Overview

Dataset statistics

Number of variables15
Number of observations39
Missing cells21
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 KiB
Average record size in memory129.4 B

Variable types

Categorical4
Numeric5
Text6

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,주된음식,영업장면적(㎡),행정동명,급수시설구분,소재지전화번호
Author금천구
URLhttps://data.seoul.go.kr/dataList/OA-10140/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
급수시설구분 is highly overall correlated with 지정년도 and 6 other fieldsHigh correlation
업태명 is highly overall correlated with 급수시설구분High correlation
행정동명 is highly overall correlated with 급수시설구분High correlation
지정년도 is highly overall correlated with 지정번호 and 3 other fieldsHigh correlation
지정번호 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
신청일자 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
지정일자 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
영업장면적(㎡) is highly overall correlated with 급수시설구분High correlation
업태명 is highly imbalanced (57.8%)Imbalance
주된음식 has 10 (25.6%) missing valuesMissing
소재지전화번호 has 11 (28.2%) missing valuesMissing
업소명 has unique valuesUnique
소재지도로명 has unique valuesUnique
소재지지번 has unique valuesUnique
허가(신고)번호 has unique valuesUnique
영업장면적(㎡) has unique valuesUnique

Reproduction

Analysis started2024-05-04 00:40:12.750557
Analysis finished2024-05-04 00:40:23.777010
Duration11.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
3170000
39 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3170000
2nd row3170000
3rd row3170000
4th row3170000
5th row3170000

Common Values

ValueCountFrequency (%)
3170000 39
100.0%

Length

2024-05-04T00:40:23.990457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T00:40:24.435683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3170000 39
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.7179
Minimum2004
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2024-05-04T00:40:25.127355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2004
5-th percentile2006
Q12008.5
median2013
Q32015
95-th percentile2016
Maximum2016
Range12
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation3.5239684
Coefficient of variation (CV)0.0017517209
Kurtosis-1.0366076
Mean2011.7179
Median Absolute Deviation (MAD)2
Skewness-0.52534813
Sum78457
Variance12.418354
MonotonicityNot monotonic
2024-05-04T00:40:26.611276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2013 9
23.1%
2015 9
23.1%
2016 4
10.3%
2008 4
10.3%
2010 3
 
7.7%
2006 3
 
7.7%
2009 2
 
5.1%
2007 2
 
5.1%
2004 1
 
2.6%
2011 1
 
2.6%
ValueCountFrequency (%)
2004 1
 
2.6%
2006 3
 
7.7%
2007 2
 
5.1%
2008 4
10.3%
2009 2
 
5.1%
2010 3
 
7.7%
2011 1
 
2.6%
2013 9
23.1%
2014 1
 
2.6%
2015 9
23.1%
ValueCountFrequency (%)
2016 4
10.3%
2015 9
23.1%
2014 1
 
2.6%
2013 9
23.1%
2011 1
 
2.6%
2010 3
 
7.7%
2009 2
 
5.1%
2008 4
10.3%
2007 2
 
5.1%
2006 3
 
7.7%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)74.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.820513
Minimum1
Maximum236
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2024-05-04T00:40:27.199210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.9
Q17
median17
Q376.5
95-th percentile217.9
Maximum236
Range235
Interquartile range (IQR)69.5

Descriptive statistics

Standard deviation84.263332
Coefficient of variation (CV)1.3854426
Kurtosis-0.27016311
Mean60.820513
Median Absolute Deviation (MAD)12
Skewness1.2503894
Sum2372
Variance7100.309
MonotonicityNot monotonic
2024-05-04T00:40:27.610980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
5 3
 
7.7%
16 2
 
5.1%
1 2
 
5.1%
7 2
 
5.1%
20 2
 
5.1%
3 2
 
5.1%
9 2
 
5.1%
19 2
 
5.1%
17 2
 
5.1%
197 1
 
2.6%
Other values (19) 19
48.7%
ValueCountFrequency (%)
1 2
5.1%
2 1
 
2.6%
3 2
5.1%
4 1
 
2.6%
5 3
7.7%
7 2
5.1%
9 2
5.1%
10 1
 
2.6%
11 1
 
2.6%
12 1
 
2.6%
ValueCountFrequency (%)
236 1
2.6%
226 1
2.6%
217 1
2.6%
214 1
2.6%
212 1
2.6%
210 1
2.6%
200 1
2.6%
197 1
2.6%
166 1
2.6%
98 1
2.6%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20117729
Minimum20040701
Maximum20161226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2024-05-04T00:40:27.975343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20040701
5-th percentile20060510
Q120085666
median20130531
Q320151023
95-th percentile20161226
Maximum20161226
Range120525
Interquartile range (IQR)65356.5

