Overview

Dataset statistics

Number of variables16
Number of observations224
Missing cells634
Missing cells (%)17.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.9 KiB
Average record size in memory136.6 B

Variable types

Categorical4
Numeric6
Unsupported1
Text5

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,취소일자,불가일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,주된음식,영업장면적(㎡),행정동명,급수시설구분
Author강동구
URLhttps://data.seoul.go.kr/dataList/OA-10698/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
지정년도 is highly overall correlated with 신청일자 and 2 other fieldsHigh correlation
신청일자 is highly overall correlated with 지정년도 and 2 other fieldsHigh correlation
지정일자 is highly overall correlated with 지정년도 and 2 other fieldsHigh correlation
취소일자 is highly overall correlated with 지정년도 and 2 other fieldsHigh correlation
업태명 is highly imbalanced (63.4%)Imbalance
지정년도 has 77 (34.4%) missing valuesMissing
지정번호 has 77 (34.4%) missing valuesMissing
지정일자 has 77 (34.4%) missing valuesMissing
취소일자 has 102 (45.5%) missing valuesMissing
불가일자 has 224 (100.0%) missing valuesMissing
주된음식 has 77 (34.4%) missing valuesMissing
불가일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-03 20:28:42.745677
Analysis finished2024-05-03 20:28:52.963715
Duration10.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
3240000
224 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3240000
2nd row3240000
3rd row3240000
4th row3240000
5th row3240000

Common Values

ValueCountFrequency (%)
3240000 224
100.0%

Length

2024-05-03T20:28:53.076344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T20:28:53.255106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3240000 224
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)9.5%
Missing77
Missing (%)34.4%
Infinite0
Infinite (%)0.0%
Mean2009.8776
Minimum2003
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-05-03T20:28:53.672887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2003
5-th percentile2003
Q12008
median2008
Q32014
95-th percentile2016
Maximum2017
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.815789
Coefficient of variation (CV)0.0018985182
Kurtosis-0.91717637
Mean2009.8776
Median Absolute Deviation (MAD)3
Skewness0.017394146
Sum295452
Variance14.560246
MonotonicityNot monotonic
2024-05-03T20:28:53.949343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2008 46
20.5%
2015 17
 
7.6%
2014 13
 
5.8%
2003 12
 
5.4%
2010 8
 
3.6%
2007 8
 
3.6%
2016 8
 
3.6%
2011 8
 
3.6%
2012 6
 
2.7%
2005 6
 
2.7%
Other values (4) 15
 
6.7%
(Missing) 77
34.4%
ValueCountFrequency (%)
2003 12
 
5.4%
2004 2
 
0.9%
2005 6
 
2.7%
2007 8
 
3.6%
2008 46
20.5%
2009 6
 
2.7%
2010 8
 
3.6%
2011 8
 
3.6%
2012 6
 
2.7%
2013 6
 
2.7%
ValueCountFrequency (%)
2017 1
 
0.4%
2016 8
 
3.6%
2015 17
 
7.6%
2014 13
 
5.8%
2013 6
 
2.7%
2012 6
 
2.7%
2011 8
 
3.6%
2010 8
 
3.6%
2009 6
 
2.7%
2008 46
20.5%

지정번호
Real number (ℝ)

MISSING 

Distinct105
Distinct (%)71.4%
Missing77
Missing (%)34.4%
Infinite0
Infinite (%)0.0%
Mean126.61905
Minimum2
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-05-03T20:28:54.352334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile13
Q165
median113
Q3129
95-th percentile394.2
Maximum2017
Range2015
Interquartile range (IQR)64

Descriptive statistics

Standard deviation178.71644
Coefficient of variation (CV)1.4114499
Kurtosis86.70429
Mean126.61905
Median Absolute Deviation (MAD)26
Skewness8.4116746
Sum18613
Variance31939.566
MonotonicityNot monotonic
2024-05-03T20:28:54.832200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 5
 
2.2%
113 5
 
2.2%
122 4
 
1.8%
121 4
 
1.8%
112 3
 
1.3%
118 3
 
1.3%
117 3
 
1.3%
176 3
 
1.3%
130 2
 
0.9%
115 2
 
0.9%
Other values (95) 113
50.4%
(Missing) 77
34.4%
ValueCountFrequency (%)
2 1
0.4%
3 1
0.4%
5 1
0.4%
8 1
0.4%
11 1
0.4%
12 2
0.9%
13 2
0.9%
15 2
0.9%
16 1
0.4%
18 1
0.4%
ValueCountFrequency (%)
2017 1
0.4%
443 1
0.4%
437 1
0.4%
428 1
0.4%
420 1
0.4%
415 1
0.4%
402 1
0.4%
399 1
0.4%
383 1
0.4%
179 1
0.4%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20082845
Minimum19970220
Maximum20171027
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-05-03T20:28:55.251751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19970220
5-th percentile20030523
Q120050614
median20070604
Q320111004
95-th percentile20151111
Maximum20171027
Range200807
Interquartile range (IQR)60390.25

