Overview

Dataset statistics

Number of variables19
Number of observations296
Missing cells555
Missing cells (%)9.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory49.3 KiB
Average record size in memory170.4 B

Variable types

Text1
Numeric18

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 부동산거래현황 중 주택 거래현황의 연도별 신탁 신탁해지(면적) 데이터입니다.- (단위 : 천㎡)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068446/fileData.do

Alerts

2006 is highly overall correlated with 2007 and 14 other fieldsHigh correlation
2007 is highly overall correlated with 2006 and 13 other fieldsHigh correlation
2008 is highly overall correlated with 2007 and 15 other fieldsHigh correlation
2009 is highly overall correlated with 2007 and 15 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 15 other fieldsHigh correlation
2018 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2019 is highly overall correlated with 2006 and 15 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 15 other fieldsHigh correlation
2023 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2006 has 83 (28.0%) missing valuesMissing
2007 has 60 (20.3%) missing valuesMissing
2008 has 51 (17.2%) missing valuesMissing
2009 has 44 (14.9%) missing valuesMissing
2010 has 35 (11.8%) missing valuesMissing
2011 has 33 (11.1%) missing valuesMissing
2012 has 28 (9.5%) missing valuesMissing
2013 has 26 (8.8%) missing valuesMissing
2014 has 35 (11.8%) missing valuesMissing
2015 has 22 (7.4%) missing valuesMissing
2016 has 18 (6.1%) missing valuesMissing
2017 has 19 (6.4%) missing valuesMissing
2018 has 17 (5.7%) missing valuesMissing
2019 has 17 (5.7%) missing valuesMissing
2020 has 17 (5.7%) missing valuesMissing
2021 has 17 (5.7%) missing valuesMissing
2022 has 17 (5.7%) missing valuesMissing
2023 has 16 (5.4%) missing valuesMissing
지역 has unique valuesUnique
2006 has 21 (7.1%) zerosZeros
2007 has 32 (10.8%) zerosZeros
2008 has 35 (11.8%) zerosZeros
2009 has 49 (16.6%) zerosZeros
2010 has 54 (18.2%) zerosZeros
2011 has 57 (19.3%) zerosZeros
2012 has 65 (22.0%) zerosZeros
2013 has 53 (17.9%) zerosZeros
2014 has 17 (5.7%) zerosZeros
2015 has 31 (10.5%) zerosZeros
2016 has 27 (9.1%) zerosZeros
2017 has 21 (7.1%) zerosZeros
2018 has 12 (4.1%) zerosZeros
2019 has 8 (2.7%) zerosZeros
2020 has 16 (5.4%) zerosZeros
2021 has 18 (6.1%) zerosZeros
2022 has 23 (7.8%) zerosZeros
2023 has 31 (10.5%) zerosZeros

Reproduction

Analysis started2024-03-30 02:42:53.511815
Analysis finished2024-03-30 02:44:35.090312
Duration1 minute and 41.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Text

UNIQUE 

Distinct296
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2024-03-30T02:44:35.574189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length6
Mean length6.2736486
Min length3

Characters and Unicode

Total characters1857
Distinct characters147
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique296 ?
Unique (%)100.0%

Sample

1st row전국
2nd row서울
3rd row서울 종로구
4th row서울 중구
5th row서울 용산구
ValueCountFrequency (%)
경기 53
 
9.2%
경남 27
 
4.7%
경북 26
 
4.5%
서울 26
 
4.5%
전남 23
 
4.0%
충남 20
 
3.5%
충북 20
 
3.5%
강원 19
 
3.3%
부산 17
 
3.0%
전북 17
 
3.0%
Other values (262) 325
56.7%
2024-03-30T02:44:36.762101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
296
 
15.9%
135
 
7.3%
121
 
6.5%
109
 
5.9%
91
 
4.9%
88
 
4.7%
71
 
3.8%
56
 
3.0%
50
 
2.7%
48
 
2.6%
Other values (137) 792
42.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1535
82.7%
Space Separator 296
 
15.9%
Close Punctuation 13
 
0.7%
Open Punctuation 13
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
135
 
8.8%
121
 
7.9%
109
 
7.1%
91
 
5.9%
88
 
5.7%
71
 
4.6%
56
 
3.6%
50
 
3.3%
48
 
3.1%
42
 
2.7%
Other values (134) 724
47.2%
Space Separator
ValueCountFrequency (%)
296
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1535
82.7%
Common 322
 
17.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
135
 
8.8%
121
 
7.9%
109
 
7.1%
91
 
5.9%
88
 
5.7%
71
 
4.6%
56
 
3.6%
50
 
3.3%
48
 
3.1%
42
 
2.7%
Other values (134) 724
47.2%
Common
ValueCountFrequency (%)
296
91.9%
) 13
 
4.0%
( 13
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1535
82.7%
ASCII 322
 
