Overview

Dataset statistics

Number of variables17
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory157.0 B

Variable types

Categorical6
Numeric9
Text1
Boolean1

Dataset

Description전북특별자치도 전주시 개별주택가격정보를 제공하며, 고유번호, 법정동명, 지번, 기준연도, 토지대장면적, 주택가격 등을 제공합니다.항목 : 고유번호, 법정동코드, 법정동명, 특수지구분코드, 특수지구분명, 지번, 건축물대장고유번호, 기준연도, 기준월, 동코드, 동명, 토지대장면적, 산정대지면적, 건물전체연면적, 건물산정연면적, 주택가격, 표준지여부
Author전북특별자치도 전주시
URLhttps://www.data.go.kr/data/3069068/fileData.do

Alerts

고유번호 has constant value ""Constant
건축물대장고유번호 has constant value ""Constant
특수지구분명 is highly overall correlated with 토지대장면적 and 1 other fieldsHigh correlation
특수지구분코드 is highly overall correlated with 토지대장면적 and 1 other fieldsHigh correlation
법정동코드 is highly overall correlated with 법정동명High correlation
동코드 is highly overall correlated with 동명 and 1 other fieldsHigh correlation
동명 is highly overall correlated with 동코드 and 1 other fieldsHigh correlation
토지대장면적 is highly overall correlated with 산정대지면적 and 3 other fieldsHigh correlation
산정대지면적 is highly overall correlated with 토지대장면적 and 1 other fieldsHigh correlation
건물전체연면적 is highly overall correlated with 건물산정연면적High correlation
건물산정연면적 is highly overall correlated with 건물전체연면적 and 1 other fieldsHigh correlation
주택가격 is highly overall correlated with 토지대장면적 and 2 other fieldsHigh correlation
법정동명 is highly overall correlated with 법정동코드High correlation
표준지여부 is highly overall correlated with 동코드 and 1 other fieldsHigh correlation
특수지구분코드 is highly imbalanced (97.2%)Imbalance
특수지구분명 is highly imbalanced (97.2%)Imbalance
기준월 is highly imbalanced (95.2%)Imbalance
표준지여부 is highly imbalanced (74.0%)Imbalance
산정대지면적 has 196 (2.0%) zerosZeros
건물산정연면적 has 189 (1.9%) zerosZeros

Reproduction

Analysis started2024-03-15 01:55:54.478415
Analysis finished2024-03-15 01:56:23.099140
Duration28.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고유번호
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
4510000000000000000
10000 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4510000000000000000
2nd row4510000000000000000
3rd row4510000000000000000
4th row4510000000000000000
5th row4510000000000000000

Common Values

ValueCountFrequency (%)
4510000000000000000 10000
100.0%

Length

2024-03-15T10:56:23.289068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:56:23.630182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4510000000000000000 10000
100.0%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2111115 × 109
Minimum5.2111101 × 109
Maximum5.211112 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T10:56:23.824472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.2111101 × 109
5-th percentile5.2111104 × 109
Q15.211111 × 109
median5.2111117 × 109
Q35.2111119 × 109
95-th percentile5.211112 × 109
Maximum5.211112 × 109
Range1900
Interquartile range (IQR)900

Descriptive statistics

Standard deviation530.2974
Coefficient of variation (CV)1.0176282 × 10-7
Kurtosis-0.43902055
Mean5.2111115 × 109
Median Absolute Deviation (MAD)200
Skewness-0.89063312
Sum5.2111115 × 1013
Variance281215.34
MonotonicityNot monotonic
2024-03-15T10:56:24.040606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
5211111900 2555
25.6%
5211111800 1322
13.2%
5211112000 842
 
