Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory1.3 MiB
Average record size in memory139.0 B

Variable types

Categorical7
Numeric7
Text1

Dataset

Description일반건축물에 대한 지방세 부과기준인 시가표준액에 대한 데이터로 시가표준 금액, 연면적, 결정일자 항목 등을 제공합니다.
URLhttps://www.data.go.kr/data/15080537/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 1 (< 0.1%) duplicate rowsDuplicates
과세년도 is highly overall correlated with 결정일자High correlation
결정일자 is highly overall correlated with 과세년도High correlation
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (98.0%)Imbalance
시가표준액 is highly skewed (γ1 = 24.15230779)Skewed
부번 has 1016 (10.2%) zerosZeros
동 has 5445 (54.4%) zerosZeros

Reproduction

Analysis started2023-12-12 00:48:19.166426
Analysis finished2023-12-12 00:48:27.874012
Duration8.71 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

2023-12-12T09:48:27.961140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:48:28.062446image/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
대덕구
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대덕구 10000
100.0%

Length

2023-12-12T09:48:28.184685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:48:28.296544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대덕구 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
30230
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30230
2nd row30230
3rd row30230
4th row30230
5th row30230

Common Values

ValueCountFrequency (%)
30230 10000
100.0%

Length

2023-12-12T09:48:28.415329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:48:28.516523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30230 10000
100.0%

과세년도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2018
4568 
2017
4453 
2019
979 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2018 4568
45.7%
2017 4453
44.5%
2019 979
 
9.8%

Length

2023-12-12T09:48:28.612398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:48:28.706767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 4568
45.7%
2017 4453
44.5%
2019 979
 
9.8%

법정동
Real number (ℝ)

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.6456
Minimum101
Maximum126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:48:28.826497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1102
median109
Q3113
95-th percentile126
Maximum126
Range25
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.7358843
Coefficient of variation (CV)0.061998685
Kurtosis0.35548167
Mean108.6456
Median Absolute Deviation (MAD)6
Skewness0.85330908
Sum1086456
Variance45.372138
MonotonicityNot monotonic
2023-12-12T09:48:28.968906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
101 1717
17.2%
109 1303
13.0%
102 1296
13.0%
107 709
 
7.1%
110 654
 
6.5%
126 585
 
5.9%
114 437
 
4.4%
113 427
 
4.3%
111 415
 
4.2%
103 378
 
3.8%
Other values (14) 2079
20.8%
ValueCountFrequency (%)
101 1717
17.2%
102 1296
13.0%
103 378
 
3.8%
104 80
 
0.8%
105 105
 
1.1%
106 165
 
1.7%
107 709
7.1%
108 349
 
3.5%
109 1303
13.0%
110 654
 
6.5%
ValueCountFrequency (%)
126 585
5.9%
125 25
 
0.2%
124 9
 
0.1%
121 4
 
< 0.1%
120 19
 
0.2%
119 22
 
0.2%
118 51
 
0.5%
117 357
3.6%
116 339
3.4%
115 310
3.1%

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

2023-12-12T09:48:29.092309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:48:29.189509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9981 
2
 
19

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 9981
99.8%
2 19
 
0.2%

Length

2023-12-12T09:48:29.292745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:48:29.391110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9981
99.8%
2 19
 
0.2%

본번
Real number (ℝ)

Distinct603
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean308.2512
Minimum1
Maximum1709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:48:29.520087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q1120
median251
Q3389
95-th percentile770
Maximum1709
Range1708
Interquartile range (IQR)269

Descriptive statistics

Standard deviation324.67946
Coefficient of variation (CV)1.053295
Kurtosis10.173387
Mean308.2512
Median Absolute Deviation (MAD)133
Skewness3.0039886
Sum3082512
Variance105416.75
MonotonicityNot monotonic
2023-12-12T09:48:29.707345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
289 439
 
4.4%
100 203
 
2.0%
40 153
 
1.5%
48 111
 
1.1%
133 102
 
1.0%
445 98
 
1.0%
1 83
 
0.8%
450 81
 
0.8%
290 77
 
0.8%
191 70
 
0.7%
Other values (593) 8583
85.8%
ValueCountFrequency (%)
1 83
0.8%
2 6
 
0.1%
3 21
 
0.2%
4 13
 
0.1%
5 4
 
< 0.1%
6 2
 
< 0.1%
7 4
 
< 0.1%
8 4
 
< 0.1%
9 13
 
0.1%
10 28
 
0.3%
ValueCountFrequency (%)
1709 1
 
< 0.1%
1696 20
0.2%
1695 17
 
0.2%
1694 8
 
0.1%
1693 12
 
0.1%
1692 7
 
0.1%
1691 1
 
< 0.1%
1690 18
 
0.2%
1689 10
 
0.1%
1688 48
0.5%

부번
Real number (ℝ)

