Overview

Dataset statistics

Number of variables19
Number of observations1196
Missing cells1532
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 아파트매매 거래현황의 연도별 매입자거주지별(면적) 데이터입니다.-(단위 : 천㎡)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068509/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 104 (8.7%) missing valuesMissing
2010 has 80 (6.7%) missing valuesMissing
2011 has 92 (7.7%) missing valuesMissing
2012 has 84 (7.0%) missing valuesMissing
2013 has 84 (7.0%) 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 = 21.8854169)Skewed
2007 is highly skewed (γ1 = 23.72630377)Skewed
2008 is highly skewed (γ1 = 23.6095566)Skewed
2009 is highly skewed (γ1 = 23.7917404)Skewed
2010 is highly skewed (γ1 = 24.6862725)Skewed
2011 is highly skewed (γ1 = 24.17900174)Skewed
2012 is highly skewed (γ1 = 25.10105808)Skewed
2013 is highly skewed (γ1 = 24.60571787)Skewed
2014 is highly skewed (γ1 = 24.7657596)Skewed
2015 is highly skewed (γ1 = 23.65398437)Skewed
2016 is highly skewed (γ1 = 23.00883656)Skewed
2017 is highly skewed (γ1 = 22.64387687)Skewed
2018 is highly skewed (γ1 = 21.86484358)Skewed
2019 is highly skewed (γ1 = 22.99473798)Skewed
2020 is highly skewed (γ1 = 21.42338245)Skewed
2021 is highly skewed (γ1 = 20.99186996)Skewed
2022 is highly skewed (γ1 = 23.11898857)Skewed
2023 is highly skewed (γ1 = 23.80139296)Skewed
아파트매매 매입자거주지별 has unique valuesUnique
2006 has 164 (13.7%) zerosZeros
2007 has 142 (11.9%) zerosZeros
2008 has 137 (11.5%) zerosZeros
2009 has 133 (11.1%) zerosZeros
2010 has 134 (11.2%) zerosZeros
2011 has 125 (10.5%) zerosZeros
2012 has 144 (12.0%) zerosZeros
2013 has 141 (11.8%) zerosZeros
2014 has 127 (10.6%) zerosZeros
2015 has 123 (10.3%) zerosZeros
2016 has 128 (10.7%) zerosZeros
2017 has 116 (9.7%) zerosZeros
2018 has 126 (10.5%) zerosZeros
2019 has 134 (11.2%) zerosZeros
2020 has 121 (10.1%) zerosZeros
2021 has 120 (10.0%) zerosZeros
2022 has 134 (11.2%) zerosZeros
2023 has 155 (13.0%) zerosZeros

Reproduction

Analysis started2024-03-23 06:31:28.451307
Analysis finished2024-03-23 06:33:40.079836
Duration2 minutes and 11.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct1196
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
2024-03-23T06:33:40.479767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length14.091137
Min length9

Characters and Unicode

Total characters16853
Distinct characters153
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1196 ?
Unique (%)100.0%

Sample

1st row전국 /관할시군구내
2nd row전국 /관할시도내
3rd row전국 /관할시도외_서울
4th row전국 /관할시도외_기타
5th row서울 /관할시군구내
ValueCountFrequency (%)
경기 212
 
8.9%
경남 108
 
4.5%
경북 104
 
4.3%
서울 104
 
4.3%
전남 92
 
3.8%
충남 80
 
3.3%
충북 80
 
3.3%
강원 76
 
3.2%
전북 68
 
2.8%
부산 68
 
2.8%
Other values (1037) 1400
58.5%
2024-03-23T06:33:42.273692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1684
 
10.0%
1200
 
7.1%
1196
 
7.1%
/ 1196
 
7.1%
1196
 
7.1%
917
 
5.4%
863
 
5.1%
671
 
4.0%
598
 
3.5%
598
 
3.5%
Other values (143) 6734
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13711
81.4%
Space Separator 1196
 
7.1%
Other Punctuation 1196
 
7.1%
Connector Punctuation 598
 
3.5%
Close Punctuation 76
 
0.5%
Open Punctuation 76
 
0.5%

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%
Space Separator
ValueCountFrequency (%)
1196
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1196
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 598
100.0%
Close Punctuation
ValueCountFrequency (%)
) 76
100.0%
Open Punctuation
ValueCountFrequency (%)
( 76
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13711
81.4%
Common 3142
 
18.6%

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 (%)
1196
38.1%
/ 1196
38.1%
_ 598
19.0%
) 76
 
2.4%
( 76
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13711
81.4%
ASCII 3142
 
18.6%

Most frequent character per block

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%
ASCII
ValueCountFrequency (%)
1196
38.1%
/ 1196
38.1%
_ 598
19.0%
) 76
 
2.4%
( 76
 
2.4%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct276
Distinct (%)25.1%
Missing96
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean151.98455
Minimum0
Maximum30458
Zeros164
Zeros (%)13.7%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:33:42.862562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median16
Q387.25
95-th percentile375.05
Maximum30458
Range30458
Interquartile range (IQR)85.25

