Overview

Dataset statistics

Number of variables19
Number of observations2718
Missing cells3312
Missing cells (%)6.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory451.4 KiB
Average record size in memory170.0 B

Variable types

Text1
Numeric18

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 부동산거래현황 중 주택매매 거래현황의 연도별 거래주체별(면적) 데이터입니다.- (단위 : 천㎡)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068291/fileData.do

Alerts

2006 is highly overall correlated with 2007 and 16 other fieldsHigh correlation
2007 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2008 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2009 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2010 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2011 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2012 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2013 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2014 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2015 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2016 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2017 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2018 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2019 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2020 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2021 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2022 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2023 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2006 has 198 (7.3%) missing valuesMissing
2007 has 243 (8.9%) missing valuesMissing
2008 has 225 (8.3%) missing valuesMissing
2009 has 225 (8.3%) missing valuesMissing
2010 has 171 (6.3%) missing valuesMissing
2011 has 198 (7.3%) missing valuesMissing
2012 has 180 (6.6%) missing valuesMissing
2013 has 180 (6.6%) missing valuesMissing
2014 has 117 (4.3%) missing valuesMissing
2015 has 153 (5.6%) missing valuesMissing
2016 has 153 (5.6%) missing valuesMissing
2017 has 180 (6.6%) missing valuesMissing
2018 has 171 (6.3%) missing valuesMissing
2019 has 180 (6.6%) missing valuesMissing
2020 has 180 (6.6%) missing valuesMissing
2021 has 180 (6.6%) missing valuesMissing
2022 has 180 (6.6%) missing valuesMissing
2023 has 198 (7.3%) missing valuesMissing
2006 is highly skewed (γ1 = 39.42959511)Skewed
2007 is highly skewed (γ1 = 40.80176837)Skewed
2008 is highly skewed (γ1 = 41.33264574)Skewed
2009 is highly skewed (γ1 = 41.29649772)Skewed
2010 is highly skewed (γ1 = 42.62439045)Skewed
2011 is highly skewed (γ1 = 42.31134223)Skewed
2012 is highly skewed (γ1 = 42.90012474)Skewed
2013 is highly skewed (γ1 = 42.86098785)Skewed
2014 is highly skewed (γ1 = 43.44254443)Skewed
2015 is highly skewed (γ1 = 42.62650712)Skewed
2016 is highly skewed (γ1 = 42.41236111)Skewed
2017 is highly skewed (γ1 = 42.33545088)Skewed
2018 is highly skewed (γ1 = 41.87260879)Skewed
2019 is highly skewed (γ1 = 42.69970282)Skewed
2020 is highly skewed (γ1 = 42.2071039)Skewed
2021 is highly skewed (γ1 = 42.60316022)Skewed
2022 is highly skewed (γ1 = 43.32626259)Skewed
2023 is highly skewed (γ1 = 43.03036714)Skewed
2006 has 1264 (46.5%) zerosZeros
2007 has 1196 (44.0%) zerosZeros
2008 has 1179 (43.4%) zerosZeros
2009 has 1073 (39.5%) zerosZeros
2010 has 1094 (40.3%) zerosZeros
2011 has 960 (35.3%) zerosZeros
2012 has 1029 (37.9%) zerosZeros
2013 has 1060 (39.0%) zerosZeros
2014 has 1157 (42.6%) zerosZeros
2015 has 1040 (38.3%) zerosZeros
2016 has 1099 (40.4%) zerosZeros
2017 has 1120 (41.2%) zerosZeros
2018 has 1104 (40.6%) zerosZeros
2019 has 1057 (38.9%) zerosZeros
2020 has 932 (34.3%) zerosZeros
2021 has 928 (34.1%) zerosZeros
2022 has 1010 (37.2%) zerosZeros
2023 has 1137 (41.8%) zerosZeros

Reproduction

Analysis started2024-04-06 08:14:51.152968
Analysis finished2024-04-06 08:16:12.538889
Duration1 minute and 21.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct2709
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size21.4 KiB
2024-04-06T17:16:12.871871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length13
Mean length13.380795
Min length9

Characters and Unicode

Total characters36369
Distinct characters154
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2700 ?
Unique (%)99.3%

Sample

1st row전국_개인->개인
2nd row전국_개인->법인
3rd row전국_개인->기타
4th row전국_법인->개인
5th row전국_법인->법인
ValueCountFrequency (%)
경기 495
 
8.9%
경남 234
 
4.2%
경북 225
 
4.0%
서울 225
 
4.0%
전남 198
 
3.6%
충남 171
 
3.1%
충북 171
 
3.1%
강원 162
 
2.9%
부산 144
 
2.6%
전북 144
 
2.6%
Other values (2504) 3393
61.0%
2024-04-06T17:16:13.669299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3786
 
10.4%
2844
 
7.8%
> 2718
 
7.5%
- 2655
 
7.3%
_ 2655
 
7.3%
2343
 
6.4%
1812
 
5.0%
1812
 
5.0%
1812
 
5.0%
1287
 
3.5%
Other values (144) 12645
34.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 25092
69.0%
Space Separator 2844
 
7.8%
Math Symbol 2718
 
7.5%
Dash Punctuation 2655
 
7.3%
Connector Punctuation 2655
 
7.3%
Close Punctuation 171
 
0.5%
Open Punctuation 171
 
0.5%
Other Punctuation 63
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3786
15.1%
2343
 
