Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows6
Duplicate rows (%)0.1%
Total size in memory1.1 MiB
Average record size in memory119.0 B

Variable types

Categorical6
Numeric5
Text1
DateTime1

Dataset

Description일반건축물에 대한 지방세 부과기준인 시가표준액에 대한 데이터로, 2018년~2022년에 대한 해당 물건지의 시가표준액, 연면적, 기준일자를 제공합니다.
Author대전광역시 동구
URLhttps://www.data.go.kr/data/15080113/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
법정리 has constant value ""Constant
기준일자 has constant value ""Constant
Dataset has 6 (0.1%) duplicate rowsDuplicates
시가표준액(원) is highly overall correlated with 연면적(제곱미터)High correlation
연면적(제곱미터) is highly overall correlated with 시가표준액(원) High correlation
특수지번 is highly imbalanced (96.1%)Imbalance
동 is highly skewed (γ1 = 33.02838569)Skewed
시가표준액(원) is highly skewed (γ1 = 45.4602597)Skewed
연면적(제곱미터) is highly skewed (γ1 = 26.14150941)Skewed
부번 has 1445 (14.4%) zerosZeros
동 has 4018 (40.2%) zerosZeros

Reproduction

Analysis started2023-12-12 21:16:03.775610
Analysis finished2023-12-12 21:16:08.136168
Duration4.36 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
대전광역시
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대전광역시 10000
100.0%

Length

2023-12-13T06:16:08.198983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:16:08.294278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동구 10000
100.0%

Length

2023-12-13T06:16:08.389120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:16:08.493455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동구 10000
100.0%

과세년도
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2019
4265 
2018
4137 
2020
1598 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2018
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 4265
42.6%
2018 4137
41.4%
2020 1598
 
16.0%

Length

2023-12-13T06:16:08.590124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:16:08.680444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 4265
42.6%
2018 4137
41.4%
2020 1598
 
16.0%

법정동
Categorical

Distinct42
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
가양동
1054 
원동
940 
용전동
790 
삼성동
747 
용운동
738 
Other values (37)
5731 

Length

Max length3
Median length3
Mean length2.7185
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row신흥동
2nd row자양동
3rd row하소동
4th row용운동
5th row가양동

Common Values

ValueCountFrequency (%)
가양동 1054
 
10.5%
원동 940
 
9.4%
용전동 790
 
7.9%
삼성동 747
 
7.5%
용운동 738
 
7.4%
가오동 648
 
6.5%
판암동 577
 
5.8%
자양동 492
 
4.9%
대동 474
 
4.7%
성남동 473
 
4.7%
Other values (32) 3067
30.7%

Length

2023-12-13T06:16:08.789053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가양동 1054
 
10.5%
원동 940
 
9.4%
용전동 790
 
7.9%
삼성동 747
 
7.5%
용운동 738
 
7.4%
가오동 648
 
6.5%
판암동 577
 
5.8%
자양동 492
 
4.9%
대동 474
 
4.7%
성남동 473
 
4.7%
Other values (32) 3067
30.7%

법정리
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2023-12-13T06:16:08.925993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:16:09.021539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

특수지번
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반
9936 
블록
 
45
산
 
19

Length

Max length2
Median length2
Mean length1.9981
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반 9936
99.4%
블록 45
 
0.4%
산 19
 
0.2%

Length

2023-12-13T06:16:09.119325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:16:09.220398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 9936
99.4%
블록 45
 
0.4%
산 19
 
0.2%

본번
Real number (ℝ)

Distinct735
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217.9761
Minimum1
Maximum1061
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:16:09.333801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q163
median157
Q3339
95-th percentile583
Maximum1061
Range1060
Interquartile range (IQR)276

Descriptive statistics

Standard deviation189.87605
Coefficient of variation (CV)0.87108655
Kurtosis0.18520577
Mean217.9761
Median Absolute Deviation (MAD)118
Skewness0.94685051
Sum2179761
Variance36052.914
MonotonicityNot monotonic
2023-12-13T06:16:09.513335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63 207
 
2.1%
40 180
 
1.8%
557 151
 
1.5%
1 151
 
1.5%
339 127
 
1.3%
85 127
 
1.3%
157 100
 
1.0%
96 96
 
1.0%
64 93
 
0.9%
68 92
 
0.9%
Other values (725) 8676
86.8%
ValueCountFrequency (%)
1 151
1.5%
2 71
0.7%
3 37
 
