Overview

Dataset statistics

Number of variables19
Number of observations1495
Missing cells1825
Missing cells (%)6.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory248.3 KiB
Average record size in memory170.1 B

Variable types

Text1
Numeric18

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 부동산거래현황 중 주택매매 거래현황의 연도별 주택유형별(면적) 데이터입니다.- (단위 : 천㎡)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068300/fileData.do

Alerts

2006 is highly overall correlated with 2007 and 16 other fieldsHigh correlation
2007 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2008 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2009 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2010 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2011 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2012 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2013 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2014 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2015 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2016 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2017 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2018 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2019 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2020 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2021 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2022 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2023 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2006 has 110 (7.4%) missing valuesMissing
2007 has 135 (9.0%) missing valuesMissing
2008 has 125 (8.4%) missing valuesMissing
2009 has 125 (8.4%) missing valuesMissing
2010 has 95 (6.4%) missing valuesMissing
2011 has 110 (7.4%) missing valuesMissing
2012 has 100 (6.7%) missing valuesMissing
2013 has 100 (6.7%) missing valuesMissing
2014 has 65 (4.3%) missing valuesMissing
2015 has 85 (5.7%) missing valuesMissing
2016 has 85 (5.7%) missing valuesMissing
2017 has 100 (6.7%) missing valuesMissing
2018 has 95 (6.4%) missing valuesMissing
2019 has 100 (6.7%) missing valuesMissing
2020 has 100 (6.7%) missing valuesMissing
2021 has 100 (6.7%) missing valuesMissing
2022 has 100 (6.7%) missing valuesMissing
2023 has 95 (6.4%) missing valuesMissing
2006 is highly skewed (γ1 = 26.22700707)Skewed
2007 is highly skewed (γ1 = 26.02922285)Skewed
2008 is highly skewed (γ1 = 27.61585402)Skewed
2009 is highly skewed (γ1 = 29.20234708)Skewed
2010 is highly skewed (γ1 = 29.67546346)Skewed
2011 is highly skewed (γ1 = 29.09153811)Skewed
2012 is highly skewed (γ1 = 28.42953958)Skewed
2013 is highly skewed (γ1 = 28.58048506)Skewed
2014 is highly skewed (γ1 = 28.49504868)Skewed
2015 is highly skewed (γ1 = 26.71413943)Skewed
2016 is highly skewed (γ1 = 26.2298998)Skewed
2017 is highly skewed (γ1 = 25.72034158)Skewed
2018 is highly skewed (γ1 = 26.23099955)Skewed
2019 is highly skewed (γ1 = 28.23757746)Skewed
2020 is highly skewed (γ1 = 29.17506661)Skewed
2021 is highly skewed (γ1 = 27.54465122)Skewed
2022 is highly skewed (γ1 = 24.92706707)Skewed
2023 is highly skewed (γ1 = 30.10397762)Skewed
지역 has unique valuesUnique
2006 has 120 (8.0%) zerosZeros
2007 has 112 (7.5%) zerosZeros
2008 has 94 (6.3%) zerosZeros
2009 has 92 (6.2%) zerosZeros
2010 has 84 (5.6%) zerosZeros
2011 has 63 (4.2%) zerosZeros
2012 has 52 (3.5%) zerosZeros
2013 has 54 (3.6%) zerosZeros
2014 has 39 (2.6%) zerosZeros
2015 has 34 (2.3%) zerosZeros
2016 has 35 (2.3%) zerosZeros
2017 has 70 (4.7%) zerosZeros
2018 has 70 (4.7%) zerosZeros
2019 has 47 (3.1%) zerosZeros
2020 has 38 (2.5%) zerosZeros
2021 has 34 (2.3%) zerosZeros
2022 has 41 (2.7%) zerosZeros
2023 has 70 (4.7%) zerosZeros

Reproduction

Analysis started2024-04-06 08:56:26.650631
Analysis finished2024-04-06 08:57:42.305318
Duration1 minute and 15.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Text

UNIQUE 

Distinct1495
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
2024-04-06T17:57:42.607277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length20
Mean length11.541137
Min length7

Characters and Unicode

Total characters17254
Distinct characters152
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1495 ?
Unique (%)100.0%

Sample

1st row전국 /단독주택
2nd row전국 /다가구주택
3rd row전국 /다세대주택
4th row전국 /연립주택
5th row전국 /아파트
ValueCountFrequency (%)
경기 265
 
8.9%
경남 135
 
4.5%
경북 130
 
4.3%
서울 130
 
4.3%
전남 115
 
3.8%
충남 100
 
3.3%
충북 100
 
3.3%
강원 95
 
3.2%
전북 85
 
2.8%
부산 85
 
2.8%
Other values (1294) 1750
58.5%
2024-04-06T17:57:43.314534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1495
 
8.7%
/ 1495
 
8.7%
1431
 
8.3%
1201
 
7.0%
1004
 
5.8%
610
 
3.5%
598
 
3.5%
545
 
3.2%
465
 
2.7%
445
 
2.6%
Other values (142) 7965
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14074
81.6%
Space Separator 1495
 
8.7%
Other Punctuation 1495
 
8.7%
Close Punctuation 95
 
0.6%
Open Punctuation 95
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1431
 
