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 건축물 거래현황의 연도별 거래주체별(동(호)수)데이터입니다.- (단위 : 동(호)수)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068212/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 = 37.39785198)Skewed
2007 is highly skewed (γ1 = 36.89204238)Skewed
2008 is highly skewed (γ1 = 37.89988451)Skewed
2009 is highly skewed (γ1 = 38.58350535)Skewed
2010 is highly skewed (γ1 = 39.49290785)Skewed
2011 is highly skewed (γ1 = 40.29760107)Skewed
2012 is highly skewed (γ1 = 40.06153012)Skewed
2013 is highly skewed (γ1 = 40.14731694)Skewed
2014 is highly skewed (γ1 = 40.62667763)Skewed
2015 is highly skewed (γ1 = 40.42507656)Skewed
2016 is highly skewed (γ1 = 39.72267736)Skewed
2017 is highly skewed (γ1 = 34.51956522)Skewed
2018 is highly skewed (γ1 = 32.20543902)Skewed
2019 is highly skewed (γ1 = 32.77433484)Skewed
2020 is highly skewed (γ1 = 36.29690778)Skewed
2021 is highly skewed (γ1 = 35.44385172)Skewed
2022 is highly skewed (γ1 = 33.61187486)Skewed
2023 is highly skewed (γ1 = 36.96103931)Skewed
지역_거래주체 has unique valuesUnique
2006 has 250 (9.3%) zerosZeros
2007 has 215 (8.0%) zerosZeros
2008 has 199 (7.4%) zerosZeros
2009 has 167 (6.2%) zerosZeros
2010 has 170 (6.3%) zerosZeros
2011 has 101 (3.8%) zerosZeros
2012 has 131 (4.9%) zerosZeros
2013 has 192 (7.1%) zerosZeros
2014 has 202 (7.5%) zerosZeros
2015 has 144 (5.4%) zerosZeros
2016 has 165 (6.1%) zerosZeros
2017 has 144 (5.4%) zerosZeros
2018 has 151 (5.6%) zerosZeros
2019 has 133 (4.9%) zerosZeros
2020 has 135 (5.0%) zerosZeros
2021 has 132 (4.9%) zerosZeros
2022 has 136 (5.1%) zerosZeros
2023 has 146 (5.4%) zerosZeros

Reproduction

Analysis started2024-03-23 05:52:52.562109
Analysis finished2024-03-23 05:53:45.416913
Duration52.85 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
2024-03-23T14:53:45.700847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length13
Mean length13.371237
Min length9

Characters and Unicode

Total characters35982
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%
2024-03-23T14:53:46.241983image/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%
1242
 
3.5%
Other values (143) 12384
34.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24768
68.8%
Space Separator 2853
 
7.9%
Connector Punctuation 2691
 
7.5%
Dash Punctuation 2691
 
7.5%
Math Symbol 2691
 
7.5%
Open Punctuation 144
 
0.4%
Close Punctuation 144
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3750
15.1%
2298
 
9.3%
1794
 
7.2%
1794
 
7.2%
1794
 
7.2%
1242
 
5.0%
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 (%)
( 144
100.0%
Close Punctuation
ValueCountFrequency (%)
) 144
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24768
68.8%
Common 11214
31.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3750
15.1%
2298
 
9.3%
1794
 
7.2%
1794
 
7.2%
1794
 
7.2%
1242
 
5.0%
1098
 
4.4%
981
 
4.0%
837
 
3.4%
801
 
3.2%
Other values (137) 8379
33.8%
Common
ValueCountFrequency (%)
2853
25.4%
_ 2691
24.0%
- 2691
24.0%
> 2691
24.0%
( 144
 
1.3%
) 144
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24768
68.8%
ASCII 11214
31.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3750
15.1%
2298
 
9.3%
1794
 
7.2%
1794
 
7.2%
1794
 
7.2%
1242
 
5.0%
1098
 
4.4%
981
 
4.0%
837
 
3.4%
801
 
3.2%
Other values (137) 8379
33.8%
ASCII
ValueCountFrequency (%)
2853
25.4%
_ 2691
24.0%
- 2691
24.0%
> 2691
24.0%
( 144
 
1.3%
) 144
 
1.3%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct813
Distinct (%)32.6%
Missing198
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean2138.9759
Minimum0
Maximum1246347
Zeros250
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:53:46.467423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median23
Q3297
95-th percentile7244.6
Maximum1246347
Range1246347
Interquartile range (IQR)294

Descriptive statistics

Standard deviation28010.245
Coefficient of variation (CV)13.095166
Kurtosis1589.8627
Mean2138.9759
Median Absolute Deviation (MAD)23
Skewness37.397852
Sum5332467
Variance7.8457384 × 108
MonotonicityNot monotonic
2024-03-23T14:53:46.728843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 250
 
