Overview

Dataset statistics

Number of variables19
Number of observations1196
Missing cells1532
Missing cells (%)6.7%
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 아파트매매 거래현황의 연도별 매입자거주지별(동(호)수) 데이터입니다.-(단위 : 동(호)수)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068507/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 96 (8.0%) missing valuesMissing
2007 has 116 (9.7%) missing valuesMissing
2008 has 104 (8.7%) missing valuesMissing
2009 has 104 (8.7%) missing valuesMissing
2010 has 80 (6.7%) missing valuesMissing
2011 has 92 (7.7%) missing valuesMissing
2012 has 84 (7.0%) missing valuesMissing
2013 has 84 (7.0%) missing valuesMissing
2014 has 60 (5.0%) missing valuesMissing
2015 has 72 (6.0%) missing valuesMissing
2016 has 72 (6.0%) missing valuesMissing
2017 has 84 (7.0%) missing valuesMissing
2018 has 80 (6.7%) missing valuesMissing
2019 has 84 (7.0%) missing valuesMissing
2020 has 84 (7.0%) 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 = 21.65650606)Skewed
2007 is highly skewed (γ1 = 23.20213156)Skewed
2008 is highly skewed (γ1 = 23.08649409)Skewed
2009 is highly skewed (γ1 = 23.68446632)Skewed
2010 is highly skewed (γ1 = 24.58427503)Skewed
2011 is highly skewed (γ1 = 23.82606922)Skewed
2012 is highly skewed (γ1 = 24.63484213)Skewed
2013 is highly skewed (γ1 = 24.4818608)Skewed
2014 is highly skewed (γ1 = 24.16433997)Skewed
2015 is highly skewed (γ1 = 23.31597654)Skewed
2016 is highly skewed (γ1 = 22.70570853)Skewed
2017 is highly skewed (γ1 = 22.21240396)Skewed
2018 is highly skewed (γ1 = 21.51745947)Skewed
2019 is highly skewed (γ1 = 22.55622568)Skewed
2020 is highly skewed (γ1 = 20.90025333)Skewed
2021 is highly skewed (γ1 = 20.2179603)Skewed
2022 is highly skewed (γ1 = 22.28974664)Skewed
2023 is highly skewed (γ1 = 23.35443223)Skewed
아파트매매 매입자거주지별 has unique valuesUnique
2006 has 49 (4.1%) zerosZeros
2007 has 44 (3.7%) zerosZeros
2008 has 52 (4.3%) zerosZeros
2009 has 46 (3.8%) zerosZeros
2010 has 43 (3.6%) zerosZeros
2011 has 46 (3.8%) zerosZeros
2012 has 50 (4.2%) zerosZeros
2013 has 45 (3.8%) zerosZeros
2014 has 41 (3.4%) zerosZeros
2015 has 48 (4.0%) zerosZeros
2016 has 48 (4.0%) zerosZeros
2017 has 42 (3.5%) zerosZeros
2018 has 42 (3.5%) zerosZeros
2019 has 38 (3.2%) zerosZeros
2020 has 43 (3.6%) zerosZeros
2021 has 47 (3.9%) zerosZeros
2022 has 49 (4.1%) zerosZeros
2023 has 59 (4.9%) zerosZeros

Reproduction

Analysis started2024-03-23 06:02:24.460675
Analysis finished2024-03-23 06:04:29.841452
Duration2 minutes and 5.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct1196
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
2024-03-23T06:04:30.176497image/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%
2024-03-23T06:04:31.254079image/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 

Distinct673
Distinct (%)61.2%
Missing96
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean2097.2845
Minimum0
Maximum411115
Zeros49
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:04:31.841608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q126
median244.5
Q31266
95-th percentile5160.75
Maximum411115
Range411115
Interquartile range (IQR)1240

Descriptive statistics

Standard deviation14740.816
Coefficient of variation (CV)7.0285246
Kurtosis559.25097
Mean2097.2845
Median Absolute Deviation (MAD)241.5
Skewness21.656506
Sum2307013
Variance2.1729166 × 108
MonotonicityNot monotonic
2024-03-23T06:04:32.318536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49
 
4.1%
1 23
 
1.9%
2 21
 
1.8%
3 17
 
1.4%
4 16
 
1.3%
6 14
 
1.2%
10 14
 
1.2%
5 13
 
1.1%
7 12
 
1.0%
15 11
 
0.9%
Other values (663) 910
76.1%
(Missing) 96
 
8.0%
ValueCountFrequency (%)
0 49
4.1%
1 23
1.9%
2 21
1.8%
3 17
 
1.4%
4 16
 
1.3%
5 13
 
1.1%
6 14
 
1.2%
7 12
 
1.0%
8 8
 
0.7%
9 6
 
0.5%
ValueCountFrequency (%)
411115 1
0.1%
165348 1
0.1%
122776 1
0.1%
88978 1
0.1%
69391 1
0.1%
61609 1
0.1%
55842 1
0.1%
47024 1
0.1%
41938 1
0.1%
25718 1
0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct657
Distinct (%)60.8%
Missing116
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean1567.6139
Minimum0
Maximum315944
Zeros44
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:04:32.925633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q137
median240
Q3903.25
95-th percentile4284.45
Maximum315944
Range315944
Interquartile range (IQR)866.25

