Overview

Dataset statistics

Number of variables19
Number of observations2392
Missing cells13027
Missing cells (%)28.7%
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 아파트 거래현황의 연도별 거래원인별(면적) 데이터입니다.-(단위 : 천㎡)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068095/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 742 (31.0%) missing valuesMissing
2007 has 772 (32.3%) missing valuesMissing
2008 has 754 (31.5%) 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 688 (28.8%) missing valuesMissing
2015 has 700 (29.3%) missing valuesMissing
2016 has 707 (29.6%) missing valuesMissing
2017 has 724 (30.3%) missing valuesMissing
2018 has 718 (30.0%) missing valuesMissing
2019 has 724 (30.3%) missing valuesMissing
2020 has 724 (30.3%) 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 = 27.44227709)Skewed
2007 is highly skewed (γ1 = 26.10854031)Skewed
2008 is highly skewed (γ1 = 29.21862882)Skewed
2009 is highly skewed (γ1 = 30.27203945)Skewed
2010 is highly skewed (γ1 = 30.00542596)Skewed
2011 is highly skewed (γ1 = 32.17196155)Skewed
2012 is highly skewed (γ1 = 30.13044167)Skewed
2013 is highly skewed (γ1 = 29.81041674)Skewed
2014 is highly skewed (γ1 = 28.65552346)Skewed
2015 is highly skewed (γ1 = 28.28150539)Skewed
2016 is highly skewed (γ1 = 28.27975915)Skewed
2017 is highly skewed (γ1 = 23.25342845)Skewed
2018 is highly skewed (γ1 = 22.89632303)Skewed
2019 is highly skewed (γ1 = 23.36201654)Skewed
2020 is highly skewed (γ1 = 27.69043074)Skewed
2021 is highly skewed (γ1 = 26.58741539)Skewed
2022 is highly skewed (γ1 = 24.30751292)Skewed
2023 is highly skewed (γ1 = 26.51916759)Skewed
지역_거래원인 has unique valuesUnique
2006 has 611 (25.5%) zerosZeros
2007 has 648 (27.1%) zerosZeros
2008 has 641 (26.8%) zerosZeros
2009 has 672 (28.1%) zerosZeros
2010 has 733 (30.6%) zerosZeros
2011 has 729 (30.5%) zerosZeros
2012 has 686 (28.7%) zerosZeros
2013 has 685 (28.6%) zerosZeros
2014 has 671 (28.1%) zerosZeros
2015 has 647 (27.0%) zerosZeros
2016 has 569 (23.8%) zerosZeros
2017 has 553 (23.1%) zerosZeros
2018 has 527 (22.0%) zerosZeros
2019 has 539 (22.5%) zerosZeros
2020 has 521 (21.8%) zerosZeros
2021 has 521 (21.8%) zerosZeros
2022 has 544 (22.7%) zerosZeros
2023 has 503 (21.0%) zerosZeros

Reproduction

Analysis started2024-03-23 05:50:52.312249
Analysis finished2024-03-23 05:51:45.785666
Duration53.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역_거래원인
Text

UNIQUE 

Distinct2392
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
2024-03-23T14:51:46.032785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length10.526338
Min length5

Characters and Unicode

Total characters25179
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 416
 
8.4%
경남 208
 
4.2%
경북 200
 
4.1%
서울 200
 
4.1%
전남 176
 
3.6%
충북 152
 
3.1%
충남 152
 
3.1%
강원 144
 
2.9%
부산 128
 
2.6%
전북 128
 
2.6%
Other values (2205) 3024
61.4%
2024-03-23T14:51:46.509452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2536
 
10.1%
_ 2392
 
9.5%
1128
 
4.5%
1046
 
4.2%
998
 
4.0%
976
 
3.9%
905
 
3.6%
897
 
3.6%
872
 
3.5%
790
 
3.1%
Other values (144) 12639
50.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19947
79.2%
Space Separator 2536
 
10.1%
Connector Punctuation 2392
 
9.5%
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 (%)
2536
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2392
100.0%
Close Punctuation
ValueCountFrequency (%)
) 152
100.0%
Open Punctuation
ValueCountFrequency (%)
( 152
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19947
79.2%
Common 5232
 
20.8%

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 (%)
2536
48.5%
_ 2392
45.7%
) 152
 
2.9%
( 152
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 19947
79.2%
ASCII 5232
 
20.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2536
48.5%
_ 2392
45.7%
) 152
 
2.9%
( 152
 
2.9%
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 

Distinct327
Distinct (%)19.8%
Missing742
Missing (%)31.0%
Infinite0
Infinite (%)0.0%
Mean161.05273
Minimum0
Maximum52234
Zeros611
Zeros (%)25.5%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:51:46.669080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q340
95-th percentile532.1
Maximum52234
Range52234
Interquartile range (IQR)40

Descriptive statistics

Standard deviation1518.5648
Coefficient of variation (CV)9.4289912
Kurtosis874.95183
Mean161.05273
Median Absolute Deviation (MAD)4
Skewness27.442277
Sum265737
Variance2306038.9
MonotonicityNot monotonic
2024-03-23T14:51:46.870157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 611
25.5%
1 128
 
