Overview

Dataset statistics

Number of variables15
Number of observations52
Missing cells10
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 KiB
Average record size in memory128.5 B

Variable types

Categorical4
Numeric5
Text6

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,주된음식,영업장면적(㎡),행정동명,급수시설구분,소재지전화번호
Author동대문구
URLhttps://data.seoul.go.kr/dataList/OA-10371/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
행정동명 is highly overall correlated with 급수시설구분High correlation
급수시설구분 is highly overall correlated with 지정년도 and 6 other fieldsHigh correlation
업태명 is highly overall correlated with 급수시설구분High 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 (66.3%)Imbalance
주된음식 has 4 (7.7%) missing valuesMissing
소재지전화번호 has 6 (11.5%) missing valuesMissing
업소명 has unique valuesUnique
소재지도로명 has unique valuesUnique
소재지지번 has unique valuesUnique
허가(신고)번호 has unique valuesUnique

Reproduction

Analysis started2024-05-11 08:01:00.706702
Analysis finished2024-05-11 08:01:06.532587
Duration5.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
3050000
52 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3050000
2nd row3050000
3rd row3050000
4th row3050000
5th row3050000

Common Values

ValueCountFrequency (%)
3050000 52
100.0%

Length

2024-05-11T17:01:06.609247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:01:06.730686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3050000 52
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.4038
Minimum2001
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-05-11T17:01:06.840898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2001
Q12003.75
median2009
Q32013
95-th percentile2015
Maximum2018
Range17
Interquartile range (IQR)9.25

Descriptive statistics

Standard deviation5.1876914
Coefficient of variation (CV)0.0025829922
Kurtosis-1.2042634
Mean2008.4038
Median Absolute Deviation (MAD)4
Skewness-0.12550987
Sum104437
Variance26.912142
MonotonicityNot monotonic
2024-05-11T17:01:06.970212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2001 11
21.2%
2013 9
17.3%
2006 7
13.5%
2012 6
11.5%
2015 4
 
7.7%
2007 3
 
5.8%
2011 2
 
3.8%
2009 2
 
3.8%
2010 2
 
3.8%
2018 2
 
3.8%
Other values (3) 4
 
7.7%
ValueCountFrequency (%)
2001 11
21.2%
2003 2
 
3.8%
2004 1
 
1.9%
2006 7
13.5%
2007 3
 
5.8%
2008 1
 
1.9%
2009 2
 
3.8%
2010 2
 
3.8%
2011 2
 
3.8%
2012 6
11.5%
ValueCountFrequency (%)
2018 2
 
3.8%
2015 4
7.7%
2013 9
17.3%
2012 6
11.5%
2011 2
 
3.8%
2010 2
 
3.8%
2009 2
 
3.8%
2008 1
 
1.9%
2007 3
 
5.8%
2006 7
13.5%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.673077
Minimum1
Maximum182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-05-11T17:01:07.119589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.55
Q112.75
median18
Q342.75
95-th percentile149.95
Maximum182
Range181
Interquartile range (IQR)30

Descriptive statistics

Standard deviation43.685921
Coefficient of variation (CV)1.2246188
Kurtosis4.3447301
Mean35.673077
Median Absolute Deviation (MAD)9
Skewness2.2244052
Sum1855
Variance1908.4597
MonotonicityNot monotonic
2024-05-11T17:01:07.272492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
9 4
 
7.7%
16 4
 
7.7%
15 3
 
5.8%
12 2
 
3.8%
26 2
 
3.8%
23 2
 
3.8%
22 2
 
3.8%
1 2
 
3.8%
13 2
 
3.8%
18 2
 
3.8%
Other values (26) 27
51.9%
ValueCountFrequency (%)
1 2
3.8%
2 1
 
1.9%
3 1
 
1.9%
4 1
 
1.9%
5 1
 
1.9%
8 1
 
1.9%
9 4
7.7%
12 2
3.8%
13 2
3.8%
14 1
 
1.9%
ValueCountFrequency (%)
182 1
1.9%
168 1
1.9%
167 1
1.9%
136 1
1.9%
124 1
1.9%
78 1
1.9%
72 1
1.9%
59 1
1.9%
57 1
1.9%
52 1
1.9%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20084762
Minimum20010701
Maximum20181116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-05-11T17:01:07.416508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010701
5-th percentile20010701
Q120038150
median20090611
Q320130627
95-th percentile20150625
Maximum20181116
Range170415
Interquartile range (IQR)92477.5

