Overview

Dataset statistics

Number of variables16
Number of observations209
Missing cells389
Missing cells (%)11.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.9 KiB
Average record size in memory136.6 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
Author노원구
URLhttps://data.seoul.go.kr/dataList/OA-11006/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
업태명 is highly overall correlated with 급수시설구분High correlation
행정동명 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 신청일자 and 3 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 3 other fieldsHigh correlation
취소일자 is highly overall correlated with 지정년도 and 2 other fieldsHigh correlation
영업장면적(㎡) is highly overall correlated with 급수시설구분High correlation
업태명 is highly imbalanced (51.4%)Imbalance
지정년도 has 18 (8.6%) missing valuesMissing
지정번호 has 18 (8.6%) missing valuesMissing
지정일자 has 18 (8.6%) missing valuesMissing
취소일자 has 108 (51.7%) missing valuesMissing
불가일자 has 209 (100.0%) missing valuesMissing
주된음식 has 18 (8.6%) missing valuesMissing
불가일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-03 23:05:57.431804
Analysis finished2024-05-03 23:06:12.096471
Duration14.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
3100000
209 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3100000
2nd row3100000
3rd row3100000
4th row3100000
5th row3100000

Common Values

ValueCountFrequency (%)
3100000 209
100.0%

Length

2024-05-03T23:06:12.349540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T23:06:12.756527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3100000 209
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)10.5%
Missing18
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean2010.5812
Minimum2002
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-05-03T23:06:13.189690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2002
5-th percentile2002
Q12004
median2008
Q32019
95-th percentile2023
Maximum2023
Range21
Interquartile range (IQR)15

Descriptive statistics

Standard deviation7.5986467
Coefficient of variation (CV)0.0037793285
Kurtosis-1.4615011
Mean2010.5812
Median Absolute Deviation (MAD)6
Skewness0.34757716
Sum384021
Variance57.739432
MonotonicityNot monotonic
2024-05-03T23:06:13.764589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2002 37
17.7%
2004 22
10.5%
2020 17
 
8.1%
2015 13
 
6.2%
2023 12
 
5.7%
2006 10
 
4.8%
2021 8
 
3.8%
2022 8
 
3.8%
2008 8
 
3.8%
2003 8
 
3.8%
Other values (10) 48
23.0%
(Missing) 18
 
8.6%
ValueCountFrequency (%)
2002 37
17.7%
2003 8
 
3.8%
2004 22
10.5%
2005 7
 
3.3%
2006 10
 
4.8%
2007 6
 
2.9%
2008 8
 
3.8%
2009 6
 
2.9%
2010 3
 
1.4%
2012 5
 
2.4%
ValueCountFrequency (%)
2023 12
5.7%
2022 8
3.8%
2021 8
3.8%
2020 17
8.1%
2019 4
 
1.9%
2018 3
 
1.4%
2017 5
 
2.4%
2015 13
6.2%
2014 3
 
1.4%
2013 6
 
2.9%

지정번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct99
Distinct (%)51.8%
Missing18
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean110.64398
Minimum1
Maximum453
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-05-03T23:06:14.265418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median19
Q3119
95-th percentile432
Maximum453
Range452
Interquartile range (IQR)112

Descriptive statistics

Standard deviation153.23109
Coefficient of variation (CV)1.3849022
Kurtosis-0.21036087
Mean110.64398
Median Absolute Deviation (MAD)17
Skewness1.2208272
Sum21133
Variance23479.767
MonotonicityNot monotonic
2024-05-03T23:06:14.760497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 10
 
4.8%
3 9
 
4.3%
1 8
 
3.8%
8 7
 
3.3%
7 7
 
3.3%
4 7
 
3.3%
5 6
 
2.9%
15 5
 
2.4%
13 5
 
2.4%
17 5
 
2.4%
Other values (89) 122
58.4%
(Missing) 18
 
8.6%
ValueCountFrequency (%)
1 8
3.8%
2 10
4.8%
3 9
4.3%
4 7
3.3%
5 6
2.9%
6 5
2.4%
7 7
3.3%
8 7
3.3%
9 3
 
1.4%
11 2
 
1.0%
ValueCountFrequency (%)
453 1
0.5%
451 1
0.5%
449 1
0.5%
448 1
0.5%
447 1
0.5%
446 1
0.5%
443 1
0.5%
442 1
0.5%
441 1
0.5%
436 1
0.5%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20101146
Minimum20020520
Maximum20231106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-05-03T23:06:15.288242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20020520
5-th percentile20020520
Q120030919
median20070322
Q320171204
95-th percentile20231031
Maximum20231106
Range210586
Interquartile range (IQR)140285