Descriptive statistics

Standard deviation35729.401
Coefficient of variation (CV)0.0017760156
Kurtosis-1.0743478
Mean20117729
Median Absolute Deviation (MAD)20492
Skewness-0.51583865
Sum7.8459142 × 108
Variance1.2765901 × 109
MonotonicityNot monotonic
2024-05-04T00:40:28.472582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
20151023 9
23.1%
20130531 8
20.5%
20161226 4
10.3%
20100813 3
 
7.7%
20060510 3
 
7.7%
20080630 3
 
7.7%
20090703 2
 
5.1%
20040701 1
 
2.6%
20070510 1
 
2.6%
20111220 1
 
2.6%
Other values (4) 4
10.3%
ValueCountFrequency (%)
20040701 1
 
2.6%
20060510 3
 
7.7%
20070410 1
 
2.6%
20070510 1
 
2.6%
20070706 1
 
2.6%
20080630 3
 
7.7%
20090703 2
 
5.1%
20100813 3
 
7.7%
20111220 1
 
2.6%
20130531 8
20.5%
ValueCountFrequency (%)
20161226 4
10.3%
20151023 9
23.1%
20141231 1
 
2.6%
20131022 1
 
2.6%
20130531 8
20.5%
20111220 1
 
2.6%
20100813 3
 
7.7%
20090703 2
 
5.1%
20080630 3
 
7.7%
20070706 1
 
2.6%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)30.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20118103
Minimum20040721
Maximum20161226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2024-05-04T00:40:28.970071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20040721
5-th percentile20060710
Q120085760
median20130716
Q320151130
95-th percentile20161226
Maximum20161226
Range120505
Interquartile range (IQR)65369.5

Descriptive statistics

Standard deviation35395.233
Coefficient of variation (CV)0.0017593722
Kurtosis-1.0386449
Mean20118103
Median Absolute Deviation (MAD)20414
Skewness-0.5209432
Sum7.8460603 × 108
Variance1.2528225 × 109
MonotonicityNot monotonic
2024-05-04T00:40:29.477875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
20151130 9
23.1%
20130716 8
20.5%
20161226 4
10.3%
20080717 4
10.3%
20100930 3
 
7.7%
20060710 3
 
7.7%
20090804 2
 
5.1%
20070726 2
 
5.1%
20040721 1
 
2.6%
20111220 1
 
2.6%
Other values (2) 2
 
5.1%
ValueCountFrequency (%)
20040721 1
 
2.6%
20060710 3
 
7.7%
20070726 2
 
5.1%
20080717 4
10.3%
20090804 2
 
5.1%
20100930 3
 
7.7%
20111220 1
 
2.6%
20130716 8
20.5%
20131210 1
 
2.6%
20141231 1
 
2.6%
ValueCountFrequency (%)
20161226 4
10.3%
20151130 9
23.1%
20141231 1
 
2.6%
20131210 1
 
2.6%
20130716 8
20.5%
20111220 1
 
2.6%
20100930 3
 
7.7%
20090804 2
 
5.1%
20080717 4
10.3%
20070726 2
 
5.1%

업소명
Text

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size444.0 B
2024-05-04T00:40:30.036148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length10
Mean length6.1538462
Min length2

Characters and Unicode

Total characters240
Distinct characters134
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)100.0%

Sample

1st row서울집
2nd row뽕잎사랑샤브샤브칼국수보쌈
3rd row본가집가마솥설렁탕
4th row동흥관
5th row백번가 코다리
ValueCountFrequency (%)
서울집 1
 
2.0%
부뚜막청국장 1
 
2.0%
큰맘할매순대국밥 1
 
2.0%
스시 1
 
2.0%
쥬베이 1
 
2.0%
고기마을 1
 
2.0%
호수 1
 
2.0%
은행골 1
 
2.0%
2 1
 
2.0%
파챠이 1
 
2.0%
Other values (41) 41
80.4%
2024-05-04T00:40:31.223014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12
 
5.0%
가 7
 
2.9%
미 7
 
2.9%
국 7
 
2.9%
점 5
 
2.1%
드 5
 
2.1%
이 5
 
2.1%
대 5
 
2.1%
정 5
 
2.1%
리 4
 
1.7%
Other values (124) 178
74.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 221
92.1%
Space Separator 12
 
5.0%
Close Punctuation 3
 
1.2%
Open Punctuation 3
 
1.2%
Decimal Number 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
가 7
 
3.2%
미 7
 
3.2%
국 7
 
3.2%
점 5
 
2.3%
드 5
 
2.3%
이 5
 
2.3%
대 5
 
2.3%
정 5
 
2.3%
리 4
 
1.8%
샤 4
 
1.8%
Other values (120) 167
75.6%
Space Separator
ValueCountFrequency (%)
12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 221
92.1%
Common 19
 
