Overview

Dataset statistics

Number of variables15
Number of observations107
Missing cells8
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.3 KiB
Average record size in memory127.2 B

Variable types

Categorical4
Numeric5
Text6

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,주된음식,영업장면적(㎡),행정동명,급수시설구분,소재지전화번호
Author노원구
URLhttps://data.seoul.go.kr/dataList/OA-10987/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
업태명 is highly overall correlated with 급수시설구분High correlation
행정동명 is highly overall correlated with 급수시설구분High correlation
급수시설구분 is highly overall correlated with 지정년도 and 6 other fieldsHigh 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 (54.0%)Imbalance
소재지전화번호 has 8 (7.5%) missing valuesMissing
소재지도로명 has unique valuesUnique
허가(신고)번호 has unique valuesUnique

Reproduction

Analysis started2024-05-11 06:35:05.960234
Analysis finished2024-05-11 06:35:13.520743
Duration7.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size988.0 B
3100000
107 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3100000
2nd row3100000
3rd row3100000
4th row3100000
5th row3100000

Common Values

ValueCountFrequency (%)
3100000 107
100.0%

Length

2024-05-11T06:35:13.801041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:35:14.132894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3100000 107
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.6449
Minimum2002
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-11T06:35:14.441125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2002
5-th percentile2002
Q12006
median2015
Q32020.5
95-th percentile2023
Maximum2023
Range21
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation7.7426195
Coefficient of variation (CV)0.003845077
Kurtosis-1.5017333
Mean2013.6449
Median Absolute Deviation (MAD)7
Skewness-0.30647719
Sum215460
Variance59.948157
MonotonicityNot monotonic
2024-05-11T06:35:15.350191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2002 14
13.1%
2020 14
13.1%
2023 12
11.2%
2022 8
 
7.5%
2021 7
 
6.5%
2015 6
 
5.6%
2003 5
 
4.7%
2009 5
 
4.7%
2017 4
 
3.7%
2013 4
 
3.7%
Other values (9) 28
26.2%
ValueCountFrequency (%)
2002 14
13.1%
2003 5
 
4.7%
2004 3
 
2.8%
2005 4
 
3.7%
2006 4
 
3.7%
2007 1
 
0.9%
2008 3
 
2.8%
2009 5
 
4.7%
2010 3
 
2.8%
2012 3
 
2.8%
ValueCountFrequency (%)
2023 12
11.2%
2022 8
7.5%
2021 7
6.5%
2020 14
13.1%
2019 4
 
3.7%
2018 3
 
2.8%
2017 4
 
3.7%
2015 6
5.6%
2013 4
 
3.7%
2012 3
 
2.8%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.364486
Minimum1
Maximum453
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-11T06:35:16.401148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median15
Q3102.5
95-th percentile433.6
Maximum453
Range452
Interquartile range (IQR)96.5

Descriptive statistics

Standard deviation145.13659
Coefficient of variation (CV)1.4754979
Kurtosis0.74819426
Mean98.364486
Median Absolute Deviation (MAD)13
Skewness1.5186589
Sum10525
Variance21064.63
MonotonicityNot monotonic
2024-05-11T06:35:17.362802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 7
 
6.5%
3 7
 
6.5%
8 5
 
4.7%
13 5
 
4.7%
1 5
 
4.7%
7 4
 
3.7%
5 4
 
3.7%
12 3
 
2.8%
15 3
 
2.8%
4 3
 
2.8%
Other values (50) 61
57.0%
ValueCountFrequency (%)
1 5
4.7%
2 7
6.5%
3 7
6.5%
4 3
2.8%
5 4
3.7%
6 3
2.8%
7 4
3.7%
8 5
4.7%
9 2
 
1.9%
11 1
 
0.9%
ValueCountFrequency (%)
453 1
0.9%
449 1
0.9%
448 1
0.9%
447 1
0.9%
443 1
0.9%
436 1
0.9%
428 1
0.9%
417 1
0.9%
397 1
0.9%
382 1
0.9%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)37.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20136957
Minimum20020520
Maximum20231106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-11T06:35:18.085518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20020520
5-th percentile20020520
Q120060620
median20151209
Q320206166
95-th percentile20231106
Maximum20231106
Range210586
Interquartile range (IQR)145546.5

Descriptive statistics

Standard deviation77926.098
Coefficient of variation (CV)0.003869805
Kurtosis-1.5117316
Mean20136957
Median Absolute Deviation (MAD)69815
Skewness-0.30146772
Sum2.1546544 × 109
Variance6.0724768 × 109
MonotonicityNot monotonic
2024-05-11T06:35:18.632926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
20201027 11
 
