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 거래주체별(필지수) 데이터입니다.- (단위 : 필지수)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068233/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 = 39.66635829)Skewed
2007 is highly skewed (γ1 = 39.53760374)Skewed
2008 is highly skewed (γ1 = 40.00672068)Skewed
2009 is highly skewed (γ1 = 40.66248851)Skewed
2010 is highly skewed (γ1 = 41.22404158)Skewed
2011 is highly skewed (γ1 = 41.44710035)Skewed
2012 is highly skewed (γ1 = 41.37237155)Skewed
2013 is highly skewed (γ1 = 41.50330854)Skewed
2014 is highly skewed (γ1 = 42.04528689)Skewed
2015 is highly skewed (γ1 = 41.69037664)Skewed
2016 is highly skewed (γ1 = 41.03256612)Skewed
2017 is highly skewed (γ1 = 37.38777915)Skewed
2018 is highly skewed (γ1 = 35.53528143)Skewed
2019 is highly skewed (γ1 = 35.97062559)Skewed
2020 is highly skewed (γ1 = 38.23022375)Skewed
2021 is highly skewed (γ1 = 38.24119871)Skewed
2022 is highly skewed (γ1 = 37.36477523)Skewed
2023 is highly skewed (γ1 = 39.11530204)Skewed
지역_거래주체 has unique valuesUnique
2006 has 47 (1.7%) zerosZeros
2007 has 32 (1.2%) zerosZeros
2008 has 27 (1.0%) zerosZeros
2013 has 31 (1.2%) zerosZeros

Reproduction

Analysis started2024-04-06 08:15:46.265950
Analysis finished2024-04-06 08:17:16.669393
Duration1 minute and 30.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역_거래주체
Text

UNIQUE 

Distinct2691
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
2024-04-06T17:17:16.993434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length13
Mean length13.377926
Min length9

Characters and Unicode

Total characters36000
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 468
 
8.5%
경남 234
 
4.2%
서울 225
 
4.1%
경북 225
 
4.1%
전남 198
 
3.6%
충남 171
 
3.1%
충북 171
 
3.1%
강원 162
 
2.9%
전북 144
 
2.6%
부산 144
 
2.6%
Other values (2486) 3375
61.2%
2024-04-06T17:17:17.715275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3750
 
10.4%
2826
 
7.8%
_ 2691
 
7.5%
> 2691
 
7.5%
- 2655
 
7.4%
2298
 
6.4%
1794
 
5.0%
1794
 
5.0%
1794
 
5.0%
1269
 
3.5%
Other values (143) 12438
34.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24795
68.9%
Space Separator 2826
 
7.8%
Connector Punctuation 2691
 
7.5%
Math Symbol 2691
 
7.5%
Dash Punctuation 2655
 
7.4%
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 (%)
2826
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2691
100.0%
Math Symbol
ValueCountFrequency (%)
> 2691
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2655
100.0%
Open Punctuation
ValueCountFrequency (%)
( 171
100.0%
Close Punctuation
ValueCountFrequency (%)
) 171
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24795
68.9%
Common 11205
31.1%

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 (%)
2826
25.2%
_ 2691
24.0%
> 2691
24.0%
- 2655
23.7%
( 171
 
1.5%
) 171
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24795
68.9%
ASCII 11205
31.1%

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 (%)
2826
25.2%
_ 2691
24.0%
> 2691
24.0%
- 2655
23.7%
( 171
 
1.5%
) 171
 
1.5%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1064
Distinct (%)42.7%
Missing198
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean3319.3301
Minimum0
Maximum1916233
Zeros47
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:17:18.021573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q119
median106
Q3741
95-th percentile10229.6
Maximum1916233
Range1916233
Interquartile range (IQR)722

Descriptive statistics

Standard deviation41856.274
Coefficient of variation (CV)12.609856
Kurtosis1765.3862
Mean3319.3301
Median Absolute Deviation (MAD)101
Skewness39.666358
Sum8275090
Variance1.7519477 × 109
MonotonicityNot monotonic
2024-04-06T17:17:18.546010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 63
 
2.3%
3 53
 
2.0%
2 48
 
1.8%
0 47
 
1.7%
6 46
 
1.7%
7 40
 
1.5%
4 38
 
1.4%
5 33
 
1.2%
9 30
 
1.1%
10 29
 
1.1%
Other values (1054) 2066
76.8%
(Missing) 198
 
7.4%
ValueCountFrequency (%)
0 47
1.7%
1 63
2.3%
2 48
1.8%
3 53
2.0%
4 38
1.4%
5 33
1.2%
6 46
1.7%
7 40
1.5%
8 25
 
0.9%
9 30
1.1%
ValueCountFrequency (%)
1916233 1
< 0.1%
508692 1
< 0.1%
401608 1
< 0.1%
325456 1
< 0.1%
180998 1
< 0.1%
145496 1
< 0.1%
135916 1
< 0.1%
110561 1
< 0.1%
107413 1
< 0.1%
90291 1
< 0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1037
Distinct (%)42.4%
Missing243
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean2845.4056
Minimum0
Maximum1582613
Zeros32
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:17:18.870659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q125
median116
Q3698.75
95-th percentile8307.3
Maximum1582613
Range1582613
Interquartile range (IQR)673.75