10.2%
1201
 
8.5%
1004
 
7.1%
610
 
4.3%
598
 
4.2%
545
 
3.9%
465
 
3.3%
445
 
3.2%
399
 
2.8%
360
 
2.6%
Other values (138) 7016
49.9%
Space Separator
ValueCountFrequency (%)
1495
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1495
100.0%
Close Punctuation
ValueCountFrequency (%)
) 95
100.0%
Open Punctuation
ValueCountFrequency (%)
( 95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14074
81.6%
Common 3180
 
18.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1431
 
10.2%
1201
 
8.5%
1004
 
7.1%
610
 
4.3%
598
 
4.2%
545
 
3.9%
465
 
3.3%
445
 
3.2%
399
 
2.8%
360
 
2.6%
Other values (138) 7016
49.9%
Common
ValueCountFrequency (%)
1495
47.0%
/ 1495
47.0%
) 95
 
3.0%
( 95
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14074
81.6%
ASCII 3180
 
18.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1495
47.0%
/ 1495
47.0%
) 95
 
3.0%
( 95
 
3.0%
Hangul
ValueCountFrequency (%)
1431
 
10.2%
1201
 
8.5%
1004
 
7.1%
610
 
4.3%
598
 
4.2%
545
 
3.9%
465
 
3.3%
445
 
3.2%
399
 
2.8%
360
 
2.6%
Other values (138) 7016
49.9%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct335
Distinct (%)24.2%
Missing110
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean193.61083
Minimum0
Maximum52234
Zeros120
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-06T17:57:43.579962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median19
Q384
95-th percentile553.8
Maximum52234
Range52234
Interquartile range (IQR)80

Descriptive statistics

Standard deviation1611.9841
Coefficient of variation (CV)8.3258985
Kurtosis802.97927
Mean193.61083
Median Absolute Deviation (MAD)18
Skewness26.227007
Sum268151
Variance2598492.8
MonotonicityNot monotonic
2024-04-06T17:57:43.886938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 120
 
8.0%
1 102
 
6.8%
2 74
 
4.9%
3 45
 
3.0%
4 40
 
2.7%
6 39
 
2.6%
5 34
 
2.3%
8 32
 
2.1%
7 28
 
1.9%
9 27
 
1.8%
Other values (325) 844
56.5%
(Missing) 110
 
7.4%
ValueCountFrequency (%)
0 120
8.0%
1 102
6.8%
2 74
4.9%
3 45
 
3.0%
4 40
 
2.7%
5 34
 
2.3%
6 39
 
2.6%
7 28
 
1.9%
8 32
 
2.1%
9 27
 
1.8%
ValueCountFrequency (%)
52234 1
0.1%
18292 1
0.1%
14168 1
0.1%
10634 1
0.1%
9134 1
0.1%
5161 1
0.1%
3569 1
0.1%
3336 1
0.1%
3188 1
0.1%
3179 1
0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct308
Distinct (%)22.6%
Missing135
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean150.67353
Minimum0
Maximum37319
Zeros112
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-06T17:57:44.132977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median20
Q370
95-th percentile410.15
Maximum37319
Range37319
Interquartile range (IQR)66

Descriptive statistics

Standard deviation1156.122
Coefficient of variation (CV)7.6730268
Kurtosis799.05451
Mean150.67353
Median Absolute Deviation (MAD)19
Skewness26.029223
Sum204916
Variance1336618.1
MonotonicityNot monotonic
2024-04-06T17:57:44.394445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 112
 
7.5%
1 91
 
6.1%
2 68
 
4.5%
4 49
 
3.3%
3 46
 
3.1%
5 34
 
2.3%
6 33
 
2.2%
10 32
 
2.1%
9 29
 
1.9%
7 24
 
1.6%
Other values (298) 842
56.3%
(Missing) 135
 
9.0%
ValueCountFrequency (%)
0 112
7.5%
1 91
6.1%
2 68
4.5%
3 46
3.1%
4 49
3.3%
5 34
 
2.3%
6 33
 
2.2%
7 24
 
1.6%
8 24
 
1.6%
9 29
 
1.9%
ValueCountFrequency (%)
37319 1
0.1%
11374 1
0.1%
9385 1
0.1%
8868 1
0.1%
5024 1
0.1%
4561 1
0.1%
3235 1
0.1%
2939 1
0.1%
2872 1
0.1%
2743 1
0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct297
Distinct (%)21.7%
Missing125
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean154.35912
Minimum0
Maximum40939
Zeros94
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-06T17:57:44.700459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median19
Q367
95-th percentile418
Maximum40939
Range40939
Interquartile range (IQR)63

Descriptive statistics

Standard deviation1232.5931
Coefficient of variation (CV)7.9852302
Kurtosis884.38458
Mean154.35912
Median Absolute Deviation (MAD)18
Skewness27.615854
Sum211472
Variance1519285.8
MonotonicityNot monotonic
2024-04-06T17:57:44.997109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 94
 
6.3%
1 91
 
6.1%
2 73
 
4.9%
3 56
 
3.7%
4 48
 
3.2%
5 39
 
2.6%
6 35
 
2.3%
16 30
 
2.0%
9 27
 
1.8%
7 26
 
1.7%
Other values (287) 851
56.9%
(Missing) 125
 
8.4%
ValueCountFrequency (%)
0 94
6.3%
1 91
6.1%
2 73
4.9%
3 56
3.7%
4 48
3.2%
5 39
2.6%
6 35
 
