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(필지수) 데이터입니다.- (단위 : 천㎡)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068327/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
지역_지목 has unique valuesUnique
2006 has 73 (4.1%) zerosZeros
2007 has 68 (3.8%) zerosZeros
2008 has 68 (3.8%) zerosZeros
2009 has 64 (3.6%) zerosZeros
2010 has 72 (4.0%) zerosZeros
2011 has 77 (4.3%) zerosZeros
2012 has 71 (4.0%) zerosZeros
2013 has 68 (3.8%) zerosZeros
2014 has 68 (3.8%) zerosZeros
2015 has 59 (3.3%) zerosZeros
2016 has 61 (3.4%) zerosZeros
2017 has 64 (3.6%) zerosZeros
2018 has 61 (3.4%) zerosZeros
2019 has 68 (3.8%) zerosZeros
2020 has 63 (3.5%) zerosZeros
2021 has 65 (3.6%) zerosZeros
2022 has 73 (4.1%) zerosZeros
2023 has 84 (4.7%) zerosZeros

Reproduction

Analysis started2024-03-23 05:34:59.464888
Analysis finished2024-03-23 05:36:05.343763
Duration1 minute and 5.88 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
2024-03-23T14:36:05.648789image/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%
2024-03-23T14:36:06.513078image/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  ZEROS 

Distinct998
Distinct (%)60.0%
Missing132
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean1865.1528
Minimum0
Maximum273497
Zeros73
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T14:36:06.825096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q168
median375
Q31098
95-th percentile3885.4
Maximum273497
Range273497
Interquartile range (IQR)1030

Descriptive statistics

Standard deviation12234.169
Coefficient of variation (CV)6.5593386
Kurtosis332.14455
Mean1865.1528
Median Absolute Deviation (MAD)359
Skewness17.30119
Sum3099884
Variance1.4967489 × 108
MonotonicityNot monotonic
2024-03-23T14:36:07.096314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 73
 
4.1%
3 22
 
1.2%
2 21
 
1.2%
1 21
 
1.2%
10 14
 
0.8%
6 12
 
0.7%
16 11
 
0.6%
9 10
 
0.6%
5 10
 
0.6%
13 10
 
0.6%
Other values (988) 1458
81.3%
(Missing) 132
 
7.4%
ValueCountFrequency (%)
0 73
4.1%
1 21
 
1.2%
2 21
 
1.2%
3 22
 
1.2%
4 10
 
0.6%
5 10
 
0.6%
6 12
 
0.7%
7 6
 
0.3%
8 9
 
0.5%
9 10
 
0.6%
ValueCountFrequency (%)
273497 1
0.1%
235177 1
0.1%
227159 1
0.1%
194558 1
0.1%
76086 1
0.1%
57441 1
0.1%
54731 1
0.1%
44428 1
0.1%
40951 1
0.1%
40764 1
0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct949
Distinct (%)58.1%
Missing162
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean1755.0864
Minimum0
Maximum257606
Zeros68
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T14:36:07.363247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q164.75
median354.5
Q31035.5
95-th percentile3489.45
Maximum257606
Range257606
Interquartile range (IQR)970.75

Descriptive statistics

Standard deviation11388.899
Coefficient of variation (CV)6.4890816
Kurtosis329.97906
Mean1755.0864
Median Absolute Deviation (MAD)336
Skewness17.190853
Sum2864301
Variance1.2970702 × 108
MonotonicityNot monotonic
2024-03-23T14:36:07.652221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68
 
3.8%
3 20
 
1.1%
1 18
 
1.0%
6 16
 
0.9%
7 16
 
0.9%
2 14
 
0.8%
5 13
 
0.7%
8 12
 
0.7%
13 10
 
0.6%
12 10
 
0.6%
Other values (939) 1435
80.0%
(Missing) 162
 
9.0%
ValueCountFrequency (%)
0 68
3.8%
1 18
 
1.0%
2 14
 
0.8%
3 20
 
1.1%
4 10
 
0.6%
5 13
 
0.7%
6 16
 
0.9%
7 16
 
0.9%
8 12
 
0.7%
9 7
 
0.4%
ValueCountFrequency (%)
257606 1
0.1%
215528 1
0.1%
208370 1
0.1%
172794 1
0.1%
74459 1
0.1%
53665 1
0.1%
45632 1
0.1%
41805 1
0.1%
39659 1
0.1%
37537 1
0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct943
Distinct (%)57.4%
Missing150
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean1804.0414
Minimum0
Maximum275401
Zeros68
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T14:36:07.899291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.15
Q164.75
median346.5
Q31059.75
95-th percentile3580.3
Maximum275401
Range275401
Interquartile range (IQR)995

