Overview

Dataset statistics

Number of variables15
Number of observations90
Missing cells4
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.2 KiB
Average record size in memory127.5 B

Variable types

Categorical4
Numeric5
Text6

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,주된음식,영업장면적(㎡),행정동명,급수시설구분,소재지전화번호
Author영등포구
URLhttps://data.seoul.go.kr/dataList/OA-10448/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
업태명 is highly overall correlated with 급수시설구분High correlation
급수시설구분 is highly overall correlated with 지정년도 and 6 other fieldsHigh correlation
행정동명 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
소재지도로명 has 2 (2.2%) missing valuesMissing
소재지전화번호 has 2 (2.2%) missing valuesMissing
허가(신고)번호 has unique valuesUnique
영업장면적(㎡) has unique valuesUnique

Reproduction

Analysis started2024-05-11 06:35:13.487125
Analysis finished2024-05-11 06:35:30.284184
Duration16.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size852.0 B
3180000
90 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3180000
2nd row3180000
3rd row3180000
4th row3180000
5th row3180000

Common Values

ValueCountFrequency (%)
3180000 90
100.0%

Length

2024-05-11T06:35:30.520312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:35:30.856558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3180000 90
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.2889
Minimum2004
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2024-05-11T06:35:31.161436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2004
5-th percentile2004
Q12004
median2008
Q32011
95-th percentile2014
Maximum2016
Range12
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.5953917
Coefficient of variation (CV)0.0017902761
Kurtosis-0.87419307
Mean2008.2889
Median Absolute Deviation (MAD)3
Skewness0.3319885
Sum180746
Variance12.926841
MonotonicityDecreasing
2024-05-11T06:35:31.615190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2004 26
28.9%
2010 10
 
11.1%
2008 10
 
11.1%
2009 8
 
8.9%
2006 8
 
8.9%
2011 7
 
7.8%
2012 6
 
6.7%
2016 4
 
4.4%
2014 4
 
4.4%
2013 4
 
4.4%
ValueCountFrequency (%)
2004 26
28.9%
2006 8
 
8.9%
2007 3
 
3.3%
2008 10
 
11.1%
2009 8
 
8.9%
2010 10
 
11.1%
2011 7
 
7.8%
2012 6
 
6.7%
2013 4
 
4.4%
2014 4
 
4.4%
ValueCountFrequency (%)
2016 4
 
4.4%
2014 4
 
4.4%
2013 4
 
4.4%
2012 6
6.7%
2011 7
7.8%
2010 10
11.1%
2009 8
8.9%
2008 10
11.1%
2007 3
 
3.3%
2006 8
8.9%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)58.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.222222
Minimum1
Maximum284
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2024-05-11T06:35:32.338982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.45
Q17
median15
Q352.5
95-th percentile219.5
Maximum284
Range283
Interquartile range (IQR)45.5

Descriptive statistics

Standard deviation76.312011
Coefficient of variation (CV)1.4612938
Kurtosis1.9073153
Mean52.222222
Median Absolute Deviation (MAD)11
Skewness1.760459
Sum4700
Variance5823.5231
MonotonicityNot monotonic
2024-05-11T06:35:32.908448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 5
 
5.6%
1 5
 
5.6%
10 5
 
5.6%
8 4
 
4.4%
5 4
 
4.4%
12 4
 
4.4%
2 3
 
3.3%
4 3
 
3.3%
7 3
 
3.3%
15 2
 
2.2%
Other values (43) 52
57.8%
ValueCountFrequency (%)
1 5
5.6%
2 3
3.3%
3 5
5.6%
4 3
3.3%
5 4
4.4%
6 1
 
1.1%
7 3
3.3%
8 4
4.4%
9 2
 
2.2%
10 5
5.6%
ValueCountFrequency (%)
284 1
1.1%
282 1
1.1%
275 1
1.1%
245 1
1.1%
224 1
1.1%
214 1
1.1%
211 1
1.1%
208 1
1.1%
207 1
1.1%
189 1
1.1%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20079561
Minimum20020601
Maximum20160610
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2024-05-11T06:35:33.432279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20020601
5-th percentile20020601
Q120043072
median20080868
Q320110609
95-th percentile20141112
Maximum20160610
Range140009
Interquartile range (IQR)67537.25

Descriptive statistics

Standard deviation40727.582
Coefficient of variation (CV)0.0020283103
Kurtosis-0.93184698
Mean20079561
Median Absolute Deviation (MAD)29841.5
Skewness0.012032236
Sum1.8071605 × 109
Variance1.6587359 × 109
MonotonicityNot monotonic
2024-05-11T06:35:33.885397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
20020601 15
16.7%
20100621 7
 
7.8%
20080630 6
 
6.7%
20110609 5
 
5.6%
20061204 5
 
5.6%
20160610 4
 
4.4%
20091026 4
 
4.4%
20090715 4
 
4.4%
20121226 4
 
4.4%
20030401 3
 
3.3%
Other values (18) 33
36.7%
ValueCountFrequency (%)
20020601 15
16.7%
20030401 3
 
