Overview

Dataset statistics

Number of variables16
Number of observations251
Missing cells737
Missing cells (%)18.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.5 KiB
Average record size in memory136.5 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
Author종로구
URLhttps://data.seoul.go.kr/dataList/OA-9851/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
지정년도 is highly overall correlated with 신청일자 and 1 other fieldsHigh correlation
신청일자 is highly overall correlated with 지정년도 and 1 other fieldsHigh correlation
지정일자 is highly overall correlated with 지정년도 and 1 other fieldsHigh correlation
지정년도 has 98 (39.0%) missing valuesMissing
지정번호 has 97 (38.6%) missing valuesMissing
지정일자 has 98 (39.0%) missing valuesMissing
취소일자 has 71 (28.3%) missing valuesMissing
불가일자 has 251 (100.0%) missing valuesMissing
소재지도로명 has 5 (2.0%) missing valuesMissing
주된음식 has 117 (46.6%) missing valuesMissing
불가일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-11 06:12:02.580231
Analysis finished2024-05-11 06:12:09.009672
Duration6.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
3000000
251 

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 251
100.0%

Length

2024-05-11T15:12:09.107176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:12:09.222150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3000000 251
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)9.2%
Missing98
Missing (%)39.0%
Infinite0
Infinite (%)0.0%
Mean2010.6078
Minimum2007
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-11T15:12:09.320891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2007
Q12007
median2009
Q32012
95-th percentile2018.4
Maximum2023
Range16
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.0461731
Coefficient of variation (CV)0.0020124129
Kurtosis0.20485157
Mean2010.6078
Median Absolute Deviation (MAD)2
Skewness1.0961718
Sum307623
Variance16.371517
MonotonicityNot monotonic
2024-05-11T15:12:09.445128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2007 46
18.3%
2008 22
 
8.8%
2012 14
 
5.6%
2010 14
 
5.6%
2009 14
 
5.6%
2016 8
 
3.2%
2018 7
 
2.8%
2014 7
 
2.8%
2011 6
 
2.4%
2015 5
 
2.0%
Other values (4) 10
 
4.0%
(Missing) 98
39.0%
ValueCountFrequency (%)
2007 46
18.3%
2008 22
8.8%
2009 14
 
5.6%
2010 14
 
5.6%
2011 6
 
2.4%
2012 14
 
5.6%
2014 7
 
2.8%
2015 5
 
2.0%
2016 8
 
3.2%
2017 2
 
0.8%
ValueCountFrequency (%)
2023 1
 
0.4%
2021 4
 
1.6%
2019 3
 
1.2%
2018 7
2.8%
2017 2
 
0.8%
2016 8
3.2%
2015 5
 
2.0%
2014 7
2.8%
2012 14
5.6%
2011 6
2.4%

지정번호
Real number (ℝ)

MISSING 

Distinct128
Distinct (%)83.1%
Missing97
Missing (%)38.6%
Infinite0
Infinite (%)0.0%
Mean100.96104
Minimum1
Maximum321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-11T15:12:09.598617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.65
Q128.25
median65.5
Q3152.75
95-th percentile299.05
Maximum321
Range320
Interquartile range (IQR)124.5

Descriptive statistics

Standard deviation96.267118
Coefficient of variation (CV)0.9535076
Kurtosis-0.28447499
Mean100.96104
Median Absolute Deviation (MAD)47
Skewness1.0226444
Sum15548
Variance9267.3579
MonotonicityNot monotonic
2024-05-11T15:12:09.780720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 3
 
1.2%
1 3
 
1.2%
89 2
 
0.8%
83 2
 
0.8%
40 2
 
0.8%
18 2
 
0.8%
126 2
 
0.8%
275 2
 
0.8%
213 2
 
0.8%
226 2
 
0.8%
Other values (118) 132
52.6%
(Missing) 97
38.6%
ValueCountFrequency (%)
1 3
1.2%
2 2
0.8%
3 1
 
0.4%
4 2
0.8%
5 2
0.8%
6 1
 
0.4%
7 2
0.8%
8 2
0.8%
9 1
 
0.4%
10 2
0.8%
ValueCountFrequency (%)
321 1
0.4%
320 1
0.4%
316 1
0.4%
315 1
0.4%
314 1
0.4%
313 1
0.4%
312 1
0.4%
301 1
0.4%
298 1
0.4%
297 1
0.4%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20096677
Minimum20071015
Maximum20231013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-11T15:12:09.957260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20071015
5-th percentile20071109
Q120071120
median20080701
Q320100901
95-th percentile20171031
Maximum20231013
Range159998
Interquartile range (IQR)29781

Descriptive statistics

Standard deviation33923.03
Coefficient of variation (CV)0.001687992
Kurtosis2.4284512
Mean20096677
Median Absolute Deviation (MAD)9592
Skewness1.7161109
Sum5.0442659 × 109
Variance1.150772 × 109
MonotonicityDecreasing
2024-05-11T15:12:10.111789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
20080701 49
19.5%
20100901 29
11.6%
20090831 26
10.4%
20071109 23
9.2%
20071120 20
8.0%
20071112 18
 