5.4%
2 44
 
1.8%
4 38
 
1.6%
3 31
 
1.3%
9 25
 
1.0%
10 23
 
1.0%
11 22
 
0.9%
7 21
 
0.9%
6 21
 
0.9%
Other values (317) 686
28.7%
(Missing) 742
31.0%
ValueCountFrequency (%)
0 611
25.5%
1 128
 
5.4%
2 44
 
1.8%
3 31
 
1.3%
4 38
 
1.6%
5 18
 
0.8%
6 21
 
0.9%
7 21
 
0.9%
8 20
 
0.8%
9 25
 
1.0%
ValueCountFrequency (%)
52234 1
< 0.1%
21138 1
< 0.1%
18292 1
< 0.1%
10634 1
< 0.1%
7877 1
< 0.1%
5048 1
< 0.1%
3336 1
< 0.1%
2949 1
< 0.1%
2540 1
< 0.1%
2346 1
< 0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct291
Distinct (%)18.0%
Missing772
Missing (%)32.3%
Infinite0
Infinite (%)0.0%
Mean126.14136
Minimum0
Maximum37319
Zeros648
Zeros (%)27.1%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:51:47.072610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q328
95-th percentile390.1
Maximum37319
Range37319
Interquartile range (IQR)28

Descriptive statistics

Standard deviation1142.4379
Coefficient of variation (CV)9.0568064
Kurtosis777.86222
Mean126.14136
Median Absolute Deviation (MAD)1.5
Skewness26.10854
Sum204349
Variance1305164.3
MonotonicityNot monotonic
2024-03-23T14:51:47.220148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 648
27.1%
1 162
 
6.8%
2 60
 
2.5%
3 45
 
1.9%
4 33
 
1.4%
10 27
 
1.1%
6 26
 
1.1%
5 24
 
1.0%
7 22
 
0.9%
8 16
 
0.7%
Other values (281) 557
23.3%
(Missing) 772
32.3%
ValueCountFrequency (%)
0 648
27.1%
1 162
 
6.8%
2 60
 
2.5%
3 45
 
1.9%
4 33
 
1.4%
5 24
 
1.0%
6 26
 
1.1%
7 22
 
0.9%
8 16
 
0.7%
9 9
 
0.4%
ValueCountFrequency (%)
37319 1
< 0.1%
21861 1
< 0.1%
9385 1
< 0.1%
4561 1
< 0.1%
4105 1
< 0.1%
3973 1
< 0.1%
3848 1
< 0.1%
3773 1
< 0.1%
3235 1
< 0.1%
2872 1
< 0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct294
Distinct (%)17.9%
Missing754
Missing (%)31.5%
Infinite0
Infinite (%)0.0%
Mean119.8254
Minimum0
Maximum40939
Zeros641
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:51:47.381386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q324
95-th percentile398.3
Maximum40939
Range40939
Interquartile range (IQR)24

Descriptive statistics

Standard deviation1160.5268
Coefficient of variation (CV)9.6851488
Kurtosis971.61743
Mean119.8254
Median Absolute Deviation (MAD)1
Skewness29.218629
Sum196274
Variance1346822.4
MonotonicityNot monotonic
2024-03-23T14:51:47.538581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 641
26.8%
1 195
 
8.2%
2 57
 
2.4%
3 53
 
2.2%
4 36
 
1.5%
8 23
 
1.0%
7 23
 
1.0%
5 23
 
1.0%
11 22
 
0.9%
9 16
 
0.7%
Other values (284) 549
23.0%
(Missing) 754
31.5%
ValueCountFrequency (%)
0 641
26.8%
1 195
 
8.2%
2 57
 
2.4%
3 53
 
2.2%
4 36
 
1.5%
5 23
 
1.0%
6 16
 
0.7%
7 23
 
1.0%
8 23
 
1.0%
9 16
 
0.7%
ValueCountFrequency (%)
40939 1
< 0.1%
17891 1
< 0.1%
9050 1
< 0.1%
4609 1
< 0.1%
4157 1
< 0.1%
3697 1
< 0.1%
3419 1
< 0.1%
2688 1
< 0.1%
2267 1
< 0.1%
2231 1
< 0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct296
Distinct (%)18.0%
Missing748
Missing (%)31.3%
Infinite0
Infinite (%)0.0%
Mean132.49696
Minimum0
Maximum47759
Zeros672
Zeros (%)28.1%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:51:47.736512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q326
95-th percentile451.95
Maximum47759
Range47759
Interquartile range (IQR)26

Descriptive statistics

Standard deviation1321.7976
Coefficient of variation (CV)9.97606
Kurtosis1045.924
Mean132.49696
Median Absolute Deviation (MAD)1
Skewness30.272039
Sum217825
Variance1747148.9
MonotonicityNot monotonic
2024-03-23T14:51:48.015148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 672
28.1%
1 171
 
7.1%
2 72
 
3.0%
3 42
 
1.8%
4 32
 
1.3%
9 28
 
1.2%
7 24
 
1.0%
5 24
 
1.0%
6 18
 
0.8%
11 15
 
0.6%
Other values (286) 546
22.8%
(Missing) 748
31.3%
ValueCountFrequency (%)
0 672
28.1%
1 171
 
