Overview

Dataset statistics

Number of variables5
Number of observations8734
Missing cells8477
Missing cells (%)19.4%
Duplicate rows9
Duplicate rows (%)0.1%
Total size in memory349.8 KiB
Average record size in memory41.0 B

Variable types

Text4
Numeric1

Dataset

Description전북특별자치도 전주시 내 다세대주택 및 오피스텔 현황으로 건물위치, 주택유형, 세대수, 건축연도 등을 제공합니다.
Author전라북도
URLhttps://www.bigdatahub.go.kr/index.jeonbuk?startPage=8&menuCd=DOM_000000103007001000&pListTypeStr=&pId=15077153

Alerts

Dataset has 9 (0.1%) duplicate rowsDuplicates
건물명(상호명) has 8477 (97.1%) missing valuesMissing
세대수(객실수) is highly skewed (γ1 = 43.87759657)Skewed
세대수(객실수) has 169 (1.9%) zerosZeros

Reproduction

Analysis started2024-03-14 03:04:22.849261
Analysis finished2024-03-14 03:04:23.451067
Duration0.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

건물명(상호명)
Text

MISSING 

Distinct238
Distinct (%)92.6%
Missing8477
Missing (%)97.1%
Memory size68.4 KiB
2024-03-14T12:04:23.682502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length25
Mean length10.568093
Min length1

Characters and Unicode

Total characters2716
Distinct characters268
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

Unique231 ?
Unique (%)89.9%

Sample

1st row휴엔하임 주상복합 아파트
2nd row덕진동1가 1267-32 공동주택
3rd row세움 펠리피아
4th row서신노블레스2단지
5th row서신노블레스1단지
ValueCountFrequency (%)
단독주택 87
 
16.1%
효자동3가 30
 
5.6%
효자동2가 20
 
3.7%
7
 
1.3%
중화산동2가 6
 
1.1%
1동 6
 
1.1%
평화동1가 6
 
1.1%
제2종근린생활시설 5
 
0.9%
오피스텔 5
 
0.9%
인후동1가 4
 
0.7%
Other values (331) 364
67.4%
2024-03-14T12:04:24.109385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
283
 
10.4%
1 133
 
4.9%
동 112
 
4.1%
주 108
 
4.0%
2 99
 
3.6%
단 98
 
3.6%
택 98
 
3.6%
독 90
 
3.3%
- 86
 
3.2%
가 84
 
3.1%
Other values (258) 1525
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1634
60.2%
Decimal Number 541
 
19.9%
Space Separator 283
 
10.4%
Dash Punctuation 86
 
3.2%
Open Punctuation 66
 
2.4%
Close Punctuation 65
 
2.4%
Uppercase Letter 26
 
1.0%
Other Punctuation 10
 
0.4%
Lowercase Letter 5
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 112
 
6.9%
주 108
 
6.6%
단 98
 
6.0%
택 98
 
6.0%
독 90
 
5.5%
가 84
 
5.1%
자 59
 
3.6%
효 58
 
3.5%
빌 50
 
3.1%
스 34
 
2.1%
Other values (226) 843
51.6%
Decimal Number
ValueCountFrequency (%)
1 133
24.6%
2 99
18.3%
3 62
11.5%
6 51
 
9.4%
5 46
 
8.5%
4 43
 
7.9%
8 30
 
5.5%
7 30
 
5.5%
0 24
 
4.4%
9 23
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
B 6
23.1%
A 5
19.2%
C 5
19.2%
J 3
11.5%
R 2
 
7.7%
H 1
 
3.8%
N 1
 
3.8%
I 1
 
3.8%
E 1
 
3.8%
D 1
 
3.8%
Lowercase Letter
ValueCountFrequency (%)
e 1
20.0%
u 1
20.0%
s 1
20.0%
o 1
20.0%
h 1
20.0%
Other Punctuation
ValueCountFrequency (%)
. 6
60.0%
, 2
 
20.0%
& 2
 
20.0%
Space Separator
ValueCountFrequency (%)
283
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 86
100.0%
Open Punctuation
ValueCountFrequency (%)
( 66
100.0%
Close Punctuation
ValueCountFrequency (%)
) 65
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1634
60.2%
Common 1051
38.7%
Latin 31
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 112
 