9.3%
1 175
 
6.5%
2 120
 
4.5%
3 101
 
3.8%
4 76
 
2.8%
5 58
 
2.2%
8 51
 
1.9%
9 47
 
1.7%
7 46
 
1.7%
6 46
 
1.7%
Other values (803) 1523
56.6%
(Missing) 198
 
7.4%
ValueCountFrequency (%)
0 250
9.3%
1 175
6.5%
2 120
4.5%
3 101
3.8%
4 76
 
2.8%
5 58
 
2.2%
6 46
 
1.7%
7 46
 
1.7%
8 51
 
1.9%
9 47
 
1.7%
ValueCountFrequency (%)
1246347 1
< 0.1%
387872 1
< 0.1%
314227 1
< 0.1%
312252 1
< 0.1%
93317 1
< 0.1%
89694 1
< 0.1%
67837 1
< 0.1%
55109 1
< 0.1%
50274 1
< 0.1%
46521 1
< 0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct799
Distinct (%)32.6%
Missing243
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean1735.5466
Minimum0
Maximum949529
Zeros215
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:53:46.935169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median25
Q3300.5
95-th percentile5404.85
Maximum949529
Range949529
Interquartile range (IQR)296.5

Descriptive statistics

Standard deviation21549.95
Coefficient of variation (CV)12.416809
Kurtosis1554.8929
Mean1735.5466
Median Absolute Deviation (MAD)25
Skewness36.892042
Sum4248618
Variance4.6440034 × 108
MonotonicityNot monotonic
2024-03-23T14:53:47.134133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 215
 
8.0%
1 131
 
4.9%
2 116
 
4.3%
3 84
 
3.1%
4 78
 
2.9%
5 65
 
2.4%
7 51
 
1.9%
6 51
 
1.9%
8 49
 
1.8%
11 41
 
1.5%
Other values (789) 1567
58.2%
(Missing) 243
 
9.0%
ValueCountFrequency (%)
0 215
8.0%
1 131
4.9%
2 116
4.3%
3 84
 
3.1%
4 78
 
2.9%
5 65
 
2.4%
6 51
 
1.9%
7 51
 
1.9%
8 49
 
1.8%
9 34
 
1.3%
ValueCountFrequency (%)
949529 1
< 0.1%
299801 1
< 0.1%
246529 1
< 0.1%
198663 1
< 0.1%
102666 1
< 0.1%
89426 1
< 0.1%
60025 1
< 0.1%
55087 1
< 0.1%
41558 1
< 0.1%
39633 1
< 0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct794
Distinct (%)32.2%
Missing225
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean1740.7652
Minimum0
Maximum967125
Zeros199
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:53:47.312316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median27
Q3277.25
95-th percentile5807.25
Maximum967125
Range967125
Interquartile range (IQR)273.25

Descriptive statistics

Standard deviation21625.51
Coefficient of variation (CV)12.422991
Kurtosis1632.0145
Mean1740.7652
Median Absolute Deviation (MAD)26
Skewness37.899885
Sum4292727
Variance4.676627 × 108
MonotonicityNot monotonic
2024-03-23T14:53:47.572834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 199
 
7.4%
1 158
 
5.9%
3 101
 
3.8%
2 99
 
3.7%
4 74
 
2.7%
5 64
 
2.4%
6 51
 
1.9%
8 47
 
1.7%
9 47
 
1.7%
7 44
 
1.6%
Other values (784) 1582
58.8%
(Missing) 225
 
8.4%
ValueCountFrequency (%)
0 199
7.4%
1 158
5.9%
2 99
3.7%
3 101
3.8%
4 74
 
2.7%
5 64
 
2.4%
6 51
 
1.9%
7 44
 
1.6%
8 47
 
1.7%
9 47
 
1.7%
ValueCountFrequency (%)
967125 1
< 0.1%
281593 1
< 0.1%
239565 1
< 0.1%
190275 1
< 0.1%
101768 1
< 0.1%
67500 1
< 0.1%
66549 1
< 0.1%
56497 1
< 0.1%
51053 1
< 0.1%
39165 1
< 0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct791
Distinct (%)32.1%
Missing225
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean1680.425
Minimum0
Maximum943534
Zeros167
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:53:47.806363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median28
Q3290.5
95-th percentile5726.25
Maximum943534
Range943534
Interquartile range (IQR)285.5

Descriptive statistics

Standard deviation20924.182
Coefficient of variation (CV)12.45172
Kurtosis1682.9571
Mean1680.425
Median Absolute Deviation (MAD)27
Skewness38.583505
Sum4143928
Variance4.3782137 × 108
MonotonicityNot monotonic
2024-03-23T14:53:48.010838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 167
 
6.2%
1 140
 
5.2%
2 114
 
4.2%
3 90
 
3.3%
4 76
 
2.8%
6 66
 
2.5%
5 60
 
2.2%
7 60
 
2.2%
9 42
 
1.6%
10 39
 
1.4%
Other values (781) 1612
59.9%
(Missing) 225
 
8.4%
ValueCountFrequency (%)
0 167
6.2%
1 140
5.2%
2 114
4.2%
3 90
3.3%
4 76
2.8%
5 60
 
