Overview

Dataset statistics

Number of variables19
Number of observations1196
Missing cells1512
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory198.7 KiB
Average record size in memory170.1 B

Variable types

Text1
Numeric18

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 부동산거래현황 중 아파트 거래현황의 연도별 매입자연령대별(면적) 데이터입니다.-(단위 : 천㎡)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068149/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 96 (8.0%) missing valuesMissing
2007 has 116 (9.7%) missing valuesMissing
2008 has 104 (8.7%) missing valuesMissing
2009 has 100 (8.4%) missing valuesMissing
2010 has 76 (6.4%) missing valuesMissing
2011 has 88 (7.4%) missing valuesMissing
2012 has 80 (6.7%) missing valuesMissing
2013 has 80 (6.7%) missing valuesMissing
2014 has 60 (5.0%) missing valuesMissing
2015 has 72 (6.0%) missing valuesMissing
2016 has 72 (6.0%) missing valuesMissing
2017 has 84 (7.0%) missing valuesMissing
2018 has 80 (6.7%) missing valuesMissing
2019 has 84 (7.0%) missing valuesMissing
2020 has 84 (7.0%) missing valuesMissing
2021 has 80 (6.7%) missing valuesMissing
2022 has 80 (6.7%) missing valuesMissing
2023 has 76 (6.4%) missing valuesMissing
2006 is highly skewed (γ1 = 21.2550788)Skewed
2007 is highly skewed (γ1 = 22.16265274)Skewed
2008 is highly skewed (γ1 = 21.88148077)Skewed
2009 is highly skewed (γ1 = 21.82577913)Skewed
2010 is highly skewed (γ1 = 22.17626305)Skewed
2011 is highly skewed (γ1 = 22.93917786)Skewed
2012 is highly skewed (γ1 = 23.30877951)Skewed
2013 is highly skewed (γ1 = 23.27757235)Skewed
2014 is highly skewed (γ1 = 24.10028908)Skewed
2015 is highly skewed (γ1 = 22.64044228)Skewed
2016 is highly skewed (γ1 = 22.17629865)Skewed
2017 is highly skewed (γ1 = 20.84573986)Skewed
2018 is highly skewed (γ1 = 20.5532306)Skewed
2019 is highly skewed (γ1 = 20.5899716)Skewed
2021 is highly skewed (γ1 = 20.09084199)Skewed
2022 is highly skewed (γ1 = 21.5727577)Skewed
2023 is highly skewed (γ1 = 21.27279927)Skewed
지역_매입자거주지 has unique valuesUnique
2006 has 141 (11.8%) zerosZeros
2007 has 129 (10.8%) zerosZeros
2008 has 124 (10.4%) zerosZeros
2009 has 129 (10.8%) zerosZeros
2010 has 134 (11.2%) zerosZeros
2011 has 123 (10.3%) zerosZeros
2012 has 137 (11.5%) zerosZeros
2013 has 132 (11.0%) zerosZeros
2014 has 117 (9.8%) zerosZeros
2015 has 108 (9.0%) zerosZeros
2016 has 109 (9.1%) zerosZeros
2017 has 94 (7.9%) zerosZeros
2018 has 101 (8.4%) zerosZeros
2019 has 111 (9.3%) zerosZeros
2020 has 102 (8.5%) zerosZeros
2021 has 102 (8.5%) zerosZeros
2022 has 115 (9.6%) zerosZeros
2023 has 128 (10.7%) zerosZeros

Reproduction

Analysis started2024-03-23 05:54:49.880449
Analysis finished2024-03-23 05:55:29.930849
Duration40.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct1196
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
2024-03-23T14:55:30.077830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length14.151338
Min length8

Characters and Unicode

Total characters16925
Distinct characters152
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1196 ?
Unique (%)100.0%

Sample

1st row전국_관할시군구내
2nd row전국_관할시도내
3rd row전국_관할시도외_서울
4th row전국_관할시도외_기타
5th row서울_관할시군구내
ValueCountFrequency (%)
경기 208
 
8.4%
경남 104
 
4.2%
경북 100
 
4.1%
서울 100
 
4.1%
전남 88
 
3.6%
충남 76
 
3.1%
충북 76
 
3.1%
강원 72
 
2.9%
전북 64
 
2.6%
부산 64
 
2.6%
Other values (1113) 1512
61.4%
2024-03-23T14:55:30.449924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 1794
 
10.6%
1684
 
9.9%
1268
 
7.5%
1200
 
7.1%
1196
 
7.1%
917
 
5.4%
863
 
5.1%
671
 
4.0%
598
 
3.5%
598
 
3.5%
Other values (142) 6136
36.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13711
81.0%
Connector Punctuation 1794
 
10.6%
Space Separator 1268
 
7.5%
Open Punctuation 76
 
0.4%
Close Punctuation 76
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1684
 
12.3%
1200
 
8.8%
1196
 
8.7%
917
 
6.7%
863
 
6.3%
671
 
4.9%
598
 
4.4%
598
 
4.4%
523
 
3.8%
471
 
3.4%
Other values (138) 4990
36.4%
Connector Punctuation
ValueCountFrequency (%)
_ 1794
100.0%
Space Separator
ValueCountFrequency (%)
1268
100.0%
Open Punctuation
ValueCountFrequency (%)
( 76
100.0%
Close Punctuation
ValueCountFrequency (%)
) 76
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13711
81.0%
Common 3214
 
