Overview

Dataset statistics

Number of variables16
Number of observations195
Missing cells379
Missing cells (%)12.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.0 KiB
Average record size in memory136.7 B

Variable types

Categorical4
Numeric6
Unsupported1
Text5

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,취소일자,불가일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,주된음식,영업장면적(㎡),행정동명,급수시설구분
Author강북구
URLhttps://data.seoul.go.kr/dataList/OA-10929/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
행정동명 is highly overall correlated with 급수시설구분High correlation
업태명 is highly overall correlated with 급수시설구분High correlation
급수시설구분 is highly overall correlated with 지정년도 and 7 other fieldsHigh 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
취소일자 is highly overall correlated with 급수시설구분High correlation
영업장면적(㎡) is highly overall correlated with 급수시설구분High correlation
업태명 is highly imbalanced (67.4%)Imbalance
지정년도 has 17 (8.7%) missing valuesMissing
지정번호 has 17 (8.7%) missing valuesMissing
지정일자 has 17 (8.7%) missing valuesMissing
취소일자 has 84 (43.1%) missing valuesMissing
불가일자 has 195 (100.0%) missing valuesMissing
주된음식 has 49 (25.1%) missing valuesMissing
불가일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
영업장면적(㎡) has 4 (2.1%) zerosZeros

Reproduction

Analysis started2024-05-18 03:26:20.848500
Analysis finished2024-05-18 03:26:36.006342
Duration15.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
3080000
195 

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 195
100.0%

Length

2024-05-18T12:26:36.284221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:26:36.633236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3080000 195
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)8.4%
Missing17
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean2010.4944
Minimum2006
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-05-18T12:26:37.053585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2006
5-th percentile2006
Q12006
median2009
Q32013
95-th percentile2022
Maximum2023
Range17
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.1346251
Coefficient of variation (CV)0.0025539117
Kurtosis-0.085702875
Mean2010.4944
Median Absolute Deviation (MAD)3
Skewness1.073694
Sum357868
Variance26.364375
MonotonicityNot monotonic
2024-05-18T12:26:37.421587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2006 61
31.3%
2009 22
 
11.3%
2008 20
 
10.3%
2018 11
 
5.6%
2022 11
 
5.6%
2010 10
 
5.1%
2012 8
 
4.1%
2011 8
 
4.1%
2017 6
 
3.1%
2021 5
 
2.6%
Other values (5) 16
 
8.2%
(Missing) 17
 
8.7%
ValueCountFrequency (%)
2006 61
31.3%
2007 3
 
1.5%
2008 20
 
10.3%
2009 22
 
11.3%
2010 10
 
5.1%
2011 8
 
4.1%
2012 8
 
4.1%
2013 4
 
2.1%
2014 3
 
1.5%
2015 5
 
2.6%
ValueCountFrequency (%)
2023 1
 
0.5%
2022 11
5.6%
2021 5
2.6%
2018 11
5.6%
2017 6
3.1%
2015 5
2.6%
2014 3
 
1.5%
2013 4
 
2.1%
2012 8
4.1%
2011 8
4.1%

지정번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct103
Distinct (%)57.9%
Missing17
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean100.95506
Minimum1
Maximum1116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-05-18T12:26:37.851909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median26
Q3110.5
95-th percentile196.3
Maximum1116
Range1115
Interquartile range (IQR)104.5

Descriptive statistics

Standard deviation227.51623
Coefficient of variation (CV)2.2536388
Kurtosis15.12036
Mean100.95506
Median Absolute Deviation (MAD)23
Skewness3.9642784
Sum17970
Variance51763.636
MonotonicityNot monotonic
2024-05-18T12:26:38.239310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 10
 
5.1%
3 9
 
4.6%
1 8
 
4.1%
2 7
 
3.6%
4 7
 
3.6%
7 6
 
3.1%
12 6
 
3.1%
11 5
 
2.6%
8 5
 
2.6%
10 5
 
2.6%
Other values (93) 110
56.4%
(Missing) 17
 
8.7%
ValueCountFrequency (%)
1 8
4.1%
2 7
3.6%
3 9
4.6%
4 7
3.6%
5 10
5.1%
6 5
2.6%
7 6
3.1%
8 5
2.6%
9 3
 
1.5%
10 5
2.6%
ValueCountFrequency (%)
1116 1
0.5%
1115 1
0.5%
1114 1
0.5%
1112 1
0.5%
1111 1
0.5%
1110 1
0.5%
1105 1
0.5%
1104 1
0.5%
198 1
0.5%
196 1
0.5%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20101989
Minimum20060627
Maximum20230925
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-05-18T12:26:38.646594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060627
5-th percentile20060627
Q120060627
median20080701
Q320121101
95-th percentile20220801
Maximum20230925
Range170298
Interquartile range (IQR)60474