Descriptive statistics

Standard deviation1086.5122
Coefficient of variation (CV)7.1488336
Kurtosis569.65807
Mean151.98455
Median Absolute Deviation (MAD)16
Skewness21.885417
Sum167183
Variance1180508.8
MonotonicityNot monotonic
2024-03-23T06:33:43.495059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 164
 
13.7%
1 101
 
8.4%
2 52
 
4.3%
3 38
 
3.2%
4 28
 
2.3%
5 26
 
2.2%
6 24
 
2.0%
8 23
 
1.9%
7 19
 
1.6%
12 15
 
1.3%
Other values (266) 610
51.0%
(Missing) 96
 
8.0%
ValueCountFrequency (%)
0 164
13.7%
1 101
8.4%
2 52
 
4.3%
3 38
 
3.2%
4 28
 
2.3%
5 26
 
2.2%
6 24
 
2.0%
7 19
 
1.6%
8 23
 
1.9%
9 13
 
1.1%
ValueCountFrequency (%)
30458 1
0.1%
11846 1
0.1%
9399 1
0.1%
6068 1
0.1%
5362 1
0.1%
4480 1
0.1%
3862 1
0.1%
3452 1
0.1%
2975 1
0.1%
1930 1
0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct231
Distinct (%)21.4%
Missing116
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean108.25741
Minimum0
Maximum22426
Zeros142
Zeros (%)11.9%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:33:44.383081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median17
Q359
95-th percentile289.05
Maximum22426
Range22426
Interquartile range (IQR)56

Descriptive statistics

Standard deviation772.65988
Coefficient of variation (CV)7.1372472
Kurtosis656.27395
Mean108.25741
Median Absolute Deviation (MAD)16
Skewness23.726304
Sum116918
Variance597003.28
MonotonicityNot monotonic
2024-03-23T06:33:44.888600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 142
 
11.9%
1 82
 
6.9%
2 45
 
3.8%
3 43
 
3.6%
4 41
 
3.4%
8 24
 
2.0%
5 23
 
1.9%
9 21
 
1.8%
14 18
 
1.5%
10 17
 
1.4%
Other values (221) 624
52.2%
(Missing) 116
 
9.7%
ValueCountFrequency (%)
0 142
11.9%
1 82
6.9%
2 45
 
3.8%
3 43
 
3.6%
4 41
 
3.4%
5 23
 
1.9%
6 16
 
1.3%
7 16
 
1.3%
8 24
 
2.0%
9 21
 
1.8%
ValueCountFrequency (%)
22426 1
0.1%
7486 1
0.1%
4886 1
0.1%
4690 1
0.1%
2718 1
0.1%
2260 1
0.1%
2034 1
0.1%
1894 1
0.1%
1868 1
0.1%
1671 1
0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct241
Distinct (%)22.1%
Missing104
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean117.96795
Minimum0
Maximum23948
Zeros137
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:33:45.419743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median18.5
Q368.25
95-th percentile306.35
Maximum23948
Range23948
Interquartile range (IQR)65.25

Descriptive statistics

Standard deviation823.99746
Coefficient of variation (CV)6.9849266
Kurtosis652.56915
Mean117.96795
Median Absolute Deviation (MAD)18.5
Skewness23.609557
Sum128821
Variance678971.82
MonotonicityNot monotonic
2024-03-23T06:33:46.094736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 137
 
11.5%
1 77
 
6.4%
3 43
 
3.6%
2 38
 
3.2%
5 29
 
2.4%
4 28
 
2.3%
7 25
 
2.1%
6 25
 
2.1%
17 17
 
1.4%
8 17
 
1.4%
Other values (231) 656
54.8%
(Missing) 104
 
8.7%
ValueCountFrequency (%)
0 137
11.5%
1 77
6.4%
2 38
 
3.2%
3 43
 
3.6%
4 28
 
2.3%
5 29
 
2.4%
6 25
 
2.1%
7 25
 
2.1%
8 17
 
1.4%
9 15
 
1.3%
ValueCountFrequency (%)
23948 1
0.1%
7876 1
0.1%
5963 1
0.1%
4750 1
0.1%
3152 1
0.1%
2354 1
0.1%
2320 1
0.1%
2174 1
0.1%
1942 1
0.1%
1699 1
0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct263
Distinct (%)24.1%
Missing104
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean139.04853
Minimum0
Maximum28593
Zeros133
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:33:46.810491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median21
Q377
95-th percentile391.9
Maximum28593
Range28593
Interquartile range (IQR)74

Descriptive statistics

Standard deviation980.68569
Coefficient of variation (CV)7.0528301
Kurtosis660.72905
Mean139.04853
Median Absolute Deviation (MAD)20
Skewness23.79174
Sum151841
Variance961744.42
MonotonicityNot monotonic
2024-03-23T06:33:47.452304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 133
 
