Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows55
Duplicate rows (%)0.5%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

Categorical6
Numeric7
Text2

Dataset

Description경상북도 군위군의 일반건축물시가표준액에 대한 데이터로 법정동, 법정리, 특수지, 본번, 부번, 동, 호, 물건지주소, 시가표준액, 연면적, 기준일자 항목을 제공합니다.
Author경상북도 군위군
URLhttps://www.data.go.kr/data/15080040/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세년도 has constant value ""Constant
결정일자 has constant value ""Constant
Dataset has 55 (0.5%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (86.5%)Imbalance
is highly skewed (γ1 = 38.17240598)Skewed
부번 has 4101 (41.0%) zerosZeros
has 325 (3.2%) zerosZeros

Reproduction

Analysis started2023-12-12 18:52:58.138774
Analysis finished2023-12-12 18:53:11.899223
Duration13.76 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

2023-12-13T03:53:12.022910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:53:12.184987image/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
군위군
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
군위군 10000
100.0%

Length

2023-12-13T03:53:12.374868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:53:12.541981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군위군 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
47720
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row47720
2nd row47720
3rd row47720
4th row47720
5th row47720

Common Values

ValueCountFrequency (%)
47720 10000
100.0%

Length

2023-12-13T03:53:12.719733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:53:12.909562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
47720 10000
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 10000
100.0%

Length

2023-12-13T03:53:13.088496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:53:13.247177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 10000
100.0%

법정동
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean309.429
Minimum250
Maximum380
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:53:13.411285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile250
Q1250
median320
Q3340
95-th percentile360
Maximum380
Range130
Interquartile range (IQR)90

Descriptive statistics

Standard deviation41.70513
Coefficient of variation (CV)0.13478094
Kurtosis-1.1468339
Mean309.429
Median Absolute Deviation (MAD)20
Skewness-0.38289874
Sum3094290
Variance1739.3179
MonotonicityNot monotonic
2023-12-13T03:53:13.665838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
250 2955
29.5%
320 2116
21.2%
330 1189
11.9%
350 1056
 
10.6%
340 894
 
8.9%
310 837
 
8.4%
380 497
 
5.0%
360 456
 
4.6%
ValueCountFrequency (%)
250 2955
29.5%
310 837
 
8.4%
320 2116
21.2%
330 1189
11.9%
340 894
 
8.9%
350 1056
 
10.6%
360 456
 
4.6%
380 497
 
5.0%
ValueCountFrequency (%)
380 497
 
5.0%
360 456
 
4.6%
350 1056
 
10.6%
340 894
 
8.9%
330 1189
11.9%
320 2116
21.2%
310 837
 
8.4%
250 2955
29.5%

법정리
Real number (ℝ)

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.887
Minimum21
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:53:14.013662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile22
Q128
median33
Q340
95-th percentile49
Maximum50
Range29
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.1542185
Coefficient of variation (CV)0.2406297
Kurtosis-0.83022176
Mean33.887
Median Absolute Deviation (MAD)6
Skewness0.22789109
Sum338870
Variance66.49128
MonotonicityNot monotonic
2023-12-13T03:53:14.301713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
22 709
 
7.1%
32 673
 
6.7%
34 604
 
6.0%
31 586
 
5.9%
25 582
 
5.8%
33 577
 
5.8%
21 455
 
4.5%
36 398
 
4.0%
50 396
 
4.0%
42 395
 
4.0%
Other values (20) 4625
46.2%
ValueCountFrequency (%)
21 455
4.5%
22 709
7.1%
23 144
 
1.4%
24 233
 
2.3%
25 582
5.8%
26 87
 
0.9%
27 190
 
1.9%
28 226
 
2.3%
29 389
3.9%
30 356
3.6%
ValueCountFrequency (%)
50 396
4.0%
49 131
 
1.3%
48 191
1.9%
47 158
 
1.6%
46 192
1.9%
45 211
2.1%
44 240
2.4%
43 156
 
1.6%
42 395
4.0%
41 334
3.3%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9812 
2
 
188

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 9812
98.1%
2 188
 
1.9%

Length

2023-12-13T03:53:14.612937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:53:14.793884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9812
98.1%
2 188
 
1.9%

본번
Real number (ℝ)

