Overview

Dataset statistics

Number of variables15
Number of observations71
Missing cells5
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.9 KiB
Average record size in memory127.9 B

Variable types

Categorical4
Numeric5
Text6

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,주된음식,영업장면적(㎡),행정동명,급수시설구분,소재지전화번호
Author종로구
URLhttps://data.seoul.go.kr/dataList/OA-9832/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 신청일자 and 2 other fieldsHigh correlation
지정번호 is highly overall correlated with 급수시설구분High correlation
신청일자 is highly overall correlated with 지정년도 and 2 other fieldsHigh correlation
지정일자 is highly overall correlated with 지정년도 and 2 other fieldsHigh correlation
영업장면적(㎡) is highly overall correlated with 급수시설구분High correlation
소재지전화번호 has 5 (7.0%) missing valuesMissing
소재지도로명 has unique valuesUnique
소재지지번 has unique valuesUnique
허가(신고)번호 has unique valuesUnique
영업장면적(㎡) has unique valuesUnique

Reproduction

Analysis started2024-05-11 02:19:19.789585
Analysis finished2024-05-11 02:19:32.223072
Duration12.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size700.0 B
3000000
71 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3000000
2nd row3000000
3rd row3000000
4th row3000000
5th row3000000

Common Values

ValueCountFrequency (%)
3000000 71
100.0%

Length

2024-05-11T02:19:32.421400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T02:19:32.643667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3000000 71
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.6197
Minimum2007
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2024-05-11T02:19:32.851743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2007
Q12007
median2010
Q32015.5
95-th percentile2021
Maximum2023
Range16
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation4.8088199
Coefficient of variation (CV)0.0023905213
Kurtosis-0.80871534
Mean2011.6197
Median Absolute Deviation (MAD)3
Skewness0.74153168
Sum142825
Variance23.124748
MonotonicityDecreasing
2024-05-11T02:19:33.063418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2007 20
28.2%
2008 8
 
11.3%
2009 7
 
9.9%
2018 6
 
8.5%
2012 6
 
8.5%
2021 4
 
5.6%
2010 4
 
5.6%
2019 3
 
4.2%
2016 3
 
4.2%
2015 3
 
4.2%
Other values (4) 7
 
9.9%
ValueCountFrequency (%)
2007 20
28.2%
2008 8
 
11.3%
2009 7
 
9.9%
2010 4
 
5.6%
2011 2
 
2.8%
2012 6
 
8.5%
2014 3
 
4.2%
2015 3
 
4.2%
2016 3
 
4.2%
2017 1
 
1.4%
ValueCountFrequency (%)
2023 1
 
1.4%
2021 4
5.6%
2019 3
4.2%
2018 6
8.5%
2017 1
 
1.4%
2016 3
4.2%
2015 3
4.2%
2014 3
4.2%
2012 6
8.5%
2011 2
 
2.8%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct68
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.28169
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2024-05-11T02:19:33.365650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q118.5
median46
Q368.5
95-th percentile84.5
Maximum90
Range89
Interquartile range (IQR)50

Descriptive statistics

Standard deviation27.691764
Coefficient of variation (CV)0.62535473
Kurtosis-1.3188365
Mean44.28169
Median Absolute Deviation (MAD)25
Skewness-0.057613817
Sum3144
Variance766.8338
MonotonicityNot monotonic
2024-05-11T02:19:33.783470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2
 
2.8%
2 2
 
2.8%
7 2
 
2.8%
23 1
 
1.4%
41 1
 
1.4%
19 1
 
1.4%
69 1
 
1.4%
36 1
 
1.4%
53 1
 
1.4%
78 1
 
1.4%
Other values (58) 58
81.7%
ValueCountFrequency (%)
1 2
2.8%
2 2
2.8%
3 1
1.4%
5 1
1.4%
6 1
1.4%
7 2
2.8%
8 1
1.4%
9 1
1.4%
10 1
1.4%
11 1
1.4%
ValueCountFrequency (%)
90 1
1.4%
89 1
1.4%
88 1
1.4%
85 1
1.4%
84 1
1.4%
83 1
1.4%
82 1
1.4%
81 1
1.4%
78 1
1.4%
77 1
1.4%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20116159
Minimum20071015
Maximum20231013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2024-05-11T02:19:34.144797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20071015
5-th percentile20071109
Q120071120
median20100901
Q320155961
95-th percentile20210668
Maximum20231013
Range159998
Interquartile range (IQR)84841

