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
Text2
Categorical4
Boolean1
DateTime1

Dataset

Description매년 1월 1일 기준 청주시 소재 단독 및 다가구 주택 공시 가격 및 면적 , 대상지번 , 구분번호 (PNU) 등을 제공합니다. 매해 업데이트 되며 해당 내용은 현재 내용가 다를수있습니다.
URLhttps://www.data.go.kr/data/3039559/fileData.do

Alerts

기준년도 has constant value ""Constant
데이터기준일자 has constant value ""Constant
특수지구분코드 is highly overall correlated with 특수지구분명High correlation
특수지구분명 is highly overall correlated with 특수지구분코드High correlation
고유번호 is highly overall correlated with 법정동코드 and 1 other fieldsHigh correlation
법정동코드 is highly overall correlated with 고유번호 and 1 other fieldsHigh correlation
건축물대장고유번호 is highly overall correlated with 고유번호 and 1 other fieldsHigh correlation
동코드 is highly overall correlated with 동명High correlation
동명 is highly overall correlated with 동코드High correlation
토지대장면적 is highly overall correlated with 산정대지면적High correlation
산정대지면적 is highly overall correlated with 토지대장면적High 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 건물전체연면적 and 1 other fieldsHigh correlation
특수지구분코드 is highly imbalanced (96.8%)Imbalance
특수지구분명 is highly imbalanced (95.0%)Imbalance
기준월 is highly imbalanced (90.6%)Imbalance
표준지여부 is highly imbalanced (69.7%)Imbalance
토지대장면적 is highly skewed (γ1 = 70.80253514)Skewed

Reproduction

Analysis started2023-12-12 22:52:41.028766
Analysis finished2023-12-12 22:52:56.397653
Duration15.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고유번호
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3112664 × 1018
Minimum4.31111 × 1018
Maximum4.31143 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:52:56.458128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.31111 × 1018
5-th percentile4.31111 × 1018
Q14.31114 × 1018
median4.31131 × 1018
Q34.31133 × 1018
95-th percentile4.31143 × 1018
Maximum4.31143 × 1018
Range3.2 × 1014
Interquartile range (IQR)1.9 × 1014

Descriptive statistics

Standard deviation1.1210898 × 1014
Coefficient of variation (CV)2.6003724 × 10-5
Kurtosis-1.3443149
Mean4.3112664 × 1018
Median Absolute Deviation (MAD)1 × 1014
Skewness0.021733998
Sum2.6226397 × 1018
Variance1.2568424 × 1028
MonotonicityNot monotonic
2023-12-13T07:52:56.576538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4311310000000000000 1887
18.9%
4311210000000000000 1722
17.2%
4311110000000000000 1392
13.9%
4311410000000000000 1283
12.8%
4311430000000000000 1099
11.0%
4311130000000000000 1021
10.2%
4311330000000000000 854
8.5%
4311230000000000000 520
 
5.2%
4311140000000000000 222
 
2.2%
ValueCountFrequency (%)
4311110000000000000 1392
13.9%
4311130000000000000 1021
10.2%
4311140000000000000 222
 
2.2%
4311210000000000000 1722
17.2%
4311230000000000000 520
 
5.2%
4311310000000000000 1887
18.9%
4311330000000000000 854
8.5%
4311410000000000000 1283
12.8%
4311430000000000000 1099
11.0%
ValueCountFrequency (%)
4311430000000000000 1099
11.0%
4311410000000000000 1283
12.8%
4311330000000000000 854
8.5%
4311310000000000000 1887
18.9%
4311230000000000000 520
 
5.2%
4311210000000000000 1722
17.2%
4311140000000000000 222
 
2.2%
4311130000000000000 1021
10.2%
4311110000000000000 1392
13.9%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct318
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3112668 × 109
Minimum4.3111101 × 109
Maximum4.311431 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:52:56.701009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.3111101 × 109
5-th percentile4.3111117 × 109
Q14.311135 × 109
median4.3113104 × 109
Q34.311332 × 109
95-th percentile4.3114253 × 109
Maximum4.311431 × 109
Range320946
Interquartile range (IQR)197002

Descriptive statistics

Standard deviation111077.85
Coefficient of variation (CV)2.576455 × 10-5
Kurtosis-1.3561187
Mean4.3112668 × 109
Median Absolute Deviation (MAD)100000
Skewness0.012636931
Sum4.3112668 × 1013
Variance1.2338289 × 1010
MonotonicityNot monotonic
2023-12-13T07:52:56.861834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4311311400 511
 
