Overview

Dataset statistics

Number of variables14
Number of observations151
Missing cells77
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.5 KiB
Average record size in memory118.9 B

Variable types

Categorical4
Numeric5
Text5

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,주된음식,영업장면적(㎡),행정동명,급수시설구분
Author서대문구
URLhttps://data.seoul.go.kr/dataList/OA-2303/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
업태명 is highly imbalanced (50.6%)Imbalance
주된음식 has 76 (50.3%) missing valuesMissing

Reproduction

Analysis started2024-05-11 04:55:34.400169
Analysis finished2024-05-11 04:55:43.716595
Duration9.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
3120000
151 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3120000
2nd row3120000
3rd row3120000
4th row3120000
5th row3120000

Common Values

ValueCountFrequency (%)
3120000 151
100.0%

Length

2024-05-11T04:55:43.983181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:55:44.319248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3120000 151
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.3775
Minimum1987
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-05-11T04:55:44.487239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1987
5-th percentile2000
Q12000
median2006
Q32011
95-th percentile2022
Maximum2023
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.0785656
Coefficient of variation (CV)0.0040244377
Kurtosis-0.44455538
Mean2007.3775
Median Absolute Deviation (MAD)6
Skewness0.54092934
Sum303114
Variance65.263223
MonotonicityDecreasing
2024-05-11T04:55:44.823367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2000 49
32.5%
2011 19
 
12.6%
2004 18
 
11.9%
2022 17
 
11.3%
2014 10
 
6.6%
2008 7
 
4.6%
2023 6
 
4.0%
2006 5
 
3.3%
2010 4
 
2.6%
2009 4
 
2.6%
Other values (5) 12
 
7.9%
ValueCountFrequency (%)
1987 1
 
0.7%
1993 3
 
2.0%
2000 49
32.5%
2004 18
 
11.9%
2005 4
 
2.6%
2006 5
 
3.3%
2007 3
 
2.0%
2008 7
 
4.6%
2009 4
 
2.6%
2010 4
 
2.6%
ValueCountFrequency (%)
2023 6
 
4.0%
2022 17
11.3%
2014 10
6.6%
2012 1
 
0.7%
2011 19
12.6%
2010 4
 
2.6%
2009 4
 
2.6%
2008 7
 
4.6%
2007 3
 
2.0%
2006 5
 
3.3%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)58.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.278146
Minimum0
Maximum187
Zeros1
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-05-11T04:55:45.261363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median74
Q3154.5
95-th percentile179.5
Maximum187
Range187
Interquartile range (IQR)144.5

Descriptive statistics

Standard deviation70.378231
Coefficient of variation (CV)0.83507095
Kurtosis-1.769521
Mean84.278146
Median Absolute Deviation (MAD)70
Skewness0.077485183
Sum12726
Variance4953.0955
MonotonicityNot monotonic
2024-05-11T04:55:45.906201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 7
 
4.6%
2 5
 
3.3%
6 5
 
3.3%
3 4
 
2.6%
1 4
 
2.6%
36 4
 
2.6%
12 4
 
2.6%
5 3
 
2.0%
11 3
 
2.0%
33 3
 
2.0%
Other values (79) 109
72.2%
ValueCountFrequency (%)
0 1
 
0.7%
1 4
2.6%
2 5
3.3%
3 4
2.6%
4 7
4.6%
5 3
2.0%
6 5
3.3%
7 2
 
1.3%
8 3
2.0%
9 3
2.0%
ValueCountFrequency (%)
187 1
0.7%
186 1
0.7%
185 1
0.7%
184 1
0.7%
183 1
0.7%
182 1
0.7%
181 1
0.7%
180 1
0.7%
179 1
0.7%
178 1
0.7%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20080655
Minimum19870513
Maximum20231109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-05-11T04:55:46.341154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19870513
5-th percentile20000101
Q120000101
median20090312
Q320111020
95-th percentile20221108
Maximum20231109
Range360596
Interquartile range (IQR)110919.5

Descriptive statistics

Standard deviation78844.009
Coefficient of variation (CV)0.0039263664
Kurtosis-0.29483901
Mean20080655
Median Absolute Deviation (MAD)50698
Skewness0.32228588
Sum3.0321789 × 109
Variance6.2163778 × 109
MonotonicityNot monotonic
2024-05-11T04:55:46.661397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
20000101 48
31.8%
20221108 16
 
