Overview

Dataset statistics

Number of variables15
Number of observations598
Missing cells941
Missing cells (%)10.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory73.1 KiB
Average record size in memory125.2 B

Variable types

Numeric5
Categorical2
Text7
DateTime1

Dataset

Description경기도 고양시 공동주택 현황 데이터는 의무, 비의무, 단지명, 위치, 사업승인일, 사용검사일, 층수, 동수, 연면적 등의 항목을 제공합니다.
Author경기도 고양시
URLhttps://www.data.go.kr/data/3079630/fileData.do

Alerts

순번 is highly overall correlated with 연면적(제곱미터)High correlation
동수 is highly overall correlated with 세대수 and 2 other fieldsHigh correlation
세대수 is highly overall correlated with 동수 and 2 other fieldsHigh correlation
연면적(제곱미터) is highly overall correlated with 순번 and 3 other fieldsHigh correlation
승강기 is highly overall correlated with 동수 and 2 other fieldsHigh correlation
건물유형 is highly imbalanced (61.7%)Imbalance
관리소전화 has 164 (27.4%) missing valuesMissing
팩스 has 194 (32.4%) missing valuesMissing
비고 has 583 (97.5%) missing valuesMissing
순번 has unique valuesUnique
승강기 has 173 (28.9%) zerosZeros

Reproduction

Analysis started2024-04-06 08:13:27.408460
Analysis finished2024-04-06 08:13:35.376592
Duration7.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct598
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean299.5
Minimum1
Maximum598
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-04-06T17:13:35.508637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile30.85
Q1150.25
median299.5
Q3448.75
95-th percentile568.15
Maximum598
Range597
Interquartile range (IQR)298.5

Descriptive statistics

Standard deviation172.77201
Coefficient of variation (CV)0.57686814
Kurtosis-1.2
Mean299.5
Median Absolute Deviation (MAD)149.5
Skewness0
Sum179101
Variance29850.167
MonotonicityStrictly increasing
2024-04-06T17:13:35.748370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
395 1
 
0.2%
397 1
 
0.2%
398 1
 
0.2%
399 1
 
0.2%
400 1
 
0.2%
401 1
 
0.2%
402 1
 
0.2%
403 1
 
0.2%
404 1
 
0.2%
Other values (588) 588
98.3%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
598 1
0.2%
597 1
0.2%
596 1
0.2%
595 1
0.2%
594 1
0.2%
593 1
0.2%
592 1
0.2%
591 1
0.2%
590 1
0.2%
589 1
0.2%

구분
Categorical

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
의무적
379 
비의무
186 
의무적(임대)
 
27
의무적(분양)
 
6

Length

Max length7
Median length3
Mean length3.2207358
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
의무적 379
63.4%
비의무 186
31.1%
의무적(임대) 27
 
4.5%
의무적(분양) 6
 
1.0%

Length

2024-04-06T17:13:36.010908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:13:36.218019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
의무적 379
63.4%
비의무 186
31.1%
의무적(임대 27
 
4.5%
의무적(분양 6
 
1.0%

단지명
Text

Distinct588
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2024-04-06T17:13:36.548131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length22
Mean length10.264214
Min length2

Characters and Unicode

Total characters6138
Distinct characters278
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique579 ?
Unique (%)96.8%

Sample

1st row삼천리연립
2nd row대당연립주택
3rd row로얄연립주택
4th row삼화주택
5th row경원연립주택
ValueCountFrequency (%)
중산마을 11
 
1.4%
백송마을 10
 
1.3%
밤가시 8
 
1.0%
정발마을 8
 
1.0%
dmc 8
 
1.0%
강촌마을 8
 
1.0%
흰돌마을 7
 
0.9%
백마마을 6
 
0.8%
하이파크시티 5
 
0.6%
삼송우미라피아노 5
 
0.6%
Other values (625) 696
90.2%
2024-04-06T17:13:37.193121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 351
 
5.7%
단 324
 
5.3%
( 315
 
5.1%
) 309
 
5.0%
마 293
 
4.8%
을 285
 
4.6%
1 187
 
3.0%
174
 
2.8%
아 110
 
1.8%
2 110
 
1.8%
Other values (268) 3680
60.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4458
72.6%
Decimal Number 622
 