Distinct1337
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean569.4123
Minimum1
Maximum1735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:53:14.994741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile30
Q1242
median513
Q3835.25
95-th percentile1300.05
Maximum1735
Range1734
Interquartile range (IQR)593.25

Descriptive statistics

Standard deviation393.94699
Coefficient of variation (CV)0.69184841
Kurtosis-0.43352298
Mean569.4123
Median Absolute Deviation (MAD)295
Skewness0.54651786
Sum5694123
Variance155194.23
MonotonicityNot monotonic
2023-12-13T03:53:15.278879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
421 133
 
1.3%
701 125
 
1.2%
17 106
 
1.1%
1408 92
 
0.9%
561 57
 
0.6%
1139 52
 
0.5%
487 46
 
0.5%
1447 45
 
0.4%
1071 43
 
0.4%
341 41
 
0.4%
Other values (1327) 9260
92.6%
ValueCountFrequency (%)
1 39
0.4%
2 24
0.2%
3 14
 
0.1%
4 23
0.2%
5 12
 
0.1%
6 13
 
0.1%
7 6
 
0.1%
8 9
 
0.1%
9 4
 
< 0.1%
10 10
 
0.1%
ValueCountFrequency (%)
1735 1
 
< 0.1%
1683 12
0.1%
1664 11
0.1%
1663 3
 
< 0.1%
1661 4
 
< 0.1%
1660 14
0.1%
1653 23
0.2%
1651 1
 
< 0.1%
1642 1
 
< 0.1%
1637 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct121
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6502
Minimum0
Maximum233
Zeros4101
Zeros (%)41.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:53:16.045557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile24
Maximum233
Range233
Interquartile range (IQR)3

Descriptive statistics

Standard deviation18.291575
Coefficient of variation (CV)3.2373322
Kurtosis63.984872
Mean5.6502
Median Absolute Deviation (MAD)1
Skewness7.1512088
Sum56502
Variance334.5817
MonotonicityNot monotonic
2023-12-13T03:53:16.294989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4101
41.0%
1 1928
19.3%
2 982
 
9.8%
3 591
 
5.9%
4 342
 
3.4%
5 255
 
2.5%
6 225
 
2.2%
7 188
 
1.9%
9 138
 
1.4%
10 102
 
1.0%
Other values (111) 1148
 
11.5%
ValueCountFrequency (%)
0 4101
41.0%
1 1928
19.3%
2 982
 
9.8%
3 591
 
5.9%
4 342
 
3.4%
5 255
 
2.5%
6 225
 
2.2%
7 188
 
1.9%
8 88
 
0.9%
9 138
 
1.4%
ValueCountFrequency (%)
233 1
 
< 0.1%
232 1
 
< 0.1%
231 2
< 0.1%
229 1
 
< 0.1%
228 4
< 0.1%
222 1
 
< 0.1%
220 2
< 0.1%
218 2
< 0.1%
217 1
 
< 0.1%
209 1
 
< 0.1%


Real number (ℝ)

SKEWED  ZEROS 

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0665
Minimum0
Maximum7011
Zeros325
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:53:16.533383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum7011
Range7011
Interquartile range (IQR)0

Descriptive statistics

Standard deviation178.82571
Coefficient of variation (CV)29.477576
Kurtosis1467.0026
Mean6.0665
Median Absolute Deviation (MAD)0
Skewness38.172406
Sum60665
Variance31978.636
MonotonicityNot monotonic
2023-12-13T03:53:16.790598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 8794
87.9%
2 554
 
5.5%
0 325
 
3.2%
3 112
 
1.1%
4 46
 
0.5%
5 32
 
0.3%
6 24
 
0.2%
7 23
 
0.2%
8 20
 
0.2%
13 14
 
0.1%
Other values (19) 56
 
0.6%
ValueCountFrequency (%)
0 325
 
3.2%
1 8794
87.9%
2 554
 
5.5%
3 112
 
1.1%
4 46
 
0.5%
5 32
 
0.3%
6 24
 
0.2%
7 23
 
0.2%
8 20
 
0.2%
9 13
 
0.1%
ValueCountFrequency (%)
7011 1
< 0.1%
7010 1
< 0.1%
7005 1
< 0.1%
7003 1
< 0.1%
7002 1
< 0.1%
7001 1
< 0.1%
5000 1
< 0.1%
719 1
< 0.1%
104 1
< 0.1%
103 1
< 0.1%