Descriptive statistics

Standard deviation34767.817
Coefficient of variation (CV)12.218931
Kurtosis1754.2512
Mean2845.4056
Median Absolute Deviation (MAD)108
Skewness39.537604
Sum6965553
Variance1.2088011 × 109
MonotonicityNot monotonic
2024-04-06T17:17:19.197637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 41
 
1.5%
3 38
 
1.4%
5 34
 
1.3%
4 33
 
1.2%
0 32
 
1.2%
6 32
 
1.2%
11 31
 
1.2%
8 29
 
1.1%
10 28
 
1.0%
1 28
 
1.0%
Other values (1027) 2122
78.9%
(Missing) 243
 
9.0%
ValueCountFrequency (%)
0 32
1.2%
1 28
1.0%
2 41
1.5%
3 38
1.4%
4 33
1.2%
5 34
1.3%
6 32
1.2%
7 22
0.8%
8 29
1.1%
9 22
0.8%
ValueCountFrequency (%)
1582613 1
< 0.1%
379491 1
< 0.1%
359114 1
< 0.1%
205410 1
< 0.1%
141660 1
< 0.1%
138831 1
< 0.1%
130886 1
< 0.1%
115823 1
< 0.1%
111147 1
< 0.1%
94234 1
< 0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1054
Distinct (%)42.7%
Missing225
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean2882.2401
Minimum0
Maximum1603741
Zeros27
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:17:19.480707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.25
Q130
median120
Q3697
95-th percentile8442.25
Maximum1603741
Range1603741
Interquartile range (IQR)667

Descriptive statistics

Standard deviation34968.771
Coefficient of variation (CV)12.132498
Kurtosis1792.6112
Mean2882.2401
Median Absolute Deviation (MAD)112
Skewness40.006721
Sum7107604
Variance1.2228149 × 109
MonotonicityNot monotonic
2024-04-06T17:17:19.885135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 35
 
1.3%
2 33
 
1.2%
6 32
 
1.2%
4 31
 
1.2%
3 29
 
1.1%
8 29
 
1.1%
0 27
 
1.0%
9 27
 
1.0%
12 27
 
1.0%
11 25
 
0.9%
Other values (1044) 2171
80.7%
(Missing) 225
 
8.4%
ValueCountFrequency (%)
0 27
1.0%
1 35
1.3%
2 33
1.2%
3 29
1.1%
4 31
1.2%
5 25
0.9%
6 32
1.2%
7 20
0.7%
8 29
1.1%
9 27
1.0%
ValueCountFrequency (%)
1603741 1
< 0.1%
362916 1
< 0.1%
350343 1
< 0.1%
199545 1
< 0.1%
152601 1
< 0.1%
139458 1
< 0.1%
138889 1
< 0.1%
115473 1
< 0.1%
96933 1
< 0.1%
94082 1
< 0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1042
Distinct (%)42.3%
Missing225
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean2827.9444
Minimum0
Maximum1601006
Zeros22
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:17:20.275551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q133
median126.5
Q3700.25
95-th percentile8609.25
Maximum1601006
Range1601006
Interquartile range (IQR)667.25

Descriptive statistics

Standard deviation34676.201
Coefficient of variation (CV)12.261981
Kurtosis1839.7003
Mean2827.9444
Median Absolute Deviation (MAD)115.5
Skewness40.662489
Sum6973711
Variance1.2024389 × 109
MonotonicityNot monotonic
2024-04-06T17:17:20.652475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 32
 
1.2%
5 31
 
1.2%
2 29
 
1.1%
6 27
 
1.0%
13 25
 
0.9%
14 25
 
0.9%
1 23
 
0.9%
11 23
 
0.9%
4 23
 
0.9%
7 22
 
0.8%
Other values (1032) 2206
82.0%
(Missing) 225
 
8.4%
ValueCountFrequency (%)
0 22
0.8%
1 23
0.9%
2 29
1.1%
3 32
1.2%
4 23
0.9%
5 31
1.2%
6 27
1.0%
7 22
0.8%
8 20
0.7%
9 19
0.7%
ValueCountFrequency (%)
1601006 1
< 0.1%
351313 1
< 0.1%
322604 1
< 0.1%
185237 1
< 0.1%
139405 1
< 0.1%
135596 1
< 0.1%
123535 1
< 0.1%
112101 1
< 0.1%
100228 1
< 0.1%
91540 1
< 0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1040
Distinct (%)41.3%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean2573.5607
Minimum0
Maximum1468232
Zeros21
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:17:21.455052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q131
median121
Q3627
95-th percentile7659.9
Maximum1468232
Range1468232
Interquartile range (IQR)596

