Overview

Dataset statistics

Number of variables15
Number of observations79
Missing cells11
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.8 KiB
Average record size in memory127.7 B

Variable types

Categorical4
Numeric5
Text6

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,주된음식,영업장면적(㎡),행정동명,급수시설구분,소재지전화번호
Author중랑구
URLhttps://data.seoul.go.kr/dataList/OA-10294/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 6 other fieldsHigh correlation
지정년도 is highly overall correlated with 지정번호 and 3 other fieldsHigh correlation
지정번호 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
신청일자 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
지정일자 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
영업장면적(㎡) is highly overall correlated with 급수시설구분High correlation
업태명 is highly imbalanced (62.5%)Imbalance
소재지전화번호 has 11 (13.9%) missing valuesMissing

Reproduction

Analysis started2024-05-03 22:55:27.170133
Analysis finished2024-05-03 22:55:39.179429
Duration12.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size764.0 B
3060000
79 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3060000
2nd row3060000
3rd row3060000
4th row3060000
5th row3060000

Common Values

ValueCountFrequency (%)
3060000 79
100.0%

Length

2024-05-03T22:55:39.416849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T22:55:39.785607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3060000 79
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.5316
Minimum2001
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2024-05-03T22:55:40.210517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2002
Q12009
median2016
Q32021
95-th percentile2023
Maximum2023
Range22
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.935297
Coefficient of variation (CV)0.0034426349
Kurtosis-1.0268746
Mean2014.5316
Median Absolute Deviation (MAD)6
Skewness-0.50683852
Sum159148
Variance48.098345
MonotonicityDecreasing
2024-05-03T22:55:40.764004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2022 10
12.7%
2016 8
 
10.1%
2023 7
 
8.9%
2021 7
 
8.9%
2017 7
 
8.9%
2002 4
 
5.1%
2011 4
 
5.1%
2019 4
 
5.1%
2015 3
 
3.8%
2009 3
 
3.8%
Other values (12) 22
27.8%
ValueCountFrequency (%)
2001 2
2.5%
2002 4
5.1%
2003 2
2.5%
2004 1
 
1.3%
2005 3
3.8%
2006 3
3.8%
2007 2
2.5%
2008 2
2.5%
2009 3
3.8%
2010 1
 
1.3%
ValueCountFrequency (%)
2023 7
8.9%
2022 10
12.7%
2021 7
8.9%
2019 4
 
5.1%
2018 2
 
2.5%
2017 7
8.9%
2016 8
10.1%
2015 3
 
3.8%
2014 1
 
1.3%
2013 2
 
2.5%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.518987
Minimum1
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2024-05-03T22:55:41.187632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q318
95-th percentile103.1
Maximum109
Range108
Interquartile range (IQR)14

Descriptive statistics

Standard deviation31.761879
Coefficient of variation (CV)1.4759932
Kurtosis2.3637249
Mean21.518987
Median Absolute Deviation (MAD)5
Skewness1.946719
Sum1700
Variance1008.8169
MonotonicityNot monotonic
2024-05-03T22:55:41.638623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1 7
 
8.9%
6 6
 
7.6%
2 6
 
7.6%
3 6
 
7.6%
5 5
 
6.3%
7 5
 
6.3%
4 4
 
5.1%
10 3
 
3.8%
12 3
 
3.8%
9 3
 
3.8%
Other values (24) 31
39.2%
ValueCountFrequency (%)
1 7
8.9%
2 6
7.6%
3 6
7.6%
4 4
5.1%
5 5
6.3%
6 6
7.6%
7 5
6.3%
8 2
 
2.5%
9 3
3.8%
10 3
3.8%
ValueCountFrequency (%)
109 1
1.3%
108 1
1.3%
107 1
1.3%
104 1
1.3%
103 1
1.3%
102 1
1.3%
98 1
1.3%
94 1
1.3%
92 1
1.3%
78 1
1.3%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20145400
Minimum20011019
Maximum20231010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2024-05-03T22:55:42.068941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20011019
5-th percentile20020626
Q120090803
median20161128
Q320211115
95-th percentile20231010
Maximum20231010
Range219991
Interquartile range (IQR)120312

Descriptive statistics

Standard deviation70477.254
Coefficient of variation (CV)0.0034984292
Kurtosis-1.0135945
Mean20145400
Median Absolute Deviation (MAD)59988
Skewness-0.51764578
Sum1.5914866 × 109
Variance4.9670433 × 109
MonotonicityNot monotonic
2024-05-03T22:55:42.494139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
20221116 10
 
