Overview

Dataset statistics

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

Variable types

Text1
Numeric18

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 부동산거래현황 중 주택 거래현황의 연도별 거래규모별(면적) 데이터입니다.- (단위 : 천㎡)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068414/fileData.do

Alerts

2006 is highly overall correlated with 2007 and 16 other fieldsHigh correlation
2007 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2008 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2009 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2010 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2011 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2012 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2013 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2014 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2015 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2016 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2017 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2018 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2019 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2020 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2021 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2022 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2023 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2006 has 198 (7.4%) missing valuesMissing
2007 has 243 (9.0%) missing valuesMissing
2008 has 225 (8.4%) missing valuesMissing
2009 has 225 (8.4%) missing valuesMissing
2010 has 171 (6.4%) missing valuesMissing
2011 has 198 (7.4%) missing valuesMissing
2012 has 180 (6.7%) missing valuesMissing
2013 has 180 (6.7%) missing valuesMissing
2014 has 117 (4.3%) missing valuesMissing
2015 has 153 (5.7%) missing valuesMissing
2016 has 153 (5.7%) missing valuesMissing
2017 has 180 (6.7%) missing valuesMissing
2018 has 171 (6.4%) missing valuesMissing
2019 has 180 (6.7%) missing valuesMissing
2020 has 180 (6.7%) missing valuesMissing
2021 has 180 (6.7%) missing valuesMissing
2022 has 180 (6.7%) missing valuesMissing
2023 has 171 (6.4%) missing valuesMissing
2006 is highly skewed (γ1 = 25.3472571)Skewed
2007 is highly skewed (γ1 = 23.63712146)Skewed
2008 is highly skewed (γ1 = 26.36568958)Skewed
2009 is highly skewed (γ1 = 25.94096262)Skewed
2010 is highly skewed (γ1 = 25.35027206)Skewed
2011 is highly skewed (γ1 = 25.75789801)Skewed
2012 is highly skewed (γ1 = 25.55944996)Skewed
2013 is highly skewed (γ1 = 28.10697565)Skewed
2014 is highly skewed (γ1 = 28.30887332)Skewed
2015 is highly skewed (γ1 = 27.65486272)Skewed
2016 is highly skewed (γ1 = 28.17919109)Skewed
2017 is highly skewed (γ1 = 31.23029911)Skewed
2018 is highly skewed (γ1 = 31.74081241)Skewed
2019 is highly skewed (γ1 = 32.72355259)Skewed
2020 is highly skewed (γ1 = 31.02949823)Skewed
2021 is highly skewed (γ1 = 30.07169633)Skewed
2022 is highly skewed (γ1 = 30.5698891)Skewed
2023 is highly skewed (γ1 = 32.73352111)Skewed
지역 및 거래규모 has unique valuesUnique
2006 has 199 (7.4%) zerosZeros
2007 has 211 (7.8%) zerosZeros
2008 has 194 (7.2%) zerosZeros
2009 has 186 (6.9%) zerosZeros
2010 has 207 (7.7%) zerosZeros
2011 has 172 (6.4%) zerosZeros
2012 has 147 (5.5%) zerosZeros
2013 has 141 (5.2%) zerosZeros
2014 has 126 (4.7%) zerosZeros
2015 has 122 (4.5%) zerosZeros
2016 has 132 (4.9%) zerosZeros
2017 has 132 (4.9%) zerosZeros
2018 has 130 (4.8%) zerosZeros
2019 has 130 (4.8%) zerosZeros
2020 has 105 (3.9%) zerosZeros
2021 has 112 (4.2%) zerosZeros
2022 has 138 (5.1%) zerosZeros
2023 has 168 (6.2%) zerosZeros

Reproduction

Analysis started2024-03-23 05:53:36.771656
Analysis finished2024-03-23 05:54:29.164726
Duration52.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct2691
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
2024-03-23T14:54:29.344715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length23
Mean length14.008175
Min length9

Characters and Unicode

Total characters37696
Distinct characters159
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
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 /20㎡이하
2nd row전국 /21~40㎡
3rd row전국 /41~60㎡
4th row전국 /61~85㎡
5th row전국 /86~100㎡
ValueCountFrequency (%)
경기 477
 
8.9%
경남 243
 
4.5%
서울 234
 
4.3%
경북 234
 
4.3%
전남 207
 
3.8%
충남 180
 
3.3%
충북 180
 
3.3%
강원 171
 
3.2%
전북 153
 
2.8%
부산 153
 
2.8%
Other values (2322) 3150
58.5%
2024-03-23T14:54:29.834132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3588
 
9.5%
2691
 
7.1%
/ 2691
 
7.1%
2691
 
7.1%
6 2093
 
5.6%
~ 2093
 
5.6%
0 1794
 
4.8%
1269
 
3.4%
8 1196
 
3.2%
1098
 
2.9%
Other values (149) 16492
43.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15227
40.4%
Decimal Number 11960
31.7%
Space Separator 2691
 
7.1%
Other Punctuation 2691
 
7.1%
Other Symbol 2691
 
7.1%
Math Symbol 2093
 
5.6%
Close Punctuation 172
 
0.5%
Open Punctuation 171
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1269
 
