Overview

Dataset statistics

Number of variables15
Number of observations80
Missing cells11
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.0 KiB
Average record size in memory127.7 B

Variable types

Categorical4
Numeric5
Text6

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,주된음식,영업장면적(㎡),행정동명,급수시설구분,소재지전화번호
Author강북구
URLhttps://data.seoul.go.kr/dataList/OA-10910/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 (74.6%)Imbalance
주된음식 has 3 (3.8%) missing valuesMissing
소재지전화번호 has 8 (10.0%) missing valuesMissing
업소명 has unique valuesUnique
소재지도로명 has unique valuesUnique
소재지지번 has unique valuesUnique
허가(신고)번호 has unique valuesUnique
영업장면적(㎡) has 2 (2.5%) zerosZeros

Reproduction

Analysis started2024-05-04 02:38:31.119995
Analysis finished2024-05-04 02:38:43.135377
Duration12.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
3080000
80 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3080000
2nd row3080000
3rd row3080000
4th row3080000
5th row3080000

Common Values

ValueCountFrequency (%)
3080000 80
100.0%

Length

2024-05-04T02:38:43.444837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:38:43.825050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3080000 80
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.225
Minimum2006
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-05-04T02:38:44.225584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2006
5-th percentile2006
Q12008
median2012
Q32018
95-th percentile2022
Maximum2023
Range17
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.8156816
Coefficient of variation (CV)0.002888739
Kurtosis-1.396101
Mean2013.225
Median Absolute Deviation (MAD)5.5
Skewness0.2646422
Sum161058
Variance33.822152
MonotonicityNot monotonic
2024-05-04T02:38:44.720159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2006 15
18.8%
2022 11
13.8%
2009 9
11.2%
2018 9
11.2%
2008 7
8.8%
2017 5
 
6.2%
2021 4
 
5.0%
2012 4
 
5.0%
2015 4
 
5.0%
2010 4
 
5.0%
Other values (4) 8
10.0%
ValueCountFrequency (%)
2006 15
18.8%
2008 7
8.8%
2009 9
11.2%
2010 4
 
5.0%
2011 3
 
3.8%
2012 4
 
5.0%
2013 2
 
2.5%
2014 2
 
2.5%
2015 4
 
5.0%
2017 5
 
6.2%
ValueCountFrequency (%)
2023 1
 
1.2%
2022 11
13.8%
2021 4
 
5.0%
2018 9
11.2%
2017 5
6.2%
2015 4
 
5.0%
2014 2
 
2.5%
2013 2
 
2.5%
2012 4
 
5.0%
2011 3
 
3.8%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)52.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.825
Minimum1
Maximum1115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-05-04T02:38:45.134177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.95
Q15
median10
Q364.25
95-th percentile188.1
Maximum1115
Range1114
Interquartile range (IQR)59.25

Descriptive statistics

Standard deviation212.11758
Coefficient of variation (CV)2.7610489
Kurtosis20.078243
Mean76.825
Median Absolute Deviation (MAD)7
Skewness4.5007859
Sum6146
Variance44993.868
MonotonicityNot monotonic
2024-05-04T02:38:45.621420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
5 7
 
8.8%
2 6
 
7.5%
3 5
 
6.2%
8 5
 
6.2%
11 5
 
6.2%
1 4
 
5.0%
4 4
 
5.0%
7 3
 
3.8%
10 3
 
3.8%
12 3
 
3.8%
Other values (32) 35
43.8%
ValueCountFrequency (%)
1 4
5.0%
2 6
7.5%
3 5
6.2%
4 4
5.0%
5 7
8.8%
6 2
 
2.5%
7 3
3.8%
8 5
6.2%
9 2
 
2.5%
10 3
3.8%
ValueCountFrequency (%)
1115 1
1.2%
1114 1
1.2%
1105 1
1.2%
190 1
1.2%
188 1
1.2%
182 1
1.2%
160 1
1.2%
151 1
1.2%
150 1
1.2%
148 1
1.2%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20131393
Minimum20060627
Maximum20230925
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-05-04T02:38:46.284418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060627
5-th percentile20060627
Q120080701
median20121101
Q320173591
95-th percentile20220826
Maximum20230925
Range170298
Interquartile range (IQR)92889.75

Descriptive statistics

Standard deviation56744.072
Coefficient of variation (CV)0.0028186858
Kurtosis-1.290051
Mean20131393
Median Absolute Deviation (MAD)45207
Skewness0.29833273
Sum1.6105115 × 109
Variance3.2198897 × 109
MonotonicityNot monotonic
2024-05-04T02:38:46.790178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
20060627 15
18.8%
20090910 9
11.2%
20080701 7
 
