Overview

Dataset statistics

Number of variables19
Number of observations1794
Missing cells2190
Missing cells (%)6.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory298.0 KiB
Average record size in memory170.1 B

Variable types

Text1
Numeric18

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 부동산거래현황 중 건축물 거래현황의 연도별 지목별(면적) 데이터입니다.- (단위 : 천㎡)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068241/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 132 (7.4%) missing valuesMissing
2007 has 162 (9.0%) missing valuesMissing
2008 has 150 (8.4%) missing valuesMissing
2009 has 150 (8.4%) missing valuesMissing
2010 has 114 (6.4%) missing valuesMissing
2011 has 132 (7.4%) missing valuesMissing
2012 has 120 (6.7%) missing valuesMissing
2013 has 120 (6.7%) missing valuesMissing
2014 has 78 (4.3%) missing valuesMissing
2015 has 102 (5.7%) missing valuesMissing
2016 has 102 (5.7%) missing valuesMissing
2017 has 120 (6.7%) missing valuesMissing
2018 has 114 (6.4%) missing valuesMissing
2019 has 120 (6.7%) missing valuesMissing
2020 has 120 (6.7%) missing valuesMissing
2021 has 120 (6.7%) missing valuesMissing
2022 has 120 (6.7%) missing valuesMissing
2023 has 114 (6.4%) missing valuesMissing
2006 is highly skewed (γ1 = 33.4603792)Skewed
2007 is highly skewed (γ1 = 34.03023683)Skewed
2008 is highly skewed (γ1 = 34.54824824)Skewed
2009 is highly skewed (γ1 = 34.3876199)Skewed
2010 is highly skewed (γ1 = 35.20551059)Skewed
2011 is highly skewed (γ1 = 35.2820134)Skewed
2013 is highly skewed (γ1 = 35.13758027)Skewed
2014 is highly skewed (γ1 = 35.70315489)Skewed
2015 is highly skewed (γ1 = 34.30672149)Skewed
2016 is highly skewed (γ1 = 34.59594702)Skewed
2017 is highly skewed (γ1 = 34.33071424)Skewed
2018 is highly skewed (γ1 = 33.89232752)Skewed
2019 is highly skewed (γ1 = 34.07007027)Skewed
2020 is highly skewed (γ1 = 34.20058695)Skewed
2021 is highly skewed (γ1 = 34.02474518)Skewed
2022 is highly skewed (γ1 = 34.10528196)Skewed
2023 is highly skewed (γ1 = 34.27862254)Skewed
지역_지목 has unique valuesUnique
2006 has 363 (20.2%) zerosZeros
2007 has 332 (18.5%) zerosZeros
2008 has 310 (17.3%) zerosZeros
2009 has 265 (14.8%) zerosZeros
2010 has 282 (15.7%) zerosZeros
2011 has 250 (13.9%) zerosZeros
2012 has 268 (14.9%) zerosZeros
2013 has 272 (15.2%) zerosZeros
2014 has 243 (13.5%) zerosZeros
2015 has 221 (12.3%) zerosZeros
2016 has 229 (12.8%) zerosZeros
2017 has 212 (11.8%) zerosZeros
2018 has 239 (13.3%) zerosZeros
2019 has 225 (12.5%) zerosZeros
2020 has 232 (12.9%) zerosZeros
2021 has 232 (12.9%) zerosZeros
2022 has 290 (16.2%) zerosZeros
2023 has 325 (18.1%) zerosZeros

Reproduction

Analysis started2024-03-23 04:53:04.989091
Analysis finished2024-03-23 04:55:23.671097
Duration2 minutes and 18.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역_지목
Text

UNIQUE 

Distinct1794
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size14.1 KiB
2024-03-23T04:55:24.264635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length9.0379041
Min length4

Characters and Unicode

Total characters16214
Distinct characters151
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1794 ?
Unique (%)100.0%

Sample

1st row전국_전
2nd row전국_답
3rd row전국_대지
4th row전국_임야
5th row전국_공장
ValueCountFrequency (%)
경기 312
 
8.4%
경남 156
 
4.2%
서울 150
 
4.1%
경북 150
 
4.1%
전남 132
 
3.6%
충북 114
 
3.1%
충남 114
 
3.1%
강원 108
 
2.9%
부산 96
 
2.6%
전북 96
 
2.6%
Other values (1659) 2268
61.4%
2024-03-23T04:55:26.021902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1902
 
11.7%
_ 1794
 
11.1%
828
 
5.1%
732
 
4.5%
654
 
4.0%
635
 
3.9%
599
 
3.7%
558
 
3.4%
534
 
3.3%
432
 
2.7%
Other values (141) 7546
46.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12326
76.0%
Space Separator 1902
 
11.7%
Connector Punctuation 1794
 
11.1%
Open Punctuation 96
 
0.6%
Close Punctuation 96
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
828
 
6.7%
732
 
5.9%
654
 
5.3%
635
 
5.2%
599
 
4.9%
558
 
4.5%
534
 
4.3%
432
 
3.5%
419
 
3.4%
329
 
2.7%
Other values (137) 6606
53.6%
Space Separator
ValueCountFrequency (%)
1902
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1794
100.0%
Open Punctuation
ValueCountFrequency (%)
( 96
100.0%
Close Punctuation
ValueCountFrequency (%)
) 96
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12326
76.0%
Common 3888
 