8.3%
1098
 
7.2%
981
 
6.4%
837
 
5.5%
801
 
5.3%
648
 
4.3%
504
 
3.3%
450
 
3.0%
432
 
2.8%
423
 
2.8%
Other values (134) 7784
51.1%
Decimal Number
ValueCountFrequency (%)
1 3588
30.0%
6 2093
17.5%
0 1794
15.0%
8 1196
 
10.0%
5 897
 
7.5%
3 598
 
5.0%
9 598
 
5.0%
2 598
 
5.0%
4 598
 
5.0%
Space Separator
ValueCountFrequency (%)
2691
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2691
100.0%
Other Symbol
ValueCountFrequency (%)
2691
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2093
100.0%
Close Punctuation
ValueCountFrequency (%)
) 172
100.0%
Open Punctuation
ValueCountFrequency (%)
( 171
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22469
59.6%
Hangul 15227
40.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1269
 
8.3%
1098
 
7.2%
981
 
6.4%
837
 
5.5%
801
 
5.3%
648
 
4.3%
504
 
3.3%
450
 
3.0%
432
 
2.8%
423
 
2.8%
Other values (134) 7784
51.1%
Common
ValueCountFrequency (%)
1 3588
16.0%
2691
12.0%
/ 2691
12.0%
2691
12.0%
6 2093
9.3%
~ 2093
9.3%
0 1794
8.0%
8 1196
 
5.3%
5 897
 
4.0%
3 598
 
2.7%
Other values (5) 2137
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19778
52.5%
Hangul 15227
40.4%
CJK Compat 2691
 
7.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3588
18.1%
2691
13.6%
/ 2691
13.6%
6 2093
10.6%
~ 2093
10.6%
0 1794
9.1%
8 1196
 
6.0%
5 897
 
4.5%
3 598
 
3.0%
9 598
 
3.0%
Other values (4) 1539
7.8%
CJK Compat
ValueCountFrequency (%)
2691
100.0%
Hangul
ValueCountFrequency (%)
1269
 
8.3%
1098
 
7.2%
981
 
6.4%
837
 
5.5%
801
 
5.3%
648
 
4.3%
504
 
3.3%
450
 
3.0%
432
 
2.8%
423
 
2.8%
Other values (134) 7784
51.1%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct419
Distinct (%)16.8%
Missing198
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean156.22222
Minimum0
Maximum44768
Zeros199
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:54:30.009680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median15
Q377
95-th percentile413.4
Maximum44768
Range44768
Interquartile range (IQR)74

Descriptive statistics

Standard deviation1245.2543
Coefficient of variation (CV)7.9710444
Kurtosis782.88403
Mean156.22222
Median Absolute Deviation (MAD)14
Skewness25.347257
Sum389462
Variance1550658.2
MonotonicityNot monotonic
2024-03-23T14:54:30.176088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 207
 
7.7%
0 199
 
7.4%
2 135
 
5.0%
3 124
 
4.6%
4 102
 
3.8%
5 93
 
3.5%
6 55
 
2.0%
7 54
 
2.0%
9 49
 
1.8%
8 45
 
1.7%
Other values (409) 1430
53.1%
(Missing) 198
 
7.4%
ValueCountFrequency (%)
0 199
7.4%
1 207
7.7%
2 135
5.0%
3 124
4.6%
4 102
3.8%
5 93
3.5%
6 55
 
2.0%
7 54
 
2.0%
8 45
 
1.7%
9 49
 
1.8%
ValueCountFrequency (%)
44768 1
< 0.1%
27546 1
< 0.1%
17167 1
< 0.1%
14634 1
< 0.1%
13433 1
< 0.1%
8898 1
< 0.1%
8444 1
< 0.1%
6190 1
< 0.1%
6021 1
< 0.1%
5001 1
< 0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct372
Distinct (%)15.2%
Missing243
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean125.22794
Minimum0
Maximum32600
Zeros211
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:54:30.317035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median13
Q359
95-th percentile333.65
Maximum32600
Range32600
Interquartile range (IQR)56

Descriptive statistics

Standard deviation967.54478
Coefficient of variation (CV)7.7262692
Kurtosis670.59574
Mean125.22794
Median Absolute Deviation (MAD)12
Skewness23.637121
Sum306558
Variance936142.91
MonotonicityNot monotonic
2024-03-23T14:54:30.463350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 211
 
7.8%
1 187
 
6.9%
2 147
 
5.5%
3 123
 
4.6%
4 111
 
4.1%
6 86
 
3.2%
5 71
 
2.6%
7 55
 
2.0%
8 54
 
2.0%
10 46
 
1.7%
Other values (362) 1357
50.4%
(Missing) 243
 
9.0%
ValueCountFrequency (%)
0 211
7.8%
1 187
6.9%
2 147
5.5%
3 123
4.6%
4 111
4.1%
5 71
 
2.6%
6 86
3.2%
7 55
 
2.0%
8 54
 
2.0%
9 41
 
1.5%
ValueCountFrequency (%)
32600 1
< 0.1%
21629 1
< 0.1%
17308 1
< 0.1%
11952 1
< 0.1%
8136 1
< 0.1%
6415 1
< 0.1%
4692 1
< 0.1%
4664 1
< 0.1%
4577 1
< 0.1%
4459 1
< 0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct374
Distinct (%)15.2%
Missing225
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean118.53812
Minimum0
Maximum33792
Zeros194
Zeros (%)7.2%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:54:30.662772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median13
Q358.75
95-th percentile345
Maximum33792
Range33792
Interquartile range (IQR)55.75

