Overview

Dataset statistics

Number of variables19
Number of observations2392
Missing cells12948
Missing cells (%)28.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory397.2 KiB
Average record size in memory170.1 B

Variable types

Text1
Numeric18

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 부동산거래현황 중 주택 거래현황의 연도별 거래원인별(면적) 데이터입니다.- (단위 : 천㎡)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068424/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 730 (30.5%) missing valuesMissing
2007 has 760 (31.8%) missing valuesMissing
2008 has 748 (31.3%) missing valuesMissing
2009 has 748 (31.3%) missing valuesMissing
2010 has 712 (29.8%) missing valuesMissing
2011 has 730 (30.5%) missing valuesMissing
2012 has 718 (30.0%) missing valuesMissing
2013 has 718 (30.0%) missing valuesMissing
2014 has 676 (28.3%) missing valuesMissing
2015 has 694 (29.0%) missing valuesMissing
2016 has 700 (29.3%) missing valuesMissing
2017 has 718 (30.0%) missing valuesMissing
2018 has 712 (29.8%) missing valuesMissing
2019 has 718 (30.0%) missing valuesMissing
2020 has 718 (30.0%) missing valuesMissing
2021 has 718 (30.0%) missing valuesMissing
2022 has 718 (30.0%) missing valuesMissing
2023 has 712 (29.8%) missing valuesMissing
2006 is highly skewed (γ1 = 29.80359287)Skewed
2007 is highly skewed (γ1 = 29.72001062)Skewed
2008 is highly skewed (γ1 = 31.74855853)Skewed
2009 is highly skewed (γ1 = 32.21011727)Skewed
2010 is highly skewed (γ1 = 32.46360502)Skewed
2011 is highly skewed (γ1 = 33.78518722)Skewed
2012 is highly skewed (γ1 = 32.72968308)Skewed
2013 is highly skewed (γ1 = 32.26528854)Skewed
2014 is highly skewed (γ1 = 31.68674484)Skewed
2015 is highly skewed (γ1 = 31.1338466)Skewed
2016 is highly skewed (γ1 = 31.27245981)Skewed
2017 is highly skewed (γ1 = 26.73750614)Skewed
2018 is highly skewed (γ1 = 24.72353954)Skewed
2019 is highly skewed (γ1 = 25.34537692)Skewed
2020 is highly skewed (γ1 = 29.9380802)Skewed
2021 is highly skewed (γ1 = 29.6948586)Skewed
2022 is highly skewed (γ1 = 26.73472612)Skewed
2023 is highly skewed (γ1 = 28.80166772)Skewed
지역 및 거래원인 has unique valuesUnique
2006 has 420 (17.6%) zerosZeros
2007 has 463 (19.4%) zerosZeros
2008 has 458 (19.1%) zerosZeros
2009 has 468 (19.6%) zerosZeros
2010 has 513 (21.4%) zerosZeros
2011 has 488 (20.4%) zerosZeros
2012 has 455 (19.0%) zerosZeros
2013 has 439 (18.4%) zerosZeros
2014 has 439 (18.4%) zerosZeros
2015 has 414 (17.3%) zerosZeros
2016 has 382 (16.0%) zerosZeros
2017 has 386 (16.1%) zerosZeros
2018 has 367 (15.3%) zerosZeros
2019 has 386 (16.1%) zerosZeros
2020 has 353 (14.8%) zerosZeros
2021 has 369 (15.4%) zerosZeros
2022 has 382 (16.0%) zerosZeros
2023 has 366 (15.3%) zerosZeros

Reproduction

Analysis started2024-03-23 05:40:10.309268
Analysis finished2024-03-23 05:41:25.943321
Duration1 minute and 15.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct2392
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
2024-03-23T14:41:26.251109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length10.466137
Min length6

Characters and Unicode

Total characters25035
Distinct characters154
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

Unique2392 ?
Unique (%)100.0%

Sample

1st row전국 /매매
2nd row전국 /판결
3rd row전국 /교환
4th row전국 /증여
5th row전국 /분양권
ValueCountFrequency (%)
경기 424
 
8.9%
경남 216
 
4.5%
서울 208
 
4.3%
경북 208
 
4.3%
전남 184
 
3.8%
충남 160
 
3.3%
충북 160
 
3.3%
강원 152
 
3.2%
전북 136
 
2.8%
부산 136
 
2.8%
Other values (2065) 2800
58.5%
2024-03-23T14:41:26.978999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2392
 
9.6%
/ 2392
 
9.6%
1128
 
4.5%
1046
 
4.2%
998
 
4.0%
976
 
3.9%
905
 
3.6%
897
 
3.6%
872
 
3.5%
790
 
3.2%
Other values (144) 12639
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19947
79.7%
Space Separator 2392
 
9.6%
Other Punctuation 2392
 
9.6%
Close Punctuation 152
 
0.6%
Open Punctuation 152
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1128
 
5.7%
1046
 
5.2%
998
 
5.0%
976
 
4.9%
905
 
4.5%
897
 
4.5%
872
 
4.4%
790
 
4.0%
744
 
3.7%
712
 
3.6%
Other values (140) 10879
54.5%
Space Separator
ValueCountFrequency (%)
2392
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2392
100.0%
Close Punctuation
ValueCountFrequency (%)
) 152
100.0%
Open Punctuation
ValueCountFrequency (%)
( 152
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19947
79.7%
Common 5088
 
