Overview

Dataset statistics

Number of variables19
Number of observations2691
Missing cells3447
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory446.9 KiB
Average record size in memory170.0 B

Variable types

Text1
Numeric18

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 부동산거래현황 중 아파트매매 거래현황의 연도별 거래주체별(동(호)수) 데이터입니다.-(단위 : 동(호)수)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068504/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 216 (8.0%) missing valuesMissing
2007 has 261 (9.7%) missing valuesMissing
2008 has 234 (8.7%) missing valuesMissing
2009 has 234 (8.7%) missing valuesMissing
2010 has 180 (6.7%) missing valuesMissing
2011 has 207 (7.7%) missing valuesMissing
2012 has 189 (7.0%) missing valuesMissing
2013 has 189 (7.0%) missing valuesMissing
2014 has 135 (5.0%) missing valuesMissing
2015 has 162 (6.0%) missing valuesMissing
2016 has 162 (6.0%) missing valuesMissing
2017 has 189 (7.0%) missing valuesMissing
2018 has 180 (6.7%) missing valuesMissing
2019 has 189 (7.0%) missing valuesMissing
2020 has 189 (7.0%) missing valuesMissing
2021 has 180 (6.7%) missing valuesMissing
2022 has 180 (6.7%) missing valuesMissing
2023 has 171 (6.4%) missing valuesMissing
2006 is highly skewed (γ1 = 38.5958741)Skewed
2007 is highly skewed (γ1 = 39.52220027)Skewed
2008 is highly skewed (γ1 = 39.98538718)Skewed
2009 is highly skewed (γ1 = 39.8648636)Skewed
2010 is highly skewed (γ1 = 41.36154245)Skewed
2011 is highly skewed (γ1 = 40.94733626)Skewed
2012 is highly skewed (γ1 = 41.37619795)Skewed
2013 is highly skewed (γ1 = 41.78471644)Skewed
2014 is highly skewed (γ1 = 42.28578847)Skewed
2015 is highly skewed (γ1 = 41.81506752)Skewed
2016 is highly skewed (γ1 = 41.62909413)Skewed
2017 is highly skewed (γ1 = 41.81435933)Skewed
2018 is highly skewed (γ1 = 41.43156341)Skewed
2019 is highly skewed (γ1 = 42.44726764)Skewed
2020 is highly skewed (γ1 = 41.75819368)Skewed
2021 is highly skewed (γ1 = 42.38400918)Skewed
2022 is highly skewed (γ1 = 43.17185344)Skewed
2023 is highly skewed (γ1 = 42.8982395)Skewed
아파트매매 거래주체별 has unique valuesUnique
2006 has 968 (36.0%) zerosZeros
2007 has 930 (34.6%) zerosZeros
2008 has 918 (34.1%) zerosZeros
2009 has 802 (29.8%) zerosZeros
2010 has 790 (29.4%) zerosZeros
2011 has 654 (24.3%) zerosZeros
2012 has 751 (27.9%) zerosZeros
2013 has 860 (32.0%) zerosZeros
2014 has 931 (34.6%) zerosZeros
2015 has 880 (32.7%) zerosZeros
2016 has 949 (35.3%) zerosZeros
2017 has 946 (35.2%) zerosZeros
2018 has 942 (35.0%) zerosZeros
2019 has 881 (32.7%) zerosZeros
2020 has 832 (30.9%) zerosZeros
2021 has 881 (32.7%) zerosZeros
2022 has 944 (35.1%) zerosZeros
2023 has 968 (36.0%) zerosZeros

Reproduction

Analysis started2024-03-23 06:23:10.643345
Analysis finished2024-03-23 06:25:35.868907
Duration2 minutes and 25.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct2691
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
2024-03-23T06:25:36.241728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length13
Mean length13.341137
Min length10

Characters and Unicode

Total characters35901
Distinct characters153
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2691 ?
Unique (%)100.0%

Sample

1st row전국 /개인->개인
2nd row전국 /개인->법인
3rd row전국 /개인->기타
4th row전국 /법인->개인
5th row전국 /법인->법인
ValueCountFrequency (%)
경기 477
 
8.9%
경남 243
 
4.5%
서울 234
 
4.3%
경북 234
 
4.3%
전남 207
 
3.8%
충남 180
 
3.3%
충북 180
 
3.3%
강원 171
 
3.2%
전북 153
 
2.8%
부산 153
 
2.8%
Other values (2322) 3150
58.5%
2024-03-23T06:25:37.757557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3750
 
10.4%
2691
 
7.5%
/ 2691
 
7.5%
- 2691
 
7.5%
> 2691
 
7.5%
2298
 
6.4%
1794
 
5.0%
1794
 
5.0%
1794
 
5.0%
1269
 
3.5%
Other values (143) 12438
34.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24795
69.1%
Space Separator 2691
 
7.5%
Other Punctuation 2691
 
7.5%
Dash Punctuation 2691
 
7.5%
Math Symbol 2691
 
7.5%
Open Punctuation 171
 
0.5%
Close Punctuation 171
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3750
15.1%
2298
 
9.3%
1794
 
7.2%
1794
 
7.2%
1794
 
7.2%
1269
 
5.1%
1098
 
4.4%
981
 
4.0%
837
 
3.4%
801
 
3.2%
Other values (137) 8379
33.8%
Space Separator
ValueCountFrequency (%)
2691
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2691
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2691
100.0%
Math Symbol
ValueCountFrequency (%)
> 2691
100.0%
Open Punctuation
ValueCountFrequency (%)
( 171
100.0%
Close Punctuation
ValueCountFrequency (%)
) 171
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24795
69.1%
Common 11106
30.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3750
15.1%
2298
 
