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(동(호)수) 데이터입니다.- (단위 : 동(호)수)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068410/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 = 26.46302225)Skewed
2007 is highly skewed (γ1 = 26.52744889)Skewed
2008 is highly skewed (γ1 = 27.05355434)Skewed
2009 is highly skewed (γ1 = 27.14701973)Skewed
2010 is highly skewed (γ1 = 27.93757035)Skewed
2011 is highly skewed (γ1 = 27.78035854)Skewed
2012 is highly skewed (γ1 = 27.75405757)Skewed
2013 is highly skewed (γ1 = 28.88474143)Skewed
2014 is highly skewed (γ1 = 29.64542939)Skewed
2015 is highly skewed (γ1 = 28.91118091)Skewed
2016 is highly skewed (γ1 = 28.50910325)Skewed
2017 is highly skewed (γ1 = 30.01428511)Skewed
2018 is highly skewed (γ1 = 30.17529569)Skewed
2019 is highly skewed (γ1 = 30.73873267)Skewed
2020 is highly skewed (γ1 = 29.3515767)Skewed
2021 is highly skewed (γ1 = 28.56380683)Skewed
2022 is highly skewed (γ1 = 28.97988342)Skewed
2023 is highly skewed (γ1 = 30.29816356)Skewed
지역 및 거래규모 has unique valuesUnique

Reproduction

Analysis started2024-03-23 04:41:38.239369
Analysis finished2024-03-23 04:44:01.382864
Duration2 minutes and 23.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct2691
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
2024-03-23T04:44:01.797636image/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%
2024-03-23T04:44:03.025599image/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 

Distinct1083
Distinct (%)43.4%
Missing198
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean1904.5415
Minimum0
Maximum555496
Zeros14
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:44:03.427774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.6
Q134
median158
Q3669
95-th percentile5410
Maximum555496
Range555496
Interquartile range (IQR)635

Descriptive statistics

Standard deviation16845.584
Coefficient of variation (CV)8.8449551
Kurtosis800.2707
Mean1904.5415
Median Absolute Deviation (MAD)144
Skewness26.463022
Sum4748022
Variance2.837737 × 108
MonotonicityNot monotonic
2024-03-23T04:44:03.852736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 36
 
1.3%
5 34
 
1.3%
4 31
 
1.2%
1 26
 
1.0%
15 25
 
0.9%
17 25
 
0.9%
14 24
 
0.9%
24 23
 
0.9%
8 21
 
0.8%
7 20
 
0.7%
Other values (1073) 2228
82.8%
(Missing) 198
 
7.4%
ValueCountFrequency (%)
0 14
 
0.5%
1 26
1.0%
2 18
0.7%
3 36
1.3%
4 31
1.2%
5 34
1.3%
6 19
0.7%
7 20
0.7%
8 21
0.8%
9 19
0.7%
ValueCountFrequency (%)
555496 1
< 0.1%
505606 1
< 0.1%
165688 1
< 0.1%
163525 1
< 0.1%
144823 1
< 0.1%
143633 1
< 0.1%
112524 1
< 0.1%
107724 1
< 0.1%
43656 1
< 0.1%
41995 1
< 0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct988
Distinct (%)40.4%
Missing243
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean1468.9383
Minimum0
Maximum404883
Zeros23
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:44:04.267301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q133
median128
Q3553.25
95-th percentile4126.65
Maximum404883
Range404883
Interquartile range (IQR)520.25

Descriptive statistics

Standard deviation12779.268
Coefficient of variation (CV)8.6996626
Kurtosis799.65464
Mean1468.9383
Median Absolute Deviation (MAD)116
Skewness26.527449
Sum3595961
Variance1.6330968 × 108
MonotonicityNot monotonic
2024-03-23T04:44:04.809259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 32
 
1.2%
2 29
 
1.1%
8 28
 
1.0%
5 28
 
1.0%
14 26
 
1.0%
7 25
 
0.9%
13 24
 
0.9%
11 23
 
0.9%
0 23
 
0.9%
1 22
 
0.8%
Other values (978) 2188
81.3%
(Missing) 243
 
9.0%
ValueCountFrequency (%)
0 23
0.9%
1 22
0.8%
2 29
1.1%
3 16
0.6%
4 20
0.7%
5 28
1.0%
6 32
1.2%
7 25
0.9%
8 28
1.0%
9 22
0.8%
ValueCountFrequency (%)
404883 1
< 0.1%
399237 1
< 0.1%
134082 1
< 0.1%
118611 1
< 0.1%
101291 1
< 0.1%
100725 1
< 0.1%
69959 1
< 0.1%
56329 1
< 0.1%
44360 1
< 0.1%
35815 1
< 0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1007
Distinct (%)40.8%
Missing225
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean1498.9234
Minimum0
Maximum419060
Zeros12
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:44:05.320113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.25
Q136.25
median128
Q3576.5
95-th percentile4365.5
Maximum419060
Range419060
Interquartile range (IQR)540.25

