Overview

Dataset statistics

Number of variables15
Number of observations49
Missing cells17
Missing cells (%)2.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.2 KiB
Average record size in memory128.7 B

Variable types

Categorical4
Numeric5
Text6

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,주된음식,영업장면적(㎡),행정동명,급수시설구분,소재지전화번호
Author도봉구
URLhttps://data.seoul.go.kr/dataList/OA-10063/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
지정년도 is highly overall correlated with 신청일자 and 1 other fieldsHigh correlation
신청일자 is highly overall correlated with 지정년도 and 1 other fieldsHigh correlation
지정일자 is highly overall correlated with 지정년도 and 1 other fieldsHigh correlation
주된음식 has 8 (16.3%) missing valuesMissing
소재지전화번호 has 9 (18.4%) missing valuesMissing
업소명 has unique valuesUnique
소재지도로명 has unique valuesUnique
소재지지번 has unique valuesUnique
허가(신고)번호 has unique valuesUnique

Reproduction

Analysis started2024-05-03 20:37:08.924579
Analysis finished2024-05-03 20:37:19.233893
Duration10.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
3090000
49 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3090000
2nd row3090000
3rd row3090000
4th row3090000
5th row3090000

Common Values

ValueCountFrequency (%)
3090000 49
100.0%

Length

2024-05-03T20:37:19.621726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T20:37:20.021934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3090000 49
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.5714
Minimum2002
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2024-05-03T20:37:20.517213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2002
5-th percentile2002
Q12007
median2012
Q32021
95-th percentile2023
Maximum2023
Range21
Interquartile range (IQR)14

Descriptive statistics

Standard deviation7.3993243
Coefficient of variation (CV)0.0036765524
Kurtosis-1.3426326
Mean2012.5714
Median Absolute Deviation (MAD)5
Skewness0.066359209
Sum98616
Variance54.75
MonotonicityNot monotonic
2024-05-03T20:37:21.007816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2007 8
16.3%
2002 7
14.3%
2022 6
12.2%
2023 5
10.2%
2012 4
8.2%
2017 3
 
6.1%
2015 3
 
6.1%
2021 3
 
6.1%
2010 3
 
6.1%
2013 2
 
4.1%
Other values (3) 5
10.2%
ValueCountFrequency (%)
2002 7
14.3%
2003 2
 
4.1%
2007 8
16.3%
2008 1
 
2.0%
2010 3
 
6.1%
2011 2
 
4.1%
2012 4
8.2%
2013 2
 
4.1%
2015 3
 
6.1%
2017 3
 
6.1%
ValueCountFrequency (%)
2023 5
10.2%
2022 6
12.2%
2021 3
6.1%
2017 3
6.1%
2015 3
6.1%
2013 2
 
4.1%
2012 4
8.2%
2011 2
 
4.1%
2010 3
6.1%
2008 1
 
2.0%

지정번호
Real number (ℝ)

Distinct38
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.836735
Minimum1
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2024-05-03T20:37:21.444798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.4
Q15
median16
Q333
95-th percentile42.6
Maximum45
Range44
Interquartile range (IQR)28

Descriptive statistics

Standard deviation14.810395
Coefficient of variation (CV)0.78625064
Kurtosis-1.3702608
Mean18.836735
Median Absolute Deviation (MAD)12
Skewness0.3546655
Sum923
Variance219.34779
MonotonicityNot monotonic
2024-05-03T20:37:21.965851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
4 3
 
6.1%
3 3
 
6.1%
2 3
 
6.1%
1 3
 
6.1%
5 3
 
6.1%
6 2
 
4.1%
11 1
 
2.0%
37 1
 
2.0%
16 1
 
2.0%
17 1
 
2.0%
Other values (28) 28
57.1%
ValueCountFrequency (%)
1 3
6.1%
2 3
6.1%
3 3
6.1%
4 3
6.1%
5 3
6.1%
6 2
4.1%
8 1
 
2.0%
9 1
 
2.0%
10 1
 
2.0%
11 1
 
2.0%
ValueCountFrequency (%)
45 1
2.0%
44 1
2.0%
43 1
2.0%
42 1
2.0%
41 1
2.0%
40 1
2.0%
39 1
2.0%
38 1
2.0%
37 1
2.0%
36 1
2.0%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20126582
Minimum20020709
Maximum20231011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2024-05-03T20:37:22.486958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20020709
5-th percentile20020709
Q120070802
median20121030
Q320211215
95-th percentile20231011
Maximum20231011
Range210302
Interquartile range (IQR)140413

Descriptive statistics

Standard deviation74169.667
Coefficient of variation (CV)0.0036851596
Kurtosis-1.3443896
Mean20126582
Median Absolute Deviation (MAD)50229
Skewness0.064988769
Sum9.8620252 × 108
Variance5.5011395 × 109
MonotonicityNot monotonic
2024-05-03T20:37:22.972677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
20221017 6
 