Descriptive statistics

Standard deviation934.03019
Coefficient of variation (CV)7.8795766
Kurtosis832.00139
Mean118.53812
Median Absolute Deviation (MAD)12
Skewness26.36569
Sum292315
Variance872412.39
MonotonicityNot monotonic
2024-03-23T14:54:30.858753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 198
 
7.4%
0 194
 
7.2%
2 141
 
5.2%
3 122
 
4.5%
4 112
 
4.2%
5 84
 
3.1%
6 69
 
2.6%
9 68
 
2.5%
7 58
 
2.2%
13 43
 
1.6%
Other values (364) 1377
51.2%
(Missing) 225
 
8.4%
ValueCountFrequency (%)
0 194
7.2%
1 198
7.4%
2 141
5.2%
3 122
4.5%
4 112
4.2%
5 84
3.1%
6 69
 
2.6%
7 58
 
2.2%
8 39
 
1.4%
9 68
 
2.5%
ValueCountFrequency (%)
33792 1
< 0.1%
22183 1
< 0.1%
12656 1
< 0.1%
10044 1
< 0.1%
7471 1
< 0.1%
5810 1
< 0.1%
4648 1
< 0.1%
4623 1
< 0.1%
4337 1
< 0.1%
3457 1
< 0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct370
Distinct (%)15.0%
Missing225
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean122.11354
Minimum0
Maximum34459
Zeros186
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:54:31.091557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median14
Q359
95-th percentile338.25
Maximum34459
Range34459
Interquartile range (IQR)56

Descriptive statistics

Standard deviation946.08216
Coefficient of variation (CV)7.7475613
Kurtosis816.58155
Mean122.11354
Median Absolute Deviation (MAD)13
Skewness25.940963
Sum301132
Variance895071.46
MonotonicityNot monotonic
2024-03-23T14:54:31.265329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 186
 
6.9%
1 181
 
6.7%
2 145
 
5.4%
3 125
 
4.6%
4 93
 
3.5%
5 88
 
3.3%
6 70
 
2.6%
7 63
 
2.3%
9 56
 
2.1%
8 48
 
1.8%
Other values (360) 1411
52.4%
(Missing) 225
 
8.4%
ValueCountFrequency (%)
0 186
6.9%
1 181
6.7%
2 145
5.4%
3 125
4.6%
4 93
3.5%
5 88
3.3%
6 70
 
2.6%
7 63
 
2.3%
8 48
 
1.8%
9 56
 
2.1%
ValueCountFrequency (%)
34459 1
< 0.1%
19676 1
< 0.1%
15756 1
< 0.1%
10289 1
< 0.1%
8549 1
< 0.1%
5738 1
< 0.1%
5327 1
< 0.1%
4476 1
< 0.1%
4286 1
< 0.1%
3374 1
< 0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct365
Distinct (%)14.5%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean114.73849
Minimum0
Maximum31674
Zeros207
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:54:31.468374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median13
Q360
95-th percentile312.05
Maximum31674
Range31674
Interquartile range (IQR)57

Descriptive statistics

Standard deviation878.71355
Coefficient of variation (CV)7.6584025
Kurtosis778.49973
Mean114.73849
Median Absolute Deviation (MAD)12
Skewness25.350272
Sum289141
Variance772137.51
MonotonicityNot monotonic
2024-03-23T14:54:31.669220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 207
 
7.7%
1 188
 
7.0%
2 127
 
4.7%
3 126
 
4.7%
4 107
 
4.0%
6 95
 
3.5%
5 90
 
3.3%
7 64
 
2.4%
8 60
 
2.2%
9 49
 
1.8%
Other values (355) 1407
52.3%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 207
7.7%
1 188
7.0%
2 127
4.7%
3 126
4.7%
4 107
4.0%
5 90
3.3%
6 95
3.5%
7 64
 
2.4%
8 60
 
2.2%
9 49
 
1.8%
ValueCountFrequency (%)
31674 1
< 0.1%
17736 1
< 0.1%
14939 1
< 0.1%
13245 1
< 0.1%
7221 1
< 0.1%
5582 1
< 0.1%
4104 1
< 0.1%
3928 1
< 0.1%
3298 1
< 0.1%
3184 1
< 0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct404
Distinct (%)16.2%
Missing198
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean134.58524
Minimum0
Maximum36820
Zeros172
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:54:31.880159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median15
Q373
95-th percentile381.4
Maximum36820
Range36820
Interquartile range (IQR)69

Descriptive statistics

Standard deviation1016.3832
Coefficient of variation (CV)7.5519661
Kurtosis800.729
Mean134.58524
Median Absolute Deviation (MAD)14
Skewness25.757898
Sum335521
Variance1033034.7
MonotonicityNot monotonic
2024-03-23T14:54:32.094701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 172
 
6.4%
1 151
 
5.6%
2 124
 
4.6%
3 122
 
4.5%
4 99
 
3.7%
6 80
 
3.0%
5 78
 
2.9%
9 63
 
2.3%
7 59
 
2.2%
8 58
 
2.2%
Other values (394) 1487
55.3%
(Missing) 198
 
7.4%
ValueCountFrequency (%)
0 172
6.4%
1 151
5.6%
2 124
4.6%
3 122
4.5%
4 99
3.7%
5 78
2.9%
6 80
3.0%
7 59
 