2.3%
7 26
 
1.7%
8 24
 
1.6%
9 27
 
1.8%
ValueCountFrequency (%)
40939 1
0.1%
10735 1
0.1%
9050 1
0.1%
8510 1
0.1%
4698 1
0.1%
4609 1
0.1%
3697 1
0.1%
3419 1
0.1%
2688 1
0.1%
2631 1
0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct299
Distinct (%)21.8%
Missing125
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean162.85693
Minimum0
Maximum47759
Zeros92
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-06T17:57:45.412795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median19
Q362
95-th percentile512.3
Maximum47759
Range47759
Interquartile range (IQR)58

Descriptive statistics

Standard deviation1406.2173
Coefficient of variation (CV)8.6346788
Kurtosis965.4949
Mean162.85693
Median Absolute Deviation (MAD)18
Skewness29.202347
Sum223114
Variance1977447.1
MonotonicityNot monotonic
2024-04-06T17:57:46.473913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 100
 
6.7%
0 92
 
6.2%
2 69
 
4.6%
3 58
 
3.9%
4 47
 
3.1%
5 42
 
2.8%
7 33
 
2.2%
8 30
 
2.0%
9 28
 
1.9%
11 26
 
1.7%
Other values (289) 845
56.5%
(Missing) 125
 
8.4%
ValueCountFrequency (%)
0 92
6.2%
1 100
6.7%
2 69
4.6%
3 58
3.9%
4 47
3.1%
5 42
2.8%
6 23
 
1.5%
7 33
 
2.2%
8 30
 
2.0%
9 28
 
1.9%
ValueCountFrequency (%)
47759 1
0.1%
11166 1
0.1%
10575 1
0.1%
6174 1
0.1%
6051 1
0.1%
4536 1
0.1%
4175 1
0.1%
3585 1
0.1%
2790 1
0.1%
2637 1
0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct297
Distinct (%)21.2%
Missing95
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean149.71357
Minimum0
Maximum44034
Zeros84
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-06T17:57:46.744318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median18
Q361
95-th percentile448.15
Maximum44034
Range44034
Interquartile range (IQR)57

Descriptive statistics

Standard deviation1279.2713
Coefficient of variation (CV)8.544792
Kurtosis996.2581
Mean149.71357
Median Absolute Deviation (MAD)16
Skewness29.675463
Sum209599
Variance1636535.1
MonotonicityNot monotonic
2024-04-06T17:57:47.031172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 97
 
6.5%
0 84
 
5.6%
2 78
 
5.2%
3 55
 
3.7%
5 52
 
3.5%
4 49
 
3.3%
6 38
 
2.5%
9 32
 
2.1%
10 31
 
2.1%
13 28
 
1.9%
Other values (287) 856
57.3%
(Missing) 95
 
6.4%
ValueCountFrequency (%)
0 84
5.6%
1 97
6.5%
2 78
5.2%
3 55
3.7%
4 49
3.3%
5 52
3.5%
6 38
 
2.5%
7 27
 
1.8%
8 25
 
1.7%
9 32
 
2.1%
ValueCountFrequency (%)
44034 1
0.1%
10859 1
0.1%
7868 1
0.1%
5646 1
0.1%
5276 1
0.1%
4592 1
0.1%
3987 1
0.1%
3674 1
0.1%
2694 1
0.1%
2534 1
0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct329
Distinct (%)23.8%
Missing110
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean190.17906
Minimum0
Maximum53545
Zeros63
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-06T17:57:47.297029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median22
Q378
95-th percentile574.8
Maximum53545
Range53545
Interquartile range (IQR)72

Descriptive statistics

Standard deviation1573.0795
Coefficient of variation (CV)8.2715706
Kurtosis963.10107
Mean190.17906
Median Absolute Deviation (MAD)20
Skewness29.091538
Sum263398
Variance2474579.2
MonotonicityNot monotonic
2024-04-06T17:57:47.616766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 75
 
5.0%
0 63
 
4.2%
2 61
 
4.1%
3 47
 
3.1%
6 40
 
2.7%
5 37
 
2.5%
4 35
 
2.3%
9 31
 
2.1%
8 31
 
2.1%
10 30
 
2.0%
Other values (319) 935
62.5%
(Missing) 110
 
7.4%
ValueCountFrequency (%)
0 63
4.2%
1 75
5.0%
2 61
4.1%
3 47
3.1%
4 35
2.3%
5 37
2.5%
6 40
2.7%
7 30
 
2.0%
8 31
2.1%
9 31
2.1%
ValueCountFrequency (%)
53545 1
0.1%
12742 1
0.1%
11641 1
0.1%
7669 1
0.1%
6427 1
0.1%
4882 1
0.1%
4495 1
0.1%
4341 1
0.1%
4211 1
0.1%
3464 1
0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct297
Distinct (%)21.3%
Missing100
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean141.1448
Minimum0
Maximum38170
Zeros52
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-06T17:57:47.897904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median19
Q363
95-th percentile400.1
Maximum38170
Range38170
Interquartile range (IQR)58

