Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells13524
Missing cells (%)11.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory110.0 B

Variable types

Categorical4
Text2
Numeric6

Dataset

Description2023.1.1~2023.5.31.기준 및 2023.6.1.고시된 일반건축물에 대한 지방세 부과기준인 시가표준액을 납세자들에게 제공함으로써 납세자들이 물건별 재산가액 확인 가능합니다. * 공란사유 -법정리 : 해당 법정 단위까지 주소가 존재하지 않음 -본번, 부번, 동, 호수 : 해당 단위까지 주소가 존재하지 않음
URLhttps://www.data.go.kr/data/15079921/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
연면적 is highly overall correlated with 시가표준액(2023.1.1~2023.5.31.) and 1 other fieldsHigh correlation
시가표준액(2023.1.1~2023.5.31.) is highly overall correlated with 연면적 and 1 other fieldsHigh correlation
시가표준액(2023.6.1.고시) is highly overall correlated with 연면적 and 1 other fieldsHigh correlation
특수지 is highly imbalanced (91.0%)Imbalance
법정리 has 4468 (44.7%) missing valuesMissing
호수 has 9056 (90.6%) missing valuesMissing
연면적 is highly skewed (γ1 = 34.10427034)Skewed
시가표준액(2023.1.1~2023.5.31.) is highly skewed (γ1 = 44.1481923)Skewed
시가표준액(2023.6.1.고시) is highly skewed (γ1 = 44.65465322)Skewed
부번 has 3286 (32.9%) zerosZeros
동 has 164 (1.6%) zerosZeros

Reproduction

Analysis started2023-12-12 02:46:53.822820
Analysis finished2023-12-12 02:46:59.733431
Duration5.91 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
전라남도
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라남도 10000
100.0%

Length

2023-12-12T11:46:59.813176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:46:59.922988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광양시 10000
100.0%

Length

2023-12-12T11:47:00.029257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:47:00.127011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광양시 10000
100.0%

법정동(면)
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
광양읍
2666 
중동
1541 
금호동
1029 
옥곡면
584 
옥룡면
558 
Other values (12)
3622 

Length

Max length3
Median length3
Mean length2.8137
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row마동
2nd row중동
3rd row중동
4th row광양읍
5th row광양읍

Common Values

ValueCountFrequency (%)
광양읍 2666
26.7%
중동 1541
15.4%
금호동 1029
 
10.3%
옥곡면 584
 
5.8%
옥룡면 558
 
5.6%
광영동 541
 
5.4%
태인동 508
 
5.1%
진상면 501
 
5.0%
진월면 418
 
4.2%
봉강면 404
 
4.0%
Other values (7) 1250
12.5%

Length

2023-12-12T11:47:00.232188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
광양읍 2666
26.7%
중동 1541
15.4%
금호동 1029
 
10.3%
옥곡면 584
 
5.8%
옥룡면 558
 
5.6%
광영동 541
 
5.4%
태인동 508
 
5.1%
진상면 501
 
5.0%
진월면 418
 
4.2%
봉강면 404
 
4.0%
Other values (7) 1250
12.5%

법정리
Text

MISSING 

Distinct59
Distinct (%)1.1%
Missing4468
Missing (%)44.7%
Memory size156.2 KiB
2023-12-12T11:47:00.465407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters16596
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
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 row용강리
2nd row인동리
3rd row추산리
4th row신금리
5th row세풍리
ValueCountFrequency (%)
덕례리 487
 
8.8%
칠성리 462
 
8.4%
신금리 296
 
5.4%
세풍리 253
 
4.6%
용강리 236
 
4.3%
목성리 193
 
3.5%
구산리 184
 
3.3%
동곡리 159
 
2.9%
인서리 156
 
2.8%
섬거리 133
 
2.4%
Other values (49) 2973
53.7%
2023-12-12T11:47:00.875951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 5532
33.3%
성 655
 
3.9%
신 599
 
3.6%
덕 580
 
3.5%
례 487
 
2.9%
금 465
 
2.8%
칠 462
 
2.8%
사 418
 
2.5%
산 413
 
2.5%
죽 367
 
2.2%
Other values (62) 6618
39.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16596
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 5532
33.3%
성 655
 
3.9%
신 599
 
3.6%
덕 580
 
3.5%
례 487
 
2.9%
금 465
 
2.8%
칠 462
 
2.8%
사 418
 
2.5%
산 413
 
2.5%
죽 367
 
2.2%
Other values (62) 6618
39.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16596
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 5532
33.3%
성 655
 
