Overview

Dataset statistics

Number of variables18
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory165.0 B

Variable types

Numeric10
Text1
Categorical6
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

기준년도 has constant value ""Constant
기준월 has constant value ""Constant
데이터기준일자 has constant value ""Constant
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 3 other fieldsHigh correlation
법정동코드 is highly overall correlated with 고유번호 and 3 other fieldsHigh correlation
건축물대장고유번호 is highly overall correlated with 고유번호 and 3 other fieldsHigh correlation
동코드 is highly overall correlated with 고유번호 and 3 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 건물산정연면적 and 1 other fieldsHigh correlation
건물산정연면적 is highly overall correlated with 건물전체연면적 and 1 other fieldsHigh correlation
주택가격 is highly overall correlated with 토지대장면적 and 3 other fieldsHigh correlation
동명 is highly overall correlated with 고유번호 and 3 other fieldsHigh correlation
특수지구분코드 is highly imbalanced (94.6%)Imbalance
특수지구분명 is highly imbalanced (94.6%)Imbalance
동명 is highly imbalanced (74.2%)Imbalance
표준지여부 is highly imbalanced (72.2%)Imbalance
동코드 is highly skewed (γ1 = 20.55077792)Skewed
토지대장면적 is highly skewed (γ1 = 28.81173825)Skewed
동코드 has 4640 (46.4%) zerosZeros

Reproduction

Analysis started2024-03-14 03:24:07.145616
Analysis finished2024-03-14 03:24:19.987185
Duration12.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고유번호
Real number (ℝ)

HIGH CORRELATION 

Distinct9910
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5112127 × 1018
Minimum4.5111101 × 1018
Maximum4.5113138 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:24:20.064468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.5111101 × 1018
5-th percentile4.5111118 × 1018
Q14.5111129 × 1018
median4.5113102 × 1018
Q34.5113107 × 1018
95-th percentile4.5113128 × 1018
Maximum4.5113138 × 1018
Range2.0370001 × 1014
Interquartile range (IQR)1.978 × 1014

Descriptive statistics

Standard deviation9.9095261 × 1013
Coefficient of variation (CV)2.1966435 × 10-5
Kurtosis-1.9998158
Mean4.5112127 × 1018
Median Absolute Deviation (MAD)3.4999978 × 1012
Skewness-0.01361788
Sum-8.6087597 × 1018
Variance9.8198708 × 1027
MonotonicityNot monotonic
2024-03-14T12:24:20.187678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4511310400100990002 14
 
0.1%
4511111800200020007 5
 
0.1%
4511112500201530001 4
 
< 0.1%
4511310500200360005 4
 
< 0.1%
4511111200100670000 3
 
< 0.1%
4511311600105710001 3
 
< 0.1%
4511111800110030000 3
 
< 0.1%
4511310200109700001 3
 
< 0.1%
4511114200108670000 3
 
< 0.1%
4511112300100070001 3
 
< 0.1%
Other values (9900) 9955
99.6%
ValueCountFrequency (%)
4511110100100070001 1
< 0.1%
4511110100100250001 1
< 0.1%
4511110100100410000 1
< 0.1%
4511110100100480001 1
< 0.1%
4511110100100530003 1
< 0.1%
4511110100100530005 1
< 0.1%
4511110100100540001 1
< 0.1%
4511110100100550001 1
< 0.1%
4511110100100550002 2
< 0.1%
4511110100100590003 1
< 0.1%
ValueCountFrequency (%)
4511313800107860004 1
< 0.1%
4511313800107860003 1
< 0.1%
4511313800107840012 1
< 0.1%
4511313800107840011 1
< 0.1%
4511313800107840007 1
< 0.1%
4511313800107840006 1
< 0.1%
4511313800107840005 1
< 0.1%
4511313800107830003 1
< 0.1%
4511313800107790005 1
< 0.1%
4511313800107790002 1
< 0.1%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5112127 × 109
Minimum4.5111101 × 109
Maximum4.5113138 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:24:20.304685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.5111101 × 109
5-th percentile4.5111118 × 109
Q14.5111129 × 109
median4.5113102 × 109
Q34.5113107 × 109
95-th percentile4.5113128 × 109
Maximum4.5113138 × 109
Range203700
Interquartile range (IQR)197800

