Overview

Dataset statistics

Number of variables15
Number of observations71
Missing cells2
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.9 KiB
Average record size in memory127.9 B

Variable types

Categorical4
Numeric5
Text6

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,주된음식,영업장면적(㎡),행정동명,급수시설구분,소재지전화번호
Author양천구
URLhttps://data.seoul.go.kr/dataList/OA-10833/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
급수시설구분 is highly overall correlated with 지정년도 and 6 other fieldsHigh correlation
행정동명 is highly overall correlated with 급수시설구분High correlation
업태명 is highly overall correlated with 급수시설구분High correlation
지정년도 is highly overall correlated with 신청일자 and 2 other fieldsHigh correlation
지정번호 is highly overall correlated with 급수시설구분High 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
업태명 is highly imbalanced (61.2%)Imbalance
소재지전화번호 has 2 (2.8%) missing valuesMissing
업소명 has unique valuesUnique
소재지도로명 has unique valuesUnique
소재지지번 has unique valuesUnique
허가(신고)번호 has unique valuesUnique

Reproduction

Analysis started2024-05-04 02:08:51.907747
Analysis finished2024-05-04 02:09:01.927249
Duration10.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size700.0 B
3140000
71 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3140000
2nd row3140000
3rd row3140000
4th row3140000
5th row3140000

Common Values

ValueCountFrequency (%)
3140000 71
100.0%

Length

2024-05-04T02:09:02.153821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:09:02.525424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3140000 71
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.8028
Minimum2001
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2024-05-04T02:09:02.840226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2003
Q12007
median2012
Q32017
95-th percentile2019
Maximum2023
Range22
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.9030736
Coefficient of variation (CV)0.0029342208
Kurtosis-1.1861876
Mean2011.8028
Median Absolute Deviation (MAD)5
Skewness-0.052633748
Sum142838
Variance34.846278
MonotonicityDecreasing
2024-05-04T02:09:03.357112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2018 9
12.7%
2017 7
 
9.9%
2005 6
 
8.5%
2008 5
 
7.0%
2019 4
 
5.6%
2013 4
 
5.6%
2009 4
 
5.6%
2007 4
 
5.6%
2016 3
 
4.2%
2014 3
 
4.2%
Other values (11) 22
31.0%
ValueCountFrequency (%)
2001 2
 
2.8%
2002 1
 
1.4%
2003 2
 
2.8%
2004 3
4.2%
2005 6
8.5%
2006 3
4.2%
2007 4
5.6%
2008 5
7.0%
2009 4
5.6%
2010 2
 
2.8%
ValueCountFrequency (%)
2023 2
 
2.8%
2022 1
 
1.4%
2019 4
5.6%
2018 9
12.7%
2017 7
9.9%
2016 3
 
4.2%
2015 2
 
2.8%
2014 3
 
4.2%
2013 4
5.6%
2012 3
 
4.2%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.760563
Minimum1
Maximum187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2024-05-04T02:09:03.772254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median10
Q394
95-th percentile153.5
Maximum187
Range186
Interquartile range (IQR)89

Descriptive statistics

Standard deviation53.321522
Coefficient of variation (CV)1.1652287
Kurtosis-0.39876471
Mean45.760563
Median Absolute Deviation (MAD)7
Skewness0.98071281
Sum3249
Variance2843.1847
MonotonicityNot monotonic
2024-05-04T02:09:04.278177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
3 7
 
9.9%
4 6
 
8.5%
5 5
 
7.0%
10 4
 
5.6%
8 4
 
5.6%
7 3
 
4.2%
16 3
 
4.2%
6 3
 
4.2%
19 2
 
2.8%
9 2
 
2.8%
Other values (32) 32
45.1%
ValueCountFrequency (%)
1 1
 
1.4%
2 1
 
1.4%
3 7
9.9%
4 6
8.5%
5 5
7.0%
6 3
4.2%
7 3
4.2%
8 4
5.6%
9 2
 
2.8%
10 4
5.6%
ValueCountFrequency (%)
187 1
1.4%
162 1
1.4%
157 1
1.4%
156 1
1.4%
151 1
1.4%
148 1
1.4%
120 1
1.4%
115 1
1.4%
113 1
1.4%
112 1
1.4%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20117652
Minimum20010601
Maximum20231101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2024-05-04T02:09:04.812442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010601
5-th percentile20025502
Q120065666
median20121002
Q320171076
95-th percentile20191011
Maximum20231101
Range220500
Interquartile range (IQR)105410

