Overview

Dataset statistics

Number of variables19
Number of observations2392
Missing cells2920
Missing cells (%)6.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory397.2 KiB
Average record size in memory170.1 B

Variable types

Text1
Numeric18

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 부동산거래현황 중 건축물 거래현황의 연도별 용도지역별(동(호)수)데이터입니다.- (단위 : 동(호)수)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068249/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 176 (7.4%) missing valuesMissing
2007 has 216 (9.0%) missing valuesMissing
2008 has 200 (8.4%) missing valuesMissing
2009 has 200 (8.4%) missing valuesMissing
2010 has 152 (6.4%) missing valuesMissing
2011 has 176 (7.4%) missing valuesMissing
2012 has 160 (6.7%) missing valuesMissing
2013 has 160 (6.7%) missing valuesMissing
2014 has 104 (4.3%) missing valuesMissing
2015 has 136 (5.7%) missing valuesMissing
2016 has 136 (5.7%) missing valuesMissing
2017 has 160 (6.7%) missing valuesMissing
2018 has 152 (6.4%) missing valuesMissing
2019 has 160 (6.7%) missing valuesMissing
2020 has 160 (6.7%) missing valuesMissing
2021 has 160 (6.7%) missing valuesMissing
2022 has 160 (6.7%) missing valuesMissing
2023 has 152 (6.4%) missing valuesMissing
2006 is highly skewed (γ1 = 37.93324262)Skewed
2007 is highly skewed (γ1 = 38.20241697)Skewed
2008 is highly skewed (γ1 = 39.10186002)Skewed
2009 is highly skewed (γ1 = 38.92144955)Skewed
2010 is highly skewed (γ1 = 40.02466629)Skewed
2011 is highly skewed (γ1 = 39.92884896)Skewed
2012 is highly skewed (γ1 = 39.95303919)Skewed
2013 is highly skewed (γ1 = 39.84639411)Skewed
2014 is highly skewed (γ1 = 40.24803945)Skewed
2015 is highly skewed (γ1 = 39.32017281)Skewed
2016 is highly skewed (γ1 = 38.80878441)Skewed
2017 is highly skewed (γ1 = 36.91870217)Skewed
2018 is highly skewed (γ1 = 36.35213838)Skewed
2019 is highly skewed (γ1 = 36.87337656)Skewed
2020 is highly skewed (γ1 = 37.51598942)Skewed
2021 is highly skewed (γ1 = 36.99794424)Skewed
2022 is highly skewed (γ1 = 36.74202558)Skewed
2023 is highly skewed (γ1 = 38.24971119)Skewed
2006 has 651 (27.2%) zerosZeros
2007 has 697 (29.1%) zerosZeros
2008 has 691 (28.9%) zerosZeros
2009 has 700 (29.3%) zerosZeros
2010 has 727 (30.4%) zerosZeros
2011 has 719 (30.1%) zerosZeros
2012 has 731 (30.6%) zerosZeros
2013 has 725 (30.3%) zerosZeros
2014 has 739 (30.9%) zerosZeros
2015 has 693 (29.0%) zerosZeros
2016 has 661 (27.6%) zerosZeros
2017 has 601 (25.1%) zerosZeros
2018 has 598 (25.0%) zerosZeros
2019 has 611 (25.5%) zerosZeros
2020 has 591 (24.7%) zerosZeros
2021 has 599 (25.0%) zerosZeros
2022 has 625 (26.1%) zerosZeros
2023 has 654 (27.3%) zerosZeros

Reproduction

Analysis started2024-03-23 05:37:55.299765
Analysis finished2024-03-23 05:39:09.365567
Duration1 minute and 14.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct2391
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
2024-03-23T14:39:09.568649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length26
Mean length15.996237
Min length7

Characters and Unicode

Total characters38263
Distinct characters158
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2390 ?
Unique (%)99.9%

Sample

1st row전국_도시지역_주거지역
2nd row전국_도시지역_상업지역
3rd row전국_도시지역_공업지역
4th row전국_도시지역_녹지지역
5th row전국_도시지역_개발제한구역
ValueCountFrequency (%)
경기 416
 
8.4%
경남 208
 
4.2%
서울 200
 
4.1%
경북 200
 
4.1%
전남 176
 
3.6%
충북 152
 
3.1%
충남 152
 
3.1%
강원 144
 
2.9%
전북 128
 
2.6%
부산 128
 
2.6%
Other values (2205) 3024
61.4%
2024-03-23T14:39:10.163523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4194
 
11.0%
_ 4186
 
10.9%
3887
 
10.2%
2770
 
7.2%
2536
 
6.6%
2133
 
5.6%
1403
 
3.7%
1171
 
3.1%
743
 
1.9%
712
 
1.9%
Other values (148) 14528
38.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31285
81.8%
Connector Punctuation 4186
 
10.9%
Space Separator 2536
 
6.6%
Open Punctuation 128
 
0.3%
Close Punctuation 128
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4194
 
13.4%
3887
 
12.4%
2770
 
8.9%
2133
 
6.8%
1403
 
4.5%
1171
 
3.7%
743
 
2.4%
712
 
2.3%
699
 
2.2%
675
 
2.2%
Other values (144) 12898
41.2%
Connector Punctuation
ValueCountFrequency (%)
_ 4186
100.0%
Space Separator
ValueCountFrequency (%)
2536
100.0%
Open Punctuation
ValueCountFrequency (%)
( 128
100.0%
Close Punctuation
ValueCountFrequency (%)
) 128
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31285
81.8%
Common 6978
 