Descriptive statistics

Standard deviation75713.595
Coefficient of variation (CV)0.0037666307
Kurtosis-1.3646413
Mean20101146
Median Absolute Deviation (MAD)49802
Skewness0.45796898
Sum4.2011395 × 109
Variance5.7325484 × 109
MonotonicityNot monotonic
2024-05-03T23:06:15.854950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20020520 25
 
12.0%
20151209 14
 
6.7%
20201027 13
 
6.2%
20060620 13
 
6.2%
20020522 12
 
5.7%
20080609 9
 
4.3%
20211105 8
 
3.8%
20221024 8
 
3.8%
20040319 8
 
3.8%
20231106 8
 
3.8%
Other values (43) 91
43.5%
ValueCountFrequency (%)
20020520 25
12.0%
20020522 12
5.7%
20020630 5
 
2.4%
20020919 3
 
1.4%
20021101 1
 
0.5%
20030319 1
 
0.5%
20030522 1
 
0.5%
20030619 3
 
1.4%
20030919 4
 
1.9%
20031216 1
 
0.5%
ValueCountFrequency (%)
20231106 8
3.8%
20231101 1
 
0.5%
20231031 2
 
1.0%
20231030 1
 
0.5%
20221024 8
3.8%
20211105 8
3.8%
20201228 2
 
1.0%
20201030 1
 
0.5%
20201028 1
 
0.5%
20201027 13
6.2%

지정일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)17.8%
Missing18
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean20106694
Minimum20020628
Maximum20231220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-05-03T23:06:16.479385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20020628
5-th percentile20020628
Q120040254
median20080710
Q320191030
95-th percentile20231220
Maximum20231220
Range210592
Interquartile range (IQR)150776.5

Descriptive statistics

Standard deviation76237.176
Coefficient of variation (CV)0.0037916316
Kurtosis-1.4636639
Mean20106694
Median Absolute Deviation (MAD)60082
Skewness0.34765286
Sum3.8403786 × 109
Variance5.812107 × 109
MonotonicityNot monotonic
2024-05-03T23:06:17.100533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
20020628 29
13.9%
20201217 15
 
7.2%
20151209 13
 
6.2%
20231220 12
 
5.7%
20060701 10
 
4.8%
20040930 10
 
4.8%
20211214 8
 
3.8%
20221202 8
 
3.8%
20080710 8
 
3.8%
20131210 6
 
2.9%
Other values (24) 72
34.4%
(Missing) 18
 
8.6%
ValueCountFrequency (%)
20020628 29
13.9%
20020703 5
 
2.4%
20021004 2
 
1.0%
20021112 1
 
0.5%
20030402 1
 
0.5%
20030630 3
 
1.4%
20030702 1
 
0.5%
20031002 3
 
1.4%
20040105 3
 
1.4%
20040402 2
 
1.0%
ValueCountFrequency (%)
20231220 12
5.7%
20221202 8
3.8%
20211214 8
3.8%
20201230 2
 
1.0%
20201217 15
7.2%
20191030 4
 
1.9%
20181221 3
 
1.4%
20171207 5
 
2.4%
20151209 13
6.2%
20141210 3
 
1.4%

취소일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct71
Distinct (%)70.3%
Missing108
Missing (%)51.7%
Infinite0
Infinite (%)0.0%
Mean20119291
Minimum20030929
Maximum20231220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-05-03T23:06:17.538744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20030929
5-th percentile20050309
Q120060927
median20111116
Q320171124
95-th percentile20211221
Maximum20231220
Range200291
Interquartile range (IQR)110197

Descriptive statistics

Standard deviation57665.672
Coefficient of variation (CV)0.002866188
Kurtosis-1.1985563
Mean20119291
Median Absolute Deviation (MAD)50406
Skewness0.32758154
Sum2.0320484 × 109
Variance3.3253298 × 109
MonotonicityNot monotonic
2024-05-03T23:06:18.213981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20111116 11
 
5.3%
20191030 8
 
3.8%
20100721 4
 
1.9%
20231220 3
 
1.4%
20171124 2
 
1.0%
20161014 2
 
1.0%
20091215 2
 
1.0%
20211217 2
 
1.0%
20221202 2
 
1.0%
20181221 2
 
1.0%
Other values (61) 63
30.1%
(Missing) 108
51.7%
ValueCountFrequency (%)
20030929 1
0.5%
20040827 1
0.5%
20050124 1
0.5%
20050207 1
0.5%
20050223 1
0.5%
20050309 1
0.5%
20050527 1
0.5%
20050601 1
0.5%
20050705 1
0.5%
20050711 1
0.5%
ValueCountFrequency (%)
20231220 3
 
1.4%
20221202 2
 
1.0%
20211221 1
 
0.5%
20211217 2
 
1.0%
20211020 1
 
0.5%
20200723 1
 
0.5%
20191030 8
3.8%
20190422 1
 
0.5%
20181221 2
 
1.0%
20180524 1
 
0.5%

불가일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing209
Missing (%)100.0%
Memory size2.0 KiB