9.3%
1794
 
7.2%
1794
 
7.2%
1794
 
7.2%
1269
 
5.1%
1098
 
4.4%
981
 
4.0%
837
 
3.4%
801
 
3.2%
Other values (137) 8379
33.8%
Common
ValueCountFrequency (%)
2691
24.2%
/ 2691
24.2%
- 2691
24.2%
> 2691
24.2%
( 171
 
1.5%
) 171
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24795
69.1%
ASCII 11106
30.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3750
15.1%
2298
 
9.3%
1794
 
7.2%
1794
 
7.2%
1794
 
7.2%
1269
 
5.1%
1098
 
4.4%
981
 
4.0%
837
 
3.4%
801
 
3.2%
Other values (137) 8379
33.8%
ASCII
ValueCountFrequency (%)
2691
24.2%
/ 2691
24.2%
- 2691
24.2%
> 2691
24.2%
( 171
 
1.5%
) 171
 
1.5%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct508
Distinct (%)20.5%
Missing216
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean932.12646
Minimum0
Maximum616289
Zeros968
Zeros (%)36.0%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:25:38.681633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q336
95-th percentile2757.1
Maximum616289
Range616289
Interquartile range (IQR)36

Descriptive statistics

Standard deviation13735.613
Coefficient of variation (CV)14.735783
Kurtosis1661.1445
Mean932.12646
Median Absolute Deviation (MAD)2
Skewness38.595874
Sum2307013
Variance1.8866707 × 108
MonotonicityNot monotonic
2024-03-23T06:25:39.384493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 968
36.0%
1 207
 
7.7%
2 95
 
3.5%
3 74
 
2.7%
4 66
 
2.5%
5 59
 
2.2%
6 45
 
1.7%
7 30
 
1.1%
9 24
 
0.9%
11 23
 
0.9%
Other values (498) 884
32.9%
(Missing) 216
 
8.0%
ValueCountFrequency (%)
0 968
36.0%
1 207
 
7.7%
2 95
 
3.5%
3 74
 
2.7%
4 66
 
2.5%
5 59
 
2.2%
6 45
 
1.7%
7 30
 
1.1%
8 19
 
0.7%
9 24
 
0.9%
ValueCountFrequency (%)
616289 1
< 0.1%
222292 1
< 0.1%
137898 1
< 0.1%
84117 1
< 0.1%
45165 1
< 0.1%
30477 1
< 0.1%
27206 1
< 0.1%
25638 1
< 0.1%
25008 1
< 0.1%
22325 1
< 0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct508
Distinct (%)20.9%
Missing261
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean696.71728
Minimum0
Maximum430358
Zeros930
Zeros (%)34.6%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:25:40.235820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q344
95-th percentile2294.95
Maximum430358
Range430358
Interquartile range (IQR)44

Descriptive statistics

Standard deviation9487.894
Coefficient of variation (CV)13.617997
Kurtosis1745.4553
Mean696.71728
Median Absolute Deviation (MAD)2
Skewness39.5222
Sum1693023
Variance90020132
MonotonicityNot monotonic
2024-03-23T06:25:41.022781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 930
34.6%
1 196
 
7.3%
2 99
 
3.7%
3 75
 
2.8%
4 60
 
2.2%
5 51
 
1.9%
6 41
 
1.5%
8 33
 
1.2%
13 24
 
0.9%
9 23
 
0.9%
Other values (498) 898
33.4%
(Missing) 261
 
9.7%
ValueCountFrequency (%)
0 930
34.6%
1 196
 
7.3%
2 99
 
3.7%
3 75
 
2.8%
4 60
 
2.2%
5 51
 
1.9%
6 41
 
1.5%
7 21
 
0.8%
8 33
 
1.2%
9 23
 
0.9%
ValueCountFrequency (%)
430358 1
< 0.1%
114376 1
< 0.1%
89794 1
< 0.1%
62799 1
< 0.1%
42597 1
< 0.1%
34980 1
< 0.1%
28808 1
< 0.1%
23587 1
< 0.1%
18684 1
< 0.1%
17772 1
< 0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct534
Distinct (%)21.7%
Missing234
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean747.31176
Minimum0
Maximum454235
Zeros918
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:25:41.841017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q346
95-th percentile2623.2
Maximum454235
Range454235
Interquartile range (IQR)46

Descriptive statistics

Standard deviation9927.107
Coefficient of variation (CV)13.283756
Kurtosis1785.5146
Mean747.31176
Median Absolute Deviation (MAD)2
Skewness39.985387
Sum1836145
Variance98547454
MonotonicityNot monotonic
2024-03-23T06:25:42.282337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 918
34.1%
1 204
 