Descriptive statistics

Standard deviation99095.26
Coefficient of variation (CV)2.1966435 × 10-5
Kurtosis-1.9998158
Mean4.5112127 × 109
Median Absolute Deviation (MAD)3500
Skewness-0.013617879
Sum4.5112127 × 1013
Variance9.8198706 × 109
MonotonicityNot monotonic
2024-03-14T12:24:20.445658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4511310300 723
 
7.2%
4511310700 675
 
6.8%
4511310200 534
 
5.3%
4511114200 491
 
4.9%
4511114000 410
 
4.1%
4511310400 401
 
4.0%
4511113700 371
 
3.7%
4511112000 353
 
3.5%
4511112900 335
 
3.4%
4511112800 313
 
3.1%
Other values (73) 5394
53.9%
ValueCountFrequency (%)
4511110100 13
 
0.1%
4511110200 3
 
< 0.1%
4511110300 7
 
0.1%
4511110400 22
0.2%
4511110500 10
 
0.1%
4511110600 14
 
0.1%
4511110700 45
0.4%
4511110800 21
0.2%
4511110900 15
 
0.1%
4511111000 35
0.4%
ValueCountFrequency (%)
4511313800 11
 
0.1%
4511313700 28
 
0.3%
4511313600 18
 
0.2%
4511313500 8
 
0.1%
4511313400 12
 
0.1%
4511313300 47
0.5%
4511313200 72
0.7%
4511313100 70
0.7%
4511313000 103
1.0%
4511312900 53
0.5%

법정동명
Text

Distinct83
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T12:24:20.674793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length17.2611
Min length15

Characters and Unicode

Total characters172611
Distinct characters68
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전라북도 전주시 덕진구 금암동
2nd row전라북도 전주시 완산구 서신동
3rd row전라북도 전주시 완산구 서완산동2가
4th row전라북도 전주시 덕진구 송천동1가
5th row전라북도 전주시 완산구 서노송동
ValueCountFrequency (%)
전라북도 10000
25.0%
전주시 10000
25.0%
덕진구 5034
12.6%
완산구 4966
12.4%
인후동1가 723
 
1.8%
금암동 675
 
1.7%
진북동 534
 
1.3%
효자동3가 491
 
1.2%
효자동1가 410
 
1.0%
인후동2가 401
 
1.0%
Other values (77) 6766
16.9%
2024-03-14T12:24:21.021057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
30000
17.4%
전 20213
11.7%
북 10534
 
6.1%
동 10426
 
6.0%
도 10042
 
5.8%
구 10041
 
5.8%
라 10000
 
5.8%
주 10000
 
5.8%
시 10000
 
5.8%
진 6062
 
3.5%
Other values (58) 45293
26.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 136990
79.4%
Space Separator 30000
 
17.4%
Decimal Number 5621
 
3.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 20213
14.8%
북 10534
 
7.7%
동 10426
 
7.6%
도 10042
 
7.3%
구 10041
 
7.3%
라 10000
 
7.3%
주 10000
 
7.3%
시 10000
 
7.3%
진 6062
 
4.4%
산 5763
 
4.2%
Other values (53) 33909
24.8%
Decimal Number
ValueCountFrequency (%)
1 2626
46.7%
2 1811
32.2%
3 1061
18.9%
4 123
 
2.2%
Space Separator
ValueCountFrequency (%)
30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 136990
79.4%
Common 35621
 
20.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 20213
14.8%
북 10534
 
7.7%
동 10426
 
7.6%
도 10042
 
7.3%
구 10041
 
7.3%
라 10000
 
7.3%
주 10000
 
7.3%
시 10000
 
7.3%
진 6062
 
4.4%
산 5763
 
4.2%
Other values (53) 33909
24.8%
Common
ValueCountFrequency (%)
30000
84.2%
1 2626
 
7.4%
2 1811
 
5.1%
3 1061
 
3.0%
4 123
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 136990
79.4%
ASCII 35621
 
20.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30000
84.2%
1 2626
 
7.4%
2 1811
 
5.1%
3 1061
 
3.0%
4 123
 
0.3%
Hangul
ValueCountFrequency (%)
전 20213
14.8%
북 10534
 
7.7%
동 10426
 
7.6%
도 10042
 
7.3%
구 10041
 
7.3%
라 10000
 
7.3%
주 10000
 
7.3%
시 10000
 
7.3%
진 6062
 
4.4%
산 5763
 
4.2%
Other values (53) 33909
24.8%

특수지구분코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9938 
2
 
62

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 9938
99.4%
2 62
 
0.6%

Length

2024-03-14T12:24:21.125779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:24:21.199072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9938
99.4%
2 62
 