Descriptive statistics

Standard deviation31408.927
Coefficient of variation (CV)12.204463
Kurtosis1890.8231
Mean2573.5607
Median Absolute Deviation (MAD)110
Skewness41.224042
Sum6485373
Variance9.8652067 × 108
MonotonicityNot monotonic
2024-04-06T17:17:21.768767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 37
 
1.4%
10 34
 
1.3%
6 30
 
1.1%
4 28
 
1.0%
5 28
 
1.0%
12 27
 
1.0%
1 25
 
0.9%
8 24
 
0.9%
13 23
 
0.9%
9 23
 
0.9%
Other values (1030) 2241
83.3%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 21
0.8%
1 25
0.9%
2 37
1.4%
3 19
0.7%
4 28
1.0%
5 28
1.0%
6 30
1.1%
7 20
0.7%
8 24
0.9%
9 23
0.9%
ValueCountFrequency (%)
1468232 1
< 0.1%
318210 1
< 0.1%
276188 1
< 0.1%
149341 1
< 0.1%
134197 1
< 0.1%
128925 1
< 0.1%
111337 1
< 0.1%
100844 1
< 0.1%
99651 1
< 0.1%
99241 1
< 0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1038
Distinct (%)41.6%
Missing198
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean2933.2647
Minimum0
Maximum1699367
Zeros18
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:17:22.130261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q136
median129
Q3665
95-th percentile8714
Maximum1699367
Range1699367
Interquartile range (IQR)629

Descriptive statistics

Standard deviation36400.201
Coefficient of variation (CV)12.40945
Kurtosis1901.1026
Mean2933.2647
Median Absolute Deviation (MAD)114
Skewness41.4471
Sum7312629
Variance1.3249747 × 109
MonotonicityNot monotonic
2024-04-06T17:17:22.554413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 32
 
1.2%
5 28
 
1.0%
6 26
 
1.0%
9 25
 
0.9%
22 22
 
0.8%
26 22
 
0.8%
8 21
 
0.8%
16 21
 
0.8%
17 21
 
0.8%
14 21
 
0.8%
Other values (1028) 2254
83.8%
(Missing) 198
 
7.4%
ValueCountFrequency (%)
0 18
0.7%
1 9
 
0.3%
2 19
0.7%
3 20
0.7%
4 16
0.6%
5 28
1.0%
6 26
1.0%
7 20
0.7%
8 21
0.8%
9 25
0.9%
ValueCountFrequency (%)
1699367 1
< 0.1%
339528 1
< 0.1%
337933 1
< 0.1%
161595 1
< 0.1%
154741 1
< 0.1%
152409 1
< 0.1%
117695 1
< 0.1%
112985 1
< 0.1%
110913 1
< 0.1%
107060 1
< 0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1050
Distinct (%)41.8%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2550.1812
Minimum0
Maximum1455983
Zeros16
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:17:22.859161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q135
median134
Q3663.5
95-th percentile7264.5
Maximum1455983
Range1455983
Interquartile range (IQR)628.5

Descriptive statistics

Standard deviation31139.274
Coefficient of variation (CV)12.210612
Kurtosis1899.2051
Mean2550.1812
Median Absolute Deviation (MAD)118
Skewness41.372372
Sum6403505
Variance9.6965438 × 108
MonotonicityNot monotonic
2024-04-06T17:17:23.279247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 25
 
0.9%
21 25
 
0.9%
5 23
 
0.9%
7 23
 
0.9%
18 23
 
0.9%
9 22
 
0.8%
26 22
 
0.8%
12 22
 
0.8%
4 20
 
0.7%
17 20
 
0.7%
Other values (1040) 2286
84.9%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 16
0.6%
1 18
0.7%
2 18
0.7%
3 15
0.6%
4 20
0.7%
5 23
0.9%
6 19
0.7%
7 23
0.9%
8 18
0.7%
9 22
0.8%
ValueCountFrequency (%)
1455983 1
< 0.1%
305449 1
< 0.1%
281238 1
< 0.1%
145466 1
< 0.1%
140568 1
< 0.1%
119639 1
< 0.1%
106927 1
< 0.1%
103811 1
< 0.1%
99880 1
< 0.1%
88870 1
< 0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1035
Distinct (%)41.2%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2803.5998
Minimum0
Maximum1622765
Zeros31
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:17:23.642032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q132
median131
Q3681
95-th percentile8413
Maximum1622765
Range1622765
Interquartile range (IQR)649

Descriptive statistics

Standard deviation34666.365
Coefficient of variation (CV)12.364948
Kurtosis1908.014
Mean2803.5998
Median Absolute Deviation (MAD)119
Skewness41.503309
Sum7039839
Variance1.2017569 × 109
MonotonicityNot monotonic
2024-04-06T17:17:24.091314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 33
 