11.1%
1 68
 
5.7%
2 48
 
4.0%
3 37
 
3.1%
5 31
 
2.6%
4 24
 
2.0%
6 23
 
1.9%
8 21
 
1.8%
7 20
 
1.7%
23 20
 
1.7%
Other values (253) 667
55.8%
(Missing) 104
 
8.7%
ValueCountFrequency (%)
0 133
11.1%
1 68
5.7%
2 48
 
4.0%
3 37
 
3.1%
4 24
 
2.0%
5 31
 
2.6%
6 23
 
1.9%
7 20
 
1.7%
8 21
 
1.8%
9 12
 
1.0%
ValueCountFrequency (%)
28593 1
0.1%
9433 1
0.1%
6504 1
0.1%
5927 1
0.1%
3805 1
0.1%
3203 1
0.1%
2573 1
0.1%
2466 1
0.1%
2361 1
0.1%
1877 1
0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct254
Distinct (%)22.8%
Missing80
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean125.21864
Minimum0
Maximum26765
Zeros134
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:33:48.085056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median18.5
Q367
95-th percentile350
Maximum26765
Range26765
Interquartile range (IQR)64

Descriptive statistics

Standard deviation897.83845
Coefficient of variation (CV)7.1701662
Kurtosis705.26284
Mean125.21864
Median Absolute Deviation (MAD)17.5
Skewness24.686272
Sum139744
Variance806113.88
MonotonicityNot monotonic
2024-03-23T06:33:48.888043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 134
 
11.2%
1 87
 
7.3%
2 48
 
4.0%
6 31
 
2.6%
3 30
 
2.5%
4 28
 
2.3%
5 28
 
2.3%
7 24
 
2.0%
8 21
 
1.8%
10 20
 
1.7%
Other values (244) 665
55.6%
(Missing) 80
 
6.7%
ValueCountFrequency (%)
0 134
11.2%
1 87
7.3%
2 48
 
4.0%
3 30
 
2.5%
4 28
 
2.3%
5 28
 
2.3%
6 31
 
2.6%
7 24
 
2.0%
8 21
 
1.8%
9 16
 
1.3%
ValueCountFrequency (%)
26765 1
0.1%
8567 1
0.1%
5793 1
0.1%
4654 1
0.1%
3135 1
0.1%
2927 1
0.1%
2909 1
0.1%
1984 1
0.1%
1568 1
0.1%
1567 1
0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct287
Distinct (%)26.0%
Missing92
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean155.32971
Minimum0
Maximum32304
Zeros125
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:33:49.467411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median24
Q389.25
95-th percentile435.4
Maximum32304
Range32304
Interquartile range (IQR)86.25

Descriptive statistics

Standard deviation1097.7372
Coefficient of variation (CV)7.0671425
Kurtosis678.44991
Mean155.32971
Median Absolute Deviation (MAD)23
Skewness24.179002
Sum171484
Variance1205026.9
MonotonicityNot monotonic
2024-03-23T06:33:50.103733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 125
 
10.5%
1 76
 
6.4%
2 46
 
3.8%
3 30
 
2.5%
6 25
 
2.1%
5 25
 
2.1%
8 23
 
1.9%
4 22
 
1.8%
7 22
 
1.8%
9 20
 
1.7%
Other values (277) 690
57.7%
(Missing) 92
 
7.7%
ValueCountFrequency (%)
0 125
10.5%
1 76
6.4%
2 46
 
3.8%
3 30
 
2.5%
4 22
 
1.8%
5 25
 
2.1%
6 25
 
2.1%
7 22
 
1.8%
8 23
 
1.9%
9 20
 
1.7%
ValueCountFrequency (%)
32304 1
0.1%
10622 1
0.1%
7498 1
0.1%
6951 1
0.1%
3122 1
0.1%
2765 1
0.1%
2722 1
0.1%
2582 1
0.1%
2459 1
0.1%
2319 1
0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct238
Distinct (%)21.4%
Missing84
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean109.6223
Minimum0
Maximum24068
Zeros144
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:33:50.722012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median15
Q357
95-th percentile309.35
Maximum24068
Range24068
Interquartile range (IQR)54

Descriptive statistics

Standard deviation801.96889
Coefficient of variation (CV)7.3157458
Kurtosis725.28086
Mean109.6223
Median Absolute Deviation (MAD)15
Skewness25.101058
Sum121900
Variance643154.11
MonotonicityNot monotonic
2024-03-23T06:33:51.380701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 144
 
12.0%
1 78
 
6.5%
2 46
 
3.8%
3 40
 
3.3%
5 34
 
2.8%
4 32
 
2.7%
8 25
 
2.1%
7 24
 
2.0%
6 23
 
1.9%
14 18
 
1.5%
Other values (228) 648
54.2%
(Missing) 84
 
7.0%
ValueCountFrequency (%)
0 144
12.0%
1 78
6.5%
2 46
 
3.8%
3 40
 
3.3%
4 32
 
2.7%
5 34
 
2.8%
6 23
 
1.9%
7 24
 
2.0%
8 25
 
2.1%
9 17
 
1.4%
ValueCountFrequency (%)
24068 1
0.1%
6914 1
0.1%
5159 1
0.1%
4914 1
0.1%
2086 1
0.1%
2044 1
0.1%
2030 1
0.1%
2029 1
0.1%
1658 1
0.1%
1619 1
0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct265
Distinct (%)23.8%
Missing84
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean132.38759
Minimum0
Maximum28984
Zeros141
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:33:52.029466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median17
Q371.25
95-th percentile358.45
Maximum28984
Range28984
Interquartile range (IQR)69.25