9.3%
1812
 
7.2%
1812
 
7.2%
1812
 
7.2%
1287
 
5.1%
1125
 
4.5%
1008
 
4.0%
837
 
3.3%
801
 
3.2%
Other values (137) 8469
33.8%
Space Separator
ValueCountFrequency (%)
2844
100.0%
Math Symbol
ValueCountFrequency (%)
> 2718
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2655
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2655
100.0%
Close Punctuation
ValueCountFrequency (%)
) 171
100.0%
Open Punctuation
ValueCountFrequency (%)
( 171
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 25092
69.0%
Common 11277
31.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3786
15.1%
2343
 
9.3%
1812
 
7.2%
1812
 
7.2%
1812
 
7.2%
1287
 
5.1%
1125
 
4.5%
1008
 
4.0%
837
 
3.3%
801
 
3.2%
Other values (137) 8469
33.8%
Common
ValueCountFrequency (%)
2844
25.2%
> 2718
24.1%
- 2655
23.5%
_ 2655
23.5%
) 171
 
1.5%
( 171
 
1.5%
/ 63
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 25092
69.0%
ASCII 11277
31.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3786
15.1%
2343
 
9.3%
1812
 
7.2%
1812
 
7.2%
1812
 
7.2%
1287
 
5.1%
1125
 
4.5%
1008
 
4.0%
837
 
3.3%
801
 
3.2%
Other values (137) 8469
33.8%
ASCII
ValueCountFrequency (%)
2844
25.2%
> 2718
24.1%
- 2655
23.5%
_ 2655
23.5%
) 171
 
1.5%
( 171
 
1.5%
/ 63
 
0.6%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct265
Distinct (%)10.5%
Missing198
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean106.63333
Minimum0
Maximum74317
Zeros1264
Zeros (%)46.5%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2024-04-06T17:16:13.964854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile302
Maximum74317
Range74317
Interquartile range (IQR)8

Descriptive statistics

Standard deviation1632.9672
Coefficient of variation (CV)15.313853
Kurtosis1724.1387
Mean106.63333
Median Absolute Deviation (MAD)0
Skewness39.429595
Sum268716
Variance2666582
MonotonicityNot monotonic
2024-04-06T17:16:14.259840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1264
46.5%
1 267
 
9.8%
2 121
 
4.5%
3 78
 
2.9%
5 42
 
1.5%
4 42
 
1.5%
6 34
 
1.3%
7 33
 
1.2%
9 27
 
1.0%
11 26
 
1.0%
Other values (255) 586
21.6%
(Missing) 198
 
7.3%
ValueCountFrequency (%)
0 1264
46.5%
1 267
 
9.8%
2 121
 
4.5%
3 78
 
2.9%
4 42
 
1.5%
5 42
 
1.5%
6 34
 
1.3%
7 33
 
1.2%
8 17
 
0.6%
9 27
 
1.0%
ValueCountFrequency (%)
74317 1
< 0.1%
25090 1
< 0.1%
19498 1
< 0.1%
6252 1
< 0.1%
5050 1
< 0.1%
3351 1
< 0.1%
3111 1
< 0.1%
3013 1
< 0.1%
2741 1
< 0.1%
2585 1
< 0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct277
Distinct (%)11.2%
Missing243
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean83.194343
Minimum0
Maximum55242
Zeros1196
Zeros (%)44.0%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2024-04-06T17:16:14.550970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38
95-th percentile280.5
Maximum55242
Range55242
Interquartile range (IQR)8

Descriptive statistics

Standard deviation1196.5596
Coefficient of variation (CV)14.382703
Kurtosis1838.999
Mean83.194343
Median Absolute Deviation (MAD)1
Skewness40.801768
Sum205906
Variance1431754.8
MonotonicityNot monotonic
2024-04-06T17:16:14.868383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1196
44.0%
1 262
 
9.6%
2 114
 
4.2%
3 76
 
2.8%
4 65
 
2.4%
5 54
 
2.0%
6 40
 
1.5%
8 31
 
1.1%
12 26
 
1.0%
10 25
 
0.9%
Other values (267) 586
21.6%
(Missing) 243
 
8.9%
ValueCountFrequency (%)
0 1196
44.0%
1 262
 
9.6%
2 114
 
4.2%
3 76
 
2.8%
4 65
 
2.4%
5 54
 
2.0%
6 40
 
1.5%
7 19
 
0.7%
8 31
 
1.1%
9 19
 
0.7%
ValueCountFrequency (%)
55242 1
< 0.1%
14692 1
< 0.1%
10915 1
< 0.1%
6615 1
< 0.1%
5430 1
< 0.1%
3556 1
< 0.1%
3155 1
< 0.1%
2651 1
< 0.1%
2490 1
< 0.1%
1772 1
< 0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct283
Distinct (%)11.4%
Missing225
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean85.202567
Minimum0
Maximum56034
Zeros1179
Zeros (%)43.4%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2024-04-06T17:16:15.145549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile290.4
Maximum56034
Range56034
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1203.7002
Coefficient of variation (CV)14.127511
Kurtosis1884.1947
Mean85.202567
Median Absolute Deviation (MAD)1
Skewness41.332646
Sum212410
Variance1448894.1
MonotonicityNot monotonic
2024-04-06T17:16:15.409833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1179
43.4%
1 289
 
