Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows28
Duplicate rows (%)0.3%
Total size in memory1.2 MiB
Average record size in memory131.0 B

Variable types

Categorical5
Numeric8
Text1

Dataset

Description일반건축물에 대한 지방세 부과기준인 시가표준액을 제공
Author경상북도 의성군
URLhttps://www.data.go.kr/data/15080608/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
Dataset has 28 (0.3%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (87.9%)Imbalance
is highly skewed (γ1 = 20.8961387)Skewed
부번 has 3468 (34.7%) zerosZeros

Reproduction

Analysis started2023-12-12 09:19:25.371253
Analysis finished2023-12-12 09:19:37.765810
Duration12.39 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

2023-12-12T18:19:37.841265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:19:37.969197image/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
의성군
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
의성군 10000
100.0%

Length

2023-12-12T18:19:38.096639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:19:38.302233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
의성군 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
47730
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row47730
2nd row47730
3rd row47730
4th row47730
5th row47730

Common Values

ValueCountFrequency (%)
47730 10000
100.0%

Length

2023-12-12T18:19:38.475209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:19:38.615720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
47730 10000
100.0%

과세년도
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020
2652 
2019
2517 
2018
2442 
2017
2389 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2018
3rd row2017
4th row2017
5th row2018

Common Values

ValueCountFrequency (%)
2020 2652
26.5%
2019 2517
25.2%
2018 2442
24.4%
2017 2389
23.9%

Length

2023-12-12T18:19:38.745815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:19:38.883053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 2652
26.5%
2019 2517
25.2%
2018 2442
24.4%
2017 2389
23.9%

법정동
Real number (ℝ)

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean364.081
Minimum250
Maximum470
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:19:39.055012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile250
Q1320
median370
Q3430
95-th percentile460
Maximum470
Range220
Interquartile range (IQR)110

Descriptive statistics

Standard deviation66.779948
Coefficient of variation (CV)0.18342058
Kurtosis-0.87588379
Mean364.081
Median Absolute Deviation (MAD)50
Skewness-0.48061953
Sum3640810
Variance4459.5614
MonotonicityNot monotonic
2023-12-12T18:19:39.218128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
250 1804
18.0%
440 984
9.8%
370 982
9.8%
380 922
9.2%
430 904
9.0%
310 467
 
4.7%
410 467
 
4.7%
390 461
 
4.6%
320 407
 
4.1%
330 382
 
3.8%
Other values (8) 2220
22.2%
ValueCountFrequency (%)
250 1804
18.0%
310 467
 
4.7%
320 407
 
4.1%
330 382
 
3.8%
340 290
 
2.9%
350 353
 
3.5%
360 351
 
3.5%
370 982
9.8%
380 922
9.2%
390 461
 
4.6%
ValueCountFrequency (%)
470 137
 
1.4%
460 367
 
3.7%
450 132
 
1.3%
440 984
9.8%
430 904
9.0%
420 249
 
2.5%
410 467
4.7%
400 341
 
3.4%
390 461
4.6%
380 922
9.2%

법정리
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.3344
Minimum21
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:19:39.398213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q130
median35
Q339
95-th percentile48
Maximum51
Range30
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.1576183
Coefficient of variation (CV)0.20846784
Kurtosis-0.30798507
Mean34.3344
Median Absolute Deviation (MAD)5
Skewness0.063425596
Sum343344
Variance51.2315
MonotonicityNot monotonic
2023-12-12T18:19:39.585226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
30 937
 
9.4%
36 829
 
8.3%
31 800
 
8.0%
37 615
 
6.2%
21 605
 
6.0%
38 599
 
6.0%
40 575
 
5.8%
34 465
 
4.7%
39 442
 
4.4%
32 439
 
4.4%
Other values (21) 3694
36.9%
ValueCountFrequency (%)
21 605
6.0%
22 278
 
2.8%
23 114
 
1.1%
24 243
 
2.4%
25 104
 
1.0%
26 69
 
0.7%
27 71
 
0.7%
28 150
 
1.5%
29 328
 
3.3%
30 937
9.4%
ValueCountFrequency (%)
51 14
 
0.1%
50 156
1.6%
49 217
2.2%
48 201
2.0%
47 72
 
0.7%
46 149
1.5%
45 158
1.6%
44 160
1.6%
43 160
1.6%
42 156
1.6%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9836 
2
 