Descriptive statistics

Standard deviation60562.2
Coefficient of variation (CV)0.003010401
Kurtosis-1.2055212
Mean20117652
Median Absolute Deviation (MAD)50381
Skewness-0.070363291
Sum1.4283533 × 109
Variance3.6677801 × 109
MonotonicityNot monotonic
2024-05-04T02:09:05.204021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
20181101 9
 
12.7%
20050620 5
 
7.0%
20171031 5
 
7.0%
20080901 5
 
7.0%
20191011 4
 
5.6%
20070621 4
 
5.6%
20131001 4
 
5.6%
20090820 3
 
4.2%
20161021 3
 
4.2%
20010601 3
 
4.2%
Other values (16) 26
36.6%
ValueCountFrequency (%)
20010601 3
4.2%
20020402 1
 
1.4%
20030602 2
 
2.8%
20040531 3
4.2%
20050620 5
7.0%
20050805 1
 
1.4%
20060710 3
4.2%
20070621 4
5.6%
20080901 5
7.0%
20090820 3
4.2%
ValueCountFrequency (%)
20231101 2
 
2.8%
20221007 1
 
1.4%
20191011 4
5.6%
20181101 9
12.7%
20171120 2
 
2.8%
20171031 5
7.0%
20161021 3
 
4.2%
20151202 1
 
1.4%
20151201 1
 
1.4%
20141112 2
 
2.8%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20119043
Minimum20010703
Maximum20231130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2024-05-04T02:09:05.725522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010703
5-th percentile20030801
Q120070921
median20121031
Q320171129
95-th percentile20191127
Maximum20231130
Range220427
Interquartile range (IQR)100208

Descriptive statistics

Standard deviation59174.186
Coefficient of variation (CV)0.0029412028
Kurtosis-1.1877287
Mean20119043
Median Absolute Deviation (MAD)50110
Skewness-0.055134007
Sum1.4284521 × 109
Variance3.5015843 × 109
MonotonicityDecreasing
2024-05-04T02:09:06.359957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
20181130 9
 
12.7%
20171129 7
 
9.9%
20050804 6
 
8.5%
20081021 5
 
7.0%
20070921 4
 
5.6%
20191127 4
 
5.6%
20131209 4
 
5.6%
20161221 3
 
4.2%
20141222 3
 
4.2%
20121031 3
 
4.2%
Other values (13) 23
32.4%
ValueCountFrequency (%)
20010703 2
 
2.8%
20020703 1
 
1.4%
20030801 2
 
2.8%
20040719 3
4.2%
20050804 6
8.5%
20060830 3
4.2%
20070921 4
5.6%
20081021 5
7.0%
20090916 3
4.2%
20091013 1
 
1.4%
ValueCountFrequency (%)
20231130 2
 
2.8%
20221130 1
 
1.4%
20191127 4
5.6%
20181130 9
12.7%
20171129 7
9.9%
20161221 3
 
4.2%
20151221 1
 
1.4%
20151210 1
 
1.4%
20141222 3
 
4.2%
20131209 4
5.6%

업소명
Text

UNIQUE 

Distinct71
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size700.0 B
2024-05-04T02:09:06.998472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length6.4788732
Min length2

Characters and Unicode

Total characters460
Distinct characters186
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)100.0%

Sample

1st row본말감자탕 신정네거리역점
2nd row우리토종삼계탕
3rd row동해해물탕
4th row중화요리 온
5th row남원골명품추어탕
ValueCountFrequency (%)
목동점 2
 
2.4%
본말감자탕 1
 
1.2%
원할머니보쌈 1
 
1.2%
광양불고기 1
 
1.2%
목동본점 1
 
1.2%
하순옥황금안동국시 1
 
1.2%
등촌샤브칼국수 1
 
1.2%
명가들깨칼국수 1
 
1.2%
바다가요리한찜 1
 
1.2%
항아리수제비 1
 
1.2%
Other values (73) 73
86.9%
2024-05-04T02:09:08.641818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 16
 
3.5%
리 15
 
3.3%
국 14
 
3.0%
점 14
 
3.0%
13
 
2.8%
목 12
 
2.6%
탕 10
 
2.2%
대 10
 
2.2%
가 9
 
2.0%
수 9
 
2.0%
Other values (176) 338
73.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 433
94.1%
Space Separator 13
 
2.8%
Lowercase Letter 5
 
1.1%
Decimal Number 3
 
0.7%
Other Punctuation 2
 
0.4%
Open Punctuation 2
 
0.4%
Close Punctuation 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 16
 
3.7%
리 15
 
3.5%
국 14
 
3.2%
점 14
 
3.2%
목 12
 
2.8%
탕 10
 
2.3%
대 10
 
2.3%
가 9
 
2.1%
수 9
 
2.1%
칼 7
 
1.6%
Other values (164) 317
73.2%
Lowercase Letter
ValueCountFrequency (%)
e 1
20.0%
l 1
20.0%
y 1
20.0%
t 1
20.0%
s 1
20.0%
Decimal Number
ValueCountFrequency (%)
5 1
33.3%
4 1
33.3%
1 1
33.3%
Space Separator
ValueCountFrequency (%)
13
100.0%
Other Punctuation
ValueCountFrequency (%)
& 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 433
94.1%
Common 22
 