10.6%
2 122
 
4.5%
3 74
 
2.7%
4 59
 
2.2%
5 41
 
1.5%
7 34
 
1.3%
6 31
 
1.1%
11 29
 
1.1%
8 24
 
0.9%
Other values (273) 611
22.5%
(Missing) 225
 
8.3%
ValueCountFrequency (%)
0 1179
43.4%
1 289
 
10.6%
2 122
 
4.5%
3 74
 
2.7%
4 59
 
2.2%
5 41
 
1.5%
6 31
 
1.1%
7 34
 
1.3%
8 24
 
0.9%
9 19
 
0.7%
ValueCountFrequency (%)
56034 1
< 0.1%
13834 1
< 0.1%
10305 1
< 0.1%
7197 1
< 0.1%
5234 1
< 0.1%
4080 1
< 0.1%
3329 1
< 0.1%
2576 1
< 0.1%
2510 1
< 0.1%
2213 1
< 0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct285
Distinct (%)11.4%
Missing225
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean89.911753
Minimum0
Maximum57506
Zeros1073
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2024-04-06T17:16:15.707562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile315.4
Maximum57506
Range57506
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1235.3722
Coefficient of variation (CV)13.73983
Kurtosis1883.1692
Mean89.911753
Median Absolute Deviation (MAD)1
Skewness41.296498
Sum224150
Variance1526144.5
MonotonicityNot monotonic
2024-04-06T17:16:16.151707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1073
39.5%
1 334
 
12.3%
2 150
 
5.5%
3 87
 
3.2%
4 64
 
2.4%
6 47
 
1.7%
5 37
 
1.4%
9 29
 
1.1%
10 28
 
1.0%
7 28
 
1.0%
Other values (275) 616
22.7%
(Missing) 225
 
8.3%
ValueCountFrequency (%)
0 1073
39.5%
1 334
 
12.3%
2 150
 
5.5%
3 87
 
3.2%
4 64
 
2.4%
5 37
 
1.4%
6 47
 
1.7%
7 28
 
1.0%
8 27
 
1.0%
9 29
 
1.1%
ValueCountFrequency (%)
57506 1
< 0.1%
14115 1
< 0.1%
10032 1
< 0.1%
8090 1
< 0.1%
4895 1
< 0.1%
3822 1
< 0.1%
3404 1
< 0.1%
2882 1
< 0.1%
2839 1
< 0.1%
2697 1
< 0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct285
Distinct (%)11.2%
Missing171
Missing (%)6.3%
Infinite0
Infinite (%)0.0%
Mean82.792697
Minimum0
Maximum53316
Zeros1094
Zeros (%)40.3%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2024-04-06T17:16:16.497885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile292.7
Maximum53316
Range53316
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1121.9252
Coefficient of variation (CV)13.551016
Kurtosis1996.9336
Mean82.792697
Median Absolute Deviation (MAD)1
Skewness42.62439
Sum210873
Variance1258716.1
MonotonicityNot monotonic
2024-04-06T17:16:16.849910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1094
40.3%
1 363
 
13.4%
2 171
 
6.3%
3 94
 
3.5%
4 58
 
2.1%
5 46
 
1.7%
6 32
 
1.2%
8 29
 
1.1%
9 23
 
0.8%
12 23
 
0.8%
Other values (275) 614
22.6%
(Missing) 171
 
6.3%
ValueCountFrequency (%)
0 1094
40.3%
1 363
 
13.4%
2 171
 
6.3%
3 94
 
3.5%
4 58
 
2.1%
5 46
 
1.7%
6 32
 
1.2%
7 19
 
0.7%
8 29
 
1.1%
9 23
 
0.8%
ValueCountFrequency (%)
53316 1
< 0.1%
10084 1
< 0.1%
8156 1
< 0.1%
6495 1
< 0.1%
5875 1
< 0.1%
4951 1
< 0.1%
3067 1
< 0.1%
3057 1
< 0.1%
2870 1
< 0.1%
2857 1
< 0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct312
Distinct (%)12.4%
Missing198
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean105.17222
Minimum0
Maximum68090
Zeros960
Zeros (%)35.3%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2024-04-06T17:16:17.123214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q310
95-th percentile385
Maximum68090
Range68090
Interquartile range (IQR)10

Descriptive statistics

Standard deviation1442.5265
Coefficient of variation (CV)13.715851
Kurtosis1966.0598
Mean105.17222
Median Absolute Deviation (MAD)1
Skewness42.311342
Sum265034
Variance2080882.7
MonotonicityNot monotonic
2024-04-06T17:16:17.672750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 960
35.3%
1 368
 
13.5%
2 209
 
7.7%
3 102
 
3.8%
4 74
 
2.7%
5 59
 
2.2%
6 41
 
1.5%
7 31
 
1.1%
8 23
 
0.8%
10 20
 
0.7%
Other values (302) 633
23.3%
(Missing) 198
 
7.3%
ValueCountFrequency (%)
0 960
35.3%
1 368
 
13.5%
2 209
 
7.7%
3 102
 
3.8%
4 74
 
2.7%
5 59
 
2.2%
6 41
 
1.5%
7 31
 
1.1%
8 23
 
0.8%
9 19
 
0.7%
ValueCountFrequency (%)
68090 1
< 0.1%
14773 1
< 0.1%
9991 1
< 0.1%
8405 1
< 0.1%
6168 1
< 0.1%
5498 1
< 0.1%
4865 1
< 0.1%
4252 1
< 0.1%
3189 1
< 0.1%
2971 1
< 0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct278
Distinct (%)11.0%
Missing180
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean78.159574
Minimum0
Maximum51393
Zeros1029
Zeros (%)37.9%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2024-04-06T17:16:18.030805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile274.6
Maximum51393
Range51393
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1080.4252
Coefficient of variation (CV)13.823325
Kurtosis2012.4484
Mean78.159574
Median Absolute Deviation (MAD)1
Skewness42.900125
Sum198369
Variance1167318.6
MonotonicityNot monotonic
2024-04-06T17:16:18.432626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1029
37.9%
1 381
 
