Overview

Dataset statistics

Number of variables15
Number of observations129
Missing cells1
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.0 KiB
Average record size in memory127.0 B

Variable types

Categorical4
Numeric5
Text6

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,주된음식,영업장면적(㎡),행정동명,급수시설구분,소재지전화번호
Author강서구
URLhttps://data.seoul.go.kr/dataList/OA-9986/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
지정년도 is highly overall correlated with 지정번호 and 2 other fieldsHigh correlation
지정번호 is highly overall correlated with 지정년도 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
영업장면적(㎡) is highly overall correlated with 급수시설구분High correlation
급수시설구분 is highly overall correlated with 영업장면적(㎡)High correlation
급수시설구분 is highly imbalanced (71.1%)Imbalance
업소명 has unique valuesUnique
허가(신고)번호 has unique valuesUnique

Reproduction

Analysis started2024-05-11 07:05:53.761522
Analysis finished2024-05-11 07:05:57.699880
Duration3.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
3150000
129 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3150000
2nd row3150000
3rd row3150000
4th row3150000
5th row3150000

Common Values

ValueCountFrequency (%)
3150000 129
100.0%

Length

2024-05-11T16:05:57.790281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T16:05:57.895763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3150000 129
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.3178
Minimum2002
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T16:05:57.998258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2002
5-th percentile2004
Q12011
median2015
Q32018
95-th percentile2021.2
Maximum2023
Range21
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.1886061
Coefficient of variation (CV)0.0025758626
Kurtosis-0.23750304
Mean2014.3178
Median Absolute Deviation (MAD)3
Skewness-0.67233553
Sum259847
Variance26.921633
MonotonicityNot monotonic
2024-05-11T16:05:58.124726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2015 16
12.4%
2017 16
12.4%
2019 12
9.3%
2008 11
8.5%
2018 11
8.5%
2020 9
 
7.0%
2013 8
 
6.2%
2009 8
 
6.2%
2016 7
 
5.4%
2002 5
 
3.9%
Other values (10) 26
20.2%
ValueCountFrequency (%)
2002 5
3.9%
2003 1
 
0.8%
2004 2
 
1.6%
2005 2
 
1.6%
2007 2
 
1.6%
2008 11
8.5%
2009 8
6.2%
2010 1
 
0.8%
2011 2
 
1.6%
2012 4
 
3.1%
ValueCountFrequency (%)
2023 4
 
3.1%
2022 3
 
2.3%
2020 9
7.0%
2019 12
9.3%
2018 11
8.5%
2017 16
12.4%
2016 7
5.4%
2015 16
12.4%
2014 5
 
3.9%
2013 8
6.2%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.837209
Minimum1
Maximum367
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T16:05:58.267492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median13
Q330
95-th percentile268.8
Maximum367
Range366
Interquartile range (IQR)25

Descriptive statistics

Standard deviation87.780603
Coefficient of variation (CV)1.8741638
Kurtosis5.5207139
Mean46.837209
Median Absolute Deviation (MAD)10
Skewness2.5698215
Sum6042
Variance7705.4342
MonotonicityNot monotonic
2024-05-11T16:05:58.445711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 9
 
7.0%
1 8
 
6.2%
2 7
 
5.4%
7 6
 
4.7%
4 6
 
4.7%
6 6
 
4.7%
8 5
 
3.9%
5 5
 
3.9%
9 4
 
3.1%
14 4
 
3.1%
Other values (46) 69
53.5%
ValueCountFrequency (%)
1 8
6.2%
2 7
5.4%
3 9
7.0%
4 6
4.7%
5 5
3.9%
6 6
4.7%
7 6
4.7%
8 5
3.9%
9 4
3.1%
10 1
 
0.8%
ValueCountFrequency (%)
367 1
0.8%
363 1
0.8%
354 1
0.8%
345 1
0.8%
334 1
0.8%
324 1
0.8%
272 1
0.8%
264 1
0.8%
261 1
0.8%
257 1
0.8%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20141964
Minimum20020315
Maximum20231024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T16:05:58.624641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20020315
5-th percentile20028429
Q120100604
median20151118
Q320181010
95-th percentile20213058
Maximum20231024
Range210709
Interquartile range (IQR)80406

Descriptive statistics

Standard deviation55244.652
Coefficient of variation (CV)0.0027427639
Kurtosis-0.37621217
Mean20141964
Median Absolute Deviation (MAD)30496
Skewness-0.71170533
Sum2.5983133 × 109
Variance3.0519715 × 109
MonotonicityNot monotonic
2024-05-11T16:05:58.771023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20151118 16
12.4%
20171124 16
12.4%
20191017 12
 
9.3%
20181010 11
 
8.5%
20131201 8
 
6.2%
20020315 7
 
5.4%
20161103 7
 
5.4%
20050622 6
 
4.7%
20141104 5
 
3.9%
20090805 5
 
3.9%
Other values (20) 36
27.9%
ValueCountFrequency (%)
20020315 7
5.4%
20040601 3
2.3%
20050622 6
4.7%
20070608 3
2.3%
20070628 1
 