ZEROS 

Distinct153
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.7171
Minimum0
Maximum976
Zeros1016
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:48:29.900029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q312
95-th percentile36
Maximum976
Range976
Interquartile range (IQR)11

Descriptive statistics

Standard deviation33.498512
Coefficient of variation (CV)2.8589422
Kurtosis225.25463
Mean11.7171
Median Absolute Deviation (MAD)4
Skewness12.899359
Sum117171
Variance1122.1503
MonotonicityNot monotonic
2023-12-12T09:48:30.068429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1862
18.6%
0 1016
 
10.2%
2 775
 
7.8%
3 656
 
6.6%
4 605
 
6.0%
5 503
 
5.0%
7 409
 
4.1%
6 407
 
4.1%
9 311
 
3.1%
8 302
 
3.0%
Other values (143) 3154
31.5%
ValueCountFrequency (%)
0 1016
10.2%
1 1862
18.6%
2 775
7.8%
3 656
 
6.6%
4 605
 
6.0%
5 503
 
5.0%
6 407
 
4.1%
7 409
 
4.1%
8 302
 
3.0%
9 311
 
3.1%
ValueCountFrequency (%)
976 1
 
< 0.1%
632 3
< 0.1%
631 1
 
< 0.1%
623 1
 
< 0.1%
617 1
 
< 0.1%
616 1
 
< 0.1%
611 1
 
< 0.1%
576 1
 
< 0.1%
575 1
 
< 0.1%
563 1
 
< 0.1%

동
Real number (ℝ)

ZEROS 

Distinct49
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.4936
Minimum0
Maximum4004
Zeros5445
Zeros (%)54.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:48:30.251601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile10.05
Maximum4004
Range4004
Interquartile range (IQR)1

Descriptive statistics

Standard deviation155.15146
Coefficient of variation (CV)6.6039882
Kurtosis160.38454
Mean23.4936
Median Absolute Deviation (MAD)0
Skewness10.134378
Sum234936
Variance24071.975
MonotonicityNot monotonic
2023-12-12T09:48:30.454581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 5445
54.4%
1 3405
34.1%
2 279
 
2.8%
900 195
 
1.9%
6 107
 
1.1%
3 92
 
0.9%
4 40
 
0.4%
16 38
 
0.4%
14 34
 
0.3%
9 29
 
0.3%
Other values (39) 336
 
3.4%
ValueCountFrequency (%)
0 5445
54.4%
1 3405
34.1%
2 279
 
2.8%
3 92
 
0.9%
4 40
 
0.4%
5 24
 
0.2%
6 107
 
1.1%
7 27
 
0.3%
8 26
 
0.3%
9 29
 
0.3%
ValueCountFrequency (%)
4004 1
 
< 0.1%
4001 1
 
< 0.1%
3008 1
 
< 0.1%
3007 2
 
< 0.1%
3006 1
 
< 0.1%
907 1
 
< 0.1%
902 3
 
< 0.1%
901 17
 
0.2%
900 195
1.9%
361 6
 
0.1%

호
Real number (ℝ)

Distinct448
Distinct (%)4.5%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean867.43079
Minimum0
Maximum8888
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:48:30.669715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile101
Q1102
median119
Q3209
95-th percentile8101
Maximum8888
Range8888
Interquartile range (IQR)107

Descriptive statistics

Standard deviation2241.9638
Coefficient of variation (CV)2.5846025
Kurtosis6.5009978
Mean867.43079
Median Absolute Deviation (MAD)18
Skewness2.9092129
Sum8672573
Variance5026401.6
MonotonicityNot monotonic
2023-12-12T09:48:30.873249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 1758
17.6%
102 1120
 
11.2%
103 603
 
6.0%
8101 534
 
5.3%
191 488
 
4.9%
104 365
 
3.6%
203 295
 
2.9%
201 289
 
2.9%
202 282
 
2.8%
105 255
 
2.5%
Other values (438) 4009
40.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 14
0.1%
2 6
0.1%
3 7
0.1%
4 4
 
< 0.1%
5 2
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
8888 1
 
< 0.1%
8519 1
 
< 0.1%
8303 1
 
< 0.1%
8301 1
 
< 0.1%
8225 1
 
< 0.1%
8221 1
 
< 0.1%
8218 1
 
< 0.1%
8204 1
 
< 0.1%
8202 3
< 0.1%
8201 7
0.1%

물건지
Text

Distinct9070
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T09:48:31.265269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length31
Mean length25.8756
Min length20