2.2%
6 66
 
2.5%
7 60
 
2.2%
8 39
 
1.4%
9 42
 
1.6%
ValueCountFrequency (%)
943534 1
< 0.1%
244796 1
< 0.1%
240001 1
< 0.1%
177243 1
< 0.1%
77000 1
< 0.1%
72446 1
< 0.1%
64138 1
< 0.1%
60323 1
< 0.1%
50308 1
< 0.1%
39756 1
< 0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct780
Distinct (%)31.0%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean1526.2782
Minimum0
Maximum866636
Zeros170
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:53:48.245372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median28
Q3260.5
95-th percentile5071.85
Maximum866636
Range866636
Interquartile range (IQR)255.5

Descriptive statistics

Standard deviation18898.04
Coefficient of variation (CV)12.38178
Kurtosis1759.0185
Mean1526.2782
Median Absolute Deviation (MAD)27
Skewness39.492908
Sum3846221
Variance3.5713592 × 108
MonotonicityNot monotonic
2024-03-23T14:53:48.889998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 170
 
6.3%
1 139
 
5.2%
2 113
 
4.2%
5 83
 
3.1%
3 75
 
2.8%
4 70
 
2.6%
6 60
 
2.2%
7 51
 
1.9%
8 49
 
1.8%
9 41
 
1.5%
Other values (770) 1669
62.0%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 170
6.3%
1 139
5.2%
2 113
4.2%
3 75
2.8%
4 70
2.6%
5 83
3.1%
6 60
 
2.2%
7 51
 
1.9%
8 49
 
1.8%
9 41
 
1.5%
ValueCountFrequency (%)
866636 1
< 0.1%
239618 1
< 0.1%
181732 1
< 0.1%
123779 1
< 0.1%
92167 1
< 0.1%
76895 1
< 0.1%
72799 1
< 0.1%
57102 1
< 0.1%
51311 1
< 0.1%
42553 1
< 0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct798
Distinct (%)32.0%
Missing198
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean1830.0931
Minimum0
Maximum1072503
Zeros101
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:53:49.132341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median31
Q3289
95-th percentile6358.8
Maximum1072503
Range1072503
Interquartile range (IQR)282

Descriptive statistics

Standard deviation23257.939
Coefficient of variation (CV)12.70861
Kurtosis1814.0805
Mean1830.0931
Median Absolute Deviation (MAD)29
Skewness40.297601
Sum4562422
Variance5.409317 × 108
MonotonicityNot monotonic
2024-03-23T14:53:49.359064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 109
 
4.1%
0 101
 
3.8%
3 100
 
3.7%
2 100
 
3.7%
6 68
 
2.5%
4 61
 
2.3%
7 55
 
2.0%
5 53
 
2.0%
10 50
 
1.9%
9 42
 
1.6%
Other values (788) 1754
65.2%
(Missing) 198
 
7.4%
ValueCountFrequency (%)
0 101
3.8%
1 109
4.1%
2 100
3.7%
3 100
3.7%
4 61
2.3%
5 53
2.0%
6 68
2.5%
7 55
2.0%
8 39
 
1.4%
9 42
 
1.6%
ValueCountFrequency (%)
1072503 1
< 0.1%
249173 1
< 0.1%
240472 1
< 0.1%
149595 1
< 0.1%
107652 1
< 0.1%
79540 1
< 0.1%
65044 1
< 0.1%
60752 1
< 0.1%
57111 1
< 0.1%
55265 1
< 0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct793
Distinct (%)31.6%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1509.2294
Minimum0
Maximum861720
Zeros131
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:53:49.567851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median32
Q3272.5
95-th percentile5098
Maximum861720
Range861720
Interquartile range (IQR)265.5

Descriptive statistics

Standard deviation18696.639
Coefficient of variation (CV)12.388202
Kurtosis1798.8744
Mean1509.2294
Median Absolute Deviation (MAD)30
Skewness40.06153
Sum3789675
Variance3.4956429 × 108
MonotonicityNot monotonic
2024-03-23T14:53:49.775084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 131
 
4.9%
1 108
 
4.0%
2 98
 
3.6%
3 86
 
3.2%
4 71
 
2.6%
6 57
 
2.1%
5 55
 
2.0%
9 54
 
2.0%
8 45
 
1.7%
7 45
 
1.7%
Other values (783) 1761
65.4%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 131
4.9%
1 108
4.0%
2 98
3.6%
3 86
3.2%
4 71
2.6%
5 55
2.0%
6 57
2.1%
7 45
 
1.7%
8 45
 
1.7%
9 54
2.0%
ValueCountFrequency (%)
861720 1
< 0.1%
222081 1
< 0.1%
186158 1
< 0.1%
115339 1
< 0.1%
80184 1
< 0.1%
64413 1
< 0.1%
56490 1
< 0.1%
52408 1
< 0.1%
50619 1
< 0.1%
50411 1
< 0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct824
Distinct (%)32.8%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1762.7244
Minimum0
Maximum1020589
Zeros192
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:53:50.044073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median31
Q3300
95-th percentile6051.5
Maximum1020589
Range1020589
Interquartile range (IQR)295

Descriptive statistics

Standard deviation22122.496
Coefficient of variation (CV)12.550173
Kurtosis1805.2797
Mean1762.7244
Median Absolute Deviation (MAD)30
Skewness40.147317
Sum4426201
Variance4.8940481 × 108
MonotonicityNot monotonic
2024-03-23T14:53:50.289994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 192
 