17.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
296
91.9%
) 13
 
4.0%
( 13
 
4.0%
Hangul
ValueCountFrequency (%)
135
 
8.8%
121
 
7.9%
109
 
7.1%
91
 
5.9%
88
 
5.7%
71
 
4.6%
56
 
3.6%
50
 
3.3%
48
 
3.1%
42
 
2.7%
Other values (134) 724
47.2%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct110
Distinct (%)51.6%
Missing83
Missing (%)28.0%
Infinite0
Infinite (%)0.0%
Mean129.94836
Minimum0
Maximum8763
Zeros21
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T02:44:37.325494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median26
Q384
95-th percentile292.4
Maximum8763
Range8763
Interquartile range (IQR)80

Descriptive statistics

Standard deviation640.52199
Coefficient of variation (CV)4.9290503
Kurtosis158.35252
Mean129.94836
Median Absolute Deviation (MAD)25
Skewness12.021608
Sum27679
Variance410268.42
MonotonicityNot monotonic
2024-03-30T02:44:37.936980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21
 
7.1%
1 16
 
5.4%
2 8
 
2.7%
4 6
 
2.0%
9 6
 
2.0%
5 5
 
1.7%
23 5
 
1.7%
3 4
 
1.4%
11 4
 
1.4%
6 4
 
1.4%
Other values (100) 134
45.3%
(Missing) 83
28.0%
ValueCountFrequency (%)
0 21
7.1%
1 16
5.4%
2 8
 
2.7%
3 4
 
1.4%
4 6
 
2.0%
5 5
 
1.7%
6 4
 
1.4%
7 2
 
0.7%
8 2
 
0.7%
9 6
 
2.0%
ValueCountFrequency (%)
8763 1
0.3%
2534 1
0.3%
1907 1
0.3%
818 1
0.3%
765 1
0.3%
546 1
0.3%
441 1
0.3%
386 1
0.3%
381 1
0.3%
332 1
0.3%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct102
Distinct (%)43.2%
Missing60
Missing (%)20.3%
Infinite0
Infinite (%)0.0%
Mean115.16525
Minimum0
Maximum8824
Zeros32
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T02:44:38.719432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median17
Q363.25
95-th percentile349.5
Maximum8824
Range8824
Interquartile range (IQR)59.25

Descriptive statistics

Standard deviation609.76091
Coefficient of variation (CV)5.2946604
Kurtosis179.61536
Mean115.16525
Median Absolute Deviation (MAD)17
Skewness12.807991
Sum27179
Variance371808.37
MonotonicityNot monotonic
2024-03-30T02:44:39.277037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32
 
10.8%
1 11
 
3.7%
8 9
 
3.0%
3 8
 
2.7%
2 7
 
2.4%
11 7
 
2.4%
6 6
 
2.0%
5 6
 
2.0%
4 5
 
1.7%
12 5
 
1.7%
Other values (92) 140
47.3%
(Missing) 60
20.3%
ValueCountFrequency (%)
0 32
10.8%
1 11
 
3.7%
2 7
 
2.4%
3 8
 
2.7%
4 5
 
1.7%
5 6
 
2.0%
6 6
 
2.0%
7 3
 
1.0%
8 9
 
3.0%
9 3
 
1.0%
ValueCountFrequency (%)
8824 1
0.3%
2355 1
0.3%
1686 1
0.3%
883 1
0.3%
666 1
0.3%
658 1
0.3%
628 1
0.3%
582 1
0.3%
500 1
0.3%
411 1
0.3%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct96
Distinct (%)39.2%
Missing51
Missing (%)17.2%
Infinite0
Infinite (%)0.0%
Mean102.3102
Minimum0
Maximum8183
Zeros35
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T02:44:40.129031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median15
Q360
95-th percentile344.2
Maximum8183
Range8183
Interquartile range (IQR)57

Descriptive statistics

Standard deviation543.41638
Coefficient of variation (CV)5.3114583
Kurtosis202.54133
Mean102.3102
Median Absolute Deviation (MAD)15
Skewness13.695725
Sum25066
Variance295301.36
MonotonicityNot monotonic
2024-03-30T02:44:40.577916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 35
 
11.8%
1 19
 
6.4%
3 15
 
5.1%
2 7
 
2.4%
8 7
 
2.4%
24 6
 
2.0%
15 6
 
2.0%
4 6
 
2.0%
6 6
 
2.0%
11 4
 
1.4%
Other values (86) 134
45.3%
(Missing) 51
 
17.2%
ValueCountFrequency (%)
0 35
11.8%
1 19
6.4%
2 7
 
2.4%
3 15
5.1%
4 6
 
2.0%
5 4
 
1.4%
6 6
 
2.0%
7 3
 
1.0%
8 7
 
2.4%
9 3
 
1.0%
ValueCountFrequency (%)
8183 1
0.3%
1221 1
0.3%
1131 1
0.3%
1120 1
0.3%
706 1
0.3%
641 1
0.3%
626 1
0.3%
566 1
0.3%
521 1
0.3%
425 1
0.3%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct98
Distinct (%)38.9%
Missing44
Missing (%)14.9%
Infinite0
Infinite (%)0.0%
Mean126.46429
Minimum0
Maximum10219
Zeros49
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T02:44:41.165787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.75
median12.5
Q367
95-th percentile508.25
Maximum10219
Range10219
Interquartile range (IQR)65.25