5.1%
4311410200 473
 
4.7%
4311311500 455
 
4.5%
4311210100 392
 
3.9%
4311410100 368
 
3.7%
4311210700 330
 
3.3%
4311210300 322
 
3.2%
4311311300 269
 
2.7%
4311112400 255
 
2.5%
4311210200 253
 
2.5%
Other values (308) 6372
63.7%
ValueCountFrequency (%)
4311110100 27
0.3%
4311110200 7
 
0.1%
4311110300 12
 
0.1%
4311110400 9
 
0.1%
4311110500 35
0.4%
4311110600 8
 
0.1%
4311110700 5
 
0.1%
4311110800 53
0.5%
4311110900 22
0.2%
4311111000 43
0.4%
ValueCountFrequency (%)
4311431046 1
 
< 0.1%
4311431045 3
 
< 0.1%
4311431044 15
0.1%
4311431043 5
 
0.1%
4311431042 18
0.2%
4311431041 7
 
0.1%
4311431040 15
0.1%
4311431039 12
0.1%
4311431038 18
0.2%
4311431037 24
0.2%
Distinct318
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T07:52:57.198360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length11
Mean length12.4866
Min length10

Characters and Unicode

Total characters124866
Distinct characters177
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st row청주시 서원구 사창동
2nd row청주시 청원구 내수읍 세교리
3rd row청주시 상당구 미원면 종암리
4th row청주시 흥덕구 가경동
5th row청주시 서원구 남이면 석판리
ValueCountFrequency (%)
청주시 10000
29.7%
흥덕구 2741
 
8.1%
상당구 2635
 
7.8%
청원구 2382
 
7.1%
서원구 2242
 
6.6%
오창읍 520
 
1.5%
복대동 511
 
1.5%
내덕동 473
 
1.4%
봉명동 455
 
1.3%
사직동 392
 
1.2%
Other values (319) 11365
33.7%
2023-12-13T07:52:57.991210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23751
19.0%
12547
 
10.0%
10157
 
8.1%
10156
 
8.1%
10033
 
8.0%
6468
 
5.2%
5101
 
4.1%
3716
 
3.0%
3546
 
2.8%
2815
 
2.3%
Other values (167) 36576
29.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 101000
80.9%
Space Separator 23751
 
19.0%
Decimal Number 71
 
0.1%
Close Punctuation 22
 
< 0.1%
Open Punctuation 22
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12547
 
12.4%
10157
 
10.1%
10156
 
10.1%
10033
 
9.9%
6468
 
6.4%
5101
 
5.1%
3716
 
3.7%
3546
 
3.5%
2815
 
2.8%
2741
 
2.7%
Other values (161) 33720
33.4%
Decimal Number
ValueCountFrequency (%)
1 42
59.2%
2 20
28.2%
3 9
 
12.7%
Space Separator
ValueCountFrequency (%)
23751
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 100956
80.9%
Common 23866
 
19.1%
Han 44
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12547
 
12.4%
10157
 
10.1%
10156
 
10.1%
10033
 
9.9%
6468
 
6.4%
5101
 
5.1%
3716
 
3.7%
3546
 
3.5%
2815
 
2.8%
2741
 
2.7%
Other values (156) 33676
33.4%
Common
ValueCountFrequency (%)
23751
99.5%
1 42
 
0.2%
) 22
 
0.1%
( 22
 
0.1%
2 20
 
0.1%
3 9
 
< 0.1%
Han
ValueCountFrequency (%)
21
47.7%
12
27.3%
9
20.5%
1
 
2.3%
1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 100956
80.9%
ASCII 23866
 
19.1%
CJK 44
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
23751
99.5%
1 42
 
0.2%
) 22
 
0.1%
( 22
 
0.1%
2 20
 
0.1%
3 9
 
< 0.1%
Hangul
ValueCountFrequency (%)
12547
 
12.4%
10157
 
10.1%
10156
 
10.1%
10033
 
9.9%
6468
 
6.4%
5101
 
5.1%
3716
 
3.7%
3546
 
3.5%
2815
 
2.8%
2741
 
2.7%
Other values (156) 33676
33.4%
CJK
ValueCountFrequency (%)
21
47.7%
12
27.3%
9
20.5%
1
 
2.3%
1
 
2.3%

특수지구분코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9944 
2
 
55
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
1 9944
99.4%
2 55
 
0.5%
6 1
 
< 0.1%

Length

2023-12-13T07:52:58.145463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:52:58.243618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9944
99.4%
2 55
 
