Overview

Dataset statistics

Number of variables15
Number of observations219
Missing cells23
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.1 KiB
Average record size in memory126.6 B

Variable types

Categorical4
Numeric5
Text6

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,주된음식,영업장면적(㎡),행정동명,급수시설구분,소재지전화번호
Author은평구
URLhttps://data.seoul.go.kr/dataList/OA-10525/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
업태명 is highly overall correlated with 급수시설구분High correlation
급수시설구분 is highly overall correlated with 지정년도 and 6 other fieldsHigh correlation
행정동명 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 (54.9%)Imbalance
급수시설구분 is highly imbalanced (67.5%)Imbalance
소재지전화번호 has 22 (10.0%) missing valuesMissing

Reproduction

Analysis started2024-05-11 06:08:54.623659
Analysis finished2024-05-11 06:09:05.748995
Duration11.13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
3110000
219 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3110000
2nd row3110000
3rd row3110000
4th row3110000
5th row3110000

Common Values

ValueCountFrequency (%)
3110000 219
100.0%

Length

2024-05-11T06:09:05.982083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:09:06.311691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3110000 219
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.4201
Minimum2004
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-05-11T06:09:06.629707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2004
5-th percentile2004
Q12009
median2011
Q32017
95-th percentile2022.1
Maximum2023
Range19
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.6808675
Coefficient of variation (CV)0.0028229034
Kurtosis-0.98550432
Mean2012.4201
Median Absolute Deviation (MAD)4
Skewness0.34648064
Sum440720
Variance32.272255
MonotonicityDecreasing
2024-05-11T06:09:07.057158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2009 55
25.1%
2004 24
11.0%
2016 22
 
10.0%
2011 15
 
6.8%
2007 12
 
5.5%
2023 11
 
5.0%
2017 11
 
5.0%
2010 11
 
5.0%
2022 9
 
4.1%
2012 9
 
4.1%
Other values (8) 40
18.3%
ValueCountFrequency (%)
2004 24
11.0%
2005 1
 
0.5%
2006 4
 
1.8%
2007 12
 
5.5%
2009 55
25.1%
2010 11
 
5.0%
2011 15
 
6.8%
2012 9
 
4.1%
2014 4
 
1.8%
2015 5
 
2.3%
ValueCountFrequency (%)
2023 11
5.0%
2022 9
4.1%
2021 8
 
3.7%
2020 8
 
3.7%
2019 4
 
1.8%
2018 6
 
2.7%
2017 11
5.0%
2016 22
10.0%
2015 5
 
2.3%
2014 4
 
1.8%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct121
Distinct (%)55.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.461187
Minimum1
Maximum237
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-05-11T06:09:07.521879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median61
Q3103
95-th percentile209.3
Maximum237
Range236
Interquartile range (IQR)93

Descriptive statistics

Standard deviation66.19877
Coefficient of variation (CV)0.95303252
Kurtosis-0.2870145
Mean69.461187
Median Absolute Deviation (MAD)50
Skewness0.8626189
Sum15212
Variance4382.2772
MonotonicityNot monotonic
2024-05-11T06:09:07.963063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 9
 
4.1%
7 8
 
3.7%
1 7
 
3.2%
5 6
 
2.7%
2 6
 
2.7%
3 6
 
2.7%
10 6
 
2.7%
11 4
 
1.8%
28 4
 
1.8%
4 4
 
1.8%
Other values (111) 159
72.6%
ValueCountFrequency (%)
1 7
3.2%
2 6
2.7%
3 6
2.7%
4 4
1.8%
5 6
2.7%
6 9
4.1%
7 8
3.7%
8 3
 
1.4%
9 4
1.8%
10 6
2.7%
ValueCountFrequency (%)
237 1
0.5%
232 1
0.5%
231 1
0.5%
228 1
0.5%
226 1
0.5%
224 1
0.5%
222 1
0.5%
216 1
0.5%
215 1
0.5%
214 1
0.5%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20125128
Minimum20040105
Maximum20231004
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-05-11T06:09:08.426730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20040105
5-th percentile20040105
Q120091204
median20111024
Q320170920
95-th percentile20222185
Maximum20231004
Range190899
Interquartile range (IQR)79716.5

Descriptive statistics

Standard deviation56956.658
Coefficient of variation (CV)0.0028301265
Kurtosis-0.97772757
Mean20125128
Median Absolute Deviation (MAD)40297
Skewness0.33758568
Sum4.407403 × 109
Variance3.2440609 × 109
MonotonicityNot monotonic
2024-05-11T06:09:08.910080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
20091204 55
25.1%
20040105 23
10.5%
20160930 22
 
