Overview

Dataset statistics

Number of variables16
Number of observations83
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.1 KiB
Average record size in memory136.6 B

Variable types

Categorical5
Numeric6
Text5

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,취소일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,지정취소사유,주된음식,영업장면적(㎡),행정동명,급수시설구분
Author노원구
URLhttps://data.seoul.go.kr/dataList/OA-11005/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
행정동명 is highly overall correlated with 급수시설구분High correlation
업태명 is highly overall correlated with 급수시설구분High correlation
급수시설구분 is highly overall correlated with 지정년도 and 8 other fieldsHigh correlation
지정취소사유 is highly overall correlated with 지정년도 and 4 other fieldsHigh correlation
지정년도 is highly overall correlated with 지정번호 and 5 other fieldsHigh correlation
지정번호 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
신청일자 is highly overall correlated with 지정년도 and 5 other fieldsHigh correlation
지정일자 is highly overall correlated with 지정년도 and 5 other fieldsHigh correlation
취소일자 is highly overall correlated with 지정년도 and 4 other fieldsHigh correlation
영업장면적(㎡) is highly overall correlated with 급수시설구분High correlation

Reproduction

Analysis started2024-05-04 02:13:22.109030
Analysis finished2024-05-04 02:13:41.091267
Duration18.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size796.0 B
3100000
83 

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 83
100.0%

Length

2024-05-04T02:13:41.250983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:13:41.520162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3100000 83
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.6627
Minimum2002
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size879.0 B
2024-05-04T02:13:41.767886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2002
5-th percentile2002
Q12002
median2004
Q32008
95-th percentile2016.8
Maximum2021
Range19
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.3311718
Coefficient of variation (CV)0.0026567355
Kurtosis0.27220965
Mean2006.6627
Median Absolute Deviation (MAD)2
Skewness1.1870998
Sum166553
Variance28.421393
MonotonicityNot monotonic
2024-05-04T02:13:42.127347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2002 23
27.7%
2004 19
22.9%
2015 7
 
8.4%
2006 6
 
7.2%
2007 5
 
6.0%
2008 4
 
4.8%
2005 3
 
3.6%
2014 3
 
3.6%
2020 3
 
3.6%
2003 3
 
3.6%
Other values (5) 7
 
8.4%
ValueCountFrequency (%)
2002 23
27.7%
2003 3
 
3.6%
2004 19
22.9%
2005 3
 
3.6%
2006 6
 
7.2%
2007 5
 
6.0%
2008 4
 
4.8%
2009 1
 
1.2%
2012 2
 
2.4%
2013 2
 
2.4%
ValueCountFrequency (%)
2021 1
 
1.2%
2020 3
3.6%
2017 1
 
1.2%
2015 7
8.4%
2014 3
3.6%
2013 2
 
2.4%
2012 2
 
2.4%
2009 1
 
1.2%
2008 4
4.8%
2007 5
6.0%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)72.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.78313
Minimum1
Maximum451
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size879.0 B
2024-05-04T02:13:42.733995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.1
Q110
median26
Q3312.5
95-th percentile415.2
Maximum451
Range450
Interquartile range (IQR)302.5

Descriptive statistics

Standard deviation162.90002
Coefficient of variation (CV)1.2748163
Kurtosis-0.99417465
Mean127.78313
Median Absolute Deviation (MAD)22
Skewness0.90853751
Sum10606
Variance26536.416
MonotonicityNot monotonic
2024-05-04T02:13:43.097092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 4
 
4.8%
17 4
 
4.8%
1 3
 
3.6%
7 3
 
3.6%
20 3
 
3.6%
8 2
 
2.4%
3 2
 
2.4%
29 2
 
2.4%
6 2
 
2.4%
15 2
 
2.4%
Other values (50) 56
67.5%
ValueCountFrequency (%)
1 3
3.6%
2 2
2.4%
3 2
2.4%
4 4
4.8%
5 2
2.4%
6 2
2.4%
7 3
3.6%
8 2
2.4%
9 1
 
1.2%
11 1
 
1.2%
ValueCountFrequency (%)
451 1
1.2%
446 1
1.2%
442 1
1.2%
441 1
1.2%
416 1
1.2%
408 1
1.2%
397 1
1.2%
392 2
2.4%
386 1
1.2%
379 1
1.2%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)38.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20067014
Minimum20020520
Maximum20211105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size879.0 B
2024-05-04T02:13:43.475493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20020520
5-th percentile20020520
Q120020919
median20040920
Q320080609
95-th percentile20169138
Maximum20211105
Range190585
Interquartile range (IQR)59690

Descriptive statistics

Standard deviation53709.992
Coefficient of variation (CV)0.0026765313
Kurtosis0.25560141
Mean20067014
Median Absolute Deviation (MAD)20398
Skewness1.1879197
Sum1.6655622 × 109
Variance2.8847633 × 109
MonotonicityNot monotonic
2024-05-04T02:13:43.996434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
20020520 9
 