0.6%

특수지구분명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반
9938 
산
 
62

Length

Max length2
Median length2
Mean length1.9938
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반 9938
99.4%
산 62
 
0.6%

Length

2024-03-14T12:24:21.277869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:24:21.377147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 9938
99.4%
산 62
 
0.6%

지번
Real number (ℝ)

Distinct8579
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6024740.8
Minimum10001
Maximum17380003
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:24:21.517387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10001
5-th percentile470022.55
Q12140021
median5565004.5
Q38370001.5
95-th percentile15710014
Maximum17380003
Range17370002
Interquartile range (IQR)6229980.5

Descriptive statistics

Standard deviation4530651.2
Coefficient of variation (CV)0.75200766
Kurtosis-0.2170957
Mean6024740.8
Median Absolute Deviation (MAD)3215004
Skewness0.78380827
Sum6.0247408 × 1010
Variance2.05268 × 1013
MonotonicityNot monotonic
2024-03-14T12:24:21.664984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990002 14
 
0.1%
1280001 6
 
0.1%
160000 6
 
0.1%
470002 6
 
0.1%
1240001 6
 
0.1%
1530001 6
 
0.1%
2220001 5
 
0.1%
1450000 5
 
0.1%
20007 5
 
0.1%
6360001 5
 
0.1%
Other values (8569) 9936
99.4%
ValueCountFrequency (%)
10001 2
< 0.1%
10002 1
< 0.1%
10003 1
< 0.1%
10004 2
< 0.1%
10005 1
< 0.1%
10008 1
< 0.1%
10017 1
< 0.1%
10021 1
< 0.1%
10037 1
< 0.1%
10050 1
< 0.1%
ValueCountFrequency (%)
17380003 1
< 0.1%
17380002 1
< 0.1%
17370001 1
< 0.1%
17360016 1
< 0.1%
17360014 1
< 0.1%
17360009 1
< 0.1%
17360007 1
< 0.1%
17360003 1
< 0.1%
17350014 1
< 0.1%
17350006 1
< 0.1%

건축물대장고유번호
Real number (ℝ)

HIGH CORRELATION 

Distinct9910
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5112127 × 1018
Minimum4.5111101 × 1018
Maximum4.5113138 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:24:21.803128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.5111101 × 1018
5-th percentile4.5111118 × 1018
Q14.5111129 × 1018
median4.5113102 × 1018
Q34.5113107 × 1018
95-th percentile4.5113128 × 1018
Maximum4.5113138 × 1018
Range2.0370001 × 1014
Interquartile range (IQR)1.978 × 1014

Descriptive statistics

Standard deviation9.9095261 × 1013
Coefficient of variation (CV)2.1966435 × 10-5
Kurtosis-1.9998158
Mean4.5112127 × 1018
Median Absolute Deviation (MAD)3.4999978 × 1012
Skewness-0.01361788
Sum-8.6087597 × 1018
Variance9.8198708 × 1027
MonotonicityNot monotonic
2024-03-14T12:24:21.926234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4511310400100990002 14
 
0.1%
4511111800200020007 5
 
0.1%
4511112500201530001 4
 
< 0.1%
4511310500200360005 4
 
< 0.1%
4511111200100670000 3
 
< 0.1%
4511311600105710001 3
 
< 0.1%
4511111800110030000 3
 
< 0.1%
4511310200109700001 3
 
< 0.1%
4511114200108670000 3
 
< 0.1%
4511112300100070001 3
 
< 0.1%
Other values (9900) 9955
99.6%
ValueCountFrequency (%)
4511110100100070001 1
< 0.1%
4511110100100250001 1
< 0.1%
4511110100100410000 1
< 0.1%
4511110100100480001 1
< 0.1%
4511110100100530003 1
< 0.1%
4511110100100530005 1
< 0.1%
4511110100100540001 1
< 0.1%
4511110100100550001 1
< 0.1%
4511110100100550002 2
< 0.1%
4511110100100590003 1
< 0.1%
ValueCountFrequency (%)
4511313800107860004 1
< 0.1%
4511313800107860003 1
< 0.1%
4511313800107840012 1
< 0.1%
4511313800107840011 1
< 0.1%
4511313800107840007 1
< 0.1%
4511313800107840006 1
< 0.1%
4511313800107840005 1
< 0.1%
4511313800107830003 1
< 0.1%
4511313800107790005 1
< 0.1%
4511313800107790002 1
< 0.1%