7.6%
2 111
 
4.1%
3 83
 
3.1%
4 66
 
2.5%
5 51
 
1.9%
6 41
 
1.5%
8 32
 
1.2%
7 32
 
1.2%
10 28
 
1.0%
Other values (524) 891
33.1%
(Missing) 234
 
8.7%
ValueCountFrequency (%)
0 918
34.1%
1 204
 
7.6%
2 111
 
4.1%
3 83
 
3.1%
4 66
 
2.5%
5 51
 
1.9%
6 41
 
1.5%
7 32
 
1.2%
8 32
 
1.2%
9 13
 
0.5%
ValueCountFrequency (%)
454235 1
< 0.1%
113379 1
< 0.1%
95873 1
< 0.1%
60727 1
< 0.1%
41801 1
< 0.1%
38115 1
< 0.1%
30366 1
< 0.1%
24343 1
< 0.1%
23798 1
< 0.1%
20043 1
< 0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct540
Distinct (%)22.0%
Missing234
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean818.11437
Minimum0
Maximum496791
Zeros802
Zeros (%)29.8%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:25:42.819571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q352
95-th percentile2811.6
Maximum496791
Range496791
Interquartile range (IQR)52

Descriptive statistics

Standard deviation10874.708
Coefficient of variation (CV)13.292405
Kurtosis1775.0901
Mean818.11437
Median Absolute Deviation (MAD)4
Skewness39.864864
Sum2010107
Variance1.1825926 × 108
MonotonicityNot monotonic
2024-03-23T06:25:43.382035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 802
29.8%
1 219
 
8.1%
2 113
 
4.2%
3 86
 
3.2%
5 58
 
2.2%
4 57
 
2.1%
6 51
 
1.9%
7 44
 
1.6%
8 35
 
1.3%
9 30
 
1.1%
Other values (530) 962
35.7%
(Missing) 234
 
8.7%
ValueCountFrequency (%)
0 802
29.8%
1 219
 
8.1%
2 113
 
4.2%
3 86
 
3.2%
4 57
 
2.1%
5 58
 
2.2%
6 51
 
1.9%
7 44
 
1.6%
8 35
 
1.3%
9 30
 
1.1%
ValueCountFrequency (%)
496791 1
< 0.1%
129476 1
< 0.1%
101016 1
< 0.1%
75085 1
< 0.1%
48686 1
< 0.1%
34579 1
< 0.1%
26551 1
< 0.1%
24365 1
< 0.1%
23454 1
< 0.1%
22808 1
< 0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct532
Distinct (%)21.2%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean734.29192
Minimum0
Maximum456014
Zeros790
Zeros (%)29.4%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:25:43.999000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q345.5
95-th percentile2552.5
Maximum456014
Range456014
Interquartile range (IQR)45.5

Descriptive statistics

Standard deviation9755.0712
Coefficient of variation (CV)13.285004
Kurtosis1898.4936
Mean734.29192
Median Absolute Deviation (MAD)4
Skewness41.361542
Sum1843807
Variance95161414
MonotonicityNot monotonic
2024-03-23T06:25:44.429553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 790
29.4%
1 212
 
7.9%
2 156
 
5.8%
3 87
 
3.2%
4 71
 
2.6%
5 53
 
2.0%
6 49
 
1.8%
7 42
 
1.6%
9 36
 
1.3%
8 36
 
1.3%
Other values (522) 979
36.4%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 790
29.4%
1 212
 
7.9%
2 156
 
5.8%
3 87
 
3.2%
4 71
 
2.6%
5 53
 
2.0%
6 49
 
1.8%
7 42
 
1.6%
8 36
 
1.3%
9 36
 
1.3%
ValueCountFrequency (%)
456014 1
< 0.1%
93591 1
< 0.1%
88653 1
< 0.1%
55194 1
< 0.1%
44797 1
< 0.1%
44349 1
< 0.1%
26505 1
< 0.1%
24007 1
< 0.1%
23995 1
< 0.1%
22954 1
< 0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct555
Distinct (%)22.3%
Missing207
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean908.5004
Minimum0
Maximum564512
Zeros654
Zeros (%)24.3%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:25:45.178053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q352
95-th percentile3141
Maximum564512
Range564512
Interquartile range (IQR)52

Descriptive statistics

Standard deviation12176.993
Coefficient of variation (CV)13.403398
Kurtosis1858.9942
Mean908.5004
Median Absolute Deviation (MAD)6
Skewness40.947336
Sum2256715
Variance1.4827915 × 108
MonotonicityNot monotonic
2024-03-23T06:25:45.618177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 654
24.3%
1 204
 
7.6%
2 142
 
5.3%
3 101
 
3.8%
4 78
 
2.9%
5 56
 
2.1%
6 55
 
2.0%
7 54
 
2.0%
8 47
 
1.7%
10 41
 
1.5%
Other values (545) 1052
39.1%
(Missing) 207
 
7.7%
ValueCountFrequency (%)
0 654
24.3%
1 204
 
7.6%
2 142
 
5.3%
3 101
 
3.8%
4 78
 
2.9%
5 56
 
2.1%
6 55
 
2.0%
7 54
 
2.0%
8 47
 
1.7%
9 40
 
1.5%
ValueCountFrequency (%)
564512 1
< 0.1%
136752 1
< 0.1%
109406 1
< 0.1%
59598 1
< 0.1%
50123 1
< 0.1%
42970 1
< 0.1%
39294 1
< 0.1%
31404 1
< 0.1%
30649 1
< 0.1%
25203 1
< 0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct508
Distinct (%)20.3%
Missing189
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean643.31575
Minimum0
Maximum403924
Zeros751
Zeros (%)27.9%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:25:46.020391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q343
95-th percentile2282.45
Maximum403924
Range403924
Interquartile range (IQR)43

