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 순수토지 거래현황의 연도별 거래주체별(필지수) 데이터입니다.- (단위 : 필지수)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068332/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 = 39.91790404)Skewed
2007 is highly skewed (γ1 = 40.23910196)Skewed
2008 is highly skewed (γ1 = 39.8282499)Skewed
2009 is highly skewed (γ1 = 40.60883744)Skewed
2010 is highly skewed (γ1 = 40.95963347)Skewed
2011 is highly skewed (γ1 = 40.70674735)Skewed
2012 is highly skewed (γ1 = 40.84410337)Skewed
2013 is highly skewed (γ1 = 41.04149715)Skewed
2014 is highly skewed (γ1 = 41.83786842)Skewed
2015 is highly skewed (γ1 = 41.24710793)Skewed
2016 is highly skewed (γ1 = 40.77173135)Skewed
2017 is highly skewed (γ1 = 40.32314775)Skewed
2018 is highly skewed (γ1 = 39.84428332)Skewed
2019 is highly skewed (γ1 = 39.46580734)Skewed
2020 is highly skewed (γ1 = 39.90861971)Skewed
2021 is highly skewed (γ1 = 40.3194829)Skewed
2022 is highly skewed (γ1 = 39.84284972)Skewed
2023 is highly skewed (γ1 = 40.08460109)Skewed
지역_거래주체 has unique valuesUnique
2006 has 105 (3.9%) zerosZeros
2007 has 81 (3.0%) zerosZeros
2008 has 73 (2.7%) zerosZeros
2009 has 57 (2.1%) zerosZeros
2010 has 67 (2.5%) zerosZeros
2011 has 48 (1.8%) zerosZeros
2012 has 48 (1.8%) zerosZeros
2013 has 56 (2.1%) zerosZeros
2014 has 50 (1.9%) zerosZeros
2015 has 44 (1.6%) zerosZeros
2016 has 52 (1.9%) zerosZeros
2017 has 33 (1.2%) zerosZeros
2018 has 49 (1.8%) zerosZeros
2019 has 36 (1.3%) zerosZeros
2020 has 36 (1.3%) zerosZeros
2022 has 36 (1.3%) zerosZeros
2023 has 43 (1.6%) zerosZeros

Reproduction

Analysis started2024-03-30 07:21:02.757016
Analysis finished2024-03-30 07:23:19.482980
Duration2 minutes and 16.73 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
2024-03-30T07:23:19.881109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length13
Mean length13.401338
Min length9

Characters and Unicode

Total characters36063
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
 
8.4%
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 (2478) 3402
61.4%
2024-03-30T07:23:20.963938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3750
 
10.4%
2853
 
7.9%
_ 2691
 
7.5%
- 2691
 
7.5%
> 2691
 
7.5%
2298
 
6.4%
1794
 
5.0%
1794
 
5.0%
1794
 
5.0%
1269
 
3.5%
Other values (143) 12438
34.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24795
68.8%
Space Separator 2853
 
7.9%
Connector Punctuation 2691
 
7.5%
Dash Punctuation 2691
 
7.5%
Math Symbol 2691
 
7.5%
Open Punctuation 171
 
0.5%
Close Punctuation 171
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3750
15.1%
2298
 
9.3%
1794
 
7.2%
1794
 
7.2%
1794
 
7.2%
1269
 
5.1%
1098
 
4.4%
981
 
4.0%
837
 
3.4%
801
 
3.2%
Other values (137) 8379
33.8%
Space Separator
ValueCountFrequency (%)
2853
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2691
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2691
100.0%
Math Symbol
ValueCountFrequency (%)
> 2691
100.0%
Open Punctuation
ValueCountFrequency (%)
( 171
100.0%
Close Punctuation
ValueCountFrequency (%)
) 171
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24795
68.8%
Common 11268
31.2%

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 (%)
2853
25.3%
_ 2691
23.9%
- 2691
23.9%
> 2691
23.9%
( 171
 
1.5%
) 171
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24795
68.8%
ASCII 11268
31.2%

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 (%)
2853
25.3%
_ 2691
23.9%
- 2691
23.9%
> 2691
23.9%
( 171
 
1.5%
) 171
 
1.5%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct788
Distinct (%)31.6%
Missing198
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean1243.4352
Minimum0
Maximum708465
Zeros105
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T07:23:21.408399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median50
Q3272
95-th percentile3962.6
Maximum708465
Range708465
Interquartile range (IQR)262

Descriptive statistics

Standard deviation15388.678
Coefficient of variation (CV)12.375939
Kurtosis1798.3189
Mean1243.4352
Median Absolute Deviation (MAD)47
Skewness39.917904
Sum3099884
Variance2.3681141 × 108
MonotonicityNot monotonic
2024-03-30T07:23:21.918651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 105
 