10.1%
Open Punctuation 315
 
5.1%
Close Punctuation 309
 
5.0%
Uppercase Letter 185
 
3.0%
Space Separator 174
 
2.8%
Other Punctuation 35
 
0.6%
Math Symbol 20
 
0.3%
Dash Punctuation 18
 
0.3%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 351
 
7.9%
단 324
 
7.3%
마 293
 
6.6%
을 285
 
6.4%
아 110
 
2.5%
파 101
 
2.3%
동 96
 
2.2%
트 95
 
2.1%
산 85
 
1.9%
삼 75
 
1.7%
Other values (232) 2643
59.3%
Uppercase Letter
ValueCountFrequency (%)
L 29
15.7%
A 23
12.4%
C 19
10.3%
H 17
9.2%
D 16
8.6%
M 16
8.6%
B 13
7.0%
P 12
6.5%
T 12
6.5%
S 8
 
4.3%
Other values (7) 20
10.8%
Decimal Number
ValueCountFrequency (%)
1 187
30.1%
2 110
17.7%
3 66
 
10.6%
5 54
 
8.7%
6 46
 
7.4%
4 37
 
5.9%
0 33
 
5.3%
7 31
 
5.0%
9 29
 
4.7%
8 29
 
4.7%
Other Punctuation
ValueCountFrequency (%)
, 29
82.9%
. 5
 
14.3%
/ 1
 
2.9%
Open Punctuation
ValueCountFrequency (%)
( 315
100.0%
Close Punctuation
ValueCountFrequency (%)
) 309
100.0%
Space Separator
ValueCountFrequency (%)
174
100.0%
Math Symbol
ValueCountFrequency (%)
~ 20
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4458
72.6%
Common 1493
 
24.3%
Latin 187
 
3.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 351
 
7.9%
단 324
 
7.3%
마 293
 
6.6%
을 285
 
6.4%
아 110
 
2.5%
파 101
 
2.3%
동 96
 
2.2%
트 95
 
2.1%
산 85
 
1.9%
삼 75
 
1.7%
Other values (232) 2643
59.3%
Common
ValueCountFrequency (%)
( 315
21.1%
) 309
20.7%
1 187
12.5%
174
11.7%
2 110
 
7.4%
3 66
 
4.4%
5 54
 
3.6%
6 46
 
3.1%
4 37
 
2.5%
0 33
 
2.2%
Other values (8) 162
10.9%
Latin
ValueCountFrequency (%)
L 29
15.5%
A 23
12.3%
C 19
10.2%
H 17
9.1%
D 16
8.6%
M 16
8.6%
B 13
7.0%
P 12
6.4%
T 12
6.4%
S 8
 
4.3%
Other values (8) 22
11.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4458
72.6%
ASCII 1680
 
27.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 351
 
7.9%
단 324
 
7.3%
마 293
 
6.6%
을 285
 
6.4%
아 110
 
2.5%
파 101
 
2.3%
동 96
 
2.2%
트 95
 
2.1%
산 85
 
1.9%
삼 75
 
1.7%
Other values (232) 2643
59.3%
ASCII
ValueCountFrequency (%)
( 315
18.8%
) 309
18.4%
1 187
11.1%
174
10.4%
2 110
 
6.5%
3 66
 
3.9%
5 54
 
3.2%
6 46
 
2.7%
4 37
 
2.2%
0 33
 
2.0%
Other values (26) 349
20.8%

지번주소
Text

Distinct589
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2024-04-06T17:13:37.602757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length32
Mean length20.832776
Min length18

Characters and Unicode

Total characters12458
Distinct characters87
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique580 ?
Unique (%)97.0%

Sample

1st row경기도 고양시 덕양구 토당동 43-4
2nd row경기도 고양시 덕양구 토당동 434-3
3rd row경기도 고양시 덕양구 토당동 292-34
4th row경기도 고양시 덕양구 토당동 392-1
5th row경기도 고양시 덕양구 행신동 618-1
ValueCountFrequency (%)
경기도 598
19.9%
고양시 598
19.9%
덕양구 305
 
10.1%
일산서구 171
 
5.7%
일산동구 121
 
4.0%
일산동 61
 
2.0%
행신동 61
 
2.0%
주교동 41
 
1.4%
주엽동 39
 
1.3%
토당동 38
 
1.3%
Other values (609) 972
32.3%
2024-04-06T17:13:38.291081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2407
19.3%
양 934
 
7.5%
동 719
 
5.8%
고 628
 
5.0%
구 614
 
4.9%
도 605
 
4.9%
시 600
 
4.8%
경 598
 
4.8%
기 598
 
4.8%
1 444
 
3.6%
Other values (77) 4311
34.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7642
61.3%
Space Separator 2407
 