7.1%
2 72
 
3.0%
3 42
 
1.8%
4 32
 
1.3%
5 24
 
1.0%
6 18
 
0.8%
7 24
 
1.0%
8 11
 
0.5%
9 28
 
1.2%
ValueCountFrequency (%)
47759 1
< 0.1%
16863 1
< 0.1%
11166 1
< 0.1%
6174 1
< 0.1%
5961 1
< 0.1%
4536 1
< 0.1%
3585 1
< 0.1%
2790 1
< 0.1%
2637 1
< 0.1%
2338 1
< 0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct293
Distinct (%)17.4%
Missing712
Missing (%)29.8%
Infinite0
Infinite (%)0.0%
Mean122.10417
Minimum0
Maximum44034
Zeros733
Zeros (%)30.6%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:51:48.199146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q325
95-th percentile375.15
Maximum44034
Range44034
Interquartile range (IQR)25

Descriptive statistics

Standard deviation1219.1785
Coefficient of variation (CV)9.9847409
Kurtosis1029.849
Mean122.10417
Median Absolute Deviation (MAD)1
Skewness30.005426
Sum205135
Variance1486396.1
MonotonicityNot monotonic
2024-03-23T14:51:48.386233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 733
30.6%
1 186
 
7.8%
2 47
 
2.0%
3 44
 
1.8%
4 22
 
0.9%
5 22
 
0.9%
11 20
 
0.8%
10 20
 
0.8%
7 18
 
0.8%
13 16
 
0.7%
Other values (283) 552
23.1%
(Missing) 712
29.8%
ValueCountFrequency (%)
0 733
30.6%
1 186
 
7.8%
2 47
 
2.0%
3 44
 
1.8%
4 22
 
0.9%
5 22
 
0.9%
6 16
 
0.7%
7 18
 
0.8%
8 15
 
0.6%
9 16
 
0.7%
ValueCountFrequency (%)
44034 1
< 0.1%
17515 1
< 0.1%
7868 1
< 0.1%
7509 1
< 0.1%
5276 1
< 0.1%
4592 1
< 0.1%
3674 1
< 0.1%
2694 1
< 0.1%
2534 1
< 0.1%
2509 1
< 0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct301
Distinct (%)18.1%
Missing730
Missing (%)30.5%
Infinite0
Infinite (%)0.0%
Mean138.20758
Minimum0
Maximum53545
Zeros729
Zeros (%)30.5%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:51:48.566936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q321
95-th percentile424
Maximum53545
Range53545
Interquartile range (IQR)21

Descriptive statistics

Standard deviation1435.8804
Coefficient of variation (CV)10.389303
Kurtosis1164.0554
Mean138.20758
Median Absolute Deviation (MAD)1
Skewness32.171962
Sum229701
Variance2061752.5
MonotonicityNot monotonic
2024-03-23T14:51:48.779888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 729
30.5%
1 158
 
6.6%
2 73
 
3.1%
3 40
 
1.7%
4 33
 
1.4%
6 28
 
1.2%
10 26
 
1.1%
7 19
 
0.8%
5 19
 
0.8%
8 15
 
0.6%
Other values (291) 522
21.8%
(Missing) 730
30.5%
ValueCountFrequency (%)
0 729
30.5%
1 158
 
6.6%
2 73
 
3.1%
3 40
 
1.7%
4 33
 
1.4%
5 19
 
0.8%
6 28
 
1.2%
7 19
 
0.8%
8 15
 
0.6%
9 15
 
0.6%
ValueCountFrequency (%)
53545 1
< 0.1%
15482 1
< 0.1%
11641 1
< 0.1%
4882 1
< 0.1%
4495 1
< 0.1%
4341 1
< 0.1%
4211 1
< 0.1%
3464 1
< 0.1%
3374 1
< 0.1%
3196 1
< 0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct274
Distinct (%)16.4%
Missing718
Missing (%)30.0%
Infinite0
Infinite (%)0.0%
Mean108.38232
Minimum0
Maximum38170
Zeros686
Zeros (%)28.7%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:51:48.981198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q322
95-th percentile347.1
Maximum38170
Range38170
Interquartile range (IQR)22

Descriptive statistics

Standard deviation1057.366
Coefficient of variation (CV)9.7558901
Kurtosis1033.3078
Mean108.38232
Median Absolute Deviation (MAD)1
Skewness30.130442
Sum181432
Variance1118022.8
MonotonicityNot monotonic
2024-03-23T14:51:49.246212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 686
28.7%
1 187
 
7.8%
2 75
 
3.1%
3 46
 
1.9%
5 34
 
1.4%
6 28
 
1.2%
7 24
 
1.0%
4 23
 
1.0%
8 22
 
0.9%
11 17
 
0.7%
Other values (264) 532
22.2%
(Missing) 718
30.0%
ValueCountFrequency (%)
0 686
28.7%
1 187
 
7.8%
2 75
 
3.1%
3 46
 
1.9%
4 23
 
1.0%
5 34
 
1.4%
6 28
 
1.2%
7 24
 
1.0%
8 22
 
0.9%
9 13
 
0.5%
ValueCountFrequency (%)
38170 1
< 0.1%
15503 1
< 0.1%
7989 1
< 0.1%
3697 1
< 0.1%
3504 1
< 0.1%
3437 1
< 0.1%
2889 1
< 0.1%
2642 1
< 0.1%
2500 1
< 0.1%
2393 1
< 0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct294
Distinct (%)17.6%
Missing718
Missing (%)30.0%
Infinite0
Infinite (%)0.0%
Mean129.02808
Minimum0
Maximum45854
Zeros685
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:51:49.438130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q319
95-th percentile441.35
Maximum45854
Range45854
Interquartile range (IQR)19