4.8%
Latin 5
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 16
 
3.7%
리 15
 
3.5%
국 14
 
3.2%
점 14
 
3.2%
목 12
 
2.8%
탕 10
 
2.3%
대 10
 
2.3%
가 9
 
2.1%
수 9
 
2.1%
칼 7
 
1.6%
Other values (164) 317
73.2%
Common
ValueCountFrequency (%)
13
59.1%
& 2
 
9.1%
( 2
 
9.1%
) 2
 
9.1%
5 1
 
4.5%
4 1
 
4.5%
1 1
 
4.5%
Latin
ValueCountFrequency (%)
e 1
20.0%
l 1
20.0%
y 1
20.0%
t 1
20.0%
s 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 433
94.1%
ASCII 27
 
5.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 16
 
3.7%
리 15
 
3.5%
국 14
 
3.2%
점 14
 
3.2%
목 12
 
2.8%
탕 10
 
2.3%
대 10
 
2.3%
가 9
 
2.1%
수 9
 
2.1%
칼 7
 
1.6%
Other values (164) 317
73.2%
ASCII
ValueCountFrequency (%)
13
48.1%
& 2
 
7.4%
( 2
 
7.4%
) 2
 
7.4%
5 1
 
3.7%
e 1
 
3.7%
l 1
 
3.7%
y 1
 
3.7%
t 1
 
3.7%
s 1
 
3.7%
Other values (2) 2
 
7.4%

소재지도로명
Text

UNIQUE 

Distinct71
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size700.0 B
2024-05-04T02:09:09.399559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length42
Mean length33.507042
Min length26

Characters and Unicode

Total characters2379
Distinct characters112
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

Unique71 ?
Unique (%)100.0%

Sample

1st row서울특별시 양천구 중앙로 259, 1층 (신정동)
2nd row서울특별시 양천구 월정로 60, 1층 (신월동)
3rd row서울특별시 양천구 목동로21길 1, 1층 (신정동)
4th row서울특별시 양천구 공항대로 630, 1층 (목동, 어바니엘)
5th row서울특별시 양천구 화곡로 95, 초원빌딩 1층 (신월동)
ValueCountFrequency (%)
서울특별시 71
 
14.3%
양천구 71
 
14.3%
1층 53
 
10.7%
신정동 26
 
5.3%
목동 25
 
5.1%
신월동 20
 
4.0%
2층 13
 
2.6%
목동서로 10
 
2.0%
지하 7
 
1.4%
목동동로 6
 
1.2%
Other values (139) 193
39.0%
2024-05-04T02:09:10.663987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
424
 
17.8%
1 132
 
5.5%
동 113
 
4.7%
, 85
 
3.6%
서 83
 
3.5%
양 77
 
3.2%
로 73
 
3.1%
( 71
 
3.0%
) 71
 
3.0%
울 71
 
3.0%
Other values (102) 1179
49.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1327
55.8%
Space Separator 424
 
17.8%
Decimal Number 381
 
16.0%
Other Punctuation 85
 
3.6%
Open Punctuation 71
 
3.0%
Close Punctuation 71
 
3.0%
Math Symbol 11
 
0.5%
Dash Punctuation 4
 
0.2%
Lowercase Letter 4
 
0.2%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 113
 
8.5%
서 83
 
6.3%
양 77
 
5.8%
로 73
 
5.5%
울 71
 
5.4%
구 71
 
5.4%
천 71
 
5.4%
시 71
 
5.4%
별 71
 
5.4%
특 71
 
5.4%
Other values (84) 555
41.8%
Decimal Number
ValueCountFrequency (%)
1 132
34.6%
2 65
17.1%
0 46
 
12.1%
5 32
 
8.4%
6 28
 
7.3%
3 23
 
6.0%
8 18
 
4.7%
7 15
 
3.9%
4 13
 
3.4%
9 9
 
2.4%
Space Separator
ValueCountFrequency (%)
424
100.0%
Other Punctuation
ValueCountFrequency (%)
, 85
100.0%
Open Punctuation
ValueCountFrequency (%)
( 71
100.0%
Close Punctuation
ValueCountFrequency (%)
) 71
100.0%
Math Symbol
ValueCountFrequency (%)
~ 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Lowercase Letter
ValueCountFrequency (%)
l 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1327
55.8%
Common 1047
44.0%
Latin 5
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 113
 