7.2%
20071121 14
 
5.6%
20120914 14
 
5.6%
20171031 8
 
3.2%
20141117 7
 
2.8%
Other values (25) 43
17.1%
ValueCountFrequency (%)
20071015 2
 
0.8%
20071101 1
 
0.4%
20071102 1
 
0.4%
20071105 2
 
0.8%
20071109 23
9.2%
20071112 18
7.2%
20071113 4
 
1.6%
20071120 20
8.0%
20071121 14
5.6%
20071122 2
 
0.8%
ValueCountFrequency (%)
20231013 1
 
0.4%
20211126 1
 
0.4%
20211025 2
 
0.8%
20210311 1
 
0.4%
20191030 3
 
1.2%
20171031 8
3.2%
20171001 1
 
0.4%
20161031 1
 
0.4%
20161010 3
 
1.2%
20161007 2
 
0.8%

지정일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)11.1%
Missing98
Missing (%)39.0%
Infinite0
Infinite (%)0.0%
Mean20107137
Minimum20071120
Maximum20231129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-11T15:12:10.273884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20071120
5-th percentile20071120
Q120071120
median20091028
Q320121127
95-th percentile20185166
Maximum20231129
Range160009
Interquartile range (IQR)50007

Descriptive statistics

Standard deviation40403.793
Coefficient of variation (CV)0.0020094254
Kurtosis0.20371407
Mean20107137
Median Absolute Deviation (MAD)19908
Skewness1.0945223
Sum3.076392 × 109
Variance1.6324665 × 109
MonotonicityNot monotonic
2024-05-11T15:12:10.431839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
20071120 46
18.3%
20081006 22
 
8.8%
20091028 14
 
5.6%
20101122 14
 
5.6%
20121127 14
 
5.6%
20161205 8
 
3.2%
20141230 7
 
2.8%
20180119 6
 
2.4%
20111125 6
 
2.4%
20150915 5
 
2.0%
Other values (7) 11
 
4.4%
(Missing) 98
39.0%
ValueCountFrequency (%)
20071120 46
18.3%
20081006 22
8.8%
20091028 14
 
5.6%
20101122 14
 
5.6%
20111125 6
 
2.4%
20121127 14
 
5.6%
20141230 7
 
2.8%
20150915 5
 
2.0%
20161205 8
 
3.2%
20171130 2
 
0.8%
ValueCountFrequency (%)
20231129 1
 
0.4%
20211230 1
 
0.4%
20211216 2
 
0.8%
20210325 1
 
0.4%
20191231 3
 
1.2%
20181122 1
 
0.4%
20180119 6
2.4%
20171130 2
 
0.8%
20161205 8
3.2%
20150915 5
2.0%

취소일자
Real number (ℝ)

MISSING 

Distinct33
Distinct (%)18.3%
Missing71
Missing (%)28.3%
Infinite0
Infinite (%)0.0%
Mean20129234
Minimum20080704
Maximum20240215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-11T15:12:10.608781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20080704
5-th percentile20090495
Q120101122
median20111125
Q320160909
95-th percentile20231129
Maximum20240215
Range159511
Interquartile range (IQR)59787

Descriptive statistics

Standard deviation42728.847
Coefficient of variation (CV)0.0021227259
Kurtosis0.67255467
Mean20129234
Median Absolute Deviation (MAD)10003
Skewness1.3141367
Sum3.6232622 × 109
Variance1.8257544 × 109
MonotonicityNot monotonic
2024-05-11T15:12:10.777157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
20111125 61
24.3%
20101122 20
 
8.0%
20161205 16
 
6.4%
20091028 14
 
5.6%
20231129 11
 
4.4%
20160909 9
 
3.6%
20110628 7
 
2.8%
20191231 7
 
2.8%
20081006 6
 
2.4%
20121127 4
 
1.6%
Other values (23) 25
 
10.0%
(Missing) 71
28.3%
ValueCountFrequency (%)
20080704 1
 
0.4%
20080923 1
 
0.4%
20081006 6
2.4%
20090323 1
 
0.4%
20090504 1
 
0.4%
20090521 1
 
0.4%
20090605 1
 
0.4%
20090706 1
 
0.4%
20091026 1
 
0.4%
20091028 14
5.6%
ValueCountFrequency (%)
20240215 1
 
0.4%
20240207 1
 
0.4%
20240119 1
 
0.4%
20231229 1
 
0.4%
20231129 11
4.4%
20231023 1
 
0.4%
20211216 1
 
0.4%
20191231 7
2.8%
20170120 3
 
1.2%
20161205 16
6.4%

불가일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing251
Missing (%)100.0%
Memory size2.3 KiB

업소명
Text

Distinct227
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-05-11T15:12:11.076827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length14
Mean length5.8007968
Min length2

Characters and Unicode

Total characters1456
Distinct characters344
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique204 ?
Unique (%)81.3%

