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 순수토지 거래현황의 연도별 거래주체별(면적) 데이터입니다.- (단위 : 천㎡)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068335/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 = 38.68651055)Skewed
2007 is highly skewed (γ1 = 38.92721906)Skewed
2008 is highly skewed (γ1 = 38.55952931)Skewed
2009 is highly skewed (γ1 = 38.8615198)Skewed
2010 is highly skewed (γ1 = 39.55314375)Skewed
2011 is highly skewed (γ1 = 39.12217437)Skewed
2012 is highly skewed (γ1 = 39.05897705)Skewed
2013 is highly skewed (γ1 = 39.58627751)Skewed
2014 is highly skewed (γ1 = 40.80240262)Skewed
2015 is highly skewed (γ1 = 40.49403952)Skewed
2016 is highly skewed (γ1 = 39.94840203)Skewed
2017 is highly skewed (γ1 = 40.05064738)Skewed
2018 is highly skewed (γ1 = 39.76663984)Skewed
2019 is highly skewed (γ1 = 39.41294741)Skewed
2020 is highly skewed (γ1 = 39.5366396)Skewed
2021 is highly skewed (γ1 = 39.3404879)Skewed
2022 is highly skewed (γ1 = 38.63499224)Skewed
2023 is highly skewed (γ1 = 38.07505599)Skewed
지역_거래주체 has unique valuesUnique
2006 has 219 (8.1%) zerosZeros
2007 has 173 (6.4%) zerosZeros
2008 has 170 (6.3%) zerosZeros
2009 has 146 (5.4%) zerosZeros
2010 has 149 (5.5%) zerosZeros
2011 has 135 (5.0%) zerosZeros
2012 has 117 (4.3%) zerosZeros
2013 has 139 (5.2%) zerosZeros
2014 has 147 (5.5%) zerosZeros
2015 has 112 (4.2%) zerosZeros
2016 has 129 (4.8%) zerosZeros
2017 has 105 (3.9%) zerosZeros
2018 has 115 (4.3%) zerosZeros
2019 has 122 (4.5%) zerosZeros
2020 has 105 (3.9%) zerosZeros
2021 has 79 (2.9%) zerosZeros
2022 has 124 (4.6%) zerosZeros
2023 has 147 (5.5%) zerosZeros

Reproduction

Analysis started2024-03-30 02:27:34.259831
Analysis finished2024-03-30 02:29:35.615505
Duration2 minutes and 1.36 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역_거래주체
Text

UNIQUE 

Distinct2691
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
2024-03-30T02:29:36.028396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length13
Mean length13.401338
Min length9

Characters and Unicode

Total characters36063
Distinct characters153
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2691 ?
Unique (%)100.0%

Sample

1st row전국_개인->개인
2nd row전국_개인->법인
3rd row전국_개인->기타
4th row전국_법인->개인
5th row전국_법인->법인
ValueCountFrequency (%)
경기 468
 
8.4%
경남 234
 
4.2%
서울 225
 
4.1%
경북 225
 
4.1%
전남 198
 
3.6%
충남 171
 
3.1%
충북 171
 
3.1%
강원 162
 
2.9%
전북 144
 
2.6%
부산 144
 
2.6%
Other values (2478) 3402
61.4%
2024-03-30T02:29:37.211573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3750
 
10.4%
2853
 
7.9%
_ 2691
 
7.5%
- 2691
 
7.5%
> 2691
 
7.5%
2298
 
6.4%
1794
 
5.0%
1794
 
5.0%
1794
 
5.0%
1269
 
3.5%
Other values (143) 12438
34.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24795
68.8%
Space Separator 2853
 
7.9%
Connector Punctuation 2691
 
7.5%
Dash Punctuation 2691
 
7.5%
Math Symbol 2691
 
7.5%
Open Punctuation 171
 
0.5%
Close Punctuation 171
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3750
15.1%
2298
 
9.3%
1794
 
7.2%
1794
 
7.2%
1794
 
7.2%
1269
 
5.1%
1098
 
4.4%
981
 
4.0%
837
 
3.4%
801
 
3.2%
Other values (137) 8379
33.8%
Space Separator
ValueCountFrequency (%)
2853
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2691
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2691
100.0%
Math Symbol
ValueCountFrequency (%)
> 2691
100.0%
Open Punctuation
ValueCountFrequency (%)
( 171
100.0%
Close Punctuation
ValueCountFrequency (%)
) 171
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24795
68.8%
Common 11268
31.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3750
15.1%
2298
 
9.3%
1794
 
7.2%
1794
 
7.2%
1794
 
7.2%
1269
 
5.1%
1098
 
4.4%
981
 
4.0%
837
 
3.4%
801
 
3.2%
Other values (137) 8379
33.8%
Common
ValueCountFrequency (%)
2853
25.3%
_ 2691
23.9%
- 2691
23.9%
> 2691
23.9%
( 171
 
1.5%
) 171
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24795
68.8%
ASCII 11268
31.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3750
15.1%
2298
 
9.3%
1794
 
7.2%
1794
 
7.2%
1794
 
7.2%
1269
 
5.1%
1098
 
4.4%
981
 
4.0%
837
 
3.4%
801
 
3.2%
Other values (137) 8379
33.8%
ASCII
ValueCountFrequency (%)
2853
25.3%
_ 2691
23.9%
- 2691
23.9%
> 2691
23.9%
( 171
 
1.5%
) 171
 
1.5%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1101
Distinct (%)44.2%
Missing198
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean3624.8997
Minimum0
Maximum1926717
Zeros219
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T02:29:38.032239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median112
Q3854
95-th percentile11265.4
Maximum1926717
Range1926717
Interquartile range (IQR)845