0.5%
6 1
 
< 0.1%

특수지구분명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반
9944 
 
56

Length

Max length2
Median length2
Mean length1.9944
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반 9944
99.4%
56
 
0.6%

Length

2023-12-13T07:52:58.361683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:52:58.471659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 9944
99.4%
56
 
0.6%

지번
Text

Distinct7791
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T07:52:58.867240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.2039
Min length1

Characters and Unicode

Total characters52039
Distinct characters39
Distinct categories7 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6269 ?
Unique (%)62.7%

Sample

1st row 305-32
2nd rowJan-67
3rd row281-2
4th row 1503-12
5th rowJun-98
ValueCountFrequency (%)
01월 33
 
0.3%
03월 17
 
0.2%
02월 16
 
0.2%
07월 14
 
0.1%
22일 12
 
0.1%
05월 11
 
0.1%
02일 11
 
0.1%
06월 10
 
0.1%
01일 10
 
0.1%
19일 9
 
0.1%
Other values (7314) 9993
98.6%
2023-12-13T07:52:59.393473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 7752
14.9%
- 6997
13.4%
2 5750
11.0%
3 4543
8.7%
4534
8.7%
4 3703
7.1%
6 3227
6.2%
5 3223
6.2%
7 2869
 
5.5%
0 2754
 
5.3%
Other values (29) 6687
12.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39119
75.2%
Dash Punctuation 6997
 
13.4%
Space Separator 4534
 
8.7%
Lowercase Letter 744
 
1.4%
Uppercase Letter 372
 
0.7%
Other Letter 272
 
0.5%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 176
23.7%
n 129
17.3%
e 98
13.2%
b 76
10.2%
r 70
 
9.4%
u 55
 
7.4%
p 41
 
5.5%
y 29
 
3.9%
c 18
 
2.4%
g 16
 
2.2%
Other values (4) 36
 
4.8%
Decimal Number
ValueCountFrequency (%)
1 7752
19.8%
2 5750
14.7%
3 4543
11.6%
4 3703
9.5%
6 3227
8.2%
5 3223
8.2%
7 2869
 
7.3%
0 2754
 
7.0%
8 2736
 
7.0%
9 2562
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
J 144
38.7%
F 76
20.4%
M 71
19.1%
A 44
 
11.8%
S 13
 
3.5%
D 9
 
2.4%
O 9
 
2.4%
N 6
 
1.6%
Other Letter
ValueCountFrequency (%)
135
49.6%
135
49.6%
1
 
0.4%
1
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
- 6997
100.0%
Space Separator
ValueCountFrequency (%)
4534
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50651
97.3%
Latin 1116
 
2.1%
Hangul 270
 
0.5%
Han 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 176
15.8%
J 144
12.9%
n 129
11.6%
e 98
8.8%
F 76
6.8%
b 76
6.8%
M 71
6.4%
r 70
 
6.3%
u 55
 
4.9%
A 44
 
3.9%
Other values (12) 177
15.9%
Common
ValueCountFrequency (%)
1 7752
15.3%
- 6997
13.8%
2 5750
11.4%
3 4543
9.0%
4534
9.0%
4 3703
7.3%
6 3227
6.4%
5 3223
6.4%
7 2869
 
5.7%
0 2754
 
5.4%
Other values (3) 5299
10.5%
Hangul
ValueCountFrequency (%)
135
50.0%
135
50.0%
Han
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51767
99.5%
Hangul 270
 
0.5%
CJK 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7752
15.0%
- 6997
13.5%
2 5750
11.1%
3 4543
8.8%
4534
8.8%
4 3703
7.2%
6 3227
6.2%
5 3223
6.2%
7 2869
 
5.5%
0 2754
 
5.3%
Other values (25) 6415
12.4%
Hangul
ValueCountFrequency (%)
135
50.0%
135
50.0%
CJK
ValueCountFrequency (%)
1
50.0%
1
50.0%

건축물대장고유번호
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3112664 × 1018
Minimum4.31111 × 1018
Maximum4.31143 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:52:59.537061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.31111 × 1018
5-th percentile4.31111 × 1018
Q14.31114 × 1018
median4.31131 × 1018
Q34.31133 × 1018
95-th percentile4.31143 × 1018
Maximum4.31143 × 1018
Range3.2 × 1014
Interquartile range (IQR)1.9 × 1014