10.8%
20040319 7
 
8.4%
20151209 7
 
8.4%
20040920 6
 
7.2%
20060620 6
 
7.2%
20020522 6
 
7.2%
20020630 5
 
6.0%
20080609 4
 
4.8%
20141121 3
 
3.6%
20050608 2
 
2.4%
Other values (22) 28
33.7%
ValueCountFrequency (%)
20020520 9
10.8%
20020522 6
7.2%
20020630 5
6.0%
20020919 2
 
2.4%
20021101 1
 
1.2%
20030619 2
 
2.4%
20030919 1
 
1.2%
20031216 1
 
1.2%
20031219 2
 
2.4%
20040319 7
8.4%
ValueCountFrequency (%)
20211105 1
 
1.2%
20201228 1
 
1.2%
20201027 2
 
2.4%
20171130 1
 
1.2%
20151209 7
8.4%
20141121 3
3.6%
20131104 2
 
2.4%
20121115 2
 
2.4%
20090609 1
 
1.2%
20080609 4
4.8%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)31.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20067406
Minimum20020628
Maximum20211214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size879.0 B
2024-05-04T02:13:44.462905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20020628
5-th percentile20020628
Q120021004
median20040930
Q320080710
95-th percentile20169207
Maximum20211214
Range190586
Interquartile range (IQR)59706

Descriptive statistics

Standard deviation53518.658
Coefficient of variation (CV)0.0026669445
Kurtosis0.27006062
Mean20067406
Median Absolute Deviation (MAD)20302
Skewness1.1891676
Sum1.6655947 × 109
Variance2.8642468 × 109
MonotonicityNot monotonic
2024-05-04T02:13:45.128030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
20020628 15
18.1%
20040930 8
 
9.6%
20151209 7
 
8.4%
20040406 6
 
7.2%
20060701 6
 
7.2%
20070423 5
 
6.0%
20020703 5
 
6.0%
20080710 4
 
4.8%
20141210 3
 
3.6%
20040105 3
 
3.6%
Other values (16) 21
25.3%
ValueCountFrequency (%)
20020628 15
18.1%
20020703 5
 
6.0%
20021004 2
 
2.4%
20021112 1
 
1.2%
20030630 1
 
1.2%
20030702 1
 
1.2%
20031002 1
 
1.2%
20040105 3
 
3.6%
20040402 1
 
1.2%
20040406 6
 
7.2%
ValueCountFrequency (%)
20211214 1
 
1.2%
20201230 1
 
1.2%
20201217 2
 
2.4%
20171207 1
 
1.2%
20151209 7
8.4%
20141210 3
3.6%
20131210 2
 
2.4%
20121228 2
 
2.4%
20090710 1
 
1.2%
20080710 4
4.8%

취소일자
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)77.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20116992
Minimum20030929
Maximum20231220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size879.0 B
2024-05-04T02:13:45.566705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20030929
5-th percentile20050232
Q120060613
median20100721
Q320175812
95-th percentile20220204
Maximum20231220
Range200291
Interquartile range (IQR)115199

Descriptive statistics

Standard deviation62205.987
Coefficient of variation (CV)0.0030922112
Kurtosis-1.3669006
Mean20116992
Median Absolute Deviation (MAD)49704
Skewness0.39648456
Sum1.6697103 × 109
Variance3.8695849 × 109
MonotonicityNot monotonic
2024-05-04T02:13:46.173725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20191030 8
 
9.6%
20100721 4
 
4.8%
20231220 3
 
3.6%
20161014 2
 
2.4%
20171124 2
 
2.4%
20111116 2
 
2.4%
20091215 2
 
2.4%
20211217 2
 
2.4%
20070705 2
 
2.4%
20221202 2
 
2.4%
Other values (54) 54
65.1%
ValueCountFrequency (%)
20030929 1
1.2%
20040827 1
1.2%
20050124 1
1.2%
20050207 1
1.2%
20050223 1
1.2%
20050309 1
1.2%
20050527 1
1.2%
20050601 1
1.2%
20050705 1
1.2%
20050711 1
1.2%
ValueCountFrequency (%)
20231220 3
 
3.6%
20221202 2
 
2.4%
20211221 1
 
1.2%
20211217 2
 
2.4%
20211020 1
 
1.2%
20200723 1
 
1.2%
20191030 8
9.6%
20190422 1
 
1.2%
20180524 1
 
1.2%
20180417 1
 
1.2%

업소명
Text

Distinct73
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size796.0 B
2024-05-04T02:13:46.879524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length6.3253012
Min length2

Characters and Unicode

Total characters525
Distinct characters203
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

Unique63 ?
Unique (%)75.9%

Sample

1st row엉터리생고기
2nd row항도
3rd row박대박
4th row범맥주 노원역점
5th row범맥주 노원역점
ValueCountFrequency (%)
노원역점 3
 