1.2%
0 31
 
1.2%
5 28
 
1.0%
12 26
 
1.0%
17 26
 
1.0%
8 26
 
1.0%
11 25
 
0.9%
3 24
 
0.9%
4 23
 
0.9%
7 22
 
0.8%
Other values (1025) 2247
83.5%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 31
1.2%
1 18
0.7%
2 21
0.8%
3 24
0.9%
4 23
0.9%
5 28
1.0%
6 33
1.2%
7 22
0.8%
8 26
1.0%
9 16
0.6%
ValueCountFrequency (%)
1622765 1
< 0.1%
329477 1
< 0.1%
322642 1
< 0.1%
157189 1
< 0.1%
156828 1
< 0.1%
149186 1
< 0.1%
109436 1
< 0.1%
104216 1
< 0.1%
100880 1
< 0.1%
99996 1
< 0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1107
Distinct (%)43.0%
Missing117
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean3241.1449
Minimum0
Maximum1942663
Zeros17
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:17:24.428360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q132
median130
Q3763.25
95-th percentile9722.15
Maximum1942663
Range1942663
Interquartile range (IQR)731.25

Descriptive statistics

Standard deviation40992.951
Coefficient of variation (CV)12.647676
Kurtosis1956.0341
Mean3241.1449
Median Absolute Deviation (MAD)119
Skewness42.045287
Sum8342707
Variance1.680422 × 109
MonotonicityNot monotonic
2024-04-06T17:17:24.824610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 32
 
1.2%
10 30
 
1.1%
9 27
 
1.0%
11 27
 
1.0%
8 26
 
1.0%
13 26
 
1.0%
12 26
 
1.0%
2 26
 
1.0%
3 26
 
1.0%
6 25
 
0.9%
Other values (1097) 2303
85.6%
(Missing) 117
 
4.3%
ValueCountFrequency (%)
0 17
0.6%
1 25
0.9%
2 26
1.0%
3 26
1.0%
4 19
0.7%
5 19
0.7%
6 25
0.9%
7 32
1.2%
8 26
1.0%
9 27
1.0%
ValueCountFrequency (%)
1942663 1
< 0.1%
404313 1
< 0.1%
390436 1
< 0.1%
199610 1
< 0.1%
183354 1
< 0.1%
175750 1
< 0.1%
126472 1
< 0.1%
116348 1
< 0.1%
114485 1
< 0.1%
112712 1
< 0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1102
Distinct (%)43.4%
Missing153
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean3838.2372
Minimum0
Maximum2318014
Zeros16
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:17:25.239362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q139
median147.5
Q3759
95-th percentile12014.1
Maximum2318014
Range2318014
Interquartile range (IQR)720

Descriptive statistics

Standard deviation49301.425
Coefficient of variation (CV)12.844809
Kurtosis1922.8119
Mean3838.2372
Median Absolute Deviation (MAD)135.5
Skewness41.690377
Sum9741446
Variance2.4306305 × 109
MonotonicityNot monotonic
2024-04-06T17:17:25.525756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 29
 
1.1%
5 28
 
1.0%
9 27
 
1.0%
2 24
 
0.9%
8 23
 
0.9%
11 22
 
0.8%
15 22
 
0.8%
10 21
 
0.8%
35 21
 
0.8%
7 20
 
0.7%
Other values (1092) 2301
85.5%
(Missing) 153
 
5.7%
ValueCountFrequency (%)
0 16
0.6%
1 19
0.7%
2 24
0.9%
3 29
1.1%
4 19
0.7%
5 28
1.0%
6 20
0.7%
7 20
0.7%
8 23
0.9%
9 27
1.0%
ValueCountFrequency (%)
2318014 1
< 0.1%
522540 1
< 0.1%
427797 1
< 0.1%
292105 1
< 0.1%
205113 1
< 0.1%
195347 1
< 0.1%
159555 1
< 0.1%
136070 1
< 0.1%
127649 1
< 0.1%
127341 1
< 0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1134
Distinct (%)44.7%
Missing153
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean3712.632
Minimum0
Maximum2200574
Zeros25
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:17:25.831956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q142
median153.5
Q3800
95-th percentile11225.45
Maximum2200574
Range2200574
Interquartile range (IQR)758

Descriptive statistics

Standard deviation47129.218
Coefficient of variation (CV)12.694288
Kurtosis1872.6813
Mean3712.632
Median Absolute Deviation (MAD)140.5
Skewness41.032566
Sum9422660
Variance2.2211632 × 109
MonotonicityNot monotonic
2024-04-06T17:17:26.163442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 35
 
1.3%
8 33
 
1.2%
5 29
 
1.1%
1 25
 
0.9%
0 25
 
0.9%
6 22
 
0.8%
20 21
 
0.8%
9 20
 
0.7%
22 19
 
0.7%
15 18
 
0.7%
Other values (1124) 2291
85.1%
(Missing) 153
 
5.7%
ValueCountFrequency (%)
0 25
0.9%
1 25
0.9%
2 17
0.6%
3 35
1.3%
4 17
0.6%
5 29
1.1%
6 22
0.8%
7 13
 