8.5%
서 83
 
6.3%
양 77
 
5.8%
로 73
 
5.5%
울 71
 
5.4%
구 71
 
5.4%
천 71
 
5.4%
시 71
 
5.4%
별 71
 
5.4%
특 71
 
5.4%
Other values (84) 555
41.8%
Common
ValueCountFrequency (%)
424
40.5%
1 132
 
12.6%
, 85
 
8.1%
( 71
 
6.8%
) 71
 
6.8%
2 65
 
6.2%
0 46
 
4.4%
5 32
 
3.1%
6 28
 
2.7%
3 23
 
2.2%
Other values (6) 70
 
6.7%
Latin
ValueCountFrequency (%)
l 4
80.0%
M 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1327
55.8%
ASCII 1052
44.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
424
40.3%
1 132
 
12.5%
, 85
 
8.1%
( 71
 
6.7%
) 71
 
6.7%
2 65
 
6.2%
0 46
 
4.4%
5 32
 
3.0%
6 28
 
2.7%
3 23
 
2.2%
Other values (8) 75
 
7.1%
Hangul
ValueCountFrequency (%)
동 113
 
8.5%
서 83
 
6.3%
양 77
 
5.8%
로 73
 
5.5%
울 71
 
5.4%
구 71
 
5.4%
천 71
 
5.4%
시 71
 
5.4%
별 71
 
5.4%
특 71
 
5.4%
Other values (84) 555
41.8%

소재지지번
Text

UNIQUE 

Distinct71
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size700.0 B
2024-05-04T02:09:11.516938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length42
Mean length32.183099
Min length25

Characters and Unicode

Total characters2285
Distinct characters93
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

Unique71 ?
Unique (%)100.0%

Sample

1st row서울특별시 양천구 신정동 1183번지 10호
2nd row서울특별시 양천구 신월동 447번지 4호
3rd row서울특별시 양천구 신정동 993번지 3호 1층
4th row서울특별시 양천구 목동 514번지 18호 어바니엘 1층
5th row서울특별시 양천구 신월동 23번지 21호 초원빌딩 1층
ValueCountFrequency (%)
서울특별시 71
 
15.4%
양천구 71
 
15.4%
1층 35
 
7.6%
신정동 26
 
5.7%
목동 25
 
5.4%
신월동 20
 
4.3%
2호 8
 
1.7%
지하 7
 
1.5%
1호 6
 
1.3%
6호 6
 
1.3%
Other values (127) 185
40.2%
2024-05-04T02:09:13.096697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
533
23.3%
1 158
 
6.9%
지 84
 
3.7%
2 76
 
3.3%
동 76
 
3.3%
양 75
 
3.3%
서 72
 
3.2%
천 71
 
3.1%
울 71
 
3.1%
시 71
 
3.1%
Other values (83) 998
43.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1216
53.2%
Space Separator 533
23.3%
Decimal Number 495
21.7%
Dash Punctuation 25
 
1.1%
Math Symbol 10
 
0.4%
Lowercase Letter 4
 
0.2%
Other Punctuation 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 84
 
6.9%
동 76
 
6.2%
양 75
 
6.2%
서 72
 
5.9%
천 71
 
5.8%
울 71
 
5.8%
시 71
 
5.8%
별 71
 
5.8%
구 71
 
5.8%
번 71
 
5.8%
Other values (67) 483
39.7%
Decimal Number
ValueCountFrequency (%)
1 158
31.9%
2 76
15.4%
0 68
13.7%
9 37
 
7.5%
5 31
 
6.3%
4 30
 
6.1%
3 28
 
5.7%
6 24
 
4.8%
8 22
 
4.4%
7 21
 
4.2%
Space Separator
ValueCountFrequency (%)
533
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%
Math Symbol
ValueCountFrequency (%)
~ 10
100.0%
Lowercase Letter
ValueCountFrequency (%)
l 4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1216
53.2%
Common 1064
46.6%
Latin 5
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 84
 
6.9%
동 76
 
6.2%
양 75
 
6.2%
서 72
 
5.9%
천 71
 
5.8%
울 71
 
5.8%
시 71
 
5.8%
별 71
 
5.8%
구 71
 
5.8%
번 71
 
5.8%
Other values (67) 483
39.7%
Common
ValueCountFrequency (%)
533
50.1%
1 158
 
14.8%
2 76
 
7.1%
0 68
 
6.4%
9 37
 
3.5%
5 31
 
2.9%
4 30
 
2.8%
3 28
 
2.6%
- 25
 
2.3%
6 24
 
2.3%
Other values (4) 54
 
5.1%
Latin
ValueCountFrequency (%)
l 4
80.0%
M 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1216
53.2%
ASCII 1069
46.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
533
49.9%
1 158
 