8.8%
20171115 5
 
6.2%
20201005 4
 
5.0%
20121101 4
 
5.0%
20220801 4
 
5.0%
20151118 4
 
5.0%
20161018 4
 
5.0%
20220826 4
 
5.0%
Other values (11) 20
25.0%
ValueCountFrequency (%)
20060627 15
18.8%
20080701 7
8.8%
20090910 9
11.2%
20100930 4
 
5.0%
20111020 3
 
3.8%
20121101 4
 
5.0%
20131129 2
 
2.5%
20141110 2
 
2.5%
20151118 4
 
5.0%
20161018 4
 
5.0%
ValueCountFrequency (%)
20230925 1
 
1.2%
20220826 4
5.0%
20220825 1
 
1.2%
20220817 1
 
1.2%
20220803 1
 
1.2%
20220801 4
5.0%
20201005 4
5.0%
20181112 1
 
1.2%
20181018 3
3.8%
20171115 5
6.2%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20133068
Minimum20060701
Maximum20231031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-05-04T02:38:47.208788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060701
5-th percentile20060701
Q120080908
median20121204
Q320180382
95-th percentile20221031
Maximum20231031
Range170330
Interquartile range (IQR)99473.75

Descriptive statistics

Standard deviation58116.628
Coefficient of variation (CV)0.0028866256
Kurtosis-1.3886285
Mean20133068
Median Absolute Deviation (MAD)53898.5
Skewness0.26314618
Sum1.6106454 × 109
Variance3.3775425 × 109
MonotonicityNot monotonic
2024-05-04T02:38:47.625151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20060701 15
18.8%
20221031 11
13.8%
20090910 9
11.2%
20080908 7
8.8%
20170103 5
 
6.2%
20180102 5
 
6.2%
20210106 4
 
5.0%
20121204 4
 
5.0%
20181221 4
 
5.0%
20151214 4
 
5.0%
Other values (5) 12
15.0%
ValueCountFrequency (%)
20060701 15
18.8%
20080908 7
8.8%
20090910 9
11.2%
20101008 4
 
5.0%
20111028 3
 
3.8%
20121204 4
 
5.0%
20131217 2
 
2.5%
20141217 2
 
2.5%
20151214 4
 
5.0%
20170103 5
 
6.2%
ValueCountFrequency (%)
20231031 1
 
1.2%
20221031 11
13.8%
20210106 4
 
5.0%
20181221 4
 
5.0%
20180102 5
6.2%
20170103 5
6.2%
20151214 4
 
5.0%
20141217 2
 
2.5%
20131217 2
 
2.5%
20121204 4
 
5.0%

업소명
Text

UNIQUE 

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
2024-05-04T02:38:48.307098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length6.0875
Min length2

Characters and Unicode

Total characters487
Distinct characters188
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

Unique80 ?
Unique (%)100.0%

Sample

1st row섬진강식당
2nd row어도참치
3rd row고메스퀘어 수유점
4th row우리가돼지갈비
5th row풍년갈비
ValueCountFrequency (%)
수유점 3
 
2.9%
오리구이 2
 
1.9%
신의주 2
 
1.9%
섬진강식당 1
 
1.0%
대가원 1
 
1.0%
광명족발 1
 
1.0%
문가네정육식당 1
 
1.0%
생생칼국수&돈까스 1
 
1.0%
털보정육식당 1
 
1.0%
김치삼겹살 1
 
1.0%
Other values (89) 89
86.4%
2024-05-04T02:38:49.631990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23
 
4.7%
점 14
 
2.9%
수 12
 
2.5%
이 11
 
2.3%
가 10
 
2.1%
유 9
 
1.8%
식 8
 
1.6%
당 8
 
1.6%
탕 8
 
1.6%
마 8
 
1.6%
Other values (178) 376
77.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 458
94.0%
Space Separator 23
 
4.7%
Open Punctuation 2
 
0.4%
Close Punctuation 2
 
0.4%
Other Punctuation 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 14
 
3.1%
수 12
 
2.6%
이 11
 
2.4%
가 10
 
2.2%
유 9
 
2.0%
식 8
 
1.7%
당 8
 
1.7%
탕 8
 
1.7%
마 8
 
1.7%
갈 7
 
1.5%
Other values (173) 363
79.3%
Other Punctuation
ValueCountFrequency (%)
& 1
50.0%
. 1
50.0%
Space Separator
ValueCountFrequency (%)
23
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 458
94.0%
Common 29
 
