Overview

Dataset statistics

Number of variables19
Number of observations2691
Missing cells3447
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory446.9 KiB
Average record size in memory170.0 B

Variable types

Text1
Numeric18

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 부동산거래현황 중 아파트매매 거래현황의 연도별 거래주체별(면적) 데이터입니다.-(단위 : 천㎡)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068505/fileData.do

Alerts

2006 is highly overall correlated with 2007 and 16 other fieldsHigh correlation
2007 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2008 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2009 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2010 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2011 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2012 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2013 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2014 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2015 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2016 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2017 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2018 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2019 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2020 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2021 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2022 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2023 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2006 has 216 (8.0%) missing valuesMissing
2007 has 261 (9.7%) missing valuesMissing
2008 has 234 (8.7%) missing valuesMissing
2009 has 234 (8.7%) missing valuesMissing
2010 has 180 (6.7%) missing valuesMissing
2011 has 207 (7.7%) missing valuesMissing
2012 has 189 (7.0%) missing valuesMissing
2013 has 189 (7.0%) missing valuesMissing
2014 has 135 (5.0%) missing valuesMissing
2015 has 162 (6.0%) missing valuesMissing
2016 has 162 (6.0%) missing valuesMissing
2017 has 189 (7.0%) missing valuesMissing
2018 has 180 (6.7%) missing valuesMissing
2019 has 189 (7.0%) missing valuesMissing
2020 has 189 (7.0%) missing valuesMissing
2021 has 180 (6.7%) missing valuesMissing
2022 has 180 (6.7%) missing valuesMissing
2023 has 171 (6.4%) missing valuesMissing
2006 is highly skewed (γ1 = 38.53686792)Skewed
2007 is highly skewed (γ1 = 39.74659386)Skewed
2008 is highly skewed (γ1 = 40.00058793)Skewed
2009 is highly skewed (γ1 = 39.53517424)Skewed
2010 is highly skewed (γ1 = 40.70756615)Skewed
2011 is highly skewed (γ1 = 40.30253794)Skewed
2012 is highly skewed (γ1 = 40.98230155)Skewed
2013 is highly skewed (γ1 = 41.40202768)Skewed
2014 is highly skewed (γ1 = 42.15985726)Skewed
2015 is highly skewed (γ1 = 41.59213347)Skewed
2016 is highly skewed (γ1 = 41.37947229)Skewed
2017 is highly skewed (γ1 = 41.53524559)Skewed
2018 is highly skewed (γ1 = 41.07447364)Skewed
2019 is highly skewed (γ1 = 42.24951739)Skewed
2020 is highly skewed (γ1 = 41.59359138)Skewed
2021 is highly skewed (γ1 = 42.2451458)Skewed
2022 is highly skewed (γ1 = 43.18453564)Skewed
2023 is highly skewed (γ1 = 42.79353831)Skewed
아파트매매 거래주체별 has unique valuesUnique
2006 has 1514 (56.3%) zerosZeros
2007 has 1470 (54.6%) zerosZeros
2008 has 1486 (55.2%) zerosZeros
2009 has 1386 (51.5%) zerosZeros
2010 has 1408 (52.3%) zerosZeros
2011 has 1309 (48.6%) zerosZeros
2012 has 1403 (52.1%) zerosZeros
2013 has 1440 (53.5%) zerosZeros
2014 has 1532 (56.9%) zerosZeros
2015 has 1426 (53.0%) zerosZeros
2016 has 1494 (55.5%) zerosZeros
2017 has 1482 (55.1%) zerosZeros
2018 has 1469 (54.6%) zerosZeros
2019 has 1413 (52.5%) zerosZeros
2020 has 1325 (49.2%) zerosZeros
2021 has 1429 (53.1%) zerosZeros
2022 has 1546 (57.5%) zerosZeros
2023 has 1597 (59.3%) zerosZeros

Reproduction

Analysis started2024-03-23 04:20:57.386361
Analysis finished2024-03-23 04:23:17.059400
Duration2 minutes and 19.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct2691
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
2024-03-23T04:23:17.495514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length12
Mean length12.354515
Min length9

Characters and Unicode

Total characters33246
Distinct characters153
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2691 ?
Unique (%)100.0%

Sample

1st row전국 /개인>개인
2nd row전국 /개인>법인
3rd row전국 /개인>기타
4th row전국 /법인>개인
5th row전국 /법인>법인
ValueCountFrequency (%)
경기 477
 
8.9%
경남 243
 
4.5%
경북 234
 
4.3%
서울 234
 
4.3%
전남 207
 
3.8%
충남 180
 
3.3%
충북 180
 
3.3%
강원 171
 
3.2%
부산 153
 
2.8%
전북 153
 
2.8%
Other values (2331) 3150
58.5%
2024-03-23T04:23:18.628109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3750
 
11.3%
2691
 
8.1%
/ 2691
 
8.1%
> 2691
 
8.1%
2298
 
6.9%
1794
 
5.4%
1794
 
5.4%
1794
 
5.4%
1269
 
3.8%
1098
 
3.3%
Other values (143) 11376
34.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24795
74.6%
Space Separator 2691
 
8.1%
Other Punctuation 2691
 
8.1%
Math Symbol 2691
 
8.1%
Open Punctuation 171
 
0.5%
Close Punctuation 171
 
0.5%
Dash Punctuation 36
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3750
15.1%
2298
 