3.9%
1 102
 
3.8%
2 68
 
2.5%
5 60
 
2.2%
3 59
 
2.2%
4 57
 
2.1%
6 53
 
2.0%
7 44
 
1.6%
11 36
 
1.3%
9 34
 
1.3%
Other values (778) 1875
69.7%
(Missing) 198
 
7.4%
ValueCountFrequency (%)
0 105
3.9%
1 102
3.8%
2 68
2.5%
3 59
2.2%
4 57
2.1%
5 60
2.2%
6 53
2.0%
7 44
1.6%
8 29
 
1.1%
9 34
 
1.3%
ValueCountFrequency (%)
708465 1
< 0.1%
144419 1
< 0.1%
128646 1
< 0.1%
101885 1
< 0.1%
93600 1
< 0.1%
83467 1
< 0.1%
68448 1
< 0.1%
67001 1
< 0.1%
55772 1
< 0.1%
54045 1
< 0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct774
Distinct (%)31.6%
Missing243
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean1170.0576
Minimum0
Maximum669176
Zeros81
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T07:23:22.397402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q113
median61
Q3263
95-th percentile3889.2
Maximum669176
Range669176
Interquartile range (IQR)250

Descriptive statistics

Standard deviation14565.563
Coefficient of variation (CV)12.448586
Kurtosis1814.5148
Mean1170.0576
Median Absolute Deviation (MAD)56
Skewness40.239102
Sum2864301
Variance2.1215563 × 108
MonotonicityNot monotonic
2024-03-30T07:23:23.058073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 81
 
3.0%
4 61
 
2.3%
1 54
 
2.0%
2 53
 
2.0%
3 53
 
2.0%
7 42
 
1.6%
11 41
 
1.5%
6 41
 
1.5%
5 41
 
1.5%
10 40
 
1.5%
Other values (764) 1941
72.1%
(Missing) 243
 
9.0%
ValueCountFrequency (%)
0 81
3.0%
1 54
2.0%
2 53
2.0%
3 53
2.0%
4 61
2.3%
5 41
1.5%
6 41
1.5%
7 42
1.6%
8 31
 
1.2%
9 34
1.3%
ValueCountFrequency (%)
669176 1
< 0.1%
119733 1
< 0.1%
103108 1
< 0.1%
102787 1
< 0.1%
86246 1
< 0.1%
82836 1
< 0.1%
75124 1
< 0.1%
56815 1
< 0.1%
55119 1
< 0.1%
50541 1
< 0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct780
Distinct (%)31.6%
Missing225
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean1202.6942
Minimum0
Maximum671194
Zeros73
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T07:23:23.665617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q115
median64
Q3264.75
95-th percentile3908.5
Maximum671194
Range671194
Interquartile range (IQR)249.75

Descriptive statistics

Standard deviation14635.402
Coefficient of variation (CV)12.168847
Kurtosis1789.1144
Mean1202.6942
Median Absolute Deviation (MAD)58
Skewness39.82825
Sum2965844
Variance2.14195 × 108
MonotonicityNot monotonic
2024-03-30T07:23:24.413000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 73
 
2.7%
1 56
 
2.1%
2 56
 
2.1%
3 52
 
1.9%
6 47
 
1.7%
5 45
 
1.7%
8 44
 
1.6%
11 40
 
1.5%
7 34
 
1.3%
13 31
 
1.2%
Other values (770) 1988
73.9%
(Missing) 225
 
8.4%
ValueCountFrequency (%)
0 73
2.7%
1 56
2.1%
2 56
2.1%
3 52
1.9%
4 31
1.2%
5 45
1.7%
6 47
1.7%
7 34
1.3%
8 44
1.6%
9 21
 
0.8%
ValueCountFrequency (%)
671194 1
< 0.1%
124258 1
< 0.1%
117172 1
< 0.1%
102941 1
< 0.1%
85959 1
< 0.1%
84296 1
< 0.1%
76120 1
< 0.1%
61090 1
< 0.1%
56721 1
< 0.1%
53693 1
< 0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct788
Distinct (%)32.0%
Missing225
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean1208.1618
Minimum0
Maximum692334
Zeros57
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T07:23:25.087849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q116
median66
Q3281
95-th percentile4020
Maximum692334
Range692334
Interquartile range (IQR)265

Descriptive statistics

Standard deviation14978.773
Coefficient of variation (CV)12.397986
Kurtosis1844.6773
Mean1208.1618
Median Absolute Deviation (MAD)60
Skewness40.608837
Sum2979327
Variance2.2436365 × 108
MonotonicityNot monotonic
2024-03-30T07:23:25.510767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 57
 
2.1%
2 56
 
2.1%
1 54
 
2.0%
5 47
 
1.7%
3 45
 
1.7%
6 38
 
1.4%
11 37
 
1.4%
7 35
 
1.3%
14 34
 
1.3%
9 33
 
1.2%
Other values (778) 2030
75.4%
(Missing) 225
 
8.4%
ValueCountFrequency (%)
0 57
2.1%
1 54
2.0%
2 56
2.1%
3 45
1.7%
4 33
1.2%
5 47
1.7%
6 38
1.4%
7 35
1.3%
8 29
1.1%
9 33
1.2%
ValueCountFrequency (%)
692334 1
< 0.1%
117750 1
< 0.1%
102312 1
< 0.1%
99165 1
< 0.1%
81951 1
< 0.1%
80856 1
< 0.1%
79388 1
< 0.1%
78702 1
< 0.1%
56101 1
< 0.1%
54180 1
< 0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct783
Distinct (%)31.1%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean1104.0504
Minimum0
Maximum635364
Zeros67
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T07:23:26.123282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q115
median64.5
Q3262
95-th percentile3707.45
Maximum635364
Range635364
Interquartile range (IQR)247

