Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows19
Duplicate rows (%)0.2%
Total size in memory1.2 MiB
Average record size in memory130.0 B

Variable types

Categorical6
Numeric6
Text1
DateTime1

Dataset

Description일반건축물에 대한 지방세 부과기준인 시가표준액을 제공 (2017~2022년)합니다. - 활용업무 : 물건별 재산가액 확인 가능
URLhttps://www.data.go.kr/data/15080628/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
법정리 has constant value ""Constant
Dataset has 19 (0.2%) duplicate rowsDuplicates
본번 is highly overall correlated with 특수지High correlation
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly overall correlated with 본번High correlation
특수지 is highly imbalanced (97.3%)Imbalance
연면적 is highly skewed (γ1 = 27.97125685)Skewed
부번 has 2469 (24.7%) zerosZeros
동 has 784 (7.8%) zerosZeros

Reproduction

Analysis started2023-12-12 02:16:14.161268
Analysis finished2023-12-12 02:16:20.244553
Duration6.08 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

2023-12-12T11:16:20.317973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:16:20.409983image/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
유성구
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
유성구 10000
100.0%

Length

2023-12-12T11:16:20.560312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:16:20.663738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유성구 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
30200
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30200
2nd row30200
3rd row30200
4th row30200
5th row30200

Common Values

ValueCountFrequency (%)
30200 10000
100.0%

Length

2023-12-12T11:16:20.766044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:16:20.860207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30200 10000
100.0%

과세년도
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2017
6709 
2018
3291 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 6709
67.1%
2018 3291
32.9%

Length

2023-12-12T11:16:20.973041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:16:21.103498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 6709
67.1%
2018 3291
32.9%

법정동
Real number (ℝ)

Distinct53
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.1888
Minimum101
Maximum153
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:16:21.233163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile102
Q1111
median120
Q3133
95-th percentile146
Maximum153
Range52
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.889415
Coefficient of variation (CV)0.11367175
Kurtosis-0.96795356
Mean122.1888
Median Absolute Deviation (MAD)9
Skewness0.51275113
Sum1221888
Variance192.91585
MonotonicityNot monotonic
2023-12-12T11:16:21.448337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111 2337
23.4%
146 945
 
9.4%
120 770
 
7.7%
117 428
 
4.3%
101 416
 
4.2%
141 380
 
3.8%
122 345
 
3.5%
127 325
 
3.2%
112 322
 
3.2%
121 242
 
2.4%
Other values (43) 3490
34.9%
ValueCountFrequency (%)
101 416
4.2%
102 95
 
0.9%
103 150
 
1.5%
104 154
 
1.5%
105 68
 
0.7%
106 46
 
0.5%
107 30
 
0.3%
108 22
 
0.2%
109 18
 
0.2%
110 49
 
0.5%
ValueCountFrequency (%)
153 2
 
< 0.1%
152 1
 
< 0.1%
151 10
 
0.1%
150 1
 
< 0.1%
149 11
 
0.1%
148 15
 
0.1%
147 121
 
1.2%
146 945
9.4%
145 100
 
1.0%
144 210
 
2.1%

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

2023-12-12T11:16:21.590469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:16:21.703361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

특수지
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9956 
2
 
40
7
 
4

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 9956
99.6%
2 40
 
0.4%
7 4
 
< 0.1%

Length

2023-12-12T11:16:21.794555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:16:21.893602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9956
99.6%
2 40
 
0.4%
7 4
 
< 0.1%

본번
Real number (ℝ)

HIGH CORRELATION 

Distinct951
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean521.8285
Minimum1
Maximum3016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:16:22.030226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile30
Q1305
median535
Q3646
95-th percentile1100
Maximum3016
Range3015
Interquartile range (IQR)341

Descriptive statistics

Standard deviation320.05602
Coefficient of variation (CV)0.61333565
Kurtosis1.0106071
Mean521.8285
Median Absolute Deviation (MAD)169
Skewness0.70252473
Sum5218285
Variance102435.86
MonotonicityNot monotonic
2023-12-12T11:16:22.238727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
536 295
 
2.9%
535 292
 
2.9%
538 190
 
1.9%
466 157
 
1.6%
1342 135
 
1.4%
1359 134
 
1.3%
551 105
 
1.1%
1352 103
 
1.0%
533 102
 
1.0%
1 97
 
1.0%
Other values (941) 8390
83.9%
ValueCountFrequency (%)
1 97
1.0%
2 4
 
< 0.1%
3 32
 
0.3%
4 56
0.6%
5 1
 
< 0.1%
6 8
 
0.1%
8 4
 
< 0.1%
9 5
 
0.1%
10 10
 
0.1%
11 8
 
0.1%
ValueCountFrequency (%)
3016 1
 
< 0.1%
3013 1
 
< 0.1%
1375 1
 
< 0.1%
1360 23
 
0.2%
1359 134
1.3%
1354 1
 
< 0.1%
1352 103
1.0%
1351 3
 
< 0.1%
1350 14
 
0.1%
1349 5
 
0.1%

부번
Real number (ℝ)