9.3%
1794
 
7.2%
1794
 
7.2%
1794
 
7.2%
1269
 
5.1%
1098
 
4.4%
981
 
4.0%
837
 
3.4%
801
 
3.2%
Other values (137) 8379
33.8%
Space Separator
ValueCountFrequency (%)
2691
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2691
100.0%
Math Symbol
ValueCountFrequency (%)
> 2691
100.0%
Open Punctuation
ValueCountFrequency (%)
( 171
100.0%
Close Punctuation
ValueCountFrequency (%)
) 171
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24795
74.6%
Common 8451
 
25.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3750
15.1%
2298
 
9.3%
1794
 
7.2%
1794
 
7.2%
1794
 
7.2%
1269
 
5.1%
1098
 
4.4%
981
 
4.0%
837
 
3.4%
801
 
3.2%
Other values (137) 8379
33.8%
Common
ValueCountFrequency (%)
2691
31.8%
/ 2691
31.8%
> 2691
31.8%
( 171
 
2.0%
) 171
 
2.0%
- 36
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24795
74.6%
ASCII 8451
 
25.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3750
15.1%
2298
 
9.3%
1794
 
7.2%
1794
 
7.2%
1794
 
7.2%
1269
 
5.1%
1098
 
4.4%
981
 
4.0%
837
 
3.4%
801
 
3.2%
Other values (137) 8379
33.8%
ASCII
ValueCountFrequency (%)
2691
31.8%
/ 2691
31.8%
> 2691
31.8%
( 171
 
2.0%
) 171
 
2.0%
- 36
 
0.4%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct235
Distinct (%)9.5%
Missing216
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean67.525657
Minimum0
Maximum45131
Zeros1514
Zeros (%)56.3%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:23:19.231826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile205
Maximum45131
Range45131
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1007.0756
Coefficient of variation (CV)14.91397
Kurtosis1654.9713
Mean67.525657
Median Absolute Deviation (MAD)0
Skewness38.536868
Sum167126
Variance1014201.3
MonotonicityNot monotonic
2024-03-23T04:23:19.963587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1514
56.3%
1 244
 
9.1%
2 94
 
3.5%
3 52
 
1.9%
4 35
 
1.3%
5 31
 
1.2%
6 27
 
1.0%
7 21
 
0.8%
8 19
 
0.7%
10 19
 
0.7%
Other values (225) 419
 
15.6%
(Missing) 216
 
8.0%
ValueCountFrequency (%)
0 1514
56.3%
1 244
 
9.1%
2 94
 
3.5%
3 52
 
1.9%
4 35
 
1.3%
5 31
 
1.2%
6 27
 
1.0%
7 21
 
0.8%
8 19
 
0.7%
9 15
 
0.6%
ValueCountFrequency (%)
45131 1
< 0.1%
16502 1
< 0.1%
10283 1
< 0.1%
5823 1
< 0.1%
3165 1
< 0.1%
2438 1
< 0.1%
1926 1
< 0.1%
1897 1
< 0.1%
1743 1
< 0.1%
1653 1
< 0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct231
Distinct (%)9.5%
Missing261
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean48.086831
Minimum0
Maximum29866
Zeros1470
Zeros (%)54.6%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:23:20.473192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile162.55
Maximum29866
Range29866
Interquartile range (IQR)3

Descriptive statistics

Standard deviation656.75361
Coefficient of variation (CV)13.65766
Kurtosis1762.3518
Mean48.086831
Median Absolute Deviation (MAD)0
Skewness39.746594
Sum116851
Variance431325.31
MonotonicityNot monotonic
2024-03-23T04:23:21.200989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1470
54.6%
1 207
 
7.7%
2 99
 
3.7%
3 68
 
2.5%
4 57
 
2.1%
5 28
 
1.0%
7 22
 
0.8%
10 19
 
0.7%
8 19
 
0.7%
6 17
 
0.6%
Other values (221) 424
 
15.8%
(Missing) 261
 
9.7%
ValueCountFrequency (%)
0 1470
54.6%
1 207
 
7.7%
2 99
 
3.7%
3 68
 
2.5%
4 57
 
2.1%
5 28
 
1.0%
6 17
 
0.6%
7 22
 
0.8%
8 19
 
0.7%
9 15
 
0.6%
ValueCountFrequency (%)
29866 1
< 0.1%
7717 1
< 0.1%
6108 1
< 0.1%
4315 1
< 0.1%
2734 1
< 0.1%
2690 1
< 0.1%
2037 1
< 0.1%
1485 1
< 0.1%
1411 1
< 0.1%
1312 1
< 0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct242
Distinct (%)9.8%
Missing234
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean52.408221
Minimum0
Maximum31822
Zeros1486
Zeros (%)55.2%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:23:21.710284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile186.4
Maximum31822
Range31822
Interquartile range (IQR)3

Descriptive statistics

Standard deviation695.22343
Coefficient of variation (CV)13.265541
Kurtosis1787.4236
Mean52.408221
Median Absolute Deviation (MAD)0
Skewness40.000588
Sum128767
Variance483335.61
MonotonicityNot monotonic
2024-03-23T04:23:22.325700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1486
55.2%
1 219
 