12.2%
20231011 4
 
8.2%
20020709 4
 
8.2%
20211215 3
 
6.1%
20020711 3
 
6.1%
20100701 3
 
6.1%
20070802 2
 
4.1%
20110501 2
 
4.1%
20121107 2
 
4.1%
20151211 2
 
4.1%
Other values (15) 18
36.7%
ValueCountFrequency (%)
20020709 4
8.2%
20020711 3
6.1%
20030118 1
 
2.0%
20030121 1
 
2.0%
20070402 1
 
2.0%
20070801 2
4.1%
20070802 2
4.1%
20070803 1
 
2.0%
20070810 2
4.1%
20080619 1
 
2.0%
ValueCountFrequency (%)
20231011 4
8.2%
20231010 1
 
2.0%
20221017 6
12.2%
20211215 3
6.1%
20171017 2
 
4.1%
20171011 1
 
2.0%
20151211 2
 
4.1%
20150811 1
 
2.0%
20131108 1
 
2.0%
20131107 1
 
2.0%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20126700
Minimum20020711
Maximum20231120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2024-05-03T20:37:23.466335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20020711
5-th percentile20020711
Q120071008
median20121204
Q320211215
95-th percentile20231120
Maximum20231120
Range210409
Interquartile range (IQR)140207

Descriptive statistics

Standard deviation74198.137
Coefficient of variation (CV)0.0036865526
Kurtosis-1.3433224
Mean20126700
Median Absolute Deviation (MAD)50196
Skewness0.063671685
Sum9.8620828 × 108
Variance5.5053636 × 109
MonotonicityNot monotonic
2024-05-03T20:37:23.780526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20020711 7
14.3%
20071008 7
14.3%
20221215 6
12.2%
20231120 5
10.2%
20121204 4
8.2%
20171206 3
6.1%
20151231 3
6.1%
20211215 3
6.1%
20100903 3
6.1%
20131224 2
 
4.1%
Other values (5) 6
12.2%
ValueCountFrequency (%)
20020711 7
14.3%
20030121 1
 
2.0%
20030123 1
 
2.0%
20070417 1
 
2.0%
20071008 7
14.3%
20080717 1
 
2.0%
20100903 3
6.1%
20110524 2
 
4.1%
20121204 4
8.2%
20131224 2
 
4.1%
ValueCountFrequency (%)
20231120 5
10.2%
20221215 6
12.2%
20211215 3
6.1%
20171206 3
6.1%
20151231 3
6.1%
20131224 2
 
4.1%
20121204 4
8.2%
20110524 2
 
4.1%
20100903 3
6.1%
20080717 1
 
2.0%

업소명
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2024-05-03T20:37:24.321633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length6.8979592
Min length2

Characters and Unicode

Total characters338
Distinct characters175
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)100.0%

Sample

1st row통나무식당
2nd row망향비빔국수
3rd row토영자갈치곰장어
4th row갈비세상 숯불갈비
5th row반값소
ValueCountFrequency (%)
반값소 2
 
3.0%
통나무식당 1
 
1.5%
삿뽀로 1
 
1.5%
갑식이네착한낙지 1
 
1.5%
방학역점 1
 
1.5%
탐나종합어시장 1
 
1.5%
힘찬장어 1
 
1.5%
주)엔타스 1
 
1.5%
창동점 1
 
1.5%
고기집 1
 
1.5%
Other values (55) 55
83.3%
2024-05-03T20:37:25.297139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17
 
5.0%
점 8
 
2.4%
어 6
 
1.8%
대 6
 
1.8%
동 6
 
1.8%
주 5
 
1.5%
장 5
 
1.5%
갈 5
 
1.5%
( 5
 
1.5%
) 5
 
1.5%
Other values (165) 270
79.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 303
89.6%
Space Separator 17
 
5.0%
Open Punctuation 5
 
1.5%
Close Punctuation 5
 
1.5%
Lowercase Letter 4
 
1.2%
Decimal Number 3
 
0.9%
Uppercase Letter 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 8
 
2.6%
어 6
 
2.0%
대 6
 
2.0%
동 6
 
2.0%
주 5
 
1.7%
장 5
 
1.7%
갈 5
 
1.7%
국 5
 
1.7%
창 5
 
1.7%
비 5
 
1.7%
Other values (155) 247
81.5%
Lowercase Letter
ValueCountFrequency (%)
o 2
50.0%
y 1
25.0%
k 1
25.0%
Decimal Number
ValueCountFrequency (%)
1 1
33.3%
3 1
33.3%
9 1
33.3%
Space Separator
ValueCountFrequency (%)
17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Uppercase Letter
ValueCountFrequency (%)
T 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 303
89.6%
Common 30
 
