Overview

Dataset statistics

Number of variables19
Number of observations2691
Missing cells3402
Missing cells (%)6.7%
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 아파트 거래현황의 연도별 거래주체별(면적) 데이터입니다.-(단위 : 천㎡)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068127/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 216 (8.0%) missing valuesMissing
2007 has 261 (9.7%) missing valuesMissing
2008 has 234 (8.7%) 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 135 (5.0%) missing valuesMissing
2015 has 162 (6.0%) missing valuesMissing
2016 has 162 (6.0%) missing valuesMissing
2017 has 189 (7.0%) missing valuesMissing
2018 has 180 (6.7%) missing valuesMissing
2019 has 189 (7.0%) missing valuesMissing
2020 has 189 (7.0%) 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 = 34.97437901)Skewed
2007 is highly skewed (γ1 = 31.76849847)Skewed
2008 is highly skewed (γ1 = 34.08495988)Skewed
2009 is highly skewed (γ1 = 35.34994085)Skewed
2010 is highly skewed (γ1 = 33.87229875)Skewed
2011 is highly skewed (γ1 = 37.42119517)Skewed
2012 is highly skewed (γ1 = 37.01581216)Skewed
2013 is highly skewed (γ1 = 38.46574487)Skewed
2014 is highly skewed (γ1 = 39.54963116)Skewed
2015 is highly skewed (γ1 = 39.82865488)Skewed
2016 is highly skewed (γ1 = 39.09024677)Skewed
2017 is highly skewed (γ1 = 33.59130071)Skewed
2018 is highly skewed (γ1 = 30.68051943)Skewed
2019 is highly skewed (γ1 = 31.15634808)Skewed
2020 is highly skewed (γ1 = 35.9582223)Skewed
2021 is highly skewed (γ1 = 34.57628211)Skewed
2022 is highly skewed (γ1 = 30.88901714)Skewed
2023 is highly skewed (γ1 = 35.41645013)Skewed
지역_거래주체 has unique valuesUnique
2006 has 1280 (47.6%) zerosZeros
2007 has 1276 (47.4%) zerosZeros
2008 has 1310 (48.7%) zerosZeros
2009 has 1250 (46.5%) zerosZeros
2010 has 1267 (47.1%) zerosZeros
2011 has 1165 (43.3%) zerosZeros
2012 has 1213 (45.1%) zerosZeros
2013 has 1273 (47.3%) zerosZeros
2014 has 1316 (48.9%) zerosZeros
2015 has 1219 (45.3%) zerosZeros
2016 has 1257 (46.7%) zerosZeros
2017 has 1193 (44.3%) zerosZeros
2018 has 1127 (41.9%) zerosZeros
2019 has 1134 (42.1%) zerosZeros
2020 has 1106 (41.1%) zerosZeros
2021 has 1195 (44.4%) zerosZeros
2022 has 1309 (48.6%) zerosZeros
2023 has 1343 (49.9%) zerosZeros

Reproduction

Analysis started2024-03-23 06:44:23.957131
Analysis finished2024-03-23 06:46:55.054989
Duration2 minutes and 31.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역_거래주체
Text

UNIQUE 

Distinct2691
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
2024-03-23T06:46:55.501500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length13
Mean length13.401338
Min length9

Characters and Unicode

Total characters36063
Distinct characters153
Distinct categories7 ?
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 468
 
8.4%
경남 234
 
4.2%
서울 225
 
4.1%
경북 225
 
4.1%
전남 198
 
3.6%
충남 171
 
3.1%
충북 171
 
3.1%
강원 162
 
2.9%
전북 144
 
2.6%
부산 144
 
2.6%
Other values (2478) 3402
61.4%
2024-03-23T06:46:57.043798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3750
 
10.4%
2853
 
7.9%
_ 2691
 
7.5%
- 2691
 
7.5%
> 2691
 
7.5%
2298
 
6.4%
1794
 
5.0%
1794
 
5.0%
1794
 
5.0%
1269
 
3.5%
Other values (143) 12438
34.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24795
68.8%
Space Separator 2853
 
7.9%
Connector Punctuation 2691
 
7.5%
Dash Punctuation 2691
 
7.5%
Math Symbol 2691
 
7.5%
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 (%)
2853
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2691
100.0%
Dash 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
68.8%
Common 11268
31.2%

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 (%)
2853
25.3%
_ 2691
23.9%
- 2691
23.9%
> 2691
23.9%
( 171
 
1.5%
) 171
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24795
68.8%
ASCII 11268
31.2%

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 (%)
2853
25.3%
_ 2691
23.9%
- 2691
23.9%
> 2691
23.9%
( 171
 
1.5%
) 171
 
1.5%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct323
Distinct (%)13.1%
Missing216
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean107.35677
Minimum0
Maximum56902
Zeros1280
Zeros (%)47.6%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:46:57.653483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile374
Maximum56902
Range56902
Interquartile range (IQR)7