Descriptive statistics

Standard deviation1.1210898 × 1014
Coefficient of variation (CV)2.6003724 × 10-5
Kurtosis-1.3443149
Mean4.3112664 × 1018
Median Absolute Deviation (MAD)1 × 1014
Skewness0.021733998
Sum2.6226397 × 1018
Variance1.2568424 × 1028
MonotonicityNot monotonic
2023-12-13T07:52:59.663670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4311310000000000000 1887
18.9%
4311210000000000000 1722
17.2%
4311110000000000000 1392
13.9%
4311410000000000000 1283
12.8%
4311430000000000000 1099
11.0%
4311130000000000000 1021
10.2%
4311330000000000000 854
8.5%
4311230000000000000 520
 
5.2%
4311140000000000000 222
 
2.2%
ValueCountFrequency (%)
4311110000000000000 1392
13.9%
4311130000000000000 1021
10.2%
4311140000000000000 222
 
2.2%
4311210000000000000 1722
17.2%
4311230000000000000 520
 
5.2%
4311310000000000000 1887
18.9%
4311330000000000000 854
8.5%
4311410000000000000 1283
12.8%
4311430000000000000 1099
11.0%
ValueCountFrequency (%)
4311430000000000000 1099
11.0%
4311410000000000000 1283
12.8%
4311330000000000000 854
8.5%
4311310000000000000 1887
18.9%
4311230000000000000 520
 
5.2%
4311210000000000000 1722
17.2%
4311140000000000000 222
 
2.2%
4311130000000000000 1021
10.2%
4311110000000000000 1392
13.9%

기준년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 10000
100.0%

Length

2023-12-13T07:52:59.841628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:52:59.958195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 10000
100.0%

기준월
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9880 
6
 
120

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 9880
98.8%
6 120
 
1.2%

Length

2023-12-13T07:53:00.079197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:53:00.217369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9880
98.8%
6 120
 
1.2%

동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5173
Minimum0
Maximum107
Zeros45
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:53:00.343353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum107
Range107
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.1752299
Coefficient of variation (CV)3.2476184
Kurtosis31.700332
Mean2.5173
Median Absolute Deviation (MAD)0
Skewness5.7180304
Sum25173
Variance66.834384
MonotonicityNot monotonic
2023-12-13T07:53:00.512824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 8937
89.4%
2 497
 
5.0%
50 280
 
2.8%
3 113
 
1.1%
4 53
 
0.5%
0 45
 
0.4%
5 25
 
0.2%
6 16
 
0.2%
7 10
 
0.1%
10 9
 
0.1%
Other values (8) 15
 
0.1%
ValueCountFrequency (%)
0 45
 
0.4%
1 8937
89.4%
2 497
 
5.0%
3 113
 
1.1%
4 53
 
0.5%
5 25
 
0.2%
6 16
 
0.2%
7 10
 
0.1%
8 5
 
0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
107 1
 
< 0.1%
52 1
 
< 0.1%
50 280
2.8%
25 1
 
< 0.1%
19 1
 
< 0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
10 9
 
0.1%
9 4
 
< 0.1%
8 5
 
0.1%

동명
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5173
Minimum0
Maximum107
Zeros45
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:53:00.666218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum107
Range107
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.1752299
Coefficient of variation (CV)3.2476184
Kurtosis31.700332
Mean2.5173
Median Absolute Deviation (MAD)0
Skewness5.7180304
Sum25173
Variance66.834384
MonotonicityNot monotonic
2023-12-13T07:53:00.821766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 8937
89.4%
2 497
 
5.0%
50 280
 
2.8%
3 113
 
1.1%
4 53
 
0.5%
0 45
 
0.4%
5 25
 
0.2%
6 16
 
0.2%
7 10
 
0.1%
10 9
 
0.1%
Other values (8) 15
 
0.1%
ValueCountFrequency (%)
0 45
 
0.4%
1 8937
89.4%
2 497
 
5.0%
3 113
 
1.1%
4 53
 
0.5%
5 25
 
0.2%
6 16
 
0.2%
7 10
 
0.1%
8 5
 
0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
107 1
 
< 0.1%
52 1
 
< 0.1%
50 280
2.8%
25 1
 
< 0.1%
19 1
 
< 0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
10 9
 
0.1%
9 4
 
< 0.1%
8 5
 
0.1%

토지대장면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3233
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean793.39186
Minimum26.8
Maximum1218409
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:53:00.979829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26.8
5-th percentile127.585
Q1189
median266
Q3505
95-th percentile1442
Maximum1218409
Range1218382.2
Interquartile range (IQR)316