ZEROS 

Distinct72
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5029
Minimum0
Maximum205
Zeros2469
Zeros (%)24.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:16:22.399638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile18
Maximum205
Range205
Interquartile range (IQR)6

Descriptive statistics

Standard deviation9.134601
Coefficient of variation (CV)1.6599613
Kurtosis95.208861
Mean5.5029
Median Absolute Deviation (MAD)3
Skewness6.7457715
Sum55029
Variance83.440936
MonotonicityNot monotonic
2023-12-12T11:16:22.588404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2469
24.7%
1 1392
13.9%
2 914
 
9.1%
5 713
 
7.1%
3 639
 
6.4%
4 530
 
5.3%
8 524
 
5.2%
6 520
 
5.2%
7 391
 
3.9%
9 378
 
3.8%
Other values (62) 1530
15.3%
ValueCountFrequency (%)
0 2469
24.7%
1 1392
13.9%
2 914
 
9.1%
3 639
 
6.4%
4 530
 
5.3%
5 713
 
7.1%
6 520
 
5.2%
7 391
 
3.9%
8 524
 
5.2%
9 378
 
3.8%
ValueCountFrequency (%)
205 3
< 0.1%
169 1
 
< 0.1%
97 1
 
< 0.1%
95 1
 
< 0.1%
94 1
 
< 0.1%
93 1
 
< 0.1%
92 2
< 0.1%
90 1
 
< 0.1%
88 3
< 0.1%
86 2
< 0.1%

동
Real number (ℝ)

ZEROS 

Distinct98
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4697
Minimum0
Maximum1118
Zeros784
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:16:22.753189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile4
Maximum1118
Range1118
Interquartile range (IQR)0

Descriptive statistics

Standard deviation60.571822
Coefficient of variation (CV)7.15159
Kurtosis127.82563
Mean8.4697
Median Absolute Deviation (MAD)0
Skewness10.839682
Sum84697
Variance3668.9456
MonotonicityNot monotonic
2023-12-12T11:16:22.918205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8022
80.2%
0 784
 
7.8%
2 491
 
4.9%
4 116
 
1.2%
3 113
 
1.1%
10 79
 
0.8%
103 69
 
0.7%
5 46
 
0.5%
101 39
 
0.4%
6 30
 
0.3%
Other values (88) 211
 
2.1%
ValueCountFrequency (%)
0 784
 
7.8%
1 8022
80.2%
2 491
 
4.9%
3 113
 
1.1%
4 116
 
1.2%
5 46
 
0.5%
6 30
 
0.3%
7 4
 
< 0.1%
8 4
 
< 0.1%
9 10
 
0.1%
ValueCountFrequency (%)
1118 1
 
< 0.1%
812 2
< 0.1%
811 2
< 0.1%
810 3
< 0.1%
809 2
< 0.1%
808 4
< 0.1%
806 1
 
< 0.1%
805 1
 
< 0.1%
803 2
< 0.1%
802 2
< 0.1%

호
Text

Distinct904
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T11:16:23.293773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length3.1436
Min length1

Characters and Unicode

Total characters31436
Distinct characters21
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique381 ?
Unique (%)3.8%

Sample

1st row315
2nd row301
3rd row1714
4th row416
5th row104
ValueCountFrequency (%)
101 1887
 
18.8%
102 698
 
7.0%
201 467
 
4.7%
103 394
 
3.9%
8101 386
 
3.8%
301 245
 
2.4%
104 239
 
2.4%
202 226
 
2.3%
203 216
 
2.2%
401 178
 
1.8%
Other values (886) 5092
50.8%
2023-12-12T11:16:23.793147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 10559
33.6%
0 8416
26.8%
2 3740
 
11.9%
3 2268
 
7.2%
4 1579
 
5.0%
8 1336
 
4.2%
5 1196
 
3.8%
6 956
 
3.0%
7 765
 
2.4%
9 473
 
1.5%
Other values (11) 148
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31288
99.5%
Other Letter 56
 
0.2%
Dash Punctuation 54
 
0.2%
Space Separator 28
 
0.1%
Lowercase Letter 6
 
< 0.1%
Uppercase Letter 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10559
33.7%
0 8416
26.9%
2 3740
 