18.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4194
 
13.4%
3887
 
12.4%
2770
 
8.9%
2133
 
6.8%
1403
 
4.5%
1171
 
3.7%
743
 
2.4%
712
 
2.3%
699
 
2.2%
675
 
2.2%
Other values (144) 12898
41.2%
Common
ValueCountFrequency (%)
_ 4186
60.0%
2536
36.3%
( 128
 
1.8%
) 128
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31285
81.8%
ASCII 6978
 
18.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
4194
 
13.4%
3887
 
12.4%
2770
 
8.9%
2133
 
6.8%
1403
 
4.5%
1171
 
3.7%
743
 
2.4%
712
 
2.3%
699
 
2.2%
675
 
2.2%
Other values (144) 12898
41.2%
ASCII
ValueCountFrequency (%)
_ 4186
60.0%
2536
36.3%
( 128
 
1.8%
) 128
 
1.8%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct666
Distinct (%)30.1%
Missing176
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean2297.7193
Minimum0
Maximum1359107
Zeros651
Zeros (%)27.2%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:39:10.536851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median14
Q3188
95-th percentile7766.75
Maximum1359107
Range1359107
Interquartile range (IQR)188

Descriptive statistics

Standard deviation31394.919
Coefficient of variation (CV)13.663514
Kurtosis1592.8927
Mean2297.7193
Median Absolute Deviation (MAD)14
Skewness37.933243
Sum5091746
Variance9.8564096 × 108
MonotonicityNot monotonic
2024-03-23T14:39:10.900379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 651
27.2%
1 122
 
5.1%
2 56
 
2.3%
4 38
 
1.6%
3 34
 
1.4%
5 32
 
1.3%
9 31
 
1.3%
6 31
 
1.3%
10 22
 
0.9%
8 21
 
0.9%
Other values (656) 1178
49.2%
(Missing) 176
 
7.4%
ValueCountFrequency (%)
0 651
27.2%
1 122
 
5.1%
2 56
 
2.3%
3 34
 
1.4%
4 38
 
1.6%
5 32
 
1.3%
6 31
 
1.3%
7 20
 
0.8%
8 21
 
0.9%
9 31
 
1.3%
ValueCountFrequency (%)
1359107 1
< 0.1%
402179 1
< 0.1%
316822 1
< 0.1%
135255 1
< 0.1%
85026 1
< 0.1%
71251 1
< 0.1%
62755 1
< 0.1%
61898 1
< 0.1%
53133 1
< 0.1%
51526 1
< 0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct648
Distinct (%)29.8%
Missing216
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean1850.6994
Minimum0
Maximum1042359
Zeros697
Zeros (%)29.1%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:39:11.226451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median14
Q3180.5
95-th percentile6423.25
Maximum1042359
Range1042359
Interquartile range (IQR)180.5

Descriptive statistics

Standard deviation24065.502
Coefficient of variation (CV)13.003463
Kurtosis1617.402
Mean1850.6994
Median Absolute Deviation (MAD)14
Skewness38.202417
Sum4027122
Variance5.7914838 × 108
MonotonicityNot monotonic
2024-03-23T14:39:11.511688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 697
29.1%
1 91
 
3.8%
2 49
 
2.0%
3 39
 
1.6%
4 33
 
1.4%
5 29
 
1.2%
6 23
 
1.0%
8 22
 
0.9%
14 19
 
0.8%
11 19
 
0.8%
Other values (638) 1155
48.3%
(Missing) 216
 
9.0%
ValueCountFrequency (%)
0 697
29.1%
1 91
 
3.8%
2 49
 
2.0%
3 39
 
1.6%
4 33
 
1.4%
5 29
 
1.2%
6 23
 
1.0%
7 16
 
0.7%
8 22
 
0.9%
9 16
 
0.7%
ValueCountFrequency (%)
1042359 1
< 0.1%
263005 1
< 0.1%
199077 1
< 0.1%
144448 1
< 0.1%
98412 1
< 0.1%
64911 1
< 0.1%
59616 1
< 0.1%
50076 1
< 0.1%
48177 1
< 0.1%
46458 1
< 0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct654
Distinct (%)29.8%
Missing200
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean1857.0844
Minimum0
Maximum1064334
Zeros691
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:39:11.763365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median14
Q3184
95-th percentile6290.1
Maximum1064334
Range1064334
Interquartile range (IQR)184

Descriptive statistics

Standard deviation24272.035
Coefficient of variation (CV)13.069969
Kurtosis1683.7464
Mean1857.0844
Median Absolute Deviation (MAD)14
Skewness39.10186
Sum4070729
Variance5.8913167 × 108
MonotonicityNot monotonic
2024-03-23T14:39:12.060972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 691
28.9%
1 95
 
4.0%
2 42
 
1.8%
5 39
 
1.6%
3 37
 
1.5%
4 34
 
1.4%
6 26
 
1.1%
17 23
 
1.0%
7 21
 
0.9%
8 21
 
0.9%
Other values (644) 1163
48.6%
(Missing) 200
 
8.4%
ValueCountFrequency (%)
0 691
28.9%
1 95
 
4.0%
2 42
 
1.8%
3 37
 
1.5%
4 34
 
1.4%
5 39
 
1.6%
6 26
 
1.1%
7 21
 
0.9%
8 21
 
0.9%
9 18
 
0.8%
ValueCountFrequency (%)
1064334 1
< 0.1%
238720 1
< 0.1%
189995 1
< 0.1%
132118 1
< 0.1%
98153 1
< 0.1%
72710 1
< 0.1%
71732 1
< 0.1%
51425 1
< 0.1%
44832 1
< 0.1%
42855 1
< 0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct650
Distinct (%)29.7%
Missing200
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean1799.5817
Minimum0
Maximum1026790
Zeros700
Zeros (%)29.3%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:39:12.373978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median14
Q3190.5
95-th percentile6143
Maximum1026790
Range1026790
Interquartile range (IQR)190.5