Characters and Unicode

Total characters258756
Distinct characters109
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

Unique8212 ?
Unique (%)82.1%

Sample

1st row[ 선비마을로 72 ] 0000동 0121호
2nd row대전광역시 대덕구 법동 187 1동 114호
3rd row대전광역시 대덕구 대화동 289-1 6동 104호
4th row[ 대전천북로 292-29 ] 0000동 0107호
5th row대전광역시 대덕구 신일동 1687-3 8102호
ValueCountFrequency (%)
11170
19.2%
대전광역시 4415
 
7.6%
대덕구 4415
 
7.6%
0000동 3746
 
6.4%
1동 1727
 
3.0%
0001동 1678
 
2.9%
0101호 1165
 
2.0%
대화동 1057
 
1.8%
0102호 833
 
1.4%
오정동 700
 
1.2%
Other values (3286) 27409
47.0%
2023-12-12T09:48:31.871486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48315
18.7%
0 36472
14.1%
1 23944
 
9.3%
동 13624
 
5.3%
대 11545
 
4.5%
호 10042
 
3.9%
2 9155
 
3.5%
3 6355
 
2.5%
] 5585
 
2.2%
[ 5585
 
2.2%
Other values (99) 88134
34.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 100685
38.9%
Other Letter 94293
36.4%
Space Separator 48315
18.7%
Close Punctuation 5585
 
2.2%
Open Punctuation 5585
 
2.2%
Dash Punctuation 4293
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 13624
14.4%
대 11545
12.2%
호 10042
 
10.6%
로 5348
 
5.7%
덕 5074
 
5.4%
전 4739
 
5.0%
시 4422
 
4.7%
구 4415
 
4.7%
역 4415
 
4.7%
광 4415
 
4.7%
Other values (85) 26254
27.8%
Decimal Number
ValueCountFrequency (%)
0 36472
36.2%
1 23944
23.8%
2 9155
 
9.1%
3 6355
 
6.3%
4 5237
 
5.2%
8 4492
 
4.5%
5 4061
 
4.0%
9 4039
 
4.0%
6 3505
 
3.5%
7 3425
 
3.4%
Space Separator
ValueCountFrequency (%)
48315
100.0%
Close Punctuation
ValueCountFrequency (%)
] 5585
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 5585
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4293
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 164463
63.6%
Hangul 94293
36.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 13624
14.4%
대 11545
12.2%
호 10042
 
10.6%
로 5348
 
5.7%
덕 5074
 
5.4%
전 4739
 
5.0%
시 4422
 
4.7%
구 4415
 
4.7%
역 4415
 
4.7%
광 4415
 
4.7%
Other values (85) 26254
27.8%
Common
ValueCountFrequency (%)
48315
29.4%
0 36472
22.2%
1 23944
14.6%
2 9155
 
5.6%
3 6355
 
3.9%
] 5585
 
3.4%
[ 5585
 
3.4%
4 5237
 
3.2%
8 4492
 
2.7%
- 4293
 
2.6%
Other values (4) 15030
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 164463
63.6%
Hangul 94293
36.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48315
29.4%
0 36472
22.2%
1 23944
14.6%
2 9155
 
5.6%
3 6355
 
3.9%
] 5585
 
3.4%
[ 5585
 
3.4%
4 5237
 
3.2%
8 4492
 
2.7%
- 4293
 
2.6%
Other values (4) 15030
 
9.1%
Hangul
ValueCountFrequency (%)
동 13624
14.4%
대 11545
12.2%
호 10042
 
10.6%
로 5348
 
5.7%
덕 5074
 
5.4%
전 4739
 
5.0%
시 4422
 
4.7%
구 4415
 
4.7%
역 4415
 
4.7%
광 4415
 
4.7%
Other values (85) 26254
27.8%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8954
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66620228
Minimum17000
Maximum1.298368 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:48:32.130992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17000
5-th percentile419995
Q15076682.5
median19647125
Q360039210
95-th percentile2.3850422 × 108
Maximum1.298368 × 1010
Range1.2983663 × 1010
Interquartile range (IQR)54962528

Descriptive statistics

Standard deviation2.5786147 × 108
Coefficient of variation (CV)3.8706182
Kurtosis890.38461
Mean66620228
Median Absolute Deviation (MAD)17499505
Skewness24.152308
Sum6.6620228 × 1011
Variance6.6492535 × 1016
MonotonicityNot monotonic
2023-12-12T09:48:32.333933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14979990 41
 
