Overview

Dataset statistics

Number of variables19
Number of observations2392
Missing cells2920
Missing cells (%)6.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory397.2 KiB
Average record size in memory170.1 B

Variable types

Text1
Numeric18

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 부동산거래현황 중 건축물 거래현황의 연도별 거래규모별(면적) 데이터입니다.- (단위 : 천㎡)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068224/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 176 (7.4%) missing valuesMissing
2007 has 216 (9.0%) missing valuesMissing
2008 has 200 (8.4%) missing valuesMissing
2009 has 200 (8.4%) missing valuesMissing
2010 has 152 (6.4%) missing valuesMissing
2011 has 176 (7.4%) missing valuesMissing
2012 has 160 (6.7%) missing valuesMissing
2013 has 160 (6.7%) missing valuesMissing
2014 has 104 (4.3%) missing valuesMissing
2015 has 136 (5.7%) missing valuesMissing
2016 has 136 (5.7%) missing valuesMissing
2017 has 160 (6.7%) missing valuesMissing
2018 has 152 (6.4%) missing valuesMissing
2019 has 160 (6.7%) missing valuesMissing
2020 has 160 (6.7%) missing valuesMissing
2021 has 160 (6.7%) missing valuesMissing
2022 has 160 (6.7%) missing valuesMissing
2023 has 152 (6.4%) missing valuesMissing
2006 is highly skewed (γ1 = 31.65324591)Skewed
2007 is highly skewed (γ1 = 30.70495449)Skewed
2008 is highly skewed (γ1 = 33.07918258)Skewed
2009 is highly skewed (γ1 = 31.32877493)Skewed
2010 is highly skewed (γ1 = 30.79590565)Skewed
2011 is highly skewed (γ1 = 31.38013556)Skewed
2012 is highly skewed (γ1 = 23.47857197)Skewed
2013 is highly skewed (γ1 = 32.8129314)Skewed
2014 is highly skewed (γ1 = 33.98724646)Skewed
2015 is highly skewed (γ1 = 31.29178631)Skewed
2016 is highly skewed (γ1 = 32.90326076)Skewed
2017 is highly skewed (γ1 = 35.07389603)Skewed
2018 is highly skewed (γ1 = 34.5631935)Skewed
2019 is highly skewed (γ1 = 34.63323953)Skewed
2020 is highly skewed (γ1 = 35.15723466)Skewed
2021 is highly skewed (γ1 = 34.29397196)Skewed
2022 is highly skewed (γ1 = 33.46243779)Skewed
2023 is highly skewed (γ1 = 35.47668636)Skewed
지역_거래규모 has unique valuesUnique
2006 has 195 (8.2%) zerosZeros
2007 has 187 (7.8%) zerosZeros
2008 has 207 (8.7%) zerosZeros
2009 has 221 (9.2%) zerosZeros
2010 has 215 (9.0%) zerosZeros
2011 has 206 (8.6%) zerosZeros
2012 has 195 (8.2%) zerosZeros
2013 has 182 (7.6%) zerosZeros
2014 has 196 (8.2%) zerosZeros
2015 has 166 (6.9%) zerosZeros
2016 has 170 (7.1%) zerosZeros
2017 has 192 (8.0%) zerosZeros
2018 has 169 (7.1%) zerosZeros
2019 has 151 (6.3%) zerosZeros
2020 has 162 (6.8%) zerosZeros
2021 has 155 (6.5%) zerosZeros
2022 has 172 (7.2%) zerosZeros
2023 has 196 (8.2%) zerosZeros

Reproduction

Analysis started2024-03-16 04:18:34.486788
Analysis finished2024-03-16 04:19:41.181392
Duration1 minute and 6.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역_거래규모
Text

UNIQUE 

Distinct2392
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
2024-03-16T13:19:41.325809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length27
Mean length16.621237
Min length9

Characters and Unicode

Total characters39758
Distinct characters156
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2392 ?
Unique (%)100.0%

Sample

1st row전국_100㎡이하
2nd row전국_101~200㎡
3rd row전국_201~300㎡
4th row전국_301~500㎡
5th row전국_501~1,000㎡
ValueCountFrequency (%)
경기 416
 
8.4%
경남 208
 
4.2%
경북 200
 
4.1%
서울 200
 
4.1%
전남 176
 
3.6%
충북 152
 
3.1%
충남 152
 
3.1%
강원 144
 
2.9%
부산 128
 
2.6%
전북 128
 
2.6%
Other values (2205) 3024
61.4%
2024-03-16T13:19:41.734112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8970
22.6%
1 3588
 
9.0%
2536
 
6.4%
_ 2392
 
6.0%
2392
 
6.0%
~ 1794
 
4.5%
, 1794
 
4.5%
3 1196
 
3.0%
1104
 
2.8%
976
 
2.5%
Other values (146) 13016
32.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14950
37.6%
Other Letter 13644
34.3%
Space Separator 2536
 
6.4%
Connector Punctuation 2392
 
6.0%
Other Symbol 2392
 
6.0%
Math Symbol 1794
 
4.5%
Other Punctuation 1794
 
4.5%
Close Punctuation 128
 
0.3%
Open Punctuation 128
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1104
 
8.1%
976
 
7.2%
872
 
6.4%
744
 
5.5%
712
 
5.2%
576
 
4.2%
448
 
3.3%
400
 
2.9%
384
 
2.8%
376
 
2.8%
Other values (134) 7052
51.7%
Decimal Number
ValueCountFrequency (%)
0 8970
60.0%
1 3588
 
24.0%
3 1196
 
8.0%
5 598
 
4.0%
2 598
 
4.0%
Space Separator
ValueCountFrequency (%)
2536
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2392
100.0%
Other Symbol
ValueCountFrequency (%)
2392
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1794
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1794
100.0%
Close Punctuation
ValueCountFrequency (%)
) 128
100.0%
Open Punctuation
ValueCountFrequency (%)
( 128
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26114
65.7%
Hangul 13644
34.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1104
 