Descriptive statistics

Standard deviation48655.804
Coefficient of variation (CV)0.0024204472
Kurtosis0.35488255
Mean20101989
Median Absolute Deviation (MAD)20074
Skewness1.2073244
Sum3.9198879 × 109
Variance2.3673872 × 109
MonotonicityNot monotonic
2024-05-18T12:26:38.996241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
20060627 66
33.8%
20090910 25
 
12.8%
20080701 23
 
11.8%
20100930 10
 
5.1%
20121101 8
 
4.1%
20111020 7
 
3.6%
20171115 7
 
3.6%
20161018 5
 
2.6%
20201005 5
 
2.6%
20151118 5
 
2.6%
Other values (16) 34
17.4%
ValueCountFrequency (%)
20060627 66
33.8%
20070530 5
 
2.6%
20070531 3
 
1.5%
20070601 1
 
0.5%
20080701 23
 
11.8%
20090910 25
 
12.8%
20100930 10
 
5.1%
20111020 7
 
3.6%
20111028 1
 
0.5%
20121101 8
 
4.1%
ValueCountFrequency (%)
20230925 1
 
0.5%
20220826 4
2.1%
20220825 1
 
0.5%
20220817 1
 
0.5%
20220803 1
 
0.5%
20220801 4
2.1%
20201005 5
2.6%
20181112 1
 
0.5%
20181018 3
1.5%
20171115 7
3.6%

지정일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)9.0%
Missing17
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean20105757
Minimum20060701
Maximum20231031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-05-18T12:26:39.370392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060701
5-th percentile20060701
Q120060701
median20090910
Q320131217
95-th percentile20221031
Maximum20231031
Range170330
Interquartile range (IQR)70516

Descriptive statistics

Standard deviation51331.918
Coefficient of variation (CV)0.0025530955
Kurtosis-0.087851683
Mean20105757
Median Absolute Deviation (MAD)30209
Skewness1.0688936
Sum3.5788247 × 109
Variance2.6349658 × 109
MonotonicityNot monotonic
2024-05-18T12:26:40.088236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
20060701 61
31.3%
20090910 22
 
11.3%
20080908 20
 
10.3%
20221031 11
 
5.6%
20101008 10
 
5.1%
20121204 8
 
4.1%
20111028 8
 
4.1%
20180102 7
 
3.6%
20170103 6
 
3.1%
20210106 5
 
2.6%
Other values (6) 20
 
10.3%
(Missing) 17
 
8.7%
ValueCountFrequency (%)
20060701 61
31.3%
20070706 3
 
1.5%
20080908 20
 
10.3%
20090910 22
 
11.3%
20101008 10
 
5.1%
20111028 8
 
4.1%
20121204 8
 
4.1%
20131217 4
 
2.1%
20141217 3
 
1.5%
20151214 5
 
2.6%
ValueCountFrequency (%)
20231031 1
 
0.5%
20221031 11
5.6%
20210106 5
2.6%
20181221 4
 
2.1%
20180102 7
3.6%
20170103 6
3.1%
20151214 5
2.6%
20141217 3
 
1.5%
20131217 4
 
2.1%
20121204 8
4.1%

취소일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)20.7%
Missing84
Missing (%)43.1%
Infinite0
Infinite (%)0.0%
Mean20179470
Minimum20060701
Maximum20221031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-05-18T12:26:40.470510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060701
5-th percentile20091112
Q120120365
median20210923
Q320210923
95-th percentile20221031
Maximum20221031
Range160330
Interquartile range (IQR)90558

Descriptive statistics

Standard deviation50164.049
Coefficient of variation (CV)0.0024858953
Kurtosis-0.57551828
Mean20179470
Median Absolute Deviation (MAD)0
Skewness-1.0769911
Sum2.2399212 × 109
Variance2.5164318 × 109
MonotonicityNot monotonic
2024-05-18T12:26:40.880938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
20210923 60
30.8%
20101008 10
 
