Overview

Dataset statistics

Number of variables19
Number of observations2691
Missing cells3285
Missing cells (%)6.4%
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 토지 거래현황의 연도별 건물용도별(필지수) 데이터입니다.- (단위 : 필지수)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068590/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 198 (7.4%) missing valuesMissing
2007 has 243 (9.0%) missing valuesMissing
2008 has 225 (8.4%) missing valuesMissing
2009 has 225 (8.4%) missing valuesMissing
2010 has 171 (6.4%) missing valuesMissing
2011 has 198 (7.4%) missing valuesMissing
2012 has 180 (6.7%) missing valuesMissing
2013 has 180 (6.7%) missing valuesMissing
2014 has 117 (4.3%) missing valuesMissing
2015 has 153 (5.7%) missing valuesMissing
2016 has 153 (5.7%) missing valuesMissing
2017 has 180 (6.7%) missing valuesMissing
2018 has 171 (6.4%) missing valuesMissing
2019 has 180 (6.7%) missing valuesMissing
2020 has 180 (6.7%) 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 = 36.05355882)Skewed
2007 is highly skewed (γ1 = 33.99779953)Skewed
2008 is highly skewed (γ1 = 35.68435255)Skewed
2009 is highly skewed (γ1 = 36.62770258)Skewed
2010 is highly skewed (γ1 = 36.99462705)Skewed
2011 is highly skewed (γ1 = 36.86678519)Skewed
2012 is highly skewed (γ1 = 35.29182628)Skewed
2013 is highly skewed (γ1 = 36.88461212)Skewed
2014 is highly skewed (γ1 = 37.64426404)Skewed
2015 is highly skewed (γ1 = 35.88773664)Skewed
2016 is highly skewed (γ1 = 34.18814654)Skewed
2017 is highly skewed (γ1 = 33.59233124)Skewed
2018 is highly skewed (γ1 = 34.46438408)Skewed
2019 is highly skewed (γ1 = 35.33498233)Skewed
2020 is highly skewed (γ1 = 36.95389364)Skewed
2021 is highly skewed (γ1 = 32.74452131)Skewed
2022 is highly skewed (γ1 = 29.24829176)Skewed
2023 is highly skewed (γ1 = 36.57995761)Skewed
지역_건물용도 has unique valuesUnique
2006 has 94 (3.5%) zerosZeros
2007 has 99 (3.7%) zerosZeros
2008 has 86 (3.2%) zerosZeros
2009 has 79 (2.9%) zerosZeros
2010 has 76 (2.8%) zerosZeros
2011 has 64 (2.4%) zerosZeros
2012 has 61 (2.3%) zerosZeros
2013 has 66 (2.5%) zerosZeros
2014 has 58 (2.2%) zerosZeros
2015 has 41 (1.5%) zerosZeros
2016 has 44 (1.6%) zerosZeros
2017 has 66 (2.5%) zerosZeros
2018 has 63 (2.3%) zerosZeros
2019 has 47 (1.7%) zerosZeros
2020 has 39 (1.4%) zerosZeros
2021 has 36 (1.3%) zerosZeros
2022 has 45 (1.7%) zerosZeros
2023 has 54 (2.0%) zerosZeros

Reproduction

Analysis started2024-04-06 08:34:05.405019
Analysis finished2024-04-06 08:35:22.602847
Duration1 minute and 17.2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역_건물용도
Text

UNIQUE 

Distinct2691
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
2024-04-06T17:35:22.948509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length11.280193
Min length5

Characters and Unicode

Total characters30355
Distinct characters156
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2691 ?
Unique (%)100.0%

Sample

1st row전국_단독주택
2nd row전국_다가구주택
3rd row전국_다세대주택
4th row전국_연립주택
5th row전국_아파트
ValueCountFrequency (%)
경기 468
 
8.5%
경남 234
 
4.2%
서울 225
 
4.1%
경북 225
 
4.1%
전남 198
 
3.6%
충북 171
 
3.1%
충남 171
 
3.1%
강원 162
 
2.9%
부산 144
 
2.6%
전북 144
 
2.6%
Other values (2477) 3375
61.2%
2024-04-06T17:35:23.964486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2826
 
9.3%
_ 2691
 
8.9%
1619
 
5.3%
1568
 
5.2%
1205
 
4.0%
1098
 
3.6%
981
 
3.2%
897
 
3.0%
837
 
2.8%
803
 
2.6%
Other values (146) 15830
52.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24496
80.7%
Space Separator 2826
 
9.3%
Connector Punctuation 2691
 
8.9%
Close Punctuation 171
 
0.6%
Open Punctuation 171
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1619
 
6.6%
1568
 
6.4%
1205
 
4.9%
1098
 
4.5%
981
 
4.0%
897
 
3.7%
837
 
3.4%
803
 
3.3%
801
 
3.3%
648
 
2.6%
Other values (142) 14039
57.3%
Space Separator
ValueCountFrequency (%)
2826
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2691
100.0%
Close Punctuation
ValueCountFrequency (%)
) 171
100.0%
Open Punctuation
ValueCountFrequency (%)
( 171
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24496
80.7%
Common 5859
 