Descriptive statistics

Standard deviation672.68475
Coefficient of variation (CV)5.3191677
Kurtosis203.96941
Mean126.46429
Median Absolute Deviation (MAD)12.5
Skewness13.687037
Sum31869
Variance452504.77
MonotonicityNot monotonic
2024-03-30T02:44:41.622958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49
 
16.6%
1 14
 
4.7%
2 11
 
3.7%
4 8
 
2.7%
18 7
 
2.4%
6 7
 
2.4%
16 6
 
2.0%
3 6
 
2.0%
22 6
 
2.0%
7 6
 
2.0%
Other values (88) 132
44.6%
(Missing) 44
 
14.9%
ValueCountFrequency (%)
0 49
16.6%
1 14
 
4.7%
2 11
 
3.7%
3 6
 
2.0%
4 8
 
2.7%
5 3
 
1.0%
6 7
 
2.4%
7 6
 
2.0%
8 4
 
1.4%
9 6
 
2.0%
ValueCountFrequency (%)
10219 1
0.3%
1761 1
0.3%
1393 1
0.3%
1179 1
0.3%
1063 1
0.3%
752 1
0.3%
713 1
0.3%
677 1
0.3%
649 1
0.3%
645 1
0.3%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct115
Distinct (%)44.1%
Missing35
Missing (%)11.8%
Infinite0
Infinite (%)0.0%
Mean138.49425
Minimum0
Maximum11212
Zeros54
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T02:44:42.044498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median16
Q379
95-th percentile482
Maximum11212
Range11212
Interquartile range (IQR)77

Descriptive statistics

Standard deviation730.44484
Coefficient of variation (CV)5.2741888
Kurtosis205.7554
Mean138.49425
Median Absolute Deviation (MAD)16
Skewness13.753345
Sum36147
Variance533549.66
MonotonicityNot monotonic
2024-03-30T02:44:42.514454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 54
 
18.2%
1 8
 
2.7%
2 7
 
2.4%
6 7
 
2.4%
3 7
 
2.4%
8 6
 
2.0%
9 6
 
2.0%
4 6
 
2.0%
10 6
 
2.0%
5 5
 
1.7%
Other values (105) 149
50.3%
(Missing) 35
 
11.8%
ValueCountFrequency (%)
0 54
18.2%
1 8
 
2.7%
2 7
 
2.4%
3 7
 
2.4%
4 6
 
2.0%
5 5
 
1.7%
6 7
 
2.4%
7 4
 
1.4%
8 6
 
2.0%
9 6
 
2.0%
ValueCountFrequency (%)
11212 1
0.3%
2951 1
0.3%
1010 1
0.3%
998 1
0.3%
915 1
0.3%
828 1
0.3%
792 1
0.3%
724 1
0.3%
712 1
0.3%
594 1
0.3%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct112
Distinct (%)42.6%
Missing33
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean135.62738
Minimum0
Maximum11057
Zeros57
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T02:44:42.914541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median14
Q364
95-th percentile465
Maximum11057
Range11057
Interquartile range (IQR)63

Descriptive statistics

Standard deviation726.7195
Coefficient of variation (CV)5.3582066
Kurtosis197.64096
Mean135.62738
Median Absolute Deviation (MAD)14
Skewness13.41553
Sum35670
Variance528121.23
MonotonicityNot monotonic
2024-03-30T02:44:43.385002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 57
 
19.3%
1 12
 
4.1%
2 9
 
3.0%
3 8
 
2.7%
12 7
 
2.4%
10 6
 
2.0%
7 5
 
1.7%
5 5
 
1.7%
8 5
 
1.7%
9 5
 
1.7%
Other values (102) 144
48.6%
(Missing) 33
 
11.1%
ValueCountFrequency (%)
0 57
19.3%
1 12
 
4.1%
2 9
 
3.0%
3 8
 
2.7%
4 4
 
1.4%
5 5
 
1.7%
6 2
 
0.7%
7 5
 
1.7%
8 5
 
1.7%
9 5
 
1.7%
ValueCountFrequency (%)
11057 1
0.3%
3309 1
0.3%
1364 1
0.3%
957 1
0.3%
923 1
0.3%
906 1
0.3%
727 1
0.3%
704 1
0.3%
676 1
0.3%
666 1
0.3%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct106
Distinct (%)39.6%
Missing28
Missing (%)9.5%
Infinite0
Infinite (%)0.0%
Mean171.87687
Minimum0
Maximum13389
Zeros65
Zeros (%)22.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T02:44:43.807997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median11
Q354
95-th percentile384.5
Maximum13389
Range13389
Interquartile range (IQR)53

Descriptive statistics

Standard deviation1012.898
Coefficient of variation (CV)5.893161
Kurtosis120.517
Mean171.87687
Median Absolute Deviation (MAD)11
Skewness10.373544
Sum46063
Variance1025962.5
MonotonicityNot monotonic
2024-03-30T02:44:44.293793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65
22.0%
1 14
 