0.5%
8 33
1.2%
9 20
0.7%
ValueCountFrequency (%)
2200574 1
< 0.1%
540237 1
< 0.1%
438540 1
< 0.1%
297252 1
< 0.1%
179930 1
< 0.1%
159203 1
< 0.1%
143242 1
< 0.1%
133510 1
< 0.1%
123169 1
< 0.1%
121283 1
< 0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1174
Distinct (%)46.8%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean4153.82
Minimum0
Maximum2164782
Zeros13
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:17:26.662394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q143
median181
Q3999
95-th percentile12127.5
Maximum2164782
Range2164782
Interquartile range (IQR)956

Descriptive statistics

Standard deviation48569.891
Coefficient of variation (CV)11.692825
Kurtosis1592.0804
Mean4153.82
Median Absolute Deviation (MAD)166
Skewness37.387779
Sum10430242
Variance2.3590343 × 109
MonotonicityNot monotonic
2024-04-06T17:17:27.149755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 30
 
1.1%
10 23
 
0.9%
14 23
 
0.9%
4 22
 
0.8%
25 22
 
0.8%
2 21
 
0.8%
3 21
 
0.8%
9 21
 
0.8%
5 18
 
0.7%
33 17
 
0.6%
Other values (1164) 2293
85.2%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 13
0.5%
1 15
0.6%
2 21
0.8%
3 21
0.8%
4 22
0.8%
5 18
0.7%
6 15
0.6%
7 15
0.6%
8 16
0.6%
9 21
0.8%
ValueCountFrequency (%)
2164782 1
< 0.1%
764771 1
< 0.1%
548859 1
< 0.1%
273838 1
< 0.1%
247015 1
< 0.1%
165566 1
< 0.1%
156996 1
< 0.1%
134876 1
< 0.1%
131059 1
< 0.1%
120864 1
< 0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1191
Distinct (%)47.3%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean3984.4119
Minimum0
Maximum1944852
Zeros18
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:17:27.500839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q144
median186
Q31098.75
95-th percentile11076.6
Maximum1944852
Range1944852
Interquartile range (IQR)1054.75

Descriptive statistics

Standard deviation44786.68
Coefficient of variation (CV)11.240474
Kurtosis1450.9309
Mean3984.4119
Median Absolute Deviation (MAD)171
Skewness35.535281
Sum10040718
Variance2.0058467 × 109
MonotonicityNot monotonic
2024-04-06T17:17:27.952156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 29
 
1.1%
2 25
 
0.9%
17 23
 
0.9%
13 22
 
0.8%
8 21
 
0.8%
14 20
 
0.7%
9 20
 
0.7%
3 19
 
0.7%
22 19
 
0.7%
0 18
 
0.7%
Other values (1181) 2304
85.6%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 18
0.7%
1 14
0.5%
2 25
0.9%
3 19
0.7%
4 29
1.1%
5 15
0.6%
6 17
0.6%
7 14
0.5%
8 21
0.8%
9 20
0.7%
ValueCountFrequency (%)
1944852 1
< 0.1%
811851 1
< 0.1%
519428 1
< 0.1%
308651 1
< 0.1%
246949 1
< 0.1%
142855 1
< 0.1%
136088 1
< 0.1%
132850 1
< 0.1%
129434 1
< 0.1%
115327 1
< 0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1165
Distinct (%)46.4%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean3642.043
Minimum0
Maximum1758244
Zeros16
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:17:28.343551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q145
median195
Q31001
95-th percentile9649.5
Maximum1758244
Range1758244
Interquartile range (IQR)956

Descriptive statistics

Standard deviation40212.374
Coefficient of variation (CV)11.041159
Kurtosis1487.5596
Mean3642.043
Median Absolute Deviation (MAD)179
Skewness35.970626
Sum9145170
Variance1.617035 × 109
MonotonicityNot monotonic
2024-04-06T17:17:28.818712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 24
 
0.9%
8 21
 
0.8%
11 21
 
0.8%
2 20
 
0.7%
16 20
 
0.7%
7 19
 
0.7%
1 19
 
0.7%
3 19
 
0.7%
14 19
 
0.7%
9 19
 
0.7%
Other values (1155) 2310
85.8%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 16
0.6%
1 19
0.7%
2 20
0.7%
3 19
0.7%
4 17
0.6%
5 24
0.9%
6 14
0.5%
7 19
0.7%
8 21
0.8%
9 19
0.7%
ValueCountFrequency (%)
1758244 1
< 0.1%
700851 1
< 0.1%
444284 1
< 0.1%
263041 1
< 0.1%
186295 1
< 0.1%
158098 1
< 0.1%
131633 1
< 0.1%
124849 1
< 0.1%
123150 1
< 0.1%
110050 1
< 0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1251
Distinct (%)49.8%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean4405.3361
Minimum0
Maximum2310295
Zeros15
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:17:29.268266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q154
median219
Q31206.5
95-th percentile12590
Maximum2310295
Range2310295
Interquartile range (IQR)1152.5