10.0%
20111024 15
 
6.8%
20070727 11
 
5.0%
20101101 11
 
5.0%
20221215 9
 
4.1%
20211112 8
 
3.7%
20120501 8
 
3.7%
20231004 7
 
3.2%
Other values (30) 50
22.8%
ValueCountFrequency (%)
20040105 23
10.5%
20040602 1
 
0.5%
20050615 1
 
0.5%
20060620 1
 
0.5%
20060628 1
 
0.5%
20060720 2
 
0.9%
20070727 11
 
5.0%
20070801 1
 
0.5%
20091204 55
25.1%
20101101 11
 
5.0%
ValueCountFrequency (%)
20231004 7
3.2%
20230920 1
 
0.5%
20230915 1
 
0.5%
20230912 1
 
0.5%
20230911 1
 
0.5%
20221215 9
4.1%
20211112 8
3.7%
20201110 1
 
0.5%
20201102 7
3.2%
20190930 1
 
0.5%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20125195
Minimum20040105
Maximum20231110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-05-11T06:09:09.346450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20040105
5-th percentile20040105
Q120091215
median20111122
Q320171117
95-th percentile20222204
Maximum20231110
Range191005
Interquartile range (IQR)79902

Descriptive statistics

Standard deviation56993.506
Coefficient of variation (CV)0.002831948
Kurtosis-0.98022558
Mean20125195
Median Absolute Deviation (MAD)40395
Skewness0.33618748
Sum4.4074177 × 109
Variance3.2482598 × 109
MonotonicityDecreasing
2024-05-11T06:09:09.757126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
20091215 54
24.7%
20040105 23
10.5%
20161115 22
10.0%
20111122 15
 
6.8%
20231110 11
 
5.0%
20070727 11
 
5.0%
20101122 11
 
5.0%
20171117 11
 
5.0%
20221215 9
 
4.1%
20120705 8
 
3.7%
Other values (12) 44
20.1%
ValueCountFrequency (%)
20040105 23
10.5%
20040630 1
 
0.5%
20050826 1
 
0.5%
20060720 4
 
1.8%
20070726 1
 
0.5%
20070727 11
 
5.0%
20091207 1
 
0.5%
20091215 54
24.7%
20101122 11
 
5.0%
20111122 15
 
6.8%
ValueCountFrequency (%)
20231110 11
5.0%
20221215 9
4.1%
20211112 8
 
3.7%
20201110 8
 
3.7%
20191122 4
 
1.8%
20181119 6
 
2.7%
20171117 11
5.0%
20161115 22
10.0%
20151112 5
 
2.3%
20141222 4
 
1.8%

업소명
Text

Distinct164
Distinct (%)74.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-05-11T06:09:10.268887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length11
Mean length6.7808219
Min length2

Characters and Unicode

Total characters1485
Distinct characters300
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122 ?
Unique (%)55.7%

Sample

1st row냉삼슈퍼
2nd row역말 장어
3rd row무한도전
4th row두루올
5th row아스론가 연신내점(Ars Longa)
ValueCountFrequency (%)
응암점 8
 
2.4%
불광점 5
 
1.5%
무안갯벌낙지 4
 
1.2%
구산점 4
 
1.2%
연신내 4
 
1.2%
주)벙구갈비 4
 
1.2%
공화춘 3
 
0.9%
생고깃간 3
 
0.9%
지호한방삼계탕 3
 
0.9%
생고기 3
 
0.9%
Other values (219) 286
87.5%
2024-05-11T06:09:11.370584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
108
 
7.3%
점 41
 
2.8%
이 28
 
1.9%
탕 25
 
1.7%
) 20
 
1.3%
( 20
 
1.3%
장 20
 
1.3%
국 19
 
1.3%
주 18
 
1.2%
고 18
 
1.2%
Other values (290) 1168
78.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1309
88.1%
Space Separator 108
 
7.3%
Close Punctuation 20
 
1.3%
Open Punctuation 20
 
1.3%
Uppercase Letter 12
 
0.8%
Other Punctuation 7
 
0.5%
Lowercase Letter 6
 
0.4%
Decimal Number 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 41
 
3.1%
이 28
 
2.1%
탕 25
 
1.9%
장 20
 
1.5%
국 19
 
1.5%
주 18
 
1.4%
고 18
 
1.4%
산 17
 
1.3%
갈 16
 
1.2%
비 16
 
1.2%
Other values (267) 1091
83.3%
Uppercase Letter
ValueCountFrequency (%)
A 2
16.7%
M 2
16.7%
D 2
16.7%
C 2
16.7%
L 1
8.3%
G 1
8.3%
U 1
8.3%
H 1
8.3%
Lowercase Letter
ValueCountFrequency (%)
a 1
16.7%
g 1
16.7%
n 1
16.7%
o 1
16.7%
s 1
16.7%
r 1
16.7%
Other Punctuation
ValueCountFrequency (%)
. 3
42.9%
? 3
42.9%
, 1
 