3.9%
신 599
 
3.6%
덕 580
 
3.5%
례 487
 
2.9%
금 465
 
2.8%
칠 462
 
2.8%
사 418
 
2.5%
산 413
 
2.5%
죽 367
 
2.2%
Other values (62) 6618
39.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16596
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 5532
33.3%
성 655
 
3.9%
신 599
 
3.6%
덕 580
 
3.5%
례 487
 
2.9%
금 465
 
2.8%
칠 462
 
2.8%
사 418
 
2.5%
산 413
 
2.5%
죽 367
 
2.2%
Other values (62) 6618
39.9%

특수지
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반번지
9813 
산번지
 
167
구획정리번지
 
20

Length

Max length6
Median length4
Mean length3.9873
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반번지 9813
98.1%
산번지 167
 
1.7%
구획정리번지 20
 
0.2%

Length

2023-12-12T11:47:01.029807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:47:01.142562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반번지 9813
98.1%
산번지 167
 
1.7%
구획정리번지 20
 
0.2%

본번
Real number (ℝ)

Distinct1745
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean931.6372
Minimum1
Maximum2795
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:47:01.245391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile85
Q1524
median846.5
Q31394
95-th percentile1814
Maximum2795
Range2794
Interquartile range (IQR)870

Descriptive statistics

Standard deviation562.953
Coefficient of variation (CV)0.60426205
Kurtosis-0.81767986
Mean931.6372
Median Absolute Deviation (MAD)439.5
Skewness0.32012597
Sum9316372
Variance316916.09
MonotonicityNot monotonic
2023-12-12T11:47:01.384252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 138
 
1.4%
1814 136
 
1.4%
645 95
 
0.9%
640 89
 
0.9%
643 79
 
0.8%
1657 67
 
0.7%
638 59
 
0.6%
639 58
 
0.6%
885 53
 
0.5%
125 52
 
0.5%
Other values (1735) 9174
91.7%
ValueCountFrequency (%)
1 8
0.1%
2 8
0.1%
3 8
0.1%
4 9
0.1%
5 2
 
< 0.1%
6 3
 
< 0.1%
7 5
0.1%
8 3
 
< 0.1%
9 6
0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
2795 1
< 0.1%
2792 1
< 0.1%
2700 1
< 0.1%
2681 1
< 0.1%
2644 1
< 0.1%
2522 1
< 0.1%
2413 1
< 0.1%
2409 1
< 0.1%
2404 1
< 0.1%
2280 1
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct137
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8377
Minimum0
Maximum433
Zeros3286
Zeros (%)32.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:47:01.534608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile17
Maximum433
Range433
Interquartile range (IQR)6

Descriptive statistics

Standard deviation19.55237
Coefficient of variation (CV)3.3493277
Kurtosis155.51953
Mean5.8377
Median Absolute Deviation (MAD)2
Skewness11.050535
Sum58377
Variance382.29519
MonotonicityNot monotonic
2023-12-12T11:47:01.663206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3286
32.9%
1 1609
16.1%
2 875
 
8.8%
3 595
 
5.9%
4 528
 
5.3%
5 442
 
4.4%
8 383
 
3.8%
6 375
 
3.8%
7 328
 
3.3%
9 225
 
2.2%
Other values (127) 1354
13.5%
ValueCountFrequency (%)
0 3286
32.9%
1 1609
16.1%
2 875
 
8.8%
3 595
 
5.9%
4 528
 
5.3%
5 442
 
4.4%
6 375
 
3.8%
7 328
 
3.3%
8 383
 
3.8%
9 225
 
2.2%
ValueCountFrequency (%)
433 1
< 0.1%
393 1
< 0.1%
374 1
< 0.1%
371 1
< 0.1%
345 2
< 0.1%
301 1
< 0.1%
291 1
< 0.1%
277 1
< 0.1%
273 2
< 0.1%
269 2
< 0.1%

동
Real number (ℝ)

ZEROS 

Distinct394
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.9308
Minimum0
Maximum9469
Zeros164
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:47:01.839623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile106
Maximum9469
Range9469
Interquartile range (IQR)1

Descriptive statistics

Standard deviation557.53101
Coefficient of variation (CV)8.7208514
Kurtosis207.01137
Mean63.9308
Median Absolute Deviation (MAD)0
Skewness13.914589
Sum639308
Variance310840.83
MonotonicityNot monotonic
2023-12-12T11:47:02.001704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6667
66.7%
2 931
 
