Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows80
Duplicate rows (%)0.8%
Total size in memory1.3 MiB
Average record size in memory139.0 B

Variable types

Categorical6
Numeric8
Text1

Dataset

Description일반건축물에 대한 취득세, 재산세 등 지방세 부과기준이 되는 시가표준액을 제공함으로써 납세자가 물건별 재산가액을 확인 가능
Author충청북도 영동군
URLhttps://www.data.go.kr/data/15079861/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
기준일자 has constant value ""Constant
Dataset has 80 (0.8%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (71.4%)Imbalance
부번 has 3041 (30.4%) zerosZeros
has 781 (7.8%) zerosZeros
has 110 (1.1%) zerosZeros

Reproduction

Analysis started2023-12-12 14:31:11.422913
Analysis finished2023-12-12 14:31:21.286481
Duration9.86 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
충청북도
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청북도 10000
100.0%

Length

2023-12-12T23:31:21.334081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:31:21.404070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청북도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영동군 10000
100.0%

Length

2023-12-12T23:31:21.479252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:31:21.546251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영동군 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
43740
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row43740
2nd row43740
3rd row43740
4th row43740
5th row43740

Common Values

ValueCountFrequency (%)
43740 10000
100.0%

Length

2023-12-12T23:31:21.622851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:31:21.692012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
43740 10000
100.0%

과세년도
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021
2245 
2020
2001 
2019
1968 
2017
1920 
2018
1866 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2021 2245
22.4%
2020 2001
20.0%
2019 1968
19.7%
2017 1920
19.2%
2018 1866
18.7%

Length

2023-12-12T23:31:21.762337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:31:21.844530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 2245
22.4%
2020 2001
20.0%
2019 1968
19.7%
2017 1920
19.2%
2018 1866
18.7%

법정동
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean319.8305
Minimum250
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:31:21.933328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile250
Q1250
median320
Q3360
95-th percentile400
Maximum400
Range150
Interquartile range (IQR)110

Descriptive statistics

Standard deviation52.41215
Coefficient of variation (CV)0.16387477
Kurtosis-1.3205545
Mean319.8305
Median Absolute Deviation (MAD)50
Skewness-0.16771528
Sum3198305
Variance2747.0335
MonotonicityNot monotonic
2023-12-12T23:31:22.030163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
250 3044
30.4%
310 1046
 
10.5%
320 1039
 
10.4%
360 1028
 
10.3%
400 673
 
6.7%
380 620
 
6.2%
350 617
 
6.2%
335 595
 
5.9%
390 555
 
5.5%
340 525
 
5.2%
ValueCountFrequency (%)
250 3044
30.4%
310 1046
 
10.5%
320 1039
 
10.4%
335 595
 
5.9%
340 525
 
5.2%
350 617
 
6.2%
360 1028
 
10.3%
370 258
 
2.6%
380 620
 
6.2%
390 555
 
5.5%
ValueCountFrequency (%)
400 673
6.7%
390 555
5.5%
380 620
6.2%
370 258
 
2.6%
360 1028
10.3%
350 617
6.2%
340 525
5.2%
335 595
5.9%
320 1039
10.4%
310 1046
10.5%

법정리
Real number (ℝ)

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.0762
Minimum21
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:31:22.134393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q121
median26
Q329
95-th percentile34
Maximum39
Range18
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.6714854
Coefficient of variation (CV)0.17914748
Kurtosis-0.57938036
Mean26.0762
Median Absolute Deviation (MAD)4
Skewness0.60604284
Sum260762
Variance21.822776
MonotonicityNot monotonic
2023-12-12T23:31:22.233347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
21 2850
28.5%
28 924
 
9.2%
27 724
 
7.2%
24 685
 
6.9%
26 609
 
6.1%
23 591
 
5.9%
29 540
 
5.4%
31 534
 
5.3%
33 434
 
4.3%
22 396
 
4.0%
Other values (9) 1713
17.1%
ValueCountFrequency (%)
21 2850
28.5%
22 396
 
4.0%
23 591
 
5.9%
24 685
 
6.9%
25 382
 
3.8%
26 609
 
6.1%
27 724
 
7.2%
28 924
 
9.2%
29 540
 
5.4%
30 307
 
3.1%
ValueCountFrequency (%)
39 22
 
0.2%
38 144
 
1.4%
37 69
 
0.7%
36 127
 
1.3%
35 134
 
1.3%
34 288
2.9%
33 434
4.3%
32 240
2.4%
31 534
5.3%
30 307
3.1%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9500 
2
 