20.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1128
 
5.7%
1046
 
5.2%
998
 
5.0%
976
 
4.9%
905
 
4.5%
897
 
4.5%
872
 
4.4%
790
 
4.0%
744
 
3.7%
712
 
3.6%
Other values (140) 10879
54.5%
Common
ValueCountFrequency (%)
2392
47.0%
/ 2392
47.0%
) 152
 
3.0%
( 152
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 19947
79.7%
ASCII 5088
 
20.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2392
47.0%
/ 2392
47.0%
) 152
 
3.0%
( 152
 
3.0%
Hangul
ValueCountFrequency (%)
1128
 
5.7%
1046
 
5.2%
998
 
5.0%
976
 
4.9%
905
 
4.5%
897
 
4.5%
872
 
4.4%
790
 
4.0%
744
 
3.7%
712
 
3.6%
Other values (140) 10879
54.5%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct372
Distinct (%)22.4%
Missing730
Missing (%)30.5%
Infinite0
Infinite (%)0.0%
Mean234.31649
Minimum0
Maximum83884
Zeros420
Zeros (%)17.6%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:41:27.238739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11
Q364
95-th percentile760.75
Maximum83884
Range83884
Interquartile range (IQR)64

Descriptive statistics

Standard deviation2329.4918
Coefficient of variation (CV)9.9416468
Kurtosis1020.6483
Mean234.31649
Median Absolute Deviation (MAD)11
Skewness29.803593
Sum389434
Variance5426531.8
MonotonicityNot monotonic
2024-03-23T14:41:27.561273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 420
17.6%
1 135
 
5.6%
2 61
 
2.6%
3 45
 
1.9%
4 42
 
1.8%
6 29
 
1.2%
11 24
 
1.0%
5 24
 
1.0%
8 22
 
0.9%
23 21
 
0.9%
Other values (362) 839
35.1%
(Missing) 730
30.5%
ValueCountFrequency (%)
0 420
17.6%
1 135
 
5.6%
2 61
 
2.6%
3 45
 
1.9%
4 42
 
1.8%
5 24
 
1.0%
6 29
 
1.2%
7 15
 
0.6%
8 22
 
0.9%
9 15
 
0.6%
ValueCountFrequency (%)
83884 1
< 0.1%
27384 1
< 0.1%
21452 1
< 0.1%
20353 1
< 0.1%
11051 1
< 0.1%
5638 1
< 0.1%
5299 1
< 0.1%
5131 1
< 0.1%
4083 1
< 0.1%
3475 1
< 0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct347
Distinct (%)21.3%
Missing760
Missing (%)31.8%
Infinite0
Infinite (%)0.0%
Mean187.83517
Minimum0
Maximum65137
Zeros463
Zeros (%)19.4%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:41:27.917814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q348
95-th percentile598.6
Maximum65137
Range65137
Interquartile range (IQR)48

Descriptive statistics

Standard deviation1819.8936
Coefficient of variation (CV)9.6887797
Kurtosis1014.2518
Mean187.83517
Median Absolute Deviation (MAD)5
Skewness29.720011
Sum306547
Variance3312012.7
MonotonicityNot monotonic
2024-03-23T14:41:28.193808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 463
19.4%
1 164
 
6.9%
2 71
 
3.0%
4 46
 
1.9%
3 41
 
1.7%
5 36
 
1.5%
7 30
 
1.3%
6 26
 
1.1%
8 19
 
0.8%
24 17
 
0.7%
Other values (337) 719
30.1%
(Missing) 760
31.8%
ValueCountFrequency (%)
0 463
19.4%
1 164
 
6.9%
2 71
 
3.0%
3 41
 
1.7%
4 46
 
1.9%
5 36
 
1.5%
6 26
 
1.1%
7 30
 
1.3%
8 19
 
0.8%
9 15
 
0.6%
ValueCountFrequency (%)
65137 1
< 0.1%
22297 1
< 0.1%
16907 1
< 0.1%
11719 1
< 0.1%
5669 1
< 0.1%
5506 1
< 0.1%
4372 1
< 0.1%
4352 1
< 0.1%
4249 1
< 0.1%
3976 1
< 0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct334
Distinct (%)20.3%
Missing748
Missing (%)31.3%
Infinite0
Infinite (%)0.0%
Mean177.79501
Minimum0
Maximum67187
Zeros458
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:41:28.470303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q339
95-th percentile525.85
Maximum67187
Range67187
Interquartile range (IQR)39

Descriptive statistics

Standard deviation1817.0167
Coefficient of variation (CV)10.219728
Kurtosis1137.1836
Mean177.79501
Median Absolute Deviation (MAD)5
Skewness31.748559
Sum292295
Variance3301549.6
MonotonicityNot monotonic
2024-03-23T14:41:28.724347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 458
19.1%
1 167
 
7.0%
2 80
 
3.3%
3 60
 
2.5%
4 45
 
1.9%
6 31
 
1.3%
5 30
 
1.3%
7 24
 
1.0%
9 23
 
1.0%
10 19
 
0.8%
Other values (324) 707
29.6%
(Missing) 748
31.3%
ValueCountFrequency (%)
0 458
19.1%
1 167
 