10.3%
20020520 8
 
7.5%
20231106 8
 
7.5%
20221024 8
 
7.5%
20211105 7
 
6.5%
20020522 6
 
5.6%
20151209 6
 
5.6%
20131104 4
 
3.7%
20060620 4
 
3.7%
20190920 4
 
3.7%
Other values (30) 41
38.3%
ValueCountFrequency (%)
20020520 8
7.5%
20020522 6
5.6%
20030319 1
 
0.9%
20030522 1
 
0.9%
20030619 1
 
0.9%
20030919 2
 
1.9%
20031219 1
 
0.9%
20040319 1
 
0.9%
20040920 2
 
1.9%
20050314 2
 
1.9%
ValueCountFrequency (%)
20231106 8
7.5%
20231101 1
 
0.9%
20231031 2
 
1.9%
20231030 1
 
0.9%
20221024 8
7.5%
20211105 7
6.5%
20201228 1
 
0.9%
20201030 1
 
0.9%
20201028 1
 
0.9%
20201027 11
10.3%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20137413
Minimum20020628
Maximum20231220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-11T06:35:19.121774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20020628
5-th percentile20020628
Q120060701
median20151209
Q320206222
95-th percentile20231220
Maximum20231220
Range210592
Interquartile range (IQR)145521

Descriptive statistics

Standard deviation77683.222
Coefficient of variation (CV)0.0038576566
Kurtosis-1.5035449
Mean20137413
Median Absolute Deviation (MAD)69993
Skewness-0.30644754
Sum2.1547031 × 109
Variance6.0346829 × 109
MonotonicityNot monotonic
2024-05-11T06:35:19.620523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
20020628 14
13.1%
20201217 13
12.1%
20231220 12
 
11.2%
20221202 8
 
7.5%
20211214 7
 
6.5%
20151209 6
 
5.6%
20090710 5
 
4.7%
20171207 4
 
3.7%
20191030 4
 
3.7%
20060701 4
 
3.7%
Other values (16) 30
28.0%
ValueCountFrequency (%)
20020628 14
13.1%
20030402 1
 
0.9%
20030630 2
 
1.9%
20031002 2
 
1.9%
20040402 1
 
0.9%
20040930 2
 
1.9%
20050105 1
 
0.9%
20050331 2
 
1.9%
20050630 1
 
0.9%
20060701 4
 
3.7%
ValueCountFrequency (%)
20231220 12
11.2%
20221202 8
7.5%
20211214 7
6.5%
20201230 1
 
0.9%
20201217 13
12.1%
20191030 4
 
3.7%
20181221 3
 
2.8%
20171207 4
 
3.7%
20151209 6
5.6%
20131210 4
 
3.7%

업소명
Text

Distinct106
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size988.0 B
2024-05-11T06:35:20.412793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length6.0560748
Min length2

Characters and Unicode

Total characters648
Distinct characters228
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

Unique105 ?
Unique (%)98.1%

Sample

1st row제일콩집
2nd row항도
3rd row향토곱창
4th row인차이나
5th row명문식당
ValueCountFrequency (%)
노원점 3
 
2.3%
신의주찹쌀순대 2
 
1.5%
소문난감자탕 1
 
0.8%
주식회사 1
 
0.8%
평창메밀막국수(본점 1
 
0.8%
달인보쌈 1
 
0.8%
횡성목장 1
 
0.8%
한동길감자탕 1
 
0.8%
조박사아구찜 1
 
0.8%
감동식당 1
 
0.8%
Other values (118) 118
90.1%
2024-05-11T06:35:21.940967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24
 
3.7%
점 17
 
2.6%
원 13
 
2.0%
수 12
 
1.9%
이 12
 
1.9%
가 10
 
1.5%
락 9
 
1.4%
한 9
 
1.4%
계 9
 
1.4%
탕 9
 
1.4%
Other values (218) 524
80.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 604
93.2%
Space Separator 24
 
3.7%
Decimal Number 9
 
1.4%
Open Punctuation 3
 
0.5%
Close Punctuation 3
 
0.5%
Uppercase Letter 3
 
0.5%
Other Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 17
 
2.8%
원 13
 
2.2%
수 12
 
2.0%
이 12
 
2.0%
가 10
 
1.7%
락 9
 
1.5%
한 9
 
1.5%
계 9
 
1.5%
탕 9
 
1.5%
시 8
 
1.3%
Other values (204) 496
82.1%
Decimal Number
ValueCountFrequency (%)
9 2
22.2%
3 2
22.2%
1 2
22.2%
5 1
11.1%
6 1
11.1%
4 1
11.1%
Uppercase Letter
ValueCountFrequency (%)
D 1
33.3%
N 1
33.3%
J 1
33.3%
Other Punctuation
ValueCountFrequency (%)
？ 1
50.0%
. 1
50.0%
Space Separator
ValueCountFrequency (%)
24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 604
93.2%
Common 41
 