12.7%
20231010 7
 
8.9%
20211115 7
 
8.9%
20171101 7
 
8.9%
20191104 4
 
5.1%
20161004 4
 
5.1%
20110907 4
 
5.1%
20151001 3
 
3.8%
20090803 3
 
3.8%
20161128 3
 
3.8%
Other values (19) 27
34.2%
ValueCountFrequency (%)
20011019 1
1.3%
20011022 2
2.5%
20020625 1
1.3%
20020626 2
2.5%
20020930 1
1.3%
20030414 1
1.3%
20030520 1
1.3%
20040616 1
1.3%
20050712 2
2.5%
20051017 1
1.3%
ValueCountFrequency (%)
20231010 7
8.9%
20221116 10
12.7%
20211115 7
8.9%
20191104 4
 
5.1%
20181026 2
 
2.5%
20171101 7
8.9%
20161128 3
 
3.8%
20161004 4
 
5.1%
20151001 3
 
3.8%
20141001 2
 
2.5%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20146361
Minimum20011019
Maximum20231102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2024-05-03T22:55:42.885654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20011019
5-th percentile20020626
Q120090918
median20161128
Q320211210
95-th percentile20231102
Maximum20231102
Range220083
Interquartile range (IQR)120292

Descriptive statistics

Standard deviation69506.406
Coefficient of variation (CV)0.0034500725
Kurtosis-1.0289704
Mean20146361
Median Absolute Deviation (MAD)60092
Skewness-0.50738451
Sum1.5915625 × 109
Variance4.8311404 × 109
MonotonicityDecreasing
2024-05-03T22:55:43.299956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
20221220 10
12.7%
20161128 8
 
10.1%
20231102 7
 
8.9%
20211210 7
 
8.9%
20171129 7
 
8.9%
20110930 4
 
5.1%
20191203 4
 
5.1%
20020626 3
 
3.8%
20090918 3
 
3.8%
20151119 3
 
3.8%
Other values (17) 23
29.1%
ValueCountFrequency (%)
20011019 1
 
1.3%
20011022 1
 
1.3%
20020626 3
3.8%
20021009 1
 
1.3%
20030807 2
2.5%
20040720 1
 
1.3%
20050729 2
2.5%
20051027 1
 
1.3%
20060630 1
 
1.3%
20060803 2
2.5%
ValueCountFrequency (%)
20231102 7
8.9%
20221220 10
12.7%
20211210 7
8.9%
20191203 4
 
5.1%
20181115 2
 
2.5%
20171129 7
8.9%
20161128 8
10.1%
20151119 3
 
3.8%
20141216 1
 
1.3%
20131213 1
 
1.3%

업소명
Text

Distinct77
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size764.0 B
2024-05-03T22:55:44.024592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length6.4936709
Min length2

Characters and Unicode

Total characters513
Distinct characters191
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique75 ?
Unique (%)94.9%

Sample

1st row소먹날 소한마리 정육식당
2nd row다함닭갈비 먹골직영점
3rd row꾸꾸가문 남원추어탕
4th row부부 부대찌개
5th row한우특가 신내점
ValueCountFrequency (%)
맹돌이생선구이쌈밥 2
 
2.0%
박가네 2
 
2.0%
갈비공장신내점 2
 
2.0%
다함닭갈비 2
 
2.0%
신내점 2
 
2.0%
지호한방삼계탕 1
 
1.0%
국밥 1
 
1.0%
전주콩나물 1
 
1.0%
두채사랑 1
 
1.0%
곽만근갈비탕상봉점 1
 
1.0%
Other values (86) 86
85.1%
2024-05-03T22:55:45.164700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22
 
4.3%
갈 13
 
2.5%
비 13
 
2.5%
이 12
 
2.3%
가 12
 
2.3%
탕 10
 
1.9%
점 10
 
1.9%
대 10
 
1.9%
한 9
 
1.8%
동 8
 
1.6%
Other values (181) 394
76.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 482
94.0%
Space Separator 22
 
4.3%
Open Punctuation 3
 
0.6%
Close Punctuation 3
 
0.6%
Decimal Number 3
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
갈 13
 
2.7%
비 13
 
2.7%
이 12
 
2.5%
가 12
 
2.5%
탕 10
 
2.1%
점 10
 
2.1%
대 10
 
2.1%
한 9
 
1.9%
동 8
 
1.7%
부 7
 
1.5%
Other values (176) 378
78.4%
Decimal Number
ValueCountFrequency (%)
0 2
66.7%
1 1
33.3%
Space Separator
ValueCountFrequency (%)
22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 477
93.0%
Common 31
 