Descriptive statistics

Standard deviation11807.95
Coefficient of variation (CV)6.5452768
Kurtosis341.60582
Mean1804.0414
Median Absolute Deviation (MAD)332.5
Skewness17.452169
Sum2965844
Variance1.3942768 × 108
MonotonicityNot monotonic
2024-03-23T14:36:08.159104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68
 
3.8%
2 21
 
1.2%
1 15
 
0.8%
5 15
 
0.8%
3 15
 
0.8%
7 13
 
0.7%
4 13
 
0.7%
8 13
 
0.7%
6 12
 
0.7%
17 11
 
0.6%
Other values (933) 1448
80.7%
(Missing) 150
 
8.4%
ValueCountFrequency (%)
0 68
3.8%
1 15
 
0.8%
2 21
 
1.2%
3 15
 
0.8%
4 13
 
0.7%
5 15
 
0.8%
6 12
 
0.7%
7 13
 
0.7%
8 13
 
0.7%
9 10
 
0.6%
ValueCountFrequency (%)
275401 1
0.1%
220372 1
0.1%
215101 1
0.1%
177651 1
0.1%
74750 1
0.1%
57458 1
0.1%
46386 1
0.1%
44004 1
0.1%
38145 1
0.1%
35581 1
0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct978
Distinct (%)59.5%
Missing150
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean1812.2427
Minimum0
Maximum263083
Zeros64
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T14:36:08.855865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q158
median362
Q31100.5
95-th percentile3413.55
Maximum263083
Range263083
Interquartile range (IQR)1042.5

Descriptive statistics

Standard deviation11797.663
Coefficient of variation (CV)6.5099798
Kurtosis327.28073
Mean1812.2427
Median Absolute Deviation (MAD)348
Skewness17.201646
Sum2979327
Variance1.3918486 × 108
MonotonicityNot monotonic
2024-03-23T14:36:09.083634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 64
 
3.6%
1 22
 
1.2%
2 19
 
1.1%
3 17
 
0.9%
6 17
 
0.9%
4 14
 
0.8%
9 12
 
0.7%
13 11
 
0.6%
5 11
 
0.6%
12 10
 
0.6%
Other values (968) 1447
80.7%
(Missing) 150
 
8.4%
ValueCountFrequency (%)
0 64
3.6%
1 22
 
1.2%
2 19
 
1.1%
3 17
 
0.9%
4 14
 
0.8%
5 11
 
0.6%
6 17
 
0.9%
7 9
 
0.5%
8 6
 
0.3%
9 12
 
0.7%
ValueCountFrequency (%)
263083 1
0.1%
216158 1
0.1%
209679 1
0.1%
207508 1
0.1%
70099 1
0.1%
56156 1
0.1%
42643 1
0.1%
41480 1
0.1%
40983 1
0.1%
36702 1
0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct945
Distinct (%)56.2%
Missing114
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean1656.0756
Minimum0
Maximum242045
Zeros72
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T14:36:09.282218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q149
median321.5
Q31012
95-th percentile3092.6
Maximum242045
Range242045
Interquartile range (IQR)963

Descriptive statistics

Standard deviation10832.177
Coefficient of variation (CV)6.5408709
Kurtosis334.33922
Mean1656.0756
Median Absolute Deviation (MAD)309.5
Skewness17.394685
Sum2782207
Variance1.1733605 × 108
MonotonicityNot monotonic
2024-03-23T14:36:09.503184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 72
 