14.0%
2 178
 
6.5%
3 105
 
3.9%
4 55
 
2.0%
5 49
 
1.8%
6 42
 
1.5%
7 33
 
1.2%
13 28
 
1.0%
9 25
 
0.9%
Other values (268) 613
22.6%
(Missing) 180
 
6.6%
ValueCountFrequency (%)
0 1029
37.9%
1 381
 
14.0%
2 178
 
6.5%
3 105
 
3.9%
4 55
 
2.0%
5 49
 
1.8%
6 42
 
1.5%
7 33
 
1.2%
8 22
 
0.8%
9 25
 
0.9%
ValueCountFrequency (%)
51393 1
< 0.1%
10736 1
< 0.1%
6814 1
< 0.1%
5827 1
< 0.1%
4232 1
< 0.1%
4060 1
< 0.1%
3828 1
< 0.1%
3567 1
< 0.1%
2821 1
< 0.1%
2628 1
< 0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct280
Distinct (%)11.0%
Missing180
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean90.56186
Minimum0
Maximum61429
Zeros1060
Zeros (%)39.0%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2024-04-06T17:16:18.729463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile330
Maximum61429
Range61429
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1292.7422
Coefficient of variation (CV)14.274687
Kurtosis2005.9339
Mean90.56186
Median Absolute Deviation (MAD)1
Skewness42.860988
Sum229846
Variance1671182.4
MonotonicityNot monotonic
2024-04-06T17:16:19.056810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1060
39.0%
1 350
 
12.9%
2 159
 
5.8%
3 107
 
3.9%
4 70
 
2.6%
5 49
 
1.8%
8 35
 
1.3%
10 33
 
1.2%
9 32
 
1.2%
6 26
 
1.0%
Other values (270) 617
22.7%
(Missing) 180
 
6.6%
ValueCountFrequency (%)
0 1060
39.0%
1 350
 
12.9%
2 159
 
5.8%
3 107
 
3.9%
4 70
 
2.6%
5 49
 
1.8%
6 26
 
1.0%
7 22
 
0.8%
8 35
 
1.3%
9 32
 
1.2%
ValueCountFrequency (%)
61429 1
< 0.1%
13988 1
< 0.1%
8014 1
< 0.1%
6505 1
< 0.1%
4885 1
< 0.1%
4858 1
< 0.1%
4517 1
< 0.1%
4135 1
< 0.1%
3279 1
< 0.1%
2923 1
< 0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct310
Distinct (%)11.9%
Missing117
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean108.14764
Minimum0
Maximum77041
Zeros1157
Zeros (%)42.6%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2024-04-06T17:16:19.646297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q310
95-th percentile404
Maximum77041
Range77041
Interquartile range (IQR)10

Descriptive statistics

Standard deviation1601.7216
Coefficient of variation (CV)14.810509
Kurtosis2056.3447
Mean108.14764
Median Absolute Deviation (MAD)1
Skewness43.442544
Sum281292
Variance2565511.9
MonotonicityNot monotonic
2024-04-06T17:16:20.033065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1157
42.6%
1 330
 
12.1%
2 117
 
4.3%
3 95
 
3.5%
4 75
 
2.8%
5 40
 
1.5%
7 39
 
1.4%
6 37
 
1.4%
9 34
 
1.3%
10 33
 
1.2%
Other values (300) 644
23.7%
(Missing) 117
 
4.3%
ValueCountFrequency (%)
0 1157
42.6%
1 330
 
12.1%
2 117
 
4.3%
3 95
 
3.5%
4 75
 
2.8%
5 40
 
1.5%
6 37
 
1.4%
7 39
 
1.4%
8 26
 
1.0%
9 34
 
1.3%
ValueCountFrequency (%)
77041 1
< 0.1%
18287 1
< 0.1%
11146 1
< 0.1%
6133 1
< 0.1%
5965 1
< 0.1%
5569 1
< 0.1%
5404 1
< 0.1%
4565 1
< 0.1%
4290 1
< 0.1%
3948 1
< 0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct303
Distinct (%)11.8%
Missing153
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean129.57778
Minimum0
Maximum93295
Zeros1040
Zeros (%)38.3%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2024-04-06T17:16:20.366389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q311
95-th percentile440.6
Maximum93295
Range93295
Interquartile range (IQR)11

Descriptive statistics

Standard deviation1964.7794
Coefficient of variation (CV)15.162935
Kurtosis1984.0071
Mean129.57778
Median Absolute Deviation (MAD)1
Skewness42.626507
Sum332367
Variance3860358.1
MonotonicityNot monotonic
2024-04-06T17:16:20.870551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1040
38.3%
1 328
 