Descriptive statistics

Standard deviation42374.175
Coefficient of variation (CV)11.689751
Kurtosis1712.7818
Mean3624.8997
Median Absolute Deviation (MAD)111
Skewness38.686511
Sum9036875
Variance1.7955707 × 109
MonotonicityNot monotonic
2024-03-30T02:29:38.966184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 219
 
8.1%
1 106
 
3.9%
2 57
 
2.1%
3 54
 
2.0%
5 44
 
1.6%
4 37
 
1.4%
8 36
 
1.3%
6 36
 
1.3%
7 25
 
0.9%
11 24
 
0.9%
Other values (1091) 1855
68.9%
(Missing) 198
 
7.4%
ValueCountFrequency (%)
0 219
8.1%
1 106
3.9%
2 57
 
2.1%
3 54
 
2.0%
4 37
 
1.4%
5 44
 
1.6%
6 36
 
1.3%
7 25
 
0.9%
8 36
 
1.3%
9 21
 
0.8%
ValueCountFrequency (%)
1926717 1
< 0.1%
445323 1
< 0.1%
374540 1
< 0.1%
278786 1
< 0.1%
250224 1
< 0.1%
249596 1
< 0.1%
207048 1
< 0.1%
190813 1
< 0.1%
169030 1
< 0.1%
137516 1
< 0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1037
Distinct (%)42.4%
Missing243
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean2973.0768
Minimum0
Maximum1584919
Zeros173
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T02:29:39.703389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median107.5
Q3713.5
95-th percentile9859.75
Maximum1584919
Range1584919
Interquartile range (IQR)702.5

Descriptive statistics

Standard deviation34940.09
Coefficient of variation (CV)11.752165
Kurtosis1725.3646
Mean2973.0768
Median Absolute Deviation (MAD)106.5
Skewness38.927219
Sum7278092
Variance1.2208099 × 109
MonotonicityNot monotonic
2024-03-30T02:29:40.153644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 173
 
6.4%
1 94
 
3.5%
3 62
 
2.3%
2 58
 
2.2%
4 49
 
1.8%
6 37
 
1.4%
5 29
 
1.1%
7 29
 
1.1%
9 25
 
0.9%
17 25
 
0.9%
Other values (1027) 1867
69.4%
(Missing) 243
 
9.0%
ValueCountFrequency (%)
0 173
6.4%
1 94
3.5%
2 58
 
2.2%
3 62
 
2.3%
4 49
 
1.8%
5 29
 
1.1%
6 37
 
1.4%
7 29
 
1.1%
8 24
 
0.9%
9 25
 
0.9%
ValueCountFrequency (%)
1584919 1
< 0.1%
313451 1
< 0.1%
296602 1
< 0.1%
219458 1
< 0.1%
212165 1
< 0.1%
187951 1
< 0.1%
187102 1
< 0.1%
184475 1
< 0.1%
123916 1
< 0.1%
121937 1
< 0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1034
Distinct (%)41.9%
Missing225
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean2698.7676
Minimum0
Maximum1400308
Zeros170
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T02:29:40.695461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median106
Q3712
95-th percentile8623.5
Maximum1400308
Range1400308
Interquartile range (IQR)702

Descriptive statistics

Standard deviation30927.346
Coefficient of variation (CV)11.459803
Kurtosis1701.4206
Mean2698.7676
Median Absolute Deviation (MAD)105
Skewness38.559529
Sum6655161
Variance9.5650073 × 108
MonotonicityNot monotonic
2024-03-30T02:29:41.159186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 170
 
6.3%
1 91
 
3.4%
2 66
 
2.5%
3 61
 
2.3%
4 43
 
1.6%
5 38
 
1.4%
7 34
 
1.3%
6 31
 
1.2%
9 30
 
1.1%
8 29
 
1.1%
Other values (1024) 1873
69.6%
(Missing) 225
 
8.4%
ValueCountFrequency (%)
0 170
6.3%
1 91
3.4%
2 66
 
2.5%
3 61
 
2.3%
4 43
 
1.6%
5 38
 
1.4%
6 31
 
1.2%
7 34
 
1.3%
8 29
 
1.1%
9 30
 
1.1%
ValueCountFrequency (%)
1400308 1
< 0.1%
296947 1
< 0.1%
291317 1
< 0.1%
184776 1
< 0.1%
176531 1
< 0.1%
169736 1
< 0.1%
160414 1
< 0.1%
157218 1
< 0.1%
128626 1
< 0.1%
111634 1
< 0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1055
Distinct (%)42.8%
Missing225
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean2650.4067
Minimum0
Maximum1371133
Zeros146
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T02:29:41.663351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median114.5
Q3682
95-th percentile9059
Maximum1371133
Range1371133
Interquartile range (IQR)668

Descriptive statistics

Standard deviation30162.197
Coefficient of variation (CV)11.380215
Kurtosis1726.6884
Mean2650.4067
Median Absolute Deviation (MAD)113.5
Skewness38.86152
Sum6535903
Variance9.0975814 × 108
MonotonicityNot monotonic
2024-03-30T02:29:42.156191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 146
 
