Overview

Dataset statistics

Number of variables19
Number of observations1794
Missing cells2190
Missing cells (%)6.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory298.0 KiB
Average record size in memory170.1 B

Variable types

Text1
Numeric18

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 부동산거래현황 중 토지 거래현황의 연도별 지목별(필지수) 데이터입니다.- (단위 : 필지수)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068088/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 132 (7.4%) missing valuesMissing
2007 has 162 (9.0%) missing valuesMissing
2008 has 150 (8.4%) missing valuesMissing
2009 has 150 (8.4%) missing valuesMissing
2010 has 114 (6.4%) missing valuesMissing
2011 has 132 (7.4%) missing valuesMissing
2012 has 120 (6.7%) missing valuesMissing
2013 has 120 (6.7%) missing valuesMissing
2014 has 78 (4.3%) missing valuesMissing
2015 has 102 (5.7%) missing valuesMissing
2016 has 102 (5.7%) missing valuesMissing
2017 has 120 (6.7%) missing valuesMissing
2018 has 114 (6.4%) missing valuesMissing
2019 has 120 (6.7%) missing valuesMissing
2020 has 120 (6.7%) missing valuesMissing
2021 has 120 (6.7%) missing valuesMissing
2022 has 120 (6.7%) missing valuesMissing
2023 has 114 (6.4%) missing valuesMissing
2006 is highly skewed (γ1 = 31.38119882)Skewed
2007 is highly skewed (γ1 = 30.98298762)Skewed
2008 is highly skewed (γ1 = 31.22302837)Skewed
2009 is highly skewed (γ1 = 31.22084904)Skewed
2010 is highly skewed (γ1 = 32.03017052)Skewed
2011 is highly skewed (γ1 = 32.78551313)Skewed
2012 is highly skewed (γ1 = 32.37645103)Skewed
2013 is highly skewed (γ1 = 32.8302015)Skewed
2014 is highly skewed (γ1 = 33.40731646)Skewed
2015 is highly skewed (γ1 = 33.10965476)Skewed
2016 is highly skewed (γ1 = 32.68479298)Skewed
2017 is highly skewed (γ1 = 32.3985582)Skewed
2018 is highly skewed (γ1 = 32.09610697)Skewed
2019 is highly skewed (γ1 = 32.00534463)Skewed
2020 is highly skewed (γ1 = 32.69889311)Skewed
2021 is highly skewed (γ1 = 31.79602357)Skewed
2022 is highly skewed (γ1 = 30.62049352)Skewed
2023 is highly skewed (γ1 = 31.7645438)Skewed
지역_지목 has unique valuesUnique
2006 has 56 (3.1%) zerosZeros
2007 has 52 (2.9%) zerosZeros
2008 has 52 (2.9%) zerosZeros
2009 has 53 (3.0%) zerosZeros
2010 has 55 (3.1%) zerosZeros
2011 has 57 (3.2%) zerosZeros
2012 has 52 (2.9%) zerosZeros
2013 has 56 (3.1%) zerosZeros
2014 has 47 (2.6%) zerosZeros
2015 has 46 (2.6%) zerosZeros
2016 has 48 (2.7%) zerosZeros
2017 has 49 (2.7%) zerosZeros
2018 has 46 (2.6%) zerosZeros
2019 has 46 (2.6%) zerosZeros
2020 has 47 (2.6%) zerosZeros
2021 has 47 (2.6%) zerosZeros
2022 has 56 (3.1%) zerosZeros
2023 has 54 (3.0%) zerosZeros

Reproduction

Analysis started2024-04-06 08:01:35.517852
Analysis finished2024-04-06 08:02:54.022846
Duration1 minute and 18.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역_지목
Text

UNIQUE 

Distinct1794
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size14.1 KiB
2024-04-06T17:02:54.321627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length9.0680045
Min length4

Characters and Unicode

Total characters16268
Distinct characters151
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1794 ?
Unique (%)100.0%

Sample

1st row전국_전
2nd row전국_답
3rd row전국_대지
4th row전국_임야
5th row전국_공장
ValueCountFrequency (%)
경기 312
 
8.4%
경남 156
 
4.2%
서울 150
 
4.1%
경북 150
 
4.1%
전남 132
 
3.6%
충북 114
 
3.1%
충남 114
 
3.1%
강원 108
 
2.9%
부산 96
 
2.6%
전북 96
 
2.6%
Other values (1659) 2268
61.4%
2024-04-06T17:02:55.027940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1902
 
11.7%
_ 1794
 
11.0%
846
 
5.2%
732
 
4.5%
654
 
4.0%
635
 
3.9%
599
 
3.7%
558
 
3.4%
534
 
3.3%
432
 
2.7%
Other values (141) 7582
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12344
75.9%
Space Separator 1902
 
11.7%
Connector Punctuation 1794
 
11.0%
Open Punctuation 114
 
0.7%
Close Punctuation 114
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
846
 
6.9%
732
 
5.9%
654
 
5.3%
635
 
5.1%
599
 
4.9%
558
 
4.5%
534
 
4.3%
432
 
3.5%
419
 
3.4%
329
 
2.7%
Other values (137) 6606
53.5%
Space Separator
ValueCountFrequency (%)
1902
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1794
100.0%
Open Punctuation
ValueCountFrequency (%)
( 114
100.0%
Close Punctuation
ValueCountFrequency (%)
) 114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12344
75.9%
Common 3924
 