3.3%
20030601 1
 
1.1%
20031016 3
 
3.3%
20040420 1
 
1.1%
20051027 3
 
3.3%
20060612 3
 
3.3%
20061204 5
 
5.6%
20070615 2
 
2.2%
20071008 1
 
1.1%
ValueCountFrequency (%)
20160610 4
4.4%
20141120 1
 
1.1%
20141103 2
 
2.2%
20131111 1
 
1.1%
20130625 3
3.3%
20121226 4
4.4%
20120702 2
 
2.2%
20111201 1
 
1.1%
20111116 2
 
2.2%
20110609 5
5.6%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20083736
Minimum20040712
Maximum20160826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2024-05-11T06:35:34.534917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20040712
5-th percentile20040712
Q120040712
median20080968
Q320110728
95-th percentile20141125
Maximum20160826
Range120114
Interquartile range (IQR)70016

Descriptive statistics

Standard deviation36026.22
Coefficient of variation (CV)0.0017938007
Kurtosis-0.87796307
Mean20083736
Median Absolute Deviation (MAD)30238.5
Skewness0.32917768
Sum1.8075363 × 109
Variance1.2978885 × 109
MonotonicityDecreasing
2024-05-11T06:35:35.174255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
20040712 26
28.9%
20100713 7
 
7.8%
20080808 6
 
6.7%
20061204 5
 
5.6%
20110728 5
 
5.6%
20160826 4
 
4.4%
20090715 4
 
4.4%
20141125 4
 
4.4%
20091026 4
 
4.4%
20121226 4
 
4.4%
Other values (10) 21
23.3%
ValueCountFrequency (%)
20040712 26
28.9%
20060630 3
 
3.3%
20061204 5
 
5.6%
20070730 2
 
2.2%
20071204 1
 
1.1%
20080515 2
 
2.2%
20080808 6
 
6.7%
20081127 2
 
2.2%
20090715 4
 
4.4%
20091026 4
 
4.4%
ValueCountFrequency (%)
20160826 4
4.4%
20141125 4
4.4%
20131129 1
 
1.1%
20130717 3
3.3%
20121226 4
4.4%
20120718 2
 
2.2%
20111206 2
 
2.2%
20110728 5
5.6%
20101206 3
3.3%
20100713 7
7.8%

업소명
Text

Distinct88
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size852.0 B
2024-05-11T06:35:36.043263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length19
Mean length6.5555556
Min length2

Characters and Unicode

Total characters590
Distinct characters239
Distinct categories7 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)95.6%

Sample

1st row돈떼목장
2nd row평가옥(여의도점)
3rd row돌배기집(영등포역점)
4th row뉴타운갈비탕
5th row선유참치
ValueCountFrequency (%)
원할머니보쌈 2
 
2.0%
여의도점 2
 
2.0%
명륜진사갈비 2
 
2.0%
나주곰탕 2
 
2.0%
대운설렁탕 1
 
1.0%
갯벌낙지 1
 
1.0%
만나 1
 
1.0%
기소야서여의도점 1
 
1.0%
태산양꼬치 1
 
1.0%
순흥골 1
 
1.0%
Other values (88) 88
86.3%
2024-05-11T06:35:37.307024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 20
 
3.4%
( 14
 
2.4%
도 14
 
2.4%
) 14
 
2.4%
이 13
 
2.2%
여 13
 
2.2%
12
 
2.0%
의 11
 
1.9%
치 10
 
1.7%
스 8
 
1.4%
Other values (229) 461
78.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 509
86.3%
Uppercase Letter 36
 
6.1%
Open Punctuation 14
 
2.4%
Close Punctuation 14
 
2.4%
Space Separator 12
 
2.0%
Decimal Number 4
 
0.7%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 20
 
3.9%
도 14
 
2.8%
이 13
 
2.6%
여 13
 
2.6%
의 11
 
2.2%
치 10
 
2.0%
스 8
 
1.6%
가 8
 
1.6%
참 8
 
1.6%
원 7
 
1.4%
Other values (205) 397
78.0%
Uppercase Letter
ValueCountFrequency (%)
H 4
11.1%
N 4
11.1%
I 3
 
8.3%
A 3
 
8.3%
U 3
 
8.3%
K 3
 
8.3%
C 2
 
5.6%
E 2
 
5.6%
O 2
 
5.6%
G 2
 
5.6%
Other values (7) 8
22.2%
Decimal Number
ValueCountFrequency (%)
3 2
50.0%
4 1
25.0%
6 1
25.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 506
85.8%
Common 45
 
7.6%
Latin 36
 
6.1%
Han 3
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 20
 
4.0%
도 14
 
2.8%
이 13
 
2.6%
여 13
 
2.6%
의 11
 
2.2%
치 10
 
2.0%
스 8
 
1.6%
가 8
 
1.6%
참 8
 
1.6%
원 7
 
1.4%
Other values (202) 394
77.9%
Latin
ValueCountFrequency (%)
H 4
11.1%
N 4
11.1%
I 3
 
