Overview

Dataset statistics

Number of variables10
Number of observations1586
Missing cells916
Missing cells (%)5.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory130.2 KiB
Average record size in memory84.1 B

Variable types

Numeric4
Categorical2
Text2
DateTime2

Dataset

Description파일 다운로드
Author서울교통공사
URLhttps://data.seoul.go.kr/dataList/OA-12927/F/1/datasetView.do

Alerts

연번 is highly overall correlated with 호선High correlation
호선 is highly overall correlated with 연번High correlation
면적(제곱미터) has 59 (3.7%) missing valuesMissing
계약시작일자 has 210 (13.2%) missing valuesMissing
계약종료일자 has 210 (13.2%) missing valuesMissing
월임대료 has 437 (27.6%) missing valuesMissing
면적(제곱미터) is highly skewed (γ1 = 30.99600944)Skewed
연번 has unique valuesUnique
상가번호 has unique valuesUnique

Reproduction

Analysis started2024-04-29 16:39:37.808092
Analysis finished2024-04-29 16:39:40.117122
Duration2.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1586
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean793.5
Minimum1
Maximum1586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.1 KiB
2024-04-30T01:39:40.185918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile80.25
Q1397.25
median793.5
Q31189.75
95-th percentile1506.75
Maximum1586
Range1585
Interquartile range (IQR)792.5

Descriptive statistics

Standard deviation457.98308
Coefficient of variation (CV)0.57716834
Kurtosis-1.2
Mean793.5
Median Absolute Deviation (MAD)396.5
Skewness0
Sum1258491
Variance209748.5
MonotonicityStrictly increasing
2024-04-30T01:39:40.315653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1067 1
 
0.1%
1065 1
 
0.1%
1064 1
 
0.1%
1063 1
 
0.1%
1062 1
 
0.1%
1061 1
 
0.1%
1060 1
 
0.1%
1059 1
 
0.1%
1058 1
 
0.1%
Other values (1576) 1576
99.4%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1586 1
0.1%
1585 1
0.1%
1584 1
0.1%
1583 1
0.1%
1582 1
0.1%
1581 1
0.1%
1580 1
0.1%
1579 1
0.1%
1578 1
0.1%
1577 1
0.1%

상가유형
Categorical

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
개별(일반)
580 
네트워크
291 
복합
226 
67일괄
216 
공실
206 
Other values (2)
67 

Length

Max length6
Median length4
Mean length4.2295082
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row개별(일반)
2nd row개별(일반)
3rd row개별(일반)
4th row개별(일반)
5th row네트워크

Common Values

ValueCountFrequency (%)
개별(일반) 580
36.6%
네트워크 291
18.3%
복합 226
 
14.2%
67일괄 216
 
13.6%
공실 206
 
13.0%
개별(대형) 34
 
2.1%
소송상가 33
 
2.1%

Length

2024-04-30T01:39:40.450567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:39:40.563347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개별(일반 580
36.6%
네트워크 291
18.3%
복합 226
 
14.2%
67일괄 216
 
13.6%
공실 206
 
13.0%
개별(대형 34
 
2.1%
소송상가 33
 
2.1%

호선
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8247163
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.1 KiB
2024-04-30T01:39:40.664791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q37
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.967764
Coefficient of variation (CV)0.40785073
Kurtosis-1.2472839
Mean4.8247163
Median Absolute Deviation (MAD)2
Skewness-0.22137988
Sum7652
Variance3.8720953
MonotonicityIncreasing
2024-04-30T01:39:40.770262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
7 392
24.7%
5 297
18.7%
2 286
18.0%
6 202
12.7%
3 172
10.8%
4 146
 
9.2%
8 64
 
4.0%
1 27
 
1.7%
ValueCountFrequency (%)
1 27
 
1.7%
2 286
18.0%
3 172
10.8%
4 146
 
9.2%
5 297
18.7%
6 202
12.7%
7 392
24.7%
8 64
 
4.0%
ValueCountFrequency (%)
8 64
 
4.0%
7 392
24.7%
6 202
12.7%
5 297
18.7%
4 146
 
9.2%
3 172
10.8%
2 286
18.0%
1 27
 
1.7%

역사명
Text

Distinct236
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
2024-04-30T01:39:41.036973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length9
Mean length5.001261
Min length3

Characters and Unicode

Total characters7932
Distinct characters203
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)1.8%

Sample

1st row서울(1)역
2nd row시청(1)역
3rd row시청(1)역
4th row시청(1)역
5th row시청(1)역
ValueCountFrequency (%)
오목교역 46
 