12.0%
3 2268
 
7.2%
4 1579
 
5.0%
8 1336
 
4.3%
5 1196
 
3.8%
6 956
 
3.1%
7 765
 
2.4%
9 473
 
1.5%
Lowercase Letter
ValueCountFrequency (%)
u 2
33.3%
n 2
33.3%
a 1
16.7%
r 1
16.7%
Uppercase Letter
ValueCountFrequency (%)
J 2
50.0%
B 1
25.0%
M 1
25.0%
Other Letter
ValueCountFrequency (%)
월 28
50.0%
일 28
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 54
100.0%
Space Separator
ValueCountFrequency (%)
28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31370
99.8%
Hangul 56
 
0.2%
Latin 10
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10559
33.7%
0 8416
26.8%
2 3740
 
11.9%
3 2268
 
7.2%
4 1579
 
5.0%
8 1336
 
4.3%
5 1196
 
3.8%
6 956
 
3.0%
7 765
 
2.4%
9 473
 
1.5%
Other values (2) 82
 
0.3%
Latin
ValueCountFrequency (%)
J 2
20.0%
u 2
20.0%
n 2
20.0%
B 1
10.0%
M 1
10.0%
a 1
10.0%
r 1
10.0%
Hangul
ValueCountFrequency (%)
월 28
50.0%
일 28
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31380
99.8%
Hangul 56
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10559
33.6%
0 8416
26.8%
2 3740
 
11.9%
3 2268
 
7.2%
4 1579
 
5.0%
8 1336
 
4.3%
5 1196
 
3.8%
6 956
 
3.0%
7 765
 
2.4%
9 473
 
1.5%
Other values (9) 92
 
0.3%
Hangul
ValueCountFrequency (%)
월 28
50.0%
일 28
50.0%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct8388
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0328132 × 108
Minimum15840
Maximum7.01584 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:16:23.977004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15840
5-th percentile1490790
Q110888712
median31433400
Q388979095
95-th percentile3.8830639 × 108
Maximum7.01584 × 109
Range7.0158242 × 109
Interquartile range (IQR)78090382

Descriptive statistics

Standard deviation2.9137041 × 108
Coefficient of variation (CV)2.8211337
Kurtosis149.62505
Mean1.0328132 × 108
Median Absolute Deviation (MAD)26521955
Skewness10.178546
Sum1.0328132 × 1012
Variance8.4896715 × 1016
MonotonicityNot monotonic
2023-12-12T11:16:24.143774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12600000 35
 
0.4%
12040000 33
 
0.3%
3371200 24
 
0.2%
12412920 24
 
0.2%
44722020 23
 
0.2%
12988690 20
 
0.2%
13008060 19
 
0.2%
1445220 18
 
0.2%
1143900 14
 
0.1%
51917060 14
 
0.1%
Other values (8378) 9776
97.8%
ValueCountFrequency (%)
15840 2
< 0.1%
18590 1
< 0.1%
35090 1
< 0.1%
44000 1
< 0.1%
46080 1
< 0.1%
47520 1
< 0.1%
48280 1
< 0.1%
49000 1
< 0.1%
49500 1
< 0.1%
51000 2
< 0.1%
ValueCountFrequency (%)
7015840020 1
< 0.1%
6325877480 1
< 0.1%
5775000000 1
< 0.1%
5357245740 1
< 0.1%
4969056960 1
< 0.1%
4935840000 1
< 0.1%
4467615460 1
< 0.1%
4430295100 1
< 0.1%
4355388440 1
< 0.1%
4287341960 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct6684
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean210.74055
Minimum0.2839
Maximum43680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:16:24.291562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2839
5-th percentile8.354705
Q131.420075
median66.085
Q3165.615
95-th percentile811.4385
Maximum43680
Range43679.716
Interquartile range (IQR)134.19493

Descriptive statistics

Standard deviation792.84218
Coefficient of variation (CV)3.7621719
Kurtosis1215.377
Mean210.74055
Median Absolute Deviation (MAD)46.7
Skewness27.971257
Sum2107405.5
Variance628598.72
MonotonicityNot monotonic
2023-12-12T11:16:24.419754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 108
 
1.1%
70.0 83
 
0.8%
42.51 45
 
0.4%
27.0 32
 
0.3%
44.33 31
 
0.3%
15.68 30
 
0.3%
6.51 29
 
0.3%
36.0 28
 
0.3%
4.65 26
 
0.3%
95.97 24
 
0.2%
Other values (6674) 9564
95.6%
ValueCountFrequency (%)
0.2839 1
 
< 0.1%
0.78 1
 
< 0.1%
0.8198 1
 
< 0.1%
0.86 1
 
< 0.1%
0.88 1
 
< 0.1%
0.92 1
 
< 0.1%
0.9726 5
0.1%
1.0 4
< 0.1%
1.0906 1
 
< 0.1%
1.12 2
 
< 0.1%
ValueCountFrequency (%)
43680.0 1
< 0.1%
26640.0 2
< 0.1%
21000.0 1
< 0.1%
11296.12 1
< 0.1%
10777.02 1
< 0.1%
9694.3 1
< 0.1%
8561.78 1
< 0.1%
7754.88 1
< 0.1%
7677.35 1
< 0.1%
7632.96 1
< 0.1%