Descriptive statistics

Standard deviation10998.147
Coefficient of variation (CV)7.015852
Kurtosis631.49927
Mean1567.6139
Median Absolute Deviation (MAD)232.5
Skewness23.202132
Sum1693023
Variance1.2095924 × 108
MonotonicityNot monotonic
2024-03-23T06:04:33.456237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 44
 
3.7%
3 17
 
1.4%
1 17
 
1.4%
2 16
 
1.3%
5 16
 
1.3%
6 12
 
1.0%
4 12
 
1.0%
8 11
 
0.9%
21 8
 
0.7%
61 8
 
0.7%
Other values (647) 919
76.8%
(Missing) 116
 
9.7%
ValueCountFrequency (%)
0 44
3.7%
1 17
 
1.4%
2 16
 
1.3%
3 17
 
1.4%
4 12
 
1.0%
5 16
 
1.3%
6 12
 
1.0%
7 7
 
0.6%
8 11
 
0.9%
9 7
 
0.6%
ValueCountFrequency (%)
315944 1
0.1%
110244 1
0.1%
71948 1
0.1%
71341 1
0.1%
41656 1
0.1%
31097 1
0.1%
31080 1
0.1%
26412 1
0.1%
25697 1
0.1%
24148 1
0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct677
Distinct (%)62.0%
Missing104
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean1681.4515
Minimum0
Maximum331624
Zeros52
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:04:34.059991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q144
median277
Q31046.25
95-th percentile4460.25
Maximum331624
Range331624
Interquartile range (IQR)1002.25

Descriptive statistics

Standard deviation11531.549
Coefficient of variation (CV)6.8580922
Kurtosis627.49518
Mean1681.4515
Median Absolute Deviation (MAD)270
Skewness23.086494
Sum1836145
Variance1.3297663 × 108
MonotonicityNot monotonic
2024-03-23T06:04:34.651597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 52
 
4.3%
2 17
 
1.4%
4 15
 
1.3%
9 14
 
1.2%
1 13
 
1.1%
6 12
 
1.0%
5 11
 
0.9%
10 10
 
0.8%
12 8
 
0.7%
7 8
 
0.7%
Other values (667) 932
77.9%
(Missing) 104
 
8.7%
ValueCountFrequency (%)
0 52
4.3%
1 13
 
1.1%
2 17
 
1.4%
3 6
 
0.5%
4 15
 
1.3%
5 11
 
0.9%
6 12
 
1.0%
7 8
 
0.7%
8 8
 
0.7%
9 14
 
1.2%
ValueCountFrequency (%)
331624 1
0.1%
114027 1
0.1%
89654 1
0.1%
67182 1
0.1%
47621 1
0.1%
32643 1
0.1%
30360 1
0.1%
29357 1
0.1%
28531 1
0.1%
25801 1
0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct697
Distinct (%)63.8%
Missing104
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean1840.7573
Minimum0
Maximum376841
Zeros46
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:04:35.322742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q146
median293
Q31085.75
95-th percentile4978.95
Maximum376841
Range376841
Interquartile range (IQR)1039.75

Descriptive statistics

Standard deviation12958.426
Coefficient of variation (CV)7.0397251
Kurtosis654.78132
Mean1840.7573
Median Absolute Deviation (MAD)282.5
Skewness23.684466
Sum2010107
Variance1.6792079 × 108
MonotonicityNot monotonic
2024-03-23T06:04:35.841234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46
 
3.8%
2 23
 
1.9%
1 12
 
1.0%
7 12
 
1.0%
3 12
 
1.0%
4 11
 
0.9%
6 11
 
0.9%
5 10
 
0.8%
35 7
 
0.6%
28 7
 
0.6%
Other values (687) 941
78.7%
(Missing) 104
 
8.7%
ValueCountFrequency (%)
0 46
3.8%
1 12
 
1.0%
2 23
1.9%
3 12
 
1.0%
4 11
 
0.9%
5 10
 
0.8%
6 11
 
0.9%
7 12
 
1.0%
8 4
 
0.3%
9 4
 
0.3%
ValueCountFrequency (%)
376841 1
0.1%
127084 1
0.1%
85573 1
0.1%
83635 1
0.1%
44698 1
0.1%
39411 1
0.1%
33454 1
0.1%
32577 1
0.1%
32522 1
0.1%
24936 1
0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct676
Distinct (%)60.6%
Missing80
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1652.1568
Minimum0
Maximum351427
Zeros43
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:04:36.398645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q140.75
median247.5
Q3901.25
95-th percentile4749.25
Maximum351427
Range351427
Interquartile range (IQR)860.5