Descriptive statistics

Standard deviation8655.0583
Coefficient of variation (CV)13.453826
Kurtosis1894.2979
Mean643.31575
Median Absolute Deviation (MAD)4
Skewness41.376198
Sum1609576
Variance74910035
MonotonicityNot monotonic
2024-03-23T06:25:46.651324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 751
27.9%
1 224
 
8.3%
2 125
 
4.6%
3 96
 
3.6%
4 76
 
2.8%
6 63
 
2.3%
5 57
 
2.1%
7 53
 
2.0%
8 48
 
1.8%
9 42
 
1.6%
Other values (498) 967
35.9%
(Missing) 189
 
7.0%
ValueCountFrequency (%)
0 751
27.9%
1 224
 
8.3%
2 125
 
4.6%
3 96
 
3.6%
4 76
 
2.8%
5 57
 
2.1%
6 63
 
2.3%
7 53
 
2.0%
8 48
 
1.8%
9 42
 
1.6%
ValueCountFrequency (%)
403924 1
< 0.1%
93940 1
< 0.1%
75287 1
< 0.1%
41540 1
< 0.1%
32857 1
< 0.1%
30633 1
< 0.1%
28108 1
< 0.1%
26475 1
< 0.1%
23990 1
< 0.1%
20633 1
< 0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct504
Distinct (%)20.1%
Missing189
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean774.31135
Minimum0
Maximum517349
Zeros860
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:25:47.362425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q340
95-th percentile2594.7
Maximum517349
Range517349
Interquartile range (IQR)40

Descriptive statistics

Standard deviation11054.124
Coefficient of variation (CV)14.276072
Kurtosis1918.1353
Mean774.31135
Median Absolute Deviation (MAD)3
Skewness41.784716
Sum1937327
Variance1.2219367 × 108
MonotonicityNot monotonic
2024-03-23T06:25:47.937904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 860
32.0%
1 207
 
7.7%
2 116
 
4.3%
3 89
 
3.3%
4 62
 
2.3%
5 53
 
2.0%
6 53
 
2.0%
9 43
 
1.6%
8 41
 
1.5%
7 34
 
1.3%
Other values (494) 944
35.1%
(Missing) 189
 
7.0%
ValueCountFrequency (%)
0 860
32.0%
1 207
 
7.7%
2 116
 
4.3%
3 89
 
3.3%
4 62
 
2.3%
5 53
 
2.0%
6 53
 
2.0%
7 34
 
1.3%
8 41
 
1.5%
9 43
 
1.6%
ValueCountFrequency (%)
517349 1
< 0.1%
134161 1
< 0.1%
67213 1
< 0.1%
64275 1
< 0.1%
40962 1
< 0.1%
37789 1
< 0.1%
36600 1
< 0.1%
32153 1
< 0.1%
27199 1
< 0.1%
23197 1
< 0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct524
Distinct (%)20.5%
Missing135
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean895.84898
Minimum0
Maximum625959
Zeros931
Zeros (%)34.6%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:25:48.646353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q343
95-th percentile3076.5
Maximum625959
Range625959
Interquartile range (IQR)43

Descriptive statistics

Standard deviation13236.709
Coefficient of variation (CV)14.775603
Kurtosis1959.3149
Mean895.84898
Median Absolute Deviation (MAD)3
Skewness42.285788
Sum2289790
Variance1.7521047 × 108
MonotonicityNot monotonic
2024-03-23T06:25:49.325747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 931
34.6%
1 233
 
8.7%
2 110
 
4.1%
3 94
 
3.5%
4 61
 
2.3%
5 58
 
2.2%
7 42
 
1.6%
6 40
 
1.5%
9 31
 
1.2%
8 25
 
0.9%
Other values (514) 931
34.6%
(Missing) 135
 
5.0%
ValueCountFrequency (%)
0 931
34.6%
1 233
 
8.7%
2 110
 
4.1%
3 94
 
3.5%
4 61
 
2.3%
5 58
 
2.2%
6 40
 
1.5%
7 42
 
1.6%
8 25
 
0.9%
9 31
 
1.2%
ValueCountFrequency (%)
625959 1
< 0.1%
170002 1
< 0.1%
86843 1
< 0.1%
62855 1
< 0.1%
51800 1
< 0.1%
46565 1
< 0.1%
39653 1
< 0.1%
35476 1
< 0.1%
27537 1
< 0.1%
27229 1
< 0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct516
Distinct (%)20.4%
Missing162
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean1026.4314
Minimum0
Maximum736130
Zeros880
Zeros (%)32.7%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:25:50.438026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q344
95-th percentile3561.4
Maximum736130
Range736130
Interquartile range (IQR)44

Descriptive statistics

Standard deviation15700.993
Coefficient of variation (CV)15.29668
Kurtosis1915.9202
Mean1026.4314
Median Absolute Deviation (MAD)4
Skewness41.815068
Sum2595845
Variance2.4652117 × 108
MonotonicityNot monotonic
2024-03-23T06:25:51.063994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 880
32.7%
1 185
 