2.7%
엉터리생고기 2
 
1.8%
범맥주 2
 
1.8%
태능갈비 2
 
1.8%
양평가 2
 
1.8%
정성담은 2
 
1.8%
박대박 2
 
1.8%
하마벌떡 2
 
1.8%
낙지&아구 2
 
1.8%
517 2
 
1.8%
Other values (86) 90
81.1%
2024-05-04T02:13:48.114688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28
 
5.3%
점 12
 
2.3%
가 11
 
2.1%
대 9
 
1.7%
탕 9
 
1.7%
장 9
 
1.7%
마 9
 
1.7%
리 9
 
1.7%
원 9
 
1.7%
수 8
 
1.5%
Other values (193) 412
78.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 477
90.9%
Space Separator 28
 
5.3%
Decimal Number 9
 
1.7%
Other Punctuation 5
 
1.0%
Open Punctuation 3
 
0.6%
Close Punctuation 3
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 12
 
2.5%
가 11
 
2.3%
대 9
 
1.9%
탕 9
 
1.9%
장 9
 
1.9%
마 9
 
1.9%
리 9
 
1.9%
원 9
 
1.9%
수 8
 
1.7%
국 8
 
1.7%
Other values (182) 384
80.5%
Decimal Number
ValueCountFrequency (%)
5 3
33.3%
7 2
22.2%
1 2
22.2%
3 1
 
11.1%
2 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
& 3
60.0%
. 1
 
20.0%
, 1
 
20.0%
Space Separator
ValueCountFrequency (%)
28
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 477
90.9%
Common 48
 
9.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 12
 
2.5%
가 11
 
2.3%
대 9
 
1.9%
탕 9
 
1.9%
장 9
 
1.9%
마 9
 
1.9%
리 9
 
1.9%
원 9
 
1.9%
수 8
 
1.7%
국 8
 
1.7%
Other values (182) 384
80.5%
Common
ValueCountFrequency (%)
28
58.3%
( 3
 
6.2%
) 3
 
6.2%
& 3
 
6.2%
5 3
 
6.2%
7 2
 
4.2%
1 2
 
4.2%
. 1
 
2.1%
3 1
 
2.1%
2 1
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 477
90.9%
ASCII 48
 
9.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28
58.3%
( 3
 
6.2%
) 3
 
6.2%
& 3
 
6.2%
5 3
 
6.2%
7 2
 
4.2%
1 2
 
4.2%
. 1
 
2.1%
3 1
 
2.1%
2 1
 
2.1%
Hangul
ValueCountFrequency (%)
점 12
 
2.5%
가 11
 
2.3%
대 9
 
1.9%
탕 9
 
1.9%
장 9
 
1.9%
마 9
 
1.9%
리 9
 
1.9%
원 9
 
1.9%
수 8
 
1.7%
국 8
 
1.7%
Other values (182) 384
80.5%

소재지도로명
Text

Distinct74
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Memory size796.0 B
2024-05-04T02:13:48.880491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length38
Mean length31.903614
Min length23

Characters and Unicode

Total characters2648
Distinct characters108
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)78.3%

Sample

1st row서울특별시 노원구 상계로7길 39, 한올빌딩 1층 (상계동)
2nd row서울특별시 노원구 노해로75길 14-22, (상계동)
3rd row서울특별시 노원구 노해로75길 14-12, 1층 (상계동)
4th row서울특별시 노원구 노해로81길 12-4, 화랑상가 2층 (상계동)
5th row서울특별시 노원구 노해로81길 12-4, 화랑상가 2층 (상계동)
ValueCountFrequency (%)
서울특별시 83
 
15.9%
노원구 83
 
15.9%
상계동 45
 
8.6%
1층 36
 
6.9%
2층 19
 
3.6%
공릉동 12
 
2.3%
하계동 11
 
2.1%
동일로 10
 
1.9%
월계동 7
 
1.3%
노원로 6
 
1.2%
Other values (147) 209
40.1%
2024-05-04T02:13:50.231499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
438
 
16.5%
1 146
 
5.5%
동 116
 
4.4%
노 105
 
4.0%
, 101
 
3.8%
계 91
 
3.4%
원 90
 
3.4%
구 84
 
3.2%
울 83
 
3.1%
2 83
 
3.1%
Other values (98) 1311
49.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1484
56.0%
Decimal Number 440
 
16.6%
Space Separator 438
 
16.5%
Other Punctuation 101
 
3.8%
Close Punctuation 83
 
3.1%
Open Punctuation 83
 
3.1%
Dash Punctuation 19
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 116
 
7.8%
노 105
 
7.1%
계 91
 
6.1%
원 90
 
6.1%
구 84
 
5.7%
울 83
 
5.6%
서 83
 
5.6%
별 83
 
5.6%
특 83
 
5.6%
시 83
 
5.6%
Other values (83) 583
39.3%
Decimal Number
ValueCountFrequency (%)
1 146
33.2%
2 83
18.9%
4 36
 