4.7%
2 12
 
4.1%
3 8
 
2.7%
5 7
 
2.4%
11 6
 
2.0%
25 6
 
2.0%
8 5
 
1.7%
17 5
 
1.7%
6 5
 
1.7%
Other values (96) 135
45.6%
(Missing) 28
 
9.5%
ValueCountFrequency (%)
0 65
22.0%
1 14
 
4.7%
2 12
 
4.1%
3 8
 
2.7%
4 4
 
1.4%
5 7
 
2.4%
6 5
 
1.7%
7 4
 
1.4%
8 5
 
1.7%
9 4
 
1.4%
ValueCountFrequency (%)
13389 1
0.3%
7323 1
0.3%
4699 1
0.3%
4639 1
0.3%
908 1
0.3%
849 1
0.3%
738 1
0.3%
622 1
0.3%
537 1
0.3%
471 1
0.3%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct93
Distinct (%)34.4%
Missing26
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean103.02963
Minimum0
Maximum8165
Zeros53
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T02:44:44.796277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median10
Q338
95-th percentile315.3
Maximum8165
Range8165
Interquartile range (IQR)37

Descriptive statistics

Standard deviation563.21742
Coefficient of variation (CV)5.4665577
Kurtosis162.66017
Mean103.02963
Median Absolute Deviation (MAD)10
Skewness12.021044
Sum27818
Variance317213.86
MonotonicityNot monotonic
2024-03-30T02:44:45.261461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 53
17.9%
1 22
 
7.4%
3 14
 
4.7%
2 10
 
3.4%
19 9
 
3.0%
5 8
 
2.7%
10 7
 
2.4%
4 7
 
2.4%
7 6
 
2.0%
6 5
 
1.7%
Other values (83) 129
43.6%
(Missing) 26
 
8.8%
ValueCountFrequency (%)
0 53
17.9%
1 22
7.4%
2 10
 
3.4%
3 14
 
4.7%
4 7
 
2.4%
5 8
 
2.7%
6 5
 
1.7%
7 6
 
2.0%
8 5
 
1.7%
9 3
 
1.0%
ValueCountFrequency (%)
8165 1
0.3%
3636 1
0.3%
1361 1
0.3%
1324 1
0.3%
877 1
0.3%
871 1
0.3%
763 1
0.3%
674 1
0.3%
592 1
0.3%
538 1
0.3%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct108
Distinct (%)41.4%
Missing35
Missing (%)11.8%
Infinite0
Infinite (%)0.0%
Mean109.11111
Minimum0
Maximum8949
Zeros17
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T02:44:45.744705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median21
Q358
95-th percentile313
Maximum8949
Range8949
Interquartile range (IQR)53

Descriptive statistics

Standard deviation587.46698
Coefficient of variation (CV)5.3841169
Kurtosis199.83806
Mean109.11111
Median Absolute Deviation (MAD)19
Skewness13.511428
Sum28478
Variance345117.45
MonotonicityNot monotonic
2024-03-30T02:44:46.236761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
5.7%
1 16
 
5.4%
2 13
 
4.4%
5 8
 
2.7%
4 8
 
2.7%
10 7
 
2.4%
8 7
 
2.4%
7 6
 
2.0%
35 6
 
2.0%
12 5
 
1.7%
Other values (98) 168
56.8%
(Missing) 35
 
11.8%
ValueCountFrequency (%)
0 17
5.7%
1 16
5.4%
2 13
4.4%
3 5
 
1.7%
4 8
2.7%
5 8
2.7%
6 5
 
1.7%
7 6
 
2.0%
8 7
2.4%
9 5
 
1.7%
ValueCountFrequency (%)
8949 1
0.3%
2442 1
0.3%
1214 1
0.3%
1049 1
0.3%
917 1
0.3%
739 1
0.3%
723 1
0.3%
535 1
0.3%
509 1
0.3%
489 1
0.3%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct115
Distinct (%)42.0%
Missing22
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean233.06204
Minimum0
Maximum20503
Zeros31
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T02:44:46.725307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median24.5
Q380
95-th percentile498
Maximum20503
Range20503
Interquartile range (IQR)76

Descriptive statistics

Standard deviation1463.1648
Coefficient of variation (CV)6.2780054
Kurtosis143.31682
Mean233.06204
Median Absolute Deviation (MAD)23.5
Skewness11.296411
Sum63859
Variance2140851.1
MonotonicityNot monotonic
2024-03-30T02:44:47.161620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31
 
10.5%
1 14
 
4.7%
3 12
 
4.1%
4 8
 
2.7%
2 7
 
2.4%
7 7
 
2.4%
13 6
 
2.0%
18 5
 
1.7%
5 5
 
1.7%
21 5
 
1.7%
Other values (105) 174
58.8%
(Missing) 22
 
7.4%
ValueCountFrequency (%)
0 31
10.5%
1 14
4.7%
2 7
 
2.4%
3 12
 
4.1%
4 8
 
2.7%
5 5
 
1.7%
6 3
 
1.0%
7 7
 
2.4%
8 4
 
1.4%
9 4
 
1.4%
ValueCountFrequency (%)
20503 1
0.3%
8853 1
0.3%
8446 1
0.3%
4149 1
0.3%
2179 1
0.3%
1098 1
0.3%
804 1
0.3%
803 1
0.3%
787 1
0.3%
767 1
0.3%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct120
Distinct (%)43.2%
Missing18
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean176.96763
Minimum0
Maximum15651
Zeros27
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T02:44:47.556518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median24.5
Q380.75
95-th percentile435.9
Maximum15651
Range15651
Interquartile range (IQR)74.75