6.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 14
 
3.1%
수 12
 
2.6%
이 11
 
2.4%
가 10
 
2.2%
유 9
 
2.0%
식 8
 
1.7%
당 8
 
1.7%
탕 8
 
1.7%
마 8
 
1.7%
갈 7
 
1.5%
Other values (173) 363
79.3%
Common
ValueCountFrequency (%)
23
79.3%
( 2
 
6.9%
) 2
 
6.9%
& 1
 
3.4%
. 1
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 458
94.0%
ASCII 29
 
6.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
23
79.3%
( 2
 
6.9%
) 2
 
6.9%
& 1
 
3.4%
. 1
 
3.4%
Hangul
ValueCountFrequency (%)
점 14
 
3.1%
수 12
 
2.6%
이 11
 
2.4%
가 10
 
2.2%
유 9
 
2.0%
식 8
 
1.7%
당 8
 
1.7%
탕 8
 
1.7%
마 8
 
1.7%
갈 7
 
1.5%
Other values (173) 363
79.3%

소재지도로명
Text

UNIQUE 

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
2024-05-04T02:38:50.412558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length40
Mean length30.7625
Min length23

Characters and Unicode

Total characters2461
Distinct characters100
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)100.0%

Sample

1st row서울특별시 강북구 한천로 1151, (수유동,(4.19길 119))
2nd row서울특별시 강북구 도봉로 296, (번동,(도봉로 296))
3rd row서울특별시 강북구 도봉로 308, (주)북한산스카이 9층 (번동)
4th row서울특별시 강북구 솔샘로 334-8, (미아동)
5th row서울특별시 강북구 솔샘로67길 132, (미아동)
ValueCountFrequency (%)
서울특별시 80
17.8%
강북구 80
17.8%
수유동 29
 
6.5%
한천로 17
 
3.8%
1층 13
 
2.9%
미아동 11
 
2.4%
도봉로 8
 
1.8%
번동 6
 
1.3%
4.19로 5
 
1.1%
7 4
 
0.9%
Other values (155) 196
43.7%
2024-05-04T02:38:51.909312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
369
 
15.0%
1 132
 
5.4%
) 117
 
4.8%
( 117
 
4.8%
, 117
 
4.8%
로 85
 
3.5%
동 84
 
3.4%
울 81
 
3.3%
서 81
 
3.3%
구 81
 
3.3%
Other values (90) 1197
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1317
53.5%
Decimal Number 413
 
16.8%
Space Separator 369
 
15.0%
Other Punctuation 125
 
5.1%
Close Punctuation 117
 
4.8%
Open Punctuation 117
 
4.8%
Dash Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 85
 
6.5%
동 84
 
6.4%
울 81
 
6.2%
서 81
 
6.2%
구 81
 
6.2%
북 81
 
6.2%
강 80
 
6.1%
시 80
 
6.1%
별 80
 
6.1%
특 80
 
6.1%
Other values (74) 504
38.3%
Decimal Number
ValueCountFrequency (%)
1 132
32.0%
2 51
 
12.3%
3 42
 
10.2%
7 36
 
8.7%
0 32
 
7.7%
9 30
 
7.3%
4 27
 
6.5%
6 26
 
6.3%
8 20
 
4.8%
5 17
 
4.1%
Other Punctuation
ValueCountFrequency (%)
, 117
93.6%
. 8
 
6.4%
Space Separator
ValueCountFrequency (%)
369
100.0%
Close Punctuation
ValueCountFrequency (%)
) 117
100.0%
Open Punctuation
ValueCountFrequency (%)
( 117
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1317
53.5%
Common 1144
46.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 85
 
6.5%
동 84
 
6.4%
울 81
 
6.2%
서 81
 
6.2%
구 81
 
6.2%
북 81
 
6.2%
강 80
 
6.1%
시 80
 
6.1%
별 80
 
6.1%
특 80
 
6.1%
Other values (74) 504
38.3%
Common
ValueCountFrequency (%)
369
32.3%
1 132
 
11.5%
) 117
 
10.2%
( 117
 
10.2%
, 117
 
10.2%
2 51
 
4.5%
3 42
 
3.7%
7 36
 
3.1%
0 32
 
2.8%
9 30
 
2.6%
Other values (6) 101
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1317
53.5%
ASCII 1144
46.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
369
32.3%
1 132
 