Descriptive statistics

Standard deviation1129.8321
Coefficient of variation (CV)8.0047726
Kurtosis929.18943
Mean141.1448
Median Absolute Deviation (MAD)17
Skewness28.42954
Sum196897
Variance1276520.5
MonotonicityNot monotonic
2024-04-06T17:57:48.190461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 80
 
5.4%
2 79
 
5.3%
3 63
 
4.2%
0 52
 
3.5%
6 45
 
3.0%
4 44
 
2.9%
5 44
 
2.9%
9 32
 
2.1%
8 31
 
2.1%
10 26
 
1.7%
Other values (287) 899
60.1%
(Missing) 100
 
6.7%
ValueCountFrequency (%)
0 52
3.5%
1 80
5.4%
2 79
5.3%
3 63
4.2%
4 44
2.9%
5 44
2.9%
6 45
3.0%
7 23
 
1.5%
8 31
 
2.1%
9 32
 
2.1%
ValueCountFrequency (%)
38170 1
0.1%
10237 1
0.1%
7989 1
0.1%
6485 1
0.1%
5231 1
0.1%
3504 1
0.1%
3437 1
0.1%
2889 1
0.1%
2642 1
0.1%
2500 1
0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct311
Distinct (%)22.3%
Missing100
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean163.48459
Minimum0
Maximum45854
Zeros54
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-06T17:57:48.507513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median21
Q368
95-th percentile492.3
Maximum45854
Range45854
Interquartile range (IQR)63

Descriptive statistics

Standard deviation1355.4367
Coefficient of variation (CV)8.2909142
Kurtosis935.07026
Mean163.48459
Median Absolute Deviation (MAD)19
Skewness28.580485
Sum228061
Variance1837208.6
MonotonicityNot monotonic
2024-04-06T17:57:48.802824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 69
 
4.6%
2 69
 
4.6%
3 59
 
3.9%
4 57
 
3.8%
0 54
 
3.6%
5 48
 
3.2%
7 34
 
2.3%
10 33
 
2.2%
6 32
 
2.1%
9 29
 
1.9%
Other values (301) 911
60.9%
(Missing) 100
 
6.7%
ValueCountFrequency (%)
0 54
3.6%
1 69
4.6%
2 69
4.6%
3 59
3.9%
4 57
3.8%
5 48
3.2%
6 32
2.1%
7 34
2.3%
8 24
 
1.6%
9 29
1.9%
ValueCountFrequency (%)
45854 1
0.1%
11889 1
0.1%
10885 1
0.1%
7244 1
0.1%
5642 1
0.1%
5306 1
0.1%
3530 1
0.1%
3416 1
0.1%
3332 1
0.1%
2757 1
0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct337
Distinct (%)23.6%
Missing65
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean195.24266
Minimum0
Maximum55081
Zeros39
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-06T17:57:49.356907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median26
Q383.75
95-th percentile550.3
Maximum55081
Range55081
Interquartile range (IQR)76.75

Descriptive statistics

Standard deviation1618.9707
Coefficient of variation (CV)8.2920954
Kurtosis934.36074
Mean195.24266
Median Absolute Deviation (MAD)23
Skewness28.495049
Sum279197
Variance2621066.2
MonotonicityNot monotonic
2024-04-06T17:57:49.772827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 70
 
4.7%
3 69
 
4.6%
2 63
 
4.2%
5 43
 
2.9%
0 39
 
2.6%
4 37
 
2.5%
7 34
 
2.3%
8 34
 
2.3%
6 32
 
2.1%
9 31
 
2.1%
Other values (327) 978
65.4%
(Missing) 65
 
4.3%
ValueCountFrequency (%)
0 39
2.6%
1 70
4.7%
2 63
4.2%
3 69
4.6%
4 37
2.5%
5 43
2.9%
6 32
2.1%
7 34
2.3%
8 34
2.3%
9 31
2.1%
ValueCountFrequency (%)
55081 1
0.1%
14747 1
0.1%
13308 1
0.1%
10075 1
0.1%
7277 1
0.1%
6423 1
0.1%
4537 1
0.1%
3824 1
0.1%
3693 1
0.1%
3403 1
0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct378
Distinct (%)26.8%
Missing85
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean234.02411
Minimum0
Maximum60994
Zeros34
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-06T17:57:50.228134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median32.5
Q3112.5
95-th percentile670.4
Maximum60994
Range60994
Interquartile range (IQR)105.5

Descriptive statistics

Standard deviation1852.1804
Coefficient of variation (CV)7.9144853
Kurtosis835.94726
Mean234.02411
Median Absolute Deviation (MAD)29.5
Skewness26.714139
Sum329974
Variance3430572.2
MonotonicityNot monotonic
2024-04-06T17:57:50.559244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 69
 
4.6%
2 64
 
4.3%
4 47
 
3.1%
3 42
 
2.8%
7 37
 
2.5%
0 34
 
2.3%
6 34
 
2.3%
5 30
 
2.0%
10 29
 
1.9%
9 26
 
1.7%
Other values (368) 998
66.8%
(Missing) 85
 
5.7%
ValueCountFrequency (%)
0 34
2.3%
1 69
4.6%
2 64
4.3%
3 42
2.8%
4 47
3.1%
5 30
2.0%
6 34
2.3%
7 37
2.5%
8 21
 