Descriptive statistics

Standard deviation11816.068
Coefficient of variation (CV)7.151905
Kurtosis699.51993
Mean1652.1568
Median Absolute Deviation (MAD)238.5
Skewness24.584275
Sum1843807
Variance1.3961947 × 108
MonotonicityNot monotonic
2024-03-23T06:04:37.001677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 43
 
3.6%
1 17
 
1.4%
2 16
 
1.3%
3 16
 
1.3%
4 13
 
1.1%
6 13
 
1.1%
11 12
 
1.0%
12 10
 
0.8%
9 10
 
0.8%
7 9
 
0.8%
Other values (666) 957
80.0%
(Missing) 80
 
6.7%
ValueCountFrequency (%)
0 43
3.6%
1 17
 
1.4%
2 16
 
1.3%
3 16
 
1.3%
4 13
 
1.1%
5 8
 
0.7%
6 13
 
1.1%
7 9
 
0.8%
8 8
 
0.7%
9 10
 
0.8%
ValueCountFrequency (%)
351427 1
0.1%
114938 1
0.1%
79060 1
0.1%
61852 1
0.1%
40724 1
0.1%
36060 1
0.1%
35485 1
0.1%
24402 1
0.1%
21363 1
0.1%
20396 1
0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct716
Distinct (%)64.9%
Missing92
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean2044.1259
Minimum0
Maximum416898
Zeros46
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:04:37.944908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q153
median331
Q31186.25
95-th percentile5474.85
Maximum416898
Range416898
Interquartile range (IQR)1133.25

Descriptive statistics

Standard deviation14267.394
Coefficient of variation (CV)6.9797041
Kurtosis660.91052
Mean2044.1259
Median Absolute Deviation (MAD)321
Skewness23.826069
Sum2256715
Variance2.0355853 × 108
MonotonicityNot monotonic
2024-03-23T06:04:38.585421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46
 
3.8%
1 17
 
1.4%
3 15
 
1.3%
2 10
 
0.8%
6 10
 
0.8%
7 10
 
0.8%
10 10
 
0.8%
4 9
 
0.8%
13 7
 
0.6%
5 7
 
0.6%
Other values (706) 963
80.5%
(Missing) 92
 
7.7%
ValueCountFrequency (%)
0 46
3.8%
1 17
 
1.4%
2 10
 
0.8%
3 15
 
1.3%
4 9
 
0.8%
5 7
 
0.6%
6 10
 
0.8%
7 10
 
0.8%
8 5
 
0.4%
9 4
 
0.3%
ValueCountFrequency (%)
416898 1
0.1%
140982 1
0.1%
105035 1
0.1%
92139 1
0.1%
42388 1
0.1%
34995 1
0.1%
34968 1
0.1%
31674 1
0.1%
31192 1
0.1%
28549 1
0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct661
Distinct (%)59.4%
Missing84
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean1447.4604
Minimum0
Maximum308961
Zeros50
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:04:39.084966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q142
median219.5
Q3761.75
95-th percentile4052.85
Maximum308961
Range308961
Interquartile range (IQR)719.75

Descriptive statistics

Standard deviation10383.981
Coefficient of variation (CV)7.1739309
Kurtosis702.08452
Mean1447.4604
Median Absolute Deviation (MAD)211.5
Skewness24.634842
Sum1609576
Variance1.0782706 × 108
MonotonicityNot monotonic
2024-03-23T06:04:39.523207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 50
 
4.2%
4 17
 
1.4%
3 15
 
1.3%
7 14
 
1.2%
2 12
 
1.0%
5 12
 
1.0%
1 12
 
1.0%
8 11
 
0.9%
6 9
 
0.8%
12 9
 
0.8%
Other values (651) 951
79.5%
(Missing) 84
 
7.0%
ValueCountFrequency (%)
0 50
4.2%
1 12
 
1.0%
2 12
 
1.0%
3 15
 
1.3%
4 17
 
1.4%
5 12
 
1.0%
6 9
 
0.8%
7 14
 
1.2%
8 11
 
0.9%
9 4
 
0.3%
ValueCountFrequency (%)
308961 1
0.1%
92986 1
0.1%
74488 1
0.1%
64087 1
0.1%
28556 1
0.1%
27152 1
0.1%
25522 1
0.1%
23701 1
0.1%
20754 1
0.1%
20444 1
0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct691
Distinct (%)62.1%
Missing84
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean1742.2005
Minimum0
Maximum376752
Zeros45
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:04:40.169388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q137
median254.5
Q3987.25
95-th percentile4672
Maximum376752
Range376752
Interquartile range (IQR)950.25

Descriptive statistics

Standard deviation12706.453
Coefficient of variation (CV)7.2933352
Kurtosis693.59034
Mean1742.2005
Median Absolute Deviation (MAD)246.5
Skewness24.481861
Sum1937327
Variance1.6145394 × 108
MonotonicityNot monotonic
2024-03-23T06:04:41.010763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 45
 
