Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells69
Missing cells (%)< 0.1%
Duplicate rows348
Duplicate rows (%)3.5%
Total size in memory1.2 MiB
Average record size in memory129.0 B

Variable types

Categorical5
Numeric8
Text1

Dataset

Description지방세법에 의한 표준지방세시스템을 통해 작성된 일반건축물에 대한 지방세 부과기준인 시가표준액을 시군구명 , 자치단체코드, 과세년도, 법정도, 법정리, 특수지, 본번, 부번, 동, 호, 물건지, 시가표준액, 연면적 등의 항목으로 제공합니다.
Author전라북도
URLhttps://www.bigdatahub.go.kr/index.jeonbuk?startPage=1&menuCd=DOM_000000103007001000&pListTypeStr=&pId=15080118

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세연도 has constant value ""Constant
Dataset has 348 (3.5%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (92.8%)Imbalance
is highly skewed (γ1 = 90.30789745)Skewed
시가표준액 is highly skewed (γ1 = 29.58897596)Skewed
부번 has 2430 (24.3%) zerosZeros
has 102 (1.0%) zerosZeros
시가표준액 has 206 (2.1%) zerosZeros
연면적 has 275 (2.8%) zerosZeros

Reproduction

Analysis started2024-03-14 03:27:10.478566
Analysis finished2024-03-14 03:27:27.511215
Duration17.03 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
전라북도
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라북도 10000
100.0%

Length

2024-03-14T12:27:27.557825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:27:27.623749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라북도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부안군 10000
100.0%

Length

2024-03-14T12:27:27.696642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:27:27.763624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부안군 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
45800
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row45800
2nd row45800
3rd row45800
4th row45800
5th row45800

Common Values

ValueCountFrequency (%)
45800 10000
100.0%

Length

2024-03-14T12:27:27.846977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:27:27.929807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
45800 10000
100.0%

과세연도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 10000
100.0%

Length

2024-03-14T12:27:28.010318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:27:28.092139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 10000
100.0%

법정동
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean335.502
Minimum250
Maximum420
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:27:28.158258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile250
Q1310
median350
Q3380
95-th percentile410
Maximum420
Range170
Interquartile range (IQR)70

Descriptive statistics

Standard deviation54.197802
Coefficient of variation (CV)0.16154241
Kurtosis-0.99915754
Mean335.502
Median Absolute Deviation (MAD)30
Skewness-0.47178928
Sum3355020
Variance2937.4017
MonotonicityNot monotonic
2024-03-14T12:27:28.263283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
250 2333
23.3%
360 1386
13.9%
350 729
 
7.3%
410 690
 
6.9%
370 683
 
6.8%
330 664
 
6.6%
380 616
 
6.2%
340 603
 
6.0%
310 569
 
5.7%
400 559
 
5.6%
Other values (3) 1168
11.7%
ValueCountFrequency (%)
250 2333
23.3%
310 569
 
5.7%
320 525
 
5.2%
330 664
 
6.6%
340 603
 
6.0%
350 729
 
7.3%
360 1386
13.9%
370 683
 
6.8%
380 616
 
6.2%
390 474
 
4.7%
ValueCountFrequency (%)
420 169
 
1.7%
410 690
6.9%
400 559
5.6%
390 474
 
4.7%
380 616
6.2%
370 683
6.8%
360 1386
13.9%
350 729
7.3%
340 603
6.0%
330 664
6.6%

법정리
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.7038
Minimum21
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:27:28.394256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q122
median23
Q325
95-th percentile29
Maximum32
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4864601
Coefficient of variation (CV)0.10489711
Kurtosis1.1174818
Mean23.7038
Median Absolute Deviation (MAD)1
Skewness1.1553828
Sum237038
Variance6.1824838
MonotonicityNot monotonic
2024-03-14T12:27:28.492063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
22 2140
21.4%
24 1965
19.7%
21 1945
19.4%
23 1122
11.2%
25 875
8.8%
26 642
 
6.4%
27 422
 
4.2%
28 296
 
3.0%
29 235
 
2.4%
30 170
 
1.7%
Other values (2) 188
 
1.9%
ValueCountFrequency (%)
21 1945
19.4%
22 2140
21.4%
23 1122
11.2%
24 1965
19.7%
25 875
8.8%
26 642
 