7.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
가 7
 
3.2%
미 7
 
3.2%
국 7
 
3.2%
점 5
 
2.3%
드 5
 
2.3%
이 5
 
2.3%
대 5
 
2.3%
정 5
 
2.3%
리 4
 
1.8%
샤 4
 
1.8%
Other values (120) 167
75.6%
Common
ValueCountFrequency (%)
12
63.2%
) 3
 
15.8%
( 3
 
15.8%
2 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 221
92.1%
ASCII 19
 
7.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12
63.2%
) 3
 
15.8%
( 3
 
15.8%
2 1
 
5.3%
Hangul
ValueCountFrequency (%)
가 7
 
3.2%
미 7
 
3.2%
국 7
 
3.2%
점 5
 
2.3%
드 5
 
2.3%
이 5
 
2.3%
대 5
 
2.3%
정 5
 
2.3%
리 4
 
1.8%
샤 4
 
1.8%
Other values (120) 167
75.6%

소재지도로명
Text

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size444.0 B
2024-05-04T00:40:31.834002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length61
Median length46
Mean length39.615385
Min length25

Characters and Unicode

Total characters1545
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

Unique39 ?
Unique (%)100.0%

Sample

1st row서울특별시 금천구 시흥대로122길 24, 지상1층 (독산동)
2nd row서울특별시 금천구 시흥대로 399, 지하1층 B110~112호 (독산동, 시티렉스)
3rd row서울특별시 금천구 시흥대로 269, 지상1층 101호 (시흥동, 대현오피스텔)
4th row서울특별시 금천구 시흥대로63길 20, (시흥동,A동 지상1층)
5th row서울특별시 금천구 시흥대로 189, 지하1층 B102호 (시흥동)
ValueCountFrequency (%)
서울특별시 39
 
14.9%
금천구 39
 
14.9%
지상1층 13
 
5.0%
가산동 13
 
5.0%
시흥대로 8
 
3.1%
시흥동 8
 
3.1%
가산디지털1로 5
 
1.9%
독산동 5
 
1.9%
벚꽃로 5
 
1.9%
지하1층 4
 
1.5%
Other values (110) 122
46.7%
2024-05-04T00:40:32.935658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
223
 
14.4%
1 93
 
6.0%
시 72
 
4.7%
, 70
 
4.5%
) 45
 
2.9%
산 45
 
2.9%
( 45
 
2.9%
동 44
 
2.8%
2 42
 
2.7%
서 40
 
2.6%
Other values (99) 826
53.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 882
57.1%
Decimal Number 261
 
16.9%
Space Separator 223
 
14.4%
Other Punctuation 70
 
4.5%
Close Punctuation 45
 
2.9%
Open Punctuation 45
 
2.9%
Uppercase Letter 15
 
1.0%
Math Symbol 3
 
0.2%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 72
 
8.2%
산 45
 
5.1%
동 44
 
5.0%
서 40
 
4.5%
울 40
 
4.5%
천 40
 
4.5%
금 40
 
4.5%
구 40
 
4.5%
로 39
 
4.4%
특 39
 
4.4%
Other values (77) 443
50.2%
Decimal Number
ValueCountFrequency (%)
1 93
35.6%
2 42
16.1%
0 26
 
10.0%
3 19
 
7.3%
4 17
 
6.5%
5 16
 
6.1%
9 14
 
5.4%
8 13
 
5.0%
6 12
 
4.6%
7 9
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
B 10
66.7%
I 1
 
6.7%
T 1
 
6.7%
S 1
 
6.7%
J 1
 
6.7%
A 1
 
6.7%
Space Separator
ValueCountFrequency (%)
223
100.0%
Other Punctuation
ValueCountFrequency (%)
, 70
100.0%
Close Punctuation
ValueCountFrequency (%)
) 45
100.0%
Open Punctuation
ValueCountFrequency (%)
( 45
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 882
57.1%
Common 648
41.9%
Latin 15
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 72
 
8.2%
산 45
 
5.1%
동 44
 
5.0%
서 40
 
4.5%
울 40
 
4.5%
천 40
 
4.5%
금 40
 
4.5%
구 40
 
4.5%
로 39
 
4.4%
특 39
 
4.4%
Other values (77) 443
50.2%
Common
ValueCountFrequency (%)
223
34.4%
1 93
14.4%
, 70
 