Descriptive statistics

Standard deviation1021.3684
Coefficient of variation (CV)5.7714986
Kurtosis193.55059
Mean176.96763
Median Absolute Deviation (MAD)22.5
Skewness13.194493
Sum49197
Variance1043193.4
MonotonicityNot monotonic
2024-03-30T02:44:48.029992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27
 
9.1%
1 12
 
4.1%
2 11
 
3.7%
5 8
 
2.7%
6 7
 
2.4%
18 7
 
2.4%
3 7
 
2.4%
24 6
 
2.0%
33 6
 
2.0%
45 5
 
1.7%
Other values (110) 182
61.5%
(Missing) 18
 
6.1%
ValueCountFrequency (%)
0 27
9.1%
1 12
4.1%
2 11
3.7%
3 7
 
2.4%
4 4
 
1.4%
5 8
 
2.7%
6 7
 
2.4%
7 2
 
0.7%
8 4
 
1.4%
9 4
 
1.4%
ValueCountFrequency (%)
15651 1
0.3%
4407 1
0.3%
4397 1
0.3%
2145 1
0.3%
1171 1
0.3%
1079 1
0.3%
1018 1
0.3%
910 1
0.3%
743 2
0.7%
703 1
0.3%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct124
Distinct (%)44.8%
Missing19
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean152.5343
Minimum0
Maximum13047
Zeros21
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T02:44:48.815072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median29
Q373
95-th percentile342.8
Maximum13047
Range13047
Interquartile range (IQR)66

Descriptive statistics

Standard deviation840.15839
Coefficient of variation (CV)5.5079967
Kurtosis203.52436
Mean152.5343
Median Absolute Deviation (MAD)26
Skewness13.543563
Sum42252
Variance705866.13
MonotonicityNot monotonic
2024-03-30T02:44:49.354752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21
 
7.1%
1 17
 
5.7%
6 9
 
3.0%
7 9
 
3.0%
9 8
 
2.7%
45 6
 
2.0%
3 6
 
2.0%
20 6
 
2.0%
2 5
 
1.7%
17 5
 
1.7%
Other values (114) 185
62.5%
(Missing) 19
 
6.4%
ValueCountFrequency (%)
0 21
7.1%
1 17
5.7%
2 5
 
1.7%
3 6
 
2.0%
4 3
 
1.0%
5 4
 
1.4%
6 9
3.0%
7 9
3.0%
8 3
 
1.0%
9 8
 
2.7%
ValueCountFrequency (%)
13047 1
0.3%
3216 1
0.3%
2771 1
0.3%
1650 1
0.3%
1509 1
0.3%
1481 1
0.3%
1392 1
0.3%
756 1
0.3%
722 1
0.3%
513 1
0.3%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct126
Distinct (%)45.2%
Missing17
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean151.38351
Minimum0
Maximum13475
Zeros12
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T02:44:49.978555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18.5
median33
Q391
95-th percentile351.2
Maximum13475
Range13475
Interquartile range (IQR)82.5

Descriptive statistics

Standard deviation860.74342
Coefficient of variation (CV)5.6858465
Kurtosis209.4818
Mean151.38351
Median Absolute Deviation (MAD)29
Skewness13.838327
Sum42236
Variance740879.23
MonotonicityNot monotonic
2024-03-30T02:44:50.536816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 18
 
6.1%
0 12
 
4.1%
2 7
 
2.4%
3 7
 
2.4%
27 7
 
2.4%
10 7
 
2.4%
6 7
 
2.4%
7 6
 
2.0%
8 6
 
2.0%
20 5
 
1.7%
Other values (116) 197
66.6%
(Missing) 17
 
5.7%
ValueCountFrequency (%)
0 12
4.1%
1 18
6.1%
2 7
 
2.4%
3 7
 
2.4%
4 4
 
1.4%
5 3
 
1.0%
6 7
 
2.4%
7 6
 
2.0%
8 6
 
2.0%
9 4
 
1.4%
ValueCountFrequency (%)
13475 1
0.3%
3429 1
0.3%
3356 1
0.3%
1165 1
0.3%
991 1
0.3%
942 1
0.3%
817 1
0.3%
773 1
0.3%
529 1
0.3%
462 1
0.3%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct138
Distinct (%)49.5%
Missing17
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean176.51971
Minimum0
Maximum15804
Zeros8
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T02:44:51.200091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19.5
median37
Q3111.5
95-th percentile413.1
Maximum15804
Range15804
Interquartile range (IQR)102

Descriptive statistics

Standard deviation994.93903
Coefficient of variation (CV)5.6364188
Kurtosis221.62485
Mean176.51971
Median Absolute Deviation (MAD)33
Skewness14.310692
Sum49249
Variance989903.67
MonotonicityNot monotonic
2024-03-30T02:44:52.197580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 11
 