Descriptive statistics

Standard deviation13005.52
Coefficient of variation (CV)8.6765743
Kurtosis829.01089
Mean1498.9234
Median Absolute Deviation (MAD)116
Skewness27.053554
Sum3696345
Variance1.6914355 × 108
MonotonicityNot monotonic
2024-03-23T04:44:05.816590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 31
 
1.2%
3 31
 
1.2%
7 28
 
1.0%
4 25
 
0.9%
6 25
 
0.9%
11 24
 
0.9%
8 24
 
0.9%
10 23
 
0.9%
5 23
 
0.9%
12 23
 
0.9%
Other values (997) 2209
82.1%
(Missing) 225
 
8.4%
ValueCountFrequency (%)
0 12
 
0.4%
1 17
0.6%
2 16
0.6%
3 31
1.2%
4 25
0.9%
5 23
0.9%
6 25
0.9%
7 28
1.0%
8 24
0.9%
9 31
1.2%
ValueCountFrequency (%)
419060 1
< 0.1%
408590 1
< 0.1%
134274 1
< 0.1%
107275 1
< 0.1%
107211 1
< 0.1%
92944 1
< 0.1%
65184 1
< 0.1%
55702 1
< 0.1%
46000 1
< 0.1%
38795 1
< 0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1005
Distinct (%)40.8%
Missing225
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean1458.2758
Minimum0
Maximum425637
Zeros14
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:44:06.271220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q138
median131.5
Q3606.25
95-th percentile3907.75
Maximum425637
Range425637
Interquartile range (IQR)568.25

Descriptive statistics

Standard deviation12418.541
Coefficient of variation (CV)8.5159071
Kurtosis841.87829
Mean1458.2758
Median Absolute Deviation (MAD)117.5
Skewness27.14702
Sum3596108
Variance1.5422016 × 108
MonotonicityNot monotonic
2024-03-23T04:44:06.730758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 31
 
1.2%
5 25
 
0.9%
24 24
 
0.9%
10 23
 
0.9%
9 23
 
0.9%
8 23
 
0.9%
16 22
 
0.8%
3 22
 
0.8%
4 21
 
0.8%
17 21
 
0.8%
Other values (995) 2231
82.9%
(Missing) 225
 
8.4%
ValueCountFrequency (%)
0 14
0.5%
1 12
 
0.4%
2 13
0.5%
3 22
0.8%
4 21
0.8%
5 25
0.9%
6 31
1.2%
7 20
0.7%
8 23
0.9%
9 23
0.9%
ValueCountFrequency (%)
425637 1
< 0.1%
359441 1
< 0.1%
133359 1
< 0.1%
105089 1
< 0.1%
97837 1
< 0.1%
97199 1
< 0.1%
56757 1
< 0.1%
56003 1
< 0.1%
38230 1
< 0.1%
36479 1
< 0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1009
Distinct (%)40.0%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean1304.4496
Minimum0
Maximum390814
Zeros6
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:44:07.224926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q136
median123
Q3549.25
95-th percentile3653.65
Maximum390814
Range390814
Interquartile range (IQR)513.25

Descriptive statistics

Standard deviation11109.487
Coefficient of variation (CV)8.516609
Kurtosis888.93551
Mean1304.4496
Median Absolute Deviation (MAD)110
Skewness27.93757
Sum3287213
Variance1.2342071 × 108
MonotonicityNot monotonic
2024-03-23T04:44:07.734134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 29
 
1.1%
5 25
 
0.9%
9 25
 
0.9%
4 25
 
0.9%
3 24
 
0.9%
16 24
 
0.9%
18 23
 
0.9%
7 23
 
0.9%
11 22
 
0.8%
8 22
 
0.8%
Other values (999) 2278
84.7%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 6
 
0.2%
1 13
0.5%
2 21
0.8%
3 24
0.9%
4 25
0.9%
5 25
0.9%
6 19
0.7%
7 23
0.9%
8 22
0.8%
9 25
0.9%
ValueCountFrequency (%)
390814 1
< 0.1%
323241 1
< 0.1%
125759 1
< 0.1%
88581 1
< 0.1%
84126 1
< 0.1%
71553 1
< 0.1%
40933 1
< 0.1%
39294 1
< 0.1%
37228 1
< 0.1%
37071 1
< 0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1079
Distinct (%)43.3%
Missing198
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean1569.9988
Minimum0
Maximum455391
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:44:08.220320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q146
median162
Q3712
95-th percentile4454
Maximum455391
Range455391
Interquartile range (IQR)666