8.4%
5211111700 729
 
7.3%
5211111000 714
 
7.1%
5211111600 513
 
5.1%
5211110700 473
 
4.7%
5211111100 452
 
4.5%
5211110400 274
 
2.7%
5211111400 268
 
2.7%
Other values (10) 1858
18.6%
ValueCountFrequency (%)
5211110100 177
 
1.8%
5211110200 74
 
0.7%
5211110300 134
 
1.3%
5211110400 274
 
2.7%
5211110500 152
 
1.5%
5211110600 135
 
1.4%
5211110700 473
4.7%
5211110800 247
 
2.5%
5211110900 252
 
2.5%
5211111000 714
7.1%
ValueCountFrequency (%)
5211112000 842
 
8.4%
5211111900 2555
25.6%
5211111800 1322
13.2%
5211111700 729
 
7.3%
5211111600 513
 
5.1%
5211111500 210
 
2.1%
5211111400 268
 
2.7%
5211111300 216
 
2.2%
5211111200 261
 
2.6%
5211111100 452
 
4.5%

법정동명
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
전북특별자치도 전주시 완산구 태평동
2555 
전북특별자치도 전주시 완산구 교동
1322 
전북특별자치도 전주시 완산구 중노송동
842 
전북특별자치도 전주시 완산구 고사동
729 
전북특별자치도 전주시 완산구 풍남동3가
714 
Other values (15)
3838 

Length

Max length21
Median length20
Mean length19.7007
Min length18

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전북특별자치도 전주시 완산구 태평동
2nd row전북특별자치도 전주시 완산구 고사동
3rd row전북특별자치도 전주시 완산구 경원동3가
4th row전북특별자치도 전주시 완산구 중노송동
5th row전북특별자치도 전주시 완산구 고사동

Common Values

ValueCountFrequency (%)
전북특별자치도 전주시 완산구 태평동 2555
25.6%
전북특별자치도 전주시 완산구 교동 1322
13.2%
전북특별자치도 전주시 완산구 중노송동 842
 
8.4%
전북특별자치도 전주시 완산구 고사동 729
 
7.3%
전북특별자치도 전주시 완산구 풍남동3가 714
 
7.1%
전북특별자치도 전주시 완산구 다가동4가 513
 
5.1%
전북특별자치도 전주시 완산구 경원동3가 473
 
4.7%
전북특별자치도 전주시 완산구 전동 452
 
4.5%
전북특별자치도 전주시 완산구 중앙동4가 274
 
2.7%
전북특별자치도 전주시 완산구 다가동2가 268
 
2.7%
Other values (10) 1858
18.6%

Length

2024-03-15T10:56:24.376529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전북특별자치도 10000
25.0%
전주시 10000
25.0%
완산구 10000
25.0%
태평동 2555
 
6.4%
교동 1322
 
3.3%
중노송동 842
 
2.1%
고사동 729
 
1.8%
풍남동3가 714
 
1.8%
다가동4가 513
 
1.3%
경원동3가 473
 
1.2%
Other values (13) 2852
 
7.1%

특수지구분코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9972 
2
 
28

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 9972
99.7%
2 28
 
0.3%

Length

2024-03-15T10:56:24.585504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:56:24.871771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9972
99.7%
2 28
 
0.3%

특수지구분명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반
9972 
 
28

Length

Max length2
Median length2
Mean length1.9972
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반 9972
99.7%
28
 
0.3%

Length

2024-03-15T10:56:25.245877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:56:25.562718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 9972
99.7%
28
 
0.3%

지번
Text

Distinct2779
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-15T10:56:26.793072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.4303
Min length1

Characters and Unicode

Total characters54303
Distinct characters36
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique577 ?
Unique (%)5.8%

Sample

1st row127-2
2nd row409-5
3rd rowFeb-82
4th row221-13
5th row324-48
ValueCountFrequency (%)
01월 254
 
2.2%
03월 174
 
1.5%
02월 165
 
1.4%
09월 142
 
1.2%
10월 141
 
1.2%
12월 122
 
1.1%
11월 104
 
0.9%
05월 102
 
0.9%
04월 100
 
0.9%
01일 96
 
0.8%
Other values (2521) 10167
87.9%
2024-03-15T10:56:28.706789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 7423
13.7%
1 7036
13.0%
2 5571
 
10.3%
3 3736
 
6.9%
0 3708
 
6.8%
4 3036
 
5.6%
5 2678
 
4.9%
6 2530
 
4.7%
8 2391
 
4.4%
9 2166
 
4.0%
Other values (26) 14028
25.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34982
64.4%
Dash Punctuation 7423
 
