Overview

Dataset statistics

Number of variables19
Number of observations1794
Missing cells2190
Missing cells (%)6.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory298.0 KiB
Average record size in memory170.1 B

Variable types

Text1
Numeric18

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 부동산거래현황 중 건축물 거래현황의 연도별 거래원인별(면적) 데이터입니다.- (단위 : 천㎡)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068219/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 132 (7.4%) missing valuesMissing
2007 has 162 (9.0%) missing valuesMissing
2008 has 150 (8.4%) missing valuesMissing
2009 has 150 (8.4%) missing valuesMissing
2010 has 114 (6.4%) missing valuesMissing
2011 has 132 (7.4%) missing valuesMissing
2012 has 120 (6.7%) missing valuesMissing
2013 has 120 (6.7%) missing valuesMissing
2014 has 78 (4.3%) missing valuesMissing
2015 has 102 (5.7%) missing valuesMissing
2016 has 102 (5.7%) missing valuesMissing
2017 has 120 (6.7%) missing valuesMissing
2018 has 114 (6.4%) missing valuesMissing
2019 has 120 (6.7%) missing valuesMissing
2020 has 120 (6.7%) missing valuesMissing
2021 has 120 (6.7%) missing valuesMissing
2022 has 120 (6.7%) missing valuesMissing
2023 has 114 (6.4%) missing valuesMissing
2006 is highly skewed (γ1 = 30.81687272)Skewed
2007 is highly skewed (γ1 = 31.33262598)Skewed
2008 is highly skewed (γ1 = 32.71303294)Skewed
2009 is highly skewed (γ1 = 33.02127193)Skewed
2010 is highly skewed (γ1 = 33.51537128)Skewed
2011 is highly skewed (γ1 = 34.15221834)Skewed
2013 is highly skewed (γ1 = 33.33873973)Skewed
2014 is highly skewed (γ1 = 33.02278559)Skewed
2015 is highly skewed (γ1 = 31.55314235)Skewed
2016 is highly skewed (γ1 = 31.45379432)Skewed
2017 is highly skewed (γ1 = 27.35002578)Skewed
2018 is highly skewed (γ1 = 25.63818694)Skewed
2019 is highly skewed (γ1 = 26.82730487)Skewed
2020 is highly skewed (γ1 = 29.92108988)Skewed
2021 is highly skewed (γ1 = 30.32327629)Skewed
2022 is highly skewed (γ1 = 29.08185233)Skewed
2023 is highly skewed (γ1 = 29.97512476)Skewed
지역_거래원인 has unique valuesUnique
2006 has 327 (18.2%) zerosZeros
2007 has 331 (18.5%) zerosZeros
2008 has 349 (19.5%) zerosZeros
2009 has 352 (19.6%) zerosZeros
2010 has 365 (20.3%) zerosZeros
2011 has 339 (18.9%) zerosZeros
2012 has 336 (18.7%) zerosZeros
2013 has 332 (18.5%) zerosZeros
2014 has 317 (17.7%) zerosZeros
2015 has 299 (16.7%) zerosZeros
2016 has 293 (16.3%) zerosZeros
2017 has 288 (16.1%) zerosZeros
2018 has 286 (15.9%) zerosZeros
2019 has 285 (15.9%) zerosZeros
2020 has 257 (14.3%) zerosZeros
2021 has 252 (14.0%) zerosZeros
2022 has 256 (14.3%) zerosZeros
2023 has 275 (15.3%) zerosZeros

Reproduction

Analysis started2024-03-16 04:21:01.418594
Analysis finished2024-03-16 04:21:38.856945
Duration37.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역_거래원인
Text

UNIQUE 

Distinct1794
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size14.1 KiB
2024-03-16T13:21:39.012648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length9
Mean length9.5379041
Min length5

Characters and Unicode

Total characters17111
Distinct characters154
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1794 ?
Unique (%)100.0%

Sample

1st row전국_매매
2nd row전국_판결
3rd row전국_교환
4th row전국_증여
5th row전국_분양권
ValueCountFrequency (%)
경기 312
 
8.4%
경남 156
 
4.2%
서울 150
 
4.1%
경북 150
 
4.1%
전남 132
 
3.6%
충북 114
 
3.1%
충남 114
 
3.1%
강원 108
 
2.9%
부산 96
 
2.6%
전북 96
 
2.6%
Other values (1659) 2268
61.4%
2024-03-16T13:21:39.433232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1902
 
11.1%
_ 1794
 
10.5%
828
 
4.8%
732
 
4.3%
654
 
3.8%
635
 
3.7%
598
 
3.5%
558
 
3.3%
534
 
3.1%
443
 
2.6%
Other values (144) 8433
49.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13223
77.3%
Space Separator 1902
 
11.1%
Connector Punctuation 1794
 
10.5%
Close Punctuation 96
 
0.6%
Open Punctuation 96
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
828
 
6.3%
732
 
5.5%
654
 
4.9%
635
 
4.8%
598
 
4.5%
558
 
4.2%
534
 
4.0%
443
 
3.4%
432
 
3.3%
323
 
2.4%
Other values (140) 7486
56.6%
Space Separator
ValueCountFrequency (%)
1902
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1794
100.0%
Close Punctuation
ValueCountFrequency (%)
) 96
100.0%
Open Punctuation
ValueCountFrequency (%)
( 96
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13223
77.3%
Common 3888
 