8.9%
Latin 5
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 8
 
2.6%
어 6
 
2.0%
대 6
 
2.0%
동 6
 
2.0%
주 5
 
1.7%
장 5
 
1.7%
갈 5
 
1.7%
국 5
 
1.7%
창 5
 
1.7%
비 5
 
1.7%
Other values (155) 247
81.5%
Common
ValueCountFrequency (%)
17
56.7%
( 5
 
16.7%
) 5
 
16.7%
1 1
 
3.3%
3 1
 
3.3%
9 1
 
3.3%
Latin
ValueCountFrequency (%)
o 2
40.0%
y 1
20.0%
k 1
20.0%
T 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 303
89.6%
ASCII 35
 
10.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17
48.6%
( 5
 
14.3%
) 5
 
14.3%
o 2
 
5.7%
1 1
 
2.9%
3 1
 
2.9%
y 1
 
2.9%
k 1
 
2.9%
T 1
 
2.9%
9 1
 
2.9%
Hangul
ValueCountFrequency (%)
점 8
 
2.6%
어 6
 
2.0%
대 6
 
2.0%
동 6
 
2.0%
주 5
 
1.7%
장 5
 
1.7%
갈 5
 
1.7%
국 5
 
1.7%
창 5
 
1.7%
비 5
 
1.7%
Other values (155) 247
81.5%

소재지도로명
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2024-05-03T20:37:25.919287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length40
Mean length31.938776
Min length23

Characters and Unicode

Total characters1565
Distinct characters91
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

Unique49 ?
Unique (%)100.0%

Sample

1st row서울특별시 도봉구 노해로60길 99, (쌍문동)
2nd row서울특별시 도봉구 도봉로 534, 1층 (창동)
3rd row서울특별시 도봉구 노해로69길 15, (창동,세정빌딩 107,108,109호(지상1층))
4th row서울특별시 도봉구 도봉로181길 29, 1층 (도봉동)
5th row서울특별시 도봉구 마들로 664-5, 101,102호 (도봉동)
ValueCountFrequency (%)
서울특별시 49
 
16.4%
도봉구 49
 
16.4%
1층 16
 
5.4%
도봉동 13
 
4.4%
창동 12
 
4.0%
방학동 10
 
3.4%
쌍문동 7
 
2.3%
도봉로 7
 
2.3%
2층 6
 
2.0%
마들로 5
 
1.7%
Other values (101) 124
41.6%
2024-05-03T20:37:27.080106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
249
 
15.9%
1 88
 
5.6%
도 84
 
5.4%
봉 84
 
5.4%
, 78
 
5.0%
) 54
 
3.5%
동 54
 
3.5%
( 54
 
3.5%
시 53
 
3.4%
서 49
 
3.1%
Other values (81) 718
45.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 837
53.5%
Decimal Number 280
 
17.9%
Space Separator 249
 
15.9%
Other Punctuation 81
 
5.2%
Close Punctuation 54
 
3.5%
Open Punctuation 54
 
3.5%
Uppercase Letter 6
 
0.4%
Dash Punctuation 4
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 84
 
10.0%
봉 84
 
10.0%
동 54
 
6.5%
시 53
 
6.3%
서 49
 
5.9%
울 49
 
5.9%
구 49
 
5.9%
별 49
 
5.9%
특 49
 
5.9%
로 45
 
5.4%
Other values (59) 272
32.5%
Decimal Number
ValueCountFrequency (%)
1 88
31.4%
0 35
 
12.5%
2 32
 
11.4%
6 29
 
10.4%
3 27
 
9.6%
5 19
 
6.8%
4 19
 
6.8%
9 15
 
5.4%
7 8
 
2.9%
8 8
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
A 2
33.3%
S 1
16.7%
E 1
16.7%
D 1
16.7%
B 1
16.7%
Other Punctuation
ValueCountFrequency (%)
, 78
96.3%
. 2
 
2.5%
/ 1
 
1.2%
Space Separator
ValueCountFrequency (%)
249
100.0%
Close Punctuation
ValueCountFrequency (%)
) 54
100.0%
Open Punctuation
ValueCountFrequency (%)
( 54
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 837
53.5%
Common 722
46.1%
Latin 6
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 84
 
10.0%
봉 84
 
10.0%
동 54
 
6.5%
시 53
 
6.3%
서 49
 
5.9%
울 49
 
5.9%
구 49
 
5.9%
별 49
 
5.9%
특 49
 
5.9%
로 45
 
5.4%
Other values (59) 272
32.5%
Common
ValueCountFrequency (%)
249
34.5%
1 88
 
12.2%
, 78
 
10.8%
) 54
 
7.5%
( 54
 
7.5%
0 35
 
4.8%
2 32
 
4.4%
6 29
 
4.0%
3 27
 
3.7%
5 19
 
2.6%
Other values (7) 57
 
7.9%
Latin
ValueCountFrequency (%)
A 2
33.3%
S 1
16.7%
E 1
16.7%
D 1
16.7%
B 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 837
53.5%
ASCII 728
46.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
249
34.2%
1 88
 