14.8%
2 76
 
7.1%
0 68
 
6.4%
9 37
 
3.5%
5 31
 
2.9%
4 30
 
2.8%
3 28
 
2.6%
- 25
 
2.3%
6 24
 
2.2%
Other values (6) 59
 
5.5%
Hangul
ValueCountFrequency (%)
지 84
 
6.9%
동 76
 
6.2%
양 75
 
6.2%
서 72
 
5.9%
천 71
 
5.8%
울 71
 
5.8%
시 71
 
5.8%
별 71
 
5.8%
구 71
 
5.8%
번 71
 
5.8%
Other values (67) 483
39.7%

허가(신고)번호
Text

UNIQUE 

Distinct71
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size700.0 B
2024-05-04T02:09:13.764557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters1562
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

Unique71 ?
Unique (%)100.0%

Sample

1st row3140000-101-2023-00159
2nd row3140000-101-2022-00099
3rd row3140000-101-2019-00190
4th row3140000-101-2004-00197
5th row3140000-101-2004-00313
ValueCountFrequency (%)
3140000-101-2023-00159 1
 
1.4%
3140000-101-2004-00201 1
 
1.4%
3140000-101-2005-00141 1
 
1.4%
3140000-101-1997-01775 1
 
1.4%
3140000-101-1995-02958 1
 
1.4%
3140000-101-1999-07505 1
 
1.4%
3140000-101-2004-00247 1
 
1.4%
3140000-101-1998-05574 1
 
1.4%
3140000-101-2007-00235 1
 
1.4%
3140000-101-2008-09138 1
 
1.4%
Other values (61) 61
85.9%
2024-05-04T02:09:14.928327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 597
38.2%
1 285
18.2%
- 213
 
13.6%
4 103
 
6.6%
3 93
 
6.0%
2 88
 
5.6%
9 60
 
3.8%
8 35
 
2.2%
5 32
 
2.0%
7 32
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1349
86.4%
Dash Punctuation 213
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 597
44.3%
1 285
21.1%
4 103
 
7.6%
3 93
 
6.9%
2 88
 
6.5%
9 60
 
4.4%
8 35
 
2.6%
5 32
 
2.4%
7 32
 
2.4%
6 24
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 213
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1562
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 597
38.2%
1 285
18.2%
- 213
 
13.6%
4 103
 
6.6%
3 93
 
6.0%
2 88
 
5.6%
9 60
 
3.8%
8 35
 
2.2%
5 32
 
2.0%
7 32
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1562
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 597
38.2%
1 285
18.2%
- 213
 
13.6%
4 103
 
6.6%
3 93
 
6.0%
2 88
 
5.6%
9 60
 
3.8%
8 35
 
2.2%
5 32
 
2.0%
7 32
 
2.0%

업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size700.0 B
한식
59 
중국식
 
5
일식
 
3
식육(숯불구이)
 
2
경양식
 
1

Length

Max length8
Median length2
Mean length2.2816901
Min length2

Unique

Unique2 ?
Unique (%)2.8%

Sample

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

Common Values

ValueCountFrequency (%)
한식 59
83.1%
중국식 5
 
7.0%
일식 3
 
4.2%
식육(숯불구이) 2
 
2.8%
경양식 1
 
1.4%
복어취급 1
 
1.4%

Length

2024-05-04T02:09:15.459727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:09:16.024642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 59
83.1%
중국식 5
 
7.0%
일식 3
 
4.2%
식육(숯불구이 2
 
2.8%
경양식 1
 
1.4%
복어취급 1
 
1.4%

주된음식
Text

Distinct50
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Memory size700.0 B
2024-05-04T02:09:16.570682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.3943662
Min length2

Characters and Unicode

Total characters241
Distinct characters87
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)54.9%

Sample

1st row감자탕
2nd row삼계탕
3rd row해물탕
4th row자장면
5th row추어탕
ValueCountFrequency (%)
칼국수 6
 
8.5%
돼지갈비 4
 
5.6%
감자탕 3
 
4.2%
자장면 3
 
4.2%
부대찌개 3
 
4.2%
순대국 3
 
4.2%
설렁탕 2
 
2.8%
삼겹살 2
 
2.8%
장어구이 2
 
2.8%
추어탕 2
 
2.8%
Other values (40) 41
57.7%
2024-05-04T02:09:17.728498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
국 16
 