6.4%
27 422
 
4.2%
28 296
 
3.0%
29 235
 
2.4%
30 170
 
1.7%
ValueCountFrequency (%)
32 155
 
1.6%
31 33
 
0.3%
30 170
 
1.7%
29 235
 
2.4%
28 296
 
3.0%
27 422
 
4.2%
26 642
 
6.4%
25 875
8.8%
24 1965
19.7%
23 1122
11.2%

특수지
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9848 
2
 
151
7
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
1 9848
98.5%
2 151
 
1.5%
7 1
 
< 0.1%

Length

2024-03-14T12:27:28.586536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:27:28.656784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9848
98.5%
2 151
 
1.5%
7 1
 
< 0.1%

본번
Real number (ℝ)

Distinct1268
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean547.1362
Minimum1
Maximum4599
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:27:28.744868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile24
Q1197
median439
Q3723
95-th percentile1333.55
Maximum4599
Range4598
Interquartile range (IQR)526

Descriptive statistics

Standard deviation594.14823
Coefficient of variation (CV)1.0859238
Kurtosis18.186383
Mean547.1362
Median Absolute Deviation (MAD)256
Skewness3.6142339
Sum5471362
Variance353012.11
MonotonicityNot monotonic
2024-03-14T12:27:28.858749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
574 134
 
1.3%
1 119
 
1.2%
455 94
 
0.9%
132 71
 
0.7%
788 67
 
0.7%
42 61
 
0.6%
260 54
 
0.5%
1121 52
 
0.5%
3 44
 
0.4%
271 44
 
0.4%
Other values (1258) 9260
92.6%
ValueCountFrequency (%)
1 119
1.2%
2 23
 
0.2%
3 44
 
0.4%
4 20
 
0.2%
5 13
 
0.1%
6 2
 
< 0.1%
7 24
 
0.2%
8 15
 
0.1%
9 18
 
0.2%
10 19
 
0.2%
ValueCountFrequency (%)
4599 1
 
< 0.1%
4557 3
 
< 0.1%
4518 3
 
< 0.1%
4515 8
0.1%
4514 2
 
< 0.1%
4513 2
 
< 0.1%
4511 3
 
< 0.1%
4510 1
 
< 0.1%
4466 6
0.1%
4455 6
0.1%

부번
Real number (ℝ)

ZEROS 

Distinct167
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.9631
Minimum0
Maximum264
Zeros2430
Zeros (%)24.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:27:28.967484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q39
95-th percentile59
Maximum264
Range264
Interquartile range (IQR)8

Descriptive statistics

Standard deviation25.358424
Coefficient of variation (CV)2.3130705
Kurtosis22.117569
Mean10.9631
Median Absolute Deviation (MAD)3
Skewness4.3096822
Sum109631
Variance643.04964
MonotonicityNot monotonic
2024-03-14T12:27:29.074466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2430
24.3%
1 1710
17.1%
2 817
 
8.2%
3 716
 
7.2%
4 531
 
5.3%
5 404
 
4.0%
6 333
 
3.3%
8 279
 
2.8%
7 278
 
2.8%
9 229
 
2.3%
Other values (157) 2273
22.7%
ValueCountFrequency (%)
0 2430
24.3%
1 1710
17.1%
2 817
 
8.2%
3 716
 
7.2%
4 531
 
5.3%
5 404
 
4.0%
6 333
 
3.3%
7 278
 
2.8%
8 279
 
2.8%
9 229
 
2.3%
ValueCountFrequency (%)
264 2
< 0.1%
258 1
< 0.1%
222 1
< 0.1%
220 2
< 0.1%
219 2
< 0.1%
218 1
< 0.1%
217 1
< 0.1%
210 1
< 0.1%
208 1
< 0.1%
207 1
< 0.1%


Real number (ℝ)

ZEROS 

Distinct52
Distinct (%)0.5%
Missing69
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean26.610311
Minimum0
Maximum7003
Zeros102
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:27:29.179740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile4
Maximum7003
Range7003
Interquartile range (IQR)0