164

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 9836
98.4%
2 164
 
1.6%

Length

2023-12-12T18:19:39.769473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:19:39.908175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9836
98.4%
2 164
 
1.6%

본번
Real number (ℝ)

Distinct1294
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean565.6936
Minimum1
Maximum1845
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:19:40.034488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile53
Q1243
median552
Q3839.25
95-th percentile1199.05
Maximum1845
Range1844
Interquartile range (IQR)596.25

Descriptive statistics

Standard deviation364.53974
Coefficient of variation (CV)0.644412
Kurtosis-0.37346441
Mean565.6936
Median Absolute Deviation (MAD)296
Skewness0.41728703
Sum5656936
Variance132889.22
MonotonicityNot monotonic
2023-12-12T18:19:40.225520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133 67
 
0.7%
1105 62
 
0.6%
858 57
 
0.6%
850 44
 
0.4%
628 41
 
0.4%
103 39
 
0.4%
916 37
 
0.4%
843 37
 
0.4%
662 36
 
0.4%
670 36
 
0.4%
Other values (1284) 9544
95.4%
ValueCountFrequency (%)
1 23
0.2%
2 20
0.2%
3 22
0.2%
4 10
0.1%
5 20
0.2%
6 16
0.2%
7 13
0.1%
8 12
0.1%
9 11
0.1%
10 7
 
0.1%
ValueCountFrequency (%)
1845 1
 
< 0.1%
1815 1
 
< 0.1%
1813 1
 
< 0.1%
1797 1
 
< 0.1%
1795 3
 
< 0.1%
1793 1
 
< 0.1%
1789 2
 
< 0.1%
1757 1
 
< 0.1%
1732 1
 
< 0.1%
1724 8
0.1%

부번
Real number (ℝ)

ZEROS 

Distinct137
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0948
Minimum0
Maximum603
Zeros3468
Zeros (%)34.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:19:40.420985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile27
Maximum603
Range603
Interquartile range (IQR)4

Descriptive statistics

Standard deviation19.224801
Coefficient of variation (CV)3.1542956
Kurtosis150.10392
Mean6.0948
Median Absolute Deviation (MAD)1
Skewness9.2845451
Sum60948
Variance369.59297
MonotonicityNot monotonic
2023-12-12T18:19:40.595835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3468
34.7%
1 1962
19.6%
2 945
 
9.4%
3 731
 
7.3%
4 483
 
4.8%
5 335
 
3.4%
6 275
 
2.8%
7 253
 
2.5%
8 159
 
1.6%
9 157
 
1.6%
Other values (127) 1232
 
12.3%
ValueCountFrequency (%)
0 3468
34.7%
1 1962
19.6%
2 945
 
9.4%
3 731
 
7.3%
4 483
 
4.8%
5 335
 
3.4%
6 275
 
2.8%
7 253
 
2.5%
8 159
 
1.6%
9 157
 
1.6%
ValueCountFrequency (%)
603 1
 
< 0.1%
291 1
 
< 0.1%
290 1
 
< 0.1%
262 2
< 0.1%
253 1
 
< 0.1%
243 4
< 0.1%
242 1
 
< 0.1%
234 1
 
< 0.1%
223 1
 
< 0.1%
216 1
 
< 0.1%


Real number (ℝ)

SKEWED 

Distinct28
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.3655
Minimum0
Maximum8001
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:19:40.797485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum8001
Range8001
Interquartile range (IQR)0

Descriptive statistics

Standard deviation334.04536
Coefficient of variation (CV)19.23615
Kurtosis436.16268
Mean17.3655
Median Absolute Deviation (MAD)0
Skewness20.896139
Sum173655
Variance111586.3
MonotonicityNot monotonic
2023-12-12T18:19:41.002281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 9523
95.2%
2 325
 
