Overview

Dataset statistics

Number of variables16
Number of observations247
Missing cells401
Missing cells (%)10.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.9 KiB
Average record size in memory136.5 B

Variable types

Categorical4
Numeric6
Unsupported1
Text5

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,취소일자,불가일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,주된음식,영업장면적(㎡),행정동명,급수시설구분
Author구로구
URLhttps://data.seoul.go.kr/dataList/OA-2330/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
업태명 is highly overall correlated with 영업장면적(㎡) and 1 other fieldsHigh correlation
행정동명 is highly overall correlated with 급수시설구분High correlation
급수시설구분 is highly overall correlated with 지정년도 and 7 other fieldsHigh correlation
지정년도 is highly overall correlated with 신청일자 and 3 other fieldsHigh correlation
지정번호 is highly overall correlated with 급수시설구분High 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
취소일자 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
영업장면적(㎡) is highly overall correlated with 업태명 and 1 other fieldsHigh correlation
업태명 is highly imbalanced (62.3%)Imbalance
급수시설구분 is highly imbalanced (59.4%)Imbalance
지정년도 has 18 (7.3%) missing valuesMissing
지정번호 has 18 (7.3%) missing valuesMissing
지정일자 has 18 (7.3%) missing valuesMissing
취소일자 has 66 (26.7%) missing valuesMissing
불가일자 has 247 (100.0%) missing valuesMissing
소재지도로명 has 11 (4.5%) missing valuesMissing
주된음식 has 23 (9.3%) missing valuesMissing
불가일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-11 06:29:38.907890
Analysis finished2024-05-11 06:29:58.884742
Duration19.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
3160000
247 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3160000
2nd row3160000
3rd row3160000
4th row3160000
5th row3160000

Common Values

ValueCountFrequency (%)
3160000 247
100.0%

Length

2024-05-11T06:29:59.182439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:29:59.645653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3160000 247
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)9.6%
Missing18
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean2006.607
Minimum1987
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-11T06:30:00.023373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1987
5-th percentile2002
Q12002
median2006
Q32010
95-th percentile2018
Maximum2023
Range36
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.3164464
Coefficient of variation (CV)0.0031478244
Kurtosis1.9180603
Mean2006.607
Median Absolute Deviation (MAD)4
Skewness-0.2847577
Sum459513
Variance39.897495
MonotonicityNot monotonic
2024-05-11T06:30:00.579622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2002 50
20.2%
2004 25
10.1%
2006 24
9.7%
2005 17
 
6.9%
2007 14
 
5.7%
2008 12
 
4.9%
2014 11
 
4.5%
2003 10
 
4.0%
2009 8
 
3.2%
2015 7
 
2.8%
Other values (12) 51
20.6%
(Missing) 18
 
7.3%
ValueCountFrequency (%)
1987 7
 
2.8%
1988 1
 
0.4%
2002 50
20.2%
2003 10
 
4.0%
2004 25
10.1%
2005 17
 
6.9%
2006 24
9.7%
2007 14
 
5.7%
2008 12
 
4.9%
2009 8
 
3.2%
ValueCountFrequency (%)
2023 1
 
0.4%
2022 2
 
0.8%
2021 2
 
0.8%
2019 3
 
1.2%
2018 6
2.4%
2016 6
2.4%
2015 7
2.8%
2014 11
4.5%
2013 5
2.0%
2012 5
2.0%

지정번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct116
Distinct (%)50.7%
Missing18
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean60.820961
Minimum1
Maximum247
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-11T06:30:01.039212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median29
Q386
95-th percentile220.6
Maximum247
Range246
Interquartile range (IQR)78

Descriptive statistics

Standard deviation73.043176
Coefficient of variation (CV)1.200954
Kurtosis0.20511433
Mean60.820961
Median Absolute Deviation (MAD)24
Skewness1.2816027
Sum13928
Variance5335.3055
MonotonicityNot monotonic
2024-05-11T06:30:01.607691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 13
 
5.3%
1 10
 
4.0%
4 8
 
3.2%
9 7
 
2.8%
5 7
 
2.8%
3 6
 
2.4%
8 6
 
2.4%
6 6
 
2.4%
11 5
 
2.0%
10 5
 
2.0%
Other values (106) 156
63.2%
(Missing) 18
 
7.3%
ValueCountFrequency (%)
1 10
4.0%
2 13
5.3%
3 6
2.4%
4 8
3.2%
5 7
2.8%
6 6
2.4%
7 3
 
1.2%
8 6
2.4%
9 7
2.8%
10 5
 
2.0%
ValueCountFrequency (%)
247 1
0.4%
243 1
0.4%
240 1
0.4%
238 1
0.4%
233 1
0.4%
228 1
0.4%
226 1
0.4%
225 1
0.4%
224 1
0.4%
223 1
0.4%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20064655
Minimum19870409
Maximum20231016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-11T06:30:02.070801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19870409
5-th percentile20020351
Q120020402
median20060619
Q320090701
95-th percentile20181120
Maximum20231016
Range360607
Interquartile range (IQR)70299.5