19.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1619
 
6.6%
1568
 
6.4%
1205
 
4.9%
1098
 
4.5%
981
 
4.0%
897
 
3.7%
837
 
3.4%
803
 
3.3%
801
 
3.3%
648
 
2.6%
Other values (142) 14039
57.3%
Common
ValueCountFrequency (%)
2826
48.2%
_ 2691
45.9%
) 171
 
2.9%
( 171
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24496
80.7%
ASCII 5859
 
19.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2826
48.2%
_ 2691
45.9%
) 171
 
2.9%
( 171
 
2.9%
Hangul
ValueCountFrequency (%)
1619
 
6.6%
1568
 
6.4%
1205
 
4.9%
1098
 
4.5%
981
 
4.0%
897
 
3.7%
837
 
3.4%
803
 
3.3%
801
 
3.3%
648
 
2.6%
Other values (142) 14039
57.3%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1109
Distinct (%)44.5%
Missing198
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean2304.0766
Minimum0
Maximum1046986
Zeros94
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:35:24.441121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q119
median167
Q3748
95-th percentile6919.4
Maximum1046986
Range1046986
Interquartile range (IQR)729

Descriptive statistics

Standard deviation23749.651
Coefficient of variation (CV)10.307665
Kurtosis1523.1077
Mean2304.0766
Median Absolute Deviation (MAD)163
Skewness36.053559
Sum5744063
Variance5.6404591 × 108
MonotonicityNot monotonic
2024-04-06T17:35:25.292215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 94
 
3.5%
2 57
 
2.1%
1 54
 
2.0%
4 51
 
1.9%
3 47
 
1.7%
5 40
 
1.5%
6 34
 
1.3%
7 26
 
1.0%
9 24
 
0.9%
11 23
 
0.9%
Other values (1099) 2043
75.9%
(Missing) 198
 
7.4%
ValueCountFrequency (%)
0 94
3.5%
1 54
2.0%
2 57
2.1%
3 47
1.7%
4 51
1.9%
5 40
1.5%
6 34
 
1.3%
7 26
 
1.0%
8 21
 
0.8%
9 24
 
0.9%
ValueCountFrequency (%)
1046986 1
< 0.1%
326941 1
< 0.1%
201100 1
< 0.1%
200719 1
< 0.1%
179994 1
< 0.1%
142757 1
< 0.1%
125536 1
< 0.1%
77904 1
< 0.1%
66691 1
< 0.1%
66258 1
< 0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1054
Distinct (%)43.1%
Missing243
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean1876.0948
Minimum0
Maximum761554
Zeros99
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:35:25.591285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q121
median161
Q3671.25
95-th percentile5658
Maximum761554
Range761554
Interquartile range (IQR)650.25

Descriptive statistics

Standard deviation17758.999
Coefficient of variation (CV)9.4659394
Kurtosis1387.8075
Mean1876.0948
Median Absolute Deviation (MAD)157
Skewness33.9978
Sum4592680
Variance3.1538206 × 108
MonotonicityNot monotonic
2024-04-06T17:35:25.867781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 99
 
3.7%
1 57
 
2.1%
4 47
 
1.7%
3 43
 
1.6%
7 36
 
1.3%
5 34
 
1.3%
2 33
 
1.2%
6 28
 
1.0%
12 28
 
1.0%
10 25
 
0.9%
Other values (1044) 2018
75.0%
(Missing) 243
 
9.0%
ValueCountFrequency (%)
0 99
3.7%
1 57
2.1%
2 33
 
1.2%
3 43
1.6%
4 47
1.7%
5 34
 
1.3%
6 28
 
1.0%
7 36
 
1.3%
8 24
 
0.9%
9 25
 
0.9%
ValueCountFrequency (%)
761554 1
< 0.1%
199924 1
< 0.1%
188329 1
< 0.1%
179948 1
< 0.1%
162452 1
< 0.1%
94415 1
< 0.1%
87598 1
< 0.1%
62822 1
< 0.1%
61186 1
< 0.1%
61057 1
< 0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1046
Distinct (%)42.4%
Missing225
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean1875.3508
Minimum0
Maximum797597
Zeros86
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:35:26.182981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q125
median163
Q3650.75
95-th percentile5748.5
Maximum797597
Range797597
Interquartile range (IQR)625.75

Descriptive statistics

Standard deviation18171.534
Coefficient of variation (CV)9.6896719
Kurtosis1506.5587
Mean1875.3508
Median Absolute Deviation (MAD)158
Skewness35.684353
Sum4624615
Variance3.3020463 × 108
MonotonicityNot monotonic
2024-04-06T17:35:26.482285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 86
 
3.2%
1 52
 
1.9%
3 47
 
1.7%
2 37
 
1.4%
4 34
 
1.3%
6 32
 
1.2%
5 31
 
1.2%
9 25
 
0.9%
7 24
 
0.9%
8 23
 
0.9%
Other values (1036) 2075
77.1%
(Missing) 225
 
8.4%
ValueCountFrequency (%)
0 86
3.2%
1 52
1.9%
2 37
1.4%
3 47
1.7%
4 34
 
1.3%
5 31
 
1.2%
6 32
 
1.2%
7 24
 
0.9%
8 23
 
0.9%
9 25
 
0.9%
ValueCountFrequency (%)
797597 1
< 0.1%
187259 1
< 0.1%
184021 1
< 0.1%
182493 1
< 0.1%
132887 1
< 0.1%
88678 1
< 0.1%
87491 1
< 0.1%
63526 1
< 0.1%
62293 1
< 0.1%
58700 1
< 0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1041
Distinct (%)42.2%
Missing225
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean1842.088
Minimum0
Maximum828391
Zeros79
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:35:26.783183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q125.25
median161
Q3634.25
95-th percentile5334.25
Maximum828391
Range828391
Interquartile range (IQR)609