6.9%
주 108
 
6.6%
단 98
 
6.0%
택 98
 
6.0%
독 90
 
5.5%
가 84
 
5.1%
자 59
 
3.6%
효 58
 
3.5%
빌 50
 
3.1%
스 34
 
2.1%
Other values (226) 843
51.6%
Common
ValueCountFrequency (%)
283
26.9%
1 133
12.7%
2 99
 
9.4%
- 86
 
8.2%
( 66
 
6.3%
) 65
 
6.2%
3 62
 
5.9%
6 51
 
4.9%
5 46
 
4.4%
4 43
 
4.1%
Other values (7) 117
11.1%
Latin
ValueCountFrequency (%)
B 6
19.4%
A 5
16.1%
C 5
16.1%
J 3
9.7%
R 2
 
6.5%
e 1
 
3.2%
u 1
 
3.2%
H 1
 
3.2%
s 1
 
3.2%
o 1
 
3.2%
Other values (5) 5
16.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1634
60.2%
ASCII 1082
39.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
283
26.2%
1 133
12.3%
2 99
 
9.1%
- 86
 
7.9%
( 66
 
6.1%
) 65
 
6.0%
3 62
 
5.7%
6 51
 
4.7%
5 46
 
4.3%
4 43
 
4.0%
Other values (22) 148
13.7%
Hangul
ValueCountFrequency (%)
동 112
 
6.9%
주 108
 
6.6%
단 98
 
6.0%
택 98
 
6.0%
독 90
 
5.5%
가 84
 
5.1%
자 59
 
3.6%
효 58
 
3.5%
빌 50
 
3.1%
스 34
 
2.1%
Other values (226) 843
51.6%

건물위치
Text

Distinct8712
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size68.4 KiB
2024-03-14T12:04:24.308164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length26
Mean length24.288642
Min length19

Characters and Unicode

Total characters212137
Distinct characters70
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

Unique8692 ?
Unique (%)99.5%

Sample

1st row전라북도 전주시 덕진구 고랑동 819-5
2nd row전라북도 전주시 덕진구 금암동 1569-6
3rd row전라북도 전주시 덕진구 금암동 1588-23
4th row전라북도 전주시 덕진구 금암동 1639-1
5th row전라북도 전주시 덕진구 금암동 525-83
ValueCountFrequency (%)
전라북도 8734
20.0%
전주시 8734
20.0%
완산구 5182
11.9%
덕진구 3552
 
8.1%
효자동3가 1642
 
3.8%
서신동 775
 
1.8%
인후동1가 735
 
1.7%
삼천동1가 699
 
1.6%
중화산동2가 611
 
1.4%
금암동 541
 
1.2%
Other values (7885) 12466
28.5%
2024-03-14T12:04:24.712609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
34937
16.5%
전 17477
 
8.2%
1 11575
 
5.5%
북 8825
 
4.2%
동 8788
 
4.1%
구 8736
 
4.1%
라 8734
 
4.1%
시 8734
 
4.1%
주 8734
 
4.1%
도 8734
 
4.1%
Other values (60) 86863
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 121022
57.0%
Decimal Number 47730
 
22.5%
Space Separator 34937
 
16.5%
Dash Punctuation 8448
 
4.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 17477
14.4%
북 8825
 
7.3%
동 8788
 
7.3%
구 8736
 
7.2%
라 8734
 
7.2%
시 8734
 
7.2%
주 8734
 
7.2%
도 8734
 
7.2%
가 6708
 
5.5%
산 5923
 
4.9%
Other values (48) 29629
24.5%
Decimal Number
ValueCountFrequency (%)
1 11575
24.3%
2 6547
13.7%
3 5217
10.9%
6 4992
10.5%
5 4035
 
8.5%
7 3499
 
7.3%
4 3392
 
7.1%
8 3130
 
6.6%
9 2787
 
5.8%
0 2556
 
5.4%
Space Separator
ValueCountFrequency (%)
34937
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8448
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 121022
57.0%
Common 91115
43.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 17477
14.4%
북 8825
 
7.3%
동 8788
 
7.3%
구 8736
 
7.2%
라 8734
 
7.2%
시 8734
 
7.2%
주 8734
 
7.2%
도 8734
 
7.2%
가 6708
 
5.5%
산 5923
 
4.9%
Other values (48) 29629
24.5%
Common
ValueCountFrequency (%)
34937
38.3%
1 11575
 