2.2%
8 58
 
2.2%
9 63
 
2.3%
ValueCountFrequency (%)
36820 1
< 0.1%
21231 1
< 0.1%
16817 1
< 0.1%
14195 1
< 0.1%
7635 1
< 0.1%
6552 1
< 0.1%
5144 1
< 0.1%
3620 1
< 0.1%
3594 1
< 0.1%
3557 1
< 0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct360
Distinct (%)14.3%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean108.57547
Minimum0
Maximum29606
Zeros147
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:54:32.283352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median13
Q356
95-th percentile300.5
Maximum29606
Range29606
Interquartile range (IQR)52

Descriptive statistics

Standard deviation819.80923
Coefficient of variation (CV)7.5505936
Kurtosis788.02612
Mean108.57547
Median Absolute Deviation (MAD)12
Skewness25.55945
Sum272633
Variance672087.17
MonotonicityNot monotonic
2024-03-23T14:54:32.452065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 160
 
5.9%
0 147
 
5.5%
3 130
 
4.8%
2 128
 
4.8%
4 111
 
4.1%
6 95
 
3.5%
5 94
 
3.5%
7 79
 
2.9%
8 74
 
2.7%
9 58
 
2.2%
Other values (350) 1435
53.3%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 147
5.5%
1 160
5.9%
2 128
4.8%
3 130
4.8%
4 111
4.1%
5 94
3.5%
6 95
3.5%
7 79
2.9%
8 74
2.7%
9 58
 
2.2%
ValueCountFrequency (%)
29606 1
< 0.1%
16762 1
< 0.1%
13172 1
< 0.1%
13161 1
< 0.1%
6337 1
< 0.1%
4706 1
< 0.1%
3695 1
< 0.1%
3185 1
< 0.1%
3142 1
< 0.1%
2770 1
< 0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct394
Distinct (%)15.7%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean125.02828
Minimum0
Maximum38390
Zeros141
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:54:32.604275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median14
Q362
95-th percentile357
Maximum38390
Range38390
Interquartile range (IQR)58

Descriptive statistics

Standard deviation995.00113
Coefficient of variation (CV)7.9582088
Kurtosis960.61879
Mean125.02828
Median Absolute Deviation (MAD)12
Skewness28.106976
Sum313946
Variance990027.24
MonotonicityNot monotonic
2024-03-23T14:54:32.752001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 142
 
5.3%
0 141
 
5.2%
2 130
 
4.8%
3 117
 
4.3%
4 113
 
4.2%
5 93
 
3.5%
7 79
 
2.9%
8 77
 
2.9%
6 74
 
2.7%
9 70
 
2.6%
Other values (384) 1475
54.8%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 141
5.2%
1 142
5.3%
2 130
4.8%
3 117
4.3%
4 113
4.2%
5 93
3.5%
6 74
2.7%
7 79
2.9%
8 77
2.9%
9 70
2.6%
ValueCountFrequency (%)
38390 1
< 0.1%
20005 1
< 0.1%
13480 1
< 0.1%
12921 1
< 0.1%
8333 1
< 0.1%
4712 1
< 0.1%
4400 1
< 0.1%
3706 1
< 0.1%
3528 1
< 0.1%
3486 1
< 0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct429
Distinct (%)16.7%
Missing117
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean153.03885
Minimum0
Maximum48039
Zeros126
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:54:32.911471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median17
Q378.75
95-th percentile433.7
Maximum48039
Range48039
Interquartile range (IQR)73.75

Descriptive statistics

Standard deviation1232.317
Coefficient of variation (CV)8.0523146
Kurtosis974.65855
Mean153.03885
Median Absolute Deviation (MAD)15
Skewness28.308873
Sum393922
Variance1518605.1
MonotonicityNot monotonic
2024-03-23T14:54:33.096475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 153
 
5.7%
0 126
 
4.7%
2 110
 
4.1%
3 95
 
3.5%
5 87
 
3.2%
6 86
 
3.2%
7 83
 
3.1%
4 83
 
3.1%
9 78
 
2.9%
8 62
 
2.3%
Other values (419) 1611
59.9%
(Missing) 117
 
4.3%
ValueCountFrequency (%)
0 126
4.7%
1 153
5.7%
2 110
4.1%
3 95
3.5%
4 83
3.1%
5 87
3.2%
6 86
3.2%
7 83
3.1%
8 62
2.3%
9 78
2.9%
ValueCountFrequency (%)
48039 1
< 0.1%
23413 1
< 0.1%
20496 1
< 0.1%
15278 1
< 0.1%
9914 1
< 0.1%
5866 1
< 0.1%
5114 1
< 0.1%
4980 1
< 0.1%
4675 1
< 0.1%
4440 1
< 0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct458
Distinct (%)18.0%
Missing153
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean182.39165
Minimum0
Maximum56011
Zeros122
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:54:33.251765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median21
Q396.75
95-th percentile504.45
Maximum56011
Range56011
Interquartile range (IQR)90.75

Descriptive statistics

Standard deviation1461.1887
Coefficient of variation (CV)8.01127
Kurtosis931.79466
Mean182.39165
Median Absolute Deviation (MAD)19
Skewness27.654863
Sum462910
Variance2135072.5
MonotonicityNot monotonic
2024-03-23T14:54:33.403559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 124
 