Descriptive statistics

Standard deviation1325.6865
Coefficient of variation (CV)12.348421
Kurtosis1407.7486
Mean107.35677
Median Absolute Deviation (MAD)0
Skewness34.974379
Sum265708
Variance1757444.8
MonotonicityNot monotonic
2024-03-23T06:46:58.474952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1280
47.6%
1 291
 
10.8%
2 112
 
4.2%
3 65
 
2.4%
4 52
 
1.9%
5 32
 
1.2%
7 23
 
0.9%
6 21
 
0.8%
9 20
 
0.7%
8 17
 
0.6%
Other values (313) 562
20.9%
(Missing) 216
 
8.0%
ValueCountFrequency (%)
0 1280
47.6%
1 291
 
10.8%
2 112
 
4.2%
3 65
 
2.4%
4 52
 
1.9%
5 32
 
1.2%
6 21
 
0.8%
7 23
 
0.9%
8 17
 
0.6%
9 20
 
0.7%
ValueCountFrequency (%)
56902 1
< 0.1%
21653 1
< 0.1%
18435 1
< 0.1%
11794 1
< 0.1%
6273 1
< 0.1%
4082 1
< 0.1%
3531 1
< 0.1%
2749 1
< 0.1%
2672 1
< 0.1%
2360 1
< 0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct278
Distinct (%)11.4%
Missing261
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean84.073251
Minimum0
Maximum38438
Zeros1276
Zeros (%)47.4%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:46:59.134007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile257.2
Maximum38438
Range38438
Interquartile range (IQR)6

Descriptive statistics

Standard deviation974.30891
Coefficient of variation (CV)11.58881
Kurtosis1141.2173
Mean84.073251
Median Absolute Deviation (MAD)0
Skewness31.768498
Sum204298
Variance949277.85
MonotonicityNot monotonic
2024-03-23T06:46:59.651068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1276
47.4%
1 265
 
9.8%
2 108
 
4.0%
3 66
 
2.5%
4 52
 
1.9%
5 32
 
1.2%
6 28
 
1.0%
12 21
 
0.8%
7 21
 
0.8%
10 19
 
0.7%
Other values (268) 542
20.1%
(Missing) 261
 
9.7%
ValueCountFrequency (%)
0 1276
47.4%
1 265
 
9.8%
2 108
 
4.0%
3 66
 
2.5%
4 52
 
1.9%
5 32
 
1.2%
6 28
 
1.0%
7 21
 
0.8%
8 17
 
0.6%
9 12
 
0.4%
ValueCountFrequency (%)
38438 1
< 0.1%
23992 1
< 0.1%
8359 1
< 0.1%
6039 1
< 0.1%
5304 1
< 0.1%
4311 1
< 0.1%
4009 1
< 0.1%
3892 1
< 0.1%
3392 1
< 0.1%
2886 1
< 0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct302
Distinct (%)12.3%
Missing234
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean79.86569
Minimum0
Maximum38522
Zeros1310
Zeros (%)48.7%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:47:00.249271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile274.8
Maximum38522
Range38522
Interquartile range (IQR)7

Descriptive statistics

Standard deviation921.40078
Coefficient of variation (CV)11.536879
Kurtosis1322.4693
Mean79.86569
Median Absolute Deviation (MAD)0
Skewness34.08496
Sum196230
Variance848979.41
MonotonicityNot monotonic
2024-03-23T06:47:00.848782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1310
48.7%
1 254
 
9.4%
2 100
 
3.7%
3 64
 
2.4%
4 56
 
2.1%
7 29
 
1.1%
6 28
 
1.0%
5 22
 
0.8%
9 17
 
0.6%
12 16
 
0.6%
Other values (292) 561
20.8%
(Missing) 234
 
8.7%
ValueCountFrequency (%)
0 1310
48.7%
1 254
 
9.4%
2 100
 
3.7%
3 64
 
2.4%
4 56
 
2.1%
5 22
 
0.8%
6 28
 
1.0%
7 29
 
1.1%
8 16
 
0.6%
9 17
 
0.6%
ValueCountFrequency (%)
38522 1
< 0.1%
19762 1
< 0.1%
8591 1
< 0.1%
5094 1
< 0.1%
4492 1
< 0.1%
3766 1
< 0.1%
2884 1
< 0.1%
2711 1
< 0.1%
2208 1
< 0.1%
2092 1
< 0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct301
Distinct (%)12.2%
Missing225
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean88.310624
Minimum0
Maximum44166
Zeros1250
Zeros (%)46.5%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:47:01.501249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile306.75
Maximum44166
Range44166
Interquartile range (IQR)8

Descriptive statistics

Standard deviation1023.9141
Coefficient of variation (CV)11.594461
Kurtosis1438.339
Mean88.310624
Median Absolute Deviation (MAD)0
Skewness35.349941
Sum217774
Variance1048400
MonotonicityNot monotonic
2024-03-23T06:47:02.132729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1250
46.5%
1 291
 
