Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows13
Duplicate rows (%)0.1%
Total size in memory1.1 MiB
Average record size in memory111.0 B

Variable types

Categorical5
Numeric6
Text1

Dataset

Description2022년 4월 15일 기준, 경상남도 산청군 일반건축물시가표준액 현황(시군명, 자치단체코드, 본번, 부번, 동, 호, 물건지 주소, 시가표준액, 연면적, 결정(조사)일자) 자료 입니다.
Author경상남도 산청군
URLhttps://www.data.go.kr/data/15080492/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
결정(조사)일자 has constant value ""Constant
데이터기준일자 has constant value ""Constant
Dataset has 13 (0.1%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
시가표준액 is highly skewed (γ1 = 57.31666629)Skewed
연면적 is highly skewed (γ1 = 45.92782117)Skewed
부번 has 3765 (37.6%) zerosZeros
동 has 345 (3.5%) zerosZeros

Reproduction

Analysis started2023-12-12 04:51:12.925043
Analysis finished2023-12-12 04:51:19.121638
Duration6.2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

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

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상남도 10000
100.0%

Length

2023-12-12T13:51:19.205633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:51:19.303084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
산청군 10000
100.0%

Length

2023-12-12T13:51:19.409242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:51:19.519866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
산청군 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
48860
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48860
2nd row48860
3rd row48860
4th row48860
5th row48860

Common Values

ValueCountFrequency (%)
48860 10000
100.0%

Length

2023-12-12T13:51:19.643864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:51:19.761229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48860 10000
100.0%

본번
Real number (ℝ)

Distinct1196
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean478.594
Minimum1
Maximum1713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:51:19.921164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile32
Q1192
median427
Q3706
95-th percentile1117
Maximum1713
Range1712
Interquartile range (IQR)514

Descriptive statistics

Standard deviation339.16414
Coefficient of variation (CV)0.70866776
Kurtosis-0.2627453
Mean478.594
Median Absolute Deviation (MAD)252
Skewness0.63952344
Sum4785940
Variance115032.31
MonotonicityNot monotonic
2023-12-12T13:51:20.138959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 77
 
0.8%
680 51
 
0.5%
6 49
 
0.5%
596 48
 
0.5%
900 47
 
0.5%
251 44
 
0.4%
344 41
 
0.4%
924 41
 
0.4%
630 36
 
0.4%
1118 36
 
0.4%
Other values (1186) 9530
95.3%
ValueCountFrequency (%)
1 29
0.3%
2 14
 
0.1%
3 8
 
0.1%
4 15
 
0.1%
5 25
0.2%
6 49
0.5%
7 7
 
0.1%
8 34
0.3%
9 8
 
0.1%
10 15
 
0.1%
ValueCountFrequency (%)
1713 1
 
< 0.1%
1706 1
 
< 0.1%
1686 3
< 0.1%
1683 2
< 0.1%
1675 1
 
< 0.1%
1673 2
< 0.1%
1672 1
 
< 0.1%
1659 1
 
< 0.1%
1567 3
< 0.1%
1548 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct90
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.91
Minimum0
Maximum426
Zeros3765
Zeros (%)37.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:51:20.332616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile15
Maximum426
Range426
Interquartile range (IQR)4

Descriptive statistics

Standard deviation11.661239
Coefficient of variation (CV)2.9824141
Kurtosis330.7368
Mean3.91
Median Absolute Deviation (MAD)1
Skewness13.886039
Sum39100
Variance135.9845
MonotonicityNot monotonic
2023-12-12T13:51:20.487656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3765
37.6%
1 1957
19.6%
2 979
 
9.8%
3 717
 
7.2%
4 469
 
4.7%
5 325
 
3.2%
6 272
 
2.7%
7 224
 
2.2%
8 176
 
1.8%
9 141
 
1.4%
Other values (80) 975
 
9.8%
ValueCountFrequency (%)
0 3765
37.6%
1 1957
19.6%
2 979
 
9.8%
3 717
 
7.2%
4 469
 
4.7%
5 325
 
3.2%
6 272
 
2.7%
7 224
 
2.2%
8 176
 
1.8%
9 141
 
1.4%
ValueCountFrequency (%)
426 1
< 0.1%
322 1
< 0.1%
281 2
< 0.1%
197 1
< 0.1%
160 1
< 0.1%
158 1
< 0.1%
154 1
< 0.1%
145 1
< 0.1%
134 2
< 0.1%
132 2
< 0.1%