4.0%
1 20
 
1.1%
2 19
 
1.1%
15 14
 
0.8%
5 14
 
0.8%
3 13
 
0.7%
6 13
 
0.7%
7 13
 
0.7%
13 12
 
0.7%
10 11
 
0.6%
Other values (935) 1479
82.4%
(Missing) 114
 
6.4%
ValueCountFrequency (%)
0 72
4.0%
1 20
 
1.1%
2 19
 
1.1%
3 13
 
0.7%
4 8
 
0.4%
5 14
 
0.8%
6 13
 
0.7%
7 13
 
0.7%
8 9
 
0.5%
9 10
 
0.6%
ValueCountFrequency (%)
242045 1
0.1%
203178 1
0.1%
201455 1
0.1%
186023 1
0.1%
66033 1
0.1%
44785 1
0.1%
41012 1
0.1%
37608 1
0.1%
36534 1
0.1%
36483 1
0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct950
Distinct (%)57.2%
Missing132
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean1756.3237
Minimum0
Maximum242146
Zeros77
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T14:36:09.718292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q154.25
median352.5
Q31077.75
95-th percentile3419.65
Maximum242146
Range242146
Interquartile range (IQR)1023.5

Descriptive statistics

Standard deviation11403.286
Coefficient of variation (CV)6.4927016
Kurtosis327.87807
Mean1756.3237
Median Absolute Deviation (MAD)337
Skewness17.2497
Sum2919010
Variance1.3003493 × 108
MonotonicityNot monotonic
2024-03-23T14:36:10.008935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 77
 
4.3%
5 19
 
1.1%
4 17
 
0.9%
2 15
 
0.8%
3 15
 
0.8%
1 15
 
0.8%
11 13
 
0.7%
9 12
 
0.7%
6 11
 
0.6%
12 9
 
0.5%
Other values (940) 1459
81.3%
(Missing) 132
 
7.4%
ValueCountFrequency (%)
0 77
4.3%
1 15
 
0.8%
2 15
 
0.8%
3 15
 
0.8%
4 17
 
0.9%
5 19
 
1.1%
6 11
 
0.6%
7 9
 
0.5%
8 8
 
0.4%
9 12
 
0.7%
ValueCountFrequency (%)
242146 1
0.1%
232906 1
0.1%
204621 1
0.1%
192793 1
0.1%
70517 1
0.1%
45142 1
0.1%
43830 1
0.1%
38641 1
0.1%
36826 1
0.1%
36821 1
0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct962
Distinct (%)57.5%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1653.3238
Minimum0
Maximum230576
Zeros71
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T14:36:10.263610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q152
median340
Q31028.75
95-th percentile3151.35
Maximum230576
Range230576
Interquartile range (IQR)976.75

Descriptive statistics

Standard deviation10776.896
Coefficient of variation (CV)6.5183214
Kurtosis334.4876
Mean1653.3238
Median Absolute Deviation (MAD)325
Skewness17.412599
Sum2767664
Variance1.1614148 × 108
MonotonicityNot monotonic
2024-03-23T14:36:10.481923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 71
 
4.0%
4 19
 
1.1%
1 17
 
0.9%
3 16
 
0.9%
5 16
 
0.9%
9 14
 
0.8%
2 14
 
0.8%
18 13
 
0.7%
8 12
 
0.7%
26 12
 
0.7%
Other values (952) 1470
81.9%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 71
4.0%
1 17
 
0.9%
2 14
 
0.8%
3 16
 
0.9%
4 19
 
1.1%
5 16
 
0.9%
6 10
 
0.6%
7 11
 
0.6%
8 12
 
0.7%
9 14
 
0.8%
ValueCountFrequency (%)
230576 1
0.1%
222990 1
0.1%
200081 1
0.1%
172838 1
0.1%
68848 1
0.1%
40718 1
0.1%
39459 1
0.1%
35060 1
0.1%
34872 1
0.1%
34730 1
0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct950
Distinct (%)56.8%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1655.7808
Minimum0
Maximum238181
Zeros68
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T14:36:10.748660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.65
Q151
median335
Q31068.5
95-th percentile3008.5
Maximum238181
Range238181
Interquartile range (IQR)1017.5