8.1%
2 80
 
3.0%
3 65
 
2.4%
4 48
 
1.8%
5 32
 
1.2%
6 27
 
1.0%
7 19
 
0.7%
10 15
 
0.6%
9 15
 
0.6%
Other values (232) 451
 
16.8%
(Missing) 234
 
8.7%
ValueCountFrequency (%)
0 1486
55.2%
1 219
 
8.1%
2 80
 
3.0%
3 65
 
2.4%
4 48
 
1.8%
5 32
 
1.2%
6 27
 
1.0%
7 19
 
0.7%
8 12
 
0.4%
9 15
 
0.6%
ValueCountFrequency (%)
31822 1
< 0.1%
7754 1
< 0.1%
6747 1
< 0.1%
4377 1
< 0.1%
3116 1
< 0.1%
2565 1
< 0.1%
2129 1
< 0.1%
1803 1
< 0.1%
1554 1
< 0.1%
1490 1
< 0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct255
Distinct (%)10.4%
Missing234
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean61.773708
Minimum0
Maximum36829
Zeros1386
Zeros (%)51.5%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:23:22.858227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile216.4
Maximum36829
Range36829
Interquartile range (IQR)4

Descriptive statistics

Standard deviation808.84462
Coefficient of variation (CV)13.093671
Kurtosis1752.3546
Mean61.773708
Median Absolute Deviation (MAD)0
Skewness39.535174
Sum151778
Variance654229.61
MonotonicityNot monotonic
2024-03-23T04:23:23.403115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1386
51.5%
1 286
 
10.6%
2 95
 
3.5%
3 61
 
2.3%
5 40
 
1.5%
4 39
 
1.4%
7 25
 
0.9%
8 18
 
0.7%
10 18
 
0.7%
9 17
 
0.6%
Other values (245) 472
 
17.5%
(Missing) 234
 
8.7%
ValueCountFrequency (%)
0 1386
51.5%
1 286
 
10.6%
2 95
 
3.5%
3 61
 
2.3%
4 39
 
1.4%
5 40
 
1.5%
6 15
 
0.6%
7 25
 
0.9%
8 18
 
0.7%
9 17
 
0.6%
ValueCountFrequency (%)
36829 1
< 0.1%
9702 1
< 0.1%
7617 1
< 0.1%
5829 1
< 0.1%
3715 1
< 0.1%
2484 1
< 0.1%
2476 1
< 0.1%
1899 1
< 0.1%
1890 1
< 0.1%
1749 1
< 0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct247
Distinct (%)9.8%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean55.618479
Minimum0
Maximum33481
Zeros1408
Zeros (%)52.3%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:23:23.894245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile190
Maximum33481
Range33481
Interquartile range (IQR)3

Descriptive statistics

Standard deviation720.90568
Coefficient of variation (CV)12.961622
Kurtosis1851.1002
Mean55.618479
Median Absolute Deviation (MAD)0
Skewness40.707566
Sum139658
Variance519704.99
MonotonicityNot monotonic
2024-03-23T04:23:24.620014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1408
52.3%
1 310
 
11.5%
2 108
 
4.0%
3 65
 
2.4%
4 39
 
1.4%
5 30
 
1.1%
7 25
 
0.9%
8 22
 
0.8%
6 20
 
0.7%
10 14
 
0.5%
Other values (237) 470
 
17.5%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 1408
52.3%
1 310
 
11.5%
2 108
 
4.0%
3 65
 
2.4%
4 39
 
1.4%
5 30
 
1.1%
6 20
 
0.7%
7 25
 
0.9%
8 22
 
0.8%
9 9
 
0.3%
ValueCountFrequency (%)
33481 1
< 0.1%
7667 1
< 0.1%
6481 1
< 0.1%
4255 1
< 0.1%
3441 1
< 0.1%
3294 1
< 0.1%
2272 1
< 0.1%
1991 1
< 0.1%
1880 1
< 0.1%
1798 1
< 0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct269
Distinct (%)10.8%
Missing207
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean69.006441
Minimum0
Maximum41515
Zeros1309
Zeros (%)48.6%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:23:25.189945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile243.7
Maximum41515
Range41515
Interquartile range (IQR)4

Descriptive statistics

Standard deviation901.33236
Coefficient of variation (CV)13.061569
Kurtosis1812.7689
Mean69.006441
Median Absolute Deviation (MAD)0
Skewness40.302538
Sum171412
Variance812400.02
MonotonicityNot monotonic
2024-03-23T04:23:25.926749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1309
48.6%
1 349
 
13.0%
2 111
 
4.1%
3 70
 
2.6%
4 45
 
1.7%
5 36
 
1.3%
9 22
 
0.8%
6 19
 
0.7%
8 17
 
0.6%
10 15
 
0.6%
Other values (259) 491
 
18.2%
(Missing) 207
 
7.7%
ValueCountFrequency (%)
0 1309
48.6%
1 349
 
13.0%
2 111
 
4.1%
3 70
 
2.6%
4 45
 
1.7%
5 36
 
1.3%
6 19
 
0.7%
7 15
 
0.6%
8 17
 
0.6%
9 22
 
0.8%
ValueCountFrequency (%)
41515 1
< 0.1%
9919 1
< 0.1%
9346 1
< 0.1%
4499 1
< 0.1%
3933 1
< 0.1%
3161 1
< 0.1%
3044 1
< 0.1%
2210 1
< 0.1%
2130 1
< 0.1%
1947 1
< 0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct240
Distinct (%)9.6%
Missing189
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean48.701839
Minimum0
Maximum30126
Zeros1403
Zeros (%)52.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:23:26.544209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile171
Maximum30126
Range30126
Interquartile range (IQR)3