Descriptive statistics

Standard deviation46919.013
Coefficient of variation (CV)0.0023324042
Kurtosis-0.66557093
Mean20116159
Median Absolute Deviation (MAD)29789
Skewness0.77281286
Sum1.4282473 × 109
Variance2.2013938 × 109
MonotonicityNot monotonic
2024-05-11T02:19:34.545229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
20071112 8
11.3%
20080701 8
11.3%
20090831 7
9.9%
20171031 6
 
8.5%
20071120 6
 
8.5%
20120914 6
 
8.5%
20100901 4
 
5.6%
20141117 3
 
4.2%
20191030 3
 
4.2%
20071109 3
 
4.2%
Other values (14) 17
23.9%
ValueCountFrequency (%)
20071015 1
 
1.4%
20071101 1
 
1.4%
20071109 3
 
4.2%
20071112 8
11.3%
20071120 6
8.5%
20071121 1
 
1.4%
20080701 8
11.3%
20090831 7
9.9%
20100901 4
5.6%
20110901 2
 
2.8%
ValueCountFrequency (%)
20231013 1
 
1.4%
20211126 1
 
1.4%
20211025 2
 
2.8%
20210311 1
 
1.4%
20191030 3
4.2%
20171031 6
8.5%
20171001 1
 
1.4%
20161010 1
 
1.4%
20161007 2
 
2.8%
20150915 1
 
1.4%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20117209
Minimum20071120
Maximum20231129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2024-05-11T02:19:34.911058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20071120
5-th percentile20071120
Q120071120
median20101122
Q320156060
95-th percentile20210770
Maximum20231129
Range160009
Interquartile range (IQR)84940

Descriptive statistics

Standard deviation47983.484
Coefficient of variation (CV)0.0023851959
Kurtosis-0.80145874
Mean20117209
Median Absolute Deviation (MAD)30002
Skewness0.74215649
Sum1.4283218 × 109
Variance2.3024147 × 109
MonotonicityDecreasing
2024-05-11T02:19:35.298280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
20071120 20
28.2%
20081006 8
 
11.3%
20091028 7
 
9.9%
20180119 6
 
8.5%
20121127 6
 
8.5%
20101122 4
 
5.6%
20150915 3
 
4.2%
20191231 3
 
4.2%
20161205 3
 
4.2%
20141230 3
 
4.2%
Other values (6) 8
 
11.3%
ValueCountFrequency (%)
20071120 20
28.2%
20081006 8
 
11.3%
20091028 7
 
9.9%
20101122 4
 
5.6%
20111125 2
 
2.8%
20121127 6
 
8.5%
20141230 3
 
4.2%
20150915 3
 
4.2%
20161205 3
 
4.2%
20171130 1
 
1.4%
ValueCountFrequency (%)
20231129 1
 
1.4%
20211230 1
 
1.4%
20211216 2
 
2.8%
20210325 1
 
1.4%
20191231 3
4.2%
20180119 6
8.5%
20171130 1
 
1.4%
20161205 3
4.2%
20150915 3
4.2%
20141230 3
4.2%

업소명
Text

Distinct70
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size700.0 B
2024-05-11T02:19:35.877654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length5.3802817
Min length2

Characters and Unicode

Total characters382
Distinct characters186
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)97.2%

Sample

1st row마산해물아구찜
2nd row늘마중
3rd row말뚜기 감자탕
4th row끄티집
5th row스윗샐러드
ValueCountFrequency (%)
신안촌 2
 
2.3%
김가네대청마루 1
 
1.1%
송전 1
 
1.1%
더레스토랑 1
 
1.1%
여자만 1
 
1.1%
인사동 1
 
1.1%
주)베이징코야 1
 
1.1%
시골해장국 1
 
1.1%
뉘조 1
 
1.1%
옥정 1
 
1.1%
Other values (76) 76
87.4%
2024-05-11T02:19:36.708868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16
 
4.2%
가 10
 
2.6%
집 10
 
2.6%
대 9
 
2.4%
동 8
 
2.1%
탕 6
 
1.6%
비 5
 
1.3%
나 5
 
1.3%
마 5
 
1.3%
옥 5
 
1.3%
Other values (176) 303
79.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 356
93.2%
Space Separator 16
 
4.2%
Close Punctuation 4
 
1.0%
Open Punctuation 4
 
1.0%
Other Punctuation 1
 
0.3%
Decimal Number 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
가 10
 
2.8%
집 10
 
2.8%
대 9
 
2.5%
동 8
 
2.2%
탕 6
 
1.7%
비 5
 
1.4%
나 5
 
1.4%
마 5
 
1.4%
옥 5
 
1.4%
진 5
 
1.4%
Other values (171) 288
80.9%
Space Separator
ValueCountFrequency (%)
16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 356
93.2%
Common 26
 