19.3%
Decimal Number 2187
 
17.6%
Dash Punctuation 177
 
1.4%
Uppercase Letter 19
 
0.2%
Close Punctuation 10
 
0.1%
Open Punctuation 9
 
0.1%
Other Punctuation 7
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
양 934
12.2%
동 719
9.4%
고 628
 
8.2%
구 614
 
8.0%
도 605
 
7.9%
시 600
 
7.9%
경 598
 
7.8%
기 598
 
7.8%
산 416
 
5.4%
일 358
 
4.7%
Other values (58) 1572
20.6%
Decimal Number
ValueCountFrequency (%)
1 444
20.3%
5 240
11.0%
0 221
10.1%
2 207
9.5%
6 192
8.8%
7 189
8.6%
9 179
8.2%
8 176
 
8.0%
3 175
 
8.0%
4 164
 
7.5%
Uppercase Letter
ValueCountFrequency (%)
B 8
42.1%
A 6
31.6%
L 4
21.1%
S 1
 
5.3%
Space Separator
ValueCountFrequency (%)
2407
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 177
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7642
61.3%
Common 4797
38.5%
Latin 19
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
양 934
12.2%
동 719
9.4%
고 628
 
8.2%
구 614
 
8.0%
도 605
 
7.9%
시 600
 
7.9%
경 598
 
7.8%
기 598
 
7.8%
산 416
 
5.4%
일 358
 
4.7%
Other values (58) 1572
20.6%
Common
ValueCountFrequency (%)
2407
50.2%
1 444
 
9.3%
5 240
 
5.0%
0 221
 
4.6%
2 207
 
4.3%
6 192
 
4.0%
7 189
 
3.9%
9 179
 
3.7%
- 177
 
3.7%
8 176
 
3.7%
Other values (5) 365
 
7.6%
Latin
ValueCountFrequency (%)
B 8
42.1%
A 6
31.6%
L 4
21.1%
S 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7642
61.3%
ASCII 4816
38.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2407
50.0%
1 444
 
9.2%
5 240
 
5.0%
0 221
 
4.6%
2 207
 
4.3%
6 192
 
4.0%
7 189
 
3.9%
9 179
 
3.7%
- 177
 
3.7%
8 176
 
3.7%
Other values (9) 384
 
8.0%
Hangul
ValueCountFrequency (%)
양 934
12.2%
동 719
9.4%
고 628
 
8.2%
구 614
 
8.0%
도 605
 
7.9%
시 600
 
7.9%
경 598
 
7.8%
기 598
 
7.8%
산 416
 
5.4%
일 358
 
4.7%
Other values (58) 1572
20.6%

사업승인일
Text

Distinct387
Distinct (%)64.7%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2024-04-06T17:13:38.773122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5980
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

Unique311 ?
Unique (%)52.0%

Sample

1st row1980-03-14
2nd row1981-06-29
3rd row1981-07-08
4th row1981-07-23
5th row1980-12-26
ValueCountFrequency (%)
1993-11-13 27
 
4.5%
1990-11-19 12
 
2.0%
1992-07-01 11
 
1.8%
1991-09-14 10
 
1.7%
1991-07-29 10
 
1.7%
1993-03-29 9
 
1.5%
2001-12-31 8
 
1.3%
1992-04-02 8
 
1.3%
1993-09-17 7
 
1.2%
1992-11-10 7
 
1.2%
Other values (377) 489
81.8%
2024-04-06T17:13:39.535256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1196
20.0%
1 1166
19.5%
0 1020
17.1%
9 842
14.1%
2 642
10.7%
3 298
 
5.0%
8 228
 
3.8%
7 166
 
2.8%
5 159
 
2.7%
4 152
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4784
80.0%
Dash Punctuation 1196
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1166
24.4%
0 1020
21.3%
9 842
17.6%
2 642
13.4%
3 298
 
6.2%
8 228
 
4.8%
7 166
 
3.5%
5 159
 
3.3%
4 152
 
3.2%
6 111
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 1196
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1196
20.0%
1 1166
19.5%
0 1020
17.1%
9 842
14.1%
2 642
10.7%
3 298
 
5.0%
8 228
 
3.8%
7 166
 
2.8%
5 159
 
2.7%
4 152
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1196
20.0%
1 1166
19.5%
0 1020
17.1%
9 842
14.1%
2 642
10.7%
3 298
 
5.0%
8 228
 
3.8%
7 166
 
2.8%
5 159
 
2.7%
4 152
 
2.5%

사용검사일
Date

Distinct495
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
Minimum1981-05-06 00:00:00
Maximum2023-09-19 00:00:00
2024-04-06T17:13:39.839923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:40.131112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