24.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
846
 
6.9%
732
 
5.9%
654
 
5.3%
635
 
5.1%
599
 
4.9%
558
 
4.5%
534
 
4.3%
432
 
3.5%
419
 
3.4%
329
 
2.7%
Other values (137) 6606
53.5%
Common
ValueCountFrequency (%)
1902
48.5%
_ 1794
45.7%
( 114
 
2.9%
) 114
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12344
75.9%
ASCII 3924
 
24.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1902
48.5%
_ 1794
45.7%
( 114
 
2.9%
) 114
 
2.9%
Hangul
ValueCountFrequency (%)
846
 
6.9%
732
 
5.9%
654
 
5.3%
635
 
5.1%
599
 
4.9%
558
 
4.5%
534
 
4.3%
432
 
3.5%
419
 
3.4%
329
 
2.7%
Other values (137) 6606
53.5%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1079
Distinct (%)64.9%
Missing132
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean4978.9952
Minimum0
Maximum1794137
Zeros56
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-04-06T17:02:55.268000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q191
median496.5
Q31686
95-th percentile16074.85
Maximum1794137
Range1794137
Interquartile range (IQR)1595

Descriptive statistics

Standard deviation48582.774
Coefficient of variation (CV)9.7575459
Kurtosis1119.5488
Mean4978.9952
Median Absolute Deviation (MAD)475.5
Skewness31.381199
Sum8275090
Variance2.3602859 × 109
MonotonicityNot monotonic
2024-04-06T17:02:55.587363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 56
 
3.1%
1 21
 
1.2%
4 16
 
0.9%
7 13
 
0.7%
35 12
 
0.7%
3 11
 
0.6%
9 11
 
0.6%
2 11
 
0.6%
13 9
 
0.5%
18 9
 
0.5%
Other values (1069) 1493
83.2%
(Missing) 132
 
7.4%
ValueCountFrequency (%)
0 56
3.1%
1 21
 
1.2%
2 11
 
0.6%
3 11
 
0.6%
4 16
 
0.9%
5 8
 
0.4%
6 6
 
0.3%
7 13
 
0.7%
8 7
 
0.4%
9 11
 
0.6%
ValueCountFrequency (%)
1794137 1
0.1%
519227 1
0.1%
388010 1
0.1%
276511 1
0.1%
237775 1
0.1%
232396 1
0.1%
113465 1
0.1%
101084 1
0.1%
94453 1
0.1%
84461 1
0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1061
Distinct (%)65.0%
Missing162
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean4268.1085
Minimum0
Maximum1447013
Zeros52
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-04-06T17:02:55.881249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q186
median482.5
Q31635.75
95-th percentile11868.15
Maximum1447013
Range1447013
Interquartile range (IQR)1549.75

Descriptive statistics

Standard deviation39512.108
Coefficient of variation (CV)9.257522
Kurtosis1099.0549
Mean4268.1085
Median Absolute Deviation (MAD)462.5
Skewness30.982988
Sum6965553
Variance1.5612067 × 109
MonotonicityNot monotonic
2024-04-06T17:02:56.181040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 52
 
2.9%
3 19
 
1.1%
2 15
 
0.8%
1 12
 
0.7%
11 12
 
0.7%
6 11
 
0.6%
4 11
 
0.6%
7 8
 
0.4%
18 8
 
0.4%
8 8
 
0.4%
Other values (1051) 1476
82.3%
(Missing) 162
 
9.0%
ValueCountFrequency (%)
0 52
2.9%
1 12
 
0.7%
2 15
 
0.8%
3 19
 
1.1%
4 11
 
0.6%
5 7
 
0.4%
6 11
 
0.6%
7 8
 
0.4%
8 8
 
0.4%
9 5
 
0.3%
ValueCountFrequency (%)
1447013 1
0.1%
371079 1
0.1%
272271 1
0.1%
262997 1
0.1%
221017 1
0.1%
213036 1
0.1%
127663 1
0.1%
87372 1
0.1%
85658 1
0.1%
81565 1
0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1062
Distinct (%)64.6%
Missing150
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean4323.3601
Minimum0
Maximum1458583
Zeros52
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-04-06T17:02:56.480752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q186.25
median473
Q31780.25
95-th percentile12008.5
Maximum1458583
Range1458583
Interquartile range (IQR)1694

Descriptive statistics

Standard deviation39583.03
Coefficient of variation (CV)9.1556172
Kurtosis1116.8603
Mean4323.3601
Median Absolute Deviation (MAD)455.5
Skewness31.223028
Sum7107604
Variance1.5668163 × 109
MonotonicityNot monotonic
2024-04-06T17:02:56.761952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 52
 
