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

Numeric11
Categorical4
Text1
Boolean1

Dataset

Description전라북도 전주시 개별주택가격정보를 제공하며, 고유번호, 법정동명, 지번, 기준연도, 토지대장면적, 주택가격 등을 제공합니다.
Author전라북도
URLhttps://www.bigdatahub.go.kr/index.jeonbuk?startPage=1&menuCd=DOM_000000103007001000&pListTypeStr=&pId=3069068

Alerts

특수지구분코드 is highly overall correlated with 토지대장면적 and 1 other fieldsHigh correlation
특수지구분명 is highly overall correlated with 토지대장면적 and 1 other fieldsHigh correlation
고유번호 is highly overall correlated with 법정동코드 and 2 other fieldsHigh correlation
법정동코드 is highly overall correlated with 고유번호 and 2 other fieldsHigh correlation
건축물대장고유번호 is highly overall correlated with 고유번호 and 2 other fieldsHigh 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 고유번호 and 2 other fieldsHigh correlation
표준지여부 is highly overall correlated with 동코드 and 1 other fieldsHigh correlation
특수지구분코드 is highly imbalanced (97.1%)Imbalance
특수지구분명 is highly imbalanced (97.1%)Imbalance
기준월 is highly imbalanced (95.4%)Imbalance
표준지여부 is highly imbalanced (72.0%)Imbalance
산정대지면적 has 228 (2.3%) zerosZeros
건물산정연면적 has 224 (2.2%) zerosZeros

Reproduction

Analysis started2024-03-14 03:23:28.805195
Analysis finished2024-03-14 03:23:42.580090
Duration13.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고유번호
Real number (ℝ)

HIGH CORRELATION 

Distinct3956
Distinct (%)39.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5111115 × 1018
Minimum4.5111101 × 1018
Maximum4.511112 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:23:42.639035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.5111101 × 1018
5-th percentile4.5111104 × 1018
Q14.511111 × 1018
median4.5111117 × 1018
Q34.5111119 × 1018
95-th percentile4.511112 × 1018
Maximum4.511112 × 1018
Range1.9000026 × 1012
Interquartile range (IQR)9.0000027 × 1011

Descriptive statistics

Standard deviation5.3210417 × 1011
Coefficient of variation (CV)1.1795412 × 10-7
Kurtosis-0.45901513
Mean4.5111115 × 1018
Median Absolute Deviation (MAD)2.0000232 × 1011
Skewness-0.884693
Sum8.8253858 × 1018
Variance2.8313485 × 1023
MonotonicityNot monotonic
2024-03-14T12:23:42.750434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4511111800200020007 30
 
0.3%
4511111800101430000 14
 
0.1%
4511111800101440000 11
 
0.1%
4511111200100670000 10
 
0.1%
4511112000102530185 9
 
0.1%
4511111700101840000 9
 
0.1%
4511111100100170003 9
 
0.1%
4511111100102200001 8
 
0.1%
4511111900101590009 8
 
0.1%
4511110900100330005 8
 
0.1%
Other values (3946) 9884
98.8%
ValueCountFrequency (%)
4511110100100010007 4
< 0.1%
4511110100100040001 1
 
< 0.1%
4511110100100050001 1
 
< 0.1%
4511110100100070001 2
 
< 0.1%
4511110100100080004 5
0.1%
4511110100100090008 1
 
< 0.1%
4511110100100150001 3
< 0.1%
4511110100100160001 2
 
< 0.1%
4511110100100190001 1
 
< 0.1%
4511110100100210003 3
< 0.1%
ValueCountFrequency (%)
4511112000102570001 1
 
< 0.1%
4511112000102550054 1
 
< 0.1%
4511112000102550046 2
< 0.1%
4511112000102550045 3
< 0.1%
4511112000102550044 3
< 0.1%
4511112000102550041 1
 
< 0.1%
4511112000102550038 1
 
< 0.1%
4511112000102550035 4
< 0.1%
4511112000102550028 4
< 0.1%
4511112000102550027 1
 
< 0.1%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5111115 × 109
Minimum4.5111101 × 109
Maximum4.511112 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:23:42.853646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.5111101 × 109
5-th percentile4.5111104 × 109
Q14.511111 × 109
median4.5111117 × 109
Q34.5111119 × 109
95-th percentile4.511112 × 109
Maximum4.511112 × 109
Range1900
Interquartile range (IQR)900