3.8%
3 18
 
1.5%
1 16
 
1.3%
2 14
 
1.2%
4 14
 
1.2%
7 13
 
1.1%
8 12
 
1.0%
5 9
 
0.8%
9 8
 
0.7%
21 8
 
0.7%
Other values (681) 955
79.8%
(Missing) 84
 
7.0%
ValueCountFrequency (%)
0 45
3.8%
1 16
 
1.3%
2 14
 
1.2%
3 18
 
1.5%
4 14
 
1.2%
5 9
 
0.8%
6 7
 
0.6%
7 13
 
1.1%
8 12
 
1.0%
9 8
 
0.7%
ValueCountFrequency (%)
376752 1
0.1%
117805 1
0.1%
90641 1
0.1%
80335 1
0.1%
39883 1
0.1%
32640 1
0.1%
30904 1
0.1%
29439 1
0.1%
28055 1
0.1%
24880 1
0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct720
Distinct (%)63.4%
Missing60
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean2015.6602
Minimum0
Maximum432868
Zeros41
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:04:41.667622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q150
median313
Q31194.75
95-th percentile5521.5
Maximum432868
Range432868
Interquartile range (IQR)1144.75

Descriptive statistics

Standard deviation14608.807
Coefficient of variation (CV)7.2476536
Kurtosis679.62999
Mean2015.6602
Median Absolute Deviation (MAD)301
Skewness24.16434
Sum2289790
Variance2.1341724 × 108
MonotonicityNot monotonic
2024-03-23T06:04:42.271555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 41
 
3.4%
1 20
 
1.7%
2 17
 
1.4%
6 12
 
1.0%
7 10
 
0.8%
3 10
 
0.8%
10 9
 
0.8%
4 9
 
0.8%
17 8
 
0.7%
5 7
 
0.6%
Other values (710) 993
83.0%
(Missing) 60
 
5.0%
ValueCountFrequency (%)
0 41
3.4%
1 20
1.7%
2 17
1.4%
3 10
 
0.8%
4 9
 
0.8%
5 7
 
0.6%
6 12
 
1.0%
7 10
 
0.8%
8 6
 
0.5%
9 7
 
0.6%
ValueCountFrequency (%)
432868 1
0.1%
144635 1
0.1%
110898 1
0.1%
95168 1
0.1%
52910 1
0.1%
38536 1
0.1%
36279 1
0.1%
36231 1
0.1%
33792 1
0.1%
26793 1
0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct738
Distinct (%)65.7%
Missing72
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean2309.4706
Minimum0
Maximum480782
Zeros48
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:04:42.889473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q153.75
median361
Q31367
95-th percentile5874
Maximum480782
Range480782
Interquartile range (IQR)1313.25

Descriptive statistics

Standard deviation16548.641
Coefficient of variation (CV)7.1655559
Kurtosis637.66385
Mean2309.4706
Median Absolute Deviation (MAD)351
Skewness23.315977
Sum2595845
Variance2.7385752 × 108
MonotonicityNot monotonic
2024-03-23T06:04:43.492462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48
 
4.0%
4 15
 
1.3%
3 13
 
1.1%
5 12
 
1.0%
2 11
 
0.9%
9 10
 
0.8%
14 10
 
0.8%
6 9
 
0.8%
29 8
 
0.7%
1 8
 
0.7%
Other values (728) 980
81.9%
(Missing) 72
 
6.0%
ValueCountFrequency (%)
0 48
4.0%
1 8
 
0.7%
2 11
 
0.9%
3 13
 
1.1%
4 15
 
1.3%
5 12
 
1.0%
6 9
 
0.8%
7 6
 
0.5%
8 3
 
0.3%
9 10
 
0.8%
ValueCountFrequency (%)
480782 1
0.1%
172150 1
0.1%
127903 1
0.1%
111258 1
0.1%
72177 1
0.1%
47531 1
0.1%
44296 1
0.1%
41349 1
0.1%
35494 1
0.1%
33988 1
0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct715
Distinct (%)63.6%
Missing72
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean1956.8879
Minimum0
Maximum396674
Zeros48
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:04:44.006996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.15
Q144.75
median317.5
Q31231
95-th percentile4679.55
Maximum396674
Range396674
Interquartile range (IQR)1186.25

Descriptive statistics

Standard deviation13827.449
Coefficient of variation (CV)7.0660404
Kurtosis608.8119
Mean1956.8879
Median Absolute Deviation (MAD)306.5
Skewness22.705709
Sum2199542
Variance1.9119835 × 108
MonotonicityNot monotonic
2024-03-23T06:04:44.614110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48
 
4.0%
2 16
 
1.3%
4 16
 
1.3%
3 10
 
0.8%
7 10
 
0.8%
24 9
 
0.8%
1 9
 
0.8%
5 9
 
0.8%
8 9
 
0.8%
28 7
 
0.6%
Other values (705) 981
82.0%
(Missing) 72
 
6.0%
ValueCountFrequency (%)
0 48
4.0%
1 9
 
0.8%
2 16
 
1.3%
3 10
 
0.8%
4 16
 
1.3%
5 9
 
0.8%
6 7
 
0.6%
7 10
 
0.8%
8 9
 
0.8%
9 6
 
0.5%
ValueCountFrequency (%)
396674 1
0.1%
150155 1
0.1%
104961 1
0.1%
99568 1
0.1%
62903 1
0.1%
42694 1
0.1%
40786 1
0.1%
38640 1
0.1%
34791 1
0.1%
28421 1
0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct695
Distinct (%)62.5%
Missing84
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean1754.6781
Minimum0
Maximum341937
Zeros42
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:04:45.193120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q140
median301.5
Q31100.75
95-th percentile4296.5
Maximum341937
Range341937
Interquartile range (IQR)1060.75