14.3%
Decimal Number
ValueCountFrequency (%)
7 1
33.3%
3 1
33.3%
8 1
33.3%
Space Separator
ValueCountFrequency (%)
108
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1305
87.9%
Common 158
 
10.6%
Latin 18
 
1.2%
Han 4
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 41
 
3.1%
이 28
 
2.1%
탕 25
 
1.9%
장 20
 
1.5%
국 19
 
1.5%
주 18
 
1.4%
고 18
 
1.4%
산 17
 
1.3%
갈 16
 
1.2%
비 16
 
1.2%
Other values (263) 1087
83.3%
Latin
ValueCountFrequency (%)
A 2
11.1%
M 2
11.1%
D 2
11.1%
C 2
11.1%
a 1
 
5.6%
g 1
 
5.6%
n 1
 
5.6%
o 1
 
5.6%
L 1
 
5.6%
s 1
 
5.6%
Other values (4) 4
22.2%
Common
ValueCountFrequency (%)
108
68.4%
) 20
 
12.7%
( 20
 
12.7%
. 3
 
1.9%
? 3
 
1.9%
7 1
 
0.6%
3 1
 
0.6%
8 1
 
0.6%
, 1
 
0.6%
Han
ValueCountFrequency (%)
品 1
25.0%
一 1
25.0%
筍 1
25.0%
海 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1305
87.9%
ASCII 176
 
11.9%
CJK 4
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
108
61.4%
) 20
 
11.4%
( 20
 
11.4%
. 3
 
1.7%
? 3
 
1.7%
A 2
 
1.1%
M 2
 
1.1%
D 2
 
1.1%
C 2
 
1.1%
7 1
 
0.6%
Other values (13) 13
 
7.4%
Hangul
ValueCountFrequency (%)
점 41
 
3.1%
이 28
 
2.1%
탕 25
 
1.9%
장 20
 
1.5%
국 19
 
1.5%
주 18
 
1.4%
고 18
 
1.4%
산 17
 
1.3%
갈 16
 
1.2%
비 16
 
1.2%
Other values (263) 1087
83.3%
CJK
ValueCountFrequency (%)
品 1
25.0%
一 1
25.0%
筍 1
25.0%
海 1
25.0%

소재지도로명
Text

Distinct166
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-05-11T06:09:12.142117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length48
Mean length29.977169
Min length23

Characters and Unicode

Total characters6565
Distinct characters120
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

Unique125 ?
Unique (%)57.1%

Sample

1st row서울특별시 은평구 갈현로 156, 1층 (구산동)
2nd row서울특별시 은평구 역말로 83-5, 2층 (역촌동)
3rd row서울특별시 은평구 은평로 6, 1층 (신사동)
4th row서울특별시 은평구 은평로9길 13, 캐스텔가든 104호 (응암동)
5th row서울특별시 은평구 연서로29길 17-3, 1층 (갈현동)
ValueCountFrequency (%)
서울특별시 219
 
16.8%
은평구 219
 
16.8%
1층 96
 
7.4%
갈현동 34
 
2.6%
녹번동 34
 
2.6%
응암동 33
 
2.5%
불광동 26
 
2.0%
1,2층 25
 
1.9%
연서로 23
 
1.8%
은평로 21
 
1.6%
Other values (260) 576
44.1%
2024-05-11T06:09:13.088556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1087
 
16.6%
1 362
 
5.5%
, 336
 
5.1%
서 289
 
4.4%
평 254
 
3.9%
은 254
 
3.9%
( 228
 
3.5%
) 228
 
3.5%
구 224
 
3.4%
동 223
 
3.4%
Other values (110) 3080
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3540
53.9%
Space Separator 1087
 
16.6%
Decimal Number 1062
 
16.2%
Other Punctuation 337
 
5.1%
Open Punctuation 228
 
3.5%
Close Punctuation 228
 
3.5%
Dash Punctuation 73
 
1.1%
Uppercase Letter 7
 
0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 289
 
8.2%
평 254
 
7.2%
은 254
 
7.2%
구 224
 
6.3%
동 223
 
6.3%
울 219
 
6.2%
시 219
 
6.2%
별 219
 
6.2%
특 219
 
6.2%
로 208
 
5.9%
Other values (87) 1212
34.2%
Decimal Number
ValueCountFrequency (%)
1 362
34.1%
2 194
18.3%
3 104
 
9.8%
0 83
 
7.8%
4 58
 
5.5%
5 58
 
5.5%
6 55
 
5.2%
7 55
 
5.2%
8 51
 
4.8%
9 42
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
B 2
28.6%
C 1
14.3%
M 1
14.3%
D 1
14.3%
S 1
14.3%
A 1
14.3%
Other Punctuation
ValueCountFrequency (%)
, 336
99.7%
. 1
 