10.8%
2 95
 
3.5%
3 67
 
2.5%
4 52
 
1.9%
5 37
 
1.4%
7 25
 
0.9%
6 21
 
0.8%
10 20
 
0.7%
14 18
 
0.7%
Other values (291) 590
21.9%
(Missing) 225
 
8.4%
ValueCountFrequency (%)
0 1250
46.5%
1 291
 
10.8%
2 95
 
3.5%
3 67
 
2.5%
4 52
 
1.9%
5 37
 
1.4%
6 21
 
0.8%
7 25
 
0.9%
8 15
 
0.6%
9 12
 
0.4%
ValueCountFrequency (%)
44166 1
< 0.1%
17800 1
< 0.1%
11900 1
< 0.1%
6854 1
< 0.1%
4610 1
< 0.1%
4285 1
< 0.1%
3258 1
< 0.1%
2872 1
< 0.1%
2698 1
< 0.1%
2282 1
< 0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct301
Distinct (%)11.9%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean81.395635
Minimum0
Maximum39091
Zeros1267
Zeros (%)47.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:47:02.950208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile263.45
Maximum39091
Range39091
Interquartile range (IQR)7

Descriptive statistics

Standard deviation931.35692
Coefficient of variation (CV)11.442345
Kurtosis1314.8641
Mean81.395635
Median Absolute Deviation (MAD)0
Skewness33.872299
Sum205117
Variance867425.71
MonotonicityNot monotonic
2024-03-23T06:47:03.652437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1267
47.1%
1 324
 
12.0%
2 117
 
4.3%
3 69
 
2.6%
4 43
 
1.6%
5 34
 
1.3%
6 27
 
1.0%
7 24
 
0.9%
8 16
 
0.6%
9 15
 
0.6%
Other values (291) 584
21.7%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 1267
47.1%
1 324
 
12.0%
2 117
 
4.3%
3 69
 
2.6%
4 43
 
1.6%
5 34
 
1.3%
6 27
 
1.0%
7 24
 
0.9%
8 16
 
0.6%
9 15
 
0.6%
ValueCountFrequency (%)
39091 1
< 0.1%
20340 1
< 0.1%
8106 1
< 0.1%
7039 1
< 0.1%
4971 1
< 0.1%
4097 1
< 0.1%
3629 1
< 0.1%
3038 1
< 0.1%
3019 1
< 0.1%
2196 1
< 0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct321
Distinct (%)12.9%
Missing198
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean92.119134
Minimum0
Maximum49074
Zeros1165
Zeros (%)43.3%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:47:04.304133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37
95-th percentile327
Maximum49074
Range49074
Interquartile range (IQR)7

Descriptive statistics

Standard deviation1103.0726
Coefficient of variation (CV)11.974414
Kurtosis1593.0328
Mean92.119134
Median Absolute Deviation (MAD)1
Skewness37.421195
Sum229653
Variance1216769.2
MonotonicityNot monotonic
2024-03-23T06:47:04.884635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1165
43.3%
1 370
 
13.7%
2 137
 
5.1%
3 76
 
2.8%
4 54
 
2.0%
6 28
 
1.0%
5 27
 
1.0%
8 22
 
0.8%
7 21
 
0.8%
9 17
 
0.6%
Other values (311) 576
21.4%
(Missing) 198
 
7.4%
ValueCountFrequency (%)
0 1165
43.3%
1 370
 
13.7%
2 137
 
5.1%
3 76
 
2.8%
4 54
 
2.0%
5 27
 
1.0%
6 28
 
1.0%
7 21
 
0.8%
8 22
 
0.8%
9 17
 
0.6%
ValueCountFrequency (%)
49074 1
< 0.1%
17951 1
< 0.1%
10999 1
< 0.1%
5464 1
< 0.1%
5089 1
< 0.1%
3951 1
< 0.1%
3681 1
< 0.1%
3425 1
< 0.1%
2455 1
< 0.1%
2451 1
< 0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct288
Distinct (%)11.5%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean72.246914
Minimum0
Maximum37619
Zeros1213
Zeros (%)45.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:47:05.426710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36.5
95-th percentile252
Maximum37619
Range37619
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation849.04262
Coefficient of variation (CV)11.751957
Kurtosis1563.0572
Mean72.246914
Median Absolute Deviation (MAD)1
Skewness37.015812
Sum181412
Variance720873.38
MonotonicityNot monotonic
2024-03-23T06:47:05.915562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1213
45.1%
1 359
 
13.3%
2 149
 
5.5%
3 59
 
2.2%
4 46
 
1.7%
5 30
 
1.1%
7 28
 
1.0%
6 27
 
1.0%
8 24
 
0.9%
9 20
 
0.7%
Other values (278) 556
20.7%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 1213
45.1%
1 359
 
13.3%
2 149
 
5.5%
3 59
 
2.2%
4 46
 
1.7%
5 30
 
1.1%
6 27
 
1.0%
7 28
 
1.0%
8 24
 
0.9%
9 20
 
0.7%
ValueCountFrequency (%)
37619 1
< 0.1%
14676 1
< 0.1%
8106 1
< 0.1%
3639 1
< 0.1%
3556 1
< 0.1%
3513 1
< 0.1%
3195 1
< 0.1%
2958 1
< 0.1%
2339 1
< 0.1%
2133 1
< 0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct305
Distinct (%)12.1%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean86
Minimum0
Maximum47997
Zeros1273
Zeros (%)47.3%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:47:06.543275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile306
Maximum47997
Range47997
Interquartile range (IQR)6