Descriptive statistics

Standard deviation1277.4853
Coefficient of variation (CV)9.9008317
Kurtosis1012.0594
Mean129.02808
Median Absolute Deviation (MAD)1
Skewness29.810417
Sum215993
Variance1631968.6
MonotonicityNot monotonic
2024-03-23T14:51:49.626423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 685
28.6%
1 184
 
7.7%
2 72
 
3.0%
4 43
 
1.8%
3 41
 
1.7%
5 33
 
1.4%
9 26
 
1.1%
10 25
 
1.0%
6 20
 
0.8%
12 19
 
0.8%
Other values (284) 526
22.0%
(Missing) 718
30.0%
ValueCountFrequency (%)
0 685
28.6%
1 184
 
7.7%
2 72
 
3.0%
3 41
 
1.7%
4 43
 
1.8%
5 33
 
1.4%
6 20
 
0.8%
7 19
 
0.8%
8 16
 
0.7%
9 26
 
1.1%
ValueCountFrequency (%)
45854 1
< 0.1%
18537 1
< 0.1%
11889 1
< 0.1%
5306 1
< 0.1%
3530 1
< 0.1%
3416 1
< 0.1%
3332 1
< 0.1%
3285 1
< 0.1%
2757 1
< 0.1%
2535 1
< 0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct320
Distinct (%)18.8%
Missing688
Missing (%)28.8%
Infinite0
Infinite (%)0.0%
Mean158.99824
Minimum0
Maximum55081
Zeros671
Zeros (%)28.1%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:51:49.787929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q328
95-th percentile503.9
Maximum55081
Range55081
Interquartile range (IQR)28

Descriptive statistics

Standard deviation1570.2664
Coefficient of variation (CV)9.8759987
Kurtosis933.29855
Mean158.99824
Median Absolute Deviation (MAD)2
Skewness28.655523
Sum270933
Variance2465736.6
MonotonicityNot monotonic
2024-03-23T14:51:50.013636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 671
28.1%
1 180
 
7.5%
2 84
 
3.5%
3 46
 
1.9%
4 35
 
1.5%
6 28
 
1.2%
5 25
 
1.0%
7 21
 
0.9%
11 21
 
0.9%
8 19
 
0.8%
Other values (310) 574
24.0%
(Missing) 688
28.8%
ValueCountFrequency (%)
0 671
28.1%
1 180
 
7.5%
2 84
 
3.5%
3 46
 
1.9%
4 35
 
1.5%
5 25
 
1.0%
6 28
 
1.2%
7 21
 
0.9%
8 19
 
0.8%
9 12
 
0.5%
ValueCountFrequency (%)
55081 1
< 0.1%
26912 1
< 0.1%
14747 1
< 0.1%
7277 1
< 0.1%
4537 1
< 0.1%
4032 1
< 0.1%
3824 1
< 0.1%
3693 1
< 0.1%
3403 1
< 0.1%
3126 1
< 0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct339
Distinct (%)20.0%
Missing700
Missing (%)29.3%
Infinite0
Infinite (%)0.0%
Mean176.06619
Minimum0
Maximum60994
Zeros647
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:51:50.263486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q331
95-th percentile555.7
Maximum60994
Range60994
Interquartile range (IQR)31

Descriptive statistics

Standard deviation1750.1662
Coefficient of variation (CV)9.9403873
Kurtosis913.46037
Mean176.06619
Median Absolute Deviation (MAD)2
Skewness28.281505
Sum297904
Variance3063081.6
MonotonicityNot monotonic
2024-03-23T14:51:50.499261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 647
27.0%
1 188
 
7.9%
2 77
 
3.2%
3 46
 
1.9%
4 35
 
1.5%
7 24
 
1.0%
5 23
 
1.0%
8 23
 
1.0%
6 22
 
0.9%
12 19
 
0.8%
Other values (329) 588
24.6%
(Missing) 700
29.3%
ValueCountFrequency (%)
0 647
27.0%
1 188
 
7.9%
2 77
 
3.2%
3 46
 
1.9%
4 35
 
1.5%
5 23
 
1.0%
6 22
 
0.9%
7 24
 
1.0%
8 23
 
1.0%
9 17
 
0.7%
ValueCountFrequency (%)
60994 1
< 0.1%
29179 1
< 0.1%
17351 1
< 0.1%
10306 1
< 0.1%
5627 1
< 0.1%
5498 1
< 0.1%
4009 1
< 0.1%
3828 1
< 0.1%
3158 1
< 0.1%
3003 1
< 0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct349
Distinct (%)20.7%
Missing707
Missing (%)29.6%
Infinite0
Infinite (%)0.0%
Mean162.10326
Minimum0
Maximum51701
Zeros569
Zeros (%)23.8%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:51:51.227632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q361
95-th percentile524.4
Maximum51701
Range51701
Interquartile range (IQR)61