업소명
Text

Distinct174
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-05-03T23:06:19.035757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length6.2105263
Min length2

Characters and Unicode

Total characters1298
Distinct characters287
Distinct categories7 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique141 ?
Unique (%)67.5%

Sample

1st row엉터리생고기
2nd row제일콩집
3rd row북경
4th row항도
5th row항도
ValueCountFrequency (%)
노원점 4
 
1.5%
노원역점 4
 
1.5%
한동길감자탕 3
 
1.1%
박대박 3
 
1.1%
한모둠순대국 2
 
0.8%
온천골 2
 
0.8%
횡성목장 2
 
0.8%
산골식당 2
 
0.8%
향림 2
 
0.8%
맛찬들3.5왕소금구이노원점 2
 
0.8%
Other values (203) 238
90.2%
2024-05-03T23:06:20.179245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
55
 
4.2%
점 32
 
2.5%
원 27
 
2.1%
수 24
 
1.8%
이 23
 
1.8%
가 21
 
1.6%
국 20
 
1.5%
탕 19
 
1.5%
대 19
 
1.5%
한 16
 
1.2%
Other values (277) 1042
80.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1197
92.2%
Space Separator 55
 
4.2%
Decimal Number 18
 
1.4%
Other Punctuation 9
 
0.7%
Close Punctuation 8
 
0.6%
Open Punctuation 8
 
0.6%
Uppercase Letter 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 32
 
2.7%
원 27
 
2.3%
수 24
 
2.0%
이 23
 
1.9%
가 21
 
1.8%
국 20
 
1.7%
탕 19
 
1.6%
대 19
 
1.6%
한 16
 
1.3%
장 16
 
1.3%
Other values (258) 980
81.9%
Decimal Number
ValueCountFrequency (%)
1 4
22.2%
5 4
22.2%
3 3
16.7%
7 2
11.1%
9 2
11.1%
2 1
 
5.6%
6 1
 
5.6%
4 1
 
5.6%
Other Punctuation
ValueCountFrequency (%)
& 3
33.3%
. 2
22.2%
, 2
22.2%
/ 1
 
11.1%
？ 1
 
11.1%
Uppercase Letter
ValueCountFrequency (%)
D 1
33.3%
J 1
33.3%
N 1
33.3%
Space Separator
ValueCountFrequency (%)
55
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1196
92.1%
Common 98
 
7.6%
Latin 3
 
0.2%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 32
 
2.7%
원 27
 
2.3%
수 24
 
2.0%
이 23
 
1.9%
가 21
 
1.8%
국 20
 
1.7%
탕 19
 
1.6%
대 19
 
1.6%
한 16
 
1.3%
장 16
 
1.3%
Other values (257) 979
81.9%
Common
ValueCountFrequency (%)
55
56.1%
) 8
 
8.2%
( 8
 
8.2%
1 4
 
4.1%
5 4
 
4.1%
& 3
 
3.1%
3 3
 
3.1%
. 2
 
2.0%
7 2
 
2.0%
, 2
 
2.0%
Other values (6) 7
 
7.1%
Latin
ValueCountFrequency (%)
D 1
33.3%
J 1
33.3%
N 1
33.3%
Han
ValueCountFrequency (%)
味 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1196
92.1%
ASCII 100
 
7.7%
CJK 1
 
0.1%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
55
55.0%
) 8
 
8.0%
( 8
 
8.0%
1 4
 
4.0%
5 4
 
4.0%
& 3
 
3.0%
3 3
 
3.0%
. 2
 
2.0%
7 2
 
2.0%
, 2
 
2.0%
Other values (8) 9
 
9.0%
Hangul
ValueCountFrequency (%)
점 32
 
2.7%
원 27
 
2.3%
수 24
 
2.0%
이 23
 
1.9%
가 21
 
1.8%
국 20
 
1.7%
탕 19
 
1.6%
대 19
 
1.6%
한 16
 
1.3%
장 16
 
1.3%
Other values (257) 979
81.9%
CJK
ValueCountFrequency (%)
味 1
100.0%
None
ValueCountFrequency (%)
？ 1
100.0%

소재지도로명
Text

Distinct174
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-05-03T23:06:21.086979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length46
Mean length32.717703
Min length23

Characters and Unicode

Total characters6838
Distinct characters165
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

Unique141 ?
Unique (%)67.5%

Sample

1st row서울특별시 노원구 상계로7길 39, 한올빌딩 1층 (상계동)
2nd row서울특별시 노원구 동일로174길 37-8, 제일빌딩 1,2층 (공릉동)
3rd row서울특별시 노원구 동일로243길 23, 1층 (상계동)
4th row서울특별시 노원구 노해로75길 14-22, (상계동)
5th row서울특별시 노원구 노해로75길 14-22, (상계동)
ValueCountFrequency (%)
서울특별시 209
 