12.1%
, 78
 
10.7%
) 54
 
7.4%
( 54
 
7.4%
0 35
 
4.8%
2 32
 
4.4%
6 29
 
4.0%
3 27
 
3.7%
5 19
 
2.6%
Other values (12) 63
 
8.7%
Hangul
ValueCountFrequency (%)
도 84
 
10.0%
봉 84
 
10.0%
동 54
 
6.5%
시 53
 
6.3%
서 49
 
5.9%
울 49
 
5.9%
구 49
 
5.9%
별 49
 
5.9%
특 49
 
5.9%
로 45
 
5.4%
Other values (59) 272
32.5%

소재지지번
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2024-05-03T20:37:27.697441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length36
Mean length29.244898
Min length21

Characters and Unicode

Total characters1433
Distinct characters73
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

Unique49 ?
Unique (%)100.0%

Sample

1st row서울특별시 도봉구 쌍문동 96번지 9호
2nd row서울특별시 도봉구 창동 700번지 33호
3rd row서울특별시 도봉구 창동 10번지 3호 세정빌딩 107,108,109호(지상1층)
4th row서울특별시 도봉구 도봉동 566번지 1층
5th row서울특별시 도봉구 도봉동 636번지 15호 101,102호
ValueCountFrequency (%)
서울특별시 49
17.8%
도봉구 49
17.8%
창동 14
 
5.1%
방학동 14
 
5.1%
도봉동 13
 
4.7%
쌍문동 8
 
2.9%
1층 7
 
2.5%
지상1층 6
 
2.2%
3호 5
 
1.8%
1호 4
 
1.4%
Other values (88) 107
38.8%
2024-05-03T20:37:28.958468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
344
24.0%
봉 64
 
4.5%
1 64
 
4.5%
도 63
 
4.4%
지 59
 
4.1%
동 54
 
3.8%
시 51
 
3.6%
서 49
 
3.4%
번 49
 
3.4%
특 49
 
3.4%
Other values (63) 587
41.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 774
54.0%
Space Separator 344
24.0%
Decimal Number 280
 
19.5%
Other Punctuation 14
 
1.0%
Open Punctuation 6
 
0.4%
Close Punctuation 6
 
0.4%
Dash Punctuation 4
 
0.3%
Uppercase Letter 4
 
0.3%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
봉 64
 
8.3%
도 63
 
8.1%
지 59
 
7.6%
동 54
 
7.0%
시 51
 
6.6%
서 49
 
6.3%
번 49
 
6.3%
특 49
 
6.3%
별 49
 
6.3%
구 49
 
6.3%
Other values (43) 238
30.7%
Decimal Number
ValueCountFrequency (%)
1 64
22.9%
2 38
13.6%
3 37
13.2%
0 28
10.0%
6 25
 
8.9%
5 24
 
8.6%
7 21
 
7.5%
4 20
 
7.1%
8 12
 
4.3%
9 11
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
A 2
50.0%
S 1
25.0%
E 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 12
85.7%
. 2
 
14.3%
Space Separator
ValueCountFrequency (%)
344
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 774
54.0%
Common 655
45.7%
Latin 4
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
봉 64
 
8.3%
도 63
 
8.1%
지 59
 
7.6%
동 54
 
7.0%
시 51
 
6.6%
서 49
 
6.3%
번 49
 
6.3%
특 49
 
6.3%
별 49
 
6.3%
구 49
 
6.3%
Other values (43) 238
30.7%
Common
ValueCountFrequency (%)
344
52.5%
1 64
 
9.8%
2 38
 
5.8%
3 37
 
5.6%
0 28
 
4.3%
6 25
 
3.8%
5 24
 
3.7%
7 21
 
3.2%
4 20
 
3.1%
, 12
 
1.8%
Other values (7) 42
 
6.4%
Latin
ValueCountFrequency (%)
A 2
50.0%
S 1
25.0%
E 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 774
54.0%
ASCII 659
46.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
344
52.2%
1 64
 
9.7%
2 38
 
5.8%
3 37
 
5.6%
0 28
 
4.2%
6 25
 
3.8%
5 24
 
3.6%
7 21
 
3.2%
4 20
 
3.0%
, 12
 
1.8%
Other values (10) 46
 
7.0%
Hangul
ValueCountFrequency (%)
봉 64
 
8.3%
도 63
 
8.1%
지 59
 
7.6%
동 54
 
7.0%
시 51
 
6.6%
서 49
 
6.3%
번 49
 
6.3%
특 49
 
6.3%
별 49
 
6.3%
구 49
 
6.3%
Other values (43) 238
30.7%

허가(신고)번호
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2024-05-03T20:37:29.532167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters1078
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

