Overview

Dataset statistics

Number of variables19
Number of observations1196
Missing cells1460
Missing cells (%)6.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory198.7 KiB
Average record size in memory170.1 B

Variable types

Text1
Numeric18

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 부동산거래현황 중 주택 거래현황의 연도별 매입자거주지별(면적) 데이터입니다.- (단위 : 천㎡)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068433/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 88 (7.4%) missing valuesMissing
2007 has 108 (9.0%) missing valuesMissing
2008 has 100 (8.4%) missing valuesMissing
2009 has 100 (8.4%) missing valuesMissing
2010 has 76 (6.4%) missing valuesMissing
2011 has 88 (7.4%) missing valuesMissing
2012 has 80 (6.7%) missing valuesMissing
2013 has 80 (6.7%) missing valuesMissing
2014 has 52 (4.3%) missing valuesMissing
2015 has 68 (5.7%) missing valuesMissing
2016 has 68 (5.7%) missing valuesMissing
2017 has 80 (6.7%) missing valuesMissing
2018 has 76 (6.4%) missing valuesMissing
2019 has 80 (6.7%) missing valuesMissing
2020 has 80 (6.7%) missing valuesMissing
2021 has 80 (6.7%) missing valuesMissing
2022 has 80 (6.7%) missing valuesMissing
2023 has 76 (6.4%) missing valuesMissing
2006 is highly skewed (γ1 = 20.63829054)Skewed
2007 is highly skewed (γ1 = 21.47875206)Skewed
2008 is highly skewed (γ1 = 21.10263448)Skewed
2009 is highly skewed (γ1 = 21.53948924)Skewed
2010 is highly skewed (γ1 = 22.2731454)Skewed
2011 is highly skewed (γ1 = 22.79282033)Skewed
2012 is highly skewed (γ1 = 23.21043293)Skewed
2013 is highly skewed (γ1 = 23.12285596)Skewed
2014 is highly skewed (γ1 = 23.70628191)Skewed
2015 is highly skewed (γ1 = 22.15746589)Skewed
2016 is highly skewed (γ1 = 21.86094078)Skewed
2017 is highly skewed (γ1 = 20.77456811)Skewed
2018 is highly skewed (γ1 = 20.54758825)Skewed
2019 is highly skewed (γ1 = 20.64270437)Skewed
2022 is highly skewed (γ1 = 21.12647396)Skewed
2023 is highly skewed (γ1 = 21.36407687)Skewed
지역 및 매입자거주지 has unique valuesUnique
2006 has 38 (3.2%) zerosZeros
2007 has 36 (3.0%) zerosZeros
2008 has 34 (2.8%) zerosZeros
2009 has 29 (2.4%) zerosZeros
2010 has 30 (2.5%) zerosZeros
2011 has 28 (2.3%) zerosZeros
2012 has 32 (2.7%) zerosZeros
2013 has 31 (2.6%) zerosZeros
2014 has 31 (2.6%) zerosZeros
2015 has 31 (2.6%) zerosZeros
2016 has 31 (2.6%) zerosZeros
2017 has 29 (2.4%) zerosZeros
2018 has 31 (2.6%) zerosZeros
2019 has 32 (2.7%) zerosZeros
2020 has 30 (2.5%) zerosZeros
2021 has 30 (2.5%) zerosZeros
2022 has 32 (2.7%) zerosZeros
2023 has 34 (2.8%) zerosZeros

Reproduction

Analysis started2024-03-30 06:13:26.225023
Analysis finished2024-03-30 06:15:31.238699
Duration2 minutes and 5.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct1196
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
2024-03-30T06:15:31.581996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length14.091137
Min length9

Characters and Unicode

Total characters16853
Distinct characters153
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1196 ?
Unique (%)100.0%

Sample

1st row전국 /관할시군구내
2nd row전국 /관할시도내
3rd row전국 /관할시도외_서울
4th row전국 /관할시도외_기타
5th row서울 /관할시군구내
ValueCountFrequency (%)
경기 212
 
8.9%
경남 108
 
4.5%
경북 104
 
4.3%
서울 104
 
4.3%
전남 92
 
3.8%
충남 80
 
3.3%
충북 80
 
3.3%
강원 76
 
3.2%
전북 68
 
2.8%
부산 68
 
2.8%
Other values (1037) 1400
58.5%
2024-03-30T06:15:32.779226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1684
 
10.0%
1200
 
7.1%
1196
 
7.1%
/ 1196
 
7.1%
1196
 
7.1%
917
 
5.4%
863
 
5.1%
671
 
4.0%
598
 
3.5%
598
 
3.5%
Other values (143) 6734
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13711
81.4%
Space Separator 1196
 
7.1%
Other Punctuation 1196
 
7.1%
Connector Punctuation 598
 
3.5%
Close Punctuation 76
 
0.5%
Open Punctuation 76
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1684
 
12.3%
1200
 
8.8%
1196
 
8.7%
917
 
6.7%
863
 
6.3%
671
 
4.9%
598
 
4.4%
598
 
4.4%
523
 
3.8%
471
 
3.4%
Other values (138) 4990
36.4%
Space Separator
ValueCountFrequency (%)
1196
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1196
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 598
100.0%
Close Punctuation
ValueCountFrequency (%)
) 76
100.0%
Open Punctuation
ValueCountFrequency (%)
( 76
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13711
81.4%
Common 3142
 