건물유형
Categorical

IMBALANCE 

Distinct13
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
아파트
435 
연립주택
98 
주상복합
 
20
공동주택(아파트)
 
17
공동주택(연립주택(도시형생활주택/단지형연립주택))
 
6
Other values (8)
 
22

Length

Max length27
Median length3
Mean length3.7173913
Min length2

Unique

Unique2 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
아파트 435
72.7%
연립주택 98
 
16.4%
주상복합 20
 
3.3%
공동주택(아파트) 17
 
2.8%
공동주택(연립주택(도시형생활주택/단지형연립주택)) 6
 
1.0%
주택 5
 
0.8%
공동주택 4
 
0.7%
도시형생활주택(연립) 4
 
0.7%
주상복합/아파트 3
 
0.5%
연립 2
 
0.3%
Other values (3) 4
 
0.7%

Length

2024-04-06T17:13:40.378005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
아파트 435
72.7%
연립주택 98
 
16.4%
주상복합 20
 
3.3%
공동주택(아파트 17
 
2.8%
공동주택(연립주택(도시형생활주택/단지형연립주택 6
 
1.0%
주택 5
 
0.8%
공동주택 4
 
0.7%
도시형생활주택(연립 4
 
0.7%
주상복합/아파트 3
 
0.5%
연립 2
 
0.3%
Other values (3) 4
 
0.7%

층수
Text

Distinct158
Distinct (%)26.4%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2024-04-06T17:13:40.970686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.1086957
Min length1

Characters and Unicode

Total characters1859
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

Unique73 ?
Unique (%)12.2%

Sample

1st row2
2nd row3
3rd row3
4th row3
5th row2
ValueCountFrequency (%)
3 61
 
10.2%
5 60
 
10.0%
4 57
 
9.5%
15 28
 
4.7%
15~20 13
 
2.2%
10~15 11
 
1.8%
18~20 11
 
1.8%
14~20 10
 
1.7%
20 10
 
1.7%
12 10
 
1.7%
Other values (148) 327
54.7%
2024-04-06T17:13:42.004807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 457
24.6%
2 300
16.1%
~ 295
15.9%
5 215
11.6%
0 137
 
7.4%
3 119
 
6.4%
4 117
 
6.3%
9 70
 
3.8%
8 58
 
3.1%
6 48
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1564
84.1%
Math Symbol 295
 
15.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 457
29.2%
2 300
19.2%
5 215
13.7%
0 137
 
8.8%
3 119
 
7.6%
4 117
 
7.5%
9 70
 
4.5%
8 58
 
3.7%
6 48
 
3.1%
7 43
 
2.7%
Math Symbol
ValueCountFrequency (%)
~ 295
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1859
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 457
24.6%
2 300
16.1%
~ 295
15.9%
5 215
11.6%
0 137
 
7.4%
3 119
 
6.4%
4 117
 
6.3%
9 70
 
3.8%
8 58
 
3.1%
6 48
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1859
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 457
24.6%
2 300
16.1%
~ 295
15.9%
5 215
11.6%
0 137
 
7.4%
3 119
 
6.4%
4 117
 
6.3%
9 70
 
3.8%
8 58
 
3.1%
6 48
 
2.6%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.091973
Minimum1
Maximum947
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-04-06T17:13:42.309398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile19
Maximum947
Range946
Interquartile range (IQR)7

Descriptive statistics

Standard deviation99.704118
Coefficient of variation (CV)4.3176959
Kurtosis44.374337
Mean23.091973
Median Absolute Deviation (MAD)3
Skewness6.5250452
Sum13809
Variance9940.9111
MonotonicityNot monotonic
2024-04-06T17:13:42.592231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1 64
10.7%
2 61
10.2%
3 55
9.2%
4 50
 
8.4%
5 50
 
8.4%
6 46
 
7.7%
8 45
 
7.5%
10 39
 
6.5%
7 32
 
5.4%
9 30
 
5.0%
Other values (34) 126
21.1%
ValueCountFrequency (%)
1 64
10.7%
2 61
10.2%
3 55
9.2%
4 50
8.4%
5 50
8.4%
6 46
7.7%
7 32
5.4%
8 45
7.5%
9 30
5.0%
10 39
6.5%
ValueCountFrequency (%)
947 1
0.2%
894 1
0.2%
777 1
0.2%
750 1
0.2%
702 1
0.2%
620 1
0.2%
583 1
0.2%
560 1
0.2%
552 1
0.2%
539 1
0.2%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct389
Distinct (%)65.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean466.3612
Minimum4
Maximum2700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-04-06T17:13:42.946120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile25
Q196.75
median356
Q3685
95-th percentile1353.3
Maximum2700
Range2696
Interquartile range (IQR)588.25