동
Real number (ℝ)

ZEROS 

Distinct53
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4926
Minimum0
Maximum107
Zeros345
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:51:20.696536image/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 deviation4.1082153
Coefficient of variation (CV)2.7523887
Kurtosis385.99563
Mean1.4926
Median Absolute Deviation (MAD)0
Skewness17.877827
Sum14926
Variance16.877433
MonotonicityNot monotonic
2023-12-12T13:51:20.889060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8231
82.3%
2 914
 
9.1%
0 345
 
3.5%
3 204
 
2.0%
4 78
 
0.8%
5 39
 
0.4%
6 33
 
0.3%
7 24
 
0.2%
9 18
 
0.2%
8 17
 
0.2%
Other values (43) 97
 
1.0%
ValueCountFrequency (%)
0 345
 
3.5%
1 8231
82.3%
2 914
 
9.1%
3 204
 
2.0%
4 78
 
0.8%
5 39
 
0.4%
6 33
 
0.3%
7 24
 
0.2%
8 17
 
0.2%
9 18
 
0.2%
ValueCountFrequency (%)
107 1
< 0.1%
106 1
< 0.1%
105 1
< 0.1%
104 1
< 0.1%
103 2
< 0.1%
102 1
< 0.1%
101 2
< 0.1%
64 1
< 0.1%
58 1
< 0.1%
56 1
< 0.1%

호
Real number (ℝ)

Distinct90
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.8849
Minimum0
Maximum8401
Zeros87
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:51:21.037912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q3101
95-th percentile201
Maximum8401
Range8401
Interquartile range (IQR)100

Descriptive statistics

Standard deviation710.80929
Coefficient of variation (CV)6.1337525
Kurtosis120.82158
Mean115.8849
Median Absolute Deviation (MAD)2
Skewness11.013402
Sum1158849
Variance505249.85
MonotonicityNot monotonic
2023-12-12T13:51:21.184485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3097
31.0%
101 2218
22.2%
2 1269
12.7%
102 644
 
6.4%
3 586
 
5.9%
201 424
 
4.2%
4 316
 
3.2%
103 246
 
2.5%
5 158
 
1.6%
104 109
 
1.1%
Other values (80) 933
 
9.3%
ValueCountFrequency (%)
0 87
 
0.9%
1 3097
31.0%
2 1269
12.7%
3 586
 
5.9%
4 316
 
3.2%
5 158
 
1.6%
6 97
 
1.0%
7 76
 
0.8%
8 42
 
0.4%
9 32
 
0.3%
ValueCountFrequency (%)
8401 1
< 0.1%
8301 1
< 0.1%
8206 1
< 0.1%
8205 1
< 0.1%
8203 1
< 0.1%
8202 1
< 0.1%
8201 1
< 0.1%
8107 1
< 0.1%
8105 1
< 0.1%
8104 1
< 0.1%

물건지
Text

Distinct9609
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T13:51:21.526102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length33
Mean length27.5062
Min length19

Characters and Unicode

Total characters275062
Distinct characters180
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9337 ?
Unique (%)93.4%

Sample

1st row경상남도 산청군 산청읍 모고리 984 1동 4호
2nd row[ 신등가회로 22-8 ] 0001동 0001호
3rd row경상남도 산청군 신안면 하정리 330 1동 101호
4th row경상남도 산청군 신등면 단계리 701-4 1동 1호
5th row경상남도 산청군 신안면 문대리 422 1동 1호
ValueCountFrequency (%)
경상남도 7309
 
10.9%
산청군 7309
 
10.9%
1동 5832
 
8.7%
5382
 
8.0%
0001동 2399
 
3.6%
1호 2253
 
3.4%
101호 1430
 
2.1%
단성면 1336
 
2.0%
시천면 1131
 
1.7%
산청읍 1081
 
1.6%
Other values (4457) 31734
47.2%
2023-12-12T13:51:22.033576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
57196
20.8%
1 25251
 