Descriptive statistics

Standard deviation532.10343
Coefficient of variation (CV)1.1795395 × 10-7
Kurtosis-0.45901227
Mean4.5111115 × 109
Median Absolute Deviation (MAD)200
Skewness-0.88469324
Sum4.5111115 × 1013
Variance283134.06
MonotonicityNot monotonic
2024-03-14T12:23:42.947957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4511111900 2436
24.4%
4511111800 1245
12.4%
4511112000 1093
10.9%
4511111700 743
 
7.4%
4511111000 666
 
6.7%
4511110700 511
 
5.1%
4511111600 510
 
5.1%
4511111100 475
 
4.8%
4511110800 277
 
2.8%
4511111400 265
 
2.6%
Other values (10) 1779
17.8%
ValueCountFrequency (%)
4511110100 187
 
1.9%
4511110200 61
 
0.6%
4511110300 118
 
1.2%
4511110400 237
 
2.4%
4511110500 146
 
1.5%
4511110600 170
 
1.7%
4511110700 511
5.1%
4511110800 277
2.8%
4511110900 241
 
2.4%
4511111000 666
6.7%
ValueCountFrequency (%)
4511112000 1093
10.9%
4511111900 2436
24.4%
4511111800 1245
12.4%
4511111700 743
 
7.4%
4511111600 510
 
5.1%
4511111500 189
 
1.9%
4511111400 265
 
2.6%
4511111300 192
 
1.9%
4511111200 238
 
2.4%
4511111100 475
 
4.8%

법정동명
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
전라북도 전주시 완산구 태평동
2436 
전라북도 전주시 완산구 교동
1245 
전라북도 전주시 완산구 중노송동
1093 
전라북도 전주시 완산구 고사동
743 
전라북도 전주시 완산구 풍남동3가
666 
Other values (15)
3817 

Length

Max length18
Median length17
Mean length16.7151
Min length15

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전라북도 전주시 완산구 고사동
2nd row전라북도 전주시 완산구 고사동
3rd row전라북도 전주시 완산구 중노송동
4th row전라북도 전주시 완산구 태평동
5th row전라북도 전주시 완산구 교동

Common Values

ValueCountFrequency (%)
전라북도 전주시 완산구 태평동 2436
24.4%
전라북도 전주시 완산구 교동 1245
12.4%
전라북도 전주시 완산구 중노송동 1093
10.9%
전라북도 전주시 완산구 고사동 743
 
7.4%
전라북도 전주시 완산구 풍남동3가 666
 
6.7%
전라북도 전주시 완산구 경원동3가 511
 
5.1%
전라북도 전주시 완산구 다가동4가 510
 
5.1%
전라북도 전주시 완산구 전동 475
 
4.8%
전라북도 전주시 완산구 풍남동1가 277
 
2.8%
전라북도 전주시 완산구 다가동2가 265
 
2.6%
Other values (10) 1779
17.8%

Length

2024-03-14T12:23:43.061008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전라북도 10000
25.0%
전주시 10000
25.0%
완산구 10000
25.0%
태평동 2436
 
6.1%
교동 1245
 
3.1%
중노송동 1093
 
2.7%
고사동 743
 
1.9%
풍남동3가 666
 
1.7%
경원동3가 511
 
1.3%
다가동4가 510
 
1.3%
Other values (13) 2796
 
7.0%

특수지구분코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9970 
2
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 9970
99.7%
2 30
 
0.3%

Length

2024-03-14T12:23:43.158320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:23:43.241915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9970
99.7%
2 30
 
0.3%

특수지구분명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반
9970 
 
30

Length

Max length2
Median length2
Mean length1.997
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반 9970
99.7%
30
 
0.3%

Length

2024-03-14T12:23:43.337262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:23:43.414482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 9970
99.7%
30
 
0.3%

지번
Text

Distinct2909
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T12:23:43.742736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.5429
Min length1

Characters and Unicode

Total characters45429
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

Unique580 ?
Unique (%)5.8%

Sample

1st row271-2
2nd row278
3rd row209-38
4th row188-22
5th row2-7
ValueCountFrequency (%)
2-7 30
 
0.3%
95-1 20
 
0.2%
4-3 19
 
0.2%
144 19
 
0.2%
143 18
 
0.2%
84-2 18
 
0.2%
53-4 17
 
0.2%
51-1 17
 
0.2%
9-6 17
 
0.2%
39-2 17
 
0.2%
Other values (2899) 9808
98.1%
2024-03-14T12:23:44.229173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 9043
19.9%
1 7694
16.9%
2 6254
13.8%
3 4306
9.5%
5 3246
 