Descriptive statistics

Standard deviation975.24196
Coefficient of variation (CV)7.3665663
Kurtosis699.75057
Mean132.38759
Median Absolute Deviation (MAD)17
Skewness24.605718
Sum147215
Variance951096.88
MonotonicityNot monotonic
2024-03-23T06:33:52.636243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 141
 
11.8%
1 87
 
7.3%
2 56
 
4.7%
3 36
 
3.0%
4 28
 
2.3%
5 25
 
2.1%
6 24
 
2.0%
8 23
 
1.9%
9 22
 
1.8%
7 21
 
1.8%
Other values (255) 649
54.3%
(Missing) 84
 
7.0%
ValueCountFrequency (%)
0 141
11.8%
1 87
7.3%
2 56
 
4.7%
3 36
 
3.0%
4 28
 
2.3%
5 25
 
2.1%
6 24
 
2.0%
7 21
 
1.8%
8 23
 
1.9%
9 22
 
1.8%
ValueCountFrequency (%)
28984 1
0.1%
8906 1
0.1%
7145 1
0.1%
5740 1
0.1%
3169 1
0.1%
2412 1
0.1%
2365 1
0.1%
2224 1
0.1%
2198 1
0.1%
2109 1
0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct279
Distinct (%)24.6%
Missing60
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean156.45335
Minimum0
Maximum34976
Zeros127
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:33:53.187505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median21
Q385
95-th percentile431.25
Maximum34976
Range34976
Interquartile range (IQR)82

Descriptive statistics

Standard deviation1166.6171
Coefficient of variation (CV)7.4566455
Kurtosis709.85801
Mean156.45335
Median Absolute Deviation (MAD)20
Skewness24.76576
Sum177731
Variance1360995.5
MonotonicityNot monotonic
2024-03-23T06:33:53.922867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 127
 
10.6%
1 83
 
6.9%
3 48
 
4.0%
2 43
 
3.6%
6 31
 
2.6%
5 30
 
2.5%
4 25
 
2.1%
7 25
 
2.1%
8 18
 
1.5%
9 17
 
1.4%
Other values (269) 689
57.6%
(Missing) 60
 
5.0%
ValueCountFrequency (%)
0 127
10.6%
1 83
6.9%
2 43
 
3.6%
3 48
 
4.0%
4 25
 
2.1%
5 30
 
2.5%
6 31
 
2.6%
7 25
 
2.1%
8 18
 
1.5%
9 17
 
1.4%
ValueCountFrequency (%)
34976 1
0.1%
10746 1
0.1%
8927 1
0.1%
6671 1
0.1%
4391 1
0.1%
2920 1
0.1%
2750 1
0.1%
2688 1
0.1%
2637 1
0.1%
2522 1
0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct299
Distinct (%)26.6%
Missing72
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean174.39769
Minimum0
Maximum37379
Zeros123
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:33:54.523640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median25
Q399.5
95-th percentile436.4
Maximum37379
Range37379
Interquartile range (IQR)96.5

Descriptive statistics

Standard deviation1277.7679
Coefficient of variation (CV)7.326748
Kurtosis654.03242
Mean174.39769
Median Absolute Deviation (MAD)24
Skewness23.653984
Sum196023
Variance1632690.8
MonotonicityNot monotonic
2024-03-23T06:33:55.111680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 123
 
10.3%
1 80
 
6.7%
2 51
 
4.3%
4 37
 
3.1%
3 29
 
2.4%
5 25
 
2.1%
8 23
 
1.9%
9 22
 
1.8%
10 22
 
1.8%
6 18
 
1.5%
Other values (289) 694
58.0%
(Missing) 72
 
6.0%
ValueCountFrequency (%)
0 123
10.3%
1 80
6.7%
2 51
4.3%
3 29
 
2.4%
4 37
 
3.1%
5 25
 
2.1%
6 18
 
1.5%
7 14
 
1.2%
8 23
 
1.9%
9 22
 
1.8%
ValueCountFrequency (%)
37379 1
0.1%
12786 1
0.1%
10294 1
0.1%
7655 1
0.1%
5877 1
0.1%
3567 1
0.1%
3375 1
0.1%
3175 1
0.1%
2762 1
0.1%
2539 1
0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct270
Distinct (%)24.0%
Missing72
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean146.9742
Minimum0
Maximum30653
Zeros128
Zeros (%)10.7%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:33:55.731840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median21
Q390
95-th percentile353.4
Maximum30653
Range30653
Interquartile range (IQR)87

Descriptive statistics

Standard deviation1061.4905
Coefficient of variation (CV)7.2222911
Kurtosis623.52
Mean146.9742
Median Absolute Deviation (MAD)20
Skewness23.008837
Sum165199
Variance1126762
MonotonicityNot monotonic
2024-03-23T06:33:56.251300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 128
 