Sample

1st row마산해물아구찜
2nd row끄티집
3rd row말뚜기 감자탕
4th row늘마중
5th row스윗샐러드
ValueCountFrequency (%)
종로점 4
 
1.2%
이문 3
 
0.9%
카페 3
 
0.9%
인사동 3
 
0.9%
설농탕 3
 
0.9%
가르텐 2
 
0.6%
백제고기나라 2
 
0.6%
종로 2
 
0.6%
국시랑만두 2
 
0.6%
병천 2
 
0.6%
Other values (283) 311
92.3%
2024-05-11T15:12:11.533262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
87
 
6.0%
집 26
 
1.8%
대 25
 
1.7%
점 24
 
1.6%
가 23
 
1.6%
동 21
 
1.4%
로 18
 
1.2%
) 17
 
1.2%
이 17
 
1.2%
( 17
 
1.2%
Other values (334) 1181
81.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1275
87.6%
Space Separator 87
 
6.0%
Uppercase Letter 36
 
2.5%
Close Punctuation 17
 
1.2%
Open Punctuation 17
 
1.2%
Decimal Number 10
 
0.7%
Lowercase Letter 9
 
0.6%
Other Punctuation 5
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
집 26
 
2.0%
대 25
 
2.0%
점 24
 
1.9%
가 23
 
1.8%
동 21
 
1.6%
로 18
 
1.4%
이 17
 
1.3%
비 16
 
1.3%
부 15
 
1.2%
정 15
 
1.2%
Other values (297) 1075
84.3%
Uppercase Letter
ValueCountFrequency (%)
E 4
 
11.1%
I 4
 
11.1%
S 3
 
8.3%
T 3
 
8.3%
V 2
 
5.6%
U 2
 
5.6%
H 2
 
5.6%
A 2
 
5.6%
O 2
 
5.6%
F 2
 
5.6%
Other values (9) 10
27.8%
Decimal Number
ValueCountFrequency (%)
0 3
30.0%
4 2
20.0%
1 1
 
10.0%
2 1
 
10.0%
6 1
 
10.0%
3 1
 
10.0%
5 1
 
10.0%
Lowercase Letter
ValueCountFrequency (%)
h 2
22.2%
e 2
22.2%
a 2
22.2%
d 1
11.1%
n 1
11.1%
r 1
11.1%
Other Punctuation
ValueCountFrequency (%)
. 3
60.0%
？ 2
40.0%
Space Separator
ValueCountFrequency (%)
87
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1273
87.4%
Common 136
 
9.3%
Latin 45
 
3.1%
Han 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
집 26
 
2.0%
대 25
 
2.0%
점 24
 
1.9%
가 23
 
1.8%
동 21
 
1.6%
로 18
 
1.4%
이 17
 
1.3%
비 16
 
1.3%
부 15
 
1.2%
정 15
 
1.2%
Other values (295) 1073
84.3%
Latin
ValueCountFrequency (%)
E 4
 
8.9%
I 4
 
8.9%
S 3
 
6.7%
T 3
 
6.7%
h 2
 
4.4%
e 2
 
4.4%
V 2
 
4.4%
U 2
 
4.4%
H 2
 
4.4%
a 2
 
4.4%
Other values (15) 19
42.2%
Common
ValueCountFrequency (%)
87
64.0%
) 17
 
12.5%
( 17
 
12.5%
. 3
 
2.2%
0 3
 
2.2%
？ 2
 
1.5%
4 2
 
1.5%
1 1
 
0.7%
2 1
 
0.7%
6 1
 
0.7%
Other values (2) 2
 
1.5%
Han
ValueCountFrequency (%)
心 1
50.0%
羊 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1273
87.4%
ASCII 179
 
12.3%
None 2
 
0.1%
CJK 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
87
48.6%
) 17
 
9.5%
( 17
 
9.5%
E 4
 
2.2%
I 4
 
2.2%
S 3
 
1.7%
. 3
 
1.7%
0 3
 
1.7%
T 3
 
1.7%
h 2
 
1.1%
Other values (26) 36
20.1%
Hangul
ValueCountFrequency (%)
집 26
 
2.0%
대 25
 
2.0%
점 24
 
1.9%
가 23
 
1.8%
동 21
 
1.6%
로 18
 
1.4%
이 17
 
1.3%
비 16
 
1.3%
부 15
 
1.2%
정 15
 
1.2%
Other values (295) 1073
84.3%
None
ValueCountFrequency (%)
？ 2
100.0%
CJK
ValueCountFrequency (%)
心 1
50.0%
羊 1
50.0%

소재지도로명
Text

MISSING 

Distinct222
Distinct (%)90.2%
Missing5
Missing (%)2.0%
Memory size2.1 KiB
2024-05-11T15:12:11.855437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length45
Mean length29.752033
Min length21

Characters and Unicode

Total characters7319
Distinct characters157
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