6.6%
탕 12
 
5.0%
수 9
 
3.7%
장 8
 
3.3%
칼 7
 
2.9%
대 7
 
2.9%
지 7
 
2.9%
자 6
 
2.5%
기 6
 
2.5%
어 6
 
2.5%
Other values (77) 157
65.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 241
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 16
 
6.6%
탕 12
 
5.0%
수 9
 
3.7%
장 8
 
3.3%
칼 7
 
2.9%
대 7
 
2.9%
지 7
 
2.9%
자 6
 
2.5%
기 6
 
2.5%
어 6
 
2.5%
Other values (77) 157
65.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 241
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 16
 
6.6%
탕 12
 
5.0%
수 9
 
3.7%
장 8
 
3.3%
칼 7
 
2.9%
대 7
 
2.9%
지 7
 
2.9%
자 6
 
2.5%
기 6
 
2.5%
어 6
 
2.5%
Other values (77) 157
65.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 241
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
국 16
 
6.6%
탕 12
 
5.0%
수 9
 
3.7%
장 8
 
3.3%
칼 7
 
2.9%
대 7
 
2.9%
지 7
 
2.9%
자 6
 
2.5%
기 6
 
2.5%
어 6
 
2.5%
Other values (77) 157
65.1%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.54676
Minimum31.9
Maximum872
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2024-05-04T02:09:18.316984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31.9
5-th percentile73.49
Q1100.2
median136.2
Q3185.085
95-th percentile420.135
Maximum872
Range840.1
Interquartile range (IQR)84.885

Descriptive statistics

Standard deviation146.27785
Coefficient of variation (CV)0.8101937
Kurtosis11.867438
Mean180.54676
Median Absolute Deviation (MAD)38.13
Skewness3.1723915
Sum12818.82
Variance21397.209
MonotonicityNot monotonic
2024-05-04T02:09:18.952545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89.0 2
 
2.8%
233.49 1
 
1.4%
297.0 1
 
1.4%
231.36 1
 
1.4%
118.51 1
 
1.4%
92.6 1
 
1.4%
98.07 1
 
1.4%
285.0 1
 
1.4%
91.26 1
 
1.4%
129.95 1
 
1.4%
Other values (60) 60
84.5%
ValueCountFrequency (%)
31.9 1
1.4%
63.0 1
1.4%
65.02 1
1.4%
71.86 1
1.4%
75.12 1
1.4%
84.0 1
1.4%
89.0 2
2.8%
91.26 1
1.4%
92.4 1
1.4%
92.6 1
1.4%
ValueCountFrequency (%)
872.0 1
1.4%
838.69 1
1.4%
519.09 1
1.4%
464.47 1
1.4%
375.8 1
1.4%
353.19 1
1.4%
309.57 1
1.4%
297.0 1
1.4%
295.7 1
1.4%
285.0 1
1.4%

행정동명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Memory size700.0 B
목1동
12 
신정4동
11 
신정2동
6 
신월2동
5 
신월5동
4 
Other values (12)
33 

Length

Max length4
Median length4
Mean length3.6478873
Min length3

Unique

Unique2 ?
Unique (%)2.8%

Sample

1st row신정3동
2nd row신월2동
3rd row신정4동
4th row목2동
5th row신월5동

Common Values

ValueCountFrequency (%)
목1동 12
16.9%
신정4동 11
15.5%
신정2동 6
8.5%
신월2동 5
 
7.0%
신월5동 4
 
5.6%
목5동 4
 
5.6%
신월6동 4
 
5.6%
목2동 4
 
5.6%
신월4동 4
 
5.6%
목3동 3
 
4.2%
Other values (7) 14
19.7%

Length

2024-05-04T02:09:19.392598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
목1동 12
16.9%
신정4동 11
15.5%
신정2동 6
8.5%
신월2동 5
 
7.0%
신월6동 4
 
5.6%
목2동 4
 
5.6%
신월4동 4
 
5.6%
목5동 4
 
5.6%
신월5동 4
 
5.6%
목3동 3
 
4.2%
Other values (7) 14
19.7%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size700.0 B
상수도전용
51 
<NA>
20 

Length

Max length5
Median length5
Mean length4.7183099
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 51
71.8%
<NA> 20
 
28.2%

Length

2024-05-04T02:09:19.758295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:09:20.175097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 51
71.8%
na 20
 
28.2%

소재지전화번호
Text

MISSING 

Distinct69
Distinct (%)100.0%
Missing2
Missing (%)2.8%
Memory size700.0 B
2024-05-04T02:09:20.749806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.202899
Min length10

Characters and Unicode

Total characters704
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

Unique69 ?
Unique (%)100.0%

Sample

1st row0226041999
2nd row0226450688
3rd row0226084655
4th row0226025515
5th row0226941100
ValueCountFrequency (%)
02 6
 