15.9%
노원구 209
 
15.9%
상계동 104
 
7.9%
1층 88
 
6.7%
2층 35
 
2.7%
공릉동 29
 
2.2%
동일로 25
 
1.9%
하계동 22
 
1.7%
중계동 18
 
1.4%
월계동 13
 
1.0%
Other values (302) 563
42.8%
2024-05-03T23:06:22.509725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1107
 
16.2%
1 360
 
5.3%
동 294
 
4.3%
, 284
 
4.2%
노 262
 
3.8%
2 228
 
3.3%
원 225
 
3.3%
계 217
 
3.2%
) 212
 
3.1%
( 212
 
3.1%
Other values (155) 3437
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3794
55.5%
Decimal Number 1182
 
17.3%
Space Separator 1107
 
16.2%
Other Punctuation 284
 
4.2%
Close Punctuation 212
 
3.1%
Open Punctuation 212
 
3.1%
Dash Punctuation 44
 
0.6%
Uppercase Letter 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 294
 
7.7%
노 262
 
6.9%
원 225
 
5.9%
계 217
 
5.7%
구 210
 
5.5%
로 210
 
5.5%
울 209
 
5.5%
서 209
 
5.5%
시 209
 
5.5%
특 209
 
5.5%
Other values (137) 1540
40.6%
Decimal Number
ValueCountFrequency (%)
1 360
30.5%
2 228
19.3%
3 102
 
8.6%
4 101
 
8.5%
7 86
 
7.3%
0 84
 
7.1%
5 74
 
6.3%
8 62
 
5.2%
9 55
 
4.7%
6 30
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
A 1
50.0%
L 1
50.0%
Space Separator
ValueCountFrequency (%)
1107
100.0%
Other Punctuation
ValueCountFrequency (%)
, 284
100.0%
Close Punctuation
ValueCountFrequency (%)
) 212
100.0%
Open Punctuation
ValueCountFrequency (%)
( 212
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3794
55.5%
Common 3042
44.5%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 294
 
7.7%
노 262
 
6.9%
원 225
 
5.9%
계 217
 
5.7%
구 210
 
5.5%
로 210
 
5.5%
울 209
 
5.5%
서 209
 
5.5%
시 209
 
5.5%
특 209
 
5.5%
Other values (137) 1540
40.6%
Common
ValueCountFrequency (%)
1107
36.4%
1 360
 
11.8%
, 284
 
9.3%
2 228
 
7.5%
) 212
 
7.0%
( 212
 
7.0%
3 102
 
3.4%
4 101
 
3.3%
7 86
 
2.8%
0 84
 
2.8%
Other values (6) 266
 
8.7%
Latin
ValueCountFrequency (%)
A 1
50.0%
L 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3794
55.5%
ASCII 3044
44.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1107
36.4%
1 360
 
11.8%
, 284
 
9.3%
2 228
 
7.5%
) 212
 
7.0%
( 212
 
7.0%
3 102
 
3.4%
4 101
 
3.3%
7 86
 
2.8%
0 84
 
2.8%
Other values (8) 268
 
8.8%
Hangul
ValueCountFrequency (%)
동 294
 
7.7%
노 262
 
6.9%
원 225
 
5.9%
계 217
 
5.7%
구 210
 
5.5%
로 210
 
5.5%
울 209
 
5.5%
서 209
 
5.5%
시 209
 
5.5%
특 209
 
5.5%
Other values (137) 1540
40.6%

소재지지번
Text

Distinct174
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-05-03T23:06:23.252136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length40
Mean length29.885167
Min length24

Characters and Unicode

Total characters6246
Distinct characters154
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

Unique140 ?
Unique (%)67.0%

Sample

1st row서울특별시 노원구 상계동 363번지 7호 한올빌딩 1층
2nd row서울특별시 노원구 공릉동 633번지 18호 제일빌딩 1,2층
3rd row서울특별시 노원구 상계동 1132번지 66호 1층
4th row서울특별시 노원구 상계동 708번지 2호 지상2층
5th row서울특별시 노원구 상계동 708번지 2호 지상2층
ValueCountFrequency (%)
서울특별시 209
16.5%
노원구 209
16.5%
상계동 119
 
9.4%
1층 74
 
5.8%
공릉동 31
 
2.4%
1호 25
 
2.0%
중계동 24
 
1.9%
하계동 22
 
1.7%
2층 21
 
1.7%
2호 21
 
1.7%
Other values (261) 512
40.4%
2024-05-03T23:06:24.337527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1520
24.3%
1 298
 
4.8%
지 228
 
3.7%
노 214
 
3.4%
동 213
 
3.4%
원 213
 
3.4%
구 211
 
3.4%
울 209
 
3.3%
번 209
 
3.3%
서 209
 
3.3%
Other values (144) 2722
43.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3503
56.1%
Space Separator 1520
24.3%
Decimal Number 1175
 