12.7%
- 8448
 
9.3%
2 6547
 
7.2%
3 5217
 
5.7%
6 4992
 
5.5%
5 4035
 
4.4%
7 3499
 
3.8%
4 3392
 
3.7%
8 3130
 
3.4%
Other values (2) 5343
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 121022
57.0%
ASCII 91115
43.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
34937
38.3%
1 11575
 
12.7%
- 8448
 
9.3%
2 6547
 
7.2%
3 5217
 
5.7%
6 4992
 
5.5%
5 4035
 
4.4%
7 3499
 
3.8%
4 3392
 
3.7%
8 3130
 
3.4%
Other values (2) 5343
 
5.9%
Hangul
ValueCountFrequency (%)
전 17477
14.4%
북 8825
 
7.3%
동 8788
 
7.3%
구 8736
 
7.2%
라 8734
 
7.2%
시 8734
 
7.2%
주 8734
 
7.2%
도 8734
 
7.2%
가 6708
 
5.5%
산 5923
 
4.9%
Other values (48) 29629
24.5%

주택유형구분
Text

Distinct741
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size68.4 KiB
2024-03-14T12:04:24.918972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length34
Mean length8.3521869
Min length2

Characters and Unicode

Total characters72948
Distinct characters126
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique493 ?
Unique (%)5.6%

Sample

1st row다세대주택(3세대)
2nd row다세대주택(3세대)
3rd row다세대주택(3세대)
4th row공동주택(아파트), 업무시설(오피스텔), 근린생활시설
5th row다세대주택
ValueCountFrequency (%)
단독주택 4288
39.6%
근린생활시설 723
 
6.7%
다가구주택 715
 
6.6%
단독주택,제1종근린생활시설 679
 
6.3%
단독주택(다가구주택 467
 
4.3%
다세대주택 390
 
3.6%
주택 343
 
3.2%
제1종근린생활시설 319
 
2.9%
단독주택(3가구 250
 
2.3%
제2종근린생활시설 226
 
2.1%
Other values (412) 2424
22.4%
2024-03-14T12:04:25.261168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 9171
 
12.6%
택 9170
 
12.6%
단 6730
 
9.2%
독 6730
 
9.2%
, 2826
 
3.9%
구 2736
 
3.8%
가 2734
 
3.7%
생 2497
 
3.4%
근 2497
 
3.4%
시 2481
 
3.4%
Other values (116) 25376
34.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 61032
83.7%
Decimal Number 3080
 
4.2%
Other Punctuation 2873
 
3.9%
Space Separator 2090
 
2.9%
Open Punctuation 1928
 
2.6%
Close Punctuation 1928
 
2.6%
Dash Punctuation 16
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 9171
15.0%
택 9170
15.0%
단 6730
11.0%
독 6730
11.0%
구 2736
 
4.5%
가 2734
 
4.5%
생 2497
 
4.1%
근 2497
 
4.1%
시 2481
 
4.1%
설 2479
 
4.1%
Other values (94) 13807
22.6%
Decimal Number
ValueCountFrequency (%)
1 1461
47.4%
2 666
21.6%
3 446
 
14.5%
5 149
 
4.8%
4 145
 
4.7%
8 53
 
1.7%
9 50
 
1.6%
6 48
 
1.6%
0 32
 
1.0%
7 30
 
1.0%
Other Punctuation
ValueCountFrequency (%)
, 2826
98.4%
. 17
 
0.6%
& 11
 
0.4%
/ 8
 
0.3%
: 6
 
0.2%
· 3
 
0.1%
' 2
 
0.1%
Space Separator
ValueCountFrequency (%)
2090
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1928
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1928
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 61032
83.7%
Common 11916
 
16.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 9171
15.0%
택 9170
15.0%
단 6730
11.0%
독 6730
11.0%
구 2736
 
4.5%
가 2734
 
4.5%
생 2497
 
4.1%
근 2497
 
4.1%
시 2481
 
4.1%
설 2479
 
4.1%
Other values (94) 13807
22.6%
Common
ValueCountFrequency (%)
, 2826
23.7%
2090
17.5%
( 1928
16.2%
) 1928
16.2%
1 1461
12.3%
2 666
 
5.6%
3 446
 
3.7%
5 149
 
1.3%
4 145
 
1.2%
8 53
 
0.4%
Other values (12) 224
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 61031
83.7%
ASCII 11913
 
16.3%
None 3
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 9171
15.0%
택 9170
15.0%
단 6730
11.0%
독 6730
11.0%
구 2736
 