Text

Distinct155
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T03:53:17.165691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length1
Mean length1.9262
Min length1

Characters and Unicode

Total characters19262
Distinct characters45
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)0.8%

Sample

1st row3
2nd row4
3rd row1
4th row0
5th row15
ValueCountFrequency (%)
1 3098
31.0%
101 2378
23.8%
2 978
 
9.8%
102 636
 
6.4%
3 470
 
4.7%
201 361
 
3.6%
103 262
 
2.6%
4 255
 
2.5%
0 165
 
1.7%
5 141
 
1.4%
Other values (145) 1256
12.6%
2023-12-13T03:53:17.805015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 9990
51.9%
0 4660
24.2%
2 2290
 
11.9%
3 943
 
4.9%
4 460
 
2.4%
5 265
 
1.4%
8 177
 
0.9%
6 172
 
0.9%
7 112
 
0.6%
9 55
 
0.3%
Other values (35) 138
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19124
99.3%
Other Letter 110
 
0.6%
Uppercase Letter 22
 
0.1%
Dash Punctuation 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
20
18.2%
20
18.2%
19
17.3%
9
8.2%
8
 
7.3%
4
 
3.6%
3
 
2.7%
2
 
1.8%
2
 
1.8%
2
 
1.8%
Other values (13) 21
19.1%
Uppercase Letter
ValueCountFrequency (%)
F 4
18.2%
B 3
13.6%
C 3
13.6%
D 3
13.6%
A 3
13.6%
K 1
 
4.5%
G 1
 
4.5%
J 1
 
4.5%
L 1
 
4.5%
I 1
 
4.5%
Decimal Number
ValueCountFrequency (%)
1 9990
52.2%
0 4660
24.4%
2 2290
 
12.0%
3 943
 
4.9%
4 460
 
2.4%
5 265
 
1.4%
8 177
 
0.9%
6 172
 
0.9%
7 112
 
0.6%
9 55
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19130
99.3%
Hangul 110
 
0.6%
Latin 22
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
20
18.2%
20
18.2%
19
17.3%
9
8.2%
8
 
7.3%
4
 
3.6%
3
 
2.7%
2
 
1.8%
2
 
1.8%
2
 
1.8%
Other values (13) 21
19.1%
Common
ValueCountFrequency (%)
1 9990
52.2%
0 4660
24.4%
2 2290
 
12.0%
3 943
 
4.9%
4 460
 
2.4%
5 265
 
1.4%
8 177
 
0.9%
6 172
 
0.9%
7 112
 
0.6%
9 55
 
0.3%
Latin
ValueCountFrequency (%)
F 4
18.2%
B 3
13.6%
C 3
13.6%
D 3
13.6%
A 3
13.6%
K 1
 
4.5%
G 1
 
4.5%
J 1
 
4.5%
L 1
 
4.5%
I 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19152
99.4%
Hangul 110
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9990
52.2%
0 4660
24.3%
2 2290
 
12.0%
3 943
 
4.9%
4 460
 
2.4%
5 265
 
1.4%
8 177
 
0.9%
6 172
 
0.9%
7 112
 
0.6%
9 55
 
0.3%
Other values (12) 28
 
0.1%
Hangul
ValueCountFrequency (%)
20
18.2%
20
18.2%
19
17.3%
9
8.2%
8
 
7.3%
4
 
3.6%
3
 
2.7%
2
 
1.8%
2
 
1.8%
2
 
1.8%
Other values (13) 21
19.1%
Distinct9055
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T03:53:18.432451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length32
Mean length27.2179
Min length19

Characters and Unicode

Total characters272179
Distinct characters185
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8503 ?
Unique (%)85.0%

Sample

1st row경상북도 군위군 효령면 고곡리 1228 1동 3호
2nd row[ 용매로 1011-14 ] 0001동 0004호
3rd row[ 화계봉산길 975-45 ] 0001동 0001호
4th row[ 가지골길 39 ] 0001동 0000호
5th row경상북도 군위군 군위읍 수서리 산 77-43 1동 15호
ValueCountFrequency (%)
경상북도 7383
 
