Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells870
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory129.0 B

Variable types

Categorical2
Text5
Numeric7

Dataset

Description경기도 광주시 관내 공동주택가격(국토교통부장관이 아파트·연립·다세대 주택 등의 공동주택에 대하여 매년 공시기준일(1월1일) 현재 적정가격을 조사·산정하여 공시한 공동주택의 가격) 현황입니다.
Author경기도 광주시
URLhttps://www.data.go.kr/data/15036900/fileData.do

Alerts

기준월 has constant value ""Constant
토지코드 has constant value ""Constant
전용면적 is highly overall correlated with 열람가격High correlation
열람가격 is highly overall correlated with 전용면적 and 1 other fieldsHigh correlation
부번 is highly overall correlated with 열람가격High correlation
아파트 has 870 (8.7%) missing valuesMissing
도로명부번 has 5072 (50.7%) zerosZeros
본번 has 333 (3.3%) zerosZeros
부번 has 4288 (42.9%) zerosZeros

Reproduction

Analysis started2023-12-12 20:50:57.088435
Analysis finished2023-12-12 20:51:04.759018
Duration7.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준월
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 10000
100.0%

Length

2023-12-13T05:51:04.821268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:51:04.920813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10000
100.0%
Distinct2373
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T05:51:05.197088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length29
Mean length22.1778
Min length15

Characters and Unicode

Total characters221778
Distinct characters159
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique785 ?
Unique (%)7.8%

Sample

1st row경충대로1422번길 42(쌍령동 360-1)
2nd row도수길 56-13(퇴촌면도수리 392-36)
3rd row능평로 116-32(오포읍능평리 194-23)
4th row태성로 107(태전동 702-0)
5th row경충대로1460번길 43-6(쌍령동 310-10)
ValueCountFrequency (%)
순암로36번길 486
 
1.6%
태전동로 410
 
1.4%
고불로 401
 
1.3%
태봉로 387
 
1.3%
경충대로 351
 
1.2%
태성로 325
 
1.1%
오포로 269
 
0.9%
새말길 252
 
0.8%
경충대로1127번길 249
 
0.8%
벼루개길42번길 233
 
0.8%
Other values (4324) 26637
88.8%
2023-12-13T05:51:05.736378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20000
 
9.0%
1 15023
 
6.8%
- 14928
 
6.7%
2 10192
 
4.6%
( 10000
 
4.5%
) 10000
 
4.5%
0 9853
 
4.4%
3 8721
 
3.9%
6 7752
 
3.5%
7478
 
3.4%
Other values (149) 107831
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 85739
38.7%
Decimal Number 81111
36.6%
Space Separator 20000
 
9.0%
Dash Punctuation 14928
 
6.7%
Open Punctuation 10000
 
4.5%
Close Punctuation 10000
 
4.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
7478
 
8.7%
6246
 
7.3%
6131
 
7.2%
4994
 
5.8%
4451
 
5.2%
3603
 
4.2%
3601
 
4.2%
3564
 
4.2%
3239
 
3.8%
2078
 
2.4%
Other values (135) 40354
47.1%
Decimal Number
ValueCountFrequency (%)
1 15023
18.5%
2 10192
12.6%
0 9853
12.1%
3 8721
10.8%
6 7752
9.6%
5 6919
8.5%
4 6903
8.5%
7 6376
7.9%
8 4851
 
6.0%
9 4521
 
5.6%
Space Separator
ValueCountFrequency (%)
20000
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14928
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10000
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 136039
61.3%
Hangul 85739
38.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
7478
 
8.7%
6246
 
7.3%
6131
 
7.2%
4994
 
5.8%
4451
 
5.2%
3603
 
4.2%
3601
 
4.2%
3564
 
4.2%
3239
 
3.8%
2078
 
2.4%
Other values (135) 40354
47.1%
Common
ValueCountFrequency (%)
20000
14.7%
1 15023
11.0%
- 14928
11.0%
2 10192
7.5%
( 10000
7.4%
) 10000
7.4%
0 9853
7.2%
3 8721
 
6.4%
6 7752
 
5.7%
5 6919
 
5.1%
Other values (4) 22651
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 136039
61.3%
Hangul 85739
38.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20000
14.7%
1 15023
11.0%
- 14928
11.0%
2 10192
7.5%
( 10000
7.4%
) 10000
7.4%
0 9853
7.2%
3 8721
 
6.4%
6 7752
 
5.7%
5 6919
 
5.1%
Other values (4) 22651
16.7%
Hangul
ValueCountFrequency (%)
7478
 
8.7%
6246
 
7.3%
6131
 
7.2%
4994
 
5.8%
4451
 
5.2%
3603
 
4.2%
3601
 
4.2%
3564
 
4.2%
3239
 
3.8%
2078
 
2.4%
Other values (135) 40354
47.1%
Distinct2197
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T05:51:06.360610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length20
Mean length10.2122
Min length2