Descriptive statistics

Standard deviation415.37066
Coefficient of variation (CV)15.609387
Kurtosis277.48735
Mean26.610311
Median Absolute Deviation (MAD)0
Skewness16.704248
Sum264267
Variance172532.78
MonotonicityNot monotonic
2024-03-14T12:27:29.286752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7520
75.2%
2 1355
 
13.6%
3 379
 
3.8%
4 167
 
1.7%
5 106
 
1.1%
0 102
 
1.0%
6 66
 
0.7%
7 35
 
0.4%
10 30
 
0.3%
8 23
 
0.2%
Other values (42) 148
 
1.5%
(Missing) 69
 
0.7%
ValueCountFrequency (%)
0 102
 
1.0%
1 7520
75.2%
2 1355
 
13.6%
3 379
 
3.8%
4 167
 
1.7%
5 106
 
1.1%
6 66
 
0.7%
7 35
 
0.4%
8 23
 
0.2%
9 23
 
0.2%
ValueCountFrequency (%)
7003 6
 
0.1%
7002 12
0.1%
7001 17
0.2%
2009 1
 
< 0.1%
301 3
 
< 0.1%
105 1
 
< 0.1%
102 1
 
< 0.1%
101 3
 
< 0.1%
71 1
 
< 0.1%
69 1
 
< 0.1%


Real number (ℝ)

SKEWED 

Distinct203
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean248.2657
Minimum0
Maximum500003
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:27:29.449873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile103
Maximum500003
Range500003
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5173.0602
Coefficient of variation (CV)20.83679
Kurtosis8713.705
Mean248.2657
Median Absolute Deviation (MAD)1
Skewness90.307897
Sum2482657
Variance26760552
MonotonicityNot monotonic
2024-03-14T12:27:29.611405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4602
46.0%
2 2083
20.8%
3 1056
 
10.6%
4 642
 
6.4%
5 350
 
3.5%
6 199
 
2.0%
9999 155
 
1.6%
7 129
 
1.3%
8 92
 
0.9%
9 66
 
0.7%
Other values (193) 626
 
6.3%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 4602
46.0%
2 2083
20.8%
3 1056
 
10.6%
4 642
 
6.4%
5 350
 
3.5%
6 199
 
2.0%
7 129
 
1.3%
8 92
 
0.9%
9 66
 
0.7%
ValueCountFrequency (%)
500003 1
 
< 0.1%
9999 155
1.6%
8202 1
 
< 0.1%
8201 1
 
< 0.1%
8126 1
 
< 0.1%
8124 1
 
< 0.1%
8122 1
 
< 0.1%
8119 1
 
< 0.1%
8118 1
 
< 0.1%
8112 1
 
< 0.1%
Distinct9323
Distinct (%)93.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T12:27:29.891568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length33
Mean length27.8174
Min length22

Characters and Unicode

Total characters278174
Distinct characters118
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8812 ?
Unique (%)88.1%

Sample

1st row전라북도 부안군 변산면 마포리 261-1 1동 3호
2nd row전라북도 부안군 하서면 석상리 622-4 1동 2호
3rd row전라북도 부안군 백산면 신평리 61-67 1동 1호
4th row전라북도 부안군 하서면 언독리 478-1 1동 3호
5th row전라북도 부안군 보안면 영전리 160 2동 2호
ValueCountFrequency (%)
전라북도 10000
 
14.3%
부안군 10000
 
14.3%
1동 7520
 
10.7%
1호 4602
 
6.6%
부안읍 2333
 
3.3%
2호 2083
 
3.0%
변산면 1386
 
2.0%
2동 1355
 
1.9%
3호 1056
 
1.5%
봉덕리 734
 
1.0%
Other values (4722) 28907
41.3%
2024-03-14T12:27:30.297716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
59976
21.6%
1 20366
 
7.3%
13882
 
5.0%
12375
 
4.4%
11262
 
4.0%
10481
 
3.8%
10277
 
3.7%
10211
 
3.7%
10188
 
3.7%
10000
 
3.6%
Other values (108) 109156
39.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 149741
53.8%
Decimal Number 60887
21.9%
Space Separator 59976
21.6%
Dash Punctuation 7570
 