Descriptive statistics

Standard deviation14248.803
Coefficient of variation (CV)17.959351
Kurtosis5632.9039
Mean793.39186
Median Absolute Deviation (MAD)106
Skewness70.802535
Sum7933918.6
Variance2.0302837 × 108
MonotonicityNot monotonic
2023-12-13T07:53:01.156194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
660.0 101
 
1.0%
198.0 34
 
0.3%
155.0 33
 
0.3%
145.0 32
 
0.3%
195.0 31
 
0.3%
192.0 30
 
0.3%
162.0 29
 
0.3%
330.0 28
 
0.3%
165.0 27
 
0.3%
182.0 27
 
0.3%
Other values (3223) 9628
96.3%
ValueCountFrequency (%)
26.8 1
< 0.1%
31.4 1
< 0.1%
33.0 1
< 0.1%
33.1 1
< 0.1%
36.7 1
< 0.1%
43.0 1
< 0.1%
44.3 1
< 0.1%
46.3 1
< 0.1%
49.0 2
< 0.1%
49.9 1
< 0.1%
ValueCountFrequency (%)
1218409.0 1
< 0.1%
567806.0 1
< 0.1%
371548.0 1
< 0.1%
164090.0 1
< 0.1%
131339.0 1
< 0.1%
94339.0 1
< 0.1%
88860.0 1
< 0.1%
76760.0 1
< 0.1%
58711.0 1
< 0.1%
43000.0 1
< 0.1%

산정대지면적
Real number (ℝ)

HIGH CORRELATION 

Distinct5099
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean316.24374
Minimum5.31
Maximum2955
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:53:01.340614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.31
5-th percentile72
Q1154
median226.85
Q3416
95-th percentile784.43
Maximum2955
Range2949.69
Interquartile range (IQR)262

Descriptive statistics

Standard deviation242.70053
Coefficient of variation (CV)0.76744771
Kurtosis5.7286585
Mean316.24374
Median Absolute Deviation (MAD)98.85
Skewness1.9098459
Sum3162437.4
Variance58903.548
MonotonicityNot monotonic
2023-12-13T07:53:01.509992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
660.0 91
 
0.9%
145.0 31
 
0.3%
330.0 30
 
0.3%
198.0 29
 
0.3%
162.0 27
 
0.3%
155.0 24
 
0.2%
192.0 24
 
0.2%
350.0 23
 
0.2%
165.0 22
 
0.2%
397.0 21
 
0.2%
Other values (5089) 9678
96.8%
ValueCountFrequency (%)
5.31 1
< 0.1%
10.78 1
< 0.1%
13.54 1
< 0.1%
13.95 1
< 0.1%
14.12 1
< 0.1%
14.95 1
< 0.1%
15.03 1
< 0.1%
16.34 1
< 0.1%
17.13 1
< 0.1%
17.34 1
< 0.1%
ValueCountFrequency (%)
2955.0 1
< 0.1%
2056.0 1
< 0.1%
1951.0 1
< 0.1%
1852.0 1
< 0.1%
1817.0 1
< 0.1%
1795.0 1
< 0.1%
1768.0 1
< 0.1%
1743.0 1
< 0.1%
1709.0 1
< 0.1%
1701.73 1
< 0.1%

건물전체연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct8080
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean220.93399
Minimum9.92
Maximum3135.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:53:01.683029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.92
5-th percentile52.5
Q188.6625
median141.68
Q3290.2725
95-th percentile619.6505
Maximum3135.8
Range3125.88
Interquartile range (IQR)201.61

Descriptive statistics

Standard deviation209.23746
Coefficient of variation (CV)0.94705872
Kurtosis17.100854
Mean220.93399
Median Absolute Deviation (MAD)65.325
Skewness2.9341558
Sum2209339.9
Variance43780.316
MonotonicityNot monotonic
2023-12-13T07:53:01.856323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.0 18
 