Descriptive statistics

Standard deviation10775.044
Coefficient of variation (CV)6.5075309
Kurtosis339.95797
Mean1655.7808
Median Absolute Deviation (MAD)321
Skewness17.514286
Sum2771777
Variance1.1610158 × 108
MonotonicityNot monotonic
2024-03-23T14:36:11.043260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68
 
3.8%
2 18
 
1.0%
4 16
 
0.9%
1 16
 
0.9%
3 16
 
0.9%
6 15
 
0.8%
13 14
 
0.8%
5 14
 
0.8%
9 14
 
0.8%
14 12
 
0.7%
Other values (940) 1471
82.0%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 68
3.8%
1 16
 
0.9%
2 18
 
1.0%
3 16
 
0.9%
4 16
 
0.9%
5 14
 
0.8%
6 15
 
0.8%
7 10
 
0.6%
8 8
 
0.4%
9 14
 
0.8%
ValueCountFrequency (%)
238181 1
0.1%
219576 1
0.1%
201987 1
0.1%
162554 1
0.1%
73706 1
0.1%
44254 1
0.1%
37206 1
0.1%
37142 1
0.1%
35845 1
0.1%
35226 1
0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct996
Distinct (%)58.0%
Missing78
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean1797.7448
Minimum0
Maximum262244
Zeros68
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T14:36:11.264115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q162.75
median378
Q31112.25
95-th percentile3566
Maximum262244
Range262244
Interquartile range (IQR)1049.5

Descriptive statistics

Standard deviation11753.507
Coefficient of variation (CV)6.5379173
Kurtosis346.88079
Mean1797.7448
Median Absolute Deviation (MAD)360.5
Skewness17.680862
Sum3084930
Variance1.3814492 × 108
MonotonicityNot monotonic
2024-03-23T14:36:11.475327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68
 
3.8%
1 21
 
1.2%
4 18
 
1.0%
7 15
 
0.8%
3 13
 
0.7%
10 13
 
0.7%
2 12
 
0.7%
6 12
 
0.7%
8 12
 
0.7%
5 11
 
0.6%
Other values (986) 1521
84.8%
(Missing) 78
 
4.3%
ValueCountFrequency (%)
0 68
3.8%
1 21
 
1.2%
2 12
 
0.7%
3 13
 
0.7%
4 18
 
1.0%
5 11
 
0.6%
6 12
 
0.7%
7 15
 
0.8%
8 12
 
0.7%
9 8
 
0.4%
ValueCountFrequency (%)
262244 1
0.1%
242158 1
0.1%
225272 1
0.1%
175028 1
0.1%
88713 1
0.1%
49142 1
0.1%
43585 1
0.1%
40162 1
0.1%
40138 1
0.1%
37661 1
0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1011
Distinct (%)59.8%
Missing102
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean2052.4858
Minimum0
Maximum285212
Zeros59
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T14:36:11.699380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q165.75
median425
Q31282.75
95-th percentile4151.15
Maximum285212
Range285212
Interquartile range (IQR)1217

Descriptive statistics

Standard deviation13319.027
Coefficient of variation (CV)6.4892176
Kurtosis338.82776
Mean2052.4858
Median Absolute Deviation (MAD)407.5
Skewness17.480682
Sum3472806
Variance1.7739648 × 108
MonotonicityNot monotonic
2024-03-23T14:36:11.994258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 59
 
3.3%
1 20
 
1.1%
7 18
 
1.0%
5 17
 
0.9%
8 13
 
0.7%
2 13
 
0.7%
3 12
 
0.7%
4 11
 
0.6%
10 10
 
0.6%
28 9
 
0.5%
Other values (1001) 1510
84.2%
(Missing) 102
 
5.7%
ValueCountFrequency (%)
0 59
3.3%
1 20
 
1.1%
2 13
 
0.7%
3 12
 
0.7%
4 11
 
0.6%
5 17
 
0.9%
6 8
 
0.4%
7 18
 
1.0%
8 13
 
0.7%
9 8
 
0.4%
ValueCountFrequency (%)
285212 1
0.1%
284594 1
0.1%
247959 1
0.1%
200100 1
0.1%
99803 1
0.1%
59706 1
0.1%
52990 1
0.1%
46409 1
0.1%
45654 1
0.1%
41040 1
0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1016
Distinct (%)60.0%
Missing102
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean2028.1584
Minimum0
Maximum287722
Zeros61
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T14:36:12.292142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q167.75
median430
Q31238
95-th percentile4001.55
Maximum287722
Range287722
Interquartile range (IQR)1170.25