0.8%
20080603 1
 
0.8%
20080801 2
 
1.6%
20090727 4
3.1%
20090805 5
3.9%
20100604 1
 
0.8%
ValueCountFrequency (%)
20231024 1
 
0.8%
20231020 1
 
0.8%
20231018 1
 
0.8%
20230817 1
 
0.8%
20221017 1
 
0.8%
20221012 2
 
1.6%
20201127 1
 
0.8%
20201126 2
 
1.6%
20201125 5
3.9%
20201027 1
 
0.8%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20144212
Minimum20020412
Maximum20231212
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T16:05:58.903989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20020412
5-th percentile20040705
Q120111031
median20151118
Q320181210
95-th percentile20213216
Maximum20231212
Range210800
Interquartile range (IQR)70179

Descriptive statistics

Standard deviation52087.303
Coefficient of variation (CV)0.0025857205
Kurtosis-0.23749725
Mean20144212
Median Absolute Deviation (MAD)30496
Skewness-0.67482974
Sum2.5986033 × 109
Variance2.7130871 × 109
MonotonicityNot monotonic
2024-05-11T16:05:59.027367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
20151118 16
12.4%
20171124 16
12.4%
20191203 12
9.3%
20080709 11
 
8.5%
20181210 11
 
8.5%
20201228 9
 
7.0%
20131202 8
 
6.2%
20161103 7
 
5.4%
20020412 5
 
3.9%
20090805 5
 
3.9%
Other values (13) 29
22.5%
ValueCountFrequency (%)
20020412 5
3.9%
20031212 1
 
0.8%
20040705 2
 
1.6%
20050712 2
 
1.6%
20070628 2
 
1.6%
20080709 11
8.5%
20090727 3
 
2.3%
20090805 5
3.9%
20100803 1
 
0.8%
20111031 2
 
1.6%
ValueCountFrequency (%)
20231212 4
 
3.1%
20221208 3
 
2.3%
20201228 9
7.0%
20191203 12
9.3%
20181210 11
8.5%
20171124 16
12.4%
20161103 7
5.4%
20151118 16
12.4%
20141216 5
 
3.9%
20131202 8
6.2%

업소명
Text

UNIQUE 

Distinct129
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-05-11T16:05:59.324006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length19
Mean length8.8914729
Min length2

Characters and Unicode

Total characters1147
Distinct characters334
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique129 ?
Unique (%)100.0%

Sample

1st row어다리횟집 발산점
2nd rowT.G.I.F 롯데백화점 김포공항점
3rd row양촌리
4th row이조갈비
5th row능라도
ValueCountFrequency (%)
김포공항점 8
 
3.7%
발산점 7
 
3.3%
주식회사 6
 
2.8%
강서점 4
 
1.9%
마곡점 4
 
1.9%
이대서울병원 4
 
1.9%
등촌점 3
 
1.4%
푸드엠파이어 3
 
1.4%
롯데몰 3
 
1.4%
애슐리퀸즈 2
 
0.9%
Other values (168) 170
79.4%
2024-05-11T16:06:00.136096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
85
 
7.4%
점 49
 
4.3%
이 31
 
2.7%
( 19
 
1.7%
) 19
 
1.7%
서 17
 
1.5%
주 16
 
1.4%
리 16
 
1.4%
포 15
 
1.3%
강 14
 
1.2%
Other values (324) 866
75.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 937
81.7%
Space Separator 85
 
7.4%
Uppercase Letter 38
 
3.3%
Lowercase Letter 31
 
2.7%
Open Punctuation 19
 
1.7%
Close Punctuation 19
 
1.7%
Decimal Number 13
 
1.1%
Other Punctuation 5
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 49
 
5.2%
이 31
 
3.3%
서 17
 
1.8%
주 16
 
1.7%
리 16
 
1.7%
포 15
 
1.6%
강 14
 
1.5%
산 14
 
1.5%
김 14
 
1.5%
공 13
 
1.4%
Other values (282) 738
78.8%
Uppercase Letter
ValueCountFrequency (%)
C 7
18.4%
E 4
10.5%
N 4
10.5%
L 3
 
7.9%
K 2
 
5.3%
A 2
 
5.3%
G 2
 
5.3%
I 2
 
5.3%
T 2
 
5.3%
F 2
 
5.3%
Other values (8) 8
21.1%
Lowercase Letter
ValueCountFrequency (%)
e 6
19.4%
a 4
12.9%
t 4
12.9%
r 3
9.7%
m 2
 
6.5%
n 2
 
6.5%
i 2
 
6.5%
c 2
 
6.5%
h 2
 
6.5%
s 1
 
3.2%
Other values (3) 3
9.7%
Decimal Number
ValueCountFrequency (%)
9 4
30.8%
7 3
23.1%
0 2
15.4%
1 2
15.4%
5 1
 
7.7%
6 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
. 3
60.0%
, 2
40.0%
Space Separator
ValueCountFrequency (%)
85
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 932
81.3%
Common 141
 