5.1%
20221031 9
 
4.6%
20181221 6
 
3.1%
20110329 3
 
1.5%
20101223 2
 
1.0%
20060701 2
 
1.0%
20211224 2
 
1.0%
20071001 2
 
1.0%
20091112 2
 
1.0%
Other values (13) 13
 
6.7%
(Missing) 84
43.1%
ValueCountFrequency (%)
20060701 2
 
1.0%
20071001 2
 
1.0%
20090910 1
 
0.5%
20091112 2
 
1.0%
20100208 1
 
0.5%
20100603 1
 
0.5%
20101008 10
5.1%
20101119 1
 
0.5%
20101223 2
 
1.0%
20110329 3
 
1.5%
ValueCountFrequency (%)
20221031 9
 
4.6%
20211224 2
 
1.0%
20210929 1
 
0.5%
20210923 60
30.8%
20190123 1
 
0.5%
20181221 6
 
3.1%
20180514 1
 
0.5%
20180101 1
 
0.5%
20160826 1
 
0.5%
20120425 1
 
0.5%

불가일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing195
Missing (%)100.0%
Memory size1.8 KiB

업소명
Text

Distinct167
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-05-18T12:26:41.697776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length6.5128205
Min length1

Characters and Unicode

Total characters1270
Distinct characters280
Distinct categories6 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique142 ?
Unique (%)72.8%

Sample

1st row섬진강식당
2nd row어도참치
3rd row고메스퀘어 수유점
4th row우리가돼지갈비
5th row풍년갈비
ValueCountFrequency (%)
수유점 7
 
2.5%
미아점 4
 
1.4%
수유역점 3
 
1.1%
오리구이 3
 
1.1%
닭한마리 3
 
1.1%
공릉동 3
 
1.1%
숯불닭갈비 3
 
1.1%
챠이니 3
 
1.1%
감탄 3
 
1.1%
수유 3
 
1.1%
Other values (213) 250
87.7%
2024-05-18T12:26:43.031231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
90
 
7.1%
점 33
 
2.6%
수 32
 
2.5%
유 23
 
1.8%
이 22
 
1.7%
탕 21
 
1.7%
리 20
 
1.6%
비 19
 
1.5%
갈 19
 
1.5%
마 17
 
1.3%
Other values (270) 974
76.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1156
91.0%
Space Separator 90
 
7.1%
Decimal Number 7
 
0.6%
Open Punctuation 6
 
0.5%
Close Punctuation 6
 
0.5%
Other Punctuation 5
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 33
 
2.9%
수 32
 
2.8%
유 23
 
2.0%
이 22
 
1.9%
탕 21
 
1.8%
리 20
 
1.7%
비 19
 
1.6%
갈 19
 
1.6%
마 17
 
1.5%
어 16
 
1.4%
Other values (260) 934
80.8%
Decimal Number
ValueCountFrequency (%)
0 2
28.6%
4 2
28.6%
1 2
28.6%
9 1
14.3%
Other Punctuation
ValueCountFrequency (%)
. 2
40.0%
! 2
40.0%
& 1
20.0%
Space Separator
ValueCountFrequency (%)
90
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1153
90.8%
Common 114
 
9.0%
Katakana 2
 
0.2%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 33
 
2.9%
수 32
 
2.8%
유 23
 
2.0%
이 22
 
1.9%
탕 21
 
1.8%
리 20
 
1.7%
비 19
 
1.6%
갈 19
 
1.6%
마 17
 
1.5%
어 16
 
1.4%
Other values (257) 931
80.7%
Common
ValueCountFrequency (%)
90
78.9%
( 6
 
5.3%
) 6
 
5.3%
0 2
 
1.8%
4 2
 
1.8%
. 2
 
1.8%
1 2
 
1.8%
! 2
 
1.8%
& 1
 
0.9%
9 1
 
0.9%
Katakana
ValueCountFrequency (%)
ネ 1
50.0%
タ 1
50.0%
Han
ValueCountFrequency (%)
樂 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1153
90.8%
ASCII 114
 
9.0%
Katakana 2
 
0.2%
CJK Compat Ideographs 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
90
78.9%
( 6
 
5.3%
) 6
 
5.3%
0 2
 
1.8%
4 2
 
1.8%
. 2
 
1.8%
1 2
 
1.8%
! 2
 
1.8%
& 1
 
0.9%
9 1
 
0.9%
Hangul
ValueCountFrequency (%)
점 33
 
2.9%
수 32
 
2.8%
유 23
 
2.0%
이 22
 
1.9%
탕 21
 
1.8%
리 20
 
1.7%
비 19
 
1.6%
갈 19
 
1.6%
마 17
 
1.5%
어 16
 
1.4%
Other values (257) 931
80.7%
CJK Compat Ideographs
ValueCountFrequency (%)
樂 1
100.0%
Katakana
ValueCountFrequency (%)
ネ 1
50.0%
タ 1
50.0%

소재지도로명
Text

Distinct167
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-05-18T12:26:43.734828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length42
Mean length30.461538
Min length23

Characters and Unicode

Total characters5940
Distinct characters130
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