Descriptive statistics

Standard deviation13133.404
Coefficient of variation (CV)8.3652321
Kurtosis876.99863
Mean1569.9988
Median Absolute Deviation (MAD)144
Skewness27.780359
Sum3914007
Variance1.7248631 × 108
MonotonicityNot monotonic
2024-03-23T04:44:08.692357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 26
 
1.0%
4 25
 
0.9%
12 22
 
0.8%
11 21
 
0.8%
6 21
 
0.8%
8 19
 
0.7%
9 18
 
0.7%
18 18
 
0.7%
24 17
 
0.6%
14 17
 
0.6%
Other values (1069) 2289
85.1%
(Missing) 198
 
7.4%
ValueCountFrequency (%)
0 3
 
0.1%
1 10
 
0.4%
2 15
0.6%
3 15
0.6%
4 25
0.9%
5 14
0.5%
6 21
0.8%
7 26
1.0%
8 19
0.7%
9 18
0.7%
ValueCountFrequency (%)
455391 1
< 0.1%
386707 1
< 0.1%
141782 1
< 0.1%
104608 1
< 0.1%
94362 1
< 0.1%
93615 1
< 0.1%
48097 1
< 0.1%
44933 1
< 0.1%
43669 1
< 0.1%
42226 1
< 0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1016
Distinct (%)40.5%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1269.4747
Minimum0
Maximum366657
Zeros6
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:44:09.262278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q143
median141
Q3603.5
95-th percentile3429
Maximum366657
Range366657
Interquartile range (IQR)560.5

Descriptive statistics

Standard deviation10497.235
Coefficient of variation (CV)8.2689594
Kurtosis878.77298
Mean1269.4747
Median Absolute Deviation (MAD)123
Skewness27.754058
Sum3187651
Variance1.1019194 × 108
MonotonicityNot monotonic
2024-03-23T04:44:09.752659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 25
 
0.9%
8 25
 
0.9%
19 23
 
0.9%
9 22
 
0.8%
3 22
 
0.8%
10 21
 
0.8%
13 20
 
0.7%
14 19
 
0.7%
17 19
 
0.7%
4 19
 
0.7%
Other values (1006) 2296
85.3%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 6
 
0.2%
1 6
 
0.2%
2 12
0.4%
3 22
0.8%
4 19
0.7%
5 25
0.9%
6 15
0.6%
7 15
0.6%
8 25
0.9%
9 22
0.8%
ValueCountFrequency (%)
366657 1
< 0.1%
306650 1
< 0.1%
111948 1
< 0.1%
94488 1
< 0.1%
78053 1
< 0.1%
67499 1
< 0.1%
36517 1
< 0.1%
34324 1
< 0.1%
31358 1
< 0.1%
31027 1
< 0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1055
Distinct (%)42.0%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1504.9383
Minimum1
Maximum474054
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:44:10.465120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q145
median155
Q3613
95-th percentile4102
Maximum474054
Range474053
Interquartile range (IQR)568

Descriptive statistics

Standard deviation12955.561
Coefficient of variation (CV)8.608699
Kurtosis953.00242
Mean1504.9383
Median Absolute Deviation (MAD)137
Skewness28.884741
Sum3778900
Variance1.6784655 × 108
MonotonicityNot monotonic
2024-03-23T04:44:10.993127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 25
 
0.9%
13 23
 
0.9%
16 23
 
0.9%
18 23
 
0.9%
9 22
 
0.8%
5 21
 
0.8%
4 21
 
0.8%
14 20
 
0.7%
6 20
 
0.7%
12 20
 
0.7%
Other values (1045) 2293
85.2%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
1 2
 
0.1%
2 6
 
0.2%
3 18
0.7%
4 21
0.8%
5 21
0.8%
6 20
0.7%
7 25
0.9%
8 12
0.4%
9 22
0.8%
10 16
0.6%
ValueCountFrequency (%)
474054 1
< 0.1%
363438 1
< 0.1%
110334 1
< 0.1%
102375 1
< 0.1%
101069 1
< 0.1%
85509 1
< 0.1%
49023 1
< 0.1%
45784 1
< 0.1%
43679 1
< 0.1%
42355 1
< 0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1161
Distinct (%)45.1%
Missing117
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean1795.6395
Minimum1
Maximum593551
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:44:11.400259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.65
Q154.25
median181
Q3755.75
95-th percentile5087.2
Maximum593551
Range593550
Interquartile range (IQR)701.5

Descriptive statistics

Standard deviation15669.847
Coefficient of variation (CV)8.726611
Kurtosis1009.4566
Mean1795.6395
Median Absolute Deviation (MAD)160
Skewness29.645429
Sum4621976
Variance2.4554411 × 108
MonotonicityNot monotonic
2024-03-23T04:44:11.865751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 23
 
