Overview

Dataset statistics

Number of variables9
Number of observations145
Missing cells28
Missing cells (%)2.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.6 KiB
Average record size in memory74.9 B

Variable types

Categorical2
Text3
Numeric2
DateTime2

Dataset

Description광진구 공동주택 현황에 대한 데이터 입니다."구분", "공동주택명", "행정동", "도로명주소", "동수", "세대수", "사용승인일", "관리사무소 전화"필드로 구성되어 있습니다.
Author서울특별시 광진구
URLhttps://www.data.go.kr/data/15122751/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
동수 is highly overall correlated with 세대수High correlation
세대수 is highly overall correlated with 동수High correlation
관리사무소전화번호 has 28 (19.3%) missing valuesMissing

Reproduction

Analysis started2023-12-12 20:21:53.701015
Analysis finished2023-12-12 20:21:55.026626
Duration1.33 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct5
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
아파트
92 
주상복합
22 
연립주택
20 
도시형생활주택
 
8
다세대
 
3

Length

Max length7
Median length3
Mean length3.5103448
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row아파트
2nd row아파트
3rd row아파트
4th row아파트
5th row연립주택

Common Values

ValueCountFrequency (%)
아파트 92
63.4%
주상복합 22
 
15.2%
연립주택 20
 
13.8%
도시형생활주택 8
 
5.5%
다세대 3
 
2.1%

Length

2023-12-13T05:21:55.134589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:21:55.278067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아파트 92
63.4%
주상복합 22
 
15.2%
연립주택 20
 
13.8%
도시형생활주택 8
 
5.5%
다세대 3
 
2.1%

공동주택명
Text

Distinct143
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T05:21:55.588163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length5.7724138
Min length2

Characters and Unicode

Total characters837
Distinct characters165
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

Unique141 ?
Unique (%)97.2%

Sample

1st row광나루현대
2nd row광장동금호
3rd row광장동현대9차
4th row광장자이
5th row광장현대11차홈타운
ValueCountFrequency (%)
한양연립 2
 
1.3%
현대빌라 2
 
1.3%
롯데캐슬리버파크 1
 
0.7%
광덕 1
 
0.7%
광나루현대 1
 
0.7%
광진한화꿈에그린 1
 
0.7%
자양9차현대홈타운 1
 
0.7%
자양강변아이파크 1
 
0.7%
자양동삼성 1
 
0.7%
자양우성1차 1
 
0.7%
Other values (141) 141
92.2%
2023-12-13T05:21:56.061606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 35
 
4.2%
차 33
 
3.9%
현 32
 
3.8%
양 32
 
3.8%
자 24
 
2.9%
리 24
 
2.9%
라 23
 
2.7%
빌 21
 
2.5%
광 21
 
2.5%
동 20
 
2.4%
Other values (155) 572
68.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 773
92.4%
Decimal Number 46
 
5.5%
Space Separator 8
 
1.0%
Close Punctuation 3
 
0.4%
Open Punctuation 3
 
0.4%
Lowercase Letter 3
 
0.4%
Letter Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 35
 
4.5%
차 33
 
4.3%
현 32
 
4.1%
양 32
 
4.1%
자 24
 
3.1%
리 24
 
3.1%
라 23
 
3.0%
빌 21
 
2.7%
광 21
 
2.7%
동 20
 
2.6%
Other values (138) 508
65.7%
Decimal Number
ValueCountFrequency (%)
1 13
28.3%
2 11
23.9%
3 5
 
10.9%
5 4
 
8.7%
7 3
 
6.5%
6 3
 
6.5%
9 2
 
4.3%
8 2
 
4.3%
4 2
 
4.3%
0 1
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
e 1
33.3%
s 1
33.3%
k 1
33.3%
Space Separator
ValueCountFrequency (%)
8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Letter Number
ValueCountFrequency (%)
Ⅲ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 773
92.4%
Common 60
 
7.2%
Latin 4
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 35
 
4.5%
차 33
 
4.3%
현 32
 
4.1%
양 32
 
4.1%
자 24
 
3.1%
리 24
 
3.1%
라 23
 
3.0%
빌 21
 
2.7%
광 21
 
2.7%
동 20
 
2.6%
Other values (138) 508
65.7%
Common
ValueCountFrequency (%)
1 13
21.7%
2 11
18.3%
8
13.3%
3 5
 
8.3%
5 4
 
6.7%
) 3
 
5.0%
7 3
 
5.0%
6 3
 
5.0%
( 3
 
5.0%
9 2
 
3.3%
Other values (3) 5
 
8.3%
Latin
ValueCountFrequency (%)
Ⅲ 1
25.0%
e 1
25.0%
s 1
25.0%
k 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 773
92.4%
ASCII 63
 