Descriptive statistics

Standard deviation62583.184
Coefficient of variation (CV)0.003119076
Kurtosis2.0445623
Mean20064655
Median Absolute Deviation (MAD)40011
Skewness-0.28888868
Sum4.9559697 × 109
Variance3.9166549 × 109
MonotonicityNot monotonic
2024-05-11T06:30:02.634329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20020401 49
19.8%
20040415 26
 
10.5%
20060619 21
 
8.5%
20070629 18
 
7.3%
20050601 15
 
6.1%
20080701 13
 
5.3%
20030401 11
 
4.5%
20090701 8
 
3.2%
20100630 7
 
2.8%
19870409 7
 
2.8%
Other values (44) 72
29.1%
ValueCountFrequency (%)
19870409 7
 
2.8%
19880525 2
 
0.8%
20020320 1
 
0.4%
20020321 1
 
0.4%
20020325 1
 
0.4%
20020330 1
 
0.4%
20020401 49
19.8%
20020402 1
 
0.4%
20020410 1
 
0.4%
20020502 1
 
0.4%
ValueCountFrequency (%)
20231016 1
 
0.4%
20221001 1
 
0.4%
20220901 1
 
0.4%
20211001 2
 
0.8%
20191010 3
1.2%
20181120 6
2.4%
20161108 6
2.4%
20151012 1
 
0.4%
20150917 1
 
0.4%
20150915 3
1.2%

지정일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)10.5%
Missing18
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean20066825
Minimum19870409
Maximum20231110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-11T06:30:03.143543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19870409
5-th percentile20020502
Q120020502
median20060702
Q320100802
95-th percentile20181228
Maximum20231110
Range360701
Interquartile range (IQR)80300

Descriptive statistics

Standard deviation63363.176
Coefficient of variation (CV)0.0031576084
Kurtosis1.9005949
Mean20066825
Median Absolute Deviation (MAD)40100
Skewness-0.28010665
Sum4.5953029 × 109
Variance4.014892 × 109
MonotonicityNot monotonic
2024-05-11T06:30:03.568074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
20020502 50
20.2%
20040702 25
10.1%
20060710 19
 
7.7%
20050720 17
 
6.9%
20070705 14
 
5.7%
20080717 12
 
4.9%
20030702 10
 
4.0%
20090803 8
 
3.2%
20151118 7
 
2.8%
19870409 7
 
2.8%
Other values (14) 60
24.3%
(Missing) 18
 
7.3%
ValueCountFrequency (%)
19870409 7
 
2.8%
19880525 1
 
0.4%
20020502 50
20.2%
20030702 10
 
4.0%
20040702 25
10.1%
20050720 17
 
6.9%
20060702 5
 
2.0%
20060710 19
 
7.7%
20070705 14
 
5.7%
20080717 12
 
4.9%
ValueCountFrequency (%)
20231110 1
 
0.4%
20221222 2
 
0.8%
20211109 2
 
0.8%
20191107 3
1.2%
20181228 6
2.4%
20161205 6
2.4%
20151118 7
2.8%
20141218 4
1.6%
20140619 7
2.8%
20131210 5
2.0%

취소일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct83
Distinct (%)45.9%
Missing66
Missing (%)26.7%
Infinite0
Infinite (%)0.0%
Mean20100514
Minimum20020501
Maximum20231110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-11T06:30:04.079747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20020501
5-th percentile20020806
Q120060119
median20100802
Q320141218
95-th percentile20191107
Maximum20231110
Range210609
Interquartile range (IQR)81099

Descriptive statistics

Standard deviation52415.04
Coefficient of variation (CV)0.0026076467
Kurtosis-0.50545673
Mean20100514
Median Absolute Deviation (MAD)40416
Skewness0.38538154
Sum3.6381931 × 109
Variance2.7473364 × 109
MonotonicityNot monotonic
2024-05-11T06:30:04.593536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20141218 18
 
7.3%
20111222 15
 
6.1%
20080717 12
 
4.9%
20100802 11
 
4.5%
20020501 8
 
3.2%
20090803 7
 
2.8%
20121122 6
 
2.4%
20191107 5
 
2.0%
20181228 5
 
2.0%
20070705 5
 
2.0%
Other values (73) 89
36.0%
(Missing) 66
26.7%
ValueCountFrequency (%)
20020501 8
3.2%
20020507 1
 
0.4%
20020806 1
 
0.4%
20020831 1
 
0.4%
20020926 1
 
0.4%
20021029 1
 
0.4%
20030212 1
 
0.4%
20030224 1
 
0.4%
20030324 1
 
0.4%
20030415 1
 
0.4%
ValueCountFrequency (%)
20231110 2
 
0.8%
20221222 1
 
0.4%
20221111 1
 
0.4%
20211109 3
1.2%
20201109 1
 
0.4%
20191107 5
2.0%
20181228 5
2.0%
20171219 2
 
0.8%
20171130 2
 
0.8%
20171121 1
 
0.4%

불가일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing247
Missing (%)100.0%
Memory size2.3 KiB