Descriptive statistics

Standard deviation23473.789
Coefficient of variation (CV)13.044026
Kurtosis1668.8461
Mean1799.5817
Median Absolute Deviation (MAD)14
Skewness38.92145
Sum3944683
Variance5.5101879 × 108
MonotonicityNot monotonic
2024-03-23T14:39:12.637832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 700
29.3%
1 75
 
3.1%
2 47
 
2.0%
3 43
 
1.8%
6 28
 
1.2%
10 26
 
1.1%
8 25
 
1.0%
4 25
 
1.0%
7 24
 
1.0%
5 23
 
1.0%
Other values (640) 1176
49.2%
(Missing) 200
 
8.4%
ValueCountFrequency (%)
0 700
29.3%
1 75
 
3.1%
2 47
 
2.0%
3 43
 
1.8%
4 25
 
1.0%
5 23
 
1.0%
6 28
 
1.2%
7 24
 
1.0%
8 25
 
1.0%
9 21
 
0.9%
ValueCountFrequency (%)
1026790 1
< 0.1%
255648 1
< 0.1%
173675 1
< 0.1%
124751 1
< 0.1%
81318 1
< 0.1%
69373 1
< 0.1%
67479 1
< 0.1%
51671 1
< 0.1%
49150 1
< 0.1%
43008 1
< 0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct658
Distinct (%)29.4%
Missing152
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean1631.9036
Minimum0
Maximum952414
Zeros727
Zeros (%)30.4%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:39:13.022846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12
Q3190.25
95-th percentile5881.5
Maximum952414
Range952414
Interquartile range (IQR)190.25

Descriptive statistics

Standard deviation21374.914
Coefficient of variation (CV)13.098148
Kurtosis1755.6047
Mean1631.9036
Median Absolute Deviation (MAD)12
Skewness40.024666
Sum3655464
Variance4.5688695 × 108
MonotonicityNot monotonic
2024-03-23T14:39:13.388101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 727
30.4%
1 83
 
3.5%
2 52
 
2.2%
3 44
 
1.8%
5 36
 
1.5%
4 32
 
1.3%
6 29
 
1.2%
9 29
 
1.2%
10 24
 
1.0%
11 24
 
1.0%
Other values (648) 1160
48.5%
(Missing) 152
 
6.4%
ValueCountFrequency (%)
0 727
30.4%
1 83
 
3.5%
2 52
 
2.2%
3 44
 
1.8%
4 32
 
1.3%
5 36
 
1.5%
6 29
 
1.2%
7 18
 
0.8%
8 16
 
0.7%
9 29
 
1.2%
ValueCountFrequency (%)
952414 1
< 0.1%
213844 1
< 0.1%
119861 1
< 0.1%
117826 1
< 0.1%
92682 1
< 0.1%
82010 1
< 0.1%
58127 1
< 0.1%
47873 1
< 0.1%
45307 1
< 0.1%
41742 2
0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct682
Distinct (%)30.8%
Missing176
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean1945.6498
Minimum0
Maximum1123796
Zeros719
Zeros (%)30.1%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:39:13.877707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13
Q3211.5
95-th percentile7022.25
Maximum1123796
Range1123796
Interquartile range (IQR)211.5

Descriptive statistics

Standard deviation25320.518
Coefficient of variation (CV)13.013913
Kurtosis1746.1567
Mean1945.6498
Median Absolute Deviation (MAD)13
Skewness39.928849
Sum4311560
Variance6.4112861 × 108
MonotonicityNot monotonic
2024-03-23T14:39:14.197558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 719
30.1%
1 70
 
2.9%
2 51
 
2.1%
3 46
 
1.9%
6 33
 
1.4%
4 29
 
1.2%
5 26
 
1.1%
8 26
 
1.1%
9 23
 
1.0%
7 22
 
0.9%
Other values (672) 1171
49.0%
(Missing) 176
 
7.4%
ValueCountFrequency (%)
0 719
30.1%
1 70
 
2.9%
2 51
 
2.1%
3 46
 
1.9%
4 29
 
1.2%
5 26
 
1.1%
6 33
 
1.4%
7 22
 
0.9%
8 26
 
1.1%
9 23
 
1.0%
ValueCountFrequency (%)
1123796 1
< 0.1%
241011 1
< 0.1%
146285 1
< 0.1%
140858 1
< 0.1%
100651 1
< 0.1%
79254 1
< 0.1%
77769 1
< 0.1%
70353 1
< 0.1%
59330 1
< 0.1%
52273 1
< 0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct668
Distinct (%)29.9%
Missing160
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1593.7249
Minimum0
Maximum905601
Zeros731
Zeros (%)30.6%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:39:14.445345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13
Q3196.25
95-th percentile5555.65
Maximum905601
Range905601
Interquartile range (IQR)196.25

Descriptive statistics

Standard deviation20348.748
Coefficient of variation (CV)12.768043
Kurtosis1752.0888
Mean1593.7249
Median Absolute Deviation (MAD)13
Skewness39.953039
Sum3557194
Variance4.1407154 × 108
MonotonicityNot monotonic
2024-03-23T14:39:14.765699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 731
30.6%
1 76
 