3.2%
3 36
 
0.4%
101 21
 
0.2%
4 18
 
0.2%
7001 11
 
0.1%
7002 10
 
0.1%
10 10
 
0.1%
5 8
 
0.1%
6 7
 
0.1%
Other values (18) 31
 
0.3%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 9523
95.2%
2 325
 
3.2%
3 36
 
0.4%
4 18
 
0.2%
5 8
 
0.1%
6 7
 
0.1%
7 4
 
< 0.1%
8 4
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
8001 1
 
< 0.1%
7002 10
0.1%
7001 11
0.1%
5001 1
 
< 0.1%
107 3
 
< 0.1%
104 1
 
< 0.1%
103 1
 
< 0.1%
102 1
 
< 0.1%
101 21
0.2%
100 1
 
< 0.1%


Real number (ℝ)

Distinct82
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.0523
Minimum0
Maximum8104
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:19:41.198089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median101
Q3102
95-th percentile201
Maximum8104
Range8104
Interquartile range (IQR)100

Descriptive statistics

Standard deviation855.14846
Coefficient of variation (CV)5.2126576
Kurtosis81.586215
Mean164.0523
Median Absolute Deviation (MAD)90
Skewness9.1091131
Sum1640523
Variance731278.89
MonotonicityNot monotonic
2023-12-12T18:19:41.391299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 3378
33.8%
1 1964
19.6%
2 1008
 
10.1%
102 965
 
9.7%
201 503
 
5.0%
3 491
 
4.9%
103 359
 
3.6%
4 273
 
2.7%
104 138
 
1.4%
5 134
 
1.3%
Other values (72) 787
 
7.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1964
19.6%
2 1008
10.1%
3 491
 
4.9%
4 273
 
2.7%
5 134
 
1.3%
6 65
 
0.7%
7 34
 
0.3%
8 18
 
0.2%
9 16
 
0.2%
ValueCountFrequency (%)
8104 1
 
< 0.1%
8103 2
 
< 0.1%
8102 2
 
< 0.1%
8101 107
1.1%
8002 2
 
< 0.1%
801 1
 
< 0.1%
601 2
 
< 0.1%
501 11
 
0.1%
404 1
 
< 0.1%
403 2
 
< 0.1%
Distinct7645
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T18:19:41.849012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length32
Mean length27.3967
Min length21

Characters and Unicode

Total characters273967
Distinct characters201
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

Unique5862 ?
Unique (%)58.6%

Sample

1st row경상북도 의성군 의성읍 후죽리 502-1 1동 201호
2nd row경상북도 의성군 금성면 산운리 726-9 1동 204호
3rd row경상북도 의성군 안계면 도덕리 729-1 1동 101호
4th row경상북도 의성군 구천면 조성리 154 1동 104호
5th row[ 용기4길 6 ] 0001동 0102호
ValueCountFrequency (%)
경상북도 7281
 
10.8%
의성군 7281
 
10.8%
1동 6889
 
10.2%
5438
 
8.1%
0001동 2634
 
3.9%
101호 2309
 
3.4%
1호 1347
 
2.0%
의성읍 1206
 
1.8%
0101호 1069
 
1.6%
2호 810
 
1.2%
Other values (4404) 31159
46.2%
2023-12-12T18:19:42.474588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
57423
21.0%
1 28463
 
10.4%
0 21311
 
7.8%
10337
 
3.8%
10209
 
3.7%
9557
 
3.5%
8553
 
3.1%
8096
 
3.0%
8091
 
3.0%
2 7655
 
2.8%
Other values (191) 104272
38.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 123934
45.2%
Decimal Number 81499
29.7%
Space Separator 57423
21.0%
Dash Punctuation 5673
 
2.1%
Open Punctuation 2719
 
1.0%
Close Punctuation 2719
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10337
 
8.3%
10209
 
8.2%
9557
 
7.7%
8553
 
6.9%
8096
 
6.5%
8091
 
6.5%
7586
 
6.1%
7426
 
6.0%
7370
 
5.9%
7324
 
5.9%
Other values (177) 39385
31.8%
Decimal Number
ValueCountFrequency (%)
1 28463
34.9%
0 21311
26.1%
2 7655
 