8.0%
0226047006 1
 
1.3%
0226455382 1
 
1.3%
0226979999 1
 
1.3%
0226462648 1
 
1.3%
0226459467 1
 
1.3%
0226533070 1
 
1.3%
0226022638 1
 
1.3%
0226044015 1
 
1.3%
0220613885 1
 
1.3%
Other values (60) 60
80.0%
2024-05-04T02:09:22.003523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 164
23.3%
0 128
18.2%
6 100
14.2%
4 58
 
8.2%
5 54
 
7.7%
9 49
 
7.0%
3 41
 
5.8%
8 39
 
5.5%
7 36
 
5.1%
1 25
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 694
98.6%
Space Separator 10
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 164
23.6%
0 128
18.4%
6 100
14.4%
4 58
 
8.4%
5 54
 
7.8%
9 49
 
7.1%
3 41
 
5.9%
8 39
 
5.6%
7 36
 
5.2%
1 25
 
3.6%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 704
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 164
23.3%
0 128
18.2%
6 100
14.2%
4 58
 
8.2%
5 54
 
7.7%
9 49
 
7.0%
3 41
 
5.8%
8 39
 
5.5%
7 36
 
5.1%
1 25
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 164
23.3%
0 128
18.2%
6 100
14.2%
4 58
 
8.2%
5 54
 
7.7%
9 49
 
7.0%
3 41
 
5.8%
8 39
 
5.5%
7 36
 
5.1%
1 25
 
3.6%

Interactions

2024-05-04T02:08:59.193338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:08:54.253246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:08:55.480575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:08:56.665260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:08:57.880708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:08:59.505698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:08:54.499856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:08:55.709807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:08:56.906144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:08:58.212339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:08:59.868116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:08:54.779108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:08:55.950019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:08:57.177094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:08:58.483089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:09:00.171827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:08:55.037665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:08:56.177612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:08:57.423910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:08:58.697421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:09:00.415477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:08:55.256387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:08:56.403849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:08:57.665045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:08:58.932933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T02:09:22.451683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명소재지전화번호
지정년도1.0000.8320.9980.9991.0001.0001.0001.0000.1360.0000.0000.6301.000
지정번호0.8321.0000.8340.8351.0001.0001.0001.0000.0000.8330.0000.3031.000
신청일자0.9980.8341.0001.0001.0001.0001.0001.0000.4630.0000.0000.5851.000
지정일자0.9990.8351.0001.0001.0001.0001.0001.0000.3370.0000.0000.5811.000
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
업태명0.1360.0000.4630.3371.0001.0001.0001.0001.0000.9880.0000.0001.000
주된음식0.0000.8330.0000.0001.0001.0001.0001.0000.9881.0000.4360.0001.000
영업장면적(㎡)0.0000.0000.0000.0001.0001.0001.0001.0000.0000.4361.0000.3451.000
행정동명0.6300.3030.5850.5811.0001.0001.0001.0000.0000.0000.3451.0001.000
소재지전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-05-04T02:09:22.998923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
급수시설구분행정동명업태명
급수시설구분1.0001.0001.000
행정동명1.0001.0000.000
업태명1.0000.0001.000