7.1%
1 134
 
5.0%
2 96
 
3.6%
3 89
 
3.3%
4 75
 
2.8%
5 57
 
2.1%
6 55
 
2.0%
7 54
 
2.0%
8 46
 
1.7%
13 35
 
1.3%
Other values (814) 1678
62.4%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 192
7.1%
1 134
5.0%
2 96
3.6%
3 89
3.3%
4 75
 
2.8%
5 57
 
2.1%
6 55
 
2.0%
7 54
 
2.0%
8 46
 
1.7%
9 34
 
1.3%
ValueCountFrequency (%)
1020589 1
< 0.1%
248132 1
< 0.1%
231631 1
< 0.1%
144746 1
< 0.1%
91579 1
< 0.1%
78538 1
< 0.1%
69534 1
< 0.1%
60754 1
< 0.1%
59252 1
< 0.1%
57679 1
< 0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct843
Distinct (%)32.8%
Missing117
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean2102.8609
Minimum0
Maximum1261874
Zeros202
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:53:50.510134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median30
Q3315.75
95-th percentile7481.05
Maximum1261874
Range1261874
Interquartile range (IQR)310.75

Descriptive statistics

Standard deviation27031.797
Coefficient of variation (CV)12.854772
Kurtosis1847.1779
Mean2102.8609
Median Absolute Deviation (MAD)29
Skewness40.626678
Sum5412764
Variance7.3071807 × 108
MonotonicityNot monotonic
2024-03-23T14:53:50.716161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 202
 
7.5%
1 149
 
5.5%
2 104
 
3.9%
3 85
 
3.2%
4 74
 
2.7%
5 69
 
2.6%
6 57
 
2.1%
7 51
 
1.9%
10 43
 
1.6%
8 43
 
1.6%
Other values (833) 1697
63.1%
(Missing) 117
 
4.3%
ValueCountFrequency (%)
0 202
7.5%
1 149
5.5%
2 104
3.9%
3 85
3.2%
4 74
 
2.7%
5 69
 
2.6%
6 57
 
2.1%
7 51
 
1.9%
8 43
 
1.6%
9 31
 
1.2%
ValueCountFrequency (%)
1261874 1
< 0.1%
300062 1
< 0.1%
298491 1
< 0.1%
193875 1
< 0.1%
115618 1
< 0.1%
95417 1
< 0.1%
77476 1
< 0.1%
75837 1
< 0.1%
66424 1
< 0.1%
52763 1
< 0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct865
Distinct (%)34.1%
Missing153
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean2536.037
Minimum0
Maximum1553461
Zeros144
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:53:50.929907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median41
Q3362.5
95-th percentile9091.7
Maximum1553461
Range1553461
Interquartile range (IQR)356.5

Descriptive statistics

Standard deviation33494.468
Coefficient of variation (CV)13.207405
Kurtosis1825.7973
Mean2536.037
Median Absolute Deviation (MAD)40
Skewness40.425077
Sum6436462
Variance1.1218794 × 109
MonotonicityNot monotonic
2024-03-23T14:53:51.154054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 144
 
5.4%
1 139
 
5.2%
2 115
 
4.3%
3 82
 
3.0%
4 68
 
2.5%
6 57
 
2.1%
5 45
 
1.7%
8 43
 
1.6%
7 42
 
1.6%
11 39
 
1.4%
Other values (855) 1764
65.6%
(Missing) 153
 
5.7%
ValueCountFrequency (%)
0 144
5.4%
1 139
5.2%
2 115
4.3%
3 82
3.0%
4 68
2.5%
5 45
 
1.7%
6 57
 
2.1%
7 42
 
1.6%
8 43
 
1.6%
9 39
 
1.4%
ValueCountFrequency (%)
1553461 1
< 0.1%
391010 1
< 0.1%
319154 1
< 0.1%
285042 1
< 0.1%
146436 1
< 0.1%
104194 1
< 0.1%
103448 1
< 0.1%
77944 1
< 0.1%
74944 1
< 0.1%
73316 1
< 0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct866
Distinct (%)34.1%
Missing153
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean2427.1608
Minimum0
Maximum1463674
Zeros165
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:53:51.373998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median41
Q3369
95-th percentile8658.5
Maximum1463674
Range1463674
Interquartile range (IQR)363

Descriptive statistics

Standard deviation31812.015
Coefficient of variation (CV)13.106678
Kurtosis1771.7858
Mean2427.1608
Median Absolute Deviation (MAD)40
Skewness39.722677
Sum6160134
Variance1.0120043 × 109
MonotonicityNot monotonic
2024-03-23T14:53:51.595853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 165
 