0.4%
4 13
 
0.1%
5 31
 
0.3%
6 13
 
0.1%
7 13
 
0.1%
8 54
 
0.5%
9 39
 
0.4%
10 52
 
0.5%
ValueCountFrequency (%)
1061 3
< 0.1%
1030 1
 
< 0.1%
1004 1
 
< 0.1%
1003 2
< 0.1%
997 1
 
< 0.1%
987 1
 
< 0.1%
940 1
 
< 0.1%
934 1
 
< 0.1%
932 1
 
< 0.1%
922 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct196
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.0629
Minimum0
Maximum636
Zeros1445
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:16:09.660834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q314
95-th percentile47
Maximum636
Range636
Interquartile range (IQR)13

Descriptive statistics

Standard deviation37.358195
Coefficient of variation (CV)2.6565072
Kurtosis95.723876
Mean14.0629
Median Absolute Deviation (MAD)4
Skewness8.5748153
Sum140629
Variance1395.6347
MonotonicityNot monotonic
2023-12-13T06:16:10.101116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1599
16.0%
0 1445
14.4%
2 654
 
6.5%
3 614
 
6.1%
4 601
 
6.0%
6 521
 
5.2%
5 379
 
3.8%
7 278
 
2.8%
8 273
 
2.7%
9 242
 
2.4%
Other values (186) 3394
33.9%
ValueCountFrequency (%)
0 1445
14.4%
1 1599
16.0%
2 654
6.5%
3 614
 
6.1%
4 601
 
6.0%
5 379
 
3.8%
6 521
 
5.2%
7 278
 
2.8%
8 273
 
2.7%
9 242
 
2.4%
ValueCountFrequency (%)
636 2
 
< 0.1%
609 2
 
< 0.1%
564 1
 
< 0.1%
555 1
 
< 0.1%
505 1
 
< 0.1%
500 3
< 0.1%
498 1
 
< 0.1%
486 5
0.1%
451 1
 
< 0.1%
446 1
 
< 0.1%

동
Real number (ℝ)

SKEWED  ZEROS 

Distinct108
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.8898
Minimum0
Maximum8002
Zeros4018
Zeros (%)40.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:16:10.234783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile8
Maximum8002
Range8002
Interquartile range (IQR)1

Descriptive statistics

Standard deviation225.68285
Coefficient of variation (CV)20.724242
Kurtosis1133.6766
Mean10.8898
Median Absolute Deviation (MAD)1
Skewness33.028386
Sum108898
Variance50932.747
MonotonicityNot monotonic
2023-12-13T06:16:10.410490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4410
44.1%
0 4018
40.2%
2 646
 
6.5%
3 185
 
1.8%
4 103
 
1.0%
5 52
 
0.5%
7 48
 
0.5%
8 35
 
0.4%
101 34
 
0.3%
6 27
 
0.3%
Other values (98) 442
 
4.4%
ValueCountFrequency (%)
0 4018
40.2%
1 4410
44.1%
2 646
 
6.5%
3 185
 
1.8%
4 103
 
1.0%
5 52
 
0.5%
6 27
 
0.3%
7 48
 
0.5%
8 35
 
0.4%
9 24
 
0.2%
ValueCountFrequency (%)
8002 2
 
< 0.1%
8001 5
0.1%
5000 1
 
< 0.1%
3202 1
 
< 0.1%
3201 2
 
< 0.1%
703 1
 
< 0.1%
614 7
0.1%
508 2
 
< 0.1%
507 1
 
< 0.1%
401 4
< 0.1%

호
Text

Distinct650
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T06:16:10.744280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.052
Min length1

Characters and Unicode

Total characters30520
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique343 ?
Unique (%)3.4%

Sample

1st row204
2nd row201
3rd row104
4th row105
5th row101
ValueCountFrequency (%)
101 2226
22.3%
102 1078
 
10.8%
103 601
 
6.0%
8101 497
 
5.0%
202 480
 
4.8%
201 334
 
3.3%
203 332
 
3.3%
104 318
 
3.2%
105 216
 
2.2%
1 177
 
1.8%
Other values (640) 3741
37.4%
2023-12-13T06:16:11.177401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 10518
34.5%
0 8963
29.4%
2 3853
 
12.6%
3 2163
 
7.1%
4 1434
 
4.7%
8 1282
 
4.2%
5 936
 
3.1%
6 637
 
2.1%
7 498
 
1.6%
9 232
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30516
> 99.9%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10518
34.5%
0 8963
29.4%
2 3853
 