5.4%
1 121
 
4.5%
2 66
 
2.5%
4 48
 
1.8%
3 43
 
1.6%
5 36
 
1.3%
7 26
 
1.0%
10 23
 
0.9%
13 23
 
0.9%
6 21
 
0.8%
Other values (1045) 1913
71.1%
(Missing) 225
 
8.4%
ValueCountFrequency (%)
0 146
5.4%
1 121
4.5%
2 66
2.5%
3 43
 
1.6%
4 48
 
1.8%
5 36
 
1.3%
6 21
 
0.8%
7 26
 
1.0%
8 18
 
0.7%
9 17
 
0.6%
ValueCountFrequency (%)
1371133 1
< 0.1%
264293 1
< 0.1%
253642 1
< 0.1%
169754 1
< 0.1%
167285 1
< 0.1%
165967 1
< 0.1%
161587 1
< 0.1%
158556 1
< 0.1%
153692 1
< 0.1%
119489 1
< 0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1025
Distinct (%)40.7%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean2253.9929
Minimum0
Maximum1194950
Zeros149
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T02:29:42.671491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median104
Q3639.25
95-th percentile7675.4
Maximum1194950
Range1194950
Interquartile range (IQR)629.25

Descriptive statistics

Standard deviation25936.605
Coefficient of variation (CV)11.50696
Kurtosis1782.6523
Mean2253.9929
Median Absolute Deviation (MAD)103
Skewness39.553144
Sum5680062
Variance6.7270747 × 108
MonotonicityNot monotonic
2024-03-30T02:29:43.117500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 149
 
5.5%
1 114
 
4.2%
2 79
 
2.9%
3 59
 
2.2%
4 54
 
2.0%
6 39
 
1.4%
5 33
 
1.2%
10 30
 
1.1%
8 30
 
1.1%
7 29
 
1.1%
Other values (1015) 1904
70.8%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 149
5.5%
1 114
4.2%
2 79
2.9%
3 59
 
2.2%
4 54
 
2.0%
5 33
 
1.2%
6 39
 
1.4%
7 29
 
1.1%
8 30
 
1.1%
9 27
 
1.0%
ValueCountFrequency (%)
1194950 1
< 0.1%
230434 1
< 0.1%
205429 1
< 0.1%
159881 1
< 0.1%
154406 1
< 0.1%
151235 1
< 0.1%
137775 1
< 0.1%
126319 1
< 0.1%
124653 1
< 0.1%
97691 1
< 0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1020
Distinct (%)40.9%
Missing198
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean2252.0172
Minimum0
Maximum1176814
Zeros135
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T02:29:43.592134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median115
Q3602
95-th percentile7194
Maximum1176814
Range1176814
Interquartile range (IQR)588

Descriptive statistics

Standard deviation25732.775
Coefficient of variation (CV)11.426544
Kurtosis1749.2639
Mean2252.0172
Median Absolute Deviation (MAD)113
Skewness39.122174
Sum5614279
Variance6.6217571 × 108
MonotonicityNot monotonic
2024-03-30T02:29:44.051884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 135
 
5.0%
1 86
 
3.2%
2 64
 
2.4%
3 56
 
2.1%
5 39
 
1.4%
6 39
 
1.4%
4 31
 
1.2%
7 30
 
1.1%
10 29
 
1.1%
8 28
 
1.0%
Other values (1010) 1956
72.7%
(Missing) 198
 
7.4%
ValueCountFrequency (%)
0 135
5.0%
1 86
3.2%
2 64
2.4%
3 56
2.1%
4 31
 
1.2%
5 39
 
1.4%
6 39
 
1.4%
7 30
 
1.1%
8 28
 
1.0%
9 28
 
1.0%
ValueCountFrequency (%)
1176814 1
< 0.1%
226006 1
< 0.1%
196519 1
< 0.1%
163045 1
< 0.1%
158961 1
< 0.1%
147087 1
< 0.1%
146914 1
< 0.1%
123169 1
< 0.1%
114364 1
< 0.1%
110386 1
< 0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1010
Distinct (%)40.2%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2076.2477
Minimum0
Maximum1065844
Zeros117
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T02:29:44.558725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q115
median119
Q3579
95-th percentile6701
Maximum1065844
Range1065844
Interquartile range (IQR)564

Descriptive statistics

Standard deviation23273.325
Coefficient of variation (CV)11.20932
Kurtosis1747.0004
Mean2076.2477
Median Absolute Deviation (MAD)117
Skewness39.058977
Sum5213458
Variance5.4164765 × 108
MonotonicityNot monotonic
2024-03-30T02:29:45.033026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 117
 
4.3%
1 101
 
3.8%
2 73
 
2.7%
3 50
 
1.9%
4 43
 
1.6%
6 40
 
1.5%
5 30
 
1.1%
10 25
 
0.9%
8 24
 
0.9%
11 23
 
0.9%
Other values (1000) 1985
73.8%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 117
4.3%
1 101
3.8%
2 73
2.7%
3 50
1.9%
4 43
 
1.6%
5 30
 
1.1%
6 40
 
1.5%
7 21
 
0.8%
8 24
 
0.9%
9 22
 
0.8%
ValueCountFrequency (%)
1065844 1
< 0.1%
213320 1
< 0.1%
188677 1
< 0.1%
155851 1
< 0.1%
143091 1
< 0.1%
139149 1
< 0.1%
108257 1
< 0.1%
105878 1
< 0.1%
101727 1
< 0.1%
96578 1
< 0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct996
Distinct (%)39.7%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2062.1705
Minimum0
Maximum1086863
Zeros139
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T02:29:45.481715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113
median100
Q3630
95-th percentile6934.5
Maximum1086863
Range1086863
Interquartile range (IQR)617

Descriptive statistics

Standard deviation23608.912
Coefficient of variation (CV)11.448574
Kurtosis1783.3775
Mean2062.1705
Median Absolute Deviation (MAD)99
Skewness39.586278
Sum5178110
Variance5.5738072 × 108
MonotonicityNot monotonic
2024-03-30T02:29:45.937692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 139
 