Descriptive statistics

Standard deviation13149.686
Coefficient of variation (CV)6.4835595
Kurtosis332.3067
Mean2028.1584
Median Absolute Deviation (MAD)409.5
Skewness17.304707
Sum3431644
Variance1.7291423 × 108
MonotonicityNot monotonic
2024-03-23T14:36:12.571404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 61
 
3.4%
1 20
 
1.1%
6 16
 
0.9%
5 16
 
0.9%
10 15
 
0.8%
2 14
 
0.8%
7 12
 
0.7%
9 12
 
0.7%
22 11
 
0.6%
11 11
 
0.6%
Other values (1006) 1504
83.8%
(Missing) 102
 
5.7%
ValueCountFrequency (%)
0 61
3.4%
1 20
 
1.1%
2 14
 
0.8%
3 8
 
0.4%
4 7
 
0.4%
5 16
 
0.9%
6 16
 
0.9%
7 12
 
0.7%
8 9
 
0.5%
9 12
 
0.7%
ValueCountFrequency (%)
287722 1
0.1%
270669 1
0.1%
239469 1
0.1%
205117 1
0.1%
101445 1
0.1%
67698 1
0.1%
54967 1
0.1%
46199 1
0.1%
43552 1
0.1%
40515 1
0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1050
Distinct (%)62.7%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2142.1774
Minimum0
Maximum291634
Zeros64
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T14:36:12.827532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q177.5
median483.5
Q31308.25
95-th percentile4195.1
Maximum291634
Range291634
Interquartile range (IQR)1230.75

Descriptive statistics

Standard deviation13753.551
Coefficient of variation (CV)6.4203602
Kurtosis321.48972
Mean2142.1774
Median Absolute Deviation (MAD)458.5
Skewness17.050752
Sum3586005
Variance1.8916015 × 108
MonotonicityNot monotonic
2024-03-23T14:36:13.150304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 64
 
3.6%
2 17
 
0.9%
1 16
 
0.9%
3 14
 
0.8%
8 13
 
0.7%
4 13
 
0.7%
7 10
 
0.6%
10 9
 
0.5%
12 9
 
0.5%
23 9
 
0.5%
Other values (1040) 1500
83.6%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 64
3.6%
1 16
 
0.9%
2 17
 
0.9%
3 14
 
0.8%
4 13
 
0.7%
5 9
 
0.5%
6 7
 
0.4%
7 10
 
0.6%
8 13
 
0.7%
9 9
 
0.5%
ValueCountFrequency (%)
291634 1
0.1%
281742 1
0.1%
247483 1
0.1%
223883 1
0.1%
108007 1
0.1%
67212 1
0.1%
63828 1
0.1%
47114 1
0.1%
44203 1
0.1%
43150 1
0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1031
Distinct (%)61.4%
Missing114
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean1998.3369
Minimum0
Maximum266277
Zeros61
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T14:36:13.492349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q174.75
median428.5
Q31193.5
95-th percentile3992.65
Maximum266277
Range266277
Interquartile range (IQR)1118.75

Descriptive statistics

Standard deviation12869.933
Coefficient of variation (CV)6.4403219
Kurtosis313.67567
Mean1998.3369
Median Absolute Deviation (MAD)407.5
Skewness16.860894
Sum3357206
Variance1.6563517 × 108
MonotonicityNot monotonic
2024-03-23T14:36:13.748923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 61
 