13.7%
Lowercase Letter 4798
 
8.8%
Other Letter 3134
 
5.8%
Uppercase Letter 2399
 
4.4%
Space Separator 1567
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 984
20.5%
n 651
13.6%
r 594
12.4%
e 542
11.3%
u 456
9.5%
b 397
8.3%
p 388
 
8.1%
y 215
 
4.5%
l 136
 
2.8%
c 128
 
2.7%
Other values (4) 307
 
6.4%
Decimal Number
ValueCountFrequency (%)
1 7036
20.1%
2 5571
15.9%
3 3736
10.7%
0 3708
10.6%
4 3036
8.7%
5 2678
 
7.7%
6 2530
 
7.2%
8 2391
 
6.8%
9 2166
 
6.2%
7 2130
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
J 787
32.8%
M 535
22.3%
F 397
16.5%
A 392
16.3%
S 114
 
4.8%
O 97
 
4.0%
N 46
 
1.9%
D 31
 
1.3%
Other Letter
ValueCountFrequency (%)
1567
50.0%
1567
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 7423
100.0%
Space Separator
ValueCountFrequency (%)
1567
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43972
81.0%
Latin 7197
 
13.3%
Hangul 3134
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 984
13.7%
J 787
10.9%
n 651
9.0%
r 594
8.3%
e 542
 
7.5%
M 535
 
7.4%
u 456
 
6.3%
F 397
 
5.5%
b 397
 
5.5%
A 392
 
5.4%
Other values (12) 1462
20.3%
Common
ValueCountFrequency (%)
- 7423
16.9%
1 7036
16.0%
2 5571
12.7%
3 3736
8.5%
0 3708
8.4%
4 3036
6.9%
5 2678
 
6.1%
6 2530
 
5.8%
8 2391
 
5.4%
9 2166
 
4.9%
Other values (2) 3697
8.4%
Hangul
ValueCountFrequency (%)
1567
50.0%
1567
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51169
94.2%
Hangul 3134
 
5.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 7423
14.5%
1 7036
13.8%
2 5571
10.9%
3 3736
 
7.3%
0 3708
 
7.2%
4 3036
 
5.9%
5 2678
 
5.2%
6 2530
 
4.9%
8 2391
 
4.7%
9 2166
 
4.2%
Other values (24) 10894
21.3%
Hangul
ValueCountFrequency (%)
1567
50.0%
1567
50.0%

건축물대장고유번호
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
4510000000000000000
10000 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4510000000000000000
2nd row4510000000000000000
3rd row4510000000000000000
4th row4510000000000000000
5th row4510000000000000000

Common Values

ValueCountFrequency (%)
4510000000000000000 10000
100.0%

Length

2024-03-15T10:56:29.166796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:56:29.466193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4510000000000000000 10000
100.0%

기준연도
Real number (ℝ)

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.9279
Minimum2005
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T10:56:29.738880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2005
Q12008
median2013
Q32017
95-th percentile2021
Maximum2022
Range17
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.1582325
Coefficient of variation (CV)0.002562552
Kurtosis-1.1770497
Mean2012.9279
Median Absolute Deviation (MAD)4
Skewness0.091738635
Sum20129279
Variance26.607362
MonotonicityNot monotonic
2024-03-15T10:56:30.169942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2005 747
 
7.5%
2006 632
 
6.3%
2007 615
 
6.2%
2014 605
 
6.0%
2008 585
 
5.9%
2012 580
 
5.8%
2017 573
 
5.7%
2009 569
 
5.7%
2011 566
 
5.7%
2015 562
 
5.6%
Other values (8) 3966
39.7%
ValueCountFrequency (%)
2005 747
7.5%
2006 632
6.3%
2007 615
6.2%
2008 585
5.9%
2009 569
5.7%
2010 537
5.4%
2011 566
5.7%
2012 580
5.8%
2013 530
5.3%
2014 605
6.0%
ValueCountFrequency (%)
2022 435
4.3%
2021 439
4.4%
2020 466
4.7%
2019 504
5.0%
2018 497
5.0%
2017 573
5.7%
2016 558
5.6%
2015 562
5.6%
2014 605
6.0%
2013 530
5.3%