Unique199 ?
Unique (%)80.9%

Sample

1st row서울특별시 종로구 북촌로5길 6, (재동)
2nd row서울특별시 종로구 삼일대로19길 20, 1~4층 (관철동)
3rd row서울특별시 종로구 종로51길 23-9, 1층 (창신동)
4th row서울특별시 종로구 인사동10길 11-5, 1층 (관훈동)
5th row서울특별시 종로구 종로1길 50, 더케이트윈타워 지하1층 (중학동)
ValueCountFrequency (%)
서울특별시 246
 
17.9%
종로구 246
 
17.9%
1층 38
 
2.8%
종로 16
 
1.2%
지하1층 16
 
1.2%
관철동 13
 
0.9%
관훈동 12
 
0.9%
평창동 11
 
0.8%
낙원동 11
 
0.8%
자하문로 9
 
0.7%
Other values (401) 756
55.0%
2024-05-11T15:12:12.402889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1128
 
15.4%
로 478
 
6.5%
1 370
 
5.1%
, 352
 
4.8%
종 319
 
4.4%
) 262
 
3.6%
( 262
 
3.6%
구 250
 
3.4%
서 250
 
3.4%
동 248
 
3.4%
Other values (147) 3400
46.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4082
55.8%
Decimal Number 1138
 
15.5%
Space Separator 1128
 
15.4%
Other Punctuation 357
 
4.9%
Close Punctuation 262
 
3.6%
Open Punctuation 262
 
3.6%
Dash Punctuation 77
 
1.1%
Math Symbol 11
 
0.2%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 478
 
11.7%
종 319
 
7.8%
구 250
 
6.1%
서 250
 
6.1%
동 248
 
6.1%
울 246
 
6.0%
시 246
 
6.0%
별 246
 
6.0%
특 246
 
6.0%
길 150
 
3.7%
Other values (128) 1403
34.4%
Decimal Number
ValueCountFrequency (%)
1 370
32.5%
2 171
15.0%
3 126
 
11.1%
4 105
 
9.2%
5 89
 
7.8%
0 68
 
6.0%
8 58
 
5.1%
9 52
 
4.6%
6 50
 
4.4%
7 49
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 352
98.6%
. 4
 
1.1%
? 1
 
0.3%
Space Separator
ValueCountFrequency (%)
1128
100.0%
Close Punctuation
ValueCountFrequency (%)
) 262
100.0%
Open Punctuation
ValueCountFrequency (%)
( 262
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 77
100.0%
Math Symbol
ValueCountFrequency (%)
~ 11
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4082
55.8%
Common 3235
44.2%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 478
 
11.7%
종 319
 
7.8%
구 250
 
6.1%
서 250
 
6.1%
동 248
 
6.1%
울 246
 
6.0%
시 246
 
6.0%
별 246
 
6.0%
특 246
 
6.0%
길 150
 
3.7%
Other values (128) 1403
34.4%
Common
ValueCountFrequency (%)
1128
34.9%
1 370
 
11.4%
, 352
 
10.9%
) 262
 
8.1%
( 262
 
8.1%
2 171
 
5.3%
3 126
 
3.9%
4 105
 
3.2%
5 89
 
2.8%
- 77
 
2.4%
Other values (8) 293
 
9.1%
Latin
ValueCountFrequency (%)
B 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4082
55.8%
ASCII 3237
44.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1128
34.8%
1 370
 
11.4%
, 352
 
10.9%
) 262
 
8.1%
( 262
 
8.1%
2 171
 
5.3%
3 126
 
3.9%
4 105
 
3.2%
5 89
 
2.7%
- 77
 
2.4%
Other values (9) 295
 
9.1%
Hangul
ValueCountFrequency (%)
로 478
 
11.7%
종 319
 
7.8%
구 250
 
6.1%
서 250
 
6.1%
동 248
 
6.1%
울 246
 
6.0%
시 246
 
6.0%
별 246
 
6.0%
특 246
 
6.0%
길 150
 
3.7%
Other values (128) 1403
34.4%

소재지지번
Text

Distinct228
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-05-11T15:12:12.743044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length42
Mean length26.645418
Min length20

Characters and Unicode

Total characters6688
Distinct characters142
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

Unique206 ?
Unique (%)82.1%

Sample

1st row서울특별시 종로구 재동 11번지 0호
2nd row서울특별시 종로구 관철동 14번지 5호
3rd row서울특별시 종로구 창신동 581번지 30호 1층
4th row서울특별시 종로구 관훈동 30번지 16호
5th row서울특별시 종로구 중학동 19번지 더케이트윈타워
ValueCountFrequency (%)
서울특별시 251
 
19.0%
종로구 251
 
19.0%
1호 40
 
3.0%
0호 24
 
1.8%
지상1층 21
 
1.6%
1층 17
 
1.3%
관철동 16
 
1.2%
5호 13
 
1.0%
낙원동 13
 
1.0%
관훈동 13
 
1.0%
Other values (293) 661
50.1%
2024-05-11T15:12:13.237216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1728
25.8%
지 339
 
5.1%
1 321
 
4.8%
로 278
 
4.2%
종 273
 
4.1%
구 255
 
3.8%
서 255
 
3.8%
번 254
 
3.8%
시 251
 
3.8%
울 251
 
3.8%
Other values (132) 2483
37.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3799
56.8%
Space Separator 1728
25.8%
Decimal Number 1072
 