2.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
13882
 
9.3%
12375
 
8.3%
11262
 
7.5%
10481
 
7.0%
10277
 
6.9%
10211
 
6.8%
10188
 
6.8%
10000
 
6.7%
10000
 
6.7%
10000
 
6.7%
Other values (96) 41065
27.4%
Decimal Number
ValueCountFrequency (%)
1 20366
33.4%
2 8662
14.2%
3 5831
 
9.6%
4 5182
 
8.5%
5 4480
 
7.4%
7 3699
 
6.1%
9 3399
 
5.6%
6 3334
 
5.5%
8 3105
 
5.1%
0 2829
 
4.6%
Space Separator
ValueCountFrequency (%)
59976
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7570
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 149741
53.8%
Common 128433
46.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
13882
 
9.3%
12375
 
8.3%
11262
 
7.5%
10481
 
7.0%
10277
 
6.9%
10211
 
6.8%
10188
 
6.8%
10000
 
6.7%
10000
 
6.7%
10000
 
6.7%
Other values (96) 41065
27.4%
Common
ValueCountFrequency (%)
59976
46.7%
1 20366
 
15.9%
2 8662
 
6.7%
- 7570
 
5.9%
3 5831
 
4.5%
4 5182
 
4.0%
5 4480
 
3.5%
7 3699
 
2.9%
9 3399
 
2.6%
6 3334
 
2.6%
Other values (2) 5934
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 149741
53.8%
ASCII 128433
46.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
59976
46.7%
1 20366
 
15.9%
2 8662
 
6.7%
- 7570
 
5.9%
3 5831
 
4.5%
4 5182
 
4.0%
5 4480
 
3.5%
7 3699
 
2.9%
9 3399
 
2.6%
6 3334
 
2.6%
Other values (2) 5934
 
4.6%
Hangul
ValueCountFrequency (%)
13882
 
9.3%
12375
 
8.3%
11262
 
7.5%
10481
 
7.0%
10277
 
6.9%
10211
 
6.8%
10188
 
6.8%
10000
 
6.7%
10000
 
6.7%
10000
 
6.7%
Other values (96) 41065
27.4%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct7762
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47772350
Minimum0
Maximum1.0104272 × 1010
Zeros206
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:27:30.417124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile350000
Q12169296.2
median9690390
Q340512480
95-th percentile1.9176537 × 108
Maximum1.0104272 × 1010
Range1.0104272 × 1010
Interquartile range (IQR)38343184

Descriptive statistics

Standard deviation1.8684706 × 108
Coefficient of variation (CV)3.9111968
Kurtosis1281.7457
Mean47772350
Median Absolute Deviation (MAD)8850390
Skewness29.588976
Sum4.777235 × 1011
Variance3.4911824 × 1016
MonotonicityNot monotonic
2024-03-14T12:27:30.538645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 206
 
2.1%
45988541 34
 
0.3%
1800000 25
 
0.2%
25389311 19
 
0.2%
1980000 15
 
0.1%
840000 15
 
0.1%
23190010 15
 
0.1%
1500000 14
 
0.1%
960000 13
 
0.1%
750000 13
 
0.1%
Other values (7752) 9631
96.3%
ValueCountFrequency (%)
0 206
2.1%
26400 1
 
< 0.1%
32500 1
 
< 0.1%
33000 1
 
< 0.1%
36000 1
 
< 0.1%
38400 1
 
< 0.1%
42000 1
 
< 0.1%
50000 1
 
< 0.1%
51200 1
 
< 0.1%
60000 2
 
< 0.1%
ValueCountFrequency (%)
10104271610 1
< 0.1%
7590898885 1
< 0.1%
5910679171 1
< 0.1%
5127139727 1
< 0.1%
3727230054 1
< 0.1%
2357884671 1
< 0.1%
2295242862 1
< 0.1%
2288550300 1
< 0.1%
1930682460 1
< 0.1%
1721749300 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5192
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198.72561
Minimum0
Maximum15946.14
Zeros275
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T12:27:30.652437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.3985
Q136
median87.6
Q3198
95-th percentile807.264
Maximum15946.14
Range15946.14
Interquartile range (IQR)162