7.1%
4 3229
 
7.1%
6 2682
 
5.9%
8 2492
 
5.5%
9 2283
 
5.0%
7 2221
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 36386
80.1%
Dash Punctuation 9043
 
19.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7694
21.1%
2 6254
17.2%
3 4306
11.8%
5 3246
8.9%
4 3229
8.9%
6 2682
 
7.4%
8 2492
 
6.8%
9 2283
 
6.3%
7 2221
 
6.1%
0 1979
 
5.4%
Dash Punctuation
ValueCountFrequency (%)
- 9043
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45429
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 9043
19.9%
1 7694
16.9%
2 6254
13.8%
3 4306
9.5%
5 3246
 
7.1%
4 3229
 
7.1%
6 2682
 
5.9%
8 2492
 
5.5%
9 2283
 
5.0%
7 2221
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45429
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 9043
19.9%
1 7694
16.9%
2 6254
13.8%
3 4306
9.5%
5 3246
 
7.1%
4 3229
 
7.1%
6 2682
 
5.9%
8 2492
 
5.5%
9 2283
 
5.0%
7 2221
 
4.9%

건축물대장고유번호
Real number (ℝ)

HIGH CORRELATION 

Distinct3957
Distinct (%)39.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5111115 × 1018
Minimum4.5111101 × 1018
Maximum4.511112 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:23:44.344533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.5111101 × 1018
5-th percentile4.5111104 × 1018
Q14.511111 × 1018
median4.5111117 × 1018
Q34.5111119 × 1018
95-th percentile4.511112 × 1018
Maximum4.511112 × 1018
Range1.9000026 × 1012
Interquartile range (IQR)9.0000027 × 1011

Descriptive statistics

Standard deviation5.3210416 × 1011
Coefficient of variation (CV)1.1795412 × 10-7
Kurtosis-0.45901509
Mean4.5111115 × 1018
Median Absolute Deviation (MAD)2.0000232 × 1011
Skewness-0.88469299
Sum8.8253858 × 1018
Variance2.8313484 × 1023
MonotonicityNot monotonic
2024-03-14T12:23:44.454182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4511111800200020007 30
 
0.3%
4511111800101430000 14
 
0.1%
4511111800101440000 11
 
0.1%
4511111200100670000 10
 
0.1%
4511111700101840000 9
 
0.1%
4511111100100170003 9
 
0.1%
4511112000102530185 9
 
0.1%
4511111100103020010 8
 
0.1%
4511111900101590009 8
 
0.1%
4511111100102200001 8
 
0.1%
Other values (3947) 9884
98.8%
ValueCountFrequency (%)
4511110100100010007 4
< 0.1%
4511110100100040001 1
 
< 0.1%
4511110100100050001 1
 
< 0.1%
4511110100100070001 2
 
< 0.1%
4511110100100080004 5
0.1%
4511110100100090008 1
 
< 0.1%
4511110100100150001 3
< 0.1%
4511110100100160001 2
 
< 0.1%
4511110100100190001 1
 
< 0.1%
4511110100100210003 3
< 0.1%
ValueCountFrequency (%)
4511112000102570001 1
 
< 0.1%
4511112000102550054 1
 
< 0.1%
4511112000102550046 2
< 0.1%
4511112000102550045 3
< 0.1%
4511112000102550044 3
< 0.1%
4511112000102550041 1
 
< 0.1%
4511112000102550038 1
 
< 0.1%
4511112000102550035 4
< 0.1%
4511112000102550028 4
< 0.1%
4511112000102550027 1
 
< 0.1%

기준연도
Real number (ℝ)

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.9642
Minimum2005
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:23:44.554186image/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 deviation5.1318761
Coefficient of variation (CV)0.0025494125
Kurtosis-1.173311
Mean2012.9642
Median Absolute Deviation (MAD)4
Skewness0.10938422
Sum20129642
Variance26.336152
MonotonicityNot monotonic
2024-03-14T12:23:44.649882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2005 649
 
6.5%
2006 644
 
6.4%
2008 619
 
6.2%
2007 613
 
6.1%
2009 610
 
6.1%
2015 597
 
6.0%
2013 578
 
5.8%
2011 568
 
5.7%
2010 567
 
5.7%
2012 564
 
5.6%
Other values (8) 3991
39.9%
ValueCountFrequency (%)
2005 649
6.5%
2006 644
6.4%
2007 613
6.1%
2008 619
6.2%
2009 610
6.1%
2010 567
5.7%
2011 568
5.7%
2012 564
5.6%
2013 578
5.8%
2014 532
5.3%
ValueCountFrequency (%)
2022 452
4.5%
2021 446
4.5%
2020 444
4.4%
2019 518
5.2%
2018 538
5.4%
2017 505
5.1%
2016 556
5.6%
2015 597
6.0%
2014 532
5.3%
2013 578
5.8%