Descriptive statistics

Standard deviation18708.479
Coefficient of variation (CV)10.156127
Kurtosis1562.5451
Mean1842.088
Median Absolute Deviation (MAD)155
Skewness36.627703
Sum4542589
Variance3.5000719 × 108
MonotonicityNot monotonic
2024-04-06T17:35:27.148111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 79
 
2.9%
2 49
 
1.8%
1 47
 
1.7%
5 43
 
1.6%
3 36
 
1.3%
8 35
 
1.3%
4 29
 
1.1%
6 26
 
1.0%
7 23
 
0.9%
14 22
 
0.8%
Other values (1031) 2077
77.2%
(Missing) 225
 
8.4%
ValueCountFrequency (%)
0 79
2.9%
1 47
1.7%
2 49
1.8%
3 36
1.3%
4 29
 
1.1%
5 43
1.6%
6 26
 
1.0%
7 23
 
0.9%
8 35
1.3%
9 16
 
0.6%
ValueCountFrequency (%)
828391 1
< 0.1%
218848 1
< 0.1%
214757 1
< 0.1%
122725 1
< 0.1%
115759 1
< 0.1%
103285 1
< 0.1%
84200 1
< 0.1%
70984 1
< 0.1%
59322 1
< 0.1%
46537 1
< 0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1040
Distinct (%)41.3%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean1681.4575
Minimum0
Maximum753516
Zeros76
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:35:27.430851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.95
Q127
median150
Q3604
95-th percentile5036.35
Maximum753516
Range753516
Interquartile range (IQR)577

Descriptive statistics

Standard deviation16836.461
Coefficient of variation (CV)10.013016
Kurtosis1595.6344
Mean1681.4575
Median Absolute Deviation (MAD)144
Skewness36.994627
Sum4237273
Variance2.8346641 × 108
MonotonicityNot monotonic
2024-04-06T17:35:27.714772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 76
 
2.8%
1 50
 
1.9%
2 48
 
1.8%
3 39
 
1.4%
5 35
 
1.3%
4 32
 
1.2%
7 30
 
1.1%
6 27
 
1.0%
9 27
 
1.0%
11 23
 
0.9%
Other values (1030) 2133
79.3%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 76
2.8%
1 50
1.9%
2 48
1.8%
3 39
1.4%
4 32
1.2%
5 35
1.3%
6 27
 
1.0%
7 30
 
1.1%
8 19
 
0.7%
9 27
 
1.0%
ValueCountFrequency (%)
753516 1
< 0.1%
210979 1
< 0.1%
176928 1
< 0.1%
114452 1
< 0.1%
110122 1
< 0.1%
83239 1
< 0.1%
78228 1
< 0.1%
69155 1
< 0.1%
65599 1
< 0.1%
41391 1
< 0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1113
Distinct (%)44.6%
Missing198
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean2004.3622
Minimum0
Maximum875470
Zeros64
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:35:27.985653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q133
median196
Q3747
95-th percentile5928.2
Maximum875470
Range875470
Interquartile range (IQR)714

Descriptive statistics

Standard deviation19638.515
Coefficient of variation (CV)9.797887
Kurtosis1586.0126
Mean2004.3622
Median Absolute Deviation (MAD)187
Skewness36.866785
Sum4996875
Variance3.8567125 × 108
MonotonicityNot monotonic
2024-04-06T17:35:28.292747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 64
 
2.4%
1 48
 
1.8%
2 46
 
1.7%
4 41
 
1.5%
3 31
 
1.2%
5 30
 
1.1%
16 20
 
0.7%
6 20
 
0.7%
14 20
 
0.7%
7 19
 
0.7%
Other values (1103) 2154
80.0%
(Missing) 198
 
7.4%
ValueCountFrequency (%)
0 64
2.4%
1 48
1.8%
2 46
1.7%
3 31
1.2%
4 41
1.5%
5 30
1.1%
6 20
 
0.7%
7 19
 
0.7%
8 18
 
0.7%
9 17
 
0.6%
ValueCountFrequency (%)
875470 1
< 0.1%
240459 1
< 0.1%
194488 1
< 0.1%
138787 1
< 0.1%
135492 1
< 0.1%
99629 1
< 0.1%
79648 1
< 0.1%
78802 1
< 0.1%
63136 1
< 0.1%
58089 1
< 0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1078
Distinct (%)42.9%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1678.5205
Minimum0
Maximum690315
Zeros61
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:35:28.561451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q132
median170
Q3662
95-th percentile4645.5
Maximum690315
Range690315
Interquartile range (IQR)630

Descriptive statistics

Standard deviation15769.412
Coefficient of variation (CV)9.3948282
Kurtosis1473.3148
Mean1678.5205
Median Absolute Deviation (MAD)163
Skewness35.291826
Sum4214765
Variance2.4867435 × 108
MonotonicityNot monotonic
2024-04-06T17:35:28.828963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 61
 