7.0%
2 80
 
3.3%
3 60
 
2.5%
4 45
 
1.9%
5 30
 
1.3%
6 31
 
1.3%
7 24
 
1.0%
8 19
 
0.8%
9 23
 
1.0%
ValueCountFrequency (%)
67187 1
< 0.1%
18433 1
< 0.1%
15528 1
< 0.1%
10999 1
< 0.1%
5459 1
< 0.1%
4832 1
< 0.1%
4731 1
< 0.1%
4655 1
< 0.1%
4297 1
< 0.1%
3127 1
< 0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct334
Distinct (%)20.3%
Missing748
Missing (%)31.3%
Infinite0
Infinite (%)0.0%
Mean183.16119
Minimum0
Maximum70338
Zeros468
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:41:28.957959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q344.25
95-th percentile583.25
Maximum70338
Range70338
Interquartile range (IQR)44.25

Descriptive statistics

Standard deviation1888.9935
Coefficient of variation (CV)10.313285
Kurtosis1166.9662
Mean183.16119
Median Absolute Deviation (MAD)5
Skewness32.210117
Sum301117
Variance3568296.6
MonotonicityNot monotonic
2024-03-23T14:41:29.186243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 468
19.6%
1 182
 
7.6%
2 83
 
3.5%
3 40
 
1.7%
4 40
 
1.7%
5 34
 
1.4%
6 25
 
1.0%
8 24
 
1.0%
7 21
 
0.9%
16 19
 
0.8%
Other values (324) 708
29.6%
(Missing) 748
31.3%
ValueCountFrequency (%)
0 468
19.6%
1 182
 
7.6%
2 83
 
3.5%
3 40
 
1.7%
4 40
 
1.7%
5 34
 
1.4%
6 25
 
1.0%
7 21
 
0.9%
8 24
 
1.0%
9 17
 
0.7%
ValueCountFrequency (%)
70338 1
< 0.1%
17206 1
< 0.1%
15991 1
< 0.1%
10778 1
< 0.1%
6098 1
< 0.1%
5837 1
< 0.1%
5089 1
< 0.1%
5053 1
< 0.1%
3962 1
< 0.1%
3846 1
< 0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct338
Distinct (%)20.1%
Missing712
Missing (%)29.8%
Infinite0
Infinite (%)0.0%
Mean172.09345
Minimum0
Maximum66147
Zeros513
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:41:30.060002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q341
95-th percentile513.2
Maximum66147
Range66147
Interquartile range (IQR)41

Descriptive statistics

Standard deviation1758.9666
Coefficient of variation (CV)10.220997
Kurtosis1188.2414
Mean172.09345
Median Absolute Deviation (MAD)4
Skewness32.463605
Sum289117
Variance3093963.5
MonotonicityNot monotonic
2024-03-23T14:41:30.393513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 513
21.4%
1 180
 
7.5%
2 87
 
3.6%
3 43
 
1.8%
4 39
 
1.6%
5 27
 
1.1%
6 23
 
1.0%
10 21
 
0.9%
9 20
 
0.8%
7 20
 
0.8%
Other values (328) 707
29.6%
(Missing) 712
29.8%
ValueCountFrequency (%)
0 513
21.4%
1 180
 
7.5%
2 87
 
3.6%
3 43
 
1.8%
4 39
 
1.6%
5 27
 
1.1%
6 23
 
1.0%
7 20
 
0.8%
8 19
 
0.8%
9 20
 
0.8%
ValueCountFrequency (%)
66147 1
< 0.1%
17988 1
< 0.1%
12170 1
< 0.1%
7625 1
< 0.1%
7078 1
< 0.1%
7037 1
< 0.1%
6358 1
< 0.1%
5098 1
< 0.1%
3914 1
< 0.1%
3878 1
< 0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct344
Distinct (%)20.7%
Missing730
Missing (%)30.5%
Infinite0
Infinite (%)0.0%
Mean201.8568
Minimum0
Maximum82390
Zeros488
Zeros (%)20.4%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:41:30.720876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q342
95-th percentile601.9
Maximum82390
Range82390
Interquartile range (IQR)42

Descriptive statistics

Standard deviation2161.5954
Coefficient of variation (CV)10.708559
Kurtosis1264.1589
Mean201.8568
Median Absolute Deviation (MAD)4
Skewness33.785187
Sum335486
Variance4672494.6
MonotonicityNot monotonic
2024-03-23T14:41:30.984985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 488
20.4%
1 195
 
8.2%
2 79
 
3.3%
3 43
 
1.8%
4 39
 
1.6%
5 27
 
1.1%
9 26
 
1.1%
7 25
 
1.0%
8 19
 
0.8%
11 18
 
0.8%
Other values (334) 703
29.4%
(Missing) 730
30.5%
ValueCountFrequency (%)
0 488
20.4%
1 195
 
8.2%
2 79
 
3.3%
3 43
 
1.8%
4 39
 
1.6%
5 27
 
1.1%
6 15
 
0.6%
7 25
 
1.0%
8 19
 
0.8%
9 26
 
1.1%
ValueCountFrequency (%)
82390 1
< 0.1%
16886 1
< 0.1%
16206 1
< 0.1%
9149 1
< 0.1%
6910 1
< 0.1%
6713 1
< 0.1%
6272 1
< 0.1%
5627 1
< 0.1%
4969 1
< 0.1%
4353 1
< 0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct336
Distinct (%)20.1%
Missing718
Missing (%)30.0%
Infinite0
Infinite (%)0.0%
Mean162.83393
Minimum0
Maximum61818
Zeros455
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:41:31.265088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q343
95-th percentile482.35
Maximum61818
Range61818
Interquartile range (IQR)43

Descriptive statistics

Standard deviation1640.3648
Coefficient of variation (CV)10.073851
Kurtosis1202.1321
Mean162.83393
Median Absolute Deviation (MAD)5
Skewness32.729683
Sum272584
Variance2690796.6
MonotonicityNot monotonic
2024-03-23T14:41:31.519672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 455
19.0%
1 177
 