8.3%
A 3
 
8.3%
U 3
 
8.3%
K 3
 
8.3%
C 2
 
5.6%
E 2
 
5.6%
O 2
 
5.6%
G 2
 
5.6%
Other values (7) 8
22.2%
Common
ValueCountFrequency (%)
( 14
31.1%
) 14
31.1%
12
26.7%
3 2
 
4.4%
, 1
 
2.2%
4 1
 
2.2%
6 1
 
2.2%
Han
ValueCountFrequency (%)
惠 1
33.3%
比 1
33.3%
壽 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 506
85.8%
ASCII 81
 
13.7%
CJK 3
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 20
 
4.0%
도 14
 
2.8%
이 13
 
2.6%
여 13
 
2.6%
의 11
 
2.2%
치 10
 
2.0%
스 8
 
1.6%
가 8
 
1.6%
참 8
 
1.6%
원 7
 
1.4%
Other values (202) 394
77.9%
ASCII
ValueCountFrequency (%)
( 14
17.3%
) 14
17.3%
12
14.8%
H 4
 
4.9%
N 4
 
4.9%
I 3
 
3.7%
A 3
 
3.7%
U 3
 
3.7%
K 3
 
3.7%
C 2
 
2.5%
Other values (14) 19
23.5%
CJK
ValueCountFrequency (%)
惠 1
33.3%
比 1
33.3%
壽 1
33.3%

소재지도로명
Text

MISSING 

Distinct88
Distinct (%)100.0%
Missing2
Missing (%)2.2%
Memory size852.0 B
2024-05-11T06:35:38.222882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length45
Mean length36.625
Min length25

Characters and Unicode

Total characters3223
Distinct characters153
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)100.0%

Sample

1st row서울특별시 영등포구 영등포로 384-1, 1층 (신길동)
2nd row서울특별시 영등포구 여의대방로 379, (여의도동, 제일빌딩 지하1층 10,11,12호)
3rd row서울특별시 영등포구 영중로4길 6-1, (영등포동3가,1층)
4th row서울특별시 영등포구 도림로 282, 1층 (신길동)
5th row서울특별시 영등포구 영신로17길 3, (영등포동,외1필지 지하1층(전체))
ValueCountFrequency (%)
서울특별시 88
 
16.3%
영등포구 88
 
16.3%
여의도동 25
 
4.6%
지하1층 15
 
2.8%
1층 12
 
2.2%
신길동 7
 
1.3%
대림동 6
 
1.1%
11 6
 
1.1%
3 6
 
1.1%
여의나루로 5
 
0.9%
Other values (214) 281
52.1%
2024-05-11T06:35:39.516654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
451
 
14.0%
1 168
 
5.2%
, 160
 
5.0%
영 114
 
3.5%
등 104
 
3.2%
포 104
 
3.2%
동 96
 
3.0%
) 92
 
2.9%
( 92
 
2.9%
시 90
 
2.8%
Other values (143) 1752
54.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1860
57.7%
Decimal Number 524
 
16.3%
Space Separator 451
 
14.0%
Other Punctuation 162
 
5.0%
Close Punctuation 92
 
2.9%
Open Punctuation 92
 
2.9%
Uppercase Letter 19
 
0.6%
Dash Punctuation 11
 
0.3%
Math Symbol 6
 
0.2%
Lowercase Letter 6
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 114
 
6.1%
등 104
 
5.6%
포 104
 
5.6%
동 96
 
5.2%
시 90
 
4.8%
별 89
 
4.8%
구 89
 
4.8%
로 89
 
4.8%
서 89
 
4.8%
특 88
 
4.7%
Other values (112) 908
48.8%
Decimal Number
ValueCountFrequency (%)
1 168
32.1%
2 64
 
12.2%
3 58
 
11.1%
0 42
 
8.0%
4 41
 
7.8%
6 38
 
7.3%
8 34
 
6.5%
7 34
 
6.5%
5 30
 
5.7%
9 15
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
B 6
31.6%
C 5
26.3%
K 2
 
10.5%
M 2
 
10.5%
D 1
 
5.3%
A 1
 
5.3%
S 1
 
5.3%
V 1
 
5.3%
Lowercase Letter
ValueCountFrequency (%)
e 2
33.3%
c 1
16.7%
n 1
16.7%
t 1
16.7%
r 1
16.7%
Other Punctuation
ValueCountFrequency (%)
, 160
98.8%
？ 1
 
0.6%
/ 1
 
0.6%
Space Separator
ValueCountFrequency (%)
451
100.0%
Close Punctuation
ValueCountFrequency (%)
) 92
100.0%
Open Punctuation
ValueCountFrequency (%)
( 92
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1860
57.7%
Common 1338
41.5%
Latin 25
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 114
 