2.3%
1 52
 
1.9%
2 40
 
1.5%
5 37
 
1.4%
3 35
 
1.3%
4 31
 
1.2%
23 28
 
1.0%
9 25
 
0.9%
6 19
 
0.7%
8 18
 
0.7%
Other values (1068) 2165
80.5%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 61
2.3%
1 52
1.9%
2 40
1.5%
3 35
1.3%
4 31
1.2%
5 37
1.4%
6 19
 
0.7%
7 18
 
0.7%
8 18
 
0.7%
9 25
0.9%
ValueCountFrequency (%)
690315 1
< 0.1%
228804 1
< 0.1%
151383 1
< 0.1%
127001 1
< 0.1%
121685 1
< 0.1%
84761 1
< 0.1%
60217 1
< 0.1%
54752 1
< 0.1%
53309 1
< 0.1%
52156 1
< 0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1107
Distinct (%)44.1%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1916.2525
Minimum0
Maximum845890
Zeros66
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:35:29.138174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q135
median177
Q3694
95-th percentile5595
Maximum845890
Range845890
Interquartile range (IQR)659

Descriptive statistics

Standard deviation18930.088
Coefficient of variation (CV)9.8787024
Kurtosis1589.6595
Mean1916.2525
Median Absolute Deviation (MAD)168
Skewness36.884612
Sum4811710
Variance3.5834823 × 108
MonotonicityNot monotonic
2024-04-06T17:35:29.471869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 66
 
2.5%
2 46
 
1.7%
1 43
 
1.6%
3 40
 
1.5%
9 27
 
1.0%
7 23
 
0.9%
6 23
 
0.9%
4 22
 
0.8%
11 21
 
0.8%
12 20
 
0.7%
Other values (1097) 2180
81.0%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 66
2.5%
1 43
1.6%
2 46
1.7%
3 40
1.5%
4 22
 
0.8%
5 20
 
0.7%
6 23
 
0.9%
7 23
 
0.9%
8 20
 
0.7%
9 27
1.0%
ValueCountFrequency (%)
845890 1
< 0.1%
224889 1
< 0.1%
195407 1
< 0.1%
140483 1
< 0.1%
134235 1
< 0.1%
89685 1
< 0.1%
88303 1
< 0.1%
76174 1
< 0.1%
72460 1
< 0.1%
64661 1
< 0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1162
Distinct (%)45.1%
Missing117
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean2254.5789
Minimum0
Maximum1024449
Zeros58
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:35:29.749307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q141
median207.5
Q3797
95-th percentile6882.65
Maximum1024449
Range1024449
Interquartile range (IQR)756

Descriptive statistics

Standard deviation22556.811
Coefficient of variation (CV)10.004889
Kurtosis1652.9156
Mean2254.5789
Median Absolute Deviation (MAD)198.5
Skewness37.644264
Sum5803286
Variance5.0880971 × 108
MonotonicityNot monotonic
2024-04-06T17:35:30.052436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58
 
2.2%
1 47
 
1.7%
4 44
 
1.6%
2 35
 
1.3%
5 33
 
1.2%
3 27
 
1.0%
13 26
 
1.0%
7 25
 
0.9%
6 23
 
0.9%
9 18
 
0.7%
Other values (1152) 2238
83.2%
(Missing) 117
 
4.3%
ValueCountFrequency (%)
0 58
2.2%
1 47
1.7%
2 35
1.3%
3 27
1.0%
4 44
1.6%
5 33
1.2%
6 23
 
0.9%
7 25
0.9%
8 16
 
0.6%
9 18
 
0.7%
ValueCountFrequency (%)
1024449 1
< 0.1%
249496 1
< 0.1%
232715 1
< 0.1%
176453 1
< 0.1%
164396 1
< 0.1%
122947 1
< 0.1%
106768 1
< 0.1%
90401 1
< 0.1%
89698 1
< 0.1%
62665 1
< 0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1236
Distinct (%)48.7%
Missing153
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean2723.8282
Minimum0
Maximum1169667
Zeros41
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:35:30.333599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q144
median265
Q3974.75
95-th percentile8348.4
Maximum1169667
Range1169667
Interquartile range (IQR)930.75

Descriptive statistics

Standard deviation26389.157
Coefficient of variation (CV)9.6882604
Kurtosis1524.7219
Mean2723.8282
Median Absolute Deviation (MAD)252
Skewness35.887737
Sum6913076
Variance6.9638761 × 108
MonotonicityNot monotonic
2024-04-06T17:35:30.726307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 43
 
1.6%
0 41
 
1.5%
1 41
 
1.5%
2 37
 
1.4%
3 29
 
1.1%
7 26
 
1.0%
10 26
 
1.0%
6 25
 
0.9%
13 24
 
0.9%
5 23
 
0.9%
Other values (1226) 2223
82.6%
(Missing) 153
 
5.7%
ValueCountFrequency (%)
0 41
1.5%
1 41
1.5%
2 37
1.4%
3 29
1.1%
4 43
1.6%
5 23
0.9%
6 25
0.9%
7 26
1.0%
8 15
 