3.2%
2 50
 
2.1%
3 46
 
1.9%
4 34
 
1.4%
5 32
 
1.3%
6 29
 
1.2%
9 26
 
1.1%
7 22
 
0.9%
17 20
 
0.8%
Other values (658) 1166
48.7%
(Missing) 160
 
6.7%
ValueCountFrequency (%)
0 731
30.6%
1 76
 
3.2%
2 50
 
2.1%
3 46
 
1.9%
4 34
 
1.4%
5 32
 
1.3%
6 29
 
1.2%
7 22
 
0.9%
8 19
 
0.8%
9 26
 
1.1%
ValueCountFrequency (%)
905601 1
< 0.1%
184881 1
< 0.1%
130233 1
< 0.1%
118700 1
< 0.1%
77004 1
< 0.1%
64763 1
< 0.1%
63637 1
< 0.1%
62526 1
< 0.1%
49050 1
< 0.1%
42721 1
< 0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct685
Distinct (%)30.7%
Missing160
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1875.211
Minimum0
Maximum1077959
Zeros725
Zeros (%)30.3%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:39:15.051903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median14
Q3237.5
95-th percentile6400.2
Maximum1077959
Range1077959
Interquartile range (IQR)237.5

Descriptive statistics

Standard deviation24255.381
Coefficient of variation (CV)12.934748
Kurtosis1743.2181
Mean1875.211
Median Absolute Deviation (MAD)14
Skewness39.846394
Sum4185471
Variance5.8832353 × 108
MonotonicityNot monotonic
2024-03-23T14:39:15.387364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 725
30.3%
1 68
 
2.8%
5 41
 
1.7%
3 39
 
1.6%
2 38
 
1.6%
6 32
 
1.3%
4 29
 
1.2%
11 24
 
1.0%
13 22
 
0.9%
7 21
 
0.9%
Other values (675) 1193
49.9%
(Missing) 160
 
6.7%
ValueCountFrequency (%)
0 725
30.3%
1 68
 
2.8%
2 38
 
1.6%
3 39
 
1.6%
4 29
 
1.2%
5 41
 
1.7%
6 32
 
1.3%
7 21
 
0.9%
8 21
 
0.9%
9 16
 
0.7%
ValueCountFrequency (%)
1077959 1
< 0.1%
233122 1
< 0.1%
155318 1
< 0.1%
135635 1
< 0.1%
99716 1
< 0.1%
84897 1
< 0.1%
81068 1
< 0.1%
64235 1
< 0.1%
56109 1
< 0.1%
49554 1
< 0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct722
Distinct (%)31.6%
Missing104
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean2244.8453
Minimum0
Maximum1325416
Zeros739
Zeros (%)30.9%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:39:16.218649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15
Q3242
95-th percentile8249.4
Maximum1325416
Range1325416
Interquartile range (IQR)242

Descriptive statistics

Standard deviation29492.493
Coefficient of variation (CV)13.137873
Kurtosis1778.9849
Mean2244.8453
Median Absolute Deviation (MAD)15
Skewness40.248039
Sum5136206
Variance8.6980712 × 108
MonotonicityNot monotonic
2024-03-23T14:39:16.493406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 739
30.9%
1 80
 
3.3%
2 42
 
1.8%
4 33
 
1.4%
3 32
 
1.3%
6 26
 
1.1%
11 25
 
1.0%
5 23
 
1.0%
15 22
 
0.9%
10 22
 
0.9%
Other values (712) 1244
52.0%
(Missing) 104
 
4.3%
ValueCountFrequency (%)
0 739
30.9%
1 80
 
3.3%
2 42
 
1.8%
3 32
 
1.3%
4 33
 
1.4%
5 23
 
1.0%
6 26
 
1.1%
7 21
 
0.9%
8 18
 
0.8%
9 18
 
0.8%
ValueCountFrequency (%)
1325416 1
< 0.1%
290158 1
< 0.1%
209103 1
< 0.1%
174514 1
< 0.1%
116547 1
< 0.1%
103910 1
< 0.1%
81856 1
< 0.1%
79432 1
< 0.1%
65966 1
< 0.1%
61449 1
< 0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct744
Distinct (%)33.0%
Missing136
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean2719.402
Minimum0
Maximum1580045
Zeros693
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:39:16.786772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median18
Q3297
95-th percentile9819.75
Maximum1580045
Range1580045
Interquartile range (IQR)297

Descriptive statistics

Standard deviation35665.452
Coefficient of variation (CV)13.115182
Kurtosis1706.272
Mean2719.402
Median Absolute Deviation (MAD)18
Skewness39.320173
Sum6134971
Variance1.2720245 × 109
MonotonicityNot monotonic
2024-03-23T14:39:17.063842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 693
29.0%
1 77
 
3.2%
2 53
 
2.2%
4 38
 
1.6%
3 38
 
1.6%
8 25
 
1.0%
5 24
 
1.0%
6 23
 
1.0%
7 23
 
1.0%
10 19
 
0.8%
Other values (734) 1243
52.0%
(Missing) 136
 
5.7%
ValueCountFrequency (%)
0 693
29.0%
1 77
 
3.2%
2 53
 
2.2%
3 38
 
1.6%
4 38
 
1.6%
5 24
 
1.0%
6 23
 
1.0%
7 23
 
1.0%
8 25
 
1.0%
9 18
 
0.8%
ValueCountFrequency (%)
1580045 1
< 0.1%
381248 1
< 0.1%
289992 1
< 0.1%
215618 1
< 0.1%
146650 1
< 0.1%
101885 1
< 0.1%
96881 1
< 0.1%
83976 1
< 0.1%
78539 1
< 0.1%
55911 1
< 0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct749
Distinct (%)33.2%
Missing136
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean2605.1924
Minimum0
Maximum1500562
Zeros661
Zeros (%)27.6%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:39:17.334105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median19
Q3320.5
95-th percentile8849
Maximum1500562
Range1500562
Interquartile range (IQR)320.5