Descriptive statistics

Standard deviation648.02752
Coefficient of variation (CV)13.306018
Kurtosis1865.9371
Mean48.701839
Median Absolute Deviation (MAD)0
Skewness40.982302
Sum121852
Variance419939.67
MonotonicityNot monotonic
2024-03-23T04:23:27.175753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1403
52.1%
1 338
 
12.6%
2 100
 
3.7%
3 46
 
1.7%
4 40
 
1.5%
5 35
 
1.3%
6 26
 
1.0%
8 24
 
0.9%
9 21
 
0.8%
10 14
 
0.5%
Other values (230) 455
 
16.9%
(Missing) 189
 
7.0%
ValueCountFrequency (%)
0 1403
52.1%
1 338
 
12.6%
2 100
 
3.7%
3 46
 
1.7%
4 40
 
1.5%
5 35
 
1.3%
6 26
 
1.0%
7 14
 
0.5%
8 24
 
0.9%
9 21
 
0.8%
ValueCountFrequency (%)
30126 1
< 0.1%
6973 1
< 0.1%
6186 1
< 0.1%
3153 1
< 0.1%
2600 1
< 0.1%
2323 1
< 0.1%
1992 1
< 0.1%
1973 1
< 0.1%
1874 1
< 0.1%
1684 1
< 0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct236
Distinct (%)9.4%
Missing189
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean58.807354
Minimum0
Maximum38675
Zeros1440
Zeros (%)53.5%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:23:27.715056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile202.85
Maximum38675
Range38675
Interquartile range (IQR)3

Descriptive statistics

Standard deviation829.32028
Coefficient of variation (CV)14.102323
Kurtosis1891.3338
Mean58.807354
Median Absolute Deviation (MAD)0
Skewness41.402028
Sum147136
Variance687772.13
MonotonicityNot monotonic
2024-03-23T04:23:28.535034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1440
53.5%
1 283
 
10.5%
2 118
 
4.4%
3 71
 
2.6%
4 34
 
1.3%
5 32
 
1.2%
6 27
 
1.0%
9 23
 
0.9%
7 21
 
0.8%
8 17
 
0.6%
Other values (226) 436
 
16.2%
(Missing) 189
 
7.0%
ValueCountFrequency (%)
0 1440
53.5%
1 283
 
10.5%
2 118
 
4.4%
3 71
 
2.6%
4 34
 
1.3%
5 32
 
1.2%
6 27
 
1.0%
7 21
 
0.8%
8 17
 
0.6%
9 23
 
0.9%
ValueCountFrequency (%)
38675 1
< 0.1%
10084 1
< 0.1%
5790 1
< 0.1%
4914 1
< 0.1%
3111 1
< 0.1%
2912 1
< 0.1%
2702 1
< 0.1%
2342 1
< 0.1%
1987 1
< 0.1%
1938 1
< 0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct258
Distinct (%)10.1%
Missing135
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean69.504695
Minimum0
Maximum48380
Zeros1532
Zeros (%)56.9%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:23:28.937162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile234
Maximum48380
Range48380
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1024.0904
Coefficient of variation (CV)14.734118
Kurtosis1951.242
Mean69.504695
Median Absolute Deviation (MAD)0
Skewness42.159857
Sum177654
Variance1048761.1
MonotonicityNot monotonic
2024-03-23T04:23:29.463431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1532
56.9%
1 227
 
8.4%
2 111
 
4.1%
3 77
 
2.9%
4 45
 
1.7%
6 31
 
1.2%
8 25
 
0.9%
5 23
 
0.9%
7 19
 
0.7%
10 18
 
0.7%
Other values (248) 448
 
16.6%
(Missing) 135
 
5.0%
ValueCountFrequency (%)
0 1532
56.9%
1 227
 
8.4%
2 111
 
4.1%
3 77
 
2.9%
4 45
 
1.7%
5 23
 
0.9%
6 31
 
1.2%
7 19
 
0.7%
8 25
 
0.9%
9 18
 
0.7%
ValueCountFrequency (%)
48380 1
< 0.1%
13064 1
< 0.1%
6868 1
< 0.1%
5089 1
< 0.1%
4077 1
< 0.1%
3360 1
< 0.1%
3046 1
< 0.1%
2899 1
< 0.1%
2752 1
< 0.1%
2012 1
< 0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct252
Distinct (%)10.0%
Missing162
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean77.491103
Minimum0
Maximum55522
Zeros1426
Zeros (%)53.0%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:23:30.019206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile266
Maximum55522
Range55522
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1187.0809
Coefficient of variation (CV)15.318932
Kurtosis1898.7722
Mean77.491103
Median Absolute Deviation (MAD)0
Skewness41.592133
Sum195975
Variance1409161.1
MonotonicityNot monotonic
2024-03-23T04:23:30.499741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1426
53.0%
1 308
 