24.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
828
 
6.7%
732
 
5.9%
654
 
5.3%
635
 
5.2%
599
 
4.9%
558
 
4.5%
534
 
4.3%
432
 
3.5%
419
 
3.4%
329
 
2.7%
Other values (137) 6606
53.6%
Common
ValueCountFrequency (%)
1902
48.9%
_ 1794
46.1%
( 96
 
2.5%
) 96
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12326
76.0%
ASCII 3888
 
24.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1902
48.9%
_ 1794
46.1%
( 96
 
2.5%
) 96
 
2.5%
Hangul
ValueCountFrequency (%)
828
 
6.7%
732
 
5.9%
654
 
5.3%
635
 
5.2%
599
 
4.9%
558
 
4.5%
534
 
4.3%
432
 
3.5%
419
 
3.4%
329
 
2.7%
Other values (137) 6606
53.6%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct329
Distinct (%)19.8%
Missing132
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean318.10289
Minimum0
Maximum151187
Zeros363
Zeros (%)20.2%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T04:55:26.943339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q331
95-th percentile1119.65
Maximum151187
Range151187
Interquartile range (IQR)30

Descriptive statistics

Standard deviation4001.795
Coefficient of variation (CV)12.580191
Kurtosis1229.0719
Mean318.10289
Median Absolute Deviation (MAD)4
Skewness33.460379
Sum528687
Variance16014364
MonotonicityNot monotonic
2024-03-23T04:55:27.903486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 363
20.2%
1 215
 
12.0%
2 117
 
6.5%
3 95
 
5.3%
4 72
 
4.0%
5 39
 
2.2%
6 36
 
2.0%
8 32
 
1.8%
9 28
 
1.6%
10 23
 
1.3%
Other values (319) 642
35.8%
(Missing) 132
 
7.4%
ValueCountFrequency (%)
0 363
20.2%
1 215
12.0%
2 117
 
6.5%
3 95
 
5.3%
4 72
 
4.0%
5 39
 
2.2%
6 36
 
2.0%
7 18
 
1.0%
8 32
 
1.8%
9 28
 
1.6%
ValueCountFrequency (%)
151187 1
0.1%
44046 1
0.1%
33265 1
0.1%
10187 1
0.1%
9367 1
0.1%
8437 1
0.1%
7664 1
0.1%
7500 1
0.1%
6067 1
0.1%
5115 1
0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct334
Distinct (%)20.5%
Missing162
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean271.77512
Minimum0
Maximum125984
Zeros332
Zeros (%)18.5%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T04:55:28.579247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q334
95-th percentile835.25
Maximum125984
Range125984
Interquartile range (IQR)33

Descriptive statistics

Standard deviation3321.44
Coefficient of variation (CV)12.22128
Kurtosis1265.7844
Mean271.77512
Median Absolute Deviation (MAD)5
Skewness34.030237
Sum443537
Variance11031964
MonotonicityNot monotonic
2024-03-23T04:55:29.259506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 332
18.5%
1 213
 
11.9%
2 123
 
6.9%
3 75
 
4.2%
4 68
 
3.8%
5 43
 
2.4%
6 39
 
2.2%
7 33
 
1.8%
9 26
 
1.4%
8 26
 
1.4%
Other values (324) 654
36.5%
(Missing) 162
 
9.0%
ValueCountFrequency (%)
0 332
18.5%
1 213
11.9%
2 123
 
6.9%
3 75
 
4.2%
4 68
 
3.8%
5 43
 
2.4%
6 39
 
2.2%
7 33
 
1.8%
8 26
 
1.4%
9 26
 
1.4%
ValueCountFrequency (%)
125984 1
0.1%
30629 1
0.1%
23663 1
0.1%
9740 1
0.1%
8975 1
0.1%
8642 1
0.1%
7843 1
0.1%
7332 1
0.1%
5815 1
0.1%
5384 1
0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct334
Distinct (%)20.3%
Missing150
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean250.05474
Minimum0
Maximum116434
Zeros310
Zeros (%)17.3%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T04:55:30.006596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q337
95-th percentile801.8
Maximum116434
Range116434
Interquartile range (IQR)36

Descriptive statistics

Standard deviation3043.0419
Coefficient of variation (CV)12.169503
Kurtosis1299.7464
Mean250.05474
Median Absolute Deviation (MAD)5
Skewness34.548248
Sum411090
Variance9260104
MonotonicityNot monotonic
2024-03-23T04:55:30.588146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 310
17.3%
1 212
 