6.1%
1 145
 
5.4%
2 96
 
3.6%
3 79
 
2.9%
4 59
 
2.2%
5 52
 
1.9%
7 51
 
1.9%
6 50
 
1.9%
9 50
 
1.9%
8 45
 
1.7%
Other values (856) 1746
64.9%
(Missing) 153
 
5.7%
ValueCountFrequency (%)
0 165
6.1%
1 145
5.4%
2 96
3.6%
3 79
2.9%
4 59
 
2.2%
5 52
 
1.9%
6 50
 
1.9%
7 51
 
1.9%
8 45
 
1.7%
9 50
 
1.9%
ValueCountFrequency (%)
1463674 1
< 0.1%
396532 1
< 0.1%
316748 1
< 0.1%
289670 1
< 0.1%
132577 1
< 0.1%
104819 1
< 0.1%
90129 1
< 0.1%
81239 1
< 0.1%
56191 1
< 0.1%
55287 1
< 0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct908
Distinct (%)36.2%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2793.2246
Minimum0
Maximum1403612
Zeros144
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:53:51.798061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median47
Q3473
95-th percentile9709.5
Maximum1403612
Range1403612
Interquartile range (IQR)466

Descriptive statistics

Standard deviation32930.759
Coefficient of variation (CV)11.789513
Kurtosis1368.822
Mean2793.2246
Median Absolute Deviation (MAD)46
Skewness34.519565
Sum7013787
Variance1.0844349 × 109
MonotonicityNot monotonic
2024-03-23T14:53:52.018165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 144
 
5.4%
1 129
 
4.8%
2 96
 
3.6%
3 82
 
3.0%
4 66
 
2.5%
5 62
 
2.3%
7 51
 
1.9%
6 47
 
1.7%
9 41
 
1.5%
8 31
 
1.2%
Other values (898) 1762
65.5%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 144
5.4%
1 129
4.8%
2 96
3.6%
3 82
3.0%
4 66
2.5%
5 62
2.3%
6 47
 
1.7%
7 51
 
1.9%
8 31
 
1.2%
9 41
 
1.5%
ValueCountFrequency (%)
1403612 1
< 0.1%
633251 1
< 0.1%
397752 1
< 0.1%
266591 1
< 0.1%
200295 1
< 0.1%
103713 1
< 0.1%
103143 1
< 0.1%
80004 1
< 0.1%
78794 1
< 0.1%
64251 1
< 0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct949
Distinct (%)37.7%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean2728.5659
Minimum0
Maximum1244914
Zeros151
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:53:52.243731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median52
Q3518.75
95-th percentile8623.15
Maximum1244914
Range1244914
Interquartile range (IQR)511.75

Descriptive statistics

Standard deviation30581.152
Coefficient of variation (CV)11.207775
Kurtosis1190.2751
Mean2728.5659
Median Absolute Deviation (MAD)51
Skewness32.205439
Sum6875986
Variance9.3520688 × 108
MonotonicityNot monotonic
2024-03-23T14:53:52.505215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 151
 
5.6%
1 128
 
4.8%
2 97
 
3.6%
3 79
 
2.9%
5 59
 
2.2%
7 50
 
1.9%
4 50
 
1.9%
6 49
 
1.8%
8 40
 
1.5%
9 33
 
1.2%
Other values (939) 1784
66.3%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 151
5.6%
1 128
4.8%
2 97
3.6%
3 79
2.9%
4 50
 
1.9%
5 59
 
2.2%
6 49
 
1.8%
7 50
 
1.9%
8 40
 
1.5%
9 33
 
1.2%
ValueCountFrequency (%)
1244914 1
< 0.1%
683023 1
< 0.1%
377944 1
< 0.1%
259783 1
< 0.1%
240519 1
< 0.1%
91602 1
< 0.1%
86849 1
< 0.1%
71232 1
< 0.1%
68830 1
< 0.1%
57235 1
< 0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct959
Distinct (%)38.2%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2462.783
Minimum0
Maximum1114599
Zeros133
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:53:52.740833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median54
Q3518
95-th percentile7659
Maximum1114599
Range1114599
Interquartile range (IQR)510

Descriptive statistics

Standard deviation27041.771
Coefficient of variation (CV)10.980168
Kurtosis1236.5255
Mean2462.783
Median Absolute Deviation (MAD)53
Skewness32.774335
Sum6184048
Variance7.3125739 × 108
MonotonicityNot monotonic
2024-03-23T14:53:52.951492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 133
 
4.9%
1 123
 
4.6%
2 96
 
3.6%
3 78
 
2.9%
7 54
 
2.0%
4 51
 
1.9%
5 47
 
1.7%
6 43
 
1.6%
8 42
 
1.6%
10 41
 
1.5%
Other values (949) 1803
67.0%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 133
4.9%
1 123
4.6%
2 96
3.6%
3 78
2.9%
4 51
 
1.9%
5 47
 
1.7%
6 43
 
1.6%
7 54
2.0%
8 42
 
1.6%
9 33
 
1.2%
ValueCountFrequency (%)
1114599 1
< 0.1%
586969 1
< 0.1%
317990 1
< 0.1%
216048 1
< 0.1%
179810 1
< 0.1%
87211 1
< 0.1%
82881 1
< 0.1%
72642 1
< 0.1%
70683 1
< 0.1%
62602 1
< 0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1033
Distinct (%)41.1%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean3090.7507
Minimum0
Maximum1588485
Zeros135
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:53:53.184624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median72
Q3723.5
95-th percentile10062
Maximum1588485
Range1588485
Interquartile range (IQR)714.5