Descriptive statistics

Standard deviation39949.271
Coefficient of variation (CV)0.0019892236
Kurtosis-0.58855105
Mean20082845
Median Absolute Deviation (MAD)20102
Skewness0.42194099
Sum4.4985574 × 109
Variance1.5959442 × 109
MonotonicityNot monotonic
2024-05-03T20:28:55.698888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
20070604 77
34.4%
20151111 16
 
7.1%
20141105 14
 
6.2%
20090508 12
 
5.4%
20100510 10
 
4.5%
20121022 8
 
3.6%
20111004 8
 
3.6%
20161111 7
 
3.1%
20030522 7
 
3.1%
20030523 7
 
3.1%
Other values (30) 58
25.9%
ValueCountFrequency (%)
19970220 1
 
0.4%
20030522 7
3.1%
20030523 7
3.1%
20030524 3
1.3%
20030526 7
3.1%
20030527 2
 
0.9%
20030528 3
1.3%
20030529 4
1.8%
20030530 1
 
0.4%
20040522 5
2.2%
ValueCountFrequency (%)
20171027 1
 
0.4%
20161111 7
3.1%
20151222 1
 
0.4%
20151111 16
7.1%
20141105 14
6.2%
20131202 3
 
1.3%
20131113 1
 
0.4%
20121031 1
 
0.4%
20121022 8
3.6%
20111004 8
3.6%

지정일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)9.5%
Missing77
Missing (%)34.4%
Infinite0
Infinite (%)0.0%
Mean20099626
Minimum20030626
Maximum20171027
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-05-03T20:28:56.075546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20030626
5-th percentile20030626
Q120080630
median20080630
Q320141212
95-th percentile20161223
Maximum20171027
Range140401
Interquartile range (IQR)60582

Descriptive statistics

Standard deviation38396.465
Coefficient of variation (CV)0.0019103075
Kurtosis-0.92729094
Mean20099626
Median Absolute Deviation (MAD)30009
Skewness0.023496471
Sum2.954645 × 109
Variance1.4742885 × 109
MonotonicityNot monotonic
2024-05-03T20:28:56.436077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
20080630 46
20.5%
20151222 17
 
7.6%
20141212 13
 
5.8%
20030626 12
 
5.4%
20100630 8
 
3.6%
20070710 8
 
3.6%
20161223 8
 
3.6%
20111031 8
 
3.6%
20121031 6
 
2.7%
20050621 6
 
2.7%
Other values (4) 15
 
6.7%
(Missing) 77
34.4%
ValueCountFrequency (%)
20030626 12
 
5.4%
20040629 2
 
0.9%
20050621 6
 
2.7%
20070710 8
 
3.6%
20080630 46
20.5%
20090630 6
 
2.7%
20100630 8
 
3.6%
20111031 8
 
3.6%
20121031 6
 
2.7%
20131202 6
 
2.7%
ValueCountFrequency (%)
20171027 1
 
0.4%
20161223 8
 
3.6%
20151222 17
 
7.6%
20141212 13
 
5.8%
20131202 6
 
2.7%
20121031 6
 
2.7%
20111031 8
 
3.6%
20100630 8
 
3.6%
20090630 6
 
2.7%
20080630 46
20.5%

취소일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct39
Distinct (%)32.0%
Missing102
Missing (%)45.5%
Infinite0
Infinite (%)0.0%
Mean20119105
Minimum20041018
Maximum20231108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-05-03T20:28:56.838286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20041018
5-th percentile20050736
Q120090630
median20111031
Q320151222
95-th percentile20211111
Maximum20231108
Range190090
Interquartile range (IQR)60592

Descriptive statistics

Standard deviation46541.855
Coefficient of variation (CV)0.0023133164
Kurtosis-0.47211572
Mean20119105
Median Absolute Deviation (MAD)40191
Skewness0.37951836
Sum2.4545308 × 109
Variance2.1661443 × 109
MonotonicityNot monotonic
2024-05-03T20:28:57.268129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
20151222 25
 
11.2%
20100630 23
 
10.3%
20111031 13
 
5.8%
20211111 7
 
3.1%
20080630 7
 
3.1%
20161223 7
 
3.1%
20090630 5
 
2.2%
20171027 3
 
1.3%
20221118 2
 
0.9%
20041026 1
 
0.4%
Other values (29) 29
 
12.9%
(Missing) 102
45.5%
ValueCountFrequency (%)
20041018 1
0.4%
20041026 1
0.4%
20041109 1
0.4%
20041208 1
0.4%
20050319 1
0.4%
20050524 1
0.4%
20050726 1
0.4%
20050926 1
0.4%
20051019 1
0.4%
20051208 1
0.4%
ValueCountFrequency (%)
20231108 1
 