11.0%
군위군 7383
 
11.0%
1동 6442
 
9.6%
5234
 
7.8%
0001동 2352
 
3.5%
군위읍 2136
 
3.2%
1호 2099
 
3.1%
효령면 1661
 
2.5%
101호 1607
 
2.4%
0001호 996
 
1.5%
Other values (4119) 29952
44.5%
2023-12-13T03:53:19.364929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
57245
21.0%
1 27077
 
9.9%
0 20313
 
7.5%
17270
 
6.3%
10390
 
3.8%
10113
 
3.7%
9639
 
3.5%
7791
 
2.9%
7547
 
2.8%
7534
 
2.8%
Other values (175) 97260
35.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 125136
46.0%
Decimal Number 79045
29.0%
Space Separator 57245
21.0%
Dash Punctuation 5497
 
2.0%
Open Punctuation 2617
 
1.0%
Close Punctuation 2617
 
1.0%
Uppercase Letter 22
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
17270
13.8%
10390
 
8.3%
10113
 
8.1%
9639
 
7.7%
7791
 
6.2%
7547
 
6.0%
7534
 
6.0%
7491
 
6.0%
7433
 
5.9%
5247
 
4.2%
Other values (150) 34681
27.7%
Uppercase Letter
ValueCountFrequency (%)
F 4
18.2%
B 3
13.6%
D 3
13.6%
C 3
13.6%
A 3
13.6%
J 1
 
4.5%
I 1
 
4.5%
K 1
 
4.5%
L 1
 
4.5%
H 1
 
4.5%
Decimal Number
ValueCountFrequency (%)
1 27077
34.3%
0 20313
25.7%
2 7522
 
9.5%
3 4961
 
6.3%
4 4005
 
5.1%
5 3346
 
4.2%
7 3215
 
4.1%
6 3087
 
3.9%
8 2938
 
3.7%
9 2581
 
3.3%
Space Separator
ValueCountFrequency (%)
57245
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5497
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 2617
100.0%
Close Punctuation
ValueCountFrequency (%)
] 2617
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147021
54.0%
Hangul 125136
46.0%
Latin 22
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
17270
13.8%
10390
 
8.3%
10113
 
8.1%
9639
 
7.7%
7791
 
6.2%
7547
 
6.0%
7534
 
6.0%
7491
 
6.0%
7433
 
5.9%
5247
 
4.2%
Other values (150) 34681
27.7%
Common
ValueCountFrequency (%)
57245
38.9%
1 27077
18.4%
0 20313
 
13.8%
2 7522
 
5.1%
- 5497
 
3.7%
3 4961
 
3.4%
4 4005
 
2.7%
5 3346
 
2.3%
7 3215
 
2.2%
6 3087
 
2.1%
Other values (4) 10753
 
7.3%
Latin
ValueCountFrequency (%)
F 4
18.2%
B 3
13.6%
D 3
13.6%
C 3
13.6%
A 3
13.6%
J 1
 
4.5%
I 1
 
4.5%
K 1
 
4.5%
L 1
 
4.5%
H 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147043
54.0%
Hangul 125136
46.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
57245
38.9%
1 27077
18.4%
0 20313
 
13.8%
2 7522
 
5.1%
- 5497
 
3.7%
3 4961
 
3.4%
4 4005
 
2.7%
5 3346
 
2.3%
7 3215
 
2.2%
6 3087
 
2.1%
Other values (15) 10775
 
7.3%
Hangul
ValueCountFrequency (%)
17270
13.8%
10390
 
8.3%
10113
 
8.1%
9639
 
7.7%
7791
 
6.2%
7547
 
6.0%
7534
 
6.0%
7491
 
6.0%
7433
 
5.9%
5247
 
4.2%
Other values (150) 34681
27.7%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct7411
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32820475
Minimum11250
Maximum3.7602335 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:53:19.656391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11250
5-th percentile425400
Q11477435
median5259100
Q322767300
95-th percentile1.370311 × 108
Maximum3.7602335 × 109
Range3.7602223 × 109
Interquartile range (IQR)21289865

Descriptive statistics

Standard deviation1.1149457 × 108
Coefficient of variation (CV)3.397104
Kurtosis341.49565
Mean32820475
Median Absolute Deviation (MAD)4561545
Skewness14.40261
Sum3.2820475 × 1011
Variance1.2431039 × 1016
MonotonicityNot monotonic
2023-12-13T03:53:19.930010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
882000 52
 