6.9%
2 106
 
3.9%
3 77
 
2.9%
4 65
 
2.4%
6 49
 
1.8%
5 49
 
1.8%
7 43
 
1.6%
10 36
 
1.3%
12 32
 
1.2%
Other values (506) 1007
37.4%
(Missing) 162
 
6.0%
ValueCountFrequency (%)
0 880
32.7%
1 185
 
6.9%
2 106
 
3.9%
3 77
 
2.9%
4 65
 
2.4%
5 49
 
1.8%
6 49
 
1.8%
7 43
 
1.6%
8 29
 
1.1%
9 31
 
1.2%
ValueCountFrequency (%)
736130 1
< 0.1%
207396 1
< 0.1%
126813 1
< 0.1%
65431 1
< 0.1%
54073 1
< 0.1%
50236 1
< 0.1%
48437 1
< 0.1%
35662 1
< 0.1%
27538 1
< 0.1%
24749 1
< 0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct510
Distinct (%)20.2%
Missing162
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean869.72796
Minimum0
Maximum622726
Zeros949
Zeros (%)35.3%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:25:51.653775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q339
95-th percentile2898.4
Maximum622726
Range622726
Interquartile range (IQR)39

Descriptive statistics

Standard deviation13310.122
Coefficient of variation (CV)15.303777
Kurtosis1900.9356
Mean869.72796
Median Absolute Deviation (MAD)3
Skewness41.629094
Sum2199542
Variance1.7715936 × 108
MonotonicityNot monotonic
2024-03-23T06:25:52.237856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 949
35.3%
1 191
 
7.1%
2 108
 
4.0%
3 76
 
2.8%
4 59
 
2.2%
5 57
 
2.1%
6 50
 
1.9%
8 40
 
1.5%
7 38
 
1.4%
11 26
 
1.0%
Other values (500) 935
34.7%
(Missing) 162
 
6.0%
ValueCountFrequency (%)
0 949
35.3%
1 191
 
7.1%
2 108
 
4.0%
3 76
 
2.8%
4 59
 
2.2%
5 57
 
2.1%
6 50
 
1.9%
7 38
 
1.4%
8 40
 
1.5%
9 25
 
0.9%
ValueCountFrequency (%)
622726 1
< 0.1%
177465 1
< 0.1%
115295 1
< 0.1%
56724 1
< 0.1%
45868 1
< 0.1%
43138 1
< 0.1%
35760 1
< 0.1%
22692 1
< 0.1%
21577 1
< 0.1%
21303 1
< 0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct456
Distinct (%)18.2%
Missing189
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean779.85691
Minimum0
Maximum567440
Zeros946
Zeros (%)35.2%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:25:52.790161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q334
95-th percentile2643.6
Maximum567440
Range567440
Interquartile range (IQR)34

Descriptive statistics

Standard deviation12144.769
Coefficient of variation (CV)15.573074
Kurtosis1909.9685
Mean779.85691
Median Absolute Deviation (MAD)2
Skewness41.814359
Sum1951202
Variance1.4749542 × 108
MonotonicityNot monotonic
2024-03-23T06:25:53.382842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 946
35.2%
1 195
 
7.2%
2 115
 
4.3%
6 65
 
2.4%
3 60
 
2.2%
4 56
 
2.1%
5 48
 
1.8%
8 37
 
1.4%
7 33
 
1.2%
12 31
 
1.2%
Other values (446) 916
34.0%
(Missing) 189
 
7.0%
ValueCountFrequency (%)
0 946
35.2%
1 195
 
7.2%
2 115
 
4.3%
3 60
 
2.2%
4 56
 
2.1%
5 48
 
1.8%
6 65
 
2.4%
7 33
 
1.2%
8 37
 
1.4%
9 23
 
0.9%
ValueCountFrequency (%)
567440 1
< 0.1%
157196 1
< 0.1%
105461 1
< 0.1%
39170 1
< 0.1%
37776 1
< 0.1%
32207 1
< 0.1%
29149 1
< 0.1%
26584 1
< 0.1%
22454 1
< 0.1%
20285 1
< 0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct460
Distinct (%)18.3%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean720.0673
Minimum0
Maximum522524
Zeros942
Zeros (%)35.0%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:25:54.076987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q338.5
95-th percentile2260.5
Maximum522524
Range522524
Interquartile range (IQR)38.5

Descriptive statistics

Standard deviation11227.286
Coefficient of variation (CV)15.591995
Kurtosis1878.3209
Mean720.0673
Median Absolute Deviation (MAD)3
Skewness41.431563
Sum1808089
Variance1.2605195 × 108
MonotonicityNot monotonic
2024-03-23T06:25:54.767258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 942
35.0%
1 206
 
7.7%
2 92
 
3.4%
3 75
 
2.8%
5 50
 
1.9%
4 49
 
1.8%
6 44
 
1.6%
8 39
 
1.4%
7 32
 
1.2%
9 29
 
1.1%
Other values (450) 953
35.4%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 942
35.0%
1 206
 
7.7%
2 92
 
3.4%
3 75
 
2.8%
4 49
 
1.8%
5 50
 
1.9%
6 44
 
1.6%
7 32
 
1.2%
8 39
 
1.4%
9 29
 
1.1%
ValueCountFrequency (%)
522524 1
< 0.1%
159635 1
< 0.1%
93191 1
< 0.1%
33593 1
< 0.1%
32047 1
< 0.1%
26101 1
< 0.1%
25466 1
< 0.1%
24012 1
< 0.1%
21370 1
< 0.1%
20564 1
< 0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct498
Distinct (%)19.9%
Missing189
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean698.3693
Minimum0
Maximum492592
Zeros881
Zeros (%)32.7%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:25:55.587952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q353
95-th percentile2288.15
Maximum492592
Range492592
Interquartile range (IQR)53