0.4%
20221118 2
 
0.9%
20211111 7
 
3.1%
20181108 1
 
0.4%
20180723 1
 
0.4%
20171027 3
 
1.3%
20161223 7
 
3.1%
20151222 25
11.2%
20150911 1
 
0.4%
20130926 1
 
0.4%

불가일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing224
Missing (%)100.0%
Memory size2.1 KiB

업소명
Text

Distinct162
Distinct (%)72.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-05-03T20:28:57.809885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length13
Mean length6.1473214
Min length2

Characters and Unicode

Total characters1377
Distinct characters298
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique104 ?
Unique (%)46.4%

Sample

1st row뚝배기손칼국수
2nd row동촌
3rd row안심한우(고덕역점)
4th row한우암소마을정육식당
5th row생활맥주
ValueCountFrequency (%)
마포집 3
 
1.1%
암사점 3
 
1.1%
큰마루 3
 
1.1%
디자인카페허브 3
 
1.1%
채선당 3
 
1.1%
풍천장어 2
 
0.7%
장수촌 2
 
0.7%
길동직영점 2
 
0.7%
청년냉쌈 2
 
0.7%
강남랭겹 2
 
0.7%
Other values (190) 251
90.9%
2024-05-03T20:28:58.783389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
52
 
3.8%
점 38
 
2.8%
동 30
 
2.2%
탕 27
 
2.0%
장 24
 
1.7%
국 23
 
1.7%
수 22
 
1.6%
어 20
 
1.5%
당 20
 
1.5%
강 19
 
1.4%
Other values (288) 1102
80.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1289
93.6%
Space Separator 52
 
3.8%
Close Punctuation 10
 
0.7%
Open Punctuation 10
 
0.7%
Lowercase Letter 10
 
0.7%
Other Punctuation 3
 
0.2%
Uppercase Letter 2
 
0.1%
Decimal Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 38
 
2.9%
동 30
 
2.3%
탕 27
 
2.1%
장 24
 
1.9%
국 23
 
1.8%
수 22
 
1.7%
어 20
 
1.6%
당 20
 
1.6%
강 19
 
1.5%
집 18
 
1.4%
Other values (274) 1048
81.3%
Lowercase Letter
ValueCountFrequency (%)
o 3
30.0%
l 2
20.0%
w 2
20.0%
e 1
 
10.0%
m 1
 
10.0%
d 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
& 2
66.7%
, 1
33.3%
Uppercase Letter
ValueCountFrequency (%)
D 1
50.0%
K 1
50.0%
Space Separator
ValueCountFrequency (%)
52
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1289
93.6%
Common 76
 
5.5%
Latin 12
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 38
 
2.9%
동 30
 
2.3%
탕 27
 
2.1%
장 24
 
1.9%
국 23
 
1.8%
수 22
 
1.7%
어 20
 
1.6%
당 20
 
1.6%
강 19
 
1.5%
집 18
 
1.4%
Other values (274) 1048
81.3%
Latin
ValueCountFrequency (%)
o 3
25.0%
l 2
16.7%
w 2
16.7%
e 1
 
8.3%
m 1
 
8.3%
d 1
 
8.3%
D 1
 
8.3%
K 1
 
8.3%
Common
ValueCountFrequency (%)
52
68.4%
) 10
 
13.2%
( 10
 
13.2%
& 2
 
2.6%
, 1
 
1.3%
3 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1289
93.6%
ASCII 88
 
6.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
52
59.1%
) 10
 
11.4%
( 10
 
11.4%
o 3
 
3.4%
& 2
 
2.3%
l 2
 
2.3%
w 2
 
2.3%
, 1
 
1.1%
e 1
 
1.1%
m 1
 
1.1%
Other values (4) 4
 
4.5%
Hangul
ValueCountFrequency (%)
점 38
 
2.9%
동 30
 
2.3%
탕 27
 
2.1%
장 24
 
1.9%
국 23
 
1.8%
수 22
 
1.7%
어 20
 
1.6%
당 20
 
1.6%
강 19
 
1.5%
집 18
 
1.4%
Other values (274) 1048
81.3%

소재지도로명
Text

Distinct160
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-05-03T20:28:59.339329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length41
Mean length28.321429
Min length23

Characters and Unicode

Total characters6344
Distinct characters109
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

Unique101 ?
Unique (%)45.1%

Sample

1st row서울특별시 강동구 천호대로 1187, (길동)
2nd row서울특별시 강동구 명일로13길 26, (둔촌동)
3rd row서울특별시 강동구 동남로73길 26, 지하1층 54호 (명일동)
4th row서울특별시 강동구 성내로6길 14-23, (성내동)
5th row서울특별시 강동구 성안로3길 128, (성내동)
ValueCountFrequency (%)
서울특별시 224
18.7%
강동구 224
18.7%
성내동 53
 