Unique49 ?
Unique (%)100.0%

Sample

1st row3090000-101-1992-00528
2nd row3090000-101-2021-00175
3rd row3090000-101-1991-03662
4th row3090000-101-2017-00125
5th row3090000-101-2012-00025
ValueCountFrequency (%)
3090000-101-1992-00528 1
 
2.0%
3090000-101-1995-04059 1
 
2.0%
3090000-101-1995-03766 1
 
2.0%
3090000-101-1996-01687 1
 
2.0%
3090000-101-1992-04540 1
 
2.0%
3090000-101-2005-00298 1
 
2.0%
3090000-101-1999-04610 1
 
2.0%
3090000-101-2006-00181 1
 
2.0%
3090000-101-2001-05561 1
 
2.0%
3090000-101-2011-00145 1
 
2.0%
Other values (39) 39
79.6%
2024-05-03T20:37:30.566610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 450
41.7%
1 165
 
15.3%
- 147
 
13.6%
9 92
 
8.5%
3 70
 
6.5%
2 69
 
6.4%
5 24
 
2.2%
8 18
 
1.7%
6 17
 
1.6%
4 17
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 931
86.4%
Dash Punctuation 147
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 450
48.3%
1 165
 
17.7%
9 92
 
9.9%
3 70
 
7.5%
2 69
 
7.4%
5 24
 
2.6%
8 18
 
1.9%
6 17
 
1.8%
4 17
 
1.8%
7 9
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 450
41.7%
1 165
 
15.3%
- 147
 
13.6%
9 92
 
8.5%
3 70
 
6.5%
2 69
 
6.4%
5 24
 
2.2%
8 18
 
1.7%
6 17
 
1.6%
4 17
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 450
41.7%
1 165
 
15.3%
- 147
 
13.6%
9 92
 
8.5%
3 70
 
6.5%
2 69
 
6.4%
5 24
 
2.2%
8 18
 
1.7%
6 17
 
1.6%
4 17
 
1.6%

업태명
Categorical

Distinct6
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
한식
33 
식육(숯불구이)
8 
중국식
4 
일식
 
2
경양식
 
1

Length

Max length8
Median length2
Mean length3.0816327
Min length2

Unique

Unique2 ?
Unique (%)4.1%

Sample

1st row한식
2nd row한식
3rd row한식
4th row식육(숯불구이)
5th row식육(숯불구이)

Common Values

ValueCountFrequency (%)
한식 33
67.3%
식육(숯불구이) 8
 
16.3%
중국식 4
 
8.2%
일식 2
 
4.1%
경양식 1
 
2.0%
회집 1
 
2.0%

Length

2024-05-03T20:37:30.982455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T20:37:31.466115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 33
67.3%
식육(숯불구이 8
 
16.3%
중국식 4
 
8.2%
일식 2
 
4.1%
경양식 1
 
2.0%
회집 1
 
2.0%

주된음식
Text

MISSING 

Distinct38
Distinct (%)92.7%
Missing8
Missing (%)16.3%
Memory size524.0 B
2024-05-03T20:37:32.037454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length3.7560976
Min length2

Characters and Unicode

Total characters154
Distinct characters82
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

Unique36 ?
Unique (%)87.8%

Sample

1st row양념갈비
2nd row비빔국수
3rd row곰장어
4th row돼지고기
5th row식육구이
ValueCountFrequency (%)
돼지갈비 3
 
7.1%
순대국 2
 
4.8%
삼겹살 1
 
2.4%
만두전골 1
 
2.4%
민물장어 1
 
2.4%
초당순두부 1
 
2.4%
오리 1
 
2.4%
추어탕 1
 
2.4%
냉면 1
 
2.4%
동태찜 1
 
2.4%
Other values (29) 29
69.0%
2024-05-03T20:37:33.175566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
비 6
 
3.9%
지 6
 
3.9%
돼 5
 
3.2%
갈 5
 
3.2%
탕 5
 
3.2%
기 5
 
3.2%
국 4
 
2.6%
식 4
 
2.6%
고 4
 
2.6%
어 4
 
2.6%
Other values (72) 106
68.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 150
97.4%
Open Punctuation 1
 
0.6%
Other Punctuation 1
 
0.6%
Space Separator 1
 
0.6%
Close Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
비 6
 
4.0%
지 6
 
4.0%
돼 5
 
3.3%
갈 5
 
3.3%
탕 5
 
3.3%
기 5
 
3.3%
국 4
 
2.7%
식 4
 
2.7%
고 4
 
2.7%
어 4
 
2.7%
Other values (68) 102
68.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 150
97.4%
Common 4
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
비 6
 