18.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1684
 
12.3%
1200
 
8.8%
1196
 
8.7%
917
 
6.7%
863
 
6.3%
671
 
4.9%
598
 
4.4%
598
 
4.4%
523
 
3.8%
471
 
3.4%
Other values (138) 4990
36.4%
Common
ValueCountFrequency (%)
1196
38.1%
/ 1196
38.1%
_ 598
19.0%
) 76
 
2.4%
( 76
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13711
81.4%
ASCII 3142
 
18.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1684
 
12.3%
1200
 
8.8%
1196
 
8.7%
917
 
6.7%
863
 
6.3%
671
 
4.9%
598
 
4.4%
598
 
4.4%
523
 
3.8%
471
 
3.4%
Other values (138) 4990
36.4%
ASCII
ValueCountFrequency (%)
1196
38.1%
/ 1196
38.1%
_ 598
19.0%
) 76
 
2.4%
( 76
 
2.4%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct419
Distinct (%)37.8%
Missing88
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean351.5009
Minimum0
Maximum64699
Zeros38
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-30T06:15:33.309215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median48
Q3234
95-th percentile788.65
Maximum64699
Range64699
Interquartile range (IQR)225

Descriptive statistics

Standard deviation2392.5772
Coefficient of variation (CV)6.8067454
Kurtosis506.42964
Mean351.5009
Median Absolute Deviation (MAD)46
Skewness20.638291
Sum389463
Variance5724425.4
MonotonicityNot monotonic
2024-03-30T06:15:34.201018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 48
 
4.0%
0 38
 
3.2%
2 38
 
3.2%
3 35
 
2.9%
4 33
 
2.8%
6 29
 
2.4%
5 25
 
2.1%
11 19
 
1.6%
9 18
 
1.5%
12 17
 
1.4%
Other values (409) 808
67.6%
(Missing) 88
 
7.4%
ValueCountFrequency (%)
0 38
3.2%
1 48
4.0%
2 38
3.2%
3 35
2.9%
4 33
2.8%
5 25
2.1%
6 29
2.4%
7 17
 
1.4%
8 8
 
0.7%
9 18
 
1.5%
ValueCountFrequency (%)
64699 1
0.1%
32412 1
0.1%
17096 1
0.1%
15790 1
0.1%
11983 1
0.1%
10146 1
0.1%
9581 1
0.1%
9144 1
0.1%
6794 1
0.1%
4693 1
0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct364
Distinct (%)33.5%
Missing108
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean281.78952
Minimum0
Maximum52720
Zeros36
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-30T06:15:34.996661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median43
Q3166
95-th percentile656.2
Maximum52720
Range52720
Interquartile range (IQR)157

Descriptive statistics

Standard deviation1909.5487
Coefficient of variation (CV)6.776507
Kurtosis547.855
Mean281.78952
Median Absolute Deviation (MAD)40
Skewness21.478752
Sum306587
Variance3646376.1
MonotonicityNot monotonic
2024-03-30T06:15:35.480604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 50
 
4.2%
2 36
 
3.0%
0 36
 
3.0%
3 32
 
2.7%
5 30
 
2.5%
7 28
 
2.3%
4 27
 
2.3%
11 17
 
1.4%
6 17
 
1.4%
12 16
 
1.3%
Other values (354) 799
66.8%
(Missing) 108
 
9.0%
ValueCountFrequency (%)
0 36
3.0%
1 50
4.2%
2 36
3.0%
3 32
2.7%
4 27
2.3%
5 30
2.5%
6 17
 
1.4%
7 28
2.3%
8 13
 
1.1%
9 10
 
0.8%
ValueCountFrequency (%)
52720 1
0.1%
23700 1
0.1%
14073 1
0.1%
10403 1
0.1%
7354 1
0.1%
6950 1
0.1%
6844 1
0.1%
5603 1
0.1%
5558 1
0.1%
4534 1
0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct365
Distinct (%)33.3%
Missing100
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean266.71077
Minimum0
Maximum48020
Zeros34
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-30T06:15:36.024963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median52
Q3166.25
95-th percentile646.5
Maximum48020
Range48020
Interquartile range (IQR)156.25

Descriptive statistics

Standard deviation1758.9643
Coefficient of variation (CV)6.5950255
Kurtosis527.00135
Mean266.71077
Median Absolute Deviation (MAD)48
Skewness21.102634
Sum292315
Variance3093955.5
MonotonicityNot monotonic
2024-03-30T06:15:36.676400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 49
 
4.1%
0 34
 
2.8%
2 34
 
2.8%
3 30
 
2.5%
4 25
 
2.1%
7 23
 
1.9%
6 22
 
1.8%
8 22
 
1.8%
10 19
 
1.6%
11 19
 
1.6%
Other values (355) 819
68.5%
(Missing) 100
 
8.4%
ValueCountFrequency (%)
0 34
2.8%
1 49
4.1%
2 34
2.8%
3 30
2.5%
4 25
2.1%
5 13
 