7.5%
Number Forms 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 35
 
4.5%
차 33
 
4.3%
현 32
 
4.1%
양 32
 
4.1%
자 24
 
3.1%
리 24
 
3.1%
라 23
 
3.0%
빌 21
 
2.7%
광 21
 
2.7%
동 20
 
2.6%
Other values (138) 508
65.7%
ASCII
ValueCountFrequency (%)
1 13
20.6%
2 11
17.5%
8
12.7%
3 5
 
7.9%
5 4
 
6.3%
) 3
 
4.8%
7 3
 
4.8%
6 3
 
4.8%
( 3
 
4.8%
9 2
 
3.2%
Other values (6) 8
12.7%
Number Forms
ValueCountFrequency (%)
Ⅲ 1
100.0%

행정동
Categorical

Distinct15
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
자양3동
28 
광장동
26 
구의3동
21 
자양2동
14 
자양4동
14 
Other values (10)
42 

Length

Max length4
Median length4
Mean length3.7241379
Min length2

Unique

Unique1 ?
Unique (%)0.7%

Sample

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

Common Values

ValueCountFrequency (%)
자양3동 28
19.3%
광장동 26
17.9%
구의3동 21
14.5%
자양2동 14
9.7%
자양4동 14
9.7%
구의2동 8
 
5.5%
화양동 7
 
4.8%
자양1동 6
 
4.1%
군자동 5
 
3.4%
중곡3동 4
 
2.8%
Other values (5) 12
8.3%

Length

2023-12-13T05:21:56.210761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
자양3동 28
19.3%
광장동 26
17.9%
구의3동 21
14.5%
자양2동 14
9.7%
자양4동 14
9.7%
구의2동 8
 
5.5%
화양동 7
 
4.8%
자양1동 6
 
4.1%
군자동 5
 
3.4%
중곡3동 4
 
2.8%
Other values (5) 12
8.3%

도로명주소
Text

Distinct143
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T05:21:56.615481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length25
Mean length18.910345
Min length16

Characters and Unicode

Total characters2742
Distinct characters58
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique141 ?
Unique (%)97.2%

Sample

1st row서울특별시 광진구 아차산로76길 31
2nd row서울특별시 광진구 광나루로 579
3rd row서울특별시 광진구 아차산로69길 19
4th row서울특별시 광진구 광나루로 595
5th row서울특별시 광진구 아차산로69길 29
ValueCountFrequency (%)
서울특별시 145
25.0%
광진구 145
25.0%
아차산로 14
 
2.4%
광나루로 9
 
1.5%
구의강변로 7
 
1.2%
능동로 6
 
1.0%
13 4
 
0.7%
32 4
 
0.7%
뚝섬로34길 4
 
0.7%
뚝섬로 4
 
0.7%
Other values (178) 239
41.1%
2023-12-13T05:21:57.222514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
437
15.9%
광 164
 
6.0%
구 157
 
5.7%
서 145
 
5.3%
특 145
 
5.3%
별 145
 
5.3%
시 145
 
5.3%
진 145
 
5.3%
울 145
 
5.3%
로 144
 
5.3%
Other values (48) 970
35.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1791
65.3%
Decimal Number 497
 
18.1%
Space Separator 437
 
15.9%
Dash Punctuation 9
 
0.3%
Close Punctuation 3
 
0.1%
Open Punctuation 3
 
0.1%
Other Punctuation 1
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 164
9.2%
구 157
8.8%
서 145
 
8.1%
특 145
 
8.1%
별 145
 
8.1%
시 145
 
8.1%
진 145
 
8.1%
울 145
 
8.1%
로 144
 
8.0%
길 90
 
5.0%
Other values (32) 366
20.4%
Decimal Number
ValueCountFrequency (%)
1 70
14.1%
3 69
13.9%
5 62
12.5%
4 61
12.3%
6 60
12.1%
2 58
11.7%
7 47
9.5%
9 29
5.8%
0 21
 
4.2%
8 20
 
4.0%
Space Separator
ValueCountFrequency (%)
437
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1791
65.3%
Common 951
34.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 164
9.2%
구 157
8.8%
서 145
 
8.1%
특 145
 
8.1%
별 145
 
8.1%
시 145
 
8.1%
진 145
 
8.1%
울 145
 
8.1%
로 144
 
8.0%
길 90
 
5.0%
Other values (32) 366
20.4%
Common
ValueCountFrequency (%)
437
46.0%
1 70
 
7.4%
3 69
 
7.3%
5 62
 
6.5%
4 61
 
6.4%
6 60
 
6.3%
2 58
 
6.1%
7 47
 
4.9%
9 29
 
3.0%
0 21
 
2.2%
Other values (6) 37
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1791
65.3%
ASCII 951
34.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
437
46.0%
1 70
 