0.2%
66.0 12
 
0.1%
40.0 10
 
0.1%
54.0 10
 
0.1%
81.0 9
 
0.1%
99.72 9
 
0.1%
84.96 8
 
0.1%
72.0 8
 
0.1%
90.0 8
 
0.1%
49.59 8
 
0.1%
Other values (8070) 9900
99.0%
ValueCountFrequency (%)
9.92 1
< 0.1%
11.08 1
< 0.1%
13.23 1
< 0.1%
14.6 1
< 0.1%
16.5 1
< 0.1%
18.0 2
< 0.1%
18.18 1
< 0.1%
18.5 1
< 0.1%
18.88 1
< 0.1%
19.0 1
< 0.1%
ValueCountFrequency (%)
3135.8 1
< 0.1%
2865.01 1
< 0.1%
2637.16 1
< 0.1%
2138.68 1
< 0.1%
2116.02 1
< 0.1%
1988.09 1
< 0.1%
1968.13 1
< 0.1%
1961.01 1
< 0.1%
1941.88 1
< 0.1%
1925.96 1
< 0.1%

건물산정연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct7801
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178.49301
Minimum7.58
Maximum1961.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:53:02.042019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.58
5-th percentile49.8
Q184.1
median123.6
Q3205.3875
95-th percentile525.849
Maximum1961.01
Range1953.43
Interquartile range (IQR)121.2875

Descriptive statistics

Standard deviation153.60246
Coefficient of variation (CV)0.86055169
Kurtosis6.8781026
Mean178.49301
Median Absolute Deviation (MAD)50.12
Skewness2.2725833
Sum1784930.1
Variance23593.715
MonotonicityNot monotonic
2023-12-13T07:53:02.216556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.0 18
 
0.2%
66.0 13
 
0.1%
99.72 11
 
0.1%
72.0 10
 
0.1%
40.0 10
 
0.1%
84.78 9
 
0.1%
81.0 9
 
0.1%
84.0 9
 
0.1%
54.0 9
 
0.1%
65.0 9
 
0.1%
Other values (7791) 9893
98.9%
ValueCountFrequency (%)
7.58 1
< 0.1%
8.79 1
< 0.1%
9.92 1
< 0.1%
10.44 1
< 0.1%
11.08 1
< 0.1%
11.55 1
< 0.1%
13.23 1
< 0.1%
14.35 1
< 0.1%
14.6 1
< 0.1%
14.88 1
< 0.1%
ValueCountFrequency (%)
1961.01 1
< 0.1%
1559.45 1
< 0.1%
1133.95 1
< 0.1%
998.41 1
< 0.1%
930.99 1
< 0.1%
906.17 1
< 0.1%
898.76 1
< 0.1%
898.53 1
< 0.1%
896.8 1
< 0.1%
895.39 1
< 0.1%

주택가격
Real number (ℝ)

HIGH CORRELATION 

Distinct1518
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4480381 × 108
Minimum581000
Maximum9.63 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:53:02.409760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum581000
5-th percentile26995000
Q159700000
median96250000
Q31.69 × 108
95-th percentile4.491 × 108
Maximum9.63 × 108
Range9.62419 × 108
Interquartile range (IQR)1.093 × 108

Descriptive statistics

Standard deviation1.3619569 × 108
Coefficient of variation (CV)0.94055322
Kurtosis4.3535301
Mean1.4480381 × 108
Median Absolute Deviation (MAD)44750000
Skewness2.033139
Sum1.4480381 × 1012
Variance1.8549267 × 1016
MonotonicityNot monotonic
2023-12-13T07:53:02.598683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000000 60
 
0.6%
110000000 56
 
0.6%
104000000 56
 
0.6%
103000000 55
 
0.5%
108000000 54
 
0.5%
124000000 49
 
0.5%
105000000 48
 
0.5%
101000000 48
 
0.5%
121000000 47
 
0.5%
111000000 47
 
0.5%
Other values (1508) 9480
94.8%
ValueCountFrequency (%)
581000 1
< 0.1%
894000 1
< 0.1%
903000 1
< 0.1%
970000 1
< 0.1%
1230000 1
< 0.1%
1270000 1
< 0.1%
1450000 1
< 0.1%
1490000 1
< 0.1%
1620000 1
< 0.1%
1760000 1
< 0.1%
ValueCountFrequency (%)
963000000 1
< 0.1%
962000000 1
< 0.1%
936000000 1
< 0.1%
913000000 2
< 0.1%
897000000 1
< 0.1%
868000000 1
< 0.1%
861000000 1
< 0.1%
823000000 1
< 0.1%
820000000 1
< 0.1%
804000000 1
< 0.1%