18.8%
Other Punctuation 27
 
0.4%
Dash Punctuation 11
 
0.2%
Open Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%
Math Symbol 2
 
< 0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 228
 
6.5%
노 214
 
6.1%
동 213
 
6.1%
원 213
 
6.1%
구 211
 
6.0%
울 209
 
6.0%
번 209
 
6.0%
서 209
 
6.0%
특 209
 
6.0%
별 209
 
6.0%
Other values (126) 1379
39.4%
Decimal Number
ValueCountFrequency (%)
1 298
25.4%
2 169
14.4%
3 145
12.3%
0 95
 
8.1%
6 94
 
8.0%
5 93
 
7.9%
7 82
 
7.0%
9 72
 
6.1%
4 72
 
6.1%
8 55
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
L 1
50.0%
A 1
50.0%
Space Separator
ValueCountFrequency (%)
1520
100.0%
Other Punctuation
ValueCountFrequency (%)
, 27
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3503
56.1%
Common 2741
43.9%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 228
 
6.5%
노 214
 
6.1%
동 213
 
6.1%
원 213
 
6.1%
구 211
 
6.0%
울 209
 
6.0%
번 209
 
6.0%
서 209
 
6.0%
특 209
 
6.0%
별 209
 
6.0%
Other values (126) 1379
39.4%
Common
ValueCountFrequency (%)
1520
55.5%
1 298
 
10.9%
2 169
 
6.2%
3 145
 
5.3%
0 95
 
3.5%
6 94
 
3.4%
5 93
 
3.4%
7 82
 
3.0%
9 72
 
2.6%
4 72
 
2.6%
Other values (6) 101
 
3.7%
Latin
ValueCountFrequency (%)
L 1
50.0%
A 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3503
56.1%
ASCII 2743
43.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1520
55.4%
1 298
 
10.9%
2 169
 
6.2%
3 145
 
5.3%
0 95
 
3.5%
6 94
 
3.4%
5 93
 
3.4%
7 82
 
3.0%
9 72
 
2.6%
4 72
 
2.6%
Other values (8) 103
 
3.8%
Hangul
ValueCountFrequency (%)
지 228
 
6.5%
노 214
 
6.1%
동 213
 
6.1%
원 213
 
6.1%
구 211
 
6.0%
울 209
 
6.0%
번 209
 
6.0%
서 209
 
6.0%
특 209
 
6.0%
별 209
 
6.0%
Other values (126) 1379
39.4%

허가(신고)번호
Text

Distinct176
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-05-03T23:06:24.850763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters4598
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

Unique144 ?
Unique (%)68.9%

Sample

1st row3100000-101-2006-00383
2nd row3100000-101-1983-00002
3rd row3100000-101-2004-00077
4th row3100000-101-1993-03282
5th row3100000-101-1993-03282
ValueCountFrequency (%)
3100000-101-2003-00268 3
 
1.4%
3100000-101-1997-06183 2
 
1.0%
3100000-101-2004-00524 2
 
1.0%
3100000-101-1994-01263 2
 
1.0%
3100000-101-1993-00911 2
 
1.0%
3100000-101-2006-00383 2
 
1.0%
3100000-101-1995-03323 2
 
1.0%
3100000-101-2012-00062 2
 
1.0%
3100000-101-1994-01137 2
 
1.0%
3100000-101-1998-02142 2
 
1.0%
Other values (166) 188
90.0%
2024-05-03T23:06:25.734939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1895
41.2%
1 855
18.6%
- 627
 
13.6%
3 313
 
6.8%
2 265
 
5.8%
9 239
 
5.2%
6 88
 
1.9%
8 87
 
1.9%
7 84
 
1.8%
4 83
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3971
86.4%
Dash Punctuation 627
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1895
47.7%
1 855
21.5%
3 313
 
7.9%
2 265
 
6.7%
9 239
 
6.0%
6 88
 
2.2%
8 87
 
2.2%
7 84
 
2.1%
4 83
 
2.1%
5 62
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 627
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4598
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1895
41.2%
1 855
18.6%
- 627
 
13.6%
3 313
 
6.8%
2 265
 
5.8%
9 239
 
5.2%
6 88
 
1.9%
8 87
 
1.9%
7 84
 
1.8%
4 83
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4598
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1895
41.2%
1 855
18.6%
- 627
 
13.6%
3 313
 
6.8%
2 265
 
5.8%
9 239
 
5.2%
6 88
 
1.9%
8 87
 
1.9%
7 84
 
1.8%
4 83
 
1.8%

업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
한식
147 
중국식
23 
일식
17 
경양식
 
5
식육(숯불구이)
 