Descriptive statistics

Standard deviation51927.237
Coefficient of variation (CV)0.0025854046
Kurtosis-1.2031722
Mean20084762
Median Absolute Deviation (MAD)40016
Skewness-0.1232499
Sum1.0444076 × 109
Variance2.6964379 × 109
MonotonicityNot monotonic
2024-05-11T17:01:07.560899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
20010701 11
21.2%
20130627 8
15.4%
20121030 6
11.5%
20060619 5
9.6%
20150625 4
 
7.7%
20100614 2
 
3.8%
20030708 2
 
3.8%
20181116 2
 
3.8%
20111020 2
 
3.8%
20130626 1
 
1.9%
Other values (9) 9
17.3%
ValueCountFrequency (%)
20010701 11
21.2%
20030708 2
 
3.8%
20040630 1
 
1.9%
20060615 1
 
1.9%
20060619 5
9.6%
20060621 1
 
1.9%
20070620 1
 
1.9%
20070621 1
 
1.9%
20070622 1
 
1.9%
20080626 1
 
1.9%
ValueCountFrequency (%)
20181116 2
 
3.8%
20150625 4
7.7%
20130627 8
15.4%
20130626 1
 
1.9%
20121030 6
11.5%
20111020 2
 
3.8%
20100614 2
 
3.8%
20090612 1
 
1.9%
20090610 1
 
1.9%
20080626 1
 
1.9%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20084915
Minimum20010701
Maximum20181224
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-05-11T17:01:07.695059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010701
5-th percentile20010701
Q120038297
median20090630
Q320131204
95-th percentile20150702
Maximum20181224
Range170523
Interquartile range (IQR)92906.75

Descriptive statistics

Standard deviation52041.184
Coefficient of variation (CV)0.0025910582
Kurtosis-1.2090419
Mean20084915
Median Absolute Deviation (MAD)40574
Skewness-0.12433888
Sum1.0444156 × 109
Variance2.7082848 × 109
MonotonicityNot monotonic
2024-05-11T17:01:07.831465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
20010701 11
21.2%
20131204 9
17.3%
20060710 7
13.5%
20121214 6
11.5%
20150702 4
 
7.7%
20070629 3
 
5.8%
20111123 2
 
3.8%
20090630 2
 
3.8%
20100630 2
 
3.8%
20181224 2
 
3.8%
Other values (3) 4
 
7.7%
ValueCountFrequency (%)
20010701 11
21.2%
20030708 2
 
3.8%
20040827 1
 
1.9%
20060710 7
13.5%
20070629 3
 
5.8%
20080630 1
 
1.9%
20090630 2
 
3.8%
20100630 2
 
3.8%
20111123 2
 
3.8%
20121214 6
11.5%
ValueCountFrequency (%)
20181224 2
 
3.8%
20150702 4
7.7%
20131204 9
17.3%
20121214 6
11.5%
20111123 2
 
3.8%
20100630 2
 
3.8%
20090630 2
 
3.8%
20080630 1
 
1.9%
20070629 3
 
5.8%
20060710 7
13.5%

업소명
Text

UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size548.0 B
2024-05-11T17:01:08.085960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length11.5
Mean length6.0769231
Min length2

Characters and Unicode

Total characters316
Distinct characters141
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

Unique52 ?
Unique (%)100.0%

Sample

1st row조영환의대양한방숯불갈비
2nd row안흥갈비 본점
3rd row이수사횟집
4th row면천추어탕
5th row그집
ValueCountFrequency (%)
장안점 2
 
3.3%
조영환의대양한방숯불갈비 1
 
1.6%
완도회집 1
 
1.6%
나주전 1
 
1.6%
최원석의돼지한판&서해쭈꾸미시립대점 1
 
1.6%
옛날할머니보쌈 1
 
1.6%
뚝배기 1
 
1.6%
양평해장국 1
 
1.6%
이천농장정육식당 1
 
1.6%
최강낙지 1
 
1.6%
Other values (50) 50
82.0%
2024-05-11T17:01:08.573123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 11
 
3.5%
9
 
2.8%
탕 8
 
2.5%
대 8
 
2.5%
점 8
 
2.5%
집 7
 
2.2%
안 7
 
2.2%
마 6
 
1.9%
지 6
 
1.9%
국 6
 
1.9%
Other values (131) 240
75.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 301
95.3%
Space Separator 9
 
