 13
 
4.1%
역삼2동 11
 
3.5%
Other values (11) 56
17.7%

Length

2024-05-11T14:27:00.189686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
역삼1동 61
19.3%
삼성1동 61
19.3%
논현1동 27
8.5%
신사동 22
 
7.0%
대치1동 19
 
6.0%
청담동 18
 
5.7%
대치2동 15
 
4.7%
대치4동 13
 
4.1%
압구정동 13
 
4.1%
역삼2동 11
 
3.5%
Other values (11) 56
17.7%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
상수도전용
204 
<NA>
112 

Length

Max length5
Median length5
Mean length4.6455696
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 204
64.6%
<NA> 112
35.4%

Length

2024-05-11T14:27:00.337363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:27:00.470415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 204
64.6%
na 112
35.4%

소재지전화번호
Text

MISSING 

Distinct189
Distinct (%)94.5%
Missing116
Missing (%)36.7%
Memory size2.6 KiB
2024-05-11T14:27:00.785676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length9.92
Min length2

Characters and Unicode

Total characters1984
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

Unique184 ?
Unique (%)92.0%

Sample

1st row02 5010738
2nd row02 5155511
3rd row02 34536008
4th row02 536 8153
5th row02555 5245
ValueCountFrequency (%)
02 143
37.8%
5015487 3
 
0.8%
565 3
 
0.8%
555 2
 
0.5%
567 2
 
0.5%
574 2
 
0.5%
545 2
 
0.5%
544 2
 
0.5%
501 2
 
0.5%
515 2
 
0.5%
Other values (211) 215
56.9%
2024-05-11T14:27:01.392412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 318
16.0%
0 313
15.8%
2 302
15.2%
228
11.5%
1 153
7.7%
4 148
7.5%
6 136
6.9%
3 116
 
5.8%
7 103
 
5.2%
8 93
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1756
88.5%
Space Separator 228
 
11.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 318
18.1%
0 313
17.8%
2 302
17.2%
1 153
8.7%
4 148
8.4%
6 136
7.7%
3 116
 
6.6%
7 103
 
5.9%
8 93
 
5.3%
9 74
 
4.2%
Space Separator
ValueCountFrequency (%)
228
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1984
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 318
16.0%
0 313
15.8%
2 302
15.2%
228
11.5%
1 153
7.7%
4 148
7.5%
6 136
6.9%
3 116
 
5.8%
7 103
 
5.2%
8 93
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1984
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 318
16.0%
0 313
15.8%
2 302
15.2%
228
11.5%
1 153
7.7%
4 148
7.5%
6 136
6.9%
3 116
 