10.7%
1 83
 
6.9%
2 54
 
4.5%
4 36
 
3.0%
3 34
 
2.8%
6 33
 
2.8%
8 22
 
1.8%
5 20
 
1.7%
7 19
 
1.6%
10 18
 
1.5%
Other values (260) 677
56.6%
(Missing) 72
 
6.0%
ValueCountFrequency (%)
0 128
10.7%
1 83
6.9%
2 54
4.5%
3 34
 
2.8%
4 36
 
3.0%
5 20
 
1.7%
6 33
 
2.8%
7 19
 
1.6%
8 22
 
1.8%
9 15
 
1.3%
ValueCountFrequency (%)
30653 1
0.1%
11198 1
0.1%
8379 1
0.1%
6807 1
0.1%
5237 1
0.1%
3116 1
0.1%
3043 1
0.1%
3038 1
0.1%
2804 1
0.1%
2103 1
0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct258
Distinct (%)23.2%
Missing84
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean132.39658
Minimum0
Maximum26664
Zeros116
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:33:56.825267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median20
Q378
95-th percentile315.35
Maximum26664
Range26664
Interquartile range (IQR)75

Descriptive statistics

Standard deviation933.04833
Coefficient of variation (CV)7.0473747
Kurtosis605.44735
Mean132.39658
Median Absolute Deviation (MAD)19
Skewness22.643877
Sum147225
Variance870579.18
MonotonicityNot monotonic
2024-03-23T06:33:57.316130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 116
 
9.7%
1 91
 
7.6%
2 63
 
5.3%
3 38
 
3.2%
5 37
 
3.1%
4 31
 
2.6%
7 25
 
2.1%
8 19
 
1.6%
9 17
 
1.4%
11 15
 
1.3%
Other values (248) 660
55.2%
(Missing) 84
 
7.0%
ValueCountFrequency (%)
0 116
9.7%
1 91
7.6%
2 63
5.3%
3 38
 
3.2%
4 31
 
2.6%
5 37
 
3.1%
6 13
 
1.1%
7 25
 
2.1%
8 19
 
1.6%
9 17
 
1.4%
ValueCountFrequency (%)
26664 1
0.1%
10047 1
0.1%
7083 1
0.1%
6394 1
0.1%
4416 1
0.1%
2950 1
0.1%
2816 1
0.1%
2632 1
0.1%
1936 1
0.1%
1888 1
0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct261
Distinct (%)23.4%
Missing80
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean124.07885
Minimum0
Maximum24118
Zeros126
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:33:57.761572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median17
Q375
95-th percentile329.5
Maximum24118
Range24118
Interquartile range (IQR)73

Descriptive statistics

Standard deviation858.25902
Coefficient of variation (CV)6.9170451
Kurtosis568.68746
Mean124.07885
Median Absolute Deviation (MAD)17
Skewness21.864844
Sum138472
Variance736608.54
MonotonicityNot monotonic
2024-03-23T06:33:58.682880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 126
 
10.5%
1 91
 
7.6%
2 65
 
5.4%
3 35
 
2.9%
4 34
 
2.8%
5 30
 
2.5%
7 29
 
2.4%
8 19
 
1.6%
9 18
 
1.5%
10 17
 
1.4%
Other values (251) 652
54.5%
(Missing) 80
 
6.7%
ValueCountFrequency (%)
0 126
10.5%
1 91
7.6%
2 65
5.4%
3 35
 
2.9%
4 34
 
2.8%
5 30
 
2.5%
6 17
 
1.4%
7 29
 
2.4%
8 19
 
1.6%
9 18
 
1.5%
ValueCountFrequency (%)
24118 1
0.1%
9800 1
0.1%
6940 1
0.1%
6094 1
0.1%
3612 1
0.1%
3161 1
0.1%
3020 1
0.1%
2611 1
0.1%
2147 1
0.1%
1663 1
0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct252
Distinct (%)22.7%
Missing84
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean119.57824
Minimum0
Maximum23957
Zeros134
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:33:59.289353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median18
Q374.25
95-th percentile314.35
Maximum23957
Range23957
Interquartile range (IQR)72.25

Descriptive statistics

Standard deviation832.93263
Coefficient of variation (CV)6.9655871
Kurtosis620.6971
Mean119.57824
Median Absolute Deviation (MAD)18
Skewness22.994738
Sum132971
Variance693776.77
MonotonicityNot monotonic
2024-03-23T06:34:00.006720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 134
 
11.2%
1 101
 
8.4%
2 48
 
4.0%
3 30
 
2.5%
4 29
 
2.4%
5 28
 
2.3%
7 26
 
2.2%
6 22
 
1.8%
8 20
 
1.7%
12 19
 
1.6%
Other values (242) 655
54.8%
(Missing) 84
 
7.0%
ValueCountFrequency (%)
0 134
11.2%
1 101
8.4%
2 48
 
4.0%
3 30
 
2.5%
4 29
 
2.4%
5 28
 
2.3%
6 22
 
1.8%
7 26
 
2.2%
8 20
 
1.7%
9 14
 
1.2%
ValueCountFrequency (%)
23957 1
0.1%
8992 1
0.1%
6133 1
0.1%
6020 1
0.1%
2599 1
0.1%
2586 1
0.1%
2306 1
0.1%
1832 1
0.1%
1754 1
0.1%
1616 1
0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct326
Distinct (%)29.3%
Missing84
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean206.46403
Minimum0
Maximum38385
Zeros121
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:34:00.792047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median33
Q3124.25
95-th percentile543.05
Maximum38385
Range38385
Interquartile range (IQR)120.25