기준년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2019
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 10000
100.0%

Length

2024-03-14T12:24:22.042750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:24:22.347916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 10000
100.0%

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

2024-03-14T12:24:22.423899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:24:22.502900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10000
100.0%

동코드
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6221
Minimum0
Maximum46
Zeros4640
Zeros (%)46.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:24:22.569487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum46
Range46
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3197976
Coefficient of variation (CV)2.1215201
Kurtosis575.15178
Mean0.6221
Median Absolute Deviation (MAD)0
Skewness20.550778
Sum6221
Variance1.7418658
MonotonicityNot monotonic
2024-03-14T12:24:22.664792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 5069
50.7%
0 4640
46.4%
2 201
 
2.0%
3 40
 
0.4%
4 10
 
0.1%
6 9
 
0.1%
5 7
 
0.1%
10 2
 
< 0.1%
9 2
 
< 0.1%
15 2
 
< 0.1%
Other values (16) 18
 
0.2%
ValueCountFrequency (%)
0 4640
46.4%
1 5069
50.7%
2 201
 
2.0%
3 40
 
0.4%
4 10
 
0.1%
5 7
 
0.1%
6 9
 
0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
46 1
< 0.1%
45 1
< 0.1%
41 1
< 0.1%
37 1
< 0.1%
35 1
< 0.1%
33 1
< 0.1%
32 1
< 0.1%
29 1
< 0.1%
25 1
< 0.1%
22 1
< 0.1%

동명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1동
5069 
0동
4640 
2동
 
201
3동
 
40
4동
 
10
Other values (21)
 
40

Length

Max length3
Median length2
Mean length2.0018
Min length2

Unique

Unique14 ?
Unique (%)0.1%

Sample

1st row0동
2nd row1동
3rd row1동
4th row0동
5th row1동

Common Values

ValueCountFrequency (%)
1동 5069
50.7%
0동 4640
46.4%
2동 201
 
2.0%
3동 40
 
0.4%
4동 10
 
0.1%
6동 9
 
0.1%
5동 7
 
0.1%
15동 2
 
< 0.1%
8동 2
 
< 0.1%
9동 2
 
< 0.1%
Other values (16) 18
 
0.2%

Length

2024-03-14T12:24:22.760459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1동 5069
50.7%
0동 4640
46.4%
2동 201
 
2.0%
3동 40
 
0.4%
4동 10
 
0.1%
6동 9
 
0.1%
5동 7
 
0.1%
7동 2
 
< 0.1%
10동 2
 
< 0.1%
9동 2
 
< 0.1%
Other values (16) 18
 
0.2%

토지대장면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2641
Distinct (%)26.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean526.18463
Minimum3
Maximum200037
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:24:22.871596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile96
Q1165
median218
Q3310
95-th percentile691.765
Maximum200037
Range200034
Interquartile range (IQR)145

Descriptive statistics

Standard deviation4825.0428
Coefficient of variation (CV)9.1698665
Kurtosis937.10852
Mean526.18463
Median Absolute Deviation (MAD)65.6
Skewness28.811738
Sum5261846.3
Variance23281038
MonotonicityNot monotonic
2024-03-14T12:24:23.021633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165.0 88
 
0.9%
162.0 82
 
0.8%
129.0 68
 
0.7%
139.0 67
 
0.7%
152.0 67
 
0.7%
188.0 66
 
0.7%
132.0 65
 
0.7%
172.0 62
 
0.6%
149.0 62
 
0.6%
182.0 62
 
0.6%
Other values (2631) 9311
93.1%
ValueCountFrequency (%)
3.0 1
 
< 0.1%
10.0 1
 
< 0.1%
11.0 1
 
< 0.1%
13.2 1
 
< 0.1%
17.0 4
< 0.1%
19.0 3
< 0.1%
19.8 3
< 0.1%
20.0 5
0.1%
23.0 4
< 0.1%
23.1 1
 
