Overview

Dataset statistics

Number of variables19
Number of observations2691
Missing cells3285
Missing cells (%)6.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory446.9 KiB
Average record size in memory170.0 B

Variable types

Text1
Numeric18

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 부동산거래현황 중 주택 거래현황의 연도별 거래주체별(동(호)수) 데이터입니다.- (단위 : 동(호)수)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068426/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 198 (7.4%) missing valuesMissing
2007 has 243 (9.0%) missing valuesMissing
2008 has 225 (8.4%) missing valuesMissing
2009 has 225 (8.4%) missing valuesMissing
2010 has 171 (6.4%) missing valuesMissing
2011 has 198 (7.4%) missing valuesMissing
2012 has 180 (6.7%) missing valuesMissing
2013 has 180 (6.7%) missing valuesMissing
2014 has 117 (4.3%) missing valuesMissing
2015 has 153 (5.7%) missing valuesMissing
2016 has 153 (5.7%) missing valuesMissing
2017 has 180 (6.7%) missing valuesMissing
2018 has 171 (6.4%) missing valuesMissing
2019 has 180 (6.7%) missing valuesMissing
2020 has 180 (6.7%) missing valuesMissing
2021 has 180 (6.7%) missing valuesMissing
2022 has 180 (6.7%) missing valuesMissing
2023 has 171 (6.4%) missing valuesMissing
2006 is highly skewed (γ1 = 37.73326997)Skewed
2007 is highly skewed (γ1 = 37.62355439)Skewed
2008 is highly skewed (γ1 = 38.36501789)Skewed
2009 is highly skewed (γ1 = 38.94569823)Skewed
2010 is highly skewed (γ1 = 39.88005231)Skewed
2011 is highly skewed (γ1 = 40.78026113)Skewed
2012 is highly skewed (γ1 = 40.67015735)Skewed
2013 is highly skewed (γ1 = 40.81547758)Skewed
2014 is highly skewed (γ1 = 41.32618864)Skewed
2015 is highly skewed (γ1 = 41.11216386)Skewed
2016 is highly skewed (γ1 = 40.49104307)Skewed
2017 is highly skewed (γ1 = 36.59795486)Skewed
2018 is highly skewed (γ1 = 34.19055222)Skewed
2019 is highly skewed (γ1 = 34.59971337)Skewed
2020 is highly skewed (γ1 = 38.02466874)Skewed
2021 is highly skewed (γ1 = 37.90092495)Skewed
2022 is highly skewed (γ1 = 35.93616948)Skewed
2023 is highly skewed (γ1 = 38.00558102)Skewed
지역 및 거래주체 has unique valuesUnique
2006 has 492 (18.3%) zerosZeros
2007 has 475 (17.7%) zerosZeros
2008 has 477 (17.7%) zerosZeros
2009 has 422 (15.7%) zerosZeros
2010 has 415 (15.4%) zerosZeros
2011 has 297 (11.0%) zerosZeros
2012 has 335 (12.4%) zerosZeros
2013 has 439 (16.3%) zerosZeros
2014 has 443 (16.5%) zerosZeros
2015 has 375 (13.9%) zerosZeros
2016 has 413 (15.3%) zerosZeros
2017 has 360 (13.4%) zerosZeros
2018 has 351 (13.0%) zerosZeros
2019 has 347 (12.9%) zerosZeros
2020 has 331 (12.3%) zerosZeros
2021 has 316 (11.7%) zerosZeros
2022 has 334 (12.4%) zerosZeros
2023 has 393 (14.6%) zerosZeros

Reproduction

Analysis started2024-03-23 05:09:11.159416
Analysis finished2024-03-23 05:11:36.539373
Duration2 minutes and 25.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct2691
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
2024-03-23T05:11:37.111320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length12
Mean length12.341137
Min length9

Characters and Unicode

Total characters33210
Distinct characters152
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2691 ?
Unique (%)100.0%

Sample

1st row전국 /개인>개인
2nd row전국 /개인>법인
3rd row전국 /개인>기타
4th row전국 /법인>개인
5th row전국 /법인>법인
ValueCountFrequency (%)
경기 477
 
8.9%
경남 243
 
4.5%
서울 234
 
4.3%
경북 234
 
4.3%
전남 207
 
3.8%
충남 180
 
3.3%
충북 180
 
3.3%
강원 171
 
3.2%
전북 153
 
2.8%
부산 153
 
2.8%
Other values (2322) 3150
58.5%
2024-03-23T05:11:38.501749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3750
 
11.3%
2691
 
8.1%
/ 2691
 
8.1%
> 2691
 
8.1%
2298
 
6.9%
1794
 
5.4%
1794
 
5.4%
1794
 
5.4%
1269
 
3.8%
1098
 
3.3%
Other values (142) 11340
34.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24795
74.7%
Space Separator 2691
 
8.1%
Other Punctuation 2691
 
8.1%
Math Symbol 2691
 
8.1%
Open Punctuation 171
 
0.5%
Close Punctuation 171
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3750
15.1%
2298
 
9.3%
1794
 
7.2%
1794
 
7.2%
1794
 
7.2%
1269
 
5.1%
1098
 
4.4%
981
 
4.0%
837
 
3.4%
801
 
3.2%
Other values (137) 8379
33.8%
Space Separator
ValueCountFrequency (%)
2691
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2691
100.0%
Math Symbol
ValueCountFrequency (%)
> 2691
100.0%
Open Punctuation
ValueCountFrequency (%)
( 171
100.0%
Close Punctuation
ValueCountFrequency (%)
) 171
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24795
74.7%
Common 8415
 