12.6%
3 2163
 
7.1%
4 1434
 
4.7%
8 1282
 
4.2%
5 936
 
3.1%
6 637
 
2.1%
7 498
 
1.6%
9 232
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30520
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10518
34.5%
0 8963
29.4%
2 3853
 
12.6%
3 2163
 
7.1%
4 1434
 
4.7%
8 1282
 
4.2%
5 936
 
3.1%
6 637
 
2.1%
7 498
 
1.6%
9 232
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10518
34.5%
0 8963
29.4%
2 3853
 
12.6%
3 2163
 
7.1%
4 1434
 
4.7%
8 1282
 
4.2%
5 936
 
3.1%
6 637
 
2.1%
7 498
 
1.6%
9 232
 
0.8%

시가표준액(원)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct9066
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61020924
Minimum4680
Maximum1.9709385 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:16:11.336173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4680
5-th percentile508080
Q13225857.5
median14607025
Q353550338
95-th percentile2.1855911 × 108
Maximum1.9709385 × 1010
Range1.970938 × 1010
Interquartile range (IQR)50324480

Descriptive statistics

Standard deviation2.6392835 × 108
Coefficient of variation (CV)4.3252106
Kurtosis3137.9966
Mean61020924
Median Absolute Deviation (MAD)13426225
Skewness45.46026
Sum6.1020924 × 1011
Variance6.9658172 × 1016
MonotonicityNot monotonic
2023-12-13T06:16:11.487019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13212820 20
 
0.2%
12924760 13
 
0.1%
29694330 12
 
0.1%
36052800 11
 
0.1%
29487840 9
 
0.1%
35802080 9
 
0.1%
36303510 9
 
0.1%
1446240 8
 
0.1%
6443000 8
 
0.1%
1998000 7
 
0.1%
Other values (9056) 9894
98.9%
ValueCountFrequency (%)
4680 1
 
< 0.1%
11070 2
< 0.1%
12000 3
< 0.1%
12300 3
< 0.1%
12700 3
< 0.1%
14700 2
< 0.1%
15240 1
 
< 0.1%
15300 1
 
< 0.1%
15600 1
 
< 0.1%
24000 2
< 0.1%
ValueCountFrequency (%)
19709384890 1
< 0.1%
5749522060 1
< 0.1%
5626689300 1
< 0.1%
5052150000 1
< 0.1%
4205593420 1
< 0.1%
2603330410 1
< 0.1%
2530516410 1
< 0.1%
2136425730 1
< 0.1%
1965310990 1
< 0.1%
1951714930 2
< 0.1%

연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5822
Distinct (%)58.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.48755
Minimum0.03
Maximum24272.642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:16:11.663625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile6.3
Q125.0525
median58.8042
Q3133.9575
95-th percentile465.023
Maximum24272.642
Range24272.612
Interquartile range (IQR)108.905

Descriptive statistics

Standard deviation429.74185
Coefficient of variation (CV)3.0159958
Kurtosis1196.7724
Mean142.48755
Median Absolute Deviation (MAD)40.8042
Skewness26.141509
Sum1424875.5
Variance184678.06
MonotonicityNot monotonic
2023-12-13T06:16:11.820303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 185
 
