Overview

Dataset statistics

Number of variables15
Number of observations182
Missing cells78
Missing cells (%)2.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.5 KiB
Average record size in memory126.7 B

Variable types

Categorical4
Numeric5
Text6

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,주된음식,영업장면적(㎡),행정동명,급수시설구분,소재지전화번호
Author마포구
URLhttps://data.seoul.go.kr/dataList/OA-11372/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
급수시설구분 is highly overall correlated with 지정년도 and 6 other fieldsHigh correlation
업태명 is highly overall correlated with 급수시설구분High 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 (51.5%)Imbalance
주된음식 has 15 (8.2%) missing valuesMissing
소재지전화번호 has 63 (34.6%) missing valuesMissing

Reproduction

Analysis started2024-05-11 06:18:53.722047
Analysis finished2024-05-11 06:19:01.731559
Duration8.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
3130000
182 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3130000
2nd row3130000
3rd row3130000
4th row3130000
5th row3130000

Common Values

ValueCountFrequency (%)
3130000 182
100.0%

Length

2024-05-11T15:19:01.873353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:19:02.080408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3130000 182
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.7198
Minimum2006
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-11T15:19:02.284922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2006
5-th percentile2006
Q12009
median2013
Q32020
95-th percentile2023
Maximum2023
Range17
Interquartile range (IQR)11

Descriptive statistics

Standard deviation5.9643086
Coefficient of variation (CV)0.0029618364
Kurtosis-1.4352701
Mean2013.7198
Median Absolute Deviation (MAD)5
Skewness0.29214419
Sum366497
Variance35.572977
MonotonicityNot monotonic
2024-05-11T15:19:02.514178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2009 40
22.0%
2006 19
10.4%
2022 18
9.9%
2023 16
 
8.8%
2016 13
 
7.1%
2008 12
 
6.6%
2014 10
 
5.5%
2013 9
 
4.9%
2007 8
 
4.4%
2021 8
 
4.4%
Other values (8) 29
15.9%
ValueCountFrequency (%)
2006 19
10.4%
2007 8
 
4.4%
2008 12
 
6.6%
2009 40
22.0%
2010 2
 
1.1%
2011 3
 
1.6%
2012 3
 
1.6%
2013 9
 
4.9%
2014 10
 
5.5%
2015 2
 
1.1%
ValueCountFrequency (%)
2023 16
8.8%
2022 18
9.9%
2021 8
4.4%
2020 5
 
2.7%
2019 7
 
3.8%
2018 3
 
1.6%
2017 4
 
2.2%
2016 13
7.1%
2015 2
 
1.1%
2014 10
5.5%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)48.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.994505
Minimum1
Maximum269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-11T15:19:02.769302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median19
Q3146.5
95-th percentile210
Maximum269
Range268
Interquartile range (IQR)140.5

Descriptive statistics

Standard deviation80.114731
Coefficient of variation (CV)1.144586
Kurtosis-0.76283571
Mean69.994505
Median Absolute Deviation (MAD)17
Skewness0.86478063
Sum12739
Variance6418.3701
MonotonicityNot monotonic
2024-05-11T15:19:03.020965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 11
 
6.0%
6 9
 
4.9%
3 8
 
4.4%
4 7
 
3.8%
5 6
 
3.3%
2 6
 
3.3%
12 6
 
3.3%
9 5
 
2.7%
183 5
 
2.7%
8 5
 
2.7%
Other values (79) 114
62.6%
ValueCountFrequency (%)
1 11
6.0%
2 6
3.3%
3 8
4.4%
4 7
3.8%
5 6
3.3%
6 9
4.9%
7 4
 
2.2%
8 5
2.7%
9 5
2.7%
10 4
 
2.2%
ValueCountFrequency (%)
269 1
0.5%
267 1
0.5%
252 1
0.5%
249 1
0.5%
248 1
0.5%
245 1
0.5%
230 1
0.5%
228 1
0.5%
217 1
0.5%
210 2
1.1%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20137566
Minimum20060621
Maximum20231108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-11T15:19:03.319919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060621
5-th percentile20060630
Q120090615
median20131212
Q320201001
95-th percentile20231108
Maximum20231108
Range170487
Interquartile range (IQR)110386

Descriptive statistics

Standard deviation59995.41
Coefficient of variation (CV)0.0029792781
Kurtosis-1.4422497
Mean20137566
Median Absolute Deviation (MAD)50102.5
Skewness0.31000786
Sum3.6650371 × 109
Variance3.5994492 × 109
MonotonicityNot monotonic
2024-05-11T15:19:03.624365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
20221123 18
 