Descriptive statistics

Standard deviation1384.892
Coefficient of variation (CV)6.7076672
Kurtosis544.84007
Mean206.46403
Median Absolute Deviation (MAD)32
Skewness21.423382
Sum229588
Variance1917925.8
MonotonicityNot monotonic
2024-03-23T06:34:01.389003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 121
 
10.1%
1 76
 
6.4%
2 41
 
3.4%
3 35
 
2.9%
5 27
 
2.3%
7 23
 
1.9%
6 19
 
1.6%
8 18
 
1.5%
4 17
 
1.4%
9 16
 
1.3%
Other values (316) 719
60.1%
(Missing) 84
 
7.0%
ValueCountFrequency (%)
0 121
10.1%
1 76
6.4%
2 41
 
3.4%
3 35
 
2.9%
4 17
 
1.4%
5 27
 
2.3%
6 19
 
1.6%
7 23
 
1.9%
8 18
 
1.5%
9 16
 
1.3%
ValueCountFrequency (%)
38385 1
0.1%
16556 1
0.1%
11332 1
0.1%
11003 1
0.1%
5742 1
0.1%
4922 1
0.1%
3499 1
0.1%
3400 1
0.1%
3088 1
0.1%
2715 1
0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct266
Distinct (%)23.8%
Missing80
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean137.86201
Minimum0
Maximum24512
Zeros120
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:34:01.937734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median26
Q386
95-th percentile380.25
Maximum24512
Range24512
Interquartile range (IQR)82

Descriptive statistics

Standard deviation892.30398
Coefficient of variation (CV)6.472443
Kurtosis525.68036
Mean137.86201
Median Absolute Deviation (MAD)25
Skewness20.99187
Sum153854
Variance796206.4
MonotonicityNot monotonic
2024-03-23T06:34:02.564319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 120
 
10.0%
1 71
 
5.9%
2 37
 
3.1%
3 34
 
2.8%
4 33
 
2.8%
5 30
 
2.5%
6 21
 
1.8%
11 18
 
1.5%
7 17
 
1.4%
26 15
 
1.3%
Other values (256) 720
60.2%
(Missing) 80
 
6.7%
ValueCountFrequency (%)
0 120
10.0%
1 71
5.9%
2 37
 
3.1%
3 34
 
2.8%
4 33
 
2.8%
5 30
 
2.5%
6 21
 
1.8%
7 17
 
1.4%
8 14
 
1.2%
9 12
 
1.0%
ValueCountFrequency (%)
24512 1
0.1%
10394 1
0.1%
8909 1
0.1%
6160 1
0.1%
4039 1
0.1%
3489 1
0.1%
2477 1
0.1%
2392 1
0.1%
1644 1
0.1%
1588 1
0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct181
Distinct (%)16.2%
Missing80
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean58.61828
Minimum0
Maximum11445
Zeros134
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:34:03.078632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median11
Q333
95-th percentile171.25
Maximum11445
Range11445
Interquartile range (IQR)31

Descriptive statistics

Standard deviation396.27303
Coefficient of variation (CV)6.7602297
Kurtosis627.52873
Mean58.61828
Median Absolute Deviation (MAD)10
Skewness23.118989
Sum65418
Variance157032.32
MonotonicityNot monotonic
2024-03-23T06:34:03.737929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 134
 
11.2%
1 100
 
8.4%
2 55
 
4.6%
3 47
 
3.9%
5 43
 
3.6%
4 41
 
3.4%
8 38
 
3.2%
11 29
 
2.4%
10 28
 
2.3%
6 27
 
2.3%
Other values (171) 574
48.0%
(Missing) 80
 
6.7%
ValueCountFrequency (%)
0 134
11.2%
1 100
8.4%
2 55
4.6%
3 47
 
3.9%
4 41
 
3.4%
5 43
 
3.6%
6 27
 
2.3%
7 22
 
1.8%
8 38
 
3.2%
9 21
 
1.8%
ValueCountFrequency (%)
11445 1
0.1%
3897 1
0.1%
3776 1
0.1%
2116 1
0.1%
1401 1
0.1%
1312 1
0.1%
1008 1
0.1%
966 1
0.1%
775 1
0.1%
739 1
0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct209
Distinct (%)18.7%
Missing76
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean86.809821
Minimum0
Maximum18118
Zeros155
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:34:04.542882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median13
Q350
95-th percentile251
Maximum18118
Range18118
Interquartile range (IQR)48