4.4%
길동 32
 
2.7%
천호동 26
 
2.2%
1층 24
 
2.0%
둔촌동 22
 
1.8%
명일동 21
 
1.8%
암사동 21
 
1.8%
올림픽로 17
 
1.4%
Other values (258) 536
44.7%
2024-05-03T20:29:00.179562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
976
 
15.4%
동 495
 
7.8%
, 292
 
4.6%
1 280
 
4.4%
강 243
 
3.8%
( 240
 
3.8%
) 240
 
3.8%
구 236
 
3.7%
울 227
 
3.6%
서 227
 
3.6%
Other values (99) 2888
45.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3563
56.2%
Decimal Number 992
 
15.6%
Space Separator 976
 
15.4%
Other Punctuation 292
 
4.6%
Open Punctuation 240
 
3.8%
Close Punctuation 240
 
3.8%
Dash Punctuation 36
 
0.6%
Uppercase Letter 3
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 495
13.9%
강 243
 
6.8%
구 236
 
6.6%
울 227
 
6.4%
서 227
 
6.4%
시 224
 
6.3%
별 224
 
6.3%
특 224
 
6.3%
로 213
 
6.0%
길 139
 
3.9%
Other values (81) 1111
31.2%
Decimal Number
ValueCountFrequency (%)
1 280
28.2%
2 141
14.2%
3 102
 
10.3%
5 84
 
8.5%
0 79
 
8.0%
7 73
 
7.4%
4 70
 
7.1%
6 58
 
5.8%
8 56
 
5.6%
9 49
 
4.9%
Lowercase Letter
ValueCountFrequency (%)
x 1
50.0%
y 1
50.0%
Space Separator
ValueCountFrequency (%)
976
100.0%
Other Punctuation
ValueCountFrequency (%)
, 292
100.0%
Open Punctuation
ValueCountFrequency (%)
( 240
100.0%
Close Punctuation
ValueCountFrequency (%)
) 240
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3563
56.2%
Common 2776
43.8%
Latin 5
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 495
13.9%
강 243
 
6.8%
구 236
 
6.6%
울 227
 
6.4%
서 227
 
6.4%
시 224
 
6.3%
별 224
 
6.3%
특 224
 
6.3%
로 213
 
6.0%
길 139
 
3.9%
Other values (81) 1111
31.2%
Common
ValueCountFrequency (%)
976
35.2%
, 292
 
10.5%
1 280
 
10.1%
( 240
 
8.6%
) 240
 
8.6%
2 141
 
5.1%
3 102
 
3.7%
5 84
 
3.0%
0 79
 
2.8%
7 73
 
2.6%
Other values (5) 269
 
9.7%
Latin
ValueCountFrequency (%)
B 3
60.0%
x 1
 
20.0%
y 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3563
56.2%
ASCII 2781
43.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
976
35.1%
, 292
 
10.5%
1 280
 
10.1%
( 240
 
8.6%
) 240
 
8.6%
2 141
 
5.1%
3 102
 
3.7%
5 84
 
3.0%
0 79
 
2.8%
7 73
 
2.6%
Other values (8) 274
 
9.9%
Hangul
ValueCountFrequency (%)
동 495
13.9%
강 243
 
6.8%
구 236
 
6.6%
울 227
 
6.4%
서 227
 
6.4%
시 224
 
6.3%
별 224
 
6.3%
특 224
 
6.3%
로 213
 
6.0%
길 139
 
3.9%
Other values (81) 1111
31.2%

소재지지번
Text

Distinct157
Distinct (%)70.1%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-05-03T20:29:00.680167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length38
Mean length26.517857
Min length22

Characters and Unicode

Total characters5940
Distinct characters95
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique97 ?
Unique (%)43.3%

Sample

1st row서울특별시 강동구 길동 228번지 9호
2nd row서울특별시 강동구 둔촌동 54번지 6호
3rd row서울특별시 강동구 명일동 47번지 12호 지하1층-54
4th row서울특별시 강동구 성내동 554번지 2호
5th row서울특별시 강동구 성내동 557번지 4호
ValueCountFrequency (%)
서울특별시 224
18.8%
강동구 224
18.8%
성내동 67
 
5.6%
길동 34
 
2.9%
명일동 30
 
2.5%
천호동 30
 
2.5%
6호 28
 
2.4%
둔촌동 28
 
2.4%
암사동 23
 
1.9%
1호 21
 
1.8%
Other values (199) 481
40.4%
2024-05-03T20:29:01.590199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1562
26.3%
동 452
 
7.6%
호 250
 
4.2%
강 229
 
3.9%
서 227
 
3.8%
울 227
 
3.8%
지 227
 
3.8%
구 225
 
3.8%
번 224
 
3.8%
특 224
 
3.8%
Other values (85) 2093
35.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3282
55.3%
Space Separator 1562
26.3%
Decimal Number 1033
 