8.1%
976
 
7.2%
872
 
6.4%
744
 
5.5%
712
 
5.2%
576
 
4.2%
448
 
3.3%
400
 
2.9%
384
 
2.8%
376
 
2.8%
Other values (134) 7052
51.7%
Common
ValueCountFrequency (%)
0 8970
34.3%
1 3588
 
13.7%
2536
 
9.7%
_ 2392
 
9.2%
2392
 
9.2%
~ 1794
 
6.9%
, 1794
 
6.9%
3 1196
 
4.6%
5 598
 
2.3%
2 598
 
2.3%
Other values (2) 256
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23722
59.7%
Hangul 13644
34.3%
CJK Compat 2392
 
6.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8970
37.8%
1 3588
 
15.1%
2536
 
10.7%
_ 2392
 
10.1%
~ 1794
 
7.6%
, 1794
 
7.6%
3 1196
 
5.0%
5 598
 
2.5%
2 598
 
2.5%
) 128
 
0.5%
CJK Compat
ValueCountFrequency (%)
2392
100.0%
Hangul
ValueCountFrequency (%)
1104
 
8.1%
976
 
7.2%
872
 
6.4%
744
 
5.5%
712
 
5.2%
576
 
4.2%
448
 
3.3%
400
 
2.9%
384
 
2.8%
376
 
2.8%
Other values (134) 7052
51.7%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct467
Distinct (%)21.1%
Missing176
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean238.58709
Minimum0
Maximum85658
Zeros195
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-16T13:19:42.274018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median30
Q393
95-th percentile717
Maximum85658
Range85658
Interquartile range (IQR)83

Descriptive statistics

Standard deviation2135.0879
Coefficient of variation (CV)8.9488825
Kurtosis1190.3054
Mean238.58709
Median Absolute Deviation (MAD)25
Skewness31.653246
Sum528709
Variance4558600.2
MonotonicityNot monotonic
2024-03-16T13:19:42.410923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 195
 
8.2%
7 55
 
2.3%
11 50
 
2.1%
4 47
 
2.0%
5 47
 
2.0%
6 46
 
1.9%
9 41
 
1.7%
3 39
 
1.6%
10 39
 
1.6%
13 37
 
1.5%
Other values (457) 1620
67.7%
(Missing) 176
 
7.4%
ValueCountFrequency (%)
0 195
8.2%
1 16
 
0.7%
2 18
 
0.8%
3 39
 
1.6%
4 47
 
2.0%
5 47
 
2.0%
6 46
 
1.9%
7 55
 
2.3%
8 34
 
1.4%
9 41
 
1.7%
ValueCountFrequency (%)
85658 1
< 0.1%
31336 1
< 0.1%
26324 1
< 0.1%
18088 1
< 0.1%
10864 1
< 0.1%
9368 1
< 0.1%
9001 1
< 0.1%
8747 1
< 0.1%
7132 1
< 0.1%
6990 1
< 0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct432
Distinct (%)19.9%
Missing216
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean203.85386
Minimum0
Maximum66838
Zeros187
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-16T13:19:42.525315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median28.5
Q386.25
95-th percentile527.75
Maximum66838
Range66838
Interquartile range (IQR)76.25

Descriptive statistics

Standard deviation1687.6722
Coefficient of variation (CV)8.2788339
Kurtosis1143.331
Mean203.85386
Median Absolute Deviation (MAD)23.5
Skewness30.704954
Sum443586
Variance2848237.6
MonotonicityNot monotonic
2024-03-16T13:19:42.647281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 187
 
7.8%
4 56
 
2.3%
7 49
 
2.0%
11 48
 
2.0%
5 48
 
2.0%
3 48
 
2.0%
8 48
 
2.0%
10 45
 
1.9%
6 41
 
1.7%
12 40
 
1.7%
Other values (422) 1566
65.5%
(Missing) 216
 
9.0%
ValueCountFrequency (%)
0 187
7.8%
1 10
 
0.4%
2 18
 
0.8%
3 48
 
2.0%
4 56
 
2.3%
5 48
 
2.0%
6 41
 
1.7%
7 49
 
2.0%
8 48
 
2.0%
9 23
 
1.0%
ValueCountFrequency (%)
66838 1
< 0.1%
23368 1
< 0.1%
18116 1
< 0.1%
13027 1
< 0.1%
11234 1
< 0.1%
9956 1
< 0.1%
8403 1
< 0.1%
8343 1
< 0.1%
6364 1
< 0.1%
6106 1
< 0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct423
Distinct (%)19.3%
Missing200
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean187.54197
Minimum0
Maximum68165
Zeros207
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-16T13:19:42.784485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median27
Q378
95-th percentile520.6
Maximum68165
Range68165
Interquartile range (IQR)69

Descriptive statistics

Standard deviation1667.2945
Coefficient of variation (CV)8.8902473
Kurtosis1286.5163
Mean187.54197
Median Absolute Deviation (MAD)22
Skewness33.079183
Sum411092
Variance2779870.9
MonotonicityNot monotonic
2024-03-16T13:19:42.954797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 207
 
8.7%
4 54
 
2.3%
10 51
 
2.1%
6 51
 
2.1%
11 49
 
2.0%
3 49
 
2.0%
5 49
 
2.0%
12 47
 
2.0%
7 45
 
1.9%
13 40
 
1.7%
Other values (413) 1550
64.8%
(Missing) 200
 
8.4%
ValueCountFrequency (%)
0 207
8.7%
1 10
 
0.4%
2 20
 
0.8%
3 49
 
2.0%
4 54
 
2.3%
5 49
 
2.0%
6 51
 
2.1%
7 45
 
1.9%
8 36
 
1.5%
9 32
 
1.3%
ValueCountFrequency (%)
68165 1
< 0.1%
24008 1
< 0.1%
16495 1
< 0.1%
10714 1
< 0.1%
8389 1
< 0.1%
7802 1
< 0.1%
7503 1
< 0.1%
5954 1
< 0.1%
5125 1
< 0.1%
5104 1
< 0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct429
Distinct (%)19.6%
Missing200
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean189.54106
Minimum0
Maximum64715
Zeros221
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-16T13:19:43.154737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median25
Q379
95-th percentile515.35
Maximum64715
Range64715
Interquartile range (IQR)70