0.4%
14952230 36
 
0.4%
14893650 34
 
0.3%
14866050 30
 
0.3%
16458900 20
 
0.2%
16489440 16
 
0.2%
270000 13
 
0.1%
16364040 13
 
0.1%
306000 12
 
0.1%
16394400 11
 
0.1%
Other values (8944) 9774
97.7%
ValueCountFrequency (%)
17000 1
< 0.1%
24000 1
< 0.1%
30000 1
< 0.1%
30150 1
< 0.1%
33600 1
< 0.1%
36000 1
< 0.1%
36300 1
< 0.1%
40800 2
< 0.1%
42110 1
< 0.1%
43200 1
< 0.1%
ValueCountFrequency (%)
12983680020 1
< 0.1%
8650659440 1
< 0.1%
6160126500 1
< 0.1%
5672260800 1
< 0.1%
5213280040 1
< 0.1%
5000221200 1
< 0.1%
4947784800 1
< 0.1%
4790446880 1
< 0.1%
4394480850 2
< 0.1%
4362667200 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct5931
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean194.00188
Minimum0.45
Maximum27577.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:48:32.511654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.45
5-th percentile6.9585
Q131.2575
median72.165
Q3157.2
95-th percentile651.1975
Maximum27577.91
Range27577.46
Interquartile range (IQR)125.9425

Descriptive statistics

Standard deviation716.63212
Coefficient of variation (CV)3.6939442
Kurtosis465.55171
Mean194.00188
Median Absolute Deviation (MAD)49.955
Skewness18.421694
Sum1940018.8
Variance513561.59
MonotonicityNot monotonic
2023-12-12T09:48:32.655416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 163
 
1.6%
43.17 75
 
0.8%
80.0 72
 
0.7%
43.09 69
 
0.7%
12.0 48
 
0.5%
27.0 47
 
0.5%
43.12 39
 
0.4%
43.2 39
 
0.4%
24.0 34
 
0.3%
36.0 29
 
0.3%
Other values (5921) 9385
93.8%
ValueCountFrequency (%)
0.45 1
 
< 0.1%
0.69 1
 
< 0.1%
0.72 2
 
< 0.1%
0.73 2
 
< 0.1%
0.8 1
 
< 0.1%
0.82 1
 
< 0.1%
0.84 1
 
< 0.1%
0.8961 1
 
< 0.1%
0.99 1
 
< 0.1%
1.0 26
0.3%
ValueCountFrequency (%)
27577.91 1
< 0.1%
18226.45 1
< 0.1%
17973.62 1
< 0.1%
17670.66 1
< 0.1%
16405.64 1
< 0.1%
16139.29 1
< 0.1%
15043.68 1
< 0.1%
14755.81 1
< 0.1%
12444.7 1
< 0.1%
11781.45 2
< 0.1%

결정일자
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2018-06-01
4568 
2017-06-01
4453 
2019-06-01
979 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-06-01
2nd row2017-06-01
3rd row2018-06-01
4th row2017-06-01
5th row2018-06-01

Common Values

ValueCountFrequency (%)
2018-06-01 4568
45.7%
2017-06-01 4453
44.5%
2019-06-01 979
 
9.8%

Length

2023-12-12T09:48:32.781554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:48:32.893271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018-06-01 4568
45.7%
2017-06-01 4453
44.5%
2019-06-01 979
 