500

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 9500
95.0%
2 500
 
5.0%

Length

2023-12-12T23:31:22.349826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:31:22.438257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9500
95.0%
2 500
 
5.0%

본번
Real number (ℝ)

Distinct982
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean417.5973
Minimum1
Maximum1643
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:31:22.566048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile30
Q1191
median400
Q3636
95-th percentile838
Maximum1643
Range1642
Interquartile range (IQR)445

Descriptive statistics

Standard deviation266.32204
Coefficient of variation (CV)0.63774848
Kurtosis-0.25445933
Mean417.5973
Median Absolute Deviation (MAD)222.5
Skewness0.3521829
Sum4175973
Variance70927.431
MonotonicityNot monotonic
2023-12-12T23:31:22.712182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
693 99
 
1.0%
27 92
 
0.9%
345 90
 
0.9%
695 86
 
0.9%
595 80
 
0.8%
42 71
 
0.7%
205 70
 
0.7%
160 56
 
0.6%
62 51
 
0.5%
12 49
 
0.5%
Other values (972) 9256
92.6%
ValueCountFrequency (%)
1 31
0.3%
2 36
0.4%
3 8
 
0.1%
4 12
 
0.1%
5 5
 
0.1%
6 22
0.2%
7 21
0.2%
8 27
0.3%
9 16
0.2%
10 10
 
0.1%
ValueCountFrequency (%)
1643 3
 
< 0.1%
1599 2
 
< 0.1%
1569 4
< 0.1%
1468 1
 
< 0.1%
1403 2
 
< 0.1%
1401 1
 
< 0.1%
1397 2
 
< 0.1%
1395 9
0.1%
1376 2
 
< 0.1%
1329 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct77
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4457
Minimum0
Maximum129
Zeros3041
Zeros (%)30.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:31:22.933433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile18
Maximum129
Range129
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.8347203
Coefficient of variation (CV)2.2121871
Kurtosis47.322932
Mean4.4457
Median Absolute Deviation (MAD)1
Skewness5.7766465
Sum44457
Variance96.721724
MonotonicityNot monotonic
2023-12-12T23:31:23.066870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3041
30.4%
1 2416
24.2%
2 971
 
9.7%
3 631
 
6.3%
4 497
 
5.0%
5 373
 
3.7%
7 259
 
2.6%
6 259
 
2.6%
9 196
 
2.0%
8 175
 
1.8%
Other values (67) 1182
 
11.8%
ValueCountFrequency (%)
0 3041
30.4%
1 2416
24.2%
2 971
 
9.7%
3 631
 
6.3%
4 497
 
5.0%
5 373
 
3.7%
6 259
 
2.6%
7 259
 
2.6%
8 175
 
1.8%
9 196
 
2.0%
ValueCountFrequency (%)
129 10
0.1%
108 1
 
< 0.1%
99 1
 
< 0.1%
97 2
 
< 0.1%
96 1
 
< 0.1%
95 4
 
< 0.1%
94 1
 
< 0.1%
89 1
 
< 0.1%
87 1
 
< 0.1%
84 1
 
< 0.1%


Real number (ℝ)

ZEROS 

Distinct48
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.3767
Minimum0
Maximum9999
Zeros781
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:31:23.204941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q310
95-th percentile20
Maximum9999
Range9999
Interquartile range (IQR)9

Descriptive statistics

Standard deviation690.80697
Coefficient of variation (CV)12.942107
Kurtosis203.36908
Mean53.3767
Median Absolute Deviation (MAD)1
Skewness14.328043
Sum533767
Variance477214.26
MonotonicityNot monotonic
2023-12-12T23:31:23.333522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1 4917
49.2%
10 3164
31.6%
0 781
 