1.8%
27.0 55
 
0.5%
62.62 35
 
0.4%
12.0 35
 
0.4%
1.0 33
 
0.3%
9.0 31
 
0.3%
36.0 31
 
0.3%
24.0 29
 
0.3%
52.3777 29
 
0.3%
13.1 27
 
0.3%
Other values (5812) 9510
95.1%
ValueCountFrequency (%)
0.03 1
 
< 0.1%
0.09 2
 
< 0.1%
0.1 10
0.1%
0.12 1
 
< 0.1%
0.2 12
0.1%
0.21 1
 
< 0.1%
0.3 6
0.1%
0.463 1
 
< 0.1%
0.5 1
 
< 0.1%
0.7 2
 
< 0.1%
ValueCountFrequency (%)
24272.6415 1
 
< 0.1%
15450.0 1
 
< 0.1%
8109.34 1
 
< 0.1%
6946.53 1
 
< 0.1%
5891.22 3
< 0.1%
5673.2678 1
 
< 0.1%
4682.67 2
< 0.1%
4573.98 3
< 0.1%
3822.37 1
 
< 0.1%
3683.43 2
< 0.1%

기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-12-31 00:00:00
Maximum2022-12-31 00:00:00
2023-12-13T06:16:11.915745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:12.029173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T06:16:07.194390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:04.880229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:05.454786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:06.026491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:06.574472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:07.297020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:04.982397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:05.566387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:06.133927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:06.679391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:07.400454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:05.085804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:05.694287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:06.242587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:06.827643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:07.517067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:05.209583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:05.822937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:06.358819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:06.948076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:07.633516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:05.332870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:05.935016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:06.479218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:16:07.089524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:16:12.097172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동특수지번본번부번동시가표준액(원)연면적(제곱미터)
과세년도1.0000.5720.1580.1130.0470.0000.0170.066
법정동0.5721.0000.4500.7840.4600.0590.0000.141
특수지번0.1580.4501.0000.1050.0000.0000.0000.000
본번0.1130.7840.1051.0000.1390.0280.0100.000
부번0.0470.4600.0000.1391.0000.0000.0000.000
동0.0000.0590.0000.0280.0001.0000.0000.000
시가표준액(원)0.0170.0000.0000.0100.0000.0001.0000.894
연면적(제곱미터)0.0660.1410.0000.0000.0000.0000.8941.000
2023-12-13T06:16:12.200466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수지번과세년도법정동
특수지번1.0000.0480.235
과세년도0.0481.0000.324
법정동0.2350.3241.000
2023-12-13T06:16:12.302458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
본번부번동시가표준액(원)연면적(제곱미터)과세년도법정동특수지번
본번1.000-0.227-0.1870.1450.0840.0670.4060.062
부번-0.2271.0000.132-0.154-0.0820.0280.1760.000
동-0.1870.1321.000-0.220-0.1180.0000.0300.000
시가표준액(원)0.145-0.154-0.2201.0000.8710.0160.0000.000
연면적(제곱미터)0.084-0.082-0.1180.8711.0000.0270.0570.000
과세년도0.0670.0280.0000.0160.0271.0000.3240.048
법정동0.4060.1760.0300.0000.0570.3241.0000.235
특수지번0.0620.0000.0000.0000.0000.0480.2351.000

Missing values

2023-12-13T06:16:07.829743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:16:08.062372image/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

시도명시군구명과세년도법정동법정리특수지번본번부번동호시가표준액(원)연면적(제곱미터)기준일자
47309대전광역시동구2019신흥동0일반1826120499285930277.3352022-12-31
55040대전광역시동구2019자양동0일반221222012160000001125.02022-12-31
39526대전광역시동구2018하소동0일반3611104417601.442022-12-31
50062대전광역시동구2019용운동0일반20631105468000012.02022-12-31
61019대전광역시동구2019가양동0일반576240101126566013.262022-12-31
39651대전광역시동구2018하소동0일반32703101828036067.322022-12-31
58272대전광역시동구2019가양동0일반1702705021231287076.432022-12-31
44348대전광역시동구2019효동0일반1687210392730028.12022-12-31
25932대전광역시동구2018성남동0일반157601291327282047.832022-12-31
94097대전광역시동구2020대동0일반403121102384384010.562022-12-31
시도명시군구명과세년도법정동법정리특수지번본번부번동호시가표준액(원)연면적(제곱미터)기준일자
68220대전광역시동구2019홍도동0일반721711026906907.82022-12-31
31713대전광역시동구2018삼성동0일반37961102847001.02022-12-31
47431대전광역시동구2019신흥동0일반1680030787120000495.02022-12-31
81922대전광역시동구2020원동0일반55170202290320038.22022-12-31
9128대전광역시동구2018판암동0일반5069010190000018.02022-12-31
10164대전광역시동구2018용운동0일반2851901014078960059.80882022-12-31
19000대전광역시동구2018가양동0일반394114053194741088.992022-12-31
55578대전광역시동구2019신안동0일반30544121620006.02022-12-31
6040대전광역시동구2018가오동0일반509002021751274025.832022-12-31
82960대전광역시동구2020원동0일반85101053525838032.6862022-12-31

Duplicate rows

Most frequently occurring

시도명시군구명과세년도법정동법정리특수지번본번부번동호시가표준액(원)연면적(제곱미터)기준일자# duplicates
1대전광역시동구2018원동0일반38103410956004.152022-12-313
0대전광역시동구2018삼성동0일반336524345600024.02022-12-312
2대전광역시동구2019신안동0일반30526114372508.252022-12-312
3대전광역시동구2019원동0일반381033264690010.382022-12-312
4대전광역시동구2020원동0일반381033254310010.382022-12-312
5대전광역시동구2020원동0일반38103410167504.152022-12-312