9.9%
20231108 16
 
8.8%
20060630 14
 
7.7%
20161021 12
 
6.6%
20090615 11
 
6.0%
20090622 10
 
5.5%
20090624 9
 
4.9%
20131212 9
 
4.9%
20211026 7
 
3.8%
20191010 7
 
3.8%
Other values (24) 69
37.9%
ValueCountFrequency (%)
20060621 5
 
2.7%
20060630 14
7.7%
20070622 2
 
1.1%
20071130 6
3.3%
20080618 6
3.3%
20081106 4
 
2.2%
20081113 2
 
1.1%
20090608 2
 
1.1%
20090612 1
 
0.5%
20090615 11
6.0%
ValueCountFrequency (%)
20231108 16
8.8%
20221123 18
9.9%
20211026 7
 
3.8%
20211016 1
 
0.5%
20201001 5
 
2.7%
20191010 7
 
3.8%
20181018 1
 
0.5%
20181017 2
 
1.1%
20171027 4
 
2.2%
20161118 1
 
0.5%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20138203
Minimum20060901
Maximum20231108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-11T15:19:03.853396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060901
5-th percentile20060901
Q120090630
median20131206
Q320201201
95-th percentile20231108
Maximum20231108
Range170207
Interquartile range (IQR)110571

Descriptive statistics

Standard deviation59776.952
Coefficient of variation (CV)0.002968336
Kurtosis-1.4391992
Mean20138203
Median Absolute Deviation (MAD)50078
Skewness0.2903638
Sum3.6651529 × 109
Variance3.573284 × 109
MonotonicityNot monotonic
2024-05-11T15:19:04.089785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
20090630 35
19.2%
20060901 19
10.4%
20221123 18
9.9%
20231108 16
 
8.8%
20161205 13
 
7.1%
20141204 9
 
4.9%
20131206 9
 
4.9%
20211126 8
 
4.4%
20191203 7
 
3.8%
20071203 6
 
3.3%
Other values (12) 42
23.1%
ValueCountFrequency (%)
20060901 19
10.4%
20070629 2
 
1.1%
20071203 6
 
3.3%
20080623 6
 
3.3%
20081128 6
 
3.3%
20090630 35
19.2%
20091201 5
 
2.7%
20100628 2
 
1.1%
20111110 3
 
1.6%
20121120 3
 
1.6%
ValueCountFrequency (%)
20231108 16
8.8%
20221123 18
9.9%
20211126 8
4.4%
20201201 5
 
2.7%
20191203 7
 
3.8%
20181203 3
 
1.6%
20171208 4
 
2.2%
20161205 13
7.1%
20151202 2
 
1.1%
20141208 1
 
0.5%

업소명
Text

Distinct147
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-05-11T15:19:04.501183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length13
Mean length5.7197802
Min length2

Characters and Unicode

Total characters1041
Distinct characters271
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique113 ?
Unique (%)62.1%

Sample

1st row신의주찹쌀순대
2nd row신의주찹쌀순대
3rd row석양집
4th row석양집
5th row홍반장순대전문점
ValueCountFrequency (%)
백년토종삼계탕 3
 
1.4%
별관 3
 
1.4%
마포활어 2
 
0.9%
놀부부대찌개 2
 
0.9%
홍이네 2
 
0.9%
마산아구찜 2
 
0.9%
청기와타운 2
 
0.9%
마포점 2
 
0.9%
램랜드 2
 
0.9%
우림집 2
 
0.9%
Other values (160) 191
89.7%
2024-05-11T15:19:05.334635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31
 
3.0%
마 24
 
2.3%
대 22
 
2.1%
이 22
 
2.1%
포 19
 
1.8%
탕 18
 
1.7%
주 16
 
1.5%
어 15
 
1.4%
양 15
 
1.4%
점 15
 
1.4%
Other values (261) 844
81.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 960
92.2%
Space Separator 31
 
3.0%
Close Punctuation 11
 
1.1%
Open Punctuation 11
 
1.1%
Decimal Number 11
 
1.1%
Other Punctuation 8
 
0.8%
Uppercase Letter 6
 
0.6%
Lowercase Letter 3
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
마 24
 
2.5%
대 22
 
2.3%
이 22
 
2.3%
포 19
 
2.0%
탕 18
 
1.9%
주 16
 
1.7%
어 15
 
1.6%
양 15
 
1.6%
점 15
 
1.6%
집 14
 
1.5%
Other values (238) 780
81.2%
Decimal Number
ValueCountFrequency (%)
2 3
27.3%
1 2
18.2%
5 1
 
9.1%
6 1
 
9.1%
3 1
 
9.1%
9 1
 
9.1%
8 1
 
9.1%
7 1
 
9.1%
Uppercase Letter
ValueCountFrequency (%)
C 1
16.7%
M 1
16.7%
D 1
16.7%
B 1
16.7%
U 1
16.7%
P 1
16.7%
Other Punctuation
ValueCountFrequency (%)
& 5
62.5%
. 2
 