1.1%
6 22
1.8%
7 23
1.9%
8 22
1.8%
9 10
 
0.8%
ValueCountFrequency (%)
48020 1
0.1%
23145 1
0.1%
14190 1
0.1%
9234 1
0.1%
7776 1
0.1%
6280 1
0.1%
5931 1
0.1%
5169 1
0.1%
4440 1
0.1%
4276 1
0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct371
Distinct (%)33.9%
Missing100
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean274.78558
Minimum0
Maximum50377
Zeros29
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-30T06:15:37.530183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q111
median51
Q3169
95-th percentile716.75
Maximum50377
Range50377
Interquartile range (IQR)158

Descriptive statistics

Standard deviation1819.1789
Coefficient of variation (CV)6.6203578
Kurtosis550.34535
Mean274.78558
Median Absolute Deviation (MAD)47
Skewness21.539489
Sum301165
Variance3309411.8
MonotonicityNot monotonic
2024-03-30T06:15:38.293293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 38
 
3.2%
1 34
 
2.8%
3 33
 
2.8%
0 29
 
2.4%
4 28
 
2.3%
6 21
 
1.8%
8 20
 
1.7%
20 18
 
1.5%
7 17
 
1.4%
12 16
 
1.3%
Other values (361) 842
70.4%
(Missing) 100
 
8.4%
ValueCountFrequency (%)
0 29
2.4%
1 34
2.8%
2 38
3.2%
3 33
2.8%
4 28
2.3%
5 16
1.3%
6 21
1.8%
7 17
1.4%
8 20
1.7%
9 15
 
1.3%
ValueCountFrequency (%)
50377 1
0.1%
22622 1
0.1%
12804 1
0.1%
11291 1
0.1%
8904 1
0.1%
6524 1
0.1%
6438 1
0.1%
4746 1
0.1%
4368 1
0.1%
4362 1
0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct362
Distinct (%)32.3%
Missing76
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean258.17768
Minimum0
Maximum49309
Zeros30
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-30T06:15:38.818822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110.75
median46
Q3157
95-th percentile646.4
Maximum49309
Range49309
Interquartile range (IQR)146.25

Descriptive statistics

Standard deviation1740.3599
Coefficient of variation (CV)6.7409388
Kurtosis586.1558
Mean258.17768
Median Absolute Deviation (MAD)42
Skewness22.273145
Sum289159
Variance3028852.7
MonotonicityNot monotonic
2024-03-30T06:15:39.347347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 49
 
4.1%
1 36
 
3.0%
0 30
 
2.5%
3 30
 
2.5%
8 24
 
2.0%
4 24
 
2.0%
5 24
 
2.0%
6 22
 
1.8%
12 22
 
1.8%
13 16
 
1.3%
Other values (352) 843
70.5%
(Missing) 76
 
6.4%
ValueCountFrequency (%)
0 30
2.5%
1 36
3.0%
2 49
4.1%
3 30
2.5%
4 24
2.0%
5 24
2.0%
6 22
1.8%
7 13
 
1.1%
8 24
2.0%
9 15
 
1.3%
ValueCountFrequency (%)
49309 1
0.1%
20868 1
0.1%
13409 1
0.1%
10563 1
0.1%
7585 1
0.1%
5715 1
0.1%
5055 1
0.1%
4558 1
0.1%
4500 1
0.1%
3629 1
0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct381
Distinct (%)34.4%
Missing88
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean302.81318
Minimum0
Maximum58460
Zeros28
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-30T06:15:39.966739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q112
median52.5
Q3186.25
95-th percentile801.3
Maximum58460
Range58460
Interquartile range (IQR)174.25

Descriptive statistics

Standard deviation2047.9358
Coefficient of variation (CV)6.7630339
Kurtosis608.35801
Mean302.81318
Median Absolute Deviation (MAD)48.5
Skewness22.79282
Sum335517
Variance4194041
MonotonicityNot monotonic
2024-03-30T06:15:40.480659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 34
 
2.8%
2 30
 
2.5%
1 29
 
2.4%
0 28
 
2.3%
5 28
 
2.3%
6 22
 
1.8%
8 21
 
1.8%
9 19
 
1.6%
4 18
 
1.5%
7 18
 
1.5%
Other values (371) 861
72.0%
(Missing) 88
 
7.4%
ValueCountFrequency (%)
0 28
2.3%
1 29
2.4%
2 30
2.5%
3 34
2.8%
4 18
1.5%
5 28
2.3%
6 22
1.8%
7 18
1.5%
8 21
1.8%
9 19
1.6%
ValueCountFrequency (%)
58460 1
0.1%
24270 1
0.1%
15161 1
0.1%
11536 1
0.1%
7326 1
0.1%
5975 1
0.1%
5172 1
0.1%
5101 1
0.1%
5049 1
0.1%
3891 1
0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct352
Distinct (%)31.5%
Missing80
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean244.28853
Minimum0
Maximum48185
Zeros32
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-30T06:15:41.418247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q111
median43
Q3146
95-th percentile665.25
Maximum48185
Range48185
Interquartile range (IQR)135

Descriptive statistics

Standard deviation1666.4702
Coefficient of variation (CV)6.8217291
Kurtosis631.42963
Mean244.28853
Median Absolute Deviation (MAD)39
Skewness23.210433
Sum272626
Variance2777122.9
MonotonicityNot monotonic
2024-03-30T06:15:42.159172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 37
 