Descriptive statistics

Standard deviation34075.066
Coefficient of variation (CV)13.079674
Kurtosis1668.2406
Mean2605.1924
Median Absolute Deviation (MAD)19
Skewness38.808784
Sum5877314
Variance1.1611101 × 109
MonotonicityNot monotonic
2024-03-23T14:39:17.596565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 661
27.6%
1 99
 
4.1%
2 53
 
2.2%
3 40
 
1.7%
6 34
 
1.4%
5 24
 
1.0%
4 23
 
1.0%
10 22
 
0.9%
11 21
 
0.9%
7 20
 
0.8%
Other values (739) 1259
52.6%
(Missing) 136
 
5.7%
ValueCountFrequency (%)
0 661
27.6%
1 99
 
4.1%
2 53
 
2.2%
3 40
 
1.7%
4 23
 
1.0%
5 24
 
1.0%
6 34
 
1.4%
7 20
 
0.8%
8 20
 
0.8%
9 17
 
0.7%
ValueCountFrequency (%)
1500562 1
< 0.1%
386366 1
< 0.1%
304668 1
< 0.1%
210750 1
< 0.1%
126892 1
< 0.1%
98448 1
< 0.1%
89725 1
< 0.1%
64005 1
< 0.1%
58764 1
< 0.1%
52609 1
< 0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct824
Distinct (%)36.9%
Missing160
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean3012.2361
Minimum0
Maximum1535646
Zeros601
Zeros (%)25.1%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:39:17.916647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median30.5
Q3502.25
95-th percentile9290.35
Maximum1535646
Range1535646
Interquartile range (IQR)502.25

Descriptive statistics

Standard deviation35677.508
Coefficient of variation (CV)11.844194
Kurtosis1540.8158
Mean3012.2361
Median Absolute Deviation (MAD)30.5
Skewness36.918702
Sum6723311
Variance1.2728846 × 109
MonotonicityNot monotonic
2024-03-23T14:39:18.296247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 601
25.1%
1 69
 
2.9%
2 56
 
2.3%
4 32
 
1.3%
5 32
 
1.3%
3 26
 
1.1%
6 22
 
0.9%
10 21
 
0.9%
11 20
 
0.8%
7 19
 
0.8%
Other values (814) 1334
55.8%
(Missing) 160
 
6.7%
ValueCountFrequency (%)
0 601
25.1%
1 69
 
2.9%
2 56
 
2.3%
3 26
 
1.1%
4 32
 
1.3%
5 32
 
1.3%
6 22
 
0.9%
7 19
 
0.8%
8 15
 
0.6%
9 16
 
0.7%
ValueCountFrequency (%)
1535646 1
< 0.1%
423533 1
< 0.1%
299714 1
< 0.1%
297510 1
< 0.1%
172432 1
< 0.1%
114222 1
< 0.1%
107272 1
< 0.1%
94872 1
< 0.1%
92359 1
< 0.1%
77486 1
< 0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct818
Distinct (%)36.5%
Missing152
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean2947.6138
Minimum0
Maximum1482525
Zeros598
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:39:18.648141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median28
Q3494.5
95-th percentile9370.35
Maximum1482525
Range1482525
Interquartile range (IQR)494.5

Descriptive statistics

Standard deviation34670.475
Coefficient of variation (CV)11.762217
Kurtosis1499.9972
Mean2947.6138
Median Absolute Deviation (MAD)28
Skewness36.352138
Sum6602655
Variance1.2020418 × 109
MonotonicityNot monotonic
2024-03-23T14:39:19.010163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 598
25.0%
1 71
 
3.0%
2 49
 
2.0%
3 42
 
1.8%
6 29
 
1.2%
4 27
 
1.1%
9 23
 
1.0%
5 23
 
1.0%
10 20
 
0.8%
7 20
 
0.8%
Other values (808) 1338
55.9%
(Missing) 152
 
6.4%
ValueCountFrequency (%)
0 598
25.0%
1 71
 
3.0%
2 49
 
2.0%
3 42
 
1.8%
4 27
 
1.1%
5 23
 
1.0%
6 29
 
1.2%
7 20
 
0.8%
8 16
 
0.7%
9 23
 
1.0%
ValueCountFrequency (%)
1482525 1
< 0.1%
459876 1
< 0.1%
289599 1
< 0.1%
267886 1
< 0.1%
193604 1
< 0.1%
111620 1
< 0.1%
101964 1
< 0.1%
92101 1
< 0.1%
87189 1
< 0.1%
72156 1
< 0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct781
Distinct (%)35.0%
Missing160
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2658.0381
Minimum0
Maximum1353797
Zeros611
Zeros (%)25.5%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:39:19.284453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median26
Q3418.5
95-th percentile8733
Maximum1353797
Range1353797
Interquartile range (IQR)418.5

Descriptive statistics

Standard deviation31487.465
Coefficient of variation (CV)11.84613
Kurtosis1535.8287
Mean2658.0381
Median Absolute Deviation (MAD)26
Skewness36.873377
Sum5932741
Variance9.9146047 × 108
MonotonicityNot monotonic
2024-03-23T14:39:19.569165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 611
25.5%
2 67
 