2.9%
2 16
 
0.9%
1 14
 
0.8%
4 13
 
0.7%
3 12
 
0.7%
5 12
 
0.7%
6 11
 
0.6%
7 10
 
0.6%
19 8
 
0.4%
17 8
 
0.4%
Other values (1052) 1488
82.9%
(Missing) 150
 
8.4%
ValueCountFrequency (%)
0 52
2.9%
1 14
 
0.8%
2 16
 
0.9%
3 12
 
0.7%
4 13
 
0.7%
5 12
 
0.7%
6 11
 
0.6%
7 10
 
0.6%
8 7
 
0.4%
9 7
 
0.4%
ValueCountFrequency (%)
1458583 1
0.1%
339716 1
0.1%
283253 1
0.1%
253145 1
0.1%
228824 1
0.1%
219333 1
0.1%
129402 1
0.1%
100046 1
0.1%
92998 1
0.1%
82590 1
0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1077
Distinct (%)65.5%
Missing150
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean4241.9167
Minimum0
Maximum1421613
Zeros53
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-04-06T17:02:57.065219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q184
median501
Q31749.5
95-th percentile11732.35
Maximum1421613
Range1421613
Interquartile range (IQR)1665.5

Descriptive statistics

Standard deviation38589.37
Coefficient of variation (CV)9.0971541
Kurtosis1116.09
Mean4241.9167
Median Absolute Deviation (MAD)482
Skewness31.220849
Sum6973711
Variance1.4891394 × 109
MonotonicityNot monotonic
2024-04-06T17:02:57.387870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 53
 
3.0%
4 15
 
0.8%
1 14
 
0.8%
2 13
 
0.7%
3 11
 
0.6%
16 11
 
0.6%
11 10
 
0.6%
7 10
 
0.6%
12 9
 
0.5%
27 8
 
0.4%
Other values (1067) 1490
83.1%
(Missing) 150
 
8.4%
ValueCountFrequency (%)
0 53
3.0%
1 14
 
0.8%
2 13
 
0.7%
3 11
 
0.6%
4 15
 
0.8%
5 7
 
0.4%
6 8
 
0.4%
7 10
 
0.6%
8 8
 
0.4%
9 7
 
0.4%
ValueCountFrequency (%)
1421613 1
0.1%
349923 1
0.1%
273523 1
0.1%
228083 1
0.1%
222244 1
0.1%
213683 1
0.1%
106432 1
0.1%
98809 1
0.1%
92591 1
0.1%
79331 1
0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1078
Distinct (%)64.2%
Missing114
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean3860.3411
Minimum0
Maximum1322543
Zeros55
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-04-06T17:02:58.888793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q176.75
median466.5
Q31626.5
95-th percentile11258.85
Maximum1322543
Range1322543
Interquartile range (IQR)1549.75

Descriptive statistics

Standard deviation35282.035
Coefficient of variation (CV)9.1396159
Kurtosis1169.4143
Mean3860.3411
Median Absolute Deviation (MAD)447.5
Skewness32.030171
Sum6485373
Variance1.244822 × 109
MonotonicityNot monotonic
2024-04-06T17:02:59.779577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 55
 
3.1%
1 15
 
0.8%
10 13
 
0.7%
2 12
 
0.7%
5 12
 
0.7%
8 12
 
0.7%
3 10
 
0.6%
6 10
 
0.6%
9 10
 
0.6%
33 8
 
0.4%
Other values (1068) 1523
84.9%
(Missing) 114
 
6.4%
ValueCountFrequency (%)
0 55
3.1%
1 15
 
0.8%
2 12
 
0.7%
3 10
 
0.6%
4 6
 
0.3%
5 12
 
0.7%
6 10
 
0.6%
7 7
 
0.4%
8 12
 
0.7%
9 10
 
0.6%
ValueCountFrequency (%)
1322543 1
0.1%
298550 1
0.1%
252267 1
0.1%
214477 1
0.1%
191326 1
0.1%
159171 1
0.1%
120307 1
0.1%
111537 1
0.1%
75669 1
0.1%
75276 1
0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1082
Distinct (%)65.1%
Missing132
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean4399.8971
Minimum0
Maximum1556710
Zeros57
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-04-06T17:03:00.081043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q185
median512.5
Q31734.25
95-th percentile12775.6
Maximum1556710
Range1556710
Interquartile range (IQR)1649.25

Descriptive statistics

Standard deviation41268.459
Coefficient of variation (CV)9.3794145
Kurtosis1210.9174
Mean4399.8971
Median Absolute Deviation (MAD)489
Skewness32.785513
Sum7312629
Variance1.7030857 × 109
MonotonicityNot monotonic
2024-04-06T17:03:00.348549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 57
 
3.2%
4 13
 
0.7%
5 13
 
0.7%
1 12
 
0.7%
3 12
 
0.7%
6 11
 
0.6%
7 10
 
0.6%
14 10
 
0.6%
2 9
 
0.5%
9 9
 
0.5%
Other values (1072) 1506
83.9%
(Missing) 132
 
7.4%
ValueCountFrequency (%)
0 57
3.2%
1 12
 
0.7%
2 9
 
0.5%
3 12
 
0.7%
4 13
 
0.7%
5 13
 
0.7%
6 11
 
0.6%
7 10
 
0.6%
8 4
 
0.2%
9 9
 
0.5%
ValueCountFrequency (%)
1556710 1
0.1%
331135 1
0.1%
254516 1
0.1%
216752 1
0.1%
201615 1
0.1%
187587 1
0.1%
134825 1
0.1%
116506 1
0.1%
97469 1
0.1%
94121 1
0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1080
Distinct (%)64.5%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean3825.2718
Minimum0
Maximum1310444
Zeros52
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-04-06T17:03:00.654552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q185
median477
Q31638.75
95-th percentile10563.65
Maximum1310444
Range1310444
Interquartile range (IQR)1553.75