Descriptive statistics

Standard deviation12087.641
Coefficient of variation (CV)6.8888085
Kurtosis584.19675
Mean1754.6781
Median Absolute Deviation (MAD)291.5
Skewness22.212404
Sum1951202
Variance1.4611107 × 108
MonotonicityNot monotonic
2024-03-23T06:04:45.692386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 42
 
3.5%
1 17
 
1.4%
9 13
 
1.1%
2 12
 
1.0%
10 12
 
1.0%
4 11
 
0.9%
12 9
 
0.8%
3 9
 
0.8%
6 8
 
0.7%
5 8
 
0.7%
Other values (685) 971
81.2%
(Missing) 84
 
7.0%
ValueCountFrequency (%)
0 42
3.5%
1 17
1.4%
2 12
 
1.0%
3 9
 
0.8%
4 11
 
0.9%
5 8
 
0.7%
6 8
 
0.7%
7 7
 
0.6%
8 5
 
0.4%
9 13
 
1.1%
ValueCountFrequency (%)
341937 1
0.1%
136186 1
0.1%
91894 1
0.1%
87646 1
0.1%
52786 1
0.1%
41137 1
0.1%
37424 1
0.1%
34293 1
0.1%
25816 1
0.1%
24352 1
0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct687
Distinct (%)61.6%
Missing80
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1620.1514
Minimum0
Maximum306108
Zeros42
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:04:46.218205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q139
median253.5
Q31006
95-th percentile4052.5
Maximum306108
Range306108
Interquartile range (IQR)967

Descriptive statistics

Standard deviation10991.363
Coefficient of variation (CV)6.7841579
Kurtosis551.52943
Mean1620.1514
Median Absolute Deviation (MAD)245.5
Skewness21.517459
Sum1808089
Variance1.2081006 × 108
MonotonicityNot monotonic
2024-03-23T06:04:46.651160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 42
 
3.5%
1 22
 
1.8%
6 14
 
1.2%
2 13
 
1.1%
11 11
 
0.9%
4 11
 
0.9%
5 9
 
0.8%
22 9
 
0.8%
69 8
 
0.7%
3 8
 
0.7%
Other values (677) 969
81.0%
(Missing) 80
 
6.7%
ValueCountFrequency (%)
0 42
3.5%
1 22
1.8%
2 13
 
1.1%
3 8
 
0.7%
4 11
 
0.9%
5 9
 
0.8%
6 14
 
1.2%
7 7
 
0.6%
8 8
 
0.7%
9 7
 
0.6%
ValueCountFrequency (%)
306108 1
0.1%
130175 1
0.1%
86119 1
0.1%
84066 1
0.1%
42770 1
0.1%
41070 1
0.1%
40841 1
0.1%
33861 1
0.1%
28245 1
0.1%
20481 1
0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct677
Distinct (%)60.9%
Missing84
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean1571.3309
Minimum0
Maximum306126
Zeros38
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:04:47.280577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q138
median262
Q31000.5
95-th percentile4121.7
Maximum306126
Range306126
Interquartile range (IQR)962.5

Descriptive statistics

Standard deviation10752.243
Coefficient of variation (CV)6.8427617
Kurtosis598.85266
Mean1571.3309
Median Absolute Deviation (MAD)255
Skewness22.556226
Sum1747320
Variance1.1561073 × 108
MonotonicityNot monotonic
2024-03-23T06:04:47.911791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38
 
3.2%
1 23
 
1.9%
2 22
 
1.8%
4 14
 
1.2%
5 12
 
1.0%
16 11
 
0.9%
3 10
 
0.8%
10 9
 
0.8%
15 9
 
0.8%
13 9
 
0.8%
Other values (667) 955
79.8%
(Missing) 84
 
7.0%
ValueCountFrequency (%)
0 38
3.2%
1 23
1.9%
2 22
1.8%
3 10
 
0.8%
4 14
 
1.2%
5 12
 
1.0%
6 8
 
0.7%
7 7
 
0.6%
8 6
 
0.5%
9 6
 
0.5%
ValueCountFrequency (%)
306126 1
0.1%
120686 1
0.1%
86805 1
0.1%
74303 1
0.1%
33871 1
0.1%
31444 1
0.1%
31364 1
0.1%
24652 1
0.1%
22974 1
0.1%
20898 1
0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct772
Distinct (%)69.4%
Missing84
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean2703.277
Minimum0
Maximum485843
Zeros43
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:04:48.579007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q157
median463
Q31697.5
95-th percentile6661.8
Maximum485843
Range485843
Interquartile range (IQR)1640.5