5.2%
1 101
 
3.8%
2 69
 
2.6%
3 54
 
2.0%
4 47
 
1.7%
5 36
 
1.3%
6 35
 
1.3%
10 34
 
1.3%
9 26
 
1.0%
7 23
 
0.9%
Other values (986) 1947
72.4%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 139
5.2%
1 101
3.8%
2 69
2.6%
3 54
 
2.0%
4 47
 
1.7%
5 36
 
1.3%
6 35
 
1.3%
7 23
 
0.9%
8 21
 
0.8%
9 26
 
1.0%
ValueCountFrequency (%)
1086863 1
< 0.1%
221025 1
< 0.1%
163525 1
< 0.1%
147834 1
< 0.1%
144084 1
< 0.1%
139473 1
< 0.1%
112061 1
< 0.1%
107046 1
< 0.1%
105851 1
< 0.1%
100640 1
< 0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1030
Distinct (%)40.0%
Missing117
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean2167.486
Minimum0
Maximum1216383
Zeros147
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T02:29:46.405072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median120.5
Q3632
95-th percentile7128.6
Maximum1216383
Range1216383
Interquartile range (IQR)618

Descriptive statistics

Standard deviation25919.885
Coefficient of variation (CV)11.958502
Kurtosis1878.493
Mean2167.486
Median Absolute Deviation (MAD)118.5
Skewness40.802403
Sum5579109
Variance6.7184045 × 108
MonotonicityNot monotonic
2024-03-30T02:29:46.977848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 147
 
5.5%
1 94
 
3.5%
2 74
 
2.7%
3 53
 
2.0%
5 40
 
1.5%
4 40
 
1.5%
8 32
 
1.2%
6 31
 
1.2%
7 29
 
1.1%
17 24
 
0.9%
Other values (1020) 2010
74.7%
(Missing) 117
 
4.3%
ValueCountFrequency (%)
0 147
5.5%
1 94
3.5%
2 74
2.7%
3 53
 
2.0%
4 40
 
1.5%
5 40
 
1.5%
6 31
 
1.2%
7 29
 
1.1%
8 32
 
1.2%
9 22
 
0.8%
ValueCountFrequency (%)
1216383 1
< 0.1%
242246 1
< 0.1%
164542 1
< 0.1%
163504 1
< 0.1%
151324 1
< 0.1%
127438 1
< 0.1%
126531 1
< 0.1%
122907 1
< 0.1%
116405 1
< 0.1%
106954 1
< 0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1062
Distinct (%)41.8%
Missing153
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean2426.3448
Minimum0
Maximum1340052
Zeros112
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T02:29:47.413576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q118
median131
Q3678.5
95-th percentile8267.45
Maximum1340052
Range1340052
Interquartile range (IQR)660.5

Descriptive statistics

Standard deviation28770.427
Coefficient of variation (CV)11.857518
Kurtosis1849.1548
Mean2426.3448
Median Absolute Deviation (MAD)129
Skewness40.49404
Sum6158063
Variance8.2773744 × 108
MonotonicityNot monotonic
2024-03-30T02:29:47.850825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 112
 
4.2%
1 81
 
3.0%
2 67
 
2.5%
4 45
 
1.7%
3 39
 
1.4%
6 38
 
1.4%
5 37
 
1.4%
7 27
 
1.0%
12 22
 
0.8%
8 20
 
0.7%
Other values (1052) 2050
76.2%
(Missing) 153
 
5.7%
ValueCountFrequency (%)
0 112
4.2%
1 81
3.0%
2 67
2.5%
3 39
 
1.4%
4 45
1.7%
5 37
 
1.4%
6 38
 
1.4%
7 27
 
1.0%
8 20
 
0.7%
9 20
 
0.7%
ValueCountFrequency (%)
1340052 1
< 0.1%
279500 1
< 0.1%
189387 1
< 0.1%
178578 1
< 0.1%
176473 1
< 0.1%
142269 1
< 0.1%
129604 1
< 0.1%
120307 1
< 0.1%
118435 1
< 0.1%
116982 1
< 0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1076
Distinct (%)42.4%
Missing153
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean2427.0461
Minimum0
Maximum1292271
Zeros129
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T02:29:48.295617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q119
median142
Q3692.75
95-th percentile8107.65
Maximum1292271
Range1292271
Interquartile range (IQR)673.75

Descriptive statistics

Standard deviation27875.588
Coefficient of variation (CV)11.485397
Kurtosis1813.9242
Mean2427.0461
Median Absolute Deviation (MAD)140
Skewness39.948402
Sum6159843
Variance7.7704838 × 108
MonotonicityNot monotonic
2024-03-30T02:29:48.761264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 129
 
4.8%
1 80
 
3.0%
2 77
 
2.9%
3 45
 
1.7%
4 36
 
1.3%
6 31
 
1.2%
7 30
 
1.1%
5 26
 
1.0%
10 25
 
0.9%
8 24
 
0.9%
Other values (1066) 2035
75.6%
(Missing) 153
 
5.7%
ValueCountFrequency (%)
0 129
4.8%
1 80
3.0%
2 77
2.9%
3 45
 
1.7%
4 36
 
1.3%
5 26
 
1.0%
6 31
 
1.2%
7 30
 
1.1%
8 24
 
0.9%
9 11
 
0.4%
ValueCountFrequency (%)
1292271 1
< 0.1%
242719 1
< 0.1%
205562 1
< 0.1%
198128 1
< 0.1%
159315 1
< 0.1%
146359 1
< 0.1%
137342 1
< 0.1%
128854 1
< 0.1%
121351 1
< 0.1%
113438 1
< 0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1073
Distinct (%)42.7%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2484.3903
Minimum0
Maximum1340576
Zeros105
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T02:29:49.280675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q121
median154
Q3698.5
95-th percentile8411
Maximum1340576
Range1340576
Interquartile range (IQR)677.5

