Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows7
Duplicate rows (%)0.1%
Total size in memory1.3 MiB
Average record size in memory139.0 B

Variable types

Categorical6
Numeric8
Text1

Dataset

Description본 데이터는 경상남도 합천군의 과세년도별, 일반건축물에 대한 물건지 (법정동, 법정리, 본번, 부번), 시가표준액, 연면적, 기준일자 등을 제공하고 있습니다.
URLhttps://www.data.go.kr/data/15089290/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
Dataset has 7 (0.1%) duplicate rowsDuplicates
기준일자 is highly overall correlated with 과세년도High correlation
과세년도 is highly overall correlated with 기준일자High correlation
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (89.3%)Imbalance
is highly skewed (γ1 = 34.15978102)Skewed
연면적 is highly skewed (γ1 = 29.88383824)Skewed
부번 has 4394 (43.9%) zerosZeros
has 186 (1.9%) zerosZeros

Reproduction

Analysis started2023-12-12 15:15:57.823988
Analysis finished2023-12-12 15:16:09.214170
Duration11.39 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
경상남도
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상남도 10000
100.0%

Length

2023-12-13T00:16:09.311172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:16:09.426185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
합천군 10000
100.0%

Length

2023-12-13T00:16:09.530692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:16:09.626709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
합천군 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
48890
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48890
2nd row48890
3rd row48890
4th row48890
5th row48890

Common Values

ValueCountFrequency (%)
48890 10000
100.0%

Length

2023-12-13T00:16:09.731788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:16:09.848850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48890 10000
100.0%

과세년도
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2019
3024 
2018
3023 
2017
2789 
2020
1164 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2019
3rd row2017
4th row2017
5th row2018

Common Values

ValueCountFrequency (%)
2019 3024
30.2%
2018 3023
30.2%
2017 2789
27.9%
2020 1164
 
11.6%

Length

2023-12-13T00:16:09.943374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:16:10.046413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 3024
30.2%
2018 3023
30.2%
2017 2789
27.9%
2020 1164
 
11.6%

법정동
Real number (ℝ)

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean367.679
Minimum250
Maximum460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:16:10.171942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile250
Q1330
median370
Q3430
95-th percentile460
Maximum460
Range210
Interquartile range (IQR)100

Descriptive statistics

Standard deviation64.33628
Coefficient of variation (CV)0.17497948
Kurtosis-0.7984673
Mean367.679
Median Absolute Deviation (MAD)50
Skewness-0.40534187
Sum3676790
Variance4139.1569
MonotonicityNot monotonic
2023-12-13T00:16:10.312004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
250 1375
13.8%
430 874
 
8.7%
330 802
 
8.0%
340 764
 
7.6%
460 685
 
6.9%
350 657
 
6.6%
390 556
 
5.6%
360 518
 
5.2%
420 508
 
5.1%
410 504
 
5.0%
Other values (7) 2757
27.6%
ValueCountFrequency (%)
250 1375
13.8%
310 352
 
3.5%
320 460
 
4.6%
330 802
8.0%
340 764
7.6%
350 657
6.6%
360 518
 
5.2%
370 294
 
2.9%
380 268
 
2.7%
390 556
5.6%
ValueCountFrequency (%)
460 685
6.9%
450 489
4.9%
440 496
5.0%
430 874
8.7%
420 508
5.1%
410 504
5.0%
400 398
4.0%
390 556
5.6%
380 268
 
2.7%
370 294
 
2.9%

법정리
Real number (ℝ)

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.2681
Minimum21
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:16:10.444829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q123
median26
Q329
95-th percentile33
Maximum37
Range16
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.0578404
Coefficient of variation (CV)0.15447788
Kurtosis-0.75001898
Mean26.2681
Median Absolute Deviation (MAD)3
Skewness0.42247478
Sum262681
Variance16.466069
MonotonicityNot monotonic
2023-12-13T00:16:10.585313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
21 1545
15.4%
23 934
9.3%
25 833
8.3%
28 755
7.5%
22 752
7.5%
27 726
7.3%
24 708
 