0.3%
Space Separator
ValueCountFrequency (%)
1087
100.0%
Open Punctuation
ValueCountFrequency (%)
( 228
100.0%
Close Punctuation
ValueCountFrequency (%)
) 228
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 73
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3540
53.9%
Common 3018
46.0%
Latin 7
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 289
 
8.2%
평 254
 
7.2%
은 254
 
7.2%
구 224
 
6.3%
동 223
 
6.3%
울 219
 
6.2%
시 219
 
6.2%
별 219
 
6.2%
특 219
 
6.2%
로 208
 
5.9%
Other values (87) 1212
34.2%
Common
ValueCountFrequency (%)
1087
36.0%
1 362
 
12.0%
, 336
 
11.1%
( 228
 
7.6%
) 228
 
7.6%
2 194
 
6.4%
3 104
 
3.4%
0 83
 
2.8%
- 73
 
2.4%
4 58
 
1.9%
Other values (7) 265
 
8.8%
Latin
ValueCountFrequency (%)
B 2
28.6%
C 1
14.3%
M 1
14.3%
D 1
14.3%
S 1
14.3%
A 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3540
53.9%
ASCII 3025
46.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1087
35.9%
1 362
 
12.0%
, 336
 
11.1%
( 228
 
7.5%
) 228
 
7.5%
2 194
 
6.4%
3 104
 
3.4%
0 83
 
2.7%
- 73
 
2.4%
4 58
 
1.9%
Other values (13) 272
 
9.0%
Hangul
ValueCountFrequency (%)
서 289
 
8.2%
평 254
 
7.2%
은 254
 
7.2%
구 224
 
6.3%
동 223
 
6.3%
울 219
 
6.2%
시 219
 
6.2%
별 219
 
6.2%
특 219
 
6.2%
로 208
 
5.9%
Other values (87) 1212
34.2%

소재지지번
Text

Distinct166
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-05-11T06:09:13.679324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length48
Mean length28.438356
Min length21

Characters and Unicode

Total characters6228
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

Unique125 ?
Unique (%)57.1%

Sample

1st row서울특별시 은평구 구산동 12번지 1호
2nd row서울특별시 은평구 역촌동 13번지 33호 2층
3rd row서울특별시 은평구 신사동 29번지 122호 1층
4th row서울특별시 은평구 응암동 89번지 1호
5th row서울특별시 은평구 갈현동 454번지 8호
ValueCountFrequency (%)
서울특별시 219
 
17.3%
은평구 219
 
17.3%
1층 73
 
5.8%
응암동 37
 
2.9%
갈현동 35
 
2.8%
녹번동 34
 
2.7%
1,2층 28
 
2.2%
불광동 28
 
2.2%
역촌동 20
 
1.6%
신사동 19
 
1.5%
Other values (242) 553
43.7%
2024-05-11T06:09:14.795350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1542
24.8%
1 337
 
5.4%
번 253
 
4.1%
지 250
 
4.0%
구 224
 
3.6%
동 223
 
3.6%
서 220
 
3.5%
은 220
 
3.5%
평 220
 
3.5%
울 219
 
3.5%
Other values (88) 2520
40.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3361
54.0%
Space Separator 1542
24.8%
Decimal Number 1220
 
19.6%
Other Punctuation 64
 
1.0%
Close Punctuation 12
 
0.2%
Open Punctuation 12
 
0.2%
Dash Punctuation 8
 
0.1%
Uppercase Letter 7
 
0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
번 253
 
7.5%
지 250
 
7.4%
구 224
 
6.7%
동 223
 
6.6%
서 220
 
6.5%
은 220
 
6.5%
평 220
 
6.5%
울 219
 
6.5%
시 219
 
6.5%
별 219
 
6.5%
Other values (66) 1094
32.5%
Decimal Number
ValueCountFrequency (%)
1 337
27.6%
2 202
16.6%
3 122
 
10.0%
4 102
 
8.4%
0 96
 
7.9%
5 88
 
7.2%
7 72
 
5.9%
6 70
 
5.7%
9 67
 
5.5%
8 64
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
B 2
28.6%
D 1
14.3%
M 1
14.3%
C 1
14.3%
S 1
14.3%
A 1
14.3%
Space Separator
ValueCountFrequency (%)
1542
100.0%
Other Punctuation
ValueCountFrequency (%)
, 64
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3361
54.0%
Common 2860
45.9%
Latin 7
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
번 253
 
7.5%
지 250
 
7.4%
구 224
 
6.7%
동 223
 
6.6%
서 220
 
6.5%
은 220
 
6.5%
평 220
 
6.5%
울 219
 
6.5%
시 219
 
6.5%
별 219
 
6.5%
Other values (66) 1094
32.5%
Common
ValueCountFrequency (%)
1542
53.9%
1 337
 