0.9%
12 21
 
0.8%
34 19
 
0.7%
7 19
 
0.7%
32 18
 
0.7%
8 18
 
0.7%
14 18
 
0.7%
5 17
 
0.6%
11 17
 
0.6%
28 16
 
0.6%
Other values (1151) 2388
88.7%
(Missing) 117
 
4.3%
ValueCountFrequency (%)
1 6
 
0.2%
2 10
0.4%
3 5
 
0.2%
4 13
0.5%
5 17
0.6%
6 16
0.6%
7 19
0.7%
8 18
0.7%
9 12
0.4%
10 13
0.5%
ValueCountFrequency (%)
593551 1
< 0.1%
428552 1
< 0.1%
131128 1
< 0.1%
125157 1
< 0.1%
122169 1
< 0.1%
107119 1
< 0.1%
62904 1
< 0.1%
62078 1
< 0.1%
57513 1
< 0.1%
55231 1
< 0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1179
Distinct (%)46.5%
Missing153
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean2161.9634
Minimum0
Maximum696783
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:44:12.443536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q164
median219
Q3899.75
95-th percentile6137
Maximum696783
Range696783
Interquartile range (IQR)835.75

Descriptive statistics

Standard deviation18883.582
Coefficient of variation (CV)8.7344598
Kurtosis958.50308
Mean2161.9634
Median Absolute Deviation (MAD)194
Skewness28.911181
Sum5487063
Variance3.5658967 × 108
MonotonicityNot monotonic
2024-03-23T04:44:12.901940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 22
 
0.8%
16 20
 
0.7%
5 17
 
0.6%
15 17
 
0.6%
21 17
 
0.6%
8 17
 
0.6%
14 17
 
0.6%
25 16
 
0.6%
26 16
 
0.6%
33 16
 
0.6%
Other values (1169) 2363
87.8%
(Missing) 153
 
5.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 4
 
0.1%
2 10
0.4%
3 10
0.4%
4 7
0.3%
5 17
0.6%
6 11
0.4%
7 11
0.4%
8 17
0.6%
9 7
0.3%
ValueCountFrequency (%)
696783 1
< 0.1%
524628 1
< 0.1%
164437 1
< 0.1%
152297 1
< 0.1%
145774 1
< 0.1%
136501 1
< 0.1%
90770 1
< 0.1%
85779 1
< 0.1%
67717 1
< 0.1%
55735 1
< 0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1150
Distinct (%)45.3%
Missing153
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean2031.9799
Minimum1
Maximum656699
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:44:13.345018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q159
median201
Q3847.75
95-th percentile5543.4
Maximum656699
Range656698
Interquartile range (IQR)788.75

Descriptive statistics

Standard deviation17928.631
Coefficient of variation (CV)8.8232326
Kurtosis934.23138
Mean2031.9799
Median Absolute Deviation (MAD)177
Skewness28.509103
Sum5157165
Variance3.2143582 × 108
MonotonicityNot monotonic
2024-03-23T04:44:13.783489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 22
 
0.8%
10 20
 
0.7%
13 19
 
0.7%
32 19
 
0.7%
24 18
 
0.7%
16 17
 
0.6%
9 17
 
0.6%
12 17
 
0.6%
8 17
 
0.6%
28 16
 
0.6%
Other values (1140) 2356
87.6%
(Missing) 153
 
5.7%
ValueCountFrequency (%)
1 3
 
0.1%
2 5
 
0.2%
3 14
0.5%
4 15
0.6%
5 8
 
0.3%
6 12
0.4%
7 10
0.4%
8 17
0.6%
9 17
0.6%
10 20
0.7%
ValueCountFrequency (%)
656699 1
< 0.1%
495144 1
< 0.1%
178104 1
< 0.1%
155933 1
< 0.1%
139518 1
< 0.1%
128215 1
< 0.1%
89618 1
< 0.1%
81617 1
< 0.1%
58781 1
< 0.1%
51583 1
< 0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1153
Distinct (%)45.9%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2225.5313
Minimum0
Maximum797675
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:44:14.259736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q158
median197
Q3861
95-th percentile6271.5
Maximum797675
Range797675
Interquartile range (IQR)803

Descriptive statistics

Standard deviation20529.563
Coefficient of variation (CV)9.2245675
Kurtosis1045.932
Mean2225.5313
Median Absolute Deviation (MAD)172
Skewness30.014285
Sum5588309
Variance4.2146298 × 108
MonotonicityNot monotonic
2024-03-23T04:44:14.873809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 23
 
0.9%
8 20
 
0.7%
10 17
 
0.6%
18 17
 
0.6%
32 17
 
0.6%
13 17
 
0.6%
36 16
 
0.6%
5 16
 
0.6%
14 15
 
0.6%
19 15
 
0.6%
Other values (1143) 2338
86.9%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 2
 