5.8%
7 103
 
5.2%
8 93
 
4.7%

Interactions

2024-05-11T14:26:52.044175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:26:49.378808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:26:50.141178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:26:50.846095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:26:51.476877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:26:52.151158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:26:49.547329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:26:50.281348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:26:50.989172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:26:51.577702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:26:52.254262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:26:49.675300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:26:50.411390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:26:51.115434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:26:51.666488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:26:52.429082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:26:49.839406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:26:50.569456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:26:51.244040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:26:51.781584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:26:52.558336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:26:49.980489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:26:50.701240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:26:51.354821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:26:51.912103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T14:27:01.548633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자업태명영업장면적(㎡)행정동명
지정년도1.0000.9460.9911.0000.0000.3860.497
지정번호0.9461.0000.9260.9490.0000.1310.606
신청일자0.9910.9261.0000.9930.0000.2420.478
지정일자1.0000.9490.9931.0000.0000.3830.511
업태명0.0000.0000.0000.0001.0000.3880.000
영업장면적(㎡)0.3860.1310.2420.3830.3881.0000.000
행정동명0.4970.6060.4780.5110.0000.0001.000
2024-05-11T14:27:01.687088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
급수시설구분행정동명업태명
급수시설구분1.0001.0001.000
행정동명1.0001.0000.000
업태명1.0000.0001.000
2024-05-11T14:27:01.797642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.0000.7670.9690.997-0.3290.0000.1821.000
지정번호0.7671.0000.7760.772-0.2740.0000.2781.000
신청일자0.9690.7761.0000.971-0.3580.0000.1741.000
지정일자0.9970.7720.9711.000-0.3300.0000.1821.000
영업장면적(㎡)-0.329-0.274-0.358-0.3301.0000.1930.0001.000
업태명0.0000.0000.0000.0000.1931.0000.0001.000
행정동명0.1820.2780.1740.1820.0000.0001.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-11T14:26:52.728176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T14:26:52.986593image/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.
2024-05-11T14:26:53.129272image/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
0322000020141602014100620141006이도곰탕서울특별시 강남구 논현로94길 29-5, 지상1층,지상2층 (역삼동)서울특별시 강남구 역삼동 671번지 17호3220000-101-2001-24197한식곰탕194.41역삼1동상수도전용02 5010738
1322000020211392018100120211116솥내음 스타필드 코엑스몰점서울특별시 강남구 영동대로 513, 코엑스 지하1층 O-107호 (삼성동)서울특별시 강남구 삼성동 159번지 코엑스3220000-101-2020-00643한식직화불백74.56삼성1동<NA><NA>
232200002010242010021020100210스시히로바서울특별시 강남구 삼성로 620, 블래스톤리조트 지상1층 (삼성동)서울특별시 강남구 삼성동 70번지 블래스톤리조트3220000-101-2002-00383일식초밥216.56삼성1동상수도전용02 5155511
332200002016892016112520161125서백자 간장게장서울특별시 강남구 삼성로 542, 지상2층 (삼성동, 석천빌딩)서울특별시 강남구 삼성동 151번지 4호 석천빌딩 2층3220000-101-2012-00416중국식간장게장260.3삼성1동<NA>02 34536008
432200002016842016112520161125주)봉산집서울특별시 강남구 삼성로 564, 지상2층,지상3층 (삼성동)서울특별시 강남구 삼성동 145번지 19호3220000-101-2011-01048한식차돌박이452.95삼성1동<NA><NA>
532200002016762016112520161125더드림서울특별시 강남구 선릉로 518, 지하1층 (삼성동)서울특별시 강남구 삼성동 140번지 25호3220000-101-2012-00882한식김치찌개110.3삼성1동<NA>02 536 8153
6322000020193682019112820190930국민한우집 삼성역점(구 칠프로칠백식당 포스코점)서울특별시 강남구 테헤란로82길 11, (대치동)서울특별시 강남구 대치동 942번지 13호3220000-101-1998-08200경양식칠백한우동123.19대치2동상수도전용02555 5245
732200002010252010021020100210이즈미서울특별시 강남구 언주로 857, 지상1층 (신사동)서울특별시 강남구 신사동 621번지 3호3220000-101-2000-21072한식회329.22신사동상수도전용02 5110107
832200002014182014100620141006홍영재 장수 청국장서울특별시 강남구 영동대로 424, 지상1층 (대치동)서울특별시 강남구 대치동 1001번지 지상1층3220000-101-2013-00292한식청국장398.68대치2동<NA><NA>
932200002010142010021020100210등대서울특별시 강남구 영동대로 342, (대치동,지상1층)서울특별시 강남구 대치동 1006번지 0호 지상1층3220000-101-2003-00016한식복요리148.76대치2동상수도전용5620505
시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
30632200002016902016112520161125순대실록 삼성역점서울특별시 강남구 삼성로100길 13, 1층 (삼성동)서울특별시 강남구 삼성동 152번지 52호3220000-101-1995-05258한식양갈비42.0삼성1동상수도전용02 5544741
30732200002014282014100620141006용수사서울특별시 강남구 삼성로86길 7, (대치동,지상2층)서울특별시 강남구 대치동 942번지 5호 지상2층3220000-101-2010-00551일식회297.75대치2동<NA>02 567 1516
308322000020212912020100120211116효미역 압구정점서울특별시 강남구 압구정로30길 17, 이소니프라자 지상1층 102,103,104-2,105,106호 (신사동)서울특별시 강남구 신사동 609번지 이소니프라자3220000-101-2008-00388한식<NA>143.15신사동상수도전용<NA>
309322000020212172018100120211116기무서울특별시 강남구 논현로 333, (역삼동,B01호)서울특별시 강남구 역삼동 797번지 3호 B01호3220000-101-2008-00782한식회30.0역삼1동상수도전용02 554 2014
3103220000201672016112520161125밀란서울특별시 강남구 개포로28길 4, (개포동,지상1층101호)서울특별시 강남구 개포동 1215번지 0호 지상1층101호3220000-101-2004-00840한식면류128.76개포4동상수도전용02 574 3216
311322000020233132023101020231123모스가든서울특별시 강남구 논현로139길 12, 지상1층 (논현동)서울특별시 강남구 논현동 30번지 6호3220000-101-2012-00814한식파스타332.0논현1동<NA><NA>
31232200002010582010021020100210삼성물산(주)10꼬르소꼬모서울카페서울특별시 강남구 압구정로 416, (청담동,지하1층)서울특별시 강남구 청담동 79번지 0호 지하1층3220000-101-2008-00093경양식이태리350.0청담동상수도전용02 30181010
313322000020232992023101020231123현대낙지집서울특별시 강남구 압구정로14길 11, (신사동)서울특별시 강남구 신사동 550번지 3호3220000-101-1985-17501한식낙지, 감자탕92.03신사동상수도전용02 5461022
31432200002010612010073020100915초심서울특별시 강남구 언주로172길 55, 지상2층 (신사동)서울특별시 강남구 신사동 662번지 6호3220000-101-2005-00412경양식곱창, 부대찌개211.26신사동상수도전용02 5174433
315322000020161722016112520161125아야진생태찌개 역삼점서울특별시 강남구 논현로 329, 지상1,2,3층 (역삼동)서울특별시 강남구 역삼동 797번지 11호 지상1,2,3,층3220000-101-2013-00416한식생태찌개253.28역삼2동<NA><NA>