4.0%
지 6
 
4.0%
돼 5
 
3.3%
갈 5
 
3.3%
탕 5
 
3.3%
기 5
 
3.3%
국 4
 
2.7%
식 4
 
2.7%
고 4
 
2.7%
어 4
 
2.7%
Other values (68) 102
68.0%
Common
ValueCountFrequency (%)
( 1
25.0%
, 1
25.0%
1
25.0%
) 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 150
97.4%
ASCII 4
 
2.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
비 6
 
4.0%
지 6
 
4.0%
돼 5
 
3.3%
갈 5
 
3.3%
탕 5
 
3.3%
기 5
 
3.3%
국 4
 
2.7%
식 4
 
2.7%
고 4
 
2.7%
어 4
 
2.7%
Other values (68) 102
68.0%
ASCII
ValueCountFrequency (%)
( 1
25.0%
, 1
25.0%
1
25.0%
) 1
25.0%

영업장면적(㎡)
Real number (ℝ)

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184.49469
Minimum53.13
Maximum853.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2024-05-03T20:37:33.794698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum53.13
5-th percentile64.416
Q191.63
median128.17
Q3234
95-th percentile450.182
Maximum853.4
Range800.27
Interquartile range (IQR)142.37

Descriptive statistics

Standard deviation150.72419
Coefficient of variation (CV)0.81695675
Kurtosis7.362582
Mean184.49469
Median Absolute Deviation (MAD)53.63
Skewness2.3619118
Sum9040.24
Variance22717.78
MonotonicityNot monotonic
2024-05-03T20:37:34.231570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
66.0 2
 
4.1%
111.18 1
 
2.0%
55.08 1
 
2.0%
87.32 1
 
2.0%
73.87 1
 
2.0%
316.8 1
 
2.0%
96.12 1
 
2.0%
377.86 1
 
2.0%
530.1 1
 
2.0%
102.0 1
 
2.0%
Other values (38) 38
77.6%
ValueCountFrequency (%)
53.13 1
2.0%
55.08 1
2.0%
63.36 1
2.0%
66.0 2
4.1%
66.03 1
2.0%
72.6 1
2.0%
73.87 1
2.0%
77.68 1
2.0%
81.0 1
2.0%
87.32 1
2.0%
ValueCountFrequency (%)
853.4 1
2.0%
530.1 1
2.0%
491.71 1
2.0%
387.89 1
2.0%
377.86 1
2.0%
337.76 1
2.0%
320.67 1
2.0%
316.8 1
2.0%
300.48 1
2.0%
295.2 1
2.0%

행정동명
Categorical

Distinct10
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
방학제1동
9 
도봉제1동
8 
창제5동
8 
도봉제2동
5 
방학제3동
5 
Other values (5)
14 

Length

Max length5
Median length5
Mean length4.7142857
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row쌍문제3동
2nd row창제1동
3rd row창제4동
4th row도봉제1동
5th row도봉제2동

Common Values

ValueCountFrequency (%)
방학제1동 9
18.4%
도봉제1동 8
16.3%
창제5동 8
16.3%
도봉제2동 5
10.2%
방학제3동 5
10.2%
쌍문제3동 3
 
6.1%
창제1동 3
 
6.1%
창제4동 3
 
6.1%
쌍문제2동 3
 
6.1%
쌍문제1동 2
 
4.1%

Length

2024-05-03T20:37:34.814334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T20:37:35.187712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
방학제1동 9
18.4%
도봉제1동 8
16.3%
창제5동 8
16.3%
도봉제2동 5
10.2%
방학제3동 5
10.2%
쌍문제3동 3
 
6.1%
창제1동 3
 
6.1%
창제4동 3
 
6.1%
쌍문제2동 3
 
6.1%
쌍문제1동 2
 
4.1%

급수시설구분
Categorical

Distinct3
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
상수도전용
33 
<NA>
15 
지하수전용
 
1

Length

Max length5
Median length5
Mean length4.6938776
Min length4

Unique

Unique1 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 33
67.3%
<NA> 15
30.6%
지하수전용 1
 
2.0%

Length

2024-05-03T20:37:35.755938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T20:37:36.408297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 33
67.3%
na 15
30.6%
지하수전용 1
 
2.0%

소재지전화번호
Text

MISSING 

Distinct40
Distinct (%)100.0%
Missing9
Missing (%)18.4%
Memory size524.0 B
2024-05-03T20:37:37.029411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.525
Min length10

Characters and Unicode

Total characters421
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

Unique40 ?
Unique (%)100.0%

Sample

1st row02 9006644
2nd row02 9992588
3rd row02 955 0366
4th row02 34931297
5th row02 955 5604
ValueCountFrequency (%)
02 34
41.5%
955 3
 
3.7%
9928479 1
 
1.2%
9916777 1
 
1.2%
9078885 1
 
1.2%
0234913334 1
 
1.2%
9915292 1
 
1.2%
9082274 1
 
1.2%
9908063 1
 
1.2%
9551554 1
 
1.2%
Other values (37) 37
45.1%
2024-05-03T20:37:38.323090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 70
16.6%
0 65
15.4%
2 64
15.2%
53
12.6%
5 38
9.0%
8 26
 