0.6%
9 13
 
0.5%
ValueCountFrequency (%)
1169667 1
< 0.1%
295006 1
< 0.1%
294532 1
< 0.1%
232437 1
< 0.1%
221638 1
< 0.1%
154680 1
< 0.1%
133609 1
< 0.1%
106815 1
< 0.1%
87534 1
< 0.1%
82993 1
< 0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1227
Distinct (%)48.3%
Missing153
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean2610.7766
Minimum0
Maximum1071179
Zeros44
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:35:31.004376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q144
median252
Q3947.75
95-th percentile7974.8
Maximum1071179
Range1071179
Interquartile range (IQR)903.75

Descriptive statistics

Standard deviation24706.339
Coefficient of variation (CV)9.4632147
Kurtosis1403.3117
Mean2610.7766
Median Absolute Deviation (MAD)240
Skewness34.188147
Sum6626151
Variance6.1040321 × 108
MonotonicityNot monotonic
2024-04-06T17:35:31.293214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 44
 
1.6%
1 40
 
1.5%
3 39
 
1.4%
2 37
 
1.4%
5 29
 
1.1%
4 27
 
1.0%
7 21
 
0.8%
9 20
 
0.7%
6 20
 
0.7%
13 19
 
0.7%
Other values (1217) 2242
83.3%
(Missing) 153
 
5.7%
ValueCountFrequency (%)
0 44
1.6%
1 40
1.5%
2 37
1.4%
3 39
1.4%
4 27
1.0%
5 29
1.1%
6 20
0.7%
7 21
0.8%
8 18
0.7%
9 20
0.7%
ValueCountFrequency (%)
1071179 1
< 0.1%
299267 1
< 0.1%
296160 1
< 0.1%
241746 1
< 0.1%
236215 1
< 0.1%
152294 1
< 0.1%
124925 1
< 0.1%
91993 1
< 0.1%
90100 1
< 0.1%
76014 1
< 0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1210
Distinct (%)48.2%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2927.7646
Minimum0
Maximum1196856
Zeros66
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:35:31.583479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q136
median234
Q31005.5
95-th percentile8364
Maximum1196856
Range1196856
Interquartile range (IQR)969.5

Descriptive statistics

Standard deviation27945.457
Coefficient of variation (CV)9.5449809
Kurtosis1355.3255
Mean2927.7646
Median Absolute Deviation (MAD)227
Skewness33.592331
Sum7351617
Variance7.8094859 × 108
MonotonicityNot monotonic
2024-04-06T17:35:31.929213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 66
 
2.5%
1 51
 
1.9%
3 43
 
1.6%
2 40
 
1.5%
4 29
 
1.1%
5 28
 
1.0%
13 22
 
0.8%
8 21
 
0.8%
6 20
 
0.7%
7 19
 
0.7%
Other values (1200) 2172
80.7%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 66
2.5%
1 51
1.9%
2 40
1.5%
3 43
1.6%
4 29
1.1%
5 28
1.0%
6 20
 
0.7%
7 19
 
0.7%
8 21
 
0.8%
9 13
 
0.5%
ValueCountFrequency (%)
1196856 1
< 0.1%
354530 1
< 0.1%
351873 1
< 0.1%
306201 1
< 0.1%
226115 1
< 0.1%
161538 1
< 0.1%
134439 1
< 0.1%
116182 1
< 0.1%
89507 1
< 0.1%
82363 1
< 0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1192
Distinct (%)47.3%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean2819.4488
Minimum0
Maximum1222877
Zeros63
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:35:32.226099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q131
median211.5
Q3917.25
95-th percentile8504.6
Maximum1222877
Range1222877
Interquartile range (IQR)886.25

Descriptive statistics

Standard deviation28272.332
Coefficient of variation (CV)10.027609
Kurtosis1407.656
Mean2819.4488
Median Absolute Deviation (MAD)205.5
Skewness34.464384
Sum7105011
Variance7.9932473 × 108
MonotonicityNot monotonic
2024-04-06T17:35:32.591339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63
 
2.3%
2 53
 
2.0%
1 46
 
1.7%
3 35
 
1.3%
5 34
 
1.3%
4 34
 
1.3%
6 25
 
0.9%
10 23
 
0.9%
9 22
 
0.8%
7 21
 
0.8%
Other values (1182) 2164
80.4%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 63
2.3%
1 46
1.7%
2 53
2.0%
3 35
1.3%
4 34
1.3%
5 34
1.3%
6 25
 
0.9%
7 21
 
0.8%
8 20
 
0.7%
9 22
 
0.8%
ValueCountFrequency (%)
1222877 1
< 0.1%
414029 1
< 0.1%
340715 1
< 0.1%
271895 1
< 0.1%
187031 1
< 0.1%
159032 1
< 0.1%
125910 1
< 0.1%
120985 1
< 0.1%
76448 1
< 0.1%
75281 1
< 0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1166
Distinct (%)46.4%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2519.1529
Minimum0
Maximum1101147
Zeros47
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:35:33.548475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q136
median208
Q3826
95-th percentile7433.5
Maximum1101147
Range1101147
Interquartile range (IQR)790

Descriptive statistics

Standard deviation25154.793
Coefficient of variation (CV)9.9854173
Kurtosis1473.4648
Mean2519.1529
Median Absolute Deviation (MAD)199
Skewness35.334982
Sum6325593
Variance6.3276362 × 108
MonotonicityNot monotonic
2024-04-06T17:35:33.816405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47
 