22.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
828
 
6.3%
732
 
5.5%
654
 
4.9%
635
 
4.8%
598
 
4.5%
558
 
4.2%
534
 
4.0%
443
 
3.4%
432
 
3.3%
323
 
2.4%
Other values (140) 7486
56.6%
Common
ValueCountFrequency (%)
1902
48.9%
_ 1794
46.1%
) 96
 
2.5%
( 96
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13223
77.3%
ASCII 3888
 
22.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1902
48.9%
_ 1794
46.1%
) 96
 
2.5%
( 96
 
2.5%
Hangul
ValueCountFrequency (%)
828
 
6.3%
732
 
5.5%
654
 
4.9%
635
 
4.8%
598
 
4.5%
558
 
4.2%
534
 
4.0%
443
 
3.4%
432
 
3.3%
323
 
2.4%
Other values (140) 7486
56.6%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct415
Distinct (%)25.0%
Missing132
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean318.09687
Minimum0
Maximum116872
Zeros327
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-16T13:21:39.562170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median19
Q3100
95-th percentile994.2
Maximum116872
Range116872
Interquartile range (IQR)99

Descriptive statistics

Standard deviation3193.9996
Coefficient of variation (CV)10.040965
Kurtosis1082.818
Mean318.09687
Median Absolute Deviation (MAD)19
Skewness30.816873
Sum528677
Variance10201633
MonotonicityNot monotonic
2024-03-16T13:21:39.722330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 327
 
18.2%
1 134
 
7.5%
2 64
 
3.6%
3 47
 
2.6%
4 30
 
1.7%
5 28
 
1.6%
6 26
 
1.4%
10 24
 
1.3%
12 19
 
1.1%
8 18
 
1.0%
Other values (405) 945
52.7%
(Missing) 132
 
7.4%
ValueCountFrequency (%)
0 327
18.2%
1 134
7.5%
2 64
 
3.6%
3 47
 
2.6%
4 30
 
1.7%
5 28
 
1.6%
6 26
 
1.4%
7 17
 
0.9%
8 18
 
1.0%
9 12
 
0.7%
ValueCountFrequency (%)
116872 1
0.1%
36309 1
0.1%
25393 1
0.1%
23991 1
0.1%
16984 1
0.1%
8338 1
0.1%
7735 1
0.1%
6429 1
0.1%
6245 1
0.1%
6072 1
0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct386
Distinct (%)23.7%
Missing162
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean271.77757
Minimum0
Maximum100097
Zeros331
Zeros (%)18.5%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-16T13:21:39.877859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median11
Q378
95-th percentile787.9
Maximum100097
Range100097
Interquartile range (IQR)77

Descriptive statistics

Standard deviation2726.9416
Coefficient of variation (CV)10.033726
Kurtosis1112.4215
Mean271.77757
Median Absolute Deviation (MAD)11
Skewness31.332626
Sum443541
Variance7436210.6
MonotonicityNot monotonic
2024-03-16T13:21:40.012780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 331
 
18.5%
1 168
 
9.4%
2 84
 
4.7%
4 48
 
2.7%
3 46
 
2.6%
7 25
 
1.4%
5 24
 
1.3%
8 23
 
1.3%
6 22
 
1.2%
10 20
 
1.1%
Other values (376) 841
46.9%
(Missing) 162
 
9.0%
ValueCountFrequency (%)
0 331
18.5%
1 168
9.4%
2 84
 
4.7%
3 46
 
2.6%
4 48
 
2.7%
5 24
 
1.3%
6 22
 
1.2%
7 25
 
1.4%
8 23
 
1.3%
9 13
 
0.7%
ValueCountFrequency (%)
100097 1
0.1%
26372 1
0.1%
24545 1
0.1%
18089 1
0.1%
8636 1
0.1%
7592 1
0.1%
7180 1
0.1%
7037 1
0.1%
6286 1
0.1%
5377 1
0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct375
Distinct (%)22.8%
Missing150
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean250.06387
Minimum0
Maximum98210
Zeros349
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-16T13:21:40.164720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median10
Q365
95-th percentile734.8
Maximum98210
Range98210
Interquartile range (IQR)64

Descriptive statistics

Standard deviation2621.064
Coefficient of variation (CV)10.481578
Kurtosis1195.6565
Mean250.06387
Median Absolute Deviation (MAD)10
Skewness32.713033
Sum411105
Variance6869976.6
MonotonicityNot monotonic
2024-03-16T13:21:40.298183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 349
19.5%
1 137
 