Descriptive statistics

Standard deviation34832.624
Coefficient of variation (CV)9.1059213
Kurtosis1189.6559
Mean3825.2718
Median Absolute Deviation (MAD)457
Skewness32.376451
Sum6403505
Variance1.2133117 × 109
MonotonicityNot monotonic
2024-04-06T17:03:00.988579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 52
 
2.9%
5 13
 
0.7%
1 13
 
0.7%
3 11
 
0.6%
4 11
 
0.6%
6 11
 
0.6%
2 11
 
0.6%
31 9
 
0.5%
15 9
 
0.5%
21 8
 
0.4%
Other values (1070) 1526
85.1%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 52
2.9%
1 13
 
0.7%
2 11
 
0.6%
3 11
 
0.6%
4 11
 
0.6%
5 13
 
0.7%
6 11
 
0.6%
7 7
 
0.4%
8 6
 
0.3%
9 8
 
0.4%
ValueCountFrequency (%)
1310444 1
0.1%
272135 1
0.1%
243377 1
0.1%
210416 1
0.1%
179177 1
0.1%
153035 1
0.1%
106508 1
0.1%
97852 1
0.1%
85692 1
0.1%
81949 1
0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1087
Distinct (%)64.9%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean4205.3996
Minimum0
Maximum1495429
Zeros56
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-04-06T17:03:01.367809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q189.25
median513.5
Q31651
95-th percentile12335.05
Maximum1495429
Range1495429
Interquartile range (IQR)1561.75

Descriptive statistics

Standard deviation39545.151
Coefficient of variation (CV)9.4034229
Kurtosis1214.6752
Mean4205.3996
Median Absolute Deviation (MAD)488.5
Skewness32.830201
Sum7039839
Variance1.563819 × 109
MonotonicityNot monotonic
2024-04-06T17:03:01.652141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 56
 
3.1%
8 20
 
1.1%
4 11
 
0.6%
3 10
 
0.6%
1 10
 
0.6%
2 10
 
0.6%
26 9
 
0.5%
15 9
 
0.5%
31 8
 
0.4%
5 8
 
0.4%
Other values (1077) 1523
84.9%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 56
3.1%
1 10
 
0.6%
2 10
 
0.6%
3 10
 
0.6%
4 11
 
0.6%
5 8
 
0.4%
6 7
 
0.4%
7 6
 
0.3%
8 20
 
1.1%
9 6
 
0.3%
ValueCountFrequency (%)
1495429 1
0.1%
324967 1
0.1%
252141 1
0.1%
214880 1
0.1%
193609 1
0.1%
170067 1
0.1%
129501 1
0.1%
122111 1
0.1%
92508 1
0.1%
90072 1
0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1134
Distinct (%)66.1%
Missing78
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean4861.7197
Minimum0
Maximum1793146
Zeros47
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-04-06T17:03:01.919095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q1112
median561
Q31821.5
95-th percentile15152
Maximum1793146
Range1793146
Interquartile range (IQR)1709.5

Descriptive statistics

Standard deviation46758.525
Coefficient of variation (CV)9.6176924
Kurtosis1253.8139
Mean4861.7197
Median Absolute Deviation (MAD)532
Skewness33.407316
Sum8342711
Variance2.1863596 × 109
MonotonicityNot monotonic
2024-04-06T17:03:02.215816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47
 
2.6%
4 13
 
0.7%
8 13
 
0.7%
1 13
 
0.7%
6 11
 
0.6%
2 11
 
0.6%
3 9
 
0.5%
26 8
 
0.4%
89 8
 
0.4%
14 8
 
0.4%
Other values (1124) 1575
87.8%
(Missing) 78
 
4.3%
ValueCountFrequency (%)
0 47
2.6%
1 13
 
0.7%
2 11
 
0.6%
3 9
 
0.5%
4 13
 
0.7%
5 4
 
0.2%
6 11
 
0.6%
7 7
 
0.4%
8 13
 
0.7%
9 7
 
0.4%
ValueCountFrequency (%)
1793146 1
0.1%
402035 1
0.1%
280835 1
0.1%
253762 1
0.1%
246594 1
0.1%
189476 1
0.1%
152810 1
0.1%
144966 1
0.1%
108853 1
0.1%
103122 1
0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1167
Distinct (%)69.0%
Missing102
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean5757.3558
Minimum0
Maximum2141968
Zeros46
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-04-06T17:03:02.490477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q1129.75
median657
Q32030
95-th percentile17124.1
Maximum2141968
Range2141968
Interquartile range (IQR)1900.25

Descriptive statistics

Standard deviation56339.038
Coefficient of variation (CV)9.7855753
Kurtosis1229.7726
Mean5757.3558
Median Absolute Deviation (MAD)616
Skewness33.109655
Sum9741446
Variance3.1740872 × 109
MonotonicityNot monotonic
2024-04-06T17:03:02.898237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46
 