Unique141 ?
Unique (%)72.3%

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 195
 
18.0%
강북구 195
 
18.0%
수유동 70
 
6.5%
1층 28
 
2.6%
미아동 25
 
2.3%
한천로 23
 
2.1%
번동 18
 
1.7%
4.19로 13
 
1.2%
도봉로 12
 
1.1%
삼양로 11
 
1.0%
Other values (267) 494
45.6%
2024-05-18T12:26:45.030492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
889
 
15.0%
1 288
 
4.8%
) 279
 
4.7%
, 279
 
4.7%
( 279
 
4.7%
로 204
 
3.4%
동 203
 
3.4%
울 198
 
3.3%
북 197
 
3.3%
서 196
 
3.3%
Other values (120) 2928
49.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3199
53.9%
Decimal Number 980
 
16.5%
Space Separator 889
 
15.0%
Other Punctuation 299
 
5.0%
Close Punctuation 279
 
4.7%
Open Punctuation 279
 
4.7%
Dash Punctuation 14
 
0.2%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 204
 
6.4%
동 203
 
6.3%
울 198
 
6.2%
북 197
 
6.2%
서 196
 
6.1%
시 196
 
6.1%
구 196
 
6.1%
특 195
 
6.1%
별 195
 
6.1%
강 195
 
6.1%
Other values (103) 1224
38.3%
Decimal Number
ValueCountFrequency (%)
1 288
29.4%
2 117
11.9%
3 101
 
10.3%
4 83
 
8.5%
9 78
 
8.0%
0 76
 
7.8%
7 76
 
7.8%
6 60
 
6.1%
8 54
 
5.5%
5 47
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 279
93.3%
. 20
 
6.7%
Space Separator
ValueCountFrequency (%)
889
100.0%
Close Punctuation
ValueCountFrequency (%)
) 279
100.0%
Open Punctuation
ValueCountFrequency (%)
( 279
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3199
53.9%
Common 2740
46.1%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 204
 
6.4%
동 203
 
6.3%
울 198
 
6.2%
북 197
 
6.2%
서 196
 
6.1%
시 196
 
6.1%
구 196
 
6.1%
특 195
 
6.1%
별 195
 
6.1%
강 195
 
6.1%
Other values (103) 1224
38.3%
Common
ValueCountFrequency (%)
889
32.4%
1 288
 
10.5%
) 279
 
10.2%
, 279
 
10.2%
( 279
 
10.2%
2 117
 
4.3%
3 101
 
3.7%
4 83
 
3.0%
9 78
 
2.8%
0 76
 
2.8%
Other values (6) 271
 
9.9%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3199
53.9%
ASCII 2741
46.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
889
32.4%
1 288
 
10.5%
) 279
 
10.2%
, 279
 
10.2%
( 279
 
10.2%
2 117
 
4.3%
3 101
 
3.7%
4 83
 
3.0%
9 78
 
2.8%
0 76
 
2.8%
Other values (7) 272
 
9.9%
Hangul
ValueCountFrequency (%)
로 204
 
6.4%
동 203
 
6.3%
울 198
 
6.2%
북 197
 
6.2%
서 196
 
6.1%
시 196
 
6.1%
구 196
 
6.1%
특 195
 
6.1%
별 195
 
6.1%
강 195
 
6.1%
Other values (103) 1224
38.3%

소재지지번
Text

Distinct167
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-05-18T12:26:45.875958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length42
Mean length30.158974
Min length22

Characters and Unicode

Total characters5881
Distinct characters120
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

Unique141 ?
Unique (%)72.3%

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 195
 
17.0%
강북구 195
 
17.0%
수유동 86
 
7.5%
미아동 57
 
5.0%
번동 35
 
3.1%
지상1층 27
 
2.4%
1호 19
 
1.7%
우이동 17
 
1.5%
10호 10
 
0.9%
2호 10
 
0.9%
Other values (274) 495
43.2%
2024-05-18T12:26:47.384057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1426
24.2%
1 276
 
4.7%
지 248
 
4.2%
번 230
 
3.9%
동 201
 
3.4%
구 198
 
3.4%
울 198
 
3.4%
북 197
 
3.3%
시 196
 
3.3%
서 196
 
3.3%
Other values (110) 2515
42.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3128
53.2%
Space Separator 1426
24.2%
Decimal Number 1115
 