19.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1684
 
12.3%
1200
 
8.8%
1196
 
8.7%
917
 
6.7%
863
 
6.3%
671
 
4.9%
598
 
4.4%
598
 
4.4%
523
 
3.8%
471
 
3.4%
Other values (138) 4990
36.4%
Common
ValueCountFrequency (%)
_ 1794
55.8%
1268
39.5%
( 76
 
2.4%
) 76
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13711
81.0%
ASCII 3214
 
19.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 1794
55.8%
1268
39.5%
( 76
 
2.4%
) 76
 
2.4%
Hangul
ValueCountFrequency (%)
1684
 
12.3%
1200
 
8.8%
1196
 
8.7%
917
 
6.7%
863
 
6.3%
671
 
4.9%
598
 
4.4%
598
 
4.4%
523
 
3.8%
471
 
3.4%
Other values (138) 4990
36.4%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct338
Distinct (%)30.7%
Missing96
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean241.59182
Minimum0
Maximum45917
Zeros141
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T14:55:30.613008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median27.5
Q3150
95-th percentile635
Maximum45917
Range45917
Interquartile range (IQR)147

Descriptive statistics

Standard deviation1674.83
Coefficient of variation (CV)6.9324784
Kurtosis533.67889
Mean241.59182
Median Absolute Deviation (MAD)27.5
Skewness21.255079
Sum265751
Variance2805055.7
MonotonicityNot monotonic
2024-03-23T14:55:30.761692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 141
 
11.8%
1 79
 
6.6%
2 43
 
3.6%
3 36
 
3.0%
5 21
 
1.8%
10 21
 
1.8%
4 20
 
1.7%
6 19
 
1.6%
7 19
 
1.6%
8 15
 
1.3%
Other values (328) 686
57.4%
(Missing) 96
 
8.0%
ValueCountFrequency (%)
0 141
11.8%
1 79
6.6%
2 43
 
3.6%
3 36
 
3.0%
4 20
 
1.7%
5 21
 
1.8%
6 19
 
1.6%
7 19
 
1.6%
8 15
 
1.3%
9 10
 
0.8%
ValueCountFrequency (%)
45917 1
0.1%
21970 1
0.1%
12968 1
0.1%
9615 1
0.1%
7111 1
0.1%
6815 1
0.1%
6053 1
0.1%
5095 1
0.1%
4442 1
0.1%
3088 1
0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct291
Distinct (%)26.9%
Missing116
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean189.22778
Minimum0
Maximum36920
Zeros129
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T14:55:30.909327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median24
Q3103.25
95-th percentile472.55
Maximum36920
Range36920
Interquartile range (IQR)100.25

Descriptive statistics

Standard deviation1316.0884
Coefficient of variation (CV)6.9550486
Kurtosis581.43189
Mean189.22778
Median Absolute Deviation (MAD)24
Skewness22.162653
Sum204366
Variance1732088.6
MonotonicityNot monotonic
2024-03-23T14:55:31.054609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 129
 
10.8%
1 74
 
6.2%
3 35
 
2.9%
2 34
 
2.8%
5 28
 
2.3%
4 26
 
2.2%
6 23
 
1.9%
7 20
 
1.7%
8 16
 
1.3%
11 14
 
1.2%
Other values (281) 681
56.9%
(Missing) 116
 
9.7%
ValueCountFrequency (%)
0 129
10.8%
1 74
6.2%
2 34
 
2.8%
3 35
 
2.9%
4 26
 
2.2%
5 28
 
2.3%
6 23
 
1.9%
7 20
 
1.7%
8 16
 
1.3%
9 10
 
0.8%
ValueCountFrequency (%)
36920 1
0.1%
15266 1
0.1%
8899 1
0.1%
6890 1
0.1%
5108 1
0.1%
4434 1
0.1%
4271 1
0.1%
4233 1
0.1%
3304 1
0.1%
2852 1
0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct299
Distinct (%)27.4%
Missing104
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean179.7848
Minimum0
Maximum33593
Zeros124
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T14:55:31.205534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median27.5
Q398.25
95-th percentile472.6
Maximum33593
Range33593
Interquartile range (IQR)94.25

Descriptive statistics

Standard deviation1207.9822
Coefficient of variation (CV)6.7190453
Kurtosis563.23083
Mean179.7848
Median Absolute Deviation (MAD)26.5
Skewness21.881481
Sum196325
Variance1459221
MonotonicityNot monotonic
2024-03-23T14:55:31.362848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 124
 
10.4%
1 75
 
6.3%
3 38
 
3.2%
2 27
 
2.3%
6 25
 
2.1%
5 23
 
1.9%
7 22
 
1.8%
4 20
 
1.7%
10 16
 
1.3%
9 14
 
1.2%
Other values (289) 708
59.2%
(Missing) 104
 
8.7%
ValueCountFrequency (%)
0 124
10.4%
1 75
6.3%
2 27
 
2.3%
3 38
 
3.2%
4 20
 
1.7%
5 23
 
1.9%
6 25
 
2.1%
7 22
 
1.8%
8 12
 
1.0%
9 14
 
1.2%
ValueCountFrequency (%)
33593 1
0.1%
15415 1
0.1%
8787 1
0.1%
6314 1
0.1%
4775 1
0.1%
3833 1
0.1%
3409 1
0.1%
3067 1
0.1%
2751 1
0.1%
2592 1
0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct309
Distinct (%)28.2%
Missing100
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean198.75091
Minimum0
Maximum37170
Zeros129
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T14:55:31.496538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median28
Q3110
95-th percentile565.25
Maximum37170
Range37170
Interquartile range (IQR)106