10.8%
) 45
 
6.9%
( 45
 
6.9%
2 42
 
6.5%
0 26
 
4.0%
3 19
 
2.9%
4 17
 
2.6%
5 16
 
2.5%
Other values (6) 52
 
8.0%
Latin
ValueCountFrequency (%)
B 10
66.7%
I 1
 
6.7%
T 1
 
6.7%
S 1
 
6.7%
J 1
 
6.7%
A 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 882
57.1%
ASCII 663
42.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
223
33.6%
1 93
14.0%
, 70
 
10.6%
) 45
 
6.8%
( 45
 
6.8%
2 42
 
6.3%
0 26
 
3.9%
3 19
 
2.9%
4 17
 
2.6%
5 16
 
2.4%
Other values (12) 67
 
10.1%
Hangul
ValueCountFrequency (%)
시 72
 
8.2%
산 45
 
5.1%
동 44
 
5.0%
서 40
 
4.5%
울 40
 
4.5%
천 40
 
4.5%
금 40
 
4.5%
구 40
 
4.5%
로 39
 
4.4%
특 39
 
4.4%
Other values (77) 443
50.2%

소재지지번
Text

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size444.0 B
2024-05-04T00:40:33.498353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length41
Mean length34.461538
Min length25

Characters and Unicode

Total characters1344
Distinct characters98
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

Unique39 ?
Unique (%)100.0%

Sample

1st row서울특별시 금천구 독산동 1013번지 9호 지상1층
2nd row서울특별시 금천구 독산동 291번지 5호 시티렉스 지하1층 B110~112호
3rd row서울특별시 금천구 시흥동 108번지 62호 대현오피스텔 상가 101호
4th row서울특별시 금천구 시흥동 115번지 10호 A동 지상1층
5th row서울특별시 금천구 시흥동 991번지 5호
ValueCountFrequency (%)
서울특별시 39
 
15.6%
금천구 39
 
15.6%
가산동 18
 
7.2%
시흥동 12
 
4.8%
독산동 9
 
3.6%
지상1층 9
 
3.6%
60번지 5
 
2.0%
10호 4
 
1.6%
345번지 3
 
1.2%
9호 3
 
1.2%
Other values (91) 109
43.6%
2024-05-04T00:40:34.752210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
296
22.0%
1 81
 
6.0%
시 56
 
4.2%
지 54
 
4.0%
호 49
 
3.6%
동 42
 
3.1%
울 40
 
3.0%
서 40
 
3.0%
천 40
 
3.0%
금 40
 
3.0%
Other values (88) 606
45.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 750
55.8%
Space Separator 296
 
22.0%
Decimal Number 266
 
19.8%
Uppercase Letter 11
 
0.8%
Close Punctuation 6
 
0.4%
Open Punctuation 6
 
0.4%
Dash Punctuation 5
 
0.4%
Other Punctuation 2
 
0.1%
Math Symbol 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 56
 
7.5%
지 54
 
7.2%
호 49
 
6.5%
동 42
 
5.6%
울 40
 
5.3%
서 40
 
5.3%
천 40
 
5.3%
금 40
 
5.3%
구 39
 
5.2%
특 39
 
5.2%
Other values (66) 311
41.5%
Decimal Number
ValueCountFrequency (%)
1 81
30.5%
0 39
14.7%
2 26
 
9.8%
3 24
 
9.0%
9 21
 
7.9%
4 21
 
7.9%
8 15
 
5.6%
5 14
 
5.3%
6 13
 
4.9%
7 12
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
B 6
54.5%
S 1
 
9.1%
T 1
 
9.1%
I 1
 
9.1%
J 1
 
9.1%
A 1
 
9.1%
Space Separator
ValueCountFrequency (%)
296
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 750
55.8%
Common 583
43.4%
Latin 11
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 56
 
7.5%
지 54
 
7.2%
호 49
 
6.5%
동 42
 
5.6%
울 40
 
5.3%
서 40
 
5.3%
천 40
 
5.3%
금 40
 
5.3%
구 39
 
5.2%
특 39
 
5.2%
Other values (66) 311
41.5%
Common
ValueCountFrequency (%)
296
50.8%
1 81
 
13.9%
0 39
 
6.7%
2 26
 
4.5%
3 24
 
4.1%
9 21
 
3.6%
4 21
 
3.6%
8 15
 
2.6%
5 14
 
2.4%
6 13
 
2.2%
Other values (6) 33
 
5.7%
Latin
ValueCountFrequency (%)
B 6
54.5%
S 1
 
9.1%
T 1
 
9.1%
I 1
 
9.1%
J 1
 
9.1%
A 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 750
55.8%
ASCII 594
44.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
296
49.8%
1 81
 