2.6%
5 13
 
0.7%
13 12
 
0.7%
1 11
 
0.6%
6 10
 
0.6%
8 9
 
0.5%
2 8
 
0.4%
11 7
 
0.4%
14 7
 
0.4%
9 7
 
0.4%
Other values (1157) 1562
87.1%
(Missing) 102
 
5.7%
ValueCountFrequency (%)
0 46
2.6%
1 11
 
0.6%
2 8
 
0.4%
3 6
 
0.3%
4 6
 
0.3%
5 13
 
0.7%
6 10
 
0.6%
7 6
 
0.3%
8 9
 
0.5%
9 7
 
0.4%
ValueCountFrequency (%)
2141968 1
0.1%
525007 1
0.1%
345304 1
0.1%
303182 1
0.1%
271869 1
0.1%
218864 1
0.1%
188317 1
0.1%
151975 1
0.1%
128441 1
0.1%
124370 1
0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1155
Distinct (%)68.3%
Missing102
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean5568.948
Minimum0
Maximum2059638
Zeros48
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-04-06T17:03:03.190698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q1132
median651
Q31984.25
95-th percentile16895.5
Maximum2059638
Range2059638
Interquartile range (IQR)1852.25

Descriptive statistics

Standard deviation54498.801
Coefficient of variation (CV)9.7861931
Kurtosis1202.5293
Mean5568.948
Median Absolute Deviation (MAD)617
Skewness32.684793
Sum9422660
Variance2.9701193 × 109
MonotonicityNot monotonic
2024-04-06T17:03:03.427919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48
 
2.7%
6 12
 
0.7%
1 10
 
0.6%
7 10
 
0.6%
9 10
 
0.6%
15 10
 
0.6%
4 9
 
0.5%
14 9
 
0.5%
2 8
 
0.4%
12 8
 
0.4%
Other values (1145) 1558
86.8%
(Missing) 102
 
5.7%
ValueCountFrequency (%)
0 48
2.7%
1 10
 
0.6%
2 8
 
0.4%
3 7
 
0.4%
4 9
 
0.5%
5 6
 
0.3%
6 12
 
0.7%
7 10
 
0.6%
8 5
 
0.3%
9 10
 
0.6%
ValueCountFrequency (%)
2059638 1
0.1%
549903 1
0.1%
355517 1
0.1%
291476 1
0.1%
267868 1
0.1%
223194 1
0.1%
162452 1
0.1%
134105 1
0.1%
133054 1
0.1%
123488 1
0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1186
Distinct (%)70.8%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean6230.73
Minimum0
Maximum2286664
Zeros49
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-04-06T17:03:03.658553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q1150.25
median763.5
Q32200.5
95-th percentile18062.75
Maximum2286664
Range2286664
Interquartile range (IQR)2050.25

Descriptive statistics

Standard deviation60957.922
Coefficient of variation (CV)9.7834318
Kurtosis1181.2208
Mean6230.73
Median Absolute Deviation (MAD)713
Skewness32.398558
Sum10430242
Variance3.7158682 × 109
MonotonicityNot monotonic
2024-04-06T17:03:03.907970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49
 
2.7%
5 13
 
0.7%
3 12
 
0.7%
4 11
 
0.6%
6 9
 
0.5%
23 8
 
0.4%
12 7
 
0.4%
18 7
 
0.4%
13 7
 
0.4%
14 7
 
0.4%
Other values (1176) 1544
86.1%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 49
2.7%
1 5
 
0.3%
2 5
 
0.3%
3 12
 
0.7%
4 11
 
0.6%
5 13
 
0.7%
6 9
 
0.5%
7 6
 
0.3%
8 3
 
0.2%
9 4
 
0.2%
ValueCountFrequency (%)
2286664 1
0.1%
652990 1
0.1%
373877 1
0.1%
311862 1
0.1%
283057 1
0.1%
256573 1
0.1%
166797 1
0.1%
162467 1
0.1%
143747 1
0.1%
141274 1
0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1180
Distinct (%)70.2%
Missing114
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean5976.6179
Minimum0
Maximum2200584
Zeros46
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-04-06T17:03:04.190961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q1143.5
median716
Q32090.5
95-th percentile17678.9
Maximum2200584
Range2200584
Interquartile range (IQR)1947

Descriptive statistics

Standard deviation58938.977
Coefficient of variation (CV)9.8615937
Kurtosis1159.2979
Mean5976.6179
Median Absolute Deviation (MAD)671
Skewness32.096107
Sum10040718
Variance3.473803 × 109
MonotonicityNot monotonic
2024-04-06T17:03:04.460823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46
 
2.6%
1 14
 
0.8%
12 13
 
0.7%
2 12
 
0.7%
7 10
 
0.6%
23 9
 
0.5%
4 8
 
0.4%
5 8
 
0.4%
6 8
 
0.4%
3 8
 
0.4%
Other values (1170) 1544
86.1%
(Missing) 114
 
6.4%
ValueCountFrequency (%)
0 46
2.6%
1 14
 
0.8%
2 12
 
0.7%
3 8
 
0.4%
4 8
 
0.4%
5 8
 
0.4%
6 8
 
0.4%
7 10
 
0.6%
8 6
 
0.3%
9 7
 
0.4%
ValueCountFrequency (%)
2200584 1
0.1%
704984 1
0.1%
346250 1
0.1%
297624 1
0.1%
258417 1
0.1%
254710 1
0.1%
149679 1
0.1%
138451 1
0.1%
129940 1
0.1%
103528 1
0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1174
Distinct (%)70.1%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean5463.0645
Minimum0
Maximum1955942
Zeros46
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-04-06T17:03:04.744292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q1150
median705
Q32009
95-th percentile15792.7
Maximum1955942
Range1955942
Interquartile range (IQR)1859