< 0.1%
ValueCountFrequency (%)
200037.0 1
 
< 0.1%
176529.0 1
 
< 0.1%
153620.0 4
< 0.1%
83702.0 1
 
< 0.1%
81525.0 4
< 0.1%
76085.0 3
< 0.1%
70747.0 1
 
< 0.1%
49534.0 1
 
< 0.1%
26148.0 1
 
< 0.1%
25361.0 1
 
< 0.1%

산정대지면적
Real number (ℝ)

HIGH CORRELATION 

Distinct4597
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219.73998
Minimum0
Maximum9666
Zeros81
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:24:23.146404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile46.8315
Q1129
median183.01
Q3257
95-th percentile507.03
Maximum9666
Range9666
Interquartile range (IQR)128

Descriptive statistics

Standard deviation199.12166
Coefficient of variation (CV)0.90616947
Kurtosis611.41634
Mean219.73998
Median Absolute Deviation (MAD)62.59
Skewness15.623286
Sum2197399.8
Variance39649.435
MonotonicityNot monotonic
2024-03-14T12:24:23.270656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165.0 81
 
0.8%
0.0 81
 
0.8%
162.0 72
 
0.7%
188.0 60
 
0.6%
139.0 60
 
0.6%
152.0 60
 
0.6%
132.0 58
 
0.6%
129.0 56
 
0.6%
169.0 56
 
0.6%
198.0 56
 
0.6%
Other values (4587) 9360
93.6%
ValueCountFrequency (%)
0.0 81
0.8%
0.01 18
 
0.2%
0.02 2
 
< 0.1%
0.1 6
 
0.1%
3.0 1
 
< 0.1%
5.0 1
 
< 0.1%
6.71 1
 
< 0.1%
7.0 1
 
< 0.1%
8.23 1
 
< 0.1%
8.73 1
 
< 0.1%
ValueCountFrequency (%)
9666.0 1
< 0.1%
6385.34 1
< 0.1%
3463.0 1
< 0.1%
2603.68 1
< 0.1%
1960.0 1
< 0.1%
1766.0 1
< 0.1%
1716.0 1
< 0.1%
1668.0 1
< 0.1%
1585.0 1
< 0.1%
1576.61 1
< 0.1%

건물전체연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct7627
Distinct (%)76.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean232.46828
Minimum7.6
Maximum11642.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:24:23.402371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.6
5-th percentile42.1095
Q179.78
median127.305
Q3300.26
95-th percentile719.246
Maximum11642.45
Range11634.85
Interquartile range (IQR)220.48

Descriptive statistics

Standard deviation284.86426
Coefficient of variation (CV)1.2253898
Kurtosis301.18496
Mean232.46828
Median Absolute Deviation (MAD)64.56
Skewness10.142675
Sum2324682.8
Variance81147.648
MonotonicityNot monotonic
2024-03-14T12:24:23.544176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.0 19
 
0.2%
49.5 16
 
0.2%
40.0 11
 
0.1%
59.4 11
 
0.1%
84.0 10
 
0.1%
63.14 8
 
0.1%
29.7 8
 
0.1%
55.1 8
 
0.1%
70.0 8
 
0.1%
99.36 8
 
0.1%
Other values (7617) 9893
98.9%
ValueCountFrequency (%)
7.6 1
< 0.1%
7.9 1
< 0.1%
7.93 1
< 0.1%
9.2 1
< 0.1%
10.9 1
< 0.1%
11.57 1
< 0.1%
12.2 2
< 0.1%
12.23 1
< 0.1%
13.2 2
< 0.1%
13.22 2
< 0.1%
ValueCountFrequency (%)
11642.45 1
< 0.1%
6351.58 1
< 0.1%
5162.28 1
< 0.1%
4123.3 1
< 0.1%
3826.43 1
< 0.1%
3293.06 1
< 0.1%
3188.12 1
< 0.1%
3123.84 1
< 0.1%
3089.49 1
< 0.1%
2855.85 1
< 0.1%

건물산정연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct7223
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.62378
Minimum0
Maximum1412.32
Zeros65
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:24:23.675664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37
Q173.6
median105.03
Q3192.1625
95-th percentile585.1965
Maximum1412.32
Range1412.32
Interquartile range (IQR)118.5625

Descriptive statistics

Standard deviation171.9647
Coefficient of variation (CV)0.99618197
Kurtosis4.4797047
Mean172.62378
Median Absolute Deviation (MAD)44.63
Skewness2.1621983
Sum1726237.8
Variance29571.858
MonotonicityNot monotonic
2024-03-14T12:24:23.829895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 65
 