11.8%
2 113
 
6.3%
3 91
 
5.1%
4 55
 
3.1%
6 47
 
2.6%
5 43
 
2.4%
7 40
 
2.2%
9 32
 
1.8%
10 29
 
1.6%
Other values (324) 672
37.5%
(Missing) 150
 
8.4%
ValueCountFrequency (%)
0 310
17.3%
1 212
11.8%
2 113
 
6.3%
3 91
 
5.1%
4 55
 
3.1%
5 43
 
2.4%
6 47
 
2.6%
7 40
 
2.2%
8 19
 
1.1%
9 32
 
1.8%
ValueCountFrequency (%)
116434 1
0.1%
26445 1
0.1%
20328 1
0.1%
9095 1
0.1%
8713 1
0.1%
8166 1
0.1%
7882 1
0.1%
5820 1
0.1%
5485 1
0.1%
5409 1
0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct332
Distinct (%)20.2%
Missing150
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean252.71107
Minimum0
Maximum116129
Zeros265
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T04:55:31.132597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q339
95-th percentile806.55
Maximum116129
Range116129
Interquartile range (IQR)38

Descriptive statistics

Standard deviation3041.4785
Coefficient of variation (CV)12.035399
Kurtosis1289.4252
Mean252.71107
Median Absolute Deviation (MAD)7
Skewness34.38762
Sum415457
Variance9250591.2
MonotonicityNot monotonic
2024-03-23T04:55:31.760490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 265
 
14.8%
1 168
 
9.4%
2 120
 
6.7%
3 83
 
4.6%
4 68
 
3.8%
5 56
 
3.1%
6 43
 
2.4%
8 35
 
2.0%
9 34
 
1.9%
7 33
 
1.8%
Other values (322) 739
41.2%
(Missing) 150
 
8.4%
ValueCountFrequency (%)
0 265
14.8%
1 168
9.4%
2 120
6.7%
3 83
 
4.6%
4 68
 
3.8%
5 56
 
3.1%
6 43
 
2.4%
7 33
 
1.8%
8 35
 
2.0%
9 34
 
1.9%
ValueCountFrequency (%)
116129 1
0.1%
27915 1
0.1%
19975 1
0.1%
9352 1
0.1%
7997 1
0.1%
7864 1
0.1%
7007 1
0.1%
6028 1
0.1%
5578 1
0.1%
5009 1
0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct334
Distinct (%)19.9%
Missing114
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean243.33631
Minimum0
Maximum114267
Zeros282
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T04:55:32.212223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q339
95-th percentile805.15
Maximum114267
Range114267
Interquartile range (IQR)38

Descriptive statistics

Standard deviation2944.1279
Coefficient of variation (CV)12.099008
Kurtosis1345.949
Mean243.33631
Median Absolute Deviation (MAD)6
Skewness35.205511
Sum408805
Variance8667889.3
MonotonicityNot monotonic
2024-03-23T04:55:32.886416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 282
 
15.7%
1 182
 
10.1%
2 113
 
6.3%
3 99
 
5.5%
4 70
 
3.9%
5 55
 
3.1%
6 55
 
3.1%
10 43
 
2.4%
7 32
 
1.8%
8 29
 
1.6%
Other values (324) 720
40.1%
(Missing) 114
 
6.4%
ValueCountFrequency (%)
0 282
15.7%
1 182
10.1%
2 113
6.3%
3 99
 
5.5%
4 70
 
3.9%
5 55
 
3.1%
6 55
 
3.1%
7 32
 
1.8%
8 29
 
1.6%
9 21
 
1.2%
ValueCountFrequency (%)
114267 1
0.1%
26107 1
0.1%
14874 1
0.1%
10496 1
0.1%
10165 1
0.1%
7887 1
0.1%
6392 1
0.1%
6028 1
0.1%
5910 1
0.1%
5047 1
0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct353
Distinct (%)21.2%
Missing132
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean282.61613
Minimum0
Maximum130747
Zeros250
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T04:55:33.364255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q345
95-th percentile910.8
Maximum130747
Range130747
Interquartile range (IQR)44

Descriptive statistics

Standard deviation3375.0239
Coefficient of variation (CV)11.942078
Kurtosis1349.138
Mean282.61613
Median Absolute Deviation (MAD)8
Skewness35.282013
Sum469708
Variance11390786
MonotonicityNot monotonic
2024-03-23T04:55:33.901639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 250
 
13.9%
1 176
 
9.8%
2 110
 
6.1%
3 92
 
5.1%
5 62
 
3.5%
4 49
 
2.7%
6 46
 
2.6%
8 38
 
2.1%
7 37
 
2.1%
11 35
 
2.0%
Other values (343) 767
42.8%
(Missing) 132
 
7.4%
ValueCountFrequency (%)
0 250
13.9%
1 176
9.8%
2 110
6.1%
3 92
 
5.1%
4 49
 
2.7%
5 62
 
3.5%
6 46
 
2.6%
7 37
 
2.1%
8 38
 
2.1%
9 31
 
1.7%
ValueCountFrequency (%)
130747 1
0.1%
26969 1
0.1%
17503 1
0.1%
12470 1
0.1%
9413 1
0.1%
8484 1
0.1%
7876 1
0.1%
7502 1
0.1%
7501 1
0.1%
6692 1
0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct350
Distinct (%)20.9%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean414.71983
Minimum0
Maximum112963
Zeros268
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T04:55:34.597339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q347
95-th percentile800.4
Maximum112963
Range112963
Interquartile range (IQR)46