Descriptive statistics

Standard deviation1631.9596
Coefficient of variation (CV)8.6100585
Kurtosis1160.6138
Mean189.54106
Median Absolute Deviation (MAD)21
Skewness31.328775
Sum415474
Variance2663292.1
MonotonicityNot monotonic
2024-03-16T13:19:43.359555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 221
 
9.2%
8 53
 
2.2%
9 52
 
2.2%
4 49
 
2.0%
5 47
 
2.0%
11 47
 
2.0%
6 47
 
2.0%
12 46
 
1.9%
14 46
 
1.9%
13 46
 
1.9%
Other values (419) 1538
64.3%
(Missing) 200
 
8.4%
ValueCountFrequency (%)
0 221
9.2%
1 6
 
0.3%
2 17
 
0.7%
3 41
 
1.7%
4 49
 
2.0%
5 47
 
2.0%
6 47
 
2.0%
7 42
 
1.8%
8 53
 
2.2%
9 52
 
2.2%
ValueCountFrequency (%)
64715 1
< 0.1%
28072 1
< 0.1%
16483 1
< 0.1%
9809 1
< 0.1%
8014 1
< 0.1%
7834 1
< 0.1%
7411 1
< 0.1%
7207 1
< 0.1%
5633 1
< 0.1%
4951 1
< 0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct423
Distinct (%)18.9%
Missing152
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean182.50893
Minimum0
Maximum59316
Zeros215
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-16T13:19:43.508994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median27
Q380
95-th percentile516
Maximum59316
Range59316
Interquartile range (IQR)70

Descriptive statistics

Standard deviation1498.5137
Coefficient of variation (CV)8.2106325
Kurtosis1133.3453
Mean182.50893
Median Absolute Deviation (MAD)22
Skewness30.795906
Sum408820
Variance2245543.5
MonotonicityNot monotonic
2024-03-16T13:19:43.736576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 215
 
9.0%
13 54
 
2.3%
7 54
 
2.3%
5 53
 
2.2%
14 50
 
2.1%
3 47
 
2.0%
11 45
 
1.9%
9 45
 
1.9%
10 43
 
1.8%
12 42
 
1.8%
Other values (413) 1592
66.6%
(Missing) 152
 
6.4%
ValueCountFrequency (%)
0 215
9.0%
1 13
 
0.5%
2 18
 
0.8%
3 47
 
2.0%
4 40
 
1.7%
5 53
 
2.2%
6 37
 
1.5%
7 54
 
2.3%
8 37
 
1.5%
9 45
 
1.9%
ValueCountFrequency (%)
59316 1
< 0.1%
26913 1
< 0.1%
13557 1
< 0.1%
9189 1
< 0.1%
8160 1
< 0.1%
8131 1
< 0.1%
7620 1
< 0.1%
7293 1
< 0.1%
6630 1
< 0.1%
5601 1
< 0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct457
Distinct (%)20.6%
Missing176
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean211.96616
Minimum0
Maximum69720
Zeros206
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-16T13:19:43.968575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median31
Q395
95-th percentile595.5
Maximum69720
Range69720
Interquartile range (IQR)84

Descriptive statistics

Standard deviation1750.4007
Coefficient of variation (CV)8.2579256
Kurtosis1168.5031
Mean211.96616
Median Absolute Deviation (MAD)25
Skewness31.380136
Sum469717
Variance3063902.7
MonotonicityNot monotonic
2024-03-16T13:19:44.289241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 206
 
8.6%
8 49
 
2.0%
4 48
 
2.0%
7 48
 
2.0%
11 45
 
1.9%
18 44
 
1.8%
12 41
 
1.7%
9 38
 
1.6%
10 38
 
1.6%
6 34
 
1.4%
Other values (447) 1625
67.9%
(Missing) 176
 
7.4%
ValueCountFrequency (%)
0 206
8.6%
1 9
 
0.4%
2 14
 
0.6%
3 30
 
1.3%
4 48
 
2.0%
5 31
 
1.3%
6 34
 
1.4%
7 48
 
2.0%
8 49
 
2.0%
9 38
 
1.6%
ValueCountFrequency (%)
69720 1
< 0.1%
30643 1
< 0.1%
15527 1
< 0.1%
10747 1
< 0.1%
9517 1
< 0.1%
9427 1
< 0.1%
8180 1
< 0.1%
6519 1
< 0.1%
6251 1
< 0.1%
5902 1
< 0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct431
Distinct (%)19.3%
Missing160
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean311.03763
Minimum0
Maximum104326
Zeros195
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-16T13:19:44.507814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median29
Q387
95-th percentile535.15
Maximum104326
Range104326
Interquartile range (IQR)77

Descriptive statistics

Standard deviation3842.0219
Coefficient of variation (CV)12.352273
Kurtosis577.69803
Mean311.03763
Median Absolute Deviation (MAD)24
Skewness23.478572
Sum694236
Variance14761132
MonotonicityNot monotonic
2024-03-16T13:19:44.715968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 195
 