업소명
Text

Distinct184
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-05-11T06:30:05.315133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length6.5587045
Min length2

Characters and Unicode

Total characters1620
Distinct characters298
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique137 ?
Unique (%)55.5%

Sample

1st row콩부자 개봉점
2nd row전주돌밥추어탕
3rd row갈비명가
4th row가빈
5th row가빈
ValueCountFrequency (%)
구로디지털단지점 6
 
1.8%
개봉점 5
 
1.5%
큰바다수산 4
 
1.2%
부뚜막 4
 
1.2%
청국장 4
 
1.2%
서울회마차 4
 
1.2%
고척점 4
 
1.2%
구로디지털단지역본점 3
 
0.9%
본가 3
 
0.9%
궁원복집 3
 
0.9%
Other values (226) 291
87.9%
2024-05-11T06:30:06.917387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
84
 
5.2%
점 47
 
2.9%
지 36
 
2.2%
구 31
 
1.9%
가 29
 
1.8%
로 28
 
1.7%
비 28
 
1.7%
식 26
 
1.6%
갈 24
 
1.5%
탕 24
 
1.5%
Other values (288) 1263
78.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1509
93.1%
Space Separator 84
 
5.2%
Uppercase Letter 11
 
0.7%
Other Punctuation 7
 
0.4%
Decimal Number 3
 
0.2%
Close Punctuation 3
 
0.2%
Open Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 47
 
3.1%
지 36
 
2.4%
구 31
 
2.1%
가 29
 
1.9%
로 28
 
1.9%
비 28
 
1.9%
식 26
 
1.7%
갈 24
 
1.6%
탕 24
 
1.6%
당 23
 
1.5%
Other values (271) 1213
80.4%
Uppercase Letter
ValueCountFrequency (%)
N 2
18.2%
I 2
18.2%
S 1
9.1%
J 1
9.1%
M 1
9.1%
K 1
9.1%
O 1
9.1%
W 1
9.1%
E 1
9.1%
Other Punctuation
ValueCountFrequency (%)
& 3
42.9%
？ 2
28.6%
? 1
 
14.3%
. 1
 
14.3%
Space Separator
ValueCountFrequency (%)
84
100.0%
Decimal Number
ValueCountFrequency (%)
2 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1509
93.1%
Common 100
 
6.2%
Latin 11
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 47
 
3.1%
지 36
 
2.4%
구 31
 
2.1%
가 29
 
1.9%
로 28
 
1.9%
비 28
 
1.9%
식 26
 
1.7%
갈 24
 
1.6%
탕 24
 
1.6%
당 23
 
1.5%
Other values (271) 1213
80.4%
Latin
ValueCountFrequency (%)
N 2
18.2%
I 2
18.2%
S 1
9.1%
J 1
9.1%
M 1
9.1%
K 1
9.1%
O 1
9.1%
W 1
9.1%
E 1
9.1%
Common
ValueCountFrequency (%)
84
84.0%
& 3
 
3.0%
2 3
 
3.0%
) 3
 
3.0%
( 3
 
3.0%
？ 2
 
2.0%
? 1
 
1.0%
. 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1509
93.1%
ASCII 109
 
6.7%
None 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
84
77.1%
& 3
 
2.8%
2 3
 
2.8%
) 3
 
2.8%
( 3
 
2.8%
N 2
 
1.8%
I 2
 
1.8%
S 1
 
0.9%
? 1
 
0.9%
J 1
 
0.9%
Other values (6) 6
 
5.5%
Hangul
ValueCountFrequency (%)
점 47
 
3.1%
지 36
 
2.4%
구 31
 
2.1%
가 29
 
1.9%
로 28
 
1.9%
비 28
 
1.9%
식 26
 
1.7%
갈 24
 
1.6%
탕 24
 
1.6%
당 23
 
1.5%
Other values (271) 1213
80.4%
None
ValueCountFrequency (%)
？ 2
100.0%

소재지도로명
Text

MISSING 

Distinct176
Distinct (%)74.6%
Missing11
Missing (%)4.5%
Memory size2.1 KiB
2024-05-11T06:30:07.724300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length66
Median length50
Mean length30.29661
Min length22

Characters and Unicode

Total characters7150
Distinct characters137
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

Unique132 ?
Unique (%)55.9%

Sample

1st row서울특별시 구로구 개봉로 56, (개봉동)
2nd row서울특별시 구로구 경인로23길 8, 2층 201호 (오류동, 대서프라자)
3rd row서울특별시 구로구 가마산로 260, (구로동)
4th row서울특별시 구로구 구로동로 174-9, (구로동)
5th row서울특별시 구로구 구로동로 174-9, (구로동)
ValueCountFrequency (%)
서울특별시 236
17.7%
구로구 236
17.7%
구로동 119
 