6.3%
Latin 3
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 17
 
2.8%
원 13
 
2.2%
수 12
 
2.0%
이 12
 
2.0%
가 10
 
1.7%
락 9
 
1.5%
한 9
 
1.5%
계 9
 
1.5%
탕 9
 
1.5%
시 8
 
1.3%
Other values (204) 496
82.1%
Common
ValueCountFrequency (%)
24
58.5%
( 3
 
7.3%
) 3
 
7.3%
9 2
 
4.9%
3 2
 
4.9%
1 2
 
4.9%
？ 1
 
2.4%
5 1
 
2.4%
. 1
 
2.4%
6 1
 
2.4%
Latin
ValueCountFrequency (%)
D 1
33.3%
N 1
33.3%
J 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 604
93.2%
ASCII 43
 
6.6%
None 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24
55.8%
( 3
 
7.0%
) 3
 
7.0%
9 2
 
4.7%
3 2
 
4.7%
1 2
 
4.7%
D 1
 
2.3%
N 1
 
2.3%
J 1
 
2.3%
5 1
 
2.3%
Other values (3) 3
 
7.0%
Hangul
ValueCountFrequency (%)
점 17
 
2.8%
원 13
 
2.2%
수 12
 
2.0%
이 12
 
2.0%
가 10
 
1.7%
락 9
 
1.5%
한 9
 
1.5%
계 9
 
1.5%
탕 9
 
1.5%
시 8
 
1.3%
Other values (204) 496
82.1%
None
ValueCountFrequency (%)
？ 1
100.0%

소재지도로명
Text

UNIQUE 

Distinct107
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size988.0 B
2024-05-11T06:35:22.719205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length46
Mean length33.084112
Min length23

Characters and Unicode

Total characters3540
Distinct characters136
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

Unique107 ?
Unique (%)100.0%

Sample

1st row서울특별시 노원구 동일로174길 37-8, 제일빌딩 1,2층 (공릉동)
2nd row서울특별시 노원구 노해로75길 14-22, (상계동)
3rd row서울특별시 노원구 동일로191길 10, (공릉동)
4th row서울특별시 노원구 공릉로 167, 1층 (공릉동)
5th row서울특별시 노원구 동일로217가길 27, (상계동)
ValueCountFrequency (%)
서울특별시 107
 
15.9%
노원구 107
 
15.9%
상계동 50
 
7.4%
1층 45
 
6.7%
공릉동 16
 
2.4%
중계동 15
 
2.2%
2층 13
 
1.9%
동일로 13
 
1.9%
하계동 10
 
1.5%
노해로75길 8
 
1.2%
Other values (205) 290
43.0%
2024-05-11T06:35:24.054493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
568
 
16.0%
1 171
 
4.8%
동 154
 
4.4%
, 152
 
4.3%
노 130
 
3.7%
2 128
 
3.6%
원 115
 
3.2%
( 108
 
3.1%
) 108
 
3.1%
로 107
 
3.0%
Other values (126) 1799
50.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1960
55.4%
Decimal Number 620
 
17.5%
Space Separator 568
 
16.0%
Other Punctuation 152
 
4.3%
Open Punctuation 108
 
3.1%
Close Punctuation 108
 
3.1%
Dash Punctuation 22
 
0.6%
Uppercase Letter 1
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 154
 
7.9%
노 130
 
6.6%
원 115
 
5.9%
로 107
 
5.5%
울 107
 
5.5%
서 107
 
5.5%
구 107
 
5.5%
시 107
 
5.5%
별 107
 
5.5%
특 107
 
5.5%
Other values (109) 812
41.4%
Decimal Number
ValueCountFrequency (%)
1 171
27.6%
2 128
20.6%
4 60
 
9.7%
3 55
 
8.9%
7 48
 
7.7%
5 40
 
6.5%
0 40
 
6.5%
9 30
 
4.8%
8 26
 
4.2%
6 22
 
3.5%
Space Separator
ValueCountFrequency (%)
568
100.0%
Other Punctuation
ValueCountFrequency (%)
, 152
100.0%
Open Punctuation
ValueCountFrequency (%)
( 108
100.0%
Close Punctuation
ValueCountFrequency (%)
) 108
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1960
55.4%
Common 1579
44.6%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 154
 