9.2%
0 20329
 
7.4%
호 10076
 
3.7%
동 9973
 
3.6%
산 9754
 
3.5%
청 8784
 
3.2%
2 8566
 
3.1%
남 7687
 
2.8%
경 7683
 
2.8%
Other values (170) 109763
39.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 126906
46.1%
Decimal Number 80057
29.1%
Space Separator 57196
20.8%
Dash Punctuation 5521
 
2.0%
Close Punctuation 2691
 
1.0%
Open Punctuation 2691
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 10076
 
7.9%
동 9973
 
7.9%
산 9754
 
7.7%
청 8784
 
6.9%
남 7687
 
6.1%
경 7683
 
6.1%
리 7619
 
6.0%
도 7435
 
5.9%
상 7426
 
5.9%
군 7309
 
5.8%
Other values (156) 43160
34.0%
Decimal Number
ValueCountFrequency (%)
1 25251
31.5%
0 20329
25.4%
2 8566
 
10.7%
3 5456
 
6.8%
4 4109
 
5.1%
5 3853
 
4.8%
6 3757
 
4.7%
7 2961
 
3.7%
8 2934
 
3.7%
9 2841
 
3.5%
Space Separator
ValueCountFrequency (%)
57196
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5521
100.0%
Close Punctuation
ValueCountFrequency (%)
] 2691
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 2691
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 148156
53.9%
Hangul 126906
46.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 10076
 
7.9%
동 9973
 
7.9%
산 9754
 
7.7%
청 8784
 
6.9%
남 7687
 
6.1%
경 7683
 
6.1%
리 7619
 
6.0%
도 7435
 
5.9%
상 7426
 
5.9%
군 7309
 
5.8%
Other values (156) 43160
34.0%
Common
ValueCountFrequency (%)
57196
38.6%
1 25251
17.0%
0 20329
 
13.7%
2 8566
 
5.8%
- 5521
 
3.7%
3 5456
 
3.7%
4 4109
 
2.8%
5 3853
 
2.6%
6 3757
 
2.5%
7 2961
 
2.0%
Other values (4) 11157
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 148156
53.9%
Hangul 126906
46.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
57196
38.6%
1 25251
17.0%
0 20329
 
13.7%
2 8566
 
5.8%
- 5521
 
3.7%
3 5456
 
3.7%
4 4109
 
2.8%
5 3853
 
2.6%
6 3757
 
2.5%
7 2961
 
2.0%
Other values (4) 11157
 
7.5%
Hangul
ValueCountFrequency (%)
호 10076
 
7.9%
동 9973
 
7.9%
산 9754
 
7.7%
청 8784
 
6.9%
남 7687
 
6.1%
경 7683
 
6.1%
리 7619
 
6.0%
도 7435
 
5.9%
상 7426
 
5.9%
군 7309
 
5.8%
Other values (156) 43160
34.0%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8123
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34581134
Minimum25300
Maximum1.4040133 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:51:22.233428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25300
5-th percentile350000
Q11579250
median6892905
Q329362095
95-th percentile1.3914558 × 108
Maximum1.4040133 × 1010
Range1.4040108 × 1010
Interquartile range (IQR)27782845

Descriptive statistics

Standard deviation1.7096799 × 108
Coefficient of variation (CV)4.9439672
Kurtosis4529.6664
Mean34581134
Median Absolute Deviation (MAD)6243725
Skewness57.316666
Sum3.4581134 × 1011
Variance2.9230054 × 1016
MonotonicityNot monotonic
2023-12-12T13:51:22.777750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1872000 24
 
0.2%
633600 17
 
0.2%
648000 17
 
0.2%
2059200 16
 
0.2%
8035500 15
 
0.1%
1000000 15
 
0.1%
1152000 14
 
0.1%
15731170 13
 
0.1%
1828800 13
 
0.1%
720000 13
 
0.1%
Other values (8113) 9843
98.4%
ValueCountFrequency (%)
25300 1
< 0.1%
26160 1
< 0.1%
26400 1
< 0.1%
29700 1
< 0.1%
36400 1
< 0.1%
38400 1
< 0.1%
40000 1
< 0.1%
41600 1
< 0.1%
45000 1
< 0.1%
51840 1
< 0.1%
ValueCountFrequency (%)
14040132970 1
< 0.1%
2955717720 1
< 0.1%
2420163590 1
< 0.1%
2372901100 1
< 0.1%
2024549670 1
< 0.1%
1851693500 1
< 0.1%
1793947100 1
< 0.1%
1756291840 1
< 0.1%
1753331720 1
< 0.1%
1492411210 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4911
Distinct (%)49.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.17347
Minimum0.64
Maximum31515.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:51:22.942177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.64
5-th percentile10.199
Q132.4
median73
Q3152
95-th percentile440.84125
Maximum31515.45
Range31514.81
Interquartile range (IQR)119.6