Descriptive statistics

Standard deviation36150.219
Coefficient of variation (CV)11.696258
Kurtosis1511.6552
Mean3090.7507
Median Absolute Deviation (MAD)71
Skewness36.296908
Sum7760875
Variance1.3068383 × 109
MonotonicityNot monotonic
2024-03-23T14:53:53.429133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 135
 
5.0%
1 123
 
4.6%
2 85
 
3.2%
4 71
 
2.6%
3 53
 
2.0%
6 43
 
1.6%
10 43
 
1.6%
7 42
 
1.6%
5 37
 
1.4%
9 36
 
1.3%
Other values (1023) 1843
68.5%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 135
5.0%
1 123
4.6%
2 85
3.2%
3 53
 
2.0%
4 71
2.6%
5 37
 
1.4%
6 43
 
1.6%
7 42
 
1.6%
8 33
 
1.2%
9 36
 
1.3%
ValueCountFrequency (%)
1588485 1
< 0.1%
579321 1
< 0.1%
462503 1
< 0.1%
235754 1
< 0.1%
191025 1
< 0.1%
132895 1
< 0.1%
119097 1
< 0.1%
100788 1
< 0.1%
86117 1
< 0.1%
82266 1
< 0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1055
Distinct (%)42.0%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2679.6647
Minimum0
Maximum1287792
Zeros132
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:53:53.692142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median96
Q3732.5
95-th percentile8140.5
Maximum1287792
Range1287792
Interquartile range (IQR)722.5

Descriptive statistics

Standard deviation29680.356
Coefficient of variation (CV)11.076146
Kurtosis1447.7038
Mean2679.6647
Median Absolute Deviation (MAD)95
Skewness35.443852
Sum6728638
Variance8.8092352 × 108
MonotonicityNot monotonic
2024-03-23T14:53:53.919734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 132
 
4.9%
1 98
 
3.6%
2 70
 
2.6%
4 65
 
2.4%
3 58
 
2.2%
5 56
 
2.1%
7 43
 
1.6%
9 34
 
1.3%
6 33
 
1.2%
8 28
 
1.0%
Other values (1045) 1894
70.4%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 132
4.9%
1 98
3.6%
2 70
2.6%
3 58
2.2%
4 65
2.4%
5 56
2.1%
6 33
 
1.2%
7 43
 
1.6%
8 28
 
1.0%
9 34
 
1.3%
ValueCountFrequency (%)
1287792 1
< 0.1%
522273 1
< 0.1%
360764 1
< 0.1%
176177 1
< 0.1%
170956 1
< 0.1%
107158 1
< 0.1%
96943 1
< 0.1%
89426 1
< 0.1%
87893 1
< 0.1%
83276 1
< 0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct952
Distinct (%)37.9%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1625.6292
Minimum0
Maximum704655
Zeros136
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:53:54.125777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median74
Q3513.5
95-th percentile4635
Maximum704655
Range704655
Interquartile range (IQR)504.5

Descriptive statistics

Standard deviation16799.642
Coefficient of variation (CV)10.334239
Kurtosis1304.5057
Mean1625.6292
Median Absolute Deviation (MAD)73
Skewness33.611875
Sum4081955
Variance2.8222796 × 108
MonotonicityNot monotonic
2024-03-23T14:53:54.332180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 136
 
5.1%
1 107
 
4.0%
2 77
 
2.9%
3 62
 
2.3%
4 57
 
2.1%
5 52
 
1.9%
6 42
 
1.6%
9 40
 
1.5%
8 37
 
1.4%
13 33
 
1.2%
Other values (942) 1868
69.4%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 136
5.1%
1 107
4.0%
2 77
2.9%
3 62
2.3%
4 57
2.1%
5 52
 
1.9%
6 42
 
1.6%
7 33
 
1.2%
8 37
 
1.4%
9 40
 
1.5%
ValueCountFrequency (%)
704655 1
< 0.1%
353494 1
< 0.1%
173130 1
< 0.1%
118478 1
< 0.1%
85135 1
< 0.1%
67384 1
< 0.1%
61761 1
< 0.1%
56441 1
< 0.1%
54299 1
< 0.1%
52021 1
< 0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct845
Distinct (%)33.5%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean1453.2179
Minimum0
Maximum735207
Zeros146
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:53:55.037564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median48
Q3341.25
95-th percentile4555.1
Maximum735207
Range735207
Interquartile range (IQR)334.25

Descriptive statistics

Standard deviation16568.215
Coefficient of variation (CV)11.401054
Kurtosis1562.4389
Mean1453.2179
Median Absolute Deviation (MAD)47
Skewness36.961039
Sum3662109
Variance2.7450574 × 108
MonotonicityNot monotonic
2024-03-23T14:53:55.241924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 146
 
5.4%
1 105
 
3.9%
3 96
 
3.6%
2 85
 
3.2%
4 62
 
2.3%
5 57
 
2.1%
6 55
 
2.0%
7 49
 
1.8%
9 42
 
1.6%
8 39
 
1.4%
Other values (835) 1784
66.3%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 146
5.4%
1 105
3.9%
2 85
3.2%
3 96
3.6%
4 62
2.3%
5 57
 