Descriptive statistics

Standard deviation5013.6374
Coefficient of variation (CV)12.089215
Kurtosis398.42314
Mean414.71983
Median Absolute Deviation (MAD)7
Skewness19.692471
Sum694241
Variance25136560
MonotonicityNot monotonic
2024-03-23T04:55:35.386849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 268
 
14.9%
1 162
 
9.0%
2 112
 
6.2%
3 79
 
4.4%
5 60
 
3.3%
4 60
 
3.3%
7 59
 
3.3%
6 53
 
3.0%
8 39
 
2.2%
11 34
 
1.9%
Other values (340) 748
41.7%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 268
14.9%
1 162
9.0%
2 112
6.2%
3 79
 
4.4%
4 60
 
3.3%
5 60
 
3.3%
6 53
 
3.0%
7 59
 
3.3%
8 39
 
2.2%
9 26
 
1.4%
ValueCountFrequency (%)
112963 1
0.1%
102514 1
0.1%
94078 1
0.1%
93902 1
0.1%
23435 1
0.1%
15826 1
0.1%
9186 1
0.1%
7670 1
0.1%
7512 1
0.1%
7289 1
0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct348
Distinct (%)20.8%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean273.92055
Minimum0
Maximum125961
Zeros272
Zeros (%)15.2%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T04:55:35.946200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q345.75
95-th percentile874.4
Maximum125961
Range125961
Interquartile range (IQR)43.75

Descriptive statistics

Standard deviation3250.5187
Coefficient of variation (CV)11.866648
Kurtosis1341.7685
Mean273.92055
Median Absolute Deviation (MAD)8
Skewness35.13758
Sum458543
Variance10565872
MonotonicityNot monotonic
2024-03-23T04:55:36.488095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 272
 
15.2%
1 135
 
7.5%
2 128
 
7.1%
3 71
 
4.0%
6 55
 
3.1%
4 52
 
2.9%
5 52
 
2.9%
8 40
 
2.2%
7 38
 
2.1%
9 33
 
1.8%
Other values (338) 798
44.5%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 272
15.2%
1 135
7.5%
2 128
7.1%
3 71
 
4.0%
4 52
 
2.9%
5 52
 
2.9%
6 55
 
3.1%
7 38
 
2.1%
8 40
 
2.2%
9 33
 
1.8%
ValueCountFrequency (%)
125961 1
0.1%
27966 1
0.1%
17177 1
0.1%
10858 1
0.1%
9542 1
0.1%
9472 1
0.1%
9208 1
0.1%
7415 1
0.1%
7031 1
0.1%
5948 1
0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct374
Distinct (%)21.8%
Missing78
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean316.4528
Minimum0
Maximum149761
Zeros243
Zeros (%)13.5%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T04:55:37.071701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median9
Q354
95-th percentile1033.25
Maximum149761
Range149761
Interquartile range (IQR)52

Descriptive statistics

Standard deviation3811.8281
Coefficient of variation (CV)12.045487
Kurtosis1383.0435
Mean316.4528
Median Absolute Deviation (MAD)9
Skewness35.703155
Sum543033
Variance14530033
MonotonicityNot monotonic
2024-03-23T04:55:37.722650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 243
 
13.5%
1 142
 
7.9%
2 97
 
5.4%
4 84
 
4.7%
5 69
 
3.8%
3 69
 
3.8%
7 58
 
3.2%
6 42
 
2.3%
10 35
 
2.0%
8 30
 
1.7%
Other values (364) 847
47.2%
(Missing) 78
 
4.3%
ValueCountFrequency (%)
0 243
13.5%
1 142
7.9%
2 97
 
5.4%
3 69
 
3.8%
4 84
 
4.7%
5 69
 
3.8%
6 42
 
2.3%
7 58
 
3.2%
8 30
 
1.7%
9 30
 
1.7%
ValueCountFrequency (%)
149761 1
0.1%
32104 1
0.1%
21658 1
0.1%
12722 1
0.1%
12403 1
0.1%
10362 1
0.1%
9748 1
0.1%
8769 1
0.1%
8192 1
0.1%
6805 1
0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct381
Distinct (%)22.5%
Missing102
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean410.49173
Minimum0
Maximum191892
Zeros221
Zeros (%)12.3%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T04:55:38.439888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median11
Q361
95-th percentile1317.7
Maximum191892
Range191892
Interquartile range (IQR)59

Descriptive statistics

Standard deviation4993.0232
Coefficient of variation (CV)12.163517
Kurtosis1289.0874
Mean410.49173
Median Absolute Deviation (MAD)11
Skewness34.306721
Sum694552
Variance24930281
MonotonicityNot monotonic
2024-03-23T04:55:39.187702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 221
 