Descriptive statistics

Standard deviation28992.509
Coefficient of variation (CV)11.669869
Kurtosis1814.8061
Mean2484.3903
Median Absolute Deviation (MAD)150
Skewness40.050647
Sum6238304
Variance8.4056556 × 108
MonotonicityNot monotonic
2024-03-30T02:29:49.803373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 105
 
3.9%
2 70
 
2.6%
1 68
 
2.5%
3 40
 
1.5%
4 38
 
1.4%
7 31
 
1.2%
5 29
 
1.1%
6 28
 
1.0%
12 27
 
1.0%
9 22
 
0.8%
Other values (1063) 2053
76.3%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 105
3.9%
1 68
2.5%
2 70
2.6%
3 40
 
1.5%
4 38
 
1.4%
5 29
 
1.1%
6 28
 
1.0%
7 31
 
1.2%
8 19
 
0.7%
9 22
 
0.8%
ValueCountFrequency (%)
1340576 1
< 0.1%
262617 1
< 0.1%
215629 1
< 0.1%
187901 1
< 0.1%
157352 1
< 0.1%
154124 1
< 0.1%
140518 1
< 0.1%
130559 1
< 0.1%
124529 1
< 0.1%
118124 1
< 0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1065
Distinct (%)42.3%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean2311.9171
Minimum0
Maximum1245880
Zeros115
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T02:29:50.255816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q120
median141.5
Q3632
95-th percentile7679.9
Maximum1245880
Range1245880
Interquartile range (IQR)612

Descriptive statistics

Standard deviation26991.259
Coefficient of variation (CV)11.674839
Kurtosis1796.3269
Mean2311.9171
Median Absolute Deviation (MAD)138.5
Skewness39.76664
Sum5826031
Variance7.2852809 × 108
MonotonicityNot monotonic
2024-03-30T02:29:50.876352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 115
 
4.3%
1 76
 
2.8%
2 62
 
2.3%
3 57
 
2.1%
5 48
 
1.8%
4 37
 
1.4%
6 26
 
1.0%
15 25
 
0.9%
7 23
 
0.9%
8 22
 
0.8%
Other values (1055) 2029
75.4%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 115
4.3%
1 76
2.8%
2 62
2.3%
3 57
2.1%
4 37
 
1.4%
5 48
1.8%
6 26
 
1.0%
7 23
 
0.9%
8 22
 
0.8%
9 12
 
0.4%
ValueCountFrequency (%)
1245880 1
< 0.1%
225440 1
< 0.1%
222673 1
< 0.1%
208827 1
< 0.1%
140835 1
< 0.1%
137124 1
< 0.1%
134364 1
< 0.1%
116502 1
< 0.1%
115661 1
< 0.1%
111059 1
< 0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1047
Distinct (%)41.7%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2098.3313
Minimum0
Maximum1098368
Zeros122
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T02:29:51.543123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q118
median154
Q3630
95-th percentile6997.5
Maximum1098368
Range1098368
Interquartile range (IQR)612

Descriptive statistics

Standard deviation23898.62
Coefficient of variation (CV)11.389345
Kurtosis1771.5224
Mean2098.3313
Median Absolute Deviation (MAD)151
Skewness39.412947
Sum5268910
Variance5.7114405 × 108
MonotonicityNot monotonic
2024-03-30T02:29:52.329985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 122
 
4.5%
1 94
 
3.5%
2 63
 
2.3%
3 44
 
1.6%
5 30
 
1.1%
8 30
 
1.1%
10 28
 
1.0%
4 27
 
1.0%
9 25
 
0.9%
13 23
 
0.9%
Other values (1037) 2025
75.3%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 122
4.5%
1 94
3.5%
2 63
2.3%
3 44
 
1.6%
4 27
 
1.0%
5 30
 
1.1%
6 23
 
0.9%
7 21
 
0.8%
8 30
 
1.1%
9 25
 
0.9%
ValueCountFrequency (%)
1098368 1
< 0.1%
199043 1
< 0.1%
193119 1
< 0.1%
182978 1
< 0.1%
131535 1
< 0.1%
121955 1
< 0.1%
115475 1
< 0.1%
111684 1
< 0.1%
111016 1
< 0.1%
106856 1
< 0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1076
Distinct (%)42.9%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2275.6479
Minimum0
Maximum1209123
Zeros105
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T02:29:53.162675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q121
median149
Q3643.5
95-th percentile7380.5
Maximum1209123
Range1209123
Interquartile range (IQR)622.5

Descriptive statistics

Standard deviation26276.234
Coefficient of variation (CV)11.546705
Kurtosis1780.1684
Mean2275.6479
Median Absolute Deviation (MAD)146
Skewness39.53664
Sum5714152
Variance6.904405 × 108
MonotonicityNot monotonic
2024-03-30T02:29:53.852711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 105
 
3.9%
1 86
 
3.2%
2 58
 
2.2%
3 42
 
1.6%
5 32
 
1.2%
8 30
 
1.1%
4 29
 
1.1%
9 27
 
1.0%
7 25
 
0.9%
13 23
 
0.9%
Other values (1066) 2054
76.3%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 105
3.9%
1 86
3.2%
2 58
2.2%
3 42
 
1.6%
4 29
 
1.1%
5 32
 
1.2%
6 23
 
0.9%
7 25
 
0.9%
8 30
 
1.1%
9 27
 
1.0%
ValueCountFrequency (%)
1209123 1
< 0.1%
211291 1
< 0.1%
204676 1
< 0.1%
204173 1
< 0.1%
156704 1
< 0.1%
140657 1
< 0.1%
136567 1
< 0.1%
125155 1
< 0.1%
113819 1
< 0.1%
109660 1
< 0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1066
Distinct (%)42.5%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2460.8343
Minimum0
Maximum1274465
Zeros79
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T02:29:54.609262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q124
median162
Q3686
95-th percentile7934
Maximum1274465
Range1274465
Interquartile range (IQR)662