8.2%
3 36
 
8.2%
7 32
 
7.3%
8 29
 
6.6%
0 28
 
6.4%
5 27
 
6.1%
9 16
 
3.6%
6 7
 
1.6%
Space Separator
ValueCountFrequency (%)
438
100.0%
Other Punctuation
ValueCountFrequency (%)
, 101
100.0%
Close Punctuation
ValueCountFrequency (%)
) 83
100.0%
Open Punctuation
ValueCountFrequency (%)
( 83
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1484
56.0%
Common 1164
44.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 116
 
7.8%
노 105
 
7.1%
계 91
 
6.1%
원 90
 
6.1%
구 84
 
5.7%
울 83
 
5.6%
서 83
 
5.6%
별 83
 
5.6%
특 83
 
5.6%
시 83
 
5.6%
Other values (83) 583
39.3%
Common
ValueCountFrequency (%)
438
37.6%
1 146
 
12.5%
, 101
 
8.7%
2 83
 
7.1%
) 83
 
7.1%
( 83
 
7.1%
4 36
 
3.1%
3 36
 
3.1%
7 32
 
2.7%
8 29
 
2.5%
Other values (5) 97
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1484
56.0%
ASCII 1164
44.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
438
37.6%
1 146
 
12.5%
, 101
 
8.7%
2 83
 
7.1%
) 83
 
7.1%
( 83
 
7.1%
4 36
 
3.1%
3 36
 
3.1%
7 32
 
2.7%
8 29
 
2.5%
Other values (5) 97
 
8.3%
Hangul
ValueCountFrequency (%)
동 116
 
7.8%
노 105
 
7.1%
계 91
 
6.1%
원 90
 
6.1%
구 84
 
5.7%
울 83
 
5.6%
서 83
 
5.6%
별 83
 
5.6%
특 83
 
5.6%
시 83
 
5.6%
Other values (83) 583
39.3%

소재지지번
Text

Distinct74
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Memory size796.0 B
2024-05-04T02:13:51.033934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length34
Mean length29.26506
Min length24

Characters and Unicode

Total characters2429
Distinct characters91
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)78.3%

Sample

1st row서울특별시 노원구 상계동 363번지 7호 한올빌딩 1층
2nd row서울특별시 노원구 상계동 708번지 2호 지상2층
3rd row서울특별시 노원구 상계동 704번지 3호 1층
4th row서울특별시 노원구 상계동 616번지 6호 화랑상가
5th row서울특별시 노원구 상계동 616번지 6호 화랑상가
ValueCountFrequency (%)
서울특별시 83
16.5%
노원구 83
16.5%
상계동 51
 
10.2%
1층 33
 
6.6%
1호 13
 
2.6%
공릉동 12
 
2.4%
하계동 11
 
2.2%
2층 9
 
1.8%
월계동 7
 
1.4%
2호 7
 
1.4%
Other values (126) 193
38.4%
2024-05-04T02:13:52.381683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
604
24.9%
1 109
 
4.5%
지 90
 
3.7%
노 85
 
3.5%
동 84
 
3.5%
원 84
 
3.5%
구 84
 
3.5%
서 83
 
3.4%
울 83
 
3.4%
호 83
 
3.4%
Other values (81) 1040
42.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1376
56.6%
Space Separator 604
24.9%
Decimal Number 438
 
18.0%
Other Punctuation 7
 
0.3%
Dash Punctuation 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 90
 
6.5%
노 85
 
6.2%
동 84
 
6.1%
원 84
 
6.1%
구 84
 
6.1%
서 83
 
6.0%
울 83
 
6.0%
호 83
 
6.0%
번 83
 
6.0%
별 83
 
6.0%
Other values (68) 534
38.8%
Decimal Number
ValueCountFrequency (%)
1 109
24.9%
3 57
13.0%
2 57
13.0%
5 40
 
9.1%
6 36
 
8.2%
7 33
 
7.5%
9 31
 
7.1%
4 26
 
5.9%
8 25
 
5.7%
0 24
 
5.5%
Space Separator
ValueCountFrequency (%)
604
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1376
56.6%
Common 1053
43.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 90
 
6.5%
노 85
 
6.2%
동 84
 
6.1%
원 84
 
6.1%
구 84
 
6.1%
서 83
 
6.0%
울 83
 
6.0%
호 83
 
6.0%
번 83
 
6.0%
별 83
 
6.0%
Other values (68) 534
38.8%
Common
ValueCountFrequency (%)
604
57.4%
1 109
 
10.4%
3 57
 
5.4%
2 57
 
5.4%
5 40
 
3.8%
6 36
 
3.4%
7 33
 
3.1%
9 31
 
2.9%
4 26
 
2.5%
8 25
 
2.4%
Other values (3) 35
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1376
56.6%
ASCII 1053
43.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
604
57.4%
1 109
 