16.0%
Other Punctuation 35
 
0.5%
Close Punctuation 18
 
0.3%
Open Punctuation 18
 
0.3%
Dash Punctuation 10
 
0.1%
Math Symbol 4
 
0.1%
Uppercase Letter 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 339
 
8.9%
로 278
 
7.3%
종 273
 
7.2%
구 255
 
6.7%
서 255
 
6.7%
번 254
 
6.7%
시 251
 
6.6%
울 251
 
6.6%
특 251
 
6.6%
별 251
 
6.6%
Other values (111) 1141
30.0%
Decimal Number
ValueCountFrequency (%)
1 321
29.9%
2 149
13.9%
3 115
 
10.7%
0 103
 
9.6%
5 83
 
7.7%
4 81
 
7.6%
8 69
 
6.4%
6 65
 
6.1%
7 47
 
4.4%
9 39
 
3.6%
Other Punctuation
ValueCountFrequency (%)
, 30
85.7%
. 3
 
8.6%
/ 1
 
2.9%
? 1
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
B 3
75.0%
D 1
 
25.0%
Space Separator
ValueCountFrequency (%)
1728
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3799
56.8%
Common 2885
43.1%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 339
 
8.9%
로 278
 
7.3%
종 273
 
7.2%
구 255
 
6.7%
서 255
 
6.7%
번 254
 
6.7%
시 251
 
6.6%
울 251
 
6.6%
특 251
 
6.6%
별 251
 
6.6%
Other values (111) 1141
30.0%
Common
ValueCountFrequency (%)
1728
59.9%
1 321
 
11.1%
2 149
 
5.2%
3 115
 
4.0%
0 103
 
3.6%
5 83
 
2.9%
4 81
 
2.8%
8 69
 
2.4%
6 65
 
2.3%
7 47
 
1.6%
Other values (9) 124
 
4.3%
Latin
ValueCountFrequency (%)
B 3
75.0%
D 1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3799
56.8%
ASCII 2889
43.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1728
59.8%
1 321
 
11.1%
2 149
 
5.2%
3 115
 
4.0%
0 103
 
3.6%
5 83
 
2.9%
4 81
 
2.8%
8 69
 
2.4%
6 65
 
2.2%
7 47
 
1.6%
Other values (11) 128
 
4.4%
Hangul
ValueCountFrequency (%)
지 339
 
8.9%
로 278
 
7.3%
종 273
 
7.2%
구 255
 
6.7%
서 255
 
6.7%
번 254
 
6.7%
시 251
 
6.6%
울 251
 
6.6%
특 251
 
6.6%
별 251
 
6.6%
Other values (111) 1141
30.0%

허가(신고)번호
Text

Distinct230
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-05-11T15:12:13.530328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters5522
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

Unique210 ?
Unique (%)83.7%

Sample

1st row3000000-101-1994-02251
2nd row3000000-101-2019-00441
3rd row3000000-101-2016-00071
4th row3000000-101-2021-00138
5th row3000000-101-2018-00205
ValueCountFrequency (%)
3000000-101-1977-00313 3
 
1.2%
3000000-101-2003-00174 2
 
0.8%
3000000-101-1999-10233 2
 
0.8%
3000000-101-1995-02514 2
 
0.8%
3000000-101-2002-11944 2
 
0.8%
3000000-101-2000-11611 2
 
0.8%
3000000-101-1998-02946 2
 
0.8%
3000000-101-1992-01017 2
 
0.8%
3000000-101-1999-10523 2
 
0.8%
3000000-101-1974-05722 2
 
0.8%
Other values (220) 230
91.6%
2024-05-11T15:12:13.995015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2359
42.7%
1 856
 
15.5%
- 753
 
13.6%
3 372
 
6.7%
9 344
 
6.2%
2 235
 
4.3%
8 131
 
2.4%
4 127
 
2.3%
6 119
 
2.2%
7 118
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4769
86.4%
Dash Punctuation 753
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2359
49.5%
1 856
 
17.9%
3 372
 
7.8%
9 344
 
7.2%
2 235
 
4.9%
8 131
 
2.7%
4 127
 
2.7%
6 119
 
2.5%
7 118
 
2.5%
5 108
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 753
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2359
42.7%
1 856
 
15.5%
- 753
 
13.6%
3 372
 
6.7%
9 344
 
6.2%
2 235
 
4.3%
8 131
 
2.4%
4 127
 
2.3%
6 119
 
2.2%
7 118
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2359
42.7%
1 856
 
15.5%
- 753
 
13.6%
3 372
 
6.7%
9 344
 
6.2%
2 235
 
4.3%
8 131
 
2.4%
4 127
 
2.3%
6 119
 
2.2%
7 118
 
2.1%

업태명
Categorical

Distinct12
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
한식
167 
경양식
18 
일식
18 
중국식
 