7.6%
3 72
 
4.0%
2 69
 
3.8%
4 40
 
2.2%
5 35
 
2.0%
7 31
 
1.7%
6 30
 
1.7%
8 26
 
1.4%
11 23
 
1.3%
Other values (365) 832
46.4%
(Missing) 150
 
8.4%
ValueCountFrequency (%)
0 349
19.5%
1 137
 
7.6%
2 69
 
3.8%
3 72
 
4.0%
4 40
 
2.2%
5 35
 
2.0%
6 30
 
1.7%
7 31
 
1.7%
8 26
 
1.4%
9 20
 
1.1%
ValueCountFrequency (%)
98210 1
0.1%
23078 1
0.1%
20631 1
0.1%
16412 1
0.1%
7438 1
0.1%
7185 1
0.1%
7049 1
0.1%
6603 1
0.1%
5627 1
0.1%
5226 1
0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct391
Distinct (%)23.8%
Missing150
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean252.69465
Minimum0
Maximum100014
Zeros352
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-16T13:21:40.430958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median10
Q367.25
95-th percentile754.25
Maximum100014
Range100014
Interquartile range (IQR)66.25

Descriptive statistics

Standard deviation2658.1019
Coefficient of variation (CV)10.519027
Kurtosis1214.8386
Mean252.69465
Median Absolute Deviation (MAD)10
Skewness33.021272
Sum415430
Variance7065505.9
MonotonicityNot monotonic
2024-03-16T13:21:40.570657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 352
19.6%
1 148
 
8.2%
2 88
 
4.9%
3 55
 
3.1%
4 46
 
2.6%
5 34
 
1.9%
7 24
 
1.3%
6 23
 
1.3%
8 22
 
1.2%
11 19
 
1.1%
Other values (381) 833
46.4%
(Missing) 150
 
8.4%
ValueCountFrequency (%)
0 352
19.6%
1 148
8.2%
2 88
 
4.9%
3 55
 
3.1%
4 46
 
2.6%
5 34
 
1.9%
6 23
 
1.3%
7 24
 
1.3%
8 22
 
1.2%
9 18
 
1.0%
ValueCountFrequency (%)
100014 1
0.1%
23208 1
0.1%
18837 1
0.1%
16259 1
0.1%
7594 1
0.1%
7384 1
0.1%
7231 1
0.1%
6475 1
0.1%
6180 1
0.1%
5093 1
0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct389
Distinct (%)23.2%
Missing114
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean243.32976
Minimum0
Maximum96553
Zeros365
Zeros (%)20.3%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-16T13:21:40.743958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median10
Q372
95-th percentile736.35
Maximum96553
Range96553
Interquartile range (IQR)71

Descriptive statistics

Standard deviation2532.1698
Coefficient of variation (CV)10.40633
Kurtosis1252.5335
Mean243.32976
Median Absolute Deviation (MAD)10
Skewness33.515371
Sum408794
Variance6411884.1
MonotonicityNot monotonic
2024-03-16T13:21:40.907695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 365
20.3%
1 168
 
9.4%
2 78
 
4.3%
3 57
 
3.2%
4 53
 
3.0%
5 28
 
1.6%
6 26
 
1.4%
9 20
 
1.1%
13 20
 
1.1%
8 19
 
1.1%
Other values (379) 846
47.2%
(Missing) 114
 
6.4%
ValueCountFrequency (%)
0 365
20.3%
1 168
9.4%
2 78
 
4.3%
3 57
 
3.2%
4 53
 
3.0%
5 28
 
1.6%
6 26
 
1.4%
7 17
 
0.9%
8 19
 
1.1%
9 20
 
1.1%
ValueCountFrequency (%)
96553 1
0.1%
19765 1
0.1%
19471 1
0.1%
11541 1
0.1%
9157 1
0.1%
8809 1
0.1%
8417 1
0.1%
7724 1
0.1%
6309 1
0.1%
5265 1
0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct379
Distinct (%)22.8%
Missing132
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean282.60469
Minimum0
Maximum115835
Zeros339
Zeros (%)18.9%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-16T13:21:41.039172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median11
Q375
95-th percentile838
Maximum115835
Range115835
Interquartile range (IQR)74

Descriptive statistics

Standard deviation3025.7251
Coefficient of variation (CV)10.706564
Kurtosis1285.9567
Mean282.60469
Median Absolute Deviation (MAD)11
Skewness34.152218
Sum469689
Variance9155012.5
MonotonicityNot monotonic
2024-03-16T13:21:41.183032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 339
 
18.9%
1 160
 
8.9%
2 81
 
4.5%
3 71
 
4.0%
4 42
 
2.3%
9 26
 
1.4%
5 23
 
1.3%
7 22
 
1.2%
6 22
 
1.2%
13 19
 
1.1%
Other values (369) 857
47.8%
(Missing) 132
 
7.4%
ValueCountFrequency (%)
0 339
18.9%
1 160
8.9%
2 81
 
4.5%
3 71
 
4.0%
4 42
 
2.3%
5 23
 
1.3%
6 22
 
1.2%
7 22
 
1.2%
8 14
 
0.8%
9 26
 
1.4%
ValueCountFrequency (%)
115835 1
0.1%
24455 1
0.1%
18288 1
0.1%
13628 1
0.1%
9841 1
0.1%
9667 1
0.1%
8796 1
0.1%
8363 1
0.1%
7759 1
0.1%
5812 1
0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct392
Distinct (%)23.4%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean414.70908
Minimum0
Maximum99773
Zeros336
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-16T13:21:41.363289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median11
Q377
95-th percentile751.05
Maximum99773
Range99773
Interquartile range (IQR)76