12.3%
1 133
 
7.4%
2 109
 
6.1%
3 73
 
4.1%
5 56
 
3.1%
6 52
 
2.9%
4 50
 
2.8%
7 38
 
2.1%
10 36
 
2.0%
8 35
 
2.0%
Other values (371) 889
49.6%
(Missing) 102
 
5.7%
ValueCountFrequency (%)
0 221
12.3%
1 133
7.4%
2 109
6.1%
3 73
 
4.1%
4 50
 
2.8%
5 56
 
3.1%
6 52
 
2.9%
7 38
 
2.1%
8 35
 
2.0%
9 33
 
1.8%
ValueCountFrequency (%)
191892 1
0.1%
52832 1
0.1%
32998 1
0.1%
15644 1
0.1%
12538 1
0.1%
12212 1
0.1%
11448 1
0.1%
10380 1
0.1%
10058 1
0.1%
9847 1
0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct386
Distinct (%)22.8%
Missing102
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean372.77837
Minimum0
Maximum173868
Zeros229
Zeros (%)12.8%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T04:55:39.869519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median11
Q354
95-th percentile1269.9
Maximum173868
Range173868
Interquartile range (IQR)52

Descriptive statistics

Standard deviation4507.8056
Coefficient of variation (CV)12.092455
Kurtosis1306.6478
Mean372.77837
Median Absolute Deviation (MAD)11
Skewness34.595947
Sum630741
Variance20320311
MonotonicityNot monotonic
2024-03-23T04:55:40.494200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 229
 
12.8%
1 127
 
7.1%
2 97
 
5.4%
3 82
 
4.6%
4 64
 
3.6%
6 51
 
2.8%
5 50
 
2.8%
7 36
 
2.0%
8 35
 
2.0%
10 33
 
1.8%
Other values (376) 888
49.5%
(Missing) 102
 
5.7%
ValueCountFrequency (%)
0 229
12.8%
1 127
7.1%
2 97
5.4%
3 82
 
4.6%
4 64
 
3.6%
5 50
 
2.8%
6 51
 
2.8%
7 36
 
2.0%
8 35
 
2.0%
9 26
 
1.4%
ValueCountFrequency (%)
173868 1
0.1%
45642 1
0.1%
30995 1
0.1%
14055 1
0.1%
12086 1
0.1%
10557 1
0.1%
10279 1
0.1%
7616 1
0.1%
7390 1
0.1%
6988 1
0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct400
Distinct (%)23.9%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean385.48327
Minimum0
Maximum176810
Zeros212
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T04:55:41.002817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median12
Q368
95-th percentile1275.7
Maximum176810
Range176810
Interquartile range (IQR)66

Descriptive statistics

Standard deviation4615.0622
Coefficient of variation (CV)11.972146
Kurtosis1286.4856
Mean385.48327
Median Absolute Deviation (MAD)12
Skewness34.330714
Sum645299
Variance21298799
MonotonicityNot monotonic
2024-03-23T04:55:41.463757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 212
 
11.8%
1 122
 
6.8%
2 87
 
4.8%
3 70
 
3.9%
4 62
 
3.5%
6 50
 
2.8%
5 48
 
2.7%
7 41
 
2.3%
8 36
 
2.0%
11 36
 
2.0%
Other values (390) 910
50.7%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 212
11.8%
1 122
6.8%
2 87
4.8%
3 70
 
3.9%
4 62
 
3.5%
5 48
 
2.7%
6 50
 
2.8%
7 41
 
2.3%
8 36
 
2.0%
9 26
 
1.4%
ValueCountFrequency (%)
176810 1
0.1%
48828 1
0.1%
29782 1
0.1%
13026 1
0.1%
11614 1
0.1%
11238 1
0.1%
10917 1
0.1%
8543 1
0.1%
7793 1
0.1%
6623 1
0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct400
Distinct (%)23.8%
Missing114
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean382.24464
Minimum0
Maximum175585
Zeros239
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T04:55:41.927888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median12
Q370.25
95-th percentile1163
Maximum175585
Range175585
Interquartile range (IQR)68.25

Descriptive statistics

Standard deviation4609.1695
Coefficient of variation (CV)12.058166
Kurtosis1256.8601
Mean382.24464
Median Absolute Deviation (MAD)12
Skewness33.892328
Sum642171
Variance21244444
MonotonicityNot monotonic
2024-03-23T04:55:42.363430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 239
 
13.3%
1 117
 
6.5%
2 94
 
5.2%
3 77
 
4.3%
4 51
 
2.8%
5 49
 
2.7%
6 48
 
2.7%
7 43
 
2.4%
8 37
 
2.1%
9 29
 
1.6%
Other values (390) 896
49.9%
(Missing) 114
 
6.4%
ValueCountFrequency (%)
0 239
13.3%
1 117
6.5%
2 94
 
5.2%
3 77
 
4.3%
4 51
 
2.8%
5 49
 
2.7%
6 48
 
2.7%
7 43
 
2.4%
8 37
 
2.1%
9 29
 
1.6%
ValueCountFrequency (%)
175585 1
0.1%
53684 1
0.1%
31410 1
0.1%
10546 1
0.1%
10439 1
0.1%
10193 1
0.1%
8400 1
0.1%
7701 1
0.1%
6629 1
0.1%
6450 1
0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct401
Distinct (%)24.0%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean354.03106
Minimum0
Maximum157189
Zeros225
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T04:55:42.848719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median12
Q375.5
95-th percentile1097.25
Maximum157189
Range157189
Interquartile range (IQR)73.5