25.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3750
15.1%
2298
 
9.3%
1794
 
7.2%
1794
 
7.2%
1794
 
7.2%
1269
 
5.1%
1098
 
4.4%
981
 
4.0%
837
 
3.4%
801
 
3.2%
Other values (137) 8379
33.8%
Common
ValueCountFrequency (%)
2691
32.0%
/ 2691
32.0%
> 2691
32.0%
( 171
 
2.0%
) 171
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24795
74.7%
ASCII 8415
 
25.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3750
15.1%
2298
 
9.3%
1794
 
7.2%
1794
 
7.2%
1794
 
7.2%
1269
 
5.1%
1098
 
4.4%
981
 
4.0%
837
 
3.4%
801
 
3.2%
Other values (137) 8379
33.8%
ASCII
ValueCountFrequency (%)
2691
32.0%
/ 2691
32.0%
> 2691
32.0%
( 171
 
2.0%
) 171
 
2.0%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct713
Distinct (%)28.6%
Missing198
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean1904.5415
Minimum0
Maximum1147064
Zeros492
Zeros (%)18.3%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T05:11:39.007214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median11
Q3184
95-th percentile6259.2
Maximum1147064
Range1147064
Interquartile range (IQR)183

Descriptive statistics

Standard deviation25682.282
Coefficient of variation (CV)13.484758
Kurtosis1613.1187
Mean1904.5415
Median Absolute Deviation (MAD)11
Skewness37.73327
Sum4748022
Variance6.5957958 × 108
MonotonicityNot monotonic
2024-03-23T05:11:39.617572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 492
 
18.3%
1 192
 
7.1%
2 120
 
4.5%
3 103
 
3.8%
4 70
 
2.6%
6 57
 
2.1%
5 54
 
2.0%
7 41
 
1.5%
8 40
 
1.5%
10 37
 
1.4%
Other values (703) 1287
47.8%
(Missing) 198
 
7.4%
ValueCountFrequency (%)
0 492
18.3%
1 192
 
7.1%
2 120
 
4.5%
3 103
 
3.8%
4 70
 
2.6%
5 54
 
2.0%
6 57
 
2.1%
7 41
 
1.5%
8 40
 
1.5%
9 36
 
1.3%
ValueCountFrequency (%)
1147064 1
< 0.1%
359245 1
< 0.1%
288646 1
< 0.1%
267143 1
< 0.1%
86358 1
< 0.1%
76478 1
< 0.1%
62306 1
< 0.1%
49067 1
< 0.1%
46651 1
< 0.1%
42730 1
< 0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct691
Distinct (%)28.2%
Missing243
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean1468.9383
Minimum0
Maximum842411
Zeros475
Zeros (%)17.7%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T05:11:40.155854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median12
Q3161
95-th percentile4552.45
Maximum842411
Range842411
Interquartile range (IQR)160

Descriptive statistics

Standard deviation18942.815
Coefficient of variation (CV)12.895582
Kurtosis1608.8901
Mean1468.9383
Median Absolute Deviation (MAD)12
Skewness37.623554
Sum3595961
Variance3.5883023 × 108
MonotonicityNot monotonic
2024-03-23T05:11:40.677073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 475
 
17.7%
1 171
 
6.4%
3 109
 
4.1%
2 103
 
3.8%
4 69
 
2.6%
5 61
 
2.3%
7 46
 
1.7%
6 46
 
1.7%
8 42
 
1.6%
9 40
 
1.5%
Other values (681) 1286
47.8%
(Missing) 243
 
9.0%
ValueCountFrequency (%)
0 475
17.7%
1 171
 
6.4%
2 103
 
3.8%
3 109
 
4.1%
4 69
 
2.6%
5 61
 
2.3%
6 46
 
1.7%
7 46
 
1.7%
8 42
 
1.6%
9 40
 
1.5%
ValueCountFrequency (%)
842411 1
< 0.1%
246933 1
< 0.1%
212452 1
< 0.1%
168228 1
< 0.1%
95816 1
< 0.1%
74913 1
< 0.1%
54323 1
< 0.1%
49193 1
< 0.1%
37759 1
< 0.1%
35234 1
< 0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct697
Distinct (%)28.3%
Missing225
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean1498.9234
Minimum0
Maximum860473
Zeros477
Zeros (%)17.7%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T05:11:41.216118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median12
Q3169.75
95-th percentile5035.25
Maximum860473
Range860473
Interquartile range (IQR)168.75

Descriptive statistics

Standard deviation19135.898
Coefficient of variation (CV)12.766429
Kurtosis1666.0321
Mean1498.9234
Median Absolute Deviation (MAD)12
Skewness38.365018
Sum3696345
Variance3.6618261 × 108
MonotonicityNot monotonic
2024-03-23T05:11:41.858367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 477
 