7.4%
3 69
 
7.3%
5 62
 
6.5%
4 61
 
6.4%
6 60
 
6.3%
2 58
 
6.1%
7 47
 
4.9%
9 29
 
3.0%
0 21
 
2.2%
Other values (6) 37
 
3.9%
Hangul
ValueCountFrequency (%)
광 164
9.2%
구 157
8.8%
서 145
 
8.1%
특 145
 
8.1%
별 145
 
8.1%
시 145
 
8.1%
진 145
 
8.1%
울 145
 
8.1%
로 144
 
8.0%
길 90
 
5.0%
Other values (32) 366
20.4%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0068966
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T05:21:57.352094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile10
Maximum15
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.9403717
Coefficient of variation (CV)0.97787589
Kurtosis5.5563309
Mean3.0068966
Median Absolute Deviation (MAD)1
Skewness2.3264989
Sum436
Variance8.6457854
MonotonicityNot monotonic
2023-12-13T05:21:57.515833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 52
35.9%
2 40
27.6%
3 17
 
11.7%
4 9
 
6.2%
5 9
 
6.2%
6 5
 
3.4%
8 3
 
2.1%
10 2
 
1.4%
11 2
 
1.4%
15 2
 
1.4%
Other values (4) 4
 
2.8%
ValueCountFrequency (%)
1 52
35.9%
2 40
27.6%
3 17
 
11.7%
4 9
 
6.2%
5 9
 
6.2%
6 5
 
3.4%
7 1
 
0.7%
8 3
 
2.1%
10 2
 
1.4%
11 2
 
1.4%
ValueCountFrequency (%)
15 2
 
1.4%
14 1
 
0.7%
13 1
 
0.7%
12 1
 
0.7%
11 2
 
1.4%
10 2
 
1.4%
8 3
 
2.1%
7 1
 
0.7%
6 5
3.4%
5 9
6.2%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct115
Distinct (%)79.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean224.68966
Minimum20
Maximum1606
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T05:21:57.686046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile24
Q150
median125
Q3264
95-th percentile829.2
Maximum1606
Range1586
Interquartile range (IQR)214

Descriptive statistics

Standard deviation280.97789
Coefficient of variation (CV)1.2505155
Kurtosis8.6251038
Mean224.68966
Median Absolute Deviation (MAD)87
Skewness2.6989608
Sum32580
Variance78948.577
MonotonicityNot monotonic
2023-12-13T05:21:57.877114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 4
 
2.8%
20 3
 
2.1%
28 3
 
2.1%
43 3
 
2.1%
24 3
 
2.1%
30 3
 
2.1%
36 2
 
1.4%
83 2
 
1.4%
67 2
 
1.4%
98 2
 
1.4%
Other values (105) 118
81.4%
ValueCountFrequency (%)
20 3
2.1%
21 1
 
0.7%
23 2
1.4%
24 3
2.1%
26 1
 
0.7%
28 3
2.1%
29 1
 
0.7%
30 3
2.1%
33 2
1.4%
34 1
 
0.7%
ValueCountFrequency (%)
1606 1
0.7%
1592 1
0.7%
1177 1
0.7%
1170 1
0.7%
1056 1
0.7%
896 1
0.7%
878 1
0.7%
854 1
0.7%
730 1
0.7%
656 1
0.7%

사용승인일
Date

Distinct139
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum1976-05-19 00:00:00
Maximum2023-06-29 00:00:00
2023-12-13T05:21:58.060994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:58.219787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

관리사무소전화번호
Text

MISSING 

Distinct115
Distinct (%)98.3%
Missing28
Missing (%)19.3%
Memory size1.3 KiB
2023-12-13T05:21:58.535212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length11.25641
Min length11

Characters and Unicode

Total characters1317
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

Unique113 ?
Unique (%)96.6%

Sample

1st row02-2201-1263
2nd row02-3437-9348
3rd row02-444-9551
4th row02-457-7051
5th row02-456-0155
ValueCountFrequency (%)
02-499-8363 2
 
1.7%
02-447-2221 2
 
1.7%
02-3437-3905 1
 
0.9%
02-462-1634 1
 
0.9%
02-457-7500 1
 
0.9%
02-3436-4385 1
 
0.9%
02-3437-9297 1
 
0.9%
02-3436-1331 1
 
0.9%
02-457-8387 1
 
0.9%
02-453-5310 1
 
0.9%
Other values (105) 105
89.7%
2023-12-13T05:21:59.156445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 234
17.8%
4 193
14.7%
0 184
14.0%
2 181
13.7%
5 99
7.5%
3 83
 