Descriptive statistics

Standard deviation13608.088
Coefficient of variation (CV)12.325604
Kurtosis1879.4491
Mean1104.0504
Median Absolute Deviation (MAD)58.5
Skewness40.959633
Sum2782207
Variance1.8518005 × 108
MonotonicityNot monotonic
2024-03-30T07:23:26.633008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67
 
2.5%
2 56
 
2.1%
3 50
 
1.9%
8 47
 
1.7%
1 46
 
1.7%
4 45
 
1.7%
7 39
 
1.4%
6 38
 
1.4%
16 38
 
1.4%
10 37
 
1.4%
Other values (773) 2057
76.4%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 67
2.5%
1 46
1.7%
2 56
2.1%
3 50
1.9%
4 45
1.7%
5 37
1.4%
6 38
1.4%
7 39
1.4%
8 47
1.7%
9 32
1.2%
ValueCountFrequency (%)
635364 1
< 0.1%
99985 1
< 0.1%
94962 1
< 0.1%
91873 1
< 0.1%
80625 1
< 0.1%
79508 1
< 0.1%
76251 1
< 0.1%
66239 1
< 0.1%
53987 1
< 0.1%
51105 1
< 0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct788
Distinct (%)31.6%
Missing198
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean1170.8825
Minimum0
Maximum662416
Zeros48
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T07:23:27.146976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q119
median69
Q3285
95-th percentile3923
Maximum662416
Range662416
Interquartile range (IQR)266

Descriptive statistics

Standard deviation14268.177
Coefficient of variation (CV)12.185832
Kurtosis1857.031
Mean1170.8825
Median Absolute Deviation (MAD)62
Skewness40.706747
Sum2919010
Variance2.0358089 × 108
MonotonicityNot monotonic
2024-03-30T07:23:27.763988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 49
 
1.8%
0 48
 
1.8%
4 47
 
1.7%
1 43
 
1.6%
3 42
 
1.6%
12 40
 
1.5%
6 37
 
1.4%
2 32
 
1.2%
7 32
 
1.2%
10 32
 
1.2%
Other values (778) 2091
77.7%
(Missing) 198
 
7.4%
ValueCountFrequency (%)
0 48
1.8%
1 43
1.6%
2 32
1.2%
3 42
1.6%
4 47
1.7%
5 49
1.8%
6 37
1.4%
7 32
1.2%
8 26
1.0%
9 28
1.0%
ValueCountFrequency (%)
662416 1
< 0.1%
104794 1
< 0.1%
98706 1
< 0.1%
92058 1
< 0.1%
88713 1
< 0.1%
85130 1
< 0.1%
80957 1
< 0.1%
66855 1
< 0.1%
56580 1
< 0.1%
49876 1
< 0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct796
Distinct (%)31.7%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1102.2159
Minimum0
Maximum627437
Zeros48
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T07:23:28.297451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q118
median70
Q3267.5
95-th percentile3603.5
Maximum627437
Range627437
Interquartile range (IQR)249.5

Descriptive statistics

Standard deviation13468.229
Coefficient of variation (CV)12.21923
Kurtosis1869.4843
Mean1102.2159
Median Absolute Deviation (MAD)62
Skewness40.844103
Sum2767664
Variance1.8139319 × 108
MonotonicityNot monotonic
2024-03-30T07:23:28.805850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48
 
1.8%
3 45
 
1.7%
4 44
 
1.6%
8 43
 
1.6%
5 41
 
1.5%
7 38
 
1.4%
1 36
 
1.3%
15 36
 
1.3%
6 35
 
1.3%
11 35
 
1.3%
Other values (786) 2110
78.4%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 48
1.8%
1 36
1.3%
2 34
1.3%
3 45
1.7%
4 44
1.6%
5 41
1.5%
6 35
1.3%
7 38
1.4%
8 43
1.6%
9 28
1.0%
ValueCountFrequency (%)
627437 1
< 0.1%
100607 1
< 0.1%
96315 1
< 0.1%
87105 1
< 0.1%
83826 1
< 0.1%
78964 1
< 0.1%
75086 1
< 0.1%
63776 1
< 0.1%
54648 1
< 0.1%
44761 1
< 0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct786
Distinct (%)31.3%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1103.8538
Minimum0
Maximum636087
Zeros56
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T07:23:29.313692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q116
median67
Q3271
95-th percentile3672.5
Maximum636087
Range636087
Interquartile range (IQR)255