5
Other values (5)
 
12

Length

Max length15
Median length2
Mean length2.4688995
Min length2

Unique

Unique2 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
한식 147
70.3%
중국식 23
 
11.0%
일식 17
 
8.1%
경양식 5
 
2.4%
식육(숯불구이) 5
 
2.4%
호프/통닭 4
 
1.9%
기타 4
 
1.9%
외국음식전문점(인도,태국등) 2
 
1.0%
복어취급 1
 
0.5%
회집 1
 
0.5%

Length

2024-05-03T23:06:26.005973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T23:06:26.310061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 147
70.3%
중국식 23
 
11.0%
일식 17
 
8.1%
경양식 5
 
2.4%
식육(숯불구이 5
 
2.4%
호프/통닭 4
 
1.9%
기타 4
 
1.9%
외국음식전문점(인도,태국등 2
 
1.0%
복어취급 1
 
0.5%
회집 1
 
0.5%

주된음식
Text

MISSING 

Distinct108
Distinct (%)56.5%
Missing18
Missing (%)8.6%
Memory size1.8 KiB
2024-05-03T23:06:26.859184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length7
Mean length3.4712042
Min length2

Characters and Unicode

Total characters663
Distinct characters134
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

Unique73 ?
Unique (%)38.2%

Sample

1st row등심
2nd row두부찌개
3rd row대구탕
4th row대구탕
5th row곱창구이
ValueCountFrequency (%)
돼지갈비 8
 
4.1%
삼겹살 8
 
4.1%
등심 5
 
2.6%
한정식 5
 
2.6%
짜장면,짬뽕 5
 
2.6%
닭갈비 5
 
2.6%
대구탕 5
 
2.6%
칼국수 4
 
2.1%
냉면 4
 
2.1%
추어탕 4
 
2.1%
Other values (100) 141
72.7%
2024-05-03T23:06:27.921757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
탕 27
 
4.1%
장 26
 
3.9%
구 23
 
3.5%
비 21
 
3.2%
국 20
 
3.0%
갈 19
 
2.9%
면 19
 
2.9%
이 16
 
2.4%
대 15
 
2.3%
삼 14
 
2.1%
Other values (124) 463
69.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 652
98.3%
Other Punctuation 8
 
1.2%
Space Separator 3
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 27
 
4.1%
장 26
 
4.0%
구 23
 
3.5%
비 21
 
3.2%
국 20
 
3.1%
갈 19
 
2.9%
면 19
 
2.9%
이 16
 
2.5%
대 15
 
2.3%
삼 14
 
2.1%
Other values (122) 452
69.3%
Other Punctuation
ValueCountFrequency (%)
, 8
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 652
98.3%
Common 11
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 27
 
4.1%
장 26
 
4.0%
구 23
 
3.5%
비 21
 
3.2%
국 20
 
3.1%
갈 19
 
2.9%
면 19
 
2.9%
이 16
 
2.5%
대 15
 
2.3%
삼 14
 
2.1%
Other values (122) 452
69.3%
Common
ValueCountFrequency (%)
, 8
72.7%
3
 
27.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 652
98.3%
ASCII 11
 
1.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
탕 27
 
4.1%
장 26
 
4.0%
구 23
 
3.5%
비 21
 
3.2%
국 20
 
3.1%
갈 19
 
2.9%
면 19
 
2.9%
이 16
 
2.5%
대 15
 
2.3%
삼 14
 
2.1%
Other values (122) 452
69.3%
ASCII
ValueCountFrequency (%)
, 8
72.7%
3
 
27.3%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct171
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176.26258
Minimum26.4
Maximum2792.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-05-03T23:06:28.455734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26.4
5-th percentile61.434
Q195.65
median131.58
Q3193.88
95-th percentile399.936
Maximum2792.2
Range2765.8
Interquartile range (IQR)98.23

Descriptive statistics

Standard deviation214.25013
Coefficient of variation (CV)1.2155168
Kurtosis107.75584
Mean176.26258
Median Absolute Deviation (MAD)42.48
Skewness9.1600622
Sum36838.88
Variance45903.12
MonotonicityNot monotonic
2024-05-03T23:06:29.137099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101.97 3
 
1.4%
148.0 3
 
1.4%
90.0 3
 
1.4%
148.2 2
 
1.0%
89.6 2
 
1.0%
486.13 2
 
1.0%
111.85 2
 
1.0%
132.84 2
 
1.0%
212.4 2
 
1.0%
89.1 2
 
1.0%
Other values (161) 186
89.0%
ValueCountFrequency (%)
26.4 1
0.5%
28.12 1
0.5%
28.47 1
0.5%
43.68 1
0.5%
44.06 1
0.5%
48.3 1
0.5%
53.86 1
0.5%
58.35 1
0.5%
58.5 2
1.0%
60.53 1
0.5%
ValueCountFrequency (%)
2792.2 1
0.5%
765.42 2
1.0%
625.37 1
0.5%
534.87 1
0.5%
486.13 2
1.0%
447.2 1
0.5%
437.2 1
0.5%
430.24 1
0.5%
419.68 1
0.5%
370.32 2
1.0%