10.6%
20090312 15
 
9.9%
20100701 13
 
8.6%
20141010 10
 
6.6%
20041018 9
 
6.0%
20231109 6
 
4.0%
20111010 4
 
2.6%
20111020 4
 
2.6%
20110101 3
 
2.0%
Other values (16) 23
15.2%
ValueCountFrequency (%)
19870513 2
 
1.3%
20000101 48
31.8%
20041018 9
 
6.0%
20041020 1
 
0.7%
20050718 1
 
0.7%
20050719 1
 
0.7%
20060703 2
 
1.3%
20070619 1
 
0.7%
20080224 1
 
0.7%
20080926 3
 
2.0%
ValueCountFrequency (%)
20231109 6
 
4.0%
20221108 16
10.6%
20141010 10
6.6%
20121030 3
 
2.0%
20111024 1
 
0.7%
20111022 1
 
0.7%
20111021 1
 
0.7%
20111020 4
 
2.6%
20111013 1
 
0.7%
20111010 4
 
2.6%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20074458
Minimum19870513
Maximum20231109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-05-11T04:55:46.891797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19870513
5-th percentile20000110
Q120000110
median20060706
Q320111201
95-th percentile20221108
Maximum20231109
Range360596
Interquartile range (IQR)111091

Descriptive statistics

Standard deviation81120.631
Coefficient of variation (CV)0.0040409875
Kurtosis-0.45547254
Mean20074458
Median Absolute Deviation (MAD)60596
Skewness0.53691569
Sum3.0312431 × 109
Variance6.5805568 × 109
MonotonicityNot monotonic
2024-05-11T04:55:47.118639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
20000110 45
29.8%
20041020 18
 
11.9%
20221108 17
 
11.3%
20111201 16
 
10.6%
20141105 10
 
6.6%
20080926 7
 
4.6%
20231109 6
 
4.0%
20060706 5
 
3.3%
20090710 4
 
2.6%
20100726 4
 
2.6%
Other values (9) 19
12.6%
ValueCountFrequency (%)
19870513 1
 
0.7%
19930308 3
 
2.0%
20000101 2
 
1.3%
20000110 45
29.8%
20000224 2
 
1.3%
20041020 18
 
11.9%
20050718 2
 
1.3%
20050719 2
 
1.3%
20060706 5
 
3.3%
20070625 3
 
2.0%
ValueCountFrequency (%)
20231109 6
 
4.0%
20221108 17
11.3%
20141105 10
6.6%
20121101 1
 
0.7%
20111201 16
10.6%
20110110 3
 
2.0%
20100726 4
 
2.6%
20090710 4
 
2.6%
20080926 7
4.6%
20070625 3
 
2.0%

업소명
Text

Distinct124
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2024-05-11T04:55:47.600091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length13
Mean length5.6423841
Min length2

Characters and Unicode

Total characters852
Distinct characters245
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106 ?
Unique (%)70.2%

Sample

1st row신촌영양센타
2nd row식껍(서대문역점)
3rd row뚜띠쿠치나
4th row한옥집
5th row삼진
ValueCountFrequency (%)
주식회사 4
 
2.1%
목란 4
 
2.1%
옛날집 3
 
1.6%
고향촌 3
 
1.6%
장군보쌈 3
 
1.6%
사조미가 3
 
1.6%
풍년갈비 3
 
1.6%
난향 3
 
1.6%
본전회관 3
 
1.6%
지호한방삼계탕 2
 
1.1%
Other values (144) 158
83.6%
2024-05-11T04:55:48.584263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
38
 
4.5%
점 17
 
2.0%
탕 16
 
1.9%
집 16
 
1.9%
비 13
 
1.5%
갈 13
 
1.5%
가 13
 
1.5%
대 13
 
1.5%
고 13
 
1.5%
촌 12
 
1.4%
Other values (235) 688
80.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 788
92.5%
Space Separator 38
 
4.5%
Lowercase Letter 11
 
1.3%
Close Punctuation 5
 
0.6%
Open Punctuation 5
 
0.6%
Uppercase Letter 2
 
0.2%
Decimal Number 2
 
0.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 17
 
2.2%
탕 16
 
2.0%
집 16
 
2.0%
비 13
 
1.6%
갈 13
 
1.6%
가 13
 
1.6%
대 13
 
1.6%
고 13
 
1.6%
촌 12
 
1.5%
식 12
 
1.5%
Other values (219) 650
82.5%
Lowercase Letter
ValueCountFrequency (%)
a 3
27.3%
o 2
18.2%
l 1
 