Descriptive statistics

Standard deviation1461.9363
Coefficient of variation (CV)9.0185497
Kurtosis939.93854
Mean162.10326
Median Absolute Deviation (MAD)5
Skewness28.279759
Sum273144
Variance2137257.9
MonotonicityNot monotonic
2024-03-23T14:51:51.493206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 569
23.8%
1 130
 
5.4%
2 59
 
2.5%
3 45
 
1.9%
6 34
 
1.4%
5 28
 
1.2%
4 27
 
1.1%
7 21
 
0.9%
8 19
 
0.8%
9 19
 
0.8%
Other values (339) 734
30.7%
(Missing) 707
29.6%
ValueCountFrequency (%)
0 569
23.8%
1 130
 
5.4%
2 59
 
2.5%
3 45
 
1.9%
4 27
 
1.1%
5 28
 
1.2%
6 34
 
1.4%
7 21
 
0.9%
8 19
 
0.8%
9 19
 
0.8%
ValueCountFrequency (%)
51701 1
< 0.1%
17303 1
< 0.1%
14706 1
< 0.1%
13361 1
< 0.1%
9878 1
< 0.1%
5435 1
< 0.1%
4737 1
< 0.1%
3432 1
< 0.1%
3105 1
< 0.1%
2878 1
< 0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct379
Distinct (%)22.7%
Missing724
Missing (%)30.3%
Infinite0
Infinite (%)0.0%
Mean187.18825
Minimum0
Maximum46055
Zeros553
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:51:51.787054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q380
95-th percentile582.15
Maximum46055
Range46055
Interquartile range (IQR)80

Descriptive statistics

Standard deviation1552.4995
Coefficient of variation (CV)8.2937871
Kurtosis613.2993
Mean187.18825
Median Absolute Deviation (MAD)6
Skewness23.253428
Sum312230
Variance2410254.6
MonotonicityNot monotonic
2024-03-23T14:51:51.962070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 553
23.1%
1 126
 
5.3%
2 52
 
2.2%
3 40
 
1.7%
4 28
 
1.2%
7 27
 
1.1%
5 25
 
1.0%
8 21
 
0.9%
9 19
 
0.8%
6 18
 
0.8%
Other values (369) 759
31.7%
(Missing) 724
30.3%
ValueCountFrequency (%)
0 553
23.1%
1 126
 
5.3%
2 52
 
2.2%
3 40
 
1.7%
4 28
 
1.2%
5 25
 
1.0%
6 18
 
0.8%
7 27
 
1.1%
8 21
 
0.9%
9 19
 
0.8%
ValueCountFrequency (%)
46055 1
< 0.1%
34824 1
< 0.1%
13839 1
< 0.1%
12885 1
< 0.1%
11236 1
< 0.1%
8570 1
< 0.1%
4474 1
< 0.1%
3172 1
< 0.1%
3143 1
< 0.1%
3007 1
< 0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct362
Distinct (%)21.6%
Missing718
Missing (%)30.0%
Infinite0
Infinite (%)0.0%
Mean191.99462
Minimum0
Maximum43216
Zeros527
Zeros (%)22.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:51:52.136044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q376
95-th percentile541.3
Maximum43216
Range43216
Interquartile range (IQR)76

Descriptive statistics

Standard deviation1632.5902
Coefficient of variation (CV)8.5033122
Kurtosis580.2544
Mean191.99462
Median Absolute Deviation (MAD)6
Skewness22.896323
Sum321399
Variance2665350.9
MonotonicityNot monotonic
2024-03-23T14:51:52.357635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 527
22.0%
1 155
 
6.5%
2 48
 
2.0%
3 38
 
1.6%
4 34
 
1.4%
7 28
 
1.2%
5 24
 
1.0%
10 20
 
0.8%
9 18
 
0.8%
11 16
 
0.7%
Other values (352) 766
32.0%
(Missing) 718
30.0%
ValueCountFrequency (%)
0 527
22.0%
1 155
 
6.5%
2 48
 
2.0%
3 38
 
1.6%
4 34
 
1.4%
5 24
 
1.0%
6 14
 
0.6%
7 28
 
1.2%
8 10
 
0.4%
9 18
 
0.8%
ValueCountFrequency (%)
43216 1
< 0.1%
43032 1
< 0.1%
16410 1
< 0.1%
13340 1
< 0.1%
10108 1
< 0.1%
7701 1
< 0.1%
3861 1
< 0.1%
3766 1
< 0.1%
3196 1
< 0.1%
3055 1
< 0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct348
Distinct (%)20.9%
Missing724
Missing (%)30.3%
Infinite0
Infinite (%)0.0%
Mean176.39568
Minimum0
Maximum41389
Zeros539
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:51:52.599743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q367.25
95-th percentile514.25
Maximum41389
Range41389
Interquartile range (IQR)67.25

Descriptive statistics

Standard deviation1505.3685
Coefficient of variation (CV)8.5340436
Kurtosis602.10789
Mean176.39568
Median Absolute Deviation (MAD)6
Skewness23.362017
Sum294228
Variance2266134.2
MonotonicityNot monotonic
2024-03-23T14:51:52.848079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 539
22.5%
1 148
 