11.5%
) 117
 
10.2%
( 117
 
10.2%
, 117
 
10.2%
2 51
 
4.5%
3 42
 
3.7%
7 36
 
3.1%
0 32
 
2.8%
9 30
 
2.6%
Other values (6) 101
 
8.8%
Hangul
ValueCountFrequency (%)
로 85
 
6.5%
동 84
 
6.4%
울 81
 
6.2%
서 81
 
6.2%
구 81
 
6.2%
북 81
 
6.2%
강 80
 
6.1%
시 80
 
6.1%
별 80
 
6.1%
특 80
 
6.1%
Other values (74) 504
38.3%

소재지지번
Text

UNIQUE 

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
2024-05-04T02:38:52.695510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length40
Mean length30.325
Min length22

Characters and Unicode

Total characters2426
Distinct characters88
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)100.0%

Sample

1st row서울특별시 강북구 수유동 273번지 11호 (4.19길 119)
2nd row서울특별시 강북구 번동 449번지 4호 (도봉로 296)
3rd row서울특별시 강북구 번동 449번지 1호 (주)북한산스카이
4th row서울특별시 강북구 미아동 65번지 9호
5th row서울특별시 강북구 미아동 317번지 19호
ValueCountFrequency (%)
서울특별시 80
 
17.0%
강북구 80
 
17.0%
수유동 38
 
8.1%
미아동 22
 
4.7%
번동 13
 
2.8%
지상1층 9
 
1.9%
우이동 7
 
1.5%
14호 5
 
1.1%
1호 5
 
1.1%
2호 5
 
1.1%
Other values (160) 207
43.9%
2024-05-04T02:38:53.917869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
585
24.1%
1 108
 
4.5%
지 97
 
4.0%
번 93
 
3.8%
동 84
 
3.5%
서 81
 
3.3%
북 81
 
3.3%
구 81
 
3.3%
울 81
 
3.3%
호 81
 
3.3%
Other values (78) 1054
43.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1288
53.1%
Space Separator 585
24.1%
Decimal Number 463
 
19.1%
Open Punctuation 40
 
1.6%
Close Punctuation 40
 
1.6%
Other Punctuation 9
 
0.4%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 97
 
7.5%
번 93
 
7.2%
동 84
 
6.5%
서 81
 
6.3%
북 81
 
6.3%
구 81
 
6.3%
울 81
 
6.3%
호 81
 
6.3%
특 80
 
6.2%
별 80
 
6.2%
Other values (62) 449
34.9%
Decimal Number
ValueCountFrequency (%)
1 108
23.3%
2 62
13.4%
4 48
10.4%
3 46
9.9%
7 46
9.9%
5 36
 
7.8%
6 35
 
7.6%
0 30
 
6.5%
8 26
 
5.6%
9 26
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 6
66.7%
. 3
33.3%
Space Separator
ValueCountFrequency (%)
585
100.0%
Open Punctuation
ValueCountFrequency (%)
( 40
100.0%
Close Punctuation
ValueCountFrequency (%)
) 40
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1288
53.1%
Common 1138
46.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 97
 
7.5%
번 93
 
7.2%
동 84
 
6.5%
서 81
 
6.3%
북 81
 
6.3%
구 81
 
6.3%
울 81
 
6.3%
호 81
 
6.3%
특 80
 
6.2%
별 80
 
6.2%
Other values (62) 449
34.9%
Common
ValueCountFrequency (%)
585
51.4%
1 108
 
9.5%
2 62
 
5.4%
4 48
 
4.2%
3 46
 
4.0%
7 46
 
4.0%
( 40
 
3.5%
) 40
 
3.5%
5 36
 
3.2%
6 35
 
3.1%
Other values (6) 92
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1288
53.1%
ASCII 1138
46.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
585
51.4%
1 108
 
9.5%
2 62
 
5.4%
4 48
 
4.2%
3 46
 
4.0%
7 46
 
4.0%
( 40
 
3.5%
) 40
 
3.5%
5 36
 
3.2%
6 35
 
3.1%
Other values (6) 92
 
8.1%
Hangul
ValueCountFrequency (%)
지 97
 
7.5%
번 93
 
7.2%
동 84
 
6.5%
서 81
 
6.3%
북 81
 
6.3%
구 81
 
6.3%
울 81
 
6.3%
호 81
 
6.3%
특 80
 
6.2%
별 80
 
6.2%
Other values (62) 449
34.9%

허가(신고)번호
Text

UNIQUE 

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
2024-05-04T02:38:54.614554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters1760
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

Unique80 ?
Unique (%)100.0%

Sample

1st row3080000-101-1984-04583
2nd row3080000-101-2007-00243
3rd row3080000-101-2018-00256
4th row3080000-101-2004-00324
5th row3080000-101-1990-00282
ValueCountFrequency (%)
3080000-101-1984-04583 1
 