Descriptive statistics

Standard deviation4745.4938
Coefficient of variation (CV)11.442946
Kurtosis386.42824
Mean414.70908
Median Absolute Deviation (MAD)11
Skewness19.415454
Sum694223
Variance22519711
MonotonicityNot monotonic
2024-03-16T13:21:41.535445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 336
 
18.7%
1 144
 
8.0%
2 84
 
4.7%
4 54
 
3.0%
3 48
 
2.7%
5 35
 
2.0%
7 26
 
1.4%
6 25
 
1.4%
9 23
 
1.3%
11 23
 
1.3%
Other values (382) 876
48.8%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 336
18.7%
1 144
8.0%
2 84
 
4.7%
3 48
 
2.7%
4 54
 
3.0%
5 35
 
2.0%
6 25
 
1.4%
7 26
 
1.4%
8 22
 
1.2%
9 23
 
1.3%
ValueCountFrequency (%)
99773 1
0.1%
94672 1
0.1%
93890 1
0.1%
93853 1
0.1%
19224 1
0.1%
18703 1
0.1%
11352 1
0.1%
8698 1
0.1%
7492 1
0.1%
7213 1
0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct404
Distinct (%)24.1%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean273.91517
Minimum0
Maximum106885
Zeros332
Zeros (%)18.5%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-16T13:21:41.694497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median13
Q382
95-th percentile770.35
Maximum106885
Range106885
Interquartile range (IQR)81

Descriptive statistics

Standard deviation2813.9779
Coefficient of variation (CV)10.273173
Kurtosis1238.723
Mean273.91517
Median Absolute Deviation (MAD)13
Skewness33.33874
Sum458534
Variance7918471.7
MonotonicityNot monotonic
2024-03-16T13:21:41.859965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 332
 
18.5%
1 140
 
7.8%
2 84
 
4.7%
3 50
 
2.8%
4 40
 
2.2%
5 29
 
1.6%
7 29
 
1.6%
10 24
 
1.3%
9 24
 
1.3%
6 23
 
1.3%
Other values (394) 899
50.1%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 332
18.5%
1 140
7.8%
2 84
 
4.7%
3 50
 
2.8%
4 40
 
2.2%
5 29
 
1.6%
6 23
 
1.3%
7 29
 
1.6%
8 19
 
1.1%
9 24
 
1.3%
ValueCountFrequency (%)
106885 1
0.1%
24100 1
0.1%
22292 1
0.1%
13317 1
0.1%
8689 1
0.1%
8199 1
0.1%
7781 1
0.1%
7516 1
0.1%
7417 1
0.1%
5941 1
0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct436
Distinct (%)25.4%
Missing78
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean316.45804
Minimum0
Maximum124356
Zeros317
Zeros (%)17.7%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-16T13:21:42.048128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median15
Q396
95-th percentile926.25
Maximum124356
Range124356
Interquartile range (IQR)95

Descriptive statistics

Standard deviation3266.1852
Coefficient of variation (CV)10.321069
Kurtosis1222.9347
Mean316.45804
Median Absolute Deviation (MAD)15
Skewness33.022786
Sum543042
Variance10667966
MonotonicityNot monotonic
2024-03-16T13:21:42.220295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 317
 
17.7%
1 162
 
9.0%
2 79
 
4.4%
3 60
 
3.3%
5 34
 
1.9%
8 33
 
1.8%
4 31
 
1.7%
6 26
 
1.4%
11 21
 
1.2%
16 21
 
1.2%
Other values (426) 932
52.0%
(Missing) 78
 
4.3%
ValueCountFrequency (%)
0 317
17.7%
1 162
9.0%
2 79
 
4.4%
3 60
 
3.3%
4 31
 
1.7%
5 34
 
1.9%
6 26
 
1.4%
7 14
 
0.8%
8 33
 
1.8%
9 20
 
1.1%
ValueCountFrequency (%)
124356 1
0.1%
31281 1
0.1%
29133 1
0.1%
16641 1
0.1%
10161 1
0.1%
9277 1
0.1%
9087 1
0.1%
8677 1
0.1%
8030 1
0.1%
7002 1
0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct472
Distinct (%)27.9%
Missing102
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean410.48227
Minimum0
Maximum149719
Zeros299
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-16T13:21:42.358670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median17
Q3114.25
95-th percentile1205.9
Maximum149719
Range149719
Interquartile range (IQR)113.25

Descriptive statistics

Standard deviation4020.0271
Coefficient of variation (CV)9.7934244
Kurtosis1136.6203
Mean410.48227
Median Absolute Deviation (MAD)17
Skewness31.553142
Sum694536
Variance16160618
MonotonicityNot monotonic
2024-03-16T13:21:42.821913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 299
 