4.6%
0 122
 
4.5%
2 107
 
4.0%
3 93
 
3.5%
4 92
 
3.4%
7 81
 
3.0%
5 75
 
2.8%
6 65
 
2.4%
9 54
 
2.0%
8 53
 
2.0%
Other values (448) 1672
62.1%
(Missing) 153
 
5.7%
ValueCountFrequency (%)
0 122
4.5%
1 124
4.6%
2 107
4.0%
3 93
3.5%
4 92
3.4%
5 75
2.8%
6 65
2.4%
7 81
3.0%
8 53
2.0%
9 54
2.0%
ValueCountFrequency (%)
56011 1
< 0.1%
28488 1
< 0.1%
23773 1
< 0.1%
16977 1
< 0.1%
13274 1
< 0.1%
7409 1
< 0.1%
6832 1
< 0.1%
6048 1
< 0.1%
5392 1
< 0.1%
5014 1
< 0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct443
Distinct (%)17.5%
Missing153
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean166.20764
Minimum0
Maximum52614
Zeros132
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:54:33.562655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median19
Q387
95-th percentile437.75
Maximum52614
Range52614
Interquartile range (IQR)82

Descriptive statistics

Standard deviation1355.0924
Coefficient of variation (CV)8.1530088
Kurtosis969.48732
Mean166.20764
Median Absolute Deviation (MAD)17
Skewness28.179191
Sum421835
Variance1836275.4
MonotonicityNot monotonic
2024-03-23T14:54:34.118304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 132
 
4.9%
1 122
 
4.5%
3 113
 
4.2%
2 105
 
3.9%
5 96
 
3.6%
4 81
 
3.0%
6 80
 
3.0%
7 79
 
2.9%
10 57
 
2.1%
8 52
 
1.9%
Other values (433) 1621
60.2%
(Missing) 153
 
5.7%
ValueCountFrequency (%)
0 132
4.9%
1 122
4.5%
2 105
3.9%
3 113
4.2%
4 81
3.0%
5 96
3.6%
6 80
3.0%
7 79
2.9%
8 52
 
1.9%
9 47
 
1.7%
ValueCountFrequency (%)
52614 1
< 0.1%
26854 1
< 0.1%
19162 1
< 0.1%
14890 1
< 0.1%
14293 1
< 0.1%
7586 1
< 0.1%
6509 1
< 0.1%
5265 1
< 0.1%
5083 1
< 0.1%
4897 1
< 0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct438
Distinct (%)17.4%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean177.92433
Minimum0
Maximum64100
Zeros132
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:54:34.277929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median19
Q385.5
95-th percentile509
Maximum64100
Range64100
Interquartile range (IQR)80.5

Descriptive statistics

Standard deviation1558.837
Coefficient of variation (CV)8.7612359
Kurtosis1179.3093
Mean177.92433
Median Absolute Deviation (MAD)17
Skewness31.230299
Sum446768
Variance2429972.9
MonotonicityNot monotonic
2024-03-23T14:54:34.425331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 132
 
4.9%
1 117
 
4.3%
3 112
 
4.2%
2 108
 
4.0%
4 94
 
3.5%
5 91
 
3.4%
6 84
 
3.1%
8 59
 
2.2%
7 57
 
2.1%
9 56
 
2.1%
Other values (428) 1601
59.5%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 132
4.9%
1 117
4.3%
2 108
4.0%
3 112
4.2%
4 94
3.5%
5 91
3.4%
6 84
3.1%
7 57
2.1%
8 59
2.2%
9 56
2.1%
ValueCountFrequency (%)
64100 1
< 0.1%
27293 1
< 0.1%
19223 1
< 0.1%
15852 1
< 0.1%
14552 1
< 0.1%
7942 1
< 0.1%
7325 1
< 0.1%
5403 1
< 0.1%
4965 1
< 0.1%
4919 1
< 0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct438
Distinct (%)17.4%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean172.54524
Minimum0
Maximum64766
Zeros130
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:54:34.634779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median17
Q377.25
95-th percentile466.55
Maximum64766
Range64766
Interquartile range (IQR)72.25

Descriptive statistics

Standard deviation1562.5217
Coefficient of variation (CV)9.0557222
Kurtosis1211.0389
Mean172.54524
Median Absolute Deviation (MAD)15
Skewness31.740812
Sum434814
Variance2441474.2
MonotonicityNot monotonic
2024-03-23T14:54:34.820206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 137
 
5.1%
0 130
 
4.8%
3 118
 
4.4%
2 111
 
4.1%
4 106
 
3.9%
5 84
 
3.1%
6 75
 
2.8%
7 74
 
2.7%
10 65
 
2.4%
8 59
 
2.2%
Other values (428) 1561
58.0%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 130
4.8%
1 137
5.1%
2 111
4.1%
3 118
4.4%
4 106
3.9%
5 84
3.1%
6 75
2.8%
7 74
2.7%
8 59
2.2%
9 53
 
2.0%
ValueCountFrequency (%)
64766 1
< 0.1%
26090 1
< 0.1%
22425 1
< 0.1%
14654 1
< 0.1%
13140 1
< 0.1%
8212 1
< 0.1%
6251 1
< 0.1%
5479 1
< 0.1%
4760 1
< 0.1%
4627 1
< 0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct414
Distinct (%)16.5%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean156.2963
Minimum0
Maximum60754
Zeros130
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:54:34.993163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median15
Q365
95-th percentile436.5
Maximum60754
Range60754
Interquartile range (IQR)60