기준월
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9949 
6
 
51

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 9949
99.5%
6 51
 
0.5%

Length

2024-03-14T12:23:44.754975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:23:44.851166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9949
99.5%
6 51
 
0.5%

동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4840.9497
Minimum0
Maximum99999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:23:44.927814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile7.05
Maximum99999
Range99999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21461.631
Coefficient of variation (CV)4.4333513
Kurtosis15.720478
Mean4840.9497
Median Absolute Deviation (MAD)0
Skewness4.2091963
Sum48409497
Variance4.6060159 × 108
MonotonicityNot monotonic
2024-03-14T12:23:45.031086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 9301
93.0%
99999 484
 
4.8%
2 141
 
1.4%
3 34
 
0.3%
4 7
 
0.1%
5 7
 
0.1%
7 5
 
0.1%
12 4
 
< 0.1%
6 4
 
< 0.1%
9 4
 
< 0.1%
Other values (5) 9
 
0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 9301
93.0%
2 141
 
1.4%
3 34
 
0.3%
4 7
 
0.1%
5 7
 
0.1%
6 4
 
< 0.1%
7 5
 
0.1%
8 3
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
99999 484
4.8%
17 1
 
< 0.1%
16 1
 
< 0.1%
12 4
 
< 0.1%
11 3
 
< 0.1%
9 4
 
< 0.1%
8 3
 
< 0.1%
7 5
 
0.1%
6 4
 
< 0.1%
5 7
 
0.1%

동명
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4840.9497
Minimum0
Maximum99999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:23:45.116621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile7.05
Maximum99999
Range99999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21461.631
Coefficient of variation (CV)4.4333513
Kurtosis15.720478
Mean4840.9497
Median Absolute Deviation (MAD)0
Skewness4.2091963
Sum48409497
Variance4.6060159 × 108
MonotonicityNot monotonic
2024-03-14T12:23:45.198128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 9301
93.0%
99999 484
 
4.8%
2 141
 
1.4%
3 34
 
0.3%
4 7
 
0.1%
5 7
 
0.1%
7 5
 
0.1%
12 4
 
< 0.1%
6 4
 
< 0.1%
9 4
 
< 0.1%
Other values (5) 9
 
0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 9301
93.0%
2 141
 
1.4%
3 34
 
0.3%
4 7
 
0.1%
5 7
 
0.1%
6 4
 
< 0.1%
7 5
 
0.1%
8 3
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
99999 484
4.8%
17 1
 
< 0.1%
16 1
 
< 0.1%
12 4
 
< 0.1%
11 3
 
< 0.1%
9 4
 
< 0.1%
8 3
 
< 0.1%
7 5
 
0.1%
6 4
 
< 0.1%
5 7
 
0.1%

토지대장면적
Real number (ℝ)

HIGH CORRELATION 

Distinct1224
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean211.6676
Minimum0
Maximum9085
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:23:45.297035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile59.19
Q1121.45
median165.3
Q3229.7
95-th percentile399.3
Maximum9085
Range9085
Interquartile range (IQR)108.25

Descriptive statistics

Standard deviation377.25934
Coefficient of variation (CV)1.7823198
Kurtosis269.13658
Mean211.6676
Median Absolute Deviation (MAD)52.9
Skewness15.311395
Sum2116676
Variance142324.61
MonotonicityNot monotonic
2024-03-14T12:23:45.423412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
162.0 119
 
1.2%
119.0 104
 
1.0%
152.1 101
 
1.0%
132.2 90
 
0.9%
115.7 88
 
0.9%
148.8 86
 
0.9%
195.0 85
 
0.9%
198.3 83
 
0.8%
142.0 83
 
0.8%
155.4 80
 
0.8%
Other values (1214) 9081
90.8%
ValueCountFrequency (%)
0.0 1
 
< 0.1%
3.0 3
 
< 0.1%
6.6 1
 
< 0.1%
9.9 2
 
< 0.1%
10.0 4
 
< 0.1%
11.0 1
 
< 0.1%
13.2 10
0.1%
16.5 6
0.1%
17.0 6
0.1%
19.8 11
0.1%
ValueCountFrequency (%)
9085.0 3
 