13.6%
0 39
 
6.6%
2 26
 
4.4%
3 24
 
4.0%
9 21
 
3.5%
4 21
 
3.5%
8 15
 
2.5%
5 14
 
2.4%
6 13
 
2.2%
Other values (12) 44
 
7.4%
Hangul
ValueCountFrequency (%)
시 56
 
7.5%
지 54
 
7.2%
호 49
 
6.5%
동 42
 
5.6%
울 40
 
5.3%
서 40
 
5.3%
천 40
 
5.3%
금 40
 
5.3%
구 39
 
5.2%
특 39
 
5.2%
Other values (66) 311
41.5%

허가(신고)번호
Text

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size444.0 B
2024-05-04T00:40:35.146913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters858
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

Unique39 ?
Unique (%)100.0%

Sample

1st row3170000-101-2014-00031
2nd row3170000-101-2005-00251
3rd row3170000-101-2016-00117
4th row3170000-101-1992-03891
5th row3170000-101-1995-04315
ValueCountFrequency (%)
3170000-101-2014-00031 1
 
2.6%
3170000-101-2013-00279 1
 
2.6%
3170000-101-2005-00142 1
 
2.6%
3170000-101-2005-00239 1
 
2.6%
3170000-101-2007-00084 1
 
2.6%
3170000-101-2014-00157 1
 
2.6%
3170000-101-2010-00242 1
 
2.6%
3170000-101-1993-03555 1
 
2.6%
3170000-101-2011-00079 1
 
2.6%
3170000-101-2007-00225 1
 
2.6%
Other values (29) 29
74.4%
2024-05-04T00:40:36.104415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 335
39.0%
1 156
18.2%
- 117
 
13.6%
2 62
 
7.2%
3 55
 
6.4%
7 52
 
6.1%
9 24
 
2.8%
5 16
 
1.9%
4 15
 
1.7%
8 14
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 741
86.4%
Dash Punctuation 117
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 335
45.2%
1 156
21.1%
2 62
 
8.4%
3 55
 
7.4%
7 52
 
7.0%
9 24
 
3.2%
5 16
 
2.2%
4 15
 
2.0%
8 14
 
1.9%
6 12
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 117
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 335
39.0%
1 156
18.2%
- 117
 
13.6%
2 62
 
7.2%
3 55
 
6.4%
7 52
 
6.1%
9 24
 
2.8%
5 16
 
1.9%
4 15
 
1.7%
8 14
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 335
39.0%
1 156
18.2%
- 117
 
13.6%
2 62
 
7.2%
3 55
 
6.4%
7 52
 
6.1%
9 24
 
2.8%
5 16
 
1.9%
4 15
 
1.7%
8 14
 
1.6%

업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size444.0 B
한식
33 
일식
 
3
중국식
 
2
회집
 
1

Length

Max length3
Median length2
Mean length2.0512821
Min length2

Unique

Unique1 ?
Unique (%)2.6%

Sample

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

Common Values

ValueCountFrequency (%)
한식 33
84.6%
일식 3
 
7.7%
중국식 2
 
5.1%
회집 1
 
2.6%

Length

2024-05-04T00:40:36.737141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T00:40:37.125278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 33
84.6%
일식 3
 
7.7%
중국식 2
 
5.1%
회집 1
 
2.6%

주된음식
Text

MISSING 

Distinct21
Distinct (%)72.4%
Missing10
Missing (%)25.6%
Memory size444.0 B
2024-05-04T00:40:37.548797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.6551724
Min length1

Characters and Unicode

Total characters77
Distinct characters41
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)65.5%

Sample

1st row식육
2nd row설렁탕
3rd row탕수육
4th row탕류
5th row선지해장국
ValueCountFrequency (%)
식육 6
20.7%
한식 4
 
13.8%
보쌈 1
 
3.4%
청국장 1
 
3.4%
갈비탕 1
 
3.4%
샤브샤브 1
 
3.4%
숯불구이 1
 
3.4%
자장면 1
 
3.4%
초밥 1
 
3.4%
국밥 1
 
3.4%
Other values (11) 11
37.9%
2024-05-04T00:40:38.565472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
식 10
 
13.0%
육 7
 
9.1%
탕 5
 
6.5%
국 4
 
5.2%
한 4
 
5.2%
장 4
 
5.2%
브 2
 
2.6%
샤 2
 
2.6%
갈 2
 
2.6%
비 2
 
2.6%
Other values (31) 35
45.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 76
98.7%
Other Punctuation 1
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
식 10
 
13.2%
육 7
 
9.2%
탕 5
 
6.6%
국 4
 
5.3%
한 4
 
5.3%
장 4
 
5.3%
브 2
 
2.6%
샤 2
 
2.6%
갈 2
 
2.6%
비 2
 
2.6%
Other values (30) 34
44.7%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 76
98.7%
Common 1
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
식 10
 