12.3%
Latin 69
 
6.0%
Han 5
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 49
 
5.3%
이 31
 
3.3%
서 17
 
1.8%
주 16
 
1.7%
리 16
 
1.7%
포 15
 
1.6%
강 14
 
1.5%
산 14
 
1.5%
김 14
 
1.5%
공 13
 
1.4%
Other values (277) 733
78.6%
Latin
ValueCountFrequency (%)
C 7
 
10.1%
e 6
 
8.7%
E 4
 
5.8%
a 4
 
5.8%
t 4
 
5.8%
N 4
 
5.8%
r 3
 
4.3%
L 3
 
4.3%
m 2
 
2.9%
n 2
 
2.9%
Other values (21) 30
43.5%
Common
ValueCountFrequency (%)
85
60.3%
( 19
 
13.5%
) 19
 
13.5%
9 4
 
2.8%
. 3
 
2.1%
7 3
 
2.1%
, 2
 
1.4%
0 2
 
1.4%
1 2
 
1.4%
5 1
 
0.7%
Han
ValueCountFrequency (%)
樂 1
20.0%
源 1
20.0%
軒 1
20.0%
萊 1
20.0%
蓬 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 932
81.3%
ASCII 210
 
18.3%
CJK 4
 
0.3%
CJK Compat Ideographs 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
85
40.5%
( 19
 
9.0%
) 19
 
9.0%
C 7
 
3.3%
e 6
 
2.9%
E 4
 
1.9%
9 4
 
1.9%
a 4
 
1.9%
t 4
 
1.9%
N 4
 
1.9%
Other values (32) 54
25.7%
Hangul
ValueCountFrequency (%)
점 49
 
5.3%
이 31
 
3.3%
서 17
 
1.8%
주 16
 
1.7%
리 16
 
1.7%
포 15
 
1.6%
강 14
 
1.5%
산 14
 
1.5%
김 14
 
1.5%
공 13
 
1.4%
Other values (277) 733
78.6%
CJK Compat Ideographs
ValueCountFrequency (%)
樂 1
100.0%
CJK
ValueCountFrequency (%)
源 1
25.0%
軒 1
25.0%
萊 1
25.0%
蓬 1
25.0%

소재지도로명
Text

Distinct117
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-05-11T16:06:00.436102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length83
Median length48
Mean length39.162791
Min length24

Characters and Unicode

Total characters5052
Distinct characters181
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

Unique109 ?
Unique (%)84.5%

Sample

1st row서울특별시 강서구 강서로54길 14, 3층 (등촌동, 3동 윤강빌딩)
2nd row서울특별시 강서구 하늘길 38, 지하 2층 (방화동, 2동 김포공항 스카이파크)
3rd row서울특별시 강서구 화곡로 109, 1~2층 (화곡동, 3동)
4th row서울특별시 강서구 양천로 353, 1층 (가양동, 1동)
5th row서울특별시 강서구 마곡동로 56, 건와빌딩 2층 202,203호 (마곡동)
ValueCountFrequency (%)
서울특별시 129
 
12.6%
강서구 129
 
12.6%
1층 48
 
4.7%
2층 34
 
3.3%
1동 31
 
3.0%
마곡동 29
 
2.8%
방화동 27
 
2.6%
등촌동 25
 
2.4%
공항대로 25
 
2.4%
화곡동 23
 
2.3%
Other values (239) 521
51.0%
2024-05-11T16:06:00.861496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
892
 
17.7%
서 294
 
5.8%
, 237
 
4.7%
동 229
 
4.5%
1 184
 
3.6%
강 162
 
3.2%
시 136
 
2.7%
울 134
 
2.7%
2 131
 
2.6%
( 129
 
2.6%
Other values (171) 2524
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2880
57.0%
Space Separator 892
 
17.7%
Decimal Number 738
 
14.6%
Other Punctuation 237
 
4.7%
Open Punctuation 129
 
2.6%
Close Punctuation 129
 
2.6%
Math Symbol 23
 
0.5%
Uppercase Letter 14
 
0.3%
Dash Punctuation 10
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 294
 
10.2%
동 229
 
8.0%
강 162
 
5.6%
시 136
 
4.7%
울 134
 
4.7%
구 129
 
4.5%
별 129
 
4.5%
특 129
 
4.5%
층 128
 
4.4%
로 110
 
3.8%
Other values (148) 1300
45.1%
Decimal Number
ValueCountFrequency (%)
1 184
24.9%
2 131
17.8%
3 94
12.7%
0 69
 
9.3%
4 54
 
7.3%
5 54
 
7.3%
6 53
 
7.2%
8 39
 
5.3%
9 38
 
5.1%
7 22
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
A 4
28.6%
F 2
14.3%
B 2
14.3%
C 2
14.3%
N 2
14.3%
G 1
 
7.1%
M 1
 
7.1%
Space Separator
ValueCountFrequency (%)
892
100.0%
Other Punctuation
ValueCountFrequency (%)
, 237
100.0%
Open Punctuation
ValueCountFrequency (%)
( 129
100.0%
Close Punctuation
ValueCountFrequency (%)
) 129
100.0%
Math Symbol
ValueCountFrequency (%)
~ 23
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2880
57.0%
Common 2158
42.7%
Latin 14
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 294
 
10.2%
동 229
 
8.0%
강 162
 
5.6%
시 136
 
4.7%
울 134
 
4.7%
구 129
 
4.5%
별 129
 
4.5%
특 129
 
4.5%
층 128
 
4.4%
로 110
 
3.8%
Other values (148) 1300
45.1%
Common
ValueCountFrequency (%)
892
41.3%
, 237
 