Descriptive statistics

Standard deviation52426.31
Coefficient of variation (CV)9.5965021
Kurtosis1157.3706
Mean5463.0645
Median Absolute Deviation (MAD)656.5
Skewness32.005345
Sum9145170
Variance2.748518 × 109
MonotonicityNot monotonic
2024-04-06T17:03:05.016578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46
 
2.6%
1 16
 
0.9%
4 14
 
0.8%
7 12
 
0.7%
16 10
 
0.6%
12 9
 
0.5%
11 9
 
0.5%
8 8
 
0.4%
5 7
 
0.4%
10 7
 
0.4%
Other values (1164) 1536
85.6%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 46
2.6%
1 16
 
0.9%
2 6
 
0.3%
3 5
 
0.3%
4 14
 
0.8%
5 7
 
0.4%
6 4
 
0.2%
7 12
 
0.7%
8 8
 
0.4%
9 5
 
0.3%
ValueCountFrequency (%)
1955942 1
0.1%
594168 1
0.1%
283303 1
0.1%
282194 1
0.1%
243774 1
0.1%
237891 1
0.1%
156318 1
0.1%
145514 1
0.1%
136450 1
0.1%
102855 1
0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1203
Distinct (%)71.9%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean6608.0042
Minimum0
Maximum2474441
Zeros47
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-04-06T17:03:05.355618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q1158.25
median760.5
Q32275.5
95-th percentile19507.4
Maximum2474441
Range2474441
Interquartile range (IQR)2117.25

Descriptive statistics

Standard deviation65749.389
Coefficient of variation (CV)9.9499617
Kurtosis1197.2568
Mean6608.0042
Median Absolute Deviation (MAD)710.5
Skewness32.698893
Sum11061799
Variance4.3229821 × 109
MonotonicityNot monotonic
2024-04-06T17:03:05.649833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47
 
2.6%
2 13
 
0.7%
7 11
 
0.6%
4 10
 
0.6%
1 8
 
0.4%
3 8
 
0.4%
6 7
 
0.4%
13 7
 
0.4%
10 7
 
0.4%
9 7
 
0.4%
Other values (1193) 1549
86.3%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 47
2.6%
1 8
 
0.4%
2 13
 
0.7%
3 8
 
0.4%
4 10
 
0.6%
5 6
 
0.3%
6 7
 
0.4%
7 11
 
0.6%
8 6
 
0.3%
9 7
 
0.4%
ValueCountFrequency (%)
2474441 1
0.1%
724422 1
0.1%
356209 1
0.1%
318488 1
0.1%
269778 1
0.1%
237680 1
0.1%
197755 1
0.1%
196413 1
0.1%
166393 1
0.1%
130865 1
0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1219
Distinct (%)72.8%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean6198.8501
Minimum0
Maximum2140023
Zeros47
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-04-06T17:03:06.037788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q1170
median815
Q32535.25
95-th percentile17398.75
Maximum2140023
Range2140023
Interquartile range (IQR)2365.25

Descriptive statistics

Standard deviation57436.59
Coefficient of variation (CV)9.2656847
Kurtosis1149.1697
Mean6198.8501
Median Absolute Deviation (MAD)768
Skewness31.796024
Sum10376875
Variance3.2989619 × 109
MonotonicityNot monotonic
2024-04-06T17:03:06.789153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47
 
2.6%
2 16
 
0.9%
3 13
 
0.7%
17 9
 
0.5%
8 8
 
0.4%
5 8
 
0.4%
9 8
 
0.4%
6 8
 
0.4%
1 6
 
0.3%
47 6
 
0.3%
Other values (1209) 1545
86.1%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 47
2.6%
1 6
 
0.3%
2 16
 
0.9%
3 13
 
0.7%
4 4
 
0.2%
5 8
 
0.4%
6 8
 
0.4%
7 1
 
0.1%
8 8
 
0.4%
9 8
 
0.4%
ValueCountFrequency (%)
2140023 1
0.1%
613874 1
0.1%
344819 1
0.1%
317562 1
0.1%
282023 1
0.1%
250391 1
0.1%
192475 1
0.1%
180379 1
0.1%
136343 1
0.1%
130348 1
0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1141
Distinct (%)68.2%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean4139.6481
Minimum0
Maximum1312664
Zeros56
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-04-06T17:03:07.081720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q1121.25
median658
Q31958.75
95-th percentile11595.95
Maximum1312664
Range1312664
Interquartile range (IQR)1837.5

Descriptive statistics

Standard deviation35723.974
Coefficient of variation (CV)8.6297127
Kurtosis1086.1856
Mean4139.6481
Median Absolute Deviation (MAD)620
Skewness30.620494
Sum6929771
Variance1.2762023 × 109
MonotonicityNot monotonic
2024-04-06T17:03:07.366384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 56
 