1.2%
3080000-101-2007-00243 1
 
1.2%
3080000-101-2021-00179 1
 
1.2%
3080000-101-2019-00089 1
 
1.2%
3080000-101-2019-00038 1
 
1.2%
3080000-101-2016-00189 1
 
1.2%
3080000-101-2005-00362 1
 
1.2%
3080000-101-1991-04567 1
 
1.2%
3080000-101-1984-04586 1
 
1.2%
3080000-101-2022-00133 1
 
1.2%
Other values (70) 70
87.5%
2024-05-04T02:38:56.182934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 722
41.0%
1 249
 
14.1%
- 240
 
13.6%
8 131
 
7.4%
3 121
 
6.9%
2 97
 
5.5%
9 73
 
4.1%
5 37
 
2.1%
6 35
 
2.0%
4 29
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1520
86.4%
Dash Punctuation 240
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 722
47.5%
1 249
 
16.4%
8 131
 
8.6%
3 121
 
8.0%
2 97
 
6.4%
9 73
 
4.8%
5 37
 
2.4%
6 35
 
2.3%
4 29
 
1.9%
7 26
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 240
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1760
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 722
41.0%
1 249
 
14.1%
- 240
 
13.6%
8 131
 
7.4%
3 121
 
6.9%
2 97
 
5.5%
9 73
 
4.1%
5 37
 
2.1%
6 35
 
2.0%
4 29
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 722
41.0%
1 249
 
14.1%
- 240
 
13.6%
8 131
 
7.4%
3 121
 
6.9%
2 97
 
5.5%
9 73
 
4.1%
5 37
 
2.1%
6 35
 
2.0%
4 29
 
1.6%

업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
한식
73 
중국식
 
3
일식
 
2
뷔페식
 
1
기타
 
1

Length

Max length3
Median length2
Mean length2.05
Min length2

Unique

Unique2 ?
Unique (%)2.5%

Sample

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

Common Values

ValueCountFrequency (%)
한식 73
91.2%
중국식 3
 
3.8%
일식 2
 
2.5%
뷔페식 1
 
1.2%
기타 1
 
1.2%

Length

2024-05-04T02:38:56.918943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:38:57.387563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 73
91.2%
중국식 3
 
3.8%
일식 2
 
2.5%
뷔페식 1
 
1.2%
기타 1
 
1.2%

주된음식
Text

MISSING 

Distinct47
Distinct (%)61.0%
Missing3
Missing (%)3.8%
Memory size772.0 B
2024-05-04T02:38:58.055129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length3.4545455
Min length1

Characters and Unicode

Total characters266
Distinct characters94
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

Unique31 ?
Unique (%)40.3%

Sample

1st row장어구이
2nd row회
3rd row생선회
4th row돼지갈비
5th row돼지갈비
ValueCountFrequency (%)
한정식 7
 
8.9%
돼지갈비 5
 
6.3%
장어구이 4
 
5.1%
낙지볶음 3
 
3.8%
설렁탕 3
 
3.8%
해물탕 3
 
3.8%
삼겹살 3
 
3.8%
칼국수 3
 
3.8%
회 2
 
2.5%
소고기 2
 
2.5%
Other values (38) 44
55.7%
2024-05-04T02:38:59.945880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
탕 10
 
3.8%
지 10
 
3.8%
구 10
 
3.8%
한 9
 
3.4%
이 9
 
3.4%
비 8
 
3.0%
기 8
 
3.0%
갈 8
 
3.0%
식 8
 
3.0%
정 7
 
2.6%
Other values (84) 179
67.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 260
97.7%
Space Separator 2
 
0.8%
Other Punctuation 2
 
0.8%
Open Punctuation 1
 
0.4%
Close Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 10
 
3.8%
지 10
 
3.8%
구 10
 
3.8%
한 9
 
3.5%
이 9
 
3.5%
비 8
 
3.1%
기 8
 
3.1%
갈 8
 
3.1%
식 8
 
3.1%
정 7
 
2.7%
Other values (80) 173
66.5%
Space Separator
ValueCountFrequency (%)
2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 260
97.7%
Common 6
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 10
 
3.8%
지 10
 
3.8%
구 10
 
3.8%
한 9
 
3.5%
이 9
 
3.5%
비 8
 
3.1%
기 8
 
3.1%
갈 8
 
3.1%
식 8
 
3.1%
정 7
 
2.7%
Other values (80) 173
66.5%
Common
ValueCountFrequency (%)
2
33.3%
, 2
33.3%
( 1
16.7%
) 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 260
97.7%
ASCII 6
 