7.8%
20 352
 
3.5%
2 340
 
3.4%
3 99
 
1.0%
30 70
 
0.7%
9999 48
 
0.5%
4 40
 
0.4%
40 37
 
0.4%
Other values (38) 152
 
1.5%
ValueCountFrequency (%)
0 781
 
7.8%
1 4917
49.2%
2 340
 
3.4%
3 99
 
1.0%
4 40
 
0.4%
5 35
 
0.4%
6 15
 
0.1%
7 7
 
0.1%
8 8
 
0.1%
9 16
 
0.2%
ValueCountFrequency (%)
9999 48
0.5%
201 6
 
0.1%
160 1
 
< 0.1%
130 1
 
< 0.1%
120 1
 
< 0.1%
110 1
 
< 0.1%
101 3
 
< 0.1%
94 2
 
< 0.1%
90 1
 
< 0.1%
86 1
 
< 0.1%


Real number (ℝ)

ZEROS 

Distinct168
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189.1875
Minimum0
Maximum8702
Zeros110
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:31:23.482186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median8
Q3101
95-th percentile201
Maximum8702
Range8702
Interquartile range (IQR)99

Descriptive statistics

Standard deviation1025.9308
Coefficient of variation (CV)5.4228257
Kurtosis55.228186
Mean189.1875
Median Absolute Deviation (MAD)7
Skewness7.5392265
Sum1891875
Variance1052534.1
MonotonicityNot monotonic
2023-12-12T23:31:23.643883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2210
22.1%
101 1391
13.9%
100 1169
11.7%
2 1133
11.3%
3 677
 
6.8%
102 600
 
6.0%
4 381
 
3.8%
103 251
 
2.5%
5 226
 
2.3%
200 157
 
1.6%
Other values (158) 1805
18.1%
ValueCountFrequency (%)
0 110
 
1.1%
1 2210
22.1%
2 1133
11.3%
3 677
 
6.8%
4 381
 
3.8%
5 226
 
2.3%
6 147
 
1.5%
7 101
 
1.0%
8 76
 
0.8%
9 62
 
0.6%
ValueCountFrequency (%)
8702 1
 
< 0.1%
8201 1
 
< 0.1%
8132 2
 
< 0.1%
8119 1
 
< 0.1%
8116 1
 
< 0.1%
8110 1
 
< 0.1%
8108 4
< 0.1%
8107 1
 
< 0.1%
8106 2
 
< 0.1%
8105 6
0.1%
Distinct7522
Distinct (%)75.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T23:31:24.028185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length31
Mean length27.4143
Min length19

Characters and Unicode

Total characters274143
Distinct characters195
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

Unique5711 ?
Unique (%)57.1%

Sample

1st row충청북도 영동군 영동읍 회동리 82-2 20동 101호
2nd row충청북도 영동군 영동읍 설계리 666-14 10동 1호
3rd row충청북도 영동군 추풍령면 지봉리 216-1 10동 100호
4th row충청북도 영동군 용산면 부릉리 산 57 10동 3호
5th row충청북도 영동군 상촌면 물한리 786-5 1동 2호
ValueCountFrequency (%)
충청북도 7629
 
11.3%
영동군 7629
 
11.3%
4742
 
7.0%
1동 4040
 
6.0%
10동 2022
 
3.0%
영동읍 1850
 
2.7%
1호 1686
 
2.5%
0010동 1142
 
1.7%
101호 1036
 
1.5%
양강면 929
 
1.4%
Other values (3814) 34697
51.5%
2023-12-12T23:31:24.522614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
57402
20.9%
1 23154
 
8.4%
0 22094
 
8.1%
19660
 
7.2%
10084
 
3.7%
9936
 
3.6%
7695
 
2.8%
7669
 
2.8%
7651
 
2.8%
7646
 
2.8%
Other values (185) 101152
36.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 128585
46.9%
Decimal Number 77534
28.3%
Space Separator 57402
20.9%
Dash Punctuation 5880
 
2.1%
Close Punctuation 2371
 
0.9%
Open Punctuation 2371
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
19660
15.3%
10084
 