6.0%
Han 5
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
갈 13
 
2.7%
비 13
 
2.7%
이 12
 
2.5%
가 12
 
2.5%
탕 10
 
2.1%
점 10
 
2.1%
대 10
 
2.1%
한 9
 
1.9%
동 8
 
1.7%
부 7
 
1.5%
Other values (171) 373
78.2%
Common
ValueCountFrequency (%)
22
71.0%
( 3
 
9.7%
) 3
 
9.7%
0 2
 
6.5%
1 1
 
3.2%
Han
ValueCountFrequency (%)
辛 1
20.0%
代 1
20.0%
三 1
20.0%
味 1
20.0%
堂 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 477
93.0%
ASCII 31
 
6.0%
CJK 5
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22
71.0%
( 3
 
9.7%
) 3
 
9.7%
0 2
 
6.5%
1 1
 
3.2%
Hangul
ValueCountFrequency (%)
갈 13
 
2.7%
비 13
 
2.7%
이 12
 
2.5%
가 12
 
2.5%
탕 10
 
2.1%
점 10
 
2.1%
대 10
 
2.1%
한 9
 
1.9%
동 8
 
1.7%
부 7
 
1.5%
Other values (171) 373
78.2%
CJK
ValueCountFrequency (%)
辛 1
20.0%
代 1
20.0%
三 1
20.0%
味 1
20.0%
堂 1
20.0%

소재지도로명
Text

Distinct77
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size764.0 B
2024-05-03T22:55:46.192522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length45
Mean length27.56962
Min length21

Characters and Unicode

Total characters2178
Distinct characters90
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

Unique75 ?
Unique (%)94.9%

Sample

1st row서울특별시 중랑구 용마산로115길 76, (망우동)
2nd row서울특별시 중랑구 공릉로 32, B06호 (묵동)
3rd row서울특별시 중랑구 용마산로 452, (망우동)
4th row서울특별시 중랑구 겸재로9길 36, 1층 (면목동)
5th row서울특별시 중랑구 봉화산로 243, 1층 (신내동)
ValueCountFrequency (%)
서울특별시 79
18.0%
중랑구 79
18.0%
면목동 29
 
6.6%
1층 18
 
4.1%
망우동 14
 
3.2%
묵동 12
 
2.7%
면목로 11
 
2.5%
겸재로 8
 
1.8%
상봉동 8
 
1.8%
신내동 7
 
1.6%
Other values (120) 174
39.6%
2024-05-03T22:55:47.428906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
360
 
16.5%
중 96
 
4.4%
, 94
 
4.3%
동 91
 
4.2%
랑 87
 
4.0%
울 79
 
3.6%
) 79
 
3.6%
( 79
 
3.6%
로 79
 
3.6%
서 79
 
3.6%
Other values (80) 1055
48.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1243
57.1%
Space Separator 360
 
16.5%
Decimal Number 312
 
14.3%
Other Punctuation 94
 
4.3%
Close Punctuation 79
 
3.6%
Open Punctuation 79
 
3.6%
Dash Punctuation 5
 
0.2%
Uppercase Letter 5
 
0.2%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 96
 
7.7%
동 91
 
7.3%
랑 87
 
7.0%
울 79
 
6.4%
로 79
 
6.4%
서 79
 
6.4%
구 79
 
6.4%
시 79
 
6.4%
별 79
 
6.4%
특 79
 
6.4%
Other values (61) 416
33.5%
Decimal Number
ValueCountFrequency (%)
1 76
24.4%
2 50
16.0%
3 32
10.3%
4 26
 
8.3%
0 26
 
8.3%
6 25
 
8.0%
5 24
 
7.7%
9 24
 
7.7%
8 17
 
5.4%
7 12
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
B 3
60.0%
D 1
 
20.0%
A 1
 
20.0%
Space Separator
ValueCountFrequency (%)
360
100.0%
Other Punctuation
ValueCountFrequency (%)
, 94
100.0%
Close Punctuation
ValueCountFrequency (%)
) 79
100.0%
Open Punctuation
ValueCountFrequency (%)
( 79
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1243
57.1%
Common 930
42.7%
Latin 5
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 96
 
7.7%
동 91
 
7.3%
랑 87
 
7.0%
울 79
 
6.4%
로 79
 
6.4%
서 79
 
6.4%
구 79
 
6.4%
시 79
 
6.4%
별 79
 
6.4%
특 79
 
6.4%
Other values (61) 416
33.5%
Common
ValueCountFrequency (%)
360
38.7%
, 94
 
10.1%
) 79
 
8.5%
( 79
 
8.5%
1 76
 
8.2%
2 50
 
5.4%
3 32
 
3.4%
4 26
 
2.8%
0 26
 
2.8%
6 25
 
2.7%
Other values (6) 83
 
8.9%
Latin
ValueCountFrequency (%)
B 3
60.0%
D 1
 
20.0%
A 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1243
57.1%
ASCII 935
42.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
360
38.5%
, 94
 