12.1%
2 153
 
5.6%
3 102
 
3.8%
4 74
 
2.7%
5 48
 
1.8%
7 42
 
1.5%
8 41
 
1.5%
6 40
 
1.5%
9 28
 
1.0%
Other values (293) 669
24.6%
(Missing) 153
 
5.6%
ValueCountFrequency (%)
0 1040
38.3%
1 328
 
12.1%
2 153
 
5.6%
3 102
 
3.8%
4 74
 
2.7%
5 48
 
1.8%
6 40
 
1.5%
7 42
 
1.5%
8 41
 
1.5%
9 28
 
1.0%
ValueCountFrequency (%)
93295 1
< 0.1%
23767 1
< 0.1%
17290 1
< 0.1%
7670 1
< 0.1%
6133 1
< 0.1%
5544 1
< 0.1%
5506 1
< 0.1%
5103 1
< 0.1%
4733 1
< 0.1%
3078 1
< 0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct285
Distinct (%)11.1%
Missing153
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean111.77076
Minimum0
Maximum80837
Zeros1099
Zeros (%)40.4%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2024-04-06T17:16:21.665442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q310
95-th percentile379.8
Maximum80837
Range80837
Interquartile range (IQR)10

Descriptive statistics

Standard deviation1706.7435
Coefficient of variation (CV)15.270035
Kurtosis1965.6209
Mean111.77076
Median Absolute Deviation (MAD)1
Skewness42.412361
Sum286692
Variance2912973.2
MonotonicityNot monotonic
2024-04-06T17:16:22.099483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1099
40.4%
1 319
 
11.7%
2 159
 
5.8%
3 105
 
3.9%
4 58
 
2.1%
5 50
 
1.8%
6 42
 
1.5%
9 32
 
1.2%
7 30
 
1.1%
8 26
 
1.0%
Other values (275) 645
23.7%
(Missing) 153
 
5.6%
ValueCountFrequency (%)
0 1099
40.4%
1 319
 
11.7%
2 159
 
5.8%
3 105
 
3.9%
4 58
 
2.1%
5 50
 
1.8%
6 42
 
1.5%
7 30
 
1.1%
8 26
 
1.0%
9 32
 
1.2%
ValueCountFrequency (%)
80837 1
< 0.1%
21212 1
< 0.1%
15939 1
< 0.1%
6785 1
< 0.1%
5119 1
< 0.1%
4850 1
< 0.1%
4155 1
< 0.1%
3333 1
< 0.1%
2950 1
< 0.1%
2889 1
< 0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct280
Distinct (%)11.0%
Missing180
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean99.890071
Minimum0
Maximum72472
Zeros1120
Zeros (%)41.2%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2024-04-06T17:16:22.399971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38
95-th percentile364.3
Maximum72472
Range72472
Interquartile range (IQR)8

Descriptive statistics

Standard deviation1536.2852
Coefficient of variation (CV)15.379759
Kurtosis1954.8809
Mean99.890071
Median Absolute Deviation (MAD)1
Skewness42.335451
Sum253521
Variance2360172.3
MonotonicityNot monotonic
2024-04-06T17:16:22.789306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1120
41.2%
1 295
 
10.9%
2 157
 
5.8%
3 98
 
3.6%
4 72
 
2.6%
5 56
 
2.1%
6 47
 
1.7%
7 37
 
1.4%
8 34
 
1.3%
11 29
 
1.1%
Other values (270) 593
21.8%
(Missing) 180
 
6.6%
ValueCountFrequency (%)
0 1120
41.2%
1 295
 
10.9%
2 157
 
5.8%
3 98
 
3.6%
4 72
 
2.6%
5 56
 
2.1%
6 47
 
1.7%
7 37
 
1.4%
8 34
 
1.3%
9 22
 
0.8%
ValueCountFrequency (%)
72472 1
< 0.1%
19075 1
< 0.1%
14123 1
< 0.1%
4833 1
< 0.1%
4465 1
< 0.1%
4024 1
< 0.1%
3835 1
< 0.1%
3118 1
< 0.1%
2796 1
< 0.1%
2712 1
< 0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct270
Distinct (%)10.6%
Missing171
Missing (%)6.3%
Infinite0
Infinite (%)0.0%
Mean89.195524
Minimum0
Maximum63969
Zeros1104
Zeros (%)40.6%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2024-04-06T17:16:23.192306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38
95-th percentile296.4
Maximum63969
Range63969
Interquartile range (IQR)8

Descriptive statistics

Standard deviation1361.832
Coefficient of variation (CV)15.267941
Kurtosis1918.6315
Mean89.195524
Median Absolute Deviation (MAD)1
Skewness41.872609
Sum227181
Variance1854586.4
MonotonicityNot monotonic
2024-04-06T17:16:23.511367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1104
40.6%
1 320
 
11.8%
2 153
 
5.6%
3 94
 
3.5%
4 74
 
2.7%
5 52
 
1.9%
6 48
 
1.8%
9 40
 
1.5%
7 37
 
1.4%
10 30
 
1.1%
Other values (260) 595
21.9%
(Missing) 171
 
6.3%
ValueCountFrequency (%)
0 1104
40.6%
1 320
 
11.8%
2 153
 
5.6%
3 94
 
3.5%
4 74
 
2.7%
5 52
 
1.9%
6 48
 
1.8%
7 37
 
1.4%
8 30
 
1.1%
9 40
 
1.5%
ValueCountFrequency (%)
63969 1
< 0.1%
18291 1
< 0.1%
12775 1
< 0.1%
3911 1
< 0.1%
3857 1
< 0.1%
2977 1
< 0.1%
2905 1
< 0.1%
2793 1
< 0.1%
2540 1
< 0.1%
2510 1
< 0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct275
Distinct (%)10.8%
Missing180
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean82.428684
Minimum0
Maximum57100
Zeros1057
Zeros (%)38.9%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2024-04-06T17:16:23.833715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q310
95-th percentile280.6
Maximum57100
Range57100
Interquartile range (IQR)10