2.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
탕 10
 
3.8%
지 10
 
3.8%
구 10
 
3.8%
한 9
 
3.5%
이 9
 
3.5%
비 8
 
3.1%
기 8
 
3.1%
갈 8
 
3.1%
식 8
 
3.1%
정 7
 
2.7%
Other values (80) 173
66.5%
ASCII
ValueCountFrequency (%)
2
33.3%
, 2
33.3%
( 1
16.7%
) 1
16.7%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173.00475
Minimum0
Maximum891
Zeros2
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-05-04T02:39:00.563558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32.94
Q168.7075
median110.16
Q3208.015
95-th percentile488.645
Maximum891
Range891
Interquartile range (IQR)139.3075

Descriptive statistics

Standard deviation170.08308
Coefficient of variation (CV)0.98311217
Kurtosis5.1936086
Mean173.00475
Median Absolute Deviation (MAD)53.185
Skewness2.1874018
Sum13840.38
Variance28928.253
MonotonicityNot monotonic
2024-05-04T02:39:01.343346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2
 
2.5%
92.4 2
 
2.5%
188.04 1
 
1.2%
57.4 1
 
1.2%
24.84 1
 
1.2%
237.42 1
 
1.2%
171.86 1
 
1.2%
160.0 1
 
1.2%
123.12 1
 
1.2%
94.85 1
 
1.2%
Other values (68) 68
85.0%
ValueCountFrequency (%)
0.0 2
2.5%
24.84 1
1.2%
29.52 1
1.2%
33.12 1
1.2%
44.1 1
1.2%
45.75 1
1.2%
48.0 1
1.2%
49.44 1
1.2%
50.0 1
1.2%
52.8 1
1.2%
ValueCountFrequency (%)
891.0 1
1.2%
726.29 1
1.2%
688.41 1
1.2%
626.3 1
1.2%
481.4 1
1.2%
445.0 1
1.2%
441.34 1
1.2%
420.81 1
1.2%
412.52 1
1.2%
378.92 1
1.2%

행정동명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
수유제3동
18 
우이동
12 
번제1동
9 
미아동
8 
수유제2동
7 
Other values (8)
26 

Length

Max length5
Median length4
Mean length3.9
Min length3

Unique

Unique3 ?
Unique (%)3.8%

Sample

1st row수유제2동
2nd row번제1동
3rd row번제1동
4th row송천동
5th row송천동

Common Values

ValueCountFrequency (%)
수유제3동 18
22.5%
우이동 12
15.0%
번제1동 9
11.2%
미아동 8
10.0%
수유제2동 7
 
8.8%
송천동 7
 
8.8%
송중동 5
 
6.2%
인수동 4
 
5.0%
수유제1동 4
 
5.0%
번제2동 3
 
3.8%
Other values (3) 3
 
3.8%

Length

2024-05-04T02:39:02.096850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수유제3동 18
22.5%
우이동 12
15.0%
번제1동 9
11.2%
미아동 8
10.0%
수유제2동 7
 
8.8%
송천동 7
 
8.8%
송중동 5
 
6.2%
인수동 4
 
5.0%
수유제1동 4
 
5.0%
번제2동 3
 
3.8%
Other values (3) 3
 
3.8%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size772.0 B
상수도전용
58 
<NA>
22 

Length

Max length5
Median length5
Mean length4.725
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 58
72.5%
<NA> 22
 
27.5%

Length

2024-05-04T02:39:02.769110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:39:03.348293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 58
72.5%
na 22
 
27.5%

소재지전화번호
Text

MISSING 

Distinct72
Distinct (%)100.0%
Missing8
Missing (%)10.0%
Memory size772.0 B
2024-05-04T02:39:03.924181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.027778
Min length10

Characters and Unicode

Total characters794
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

Unique72 ?
Unique (%)100.0%

Sample

1st row02 9939545
2nd row02 9000037
3rd row02 69566275
4th row02 981 9800
5th row02 9801631
ValueCountFrequency (%)
02 66
37.7%
983 5
 
2.9%
980 5
 
2.9%
0092 2
 
1.1%
905 2
 
1.1%
986 2
 
1.1%
02993 2
 
1.1%
996 2
 
1.1%
9893392 1
 
0.6%
982 1
 
0.6%
Other values (87) 87
49.7%
2024-05-04T02:39:05.303940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 141
17.8%
141
17.8%
9 137
17.3%
2 118
14.9%
8 50
 