15
기타
 
14
Other values (7)
19 

Length

Max length15
Median length2
Mean length2.2709163
Min length2

Unique

Unique5 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
한식 167
66.5%
경양식 18
 
7.2%
일식 18
 
7.2%
중국식 15
 
6.0%
기타 14
 
5.6%
분식 11
 
4.4%
호프/통닭 3
 
1.2%
식육(숯불구이) 1
 
0.4%
탕류(보신용) 1
 
0.4%
복어취급 1
 
0.4%
Other values (2) 2
 
0.8%

Length

2024-05-11T15:12:14.168469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 167
66.5%
경양식 18
 
7.2%
일식 18
 
7.2%
중국식 15
 
6.0%
기타 14
 
5.6%
분식 11
 
4.4%
호프/통닭 3
 
1.2%
식육(숯불구이 1
 
0.4%
탕류(보신용 1
 
0.4%
복어취급 1
 
0.4%
Other values (2) 2
 
0.8%

주된음식
Text

MISSING 

Distinct85
Distinct (%)63.4%
Missing117
Missing (%)46.6%
Memory size2.1 KiB
2024-05-11T15:12:14.463580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length3.6268657
Min length1

Characters and Unicode

Total characters486
Distinct characters123
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

Unique61 ?
Unique (%)45.5%

Sample

1st row해물
2nd row전골, 샤브샤브
3rd row감자탕
4th row막걸리, 전
5th row샐러드
ValueCountFrequency (%)
한정식 14
 
9.6%
자장면 6
 
4.1%
갈비탕 4
 
2.7%
감자탕 3
 
2.1%
낙지 3
 
2.1%
전골 3
 
2.1%
샤브샤브 3
 
2.1%
불고기 3
 
2.1%
삼계탕 3
 
2.1%
스테이크 3
 
2.1%
Other values (77) 101
69.2%
2024-05-11T15:12:14.932055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 19
 
3.9%
탕 19
 
3.9%
한 18
 
3.7%
식 17
 
3.5%
정 17
 
3.5%
갈 15
 
3.1%
비 15
 
3.1%
밥 13
 
2.7%
12
 
2.5%
, 12
 
2.5%
Other values (113) 329
67.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 462
95.1%
Space Separator 12
 
2.5%
Other Punctuation 12
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 19
 
4.1%
탕 19
 
4.1%
한 18
 
3.9%
식 17
 
3.7%
정 17
 
3.7%
갈 15
 
3.2%
비 15
 
3.2%
밥 13
 
2.8%
면 11
 
2.4%
회 10
 
2.2%
Other values (111) 308
66.7%
Space Separator
ValueCountFrequency (%)
12
100.0%
Other Punctuation
ValueCountFrequency (%)
, 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 462
95.1%
Common 24
 
4.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 19
 
4.1%
탕 19
 
4.1%
한 18
 
3.9%
식 17
 
3.7%
정 17
 
3.7%
갈 15
 
3.2%
비 15
 
3.2%
밥 13
 
2.8%
면 11
 
2.4%
회 10
 
2.2%
Other values (111) 308
66.7%
Common
ValueCountFrequency (%)
12
50.0%
, 12
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 462
95.1%
ASCII 24
 
4.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 19
 
4.1%
탕 19
 
4.1%
한 18
 
3.9%
식 17
 
3.7%
정 17
 
3.7%
갈 15
 
3.2%
비 15
 
3.2%
밥 13
 
2.8%
면 11
 
2.4%
회 10
 
2.2%
Other values (111) 308
66.7%
ASCII
ValueCountFrequency (%)
12
50.0%
, 12
50.0%

영업장면적(㎡)
Real number (ℝ)

Distinct226
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160.64024
Minimum23.14
Maximum2768.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-11T15:12:15.102112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.14
5-th percentile47.105
Q177.845
median118.4
Q3183.655
95-th percentile356.715
Maximum2768.5
Range2745.36
Interquartile range (IQR)105.81

Descriptive statistics

Standard deviation201.12141
Coefficient of variation (CV)1.2519989
Kurtosis114.004
Mean160.64024
Median Absolute Deviation (MAD)45.46
Skewness9.2235473
Sum40320.7
Variance40449.821
MonotonicityNot monotonic
2024-05-11T15:12:15.256954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.26 3
 
1.2%
74.57 2
 
0.8%
168.21 2
 
0.8%
75.5 2
 
0.8%
206.01 2
 
0.8%
119.7 2
 
0.8%
107.36 2
 
0.8%
115.71 2
 
0.8%
60.0 2
 
0.8%
167.05 2
 
0.8%
Other values (216) 230
91.6%
ValueCountFrequency (%)
23.14 1
0.4%
29.37 1
0.4%
35.8 1
0.4%
37.98 1
0.4%
40.0 1
0.4%
40.5 1
0.4%
41.7 1
0.4%
42.81 1
0.4%
43.1 1
0.4%
43.55 1
0.4%
ValueCountFrequency (%)
2768.5 1
0.4%
793.39 2
0.8%
621.0 1
0.4%
558.55 2
0.8%
552.75 1
0.4%
451.81 1
0.4%
445.45 1
0.4%
440.86 1
0.4%
426.92 1
0.4%
408.19 1
0.4%