Descriptive statistics

Standard deviation27747.751
Coefficient of variation (CV)11.275749
Kurtosis1766.9085
Mean2460.8343
Median Absolute Deviation (MAD)158
Skewness39.340488
Sum6179155
Variance7.6993767 × 108
MonotonicityNot monotonic
2024-03-30T02:29:55.380443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 93
 
3.5%
0 79
 
2.9%
4 43
 
1.6%
2 41
 
1.5%
3 39
 
1.4%
7 26
 
1.0%
5 25
 
0.9%
6 23
 
0.9%
9 23
 
0.9%
12 22
 
0.8%
Other values (1056) 2097
77.9%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 79
2.9%
1 93
3.5%
2 41
1.5%
3 39
1.4%
4 43
1.6%
5 25
 
0.9%
6 23
 
0.9%
7 26
 
1.0%
8 22
 
0.8%
9 23
 
0.9%
ValueCountFrequency (%)
1274465 1
< 0.1%
242854 1
< 0.1%
210897 1
< 0.1%
195300 1
< 0.1%
172635 1
< 0.1%
156580 1
< 0.1%
144849 1
< 0.1%
138714 1
< 0.1%
123435 1
< 0.1%
112117 1
< 0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1056
Distinct (%)42.1%
Missing180
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2046.8929
Minimum0
Maximum1008399
Zeros124
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T02:29:56.092603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q118
median139
Q3605.5
95-th percentile6548
Maximum1008399
Range1008399
Interquartile range (IQR)587.5

Descriptive statistics

Standard deviation22105.486
Coefficient of variation (CV)10.799533
Kurtosis1719.4496
Mean2046.8929
Median Absolute Deviation (MAD)136
Skewness38.634992
Sum5139748
Variance4.8865253 × 108
MonotonicityNot monotonic
2024-03-30T02:29:56.595950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 124
 
4.6%
1 87
 
3.2%
2 66
 
2.5%
9 39
 
1.4%
3 34
 
1.3%
5 34
 
1.3%
4 29
 
1.1%
6 27
 
1.0%
23 24
 
0.9%
8 23
 
0.9%
Other values (1046) 2024
75.2%
(Missing) 180
 
6.7%
ValueCountFrequency (%)
0 124
4.6%
1 87
3.2%
2 66
2.5%
3 34
 
1.3%
4 29
 
1.1%
5 34
 
1.3%
6 27
 
1.0%
7 21
 
0.8%
8 23
 
0.9%
9 39
 
1.4%
ValueCountFrequency (%)
1008399 1
< 0.1%
202651 1
< 0.1%
174665 1
< 0.1%
157175 1
< 0.1%
126325 1
< 0.1%
122326 1
< 0.1%
120773 1
< 0.1%
111282 1
< 0.1%
109896 1
< 0.1%
103956 1
< 0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct946
Distinct (%)37.5%
Missing171
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean1535.7996
Minimum0
Maximum732678
Zeros147
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size23.8 KiB
2024-03-30T02:29:57.261461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113
median108.5
Q3437.25
95-th percentile5178.15
Maximum732678
Range732678
Interquartile range (IQR)424.25

Descriptive statistics

Standard deviation16129.7
Coefficient of variation (CV)10.502477
Kurtosis1684.3487
Mean1535.7996
Median Absolute Deviation (MAD)106.5
Skewness38.075056
Sum3870215
Variance2.6016724 × 108
MonotonicityNot monotonic
2024-03-30T02:29:57.789687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 147
 
5.5%
1 114
 
4.2%
3 54
 
2.0%
2 51
 
1.9%
4 44
 
1.6%
5 38
 
1.4%
6 33
 
1.2%
7 30
 
1.1%
11 27
 
1.0%
8 25
 
0.9%
Other values (936) 1957
72.7%
(Missing) 171
 
6.4%
ValueCountFrequency (%)
0 147
5.5%
1 114
4.2%
2 51
 
1.9%
3 54
 
2.0%
4 44
 
1.6%
5 38
 
1.4%
6 33
 
1.2%
7 30
 
1.1%
8 25
 
0.9%
9 21
 
0.8%
ValueCountFrequency (%)
732678 1
< 0.1%
136315 1
< 0.1%
131263 1
< 0.1%
123089 1
< 0.1%
107728 1
< 0.1%
103907 1
< 0.1%
82816 1
< 0.1%
78225 1
< 0.1%
76901 1
< 0.1%
76305 1
< 0.1%