3.1%
0 32
 
2.7%
3 31
 
2.6%
1 30
 
2.5%
5 29
 
2.4%
4 28
 
2.3%
9 21
 
1.8%
15 20
 
1.7%
7 20
 
1.7%
6 17
 
1.4%
Other values (342) 851
71.2%
(Missing) 80
 
6.7%
ValueCountFrequency (%)
0 32
2.7%
1 30
2.5%
2 37
3.1%
3 31
2.6%
4 28
2.3%
5 29
2.4%
6 17
1.4%
7 20
1.7%
8 14
 
1.2%
9 21
1.8%
ValueCountFrequency (%)
48185 1
0.1%
18550 1
0.1%
12449 1
0.1%
9336 1
0.1%
6358 1
0.1%
4706 1
0.1%
4218 1
0.1%
4180 1
0.1%
3679 1
0.1%
3611 1
0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct383
Distinct (%)34.3%
Missing80
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean281.2948
Minimum0
Maximum56094
Zeros31
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-30T06:15:42.871581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median45
Q3166.25
95-th percentile718.25
Maximum56094
Range56094
Interquartile range (IQR)155.25

Descriptive statistics

Standard deviation1947.0551
Coefficient of variation (CV)6.9217598
Kurtosis624.95149
Mean281.2948
Median Absolute Deviation (MAD)41
Skewness23.122856
Sum313925
Variance3791023.5
MonotonicityNot monotonic
2024-03-30T06:15:43.572448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 40
 
3.3%
4 36
 
3.0%
3 35
 
2.9%
0 31
 
2.6%
1 23
 
1.9%
7 20
 
1.7%
10 20
 
1.7%
18 18
 
1.5%
5 18
 
1.5%
6 17
 
1.4%
Other values (373) 858
71.7%
(Missing) 80
 
6.7%
ValueCountFrequency (%)
0 31
2.6%
1 23
1.9%
2 40
3.3%
3 35
2.9%
4 36
3.0%
5 18
1.5%
6 17
1.4%
7 20
1.7%
8 11
 
0.9%
9 17
1.4%
ValueCountFrequency (%)
56094 1
0.1%
22626 1
0.1%
14071 1
0.1%
11426 1
0.1%
6255 1
0.1%
5539 1
0.1%
5314 1
0.1%
5214 1
0.1%
4347 1
0.1%
4206 1
0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct413
Distinct (%)36.1%
Missing52
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean344.34353
Minimum0
Maximum71629
Zeros31
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-30T06:15:44.149778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q114
median57
Q3191.5
95-th percentile881.85
Maximum71629
Range71629
Interquartile range (IQR)177.5

Descriptive statistics

Standard deviation2440.5043
Coefficient of variation (CV)7.0874114
Kurtosis655.16473
Mean344.34353
Median Absolute Deviation (MAD)52.5
Skewness23.706282
Sum393929
Variance5956061.1
MonotonicityNot monotonic
2024-03-30T06:15:44.544762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 31
 
2.6%
0 31
 
2.6%
3 29
 
2.4%
4 27
 
2.3%
5 25
 
2.1%
1 21
 
1.8%
11 19
 
1.6%
12 18
 
1.5%
10 18
 
1.5%
6 17
 
1.4%
Other values (403) 908
75.9%
(Missing) 52
 
4.3%
ValueCountFrequency (%)
0 31
2.6%
1 21
1.8%
2 31
2.6%
3 29
2.4%
4 27
2.3%
5 25
2.1%
6 17
1.4%
7 14
1.2%
8 13
1.1%
9 7
 
0.6%
ValueCountFrequency (%)
71629 1
0.1%
28124 1
0.1%
17029 1
0.1%
14482 1
0.1%
8924 1
0.1%
7355 1
0.1%
6652 1
0.1%
5931 1
0.1%
5344 1
0.1%
5146 1
0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct451
Distinct (%)40.0%
Missing68
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean410.38298
Minimum0
Maximum79592
Zeros31
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-30T06:15:45.087827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q116
median71
Q3254.25
95-th percentile1033.8
Maximum79592
Range79592
Interquartile range (IQR)238.25

Descriptive statistics

Standard deviation2819.3738
Coefficient of variation (CV)6.8701041
Kurtosis577.79101
Mean410.38298
Median Absolute Deviation (MAD)66
Skewness22.157466
Sum462912
Variance7948868.6
MonotonicityNot monotonic
2024-03-30T06:15:45.563020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31
 
2.6%
2 27
 
2.3%
4 26
 
2.2%
3 25
 
2.1%
5 23
 
1.9%
7 19
 
1.6%
8 16
 
1.3%
1 15
 
1.3%
6 14
 
1.2%
28 13
 
1.1%
Other values (441) 919
76.8%
(Missing) 68
 
5.7%
ValueCountFrequency (%)
0 31
2.6%
1 15
1.3%
2 27
2.3%
3 25
2.1%
4 26
2.2%
5 23
1.9%
6 14
1.2%
7 19
1.6%
8 16
1.3%
9 12
 