13.2%
육 7
 
9.2%
탕 5
 
6.6%
국 4
 
5.3%
한 4
 
5.3%
장 4
 
5.3%
브 2
 
2.6%
샤 2
 
2.6%
갈 2
 
2.6%
비 2
 
2.6%
Other values (30) 34
44.7%
Common
ValueCountFrequency (%)
, 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 76
98.7%
ASCII 1
 
1.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
식 10
 
13.2%
육 7
 
9.2%
탕 5
 
6.6%
국 4
 
5.3%
한 4
 
5.3%
장 4
 
5.3%
브 2
 
2.6%
샤 2
 
2.6%
갈 2
 
2.6%
비 2
 
2.6%
Other values (30) 34
44.7%
ASCII
ValueCountFrequency (%)
, 1
100.0%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean245.80897
Minimum41.58
Maximum1262.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2024-05-04T00:40:39.068905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41.58
5-th percentile49.865
Q196.15
median144.48
Q3195.96
95-th percentile729.589
Maximum1262.78
Range1221.2
Interquartile range (IQR)99.81

Descriptive statistics

Standard deviation265.48047
Coefficient of variation (CV)1.0800276
Kurtosis4.7792886
Mean245.80897
Median Absolute Deviation (MAD)50.16
Skewness2.151632
Sum9586.55
Variance70479.88
MonotonicityNot monotonic
2024-05-04T00:40:39.547979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
90.48 1
 
2.6%
107.67 1
 
2.6%
136.89 1
 
2.6%
601.5 1
 
2.6%
52.89 1
 
2.6%
41.58 1
 
2.6%
75.26 1
 
2.6%
270.22 1
 
2.6%
154.75 1
 
2.6%
197.28 1
 
2.6%
Other values (29) 29
74.4%
ValueCountFrequency (%)
41.58 1
2.6%
47.48 1
2.6%
50.13 1
2.6%
52.89 1
2.6%
75.26 1
2.6%
79.38 1
2.6%
85.95 1
2.6%
90.0 1
2.6%
90.48 1
2.6%
94.4 1
2.6%
ValueCountFrequency (%)
1262.78 1
2.6%
735.52 1
2.6%
728.93 1
2.6%
677.0 1
2.6%
646.92 1
2.6%
634.08 1
2.6%
601.5 1
2.6%
382.49 1
2.6%
270.22 1
2.6%
197.28 1
2.6%

행정동명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size444.0 B
가산동
18 
시흥제1동
9 
독산제1동
5 
독산제4동
3 
시흥제3동
2 
Other values (2)
2 

Length

Max length5
Median length5
Mean length4.0769231
Min length3

Unique

Unique2 ?
Unique (%)5.1%

Sample

1st row독산제4동
2nd row독산제1동
3rd row시흥제1동
4th row시흥제1동
5th row시흥제1동

Common Values

ValueCountFrequency (%)
가산동 18
46.2%
시흥제1동 9
23.1%
독산제1동 5
 
12.8%
독산제4동 3
 
7.7%
시흥제3동 2
 
5.1%
독산제3동 1
 
2.6%
시흥제2동 1
 
2.6%

Length

2024-05-04T00:40:39.970143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T00:40:40.395234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가산동 18
46.2%
시흥제1동 9
23.1%
독산제1동 5
 
12.8%
독산제4동 3
 
7.7%
시흥제3동 2
 
5.1%
독산제3동 1
 
2.6%
시흥제2동 1
 
2.6%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size444.0 B
상수도전용
31 
<NA>
8 

Length

Max length5
Median length5
Mean length4.7948718
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 31
79.5%
<NA> 8
 
20.5%

Length

2024-05-04T00:40:40.899132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T00:40:41.314941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 31
79.5%
na 8
 
20.5%

소재지전화번호
Text

MISSING 

Distinct28
Distinct (%)100.0%
Missing11
Missing (%)28.2%
Memory size444.0 B
2024-05-04T00:40:41.688799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.285714
Min length8

Characters and Unicode

Total characters288
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

Unique28 ?
Unique (%)100.0%

Sample

1st row02 804 1175
2nd row02 8033759
3rd row02 8083557
4th row0220296260
5th row02 63436969
ValueCountFrequency (%)
02 20
40.0%
8659233 1
 