7.9%
노 130
 
6.6%
원 115
 
5.9%
로 107
 
5.5%
울 107
 
5.5%
서 107
 
5.5%
구 107
 
5.5%
시 107
 
5.5%
별 107
 
5.5%
특 107
 
5.5%
Other values (109) 812
41.4%
Common
ValueCountFrequency (%)
568
36.0%
1 171
 
10.8%
, 152
 
9.6%
2 128
 
8.1%
( 108
 
6.8%
) 108
 
6.8%
4 60
 
3.8%
3 55
 
3.5%
7 48
 
3.0%
5 40
 
2.5%
Other values (6) 141
 
8.9%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1960
55.4%
ASCII 1580
44.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
568
35.9%
1 171
 
10.8%
, 152
 
9.6%
2 128
 
8.1%
( 108
 
6.8%
) 108
 
6.8%
4 60
 
3.8%
3 55
 
3.5%
7 48
 
3.0%
5 40
 
2.5%
Other values (7) 142
 
9.0%
Hangul
ValueCountFrequency (%)
동 154
 
7.9%
노 130
 
6.6%
원 115
 
5.9%
로 107
 
5.5%
울 107
 
5.5%
서 107
 
5.5%
구 107
 
5.5%
시 107
 
5.5%
별 107
 
5.5%
특 107
 
5.5%
Other values (109) 812
41.4%

소재지지번
Text

Distinct106
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size988.0 B
2024-05-11T06:35:24.769669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length41
Mean length29.841121
Min length24

Characters and Unicode

Total characters3193
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

Unique105 ?
Unique (%)98.1%

Sample

1st row서울특별시 노원구 공릉동 633번지 18호 제일빌딩 1,2층
2nd row서울특별시 노원구 상계동 708번지 2호 지상2층
3rd row서울특별시 노원구 공릉동 383번지 18호
4th row서울특별시 노원구 공릉동 411번지 4호 1층
5th row서울특별시 노원구 상계동 735번지 5호 1층
ValueCountFrequency (%)
서울특별시 107
16.7%
노원구 107
16.7%
상계동 57
 
8.9%
1층 34
 
5.3%
공릉동 18
 
2.8%
중계동 18
 
2.8%
2호 11
 
1.7%
하계동 10
 
1.6%
1호 9
 
1.4%
2층 9
 
1.4%
Other values (183) 261
40.7%
2024-05-11T06:35:26.154908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
772
24.2%
1 147
 
4.6%
지 114
 
3.6%
동 110
 
3.4%
원 110
 
3.4%
노 109
 
3.4%
구 108
 
3.4%
번 107
 
3.4%
서 107
 
3.4%
시 107
 
3.4%
Other values (110) 1402
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1791
56.1%
Space Separator 772
24.2%
Decimal Number 606
 
19.0%
Other Punctuation 14
 
0.4%
Dash Punctuation 6
 
0.2%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 114
 
6.4%
동 110
 
6.1%
원 110
 
6.1%
노 109
 
6.1%
구 108
 
6.0%
번 107
 
6.0%
서 107
 
6.0%
시 107
 
6.0%
별 107
 
6.0%
특 107
 
6.0%
Other values (93) 705
39.4%
Decimal Number
ValueCountFrequency (%)
1 147
24.3%
2 89
14.7%
3 75
12.4%
0 57
 
9.4%
5 49
 
8.1%
6 47
 
7.8%
7 42
 
6.9%
4 41
 
6.8%
9 32
 
5.3%
8 27
 
4.5%
Space Separator
ValueCountFrequency (%)
772
100.0%
Other Punctuation
ValueCountFrequency (%)
, 14
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1791
56.1%
Common 1401
43.9%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 114
 
6.4%
동 110
 
6.1%
원 110
 
6.1%
노 109
 
6.1%
구 108
 
6.0%
번 107
 
6.0%
서 107
 
6.0%
시 107
 
6.0%
별 107
 
6.0%
특 107
 
6.0%
Other values (93) 705
39.4%
Common
ValueCountFrequency (%)
772
55.1%
1 147
 
10.5%
2 89
 
6.4%
3 75
 
5.4%
0 57
 
4.1%
5 49
 
3.5%
6 47
 
3.4%
7 42
 
3.0%
4 41
 
2.9%
9 32
 
2.3%
Other values (6) 50
 
3.6%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1791
56.1%
ASCII 1402
43.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
772
55.1%
1 147
 