Descriptive statistics

Standard deviation1205.0434
Coefficient of variation (CV)14.619224
Kurtosis1986.8884
Mean82.428684
Median Absolute Deviation (MAD)1
Skewness42.699703
Sum209204
Variance1452129.7
MonotonicityNot monotonic
2024-04-06T17:16:24.218656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1057
38.9%
1 311
 
11.4%
2 141
 
5.2%
3 108
 
4.0%
5 65
 
2.4%
4 61
 
2.2%
6 53
 
1.9%
8 35
 
1.3%
9 35
 
1.3%
7 31
 
1.1%
Other values (265) 641
23.6%
(Missing) 180
 
6.6%
ValueCountFrequency (%)
0 1057
38.9%
1 311
 
11.4%
2 141
 
5.2%
3 108
 
4.0%
4 61
 
2.2%
5 65
 
2.4%
6 53
 
1.9%
7 31
 
1.1%
8 35
 
1.3%
9 35
 
1.3%
ValueCountFrequency (%)
57100 1
< 0.1%
14605 1
< 0.1%
8786 1
< 0.1%
3954 1
< 0.1%
3583 1
< 0.1%
3499 1
< 0.1%
3397 1
< 0.1%
3152 1
< 0.1%
3029 1
< 0.1%
2623 1
< 0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct324
Distinct (%)12.8%
Missing180
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean129.92671
Minimum0
Maximum88463
Zeros932
Zeros (%)34.3%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2024-04-06T17:16:24.619572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q317
95-th percentile445.45
Maximum88463
Range88463
Interquartile range (IQR)17

Descriptive statistics

Standard deviation1877.846
Coefficient of variation (CV)14.453117
Kurtosis1945.4802
Mean129.92671
Median Absolute Deviation (MAD)2
Skewness42.207104
Sum329754
Variance3526305.8
MonotonicityNot monotonic
2024-04-06T17:16:25.036546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 932
34.3%
1 305
 
11.2%
2 143
 
5.3%
3 102
 
3.8%
4 59
 
2.2%
5 55
 
2.0%
6 50
 
1.8%
7 39
 
1.4%
8 38
 
1.4%
10 34
 
1.3%
Other values (314) 781
28.7%
(Missing) 180
 
6.6%
ValueCountFrequency (%)
0 932
34.3%
1 305
 
11.2%
2 143
 
5.3%
3 102
 
3.8%
4 59
 
2.2%
5 55
 
2.0%
6 50
 
1.8%
7 39
 
1.4%
8 38
 
1.4%
9 22
 
0.8%
ValueCountFrequency (%)
88463 1
< 0.1%
25548 1
< 0.1%
11493 1
< 0.1%
7522 1
< 0.1%
5685 1
< 0.1%
5288 1
< 0.1%
5259 1
< 0.1%
5056 1
< 0.1%
4759 1
< 0.1%
4039 1
< 0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct310
Distinct (%)12.2%
Missing180
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean99.659968
Minimum0
Maximum66188
Zeros928
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2024-04-06T17:16:25.364452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q315
95-th percentile330
Maximum66188
Range66188
Interquartile range (IQR)15

Descriptive statistics

Standard deviation1398.4956
Coefficient of variation (CV)14.032672
Kurtosis1978.5506
Mean99.659968
Median Absolute Deviation (MAD)2
Skewness42.60316
Sum252937
Variance1955790
MonotonicityNot monotonic
2024-04-06T17:16:25.686731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 928
34.1%
1 317
 
11.7%
2 145
 
5.3%
3 99
 
3.6%
4 82
 
3.0%
6 61
 
2.2%
5 50
 
1.8%
7 37
 
1.4%
10 31
 
1.1%
9 29
 
1.1%
Other values (300) 759
27.9%
(Missing) 180
 
6.6%
ValueCountFrequency (%)
0 928
34.1%
1 317
 
11.7%
2 145
 
5.3%
3 99
 
3.6%
4 82
 
3.0%
5 50
 
1.8%
6 61
 
2.2%
7 37
 
1.4%
8 24
 
0.9%
9 29
 
1.1%
ValueCountFrequency (%)
66188 1
< 0.1%
17993 1
< 0.1%
7322 1
< 0.1%
5116 1
< 0.1%
4620 1
< 0.1%
4267 1
< 0.1%
4106 1
< 0.1%
4012 1
< 0.1%
3798 1
< 0.1%
3517 1
< 0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct233
Distinct (%)9.2%
Missing180
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean49.921986
Minimum0
Maximum32165
Zeros1010
Zeros (%)37.2%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2024-04-06T17:16:26.032096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39.75
95-th percentile153.3
Maximum32165
Range32165
Interquartile range (IQR)9.75

Descriptive statistics

Standard deviation673.64671
Coefficient of variation (CV)13.493989
Kurtosis2042.6365
Mean49.921986
Median Absolute Deviation (MAD)1
Skewness43.326263
Sum126702
Variance453799.88
MonotonicityNot monotonic
2024-04-06T17:16:26.361866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1010
37.2%
1 312
 