3.1%
1 14
 
0.8%
2 13
 
0.7%
7 12
 
0.7%
9 10
 
0.6%
6 9
 
0.5%
17 9
 
0.5%
12 8
 
0.4%
8 8
 
0.4%
28 7
 
0.4%
Other values (1131) 1528
85.2%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 56
3.1%
1 14
 
0.8%
2 13
 
0.7%
3 5
 
0.3%
4 4
 
0.2%
5 5
 
0.3%
6 9
 
0.5%
7 12
 
0.7%
8 8
 
0.4%
9 10
 
0.6%
ValueCountFrequency (%)
1312664 1
0.1%
355090 1
0.1%
253405 1
0.1%
247173 1
0.1%
200770 1
0.1%
160188 1
0.1%
156949 1
0.1%
102803 1
0.1%
88105 1
0.1%
85392 1
0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1080
Distinct (%)64.3%
Missing114
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean3415.6232
Minimum0
Maximum1147941
Zeros54
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-04-06T17:03:07.797975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q188
median482.5
Q31488.5
95-th percentile8852.4
Maximum1147941
Range1147941
Interquartile range (IQR)1400.5

Descriptive statistics

Standard deviation30745.619
Coefficient of variation (CV)9.0014669
Kurtosis1152.3359
Mean3415.6232
Median Absolute Deviation (MAD)458.5
Skewness31.764544
Sum5738247
Variance9.4529311 × 108
MonotonicityNot monotonic
2024-04-06T17:03:08.345616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 54
 
3.0%
1 21
 
1.2%
4 16
 
0.9%
2 12
 
0.7%
3 12
 
0.7%
5 11
 
0.6%
7 11
 
0.6%
13 10
 
0.6%
11 10
 
0.6%
12 9
 
0.5%
Other values (1070) 1514
84.4%
(Missing) 114
 
6.4%
ValueCountFrequency (%)
0 54
3.0%
1 21
 
1.2%
2 12
 
0.7%
3 12
 
0.7%
4 16
 
0.9%
5 11
 
0.6%
6 7
 
0.4%
7 11
 
0.6%
8 8
 
0.4%
9 6
 
0.3%
ValueCountFrequency (%)
1147941 1
0.1%
299697 1
0.1%
192933 1
0.1%
171549 1
0.1%
158436 1
0.1%
156577 1
0.1%
121538 1
0.1%
81172 1
0.1%
76155 1
0.1%
71103 1
0.1%