11.4%
2 110
 
4.1%
3 72
 
2.7%
4 54
 
2.0%
5 46
 
1.7%
7 32
 
1.2%
6 23
 
0.9%
8 17
 
0.6%
11 15
 
0.6%
Other values (242) 426
 
15.8%
(Missing) 162
 
6.0%
ValueCountFrequency (%)
0 1426
53.0%
1 308
 
11.4%
2 110
 
4.1%
3 72
 
2.7%
4 54
 
2.0%
5 46
 
1.7%
6 23
 
0.9%
7 32
 
1.2%
8 17
 
0.6%
9 15
 
0.6%
ValueCountFrequency (%)
55522 1
< 0.1%
15958 1
< 0.1%
9846 1
< 0.1%
5207 1
< 0.1%
4126 1
< 0.1%
3692 1
< 0.1%
3514 1
< 0.1%
2844 1
< 0.1%
1897 1
< 0.1%
1834 1
< 0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct246
Distinct (%)9.7%
Missing162
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean65.303282
Minimum0
Maximum46881
Zeros1494
Zeros (%)55.5%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:23:30.975954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile214.2
Maximum46881
Range46881
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1004.8984
Coefficient of variation (CV)15.388176
Kurtosis1881.0206
Mean65.303282
Median Absolute Deviation (MAD)0
Skewness41.379472
Sum165152
Variance1009820.7
MonotonicityNot monotonic
2024-03-23T04:23:31.658289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1494
55.5%
1 266
 
9.9%
2 104
 
3.9%
3 75
 
2.8%
4 42
 
1.6%
5 42
 
1.6%
6 34
 
1.3%
9 20
 
0.7%
7 18
 
0.7%
8 16
 
0.6%
Other values (236) 418
 
15.5%
(Missing) 162
 
6.0%
ValueCountFrequency (%)
0 1494
55.5%
1 266
 
9.9%
2 104
 
3.9%
3 75
 
2.8%
4 42
 
1.6%
5 42
 
1.6%
6 34
 
1.3%
7 18
 
0.7%
8 16
 
0.6%
9 20
 
0.7%
ValueCountFrequency (%)
46881 1
< 0.1%
13690 1
< 0.1%
9084 1
< 0.1%
4441 1
< 0.1%
3225 1
< 0.1%
3127 1
< 0.1%
2587 1
< 0.1%
1750 1
< 0.1%
1582 1
< 0.1%
1568 1
< 0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct236
Distinct (%)9.4%
Missing189
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean58.82534
Minimum0
Maximum42887
Zeros1482
Zeros (%)55.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:23:32.307230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile185.9
Maximum42887
Range42887
Interquartile range (IQR)2

Descriptive statistics

Standard deviation920.74534
Coefficient of variation (CV)15.652189
Kurtosis1888.1078
Mean58.82534
Median Absolute Deviation (MAD)0
Skewness41.535246
Sum147181
Variance847771.98
MonotonicityNot monotonic
2024-03-23T04:23:32.871599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1482
55.1%
1 276
 
10.3%
2 136
 
5.1%
3 69
 
2.6%
4 46
 
1.7%
5 39
 
1.4%
6 27
 
1.0%
7 22
 
0.8%
12 16
 
0.6%
8 14
 
0.5%
Other values (226) 375
 
13.9%
(Missing) 189
 
7.0%
ValueCountFrequency (%)
0 1482
55.1%
1 276
 
10.3%
2 136
 
5.1%
3 69
 
2.6%
4 46
 
1.7%
5 39
 
1.4%
6 27
 
1.0%
7 22
 
0.8%
8 14
 
0.5%
9 14
 
0.5%
ValueCountFrequency (%)
42887 1
< 0.1%
12196 1
< 0.1%
8356 1
< 0.1%
2945 1
< 0.1%
2746 1
< 0.1%
2583 1
< 0.1%
2110 1
< 0.1%
1969 1
< 0.1%
1699 1
< 0.1%
1572 1
< 0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct215
Distinct (%)8.6%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean55.124253
Minimum0
Maximum40198
Zeros1469
Zeros (%)54.6%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:23:33.424835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile168.5
Maximum40198
Range40198
Interquartile range (IQR)3

Descriptive statistics

Standard deviation867.34602
Coefficient of variation (CV)15.734381
Kurtosis1849.933
Mean55.124253
Median Absolute Deviation (MAD)0
Skewness41.074474
Sum138417
Variance752289.12
MonotonicityNot monotonic
2024-03-23T04:23:33.881622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1469
54.6%
1 275
 
10.2%
2 122
 
4.5%
3 94
 
3.5%
4 58
 
2.2%
5 43
 
1.6%
6 34
 
1.3%
7 23
 
0.9%
8 23
 
0.9%
10 14
 
0.5%
Other values (205) 356
 
13.2%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 1469
54.6%
1 275
 
10.2%
2 122
 
4.5%
3 94
 
3.5%
4 58
 
2.2%
5 43
 
1.6%
6 34
 
1.3%
7 23
 
0.9%
8 23
 
0.9%
9 10
 
0.4%
ValueCountFrequency (%)
40198 1
< 0.1%
12740 1
< 0.1%
7462 1
< 0.1%
2652 1
< 0.1%
2370 1
< 0.1%
2028 1
< 0.1%
1991 1
< 0.1%
1930 1
< 0.1%
1749 1
< 0.1%
1645 1
< 0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct227
Distinct (%)9.1%
Missing189
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean53.1247
Minimum0
Maximum37685
Zeros1413
Zeros (%)52.5%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:23:34.369772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile175.85
Maximum37685
Range37685
Interquartile range (IQR)3