6.1%
등 104
 
5.6%
포 104
 
5.6%
동 96
 
5.2%
시 90
 
4.8%
별 89
 
4.8%
구 89
 
4.8%
로 89
 
4.8%
서 89
 
4.8%
특 88
 
4.7%
Other values (112) 908
48.8%
Common
ValueCountFrequency (%)
451
33.7%
1 168
 
12.6%
, 160
 
12.0%
) 92
 
6.9%
( 92
 
6.9%
2 64
 
4.8%
3 58
 
4.3%
0 42
 
3.1%
4 41
 
3.1%
6 38
 
2.8%
Other values (8) 132
 
9.9%
Latin
ValueCountFrequency (%)
B 6
24.0%
C 5
20.0%
e 2
 
8.0%
K 2
 
8.0%
M 2
 
8.0%
D 1
 
4.0%
A 1
 
4.0%
S 1
 
4.0%
V 1
 
4.0%
c 1
 
4.0%
Other values (3) 3
12.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1860
57.7%
ASCII 1362
42.3%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
451
33.1%
1 168
 
12.3%
, 160
 
11.7%
) 92
 
6.8%
( 92
 
6.8%
2 64
 
4.7%
3 58
 
4.3%
0 42
 
3.1%
4 41
 
3.0%
6 38
 
2.8%
Other values (20) 156
 
11.5%
Hangul
ValueCountFrequency (%)
영 114
 
6.1%
등 104
 
5.6%
포 104
 
5.6%
동 96
 
5.2%
시 90
 
4.8%
별 89
 
4.8%
구 89
 
4.8%
로 89
 
4.8%
서 89
 
4.8%
특 88
 
4.7%
Other values (112) 908
48.8%
None
ValueCountFrequency (%)
？ 1
100.0%

소재지지번
Text

Distinct88
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size852.0 B
2024-05-11T06:35:40.234147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length42
Mean length32.766667
Min length23

Characters and Unicode

Total characters2949
Distinct characters137
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)95.6%

Sample

1st row서울특별시 영등포구 신길동 65번지 40호 1층
2nd row서울특별시 영등포구 여의도동 44번지 35호 제일빌딩 지하1층 10,11,12호
3rd row서울특별시 영등포구 영등포동3가 10번지 27호 1층
4th row서울특별시 영등포구 신길동 342번지 134호
5th row서울특별시 영등포구 영등포동 618번지 55호 외1필지 지하1층(전체)
ValueCountFrequency (%)
서울특별시 90
 
16.7%
영등포구 90
 
16.7%
여의도동 44
 
8.2%
1층 14
 
2.6%
지하1층 11
 
2.0%
신길동 9
 
1.7%
2호 7
 
1.3%
대림동 6
 
1.1%
영등포동3가 6
 
1.1%
13번지 6
 
1.1%
Other values (181) 255
47.4%
2024-05-11T06:35:41.216860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
649
22.0%
1 142
 
4.8%
지 120
 
4.1%
영 106
 
3.6%
등 103
 
3.5%
포 103
 
3.5%
동 94
 
3.2%
서 91
 
3.1%
시 91
 
3.1%
구 91
 
3.1%
Other values (127) 1359
46.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1734
58.8%
Space Separator 649
 
22.0%
Decimal Number 511
 
17.3%
Other Punctuation 19
 
0.6%
Uppercase Letter 14
 
0.5%
Lowercase Letter 6
 
0.2%
Dash Punctuation 5
 
0.2%
Close Punctuation 4
 
0.1%
Open Punctuation 4
 
0.1%
Math Symbol 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 120
 
6.9%
영 106
 
6.1%
등 103
 
5.9%
포 103
 
5.9%
동 94
 
5.4%
서 91
 
5.2%
시 91
 
5.2%
구 91
 
5.2%
울 91
 
5.2%
번 90
 
5.2%
Other values (96) 754
43.5%
Decimal Number
ValueCountFrequency (%)
1 142
27.8%
2 78
15.3%
3 61
11.9%
4 56
 
11.0%
0 46
 
9.0%
6 35
 
6.8%
5 32
 
6.3%
7 29
 
5.7%
9 18
 
3.5%
8 14
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
C 4
28.6%
M 2
14.3%
K 2
14.3%
B 2
14.3%
A 1
 
7.1%
S 1
 
7.1%
V 1
 
7.1%
D 1
 
7.1%
Lowercase Letter
ValueCountFrequency (%)
e 2
33.3%
r 1
16.7%
t 1
16.7%
c 1
16.7%
n 1
16.7%
Other Punctuation
ValueCountFrequency (%)
, 17
89.5%
/ 1
 
5.3%
？ 1
 
5.3%
Space Separator
ValueCountFrequency (%)
649
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1734
58.8%
Common 1195
40.5%
Latin 20
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 120
 
6.9%
영 106
 
6.1%
등 103
 
5.9%
포 103
 
5.9%
동 94
 
5.4%
서 91
 
5.2%
시 91
 
5.2%
구 91
 
5.2%
울 91
 
5.2%
번 90
 
5.2%
Other values (96) 754
43.5%
Common
ValueCountFrequency (%)
649
54.3%
1 142
 
11.9%
2 78
 
6.5%
3 61
 
5.1%
4 56
 
4.7%
0 46
 
3.8%
6 35
 
2.9%
5 32
 
2.7%
7 29
 
2.4%
9 18
 
1.5%
Other values (8) 49
 
4.1%
Latin
ValueCountFrequency (%)
C 4
20.0%
M 2
10.0%
K 2
10.0%
e 2
10.0%
B 2
10.0%
r 1
 