11.5%
2 158
 
5.8%
3 108
 
4.0%
4 80
 
2.9%
5 75
 
2.8%
6 57
 
2.1%
8 50
 
1.8%
7 34
 
1.3%
10 27
 
1.0%
Other values (223) 627
23.1%
(Missing) 180
 
6.6%
ValueCountFrequency (%)
0 1010
37.2%
1 312
 
11.5%
2 158
 
5.8%
3 108
 
4.0%
4 80
 
2.9%
5 75
 
2.8%
6 57
 
2.1%
7 34
 
1.3%
8 50
 
1.8%
9 19
 
0.7%
ValueCountFrequency (%)
32165 1
< 0.1%
6884 1
< 0.1%
2950 1
< 0.1%
2716 1
< 0.1%
2670 1
< 0.1%
2651 1
< 0.1%
2304 1
< 0.1%
2115 1
< 0.1%
1769 1
< 0.1%
1753 1
< 0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct239
Distinct (%)9.5%
Missing198
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean54.505159
Minimum0
Maximum38564
Zeros1137
Zeros (%)41.8%
Negative0
Negative (%)0.0%
Memory size24.0 KiB
2024-04-06T17:16:26.703027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35
95-th percentile181
Maximum38564
Range38564
Interquartile range (IQR)5

Descriptive statistics

Standard deviation813.29084
Coefficient of variation (CV)14.921355
Kurtosis2006.6475
Mean54.505159
Median Absolute Deviation (MAD)1
Skewness43.030367
Sum137353
Variance661442
MonotonicityNot monotonic
2024-04-06T17:16:27.080236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1137
41.8%
1 343
 
12.6%
2 184
 
6.8%
3 102
 
3.8%
4 76
 
2.8%
5 55
 
2.0%
6 50
 
1.8%
7 37
 
1.4%
8 32
 
1.2%
9 31
 
1.1%
Other values (229) 473
17.4%
(Missing) 198
 
7.3%
ValueCountFrequency (%)
0 1137
41.8%
1 343
 
12.6%
2 184
 
6.8%
3 102
 
3.8%
4 76
 
2.8%
5 55
 
2.0%
6 50
 
1.8%
7 37
 
1.4%
8 32
 
1.2%
9 31
 
1.1%
ValueCountFrequency (%)
38564 1
< 0.1%
10015 1
< 0.1%
3853 1
< 0.1%
2780 1
< 0.1%
2506 1
< 0.1%
2453 1
< 0.1%
2287 1
< 0.1%
2271 1
< 0.1%
2086 1
< 0.1%
1956 1
< 0.1%