6.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
가 10
 
2.8%
집 10
 
2.8%
대 9
 
2.5%
동 8
 
2.2%
탕 6
 
1.7%
비 5
 
1.4%
나 5
 
1.4%
마 5
 
1.4%
옥 5
 
1.4%
진 5
 
1.4%
Other values (171) 288
80.9%
Common
ValueCountFrequency (%)
16
61.5%
) 4
 
15.4%
( 4
 
15.4%
. 1
 
3.8%
2 1
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 356
93.2%
ASCII 26
 
6.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16
61.5%
) 4
 
15.4%
( 4
 
15.4%
. 1
 
3.8%
2 1
 
3.8%
Hangul
ValueCountFrequency (%)
가 10
 
2.8%
집 10
 
2.8%
대 9
 
2.5%
동 8
 
2.2%
탕 6
 
1.7%
비 5
 
1.4%
나 5
 
1.4%
마 5
 
1.4%
옥 5
 
1.4%
진 5
 
1.4%
Other values (171) 288
80.9%

소재지도로명
Text

UNIQUE 

Distinct71
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size700.0 B
2024-05-11T02:19:37.279953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length41
Mean length31.070423
Min length22

Characters and Unicode

Total characters2206
Distinct characters124
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

Unique71 ?
Unique (%)100.0%

Sample

1st row서울특별시 종로구 북촌로5길 6, (재동)
2nd row서울특별시 종로구 인사동10길 11-5, 1층 (관훈동)
3rd row서울특별시 종로구 종로51길 23-9, 1층 (창신동)
4th row서울특별시 종로구 삼일대로19길 20, 1~4층 (관철동)
5th row서울특별시 종로구 종로1길 50, 더케이트윈타워 지하1층 (중학동)
ValueCountFrequency (%)
서울특별시 71
 
17.2%
종로구 71
 
17.2%
1층 14
 
3.4%
관훈동 7
 
1.7%
지하1층 5
 
1.2%
숭인동 4
 
1.0%
1,2층 4
 
1.0%
평창동 4
 
1.0%
6 4
 
1.0%
삼청로 4
 
1.0%
Other values (181) 225
54.5%
2024-05-11T02:19:38.130199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
342
 
15.5%
로 134
 
6.1%
1 127
 
5.8%
, 109
 
4.9%
종 85
 
3.9%
동 78
 
3.5%
) 76
 
3.4%
( 76
 
3.4%
구 73
 
3.3%
서 72
 
3.3%
Other values (114) 1034
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1198
54.3%
Decimal Number 368
 
16.7%
Space Separator 342
 
15.5%
Other Punctuation 111
 
5.0%
Close Punctuation 76
 
3.4%
Open Punctuation 76
 
3.4%
Dash Punctuation 28
 
1.3%
Math Symbol 6
 
0.3%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 134
 
11.2%
종 85
 
7.1%
동 78
 
6.5%
구 73
 
6.1%
서 72
 
6.0%
특 71
 
5.9%
별 71
 
5.9%
시 71
 
5.9%
울 71
 
5.9%
층 49
 
4.1%
Other values (96) 423
35.3%
Decimal Number
ValueCountFrequency (%)
1 127
34.5%
2 56
15.2%
4 35
 
9.5%
3 35
 
9.5%
5 30
 
8.2%
0 26
 
7.1%
9 17
 
4.6%
8 16
 
4.3%
6 16
 
4.3%
7 10
 
2.7%
Other Punctuation
ValueCountFrequency (%)
, 109
98.2%
. 2
 
1.8%
Space Separator
ValueCountFrequency (%)
342
100.0%
Close Punctuation
ValueCountFrequency (%)
) 76
100.0%
Open Punctuation
ValueCountFrequency (%)
( 76
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1198
54.3%
Common 1007
45.6%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 134
 
11.2%
종 85
 
7.1%
동 78
 
6.5%
구 73
 
6.1%
서 72
 
6.0%
특 71
 
5.9%
별 71
 
5.9%
시 71
 
5.9%
울 71
 
5.9%
층 49
 
4.1%
Other values (96) 423
35.3%
Common
ValueCountFrequency (%)
342
34.0%
1 127
 
12.6%
, 109
 
10.8%
) 76
 
7.5%
( 76
 
7.5%
2 56
 
5.6%
4 35
 
3.5%
3 35
 
3.5%
5 30
 
3.0%
- 28
 
2.8%
Other values (7) 93
 
9.2%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1198
54.3%
ASCII 1008
45.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
342
33.9%
1 127
 