0.5%
792000 44
 
0.4%
612000 42
 
0.4%
540000 40
 
0.4%
450000 38
 
0.4%
972000 37
 
0.4%
702000 36
 
0.4%
630000 35
 
0.4%
1638000 30
 
0.3%
360000 24
 
0.2%
Other values (7401) 9622
96.2%
ValueCountFrequency (%)
11250 1
< 0.1%
17640 1
< 0.1%
26880 1
< 0.1%
30800 1
< 0.1%
39200 1
< 0.1%
42000 1
< 0.1%
45000 1
< 0.1%
45900 1
< 0.1%
48000 1
< 0.1%
48050 1
< 0.1%
ValueCountFrequency (%)
3760233510 1
< 0.1%
3628080120 1
< 0.1%
2656163610 1
< 0.1%
2512888840 1
< 0.1%
2104408020 1
< 0.1%
2010695400 1
< 0.1%
1665686400 1
< 0.1%
1632365670 1
< 0.1%
1482384000 1
< 0.1%
1467051600 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct4671
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160.72317
Minimum0.9
Maximum10532.867
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:53:20.198961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile12
Q129.75
median78.52
Q3172.8
95-th percentile563.048
Maximum10532.867
Range10531.967
Interquartile range (IQR)143.05

Descriptive statistics

Standard deviation324.59705
Coefficient of variation (CV)2.0196033
Kurtosis246.20803
Mean160.72317
Median Absolute Deviation (MAD)58.53
Skewness11.579339
Sum1607231.7
Variance105363.24
MonotonicityNot monotonic
2023-12-13T03:53:20.464892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 759
 
7.6%
20.0 85
 
0.9%
15.0 62
 
0.6%
19.8 60
 
0.6%
198.0 55
 
0.5%
12.0 53
 
0.5%
24.0 52
 
0.5%
192.0 50
 
0.5%
36.0 47
 
0.5%
96.0 47
 
0.5%
Other values (4661) 8730
87.3%
ValueCountFrequency (%)
0.9 1
 
< 0.1%
1.0 4
< 0.1%
1.1 1
 
< 0.1%
1.2 1
 
< 0.1%
1.21 1
 
< 0.1%
1.43 1
 
< 0.1%
1.68 1
 
< 0.1%
1.74 1
 
< 0.1%
1.79 1
 
< 0.1%
1.8 1
 
< 0.1%
ValueCountFrequency (%)
10532.867 1
< 0.1%
9127.71 1
< 0.1%
7574.28 1
< 0.1%
6716.68 1
< 0.1%
6077.0 1
< 0.1%
4925.46 1
< 0.1%
4830.3 1
< 0.1%
4630.5 1
< 0.1%
3818.98 1
< 0.1%
3709.8 1
< 0.1%

결정일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-06-01
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-06-01
2nd row2021-06-01
3rd row2021-06-01
4th row2021-06-01
5th row2021-06-01

Common Values

ValueCountFrequency (%)
2021-06-01 10000
100.0%

Length

2023-12-13T03:53:20.713912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:53:20.893739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-06-01 10000
100.0%

Interactions

2023-12-13T03:53:10.020600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:01.623487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:02.897780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:04.687034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:06.109510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:07.388366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:08.710077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:10.185621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:01.794227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:03.074371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:04.910295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:06.272769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:07.582924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:08.885658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:10.341826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:01.989635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:03.235883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:05.148284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:06.442961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:07.780870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:09.084107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:10.533539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:02.186421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:03.419380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:05.354122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:06.627704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:07.984009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:09.254202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:10.702941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:02.360269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:03.579122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:05.538199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:06.786960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:08.132934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:09.427803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:10.875294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:02.533060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:03.759716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:05.737231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:06.978096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:08.325737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:09.617180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:11.059265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:02.726890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:04.482464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:05.944484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:07.206602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:08.516848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:09.816098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:53:21.014729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리특수지본번부번시가표준액연면적
법정동1.0000.7870.1160.2760.1760.0320.0340.028
법정리0.7871.0000.1550.4720.3050.0590.0370.091
특수지0.1160.1551.0000.3650.0610.0000.1070.050
본번0.2760.4720.3651.0000.5150.0000.0630.062
부번0.1760.3050.0610.5151.0000.0000.0000.000
0.0320.0590.0000.0000.0001.0000.0000.000
시가표준액0.0340.0370.1070.0630.0000.0001.0000.835
연면적0.0280.0910.0500.0620.0000.0000.8351.000
2023-12-13T03:53:21.205566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적특수지
법정동1.0000.2160.226-0.132-0.053-0.134-0.0590.074
법정리0.2161.0000.175-0.085-0.025-0.1040.0620.119
본번0.2260.1751.0000.040-0.004-0.053-0.0140.280
부번-0.132-0.0850.0401.000-0.0140.1100.0690.046
-0.053-0.025-0.004-0.0141.0000.026-0.0110.000
시가표준액-0.134-0.104-0.0530.1100.0261.0000.6500.107
연면적-0.0590.062-0.0140.069-0.0110.6501.0000.038
특수지0.0740.1190.2800.0460.0000.1070.0381.000