2.9%
고속터미널(3)역 39
 
2.5%
공덕(5)역 29
 
1.8%
천호(5)역 27
 
1.7%
잠실(8)역 26
 
1.6%
사당(4)역 25
 
1.6%
노원(7)역 22
 
1.4%
강남구청역 21
 
1.3%
고속터미널(7)역 19
 
1.2%
총신대입구역 19
 
1.2%
Other values (226) 1313
82.8%
2024-04-30T01:39:41.394445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 1613
 
20.3%
) 556
 
7.0%
( 556
 
7.0%
대 209
 
2.6%
구 184
 
2.3%
신 127
 
1.6%
2 114
 
1.4%
입 113
 
1.4%
사 110
 
1.4%
5 106
 
1.3%
Other values (193) 4244
53.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6222
78.4%
Decimal Number 598
 
7.5%
Close Punctuation 556
 
7.0%
Open Punctuation 556
 
7.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 1613
25.9%
대 209
 
3.4%
구 184
 
3.0%
신 127
 
2.0%
입 113
 
1.8%
사 110
 
1.8%
산 94
 
1.5%
공 93
 
1.5%
동 91
 
1.5%
미 86
 
1.4%
Other values (183) 3502
56.3%
Decimal Number
ValueCountFrequency (%)
2 114
19.1%
5 106
17.7%
3 106
17.7%
7 99
16.6%
6 68
11.4%
4 62
10.4%
8 31
 
5.2%
1 12
 
2.0%
Close Punctuation
ValueCountFrequency (%)
) 556
100.0%
Open Punctuation
ValueCountFrequency (%)
( 556
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6222
78.4%
Common 1710
 
21.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 1613
25.9%
대 209
 
3.4%
구 184
 
3.0%
신 127
 
2.0%
입 113
 
1.8%
사 110
 
1.8%
산 94
 
1.5%
공 93
 
1.5%
동 91
 
1.5%
미 86
 
1.4%
Other values (183) 3502
56.3%
Common
ValueCountFrequency (%)
) 556
32.5%
( 556
32.5%
2 114
 
6.7%
5 106
 
6.2%
3 106
 
6.2%
7 99
 
5.8%
6 68
 
4.0%
4 62
 
3.6%
8 31
 
1.8%
1 12
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6222
78.4%
ASCII 1710
 
21.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 1613
25.9%
대 209
 
3.4%
구 184
 
3.0%
신 127
 
2.0%
입 113
 
1.8%
사 110
 
1.8%
산 94
 
1.5%
공 93
 
1.5%
동 91
 
1.5%
미 86
 
1.4%
Other values (183) 3502
56.3%
ASCII
ValueCountFrequency (%)
) 556
32.5%
( 556
32.5%
2 114
 
6.7%
5 106
 
6.2%
3 106
 
6.2%
7 99
 
5.8%
6 68
 
4.0%
4 62
 
3.6%
8 31
 
1.8%
1 12
 
0.7%

상가번호
Text

UNIQUE 

Distinct1586
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
2024-04-30T01:39:41.667868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters11102
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1586 ?
Unique (%)100.0%

Sample

1st row150-107
2nd row151-101
3rd row151-103
4th row151-104
5th row151-105
ValueCountFrequency (%)
150-107 1
 
0.1%
638-101 1
 
0.1%
636-207 1
 
0.1%
636-206 1
 
0.1%
636-205 1
 
0.1%
636-201 1
 
0.1%
635-203 1
 
0.1%
635-202 1
 
0.1%
635-201 1
 
0.1%
635-106 1
 
0.1%
Other values (1576) 1576
99.4%
2024-04-30T01:39:42.381380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2250
20.3%
2 1632
14.7%
- 1586
14.3%
0 1559
14.0%
3 1055
9.5%
4 743
 
6.7%
7 635
 
5.7%
5 628
 
5.7%
6 486
 
4.4%
8 272
 
2.5%
Other values (2) 256
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9514
85.7%
Dash Punctuation 1586
 
14.3%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2250
23.6%
2 1632
17.2%
0 1559
16.4%
3 1055
11.1%
4 743
 
7.8%
7 635
 
6.7%
5 628
 
6.6%
6 486
 
5.1%
8 272
 
2.9%
9 254
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 1586
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11100
> 99.9%
Latin 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2250
20.3%
2 1632
14.7%
- 1586
14.3%
0 1559
14.0%
3 1055
9.5%
4 743
 