Descriptive statistics

Standard deviation17784.655
Coefficient of variation (CV)6.5789244
Kurtosis519.16983
Mean2703.277
Median Absolute Deviation (MAD)454
Skewness20.900253
Sum3006044
Variance3.1629395 × 108
MonotonicityNot monotonic
2024-03-23T06:04:49.243296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 43
 
3.6%
3 17
 
1.4%
2 13
 
1.1%
4 12
 
1.0%
9 10
 
0.8%
8 10
 
0.8%
6 10
 
0.8%
10 8
 
0.7%
7 8
 
0.7%
1 8
 
0.7%
Other values (762) 973
81.4%
(Missing) 84
 
7.0%
ValueCountFrequency (%)
0 43
3.6%
1 8
 
0.7%
2 13
 
1.1%
3 17
 
1.4%
4 12
 
1.0%
5 5
 
0.4%
6 10
 
0.8%
7 8
 
0.7%
8 10
 
0.8%
9 10
 
0.8%
ValueCountFrequency (%)
485843 1
0.1%
220647 1
0.1%
160588 1
0.1%
134865 1
0.1%
74601 1
0.1%
67000 1
0.1%
45959 1
0.1%
41457 1
0.1%
38116 1
0.1%
35025 1
0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct744
Distinct (%)66.7%
Missing80
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1924.431
Minimum0
Maximum326404
Zeros47
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:04:49.908123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q162
median387.5
Q31200.5
95-th percentile5230.75
Maximum326404
Range326404
Interquartile range (IQR)1138.5

Descriptive statistics

Standard deviation12164.262
Coefficient of variation (CV)6.3209656
Kurtosis487.33751
Mean1924.431
Median Absolute Deviation (MAD)373.5
Skewness20.21796
Sum2147665
Variance1.4796927 × 108
MonotonicityNot monotonic
2024-03-23T06:04:50.717837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47
 
3.9%
1 14
 
1.2%
4 14
 
1.2%
3 13
 
1.1%
5 12
 
1.0%
8 11
 
0.9%
7 8
 
0.7%
14 8
 
0.7%
19 7
 
0.6%
12 7
 
0.6%
Other values (734) 975
81.5%
(Missing) 80
 
6.7%
ValueCountFrequency (%)
0 47
3.9%
1 14
 
1.2%
2 5
 
0.4%
3 13
 
1.1%
4 14
 
1.2%
5 12
 
1.0%
6 6
 
0.5%
7 8
 
0.7%
8 11
 
0.9%
9 4
 
0.3%
ValueCountFrequency (%)
326404 1
0.1%
146781 1
0.1%
136522 1
0.1%
78325 1
0.1%
59475 1
0.1%
48146 1
0.1%
33833 1
0.1%
32300 1
0.1%
22078 1
0.1%
21791 1
0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct601
Distinct (%)53.9%
Missing80
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean855.73835
Minimum0
Maximum159770
Zeros49
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:04:51.425504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q135
median162
Q3483.5
95-th percentile2581.75
Maximum159770
Range159770
Interquartile range (IQR)448.5

Descriptive statistics

Standard deviation5643.6632
Coefficient of variation (CV)6.5950803
Kurtosis585.48575
Mean855.73835
Median Absolute Deviation (MAD)151
Skewness22.289747
Sum955004
Variance31850934
MonotonicityNot monotonic
2024-03-23T06:04:52.086082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49
 
4.1%
1 21
 
1.8%
4 17
 
1.4%
2 16
 
1.3%
11 13
 
1.1%
13 12
 
1.0%
9 10
 
0.8%
7 10
 
0.8%
3 9
 
0.8%
23 9
 
0.8%
Other values (591) 950
79.4%
(Missing) 80
 
6.7%
ValueCountFrequency (%)
0 49
4.1%
1 21
1.8%
2 16
 
1.3%
3 9
 
0.8%
4 17
 
1.4%
5 5
 
0.4%
6 8
 
0.7%
7 10
 
0.8%
8 6
 
0.5%
9 10
 
0.8%
ValueCountFrequency (%)
159770 1
0.1%
60246 1
0.1%
58354 1
0.1%
28830 1
0.1%
20211 1
0.1%
18937 1
0.1%
14128 1
0.1%
13984 1
0.1%
11395 1
0.1%
10097 1
0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct631
Distinct (%)56.3%
Missing76
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean1179.1634
Minimum0
Maximum239081
Zeros59
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-03-23T06:04:52.689341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130
median196
Q3688.5
95-th percentile3374.75
Maximum239081
Range239081
Interquartile range (IQR)658.5

Descriptive statistics

Standard deviation8244.7031
Coefficient of variation (CV)6.9919937
Kurtosis636.94026
Mean1179.1634
Median Absolute Deviation (MAD)190
Skewness23.354432
Sum1320663
Variance67975129
MonotonicityNot monotonic
2024-03-23T06:04:53.507034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 59
 