0.7%
66.0 19
 
0.2%
49.5 15
 
0.1%
40.0 11
 
0.1%
29.7 11
 
0.1%
59.4 9
 
0.1%
78.6 9
 
0.1%
63.14 9
 
0.1%
84.0 9
 
0.1%
70.0 8
 
0.1%
Other values (7213) 9835
98.4%
ValueCountFrequency (%)
0.0 65
0.7%
5.9 1
 
< 0.1%
7.6 1
 
< 0.1%
7.9 1
 
< 0.1%
7.93 1
 
< 0.1%
8.2 1
 
< 0.1%
8.9 1
 
< 0.1%
9.2 2
 
< 0.1%
10.9 1
 
< 0.1%
11.38 1
 
< 0.1%
ValueCountFrequency (%)
1412.32 1
< 0.1%
1076.42 1
< 0.1%
1046.63 1
< 0.1%
1044.28 1
< 0.1%
983.5 1
< 0.1%
946.25 1
< 0.1%
920.23 1
< 0.1%
913.68 1
< 0.1%
907.7 1
< 0.1%
903.25 1
< 0.1%

주택가격
Real number (ℝ)

HIGH CORRELATION 

Distinct1663
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3244569 × 108
Minimum0
Maximum1.71 × 109
Zeros65
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:24:23.949722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17900000
Q145300000
median72900000
Q31.43 × 108
95-th percentile4.91 × 108
Maximum1.71 × 109
Range1.71 × 109
Interquartile range (IQR)97700000

Descriptive statistics

Standard deviation1.4885279 × 108
Coefficient of variation (CV)1.123878
Kurtosis5.5588797
Mean1.3244569 × 108
Median Absolute Deviation (MAD)36100000
Skewness2.1825568
Sum1.3244569 × 1012
Variance2.2157152 × 1016
MonotonicityNot monotonic
2024-03-14T12:24:24.099611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65
 
0.7%
102000000 44
 
0.4%
108000000 41
 
0.4%
114000000 39
 
0.4%
103000000 37
 
0.4%
115000000 36
 
0.4%
106000000 35
 
0.4%
111000000 35
 
0.4%
113000000 34
 
0.3%
112000000 32
 
0.3%
Other values (1653) 9602
96.0%
ValueCountFrequency (%)
0 65
0.7%
366000 1
 
< 0.1%
417000 1
 
< 0.1%
623000 1
 
< 0.1%
903000 1
 
< 0.1%
913000 1
 
< 0.1%
967000 1
 
< 0.1%
1030000 1
 
< 0.1%
1130000 1
 
< 0.1%
1270000 1
 
< 0.1%
ValueCountFrequency (%)
1710000000 1
< 0.1%
1360000000 1
< 0.1%
1240000000 1
< 0.1%
1220000000 1
< 0.1%
1040000000 1
< 0.1%
918000000 1
< 0.1%
871000000 1
< 0.1%
869000000 1
< 0.1%
850000000 1
< 0.1%
832000000 1
< 0.1%

표준지여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
False
9519 
True
 
481
ValueCountFrequency (%)
False 9519
95.2%
True 481
 
4.8%
2024-03-14T12:24:24.216951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2019-05-08
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-05-08
2nd row2019-05-08
3rd row2019-05-08
4th row2019-05-08
5th row2019-05-08

Common Values

ValueCountFrequency (%)
2019-05-08 10000
100.0%

Length

2024-03-14T12:24:24.298999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:24:24.379818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-05-08 10000
100.0%