Characters and Unicode

Total characters102122
Distinct characters337
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique690 ?
Unique (%)6.9%

Sample

1st row현대모닝사이드2
2nd row주목샤넬카운티3동
3rd row엔카운티(101동)
4th row힐스테이트 태전6지구(1601동~1608동)
5th row태성슈퍼빌104동
ValueCountFrequency (%)
힐스테이트 915
 
7.1%
오포문형 229
 
1.8%
아파트 229
 
1.8%
양우내안애 229
 
1.8%
태전2차 217
 
1.7%
에듀포레 217
 
1.7%
현대 216
 
1.7%
현대모닝사이드2 205
 
1.6%
태전7지구 160
 
1.2%
태전파크자이 160
 
1.2%
Other values (2209) 10059
78.4%
2023-12-13T05:51:06.899352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6420
 
6.3%
5293
 
5.2%
) 4220
 
4.1%
( 4220
 
4.1%
0 3755
 
3.7%
3019
 
3.0%
2994
 
2.9%
2913
 
2.9%
2836
 
2.8%
2 2427
 
2.4%
Other values (327) 64025
62.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67095
65.7%
Decimal Number 18451
 
18.1%
Close Punctuation 4220
 
4.1%
Open Punctuation 4220
 
4.1%
Space Separator 2836
 
2.8%
Uppercase Letter 2696
 
2.6%
Math Symbol 874
 
0.9%
Lowercase Letter 691
 
0.7%
Dash Punctuation 606
 
0.6%
Other Punctuation 431
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
5293
 
7.9%
3019
 
4.5%
2994
 
4.5%
2913
 
4.3%
2133
 
3.2%
1973
 
2.9%
1914
 
2.9%
1564
 
2.3%
1542
 
2.3%
1526
 
2.3%
Other values (281) 42224
62.9%
Uppercase Letter
ValueCountFrequency (%)
C 829
30.7%
B 795
29.5%
L 438
16.2%
A 416
15.4%
D 98
 
3.6%
E 42
 
1.6%
I 24
 
0.9%
M 17
 
0.6%
F 15
 
0.6%
G 8
 
0.3%
Other values (5) 14
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
e 239
34.6%
u 71
 
10.3%
a 67
 
9.7%
i 67
 
9.7%
t 65
 
9.4%
m 61
 
8.8%
r 61
 
8.8%
o 14
 
2.0%
l 12
 
1.7%
s 10
 
1.4%
Other values (4) 24
 
3.5%
Decimal Number
ValueCountFrequency (%)
1 6420
34.8%
0 3755
20.4%
2 2427
 
13.2%
6 1691
 
9.2%
5 1376
 
7.5%
3 956
 
5.2%
4 883
 
4.8%
7 482
 
2.6%
8 260
 
1.4%
9 201
 
1.1%
Close Punctuation
ValueCountFrequency (%)
) 4220
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4220
100.0%
Space Separator
ValueCountFrequency (%)
2836
100.0%
Math Symbol
ValueCountFrequency (%)
~ 874
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 606
100.0%
Other Punctuation
ValueCountFrequency (%)
, 431
100.0%
Letter Number
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67093
65.7%
Common 31638
31.0%
Latin 3389
 
3.3%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
5293
 
7.9%
3019
 
4.5%
2994
 
4.5%
2913
 
4.3%
2133
 
3.2%
1973
 
2.9%
1914
 
2.9%
1564
 
2.3%
1542
 
2.3%
1526
 
2.3%
Other values (279) 42222
62.9%
Latin
ValueCountFrequency (%)
C 829
24.5%
B 795
23.5%
L 438
12.9%
A 416
12.3%
e 239
 
7.1%
D 98
 
2.9%
u 71
 
2.1%
a 67
 
2.0%
i 67
 
2.0%
t 65
 
1.9%
Other values (20) 304
 
9.0%
Common
ValueCountFrequency (%)
1 6420
20.3%
) 4220
13.3%
( 4220
13.3%
0 3755
11.9%
2836
9.0%
2 2427
 
7.7%
6 1691
 
5.3%
5 1376
 
4.3%
3 956
 
3.0%
4 883
 
2.8%
Other values (6) 2854
9.0%
Han
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67093
65.7%
ASCII 35025
34.3%
Number Forms 2
 
< 0.1%
CJK 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6420
18.3%
) 4220
12.0%
( 4220
12.0%
0 3755
10.7%
2836
8.1%
2 2427
 