17.4%
Open Punctuation 17
 
0.3%
Close Punctuation 17
 
0.3%
Dash Punctuation 12
 
0.2%
Other Punctuation 11
 
0.2%
Math Symbol 2
 
< 0.1%
Uppercase Letter 2
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 452
13.8%
호 250
 
7.6%
강 229
 
7.0%
서 227
 
6.9%
울 227
 
6.9%
지 227
 
6.9%
구 225
 
6.9%
번 224
 
6.8%
특 224
 
6.8%
별 224
 
6.8%
Other values (66) 773
23.6%
Decimal Number
ValueCountFrequency (%)
4 169
16.4%
1 167
16.2%
3 130
12.6%
5 127
12.3%
2 116
11.2%
6 82
7.9%
8 72
7.0%
9 62
 
6.0%
0 55
 
5.3%
7 53
 
5.1%
Lowercase Letter
ValueCountFrequency (%)
y 1
50.0%
x 1
50.0%
Space Separator
ValueCountFrequency (%)
1562
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Other Punctuation
ValueCountFrequency (%)
, 11
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3282
55.3%
Common 2654
44.7%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 452
13.8%
호 250
 
7.6%
강 229
 
7.0%
서 227
 
6.9%
울 227
 
6.9%
지 227
 
6.9%
구 225
 
6.9%
번 224
 
6.8%
특 224
 
6.8%
별 224
 
6.8%
Other values (66) 773
23.6%
Common
ValueCountFrequency (%)
1562
58.9%
4 169
 
6.4%
1 167
 
6.3%
3 130
 
4.9%
5 127
 
4.8%
2 116
 
4.4%
6 82
 
3.1%
8 72
 
2.7%
9 62
 
2.3%
0 55
 
2.1%
Other values (6) 112
 
4.2%
Latin
ValueCountFrequency (%)
B 2
50.0%
y 1
25.0%
x 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3282
55.3%
ASCII 2658
44.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1562
58.8%
4 169
 
6.4%
1 167
 
6.3%
3 130
 
4.9%
5 127
 
4.8%
2 116
 
4.4%
6 82
 
3.1%
8 72
 
2.7%
9 62
 
2.3%
0 55
 
2.1%
Other values (9) 116
 
4.4%
Hangul
ValueCountFrequency (%)
동 452
13.8%
호 250
 
7.6%
강 229
 
7.0%
서 227
 
6.9%
울 227
 
6.9%
지 227
 
6.9%
구 225
 
6.9%
번 224
 
6.8%
특 224
 
6.8%
별 224
 
6.8%
Other values (66) 773
23.6%

허가(신고)번호
Text

Distinct163
Distinct (%)72.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-05-03T20:29:02.021912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters4928
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

Unique105 ?
Unique (%)46.9%

Sample

1st row3240000-101-1998-09841
2nd row3240000-101-2002-13890
3rd row3240000-101-2012-00004
4th row3240000-101-2005-00348
5th row3240000-101-1995-07399
ValueCountFrequency (%)
3240000-101-2002-13601 3
 
1.3%
3240000-101-1995-07328 3
 
1.3%
3240000-101-2004-00022 3
 
1.3%
3240000-101-1989-02630 2
 
0.9%
3240000-101-1988-01693 2
 
0.9%
3240000-101-1998-09841 2
 
0.9%
3240000-101-2000-11679 2
 
0.9%
3240000-101-1988-03009 2
 
0.9%
3240000-101-2003-13724 2
 
0.9%
3240000-101-1992-02053 2
 
0.9%
Other values (153) 201
89.7%
2024-05-03T20:29:02.897590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1662
33.7%
1 774
15.7%
- 672
13.6%
2 496
 
10.1%
3 353
 
7.2%
4 313
 
6.4%
9 298
 
6.0%
8 103
 
2.1%
5 91
 
1.8%
7 85
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4256
86.4%
Dash Punctuation 672
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1662
39.1%
1 774
18.2%
2 496
 
11.7%
3 353
 
8.3%
4 313
 
7.4%
9 298
 
7.0%
8 103
 
2.4%
5 91
 
2.1%
7 85
 
2.0%
6 81
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4928
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1662
33.7%
1 774
15.7%
- 672
13.6%
2 496
 
10.1%
3 353
 
7.2%
4 313
 
6.4%
9 298
 
6.0%
8 103
 
2.1%
5 91
 
1.8%
7 85
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4928
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1662
33.7%
1 774
15.7%
- 672
13.6%
2 496
 
10.1%
3 353
 
7.2%
4 313
 
6.4%
9 298
 
6.0%
8 103
 
2.1%
5 91
 
1.8%
7 85
 
1.7%

업태명
Categorical

IMBALANCE 

Distinct11
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
한식
182 
중국식
 
10
일식
 
9
호프/통닭
 
4
분식
 
4
Other values (6)
 
15

Length

Max length8
Median length2
Mean length2.21875
Min length2

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
한식 182
81.2%
중국식 10
 
4.5%
일식 9
 
4.0%
호프/통닭 4
 
1.8%
분식 4
 
1.8%
뷔페식 3
 
1.3%
식육(숯불구이) 3
 
1.3%
회집 3
 
1.3%
기타 3
 
1.3%
라이브카페 2
 
0.9%

Length

2024-05-03T20:29:03.334313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 182
81.2%
중국식 10
 