6.3%
3 43
 
5.4%
5 36
 
4.5%
4 35
 
4.4%
1 34
 
4.3%
6 31
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 653
82.2%
Space Separator 141
 
17.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 141
21.6%
9 137
21.0%
2 118
18.1%
8 50
 
7.7%
3 43
 
6.6%
5 36
 
5.5%
4 35
 
5.4%
1 34
 
5.2%
6 31
 
4.7%
7 28
 
4.3%
Space Separator
ValueCountFrequency (%)
141
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 794
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 141
17.8%
141
17.8%
9 137
17.3%
2 118
14.9%
8 50
 
6.3%
3 43
 
5.4%
5 36
 
4.5%
4 35
 
4.4%
1 34
 
4.3%
6 31
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 794
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 141
17.8%
141
17.8%
9 137
17.3%
2 118
14.9%
8 50
 
6.3%
3 43
 
5.4%
5 36
 
4.5%
4 35
 
4.4%
1 34
 
4.3%
6 31
 
3.9%

Interactions

2024-05-04T02:38:39.935017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:38:34.035170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:38:35.609373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:38:37.025451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:38:38.573467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:38:40.187598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:38:34.403439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:38:35.889871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:38:37.370871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:38:38.900365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:38:40.469965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:38:34.635090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:38:36.210142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:38:37.640145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:38:39.152002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:38:40.761513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:38:34.917562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:38:36.500340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:38:37.905439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:38:39.421921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:38:41.046337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:38:35.247598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:38:36.788722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:38:38.222641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:38:39.698516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T02:39:05.681541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명소재지전화번호
지정년도1.0000.8320.9981.0001.0001.0001.0001.0000.0000.7650.4020.4401.000
지정번호0.8321.0000.8320.8321.0001.0001.0001.0000.0000.0000.7370.0001.000
신청일자0.9980.8321.0000.9981.0001.0001.0001.0000.0000.7500.4600.4231.000
지정일자1.0000.8320.9981.0001.0001.0001.0001.0000.0000.7650.4020.4401.000
업소명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지지번1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
허가(신고)번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
업태명0.0000.0000.0000.0001.0001.0001.0001.0001.0000.9500.6610.0001.000
주된음식0.7650.0000.7500.7651.0001.0001.0001.0000.9501.0000.0000.3401.000
영업장면적(㎡)0.4020.7370.4600.4021.0001.0001.0001.0000.6610.0001.0000.0001.000
행정동명0.4400.0000.4230.4401.0001.0001.0001.0000.0000.3400.0001.0001.000
소재지전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-05-04T02:39:06.104568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
급수시설구분업태명행정동명
급수시설구분1.0001.0001.000
업태명1.0001.0000.000
행정동명1.0000.0001.000
2024-05-04T02:39:06.493032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.000-0.6740.9980.9990.1520.0000.2211.000