4.5%
가 2734
 
4.5%
생 2497
 
4.1%
근 2497
 
4.1%
시 2481
 
4.1%
설 2479
 
4.1%
Other values (93) 13806
22.6%
ASCII
ValueCountFrequency (%)
, 2826
23.7%
2090
17.5%
( 1928
16.2%
) 1928
16.2%
1 1461
12.3%
2 666
 
5.6%
3 446
 
3.7%
5 149
 
1.3%
4 145
 
1.2%
8 53
 
0.4%
Other values (11) 221
 
1.9%
None
ValueCountFrequency (%)
· 3
100.0%
Compat Jamo
ValueCountFrequency (%)
ㄱ 1
100.0%

세대수(객실수)
Real number (ℝ)

SKEWED  ZEROS 

Distinct43
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1357912
Minimum0
Maximum826
Zeros169
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size76.9 KiB
2024-03-14T12:04:25.401134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median8
Q311
95-th percentile16
Maximum826
Range826
Interquartile range (IQR)8

Descriptive statistics

Standard deviation11.724445
Coefficient of variation (CV)1.4410946
Kurtosis2806.6615
Mean8.1357912
Median Absolute Deviation (MAD)4
Skewness43.877597
Sum71058
Variance137.46261
MonotonicityNot monotonic
2024-03-14T12:04:25.497833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3 1332
15.3%
10 1066
12.2%
11 788
9.0%
9 643
 
7.4%
4 616
 
7.1%
5 576
 
6.6%
8 542
 
6.2%
2 497
 
5.7%
6 423
 
4.8%
12 349
 
4.0%
Other values (33) 1902
21.8%
ValueCountFrequency (%)
0 169
 
1.9%
1 192
 
2.2%
2 497
 
5.7%
3 1332
15.3%
4 616
7.1%
5 576
6.6%
6 423
 
4.8%
7 302
 
3.5%
8 542
6.2%
9 643
7.4%
ValueCountFrequency (%)
826 1
< 0.1%
288 1
< 0.1%
276 1
< 0.1%
191 1
< 0.1%
182 1
< 0.1%
172 1
< 0.1%
167 1
< 0.1%
159 2
< 0.1%
110 1
< 0.1%
77 1
< 0.1%

건축연도
Text

Distinct4730
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Memory size68.4 KiB
2024-03-14T12:04:25.759949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9910694
Min length4

Characters and Unicode

Total characters87262
Distinct characters15
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

Unique2574 ?
Unique (%)29.5%

Sample

1st row1992-08-10
2nd row1989-12-06
3rd row1989-08-03
4th row2016-11-28
5th row1989-11-21
ValueCountFrequency (%)
2011-01-04 13
 
0.1%
확인불가 12
 
0.1%
2011-01-05 11
 
0.1%
2012-06-11 11
 
0.1%
2011-12-29 11
 
0.1%
2011-02-23 10
 
0.1%
2011-11-25 10
 
0.1%
2012-02-29 10
 
0.1%
2012-03-07 9
 
0.1%
2011-12-28 9
 
0.1%
Other values (4720) 8628
98.8%
2024-03-14T12:04:26.127512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 19979
22.9%
- 17442
20.0%
1 15406
17.7%
2 13288
15.2%
9 6196
 
7.1%
8 2950
 
3.4%
3 2646
 
3.0%
7 2491
 
2.9%
4 2389
 
2.7%
6 2279
 
2.6%
Other values (5) 2196
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69772
80.0%
Dash Punctuation 17442
 
20.0%
Other Letter 48
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19979
28.6%
1 15406
22.1%
2 13288
19.0%
9 6196
 
8.9%
8 2950
 
4.2%
3 2646
 
3.8%
7 2491
 
3.6%
4 2389
 
3.4%
6 2279
 
3.3%
5 2148
 
3.1%
Other Letter
ValueCountFrequency (%)
확 12
25.0%
인 12
25.0%
불 12
25.0%
가 12
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 17442
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 87214
99.9%
Hangul 48
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19979
22.9%
- 17442
20.0%
1 15406
17.7%
2 13288
15.2%
9 6196
 
7.1%
8 2950
 
3.4%
3 2646
 
3.0%
7 2491
 
2.9%
4 2389
 
2.7%
6 2279
 
2.6%
Hangul
ValueCountFrequency (%)
확 12
25.0%
인 12
25.0%
불 12
25.0%
가 12
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87214
99.9%
Hangul 48
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19979
22.9%
- 17442
20.0%
1 15406
17.7%
2 13288
15.2%
9 6196
 