2.8%
1 59
 
2.5%
3 43
 
1.8%
4 31
 
1.3%
8 25
 
1.0%
6 23
 
1.0%
5 20
 
0.8%
11 17
 
0.7%
16 17
 
0.7%
Other values (771) 1319
55.1%
(Missing) 160
 
6.7%
ValueCountFrequency (%)
0 611
25.5%
1 59
 
2.5%
2 67
 
2.8%
3 43
 
1.8%
4 31
 
1.3%
5 20
 
0.8%
6 23
 
1.0%
7 17
 
0.7%
8 25
 
1.0%
9 10
 
0.4%
ValueCountFrequency (%)
1353797 1
< 0.1%
401392 1
< 0.1%
264802 1
< 0.1%
215282 1
< 0.1%
155852 1
< 0.1%
107839 1
< 0.1%
98195 1
< 0.1%
86500 1
< 0.1%
78762 1
< 0.1%
69655 1
< 0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct818
Distinct (%)36.6%
Missing160
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean3335.2079
Minimum0
Maximum1758594
Zeros591
Zeros (%)24.7%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:39:19.805925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median30
Q3486
95-th percentile10741.2
Maximum1758594
Range1758594
Interquartile range (IQR)486

Descriptive statistics

Standard deviation40624
Coefficient of variation (CV)12.18035
Kurtosis1577.49
Mean3335.2079
Median Absolute Deviation (MAD)30
Skewness37.515989
Sum7444184
Variance1.6503094 × 109
MonotonicityNot monotonic
2024-03-23T14:39:20.184073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 591
24.7%
1 65
 
2.7%
2 48
 
2.0%
4 40
 
1.7%
3 34
 
1.4%
6 27
 
1.1%
5 25
 
1.0%
15 24
 
1.0%
7 23
 
1.0%
9 20
 
0.8%
Other values (808) 1335
55.8%
(Missing) 160
 
6.7%
ValueCountFrequency (%)
0 591
24.7%
1 65
 
2.7%
2 48
 
2.0%
3 34
 
1.4%
4 40
 
1.7%
5 25
 
1.0%
6 27
 
1.1%
7 23
 
1.0%
8 14
 
0.6%
9 20
 
0.8%
ValueCountFrequency (%)
1758594 1
< 0.1%
515091 1
< 0.1%
318316 1
< 0.1%
274799 1
< 0.1%
155590 1
< 0.1%
144979 1
< 0.1%
137197 1
< 0.1%
98231 1
< 0.1%
90083 1
< 0.1%
77551 1
< 0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct825
Distinct (%)37.0%
Missing160
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2849.392
Minimum0
Maximum1420843
Zeros599
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:39:20.673744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median32
Q3543.75
95-th percentile8829
Maximum1420843
Range1420843
Interquartile range (IQR)543.75

Descriptive statistics

Standard deviation32979.126
Coefficient of variation (CV)11.574092
Kurtosis1546.4594
Mean2849.392
Median Absolute Deviation (MAD)32
Skewness36.997944
Sum6359843
Variance1.0876228 × 109
MonotonicityNot monotonic
2024-03-23T14:39:21.140964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 599
25.0%
1 63
 
2.6%
2 50
 
2.1%
5 32
 
1.3%
3 28
 
1.2%
8 27
 
1.1%
4 26
 
1.1%
6 22
 
0.9%
20 18
 
0.8%
10 18
 
0.8%
Other values (815) 1349
56.4%
(Missing) 160
 
6.7%
ValueCountFrequency (%)
0 599
25.0%
1 63
 
2.6%
2 50
 
2.1%
3 28
 
1.2%
4 26
 
1.1%
5 32
 
1.3%
6 22
 
0.9%
7 16
 
0.7%
8 27
 
1.1%
9 18
 
0.8%
ValueCountFrequency (%)
1420843 1
< 0.1%
396225 1
< 0.1%
306784 1
< 0.1%
209529 1
< 0.1%
150127 1
< 0.1%
122398 1
< 0.1%
94308 1
< 0.1%
90932 1
< 0.1%
85562 1
< 0.1%
71835 1
< 0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct759
Distinct (%)34.0%
Missing160
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1697.5385
Minimum0
Maximum822098
Zeros625
Zeros (%)26.1%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:39:21.436788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23
Q3358
95-th percentile5103
Maximum822098
Range822098
Interquartile range (IQR)358

Descriptive statistics

Standard deviation19128.134
Coefficient of variation (CV)11.268159
Kurtosis1531.1125
Mean1697.5385
Median Absolute Deviation (MAD)23
Skewness36.742026
Sum3788906
Variance3.658855 × 108
MonotonicityNot monotonic
2024-03-23T14:39:21.690896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 625
26.1%
1 65
 
2.7%
2 54
 
2.3%
3 38
 
1.6%
5 38
 
1.6%
6 33
 
1.4%
4 28
 
1.2%
9 25
 
1.0%
15 21
 
0.9%
11 21
 
0.9%
Other values (749) 1284
53.7%
(Missing) 160
 
6.7%
ValueCountFrequency (%)
0 625
26.1%
1 65
 
2.7%
2 54
 
2.3%
3 38
 
1.6%
4 28
 
1.2%
5 38
 
1.6%
6 33
 
1.4%
7 16
 
0.7%
8 17
 
0.7%
9 25
 
1.0%
ValueCountFrequency (%)
822098 1
< 0.1%
220302 1
< 0.1%
195189 1
< 0.1%
111073 1
< 0.1%
93532 1
< 0.1%
65623 1
< 0.1%
56786 1
< 0.1%
54122 1
< 0.1%
49742 1
< 0.1%
45589 1
< 0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct713
Distinct (%)31.8%
Missing152
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean1540.4656
Minimum0
Maximum819834
Zeros654
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-23T14:39:22.029547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median19
Q3273
95-th percentile4857.65
Maximum819834
Range819834
Interquartile range (IQR)273