8.2%
7 52
 
2.2%
10 52
 
2.2%
8 48
 
2.0%
5 47
 
2.0%
16 42
 
1.8%
9 41
 
1.7%
13 41
 
1.7%
4 40
 
1.7%
3 39
 
1.6%
Other values (421) 1635
68.4%
(Missing) 160
 
6.7%
ValueCountFrequency (%)
0 195
8.2%
1 11
 
0.5%
2 18
 
0.8%
3 39
 
1.6%
4 40
 
1.7%
5 47
 
2.0%
6 35
 
1.5%
7 52
 
2.2%
8 48
 
2.0%
9 41
 
1.7%
ValueCountFrequency (%)
104326 1
< 0.1%
94021 1
< 0.1%
93888 1
< 0.1%
56991 1
< 0.1%
24019 1
< 0.1%
12389 1
< 0.1%
9889 1
< 0.1%
8958 1
< 0.1%
8660 1
< 0.1%
6342 1
< 0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct453
Distinct (%)20.3%
Missing160
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean205.44624
Minimum0
Maximum70281
Zeros182
Zeros (%)7.6%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-16T13:19:44.896112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median31
Q392
95-th percentile597.9
Maximum70281
Range70281
Interquartile range (IQR)81

Descriptive statistics

Standard deviation1711.0079
Coefficient of variation (CV)8.3282513
Kurtosis1283.221
Mean205.44624
Median Absolute Deviation (MAD)25
Skewness32.812931
Sum458556
Variance2927548
MonotonicityNot monotonic
2024-03-16T13:19:45.160351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 182
 
7.6%
3 53
 
2.2%
11 48
 
2.0%
12 44
 
1.8%
9 42
 
1.8%
8 42
 
1.8%
7 41
 
1.7%
6 38
 
1.6%
5 38
 
1.6%
20 36
 
1.5%
Other values (443) 1668
69.7%
(Missing) 160
 
6.7%
ValueCountFrequency (%)
0 182
7.6%
1 5
 
0.2%
2 13
 
0.5%
3 53
 
2.2%
4 29
 
1.2%
5 38
 
1.6%
6 38
 
1.6%
7 41
 
1.7%
8 42
 
1.8%
9 42
 
1.8%
ValueCountFrequency (%)
70281 1
< 0.1%
23851 1
< 0.1%
15692 1
< 0.1%
10353 1
< 0.1%
9843 1
< 0.1%
9298 1
< 0.1%
9124 1
< 0.1%
8338 1
< 0.1%
6425 1
< 0.1%
6147 1
< 0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct487
Distinct (%)21.3%
Missing104
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean237.35184
Minimum0
Maximum86136
Zeros196
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-16T13:19:45.430385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median35
Q3106
95-th percentile728.65
Maximum86136
Range86136
Interquartile range (IQR)94

Descriptive statistics

Standard deviation2051.3183
Coefficient of variation (CV)8.6425211
Kurtosis1364.8281
Mean237.35184
Median Absolute Deviation (MAD)29
Skewness33.987246
Sum543061
Variance4207906.6
MonotonicityNot monotonic
2024-03-16T13:19:45.659909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 196
 
8.2%
10 45
 
1.9%
16 44
 
1.8%
5 41
 
1.7%
12 39
 
1.6%
11 39
 
1.6%
7 38
 
1.6%
24 35
 
1.5%
8 34
 
1.4%
4 33
 
1.4%
Other values (477) 1744
72.9%
(Missing) 104
 
4.3%
ValueCountFrequency (%)
0 196
8.2%
1 9
 
0.4%
2 10
 
0.4%
3 29
 
1.2%
4 33
 
1.4%
5 41
 
1.7%
6 32
 
1.3%
7 38
 
1.6%
8 34
 
1.4%
9 31
 
1.3%
ValueCountFrequency (%)
86136 1
< 0.1%
27960 1
< 0.1%
19269 1
< 0.1%
12079 1
< 0.1%
11050 1
< 0.1%
10946 1
< 0.1%
10710 1
< 0.1%
9826 1
< 0.1%
7618 1
< 0.1%
7464 1
< 0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct525
Distinct (%)23.3%
Missing136
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean307.88209
Minimum0
Maximum102815
Zeros166
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-16T13:19:45.867730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115
median43
Q3127
95-th percentile830.5
Maximum102815
Range102815
Interquartile range (IQR)112

Descriptive statistics

Standard deviation2540.6658
Coefficient of variation (CV)8.2520739
Kurtosis1196.2286
Mean307.88209
Median Absolute Deviation (MAD)35
Skewness31.291786
Sum694582
Variance6454982.5
MonotonicityNot monotonic
2024-03-16T13:19:46.127360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 166
 
6.9%
11 41
 
1.7%
12 38
 
1.6%
16 36
 
1.5%
9 36
 
1.5%
23 35
 
1.5%
15 33
 
1.4%
8 32
 
1.3%
7 32
 
1.3%
10 31
 
1.3%
Other values (515) 1776
74.2%
(Missing) 136
 
5.7%
ValueCountFrequency (%)
0 166
6.9%
1 5
 
0.2%
2 11
 
0.5%
3 29
 
1.2%
4 26
 
1.1%
5 29
 
1.2%
6 24
 
1.0%
7 32
 
1.3%
8 32
 
1.3%
9 36
 
1.5%
ValueCountFrequency (%)
102815 1
< 0.1%
32299 1
< 0.1%
25350 1
< 0.1%
24115 1
< 0.1%
15238 1
< 0.1%
15068 1
< 0.1%
13575 1
< 0.1%
12480 1
< 0.1%
12210 1
< 0.1%
9224 1
< 0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct502
Distinct (%)22.3%
Missing136
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean279.57979
Minimum0
Maximum99220
Zeros170
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-16T13:19:46.437832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median42
Q3115
95-th percentile777.75
Maximum99220
Range99220
Interquartile range (IQR)101

Descriptive statistics

Standard deviation2404.0495
Coefficient of variation (CV)8.5987957
Kurtosis1292.3565
Mean279.57979
Median Absolute Deviation (MAD)34
Skewness32.903261
Sum630732
Variance5779453.8
MonotonicityNot monotonic
2024-03-16T13:19:46.643217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 170
 