Descriptive statistics

Standard deviation51208.265
Coefficient of variation (CV)11.624145
Kurtosis1660.7946
Mean4405.3361
Median Absolute Deviation (MAD)203
Skewness38.230224
Sum11061799
Variance2.6222864 × 109
MonotonicityNot monotonic
2024-04-06T17:17:29.681173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 23
 
0.9%
16 21
 
0.8%
2 19
 
0.7%
17 18
 
0.7%
12 18
 
0.7%
13 18
 
0.7%
3 17
 
0.6%
32 17
 
0.6%
14 17
 
0.6%
7 17
 
0.6%
Other values (1241) 2326
86.4%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 15
0.6%
1 16
0.6%
2 19
0.7%
3 17
0.6%
4 23
0.9%
5 12
0.4%
6 15
0.6%
7 17
0.6%
8 14
0.5%
9 15
0.6%
ValueCountFrequency (%)
2310295 1
< 0.1%
691817 1
< 0.1%
618473 1
< 0.1%
243197 1
< 0.1%
236354 1
< 0.1%
209752 1
< 0.1%
152320 1
< 0.1%
149808 1
< 0.1%
146268 1
< 0.1%
142000 1
< 0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1274
Distinct (%)50.7%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean4132.5667
Minimum0
Maximum2109527
Zeros10
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:17:30.120400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q164
median248
Q31295.5
95-th percentile11391.5
Maximum2109527
Range2109527
Interquartile range (IQR)1231.5

Descriptive statistics

Standard deviation46707.951
Coefficient of variation (CV)11.302407
Kurtosis1665.6132
Mean4132.5667
Median Absolute Deviation (MAD)228
Skewness38.241199
Sum10376875
Variance2.1816326 × 109
MonotonicityNot monotonic
2024-04-06T17:17:31.028838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 20
 
0.7%
7 18
 
0.7%
10 17
 
0.6%
19 16
 
0.6%
8 16
 
0.6%
13 15
 
0.6%
12 15
 
0.6%
14 15
 
0.6%
4 15
 
0.6%
16 15
 
0.6%
Other values (1264) 2349
87.3%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 10
0.4%
1 13
0.5%
2 11
0.4%
3 14
0.5%
4 15
0.6%
5 20
0.7%
6 10
0.4%
7 18
0.7%
8 16
0.6%
9 7
 
0.3%
ValueCountFrequency (%)
2109527 1
< 0.1%
616835 1
< 0.1%
550065 1
< 0.1%
219858 1
< 0.1%
201181 1
< 0.1%
183045 1
< 0.1%
165672 1
< 0.1%
164442 1
< 0.1%
153760 1
< 0.1%
145789 1
< 0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1198
Distinct (%)47.7%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2759.7654
Minimum0
Maximum1314848
Zeros17
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:17:31.439677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q159
median213
Q3957.5
95-th percentile7453.5
Maximum1314848
Range1314848
Interquartile range (IQR)898.5

Descriptive statistics

Standard deviation29384.55
Coefficient of variation (CV)10.647481
Kurtosis1607.0021
Mean2759.7654
Median Absolute Deviation (MAD)193
Skewness37.364775
Sum6929771
Variance8.6345177 × 108
MonotonicityNot monotonic
2024-04-06T17:17:31.779983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 22
 
0.8%
6 21
 
0.8%
5 21
 
0.8%
12 19
 
0.7%
14 19
 
0.7%
9 18
 
0.7%
17 18
 
0.7%
15 17
 
0.6%
0 17
 
0.6%
3 17
 
0.6%
Other values (1188) 2322
86.3%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 17
0.6%
1 22
0.8%
2 10
0.4%
3 17
0.6%
4 14
0.5%
5 21
0.8%
6 21
0.8%
7 11
0.4%
8 16
0.6%
9 18
0.7%
ValueCountFrequency (%)
1314848 1
< 0.1%
425526 1
< 0.1%
300592 1
< 0.1%
166214 1
< 0.1%
140803 1
< 0.1%
121075 1
< 0.1%
114634 1
< 0.1%
112946 1
< 0.1%
109822 1
< 0.1%
90263 1
< 0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1061
Distinct (%)42.1%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean2277.0821
Minimum0
Maximum1174079
Zeros16
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-04-06T17:17:32.136463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q147
median156
Q3641
95-th percentile6482.9
Maximum1174079
Range1174079
Interquartile range (IQR)594

Descriptive statistics

Standard deviation25693.91
Coefficient of variation (CV)11.283699
Kurtosis1733.8501
Mean2277.0821
Median Absolute Deviation (MAD)137
Skewness39.115302
Sum5738247
Variance6.6017701 × 108
MonotonicityNot monotonic
2024-04-06T17:17:32.562106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 23
 