6.9%
6 1691
 
4.8%
5 1376
 
3.9%
3 956
 
2.7%
4 883
 
2.5%
Other values (35) 6241
17.8%
Hangul
ValueCountFrequency (%)
5293
 
7.9%
3019
 
4.5%
2994
 
4.5%
2913
 
4.3%
2133
 
3.2%
1973
 
2.9%
1914
 
2.9%
1564
 
2.3%
1542
 
2.3%
1526
 
2.3%
Other values (279) 42222
62.9%
Number Forms
ValueCountFrequency (%)
2
100.0%
CJK
ValueCountFrequency (%)
1
50.0%
1
50.0%

아파트
Text

MISSING 

Distinct256
Distinct (%)2.8%
Missing870
Missing (%)8.7%
Memory size156.2 KiB
2023-12-13T05:51:07.368254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length4
Mean length3.747207
Min length2

Characters and Unicode

Total characters34212
Distinct characters114
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

Unique43 ?
Unique (%)0.5%

Sample

1st row204동
2nd row3동
3rd row101동
4th row1604동
5th row104동
ValueCountFrequency (%)
102동 872
 
9.6%
101동 854
 
9.4%
103동 787
 
8.6%
104동 545
 
6.0%
105동 500
 
5.5%
1동 435
 
4.8%
106동 382
 
4.2%
b동 326
 
3.6%
a동 291
 
3.2%
107동 259
 
2.8%
Other values (246) 3879
42.5%
2023-12-13T05:51:08.268711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9126
26.7%
1 8160
23.9%
0 6681
19.5%
2 2605
 
7.6%
3 1578
 
4.6%
5 1217
 
3.6%
4 1087
 
3.2%
6 1050
 
3.1%
7 376
 
1.1%
B 331
 
1.0%
Other values (104) 2001
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23216
67.9%
Other Letter 10082
29.5%
Uppercase Letter 885
 
2.6%
Close Punctuation 11
 
< 0.1%
Open Punctuation 11
 
< 0.1%
Letter Number 5
 
< 0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
9126
90.5%
122
 
1.2%
105
 
1.0%
80
 
0.8%
53
 
0.5%
50
 
0.5%
48
 
0.5%
34
 
0.3%
29
 
0.3%
23
 
0.2%
Other values (82) 412
 
4.1%
Decimal Number
ValueCountFrequency (%)
1 8160
35.1%
0 6681
28.8%
2 2605
 
11.2%
3 1578
 
6.8%
5 1217
 
5.2%
4 1087
 
4.7%
6 1050
 
4.5%
7 376
 
1.6%
8 279
 
1.2%
9 183
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
B 331
37.4%
A 296
33.4%
C 117
 
13.2%
D 92
 
10.4%
E 40
 
4.5%
F 7
 
0.8%
G 2
 
0.2%
Letter Number
ValueCountFrequency (%)
3
60.0%
2
40.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23240
67.9%
Hangul 10082
29.5%
Latin 890
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
9126
90.5%
122
 
1.2%
105
 
1.0%
80
 
0.8%
53
 
0.5%
50
 
0.5%
48
 
0.5%
34
 
0.3%
29
 
0.3%
23
 
0.2%
Other values (82) 412
 
4.1%
Common
ValueCountFrequency (%)
1 8160
35.1%
0 6681
28.7%
2 2605
 
11.2%
3 1578
 
6.8%
5 1217
 
5.2%
4 1087
 
4.7%
6 1050
 
4.5%
7 376
 
1.6%
8 279
 
1.2%
9 183
 
0.8%
Other values (3) 24
 
0.1%
Latin
ValueCountFrequency (%)
B 331
37.2%
A 296
33.3%
C 117
 
13.1%
D 92
 
10.3%
E 40
 
4.5%
F 7
 
0.8%
3
 
0.3%
G 2
 
0.2%
2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24125
70.5%
Hangul 10082
29.5%
Number Forms 5
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
9126
90.5%
122
 
1.2%
105
 
1.0%
80
 
0.8%
53
 
0.5%
50
 
0.5%
48
 
0.5%
34
 
0.3%
29
 
0.3%
23
 
0.2%
Other values (82) 412
 
4.1%
ASCII
ValueCountFrequency (%)
1 8160
33.8%
0 6681
27.7%
2 2605
 
10.8%
3 1578
 
6.5%
5 1217
 
5.0%
4 1087
 
4.5%
6 1050
 
4.4%
7 376
 
1.6%
B 331
 
1.4%
A 296
 
1.2%
Other values (10) 744
 
3.1%
Number Forms
ValueCountFrequency (%)
3
60.0%
2
40.0%
Distinct305
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T05:51:08.723776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length4
Mean length4.2527
Min length2