11.0%
1 184
 
8.5%
2 131
 
6.1%
( 129
 
6.0%
) 129
 
6.0%
3 94
 
4.4%
0 69
 
3.2%
4 54
 
2.5%
5 54
 
2.5%
Other values (6) 185
 
8.6%
Latin
ValueCountFrequency (%)
A 4
28.6%
F 2
14.3%
B 2
14.3%
C 2
14.3%
N 2
14.3%
G 1
 
7.1%
M 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2880
57.0%
ASCII 2172
43.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
892
41.1%
, 237
 
10.9%
1 184
 
8.5%
2 131
 
6.0%
( 129
 
5.9%
) 129
 
5.9%
3 94
 
4.3%
0 69
 
3.2%
4 54
 
2.5%
5 54
 
2.5%
Other values (13) 199
 
9.2%
Hangul
ValueCountFrequency (%)
서 294
 
10.2%
동 229
 
8.0%
강 162
 
5.6%
시 136
 
4.7%
울 134
 
4.7%
구 129
 
4.5%
별 129
 
4.5%
특 129
 
4.5%
층 128
 
4.4%
로 110
 
3.8%
Other values (148) 1300
45.1%

소재지지번
Text

Distinct118
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-05-11T16:06:01.156646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length49
Mean length39.131783
Min length24

Characters and Unicode

Total characters5048
Distinct characters169
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

Unique111 ?
Unique (%)86.0%

Sample

1st row서울특별시 강서구 등촌동 673번지 3호 윤강빌딩 (지상 3층)
2nd row서울특별시 강서구 방화동 886번지 0호 외 6필지 김포공항 스카이파크 (지하 2층)
3rd row서울특별시 강서구 화곡동 1069번지 3호 (지상 1층~2층)
4th row서울특별시 강서구 가양동 138번지 9호 외 2필지 (지상 1층)
5th row서울특별시 강서구 마곡동 797번지 6호 건와빌딩 (지상 2층)-202,203
ValueCountFrequency (%)
서울특별시 129
 
13.0%
강서구 129
 
13.0%
지상 80
 
8.1%
1층 43
 
4.3%
마곡동 30
 
3.0%
방화동 27
 
2.7%
등촌동 25
 
2.5%
화곡동 23
 
2.3%
0호 22
 
2.2%
2층 18
 
1.8%
Other values (219) 463
46.8%
2024-05-11T16:06:01.609552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1117
22.1%
서 274
 
5.4%
지 255
 
5.1%
1 191
 
3.8%
강 143
 
2.8%
시 136
 
2.7%
울 134
 
2.7%
동 134
 
2.7%
특 129
 
2.6%
별 129
 
2.6%
Other values (159) 2406
47.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2808
55.6%
Space Separator 1117
 
22.1%
Decimal Number 838
 
16.6%
Open Punctuation 100
 
2.0%
Close Punctuation 100
 
2.0%
Dash Punctuation 26
 
0.5%
Math Symbol 23
 
0.5%
Other Punctuation 22
 
0.4%
Uppercase Letter 14
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 274
 
9.8%
지 255
 
9.1%
강 143
 
5.1%
시 136
 
4.8%
울 134
 
4.8%
동 134
 
4.8%
특 129
 
4.6%
별 129
 
4.6%
구 129
 
4.6%
번 129
 
4.6%
Other values (136) 1216
43.3%
Decimal Number
ValueCountFrequency (%)
1 191
22.8%
2 113
13.5%
0 95
11.3%
6 93
11.1%
8 68
 
8.1%
7 68
 
8.1%
3 65
 
7.8%
4 61
 
7.3%
9 55
 
6.6%
5 29
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
A 4
28.6%
F 2
14.3%
B 2
14.3%
C 2
14.3%
N 2
14.3%
M 1
 
7.1%
G 1
 
7.1%
Space Separator
ValueCountFrequency (%)
1117
100.0%
Open Punctuation
ValueCountFrequency (%)
( 100
100.0%
Close Punctuation
ValueCountFrequency (%)
) 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%
Math Symbol
ValueCountFrequency (%)
~ 23
100.0%
Other Punctuation
ValueCountFrequency (%)
, 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2808
55.6%
Common 2226
44.1%
Latin 14
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 274
 
9.8%
지 255
 
9.1%
강 143
 
5.1%
시 136
 
4.8%
울 134
 
4.8%
동 134
 
4.8%
특 129
 
4.6%
별 129
 
4.6%
구 129
 
4.6%
번 129
 
4.6%
Other values (136) 1216
43.3%
Common
ValueCountFrequency (%)
1117
50.2%
1 191
 
8.6%
2 113
 
5.1%
( 100
 
4.5%
) 100
 
4.5%
0 95
 
4.3%
6 93
 
4.2%
8 68
 
3.1%
7 68
 
3.1%
3 65
 
2.9%
Other values (6) 216
 
9.7%
Latin
ValueCountFrequency (%)
A 4
28.6%
F 2
14.3%
B 2
14.3%
C 2
14.3%
N 2
14.3%
M 1
 