Missing values

2023-12-13T03:53:11.332064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:53:11.725650image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적결정일자
6147경상북도군위군4772020213205011228013경상북도 군위군 효령면 고곡리 1228 1동 3호14193000171.02021-06-01
3715경상북도군위군477202021320501140822814[ 용매로 1011-14 ] 0001동 0004호2007360061.22021-06-01
10706경상북도군위군47720202134030112928711[ 화계봉산길 975-45 ] 0001동 0001호1111110077.72021-06-01
9902경상북도군위군4772020213503511187210[ 가지골길 39 ] 0001동 0000호2497110108.572021-06-01
3008경상북도군위군4772020212502527743115경상북도 군위군 군위읍 수서리 산 77-43 1동 15호146454001046.12021-06-01
8492경상북도군위군477202021330301103531102경상북도 군위군 부계면 가호리 1035-3 1동 102호3317602.862021-06-01
8033경상북도군위군47720202133029298451경상북도 군위군 부계면 창평리 산 98-4 5동 1호1829880023.42021-06-01
750경상북도군위군47720202125027164501101경상북도 군위군 군위읍 내량리 645 1동 101호16637400205.42021-06-01
6085경상북도군위군47720202132041111201102[ 중내길 141 ] 0001동 0102호496800092.02021-06-01
11104경상북도군위군477202021380221127011101경상북도 군위군 삼국유사면 화북리 1270-1 1동 101호201171220251.72021-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적결정일자
5313경상북도군위군47720202131041126101104경상북도 군위군 소보면 송원리 261 1동 104호223440053.22021-06-01
2086경상북도군위군477202021250221493211[ 중앙6길 13-57 ] 0001동 0001호72722750115.252021-06-01
3013경상북도군위군477202021250252772213경상북도 군위군 군위읍 수서리 산 77-22 1동 3호383600002740.02021-06-01
12377경상북도군위군4772020213603311231611[ 산성가음로 697 ] 0001동 0001호636909069.992021-06-01
4539경상북도군위군47720202132049213701101경상북도 군위군 효령면 매곡리 산 137 1동 101호6543279601749.542021-06-01
1101경상북도군위군47720202125036164911101경상북도 군위군 군위읍 광현리 649-1 1동 101호391776093.282021-06-01
5347경상북도군위군477202021310431302102경상북도 군위군 소보면 도산리 302-1 2호50400018.02021-06-01
5204경상북도군위군47720202125027150621101경상북도 군위군 군위읍 내량리 506-2 1동 101호170000017.02021-06-01
12440경상북도군위군477202021350331579011경상북도 군위군 의흥면 수북리 579 1동 1호118800018.02021-06-01
12583경상북도군위군477202021350381843111[ 지호2길 12 ] 0001동 0001호11718000126.02021-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적결정일자# duplicates
44경상북도군위군47720202134033142000경상북도 군위군 우보면 선곡리 4230000000400.02021-06-016
14경상북도군위군477202021310371201101경상북도 군위군 소보면 보현리 2 1동 101호21285009.92021-06-015
36경상북도군위군477202021330341860311경상북도 군위군 부계면 남산리 860-3 1동 1호13018608.412021-06-015
10경상북도군위군47720202125027164501101경상북도 군위군 군위읍 내량리 645 1동 101호16637400205.42021-06-014
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