Characters and Unicode

Total characters42527
Distinct characters36
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

Unique123 ?
Unique (%)1.2%

Sample

1st row105호
2nd row301호
3rd row301호
4th row1202호
5th row302호
ValueCountFrequency (%)
202호 674
 
6.7%
302호 624
 
6.2%
201호 624
 
6.2%
402호 622
 
6.2%
301호 619
 
6.2%
401호 592
 
5.9%
101호 581
 
5.8%
102호 578
 
5.8%
303호 176
 
1.8%
403호 162
 
1.6%
Other values (280) 4782
47.7%
2023-12-13T05:51:09.294085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 10335
24.3%
10000
23.5%
1 7402
17.4%
2 5561
13.1%
3 3330
 
7.8%
4 2687
 
6.3%
5 899
 
2.1%
6 644
 
1.5%
7 472
 
1.1%
8 454
 
1.1%
Other values (26) 743
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32182
75.7%
Other Letter 10125
 
23.8%
Uppercase Letter 153
 
0.4%
Space Separator 34
 
0.1%
Dash Punctuation 26
 
0.1%
Lowercase Letter 5
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10000
98.8%
41
 
0.4%
23
 
0.2%
12
 
0.1%
11
 
0.1%
8
 
0.1%
8
 
0.1%
6
 
0.1%
3
 
< 0.1%
3
 
< 0.1%
Other values (7) 10
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 10335
32.1%
1 7402
23.0%
2 5561
17.3%
3 3330
 
10.3%
4 2687
 
8.3%
5 899
 
2.8%
6 644
 
2.0%
7 472
 
1.5%
8 454
 
1.4%
9 398
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
B 130
85.0%
A 19
 
12.4%
O 3
 
2.0%
C 1
 
0.7%
Space Separator
ValueCountFrequency (%)
34
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%
Lowercase Letter
ValueCountFrequency (%)
b 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32244
75.8%
Hangul 10125
 
23.8%
Latin 158
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10000
98.8%
41
 
0.4%
23
 
0.2%
12
 
0.1%
11
 
0.1%
8
 
0.1%
8
 
0.1%
6
 
0.1%
3
 
< 0.1%
3
 
< 0.1%
Other values (7) 10
 
0.1%
Common
ValueCountFrequency (%)
0 10335
32.1%
1 7402
23.0%
2 5561
17.2%
3 3330
 
10.3%
4 2687
 
8.3%
5 899
 
2.8%
6 644
 
2.0%
7 472
 
1.5%
8 454
 
1.4%
9 398
 
1.2%
Other values (4) 62
 
0.2%
Latin
ValueCountFrequency (%)
B 130
82.3%
A 19
 
12.0%
b 5
 
3.2%
O 3
 
1.9%
C 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32402
76.2%
Hangul 10125
 
23.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10335
31.9%
1 7402
22.8%
2 5561
17.2%
3 3330
 
10.3%
4 2687
 
8.3%
5 899
 
2.8%
6 644
 
2.0%
7 472
 
1.5%
8 454
 
1.4%
9 398
 
1.2%
Other values (9) 220
 
0.7%
Hangul
ValueCountFrequency (%)
10000
98.8%
41
 
0.4%
23
 
0.2%
12
 
0.1%
11
 
0.1%
8
 
0.1%
8
 
0.1%
6
 
0.1%
3
 
< 0.1%
3
 
< 0.1%
Other values (7) 10
 
0.1%

전용면적
Real number (ℝ)

HIGH CORRELATION 

Distinct125
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.9003
Minimum18
Maximum187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:51:09.457448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile50
Q159
median73
Q385
95-th percentile123
Maximum187
Range169
Interquartile range (IQR)26

Descriptive statistics

Standard deviation21.542905
Coefficient of variation (CV)0.2915131
Kurtosis5.0079881
Mean73.9003
Median Absolute Deviation (MAD)12
Skewness1.7105917
Sum739003
Variance464.09677
MonotonicityNot monotonic
2023-12-13T05:51:09.635690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 2530
25.3%
60 873
 
8.7%
73 681
 
6.8%
59 336
 
3.4%
77 296
 
3.0%
76 288
 
2.9%
75 252
 
2.5%
52 249
 
2.5%
54 232
 
2.3%
55 227
 
2.3%
Other values (115) 4036
40.4%
ValueCountFrequency (%)
18 3
 
< 0.1%
19 2
 
< 0.1%
20 5
0.1%
21 6
0.1%
22 3
 
< 0.1%
23 1
 
< 0.1%
24 4
 
< 0.1%
25 12
0.1%
26 1
 
< 0.1%
29 6
0.1%
ValueCountFrequency (%)
187 10
 
0.1%
185 2
 
< 0.1%
177 1
 
< 0.1%
176 17
0.2%
175 4
 
< 0.1%
172 1
 
< 0.1%
171 1
 
< 0.1%
170 16
0.2%
166 2
 
< 0.1%
162 37
0.4%

열람가격
Real number (ℝ)