< 0.1%
9045.8 1
 
< 0.1%
5851.0 30
0.3%
1874.1 1
 
< 0.1%
1874.0 1
 
< 0.1%
1593.4 4
 
< 0.1%
1325.0 1
 
< 0.1%
1229.0 3
 
< 0.1%
1186.8 2
 
< 0.1%
1183.3 1
 
< 0.1%

산정대지면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2480
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.15611
Minimum0
Maximum1593.4
Zeros228
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:23:45.528610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q166.13
median129
Q3191.7
95-th percentile315.3335
Maximum1593.4
Range1593.4
Interquartile range (IQR)125.57

Descriptive statistics

Standard deviation105.76446
Coefficient of variation (CV)0.74400222
Kurtosis19.735601
Mean142.15611
Median Absolute Deviation (MAD)62.7
Skewness2.5882067
Sum1421561.1
Variance11186.122
MonotonicityNot monotonic
2024-03-14T12:23:45.636963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 228
 
2.3%
142.0 79
 
0.8%
119.0 74
 
0.7%
162.0 66
 
0.7%
139.0 64
 
0.6%
145.0 64
 
0.6%
158.7 62
 
0.6%
155.0 61
 
0.6%
152.0 60
 
0.6%
165.0 58
 
0.6%
Other values (2470) 9184
91.8%
ValueCountFrequency (%)
0.0 228
2.3%
0.01 8
 
0.1%
0.02 6
 
0.1%
0.1 25
 
0.2%
1.93 1
 
< 0.1%
1.95 2
 
< 0.1%
3.0 1
 
< 0.1%
3.38 1
 
< 0.1%
3.41 2
 
< 0.1%
4.11 1
 
< 0.1%
ValueCountFrequency (%)
1593.4 4
< 0.1%
1081.0 1
 
< 0.1%
1042.0 1
 
< 0.1%
985.1 1
 
< 0.1%
872.7 7
0.1%
813.2 3
< 0.1%
813.0 1
 
< 0.1%
770.2 3
< 0.1%
732.0 1
 
< 0.1%
718.36 1
 
< 0.1%

건물전체연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct3395
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.91033
Minimum0
Maximum4281.32
Zeros16
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:23:45.754798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34.296
Q160.52
median95
Q3175.3
95-th percentile558.95
Maximum4281.32
Range4281.32
Interquartile range (IQR)114.78

Descriptive statistics

Standard deviation218.05259
Coefficient of variation (CV)1.2986252
Kurtosis37.944238
Mean167.91033
Median Absolute Deviation (MAD)43.43
Skewness4.6511259
Sum1679103.3
Variance47546.931
MonotonicityNot monotonic
2024-03-14T12:23:45.900731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.93 38
 
0.4%
46.08 36
 
0.4%
66.0 30
 
0.3%
56.19 24
 
0.2%
40.0 22
 
0.2%
16.52 22
 
0.2%
23.14 22
 
0.2%
51.57 22
 
0.2%
26.44 20
 
0.2%
59.5 20
 
0.2%
Other values (3385) 9744
97.4%
ValueCountFrequency (%)
0.0 16
0.2%
3.96 3
 
< 0.1%
6.94 3
 
< 0.1%
8.26 1
 
< 0.1%
8.28 2
 
< 0.1%
8.92 1
 
< 0.1%
9.9 1
 
< 0.1%
11.57 2
 
< 0.1%
11.9 6
 
0.1%
12.23 5
 
0.1%
ValueCountFrequency (%)
4281.32 1
 
< 0.1%
2763.52 2
< 0.1%
2705.26 1
 
< 0.1%
2555.58 3
< 0.1%
2390.89 2
< 0.1%
2390.0 1
 
< 0.1%
2247.01 3
< 0.1%
2108.74 1
 
< 0.1%
1995.31 1
 
< 0.1%
1987.0 1
 
< 0.1%

건물산정연면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3373
Distinct (%)33.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.464454
Minimum0
Maximum1087.51
Zeros224
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:23:46.065639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20.1885
Q150.5
median74.9
Q3115.02
95-th percentile201.808
Maximum1087.51
Range1087.51
Interquartile range (IQR)64.52