12.6%
, 109
 
10.8%
) 76
 
7.5%
( 76
 
7.5%
2 56
 
5.6%
4 35
 
3.5%
3 35
 
3.5%
5 30
 
3.0%
- 28
 
2.8%
Other values (8) 94
 
9.3%
Hangul
ValueCountFrequency (%)
로 134
 
11.2%
종 85
 
7.1%
동 78
 
6.5%
구 73
 
6.1%
서 72
 
6.0%
특 71
 
5.9%
별 71
 
5.9%
시 71
 
5.9%
울 71
 
5.9%
층 49
 
4.1%
Other values (96) 423
35.3%

소재지지번
Text

UNIQUE 

Distinct71
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size700.0 B
2024-05-11T02:19:38.631607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length36
Mean length26.661972
Min length21

Characters and Unicode

Total characters1893
Distinct characters102
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

Unique71 ?
Unique (%)100.0%

Sample

1st row서울특별시 종로구 재동 11번지 0호
2nd row서울특별시 종로구 관훈동 30번지 16호
3rd row서울특별시 종로구 창신동 581번지 30호 1층
4th row서울특별시 종로구 관철동 14번지 5호
5th row서울특별시 종로구 중학동 19번지 더케이트윈타워
ValueCountFrequency (%)
서울특별시 71
 
19.4%
종로구 71
 
19.4%
1호 10
 
2.7%
관훈동 7
 
1.9%
숭인동 5
 
1.4%
관철동 4
 
1.1%
1층 4
 
1.1%
8호 4
 
1.1%
지상1층 4
 
1.1%
84번지 4
 
1.1%
Other values (143) 182
49.7%
2024-05-11T02:19:39.689518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
483
25.5%
1 105
 
5.5%
지 92
 
4.9%
로 76
 
4.0%
종 74
 
3.9%
구 73
 
3.9%
서 72
 
3.8%
번 72
 
3.8%
울 71
 
3.8%
시 71
 
3.8%
Other values (92) 704
37.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1062
56.1%
Space Separator 483
25.5%
Decimal Number 314
 
16.6%
Other Punctuation 14
 
0.7%
Close Punctuation 6
 
0.3%
Open Punctuation 6
 
0.3%
Dash Punctuation 4
 
0.2%
Math Symbol 3
 
0.2%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 92
 
8.7%
로 76
 
7.2%
종 74
 
7.0%
구 73
 
6.9%
서 72
 
6.8%
번 72
 
6.8%
울 71
 
6.7%
시 71
 
6.7%
별 71
 
6.7%
특 71
 
6.7%
Other values (74) 319
30.0%
Decimal Number
ValueCountFrequency (%)
1 105
33.4%
2 36
 
11.5%
0 29
 
9.2%
3 28
 
8.9%
8 26
 
8.3%
4 25
 
8.0%
5 22
 
7.0%
9 17
 
5.4%
6 16
 
5.1%
7 10
 
3.2%
Other Punctuation
ValueCountFrequency (%)
, 12
85.7%
. 2
 
14.3%
Space Separator
ValueCountFrequency (%)
483
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1062
56.1%
Common 830
43.8%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 92
 
8.7%
로 76
 
7.2%
종 74
 
7.0%
구 73
 
6.9%
서 72
 
6.8%
번 72
 
6.8%
울 71
 
6.7%
시 71
 
6.7%
별 71
 
6.7%
특 71
 
6.7%
Other values (74) 319
30.0%
Common
ValueCountFrequency (%)
483
58.2%
1 105
 
12.7%
2 36
 
4.3%
0 29
 
3.5%
3 28
 
3.4%
8 26
 
3.1%
4 25
 
3.0%
5 22
 
2.7%
9 17
 
2.0%
6 16
 
1.9%
Other values (7) 43
 
5.2%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1062
56.1%
ASCII 831
43.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
483
58.1%
1 105
 
12.6%
2 36
 
4.3%
0 29
 
3.5%
3 28
 
3.4%
8 26
 
3.1%
4 25
 
3.0%
5 22
 
2.6%
9 17
 
2.0%
6 16
 
1.9%
Other values (8) 44
 
5.3%
Hangul
ValueCountFrequency (%)
지 92
 
8.7%
로 76
 
7.2%
종 74
 
7.0%
구 73
 
6.9%
서 72
 
6.8%
번 72
 
6.8%
울 71
 
6.7%
시 71
 
6.7%
별 71
 
6.7%
특 71
 
6.7%
Other values (74) 319
30.0%

허가(신고)번호
Text

UNIQUE 

Distinct71
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size700.0 B
2024-05-11T02:19:40.153630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters1562
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