Descriptive statistics

Standard deviation1332.7159
Coefficient of variation (CV)6.7054579
Kurtosis564.16723
Mean198.75091
Median Absolute Deviation (MAD)27
Skewness21.825779
Sum217831
Variance1776131.6
MonotonicityNot monotonic
2024-03-23T14:55:31.646698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 129
 
10.8%
1 67
 
5.6%
2 36
 
3.0%
3 33
 
2.8%
5 25
 
2.1%
6 23
 
1.9%
7 19
 
1.6%
4 19
 
1.6%
9 16
 
1.3%
11 15
 
1.3%
Other values (299) 714
59.7%
(Missing) 100
 
8.4%
ValueCountFrequency (%)
0 129
10.8%
1 67
5.6%
2 36
 
3.0%
3 33
 
2.8%
4 19
 
1.6%
5 25
 
2.1%
6 23
 
1.9%
7 19
 
1.6%
8 10
 
0.8%
9 16
 
1.3%
ValueCountFrequency (%)
37170 1
0.1%
16310 1
0.1%
8919 1
0.1%
8356 1
0.1%
6469 1
0.1%
4864 1
0.1%
3967 1
0.1%
3220 1
0.1%
3127 1
0.1%
3019 1
0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct304
Distinct (%)27.1%
Missing76
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean183.20714
Minimum0
Maximum35133
Zeros134
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T14:55:31.846870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median28
Q3106
95-th percentile473.05
Maximum35133
Range35133
Interquartile range (IQR)102

Descriptive statistics

Standard deviation1241.5352
Coefficient of variation (CV)6.7766744
Kurtosis582.87281
Mean183.20714
Median Absolute Deviation (MAD)27
Skewness22.176263
Sum205192
Variance1541409.5
MonotonicityNot monotonic
2024-03-23T14:55:32.378375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 134
 
11.2%
1 82
 
6.9%
2 37
 
3.1%
4 31
 
2.6%
7 27
 
2.3%
3 25
 
2.1%
5 23
 
1.9%
8 21
 
1.8%
6 19
 
1.6%
12 15
 
1.3%
Other values (294) 706
59.0%
(Missing) 76
 
6.4%
ValueCountFrequency (%)
0 134
11.2%
1 82
6.9%
2 37
 
3.1%
3 25
 
2.1%
4 31
 
2.6%
5 23
 
1.9%
6 19
 
1.6%
7 27
 
2.3%
8 21
 
1.8%
9 11
 
0.9%
ValueCountFrequency (%)
35133 1
0.1%
14771 1
0.1%
9199 1
0.1%
8091 1
0.1%
5451 1
0.1%
4293 1
0.1%
3804 1
0.1%
3450 1
0.1%
2692 1
0.1%
2549 1
0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct335
Distinct (%)30.2%
Missing88
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean207.33484
Minimum0
Maximum40520
Zeros123
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T14:55:32.532808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median30
Q3128
95-th percentile551
Maximum40520
Range40520
Interquartile range (IQR)124

Descriptive statistics

Standard deviation1414.7123
Coefficient of variation (CV)6.8233216
Kurtosis615.63571
Mean207.33484
Median Absolute Deviation (MAD)29
Skewness22.939178
Sum229727
Variance2001410.8
MonotonicityNot monotonic
2024-03-23T14:55:32.830294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 123
 
10.3%
1 69
 
5.8%
2 43
 
3.6%
8 31
 
2.6%
3 29
 
2.4%
5 23
 
1.9%
4 23
 
1.9%
7 21
 
1.8%
6 19
 
1.6%
9 14
 
1.2%
Other values (325) 713
59.6%
(Missing) 88
 
7.4%
ValueCountFrequency (%)
0 123
10.3%
1 69
5.8%
2 43
 
3.6%
3 29
 
2.4%
4 23
 
1.9%
5 23
 
1.9%
6 19
 
1.6%
7 21
 
1.8%
8 31
 
2.6%
9 14
 
1.2%
ValueCountFrequency (%)
40520 1
0.1%
16617 1
0.1%
9921 1
0.1%
8561 1
0.1%
4863 1
0.1%
3673 1
0.1%
3504 1
0.1%
3347 1
0.1%
3275 1
0.1%
2782 1
0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct279
Distinct (%)25.0%
Missing80
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean162.58871
Minimum0
Maximum32400
Zeros137
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T14:55:33.051562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median22
Q388
95-th percentile509.5
Maximum32400
Range32400
Interquartile range (IQR)84

Descriptive statistics

Standard deviation1117.309
Coefficient of variation (CV)6.8719961
Kurtosis637.55696
Mean162.58871
Median Absolute Deviation (MAD)21
Skewness23.30878
Sum181449
Variance1248379.4
MonotonicityNot monotonic
2024-03-23T14:55:33.244305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 137
 