2.0%
33970503 1
 
2.0%
8023114 1
 
2.0%
0220672454 1
 
2.0%
64435353 1
 
2.0%
66790044 1
 
2.0%
8081888 1
 
2.0%
5416 1
 
2.0%
02864 1
 
2.0%
Other values (21) 21
42.0%
2024-05-04T00:40:42.608021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 51
17.7%
2 45
15.6%
8 29
10.1%
27
9.4%
3 24
8.3%
4 22
7.6%
5 22
7.6%
6 21
7.3%
7 18
 
6.2%
9 17
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 261
90.6%
Space Separator 27
 
9.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 51
19.5%
2 45
17.2%
8 29
11.1%
3 24
9.2%
4 22
8.4%
5 22
8.4%
6 21
8.0%
7 18
 
6.9%
9 17
 
6.5%
1 12
 
4.6%
Space Separator
ValueCountFrequency (%)
27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 51
17.7%
2 45
15.6%
8 29
10.1%
27
9.4%
3 24
8.3%
4 22
7.6%
5 22
7.6%
6 21
7.3%
7 18
 
6.2%
9 17
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 51
17.7%
2 45
15.6%
8 29
10.1%
27
9.4%
3 24
8.3%
4 22
7.6%
5 22
7.6%
6 21
7.3%
7 18
 