Interactions

2024-03-30T02:29:27.355254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:42.010434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:48.271512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:54.058723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:59.606609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:05.423214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:14.708013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:20.648955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:26.311539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:32.857601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:39.587832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:45.724866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:52.602989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:58.295322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:03.959116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:09.302117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:14.664771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:21.200073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:27.651968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:42.335469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:48.646422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:54.349457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:59.971984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:05.830807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:15.090914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:20.948662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:26.636931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:33.195555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:39.934776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:46.317874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:52.901296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:58.601793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:04.360134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:09.624977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:14.997507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:21.604754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:27.967275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:42.653351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:48.958270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:54.661105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:00.274719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:06.220677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:15.571713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:21.250027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:26.936771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:33.522708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:40.242335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:46.810682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:53.279909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:58.900731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:04.646692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:09.980726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:15.301938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:21.969498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:28.295139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:42.986435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:49.249444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:55.039676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:00.569092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:06.524744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:16.053991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:21.554855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:27.533325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:33.818588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:40.519612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:47.367359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:53.598249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:59.164083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:04.962463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:10.313774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:15.594818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:22.462077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:28.650080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:43.302632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:49.536980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:55.468479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:00.904939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:06.831656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:16.343813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:21.848305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:27.971221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:34.122974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:40.811708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:47.837952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:53.874499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:59.541295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:05.236227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:10.596129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:15.928485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:22.781423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:28.976248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:43.631826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:49.836301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:55.762976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:01.225881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:07.137367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:16.648610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:22.195751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:28.394810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:34.417846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:41.134719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:48.224673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:54.172302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:59.926384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:05.548518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:10.895992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:16.263862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:23.079795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:29.262687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:43.928559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:50.148912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:56.047584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:01.734690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:07.449082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:16.989163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:22.515353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:28.865311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:34.752079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:41.434707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:48.575304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:54.495481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:00.227828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:05.875788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:11.176514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:16.631813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:23.473907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:29.555720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:44.298683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:50.439417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:56.372222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:02.025629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:07.747094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:17.276930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:22.783626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:29.274333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:35.058438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:41.715614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:48.877408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:54.769421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:00.560022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:06.180870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:11.471644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:16.909836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:23.854427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:29.863187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:44.852312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:50.723090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:56.648264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:02.300044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:08.013883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:17.535416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:23.072929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:29.689808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:35.397347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:42.015285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:49.441299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:55.038862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:00.822535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:06.538361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:11.745757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:17.185798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:24.249234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:30.267355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:45.215239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:51.049124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:56.942435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:02.656373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:08.335443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:17.827374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:23.581638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:30.041385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:35.786582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:42.366420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:49.776007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:55.384811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:01.106621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:06.838938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:12.035317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:17.468429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:24.589984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:30.589237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:45.598187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:51.398504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:57.230054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:02.956414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:08.847245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:18.098877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:23.902951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:30.299342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:36.440257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:42.653031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:50.101517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:55.705936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:01.450718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:07.110359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:12.318799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:17.826018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:24.915449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:30.972307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:45.904219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:51.758622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:57.537709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:03.246014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:09.539194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:18.399160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:24.153984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:30.583620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:36.957911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:42.941232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:50.435059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:55.996505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:01.729681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:07.377623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:12.631512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:18.180686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:25.207494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:31.301686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:46.199902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:52.125333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:57.816383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:03.548764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:10.340215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:18.761893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:24.461539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:30.890709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:37.428031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:43.467342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:50.745346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:56.343588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:02.259287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:07.661532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:12.928184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:18.501292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:25.516282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:31.718969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:46.557553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:52.420895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:58.068434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:03.890603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:10.959199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:19.088691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:24.755864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:31.193604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:37.866952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:43.865129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:51.046481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:56.650499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:02.579455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:07.943057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:13.208304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:18.847264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:25.795558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:32.044818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:46.847159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:52.764051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:58.392788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:04.159657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:12.959778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:19.374229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:25.064373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:31.501565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:38.287462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:44.209977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:51.376850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:56.995898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:02.843546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:08.210279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:13.485096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:19.146508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:26.085022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:32.401868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:47.167688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:53.134557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:58.768515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:04.466460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:13.465010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:19.656343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:25.353069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:31.808854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:38.675900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:44.575129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:51.645523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:57.306522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:03.126641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:08.503009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:13.787137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:19.480911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:26.353039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:32.718413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:47.475899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:53.420861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:59.048698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:04.791593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:13.985963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:19.939809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:25.647922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:32.146338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:38.969945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:44.899755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:51.973027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:57.668423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:03.408568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:08.773912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:14.066962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:19.871955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:26.659387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:33.025071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:47.860659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:53.722131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:27:59.311239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:05.121727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:14.378886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:20.285320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:26.013243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:32.548096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:39.300453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:45.282476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:52.316407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:28:57.978661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:03.681689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:09.043378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:14.354252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:20.354583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T02:29:26.965090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-30T02:29:58.316698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.8910.9900.8620.8620.8910.9700.9570.8260.9770.8501.0001.0000.8620.8260.8260.9980.842
20070.8911.0001.0000.9990.9991.0000.8910.8260.9970.8530.9920.9990.9990.9940.9890.9890.8710.998
20080.9901.0001.0000.8960.8961.0000.9900.9800.8610.9850.8340.8940.8940.8380.8130.8130.9890.869
20090.8620.9990.8961.0001.0000.9990.8620.8090.9940.8320.9890.9970.9970.9980.9940.9940.9010.999
20100.8620.9990.8961.0001.0000.9990.8620.8090.9940.8320.9890.9970.9970.9980.9940.9940.9010.999
20110.8911.0001.0000.9990.9991.0000.8910.8260.9970.8530.9920.9990.9990.9940.9890.9890.8710.998
20120.9700.8910.9900.8620.8620.8911.0000.9980.8260.9990.8500.8500.8500.8090.8260.7790.9680.842
20130.9570.8260.9800.8090.8090.8260.9981.0000.8910.9950.8500.7950.7950.8090.8260.7790.9680.842
20140.8260.9970.8610.9940.9940.9970.8260.8911.0000.7940.9920.9920.9920.9940.9890.9890.8710.998
20150.9770.8530.9850.8320.8320.8530.9990.9950.7941.0000.8840.8840.8840.8320.8530.7940.9640.816
20160.8500.9920.8340.9890.9890.9920.8500.8500.9920.8841.0000.9960.9960.9970.9990.9920.8470.995
20171.0000.9990.8940.9970.9970.9990.8500.7950.9920.8840.9961.0001.0000.9970.9920.9920.8470.995
20181.0000.9990.8940.9970.9970.9990.8500.7950.9920.8840.9961.0001.0000.9970.9920.9920.8470.995
20190.8620.9940.8380.9980.9980.9940.8090.8090.9940.8320.9970.9970.9971.0000.9990.9990.9010.999
20200.8260.9890.8130.9940.9940.9890.8260.8260.9890.8530.9990.9920.9920.9991.0000.9970.8710.998
20210.8260.9890.8130.9940.9940.9890.7790.7790.9890.7940.9920.9920.9920.9990.9971.0000.8710.998
20220.9980.8710.9890.9010.9010.8710.9680.9680.8710.9640.8470.8470.8470.9010.8710.8711.0001.000
20230.8420.9980.8690.9990.9990.9980.8420.8420.9980.8160.9950.9950.9950.9990.9980.9981.0001.000
2024-03-30T02:29:58.908425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.8250.8080.7750.7700.7710.7510.7660.7720.7670.7640.7730.7670.7690.7670.7600.7510.723
20070.8251.0000.8390.7990.7930.8020.7850.7930.7860.7800.7850.7740.7860.7730.7780.7680.7720.748
20080.8080.8391.0000.8190.8080.8130.8090.8020.8000.7940.7940.7850.8020.7890.7900.7800.7800.774
20090.7750.7990.8191.0000.8360.8300.8120.8030.8070.8020.8050.8000.8080.7990.7990.7880.7900.780
20100.7700.7930.8080.8361.0000.8350.8160.8030.7980.7900.7940.8000.8060.7940.7950.7820.7900.779
20110.7710.8020.8130.8300.8351.0000.8440.8300.8200.8180.8160.8110.8050.8020.8030.7970.7910.785
20120.7510.7850.8090.8120.8160.8441.0000.8310.8320.8120.8140.8080.8140.8100.8060.7880.7840.794
20130.7660.7930.8020.8030.8030.8300.8311.0000.8390.8240.8270.8070.8150.8100.8080.7940.7960.795
20140.7720.7860.8000.8070.7980.8200.8320.8391.0000.8580.8300.8250.8240.8210.8100.8000.7860.781
20150.7670.7800.7940.8020.7900.8180.8120.8240.8581.0000.8430.8310.8270.8260.8080.7950.7870.787
20160.7640.7850.7940.8050.7940.8160.8140.8270.8300.8431.0000.8530.8410.8240.8170.7950.7830.792
20170.7730.7740.7850.8000.8000.8110.8080.8070.8250.8310.8531.0000.8530.8330.8310.8060.7960.806
20180.7670.7860.8020.8080.8060.8050.8140.8150.8240.8270.8410.8531.0000.8530.8410.8270.8170.823
20190.7690.7730.7890.7990.7940.8020.8100.8100.8210.8260.8240.8330.8531.0000.8480.8240.8100.806
20200.7670.7780.7900.7990.7950.8030.8060.8080.8100.8080.8170.8310.8410.8481.0000.8400.8280.814
20210.7600.7680.7800.7880.7820.7970.7880.7940.8000.7950.7950.8060.8270.8240.8401.0000.8440.811
20220.7510.7720.7800.7900.7900.7910.7840.7960.7860.7870.7830.7960.8170.8100.8280.8441.0000.838
20230.7230.7480.7740.7800.7790.7850.7940.7950.7810.7870.7920.8060.8230.8060.8140.8110.8381.000