7.4%
2 80
 
3.3%
3 57
 
2.4%
4 39
 
1.6%
5 32
 
1.3%
7 29
 
1.2%
6 26
 
1.1%
10 25
 
1.0%
13 21
 
0.9%
Other values (326) 733
30.6%
(Missing) 718
30.0%
ValueCountFrequency (%)
0 455
19.0%
1 177
 
7.4%
2 80
 
3.3%
3 57
 
2.4%
4 39
 
1.6%
5 32
 
1.3%
6 26
 
1.1%
7 29
 
1.2%
8 20
 
0.8%
9 17
 
0.7%
ValueCountFrequency (%)
61818 1
< 0.1%
16443 1
< 0.1%
12134 1
< 0.1%
6499 1
< 0.1%
5274 1
< 0.1%
4927 1
< 0.1%
4672 1
< 0.1%
4613 1
< 0.1%
4557 1
< 0.1%
4010 1
< 0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct357
Distinct (%)21.3%
Missing718
Missing (%)30.0%
Infinite0
Infinite (%)0.0%
Mean187.52688
Minimum0
Maximum71395
Zeros439
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:41:31.748864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q347
95-th percentile542
Maximum71395
Range71395
Interquartile range (IQR)47

Descriptive statistics

Standard deviation1907.9951
Coefficient of variation (CV)10.174515
Kurtosis1171.1104
Mean187.52688
Median Absolute Deviation (MAD)6
Skewness32.265289
Sum313920
Variance3640445.2
MonotonicityNot monotonic
2024-03-23T14:41:31.969235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 439
18.4%
1 183
 
7.7%
2 87
 
3.6%
3 48
 
2.0%
4 44
 
1.8%
5 32
 
1.3%
6 28
 
1.2%
7 27
 
1.1%
10 25
 
1.0%
8 24
 
1.0%
Other values (347) 737
30.8%
(Missing) 718
30.0%
ValueCountFrequency (%)
0 439
18.4%
1 183
7.7%
2 87
 
3.6%
3 48
 
2.0%
4 44
 
1.8%
5 32
 
1.3%
6 28
 
1.2%
7 27
 
1.1%
8 24
 
1.0%
9 15
 
0.6%
ValueCountFrequency (%)
71395 1
< 0.1%
19957 1
< 0.1%
16282 1
< 0.1%
8785 1
< 0.1%
5542 1
< 0.1%
5493 1
< 0.1%
5333 1
< 0.1%
4897 1
< 0.1%
4667 1
< 0.1%
3956 1
< 0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct383
Distinct (%)22.3%
Missing676
Missing (%)28.3%
Infinite0
Infinite (%)0.0%
Mean229.48019
Minimum0
Maximum86983
Zeros439
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:41:32.272009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q357
95-th percentile723.25
Maximum86983
Range86983
Interquartile range (IQR)57

Descriptive statistics

Standard deviation2330.7955
Coefficient of variation (CV)10.156849
Kurtosis1136.7672
Mean229.48019
Median Absolute Deviation (MAD)7
Skewness31.686745
Sum393788
Variance5432607.6
MonotonicityNot monotonic
2024-03-23T14:41:32.626830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 439
18.4%
1 191
 
8.0%
2 88
 
3.7%
3 48
 
2.0%
4 37
 
1.5%
5 30
 
1.3%
7 26
 
1.1%
10 24
 
1.0%
6 22
 
0.9%
8 20
 
0.8%
Other values (373) 791
33.1%
(Missing) 676
28.3%
ValueCountFrequency (%)
0 439
18.4%
1 191
8.0%
2 88
 
3.7%
3 48
 
2.0%
4 37
 
1.5%
5 30
 
1.3%
6 22
 
0.9%
7 26
 
1.1%
8 20
 
0.8%
9 19
 
0.8%
ValueCountFrequency (%)
86983 1
< 0.1%
28611 1
< 0.1%
20498 1
< 0.1%
12034 1
< 0.1%
6951 1
< 0.1%
6238 1
< 0.1%
5998 1
< 0.1%
5812 1
< 0.1%
5425 1
< 0.1%
5003 1
< 0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct392
Distinct (%)23.1%
Missing694
Missing (%)29.0%
Infinite0
Infinite (%)0.0%
Mean272.55359
Minimum0
Maximum102993
Zeros414
Zeros (%)17.3%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:41:32.889636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q362
95-th percentile832.75
Maximum102993
Range102993
Interquartile range (IQR)61

Descriptive statistics

Standard deviation2789.5721
Coefficient of variation (CV)10.234949
Kurtosis1101.5461
Mean272.55359
Median Absolute Deviation (MAD)7
Skewness31.133847
Sum462796
Variance7781712.5
MonotonicityNot monotonic
2024-03-23T14:41:33.117672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 414
17.3%
1 186
 
7.8%
2 87
 
3.6%
3 44
 
1.8%
4 39
 
1.6%
5 33
 
1.4%
6 30
 
1.3%
7 24
 
1.0%
13 19
 
0.8%
8 19
 
0.8%
Other values (382) 803
33.6%
(Missing) 694
29.0%
ValueCountFrequency (%)
0 414
17.3%
1 186
7.8%
2 87
 