11.8%
2 202
 
7.1%
3 122
 
4.3%
4 102
 
3.6%
0 96
 
3.4%
5 88
 
3.1%
7 72
 
2.5%
6 70
 
2.4%
9 67
 
2.3%
Other values (6) 162
 
5.7%
Latin
ValueCountFrequency (%)
B 2
28.6%
D 1
14.3%
M 1
14.3%
C 1
14.3%
S 1
14.3%
A 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3361
54.0%
ASCII 2867
46.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1542
53.8%
1 337
 
11.8%
2 202
 
7.0%
3 122
 
4.3%
4 102
 
3.6%
0 96
 
3.3%
5 88
 
3.1%
7 72
 
2.5%
6 70
 
2.4%
9 67
 
2.3%
Other values (12) 169
 
5.9%
Hangul
ValueCountFrequency (%)
번 253
 
7.5%
지 250
 
7.4%
구 224
 
6.7%
동 223
 
6.6%
서 220
 
6.5%
은 220
 
6.5%
평 220
 
6.5%
울 219
 
6.5%
시 219
 
6.5%
별 219
 
6.5%
Other values (66) 1094
32.5%

허가(신고)번호
Text

Distinct166
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-05-11T06:09:15.351081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters4818
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

Unique125 ?
Unique (%)57.1%

Sample

1st row3110000-101-2021-00113
2nd row3110000-101-2003-00271
3rd row3110000-101-2023-00104
4th row3110000-101-2021-00299
5th row3110000-101-2021-00457
ValueCountFrequency (%)
3110000-101-1986-00084 4
 
1.8%
3110000-101-1995-01922 3
 
1.4%
3110000-101-2003-00476 3
 
1.4%
3110000-101-1999-06044 3
 
1.4%
3110000-101-1998-06277 3
 
1.4%
3110000-101-1997-06406 3
 
1.4%
3110000-101-2006-00183 3
 
1.4%
3110000-101-1994-05657 3
 
1.4%
3110000-101-1998-01373 3
 
1.4%
3110000-101-1994-00347 3
 
1.4%
Other values (156) 188
85.8%
2024-05-11T06:09:16.338582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1752
36.4%
1 1139
23.6%
- 657
 
13.6%
3 319
 
6.6%
2 246
 
5.1%
9 221
 
4.6%
4 101
 
2.1%
7 100
 
2.1%
8 97
 
2.0%
5 96
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4161
86.4%
Dash Punctuation 657
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1752
42.1%
1 1139
27.4%
3 319
 
7.7%
2 246
 
5.9%
9 221
 
5.3%
4 101
 
2.4%
7 100
 
2.4%
8 97
 
2.3%
5 96
 
2.3%
6 90
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 657
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4818
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1752
36.4%
1 1139
23.6%
- 657
 
13.6%
3 319
 
6.6%
2 246
 
5.1%
9 221
 
4.6%
4 101
 
2.1%
7 100
 
2.1%
8 97
 
2.0%
5 96
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4818
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1752
36.4%
1 1139
23.6%
- 657
 
13.6%
3 319
 
6.6%
2 246
 
5.1%
9 221
 
4.6%
4 101
 
2.1%
7 100
 
2.1%
8 97
 
2.0%
5 96
 
2.0%

업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
한식
160 
일식
21 
중국식
18 
경양식
 
6
식육(숯불구이)
 
4
Other values (5)
 
10

Length

Max length8
Median length2
Mean length2.2785388
Min length2

Unique

Unique2 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
한식 160
73.1%
일식 21
 
9.6%
중국식 18
 
8.2%
경양식 6
 
2.7%
식육(숯불구이) 4
 
1.8%
호프/통닭 4
 
1.8%
기타 2
 
0.9%
분식 2
 
0.9%
뷔페식 1
 
0.5%
회집 1
 
0.5%

Length

2024-05-11T06:09:16.782947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:09:17.170692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 160
73.1%
일식 21
 
9.6%
중국식 18
 
8.2%
경양식 6
 
2.7%
식육(숯불구이 4
 
1.8%
호프/통닭 4
 
1.8%
기타 2
 
0.9%
분식 2
 
0.9%
뷔페식 1
 
0.5%
회집 1
 
0.5%

주된음식
Text

Distinct128
Distinct (%)58.7%
Missing1
Missing (%)0.5%
Memory size1.8 KiB
2024-05-11T06:09:17.867111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length3.6972477
Min length1

Characters and Unicode

Total characters806
Distinct characters158
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)41.3%

Sample

1st row삼겹살
2nd row장어구이
3rd row돼지갈비, 특수부위
4th row순대국
5th row파스타, 피자
ValueCountFrequency (%)
삼계탕 10
 