19.0%
Close Punctuation 96
 
1.6%
Open Punctuation 96
 
1.6%
Other Punctuation 16
 
0.3%
Dash Punctuation 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 248
 
7.9%
번 230
 
7.4%
동 201
 
6.4%
구 198
 
6.3%
울 198
 
6.3%
북 197
 
6.3%
시 196
 
6.3%
서 196
 
6.3%
강 195
 
6.2%
별 195
 
6.2%
Other values (94) 1074
34.3%
Decimal Number
ValueCountFrequency (%)
1 276
24.8%
2 141
12.6%
4 129
11.6%
7 101
 
9.1%
3 98
 
8.8%
5 92
 
8.3%
6 82
 
7.4%
0 69
 
6.2%
8 68
 
6.1%
9 59
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 10
62.5%
. 6
37.5%
Space Separator
ValueCountFrequency (%)
1426
100.0%
Close Punctuation
ValueCountFrequency (%)
) 96
100.0%
Open Punctuation
ValueCountFrequency (%)
( 96
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3128
53.2%
Common 2753
46.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 248
 
7.9%
번 230
 
7.4%
동 201
 
6.4%
구 198
 
6.3%
울 198
 
6.3%
북 197
 
6.3%
시 196
 
6.3%
서 196
 
6.3%
강 195
 
6.2%
별 195
 
6.2%
Other values (94) 1074
34.3%
Common
ValueCountFrequency (%)
1426
51.8%
1 276
 
10.0%
2 141
 
5.1%
4 129
 
4.7%
7 101
 
3.7%
3 98
 
3.6%
) 96
 
3.5%
( 96
 
3.5%
5 92
 
3.3%
6 82
 
3.0%
Other values (6) 216
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3128
53.2%
ASCII 2753
46.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1426
51.8%
1 276
 
10.0%
2 141
 
5.1%
4 129
 
4.7%
7 101
 
3.7%
3 98
 
3.6%
) 96
 
3.5%
( 96
 
3.5%
5 92
 
3.3%
6 82
 
3.0%
Other values (6) 216
 
7.8%
Hangul
ValueCountFrequency (%)
지 248
 
7.9%
번 230
 
7.4%
동 201
 
6.4%
구 198
 
6.3%
울 198
 
6.3%
북 197
 
6.3%
시 196
 
6.3%
서 196
 
6.3%
강 195
 
6.2%
별 195
 
6.2%
Other values (94) 1074
34.3%

허가(신고)번호
Text

Distinct168
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-05-18T12:26:48.050938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters4290
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

Unique143 ?
Unique (%)73.3%

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 (%)
3080000-101-1992-04582 3
 
1.5%
3080000-101-1999-07682 3
 
1.5%
3080000-101-1995-06549 2
 
1.0%
3080000-101-1997-05885 2
 
1.0%
3080000-101-2006-00346 2
 
1.0%
3080000-101-2008-00136 2
 
1.0%
3080000-101-2000-00106 2
 
1.0%
3080000-101-1986-00344 2
 
1.0%
3080000-101-2003-00005 2
 
1.0%
3080000-101-2006-00154 2
 
1.0%
Other values (158) 173
88.7%
2024-05-18T12:26:48.992704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1752
40.8%
1 596
 
13.9%
- 585
 
13.6%
8 315
 
7.3%
3 275
 
6.4%
9 207
 
4.8%
2 200
 
4.7%
5 103
 
2.4%
4 93
 
2.2%
6 91
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3705
86.4%
Dash Punctuation 585
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1752
47.3%
1 596
 
16.1%
8 315
 
8.5%
3 275
 
7.4%
9 207
 
5.6%
2 200
 
5.4%
5 103
 
2.8%
4 93
 
2.5%
6 91
 
2.5%
7 73
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4290
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1752
40.8%
1 596
 
13.9%
- 585
 
13.6%
8 315
 
7.3%
3 275
 
6.4%
9 207
 
4.8%
2 200
 
4.7%
5 103
 
2.4%
4 93
 
2.2%
6 91
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4290
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1752
40.8%
1 596
 
13.9%
- 585
 
13.6%
8 315
 
7.3%
3 275
 
6.4%
9 207
 
4.8%
2 200
 
4.7%
5 103
 
2.4%
4 93
 
2.2%
6 91
 
2.1%

업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
한식
165 
중국식
 
11
일식
 
9
기타
 
3
탕류(보신용)
 
2
Other values (3)
 
5

Length

Max length10
Median length2
Mean length2.225641
Min length2

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
한식 165
84.6%
중국식 11
 
5.6%
일식 9
 
4.6%
기타 3
 
1.5%
탕류(보신용) 2
 
1.0%
정종/대포집/소주방 2
 
1.0%
호프/통닭 2
 
1.0%
뷔페식 1
 
0.5%

Length

2024-05-18T12:26:49.500733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:26:50.083161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 165
84.6%
중국식 11
 