Unique71 ?
Unique (%)100.0%

Sample

1st row3000000-101-1994-02251
2nd row3000000-101-2021-00138
3rd row3000000-101-2016-00071
4th row3000000-101-2019-00441
5th row3000000-101-2018-00205
ValueCountFrequency (%)
3000000-101-1994-02251 1
 
1.4%
3000000-101-1998-07944 1
 
1.4%
3000000-101-1999-10390 1
 
1.4%
3000000-101-2007-00059 1
 
1.4%
3000000-101-2006-00306 1
 
1.4%
3000000-101-1996-02678 1
 
1.4%
3000000-101-1994-00298 1
 
1.4%
3000000-101-2002-11958 1
 
1.4%
3000000-101-1985-06207 1
 
1.4%
3000000-101-1981-01269 1
 
1.4%
Other values (61) 61
85.9%
2024-05-11T02:19:40.977082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 686
43.9%
1 238
 
15.2%
- 213
 
13.6%
3 102
 
6.5%
9 90
 
5.8%
2 71
 
4.5%
6 45
 
2.9%
4 31
 
2.0%
8 31
 
2.0%
7 28
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1349
86.4%
Dash Punctuation 213
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 686
50.9%
1 238
 
17.6%
3 102
 
7.6%
9 90
 
6.7%
2 71
 
5.3%
6 45
 
3.3%
4 31
 
2.3%
8 31
 
2.3%
7 28
 
2.1%
5 27
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 213
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1562
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 686
43.9%
1 238
 
15.2%
- 213
 
13.6%
3 102
 
6.5%
9 90
 
5.8%
2 71
 
4.5%
6 45
 
2.9%
4 31
 
2.0%
8 31
 
2.0%
7 28
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1562
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 686
43.9%
1 238
 
15.2%
- 213
 
13.6%
3 102
 
6.5%
9 90
 
5.8%
2 71
 
4.5%
6 45
 
2.9%
4 31
 
2.0%
8 31
 
2.0%
7 28
 
1.8%

업태명
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Memory size700.0 B
한식
51 
기타
 
5
분식
 
4
경양식
 
3
일식
 
3
Other values (4)
 
5

Length

Max length8
Median length2
Mean length2.2535211
Min length2

Unique

Unique3 ?
Unique (%)4.2%

Sample

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

Common Values

ValueCountFrequency (%)
한식 51
71.8%
기타 5
 
7.0%
분식 4
 
5.6%
경양식 3
 
4.2%
일식 3
 
4.2%
중국식 2
 
2.8%
식육(숯불구이) 1
 
1.4%
탕류(보신용) 1
 
1.4%
복어취급 1
 
1.4%

Length

2024-05-11T02:19:41.381990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T02:19:41.760155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 51
71.8%
기타 5
 
7.0%
분식 4
 
5.6%
경양식 3
 
4.2%
일식 3
 
4.2%
중국식 2
 
2.8%
식육(숯불구이 1
 
1.4%
탕류(보신용 1
 
1.4%
복어취급 1
 
1.4%

주된음식
Text

Distinct57
Distinct (%)80.3%
Missing0
Missing (%)0.0%
Memory size700.0 B
2024-05-11T02:19:42.267241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length3.915493
Min length1

Characters and Unicode

Total characters278
Distinct characters97
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)67.6%

Sample

1st row해물
2nd row막걸리, 전
3rd row감자탕
4th row전골, 샤브샤브
5th row샐러드
ValueCountFrequency (%)
한정식 7
 
8.4%
낙지 3
 
3.6%
전골 3
 
3.6%
갈비탕 3
 
3.6%
샤브샤브 3
 
3.6%
홍어 2
 
2.4%
장어 2
 
2.4%
해장국 2
 
2.4%
추어탕 2
 
2.4%
칼국수 2
 
2.4%
Other values (50) 54
65.1%
2024-05-11T02:19:43.191176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
탕 13
 
4.7%
, 12
 
4.3%
12
 
4.3%
갈 10
 
3.6%
비 10
 
3.6%
한 8
 
2.9%
식 8
 
2.9%
정 8
 
2.9%
장 8
 
2.9%
지 8
 
2.9%
Other values (87) 181
65.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 254
91.4%
Other Punctuation 12
 
4.3%
Space Separator 12
 
4.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 13
 
5.1%
갈 10
 
3.9%
비 10
 
3.9%
한 8
 
3.1%
식 8
 
3.1%
정 8
 
3.1%
장 8
 
3.1%
지 8
 
3.1%
어 7
 
2.8%
리 6
 
2.4%
Other values (85) 168
66.1%
Other Punctuation
ValueCountFrequency (%)
, 12
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 254
91.4%
Common 24
 