9.1%
i 1
 
9.1%
r 1
 
9.1%
m 1
 
9.1%
e 1
 
9.1%
d 1
 
9.1%
Uppercase Letter
ValueCountFrequency (%)
N 1
50.0%
P 1
50.0%
Decimal Number
ValueCountFrequency (%)
0 1
50.0%
6 1
50.0%
Space Separator
ValueCountFrequency (%)
38
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 788
92.5%
Common 51
 
6.0%
Latin 13
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 17
 
2.2%
탕 16
 
2.0%
집 16
 
2.0%
비 13
 
1.6%
갈 13
 
1.6%
가 13
 
1.6%
대 13
 
1.6%
고 13
 
1.6%
촌 12
 
1.5%
식 12
 
1.5%
Other values (219) 650
82.5%
Latin
ValueCountFrequency (%)
a 3
23.1%
o 2
15.4%
N 1
 
7.7%
P 1
 
7.7%
l 1
 
7.7%
i 1
 
7.7%
r 1
 
7.7%
m 1
 
7.7%
e 1
 
7.7%
d 1
 
7.7%
Common
ValueCountFrequency (%)
38
74.5%
) 5
 
9.8%
( 5
 
9.8%
0 1
 
2.0%
6 1
 
2.0%
, 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 788
92.5%
ASCII 64
 
7.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
38
59.4%
) 5
 
7.8%
( 5
 
7.8%
a 3
 
4.7%
o 2
 
3.1%
N 1
 
1.6%
0 1
 
1.6%
6 1
 
1.6%
P 1
 
1.6%
l 1
 
1.6%
Other values (6) 6
 
9.4%
Hangul
ValueCountFrequency (%)
점 17
 
2.2%
탕 16
 
2.0%
집 16
 
2.0%
비 13
 
1.6%
갈 13
 
1.6%
가 13
 
1.6%
대 13
 
1.6%
고 13
 
1.6%
촌 12
 
1.5%
식 12
 
1.5%
Other values (219) 650
82.5%

소재지도로명
Text

Distinct123
Distinct (%)82.0%
Missing1
Missing (%)0.7%
Memory size1.3 KiB
2024-05-11T04:55:49.079274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length41
Mean length30.913333
Min length23

Characters and Unicode

Total characters4637
Distinct characters120
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

Unique105 ?
Unique (%)70.0%

Sample

1st row서울특별시 서대문구 연세로4길 34, (창천동,(지상1층))
2nd row서울특별시 서대문구 통일로9안길 26, 1층 (충정로2가)
3rd row서울특별시 서대문구 충정로 70, 1층 103호 (미근동)
4th row서울특별시 서대문구 통일로9길 12, 1층 (냉천동)
5th row서울특별시 서대문구 모래내로 279, 1층 (홍은동)
ValueCountFrequency (%)
서울특별시 150
 
18.0%
서대문구 150
 
18.0%
1층 29
 
3.5%
연희동 28
 
3.4%
통일로 16
 
1.9%
충정로2가 15
 
1.8%
북가좌동 12
 
1.4%
연희로 11
 
1.3%
응암로 10
 
1.2%
홍은동 9
 
1.1%
Other values (214) 402
48.3%
2024-05-11T04:55:50.087207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
682
 
14.7%
서 306
 
6.6%
, 226
 
4.9%
1 193
 
4.2%
) 173
 
3.7%
( 173
 
3.7%
대 170
 
3.7%
로 158
 
3.4%
문 157
 
3.4%
시 152
 
3.3%
Other values (110) 2247
48.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2675
57.7%
Space Separator 682
 
14.7%
Decimal Number 646
 
13.9%
Other Punctuation 226
 
4.9%
Close Punctuation 173
 
3.7%
Open Punctuation 173
 
3.7%
Dash Punctuation 46
 
1.0%
Uppercase Letter 9
 
0.2%
Math Symbol 7
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 306
 
11.4%
대 170
 
6.4%
로 158
 
5.9%
문 157
 
5.9%
시 152
 
5.7%
울 152
 
5.7%
구 151
 
5.6%
특 150
 
5.6%
별 150
 
5.6%
동 134
 
5.0%
Other values (88) 995
37.2%
Decimal Number
ValueCountFrequency (%)
1 193
29.9%
2 126
19.5%
3 63
 