2.1%
6 55
 
2.0%
7 49
 
1.8%
8 39
 
1.4%
9 42
 
1.6%
ValueCountFrequency (%)
735207 1
< 0.1%
269409 1
< 0.1%
190701 1
< 0.1%
87931 1
< 0.1%
80748 1
< 0.1%
55025 1
< 0.1%
50195 1
< 0.1%
44659 1
< 0.1%
43862 1
< 0.1%
41778 1
< 0.1%

Interactions

2024-03-23T14:53:40.504882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:52:55.697041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:52:58.530601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T14:53:03.199079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:05.700524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:08.423567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:10.684859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:12.949514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:15.673742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T14:53:26.823464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:29.754301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:32.202295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T14:53:29.914533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:32.321569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:35.269412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:38.093339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T14:53:13.192317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:15.941526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T14:53:21.176544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:24.332661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:27.215085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:30.071707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:32.450887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:35.383793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:38.229684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:41.435821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T14:53:19.535609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:22.303763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:25.538030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:28.565455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:31.052587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:34.000898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:36.494845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:39.184781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:42.839737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:52:57.235913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:52:59.894022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:02.273834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:04.635302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:07.471355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:09.810038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:12.083522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:14.300177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:17.210297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:19.684196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:22.492966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:25.720694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:28.710442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:31.183389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:34.158208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:36.642207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:39.294762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:43.061068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:52:57.381228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:00.029413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:02.437617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:04.775436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:07.617580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:09.949094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:12.189734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:14.438591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:17.356007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:19.872119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:22.671496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:25.881336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:28.875410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:31.304716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:34.274328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:36.775770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:39.490977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:43.249283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:52:57.525151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:00.186810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:02.580748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:04.902415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:07.730198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:10.063391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:12.299204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:14.564611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:17.548516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:20.027659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:22.849085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:26.028434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:29.027134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:31.448454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:34.413526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:36.945876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:39.655564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:43.455184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:52:57.673004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:00.319130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:02.694340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:05.033823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:07.844682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:10.166348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:12.407876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:14.693394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:17.712631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:20.165916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:22.989824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:26.158831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:29.193737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:31.613657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:34.569662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:37.157499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:39.783631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:43.664199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:52:57.823238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:00.480594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:02.820138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:05.175771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:07.975789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:10.281128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:12.522723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:14.858949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:17.873151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:20.298368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:23.175498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:26.323039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:29.334892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:31.767659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:34.713212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:37.378095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:39.937366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:43.849161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:52:57.940921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:00.618683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:02.945365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:05.333637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:08.106484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:10.390051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:12.649791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:15.025860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:17.979625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:20.453107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:23.327168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:26.469584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:29.481653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:31.906225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:34.849112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:37.571518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:40.118309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:44.049002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:52:58.071194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:00.753902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:03.090348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:05.542293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:08.260629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:10.537591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:12.792195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:15.552130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:18.103092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:20.617606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:23.906509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:26.632951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:29.608066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:32.056442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:35.011864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:37.777933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:40.242183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T14:53:55.483595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.8760.9820.9900.9820.9820.9900.9900.9900.9900.9900.9381.0000.9380.9380.9380.9380.938
20070.8761.0000.9070.8760.8410.8410.8760.8760.8760.8760.8760.9860.9130.9860.9860.9860.9860.986
20080.9820.9071.0000.9960.9900.9900.9960.9960.9960.9960.9960.8760.9830.8760.8760.8760.8760.876
20090.9900.8760.9961.0000.9960.9961.0001.0001.0001.0001.0000.9071.0000.9070.9070.9070.9070.907
20100.9820.8410.9900.9961.0001.0000.9960.9960.9960.9960.9960.8760.9830.8760.8760.8760.8760.876
20110.9820.8410.9900.9961.0001.0000.9960.9960.9960.9960.9960.8760.9830.8760.8760.8760.8760.876
20120.9900.8760.9961.0000.9960.9961.0001.0001.0001.0001.0000.9071.0000.9070.9070.9070.9070.907
20130.9900.8760.9961.0000.9960.9961.0001.0001.0001.0001.0000.9071.0000.9070.9070.9070.9070.907
20140.9900.8760.9961.0000.9960.9961.0001.0001.0001.0001.0000.9071.0000.9070.9070.9070.9070.907
20150.9900.8760.9961.0000.9960.9961.0001.0001.0001.0001.0000.9071.0000.9070.9070.9070.9070.907
20160.9900.8760.9961.0000.9960.9961.0001.0001.0001.0001.0000.9071.0000.9070.9070.9070.9070.907
20170.9380.9860.8760.9070.8760.8760.9070.9070.9070.9070.9071.0001.0001.0001.0001.0001.0001.000
20181.0000.9130.9831.0000.9830.9831.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20190.9380.9860.8760.9070.8760.8760.9070.9070.9070.9070.9071.0001.0001.0001.0001.0001.0001.000
20200.9380.9860.8760.9070.8760.8760.9070.9070.9070.9070.9071.0001.0001.0001.0001.0001.0001.000
20210.9380.9860.8760.9070.8760.8760.9070.9070.9070.9070.9071.0001.0001.0001.0001.0001.0001.000
20220.9380.9860.8760.9070.8760.8760.9070.9070.9070.9070.9071.0001.0001.0001.0001.0001.0001.000
20230.9380.9860.8760.9070.8760.8760.9070.9070.9070.9070.9071.0001.0001.0001.0001.0001.0001.000
2024-03-23T14:53:55.874975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9130.8840.8730.8820.8760.8730.8780.8690.8750.8760.8790.8760.8750.8720.8590.8540.834
20070.9131.0000.9050.8790.8870.8750.8700.8700.8730.8750.8770.8730.8710.8690.8680.8490.8510.837
20080.8840.9051.0000.8950.8900.8740.8700.8720.8640.8670.8720.8620.8640.8560.8510.8370.8420.826
20090.8730.8790.8951.0000.8920.8760.8740.8700.8680.8760.8640.8620.8540.8500.8490.8390.8350.832
20100.8820.8870.8900.8921.0000.9130.8980.8940.8840.8860.8840.8790.8730.8660.8640.8530.8520.842
20110.8760.8750.8740.8760.9131.0000.9160.8970.8830.8850.8780.8800.8670.8600.8570.8450.8410.832
20120.8730.8700.8700.8740.8980.9161.0000.9150.8910.8880.8830.8770.8690.8630.8600.8530.8520.834
20130.8780.8700.8720.8700.8940.8970.9151.0000.9140.9030.8960.8930.8850.8740.8690.8620.8540.847
20140.8690.8730.8640.8680.8840.8830.8910.9141.0000.9150.9020.8970.8880.8750.8690.8550.8550.838
20150.8750.8750.8670.8760.8860.8850.8880.9030.9151.0000.9260.9120.8990.8840.8780.8640.8610.848
20160.8760.8770.8720.8640.8840.8780.8830.8960.9020.9261.0000.9250.9090.8900.8800.8680.8700.852
20170.8790.8730.8620.8620.8790.8800.8770.8930.8970.9120.9251.0000.9180.9020.8890.8850.8700.859
20180.8760.8710.8640.8540.8730.8670.8690.8850.8880.8990.9090.9181.0000.9200.8990.8900.8840.864
20190.8750.8690.8560.8500.8660.8600.8630.8740.8750.8840.8900.9020.9201.0000.9160.8890.8790.871
20200.8720.8680.8510.8490.8640.8570.8600.8690.8690.8780.8800.8890.8990.9161.0000.9190.9030.890
20210.8590.8490.8370.8390.8530.8450.8530.8620.8550.8640.8680.8850.8900.8890.9191.0000.9200.891
20220.8540.8510.8420.8350.8520.8410.8520.8540.8550.8610.8700.8700.8840.8790.9030.9201.0000.904
20230.8340.8370.8260.8320.8420.8320.8340.8470.8380.8480.8520.8590.8640.8710.8900.8910.9041.000