1.0%
ValueCountFrequency (%)
79592 1
0.1%
35216 1
0.1%
21852 1
0.1%
18885 1
0.1%
12209 1
0.1%
8303 1
0.1%
8005 1
0.1%
7515 1
0.1%
6321 1
0.1%
6177 1
0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct433
Distinct (%)38.4%
Missing68
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean373.9805
Minimum0
Maximum71974
Zeros31
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-30T06:15:46.067196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q115
median65
Q3234.75
95-th percentile902.3
Maximum71974
Range71974
Interquartile range (IQR)219.75

Descriptive statistics

Standard deviation2566.2583
Coefficient of variation (CV)6.8620109
Kurtosis564.31999
Mean373.9805
Median Absolute Deviation (MAD)60
Skewness21.860941
Sum421850
Variance6585681.4
MonotonicityNot monotonic
2024-03-30T06:15:46.508082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31
 
2.6%
4 29
 
2.4%
5 25
 
2.1%
3 23
 
1.9%
2 22
 
1.8%
1 20
 
1.7%
10 19
 
1.6%
23 17
 
1.4%
6 17
 
1.4%
12 16
 
1.3%
Other values (423) 909
76.0%
(Missing) 68
 
5.7%
ValueCountFrequency (%)
0 31
2.6%
1 20
1.7%
2 22
1.8%
3 23
1.9%
4 29
2.4%
5 25
2.1%
6 17
1.4%
7 16
1.3%
8 13
1.1%
9 14
1.2%
ValueCountFrequency (%)
71974 1
0.1%
32294 1
0.1%
19694 1
0.1%
17962 1
0.1%
11390 1
0.1%
8568 1
0.1%
8409 1
0.1%
7241 1
0.1%
5597 1
0.1%
5579 1
0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct433
Distinct (%)38.8%
Missing80
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean400.3647
Minimum0
Maximum72465
Zeros29
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-30T06:15:46.925900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q115
median68
Q3260
95-th percentile963.25
Maximum72465
Range72465
Interquartile range (IQR)245

Descriptive statistics

Standard deviation2674.3738
Coefficient of variation (CV)6.6798442
Kurtosis509.44402
Mean400.3647
Median Absolute Deviation (MAD)63.5
Skewness20.774568
Sum446807
Variance7152275.1
MonotonicityNot monotonic
2024-03-30T06:15:47.379665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29
 
2.4%
4 28
 
2.3%
2 25
 
2.1%
5 25
 
2.1%
3 24
 
2.0%
6 22
 
1.8%
1 19
 
1.6%
33 17
 
1.4%
10 17
 
1.4%
8 15
 
1.3%
Other values (423) 895
74.8%
(Missing) 80
 
6.7%
ValueCountFrequency (%)
0 29
2.4%
1 19
1.6%
2 25
2.1%
3 24
2.0%
4 28
2.3%
5 25
2.1%
6 22
1.8%
7 15
1.3%
8 15
1.3%
9 12
1.0%
ValueCountFrequency (%)
72465 1
0.1%
36710 1
0.1%
21657 1
0.1%
19294 1
0.1%
10643 1
0.1%
9961 1
0.1%
9545 1
0.1%
7367 1
0.1%
6252 1
0.1%
5687 1
0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct442
Distinct (%)39.5%
Missing76
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean388.24464
Minimum0
Maximum70264
Zeros31
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-30T06:15:47.757852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q115
median68.5
Q3239.5
95-th percentile944
Maximum70264
Range70264
Interquartile range (IQR)224.5

Descriptive statistics

Standard deviation2608.8422
Coefficient of variation (CV)6.7195833
Kurtosis498.5546
Mean388.24464
Median Absolute Deviation (MAD)63.5
Skewness20.547588
Sum434834
Variance6806057.8
MonotonicityNot monotonic
2024-03-30T06:15:48.361091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31
 
2.6%
2 28
 
2.3%
5 26
 
2.2%
4 25
 
2.1%
3 24
 
2.0%
1 20
 
1.7%
7 18
 
1.5%
6 18
 
1.5%
12 14
 
1.2%
10 14
 
1.2%
Other values (432) 902
75.4%
(Missing) 76
 
6.4%
ValueCountFrequency (%)
0 31
2.6%
1 20
1.7%
2 28
2.3%
3 24
2.0%
4 25
2.1%
5 26
2.2%
6 18
1.5%
7 18
1.5%
8 13
1.1%
9 11
 
0.9%
ValueCountFrequency (%)
70264 1
0.1%
36258 1
0.1%
21167 1
0.1%
19814 1
0.1%
11726 1
0.1%
10069 1
0.1%
8585 1
0.1%
7581 1
0.1%
6533 1
0.1%
4012 1
0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct415
Distinct (%)37.2%
Missing80
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean351.67473
Minimum0
Maximum63383
Zeros32
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-30T06:15:48.797299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q114
median58.5
Q3213.25
95-th percentile838.25
Maximum63383
Range63383
Interquartile range (IQR)199.25

Descriptive statistics

Standard deviation2353.8526
Coefficient of variation (CV)6.6932662
Kurtosis501.26597
Mean351.67473
Median Absolute Deviation (MAD)54.5
Skewness20.642704
Sum392469
Variance5540622.1
MonotonicityNot monotonic
2024-03-30T06:15:49.235783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32
 
