Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows12
Duplicate rows (%)0.1%
Total size in memory1.3 MiB
Average record size in memory137.0 B

Variable types

Categorical7
Numeric6
Text2

Dataset

Description2017년~2020년 일반건축물에 대한 지방세 부과기준인 시가표준액을 물건지, 시가표준금액, 연면적, 결정일자 항목으로 공개
Author경상북도 청도군
URLhttps://www.data.go.kr/data/15080485/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
Dataset has 12 (0.1%) duplicate rowsDuplicates
기준일자 is highly overall correlated with 과세년도High correlation
과세년도 is highly overall correlated with 기준일자High correlation
법정동 is highly overall correlated with 법정리High correlation
법정리 is highly overall correlated with 법정동High correlation
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (90.6%)Imbalance
동 is highly imbalanced (86.3%)Imbalance
시가표준액 is highly skewed (γ1 = 24.28523006)Skewed
부번 has 4358 (43.6%) zerosZeros

Reproduction

Analysis started2023-12-12 18:13:36.110251
Analysis finished2023-12-12 18:13:42.393656
Duration6.28 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
경상북도
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 10000
100.0%

Length

2023-12-13T03:13:42.462620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:13:42.583196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
청도군 10000
100.0%

Length

2023-12-13T03:13:42.730131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:13:42.870475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청도군 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
47820
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row47820
2nd row47820
3rd row47820
4th row47820
5th row47820

Common Values

ValueCountFrequency (%)
47820 10000
100.0%

Length

2023-12-13T03:13:42.994119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:13:43.092454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
47820 10000
100.0%

과세년도
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020
2754 
2019
2609 
2018
2336 
2017
2301 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2018
3rd row2020
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2020 2754
27.5%
2019 2609
26.1%
2018 2336
23.4%
2017 2301
23.0%

Length

2023-12-13T03:13:43.205808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:13:43.330078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 2754
27.5%
2019 2609
26.1%
2018 2336
23.4%
2017 2301
23.0%

법정동
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean309.1227
Minimum250
Maximum370
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:13:43.504629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile250
Q1253
median320
Q3350
95-th percentile370
Maximum370
Range120
Interquartile range (IQR)97

Descriptive statistics

Standard deviation46.143042
Coefficient of variation (CV)0.14927096
Kurtosis-1.5851794
Mean309.1227
Median Absolute Deviation (MAD)40
Skewness-0.23120774
Sum3091227
Variance2129.1804
MonotonicityNot monotonic
2023-12-13T03:13:43.906173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
253 2079
20.8%
250 1559
15.6%
340 1184
11.8%
360 1089
10.9%
320 1086
10.9%
370 1029
10.3%
330 747
 
7.5%
350 644
 
6.4%
310 583
 
5.8%
ValueCountFrequency (%)
250 1559
15.6%
253 2079
20.8%
310 583
 
5.8%
320 1086
10.9%
330 747
 
7.5%
340 1184
11.8%
350 644
 
6.4%
360 1089
10.9%
370 1029
10.3%
ValueCountFrequency (%)
370 1029
10.3%
360 1089
10.9%
350 644
 
6.4%
340 1184
11.8%
330 747
 
7.5%
320 1086
10.9%
310 583
 
5.8%
253 2079
20.8%
250 1559
15.6%

법정리
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.3751
Minimum21
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:13:44.033044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q131
median35
Q340
95-th percentile53
Maximum60
Range39
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.9445179
Coefficient of variation (CV)0.25284785
Kurtosis-0.11665826
Mean35.3751
Median Absolute Deviation (MAD)5
Skewness0.39515049
Sum353751
Variance80.0044
MonotonicityNot monotonic
2023-12-13T03:13:44.197168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
21 968
 
9.7%
36 868
 
8.7%
31 642
 
6.4%
33 634
 
6.3%
34 605
 
6.0%
32 540
 
5.4%
35 421
 
4.2%
44 401
 
4.0%
40 368
 
3.7%
37 351
 
3.5%
Other values (30) 4202
42.0%
ValueCountFrequency (%)
21 968
9.7%
22 166
 
1.7%
23 95
 
0.9%
24 57
 
0.6%
25 209
 
2.1%
26 162
 
1.6%
27 328
 
3.3%
28 124
 
1.2%
29 241
 
2.4%
30 124
 
1.2%
ValueCountFrequency (%)
60 18
 
0.2%
59 57
0.6%
58 31
 
0.3%
57 87
0.9%
56 42
 
0.4%
55 88
0.9%
54 109
1.1%
53 96
1.0%
52 98
1.0%
51 83
0.8%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9879 
2
 