10.4%
3 57
 
5.4%
2 57
 
5.4%
5 40
 
3.8%
6 36
 
3.4%
7 33
 
3.1%
9 31
 
2.9%
4 26
 
2.5%
8 25
 
2.4%
Other values (3) 35
 
3.3%
Hangul
ValueCountFrequency (%)
지 90
 
6.5%
노 85
 
6.2%
동 84
 
6.1%
원 84
 
6.1%
구 84
 
6.1%
서 83
 
6.0%
울 83
 
6.0%
호 83
 
6.0%
번 83
 
6.0%
별 83
 
6.0%
Other values (68) 534
38.8%

허가(신고)번호
Text

Distinct74
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Memory size796.0 B
2024-05-04T02:13:52.922237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters1826
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

Unique65 ?
Unique (%)78.3%

Sample

1st row3100000-101-2006-00383
2nd row3100000-101-1993-03282
3rd row3100000-101-1992-00563
4th row3100000-101-1995-01460
5th row3100000-101-1995-01460
ValueCountFrequency (%)
3100000-101-2006-00383 2
 
2.4%
3100000-101-1997-06183 2
 
2.4%
3100000-101-2004-00309 2
 
2.4%
3100000-101-1994-01263 2
 
2.4%
3100000-101-2003-00268 2
 
2.4%
3100000-101-1995-03323 2
 
2.4%
3100000-101-1995-01460 2
 
2.4%
3100000-101-1998-02142 2
 
2.4%
3100000-101-1999-06295 2
 
2.4%
3100000-101-2004-00032 1
 
1.2%
Other values (64) 64
77.1%
2024-05-04T02:13:54.126012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 742
40.6%
1 334
18.3%
- 249
 
13.6%
3 133
 
7.3%
9 106
 
5.8%
2 101
 
5.5%
6 40
 
2.2%
8 36
 
2.0%
4 33
 
1.8%
7 32
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1577
86.4%
Dash Punctuation 249
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 742
47.1%
1 334
21.2%
3 133
 
8.4%
9 106
 
6.7%
2 101
 
6.4%
6 40
 
2.5%
8 36
 
2.3%
4 33
 
2.1%
7 32
 
2.0%
5 20
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 249
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1826
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 742
40.6%
1 334
18.3%
- 249
 
13.6%
3 133
 
7.3%
9 106
 
5.8%
2 101
 
5.5%
6 40
 
2.2%
8 36
 
2.0%
4 33
 
1.8%
7 32
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1826
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 742
40.6%
1 334
18.3%
- 249
 
13.6%
3 133
 
7.3%
9 106
 
5.8%
2 101
 
5.5%
6 40
 
2.2%
8 36
 
2.0%
4 33
 
1.8%
7 32
 
1.8%

업태명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Memory size796.0 B
한식
54 
중국식
12 
일식
8 
호프/통닭
 
2
식육(숯불구이)
 
2
Other values (3)
 
5

Length

Max length8
Median length2
Mean length2.4096386
Min length2

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row한식
2nd row일식
3rd row한식
4th row호프/통닭
5th row호프/통닭

Common Values

ValueCountFrequency (%)
한식 54
65.1%
중국식 12
 
14.5%
일식 8
 
9.6%
호프/통닭 2
 
2.4%
식육(숯불구이) 2
 
2.4%
경양식 2
 
2.4%
기타 2
 
2.4%
복어취급 1
 
1.2%

Length

2024-05-04T02:13:54.769588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:13:55.325977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 54
65.1%
중국식 12
 
14.5%
일식 8
 
9.6%
호프/통닭 2
 
2.4%
식육(숯불구이 2
 
2.4%
경양식 2
 
2.4%
기타 2
 
2.4%
복어취급 1
 
1.2%

지정취소사유
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Memory size796.0 B
영업자지위승계
30 
지위승계
11 
기준 부적합
7 
행정처분
7 
기준미달
4 
Other values (17)
24 

Length

Max length26
Median length17
Mean length7.2048193
Min length4

Unique

Unique11 ?
Unique (%)13.3%

Sample

1st row위생등급 미달
2nd row영업자지위승계
3rd row영업자지위승계
4th row영업자지위승계
5th row기준미달

Common Values

ValueCountFrequency (%)
영업자지위승계 30
36.1%
지위승계 11
 
13.3%
기준 부적합 7
 
8.4%
행정처분 7
 
8.4%
기준미달 4
 
4.8%
지정기준 미달 3
 
3.6%
지위승계 후 모범음식점 승계포기 2
 
2.4%
재지정심사에 따른 점검점수가 지정기준점수에 미달 2
 
2.4%
<NA> 2
 
2.4%
지위승계 포기 2
 
2.4%
Other values (12) 13
15.7%

Length

2024-05-04T02:13:55.946079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
영업자지위승계 30
25.4%
지위승계 16
13.6%
부적합 8
 
6.8%
기준 7
 
5.9%
행정처분 7
 
5.9%
미달 6
 
5.1%
기준미달 4
 
3.4%
지정기준 3
 
2.5%
모범음식점 3
 
2.5%
승계포기 3
 
2.5%
Other values (23) 31
26.3%

주된음식
Text

Distinct62
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Memory size796.0 B
2024-05-04T02:13:56.654482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.5542169
Min length2

Characters and Unicode

Total characters295
Distinct characters103
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