Descriptive statistics

Standard deviation10469.176
Coefficient of variation (CV)14.990888
Kurtosis1960.6419
Mean698.3693
Median Absolute Deviation (MAD)4
Skewness42.447268
Sum1747320
Variance1.0960365 × 108
MonotonicityNot monotonic
2024-03-23T06:25:56.097785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 881
32.7%
1 185
 
6.9%
2 106
 
3.9%
3 70
 
2.6%
4 59
 
2.2%
6 52
 
1.9%
5 43
 
1.6%
8 36
 
1.3%
7 31
 
1.2%
12 29
 
1.1%
Other values (488) 1010
37.5%
(Missing) 189
 
7.0%
ValueCountFrequency (%)
0 881
32.7%
1 185
 
6.9%
2 106
 
3.9%
3 70
 
2.6%
4 59
 
2.2%
5 43
 
1.6%
6 52
 
1.9%
7 31
 
1.2%
8 36
 
1.3%
9 24
 
0.9%
ValueCountFrequency (%)
492592 1
< 0.1%
130713 1
< 0.1%
67523 1
< 0.1%
34170 1
< 0.1%
31462 1
< 0.1%
30766 1
< 0.1%
28641 1
< 0.1%
26508 1
< 0.1%
23891 1
< 0.1%
22321 1
< 0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct608
Distinct (%)24.3%
Missing189
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean1201.4564
Minimum0
Maximum822185
Zeros832
Zeros (%)30.9%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:25:56.641620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q399.75
95-th percentile3664.5
Maximum822185
Range822185
Interquartile range (IQR)99.75

Descriptive statistics

Standard deviation17616.848
Coefficient of variation (CV)14.66291
Kurtosis1903.7738
Mean1201.4564
Median Absolute Deviation (MAD)5
Skewness41.758194
Sum3006044
Variance3.1035334 × 108
MonotonicityNot monotonic
2024-03-23T06:25:57.257958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 832
30.9%
1 177
 
6.6%
2 88
 
3.3%
3 64
 
2.4%
4 52
 
1.9%
6 48
 
1.8%
5 48
 
1.8%
8 32
 
1.2%
7 29
 
1.1%
10 24
 
0.9%
Other values (598) 1108
41.2%
(Missing) 189
 
7.0%
ValueCountFrequency (%)
0 832
30.9%
1 177
 
6.6%
2 88
 
3.3%
3 64
 
2.4%
4 52
 
1.9%
5 48
 
1.8%
6 48
 
1.8%
7 29
 
1.1%
8 32
 
1.2%
9 22
 
0.8%
ValueCountFrequency (%)
822185 1
< 0.1%
249782 1
< 0.1%
88700 1
< 0.1%
73441 1
< 0.1%
54861 1
< 0.1%
51295 1
< 0.1%
50014 1
< 0.1%
47616 1
< 0.1%
35200 1
< 0.1%
35153 1
< 0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct585
Distinct (%)23.3%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean855.30267
Minimum0
Maximum574203
Zeros881
Zeros (%)32.7%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:25:57.994309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q375
95-th percentile2758.5
Maximum574203
Range574203
Interquartile range (IQR)75

Descriptive statistics

Standard deviation12196.983
Coefficient of variation (CV)14.260429
Kurtosis1958.1655
Mean855.30267
Median Absolute Deviation (MAD)4
Skewness42.384009
Sum2147665
Variance1.487664 × 108
MonotonicityNot monotonic
2024-03-23T06:25:58.611866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 881
32.7%
1 195
 
7.2%
2 94
 
3.5%
3 81
 
3.0%
4 57
 
2.1%
6 47
 
1.7%
5 41
 
1.5%
9 37
 
1.4%
8 35
 
1.3%
12 27
 
1.0%
Other values (575) 1016
37.8%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 881
32.7%
1 195
 
7.2%
2 94
 
3.5%
3 81
 
3.0%
4 57
 
2.1%
5 41
 
1.5%
6 47
 
1.7%
7 25
 
0.9%
8 35
 
1.3%
9 37
 
1.4%
ValueCountFrequency (%)
574203 1
< 0.1%
157333 1
< 0.1%
50250 1
< 0.1%
45607 1
< 0.1%
40733 1
< 0.1%
40204 1
< 0.1%
37049 1
< 0.1%
34384 1
< 0.1%
33096 1
< 0.1%
26205 1
< 0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct477
Distinct (%)19.0%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean380.32816
Minimum0
Maximum247501
Zeros944
Zeros (%)35.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:25:59.212271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q344
95-th percentile1207
Maximum247501
Range247501
Interquartile range (IQR)44

Descriptive statistics

Standard deviation5204.8881
Coefficient of variation (CV)13.685256
Kurtosis2029.7557
Mean380.32816
Median Absolute Deviation (MAD)2
Skewness43.171853
Sum955004
Variance27090860
MonotonicityNot monotonic
2024-03-23T06:25:59.707586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 944
35.1%
1 209
 
7.8%
2 135
 
5.0%
3 85
 
3.2%
4 56
 
2.1%
5 40
 
1.5%
6 37
 
1.4%
8 31
 
1.2%
7 31
 
1.2%
9 20
 
0.7%
Other values (467) 923
34.3%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 944
35.1%
1 209
 