Descriptive statistics

Standard deviation415.20807
Coefficient of variation (CV)2.8799201
Kurtosis3290.5297
Mean144.17347
Median Absolute Deviation (MAD)49.4
Skewness45.927821
Sum1441734.7
Variance172397.74
MonotonicityNot monotonic
2023-12-12T13:51:23.108088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 391
 
3.9%
10.0 109
 
1.1%
16.5 86
 
0.9%
66.0 52
 
0.5%
50.0 52
 
0.5%
36.0 51
 
0.5%
72.0 44
 
0.4%
40.0 42
 
0.4%
33.0 41
 
0.4%
27.0 41
 
0.4%
Other values (4901) 9091
90.9%
ValueCountFrequency (%)
0.64 1
 
< 0.1%
1.0 2
< 0.1%
1.1 1
 
< 0.1%
1.44 2
< 0.1%
1.49 1
 
< 0.1%
1.5 2
< 0.1%
1.92 1
 
< 0.1%
1.96 2
< 0.1%
1.98 1
 
< 0.1%
2.0 3
< 0.1%
ValueCountFrequency (%)
31515.45 1
< 0.1%
7464.0 1
< 0.1%
7032.4 1
< 0.1%
6414.98 1
< 0.1%
4770.74 1
< 0.1%
4719.23 1
< 0.1%
4653.9 1
< 0.1%
4400.0 1
< 0.1%
3416.38 1
< 0.1%
3300.0 2
< 0.1%

결정(조사)일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020-06-01
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-06-01
2nd row2020-06-01
3rd row2020-06-01
4th row2020-06-01
5th row2020-06-01

Common Values

ValueCountFrequency (%)
2020-06-01 10000
100.0%

Length

2023-12-12T13:51:23.300240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:51:23.394999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-06-01 10000
100.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022-04-15
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-04-15
2nd row2022-04-15
3rd row2022-04-15
4th row2022-04-15
5th row2022-04-15

Common Values

ValueCountFrequency (%)
2022-04-15 10000
100.0%

Length

2023-12-12T13:51:23.514189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:51:23.661049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-04-15 10000
100.0%

Interactions

2023-12-12T13:51:18.158601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:14.381390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:15.237125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:15.916119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:16.657700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:17.425291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:18.262624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:14.500022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:15.360764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:16.048828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:16.786371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:17.590380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:18.355619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:14.584933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:15.458054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:16.164166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:16.891272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:17.709686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:18.454498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:14.677734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:15.552215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:16.275709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:16.995893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:17.834676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:18.544282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:14.775664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:15.666887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:16.380384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:17.109674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:17.931322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:18.648432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:15.138766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:15.801097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:16.516631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:17.318344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:18.040215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:51:23.751853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
본번부번동호시가표준액연면적
본번1.0000.1920.0600.0560.0980.108
부번0.1921.0000.0000.0000.0000.000
동0.0600.0001.0000.0000.0000.000
호0.0560.0000.0001.0000.0000.000
시가표준액0.0980.0000.0000.0001.0000.947
연면적0.1080.0000.0000.0000.9471.000
2023-12-12T13:51:23.874335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
본번부번동호시가표준액연면적
본번1.000-0.005-0.0430.0580.046-0.005
부번-0.0051.000-0.0160.0760.1600.015
동-0.043-0.0161.0000.0140.031-0.036
호0.0580.0760.0141.0000.190-0.076
시가표준액0.0460.1600.0310.1901.0000.597
연면적-0.0050.015-0.036-0.0760.5971.000

Missing values

2023-12-12T13:51:18.798102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:51:19.003808image/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