Descriptive statistics

Standard deviation13629.395
Coefficient of variation (CV)12.347101
Kurtosis1882.9557
Mean1103.8538
Median Absolute Deviation (MAD)60
Skewness41.041497
Sum2771777
Variance1.857604 × 108
MonotonicityNot monotonic
2024-03-30T07:23:29.798767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 56
 
2.1%
2 49
 
1.8%
5 43
 
1.6%
4 42
 
1.6%
3 42
 
1.6%
6 40
 
1.5%
9 39
 
1.4%
1 37
 
1.4%
14 37
 
1.4%
13 36
 
1.3%
Other values (776) 2090
77.7%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 56
2.1%
1 37
1.4%
2 49
1.8%
3 42
1.6%
4 42
1.6%
5 43
1.6%
6 40
1.5%
7 35
1.3%
8 30
1.1%
9 39
1.4%
ValueCountFrequency (%)
636087 1
< 0.1%
101195 1
< 0.1%
96566 1
< 0.1%
84922 1
< 0.1%
84210 1
< 0.1%
81853 1
< 0.1%
77284 1
< 0.1%
60456 1
< 0.1%
55084 1
< 0.1%
43397 1
< 0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct810
Distinct (%)31.5%
Missing117
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean1198.4965
Minimum0
Maximum717491
Zeros50
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T07:23:30.274352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q117
median70
Q3295
95-th percentile4041.55
Maximum717491
Range717491
Interquartile range (IQR)278

Descriptive statistics

Standard deviation15145.653
Coefficient of variation (CV)12.63721
Kurtosis1950.1727
Mean1198.4965
Median Absolute Deviation (MAD)64
Skewness41.837868
Sum3084930
Variance2.2939079 × 108
MonotonicityNot monotonic
2024-03-30T07:23:30.863195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 51
 
1.9%
0 50
 
1.9%
1 50
 
1.9%
5 47
 
1.7%
6 45
 
1.7%
4 44
 
1.6%
3 40
 
1.5%
9 36
 
1.3%
7 34
 
1.3%
10 32
 
1.2%
Other values (800) 2145
79.7%
(Missing) 117
 
4.3%
ValueCountFrequency (%)
0 50
1.9%
1 50
1.9%
2 51
1.9%
3 40
1.5%
4 44
1.6%
5 47
1.7%
6 45
1.7%
7 34
1.3%
8 25
0.9%
9 36
1.3%
ValueCountFrequency (%)
717491 1
< 0.1%
113414 1
< 0.1%
112128 1
< 0.1%
92598 1
< 0.1%
91184 1
< 0.1%
84423 1
< 0.1%
84222 1
< 0.1%
67560 1
< 0.1%
58631 1
< 0.1%
47902 1
< 0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct853
Distinct (%)33.6%
Missing153
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean1368.3239
Minimum0
Maximum802719
Zeros44
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T07:23:31.421101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q120
median76
Q3326
95-th percentile4521.9
Maximum802719
Range802719
Interquartile range (IQR)306

Descriptive statistics

Standard deviation17114.272
Coefficient of variation (CV)12.507471
Kurtosis1901.037
Mean1368.3239
Median Absolute Deviation (MAD)68
Skewness41.247108
Sum3472806
Variance2.928983 × 108
MonotonicityNot monotonic
2024-03-30T07:23:32.120919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 46
 
1.7%
3 44
 
1.6%
0 44
 
1.6%
2 42
 
1.6%
4 39
 
1.4%
9 38
 
1.4%
8 37
 
1.4%
6 31
 
1.2%
7 30
 
1.1%
5 30
 
1.1%
Other values (843) 2157
80.2%
(Missing) 153
 
5.7%
ValueCountFrequency (%)
0 44
1.6%
1 46
1.7%
2 42
1.6%
3 44
1.6%
4 39
1.4%
5 30
1.1%
6 31
1.2%
7 30
1.1%
8 37
1.4%
9 38
1.4%
ValueCountFrequency (%)
802719 1
< 0.1%
138070 1
< 0.1%
124391 1
< 0.1%
111281 1
< 0.1%
103979 1
< 0.1%
102120 1
< 0.1%
91049 1
< 0.1%
75117 1
< 0.1%
58448 1
< 0.1%
53609 1
< 0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct846
Distinct (%)33.3%
Missing153
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean1352.1056
Minimum0
Maximum775303
Zeros52
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T07:23:32.602676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q121
median77
Q3328
95-th percentile4259.2
Maximum775303
Range775303
Interquartile range (IQR)307

Descriptive statistics

Standard deviation16606.123
Coefficient of variation (CV)12.281676
Kurtosis1867.0232
Mean1352.1056
Median Absolute Deviation (MAD)70
Skewness40.771731
Sum3431644
Variance2.7576332 × 108
MonotonicityNot monotonic
2024-03-30T07:23:33.295868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 62
 