25.0%
% 1
 
12.5%
Lowercase Letter
ValueCountFrequency (%)
b 1
33.3%
i 1
33.3%
j 1
33.3%
Space Separator
ValueCountFrequency (%)
31
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 959
92.1%
Common 72
 
6.9%
Latin 9
 
0.9%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
마 24
 
2.5%
대 22
 
2.3%
이 22
 
2.3%
포 19
 
2.0%
탕 18
 
1.9%
주 16
 
1.7%
어 15
 
1.6%
양 15
 
1.6%
점 15
 
1.6%
집 14
 
1.5%
Other values (237) 779
81.2%
Common
ValueCountFrequency (%)
31
43.1%
) 11
 
15.3%
( 11
 
15.3%
& 5
 
6.9%
2 3
 
4.2%
. 2
 
2.8%
1 2
 
2.8%
5 1
 
1.4%
6 1
 
1.4%
3 1
 
1.4%
Other values (4) 4
 
5.6%
Latin
ValueCountFrequency (%)
b 1
11.1%
i 1
11.1%
j 1
11.1%
C 1
11.1%
M 1
11.1%
D 1
11.1%
B 1
11.1%
U 1
11.1%
P 1
11.1%
Han
ValueCountFrequency (%)
愛 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 959
92.1%
ASCII 81
 
7.8%
CJK 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31
38.3%
) 11
 
13.6%
( 11
 
13.6%
& 5
 
6.2%
2 3
 
3.7%
. 2
 
2.5%
1 2
 
2.5%
5 1
 
1.2%
b 1
 
1.2%
i 1
 
1.2%
Other values (13) 13
16.0%
Hangul
ValueCountFrequency (%)
마 24
 
2.5%
대 22
 
2.3%
이 22
 
2.3%
포 19
 
2.0%
탕 18
 
1.9%
주 16
 
1.7%
어 15
 
1.6%
양 15
 
1.6%
점 15
 
1.6%
집 14
 
1.5%
Other values (237) 779
81.2%
CJK
ValueCountFrequency (%)
愛 1
100.0%

소재지도로명
Text

Distinct147
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-05-11T15:19:05.892877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length72
Median length50
Mean length31.291209
Min length23

Characters and Unicode

Total characters5695
Distinct characters156
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

Unique113 ?
Unique (%)62.1%

Sample

1st row서울특별시 마포구 큰우물로 66, (용강동)
2nd row서울특별시 마포구 큰우물로 66, (용강동)
3rd row서울특별시 마포구 토정로37길 9, E동 8, 9호 (용강동, 우성연립)
4th row서울특별시 마포구 토정로37길 9, E동 8, 9호 (용강동, 우성연립)
5th row서울특별시 마포구 동교로 213, 1층 (동교동)
ValueCountFrequency (%)
서울특별시 182
 
16.7%
마포구 182
 
16.7%
1층 47
 
4.3%
용강동 28
 
2.6%
토정로 21
 
1.9%
서교동 17
 
1.6%
상암동 17
 
1.6%
2층 17
 
1.6%
공덕동 16
 
1.5%
도화동 14
 
1.3%
Other values (287) 551
50.5%
2024-05-11T15:19:06.621553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
910
 
16.0%
, 293
 
5.1%
1 292
 
5.1%
마 222
 
3.9%
포 220
 
3.9%
서 208
 
3.7%
동 206
 
3.6%
울 185
 
3.2%
) 185
 
3.2%
( 185
 
3.2%
Other values (146) 2789
49.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3195
56.1%
Space Separator 910
 
16.0%
Decimal Number 877
 
15.4%
Other Punctuation 297
 
5.2%
Close Punctuation 185
 
3.2%
Open Punctuation 185
 
3.2%
Uppercase Letter 27
 
0.5%
Dash Punctuation 16
 
0.3%
Math Symbol 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
마 222
 
6.9%
포 220
 
6.9%
서 208
 
6.5%
동 206
 
6.4%
울 185
 
5.8%
시 184
 
5.8%
구 183
 
5.7%
특 182
 
5.7%
별 182
 
5.7%
로 178
 
5.6%
Other values (118) 1245
39.0%
Uppercase Letter
ValueCountFrequency (%)
B 13
48.1%
A 3
 
11.1%
R 2
 
7.4%
E 2
 
7.4%
F 1
 
3.7%
Y 1
 
3.7%
T 1
 
3.7%
N 1
 
3.7%
D 1
 
3.7%
C 1
 
3.7%
Decimal Number
ValueCountFrequency (%)
1 292
33.3%
2 124
14.1%
3 93
 
10.6%
0 78
 
8.9%
4 70
 
8.0%
5 50
 
5.7%
6 49
 
5.6%
8 43
 
4.9%
7 42
 
4.8%
9 36
 
4.1%
Other Punctuation
ValueCountFrequency (%)
, 293
98.7%
. 4
 
1.3%
Space Separator
ValueCountFrequency (%)
910
100.0%
Close Punctuation
ValueCountFrequency (%)
) 185
100.0%
Open Punctuation
ValueCountFrequency (%)
( 185
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3195
56.1%
Common 2473
43.4%
Latin 27
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
마 222
 