Descriptive statistics

Standard deviation446.15285
Coefficient of variation (CV)0.95666803
Kurtosis3.112837
Mean466.3612
Median Absolute Deviation (MAD)271.5
Skewness1.5341221
Sum278884
Variance199052.37
MonotonicityNot monotonic
2024-04-06T17:13:43.212654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 12
 
2.0%
30 11
 
1.8%
60 8
 
1.3%
48 7
 
1.2%
96 6
 
1.0%
120 6
 
1.0%
80 6
 
1.0%
90 5
 
0.8%
720 5
 
0.8%
42 5
 
0.8%
Other values (379) 527
88.1%
ValueCountFrequency (%)
4 1
 
0.2%
5 1
 
0.2%
10 1
 
0.2%
14 1
 
0.2%
18 1
 
0.2%
20 5
0.8%
21 3
 
0.5%
22 1
 
0.2%
23 1
 
0.2%
24 12
2.0%
ValueCountFrequency (%)
2700 1
0.2%
2588 1
0.2%
2416 1
0.2%
2404 1
0.2%
2032 1
0.2%
2008 1
0.2%
1975 1
0.2%
1890 1
0.2%
1813 1
0.2%
1802 1
0.2%

관리소전화
Text

MISSING 

Distinct423
Distinct (%)97.5%
Missing164
Missing (%)27.4%
Memory size4.8 KiB
2024-04-06T17:13:43.811480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.951613
Min length11

Characters and Unicode

Total characters5187
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

Unique414 ?
Unique (%)95.4%

Sample

1st row031-962-2531
2nd row031-963-2252
3rd row031-921-7058
4th row031-972-4731
5th row031-972-3343
ValueCountFrequency (%)
02-6952-6804 3
 
0.7%
031-905-2033 3
 
0.7%
031-926-3058 2
 
0.5%
031-917-2697 2
 
0.5%
031-968-9205 2
 
0.5%
031-905-4335 2
 
0.5%
031-914-1312 2
 
0.5%
031-922-0300 2
 
0.5%
02-381-9806 2
 
0.5%
031-977-1646 1
 
0.2%
Other values (413) 413
95.2%
2024-04-06T17:13:44.613481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 868
16.7%
1 788
15.2%
0 731
14.1%
3 701
13.5%
9 546
10.5%
2 332
 
6.4%
7 292
 
5.6%
6 253
 
4.9%
5 250
 
4.8%
4 219
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4319
83.3%
Dash Punctuation 868
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 788
18.2%
0 731
16.9%
3 701
16.2%
9 546
12.6%
2 332
7.7%
7 292
 
6.8%
6 253
 
5.9%
5 250
 
5.8%
4 219
 
5.1%
8 207
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 868
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5187
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 868
16.7%
1 788
15.2%
0 731
14.1%
3 701
13.5%
9 546
10.5%
2 332
 
6.4%
7 292
 
5.6%
6 253
 
4.9%
5 250
 
4.8%
4 219
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5187
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 868
16.7%
1 788
15.2%
0 731
14.1%
3 701
13.5%
9 546
10.5%
2 332
 
6.4%
7 292
 
5.6%
6 253
 
4.9%
5 250
 
4.8%
4 219
 
4.2%

팩스
Text

MISSING 

Distinct395
Distinct (%)97.8%
Missing194
Missing (%)32.4%
Memory size4.8 KiB
2024-04-06T17:13:45.167639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.95297
Min length11

Characters and Unicode

Total characters4829
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

Unique388 ?
Unique (%)96.0%

Sample

1st row031-963-8407
2nd row031-963-2252
3rd row031-921-7058
4th row031-972-4731
5th row031-819-3343
ValueCountFrequency (%)
031-905-2034 3
 
0.7%
031-908-2234 3
 
0.7%
031-926-3059 2
 
0.5%
031-911-7608 2
 
0.5%
031-922-0308 2
 
0.5%
031-918-1821 2
 
0.5%
031-905-4337 2
 
0.5%
031-922-1513 1
 
0.2%
031-813-7090 1
 
0.2%
031-922-6177 1
 
0.2%
Other values (385) 385
95.3%
2024-04-06T17:13:45.884452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 808
16.7%
1 767
15.9%
3 646
13.4%
0 634
13.1%
9 490
10.1%
8 290
 