Descriptive statistics

Standard deviation1444.0312
Coefficient of variation (CV)9.2390621
Kurtosis1279.9743
Mean156.2963
Median Absolute Deviation (MAD)13
Skewness32.723553
Sum392460
Variance2085226.1
MonotonicityNot monotonic
2024-03-23T14:54:35.162815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 135
 
5.0%
0 130
 
4.8%
2 125
 
4.6%
3 125
 
4.6%
4 108
 
4.0%
5 106
 
3.9%
8 69
 
2.6%
6 68
 
2.5%
9 66
 
2.5%
7 62
 
2.3%
Other values (404) 1517
56.4%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 130
4.8%
1 135
5.0%
2 125
4.6%
3 125
4.6%
4 108
4.0%
5 106
3.9%
6 68
2.5%
7 62
2.3%
8 69
2.6%
9 66
2.5%
ValueCountFrequency (%)
60754 1
< 0.1%
23531 1
< 0.1%
19416 1
< 0.1%
13182 1
< 0.1%
11001 1
< 0.1%
7525 1
< 0.1%
4993 1
< 0.1%
4632 1
< 0.1%
4487 1
< 0.1%
4206 1
< 0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct462
Distinct (%)18.4%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean200.66706
Minimum0
Maximum72357
Zeros105
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:54:35.333893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median20
Q389
95-th percentile578
Maximum72357
Range72357
Interquartile range (IQR)83

Descriptive statistics

Standard deviation1773.6711
Coefficient of variation (CV)8.8388751
Kurtosis1156.643
Mean200.66706
Median Absolute Deviation (MAD)18
Skewness31.029498
Sum503875
Variance3145909.2
MonotonicityNot monotonic
2024-03-23T14:54:35.510269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 127
 
4.7%
2 105
 
3.9%
0 105
 
3.9%
3 93
 
3.5%
4 91
 
3.4%
5 77
 
2.9%
7 72
 
2.7%
6 71
 
2.6%
8 68
 
2.5%
9 51
 
1.9%
Other values (452) 1651
61.4%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 105
3.9%
1 127
4.7%
2 105
3.9%
3 93
3.5%
4 91
3.4%
5 77
2.9%
6 71
2.6%
7 72
2.7%
8 68
2.5%
9 51
1.9%
ValueCountFrequency (%)
72357 1
< 0.1%
33745 1
< 0.1%
21536 1
< 0.1%
17977 1
< 0.1%
14103 1
< 0.1%
10473 1
< 0.1%
5924 1
< 0.1%
5744 1
< 0.1%
5728 1
< 0.1%
5684 1
< 0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct421
Distinct (%)16.8%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean156.36758
Minimum0
Maximum53520
Zeros112
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:54:35.680298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median18
Q374.5
95-th percentile424.5
Maximum53520
Range53520
Interquartile range (IQR)68.5

Descriptive statistics

Standard deviation1347.6544
Coefficient of variation (CV)8.6185024
Kurtosis1077.8434
Mean156.36758
Median Absolute Deviation (MAD)16
Skewness30.071696
Sum392639
Variance1816172.3
MonotonicityNot monotonic
2024-03-23T14:54:35.847773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 120
 
4.5%
0 112
 
4.2%
2 102
 
3.8%
3 99
 
3.7%
4 93
 
3.5%
5 85
 
3.2%
6 79
 
2.9%
8 75
 
2.8%
7 69
 
2.6%
9 62
 
2.3%
Other values (411) 1615
60.0%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 112
4.2%
1 120
4.5%
2 102
3.8%
3 99
3.7%
4 93
3.5%
5 85
3.2%
6 79
2.9%
7 69
2.6%
8 75
2.8%
9 62
2.3%
ValueCountFrequency (%)
53520 1
< 0.1%
29363 1
< 0.1%
14327 1
< 0.1%
12924 1
< 0.1%
11069 1
< 0.1%
8984 1
< 0.1%
5684 1
< 0.1%
4969 1
< 0.1%
4037 1
< 0.1%
4033 1
< 0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct317
Distinct (%)12.6%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean90.812824
Minimum0
Maximum31203
Zeros138
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:54:35.985250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median11
Q342
95-th percentile258
Maximum31203
Range31203
Interquartile range (IQR)38

Descriptive statistics

Standard deviation772.93184
Coefficient of variation (CV)8.5112631
Kurtosis1122.4158
Mean90.812824
Median Absolute Deviation (MAD)9
Skewness30.569889
Sum228031
Variance597423.63
MonotonicityNot monotonic
2024-03-23T14:54:36.135210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 162
 
6.0%
0 138
 
5.1%
1 134
 
5.0%
3 132
 
4.9%
5 120
 
4.5%
4 117
 
4.3%
6 112
 
4.2%
7 103
 
3.8%
8 73
 
2.7%
10 71
 
2.6%
Other values (307) 1349
50.1%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 138
5.1%
1 134
5.0%
2 162
6.0%
3 132
4.9%
4 117
4.3%
5 120
4.5%
6 112
4.2%
7 103
3.8%
8 73
2.7%
9 68
2.5%
ValueCountFrequency (%)
31203 1
< 0.1%
15598 1
< 0.1%
8506 1
< 0.1%
7883 1
< 0.1%
6502 1
< 0.1%
4100 1
< 0.1%
3462 1
< 0.1%
2673 1
< 0.1%
2448 1
< 0.1%
2433 1
< 0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct311
Distinct (%)12.3%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean89.844444
Minimum0
Maximum34241
Zeros168
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T14:54:36.290389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median9
Q337
95-th percentile252.05
Maximum34241
Range34241
Interquartile range (IQR)34