2.3%
0 52
 
1.9%
7 37
 
1.4%
3 37
 
1.4%
9 35
 
1.3%
8 35
 
1.3%
4 34
 
1.3%
1 34
 
1.3%
6 34
 
1.3%
5 31
 
1.2%
Other values (836) 2147
79.8%
(Missing) 153
 
5.7%
ValueCountFrequency (%)
0 52
1.9%
1 34
1.3%
2 62
2.3%
3 37
1.4%
4 34
1.3%
5 31
1.2%
6 34
1.3%
7 37
1.4%
8 35
1.3%
9 35
1.3%
ValueCountFrequency (%)
775303 1
< 0.1%
151048 1
< 0.1%
125137 1
< 0.1%
107533 1
< 0.1%
96256 1
< 0.1%
92725 1
< 0.1%
87666 1
< 0.1%
73766 1
< 0.1%
56783 1
< 0.1%
55828 1
< 0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct887
Distinct (%)35.3%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1428.1183
Minimum0
Maximum799155
Zeros33
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T07:23:33.923996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q123
median91
Q3358.5
95-th percentile4572.5
Maximum799155
Range799155
Interquartile range (IQR)335.5

Descriptive statistics

Standard deviation17246.295
Coefficient of variation (CV)12.076237
Kurtosis1831.1969
Mean1428.1183
Median Absolute Deviation (MAD)82
Skewness40.323148
Sum3586005
Variance2.974347 × 108
MonotonicityNot monotonic
2024-03-30T07:23:34.746054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 42
 
1.6%
1 36
 
1.3%
9 33
 
1.2%
0 33
 
1.2%
10 33
 
1.2%
7 32
 
1.2%
5 31
 
1.2%
4 31
 
1.2%
6 29
 
1.1%
3 29
 
1.1%
Other values (877) 2182
81.1%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 33
1.2%
1 36
1.3%
2 27
1.0%
3 29
1.1%
4 31
1.2%
5 31
1.2%
6 29
1.1%
7 32
1.2%
8 42
1.6%
9 33
1.2%
ValueCountFrequency (%)
799155 1
< 0.1%
158104 1
< 0.1%
136501 1
< 0.1%
105390 1
< 0.1%
97655 1
< 0.1%
95320 1
< 0.1%
89748 1
< 0.1%
78664 1
< 0.1%
64176 1
< 0.1%
60400 1
< 0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct860
Distinct (%)34.1%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean1332.2246
Minimum0
Maximum736149
Zeros49
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T07:23:35.600556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q122
median84
Q3341.25
95-th percentile4365.1
Maximum736149
Range736149
Interquartile range (IQR)319.25

Descriptive statistics

Standard deviation15944.979
Coefficient of variation (CV)11.968687
Kurtosis1798.9139
Mean1332.2246
Median Absolute Deviation (MAD)75
Skewness39.844283
Sum3357206
Variance2.5424237 × 108
MonotonicityNot monotonic
2024-03-30T07:23:35.995646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49
 
1.8%
1 39
 
1.4%
13 36
 
1.3%
9 36
 
1.3%
2 33
 
1.2%
4 32
 
1.2%
10 31
 
1.2%
3 30
 
1.1%
6 29
 
1.1%
7 29
 
1.1%
Other values (850) 2176
80.9%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 49
1.8%
1 39
1.4%
2 33
1.2%
3 30
1.1%
4 32
1.2%
5 28
1.0%
6 29
1.1%
7 29
1.1%
8 25
0.9%
9 36
1.3%
ValueCountFrequency (%)
736149 1
< 0.1%
148429 1
< 0.1%
138189 1
< 0.1%
97321 1
< 0.1%
96002 1
< 0.1%
92471 1
< 0.1%
75140 1
< 0.1%
72144 1
< 0.1%
65473 1
< 0.1%
56557 1
< 0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct836
Distinct (%)33.3%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1261.9255
Minimum0
Maximum680649
Zeros36
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T07:23:36.504210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q122
median85
Q3324.5
95-th percentile4105
Maximum680649
Range680649
Interquartile range (IQR)302.5

Descriptive statistics

Standard deviation14811.172
Coefficient of variation (CV)11.736962
Kurtosis1772.1316
Mean1261.9255
Median Absolute Deviation (MAD)77
Skewness39.465807
Sum3168695
Variance2.1937081 × 108
MonotonicityNot monotonic
2024-03-30T07:23:37.322287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 45
 
1.7%
5 39
 
1.4%
1 38
 
1.4%
6 37
 
1.4%
3 37
 
1.4%
0 36
 
1.3%
7 35
 
1.3%
8 35
 
1.3%
4 32
 
1.2%
12 27
 
1.0%
Other values (826) 2150
79.9%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 36
1.3%
1 38
1.4%
2 45
1.7%
3 37
1.4%
4 32
1.2%
5 39
1.4%
6 37
1.4%
7 35
1.3%
8 35
1.3%
9 26
1.0%
ValueCountFrequency (%)
680649 1
< 0.1%
133892 1
< 0.1%
130202 1
< 0.1%
96312 1
< 0.1%
93081 1
< 0.1%
83446 1
< 0.1%
71345 1
< 0.1%
64792 1
< 0.1%
62023 1
< 0.1%
61221 1
< 0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct870
Distinct (%)34.6%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1392.3684
Minimum0
Maximum765032
Zeros36
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T07:23:38.037475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q124
median89
Q3374.5
95-th percentile4438.5
Maximum765032
Range765032
Interquartile range (IQR)350.5