2.8%
Close Punctuation 2
 
0.6%
Open Punctuation 2
 
0.6%
Other Punctuation 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 11
 
3.7%
탕 8
 
2.7%
대 8
 
2.7%
점 8
 
2.7%
집 7
 
2.3%
안 7
 
2.3%
마 6
 
2.0%
지 6
 
2.0%
국 6
 
2.0%
육 5
 
1.7%
Other values (127) 229
76.1%
Space Separator
ValueCountFrequency (%)
9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Other Punctuation
ValueCountFrequency (%)
& 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 301
95.3%
Common 15
 
4.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 11
 
3.7%
탕 8
 
2.7%
대 8
 
2.7%
점 8
 
2.7%
집 7
 
2.3%
안 7
 
2.3%
마 6
 
2.0%
지 6
 
2.0%
국 6
 
2.0%
육 5
 
1.7%
Other values (127) 229
76.1%
Common
ValueCountFrequency (%)
9
60.0%
) 2
 
13.3%
( 2
 
13.3%
& 2
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 301
95.3%
ASCII 15
 
4.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 11
 
3.7%
탕 8
 
2.7%
대 8
 
2.7%
점 8
 
2.7%
집 7
 
2.3%
안 7
 
2.3%
마 6
 
2.0%
지 6
 
2.0%
국 6
 
2.0%
육 5
 
1.7%
Other values (127) 229
76.1%
ASCII
ValueCountFrequency (%)
9
60.0%
) 2
 
13.3%
( 2
 
13.3%
& 2
 
13.3%

소재지도로명
Text

UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size548.0 B
2024-05-11T17:01:08.854863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length36
Mean length29.788462
Min length24

Characters and Unicode

Total characters1549
Distinct characters75
Distinct categories8 ?
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 (%)100.0%

Sample

1st row서울특별시 동대문구 고산자로 399, (용두동,동양프라자 204호~208호)
2nd row서울특별시 동대문구 장한로 153, 1층 (장안동)
3rd row서울특별시 동대문구 천호대로85길 10, (장안동,1층)
4th row서울특별시 동대문구 천호대로83길 19, 1층 (장안동)
5th row서울특별시 동대문구 천장산로7길 56, (이문동)
ValueCountFrequency (%)
서울특별시 52
17.8%
동대문구 52
17.8%
1층 17
 
5.8%
장안동 17
 
5.8%
장한로 11
 
3.8%
답십리동 4
 
1.4%
이문동 3
 
1.0%
전농로 3
 
1.0%
천호대로 3
 
1.0%
제기동 3
 
1.0%
Other values (102) 127
43.5%
2024-05-11T17:01:09.298056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
240
 
15.5%
동 105
 
6.8%
1 78
 
5.0%
, 66
 
4.3%
대 63
 
4.1%
) 57
 
3.7%
( 57
 
3.7%
로 56
 
3.6%
문 56
 
3.6%
울 54
 
3.5%
Other values (65) 717
46.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 894
57.7%
Space Separator 240
 
15.5%
Decimal Number 223
 
14.4%
Other Punctuation 67
 
4.3%
Close Punctuation 57
 
3.7%
Open Punctuation 57
 
3.7%
Dash Punctuation 8
 
0.5%
Math Symbol 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 105
 
11.7%
대 63
 
7.0%
로 56
 
6.3%
문 56
 
6.3%
울 54
 
6.0%
서 54
 
6.0%
시 54
 
6.0%
특 52
 
5.8%
구 52
 
5.8%
별 52
 
5.8%
Other values (48) 296
33.1%
Decimal Number
ValueCountFrequency (%)
1 78
35.0%
2 33
14.8%
3 19
 
8.5%
0 17
 
7.6%
8 16
 
7.2%
7 15
 
6.7%
4 14
 
6.3%
5 13
 
5.8%
6 11
 
4.9%
9 7
 
3.1%
Other Punctuation
ValueCountFrequency (%)
, 66
98.5%
/ 1
 
1.5%
Space Separator
ValueCountFrequency (%)
240
100.0%
Close Punctuation
ValueCountFrequency (%)
) 57
100.0%
Open Punctuation
ValueCountFrequency (%)
( 57
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 894
57.7%
Common 655
42.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 105
 
11.7%
대 63
 
7.0%
로 56
 
6.3%
문 56
 
6.3%
울 54
 
6.0%
서 54
 
6.0%
시 54
 
6.0%
특 52
 
5.8%
구 52
 
5.8%
별 52
 
5.8%
Other values (48) 296
33.1%
Common
ValueCountFrequency (%)
240
36.6%
1 78
 
11.9%
, 66
 
10.1%
) 57
 
8.7%
( 57
 
8.7%
2 33
 
5.0%
3 19
 
2.9%
0 17
 
2.6%
8 16
 
2.4%
7 15
 
2.3%
Other values (7) 57
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 894
57.7%
ASCII 655
42.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
240
36.6%
1 78
 