6.9%
포 220
 
6.9%
서 208
 
6.5%
동 206
 
6.4%
울 185
 
5.8%
시 184
 
5.8%
구 183
 
5.7%
특 182
 
5.7%
별 182
 
5.7%
로 178
 
5.6%
Other values (118) 1245
39.0%
Common
ValueCountFrequency (%)
910
36.8%
, 293
 
11.8%
1 292
 
11.8%
) 185
 
7.5%
( 185
 
7.5%
2 124
 
5.0%
3 93
 
3.8%
0 78
 
3.2%
4 70
 
2.8%
5 50
 
2.0%
Other values (7) 193
 
7.8%
Latin
ValueCountFrequency (%)
B 13
48.1%
A 3
 
11.1%
R 2
 
7.4%
E 2
 
7.4%
F 1
 
3.7%
Y 1
 
3.7%
T 1
 
3.7%
N 1
 
3.7%
D 1
 
3.7%
C 1
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3195
56.1%
ASCII 2500
43.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
910
36.4%
, 293
 
11.7%
1 292
 
11.7%
) 185
 
7.4%
( 185
 
7.4%
2 124
 
5.0%
3 93
 
3.7%
0 78
 
3.1%
4 70
 
2.8%
5 50
 
2.0%
Other values (18) 220
 
8.8%
Hangul
ValueCountFrequency (%)
마 222
 
6.9%
포 220
 
6.9%
서 208
 
6.5%
동 206
 
6.4%
울 185
 
5.8%
시 184
 
5.8%
구 183
 
5.7%
특 182
 
5.7%
별 182
 
5.7%
로 178
 
5.6%
Other values (118) 1245
39.0%

소재지지번
Text

Distinct143
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-05-11T15:19:07.037206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length44
Mean length27.725275
Min length22

Characters and Unicode

Total characters5046
Distinct characters118
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

Unique107 ?
Unique (%)58.8%

Sample

1st row서울특별시 마포구 용강동 19번지 5호
2nd row서울특별시 마포구 용강동 19번지 5호
3rd row서울특별시 마포구 용강동 39번지 1호 우성연립
4th row서울특별시 마포구 용강동 39번지 1호 우성연립
5th row서울특별시 마포구 동교동 198번지 27호 1층
ValueCountFrequency (%)
서울특별시 182
18.4%
마포구 182
18.4%
1층 34
 
3.4%
용강동 28
 
2.8%
공덕동 23
 
2.3%
1호 23
 
2.3%
상암동 21
 
2.1%
서교동 21
 
2.1%
2호 19
 
1.9%
도화동 18
 
1.8%
Other values (224) 440
44.4%
2024-05-11T15:19:08.129341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1272
25.2%
1 210
 
4.2%
지 206
 
4.1%
서 205
 
4.1%
마 190
 
3.8%
포 188
 
3.7%
동 185
 
3.7%
시 184
 
3.6%
울 183
 
3.6%
구 183
 
3.6%
Other values (108) 2040
40.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2815
55.8%
Space Separator 1272
25.2%
Decimal Number 916
 
18.2%
Other Punctuation 21
 
0.4%
Dash Punctuation 8
 
0.2%
Uppercase Letter 6
 
0.1%
Open Punctuation 4
 
0.1%
Close Punctuation 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 206
 
7.3%
서 205
 
7.3%
마 190
 
6.7%
포 188
 
6.7%
동 185
 
6.6%
시 184
 
6.5%
울 183
 
6.5%
구 183
 
6.5%
특 182
 
6.5%
번 182
 
6.5%
Other values (87) 927
32.9%
Decimal Number
ValueCountFrequency (%)
1 210
22.9%
2 148
16.2%
3 98
10.7%
5 97
10.6%
4 90
9.8%
0 79
 
8.6%
6 59
 
6.4%
9 47
 
5.1%
8 45
 
4.9%
7 43
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
B 2
33.3%
F 1
16.7%
D 1
16.7%
M 1
16.7%
C 1
16.7%
Other Punctuation
ValueCountFrequency (%)
, 17
81.0%
. 4
 
19.0%
Space Separator
ValueCountFrequency (%)
1272
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2815
55.8%
Common 2225
44.1%
Latin 6
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 206
 