Descriptive statistics

Standard deviation16577.821
Coefficient of variation (CV)11.906203
Kurtosis1802.0125
Mean1392.3684
Median Absolute Deviation (MAD)80
Skewness39.90862
Sum3496237
Variance2.7482414 × 108
MonotonicityNot monotonic
2024-03-30T07:23:38.522496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 40
 
1.5%
13 36
 
1.3%
0 36
 
1.3%
1 35
 
1.3%
6 34
 
1.3%
15 31
 
1.2%
2 31
 
1.2%
10 30
 
1.1%
3 29
 
1.1%
5 29
 
1.1%
Other values (860) 2180
81.0%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 36
1.3%
1 35
1.3%
2 31
1.2%
3 29
1.1%
4 40
1.5%
5 29
1.1%
6 34
1.3%
7 29
1.1%
8 19
0.7%
9 27
1.0%
ValueCountFrequency (%)
765032 1
< 0.1%
163849 1
< 0.1%
116534 1
< 0.1%
116258 1
< 0.1%
101711 1
< 0.1%
88431 1
< 0.1%
81082 1
< 0.1%
69443 1
< 0.1%
68836 1
< 0.1%
55424 1
< 0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct878
Distinct (%)35.0%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1534.0777
Minimum0
Maximum867506
Zeros22
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T07:23:39.205106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q126.5
median94
Q3367
95-th percentile4745
Maximum867506
Range867506
Interquartile range (IQR)340.5

Descriptive statistics

Standard deviation18731.352
Coefficient of variation (CV)12.210172
Kurtosis1828.6216
Mean1534.0777
Median Absolute Deviation (MAD)83
Skewness40.319483
Sum3852069
Variance3.5086353 × 108
MonotonicityNot monotonic
2024-03-30T07:23:39.919721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 41
 
1.5%
3 32
 
1.2%
6 32
 
1.2%
12 32
 
1.2%
4 30
 
1.1%
7 29
 
1.1%
8 29
 
1.1%
10 28
 
1.0%
5 28
 
1.0%
2 26
 
1.0%
Other values (868) 2204
81.9%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 22
0.8%
1 23
0.9%
2 26
1.0%
3 32
1.2%
4 30
1.1%
5 28
1.0%
6 32
1.2%
7 29
1.1%
8 29
1.1%
9 19
0.7%
ValueCountFrequency (%)
867506 1
< 0.1%
198020 1
< 0.1%
130878 1
< 0.1%
107769 1
< 0.1%
100383 1
< 0.1%
97998 1
< 0.1%
92212 1
< 0.1%
79975 1
< 0.1%
71134 1
< 0.1%
66336 1
< 0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct823
Distinct (%)32.8%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1198.6268
Minimum0
Maximum645159
Zeros36
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T07:23:40.458108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q123
median89
Q3305.5
95-th percentile3792.5
Maximum645159
Range645159
Interquartile range (IQR)282.5

Descriptive statistics

Standard deviation13984.398
Coefficient of variation (CV)11.667016
Kurtosis1799.2157
Mean1198.6268
Median Absolute Deviation (MAD)79
Skewness39.84285
Sum3009752
Variance1.9556339 × 108
MonotonicityNot monotonic
2024-03-30T07:23:40.972166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 41
 
1.5%
6 40
 
1.5%
5 36
 
1.3%
8 36
 
1.3%
0 36
 
1.3%
3 34
 
1.3%
9 33
 
1.2%
2 33
 
1.2%
4 32
 
1.2%
10 28
 
1.0%
Other values (813) 2162
80.3%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 36
1.3%
1 41
1.5%
2 33
1.2%
3 34
1.3%
4 32
1.2%
5 36
1.3%
6 40
1.5%
7 28
1.0%
8 36
1.3%
9 33
1.2%
ValueCountFrequency (%)
645159 1
< 0.1%
133435 1
< 0.1%
110512 1
< 0.1%
84253 1
< 0.1%
79034 1
< 0.1%
74553 1
< 0.1%
71830 1
< 0.1%
62867 1
< 0.1%
55871 1
< 0.1%
50053 1
< 0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct745
Distinct (%)29.6%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean871.77857
Minimum0
Maximum466409
Zeros43
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T07:23:41.447382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q119
median73.5
Q3232.25
95-th percentile2802.75
Maximum466409
Range466409
Interquartile range (IQR)213.25

Descriptive statistics

Standard deviation10069.129
Coefficient of variation (CV)11.550099
Kurtosis1820.541
Mean871.77857
Median Absolute Deviation (MAD)64.5
Skewness40.084601
Sum2196882
Variance1.0138736 × 108
MonotonicityNot monotonic
2024-03-30T07:23:41.836702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 46
 