7.1%
G 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2808
55.6%
ASCII 2240
44.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1117
49.9%
1 191
 
8.5%
2 113
 
5.0%
( 100
 
4.5%
) 100
 
4.5%
0 95
 
4.2%
6 93
 
4.2%
8 68
 
3.0%
7 68
 
3.0%
3 65
 
2.9%
Other values (13) 230
 
10.3%
Hangul
ValueCountFrequency (%)
서 274
 
9.8%
지 255
 
9.1%
강 143
 
5.1%
시 136
 
4.8%
울 134
 
4.8%
동 134
 
4.8%
특 129
 
4.6%
별 129
 
4.6%
구 129
 
4.6%
번 129
 
4.6%
Other values (136) 1216
43.3%

허가(신고)번호
Text

UNIQUE 

Distinct129
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-05-11T16:06:01.881694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2838
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

Unique129 ?
Unique (%)100.0%

Sample

1st row3150000-101-2014-00430
2nd row3150000-101-2011-00391
3rd row3150000-101-1997-04510
4th row3150000-101-1996-04864
5th row3150000-101-2018-00236
ValueCountFrequency (%)
3150000-101-2014-00430 1
 
0.8%
3150000-101-2020-00141 1
 
0.8%
3150000-101-2017-00227 1
 
0.8%
3150000-101-2014-00010 1
 
0.8%
3150000-101-2014-00062 1
 
0.8%
3150000-101-2005-00181 1
 
0.8%
3150000-101-2018-00077 1
 
0.8%
3150000-101-2013-00241 1
 
0.8%
3150000-101-2013-00240 1
 
0.8%
3150000-101-2016-00233 1
 
0.8%
Other values (119) 119
92.2%
2024-05-11T16:06:02.296742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1049
37.0%
1 558
19.7%
- 387
 
13.6%
5 178
 
6.3%
3 175
 
6.2%
2 170
 
6.0%
9 85
 
3.0%
4 82
 
2.9%
7 61
 
2.1%
8 51
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2451
86.4%
Dash Punctuation 387
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1049
42.8%
1 558
22.8%
5 178
 
7.3%
3 175
 
7.1%
2 170
 
6.9%
9 85
 
3.5%
4 82
 
3.3%
7 61
 
2.5%
8 51
 
2.1%
6 42
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 387
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2838
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1049
37.0%
1 558
19.7%
- 387
 
13.6%
5 178
 
6.3%
3 175
 
6.2%
2 170
 
6.0%
9 85
 
3.0%
4 82
 
2.9%
7 61
 
2.1%
8 51
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2838
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1049
37.0%
1 558
19.7%
- 387
 
13.6%
5 178
 
6.3%
3 175
 
6.2%
2 170
 
6.0%
9 85
 
3.0%
4 82
 
2.9%
7 61
 
2.1%
8 51
 
1.8%

업태명
Categorical

Distinct10
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
한식
86 
경양식
12 
일식
11 
중국식
 
7
뷔페식
 
5
Other values (5)
 
8

Length

Max length15
Median length2
Mean length2.6744186
Min length2

Unique

Unique4 ?
Unique (%)3.1%

Sample

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

Common Values

ValueCountFrequency (%)
한식 86
66.7%
경양식 12
 
9.3%
일식 11
 
8.5%
중국식 7
 
5.4%
뷔페식 5
 
3.9%
외국음식전문점(인도,태국등) 4
 
3.1%
통닭(치킨) 1
 
0.8%
복어취급 1
 
0.8%
기타 1
 
0.8%
패밀리레스트랑 1
 
0.8%

Length

2024-05-11T16:06:02.465507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T16:06:02.611090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 86
66.7%
경양식 12
 
9.3%
일식 11
 
8.5%
중국식 7
 
5.4%
뷔페식 5
 
3.9%
외국음식전문점(인도,태국등 4
 
3.1%
통닭(치킨 1
 
0.8%
복어취급 1
 
0.8%
기타 1
 
0.8%
패밀리레스트랑 1
 
0.8%

주된음식
Text

Distinct88
Distinct (%)68.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-05-11T16:06:02.913867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length4.0387597
Min length2

Characters and Unicode

Total characters521
Distinct characters147
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67 ?
Unique (%)51.9%

Sample

1st row모듬회
2nd row스테이크
3rd row돼지갈비, 삼겹살
4th row갈비탕
5th row평양냉면
ValueCountFrequency (%)
뷔페 8
 
5.4%
삼겹살 7
 
4.7%
칼국수 7
 
4.7%
샤브샤브 6
 
4.0%
파스타 6
 
4.0%
스테이크 4
 
2.7%
아구찜 3
 
2.0%
갈비탕 3
 
2.0%
피자 3
 
2.0%
돼지갈비 3
 
2.0%
Other values (81) 99
66.4%
2024-05-11T16:06:03.418723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20
 
3.8%
탕 18
 
3.5%
, 16
 
3.1%
갈 15
 
2.9%
비 15
 
2.9%
수 13
 
2.5%
스 13
 
2.5%
찜 12
 
2.3%
샤 12
 
2.3%
브 12
 
2.3%
Other values (137) 375
72.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 484
92.9%
Space Separator 20
 