7.8%
9936
 
7.7%
7695
 
6.0%
7669
 
6.0%
7651
 
6.0%
7646
 
5.9%
7629
 
5.9%
7629
 
5.9%
5779
 
4.5%
Other values (171) 37207
28.9%
Decimal Number
ValueCountFrequency (%)
1 23154
29.9%
0 22094
28.5%
2 7242
 
9.3%
3 5385
 
6.9%
4 4216
 
5.4%
5 3897
 
5.0%
6 3538
 
4.6%
7 2800
 
3.6%
8 2661
 
3.4%
9 2547
 
3.3%
Space Separator
ValueCountFrequency (%)
57402
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5880
100.0%
Close Punctuation
ValueCountFrequency (%)
] 2371
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 2371
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 145558
53.1%
Hangul 128585
46.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
19660
15.3%
10084
 
7.8%
9936
 
7.7%
7695
 
6.0%
7669
 
6.0%
7651
 
6.0%
7646
 
5.9%
7629
 
5.9%
7629
 
5.9%
5779
 
4.5%
Other values (171) 37207
28.9%
Common
ValueCountFrequency (%)
57402
39.4%
1 23154
15.9%
0 22094
 
15.2%
2 7242
 
5.0%
- 5880
 
4.0%
3 5385
 
3.7%
4 4216
 
2.9%
5 3897
 
2.7%
6 3538
 
2.4%
7 2800
 
1.9%
Other values (4) 9950
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145558
53.1%
Hangul 128585
46.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
57402
39.4%
1 23154
15.9%
0 22094
 
15.2%
2 7242
 
5.0%
- 5880
 
4.0%
3 5385
 
3.7%
4 4216
 
2.9%
5 3897
 
2.7%
6 3538
 
2.4%
7 2800
 
1.9%
Other values (4) 9950
 
6.8%
Hangul
ValueCountFrequency (%)
19660
15.3%
10084
 
7.8%
9936
 
7.7%
7695
 
6.0%
7669
 
6.0%
7651
 
6.0%
7646
 
5.9%
7629
 
5.9%
7629
 
5.9%
5779
 
4.5%
Other values (171) 37207
28.9%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct7900
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36671705
Minimum14400
Maximum4.3132891 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:31:24.687650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14400
5-th percentile187167.5
Q11263510
median6298280
Q327807700
95-th percentile1.4398122 × 108
Maximum4.3132891 × 109
Range4.3132747 × 109
Interquartile range (IQR)26544190

Descriptive statistics

Standard deviation1.3465891 × 108
Coefficient of variation (CV)3.6720112
Kurtosis328.12624
Mean36671705
Median Absolute Deviation (MAD)5876920
Skewness15.042528
Sum3.6671705 × 1011
Variance1.8133022 × 1016
MonotonicityNot monotonic
2023-12-12T23:31:24.839140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8266720 34
 
0.3%
72000 19
 
0.2%
7971480 19
 
0.2%
360000 18
 
0.2%
112710 17
 
0.2%
7381000 17
 
0.2%
7676240 16
 
0.2%
648000 15
 
0.1%
270000 14
 
0.1%
1080000 14
 
0.1%
Other values (7890) 9817
98.2%
ValueCountFrequency (%)
14400 1
< 0.1%
20000 1
< 0.1%
21600 1
< 0.1%
22120 1
< 0.1%
24000 1
< 0.1%
24960 2
< 0.1%
25480 1
< 0.1%
26880 1
< 0.1%
28000 1
< 0.1%
31200 1
< 0.1%
ValueCountFrequency (%)
4313289100 1
< 0.1%
3817070000 1
< 0.1%
3374813160 1
< 0.1%
3012777990 1
< 0.1%
2810004900 1
< 0.1%
2801897150 1
< 0.1%
2739113720 1
< 0.1%
2544014360 1
< 0.1%
2258462580 1
< 0.1%
2154449960 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct4066
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.45454
Minimum1.44
Maximum9860.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:31:24.988585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.44
5-th percentile9.9
Q133
median79.3
Q3161
95-th percentile440.02
Maximum9860.18
Range9858.74
Interquartile range (IQR)128