Descriptive statistics

Standard deviation82.475589
Coefficient of variation (CV)0.88242734
Kurtosis38.226344
Mean93.464454
Median Absolute Deviation (MAD)28.82
Skewness4.8406478
Sum934644.54
Variance6802.2229
MonotonicityNot monotonic
2024-03-14T12:23:46.189987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 224
 
2.2%
47.93 44
 
0.4%
46.08 37
 
0.4%
66.0 36
 
0.4%
52.89 26
 
0.3%
26.44 25
 
0.2%
55.2 25
 
0.2%
40.0 25
 
0.2%
42.97 24
 
0.2%
16.52 22
 
0.2%
Other values (3363) 9512
95.1%
ValueCountFrequency (%)
0.0 224
2.2%
2.5 1
 
< 0.1%
3.96 3
 
< 0.1%
6.0 1
 
< 0.1%
6.42 1
 
< 0.1%
6.84 3
 
< 0.1%
6.94 3
 
< 0.1%
7.0 1
 
< 0.1%
7.68 3
 
< 0.1%
7.9 2
 
< 0.1%
ValueCountFrequency (%)
1087.51 2
< 0.1%
1076.42 3
< 0.1%
910.57 2
< 0.1%
902.59 1
 
< 0.1%
895.13 2
< 0.1%
888.26 1
 
< 0.1%
886.95 3
< 0.1%
885.18 2
< 0.1%
874.21 1
 
< 0.1%
863.8 1
 
< 0.1%

주택가격
Real number (ℝ)

HIGH CORRELATION 

Distinct1553
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70224398
Minimum402000
Maximum2.836 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:23:46.301075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum402000
5-th percentile12300000
Q132700000
median49300000
Q378000000
95-th percentile1.9 × 108
Maximum2.836 × 109
Range2.835598 × 109
Interquartile range (IQR)45300000

Descriptive statistics

Standard deviation85306535
Coefficient of variation (CV)1.2147706
Kurtosis154.27435
Mean70224398
Median Absolute Deviation (MAD)20300000
Skewness8.2002158
Sum7.0224398 × 1011
Variance7.2772049 × 1015
MonotonicityNot monotonic
2024-03-14T12:23:46.414812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103000000 34
 
0.3%
100000000 33
 
0.3%
104000000 32
 
0.3%
105000000 27
 
0.3%
101000000 26
 
0.3%
115000000 26
 
0.3%
38900000 26
 
0.3%
39500000 25
 
0.2%
113000000 25
 
0.2%
40300000 25
 
0.2%
Other values (1543) 9721
97.2%
ValueCountFrequency (%)
402000 1
< 0.1%
412000 1
< 0.1%
423000 1
< 0.1%
440000 1
< 0.1%
465000 1
< 0.1%
468000 2
< 0.1%
473000 2
< 0.1%
488000 1
< 0.1%
496000 1
< 0.1%
518000 1
< 0.1%
ValueCountFrequency (%)
2836000000 1
< 0.1%
1479000000 1
< 0.1%
1445000000 1
< 0.1%
1420000000 1
< 0.1%
1340000000 1
< 0.1%
1140000000 1
< 0.1%
1020000000 1
< 0.1%
954000000 1
< 0.1%
915000000 1
< 0.1%
861000000 1
< 0.1%