8.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 13
 
5.1%
갈 10
 
3.9%
비 10
 
3.9%
한 8
 
3.1%
식 8
 
3.1%
정 8
 
3.1%
장 8
 
3.1%
지 8
 
3.1%
어 7
 
2.8%
리 6
 
2.4%
Other values (85) 168
66.1%
Common
ValueCountFrequency (%)
, 12
50.0%
12
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 254
91.4%
ASCII 24
 
8.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
탕 13
 
5.1%
갈 10
 
3.9%
비 10
 
3.9%
한 8
 
3.1%
식 8
 
3.1%
정 8
 
3.1%
장 8
 
3.1%
지 8
 
3.1%
어 7
 
2.8%
리 6
 
2.4%
Other values (85) 168
66.1%
ASCII
ValueCountFrequency (%)
, 12
50.0%
12
50.0%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct71
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189.18944
Minimum23.14
Maximum793.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2024-05-11T02:19:43.628912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.14
5-th percentile52.9
Q189.085
median122
Q3243.48
95-th percentile502.28
Maximum793.39
Range770.25
Interquartile range (IQR)154.395

Descriptive statistics

Standard deviation155.085
Coefficient of variation (CV)0.81973391
Kurtosis3.0734059
Mean189.18944
Median Absolute Deviation (MAD)49.06
Skewness1.7368857
Sum13432.45
Variance24051.356
MonotonicityNot monotonic
2024-05-11T02:19:44.092129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83.8 1
 
1.4%
59.5 1
 
1.4%
148.12 1
 
1.4%
408.19 1
 
1.4%
137.82 1
 
1.4%
621.0 1
 
1.4%
77.43 1
 
1.4%
53.02 1
 
1.4%
552.75 1
 
1.4%
234.66 1
 
1.4%
Other values (61) 61
85.9%
ValueCountFrequency (%)
23.14 1
1.4%
29.37 1
1.4%
48.92 1
1.4%
52.78 1
1.4%
53.02 1
1.4%
57.95 1
1.4%
59.5 1
1.4%
64.81 1
1.4%
72.49 1
1.4%
72.94 1
1.4%
ValueCountFrequency (%)
793.39 1
1.4%
621.0 1
1.4%
558.55 1
1.4%
552.75 1
1.4%
451.81 1
1.4%
445.45 1
1.4%
440.86 1
1.4%
426.92 1
1.4%
408.19 1
1.4%
343.0 1
1.4%

행정동명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Memory size700.0 B
종로1.2.3.4가동
29 
사직동
15 
평창동
5 
삼청동
4 
혜화동
3 
Other values (9)
15 

Length

Max length11
Median length7
Mean length6.6338028
Min length3

Unique

Unique4 ?
Unique (%)5.6%

Sample

1st row가회동
2nd row종로1.2.3.4가동
3rd row창신제1동
4th row종로1.2.3.4가동
5th row종로1.2.3.4가동

Common Values

ValueCountFrequency (%)
종로1.2.3.4가동 29
40.8%
사직동 15
21.1%
평창동 5
 
7.0%
삼청동 4
 
5.6%
혜화동 3
 
4.2%
숭인제1동 3
 
4.2%
가회동 2
 
2.8%
종로5.6가동 2
 
2.8%
숭인제2동 2
 
2.8%
청운효자동 2
 
2.8%
Other values (4) 4
 
5.6%

Length

2024-05-11T02:19:44.551949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
종로1.2.3.4가동 29
40.8%
사직동 15
21.1%
평창동 5
 
7.0%
삼청동 4
 
5.6%
혜화동 3
 
4.2%
숭인제1동 3
 
4.2%
가회동 2
 
2.8%
종로5.6가동 2
 
2.8%
숭인제2동 2
 
2.8%
청운효자동 2
 
2.8%
Other values (4) 4
 
5.6%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size700.0 B
상수도전용
47 
<NA>
24 

Length

Max length5
Median length5
Mean length4.6619718
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 47
66.2%
<NA> 24
33.8%

Length

2024-05-11T02:19:44.974486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T02:19:45.291424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 47
66.2%
na 24
33.8%

소재지전화번호
Text

MISSING 

Distinct66
Distinct (%)100.0%
Missing5
Missing (%)7.0%
Memory size700.0 B
2024-05-11T02:19:45.816362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.5
Min length10

Characters and Unicode

Total characters693
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

Unique66 ?
Unique (%)100.0%

Sample

1st row02 7412109
2nd row02 730 2985
3rd row02 741 0607
4th row02725 7772
5th row02 733 3225
ValueCountFrequency (%)
02 54
38.6%
733 4
 