1.7%
2 46
 
1.7%
4 44
 
1.6%
0 43
 
1.6%
7 43
 
1.6%
3 42
 
1.6%
8 39
 
1.4%
14 35
 
1.3%
5 33
 
1.2%
6 31
 
1.2%
Other values (735) 2118
78.7%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 43
1.6%
1 46
1.7%
2 46
1.7%
3 42
1.6%
4 44
1.6%
5 33
1.2%
6 31
1.2%
7 43
1.6%
8 39
1.4%
9 29
1.1%
ValueCountFrequency (%)
466409 1
< 0.1%
86778 1
< 0.1%
69461 1
< 0.1%
65756 1
< 0.1%
58723 1
< 0.1%
57276 1
< 0.1%
52317 1
< 0.1%
45815 1
< 0.1%
43631 1
< 0.1%
41182 1
< 0.1%

Interactions

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2024-03-30T07:22:50.600408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:57.724198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:04.012821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:09.266669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:14.914616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:16.228592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:23.375257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:30.458804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:37.867194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:44.904239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:52.464890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:59.269623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:06.872869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:14.339020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:21.088821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:28.646740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:35.836141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:42.928742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:50.928928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:58.203222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:04.294999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:09.552323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:15.195187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:16.627833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:23.726135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:30.844509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:38.320638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:45.267390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:52.888353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:59.645298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:07.217809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:14.736372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:21.554873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:29.009850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:36.149871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:43.492665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:51.372753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:58.772526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:04.669892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:09.853100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:15.434571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:17.024969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:24.095878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:31.290253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:38.842980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:45.631556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:53.418971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:00.028478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:07.633631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:15.183465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:22.123823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:29.425246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:36.621655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:43.932871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:51.688716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:59.245915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:04.935580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:10.135503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:15.702477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:17.402653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:24.512033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:31.706975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:39.195520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:46.049530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:53.730151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:00.519831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:08.053797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:15.558069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:22.503778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:29.813123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:36.999801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:44.507656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:52.046197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:59.576910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:05.207545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:10.406990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:15.997948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:17.735496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:24.961769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:32.036773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:39.599295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:46.416255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:54.096189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:01.087228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:08.442122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:16.018938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:23.034289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:30.634642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:37.319142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:45.103711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:52.477737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:00.033867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:05.487538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:10.694443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:16.246725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:18.140741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:25.292593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:32.350208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:39.879019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:46.774980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:54.438115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:01.637290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:08.793966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:16.363098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:23.394724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:30.983510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:37.714478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:45.488428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:52.786600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:00.434577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:05.734300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:11.068174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:16.531415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:18.583833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:25.617587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:32.872251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:40.329890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:47.118548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:54.801736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:02.162773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:09.299291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:16.727538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:24.021280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:31.368588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:38.124091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:45.782950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:53.234459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:00.775610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:06.001258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:23:11.358303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-30T07:23:42.153580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.8840.8840.8530.8530.8530.8530.8840.8840.8840.8840.8840.9770.8120.9630.9630.9630.812
20070.8841.0001.0000.9990.9990.9990.9991.0001.0001.0001.0001.0001.0000.9960.8500.8500.8500.996
20080.8841.0001.0000.9990.9990.9990.9991.0001.0001.0001.0001.0001.0000.9960.8500.8500.8500.996
20090.8530.9990.9991.0001.0001.0001.0000.9990.9990.9990.9990.9990.8910.9990.8910.8910.8910.999
20100.8530.9990.9991.0001.0001.0001.0000.9990.9990.9990.9990.9990.8910.9990.8910.8910.8910.999
20110.8530.9990.9991.0001.0001.0001.0000.9990.9990.9990.9990.9990.8910.9990.8910.8910.8910.999
20120.8530.9990.9991.0001.0001.0001.0000.9990.9990.9990.9990.9990.8910.9990.8910.8910.8910.999
20130.8841.0001.0000.9990.9990.9990.9991.0001.0001.0001.0001.0001.0000.9960.8500.8500.8500.996
20140.8841.0001.0000.9990.9990.9990.9991.0001.0001.0001.0001.0001.0000.9960.8500.8500.8500.996
20150.8841.0001.0000.9990.9990.9990.9991.0001.0001.0001.0001.0001.0000.9960.8500.8500.8500.996
20160.8841.0001.0000.9990.9990.9990.9991.0001.0001.0001.0001.0001.0000.9960.8500.8500.8500.996
20170.8841.0001.0000.9990.9990.9990.9991.0001.0001.0001.0001.0001.0000.9960.8500.8500.8500.996
20180.9771.0001.0000.8910.8910.8910.8911.0001.0001.0001.0001.0001.0000.8500.9980.9980.9980.850
20190.8120.9960.9960.9990.9990.9990.9990.9960.9960.9960.9960.9960.8501.0001.0001.0001.0001.000
20200.9630.8500.8500.8910.8910.8910.8910.8500.8500.8500.8500.8500.9981.0001.0001.0001.0001.000
20210.9630.8500.8500.8910.8910.8910.8910.8500.8500.8500.8500.8500.9981.0001.0001.0001.0001.000
20220.9630.8500.8500.8910.8910.8910.8910.8500.8500.8500.8500.8500.9981.0001.0001.0001.0001.000
20230.8120.9960.9960.9990.9990.9990.9990.9960.9960.9960.9960.9960.8501.0001.0001.0001.0001.000
2024-03-30T07:23:42.600219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.8990.8730.8410.8220.8360.8220.8280.8330.8330.8210.8230.8240.8120.8090.8180.7950.777
20070.8991.0000.9020.8610.8380.8460.8340.8440.8360.8380.8260.8220.8180.8050.8050.8070.7900.774
20080.8730.9021.0000.8750.8520.8560.8470.8370.8350.8320.8250.8140.8170.8050.8020.8030.7880.773
20090.8410.8610.8751.0000.8750.8580.8480.8410.8360.8320.8380.8240.8280.8110.8090.8100.8080.796
20100.8220.8380.8520.8751.0000.8810.8640.8540.8350.8340.8290.8290.8230.8120.8050.8000.7930.792
20110.8360.8460.8560.8580.8811.0000.8910.8680.8600.8520.8440.8400.8270.8140.8060.8110.8010.803
20120.8220.8340.8470.8480.8640.8911.0000.8880.8690.8590.8460.8380.8330.8210.8150.8150.8080.808
20130.8280.8440.8370.8410.8540.8680.8881.0000.8880.8700.8650.8480.8430.8260.8160.8180.8160.815
20140.8330.8360.8350.8360.8350.8600.8690.8881.0000.8990.8810.8650.8570.8450.8290.8290.8180.811
20150.8330.8380.8320.8320.8340.8520.8590.8700.8991.0000.9080.8880.8790.8580.8460.8390.8340.819
20160.8210.8260.8250.8380.8290.8440.8460.8650.8810.9081.0000.9040.8850.8650.8530.8410.8320.829
20170.8230.8220.8140.8240.8290.8400.8380.8480.8650.8880.9041.0000.9010.8750.8610.8480.8270.837
20180.8240.8180.8170.8280.8230.8270.8330.8430.8570.8790.8850.9011.0000.8990.8780.8560.8440.841
20190.8120.8050.8050.8110.8120.8140.8210.8260.8450.8580.8650.8750.8991.0000.8930.8690.8490.845
20200.8090.8050.8020.8090.8050.8060.8150.8160.8290.8460.8530.8610.8780.8931.0000.8890.8630.842
20210.8180.8070.8030.8100.8000.8110.8150.8180.8290.8390.8410.8480.8560.8690.8891.0000.8910.849
20220.7950.7900.7880.8080.7930.8010.8080.8160.8180.8340.8320.8270.8440.8490.8630.8911.0000.874
20230.7770.7740.7730.7960.7920.8030.8080.8150.8110.8190.8290.8370.8410.8450.8420.8490.8741.000