7.1%
26 706
 
7.1%
30 642
 
6.4%
29 626
 
6.3%
Other values (7) 1773
17.7%
ValueCountFrequency (%)
21 1545
15.4%
22 752
7.5%
23 934
9.3%
24 708
7.1%
25 833
8.3%
26 706
7.1%
27 726
7.3%
28 755
7.5%
29 626
6.3%
30 642
6.4%
ValueCountFrequency (%)
37 123
 
1.2%
36 15
 
0.1%
35 15
 
0.1%
34 294
 
2.9%
33 313
3.1%
32 537
5.4%
31 476
4.8%
30 642
6.4%
29 626
6.3%
28 755
7.5%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9859 
2
 
141

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 9859
98.6%
2 141
 
1.4%

Length

2023-12-13T00:16:10.719612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:16:10.827246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9859
98.6%
2 141
 
1.4%

본번
Real number (ℝ)

Distinct1238
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean494.3513
Minimum1
Maximum1910
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:16:10.944825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile40
Q1230
median452
Q3698.25
95-th percentile1108
Maximum1910
Range1909
Interquartile range (IQR)468.25

Descriptive statistics

Standard deviation336.69296
Coefficient of variation (CV)0.68108035
Kurtosis0.52062383
Mean494.3513
Median Absolute Deviation (MAD)233.5
Skewness0.77432529
Sum4943513
Variance113362.15
MonotonicityNot monotonic
2023-12-13T00:16:11.085221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
473 72
 
0.7%
418 69
 
0.7%
433 59
 
0.6%
1230 47
 
0.5%
666 39
 
0.4%
493 38
 
0.4%
337 36
 
0.4%
18 35
 
0.4%
315 34
 
0.3%
364 32
 
0.3%
Other values (1228) 9539
95.4%
ValueCountFrequency (%)
1 13
0.1%
2 5
 
0.1%
3 15
0.1%
4 6
 
0.1%
5 5
 
0.1%
6 9
 
0.1%
7 7
 
0.1%
8 16
0.2%
9 10
 
0.1%
10 26
0.3%
ValueCountFrequency (%)
1910 1
 
< 0.1%
1878 1
 
< 0.1%
1853 1
 
< 0.1%
1849 3
 
< 0.1%
1840 8
0.1%
1822 2
 
< 0.1%
1790 4
< 0.1%
1788 1
 
< 0.1%
1787 2
 
< 0.1%
1766 5
0.1%

부번
Real number (ℝ)

ZEROS 

Distinct107
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.093
Minimum0
Maximum325
Zeros4394
Zeros (%)43.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:16:11.245934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile14
Maximum325
Range325
Interquartile range (IQR)3

Descriptive statistics

Standard deviation15.128742
Coefficient of variation (CV)3.6962478
Kurtosis173.4328
Mean4.093
Median Absolute Deviation (MAD)1
Skewness11.154369
Sum40930
Variance228.87884
MonotonicityNot monotonic
2023-12-13T00:16:11.382388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4394
43.9%
1 1780
17.8%
2 931
 
9.3%
3 688
 
6.9%
4 382
 
3.8%
5 319
 
3.2%
6 224
 
2.2%
7 165
 
1.7%
9 133
 
1.3%
8 131
 
1.3%
Other values (97) 853
 
8.5%
ValueCountFrequency (%)
0 4394
43.9%
1 1780
17.8%
2 931
 
9.3%
3 688
 
6.9%
4 382
 
3.8%
5 319
 
3.2%
6 224
 
2.2%
7 165
 
1.7%
8 131
 
1.3%
9 133
 
1.3%
ValueCountFrequency (%)
325 2
< 0.1%
305 4
< 0.1%
302 2
< 0.1%
300 1
 
< 0.1%
163 2
< 0.1%
152 2
< 0.1%
151 2
< 0.1%
144 1
 
< 0.1%
133 2
< 0.1%
132 1
 
< 0.1%


Real number (ℝ)

SKEWED  ZEROS 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1832
Minimum0
Maximum101
Zeros186
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:16:11.510094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum101
Range101
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.737807
Coefficient of variation (CV)2.3139005
Kurtosis1235.9599
Mean1.1832
Median Absolute Deviation (MAD)0
Skewness34.159781
Sum11832
Variance7.4955873
MonotonicityNot monotonic
2023-12-13T00:16:11.624422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 9015
90.1%
2 590
 