7.1%
11 46
 
1.9%
10 38
 
1.6%
7 36
 
1.5%
12 35
 
1.5%
4 35
 
1.5%
18 34
 
1.4%
13 33
 
1.4%
8 31
 
1.3%
21 29
 
1.2%
Other values (492) 1769
74.0%
(Missing) 136
 
5.7%
ValueCountFrequency (%)
0 170
7.1%
1 8
 
0.3%
2 11
 
0.5%
3 27
 
1.1%
4 35
 
1.5%
5 25
 
1.0%
6 28
 
1.2%
7 36
 
1.5%
8 31
 
1.3%
9 27
 
1.1%
ValueCountFrequency (%)
99220 1
< 0.1%
29340 1
< 0.1%
27546 1
< 0.1%
16375 1
< 0.1%
15411 1
< 0.1%
13816 1
< 0.1%
12633 1
< 0.1%
12143 1
< 0.1%
8215 1
< 0.1%
8167 1
< 0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct487
Distinct (%)21.8%
Missing160
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean289.10753
Minimum0
Maximum116592
Zeros192
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-16T13:19:46.875353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median40
Q3112
95-th percentile838.45
Maximum116592
Range116592
Interquartile range (IQR)98

Descriptive statistics

Standard deviation2764.3806
Coefficient of variation (CV)9.5617733
Kurtosis1422.6385
Mean289.10753
Median Absolute Deviation (MAD)33
Skewness35.073896
Sum645288
Variance7641800.3
MonotonicityNot monotonic
2024-03-16T13:19:47.096976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 192
 
8.0%
12 43
 
1.8%
8 38
 
1.6%
14 36
 
1.5%
3 34
 
1.4%
7 33
 
1.4%
5 33
 
1.4%
16 32
 
1.3%
4 31
 
1.3%
9 31
 
1.3%
Other values (477) 1729
72.3%
(Missing) 160
 
6.7%
ValueCountFrequency (%)
0 192
8.0%
1 3
 
0.1%
2 12
 
0.5%
3 34
 
1.4%
4 31
 
1.3%
5 33
 
1.4%
6 26
 
1.1%
7 33
 
1.4%
8 38
 
1.6%
9 31
 
1.3%
ValueCountFrequency (%)
116592 1
< 0.1%
35131 1
< 0.1%
28592 1
< 0.1%
16882 1
< 0.1%
12622 1
< 0.1%
12018 1
< 0.1%
11835 1
< 0.1%
8640 1
< 0.1%
8355 1
< 0.1%
8112 1
< 0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct475
Distinct (%)21.2%
Missing152
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean286.68482
Minimum0
Maximum115354
Zeros169
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-16T13:19:47.346547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113.75
median37
Q3104
95-th percentile771
Maximum115354
Range115354
Interquartile range (IQR)90.25

Descriptive statistics

Standard deviation2754.8952
Coefficient of variation (CV)9.6094908
Kurtosis1383.8021
Mean286.68482
Median Absolute Deviation (MAD)29
Skewness34.563194
Sum642174
Variance7589447.3
MonotonicityNot monotonic
2024-03-16T13:19:47.957449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 169
 
7.1%
7 50
 
2.1%
10 44
 
1.8%
8 40
 
1.7%
11 39
 
1.6%
28 36
 
1.5%
12 35
 
1.5%
4 34
 
1.4%
19 34
 
1.4%
20 31
 
1.3%
Other values (465) 1728
72.2%
(Missing) 152
 
6.4%
ValueCountFrequency (%)
0 169
7.1%
1 7
 
0.3%
2 9
 
0.4%
3 21
 
0.9%
4 34
 
1.4%
5 26
 
1.1%
6 29
 
1.2%
7 50
 
2.1%
8 40
 
1.7%
9 29
 
1.2%
ValueCountFrequency (%)
115354 1
< 0.1%
39482 1
< 0.1%
27624 1
< 0.1%
15292 1
< 0.1%
14511 1
< 0.1%
11613 1
< 0.1%
10045 1
< 0.1%
10006 1
< 0.1%
8647 1
< 0.1%
7828 1
< 0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct474
Distinct (%)21.2%
Missing160
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean265.53002
Minimum0
Maximum105099
Zeros151
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-16T13:19:48.198916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113
median35
Q398
95-th percentile782.45
Maximum105099
Range105099
Interquartile range (IQR)85

Descriptive statistics

Standard deviation2506.6127
Coefficient of variation (CV)9.4400351
Kurtosis1392.3719
Mean265.53002
Median Absolute Deviation (MAD)27
Skewness34.63324
Sum592663
Variance6283107.2
MonotonicityNot monotonic
2024-03-16T13:19:48.481487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 151
 
6.3%
11 52
 
2.2%
13 45
 
1.9%
10 40
 
1.7%
16 39
 
1.6%
4 39
 
1.6%
14 37
 
1.5%
9 37
 
1.5%
17 36
 
1.5%
20 34
 
1.4%
Other values (464) 1722
72.0%
(Missing) 160
 
6.7%
ValueCountFrequency (%)
0 151
6.3%
1 8
 
0.3%
2 18
 
0.8%
3 32
 
1.3%
4 39
 
1.6%
5 32
 
1.3%
6 33
 
1.4%
7 29
 
1.2%
8 30
 
1.3%
9 37
 
1.5%
ValueCountFrequency (%)
105099 1
< 0.1%
34231 1
< 0.1%
24411 1
< 0.1%
15217 1
< 0.1%
12425 1
< 0.1%
11197 1
< 0.1%
9383 1
< 0.1%
8966 1
< 0.1%
8589 1
< 0.1%
7465 1
< 0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct520
Distinct (%)23.3%
Missing160
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean322.3181
Minimum0
Maximum131111
Zeros162
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-16T13:19:48.736521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114.75
median40
Q3122
95-th percentile897.35
Maximum131111
Range131111
Interquartile range (IQR)107.25

Descriptive statistics

Standard deviation3106.7038
Coefficient of variation (CV)9.6386266
Kurtosis1426.9232
Mean322.3181
Median Absolute Deviation (MAD)32
Skewness35.157235
Sum719414
Variance9651608.7
MonotonicityNot monotonic
2024-03-16T13:19:48.963878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 162
 