HIGH CORRELATION 

Distinct890
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0969538 × 108
Minimum13800000
Maximum9.1 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:51:10.077420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13800000
5-th percentile70500000
Q11.11 × 108
median1.68 × 108
Q33.15 × 108
95-th percentile4.4 × 108
Maximum9.1 × 108
Range8.962 × 108
Interquartile range (IQR)2.04 × 108

Descriptive statistics

Standard deviation1.2061807 × 108
Coefficient of variation (CV)0.57520614
Kurtosis-0.68782666
Mean2.0969538 × 108
Median Absolute Deviation (MAD)73250000
Skewness0.688821
Sum2.0969538 × 1012
Variance1.4548719 × 1016
MonotonicityNot monotonic
2023-12-13T05:51:10.222581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
381000000 96
 
1.0%
111000000 94
 
0.9%
141000000 93
 
0.9%
468000000 92
 
0.9%
107000000 85
 
0.9%
106000000 81
 
0.8%
105000000 78
 
0.8%
129000000 74
 
0.7%
119000000 73
 
0.7%
123000000 72
 
0.7%
Other values (880) 9162
91.6%
ValueCountFrequency (%)
13800000 1
 
< 0.1%
18800000 2
< 0.1%
22900000 1
 
< 0.1%
26400000 1
 
< 0.1%
27000000 1
 
< 0.1%
27200000 1
 
< 0.1%
27500000 1
 
< 0.1%
28200000 1
 
< 0.1%
28500000 4
< 0.1%
29000000 1
 
< 0.1%
ValueCountFrequency (%)
910000000 1
 
< 0.1%
578000000 1
 
< 0.1%
540000000 3
 
< 0.1%
530000000 15
0.1%
526000000 3
 
< 0.1%
522000000 3
 
< 0.1%
519000000 2
 
< 0.1%
516000000 14
0.1%
512000000 5
 
0.1%
507000000 5
 
0.1%

토지코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
4160000000000000000
10000 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4160000000000000000
2nd row4160000000000000000
3rd row4160000000000000000
4th row4160000000000000000
5th row4160000000000000000

Common Values

ValueCountFrequency (%)
4160000000000000000 10000
100.0%

Length

2023-12-13T05:51:10.370146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:51:10.542771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4160000000000000000 10000
100.0%
Distinct243
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T05:51:10.777227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.0183
Min length3

Characters and Unicode

Total characters50183
Distinct characters142
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row경충대로1422번길
2nd row도수길
3rd row능평로
4th row태성로
5th row경충대로1460번길
ValueCountFrequency (%)
순암로36번길 486
 
4.9%
태전동로 410
 
4.1%
고불로 401
 
4.0%
태봉로 387
 
3.9%
경충대로 351
 
3.5%
태성로 325
 
3.2%
오포로 269
 
2.7%
새말길 252
 
2.5%
경충대로1127번길 249
 
2.5%
벼루개길42번길 233
 
2.3%
Other values (233) 6637
66.4%
2023-12-13T05:51:11.191572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7478
 
14.9%
6131
 
12.2%
3601
 
7.2%
1 1919
 
3.8%
1571
 
3.1%
2 1473
 
2.9%
3 1286
 
2.6%
1084
 
2.2%
1049
 
2.1%
6 912
 
1.8%
Other values (132) 23679
47.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 41199
82.1%
Decimal Number 8984
 
17.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
7478
18.2%
6131
 
14.9%
3601
 
8.7%
1571
 
3.8%
1084
 
2.6%
1049
 
2.5%
796
 
1.9%
782
 
1.9%
641
 
1.6%
640
 
1.6%
Other values (122) 17426
42.3%
Decimal Number
ValueCountFrequency (%)
1 1919
21.4%
2 1473
16.4%
3 1286
14.3%
6 912
10.2%
4 890
9.9%
7 859
9.6%
5 546
 
6.1%
8 458
 
5.1%
9 354
 
3.9%
0 287
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 41199
82.1%
Common 8984
 
17.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
7478
18.2%
6131
 
14.9%
3601
 
8.7%
1571
 
3.8%
1084
 
2.6%
1049
 
2.5%
796
 
1.9%
782
 
1.9%
641
 
1.6%
640
 
1.6%
Other values (122) 17426
42.3%
Common
ValueCountFrequency (%)
1 1919
21.4%
2 1473
16.4%
3 1286
14.3%
6 912
10.2%
4 890
9.9%
7 859
9.6%
5 546
 