기준일자
Date

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2017-06-01 00:00:00
Maximum2018-06-01 00:00:00
2023-12-12T11:16:24.547627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:24.658911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

Interactions

2023-12-12T11:16:19.236439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:15.841516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:16.585948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:17.209677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:17.734208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:18.655053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:19.346299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:15.982989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:16.685928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:17.310081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:17.832712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:18.757226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:19.457405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:16.106109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:16.781058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:17.401108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:17.934144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:18.841596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:19.559792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:16.229728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:16.909509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:17.484839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:18.015351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:18.936234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:19.664154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:16.363691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:17.002439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:17.571006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:18.129843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:19.025642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:19.792196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:16.478556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:17.108837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:17.657355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:18.539872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:16:19.134862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:16:25.171205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동특수지본번부번동시가표준액연면적기준일자
과세년도1.0000.3030.0090.2080.0200.0350.0540.0001.000
법정동0.3031.0000.3640.6380.1330.3380.1420.0490.303
특수지0.0090.3641.0000.8210.0000.0000.0000.0000.009
본번0.2080.6380.8211.0000.0810.1160.0880.0300.208
부번0.0200.1330.0000.0811.0000.0000.0000.0000.020
동0.0350.3380.0000.1160.0001.0000.0870.0000.035
시가표준액0.0540.1420.0000.0880.0000.0871.0000.8760.054
연면적0.0000.0490.0000.0300.0000.0000.8761.0000.000
기준일자1.0000.3030.0090.2080.0200.0350.0540.0001.000
2023-12-12T11:16:25.302934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도특수지
과세년도1.0000.015
특수지0.0151.000
2023-12-12T11:16:25.420510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동본번부번동시가표준액연면적과세년도특수지
법정동1.0000.110-0.3980.1540.1040.0630.2320.234
본번0.1101.000-0.175-0.1440.021-0.1080.1500.506
부번-0.398-0.1751.000-0.197-0.112-0.1090.0260.000
동0.154-0.144-0.1971.0000.1290.1410.0260.000
시가표준액0.1040.021-0.1120.1291.0000.9050.0410.000
연면적0.063-0.108-0.1090.1410.9051.0000.0000.000
과세년도0.2320.1500.0260.0260.0410.0001.0000.015
특수지0.2340.5060.0000.0000.0000.0000.0151.000

Missing values

2023-12-12T11:16:19.932866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:16:20.161114image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호시가표준액연면적기준일자
76711대전광역시유성구3020020181270139581315862148016.87182018-06-01
71473대전광역시유성구30200201811301161130110113421602059.762018-06-01
70728대전광역시유성구30200201811101538801714256665010.652018-06-01
8188대전광역시유성구302002017111015517041614452206.512017-06-01
53096대전광역시유성구30200201714101336111041987462040.332017-06-01
27024대전광역시유성구302002017124012304301253289180729.942017-06-01
3770대전광역시유성구302002017111014465110250973005.512017-06-01
57398대전광역시유성구3020020171330153218081014472202095.972017-06-01
1440대전광역시유성구3020020171110153653314147131280183.2272017-06-01
39413대전광역시유성구3020020171010188511013938346054.24722017-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호시가표준액연면적기준일자
93053대전광역시유성구30200201814501196211022796127057.382018-06-01
30395대전광역시유성구302002017126011610123184331800289.82017-06-01
40773대전광역시유성구302002017110023701101372960066.62017-06-01
61789대전광역시유성구302002017121016640120122990522502688.952017-06-01
10511대전광역시유성구30200201712001104001061013624011042.51042017-06-01
37873대전광역시유성구30200201713901522118101190029780285.332017-06-01
5720대전광역시유성구302002017111015491150983698240104.62282017-06-01
68516대전광역시유성구3020020181110154751510368982760680.782018-06-01
82836대전광역시유성구30200201810201643611013696440068.22018-06-01
3054대전광역시유성구3020020171110153550701533654021.012017-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호시가표준액연면적기준일자# duplicates
10대전광역시유성구3020020171350112301201135936770342.412017-06-013
0대전광역시유성구3020020171010110101201260000070.02017-06-012
1대전광역시유성구3020020171010110101881260000070.02017-06-012
2대전광역시유성구3020020171010110101971260000070.02017-06-012
3대전광역시유성구30200201712101705311101390094029.53252017-06-012
4대전광역시유성구302002017122012200210121600012.02017-06-012
5대전광역시유성구3020020171240123041012207740066.12017-06-012
6대전광역시유성구302002017134012230120183721150242.672017-06-012
7대전광역시유성구30200201713401235021013402002.12017-06-012
8대전광역시유성구30200201713401235021011176000049.02017-06-012