5.9%
0 186
 
1.9%
3 110
 
1.1%
4 37
 
0.4%
5 16
 
0.2%
6 11
 
0.1%
9 9
 
0.1%
101 7
 
0.1%
8 6
 
0.1%
Other values (7) 13
 
0.1%
ValueCountFrequency (%)
0 186
 
1.9%
1 9015
90.1%
2 590
 
5.9%
3 110
 
1.1%
4 37
 
0.4%
5 16
 
0.2%
6 11
 
0.1%
8 6
 
0.1%
9 9
 
0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
101 7
0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
16 1
 
< 0.1%
13 3
 
< 0.1%
12 1
 
< 0.1%
11 3
 
< 0.1%
10 3
 
< 0.1%
9 9
0.1%
8 6
0.1%


Real number (ℝ)

Distinct113
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean215.91
Minimum0
Maximum9401
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:16:11.780416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile101
Q1101
median102
Q3103
95-th percentile201
Maximum9401
Range9401
Interquartile range (IQR)2

Descriptive statistics

Standard deviation925.77021
Coefficient of variation (CV)4.2877598
Kurtosis74.304524
Mean215.91
Median Absolute Deviation (MAD)1
Skewness8.7069051
Sum2159100
Variance857050.48
MonotonicityNot monotonic
2023-12-13T00:16:11.954628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 4573
45.7%
102 2029
20.3%
103 1061
 
10.6%
104 499
 
5.0%
201 412
 
4.1%
105 270
 
2.7%
1 165
 
1.7%
106 143
 
1.4%
301 119
 
1.2%
202 82
 
0.8%
Other values (103) 647
 
6.5%
ValueCountFrequency (%)
0 8
 
0.1%
1 165
1.7%
2 10
 
0.1%
3 8
 
0.1%
4 4
 
< 0.1%
5 1
 
< 0.1%
6 3
 
< 0.1%
8 1
 
< 0.1%
10 2
 
< 0.1%
11 4
 
< 0.1%
ValueCountFrequency (%)
9401 1
 
< 0.1%
9301 2
 
< 0.1%
9201 2
 
< 0.1%
9103 4
 
< 0.1%
9102 4
 
< 0.1%
9101 18
0.2%
8301 1
 
< 0.1%
8201 2
 
< 0.1%
8105 2
 
< 0.1%
8104 3
 
< 0.1%
Distinct8313
Distinct (%)83.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T00:16:12.330462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length33
Mean length27.8188
Min length21

Characters and Unicode

Total characters278188
Distinct characters196
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

Unique6876 ?
Unique (%)68.8%

Sample

1st row경상남도 합천군 적중면 상부리 787-13 1동 101호
2nd row경상남도 합천군 덕곡면 율지리 117-16 1동 101호
3rd row경상남도 합천군 합천읍 합천리 155-19 1동 8101호
4th row경상남도 합천군 합천읍 서산리 709-1 1동 101호
5th row[ 황계폭포로 1061-12 ] 0001동 0101호
ValueCountFrequency (%)
경상남도 7573
 
11.2%
합천군 7573
 
11.2%
1동 6788
 
10.0%
4854
 
7.2%
101호 3233
 
4.8%
0001동 2227
 
3.3%
102호 1645
 
2.4%
0101호 1340
 
2.0%
103호 904
 
1.3%
합천읍 874
 
1.3%
Other values (4333) 30542
45.2%
2023-12-13T00:16:12.935088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
57555
20.7%
1 30488
 
11.0%
0 21795
 
7.8%
10196
 
3.7%
10154
 
3.7%
9272
 
3.3%
9025
 
3.2%
7803
 
2.8%
2 7783
 
2.8%
7740
 
2.8%
Other values (186) 106377
38.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 126278
45.4%
Decimal Number 84431
30.4%
Space Separator 57555
20.7%
Dash Punctuation 5070
 
1.8%
Close Punctuation 2427
 
0.9%
Open Punctuation 2427
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10196
 
8.1%
10154
 
8.0%
9272
 
7.3%
9025
 
7.1%
7803
 
6.2%
7740
 
6.1%
7686
 
6.1%
7608
 
6.0%
7573
 
6.0%
7573
 
6.0%
Other values (172) 41648
33.0%
Decimal Number
ValueCountFrequency (%)
1 30488
36.1%
0 21795
25.8%
2 7783
 