1.4%
9 26
 
1.7%
ValueCountFrequency (%)
60994 1
0.1%
17757 1
0.1%
17351 1
0.1%
12983 1
0.1%
10306 1
0.1%
8649 1
0.1%
5627 1
0.1%
4009 1
0.1%
3828 1
0.1%
3003 1
0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct351
Distinct (%)24.9%
Missing85
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean201.77376
Minimum0
Maximum51701
Zeros35
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-06T17:57:50.817984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median30
Q397
95-th percentile571.75
Maximum51701
Range51701
Interquartile range (IQR)90

Descriptive statistics

Standard deviation1582.5659
Coefficient of variation (CV)7.8432693
Kurtosis810.76896
Mean201.77376
Median Absolute Deviation (MAD)27
Skewness26.2299
Sum284501
Variance2504514.9
MonotonicityNot monotonic
2024-04-06T17:57:51.076502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 71
 
4.7%
3 58
 
3.9%
2 53
 
3.5%
4 52
 
3.5%
5 45
 
3.0%
0 35
 
2.3%
8 32
 
2.1%
9 31
 
2.1%
6 28
 
1.9%
10 22
 
1.5%
Other values (341) 983
65.8%
(Missing) 85
 
5.7%
ValueCountFrequency (%)
0 35
2.3%
1 71
4.7%
2 53
3.5%
3 58
3.9%
4 52
3.5%
5 45
3.0%
6 28
 
1.9%
7 20
 
1.3%
8 32
2.1%
9 31
2.1%
ValueCountFrequency (%)
51701 1
0.1%
15396 1
0.1%
14706 1
0.1%
11495 1
0.1%
9878 1
0.1%
8276 1
0.1%
4737 1
0.1%
3432 1
0.1%
2878 1
0.1%
2629 1
0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct336
Distinct (%)24.1%
Missing100
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean180.66022
Minimum0
Maximum46055
Zeros70
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-06T17:57:51.464152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.7
Q15
median24
Q390
95-th percentile523.6
Maximum46055
Range46055
Interquartile range (IQR)85

Descriptive statistics

Standard deviation1437.5801
Coefficient of variation (CV)7.9573697
Kurtosis770.52142
Mean180.66022
Median Absolute Deviation (MAD)22
Skewness25.720342
Sum252021
Variance2066636.6
MonotonicityNot monotonic
2024-04-06T17:57:51.804402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 80
 
5.4%
0 70
 
4.7%
3 60
 
4.0%
4 53
 
3.5%
2 51
 
3.4%
5 42
 
2.8%
6 33
 
2.2%
7 32
 
2.1%
8 29
 
1.9%
9 29
 
1.9%
Other values (326) 916
61.3%
(Missing) 100
 
6.7%
ValueCountFrequency (%)
0 70
4.7%
1 80
5.4%
2 51
3.4%
3 60
4.0%
4 53
3.5%
5 42
2.8%
6 33
2.2%
7 32
 
2.1%
8 29
 
1.9%
9 29
 
1.9%
ValueCountFrequency (%)
46055 1
0.1%
18881 1
0.1%
12885 1
0.1%
8570 1
0.1%
7473 1
0.1%
4373 1
0.1%
3407 1
0.1%
3143 1
0.1%
2923 1
0.1%
2675 1
0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct304
Distinct (%)21.7%
Missing95
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean161.14786
Minimum0
Maximum43032
Zeros70
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-06T17:57:52.175913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.95
Q14
median20
Q374
95-th percentile465.75
Maximum43032
Range43032
Interquartile range (IQR)70

Descriptive statistics

Standard deviation1327.4945
Coefficient of variation (CV)8.2377422
Kurtosis799.6487
Mean161.14786
Median Absolute Deviation (MAD)18
Skewness26.231
Sum225607
Variance1762241.6
MonotonicityNot monotonic
2024-04-06T17:57:52.433384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 94
 
6.3%
3 84
 
5.6%
0 70
 
4.7%
2 69
 
4.6%
4 48
 
3.2%
5 36
 
2.4%
6 31
 
2.1%
9 30
 
2.0%
10 30
 
2.0%
8 27
 
1.8%
Other values (294) 881
58.9%
(Missing) 95
 
6.4%
ValueCountFrequency (%)
0 70
4.7%
1 94
6.3%
2 69
4.6%
3 84
5.6%
4 48
3.2%
5 36
 
2.4%
6 31
 
2.1%
7 27
 
1.8%
8 27
 
1.8%
9 30
 
2.0%
ValueCountFrequency (%)
43032 1
0.1%
15736 1
0.1%
13340 1
0.1%
7701 1
0.1%
6630 1
0.1%
3187 1
0.1%
2847 1
0.1%
2795 1
0.1%
2640 1
0.1%
2460 1
0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct292
Distinct (%)20.9%
Missing100
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean149.02294
Minimum0
Maximum41389
Zeros47
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-06T17:57:52.747018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median20
Q362.5
95-th percentile449
Maximum41389
Range41389
Interquartile range (IQR)57.5

Descriptive statistics

Standard deviation1230.1015
Coefficient of variation (CV)8.2544438
Kurtosis916.11736
Mean149.02294
Median Absolute Deviation (MAD)18
Skewness28.237577
Sum207887
Variance1513149.6
MonotonicityNot monotonic
2024-04-06T17:57:53.026086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 89
 