2.7%
2 29
 
2.4%
5 27
 
2.3%
3 27
 
2.3%
1 23
 
1.9%
4 21
 
1.8%
9 20
 
1.7%
7 17
 
1.4%
8 17
 
1.4%
6 16
 
1.3%
Other values (405) 887
74.2%
(Missing) 80
 
6.7%
ValueCountFrequency (%)
0 32
2.7%
1 23
1.9%
2 29
2.4%
3 27
2.3%
4 21
1.8%
5 27
2.3%
6 16
1.3%
7 17
1.4%
8 17
1.4%
9 20
1.7%
ValueCountFrequency (%)
63383 1
0.1%
33165 1
0.1%
18609 1
0.1%
17926 1
0.1%
10347 1
0.1%
8201 1
0.1%
6562 1
0.1%
5771 1
0.1%
5315 1
0.1%
4440 1
0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct472
Distinct (%)42.3%
Missing80
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean451.51075
Minimum0
Maximum76241
Zeros30
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-30T06:15:49.724381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q118
median89
Q3285.75
95-th percentile1098.25
Maximum76241
Range76241
Interquartile range (IQR)267.75

Descriptive statistics

Standard deviation2921.1031
Coefficient of variation (CV)6.4696203
Kurtosis456.42174
Mean451.51075
Median Absolute Deviation (MAD)82
Skewness19.705932
Sum503886
Variance8532843.4
MonotonicityNot monotonic
2024-03-30T06:15:50.228732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30
 
2.5%
7 24
 
2.0%
2 23
 
1.9%
3 23
 
1.9%
15 21
 
1.8%
4 21
 
1.8%
5 18
 
1.5%
8 17
 
1.4%
14 13
 
1.1%
1 13
 
1.1%
Other values (462) 913
76.3%
(Missing) 80
 
6.7%
ValueCountFrequency (%)
0 30
2.5%
1 13
1.1%
2 23
1.9%
3 23
1.9%
4 21
1.8%
5 18
1.5%
6 12
 
1.0%
7 24
2.0%
8 17
1.4%
9 11
 
0.9%
ValueCountFrequency (%)
76241 1
0.1%
43323 1
0.1%
27099 1
0.1%
20698 1
0.1%
13304 1
0.1%
11554 1
0.1%
7700 1
0.1%
7504 1
0.1%
7041 1
0.1%
5688 1
0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct411
Distinct (%)36.8%
Missing80
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean351.80108
Minimum0
Maximum59372
Zeros30
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-30T06:15:50.668580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q119
median80
Q3212
95-th percentile852.5
Maximum59372
Range59372
Interquartile range (IQR)193

Descriptive statistics

Standard deviation2249.9981
Coefficient of variation (CV)6.3956544
Kurtosis467.71623
Mean351.80108
Median Absolute Deviation (MAD)71
Skewness19.904468
Sum392610
Variance5062491.5
MonotonicityNot monotonic
2024-03-30T06:15:51.113697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30
 
2.5%
4 26
 
2.2%
3 19
 
1.6%
5 19
 
1.6%
1 19
 
1.6%
6 17
 
1.4%
15 15
 
1.3%
9 15
 
1.3%
2 15
 
1.3%
8 14
 
1.2%
Other values (401) 927
77.5%
(Missing) 80
 
6.7%
ValueCountFrequency (%)
0 30
2.5%
1 19
1.6%
2 15
1.3%
3 19
1.6%
4 26
2.2%
5 19
1.6%
6 17
1.4%
7 13
1.1%
8 14
1.2%
9 15
1.3%
ValueCountFrequency (%)
59372 1
0.1%
31354 1
0.1%
22864 1
0.1%
15047 1
0.1%
9799 1
0.1%
9441 1
0.1%
5554 1
0.1%
5223 1
0.1%
5182 1
0.1%
5094 1
0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct313
Distinct (%)28.0%
Missing80
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean204.34857
Minimum0
Maximum36662
Zeros32
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-30T06:15:51.591475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.75
Q113
median44
Q3117
95-th percentile575
Maximum36662
Range36662
Interquartile range (IQR)104

Descriptive statistics

Standard deviation1338.4266
Coefficient of variation (CV)6.5497237
Kurtosis526.3485
Mean204.34857
Median Absolute Deviation (MAD)38
Skewness21.126474
Sum228053
Variance1791385.9
MonotonicityNot monotonic
2024-03-30T06:15:51.981150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 37
 
3.1%
0 32
 
2.7%
3 27
 
2.3%
5 27
 
2.3%
1 24
 
2.0%
9 21
 
1.8%
4 21
 
1.8%
6 18
 
1.5%
11 17
 
1.4%
12 16
 
1.3%
Other values (303) 876
73.2%
(Missing) 80
 
6.7%
ValueCountFrequency (%)
0 32
2.7%
1 24
2.0%
2 37
3.1%
3 27
2.3%
4 21
1.8%
5 27
2.3%
6 18
1.5%
7 14
 
1.2%
8 11
 
0.9%
9 21
1.8%
ValueCountFrequency (%)
36662 1
0.1%
17408 1
0.1%
12637 1
0.1%
8345 1
0.1%
5097 1
0.1%
4817 1
0.1%
3286 1
0.1%
3147 1
0.1%
2582 1
0.1%
2436 1
0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct327
Distinct (%)29.2%
Missing76
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean202.1625
Minimum0
Maximum37445
Zeros34
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-30T06:15:52.403323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median36
Q3115.25
95-th percentile544.25
Maximum37445
Range37445
Interquartile range (IQR)105.25