Descriptive statistics

Standard deviation344.07917
Coefficient of variation (CV)2.2869312
Kurtosis263.66594
Mean150.45454
Median Absolute Deviation (MAD)54.05
Skewness13.244792
Sum1504545.4
Variance118390.48
MonotonicityNot monotonic
2023-12-12T23:31:25.136068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 359
 
3.6%
295.24 86
 
0.9%
66.0 74
 
0.7%
27.0 72
 
0.7%
36.0 66
 
0.7%
2.89 65
 
0.7%
20.0 51
 
0.5%
24.0 49
 
0.5%
21.0 48
 
0.5%
207.48 46
 
0.5%
Other values (4056) 9084
90.8%
ValueCountFrequency (%)
1.44 1
 
< 0.1%
1.5 3
 
< 0.1%
1.7 1
 
< 0.1%
1.8 2
 
< 0.1%
1.9 1
 
< 0.1%
1.96 1
 
< 0.1%
2.0 10
0.1%
2.25 14
0.1%
2.38 1
 
< 0.1%
2.41 1
 
< 0.1%
ValueCountFrequency (%)
9860.18 1
< 0.1%
9643.34 1
< 0.1%
7776.0 1
< 0.1%
7634.14 2
< 0.1%
7441.5 1
< 0.1%
6944.06 1
< 0.1%
5052.0 1
< 0.1%
4973.46 1
< 0.1%
4959.11 1
< 0.1%
4788.0 1
< 0.1%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022-07-12
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-07-12
2nd row2022-07-12
3rd row2022-07-12
4th row2022-07-12
5th row2022-07-12

Common Values

ValueCountFrequency (%)
2022-07-12 10000
100.0%

Length

2023-12-12T23:31:25.263671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:31:25.372002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-07-12 10000
100.0%

Interactions

2023-12-12T23:31:20.155728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:14.083318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:14.962935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:15.782123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:16.689753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:17.481020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:18.366498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:19.332698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:20.261122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:14.208345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:15.076900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:15.907426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:16.789382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:17.593056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:18.525659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:19.489576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:20.338356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:14.310805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:15.171665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:16.023887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:16.913932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:17.693099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:18.657534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:19.619553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:20.636133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:14.411875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:15.282735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:16.131464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:17.035987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:17.797121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:18.783250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:19.725461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:20.721164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:14.502766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:15.374197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:16.260101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:17.125838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:17.890936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:18.889472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:19.811527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:20.812850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:14.646847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:15.473206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:16.374438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:17.219146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:18.005954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:18.993947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:19.885411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:20.883586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:14.758000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:15.571101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:16.479104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:17.302259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:18.106481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:19.099261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:19.976009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:20.953650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:14.848477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:15.678513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:16.584823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:17.387758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:18.224972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:19.208795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:20.059353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:31:25.451023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동법정리특수지본번부번시가표준액연면적
과세년도1.0000.0230.0250.0000.0390.0080.0300.0000.0000.016
법정동0.0231.0000.5100.2630.3590.1310.0400.0260.0370.064
법정리0.0250.5101.0000.3190.4220.2460.0590.1560.1010.083
특수지0.0000.2630.3191.0000.5470.1890.0120.0020.0720.067
본번0.0390.3590.4220.5471.0000.2710.0640.1350.0310.000
부번0.0080.1310.2460.1890.2711.0000.1220.0810.0000.000
0.0300.0400.0590.0120.0640.1221.0000.0000.0000.000
0.0000.0260.1560.0020.1350.0810.0001.0000.0000.000
시가표준액0.0000.0370.1010.0720.0310.0000.0000.0001.0000.811
연면적0.0160.0640.0830.0670.0000.0000.0000.0000.8111.000
2023-12-12T23:31:25.586569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도특수지
과세년도1.0000.000
특수지0.0001.000
2023-12-12T23:31:26.029674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적과세년도특수지
법정동1.0000.059-0.050-0.249-0.0640.043-0.232-0.0740.0230.273
법정리0.0591.000-0.237-0.266-0.107-0.011-0.1140.0810.0130.228
본번-0.050-0.2371.0000.1450.0950.0070.016-0.0510.0160.422
부번-0.249-0.2660.1451.0000.104-0.0310.085-0.0520.0030.145
-0.064-0.1070.0950.1041.0000.2910.044-0.0990.0360.008
0.043-0.0110.007-0.0310.2911.0000.138-0.0310.0000.003
시가표준액-0.232-0.1140.0160.0850.0440.1381.0000.6520.0000.055
연면적-0.0740.081-0.051-0.052-0.099-0.0310.6521.0000.0100.050
과세년도0.0230.0130.0160.0030.0360.0000.0000.0101.0000.000
특수지0.2730.2280.4220.1450.0080.0030.0550.0500.0001.000