3.6%
3 44
 
1.8%
4 39
 
1.6%
5 33
 
1.4%
6 30
 
1.3%
7 24
 
1.0%
8 19
 
0.8%
9 13
 
0.5%
ValueCountFrequency (%)
102993 1
< 0.1%
33507 1
< 0.1%
25859 1
< 0.1%
18454 1
< 0.1%
8499 1
< 0.1%
6719 1
< 0.1%
6699 1
< 0.1%
6450 1
< 0.1%
5982 1
< 0.1%
5897 1
< 0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct411
Distinct (%)24.3%
Missing700
Missing (%)29.3%
Infinite0
Infinite (%)0.0%
Mean249.21631
Minimum0
Maximum89266
Zeros382
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:41:33.347891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median15
Q396.25
95-th percentile764.35
Maximum89266
Range89266
Interquartile range (IQR)95.25

Descriptive statistics

Standard deviation2406.359
Coefficient of variation (CV)9.6557043
Kurtosis1119.0646
Mean249.21631
Median Absolute Deviation (MAD)15
Skewness31.27246
Sum421674
Variance5790563.7
MonotonicityNot monotonic
2024-03-23T14:41:33.626099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 382
16.0%
1 151
 
6.3%
2 55
 
2.3%
3 42
 
1.8%
4 29
 
1.2%
6 28
 
1.2%
8 26
 
1.1%
10 21
 
0.9%
5 19
 
0.8%
13 19
 
0.8%
Other values (401) 920
38.5%
(Missing) 700
29.3%
ValueCountFrequency (%)
0 382
16.0%
1 151
 
6.3%
2 55
 
2.3%
3 42
 
1.8%
4 29
 
1.2%
5 19
 
0.8%
6 28
 
1.2%
7 14
 
0.6%
8 26
 
1.1%
9 17
 
0.7%
ValueCountFrequency (%)
89266 1
< 0.1%
22856 1
< 0.1%
21871 1
< 0.1%
17423 1
< 0.1%
13501 1
< 0.1%
7456 1
< 0.1%
7184 1
< 0.1%
6852 1
< 0.1%
5571 1
< 0.1%
5379 1
< 0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct434
Distinct (%)25.9%
Missing718
Missing (%)30.0%
Infinite0
Infinite (%)0.0%
Mean266.89247
Minimum0
Maximum79151
Zeros386
Zeros (%)16.1%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:41:33.934366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median17
Q3113.75
95-th percentile754.2
Maximum79151
Range79151
Interquartile range (IQR)112.75

Descriptive statistics

Standard deviation2336.0584
Coefficient of variation (CV)8.7528074
Kurtosis831.60359
Mean266.89247
Median Absolute Deviation (MAD)17
Skewness26.737506
Sum446778
Variance5457168.9
MonotonicityNot monotonic
2024-03-23T14:41:34.184977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 386
16.1%
1 154
 
6.4%
2 59
 
2.5%
3 33
 
1.4%
4 29
 
1.2%
7 22
 
0.9%
5 22
 
0.9%
10 19
 
0.8%
19 17
 
0.7%
12 15
 
0.6%
Other values (424) 918
38.4%
(Missing) 718
30.0%
ValueCountFrequency (%)
0 386
16.1%
1 154
 
6.4%
2 59
 
2.5%
3 33
 
1.4%
4 29
 
1.2%
5 22
 
0.9%
6 13
 
0.5%
7 22
 
0.9%
8 14
 
0.6%
9 14
 
0.6%
ValueCountFrequency (%)
79151 1
< 0.1%
39761 1
< 0.1%
20413 1
< 0.1%
14948 1
< 0.1%
14029 1
< 0.1%
13084 1
< 0.1%
7048 1
< 0.1%
5404 1
< 0.1%
4786 1
< 0.1%
4565 1
< 0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct415
Distinct (%)24.7%
Missing712
Missing (%)29.8%
Infinite0
Infinite (%)0.0%
Mean258.81012
Minimum0
Maximum70657
Zeros367
Zeros (%)15.3%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:41:34.469707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median17
Q3104.25
95-th percentile716.4
Maximum70657
Range70657
Interquartile range (IQR)103.25

Descriptive statistics

Standard deviation2238.7413
Coefficient of variation (CV)8.6501306
Kurtosis697.34637
Mean258.81012
Median Absolute Deviation (MAD)17
Skewness24.72354
Sum434801
Variance5011962.7
MonotonicityNot monotonic
2024-03-23T14:41:34.691457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 367
 
15.3%
1 162
 
6.8%
2 64
 
2.7%
3 36
 
1.5%
4 32
 
1.3%
5 22
 
0.9%
17 17
 
0.7%
10 17
 
0.7%
9 17
 
0.7%
11 17
 
0.7%
Other values (405) 929
38.8%
(Missing) 712
29.8%
ValueCountFrequency (%)
0 367
15.3%
1 162
6.8%
2 64
 
2.7%
3 36
 
1.5%
4 32
 
1.3%
5 22
 
0.9%
6 11
 
0.5%
7 14
 
0.6%
8 14
 
0.6%
9 17
 
0.7%
ValueCountFrequency (%)
70657 1
< 0.1%
46533 1
< 0.1%
19715 1
< 0.1%
17745 1
< 0.1%
13826 1
< 0.1%
10213 1
< 0.1%
8473 1
< 0.1%
4402 1
< 0.1%
4128 1
< 0.1%
3918 1
< 0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct413
Distinct (%)24.7%
Missing718
Missing (%)30.0%
Infinite0
Infinite (%)0.0%
Mean234.43967
Minimum0
Maximum65198
Zeros386
Zeros (%)16.1%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:41:34.977103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median15
Q391.75
95-th percentile625.65
Maximum65198
Range65198
Interquartile range (IQR)90.75