8.9%
개봉동 37
 
2.8%
고척동 37
 
2.8%
1층 33
 
2.5%
디지털로32길 22
 
1.6%
경인로 18
 
1.3%
오류동 15
 
1.1%
고척로 12
 
0.9%
Other values (287) 570
42.7%
2024-05-11T06:30:09.345727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1099
 
15.4%
구 641
 
9.0%
로 641
 
9.0%
, 316
 
4.4%
1 276
 
3.9%
동 257
 
3.6%
서 243
 
3.4%
시 240
 
3.4%
( 240
 
3.4%
) 240
 
3.4%
Other values (127) 2957
41.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4056
56.7%
Decimal Number 1132
 
15.8%
Space Separator 1099
 
15.4%
Other Punctuation 320
 
4.5%
Open Punctuation 240
 
3.4%
Close Punctuation 240
 
3.4%
Dash Punctuation 49
 
0.7%
Uppercase Letter 13
 
0.2%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 641
15.8%
로 641
15.8%
동 257
 
6.3%
서 243
 
6.0%
시 240
 
5.9%
별 236
 
5.8%
특 236
 
5.8%
울 236
 
5.8%
길 143
 
3.5%
봉 66
 
1.6%
Other values (106) 1117
27.5%
Decimal Number
ValueCountFrequency (%)
1 276
24.4%
2 214
18.9%
3 147
13.0%
0 90
 
8.0%
7 81
 
7.2%
4 71
 
6.3%
6 68
 
6.0%
5 64
 
5.7%
9 61
 
5.4%
8 60
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
B 10
76.9%
K 1
 
7.7%
N 1
 
7.7%
J 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
, 316
98.8%
. 4
 
1.2%
Space Separator
ValueCountFrequency (%)
1099
100.0%
Open Punctuation
ValueCountFrequency (%)
( 240
100.0%
Close Punctuation
ValueCountFrequency (%)
) 240
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4056
56.7%
Common 3081
43.1%
Latin 13
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 641
15.8%
로 641
15.8%
동 257
 
6.3%
서 243
 
6.0%
시 240
 
5.9%
별 236
 
5.8%
특 236
 
5.8%
울 236
 
5.8%
길 143
 
3.5%
봉 66
 
1.6%
Other values (106) 1117
27.5%
Common
ValueCountFrequency (%)
1099
35.7%
, 316
 
10.3%
1 276
 
9.0%
( 240
 
7.8%
) 240
 
7.8%
2 214
 
6.9%
3 147
 
4.8%
0 90
 
2.9%
7 81
 
2.6%
4 71
 
2.3%
Other values (7) 307
 
10.0%
Latin
ValueCountFrequency (%)
B 10
76.9%
K 1
 
7.7%
N 1
 
7.7%
J 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4056
56.7%
ASCII 3094
43.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1099
35.5%
, 316
 
10.2%
1 276
 
8.9%
( 240
 
7.8%
) 240
 
7.8%
2 214
 
6.9%
3 147
 
4.8%
0 90
 
2.9%
7 81
 
2.6%
4 71
 
2.3%
Other values (11) 320
 
10.3%
Hangul
ValueCountFrequency (%)
구 641
15.8%
로 641
15.8%
동 257
 
6.3%
서 243
 
6.0%
시 240
 
5.9%
별 236
 
5.8%
특 236
 
5.8%
울 236
 
5.8%
길 143
 
3.5%
봉 66
 
1.6%
Other values (106) 1117
27.5%

소재지지번
Text

Distinct178
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-05-11T06:30:10.357333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length47
Mean length28.534413
Min length22

Characters and Unicode

Total characters7048
Distinct characters131
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

Unique127 ?
Unique (%)51.4%

Sample

1st row서울특별시 구로구 개봉동 403번지 65호
2nd row서울특별시 구로구 오류동 38번지 1호 대서프라자-201
3rd row서울특별시 구로구 구로동 98번지 1호
4th row서울특별시 구로구 구로동 416번지 23호
5th row서울특별시 구로구 구로동 416번지 23호
ValueCountFrequency (%)
구로구 248
18.4%
서울특별시 247
18.4%
구로동 135
 
10.0%
개봉동 43
 
3.2%
고척동 40
 
3.0%
1124번지 23
 
1.7%
1호 19
 
1.4%
3호 17
 
1.3%
1층 16
 
1.2%
오류동 16
 
1.2%
Other values (265) 542
40.3%
2024-05-11T06:30:11.946357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1765
25.0%
구 639
 
9.1%
로 389
 
5.5%
1 369
 
5.2%
지 262
 
3.7%
동 254
 
3.6%
호 251
 
3.6%
서 250
 
3.5%
번 247
 
3.5%
울 247
 
3.5%
Other values (121) 2375
33.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3877
55.0%
Space Separator 1765
25.0%
Decimal Number 1333
 