17.7%
1 187
 
6.9%
2 125
 
4.6%
3 81
 
3.0%
4 76
 
2.8%
5 65
 
2.4%
6 50
 
1.9%
7 44
 
1.6%
9 32
 
1.2%
8 27
 
1.0%
Other values (687) 1302
48.4%
(Missing) 225
 
8.4%
ValueCountFrequency (%)
0 477
17.7%
1 187
 
6.9%
2 125
 
4.6%
3 81
 
3.0%
4 76
 
2.8%
5 65
 
2.4%
6 50
 
1.9%
7 44
 
1.6%
8 27
 
1.0%
9 32
 
1.2%
ValueCountFrequency (%)
860473 1
< 0.1%
242980 1
< 0.1%
207654 1
< 0.1%
158526 1
< 0.1%
93952 1
< 0.1%
61587 1
< 0.1%
56025 1
< 0.1%
50483 1
< 0.1%
36498 1
< 0.1%
35551 1
< 0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct705
Distinct (%)28.6%
Missing225
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean1458.2758
Minimum0
Maximum840327
Zeros422
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T05:11:42.322265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median14
Q3175.75
95-th percentile5040.25
Maximum840327
Range840327
Interquartile range (IQR)173.75

Descriptive statistics

Standard deviation18560.128
Coefficient of variation (CV)12.727447
Kurtosis1709.2255
Mean1458.2758
Median Absolute Deviation (MAD)14
Skewness38.945698
Sum3596108
Variance3.4447834 × 108
MonotonicityNot monotonic
2024-03-23T05:11:42.811519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 422
 
15.7%
1 189
 
7.0%
2 118
 
4.4%
3 97
 
3.6%
6 60
 
2.2%
4 60
 
2.2%
5 59
 
2.2%
7 42
 
1.6%
9 36
 
1.3%
8 36
 
1.3%
Other values (695) 1347
50.1%
(Missing) 225
 
8.4%
ValueCountFrequency (%)
0 422
15.7%
1 189
7.0%
2 118
 
4.4%
3 97
 
3.6%
4 60
 
2.2%
5 59
 
2.2%
6 60
 
2.2%
7 42
 
1.6%
8 36
 
1.3%
9 36
 
1.3%
ValueCountFrequency (%)
840327 1
< 0.1%
211595 1
< 0.1%
211364 1
< 0.1%
149245 1
< 0.1%
70096 1
< 0.1%
65149 1
< 0.1%
55442 1
< 0.1%
53798 1
< 0.1%
36209 1
< 0.1%
35945 1
< 0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct698
Distinct (%)27.7%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean1304.4496
Minimum0
Maximum761361
Zeros415
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T05:11:43.612237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median14
Q3153.25
95-th percentile4407.6
Maximum761361
Range761361
Interquartile range (IQR)151.25

Descriptive statistics

Standard deviation16533.604
Coefficient of variation (CV)12.674774
Kurtosis1787.347
Mean1304.4496
Median Absolute Deviation (MAD)14
Skewness39.880052
Sum3287213
Variance2.7336005 × 108
MonotonicityNot monotonic
2024-03-23T05:11:44.341553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 415
 
15.4%
1 179
 
6.7%
2 128
 
4.8%
3 93
 
3.5%
4 79
 
2.9%
5 70
 
2.6%
8 53
 
2.0%
7 46
 
1.7%
6 42
 
1.6%
9 33
 
1.2%
Other values (688) 1382
51.4%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 415
15.4%
1 179
6.7%
2 128
 
4.8%
3 93
 
3.5%
4 79
 
2.9%
5 70
 
2.6%
6 42
 
1.6%
7 46
 
1.7%
8 53
 
2.0%
9 33
 
1.2%
ValueCountFrequency (%)
761361 1
< 0.1%
206104 1
< 0.1%
153604 1
< 0.1%
98024 1
< 0.1%
83922 1
< 0.1%
66159 1
< 0.1%
65781 1
< 0.1%
50228 1
< 0.1%
38307 1
< 0.1%
37655 1
< 0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct699
Distinct (%)28.0%
Missing198
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean1569.9988
Minimum0
Maximum953965
Zeros297
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T05:11:45.019172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median18
Q3154
95-th percentile5620.6
Maximum953965
Range953965
Interquartile range (IQR)151

Descriptive statistics

Standard deviation20584.743
Coefficient of variation (CV)13.111312
Kurtosis1849.3587
Mean1569.9988
Median Absolute Deviation (MAD)18
Skewness40.780261
Sum3914007
Variance4.2373166 × 108
MonotonicityNot monotonic
2024-03-23T05:11:45.943431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 297
 
11.0%
1 188
 
7.0%
2 111
 
4.1%
3 92
 
3.4%
4 70
 
2.6%
5 63
 
2.3%
6 62
 
2.3%
7 58
 
2.2%
8 49
 
1.8%
11 37
 
1.4%
Other values (689) 1466
54.5%
(Missing) 198
 
7.4%
ValueCountFrequency (%)
0 297
11.0%
1 188
7.0%
2 111
 
4.1%
3 92
 
3.4%
4 70
 
2.6%
5 63
 
2.3%
6 62
 
2.3%
7 58
 
2.2%
8 49
 
1.8%
9 27
 
1.0%
ValueCountFrequency (%)
953965 1
< 0.1%
209564 1
< 0.1%
208357 1
< 0.1%
123533 1
< 0.1%
96284 1
< 0.1%
70726 1
< 0.1%
57404 1
< 0.1%
57342 1
< 0.1%
52330 1
< 0.1%
45274 1
< 0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct693
Distinct (%)27.6%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1269.4747
Minimum0
Maximum756951
Zeros335
Zeros (%)12.4%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T05:11:46.748509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median16
Q3162
95-th percentile4337.5
Maximum756951
Range756951
Interquartile range (IQR)160