11.9%
, 66
 
10.1%
) 57
 
8.7%
( 57
 
8.7%
2 33
 
5.0%
3 19
 
2.9%
0 17
 
2.6%
8 16
 
2.4%
7 15
 
2.3%
Other values (7) 57
 
8.7%
Hangul
ValueCountFrequency (%)
동 105
 
11.7%
대 63
 
7.0%
로 56
 
6.3%
문 56
 
6.3%
울 54
 
6.0%
서 54
 
6.0%
시 54
 
6.0%
특 52
 
5.8%
구 52
 
5.8%
별 52
 
5.8%
Other values (48) 296
33.1%

소재지지번
Text

UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size548.0 B
2024-05-11T17:01:09.636582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length37
Mean length28.75
Min length24

Characters and Unicode

Total characters1495
Distinct characters58
Distinct categories8 ?
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 (%)100.0%

Sample

1st row서울특별시 동대문구 용두동 40번지 13호 동양프라자 204호~208호
2nd row서울특별시 동대문구 장안동 306번지 1호 1층
3rd row서울특별시 동대문구 장안동 463번지 3호 1층
4th row서울특별시 동대문구 장안동 417번지 3호 1층
5th row서울특별시 동대문구 이문동 264번지 407호
ValueCountFrequency (%)
서울특별시 52
18.3%
동대문구 52
18.3%
장안동 21
 
7.4%
1층 8
 
2.8%
3호 6
 
2.1%
용두동 6
 
2.1%
2호 5
 
1.8%
1호 5
 
1.8%
306번지 4
 
1.4%
신설동 4
 
1.4%
Other values (92) 121
42.6%
2024-05-11T17:01:10.112217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
368
24.6%
동 105
 
7.0%
1 62
 
4.1%
지 56
 
3.7%
문 55
 
3.7%
서 52
 
3.5%
번 52
 
3.5%
특 52
 
3.5%
별 52
 
3.5%
시 52
 
3.5%
Other values (48) 589
39.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 828
55.4%
Space Separator 368
24.6%
Decimal Number 278
 
18.6%
Other Punctuation 6
 
0.4%
Open Punctuation 5
 
0.3%
Close Punctuation 5
 
0.3%
Math Symbol 3
 
0.2%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 105
12.7%
지 56
 
6.8%
문 55
 
6.6%
서 52
 
6.3%
번 52
 
6.3%
특 52
 
6.3%
별 52
 
6.3%
시 52
 
6.3%
대 52
 
6.3%
구 52
 
6.3%
Other values (32) 248
30.0%
Decimal Number
ValueCountFrequency (%)
1 62
22.3%
2 46
16.5%
3 37
13.3%
4 33
11.9%
0 25
9.0%
6 19
 
6.8%
7 18
 
6.5%
9 17
 
6.1%
5 14
 
5.0%
8 7
 
2.5%
Space Separator
ValueCountFrequency (%)
368
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 828
55.4%
Common 667
44.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 105
12.7%
지 56
 
6.8%
문 55
 
6.6%
서 52
 
6.3%
번 52
 
6.3%
특 52
 
6.3%
별 52
 
6.3%
시 52
 
6.3%
대 52
 
6.3%
구 52
 
6.3%
Other values (32) 248
30.0%
Common
ValueCountFrequency (%)
368
55.2%
1 62
 
9.3%
2 46
 
6.9%
3 37
 
5.5%
4 33
 
4.9%
0 25
 
3.7%
6 19
 
2.8%
7 18
 
2.7%
9 17
 
2.5%
5 14
 
2.1%
Other values (6) 28
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 828
55.4%
ASCII 667
44.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
368
55.2%
1 62
 
9.3%
2 46
 
6.9%
3 37
 
5.5%
4 33
 
4.9%
0 25
 
3.7%
6 19
 
2.8%
7 18
 
2.7%
9 17
 
2.5%
5 14
 
2.1%
Other values (6) 28
 
4.2%
Hangul
ValueCountFrequency (%)
동 105
12.7%
지 56
 
6.8%
문 55
 
6.6%
서 52
 
6.3%
번 52
 
6.3%
특 52
 
6.3%
별 52
 
6.3%
시 52
 
6.3%
대 52
 
6.3%
구 52
 
6.3%
Other values (32) 248
30.0%

허가(신고)번호
Text

UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size548.0 B
2024-05-11T17:01:10.334996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters1144
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