10.5%
2 89
 
6.3%
3 75
 
5.3%
0 57
 
4.1%
5 49
 
3.5%
6 47
 
3.4%
7 42
 
3.0%
4 41
 
2.9%
9 32
 
2.3%
Other values (7) 51
 
3.6%
Hangul
ValueCountFrequency (%)
지 114
 
6.4%
동 110
 
6.1%
원 110
 
6.1%
노 109
 
6.1%
구 108
 
6.0%
번 107
 
6.0%
서 107
 
6.0%
시 107
 
6.0%
별 107
 
6.0%
특 107
 
6.0%
Other values (93) 705
39.4%

허가(신고)번호
Text

UNIQUE 

Distinct107
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size988.0 B
2024-05-11T06:35:26.769439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2354
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

Unique107 ?
Unique (%)100.0%

Sample

1st row3100000-101-1983-00002
2nd row3100000-101-1993-03282
3rd row3100000-101-1998-02143
4th row3100000-101-2008-00062
5th row3100000-101-1990-00361
ValueCountFrequency (%)
3100000-101-1983-00002 1
 
0.9%
3100000-101-1994-01103 1
 
0.9%
3100000-101-2009-00318 1
 
0.9%
3100000-101-1994-01137 1
 
0.9%
3100000-101-2003-00268 1
 
0.9%
3100000-101-1990-00323 1
 
0.9%
3100000-101-2019-00095 1
 
0.9%
3100000-101-2016-00072 1
 
0.9%
3100000-101-1995-03327 1
 
0.9%
3100000-101-1994-03296 1
 
0.9%
Other values (97) 97
90.7%
2024-05-11T06:35:27.952332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 979
41.6%
1 447
19.0%
- 321
 
13.6%
3 155
 
6.6%
2 144
 
6.1%
9 110
 
4.7%
8 42
 
1.8%
4 41
 
1.7%
7 41
 
1.7%
6 38
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2033
86.4%
Dash Punctuation 321
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 979
48.2%
1 447
22.0%
3 155
 
7.6%
2 144
 
7.1%
9 110
 
5.4%
8 42
 
2.1%
4 41
 
2.0%
7 41
 
2.0%
6 38
 
1.9%
5 36
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 321
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 979
41.6%
1 447
19.0%
- 321
 
13.6%
3 155
 
6.6%
2 144
 
6.1%
9 110
 
4.7%
8 42
 
1.8%
4 41
 
1.7%
7 41
 
1.7%
6 38
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 979
41.6%
1 447
19.0%
- 321
 
13.6%
3 155
 
6.6%
2 144
 
6.1%
9 110
 
4.7%
8 42
 
1.8%
4 41
 
1.7%
7 41
 
1.7%
6 38
 
1.6%

업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Memory size988.0 B
한식
80 
일식
 
8
중국식
 
8
경양식
 
3
식육(숯불구이)
 
3
Other values (4)
 
5

Length

Max length15
Median length2
Mean length2.5420561
Min length2

Unique

Unique3 ?
Unique (%)2.8%

Sample

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

Common Values

ValueCountFrequency (%)
한식 80
74.8%
일식 8
 
7.5%
중국식 8
 
7.5%
경양식 3
 
2.8%
식육(숯불구이) 3
 
2.8%
외국음식전문점(인도,태국등) 2
 
1.9%
호프/통닭 1
 
0.9%
회집 1
 
0.9%
기타 1
 
0.9%

Length

2024-05-11T06:35:28.487785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:35:28.882609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 80
74.8%
일식 8
 
7.5%
중국식 8
 
7.5%
경양식 3
 
2.8%
식육(숯불구이 3
 
2.8%
외국음식전문점(인도,태국등 2
 
1.9%
호프/통닭 1
 
0.9%
회집 1
 
0.9%
기타 1
 
0.9%

주된음식
Text

Distinct69
Distinct (%)64.5%
Missing0
Missing (%)0.0%
Memory size988.0 B
2024-05-11T06:35:29.780042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length7
Mean length3.411215
Min length2

Characters and Unicode

Total characters365
Distinct characters106
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

Unique49 ?
Unique (%)45.8%

Sample

1st row두부찌개
2nd row대구탕
3rd row곱창구이
4th row면류
5th row부대찌개
ValueCountFrequency (%)
돼지갈비 6
 
5.5%
삼겹살 6
 
5.5%
삼계탕 4
 
3.7%
닭갈비 4
 
3.7%
대구탕 3
 
2.8%
순대국 3
 
2.8%
샤브샤브 3
 
2.8%
양꼬치 3
 
2.8%
추어탕 3
 
2.8%
참치회 3
 
2.8%
Other values (60) 71
65.1%
2024-05-11T06:35:31.534575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
비 16
 
4.4%
갈 15
 
4.1%
탕 15
 
4.1%
국 12
 
3.3%
구 12
 
3.3%
장 12
 
3.3%
삼 10
 
2.7%
이 9
 
2.5%
대 9
 
2.5%
면 8
 
2.2%
Other values (96) 247
67.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 359
98.4%
Other Punctuation 4
 