Descriptive statistics

Standard deviation18755.375
Coefficient of variation (CV)12.175134
Kurtosis1630.9226
Mean1540.4656
Median Absolute Deviation (MAD)19
Skewness38.249711
Sum3450643
Variance3.5176411 × 108
MonotonicityNot monotonic
2024-03-23T14:39:22.295234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 654
27.3%
1 76
 
3.2%
2 51
 
2.1%
4 38
 
1.6%
3 37
 
1.5%
7 33
 
1.4%
6 33
 
1.4%
5 31
 
1.3%
8 27
 
1.1%
16 21
 
0.9%
Other values (703) 1239
51.8%
(Missing) 152
 
6.4%
ValueCountFrequency (%)
0 654
27.3%
1 76
 
3.2%
2 51
 
2.1%
3 37
 
1.5%
4 38
 
1.6%
5 31
 
1.3%
6 33
 
1.4%
7 33
 
1.4%
8 27
 
1.1%
9 15
 
0.6%
ValueCountFrequency (%)
819834 1
< 0.1%
224612 1
< 0.1%
138977 1
< 0.1%
122313 1
< 0.1%
62971 1
< 0.1%
61052 1
< 0.1%
52559 1
< 0.1%
48435 1
< 0.1%
38658 1
< 0.1%
38578 1
< 0.1%

Interactions

2024-03-23T14:39:03.363581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:37:59.676258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T14:38:06.674620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:10.513310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:13.997124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:17.464910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:21.166810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:24.635819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T14:38:31.837201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:35.531510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:39.522424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:43.674403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:47.435547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:51.166262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:55.316433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T14:38:14.157945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T14:38:21.346833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T14:38:47.596165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:51.357675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:55.564146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:59.491534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T14:38:25.043059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:28.152467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:32.296792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:35.933564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:39.883935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T14:38:47.764074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:51.557249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:55.794918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:59.645934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T14:38:37.611934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:41.629887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:45.698868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:49.599873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:53.367334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:57.511230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:01.260246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:05.828963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:02.167418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:05.633747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:09.071457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:12.689302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:16.086042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:19.798218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:23.335623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:26.496431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:30.025275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:34.070953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:37.829245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:41.825566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:46.011476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:49.765638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:53.614129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:57.725604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:01.459788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:06.089243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:02.360988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:05.800959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:09.240603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:12.905001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:16.254646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:20.078220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:23.508904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:26.700191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:30.217163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:34.302907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:38.091202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:42.058579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:46.322424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:49.928905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:53.806300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:57.997178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:01.645021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:06.424508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:02.577568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:05.973629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:09.428778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:13.088428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:16.430454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:20.241894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:23.685968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:26.889112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:30.411903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:34.529278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:38.287944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:42.277733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:46.546885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:50.092728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:54.433741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:58.350491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:01.864848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:06.730757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:02.793893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:06.122887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:09.662300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:13.285661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:16.621103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:20.415465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:23.863389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:27.116734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:30.584952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:34.726762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:38.544099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:42.897929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:46.741276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:50.282137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:54.598319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:58.596290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:02.047578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:06.925886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:02.966908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:06.278495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:09.906135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:13.490287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:16.841449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:20.611584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:24.113162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:27.285031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:31.249792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:34.961850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:38.852632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:43.082789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:46.931332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:50.459389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:54.786590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:58.822036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:02.253812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:07.564923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:03.200122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:06.422990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:10.105783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:13.682082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:17.077405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:20.801775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:24.302083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:27.440826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:31.455508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:35.164958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:39.108495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:43.258697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:47.104839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:50.690584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:54.981572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:58.999199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:02.525679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:07.738785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:03.372537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:06.557867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:10.334414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:13.854119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:17.249234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:20.979201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:24.452950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:27.624273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:31.639018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:35.343943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:39.298484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:43.460000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:47.276230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:50.936492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:55.162529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:38:59.172003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:39:02.865272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T14:39:22.554520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.8620.8620.8620.8620.8620.8620.8620.8620.8621.0000.8420.8420.8420.8620.8030.8030.862
20070.8621.0001.0001.0001.0001.0001.0001.0001.0001.0000.9900.9980.9980.9981.0000.9820.9821.000
20080.8621.0001.0001.0001.0001.0001.0001.0001.0001.0000.9900.9980.9980.9981.0000.9820.9821.000
20090.8621.0001.0001.0001.0001.0001.0001.0001.0001.0000.9900.9980.9980.9981.0000.9820.9821.000
20100.8621.0001.0001.0001.0001.0001.0001.0001.0001.0000.9900.9980.9980.9981.0000.9820.9821.000
20110.8621.0001.0001.0001.0001.0001.0001.0001.0001.0000.9900.9980.9980.9981.0000.9820.9821.000
20120.8621.0001.0001.0001.0001.0001.0001.0001.0001.0000.9900.9980.9980.9981.0000.9820.9821.000
20130.8621.0001.0001.0001.0001.0001.0001.0001.0001.0000.9900.9980.9980.9981.0000.9820.9821.000
20140.8621.0001.0001.0001.0001.0001.0001.0001.0001.0000.9900.9980.9980.9981.0000.9820.9821.000
20150.8621.0001.0001.0001.0001.0001.0001.0001.0001.0000.9900.9980.9980.9981.0000.9820.9821.000
20161.0000.9900.9900.9900.9900.9900.9900.9900.9900.9901.0000.9820.9820.9820.9900.9820.9820.990
20170.8420.9980.9980.9980.9980.9980.9980.9980.9980.9980.9821.0001.0001.0000.9980.9930.9930.998
20180.8420.9980.9980.9980.9980.9980.9980.9980.9980.9980.9821.0001.0001.0000.9980.9930.9930.998
20190.8420.9980.9980.9980.9980.9980.9980.9980.9980.9980.9821.0001.0001.0000.9980.9930.9930.998
20200.8621.0001.0001.0001.0001.0001.0001.0001.0001.0000.9900.9980.9980.9981.0000.9820.9821.000
20210.8030.9820.9820.9820.9820.9820.9820.9820.9820.9820.9820.9930.9930.9930.9821.0001.0000.982
20220.8030.9820.9820.9820.9820.9820.9820.9820.9820.9820.9820.9930.9930.9930.9821.0001.0000.982
20230.8621.0001.0001.0001.0001.0001.0001.0001.0001.0000.9900.9980.9980.9981.0000.9820.9821.000
2024-03-23T14:39:22.944220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9580.9430.9350.9260.9250.9180.9180.9120.9140.9110.8520.8460.8510.8630.8640.8750.867
20070.9581.0000.9590.9450.9410.9390.9300.9280.9250.9260.9150.8510.8480.8480.8590.8620.8680.863
20080.9430.9591.0000.9620.9510.9530.9420.9350.9350.9350.9260.8590.8560.8610.8710.8670.8760.872
20090.9350.9450.9621.0000.9680.9640.9530.9410.9380.9390.9280.8620.8570.8610.8700.8680.8750.872
20100.9260.9410.9510.9681.0000.9710.9630.9470.9410.9400.9250.8560.8510.8550.8620.8610.8710.867
20110.9250.9390.9530.9640.9711.0000.9690.9530.9420.9440.9300.8580.8540.8570.8660.8670.8770.876
20120.9180.9300.9420.9530.9630.9691.0000.9660.9500.9480.9350.8640.8590.8600.8670.8660.8800.876
20130.9180.9280.9350.9410.9470.9530.9661.0000.9570.9510.9380.8760.8720.8700.8780.8720.8870.881
20140.9120.9250.9350.9380.9410.9420.9500.9571.0000.9680.9490.8840.8790.8810.8900.8850.8920.887
20150.9140.9260.9350.9390.9400.9440.9480.9510.9681.0000.9580.8900.8850.8870.8950.8920.8970.894
20160.9110.9150.9260.9280.9250.9300.9350.9380.9490.9581.0000.9210.9080.9120.9200.9100.9140.908
20170.8520.8510.8590.8620.8560.8580.8640.8760.8840.8900.9211.0000.9580.9400.9380.9250.9180.914
20180.8460.8480.8560.8570.8510.8540.8590.8720.8790.8850.9080.9581.0000.9530.9470.9290.9230.918
20190.8510.8480.8610.8610.8550.8570.8600.8700.8810.8870.9120.9400.9531.0000.9570.9380.9340.926
20200.8630.8590.8710.8700.8620.8660.8670.8780.8900.8950.9200.9380.9470.9571.0000.9590.9450.938
20210.8640.8620.8670.8680.8610.8670.8660.8720.8850.8920.9100.9250.9290.9380.9591.0000.9500.944
20220.8750.8680.8760.8750.8710.8770.8800.8870.8920.8970.9140.9180.9230.9340.9450.9501.0000.960
20230.8670.8630.8720.8720.8670.8760.8760.8810.8870.8940.9080.9140.9180.9260.9380.9440.9601.000