기준월
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9946 
6
 
54

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 9946
99.5%
6 54
 
0.5%

Length

2024-03-15T10:56:30.590694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:56:30.923910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9946
99.5%
6 54
 
0.5%

동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4400.9662
Minimum0
Maximum99999
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T10:56:31.227051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum99999
Range99999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20510.114
Coefficient of variation (CV)4.6603661
Kurtosis17.782788
Mean4400.9662
Median Absolute Deviation (MAD)0
Skewness4.4473848
Sum44009662
Variance4.2066476 × 108
MonotonicityNot monotonic
2024-03-15T10:56:31.605304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 9300
93.0%
99999 440
 
4.4%
2 163
 
1.6%
3 33
 
0.3%
5 12
 
0.1%
4 12
 
0.1%
6 12
 
0.1%
0 6
 
0.1%
8 6
 
0.1%
10 5
 
0.1%
Other values (4) 11
 
0.1%
ValueCountFrequency (%)
0 6
 
0.1%
1 9300
93.0%
2 163
 
1.6%
3 33
 
0.3%
4 12
 
0.1%
5 12
 
0.1%
6 12
 
0.1%
7 4
 
< 0.1%
8 6
 
0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
99999 440
4.4%
12 2
 
< 0.1%
11 1
 
< 0.1%
10 5
 
0.1%
9 4
 
< 0.1%
8 6
 
0.1%
7 4
 
< 0.1%
6 12
 
0.1%
5 12
 
0.1%
4 12
 
0.1%

동명
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4400.9662
Minimum0
Maximum99999
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T10:56:31.983056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum99999
Range99999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20510.114
Coefficient of variation (CV)4.6603661
Kurtosis17.782788
Mean4400.9662
Median Absolute Deviation (MAD)0
Skewness4.4473848
Sum44009662
Variance4.2066476 × 108
MonotonicityNot monotonic
2024-03-15T10:56:32.383856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 9300
93.0%
99999 440
 
4.4%
2 163
 
1.6%
3 33
 
0.3%
5 12
 
0.1%
4 12
 
0.1%
6 12
 
0.1%
0 6
 
0.1%
8 6
 
0.1%
10 5
 
0.1%
Other values (4) 11
 
0.1%
ValueCountFrequency (%)
0 6
 
0.1%
1 9300
93.0%
2 163
 
1.6%
3 33
 
0.3%
4 12
 
0.1%
5 12
 
0.1%
6 12
 
0.1%
7 4
 
< 0.1%
8 6
 
0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
99999 440
4.4%
12 2
 
< 0.1%
11 1
 
< 0.1%
10 5
 
0.1%
9 4
 
< 0.1%
8 6
 
0.1%
7 4
 
< 0.1%
6 12
 
0.1%
5 12
 
0.1%
4 12
 
0.1%

토지대장면적
Real number (ℝ)

HIGH CORRELATION 

Distinct1259
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217.16887
Minimum3
Maximum9085
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T10:56:32.744076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile59
Q1121.6
median167
Q3235
95-th percentile426
Maximum9085
Range9082
Interquartile range (IQR)113.4

Descriptive statistics

Standard deviation392.17399
Coefficient of variation (CV)1.8058481
Kurtosis271.1915
Mean217.16887
Median Absolute Deviation (MAD)54.6
Skewness15.272565
Sum2171688.7
Variance153800.44
MonotonicityNot monotonic
2024-03-15T10:56:33.000258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
162.0 117
 