7.3%
서 205
 
7.3%
마 190
 
6.7%
포 188
 
6.7%
동 185
 
6.6%
시 184
 
6.5%
울 183
 
6.5%
구 183
 
6.5%
특 182
 
6.5%
번 182
 
6.5%
Other values (87) 927
32.9%
Common
ValueCountFrequency (%)
1272
57.2%
1 210
 
9.4%
2 148
 
6.7%
3 98
 
4.4%
5 97
 
4.4%
4 90
 
4.0%
0 79
 
3.6%
6 59
 
2.7%
9 47
 
2.1%
8 45
 
2.0%
Other values (6) 80
 
3.6%
Latin
ValueCountFrequency (%)
B 2
33.3%
F 1
16.7%
D 1
16.7%
M 1
16.7%
C 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2815
55.8%
ASCII 2231
44.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1272
57.0%
1 210
 
9.4%
2 148
 
6.6%
3 98
 
4.4%
5 97
 
4.3%
4 90
 
4.0%
0 79
 
3.5%
6 59
 
2.6%
9 47
 
2.1%
8 45
 
2.0%
Other values (11) 86
 
3.9%
Hangul
ValueCountFrequency (%)
지 206
 
7.3%
서 205
 
7.3%
마 190
 
6.7%
포 188
 
6.7%
동 185
 
6.6%
시 184
 
6.5%
울 183
 
6.5%
구 183
 
6.5%
특 182
 
6.5%
번 182
 
6.5%
Other values (87) 927
32.9%

허가(신고)번호
Text

Distinct147
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-05-11T15:19:08.546210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters4004
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

Unique113 ?
Unique (%)62.1%

Sample

1st row3130000-101-1985-05463
2nd row3130000-101-1985-05463
3rd row3130000-101-1984-00745
4th row3130000-101-1984-00745
5th row3130000-101-2017-00702
ValueCountFrequency (%)
3130000-101-1990-01374 3
 
1.6%
3130000-101-1990-00780 2
 
1.1%
3130000-101-2007-09584 2
 
1.1%
3130000-101-2006-00452 2
 
1.1%
3130000-101-2008-00073 2
 
1.1%
3130000-101-1994-00393 2
 
1.1%
3130000-101-1997-03503 2
 
1.1%
3130000-101-2006-00078 2
 
1.1%
3130000-101-1996-00811 2
 
1.1%
3130000-101-2005-00084 2
 
1.1%
Other values (137) 161
88.5%
2024-05-11T15:19:09.075961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1496
37.4%
1 738
18.4%
- 546
 
13.6%
3 456
 
11.4%
2 197
 
4.9%
9 169
 
4.2%
4 90
 
2.2%
8 85
 
2.1%
7 82
 
2.0%
5 77
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3458
86.4%
Dash Punctuation 546
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1496
43.3%
1 738
21.3%
3 456
 
13.2%
2 197
 
5.7%
9 169
 
4.9%
4 90
 
2.6%
8 85
 
2.5%
7 82
 
2.4%
5 77
 
2.2%
6 68
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 546
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4004
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1496
37.4%
1 738
18.4%
- 546
 
13.6%
3 456
 
11.4%
2 197
 
4.9%
9 169
 
4.2%
4 90
 
2.2%
8 85
 
2.1%
7 82
 
2.0%
5 77
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4004
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1496
37.4%
1 738
18.4%
- 546
 
13.6%
3 456
 
11.4%
2 197
 
4.9%
9 169
 
4.2%
4 90
 
2.2%
8 85
 
2.1%
7 82
 
2.0%
5 77
 
1.9%

업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
한식
131 
일식
25 
중국식
 
10
기타
 
7
복어취급
 
3
Other values (3)
 
6

Length

Max length5
Median length2
Mean length2.1263736
Min length2

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
한식 131
72.0%
일식 25
 
13.7%
중국식 10
 
5.5%
기타 7
 
3.8%
복어취급 3
 
1.6%
분식 3
 
1.6%
호프/통닭 2
 
1.1%
경양식 1
 
0.5%

Length

2024-05-11T15:19:09.293483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:19:09.503617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 131
72.0%
일식 25
 
13.7%
중국식 10
 
5.5%
기타 7
 
3.8%
복어취급 3
 
1.6%
분식 3
 
1.6%
호프/통닭 2
 
1.1%
경양식 1
 
0.5%

주된음식
Text

MISSING 

Distinct93
Distinct (%)55.7%
Missing15
Missing (%)8.2%
Memory size1.6 KiB
2024-05-11T15:19:10.057542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length9
Mean length3.4550898
Min length1

Characters and Unicode

Total characters577
Distinct characters134
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

Unique58 ?
Unique (%)34.7%

Sample

1st row순대국
2nd row순대국
3rd row돼지갈비
4th row돼지갈비
5th row회
ValueCountFrequency (%)
돼지갈비 10
 
5.8%
회 7
 
4.1%
소갈비 7
 
4.1%
설렁탕 6
 
3.5%
부대찌개 5
 
2.9%
추어탕 5
 
2.9%
킹크랩 4
 
2.3%
참치회 4
 
2.3%
한정식 4
 
2.3%
콩나물국밥 4
 
2.3%
Other values (86) 115
67.3%
2024-05-11T15:19:10.898250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
비 29
 