Unique52 ?
Unique (%)100.0%

Sample

1st row3050000-101-2010-00333
2nd row3050000-101-1984-01262
3rd row3050000-101-2010-00352
4th row3050000-101-2004-00359
5th row3050000-101-1991-02183
ValueCountFrequency (%)
3050000-101-2010-00333 1
 
1.9%
3050000-101-1984-01262 1
 
1.9%
3050000-101-2011-00287 1
 
1.9%
3050000-101-1999-10728 1
 
1.9%
3050000-101-2007-00262 1
 
1.9%
3050000-101-1996-03103 1
 
1.9%
3050000-101-2005-00133 1
 
1.9%
3050000-101-2011-00253 1
 
1.9%
3050000-101-2010-00019 1
 
1.9%
3050000-101-1996-03076 1
 
1.9%
Other values (42) 42
80.8%
2024-05-11T17:01:10.709460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 440
38.5%
1 192
16.8%
- 156
 
13.6%
3 83
 
7.3%
5 68
 
5.9%
9 60
 
5.2%
2 48
 
4.2%
8 34
 
3.0%
4 21
 
1.8%
6 21
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 988
86.4%
Dash Punctuation 156
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 440
44.5%
1 192
19.4%
3 83
 
8.4%
5 68
 
6.9%
9 60
 
6.1%
2 48
 
4.9%
8 34
 
3.4%
4 21
 
2.1%
6 21
 
2.1%
7 21
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 156
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1144
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 440
38.5%
1 192
16.8%
- 156
 
13.6%
3 83
 
7.3%
5 68
 
5.9%
9 60
 
5.2%
2 48
 
4.2%
8 34
 
3.0%
4 21
 
1.8%
6 21
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 440
38.5%
1 192
16.8%
- 156
 
13.6%
3 83
 
7.3%
5 68
 
5.9%
9 60
 
5.2%
2 48
 
4.2%
8 34
 
3.0%
4 21
 
1.8%
6 21
 
1.8%

업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size548.0 B
한식
45 
일식
 
2
경양식
 
2
중국식
 
1
냉면집
 
1

Length

Max length3
Median length2
Mean length2.0769231
Min length2

Unique

Unique3 ?
Unique (%)5.8%

Sample

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

Common Values

ValueCountFrequency (%)
한식 45
86.5%
일식 2
 
3.8%
경양식 2
 
3.8%
중국식 1
 
1.9%
냉면집 1
 
1.9%
회집 1
 
1.9%

Length

2024-05-11T17:01:10.864011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:01:10.989737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 45
86.5%
일식 2
 
3.8%
경양식 2
 
3.8%
중국식 1
 
1.9%
냉면집 1
 
1.9%
회집 1
 
1.9%

주된음식
Text

MISSING 

Distinct40
Distinct (%)83.3%
Missing4
Missing (%)7.7%
Memory size548.0 B
2024-05-11T17:01:11.205163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.8125
Min length2

Characters and Unicode

Total characters183
Distinct characters85
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

Unique35 ?
Unique (%)72.9%

Sample

1st row돼지갈비
2nd row돼지갈비
3rd row생선회
4th row추어탕
5th row설렁탕
ValueCountFrequency (%)
돼지갈비 4
 
7.8%
설렁탕 4
 
7.8%
삼계탕 2
 
3.9%
돈까스 2
 
3.9%
추어탕 2
 
3.9%
보쌈 2
 
3.9%
뼈다귀감자탕 1
 
2.0%
홍어찜 1
 
2.0%
아구찜 1
 
2.0%
낙지비빔밥 1
 
2.0%
Other values (31) 31
60.8%
2024-05-11T17:01:11.589946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
탕 11
 
6.0%
비 9
 
4.9%
갈 8
 
4.4%
어 7
 
3.8%
지 6
 
3.3%
밥 5
 
2.7%
돼 4
 
2.2%
구 4
 
2.2%
찜 4
 
2.2%
쌈 4
 
2.2%
Other values (75) 121
66.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 176
96.2%
Space Separator 3
 
1.6%
Other Punctuation 2
 
1.1%
Open Punctuation 1
 
0.5%
Close Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 11
 
6.2%
비 9
 
5.1%
갈 8
 
4.5%
어 7
 
4.0%
지 6
 
3.4%
밥 5
 
2.8%
돼 4
 
2.3%
구 4
 
2.3%
찜 4
 
2.3%
쌈 4
 
2.3%
Other values (71) 114
64.8%
Space Separator
ValueCountFrequency (%)
3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 176
96.2%
Common 7
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 11
 