Descriptive statistics

Standard deviation1061.1611
Coefficient of variation (CV)12.339083
Kurtosis1678.1858
Mean86
Median Absolute Deviation (MAD)0
Skewness38.465745
Sum215946
Variance1126062.9
MonotonicityNot monotonic
2024-03-23T06:47:07.119053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1273
47.3%
1 315
 
11.7%
2 131
 
4.9%
3 73
 
2.7%
4 47
 
1.7%
6 36
 
1.3%
5 36
 
1.3%
9 28
 
1.0%
8 22
 
0.8%
10 15
 
0.6%
Other values (295) 535
19.9%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 1273
47.3%
1 315
 
11.7%
2 131
 
4.9%
3 73
 
2.7%
4 47
 
1.7%
5 36
 
1.3%
6 36
 
1.3%
7 13
 
0.5%
8 22
 
0.8%
9 28
 
1.0%
ValueCountFrequency (%)
47997 1
< 0.1%
15205 1
< 0.1%
11055 1
< 0.1%
5519 1
< 0.1%
4344 1
< 0.1%
4056 1
< 0.1%
4016 1
< 0.1%
3887 1
< 0.1%
2604 1
< 0.1%
2515 1
< 0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct330
Distinct (%)12.9%
Missing135
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean105.99648
Minimum0
Maximum61615
Zeros1316
Zeros (%)48.9%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:47:07.800004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile384.5
Maximum61615
Range61615
Interquartile range (IQR)7

Descriptive statistics

Standard deviation1338.1201
Coefficient of variation (CV)12.624194
Kurtosis1765.305
Mean105.99648
Median Absolute Deviation (MAD)0
Skewness39.549631
Sum270927
Variance1790565.5
MonotonicityNot monotonic
2024-03-23T06:47:08.216897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1316
48.9%
1 297
 
11.0%
2 110
 
4.1%
3 80
 
3.0%
4 55
 
2.0%
5 35
 
1.3%
7 26
 
1.0%
8 19
 
0.7%
6 19
 
0.7%
10 17
 
0.6%
Other values (320) 582
21.6%
(Missing) 135
 
5.0%
ValueCountFrequency (%)
0 1316
48.9%
1 297
 
11.0%
2 110
 
4.1%
3 80
 
3.0%
4 55
 
2.0%
5 35
 
1.3%
6 19
 
0.7%
7 26
 
1.0%
8 19
 
0.7%
9 16
 
0.6%
ValueCountFrequency (%)
61615 1
< 0.1%
17398 1
< 0.1%
14422 1
< 0.1%
7696 1
< 0.1%
5835 1
< 0.1%
5106 1
< 0.1%
4781 1
< 0.1%
3734 1
< 0.1%
3414 1
< 0.1%
3308 1
< 0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct339
Distinct (%)13.4%
Missing162
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean117.79992
Minimum0
Maximum71637
Zeros1219
Zeros (%)45.3%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:47:08.613263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37
95-th percentile418.6
Maximum71637
Range71637
Interquartile range (IQR)7

Descriptive statistics

Standard deviation1555.9386
Coefficient of variation (CV)13.208316
Kurtosis1781.4031
Mean117.79992
Median Absolute Deviation (MAD)1
Skewness39.828655
Sum297916
Variance2420945
MonotonicityNot monotonic
2024-03-23T06:47:09.058478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1219
45.3%
1 332
 
12.3%
2 128
 
4.8%
3 85
 
3.2%
4 60
 
2.2%
5 33
 
1.2%
7 28
 
1.0%
6 27
 
1.0%
8 21
 
0.8%
10 17
 
0.6%
Other values (329) 579
21.5%
(Missing) 162
 
6.0%
ValueCountFrequency (%)
0 1219
45.3%
1 332
 
12.3%
2 128
 
4.8%
3 85
 
3.2%
4 60
 
2.2%
5 33
 
1.2%
6 27
 
1.0%
7 28
 
1.0%
8 21
 
0.8%
9 15
 
0.6%
ValueCountFrequency (%)
71637 1
< 0.1%
18347 1
< 0.1%
17155 1
< 0.1%
10879 1
< 0.1%
7483 1
< 0.1%
5211 1
< 0.1%
4286 1
< 0.1%
4158 1
< 0.1%
4118 1
< 0.1%
3233 1
< 0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct328
Distinct (%)13.0%
Missing162
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean108.00633
Minimum0
Maximum64193
Zeros1257
Zeros (%)46.7%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:47:09.481597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37
95-th percentile392.6
Maximum64193
Range64193
Interquartile range (IQR)7

Descriptive statistics

Standard deviation1406.3088
Coefficient of variation (CV)13.020615
Kurtosis1725.027
Mean108.00633
Median Absolute Deviation (MAD)1
Skewness39.090247
Sum273148
Variance1977704.5
MonotonicityNot monotonic
2024-03-23T06:47:09.929450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1257
46.7%
1 295
 
