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 순수토지 거래현황의 연도별 지목별(면적) 데이터입니다.- (단위 : 천㎡)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068329/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 = 30.76572527)Skewed
2007 is highly skewed (γ1 = 30.06823139)Skewed
2008 is highly skewed (γ1 = 29.22802169)Skewed
2009 is highly skewed (γ1 = 29.56306927)Skewed
2010 is highly skewed (γ1 = 29.59716108)Skewed
2011 is highly skewed (γ1 = 28.98859887)Skewed
2012 is highly skewed (γ1 = 28.71631624)Skewed
2013 is highly skewed (γ1 = 28.25180632)Skewed
2014 is highly skewed (γ1 = 28.14145165)Skewed
2015 is highly skewed (γ1 = 28.1935003)Skewed
2016 is highly skewed (γ1 = 27.65921345)Skewed
2017 is highly skewed (γ1 = 28.43432482)Skewed
2018 is highly skewed (γ1 = 28.104904)Skewed
2019 is highly skewed (γ1 = 27.51405572)Skewed
2020 is highly skewed (γ1 = 26.26809482)Skewed
2021 is highly skewed (γ1 = 26.50467222)Skewed
2022 is highly skewed (γ1 = 28.1145584)Skewed
2023 is highly skewed (γ1 = 28.87227768)Skewed
지역_지목 has unique valuesUnique
2006 has 102 (5.7%) zerosZeros
2007 has 90 (5.0%) zerosZeros
2008 has 95 (5.3%) zerosZeros
2009 has 91 (5.1%) zerosZeros
2010 has 100 (5.6%) zerosZeros
2011 has 100 (5.6%) zerosZeros
2012 has 103 (5.7%) zerosZeros
2013 has 90 (5.0%) zerosZeros
2014 has 93 (5.2%) zerosZeros
2015 has 85 (4.7%) zerosZeros
2016 has 84 (4.7%) zerosZeros
2017 has 89 (5.0%) zerosZeros
2018 has 95 (5.3%) zerosZeros
2019 has 90 (5.0%) zerosZeros
2020 has 91 (5.1%) zerosZeros
2021 has 103 (5.7%) zerosZeros
2022 has 99 (5.5%) zerosZeros
2023 has 115 (6.4%) zerosZeros

Reproduction

Analysis started2024-03-23 06:10:02.603783
Analysis finished2024-03-23 06:12:11.526071
Duration2 minutes and 8.92 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
2024-03-23T06:12:12.137561image/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%
2024-03-23T06:12:13.282819image/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 

Distinct1062
Distinct (%)63.9%
Missing132
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean5437.352
Minimum0
Maximum1917271
Zeros102
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T06:12:13.974940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q173
median400.5
Q31928.5
95-th percentile14963.6
Maximum1917271
Range1917271
Interquartile range (IQR)1855.5

Descriptive statistics

Standard deviation52161.548
Coefficient of variation (CV)9.5931894
Kurtosis1094.9102
Mean5437.352
Median Absolute Deviation (MAD)391.5
Skewness30.765725
Sum9036879
Variance2.720827 × 109
MonotonicityNot monotonic
2024-03-23T06:12:14.563011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 102
 
5.7%
1 22
 
1.2%
6 16
 
0.9%
5 12
 
0.7%
13 12
 
0.7%
14 10
 
0.6%
7 10
 
0.6%
9 9
 
0.5%
8 9
 
0.5%
4 9
 
0.5%
Other values (1052) 1451
80.9%
(Missing) 132
 
7.4%
ValueCountFrequency (%)
0 102
5.7%
1 22
 
1.2%
2 8
 
0.4%
3 7
 
0.4%
4 9
 
0.5%
5 12
 
0.7%
6 16
 
0.9%
7 10
 
0.6%
8 9
 
0.5%
9 9
 
0.5%
ValueCountFrequency (%)
1917271 1
0.1%
469423 1
0.1%
359543 1
0.1%
306683 1
0.1%
306391 1
0.1%
295170 1
0.1%
255285 1
0.1%
177358 1
0.1%
154905 1
0.1%
141732 1
0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1000
Distinct (%)61.3%
Missing162
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean4459.625
Minimum0
Maximum1514145
Zeros90
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T06:12:15.058028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q156.75
median324.5
Q31664.5
95-th percentile12184.5
Maximum1514145
Range1514145
Interquartile range (IQR)1607.75

Descriptive statistics

Standard deviation41829.305
Coefficient of variation (CV)9.3795565
Kurtosis1050.1024
Mean4459.625
Median Absolute Deviation (MAD)317.5
Skewness30.068231
Sum7278108
Variance1.7496907 × 109
MonotonicityNot monotonic
2024-03-23T06:12:15.545984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 90
 