9.8%

Interactions

2023-12-12T09:48:26.287580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:21.222438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:21.928135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:22.793940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:23.653441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:24.639552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:25.428492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:26.426655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:21.312055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:22.031609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:22.915214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:23.772188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:24.740820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:25.535993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:26.569594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:21.426125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:22.156671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:23.026623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:23.915554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:24.840950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:25.641975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:26.721230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:21.520405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:22.274329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:23.141798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:24.049988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:24.942566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:25.765777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:26.848256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:21.626468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:22.435277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:23.250549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:24.187538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:25.060090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:25.911638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:26.943156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:21.725413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:22.576277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:23.355404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:24.326723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:25.177789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:26.024513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:27.075257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:21.826458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:22.690686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:23.515271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:24.481222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:25.291577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:48:26.148521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:48:32.988691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동특수지본번부번동호시가표준액연면적결정일자
과세년도1.0000.4540.0000.0900.0120.0240.0130.0000.0101.000
법정동0.4541.0000.0780.5310.0820.1660.1080.0470.0720.454
특수지0.0000.0781.0000.0480.0000.0560.0000.0000.0000.000
본번0.0900.5310.0481.0000.0800.0210.2000.0650.0710.090
부번0.0120.0820.0000.0801.0000.0650.0000.0000.0000.012
동0.0240.1660.0560.0210.0651.0000.0700.0000.0000.024
호0.0130.1080.0000.2000.0000.0701.0000.0000.0000.013
시가표준액0.0000.0470.0000.0650.0000.0000.0001.0000.8240.000
연면적0.0100.0720.0000.0710.0000.0000.0000.8241.0000.010
결정일자1.0000.4540.0000.0900.0120.0240.0130.0000.0101.000
2023-12-12T09:48:33.137973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도결정일자특수지
과세년도1.0001.0000.000
결정일자1.0001.0000.000
특수지0.0000.0001.000
2023-12-12T09:48:33.272262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동본번부번동호시가표준액연면적과세년도특수지결정일자
법정동1.000-0.103-0.036-0.044-0.0050.0520.0320.2220.0740.222
본번-0.1031.000-0.042-0.1200.0100.046-0.0120.0600.0520.060
부번-0.036-0.0421.000-0.190-0.150-0.035-0.0170.0070.0000.007
동-0.044-0.120-0.1901.0000.052-0.142-0.0940.0220.0370.022
호-0.0050.010-0.1500.0521.0000.0920.0400.0120.0000.012
시가표준액0.0520.046-0.035-0.1420.0921.0000.8560.0000.0000.000
연면적0.032-0.012-0.017-0.0940.0400.8561.0000.0060.0000.006
과세년도0.2220.0600.0070.0220.0120.0000.0061.0000.0001.000
특수지0.0740.0520.0000.0370.0000.0000.0000.0001.0000.000
결정일자0.2220.0600.0070.0220.0120.0000.0061.0000.0001.000

Missing values

2023-12-12T09:48:27.532949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:48:27.763757image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적결정일자
22214대전광역시대덕구3023020171070151000121[ 선비마을로 72 ] 0000동 0121호28599009.38292017-06-01
27309대전광역시대덕구3023020171080118701114대전광역시 대덕구 법동 187 1동 114호380880033.122017-06-01
59532대전광역시대덕구3023020181020128916104대전광역시 대덕구 대화동 289-1 6동 104호1636404043.122018-06-01
23611대전광역시대덕구3023020171010130450107[ 대전천북로 292-29 ] 0000동 0107호788025033.252017-06-01
78778대전광역시대덕구302302018114011687308102대전광역시 대덕구 신일동 1687-3 8102호226200000780.02018-06-01
8248대전광역시대덕구30230201711101182180203[ 석봉로38번길 38 ] 0000동 0203호51998400125.62017-06-01
24318대전광역시대덕구302302017101012335008103[ 한남로59번길 12 ] 0000동 8103호655038027.182017-06-01
70329대전광역시대덕구3023020181170117000104대전광역시 대덕구 평촌동 170 104호495040020.82018-06-01
71816대전광역시대덕구3023020181260183103109대전광역시 대덕구 신탄진동 83-10 3동 109호111073035.832018-06-01
36167대전광역시대덕구302302017101017052020211대전광역시 대덕구 오정동 705-202 211호29952000128.02017-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적결정일자
7927대전광역시대덕구302302017113014210304대전광역시 대덕구 문평동 42-1 304호216172800648.02017-06-01
87854대전광역시대덕구3023020191010137930102[ 오정로 28 ] 0000동 0102호48632670120.142019-06-01
65188대전광역시대덕구30230201810901241100102[ 홍도로113번길 22 ] 0000동 0102호1238218041.862018-06-01
54841대전광역시대덕구302302018101019331304[ 옛신탄진로 256 ] 0001동 0304호232170032.72018-06-01
13658대전광역시대덕구3023020171070144540133[ 동춘당로114번길 9 ] 0000동 0133호66056608.712017-06-01
30909대전광역시대덕구3023020171180177801191대전광역시 대덕구 장동 778 1동 191호10800018.02017-06-01
34257대전광역시대덕구30230201710201250191102[ 생산5길 42 ] 0001동 0102호2661120063.02017-06-01
50787대전광역시대덕구30230201810201330121대전광역시 대덕구 대화동 3-3 121호95729630318.782018-06-01
67469대전광역시대덕구30230201810601772900191대전광역시 대덕구 와동 77-2 900동 191호126000018.02018-06-01
83992대전광역시대덕구3023020191100114981104대전광역시 대덕구 비래동 149-8 1동 104호159000015.02019-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적결정일자# duplicates
0대전광역시대덕구30230201810101381160102[ 대전로1087번길 27 ] 0000동 0102호1809180026.222018-06-012