9.3%
3 309
 
3.1%
101 221
 
2.2%
0 164
 
1.6%
4 163
 
1.6%
5 100
 
1.0%
100 62
 
0.6%
6 61
 
0.6%
7 47
 
0.5%
Other values (384) 1275
 
12.8%
ValueCountFrequency (%)
0 164
 
1.6%
1 6667
66.7%
2 931
 
9.3%
3 309
 
3.1%
4 163
 
1.6%
5 100
 
1.0%
6 61
 
0.6%
7 47
 
0.5%
8 46
 
0.5%
9 39
 
0.4%
ValueCountFrequency (%)
9469 1
< 0.1%
9468 1
< 0.1%
9466 1
< 0.1%
9464 1
< 0.1%
9463 1
< 0.1%
9461 1
< 0.1%
9444 1
< 0.1%
9424 1
< 0.1%
9422 1
< 0.1%
9419 1
< 0.1%

호수
Text

MISSING 

Distinct203
Distinct (%)21.5%
Missing9056
Missing (%)90.6%
Memory size156.2 KiB
2023-12-12T11:47:02.395067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.2415254
Min length1

Characters and Unicode

Total characters3060
Distinct characters13
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

Unique138 ?
Unique (%)14.6%

Sample

1st row201
2nd row102
3rd row303
4th row지하 101
5th row310
ValueCountFrequency (%)
101 107
 
10.6%
지하 66
 
6.5%
102 64
 
6.3%
201 52
 
5.1%
104 49
 
4.9%
103 45
 
4.5%
202 37
 
3.7%
105 35
 
3.5%
106 31
 
3.1%
204 30
 
3.0%
Other values (178) 494
48.9%
2023-12-12T11:47:02.946247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 895
29.2%
0 825
27.0%
2 417
13.6%
3 202
 
6.6%
4 141
 
4.6%
5 101
 
3.3%
6 95
 
3.1%
7 68
 
2.2%
지 66
 
2.2%
하 66
 
2.2%
Other values (3) 184
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2862
93.5%
Other Letter 132
 
4.3%
Space Separator 66
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 895
31.3%
0 825
28.8%
2 417
14.6%
3 202
 
7.1%
4 141
 
4.9%
5 101
 
3.5%
6 95
 
3.3%
7 68
 
2.4%
8 65
 
2.3%
9 53
 
1.9%
Other Letter
ValueCountFrequency (%)
지 66
50.0%
하 66
50.0%
Space Separator
ValueCountFrequency (%)
66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2928
95.7%
Hangul 132
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 895
30.6%
0 825
28.2%
2 417
14.2%
3 202
 
6.9%
4 141
 
4.8%
5 101
 
3.4%
6 95
 
3.2%
7 68
 
2.3%
66
 
2.3%
8 65
 
2.2%
Hangul
ValueCountFrequency (%)
지 66
50.0%
하 66
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2928
95.7%
Hangul 132
 
4.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 895
30.6%
0 825
28.2%
2 417
14.2%
3 202
 
6.9%
4 141
 
4.8%
5 101
 
3.4%
6 95
 
3.2%
7 68
 
2.3%
66
 
2.3%
8 65
 
2.2%
Hangul
ValueCountFrequency (%)
지 66
50.0%
하 66
50.0%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct6463
Distinct (%)64.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean659.4753
Minimum2
Maximum295143.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:47:03.121010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile17.4995
Q147.96
median125.57
Q3324
95-th percentile1850.413
Maximum295143.06
Range295141.06
Interquartile range (IQR)276.04

Descriptive statistics

Standard deviation5556.0977
Coefficient of variation (CV)8.4250277
Kurtosis1422.2709
Mean659.4753
Median Absolute Deviation (MAD)95.36
Skewness34.10427
Sum6594753
Variance30870221
MonotonicityNot monotonic
2023-12-12T11:47:03.340359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 453
 
4.5%
10.0 132
 
1.3%
20.0 75
 
0.8%
37.2974 70
 
0.7%
27.0 60
 
0.6%
47.2268 51
 
0.5%
36.0 51
 
0.5%
54.0 46
 
0.5%
19.8 43
 
0.4%
72.0 38
 
0.4%
Other values (6453) 8981
89.8%
ValueCountFrequency (%)
2.0 1
 
< 0.1%
2.3 1
 
< 0.1%
2.7 3
< 0.1%
2.72 1
 
< 0.1%
3.0 1
 
< 0.1%
3.28 1
 
< 0.1%
4.19 1
 
< 0.1%
4.56 5
0.1%
4.8 1
 
< 0.1%
5.0 3
< 0.1%
ValueCountFrequency (%)
295143.06 1
< 0.1%
229959.78 1
< 0.1%
186703.61 1
< 0.1%
181577.69 1
< 0.1%
160967.0 1
< 0.1%
103181.82 1
< 0.1%
99127.05 1
< 0.1%
93587.57 1
< 0.1%
74455.44 1
< 0.1%
68366.93 1
< 0.1%