5.0%
1 25
 
1.4%
3 23
 
1.3%
2 17
 
0.9%
4 15
 
0.8%
6 13
 
0.7%
7 12
 
0.7%
24 12
 
0.7%
8 9
 
0.5%
5 8
 
0.4%
Other values (990) 1408
78.5%
(Missing) 162
 
9.0%
ValueCountFrequency (%)
0 90
5.0%
1 25
 
1.4%
2 17
 
0.9%
3 23
 
1.3%
4 15
 
0.8%
5 8
 
0.4%
6 13
 
0.7%
7 12
 
0.7%
8 9
 
0.5%
9 2
 
0.1%
ValueCountFrequency (%)
1514145 1
0.1%
401547 1
0.1%
312280 1
0.1%
256097 1
0.1%
219143 1
0.1%
213885 1
0.1%
202727 1
0.1%
157522 1
0.1%
117854 1
0.1%
107383 1
0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct973
Distinct (%)59.2%
Missing150
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean4048.1545
Minimum0
Maximum1328592
Zeros95
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T06:12:16.129370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q148
median285
Q31570
95-th percentile10826.35
Maximum1328592
Range1328592
Interquartile range (IQR)1522

Descriptive statistics

Standard deviation37127.714
Coefficient of variation (CV)9.1715161
Kurtosis1000.2598
Mean4048.1545
Median Absolute Deviation (MAD)280
Skewness29.228022
Sum6655166
Variance1.3784672 × 109
MonotonicityNot monotonic
2024-03-23T06:12:16.600237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 95
 
5.3%
1 20
 
1.1%
2 18
 
1.0%
3 17
 
0.9%
4 15
 
0.8%
6 14
 
0.8%
8 12
 
0.7%
11 12
 
0.7%
5 11
 
0.6%
9 11
 
0.6%
Other values (963) 1419
79.1%
(Missing) 150
 
8.4%
ValueCountFrequency (%)
0 95
5.3%
1 20
 
1.1%
2 18
 
1.0%
3 17
 
0.9%
4 15
 
0.8%
5 11
 
0.6%
6 14
 
0.8%
7 8
 
0.4%
8 12
 
0.7%
9 11
 
0.6%
ValueCountFrequency (%)
1328592 1
0.1%
417959 1
0.1%
288439 1
0.1%
247951 1
0.1%
180432 1
0.1%
178023 1
0.1%
169622 1
0.1%
140492 1
0.1%
98186 1
0.1%
98084 1
0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct987
Distinct (%)60.0%
Missing150
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean3975.6046
Minimum0
Maximum1319531
Zeros91
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T06:12:17.221546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q146
median288.5
Q31535
95-th percentile10530.65
Maximum1319531
Range1319531
Interquartile range (IQR)1489

Descriptive statistics

Standard deviation36646.992
Coefficient of variation (CV)9.2179669
Kurtosis1022.7074
Mean3975.6046
Median Absolute Deviation (MAD)283.5
Skewness29.563069
Sum6535894
Variance1.343002 × 109
MonotonicityNot monotonic
2024-03-23T06:12:18.141421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 91
 
5.1%
1 22
 
1.2%
3 22
 
1.2%
2 18
 
1.0%
4 16
 
0.9%
7 13
 
0.7%
9 11
 
0.6%
8 10
 
0.6%
10 10
 
0.6%
40 10
 
0.6%
Other values (977) 1421
79.2%
(Missing) 150
 
8.4%
ValueCountFrequency (%)
0 91
5.1%
1 22
 
1.2%
2 18
 
1.0%
3 22
 
1.2%
4 16
 
0.9%
5 8
 
0.4%
6 8
 
0.4%
7 13
 
0.7%
8 10
 
0.6%
9 11
 
0.6%
ValueCountFrequency (%)
1319531 1
0.1%
394873 1
0.1%
247540 1
0.1%
234035 1
0.1%
182507 1
0.1%
167663 1
0.1%
161769 1
0.1%
147655 1
0.1%
139927 1
0.1%
120116 1
0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct972
Distinct (%)57.9%
Missing114
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean3380.9935
Minimum0
Maximum1116184
Zeros100
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T06:12:19.063421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q140
median258
Q31350.5
95-th percentile9578.8
Maximum1116184
Range1116184
Interquartile range (IQR)1310.5

Descriptive statistics

Standard deviation30812.55
Coefficient of variation (CV)9.1134603
Kurtosis1026.9886
Mean3380.9935
Median Absolute Deviation (MAD)253
Skewness29.597161
Sum5680069
Variance9.4941321 × 108
MonotonicityNot monotonic
2024-03-23T06:12:19.769976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 100
 