5.0%
A 1
 
5.0%
t 1
 
5.0%
S 1
 
5.0%
V 1
 
5.0%
Other values (3) 3
15.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1734
58.8%
ASCII 1214
41.2%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
649
53.5%
1 142
 
11.7%
2 78
 
6.4%
3 61
 
5.0%
4 56
 
4.6%
0 46
 
3.8%
6 35
 
2.9%
5 32
 
2.6%
7 29
 
2.4%
9 18
 
1.5%
Other values (20) 68
 
5.6%
Hangul
ValueCountFrequency (%)
지 120
 
6.9%
영 106
 
6.1%
등 103
 
5.9%
포 103
 
5.9%
동 94
 
5.4%
서 91
 
5.2%
시 91
 
5.2%
구 91
 
5.2%
울 91
 
5.2%
번 90
 
5.2%
Other values (96) 754
43.5%
None
ValueCountFrequency (%)
？ 1
100.0%

허가(신고)번호
Text

UNIQUE 

Distinct90
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size852.0 B
2024-05-11T06:35:41.689529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters1980
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

Unique90 ?
Unique (%)100.0%

Sample

1st row3180000-101-2015-00336
2nd row3180000-101-2014-00317
3rd row3180000-101-1986-10338
4th row3180000-101-2000-13108
5th row3180000-101-2011-00425
ValueCountFrequency (%)
3180000-101-2015-00336 1
 
1.1%
3180000-101-1999-12086 1
 
1.1%
3180000-101-2003-00658 1
 
1.1%
3180000-101-2001-14552 1
 
1.1%
3180000-101-1988-07968 1
 
1.1%
3180000-101-1988-10400 1
 
1.1%
3180000-101-1996-03426 1
 
1.1%
3180000-101-2005-00309 1
 
1.1%
3180000-101-1997-08398 1
 
1.1%
3180000-101-1979-07960 1
 
1.1%
Other values (80) 80
88.9%
2024-05-11T06:35:42.669741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 721
36.4%
1 376
19.0%
- 270
 
13.6%
8 147
 
7.4%
3 128
 
6.5%
2 104
 
5.3%
9 77
 
3.9%
5 44
 
2.2%
4 43
 
2.2%
6 35
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1710
86.4%
Dash Punctuation 270
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 721
42.2%
1 376
22.0%
8 147
 
8.6%
3 128
 
7.5%
2 104
 
6.1%
9 77
 
4.5%
5 44
 
2.6%
4 43
 
2.5%
6 35
 
2.0%
7 35
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 270
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 721
36.4%
1 376
19.0%
- 270
 
13.6%
8 147
 
7.4%
3 128
 
6.5%
2 104
 
5.3%
9 77
 
3.9%
5 44
 
2.2%
4 43
 
2.2%
6 35
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 721
36.4%
1 376
19.0%
- 270
 
13.6%
8 147
 
7.4%
3 128
 
6.5%
2 104
 
5.3%
9 77
 
3.9%
5 44
 
2.2%
4 43
 
2.2%
6 35
 
1.8%

업태명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size852.0 B
한식
60 
일식
15 
중국식
7 
경양식
 
5
복어취급
 
2

Length

Max length4
Median length2
Mean length2.1888889
Min length2

Unique

Unique1 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
한식 60
66.7%
일식 15
 
16.7%
중국식 7
 
7.8%
경양식 5
 
5.6%
복어취급 2
 
2.2%
뷔페식 1
 
1.1%

Length

2024-05-11T06:35:43.075895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:35:43.562723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 60
66.7%
일식 15
 
16.7%
중국식 7
 
7.8%
경양식 5
 
5.6%
복어취급 2
 
2.2%
뷔페식 1
 
1.1%

주된음식
Text

Distinct64
Distinct (%)71.1%
Missing0
Missing (%)0.0%
Memory size852.0 B
2024-05-11T06:35:44.123462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length3.8111111
Min length2

Characters and Unicode

Total characters343
Distinct characters110
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

Unique49 ?
Unique (%)54.4%

Sample

1st row돼지갈비
2nd row어복쟁반
3rd row차돌박이
4th row삼계탕
5th row한정식
ValueCountFrequency (%)
한정식 6
 
6.5%
생선회 4
 
4.3%
자장면 4
 
4.3%
돼지갈비 3
 
3.3%
갈비 3
 
3.3%
참치회 3
 
3.3%
보쌈,족발 2
 
2.2%
샤브샤브 2
 
2.2%
꼬리곰탕 2
 
2.2%
순대국 2
 
2.2%
Other values (55) 61
66.3%
2024-05-11T06:35:45.572170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
식 14
 