7.8%
2 135
 
5.0%
3 85
 
3.2%
4 56
 
2.1%
5 40
 
1.5%
6 37
 
1.4%
7 31
 
1.2%
8 31
 
1.2%
9 20
 
0.7%
ValueCountFrequency (%)
247501 1
< 0.1%
47022 1
< 0.1%
27305 1
< 0.1%
26878 1
< 0.1%
19372 1
< 0.1%
18623 1
< 0.1%
17048 1
< 0.1%
14908 1
< 0.1%
14407 1
< 0.1%
13414 1
< 0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct438
Distinct (%)17.4%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean524.07262
Minimum0
Maximum376363
Zeros968
Zeros (%)36.0%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:26:00.147724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q328
95-th percentile1656.9
Maximum376363
Range376363
Interquartile range (IQR)28

Descriptive statistics

Standard deviation7947.6988
Coefficient of variation (CV)15.165263
Kurtosis1996.87
Mean524.07262
Median Absolute Deviation (MAD)2
Skewness42.89824
Sum1320663
Variance63165917
MonotonicityNot monotonic
2024-03-23T06:26:00.608351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 968
36.0%
1 208
 
7.7%
2 124
 
4.6%
3 100
 
3.7%
4 70
 
2.6%
5 54
 
2.0%
6 48
 
1.8%
7 42
 
1.6%
8 32
 
1.2%
9 28
 
1.0%
Other values (428) 846
31.4%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 968
36.0%
1 208
 
7.7%
2 124
 
4.6%
3 100
 
3.7%
4 70
 
2.6%
5 54
 
2.0%
6 48
 
1.8%
7 42
 
1.6%
8 32
 
1.2%
9 28
 
1.0%
ValueCountFrequency (%)
376363 1
< 0.1%
99401 1
< 0.1%
34129 1
< 0.1%
27827 1
< 0.1%
25119 1
< 0.1%
25046 1
< 0.1%
23807 1
< 0.1%
21325 1
< 0.1%
21067 1
< 0.1%
20973 1
< 0.1%