16.7%
1 149
 
8.3%
2 83
 
4.6%
3 62
 
3.5%
4 35
 
2.0%
5 26
 
1.4%
6 24
 
1.3%
9 22
 
1.2%
8 20
 
1.1%
13 20
 
1.1%
Other values (462) 952
53.1%
(Missing) 102
 
5.7%
ValueCountFrequency (%)
0 299
16.7%
1 149
8.3%
2 83
 
4.6%
3 62
 
3.5%
4 35
 
2.0%
5 26
 
1.4%
6 24
 
1.3%
7 19
 
1.1%
8 20
 
1.1%
9 22
 
1.2%
ValueCountFrequency (%)
149719 1
0.1%
39664 1
0.1%
36295 1
0.1%
25241 1
0.1%
14573 1
0.1%
12127 1
0.1%
10458 1
0.1%
10280 1
0.1%
10001 1
0.1%
9003 1
0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct467
Distinct (%)27.6%
Missing102
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean372.76773
Minimum0
Maximum138590
Zeros293
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-16T13:21:42.977584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median18
Q3117.5
95-th percentile1083.7
Maximum138590
Range138590
Interquartile range (IQR)116.5

Descriptive statistics

Standard deviation3734.6272
Coefficient of variation (CV)10.018644
Kurtosis1123.7864
Mean372.76773
Median Absolute Deviation (MAD)18
Skewness31.453794
Sum630723
Variance13947440
MonotonicityNot monotonic
2024-03-16T13:21:43.124765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 293
 
16.3%
1 148
 
8.2%
2 78
 
4.3%
3 44
 
2.5%
4 40
 
2.2%
6 28
 
1.6%
5 26
 
1.4%
7 24
 
1.3%
10 22
 
1.2%
11 21
 
1.2%
Other values (457) 968
54.0%
(Missing) 102
 
5.7%
ValueCountFrequency (%)
0 293
16.3%
1 148
8.2%
2 78
 
4.3%
3 44
 
2.5%
4 40
 
2.2%
5 26
 
1.4%
6 28
 
1.6%
7 24
 
1.3%
8 19
 
1.1%
9 13
 
0.7%
ValueCountFrequency (%)
138590 1
0.1%
39181 1
0.1%
36528 1
0.1%
24535 1
0.1%
12492 1
0.1%
11721 1
0.1%
11237 1
0.1%
8797 1
0.1%
8212 1
0.1%
6620 1
0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct461
Distinct (%)27.5%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean385.49164
Minimum0
Maximum122756
Zeros288
Zeros (%)16.1%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-16T13:21:43.265901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median18
Q3130
95-th percentile1100.8
Maximum122756
Range122756
Interquartile range (IQR)129

Descriptive statistics

Standard deviation3606.2814
Coefficient of variation (CV)9.3550184
Kurtosis855.53944
Mean385.49164
Median Absolute Deviation (MAD)18
Skewness27.350026
Sum645313
Variance13005265
MonotonicityNot monotonic
2024-03-16T13:21:43.431716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 288
 
16.1%
1 160
 
8.9%
2 77
 
4.3%
3 44
 
2.5%
5 36
 
2.0%
4 34
 
1.9%
6 27
 
1.5%
7 26
 
1.4%
8 24
 
1.3%
10 19
 
1.1%
Other values (451) 939
52.3%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 288
16.1%
1 160
8.9%
2 77
 
4.3%
3 44
 
2.5%
4 34
 
1.9%
5 36
 
2.0%
6 27
 
1.5%
7 26
 
1.4%
8 24
 
1.3%
9 13
 
0.7%
ValueCountFrequency (%)
122756 1
0.1%
64109 1
0.1%
31492 1
0.1%
21456 1
0.1%
20975 1
0.1%
10873 1
0.1%
8622 1
0.1%
8273 1
0.1%
7182 1
0.1%
6939 1
0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct481
Distinct (%)28.6%
Missing114
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean382.23155
Minimum0
Maximum111819
Zeros286
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-16T13:21:43.569132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median19
Q3141
95-th percentile1076.65
Maximum111819
Range111819
Interquartile range (IQR)140

Descriptive statistics

Standard deviation3448.8999
Coefficient of variation (CV)9.0230644
Kurtosis747.7891
Mean382.23155
Median Absolute Deviation (MAD)19
Skewness25.638187
Sum642149
Variance11894910
MonotonicityNot monotonic
2024-03-16T13:21:43.742877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 286
 
15.9%
1 147
 
8.2%
2 87
 
4.8%
3 42
 
2.3%
4 39
 
2.2%
5 29
 
1.6%
7 26
 
1.4%
6 25
 
1.4%
9 21
 
1.2%
16 16
 
0.9%
Other values (471) 962
53.6%
(Missing) 114
 
6.4%
ValueCountFrequency (%)
0 286
15.9%
1 147
8.2%
2 87
 
4.8%
3 42
 
2.3%
4 39
 
2.2%
5 29
 
1.6%
6 25
 
1.4%
7 26
 
1.4%
8 13
 
0.7%
9 21
 
1.2%
ValueCountFrequency (%)
111819 1
0.1%
68798 1
0.1%
31378 1
0.1%
26999 1
0.1%
19993 1
0.1%
12468 1
0.1%
6508 1
0.1%
6144 1
0.1%
6011 1
0.1%
5748 1
0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct465
Distinct (%)27.8%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean354.01792
Minimum0
Maximum108526
Zeros285
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-16T13:21:43.864977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median20
Q3120
95-th percentile1020.05
Maximum108526
Range108526
Interquartile range (IQR)119