6.0%
2 281
 
5.8%
7 235
 
4.9%
6 230
 
4.8%
4 226
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4021
83.3%
Dash Punctuation 808
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 767
19.1%
3 646
16.1%
0 634
15.8%
9 490
12.2%
8 290
 
7.2%
2 281
 
7.0%
7 235
 
5.8%
6 230
 
5.7%
4 226
 
5.6%
5 222
 
5.5%
Dash Punctuation
ValueCountFrequency (%)
- 808
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 808
16.7%
1 767
15.9%
3 646
13.4%
0 634
13.1%
9 490
10.1%
8 290
 
6.0%
2 281
 
5.8%
7 235
 
4.9%
6 230
 
4.8%
4 226
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 808
16.7%
1 767
15.9%
3 646
13.4%
0 634
13.1%
9 490
10.1%
8 290
 
6.0%
2 281
 
5.8%
7 235
 
4.9%
6 230
 
4.8%
4 226
 
4.7%

연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct587
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean379610.38
Minimum871
Maximum94840602
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-04-06T17:13:46.275341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum871
5-th percentile2121.9
Q114530.5
median49131
Q387242.25
95-th percentile166446.45
Maximum94840602
Range94839731
Interquartile range (IQR)72711.75

Descriptive statistics

Standard deviation5460952.2
Coefficient of variation (CV)14.385677
Kurtosis296.39382
Mean379610.38
Median Absolute Deviation (MAD)36948.5
Skewness17.243789
Sum2.2700701 × 108
Variance2.9821999 × 1013
MonotonicityNot monotonic
2024-04-06T17:13:46.572528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3113.0 2
 
0.3%
27448.0 2
 
0.3%
7158.0 2
 
0.3%
73092.0 2
 
0.3%
14920.0 2
 
0.3%
34190.0 2
 
0.3%
118660.0 2
 
0.3%
113901.0 2
 
0.3%
87036.0 2
 
0.3%
1675.0 2
 
0.3%
Other values (577) 578
96.7%
ValueCountFrequency (%)
871.0 1
0.2%
1097.0 1
0.2%
1120.0 1
0.2%
1168.0 1
0.2%
1526.0 1
0.2%
1527.0 1
0.2%
1634.0 1
0.2%
1655.0 1
0.2%
1675.0 2
0.3%
1705.0 1
0.2%
ValueCountFrequency (%)
94840602.0 1
0.2%
94285171.0 1
0.2%
659966.0 1
0.2%
561961.0 1
0.2%
488099.0 1
0.2%
458790.0 1
0.2%
369459.0 1
0.2%
360658.0 1
0.2%
341425.0 1
0.2%
341076.0 1
0.2%

승강기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.77592
Minimum0
Maximum136
Zeros173
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-04-06T17:13:47.404055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q318
95-th percentile34
Maximum136
Range136
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.678473
Coefficient of variation (CV)1.076644
Kurtosis16.475123
Mean11.77592
Median Absolute Deviation (MAD)10
Skewness2.5340893
Sum7042
Variance160.74367
MonotonicityNot monotonic
2024-04-06T17:13:47.706141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 173
28.9%
12 34
 
5.7%
13 21
 
3.5%
8 20
 
3.3%
18 20
 
3.3%
4 19
 
3.2%
10 18
 
3.0%
9 17
 
2.8%
16 17
 
2.8%
6 16
 
2.7%
Other values (40) 243
40.6%
ValueCountFrequency (%)
0 173
28.9%
1 4
 
0.7%
2 7
 
1.2%
3 10
 
1.7%
4 19
 
3.2%
5 14
 
2.3%
6 16
 
2.7%
7 14
 
2.3%
8 20
 
3.3%
9 17
 
2.8%
ValueCountFrequency (%)
136 1
0.2%
71 1
0.2%
66 1
0.2%
64 1
0.2%
60 1
0.2%
54 1
0.2%
52 1
0.2%
50 1
0.2%
44 1
0.2%
43 1
0.2%

비고
Text

MISSING 

Distinct11
Distinct (%)73.3%
Missing583
Missing (%)97.5%
Memory size4.8 KiB
2024-04-06T17:13:48.105951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length34
Mean length28.8
Min length4