9.8%
5 46
 
7.1%
7 46
 
7.1%
9 37
 
5.7%
4 35
 
5.4%
0 34
 
5.3%
6 33
 
5.1%
8 33
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
M 2
22.2%
D 2
22.2%
C 2
22.2%
A 1
11.1%
P 1
11.1%
T 1
11.1%
Space Separator
ValueCountFrequency (%)
682
100.0%
Other Punctuation
ValueCountFrequency (%)
, 226
100.0%
Close Punctuation
ValueCountFrequency (%)
) 173
100.0%
Open Punctuation
ValueCountFrequency (%)
( 173
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2675
57.7%
Common 1953
42.1%
Latin 9
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 306
 
11.4%
대 170
 
6.4%
로 158
 
5.9%
문 157
 
5.9%
시 152
 
5.7%
울 152
 
5.7%
구 151
 
5.6%
특 150
 
5.6%
별 150
 
5.6%
동 134
 
5.0%
Other values (88) 995
37.2%
Common
ValueCountFrequency (%)
682
34.9%
, 226
 
11.6%
1 193
 
9.9%
) 173
 
8.9%
( 173
 
8.9%
2 126
 
6.5%
3 63
 
3.2%
5 46
 
2.4%
- 46
 
2.4%
7 46
 
2.4%
Other values (6) 179
 
9.2%
Latin
ValueCountFrequency (%)
M 2
22.2%
D 2
22.2%
C 2
22.2%
A 1
11.1%
P 1
11.1%
T 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2675
57.7%
ASCII 1962
42.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
682
34.8%
, 226
 
11.5%
1 193
 
9.8%
) 173
 
8.8%
( 173
 
8.8%
2 126
 
6.4%
3 63
 
3.2%
5 46
 
2.3%
- 46
 
2.3%
7 46
 
2.3%
Other values (12) 188
 
9.6%
Hangul
ValueCountFrequency (%)
서 306
 
11.4%
대 170
 
6.4%
로 158
 
5.9%
문 157
 
5.9%
시 152
 
5.7%
울 152
 
5.7%
구 151
 
5.6%
특 150
 
5.6%
별 150
 
5.6%
동 134
 
5.0%
Other values (88) 995
37.2%

소재지지번
Text

Distinct124
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2024-05-11T04:55:50.544686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length43
Mean length30.364238
Min length24

Characters and Unicode

Total characters4585
Distinct characters98
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

Unique106 ?
Unique (%)70.2%

Sample

1st row서울특별시 서대문구 창천동 13번지 37호 (지상1층)
2nd row서울특별시 서대문구 충정로2가 16번지 13호 1층
3rd row서울특별시 서대문구 미근동 332번지 1층 103호 웨스트게이트타워
4th row서울특별시 서대문구 냉천동 20번지 1호 1층
5th row서울특별시 서대문구 대신동 85번지 1호 ,2,4,5,6,8
ValueCountFrequency (%)
서울특별시 151
 
17.6%
서대문구 151
 
17.6%
연희동 33
 
3.9%
1층 20
 
2.3%
충정로2가 16
 
1.9%
창천동 16
 
1.9%
북가좌동 15
 
1.8%
홍은동 15
 
1.8%
1호 14
 
1.6%
홍제동 13
 
1.5%
Other values (198) 413
48.2%
2024-05-11T04:55:51.448056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1067
23.3%
서 303
 
6.6%
1 235
 
5.1%
지 196
 
4.3%
대 164
 
3.6%
번 152
 
3.3%
시 152
 
3.3%
문 152
 
3.3%
구 152
 
3.3%
울 151
 
3.3%
Other values (88) 1861
40.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2572
56.1%
Space Separator 1067
23.3%
Decimal Number 829
 
18.1%
Other Punctuation 34
 
0.7%
Close Punctuation 27
 
0.6%
Open Punctuation 27
 
0.6%
Dash Punctuation 14
 
0.3%
Uppercase Letter 9
 
0.2%
Math Symbol 6
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 303
 
11.8%
지 196
 
7.6%
대 164
 
6.4%
번 152
 
5.9%
시 152
 
5.9%
문 152
 
5.9%
구 152
 
5.9%
울 151
 
5.9%
별 151
 
5.9%
특 151
 
5.9%
Other values (66) 848
33.0%
Decimal Number
ValueCountFrequency (%)
1 235
28.3%
2 145
17.5%
3 106
12.8%
0 64
 