Descriptive statistics

Standard deviation16317.822
Coefficient of variation (CV)12.853995
Kurtosis1843.947
Mean1269.4747
Median Absolute Deviation (MAD)16
Skewness40.670157
Sum3187651
Variance2.6627131 × 108
MonotonicityNot monotonic
2024-03-23T05:11:47.233389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 335
 
12.4%
1 170
 
6.3%
2 133
 
4.9%
3 78
 
2.9%
4 77
 
2.9%
5 66
 
2.5%
6 62
 
2.3%
7 60
 
2.2%
9 42
 
1.6%
10 40
 
1.5%
Other values (683) 1448
53.8%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 335
12.4%
1 170
6.3%
2 133
 
4.9%
3 78
 
2.9%
4 77
 
2.9%
5 66
 
2.5%
6 62
 
2.3%
7 60
 
2.2%
8 39
 
1.4%
9 42
 
1.6%
ValueCountFrequency (%)
756951 1
< 0.1%
180171 1
< 0.1%
160461 1
< 0.1%
93799 1
< 0.1%
70339 1
< 0.1%
56747 1
< 0.1%
52896 1
< 0.1%
47643 1
< 0.1%
44250 1
< 0.1%
40566 1
< 0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct715
Distinct (%)28.5%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1504.9383
Minimum0
Maximum914367
Zeros439
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T05:11:47.793783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median16
Q3161
95-th percentile5313
Maximum914367
Range914367
Interquartile range (IQR)159

Descriptive statistics

Standard deviation19682.654
Coefficient of variation (CV)13.078712
Kurtosis1854.333
Mean1504.9383
Median Absolute Deviation (MAD)16
Skewness40.815478
Sum3778900
Variance3.8740686 × 108
MonotonicityNot monotonic
2024-03-23T05:11:48.330316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 439
 
16.3%
1 181
 
6.7%
2 102
 
3.8%
3 81
 
3.0%
4 70
 
2.6%
5 63
 
2.3%
7 55
 
2.0%
6 35
 
1.3%
9 33
 
1.2%
14 29
 
1.1%
Other values (705) 1423
52.9%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 439
16.3%
1 181
6.7%
2 102
 
3.8%
3 81
 
3.0%
4 70
 
2.6%
5 63
 
2.3%
6 35
 
1.3%
7 55
 
2.0%
8 27
 
1.0%
9 33
 
1.2%
ValueCountFrequency (%)
914367 1
< 0.1%
205557 1
< 0.1%
199126 1
< 0.1%
123832 1
< 0.1%
81512 1
< 0.1%
70589 1
< 0.1%
65426 1
< 0.1%
54164 1
< 0.1%
53803 1
< 0.1%
46253 1
< 0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct750
Distinct (%)29.1%
Missing117
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean1795.6348
Minimum0
Maximum1127602
Zeros443
Zeros (%)16.5%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T05:11:48.881219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median15
Q3192.75
95-th percentile6756.75
Maximum1127602
Range1127602
Interquartile range (IQR)190.75

Descriptive statistics

Standard deviation23981.851
Coefficient of variation (CV)13.355639
Kurtosis1899.2022
Mean1795.6348
Median Absolute Deviation (MAD)15
Skewness41.326189
Sum4621964
Variance5.7512919 × 108
MonotonicityNot monotonic
2024-03-23T05:11:49.424405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 443
 
16.5%
1 167
 
6.2%
2 119
 
4.4%
3 105
 
3.9%
4 76
 
2.8%
5 59
 
2.2%
6 50
 
1.9%
7 48
 
1.8%
8 40
 
1.5%
10 39
 
1.4%
Other values (740) 1428
53.1%
(Missing) 117
 
4.3%
ValueCountFrequency (%)
0 443
16.5%
1 167
 
6.2%
2 119
 
4.4%
3 105
 
3.9%
4 76
 
2.8%
5 59
 
2.2%
6 50
 
1.9%
7 48
 
1.8%
8 40
 
1.5%
9 26
 
1.0%
ValueCountFrequency (%)
1127602 1
< 0.1%
264197 1
< 0.1%
236687 1
< 0.1%
166848 1
< 0.1%
102936 1
< 0.1%
86086 1
< 0.1%
70186 1
< 0.1%
68443 1
< 0.1%
59898 1
< 0.1%
47870 1
< 0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct757
Distinct (%)29.8%
Missing153
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean2161.9634
Minimum0
Maximum1389712
Zeros375
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T05:11:49.955747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median20
Q3232.5
95-th percentile8184.1
Maximum1389712
Range1389712
Interquartile range (IQR)230.5

Descriptive statistics

Standard deviation29757.922
Coefficient of variation (CV)13.764305
Kurtosis1875.5165
Mean2161.9634
Median Absolute Deviation (MAD)20
Skewness41.112164
Sum5487063
Variance8.8553394 × 108
MonotonicityNot monotonic
2024-03-23T05:11:50.656698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 375
 