Characters and Unicode

Total characters432
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)53.3%

Sample

1st row사용검사일 92.09.29(삼93.08.09(극93.08.19
2nd row사용검사일 1차:92.10.30/ 2차:93.05.29
3rd row사용검사일 1차:92.10.30/ 2차:93.04.30
4th row사용검사일 1993-01-29(1차), 1993-07-09(2차)
5th row사용검사일 1차:93.05.13/ 2차:93.08.30
ValueCountFrequency (%)
사용검사일 13
33.3%
2011-05-04(전체 3
 
7.7%
2011-03-31(동별 3
 
7.7%
통합관리 2
 
5.1%
2011-01-14(동별 2
 
5.1%
2011-04-15(전체 2
 
5.1%
1차:92.10.30 2
 
5.1%
2차:93.08.30 1
 
2.6%
2002-12-31 1
 
2.6%
2002-06-26 1
 
2.6%
Other values (9) 9
23.1%
2024-04-06T17:13:48.674390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 55
12.7%
1 45
 
10.4%
- 32
 
7.4%
2 30
 
6.9%
사 26
 
6.0%
3 24
 
5.6%
24
 
5.6%
9 23
 
5.3%
. 22
 
5.1%
( 16
 
3.7%
Other values (25) 135
31.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202
46.8%
Other Letter 105
24.3%
Other Punctuation 39
 
9.0%
Dash Punctuation 32
 
7.4%
Space Separator 24
 
5.6%
Open Punctuation 16
 
3.7%
Close Punctuation 14
 
3.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 26
24.8%
일 13
12.4%
검 13
12.4%
용 13
12.4%
차 8
 
7.6%
전 5
 
4.8%
체 5
 
4.8%
별 5
 
4.8%
동 5
 
4.8%
합 2
 
1.9%
Other values (7) 10
 
9.5%
Decimal Number
ValueCountFrequency (%)
0 55
27.2%
1 45
22.3%
2 30
14.9%
3 24
11.9%
9 23
11.4%
4 11
 
5.4%
5 7
 
3.5%
8 3
 
1.5%
7 2
 
1.0%
6 2
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 22
56.4%
, 7
 
17.9%
: 6
 
15.4%
/ 4
 
10.3%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%
Space Separator
ValueCountFrequency (%)
24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 327
75.7%
Hangul 105
 
24.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 55
16.8%
1 45
13.8%
- 32
9.8%
2 30
9.2%
3 24
7.3%
24
7.3%
9 23
7.0%
. 22
 
6.7%
( 16
 
4.9%
) 14
 
4.3%
Other values (8) 42
12.8%
Hangul
ValueCountFrequency (%)
사 26
24.8%
일 13
12.4%
검 13
12.4%
용 13
12.4%
차 8
 
7.6%
전 5
 
4.8%
체 5
 
4.8%
별 5
 
4.8%
동 5
 
4.8%
합 2
 
1.9%
Other values (7) 10
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 327
75.7%
Hangul 105
 
24.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 55
16.8%
1 45
13.8%
- 32
9.8%
2 30
9.2%
3 24
7.3%
24
7.3%
9 23
7.0%
. 22
 
6.7%
( 16
 
4.9%
) 14
 
4.3%
Other values (8) 42
12.8%
Hangul
ValueCountFrequency (%)
사 26
24.8%
일 13
12.4%
검 13
12.4%
용 13
12.4%
차 8
 
7.6%
전 5
 
4.8%
체 5
 
4.8%
별 5
 
4.8%
동 5
 
4.8%
합 2
 
1.9%
Other values (7) 10
 
9.5%

Interactions

2024-04-06T17:13:33.462535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:29.067207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:30.351101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:31.262556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:32.584140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:33.633217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:29.346815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:30.519059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:31.446698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:32.731394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:33.802134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:29.614171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:30.717660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:31.987038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:32.892371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:33.992108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:29.826196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:30.869806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:32.218267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:33.067287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:34.228452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:30.046336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:31.093176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:32.416950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:13:33.282205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:13:48.874566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번구분건물유형동수세대수연면적(제곱미터)승강기비고
순번1.0000.6370.5880.3010.6200.1640.3731.000
구분0.6371.0000.5120.0000.6970.0000.387NaN
건물유형0.5880.5121.0000.5540.3820.0000.599NaN
동수0.3010.0000.5541.0000.0000.0000.297NaN
세대수0.6200.6970.3820.0001.0000.0000.8070.000
연면적(제곱미터)0.1640.0000.0000.0000.0001.0000.000NaN
승강기0.3730.3870.5990.2970.8070.0001.0000.000
비고1.000NaNNaNNaN0.000NaN0.0001.000
2024-04-06T17:13:49.094001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분건물유형
구분1.0000.322
건물유형0.3221.000
2024-04-06T17:13:49.261872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번동수세대수연면적(제곱미터)승강기구분건물유형
순번1.0000.4130.3360.5010.4530.4360.289
동수0.4131.0000.6340.7420.7380.0000.275
세대수0.3360.6341.0000.8520.8390.4960.168
연면적(제곱미터)0.5010.7420.8521.0000.8900.0000.000
승강기0.4530.7380.8390.8901.0000.2740.329
구분0.4360.0000.4960.0000.2741.0000.322
건물유형0.2890.2750.1680.0000.3290.3221.000