행정동명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
상계2동
41 
상계6.7동
39 
상계1동
30 
공릉1동
26 
하계1동
22 
Other values (8)
51 

Length

Max length6
Median length4
Mean length4.4593301
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row상계2동
2nd row공릉1동
3rd row상계1동
4th row상계6.7동
5th row상계6.7동

Common Values

ValueCountFrequency (%)
상계2동 41
19.6%
상계6.7동 39
18.7%
상계1동 30
14.4%
공릉1동 26
12.4%
하계1동 22
10.5%
월계1동 13
 
6.2%
중계4동 10
 
4.8%
공릉2동 5
 
2.4%
중계2.3동 5
 
2.4%
중계본동 5
 
2.4%
Other values (3) 13
 
6.2%

Length

2024-05-03T23:06:29.596737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
상계2동 41
19.6%
상계6.7동 39
18.7%
상계1동 30
14.4%
공릉1동 26
12.4%
하계1동 22
10.5%
월계1동 13
 
6.2%
중계4동 10
 
4.8%
공릉2동 5
 
2.4%
중계2.3동 5
 
2.4%
중계본동 5
 
2.4%
Other values (3) 13
 
6.2%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
상수도전용
146 
<NA>
63 

Length

Max length5
Median length5
Mean length4.6985646
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 146
69.9%
<NA> 63
30.1%

Length

2024-05-03T23:06:30.049023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T23:06:30.397429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 146
69.9%
na 63
30.1%