3.8%
Other Punctuation 17
 
3.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 18
 
3.7%
갈 15
 
3.1%
비 15
 
3.1%
수 13
 
2.7%
스 13
 
2.7%
찜 12
 
2.5%
샤 12
 
2.5%
브 12
 
2.5%
삼 11
 
2.3%
국 11
 
2.3%
Other values (134) 352
72.7%
Other Punctuation
ValueCountFrequency (%)
, 16
94.1%
？ 1
 
5.9%
Space Separator
ValueCountFrequency (%)
20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 484
92.9%
Common 37
 
7.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 18
 
3.7%
갈 15
 
3.1%
비 15
 
3.1%
수 13
 
2.7%
스 13
 
2.7%
찜 12
 
2.5%
샤 12
 
2.5%
브 12
 
2.5%
삼 11
 
2.3%
국 11
 
2.3%
Other values (134) 352
72.7%
Common
ValueCountFrequency (%)
20
54.1%
, 16
43.2%
？ 1
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 484
92.9%
ASCII 36
 
6.9%
None 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20
55.6%
, 16
44.4%
Hangul
ValueCountFrequency (%)
탕 18
 
3.7%
갈 15
 
3.1%
비 15
 
3.1%
수 13
 
2.7%
스 13
 
2.7%
찜 12
 
2.5%
샤 12
 
2.5%
브 12
 
2.5%
삼 11
 
2.3%
국 11
 
2.3%
Other values (134) 352
72.7%
None
ValueCountFrequency (%)
？ 1
100.0%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct126
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean349.33752
Minimum30
Maximum3649.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T16:06:03.588580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile91.828
Q1153.95
median244.9
Q3359.55
95-th percentile821.364
Maximum3649.97
Range3619.97
Interquartile range (IQR)205.6

Descriptive statistics

Standard deviation431.99079
Coefficient of variation (CV)1.2366
Kurtosis34.540896
Mean349.33752
Median Absolute Deviation (MAD)103.78
Skewness5.2619992
Sum45064.54
Variance186616.04
MonotonicityNot monotonic
2024-05-11T16:06:03.746390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138.73 2
 
1.6%
729.26 2
 
1.6%
348.43 2
 
1.6%
285.9 1
 
0.8%
291.89 1
 
0.8%
200.0 1
 
0.8%
288.0 1
 
0.8%
570.0 1
 
0.8%
196.29 1
 
0.8%
227.7 1
 
0.8%
Other values (116) 116
89.9%
ValueCountFrequency (%)
30.0 1
0.8%
49.5 1
0.8%
52.31 1
0.8%
75.86 1
0.8%
76.61 1
0.8%
89.64 1
0.8%
91.2 1
0.8%
92.77 1
0.8%
92.83 1
0.8%
95.37 1
0.8%
ValueCountFrequency (%)
3649.97 1
0.8%
2855.92 1
0.8%
1298.52 1
0.8%
1146.19 1
0.8%
964.75 1
0.8%
876.25 1
0.8%
823.0 1
0.8%
818.91 1
0.8%
818.8 1
0.8%
754.05 1
0.8%

행정동명
Categorical

Distinct15
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
가양제1동
30 
방화제2동
21 
등촌제3동
16 
발산제1동
12 
화곡제6동
11 
Other values (10)
39 

Length

Max length5
Median length5
Mean length4.8139535
Min length3

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row등촌제3동
2nd row방화제2동
3rd row화곡제3동
4th row가양제1동
5th row가양제1동

Common Values

ValueCountFrequency (%)
가양제1동 30
23.3%
방화제2동 21
16.3%
등촌제3동 16
12.4%
발산제1동 12
 
9.3%
화곡제6동 11
 
8.5%
우장산동 8
 
6.2%
등촌제1동 8
 
6.2%
공항동 5
 
3.9%
화곡제3동 3
 
2.3%
방화제3동 3
 
2.3%
Other values (5) 12
 
9.3%

Length

2024-05-11T16:06:03.938694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가양제1동 30
23.3%
방화제2동 21
16.3%
등촌제3동 16
12.4%
발산제1동 12
 
9.3%
화곡제6동 11
 
8.5%
우장산동 8
 
6.2%
등촌제1동 8
 
6.2%
공항동 5
 
3.9%
화곡제3동 3
 
2.3%
방화제3동 3
 
2.3%
Other values (5) 12
 
9.3%

급수시설구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
상수도전용
118 
<NA>
 
10
지하수전용
 
1

Length

Max length5
Median length5
Mean length4.9224806
Min length4

Unique

Unique1 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 118
91.5%
<NA> 10
 
7.8%
지하수전용 1
 
0.8%

Length

2024-05-11T16:06:04.110179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T16:06:04.223000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 118
91.5%
na 10
 
7.8%
지하수전용 1
 
0.8%

소재지전화번호
Text

Distinct125
Distinct (%)97.7%
Missing1
Missing (%)0.8%
Memory size1.1 KiB
2024-05-11T16:06:04.488651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.03125
Min length10

Characters and Unicode

Total characters1284
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

Unique123 ?
Unique (%)96.1%

Sample

1st row0236640015
2nd row0261165711
3rd row0226988592
4th row0236613457
5th row0269588939
ValueCountFrequency (%)
0221352465 3
 