Missing values

2024-04-06T17:13:34.586916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:13:35.053074image/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.
2024-04-06T17:13:35.280793image/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

순번구분단지명지번주소사업승인일사용검사일건물유형층수동수세대수관리소전화팩스연면적(제곱미터)승강기비고
01비의무삼천리연립경기도 고양시 덕양구 토당동 43-41980-03-141981-05-06연립주택2320<NA><NA>1097.00<NA>
12비의무대당연립주택경기도 고양시 덕양구 토당동 434-31981-06-291981-12-08연립주택3336<NA><NA>2855.00<NA>
23비의무로얄연립주택경기도 고양시 덕양구 토당동 292-341981-07-081981-12-15연립주택3236<NA><NA>2318.00<NA>
34비의무삼화주택경기도 고양시 덕양구 토당동 392-11981-07-231982-03-03연립주택3463<NA><NA>2791.00<NA>
45비의무경원연립주택경기도 고양시 덕양구 행신동 618-11980-12-261982-03-18연립주택2424<NA><NA>2071.00<NA>
56비의무삼진연립경기도 고양시 덕양구 행신동 173-11981-05-261982-05-24연립주택3474<NA><NA>6112.00<NA>
67비의무제민연립경기도 고양시 덕양구 행신동 235-11981-02-141982-05-25연립주택3227<NA><NA>1898.00<NA>
78비의무제민연립경기도 고양시 덕양구 행신동 235-31981-08-171982-05-25연립주택3230<NA><NA>2214.00<NA>
89비의무삼화주택경기도 고양시 덕양구 토당동 399-01982-10-081983-09-19연립주택3351<NA><NA>4070.00<NA>
910비의무성산주택경기도 고양시 덕양구 주교동 597-121983-02-261983-11-23주택4248<NA><NA>2723.00<NA>
순번구분단지명지번주소사업승인일사용검사일건물유형층수동수세대수관리소전화팩스연면적(제곱미터)승강기비고
588589의무적대곡역 두산위브 1단지경기도 고양시 덕양구 토당동 9132016-10-262023-01-17공동주택(아파트)549334<NA><NA>71294.5612<NA>
589590의무적힐스테이트라피아노삼송경기도 고양시 덕양구 오금동 648외2필지2020-10-152023-03-31공동주택(연립주택(도시형생활주택/단지형연립주택))81452144<NA><NA>18788.69960<NA>
590591의무적지축역수피움경기도 고양시 덕양구 지축동 9682018-12-312023-07-14공동주택(아파트)1058321<NA><NA>76912.3420<NA>
591592의무적DMC한강 삼정그린코아 더베스트경기도 고양시 덕양구 덕은동 7102020-06-152023-09-19공동주택(아파트)536625<NA><NA>76199.6615<NA>
592593비의무지축 위스테이경기도 고양시 덕양구 지축동 9402019-02-202022-02-03아파트653920<NA><NA>84089.8813<NA>
593594비의무삼송 원흥마을 LH 13단지경기도 고양시 덕양구 원흥동 6002019-12-272022-03-16아파트169475<NA><NA>65937.1220<NA>
594595비의무덕은 LH 1단지경기도 고양시 덕양구 덕은동 대덕산로 1472018-12-242022-04-15아파트436414<NA><NA>5038.338<NA>
595596비의무삼송 LH 10단지경기도 고양시 덕양구 원흥동 6142018-12-312022-04-27아파트477710<NA><NA>50486.9912<NA>
596597비의무대곡역 두산위브 2단지경기도 고양시 덕양구 토당동 9142016-10-262023-01-17아파트215018<NA><NA>18252.093<NA>
597598비의무삼송비아티움경기도 고양시 덕양구오금동 5952019-11-292023-02-28연립335284<NA><NA>91988.82136<NA>