Descriptive statistics

Standard deviation2028.2768
Coefficient of variation (CV)8.6515938
Kurtosis733.38863
Mean234.43967
Median Absolute Deviation (MAD)15
Skewness25.345377
Sum392452
Variance4113906.6
MonotonicityNot monotonic
2024-03-23T14:41:35.264403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 386
16.1%
1 147
 
6.1%
2 66
 
2.8%
3 42
 
1.8%
17 27
 
1.1%
4 26
 
1.1%
5 23
 
1.0%
11 21
 
0.9%
15 19
 
0.8%
8 19
 
0.8%
Other values (403) 898
37.5%
(Missing) 718
30.0%
ValueCountFrequency (%)
0 386
16.1%
1 147
 
6.1%
2 66
 
2.8%
3 42
 
1.8%
4 26
 
1.1%
5 23
 
1.0%
6 14
 
0.6%
7 17
 
0.7%
8 19
 
0.8%
9 14
 
0.6%
ValueCountFrequency (%)
65198 1
< 0.1%
41213 1
< 0.1%
16343 1
< 0.1%
15824 1
< 0.1%
10043 1
< 0.1%
8262 1
< 0.1%
8164 1
< 0.1%
4249 1
< 0.1%
4079 1
< 0.1%
4031 1
< 0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct433
Distinct (%)25.9%
Missing718
Missing (%)30.0%
Infinite0
Infinite (%)0.0%
Mean300.99044
Minimum0
Maximum102420
Zeros353
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:41:35.516038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median17
Q3107.5
95-th percentile887.4
Maximum102420
Range102420
Interquartile range (IQR)106.5

Descriptive statistics

Standard deviation2832.0816
Coefficient of variation (CV)9.4092076
Kurtosis1030.9939
Mean300.99044
Median Absolute Deviation (MAD)17
Skewness29.93808
Sum503858
Variance8020686
MonotonicityNot monotonic
2024-03-23T14:41:35.809277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 353
 
14.8%
1 173
 
7.2%
2 68
 
2.8%
3 43
 
1.8%
4 26
 
1.1%
11 20
 
0.8%
9 18
 
0.8%
7 17
 
0.7%
5 16
 
0.7%
6 15
 
0.6%
Other values (423) 925
38.7%
(Missing) 718
30.0%
ValueCountFrequency (%)
0 353
14.8%
1 173
7.2%
2 68
 
2.8%
3 43
 
1.8%
4 26
 
1.1%
5 16
 
0.7%
6 15
 
0.6%
7 17
 
0.7%
8 10
 
0.4%
9 18
 
0.8%
ValueCountFrequency (%)
102420 1
< 0.1%
34287 1
< 0.1%
29538 1
< 0.1%
13281 1
< 0.1%
11237 1
< 0.1%
11058 1
< 0.1%
9621 1
< 0.1%
8472 1
< 0.1%
6563 1
< 0.1%
6067 1
< 0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct395
Distinct (%)23.6%
Missing718
Missing (%)30.0%
Infinite0
Infinite (%)0.0%
Mean234.51732
Minimum0
Maximum78634
Zeros369
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:41:36.161940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median15
Q381
95-th percentile637.4
Maximum78634
Range78634
Interquartile range (IQR)80

Descriptive statistics

Standard deviation2186.6741
Coefficient of variation (CV)9.3241476
Kurtosis1012.8914
Mean234.51732
Median Absolute Deviation (MAD)15
Skewness29.694859
Sum392582
Variance4781543.8
MonotonicityNot monotonic
2024-03-23T14:41:36.619594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 369
15.4%
1 164
 
6.9%
2 55
 
2.3%
3 55
 
2.3%
4 32
 
1.3%
8 18
 
0.8%
6 18
 
0.8%
20 18
 
0.8%
7 17
 
0.7%
9 17
 
0.7%
Other values (385) 911
38.1%
(Missing) 718
30.0%
ValueCountFrequency (%)
0 369
15.4%
1 164
6.9%
2 55
 
2.3%
3 55
 
2.3%
4 32
 
1.3%
5 15
 
0.6%
6 18
 
0.8%
7 17
 
0.7%
8 18
 
0.8%
9 17
 
0.7%
ValueCountFrequency (%)
78634 1
< 0.1%
29600 1
< 0.1%
20590 1
< 0.1%
9542 1
< 0.1%
9320 1
< 0.1%
9085 1
< 0.1%
5908 1
< 0.1%
5567 1
< 0.1%
5094 1
< 0.1%
4954 1
< 0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct323
Distinct (%)19.3%
Missing718
Missing (%)30.0%
Infinite0
Infinite (%)0.0%
Mean136.21207
Minimum0
Maximum39423
Zeros382
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:41:36.879906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median10
Q352
95-th percentile388.45
Maximum39423
Range39423
Interquartile range (IQR)51

Descriptive statistics

Standard deviation1177.8098
Coefficient of variation (CV)8.6468829
Kurtosis818.24536
Mean136.21207
Median Absolute Deviation (MAD)10
Skewness26.734726
Sum228019
Variance1387235.9
MonotonicityNot monotonic
2024-03-23T14:41:37.730632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 382
16.0%
1 188
 