5.6%
1 22
 
1.2%
4 19
 
1.1%
3 18
 
1.0%
2 16
 
0.9%
6 16
 
0.9%
5 14
 
0.8%
10 12
 
0.7%
11 11
 
0.6%
12 11
 
0.6%
Other values (962) 1441
80.3%
(Missing) 114
 
6.4%
ValueCountFrequency (%)
0 100
5.6%
1 22
 
1.2%
2 16
 
0.9%
3 18
 
1.0%
4 19
 
1.1%
5 14
 
0.8%
6 16
 
0.9%
7 4
 
0.2%
8 8
 
0.4%
9 10
 
0.6%
ValueCountFrequency (%)
1116184 1
0.1%
346599 1
0.1%
219137 1
0.1%
218951 1
0.1%
147349 1
0.1%
142194 1
0.1%
126730 1
0.1%
124381 1
0.1%
104058 1
0.1%
100013 1
0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct972
Distinct (%)58.5%
Missing132
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean3378.0259
Minimum0
Maximum1079433
Zeros100
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T06:12:20.418573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q142
median283.5
Q31427.25
95-th percentile9309.6
Maximum1079433
Range1079433
Interquartile range (IQR)1385.25

Descriptive statistics

Standard deviation30153.903
Coefficient of variation (CV)8.9264867
Kurtosis990.92719
Mean3378.0259
Median Absolute Deviation (MAD)278.5
Skewness28.988599
Sum5614279
Variance9.0925786 × 108
MonotonicityNot monotonic
2024-03-23T06:12:21.099519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 100
 
5.6%
1 26
 
1.4%
3 19
 
1.1%
5 19
 
1.1%
6 16
 
0.9%
4 15
 
0.8%
2 14
 
0.8%
9 11
 
0.6%
24 11
 
0.6%
28 11
 
0.6%
Other values (962) 1420
79.2%
(Missing) 132
 
7.4%
ValueCountFrequency (%)
0 100
5.6%
1 26
 
1.4%
2 14
 
0.8%
3 19
 
1.1%
4 15
 
0.8%
5 19
 
1.1%
6 16
 
0.9%
7 7
 
0.4%
8 6
 
0.3%
9 11
 
0.6%
ValueCountFrequency (%)
1079433 1
0.1%
345389 1
0.1%
213977 1
0.1%
210435 1
0.1%
155667 1
0.1%
146974 1
0.1%
127816 1
0.1%
126793 1
0.1%
109570 1
0.1%
94430 1
0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct961
Distinct (%)57.4%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean3114.374
Minimum0
Maximum976722
Zeros103
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T06:12:21.715823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q141
median276.5
Q31363.75
95-th percentile8488.5
Maximum976722
Range976722
Interquartile range (IQR)1322.75

Descriptive statistics

Standard deviation27354.191
Coefficient of variation (CV)8.7832069
Kurtosis975.63831
Mean3114.374
Median Absolute Deviation (MAD)272.5
Skewness28.716316
Sum5213462
Variance7.4825176 × 108
MonotonicityNot monotonic
2024-03-23T06:12:22.485140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 103
 
5.7%
1 26
 
1.4%
2 19
 
1.1%
4 18
 
1.0%
3 14
 
0.8%
5 12
 
0.7%
8 11
 
0.6%
16 11
 
0.6%
19 11
 
0.6%
10 10
 
0.6%
Other values (951) 1439
80.2%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 103
5.7%
1 26
 
1.4%
2 19
 
1.1%
3 14
 
0.8%
4 18
 
1.0%
5 12
 
0.7%
6 10
 
0.6%
7 10
 
0.6%
8 11
 
0.6%
9 7
 
0.4%
ValueCountFrequency (%)
976722 1
0.1%
323172 1
0.1%
205851 1
0.1%
202617 1
0.1%
138080 1
0.1%
120983 1
0.1%
114233 1
0.1%
112534 1
0.1%
107385 1
0.1%
86883 1
0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct968
Distinct (%)57.8%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean3093.2575
Minimum0
Maximum940684
Zeros90
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T06:12:23.160278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q142
median286.5
Q31407.25
95-th percentile8122.95
Maximum940684
Range940684
Interquartile range (IQR)1365.25

Descriptive statistics

Standard deviation26562.168
Coefficient of variation (CV)8.5871183
Kurtosis947.3527
Mean3093.2575
Median Absolute Deviation (MAD)281.5
Skewness28.251806
Sum5178113
Variance7.0554876 × 108
MonotonicityNot monotonic
2024-03-23T06:12:23.701845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 90
 
5.0%
1 26
 
1.4%
2 24
 
1.3%
3 20
 
1.1%
9 17
 
0.9%
14 14
 
0.8%
11 13
 
0.7%
10 12
 
0.7%
4 12
 
0.7%
37 10
 
0.6%
Other values (958) 1436
80.0%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 90
5.0%
1 26
 