6.1%
8 458
 
5.1%
9 354
 
3.9%
0 287
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 41199
82.1%
ASCII 8984
 
17.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
7478
18.2%
6131
 
14.9%
3601
 
8.7%
1571
 
3.8%
1084
 
2.6%
1049
 
2.5%
796
 
1.9%
782
 
1.9%
641
 
1.6%
640
 
1.6%
Other values (122) 17426
42.3%
ASCII
ValueCountFrequency (%)
1 1919
21.4%
2 1473
16.4%
3 1286
14.3%
6 912
10.2%
4 890
9.9%
7 859
9.6%
5 546
 
6.1%
8 458
 
5.1%
9 354
 
3.9%
0 287
 
3.2%

도로명본번
Real number (ℝ)

Distinct262
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.233
Minimum1
Maximum1946
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:51:11.359569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q123
median50
Q3106
95-th percentile701
Maximum1946
Range1945
Interquartile range (IQR)83

Descriptive statistics

Standard deviation242.71344
Coefficient of variation (CV)1.8354982
Kurtosis12.876535
Mean132.233
Median Absolute Deviation (MAD)34
Skewness3.4672778
Sum1322330
Variance58909.812
MonotonicityNot monotonic
2023-12-13T05:51:11.524084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 378
 
3.8%
12 265
 
2.6%
87 250
 
2.5%
17 239
 
2.4%
28 226
 
2.3%
20 226
 
2.3%
14 223
 
2.2%
65 219
 
2.2%
21 186
 
1.9%
33 184
 
1.8%
Other values (252) 7604
76.0%
ValueCountFrequency (%)
1 10
 
0.1%
2 14
 
0.1%
3 30
 
0.3%
4 27
 
0.3%
5 101
1.0%
6 97
1.0%
7 110
1.1%
8 37
 
0.4%
9 184
1.8%
10 83
0.8%
ValueCountFrequency (%)
1946 5
 
0.1%
1524 9
 
0.1%
1514 6
 
0.1%
1498 29
0.3%
1471 2
 
< 0.1%
1468 3
 
< 0.1%
1442 15
0.1%
1340 7
 
0.1%
1290 8
 
0.1%
1252 17
0.2%

도로명부번
Real number (ℝ)

ZEROS 

Distinct122
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8915
Minimum0
Maximum258
Zeros5072
Zeros (%)50.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:51:11.693066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q313
95-th percentile46
Maximum258
Range258
Interquartile range (IQR)13

Descriptive statistics

Standard deviation21.55904
Coefficient of variation (CV)2.1795521
Kurtosis36.949298
Mean9.8915
Median Absolute Deviation (MAD)0
Skewness4.9846927
Sum98915
Variance464.79221
MonotonicityNot monotonic
2023-12-13T05:51:11.839643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5072
50.7%
1 947
 
9.5%
24 225
 
2.2%
5 201
 
2.0%
6 191
 
1.9%
15 187
 
1.9%
14 152
 
1.5%
13 145
 
1.5%
4 140
 
1.4%
10 133
 
1.3%
Other values (112) 2607
26.1%
ValueCountFrequency (%)
0 5072
50.7%
1 947
 
9.5%
2 90
 
0.9%
3 121
 
1.2%
4 140
 
1.4%
5 201
 
2.0%
6 191
 
1.9%
7 125
 
1.2%
8 120
 
1.2%
9 100
 
1.0%
ValueCountFrequency (%)
258 2
 
< 0.1%
246 4
< 0.1%
240 2
 
< 0.1%
238 3
 
< 0.1%
214 2
 
< 0.1%
212 4
< 0.1%
194 2
 
< 0.1%
192 5
0.1%
190 9
0.1%
188 4
< 0.1%

읍면동
Real number (ℝ)

Distinct43
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1610184 × 109
Minimum4.1610101 × 109
Maximum4.161034 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:51:12.001298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1610101 × 109
5-th percentile4.1610102 × 109
Q14.161011 × 109
median4.1610112 × 109
Q34.161025 × 109
95-th percentile4.161033 × 109
Maximum4.161034 × 109
Range23929
Interquartile range (IQR)14025

Descriptive statistics

Standard deviation7895.0632
Coefficient of variation (CV)1.8973872 × 10-6
Kurtosis-1.5429895
Mean4.1610184 × 109
Median Absolute Deviation (MAD)1100
Skewness0.21941085
Sum4.1610184 × 1013
Variance62332023
MonotonicityNot monotonic
2023-12-13T05:51:12.153883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
4161011000 1668
16.7%
4161025022 1103
 