2024-05-04T02:09:23.343983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.000-0.1290.9861.000-0.1740.0000.2931.000
지정번호-0.1291.000-0.142-0.128-0.0970.0000.0981.000
신청일자0.986-0.1421.0000.986-0.1490.2300.2431.000
지정일자1.000-0.1280.9861.000-0.1710.1420.2621.000
영업장면적(㎡)-0.174-0.097-0.149-0.1711.0000.0000.1381.000
업태명0.0000.0000.2300.1420.0001.0000.0001.000
행정동명0.2930.0980.2430.2620.1380.0001.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-04T02:09:00.928054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T02:09:01.673468image/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
031400002023722023110120231130본말감자탕 신정네거리역점서울특별시 양천구 중앙로 259, 1층 (신정동)서울특별시 양천구 신정동 1183번지 10호3140000-101-2023-00159한식감자탕233.49신정3동<NA><NA>
131400002023732023110120231130우리토종삼계탕서울특별시 양천구 월정로 60, 1층 (신월동)서울특별시 양천구 신월동 447번지 4호3140000-101-2022-00099한식삼계탕105.64신월2동<NA><NA>
231400002022812022100720221130동해해물탕서울특별시 양천구 목동로21길 1, 1층 (신정동)서울특별시 양천구 신정동 993번지 3호 1층3140000-101-2019-00190한식해물탕98.45신정4동<NA>0226041999
33140000201932019101120191127중화요리 온서울특별시 양천구 공항대로 630, 1층 (목동, 어바니엘)서울특별시 양천구 목동 514번지 18호 어바니엘 1층3140000-101-2004-00197중국식자장면105.4목2동상수도전용0226450688
43140000201942019101120191127남원골명품추어탕서울특별시 양천구 화곡로 95, 초원빌딩 1층 (신월동)서울특별시 양천구 신월동 23번지 21호 초원빌딩 1층3140000-101-2004-00313한식추어탕124.03신월5동상수도전용0226084655
53140000201952019101120191127등촌칼국수서울특별시 양천구 화곡로 100, 1층 (신월동)서울특별시 양천구 신월동 64번지 2호 1층3140000-101-2007-00027한식칼국수295.7신월5동상수도전용0226025515
63140000201972019101120191127섬어부밥상서울특별시 양천구 신정중앙로 85, 로데오프라자 2층 204~205호 (신정동)서울특별시 양천구 신정동 886번지 6호 로데오프라자 2층-204~2053140000-101-2015-00258한식낙지볶음167.64신정4동<NA>0226941100
7314000020181062018110120181130명태어장서울특별시 양천구 중앙로 311, 지하 1층 101호 (신월동, 리슈리안)서울특별시 양천구 신월동 500번지 21호 리슈리안 지하 1층-1013140000-101-2016-00088한식명태조림138.91신월2동<NA>0220658835
8314000020181092018110120181130순천식당서울특별시 양천구 지양로 26, 1층 102호 (신월동)서울특별시 양천구 신월동 992번지 4호 1층-1023140000-101-2016-00174한식오리주물럭31.9신월7동상수도전용0220654465
9314000020181012018110120181130대가전주콩나물국밥서울특별시 양천구 월정로 8, 101동 지하 1층 101호 (신월동, 목동M타운)서울특별시 양천구 신월동 512번지 1호 101 목동M타운 지하 1층-1013140000-101-2018-00199한식돼지고기153.59신월2동상수도전용0226047006
시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
61314000020051202005062020050804북해도서울특별시 양천구 오목로50길 37, 서홍빌딩 1층 (신정동)서울특별시 양천구 신정동 1007번지 6호 서홍빌딩 1층3140000-101-2002-00579일식참치회100.0신정4동<NA>02 20657147
62314000020051562005080520050804교담집서울특별시 양천구 목동동로 260, 2층 201호 (목동, 양지타운)서울특별시 양천구 목동 406번지 202호 양지타운 2층-2013140000-101-2005-00011한식소고기133.64목1동상수도전용0226468592
6331400002004832004053120040719먹자대게서울특별시 양천구 목동서로 213, 세신비젼프라자 1층 108호 (목동)서울특별시 양천구 목동 923번지 세신비젼프라자 1층-1083140000-101-1999-07186한식대게165.23목1동상수도전용0226471500
643140000200452004053120040719곰달래감자탕서울특별시 양천구 남부순환로 368, 1층 (신월동)서울특별시 양천구 신월동 166번지 10호 1층3140000-101-1987-00819한식감자탕156.48신월3동상수도전용0226083350
6531400002004902004053120040719신의주순대와쭈꾸미목동점서울특별시 양천구 목동동로 385, 1층 108호 (목동, 벽산미라지타워)서울특별시 양천구 목동 907번지 14호 벽산미라지타워 1층-1083140000-101-2000-07650한식순대국156.18목5동상수도전용0226493339
6631400002003222003060220030801향촌서울특별시 양천구 목동중앙서로 46, 지하 1층 (목동)서울특별시 양천구 목동 799번지 5호 지하 1층3140000-101-1992-04166한식돼지갈비145.0목4동상수도전용0226539020
673140000200332003060220030801명륜진사갈비 염창점서울특별시 양천구 공항대로 626, 1층 (목동)서울특별시 양천구 목동 514번지 4호 1층3140000-101-1985-00150식육(숯불구이)광어회170.33목2동상수도전용0226467825
6831400002002162001060120020703홍농숯불갈비서울특별시 양천구 신월로15길 1, 1~2층 (신월동)서울특별시 양천구 신월동 544번지 4호 1~2층3140000-101-1992-00440한식돼지갈비205.51신월4동상수도전용0226976979
693140000200142001060120010703어촌마을서울특별시 양천구 오목로 58, 1층 (신월동)서울특별시 양천구 신월동 510번지 1호 1층3140000-101-1986-03045한식생선구이219.28신월2동상수도전용0226908658
7031400002001192001060120010703착한낙지목동점서울특별시 양천구 국회대로 289, 1층 (목동)서울특별시 양천구 목동 802번지 9호 1층3140000-101-1992-00946한식낙지전골309.57목4동상수도전용0226490112