1.4%
2 24
 
1.3%
3 20
 
1.1%
4 12
 
0.7%
5 7
 
0.4%
6 10
 
0.6%
7 7
 
0.4%
8 9
 
0.5%
9 17
 
0.9%
ValueCountFrequency (%)
940684 1
0.1%
333355 1
0.1%
206584 1
0.1%
195217 1
0.1%
121387 1
0.1%
120228 1
0.1%
108245 1
0.1%
108136 1
0.1%
107052 1
0.1%
82771 1
0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1040
Distinct (%)60.6%
Missing78
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean3251.2378
Minimum0
Maximum985359
Zeros93
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T06:12:24.293561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q152
median340
Q31479.25
95-th percentile8353.25
Maximum985359
Range985359
Interquartile range (IQR)1427.25

Descriptive statistics

Standard deviation27683.562
Coefficient of variation (CV)8.5147764
Kurtosis944.86446
Mean3251.2378
Median Absolute Deviation (MAD)333
Skewness28.141452
Sum5579124
Variance7.6637963 × 108
MonotonicityNot monotonic
2024-03-23T06:12:24.915204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 93
 
5.2%
1 27
 
1.5%
2 20
 
1.1%
4 17
 
0.9%
6 15
 
0.8%
3 15
 
0.8%
5 15
 
0.8%
10 13
 
0.7%
11 13
 
0.7%
17 11
 
0.6%
Other values (1030) 1477
82.3%
(Missing) 78
 
4.3%
ValueCountFrequency (%)
0 93
5.2%
1 27
 
1.5%
2 20
 
1.1%
3 15
 
0.8%
4 17
 
0.9%
5 15
 
0.8%
6 15
 
0.8%
7 8
 
0.4%
8 4
 
0.2%
9 8
 
0.4%
ValueCountFrequency (%)
985359 1
0.1%
355640 1
0.1%
233252 1
0.1%
204706 1
0.1%
137278 1
0.1%
130594 1
0.1%
126191 1
0.1%
113265 1
0.1%
99114 1
0.1%
91351 1
0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1029
Distinct (%)60.8%
Missing102
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean3639.5083
Minimum0
Maximum1103089
Zeros85
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T06:12:25.392287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.55
Q160
median380
Q31600.75
95-th percentile10036.65
Maximum1103089
Range1103089
Interquartile range (IQR)1540.75

Descriptive statistics

Standard deviation31069.013
Coefficient of variation (CV)8.5365963
Kurtosis946.5406
Mean3639.5083
Median Absolute Deviation (MAD)372
Skewness28.1935
Sum6158048
Variance9.6528356 × 108
MonotonicityNot monotonic
2024-03-23T06:12:25.893780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 85
 
4.7%
1 27
 
1.5%
2 18
 
1.0%
3 16
 
0.9%
7 15
 
0.8%
18 15
 
0.8%
4 12
 
0.7%
30 12
 
0.7%
5 11
 
0.6%
12 11
 
0.6%
Other values (1019) 1470
81.9%
(Missing) 102
 
5.7%
ValueCountFrequency (%)
0 85
4.7%
1 27
 
1.5%
2 18
 
1.0%
3 16
 
0.9%
4 12
 
0.7%
5 11
 
0.6%
6 10
 
0.6%
7 15
 
0.8%
8 9
 
0.5%
9 8
 
0.4%
ValueCountFrequency (%)
1103089 1
0.1%
382225 1
0.1%
256025 1
0.1%
239456 1
0.1%
159752 1
0.1%
148446 1
0.1%
131487 1
0.1%
128522 1
0.1%
126259 1
0.1%
90943 1
0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1013
Distinct (%)59.9%
Missing102
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean3640.552
Minimum0
Maximum1070868
Zeros84
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T06:12:26.442021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q153
median344
Q31578.5
95-th percentile9976.7
Maximum1070868
Range1070868
Interquartile range (IQR)1525.5

Descriptive statistics

Standard deviation30418.738
Coefficient of variation (CV)8.355529
Kurtosis917.01115
Mean3640.552
Median Absolute Deviation (MAD)338
Skewness27.659213
Sum6159814
Variance9.2529961 × 108
MonotonicityNot monotonic
2024-03-23T06:12:27.061037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 84
 
4.7%
1 25
 
1.4%
3 24
 
1.3%
2 19
 
1.1%
6 15
 
0.8%
13 12
 
0.7%
11 12
 
0.7%
5 12
 
0.7%
8 12
 
0.7%
4 11
 
0.6%
Other values (1003) 1466
81.7%
(Missing) 102
 
5.7%
ValueCountFrequency (%)
0 84
4.7%
1 25
 
1.4%
2 19
 
1.1%
3 24
 
1.3%
4 11
 
0.6%
5 12
 
0.7%
6 15
 
0.8%
7 6
 
0.3%
8 12
 
0.7%
9 10
 
0.6%
ValueCountFrequency (%)
1070868 1
0.1%
388534 1
0.1%
240897 1
0.1%
227854 1
0.1%
180419 1
0.1%
143130 1
0.1%
130942 1
0.1%
130553 1
0.1%
127560 1
0.1%
103029 1
0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1046
Distinct (%)62.5%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean3726.5878
Minimum0
Maximum1136909
Zeros89
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T06:12:27.901190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q157
median397
Q31739
95-th percentile9813.15
Maximum1136909
Range1136909
Interquartile range (IQR)1682