1.2%
148.8 103
 
1.0%
198.3 102
 
1.0%
132.2 99
 
1.0%
152.1 95
 
0.9%
119.0 90
 
0.9%
195.0 89
 
0.9%
99.2 81
 
0.8%
155.4 80
 
0.8%
115.7 79
 
0.8%
Other values (1249) 9065
90.6%
ValueCountFrequency (%)
3.0 5
 
0.1%
9.0 1
 
< 0.1%
9.9 3
 
< 0.1%
10.0 2
 
< 0.1%
10.9 2
 
< 0.1%
13.0 4
 
< 0.1%
13.2 18
0.2%
16.0 1
 
< 0.1%
16.5 1
 
< 0.1%
17.0 7
 
0.1%
ValueCountFrequency (%)
9085.0 5
 
0.1%
9045.8 1
 
< 0.1%
5851.0 27
0.3%
3993.3 2
 
< 0.1%
3754.5 1
 
< 0.1%
1874.1 2
 
< 0.1%
1593.4 2
 
< 0.1%
1325.0 4
 
< 0.1%
1229.0 1
 
< 0.1%
1186.8 1
 
< 0.1%

산정대지면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2568
Distinct (%)25.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.8408
Minimum0
Maximum1593.4
Zeros196
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T10:56:33.350759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q166.4
median129
Q3192
95-th percentile320.7
Maximum1593.4
Range1593.4
Interquartile range (IQR)125.6

Descriptive statistics

Standard deviation105.50519
Coefficient of variation (CV)0.73862083
Kurtosis13.290628
Mean142.8408
Median Absolute Deviation (MAD)62.9
Skewness2.1738555
Sum1428408
Variance11131.345
MonotonicityNot monotonic
2024-03-15T10:56:34.056894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 196
 
2.0%
162.0 73
 
0.7%
119.0 72
 
0.7%
132.2 69
 
0.7%
198.3 68
 
0.7%
155.0 67
 
0.7%
142.0 65
 
0.7%
155.4 63
 
0.6%
158.7 61
 
0.6%
112.0 61
 
0.6%
Other values (2558) 9205
92.0%
ValueCountFrequency (%)
0.0 196
2.0%
0.01 14
 
0.1%
0.02 6
 
0.1%
0.1 21
 
0.2%
1.12 1
 
< 0.1%
1.85 1
 
< 0.1%
2.92 1
 
< 0.1%
3.0 2
 
< 0.1%
3.41 2
 
< 0.1%
3.45 1
 
< 0.1%
ValueCountFrequency (%)
1593.4 2
< 0.1%
1154.06 1
 
< 0.1%
1087.6 1
 
< 0.1%
1070.4 1
 
< 0.1%
985.1 1
 
< 0.1%
872.7 3
< 0.1%
813.2 2
< 0.1%
770.2 4
< 0.1%
741.51 1
 
< 0.1%
732.0 2
< 0.1%

건물전체연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct3416
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.27364
Minimum0
Maximum4281.32
Zeros12
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T10:56:34.503843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34.2095
Q161.15
median95.4
Q3180.97
95-th percentile587.384
Maximum4281.32
Range4281.32
Interquartile range (IQR)119.82

Descriptive statistics

Standard deviation242.0028
Coefficient of variation (CV)1.3807142
Kurtosis38.399782
Mean175.27364
Median Absolute Deviation (MAD)44.25
Skewness4.9291441
Sum1752736.4
Variance58565.355
MonotonicityNot monotonic
2024-03-15T10:56:34.929549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.0 35
 
0.4%
16.52 32
 
0.3%
40.0 31
 
0.3%
42.97 28
 
0.3%
36.36 26
 
0.3%
47.93 26
 
0.3%
29.75 24
 
0.2%
67.43 22
 
0.2%
55.2 22
 
0.2%
45.28 21
 
0.2%
Other values (3406) 9733
97.3%
ValueCountFrequency (%)
0.0 12
0.1%
3.96 2
 
< 0.1%
5.95 3
 
< 0.1%
6.94 2
 
< 0.1%
7.6 1
 
< 0.1%
8.26 1
 
< 0.1%
8.28 1
 
< 0.1%
8.92 5
0.1%
9.9 1
 
< 0.1%
10.5 1
 
< 0.1%
ValueCountFrequency (%)
4281.32 1
 
< 0.1%
2774.75 4
< 0.1%
2763.51 1
 
< 0.1%
2705.26 4
< 0.1%
2555.58 3
< 0.1%
2527.58 1
 
< 0.1%
2478.81 3
< 0.1%
2475.29 1
 
< 0.1%
2393.75 1
 
< 0.1%
2390.89 1
 
< 0.1%

건물산정연면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3379
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.031277
Minimum0
Maximum1087.51
Zeros189
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T10:56:35.306263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20.157
Q150.46
median75.7
Q3115.23
95-th percentile207.1
Maximum1087.51
Range1087.51
Interquartile range (IQR)64.77