Descriptive statistics

Standard deviation3222.4627
Coefficient of variation (CV)9.1025412
Kurtosis826.13816
Mean354.01792
Median Absolute Deviation (MAD)20
Skewness26.827305
Sum592626
Variance10384266
MonotonicityNot monotonic
2024-03-16T13:21:44.004128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 285
 
15.9%
1 140
 
7.8%
2 76
 
4.2%
3 67
 
3.7%
4 37
 
2.1%
5 30
 
1.7%
7 23
 
1.3%
9 20
 
1.1%
10 17
 
0.9%
6 17
 
0.9%
Other values (455) 962
53.6%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 285
15.9%
1 140
7.8%
2 76
 
4.2%
3 67
 
3.7%
4 37
 
2.1%
5 30
 
1.7%
6 17
 
0.9%
7 23
 
1.3%
8 15
 
0.8%
9 20
 
1.1%
ValueCountFrequency (%)
108526 1
0.1%
58119 1
0.1%
29122 1
0.1%
21860 1
0.1%
16092 1
0.1%
12647 1
0.1%
7797 1
0.1%
6505 1
0.1%
6192 1
0.1%
5824 1
0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct487
Distinct (%)29.1%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean429.73716
Minimum0
Maximum151091
Zeros257
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-16T13:21:44.166254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median21
Q3141
95-th percentile1235.05
Maximum151091
Range151091
Interquartile range (IQR)139

Descriptive statistics

Standard deviation4186.0782
Coefficient of variation (CV)9.7410199
Kurtosis1025.8219
Mean429.73716
Median Absolute Deviation (MAD)21
Skewness29.92109
Sum719380
Variance17523251
MonotonicityNot monotonic
2024-03-16T13:21:44.313811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 257
 
14.3%
1 160
 
8.9%
2 77
 
4.3%
3 54
 
3.0%
4 37
 
2.1%
5 32
 
1.8%
6 24
 
1.3%
7 20
 
1.1%
12 20
 
1.1%
8 19
 
1.1%
Other values (477) 974
54.3%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 257
14.3%
1 160
8.9%
2 77
 
4.3%
3 54
 
3.0%
4 37
 
2.1%
5 32
 
1.8%
6 24
 
1.3%
7 20
 
1.1%
8 19
 
1.1%
9 18
 
1.0%
ValueCountFrequency (%)
151091 1
0.1%
52240 1
0.1%
44715 1
0.1%
20695 1
0.1%
17412 1
0.1%
15981 1
0.1%
11330 1
0.1%
9700 1
0.1%
8267 1
0.1%
7856 1
0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct473
Distinct (%)28.3%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean392.19654
Minimum0
Maximum139090
Zeros252
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-16T13:21:44.448604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median21
Q3129
95-th percentile1035.3
Maximum139090
Range139090
Interquartile range (IQR)128

Descriptive statistics

Standard deviation3828.5066
Coefficient of variation (CV)9.7617044
Kurtosis1050.4103
Mean392.19654
Median Absolute Deviation (MAD)21
Skewness30.323276
Sum656537
Variance14657463
MonotonicityNot monotonic
2024-03-16T13:21:44.578917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 252
 
14.0%
1 174
 
9.7%
2 75
 
4.2%
3 45
 
2.5%
4 44
 
2.5%
5 36
 
2.0%
7 26
 
1.4%
11 22
 
1.2%
6 20
 
1.1%
8 17
 
0.9%
Other values (463) 963
53.7%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 252
14.0%
1 174
9.7%
2 75
 
4.2%
3 45
 
2.5%
4 44
 
2.5%
5 36
 
2.0%
6 20
 
1.1%
7 26
 
1.4%
8 17
 
0.9%
9 15
 
0.8%
ValueCountFrequency (%)
139090 1
0.1%
46760 1
0.1%
39711 1
0.1%
16697 1
0.1%
15935 1
0.1%
14208 1
0.1%
9656 1
0.1%
9641 1
0.1%
8633 1
0.1%
7874 1
0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct416
Distinct (%)24.9%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean245.74014
Minimum0
Maximum80605
Zeros256
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-16T13:21:44.709114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median17
Q395
95-th percentile678.5
Maximum80605
Range80605
Interquartile range (IQR)94

Descriptive statistics

Standard deviation2266.749
Coefficient of variation (CV)9.2241705
Kurtosis974.65836
Mean245.74014
Median Absolute Deviation (MAD)17
Skewness29.081852
Sum411369
Variance5138150.9
MonotonicityNot monotonic
2024-03-16T13:21:44.847533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 256
 
14.3%
1 164
 
9.1%
2 104
 
5.8%
3 61
 
3.4%
4 48
 
2.7%
6 32
 
1.8%
5 23
 
1.3%
9 18
 
1.0%
12 18
 
1.0%
8 17
 
0.9%
Other values (406) 933
52.0%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 256
14.3%
1 164
9.1%
2 104
5.8%
3 61
 