Descriptive statistics

Standard deviation802.69206
Coefficient of variation (CV)15.109583
Kurtosis1944.8139
Mean53.1247
Median Absolute Deviation (MAD)0
Skewness42.249517
Sum132918
Variance644314.54
MonotonicityNot monotonic
2024-03-23T04:23:35.036832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1413
52.5%
1 258
 
9.6%
2 122
 
4.5%
3 91
 
3.4%
4 53
 
2.0%
5 52
 
1.9%
6 34
 
1.3%
8 25
 
0.9%
7 24
 
0.9%
11 17
 
0.6%
Other values (217) 413
 
15.3%
(Missing) 189
 
7.0%
ValueCountFrequency (%)
0 1413
52.5%
1 258
 
9.6%
2 122
 
4.5%
3 91
 
3.4%
4 53
 
2.0%
5 52
 
1.9%
6 34
 
1.3%
7 24
 
0.9%
8 25
 
0.9%
9 17
 
0.6%
ValueCountFrequency (%)
37685 1
< 0.1%
10315 1
< 0.1%
5273 1
< 0.1%
2570 1
< 0.1%
2457 1
< 0.1%
2300 1
< 0.1%
2212 1
< 0.1%
2127 1
< 0.1%
1807 1
< 0.1%
1645 1
< 0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct272
Distinct (%)10.9%
Missing189
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean91.741407
Minimum0
Maximum63300
Zeros1325
Zeros (%)49.2%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:23:35.526783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile275.95
Maximum63300
Range63300
Interquartile range (IQR)6

Descriptive statistics

Standard deviation1359.1151
Coefficient of variation (CV)14.814631
Kurtosis1889.9697
Mean91.741407
Median Absolute Deviation (MAD)0
Skewness41.593591
Sum229537
Variance1847193.9
MonotonicityNot monotonic
2024-03-23T04:23:36.127831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1325
49.2%
1 240
 
8.9%
2 104
 
3.9%
3 78
 
2.9%
4 55
 
2.0%
5 45
 
1.7%
7 32
 
1.2%
6 30
 
1.1%
8 29
 
1.1%
10 23
 
0.9%
Other values (262) 541
20.1%
(Missing) 189
 
7.0%
ValueCountFrequency (%)
0 1325
49.2%
1 240
 
8.9%
2 104
 
3.9%
3 78
 
2.9%
4 55
 
2.0%
5 45
 
1.7%
6 30
 
1.1%
7 32
 
1.2%
8 29
 
1.1%
9 17
 
0.6%
ValueCountFrequency (%)
63300 1
< 0.1%
19737 1
< 0.1%
6788 1
< 0.1%
5833 1
< 0.1%
4163 1
< 0.1%
3864 1
< 0.1%
3768 1
< 0.1%
3678 1
< 0.1%
2623 1
< 0.1%
2614 1
< 0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct248
Distinct (%)9.9%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean61.248108
Minimum0
Maximum41512
Zeros1429
Zeros (%)53.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:23:36.749950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile202.5
Maximum41512
Range41512
Interquartile range (IQR)5

Descriptive statistics

Standard deviation883.26865
Coefficient of variation (CV)14.421158
Kurtosis1946.3823
Mean61.248108
Median Absolute Deviation (MAD)0
Skewness42.245146
Sum153794
Variance780163.51
MonotonicityNot monotonic
2024-03-23T04:23:37.286828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1429
53.1%
1 268
 
10.0%
2 77
 
2.9%
3 62
 
2.3%
4 42
 
1.6%
5 42
 
1.6%
6 32
 
1.2%
7 31
 
1.2%
8 22
 
0.8%
9 17
 
0.6%
Other values (238) 489
 
18.2%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 1429
53.1%
1 268
 
10.0%
2 77
 
2.9%
3 62
 
2.3%
4 42
 
1.6%
5 42
 
1.6%
6 32
 
1.2%
7 31
 
1.2%
8 22
 
0.8%
9 17
 
0.6%
ValueCountFrequency (%)
41512 1
< 0.1%
11698 1
< 0.1%
3736 1
< 0.1%
3367 1
< 0.1%
2911 1
< 0.1%
2852 1
< 0.1%
2586 1
< 0.1%
2371 1
< 0.1%
2357 1
< 0.1%
1882 1
< 0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct172
Distinct (%)6.8%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean26.026683
Minimum0
Maximum17235
Zeros1546
Zeros (%)57.5%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:23:37.802883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile82
Maximum17235
Range17235
Interquartile range (IQR)3

Descriptive statistics

Standard deviation362.47367
Coefficient of variation (CV)13.927002
Kurtosis2029.675
Mean26.026683
Median Absolute Deviation (MAD)0
Skewness43.184536
Sum65353
Variance131387.16
MonotonicityNot monotonic
2024-03-23T04:23:38.357543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1546
57.5%
1 208
 