Descriptive statistics

Standard deviation4116.5349
Coefficient of variation (CV)11.627609
Kurtosis1270.5646
Mean354.03106
Median Absolute Deviation (MAD)12
Skewness34.07007
Sum592648
Variance16945859
MonotonicityNot monotonic
2024-03-23T04:55:43.497009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 225
 
12.5%
1 148
 
8.2%
2 100
 
5.6%
3 54
 
3.0%
5 52
 
2.9%
8 49
 
2.7%
6 45
 
2.5%
4 43
 
2.4%
7 40
 
2.2%
9 32
 
1.8%
Other values (391) 886
49.4%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 225
12.5%
1 148
8.2%
2 100
5.6%
3 54
 
3.0%
4 43
 
2.4%
5 52
 
2.9%
6 45
 
2.5%
7 40
 
2.2%
8 49
 
2.7%
9 32
 
1.8%
ValueCountFrequency (%)
157189 1
0.1%
45858 1
0.1%
23468 1
0.1%
12551 1
0.1%
11035 1
0.1%
10859 1
0.1%
8614 1
0.1%
8402 1
0.1%
7875 1
0.1%
5932 1
0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct417
Distinct (%)24.9%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean429.75926
Minimum0
Maximum194670
Zeros232
Zeros (%)12.9%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T04:55:43.955576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median14
Q382
95-th percentile1365.05
Maximum194670
Range194670
Interquartile range (IQR)80

Descriptive statistics

Standard deviation5090.2818
Coefficient of variation (CV)11.844496
Kurtosis1278.2058
Mean429.75926
Median Absolute Deviation (MAD)14
Skewness34.200587
Sum719417
Variance25910969
MonotonicityNot monotonic
2024-03-23T04:55:44.524236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 232
 
12.9%
1 119
 
6.6%
2 92
 
5.1%
5 57
 
3.2%
4 49
 
2.7%
3 48
 
2.7%
6 44
 
2.5%
8 36
 
2.0%
11 31
 
1.7%
12 30
 
1.7%
Other values (407) 936
52.2%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 232
12.9%
1 119
6.6%
2 92
 
5.1%
3 48
 
2.7%
4 49
 
2.7%
5 57
 
3.2%
6 44
 
2.5%
7 24
 
1.3%
8 36
 
2.0%
9 26
 
1.4%
ValueCountFrequency (%)
194670 1
0.1%
56143 1
0.1%
28578 1
0.1%
15552 1
0.1%
14884 1
0.1%
13237 1
0.1%
11844 1
0.1%
9728 1
0.1%
7968 1
0.1%
7852 1
0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct437
Distinct (%)26.1%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean392.19415
Minimum0
Maximum167616
Zeros232
Zeros (%)12.9%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T04:55:45.055957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median15
Q394.75
95-th percentile1251.35
Maximum167616
Range167616
Interquartile range (IQR)92.75

Descriptive statistics

Standard deviation4387.2468
Coefficient of variation (CV)11.186416
Kurtosis1271.291
Mean392.19415
Median Absolute Deviation (MAD)15
Skewness34.024745
Sum656533
Variance19247935
MonotonicityNot monotonic
2024-03-23T04:55:45.567521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 232
 
12.9%
1 116
 
6.5%
2 78
 
4.3%
3 54
 
3.0%
5 49
 
2.7%
4 46
 
2.6%
10 42
 
2.3%
6 41
 
2.3%
7 34
 
1.9%
9 31
 
1.7%
Other values (427) 951
53.0%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 232
12.9%
1 116
6.5%
2 78
 
4.3%
3 54
 
3.0%
4 46
 
2.6%
5 49
 
2.7%
6 41
 
2.3%
7 34
 
1.9%
8 29
 
1.6%
9 31
 
1.7%
ValueCountFrequency (%)
167616 1
0.1%
46547 1
0.1%
24295 1
0.1%
18031 1
0.1%
13210 1
0.1%
10668 1
0.1%
10076 1
0.1%
8489 1
0.1%
8439 1
0.1%
8128 1
0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct376
Distinct (%)22.5%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean245.73835
Minimum0
Maximum101790
Zeros290
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T04:55:46.116100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median11
Q372
95-th percentile724.2
Maximum101790
Range101790
Interquartile range (IQR)70

Descriptive statistics

Standard deviation2659.33
Coefficient of variation (CV)10.821795
Kurtosis1279.0228
Mean245.73835
Median Absolute Deviation (MAD)11
Skewness34.105282
Sum411366
Variance7072036.1
MonotonicityNot monotonic
2024-03-23T04:55:46.688659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 290
 
16.2%
1 128
 
7.1%
2 67
 
3.7%
4 59
 
3.3%
3 55
 
3.1%
6 51
 
2.8%
8 43
 
2.4%
7 38
 
2.1%
5 33
 
1.8%
9 32
 
1.8%
Other values (366) 878
48.9%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 290
16.2%
1 128
7.1%
2 67
 