1.7%
4 40
 
1.5%
1 39
 
1.4%
2 33
 
1.2%
3 32
 
1.2%
6 30
 
1.1%
5 28
 
1.0%
8 24
 
0.9%
14 22
 
0.8%
12 20
 
0.7%
Other values (1156) 2196
81.6%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 47
1.7%
1 39
1.4%
2 33
1.2%
3 32
1.2%
4 40
1.5%
5 28
1.0%
6 30
1.1%
7 18
 
0.7%
8 24
0.9%
9 20
0.7%
ValueCountFrequency (%)
1101147 1
< 0.1%
339306 1
< 0.1%
275990 1
< 0.1%
250235 1
< 0.1%
163115 1
< 0.1%
125051 1
< 0.1%
99843 1
< 0.1%
97237 1
< 0.1%
87189 1
< 0.1%
86339 1
< 0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1236
Distinct (%)49.2%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean3180.6344
Minimum0
Maximum1470022
Zeros39
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:35:34.084908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q144
median257
Q3999
95-th percentile9309.5
Maximum1470022
Range1470022
Interquartile range (IQR)955

Descriptive statistics

Standard deviation32924.114
Coefficient of variation (CV)10.35143
Kurtosis1588.1836
Mean3180.6344
Median Absolute Deviation (MAD)246
Skewness36.953894
Sum7986573
Variance1.0839973 × 109
MonotonicityNot monotonic
2024-04-06T17:35:34.388337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 43
 
1.6%
0 39
 
1.4%
2 37
 
1.4%
4 29
 
1.1%
3 28
 
1.0%
5 28
 
1.0%
6 23
 
0.9%
7 22
 
0.8%
11 21
 
0.8%
13 20
 
0.7%
Other values (1226) 2221
82.5%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 39
1.4%
1 43
1.6%
2 37
1.4%
3 28
1.0%
4 29
1.1%
5 28
1.0%
6 23
0.9%
7 22
0.8%
8 15
 
0.6%
9 13
 
0.5%
ValueCountFrequency (%)
1470022 1
< 0.1%
435878 1
< 0.1%
301477 1
< 0.1%
281996 1
< 0.1%
206233 1
< 0.1%
161878 1
< 0.1%
128995 1
< 0.1%
123334 1
< 0.1%
116907 1
< 0.1%
98555 1
< 0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1242
Distinct (%)49.5%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2770.5536
Minimum0
Maximum1063519
Zeros36
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:35:34.680684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q149
median271
Q31040.5
95-th percentile8029
Maximum1063519
Range1063519
Interquartile range (IQR)991.5

Descriptive statistics

Standard deviation25108.381
Coefficient of variation (CV)9.0625863
Kurtosis1299.7569
Mean2770.5536
Median Absolute Deviation (MAD)259
Skewness32.744521
Sum6956860
Variance6.3043079 × 108
MonotonicityNot monotonic
2024-04-06T17:35:34.950655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36
 
1.3%
4 32
 
1.2%
3 30
 
1.1%
1 28
 
1.0%
6 25
 
0.9%
2 25
 
0.9%
8 25
 
0.9%
7 23
 
0.9%
13 20
 
0.7%
5 19
 
0.7%
Other values (1232) 2248
83.5%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 36
1.3%
1 28
1.0%
2 25
0.9%
3 30
1.1%
4 32
1.2%
5 19
0.7%
6 25
0.9%
7 23
0.9%
8 25
0.9%
9 16
0.6%
ValueCountFrequency (%)
1063519 1
< 0.1%
340742 1
< 0.1%
301723 1
< 0.1%
287751 1
< 0.1%
218094 1
< 0.1%
118104 1
< 0.1%
116318 1
< 0.1%
91495 1
< 0.1%
84808 1
< 0.1%
83784 1
< 0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1095
Distinct (%)43.6%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1698.0052
Minimum0
Maximum565734
Zeros45
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:35:35.222857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q137
median178
Q3705
95-th percentile4649.5
Maximum565734
Range565734
Interquartile range (IQR)668

Descriptive statistics

Standard deviation14190.152
Coefficient of variation (CV)8.3569544
Kurtosis1053.9751
Mean1698.0052
Median Absolute Deviation (MAD)168
Skewness29.248292
Sum4263691
Variance2.0136041 × 108
MonotonicityNot monotonic
2024-04-06T17:35:35.495477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 45
 
1.7%
3 40
 
1.5%
1 37
 
1.4%
2 30
 
1.1%
4 26
 
1.0%
6 25
 
0.9%
20 23
 
0.9%
16 22
 
0.8%
7 22
 
0.8%
5 22
 
0.8%
Other values (1085) 2219
82.5%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 45
1.7%
1 37
1.4%
2 30
1.1%
3 40
1.5%
4 26
1.0%
5 22
0.8%
6 25
0.9%
7 22
0.8%
8 21
0.8%
9 17
 
0.6%
ValueCountFrequency (%)
565734 1
< 0.1%
231588 1
< 0.1%
228039 1
< 0.1%
131799 1
< 0.1%
131066 1
< 0.1%
79702 1
< 0.1%
78558 1
< 0.1%
56147 1
< 0.1%
53242 1
< 0.1%
47360 1
< 0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1001
Distinct (%)39.7%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean1482.023
Minimum0
Maximum659471
Zeros54
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:35:35.784495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q127
median127.5
Q3509.25
95-th percentile4243.25
Maximum659471
Range659471
Interquartile range (IQR)482.25