Interactions

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2024-04-06T17:01:39.812918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-04-06T17:02:34.143674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:38.748908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:42.670022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:46.836915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:51.521661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:42.172103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:46.311751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:49.857669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:53.959454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:57.980745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:02.592231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:06.613398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:10.651052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:14.100514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:17.662168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:22.053018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:25.788610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:29.810147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:34.345222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:38.905950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:42.840286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:47.090800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:51.685057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:42.366081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:46.483905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:50.012407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:54.239505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:58.215121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:02.785356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:06.820080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:10.844369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:14.297987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:17.848307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:22.202278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:25.964537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:30.010355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:34.545135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:39.201013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:42.999026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:47.321976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:51.851643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:42.530400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:46.678411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:50.203787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:54.466975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:58.444500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:02.974002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:06.992050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:11.015842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:14.514102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:18.007766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:22.373281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:26.148886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:30.249792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:34.816302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:39.436796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:43.159132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:47.504604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:52.047168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:42.740237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:46.872586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:50.453348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:54.730756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:58.750109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:03.185878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:07.196817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:11.209746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:14.825538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:18.221644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:22.556594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:26.348365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:30.468622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:35.128419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:39.659658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:43.476515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:47.737331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:52.180292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:42.945173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:47.056215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:50.657509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:54.932742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:58.948173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:03.426468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:07.386485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:11.381579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:15.011558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:18.402768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:22.721366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:26.528902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:30.784328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:35.838374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:39.877914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:43.789800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:48.007593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:52.335080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:43.138814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:47.266740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:50.827491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:55.112371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:59.216750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:03.625172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:07.542565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:11.541025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:15.188617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:18.614362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:22.864853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:26.705007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:31.081784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:36.111127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:40.051711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:43.965087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:48.217524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:52.510628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:43.335479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:47.513623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:51.038119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:55.301415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:59.521260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:03.864076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:07.729212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:11.721631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:15.383370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:18.854352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:23.035126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:26.887075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:31.297754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:36.368641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:40.289036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:44.182705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:48.407272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:03:08.575105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9940.9940.9940.9940.9940.9940.9940.9940.9940.9940.9940.9940.9940.9860.9940.9890.989
20070.9941.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9991.0000.9990.999
20080.9941.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9991.0000.9990.999
20090.9941.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9991.0000.9990.999
20100.9941.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9991.0000.9990.999
20110.9941.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9991.0000.9990.999
20120.9941.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9991.0000.9990.999
20130.9941.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9991.0000.9990.999
20140.9941.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9991.0000.9990.999
20150.9941.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9991.0000.9990.999
20160.9941.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9991.0000.9990.999
20170.9941.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9991.0000.9990.999
20180.9941.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9991.0000.9990.999
20190.9941.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9991.0000.9990.999
20200.9860.9990.9990.9990.9990.9990.9990.9990.9990.9990.9990.9990.9990.9991.0000.9990.9970.997
20210.9941.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9991.0000.9990.999
20220.9890.9990.9990.9990.9990.9990.9990.9990.9990.9990.9990.9990.9990.9990.9970.9991.0001.000
20230.9890.9990.9990.9990.9990.9990.9990.9990.9990.9990.9990.9990.9990.9990.9970.9991.0001.000
2024-04-06T17:03:08.883233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9790.9710.9630.9620.9640.9620.9600.9590.9600.9560.9460.9410.9290.9420.9420.9410.931
20070.9791.0000.9850.9710.9700.9710.9680.9670.9630.9630.9610.9500.9440.9330.9420.9460.9460.937
20080.9710.9851.0000.9740.9690.9740.9700.9670.9620.9600.9570.9440.9380.9270.9360.9370.9390.928
20090.9630.9710.9741.0000.9800.9760.9710.9680.9620.9630.9590.9500.9440.9350.9430.9440.9440.934
20100.9620.9700.9690.9801.0000.9810.9740.9690.9640.9640.9600.9500.9450.9350.9420.9450.9460.937
20110.9640.9710.9740.9760.9811.0000.9850.9800.9730.9700.9660.9550.9470.9380.9430.9460.9460.940
20120.9620.9680.9700.9710.9740.9851.0000.9840.9770.9740.9690.9560.9490.9400.9460.9470.9480.942
20130.9600.9670.9670.9680.9690.9800.9841.0000.9810.9770.9720.9590.9530.9420.9480.9460.9440.939
20140.9590.9630.9620.9620.9640.9730.9770.9811.0000.9840.9760.9620.9550.9420.9490.9470.9460.937
20150.9600.9630.9600.9630.9640.9700.9740.9770.9841.0000.9830.9700.9620.9470.9560.9540.9500.940
20160.9560.9610.9570.9590.9600.9660.9690.9720.9760.9831.0000.9790.9680.9530.9610.9610.9550.947
20170.9460.9500.9440.9500.9500.9550.9560.9590.9620.9700.9791.0000.9740.9590.9620.9590.9540.949
20180.9410.9440.9380.9440.9450.9470.9490.9530.9550.9620.9680.9741.0000.9660.9670.9570.9530.949
20190.9290.9330.9270.9350.9350.9380.9400.9420.9420.9470.9530.9590.9661.0000.9670.9520.9470.944
20200.9420.9420.9360.9430.9420.9430.9460.9480.9490.9560.9610.9620.9670.9671.0000.9680.9570.953
20210.9420.9460.9370.9440.9450.9460.9470.9460.9470.9540.9610.9590.9570.9520.9681.0000.9660.961
20220.9410.9460.9390.9440.9460.9460.9480.9440.9460.9500.9550.9540.9530.9470.9570.9661.0000.962
20230.9310.9370.9280.9340.9370.9400.9420.9390.9370.9400.9470.9490.9490.9440.9530.9610.9621.000

Missing values

2024-04-06T17:02:52.790186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:02:53.240076image/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.
2024-04-06T17:02:53.630190image/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_지목200620072008200920102011201220132014201520162017201820192020202120222023
0전국_전232396221017228824228083214477216752210416214880246594271869267868283057258417237891269778317562247173171549
1전국_답276511262997283253273523252267254516243377252141280835303182291476311862297624282194318488344819253405192933
2전국_대지179413714470131458583142161313225431556710131044414954291793146214196820596382286664220058419559422474441214002313126641147941
3전국_임야237775213036219333213683191326201615179177170067189476218864223194256573254710243774237680250391200770158436
4전국_공장179841800116710143711546917072195991939024718262762949532898359183649539333513523497533331
5전국_기타8446181565825907933175175825348194990072108853124370123488143747138451145514166393192475160188121538
6서울_전68551665422191827129182266581219281412158078813521819831612376
7서울_답459322371344315257834303135207896881037102610979421389386283
8서울_대지388010272271253145222244159171187587153035193609253762345304355517373877346250283303356209282023156949156577
9서울_임야12761954125917811241118311031391146219891689190927322965259019549891143
지역_지목200620072008200920102011201220132014201520162017201820192020202120222023
1784제주 제주시_대지49298174745886871253012779131831259215733179261894822655194681376814286175191514210321
1785제주 제주시_임야277934013091282526893500332141265795708572365935521940943512402736072487
1786제주 제주시_공장163119284429221847484325201919293317
1787제주 제주시_기타195427112296204322512595315452225263538152024592359331573050355633801966
1788제주 서귀포시_전174534892317247824732558331948426487793863364763476932402866340827881688
1789제주 서귀포시_답18934317843513813510615229527830417118811510620319250
1790제주 서귀포시_대지132328342686291232423631364847296146101021063510856983162665436705056175047
1791제주 서귀포시_임야178829912310206824102437328335284979842583906015393730442420234820731490
1792제주 서귀포시_공장1917242519131825323092611101318711
1793제주 서귀포시_기타144628141985205817442773275532364534609853084028364234742564291428871843