10.1%
) 79
 
8.4%
( 79
 
8.4%
1 76
 
8.1%
2 50
 
5.3%
3 32
 
3.4%
4 26
 
2.8%
0 26
 
2.8%
6 25
 
2.7%
Other values (9) 88
 
9.4%
Hangul
ValueCountFrequency (%)
중 96
 
7.7%
동 91
 
7.3%
랑 87
 
7.0%
울 79
 
6.4%
로 79
 
6.4%
서 79
 
6.4%
구 79
 
6.4%
시 79
 
6.4%
별 79
 
6.4%
특 79
 
6.4%
Other values (61) 416
33.5%

소재지지번
Text

Distinct77
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size764.0 B
2024-05-03T22:55:48.207370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length37
Mean length26.670886
Min length22

Characters and Unicode

Total characters2107
Distinct characters71
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique75 ?
Unique (%)94.9%

Sample

1st row서울특별시 중랑구 망우동 490번지 31호
2nd row서울특별시 중랑구 묵동 171번지 4호 -B06
3rd row서울특별시 중랑구 망우동 442번지 37호
4th row서울특별시 중랑구 면목동 179번지 115호
5th row서울특별시 중랑구 신내동 396번지 32호
ValueCountFrequency (%)
서울특별시 79
19.0%
중랑구 79
19.0%
면목동 30
 
7.2%
망우동 14
 
3.4%
묵동 13
 
3.1%
상봉동 8
 
1.9%
신내동 7
 
1.7%
1호 7
 
1.7%
중화동 7
 
1.7%
4호 5
 
1.2%
Other values (121) 166
40.0%
2024-05-03T22:55:49.473771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
556
26.4%
1 88
 
4.2%
중 86
 
4.1%
지 81
 
3.8%
호 80
 
3.8%
동 80
 
3.8%
서 79
 
3.7%
특 79
 
3.7%
별 79
 
3.7%
시 79
 
3.7%
Other values (61) 820
38.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1160
55.1%
Space Separator 556
26.4%
Decimal Number 384
 
18.2%
Uppercase Letter 3
 
0.1%
Other Punctuation 2
 
0.1%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 86
 
7.4%
지 81
 
7.0%
호 80
 
6.9%
동 80
 
6.9%
서 79
 
6.8%
특 79
 
6.8%
별 79
 
6.8%
시 79
 
6.8%
랑 79
 
6.8%
구 79
 
6.8%
Other values (47) 359
30.9%
Decimal Number
ValueCountFrequency (%)
1 88
22.9%
2 48
12.5%
3 44
11.5%
0 39
10.2%
4 34
 
8.9%
6 32
 
8.3%
9 29
 
7.6%
5 26
 
6.8%
7 23
 
6.0%
8 21
 
5.5%
Space Separator
ValueCountFrequency (%)
556
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1160
55.1%
Common 944
44.8%
Latin 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 86
 
7.4%
지 81
 
7.0%
호 80
 
6.9%
동 80
 
6.9%
서 79
 
6.8%
특 79
 
6.8%
별 79
 
6.8%
시 79
 
6.8%
랑 79
 
6.8%
구 79
 
6.8%
Other values (47) 359
30.9%
Common
ValueCountFrequency (%)
556
58.9%
1 88
 
9.3%
2 48
 
5.1%
3 44
 
4.7%
0 39
 
4.1%
4 34
 
3.6%
6 32
 
3.4%
9 29
 
3.1%
5 26
 
2.8%
7 23
 
2.4%
Other values (3) 25
 
2.6%
Latin
ValueCountFrequency (%)
B 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1160
55.1%
ASCII 947
44.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
556
58.7%
1 88
 
9.3%
2 48
 
5.1%
3 44
 
4.6%
0 39
 
4.1%
4 34
 
3.6%
6 32
 
3.4%
9 29
 
3.1%
5 26
 
2.7%
7 23
 
2.4%
Other values (4) 28
 
3.0%
Hangul
ValueCountFrequency (%)
중 86
 
7.4%
지 81
 
7.0%
호 80
 
6.9%
동 80
 
6.9%
서 79
 
6.8%
특 79
 
6.8%
별 79
 
6.8%
시 79
 
6.8%
랑 79
 
6.8%
구 79
 
6.8%
Other values (47) 359
30.9%

허가(신고)번호
Text

Distinct77
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size764.0 B
2024-05-03T22:55:50.148678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters1738
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

Unique75 ?
Unique (%)94.9%

Sample

1st row3060000-101-2019-00036
2nd row3060000-101-2014-00103
3rd row3060000-101-1995-01994
4th row3060000-101-2019-00017
5th row3060000-101-2022-00253
ValueCountFrequency (%)
3060000-101-1994-03515 2
 