5.6%
일식 9
 
4.6%
기타 3
 
1.5%
탕류(보신용 2
 
1.0%
정종/대포집/소주방 2
 
1.0%
호프/통닭 2
 
1.0%
뷔페식 1
 
0.5%

주된음식
Text

MISSING 

Distinct79
Distinct (%)54.1%
Missing49
Missing (%)25.1%
Memory size1.7 KiB
2024-05-18T12:26:50.681560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length3.5136986
Min length1

Characters and Unicode

Total characters513
Distinct characters114
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

Unique53 ?
Unique (%)36.3%

Sample

1st row장어구이
2nd row회
3rd row생선회
4th row돼지갈비
5th row돼지갈비
ValueCountFrequency (%)
돼지갈비 14
 
9.3%
한정식 10
 
6.7%
삼겹살 7
 
4.7%
장어구이 6
 
4.0%
해물탕 5
 
3.3%
칼국수 5
 
3.3%
설렁탕 4
 
2.7%
오리구이 3
 
2.0%
추어탕 3
 
2.0%
생등심 3
 
2.0%
Other values (69) 90
60.0%
2024-05-18T12:26:51.752127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
갈 22
 
4.3%
비 22
 
4.3%
탕 20
 
3.9%
지 20
 
3.9%
국 17
 
3.3%
돼 16
 
3.1%
구 15
 
2.9%
식 14
 
2.7%
정 13
 
2.5%
장 13
 
2.5%
Other values (104) 341
66.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 502
97.9%
Other Punctuation 5
 
1.0%
Space Separator 4
 
0.8%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
갈 22
 
4.4%
비 22
 
4.4%
탕 20
 
4.0%
지 20
 
4.0%
국 17
 
3.4%
돼 16
 
3.2%
구 15
 
3.0%
식 14
 
2.8%
정 13
 
2.6%
장 13
 
2.6%
Other values (100) 330
65.7%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 502
97.9%
Common 11
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
갈 22
 
4.4%
비 22
 
4.4%
탕 20
 
4.0%
지 20
 
4.0%
국 17
 
3.4%
돼 16
 
3.2%
구 15
 
3.0%
식 14
 
2.8%
정 13
 
2.6%
장 13
 
2.6%
Other values (100) 330
65.7%
Common
ValueCountFrequency (%)
, 5
45.5%
4
36.4%
( 1
 
9.1%
) 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 502
97.9%
ASCII 11
 
2.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
갈 22
 
4.4%
비 22
 
4.4%
탕 20
 
4.0%
지 20
 
4.0%
국 17
 
3.4%
돼 16
 
3.2%
구 15
 
3.0%
식 14
 
2.8%
정 13
 
2.6%
장 13
 
2.6%
Other values (100) 330
65.7%
ASCII
ValueCountFrequency (%)
, 5
45.5%
4
36.4%
( 1
 
9.1%
) 1
 
9.1%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct163
Distinct (%)83.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.44985
Minimum0
Maximum891
Zeros4
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-05-18T12:26:52.188002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile47.325
Q172.815
median95.6
Q3157.475
95-th percentile415.007
Maximum891
Range891
Interquartile range (IQR)84.66

Descriptive statistics

Standard deviation125.38643
Coefficient of variation (CV)0.90564513
Kurtosis11.304842
Mean138.44985
Median Absolute Deviation (MAD)29.74
Skewness2.9972242
Sum26997.72
Variance15721.757
MonotonicityNot monotonic
2024-05-18T12:26:52.608112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4
 
2.1%
95.07 3
 
1.5%
101.29 3
 
1.5%
420.81 2
 
1.0%
109.66 2
 
1.0%
64.74 2
 
1.0%
280.22 2
 
1.0%
160.0 2
 
1.0%
94.19 2
 
1.0%
95.6 2
 
1.0%
Other values (153) 171
87.7%
ValueCountFrequency (%)
0.0 4
2.1%
24.84 1
 
0.5%
29.52 1
 
0.5%
33.12 1
 
0.5%
43.26 1
 
0.5%
44.1 1
 
0.5%
45.75 1
 
0.5%
48.0 2
1.0%
48.58 1
 
0.5%
49.44 1
 
0.5%
ValueCountFrequency (%)
891.0 1
0.5%
726.29 1
0.5%
688.41 1
0.5%
626.3 1
0.5%
481.4 2
1.0%
445.0 1
0.5%
441.34 1
0.5%
420.81 2
1.0%
412.52 1
0.5%
378.92 1
0.5%

행정동명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
수유제3동
49 
우이동
31 
번제1동
22 
미아동
19 
송천동
18 
Other values (8)
56 