6.8%
9 45
 
1.9%
13 41
 
1.7%
14 40
 
1.7%
19 32
 
1.3%
12 32
 
1.3%
10 31
 
1.3%
18 31
 
1.3%
11 31
 
1.3%
17 30
 
1.3%
Other values (510) 1757
73.5%
(Missing) 160
 
6.7%
ValueCountFrequency (%)
0 162
6.8%
1 10
 
0.4%
2 13
 
0.5%
3 20
 
0.8%
4 26
 
1.1%
5 29
 
1.2%
6 29
 
1.2%
7 23
 
1.0%
8 26
 
1.1%
9 45
 
1.9%
ValueCountFrequency (%)
131111 1
< 0.1%
39883 1
< 0.1%
33048 1
< 0.1%
15566 1
< 0.1%
13580 1
< 0.1%
11997 1
< 0.1%
11135 1
< 0.1%
11060 1
< 0.1%
10827 1
< 0.1%
10375 1
< 0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct508
Distinct (%)22.8%
Missing160
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean294.15547
Minimum0
Maximum109796
Zeros155
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-16T13:19:49.173663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117
median46
Q3126
95-th percentile806.05
Maximum109796
Range109796
Interquartile range (IQR)109

Descriptive statistics

Standard deviation2621.6653
Coefficient of variation (CV)8.912516
Kurtosis1380.4393
Mean294.15547
Median Absolute Deviation (MAD)36
Skewness34.293972
Sum656555
Variance6873129
MonotonicityNot monotonic
2024-03-16T13:19:49.328112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 155
 
6.5%
12 40
 
1.7%
14 34
 
1.4%
11 33
 
1.4%
7 31
 
1.3%
27 30
 
1.3%
17 29
 
1.2%
10 29
 
1.2%
30 29
 
1.2%
24 28
 
1.2%
Other values (498) 1794
75.0%
(Missing) 160
 
6.7%
ValueCountFrequency (%)
0 155
6.5%
1 6
 
0.3%
2 8
 
0.3%
3 16
 
0.7%
4 24
 
1.0%
5 28
 
1.2%
6 27
 
1.1%
7 31
 
1.3%
8 24
 
1.0%
9 25
 
1.0%
ValueCountFrequency (%)
109796 1
< 0.1%
32384 1
< 0.1%
25131 1
< 0.1%
16773 1
< 0.1%
13933 1
< 0.1%
12530 1
< 0.1%
11549 1
< 0.1%
11422 1
< 0.1%
10608 1
< 0.1%
9829 1
< 0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct423
Distinct (%)19.0%
Missing160
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean184.30645
Minimum0
Maximum65101
Zeros172
Zeros (%)7.2%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-16T13:19:49.489362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113
median31
Q384
95-th percentile508.7
Maximum65101
Range65101
Interquartile range (IQR)71

Descriptive statistics

Standard deviation1568.5325
Coefficient of variation (CV)8.5104591
Kurtosis1330.798
Mean184.30645
Median Absolute Deviation (MAD)24
Skewness33.462438
Sum411372
Variance2460294.3
MonotonicityNot monotonic
2024-03-16T13:19:49.726631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 172
 
7.2%
13 51
 
2.1%
18 48
 
2.0%
7 45
 
1.9%
11 42
 
1.8%
12 42
 
1.8%
10 38
 
1.6%
8 38
 
1.6%
24 36
 
1.5%
25 34
 
1.4%
Other values (413) 1686
70.5%
(Missing) 160
 
6.7%
ValueCountFrequency (%)
0 172
7.2%
1 11
 
0.5%
2 17
 
0.7%
3 29
 
1.2%
4 33
 
1.4%
5 24
 
1.0%
6 34
 
1.4%
7 45
 
1.9%
8 38
 
1.6%
9 28
 
1.2%
ValueCountFrequency (%)
65101 1
< 0.1%
18349 1
< 0.1%
15542 1
< 0.1%
11754 1
< 0.1%
9018 1
< 0.1%
8882 1
< 0.1%
7980 1
< 0.1%
7266 1
< 0.1%
5965 1
< 0.1%
5710 1
< 0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct384
Distinct (%)17.1%
Missing152
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean158.92812
Minimum0
Maximum62760
Zeros196
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size21.2 KiB
2024-03-16T13:19:49.901337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18.75
median22
Q364
95-th percentile443
Maximum62760
Range62760
Interquartile range (IQR)55.25

Descriptive statistics

Standard deviation1476.3186
Coefficient of variation (CV)9.2892219
Kurtosis1458.019
Mean158.92812
Median Absolute Deviation (MAD)17
Skewness35.476686
Sum355999
Variance2179516.7
MonotonicityNot monotonic
2024-03-16T13:19:50.094572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 196
 
8.2%
6 59
 
2.5%
4 58
 
2.4%
7 57
 
2.4%
8 56
 
2.3%
14 54
 
2.3%
5 52
 
2.2%
11 51
 
2.1%
16 51
 
2.1%
10 50
 
2.1%
Other values (374) 1556
65.1%
(Missing) 152
 
6.4%
ValueCountFrequency (%)
0 196
8.2%
1 13
 
0.5%
2 29
 
1.2%
3 40
 
1.7%
4 58
 
2.4%
5 52
 
2.2%
6 59
 
2.5%
7 57
 
2.4%
8 56
 
2.3%
9 49
 
2.0%
ValueCountFrequency (%)
62760 1
< 0.1%
17546 1
< 0.1%
13845 1
< 0.1%
8212 1
< 0.1%
7269 1
< 0.1%
6887 1
< 0.1%
6103 1
< 0.1%
5720 1
< 0.1%
5597 1
< 0.1%
4865 1
< 0.1%