121

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 9879
98.8%
2 121
 
1.2%

Length

2023-12-13T03:13:44.387799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:13:44.511951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9879
98.8%
2 121
 
1.2%

본번
Real number (ℝ)

Distinct1417
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean601.8605
Minimum1
Maximum2202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:13:44.628424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile51
Q1270
median533
Q3824.25
95-th percentile1419.05
Maximum2202
Range2201
Interquartile range (IQR)554.25

Descriptive statistics

Standard deviation426.33786
Coefficient of variation (CV)0.70836658
Kurtosis0.81625194
Mean601.8605
Median Absolute Deviation (MAD)277
Skewness0.94021448
Sum6018605
Variance181763.97
MonotonicityNot monotonic
2023-12-13T03:13:44.800770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
478 80
 
0.8%
300 78
 
0.8%
1462 49
 
0.5%
143 45
 
0.4%
969 45
 
0.4%
752 42
 
0.4%
1842 40
 
0.4%
453 35
 
0.4%
1143 34
 
0.3%
15 33
 
0.3%
Other values (1407) 9519
95.2%
ValueCountFrequency (%)
1 9
 
0.1%
2 29
0.3%
3 28
0.3%
4 3
 
< 0.1%
5 24
0.2%
6 17
0.2%
7 15
0.1%
8 12
0.1%
9 7
 
0.1%
10 7
 
0.1%
ValueCountFrequency (%)
2202 1
 
< 0.1%
2145 5
0.1%
2134 3
< 0.1%
2112 2
 
< 0.1%
2084 1
 
< 0.1%
2081 2
 
< 0.1%
2078 5
0.1%
2076 1
 
< 0.1%
2075 1
 
< 0.1%
2074 2
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct139
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5534
Minimum0
Maximum340
Zeros4358
Zeros (%)43.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:13:44.969604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile22
Maximum340
Range340
Interquartile range (IQR)3

Descriptive statistics

Standard deviation19.005996
Coefficient of variation (CV)3.4224071
Kurtosis66.531322
Mean5.5534
Median Absolute Deviation (MAD)1
Skewness7.032246
Sum55534
Variance361.22787
MonotonicityNot monotonic
2023-12-13T03:13:45.196752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4358
43.6%
1 1870
18.7%
2 909
 
9.1%
3 579
 
5.8%
4 415
 
4.2%
5 278
 
2.8%
7 193
 
1.9%
6 191
 
1.9%
8 135
 
1.4%
11 98
 
1.0%
Other values (129) 974
 
9.7%
ValueCountFrequency (%)
0 4358
43.6%
1 1870
18.7%
2 909
 
9.1%
3 579
 
5.8%
4 415
 
4.2%
5 278
 
2.8%
6 191
 
1.9%
7 193
 
1.9%
8 135
 
1.4%
9 95
 
0.9%
ValueCountFrequency (%)
340 3
< 0.1%
283 1
 
< 0.1%
224 2
 
< 0.1%
186 1
 
< 0.1%
179 1
 
< 0.1%
174 5
0.1%
173 1
 
< 0.1%
172 5
0.1%
170 2
 
< 0.1%
169 3
< 0.1%

동
Categorical

IMBALANCE 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9040 
0
 
541
2
 
322
3
 
51
4
 
15
Other values (17)
 
31

Length

Max length4
Median length1
Mean length1.0036
Min length1

Unique

Unique9 ?
Unique (%)0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9040
90.4%
0 541
 
5.4%
2 322
 
3.2%
3 51
 
0.5%
4 15
 
0.1%
101 5
 
0.1%
8 3
 
< 0.1%
11 3
 
< 0.1%
16 3
 
< 0.1%
14 2
 
< 0.1%
Other values (12) 15
 
0.1%

Length

2023-12-13T03:13:45.397447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 9041
90.4%
0 542
 