2.9%
723 2
 
1.4%
741 1
 
0.7%
0607 1
 
0.7%
3955288 1
 
0.7%
2495 1
 
0.7%
943 1
 
0.7%
3796276 1
 
0.7%
7204323 1
 
0.7%
Other values (73) 73
52.1%
2024-05-11T02:19:46.999893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 121
17.5%
0 105
15.2%
7 89
12.8%
87
12.6%
3 86
12.4%
5 42
 
6.1%
4 42
 
6.1%
9 35
 
5.1%
6 32
 
4.6%
1 29
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 606
87.4%
Space Separator 87
 
12.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 121
20.0%
0 105
17.3%
7 89
14.7%
3 86
14.2%
5 42
 
6.9%
4 42
 
6.9%
9 35
 
5.8%
6 32
 
5.3%
1 29
 
4.8%
8 25
 
4.1%
Space Separator
ValueCountFrequency (%)
87
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 693
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 121
17.5%
0 105
15.2%
7 89
12.8%
87
12.6%
3 86
12.4%
5 42
 
6.1%
4 42
 
6.1%
9 35
 
5.1%
6 32
 
4.6%
1 29
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 693
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 121
17.5%
0 105
15.2%
7 89
12.8%
87
12.6%
3 86
12.4%
5 42
 
6.1%
4 42
 
6.1%
9 35
 
5.1%
6 32
 
4.6%
1 29
 
4.2%

Interactions

2024-05-11T02:19:29.633787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:19:24.292580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:19:25.778674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:19:27.232249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:19:28.535803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:19:29.901306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:19:24.569745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:19:26.038475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:19:27.546947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:19:28.768297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:19:30.370331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:19:24.821905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:19:26.357811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:19:27.783419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:19:28.957830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:19:30.653889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:19:25.166170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:19:26.668712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:19:28.036209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:19:29.144972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:19:30.935227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:19:25.493006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:19:26.941862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:19:28.258436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:19:29.356434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T02:19:47.328393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명소재지전화번호
지정년도1.0000.6261.0001.0001.0001.0001.0001.0000.0000.8890.0000.6421.000
지정번호0.6261.0000.6110.6261.0001.0001.0001.0000.0000.7400.2460.0001.000
신청일자1.0000.6111.0001.0001.0001.0001.0001.0000.1190.9470.0000.4101.000
지정일자1.0000.6261.0001.0001.0001.0001.0001.0000.0000.8890.0000.6421.000
업소명1.0001.0001.0001.0001.0001.0001.0001.0000.9191.0000.9381.0001.000
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
업태명0.0000.0000.1190.0000.9191.0001.0001.0001.0000.7530.3280.6291.000
주된음식0.8890.7400.9470.8891.0001.0001.0001.0000.7531.0000.8510.0001.000
영업장면적(㎡)0.0000.2460.0000.0000.9381.0001.0001.0000.3280.8511.0000.0001.000
행정동명0.6420.0000.4100.6421.0001.0001.0001.0000.6290.0000.0001.0001.000
소재지전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-05-11T02:19:47.747860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
급수시설구분업태명행정동명
급수시설구분1.0001.0001.000
업태명1.0001.0000.308
행정동명1.0000.3081.000
2024-05-11T02:19:48.015032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.0000.1040.9891.000-0.2750.0310.1721.000
지정번호0.1041.0000.0950.1010.1220.0000.0001.000
신청일자0.9890.0951.0000.989-0.2820.0310.1721.000
지정일자1.0000.1010.9891.000-0.2750.0310.1721.000
영업장면적(㎡)-0.2750.122-0.282-0.2751.0000.1020.0001.000
업태명0.0310.0000.0310.0310.1021.0000.3081.000
행정동명0.1720.0000.1720.1720.0000.3081.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-11T02:19:31.344745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T02:19:31.976432image/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.