Interactions

2024-05-03T23:06:08.218195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:05:59.147811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:00.652737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:02.191419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:03.893662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:05.708815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:08.453163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:05:59.381537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:00.890901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:02.453520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:04.157669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:06.321033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:08.854002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:05:59.633695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:01.122611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:02.613453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:04.436040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:06.713219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:09.175308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:05:59.875495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:01.401057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:02.824140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:04.666890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:07.096298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:09.493616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:00.114836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:01.684545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:03.095923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:04.948632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:07.455662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:09.904328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:00.405692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:01.952804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:03.592166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:05.369220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:06:07.873804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T23:06:30.617564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자업태명영업장면적(㎡)행정동명
지정년도1.0000.8441.0001.0000.8930.1070.0000.136
지정번호0.8441.0000.6050.6900.5390.0730.0000.267
신청일자1.0000.6051.0001.0000.8560.1840.0000.176
지정일자1.0000.6901.0001.0000.8430.0950.0000.179
취소일자0.8930.5390.8560.8431.0000.0000.0000.324
업태명0.1070.0730.1840.0950.0001.0000.1450.405
영업장면적(㎡)0.0000.0000.0000.0000.0000.1451.0000.000
행정동명0.1360.2670.1760.1790.3240.4050.0001.000
2024-05-03T23:06:30.919231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태명행정동명급수시설구분
업태명1.0000.1781.000
행정동명0.1781.0001.000
급수시설구분1.0001.0001.000
2024-05-03T23:06:31.206181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.000-0.4530.9950.9970.550-0.0560.0470.1041.000
지정번호-0.4531.000-0.450-0.448-0.229-0.0590.0290.1151.000
신청일자0.995-0.4501.0000.9980.453-0.0580.0310.0731.000
지정일자0.997-0.4480.9981.0000.523-0.0580.0470.1041.000
취소일자0.550-0.2290.4530.5231.0000.1520.0000.1281.000
영업장면적(㎡)-0.056-0.059-0.058-0.0580.1521.0000.0840.0001.000
업태명0.0470.0290.0310.0470.0000.0841.0000.1781.000
행정동명0.1040.1150.0730.1040.1280.0000.1781.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-03T23:06:10.393548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T23:06:11.237163image/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.
2024-05-03T23:06:11.837585image/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
0310000020089200806092008071020131230<NA>엉터리생고기서울특별시 노원구 상계로7길 39, 한올빌딩 1층 (상계동)서울특별시 노원구 상계동 363번지 7호 한올빌딩 1층3100000-101-2006-00383한식등심148.2상계2동<NA>
13100000200222002052220020628<NA><NA>제일콩집서울특별시 노원구 동일로174길 37-8, 제일빌딩 1,2층 (공릉동)서울특별시 노원구 공릉동 633번지 18호 제일빌딩 1,2층3100000-101-1983-00002한식두부찌개214.46공릉1동상수도전용
23100000<NA><NA>20050314<NA>20100504<NA>북경서울특별시 노원구 동일로243길 23, 1층 (상계동)서울특별시 노원구 상계동 1132번지 66호 1층3100000-101-2004-00077중국식<NA>176.98상계1동상수도전용
33100000200219200206302002070320051017<NA>항도서울특별시 노원구 노해로75길 14-22, (상계동)서울특별시 노원구 상계동 708번지 2호 지상2층3100000-101-1993-03282일식대구탕126.42상계6.7동상수도전용
431000002009142009032420090710<NA><NA>항도서울특별시 노원구 노해로75길 14-22, (상계동)서울특별시 노원구 상계동 708번지 2호 지상2층3100000-101-1993-03282일식대구탕126.42상계6.7동상수도전용
5310000020021302002052220020628<NA><NA>향토곱창서울특별시 노원구 동일로191길 10, (공릉동)서울특별시 노원구 공릉동 383번지 18호3100000-101-1998-02143한식곱창구이66.36공릉1동상수도전용
63100000200420200403192004040620051108<NA>박대박서울특별시 노원구 노해로75길 14-12, 1층 (상계동)서울특별시 노원구 상계동 704번지 3호 1층3100000-101-1992-00563한식부대찌게119.04상계6.7동상수도전용
73100000201582015120920151209<NA><NA>인차이나서울특별시 노원구 공릉로 167, 1층 (공릉동)서울특별시 노원구 공릉동 411번지 4호 1층3100000-101-2008-00062중국식면류195.7공릉2동<NA>
8310000020034282003091920031002<NA><NA>명문식당서울특별시 노원구 동일로217가길 27, (상계동)서울특별시 노원구 상계동 735번지 5호 1층3100000-101-1990-00361한식부대찌개88.5상계6.7동상수도전용
931000002015162015120920151209<NA><NA>육대장 상계점서울특별시 노원구 수락산로 212, 지상1층 우측호 (상계동)서울특별시 노원구 상계동 963번지 2호3100000-101-2012-00022한식육개장67.0상계1동<NA>
시군구코드지정년도지정번호신청일자지정일자취소일자불가일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분
19931000002002378200205202002062820131104<NA>대박짬뽕마을서울특별시 노원구 공릉로58가길 17, 1층 (하계동)서울특별시 노원구 하계동 179번지 27호 1층3100000-101-2000-06891중국식우거지탕140.4하계1동상수도전용
2003100000200719200703222007042320160805<NA>동트팔팔장어서울특별시 노원구 노원로 192, 2층 (하계동)서울특별시 노원구 하계동 69번지 8호3100000-101-2006-00168한식우럭회무침161.82하계1동<NA>
201310000020023452002052020020628<NA><NA>원명품생태서울특별시 노원구 노해로75길 14-27, (상계동)서울특별시 노원구 상계동 707번지 4호 1층3100000-101-1992-00632한식생태찌개108.31상계6.7동상수도전용
20231000002002240200205222002062820130520<NA>온고을&이지쿡서울특별시 노원구 동일로 1065, (공릉동)서울특별시 노원구 공릉동 398번지 5호3100000-101-2000-06797한식콩나물해장국60.53공릉1동<NA>
2033100000200520200506082005063020061207<NA>신의 한국수 (노원점)서울특별시 노원구 동일로217가길 13, (상계동,101호)서울특별시 노원구 상계동 733번지 1호 101호3100000-101-2003-00406한식생선구이80.92상계6.7동상수도전용
204310000020023712002052020020628<NA><NA>DNJ서울특별시 노원구 노원로 244, 보스턴 산부인과 1층 (하계동)서울특별시 노원구 하계동 256번지 11호 보스턴 산부인과3100000-101-1997-01969중국식짜장면,짬뽕341.66하계1동상수도전용
2053100000<NA><NA>20020520<NA>20111116<NA>호남한식뷔폐서울특별시 노원구 노해로 490, (상계동, 길빌딩 지하1층 101호)서울특별시 노원구 상계동 724번지 1호 길빌딩 지하1층 101호3100000-101-1990-00325한식<NA>90.77상계6.7동상수도전용
206310000020141201411212014121020191030<NA>여기,꼬치네서울특별시 노원구 동일로192길 62, 2층 (공릉동)서울특별시 노원구 공릉동 392번지 25호 지상2층3100000-101-2013-00048경양식파스타179.0공릉1동<NA>
2073100000202162021110520211214<NA><NA>스시웨이서울특별시 노원구 동일로 1374, (상계동, 2층)서울특별시 노원구 상계동 749번지 2층3100000-101-2012-00159일식초밥90.0상계6.7동<NA>
2083100000200822008060920080710<NA><NA>아바이토종순대국서울특별시 노원구 동일로203가길 29, (중계동,브라운스톤 중계 107,108,109호)서울특별시 노원구 중계동 506번지 브라운스톤 중계 107,108,109호3100000-101-2006-00267한식추어탕103.75중계2.3동<NA>