5.4%
2 322
 
3.2%
3 51
 
0.5%
4 15
 
0.1%
101 5
 
< 0.1%
8 3
 
< 0.1%
11 3
 
< 0.1%
16 3
 
< 0.1%
10 2
 
< 0.1%
Other values (11) 14
 
0.1%

호
Text

Distinct72
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T03:13:45.605258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length1
Mean length1.3228
Min length1

Characters and Unicode

Total characters13228
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)0.2%

Sample

1st row3
2nd row3
3rd row10
4th row8101
5th row1
ValueCountFrequency (%)
1 3783
37.8%
2 1799
18.0%
3 1014
 
10.1%
0 676
 
6.8%
101 546
 
5.5%
4 503
 
5.0%
201 307
 
3.1%
5 253
 
2.5%
102 177
 
1.8%
6 158
 
1.6%
Other values (62) 784
 
7.8%
2023-12-13T03:13:45.921436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6011
45.4%
2 2434
18.4%
0 2198
 
16.6%
3 1213
 
9.2%
4 559
 
4.2%
5 298
 
2.3%
8 177
 
1.3%
6 175
 
1.3%
7 109
 
0.8%
9 52
 
0.4%
Other values (2) 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13226
> 99.9%
Other Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6011
45.4%
2 2434
18.4%
0 2198
 
16.6%
3 1213
 
9.2%
4 559
 
4.2%
5 298
 
2.3%
8 177
 
1.3%
6 175
 
1.3%
7 109
 
0.8%
9 52
 
0.4%
Other Letter
ValueCountFrequency (%)
농 1
50.0%
막 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13226
> 99.9%
Hangul 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6011
45.4%
2 2434
18.4%
0 2198
 
16.6%
3 1213
 
9.2%
4 559
 
4.2%
5 298
 
2.3%
8 177
 
1.3%
6 175
 
1.3%
7 109
 
0.8%
9 52
 
0.4%
Hangul
ValueCountFrequency (%)
농 1
50.0%
막 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13226
> 99.9%
Hangul 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6011
45.4%
2 2434
18.4%
0 2198
 
16.6%
3 1213
 
9.2%
4 559
 
4.2%
5 298
 
2.3%
8 177
 
1.3%
6 175
 
1.3%
7 109
 
0.8%
9 52
 
0.4%
Hangul
ValueCountFrequency (%)
농 1
50.0%
막 1
50.0%

물건지
Text

Distinct7691
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T03:13:46.264497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length33
Mean length26.3333
Min length18

Characters and Unicode

Total characters263333
Distinct characters189
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

Unique5866 ?
Unique (%)58.7%

Sample

1st row[ 남성현로 167-17 ] 0001동 0003호
2nd row경상북도 청도군 이서면 구라리 787 1동 3호
3rd row경상북도 청도군 각북면 우산리 763 1동 10호
4th row경상북도 청도군 화양읍 삼신리 693-2 1동 8101호
5th row경상북도 청도군 이서면 칠곡리 68 1동 1호
ValueCountFrequency (%)
경상북도 7350
 
11.0%
청도군 7350
 
11.0%
1동 6648
 
10.0%
5300
 
7.9%
1호 2634
 
3.9%
0001동 2393
 
3.6%
2호 1408
 
2.1%
청도읍 1333
 
2.0%
화양읍 1164
 
1.7%
0001호 1148
 
1.7%
Other values (3988) 29974
44.9%
2023-12-13T03:13:46.767530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
56702
21.5%
1 22828
 
8.7%
0 19210
 
7.3%
도 16161
 
6.1%
동 10212
 
3.9%
호 9813
 
3.7%
청 9108
 
3.5%
북 7990
 
3.0%
상 7553
 
2.9%
리 7511
 
2.9%
Other values (179) 96245
36.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 123521
46.9%
Decimal Number 72814
27.7%
Space Separator 56702
21.5%
Dash Punctuation 4996
 
1.9%
Open Punctuation 2650
 
1.0%
Close Punctuation 2650
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 16161
13.1%
동 10212
 
8.3%
호 9813
 
7.9%
청 9108
 
7.4%
북 7990
 
6.5%
상 7553
 
6.1%
리 7511
 
6.1%
경 7350
 
6.0%
군 7350
 
6.0%
면 4853
 
3.9%
Other values (165) 35620
28.8%
Decimal Number
ValueCountFrequency (%)
1 22828
31.4%
0 19210
26.4%
2 7190
 