6.2%
4 25
 
5.9%
6 22
 
5.2%
7 22
 
5.2%
3 19
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 368
87.4%
Space Separator 53
 
12.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 70
19.0%
0 65
17.7%
2 64
17.4%
5 38
10.3%
8 26
 
7.1%
4 25
 
6.8%
6 22
 
6.0%
7 22
 
6.0%
3 19
 
5.2%
1 17
 
4.6%
Space Separator
ValueCountFrequency (%)
53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 421
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 70
16.6%
0 65
15.4%
2 64
15.2%
53
12.6%
5 38
9.0%
8 26
 
6.2%
4 25
 
5.9%
6 22
 
5.2%
7 22
 
5.2%
3 19
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 421
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 70
16.6%
0 65
15.4%
2 64
15.2%
53
12.6%
5 38
9.0%
8 26
 
6.2%
4 25
 
5.9%
6 22
 
5.2%
7 22
 
5.2%
3 19
 
4.5%

Interactions

2024-05-03T20:37:16.221635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:37:10.544099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:37:11.756587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:37:13.003754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:37:14.633554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:37:16.548724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:37:10.776271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:37:11.981129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:37:13.415443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:37:14.899682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:37:16.815828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:37:10.999269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:37:12.302388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:37:13.701973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:37:15.110167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:37:17.083928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:37:11.235070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:37:12.511514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:37:13.990216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:37:15.375762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:37:17.345702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:37:11.502293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:37:12.744986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:37:14.252287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:37:15.923728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T20:37:38.917144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
지정년도1.0000.8901.0001.0001.0001.0001.0001.0000.0000.7580.0000.3750.0001.000
지정번호0.8901.0000.8770.8901.0001.0001.0001.0000.0000.8460.3140.6080.7011.000
신청일자1.0000.8771.0001.0001.0001.0001.0001.0000.0000.9050.0000.3710.0001.000
지정일자1.0000.8901.0001.0001.0001.0001.0001.0000.0000.7580.0000.3750.0001.000
업소명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지지번1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
허가(신고)번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
업태명0.0000.0000.0000.0001.0001.0001.0001.0001.0000.9790.3240.0000.0001.000
주된음식0.7580.8460.9050.7581.0001.0001.0001.0000.9791.0000.2930.926NaN1.000
영업장면적(㎡)0.0000.3140.0000.0001.0001.0001.0001.0000.3240.2931.0000.0000.2321.000
행정동명0.3750.6080.3710.3751.0001.0001.0001.0000.0000.9260.0001.0000.0001.000
급수시설구분0.0000.7010.0000.0001.0001.0001.0001.0000.000NaN0.2320.0001.0001.000
소재지전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-05-03T20:37:39.499924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명업태명급수시설구분
행정동명1.0000.0000.000
업태명0.0001.0000.000
급수시설구분0.0000.0001.000
2024-05-03T20:37:39.918916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.0000.0580.9960.999-0.0190.0000.1370.000
지정번호0.0581.0000.0600.0590.1960.0000.2280.468
신청일자0.9960.0601.0000.997-0.0280.0000.1370.000
지정일자0.9990.0590.9971.000-0.0160.0000.1370.000
영업장면적(㎡)-0.0190.196-0.028-0.0161.0000.1900.0000.217
업태명0.0000.0000.0000.0000.1901.0000.0000.000
행정동명0.1370.2280.1370.1370.0000.0001.0000.000
급수시설구분0.0000.4680.0000.0000.2170.0000.0001.000