6.2%
2 50
 
2.1%
3 42
 
1.8%
7 29
 
1.2%
4 28
 
1.2%
6 26
 
1.1%
5 24
 
1.0%
11 19
 
0.8%
9 17
 
0.7%
Other values (338) 746
31.2%
(Missing) 724
30.3%
ValueCountFrequency (%)
0 539
22.5%
1 148
 
6.2%
2 50
 
2.1%
3 42
 
1.8%
4 28
 
1.2%
5 24
 
1.0%
6 26
 
1.1%
7 29
 
1.2%
8 15
 
0.6%
9 17
 
0.7%
ValueCountFrequency (%)
41389 1
< 0.1%
38739 1
< 0.1%
14761 1
< 0.1%
11182 1
< 0.1%
8074 1
< 0.1%
5537 1
< 0.1%
3737 1
< 0.1%
3416 1
< 0.1%
3072 1
< 0.1%
3057 1
< 0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct381
Distinct (%)22.8%
Missing724
Missing (%)30.3%
Infinite0
Infinite (%)0.0%
Mean226.80156
Minimum0
Maximum71196
Zeros521
Zeros (%)21.8%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:51:52.999972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q380
95-th percentile714.3
Maximum71196
Range71196
Interquartile range (IQR)80

Descriptive statistics

Standard deviation2060.0922
Coefficient of variation (CV)9.0832367
Kurtosis887.47813
Mean226.80156
Median Absolute Deviation (MAD)7
Skewness27.690431
Sum378305
Variance4243980
MonotonicityNot monotonic
2024-03-23T14:51:53.184287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 521
21.8%
1 153
 
6.4%
2 61
 
2.6%
3 39
 
1.6%
8 28
 
1.2%
4 24
 
1.0%
5 21
 
0.9%
11 20
 
0.8%
7 16
 
0.7%
12 15
 
0.6%
Other values (371) 770
32.2%
(Missing) 724
30.3%
ValueCountFrequency (%)
0 521
21.8%
1 153
 
6.4%
2 61
 
2.6%
3 39
 
1.6%
4 24
 
1.0%
5 21
 
0.9%
6 13
 
0.5%
7 16
 
0.7%
8 28
 
1.2%
9 12
 
0.5%
ValueCountFrequency (%)
71196 1
< 0.1%
31896 1
< 0.1%
22467 1
< 0.1%
10038 1
< 0.1%
9531 1
< 0.1%
7107 1
< 0.1%
6322 1
< 0.1%
5383 1
< 0.1%
4654 1
< 0.1%
4337 1
< 0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct334
Distinct (%)20.0%
Missing718
Missing (%)30.0%
Infinite0
Infinite (%)0.0%
Mean162.02688
Minimum0
Maximum47855
Zeros521
Zeros (%)21.8%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:51:53.362942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q355
95-th percentile503.7
Maximum47855
Range47855
Interquartile range (IQR)55

Descriptive statistics

Standard deviation1436.8431
Coefficient of variation (CV)8.8679306
Kurtosis806.75403
Mean162.02688
Median Absolute Deviation (MAD)6
Skewness26.587415
Sum271233
Variance2064518.2
MonotonicityNot monotonic
2024-03-23T14:51:53.573274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 521
21.8%
1 161
 
6.7%
2 59
 
2.5%
3 35
 
1.5%
4 30
 
1.3%
7 27
 
1.1%
5 23
 
1.0%
11 23
 
1.0%
6 22
 
0.9%
8 21
 
0.9%
Other values (324) 752
31.4%
(Missing) 718
30.0%
ValueCountFrequency (%)
0 521
21.8%
1 161
 
6.7%
2 59
 
2.5%
3 35
 
1.5%
4 30
 
1.3%
5 23
 
1.0%
6 22
 
0.9%
7 27
 
1.1%
8 21
 
0.9%
9 13
 
0.5%
ValueCountFrequency (%)
47855 1
< 0.1%
27318 1
< 0.1%
13312 1
< 0.1%
8166 1
< 0.1%
5028 1
< 0.1%
4418 1
< 0.1%
4296 1
< 0.1%
3684 1
< 0.1%
3234 1
< 0.1%
3194 1
< 0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct263
Distinct (%)15.7%
Missing718
Missing (%)30.0%
Infinite0
Infinite (%)0.0%
Mean88.902628
Minimum0
Maximum20611
Zeros544
Zeros (%)22.7%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:51:53.938190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q328
95-th percentile279.4
Maximum20611
Range20611
Interquartile range (IQR)28

Descriptive statistics

Standard deviation754.24335
Coefficient of variation (CV)8.4839263
Kurtosis646.30747
Mean88.902628
Median Absolute Deviation (MAD)3
Skewness24.307513
Sum148823
Variance568883.03
MonotonicityNot monotonic
2024-03-23T14:51:54.230194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 544
22.7%
1 177
 
7.4%
2 78
 
3.3%
3 46
 
1.9%
6 39
 
1.6%
4 35
 
1.5%
5 32
 
1.3%
13 21
 
0.9%
22 19
 
0.8%
20 19
 
0.8%
Other values (253) 664
27.8%
(Missing) 718
30.0%
ValueCountFrequency (%)
0 544
22.7%
1 177
 