Interactions

2024-03-23T06:25:24.652283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:19.784630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T06:24:38.523757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:45.752622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:52.994115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:00.687273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:07.146352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:14.727040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:21.435591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:29.485491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:24.437570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:31.972265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:38.895930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:46.226916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:54.231418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:01.384597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:08.494030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:15.571741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:23.379335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:30.827020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:38.888219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:46.114165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:53.333705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:01.058789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:07.527991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:15.133552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:21.835595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:29.890088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:24.857005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:32.380322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:39.254494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:46.762428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:54.627863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:01.702521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:08.856162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:15.992436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:23.912815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:31.240190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:39.186383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:46.508744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:53.609583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:01.397203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:07.904166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:15.524400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:22.225456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:30.376439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:25.212999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:32.684589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:39.667771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:47.311004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:55.009097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:02.327922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:09.242325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:16.495684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:24.338703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:31.606618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:39.600799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:47.058603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:53.942763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:01.732615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:08.241772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:15.816837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:22.624206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:30.760190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:25.630788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:33.131007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:40.218450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:47.807291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:55.316929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:02.736569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:09.627490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:16.768341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:24.768932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:31.996280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:39.960952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:47.401543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:54.490159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:02.036891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:08.991968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:16.208358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:23.064693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:31.166663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:26.050334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:33.636516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:40.628171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:48.252226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:55.684351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:03.110307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:10.010773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:17.039800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:25.180750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:32.302072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:40.312368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:47.745489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:54.895916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:02.381002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:09.525515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:16.538293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:23.420468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:31.676749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:26.723358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:33.986880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:40.925376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:48.802288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:56.061255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:03.476446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:10.438788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:17.769669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:25.595095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:32.607418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:40.760614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:48.343941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:55.229001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:02.686368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:09.929101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:16.979558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:23.784899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:32.132705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:27.504793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:34.340473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:41.284739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:49.140582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:23:56.374784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:03.927580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:10.748468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:18.169608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:25.911270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:33.472434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:41.165859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:48.750960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:24:55.876261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:02.978265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:10.317127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:17.361124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:25:24.157391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T06:26:01.055117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0001.0001.0001.0000.9071.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.8691.000
20071.0001.0001.0001.0000.9690.9900.9900.9900.9900.9960.9960.9960.9960.9960.9960.8030.8420.803
20081.0001.0001.0001.0000.9690.9900.9900.9900.9900.9960.9960.9960.9960.9960.9960.8030.8420.803
20091.0001.0001.0001.0000.9690.9900.9900.9900.9900.9960.9960.9960.9960.9960.9960.8030.8420.803
20100.9070.9690.9690.9691.0000.9820.9820.9820.9820.9520.9520.9520.9520.9520.9520.8030.8030.803
20111.0000.9900.9900.9900.9821.0001.0001.0001.0000.9960.9960.9960.9960.9960.9961.0000.8031.000
20121.0000.9900.9900.9900.9821.0001.0001.0001.0000.9960.9960.9960.9960.9960.9961.0000.8031.000
20131.0000.9900.9900.9900.9821.0001.0001.0001.0000.9960.9960.9960.9960.9960.9961.0000.8031.000
20141.0000.9900.9900.9900.9821.0001.0001.0001.0000.9960.9960.9960.9960.9960.9961.0000.8031.000
20151.0000.9960.9960.9960.9520.9960.9960.9960.9961.0001.0001.0001.0001.0001.0001.0000.7631.000
20161.0000.9960.9960.9960.9520.9960.9960.9960.9961.0001.0001.0001.0001.0001.0001.0000.7631.000
20171.0000.9960.9960.9960.9520.9960.9960.9960.9961.0001.0001.0001.0001.0001.0001.0000.7631.000
20181.0000.9960.9960.9960.9520.9960.9960.9960.9961.0001.0001.0001.0001.0001.0001.0000.7631.000
20191.0000.9960.9960.9960.9520.9960.9960.9960.9961.0001.0001.0001.0001.0001.0001.0000.7631.000
20201.0000.9960.9960.9960.9520.9960.9960.9960.9961.0001.0001.0001.0001.0001.0001.0000.7631.000
20211.0000.8030.8030.8030.8031.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9781.000
20220.8690.8420.8420.8420.8030.8030.8030.8030.8030.7630.7630.7630.7630.7630.7630.9781.0000.978
20231.0000.8030.8030.8030.8031.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9781.000
2024-03-23T06:26:01.772752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.8560.8440.8160.8270.8160.8030.8360.8280.8390.8530.8410.8390.8330.8310.8070.7970.793
20070.8561.0000.8630.8140.8230.7890.7740.8070.8080.8200.8240.8150.8140.8040.8070.7930.8000.777
20080.8440.8631.0000.8430.8360.8010.7970.8200.8060.8270.8370.8300.8240.8010.8020.7920.7980.778
20090.8160.8140.8431.0000.8600.8240.8080.8240.8010.8350.8340.8210.8110.7880.8020.7900.7850.781
20100.8270.8230.8360.8601.0000.8690.8440.8510.8330.8490.8490.8380.8340.8190.8260.8060.7990.798
20110.8160.7890.8010.8240.8691.0000.8760.8530.8260.8450.8300.8170.8240.8010.8190.8040.7820.785
20120.8030.7740.7970.8080.8440.8761.0000.8550.8290.8380.8270.8190.8080.7880.8000.7880.7770.784
20130.8360.8070.8200.8240.8510.8530.8551.0000.8690.8740.8690.8600.8470.8400.8570.8230.8040.814
20140.8280.8080.8060.8010.8330.8260.8290.8691.0000.8740.8610.8510.8440.8320.8400.8120.7960.803
20150.8390.8200.8270.8350.8490.8450.8380.8740.8741.0000.8920.8710.8720.8520.8600.8350.8180.824
20160.8530.8240.8370.8340.8490.8300.8270.8690.8610.8921.0000.8940.8870.8630.8710.8370.8160.829
20170.8410.8150.8300.8210.8380.8170.8190.8600.8510.8710.8941.0000.8880.8660.8680.8460.8260.833
20180.8390.8140.8240.8110.8340.8240.8080.8470.8440.8720.8870.8881.0000.8770.8710.8420.8180.822
20190.8330.8040.8010.7880.8190.8010.7880.8400.8320.8520.8630.8660.8771.0000.8870.8260.8110.818
20200.8310.8070.8020.8020.8260.8190.8000.8570.8400.8600.8710.8680.8710.8871.0000.8660.8400.840
20210.8070.7930.7920.7900.8060.8040.7880.8230.8120.8350.8370.8460.8420.8260.8661.0000.8740.843
20220.7970.8000.7980.7850.7990.7820.7770.8040.7960.8180.8160.8260.8180.8110.8400.8741.0000.846
20230.7930.7770.7780.7810.7980.7850.7840.8140.8030.8240.8290.8330.8220.8180.8400.8430.8461.000

Missing values

2024-03-23T06:25:33.232967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T06:25:34.191967image/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.
2024-03-23T06:25:34.996294image/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 거래주체별200620072008200920102011201220132014201520162017201820192020202120222023
0전국 /개인->개인616289430358454235496791456014564512403924517349625959736130622726567440522524492592822185574203247501376363
1전국 /개인->법인615148974582373836795496444446705543536264966111794916129316642191387223079
2전국 /개인->기타75371378914651434171413051328728178116421349153219273924328022031621
3전국 /법인->개인84117897949587310101693591109406752876721362855540734586826584186812389150014370492687823807
4전국 /법인->법인106021132524343234542295419136157951127011503853910553754810968743611020655539552385
5전국 /법인->기타12096484112074242531375264519222891022506751125111417531375
6전국 /기타->개인19141596247034092763377615542019163122031669197414961772139382457773572986
7전국 /기타->법인35963848601633710826101202854444164433174110
8전국 /기타->기타2133651852631735698661101867528181819544583886
9서울 /개인->개인1378986279960727750854434959598415406427586843126813115295105461931916752388700456071197734129
아파트매매 거래주체별200620072008200920102011201220132014201520162017201820192020202120222023
2681제주 제주시/기타->기타000204103200000000
2682제주 서귀포시/개인->개인1463513913543655574034805596107126905824818131110639574
2683제주 서귀포시/개인->법인41598465611010342325216824507
2684제주 서귀포시/개인->기타012116253142001422124
2685제주 서귀포시/법인->개인1616753615715437032338714532238816310871918612160
2686제주 서귀포시/법인->법인96158222316553302282922755121
2687제주 서귀포시/법인->기타00146021204500002200
2688제주 서귀포시/기타->개인01115112105295639862
2689제주 서귀포시/기타->법인000000000000060001
2690제주 서귀포시/기타->기타000010000000000100