Length

Max length5
Median length4
Mean length3.8769231
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
수유제3동 49
25.1%
우이동 31
15.9%
번제1동 22
11.3%
미아동 19
 
9.7%
송천동 18
 
9.2%
송중동 15
 
7.7%
수유제2동 10
 
5.1%
수유제1동 8
 
4.1%
번제2동 7
 
3.6%
번제3동 6
 
3.1%
Other values (3) 10
 
5.1%

Length

2024-05-18T12:26:53.157310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수유제3동 49
25.1%
우이동 31
15.9%
번제1동 22
11.3%
미아동 19
 
9.7%
송천동 18
 
9.2%
송중동 15
 
7.7%
수유제2동 10
 
5.1%
수유제1동 8
 
4.1%
번제2동 7
 
3.6%
번제3동 6
 
3.1%
Other values (3) 10
 
5.1%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
상수도전용
157 
<NA>
38 

Length

Max length5
Median length5
Mean length4.8051282
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 157
80.5%
<NA> 38
 
19.5%

Length

2024-05-18T12:26:53.647420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:26:54.135000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 157
80.5%
na 38
 
19.5%

Interactions

2024-05-18T12:26:32.453783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:22.544191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:24.884350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:27.027948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:28.882884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:30.719216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:32.756161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:23.086082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:25.152984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:27.497857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:29.263075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:31.037624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:33.007138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:23.418662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:25.421248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:27.782906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:29.655109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:31.330108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:33.299973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:23.770735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:25.820600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:28.105866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:29.914389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:31.615762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:33.654648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:24.220319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:26.241334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:28.365572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:30.177193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:31.927426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:33.947992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:24.644865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:26.734258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:28.639683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:30.458153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:26:32.200830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T12:26:54.430814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자업태명주된음식영업장면적(㎡)행정동명
지정년도1.0000.7330.9991.0000.0000.0000.8320.2850.342
지정번호0.7331.0000.7330.7330.0000.0000.4620.5500.099
신청일자0.9990.7331.0000.9990.0000.1590.8590.3550.317
지정일자1.0000.7330.9991.0000.0000.0000.8320.2850.342
취소일자0.0000.0000.0000.0001.0000.1800.5100.0000.000
업태명0.0000.0000.1590.0000.1801.0000.9100.6000.000
주된음식0.8320.4620.8590.8320.5100.9101.0000.0000.000
영업장면적(㎡)0.2850.5500.3550.2850.0000.6000.0001.0000.000
행정동명0.3420.0990.3170.3420.0000.0000.0000.0001.000
2024-05-18T12:26:54.823985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명업태명급수시설구분
행정동명1.0000.0001.000
업태명0.0001.0001.000
급수시설구분1.0001.0001.000
2024-05-18T12:26:55.183785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.000-0.5761.0001.0000.2390.1170.0000.1721.000
지정번호-0.5761.000-0.574-0.5750.146-0.0980.0000.0501.000
신청일자1.000-0.5741.0001.0000.2260.1020.0000.1681.000
지정일자1.000-0.5751.0001.0000.2390.1160.0000.1721.000
취소일자0.2390.1460.2260.2391.0000.0450.1720.0001.000
영업장면적(㎡)0.117-0.0980.1020.1160.0451.0000.3460.0001.000
업태명0.0000.0000.0000.0000.1720.3461.0000.0001.000
행정동명0.1720.0500.1680.1720.0000.0000.0001.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-18T12:26:34.370065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T12:26:35.146989image/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.
2024-05-18T12:26:35.747575image/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