4.5%
일식 9
 
4.0%
호프/통닭 4
 
1.8%
분식 4
 
1.8%
뷔페식 3
 
1.3%
식육(숯불구이 3
 
1.3%
회집 3
 
1.3%
기타 3
 
1.3%
라이브카페 2
 
0.9%

주된음식
Text

MISSING 

Distinct90
Distinct (%)61.2%
Missing77
Missing (%)34.4%
Memory size1.9 KiB
2024-05-03T20:29:04.057693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length10
Mean length3.9727891
Min length1

Characters and Unicode

Total characters584
Distinct characters136
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

Unique67 ?
Unique (%)45.6%

Sample

1st row칼국수
2nd row한정식
3rd row아구찜
4th row냉면,만두,갈비탕
5th row부페
ValueCountFrequency (%)
돼지갈비 9
 
5.7%
한정식 7
 
4.5%
칼국수 7
 
4.5%
샤브샤브 5
 
3.2%
추어탕 5
 
3.2%
삼계탕 4
 
2.5%
부대찌개 4
 
2.5%
삼겹살 4
 
2.5%
중화요리 4
 
2.5%
아구찜 3
 
1.9%
Other values (87) 105
66.9%
2024-05-03T20:29:05.191046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 24
 
4.1%
탕 21
 
3.6%
비 18
 
3.1%
기 17
 
2.9%
갈 16
 
2.7%
고 16
 
2.7%
샤 15
 
2.6%
브 15
 
2.6%
지 15
 
2.6%
국 15
 
2.6%
Other values (126) 412
70.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 550
94.2%
Other Punctuation 24
 
4.1%
Space Separator 10
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 21
 
3.8%
비 18
 
3.3%
기 17
 
3.1%
갈 16
 
2.9%
고 16
 
2.9%
샤 15
 
2.7%
브 15
 
2.7%
지 15
 
2.7%
국 15
 
2.7%
리 13
 
2.4%
Other values (124) 389
70.7%
Other Punctuation
ValueCountFrequency (%)
, 24
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 550
94.2%
Common 34
 
5.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 21
 
3.8%
비 18
 
3.3%
기 17
 
3.1%
갈 16
 
2.9%
고 16
 
2.9%
샤 15
 
2.7%
브 15
 
2.7%
지 15
 
2.7%
국 15
 
2.7%
리 13
 
2.4%
Other values (124) 389
70.7%
Common
ValueCountFrequency (%)
, 24
70.6%
10
29.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 550
94.2%
ASCII 34
 
5.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 24
70.6%
10
29.4%
Hangul
ValueCountFrequency (%)
탕 21
 
3.8%
비 18
 
3.3%
기 17
 
3.1%
갈 16
 
2.9%
고 16
 
2.9%
샤 15
 
2.7%
브 15
 
2.7%
지 15
 
2.7%
국 15
 
2.7%
리 13
 
2.4%
Other values (124) 389
70.7%

영업장면적(㎡)
Real number (ℝ)

Distinct146
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.02638
Minimum23.22
Maximum1504.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-05-03T20:29:05.892685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.22
5-th percentile48.484
Q182.5
median109.51
Q3174
95-th percentile287.87
Maximum1504.4
Range1481.18
Interquartile range (IQR)91.5

Descriptive statistics

Standard deviation138.37163
Coefficient of variation (CV)0.95411346
Kurtosis53.784161
Mean145.02638
Median Absolute Deviation (MAD)40.65
Skewness6.2786494
Sum32485.91
Variance19146.707
MonotonicityNot monotonic
2024-05-03T20:29:06.473058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82.5 9
 
4.0%
132.0 7
 
3.1%
231.0 4
 
1.8%
165.0 4
 
1.8%
133.0 3
 
1.3%
109.0 3
 
1.3%
174.0 3
 
1.3%
112.0 3
 
1.3%
191.93 3
 
1.3%
82.56 2
 
0.9%
Other values (136) 183
81.7%
ValueCountFrequency (%)
23.22 1
0.4%
25.7 1
0.4%
28.12 1
0.4%
32.38 2
0.9%
39.6 2
0.9%
42.0 1
0.4%
45.0 1
0.4%
46.64 1
0.4%
48.2 1
0.4%
48.4 1
0.4%
ValueCountFrequency (%)
1504.4 1
0.4%
1160.97 1
0.4%
461.4 2
0.9%
396.0 2
0.9%
377.9 1
0.4%
350.27 1
0.4%
320.7 1
0.4%
299.15 2
0.9%
288.2 1
0.4%
286.0 1
0.4%