2.5%
3060000-101-1999-08674 2
 
2.5%
3060000-101-2002-00675 1
 
1.3%
3060000-101-1990-03513 1
 
1.3%
3060000-101-2010-00111 1
 
1.3%
3060000-101-2008-00102 1
 
1.3%
3060000-101-2006-00206 1
 
1.3%
3060000-101-2009-00247 1
 
1.3%
3060000-101-2008-00050 1
 
1.3%
3060000-101-2010-00084 1
 
1.3%
Other values (67) 67
84.8%
2024-05-03T22:55:51.428552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 739
42.5%
1 252
 
14.5%
- 237
 
13.6%
6 113
 
6.5%
3 109
 
6.3%
2 109
 
6.3%
9 69
 
4.0%
4 36
 
2.1%
5 30
 
1.7%
8 24
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1501
86.4%
Dash Punctuation 237
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 739
49.2%
1 252
 
16.8%
6 113
 
7.5%
3 109
 
7.3%
2 109
 
7.3%
9 69
 
4.6%
4 36
 
2.4%
5 30
 
2.0%
8 24
 
1.6%
7 20
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 237
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1738
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 739
42.5%
1 252
 
14.5%
- 237
 
13.6%
6 113
 
6.5%
3 109
 
6.3%
2 109
 
6.3%
9 69
 
4.0%
4 36
 
2.1%
5 30
 
1.7%
8 24
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1738
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 739
42.5%
1 252
 
14.5%
- 237
 
13.6%
6 113
 
6.5%
3 109
 
6.3%
2 109
 
6.3%
9 69
 
4.0%
4 36
 
2.1%
5 30
 
1.7%
8 24
 
1.4%

업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Memory size764.0 B
한식
64 
일식
 
5
분식
 
2
회집
 
2
식육(숯불구이)
 
1
Other values (5)
 
5

Length

Max length10
Median length2
Mean length2.2531646
Min length2

Unique

Unique6 ?
Unique (%)7.6%

Sample

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

Common Values

ValueCountFrequency (%)
한식 64
81.0%
일식 5
 
6.3%
분식 2
 
2.5%
회집 2
 
2.5%
식육(숯불구이) 1
 
1.3%
냉면집 1
 
1.3%
중국식 1
 
1.3%
호프/통닭 1
 
1.3%
정종/대포집/소주방 1
 
1.3%
뷔페식 1
 
1.3%

Length

2024-05-03T22:55:52.025668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T22:55:52.457221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 64
81.0%
일식 5
 
6.3%
분식 2
 
2.5%
회집 2
 
2.5%
식육(숯불구이 1
 
1.3%
냉면집 1
 
1.3%
중국식 1
 
1.3%
호프/통닭 1
 
1.3%
정종/대포집/소주방 1
 
1.3%
뷔페식 1
 
1.3%

주된음식
Text

Distinct62
Distinct (%)78.5%
Missing0
Missing (%)0.0%
Memory size764.0 B
2024-05-03T22:55:52.971426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length3.8987342
Min length2

Characters and Unicode

Total characters308
Distinct characters110
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

Unique52 ?
Unique (%)65.8%

Sample

1st row고기
2nd row닭갈비
3rd row추어탕
4th row부대찌개
5th row고기
ValueCountFrequency (%)
돼지갈비 6
 
6.8%
갈비탕 4
 
4.5%
추어탕 3
 
3.4%
닭갈비 3
 
3.4%
해물찜 2
 
2.3%
돼지고기 2
 
2.3%
칼국수 2
 
2.3%
설렁탕 2
 
2.3%
참치 2
 
2.3%
쌈밥 2
 
2.3%
Other values (58) 60
68.2%
2024-05-03T22:55:53.796940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
비 19
 
6.2%
갈 18
 
5.8%
탕 16
 
5.2%
지 13
 
4.2%
돼 10
 
3.2%
9
 
2.9%
찜 7
 
2.3%
고 7
 
2.3%
기 7
 
2.3%
밥 7
 
2.3%
Other values (100) 195
63.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 292
94.8%
Space Separator 9
 
2.9%
Other Punctuation 7
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
비 19
 
6.5%
갈 18
 
6.2%
탕 16
 
5.5%
지 13
 
4.5%
돼 10
 
3.4%
찜 7
 
2.4%
고 7
 
2.4%
기 7
 
2.4%
밥 7
 
2.4%
수 7
 
2.4%
Other values (97) 181
62.0%
Other Punctuation
ValueCountFrequency (%)
, 6
85.7%
. 1
 