1.1%
Space Separator 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
비 16
 
4.5%
갈 15
 
4.2%
탕 15
 
4.2%
국 12
 
3.3%
구 12
 
3.3%
장 12
 
3.3%
삼 10
 
2.8%
이 9
 
2.5%
대 9
 
2.5%
면 8
 
2.2%
Other values (94) 241
67.1%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 359
98.4%
Common 6
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
비 16
 
4.5%
갈 15
 
4.2%
탕 15
 
4.2%
국 12
 
3.3%
구 12
 
3.3%
장 12
 
3.3%
삼 10
 
2.8%
이 9
 
2.5%
대 9
 
2.5%
면 8
 
2.2%
Other values (94) 241
67.1%
Common
ValueCountFrequency (%)
, 4
66.7%
2
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 359
98.4%
ASCII 6
 
1.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
비 16
 
4.5%
갈 15
 
4.2%
탕 15
 
4.2%
국 12
 
3.3%
구 12
 
3.3%
장 12
 
3.3%
삼 10
 
2.8%
이 9
 
2.5%
대 9
 
2.5%
면 8
 
2.2%
Other values (94) 241
67.1%
ASCII
ValueCountFrequency (%)
, 4
66.7%
2
33.3%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct103
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190.36794
Minimum28.12
Maximum2792.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-11T06:35:32.131066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28.12
5-th percentile65.478
Q189.8
median131.58
Q3199.53
95-th percentile431.944
Maximum2792.2
Range2764.08
Interquartile range (IQR)109.73

Descriptive statistics

Standard deviation280.84434
Coefficient of variation (CV)1.4752712
Kurtosis70.749259
Mean190.36794
Median Absolute Deviation (MAD)47.42
Skewness7.7828648
Sum20369.37
Variance78873.543
MonotonicityNot monotonic
2024-05-11T06:35:32.718196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.0 3
 
2.8%
148.0 2
 
1.9%
79.04 2
 
1.9%
214.46 1
 
0.9%
105.35 1
 
0.9%
101.97 1
 
0.9%
77.4 1
 
0.9%
111.8 1
 
0.9%
69.5 1
 
0.9%
147.43 1
 
0.9%
Other values (93) 93
86.9%
ValueCountFrequency (%)
28.12 1
0.9%
28.47 1
0.9%
43.68 1
0.9%
44.06 1
0.9%
58.35 1
0.9%
65.1 1
0.9%
66.36 1
0.9%
67.0 1
0.9%
69.5 1
0.9%
72.0 1
0.9%
ValueCountFrequency (%)
2792.2 1
0.9%
765.42 1
0.9%
625.37 1
0.9%
534.87 1
0.9%
486.13 1
0.9%
437.2 1
0.9%
419.68 1
0.9%
357.31 1
0.9%
341.66 1
0.9%
330.48 1
0.9%

행정동명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Memory size988.0 B
상계6.7동
20 
상계1동
18 
상계2동
15 
공릉1동
14 
하계1동
10 
Other values (8)
30 

Length

Max length6
Median length4
Mean length4.4672897
Min length4

Unique

Unique1 ?
Unique (%)0.9%

Sample

1st row공릉1동
2nd row상계6.7동
3rd row공릉1동
4th row공릉2동
5th row상계6.7동

Common Values

ValueCountFrequency (%)
상계6.7동 20
18.7%
상계1동 18
16.8%
상계2동 15
14.0%
공릉1동 14
13.1%
하계1동 10
9.3%
중계4동 9
8.4%
공릉2동 4
 
3.7%
중계2.3동 4
 
3.7%
월계1동 4
 
3.7%
중계본동 3
 
2.8%
Other values (3) 6
 
5.6%

Length

2024-05-11T06:35:33.282437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
상계6.7동 20
18.7%
상계1동 18
16.8%
상계2동 15
14.0%
공릉1동 14
13.1%
하계1동 10
9.3%
중계4동 9
8.4%
공릉2동 4
 
3.7%
중계2.3동 4
 
3.7%
월계1동 4
 
3.7%
중계본동 3
 
2.8%
Other values (3) 6
 
5.6%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size988.0 B
상수도전용
69 
<NA>
38 

Length

Max length5
Median length5
Mean length4.6448598
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 69
64.5%
<NA> 38
35.5%

Length

2024-05-11T06:35:34.005315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:35:34.528413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 69
64.5%
na 38
35.5%