7.1%
8 2950
 
3.4%
3 2646
 
3.0%
7 2491
 
2.9%
4 2389
 
2.7%
6 2279
 
2.6%
Hangul
ValueCountFrequency (%)
확 12
25.0%
인 12
25.0%
불 12
25.0%
가 12
25.0%

Interactions

2024-03-14T12:04:23.254335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2024-03-14T12:04:23.338093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T12:04:23.412697image/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

건물명(상호명)건물위치주택유형구분세대수(객실수)건축연도
0<NA>전라북도 전주시 덕진구 고랑동 819-5다세대주택(3세대)31992-08-10
1<NA>전라북도 전주시 덕진구 금암동 1569-6다세대주택(3세대)61989-12-06
2<NA>전라북도 전주시 덕진구 금암동 1588-23다세대주택(3세대)31989-08-03
3휴엔하임 주상복합 아파트전라북도 전주시 덕진구 금암동 1639-1공동주택(아파트), 업무시설(오피스텔), 근린생활시설302016-11-28
4<NA>전라북도 전주시 덕진구 금암동 525-83다세대주택41989-11-21
5덕진동1가 1267-32 공동주택전라북도 전주시 덕진구 덕진동1가 1267-32공동주택22013-01-31
6<NA>전라북도 전주시 덕진구 덕진동1가 1360-18다세대주택21989-12-26
7세움 펠리피아전라북도 전주시 덕진구 반월동 1231아파트,업무시설,근린생활시설282016-02-11
8<NA>전라북도 전주시 덕진구 송천동1가 367다세대주택41988-10-25
9<NA>전라북도 전주시 덕진구 여의동 681-1다세대주택(2세대)21991-10-28
건물명(상호명)건물위치주택유형구분세대수(객실수)건축연도
87245전라북도 전주시 완산구 효자동1가 690-2교육연구시설, 노유자시설, 단독주택, 종교시설151998-02-02
8725세움 펠리피아 오피스텔전라북도 전주시 완산구 효자동2가 1227-3업무시설(오피스텔),판매시설,근린생활시설1912013-10-15
8726에벤에셀빌딩전라북도 전주시 완산구 효자동2가 1230-1업무시설,제1종근린생활시설162013-01-28
8727로자벨시티전라북도 전주시 완산구 효자동3가 1525-1업무시설,제1,2종근린생활시설1672013-01-31
8728전주상공회의소전라북도 전주시 완산구 효자동3가 1525-2업무시설, 제1,2종근린생활시설732017-11-20
8729효자 스위트엠씨즈 오피스텔전라북도 전주시 완산구 효자동3가 1525-3업무시설(오피스텔),판매시설,업무시설(사무소)2762019-10-18
8730로뎀트리빌딩전라북도 전주시 완산구 효자동3가 1544-2업무시설,제1,2종근린생활시설302015-12-07
8731센텀퍼스트전라북도 전주시 완산구 효자동3가 1544-4업무시설1102013-06-04
8732태왕오피스텔전라북도 전주시 완산구 효자동3가 1627-9업무시설, 제1,2종근린생활시설12018-01-23
8733웨스트빌 오피스텔전라북도 전주시 완산구 효자동3가 1693-5업무시설(오피스텔)1822013-08-28

Duplicate rows

Most frequently occurring

건물명(상호명)건물위치주택유형구분세대수(객실수)건축연도# duplicates
0대방 디엠시티전라북도 전주시 덕진구 장동 1111오피스텔1592020-07-242
1<NA>전라북도 전주시 덕진구 송천동1가 251단독주택(다가구주택)92020-02-112
2<NA>전라북도 전주시 덕진구 전미동1가 565-3다가구주택12009-09-012
3<NA>전라북도 전주시 덕진구 팔복동3가 108-1주택11952-11-062
4<NA>전라북도 전주시 완산구 대성동 132-8단독주택(다가구주택)12018-09-072
5<NA>전라북도 전주시 완산구 동완산동 20-4단독주택22014-03-212
6<NA>전라북도 전주시 완산구 서완산동2가 334-4단독주택(다가구주택)92019-12-042
7<NA>전라북도 전주시 완산구 효자동2가 926단독주택(다가구주택)12017-08-022
8<NA>전라북도 전주시 완산구 효자동3가 1116단독주택41999-04-122