11.5%
1 77
 
6.4%
2 33
 
2.8%
4 32
 
2.7%
3 29
 
2.4%
5 28
 
2.3%
9 24
 
2.0%
6 24
 
2.0%
7 21
 
1.8%
8 19
 
1.6%
Other values (269) 692
57.9%
(Missing) 80
 
6.7%
ValueCountFrequency (%)
0 137
11.5%
1 77
6.4%
2 33
 
2.8%
3 29
 
2.4%
4 32
 
2.7%
5 28
 
2.3%
6 24
 
2.0%
7 21
 
1.8%
8 19
 
1.6%
9 24
 
2.0%
ValueCountFrequency (%)
32400 1
0.1%
12248 1
0.1%
7788 1
0.1%
6664 1
0.1%
4290 1
0.1%
2953 1
0.1%
2807 1
0.1%
2588 1
0.1%
2538 1
0.1%
2491 1
0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct307
Distinct (%)27.5%
Missing80
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean193.57079
Minimum0
Maximum39305
Zeros132
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T14:55:33.440578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median22
Q3109.25
95-th percentile541.75
Maximum39305
Range39305
Interquartile range (IQR)106.25

Descriptive statistics

Standard deviation1359.9998
Coefficient of variation (CV)7.0258526
Kurtosis632.44045
Mean193.57079
Median Absolute Deviation (MAD)22
Skewness23.277572
Sum216025
Variance1849599.5
MonotonicityNot monotonic
2024-03-23T14:55:33.628739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 132
 
11.0%
1 75
 
6.3%
2 43
 
3.6%
3 36
 
3.0%
8 28
 
2.3%
5 28
 
2.3%
6 24
 
2.0%
4 24
 
2.0%
10 18
 
1.5%
9 15
 
1.3%
Other values (297) 693
57.9%
(Missing) 80
 
6.7%
ValueCountFrequency (%)
0 132
11.0%
1 75
6.3%
2 43
 
3.6%
3 36
 
3.0%
4 24
 
2.0%
5 28
 
2.3%
6 24
 
2.0%
7 13
 
1.1%
8 28
 
2.3%
9 15
 
1.3%
ValueCountFrequency (%)
39305 1
0.1%
15797 1
0.1%
8910 1
0.1%
8583 1
0.1%
3933 1
0.1%
3757 1
0.1%
3706 1
0.1%
3353 1
0.1%
3141 1
0.1%
2992 1
0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct347
Distinct (%)30.5%
Missing60
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean238.54313
Minimum0
Maximum51065
Zeros117
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T14:55:33.807076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median31.5
Q3133.25
95-th percentile647.25
Maximum51065
Range51065
Interquartile range (IQR)128.25

Descriptive statistics

Standard deviation1728.5934
Coefficient of variation (CV)7.2464605
Kurtosis674.44226
Mean238.54313
Median Absolute Deviation (MAD)30.5
Skewness24.100289
Sum270985
Variance2988035.1
MonotonicityNot monotonic
2024-03-23T14:55:33.984129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 117
 
9.8%
1 71
 
5.9%
2 36
 
3.0%
4 31
 
2.6%
7 30
 
2.5%
5 29
 
2.4%
9 25
 
2.1%
3 25
 
2.1%
6 21
 
1.8%
16 17
 
1.4%
Other values (337) 734
61.4%
(Missing) 60
 
5.0%
ValueCountFrequency (%)
0 117
9.8%
1 71
5.9%
2 36
 
3.0%
3 25
 
2.1%
4 31
 
2.6%
5 29
 
2.4%
6 21
 
1.8%
7 30
 
2.5%
8 13
 
1.1%
9 25
 
2.1%
ValueCountFrequency (%)
51065 1
0.1%
19165 1
0.1%
10791 1
0.1%
10664 1
0.1%
5713 1
0.1%
5640 1
0.1%
3937 1
0.1%
3935 1
0.1%
3850 1
0.1%
3654 1
0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct356
Distinct (%)31.7%
Missing72
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean265.07918
Minimum0
Maximum52982
Zeros108
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T14:55:34.126065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median37
Q3157
95-th percentile679.2
Maximum52982
Range52982
Interquartile range (IQR)152

Descriptive statistics

Standard deviation1856.1021
Coefficient of variation (CV)7.0020665
Kurtosis601.561
Mean265.07918
Median Absolute Deviation (MAD)36
Skewness22.640442
Sum297949
Variance3445114.9
MonotonicityNot monotonic
2024-03-23T14:55:34.275080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 108
 
9.0%
1 79
 
6.6%
3 32
 
2.7%
2 31
 
2.6%
5 22
 
1.8%
9 21
 
1.8%
7 21
 
1.8%
6 21
 
1.8%
4 18
 
1.5%
12 16
 
1.3%
Other values (346) 755
63.1%
(Missing) 72
 
6.0%
ValueCountFrequency (%)
0 108
9.0%
1 79
6.6%
2 31
 
2.6%
3 32
 
2.7%
4 18
 
1.5%
5 22
 
1.8%
6 21
 
1.8%
7 21
 
1.8%
8 16
 
1.3%
9 21
 
1.8%
ValueCountFrequency (%)
52982 1
0.1%
22155 1
0.1%
13437 1
0.1%
13233 1
0.1%
6723 1
0.1%
5223 1
0.1%
4759 1
0.1%
4655 1
0.1%
4337 1
0.1%
3417 1
0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct352
Distinct (%)31.3%
Missing72
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean243.06317
Minimum0
Maximum47815
Zeros109
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T14:55:34.434404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median35
Q3149.25
95-th percentile617.7
Maximum47815
Range47815
Interquartile range (IQR)145.25