9.4%
3 5161
 
6.3%
4 3760
 
4.6%
5 3587
 
4.4%
6 3191
 
3.9%
7 2984
 
3.7%
8 2925
 
3.6%
9 2462
 
3.0%
Space Separator
ValueCountFrequency (%)
57423
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5673
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 2719
100.0%
Close Punctuation
ValueCountFrequency (%)
] 2719
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 150033
54.8%
Hangul 123934
45.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10337
 
8.3%
10209
 
8.2%
9557
 
7.7%
8553
 
6.9%
8096
 
6.5%
8091
 
6.5%
7586
 
6.1%
7426
 
6.0%
7370
 
5.9%
7324
 
5.9%
Other values (177) 39385
31.8%
Common
ValueCountFrequency (%)
57423
38.3%
1 28463
19.0%
0 21311
 
14.2%
2 7655
 
5.1%
- 5673
 
3.8%
3 5161
 
3.4%
4 3760
 
2.5%
5 3587
 
2.4%
6 3191
 
2.1%
7 2984
 
2.0%
Other values (4) 10825
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 150033
54.8%
Hangul 123934
45.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
57423
38.3%
1 28463
19.0%
0 21311
 
14.2%
2 7655
 
5.1%
- 5673
 
3.8%
3 5161
 
3.4%
4 3760
 
2.5%
5 3587
 
2.4%
6 3191
 
2.1%
7 2984
 
2.0%
Other values (4) 10825
 
7.2%
Hangul
ValueCountFrequency (%)
10337
 
8.3%
10209
 
8.2%
9557
 
7.7%
8553
 
6.9%
8096
 
6.5%
8091
 
6.5%
7586
 
6.1%
7426
 
6.0%
7370
 
5.9%
7324
 
5.9%
Other values (177) 39385
31.8%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct8071
Distinct (%)80.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27797516
Minimum12000
Maximum2.0259798 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:19:42.678301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12000
5-th percentile199200
Q1936000
median4065900
Q319440000
95-th percentile1.3111865 × 108
Maximum2.0259798 × 109
Range2.0259678 × 109
Interquartile range (IQR)18504000

Descriptive statistics

Standard deviation84420222
Coefficient of variation (CV)3.03697
Kurtosis165.23276
Mean27797516
Median Absolute Deviation (MAD)3717060
Skewness10.25968
Sum2.7797516 × 1011
Variance7.1267739 × 1015
MonotonicityNot monotonic
2023-12-12T18:19:42.927692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
720000 23
 
0.2%
288000 21
 
0.2%
384000 21
 
0.2%
676800 19
 
0.2%
576000 17
 
0.2%
172800 17
 
0.2%
768000 17
 
0.2%
216000 15
 
0.1%
1620000 15
 
0.1%
230400 14
 
0.1%
Other values (8061) 9821
98.2%
ValueCountFrequency (%)
12000 2
< 0.1%
17400 1
< 0.1%
18750 1
< 0.1%
19380 1
< 0.1%
20000 1
< 0.1%
25000 2
< 0.1%
26000 1
< 0.1%
29800 1
< 0.1%
30000 1
< 0.1%
31500 1
< 0.1%
ValueCountFrequency (%)
2025979800 1
< 0.1%
1934354620 1
< 0.1%
1925944380 1
< 0.1%
1708448280 1
< 0.1%
1642126530 1
< 0.1%
1547826280 1
< 0.1%
1535708640 1
< 0.1%
1363427320 1
< 0.1%
1072284480 1
< 0.1%
1037334640 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct4479
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean163.80001
Minimum0.8
Maximum10915.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:19:43.142302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile13.95
Q142.315
median85.07
Q3180
95-th percentile539.05
Maximum10915.19
Range10914.39
Interquartile range (IQR)137.685