Sample

시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
03000000202312023101320231129마산해물아구찜서울특별시 종로구 북촌로5길 6, (재동)서울특별시 종로구 재동 11번지 0호3000000-101-1994-02251한식해물83.8가회동상수도전용02 7412109
130000002021132021102520211230늘마중서울특별시 종로구 인사동10길 11-5, 1층 (관훈동)서울특별시 종로구 관훈동 30번지 16호3000000-101-2021-00138한식막걸리, 전59.5종로1.2.3.4가동<NA>02 730 2985
230000002021892021102520211216말뚜기 감자탕서울특별시 종로구 종로51길 23-9, 1층 (창신동)서울특별시 종로구 창신동 581번지 30호 1층3000000-101-2016-00071식육(숯불구이)감자탕48.92창신제1동<NA>02 741 0607
330000002021902021112620211216끄티집서울특별시 종로구 삼일대로19길 20, 1~4층 (관철동)서울특별시 종로구 관철동 14번지 5호3000000-101-2019-00441한식전골, 샤브샤브445.45종로1.2.3.4가동<NA>02725 7772
430000002021882021031120210325스윗샐러드서울특별시 종로구 종로1길 50, 더케이트윈타워 지하1층 (중학동)서울특별시 종로구 중학동 19번지 더케이트윈타워3000000-101-2018-00205경양식샐러드64.81종로1.2.3.4가동<NA>02 733 3225
530000002019572019103020191231이대감 고깃집서울특별시 종로구 수표로 96, (관수동)서울특별시 종로구 관수동 20번지3000000-101-2006-00326한식갈비탕, 갈빗살, 불고기793.39종로1.2.3.4가동<NA>02 22651400
630000002019152019103020191231대가곱창서울특별시 종로구 명륜길 53, (명륜3가,(지상1층))서울특별시 종로구 명륜3가 1번지 1143호 (지상1층)3000000-101-2009-00015한식곱창, 막창29.37혜화동<NA>02 747 3827
730000002019482019103020191231엔차이서울특별시 종로구 새문안로3길 36, (내수동,용비어천가 지층 B103호)서울특별시 종로구 내수동 75번지 용비어천가 지층 B103호3000000-101-2007-02099중국식짜장면135.15사직동<NA><NA>
830000002018262017103120180119만가서울특별시 종로구 사직로 103-19, (필운동,(지상1층))서울특별시 종로구 필운동 206번지 (지상1층)3000000-101-1991-06583한식장어, 생등심110.0사직동상수도전용02720 5797
930000002018302017103120180119박가네서울특별시 종로구 종로32길 7, 2층 (종로5가)서울특별시 종로구 종로5가 138번지 10호3000000-101-2014-00149분식빈대떡91.8종로5.6가동<NA><NA>
시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
6130000002007722007110920071120(주)토속촌삼계탕서울특별시 종로구 자하문로5길 5, 지상1층 (체부동, (체부동 86-1,2,3))서울특별시 종로구 체부동 85번지 1호 지상1층(체부동 86-1,2,3)3000000-101-1990-00725한식삼계탕440.86사직동상수도전용0207377444
6230000002007622007111220071120일품당샤브샤브서울특별시 종로구 세종대로23길 25, 1,2층 (당주동)서울특별시 종로구 당주동 16번지 1호3000000-101-2003-00426한식샤브샤브177.64사직동<NA>02 7337949
633000000200762007112020071120금해 복집서울특별시 종로구 진흥로 461, 1,2층 (구기동)서울특별시 종로구 구기동 67번지 19호3000000-101-1997-07563일식활지리341.45평창동상수도전용02 3955656
6430000002007832007111220071120함흥곰보냉면서울특별시 종로구 창경궁로 109, 401,405호 (인의동)서울특별시 종로구 인의동 112번지 14호 401,405호3000000-101-1968-06103한식냉면334.75종로1.2.3.4가동상수도전용0222732833
653000000200752007111220071120궁나라냉면.묵밥서울특별시 종로구 지봉로12길 6, 1층 (숭인동)서울특별시 종로구 숭인동 56번지 38호3000000-101-1992-01124한식냉면,묵밥115.5숭인제1동상수도전용02744 4701
6630000002007472007112020071120양반댁서울특별시 종로구 인사동길 19-18, 1층 (인사동)서울특별시 종로구 인사동 193번지 5호3000000-101-1983-00641한식한정식72.49종로1.2.3.4가동상수도전용02 7301112
673000000200722007112020071120(주)석파랑서울특별시 종로구 자하문로 309, 1층 (홍지동)서울특별시 종로구 홍지동 125번지3000000-101-1993-00149한식한정식183.42부암동상수도전용02395 2500
6830000002007842007110920071120향가서울특별시 종로구 계동길 19-6, (재동)서울특별시 종로구 재동 84번지 1호3000000-101-2000-11447한식시골밥상200.65가회동상수도전용02 7473368
6930000002007122007112020071120뉘조서울특별시 종로구 인사동14길 27, 1층 (관훈동)서울특별시 종로구 관훈동 84번지 13호3000000-101-1993-00086한식한정식95.01종로1.2.3.4가동상수도전용02 7309311
7030000002007112007111220071120눈나무집서울특별시 종로구 삼청로 136-1, 1,2,3층 (삼청동)서울특별시 종로구 삼청동 20번지 8호3000000-101-2005-00056한식떡갈비,김치말이국수103.56삼청동상수도전용02 7396742