4.9%
5 17
 
1.4%
4 16
 
1.3%
1 16
 
1.3%
3 15
 
1.3%
6 13
 
1.1%
27 10
 
0.8%
9 9
 
0.8%
28 9
 
0.8%
12 9
 
0.8%
Other values (621) 947
79.2%
(Missing) 76
 
6.4%
ValueCountFrequency (%)
0 59
4.9%
1 16
 
1.3%
2 8
 
0.7%
3 15
 
1.3%
4 16
 
1.3%
5 17
 
1.4%
6 13
 
1.1%
7 9
 
0.8%
8 7
 
0.6%
9 9
 
0.8%
ValueCountFrequency (%)
239081 1
0.1%
91408 1
0.1%
59770 1
0.1%
55525 1
0.1%
27277 1
0.1%
21553 1
0.1%
21078 1
0.1%
16304 1
0.1%
14716 1
0.1%
14537 1
0.1%

Interactions

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2024-03-23T06:04:05.253240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:11.541349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:18.569538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:24.847478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:37.310981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:43.093694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:49.482723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:55.627484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:02.404230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:08.130371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:14.808234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:21.687298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:27.947954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:33.998569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:41.602072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:47.841617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:53.566540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:59.617367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:05.533021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:12.004097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:18.940032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:25.264445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:37.718965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:43.439645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:49.872666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:55.997358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:02.692402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:08.455160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:15.187111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:22.082202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:28.236765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:34.392751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:41.915836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:48.167615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:53.989617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:59.908232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:05.819114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:12.404424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:19.387899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:25.625891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:38.055210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:43.683275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:50.214255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:56.314080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:03.005935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:08.770672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:15.487711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:22.494822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:28.510302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:34.851762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:42.309365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:48.478509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:54.300857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:00.272852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:06.181025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:12.710702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:19.731818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:25.895223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:38.359987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:43.962394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:50.564430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:56.632766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:03.276497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:09.162315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:15.916119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:22.819009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:28.860943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:35.418214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:42.648458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:48.803381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:54.620875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:00.559951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:06.561651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:13.025963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:20.145335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:26.154379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:38.609670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:44.242822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:50.847183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:57.002906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:03.554727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:09.437405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:16.381938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:23.132261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:29.217102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:35.966666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:42.980133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:49.083569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:54.948514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:00.866458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:06.987137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:13.358048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:20.554439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:26.433732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:38.854151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:44.486725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:51.109673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:57.323027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:03.833501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:09.866924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:16.716894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:23.433615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:29.682980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:36.400588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:43.318461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:49.372407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:55.244167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:01.189787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:07.383771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:13.642097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:20.902002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:26.809732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:39.211372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:44.774602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:51.550619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:02:57.693948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:04.372395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:10.214852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:17.001992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:23.813523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:30.036639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:36.812783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:43.819514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:49.791026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:03:55.518834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:01.603215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:07.735646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:13.993121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:21.301349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T06:04:53.871075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9950.9950.9970.9840.9951.0000.9950.9950.9950.9991.0001.0000.9990.9430.9880.8380.995
20070.9951.0001.0000.9980.9891.0001.0000.9910.9910.9910.9960.9950.9950.9960.9500.9810.8750.991
20080.9951.0001.0000.9980.9891.0001.0000.9910.9910.9910.9960.9950.9950.9960.9500.9810.8750.991
20090.9970.9980.9981.0000.9860.9981.0000.9980.9980.9980.9990.9980.9980.9990.9320.9760.8530.991
20100.9840.9890.9890.9861.0000.9890.9220.9830.9830.9830.9850.9840.9840.9850.9410.9720.9380.983
20110.9951.0001.0000.9980.9891.0001.0000.9910.9910.9910.9960.9950.9950.9960.9500.9810.8750.991
20121.0001.0001.0001.0000.9221.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.8750.9781.000
20130.9950.9910.9910.9980.9830.9911.0001.0001.0001.0000.9960.9950.9950.9960.9130.9690.8290.991
20140.9950.9910.9910.9980.9830.9911.0001.0001.0001.0000.9960.9950.9950.9960.9130.9690.8290.991
20150.9950.9910.9910.9980.9830.9911.0001.0001.0001.0000.9960.9950.9950.9960.9130.9690.8290.991
20160.9990.9960.9960.9990.9850.9961.0000.9960.9960.9961.0001.0001.0001.0000.9610.9840.8450.996
20171.0000.9950.9950.9980.9840.9951.0000.9950.9950.9951.0001.0001.0001.0000.9500.9810.8410.995
20181.0000.9950.9950.9980.9840.9951.0000.9950.9950.9951.0001.0001.0001.0000.9500.9810.8410.995
20190.9990.9960.9960.9990.9850.9961.0000.9960.9960.9961.0001.0001.0001.0000.9610.9840.8450.996
20200.9430.9500.9500.9320.9410.9501.0000.9130.9130.9130.9610.9500.9500.9611.0000.9610.9830.950
20210.9880.9810.9810.9760.9720.9810.8750.9690.9690.9690.9840.9810.9810.9840.9611.0000.9000.981
20220.8380.8750.8750.8530.9380.8750.9780.8290.8290.8290.8450.8410.8410.8450.9830.9001.0000.829
20230.9950.9910.9910.9910.9830.9911.0000.9910.9910.9910.9960.9950.9950.9960.9500.9810.8291.000
2024-03-23T06:04:54.442328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9550.9340.9330.9130.9220.9200.9470.9550.9600.9570.9570.9560.9560.9530.9240.8690.937
20070.9551.0000.9650.9410.9390.9400.9380.9500.9530.9540.9520.9500.9400.9510.9530.9470.9120.945
20080.9340.9651.0000.9530.9460.9410.9370.9510.9490.9500.9470.9460.9390.9450.9450.9460.9190.943
20090.9330.9410.9531.0000.9590.9540.9420.9550.9530.9560.9540.9530.9470.9520.9490.9360.9080.946
20100.9130.9390.9460.9591.0000.9670.9560.9600.9540.9510.9430.9410.9290.9470.9460.9410.9290.949
20110.9220.9400.9410.9540.9671.0000.9690.9660.9600.9570.9460.9480.9380.9540.9550.9460.9310.956
20120.9200.9380.9370.9420.9560.9691.0000.9730.9620.9590.9450.9470.9340.9480.9470.9380.9260.953
20130.9470.9500.9510.9550.9600.9660.9731.0000.9820.9790.9690.9690.9610.9690.9660.9460.9260.969
20140.9550.9530.9490.9530.9540.9600.9620.9821.0000.9840.9720.9700.9620.9690.9670.9430.9140.966
20150.9600.9540.9500.9560.9510.9570.9590.9790.9841.0000.9810.9770.9690.9710.9690.9440.9080.967
20160.9570.9520.9470.9540.9430.9460.9450.9690.9720.9811.0000.9850.9740.9730.9690.9420.9040.964
20170.9570.9500.9460.9530.9410.9480.9470.9690.9700.9770.9851.0000.9810.9770.9700.9410.9050.966
20180.9560.9400.9390.9470.9290.9380.9340.9610.9620.9690.9740.9811.0000.9790.9660.9300.8890.960
20190.9560.9510.9450.9520.9470.9540.9480.9690.9690.9710.9730.9770.9791.0000.9810.9490.9170.975
20200.9530.9530.9450.9490.9460.9550.9470.9660.9670.9690.9690.9700.9660.9811.0000.9670.9280.976
20210.9240.9470.9460.9360.9410.9460.9380.9460.9430.9440.9420.9410.9300.9490.9671.0000.9690.965
20220.8690.9120.9190.9080.9290.9310.9260.9260.9140.9080.9040.9050.8890.9170.9280.9691.0000.950
20230.9370.9450.9430.9460.9490.9560.9530.9690.9660.9670.9640.9660.9600.9750.9760.9650.9501.000