6.0%
2 78
 
5.2%
3 63
 
4.2%
4 57
 
3.8%
0 47
 
3.1%
5 43
 
2.9%
7 34
 
2.3%
6 34
 
2.3%
13 25
 
1.7%
11 25
 
1.7%
Other values (282) 900
60.2%
(Missing) 100
 
6.7%
ValueCountFrequency (%)
0 47
3.1%
1 89
6.0%
2 78
5.2%
3 63
4.2%
4 57
3.8%
5 43
2.9%
6 34
 
2.3%
7 34
 
2.3%
8 21
 
1.4%
9 23
 
1.5%
ValueCountFrequency (%)
41389 1
0.1%
11182 1
0.1%
10254 1
0.1%
6015 1
0.1%
5766 1
0.1%
5537 1
0.1%
2728 1
0.1%
2655 1
0.1%
2560 1
0.1%
2339 1
0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct339
Distinct (%)24.3%
Missing100
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean235.1871
Minimum0
Maximum71196
Zeros38
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-06T17:57:53.314715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median27
Q389
95-th percentile730
Maximum71196
Range71196
Interquartile range (IQR)83

Descriptive statistics

Standard deviation2092.7985
Coefficient of variation (CV)8.8984409
Kurtosis959.92628
Mean235.1871
Median Absolute Deviation (MAD)24
Skewness29.175067
Sum328086
Variance4379805.5
MonotonicityNot monotonic
2024-04-06T17:57:53.574951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 81
 
5.4%
2 72
 
4.8%
3 47
 
3.1%
5 43
 
2.9%
6 42
 
2.8%
4 40
 
2.7%
0 38
 
2.5%
11 30
 
2.0%
8 28
 
1.9%
9 23
 
1.5%
Other values (329) 951
63.6%
(Missing) 100
 
6.7%
ValueCountFrequency (%)
0 38
2.5%
1 81
5.4%
2 72
4.8%
3 47
3.1%
4 40
2.7%
5 43
2.9%
6 42
2.8%
7 21
 
1.4%
8 28
 
1.9%
9 23
 
1.5%
ValueCountFrequency (%)
71196 1
0.1%
22467 1
0.1%
13130 1
0.1%
8180 1
0.1%
7270 1
0.1%
7107 1
0.1%
6322 1
0.1%
4654 1
0.1%
4337 1
0.1%
4015 1
0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct324
Distinct (%)23.2%
Missing100
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean179.87742
Minimum0
Maximum47855
Zeros34
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-06T17:57:53.838054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median27
Q384
95-th percentile504.2
Maximum47855
Range47855
Interquartile range (IQR)77

Descriptive statistics

Standard deviation1437.7926
Coefficient of variation (CV)7.9931801
Kurtosis878.94273
Mean179.87742
Median Absolute Deviation (MAD)23
Skewness27.544651
Sum250929
Variance2067247.6
MonotonicityNot monotonic
2024-04-06T17:57:54.606720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 87
 
5.8%
2 59
 
3.9%
3 43
 
2.9%
6 42
 
2.8%
4 38
 
2.5%
7 37
 
2.5%
5 35
 
2.3%
0 34
 
2.3%
8 29
 
1.9%
13 27
 
1.8%
Other values (314) 964
64.5%
(Missing) 100
 
6.7%
ValueCountFrequency (%)
0 34
 
2.3%
1 87
5.8%
2 59
3.9%
3 43
2.9%
4 38
2.5%
5 35
2.3%
6 42
2.8%
7 37
2.5%
8 29
 
1.9%
9 23
 
1.5%
ValueCountFrequency (%)
47855 1
0.1%
13312 1
0.1%
13074 1
0.1%
8718 1
0.1%
6598 1
0.1%
4296 1
0.1%
3684 1
0.1%
3234 1
0.1%
3194 1
0.1%
2853 1
0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct238
Distinct (%)17.1%
Missing100
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean89.835125
Minimum0
Maximum20429
Zeros41
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-06T17:57:54.871027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median17
Q346
95-th percentile227.3
Maximum20429
Range20429
Interquartile range (IQR)42

Descriptive statistics

Standard deviation643.77137
Coefficient of variation (CV)7.1661432
Kurtosis738.64537
Mean89.835125
Median Absolute Deviation (MAD)14
Skewness24.927067
Sum125320
Variance414441.58
MonotonicityNot monotonic
2024-04-06T17:57:55.175942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 100
 
6.7%
2 83
 
5.6%
3 66
 
4.4%
4 63
 
4.2%
6 45
 
3.0%
5 44
 
2.9%
8 42
 
2.8%
7 41
 
2.7%
0 41
 
2.7%
9 36
 
2.4%
Other values (228) 834
55.8%
(Missing) 100
 
6.7%
ValueCountFrequency (%)
0 41
2.7%
1 100
6.7%
2 83
5.6%
3 66
4.4%
4 63
4.2%
5 44
2.9%
6 45
3.0%
7 41
2.7%
8 42
2.8%
9 36
 