Descriptive statistics

Standard deviation1693.4092
Coefficient of variation (CV)6.9669511
Kurtosis579.25881
Mean243.06317
Median Absolute Deviation (MAD)34
Skewness22.176299
Sum273203
Variance2867634.7
MonotonicityNot monotonic
2024-03-23T14:55:34.582626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 109
 
9.1%
1 66
 
5.5%
2 50
 
4.2%
3 33
 
2.8%
4 27
 
2.3%
6 23
 
1.9%
11 22
 
1.8%
8 21
 
1.8%
5 17
 
1.4%
7 15
 
1.3%
Other values (342) 741
62.0%
(Missing) 72
 
6.0%
ValueCountFrequency (%)
0 109
9.1%
1 66
5.5%
2 50
4.2%
3 33
 
2.8%
4 27
 
2.3%
5 17
 
1.4%
6 23
 
1.9%
7 15
 
1.3%
8 21
 
1.8%
9 14
 
1.2%
ValueCountFrequency (%)
47815 1
0.1%
20847 1
0.1%
12735 1
0.1%
11948 1
0.1%
6225 1
0.1%
5628 1
0.1%
5092 1
0.1%
4068 1
0.1%
4001 1
0.1%
3660 1
0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct375
Distinct (%)33.7%
Missing84
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean280.82374
Minimum0
Maximum51366
Zeros94
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T14:55:34.733550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median41
Q3175
95-th percentile745.8
Maximum51366
Range51366
Interquartile range (IQR)170

Descriptive statistics

Standard deviation1895.8769
Coefficient of variation (CV)6.7511277
Kurtosis511.7367
Mean280.82374
Median Absolute Deviation (MAD)40
Skewness20.84574
Sum312276
Variance3594349.4
MonotonicityNot monotonic
2024-03-23T14:55:34.876255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 94
 
7.9%
1 69
 
5.8%
2 42
 
3.5%
3 35
 
2.9%
4 30
 
2.5%
7 22
 
1.8%
5 21
 
1.8%
10 18
 
1.5%
6 17
 
1.4%
9 16
 
1.3%
Other values (365) 748
62.5%
(Missing) 84
 
7.0%
ValueCountFrequency (%)
0 94
7.9%
1 69
5.8%
2 42
3.5%
3 35
 
2.9%
4 30
 
2.5%
5 21
 
1.8%
6 17
 
1.4%
7 22
 
1.8%
8 15
 
1.3%
9 16
 
1.3%
ValueCountFrequency (%)
51366 1
0.1%
26221 1
0.1%
14587 1
0.1%
14092 1
0.1%
8001 1
0.1%
6134 1
0.1%
5900 1
0.1%
4501 1
0.1%
4353 1
0.1%
4332 1
0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct378
Distinct (%)33.9%
Missing80
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean288.00896
Minimum0
Maximum52644
Zeros101
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T14:55:35.038011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median41
Q3171.25
95-th percentile734
Maximum52644
Range52644
Interquartile range (IQR)166.25

Descriptive statistics

Standard deviation1958.9232
Coefficient of variation (CV)6.8016051
Kurtosis497.4154
Mean288.00896
Median Absolute Deviation (MAD)40
Skewness20.553231
Sum321418
Variance3837380.2
MonotonicityNot monotonic
2024-03-23T14:55:35.210599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 101
 
8.4%
1 60
 
5.0%
3 39
 
3.3%
2 37
 
3.1%
5 32
 
2.7%
7 21
 
1.8%
4 19
 
1.6%
8 17
 
1.4%
10 17
 
1.4%
6 16
 
1.3%
Other values (368) 757
63.3%
(Missing) 80
 
6.7%
ValueCountFrequency (%)
0 101
8.4%
1 60
5.0%
2 37
 
3.1%
3 39
 
3.3%
4 19
 
1.6%
5 32
 
2.7%
6 16
 
1.3%
7 21
 
1.8%
8 17
 
1.4%
9 15
 
1.3%
ValueCountFrequency (%)
52644 1
0.1%
27349 1
0.1%
17258 1
0.1%
13224 1
0.1%
9479 1
0.1%
7207 1
0.1%
5050 1
0.1%
5002 1
0.1%
4727 1
0.1%
3146 1
0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct360
Distinct (%)32.4%
Missing84
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean264.61421
Minimum0
Maximum48083
Zeros111
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T14:55:35.368800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median33.5
Q3155.25
95-th percentile700.15
Maximum48083
Range48083
Interquartile range (IQR)151.25

Descriptive statistics

Standard deviation1793.5645
Coefficient of variation (CV)6.7780354
Kurtosis497.29013
Mean264.61421
Median Absolute Deviation (MAD)33.5
Skewness20.589972
Sum294251
Variance3216873.5
MonotonicityNot monotonic
2024-03-23T14:55:35.551382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 111
 