11.0%
4161025023 682
 
6.8%
4161011200 576
 
5.8%
4161010300 473
 
4.7%
4161025322 463
 
4.6%
4161025024 462
 
4.6%
4161010500 337
 
3.4%
4161011100 331
 
3.3%
4161010700 320
 
3.2%
Other values (33) 3585
35.9%
ValueCountFrequency (%)
4161010100 273
 
2.7%
4161010200 255
 
2.5%
4161010300 473
 
4.7%
4161010400 312
 
3.1%
4161010500 337
 
3.4%
4161010600 313
 
3.1%
4161010700 320
 
3.2%
4161010800 149
 
1.5%
4161010900 2
 
< 0.1%
4161011000 1668
16.7%
ValueCountFrequency (%)
4161034029 5
 
0.1%
4161034025 87
0.9%
4161034022 189
1.9%
4161034021 118
1.2%
4161033028 93
0.9%
4161033027 8
 
0.1%
4161033026 1
 
< 0.1%
4161033022 10
 
0.1%
4161033021 20
 
0.2%
4161025936 167
1.7%

본번
Real number (ℝ)

ZEROS 

Distinct540
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean421.8403
Minimum0
Maximum1237
Zeros333
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:51:12.298833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18
Q1185
median393
Q3681
95-th percentile774
Maximum1237
Range1237
Interquartile range (IQR)496

Descriptive statistics

Standard deviation278.5028
Coefficient of variation (CV)0.66020909
Kurtosis-0.12768794
Mean421.8403
Median Absolute Deviation (MAD)236
Skewness0.47922442
Sum4218403
Variance77563.81
MonotonicityNot monotonic
2023-12-13T05:51:12.475981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 333
 
3.3%
586 253
 
2.5%
692 236
 
2.4%
705 160
 
1.6%
732 155
 
1.6%
699 152
 
1.5%
395 147
 
1.5%
702 144
 
1.4%
703 144
 
1.4%
681 139
 
1.4%
Other values (530) 8137
81.4%
ValueCountFrequency (%)
0 333
3.3%
1 19
 
0.2%
3 1
 
< 0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%
8 12
 
0.1%
9 1
 
< 0.1%
11 4
 
< 0.1%
12 7
 
0.1%
14 20
 
0.2%
ValueCountFrequency (%)
1237 25
 
0.2%
1236 115
1.1%
1230 35
 
0.4%
1229 61
0.6%
1078 12
 
0.1%
1006 1
 
< 0.1%
1004 8
 
0.1%
1001 8
 
0.1%
1000 4
 
< 0.1%
997 1
 
< 0.1%

부번
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct138
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9348
Minimum0
Maximum196
Zeros4288
Zeros (%)42.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:51:12.645420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38
95-th percentile51
Maximum196
Range196
Interquartile range (IQR)8

Descriptive statistics

Standard deviation21.017324
Coefficient of variation (CV)2.3522994
Kurtosis20.760017
Mean8.9348
Median Absolute Deviation (MAD)1
Skewness4.1515063
Sum89348
Variance441.72792
MonotonicityNot monotonic
2023-12-13T05:51:12.827394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4288
42.9%
1 1340
 