6.2%
비 9
 
5.1%
갈 8
 
4.5%
어 7
 
4.0%
지 6
 
3.4%
밥 5
 
2.8%
돼 4
 
2.3%
구 4
 
2.3%
찜 4
 
2.3%
쌈 4
 
2.3%
Other values (71) 114
64.8%
Common
ValueCountFrequency (%)
3
42.9%
, 2
28.6%
( 1
 
14.3%
) 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 176
96.2%
ASCII 7
 
3.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
탕 11
 
6.2%
비 9
 
5.1%
갈 8
 
4.5%
어 7
 
4.0%
지 6
 
3.4%
밥 5
 
2.8%
돼 4
 
2.3%
구 4
 
2.3%
찜 4
 
2.3%
쌈 4
 
2.3%
Other values (71) 114
64.8%
ASCII
ValueCountFrequency (%)
3
42.9%
, 2
28.6%
( 1
 
14.3%
) 1
 
14.3%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.98058
Minimum44.2
Maximum842.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-05-11T17:01:11.764121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum44.2
5-th percentile56.823
Q195
median126.415
Q3164.25
95-th percentile369.1325
Maximum842.88
Range798.68
Interquartile range (IQR)69.25

Descriptive statistics

Standard deviation150.48039
Coefficient of variation (CV)0.91210973
Kurtosis12.189499
Mean164.98058
Median Absolute Deviation (MAD)35.225
Skewness3.2932732
Sum8578.99
Variance22644.347
MonotonicityNot monotonic
2024-05-11T17:01:12.199878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95.0 2
 
3.8%
343.55 1
 
1.9%
95.2 1
 
1.9%
195.0 1
 
1.9%
82.5 1
 
1.9%
159.28 1
 
1.9%
132.9 1
 
1.9%
103.2 1
 
1.9%
52.94 1
 
1.9%
115.92 1
 
1.9%
Other values (41) 41
78.8%
ValueCountFrequency (%)
44.2 1
1.9%
50.49 1
1.9%
52.94 1
1.9%
60.0 1
1.9%
66.0 1
1.9%
68.0 1
1.9%
70.91 1
1.9%
71.76 1
1.9%
75.0 1
1.9%
77.52 1
1.9%
ValueCountFrequency (%)
842.88 1
1.9%
776.57 1
1.9%
400.4 1
1.9%
343.55 1
1.9%
314.48 1
1.9%
273.65 1
1.9%
271.72 1
1.9%
230.59 1
1.9%
230.49 1
1.9%
207.94 1
1.9%

행정동명
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Memory size548.0 B
장안제1동
18 
용신동
10 
답십리제1동
4 
전농제1동
4 
이문제1동
3 
Other values (5)
13 

Length

Max length6
Median length5
Mean length4.4230769
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row용신동
2nd row장안제1동
3rd row장안제1동
4th row장안제1동
5th row이문제1동

Common Values

ValueCountFrequency (%)
장안제1동 18
34.6%
용신동 10
19.2%
답십리제1동 4
 
7.7%
전농제1동 4
 
7.7%
이문제1동 3
 
5.8%
제기동 3
 
5.8%
회기동 3
 
5.8%
장안제2동 3
 
5.8%
청량리동 2
 
3.8%
휘경제1동 2
 
3.8%

Length

2024-05-11T17:01:12.345896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:01:12.477945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
장안제1동 18
34.6%
용신동 10
19.2%
답십리제1동 4
 
7.7%
전농제1동 4
 
7.7%
이문제1동 3
 
5.8%
제기동 3
 
5.8%
회기동 3
 
5.8%
장안제2동 3
 
5.8%
청량리동 2
 
3.8%
휘경제1동 2
 
3.8%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size548.0 B
상수도전용
40 
<NA>
12 

Length

Max length5
Median length5
Mean length4.7692308
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 40
76.9%
<NA> 12
 
23.1%

Length

2024-05-11T17:01:12.625917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:01:12.734432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 40
76.9%
na 12
 
23.1%

소재지전화번호
Text

MISSING 

Distinct46
Distinct (%)100.0%
Missing6
Missing (%)11.5%
Memory size548.0 B
2024-05-11T17:01:12.938717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.108696
Min length7

Characters and Unicode

Total characters465
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

Unique46 ?
Unique (%)100.0%

Sample

1st row02 9649595
2nd row0222467896
3rd row0222124789
4th row029673110
5th row0222430042
ValueCountFrequency (%)
02 18
27.3%
0222421310 1
 