2.4%
ValueCountFrequency (%)
20429 1
0.1%
8258 1
0.1%
5105 1
0.1%
4189 1
0.1%
4092 1
0.1%
2295 1
0.1%
1826 1
0.1%
1551 1
0.1%
1441 1
0.1%
1436 1
0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct241
Distinct (%)17.2%
Missing95
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean98.157857
Minimum0
Maximum30280
Zeros70
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-06T17:57:55.491053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.95
Q13
median11
Q338
95-th percentile292.55
Maximum30280
Range30280
Interquartile range (IQR)35

Descriptive statistics

Standard deviation875.94243
Coefficient of variation (CV)8.9238137
Kurtosis1014.9793
Mean98.157857
Median Absolute Deviation (MAD)9
Skewness30.103978
Sum137421
Variance767275.15
MonotonicityNot monotonic
2024-04-06T17:57:55.811036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 111
 
7.4%
2 92
 
6.2%
3 86
 
5.8%
0 70
 
4.7%
4 67
 
4.5%
6 55
 
3.7%
5 52
 
3.5%
7 43
 
2.9%
10 38
 
2.5%
8 36
 
2.4%
Other values (231) 750
50.2%
(Missing) 95
 
6.4%
ValueCountFrequency (%)
0 70
4.7%
1 111
7.4%
2 92
6.2%
3 86
5.8%
4 67
4.5%
5 52
3.5%
6 55
3.7%
7 43
 
2.9%
8 36
 
2.4%
9 27
 
1.8%
ValueCountFrequency (%)
30280 1
0.1%
7918 1
0.1%
5740 1
0.1%
3280 1
0.1%
2667 1
0.1%
2527 1
0.1%
2269 1
0.1%
2026 1
0.1%
1933 1
0.1%
1746 1
0.1%

Interactions

2024-04-06T17:57:37.193612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:30.287934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:33.741914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:38.069062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-04-06T17:56:47.488904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:51.630038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:55.759543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:59.703422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:03.092415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:07.080741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:10.817398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:14.595546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:18.008785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:21.851507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:26.322309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:31.161621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:35.027548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:39.728621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:32.611626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:36.801641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:40.788081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:44.118889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:47.661266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:51.784680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:55.952468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:59.894967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:03.273335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:07.276534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:10.988991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:14.772121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:18.158315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:22.289819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:26.759429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:31.347411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:35.246777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:39.921177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:32.777729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:36.980108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:40.962857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:44.361259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:47.844718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:51.941051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:56.162177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:00.095468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:03.461349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:07.449884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:11.252184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:14.954714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:18.327511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:22.487465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:27.069922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:31.535321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:35.440949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:40.071611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:33.000772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:37.171255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:41.109652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:44.526440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:48.011714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:52.102823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:56.347751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:00.279373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:03.628601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:07.614407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:11.475507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:15.183917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:18.505831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:22.649176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:27.356350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:31.707464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:35.671161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:40.292961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:33.180342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:37.396115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:41.304459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:44.741390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:48.231346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:52.292681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:56.532579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:00.491799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:03.842363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:07.820235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:11.755047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:15.367793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:18.726159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:22.844591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:27.685264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:31.928895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:35.933026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:40.468940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:33.373547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:37.610058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:41.463230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:44.937832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:48.439664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:52.466794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:56.725060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:00.666849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:04.134766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:08.011879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:11.925295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:15.559622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:18.889074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:23.027360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:27.942986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:32.102460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:36.719778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:40.707221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:33.560975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:37.845077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:41.653293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:45.118189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:48.744136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:52.668341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:56.938864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:00.868289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:04.346836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:08.246611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:12.140881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:15.771487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:19.080249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:23.226972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:28.232748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:32.349040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:57:36.937620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:57:56.043933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9860.8530.9070.8410.8750.8750.8750.8750.8750.8750.9950.9070.8750.9070.8750.9650.938
20070.9861.0001.0000.8750.9070.8750.8750.9170.9170.8640.8640.9910.8750.9170.8640.8750.9880.875
20080.8531.0001.0000.9850.9640.9850.9850.9950.9950.9830.9830.8530.9850.9950.9790.9850.8330.982
20090.9070.8750.9851.0000.9690.9900.9900.9980.9980.9980.9981.0001.0000.9980.9740.9900.8160.982
20100.8410.9070.9640.9691.0000.9690.9690.9670.9670.9550.9550.8910.9690.9670.9640.9690.9170.976
20110.8750.8750.9850.9900.9691.0001.0000.9980.9980.9850.9850.8750.9900.9980.9931.0000.8860.982
20120.8750.8750.9850.9900.9691.0001.0000.9980.9980.9850.9850.8750.9900.9980.9931.0000.8860.982
20130.8750.9170.9950.9980.9670.9980.9981.0001.0000.9950.9950.9270.9981.0000.9860.9980.8570.978
20140.8750.9170.9950.9980.9670.9980.9981.0001.0000.9950.9950.9270.9981.0000.9860.9980.8570.978
20150.8750.8640.9830.9980.9550.9850.9850.9950.9951.0001.0000.9270.9980.9950.9790.9850.8330.969
20160.8750.8640.9830.9980.9550.9850.9850.9950.9951.0001.0000.9270.9980.9950.9790.9850.8330.969
20170.9950.9910.8531.0000.8910.8750.8750.9270.9270.9270.9271.0001.0000.9270.9070.8750.9790.938
20180.9070.8750.9851.0000.9690.9900.9900.9980.9980.9980.9981.0001.0000.9980.9740.9900.8160.982
20190.8750.9170.9950.9980.9670.9980.9981.0001.0000.9950.9950.9270.9981.0000.9860.9980.8570.978
20200.9070.8640.9790.9740.9640.9930.9930.9860.9860.9790.9790.9070.9740.9861.0000.9930.8570.998
20210.8750.8750.9850.9900.9691.0001.0000.9980.9980.9850.9850.8750.9900.9980.9931.0000.8860.982
20220.9650.9880.8330.8160.9170.8860.8860.8570.8570.8330.8330.9790.8160.8570.8570.8861.0000.841
20230.9380.8750.9820.9820.9760.9820.9820.9780.9780.9690.9690.9380.9820.9780.9980.9820.8411.000
2024-04-06T17:57:56.400957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9670.9520.9430.9180.8970.8760.8830.8900.9080.9080.9390.9400.9120.9140.9010.8520.859
20070.9671.0000.9810.9610.9450.9240.9080.9090.9120.9220.9200.9440.9410.9230.9250.9270.8960.894
20080.9520.9811.0000.9730.9560.9370.9220.9220.9230.9310.9290.9490.9460.9340.9340.9360.9100.909
20090.9430.9610.9731.0000.9750.9580.9420.9430.9430.9470.9430.9630.9570.9450.9450.9450.9200.929
20100.9180.9450.9560.9751.0000.9700.9570.9560.9520.9480.9410.9590.9490.9440.9460.9500.9340.935
20110.8970.9240.9370.9580.9701.0000.9800.9760.9730.9640.9550.9450.9270.9490.9510.9540.9340.929
20120.8760.9080.9220.9420.9570.9801.0000.9790.9710.9600.9500.9320.9130.9420.9440.9510.9350.928
20130.8830.9090.9220.9430.9560.9760.9791.0000.9800.9670.9570.9370.9200.9490.9500.9520.9330.929
20140.8900.9120.9230.9430.9520.9730.9710.9801.0000.9810.9700.9410.9230.9560.9560.9570.9320.925
20150.9080.9220.9310.9470.9480.9640.9600.9670.9811.0000.9830.9520.9360.9630.9620.9600.9290.920
20160.9080.9200.9290.9430.9410.9550.9500.9570.9700.9831.0000.9560.9390.9660.9640.9610.9280.920
20170.9390.9440.9490.9630.9590.9450.9320.9370.9410.9520.9561.0000.9830.9570.9560.9530.9230.928
20180.9400.9410.9460.9570.9490.9270.9130.9200.9230.9360.9390.9831.0000.9600.9560.9480.9180.927
20190.9120.9230.9340.9450.9440.9490.9420.9490.9560.9630.9660.9570.9601.0000.9820.9730.9430.942
20200.9140.9250.9340.9450.9460.9510.9440.9500.9560.9620.9640.9560.9560.9821.0000.9780.9420.941
20210.9010.9270.9360.9450.9500.9540.9510.9520.9570.9600.9610.9530.9480.9730.9781.0000.9690.958
20220.8520.8960.9100.9200.9340.9340.9350.9330.9320.9290.9280.9230.9180.9430.9420.9691.0000.964
20230.8590.8940.9090.9290.9350.9290.9280.9290.9250.9200.9200.9280.9270.9420.9410.9580.9641.000