Descriptive statistics

Standard deviation278.03669
Coefficient of variation (CV)1.6974156
Kurtosis255.91432
Mean163.80001
Median Absolute Deviation (MAD)55.07
Skewness10.018999
Sum1638000.1
Variance77304.402
MonotonicityNot monotonic
2023-12-12T18:19:43.338962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 279
 
2.8%
160.0 82
 
0.8%
192.0 62
 
0.6%
96.0 60
 
0.6%
72.0 57
 
0.6%
48.0 49
 
0.5%
384.0 44
 
0.4%
60.0 41
 
0.4%
36.0 41
 
0.4%
198.0 36
 
0.4%
Other values (4469) 9249
92.5%
ValueCountFrequency (%)
0.8 3
< 0.1%
1.0 2
 
< 0.1%
1.14 5
0.1%
1.2 1
 
< 0.1%
1.5 1
 
< 0.1%
1.6 3
< 0.1%
2.0 4
< 0.1%
2.03 1
 
< 0.1%
2.1 1
 
< 0.1%
2.12 1
 
< 0.1%
ValueCountFrequency (%)
10915.19 1
 
< 0.1%
3983.62 3
< 0.1%
3720.0 2
< 0.1%
3200.6 1
 
< 0.1%
3041.05 1
 
< 0.1%
2846.25 1
 
< 0.1%
2819.5 2
< 0.1%
2652.87 2
< 0.1%
2550.0 1
 
< 0.1%
2437.75 2
< 0.1%

Interactions

2023-12-12T18:19:36.268200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:28.400516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:29.490014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:30.652273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:31.884771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:32.999062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:34.124634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:35.061121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:36.374029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:28.540775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:29.615831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:30.772233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:32.008340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:33.122754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:34.253621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:35.153458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:36.501283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:28.696590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:29.758102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:30.941733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:32.131112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:33.239741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:34.383080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:35.257160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:36.641736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:28.827760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:29.918330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:31.082086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:32.270610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:33.372724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:34.506378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:35.365307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:36.761926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:28.967094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:30.036270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:31.244360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:32.412507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:33.535402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:34.620336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:35.494413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:36.893867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:29.102126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:30.180274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:31.422484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:32.555539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:33.675226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:34.731183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:35.616883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:37.016070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:29.222780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:30.339932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:31.586173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:32.703584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:33.797890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:34.843615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:35.726430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:37.188972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:29.352828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:30.520163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:31.745606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:32.867988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:33.971639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:34.968622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:19:36.159569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:19:43.473125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동법정리특수지본번부번시가표준액연면적
과세년도1.0000.0000.0000.0000.0000.0150.0000.0000.0000.000
법정동0.0001.0000.6030.0880.2820.1440.0000.0680.0670.056
법정리0.0000.6031.0000.0990.4780.0800.1140.1170.0570.031
특수지0.0000.0880.0991.0000.3130.0000.0000.0000.1810.058
본번0.0000.2820.4780.3131.0000.1350.0760.0420.0600.060
부번0.0150.1440.0800.0000.1351.0000.0000.0000.3020.112
0.0000.0000.1140.0000.0760.0001.0000.0000.0000.000
0.0000.0680.1170.0000.0420.0000.0001.0000.1850.032
시가표준액0.0000.0670.0570.1810.0600.3020.0000.1851.0000.795
연면적0.0000.0560.0310.0580.0600.1120.0000.0320.7951.000
2023-12-12T18:19:43.980683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수지과세년도
특수지1.0000.000
과세년도0.0001.000
2023-12-12T18:19:44.108090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적과세년도특수지
법정동1.0000.462-0.040-0.021-0.021-0.135-0.0940.0610.0000.085
법정리0.4621.000-0.134-0.124-0.027-0.037-0.1430.0540.0000.093
본번-0.040-0.1341.0000.0180.0020.0400.0060.0000.0000.240
부번-0.021-0.1240.0181.0000.0030.0600.131-0.0420.0100.000
-0.021-0.0270.0020.0031.000-0.031-0.038-0.0580.0000.000
-0.135-0.0370.0400.060-0.0311.0000.3270.0170.0000.000
시가표준액-0.094-0.1430.0060.131-0.0380.3271.0000.5230.0000.139
연면적0.0610.0540.000-0.042-0.0580.0170.5231.0000.0000.071
과세년도0.0000.0000.0000.0100.0000.0000.0000.0001.0000.000
특수지0.0850.0930.2400.0000.0000.0000.1390.0710.0001.000