9.3%
1 67
 
5.6%
2 52
 
4.3%
3 27
 
2.3%
6 25
 
2.1%
4 22
 
1.8%
5 20
 
1.7%
7 19
 
1.6%
11 18
 
1.5%
10 15
 
1.3%
Other values (350) 736
61.5%
(Missing) 84
 
7.0%
ValueCountFrequency (%)
0 111
9.3%
1 67
5.6%
2 52
4.3%
3 27
 
2.3%
4 22
 
1.8%
5 20
 
1.7%
6 25
 
2.1%
7 19
 
1.6%
8 9
 
0.8%
9 11
 
0.9%
ValueCountFrequency (%)
48083 1
0.1%
25716 1
0.1%
14728 1
0.1%
12500 1
0.1%
8515 1
0.1%
5825 1
0.1%
4106 1
0.1%
3950 1
0.1%
3788 1
0.1%
3650 1
0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct410
Distinct (%)36.9%
Missing84
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean340.2473
Minimum0
Maximum57777
Zeros102
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T14:55:35.743520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median58.5
Q3212
95-th percentile870.4
Maximum57777
Range57777
Interquartile range (IQR)205

Descriptive statistics

Standard deviation2216.0456
Coefficient of variation (CV)6.5130438
Kurtosis457.09613
Mean340.2473
Median Absolute Deviation (MAD)57.5
Skewness19.72904
Sum378355
Variance4910858
MonotonicityNot monotonic
2024-03-23T14:55:35.891872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 102
 
8.5%
1 63
 
5.3%
2 36
 
3.0%
4 22
 
1.8%
3 19
 
1.6%
6 17
 
1.4%
5 16
 
1.3%
12 15
 
1.3%
9 15
 
1.3%
8 15
 
1.3%
Other values (400) 792
66.2%
(Missing) 84
 
7.0%
ValueCountFrequency (%)
0 102
8.5%
1 63
5.3%
2 36
 
3.0%
3 19
 
1.6%
4 22
 
1.8%
5 16
 
1.3%
6 17
 
1.4%
7 15
 
1.3%
8 15
 
1.3%
9 15
 
1.3%
ValueCountFrequency (%)
57777 1
0.1%
33236 1
0.1%
18870 1
0.1%
16642 1
0.1%
10696 1
0.1%
8495 1
0.1%
5371 1
0.1%
4859 1
0.1%
4622 1
0.1%
4448 1
0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct352
Distinct (%)31.5%
Missing80
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean243.04032
Minimum0
Maximum41569
Zeros102
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T14:55:36.041856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median46
Q3143.25
95-th percentile660.25
Maximum41569
Range41569
Interquartile range (IQR)137.25

Descriptive statistics

Standard deviation1565.9957
Coefficient of variation (CV)6.4433577
Kurtosis476.97425
Mean243.04032
Median Absolute Deviation (MAD)44
Skewness20.090842
Sum271233
Variance2452342.6
MonotonicityNot monotonic
2024-03-23T14:55:36.181875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 102
 
8.5%
1 55
 
4.6%
2 31
 
2.6%
3 27
 
2.3%
5 24
 
2.0%
6 21
 
1.8%
4 20
 
1.7%
13 19
 
1.6%
7 19
 
1.6%
8 17
 
1.4%
Other values (342) 781
65.3%
(Missing) 80
 
6.7%
ValueCountFrequency (%)
0 102
8.5%
1 55
4.6%
2 31
 
2.6%
3 27
 
2.3%
4 20
 
1.7%
5 24
 
2.0%
6 21
 
1.8%
7 19
 
1.6%
8 17
 
1.4%
9 11
 
0.9%
ValueCountFrequency (%)
41569 1
0.1%
22030 1
0.1%
14539 1
0.1%
10948 1
0.1%
6832 1
0.1%
6721 1
0.1%
4113 1
0.1%
3974 1
0.1%
3177 1
0.1%
2836 1
0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct267
Distinct (%)23.9%
Missing80
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean133.36828
Minimum0
Maximum24628
Zeros115
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T14:55:36.320971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median21
Q369
95-th percentile393.25
Maximum24628
Range24628
Interquartile range (IQR)65

Descriptive statistics

Standard deviation888.84427
Coefficient of variation (CV)6.6645852
Kurtosis548.13472
Mean133.36828
Median Absolute Deviation (MAD)20
Skewness21.572758
Sum148839
Variance790044.13
MonotonicityNot monotonic
2024-03-23T14:55:36.488569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 115
 
9.6%
1 75
 
6.3%
2 40
 
3.3%
4 35
 
2.9%
6 29
 
2.4%
3 28
 
2.3%
5 27
 
2.3%
7 24
 
2.0%
16 22
 
1.8%
11 20
 
1.7%
Other values (257) 701
58.6%
(Missing) 80
 
6.7%
ValueCountFrequency (%)
0 115
9.6%
1 75
6.3%
2 40
 
3.3%
3 28
 
2.3%
4 35
 
2.9%
5 27
 
2.3%
6 29
 
2.4%
7 24
 
2.0%
8 19
 
1.6%
9 16
 
1.3%
ValueCountFrequency (%)
24628 1
0.1%
11718 1
0.1%
7083 1
0.1%
5629 1
0.1%
3273 1
0.1%
3211 1
0.1%
2487 1
0.1%
2164 1
0.1%
1772 1
0.1%
1680 1
0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct281
Distinct (%)25.1%
Missing76
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean154.73661
Minimum0
Maximum28767
Zeros128
Zeros (%)10.7%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T14:55:36.998633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median22
Q389
95-th percentile435
Maximum28767
Range28767
Interquartile range (IQR)86