소재지전화번호
Text

MISSING 

Distinct99
Distinct (%)100.0%
Missing8
Missing (%)7.5%
Memory size988.0 B
2024-05-11T06:35:35.627718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.292929
Min length7

Characters and Unicode

Total characters1019
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

Unique99 ?
Unique (%)100.0%

Sample

1st row02 9727016
2nd row02 9388182
3rd row02 9723492
4th row02 971 4009
5th row02 936 2651
ValueCountFrequency (%)
02 79
38.0%
951 4
 
1.9%
976 3
 
1.4%
974 2
 
1.0%
975 2
 
1.0%
952 2
 
1.0%
932 1
 
0.5%
9349926 1
 
0.5%
9488466 1
 
0.5%
9794042 1
 
0.5%
Other values (112) 112
53.8%
2024-05-11T06:35:36.910673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 145
14.2%
2 145
14.2%
144
14.1%
9 138
13.5%
3 104
10.2%
7 74
7.3%
5 72
7.1%
1 63
6.2%
4 59
5.8%
8 46
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 875
85.9%
Space Separator 144
 
14.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 145
16.6%
2 145
16.6%
9 138
15.8%
3 104
11.9%
7 74
8.5%
5 72
8.2%
1 63
7.2%
4 59
6.7%
8 46
 
5.3%
6 29
 
3.3%
Space Separator
ValueCountFrequency (%)
144
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1019
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 145
14.2%
2 145
14.2%
144
14.1%
9 138
13.5%
3 104
10.2%
7 74
7.3%
5 72
7.1%
1 63
6.2%
4 59
5.8%
8 46
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1019
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 145
14.2%
2 145
14.2%
144
14.1%
9 138
13.5%
3 104
10.2%
7 74
7.3%
5 72
7.1%
1 63
6.2%
4 59
5.8%
8 46
 