18.9%
Other Punctuation 24
 
0.3%
Dash Punctuation 23
 
0.3%
Uppercase Letter 16
 
0.2%
Close Punctuation 5
 
0.1%
Open Punctuation 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 639
16.5%
로 389
10.0%
지 262
 
6.8%
동 254
 
6.6%
호 251
 
6.5%
서 250
 
6.4%
번 247
 
6.4%
울 247
 
6.4%
시 247
 
6.4%
별 247
 
6.4%
Other values (99) 844
21.8%
Decimal Number
ValueCountFrequency (%)
1 369
27.7%
2 191
14.3%
3 147
 
11.0%
4 128
 
9.6%
0 118
 
8.9%
7 90
 
6.8%
5 86
 
6.5%
6 77
 
5.8%
8 71
 
5.3%
9 56
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
B 11
68.8%
N 1
 
6.2%
J 1
 
6.2%
K 1
 
6.2%
C 1
 
6.2%
D 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 20
83.3%
. 4
 
16.7%
Space Separator
ValueCountFrequency (%)
1765
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3877
55.0%
Common 3155
44.8%
Latin 16
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 639
16.5%
로 389
10.0%
지 262
 
6.8%
동 254
 
6.6%
호 251
 
6.5%
서 250
 
6.4%
번 247
 
6.4%
울 247
 
6.4%
시 247
 
6.4%
별 247
 
6.4%
Other values (99) 844
21.8%
Common
ValueCountFrequency (%)
1765
55.9%
1 369
 
11.7%
2 191
 
6.1%
3 147
 
4.7%
4 128
 
4.1%
0 118
 
3.7%
7 90
 
2.9%
5 86
 
2.7%
6 77
 
2.4%
8 71
 
2.3%
Other values (6) 113
 
3.6%
Latin
ValueCountFrequency (%)
B 11
68.8%
N 1
 
6.2%
J 1
 
6.2%
K 1
 
6.2%
C 1
 
6.2%
D 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3877
55.0%
ASCII 3171
45.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1765
55.7%
1 369
 
11.6%
2 191
 
6.0%
3 147
 
4.6%
4 128
 
4.0%
0 118
 
3.7%
7 90
 
2.8%
5 86
 
2.7%
6 77
 
2.4%
8 71
 
2.2%
Other values (12) 129
 
4.1%
Hangul
ValueCountFrequency (%)
구 639
16.5%
로 389
10.0%
지 262
 
6.8%
동 254
 
6.6%
호 251
 
6.5%
서 250
 
6.4%
번 247
 
6.4%
울 247
 
6.4%
시 247
 
6.4%
별 247
 
6.4%
Other values (99) 844
21.8%

허가(신고)번호
Text

Distinct186
Distinct (%)75.3%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-05-11T06:30:12.647596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters5434
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

Unique139 ?
Unique (%)56.3%

Sample

1st row3160000-101-2005-00362
2nd row3160000-101-2000-08432
3rd row3160000-101-2006-00197
4th row3160000-101-2003-00486
5th row3160000-101-2003-00486
ValueCountFrequency (%)
3160000-101-2004-00157 4
 
1.6%
3160000-101-1984-08300 4
 
1.6%
3160000-101-2000-08811 3
 
1.2%
3160000-101-1998-01990 3
 
1.2%
3160000-101-2000-08823 3
 
1.2%
3160000-101-2000-08627 3
 
1.2%
3160000-101-1984-07962 3
 
1.2%
3160000-101-1996-01877 3
 
1.2%
3160000-101-2006-00197 3
 
1.2%
3160000-101-1998-01306 3
 
1.2%
Other values (176) 215
87.0%
2024-05-11T06:30:13.820899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1955
36.0%
1 1027
18.9%
- 741
 
13.6%
3 372
 
6.8%
6 339
 
6.2%
9 282
 
5.2%
2 273
 
5.0%
8 139
 
2.6%
7 110
 
2.0%
4 105
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4693
86.4%
Dash Punctuation 741
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1955
41.7%
1 1027
21.9%
3 372
 
7.9%
6 339
 
7.2%
9 282
 
6.0%
2 273
 
5.8%
8 139
 
3.0%
7 110
 
2.3%
4 105
 
2.2%
5 91
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 741
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5434
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1955
36.0%
1 1027
18.9%
- 741
 
13.6%
3 372
 
6.8%
6 339
 
6.2%
9 282
 
5.2%
2 273
 
5.0%
8 139
 
2.6%
7 110
 
2.0%
4 105
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5434
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1955
36.0%
1 1027
18.9%
- 741
 
13.6%
3 372
 
6.8%
6 339
 
6.2%
9 282
 
5.2%
2 273
 
5.0%
8 139
 
2.6%
7 110
 
2.0%
4 105
 
1.9%

업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
한식
196 
일식
 
14
중국식
 
10
호프/통닭
 
8
통닭(치킨)
 