1.5%
965 1
 
1.5%
7898 1
 
1.5%
22482645 1
 
1.5%
0222135592 1
 
1.5%
9675292 1
 
1.5%
0222176735 1
 
1.5%
0222123010 1
 
1.5%
0260806255 1
 
1.5%
Other values (39) 39
59.1%
2024-05-11T17:01:13.331506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 120
25.8%
0 66
14.2%
9 49
10.5%
6 41
 
8.8%
4 36
 
7.7%
7 30
 
6.5%
3 30
 
6.5%
5 27
 
5.8%
26
 
5.6%
1 23
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 439
94.4%
Space Separator 26
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 120
27.3%
0 66
15.0%
9 49
11.2%
6 41
 
9.3%
4 36
 
8.2%
7 30
 
6.8%
3 30
 
6.8%
5 27
 
6.2%
1 23
 
5.2%
8 17
 
3.9%
Space Separator
ValueCountFrequency (%)
26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 465
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 120
25.8%
0 66
14.2%
9 49
10.5%
6 41
 
8.8%
4 36
 
7.7%
7 30
 
6.5%
3 30
 
6.5%
5 27
 
5.8%
26
 
5.6%
1 23
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 465
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 120
25.8%
0 66
14.2%
9 49
10.5%
6 41
 
8.8%
4 36
 
7.7%
7 30
 
6.5%
3 30
 
6.5%
5 27
 
5.8%
26
 
5.6%
1 23
 
4.9%

Interactions

2024-05-11T17:01:05.441170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:01:02.822147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:01:03.409576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:01:04.302953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:01:04.869096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:01:05.548320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:01:02.959703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:01:03.532074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:01:04.401789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:01:04.995877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:01:05.657734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:01:03.077979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:01:03.961718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:01:04.503701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:01:05.105896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:01:05.767659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:01:03.184769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:01:04.081303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:01:04.628544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:01:05.217887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:01:05.869758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:01:03.300944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:01:04.202513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:01:04.742450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:01:05.336996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T17:01:13.461077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명소재지전화번호
지정년도1.0000.3751.0001.0001.0001.0001.0001.0000.0000.8350.5520.5111.000
지정번호0.3751.0000.3750.3751.0001.0001.0001.0000.0000.9610.0000.4811.000
신청일자1.0000.3751.0001.0001.0001.0001.0001.0000.0000.8350.5520.5111.000
지정일자1.0000.3751.0001.0001.0001.0001.0001.0000.0000.8350.5520.5111.000
업소명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지지번1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
허가(신고)번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
업태명0.0000.0000.0000.0001.0001.0001.0001.0001.0000.9710.0000.3951.000
주된음식0.8350.9610.8350.8351.0001.0001.0001.0000.9711.0000.0000.0001.000
영업장면적(㎡)0.5520.0000.5520.5521.0001.0001.0001.0000.0000.0001.0000.5511.000
행정동명0.5110.4810.5110.5111.0001.0001.0001.0000.3950.0000.5511.0001.000
소재지전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-05-11T17:01:13.597326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명급수시설구분업태명
행정동명1.0001.0000.202
급수시설구분1.0001.0001.000
업태명0.2021.0001.000
2024-05-11T17:01:13.715072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.000-0.6400.9981.0000.2870.0000.0921.000
지정번호-0.6401.000-0.648-0.640-0.2110.0000.2401.000
신청일자0.998-0.6481.0000.9980.2870.0000.0921.000
지정일자1.000-0.6400.9981.0000.2870.0000.0921.000
영업장면적(㎡)0.287-0.2110.2870.2871.0000.0000.3101.000
업태명0.0000.0000.0000.0000.0001.0000.2021.000
행정동명0.0920.2400.0920.0920.3100.2021.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-11T17:01:06.039524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T17:01:06.287260image/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.
2024-05-11T17:01:06.457350image/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