Descriptive statistics

Standard deviation14800.419
Coefficient of variation (CV)9.9866324
Kurtosis1567.8149
Mean1482.023
Median Absolute Deviation (MAD)119.5
Skewness36.579958
Sum3734698
Variance2.1905241 × 108
MonotonicityNot monotonic
2024-04-06T17:35:36.180367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 54
 
2.0%
1 46
 
1.7%
2 42
 
1.6%
5 33
 
1.2%
3 31
 
1.2%
12 28
 
1.0%
7 27
 
1.0%
10 26
 
1.0%
4 26
 
1.0%
14 25
 
0.9%
Other values (991) 2182
81.1%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 54
2.0%
1 46
1.7%
2 42
1.6%
3 31
1.2%
4 26
1.0%
5 33
1.2%
6 23
0.9%
7 27
1.0%
8 23
0.9%
9 24
0.9%
ValueCountFrequency (%)
659471 1
< 0.1%
167170 1
< 0.1%
163888 1
< 0.1%
142846 1
< 0.1%
82956 1
< 0.1%
81520 1
< 0.1%
57648 1
< 0.1%
48744 1
< 0.1%
48495 1
< 0.1%
45735 1
< 0.1%

Interactions

2024-04-06T17:35:17.140103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:12.067984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-04-06T17:35:04.121339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:07.808206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:11.649541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:15.685226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:19.513893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:14.405935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:18.321037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:21.993633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:26.384693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:30.302451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:34.246887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:38.849792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:42.405602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:46.098143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:49.936687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:53.849742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:57.302599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:00.404701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:04.340011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:08.032727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:11.834657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:15.864378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:19.723307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:14.572423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:18.532539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:22.204594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:26.587538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:30.611589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:34.473796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:39.107736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:42.590014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:46.358001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:50.130491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:54.022622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:57.505507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:00.592836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:04.528873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:08.285116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:12.019921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:16.044561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:19.944649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:14.763259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:18.747432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:22.448449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:26.817014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:30.795533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:34.740671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:39.306342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:42.776095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:46.592431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:50.333840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:54.257227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:57.737486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:00.788485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:04.708514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:08.491094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:12.203695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:16.228315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:20.183831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:14.993035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:18.936746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:22.645887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:27.013704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:30.984989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:35.004244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:39.527251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:42.949805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:46.796260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:50.956581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:54.454149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:57.894777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:01.130879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:04.867528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:08.829702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:12.409105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:16.390456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:20.401589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:15.208723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:19.149513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:22.947763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:27.208319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:31.240654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:35.191373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:39.768515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:43.129389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:46.995003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:51.135651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:54.740358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:58.052189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:01.340144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:05.053490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:09.119686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:12.628769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:16.601043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:20.615035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:15.405450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:19.388570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:23.615046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:27.406674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:31.530703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:35.379199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:39.952077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:43.297732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:47.197062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:51.299947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:54.926115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:58.212867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:01.501209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:05.214025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:09.318729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:12.835834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:16.786005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:20.812678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:15.642253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:19.566553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:23.878213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:27.665548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:31.758711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:35.541932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:40.133482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:43.458529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:47.378244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:51.463143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:55.071924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:34:58.379856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:01.670354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:05.366998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:09.512273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:13.035918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:35:16.971826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:35:36.392259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9860.9890.9940.9840.9890.9440.9940.9940.9940.9860.9890.9270.9440.9890.8260.8550.982
20070.9861.0000.9960.9900.9760.9820.8410.9900.9900.9901.0000.9960.8410.8590.9820.8760.8760.986
20080.9890.9961.0000.9960.9850.9900.8720.9960.9960.9960.9960.9860.8250.8410.9770.9070.8640.977
20090.9940.9900.9961.0000.9970.9990.9381.0001.0001.0000.9900.9960.8700.8920.9760.8590.8230.990
20100.9840.9760.9850.9971.0000.9990.9380.9970.9970.9970.9760.9850.8350.8410.9610.8590.8230.976
20110.9890.9820.9900.9990.9991.0001.0000.9990.9990.9990.9820.9900.8450.8530.9660.8790.8410.979
20120.9440.8410.8720.9380.9381.0001.0000.9380.9380.9380.8410.8720.9870.9880.8570.9700.9790.833
20130.9940.9900.9961.0000.9970.9990.9381.0001.0001.0000.9900.9960.8700.8920.9760.8590.8230.990
20140.9940.9900.9961.0000.9970.9990.9381.0001.0001.0000.9900.9960.8700.8920.9760.8590.8230.990
20150.9940.9900.9961.0000.9970.9990.9381.0001.0001.0000.9900.9960.8700.8920.9760.8590.8230.990
20160.9861.0000.9960.9900.9760.9820.8410.9900.9900.9901.0000.9960.8410.8590.9820.8760.8760.986
20170.9890.9960.9860.9960.9850.9900.8720.9960.9960.9960.9961.0000.9000.9270.9900.8490.8640.998
20180.9270.8410.8250.8700.8350.8450.9870.8700.8700.8700.8410.9001.0000.9990.8900.9700.9880.927
20190.9440.8590.8410.8920.8410.8530.9880.8920.8920.8920.8590.9270.9991.0000.9170.9660.9861.000
20200.9890.9820.9770.9760.9610.9660.8570.9760.9760.9760.9820.9900.8900.9171.0000.8570.8160.995
20210.8260.8760.9070.8590.8590.8790.9700.8590.8590.8590.8760.8490.9700.9660.8571.0000.9960.833
20220.8550.8760.8640.8230.8230.8410.9790.8230.8230.8230.8760.8640.9880.9860.8160.9961.0000.853
20230.9820.9860.9770.9900.9760.9790.8330.9900.9900.9900.9860.9980.9271.0000.9950.8330.8531.000
2024-04-06T17:35:36.751704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9720.9510.9420.9320.9280.9140.9170.9180.9300.9280.9240.9230.9100.9170.8980.8750.875
20070.9721.0000.9710.9580.9470.9410.9320.9340.9340.9390.9370.9330.9320.9200.9240.9140.8980.896
20080.9510.9711.0000.9740.9590.9480.9410.9400.9390.9420.9410.9370.9360.9280.9290.9190.9040.902
20090.9420.9580.9741.0000.9750.9620.9540.9540.9510.9490.9490.9460.9460.9390.9380.9280.9170.916
20100.9320.9470.9590.9751.0000.9720.9620.9600.9540.9520.9480.9480.9470.9400.9390.9320.9230.922
20110.9280.9410.9480.9620.9721.0000.9780.9740.9700.9640.9580.9440.9390.9420.9430.9330.9210.917
20120.9140.9320.9410.9540.9620.9781.0000.9810.9730.9630.9540.9410.9360.9390.9410.9320.9250.921
20130.9170.9340.9400.9540.9600.9740.9811.0000.9780.9700.9600.9470.9420.9450.9460.9370.9270.923
20140.9180.9340.9390.9510.9540.9700.9730.9781.0000.9790.9690.9500.9430.9480.9480.9370.9240.920
20150.9300.9390.9420.9490.9520.9640.9630.9700.9791.0000.9810.9600.9520.9530.9560.9430.9250.919
20160.9280.9370.9410.9490.9480.9580.9540.9600.9690.9811.0000.9700.9580.9570.9590.9460.9300.925
20170.9240.9330.9370.9460.9480.9440.9410.9470.9500.9600.9701.0000.9770.9630.9590.9510.9370.935
20180.9230.9320.9360.9460.9470.9390.9360.9420.9430.9520.9580.9771.0000.9710.9650.9560.9430.939
20190.9100.9200.9280.9390.9400.9420.9390.9450.9480.9530.9570.9630.9711.0000.9750.9610.9470.944
20200.9170.9240.9290.9380.9390.9430.9410.9460.9480.9560.9590.9590.9650.9751.0000.9730.9520.949
20210.8980.9140.9190.9280.9320.9330.9320.9370.9370.9430.9460.9510.9560.9610.9731.0000.9710.960
20220.8750.8980.9040.9170.9230.9210.9250.9270.9240.9250.9300.9370.9430.9470.9520.9711.0000.972
20230.8750.8960.9020.9160.9220.9170.9210.9230.9200.9190.9250.9350.9390.9440.9490.9600.9721.000