7.7%
2 124
 
4.6%
3 68
 
2.5%
4 48
 
1.8%
5 33
 
1.2%
6 28
 
1.0%
8 24
 
0.9%
7 19
 
0.7%
9 16
 
0.6%
Other values (162) 397
 
14.8%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 1546
57.5%
1 208
 
7.7%
2 124
 
4.6%
3 68
 
2.5%
4 48
 
1.8%
5 33
 
1.2%
6 28
 
1.0%
7 19
 
0.7%
8 24
 
0.9%
9 16
 
0.6%
ValueCountFrequency (%)
17235 1
< 0.1%
3366 1
< 0.1%
1977 1
< 0.1%
1767 1
< 0.1%
1349 1
< 0.1%
1220 1
< 0.1%
1215 1
< 0.1%
1035 1
< 0.1%
955 1
< 0.1%
858 1
< 0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct202
Distinct (%)8.0%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean38.554762
Minimum0
Maximum27999
Zeros1597
Zeros (%)59.3%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:23:38.802190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile122.1
Maximum27999
Range27999
Interquartile range (IQR)2

Descriptive statistics

Standard deviation592.0156
Coefficient of variation (CV)15.355188
Kurtosis1987.7796
Mean38.554762
Median Absolute Deviation (MAD)0
Skewness42.793538
Sum97158
Variance350482.47
MonotonicityNot monotonic
2024-03-23T04:23:39.279505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1597
59.3%
1 269
 
10.0%
2 104
 
3.9%
3 62
 
2.3%
5 36
 
1.3%
6 35
 
1.3%
4 34
 
1.3%
7 18
 
0.7%
8 17
 
0.6%
10 12
 
0.4%
Other values (192) 336
 
12.5%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 1597
59.3%
1 269
 
10.0%
2 104
 
3.9%
3 62
 
2.3%
4 34
 
1.3%
5 36
 
1.3%
6 35
 
1.3%
7 18
 
0.7%
8 17
 
0.6%
9 12
 
0.4%
ValueCountFrequency (%)
27999 1
< 0.1%
7583 1
< 0.1%
2549 1
< 0.1%
2076 1
< 0.1%
1919 1
< 0.1%
1876 1
< 0.1%
1670 1
< 0.1%
1609 1
< 0.1%
1526 1
< 0.1%
1435 1
< 0.1%