행정동명
Categorical

Distinct16
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
길동
34 
성내제1동
33 
둔촌제2동
28 
천호제2동
20 
성내제3동
18 
Other values (11)
91 

Length

Max length5
Median length5
Mean length4.5267857
Min length2

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row길동
2nd row둔촌제2동
3rd row명일제2동
4th row성내제1동
5th row성내제1동

Common Values

ValueCountFrequency (%)
길동 34
15.2%
성내제1동 33
14.7%
둔촌제2동 28
12.5%
천호제2동 20
8.9%
성내제3동 18
8.0%
성내제2동 16
7.1%
명일제2동 15
6.7%
명일제1동 15
6.7%
암사제2동 12
 
5.4%
암사제1동 11
 
4.9%
Other values (6) 22
9.8%

Length

2024-05-03T20:29:07.023446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
길동 34
15.2%
성내제1동 33
14.7%
둔촌제2동 28
12.5%
천호제2동 20
8.9%
성내제3동 18
8.0%
성내제2동 16
7.1%
명일제2동 15
6.7%
명일제1동 15
6.7%
암사제2동 12
 
5.4%
암사제1동 11
 
4.9%
Other values (6) 22
9.8%

급수시설구분
Categorical

Distinct4
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
상수도전용
143 
<NA>
78 
지하수전용
 
2
상수도(음용)지하수(주방용)겸용
 
1

Length

Max length17
Median length5
Mean length4.7053571
Min length4

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 143
63.8%
<NA> 78
34.8%
지하수전용 2
 
0.9%
상수도(음용)지하수(주방용)겸용 1
 
0.4%

Length

2024-05-03T20:29:07.439138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T20:29:07.819559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 143
63.8%
na 78
34.8%
지하수전용 2
 
0.9%
상수도(음용)지하수(주방용)겸용 1
 
0.4%

Interactions

2024-05-03T20:28:50.879200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:44.525967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:45.587175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:46.767051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:48.443295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:49.640947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:51.047285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:44.690053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:45.761957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:47.024205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:48.690239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:49.814221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:51.223390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:44.867653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:45.945471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:47.294522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:48.910780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:49.965743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:51.453484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:45.036931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:46.119384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:47.633327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:49.077766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:50.141528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:51.638323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:45.204495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:46.296725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:47.897932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:49.286433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:50.355180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:51.810061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:45.377749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:46.493447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:48.167766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:49.461917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:28:50.729375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T20:29:08.196440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자업태명주된음식영업장면적(㎡)행정동명급수시설구분
지정년도1.0000.8650.9201.0000.8870.2040.5740.3740.1880.619
지정번호0.8651.0000.2780.8650.4440.0000.0000.0000.0000.811
신청일자0.9200.2781.0000.9200.7160.2340.5650.1800.3430.000
지정일자1.0000.8650.9201.0000.8870.2040.5740.3740.1880.619
취소일자0.8870.4440.7160.8871.0000.3810.8160.0390.2360.180
업태명0.2040.0000.2340.2040.3811.0000.9460.6230.4300.000
주된음식0.5740.0000.5650.5740.8160.9461.0000.0000.5010.000
영업장면적(㎡)0.3740.0000.1800.3740.0390.6230.0001.0000.3320.133
행정동명0.1880.0000.3430.1880.2360.4300.5010.3321.0000.000
급수시설구분0.6190.8110.0000.6190.1800.0000.0000.1330.0001.000
2024-05-03T20:29:09.212415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명급수시설구분업태명
행정동명1.0000.0000.175
급수시설구분0.0001.0000.000
업태명0.1750.0001.000
2024-05-03T20:29:09.639751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.0000.0250.9061.0000.873-0.0290.1030.0980.434
지정번호0.0251.0000.1220.025-0.0220.0740.0000.0000.478
신청일자0.9060.1221.0000.9060.748-0.0130.1440.1890.000
지정일자1.0000.0250.9061.0000.873-0.0290.1030.0980.434
취소일자0.873-0.0220.7480.8731.0000.0900.2010.0880.120
영업장면적(㎡)-0.0290.074-0.013-0.0290.0901.0000.3980.1710.125
업태명0.1030.0000.1440.1030.2010.3981.0000.1750.000
행정동명0.0980.0000.1890.0980.0880.1710.1751.0000.000
급수시설구분0.4340.4780.0000.4340.1200.1250.0000.0001.000