Descriptive statistics

Standard deviation85.289251
Coefficient of variation (CV)0.89748611
Kurtosis34.195903
Mean95.031277
Median Absolute Deviation (MAD)29.49
Skewness4.6579842
Sum950312.77
Variance7274.2564
MonotonicityNot monotonic
2024-03-15T10:56:35.751373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 189
 
1.9%
66.0 41
 
0.4%
40.0 39
 
0.4%
16.52 33
 
0.3%
47.93 32
 
0.3%
36.36 31
 
0.3%
42.97 31
 
0.3%
29.75 25
 
0.2%
46.94 25
 
0.2%
45.28 24
 
0.2%
Other values (3369) 9530
95.3%
ValueCountFrequency (%)
0.0 189
1.9%
3.6 1
 
< 0.1%
3.96 2
 
< 0.1%
5.9 3
 
< 0.1%
5.95 3
 
< 0.1%
6.84 5
 
0.1%
6.94 2
 
< 0.1%
7.0 1
 
< 0.1%
7.23 1
 
< 0.1%
7.6 1
 
< 0.1%
ValueCountFrequency (%)
1087.51 2
< 0.1%
1076.42 3
< 0.1%
910.57 1
 
< 0.1%
902.59 2
< 0.1%
895.13 3
< 0.1%
888.26 1
 
< 0.1%
885.18 2
< 0.1%
875.5 1
 
< 0.1%
862.84 2
< 0.1%
861.7 3
< 0.1%

주택가격
Real number (ℝ)

HIGH CORRELATION 

Distinct1590
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71590746
Minimum161000
Maximum1.904 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T10:56:36.163912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum161000
5-th percentile11400000
Q132300000
median49900000
Q379600000
95-th percentile2 × 108
Maximum1.904 × 109
Range1.903839 × 109
Interquartile range (IQR)47300000

Descriptive statistics

Standard deviation83158665
Coefficient of variation (CV)1.161584
Kurtosis67.884531
Mean71590746
Median Absolute Deviation (MAD)21500000
Skewness5.8946482
Sum7.1590746 × 1011
Variance6.9153636 × 1015
MonotonicityNot monotonic
2024-03-15T10:56:36.624261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101000000 30
 
0.3%
35200000 30
 
0.3%
126000000 27
 
0.3%
106000000 27
 
0.3%
111000000 26
 
0.3%
28700000 26
 
0.3%
104000000 26
 
0.3%
39000000 26
 
0.3%
44600000 25
 
0.2%
105000000 25
 
0.2%
Other values (1580) 9732
97.3%
ValueCountFrequency (%)
161000 1
< 0.1%
406000 1
< 0.1%
423000 1
< 0.1%
468000 1
< 0.1%
473000 1
< 0.1%
499000 2
< 0.1%
559000 2
< 0.1%
568000 1
< 0.1%
572000 1
< 0.1%
613000 1
< 0.1%
ValueCountFrequency (%)
1904000000 1
< 0.1%
1603000000 1
< 0.1%
1500000000 1
< 0.1%
1240000000 1
< 0.1%
1170000000 1
< 0.1%
998000000 1
< 0.1%
975000000 1
< 0.1%
941000000 1
< 0.1%
899000000 1
< 0.1%
893000000 1
< 0.1%