Missing values

2024-03-23T14:39:08.106728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T14:39:08.575291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-23T14:39:08.973740image/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_용도지역200620072008200920102011201220132014201520162017201820192020202120222023
0전국_도시지역_주거지역13591071042359106433410267909524141123796905601107795913254161580045150056215356461482525135379717585941420843822098819834
1전국_도시지역_상업지역135255144448132118124751117826140858130233135635174514215618210750299714289599264802318316306784195189138977
2전국_도시지역_공업지역505714037340890349273243635610302753397739971484144324548806504073834450020537743762630747
3전국_도시지역_녹지지역299832723523846281642523930403273723285731367356403629745465399633197444008527543528227103
4전국_도시지역_개발제한구역293517721398153313021638159317141809204823506665826612720171216921273754
5전국_도시지역_용도미지정16300192392697719105155281406517655247162461230486458421724321936041558521555901501279353262971
6전국_농림지역461143263847450735693696346035933959442143864685465849676307725961904540
7전국_자연환경보전지역1873159818652101177716791435133913311399235092511279329791052953740
8서울_도시지역_주거지역316822199077189995173675119861146285118700155318209103289992304668297510267886215282274799209529111073122313
9서울_도시지역_상업지역378654817734545274132386525518213092337626304321742935048109429034395349193475943158421060
지역_용도지역200620072008200920102011201220132014201520162017201820192020202120222023
2382제주 제주시_농림지역222232000000010000
2383제주 제주시_자연환경보전지역200100000000000427
2384제주 서귀포시_도시지역_주거지역53811991549119812561745172323872883486250073932306231382701326521941628
2385제주 서귀포시_도시지역_상업지역76188169196232364376800585150122812400261812651094804773785
2386제주 서귀포시_도시지역_공업지역13496327913913232754
2387제주 서귀포시_도시지역_녹지지역1844973114044585064744721223171214301334133081792212101127683
2388제주 서귀포시_도시지역_개발제한구역000000000000000000
2389제주 서귀포시_도시지역_용도미지정000000001227255451313206
2390제주 서귀포시_농림지역21291276000000011000
2391제주 서귀포시_자연환경보전지역100000000000001010