9.2%
3 5424
 
6.4%
4 4057
 
4.8%
5 3470
 
4.1%
6 3053
 
3.6%
8 2929
 
3.5%
7 2763
 
3.3%
9 2669
 
3.2%
Space Separator
ValueCountFrequency (%)
57555
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5070
100.0%
Close Punctuation
ValueCountFrequency (%)
] 2427
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 2427
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 151910
54.6%
Hangul 126278
45.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10196
 
8.1%
10154
 
8.0%
9272
 
7.3%
9025
 
7.1%
7803
 
6.2%
7740
 
6.1%
7686
 
6.1%
7608
 
6.0%
7573
 
6.0%
7573
 
6.0%
Other values (172) 41648
33.0%
Common
ValueCountFrequency (%)
57555
37.9%
1 30488
20.1%
0 21795
 
14.3%
2 7783
 
5.1%
3 5424
 
3.6%
- 5070
 
3.3%
4 4057
 
2.7%
5 3470
 
2.3%
6 3053
 
2.0%
8 2929
 
1.9%
Other values (4) 10286
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 151910
54.6%
Hangul 126278
45.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
57555
37.9%
1 30488
20.1%
0 21795
 
14.3%
2 7783
 
5.1%
3 5424
 
3.6%
- 5070
 
3.3%
4 4057
 
2.7%
5 3470
 
2.3%
6 3053
 
2.0%
8 2929
 
1.9%
Other values (4) 10286
 
6.8%
Hangul
ValueCountFrequency (%)
10196
 
8.1%
10154
 
8.0%
9272
 
7.3%
9025
 
7.1%
7803
 
6.2%
7740
 
6.1%
7686
 
6.1%
7608
 
6.0%
7573
 
6.0%
7573
 
6.0%
Other values (172) 41648
33.0%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct7401
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24472910
Minimum14400
Maximum2.0714512 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:16:13.225782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14400
5-th percentile264000
Q11141280
median3907035
Q318428235
95-th percentile1.109831 × 108
Maximum2.0714512 × 109
Range2.0714368 × 109
Interquartile range (IQR)17286955

Descriptive statistics

Standard deviation72581406
Coefficient of variation (CV)2.9657857
Kurtosis204.65048
Mean24472910
Median Absolute Deviation (MAD)3435035
Skewness10.984321
Sum2.447291 × 1011
Variance5.2680606 × 1015
MonotonicityNot monotonic
2023-12-13T00:16:13.398093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2160000 23
 
0.2%
1440000 22
 
0.2%
1200000 19
 
0.2%
660000 18
 
0.2%
2700000 17
 
0.2%
648000 16
 
0.2%
2448000 16
 
0.2%
360000 16
 
0.2%
594000 16
 
0.2%
3960000 15
 
0.1%
Other values (7391) 9822
98.2%
ValueCountFrequency (%)
14400 1
< 0.1%
19200 1
< 0.1%
21000 1
< 0.1%
24960 1
< 0.1%
26400 1
< 0.1%
27200 1
< 0.1%
28000 2
< 0.1%
30000 1
< 0.1%
37000 1
< 0.1%
39600 1
< 0.1%
ValueCountFrequency (%)
2071451200 1
< 0.1%
2060624800 1
< 0.1%
1643365280 1
< 0.1%
1268559810 1
< 0.1%
1208152200 1
< 0.1%
1123655500 1
< 0.1%
1058040580 1
< 0.1%
957282750 1
< 0.1%
949774650 1
< 0.1%
925490720 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3891
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean147.99613
Minimum0.81
Maximum20135.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:16:13.587772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.81
5-th percentile12
Q131.4875
median72
Q3164.325
95-th percentile468.18
Maximum20135.87
Range20135.06
Interquartile range (IQR)132.8375

Descriptive statistics

Standard deviation398.49469
Coefficient of variation (CV)2.6926021
Kurtosis1342.4512
Mean147.99613
Median Absolute Deviation (MAD)50.08
Skewness29.883838
Sum1479961.3
Variance158798.02
MonotonicityNot monotonic
2023-12-13T00:16:13.782416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 337
 