3.4%
4 48
 
2.7%
5 23
 
1.3%
6 32
 
1.8%
7 14
 
0.8%
8 17
 
0.9%
9 18
 
1.0%
ValueCountFrequency (%)
80605 1
0.1%
32740 1
0.1%
20632 1
0.1%
11480 1
0.1%
10968 1
0.1%
8550 1
0.1%
6049 1
0.1%
5896 1
0.1%
5197 1
0.1%
4771 1
0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct391
Distinct (%)23.3%
Missing114
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean211.88274
Minimum0
Maximum72389
Zeros275
Zeros (%)15.3%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-16T13:21:45.010374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median13
Q381
95-th percentile581.15
Maximum72389
Range72389
Interquartile range (IQR)80

Descriptive statistics

Standard deviation2002.3433
Coefficient of variation (CV)9.4502427
Kurtosis1029.6782
Mean211.88274
Median Absolute Deviation (MAD)13
Skewness29.975125
Sum355963
Variance4009378.7
MonotonicityNot monotonic
2024-03-16T13:21:45.139468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 275
 
15.3%
1 159
 
8.9%
2 85
 
4.7%
3 62
 
3.5%
4 49
 
2.7%
7 36
 
2.0%
5 33
 
1.8%
6 32
 
1.8%
8 24
 
1.3%
10 24
 
1.3%
Other values (381) 901
50.2%
(Missing) 114
 
6.4%
ValueCountFrequency (%)
0 275
15.3%
1 159
8.9%
2 85
 
4.7%
3 62
 
3.5%
4 49
 
2.7%
5 33
 
1.8%
6 32
 
1.8%
7 36
 
2.0%
8 24
 
1.3%
9 15
 
0.8%
ValueCountFrequency (%)
72389 1
0.1%
26573 1
0.1%
19450 1
0.1%
8384 1
0.1%
7987 1
0.1%
7438 1
0.1%
5450 1
0.1%
4921 1
0.1%
4649 1
0.1%
4070 1
0.1%