0.9%
13 23
 
0.9%
26 22
 
0.8%
8 22
 
0.8%
2 21
 
0.8%
3 21
 
0.8%
12 20
 
0.7%
1 19
 
0.7%
7 18
 
0.7%
22 18
 
0.7%
Other values (1051) 2313
86.0%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 16
0.6%
1 19
0.7%
2 21
0.8%
3 21
0.8%
4 16
0.6%
5 23
0.9%
6 16
0.6%
7 18
0.7%
8 22
0.8%
9 15
0.6%
ValueCountFrequency (%)
1174079 1
< 0.1%
324880 1
< 0.1%
272913 1
< 0.1%
101316 1
< 0.1%
100878 1
< 0.1%
99334 1
< 0.1%
93905 1
< 0.1%
91370 1
< 0.1%
91365 1
< 0.1%
91044 1
< 0.1%

Interactions

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2024-04-06T17:17:09.205273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:13.598890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:55.236017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:59.637228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:04.309628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:08.418190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:12.810996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:18.862090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:24.123961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:29.285934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:34.484108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:38.752875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:42.934129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:47.299151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:51.252959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:55.640366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:00.876172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:05.113947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:09.402083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:13.789136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:55.490186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:59.829587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:04.493338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:08.632792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:12.989401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:19.198545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:24.407166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:29.485334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:34.762570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:38.972082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:43.118921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:47.479322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:51.468334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:55.954741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:01.043117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:05.313639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:09.574142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:14.005149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:55.739767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:00.036559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:04.809149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:08.950635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:13.250435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:19.627987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:24.741760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:29.699123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:34.985181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:39.228595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:43.413855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:47.688793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:51.743795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:56.257972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:01.253156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:05.516057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:09.787016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:14.205064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:56.028661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:00.279349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:04.990651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:09.298856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:13.507447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:19.918899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:24.990126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:30.373568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:35.213328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:39.454142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:43.659110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:47.888218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:51.970211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:56.587832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:01.535865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:05.726642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:09.970078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:14.369775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:56.311636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:00.450689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:05.173661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:09.580426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:13.706068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:20.139757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:25.262548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:30.565586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:35.439157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:39.680876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:44.287457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:48.074001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:52.208646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:57.281891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:01.806401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:05.919682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:10.193483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:14.547590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:56.571165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:00.628880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:05.324444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:09.775662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:13.941018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:20.388386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:25.641813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:30.769084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:35.621193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:39.886223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:44.448493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:48.257468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:52.417213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:57.536156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:02.017283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:06.103789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:10.382867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:14.885644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:56.796901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:00.846814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:05.525043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:09.967684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:14.144069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:20.660602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:25.895193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:31.081619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:35.829557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:40.099523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:44.641645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:48.424702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:52.643980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:57.758847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:02.214530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:06.312715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:17:10.569713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:17:32.815089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0001.0001.0001.0000.9691.0000.9820.9821.0000.9900.9900.9070.9070.9070.9960.9820.8411.000
20071.0001.0001.0001.0000.9691.0000.9820.9821.0000.9900.9900.9070.9070.9070.9960.9820.8411.000
20081.0001.0001.0001.0000.9691.0000.9820.9821.0000.9900.9900.9070.9070.9070.9960.9820.8411.000
20091.0001.0001.0001.0000.9691.0000.9820.9821.0000.9900.9900.9070.9070.9070.9960.9820.8411.000
20100.9690.9690.9690.9691.0000.8620.9960.9960.9690.9820.9820.9070.9070.9070.9690.9690.9070.862
20111.0001.0001.0001.0000.8621.0001.0001.0001.0000.8620.8621.0001.0001.0001.0001.0001.0001.000
20120.9820.9820.9820.9820.9961.0001.0001.0000.9820.9960.9961.0001.0001.0000.9820.9821.0001.000
20130.9820.9820.9820.9820.9961.0001.0001.0000.9820.9960.9961.0001.0001.0000.9820.9821.0001.000
20141.0001.0001.0001.0000.9691.0000.9820.9821.0000.9900.9900.9070.9070.9070.9960.9820.8411.000
20150.9900.9900.9900.9900.9820.8620.9960.9960.9901.0001.0000.9380.9380.9380.9820.9690.9070.862
20160.9900.9900.9900.9900.9820.8620.9960.9960.9901.0001.0000.9380.9380.9380.9820.9690.9070.862
20170.9070.9070.9070.9070.9071.0001.0001.0000.9070.9380.9381.0001.0001.0001.0000.8410.9951.000
20180.9070.9070.9070.9070.9071.0001.0001.0000.9070.9380.9381.0001.0001.0001.0000.8410.9951.000
20190.9070.9070.9070.9070.9071.0001.0001.0000.9070.9380.9381.0001.0001.0001.0000.8410.9951.000
20200.9960.9960.9960.9960.9691.0000.9820.9820.9960.9820.9821.0001.0001.0001.0000.9820.8761.000
20210.9820.9820.9820.9820.9691.0000.9820.9820.9820.9690.9690.8410.8410.8410.9821.0000.9071.000
20220.8410.8410.8410.8410.9071.0001.0001.0000.8410.9070.9070.9950.9950.9950.8760.9071.0001.000
20231.0001.0001.0001.0000.8621.0001.0001.0001.0000.8620.8621.0001.0001.0001.0001.0001.0001.000
2024-04-06T17:17:33.322221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9240.8960.8760.8710.8730.8640.8690.8720.8740.8660.8650.8720.8660.8670.8550.8430.820
20070.9241.0000.9220.8900.8830.8820.8760.8790.8820.8810.8750.8700.8680.8550.8520.8420.8350.821
20080.8960.9221.0000.9050.8910.8840.8790.8710.8720.8670.8660.8540.8590.8440.8370.8250.8230.808
20090.8760.8900.9051.0000.8970.8870.8770.8710.8720.8690.8680.8600.8540.8410.8370.8260.8290.822
20100.8710.8830.8910.8971.0000.9180.8980.8940.8820.8770.8750.8680.8580.8510.8400.8310.8300.826
20110.8730.8820.8840.8870.9181.0000.9190.8970.8880.8860.8770.8770.8610.8480.8390.8310.8350.829
20120.8640.8760.8790.8770.8980.9191.0000.9140.8960.8860.8770.8680.8630.8510.8460.8380.8420.831
20130.8690.8790.8710.8710.8940.8970.9141.0000.9120.9000.8930.8820.8750.8580.8460.8420.8400.836
20140.8720.8820.8720.8720.8820.8880.8960.9121.0000.9170.9010.8910.8840.8710.8520.8430.8480.838
20150.8740.8810.8670.8690.8770.8860.8860.9000.9171.0000.9290.9120.8990.8800.8680.8580.8580.839
20160.8660.8750.8660.8680.8750.8770.8770.8930.9010.9291.0000.9240.9080.8890.8710.8570.8570.845
20170.8650.8700.8540.8600.8680.8770.8680.8820.8910.9120.9241.0000.9200.8990.8800.8720.8570.853
20180.8720.8680.8590.8540.8580.8610.8630.8750.8840.8990.9080.9201.0000.9180.8940.8780.8710.857
20190.8660.8550.8440.8410.8510.8480.8510.8580.8710.8800.8890.8990.9181.0000.9150.8890.8740.866
20200.8670.8520.8370.8370.8400.8390.8460.8460.8520.8680.8710.8800.8940.9151.0000.9140.8940.879
20210.8550.8420.8250.8260.8310.8310.8380.8420.8430.8580.8570.8720.8780.8890.9141.0000.9120.877
20220.8430.8350.8230.8290.8300.8350.8420.8400.8480.8580.8570.8570.8710.8740.8940.9121.0000.894
20230.8200.8210.8080.8220.8260.8290.8310.8360.8380.8390.8450.8530.8570.8660.8790.8770.8941.000