표준지여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
False
9461 
True
 
539
ValueCountFrequency (%)
False 9461
94.6%
True 539
 
5.4%
2023-12-13T07:53:02.726776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-01-01 00:00:00
Maximum2022-01-01 00:00:00
2023-12-13T07:53:02.823526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:02.911764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T07:52:54.715387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:44.810553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:45.995722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:47.124727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:48.117861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:49.274645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:50.239870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:51.570903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:52.582849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:53.635628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:54.818336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:44.906470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:46.124581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:47.219618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:48.229487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:49.391587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:50.345293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:51.694027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:52.676179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:53.736220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:54.928738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:45.036729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:46.240202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:47.305162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:48.350488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:49.481045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:50.442531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:51.792022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:52.778236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:53.836232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:55.035860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:45.153555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:46.361269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:47.410124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:48.521889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:49.571639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:50.536842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:51.891343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:52.877402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:53.956791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:55.166544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:45.265765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:46.471250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:47.503886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:48.656010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:49.654588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:50.628742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:51.985449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:52.964785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:54.051817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:55.275435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:45.361658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:46.589271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:47.609004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:48.753276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:49.765714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:51.011351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:52.070601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:53.063725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:54.152562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:55.481364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:45.490578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:46.695298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:47.721636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:48.853467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:49.851788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:51.121380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:52.153364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:53.187090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:54.273121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:55.602095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:45.625716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:46.812891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:47.822880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:48.966915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:49.933116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:51.232126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:52.251222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:53.302581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:54.386415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:55.710595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:45.733853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:46.907875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:47.907226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:49.059278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:50.041396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:51.331213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:52.361730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:53.415829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:54.493154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:55.851552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:45.881830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:47.021965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:47.999732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:49.167851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:50.143559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:51.471161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:52.470614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:53.528739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:54.611233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:53:02.994595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호법정동코드특수지구분코드특수지구분명건축물대장고유번호기준월동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격표준지여부
고유번호1.0001.0000.0610.0611.0000.0600.1100.1100.0220.4320.2530.1970.3220.020
법정동코드1.0001.0000.0210.0441.0000.0620.1200.1200.0000.1930.0860.2110.2800.026
특수지구분코드0.0610.0211.0001.0000.0610.0050.0000.0000.2710.0350.0770.0510.1200.000
특수지구분명0.0610.0441.0001.0000.0610.0030.0000.0000.2430.0510.0190.0420.0640.000
건축물대장고유번호1.0001.0000.0610.0611.0000.0600.1100.1100.0220.4320.2530.1970.3220.020
기준월0.0600.0620.0050.0030.0601.0000.0030.0030.0000.0690.0000.0550.1120.028
동코드0.1100.1200.0000.0000.1100.0031.0001.0000.0000.0000.1350.1550.2900.029
동명0.1100.1200.0000.0000.1100.0031.0001.0000.0000.0000.1350.1550.2900.029
토지대장면적0.0220.0000.2710.2430.0220.0000.0000.0001.0000.0330.0000.0000.0000.000
산정대지면적0.4320.1930.0350.0510.4320.0690.0000.0000.0331.0000.2040.3100.1710.040
건물전체연면적0.2530.0860.0770.0190.2530.0000.1350.1350.0000.2041.0000.6840.5820.000
건물산정연면적0.1970.2110.0510.0420.1970.0550.1550.1550.0000.3100.6841.0000.7160.123
주택가격0.3220.2800.1200.0640.3220.1120.2900.2900.0000.1710.5820.7161.0000.000
표준지여부0.0200.0260.0000.0000.0200.0280.0290.0290.0000.0400.0000.1230.0001.000
2023-12-13T07:53:03.165867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수지구분코드특수지구분명기준월표준지여부
특수지구분코드1.0001.0000.0090.000
특수지구분명1.0001.0000.0020.000
기준월0.0090.0021.0000.018
표준지여부0.0000.0000.0181.000
2023-12-13T07:53:03.278954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호법정동코드건축물대장고유번호동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격특수지구분코드특수지구분명기준월표준지여부
고유번호1.0000.9901.0000.0070.0070.1320.1570.0220.0430.0750.0190.0290.0410.017
법정동코드0.9901.0000.9900.0120.0120.1620.1860.0260.0520.0880.0190.0290.0410.017
건축물대장고유번호1.0000.9901.0000.0070.0070.1320.1570.0220.0430.0750.0190.0290.0410.017
동코드0.0070.0120.0071.0001.0000.1960.101-0.046-0.0330.0620.0000.0000.0030.036
동명0.0070.0120.0071.0001.0000.1960.101-0.046-0.0330.0620.0000.0000.0030.036
토지대장면적0.1320.1620.1320.1960.1961.0000.816-0.034-0.0100.1380.2120.2970.0000.000
산정대지면적0.1570.1860.1570.1010.1010.8161.000-0.1530.0480.2040.0220.0380.0520.030
건물전체연면적0.0220.0260.022-0.046-0.046-0.034-0.1531.0000.8960.6550.0330.0190.0000.000
건물산정연면적0.0430.0520.043-0.033-0.033-0.0100.0480.8961.0000.7520.0320.0310.0410.092
주택가격0.0750.0880.0750.0620.0620.1380.2040.6550.7521.0000.0710.0490.0860.000
특수지구분코드0.0190.0190.0190.0000.0000.2120.0220.0330.0320.0711.0001.0000.0090.000
특수지구분명0.0290.0290.0290.0000.0000.2970.0380.0190.0310.0491.0001.0000.0020.000
기준월0.0410.0410.0410.0030.0030.0000.0520.0000.0410.0860.0090.0021.0000.018
표준지여부0.0170.0170.0170.0360.0360.0000.0300.0000.0920.0000.0000.0000.0181.000