6.3%
1 82
 
6.2%
6 76
 
5.8%
7 70
 
5.3%
9 63
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1083
82.2%
Dash Punctuation 234
 
17.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 193
17.8%
0 184
17.0%
2 181
16.7%
5 99
9.1%
3 83
7.7%
1 82
7.6%
6 76
 
7.0%
7 70
 
6.5%
9 63
 
5.8%
8 52
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 234
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1317
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 234
17.8%
4 193
14.7%
0 184
14.0%
2 181
13.7%
5 99
7.5%
3 83
 
6.3%
1 82
 
6.2%
6 76
 
5.8%
7 70
 
5.3%
9 63
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1317
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 234
17.8%
4 193
14.7%
0 184
14.0%
2 181
13.7%
5 99
7.5%
3 83
 
6.3%
1 82
 
6.2%
6 76
 
5.8%
7 70
 
5.3%
9 63
 
4.8%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2023-09-12 00:00:00
Maximum2023-09-12 00:00:00
2023-12-13T05:21:59.309994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:59.439869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T05:21:54.401689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:54.128025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:54.531087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:21:54.259053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:21:59.578243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분행정동동수세대수
구분1.0000.8320.0000.171
행정동0.8321.0000.0000.000
동수0.0000.0001.0000.822
세대수0.1710.0000.8221.000
2023-12-13T05:21:59.732064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분행정동
구분1.0000.493
행정동0.4931.000
2023-12-13T05:21:59.865232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동수세대수구분행정동
동수1.0000.5820.0000.000
세대수0.5821.0000.0960.000
구분0.0000.0961.0000.493
행정동0.0000.0000.4931.000

Missing values

2023-12-13T05:21:54.726703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:21:54.937931image/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

구분공동주택명행정동도로명주소동수세대수사용승인일관리사무소전화번호데이터기준일자
0아파트광나루현대광장동서울특별시 광진구 아차산로76길 3133801996-04-1702-2201-12632023-09-12
1아파트광장동금호광장동서울특별시 광진구 광나루로 57942422001-06-1802-3437-93482023-09-12
2아파트광장동현대9차광장동서울특별시 광진구 아차산로69길 1954371999-04-3002-444-95512023-09-12
3아파트광장자이광장동서울특별시 광진구 광나루로 59521222008-12-0202-457-70512023-09-12
4연립주택광장현대11차홈타운광장동서울특별시 광진구 아차산로69길 2981592003-10-2802-456-01552023-09-12
5아파트광장현대12차홈타운광장동서울특별시 광진구 천호대로 80821192004-09-0102-444-13362023-09-12
6아파트광장현대3단지광장동서울특별시 광진구 아차산로70길 621010561990-10-2002-454-12882023-09-12
7아파트광장현대5단지광장동서울특별시 광진구 아차산로70길 6165811989-04-2102-458-27782023-09-12
8아파트광장현대8단지광장동서울특별시 광진구 아차산로 52235371995-03-2702-456-58632023-09-12
9아파트광장현대파크빌광장동서울특별시 광진구 아차산로 5491311702000-08-3102-458-74802023-09-12
구분공동주택명행정동도로명주소동수세대수사용승인일관리사무소전화번호데이터기준일자
135아파트테라팰리스건대1차화양동서울특별시 광진구 아차산로25길 601542019-01-2402-462-99172023-09-12
136아파트화양동현대화양동서울특별시 광진구 아차산로21길 4732531999-05-2002-499-81212023-09-12
137주상복합화양타워화양동서울특별시 광진구 능동로19길 471381999-06-2802-462-16342023-09-12
138다세대송일빌라트능동서울특별시 광진구 능동로36길 771242003-02-25<NA>2023-09-12
139도시형생활주택채움하우스화양동서울특별시 광진구 능동로15길 222362012-09-25<NA>2023-09-12
140아파트e편한세상 광진 그랜드파크구의1동서울특별시 광진구 광나루로 458117302021-12-3102-447-09912023-09-12
141아파트하늘채베르자양1동서울특별시 광진구 자양로 5521652023-02-27<NA>2023-09-12
142아파트자양호반써밋자양4동서울특별시 광진구 능동로 6923052021-08-30<NA>2023-09-12
143도시형생활주택아스하임4차화양동서울특별시 광진구 광나루로 38211622018-11-16<NA>2023-09-12
144아파트롯데캐슬리버파크 시그니처자양4동서울특별시 광진구 뚝섬로 46768782023-06-29<NA>2023-09-12