7.9%
2 85
 
3.6%
3 43
 
1.8%
4 35
 
1.5%
5 27
 
1.1%
12 22
 
0.9%
8 20
 
0.8%
6 20
 
0.8%
19 20
 
0.8%
Other values (313) 832
34.8%
(Missing) 718
30.0%
ValueCountFrequency (%)
0 382
16.0%
1 188
7.9%
2 85
 
3.6%
3 43
 
1.8%
4 35
 
1.5%
5 27
 
1.1%
6 20
 
0.8%
7 13
 
0.5%
8 20
 
0.8%
9 15
 
0.6%
ValueCountFrequency (%)
39423 1
< 0.1%
22333 1
< 0.1%
8417 1
< 0.1%
6983 1
< 0.1%
6928 1
< 0.1%
3811 1
< 0.1%
3421 1
< 0.1%
3080 1
< 0.1%
2567 1
< 0.1%
2502 1
< 0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct314
Distinct (%)18.7%
Missing712
Missing (%)29.8%
Infinite0
Infinite (%)0.0%
Mean134.76845
Minimum0
Maximum42987
Zeros366
Zeros (%)15.3%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:41:38.028553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median9
Q350
95-th percentile407.1
Maximum42987
Range42987
Interquartile range (IQR)49

Descriptive statistics

Standard deviation1215.2634
Coefficient of variation (CV)9.0174175
Kurtosis956.54338
Mean134.76845
Median Absolute Deviation (MAD)9
Skewness28.801668
Sum226411
Variance1476865.1
MonotonicityNot monotonic
2024-03-23T14:41:38.284091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 366
15.3%
1 162
 
6.8%
2 105
 
4.4%
3 53
 
2.2%
4 39
 
1.6%
5 27
 
1.1%
6 25
 
1.0%
10 25
 
1.0%
14 24
 
1.0%
15 24
 
1.0%
Other values (304) 830
34.7%
(Missing) 712
29.8%
ValueCountFrequency (%)
0 366
15.3%
1 162
6.8%
2 105
 
4.4%
3 53
 
2.2%
4 39
 
1.6%
5 27
 
1.1%
6 25
 
1.0%
7 20
 
0.8%
8 23
 
1.0%
9 23
 
1.0%
ValueCountFrequency (%)
42987 1
< 0.1%
18510 1
< 0.1%
10796 1
< 0.1%
5648 1
< 0.1%
5103 1
< 0.1%
4448 1
< 0.1%
4125 1
< 0.1%
3068 1
< 0.1%
2791 1
< 0.1%
2749 1
< 0.1%