14.3%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 292
94.8%
Common 16
 
5.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
비 19
 
6.5%
갈 18
 
6.2%
탕 16
 
5.5%
지 13
 
4.5%
돼 10
 
3.4%
찜 7
 
2.4%
고 7
 
2.4%
기 7
 
2.4%
밥 7
 
2.4%
수 7
 
2.4%
Other values (97) 181
62.0%
Common
ValueCountFrequency (%)
9
56.2%
, 6
37.5%
. 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 292
94.8%
ASCII 16
 
5.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
비 19
 
6.5%
갈 18
 
6.2%
탕 16
 
5.5%
지 13
 
4.5%
돼 10
 
3.4%
찜 7
 
2.4%
고 7
 
2.4%
기 7
 
2.4%
밥 7
 
2.4%
수 7
 
2.4%
Other values (97) 181
62.0%
ASCII
ValueCountFrequency (%)
9
56.2%
, 6
37.5%
. 1
 
6.2%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean131.96051
Minimum49.49
Maximum663.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2024-05-03T22:55:54.087788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49.49
5-th percentile60.168
Q188.2
median108.5
Q3152.25
95-th percentile229.528
Maximum663.84
Range614.35
Interquartile range (IQR)64.05

Descriptive statistics

Standard deviation91.030997
Coefficient of variation (CV)0.68983516
Kurtosis19.402075
Mean131.96051
Median Absolute Deviation (MAD)31.5
Skewness3.9304909
Sum10424.88
Variance8286.6424
MonotonicityNot monotonic
2024-05-03T22:55:54.405509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.8 2
 
2.5%
165.0 2
 
2.5%
131.41 2
 
2.5%
98.0 2
 
2.5%
214.68 1
 
1.3%
117.82 1
 
1.3%
549.12 1
 
1.3%
114.3 1
 
1.3%
114.81 1
 
1.3%
193.7 1
 
1.3%
Other values (65) 65
82.3%
ValueCountFrequency (%)
49.49 1
1.3%
59.4 1
1.3%
60.0 1
1.3%
60.15 1
1.3%
60.17 1
1.3%
62.64 1
1.3%
66.0 1
1.3%
66.7 1
1.3%
67.5 1
1.3%
69.98 1
1.3%
ValueCountFrequency (%)
663.84 1
1.3%
549.12 1
1.3%
287.74 1
1.3%
253.0 1
1.3%
226.92 1
1.3%
214.68 1
1.3%
196.51 1
1.3%
193.7 1
1.3%
188.1 1
1.3%
182.4 1
1.3%

행정동명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Memory size764.0 B
묵제1동
11 
면목제3.8동
11 
망우본동
10 
면목본동
9 
신내1동
6 
Other values (11)
32 

Length

Max length7
Median length5
Mean length4.7848101
Min length4

Unique

Unique4 ?
Unique (%)5.1%

Sample

1st row망우본동
2nd row묵제1동
3rd row망우제3동
4th row면목제2동
5th row신내1동

Common Values

ValueCountFrequency (%)
묵제1동 11
13.9%
면목제3.8동 11
13.9%
망우본동 10
12.7%
면목본동 9
11.4%
신내1동 6
7.6%
중화제2동 6
7.6%
상봉제2동 6
7.6%
면목제7동 5
6.3%
망우제3동 4
 
5.1%
면목제2동 3
 
3.8%
Other values (6) 8
10.1%

Length

2024-05-03T22:55:54.782281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
묵제1동 11
13.9%
면목제3.8동 11
13.9%
망우본동 10
12.7%
면목본동 9
11.4%
신내1동 6
7.6%
중화제2동 6
7.6%
상봉제2동 6
7.6%
면목제7동 5
6.3%
망우제3동 4
 
5.1%
면목제2동 3
 
3.8%
Other values (6) 8
10.1%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size764.0 B
상수도전용
54 
<NA>
25 

Length

Max length5
Median length5
Mean length4.6835443
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 54
68.4%
<NA> 25
31.6%

Length

2024-05-03T22:55:55.080525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T22:55:55.442994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 54
68.4%
na 25
31.6%

소재지전화번호
Text

MISSING 

Distinct66
Distinct (%)97.1%
Missing11
Missing (%)13.9%
Memory size764.0 B
2024-05-03T22:55:55.893338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.073529
Min length9

Characters and Unicode

Total characters753
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

Unique64 ?
Unique (%)94.1%

Sample

1st row02 977 7825
2nd row0215669971
3rd row024370666
4th row02 4352652
5th row02 491 6601
ValueCountFrequency (%)
02 63
38.2%
435 4
 