7.7%
4 56
 
6.8%
5 49
 
5.9%
6 47
 
5.7%
9 46
 
5.5%
7 44
 
5.3%
8 37
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
D 2
22.2%
M 2
22.2%
C 2
22.2%
T 1
11.1%
P 1
11.1%
A 1
11.1%
Space Separator
ValueCountFrequency (%)
1067
100.0%
Other Punctuation
ValueCountFrequency (%)
, 34
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2572
56.1%
Common 2004
43.7%
Latin 9
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 303
 
11.8%
지 196
 
7.6%
대 164
 
6.4%
번 152
 
5.9%
시 152
 
5.9%
문 152
 
5.9%
구 152
 
5.9%
울 151
 
5.9%
별 151
 
5.9%
특 151
 
5.9%
Other values (66) 848
33.0%
Common
ValueCountFrequency (%)
1067
53.2%
1 235
 
11.7%
2 145
 
7.2%
3 106
 
5.3%
0 64
 
3.2%
4 56
 
2.8%
5 49
 
2.4%
6 47
 
2.3%
9 46
 
2.3%
7 44
 
2.2%
Other values (6) 145
 
7.2%
Latin
ValueCountFrequency (%)
D 2
22.2%
M 2
22.2%
C 2
22.2%
T 1
11.1%
P 1
11.1%
A 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2572
56.1%
ASCII 2013
43.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1067
53.0%
1 235
 
11.7%
2 145
 
7.2%
3 106
 
5.3%
0 64
 
3.2%
4 56
 
2.8%
5 49
 
2.4%
6 47
 
2.3%
9 46
 
2.3%
7 44
 
2.2%
Other values (12) 154
 
7.7%
Hangul
ValueCountFrequency (%)
서 303
 
11.8%
지 196
 
7.6%
대 164
 
6.4%
번 152
 
5.9%
시 152
 
5.9%
문 152
 
5.9%
구 152
 
5.9%
울 151
 
5.9%
별 151
 
5.9%
특 151
 
5.9%
Other values (66) 848
33.0%

허가(신고)번호
Text

Distinct124
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2024-05-11T04:55:51.935313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters3322
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

Unique106 ?
Unique (%)70.2%

Sample

1st row3120000-101-1997-07601
2nd row3120000-101-2000-04932
3rd row3120000-101-2014-00083
4th row3120000-101-1996-00535
5th row3120000-101-1995-07469
ValueCountFrequency (%)
3120000-101-2004-00386 4
 
2.6%
3120000-101-1996-05859 3
 
2.0%
3120000-101-1981-10346 3
 
2.0%
3120000-101-1999-09953 3
 
2.0%
3120000-101-1987-00011 3
 
2.0%
3120000-101-1993-09664 3
 
2.0%
3120000-101-1988-00008 3
 
2.0%
3120000-101-1996-09605 3
 
2.0%
3120000-101-2007-00253 2
 
1.3%
3120000-101-1996-00554 2
 
1.3%
Other values (114) 122
80.8%
2024-05-11T04:55:52.874653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1196
36.0%
1 639
19.2%
- 453
 
13.6%
2 303
 
9.1%
3 231
 
7.0%
9 175
 
5.3%
6 79
 
2.4%
5 77
 
2.3%
8 65
 
2.0%
4 54
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2869
86.4%
Dash Punctuation 453
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1196
41.7%
1 639
22.3%
2 303
 
10.6%
3 231
 
8.1%
9 175
 
6.1%
6 79
 
2.8%
5 77
 
2.7%
8 65
 
2.3%
4 54
 
1.9%
7 50
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 453
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3322
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1196
36.0%
1 639
19.2%
- 453
 
13.6%
2 303
 
9.1%
3 231
 
7.0%
9 175
 
5.3%
6 79
 
2.4%
5 77
 
2.3%
8 65
 
2.0%
4 54
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1196
36.0%
1 639
19.2%
- 453
 
13.6%
2 303
 
9.1%
3 231
 
7.0%
9 175
 
5.3%
6 79
 
2.4%
5 77
 
2.3%
8 65
 
2.0%
4 54
 
1.6%

업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
한식
106 
분식
12 
일식
 
10
중국식
 
8
경양식
 
6
Other values (6)
 
9

Length

Max length10
Median length2
Mean length2.2582781
Min length2

Unique

Unique3 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
한식 106
70.2%
분식 12
 
7.9%
일식 10
 
6.6%
중국식 8
 
5.3%
경양식 6
 
4.0%
까페 2
 
1.3%
호프/통닭 2
 
1.3%
기타 2
 
1.3%
식육(숯불구이) 1
 
0.7%
정종/대포집/소주방 1
 
0.7%

Length

2024-05-11T04:55:53.328449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 106
70.2%
분식 12
 