Descriptive statistics

Standard deviation618.99565
Coefficient of variation (CV)7.1304795
Kurtosis658.84257
Mean86.809821
Median Absolute Deviation (MAD)13
Skewness23.801393
Sum97227
Variance383155.62
MonotonicityNot monotonic
2024-03-23T06:34:05.414985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 155
 
13.0%
1 90
 
7.5%
2 53
 
4.4%
3 43
 
3.6%
4 40
 
3.3%
5 34
 
2.8%
7 27
 
2.3%
13 26
 
2.2%
8 23
 
1.9%
10 20
 
1.7%
Other values (199) 609
50.9%
(Missing) 76
 
6.4%
ValueCountFrequency (%)
0 155
13.0%
1 90
7.5%
2 53
 
4.4%
3 43
 
3.6%
4 40
 
3.3%
5 34
 
2.8%
6 19
 
1.6%
7 27
 
2.3%
8 23
 
1.9%
9 18
 
1.5%
ValueCountFrequency (%)
18118 1
0.1%
6633 1
0.1%
4352 1
0.1%
4026 1
0.1%
2021 1
0.1%
1597 1
0.1%
1503 1
0.1%
1202 1
0.1%
1148 1
0.1%
1138 1
0.1%

Interactions

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2024-03-23T06:31:33.314334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T06:31:37.382236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:31:43.888096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:31:50.475975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:31:58.360062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:05.607145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:12.183995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:18.959486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:25.914048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:33.900041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:40.486550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:47.973615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:55.550488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:02.250462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:09.125118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:15.144539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:21.495966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:29.062199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:35.334972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:31:37.697471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:31:44.235282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:31:50.985843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:31:58.681419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:06.036504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:12.561601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:19.407368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:26.354381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:34.312016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:41.080800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:48.252510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:55.817629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:02.629146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:09.422186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:15.510325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:21.867834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:29.447191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:35.629688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:31:37.974513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:31:44.567478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:31:51.381670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:31:59.105016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:06.318019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:12.952017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:19.854139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:26.809067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:34.735432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:41.409434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:48.589464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:56.144038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:03.041929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:09.713590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:15.848568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:22.265611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:29.764069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:35.942540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:31:38.336697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:31:44.990095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:31:51.772203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:31:59.654443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:06.617193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:13.393756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:20.187634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:27.218809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:35.230124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:41.686153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:48.964779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:56.405626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:03.456946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:09.986039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:16.120473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:22.702219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:30.022000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:36.260763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:31:38.732022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:31:45.410557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:31:52.063329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:00.208483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:06.954118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:13.730036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:20.822519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:28.038986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:35.590149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:41.994160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:49.447481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:56.720534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:03.849361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:10.376743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:16.433422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:23.145819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:30.435329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:36.621716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:31:39.093900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:31:45.820478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:31:52.602621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:00.656443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:07.278067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:14.142868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:21.118018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:28.556554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:35.879394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:42.258064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:50.184146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:56.977835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:04.143372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:10.872602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:16.773068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:23.446620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:30.702003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:37.019781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:31:39.431377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:31:46.095499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:31:53.210987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:01.069377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:07.667623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:14.782024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:21.460762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:28.890664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:36.415548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:42.589430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:50.872030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:32:57.361716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:04.406994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:11.159068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:17.094844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:23.835624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:33:30.980301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T06:34:06.141018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.8750.8480.8750.8220.9750.8580.8940.8940.8480.8750.9010.9250.8750.8750.9800.9580.848
20070.8751.0000.9911.0000.9861.0001.0000.9910.9910.9980.9950.9990.9980.9950.9950.9260.8530.991
20080.8480.9911.0000.9910.9980.9380.9070.9910.9910.9860.9860.9900.9890.9860.9910.9610.9270.986
20090.8751.0000.9911.0000.9861.0001.0000.9910.9910.9980.9950.9990.9980.9950.9950.9260.8530.991
20100.8220.9860.9980.9861.0000.9220.8870.9860.9860.9830.9830.9850.9840.9830.9860.9550.9380.983
20110.9751.0000.9381.0000.9221.0001.0000.9380.9381.0001.0001.0001.0001.0001.0001.0000.9781.000
20120.8581.0000.9071.0000.8871.0001.0000.9070.9071.0001.0001.0001.0001.0001.0001.0000.8691.000
20130.8940.9910.9910.9910.9860.9380.9071.0001.0000.9950.9910.9900.9890.9910.9860.9500.8530.986
20140.8940.9910.9910.9910.9860.9380.9071.0001.0000.9950.9910.9900.9890.9910.9860.9500.8530.986
20150.8480.9980.9860.9980.9831.0001.0000.9950.9951.0000.9980.9960.9950.9980.9910.9130.8290.991
20160.8750.9950.9860.9950.9831.0001.0000.9910.9910.9981.0000.9940.9980.9950.9910.9130.8290.991
20170.9010.9990.9900.9990.9851.0001.0000.9900.9900.9960.9941.0001.0000.9990.9990.9460.8450.996
20180.9250.9980.9890.9980.9841.0001.0000.9890.9890.9950.9981.0001.0000.9980.9980.9380.8410.995
20190.8750.9950.9860.9950.9831.0001.0000.9910.9910.9980.9950.9990.9981.0000.9950.9260.8290.998
20200.8750.9950.9910.9950.9861.0001.0000.9860.9860.9910.9910.9990.9980.9951.0000.9610.8530.998
20210.9800.9260.9610.9260.9551.0001.0000.9500.9500.9130.9130.9460.9380.9260.9611.0000.9970.938
20220.9580.8530.9270.8530.9380.9780.8690.8530.8530.8290.8290.8450.8410.8290.8530.9971.0000.829
20230.8480.9910.9860.9910.9831.0001.0000.9860.9860.9910.9910.9960.9950.9980.9980.9380.8291.000
2024-03-23T06:34:07.110677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9520.9300.9230.9060.9180.9190.9450.9520.9580.9570.9570.9560.9560.9530.9240.8660.935
20070.9521.0000.9630.9320.9330.9370.9360.9470.9490.9510.9480.9470.9360.9470.9510.9430.9070.942
20080.9300.9631.0000.9460.9420.9360.9320.9480.9450.9460.9420.9410.9340.9420.9420.9410.9140.940
20090.9230.9320.9461.0000.9520.9480.9360.9490.9460.9490.9470.9440.9380.9440.9440.9300.9020.939
20100.9060.9330.9420.9521.0000.9620.9510.9540.9500.9440.9360.9330.9210.9400.9410.9350.9210.942
20110.9180.9370.9360.9480.9621.0000.9690.9650.9570.9540.9430.9430.9340.9500.9550.9440.9280.953
20120.9190.9360.9320.9360.9510.9691.0000.9710.9600.9570.9430.9430.9320.9470.9480.9370.9220.951
20130.9450.9470.9480.9490.9540.9650.9711.0000.9810.9770.9670.9660.9580.9690.9670.9460.9240.968
20140.9520.9490.9450.9460.9500.9570.9600.9811.0000.9830.9720.9700.9610.9680.9680.9440.9110.966
20150.9580.9510.9460.9490.9440.9540.9570.9770.9831.0000.9810.9770.9680.9690.9700.9440.9050.966
20160.9570.9480.9420.9470.9360.9430.9430.9670.9720.9811.0000.9840.9740.9720.9690.9420.9000.963
20170.9570.9470.9410.9440.9330.9430.9430.9660.9700.9770.9841.0000.9810.9760.9700.9410.9010.965
20180.9560.9360.9340.9380.9210.9340.9320.9580.9610.9680.9740.9811.0000.9780.9650.9320.8860.959
20190.9560.9470.9420.9440.9400.9500.9470.9690.9680.9690.9720.9760.9781.0000.9810.9500.9160.974
20200.9530.9510.9420.9440.9410.9550.9480.9670.9680.9700.9690.9700.9650.9811.0000.9680.9290.977
20210.9240.9430.9410.9300.9350.9440.9370.9460.9440.9440.9420.9410.9320.9500.9681.0000.9680.967
20220.8660.9070.9140.9020.9210.9280.9220.9240.9110.9050.9000.9010.8860.9160.9290.9681.0000.951
20230.9350.9420.9400.9390.9420.9530.9510.9680.9660.9660.9630.9650.9590.9740.9770.9670.9511.000