시가표준액(2023.1.1~2023.5.31.)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8609
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2782483 × 108
Minimum76160
Maximum1.26 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:47:03.509177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76160
5-th percentile807556.15
Q16443862.5
median31163518
Q31.1705969 × 108
95-th percentile7.0971861 × 108
Maximum1.26 × 1011
Range1.2599992 × 1011
Interquartile range (IQR)1.1061582 × 108

Descriptive statistics

Standard deviation1.9194478 × 109
Coefficient of variation (CV)8.4251035
Kurtosis2440.9062
Mean2.2782483 × 108
Median Absolute Deviation (MAD)29022518
Skewness44.148192
Sum2.2782483 × 1012
Variance3.6842798 × 1018
MonotonicityNot monotonic
2023-12-12T11:47:03.652596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32523331 70
 
0.7%
41181768 51
 
0.5%
676800 18
 
0.2%
777600 18
 
0.2%
748800 17
 
0.2%
2754000 15
 
0.1%
763200 14
 
0.1%
864000 12
 
0.1%
475200 11
 
0.1%
1470000 11
 
0.1%
Other values (8599) 9763
97.6%
ValueCountFrequency (%)
76160 1
< 0.1%
90000 1
< 0.1%
100800 1
< 0.1%
111520 1
< 0.1%
114240 1
< 0.1%
115200 1
< 0.1%
129920 1
< 0.1%
130200 1
< 0.1%
145200 1
< 0.1%
150000 1
< 0.1%
ValueCountFrequency (%)
126000000000 1
< 0.1%
82940593449 1
< 0.1%
73571805002 1
< 0.1%
45315101384 1
< 0.1%
26232868258 1
< 0.1%
23789055696 1
< 0.1%
21144149801 1
< 0.1%
16240247100 1
< 0.1%
15995889420 1
< 0.1%
14344012918 1
< 0.1%

시가표준액(2023.6.1.고시)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8592
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2204922 × 108
Minimum76160
Maximum1.27 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:47:03.790131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76160
5-th percentile773311.5
Q16256193.8
median29806275
Q31.1215562 × 108
95-th percentile6.9055047 × 108
Maximum1.27 × 1011
Range1.2699992 × 1011
Interquartile range (IQR)1.0589943 × 108

Descriptive statistics

Standard deviation1.9174732 × 109
Coefficient of variation (CV)8.6353523
Kurtosis2491.8887
Mean2.2204922 × 108
Median Absolute Deviation (MAD)27772275
Skewness44.654653
Sum2.2204922 × 1012
Variance3.6767036 × 1018
MonotonicityNot monotonic
2023-12-12T11:47:03.941004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31553600 70
 
0.7%
39953871 51
 
0.5%
720000 22
 
0.2%
633600 18
 
0.2%
792000 18
 
0.2%
705600 17
 
0.2%
748800 16
 
0.2%
1980000 15
 
0.1%
360000 14
 
0.1%
7344000 13
 
0.1%
Other values (8582) 9746
97.5%
ValueCountFrequency (%)
76160 1
< 0.1%
90000 1
< 0.1%
100800 2
< 0.1%
107520 1
< 0.1%
111520 1
< 0.1%
129920 1
< 0.1%
130200 1
< 0.1%
138600 1
< 0.1%
144000 1
< 0.1%
150000 1
< 0.1%
ValueCountFrequency (%)
127000000000 1
< 0.1%
80897696877 1
< 0.1%
74622867218 1
< 0.1%
45820474259 1
< 0.1%
26520925902 1
< 0.1%
23100269881 1
< 0.1%
20855002424 1
< 0.1%
16452076410 1
< 0.1%
15923873240 1
< 0.1%
14543186662 1
< 0.1%