0.1%
1 4
 
0.1%
2 13
0.5%
3 7
 
0.3%
4 10
0.4%
5 16
0.6%
6 13
0.5%
7 13
0.5%
8 20
0.7%
9 12
0.4%
ValueCountFrequency (%)
797675 1
< 0.1%
500640 1
< 0.1%
238867 1
< 0.1%
153221 1
< 0.1%
145493 1
< 0.1%
125753 1
< 0.1%
90756 1
< 0.1%
88244 1
< 0.1%
61237 1
< 0.1%
58714 1
< 0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1122
Distinct (%)44.5%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean2170.3702
Minimum0
Maximum803831
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:44:15.334583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q153
median177
Q3795
95-th percentile5712.3
Maximum803831
Range803831
Interquartile range (IQR)742

Descriptive statistics

Standard deviation20495.657
Coefficient of variation (CV)9.443392
Kurtosis1059.7837
Mean2170.3702
Median Absolute Deviation (MAD)155
Skewness30.175296
Sum5469333
Variance4.2007195 × 108
MonotonicityNot monotonic
2024-03-23T04:44:15.992616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 19
 
0.7%
21 19
 
0.7%
6 19
 
0.7%
11 18
 
0.7%
8 18
 
0.7%
12 17
 
0.6%
9 17
 
0.6%
20 16
 
0.6%
30 16
 
0.6%
18 16
 
0.6%
Other values (1112) 2345
87.1%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 3
 
0.1%
2 8
0.3%
3 11
0.4%
4 11
0.4%
5 10
0.4%
6 19
0.7%
7 15
0.6%
8 18
0.7%
9 17
0.6%
ValueCountFrequency (%)
803831 1
< 0.1%
478019 1
< 0.1%
277957 1
< 0.1%
150647 1
< 0.1%
140406 1
< 0.1%
127250 1
< 0.1%
87144 1
< 0.1%
77955 1
< 0.1%
57351 1
< 0.1%
47567 1
< 0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1087
Distinct (%)43.3%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1988.2397
Minimum0
Maximum752171
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:44:16.511192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q151.5
median158
Q3660
95-th percentile5806.5
Maximum752171
Range752171
Interquartile range (IQR)608.5

Descriptive statistics

Standard deviation18930.568
Coefficient of variation (CV)9.5212704
Kurtosis1101.1766
Mean1988.2397
Median Absolute Deviation (MAD)136
Skewness30.738733
Sum4992470
Variance3.5836642 × 108
MonotonicityNot monotonic
2024-03-23T04:44:17.102321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 27
 
1.0%
10 24
 
0.9%
7 19
 
0.7%
15 19
 
0.7%
13 18
 
0.7%
17 18
 
0.7%
36 17
 
0.6%
25 17
 
0.6%
27 17
 
0.6%
5 17
 
0.6%
Other values (1077) 2318
86.1%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 2
 
0.1%
1 4
 
0.1%
2 8
0.3%
3 11
0.4%
4 12
0.4%
5 17
0.6%
6 12
0.4%
7 19
0.7%
8 13
0.5%
9 16
0.6%
ValueCountFrequency (%)
752171 1
< 0.1%
432269 1
< 0.1%
240803 1
< 0.1%
138397 1
< 0.1%
128688 1
< 0.1%
114384 1
< 0.1%
70489 1
< 0.1%
61972 1
< 0.1%
57442 1
< 0.1%
55504 1
< 0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1182
Distinct (%)47.1%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2562.1987
Minimum0
Maximum895189
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:44:17.841932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q165
median210
Q3948.5
95-th percentile7366.5
Maximum895189
Range895189
Interquartile range (IQR)883.5

Descriptive statistics

Standard deviation23721.776
Coefficient of variation (CV)9.258367
Kurtosis989.7657
Mean2562.1987
Median Absolute Deviation (MAD)183
Skewness29.351577
Sum6433681
Variance5.6272266 × 108
MonotonicityNot monotonic
2024-03-23T04:44:18.581436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 19
 
0.7%
17 18
 
0.7%
22 17
 
0.6%
20 17
 
0.6%
27 17
 
0.6%
21 16
 
0.6%
30 15
 
0.6%
8 15
 
0.6%
25 15
 
0.6%
19 14
 
0.5%
Other values (1172) 2348
87.3%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 3
 
0.1%
2 1
 
< 0.1%
3 3
 
0.1%
4 5
 
0.2%
5 7
0.3%
6 10
0.4%
7 12
0.4%
8 15
0.6%
9 14
0.5%
ValueCountFrequency (%)
895189 1
< 0.1%
619223 1
< 0.1%
266275 1
< 0.1%
191442 1
< 0.1%
173143 1
< 0.1%
154743 1
< 0.1%
99197 1
< 0.1%
73937 1
< 0.1%
69982 1
< 0.1%
66436 1
< 0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1108
Distinct (%)44.1%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2051.6973
Minimum0
Maximum666747
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:44:18.994174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q161
median183
Q3769
95-th percentile5742.5
Maximum666747
Range666747
Interquartile range (IQR)708