Missing values

2024-03-30T02:29:33.535415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T02:29:34.366236image/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.
2024-03-30T02:29:35.006174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

지역_거래주체200620072008200920102011201220132014201520162017201820192020202120222023
0전국_개인->개인19267171584919140030813711331194950117681410658441086863121638313400521292271134057612458801098368120912312744651008399732678
1전국_개인->법인445323296602296947253642205429196519188677163525164542189387205562215629222673199043211291242854202651136315
2전국_개인->기타16903018795115721816158715988116304515585114408412290711843512135110204810872310178699553100530122326107728
3전국_법인->개인1908131219371116341000648261979817907057966781903104462110137103875960728002683090768535613744875
4전국_법인->법인106356104450931001194899364511436483037112061126531120307137342124529111059121955140657156580120773103907
5전국_법인->기타183731864629123323322656219749250711942625603397283304838657235841642614108343532854233311
6전국_기타->개인551084273643553448404534239701409254094438267435354750051908407393795136266348144065034190
7전국_기타->법인435552750130654266672972022129312022204326917283903203334928277582653332229481995073323689
8전국_기타->기타271271563923283410262548531919303113024524614296182734525745302873499037897474504541247096
9서울_개인->개인1500848973179226319191163121414732351338228182783348126203370467934151209
지역_거래주체200620072008200920102011201220132014201520162017201820192020202120222023
2681제주 제주시_기타->기타47114131669316267246122399141315188122428251524191910152892
2682제주 서귀포시_개인->개인1134816332106911047810893110091131214362197672401018649148931359211403100981025389646153
2683제주 서귀포시_개인->법인182644342466397922482122238529304658633237662283140212191451164216501352
2684제주 서귀포시_개인->기타104101231146197293175682946219130253333326328152140313
2685제주 서귀포시_법인->개인641108493974310099751613161823954521294218121514639519687362296
2686제주 서귀포시_법인->법인33265528771261512205243839861844290823551394868740321535515310
2687제주 서귀포시_법인->기타011514952140612662713151279726819175
2688제주 서귀포시_기타->개인18024262234344330312468206768142019224512089164157166
2689제주 서귀포시_기타->법인348645393922384146782552743098285664222614323836
2690제주 서귀포시_기타->기타66634911814771239490542385363590143319129963723