9.9%
3 5142
 
7.1%
4 4005
 
5.5%
5 3422
 
4.7%
7 2993
 
4.1%
6 2972
 
4.1%
8 2714
 
3.7%
9 2338
 
3.2%
Space Separator
ValueCountFrequency (%)
56702
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4996
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 2650
100.0%
Close Punctuation
ValueCountFrequency (%)
] 2650
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 139812
53.1%
Hangul 123521
46.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 16161
13.1%
동 10212
 
8.3%
호 9813
 
7.9%
청 9108
 
7.4%
북 7990
 
6.5%
상 7553
 
6.1%
리 7511
 
6.1%
경 7350
 
6.0%
군 7350
 
6.0%
면 4853
 
3.9%
Other values (165) 35620
28.8%
Common
ValueCountFrequency (%)
56702
40.6%
1 22828
16.3%
0 19210
 
13.7%
2 7190
 
5.1%
3 5142
 
3.7%
- 4996
 
3.6%
4 4005
 
2.9%
5 3422
 
2.4%
7 2993
 
2.1%
6 2972
 
2.1%
Other values (4) 10352
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 139812
53.1%
Hangul 123521
46.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
56702
40.6%
1 22828
16.3%
0 19210
 
13.7%
2 7190
 
5.1%
3 5142
 
3.7%
- 4996
 
3.6%
4 4005
 
2.9%
5 3422
 
2.4%
7 2993
 
2.1%
6 2972
 
2.1%
Other values (4) 10352
 
7.4%
Hangul
ValueCountFrequency (%)
도 16161
13.1%
동 10212
 
8.3%
호 9813
 
7.9%
청 9108
 
7.4%
북 7990
 
6.5%
상 7553
 
6.1%
리 7511
 
6.1%
경 7350
 
6.0%
군 7350
 
6.0%
면 4853
 
3.9%
Other values (165) 35620
28.8%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct7893
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33714067
Minimum15000
Maximum6.1179076 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:13:46.939560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15000
5-th percentile288000
Q11077695
median5616750
Q326775630
95-th percentile1.3774716 × 108
Maximum6.1179076 × 109
Range6.1178926 × 109
Interquartile range (IQR)25697935

Descriptive statistics

Standard deviation1.3814623 × 108
Coefficient of variation (CV)4.0975844
Kurtosis880.46991
Mean33714067
Median Absolute Deviation (MAD)5137630
Skewness24.28523
Sum3.3714067 × 1011
Variance1.9084382 × 1016
MonotonicityNot monotonic
2023-12-13T03:13:47.110743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
792000 53
 
0.5%
756000 32
 
0.3%
846000 26
 
0.3%
720000 25
 
0.2%
684000 24
 
0.2%
936000 23
 
0.2%
594000 23
 
0.2%
540000 22
 
0.2%
576000 22
 
0.2%
396000 20
 
0.2%
Other values (7883) 9730
97.3%
ValueCountFrequency (%)
15000 1
 
< 0.1%
15200 2
< 0.1%
15840 1
 
< 0.1%
22000 1
 
< 0.1%
22500 1
 
< 0.1%
23040 1
 
< 0.1%
28800 3
< 0.1%
31680 2
< 0.1%
32400 1
 
< 0.1%
32760 1
 
< 0.1%
ValueCountFrequency (%)
6117907580 1
< 0.1%
6074671480 1
< 0.1%
3678924310 1
< 0.1%
3583615390 1
< 0.1%
2567588240 1
< 0.1%
2548874040 1
< 0.1%
1979051140 2
< 0.1%
1881764910 1
< 0.1%
1819741140 1
< 0.1%
1815550220 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct4229
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157.64591
Minimum0.8
Maximum13511.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:13:47.268387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile13.088
Q136
median87.4
Q3170.055
95-th percentile475.01
Maximum13511.28
Range13510.48
Interquartile range (IQR)134.055

Descriptive statistics

Standard deviation348.9799
Coefficient of variation (CV)2.2136946
Kurtosis507.21686
Mean157.64591
Median Absolute Deviation (MAD)58.505
Skewness16.916501
Sum1576459.1
Variance121786.97
MonotonicityNot monotonic
2023-12-13T03:13:47.470999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 473
 