4
Other values (7)
 
15

Length

Max length8
Median length2
Mean length2.3198381
Min length2

Unique

Unique2 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
한식 196
79.4%
일식 14
 
5.7%
중국식 10
 
4.0%
호프/통닭 8
 
3.2%
통닭(치킨) 4
 
1.6%
복어취급 4
 
1.6%
분식 3
 
1.2%
식육(숯불구이) 2
 
0.8%
뷔페식 2
 
0.8%
패스트푸드 2
 
0.8%
Other values (2) 2
 
0.8%

Length

2024-05-11T06:30:14.392934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 196
79.4%
일식 14
 
5.7%
중국식 10
 
4.0%
호프/통닭 8
 
3.2%
통닭(치킨 4
 
1.6%
복어취급 4
 
1.6%
분식 3
 
1.2%
식육(숯불구이 2
 
0.8%
뷔페식 2
 
0.8%
패스트푸드 2
 
0.8%
Other values (2) 2
 
0.8%

주된음식
Text

MISSING 

Distinct126
Distinct (%)56.2%
Missing23
Missing (%)9.3%
Memory size2.1 KiB
2024-05-11T06:30:15.309544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length3.75
Min length1

Characters and Unicode

Total characters840
Distinct characters147
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

Unique92 ?
Unique (%)41.1%

Sample

1st row묵은지 등갈비
2nd row돼지갈비
3rd row삼겹살
4th row오리고기구이
5th row왕소갈비
ValueCountFrequency (%)
돼지갈비 17
 
7.3%
감자탕 16
 
6.9%
냉면 8
 
3.4%
팔보채 7
 
3.0%
설렁탕 6
 
2.6%
삼겹살 5
 
2.2%
생삼겹살 5
 
2.2%
자장면 5
 
2.2%
소갈비살 4
 
1.7%
회덮밥 4
 
1.7%
Other values (115) 155
66.8%
2024-05-11T06:30:16.505570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
탕 45
 
5.4%
갈 39
 
4.6%
비 39
 
4.6%
지 31
 
3.7%
돼 22
 
2.6%
자 21
 
2.5%
기 20
 
2.4%
생 20
 
2.4%
살 18
 
2.1%
회 18
 
2.1%
Other values (137) 567
67.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 814
96.9%
Other Punctuation 10
 
1.2%
Space Separator 8
 
1.0%
Open Punctuation 4
 
0.5%
Close Punctuation 4
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 45
 
5.5%
갈 39
 
4.8%
비 39
 
4.8%
지 31
 
3.8%
돼 22
 
2.7%
자 21
 
2.6%
기 20
 
2.5%
생 20
 
2.5%
살 18
 
2.2%
회 18
 
2.2%
Other values (133) 541
66.5%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 814
96.9%
Common 26
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 45
 
5.5%
갈 39
 
4.8%
비 39
 
4.8%
지 31
 
3.8%
돼 22
 
2.7%
자 21
 
2.6%
기 20
 
2.5%
생 20
 
2.5%
살 18
 
2.2%
회 18
 
2.2%
Other values (133) 541
66.5%
Common
ValueCountFrequency (%)
, 10
38.5%
8
30.8%
( 4
 
15.4%
) 4
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 814
96.9%
ASCII 26
 
3.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
탕 45
 
5.5%
갈 39
 
4.8%
비 39
 
4.8%
지 31
 
3.8%
돼 22
 
2.7%
자 21
 
2.6%
기 20
 
2.5%
생 20
 
2.5%
살 18
 
2.2%
회 18
 
2.2%
Other values (133) 541
66.5%
ASCII
ValueCountFrequency (%)
, 10
38.5%
8
30.8%
( 4
 
15.4%
) 4
 
15.4%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct175
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean238.7913
Minimum0
Maximum22275.8
Zeros2
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-11T06:30:16.925850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34.905
Q166.84
median100.5
Q3162.375
95-th percentile299.718
Maximum22275.8
Range22275.8
Interquartile range (IQR)95.535

Descriptive statistics

Standard deviation1447.8543
Coefficient of variation (CV)6.0632624
Kurtosis221.07162
Mean238.7913
Median Absolute Deviation (MAD)40.1
Skewness14.612442
Sum58981.45
Variance2096282
MonotonicityNot monotonic
2024-05-11T06:30:17.362541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.2 4
 
1.6%
73.16 4
 
1.6%
87.72 3
 
1.2%
87.59 3
 
1.2%
164.26 3
 
1.2%
157.67 3
 
1.2%
86.25 3
 
1.2%
278.06 3
 
1.2%
158.0 3
 
1.2%
123.76 3
 
1.2%
Other values (165) 215
87.0%
ValueCountFrequency (%)
0.0 2
0.8%
15.0 1
0.4%
18.75 1
0.4%
22.32 1
0.4%
25.0 1
0.4%
27.41 1
0.4%
29.6 1
0.4%
32.0 2
0.8%
32.52 1
0.4%
33.0 1
0.4%
ValueCountFrequency (%)
22275.8 1
 