6.7%
7 635
 
5.7%
5 628
 
5.7%
6 486
 
4.4%
8 272
 
2.5%
Latin
ValueCountFrequency (%)
M 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2250
20.3%
2 1632
14.7%
- 1586
14.3%
0 1559
14.0%
3 1055
9.5%
4 743
 
6.7%
7 635
 
5.7%
5 628
 
5.7%
6 486
 
4.4%
8 272
 
2.5%
Other values (2) 256
 
2.3%

면적(제곱미터)
Real number (ℝ)

MISSING  SKEWED 

Distinct814
Distinct (%)53.3%
Missing59
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean50.575927
Minimum7.61
Maximum7475.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.1 KiB
2024-04-30T01:39:42.519941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.61
5-th percentile13.03
Q122
median31.64
Q344
95-th percentile100
Maximum7475.19
Range7467.58
Interquartile range (IQR)22

Descriptive statistics

Standard deviation206.66927
Coefficient of variation (CV)4.086317
Kurtosis1095.5981
Mean50.575927
Median Absolute Deviation (MAD)10.59
Skewness30.996009
Sum77229.44
Variance42712.188
MonotonicityNot monotonic
2024-04-30T01:39:42.635916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.0 46
 
2.9%
33.0 37
 
2.3%
40.0 28
 
1.8%
20.0 20
 
1.3%
50.0 18
 
1.1%
35.0 17
 
1.1%
37.0 16
 
1.0%
31.0 15
 
0.9%
25.0 15
 
0.9%
32.0 14
 
0.9%
Other values (804) 1301
82.0%
(Missing) 59
 
3.7%
ValueCountFrequency (%)
7.61 1
0.1%
8.0 1
0.1%
8.15 1
0.1%
8.25 1
0.1%
9.01 1
0.1%
9.05 1
0.1%
9.06 1
0.1%
9.2 1
0.1%
9.36 1
0.1%
9.41 1
0.1%
ValueCountFrequency (%)
7475.19 1
0.1%
1351.0 1
0.1%
1260.58 1
0.1%
900.39 1
0.1%
871.4 1
0.1%
867.64 1
0.1%
849.0 1
0.1%
808.0 1
0.1%
708.0 1
0.1%
592.0 1
0.1%

영업업종
Categorical

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
<NA>
422 
의류
269 
기타
201 
편의점
163 
식음료
133 
Other values (7)
398 

Length

Max length5
Median length4
Mean length2.9552333
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사무실
2nd row의류
3rd row기타
4th row플라워
5th row식음료

Common Values

ValueCountFrequency (%)
<NA> 422
26.6%
의류 269
17.0%
기타 201
12.7%
편의점 163
 
10.3%
식음료 133
 
8.4%
제과 133
 
8.4%
액세서리 92
 
5.8%
플라워 51
 
3.2%
화장품 46
 
2.9%
사무실 34
 
2.1%
Other values (2) 42
 
2.6%

Length

2024-04-30T01:39:42.771324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 422
26.6%
의류 269
17.0%
기타 201
12.7%
편의점 163
 
10.3%
식음료 133
 
8.4%
제과 133
 
8.4%
액세서리 92
 
5.8%
플라워 51
 
3.2%
화장품 46
 
2.9%
사무실 34
 
2.1%
Other values (2) 42
 
2.6%

계약시작일자
Date

MISSING 

Distinct320
Distinct (%)23.3%
Missing210
Missing (%)13.2%
Memory size12.5 KiB
Minimum2008-12-01 00:00:00
Maximum2022-03-11 00:00:00
2024-04-30T01:39:42.898052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:43.005260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

계약종료일자
Date

MISSING 

Distinct335
Distinct (%)24.3%
Missing210
Missing (%)13.2%
Memory size12.5 KiB
Minimum2014-03-01 00:00:00
Maximum2027-05-24 00:00:00
2024-04-30T01:39:43.137578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:43.337651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

월임대료
Real number (ℝ)

MISSING 

Distinct1035
Distinct (%)90.1%
Missing437
Missing (%)27.6%
Infinite0
Infinite (%)0.0%
Mean6180317.7
Minimum153600
Maximum2.8462293 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.1 KiB
2024-04-30T01:39:43.493463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum153600
5-th percentile676220
Q12124009
median3861593
Q36806251
95-th percentile15445914
Maximum2.8462293 × 108
Range2.8446933 × 108
Interquartile range (IQR)4682242

Descriptive statistics

Standard deviation14017130
Coefficient of variation (CV)2.2680275
Kurtosis217.29115
Mean6180317.7
Median Absolute Deviation (MAD)2071698
Skewness13.414855
Sum7.101185 × 109
Variance1.9647994 × 1014
MonotonicityNot monotonic
2024-04-30T01:39:43.642956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2200000 5
 