11.0%
2 121
 
4.5%
3 80
 
3.0%
4 54
 
2.0%
5 48
 
1.8%
6 33
 
1.2%
7 23
 
0.9%
13 16
 
0.6%
8 13
 
0.5%
Other values (318) 589
21.9%
(Missing) 162
 
6.0%
ValueCountFrequency (%)
0 1257
46.7%
1 295
 
11.0%
2 121
 
4.5%
3 80
 
3.0%
4 54
 
2.0%
5 48
 
1.8%
6 33
 
1.2%
7 23
 
0.9%
8 13
 
0.5%
9 11
 
0.4%
ValueCountFrequency (%)
64193 1
< 0.1%
17850 1
< 0.1%
16132 1
< 0.1%
10460 1
< 0.1%
6532 1
< 0.1%
4465 1
< 0.1%
4355 1
< 0.1%
3861 1
< 0.1%
3389 1
< 0.1%
2850 1
< 0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct353
Distinct (%)14.1%
Missing189
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean124.78817
Minimum0
Maximum61541
Zeros1193
Zeros (%)44.3%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:47:10.651268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38
95-th percentile435.85
Maximum61541
Range61541
Interquartile range (IQR)8

Descriptive statistics

Standard deviation1474.2614
Coefficient of variation (CV)11.814112
Kurtosis1291.3994
Mean124.78817
Median Absolute Deviation (MAD)1
Skewness33.591301
Sum312220
Variance2173446.5
MonotonicityNot monotonic
2024-03-23T06:47:11.122256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1193
44.3%
1 303
 
11.3%
2 138
 
5.1%
3 72
 
2.7%
4 53
 
2.0%
5 44
 
1.6%
6 28
 
1.0%
8 25
 
0.9%
7 22
 
0.8%
10 17
 
0.6%
Other values (343) 607
22.6%
(Missing) 189
 
7.0%
ValueCountFrequency (%)
0 1193
44.3%
1 303
 
11.3%
2 138
 
5.1%
3 72
 
2.7%
4 53
 
2.0%
5 44
 
1.6%
6 28
 
1.0%
7 22
 
0.8%
8 25
 
0.9%
9 10
 
0.4%
ValueCountFrequency (%)
61541 1
< 0.1%
30973 1
< 0.1%
18152 1
< 0.1%
9633 1
< 0.1%
9478 1
< 0.1%
4441 1
< 0.1%
3814 1
< 0.1%
3709 1
< 0.1%
3684 1
< 0.1%
3633 1
< 0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct364
Distinct (%)14.5%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean127.98487
Minimum0
Maximum55821
Zeros1127
Zeros (%)41.9%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:47:11.638885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q312.5
95-th percentile398
Maximum55821
Range55821
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation1438.3503
Coefficient of variation (CV)11.238441
Kurtosis1065.7907
Mean127.98487
Median Absolute Deviation (MAD)1
Skewness30.680519
Sum321370
Variance2068851.6
MonotonicityNot monotonic
2024-03-23T06:47:12.033391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1127
41.9%
1 315
 
11.7%
2 103
 
3.8%
3 92
 
3.4%
4 58
 
2.2%
6 42
 
1.6%
5 35
 
1.3%
7 24
 
0.9%
9 20
 
0.7%
8 20
 
0.7%
Other values (354) 675
25.1%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 1127
41.9%
1 315
 
11.7%
2 103
 
3.8%
3 92
 
3.4%
4 58
 
2.2%
5 35
 
1.3%
6 42
 
1.6%
7 24
 
0.9%
8 20
 
0.7%
9 20
 
0.7%
ValueCountFrequency (%)
55821 1
< 0.1%
36172 1
< 0.1%
18208 1
< 0.1%
13584 1
< 0.1%
8589 1
< 0.1%
5674 1
< 0.1%
3275 1
< 0.1%
3210 1
< 0.1%
3035 1
< 0.1%
2851 1
< 0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct358
Distinct (%)14.3%
Missing189
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean117.58553
Minimum0
Maximum51136
Zeros1134
Zeros (%)42.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:47:12.471864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q312
95-th percentile370
Maximum51136
Range51136
Interquartile range (IQR)12

Descriptive statistics

Standard deviation1304.9277
Coefficient of variation (CV)11.09769
Kurtosis1099.666
Mean117.58553
Median Absolute Deviation (MAD)1
Skewness31.156348
Sum294199
Variance1702836.4
MonotonicityNot monotonic
2024-03-23T06:47:12.986540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1134
42.1%
1 277
 