Interactions

2024-04-06T17:16:06.505158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:14:56.724532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:00.946178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-04-06T17:15:09.614996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:13.167132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:17.300799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:21.741119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-04-06T17:15:51.194916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-04-06T17:15:39.667258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-04-06T17:15:51.545451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:55.567095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:59.651033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-04-06T17:15:53.140779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:57.495139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:01.561184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:05.051466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:08.692565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:14:59.208970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:03.336205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:07.881246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:11.726255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:15.672252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:20.071152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:25.016304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:29.050752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:32.797960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:37.629030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:41.488066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:45.204399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:49.686654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:53.357189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:57.697330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:01.740000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:05.229638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:08.970103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:14:59.430339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:03.634054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:08.080856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:12.008108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:15.873670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:20.296650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:25.202327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:29.264250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:33.044978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:37.931338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:41.701215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:45.429823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:49.909008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:53.586662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:58.032135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:01.903110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:05.391468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:09.315581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:14:59.795913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:03.922779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:08.256328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:12.250772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:16.059633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:20.536482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:25.440982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:29.420742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:33.272150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:38.181062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:41.923916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:45.647738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:50.115328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:53.830483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:58.296496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:02.085994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:05.553434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:09.494998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:00.006186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:04.133626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:08.438911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:12.459434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:16.277144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:20.732579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:25.657805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:29.574243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:33.499002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:38.409850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:42.097552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:45.830996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:50.361149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:54.018352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:58.474136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:02.276368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:05.703336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:09.683030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:00.189774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:04.404465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:09.082029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:12.608650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:16.458057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:21.028047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:25.969557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:29.730978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:33.708054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:38.596742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:42.288593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:46.019260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:50.555455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:54.216257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:58.631406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:02.486200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:05.854289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:09.875507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:00.405987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:04.660725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:09.250554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:12.780425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:16.707718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:21.241911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:26.271326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:29.896415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:33.913450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:38.772625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:42.556552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:46.236923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:50.765214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:54.424158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:58.849553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:02.835726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:06.061862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:10.085730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:00.729635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:04.933193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:09.430580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:12.941730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:16.906255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:21.493663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:26.444242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:30.060470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:34.351681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:38.949217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:42.759317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:46.429527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:50.961835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:54.713660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:15:59.055629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:03.058560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:16:06.250442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:16:27.355801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9960.9960.9960.8030.9960.9960.9961.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20070.9961.0001.0001.0000.8621.0001.0001.0000.9960.9960.9960.9960.9960.9960.9960.9961.0001.000
20080.9961.0001.0001.0000.8621.0001.0001.0000.9960.9960.9960.9960.9960.9960.9960.9961.0001.000
20090.9961.0001.0001.0000.8621.0001.0001.0000.9960.9960.9960.9960.9960.9960.9960.9961.0001.000
20100.8030.8620.8620.8621.0000.8620.8620.8620.8030.8030.8030.8030.8030.8030.8030.8030.9710.971
20110.9961.0001.0001.0000.8621.0001.0001.0000.9960.9960.9960.9960.9960.9960.9960.9961.0001.000
20120.9961.0001.0001.0000.8621.0001.0001.0000.9960.9960.9960.9960.9960.9960.9960.9961.0001.000
20130.9961.0001.0001.0000.8621.0001.0001.0000.9960.9960.9960.9960.9960.9960.9960.9961.0001.000
20141.0000.9960.9960.9960.8030.9960.9960.9961.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20151.0000.9960.9960.9960.8030.9960.9960.9961.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20161.0000.9960.9960.9960.8030.9960.9960.9961.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20171.0000.9960.9960.9960.8030.9960.9960.9961.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20181.0000.9960.9960.9960.8030.9960.9960.9961.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20191.0000.9960.9960.9960.8030.9960.9960.9961.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20201.0000.9960.9960.9960.8030.9960.9960.9961.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20211.0000.9960.9960.9960.8030.9960.9960.9961.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20221.0001.0001.0001.0000.9711.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20231.0001.0001.0001.0000.9711.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-04-06T17:16:27.865509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.8730.8530.8290.8510.8460.8340.8450.8460.8630.8600.8520.8500.8430.8390.8110.8000.805
20070.8731.0000.8680.8330.8440.8280.8240.8320.8310.8490.8470.8420.8400.8380.8350.8010.8000.797
20080.8530.8681.0000.8630.8610.8370.8300.8430.8240.8370.8450.8370.8320.8230.8210.8000.7900.790
20090.8290.8330.8631.0000.8660.8360.8300.8380.8210.8390.8420.8320.8260.8170.8160.8040.7810.787
20100.8510.8440.8610.8661.0000.8790.8590.8760.8440.8650.8640.8530.8460.8360.8440.8110.7940.793
20110.8460.8280.8370.8360.8791.0000.8880.8620.8350.8650.8470.8420.8410.8240.8320.8100.7860.791
20120.8340.8240.8300.8300.8590.8881.0000.8700.8450.8650.8570.8490.8380.8300.8250.8080.7870.792
20130.8450.8320.8430.8380.8760.8620.8701.0000.8740.8810.8770.8710.8610.8490.8520.8190.8050.798
20140.8460.8310.8240.8210.8440.8350.8450.8741.0000.8730.8620.8550.8540.8410.8380.8030.7970.793
20150.8630.8490.8370.8390.8650.8650.8650.8810.8731.0000.9040.8790.8760.8680.8640.8410.8240.815
20160.8600.8470.8450.8420.8640.8470.8570.8770.8620.9041.0000.8960.8840.8660.8680.8390.8270.819
20170.8520.8420.8370.8320.8530.8420.8490.8710.8550.8790.8961.0000.8850.8790.8680.8310.8210.820
20180.8500.8400.8320.8260.8460.8410.8380.8610.8540.8760.8840.8851.0000.8840.8750.8420.8230.815
20190.8430.8380.8230.8170.8360.8240.8300.8490.8410.8680.8660.8790.8841.0000.8860.8360.8240.820
20200.8390.8350.8210.8160.8440.8320.8250.8520.8380.8640.8680.8680.8750.8861.0000.8870.8680.850
20210.8110.8010.8000.8040.8110.8100.8080.8190.8030.8410.8390.8310.8420.8360.8871.0000.8940.849
20220.8000.8000.7900.7810.7940.7860.7870.8050.7970.8240.8270.8210.8230.8240.8680.8941.0000.862
20230.8050.7970.7900.7870.7930.7910.7920.7980.7930.8150.8190.8200.8150.8200.8500.8490.8621.000

Missing values

2024-04-06T17:16:10.439950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:16:10.914836image/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.
2024-04-06T17:16:11.918688image/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->개인743175524256034575065331668090513936142977041932958083772472639695710088463661883216538564
1전국_개인->법인18641615128410951082121012431398169922082103196425103583528842672304881
2전국_개인->기타27327029136532840336637527352350659363575211561119680424
3전국_법인->개인625266157197809081569991681465055965510341552796210424674759379826702271
4전국_법인->법인839105220152697285720801554126016301397128396911328881240760437294
5전국_법인->기타93986117132594334745549504885269377461228
6전국_기타->개인20519125635530241527430823233228127522124011922012600284
7전국_기타->법인151518142829165327523215155232808027
8전국_기타->기타27403045451131163343292016223120322614
9서울_개인->개인1949810915103051003264958405582780141114617290159391412312775878611493732226513853
지역_거래주체200620072008200920102011201220132014201520162017201820192020202120222023
2708제주 제주시_기타->기타00501121001000000<NA>
2709제주 서귀포시_개인->개인48888393105125120144189256244193175121165225169<NA>
2710제주 서귀포시_개인->법인142333613101222152016161310<NA>
2711제주 서귀포시_개인->기타11002112441011452<NA>
2712제주 서귀포시_법인->개인214341716322433194149603721374438<NA>
2713제주 서귀포시_법인->법인11277101246241761591485<NA>
2714제주 서귀포시_법인->기타000100000123000260<NA>
2715제주 서귀포시_기타->개인00001112112211473<NA>
2716제주 서귀포시_기타->법인00000100000000010<NA>
2717제주 서귀포시_기타->기타00000000000000030<NA>