13.9%
1 178
 
6.6%
2 126
 
4.7%
3 72
 
2.7%
4 67
 
2.5%
6 57
 
2.1%
5 56
 
2.1%
7 45
 
1.7%
8 36
 
1.3%
11 31
 
1.2%
Other values (747) 1495
55.6%
(Missing) 153
 
5.7%
ValueCountFrequency (%)
0 375
13.9%
1 178
6.6%
2 126
 
4.7%
3 72
 
2.7%
4 67
 
2.5%
5 56
 
2.1%
6 57
 
2.1%
7 45
 
1.7%
8 36
 
1.3%
9 28
 
1.0%
ValueCountFrequency (%)
1389712 1
< 0.1%
346381 1
< 0.1%
250104 1
< 0.1%
242132 1
< 0.1%
131123 1
< 0.1%
92936 1
< 0.1%
91548 1
< 0.1%
71587 1
< 0.1%
68012 1
< 0.1%
51824 1
< 0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct768
Distinct (%)30.3%
Missing153
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean2031.9799
Minimum0
Maximum1288104
Zeros413
Zeros (%)15.3%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T05:11:51.290778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median19
Q3239
95-th percentile7374.7
Maximum1288104
Range1288104
Interquartile range (IQR)237

Descriptive statistics

Standard deviation27773.909
Coefficient of variation (CV)13.668397
Kurtosis1827.3071
Mean2031.9799
Median Absolute Deviation (MAD)19
Skewness40.491043
Sum5157165
Variance7.7138999 × 108
MonotonicityNot monotonic
2024-03-23T05:11:52.018510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 413
 
15.3%
1 165
 
6.1%
2 100
 
3.7%
4 88
 
3.3%
3 74
 
2.7%
5 53
 
2.0%
6 53
 
2.0%
7 48
 
1.8%
8 38
 
1.4%
10 33
 
1.2%
Other values (758) 1473
54.7%
(Missing) 153
 
5.7%
ValueCountFrequency (%)
0 413
15.3%
1 165
 
6.1%
2 100
 
3.7%
3 74
 
2.7%
4 88
 
3.3%
5 53
 
2.0%
6 53
 
2.0%
7 48
 
1.8%
8 38
 
1.4%
9 31
 
1.2%
ValueCountFrequency (%)
1288104 1
< 0.1%
345315 1
< 0.1%
249111 1
< 0.1%
235376 1
< 0.1%
117777 1
< 0.1%
91608 1
< 0.1%
80001 1
< 0.1%
58790 1
< 0.1%
48795 1
< 0.1%
47824 1
< 0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct794
Distinct (%)31.6%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2225.5313
Minimum0
Maximum1215828
Zeros360
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T05:11:52.581087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median22
Q3284
95-th percentile7879.5
Maximum1215828
Range1215828
Interquartile range (IQR)282

Descriptive statistics

Standard deviation27578.716
Coefficient of variation (CV)12.39197
Kurtosis1530.7362
Mean2225.5313
Median Absolute Deviation (MAD)22
Skewness36.597955
Sum5588309
Variance7.6058556 × 108
MonotonicityNot monotonic
2024-03-23T05:11:52.982109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 360
 
13.4%
1 196
 
7.3%
2 112
 
4.2%
3 87
 
3.2%
4 67
 
2.5%
5 57
 
2.1%
7 42
 
1.6%
6 41
 
1.5%
12 27
 
1.0%
17 27
 
1.0%
Other values (784) 1495
55.6%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 360
13.4%
1 196
7.3%
2 112
 
4.2%
3 87
 
3.2%
4 67
 
2.5%
5 57
 
2.1%
6 41
 
1.5%
7 42
 
1.6%
8 26
 
1.0%
9 23
 
0.9%
ValueCountFrequency (%)
1215828 1
< 0.1%
439036 1
< 0.1%
341238 1
< 0.1%
223402 1
< 0.1%
132109 1
< 0.1%
88851 1
< 0.1%
86093 1
< 0.1%
69265 1
< 0.1%
58645 1
< 0.1%
50951 1
< 0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct850
Distinct (%)33.7%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean2170.3702
Minimum0
Maximum1080183
Zeros351
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T05:11:53.534428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median25
Q3343.25
95-th percentile6954.9
Maximum1080183
Range1080183
Interquartile range (IQR)340.25

Descriptive statistics

Standard deviation25433.467
Coefficient of variation (CV)11.718493
Kurtosis1346.732
Mean2170.3702
Median Absolute Deviation (MAD)25
Skewness34.190552
Sum5469333
Variance6.4686126 × 108
MonotonicityNot monotonic
2024-03-23T05:11:54.077166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 351
 
13.0%
1 155
 
5.8%
2 120
 
4.5%
3 66
 
2.5%
5 63
 
2.3%
4 63
 
2.3%
6 48
 
1.8%
7 36
 
1.3%
9 34
 
1.3%
12 31
 
1.2%
Other values (840) 1553
57.7%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 351
13.0%
1 155
5.8%
2 120
 
4.5%
3 66
 
2.5%
4 63
 
2.3%
5 63
 
2.3%
6 48
 
1.8%
7 36
 
1.3%
8 31
 
1.2%
9 34
 
1.3%
ValueCountFrequency (%)
1080183 1
< 0.1%
487211 1
< 0.1%
323311 1
< 0.1%
202923 1
< 0.1%
182632 1
< 0.1%
75175 1
< 0.1%
72758 1
< 0.1%
58816 1
< 0.1%
52972 1
< 0.1%
49441 1
< 0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct850
Distinct (%)33.9%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1988.2397
Minimum0
Maximum974292
Zeros347
Zeros (%)12.9%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T05:11:54.524092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median28
Q3317
95-th percentile6565
Maximum974292
Range974292
Interquartile range (IQR)314