Descriptive statistics

Standard deviation31971.837
Coefficient of variation (CV)8.5793865
Kurtosis960.03257
Mean3726.5878
Median Absolute Deviation (MAD)389
Skewness28.434325
Sum6238308
Variance1.0221984 × 109
MonotonicityNot monotonic
2024-03-23T06:12:28.653537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 89
 
5.0%
3 19
 
1.1%
1 19
 
1.1%
4 18
 
1.0%
10 16
 
0.9%
2 13
 
0.7%
6 12
 
0.7%
5 11
 
0.6%
7 11
 
0.6%
13 10
 
0.6%
Other values (1036) 1456
81.2%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 89
5.0%
1 19
 
1.1%
2 13
 
0.7%
3 19
 
1.1%
4 18
 
1.0%
5 11
 
0.6%
6 12
 
0.7%
7 11
 
0.6%
8 9
 
0.5%
9 6
 
0.3%
ValueCountFrequency (%)
1136909 1
0.1%
377349 1
0.1%
245636 1
0.1%
243425 1
0.1%
167347 1
0.1%
150796 1
0.1%
142611 1
0.1%
133485 1
0.1%
132000 1
0.1%
94107 1
0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1025
Distinct (%)61.0%
Missing114
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean3467.8887
Minimum0
Maximum1049899
Zeros95
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T06:12:29.160887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q151.75
median362
Q31562.5
95-th percentile9300.45
Maximum1049899
Range1049899
Interquartile range (IQR)1510.75

Descriptive statistics

Standard deviation29670.951
Coefficient of variation (CV)8.5559121
Kurtosis940.67509
Mean3467.8887
Median Absolute Deviation (MAD)355
Skewness28.104904
Sum5826053
Variance8.8036531 × 108
MonotonicityNot monotonic
2024-03-23T06:12:29.814247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 95
 
5.3%
1 22
 
1.2%
2 22
 
1.2%
3 18
 
1.0%
6 13
 
0.7%
5 12
 
0.7%
7 12
 
0.7%
11 11
 
0.6%
4 10
 
0.6%
23 10
 
0.6%
Other values (1015) 1455
81.1%
(Missing) 114
 
6.4%
ValueCountFrequency (%)
0 95
5.3%
1 22
 
1.2%
2 22
 
1.2%
3 18
 
1.0%
4 10
 
0.6%
5 12
 
0.7%
6 13
 
0.7%
7 12
 
0.7%
8 7
 
0.4%
9 8
 
0.4%
ValueCountFrequency (%)
1049899 1
0.1%
370839 1
0.1%
228850 1
0.1%
209386 1
0.1%
160551 1
0.1%
141274 1
0.1%
140567 1
0.1%
129994 1
0.1%
122667 1
0.1%
94732 1
0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1016
Distinct (%)60.7%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean3147.506
Minimum0
Maximum917231
Zeros90
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T06:12:30.356247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q152
median342.5
Q31491.25
95-th percentile7937.8
Maximum917231
Range917231
Interquartile range (IQR)1439.25

Descriptive statistics

Standard deviation26224.19
Coefficient of variation (CV)8.3317365
Kurtosis905.2235
Mean3147.506
Median Absolute Deviation (MAD)336.5
Skewness27.514056
Sum5268925
Variance6.8770817 × 108
MonotonicityNot monotonic
2024-03-23T06:12:30.761849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 90
 
5.0%
1 22
 
1.2%
2 22
 
1.2%
4 18
 
1.0%
3 17
 
0.9%
6 13
 
0.7%
10 12
 
0.7%
9 11
 
0.6%
8 11
 
0.6%
5 10
 
0.6%
Other values (1006) 1448
80.7%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 90
5.0%
1 22
 
1.2%
2 22
 
1.2%
3 17
 
0.9%
4 18
 
1.0%
5 10
 
0.6%
6 13
 
0.7%
7 4
 
0.2%
8 11
 
0.6%
9 11
 
0.6%
ValueCountFrequency (%)
917231 1
0.1%
347701 1
0.1%
208644 1
0.1%
172907 1
0.1%
133688 1
0.1%
133501 1
0.1%
120886 1
0.1%
118045 1
0.1%
103210 1
0.1%
88122 1
0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1039
Distinct (%)62.1%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean3413.4719
Minimum0
Maximum945321
Zeros91
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T06:12:31.456810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q160
median383.5
Q31643.5
95-th percentile8308.15
Maximum945321
Range945321
Interquartile range (IQR)1583.5

Descriptive statistics

Standard deviation27678.106
Coefficient of variation (CV)8.1084909
Kurtosis832.2558
Mean3413.4719
Median Absolute Deviation (MAD)374.5
Skewness26.268095
Sum5714152
Variance7.6607755 × 108
MonotonicityNot monotonic
2024-03-23T06:12:32.007329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 91
 