4.1%
, 14
 
4.1%
탕 13
 
3.8%
회 12
 
3.5%
심 12
 
3.5%
비 11
 
3.2%
갈 11
 
3.2%
한 10
 
2.9%
정 9
 
2.6%
지 9
 
2.6%
Other values (100) 228
66.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 327
95.3%
Other Punctuation 14
 
4.1%
Space Separator 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
식 14
 
4.3%
탕 13
 
4.0%
회 12
 
3.7%
심 12
 
3.7%
비 11
 
3.4%
갈 11
 
3.4%
한 10
 
3.1%
정 9
 
2.8%
지 9
 
2.8%
등 9
 
2.8%
Other values (98) 217
66.4%
Other Punctuation
ValueCountFrequency (%)
, 14
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 327
95.3%
Common 16
 
4.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
식 14
 
4.3%
탕 13
 
4.0%
회 12
 
3.7%
심 12
 
3.7%
비 11
 
3.4%
갈 11
 
3.4%
한 10
 
3.1%
정 9
 
2.8%
지 9
 
2.8%
등 9
 
2.8%
Other values (98) 217
66.4%
Common
ValueCountFrequency (%)
, 14
87.5%
2
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 327
95.3%
ASCII 16
 
4.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
식 14
 
4.3%
탕 13
 
4.0%
회 12
 
3.7%
심 12
 
3.7%
비 11
 
3.4%
갈 11
 
3.4%
한 10
 
3.1%
정 9
 
2.8%
지 9
 
2.8%
등 9
 
2.8%
Other values (98) 217
66.4%
ASCII
ValueCountFrequency (%)
, 14
87.5%
2
 
12.5%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct90
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean282.25011
Minimum66.15
Maximum1840.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2024-05-11T06:35:46.528684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum66.15
5-th percentile78.3305
Q1114.025
median191.665
Q3332.825
95-th percentile674.7705
Maximum1840.8
Range1774.65
Interquartile range (IQR)218.8

Descriptive statistics

Standard deviation276.65242
Coefficient of variation (CV)0.98016762
Kurtosis13.977615
Mean282.25011
Median Absolute Deviation (MAD)89.23
Skewness3.2840387
Sum25402.51
Variance76536.561
MonotonicityNot monotonic
2024-05-11T06:35:47.301055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92.0 1
 
1.1%
110.44 1
 
1.1%
168.07 1
 
1.1%
214.0 1
 
1.1%
112.7 1
 
1.1%
98.65 1
 
1.1%
534.87 1
 
1.1%
89.25 1
 
1.1%
94.65 1
 
1.1%
78.71 1
 
1.1%
Other values (80) 80
88.9%
ValueCountFrequency (%)
66.15 1
1.1%
71.44 1
1.1%
74.35 1
1.1%
75.97 1
1.1%
78.02 1
1.1%
78.71 1
1.1%
85.0 1
1.1%
86.94 1
1.1%
89.25 1
1.1%
91.83 1
1.1%
ValueCountFrequency (%)
1840.8 1
1.1%
1461.0 1
1.1%
974.67 1
1.1%
896.66 1
1.1%
704.16 1
1.1%
638.85 1
1.1%
590.0 1
1.1%
561.15 1
1.1%
546.78 1
1.1%
542.2 1
1.1%

행정동명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Memory size852.0 B
여의동
44 
영등포동
11 
문래동
5 
당산제1동
5 
양평제2동
 
4
Other values (12)
21 

Length

Max length6
Median length3
Mean length3.8
Min length3

Unique

Unique5 ?
Unique (%)5.6%

Sample

1st row신길제1동
2nd row여의동
3rd row영등포동
4th row신길제5동
5th row영등포제2동

Common Values

ValueCountFrequency (%)
여의동 44
48.9%
영등포동 11
 
12.2%
문래동 5
 
5.6%
당산제1동 5
 
5.6%
양평제2동 4
 
4.4%
대림제1동 3
 
3.3%
당산제2동 3
 
3.3%
대림제3동 2
 
2.2%
신길제7동 2
 
2.2%
신길제6동 2
 
2.2%
Other values (7) 9
 
10.0%

Length

2024-05-11T06:35:47.954965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
여의동 44
48.9%
영등포동 11
 
12.2%
문래동 5
 
5.6%
당산제1동 5
 
5.6%
양평제2동 4
 
4.4%
대림제1동 3
 
3.3%
당산제2동 3
 
3.3%
신길제1동 2
 
2.2%
영등포본동 2
 
2.2%
신길제7동 2
 
2.2%
Other values (7) 9
 
10.0%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size852.0 B
상수도전용
50 
<NA>
40 

Length

Max length5
Median length5
Mean length4.5555556
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 50
55.6%
<NA> 40
44.4%

Length

2024-05-11T06:35:48.452116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:35:48.815547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 50
55.6%
na 40
44.4%

소재지전화번호
Text

MISSING 

Distinct88
Distinct (%)100.0%
Missing2
Missing (%)2.2%
Memory size852.0 B
2024-05-11T06:35:49.686499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.090909
Min length9