7.9%
일식 10
 
6.6%
중국식 8
 
5.3%
경양식 6
 
4.0%
까페 2
 
1.3%
호프/통닭 2
 
1.3%
기타 2
 
1.3%
식육(숯불구이 1
 
0.7%
정종/대포집/소주방 1
 
0.7%

주된음식
Text

MISSING 

Distinct47
Distinct (%)62.7%
Missing76
Missing (%)50.3%
Memory size1.3 KiB
2024-05-11T04:55:53.779180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.3733333
Min length2

Characters and Unicode

Total characters253
Distinct characters88
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

Unique36 ?
Unique (%)48.0%

Sample

1st row삼계탕
2nd row고기
3rd row소갈비
4th row돼지고기구이
5th row삼계탕
ValueCountFrequency (%)
한식 11
 
14.5%
삼계탕 5
 
6.6%
추어탕 4
 
5.3%
소갈비 3
 
3.9%
샤브샤브 3
 
3.9%
갈비탕 3
 
3.9%
해장국.감자탕 2
 
2.6%
돼지갈비 2
 
2.6%
냉면 2
 
2.6%
돼지고기구이 2
 
2.6%
Other values (38) 39
51.3%
2024-05-11T04:55:54.602131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
식 20
 
7.9%
탕 18
 
7.1%
한 13
 
5.1%
비 11
 
4.3%
갈 10
 
4.0%
브 7
 
2.8%
삼 7
 
2.8%
샤 7
 
2.8%
회 6
 
2.4%
지 6
 
2.4%
Other values (78) 148
58.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 246
97.2%
Other Punctuation 6
 
2.4%
Space Separator 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
식 20
 
8.1%
탕 18
 
7.3%
한 13
 
5.3%
비 11
 
4.5%
갈 10
 
4.1%
브 7
 
2.8%
삼 7
 
2.8%
샤 7
 
2.8%
회 6
 
2.4%
지 6
 
2.4%
Other values (76) 141
57.3%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 246
97.2%
Common 7
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
식 20
 
8.1%
탕 18
 
7.3%
한 13
 
5.3%
비 11
 
4.5%
갈 10
 
4.1%
브 7
 
2.8%
삼 7
 
2.8%
샤 7
 
2.8%
회 6
 
2.4%
지 6
 
2.4%
Other values (76) 141
57.3%
Common
ValueCountFrequency (%)
. 6
85.7%
1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 246
97.2%
ASCII 7
 
2.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
식 20
 
8.1%
탕 18
 
7.3%
한 13
 
5.3%
비 11
 
4.5%
갈 10
 
4.1%
브 7
 
2.8%
삼 7
 
2.8%
샤 7
 
2.8%
회 6
 
2.4%
지 6
 
2.4%
Other values (76) 141
57.3%
ASCII
ValueCountFrequency (%)
. 6
85.7%
1
 
14.3%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct122
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.68828
Minimum15.54
Maximum618.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-05-11T04:55:55.024005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15.54
5-th percentile33.13
Q172.73
median105
Q3182.675
95-th percentile470.03
Maximum618.6
Range603.06
Interquartile range (IQR)109.945

Descriptive statistics

Standard deviation129.53416
Coefficient of variation (CV)0.86535944
Kurtosis3.6845574
Mean149.68828
Median Absolute Deviation (MAD)40.4
Skewness1.9908059
Sum22602.93
Variance16779.1
MonotonicityNot monotonic
2024-05-11T04:55:55.534670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
205.32 4
 
2.6%
618.6 3
 
2.0%
470.03 3
 
2.0%
167.0 3
 
2.0%
96.85 3
 
2.0%
539.1 3
 
2.0%
51.89 3
 
2.0%
88.54 3
 
2.0%
184.36 2
 
1.3%
64.6 2
 
1.3%
Other values (112) 122
80.8%
ValueCountFrequency (%)
15.54 1
0.7%
21.78 1
0.7%
29.7 1
0.7%
30.0 1
0.7%
32.55 1
0.7%
32.9 1
0.7%
33.0 1
0.7%
33.06 1
0.7%
33.2 1
0.7%
33.3 1
0.7%
ValueCountFrequency (%)
618.6 3
2.0%
539.1 3
2.0%
470.03 3
2.0%
408.62 1
 