4.2%
돼지갈비 8
 
3.3%
자장면 7
 
2.9%
삼겹살 6
 
2.5%
냉면 6
 
2.5%
아구찜 5
 
2.1%
순대국 5
 
2.1%
갈비 5
 
2.1%
짜장면 5
 
2.1%
추어탕 4
 
1.7%
Other values (122) 178
74.5%
2024-05-11T06:09:19.187927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
탕 34
 
4.2%
, 25
 
3.1%
지 24
 
3.0%
장 23
 
2.9%
비 22
 
2.7%
21
 
2.6%
면 21
 
2.6%
갈 20
 
2.5%
국 19
 
2.4%
삼 17
 
2.1%
Other values (148) 580
72.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 756
93.8%
Other Punctuation 29
 
3.6%
Space Separator 21
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 34
 
4.5%
지 24
 
3.2%
장 23
 
3.0%
비 22
 
2.9%
면 21
 
2.8%
갈 20
 
2.6%
국 19
 
2.5%
삼 17
 
2.2%
어 15
 
2.0%
자 15
 
2.0%
Other values (145) 546
72.2%
Other Punctuation
ValueCountFrequency (%)
, 25
86.2%
. 4
 
13.8%
Space Separator
ValueCountFrequency (%)
21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 756
93.8%
Common 50
 
6.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 34
 
4.5%
지 24
 
3.2%
장 23
 
3.0%
비 22
 
2.9%
면 21
 
2.8%
갈 20
 
2.6%
국 19
 
2.5%
삼 17
 
2.2%
어 15
 
2.0%
자 15
 
2.0%
Other values (145) 546
72.2%
Common
ValueCountFrequency (%)
, 25
50.0%
21
42.0%
. 4
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 756
93.8%
ASCII 50
 
6.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
탕 34
 
4.5%
지 24
 
3.2%
장 23
 
3.0%
비 22
 
2.9%
면 21
 
2.8%
갈 20
 
2.6%
국 19
 
2.5%
삼 17
 
2.2%
어 15
 
2.0%
자 15
 
2.0%
Other values (145) 546
72.2%
ASCII
ValueCountFrequency (%)
, 25
50.0%
21
42.0%
. 4
 
8.0%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct160
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.88091
Minimum18.2
Maximum1976.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-05-11T06:09:19.682228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18.2
5-th percentile46.161
Q186.785
median121.91
Q3199.17
95-th percentile485.456
Maximum1976.62
Range1958.42
Interquartile range (IQR)112.385

Descriptive statistics

Standard deviation184.09582
Coefficient of variation (CV)1.0526924
Kurtosis44.038912
Mean174.88091
Median Absolute Deviation (MAD)43.91
Skewness5.4168537
Sum38298.92
Variance33891.269
MonotonicityNot monotonic
2024-05-11T06:09:20.161849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
204.92 4
 
1.8%
191.74 3
 
1.4%
203.0 3
 
1.4%
69.36 3
 
1.4%
74.1 3
 
1.4%
287.88 3
 
1.4%
149.8 3
 
1.4%
217.53 3
 
1.4%
177.93 3
 
1.4%
165.0 3
 
1.4%
Other values (150) 188
85.8%
ValueCountFrequency (%)
18.2 1
0.5%
25.0 1
0.5%
25.55 2
0.9%
31.03 1
0.5%
33.27 1
0.5%
37.0 1
0.5%
40.0 1
0.5%
44.91 1
0.5%
45.0 2
0.9%
46.29 1
0.5%
ValueCountFrequency (%)
1976.62 1
 
0.5%
993.0 1
 
0.5%
746.44 1
 
0.5%
735.0 2
0.9%
654.0 1
 
0.5%
613.15 1
 
0.5%
558.15 1
 
0.5%
494.96 3
1.4%
484.4 1
 
0.5%
431.6 1
 
0.5%

행정동명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
응암제1동
36 
녹번동
34 
갈현제1동
30 
불광제1동
24 
역촌동
20 
Other values (10)
75 

Length

Max length5
Median length5
Mean length4.086758
Min length3

Unique

Unique2 ?
Unique (%)0.9%

Sample

1st row구산동
2nd row역촌동
3rd row신사제1동
4th row응암제1동
5th row갈현제1동

Common Values

ValueCountFrequency (%)
응암제1동 36
16.4%
녹번동 34
15.5%
갈현제1동 30
13.7%
불광제1동 24
11.0%
역촌동 20
9.1%
대조동 17
7.8%
진관동 17
7.8%
신사제1동 14
 
6.4%
증산동 6
 
2.7%
구산동 5
 
2.3%
Other values (5) 16
7.3%

Length

2024-05-11T06:09:20.710499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
응암제1동 36
16.4%
녹번동 34
15.5%
갈현제1동 30
13.7%
불광제1동 24
11.0%
역촌동 20
9.1%
대조동 17
7.8%
진관동 17
7.8%
신사제1동 14
 