Interactions

2024-03-16T13:21:35.994917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-16T13:21:10.614596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:12.573300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:14.641109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:16.350735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:18.196618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:20.185597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:22.393994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:24.553548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:26.226639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:28.333144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:29.875488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:31.578329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:33.405566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:35.176290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:37.194835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:04.750935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:06.769154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:08.947357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:10.748508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:12.691838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:14.748221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:16.430660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:18.287975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:20.325075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:22.475096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:24.637474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:26.308282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:28.410754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:29.956479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:31.672563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:33.495537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:35.310111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:37.274098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:04.911155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:06.869205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:09.024441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:10.882890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:12.814548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:14.855633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:16.504597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:18.411478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:20.442577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:22.556503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:24.721198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:26.405971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:28.484238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:30.030881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:31.770873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:33.599936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:35.433849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:37.355040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:05.004075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:06.974626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:09.126144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:11.001172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:12.911771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:14.971138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:16.586526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:18.509208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:20.551361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:22.647146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:24.799811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:26.510648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:28.563081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:30.105661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:31.851940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:33.710795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:35.532716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:37.442506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:05.121726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:07.088803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:09.212516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:11.114241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:13.006093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:15.076345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:16.678169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:18.606712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:20.964751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:22.875024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:24.899636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:26.611413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:28.643366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:30.181866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:31.930889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:33.831646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:35.639983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:37.544447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:05.293292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:07.188799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:09.294035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:11.234584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:13.093227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:15.167462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:16.765428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:18.730362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:21.055445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:23.195440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:25.015574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:26.726690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:28.732927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:30.282143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:32.009931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:33.936353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:35.750485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:37.660605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:05.394998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:07.321184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:09.378556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:11.347966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:13.183034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:15.259578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:16.905382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:18.880993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:21.185091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:23.326642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:25.130459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:27.139019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:28.821105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:30.401621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:32.106083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:34.039318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:21:35.868227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-16T13:21:45.259874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.8760.8761.0000.8761.0000.8330.8750.8750.8750.8750.9860.9690.9690.9840.9840.9840.984
20070.8761.0001.0000.9901.0000.9900.7421.0001.0001.0001.0000.9070.8750.8750.8860.8860.8860.886
20080.8761.0001.0000.9901.0000.9900.7421.0001.0001.0001.0000.9070.8750.8750.8860.8860.8860.886
20091.0000.9900.9901.0000.9901.0000.7420.9900.9900.9900.9900.9380.8750.8750.9270.9270.9270.927
20100.8761.0001.0000.9901.0000.9900.7421.0001.0001.0001.0000.9070.8750.8750.8860.8860.8860.886
20111.0000.9900.9901.0000.9901.0000.7420.9900.9900.9900.9900.9380.8750.8750.9270.9270.9270.927
20120.8330.7420.7420.7420.7420.7421.0000.7420.7420.7420.7420.7380.6950.6950.7240.7240.7240.724
20130.8751.0001.0000.9901.0000.9900.7421.0001.0001.0001.0000.9070.8750.8750.8860.8860.8860.886
20140.8751.0001.0000.9901.0000.9900.7421.0001.0001.0001.0000.9070.8750.8750.8860.8860.8860.886
20150.8751.0001.0000.9901.0000.9900.7421.0001.0001.0001.0000.9070.8750.8750.8860.8860.8860.886
20160.8751.0001.0000.9901.0000.9900.7421.0001.0001.0001.0000.9070.8750.8750.8860.8860.8860.886
20170.9860.9070.9070.9380.9070.9380.7380.9070.9070.9070.9071.0000.9910.9910.9990.9990.9990.999
20180.9690.8750.8750.8750.8750.8750.6950.8750.8750.8750.8750.9911.0001.0000.9960.9960.9960.996
20190.9690.8750.8750.8750.8750.8750.6950.8750.8750.8750.8750.9911.0001.0000.9960.9960.9960.996
20200.9840.8860.8860.9270.8860.9270.7240.8860.8860.8860.8860.9990.9960.9961.0001.0001.0001.000
20210.9840.8860.8860.9270.8860.9270.7240.8860.8860.8860.8860.9990.9960.9961.0001.0001.0001.000
20220.9840.8860.8860.9270.8860.9270.7240.8860.8860.8860.8860.9990.9960.9961.0001.0001.0001.000
20230.9840.8860.8860.9270.8860.9270.7240.8860.8860.8860.8860.9990.9960.9961.0001.0001.0001.000
2024-03-16T13:21:45.441845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.8900.8780.8640.8580.8580.8630.8680.8540.8640.8590.8600.8470.8550.8510.8450.8410.837
20070.8901.0000.9160.8940.8820.8810.8810.8740.8740.8740.8850.8780.8710.8810.8740.8770.8700.869
20080.8780.9161.0000.9170.8940.8950.8980.8860.8850.8820.8850.8850.8740.8850.8860.8780.8770.878
20090.8640.8940.9171.0000.9060.8930.8920.8860.8800.8890.8890.8850.8790.8840.8850.8840.8790.885
20100.8580.8820.8940.9061.0000.9090.8920.8830.8710.8720.8770.8730.8680.8800.8770.8720.8670.865
20110.8580.8810.8950.8930.9091.0000.9120.8950.8880.8760.8830.8780.8680.8790.8840.8720.8650.874
20120.8630.8810.8980.8920.8920.9121.0000.9160.8990.8920.8910.8900.8790.8930.8910.8860.8810.883
20130.8680.8740.8860.8860.8830.8950.9161.0000.9090.9030.8980.8990.8840.8950.8870.8840.8770.880
20140.8540.8740.8850.8800.8710.8880.8990.9091.0000.9110.9040.9120.9010.9030.8980.8880.8860.881
20150.8640.8740.8820.8890.8720.8760.8920.9030.9111.0000.9240.9190.9040.9100.9030.9010.8920.887
20160.8590.8850.8850.8890.8770.8830.8910.8980.9040.9241.0000.9260.9110.9160.9100.9090.8980.897
20170.8600.8780.8850.8850.8730.8780.8900.8990.9120.9190.9261.0000.9390.9310.9220.9180.9080.902
20180.8470.8710.8740.8790.8680.8680.8790.8840.9010.9040.9110.9391.0000.9400.9240.9210.9140.897
20190.8550.8810.8850.8840.8800.8790.8930.8950.9030.9100.9160.9310.9401.0000.9360.9250.9130.911
20200.8510.8740.8860.8850.8770.8840.8910.8870.8980.9030.9100.9220.9240.9361.0000.9340.9210.914
20210.8450.8770.8780.8840.8720.8720.8860.8840.8880.9010.9090.9180.9210.9250.9341.0000.9330.921
20220.8410.8700.8770.8790.8670.8650.8810.8770.8860.8920.8980.9080.9140.9130.9210.9331.0000.931
20230.8370.8690.8780.8850.8650.8740.8830.8800.8810.8870.8970.9020.8970.9110.9140.9210.9311.000

Missing values

2024-03-16T13:21:38.139640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-16T13:21:38.402828image/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.
2024-03-16T13:21:38.647635image/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_거래원인200620072008200920102011201220132014201520162017201820192020202120222023
0전국_매매1168721000979821010001496553115835946721068851243561497191385901227561118191085261510911390908060572389
1전국_판결594107874474993192661411367067591761722794107510688957101010
2전국_교환22915014315617018214751474997802101692919179298187187197
3전국_증여83388636704973847724879686987781908710001124921087312468126471598114208109687987
4전국_분양권239912454520631188371976518288187032229231281396643918164109687985811952240467603274026573
5전국_기타169847037434741954092370499773594143441457356854579472755755497550747514008
6서울_매매2539318089164121625911541136281135213317166412524124535209751999316092206951669785507438
7서울_판결162560166207180339130287120158274238225341234214200187
8서울_교환284029292127154171925266027564833332033
9서울_증여20611895133014031277143124011337162318612102188326972382325323061589951
지역_거래원인200620072008200920102011201220132014201520162017201820192020202120222023
1784제주 제주시_교환02241151101412311129
1785제주 제주시_증여4292698087898783115146188208200204197183168117
1786제주 제주시_분양권1113979918111484120200657435179105206242105
1787제주 제주시_기타423419221218216416447824332524131219
1788제주 서귀포시_매매108182191199206285254295345526465407418260331495347263
1789제주 서귀포시_판결021013218208554262
1790제주 서귀포시_교환000000010011200010
1791제주 서귀포시_증여132829272936315445688783817670796670
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