2.3%
0236636639 2
 
1.6%
0226679860 1
 
0.8%
0220847000 1
 
0.8%
0236640015 1
 
0.8%
0261165832 1
 
0.8%
07049479859 1
 
0.8%
0226993592 1
 
0.8%
0226669386 1
 
0.8%
0226684114 1
 
0.8%
Other values (116) 116
89.9%
2024-05-11T16:06:04.997892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 258
20.1%
6 251
19.5%
0 212
16.5%
3 110
8.6%
9 101
 
7.9%
5 98
 
7.6%
1 85
 
6.6%
8 64
 
5.0%
7 62
 
4.8%
4 41
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1282
99.8%
Space Separator 2
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 258
20.1%
6 251
19.6%
0 212
16.5%
3 110
8.6%
9 101
 
7.9%
5 98
 
7.6%
1 85
 
6.6%
8 64
 
5.0%
7 62
 
4.8%
4 41
 
3.2%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1284
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 258
20.1%
6 251
19.5%
0 212
16.5%
3 110
8.6%
9 101
 
7.9%
5 98
 
7.6%
1 85
 
6.6%
8 64
 
5.0%
7 62
 
4.8%
4 41
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1284
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 258
20.1%
6 251
19.5%
0 212
16.5%
3 110
8.6%
9 101
 
7.9%
5 98
 
7.6%
1 85
 
6.6%
8 64
 
5.0%
7 62
 
4.8%
4 41
 
3.2%

Interactions

2024-05-11T16:05:56.695456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:05:54.644003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:05:55.138083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:05:55.718422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:05:56.208994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:05:56.814782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:05:54.728457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:05:55.253850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:05:55.805053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:05:56.293158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:05:56.951381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:05:54.842179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:05:55.373134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:05:55.896377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:05:56.392177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:05:57.058197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:05:54.937913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:05:55.490787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:05:55.993210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:05:56.485765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:05:57.158131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:05:55.026976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:05:55.593311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:05:56.091826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:05:56.588897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T16:06:05.130205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자업태명주된음식영업장면적(㎡)행정동명급수시설구분
지정년도1.0000.7500.9971.0000.0000.5820.0000.6980.000
지정번호0.7501.0000.7750.7500.0000.8670.0000.6680.332
신청일자0.9970.7751.0000.9970.2410.2160.0000.642NaN
지정일자1.0000.7500.9971.0000.0000.5820.0000.6980.000
업태명0.0000.0000.2410.0001.0000.9560.5160.0000.176
주된음식0.5820.8670.2160.5820.9561.0000.0000.3580.000
영업장면적(㎡)0.0000.0000.0000.0000.5160.0001.0000.0000.877
행정동명0.6980.6680.6420.6980.0000.3580.0001.0000.000
급수시설구분0.0000.332NaN0.0000.1760.0000.8770.0001.000
2024-05-11T16:06:05.290413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
급수시설구분업태명행정동명
급수시설구분1.0000.1270.000
업태명0.1271.0000.000
행정동명0.0000.0001.000
2024-05-11T16:06:05.409134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.000-0.6190.9971.000-0.0590.0000.3230.000
지정번호-0.6191.000-0.613-0.6190.0530.0000.3110.245
신청일자0.997-0.6131.0000.997-0.0610.0940.2770.127
지정일자1.000-0.6190.9971.000-0.0580.0000.3230.000
영업장면적(㎡)-0.0590.053-0.061-0.0581.0000.2970.0000.676
업태명0.0000.0000.0940.0000.2971.0000.0000.127
행정동명0.3230.3110.2770.3230.0000.0001.0000.000
급수시설구분0.0000.2450.1270.0000.6760.1270.0001.000