Missing values

2023-12-12T23:31:21.057253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:31:21.215332image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자
15310충청북도영동군43740201725023182220101충청북도 영동군 영동읍 회동리 82-2 20동 101호232200018.02022-07-12
58019충청북도영동군43740202025031166614101충청북도 영동군 영동읍 설계리 666-14 10동 1호955539093.362022-07-12
13995충청북도영동군437402017335291216110100충청북도 영동군 추풍령면 지봉리 216-1 10동 100호35840760175.692022-07-12
194충청북도영동군437402017310252570103충청북도 영동군 용산면 부릉리 산 57 10동 3호16800028.02022-07-12
9408충청북도영동군437402017350271786512충청북도 영동군 상촌면 물한리 786-5 1동 2호68400018.02022-07-12
85006충청북도영동군4374020214002918101충청북도 영동군 심천면 명천리 8-1 1호99000099.02022-07-12
5873충청북도영동군437402017250311635112충청북도 영동군 영동읍 설계리 635-1 1동 2호6208488087.12022-07-12
47438충청북도영동군437402019250241354031충청북도 영동군 영동읍 화신리 354 3동 1호504000126.02022-07-12
26212충청북도영동군437402018310241230111충청북도 영동군 용산면 신항리 230-1 1동 1호1489800148.982022-07-12
53125충청북도영동군43740202031037189710103충청북도 영동군 용산면 상용리 89-7 10동 103호359100027.02022-07-12
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자
34154충청북도영동군4374020193602414028101충청북도 영동군 양강면 죽촌리 402-8 10동 1호82600082.62022-07-12
58656충청북도영동군4374020202503111711211충청북도 영동군 영동읍 설계리 171-1 2동 11호530880132.722022-07-12
55071충청북도영동군43740202031036151119충청북도 영동군 용산면 매금리 51-1 1동 9호972800243.22022-07-12
37461충청북도영동군437402019390261295710102충청북도 영동군 양산면 수두리 295-7 10동 102호80240080.242022-07-12
31832충청북도영동군43740201825032135111103충청북도 영동군 영동읍 심원리 351-1 1동 103호5304000132.62022-07-12
16585충청북도영동군43740201839024171631104[ 양산심천로 308-68 ] 0001동 0104호1920000400.02022-07-12
39407충청북도영동군43740201933526121702101충청북도 영동군 추풍령면 작점리 217 2동 101호619750018.52022-07-12
54868충청북도영동군437402020310381311111충청북도 영동군 용산면 백자전리 311-1 1동 1호71250000750.02022-07-12
67494충청북도영동군437402020360261101011충청북도 영동군 양강면 산막리 101 1동 1호1004300100.432022-07-12
60474충청북도영동군437402020335221734512[ 추풍령로 257 ] 0001동 0002호210000030.02022-07-12

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자# duplicates
7충청북도영동군4374020173603322701101충청북도 영동군 양강면 쌍암리 산 27 1동 101호7381000295.242022-07-125
28충청북도영동군4374020193603322701101충청북도 영동군 양강면 쌍암리 산 27 1동 101호7971480295.242022-07-125
2충청북도영동군4374020173602127411101충청북도 영동군 양강면 괴목리 산 74-1 1동 101호31951920207.482022-07-124
27충청북도영동군4374020193603321501101충청북도 영동군 양강면 쌍암리 산 15 1동 101호7971480295.242022-07-124
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