5.0%
갈 27
 
4.7%
탕 27
 
4.7%
식 19
 
3.3%
회 18
 
3.1%
지 17
 
2.9%
국 17
 
2.9%
한 14
 
2.4%
밥 14
 
2.4%
돼 13
 
2.3%
Other values (124) 382
66.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 559
96.9%
Other Punctuation 8
 
1.4%
Space Separator 4
 
0.7%
Open Punctuation 3
 
0.5%
Close Punctuation 3
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
비 29
 
5.2%
갈 27
 
4.8%
탕 27
 
4.8%
식 19
 
3.4%
회 18
 
3.2%
지 17
 
3.0%
국 17
 
3.0%
한 14
 
2.5%
밥 14
 
2.5%
돼 13
 
2.3%
Other values (119) 364
65.1%
Other Punctuation
ValueCountFrequency (%)
, 7
87.5%
. 1
 
12.5%
Space Separator
ValueCountFrequency (%)
4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 559
96.9%
Common 18
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
비 29
 
5.2%
갈 27
 
4.8%
탕 27
 
4.8%
식 19
 
3.4%
회 18
 
3.2%
지 17
 
3.0%
국 17
 
3.0%
한 14
 
2.5%
밥 14
 
2.5%
돼 13
 
2.3%
Other values (119) 364
65.1%
Common
ValueCountFrequency (%)
, 7
38.9%
4
22.2%
( 3
16.7%
) 3
16.7%
. 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 559
96.9%
ASCII 18
 
3.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
비 29
 
5.2%
갈 27
 
4.8%
탕 27
 
4.8%
식 19
 
3.4%
회 18
 
3.2%
지 17
 
3.0%
국 17
 
3.0%
한 14
 
2.5%
밥 14
 
2.5%
돼 13
 
2.3%
Other values (119) 364
65.1%
ASCII
ValueCountFrequency (%)
, 7
38.9%
4
22.2%
( 3
16.7%
) 3
16.7%
. 1
 
5.6%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct145
Distinct (%)79.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.6461
Minimum22
Maximum3310.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-11T15:19:11.169302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile36.81
Q173.09
median105.36
Q3183.97
95-th percentile390.9215
Maximum3310.87
Range3288.87
Interquartile range (IQR)110.88

Descriptive statistics

Standard deviation275.87433
Coefficient of variation (CV)1.616646
Kurtosis94.387353
Mean170.6461
Median Absolute Deviation (MAD)42.39
Skewness8.7170109
Sum31057.59
Variance76106.647
MonotonicityNot monotonic
2024-05-11T15:19:11.420352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.44 3
 
1.6%
148.0 2
 
1.1%
68.0 2
 
1.1%
133.26 2
 
1.1%
110.1 2
 
1.1%
165.0 2
 
1.1%
205.57 2
 
1.1%
169.55 2
 
1.1%
86.71 2
 
1.1%
34.4 2
 
1.1%
Other values (135) 161
88.5%
ValueCountFrequency (%)
22.0 1
0.5%
25.36 1
0.5%
25.6 1
0.5%
29.58 1
0.5%
31.58 1
0.5%
34.4 2
1.1%
34.48 1
0.5%
35.19 1
0.5%
36.73 1
0.5%
38.33 1
0.5%
ValueCountFrequency (%)
3310.87 1
0.5%
1198.48 1
0.5%
900.69 1
0.5%
688.26 1
0.5%
647.68 1
0.5%
541.21 1
0.5%
464.64 1
0.5%
403.16 2
1.1%
390.99 1
0.5%
389.62 1
0.5%

행정동명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
용강동
32 
서교동
25 
공덕동
22 
상암동
21 
도화동
17 
Other values (10)
65 

Length

Max length5
Median length3
Mean length3.2087912
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row용강동
2nd row용강동
3rd row용강동
4th row용강동
5th row서교동

Common Values

ValueCountFrequency (%)
용강동 32
17.6%
서교동 25
13.7%
공덕동 22
12.1%
상암동 21
11.5%
도화동 17
9.3%
아현동 11
 
6.0%
성산제1동 10
 
5.5%
합정동 9
 
4.9%
대흥동 8
 
4.4%
연남동 7
 
3.8%
Other values (5) 20
11.0%

Length

2024-05-11T15:19:11.722360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
용강동 32
17.6%
서교동 25
13.7%
공덕동 22
12.1%
상암동 21
11.5%
도화동 17
9.3%
아현동 11
 
6.0%
성산제1동 10
 
5.5%
합정동 9
 
4.9%
대흥동 8
 
4.4%
연남동 7
 
3.8%
Other values (5) 20
11.0%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
상수도전용
99 
<NA>
83 