Descriptive statistics

Standard deviation18674.305
Coefficient of variation (CV)9.1018812
Kurtosis921.9801
Mean2051.6973
Median Absolute Deviation (MAD)155
Skewness28.563807
Sum5151812
Variance3.4872968 × 108
MonotonicityNot monotonic
2024-03-23T04:44:19.528722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 18
 
0.7%
6 18
 
0.7%
19 17
 
0.6%
12 16
 
0.6%
13 16
 
0.6%
25 16
 
0.6%
20 15
 
0.6%
16 15
 
0.6%
11 15
 
0.6%
31 15
 
0.6%
Other values (1098) 2350
87.3%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 2
 
0.1%
2 3
 
0.1%
4 10
0.4%
5 12
0.4%
6 18
0.7%
7 9
0.3%
8 6
 
0.2%
9 9
0.3%
10 10
0.4%
ValueCountFrequency (%)
666747 1
< 0.1%
540110 1
< 0.1%
179396 1
< 0.1%
171839 1
< 0.1%
164136 1
< 0.1%
95446 1
< 0.1%
63406 1
< 0.1%
61206 1
< 0.1%
53635 1
< 0.1%
49849 1
< 0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct953
Distinct (%)38.0%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1178.2178
Minimum0
Maximum387950
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:44:20.270480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q140
median114
Q3473
95-th percentile3241.5
Maximum387950
Range387950
Interquartile range (IQR)433

Descriptive statistics

Standard deviation10520.628
Coefficient of variation (CV)8.929272
Kurtosis958.48177
Mean1178.2178
Median Absolute Deviation (MAD)94
Skewness28.979883
Sum2958505
Variance1.106836 × 108
MonotonicityNot monotonic
2024-03-23T04:44:21.063401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 29
 
1.1%
36 25
 
0.9%
16 24
 
0.9%
9 22
 
0.8%
23 21
 
0.8%
21 21
 
0.8%
27 21
 
0.8%
12 20
 
0.7%
44 20
 
0.7%
18 19
 
0.7%
Other values (943) 2289
85.1%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 2
 
0.1%
1 2
 
0.1%
2 5
 
0.2%
3 3
 
0.1%
4 14
0.5%
5 11
0.4%
6 16
0.6%
7 18
0.7%
8 15
0.6%
9 22
0.8%
ValueCountFrequency (%)
387950 1
< 0.1%
288970 1
< 0.1%
105842 1
< 0.1%
98465 1
< 0.1%
75779 1
< 0.1%
56596 1
< 0.1%
33260 1
< 0.1%
33011 1
< 0.1%
30062 1
< 0.1%
30017 1
< 0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct922
Distinct (%)36.6%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean1168.8448
Minimum0
Maximum422263
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-23T04:44:22.034483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q131
median98
Q3436.25
95-th percentile3145.5
Maximum422263
Range422263
Interquartile range (IQR)405.25

Descriptive statistics

Standard deviation10929.82
Coefficient of variation (CV)9.3509589
Kurtosis1053.1043
Mean1168.8448
Median Absolute Deviation (MAD)83
Skewness30.298164
Sum2945489
Variance1.1946097 × 108
MonotonicityNot monotonic
2024-03-23T04:44:22.645576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 32
 
1.2%
18 27
 
1.0%
5 27
 
1.0%
6 27
 
1.0%
21 27
 
1.0%
23 26
 
1.0%
10 25
 
0.9%
7 25
 
0.9%
20 25
 
0.9%
33 24
 
0.9%
Other values (912) 2255
83.8%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 2
 
0.1%
1 6
 
0.2%
2 10
 
0.4%
3 18
0.7%
4 19
0.7%
5 27
1.0%
6 27
1.0%
7 25
0.9%
8 16
0.6%
9 16
0.6%
ValueCountFrequency (%)
422263 1
< 0.1%
281682 1
< 0.1%
108982 1
< 0.1%
83227 1
< 0.1%
79963 1
< 0.1%
61411 1
< 0.1%
42366 1
< 0.1%
32259 1
< 0.1%
29884 1
< 0.1%
29375 1
< 0.1%