Interactions

2024-03-16T13:19:37.520562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:39.161806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:42.494130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:46.388038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:51.359249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:54.630384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:58.744361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:02.704821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:05.893808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:09.081114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:12.506807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:16.620208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:19.463839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:23.082524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:26.369252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:29.099608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:31.717418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:34.791784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:37.616813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:39.412729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:42.646979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:46.697846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:51.539174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:54.842841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:58.933844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:02.866478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:06.036859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:09.240538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:12.651566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:16.805808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:19.568028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:23.228407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:26.492462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:29.216976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:31.861762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:34.937955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:37.749052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:39.610434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:42.830721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:46.997813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:51.691374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:55.066132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:59.145755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:03.039378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:06.229716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:09.412367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:12.808400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:17.034663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:19.734311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:23.395953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:26.630348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:29.389956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:32.024033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:35.112509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:37.901518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:39.791921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:43.005952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:47.257774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:51.890234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:55.339640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:59.327760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:03.211150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:06.413810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:09.564515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:13.061287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:17.230619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:20.064533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:23.627186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:26.761374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:29.547856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:32.164604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:35.333558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:38.056760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:39.988142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:43.148176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:47.470169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:52.110349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:55.783789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:59.546009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:03.411373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:06.565323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:09.814029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:14.002456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:17.375619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:20.509551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:23.883869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:26.890754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:29.710363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:32.279099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:35.456484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:38.207519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:40.322899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:43.372565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:47.717634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:52.277799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:56.005644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:59.726731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:03.691012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:06.751353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:10.070951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:14.178024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:17.542381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:20.898806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:24.045466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:27.054043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:29.889101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:32.424736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:35.603169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:38.361696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:40.459770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:43.550691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:47.999852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:52.454486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:56.253266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:59.986155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:03.925103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:07.056939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:10.222908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:14.349319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:17.694191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:21.081463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:24.293972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:27.224129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:30.029749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:32.567429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:35.779299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:38.522395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:40.632601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:43.711873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:48.241742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:52.617902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:56.449656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:00.192052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:04.084002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:07.216475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:10.348420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:14.554253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:17.838924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:21.211391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:24.410338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:27.372327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:30.165892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:32.757991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:35.924484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:38.691124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:40.823005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:43.905028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:48.537510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:52.811379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:56.692905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:00.424539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:04.240743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:07.379968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:10.583941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:14.773647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:17.969805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:21.393690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:24.534513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:27.606484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:30.298246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:33.031565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:36.097455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:38.847928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:40.996101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:44.096250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:48.747175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:52.972993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:56.855096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:00.625527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:04.396597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:07.603923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:10.823693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:14.941742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:18.119645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:21.630771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:25.017137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:27.733342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:30.444972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:33.148728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:36.240262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:38.986092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:41.147171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:44.314625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:48.933453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:53.210748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:57.000398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:00.841476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:04.611149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:07.794399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:11.047725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:15.085702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:18.273523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:21.764849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:25.158829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:27.884655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:30.607958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:33.287622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:36.367507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:39.121686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:41.316209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:44.606912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:49.583469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:53.466395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:57.217255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:01.022839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:04.811153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:07.942463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:11.219882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:15.213511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:18.437989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:21.934071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:25.319844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:27.990298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:30.739645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:33.414815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:36.520457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:39.266245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:41.502055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:44.837953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:49.807917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:53.646556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:57.436729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:01.221604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:04.958781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:08.078825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:11.407042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:15.431898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:18.575505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:22.088272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:25.474064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:28.174822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:30.868395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:33.541013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:36.675342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:39.425628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:41.661650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:45.058824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:50.031602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:53.803343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:57.713278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:01.402388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:05.099773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:08.209929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:11.654743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:15.605101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:18.708143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:22.231578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:25.624433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:28.378806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:31.028471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:33.673836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:36.809816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:39.610869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:41.850987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:45.247123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:50.265108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:53.937842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:57.897746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:01.938376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:05.265431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:08.327054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:11.818892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:15.750734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:18.859734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:22.380447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:25.749648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:28.512530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:31.176239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:33.811164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:36.935044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:39.770289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:42.024393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:45.513327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:50.510007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:54.102102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:58.124792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:02.075613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:05.427953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:08.553419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:11.967697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:16.013029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:19.003293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:22.543693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:25.924812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:28.637944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:31.307132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:33.999901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:37.081081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:39.897936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:42.177249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:45.893940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:50.819465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:54.280331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:58.292296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:02.302092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:05.614760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:08.715615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:12.122207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:16.280555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:19.207027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:22.716181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:26.081346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:28.789076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:31.423884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:34.114680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:37.237162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:40.061560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:42.337064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:46.151180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:51.056222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:54.455834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:58.496617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:02.566842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:05.742590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:08.878093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:12.347036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:16.437052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:19.358024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:22.910786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:26.236332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:28.967282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:31.553757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:34.634360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:37.411390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-16T13:19:50.202088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9830.9880.9910.9880.9880.8130.9830.9830.9750.9250.9880.9760.9760.9830.9250.9010.897
20070.9831.0001.0000.9991.0001.0000.9211.0001.0000.9951.0001.0000.9990.9990.9971.0000.9240.919
20080.9881.0001.0001.0000.9991.0000.9131.0001.0000.9960.9441.0000.9960.9960.9980.9440.9010.886
20090.9910.9991.0001.0001.0001.0000.9130.9990.9990.9940.9241.0000.9960.9960.9970.9240.8900.878
20100.9881.0000.9991.0001.0000.9990.9191.0001.0000.9930.9490.9990.9980.9980.9960.9490.9110.907
20110.9881.0001.0001.0000.9991.0000.9131.0001.0000.9960.9441.0000.9960.9960.9980.9440.9010.886
20120.8130.9210.9130.9130.9190.9131.0000.9210.9210.8920.9390.9130.9260.9260.9130.9390.9390.948
20130.9831.0001.0000.9991.0001.0000.9211.0001.0000.9951.0001.0000.9990.9990.9971.0000.9240.919
20140.9831.0001.0000.9991.0001.0000.9211.0001.0000.9951.0001.0000.9990.9990.9971.0000.9240.919
20150.9750.9950.9960.9940.9930.9960.8920.9950.9951.0000.9060.9960.9900.9900.9890.9060.8700.867
20160.9251.0000.9440.9240.9490.9440.9391.0001.0000.9061.0000.9440.9190.9190.8951.0000.9980.997
20170.9881.0001.0001.0000.9991.0000.9131.0001.0000.9960.9441.0000.9960.9960.9980.9440.9010.886
20180.9760.9990.9960.9960.9980.9960.9260.9990.9990.9900.9190.9961.0001.0000.9990.9190.8781.000
20190.9760.9990.9960.9960.9980.9960.9260.9990.9990.9900.9190.9961.0001.0000.9990.9190.8781.000
20200.9830.9970.9980.9970.9960.9980.9130.9970.9970.9890.8950.9980.9990.9991.0000.8950.8550.927
20210.9251.0000.9440.9240.9490.9440.9391.0001.0000.9061.0000.9440.9190.9190.8951.0000.9980.997
20220.9010.9240.9010.8900.9110.9010.9390.9240.9240.8700.9980.9010.8780.8780.8550.9981.0000.991
20230.8970.9190.8860.8780.9070.8860.9480.9190.9190.8670.9970.8861.0001.0000.9270.9970.9911.000
2024-03-16T13:19:50.382160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.8900.8760.8680.8490.8610.8540.8530.8590.8730.8590.8820.8680.8480.8700.8580.8240.799
20070.8901.0000.8910.8710.8670.8620.8670.8600.8620.8690.8720.8770.8730.8600.8650.8670.8290.815
20080.8760.8911.0000.8860.8630.8730.8640.8620.8700.8770.8730.8770.8620.8610.8660.8670.8390.810
20090.8680.8710.8861.0000.8870.8780.8720.8620.8710.8810.8750.8860.8720.8520.8650.8640.8460.829
20100.8490.8670.8630.8871.0000.8840.8700.8680.8610.8690.8610.8740.8530.8410.8600.8520.8260.812
20110.8610.8620.8730.8780.8841.0000.8950.8790.8840.8730.8700.8900.8670.8430.8630.8500.8280.820
20120.8540.8670.8640.8720.8700.8951.0000.8770.8920.8750.8700.8730.8720.8490.8730.8690.8420.821
20130.8530.8600.8620.8620.8680.8790.8771.0000.8960.8810.8620.8730.8750.8600.8630.8670.8420.810
20140.8590.8620.8700.8710.8610.8840.8920.8961.0000.8840.8840.8880.8660.8480.8670.8650.8420.817
20150.8730.8690.8770.8810.8690.8730.8750.8810.8841.0000.9040.9020.8830.8660.8820.8760.8560.825
20160.8590.8720.8730.8750.8610.8700.8700.8620.8840.9041.0000.8970.8850.8710.8850.8770.8480.811
20170.8820.8770.8770.8860.8740.8900.8730.8730.8880.9020.8971.0000.9000.8770.8950.8890.8630.849
20180.8680.8730.8620.8720.8530.8670.8720.8750.8660.8830.8850.9001.0000.8900.8980.8900.8600.835
20190.8480.8600.8610.8520.8410.8430.8490.8600.8480.8660.8710.8770.8901.0000.8900.8890.8620.849
20200.8700.8650.8660.8650.8600.8630.8730.8630.8670.8820.8850.8950.8980.8901.0000.9110.8670.848
20210.8580.8670.8670.8640.8520.8500.8690.8670.8650.8760.8770.8890.8900.8890.9111.0000.8980.862
20220.8240.8290.8390.8460.8260.8280.8420.8420.8420.8560.8480.8630.8600.8620.8670.8981.0000.874
20230.7990.8150.8100.8290.8120.8200.8210.8100.8170.8250.8110.8490.8350.8490.8480.8620.8741.000