Interactions

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2024-03-23T14:41:22.724979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:15.939631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:20.356856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:23.857859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:27.500663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:31.895226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:35.811118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:39.647284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:44.005359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:47.285095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:50.576638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:54.252284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:58.492579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:02.847788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:06.826102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:11.017207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:14.523222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:18.293845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:23.024978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:16.111207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:20.577908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:24.047404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:27.716586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:32.199784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:36.044224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:39.853490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:44.190526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:47.503203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:50.812406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:54.435569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:58.756402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:03.049598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:06.993043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:11.184844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:14.775331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:18.467154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:23.247291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:16.287565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:20.781996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:24.254005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:27.902271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:32.396046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:36.265836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:40.015767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:44.397842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:47.652669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:51.030599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:54.640014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:58.954929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:03.369316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:07.186711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:11.348982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:14.967578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:18.675953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:23.482342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:16.542950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:20.949259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:24.432743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:28.086802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:32.621292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:36.496318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:40.181736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:44.577934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:47.805897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:51.247595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:55.275864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:59.153825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:03.633057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:07.404006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:11.544520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:15.172054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:18.839425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:23.663997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:16.932960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:21.117197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:24.620609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:28.254678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:32.836537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:36.739220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:40.343912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:44.743262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:47.947319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:51.428497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:55.513514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:59.359769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:03.836504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:07.603310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:11.697896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:15.421682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:19.079424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:23.837891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:17.201561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:21.285150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:24.821757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:28.489722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:33.116136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:36.984015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:40.544517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:44.934304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:48.145159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:51.649759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:55.714326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:59.561120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:04.024516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:08.470776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:11.908001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:15.722591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:19.797343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:24.032550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:17.389428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:21.536746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:25.043445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:28.723960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:33.359492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:37.163877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:40.735041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:45.132667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:48.371064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:51.834944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:55.975606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:40:59.775668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:04.233559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:08.731210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:12.088916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:15.945664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:41:20.039197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T14:41:38.590233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9910.8760.8760.8221.0000.8750.8750.9910.9911.0001.0000.9960.9960.9960.9960.9820.996
20070.9911.0001.0001.0000.9071.0001.0001.0001.0001.0001.0001.0000.9960.9960.9960.9960.9820.996
20080.8761.0001.0001.0000.9760.9900.9901.0001.0001.0001.0001.0000.9960.9960.9960.9960.9820.996
20090.8761.0001.0001.0000.9760.9900.9901.0001.0001.0001.0001.0000.9960.9960.9960.9960.9820.996
20100.8220.9070.9760.9761.0000.9760.9960.9760.9070.9070.8420.8420.8020.8020.8020.8020.7630.802
20111.0001.0000.9900.9900.9761.0000.9900.9901.0001.0001.0001.0000.9960.9960.9960.9960.9820.996
20120.8751.0000.9900.9900.9960.9901.0000.9901.0001.0001.0001.0000.8620.8620.8620.8620.8030.862
20130.8751.0001.0001.0000.9760.9900.9901.0001.0001.0001.0001.0000.9960.9960.9960.9960.9820.996
20140.9911.0001.0001.0000.9071.0001.0001.0001.0001.0001.0001.0000.9960.9960.9960.9960.9820.996
20150.9911.0001.0001.0000.9071.0001.0001.0001.0001.0001.0001.0000.9960.9960.9960.9960.9820.996
20161.0001.0001.0001.0000.8421.0001.0001.0001.0001.0001.0000.9270.8970.8970.8640.8640.8410.864
20171.0001.0001.0001.0000.8421.0001.0001.0001.0001.0000.9271.0000.9930.9930.9980.9980.9940.998
20180.9960.9960.9960.9960.8020.9960.8620.9960.9960.9960.8970.9931.0001.0000.9930.9930.9900.993
20190.9960.9960.9960.9960.8020.9960.8620.9960.9960.9960.8970.9931.0001.0000.9930.9930.9900.993
20200.9960.9960.9960.9960.8020.9960.8620.9960.9960.9960.8640.9980.9930.9931.0001.0000.9991.000
20210.9960.9960.9960.9960.8020.9960.8620.9960.9960.9960.8640.9980.9930.9931.0001.0000.9991.000
20220.9820.9820.9820.9820.7630.9820.8030.9820.9820.9820.8410.9940.9900.9900.9990.9991.0000.999
20230.9960.9960.9960.9960.8020.9960.8620.9960.9960.9960.8640.9980.9930.9931.0001.0000.9991.000
2024-03-23T14:41:38.981918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.8880.8670.8500.8370.8310.8470.8450.8400.8540.9430.9450.9420.9420.9440.9440.9420.939
20070.8881.0000.9270.9050.8900.8810.8910.8820.8860.8940.9560.9550.9550.9550.9530.9580.9530.953
20080.8670.9271.0000.9280.9040.8940.9030.8910.8910.8970.9560.9610.9550.9570.9540.9580.9520.957
20090.8500.9050.9281.0000.9210.9010.8990.8940.8930.8980.9520.9620.9550.9550.9530.9530.9510.953
20100.8370.8900.9040.9211.0000.9170.9050.8940.8770.8880.9480.9520.9470.9440.9440.9510.9490.950
20110.8310.8810.8940.9010.9171.0000.9190.9040.8970.8920.9520.9530.9500.9530.9500.9510.9480.952
20120.8470.8910.9030.8990.9050.9191.0000.9290.9160.9130.9580.9600.9550.9600.9530.9570.9510.955
20130.8450.8820.8910.8940.8940.9040.9291.0000.9220.9250.9540.9570.9530.9550.9460.9510.9450.954
20140.8400.8860.8910.8930.8770.8970.9160.9221.0000.9340.9480.9590.9540.9580.9550.9550.9500.954
20150.8540.8940.8970.8980.8880.8920.9130.9250.9341.0000.9550.9660.9600.9610.9590.9610.9520.957
20160.9430.9560.9560.9520.9480.9520.9580.9540.9480.9551.0000.9450.9260.9170.8960.8850.8470.876
20170.9450.9550.9610.9620.9520.9530.9600.9570.9590.9660.9451.0000.9540.9390.9140.8920.8590.886
20180.9420.9550.9550.9550.9470.9500.9550.9530.9540.9600.9260.9541.0000.9540.9250.9080.8820.887
20190.9420.9550.9570.9550.9440.9530.9600.9550.9580.9610.9170.9390.9541.0000.9420.9090.8830.901
20200.9440.9530.9540.9530.9440.9500.9530.9460.9550.9590.8960.9140.9250.9421.0000.9300.8950.904
20210.9440.9580.9580.9530.9510.9510.9570.9510.9550.9610.8850.8920.9080.9090.9301.0000.9350.927
20220.9420.9530.9520.9510.9490.9480.9510.9450.9500.9520.8470.8590.8820.8830.8950.9351.0000.929
20230.9390.9530.9570.9530.9500.9520.9550.9540.9540.9570.8760.8860.8870.9010.9040.9270.9291.000

Missing values

2024-03-23T14:41:24.428470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T14:41:24.938948image/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.
2024-03-23T14:41:25.483041image/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

지역 및 거래원인200620072008200920102011201220132014201520162017201820192020202120222023
0전국 /매매83884651376718770338661478239061818713958698310299389266791517065765198102420786343942342987
1전국 /판결359469385339460512365789326323942333447446733431406452
2전국 /교환9964606466646051179689756817698889492
3전국 /증여5638550646555089509849694927466759986450685270488473826211058954269835103
4전국 /분양권21452222971843317206179881620616443199572861133507<NA><NA><NA><NA><NA><NA><NA><NA>
5전국 /분양권전매<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>13501140291021381649621509425674125
6전국 /기타11051437224111672141210761928147013381622<NA><NA><NA><NA><NA><NA><NA><NA>
7전국 /기타소유권이전<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2187139761465334121334287296002233318510
8서울 /매매20353117191099910778707891496499878512034184541742314948138261004313281908538114448
9서울 /판결1042521137667167801998287157951351121498211695
지역 및 거래원인200620072008200920102011201220132014201520162017201820192020202120222023
2382제주 제주시/기타720434118541028<NA><NA><NA><NA><NA><NA><NA><NA>
2383제주 제주시/기타소유권이전<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2104172641337212818691
2384제주 서귀포시/매매53107121122135174165197229350338277249168242312227184
2385제주 서귀포시/판결011001111104323242
2386제주 서귀포시/교환000000010011000010
2387제주 서귀포시/증여82017191921192834505159565348454240
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