3.4%
1 19
 
1.1%
3 18
 
1.0%
2 15
 
0.8%
10 14
 
0.8%
4 12
 
0.7%
6 12
 
0.7%
5 12
 
0.7%
9 11
 
0.6%
17 10
 
0.6%
Other values (1021) 1496
83.4%
(Missing) 114
 
6.4%
ValueCountFrequency (%)
0 61
3.4%
1 19
 
1.1%
2 15
 
0.8%
3 18
 
1.0%
4 12
 
0.7%
5 12
 
0.7%
6 12
 
0.7%
7 8
 
0.4%
8 7
 
0.4%
9 11
 
0.6%
ValueCountFrequency (%)
266277 1
0.1%
262718 1
0.1%
225587 1
0.1%
222711 1
0.1%
104216 1
0.1%
71030 1
0.1%
64557 1
0.1%
45951 1
0.1%
43649 1
0.1%
39137 1
0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1018
Distinct (%)60.8%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1892.8883
Minimum0
Maximum248258
Zeros68
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T14:36:13.985720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q170.5
median431.5
Q31104.5
95-th percentile3677
Maximum248258
Range248258
Interquartile range (IQR)1034

Descriptive statistics

Standard deviation12089.251
Coefficient of variation (CV)6.3866689
Kurtosis302.67058
Mean1892.8883
Median Absolute Deviation (MAD)407.5
Skewness16.572408
Sum3168695
Variance1.4614998 × 108
MonotonicityNot monotonic
2024-03-23T14:36:14.219194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68
 
3.8%
1 18
 
1.0%
5 17
 
0.9%
7 16
 
0.9%
9 14
 
0.8%
3 12
 
0.7%
6 12
 
0.7%
8 10
 
0.6%
4 9
 
0.5%
15 9
 
0.5%
Other values (1008) 1489
83.0%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 68
3.8%
1 18
 
1.0%
2 9
 
0.5%
3 12
 
0.7%
4 9
 
0.5%
5 17
 
0.9%
6 12
 
0.7%
7 16
 
0.9%
8 10
 
0.6%
9 14
 
0.8%
ValueCountFrequency (%)
248258 1
0.1%
236719 1
0.1%
217614 1
0.1%
208478 1
0.1%
105431 1
0.1%
77902 1
0.1%
58930 1
0.1%
43007 1
0.1%
40861 1
0.1%
36933 1
0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1054
Distinct (%)63.0%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2088.5526
Minimum0
Maximum274057
Zeros63
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T14:36:14.408353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q182.25
median480
Q31244
95-th percentile4014.85
Maximum274057
Range274057
Interquartile range (IQR)1161.75

Descriptive statistics

Standard deviation13300.234
Coefficient of variation (CV)6.3681584
Kurtosis306.43163
Mean2088.5526
Median Absolute Deviation (MAD)457
Skewness16.630678
Sum3496237
Variance1.7689621 × 108
MonotonicityNot monotonic
2024-03-23T14:36:14.663851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63
 
3.5%
2 17
 
0.9%
4 16
 
0.9%
3 16
 
0.9%
13 14
 
0.8%
1 14
 
0.8%
7 14
 
0.8%
8 13
 
0.7%
14 11
 
0.6%
5 9
 
0.5%
Other values (1044) 1487
82.9%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 63
3.5%
1 14
 
0.8%
2 17
 
0.9%
3 16
 
0.9%
4 16
 
0.9%
5 9
 
0.5%
6 5
 
0.3%
7 14
 
0.8%
8 13
 
0.7%
9 6
 
0.3%
ValueCountFrequency (%)
274057 1
0.1%
272188 1
0.1%
238249 1
0.1%
211897 1
0.1%
126035 1
0.1%
70393 1
0.1%
67253 1
0.1%
49984 1
0.1%
47865 1
0.1%
44757 1
0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1063
Distinct (%)63.5%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2301.1165
Minimum0
Maximum299309
Zeros65
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T14:36:15.422987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q189
median515
Q31382.75
95-th percentile4497.8
Maximum299309
Range299309
Interquartile range (IQR)1293.75

Descriptive statistics

Standard deviation14601.244
Coefficient of variation (CV)6.3452868
Kurtosis301.67995
Mean2301.1165
Median Absolute Deviation (MAD)488.5
Skewness16.49243
Sum3852069
Variance2.1319633 × 108
MonotonicityNot monotonic
2024-03-23T14:36:15.758716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65
 