3.7%
1 10
 
3.4%
0 8
 
2.7%
3 8
 
2.7%
9 6
 
2.0%
5 6
 
2.0%
8 6
 
2.0%
4 6
 
2.0%
6 6
 
2.0%
10 6
 
2.0%
Other values (128) 206
69.6%
(Missing) 17
 
5.7%
ValueCountFrequency (%)
0 8
2.7%
1 10
3.4%
2 11
3.7%
3 8
2.7%
4 6
2.0%
5 6
2.0%
6 6
2.0%
7 3
 
1.0%
8 6
2.0%
9 6
2.0%
ValueCountFrequency (%)
15804 1
0.3%
3993 1
0.3%
2602 1
0.3%
1170 1
0.3%
1157 1
0.3%
1142 1
0.3%
956 1
0.3%
925 1
0.3%
900 1
0.3%
733 1
0.3%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct141
Distinct (%)50.5%
Missing17
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean166.6129
Minimum0
Maximum14977
Zeros16
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T02:44:53.154312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.5
median39
Q396.5
95-th percentile428.1
Maximum14977
Range14977
Interquartile range (IQR)87

Descriptive statistics

Standard deviation950.90218
Coefficient of variation (CV)5.7072542
Kurtosis214.80009
Mean166.6129
Median Absolute Deviation (MAD)34
Skewness14.070208
Sum46485
Variance904214.96
MonotonicityNot monotonic
2024-03-30T02:44:53.883041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
5.4%
3 13
 
4.4%
1 10
 
3.4%
6 7
 
2.4%
16 7
 
2.4%
5 7
 
2.4%
4 6
 
2.0%
49 6
 
2.0%
12 5
 
1.7%
2 5
 
1.7%
Other values (131) 197
66.6%
(Missing) 17
 
5.7%
ValueCountFrequency (%)
0 16
5.4%
1 10
3.4%
2 5
 
1.7%
3 13
4.4%
4 6
 
2.0%
5 7
2.4%
6 7
2.4%
7 1
 
0.3%
8 3
 
1.0%
9 2
 
0.7%
ValueCountFrequency (%)
14977 1
0.3%
4310 1
0.3%
2752 1
0.3%
1158 1
0.3%
977 1
0.3%
888 1
0.3%
886 1
0.3%
603 1
0.3%
553 1
0.3%
541 1
0.3%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct135
Distinct (%)48.4%
Missing17
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean259.73835
Minimum0
Maximum23541
Zeros18
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T02:44:54.883717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median31
Q388
95-th percentile571
Maximum23541
Range23541
Interquartile range (IQR)83

Descriptive statistics

Standard deviation1693.2519
Coefficient of variation (CV)6.5190678
Kurtosis141.22987
Mean259.73835
Median Absolute Deviation (MAD)28
Skewness11.342896
Sum72467
Variance2867102
MonotonicityNot monotonic
2024-03-30T02:44:55.512761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 19
 
6.4%
1 19
 
6.4%
0 18
 
6.1%
2 6
 
2.0%
4 6
 
2.0%
5 5
 
1.7%
35 5
 
1.7%
9 5
 
1.7%
16 4
 
1.4%
8 4
 
1.4%
Other values (125) 188
63.5%
(Missing) 17
 
5.7%
ValueCountFrequency (%)
0 18
6.1%
1 19
6.4%
2 6
 
2.0%
3 19
6.4%
4 6
 
2.0%
5 5
 
1.7%
6 1
 
0.3%
7 3
 
1.0%
8 4
 
1.4%
9 5
 
1.7%
ValueCountFrequency (%)
23541 1
0.3%
12391 1
0.3%
9454 1
0.3%
2841 1
0.3%
1437 1
0.3%
961 1
0.3%
903 1
0.3%
878 1
0.3%
870 1
0.3%
739 1
0.3%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct122
Distinct (%)43.7%
Missing17
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean1698.7885
Minimum0
Maximum157084
Zeros23
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T02:44:56.111851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median21
Q372
95-th percentile489.2
Maximum157084
Range157084
Interquartile range (IQR)68

Descriptive statistics

Standard deviation13565.589
Coefficient of variation (CV)7.9854492
Kurtosis94.732596
Mean1698.7885
Median Absolute Deviation (MAD)20
Skewness9.5813346
Sum473962
Variance1.8402522 × 108
MonotonicityNot monotonic
2024-03-30T02:44:56.642291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
 