Missing values

2023-12-13T07:52:56.018544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:52:56.271024image/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
2157343112100000000000004311210200청주시 서원구 사창동1일반305-3243112100000000000002022111262.157.74738.27199.9861500000N2022-01-01
6046943114300000000000004311425027청주시 청원구 내수읍 세교리1일반Jan-6743114300000000000002022111757.0757.0115.5115.5131000000N2022-01-01
1149343111300000000000004311132036청주시 상당구 미원면 종암리1일반281-2431113000000000000020221111455.01455.056.056.063800000N2022-01-01
3514543113100000000000004311311300청주시 흥덕구 가경동1일반1503-1243113100000000000002022111190.0190.0402.78402.78265000000N2022-01-01
3040543112300000000000004311231034청주시 서원구 남이면 석판리1일반Jun-9843112300000000000002022111665.0665.0129.52129.52245000000N2022-01-01
2583543112100000000000004311210700청주시 서원구 수곡동1일반47-1143112100000000000002022111113.9113.9140.25140.2553700000N2022-01-01
1373643111300000000000004311133031청주시 상당구 가덕면 병암리202월 17일4311130000000000000202261143000.0356.471.2871.2821800000N2022-01-01
3383243113100000000000004311311300청주시 흥덕구 가경동1일반309-243113100000000000002022111309.0309.0128.76128.76175000000N2022-01-01
5658543114100000000000004311410200청주시 청원구 내덕동1일반729-1243114100000000000002022111140.0140.081.5881.5846300000N2022-01-01
885843111100000000000004311112900청주시 상당구 운동동1일반214-143111100000000000002022111608.0247.21152.8762.1575900000N2022-01-01
고유번호법정동코드법정동명특수지구분코드특수지구분명지번건축물대장고유번호기준년도기준월동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격표준지여부데이터기준일자
5720743114100000000000004311410300청주시 청원구 율량동1일반131343114100000000000002022611273.0142.64199.83104.41162000000N2022-01-01
1063943111300000000000004311132024청주시 상당구 미원면 내산리1일반136-143111300000000000002022111660.0551.76149.3124.8265900000N2022-01-01
6223443114300000000000004311425327청주시 청원구 오창읍 원리1일반247-3243114300000000000002022611477.0477.0189.36189.36239000000N2022-01-01
5103743113300000000000004311332035청주시 흥덕구 옥산면 국사리1일반40043113300000000000002022111481.0481.0184.71184.7192100000N2022-01-01
1052243111300000000000004311132023청주시 상당구 미원면 중리1일반173-343111300000000000002022111652.0652.086.1286.1241800000N2022-01-01
2130543112100000000000004311210200청주시 서원구 사창동1일반266-1443112100000000000002022111124.055.48131.2559.3956400000N2022-01-01
5014043113300000000000004311332025청주시 흥덕구 옥산면 가락리1일반134-243113300000000000002022111695.0695.0141.6141.668700000N2022-01-01
1891043112100000000000004311210100청주시 서원구 사직동1일반262-443112100000000000002022111203.6203.6134.85134.8588400000N2022-01-01
3912543113100000000000004311311500청주시 흥덕구 봉명동1일반203-643113100000000000002022111158.5158.5146.96146.9672700000N2022-01-01
2319843112100000000000004311210300청주시 서원구 모충동1일반252-143112100000000000002022111145.0145.0328.04328.04192000000N2022-01-01