6.4%
증산동 6
 
2.7%
구산동 5
 
2.3%
Other values (5) 16
7.3%

급수시설구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
상수도전용
206 
<NA>
 
13

Length

Max length5
Median length5
Mean length4.9406393
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 206
94.1%
<NA> 13
 
5.9%

Length

2024-05-11T06:09:21.188302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:09:21.561937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 206
94.1%
na 13
 
5.9%

소재지전화번호
Text

MISSING 

Distinct148
Distinct (%)75.1%
Missing22
Missing (%)10.0%
Memory size1.8 KiB
2024-05-11T06:09:22.384842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.269036
Min length7

Characters and Unicode

Total characters2023
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

Unique110 ?
Unique (%)55.8%

Sample

1st row02352 2345
2nd row02 3559518
3rd row02355 8255
4th row07040156772
5th row02 352 3920
ValueCountFrequency (%)
02 159
39.6%
358 5
 
1.2%
302 4
 
1.0%
02389 4
 
1.0%
3923292 4
 
1.0%
0807 3
 
0.7%
6586 3
 
0.7%
388 3
 
0.7%
0260136992 3
 
0.7%
7557776 3
 
0.7%
Other values (164) 211
52.5%
2024-05-11T06:09:23.506840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 348
17.2%
2 324
16.0%
3 286
14.1%
230
11.4%
5 197
9.7%
8 168
8.3%
7 129
 
6.4%
9 115
 
5.7%
6 95
 
4.7%
1 78
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1793
88.6%
Space Separator 230
 
11.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 348
19.4%
2 324
18.1%
3 286
16.0%
5 197
11.0%
8 168
9.4%
7 129
 
7.2%
9 115
 
6.4%
6 95
 
5.3%
1 78
 
4.4%
4 53
 
3.0%
Space Separator
ValueCountFrequency (%)
230
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2023
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 348
17.2%
2 324
16.0%
3 286
14.1%
230
11.4%
5 197
9.7%
8 168
8.3%
7 129
 
6.4%
9 115
 
5.7%
6 95
 
4.7%
1 78
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2023
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 348
17.2%
2 324
16.0%
3 286
14.1%
230
11.4%
5 197
9.7%
8 168
8.3%
7 129
 