7.8%
1 15
 
5.1%
2 15
 
5.1%
3 13
 
4.4%
5 8
 
2.7%
6 8
 
2.7%
8 6
 
2.0%
12 6
 
2.0%
11 6
 
2.0%
4 5
 
1.7%
Other values (112) 174
58.8%
(Missing) 17
 
5.7%
ValueCountFrequency (%)
0 23
7.8%
1 15
5.1%
2 15
5.1%
3 13
4.4%
4 5
 
1.7%
5 8
 
2.7%
6 8
 
2.7%
7 5
 
1.7%
8 6
 
2.0%
9 5
 
1.7%
ValueCountFrequency (%)
157084 1
0.3%
114448 1
0.3%
113392 1
0.3%
24763 1
0.3%
24654 1
0.3%
6379 1
0.3%
5919 1
0.3%
3658 1
0.3%
3481 1
0.3%
1910 1
0.3%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct113
Distinct (%)40.4%
Missing16
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean13254.393
Minimum0
Maximum1236384
Zeros31
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T02:44:57.120818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median19.5
Q350
95-th percentile576.9
Maximum1236384
Range1236384
Interquartile range (IQR)47

Descriptive statistics

Standard deviation125156.97
Coefficient of variation (CV)9.4426787
Kurtosis89.986463
Mean13254.393
Median Absolute Deviation (MAD)18.5
Skewness9.5552829
Sum3711230
Variance1.5664268 × 1010
MonotonicityNot monotonic
2024-03-30T02:44:57.724046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31
 
10.5%
1 24
 
8.1%
3 10
 
3.4%
6 9
 
3.0%
8 7
 
2.4%
5 7
 
2.4%
2 7
 
2.4%
4 6
 
2.0%
44 6
 
2.0%
13 6
 
2.0%
Other values (103) 167
56.4%
(Missing) 16
 
5.4%
ValueCountFrequency (%)
0 31
10.5%
1 24
8.1%
2 7
 
2.4%
3 10
 
3.4%
4 6
 
2.0%
5 7
 
2.4%
6 9
 
3.0%
7 4
 
1.4%
8 7
 
2.4%
9 4
 
1.4%
ValueCountFrequency (%)
1236384 1
0.3%
1202162 1
0.3%
1201906 1
0.3%
23277 1
0.3%
23168 1
0.3%
3068 1
0.3%
2646 1
0.3%
1662 1
0.3%
965 1
0.3%
878 1
0.3%