4.5%

Interactions

2024-05-11T06:35:11.178915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:07.399822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:08.096042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:09.096740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:10.191372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:11.408028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:07.532487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:08.245180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:09.322640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:10.325110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:11.693905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:07.666289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:08.394000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:09.567021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:10.529752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:11.929761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:07.799454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:08.631069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:09.796852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:10.757413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:12.171032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:07.935134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:08.861820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:10.035132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:35:10.990142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T06:35:37.322377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자업태명주된음식영업장면적(㎡)행정동명소재지전화번호
지정년도1.0000.8101.0001.0000.0000.7190.0000.0001.000
지정번호0.8101.0000.6610.8100.0000.7710.0000.2921.000
신청일자1.0000.6611.0001.0000.0000.7280.0000.1951.000
지정일자1.0000.8101.0001.0000.0000.7190.0000.0001.000
업태명0.0000.0000.0000.0001.0000.9340.1710.2971.000
주된음식0.7190.7710.7280.7190.9341.0000.0000.0001.000
영업장면적(㎡)0.0000.0000.0000.0000.1710.0001.0000.0001.000
행정동명0.0000.2920.1950.0000.2970.0000.0001.0001.000
소재지전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-05-11T06:35:37.784518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태명행정동명급수시설구분
업태명1.0000.1251.000
행정동명0.1251.0001.000
급수시설구분1.0001.0001.000
2024-05-11T06:35:38.178693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.000-0.2990.9981.000-0.0620.0000.0981.000
지정번호-0.2991.000-0.298-0.295-0.0470.0000.1401.000
신청일자0.998-0.2981.0000.998-0.0710.0000.0841.000
지정일자1.000-0.2950.9981.000-0.0660.0000.0981.000
영업장면적(㎡)-0.062-0.047-0.071-0.0661.0000.1040.0001.000
업태명0.0000.0000.0000.0000.1041.0000.1251.000
행정동명0.0980.1400.0840.0980.0000.1251.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-11T06:35:12.580319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T06:35:13.283196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
03100000200222002052220020628제일콩집서울특별시 노원구 동일로174길 37-8, 제일빌딩 1,2층 (공릉동)서울특별시 노원구 공릉동 633번지 18호 제일빌딩 1,2층3100000-101-1983-00002한식두부찌개214.46공릉1동상수도전용02 9727016
131000002009142009032420090710항도서울특별시 노원구 노해로75길 14-22, (상계동)서울특별시 노원구 상계동 708번지 2호 지상2층3100000-101-1993-03282일식대구탕126.42상계6.7동상수도전용02 9388182
2310000020021302002052220020628향토곱창서울특별시 노원구 동일로191길 10, (공릉동)서울특별시 노원구 공릉동 383번지 18호3100000-101-1998-02143한식곱창구이66.36공릉1동상수도전용02 9723492
33100000201582015120920151209인차이나서울특별시 노원구 공릉로 167, 1층 (공릉동)서울특별시 노원구 공릉동 411번지 4호 1층3100000-101-2008-00062중국식면류195.7공릉2동<NA>02 971 4009
4310000020034282003091920031002명문식당서울특별시 노원구 동일로217가길 27, (상계동)서울특별시 노원구 상계동 735번지 5호 1층3100000-101-1990-00361한식부대찌개88.5상계6.7동상수도전용02 936 2651
531000002015162015120920151209육대장 상계점서울특별시 노원구 수락산로 212, 지상1층 우측호 (상계동)서울특별시 노원구 상계동 963번지 2호3100000-101-2012-00022한식육개장67.0상계1동<NA>02 930 0777
631000002009122009060220090710신의주찹쌀순대서울특별시 노원구 노해로75길 14-18, (상계동)서울특별시 노원구 상계동 708번지 0호3100000-101-1994-01269한식순대국94.89상계6.7동상수도전용02 9317766
731000002015192015120920151209포앤시드니중계점서울특별시 노원구 동일로203가길 29, (중계동, 브라운스톤 중계상가 110호, 111호)서울특별시 노원구 중계동 506번지 브라운스톤 중계상가 110호, 111호3100000-101-2015-00107외국음식전문점(인도,태국등)쌀국수79.04중계2.3동상수도전용02 979 1555
83100000201932019092020191030착한낙지 수락산역점서울특별시 노원구 동일로242가길 35, 1층 (상계동)서울특별시 노원구 상계동 1115번지 4호3100000-101-2018-00258한식삼계탕200.34상계1동<NA>02 9358819
9310000020023242002052220020628부일관서울특별시 노원구 노원로26길 119, 부일관 1층 (상계동)서울특별시 노원구 상계동 180번지 3호 부일관3100000-101-1996-01704한식돼지갈비328.4상계2동상수도전용02 9526634
시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
973100000202152021110520211214장안동본참치 태릉직영점서울특별시 노원구 동일로174길 7, 1층 (공릉동)서울특별시 노원구 공릉동 617번지 18호 1층3100000-101-2011-00284한식참치회148.0공릉1동<NA>02 4959544
983100000202182021110520211214신라강호서울특별시 노원구 한글비석로 269, 마들플라자 2층 207,208호 (중계동)서울특별시 노원구 중계동 359번지 9호 마들플라자3100000-101-2021-00236외국음식전문점(인도,태국등)마라탕97.5중계1동<NA>02 9368887
993100000202112021110520211214전주콩나루콩나물국밥서울특별시 노원구 노해로75길 14-25, (상계동)서울특별시 노원구 상계동 707번지 3호3100000-101-1993-04077한식콩나물국밥92.29상계6.7동상수도전용02 9307111
1003100000202122021110520211214산골식당서울특별시 노원구 한글비석로15길 11, 1층 (중계동)서울특별시 노원구 중계동 450번지 18호 1층3100000-101-1999-05943한식만두전골179.58중계4동상수도전용02 9370333
10131000002023962023110120231220숟가락반상마실서울특별시 노원구 노원로 412, 2층 (상계동)서울특별시 노원구 상계동 318번지 1호 2층3100000-101-2003-00019한식한정식357.31상계2동상수도전용9305111
1023100000201912019092020191030청춘남원추어탕서울특별시 노원구 동일로 1383, 108호 (상계동, 상계주공3단지아파트)서울특별시 노원구 상계동 730번지 2호 상계주공3단지아파트-1083100000-101-1998-02513한식추어탕28.47상계6.7동상수도전용02 9318242
10331000002018932018103020181221팔각도 노원역점서울특별시 노원구 노해로 517, 1층 (상계동)서울특별시 노원구 상계동 323번지 16호 1층3100000-101-2017-00262한식함흥냉면, 눈꽃등심207.9상계2동<NA>02 932 3775
104310000020023452002052020020628원명품생태서울특별시 노원구 노해로75길 14-27, (상계동)서울특별시 노원구 상계동 707번지 4호 1층3100000-101-1992-00632한식생태찌개108.31상계6.7동상수도전용02 9394208
105310000020023712002052020020628DNJ서울특별시 노원구 노원로 244, 보스턴 산부인과 1층 (하계동)서울특별시 노원구 하계동 256번지 11호 보스턴 산부인과3100000-101-1997-01969중국식짜장면,짬뽕341.66하계1동상수도전용02 9396700
1063100000202162021110520211214스시웨이서울특별시 노원구 동일로 1374, (상계동, 2층)서울특별시 노원구 상계동 749번지 2층3100000-101-2012-00159일식초밥90.0상계6.7동<NA>02 9353774