행정동명
Categorical

Distinct15
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
종로1.2.3.4가동
102 
사직동
36 
종로5.6가동
23 
혜화동
17 
평창동
15 
Other values (10)
58 

Length

Max length11
Median length7
Mean length6.7768924
Min length3

Unique

Unique0 ?
Unique (%)0.0%

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 102
40.6%
사직동 36
 
14.3%
종로5.6가동 23
 
9.2%
혜화동 17
 
6.8%
평창동 15
 
6.0%
가회동 10
 
4.0%
부암동 9
 
3.6%
삼청동 9
 
3.6%
숭인제1동 9
 
3.6%
이화동 8
 
3.2%
Other values (5) 13
 
5.2%

Length

2024-05-11T15:12:15.440266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
종로1.2.3.4가동 102
40.6%
사직동 36
 
14.3%
종로5.6가동 23
 
9.2%
혜화동 17
 
6.8%
평창동 15
 
6.0%
가회동 10
 
4.0%
부암동 9
 
3.6%
삼청동 9
 
3.6%
숭인제1동 9
 
3.6%
이화동 8
 
3.2%
Other values (5) 13
 
5.2%

급수시설구분
Categorical

Distinct3
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
상수도전용
194 
<NA>
56 
상수도(음용)지하수(주방용)겸용
 
1

Length

Max length17
Median length5
Mean length4.8247012
Min length4

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 194
77.3%
<NA> 56
 
22.3%
상수도(음용)지하수(주방용)겸용 1
 
0.4%

Length

2024-05-11T15:12:15.618596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:12:15.752897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 194
77.3%
na 56
 
22.3%
상수도(음용)지하수(주방용)겸용 1
 
0.4%

Interactions

2024-05-11T15:12:07.423077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:03.600142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:04.317405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:05.118827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:05.994023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:06.696100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:07.530260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:03.714476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:04.433791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:05.253284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:06.114510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:06.843806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:07.681131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:03.838978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:04.533796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:05.409814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:06.249080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:06.958106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:07.817093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:03.958959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:04.701387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:05.574997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:06.366592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:07.052207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:08.180786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:04.073093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:04.835202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:05.716408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:06.476675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:07.189527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:08.305832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:04.186913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:04.981495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:05.890404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:06.586768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:12:07.327476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T15:12:15.877757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자업태명주된음식영업장면적(㎡)행정동명급수시설구분
지정년도1.0000.7471.0001.0000.8150.0000.9030.1630.495NaN
지정번호0.7471.0000.6790.7470.7070.0000.3830.0800.2570.000
신청일자1.0000.6791.0001.0000.7530.1600.9070.1720.1900.000
지정일자1.0000.7471.0001.0000.8150.0000.9030.1630.495NaN
취소일자0.8150.7070.7530.8151.0000.0000.5260.0000.2950.099
업태명0.0000.0000.1600.0000.0001.0000.8790.0000.6020.000
주된음식0.9030.3830.9070.9030.5260.8791.0000.8970.0001.000
영업장면적(㎡)0.1630.0800.1720.1630.0000.0000.8971.0000.1310.000
행정동명0.4950.2570.1900.4950.2950.6020.0000.1311.0000.000
급수시설구분NaN0.0000.000NaN0.0990.0001.0000.0000.0001.000
2024-05-11T15:12:16.364721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
급수시설구분업태명행정동명
급수시설구분1.0000.0000.000
업태명0.0001.0000.266
행정동명0.0000.2661.000
2024-05-11T15:12:16.480789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.000-0.0350.9871.0000.323-0.2020.0000.1410.000
지정번호-0.0351.000-0.065-0.035-0.4930.0150.0000.0910.000
신청일자0.987-0.0651.0000.9870.367-0.1200.0690.0730.000
지정일자1.000-0.0350.9871.0000.323-0.2030.0000.1410.000
취소일자0.323-0.4930.3670.3231.000-0.0690.0000.1090.071
영업장면적(㎡)-0.2020.015-0.120-0.203-0.0691.0000.0000.0710.000
업태명0.0000.0000.0690.0000.0000.0001.0000.2660.000
행정동명0.1410.0910.0730.1410.1090.0710.2661.0000.000
급수시설구분0.0000.0000.0000.0000.0710.0000.0000.0001.000