Interactions

2024-03-14T12:24:18.631979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:09.809056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:10.831720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:11.752337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:12.940182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:13.845191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:14.702450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:15.652867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:16.590209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:17.724923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:18.769107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:09.902441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:10.935349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:11.842494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:13.032899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:13.930037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:14.832343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:15.769751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:16.673256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:17.833798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:18.858160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:09.994938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:11.038818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:11.928903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:13.127423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:14.030370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:14.964184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:15.855264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:16.758581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:17.920201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:18.943425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:10.076952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:11.121463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:12.255837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:13.210911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:14.127186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:15.052088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:15.933530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:16.836392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:18.004982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:19.032084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:10.167220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:11.209288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:12.344911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:13.297359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:14.212585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:15.137853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:16.021317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:16.939778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:18.089216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:19.107161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:10.274546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:11.292175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:12.416536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:13.371749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:14.283325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:15.216197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:16.112476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:17.051096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:18.162518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:19.246850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:10.424629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:11.375827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:12.534345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:13.464892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:14.368987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:15.308991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:16.209491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:17.172892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:18.274535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:19.369700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:10.529393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:11.463615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:12.653856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:13.551975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:14.457692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:15.387710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:16.289451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:17.245957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:18.356108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:19.461261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:10.635646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:11.573904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:12.749842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:13.651486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:14.548091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:15.475430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:16.390549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:17.564886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:18.440307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:19.545187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:10.737302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:11.665274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:12.847533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:13.740462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:14.619677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:15.557469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:16.501039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:17.642505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:24:18.531871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T12:24:24.449481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호법정동코드법정동명특수지구분코드특수지구분명지번건축물대장고유번호동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격표준지여부
고유번호1.0001.0001.0000.0070.0070.2821.0000.0210.9950.0410.0000.0120.1280.1220.012
법정동코드1.0001.0001.0000.0070.0070.2811.0000.0210.9950.0410.0000.0120.1290.1230.012
법정동명1.0001.0001.0000.1870.1870.8731.0000.0000.6630.2130.3660.1260.5790.5470.031
특수지구분코드0.0070.0070.1871.0001.0000.1760.0070.3020.4290.6750.0260.0000.0330.0250.017
특수지구분명0.0070.0070.1871.0001.0000.1760.0070.3020.4290.6750.0260.0000.0330.0250.017
지번0.2820.2810.8730.1760.1761.0000.2820.0510.1880.0300.0360.0000.3520.3250.000
건축물대장고유번호1.0001.0001.0000.0070.0070.2821.0000.0210.9950.0410.0000.0120.1280.1220.012
동코드0.0210.0210.0000.3020.3020.0510.0211.0001.0000.3590.0000.0000.0000.1530.000
동명0.9950.9950.6630.4290.4290.1880.9951.0001.0000.6440.1620.0000.0930.3490.000
토지대장면적0.0410.0410.2130.6750.6750.0300.0410.3590.6441.0000.3150.0000.0000.0000.000
산정대지면적0.0000.0000.3660.0260.0260.0360.0000.0000.1620.3151.0000.0000.0540.5710.000
건물전체연면적0.0120.0120.1260.0000.0000.0000.0120.0000.0000.0000.0001.0000.1430.0200.000
건물산정연면적0.1280.1290.5790.0330.0330.3520.1280.0000.0930.0000.0540.1431.0000.8510.021
주택가격0.1220.1230.5470.0250.0250.3250.1220.1530.3490.0000.5710.0200.8511.0000.021
표준지여부0.0120.0120.0310.0170.0170.0000.0120.0000.0000.0000.0000.0000.0210.0211.000
2024-03-14T12:24:24.574308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수지구분코드동명특수지구분명표준지여부
특수지구분코드1.0000.3400.9920.011
동명0.3401.0000.3400.000
특수지구분명0.9920.3401.0000.011
표준지여부0.0110.0000.0111.000
2024-03-14T12:24:24.663654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호법정동코드지번건축물대장고유번호동코드토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격특수지구분코드특수지구분명동명표준지여부
고유번호1.0000.9990.2611.000-0.7500.2620.2500.0730.1010.0070.0040.0040.9410.007
법정동코드0.9991.0000.2420.999-0.7500.2580.2480.0640.0930.0010.0040.0040.9410.007
지번0.2610.2421.0000.261-0.1660.1570.1130.3670.3790.3470.1360.1360.0690.000
건축물대장고유번호1.0000.9990.2611.000-0.7500.2620.2500.0730.1010.0070.0040.0040.9410.007
동코드-0.750-0.750-0.166-0.7501.0000.048-0.093-0.000-0.0310.0560.2320.2320.9990.000
토지대장면적0.2620.2580.1570.2620.0481.0000.6870.4430.4230.5010.5140.5140.3210.000
산정대지면적0.2500.2480.1130.250-0.0930.6871.0000.1710.4260.5170.0180.0180.0730.000
건물전체연면적0.0730.0640.3670.073-0.0000.4430.1711.0000.8750.7080.0000.0000.0000.000
건물산정연면적0.1010.0930.3790.101-0.0310.4230.4260.8751.0000.8070.0330.0330.0350.021
주택가격0.0070.0010.3470.0070.0560.5010.5170.7080.8071.0000.0250.0250.1380.021
특수지구분코드0.0040.0040.1360.0040.2320.5140.0180.0000.0330.0251.0000.9920.3400.011
특수지구분명0.0040.0040.1360.0040.2320.5140.0180.0000.0330.0250.9921.0000.3400.011
동명0.9410.9410.0690.9410.9990.3210.0730.0000.0350.1380.3400.3401.0000.000
표준지여부0.0070.0070.0000.0070.0000.0000.0000.0000.0210.0210.0110.0110.0001.000