Unique46 ?
Unique (%)55.4%

Sample

1st row등심
2nd row대구탕
3rd row부대찌게
4th row닭갈비
5th row해물찜
ValueCountFrequency (%)
한정식 4
 
4.8%
짜장면,짬뽕 3
 
3.6%
등심 3
 
3.6%
칼국수 3
 
3.6%
자장면 2
 
2.4%
해물된장 2
 
2.4%
활어회 2
 
2.4%
대구탕 2
 
2.4%
삼겹살 2
 
2.4%
돼지갈비 2
 
2.4%
Other values (53) 59
70.2%
2024-05-04T02:13:57.950468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 14
 
4.7%
탕 11
 
3.7%
구 11
 
3.7%
면 11
 
3.7%
국 8
 
2.7%
식 7
 
2.4%
이 7
 
2.4%
회 7
 
2.4%
대 6
 
2.0%
해 6
 
2.0%
Other values (93) 207
70.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 290
98.3%
Other Punctuation 4
 
1.4%
Space Separator 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 14
 
4.8%
탕 11
 
3.8%
구 11
 
3.8%
면 11
 
3.8%
국 8
 
2.8%
식 7
 
2.4%
이 7
 
2.4%
회 7
 
2.4%
대 6
 
2.1%
해 6
 
2.1%
Other values (91) 202
69.7%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 290
98.3%
Common 5
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 14
 
4.8%
탕 11
 
3.8%
구 11
 
3.8%
면 11
 
3.8%
국 8
 
2.8%
식 7
 
2.4%
이 7
 
2.4%
회 7
 
2.4%
대 6
 
2.1%
해 6
 
2.1%
Other values (91) 202
69.7%
Common
ValueCountFrequency (%)
, 4
80.0%
1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 290
98.3%
ASCII 5
 
1.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 14
 
4.8%
탕 11
 
3.8%
구 11
 
3.8%
면 11
 
3.8%
국 8
 
2.8%
식 7
 
2.4%
이 7
 
2.4%
회 7
 
2.4%
대 6
 
2.1%
해 6
 
2.1%
Other values (91) 202
69.7%
ASCII
ValueCountFrequency (%)
, 4
80.0%
1
 
20.0%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct74
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160.77229
Minimum26.4
Maximum765.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size879.0 B
2024-05-04T02:13:58.574347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26.4
5-th percentile58.703
Q198.875
median132.84
Q3181.365
95-th percentile356.288
Maximum765.42
Range739.02
Interquartile range (IQR)82.49

Descriptive statistics

Standard deviation109.40967
Coefficient of variation (CV)0.6805257
Kurtosis11.468033
Mean160.77229
Median Absolute Deviation (MAD)37.65
Skewness2.8314662
Sum13344.1
Variance11970.477
MonotonicityNot monotonic
2024-05-04T02:13:59.144822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148.2 2
 
2.4%
253.93 2
 
2.4%
111.85 2
 
2.4%
188.22 2
 
2.4%
101.97 2
 
2.4%
58.5 2
 
2.4%
118.32 2
 
2.4%
112.81 2
 
2.4%
152.35 2
 
2.4%
48.3 1
 
1.2%
Other values (64) 64
77.1%
ValueCountFrequency (%)
26.4 1
1.2%
48.3 1
1.2%
53.86 1
1.2%
58.5 2
2.4%
60.53 1
1.2%
62.79 1
1.2%
72.95 1
1.2%
79.4 1
1.2%
80.92 1
1.2%
82.65 1
1.2%
ValueCountFrequency (%)
765.42 1
1.2%
486.13 1
1.2%
447.2 1
1.2%
370.32 1
1.2%
357.31 1
1.2%
347.09 1
1.2%
312.0 1
1.2%
310.96 1
1.2%
274.15 1
1.2%
253.93 2
2.4%

행정동명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size796.0 B
상계2동
22 
상계6.7동
16 
공릉1동
11 
하계1동
11 
상계1동
10 
Other values (6)
13 

Length

Max length6
Median length4
Mean length4.4337349
Min length4

Unique

Unique4 ?
Unique (%)4.8%

Sample

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

Common Values

ValueCountFrequency (%)
상계2동 22
26.5%
상계6.7동 16
19.3%
공릉1동 11
13.3%
하계1동 11
13.3%
상계1동 10
12.0%
월계1동 7
 
8.4%
상계3.4동 2
 
2.4%
중계1동 1
 
1.2%
중계4동 1
 
1.2%
상계5동 1
 
1.2%

Length

2024-05-04T02:13:59.628734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
상계2동 22
26.5%
상계6.7동 16
19.3%
공릉1동 11
13.3%
하계1동 11
13.3%
상계1동 10
12.0%
월계1동 7
 
8.4%
상계3.4동 2
 
2.4%
중계1동 1
 
1.2%
중계4동 1
 
1.2%
상계5동 1
 
1.2%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size796.0 B
상수도전용
62 
<NA>
21 

Length

Max length5
Median length5
Mean length4.746988
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 62
74.7%
<NA> 21
 