10.3%
2 115
 
4.3%
3 81
 
3.0%
4 64
 
2.4%
5 56
 
2.1%
6 35
 
1.3%
9 27
 
1.0%
8 23
 
0.9%
7 23
 
0.9%
Other values (348) 667
24.8%
(Missing) 189
 
7.0%
ValueCountFrequency (%)
0 1134
42.1%
1 277
 
10.3%
2 115
 
4.3%
3 81
 
3.0%
4 64
 
2.4%
5 56
 
2.1%
6 35
 
1.3%
7 23
 
0.9%
8 23
 
0.9%
9 27
 
1.0%
ValueCountFrequency (%)
51136 1
< 0.1%
32745 1
< 0.1%
14910 1
< 0.1%
11770 1
< 0.1%
6237 1
< 0.1%
5242 1
< 0.1%
3618 1
< 0.1%
3474 1
< 0.1%
3311 1
< 0.1%
3225 1
< 0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct364
Distinct (%)14.5%
Missing189
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean151.19384
Minimum0
Maximum79330
Zeros1106
Zeros (%)41.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:47:13.435696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q318
95-th percentile489.6
Maximum79330
Range79330
Interquartile range (IQR)18

Descriptive statistics

Standard deviation1819.1806
Coefficient of variation (CV)12.032108
Kurtosis1479.7738
Mean151.19384
Median Absolute Deviation (MAD)1
Skewness35.958222
Sum378287
Variance3309418.2
MonotonicityNot monotonic
2024-03-23T06:47:13.908619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1106
41.1%
1 245
 
9.1%
2 117
 
4.3%
3 72
 
2.7%
4 67
 
2.5%
5 42
 
1.6%
9 29
 
1.1%
11 26
 
1.0%
6 26
 
1.0%
8 25
 
0.9%
Other values (354) 747
27.8%
(Missing) 189
 
7.0%
ValueCountFrequency (%)
0 1106
41.1%
1 245
 
9.1%
2 117
 
4.3%
3 72
 
2.7%
4 67
 
2.5%
5 42
 
1.6%
6 26
 
1.0%
7 25
 
0.9%
8 25
 
0.9%
9 29
 
1.1%
ValueCountFrequency (%)
79330 1
< 0.1%
30735 1
< 0.1%
23959 1
< 0.1%
9744 1
< 0.1%
8301 1
< 0.1%
7380 1
< 0.1%
5325 1
< 0.1%
5050 1
< 0.1%
4794 1
< 0.1%
3351 1
< 0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct358
Distinct (%)14.3%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean107.99323
Minimum0
Maximum52164
Zeros1195
Zeros (%)44.4%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:47:14.334195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q314
95-th percentile344.5
Maximum52164
Range52164
Interquartile range (IQR)14

Descriptive statistics

Standard deviation1225.1111
Coefficient of variation (CV)11.344333
Kurtosis1370.6877
Mean107.99323
Median Absolute Deviation (MAD)1
Skewness34.576282
Sum271171
Variance1500897.3
MonotonicityNot monotonic
2024-03-23T06:47:14.766587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1195
44.4%
1 250
 
9.3%
2 109
 
4.1%
3 60
 
2.2%
4 53
 
2.0%
5 40
 
1.5%
7 35
 
1.3%
6 32
 
1.2%
8 26
 
1.0%
10 24
 
0.9%
Other values (348) 687
25.5%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 1195
44.4%
1 250
 
9.3%
2 109
 
4.1%
3 60
 
2.2%
4 53
 
2.0%
5 40
 
1.5%
6 32
 
1.2%
7 35
 
1.3%
8 26
 
1.0%
9 14
 
0.5%
ValueCountFrequency (%)
52164 1
< 0.1%
24649 1
< 0.1%
14149 1
< 0.1%
6601 1
< 0.1%
5182 1
< 0.1%
4766 1
< 0.1%
4302 1
< 0.1%
3636 1
< 0.1%
3544 1
< 0.1%
3244 1
< 0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct268
Distinct (%)10.7%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean59.248905
Minimum0
Maximum23706
Zeros1309
Zeros (%)48.6%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:47:15.187109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile196
Maximum23706
Range23706
Interquartile range (IQR)8

Descriptive statistics

Standard deviation628.16365
Coefficient of variation (CV)10.602114
Kurtosis1060.283
Mean59.248905
Median Absolute Deviation (MAD)0
Skewness30.889017
Sum148774
Variance394589.57
MonotonicityNot monotonic
2024-03-23T06:47:15.730953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1309
48.6%
1 234
 
8.7%
2 114
 
4.2%
3 76
 
2.8%
4 53
 
2.0%
5 41
 
1.5%
7 30
 
1.1%
6 25
 
0.9%
8 21
 
0.8%
10 16
 
0.6%
Other values (258) 592
22.0%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 1309
48.6%
1 234
 
8.7%
2 114
 
4.2%
3 76
 
2.8%
4 53
 
2.0%
5 41
 
1.5%
6 25
 
0.9%
7 30
 
1.1%
8 21
 
0.8%
9 14
 
0.5%
ValueCountFrequency (%)
23706 1
< 0.1%
17863 1
< 0.1%
5134 1
< 0.1%
4855 1
< 0.1%
3383 1
< 0.1%
2548 1
< 0.1%
1834 1
< 0.1%
1687 1
< 0.1%
1659 1
< 0.1%
1624 1
< 0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct283
Distinct (%)11.2%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean68.743651
Minimum0
Maximum35314
Zeros1343
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T06:47:16.293799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile222.15
Maximum35314
Range35314
Interquartile range (IQR)5