시군구코드지정년도지정번호신청일자지정일자취소일자불가일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분
030800002006892006062720060701<NA><NA>섬진강식당서울특별시 강북구 한천로 1151, (수유동,(4.19길 119))서울특별시 강북구 수유동 273번지 11호 (4.19길 119)3080000-101-1984-04583한식장어구이188.04수유제2동상수도전용
1308000020081882008070120080908<NA><NA>어도참치서울특별시 강북구 도봉로 296, (번동,(도봉로 296))서울특별시 강북구 번동 449번지 4호 (도봉로 296)3080000-101-2007-00243일식회297.0번제1동상수도전용
23080000202112020100520210106<NA><NA>고메스퀘어 수유점서울특별시 강북구 도봉로 308, (주)북한산스카이 9층 (번동)서울특별시 강북구 번동 449번지 1호 (주)북한산스카이3080000-101-2018-00256뷔페식생선회891.0번제1동<NA>
330800002012122012110120121204<NA><NA>우리가돼지갈비서울특별시 강북구 솔샘로 334-8, (미아동)서울특별시 강북구 미아동 65번지 9호3080000-101-2004-00324한식돼지갈비230.64송천동상수도전용
430800002006592006062720060701<NA><NA>풍년갈비서울특별시 강북구 솔샘로67길 132, (미아동)서울특별시 강북구 미아동 317번지 19호3080000-101-1990-00282한식돼지갈비118.9송천동상수도전용
5308000020061342006062720060701<NA><NA>시골쌈밥서울특별시 강북구 덕릉로19길 8, (수유동,(원앙길 4))서울특별시 강북구 수유동 47번지 14호 (원앙길 4)3080000-101-2001-08750한식쌈밥101.79인수동상수도전용
630800002009192009091020090910<NA><NA>갑식이네착한낙지서울특별시 강북구 한천로 1124, (수유동)서울특별시 강북구 수유동 254번지 32호3080000-101-2000-08535한식낙지볶음67.2수유제2동상수도전용
730800002018112018101820181221<NA><NA>기품서울특별시 강북구 도봉로49길 7, (미아동,(밤꽃3길 5)(지상1층))서울특별시 강북구 미아동 304번지 16호 (밤꽃3길 5)(지상1층)3080000-101-2002-00727한식장어구이200.9미아동<NA>
83080000202152020100520210106<NA><NA>명인갈비(미아점)서울특별시 강북구 솔샘로 327, 테마빌딩 (미아동)서울특별시 강북구 미아동 374번지 13호 테마빌딩3080000-101-2019-00291한식돼지갈비626.3미아동<NA>
930800002009132009091020090910<NA><NA>부잣집설렁탕서울특별시 강북구 도봉로8길 9, (미아동,(하천길 5))서울특별시 강북구 미아동 860번지 223호 (하천길 5)3080000-101-1998-05722한식설렁탕89.6송중동상수도전용
시군구코드지정년도지정번호신청일자지정일자취소일자불가일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분
1853080000200683200606272006070120210923<NA>개성손만두서울특별시 강북구 한천로109길 59, (번동)서울특별시 강북구 번동 169번지 1호3080000-101-2001-08938한식감자국161.3번제3동상수도전용
186308000020105201009302010100820210923<NA>생마차 수유역점서울특별시 강북구 한천로139길 29, (수유동)서울특별시 강북구 수유동 191번지 67호3080000-101-1994-00204한식<NA>106.06수유제3동상수도전용
18730800002022102022080320221031<NA><NA>낙지왕궁서울특별시 강북구 덕릉로24길 6, 1층 (수유동)서울특별시 강북구 수유동 48번지 23호3080000-101-2021-00182한식낙지볶음252.98수유제1동<NA>
1883080000202282022080120221031<NA><NA>진송추어탕 서울본점서울특별시 강북구 한천로 1161, 대성빌딩 1층 (수유동)서울특별시 강북구 수유동 273번지 72호 대성빌딩3080000-101-2020-00205한식추어탕193.8수유제2동<NA>
1893080000200628200606272006070120210923<NA>여행호프서울특별시 강북구 4.19로 29, (수유동)서울특별시 강북구 수유동 570번지3080000-101-2001-08751한식버섯매운탕칼국수74.2우이동상수도전용
1903080000200638200606272006070120210923<NA>수유 감탄 숯불닭갈비서울특별시 강북구 덕릉로 94, (미아동,(지상1층))서울특별시 강북구 미아동 159번지 10호 (지상1층)3080000-101-1992-04582한식돼지갈비95.07미아동상수도전용
19130800002008195200807012008090820210923<NA>수유 감탄 숯불닭갈비서울특별시 강북구 덕릉로 94, (미아동,(지상1층))서울특별시 강북구 미아동 159번지 10호 (지상1층)3080000-101-1992-04582한식<NA>95.07미아동상수도전용
192308000020102201009302010100820210923<NA>수유 감탄 숯불닭갈비서울특별시 강북구 덕릉로 94, (미아동,(지상1층))서울특별시 강북구 미아동 159번지 10호 (지상1층)3080000-101-1992-04582한식<NA>95.07미아동상수도전용
19330800002006115200606272006070120210923<NA>칸 스시 앤 이자카야서울특별시 강북구 도봉로89길 5, (수유동)서울특별시 강북구 수유동 191번지 25호3080000-101-1998-04508일식참치168.96수유제3동상수도전용
19430800002008162200807012008090820210923<NA>칸 스시 앤 이자카야서울특별시 강북구 도봉로89길 5, (수유동)서울특별시 강북구 수유동 191번지 25호3080000-101-1998-04508일식<NA>168.96수유제3동상수도전용