Characters and Unicode

Total characters888
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

Unique88 ?
Unique (%)100.0%

Sample

1st row0226766070
2nd row02 8314111
3rd row02 847 5888
4th row0207848877
5th row0226349288
ValueCountFrequency (%)
02 53
36.8%
0226766070 1
 
0.7%
7816193 1
 
0.7%
028444888 1
 
0.7%
7831324 1
 
0.7%
8333011 1
 
0.7%
7805393 1
 
0.7%
0226783463 1
 
0.7%
7825638 1
 
0.7%
7615454 1
 
0.7%
Other values (82) 82
56.9%
2024-05-11T06:35:51.641549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 157
17.7%
0 147
16.6%
7 97
10.9%
8 87
9.8%
6 82
9.2%
3 81
9.1%
60
 
6.8%
4 53
 
6.0%
5 47
 
5.3%
1 39
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 828
93.2%
Space Separator 60
 
6.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 157
19.0%
0 147
17.8%
7 97
11.7%
8 87
10.5%
6 82
9.9%
3 81
9.8%
4 53
 
6.4%
5 47
 
5.7%
1 39
 
4.7%
9 38
 
4.6%
Space Separator
ValueCountFrequency (%)
60
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 888
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 157
17.7%
0 147
16.6%
7 97
10.9%
8 87
9.8%
6 82
9.2%
3 81
9.1%
60
 
6.8%
4 53
 
6.0%
5 47
 
5.3%
1 39
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 157
17.7%
0 147
16.6%
7 97
10.9%
8 87
9.8%
6 82
9.2%
3 81
9.1%
60
 