25.3%

Length

2024-05-04T02:14:00.170881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:14:00.612206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 62
74.7%
na 21
 
25.3%

Interactions

2024-05-04T02:13:38.476768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:29.397157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:31.660681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:33.298674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:35.181127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:36.727298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:38.725241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:29.863163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:31.929111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:33.624779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:35.448946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:37.023729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:38.969366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:30.218313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:32.221919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:33.881324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:35.689825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:37.278617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:39.247891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:30.592530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:32.483536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:34.161030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:35.965879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:37.558530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:39.523171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:30.933217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:32.742696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:34.553492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:36.187494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:37.912769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:39.821829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:31.375253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:33.037232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:34.907340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:36.454527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:13:38.194211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T02:14:00.985511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자업소명소재지도로명소재지지번허가(신고)번호업태명지정취소사유주된음식영업장면적(㎡)행정동명
지정년도1.0000.8470.9991.0000.8150.9320.9370.9370.9370.0000.9160.0000.3430.000
지정번호0.8471.0000.7580.8240.5420.9040.8740.8740.8740.0000.7680.0000.3650.000
신청일자0.9990.7581.0001.0000.7540.8910.8970.8970.8970.3770.8690.6590.5610.044
지정일자1.0000.8241.0001.0000.7470.9090.9100.9100.9100.0000.9220.3870.3810.000
취소일자0.8150.5420.7540.7471.0000.8550.8620.8620.8620.3210.9220.8530.0000.398
업소명0.9320.9040.8910.9090.8551.0001.0001.0001.0001.0000.9400.9641.0000.998
소재지도로명0.9370.8740.8970.9100.8621.0001.0001.0001.0001.0000.9710.9771.0001.000
소재지지번0.9370.8740.8970.9100.8621.0001.0001.0001.0001.0000.9710.9771.0001.000
허가(신고)번호0.9370.8740.8970.9100.8621.0001.0001.0001.0001.0000.9710.9771.0001.000
업태명0.0000.0000.3770.0000.3211.0001.0001.0001.0001.0000.8070.9520.0000.256
지정취소사유0.9160.7680.8690.9220.9220.9400.9710.9710.9710.8071.0000.9470.0770.000
주된음식0.0000.0000.6590.3870.8530.9640.9770.9770.9770.9520.9471.0000.0000.000
영업장면적(㎡)0.3430.3650.5610.3810.0001.0001.0001.0001.0000.0000.0770.0001.0000.000
행정동명0.0000.0000.0440.0000.3980.9981.0001.0001.0000.2560.0000.0000.0001.000
2024-05-04T02:14:01.420684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명업태명급수시설구분지정취소사유
행정동명1.0000.1141.0000.000
업태명0.1141.0001.0000.451
급수시설구분1.0001.0001.0001.000
지정취소사유0.0000.4511.0001.000
2024-05-04T02:14:01.927877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자영업장면적(㎡)업태명지정취소사유행정동명급수시설구분
지정년도1.000-0.6270.9840.9870.5500.0530.0000.5080.0001.000
지정번호-0.6271.000-0.612-0.609-0.229-0.1020.0000.3860.0001.000
신청일자0.984-0.6121.0000.9970.5200.0600.0000.5080.0001.000
지정일자0.987-0.6090.9971.0000.5230.0540.0000.5080.0001.000
취소일자0.550-0.2290.5200.5231.0000.1340.1580.6180.1681.000
영업장면적(㎡)0.053-0.1020.0600.0540.1341.0000.0000.0000.0001.000
업태명0.0000.0000.0000.0000.1580.0001.0000.4510.1141.000
지정취소사유0.5080.3860.5080.5080.6180.0000.4511.0000.0001.000
행정동명0.0000.0000.0000.0000.1680.0000.1140.0001.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-04T02:13:40.187903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T02:13:40.834385image/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