5.1%
1 27
 
1.5%
4 14
 
0.8%
8 13
 
0.7%
6 12
 
0.7%
2 11
 
0.6%
12 11
 
0.6%
30 10
 
0.6%
17 10
 
0.6%
3 9
 
0.5%
Other values (1029) 1466
81.7%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 91
5.1%
1 27
 
1.5%
2 11
 
0.6%
3 9
 
0.5%
4 14
 
0.8%
5 8
 
0.4%
6 12
 
0.7%
7 8
 
0.4%
8 13
 
0.7%
9 5
 
0.3%
ValueCountFrequency (%)
945321 1
0.1%
394110 1
0.1%
257024 1
0.1%
185008 1
0.1%
152070 1
0.1%
140190 1
0.1%
121950 1
0.1%
120300 1
0.1%
102229 1
0.1%
91792 1
0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1069
Distinct (%)63.9%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean3691.2569
Minimum0
Maximum1022236
Zeros103
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T06:12:32.490651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q170
median439
Q31807.75
95-th percentile9274
Maximum1022236
Range1022236
Interquartile range (IQR)1737.75

Descriptive statistics

Standard deviation29766.578
Coefficient of variation (CV)8.0640765
Kurtosis847.1882
Mean3691.2569
Median Absolute Deviation (MAD)428
Skewness26.504672
Sum6179164
Variance8.8604916 × 108
MonotonicityNot monotonic
2024-03-23T06:12:33.039065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 103
 
5.7%
1 18
 
1.0%
7 16
 
0.9%
5 12
 
0.7%
10 11
 
0.6%
4 10
 
0.6%
6 9
 
0.5%
25 9
 
0.5%
28 9
 
0.5%
13 9
 
0.5%
Other values (1059) 1468
81.8%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 103
5.7%
1 18
 
1.0%
2 7
 
0.4%
3 9
 
0.5%
4 10
 
0.6%
5 12
 
0.7%
6 9
 
0.5%
7 16
 
0.9%
8 6
 
0.3%
9 5
 
0.3%
ValueCountFrequency (%)
1022236 1
0.1%
411027 1
0.1%
273057 1
0.1%
196927 1
0.1%
178722 1
0.1%
138376 1
0.1%
130400 1
0.1%
129777 1
0.1%
109216 1
0.1%
105549 1
0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct984
Distinct (%)58.8%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean3070.3584
Minimum0
Maximum913541
Zeros99
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T06:12:33.637161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q144
median313.5
Q31410
95-th percentile7919.5
Maximum913541
Range913541
Interquartile range (IQR)1366

Descriptive statistics

Standard deviation25798.328
Coefficient of variation (CV)8.402383
Kurtosis944.10424
Mean3070.3584
Median Absolute Deviation (MAD)306.5
Skewness28.114558
Sum5139780
Variance6.655537 × 108
MonotonicityNot monotonic
2024-03-23T06:12:34.138125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 99
 
5.5%
1 25
 
1.4%
3 20
 
1.1%
8 13
 
0.7%
2 13
 
0.7%
4 12
 
0.7%
6 12
 
0.7%
10 11
 
0.6%
5 10
 
0.6%
43 10
 
0.6%
Other values (974) 1449
80.8%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 99
5.5%
1 25
 
1.4%
2 13
 
0.7%
3 20
 
1.1%
4 12
 
0.7%
5 10
 
0.6%
6 12
 
0.7%
7 9
 
0.5%
8 13
 
0.7%
9 8
 
0.4%
ValueCountFrequency (%)
913541 1
0.1%
306456 1
0.1%
207147 1
0.1%
176363 1
0.1%
131916 1
0.1%
119906 1
0.1%
117199 1
0.1%
115872 1
0.1%
103049 1
0.1%
94583 1
0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct897
Distinct (%)53.4%
Missing114
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean2303.7131
Minimum0
Maximum721886
Zeros115
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T06:12:34.620802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median223.5
Q3997.25
95-th percentile6013.55
Maximum721886
Range721886
Interquartile range (IQR)966.25

Descriptive statistics

Standard deviation20134.236
Coefficient of variation (CV)8.739906
Kurtosis987.02604
Mean2303.7131
Median Absolute Deviation (MAD)218.5
Skewness28.872278
Sum3870238
Variance4.0538745 × 108
MonotonicityNot monotonic
2024-03-23T06:12:35.182474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 115
 