Missing values

2024-04-06T17:57:40.977743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:57:41.478712image/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.
2024-04-06T17:57:41.929249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

지역200620072008200920102011201220132014201520162017201820192020202120222023
0전국 /단독주택1416811374107351057510859127421023710885133081775715396188811573610254131301307482585740
1전국 /다가구주택516150244698417539877669648572441007512983114954373318757667270659841892527
2전국 /다세대주택913488688510605156466427523156426423864982767473663060158180871851053280
3전국 /연립주택318825522305177716212007169517712096261023992369207117742644238914411160
4전국 /아파트522343731940939477594403453545381704585455081609945170146055430324138971196478552042930280
5서울 /단독주택3046204318491367113115021001114116222567226925952640146518541512773428
6서울 /다가구주택1916138512608565958026006791043224819968847678501131980539267
7서울 /다세대주택35692939263119351376161312061353165326582629228521591781263224921268909
8서울 /연립주택1188790650447302351255305440676651613559411557417226178
9서울 /아파트10634456146096174367448823437530672771030698788570770155377107368410042667
지역200620072008200920102011201220132014201520162017201820192020202120222023
1485제주 제주시/단독주택6698115134136195184230274335287208215159150228158128
1486제주 제주시/다가구주택71510102435465673757015163644542918
1487제주 제주시/다세대주택254849486784859810910611271738391988057
1488제주 제주시/연립주택163846516278677983828351736198985960
1489제주 제주시/아파트149216163173253277218247311264234155190169222242160114
1490제주 서귀포시/단독주택23373647515963841071381271091025478886853
1491제주 서귀포시/다가구주택132326101426535014121914251916
1492제주 서귀포시/다세대주택6109121512131422283031341924252518
1493제주 서귀포시/연립주택101814233134342328525869503655846251
1494제주 서귀포시/아파트123960363564466245797354524071905246