Missing values

2024-03-14T12:24:19.676831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T12:24:19.866059image/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

고유번호법정동코드법정동명특수지구분코드특수지구분명지번건축물대장고유번호기준년도기준월동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격표준지여부데이터기준일자
3322445113107001014500834511310700전라북도 전주시 덕진구 금암동1일반145008345113107001014500832019100동202.0202.0123.96123.9697900000N2019-05-08
1248045111129001100400014511112900전라북도 전주시 완산구 서신동1일반1004000145111129001100400012019111동332.2230.78326.64236.49288000000N2019-05-08
746745111124001012800204511112400전라북도 전주시 완산구 서완산동2가1일반128002045111124001012800202019111동66.066.076.076.026500000N2019-05-08
4093745113121001024700144511312100전라북도 전주시 덕진구 송천동1가1일반247001445113121001024700142019100동163.0163.098.6698.6654600000N2019-05-08
2276345111147001070800024511114700전라북도 전주시 완산구 서노송동1일반708000245111147001070800022019111동116.0116.069.5869.5822900000N2019-05-08
3039945113104001157800074511310400전라북도 전주시 덕진구 인후동2가1일반1578000745113104001157800072019100동221.7221.7148.2148.281700000N2019-05-08
1863945111140001062300064511114000전라북도 전주시 완산구 효자동1가1일반623000645111140001062300062019111동299.4299.4311.24311.24337000000N2019-05-08
3679345113109001068900034511310900전라북도 전주시 덕진구 팔복동2가1일반689000345113109001068900032019100동334.0334.079.879.857400000N2019-05-08
2821145113103001087600024511310300전라북도 전주시 덕진구 인후동1가1일반876000245113103001087600022019100동163.5163.5376.47376.47234000000N2019-05-08
3711945113112001002700034511311200전라북도 전주시 덕진구 금상동1일반27000345113112001002700032019100동200.0200.066.1966.1919400000N2019-05-08
고유번호법정동코드법정동명특수지구분코드특수지구분명지번건축물대장고유번호기준년도기준월동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격표준지여부데이터기준일자
71645111110001000100534511111000전라북도 전주시 완산구 풍남동3가1일반1005345111110001000100532019111동191.7191.762.4762.4793600000N2019-05-08
2777445113103001077200044511310300전라북도 전주시 덕진구 인후동1가1일반772000445113103001077200042019100동488.5159.93995.3325.9254000000N2019-05-08
3627445113109001012100074511310900전라북도 전주시 덕진구 팔복동2가1일반121000745113109001012100072019100동595.00.0171.00.00N2019-05-08
4426645113130001100200044511313000전라북도 전주시 덕진구 여의동1일반1002000445113130001100200042019133동800.0266.837.2437.2456900000N2019-05-08
933945111127001016100154511112700전라북도 전주시 완산구 중화산동1가1일반161001545111127001016100152019111동165.0165.068.3268.3244500000N2019-05-08
199245111118001005000364511111800전라북도 전주시 완산구 교동1일반50003645111118001005000362019100동123.0123.068.368.3121000000N2019-05-08
1879445111140001068300104511114000전라북도 전주시 완산구 효자동1가1일반683001045111140001068300102019111동209.062.6295.8496.159400000Y2019-05-08
731545111124001007400024511112400전라북도 전주시 완산구 서완산동2가1일반74000245111124001007400022019111동132.0430.0896.99896.99491000000N2019-05-08
3174645113105002003600064511310500전라북도 전주시 덕진구 덕진동1가2산36000645113105002003600062019144동76085.0243.9948.848.82250000N2019-05-08
3996445113118001069900004511311800전라북도 전주시 덕진구 호성동3가1일반699000045113118001069900002019100동212.0212.028.428.419800000N2019-05-08