6.4%
1 36
 
2.0%
2 20
 
1.1%
7 19
 
1.1%
12 16
 
0.9%
4 15
 
0.8%
5 15
 
0.8%
6 14
 
0.8%
13 13
 
0.7%
8 13
 
0.7%
Other values (887) 1404
78.3%
(Missing) 114
 
6.4%
ValueCountFrequency (%)
0 115
6.4%
1 36
 
2.0%
2 20
 
1.1%
3 12
 
0.7%
4 15
 
0.8%
5 15
 
0.8%
6 14
 
0.8%
7 19
 
1.1%
8 13
 
0.7%
9 9
 
0.5%
ValueCountFrequency (%)
721886 1
0.1%
235584 1
0.1%
145366 1
0.1%
138806 1
0.1%
101277 1
0.1%
97132 1
0.1%
83222 1
0.1%
82690 1
0.1%
81991 1
0.1%
63951 1
0.1%

Interactions

2024-03-23T06:12:02.213278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:08.872535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T06:11:20.204122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:27.111996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T06:11:38.811272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T06:11:40.962133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:46.306185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:53.521509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:59.421939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:12:07.066664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:13.565775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:21.148041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:28.563540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:35.928002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:43.225528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:49.767989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:57.089915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:03.356389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:10.380808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:17.024300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:23.691483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:30.840727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:36.362985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:41.214752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:46.634431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:53.847875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:59.730748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:12:07.351559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:13.901210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:21.842385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:28.922431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:36.389410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:43.805525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:50.163213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:57.573794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:03.647498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:10.698021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:17.293894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:23.988596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:31.149417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:36.622406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:41.473825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:46.979300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:54.176814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:12:00.249299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:12:07.777003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:14.401482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:22.228518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:29.296982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:37.002764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:44.166965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:50.532919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:57.980202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:03.975161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:11.070439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:17.785967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:24.358454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:31.485394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:36.977862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:41.745912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:47.320269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:54.578465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:12:00.673585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:12:08.103345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:14.782252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:22.574464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:29.670578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:37.491873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:44.533545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:50.914083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:58.399068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:04.331444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:11.468998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:18.102560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:24.720415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:31.806522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:37.197505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:41.978715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:47.576004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:54.838867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:12:00.975723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:12:08.364535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:15.214857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:23.038366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:29.999976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:37.905538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:44.883698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:51.277409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:58.816525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:04.622139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:11.780230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:18.416796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:25.217660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:32.458552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:37.450549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:42.217474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:47.828645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:55.055945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:12:01.265797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:12:08.564034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:15.617673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:23.407458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:30.489237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:38.363616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:45.254400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:51.662632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:59.184595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:05.036321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:12.283982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:18.777515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:25.571417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:32.737098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:37.715813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:42.478482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:48.107346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:55.398685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:12:01.537431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:12:08.883942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:15.918666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:23.787501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:30.868782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:38.717667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:45.599008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:52.087102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:10:59.454365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:05.403993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:12.715467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:19.095290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:25.896224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:32.980922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:37.967423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:42.799164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:48.435676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:11:55.836937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:12:01.838299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T06:12:35.651656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9900.9500.9991.0000.9990.9390.9390.9390.9390.9390.9390.9360.9360.9360.9260.9190.936
20070.9901.0001.0000.9910.9940.9910.9060.9060.9060.9060.9060.9060.8840.8840.8840.8730.8640.884
20080.9501.0001.0000.9521.0000.9520.9950.9950.9950.9950.9950.9950.9890.9890.9890.9870.9850.989
20090.9990.9910.9521.0001.0000.9990.9370.9370.9370.9370.9370.9370.9360.9360.9360.9260.9180.936
20101.0000.9941.0001.0001.0001.0000.9530.9530.9530.9530.9530.9530.9530.9530.9530.9390.9300.953
20110.9990.9910.9520.9991.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9550.9421.000
20120.9390.9060.9950.9370.9531.0001.0001.0001.0001.0001.0001.0000.9970.9970.9970.9960.9950.997
20130.9390.9060.9950.9370.9531.0001.0001.0001.0001.0001.0001.0000.9970.9970.9970.9960.9950.997
20140.9390.9060.9950.9370.9531.0001.0001.0001.0001.0001.0001.0000.9970.9970.9970.9960.9950.997
20150.9390.9060.9950.9370.9531.0001.0001.0001.0001.0001.0001.0000.9970.9970.9970.9960.9950.997
20160.9390.9060.9950.9370.9531.0001.0001.0001.0001.0001.0001.0000.9970.9970.9970.9960.9950.997
20170.9390.9060.9950.9370.9531.0001.0001.0001.0001.0001.0001.0000.9970.9970.9970.9960.9950.997
20180.9360.8840.9890.9360.9531.0000.9970.9970.9970.9970.9970.9971.0001.0001.0001.0001.0001.000
20190.9360.8840.9890.9360.9531.0000.9970.9970.9970.9970.9970.9971.0001.0001.0001.0001.0001.000
20200.9360.8840.9890.9360.9531.0000.9970.9970.9970.9970.9970.9971.0001.0001.0001.0001.0001.000
20210.9260.8730.9870.9260.9390.9550.9960.9960.9960.9960.9960.9961.0001.0001.0001.0001.0001.000
20220.9190.8640.9850.9180.9300.9420.9950.9950.9950.9950.9950.9951.0001.0001.0001.0001.0001.000
20230.9360.8840.9890.9360.9531.0000.9970.9970.9970.9970.9970.9971.0001.0001.0001.0001.0001.000
2024-03-23T06:12:36.282445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9460.9260.9180.9160.9230.9170.9170.9140.9250.9190.9170.9180.9110.9120.9130.9110.903
20070.9461.0000.9500.9360.9300.9340.9360.9320.9310.9370.9320.9360.9350.9290.9330.9280.9280.922
20080.9260.9501.0000.9470.9380.9400.9400.9370.9370.9450.9400.9380.9360.9300.9330.9300.9310.929
20090.9180.9360.9471.0000.9570.9540.9520.9460.9460.9500.9460.9460.9460.9380.9420.9430.9400.937
20100.9160.9300.9380.9571.0000.9550.9500.9420.9350.9380.9390.9420.9410.9310.9380.9360.9330.932
20110.9230.9340.9400.9540.9551.0000.9610.9570.9500.9500.9450.9480.9490.9380.9460.9430.9380.940
20120.9170.9360.9400.9520.9500.9611.0000.9620.9520.9540.9520.9510.9480.9400.9470.9440.9420.943
20130.9170.9320.9370.9460.9420.9570.9621.0000.9580.9570.9510.9550.9500.9410.9470.9430.9430.940
20140.9140.9310.9370.9460.9350.9500.9520.9581.0000.9620.9500.9500.9460.9400.9440.9380.9370.931
20150.9250.9370.9450.9500.9380.9500.9540.9570.9621.0000.9630.9550.9550.9470.9520.9490.9430.938
20160.9190.9320.9400.9460.9390.9450.9520.9510.9500.9631.0000.9630.9550.9500.9530.9480.9430.941
20170.9170.9360.9380.9460.9420.9480.9510.9550.9500.9550.9631.0000.9640.9580.9590.9510.9500.945
20180.9180.9350.9360.9460.9410.9490.9480.9500.9460.9550.9550.9641.0000.9640.9600.9550.9520.951
20190.9110.9290.9300.9380.9310.9380.9400.9410.9400.9470.9500.9580.9641.0000.9600.9530.9490.948
20200.9120.9330.9330.9420.9380.9460.9470.9470.9440.9520.9530.9590.9600.9601.0000.9630.9530.949
20210.9130.9280.9300.9430.9360.9430.9440.9430.9380.9490.9480.9510.9550.9530.9631.0000.9640.952
20220.9110.9280.9310.9400.9330.9380.9420.9430.9370.9430.9430.9500.9520.9490.9530.9641.0000.957
20230.9030.9220.9290.9370.9320.9400.9430.9400.9310.9380.9410.9450.9510.9480.9490.9520.9571.000