Interactions

2024-03-23T04:43:51.557813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:41:50.992116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:41:57.653483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:04.743793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:12.712501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:19.461238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:26.365807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:33.924208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:41.970647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:49.313142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:56.314020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:03.139614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T04:43:19.936818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:26.085319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:31.121574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:36.526924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T04:42:19.826029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:26.930499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:34.332945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:42.376841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T04:43:03.602554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T04:43:26.350664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:31.420251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:36.804500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:44.081146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T04:42:42.803700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:50.044546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:57.244732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:04.021419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:11.290122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:20.785529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:26.644406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:31.727528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:37.102215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:44.432743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:52.984501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T04:41:52.772113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T04:43:23.748763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:28.830306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:33.871745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:40.171288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:47.853805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:55.424520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:41:54.951839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:02.053502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:09.870628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:16.865229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:23.776012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:31.449723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:38.370599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:46.526564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:53.530679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:00.293893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:07.094042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:16.286431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:24.022717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:29.132783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:34.145456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:40.723520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:48.282566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:55.762114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:41:55.384337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:02.428987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:10.471007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:17.285665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:24.164730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:31.807015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:38.850086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:46.921030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:54.150074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:00.742241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:07.750809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:16.788389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:24.341262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:29.436790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:34.444277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:41.094784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:48.805841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:56.197287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:41:55.775730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:02.877335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:10.963795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:17.600149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:24.505686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:32.190512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:39.192969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:47.300206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:54.541451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:01.074631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:08.271632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:17.880877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:24.641604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:29.746845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:34.981639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:41.933511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:49.308060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:56.977990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:41:56.125676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:03.213343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:11.413111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:17.983925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:24.819250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:32.570931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:39.752969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:47.610794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:54.886273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:01.471752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:08.710124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:18.298865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:24.959574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:30.027070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:35.295145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:42.249329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:49.856606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:57.338506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:41:56.522198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:03.503309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:11.681051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:18.305143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:25.121391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:32.904295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:40.564657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:48.073037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:55.296223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:01.845982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:09.057850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:18.822174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:25.219942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:30.281199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:35.572360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:42.554163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:50.256831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:57.752546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:41:56.885051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:03.907972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:12.047915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:18.662203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:25.530452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:33.305565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:41.038047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:48.437928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:55.635144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:02.269592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:09.483998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:19.245836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:25.518054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:30.568425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:35.881196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:42.912253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:50.776026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:58.228487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:41:57.276079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:04.335776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:12.400986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:19.139967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:25.939803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:33.641280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:41.558859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:48.972783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:42:55.994468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:02.756272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:09.838407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:19.651306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:25.822561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:30.872182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:36.183724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:43.303302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:43:51.206339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T04:44:23.168660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.8690.8690.9740.8730.9240.8940.8410.8690.8690.8690.8470.8410.8110.8410.8110.8110.841
20070.8691.0001.0000.8940.8270.8630.8520.9630.9730.9730.9730.9610.9570.9630.9680.9630.9730.957
20080.8691.0001.0000.8940.8270.8630.8520.9630.9730.9730.9730.9610.9570.9630.9680.9630.9730.957
20090.9740.8940.8941.0000.9930.9990.9950.9280.9540.9541.0000.9280.9160.9220.9341.0000.9380.916
20100.8730.8270.8270.9931.0000.9930.9980.9260.9220.9220.9220.9020.9100.9220.9060.9690.9610.910
20110.9240.8630.8630.9990.9931.0000.9950.9460.9300.9300.9610.9130.9260.9220.9461.0000.9380.926
20120.8940.8520.8520.9950.9980.9951.0000.9500.9380.9380.9380.9130.9230.9380.9321.0001.0000.923
20130.8410.9630.9630.9280.9260.9460.9501.0000.9990.9990.9890.9910.9940.9900.9920.9900.9930.994
20140.8690.9730.9730.9540.9220.9300.9380.9991.0001.0000.9930.9950.9910.9890.9890.9890.9910.991
20150.8690.9730.9730.9540.9220.9300.9380.9991.0001.0000.9930.9950.9910.9890.9890.9890.9910.991
20160.8690.9730.9731.0000.9220.9610.9380.9890.9930.9931.0000.9950.9910.9890.9950.9960.9910.991
20170.8470.9610.9610.9280.9020.9130.9130.9910.9950.9950.9951.0001.0000.9990.9950.9870.9911.000
20180.8410.9570.9570.9160.9100.9260.9230.9940.9910.9910.9911.0001.0001.0000.9970.9900.9941.000
20190.8110.9630.9630.9220.9220.9220.9380.9900.9890.9890.9890.9991.0001.0000.9930.9930.9961.000
20200.8410.9680.9680.9340.9060.9460.9320.9920.9890.9890.9950.9950.9970.9931.0000.9930.9880.997
20210.8110.9630.9631.0000.9691.0001.0000.9900.9890.9890.9960.9870.9900.9930.9931.0000.9960.990
20220.8110.9730.9730.9380.9610.9381.0000.9930.9910.9910.9910.9910.9940.9960.9880.9961.0000.994
20230.8410.9570.9570.9160.9100.9260.9230.9940.9910.9910.9911.0001.0001.0000.9970.9900.9941.000
2024-03-23T04:44:23.989222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9610.9480.9500.9360.9340.9170.9160.9220.9370.9350.9300.9230.9210.9320.9060.8550.874
20070.9611.0000.9690.9560.9490.9470.9330.9270.9310.9390.9340.9320.9230.9250.9330.9240.8890.896
20080.9480.9691.0000.9670.9540.9490.9350.9270.9300.9380.9330.9290.9230.9260.9330.9270.8910.900
20090.9500.9560.9671.0000.9690.9610.9410.9350.9370.9440.9420.9360.9330.9360.9430.9280.8870.909
20100.9360.9490.9540.9691.0000.9660.9480.9410.9370.9390.9340.9290.9220.9270.9360.9250.8900.905
20110.9340.9470.9490.9610.9661.0000.9650.9540.9500.9500.9380.9330.9220.9290.9410.9290.8990.906
20120.9170.9330.9350.9410.9480.9651.0000.9730.9640.9580.9470.9440.9360.9410.9490.9470.9200.925
20130.9160.9270.9270.9350.9410.9540.9731.0000.9730.9660.9560.9530.9450.9480.9530.9450.9140.924
20140.9220.9310.9300.9370.9370.9500.9640.9731.0000.9760.9630.9600.9500.9520.9580.9460.9140.924
20150.9370.9390.9380.9440.9390.9500.9580.9660.9761.0000.9790.9720.9610.9580.9630.9490.9100.921
20160.9350.9340.9330.9420.9340.9380.9470.9560.9630.9791.0000.9780.9680.9620.9630.9500.9130.924
20170.9300.9320.9290.9360.9290.9330.9440.9530.9600.9720.9781.0000.9750.9690.9670.9530.9170.934
20180.9230.9230.9230.9330.9220.9220.9360.9450.9500.9610.9680.9751.0000.9760.9700.9570.9200.939
20190.9210.9250.9260.9360.9270.9290.9410.9480.9520.9580.9620.9690.9761.0000.9790.9620.9300.950
20200.9320.9330.9330.9430.9360.9410.9490.9530.9580.9630.9630.9670.9700.9791.0000.9690.9320.951
20210.9060.9240.9270.9280.9250.9290.9470.9450.9460.9490.9500.9530.9570.9620.9691.0000.9600.961
20220.8550.8890.8910.8870.8900.8990.9200.9140.9140.9100.9130.9170.9200.9300.9320.9601.0000.959
20230.8740.8960.9000.9090.9050.9060.9250.9240.9240.9210.9240.9340.9390.9500.9510.9610.9591.000