4.7%
198.0 92
 
0.9%
99.0 58
 
0.6%
192.0 55
 
0.5%
35.0 52
 
0.5%
36.0 51
 
0.5%
96.0 49
 
0.5%
165.0 48
 
0.5%
40.0 47
 
0.5%
180.0 45
 
0.4%
Other values (4219) 9030
90.3%
ValueCountFrequency (%)
0.8 5
0.1%
1.0 5
0.1%
1.1 1
 
< 0.1%
1.32 1
 
< 0.1%
1.4 1
 
< 0.1%
1.43 1
 
< 0.1%
1.44 5
0.1%
1.5 1
 
< 0.1%
1.8 1
 
< 0.1%
1.96 1
 
< 0.1%
ValueCountFrequency (%)
13511.28 2
< 0.1%
7942.41 2
< 0.1%
5310.06 1
< 0.1%
5057.84 1
< 0.1%
4477.22 1
< 0.1%
4435.4 1
< 0.1%
4046.59 1
< 0.1%
4033.0 1
< 0.1%
3742.84 2
< 0.1%
3695.06 1
< 0.1%

기준일자
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020-06-01
2754 
2019-06-01
2609 
2018-06-01
2336 
2017-06-01
2301 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-06-01
2nd row2018-06-01
3rd row2020-06-01
4th row2018-06-01
5th row2018-06-01

Common Values

ValueCountFrequency (%)
2020-06-01 2754
27.5%
2019-06-01 2609
26.1%
2018-06-01 2336
23.4%
2017-06-01 2301
23.0%

Length

2023-12-13T03:13:47.653787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:13:47.784911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-06-01 2754
27.5%
2019-06-01 2609
26.1%
2018-06-01 2336
23.4%
2017-06-01 2301
23.0%

Interactions

2023-12-13T03:13:41.241554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:37.955653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:38.554452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:39.139608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:39.772762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:40.533284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:41.346359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:38.046730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:38.645050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:39.220870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:39.884913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:40.648660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:41.480417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:38.161465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:38.743700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:39.312827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:40.021503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:40.774493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:41.586151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:38.275431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:38.830810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:39.405051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:40.113635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:40.865099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:41.722442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:38.373176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:38.924867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:39.508471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:40.247229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:40.974145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:41.866116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:38.467255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:39.039330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:39.660208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:40.416056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:41.081916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:13:47.895391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동법정리특수지본번부번동호시가표준액연면적기준일자
과세년도1.0000.0000.0000.0140.0120.0210.0000.0470.0140.0181.000
법정동0.0001.0000.7110.1050.3650.1530.1320.3410.0430.0370.000
법정리0.0000.7111.0000.1200.3890.1220.1290.2810.0500.0480.000
특수지0.0140.1050.1201.0000.2680.0000.2260.0000.1850.1060.014
본번0.0120.3650.3890.2681.0000.2490.0850.1560.0160.0180.012
부번0.0210.1530.1220.0000.2491.0000.0000.0000.0000.0000.021
동0.0000.1320.1290.2260.0850.0001.0000.5070.7000.2930.000
호0.0470.3410.2810.0000.1560.0000.5071.0000.3940.5070.047
시가표준액0.0140.0430.0500.1850.0160.0000.7000.3941.0000.8530.014
연면적0.0180.0370.0480.1060.0180.0000.2930.5070.8531.0000.018
기준일자1.0000.0000.0000.0140.0120.0210.0000.0470.0140.0181.000
2023-12-13T03:13:48.074471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수지기준일자과세년도동
특수지1.0000.0090.0090.179
기준일자0.0091.0001.0000.000
과세년도0.0091.0001.0000.000
동0.1790.0000.0001.000
2023-12-13T03:13:48.204035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적과세년도특수지동기준일자
법정동1.0000.6440.166-0.093-0.130-0.0520.0000.1080.0760.000
법정리0.6441.0000.030-0.166-0.175-0.0200.0000.0920.0480.000
본번0.1660.0301.0000.0340.0050.0050.0070.2050.0320.007
부번-0.093-0.1660.0341.0000.127-0.0160.0150.0000.0000.015
시가표준액-0.130-0.1750.0050.1271.0000.5270.0060.1390.3750.006
연면적-0.052-0.0200.005-0.0160.5271.0000.0120.0770.1380.012
과세년도0.0000.0000.0070.0150.0060.0121.0000.0090.0001.000
특수지0.1080.0920.2050.0000.1390.0770.0091.0000.1790.009
동0.0760.0480.0320.0000.3750.1380.0000.1791.0000.000
기준일자0.0000.0000.0070.0150.0060.0121.0000.0090.0001.000