0.7%
403.88 1
 
0.7%
379.55 1
 
0.7%
379.0 1
 
0.7%
376.86 1
 
0.7%
357.17 2
1.3%
319.07 1
 
0.7%

행정동명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
충현동
33 
연희동
33 
신촌동
27 
홍은제2동
12 
북가좌제2동
8 
Other values (9)
38 

Length

Max length6
Median length3
Mean length3.8807947
Min length3

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row신촌동
2nd row충현동
3rd row충현동
4th row천연동
5th row신촌동

Common Values

ValueCountFrequency (%)
충현동 33
21.9%
연희동 33
21.9%
신촌동 27
17.9%
홍은제2동 12
 
7.9%
북가좌제2동 8
 
5.3%
북가좌제1동 7
 
4.6%
홍제제1동 7
 
4.6%
홍제제3동 5
 
3.3%
남가좌제2동 5
 
3.3%
남가좌제1동 5
 
3.3%
Other values (4) 9
 
6.0%

Length

2024-05-11T04:55:55.962035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
충현동 33
21.9%
연희동 33
21.9%
신촌동 27
17.9%
홍은제2동 12
 
7.9%
북가좌제2동 8
 
5.3%
북가좌제1동 7
 
4.6%
홍제제1동 7
 
4.6%
홍제제3동 5
 
3.3%
남가좌제2동 5
 
3.3%
남가좌제1동 5
 
3.3%
Other values (4) 9
 
6.0%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
상수도전용
99 
<NA>
52 

Length

Max length5
Median length5
Mean length4.6556291
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 99
65.6%
<NA> 52
34.4%

Length

2024-05-11T04:55:56.354238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:55:56.690694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 99
65.6%
na 52
34.4%