Missing values

2024-04-06T17:35:21.149987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:35:21.684017image/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.
2024-04-06T17:35:22.130243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

지역_건물용도200620072008200920102011201220132014201520162017201820192020202120222023
0전국_단독주택12553694415886788420083239996298476188303106768133609124925134439120985972371233341163187970257648
1전국_다가구주택21185170021568412776113732323220076225753058438477352051382310850197772579821748134198314
2전국_다세대주택20110018832918725912272511012213549212168513423516439623243724174622611518703116311520623321809413106681520
3전국_연립주택570454363739053288642594931821268652782532784433104188044260348993144044843419762548420019
4전국_아파트104698676155479759782839175351687547069031584589010244491169667107117911968561222877110114714700221063519565734659471
5전국_상업업무용142757162452132887115759114452138787127001140483176453221638236215351873340715275990301477340742228039142846
6전국_공업용1177110317983789851083911993125551301112668151931691322483258602913437207614615324234095
7전국_기타건물5022736020277195271754316899179811760820266189072180323512213902436222864257941936513471
8전국_나지200719179948184021214757210979240459228804224889249496295006299267306201271895250235281996301723231588163888
9서울_단독주택2679816777153771154090591133580098613117311854917597185891780111447143221049460653209
지역_건물용도200620072008200920102011201220132014201520162017201820192020202120222023
2681제주 제주시_나지69415371338180220731973205420383467462448935124440335602926391143343111
2682제주 서귀포시_단독주택2284383733553844805037529371095945801709496726800781538
2683제주 서귀포시_다가구주택396861519386010110527245436664552
2684제주 서귀포시_다세대주택96195162213212183177208252360525467555380369428362268
2685제주 서귀포시_연립주택1492382122653463864493026841348108212868406788541347954855
2686제주 서귀포시_아파트175546985543572833751103313483047311819431252105111981546911749
2687제주 서귀포시_상업업무용6733616123625929136967464218092705297138011490117510861043623
2688제주 서귀포시_공업용0323421586134568301128927
2689제주 서귀포시_기타건물47182119292858463221113943273252413
2690제주 서귀포시_나지6201079791129314591425136916842201233421423373261815161034165113871533