Missing values

2024-03-23T06:33:37.547129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T06:33:38.526545image/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.
2024-03-23T06:33:39.130783image/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

아파트매매 매입자거주지별200620072008200920102011201220132014201520162017201820192020202120222023
0전국 /관할시군구내304582242623948285932676532304240682898434976373793065326664241182395738385245121144518118
1전국 /관할시도내11846748678769433856710622691489061074612786111981004798008992165561039438976633
2전국 /관할시도외_서울386227183152380529093122203022242688317530432950302023064922403913121503
3전국 /관할시도외_기타6068469059635927579374985159574066717655680763946094613311332890937764026
4서울 /관할시군구내53622260232032031984276520293169439158775237441636122586308815694581138
5서울 /관할시도내345215291516187710721366892136218412762311626322611183225551424354933
6서울 /관할시도외_서울000000000000000000
7서울 /관할시도외_기타181977277410946187515167751045166715251522147811191465691192596
8서울 종로구/관할시군구내25141621142421242841333027192112310
9서울 종로구/관할시도내32171920111611171826272640243621511
아파트매매 매입자거주지별200620072008200920102011201220132014201520162017201820192020202120222023
1186제주 /관할시도외_서울4986813141317151912159121989
1187제주 /관할시도외_기타121513142026303536503431231936522513
1188제주 제주시/관할시군구내12918513514321823217619924920818812416114418519713699
1189제주 제주시/관할시도내61212141317121419161277710854
1190제주 제주시/관할시도외_서울375561010813111377681144
1191제주 제주시/관할시도외_기타10121012161921263028201715122026148
1192제주 서귀포시/관할시군구내83352302752294332504731322544503433
1193제주 서귀포시/관할시도내223233253465347743
1194제주 서귀포시/관할시도외_서울122123554465834845
1195제주 서귀포시/관할시도외_기타1333461096211413971626115