7.4%
2 78
 
3.3%
3 46
 
1.9%
4 35
 
1.5%
5 32
 
1.3%
6 39
 
1.6%
7 17
 
0.7%
8 19
 
0.8%
9 17
 
0.7%
ValueCountFrequency (%)
20611 1
< 0.1%
20429 1
< 0.1%
6132 1
< 0.1%
4092 1
< 0.1%
2844 1
< 0.1%
2528 1
< 0.1%
2295 2
0.1%
1551 1
< 0.1%
1436 1
< 0.1%
1350 1
< 0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct284
Distinct (%)16.9%
Missing712
Missing (%)29.8%
Infinite0
Infinite (%)0.0%
Mean103.16071
Minimum0
Maximum30280
Zeros503
Zeros (%)21.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:51:54.438627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q336
95-th percentile328.25
Maximum30280
Range30280
Interquartile range (IQR)36

Descriptive statistics

Standard deviation909.79189
Coefficient of variation (CV)8.8191701
Kurtosis804.17206
Mean103.16071
Median Absolute Deviation (MAD)3
Skewness26.519168
Sum173310
Variance827721.29
MonotonicityNot monotonic
2024-03-23T14:51:54.642835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 503
21.0%
1 194
 
8.1%
2 86
 
3.6%
3 58
 
2.4%
4 44
 
1.8%
6 33
 
1.4%
7 33
 
1.4%
10 25
 
1.0%
5 25
 
1.0%
11 22
 
0.9%
Other values (274) 657
27.5%
(Missing) 712
29.8%
ValueCountFrequency (%)
0 503
21.0%
1 194
 
8.1%
2 86
 
3.6%
3 58
 
2.4%
4 44
 
1.8%
5 25
 
1.0%
6 33
 
1.4%
7 33
 
1.4%
8 22
 
0.9%
9 21
 
0.9%
ValueCountFrequency (%)
30280 1
< 0.1%
17477 1
< 0.1%
7918 1
< 0.1%
5143 1
< 0.1%
4097 1
< 0.1%
2667 1
< 0.1%
2603 1
< 0.1%
2292 1
< 0.1%
2269 1
< 0.1%
2026 1
< 0.1%