표준지여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
False
9560 
True
 
440
ValueCountFrequency (%)
False 9560
95.6%
True 440
 
4.4%
2024-03-15T10:56:36.985698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2024-03-15T10:56:19.591997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:55:59.831532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:02.031732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:04.582226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:07.260982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:09.489290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:12.137856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:14.682084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:17.141787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:19.881432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:00.039997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:02.322600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:04.861048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:07.547247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:09.779654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:12.438473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:14.964329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:17.419423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:20.172671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:00.314075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:02.604974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:05.138594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:07.797490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:10.093259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:12.669760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:15.249384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:17.650550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:20.347926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:00.590415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:02.873857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:05.611087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:07.956486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:10.416825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:12.942281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:15.513332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:17.905582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:20.547710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:00.813421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:03.147803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:05.867947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:08.112457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:10.737254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:13.217351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:15.772110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:18.121459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:20.815381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:00.996625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:03.467237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:06.122189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:08.326441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:11.007286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:13.551705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:16.111064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:18.303550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:21.108101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:01.214594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:03.757216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:06.410077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:08.537647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:11.304912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:13.845526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:16.384335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:18.590921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:21.372190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:01.477477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:04.019811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:06.721792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:08.913181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:11.561437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:14.113665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:16.637520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:19.045515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:21.645867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:01.748579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:04.295779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:06.983006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:09.189892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:11.832989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:14.386722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:16.922716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:56:19.305709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T10:56:37.288409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드법정동명특수지구분코드특수지구분명기준연도기준월동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격표준지여부
법정동코드1.0001.0000.1300.1300.0480.0350.0310.0310.1290.1680.2210.2670.1990.031
법정동명1.0001.0000.1630.1630.0230.0410.0620.0620.2150.2430.3280.3230.2230.062
특수지구분코드0.1300.1631.0001.0000.0000.0000.0000.0001.0000.0280.0000.0180.0000.000
특수지구분명0.1300.1631.0001.0000.0000.0000.0000.0001.0000.0280.0000.0180.0000.000
기준연도0.0480.0230.0000.0001.0000.0600.0640.0640.0000.0000.0230.0720.1980.064
기준월0.0350.0410.0000.0000.0601.0000.0120.0120.0000.0250.0140.0880.0340.012
동코드0.0310.0620.0000.0000.0640.0121.0001.0000.0000.0420.0420.0600.0591.000
동명0.0310.0620.0000.0000.0640.0121.0001.0000.0000.0420.0420.0600.0591.000
토지대장면적0.1290.2151.0001.0000.0000.0000.0000.0001.0000.5310.1890.0000.0990.000
산정대지면적0.1680.2430.0280.0280.0000.0250.0420.0420.5311.0000.0920.2980.4760.042
건물전체연면적0.2210.3280.0000.0000.0230.0140.0420.0420.1890.0921.0000.4430.1520.042
건물산정연면적0.2670.3230.0180.0180.0720.0880.0600.0600.0000.2980.4431.0000.6120.060
주택가격0.1990.2230.0000.0000.1980.0340.0590.0590.0990.4760.1520.6121.0000.059
표준지여부0.0310.0620.0000.0000.0640.0121.0001.0000.0000.0420.0420.0600.0591.000
2024-03-15T10:56:37.623580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준월표준지여부법정동명특수지구분명특수지구분코드
기준월1.0000.0070.0320.0000.000
표준지여부0.0071.0000.0490.0000.000
법정동명0.0320.0491.0000.1290.129
특수지구분명0.0000.0000.1291.0000.982
특수지구분코드0.0000.0000.1290.9821.000
2024-03-15T10:56:37.955019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드기준연도동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격법정동명특수지구분코드특수지구분명기준월표준지여부
법정동코드1.000-0.0050.0200.020-0.0260.139-0.215-0.081-0.0610.9990.1000.1000.0260.028
기준연도-0.0051.0000.0320.0320.0050.0200.0590.0970.2640.0080.0120.0120.0490.053
동코드0.0200.0321.0001.0000.082-0.165-0.082-0.148-0.0260.0490.0000.0000.0070.999
동명0.0200.0321.0001.0000.082-0.165-0.082-0.148-0.0260.0490.0000.0000.0070.999
토지대장면적-0.0260.0050.0820.0821.0000.5730.4080.4630.5850.1010.9820.9820.0000.000
산정대지면적0.1390.020-0.165-0.1650.5731.000-0.0610.4130.6070.0980.0270.0270.0250.042
건물전체연면적-0.2150.059-0.082-0.0820.408-0.0611.0000.7470.3660.1370.0000.0000.0100.032
건물산정연면적-0.0810.097-0.148-0.1480.4630.4130.7471.0000.6080.1080.0140.0140.0680.046
주택가격-0.0610.264-0.026-0.0260.5850.6070.3660.6081.0000.0720.0000.0000.0260.045
법정동명0.9990.0080.0490.0490.1010.0980.1370.1080.0721.0000.1290.1290.0320.049
특수지구분코드0.1000.0120.0000.0000.9820.0270.0000.0140.0000.1291.0000.9820.0000.000
특수지구분명0.1000.0120.0000.0000.9820.0270.0000.0140.0000.1290.9821.0000.0000.000
기준월0.0260.0490.0070.0070.0000.0250.0100.0680.0260.0320.0000.0001.0000.007
표준지여부0.0280.0530.9990.9990.0000.0420.0320.0460.0450.0490.0000.0000.0071.000