표준지여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
False
9516 
True
 
484
ValueCountFrequency (%)
False 9516
95.2%
True 484
 
4.8%
2024-03-14T12:23:46.498646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2024-03-14T12:23:41.297600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:31.765927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:32.790535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:33.769141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:34.687545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:35.764418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:36.622199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:37.605168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:38.503691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:39.506276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:40.409253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:41.390127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:31.849033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:32.890558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:33.851723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:34.783181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:35.851479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:36.722104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:37.695911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:38.589500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:39.605128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:40.496356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:41.508948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:31.934020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:32.977260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:33.941334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:34.932168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:35.938391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:36.800210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:37.784325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:38.679245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:39.690924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:40.575607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:41.625610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:32.020503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:33.071658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:34.024483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:35.041956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:36.016404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:36.885789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:37.875628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:38.764571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:39.776412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:40.656008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:41.744160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:32.102590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:33.179651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:34.109072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:35.147640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:36.092711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:36.987486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:37.958465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:38.845160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:39.873691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:40.739561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:41.822407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:32.182790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:33.267344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:34.202988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:35.232223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:36.159063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:37.092106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:38.034664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:38.926851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:39.954760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:40.835342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:41.905556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:32.259305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:33.344939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:34.288511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:35.352134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:36.229425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:37.185009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:38.119894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:39.012428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:40.024366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:40.915857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:41.982928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:32.343251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:33.423575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:34.363959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:35.442879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:36.297973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:37.257668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:38.211196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:39.097043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:40.097012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:40.982368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:42.066606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:32.461140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:33.510318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:34.461106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:35.537770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:36.376510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:37.337002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:38.287346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:39.193107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:40.182122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:41.060728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:42.137052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:32.546814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:33.590628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:34.535183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:35.609308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:36.442648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:37.416437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:38.356796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:39.297848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:40.254518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:41.129359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:42.210643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:32.663247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:33.674145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:34.608504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:35.686810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:36.520852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:37.507457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:38.431429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:39.403889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:40.325566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:23:41.213583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T12:23:46.554430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호법정동코드법정동명특수지구분코드특수지구분명건축물대장고유번호기준연도기준월동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격표준지여부
고유번호1.0001.0001.0000.1380.1381.0000.0210.0280.0490.0490.1670.1770.2160.2540.1080.049
법정동코드1.0001.0001.0000.1380.1381.0000.0300.0290.0460.0460.1660.1740.1850.2400.1080.046
법정동명1.0001.0001.0000.1760.1761.0000.0170.0510.0570.0570.2310.2680.3240.2970.1440.057
특수지구분코드0.1380.1380.1761.0001.0000.1380.0260.0000.0000.0001.0000.0420.0000.0210.0000.000
특수지구분명0.1380.1380.1761.0001.0000.1380.0260.0000.0000.0001.0000.0420.0000.0210.0000.000
건축물대장고유번호1.0001.0001.0000.1380.1381.0000.0210.0280.0490.0490.1670.1770.2160.2540.1080.049
기준연도0.0210.0300.0170.0260.0260.0211.0000.0820.0620.0620.0000.0000.0350.0700.1210.062
기준월0.0280.0290.0510.0000.0000.0280.0821.0000.0130.0130.0000.0000.0000.0530.0000.013
동코드0.0490.0460.0570.0000.0000.0490.0620.0131.0001.0000.0000.1050.0240.0740.0401.000
동명0.0490.0460.0570.0000.0000.0490.0620.0131.0001.0000.0000.1050.0240.0740.0401.000
토지대장면적0.1670.1660.2311.0001.0000.1670.0000.0000.0000.0001.0000.3690.1270.0310.1390.000
산정대지면적0.1770.1740.2680.0420.0420.1770.0000.0000.1050.1050.3691.0000.1620.3510.5290.105
건물전체연면적0.2160.1850.3240.0000.0000.2160.0350.0000.0240.0240.1270.1621.0000.4570.1350.024
건물산정연면적0.2540.2400.2970.0210.0210.2540.0700.0530.0740.0740.0310.3510.4571.0000.4620.074
주택가격0.1080.1080.1440.0000.0000.1080.1210.0000.0400.0400.1390.5290.1350.4621.0000.040
표준지여부0.0490.0460.0570.0000.0000.0490.0620.0131.0001.0000.0000.1050.0240.0740.0401.000
2024-03-14T12:23:46.673556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동명기준월특수지구분코드표준지여부특수지구분명
법정동명1.0000.0400.1390.0450.139
기준월0.0401.0000.0000.0080.000
특수지구분코드0.1390.0001.0000.0000.983
표준지여부0.0450.0080.0001.0000.000
특수지구분명0.1390.0000.9830.0001.000
2024-03-14T12:23:46.766093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호법정동코드건축물대장고유번호기준연도동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격법정동명특수지구분코드특수지구분명기준월표준지여부
고유번호1.0000.9911.0000.0040.0160.016-0.0090.150-0.204-0.076-0.0490.9990.1060.1060.0220.035
법정동코드0.9911.0000.9910.0070.0130.013-0.0090.145-0.193-0.069-0.0430.9990.1060.1060.0220.035
건축물대장고유번호1.0000.9911.0000.0040.0160.016-0.0090.150-0.204-0.076-0.0490.9990.1060.1060.0220.035
기준연도0.0040.0070.0041.0000.0370.0370.0220.0210.0650.0980.2650.0000.0260.0260.0650.052
동코드0.0160.0130.0160.0371.0001.0000.062-0.195-0.072-0.163-0.0280.0450.0000.0000.0080.999
동명0.0160.0130.0160.0371.0001.0000.062-0.195-0.072-0.163-0.0280.0450.0000.0000.0080.999
토지대장면적-0.009-0.009-0.0090.0220.0620.0621.0000.5890.4050.4630.5930.1011.0001.0000.0000.000
산정대지면적0.1500.1450.1500.021-0.195-0.1950.5891.000-0.0590.4100.5920.1100.0310.0310.0000.079
건물전체연면적-0.204-0.193-0.2040.065-0.072-0.0720.405-0.0591.0000.7470.3710.1350.0000.0000.0000.018
건물산정연면적-0.076-0.069-0.0760.098-0.163-0.1630.4630.4100.7471.0000.5970.0980.0160.0160.0400.057
주택가격-0.049-0.043-0.0490.265-0.028-0.0280.5930.5920.3710.5971.0000.0620.0000.0000.0000.043
법정동명0.9990.9990.9990.0000.0450.0450.1010.1100.1350.0980.0621.0000.1390.1390.0400.045
특수지구분코드0.1060.1060.1060.0260.0000.0001.0000.0310.0000.0160.0000.1391.0000.9830.0000.000
특수지구분명0.1060.1060.1060.0260.0000.0001.0000.0310.0000.0160.0000.1390.9831.0000.0000.000
기준월0.0220.0220.0220.0650.0080.0080.0000.0000.0000.0400.0000.0400.0000.0001.0000.008
표준지여부0.0350.0350.0350.0520.9990.9990.0000.0790.0180.0570.0430.0450.0000.0000.0081.000