Descriptive statistics

Standard deviation1054.007
Coefficient of variation (CV)6.8116205
Kurtosis527.54619
Mean154.73661
Median Absolute Deviation (MAD)21
Skewness21.272799
Sum173305
Variance1110930.8
MonotonicityNot monotonic
2024-03-23T14:55:37.131906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 128
 
10.7%
1 67
 
5.6%
2 55
 
4.6%
4 38
 
3.2%
3 31
 
2.6%
6 22
 
1.8%
5 21
 
1.8%
14 20
 
1.7%
8 19
 
1.6%
13 17
 
1.4%
Other values (271) 702
58.7%
(Missing) 76
 
6.4%
ValueCountFrequency (%)
0 128
10.7%
1 67
5.6%
2 55
4.6%
3 31
 
2.6%
4 38
 
3.2%
5 21
 
1.8%
6 22
 
1.8%
7 16
 
1.3%
8 19
 
1.6%
9 16
 
1.3%
ValueCountFrequency (%)
28767 1
0.1%
15287 1
0.1%
7602 1
0.1%
7323 1
0.1%
4198 1
0.1%
2781 1
0.1%
2629 1
0.1%
2290 1
0.1%
1845 1
0.1%
1822 1
0.1%

Interactions

2024-03-23T14:55:26.811785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:51.325441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:53.429851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T14:54:54.605807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:56.741197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:58.768589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:01.049465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:03.084113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:04.930942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:07.572189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:09.647055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:11.769929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:14.092195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:16.055042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:18.023747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:19.935869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:22.269769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:24.161021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:26.117563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:28.461045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:52.575311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:54.698747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:56.866682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:58.873065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:01.144040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:03.190309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:05.031058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:07.697019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:09.782891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:11.857532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:14.184415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:16.133495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:18.135623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:20.453410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:22.361664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:24.256536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:26.210974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:28.578420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:52.658937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:54.796600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:56.981324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:58.979638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:01.260198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:03.292507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:05.160274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:07.823676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:09.932335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:11.940069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:14.285370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:16.214502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:18.227746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:20.547661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:22.442501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:24.365248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:26.312145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:28.710532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:53.063396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:54.913797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:57.088809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:59.083307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:01.360431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:03.385417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:05.295165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:07.947325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:10.048449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:12.045585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:14.423599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:16.310292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:18.325932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:20.655242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:22.541622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:24.471383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:26.412763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:28.835281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:53.164407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:55.055771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:57.192365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:59.179633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:01.456344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:03.488200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:05.463520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:08.086212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:10.160991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:12.161078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:14.566046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:16.404048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:18.424216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:20.755422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:22.651680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:24.584961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:26.518573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:28.934358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:53.263792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:55.160737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:57.289143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:59.679064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:01.562379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:03.595801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:05.965188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:08.203596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:10.263663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:12.272783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:14.721897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:16.507886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:18.512190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:20.853182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:22.748773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:24.693709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:26.622642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:29.027550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:53.347775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:55.286773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:57.400054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:59.775803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:01.668159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:03.702888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:06.065493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:08.332726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:10.380978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:12.375770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:14.840311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:16.649932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:18.612685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:20.945567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:22.872595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:24.789239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:55:26.718441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T14:55:37.256359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9870.9891.0000.9960.9950.9950.9910.9940.9951.0001.0000.9500.9500.9500.9500.9960.995
20070.9871.0000.9980.9890.9890.9870.9870.9820.9820.9820.9890.9890.9570.9570.9570.9570.9890.987
20080.9890.9981.0000.9910.9970.9890.9890.9890.9860.9820.9910.9910.9670.9670.9670.9670.9970.989
20091.0000.9890.9911.0000.9980.9960.9960.9910.9940.9961.0001.0000.9610.9610.9610.9610.9980.996
20100.9960.9890.9970.9981.0000.9960.9960.9960.9940.9910.9980.9980.9610.9610.9610.9611.0000.996
20110.9950.9870.9890.9960.9961.0001.0000.9910.9910.9910.9960.9960.9500.9500.9500.9500.9960.991
20120.9950.9870.9890.9960.9961.0001.0000.9910.9910.9910.9960.9960.9500.9500.9500.9500.9960.991
20130.9910.9820.9890.9910.9960.9910.9911.0000.9980.9910.9910.9910.9130.9130.9130.9130.9960.991
20140.9940.9820.9860.9940.9940.9910.9910.9981.0000.9980.9940.9940.9130.9130.9130.9130.9940.991
20150.9950.9820.9820.9960.9910.9910.9910.9910.9981.0000.9960.9960.9130.9130.9130.9130.9910.991
20161.0000.9890.9911.0000.9980.9960.9960.9910.9940.9961.0001.0000.9610.9610.9610.9610.9980.996
20171.0000.9890.9911.0000.9980.9960.9960.9910.9940.9961.0001.0000.9610.9610.9610.9610.9980.996
20180.9500.9570.9670.9610.9610.9500.9500.9130.9130.9130.9610.9611.0001.0001.0001.0000.9610.950
20190.9500.9570.9670.9610.9610.9500.9500.9130.9130.9130.9610.9611.0001.0001.0001.0000.9610.950
20200.9500.9570.9670.9610.9610.9500.9500.9130.9130.9130.9610.9611.0001.0001.0001.0000.9610.950
20210.9500.9570.9670.9610.9610.9500.9500.9130.9130.9130.9610.9611.0001.0001.0001.0000.9610.950
20220.9960.9890.9970.9981.0000.9960.9960.9960.9940.9910.9980.9980.9610.9610.9610.9611.0000.996
20230.9950.9870.9890.9960.9960.9910.9910.9910.9910.9910.9960.9960.9500.9500.9500.9500.9961.000
2024-03-23T14:55:37.483927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9560.9390.9330.9300.9360.9290.9410.9420.9490.9410.9430.9410.9460.9500.9170.8790.927
20070.9561.0000.9570.9350.9350.9430.9320.9380.9370.9370.9300.9290.9240.9280.9360.9200.9000.927
20080.9390.9571.0000.9450.9420.9420.9350.9420.9370.9360.9250.9230.9220.9240.9330.9220.8980.920
20090.9330.9350.9451.0000.9510.9480.9350.9380.9350.9350.9260.9230.9180.9260.9350.9100.8830.916
20100.9300.9350.9420.9511.0000.9610.9500.9470.9370.9320.9210.9200.9190.9280.9310.9200.8990.921
20110.9360.9430.9420.9480.9611.0000.9640.9600.9530.9480.9350.9350.9280.9400.9430.9280.9130.933
20120.9290.9320.9350.9350.9500.9641.0000.9700.9590.9500.9350.9330.9290.9380.9440.9310.9140.932
20130.9410.9380.9420.9380.9470.9600.9701.0000.9730.9660.9540.9500.9450.9520.9510.9300.9130.944
20140.9420.9370.9370.9350.9370.9530.9590.9731.0000.9770.9630.9570.9470.9510.9480.9230.9110.940
20150.9490.9370.9360.9350.9320.9480.9500.9660.9771.0000.9750.9650.9560.9550.9540.9280.9050.939
20160.9410.9300.9250.9260.9210.9350.9350.9540.9630.9751.0000.9750.9600.9580.9480.9270.9000.939
20170.9430.9290.9230.9230.9200.9350.9330.9500.9570.9650.9751.0000.9670.9640.9560.9320.9020.944
20180.9410.9240.9220.9180.9190.9280.9290.9450.9470.9560.9600.9671.0000.9720.9590.9360.9090.943
20190.9460.9280.9240.9260.9280.9400.9380.9520.9510.9550.9580.9640.9721.0000.9690.9370.9080.952
20200.9500.9360.9330.9350.9310.9430.9440.9510.9480.9540.9480.9560.9590.9691.0000.9570.9220.954
20210.9170.9200.9220.9100.9200.9280.9310.9300.9230.9280.9270.9320.9360.9370.9571.0000.9490.950
20220.8790.9000.8980.8830.8990.9130.9140.9130.9110.9050.9000.9020.9090.9080.9220.9491.0000.947
20230.9270.9270.9200.9160.9210.9330.9320.9440.9400.9390.9390.9440.9430.9520.9540.9500.9471.000