Descriptive statistics

Standard deviation818.01756
Coefficient of variation (CV)11.899536
Kurtosis1432.3356
Mean68.743651
Median Absolute Deviation (MAD)0
Skewness35.41645
Sum173234
Variance669152.72
MonotonicityNot monotonic
2024-03-23T06:47:16.701057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1343
49.9%
1 308
 
11.4%
2 125
 
4.6%
3 59
 
2.2%
5 37
 
1.4%
4 36
 
1.3%
6 28
 
1.0%
7 27
 
1.0%
9 19
 
0.7%
8 16
 
0.6%
Other values (273) 522
 
19.4%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 1343
49.9%
1 308
 
11.4%
2 125
 
4.6%
3 59
 
2.2%
4 36
 
1.3%
5 37
 
1.4%
6 28
 
1.0%
7 27
 
1.0%
8 16
 
0.6%
9 19
 
0.7%
ValueCountFrequency (%)
35314 1
< 0.1%
16008 1
< 0.1%
9147 1
< 0.1%
4340 1
< 0.1%
3000 1
< 0.1%
2646 1
< 0.1%
2565 1
< 0.1%
2372 1
< 0.1%
2274 1
< 0.1%
2199 1
< 0.1%

Interactions

2024-03-23T06:46:42.460980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:44:34.277469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T06:46:16.691578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:24.726927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:33.256989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:39.518166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:46.869746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:44:40.253848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:44:47.022897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:44:54.164947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:00.747216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:11.999845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:18.921416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:26.133164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:32.410164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:39.345532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:47.504663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:56.012189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:02.454273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:09.311769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:17.148841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:25.194560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:33.590486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:39.858283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:47.355815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:44:40.592848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:44:47.454183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:44:54.498856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:01.240944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:13.027051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:19.219051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:26.392259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:32.744476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:39.768067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:47.950924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:56.320023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:02.764974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:09.726058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:17.569548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:25.814651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:33.918973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:40.247849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:47.706597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:44:41.059738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:44:47.787106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:44:54.899367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:01.660042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:13.563450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:19.578730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:26.866081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:33.085386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:40.320712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:48.330115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:56.661132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:03.126333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:10.056233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:18.018949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:26.506114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:34.231313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:40.588614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:48.137989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:44:41.450423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:44:48.076203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:44:55.191386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:02.200866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:13.850031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:19.935789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:27.168437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:33.413545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:40.696582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:48.736104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:57.029308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:03.518354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:10.379380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:18.562620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:26.954227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:34.607283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:41.041753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:48.578342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:44:41.751734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:44:48.414160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:44:55.611798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:02.871349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:14.155216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:20.399670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:27.557095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:33.776885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:41.280607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:49.037638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:57.383391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:03.909463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:10.779948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:19.025079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:27.671182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:34.980091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:41.434162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:49.457980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:44:42.024927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:44:48.803431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:44:56.203758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:03.650065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:14.588439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:20.784048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:27.908016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:34.151813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:41.760300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:49.326782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:57.683023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:04.265739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:11.099152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:19.504758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:28.056790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:35.269560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:41.805099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:50.388582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:44:42.375365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:44:49.231161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:44:56.492279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:04.288131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:14.821617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:21.041163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:28.280308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:34.547582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:42.367431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:49.668514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:45:58.018435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:04.702892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:11.393679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:19.822397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:28.665873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:35.609879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:46:42.103599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T06:47:17.117123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9750.9910.9910.9840.9790.8410.9911.0000.9070.9070.9910.9500.9861.0000.9910.9690.991
20070.9751.0000.9970.9970.9960.9931.0000.9950.8690.8550.8550.9970.9360.9850.8950.9950.9850.995
20080.9910.9971.0001.0000.9990.9951.0000.9980.9070.8760.8761.0000.9670.9911.0000.9980.9860.998
20090.9910.9971.0001.0000.9990.9951.0000.9980.9070.8760.8761.0000.9670.9911.0000.9980.9860.998
20100.9840.9960.9990.9991.0000.9991.0000.9960.8860.9270.9270.9990.9500.9880.9270.9960.9840.996
20110.9790.9930.9950.9950.9991.0001.0000.9980.9071.0001.0000.9950.9320.9860.8760.9910.9790.991
20120.8411.0001.0001.0001.0001.0001.0001.0000.9820.9820.9821.0001.0000.9070.9821.0000.9071.000
20130.9910.9950.9980.9980.9960.9981.0001.0001.0000.9070.9070.9980.9500.9910.9070.9910.9790.991
20141.0000.8690.9070.9070.8860.9070.9821.0001.0000.9960.9960.9070.9830.8760.9960.8410.8030.841
20150.9070.8550.8760.8760.9271.0000.9820.9070.9961.0001.0000.8760.9580.8410.9900.8410.8030.841
20160.9070.8550.8760.8760.9271.0000.9820.9070.9961.0001.0000.8760.9580.8410.9900.8410.8030.841
20170.9910.9971.0001.0000.9990.9951.0000.9980.9070.8760.8761.0000.9670.9911.0000.9980.9860.998
20180.9500.9360.9670.9670.9500.9321.0000.9500.9830.9580.9580.9671.0001.0000.9961.0000.9321.000
20190.9860.9850.9910.9910.9880.9860.9070.9910.8760.8410.8410.9911.0001.0000.8760.9910.9950.991
20201.0000.8951.0001.0000.9270.8760.9820.9070.9960.9900.9901.0000.9960.8761.0000.9070.8410.907
20210.9910.9950.9980.9980.9960.9911.0000.9910.8410.8410.8410.9981.0000.9910.9071.0000.9911.000
20220.9690.9850.9860.9860.9840.9790.9070.9790.8030.8030.8030.9860.9320.9950.8410.9911.0000.991
20230.9910.9950.9980.9980.9960.9911.0000.9910.8410.8410.8410.9981.0000.9910.9071.0000.9911.000
2024-03-23T06:47:17.592090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.8500.8160.8060.8110.8080.7790.8050.7950.8140.8080.8130.8050.8120.8160.7790.7710.750
20070.8501.0000.8470.8110.8080.7900.7760.7870.7900.8030.7950.7940.7920.7870.7990.7730.7680.744
20080.8160.8471.0000.8430.8170.7920.7790.7900.7830.7920.7960.7840.7810.7730.7760.7560.7560.743
20090.8060.8110.8431.0000.8340.8060.8010.8010.7830.8090.7900.7860.7720.7680.7820.7570.7560.748
20100.8110.8080.8170.8341.0000.8550.8290.8260.8040.8150.7940.8010.7850.7910.7960.7710.7650.754
20110.8080.7900.7920.8060.8551.0000.8610.8390.8080.8140.8040.8080.7970.7820.7960.7650.7600.755
20120.7790.7760.7790.8010.8290.8611.0000.8510.8180.8250.7980.8080.7970.7930.7950.7790.7670.754
20130.8050.7870.7900.8010.8260.8390.8511.0000.8510.8420.8290.8380.8080.8010.8140.7770.7750.760
20140.7950.7900.7830.7830.8040.8080.8180.8511.0000.8470.8190.8210.8050.8080.7990.7600.7680.752
20150.8140.8030.7920.8090.8150.8140.8250.8420.8471.0000.8640.8480.8370.8140.8180.7900.7860.767
20160.8080.7950.7960.7900.7940.8040.7980.8290.8190.8641.0000.8640.8420.8270.8240.7910.7750.776
20170.8130.7940.7840.7860.8010.8080.8080.8380.8210.8480.8641.0000.8530.8380.8240.8070.7940.764
20180.8050.7920.7810.7720.7850.7970.7970.8080.8050.8370.8420.8531.0000.8630.8400.8070.7890.770
20190.8120.7870.7730.7680.7910.7820.7930.8010.8080.8140.8270.8380.8631.0000.8620.8100.7890.772
20200.8160.7990.7760.7820.7960.7960.7950.8140.7990.8180.8240.8240.8400.8621.0000.8520.8190.806
20210.7790.7730.7560.7570.7710.7650.7790.7770.7600.7900.7910.8070.8070.8100.8521.0000.8630.828
20220.7710.7680.7560.7560.7650.7600.7670.7750.7680.7860.7750.7940.7890.7890.8190.8631.0000.835
20230.7500.7440.7430.7480.7540.7550.7540.7600.7520.7670.7760.7640.7700.7720.8060.8280.8351.000