Descriptive statistics

Standard deviation816.82927
Coefficient of variation (CV)9.0915946
Kurtosis1272.1391
Mean89.844444
Median Absolute Deviation (MAD)8
Skewness32.733521
Sum226408
Variance667210.06
MonotonicityNot monotonic
2024-03-23T14:54:36.441120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 183
 
6.8%
2 177
 
6.6%
0 168
 
6.2%
3 155
 
5.8%
4 134
 
5.0%
5 126
 
4.7%
6 110
 
4.1%
8 81
 
3.0%
7 79
 
2.9%
10 58
 
2.2%
Other values (301) 1249
46.4%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 168
6.2%
1 183
6.8%
2 177
6.6%
3 155
5.8%
4 134
5.0%
5 126
4.7%
6 110
4.1%
7 79
2.9%
8 81
3.0%
9 57
 
2.1%
ValueCountFrequency (%)
34241 1
< 0.1%
15511 1
< 0.1%
8790 1
< 0.1%
7067 1
< 0.1%
5603 1
< 0.1%
4417 1
< 0.1%
2736 1
< 0.1%
2704 1
< 0.1%
2611 1
< 0.1%
2413 1
< 0.1%

Interactions

2024-03-23T14:54:25.472793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:40.364047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T14:54:18.737548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:20.715193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T14:54:13.330790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:15.755927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:17.686203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:19.611467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:22.195088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:24.364245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:26.866664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:42.231864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:45.283743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:48.421792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:51.309072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:54.134817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:57.545488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:00.244114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:03.087858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:05.807694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:08.329118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:11.009703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:13.479539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:15.904409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:17.783824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:19.722260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:22.305356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:24.493084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:26.983935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:42.373724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:45.443970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:48.581132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:51.479898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:54.273229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:57.674175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:00.386621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:03.213092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:05.930461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:08.518676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:11.177754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:13.626127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:16.016767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:17.882110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:19.847729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:22.400796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:24.651552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:27.116612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:42.508278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:45.628043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:48.752090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:51.617390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:54.444555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:57.824372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:00.561528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:03.344759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:06.364577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:08.653886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:11.332118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:13.747556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:16.122082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:18.014655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:19.950818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:22.540627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:24.818918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:27.250809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:42.660881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:45.756847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:48.940469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:51.762402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:54.609937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:57.972781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:00.713470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:03.467991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:06.468678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:08.781549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:11.475648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:13.870076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:16.221930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:18.110924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:20.052454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:22.664354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:24.981871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:27.408753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:42.868032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:45.900679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:49.123895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:51.918206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:54.812527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:58.155839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:00.864583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:03.603648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:06.590633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:08.897958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:11.629314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:13.995670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:16.336515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:18.230408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:20.162289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:22.787945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:25.118090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:27.560002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:43.060890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:46.048662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:49.264659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:52.072389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:54.976407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:58.316548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:00.990033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:03.759921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:06.707159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:08.989361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:11.760253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:14.107147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:16.451262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:18.322295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:20.273293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:22.884253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:25.210891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:27.700209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:43.234833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:46.192067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:49.405222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:52.223930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:55.129139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:53:58.458861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:01.130355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:04.009541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:06.821557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:09.084563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:11.874106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:14.229323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:16.573111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:18.423682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:20.382240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:22.980476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:54:25.345758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T14:54:36.562213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9590.9650.9550.9330.9320.9550.9350.8930.9430.9450.9960.9240.9690.9900.9960.9960.990
20070.9591.0000.9750.9600.9520.9980.9600.9590.9640.9990.9640.9300.9530.9530.9530.9300.9300.953
20080.9650.9751.0000.9990.9870.9650.9920.9910.9940.9740.9940.9430.9870.9210.9210.9430.9430.921
20090.9550.9600.9991.0000.9920.9700.9930.9920.9950.9810.9950.9500.9880.9230.9230.9230.9230.923
20100.9330.9520.9870.9921.0000.9650.9990.9980.9890.9590.9890.9210.9970.9010.9210.9210.9210.921
20110.9320.9980.9650.9700.9651.0000.9701.0000.9771.0000.9770.9300.9700.9670.9670.9300.9300.967
20120.9550.9600.9920.9930.9990.9701.0000.9990.9900.9620.9900.9230.9970.9030.9230.9500.9500.923
20130.9350.9590.9910.9920.9981.0000.9991.0000.9940.9770.9940.9320.9990.9130.9320.9320.9320.932
20140.8930.9640.9940.9950.9890.9770.9900.9941.0001.0000.9950.9130.9900.9260.9060.8690.8690.906
20150.9430.9990.9740.9810.9591.0000.9620.9771.0001.0001.0000.9610.9620.9610.9610.9240.9240.961
20160.9450.9640.9940.9950.9890.9770.9900.9940.9951.0001.0000.9610.9900.9060.9610.9260.9260.961
20170.9960.9300.9430.9500.9210.9300.9230.9320.9130.9610.9611.0000.9500.9760.9930.9950.9950.993
20180.9240.9530.9870.9880.9970.9700.9970.9990.9900.9620.9900.9501.0000.9320.9500.9500.9500.950
20190.9690.9530.9210.9230.9010.9670.9030.9130.9260.9610.9060.9760.9321.0000.9950.9760.9760.995
20200.9900.9530.9210.9230.9210.9670.9230.9320.9060.9610.9610.9930.9500.9951.0000.9930.9931.000
20210.9960.9300.9430.9230.9210.9300.9500.9320.8690.9240.9260.9950.9500.9760.9931.0001.0000.993
20220.9960.9300.9430.9230.9210.9300.9500.9320.8690.9240.9260.9950.9500.9760.9931.0001.0000.993
20230.9900.9530.9210.9230.9210.9670.9230.9320.9060.9610.9610.9930.9500.9951.0000.9930.9931.000
2024-03-23T14:54:36.740517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9620.9540.9530.9380.9390.9260.9260.9320.9440.9450.9420.9360.9360.9460.9220.8760.895
20070.9621.0000.9720.9560.9480.9490.9380.9350.9370.9430.9410.9400.9330.9340.9440.9350.9050.910
20080.9540.9721.0000.9690.9560.9540.9410.9350.9360.9440.9420.9390.9330.9360.9440.9390.9060.916
20090.9530.9560.9691.0000.9700.9650.9480.9440.9450.9510.9500.9460.9430.9460.9540.9390.9030.923
20100.9380.9480.9560.9701.0000.9690.9540.9480.9440.9470.9430.9380.9320.9380.9460.9360.9040.918
20110.9390.9490.9540.9650.9691.0000.9710.9610.9570.9560.9470.9410.9310.9390.9510.9400.9120.919
20120.9260.9380.9410.9480.9540.9711.0000.9730.9620.9570.9480.9440.9350.9420.9490.9460.9210.926
20130.9260.9350.9350.9440.9480.9610.9731.0000.9730.9670.9570.9530.9440.9480.9530.9440.9130.924
20140.9320.9370.9360.9450.9440.9570.9620.9731.0000.9750.9630.9600.9480.9520.9570.9440.9120.923
20150.9440.9430.9440.9510.9470.9560.9570.9670.9751.0000.9780.9710.9600.9600.9650.9500.9140.926
20160.9450.9410.9420.9500.9430.9470.9480.9570.9630.9781.0000.9790.9690.9660.9680.9540.9190.932
20170.9420.9400.9390.9460.9380.9410.9440.9530.9600.9710.9791.0000.9770.9730.9710.9560.9200.939
20180.9360.9330.9330.9430.9320.9310.9350.9440.9480.9600.9690.9771.0000.9790.9730.9590.9210.941
20190.9360.9340.9360.9460.9380.9390.9420.9480.9520.9600.9660.9730.9791.0000.9800.9630.9290.949
20200.9460.9440.9440.9540.9460.9510.9490.9530.9570.9650.9680.9710.9730.9801.0000.9710.9340.954
20210.9220.9350.9390.9390.9360.9400.9460.9440.9440.9500.9540.9560.9590.9630.9711.0000.9600.961
20220.8760.9050.9060.9030.9040.9120.9210.9130.9120.9140.9190.9200.9210.9290.9340.9601.0000.956
20230.8950.9100.9160.9230.9180.9190.9260.9240.9230.9260.9320.9390.9410.9490.9540.9610.9561.000