Descriptive statistics

Standard deviation423.83874
Coefficient of variation (CV)2.1327837
Kurtosis346.61074
Mean198.72561
Median Absolute Deviation (MAD)63.6
Skewness13.324852
Sum1987256.1
Variance179639.28
MonotonicityNot monotonic
2024-03-14T12:27:30.765992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 275
 
2.8%
18.0 231
 
2.3%
27.0 58
 
0.6%
100.0 48
 
0.5%
10.0 47
 
0.5%
30.0 46
 
0.5%
330.0 44
 
0.4%
150.0 44
 
0.4%
40.0 41
 
0.4%
48.0 40
 
0.4%
Other values (5182) 9126
91.3%
ValueCountFrequency (%)
0.0 275
2.8%
0.81 1
 
< 0.1%
0.8772 1
 
< 0.1%
1.0 1
 
< 0.1%
1.1 1
 
< 0.1%
1.2 1
 
< 0.1%
1.44 1
 
< 0.1%
1.5 2
 
< 0.1%
1.56 1
 
< 0.1%
1.59 1
 
< 0.1%
ValueCountFrequency (%)
15946.14 1
< 0.1%
11979.64 1
< 0.1%
10789.84 1
< 0.1%
9926.4 1
< 0.1%
8130.2327 1
< 0.1%
7052.4618 1
< 0.1%
5126.8639 1
< 0.1%
4736.61 1
< 0.1%
4619.68 1
< 0.1%
4554.0533 1
< 0.1%

Interactions

2024-03-14T12:27:25.593641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:12.011471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:13.591383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:14.971640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:16.262537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:17.794871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:19.888961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:24.232166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:25.687713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:12.091071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:13.667638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:15.055722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:16.351728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:17.956159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:20.293156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:24.320346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:25.789238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:12.171606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:13.744072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:15.139765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:16.426664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:18.099708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:20.730942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:24.409579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:25.864698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:12.243500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:13.810584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:15.210168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:16.496736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:18.231302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:21.115720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:24.479876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:25.953532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:12.321695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:13.897458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:15.279588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:16.830083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:18.421816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:21.800332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:24.550402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:26.176129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:12.534197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:14.150748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:15.459005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:17.013116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:18.687491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:22.264378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:24.738470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:26.764288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:13.417041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:14.770933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:16.048645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:17.624216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:19.587893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:23.192933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:25.446250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:27.066525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:13.490675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:14.869407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:16.146774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:17.699051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:19.725516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:23.563847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:27:25.517116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T12:27:30.851743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리특수지본번부번시가표준액연면적
법정동1.0000.4000.0950.3710.2040.158NaN0.0140.014
법정리0.4001.0000.1410.3670.1880.1340.0000.0000.039
특수지0.0950.1411.0000.1290.1850.0000.0000.0000.000
본번0.3710.3670.1291.0000.0910.0930.0000.0000.098
부번0.2040.1880.1850.0911.0000.0000.0000.0000.000
0.1580.1340.0000.0930.0001.000NaN0.0000.000
NaN0.0000.0000.0000.000NaN1.0000.0000.000
시가표준액0.0140.0000.0000.0000.0000.0000.0001.0000.943
연면적0.0140.0390.0000.0980.0000.0000.0000.9431.000
2024-03-14T12:27:30.958504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적특수지
법정동1.000-0.0540.084-0.0370.063-0.065-0.1140.0020.066
법정리-0.0541.0000.011-0.0890.011-0.037-0.1050.0800.084
본번0.0840.0111.000-0.0980.011-0.0110.0760.1100.077
부번-0.037-0.089-0.0981.000-0.063-0.0440.002-0.0480.112
0.0630.0110.011-0.0631.000-0.140-0.0020.0310.064
-0.065-0.037-0.011-0.044-0.1401.000-0.048-0.1510.000
시가표준액-0.114-0.1050.0760.002-0.002-0.0481.0000.6020.000
연면적0.0020.0800.110-0.0480.031-0.1510.6021.0000.000
특수지0.0660.0840.0770.1120.0640.0000.0000.0001.000