Missing values

2024-05-03T20:37:17.795022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T20:37:18.451157image/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.
2024-05-03T20:37:18.983832image/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
030900002007112007040220070417통나무식당서울특별시 도봉구 노해로60길 99, (쌍문동)서울특별시 도봉구 쌍문동 96번지 9호3090000-101-1992-00528한식양념갈비111.18쌍문제3동상수도전용02 9006644
13090000202262022101720221215망향비빔국수서울특별시 도봉구 도봉로 534, 1층 (창동)서울특별시 도봉구 창동 700번지 33호3090000-101-2021-00175한식비빔국수197.4창제1동<NA><NA>
23090000200242002071120020711토영자갈치곰장어서울특별시 도봉구 노해로69길 15, (창동,세정빌딩 107,108,109호(지상1층))서울특별시 도봉구 창동 10번지 3호 세정빌딩 107,108,109호(지상1층)3090000-101-1991-03662한식곰장어92.83창제4동상수도전용02 9992588
330900002017422017101720171206갈비세상 숯불갈비서울특별시 도봉구 도봉로181길 29, 1층 (도봉동)서울특별시 도봉구 도봉동 566번지 1층3090000-101-2017-00125식육(숯불구이)돼지고기177.48도봉제1동<NA>02 955 0366
430900002013362013110820131224반값소서울특별시 도봉구 마들로 664-5, 101,102호 (도봉동)서울특별시 도봉구 도봉동 636번지 15호 101,102호3090000-101-2012-00025식육(숯불구이)식육구이114.24도봉제2동상수도전용02 34931297
530900002015382015081120151231더맛있는족발보쌈(방학점)서울특별시 도봉구 도당로 120, 1층 (방학동)서울특별시 도봉구 방학동 701번지 24호3090000-101-2000-05108한식돼지족발72.6방학제1동상수도전용02 955 5604
630900002012292012103020121204한가향서울특별시 도봉구 도봉로169길 202, 1층 (도봉동)서울특별시 도봉구 도봉동 470번지 3호 1층3090000-101-2012-00045경양식코스요리283.7도봉제1동상수도전용02 9557722
730900002017412017101120171206반값소 푸드컴퍼니서울특별시 도봉구 방학로 173, 2층 201호 (방학동)서울특별시 도봉구 방학동 669번지 3호 2층-2013090000-101-2016-00134식육(숯불구이)소고기181.8방학제3동<NA>02 955 1297
830900002021452021121520211215신의주찹쌀순대도봉구청점서울특별시 도봉구 마들로 664-5, 104,105호 (도봉동)서울특별시 도봉구 도봉동 636번지 15호3090000-101-2021-00051한식<NA>91.63도봉제2동<NA><NA>
93090000202252022101720221215오섭생연구소서울특별시 도봉구 노해로 341, 신원리베르텔 1층 101호 (창동)서울특별시 도봉구 창동 338번지 신원리베르텔3090000-101-2018-00143한식해물뚝배기143.82창제5동상수도전용<NA>
시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
3930900002011282011050120110524문성해물짬뽕서울특별시 도봉구 우이천로 294, A동 (쌍문동)서울특별시 도봉구 쌍문동 103번지 153호 A3090000-101-2000-05333중국식<NA>92.4쌍문제3동<NA>02905 8999
4030900002017402017101720171206봉평메밀막국수서울특별시 도봉구 도봉산4길 15, 1층 (도봉동)서울특별시 도봉구 도봉동 282번지 309호 1층3090000-101-2012-00101한식막국수90.55도봉제1동상수도전용02 9562080
4130900002012312012102420121204은행골서울특별시 도봉구 마들로 684, (도봉동, 안세빌딩 101,102호(지상1층))서울특별시 도봉구 도봉동 635번지 안세빌딩 101,102호3090000-101-2009-00020한식모듬초밥63.36도봉제2동<NA>02 9554988
4230900002013352013110720131224쌈촌창동역점서울특별시 도봉구 노해로65길 11, 2층 2,3,4호 (창동, 한성빌딩)서울특별시 도봉구 창동 333번지 2호 한성빌딩 2층-2,3,43090000-101-2012-00055한식샤브샤브295.2창제5동상수도전용02 9004929
433090000202312023101020231120본 만두(쌍문점)서울특별시 도봉구 해등로 157, (쌍문동,(1층))서울특별시 도봉구 쌍문동 651번지 (1층)3090000-101-2003-00061식육(숯불구이)만두전골95.2쌍문제2동상수도전용02 9078885
443090000202342023101120231120파삼 솥뚜껑 삼겹살 쌍문점서울특별시 도봉구 도당로2길 13, 쌍문동근린생활시설 1층 (쌍문동)서울특별시 도봉구 쌍문동 19번지 22호 쌍문동근린생활시설3090000-101-2022-00105한식삼겹살95.24쌍문제2동<NA><NA>
453090000202352023101120231120콩사랑두부서울특별시 도봉구 도봉산4길 10, 지하1,지상2,3층 (도봉동)서울특별시 도봉구 도봉동 281번지 10호3090000-101-2022-00172한식초당순두부387.89도봉제1동<NA><NA>
463090000202322023101120231120우거지 품은 순대국서울특별시 도봉구 도봉산3길 31, 1층 (도봉동)서울특별시 도봉구 도봉동 558번지 4호3090000-101-2023-00004한식순대국144.78도봉제1동<NA><NA>
473090000202332023101120231120해등로139서울특별시 도봉구 해등로 139, 1층 113호 (창동, 미소애아파트)서울특별시 도봉구 창동 715번지 3호 미소애아파트3090000-101-2022-00102한식등심, 생삼겹살128.17창제5동<NA><NA>
4830900002015392015121120151231창원서울특별시 도봉구 도봉로180길 6, 지상1층 (도봉동, 태봉운동시설2동)서울특별시 도봉구 도봉동 62번지 4호 태봉운동시설2동, 지상1층3090000-101-2015-00034한식식육취급(소고기)234.0도봉제2동상수도전용02 34927892