Interactions

2023-12-12T11:46:58.310379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:55.146879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:55.789618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:56.383747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:57.021462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:57.651952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:58.417489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:55.239207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:55.873298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:56.479078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:57.116877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:57.775132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:58.520458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:55.327057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:55.968292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:56.583584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:57.234760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:57.863826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:58.611354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:55.423017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:56.070550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:56.678646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:57.332891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:57.956421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:58.733291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:55.556121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:56.171198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:56.799687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:57.444598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:58.058480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:58.858132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:55.676341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:56.279919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:56.930691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:57.545253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:46:58.194473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:47:04.044158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동(면)법정리특수지본번부번동연면적시가표준액(2023.1.1~2023.5.31.)시가표준액(2023.6.1.고시)
법정동(면)1.0001.0000.5240.6810.2200.2250.0000.0000.000
법정리1.0001.0000.3230.8460.4250.0280.0000.0000.000
특수지0.5240.3231.0000.3400.0000.2140.0000.0000.000
본번0.6810.8460.3401.0000.1380.1680.0450.0540.054
부번0.2200.4250.0000.1381.0000.0000.0000.0000.000
동0.2250.0280.2140.1680.0001.0000.4810.0900.090
연면적0.0000.0000.0000.0450.0000.4811.0000.9120.912
시가표준액(2023.1.1~2023.5.31.)0.0000.0000.0000.0540.0000.0900.9121.0001.000
시가표준액(2023.6.1.고시)0.0000.0000.0000.0540.0000.0900.9121.0001.000
2023-12-12T11:47:04.161467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수지법정동(면)
특수지1.0000.333
법정동(면)0.3331.000
2023-12-12T11:47:04.240046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
본번부번동연면적시가표준액(2023.1.1~2023.5.31.)시가표준액(2023.6.1.고시)법정동(면)특수지
본번1.0000.0650.0050.1310.2540.2570.3450.217
부번0.0651.000-0.3450.0950.0950.0940.0870.000
동0.005-0.3451.000-0.040-0.0000.0060.0890.130
연면적0.1310.095-0.0401.0000.8430.8430.0000.000
시가표준액(2023.1.1~2023.5.31.)0.2540.095-0.0000.8431.0001.0000.0000.000
시가표준액(2023.6.1.고시)0.2570.0940.0060.8431.0001.0000.0000.000
법정동(면)0.3450.0870.0890.0000.0000.0001.0000.333
특수지0.2170.0000.1300.0000.0000.0000.3331.000

Missing values

2023-12-12T11:46:59.009352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:46:59.210322image/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.
2023-12-12T11:46:59.665966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시도명시군구명법정동(면)법정리특수지본번부번동호수연면적시가표준액(2023.1.1~2023.5.31.)시가표준액(2023.6.1.고시)
148전라남도광양시마동<NA>일반번지10310612201117.517203363069213390
1729전라남도광양시중동<NA>일반번지15970110237.82116800020223000
1375전라남도광양시중동<NA>일반번지142741<NA>362.88147069524134102875
5870전라남도광양시광양읍용강리일반번지8930030358.92827200026681700
6011전라남도광양시광양읍인동리일반번지26851<NA>87.486219828060273720
9491전라남도광양시금호동<NA>일반번지69812<NA>4416.1829986159802691883350
15935전라남도광양시태인동<NA>일반번지125491<NA>409.15125326184112286408
13590전라남도광양시옥룡면추산리일반번지90121<NA>82.082109456020684160
7711전라남도광양시광영동<NA>일반번지18101<NA>34.053856005293800
1354전라남도광양시중동<NA>일반번지141921<NA>1199.7505570445464134840
시도명시군구명법정동(면)법정리특수지본번부번동호수연면적시가표준액(2023.1.1~2023.5.31.)시가표준액(2023.6.1.고시)
4457전라남도광양시광양읍칠성리일반번지1009111<NA>311.17497510073419600
7704전라남도광양시광영동<NA>산번지6121<NA>196.117334514064324080
7900전라남도광양시광영동<NA>일반번지685112<NA>68.4234210003352580
14607전라남도광양시진상면비평리일반번지26006<NA>6.017118001733400
10835전라남도광양시다압면금천리일반번지28441<NA>43.275168007603200
2957전라남도광양시중동<NA>일반번지182931<NA>277.52206693485201686212
12653전라남도광양시옥곡면신금리일반번지18481<NA>194.43071520028965600
9470전라남도광양시금호동<NA>일반번지67803<NA>1261.99753408030687784550
11206전라남도광양시도이동<NA>일반번지825012<NA>3782.8519330363501955733450
11607전라남도광양시봉강면부저리일반번지56801<NA>67.6410822401014600