Interactions

2024-03-30T02:44:27.524372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:42:55.896745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:01.616135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:06.696539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:12.101051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:17.082600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:22.379921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:27.179154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:33.610990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:39.498089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:44.774160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:49.786199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:55.181362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:00.157137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:05.834717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:11.488418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:17.255955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:22.162966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:27.767831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-30T02:43:15.433301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:20.557094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:25.507184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:31.395584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:37.233000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:43.107497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:48.087568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:53.425103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:58.457757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:03.674394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:09.652917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:15.022608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:20.561272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:25.698446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:31.392929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:00.155671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:05.175781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:10.455856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:15.710356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:20.842989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:25.770994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:31.795351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:37.713530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:43.431755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:48.344518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:53.714725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:58.712761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:03.964324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:09.939300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:15.375419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:20.813185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:25.968281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:31.668185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:00.438154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:05.511393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:10.878654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:15.986043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:21.122023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:26.042976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:32.436951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:38.010602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:43.751207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:48.687228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:54.014805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:59.094572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:04.435082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:10.236614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:15.787488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:21.129436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:26.298398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:31.936508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:00.743902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:05.854365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:11.216142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:16.239434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:21.496196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:26.277865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:32.759409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:38.306545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:44.014081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:48.973033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:54.314848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:59.370770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:04.777081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:10.533327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:16.155606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:21.387716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:26.587054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:32.204161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:01.098444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:06.109857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:11.515048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:16.482589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:21.858483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:26.529699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:33.020918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:38.688974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:44.216814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:49.221524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:54.606734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:59.617989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:05.103638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:10.917800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:16.460520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:21.634058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:26.862296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:32.533545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:01.369320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:06.407796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:11.845368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:16.819342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:22.128343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:26.807033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:33.324797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:39.073811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:44.488463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:49.530335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:54.908191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:43:59.895413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:05.433571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:11.203740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:16.892294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:21.890496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:44:27.140976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-30T02:44:58.042096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.8610.9620.9570.9880.8010.8010.8300.8401.0001.0001.0001.0001.0001.0001.0000.6720.357
20070.8611.0000.7610.7060.8010.9510.9510.9680.9730.9060.9680.9680.8610.9960.9960.9060.8910.775
20080.9620.7611.0000.9770.9420.7610.6730.6950.7870.7940.7170.7180.9620.7610.7610.7940.7180.358
20090.9570.7060.9771.0000.9700.8010.7400.7400.7590.7750.7060.7060.9570.7400.7400.7750.7060.405
20100.9880.8010.9420.9701.0001.0001.0001.0001.0001.0000.8010.8010.9881.0001.0001.0000.6730.358
20110.8010.9510.7610.8011.0001.0000.9810.9810.9810.8390.9510.9510.8010.9810.9810.8390.8910.777
20120.8010.9510.6730.7401.0000.9811.0000.9960.9810.8390.9510.9510.8010.9810.9810.8390.8910.777
20130.8300.9680.6950.7401.0000.9810.9961.0000.9840.8740.9600.9600.8310.9900.9900.8740.8920.777
20140.8400.9730.7870.7591.0000.9810.9810.9841.0000.8850.9630.9630.8410.9920.9920.8850.9150.777
20151.0000.9060.7940.7751.0000.8390.8390.8740.8851.0000.8390.8391.0001.0001.0000.9910.6390.466
20161.0000.9680.7170.7060.8010.9510.9510.9600.9630.8391.0000.9811.0000.9810.9810.8390.8920.777
20171.0000.9680.7180.7060.8010.9510.9510.9600.9630.8390.9811.0001.0000.9810.9810.8390.8920.777
20181.0000.8610.9620.9570.9880.8010.8010.8310.8411.0001.0001.0001.0001.0001.0001.0000.6730.358
20191.0000.9960.7610.7401.0000.9810.9810.9900.9921.0000.9810.9811.0001.0001.0001.0000.8920.777
20201.0000.9960.7610.7401.0000.9810.9810.9900.9921.0000.9810.9811.0001.0001.0001.0000.8920.777
20211.0000.9060.7940.7751.0000.8390.8390.8740.8850.9910.8390.8391.0001.0001.0001.0000.6390.466
20220.6720.8910.7180.7060.6730.8910.8910.8920.9150.6390.8920.8920.6730.8920.8920.6391.0000.777
20230.3570.7750.3580.4050.3580.7770.7770.7770.7770.4660.7770.7770.3580.7770.7770.4660.7771.000
2024-03-30T02:44:58.462476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.6090.4860.4970.5260.5160.5660.5490.6170.6070.5700.5230.5290.5160.5630.5780.5680.575
20070.6091.0000.6150.5040.5080.5150.5780.5690.5130.5290.5160.4750.5220.4960.5430.5580.4700.523
20080.4860.6151.0000.6930.6060.5950.5540.5580.5210.5560.5320.5050.5160.5420.5850.5730.5570.549
20090.4970.5040.6931.0000.7140.6440.5920.5590.5380.6350.5510.5400.5820.6040.6170.6300.6380.584
20100.5260.5080.6060.7141.0000.7410.7160.6080.5340.6080.6100.5720.6220.6020.6080.6060.5900.575
20110.5160.5150.5950.6440.7411.0000.7420.6810.6040.6610.6110.6060.6500.6590.6670.6380.6260.621
20120.5660.5780.5540.5920.7160.7421.0000.7890.6410.6950.6610.6390.6500.6660.6730.6410.6280.608
20130.5490.5690.5580.5590.6080.6810.7891.0000.7130.7190.6690.6660.6590.6560.6440.6530.6440.626
20140.6170.5130.5210.5380.5340.6040.6410.7131.0000.7740.6760.6770.6760.6790.6350.6650.6640.667
20150.6070.5290.5560.6350.6080.6610.6950.7190.7741.0000.7720.7810.7550.7150.6670.7070.7190.686
20160.5700.5160.5320.5510.6100.6110.6610.6690.6760.7721.0000.8560.8050.7370.7160.7480.7200.692
20170.5230.4750.5050.5400.5720.6060.6390.6660.6770.7810.8561.0000.8470.7840.7620.7860.7450.741
20180.5290.5220.5160.5820.6220.6500.6500.6590.6760.7550.8050.8471.0000.8410.8010.7950.7630.738
20190.5160.4960.5420.6040.6020.6590.6660.6560.6790.7150.7370.7840.8411.0000.8510.7980.7720.743
20200.5630.5430.5850.6170.6080.6670.6730.6440.6350.6670.7160.7620.8010.8511.0000.8220.8020.799
20210.5780.5580.5730.6300.6060.6380.6410.6530.6650.7070.7480.7860.7950.7980.8221.0000.8200.798
20220.5680.4700.5570.6380.5900.6260.6280.6440.6640.7190.7200.7450.7630.7720.8020.8201.0000.834
20230.5750.5230.5490.5840.5750.6210.6080.6260.6670.6860.6920.7410.7380.7430.7990.7980.8341.000

Missing values

2024-03-30T02:44:32.958849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T02:44:33.890965image/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.
2024-03-30T02:44:34.483488image/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

지역200620072008200920102011201220132014201520162017201820192020202120222023
0전국8763882481831021911212110571338981658949205031565113047134751580414977235411570841236384
1서울1907235511317139159579088771214217943972771342926022752284134813068
2서울 종로구524119171133618192017242332208
3서울 중구2125212010921139645523122128237314
4서울 용산구2072740115811318237188596948697913137312
5서울 성동구48103901817103326167947872182132
6서울 광진구872724172091120118266751207649996547
7서울 동대문구618831383382533281013345917480867243
8서울 중랑구29151299451094684808053515045393558
9서울 성북구11483771707317519527869908294757240
지역200620072008200920102011201220132014201520162017201820192020202120222023
286경남 하동군<NA><NA><NA>13250002311845254
287경남 산청군<NA><NA>3100000121183114
288경남 함양군<NA>8121791415191000120030
289경남 거창군3000253140662112201530
290경남 합천군<NA><NA><NA><NA><NA><NA>007202031001521
291(구)제주0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
292(구)제주 (구)제주시0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
293제주08232430882745657716243865237532924763965
294제주 제주시08156232133851579429029018316610987
295제주 서귀포시<NA>0818275623814206814836219216424654878