Descriptive statistics

Standard deviation1363.3551
Coefficient of variation (CV)6.7438575
Kurtosis534.29534
Mean202.1625
Median Absolute Deviation (MAD)33
Skewness21.364077
Sum226422
Variance1858737.1
MonotonicityNot monotonic
2024-03-30T06:15:52.862647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 43
 
3.6%
2 38
 
3.2%
0 34
 
2.8%
4 34
 
2.8%
1 33
 
2.8%
5 25
 
2.1%
7 22
 
1.8%
10 20
 
1.7%
6 20
 
1.7%
20 16
 
1.3%
Other values (317) 835
69.8%
(Missing) 76
 
6.4%
ValueCountFrequency (%)
0 34
2.8%
1 33
2.8%
2 38
3.2%
3 43
3.6%
4 34
2.8%
5 25
2.1%
6 20
1.7%
7 22
1.8%
8 15
 
1.3%
9 14
 
1.2%
ValueCountFrequency (%)
37445 1
0.1%
18834 1
0.1%
11060 1
0.1%
9249 1
0.1%
5200 1
0.1%
3931 1
0.1%
3344 1
0.1%
2896 1
0.1%
2819 1
0.1%
2503 1
0.1%

Interactions

2024-03-30T06:15:22.625347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:30.883320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-30T06:13:47.345914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:54.144203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:59.568236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:04.636486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:09.772795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:15.062988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:21.781871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:32.302258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:38.824149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:44.888970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:52.063414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:59.016318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:06.680996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:13.689453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:19.995570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:26.345572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:35.035914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:41.440022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:47.744900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:54.553543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:59.835947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:04.907734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:10.036392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:15.437737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:22.274633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:32.615446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:39.121235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:45.197697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:52.385771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:59.730849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:07.091500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:14.079528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:20.382608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:26.640633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:35.335184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:41.730451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:48.105258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:54.888119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:00.124786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:05.376458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:10.341288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:15.735015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:22.685708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:32.965241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:39.412998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:45.542866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:52.733739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:00.427881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:07.540912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:14.368028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:20.836482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:27.225758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:35.622316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:42.077250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:48.502087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:55.186294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:00.477581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:05.646788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:10.628829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:16.046417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:23.174675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:33.435598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:39.882902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:46.009550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:53.087681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:00.997791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:07.980011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:14.674562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:21.288177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:27.551455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:35.989069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:42.617525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:48.954752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:55.432027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:00.753877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:05.867649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:10.940462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:16.375926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:23.618736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:33.877492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:40.199059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:46.682520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:53.533215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:01.468702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:08.816246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:15.018691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:21.684974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:27.897365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:36.357937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:42.902985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:49.230123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:55.690768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:00.988929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:06.042255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:11.245995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:16.721354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:25.105808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:34.173050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:40.490034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:46.968220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:53.950870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:01.878427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:09.145941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:15.304418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:21.981193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:28.279683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:36.712629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:43.262916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:49.579933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:13:55.962709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:01.275380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:06.272795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:11.662094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:16.991904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:27.170509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:34.457487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:40.843673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:47.384192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:14:54.511879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:02.214780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:09.551155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:15.622091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T06:15:22.270524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-30T06:15:53.152307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9930.9950.9990.9960.9900.9880.9950.9930.9991.0001.0000.9710.9990.9570.9430.9430.997
20070.9931.0001.0000.9930.9870.9980.9960.9860.9840.9930.9950.9950.9730.9930.9610.9500.9500.990
20080.9951.0001.0000.9950.9890.9990.9970.9870.9850.9950.9970.9971.0000.9950.9710.9570.9570.991
20090.9990.9930.9951.0000.9980.9930.9900.9960.9941.0001.0001.0000.9671.0001.0000.9610.9610.999
20100.9960.9870.9890.9981.0000.9870.9900.9910.9940.9980.9960.9960.9380.9980.9460.9610.9610.999
20110.9900.9980.9990.9930.9871.0000.9990.9910.9880.9930.9910.9910.9610.9930.9690.9500.9500.988
20120.9880.9960.9970.9900.9900.9991.0000.9860.9860.9900.9890.9890.9580.9900.9610.9670.9670.991
20130.9950.9860.9870.9960.9910.9910.9861.0000.9980.9960.9950.9950.9320.9960.9380.9130.9130.991
20140.9930.9840.9850.9940.9940.9880.9860.9981.0000.9940.9940.9940.9220.9940.9260.9130.9130.991
20150.9990.9930.9951.0000.9980.9930.9900.9960.9941.0001.0001.0000.9671.0001.0000.9610.9610.999
20161.0000.9950.9971.0000.9960.9910.9890.9950.9941.0001.0001.0001.0001.0000.9670.9500.9500.998
20171.0000.9950.9971.0000.9960.9910.9890.9950.9941.0001.0001.0001.0001.0000.9670.9500.9500.998
20180.9710.9731.0000.9670.9380.9610.9580.9320.9220.9671.0001.0001.0000.9671.0000.9990.9990.950
20190.9990.9930.9951.0000.9980.9930.9900.9960.9941.0001.0001.0000.9671.0001.0000.9610.9610.999
20200.9570.9610.9711.0000.9460.9690.9610.9380.9261.0000.9670.9671.0001.0001.0001.0001.0000.961
20210.9430.9500.9570.9610.9610.9500.9670.9130.9130.9610.9500.9500.9990.9611.0001.0001.0001.000
20220.9430.9500.9570.9610.9610.9500.9670.9130.9130.9610.9500.9500.9990.9611.0001.0001.0001.000
20230.9970.9900.9910.9990.9990.9880.9910.9910.9910.9990.9980.9980.9500.9990.9611.0001.0001.000
2024-03-30T06:15:53.756384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9650.9520.9430.9360.9380.9300.9370.9360.9480.9490.9460.9450.9500.9540.9340.8920.920
20070.9651.0000.9710.9490.9470.9510.9420.9460.9440.9500.9510.9480.9410.9460.9520.9450.9190.932
20080.9520.9711.0000.9600.9510.9530.9460.9490.9470.9500.9460.9440.9380.9420.9500.9470.9180.930
20090.9430.9490.9601.0000.9590.9580.9450.9500.9480.9490.9450.9420.9370.9400.9470.9330.9030.926
20100.9360.9470.9510.9591.0000.9680.9610.9560.9470.9430.9370.9350.9300.9390.9410.9360.9140.927
20110.9380.9510.9530.9580.9681.0000.9730.9680.9630.9580.9490.9490.9400.9480.9500.9440.9250.938
20120.9300.9420.9460.9450.9610.9731.0000.9760.9670.9610.9500.9490.9390.9470.9510.9470.9310.941
20130.9370.9460.9490.9500.9560.9680.9761.0000.9770.9700.9630.9600.9510.9560.9540.9440.9300.947
20140.9360.9440.9470.9480.9470.9630.9670.9771.0000.9770.9690.9650.9510.9560.9530.9430.9300.947
20150.9480.9500.9500.9490.9430.9580.9610.9700.9771.0000.9790.9730.9620.9620.9600.9500.9300.948
20160.9490.9510.9460.9450.9370.9490.9500.9630.9690.9791.0000.9840.9720.9710.9620.9520.9300.953
20170.9460.9480.9440.9420.9350.9490.9490.9600.9650.9730.9841.0000.9780.9750.9680.9540.9310.956
20180.9450.9410.9380.9370.9300.9400.9390.9510.9510.9620.9720.9781.0000.9790.9670.9530.9280.953
20190.9500.9460.9420.9400.9390.9480.9470.9560.9560.9620.9710.9750.9791.0000.9770.9560.9290.958
20200.9540.9520.9500.9470.9410.9500.9510.9540.9530.9600.9620.9680.9670.9771.0000.9670.9350.957
20210.9340.9450.9470.9330.9360.9440.9470.9440.9430.9500.9520.9540.9530.9560.9671.0000.9630.962
20220.8920.9190.9180.9030.9140.9250.9310.9300.9300.9300.9300.9310.9280.9290.9350.9631.0000.963
20230.9200.9320.9300.9260.9270.9380.9410.9470.9470.9480.9530.9560.9530.9580.9570.9620.9631.000