Missing values

2024-03-30T07:23:16.922199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T07:23:17.791624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-30T07:23:18.743516image/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->개인708465669176671194692334635364662416627437636087717491802719775303799155736149680649765032867506645159466409
1전국_개인->법인14441910278712425810231291873887138382684922842221021209625697655960029308111653413087811051269461
2전국_개인->기타162761910523092262202602725267271842525128301273302990734753363603741642331518115005341182
3전국_법인->개인936008283684296808568062592058871058421092598111281125137136501138189130202116258979987455357276
4전국_법인->법인190832181621234204862171026015225662472329811279762972732667301363317840869435733560226844
5전국_법인->기타182222842708447343934159394836105451531757496009556054905095650157024799
6전국_기타->개인210122525026823277182823432549293652679725622295012961932397263472226221924228382311220929
7전국_기타->법인7564998011987100771077599591151596811118810989123341335311241119611193414341150819943
8전국_기타->기타2780406849367839600277538023694063877453794282178686949710592126381403313249
9서울_개인->개인19170135231423112228878888887444761493201077711910119171108310333115491043368194956
지역_거래주체200620072008200920102011201220132014201520162017201820192020202120222023
2681제주 제주시_기타->기타2552871201741181191158094128155207319299235346304
2682제주 서귀포시_개인->개인4404703552435373563762626534842111450135401183410191837773536308747562214422
2683제주 서귀포시_개인->법인46515168061147780809114111641824242517301121855612566762879699
2684제주 서귀포시_개인->기타42731041051081101102162371398811610113112791149191
2685제주 서귀포시_법인->개인4891107782508981123421222050296859995856447830571423696644492330
2686제주 서귀포시_법인->법인1185522676791731461344345139301014473546305212233213139
2687제주 서귀포시_법인->기타062537341222131961313216191297
2688제주 서귀포시_기타->개인6814710022027633631632325933832722216722387122189147
2689제주 서귀포시_기타->법인10169709355433022451413124141139442936
2690제주 서귀포시_기타->기타133121223944563837252278552018796245