0.4%
5170.8 1
 
0.4%
1117.38 1
 
0.4%
680.4 1
 
0.4%
451.93 1
 
0.4%
423.17 3
1.2%
369.6 1
 
0.4%
364.31 1
 
0.4%
363.0 1
 
0.4%
334.48 1
 
0.4%

행정동명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
구로제3동
50 
구로제2동
37 
구로제5동
31 
고척제1동
21 
개봉제1동
21 
Other values (10)
87 

Length

Max length5
Median length5
Mean length4.9352227
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row개봉제2동
2nd row오류제1동
3rd row구로제4동
4th row구로제2동
5th row구로제2동

Common Values

ValueCountFrequency (%)
구로제3동 50
20.2%
구로제2동 37
15.0%
구로제5동 31
12.6%
고척제1동 21
8.5%
개봉제1동 21
8.5%
고척제2동 19
 
7.7%
구로제4동 15
 
6.1%
개봉제2동 14
 
5.7%
오류제1동 8
 
3.2%
개봉제3동 8
 
3.2%
Other values (5) 23
9.3%

Length

2024-05-11T06:30:17.885975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
구로제3동 50
20.2%
구로제2동 37
15.0%
구로제5동 31
12.6%
고척제1동 21
8.5%
개봉제1동 21
8.5%
고척제2동 19
 
7.7%
구로제4동 15
 
6.1%
개봉제2동 14
 
5.7%
오류제1동 8
 
3.2%
개봉제3동 8
 
3.2%
Other values (5) 23
9.3%

급수시설구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
상수도전용
227 
<NA>
 
20

Length

Max length5
Median length5
Mean length4.9190283
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 227
91.9%
<NA> 20
 
8.1%

Length

2024-05-11T06:30:18.549134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:30:18.957490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 227
91.9%
na 20
 