Interactions

2024-05-11T04:55:40.933054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:55:35.718010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:55:37.022309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:55:38.332638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:55:39.656331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:55:41.303864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:55:35.974781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:55:37.285719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:55:38.591137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:55:39.976487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:55:41.580149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:55:36.241067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:55:37.544350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:55:38.852951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:55:40.149682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:55:41.844106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:55:36.504764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:55:37.805543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:55:39.115055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:55:40.403135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:55:42.112008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:55:36.763813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:55:38.067750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:55:39.345875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:55:40.673886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T04:55:56.895330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자업태명주된음식영업장면적(㎡)행정동명
지정년도1.0000.6850.9161.0000.2250.6090.7200.652
지정번호0.6851.0000.6550.6850.4030.8960.5000.070
신청일자0.9160.6551.0000.9160.1880.8140.2800.000
지정일자1.0000.6850.9161.0000.2250.6090.7200.652
업태명0.2250.4030.1880.2251.0000.8480.0000.157
주된음식0.6090.8960.8140.6090.8481.0000.8380.000
영업장면적(㎡)0.7200.5000.2800.7200.0000.8381.0000.502
행정동명0.6520.0700.0000.6520.1570.0000.5021.000
2024-05-11T04:55:57.182973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태명급수시설구분행정동명
업태명1.0001.0000.056
급수시설구분1.0001.0001.000
행정동명0.0561.0001.000
2024-05-11T04:55:57.443769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.000-0.6540.9230.996-0.1020.0330.2681.000
지정번호-0.6541.000-0.629-0.656-0.0190.1820.0131.000
신청일자0.923-0.6291.0000.928-0.0910.0000.0001.000
지정일자0.996-0.6560.9281.000-0.1100.0330.2681.000
영업장면적(㎡)-0.102-0.019-0.091-0.1101.0000.0000.2241.000
업태명0.0330.1820.0000.0330.0001.0000.0561.000
행정동명0.2680.0130.0000.2680.2240.0561.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-11T04:55:42.552881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T04:55:43.174159image/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.
2024-05-11T04:55:43.560335image/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(㎡)행정동명급수시설구분
03120000202342023110920231109신촌영양센타서울특별시 서대문구 연세로4길 34, (창천동,(지상1층))서울특별시 서대문구 창천동 13번지 37호 (지상1층)3120000-101-1997-07601한식삼계탕43.75신촌동상수도전용
13120000202362023110920231109식껍(서대문역점)서울특별시 서대문구 통일로9안길 26, 1층 (충정로2가)서울특별시 서대문구 충정로2가 16번지 13호 1층3120000-101-2000-04932한식고기82.5충현동상수도전용
23120000202332023110920231109뚜띠쿠치나서울특별시 서대문구 충정로 70, 1층 103호 (미근동)서울특별시 서대문구 미근동 332번지 1층 103호 웨스트게이트타워3120000-101-2014-00083경양식<NA>130.0충현동<NA>
33120000202322023110920231109한옥집서울특별시 서대문구 통일로9길 12, 1층 (냉천동)서울특별시 서대문구 냉천동 20번지 1호 1층3120000-101-1996-00535한식<NA>108.9천연동상수도전용
43120000202312023110920231109삼진<NA>서울특별시 서대문구 대신동 85번지 1호 ,2,4,5,6,83120000-101-1995-07469분식<NA>84.03신촌동상수도전용
53120000202352023110920231109황소서서갈비서울특별시 서대문구 모래내로 279, 1층 (홍은동)서울특별시 서대문구 홍은동 405번지 24호 송산빌딩3120000-101-2013-00191한식소갈비84.0홍은제2동<NA>
631200002022142022110820221108포방터쭈꾸미서울특별시 서대문구 홍은중앙로 100-35, 1층 (홍은동)서울특별시 서대문구 홍은동 9번지 77호 외 2필지(9-79, 9-81) 1층 좌측3번째3120000-101-2016-00145한식<NA>29.7홍은제1동<NA>
73120000202222022110820221108한촌설렁탕 북가좌점서울특별시 서대문구 응암로 67, (북가좌동)서울특별시 서대문구 북가좌동 307번지 1호3120000-101-1994-05535한식<NA>109.09북가좌제1동상수도전용
831200002022112022110820221108연희곰탕서울특별시 서대문구 증가로 17, 1층 (연희동)서울특별시 서대문구 연희동 122번지 4호 1층3120000-101-2019-00335한식<NA>60.0연희동<NA>
93120000202242022110820221108남길서울특별시 서대문구 통일로 107-39, 1층 102호 (충정로2가)서울특별시 서대문구 충정로2가 157번지 1층-1023120000-101-2017-00123한식<NA>135.52충현동상수도전용
시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분
141312000020001872000010120000110연희김밥서울특별시 서대문구 연희로11가길 2, (연희동)서울특별시 서대문구 연희동 129번지 3호3120000-101-1993-07356분식<NA>15.54연희동상수도전용
142312000020001862000010120000110목포세발낙지서울특별시 서대문구 통일로 451-39, (홍제동)서울특별시 서대문구 홍제동 330번지 37호3120000-101-1991-03575한식<NA>72.79홍제제1동상수도전용
143312000020001852000010120000110원당감자탕서울특별시 서대문구 북아현로 54-1, 지하1층 (북아현동)서울특별시 서대문구 북아현동 3번지 130호 지하1층3120000-101-1991-06356한식<NA>156.6충현동상수도전용
144312000020001842000010120000110스시마쯔서울특별시 서대문구 연희로 166, 1층 (연희동)서울특별시 서대문구 연희동 79번지 31호 1층3120000-101-2016-00093일식<NA>89.1연희동상수도전용
145312000020001832000010120000110서대문불고기서울특별시 서대문구 간호대로 11-19, 1층 (홍제동)서울특별시 서대문구 홍제동 278번지 16호 1층3120000-101-2016-00290한식<NA>72.73홍제제3동<NA>
146312000020001432000010120000110웅네 서서갈비서울특별시 서대문구 신촌역로 13-1, 1층 (대현동)서울특별시 서대문구 대현동 110번지 25호 1층3120000-101-2009-00183한식<NA>140.4신촌동<NA>
14731200001993361987051319930308본전회관서울특별시 서대문구 수색로2길 15, (남가좌동,,30,45)서울특별시 서대문구 남가좌동 291번지 51호 ,30,453120000-101-1988-00008한식갈비탕618.6남가좌제2동상수도전용
148312000019931422010070119930308본전회관서울특별시 서대문구 수색로2길 15, (남가좌동,,30,45)서울특별시 서대문구 남가좌동 291번지 51호 ,30,453120000-101-1988-00008한식돼지갈비618.6남가좌제2동상수도전용
14931200001993362009031219930308본전회관서울특별시 서대문구 수색로2길 15, (남가좌동,,30,45)서울특별시 서대문구 남가좌동 291번지 51호 ,30,453120000-101-1988-00008한식부페618.6남가좌제2동상수도전용
1503120000198701987051319870513풍년갈비서울특별시 서대문구 통일로 495, (홍은동,,92-3)서울특별시 서대문구 홍은동 137번지 50호 ,92-33120000-101-1987-00011한식갈비탕51.89홍은제2동상수도전용