Missing values

2024-03-23T14:55:29.178427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T14:55:29.423312image/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.
2024-03-23T14:55:29.703717image/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_매입자거주지200620072008200920102011201220132014201520162017201820192020202120222023
0전국_관할시군구내459173692033593371703513340520324003930551065529824781551366526444808357777415692462828767
1전국_관할시도내219701526615415163101477116617122481579719165221552084726221273492571633236220301171815287
2전국_관할시도외_서울605344344775646954514863429033533850475950926134720758258495672132112781
3전국_관할시도외_기타96158899878783569199992177888910106641323311948140921322412500188701453970837602
4서울_관할시군구내68153304306739672549327523933706564067236225590050503950444824709771845
5서울_관할시도내509526192500289819682081138018772615341740014501472737884859283610742629
6서울_관할시도외_서울000000000000000000
7서울_관할시도외_기타241911541093147088210017101044146520282001237122031722221613044701210
8서울 종로구_관할시군구내35232825162522263051376631282915511
9서울 종로구_관할시도내453680291216132222473980443748271212
지역_매입자거주지200620072008200920102011201220132014201520162017201820192020202120222023
1186제주_관할시도외_서울59871114162027393123332318321714
1187제주_관할시도외_기타1215151523293848551027266523762894224
1188제주 제주시_관할시군구내135192143151316310298298318262241276238186208269197140
1189제주 제주시_관할시도내612121519201918242015211511121386
1190제주 제주시_관할시도외_서울58659111211151715141813918117
1191제주 제주시_관할시도외_기타111311121922273034362539302028432416
1192제주 서귀포시_관할시군구내9345431285337655512512886574654725049
1193제주 서귀포시_관할시도내223233510136080381712101484
1194제주 서귀포시_관할시도외_서울1231335101222169161091366
1195제주 서귀포시_관할시도외_기타13434711182167472823173446178