3.7%
3 55
 
3.1%
4 59
 
3.3%
5 33
 
1.8%
6 51
 
2.8%
7 38
 
2.1%
8 43
 
2.4%
9 32
 
1.8%
ValueCountFrequency (%)
101790 1
0.1%
26946 1
0.1%
12652 1
0.1%
12484 1
0.1%
7763 1
0.1%
6339 1
0.1%
6263 1
0.1%
6250 1
0.1%
5963 1
0.1%
5794 1
0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct380
Distinct (%)22.6%
Missing114
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean211.89762
Minimum0
Maximum88679
Zeros325
Zeros (%)18.1%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T04:55:47.384415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median9
Q361
95-th percentile632.2
Maximum88679
Range88679
Interquartile range (IQR)60

Descriptive statistics

Standard deviation2309.4098
Coefficient of variation (CV)10.898706
Kurtosis1290.5147
Mean211.89762
Median Absolute Deviation (MAD)9
Skewness34.278623
Sum355988
Variance5333373.6
MonotonicityNot monotonic
2024-03-23T04:55:48.106681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 325
18.1%
1 151
 
8.4%
2 82
 
4.6%
3 75
 
4.2%
5 52
 
2.9%
4 47
 
2.6%
6 40
 
2.2%
9 38
 
2.1%
7 30
 
1.7%
12 29
 
1.6%
Other values (370) 811
45.2%
(Missing) 114
 
6.4%
ValueCountFrequency (%)
0 325
18.1%
1 151
8.4%
2 82
 
4.6%
3 75
 
4.2%
4 47
 
2.6%
5 52
 
2.9%
6 40
 
2.2%
7 30
 
1.7%
8 24
 
1.3%
9 38
 
2.1%
ValueCountFrequency (%)
88679 1
0.1%
22978 1
0.1%
11911 1
0.1%
10469 1
0.1%
6137 1
0.1%
5769 1
0.1%
5281 1
0.1%
5202 1
0.1%
4814 1
0.1%
4575 1
0.1%