Length

Max length5
Median length5
Mean length4.543956
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 99
54.4%
<NA> 83
45.6%

Length

2024-05-11T15:19:12.000669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:19:12.180376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 99
54.4%
na 83
45.6%

소재지전화번호
Text

MISSING 

Distinct89
Distinct (%)74.8%
Missing63
Missing (%)34.6%
Memory size1.6 KiB
2024-05-11T15:19:12.631066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10
Min length7

Characters and Unicode

Total characters1190
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

Unique59 ?
Unique (%)49.6%

Sample

1st row02 32751780
2nd row02 32751780
3rd row02 7166847
4th row02 7166847
5th row02 3230572
ValueCountFrequency (%)
02 94
39.7%
715 5
 
2.1%
711 4
 
1.7%
716 3
 
1.3%
3331977 2
 
0.8%
3651595 2
 
0.8%
0888 2
 
0.8%
7140116 2
 
0.8%
704 2
 
0.8%
7193001 2
 
0.8%
Other values (94) 119
50.2%
2024-05-11T15:19:13.290181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 186
15.6%
0 171
14.4%
144
12.1%
3 140
11.8%
7 126
10.6%
1 125
10.5%
4 65
 
5.5%
5 65
 
5.5%
9 63
 
5.3%
6 59
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1046
87.9%
Space Separator 144
 
12.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 186
17.8%
0 171
16.3%
3 140
13.4%
7 126
12.0%
1 125
12.0%
4 65
 
6.2%
5 65
 
6.2%
9 63
 
6.0%
6 59
 
5.6%
8 46
 
4.4%
Space Separator
ValueCountFrequency (%)
144
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1190
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 186
15.6%
0 171
14.4%
144
12.1%
3 140
11.8%
7 126
10.6%
1 125
10.5%
4 65
 
5.5%
5 65
 
5.5%
9 63
 
5.3%
6 59
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1190
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 186
15.6%
0 171
14.4%
144
12.1%
3 140
11.8%
7 126
10.6%
1 125
10.5%
4 65
 