2.4%
491 3
 
1.8%
436 3
 
1.8%
977 3
 
1.8%
433 2
 
1.2%
496 2
 
1.2%
437 2
 
1.2%
439 2
 
1.2%
4918221 2
 
1.2%
Other values (77) 79
47.9%
2024-05-03T22:55:56.912942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
135
17.9%
2 108
14.3%
0 103
13.7%
3 74
9.8%
4 72
9.6%
9 63
8.4%
6 51
 
6.8%
7 50
 
6.6%
5 37
 
4.9%
1 31
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 618
82.1%
Space Separator 135
 
17.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 108
17.5%
0 103
16.7%
3 74
12.0%
4 72
11.7%
9 63
10.2%
6 51
8.3%
7 50
8.1%
5 37
 
6.0%
1 31
 
5.0%
8 29
 
4.7%
Space Separator
ValueCountFrequency (%)
135
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 753
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
135
17.9%
2 108
14.3%
0 103
13.7%
3 74
9.8%
4 72
9.6%
9 63
8.4%
6 51
 
6.8%
7 50
 
6.6%
5 37
 
4.9%
1 31
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 753
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
135
17.9%
2 108
14.3%
0 103
13.7%
3 74
9.8%
4 72
9.6%
9 63
8.4%
6 51
 