Missing values

2024-03-23T04:43:58.811121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T04:43:59.929992image/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.
2024-03-23T04:44:00.615653image/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㎡이하107169346111389414920216510271463423239773381193321135628328873070435817320712311517649
1전국 /21~40㎡14363313408213427497837841261046089448810106912515715229715593315322114040612868817314317183910584283227
2전국 /41~60㎡505606399237408590359441323241386707306650363438428552524628495144500640478019432269619223540110288970281682
3전국 /61~85㎡555496404883419060425637390814455391366657474054593551696783656699797675803831752171895189666747387950422263
4전국 /86~100㎡364412638426898277982544828890237652911436518474285158356634573514373050942391992575728314
5전국 /101~135㎡144823101291107275133359125759141782111948110334131128145774128215125753127250114384154743954465659661411
6전국 /136~165㎡412642981830842382303722843669313582931934179404043517231435302222591538299270221463413345
7전국 /166~198㎡1989514367138141473614219168071262413643159891989318123164981522712631181541464387636587
8전국 /198㎡초과370612916627851271852719237029293703207642276554694995242177340382900636355337042172014317
9서울 /20㎡이하38002954303529512207333576271019210827105971069711306112888052115341090080126063
지역 및 거래규모200620072008200920102011201220132014201520162017201820192020202120222023
2681제주 제주시/198㎡초과90127136183195315322415514597519303328331296345276212
2682제주 서귀포시/20㎡이하2420813131251636940646219941680141148116116
2683제주 서귀포시/21~40㎡89222188213198263282440419692626702708659459365365240
2684제주 서귀포시/41~60㎡175383859473572723680671111514909641304733686767963588641
2685제주 서귀포시/61~85㎡26460652959164895997514751699383741092819236715421973263418831273
2686제주 서귀포시/86~100㎡57127117133149170158186274336300351417306256367369218
2687제주 서귀포시/101~135㎡56152124125118136124179199254415330209193269351322157
2688제주 서귀포시/136~165㎡41545054921048698107148160143156120152158169131
2689제주 서귀포시/166~198㎡15331631353428575293766865511031028471
2690제주 서귀포시/198㎡초과27386160576893108183295259176179154120172107107