Interactions

2024-03-23T04:55:13.042324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:10.999923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:17.893629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T04:53:52.086382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:59.029541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:05.483809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:13.339746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:20.907343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:28.826705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:36.980425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:44.376487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:50.134465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:55.532489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:03.446021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:10.545932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:19.443172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:15.842309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:22.363699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:30.515880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:38.658348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:45.577658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:52.495286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:59.489604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:05.890102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:13.775370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:21.443585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:29.241201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:37.472678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:44.721505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:50.442321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:56.060285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:03.803350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:10.950544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:19.734588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:16.123987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:22.703339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:30.825295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:38.968949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:46.035761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:52.835841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:59.826728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:06.577085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:14.231980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:21.847854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:29.800032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:37.825828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:45.074503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:50.759746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:56.708880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:04.142984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:11.346577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:20.076655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:16.528998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:23.084627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:31.374635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:39.339493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:46.432474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:53.252802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:00.152843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:07.532640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:14.644632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:22.321826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:30.456217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:38.190253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:45.388073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:51.076029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:57.134867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:04.507176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:11.723454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:20.527423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:16.869750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:23.468965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:31.858795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:39.713491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:46.808660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:53.672926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:00.569042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:07.849998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:15.115478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:22.734810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:31.050167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:38.508632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:45.762303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:51.370681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:57.742104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:04.862277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:12.012568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:20.860751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:17.180367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:24.010569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:32.180414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:40.131793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:47.271551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:54.080672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:00.937576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:08.203968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:15.423057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:23.214491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:31.663609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:38.784829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:46.067279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:51.655290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:58.346077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:05.190635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:12.371478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:21.128261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:17.459565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:24.326597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:32.531554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:40.492104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:47.541548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:53:54.460007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:01.253141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:08.784532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:15.846930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:23.656128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:32.088921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:39.063797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:46.333262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:51.944725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:54:58.807317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:05.571579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:55:12.738102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T04:55:48.777092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0001.0001.0001.0001.0001.0000.8331.0001.0001.0001.0001.0001.0001.0001.0000.8620.8620.862
20071.0001.0001.0001.0001.0001.0000.9071.0001.0001.0001.0001.0001.0001.0001.0000.9960.9960.996
20081.0001.0001.0001.0001.0001.0000.9071.0001.0001.0001.0001.0001.0001.0001.0000.9960.9960.996
20091.0001.0001.0001.0001.0001.0000.9071.0001.0001.0001.0001.0001.0001.0001.0000.9960.9960.996
20101.0001.0001.0001.0001.0001.0000.9071.0001.0001.0001.0001.0001.0001.0001.0000.9960.9960.996
20111.0001.0001.0001.0001.0001.0000.9071.0001.0001.0001.0001.0001.0001.0001.0000.9960.9960.996
20120.8330.9070.9070.9070.9070.9071.0000.9070.9070.9070.9070.9070.9070.9070.9070.8760.8760.876
20131.0001.0001.0001.0001.0001.0000.9071.0001.0001.0001.0001.0001.0001.0001.0000.9960.9960.996
20141.0001.0001.0001.0001.0001.0000.9071.0001.0001.0001.0001.0001.0001.0001.0000.9960.9960.996
20151.0001.0001.0001.0001.0001.0000.9071.0001.0001.0001.0001.0001.0001.0001.0000.9960.9960.996
20161.0001.0001.0001.0001.0001.0000.9071.0001.0001.0001.0001.0001.0001.0001.0000.9960.9960.996
20171.0001.0001.0001.0001.0001.0000.9071.0001.0001.0001.0001.0001.0001.0001.0000.9960.9960.996
20181.0001.0001.0001.0001.0001.0000.9071.0001.0001.0001.0001.0001.0001.0001.0000.9960.9960.996
20191.0001.0001.0001.0001.0001.0000.9071.0001.0001.0001.0001.0001.0001.0001.0000.9960.9960.996
20201.0001.0001.0001.0001.0001.0000.9071.0001.0001.0001.0001.0001.0001.0001.0000.9960.9960.996
20210.8620.9960.9960.9960.9960.9960.8760.9960.9960.9960.9960.9960.9960.9960.9961.0001.0001.000
20220.8620.9960.9960.9960.9960.9960.8760.9960.9960.9960.9960.9960.9960.9960.9961.0001.0001.000
20230.8620.9960.9960.9960.9960.9960.8760.9960.9960.9960.9960.9960.9960.9960.9961.0001.0001.000
2024-03-23T04:55:49.480963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9050.8950.8820.8790.8830.8800.8790.8670.8670.8580.8410.8420.8320.8350.8310.8310.837
20070.9051.0000.9110.8860.8810.8880.8830.8820.8760.8750.8680.8470.8350.8350.8260.8380.8310.834
20080.8950.9111.0000.8990.8940.8980.8890.8970.8840.8820.8800.8530.8500.8460.8480.8540.8470.848
20090.8820.8860.8991.0000.9180.9030.9020.8960.8760.8760.8780.8620.8540.8570.8570.8490.8450.856
20100.8790.8810.8940.9181.0000.9150.9010.8970.8840.8830.8780.8590.8560.8510.8500.8480.8450.847
20110.8830.8880.8980.9030.9151.0000.9200.9160.8990.8930.8910.8670.8580.8480.8540.8450.8470.845
20120.8800.8830.8890.9020.9010.9201.0000.9210.9130.8970.8890.8680.8640.8540.8590.8580.8570.857
20130.8790.8820.8970.8960.8970.9160.9211.0000.9240.9130.9030.8800.8740.8620.8650.8530.8580.852
20140.8670.8760.8840.8760.8840.8990.9130.9241.0000.9290.9120.8860.8770.8600.8650.8620.8570.846
20150.8670.8750.8820.8760.8830.8930.8970.9130.9291.0000.9250.9010.8890.8680.8770.8720.8580.849
20160.8580.8680.8800.8780.8780.8910.8890.9030.9120.9251.0000.9140.9020.8730.8780.8790.8680.856
20170.8410.8470.8530.8620.8590.8670.8680.8800.8860.9010.9141.0000.9150.8780.8690.8640.8580.849
20180.8420.8350.8500.8540.8560.8580.8640.8740.8770.8890.9020.9151.0000.9100.8940.8820.8730.860
20190.8320.8350.8460.8570.8510.8480.8540.8620.8600.8680.8730.8780.9101.0000.9100.8760.8770.856
20200.8350.8260.8480.8570.8500.8540.8590.8650.8650.8770.8780.8690.8940.9101.0000.9020.8800.865
20210.8310.8380.8540.8490.8480.8450.8580.8530.8620.8720.8790.8640.8820.8760.9021.0000.9060.892
20220.8310.8310.8470.8450.8450.8470.8570.8580.8570.8580.8680.8580.8730.8770.8800.9061.0000.906
20230.8370.8340.8480.8560.8470.8450.8570.8520.8460.8490.8560.8490.8600.8560.8650.8920.9061.000

Missing values

2024-03-23T04:55:21.603215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T04:55:22.487603image/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.
2024-03-23T04:55:23.095653image/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_지목200620072008200920102011201220132014201520162017201820192020202120222023
0전국_전7317791051143714621507144915752113235225593129340729793241434133102541
1전국_답5676891006128212681618171916871958208323872871309135714702485633163228
2전국_대지15118712598411643411612911426713074711296312596114976119189217386817681017558515718919467016761610179088679
3전국_임야599627631942729107112089681502223015142706237427332357336326312433
4전국_공장1018797408713799778878484102514954210362114481208611238104391103513237180311265210469
5전국_기타373737243288354836214303408244484579549255056453662986147968843962634814
6서울_전189965503319202450409126837319107
7서울_답364242818556153615494050377853
8서울_대지332652366320328199751487417503158261717721658329983099529782314102346828578242951248411911
9서울_임야25301325282713333635222430162224728
지역_지목200620072008200920102011201220132014201520162017201820192020202120222023
1784제주 제주시_대지44571863068292910481104111912881356139613871220975106313731083751
1785제주 제주시_임야341711108202516252626351714294716
1786제주 제주시_공장5121717141716101722191171829156
1787제주 제주시_기타2876474368657716210113811214110792521099751
1788제주 서귀포시_전499141011204125434650513337512615
1789제주 서귀포시_답000000000001200591
1790제주 서귀포시_대지831771851942032732373533917627076035872101380583401336
1791제주 서귀포시_임야3251135565373017206104173
1792제주 서귀포시_공장20502266659516319245
1793제주 서귀포시_기타153239292245404057619065784740598231