시군구코드지정년도지정번호신청일자지정일자취소일자업소명소재지도로명소재지지번허가(신고)번호업태명지정취소사유주된음식영업장면적(㎡)행정동명급수시설구분
0310000020089200806092008071020131230엉터리생고기서울특별시 노원구 상계로7길 39, 한올빌딩 1층 (상계동)서울특별시 노원구 상계동 363번지 7호 한올빌딩 1층3100000-101-2006-00383한식위생등급 미달등심148.2상계2동<NA>
13100000200219200206302002070320051017항도서울특별시 노원구 노해로75길 14-22, (상계동)서울특별시 노원구 상계동 708번지 2호 지상2층3100000-101-1993-03282일식영업자지위승계대구탕126.42상계6.7동상수도전용
23100000200420200403192004040620051108박대박서울특별시 노원구 노해로75길 14-12, 1층 (상계동)서울특별시 노원구 상계동 704번지 3호 1층3100000-101-1992-00563한식영업자지위승계부대찌게119.04상계6.7동상수도전용
331000002002336200205202002062820060503범맥주 노원역점서울특별시 노원구 노해로81길 12-4, 화랑상가 2층 (상계동)서울특별시 노원구 상계동 616번지 6호 화랑상가3100000-101-1995-01460호프/통닭영업자지위승계닭갈비152.35상계2동상수도전용
4310000020078200703202007042320100721범맥주 노원역점서울특별시 노원구 노해로81길 12-4, 화랑상가 2층 (상계동)서울특별시 노원구 상계동 616번지 6호 화랑상가3100000-101-1995-01460호프/통닭기준미달해물찜152.35상계2동상수도전용
53100000200423200403192004040620071116양푼이생태탕서울특별시 노원구 동일로218길 35, (상계동)서울특별시 노원구 상계동 725번지 3호3100000-101-1992-00587한식지위승계샤브샤브98.74상계6.7동상수도전용
631000002002310200206302002070320180417천하감자탕서울특별시 노원구 동일로 1595, 1층 (상계동)서울특별시 노원구 상계동 963번지 1호 1층3100000-101-2000-07299한식지위승계감자탕312.0상계1동상수도전용
73100000200416200403192004040620050207경성양꼬치서울특별시 노원구 노해로83길 10-1, 2층 201호 (상계동)서울특별시 노원구 상계동 328번지 1호 -2013100000-101-2003-00397중국식영업자지위승계아구찜153.48상계2동상수도전용
831000002013451201311042013121020171124북경2서울특별시 노원구 동일로217가길 13, (상계동)서울특별시 노원구 상계동 733번지 1호3100000-101-2006-00261중국식지위승계짜장면짬뽕79.4상계6.7동<NA>
93100000201798201711302017120720211020(주)세원외식산업 산채향서울특별시 노원구 노해로77길 22, (상계동,노블레스호텔 2층)서울특별시 노원구 상계동 711번지 9호 노블레스호텔 2층3100000-101-2008-00058일식시설물 멸실한정식370.32상계6.7동<NA>
시군구코드지정년도지정번호신청일자지정일자취소일자업소명소재지도로명소재지지번허가(신고)번호업태명지정취소사유주된음식영업장면적(㎡)행정동명급수시설구분
73310000020061200606202006070120091215숟가락반상마실서울특별시 노원구 노원로 412, 2층 (상계동)서울특별시 노원구 상계동 318번지 1호 2층3100000-101-2003-00019한식지위승계대게찜357.31상계2동상수도전용
74310000020097200906092009071020191030우리수산서울특별시 노원구 덕릉로 808, 101동 제지1층 제비02호 (상계동, 더힐탑유앤아이)서울특별시 노원구 상계동 76번지 11호 더힐탑유앤아이3100000-101-2008-00212일식기준 부적합활어회98.86상계3.4동<NA>
7531000002002315200206302002070320110304감악산 왕솥뚜껑서울특별시 노원구 상계로7길 23, 문화빌딩 1층 (상계동)서울특별시 노원구 상계동 363번지 3호 문화빌딩3100000-101-2002-07553한식<NA>오리구이150.66상계2동상수도전용
76310000020144201411212014121020171207전민규의 황제누룽지탕서울특별시 노원구 노해로77길 14-8, (상계동, 1층 2층)서울특별시 노원구 상계동 710번지 3호 1,2층3100000-101-1992-00672한식행정처분돌솥밥447.2상계6.7동상수도전용
773100000201515201512092015120920190422장안동본참치 태릉직영점서울특별시 노원구 동일로174길 7, 1층 (공릉동)서울특별시 노원구 공릉동 617번지 18호 1층3100000-101-2011-00284한식지위승계활어회148.0공릉1동<NA>
7831000002002378200205202002062820131104대박짬뽕마을서울특별시 노원구 공릉로58가길 17, 1층 (하계동)서울특별시 노원구 하계동 179번지 27호 1층3100000-101-2000-06891중국식지위승계 후 모범음식점 승계포기우거지탕140.4하계1동상수도전용
793100000200719200703222007042320160805동트팔팔장어서울특별시 노원구 노원로 192, 2층 (하계동)서울특별시 노원구 하계동 69번지 8호3100000-101-2006-00168한식영업장소재지변경 승계포기우럭회무침161.82하계1동<NA>
8031000002002240200205222002062820130520온고을&이지쿡서울특별시 노원구 동일로 1065, (공릉동)서울특별시 노원구 공릉동 398번지 5호3100000-101-2000-06797한식행정처분콩나물해장국60.53공릉1동<NA>
813100000200520200506082005063020061207신의 한국수 (노원점)서울특별시 노원구 동일로217가길 13, (상계동,101호)서울특별시 노원구 상계동 733번지 1호 101호3100000-101-2003-00406한식영업자지위승계생선구이80.92상계6.7동상수도전용
82310000020141201411212014121020191030여기,꼬치네서울특별시 노원구 동일로192길 62, 2층 (공릉동)서울특별시 노원구 공릉동 392번지 25호 지상2층3100000-101-2013-00048경양식기준 부적합파스타179.0공릉1동<NA>