Missing values

2024-03-15T10:56:22.066681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T10:56:22.815846image/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

고유번호법정동코드법정동명특수지구분코드특수지구분명지번건축물대장고유번호기준연도기준월동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격표준지여부
5300945100000000000000005211111900전북특별자치도 전주시 완산구 태평동1일반127-245100000000000000002016111195.0195.043.243.273300000N
3553645100000000000000005211111700전북특별자치도 전주시 완산구 고사동1일반409-545100000000000000002013111148.826.1517.8690.8225100000N
820745100000000000000005211110700전북특별자치도 전주시 완산구 경원동3가1일반Feb-824510000000000000000200911182.682.650.450.422000000N
6539245100000000000000005211112000전북특별자치도 전주시 완산구 중노송동1일반221-1345100000000000000002019111185.0185.045.5545.5547500000N
3444745100000000000000005211111700전북특별자치도 전주시 완산구 고사동1일반324-4845100000000000000002006111142.1142.148.2448.2436200000N
504545100000000000000005211110500전북특별자치도 전주시 완산구 경원동1가1일반126-484510000000000000000201411182.621.63183.147.9615400000N
3266845100000000000000005211111700전북특별자치도 전주시 완산구 고사동1일반246-14510000000000000000200711185.285.287.1387.1365900000N
5879845100000000000000005211111900전북특별자치도 전주시 완산구 태평동1일반214-745100000000000000002013111262.0262.0167.0167.071500000N
975145100000000000000005211110800전북특별자치도 전주시 완산구 풍남동1가1일반01월 14일45100000000000000002016111231.4102.63185.5282.2832200000N
5888445100000000000000005211111900전북특별자치도 전주시 완산구 태평동1일반215-545100000000000000002017111284.0284.0128.09128.0972100000N
고유번호법정동코드법정동명특수지구분코드특수지구분명지번건축물대장고유번호기준연도기준월동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격표준지여부
6241145100000000000000005211112000전북특별자치도 전주시 완산구 중노송동1일반10월 22일4510000000000000000201619999999999129.0129.064.5864.5836600000Y
3291145100000000000000005211111700전북특별자치도 전주시 완산구 고사동1일반264-245100000000000000002016111132.2132.0102.78102.7836400000N
2327045100000000000000005211111300전북특별자치도 전주시 완산구 다가동1가1일반Jan-6145100000000000000002015111459.5459.5174.01174.01141000000N
534145100000000000000005211110600전북특별자치도 전주시 완산구 경원동2가1일반03월 18일4510000000000000000202211179.379.347.9847.9839600000N
2286545100000000000000005211111300전북특별자치도 전주시 완산구 다가동1가1일반2845100000000000000002006111396.7115.84217.5651.6645900000N
2210045100000000000000005211111200전북특별자치도 전주시 완산구 전동3가1일반Feb-8945100000000000000002021111198.3198.378.6778.6768700000N
4604945100000000000000005211111900전북특별자치도 전주시 완산구 태평동1일반04월 11일45100000000000000002018111188.0131.41154.14107.7457700000N
6171745100000000000000005211111900전북특별자치도 전주시 완산구 태평동1일반263-5645100000000000000002007111144.7144.756.1648.631100000N
1256945100000000000000005211110900전북특별자치도 전주시 완산구 풍남동2가1일반Feb-4045100000000000000002021111112.4112.4124.92124.92116000000N
6547045100000000000000005211112000전북특별자치도 전주시 완산구 중노송동1일반2224510000000000000000200719999999999133.00.070.20.026100000Y