3.4%
66.0 86
 
0.9%
15.0 81
 
0.8%
12.0 74
 
0.7%
40.0 66
 
0.7%
30.0 64
 
0.6%
24.0 58
 
0.6%
33.0 56
 
0.6%
60.0 56
 
0.6%
35.0 54
 
0.5%
Other values (3881) 9068
90.7%
ValueCountFrequency (%)
0.81 4
< 0.1%
0.9 2
< 0.1%
1.0 1
 
< 0.1%
1.44 1
 
< 0.1%
1.5 1
 
< 0.1%
1.56 1
 
< 0.1%
1.76 1
 
< 0.1%
1.8 2
< 0.1%
1.87 1
 
< 0.1%
1.95 1
 
< 0.1%
ValueCountFrequency (%)
20135.87 2
< 0.1%
8730.0 2
< 0.1%
7811.3 2
< 0.1%
4447.23 1
< 0.1%
4238.6 2
< 0.1%
3608.8 2
< 0.1%
3306.0 1
< 0.1%
3114.64 1
< 0.1%
3093.48 1
< 0.1%
2962.65 1
< 0.1%

기준일자
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2019-06-01
3024 
2018-06-01
3023 
2017-06-01
2789 
2020-06-01
1164 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-06-01
2nd row2019-06-01
3rd row2017-06-01
4th row2017-06-01
5th row2018-06-01

Common Values

ValueCountFrequency (%)
2019-06-01 3024
30.2%
2018-06-01 3023
30.2%
2017-06-01 2789
27.9%
2020-06-01 1164
 
11.6%

Length

2023-12-13T00:16:13.977481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:16:14.099925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-06-01 3024
30.2%
2018-06-01 3023
30.2%
2017-06-01 2789
27.9%
2020-06-01 1164
 
11.6%

Interactions

2023-12-13T00:16:07.282515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:00.873124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:01.748481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:02.899149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:03.695410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:04.430988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:05.227098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:06.254057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:07.422375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:00.966934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:01.917175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:02.991225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:03.775585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:04.523684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:05.349521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:06.375018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:07.566483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:01.092956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:02.059401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:03.112566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:03.870515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:04.614171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:05.512528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:06.487941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:07.715289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:01.230559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:02.196630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:03.210533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:03.963892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:04.725315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:05.655476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:06.629540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:07.826049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:01.325757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:02.337728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:03.303680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:04.053715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:04.811876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:05.772639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:06.759955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:07.930400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:01.426017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:02.486933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:03.396334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:04.138288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:04.902279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:05.893013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:06.890165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:08.442743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:01.531651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:02.635005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:03.489932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:04.224900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:05.001810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:06.008981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:07.003922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:08.590523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:01.634673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:02.769811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:03.594713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:04.330702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:05.106963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:06.130444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:07.129567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:16:14.198617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동법정리특수지본번부번시가표준액연면적기준일자
과세년도1.0000.3760.1110.0200.0280.0250.0290.0000.0250.0001.000
법정동0.3761.0000.4210.1430.2660.1070.0390.0580.0290.0470.376
법정리0.1110.4211.0000.1640.3250.2100.0750.0810.0760.0760.111
특수지0.0200.1430.1641.0000.2760.0000.1960.0000.1160.0480.020
본번0.0280.2660.3250.2761.0000.2310.0000.0510.0430.0100.028
부번0.0250.1070.2100.0000.2311.0000.0000.0870.0720.0480.025
0.0290.0390.0750.1960.0000.0001.0000.0300.0000.0000.029
0.0000.0580.0810.0000.0510.0870.0301.0000.0510.0000.000
시가표준액0.0250.0290.0760.1160.0430.0720.0000.0511.0000.8350.025
연면적0.0000.0470.0760.0480.0100.0480.0000.0000.8351.0000.000
기준일자1.0000.3760.1110.0200.0280.0250.0290.0000.0250.0001.000
2023-12-13T00:16:14.358883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수지기준일자과세년도
특수지1.0000.0130.013
기준일자0.0131.0001.000
과세년도0.0131.0001.000
2023-12-13T00:16:14.462144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적과세년도특수지기준일자
법정동1.0000.1860.047-0.121-0.086-0.093-0.095-0.0140.1900.1080.190
법정리0.1861.000-0.082-0.083-0.016-0.069-0.1250.0160.0670.1240.067
본번0.047-0.0821.0000.003-0.033-0.001-0.001-0.0320.0170.2120.017
부번-0.121-0.0830.0031.0000.0050.0380.1790.0380.0170.0000.017
-0.086-0.016-0.0330.0051.000-0.026-0.026-0.0210.0110.1300.011
-0.093-0.069-0.0010.038-0.0261.0000.021-0.1140.0000.0000.000
시가표준액-0.095-0.125-0.0010.179-0.0260.0211.0000.6250.0160.1160.016
연면적-0.0140.016-0.0320.038-0.021-0.1140.6251.0000.0000.0340.000
과세년도0.1900.0670.0170.0170.0110.0000.0160.0001.0000.0131.000
특수지0.1080.1240.2120.0000.1300.0000.1160.0340.0131.0000.013
기준일자0.1900.0670.0170.0170.0110.0000.0160.0001.0000.0131.000