시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
03050000201192011102020111123조영환의대양한방숯불갈비서울특별시 동대문구 고산자로 399, (용두동,동양프라자 204호~208호)서울특별시 동대문구 용두동 40번지 13호 동양프라자 204호~208호3050000-101-2010-00333한식돼지갈비343.55용신동<NA>02 9649595
130500002013132013062720131204안흥갈비 본점서울특별시 동대문구 장한로 153, 1층 (장안동)서울특별시 동대문구 장안동 306번지 1호 1층3050000-101-1984-01262한식돼지갈비95.6장안제1동상수도전용0222467896
230500002013202013062720131204이수사횟집서울특별시 동대문구 천호대로85길 10, (장안동,1층)서울특별시 동대문구 장안동 463번지 3호 1층3050000-101-2010-00352일식생선회148.0장안제1동<NA>0222124789
330500002007262007062120070629면천추어탕서울특별시 동대문구 천호대로83길 19, 1층 (장안동)서울특별시 동대문구 장안동 417번지 3호 1층3050000-101-2004-00359한식추어탕66.0장안제1동상수도전용<NA>
430500002007522007062020070629그집서울특별시 동대문구 천장산로7길 56, (이문동)서울특별시 동대문구 이문동 264번지 407호3050000-101-1991-02183한식설렁탕95.0이문제1동상수도전용029673110
530500002006162006061920060710샛집남원추어탕서울특별시 동대문구 답십리로 310, 1층 (장안동)서울특별시 동대문구 장안동 334번지 2호3050000-101-1998-03627한식추어탕141.9장안제1동상수도전용0222430042
630500002006232006061920060710신마포갈매기서울특별시 동대문구 한천로 141, 지상1층 (답십리동)서울특별시 동대문구 답십리동 1번지 49호3050000-101-2000-11826한식숯불갈비196.49답십리제1동상수도전용0222475303
730500002006222006061920060710홍어집서울특별시 동대문구 청계천로5길 9, (신설동)서울특별시 동대문구 신설동 92번지 60호3050000-101-2000-11757한식홍어찜50.49용신동상수도전용0222341644
830500002001422001070120010701대흥설농탕서울특별시 동대문구 한천로 60, (장안동)서울특별시 동대문구 장안동 192번지 75호3050000-101-1987-01478한식설렁탕122.31장안제1동상수도전용0222493939
93050000200912009061020090630영화장서울특별시 동대문구 휘경로 3-8, (이문동)서울특별시 동대문구 이문동 288번지 23호3050000-101-2008-00380중국식굴짬뽕114.74이문제1동<NA>9679595
시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
42305000020011822001070120010701다복서울특별시 동대문구 망우로 117, 101호 (휘경동)서울특별시 동대문구 휘경동 72번지 7호3050000-101-1991-02004한식돼지갈비95.0휘경제1동상수도전용02 9651332
43305000020011362001070120010701이대감서울특별시 동대문구 하정로3길 16, (신설동,(신설2길 1))서울특별시 동대문구 신설동 98번지 14호 (신설2길 1)3050000-101-1989-05128한식오징어철판구이70.91용신동상수도전용02 9277751
4430500002001722001070120010701장안삼계탕서울특별시 동대문구 전농로 50, 1층 (답십리동)서울특별시 동대문구 답십리동 35번지 10호3050000-101-1987-00905한식삼계탕75.0답십리제1동상수도전용0222497297
45305000020011672001070120010701초우마을서울특별시 동대문구 회기로28길 8, 2층 (휘경동)서울특별시 동대문구 휘경동 329번지 2층3050000-101-1996-02738한식상추쌈밥샤브샤브776.57휘경제1동상수도전용02 9572266
4630500002013272013062720131204최가네서울특별시 동대문구 천호대로 281, 1층 (답십리동)서울특별시 동대문구 답십리동 495번지 1호 1층3050000-101-1998-03027한식동태찜165.0답십리제1동상수도전용0222466029
47305000020011682001070120010701유성관서울특별시 동대문구 경희대로3길 30, 1층 (회기동)서울특별시 동대문구 회기동 42번지 23호3050000-101-1995-02672한식생삼겹살230.49회기동상수도전용0209646849
483050000200692006061920060710함경면옥서울특별시 동대문구 왕산로 87, (제기동)서울특별시 동대문구 제기동 1158번지 49호3050000-101-1994-00455한식냉면271.72제기동상수도전용0209217300
493050000201512015062520150702육전국밥 장안점서울특별시 동대문구 장한로 160, 1층 (장안동)서울특별시 동대문구 장안동 310번지 3호3050000-101-2006-00354한식<NA>140.0장안제1동상수도전용0233944598
5030500002010152010061420100630팔각도 서울장안점서울특별시 동대문구 장한로 126, 1층 (장안동)서울특별시 동대문구 장안동 367번지 1호 1층3050000-101-2008-00385한식족발 보쌈100.0장안제1동<NA><NA>
5130500002006452006061920060710정대감감자탕서울특별시 동대문구 천호대로83길 46, 1층 (장안동)서울특별시 동대문구 장안동 431번지 1호3050000-101-2005-00193한식뼈해장국207.94장안제1동상수도전용02 22422533