Missing values

2024-03-23T14:54:27.925619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T14:54:28.194071image/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.
2024-03-23T14:54:28.915645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

지역 및 거래규모200620072008200920102011201220132014201520162017201820192020202120222023
0전국 /20㎡이하147137143125127235412526600571488529464378437419312233
1전국 /21~40㎡498246644648337428933594318533694132499850834965454842065744568434622736
2전국 /41~60㎡275462162922183196761773621231167622000523413284882685427293260902353133745293631559815511
3전국 /61~85㎡447683260033792344593167436820296063839048039560115261464100647666075472357535203120334241
4전국 /86~100㎡343824972544263224142734225127643464450048975403547941544852372624482704
5전국 /101~135㎡1716711952126561575614939168171317212921152781697714890145521465413182179771106965027067
6전국 /136~165㎡619044594623573855826552470644005114604852654708451638715728403321821983
7전국 /166~198㎡361026012499265925613038228524752897359732772975274722813275265015911191
8전국 /198㎡초과1463417308100441028913245141951316113480204962377319162158521314011001141031292485065603
9서울 /20㎡이하55433936294511014815415015316515811115115111886
지역 및 거래규모200620072008200920102011201220132014201520162017201820192020202120222023
2681제주 제주시/198㎡초과31534567671161322082042532131181521381191259476
2682제주 서귀포시/20㎡이하000000111663712222
2683제주 서귀포시/21~40㎡38777910141321192220191412128
2684제주 서귀포시/41~60㎡92147253139363561815068383541523134
2685제주 서귀포시/61~85㎡21484146517675114134305332224184121157210147101
2686제주 서귀포시/86~100㎡51211131416151726322833402924353621
2687제주 서귀포시/101~135㎡71815151416152123304737252331413819
2688제주 서귀포시/136~165㎡68781315131516222421231823232519
2689제주 서귀포시/166~198㎡363666510917141212919191513
2690제주 서귀포시/198㎡초과9121724202230396612311367635139544246