Missing values

2023-12-13T00:16:08.841154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:16:09.104680image/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
67232경상남도합천군488902020400281787131101경상남도 합천군 적중면 상부리 787-13 1동 101호1326600120.62020-06-01
55367경상남도합천군488902019380251117161101경상남도 합천군 덕곡면 율지리 117-16 1동 101호74299500495.02019-06-01
20824경상남도합천군4889020172502111551918101경상남도 합천군 합천읍 합천리 155-19 1동 8101호6611860301246.582017-06-01
6697경상남도합천군48890201725025170911101경상남도 합천군 합천읍 서산리 709-1 1동 101호14042500342.52017-06-01
41111경상남도합천군48890201846028113411101[ 황계폭포로 1061-12 ] 0001동 0101호41569630148.042018-06-01
3127경상남도합천군48890201733026148211101[ 매안2길 5 ] 0001동 0101호12892620330.582017-06-01
65895경상남도합천군488902019410231110801108경상남도 합천군 대양면 대목리 1108 1동 108호373200012.02019-06-01
31455경상남도합천군48890201831031132503101경상남도 합천군 봉산면 노곡리 325 3동 101호300528041.742018-06-01
14926경상남도합천군48890201741024113001101경상남도 합천군 대양면 무곡리 130 1동 101호7840000112.02017-06-01
68273경상남도합천군48890202039023130801101경상남도 합천군 청덕면 두곡리 308 1동 101호1386000115.52020-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자
15859경상남도합천군48890201741028157301101[ 대야로 84 ] 0001동 0101호127040039.72017-06-01
16030경상남도합천군4889020174402512101104경상남도 합천군 가회면 중촌리 21 1동 104호15642000198.02017-06-01
69394경상남도합천군48890202041023179201104경상남도 합천군 대양면 대목리 792 1동 104호101790000390.02020-06-01
43189경상남도합천군4889020193302612301201경상남도 합천군 가야면 매안리 23 1동 201호18365200187.42019-06-01
29334경상남도합천군48890201831033182321102[ 도곡길 48-1 ] 0001동 0102호460000115.02018-06-01
57897경상남도합천군48890201941025170631102경상남도 합천군 대양면 덕정리 706-3 1동 102호1629000090.02019-06-01
49839경상남도합천군48890201925028182501105경상남도 합천군 합천읍 장계리 825 1동 105호151486800246.322019-06-01
62555경상남도합천군48890201946036118481103경상남도 합천군 용주면 죽죽리 184-8 1동 103호241007049.592019-06-01
50380경상남도합천군488902019250211669241101경상남도 합천군 합천읍 합천리 669-24 1동 101호19411310228.12019-06-01
14396경상남도합천군48890201743033147421104[ 남명로 401 ] 0001동 0104호88704024.642017-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자# duplicates
0경상남도합천군48890201735024140801101경상남도 합천군 율곡면 갑산리 408 1동 101호3912480434.722017-06-012
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4경상남도합천군48890201939032188201101경상남도 합천군 청덕면 초곡리 882 1동 101호201810032.552019-06-012
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