Missing values

2023-12-13T03:13:42.025814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:13:42.296424image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
55884경상북도청도군478202020250321515413[ 남성현로 167-17 ] 0001동 0003호111780081.02020-06-01
25032경상북도청도군478202018340411787013경상북도 청도군 이서면 구라리 787 1동 3호143724000406.02018-06-01
57947경상북도청도군4782020203303017630110경상북도 청도군 각북면 우산리 763 1동 10호17928000216.02020-06-01
20138경상북도청도군478202018250331693218101경상북도 청도군 화양읍 삼신리 693-2 1동 8101호36789243107942.412018-06-01
30148경상북도청도군47820201834052168011경상북도 청도군 이서면 칠곡리 68 1동 1호5646384081.362018-06-01
44725경상북도청도군478202019350361354212경상북도 청도군 운문면 신원리 354-2 1동 2호2624144065.442019-06-01
12428경상북도청도군47820201735036169851101경상북도 청도군 운문면 신원리 698-5 1동 101호29484098.282017-06-01
58242경상북도청도군4782020203204111636111경상북도 청도군 풍각면 흑석리 1636-1 1동 1호2349900063.02020-06-01
43551경상북도청도군478202019250241112002[ 동천3길 67 ] 0000동 0002호70217100115.112019-06-01
52530경상북도청도군47820202035036178761102경상북도 청도군 운문면 신원리 787-6 1동 102호945000045.02020-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
50383경상북도청도군4782020203603511122011경상북도 청도군 금천면 소천리 1122 1동 1호95400018.02020-06-01
39004경상북도청도군4782020192503311193014경상북도 청도군 화양읍 삼신리 1193 1동 4호71448240260.762019-06-01
18329경상북도청도군478202018253211183013경상북도 청도군 청도읍 고수리 183 1동 3호86000020.02018-06-01
7293경상북도청도군47820201735036229681경상북도 청도군 운문면 신원리 산 29-6 8동 1호3251287084.892017-06-01
34389경상북도청도군478202019370571788211[ 관방로 153-8 ] 0001동 0001호1420497057.512019-06-01
57915경상북도청도군478202020330301625013경상북도 청도군 각북면 우산리 625 1동 3호29640000195.02020-06-01
4309경상북도청도군478202017253211478631102경상북도 청도군 청도읍 고수리 478-63 1동 102호1034002.02017-06-01
14960경상북도청도군47820201736031151911101경상북도 청도군 금천면 동곡리 519-1 1동 101호480000160.02017-06-01
21526경상북도청도군478202018350411960001경상북도 청도군 운문면 정상리 960 1호73800018.02018-06-01
21003경상북도청도군47820201825028115211경상북도 청도군 화양읍 송북리 15-2 1동 1호205800049.02018-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자# duplicates
5경상북도청도군478202017330301763010경상북도 청도군 각북면 우산리 763 1동2808000108.02017-06-013
0경상북도청도군478202017250321316213경상북도 청도군 화양읍 진라리 316-2 1동 3호98872650244.132017-06-012
1경상북도청도군47820201731034132811경상북도 청도군 각남면 구곡리 3-28 1동 1호3080000308.02017-06-012
2경상북도청도군478202017320331558010경상북도 청도군 풍각면 송서리 558 1동63916280122.682017-06-012
3경상북도청도군4782020173204111366011경상북도 청도군 풍각면 흑석리 1366 1동 1호6308280233.642017-06-012
4경상북도청도군4782020173204111401011경상북도 청도군 풍각면 흑석리 1401 1동 1호4836260372.022017-06-012
6경상북도청도군47820201734040117111경상북도 청도군 이서면 가금리 17-1 1동 1호17304300198.92017-06-012
7경상북도청도군4782020183204111401011경상북도 청도군 풍각면 흑석리 1401 1동 1호4092220372.022018-06-012
8경상북도청도군47820201834040117111경상북도 청도군 이서면 가금리 17-1 1동 1호16906500198.92018-06-012
9경상북도청도군478202018360381213214경상북도 청도군 금천면 신지리 213-2 1동 4호265500045.02018-06-012