13.4%
2 619
 
6.2%
4 374
 
3.7%
3 333
 
3.3%
5 224
 
2.2%
11 223
 
2.2%
12 190
 
1.9%
6 154
 
1.5%
7 142
 
1.4%
Other values (128) 2113
21.1%
ValueCountFrequency (%)
0 4288
42.9%
1 1340
 
13.4%
2 619
 
6.2%
3 333
 
3.3%
4 374
 
3.7%
5 224
 
2.2%
6 154
 
1.5%
7 142
 
1.4%
8 124
 
1.2%
9 141
 
1.4%
ValueCountFrequency (%)
196 1
 
< 0.1%
179 2
< 0.1%
178 2
< 0.1%
177 1
 
< 0.1%
176 2
< 0.1%
171 1
 
< 0.1%
168 3
< 0.1%
167 2
< 0.1%
166 1
 
< 0.1%
165 2
< 0.1%

Interactions

2023-12-13T05:51:03.525340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:59.108968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:59.822537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:00.678963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:01.394755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:02.076947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:02.785075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:03.632214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:59.212603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:59.933227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:00.777222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:01.495196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:02.177121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:02.906431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:03.765124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:59.308992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:00.011919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:00.867198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:01.573896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:02.273718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:03.019240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:03.912497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:59.430127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:00.114552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:00.964985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:01.685212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:02.374038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:03.125604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:04.047036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:59.520087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:00.188050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:01.060627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:01.787341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:02.472284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:03.216234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:04.156742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:59.615947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:00.263888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:01.160575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:01.879318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:02.572333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:03.307990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:04.249146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:50:59.716492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:00.585623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:01.266001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:01.977807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:02.678183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:51:03.419423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:51:12.952460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전용면적열람가격도로명본번도로명부번읍면동본번부번
전용면적1.0000.5120.1290.2780.2090.4680.323
열람가격0.5121.0000.2940.2110.4440.5390.232
도로명본번0.1290.2941.0000.1530.4170.4360.113
도로명부번0.2780.2110.1531.0000.0980.2950.613
읍면동0.2090.4440.4170.0981.0000.4500.157
본번0.4680.5390.4360.2950.4501.0000.252
부번0.3230.2320.1130.6130.1570.2521.000
2023-12-13T05:51:13.083404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전용면적열람가격도로명본번도로명부번읍면동본번부번
전용면적1.0000.6110.116-0.3630.0290.150-0.407
열람가격0.6111.0000.145-0.350-0.1440.341-0.552
도로명본번0.1160.1451.000-0.097-0.0420.033-0.093
도로명부번-0.363-0.350-0.0971.0000.090-0.0830.418
읍면동0.029-0.144-0.0420.0901.000-0.0020.040
본번0.1500.3410.033-0.083-0.0021.000-0.263
부번-0.407-0.552-0.0930.4180.040-0.2631.000

Missing values

2023-12-13T05:51:04.437652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:51:04.673892image/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

기준월소재지소재지상세아파트동호명전용면적열람가격토지코드도로명도로명본번도로명부번읍면동본번부번
357361경충대로1422번길 42(쌍령동 360-1)현대모닝사이드2204동105호851660000004160000000000000000경충대로1422번길42041610102003601
167271도수길 56-13(퇴촌면도수리 392-36)주목샤넬카운티3동3동301호68871000004160000000000000000도수길5613416103402539233
1831능평로 116-32(오포읍능평리 194-23)엔카운티(101동)101동301호752030000004160000000000000000능평로11632416102502319423
449611태성로 107(태전동 702-0)힐스테이트 태전6지구(1601동~1608동)1604동1202호854400000004160000000000000000태성로107041610110007020
231781경충대로1460번길 43-6(쌍령동 310-10)태성슈퍼빌104동104동302호741140000004160000000000000000경충대로1460번길436416101020031010
418261태전동로 50(태전동 699-0)힐스테이트 태전(C5,1507~1514동)1512동1804호603230000004160000000000000000태전동로50041610110006990
402351능평로30번길 12-14(오포읍능평리 456-32)휴먼테라스빌(202동)202동402호591970000004160000000000000000능평로30번길1214416102502345632
276871매봉재길 17-17(오포읍매산리 583-28)펠리스빌(204동)204동102호571390000004160000000000000000매봉재길1717416102502658328
37641수렁개들길32번길 50(오포읍문형리 600-0)오포문형 양우내안애 아파트109동203호852460000004160000000000000000수렁개들길32번길500416102502400
432571태봉로 163-1(태전동 732-0)힐스테이트 태전2차 에듀포레 C11BL2107동1303호733810000004160000000000000000태봉로163141610110007320
기준월소재지소재지상세아파트동호명전용면적열람가격토지코드도로명도로명본번도로명부번읍면동본번부번
362471새말길 93(오포읍신현리 588-1)현대모닝사이드2104동804호852630000004160000000000000000새말길93041610250225881
446631태성로 130(태전동 704-0)힐스테이트 태전6(1615동~1620동)1620동1903호603330000004160000000000000000태성로130041610110007040
129081태재로 20-15(오포읍신현리 1236-0)이편한세상태재1단지103동1201호854400000004160000000000000000태재로2015416102502212360
108841순암로36번길 88(역동 243-0)이편한세상광주역1단지104동1803호603760000004160000000000000000순암로36번길88041610112002430
106881순암로36번길 88(역동 243-0)이편한세상광주역1단지102동503호603700000004160000000000000000순암로36번길88041610112002430
72891무들로 28(초월읍대쌍령리 61-1)우림푸른마을101동503호851520000004160000000000000000무들로2804161025321611
128321태재로 20-15(오포읍신현리 1236-0)이편한세상태재1단지102동1101호854400000004160000000000000000태재로2015416102502212360
70701탄벌길 60(탄벌동 762-0)우림루미아트103동902호1261990000004160000000000000000탄벌길60041610105007620
272151순암로264번길 21-14(중대동 158-5)파인팰리스(B동)B동301호501190000004160000000000000000순암로264번길211441610108001585
95361모개미길 64(목현동 598-0)유로캐슬1동302호461090000004160000000000000000모개미길64041610106005980