Missing values

2024-05-11T16:05:57.362860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T16:05:57.589182image/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
031500002015232015111820151118어다리횟집 발산점서울특별시 강서구 강서로54길 14, 3층 (등촌동, 3동 윤강빌딩)서울특별시 강서구 등촌동 673번지 3호 윤강빌딩 (지상 3층)3150000-101-2014-00430일식모듬회285.9등촌제3동상수도전용0236640015
13150000201562015111820151118T.G.I.F 롯데백화점 김포공항점서울특별시 강서구 하늘길 38, 지하 2층 (방화동, 2동 김포공항 스카이파크)서울특별시 강서구 방화동 886번지 0호 외 6필지 김포공항 스카이파크 (지하 2층)3150000-101-2011-00391경양식스테이크583.87방화제2동상수도전용0261165711
2315000020021212002031520020412양촌리서울특별시 강서구 화곡로 109, 1~2층 (화곡동, 3동)서울특별시 강서구 화곡동 1069번지 3호 (지상 1층~2층)3150000-101-1997-04510한식돼지갈비, 삼겹살456.71화곡제3동상수도전용0226988592
3315000020031982002031520031212이조갈비서울특별시 강서구 양천로 353, 1층 (가양동, 1동)서울특별시 강서구 가양동 138번지 9호 외 2필지 (지상 1층)3150000-101-1996-04864한식갈비탕266.45가양제1동상수도전용0236613457
43150000201892018101020181210능라도서울특별시 강서구 마곡동로 56, 건와빌딩 2층 202,203호 (마곡동)서울특별시 강서구 마곡동 797번지 6호 건와빌딩 (지상 2층)-202,2033150000-101-2018-00236한식평양냉면536.0가양제1동상수도전용0269588939
5315000020042722004060120040705신고집해물찜서울특별시 강서구 강서로 188, 1층 (화곡동, 우장산동)서울특별시 강서구 화곡동 1052번지 0호 외 1필지 (지상 1층)3150000-101-2003-00593한식해물찜158.54우장산동상수도전용0226683359
631500002013292013120120131202명동가츠라서울특별시 강서구 하늘길 38, 지하 2층 (방화동, 2동 김포공항 스카이파크)서울특별시 강서구 방화동 886번지 0호 외 6필지 김포공항 스카이파크 (지하 2층)3150000-101-2011-00415일식우동165.75방화제2동상수도전용0261165539
7315000020022642002031520020412마산아구찜서울특별시 강서구 금낭화로 135, 3층 (방화동, 3동)서울특별시 강서구 방화동 829번지 4호 (지상 3층)3150000-101-1997-04441한식대구탕102.86방화제3동상수도전용0226651527
83150000201052010060420100803망향비빔국수서울특별시 강서구 양천로 720, 가동 1층 (염창동, 유동빌딩)서울특별시 강서구 염창동 263번지 8호 가 유동빌딩 (지상 1층)3150000-101-2009-00199한식비빔국수264.0염창동상수도전용0226596599
931500002015302015111820151118맛남굼터서울특별시 강서구 강서로45가길 10, 2층 (화곡동, 3동)서울특별시 강서구 화곡동 1007번지 4호 (지상2층)3150000-101-2015-00136한식숙성삼겹살174.96화곡제3동<NA>0226996692
시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
1193150000201452014110420141216현대옥 강서점서울특별시 강서구 화곡로 346, 1층 101~102호 (화곡동, 6동 귀뚜라미홈시스템)서울특별시 강서구 화곡동 1111번지 2호 귀뚜라미홈시스템 (지상 1층) 101~102호3150000-101-2014-00040한식전주비빔밥92.83화곡제6동상수도전용0226020069
12031500002016112016110320161103으뜸 착한낙지 등촌점서울특별시 강서구 공항대로 353, 2층 (등촌동, 3동)서울특별시 강서구 등촌동 661번지 2호 (지상 2층)3150000-101-2016-00275한식낙지덮밥244.9등촌제3동상수도전용0236655333
12131500002015192015111820151118낙원(樂源)서울특별시 강서구 방화대로 94, 1층 (외발산동, 1동 메이필드호텔)서울특별시 강서구 외발산동 426번지 1호 메이필드호텔 (지상 1층)3150000-101-1983-06229한식갈비614.69발산제1동상수도전용0226609010
12231500002019112019101720191203채선당 월남쌈 샤브(마곡점)서울특별시 강서구 마곡동로 56, 건와빌딩 2층 201호 (마곡동)서울특별시 강서구 마곡동 797번지 6호 건와빌딩 2층-2013150000-101-2019-00146한식샤브샤브288.26가양제1동상수도전용0226580701
12331500002017102017112420171124청록미나리식당 마곡점서울특별시 강서구 마곡동로 55, 2층 202호 (마곡동, 가양 1동 마커스빌딩)서울특별시 강서구 마곡동 774번지 12호 마커스빌딩 (지상 2층)-2023150000-101-2017-00481한식샤브샤브234.61가양제1동상수도전용0269084900
1243150000201862018101020181210육시리 마곡본점서울특별시 강서구 마곡중앙6로 40, 장흥빌딩 1층 102, 103호 (마곡동)서울특별시 강서구 마곡동 774번지 8호 장흥빌딩3150000-101-2017-00739한식육류249.47가양제1동<NA>0236629797
125315000020042572004060120040705주식회사 명가원설농탕서울특별시 강서구 강서로 195, 1층 (화곡동, 3동)서울특별시 강서구 화곡동 1032번지 19호 외 1필지 (지상 1층)3150000-101-2003-00780한식설농탕195.0화곡제3동상수도전용0226930055
1263150000202232022101220221208미스터피자 발산점서울특별시 강서구 강서로 289, 연빌딩 1층 (내발산동)서울특별시 강서구 내발산동 701번지 9호 연빌딩, 1층3150000-101-2021-00475패밀리레스트랑포테이토 피자140.9발산제1동상수도전용0226651067
1273150000201992019101720191203(주)금고깃집 마곡본점서울특별시 강서구 마곡동로 61, 에이스프라자 1~2층 109,203~205호 (마곡동)서울특별시 강서구 마곡동 772번지 8호 에이스프라자 (지상 1~2층) 109호, 203~205호3150000-101-2019-00391한식삼겹살301.8가양제1동상수도전용0236628295
1283150000201932019101720191203익스퍼랩(EXPER LAB)서울특별시 강서구 마곡서로 152, 두산더랜드타워 B동 2층 221호 (마곡동)서울특별시 강서구 마곡동 759번지 1호 두산더랜드타워B동 221호3150000-101-2019-00397경양식스파게티97.45가양제1동상수도전용0269899388