Missing values

2024-03-23T06:12:09.339470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T06:12:10.219012image/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.
2024-03-23T06:12:10.916939image/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_전295170256097247951234035219137213977205851206584233252256025240897245636228850208644257024273057207147145366
1전국_답469423401547417959394873346599345389323172333355355640382225388534377349370839347701394110411027306456235584
2전국_대지154905107383935011115529429910957012098312138713727815975218041916734714127413368815207017872213191681991
3전국_임야19172711514145132859213195311116184107943397672294068498535911030891070868113690910498999172319453211022236913541721886
4전국_공장318473598023239154451652618877155522523226103218822284024593211802169223898255072198115010
5전국_기타113787852287457875345708887681269344716189003690943103029860619473288122917921055499458363951
6서울_전29024025256664324515316528525639324722417019929917371
7서울_답3852342242511746317140167210352338182295219191447208106
8서울_대지1139447403279195922011767170524172898294829014397415959895822608474762012
9서울_임야61512612130619091757108449311116157225231958127224161776107021931470524
지역_지목200620072008200920102011201220132014201520162017201820192020202120222023
1784제주 제주시_대지67261755879212087969188441313243524292670210016121307193414901051
1785제주 제주시_임야172271566510857716710921823076601044316147212011638611293976293887817973771766759
1786제주 제주시_공장15384651402716839666635101623312012
1787제주 제주시_기타4235631554635556587555165076561984409331102125379636840313260492050802503
1788제주 서귀포시_전3587681042314864460245244883671694181015375545827486339423406411634532247
1789제주 서귀포시_답1803021663811279873112311263370142127841131348234
1790제주 서귀포시_대지367527334569839715640334712151264108617181198640371794620640
1791제주 서귀포시_임야106971014098187168683681906548949112476217981390911894672477505365519149087565
1792제주 서귀포시_공장35202348161634983039193117181113611
1793제주 서귀포시_기타305756814132617133484126432848356558863370084929516348593577331428722027