6.8%
7 50
 
6.6%
5 37
 
4.9%
1 31
 
4.1%

Interactions

2024-05-03T22:55:36.770720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:55:31.776721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:55:33.014088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:55:34.375551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:55:35.617607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:55:37.059004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:55:32.064680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:55:33.221606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:55:34.604972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:55:35.808644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:55:37.327932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:55:32.333563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:55:33.703607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:55:34.853062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:55:36.070334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:55:37.580093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:55:32.567666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:55:33.903792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:55:35.171434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:55:36.298290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:55:37.837644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:55:32.782092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:55:34.133284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:55:35.408494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:55:36.529068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T22:55:57.478765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명소재지전화번호
지정년도1.0000.6520.9991.0000.7040.7040.7040.7040.0000.8720.1910.0000.846
지정번호0.6521.0000.6450.6450.9140.9140.9140.9140.0000.0000.0000.0000.921
신청일자0.9990.6451.0000.9990.0000.0000.0000.0000.0000.8580.2380.0000.251
지정일자1.0000.6450.9991.0000.6760.6760.6760.6760.0000.8660.3020.0000.841
업소명0.7040.9140.0000.6761.0001.0001.0001.0001.0000.9781.0001.0001.000
소재지도로명0.7040.9140.0000.6761.0001.0001.0001.0001.0000.9781.0001.0001.000
소재지지번0.7040.9140.0000.6761.0001.0001.0001.0001.0000.9781.0001.0001.000
허가(신고)번호0.7040.9140.0000.6761.0001.0001.0001.0001.0000.9781.0001.0001.000
업태명0.0000.0000.0000.0001.0001.0001.0001.0001.0000.8850.0000.7211.000
주된음식0.8720.0000.8580.8660.9780.9780.9780.9780.8851.0000.0000.8910.977
영업장면적(㎡)0.1910.0000.2380.3021.0001.0001.0001.0000.0000.0001.0000.0001.000
행정동명0.0000.0000.0000.0001.0001.0001.0001.0000.7210.8910.0001.0001.000
소재지전화번호0.8460.9210.2510.8411.0001.0001.0001.0001.0000.9771.0001.0001.000
2024-05-03T22:55:57.882119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태명행정동명급수시설구분
업태명1.0000.3641.000
행정동명0.3641.0001.000
급수시설구분1.0001.0001.000
2024-05-03T22:55:58.194393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.000-0.5170.9971.000-0.1960.0000.0001.000
지정번호-0.5171.000-0.510-0.515-0.0060.0000.0001.000
신청일자0.997-0.5101.0000.998-0.2000.0000.0001.000
지정일자1.000-0.5150.9981.000-0.1980.0000.0001.000
영업장면적(㎡)-0.196-0.006-0.200-0.1981.0000.0000.0001.000
업태명0.0000.0000.0000.0000.0001.0000.3641.000
행정동명0.0000.0000.0000.0000.0000.3641.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-03T22:55:38.261451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T22:55:38.901248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
03060000202362023101020231102소먹날 소한마리 정육식당서울특별시 중랑구 용마산로115길 76, (망우동)서울특별시 중랑구 망우동 490번지 31호3060000-101-2019-00036한식고기82.4망우본동상수도전용<NA>
13060000202332023101020231102다함닭갈비 먹골직영점서울특별시 중랑구 공릉로 32, B06호 (묵동)서울특별시 중랑구 묵동 171번지 4호 -B063060000-101-2014-00103한식닭갈비98.0묵제1동<NA>02 977 7825
23060000202312023101020231102꾸꾸가문 남원추어탕서울특별시 중랑구 용마산로 452, (망우동)서울특별시 중랑구 망우동 442번지 37호3060000-101-1995-01994한식추어탕125.6망우제3동상수도전용0215669971
33060000202352023101020231102부부 부대찌개서울특별시 중랑구 겸재로9길 36, 1층 (면목동)서울특별시 중랑구 면목동 179번지 115호3060000-101-2019-00017한식부대찌개49.49면목제2동상수도전용024370666
43060000202372023101020231102한우특가 신내점서울특별시 중랑구 봉화산로 243, 1층 (신내동)서울특별시 중랑구 신내동 396번지 32호3060000-101-2022-00253식육(숯불구이)고기164.18신내1동<NA><NA>
53060000202342023101020231102밥묵고돈내고서울특별시 중랑구 동일로123길 27, 1층 (중화동)서울특별시 중랑구 중화동 299번지 148호3060000-101-2013-00123한식백반77.14중화제2동<NA>02 4352652
63060000202322023101020231102능이마을 사가정점서울특별시 중랑구 면목로39길 24, (면목동)서울특별시 중랑구 면목동 648번지 31호3060000-101-2019-00055한식능이오리117.84면목제7동상수도전용<NA>
73060000202222022111620221220유가네한우곰탕서울특별시 중랑구 동일로 608, 1층 (면목동)서울특별시 중랑구 면목동 161번지 34호3060000-101-2021-00263한식한우곰탕96.61면목제5동<NA><NA>
83060000202232022111620221220무교동 낙지나라서울특별시 중랑구 면목로 302, (면목동)서울특별시 중랑구 면목동 626번지 17호 외 1필지3060000-101-1970-00857한식낚지볶음157.99면목제7동상수도전용02 491 6601
93060000202252022111620221220미스터아구 신내점서울특별시 중랑구 봉화산로 250, 1층 (신내동)서울특별시 중랑구 신내동 385번지 7호3060000-101-2021-00326한식아귀찜181.7신내1동<NA><NA>
시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
6930600002005122005071220050729옛골참숯불갈비서울특별시 중랑구 상봉중앙로6가길 34, (상봉동)서울특별시 중랑구 상봉동 184번지 27호3060000-101-2002-00675한식돼지왕갈비90.7상봉제1동상수도전용02 491 5545
7030600002004182004061620040720맹돌이생선구이쌈밥서울특별시 중랑구 용마산로115길 59, 2층 (망우동)서울특별시 중랑구 망우동 491번지 39호 2층3060000-101-1994-03515한식쌈밥88.8망우본동상수도전용02 4918221
713060000200392003052020030807옛가 칼국수서울특별시 중랑구 중랑천로10길 39, (상봉동)서울특별시 중랑구 상봉동 130번지 80호3060000-101-2000-09744한식칼국수108.09상봉제2동상수도전용02 4921555
723060000200362003041420030807세상만사서울특별시 중랑구 겸재로 196, (면목동)서울특별시 중랑구 면목동 105번지 6호3060000-101-2002-00410한식감자탕155.1면목본동상수도전용02 4354955
7330600002002782002093020021009춘향골남원추어탕서울특별시 중랑구 중랑역로 269, (묵동)서울특별시 중랑구 묵동 166번지 12호3060000-101-2002-00181한식추어탕123.69묵제1동상수도전용02 973 9526
7430600002002202002062620020626개성숯불갈비서울특별시 중랑구 겸재로 261, (망우동)서울특별시 중랑구 망우동 526번지 2호3060000-101-1984-04056한식돼지갈비180.63망우제3동상수도전용02 4937833
7530600002002452002062620020626신선한우촌서울특별시 중랑구 용마산로 707, (신내동)서울특별시 중랑구 신내동 352번지 3호3060000-101-2001-10476한식한우고기148.5신내1동<NA>02 492 3335
7630600002002122002062520020626농부보쌈서울특별시 중랑구 용마산로 389, (면목동)서울특별시 중랑구 면목동 50번지 46호3060000-101-1991-01148한식보쌈정식214.68면목제3.8동상수도전용02 4968860
7730600002001182001102220011022회를 품은 달서울특별시 중랑구 신내로 61, (신내동)서울특별시 중랑구 신내동 547번지 1층3060000-101-1999-08519회집우거지탕196.51신내2동상수도전용02 494 0133
7830600002001262001101920011019할매보쌈서울특별시 중랑구 면목로 352, (면목동)서울특별시 중랑구 면목동 458번지 1호3060000-101-1994-01809한식보쌈 쟁반국수105.68면목제3.8동상수도전용02 435 4632