Interactions

2024-03-23T04:23:09.134830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:10.952830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T04:21:51.723840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T04:22:49.123996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T04:22:53.386658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:59.862556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:23:06.888527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:23:12.473869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:14.995931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:22.258135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:28.823717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:36.099307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:43.930637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:49.408630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:55.676591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:02.140774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:09.211731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:17.815122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:25.543403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:32.840954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:38.852085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:45.576617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:53.741795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:23:00.294238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:23:07.223517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:23:12.729433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:15.432720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:22.542463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:29.105935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:36.375872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:44.279460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:49.654901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:55.971492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:02.506501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:09.564653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:18.145005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:25.964101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:33.129281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:39.124197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:45.877027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:54.090180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:23:00.870937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:23:07.462274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:23:13.035175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:15.952781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:22.954421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:29.656785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:36.835948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:44.629001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:50.166962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:56.249898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:03.051032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:10.022557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:18.627114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:26.445230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:33.578464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:39.556109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:46.178977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:54.433961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:23:01.429041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:23:07.778381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:23:13.338117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:16.472980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:23.315310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:30.093869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:37.237626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:44.991168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:50.477731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:56.530941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:03.368079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:10.595919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:19.140320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:26.800387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:33.879939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:39.909368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:46.494505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:54.863136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:23:01.819173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:23:08.066929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:23:13.628229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:16.812869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:23.955748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:30.533871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:37.654115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:45.403891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:50.742076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:56.796476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:03.736421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:10.977149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:19.599586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:27.095540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:34.190657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:40.295482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:46.904092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:55.226541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:23:02.232272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:23:08.336416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:23:13.881047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:17.223574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:24.269499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:30.926100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:38.040118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:45.925232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:51.044248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:57.049935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:04.079606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:11.347795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:19.997667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:27.478527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:34.499975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:40.803379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:47.295189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:55.530561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:23:02.590625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:23:08.552253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:23:14.154577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:17.665406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:24.682391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:31.315788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:38.435999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:46.242779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:51.412708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:21:57.374701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:04.464564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:11.681717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:20.490326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:27.898394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:34.782280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:41.270920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:47.745940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:22:55.924461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:23:03.108429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:23:08.856429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T04:23:39.618579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0001.0001.0000.9070.9271.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.8691.000
20071.0001.0001.0000.9960.9821.0001.0000.9900.9900.9960.9960.9960.9960.9960.9960.8030.8420.803
20081.0001.0001.0000.9960.9821.0001.0000.9900.9900.9960.9960.9960.9960.9960.9960.8030.8420.803
20090.9070.9960.9961.0000.9930.9960.9960.9820.9820.9820.9820.9820.9820.9820.9820.8030.8420.803
20100.9270.9820.9820.9931.0000.9820.9820.9820.9820.9640.9640.9640.9640.9640.9640.7630.7900.763
20111.0001.0001.0000.9960.9821.0001.0000.9900.9900.9960.9960.9960.9960.9960.9960.8030.8420.803
20121.0001.0001.0000.9960.9821.0001.0000.9900.9900.9960.9960.9960.9960.9960.9960.8030.8420.803
20131.0000.9900.9900.9820.9820.9900.9901.0001.0000.9960.9960.9960.9960.9960.9961.0000.8031.000
20141.0000.9900.9900.9820.9820.9900.9901.0001.0000.9960.9960.9960.9960.9960.9961.0000.8031.000
20151.0000.9960.9960.9820.9640.9960.9960.9960.9961.0001.0001.0001.0001.0001.0001.0000.7631.000
20161.0000.9960.9960.9820.9640.9960.9960.9960.9961.0001.0001.0001.0001.0001.0001.0000.7631.000
20171.0000.9960.9960.9820.9640.9960.9960.9960.9961.0001.0001.0001.0001.0001.0001.0000.7631.000
20181.0000.9960.9960.9820.9640.9960.9960.9960.9961.0001.0001.0001.0001.0001.0001.0000.7631.000
20191.0000.9960.9960.9820.9640.9960.9960.9960.9961.0001.0001.0001.0001.0001.0001.0000.7631.000
20201.0000.9960.9960.9820.9640.9960.9960.9960.9961.0001.0001.0001.0001.0001.0001.0000.7631.000
20211.0000.8030.8030.8030.7630.8030.8031.0001.0001.0001.0001.0001.0001.0001.0001.0000.9781.000
20220.8690.8420.8420.8420.7900.8420.8420.8030.8030.7630.7630.7630.7630.7630.7630.9781.0000.978
20231.0000.8030.8030.8030.7630.8030.8031.0001.0001.0001.0001.0001.0001.0001.0001.0000.9781.000
2024-03-23T04:23:40.100002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.8540.8340.8030.8070.8100.7890.8220.8260.8210.8220.8140.8220.8000.7990.7770.7780.774
20070.8541.0000.8630.8120.8160.7900.7820.8040.8040.8060.7980.8020.8030.7950.7900.7680.7780.760
20080.8340.8631.0000.8370.8200.7920.7840.8010.7910.8010.8060.7950.7920.7700.7640.7690.7810.753
20090.8030.8120.8371.0000.8430.8160.7990.8080.7870.8180.8090.7960.7920.7730.7700.7700.7590.758
20100.8070.8160.8200.8431.0000.8640.8270.8370.8140.8290.8260.8140.8030.7960.8090.7820.7740.770
20110.8100.7900.7920.8160.8641.0000.8630.8450.8090.8370.8210.8100.8160.7980.8160.7870.7770.769
20120.7890.7820.7840.7990.8270.8631.0000.8440.8160.8410.8190.8120.8070.7920.7900.7730.7670.761
20130.8220.8040.8010.8080.8370.8450.8441.0000.8540.8540.8490.8420.8270.8230.8280.7860.7710.773
20140.8260.8040.7910.7870.8140.8090.8160.8541.0000.8530.8350.8360.8230.8240.8110.7730.7610.765
20150.8210.8060.8010.8180.8290.8370.8410.8540.8531.0000.8720.8640.8530.8370.8380.8140.7920.787
20160.8220.7980.8060.8090.8260.8210.8190.8490.8350.8721.0000.8660.8610.8390.8380.8040.7800.792
20170.8140.8020.7950.7960.8140.8100.8120.8420.8360.8640.8661.0000.8640.8470.8430.8130.7950.796
20180.8220.8030.7920.7920.8030.8160.8070.8270.8230.8530.8610.8641.0000.8560.8520.8250.7940.786
20190.8000.7950.7700.7730.7960.7980.7920.8230.8240.8370.8390.8470.8561.0000.8660.8000.7880.775
20200.7990.7900.7640.7700.8090.8160.7900.8280.8110.8380.8380.8430.8520.8661.0000.8550.8150.803
20210.7770.7680.7690.7700.7820.7870.7730.7860.7730.8140.8040.8130.8250.8000.8551.0000.8670.826
20220.7780.7780.7810.7590.7740.7770.7670.7710.7610.7920.7800.7950.7940.7880.8150.8671.0000.843
20230.7740.7600.7530.7580.7700.7690.7610.7730.7650.7870.7920.7960.7860.7750.8030.8260.8431.000

Missing values

2024-03-23T04:23:14.938836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T04:23:15.814368image/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.
2024-03-23T04:23:16.399059image/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

아파트매매 거래주체별200620072008200920102011201220132014201520162017201820192020202120222023
0전국 /개인>개인451312986631822368293348141515301263867548380555224688142887401983768563300415121723527999
1전국 /개인>법인373313293291270385324318395369437416536110921661264500181
2전국 /개인>기타55515610810612498975212611698109129235194143103
3전국 /법인>개인582361086747761776679346618657905089412632251969137818073678258617671609
4전국 /법인>법인62079018032484227218271221770983650892509672452663350214120
5전국 /법인>기타76373115421443020351879122447357455
6전국 /기타>개인13812517225218925212116712817213916412114310951882485205
7전국 /기타>법인3734161229651242992995
8전국 /기타>기타142313201340618135221123423
9서울 /개인>개인102834315437758293441449931534914686898469084835674625273678833678182549
아파트매매 거래주체별200620072008200920102011201220132014201520162017201820192020202120222023
2681제주 제주시/기타>기타000000000000000000
2682제주 서귀포시/개인>개인102626242438273135414643383255764238
2683제주 서귀포시/개인>법인010100031012213120
2684제주 서귀포시/개인>기타000000000100001002
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