Missing values

2024-03-14T12:23:42.318488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T12:23:42.496971image/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
3304245111117001027100024511111700전라북도 전주시 완산구 고사동1일반271-24511111700102710002202011188.346.75241.854.827700000N
3317245111117001027800004511111700전라북도 전주시 완산구 고사동1일반27845111117001027800002005111170.4170.454.8754.8741900000N
6493745111120001020900384511112000전라북도 전주시 완산구 중노송동1일반209-3845111120001020900382006122109.053.8515.5315.537600000N
5700845111119001018800224511111900전라북도 전주시 완산구 태평동1일반188-224511111900101880022201619999999999172.0172.079.879.853000000Y
4484945111118002000200074511111800전라북도 전주시 완산구 교동22-745111118002000200072017111115851.00.134.834.81720000N
4568245111119001000100954511111900전라북도 전주시 완산구 태평동1일반1-9545111119001000100952011111238.0238.0157.63157.6369100000N
2042745111111001027500014511111100전라북도 전주시 완산구 전동1일반275-14511111100102750001200511169.023.0209.769.922800000N
420545111105001000200204511110500전라북도 전주시 완산구 경원동1가1일반2-204511110500100020020200611143.028.9101.3868.0830600000N
6161945111119001026300444511111900전라북도 전주시 완산구 태평동1일반263-4445111119001026300442014111145.574.16149.876.3543600000N
4609445111119001000500004511111900전라북도 전주시 완산구 태평동1일반545111119001000500002009111116.0116.051.5751.5737700000N
고유번호법정동코드법정동명특수지구분코드특수지구분명지번건축물대장고유번호기준연도기준월동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격표준지여부
6619345111120001023200134511112000전라북도 전주시 완산구 중노송동1일반232-1345111120001023200132016111179.0179.032.3932.3933600000N
5134245111119001009200054511111900전라북도 전주시 완산구 태평동1일반92-545111119001009200052005111119.0119.052.549.223300000N
2474445111114001007400044511111400전라북도 전주시 완산구 다가동2가1일반74-445111114001007400042014111128.9128.9123.2123.244100000N
4367645111118001096300224511111800전라북도 전주시 완산구 교동1일반963-224511111800109630022200819999999999122.00.055.00.020900000Y
856045111107001009200324511110700전라북도 전주시 완산구 경원동3가1일반92-3245111107001009200322010111126.843.53397.85136.662200000N
329745111104001005100064511110400전라북도 전주시 완산구 중앙동4가1일반51-645111104001005100062020111115.757.42164.281.529200000N
736345111107001001600054511110700전라북도 전주시 완산구 경원동3가1일반16-545111107001001600052008111214.936.62587.53100.1441300000N
5816245111119001020900424511111900전라북도 전주시 완산구 태평동1일반209-4245111119001020900422016111155.0135.63115.2100.846300000N
3371445111117001031000014511111700전라북도 전주시 완산구 고사동1일반310-145111117001031000012020111396.745.46898.56102.9635200000N
732145111107001001500144511110700전라북도 전주시 완산구 경원동3가1일반15-144511110700100150014200711123.143.049.149.113600000N