Missing values

2024-03-16T13:19:40.286721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-16T13:19:40.515823image/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.
2024-03-16T13:19:40.838721image/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_거래규모200620072008200920102011201220132014201520162017201820192020202120222023
0전국_100㎡이하856586683868165647155931669720569917028186136102815992201165921153541050991311111097966510162760
1전국_101~200㎡313362336824008280722691330643240192385127960322992934028592276242441133048251311554213845
2전국_201~300㎡666052465028470446565887475050076320799675966954598852126473645344193039
3전국_301~500㎡93688343780280148131107478958984312079150681381612622100458966108271142279805597
4전국_501~1,000㎡9001840375037411762095178660912410946135751263311835100069383111351253090186103
5전국_1,001~3,000㎡10864995683897834816094279889103531071012480121431201811613111971358016773117548212
6전국_3,001~10,000㎡7132636451254951525055386342642562967149679868906384663580041060872665720
7전국_10,000㎡초과699013027510456339189625110432692989826241151637577061451115217119971393388826887
8서울_100㎡이하18088112341071498096630818063068338110501523815411168821529212425155661154959657269
9서울_101~200㎡649137693437383829003023243631084385584162255400485435594259298513061568
지역_거래규모200620072008200920102011201220132014201520162017201820192020202120222023
2382제주 제주시_3,001~10,000㎡253230314656656495498144706628716470
2383제주 제주시_10,000㎡초과3660250001037025150161833745116
2384제주 서귀포시_100㎡이하46108122110121163163222285524538469416293293368284204
2385제주 서귀포시_101~200㎡2047353845504766119117112101117719912611671
2386제주 서귀포시_201~300㎡71220171417222428414132362320292017
2387제주 서귀포시_301~500㎡111817281925243245694642353427402824
2388제주 서귀포시_501~1,000㎡101810231927273041465641311918293525
2389제주 서귀포시_1,001~3,000㎡92311231425183225433926251713192528
2390제주 서귀포시_3,001~10,000㎡4011410327441141301936491221
2391제주 서귀포시_10,000㎡초과17012180003405600641728044180