Missing values

2024-03-23T06:04:27.235796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T06:04:28.211582image/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.
2024-03-23T06:04:29.047278image/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

아파트매매 매입자거주지별200620072008200920102011201220132014201520162017201820192020202120222023
0전국 /관할시군구내411115315944331624376841351427416898308961376752432868480782396674341937306108306126485843326404159770239081
1전국 /관할시도내165348110244114027127084114938140982929861178051446351721501501551361861301751206862206471467815835491408
2전국 /관할시도외_서울558424165647621446983548542388271522943936279442964269441137410703144467000594752021121553
3전국 /관할시도외_기타8897871948896548363579060105035744888033595168111258995689189486119868051605881365226024659770
4서울 /관할시군구내69391310973036039411244023496825522398835291072177629035278642770313643811619383621514350
5서울 /관할시도내47024229662166624936140161825311962181232412935494386403429333861246523487120253574413134
6서울 /관할시도외_서울000000000000000000
7서울 /관할시도외_기타25397117541132114695825410401728710696146572374221063208181999115718207971011534258955
8서울 종로구/관할시군구내32919219726117429721326333546239334330422423013349117
9서울 종로구/관할시도내45223625330616020914122324033337033759329246630687147
아파트매매 매입자거주지별200620072008200920102011201220132014201520162017201820192020202120222023
1186제주 /관할시도외_서울5111411880107177248213273232311204248153181288128134
1187제주 /관할시도외_기타152199188209310389545659572842593511362314607819415227
1188제주 제주시/관할시군구내151922901764181630903375258128193444289726431716217319702446268318151363
1189제주 제주시/관할시도내711681661961762401842062972291801071131001381117356
1190제주 제주시/관할시도외_서울4087786279135150130205169215114109951121676863
1191제주 제주시/관할시도외_기타133157139156239280350482459427349273224192321418231126
1192제주 서귀포시/관할시군구내122442824431404766424644490704675463485355619690498453
1193제주 서귀포시/관할시도내2330553349483488501211548954771601127744
1194제주 서귀포시/관할시도외_서울11274018284298836863969013958691216071
1195제주 서귀포시/관할시도외_기타1942495371109195177113415244238138122286401184101