Missing values

2024-03-23T14:53:44.361521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T14:53:44.772928image/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.
2024-03-23T14:53:45.084922image/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->개인12463479495299671259435348666361072503861720102058912618741553461146367414036121244914111459915884851287792704655735207
1전국_개인->법인4085531578320432477223191263312689129781333343954742424420065263870683100788969436176135624
2전국_개인->기타6219620562137000650875217662735661109661920110098109461417720850232411630410813
3전국_법인->개인312252299801281593244796239618249173222081248132300062319154316748633251683023586969579321522273353494269409
4전국_법인->법인335183963351053503085131146634409703958940080382404075446967564206036371256647465644140466
5전국_법인->기타3147453935433772203028823174285429013702412458457241748118041192481745215444
6전국_기타->개인335261859222921296261965724442263593868344853474105619161189916028288153004894266738437745
7전국_기타->법인9681947103685514631281148415971040164018071616785435613129684967643663
8전국_기타->기타257624502532231321913380335018622146301226063945438442103572379135416278
9서울_개인->개인3142271986631902751772431237791495951153391447461938752850422896702665912405191798102357541761778513587931
지역_거래주체200620072008200920102011201220132014201520162017201820192020202120222023
2681제주 제주시_기타->기타3125731261141272417273437515331913129
2682제주 서귀포시_개인->개인86316941611174719852294220827173468513556204542408629043260432931952465
2683제주 서귀포시_개인->법인411668312563152206216145174242226518302498430269165
2684제주 서귀포시_개인->기타6167131929192430554321143861382080
2685제주 서귀포시_법인->개인332466052832594976321126153341854534458842932544126316021449774
2686제주 서귀포시_법인->법인26148110127113187157193619449229536904806780463492549
2687제주 서귀포시_법인->기타152112513101181275225341733964012
2688제주 서귀포시_기타->개인516912283129432028223925287612526281
2689제주 서귀포시_기타->법인130457213546391148182
2690제주 서귀포시_기타->기타4025571191544331441362