Descriptive statistics

Standard deviation22809.501
Coefficient of variation (CV)11.472208
Kurtosis1377.7931
Mean1988.2397
Median Absolute Deviation (MAD)28
Skewness34.599713
Sum4992470
Variance5.2027332 × 108
MonotonicityNot monotonic
2024-03-23T05:11:55.107794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 347
 
12.9%
1 176
 
6.5%
2 96
 
3.6%
4 73
 
2.7%
3 65
 
2.4%
5 45
 
1.7%
6 41
 
1.5%
8 40
 
1.5%
9 37
 
1.4%
7 31
 
1.2%
Other values (840) 1560
58.0%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 347
12.9%
1 176
6.5%
2 96
 
3.6%
3 65
 
2.4%
4 73
 
2.7%
5 45
 
1.7%
6 41
 
1.5%
7 31
 
1.2%
8 40
 
1.5%
9 37
 
1.4%
ValueCountFrequency (%)
974292 1
< 0.1%
436701 1
< 0.1%
271772 1
< 0.1%
157276 1
< 0.1%
149851 1
< 0.1%
74088 1
< 0.1%
69423 1
< 0.1%
63355 1
< 0.1%
54901 1
< 0.1%
49713 1
< 0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct899
Distinct (%)35.8%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2562.1987
Minimum0
Maximum1417485
Zeros331
Zeros (%)12.3%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T05:11:55.616427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median37
Q3502
95-th percentile8494.5
Maximum1417485
Range1417485
Interquartile range (IQR)499

Descriptive statistics

Standard deviation31527.091
Coefficient of variation (CV)12.304702
Kurtosis1641.5402
Mean2562.1987
Median Absolute Deviation (MAD)37
Skewness38.024669
Sum6433681
Variance9.9395747 × 108
MonotonicityNot monotonic
2024-03-23T05:11:56.293230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 331
 
12.3%
1 158
 
5.9%
2 103
 
3.8%
3 58
 
2.2%
4 56
 
2.1%
5 47
 
1.7%
6 43
 
1.6%
7 40
 
1.5%
8 37
 
1.4%
10 32
 
1.2%
Other values (889) 1606
59.7%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 331
12.3%
1 158
5.9%
2 103
 
3.8%
3 58
 
2.2%
4 56
 
2.1%
5 47
 
1.7%
6 43
 
1.6%
7 40
 
1.5%
8 37
 
1.4%
9 31
 
1.2%
ValueCountFrequency (%)
1417485 1
< 0.1%
423356 1
< 0.1%
406623 1
< 0.1%
201305 1
< 0.1%
135115 1
< 0.1%
120273 1
< 0.1%
101031 1
< 0.1%
78849 1
< 0.1%
77399 1
< 0.1%
70271 1
< 0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct893
Distinct (%)35.6%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2051.6973
Minimum0
Maximum1092862
Zeros316
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T05:11:57.246158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median37
Q3441
95-th percentile6311.5
Maximum1092862
Range1092862
Interquartile range (IQR)438

Descriptive statistics

Standard deviation24337.154
Coefficient of variation (CV)11.861961
Kurtosis1633.8018
Mean2051.6973
Median Absolute Deviation (MAD)37
Skewness37.900925
Sum5151812
Variance5.9229707 × 108
MonotonicityNot monotonic
2024-03-23T05:11:58.182800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 316
 
11.7%
1 150
 
5.6%
2 94
 
3.5%
3 81
 
3.0%
5 66
 
2.5%
4 65
 
2.4%
6 44
 
1.6%
7 43
 
1.6%
8 32
 
1.2%
10 28
 
1.0%
Other values (883) 1592
59.2%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 316
11.7%
1 150
5.6%
2 94
 
3.5%
3 81
 
3.0%
4 65
 
2.4%
5 66
 
2.5%
6 44
 
1.6%
7 43
 
1.6%
8 32
 
1.2%
9 27
 
1.0%
ValueCountFrequency (%)
1092862 1
< 0.1%
346547 1
< 0.1%
295075 1
< 0.1%
137163 1
< 0.1%
96576 1
< 0.1%
87837 1
< 0.1%
80430 1
< 0.1%
73378 1
< 0.1%
69985 1
< 0.1%
63808 1
< 0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct781
Distinct (%)31.1%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1178.2178
Minimum0
Maximum571235
Zeros334
Zeros (%)12.4%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T05:11:59.027524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median28
Q3269
95-th percentile3525.5
Maximum571235
Range571235
Interquartile range (IQR)266

Descriptive statistics

Standard deviation13110.681
Coefficient of variation (CV)11.127552
Kurtosis1477.268
Mean1178.2178
Median Absolute Deviation (MAD)28
Skewness35.936169
Sum2958505
Variance1.7188995 × 108
MonotonicityNot monotonic
2024-03-23T05:11:59.742090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 334
 
12.4%
1 156
 
5.8%
2 102
 
3.8%
3 78
 
2.9%
4 75
 
2.8%
5 55
 
2.0%
6 46
 
1.7%
7 46
 
1.7%
8 31
 
1.2%
12 27
 
1.0%
Other values (771) 1561
58.0%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 334
12.4%
1 156
5.8%
2 102
 
3.8%
3 78
 
2.9%
4 75
 
2.8%
5 55
 
2.0%
6 46
 
1.7%
7 46
 
1.7%
8 31
 
1.2%
9 25
 
0.9%
ValueCountFrequency (%)
571235 1
< 0.1%
245938 1
< 0.1%
129318 1
< 0.1%
70881 1
< 0.1%
59229 1
< 0.1%
48806 1
< 0.1%
48190 1
< 0.1%
40815 1
< 0.1%
39469 1
< 0.1%
35820 1
< 0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct704
Distinct (%)27.9%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean1168.8448
Minimum0
Maximum641766
Zeros393
Zeros (%)14.6%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T05:12:00.263589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median18
Q3145
95-th percentile3858.5
Maximum641766
Range641766
Interquartile range (IQR)143