Interactions

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2024-03-23T14:51:40.455961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:43.300582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:50:56.401325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:50:59.159678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:01.769792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:04.463008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:07.077807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:09.686003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:12.025058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:14.412925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:17.280371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:20.358889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:23.327370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:26.093277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:29.238425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:31.790498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:34.460633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:37.682831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:40.677484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:43.418513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:50:56.536876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:50:59.275231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:01.930759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:04.605331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:07.200873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:09.792715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:12.139800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:14.540584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:17.394850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:20.496289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:23.466238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:26.226854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:29.354407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:31.942308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:34.600138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:37.813737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:40.883174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:43.532864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:50:56.643108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:50:59.383961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:02.063896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:04.745658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:07.648215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:09.910849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:12.259983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:14.682143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:17.575190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:20.608210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:23.595492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:26.409327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:29.458086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:32.080375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:34.725175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:37.937588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:41.034818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:43.639327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:50:56.765401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:50:59.497006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:02.225949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:04.883526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:07.807646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:10.050866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:12.415823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:14.824210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:17.723682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:20.773101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:23.760631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:26.602663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:29.557925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:32.220514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:34.831139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:38.065466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:41.179538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:43.766370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:50:56.907171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:50:59.613447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:02.398196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:05.017595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:07.977072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:10.185290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:12.570887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:14.988990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:17.916595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:21.032173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:23.953517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:27.209346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:29.674853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:32.355179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:35.040630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:38.295859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:41.347258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:43.883565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:50:57.034466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:50:59.739122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:02.520145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:05.169428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:08.110703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:10.295480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:12.705021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:15.123794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:18.100847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:21.246025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:24.142248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:27.350507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:29.779964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:32.475014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:35.254825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:38.477743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:41.523018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:43.996754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:50:57.180827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:50:59.889495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:02.669972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:05.363703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:08.244521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:10.424250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:12.852555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:15.265728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:18.286275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:21.476845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:24.316676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:27.493579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:29.923660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:32.640896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:35.402573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:38.597170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:51:41.672352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T14:51:54.849033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9430.9500.9500.9581.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.7631.000
20070.9431.0000.9970.9970.8821.0001.0000.9950.9950.9951.0001.0001.0001.0001.0001.0000.7631.000
20080.9500.9971.0001.0000.8911.0001.0000.9980.9980.9981.0001.0001.0001.0001.0001.0000.7631.000
20090.9500.9971.0001.0000.8911.0001.0000.9980.9980.9981.0001.0001.0001.0001.0001.0000.7631.000
20100.9580.8820.8910.8911.0000.8320.9900.8750.8750.8750.7630.7630.7630.7630.9780.9780.9850.978
20111.0001.0001.0001.0000.8321.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9781.000
20121.0001.0001.0001.0000.9901.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9781.000
20131.0000.9950.9980.9980.8751.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.7631.000
20141.0000.9950.9980.9980.8751.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.7631.000
20151.0000.9950.9980.9980.8751.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.7631.000
20161.0001.0001.0001.0000.7631.0001.0001.0001.0001.0001.0000.9500.9790.9790.9910.9910.8260.991
20171.0001.0001.0001.0000.7631.0001.0001.0001.0001.0000.9501.0001.0000.9130.9320.9320.8940.932
20181.0001.0001.0001.0000.7631.0001.0001.0001.0001.0000.9791.0001.0000.9910.9690.9690.8110.969
20191.0001.0001.0001.0000.7631.0001.0001.0001.0001.0000.9790.9130.9911.0000.9860.9860.8820.986
20201.0001.0001.0001.0000.9781.0001.0001.0001.0001.0000.9910.9320.9690.9861.0001.0000.8111.000
20211.0001.0001.0001.0000.9781.0001.0001.0001.0001.0000.9910.9320.9690.9861.0001.0000.8111.000
20220.7630.7630.7630.7630.9850.9780.9780.7630.7630.7630.8260.8940.8110.8820.8110.8111.0000.811
20231.0001.0001.0001.0000.9781.0001.0001.0001.0001.0000.9910.9320.9690.9861.0001.0000.8111.000
2024-03-23T14:51:55.124820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.8780.8360.8260.8190.8060.8250.8100.8210.8270.9220.9280.9310.9280.9300.9270.9180.909
20070.8781.0000.9110.8830.8700.8550.8720.8540.8680.8700.9390.9400.9430.9420.9420.9370.9310.928
20080.8360.9111.0000.9080.8790.8670.8720.8600.8660.8630.9280.9320.9340.9310.9370.9310.9200.923
20090.8260.8830.9081.0000.9110.8880.8840.8720.8730.8740.9270.9290.9380.9340.9310.9310.9230.919
20100.8190.8700.8790.9111.0000.9080.8950.8830.8720.8740.9310.9400.9390.9380.9390.9340.9320.929
20110.8060.8550.8670.8880.9081.0000.9010.8890.8850.8680.9330.9410.9340.9330.9400.9350.9300.927
20120.8250.8720.8720.8840.8950.9011.0000.9170.8970.8990.9410.9440.9490.9450.9430.9420.9340.931
20130.8100.8540.8600.8720.8830.8890.9171.0000.9090.9060.9390.9410.9410.9400.9390.9350.9290.930
20140.8210.8680.8660.8730.8720.8850.8970.9091.0000.9230.9450.9510.9470.9510.9450.9440.9310.931
20150.8270.8700.8630.8740.8740.8680.8990.9060.9231.0000.9530.9540.9510.9490.9490.9450.9310.931
20160.9220.9390.9280.9270.9310.9330.9410.9390.9450.9531.0000.9320.9050.8960.8740.8590.8140.849
20170.9280.9400.9320.9290.9400.9410.9440.9410.9510.9540.9321.0000.9450.9300.9020.8800.8400.867
20180.9310.9430.9340.9380.9390.9340.9490.9410.9470.9510.9050.9451.0000.9510.9190.8940.8580.871
20190.9280.9420.9310.9340.9380.9330.9450.9400.9510.9490.8960.9300.9511.0000.9370.8980.8610.888
20200.9300.9420.9370.9310.9390.9400.9430.9390.9450.9490.8740.9020.9190.9371.0000.9260.8800.892
20210.9270.9370.9310.9310.9340.9350.9420.9350.9440.9450.8590.8800.8940.8980.9261.0000.9200.910
20220.9180.9310.9200.9230.9320.9300.9340.9290.9310.9310.8140.8400.8580.8610.8800.9201.0000.919
20230.9090.9280.9230.9190.9290.9270.9310.9300.9310.9310.8490.8670.8710.8880.8920.9100.9191.000

Missing values

2024-03-23T14:51:44.214759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T14:51:44.689602image/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.
2024-03-23T14:51:45.414240image/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_거래원인200620072008200920102011201220132014201520162017201820192020202120222023
0전국_매매522343731940939477594403453545381704585455081609945170146055430324138971196478552042930280
1전국_판결227316217186291370137460122121634129179156333209171236
2전국_교환2119302720182017143243625272938305856
3전국_증여205721561863231118341941172717262080227026682942386137375383441828442292
4전국_분양권21138218611789116863175151548215503185372691229179<NA><NA><NA><NA><NA><NA><NA><NA>
5전국_분양권전매<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>13361138391010880749531502825284097
6전국_기타78773848162811588605661170770406541<NA><NA><NA><NA><NA><NA><NA><NA>
7전국_기타소유권이전<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1730334824432163873931896273182061117477
8서울_매매10634456146096174367448823437530672771030698788570770155377107368410042667
9서울_판결70212543134127243722293428332933382856
지역_거래원인200620072008200920102011201220132014201520162017201820192020202120222023
2382제주 제주시_기타32112133216<NA><NA><NA><NA><NA><NA><NA><NA>
2383제주 제주시_기타소유권이전<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>40138763617625837
2384제주 서귀포시_매매123960363564466245797354524071905246
2385제주 서귀포시_판결000000000000000000
2386제주 서귀포시_교환000000000000000000
2387제주 서귀포시_증여012112222746432114
2388제주 서귀포시_분양권00000063754187<NA><NA><NA><NA><NA><NA><NA><NA>
2389제주 서귀포시_분양권전매<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>751710332364
2390제주 서귀포시_기타0010103100<NA><NA><NA><NA><NA><NA><NA><NA>
2391제주 서귀포시_기타소유권이전<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>11883463732312213