5.5%
5 65
 
5.5%
9 63
 
5.3%
6 59
 
5.0%

Interactions

2024-05-11T15:18:59.649108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:18:55.716669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:18:56.780624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:18:57.691152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:18:58.682808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:18:59.882026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:18:55.896950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:18:56.964601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:18:57.898141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:18:58.853308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:19:00.060059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:18:56.118818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:18:57.149159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:18:58.067688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:18:59.012321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:19:00.245192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:18:56.390867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:18:57.327057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:18:58.254286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:18:59.227473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:19:00.421350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:18:56.591911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:18:57.495814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:18:58.490605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:18:59.446976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T15:19:13.465183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자업태명주된음식영업장면적(㎡)행정동명소재지전화번호
지정년도1.0000.7101.0001.0000.1970.7670.5890.3780.973
지정번호0.7101.0000.7710.7100.0000.0000.1430.2160.000
신청일자1.0000.7711.0001.0000.2810.8060.6090.2830.969
지정일자1.0000.7101.0001.0000.1970.7670.5890.3780.973
업태명0.1970.0000.2810.1971.0000.9530.0000.2261.000
주된음식0.7670.0000.8060.7670.9531.0000.9610.8921.000
영업장면적(㎡)0.5890.1430.6090.5890.0000.9611.0000.3001.000
행정동명0.3780.2160.2830.3780.2260.8920.3001.0001.000
소재지전화번호0.9730.0000.9690.9731.0001.0001.0001.0001.000
2024-05-11T15:19:13.648879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
급수시설구분업태명행정동명
급수시설구분1.0001.0001.000
업태명1.0001.0000.094
행정동명1.0000.0941.000
2024-05-11T15:19:13.816377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.000-0.6550.9930.998-0.0750.1340.0991.000
지정번호-0.6551.000-0.645-0.6450.0240.0000.0781.000
신청일자0.993-0.6451.0000.995-0.0660.1340.1061.000
지정일자0.998-0.6450.9951.000-0.0720.1340.0991.000
영업장면적(㎡)-0.0750.024-0.066-0.0721.0000.0000.1261.000
업태명0.1340.0000.1340.1340.0001.0000.0941.000
행정동명0.0990.0780.1060.0990.1260.0941.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-11T15:19:00.685598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T15:19:01.317113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-11T15:19:01.583616image/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
031300002008462008061820080623신의주찹쌀순대서울특별시 마포구 큰우물로 66, (용강동)서울특별시 마포구 용강동 19번지 5호3130000-101-1985-05463한식순대국75.58용강동상수도전용02 32751780
131300002009662009062320090630신의주찹쌀순대서울특별시 마포구 큰우물로 66, (용강동)서울특별시 마포구 용강동 19번지 5호3130000-101-1985-05463한식순대국75.58용강동상수도전용02 32751780
2313000020061632006063020060901석양집서울특별시 마포구 토정로37길 9, E동 8, 9호 (용강동, 우성연립)서울특별시 마포구 용강동 39번지 1호 우성연립3130000-101-1984-00745한식돼지갈비165.65용강동상수도전용02 7166847
3313000020091782009062220090630석양집서울특별시 마포구 토정로37길 9, E동 8, 9호 (용강동, 우성연립)서울특별시 마포구 용강동 39번지 1호 우성연립3130000-101-1984-00745한식돼지갈비165.65용강동상수도전용02 7166847
43130000201942019101020191203홍반장순대전문점서울특별시 마포구 동교로 213, 1층 (동교동)서울특별시 마포구 동교동 198번지 27호 1층3130000-101-2017-00702일식회89.67서교동<NA><NA>
5313000020141812014120820141208덕승루서울특별시 마포구 백범로1길 12, 1층 (노고산동)서울특별시 마포구 노고산동 31번지 33호3130000-101-2022-00623중국식<NA>64.07대흥동<NA><NA>
63130000202232022112320221123예촌서울특별시 마포구 마포대로4나길 7, 1층동 (도화동)서울특별시 마포구 도화동 290번지 10호 1층3130000-101-1990-03418한식생태탕89.7도화동상수도전용02 3230572
73130000201222012102520121120(주)거구상사(거구장)서울특별시 마포구 백범로 23, (신수동,외 8필지)서울특별시 마포구 신수동 63번지 14호 외 8필지3130000-101-1993-01008한식갈비.불고기3310.87대흥동상수도전용02 7153611
83130000201562015101220151202시골보쌈앤감자옹심이서울특별시 마포구 월드컵북로 400, 지층 01호 (상암동, 서울산업진흥원)서울특별시 마포구 상암동 1602번지 서울산업진흥원, 지층-013130000-101-2014-00935한식구이294.84상암동<NA><NA>
931300002006262006062120060901쌍마막회서울특별시 마포구 토정로 257, (용강동)서울특별시 마포구 용강동 494번지 36호3130000-101-1993-00687한식해장국92.34용강동상수도전용02 7141233
시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
1723130000202352023110820231108봄이보리밥 합정점서울특별시 마포구 월드컵로3길 14, 지1층 B155호,B156호,B157호,B158호일부호 (합정동, 마포 한강 2차 푸르지오)서울특별시 마포구 합정동 473번지 마포 한강 2차 푸르지오3130000-101-2022-01031한식보리밥132.83합정동<NA><NA>
1733130000202362023110820231108어제그쭈꾸미.jib서울특별시 마포구 매봉산로2길 69, (상암동, 1층)서울특별시 마포구 상암동 23번지 1호3130000-101-2012-00007한식주꾸미68.81상암동<NA><NA>
1743130000202372023110820231108을밀대서울특별시 마포구 숭문길 24, (염리동)서울특별시 마포구 염리동 147번지 6호3130000-101-2000-08997호프/통닭냉면25.36염리동<NA>02 7171922
1753130000202382023110820231108매일스시 횟집서울특별시 마포구 숭문길 34, 1층 (염리동)서울특별시 마포구 염리동 95번지 1호3130000-101-2017-00620일식회90.55염리동<NA>02 32738289
1763130000202392023110820231108시카노이에서울특별시 마포구 성암로13길 16, 1층 (상암동)서울특별시 마포구 상암동 1466번지3130000-101-2021-00249한식덮밥38.9상암동<NA><NA>
1773130000201952019101020191203바다애(愛)서울특별시 마포구 동교로18길 4, A동 1층 (서교동)서울특별시 마포구 서교동 465번지 2호3130000-101-2015-00268일식생선회141.16서교동<NA><NA>
1783130000201712017102720171208정성한줄서울특별시 마포구 마포대로 195, 상가4동 108호 (아현동, 마포 래미안 푸르지오)서울특별시 마포구 아현동 774번지 마포 래미안 푸르지오3130000-101-2016-00909분식분식45.0아현동<NA><NA>
1793130000201922019101020191203원당감자탕 마포본점서울특별시 마포구 도화길 35, 1층일부 (도화동)서울특별시 마포구 도화동 179번지 1호3130000-101-2018-00021한식감자탕257.4도화동<NA><NA>
180313000020072522007113020071203마포 대파 솥뚜껑삼겹서울특별시 마포구 삼개로3길 6, (도화동)서울특별시 마포구 도화동 204번지 11호3130000-101-1998-00686한식참이맛감자탕83.72도화동상수도전용703 6800
18131300002013322013121220131206불광설 주식회사서울특별시 마포구 양화로15길 12, (서교동, 1층)서울특별시 마포구 서교동 352번지 26호3130000-101-2003-00468중국식참치회207.23서교동상수도전용0231436811