Missing values

2024-05-11T15:12:08.465649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T15:12:08.713741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-11T15:12:08.888992image/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
03000000202312023101320231129<NA><NA>마산해물아구찜서울특별시 종로구 북촌로5길 6, (재동)서울특별시 종로구 재동 11번지 0호3000000-101-1994-02251한식해물83.8가회동상수도전용
130000002021902021112620211216<NA><NA>끄티집서울특별시 종로구 삼일대로19길 20, 1~4층 (관철동)서울특별시 종로구 관철동 14번지 5호3000000-101-2019-00441한식전골, 샤브샤브445.45종로1.2.3.4가동<NA>
230000002021892021102520211216<NA><NA>말뚜기 감자탕서울특별시 종로구 종로51길 23-9, 1층 (창신동)서울특별시 종로구 창신동 581번지 30호 1층3000000-101-2016-00071식육(숯불구이)감자탕48.92창신제1동<NA>
330000002021132021102520211230<NA><NA>늘마중서울특별시 종로구 인사동10길 11-5, 1층 (관훈동)서울특별시 종로구 관훈동 30번지 16호3000000-101-2021-00138한식막걸리, 전59.5종로1.2.3.4가동<NA>
430000002021882021031120210325<NA><NA>스윗샐러드서울특별시 종로구 종로1길 50, 더케이트윈타워 지하1층 (중학동)서울특별시 종로구 중학동 19번지 더케이트윈타워3000000-101-2018-00205경양식샐러드64.81종로1.2.3.4가동<NA>
530000002019482019103020191231<NA><NA>엔차이서울특별시 종로구 새문안로3길 36, (내수동,용비어천가 지층 B103호)서울특별시 종로구 내수동 75번지 용비어천가 지층 B103호3000000-101-2007-02099중국식짜장면135.15사직동<NA>
630000002019572019103020191231<NA><NA>이대감 고깃집서울특별시 종로구 수표로 96, (관수동)서울특별시 종로구 관수동 20번지3000000-101-2006-00326한식갈비탕, 갈빗살, 불고기793.39종로1.2.3.4가동<NA>
730000002019152019103020191231<NA><NA>대가곱창서울특별시 종로구 명륜길 53, (명륜3가,(지상1층))서울특별시 종로구 명륜3가 1번지 1143호 (지상1층)3000000-101-2009-00015한식곱창, 막창29.37혜화동<NA>
830000002018432017103120180119<NA><NA>신안촌서울특별시 종로구 사직로12길 8, (내자동,,153,154-1,155-1 (1층))서울특별시 종로구 내자동 152번지 ,153,154-1,155-1 (1층)3000000-101-1986-06323분식낙지, 홍어99.98사직동상수도전용
93000000201872017103120180119<NA><NA>오두막서울특별시 종로구 새문안로9길 24-5, (도렴동)서울특별시 종로구 도렴동 150번지 6호3000000-101-2014-00418기타곤드레밥23.14사직동<NA>
시군구코드지정년도지정번호신청일자지정일자취소일자불가일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분
2413000000<NA><NA>20071109<NA>20111125<NA>등촌 샤브칼국수서울특별시 종로구 대학로8가길 111, (동숭동,지상2층)서울특별시 종로구 동숭동 1번지 100호 지상2층3000000-101-1988-04826한식<NA>139.31이화동상수도전용
24230000002007182200711092007112020161205<NA>부산돼지국밥서울특별시 종로구 삼봉로 81, 지하109호 (수송동)서울특별시 종로구 수송동 58번지3000000-101-2005-00161한식동태찜86.07종로1.2.3.4가동<NA>
24330000002007842007110920071120<NA><NA>향가서울특별시 종로구 계동길 19-6, (재동)서울특별시 종로구 재동 84번지 1호3000000-101-2000-11447한식시골밥상200.65가회동상수도전용
2443000000200732007110920071120<NA><NA>감촌서울특별시 종로구 종로 19, 5층 511호 일부, 512호 (종로1가)서울특별시 종로구 종로1가 24번지3000000-101-1999-00629한식순두부244.56종로1.2.3.4가동상수도전용
2453000000<NA><NA>20071105<NA>20111125<NA>미스 사이공서울특별시 종로구 동숭길 55, (동숭동,지상1층)서울특별시 종로구 동숭동 1번지 145호 지상1층3000000-101-2005-00361외국음식전문점(인도,태국등)<NA>85.2이화동<NA>
2463000000<NA><NA>20071105<NA>20091028<NA>종로 허서방서울특별시 종로구 지봉로12길 3, (숭인동)서울특별시 종로구 숭인동 56번지 25호3000000-101-2001-11782한식<NA>102.48숭인제1동<NA>
2473000000<NA><NA>20071102<NA>20101122<NA>(주)달개비자연음식전문점서울특별시 종로구 북촌로5길 11, (재동)서울특별시 종로구 재동 32번지 10호3000000-101-2002-12098한식<NA>185.16가회동상수도전용
24830000002007342007110120071120<NA><NA>별미삼청수제비서울특별시 종로구 삼청로 101-1, (삼청동)서울특별시 종로구 삼청동 99번지3000000-101-1997-02854한식수제비106.4삼청동상수도전용
24930000002007412007101520071120<NA><NA>송전서울특별시 종로구 종로 14, (서린동)서울특별시 종로구 서린동 136번지3000000-101-1999-05788일식생선회148.12종로1.2.3.4가동상수도전용
2503000000200767200710152007112020231129<NA>허서방네서울특별시 종로구 종로 332, (창신동)서울특별시 종로구 창신동 328번지 3호3000000-101-1988-00882한식된장찌개158.34창신제2동상수도전용