Missing values

2024-05-03T20:28:52.091356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T20:28:52.528877image/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.
2024-05-03T20:28:52.819476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시군구코드지정년도지정번호신청일자지정일자취소일자불가일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분
032400002010119201005102010063020151222<NA>뚝배기손칼국수서울특별시 강동구 천호대로 1187, (길동)서울특별시 강동구 길동 228번지 9호3240000-101-1998-09841한식칼국수80.24길동상수도전용
132400002008120200706042008063020151222<NA>동촌서울특별시 강동구 명일로13길 26, (둔촌동)서울특별시 강동구 둔촌동 54번지 6호3240000-101-2002-13890한식한정식82.5둔촌제2동상수도전용
23240000<NA><NA>20121022<NA>20161223<NA>안심한우(고덕역점)서울특별시 강동구 동남로73길 26, 지하1층 54호 (명일동)서울특별시 강동구 명일동 47번지 12호 지하1층-543240000-101-2012-00004한식<NA>213.07명일제2동<NA>
33240000<NA><NA>20070604<NA>20100630<NA>한우암소마을정육식당서울특별시 강동구 성내로6길 14-23, (성내동)서울특별시 강동구 성내동 554번지 2호3240000-101-2005-00348한식<NA>58.39성내제1동<NA>
43240000<NA><NA>20040522<NA><NA><NA>생활맥주서울특별시 강동구 성안로3길 128, (성내동)서울특별시 강동구 성내동 557번지 4호3240000-101-1995-07399호프/통닭<NA>67.4성내제1동상수도전용
53240000200792200706042007071020080630<NA>생활맥주서울특별시 강동구 성안로3길 128, (성내동)서울특별시 강동구 성내동 557번지 4호3240000-101-1995-07399호프/통닭아구찜67.4성내제1동상수도전용
63240000<NA><NA>20030526<NA><NA><NA>함경면옥서울특별시 강동구 강동대로 215, (성내동)서울특별시 강동구 성내동 448번지 26호3240000-101-1996-11213한식<NA>239.54성내제3동상수도전용
73240000200859200706042008063020151222<NA>함경면옥서울특별시 강동구 강동대로 215, (성내동)서울특별시 강동구 성내동 448번지 26호3240000-101-1996-11213한식냉면,만두,갈비탕239.54성내제3동상수도전용
8324000020121202012102220121031<NA><NA>더 파크뷰서울특별시 강동구 천호대로 1102, (성내동)서울특별시 강동구 성내동 199번지 10호3240000-101-1998-00966뷔페식부페1504.4성내제2동상수도전용
9324000020121132012102220121031<NA><NA>길조서울특별시 강동구 천호대로 1173, (길동)서울특별시 강동구 길동 236번지 8호3240000-101-1989-10414한식해물탕, 찜160.18길동<NA>
시군구코드지정년도지정번호신청일자지정일자취소일자불가일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분
214324000020151322015111120151222<NA><NA>웅골서울특별시 강동구 강동대로53길 18, 2층 (성내동)서울특별시 강동구 성내동 442번지 16호3240000-101-2015-00006한식샤브샤브214.53성내제3동<NA>
2153240000<NA><NA>20040522<NA><NA><NA>고집서울특별시 강동구 천호대로 1027, (천호동)서울특별시 강동구 천호동 454번지 15호3240000-101-2003-13952한식<NA>98.0천호제2동상수도전용
21632400002008136200706042008063020151222<NA>고집서울특별시 강동구 천호대로 1027, (천호동)서울특별시 강동구 천호동 454번지 15호3240000-101-2003-13952한식부대찌개98.0천호제2동상수도전용
217324000020151222015111120151222<NA><NA>윌리엄커피서울특별시 강동구 풍성로 92, (성내동)서울특별시 강동구 성내동 111번지 6호3240000-101-2011-00164까페샌드위치,커피168.28성내제2동<NA>
218324000020151292015111120151222<NA><NA>김선장횟집서울특별시 강동구 올림픽로98길 49, (암사동)서울특별시 강동구 암사동 495번지3240000-101-2014-00188일식회96.0암사제1동<NA>
219324000020121172012102220121031<NA><NA>마실서울특별시 강동구 동남로73길 31, 2층 (명일동)서울특별시 강동구 명일동 48번지 1호 2층3240000-101-2012-00085한식한정식320.7명일제2동<NA>
220324000020101392010051020100630<NA><NA>원할머니보쌈,족발&박가부대 암사점서울특별시 강동구 올림픽로 814, (암사동)서울특별시 강동구 암사동 463번지 1호3240000-101-2006-00188한식보쌈147.31암사제1동<NA>
2213240000200915200905082009063020231108<NA>그린로프트서울특별시 강동구 아리수로 181-19, (고덕동)서울특별시 강동구 고덕동 593번지 0호3240000-101-2000-11567기타민물장어93.0고덕제1동상수도전용
22232400002008121200706042008063020151222<NA>두레통닭 명일역점서울특별시 강동구 양재대로 1609, 조희빌딩 1층 (천호동)서울특별시 강동구 천호동 25번지 6호 조희빌딩3240000-101-2002-13922호프/통닭참치회85.95천호제1동상수도전용
22332400002007157200706042007071020080630<NA>용호관서울특별시 강동구 천호대로159길 51, 1층 (천호동)서울특별시 강동구 천호동 409번지 38호3240000-101-1990-02971중국식백반48.2천호제3동상수도전용