Missing values

2024-04-06T17:17:15.194447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:17:15.751316image/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.
2024-04-06T17:17:16.236196image/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_거래주체200620072008200920102011201220132014201520162017201820192020202120222023
0전국_개인->개인191623315826131603741160100614682321699367145598316227651942663231801422005742164782194485217582442310295210952713148481174079
1전국_개인->법인180998130886152601123535111337110913106927109436112712136070133510134876142855158098209752219858166214101316
2전국_개인->기타217572447028288321643159231751338383150433327358523799043772460665028361426727076276449523
3전국_법인->개인401608379491362916322604318210337933305449329477390436427797438540764771811851700851691817616835425526324880
4전국_법인->법인50587585566721766200689456447257512586786615461562658637554880640888301066321035668772264507
5전국_법인->기타4899672856077942630568796869627582338833960211604125721255522687255722292719959
6전국_기타->개인535914308449010565274724056237550876472669912762338498892999115327104566749071119629026358993
7전국_기타->법인84411152212845107831148310930124041069011954122131391314646188591520314920207192182813733
8전국_기타->기타5150627970689843791310717108938428823199551017911803126821318013677158761708318738
9서울_개인->개인3254562054101995451852371289251547411196391491861996102921052972522738382469491862952431971830458936491044
지역_거래주체200620072008200920102011201220132014201520162017201820192020202120222023
2681제주 제주시_기타->기타27609513719122822012992119154181248358311311363318
2682제주 서귀포시_개인->개인5113843365346748718980468264107701456818183169011408911865966191221126490946641
2683제주 서귀포시_개인->법인500166587111818059221282129119452581193913151338892103311631124848
2684제주 서귀포시_개인->기타4582111113119126122238266193130130112139184127167267
2685제주 서귀포시_법인->개인522134213787761231168127483148447310153103448967731239431929220419121085
2686제주 서귀포시_법인->법인1416943628012732762636118341086122496114431083912665681679
2687제주 서귀포시_법인->기타11145121244213320144473847383911524105
2688제주 서귀포시_기타->개인70159107226299356338354272361342254187250158255461241
2689제주 서귀포시_기타->법인102717096574932224814332261712195737217
2690제주 서귀포시_기타->기타163122234147594547272379572219916446