6.2%
9 17
 
5.9%

Interactions

2024-05-04T00:40:20.661631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:14.183436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:15.680516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:17.347578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:19.126412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:21.012127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:14.455901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:15.999503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:17.696746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:19.474279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:21.288280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:14.726831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:16.283855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:17.997226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:19.776503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:21.634043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:15.110132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:16.593475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:18.392462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:20.056182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:21.952235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:15.399033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:16.974940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:18.724121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:40:20.318753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T00:40:42.886168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명소재지전화번호
지정년도1.0000.6730.9991.0001.0001.0001.0001.0000.0001.0000.0000.0001.000
지정번호0.6731.0000.6970.6731.0001.0001.0001.0000.4931.0000.5400.0001.000
신청일자0.9990.6971.0000.9991.0001.0001.0001.0000.0001.0000.0000.0001.000
지정일자1.0000.6730.9991.0001.0001.0001.0001.0000.0001.0000.0000.0001.000
업소명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지지번1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
허가(신고)번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
업태명0.0000.4930.0000.0001.0001.0001.0001.0001.0001.0000.0000.0001.000
주된음식1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.8591.000
영업장면적(㎡)0.0000.5400.0000.0001.0001.0001.0001.0000.0000.0001.0000.0001.000
행정동명0.0000.0000.0000.0001.0001.0001.0001.0000.0000.8590.0001.0001.000
소재지전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-05-04T00:40:43.268998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
급수시설구분업태명행정동명
급수시설구분1.0001.0001.000
업태명1.0001.0000.000
행정동명1.0000.0001.000
2024-05-04T00:40:43.463698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.000-0.6780.9970.998-0.2700.2520.0001.000
지정번호-0.6781.000-0.675-0.6680.1820.3400.0001.000
신청일자0.997-0.6751.0000.998-0.2690.2730.0001.000
지정일자0.998-0.6680.9981.000-0.2670.2520.0001.000
영업장면적(㎡)-0.2700.182-0.269-0.2671.0000.0000.0001.000
업태명0.2520.3400.2730.2520.0001.0000.0001.000
행정동명0.0000.0000.0000.0000.0000.0001.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-04T00:40:22.343805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T00:40:23.238825image/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.
2024-05-04T00:40:23.614535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
03170000201652016122620161226서울집서울특별시 금천구 시흥대로122길 24, 지상1층 (독산동)서울특별시 금천구 독산동 1013번지 9호 지상1층3170000-101-2014-00031한식식육90.48독산제4동상수도전용<NA>
1317000020102262010081320100930뽕잎사랑샤브샤브칼국수보쌈서울특별시 금천구 시흥대로 399, 지하1층 B110~112호 (독산동, 시티렉스)서울특별시 금천구 독산동 291번지 5호 시티렉스 지하1층 B110~112호3170000-101-2005-00251한식<NA>107.67독산제1동상수도전용<NA>
23170000201622016122620161226본가집가마솥설렁탕서울특별시 금천구 시흥대로 269, 지상1층 101호 (시흥동, 대현오피스텔)서울특별시 금천구 시흥동 108번지 62호 대현오피스텔 상가 101호3170000-101-2016-00117한식설렁탕180.05시흥제1동<NA>02 804 1175
331700002004982004070120040721동흥관서울특별시 금천구 시흥대로63길 20, (시흥동,A동 지상1층)서울특별시 금천구 시흥동 115번지 10호 A동 지상1층3170000-101-1992-03891중국식탕수육151.04시흥제1동상수도전용02 8033759
431700002006172006051020060710백번가 코다리서울특별시 금천구 시흥대로 189, 지하1층 B102호 (시흥동)서울특별시 금천구 시흥동 991번지 5호3170000-101-1995-04315한식탕류103.23시흥제1동상수도전용02 8083557
531700002013132013053120130716청진동 해장국서울특별시 금천구 시흥대로 102, 101호 (시흥동)서울특별시 금천구 시흥동 940번지 4호 -1013170000-101-2008-00226한식선지해장국109.2시흥제3동상수도전용<NA>
631700002013122013053120130716델리명가서울특별시 금천구 가산디지털2로 14, (가산동,대륭테크노타운12차 B211~B214호(가산디지털2길185))서울특별시 금천구 가산동 327번지 32호 대륭테크노타운12차 B211~B214호(가산디지털2길185)3170000-101-2008-00141한식한식677.0가산동상수도전용0220296260
731700002013162013053120130716델리명가한라점서울특별시 금천구 가산디지털2로 53, (가산동,한라시그마밸리 B115호)서울특별시 금천구 가산동 345번지 90호 한라시그마밸리 B115호3170000-101-2010-00007한식한식735.52가산동상수도전용02 63436969
8317000020081972008063020080717우미정서울특별시 금천구 시흥대로138길 19, 지상1층 (독산동)서울특별시 금천구 독산동 988번지 11호3170000-101-2001-07048한식생선회94.4독산제3동상수도전용02 8520107
9317000020081662008063020080717주식회사 대성푸드빌(월드점)서울특별시 금천구 벚꽃로 254, 지하1층 B104호 (가산동, 월드메르디앙벤처센타)서울특별시 금천구 가산동 60번지 24호 월드메르디앙벤처센타 B104호3170000-101-2004-00036한식백반646.92가산동상수도전용0221137799
시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
293170000201512015102320151130풍년갈비서울특별시 금천구 디지털로12길 33, 지상1층 (가산동)서울특별시 금천구 가산동 143번지 28호 지상1층3170000-101-1989-05196한식<NA>197.28가산동상수도전용02864 5416
3031700002007392007070620070726강강술래시흥점서울특별시 금천구 시흥대로 193, 아람아이씨티타워 지상1,2층 201~216호 (시흥동)서울특별시 금천구 시흥동 991번지 아람아이씨티타워3170000-101-1997-04142한식숯불구이728.93시흥제1동상수도전용02 8081888
31317000020102142010081320100930미각서울특별시 금천구 가산디지털1로 151, 104호 (가산동, 이노플렉스1차)서울특별시 금천구 가산동 371번지 47호 이노플렉스1차-1043170000-101-2009-00142한식<NA>155.93가산동상수도전용02 66790044
323170000201412014123120141231엘에이북창동순두부서울특별시 금천구 가산디지털2로 53, (가산동,한라시그마밸리 206호)서울특별시 금천구 가산동 345번지 90호 한라시그마밸리 206호3170000-101-2009-00324한식<NA>144.48가산동상수도전용02 64435353
3331700002013302013102220131210바르미샤브샤브서울특별시 금천구 벚꽃로 266, (가산동, 마리오아울렛3관 13층)서울특별시 금천구 가산동 60번지 20호 마리오아울렛3관 13층3170000-101-2012-00212한식샤브샤브382.49가산동상수도전용0220672454
34317000020102362010081320100930미미초밥서울특별시 금천구 시흥대로 192, (시흥동)서울특별시 금천구 시흥동 903번지 1호3170000-101-2001-07006회집<NA>85.95시흥제1동상수도전용<NA>
353170000201352013053120130716무드무드서울특별시 금천구 탑골로4길 58, (시흥동, 지상1,2층)서울특별시 금천구 시흥동 243번지 46호 지상1,2층3170000-101-2003-00320한식<NA>123.55시흥제2동상수도전용02 8023114
3631700002006552006051020060710내조국국밥서울특별시 금천구 벚꽃로 278, SJ테크노빌 B144,B145호 (가산동)서울특별시 금천구 가산동 60번지 19호 SJ테크노빌3170000-101-2005-00186한식갈비탕194.64가산동상수도전용33970503
373170000201632016122620161226밀면의 법칙 (독산점)서울특별시 금천구 독산로75길 20, 지상1층 (독산동)서울특별시 금천구 독산동 196번지 33호3170000-101-2014-00227일식회79.38독산제1동<NA><NA>
38317000020092122009070320090804대촌 불쭈꾸미 본점서울특별시 금천구 벚꽃로 312, (가산동, 지상1층)서울특별시 금천구 가산동 41번지 11호 지상1층3170000-101-2008-00266한식<NA>186.21가산동상수도전용02 8557417