Missing values

2023-12-12T18:19:37.376240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:19:37.647943image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적
70698경상북도의성군47730202025021150211201경상북도 의성군 의성읍 후죽리 502-1 1동 201호4391496087.48
27919경상북도의성군47730201837049172691204경상북도 의성군 금성면 산운리 726-9 1동 204호978810055.3
11364경상북도의성군47730201743036172911101경상북도 의성군 안계면 도덕리 729-1 1동 101호45000018.0
7218경상북도의성군47730201740033115401104경상북도 의성군 구천면 조성리 154 1동 104호330480015.3
23252경상북도의성군47730201843030191501102[ 용기4길 6 ] 0001동 0102호69924470129.73
53813경상북도의성군47730202025029111411경상북도 의성군 의성읍 팔성리 11-4 1동 1호72296025.82
16680경상북도의성군477302017430301507321101경상북도 의성군 안계면 용기리 507-32 1동 101호1323000042.0
66052경상북도의성군477302020400341154016경상북도 의성군 구천면 장국리 154 1동 6호420000175.0
13323경상북도의성군477302017440511696011경상북도 의성군 다인면 용무리 696 1동 1호9561053.12
25347경상북도의성군47730201825021148801102[ 후죽1길 11 ] 0001동 0102호43390600121.0
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적
41771경상북도의성군477302019430301469171102경상북도 의성군 안계면 용기리 469-17 1동 102호57217140116.52
43468경상북도의성군477302019430311212111[ 토매1길 120 ] 0001동 0001호1493820077.4
45255경상북도의성군47730201936033166801101경상북도 의성군 가음면 순호리 668 1동 101호74880018.0
39416경상북도의성군47730201936031191401101[ 가산1길 5 ] 0001동 0101호396000165.0
28593경상북도의성군47730201838038153001102[ 농공신동길 130-59 ] 0001동 0102호2112000192.0
65197경상북도의성군477302020360341107961101[ 빙계계곡길 14-6 ] 0001동 0101호1599600133.3
16169경상북도의성군47730201739040135031102경상북도 의성군 비안면 이두리 350-3 1동 102호96912000242.28
31576경상북도의성군47730201844037168301101경상북도 의성군 다인면 산내리 683 1동 101호5400000300.0
70931경상북도의성군47730202043030184210011경상북도 의성군 안계면 용기리 842-100 1동 1호2655166045.31
32309경상북도의성군4773020184404019895711[ 서부로 2858-30 ] 0001동 0001호84990400193.6

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적# duplicates
8경상북도의성군47730201837042167011101경상북도 의성군 금성면 하리 670-1 1동 101호44755200336.03
15경상북도의성군47730201941031147011101[ 구단길 112 ] 0001동 0101호4720000100.03
0경상북도의성군47730201725026171401101경상북도 의성군 의성읍 치선리 714 1동 101호6594580180.182
1경상북도의성군477302017310381370141101경상북도 의성군 단촌면 후평리 370-14 1동 101호595200198.42
2경상북도의성군47730201742034123001101경상북도 의성군 단북면 신하리 230 1동 101호3360000350.02
3경상북도의성군47730201744036152901101경상북도 의성군 다인면 서릉리 529 1동 101호1728086501319.152
4경상북도의성군47730201744037168301101경상북도 의성군 다인면 산내리 683 1동 101호5220000300.02
5경상북도의성군4773020174404914851101경상북도 의성군 다인면 외정리 48-5 1동 101호26092800288.02
6경상북도의성군477302017460381120301101경상북도 의성군 안평면 신월리 1203 1동 101호39347000539.02
7경상북도의성군47730201831033157601101경상북도 의성군 단촌면 관덕리 576 1동 101호14040000900.02