6.4%
9 115
 
5.7%
6 95
 
4.7%
1 78
 
3.9%

Interactions

2024-05-11T06:09:02.570174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:08:56.512022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:08:58.074755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:08:59.433336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:09:00.896118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:09:02.876646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:08:56.905829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:08:58.341778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:08:59.749796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:09:01.182474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:09:03.151012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:08:57.171121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:08:58.582228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:09:00.014618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:09:01.443229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:09:03.475114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:08:57.497963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:08:58.870949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:09:00.302192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:09:01.944005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:09:04.057491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:08:57.783769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:08:59.156415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:09:00.590501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:09:02.269585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T06:09:23.936009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자업태명영업장면적(㎡)행정동명
지정년도1.0000.8261.0001.0000.0000.2200.189
지정번호0.8261.0000.8320.8320.0000.0000.000
신청일자1.0000.8321.0001.0000.0000.1880.304
지정일자1.0000.8321.0001.0000.0000.1880.304
업태명0.0000.0000.0000.0001.0000.2600.593
영업장면적(㎡)0.2200.0000.1880.1880.2601.0000.337
행정동명0.1890.0000.3040.3040.5930.3371.000
2024-05-11T06:09:24.318444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태명급수시설구분행정동명
업태명1.0001.0000.262
급수시설구분1.0001.0001.000
행정동명0.2621.0001.000
2024-05-11T06:09:24.622154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.000-0.4661.0000.999-0.0240.0000.0921.000
지정번호-0.4661.000-0.463-0.4650.0930.0000.0001.000
신청일자1.000-0.4631.0000.999-0.0260.0000.0921.000
지정일자0.999-0.4650.9991.000-0.0270.0000.0921.000
영업장면적(㎡)-0.0240.093-0.026-0.0271.0000.1380.1601.000
업태명0.0000.0000.0000.0000.1381.0000.2621.000
행정동명0.0920.0000.0920.0920.1600.2621.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-11T06:09:04.488845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T06:09:05.171730image/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.
2024-05-11T06:09:05.585716image/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
031100002023112023100420231110냉삼슈퍼서울특별시 은평구 갈현로 156, 1층 (구산동)서울특별시 은평구 구산동 12번지 1호3110000-101-2021-00113한식삼겹살25.0구산동상수도전용02352 2345
13110000202382023100420231110역말 장어서울특별시 은평구 역말로 83-5, 2층 (역촌동)서울특별시 은평구 역촌동 13번지 33호 2층3110000-101-2003-00271한식장어구이147.63역촌동상수도전용02 3559518
23110000202372023100420231110무한도전서울특별시 은평구 은평로 6, 1층 (신사동)서울특별시 은평구 신사동 29번지 122호 1층3110000-101-2023-00104한식돼지갈비, 특수부위118.0신사제1동상수도전용<NA>
33110000202352023091120231110두루올서울특별시 은평구 은평로9길 13, 캐스텔가든 104호 (응암동)서울특별시 은평구 응암동 89번지 1호3110000-101-2021-00299한식순대국81.9응암제1동상수도전용02355 8255
43110000202312023091220231110아스론가 연신내점(Ars Longa)서울특별시 은평구 연서로29길 17-3, 1층 (갈현동)서울특별시 은평구 갈현동 454번지 8호3110000-101-2021-00457경양식파스타, 피자93.96갈현제1동상수도전용07040156772
53110000202322023091520231110순(筍)서울특별시 은평구 진흥로 180-1, 1층 (녹번동)서울특별시 은평구 녹번동 118번지 69호3110000-101-2022-00160일식어류18.2녹번동상수도전용<NA>
63110000202332023100420231110인생돈카츠서울특별시 은평구 통일로65길 12-10, 1층 (대조동)서울특별시 은평구 대조동 15번지 39호3110000-101-2015-00081일식수제 돈가스46.29대조동상수도전용02 352 3920
73110000202342023092020231110자금성서울특별시 은평구 응암로 288, (응암동, 1층)서울특별시 은평구 응암동 107번지 5호 1층3110000-101-2011-00327중국식짜장면, 짬뽕, 탕수육126.0응암제1동상수도전용02 355 7917
83110000202362023100420231110혼양서울특별시 은평구 역말로 105, 1층 102호 (역촌동)서울특별시 은평구 역촌동 2번지 50호3110000-101-2021-00227중국식양고기70.0역촌동상수도전용<NA>
93110000202392023100420231110보들이 족발서울특별시 은평구 연서로 230-10, (대조동)서울특별시 은평구 대조동 185번지 43호3110000-101-1995-03364한식족발119.46대조동상수도전용02 3887610
시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
20931100002004812004010520040105이조삼계탕. 고기서울특별시 은평구 서오릉로 103, (역촌동)서울특별시 은평구 역촌동 2번지 131호3110000-101-1994-00347한식삼계탕74.1역촌동상수도전용02 359 8785
21031100002004192004010520040105은행나무샤브서울특별시 은평구 연서로 183-4, 1층 (갈현동)서울특별시 은평구 갈현동 462번지 41호3110000-101-2002-00500한식해장국78.0갈현제1동상수도전용02 3538628
21131100002004362004010520040105유진서울특별시 은평구 갈현로 219, 1층 (갈현동)서울특별시 은평구 갈현동 505번지 22호 1층3110000-101-2001-07863중국식쟁반짜장124.0갈현제1동상수도전용02 3527252
2123110000200462004010520040105사이고로서울특별시 은평구 연서로27길 27-4, (갈현동)서울특별시 은평구 갈현동 110번지 22호3110000-101-1995-06862한식냉면115.7갈현제1동상수도전용02 3851817
21331100002004822004010520040105(주)벙구갈비서울특별시 은평구 은평로 32, (신사동, 1층,2층)서울특별시 은평구 신사동 29번지 177호 1,2층3110000-101-1995-01922식육(숯불구이)생선초밥287.88신사제1동상수도전용<NA>
21431100002004872004010520040105꾸워서울특별시 은평구 은평로 34, 1층 (신사동)서울특별시 은평구 신사동 29번지 1호3110000-101-1999-06047한식해장국101.7신사제1동상수도전용<NA>
215311000020041102004010520040105은평감자국서울특별시 은평구 응암로 287, (응암동)서울특별시 은평구 응암동 117번지 9호3110000-101-2001-08246한식감자탕359.63응암제1동상수도전용02389 4458
21631100002004112004010520040105새만포면옥서울특별시 은평구 연서로 171, (갈현동, 지상1,2층)서울특별시 은평구 갈현동 463번지 1호 지상1,2층3110000-101-1993-05758한식사골탕306.09갈현제1동상수도전용02389 3917
217311000020041302004010520040105북한산갈비서울특별시 은평구 북한산로 281-3, 1층 (진관동)서울특별시 은평구 진관동 296번지 8호 1층3110000-101-1981-05180한식돼지갈비124.25진관동상수도전용02 3840398
21831100002004962004010520040105무안갯벌낙지서울특별시 은평구 진흥로 11, 1,2층 (역촌동)서울특별시 은평구 역촌동 44번지 34호 1,2층3110000-101-1986-00084한식돼지갈비204.92역촌동상수도전용02 3923292