8.1%

Interactions

2024-05-11T06:29:52.350872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:43.061095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:44.638723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:46.165261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:48.376911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:50.247584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:53.027689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:43.333709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:44.925372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:46.457900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:48.665284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:50.573282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:53.417652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:43.579582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:45.176386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:46.739957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:48.947026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:50.921754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:54.044839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:43.858337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:45.458281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:47.039430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:49.256800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:51.184390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:54.754470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:44.136158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:45.746484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:47.432097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:49.568735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:51.570567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:55.169378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:44.396590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:45.953285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:47.853587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:49.880836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:29:51.956437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T06:30:19.184304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자업태명영업장면적(㎡)행정동명
지정년도1.0000.5571.0001.0000.7870.0000.1280.148
지정번호0.5571.0000.5570.5570.3880.1170.3310.429
신청일자1.0000.5571.0001.0000.6110.1940.1360.218
지정일자1.0000.5571.0001.0000.7870.0000.1280.148
취소일자0.7870.3880.6110.7871.0000.0000.0000.000
업태명0.0000.1170.1940.0000.0001.0000.9230.265
영업장면적(㎡)0.1280.3310.1360.1280.0000.9231.0000.000
행정동명0.1480.4290.2180.1480.0000.2650.0001.000
2024-05-11T06:30:19.497722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태명행정동명급수시설구분
업태명1.0000.0981.000
행정동명0.0981.0001.000
급수시설구분1.0001.0001.000
2024-05-11T06:30:19.789842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.000-0.3640.9981.0000.6960.1810.0760.1101.000
지정번호-0.3641.000-0.366-0.361-0.1510.0750.0480.1721.000
신청일자0.998-0.3661.0000.9970.6350.1990.0760.1151.000
지정일자1.000-0.3610.9971.0000.6960.1810.0760.1101.000
취소일자0.696-0.1510.6350.6961.0000.1080.0000.0371.000
영업장면적(㎡)0.1810.0750.1990.1810.1081.0000.6780.0001.000
업태명0.0760.0480.0760.0760.0000.6781.0000.0981.000
행정동명0.1100.1720.1150.1100.0370.0000.0981.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-11T06:29:55.796829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T06:29:57.076597image/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.
2024-05-11T06:29:58.321636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시군구코드지정년도지정번호신청일자지정일자취소일자불가일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분
03160000200619200606192006071020090803<NA>콩부자 개봉점서울특별시 구로구 개봉로 56, (개봉동)서울특별시 구로구 개봉동 403번지 65호3160000-101-2005-00362한식묵은지 등갈비110.0개봉제2동상수도전용
13160000<NA><NA>20020401<NA>20110630<NA>전주돌밥추어탕서울특별시 구로구 경인로23길 8, 2층 201호 (오류동, 대서프라자)서울특별시 구로구 오류동 38번지 1호 대서프라자-2013160000-101-2000-08432한식<NA>120.0오류제1동상수도전용
23160000200748200706292007070520090803<NA>갈비명가서울특별시 구로구 가마산로 260, (구로동)서울특별시 구로구 구로동 98번지 1호3160000-101-2006-00197한식돼지갈비423.17구로제4동상수도전용
33160000200846200807012008071720151118<NA>가빈서울특별시 구로구 구로동로 174-9, (구로동)서울특별시 구로구 구로동 416번지 23호3160000-101-2003-00486한식삼겹살83.0구로제2동상수도전용
431600002015112015101220151118<NA><NA>가빈서울특별시 구로구 구로동로 174-9, (구로동)서울특별시 구로구 구로동 416번지 23호3160000-101-2003-00486한식오리고기구이83.0구로제2동상수도전용
53160000<NA><NA>20070629<NA>20111222<NA>꿀꿀이생고기맛집서울특별시 구로구 중앙로 32, 2층 (고척동)서울특별시 구로구 고척동 52번지 346호3160000-101-1997-05349한식<NA>105.18고척제1동상수도전용
631600002002139200203212002050220051102<NA>황제갈비서울특별시 구로구 경인로40길 12, (개봉동)서울특별시 구로구 개봉동 178번지 13호3160000-101-1996-01877한식왕소갈비103.72개봉제1동상수도전용
73160000200712200706292007070520080717<NA>황제갈비서울특별시 구로구 경인로40길 12, (개봉동)서울특별시 구로구 개봉동 178번지 13호3160000-101-1996-01877한식낙지한마리103.72개봉제1동상수도전용
8316000020092122009070120090803<NA><NA>황제갈비서울특별시 구로구 경인로40길 12, (개봉동)서울특별시 구로구 개봉동 178번지 13호3160000-101-1996-01877한식냉면,갈비103.72개봉제1동상수도전용
9316000020157201509152015111820191107<NA>옛날황소곱창서울특별시 구로구 디지털로32나길 16, (구로동)서울특별시 구로구 구로동 1124번지 37호3160000-101-1985-00599한식곱창구이,전골165.0구로제3동상수도전용
시군구코드지정년도지정번호신청일자지정일자취소일자불가일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분
23731600002003238200304012003070220061201<NA>속초오징어어시장 구로디지털단지점서울특별시 구로구 디지털로32나길 34, (구로동)서울특별시 구로구 구로동 1124번지 41호 케이제이빌딩1층3160000-101-2002-00395한식감자탕218.79구로제3동상수도전용
2383160000200617200606252006070220100802<NA>속초오징어어시장 구로디지털단지점서울특별시 구로구 디지털로32나길 34, (구로동)서울특별시 구로구 구로동 1124번지 41호 케이제이빌딩1층3160000-101-2002-00395한식감자탕218.79구로제3동상수도전용
2393160000200441200404152004070220181228<NA>우림 더이룸푸드서울특별시 구로구 디지털로33길 28, B108호 (구로동, 우림이비즈센터)서울특별시 구로구 구로동 170번지 5호 우림이비지센터-B1083160000-101-2003-00112한식백반369.6구로제3동상수도전용
240316000020078200706292007070520181228<NA>고기에미친남자서울특별시 구로구 개봉로 6, (개봉동)서울특별시 구로구 개봉동 290번지 5호3160000-101-1984-02992한식쭈꾸미볶음141.95개봉제3동상수도전용
24131600002006142006061920060710<NA><NA>누나밥쭈서울특별시 구로구 도림로 31-1, (구로동)서울특별시 구로구 구로동 771번지 33호3160000-101-2004-00223한식감자탕129.58구로제4동상수도전용
2423160000202312023101620231110<NA><NA>백채김치찌개 동양미래대학점서울특별시 구로구 경인로47길 67, 1층 103호 (고척동)서울특별시 구로구 고척동 52번지 48호3160000-101-2021-00310한식김치찌개32.0고척제1동<NA>
243316000020114201110062011122220231110<NA>마부식당서울특별시 구로구 오류로8길 60, B동 (오류동)서울특별시 구로구 오류동 148번지 3호 B3160000-101-2005-00051한식감자탕59.4오류제2동상수도전용
2443160000201352013100820131210<NA><NA>누리한방삼계탕서울특별시 구로구 디지털로 288, 대륭포스트타워1차 지하1층 B131호 (구로동)서울특별시 구로구 구로동 212번지 8호 대륭포스트타워1차 지하1층 B131호3160000-101-2005-00435한식한방삼계탕114.27구로제3동<NA>
245316000020049200404152004070220100802<NA>사천성서울특별시 구로구 디지털로27길 47, (가리봉동)서울특별시 구로구 가리봉동 115번지 109호3160000-101-1991-02335중국식짜장면50.74가리봉동상수도전용
2463160000200254200204012002050220090803<NA>육미제당 구로디지털단지점서울특별시 구로구 디지털로32나길 12, 1층 (구로동)서울특별시 구로구 구로동 1124번지 36호3160000-101-2000-08677한식한방닭180.52구로제3동상수도전용