시도명시군구명자치단체코드본번부번동호물건지시가표준액연면적결정(조사)일자데이터기준일자
11065경상남도산청군48860984014경상남도 산청군 산청읍 모고리 984 1동 4호585600032.02020-06-012022-04-15
7712경상남도산청군48860687011[ 신등가회로 22-8 ] 0001동 0001호6746544099.362020-06-012022-04-15
3774경상남도산청군4886033001101경상남도 산청군 신안면 하정리 330 1동 101호211680018.02020-06-012022-04-15
7822경상남도산청군48860701411경상남도 산청군 신등면 단계리 701-4 1동 1호18000045.02020-06-012022-04-15
5417경상남도산청군48860422011경상남도 산청군 신안면 문대리 422 1동 1호41147100349.02020-06-012022-04-15
6383경상남도산청군4886034901201경상남도 산청군 산청읍 지리 349 1동 201호2324138080.422020-06-012022-04-15
6522경상남도산청군48860721021경상남도 산청군 생초면 노은리 721 2동 1호1152000115.22020-06-012022-04-15
5305경상남도산청군488601101101경상남도 산청군 신안면 청현리 11 1동 101호290491890649.872020-06-012022-04-15
2187경상남도산청군4886063851101[ 동의보감로 839 ] 0001동 0101호994884075.372020-06-012022-04-15
11318경상남도산청군488601168011경상남도 산청군 산청읍 모고리 1168 1동 1호370575067.52020-06-012022-04-15
시도명시군구명자치단체코드본번부번동호물건지시가표준액연면적결정(조사)일자데이터기준일자
9770경상남도산청군48860763161102[ 친환경로 3603 ] 0001동 0102호10344250128.52020-06-012022-04-15
6689경상남도산청군48860591012경상남도 산청군 생초면 노은리 591 1동 2호117290031.72020-06-012022-04-15
7044경상남도산청군4886034511102[ 새실로 13-6 ] 0001동 0102호669600074.42020-06-012022-04-15
6842경상남도산청군4886071522101경상남도 산청군 생초면 계남리 715-2 2동 101호105000010.02020-06-012022-04-15
9243경상남도산청군48860183011경상남도 산청군 생비량면 도리 183 1동 1호25542023.222020-06-012022-04-15
16029경상남도산청군48860116301201[ 삼신봉로900번길 3 ] 0001동 0201호966504062.762020-06-012022-04-15
5465경상남도산청군48860168311101[ 성철로 18-38 ] 0001동 0101호63360018.02020-06-012022-04-15
1980경상남도산청군48860246311경상남도 산청군 삼장면 평촌리 246-3 1동 1호66100066.12020-06-012022-04-15
139경상남도산청군4886024202104경상남도 산청군 금서면 화계리 242 2동 104호12852270116.12020-06-012022-04-15
974경상남도산청군488601290313경상남도 산청군 금서면 매촌리 1290-3 1동 3호1873901043.642020-06-012022-04-15

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드본번부번동호물건지시가표준액연면적결정(조사)일자데이터기준일자# duplicates
4경상남도산청군4886013071101경상남도 산청군 금서면 신아리 130-7 1동 101호75627600121.982020-06-012022-04-155
2경상남도산청군4886039200경상남도 산청군 차황면 장박리 39-26512400162.02020-06-012022-04-153
11경상남도산청군4886099501101경상남도 산청군 금서면 매촌리 995 1동 101호42486000194.02020-06-012022-04-153
0경상남도산청군488601101101경상남도 산청군 신안면 청현리 11 1동 101호290491890649.872020-06-012022-04-152
1경상남도산청군488602701102경상남도 산청군 오부면 오전리 27 1동 102호266000028.02020-06-012022-04-152
3경상남도산청군4886090100경상남도 산청군 차황면 우사리 90-114952000311.52020-06-012022-04-152
5경상남도산청군48860141011경상남도 산청군 단성면 성내리 141 1동 1호32728500467.552020-06-012022-04-152
6경상남도산청군48860335211경상남도 산청군 삼장면 대하리 335-2 1동 1호438900077.02020-06-012022-04-152
7경상남도산청군48860397111[ 사직단로85번길 4 ] 0001동 0001호451200024.02020-06-012022-04-152
8경상남도산청군48860548011[ 오동로565번길 24 ] 0001동 0001호1568000392.02020-06-012022-04-152