Missing values

2024-03-23T06:46:51.083805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T06:46:52.402405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-23T06:46:54.114909image/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_거래주체200620072008200920102011201220132014201520162017201820192020202120222023
0전국_개인->개인569023843838522441663909149074376194799761615716376419361541558215113679330521642370635314
1전국_개인->법인1419435452380364427377390431432520516669123524261512554220
2전국_개인->기타123868015013515613913181159153133149181282247201140
3전국_법인->개인216532399219762178002034017951146761520517398171551613230973361723274530735246491786316008
4전국_법인->법인83898722083258301924552126100413038981059732123611241883734578321
5전국_법인->기타13422482182651131096491116139158194202495217257322
6전국_기타->개인236012331397228213831629155425153734261033893633567452423117518233831973
7전국_기타->법인15351011762828141918471438310449746316
8전국_기타->기타111895575819099457310471114126156597935124
9서울_개인->개인117945304509468544097508936395519769610879104609633858962378301430212343000
지역_거래주체200620072008200920102011201220132014201520162017201820192020202120222023
2681제주 제주시_기타->기타000001100000000000
2682제주 서귀포시_개인->개인10272725254034495611113572524060975045
2683제주 서귀포시_개인->법인010100031112623121
2684제주 서귀포시_개인->기타000000000100001002
2685제주 서귀포시_법인->개인1113411102422464414213285463834382810
2686제주 서귀포시_법인->법인1010111311411735308
2687제주 서귀포시_법인->기타000100000400000000
2688제주 서귀포시_기타->개인000000000000104610
2689제주 서귀포시_기타->법인000000000000000000
2690제주 서귀포시_기타->기타000000000000000000