0.3%
1700000 5
 
0.3%
2500000 4
 
0.3%
2810000 4
 
0.3%
4310000 4
 
0.3%
2300000 4
 
0.3%
1200000 4
 
0.3%
500000 3
 
0.2%
4200000 3
 
0.2%
2516600 3
 
0.2%
Other values (1025) 1110
70.0%
(Missing) 437
 
27.6%
ValueCountFrequency (%)
153600 1
0.1%
163900 1
0.1%
186000 1
0.1%
233500 1
0.1%
252000 1
0.1%
300000 1
0.1%
302500 1
0.1%
311667 1
0.1%
328100 1
0.1%
330000 1
0.1%
ValueCountFrequency (%)
284622927 1
0.1%
217793378 1
0.1%
176500000 1
0.1%
152935000 1
0.1%
145000000 1
0.1%
61517300 1
0.1%
48204012 1
0.1%
40550000 1
0.1%
40100000 1
0.1%
33866781 1
0.1%

Interactions

2024-04-30T01:39:39.457713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:38.299887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:38.707700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:39.101755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:39.545076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:38.401679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:38.810387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:39.186332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:39.625226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:38.501005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:38.916631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:39.273564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:39.704150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:38.605718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:39.024102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:39:39.368298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T01:39:43.742470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번상가유형호선면적(제곱미터)영업업종월임대료
연번1.0000.4650.9460.0580.3430.000
상가유형0.4651.0000.4550.3450.5740.351
호선0.9460.4551.0000.0490.3330.062
면적(제곱미터)0.0580.3450.0491.0000.1720.807
영업업종0.3430.5740.3330.1721.0000.000
월임대료0.0000.3510.0620.8070.0001.000
2024-04-30T01:39:43.851142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업업종상가유형
영업업종1.0000.360
상가유형0.3601.000
2024-04-30T01:39:43.936465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번호선면적(제곱미터)월임대료상가유형영업업종
연번1.0000.9840.3840.1010.2570.153
호선0.9841.0000.3860.0760.2640.164
면적(제곱미터)0.3840.3861.0000.3500.2470.101
월임대료0.1010.0760.3501.0000.2180.000
상가유형0.2570.2640.2470.2181.0000.360
영업업종0.1530.1640.1010.0000.3601.000

Missing values

2024-04-30T01:39:39.824923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T01:39:39.955124image/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.
2024-04-30T01:39:40.056045image/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

연번상가유형호선역사명상가번호면적(제곱미터)영업업종계약시작일자계약종료일자월임대료
01개별(일반)1서울(1)역150-10733.0사무실2019-05-082024-06-06527100
12개별(일반)1시청(1)역151-10129.73의류2017-04-042022-05-033858954
23개별(일반)1시청(1)역151-10357.6기타2020-02-012025-01-311858300
34개별(일반)1시청(1)역151-10425.0플라워2020-12-312026-01-302470600
45네트워크1시청(1)역151-10525.0식음료2021-06-032026-08-024145884
56개별(일반)1시청(1)역151-10614.0액세서리2017-09-192022-11-171801800
67개별(일반)1시청(1)역151-10722.0의류2020-09-182025-10-182613800
78공실1종각역152-10136.85<NA><NA><NA><NA>
89공실1종각역152-10418.64<NA><NA><NA><NA>
910개별(일반)1종각역152-10529.3편의점2017-04-182022-04-176549400
연번상가유형호선역사명상가번호면적(제곱미터)영업업종계약시작일자계약종료일자월임대료
15761577네트워크8남한산성입구역822-20434.18액세서리2018-05-092023-07-071977511
15771578공실8남한산성입구역822-20517.0<NA><NA><NA><NA>
15781579개별(일반)8단대오거리역823-10236.78기타2021-01-212026-02-201700000
15791580네트워크8단대오거리역823-20132.5편의점2022-02-032027-05-048712991
15801581공실8단대오거리역823-20228.97<NA><NA><NA><NA>
15811582개별(일반)8단대오거리역823-20354.03식음료2018-08-312023-09-297630000
15821583개별(일반)8단대오거리역823-20475.09의류2021-03-182026-04-173780000
15831584네트워크8신흥역824-10140.0편의점2022-02-032027-05-046124682
15841585네트워크8수진역825-10140.0편의점2022-02-032027-05-045575875
15851586네트워크8모란역826-10150.0편의점2022-02-032027-05-045831070