Missing values

2024-03-30T06:15:28.811970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T06:15:29.644624image/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.
2024-03-30T06:15:30.316020image/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 및 매입자거주지200620072008200920102011201220132014201520162017201820192020202120222023
0전국 /관할시군구내646995272048020503774930958460481855609471629795927197472465702646338376241593723666237445
1전국 /관할시도내324122370023145226222086824270185502262628124352163229436710362583316543323313541740818834
2전국 /관할시도외_서울95817354777689047585732663585539665283038568954510069820111554979950973931
3전국 /관할시도외_기타157901407314190128041340915161124491407117029218521969421657198141860927099228641263711060
4서울 /관할시군구내119836950628064384500597547066255892412209113909961858565627700509424262819
5서울 /관할시도내914455585169474633643673261432644424751572417367758157717504518223753344
6서울 /관할시도외_서울000100000000000000
7서울 /관할시도외_기타469327682544245916011830130317242391352335433862376130674168307415361818
8서울 종로구/관할시군구내1281081079273838193103145114140998391714741
9서울 종로구/관할시도내13411914981525949577010810114010987118974636
지역 및 매입자거주지200620072008200920102011201220132014201520162017201820192020202120222023
1186제주 /관할시도외_서울214230394453641089914512510611687931189756
1187제주 /관할시도외_기타3363607488108129161188282259273239185235293208130
1188제주 제주시/관할시군구내248394337367565679643716796840880814733614625731594422
1189제주 제주시/관할시도내152732333845454657655762483335413324
1190제주 제주시/관할시도외_서울153318192532388059686860675152605730
1191제주 제주시/관할시도외_기타2442445063708810611512413815613510413015211677
1192제주 서귀포시/관할시군구내399211187101124111157187302310245199151170203176159
1193제주 서귀포시/관할시도내81181413172026499914480613935464024
1194제주 서귀포시/관할시도외_서울7912191921262840775846503540584026
1195제주 서귀포시/관할시도외_기타92116232538415573158122117104811041419253