6.8%
4 53
 
6.0%
5 47
 
5.3%
1 39
 
4.4%

Interactions

2024-05-11T06:35:26.965664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:19.468433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:21.582734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:23.346960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:25.046014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:27.351141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:19.927597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:22.003856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:23.717574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:25.476397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:27.668583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:20.342450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:22.311293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:24.050497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:25.788090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:27.954428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:20.753520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:22.657200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:24.406025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:26.065362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:28.289307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:21.231257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:22.989004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:24.683581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:26.651672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T06:35:52.107175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명소재지전화번호
지정년도1.0000.5310.9891.0001.0001.0000.9471.0000.1580.8300.0000.3741.000
지정번호0.5311.0000.6200.5321.0001.0000.9911.0000.1830.8920.3080.3931.000
신청일자0.9890.6201.0000.9791.0001.0000.9651.0000.0000.5680.0000.6421.000
지정일자1.0000.5320.9791.0001.0001.0000.9471.0000.2140.7870.0000.3551.000
업소명1.0001.0001.0001.0001.0001.0000.9961.0001.0001.0001.0000.0001.000
소재지도로명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지지번0.9470.9910.9650.9470.9961.0001.0001.0000.0000.9770.0001.0001.000
허가(신고)번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
업태명0.1580.1830.0000.2141.0001.0000.0001.0001.0001.0000.6820.0001.000
주된음식0.8300.8920.5680.7871.0001.0000.9771.0001.0001.0000.9740.0001.000
영업장면적(㎡)0.0000.3080.0000.0001.0001.0000.0001.0000.6820.9741.0000.0001.000
행정동명0.3740.3930.6420.3550.0001.0001.0001.0000.0000.0000.0001.0001.000
소재지전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-05-11T06:35:52.679564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태명급수시설구분행정동명
업태명1.0001.0000.000
급수시설구분1.0001.0001.000
행정동명0.0001.0001.000
2024-05-11T06:35:53.142911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.000-0.7200.9870.9980.0600.0000.1231.000
지정번호-0.7201.000-0.730-0.6970.0280.0880.1491.000
신청일자0.987-0.7301.0000.9890.0250.0000.2481.000
지정일자0.998-0.6970.9891.0000.0610.0000.1061.000
영업장면적(㎡)0.0600.0280.0250.0611.0000.4600.0001.000
업태명0.0000.0880.0000.0000.4601.0000.0001.000
행정동명0.1230.1490.2480.1060.0000.0001.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-11T06:35:28.847976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T06:35:29.583028image/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.
2024-05-11T06:35:30.045221image/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
03180000201632016061020160826돈떼목장서울특별시 영등포구 영등포로 384-1, 1층 (신길동)서울특별시 영등포구 신길동 65번지 40호 1층3180000-101-2015-00336한식돼지갈비92.0신길제1동<NA><NA>
13180000201612016061020160826평가옥(여의도점)서울특별시 영등포구 여의대방로 379, (여의도동, 제일빌딩 지하1층 10,11,12호)서울특별시 영등포구 여의도동 44번지 35호 제일빌딩 지하1층 10,11,12호3180000-101-2014-00317한식어복쟁반401.95여의동<NA><NA>
23180000201622016061020160826돌배기집(영등포역점)서울특별시 영등포구 영중로4길 6-1, (영등포동3가,1층)서울특별시 영등포구 영등포동3가 10번지 27호 1층3180000-101-1986-10338한식차돌박이164.3영등포동상수도전용0226766070
33180000201642016061020160826뉴타운갈비탕서울특별시 영등포구 도림로 282, 1층 (신길동)서울특별시 영등포구 신길동 342번지 134호3180000-101-2000-13108한식삼계탕66.15신길제5동상수도전용02 8314111
43180000201412014110320141125선유참치서울특별시 영등포구 영신로17길 3, (영등포동,외1필지 지하1층(전체))서울특별시 영등포구 영등포동 618번지 55호 외1필지 지하1층(전체)3180000-101-2011-00425한식한정식322.75영등포제2동상수도전용02 847 5888
53180000201422014110320141125온화정서울특별시 영등포구 국제금융로6길 30, 백상빌딩 1층 117, 118, 119, 120호 (여의도동)서울특별시 영등포구 여의도동 35번지 2호 백상빌딩3180000-101-1998-06410일식생선회104.79여의동상수도전용0207848877
63180000201432014112020141125값진식육서울특별시 영등포구 선유로 58-4, 1층 (문래동3가)서울특별시 영등포구 문래동3가 77번지 43호 1층3180000-101-2012-00480한식식육160.9문래동상수도전용0226349288
73180000201482011120120141125(유)아웃백스테이크하우스코리아 여의도점서울특별시 영등포구 국제금융로8길 6, (여의도동,신영증권 빌딩 지하1층)서울특별시 영등포구 여의도동 34번지 12호 신영증권 빌딩 지하1층3180000-101-2003-00684경양식스테이크561.15여의동<NA>02 20548365
83180000201372013111120131129이태원천상여의도점서울특별시 영등포구 국회대로68길 17, (여의도동)서울특별시 영등포구 여의도동 14번지 34호3180000-101-2000-13860일식회,스시163.0여의동상수도전용02 7853171
93180000201352013062520130717나주곰탕서울특별시 영등포구 영등포로 216-1, 1층 (영등포동3가)서울특별시 영등포구 영등포동3가 6번지 11호 1층3180000-101-2012-00458한식곰탕231.0영등포동<NA>0226787898
시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
80318000020042452002060120040712이강순실비집서울특별시 영등포구 영등포로46길 8, (영등포동3가,1~2층)서울특별시 영등포구 영등포동3가 17번지 4호 1~2층3180000-101-1975-06400한식낚지볶음193.45영등포동상수도전용0226781969
81318000020042072002060120040712슈치쿠(SHUCHIKU)서울특별시 영등포구 63로 50, (여의도동,63빌딩 지상58층)서울특별시 영등포구 여의도동 60번지 63빌딩 지상58층3180000-101-1985-06422일식우동590.0여의동상수도전용02 7895753
82318000020042112002060120040712워킹온더클라우드(WALKINGONTHECLOUD)서울특별시 영등포구 63로 50, 63한화생명빌딩 59층 (여의도동)서울특별시 영등포구 여의도동 60번지 63한화생명빌딩3180000-101-1985-07592경양식스파게티974.67여의동상수도전용02 7895941
83318000020042822003101620040712함흥냉면서울특별시 영등포구 영등포로42길 6, (영등포동3가,1층)서울특별시 영등포구 영등포동3가 7번지 32호 1층3180000-101-1972-10406한식냉면99.8영등포동상수도전용0226782722
84318000020041372002060120040712동해도본점서울특별시 영등포구 은행로 25, 안원빌딩 (여의도동)서울특별시 영등포구 여의도동 14번지 15호 안원빌딩 지하3180000-101-2001-07889일식알탕302.22여의동상수도전용02 7616300
85318000020041792002060120040712홍보석서울특별시 영등포구 의사당대로 108, (여의도동)서울특별시 영등포구 여의도동 37번지3180000-101-1980-08107중국식잡탕밥542.2여의동상수도전용02 7836622
86318000020041702002060120040712여일정서울특별시 영등포구 국제금융로6길 7, (여의도동,한양빌딩 지하101호)서울특별시 영등포구 여의도동 34번지 11호 한양빌딩 지하101호3180000-101-1987-11208한식설렁탕154.85여의동상수도전용02 7806273
87318000020041382003040120040712이도식당서울특별시 영등포구 국회대로68길 11, (여의도동, 지하1층)서울특별시 영등포구 여의도동 14번지 24호 지하1층3180000-101-1993-11823한식샤브샤브237.7여의동상수도전용02 7864166
88318000020041012002060120040712대운설렁탕서울특별시 영등포구 여의대방로23길 3, (신길동)서울특별시 영등포구 신길동 4246번지3180000-101-2002-00453한식설렁탕181.0신길제6동<NA>02 8493077
89318000020042752003101620040712복먹고복받고서울특별시 영등포구 양산로19길 8, (당산동3가)서울특별시 영등포구 당산동3가 370번지 0호3180000-101-1996-09051복어취급복지리359.28당산제1동상수도전용0226752221