지정번호-0.6741.000-0.659-0.668-0.1130.0000.0001.000
신청일자0.998-0.6591.0000.9990.1440.0000.2131.000
지정일자0.999-0.6680.9991.0000.1470.0000.2211.000
영업장면적(㎡)0.152-0.1130.1440.1471.0000.4460.0001.000
업태명0.0000.0000.0000.0000.4461.0000.0001.000
행정동명0.2210.0000.2130.2210.0000.0001.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-04T02:38:41.334020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T02:38:42.346957image/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.
2024-05-04T02:38:42.956212image/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
030800002006892006062720060701섬진강식당서울특별시 강북구 한천로 1151, (수유동,(4.19길 119))서울특별시 강북구 수유동 273번지 11호 (4.19길 119)3080000-101-1984-04583한식장어구이188.04수유제2동상수도전용02 9939545
1308000020081882008070120080908어도참치서울특별시 강북구 도봉로 296, (번동,(도봉로 296))서울특별시 강북구 번동 449번지 4호 (도봉로 296)3080000-101-2007-00243일식회297.0번제1동상수도전용02 9000037
23080000202112020100520210106고메스퀘어 수유점서울특별시 강북구 도봉로 308, (주)북한산스카이 9층 (번동)서울특별시 강북구 번동 449번지 1호 (주)북한산스카이3080000-101-2018-00256뷔페식생선회891.0번제1동<NA>02 69566275
330800002012122012110120121204우리가돼지갈비서울특별시 강북구 솔샘로 334-8, (미아동)서울특별시 강북구 미아동 65번지 9호3080000-101-2004-00324한식돼지갈비230.64송천동상수도전용02 981 9800
430800002006592006062720060701풍년갈비서울특별시 강북구 솔샘로67길 132, (미아동)서울특별시 강북구 미아동 317번지 19호3080000-101-1990-00282한식돼지갈비118.9송천동상수도전용02 9801631
5308000020061342006062720060701시골쌈밥서울특별시 강북구 덕릉로19길 8, (수유동,(원앙길 4))서울특별시 강북구 수유동 47번지 14호 (원앙길 4)3080000-101-2001-08750한식쌈밥101.79인수동상수도전용02 9081264
630800002009192009091020090910갑식이네착한낙지서울특별시 강북구 한천로 1124, (수유동)서울특별시 강북구 수유동 254번지 32호3080000-101-2000-08535한식낙지볶음67.2수유제2동상수도전용02 9977779
730800002018112018101820181221기품서울특별시 강북구 도봉로49길 7, (미아동,(밤꽃3길 5)(지상1층))서울특별시 강북구 미아동 304번지 16호 (밤꽃3길 5)(지상1층)3080000-101-2002-00727한식장어구이200.9미아동<NA>02 985 7744
83080000202152020100520210106명인갈비(미아점)서울특별시 강북구 솔샘로 327, 테마빌딩 (미아동)서울특별시 강북구 미아동 374번지 13호 테마빌딩3080000-101-2019-00291한식돼지갈비626.3미아동<NA>02 986 9292
930800002009132009091020090910부잣집설렁탕서울특별시 강북구 도봉로8길 9, (미아동,(하천길 5))서울특별시 강북구 미아동 860번지 223호 (하천길 5)3080000-101-1998-05722한식설렁탕89.6송중동상수도전용02 9829944
시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
703080000202152020100520210106오마니 오리구이서울특별시 강북구 도봉로10라길 2, (미아동,(지상1층))서울특별시 강북구 미아동 90번지 12호 (지상1층)3080000-101-2005-00361한식오리로스48.0송중동상수도전용02 989 5292
713080000200982009091020090910토종집(빨래골옻닭)서울특별시 강북구 인수봉로19나길 32, (수유동,(산마루길 20-2))서울특별시 강북구 수유동 486번지 726호 (산마루길 20-2)3080000-101-1994-06983한식한정식33.12수유제1동상수도전용0209813104
72308000020061162006062720060701춘천집서울특별시 강북구 도봉로87길 28, (수유동,(장수길 20)(지상1,2층))서울특별시 강북구 수유동 192번지 55호 (장수길 20)(지상1,2층)3080000-101-1995-00864한식닭갈비114.95수유제3동상수도전용02 9988512
73308000020081822008070120080908왕가서울특별시 강북구 삼양로 681, (우이동,(우이동길 337)(지상1,2층))서울특별시 강북구 우이동 4번지 8호 (우이동길 337)(지상1,2층)3080000-101-2007-00099한식소고기481.4우이동상수도전용02 9089277
743080000201822017111520180102중국요리 귀종서울특별시 강북구 솔샘로67길 138, (미아동)서울특별시 강북구 미아동 317번지 22호3080000-101-2014-00223중국식해물짬뽕133.43미아동상수도전용02 983 8388
753080000201032010093020101008수유감자탕서울특별시 강북구 수유로 82, 1층 (수유동)서울특별시 강북구 수유동 7번지 17호 지상1층3080000-101-1993-00215한식한정식101.71인수동상수도전용02 9040685
76308000020081602008070120080908마라상회서울특별시 강북구 오패산로77길 14, (번동,(울타리길 12)(지상1층))서울특별시 강북구 번동 446번지 2호 (울타리길 12)(지상1층)3080000-101-1997-05885한식<NA>66.24번제1동상수도전용02 9955030
773080000202312023092520231031대찬식당서울특별시 강북구 한천로139나길 20, 1층 (수유동)서울특별시 강북구 수유동 192번지 71호3080000-101-2007-00341한식곱창구이, 전골95.37수유제3동상수도전용0263695618
7830800002022102022080320221031낙지왕궁서울특별시 강북구 덕릉로24길 6, 1층 (수유동)서울특별시 강북구 수유동 48번지 23호3080000-101-2021-00182한식낙지볶음252.98수유제1동<NA><NA>
793080000202282022080120221031진송추어탕 서울본점서울특별시 강북구 한천로 1161, 대성빌딩 1층 (수유동)서울특별시 강북구 수유동 273번지 72호 대성빌딩3080000-101-2020-00205한식추어탕193.8수유제2동<NA><NA>