Descriptive statistics

Standard deviation14275.746
Coefficient of variation (CV)12.213551
Kurtosis1638.5431
Mean1168.8448
Median Absolute Deviation (MAD)18
Skewness38.005581
Sum2945489
Variance2.0379692 × 108
MonotonicityNot monotonic
2024-03-23T05:12:00.750808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 393
 
14.6%
1 185
 
6.9%
2 121
 
4.5%
3 84
 
3.1%
4 71
 
2.6%
5 60
 
2.2%
6 45
 
1.7%
7 42
 
1.6%
8 35
 
1.3%
11 34
 
1.3%
Other values (694) 1450
53.9%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 393
14.6%
1 185
6.9%
2 121
 
4.5%
3 84
 
3.1%
4 71
 
2.6%
5 60
 
2.2%
6 45
 
1.7%
7 42
 
1.6%
8 35
 
1.3%
9 28
 
1.0%
ValueCountFrequency (%)
641766 1
< 0.1%
218111 1
< 0.1%
161738 1
< 0.1%
70869 1
< 0.1%
60035 1
< 0.1%
46815 1
< 0.1%
45392 1
< 0.1%
39653 1
< 0.1%
39077 1
< 0.1%
37499 1
< 0.1%

Interactions

2024-03-23T05:11:26.487516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:23.479576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T05:10:48.732031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:54.896600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:03.089702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:10.136221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:17.265273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:23.665061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:29.952267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:29.364105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:37.529653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:47.047134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:53.748463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:00.539360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:07.859911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:15.642414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:23.973723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:29.383195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:36.964605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:43.205833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:49.096114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:55.207350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:03.474455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:10.716020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:17.650580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:24.009449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:30.280392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:29.822046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:37.978518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:47.350124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:54.123089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:00.914530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:08.340529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:16.243949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:24.231566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:29.655280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:37.273852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:43.639327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:49.505816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:55.480444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:03.835418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:11.205101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:18.121697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:24.384681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:30.577766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:30.293144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:38.370910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:47.667613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:54.517861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:01.375184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:08.895336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:16.795663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:24.538171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:29.950499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:37.641563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:43.922422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:49.914722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:55.805776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:04.209349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:11.697276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:18.578726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:24.868186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:30.896424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:30.740212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:38.769011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:48.011107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:54.903677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:01.882042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:09.290983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:17.356321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:24.812437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:30.265266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:38.049721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:44.263494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:50.329451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:56.156222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:04.649919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:12.095301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:18.893526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:25.172293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:31.243001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:31.380071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:39.205701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:48.416980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:55.285890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:02.314291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:09.706045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:17.952740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:25.186621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:30.602513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:38.388373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:44.538713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:50.660914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:57.244266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:05.043814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:12.403671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:19.506380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:25.547132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:31.794671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:32.203551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:39.575011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:48.770064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:56.030823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:02.786640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:10.068047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:18.616498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:25.473090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:31.065059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:38.783663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:44.907294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:50.996716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:57.821301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:05.499367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:12.887416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:19.766869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:25.848920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:32.675146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:32.794519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:39.846651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:49.120395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:09:56.291263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:03.233814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:10.551878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:19.061754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:25.740263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:31.457993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:39.211793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:45.167437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:51.434357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:10:58.308390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:05.875530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:13.303537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:20.063569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:11:26.168208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T05:12:01.073837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9820.9820.9900.9820.9820.9900.9900.9901.0001.0000.9380.9380.9380.9901.0000.9381.000
20070.9821.0001.0000.9960.9900.9900.9960.9960.9960.9820.9820.8760.8760.8760.9960.9070.8760.907
20080.9821.0001.0000.9960.9900.9900.9960.9960.9960.9820.9820.8760.8760.8760.9960.9070.8760.907
20090.9900.9960.9961.0000.9960.9961.0001.0001.0000.9900.9900.9070.9070.9071.0001.0000.9071.000
20100.9820.9900.9900.9961.0001.0000.9960.9960.9960.9820.9820.8760.8760.8760.9960.9070.8760.907
20110.9820.9900.9900.9961.0001.0000.9960.9960.9960.9820.9820.8760.8760.8760.9960.9070.8760.907
20120.9900.9960.9961.0000.9960.9961.0001.0001.0000.9900.9900.9070.9070.9071.0001.0000.9071.000
20130.9900.9960.9961.0000.9960.9961.0001.0001.0000.9900.9900.9070.9070.9071.0001.0000.9071.000
20140.9900.9960.9961.0000.9960.9961.0001.0001.0000.9900.9900.9070.9070.9071.0001.0000.9071.000
20151.0000.9820.9820.9900.9820.9820.9900.9900.9901.0001.0000.9380.9380.9380.9901.0000.9381.000
20161.0000.9820.9820.9900.9820.9820.9900.9900.9901.0001.0000.9380.9380.9380.9901.0000.9381.000
20170.9380.8760.8760.9070.8760.8760.9070.9070.9070.9380.9381.0001.0001.0000.9070.9981.0000.998
20180.9380.8760.8760.9070.8760.8760.9070.9070.9070.9380.9381.0001.0001.0000.9070.9981.0000.998
20190.9380.8760.8760.9070.8760.8760.9070.9070.9070.9380.9381.0001.0001.0000.9070.9981.0000.998
20200.9900.9960.9961.0000.9960.9961.0001.0001.0000.9900.9900.9070.9070.9071.0001.0000.9071.000
20211.0000.9070.9071.0000.9070.9071.0001.0001.0001.0001.0000.9980.9980.9981.0001.0000.9981.000
20220.9380.8760.8760.9070.8760.8760.9070.9070.9070.9380.9381.0001.0001.0000.9070.9981.0000.998
20231.0000.9070.9071.0000.9070.9071.0001.0001.0001.0001.0000.9980.9980.9981.0001.0000.9981.000
2024-03-23T05:12:01.743712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9000.8670.8520.8590.8560.8490.8600.8530.8600.8570.8630.8590.8550.8540.8440.8340.804
20070.9001.0000.8870.8530.8640.8450.8430.8480.8500.8600.8610.8560.8570.8510.8530.8320.8330.810
20080.8670.8871.0000.8750.8690.8450.8420.8450.8380.8430.8450.8410.8420.8290.8290.8150.8140.792
20090.8520.8530.8751.0000.8730.8500.8450.8490.8370.8480.8450.8380.8340.8210.8250.8140.8120.798
20100.8590.8640.8690.8731.0000.8920.8720.8680.8580.8660.8560.8560.8470.8360.8370.8280.8230.808
20110.8560.8450.8450.8500.8921.0000.9010.8740.8520.8610.8460.8500.8430.8260.8310.8240.8190.807
20120.8490.8430.8420.8450.8720.9011.0000.8960.8710.8660.8590.8610.8510.8420.8430.8390.8330.816
20130.8600.8480.8450.8490.8680.8740.8961.0000.8940.8860.8790.8830.8670.8530.8530.8430.8360.828
20140.8530.8500.8380.8370.8580.8520.8710.8941.0000.8930.8770.8740.8670.8490.8500.8340.8370.816
20150.8600.8600.8430.8480.8660.8610.8660.8860.8931.0000.9090.8940.8840.8610.8550.8440.8380.824
20160.8570.8610.8450.8450.8560.8460.8590.8790.8770.9091.0000.9100.8940.8730.8630.8520.8460.831
20170.8630.8560.8410.8380.8560.8500.8610.8830.8740.8940.9101.0000.9040.8860.8740.8690.8490.839
20180.8590.8570.8420.8340.8470.8430.8510.8670.8670.8840.8940.9041.0000.9070.8850.8670.8600.840
20190.8550.8510.8290.8210.8360.8260.8420.8530.8490.8610.8730.8860.9071.0000.9050.8690.8570.838
20200.8540.8530.8290.8250.8370.8310.8430.8530.8500.8550.8630.8740.8850.9051.0000.9020.8850.865
20210.8440.8320.8150.8140.8280.8240.8390.8430.8340.8440.8520.8690.8670.8690.9021.0000.9020.866
20220.8340.8330.8140.8120.8230.8190.8330.8360.8370.8380.8460.8490.8600.8570.8850.9021.0000.885
20230.8040.8100.7920.7980.8080.8070.8160.8280.8160.8240.8310.8390.8400.8380.8650.8660.8851.000