Missing values

2024-03-14T12:27:27.251777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T12:27:27.442303image/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

시도명시군구명자치단체코드과세연도법정동법정리특수지본번부번물건지시가표준액연면적
15876전라북도부안군458002022360231261113전라북도 부안군 변산면 마포리 261-1 1동 3호1174800066.0
22315전라북도부안군458002022400231622412전라북도 부안군 하서면 석상리 622-4 1동 2호250000020.0
18996전라북도부안군458002022380311616711전라북도 부안군 백산면 신평리 61-67 1동 1호1834434086.94
21634전라북도부안군458002022400221478113전라북도 부안군 하서면 언독리 478-1 1동 3호50048000136.0
13330전라북도부안군458002022350211160022전라북도 부안군 보안면 영전리 160 2동 2호1200000240.0
18957전라북도부안군4580020223803211047111전라북도 부안군 백산면 죽림리 1047-1 1동 1호614304019.44
18234전라북도부안군45800202237023174521전라북도 부안군 진서면 운호리 74-5 2동 1호124501805252.36
1497전라북도부안군458002022250221185012전라북도 부안군 부안읍 서외리 185 1동 2호142560017.82
5344전라북도부안군458002022250321308013전라북도 부안군 부안읍 신흥리 308 1동 3호5132160155.52
16936전라북도부안군458002022360221241111전라북도 부안군 변산면 격포리 241-1 1동 1호5805540070.2
시도명시군구명자치단체코드과세연도법정동법정리특수지본번부번물건지시가표준액연면적
4945전라북도부안군458002022250281176016전라북도 부안군 부안읍 연곡리 176 1동 6호1114560030.96
14966전라북도부안군458002022360231301413전라북도 부안군 변산면 마포리 30-14 1동 3호73476000141.3
24135전라북도부안군458002022410221471211전라북도 부안군 줄포면 장동리 471-2 1동 1호30292080110.96
12682전라북도부안군4580020223502417762913전라북도 부안군 보안면 우동리 776-29 1동 3호41040000273.6
6782전라북도부안군4580020223102611207514전라북도 부안군 주산면 사산리 1207-5 1동 4호3555000711.0
7245전라북도부안군458002022310251357214전라북도 부안군 주산면 갈촌리 357-2 1동 4호5342400166.95
21527전라북도부안군458002022400221233231전라북도 부안군 하서면 언독리 233-2 3동 1호55177200524.0
14672전라북도부안군45800202236023180932전라북도 부안군 변산면 마포리 80-9 3동 2호8394126501273.7673
2241전라북도부안군458002022250221336124전라북도 부안군 부안읍 서외리 336-1 2동 4호1631800049.75
872전라북도부안군458002022250221425812전라북도 부안군 부안읍 서외리 42-58 1동 2호225861028.59

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세연도법정동법정리특수지본번부번물건지시가표준액연면적# duplicates
304전라북도부안군4580020223902714951011전라북도 부안군 상서면 청림리 495-10 1동 1호146835097.897
132전라북도부안군45800202232024191419999전라북도 부안군 동진면 장등리 91-4 1동 9999호00.06
266전라북도부안군458002022370221697119999전라북도 부안군 진서면 석포리 697-1 1동 9999호00.06
115전라북도부안군458002022310271382719999전라북도 부안군 주산면 돈계리 38-27 1동 9999호00.05
232전라북도부안군458002022360231112011전라북도 부안군 변산면 마포리 112 1동 1호7312422093.995
334전라북도부안군458002022410211735119999전라북도 부안군 줄포면 줄포리 735-1 1동 9999호00.05
4전라북도부안군458002022250211118014전라북도 부안군 부안읍 동중리 118 1동 4호68899490272.334
35전라북도부안군45800202225022124521전라북도 부안군 부안읍 서외리 24-5 2동 1호8300320148.224
60전라북도부안군458002022250231167119999전라북도 부안군 부안읍 선은리 167-1 1동 9999호00.04
142전라북도부안군458002022330221347119999전라북도 부안군 행안면 삼간리 347-1 1동 9999호00.04