Missing values

2024-03-23T05:11:33.833788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T05:11:35.008133image/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.
2024-03-23T05:11:35.806664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

지역 및 거래주체200620072008200920102011201220132014201520162017201820192020202120222023
0전국 /개인>개인11470648424118604738403277613619539657569519143671127602138971212881041215828108018397429214174851092862571235641766
1전국 /개인>법인280621878121146148181404714193152221761819062234992559624394309334466170271563922675610851
2전국 /개인>기타4106384637694549428750664705484239047110660372248462106641628517775111106747
3전국 /법인>개인267143246933242980211595206104208357180171199126236687242132235376439036487211436701423356346547245938218111
4전국 /법인>법인15725158943048432319320552585421065175681715915244164151635924737243693193918751135128201
5전국 /법인>기타212333551568255289113681404111817502014238130883932475514131117351291110948
6전국 /기타>개인289431512218125260181666120140221093134139261386874707050951751756942345683733784880628675
7전국 /기타>법인296765274276709592385360420707103346963801758111013561809622
8전국 /기타>기타1473146792311831114185819949391278169014542312221828751605198512702874
9서울 /개인>개인28864616822815852614924598024123533937991238321668482501042491112234022029231498512013051371635922970869
지역 및 거래주체200620072008200920102011201220132014201520162017201820192020202120222023
2681제주 제주시/기타>기타39481012344216915137254618191818
2682제주 서귀포시/개인>개인67013281276138915381803172622052878430244393532293322162714367726312020
2683제주 서귀포시/개인>법인289555342233631289412016913240818222713311756
2684제주 서귀포시/개인>기타37559181020285341910658351173
2685제주 서귀포시/법인>개인271945732122264765357771026256925402267151211148801092912619
2686제주 서귀포시/법인>법인1882635681151241197737914112336324226410237138
2687제주 서귀포시/법인>기타0014815222110204532487356
2688제주 서귀포시/기타>개인1225161313251115182117197011525439
2689제주 서귀포시/기타>법인010303201213060853
2690제주 서귀포시/기타>기타1012232101212331110