3.6%
1 20
 
1.1%
2 15
 
0.8%
7 14
 
0.8%
5 13
 
0.7%
3 12
 
0.7%
22 12
 
0.7%
6 11
 
0.6%
13 11
 
0.6%
10 9
 
0.5%
Other values (1053) 1492
83.2%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 65
3.6%
1 20
 
1.1%
2 15
 
0.8%
3 12
 
0.7%
4 6
 
0.3%
5 13
 
0.7%
6 11
 
0.6%
7 14
 
0.8%
8 6
 
0.3%
9 6
 
0.3%
ValueCountFrequency (%)
299309 1
0.1%
290629 1
0.1%
277055 1
0.1%
214981 1
0.1%
156407 1
0.1%
71758 1
0.1%
63114 1
0.1%
61261 1
0.1%
60003 1
0.1%
57467 1
0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct984
Distinct (%)58.8%
Missing120
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1797.9403
Minimum0
Maximum225468
Zeros73
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T14:36:16.070776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q167.25
median397.5
Q31072.75
95-th percentile3328
Maximum225468
Range225468
Interquartile range (IQR)1005.5

Descriptive statistics

Standard deviation11320.928
Coefficient of variation (CV)6.2966095
Kurtosis294.02193
Mean1797.9403
Median Absolute Deviation (MAD)376
Skewness16.317714
Sum3009752
Variance1.2816341 × 108
MonotonicityNot monotonic
2024-03-23T14:36:16.313665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 73
 
4.1%
2 18
 
1.0%
7 16
 
0.9%
1 16
 
0.9%
3 14
 
0.8%
5 13
 
0.7%
4 13
 
0.7%
6 12
 
0.7%
18 12
 
0.7%
8 10
 
0.6%
Other values (974) 1477
82.3%
(Missing) 120
 
6.7%
ValueCountFrequency (%)
0 73
4.1%
1 16
 
0.9%
2 18
 
1.0%
3 14
 
0.8%
4 13
 
0.7%
5 13
 
0.7%
6 12
 
0.7%
7 16
 
0.9%
8 10
 
0.6%
9 5
 
0.3%
ValueCountFrequency (%)
225468 1
0.1%
223004 1
0.1%
214917 1
0.1%
174485 1
0.1%
128460 1
0.1%
52791 1
0.1%
49182 1
0.1%
44537 1
0.1%
43480 1
0.1%
40293 1
0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct893
Distinct (%)53.2%
Missing114
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean1307.6679
Minimum0
Maximum168395
Zeros84
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2024-03-23T14:36:16.586095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.95
Q144
median301
Q3797.25
95-th percentile2266.55
Maximum168395
Range168395
Interquartile range (IQR)753.25

Descriptive statistics

Standard deviation8226.8091
Coefficient of variation (CV)6.2912069
Kurtosis291.60434
Mean1307.6679
Median Absolute Deviation (MAD)285
Skewness16.256654
Sum2196882
Variance67680387
MonotonicityNot monotonic
2024-03-23T14:36:16.824220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 84
 
4.7%
1 30
 
1.7%
4 21
 
1.2%
2 20
 
1.1%
6 17
 
0.9%
3 15
 
0.8%
5 15
 
0.8%
12 13
 
0.7%
23 13
 
0.7%
9 13
 
0.7%
Other values (883) 1439
80.2%
(Missing) 114
 
6.4%
ValueCountFrequency (%)
0 84
4.7%
1 30
 
1.7%
2 20
 
1.1%
3 15
 
0.8%
4 21
 
1.2%
5 15
 
0.8%
6 17
 
0.9%
7 6
 
0.3%
8 8
 
0.4%
9 13
 
0.7%
ValueCountFrequency (%)
168395 1
0.1%
158080 1
0.1%
150642 1
0.1%
131516 1
0.1%
96607 1
0.1%
37979 1
0.1%
34167 1
0.1%
33213 1
0.1%
29800 1
0.1%
27363 1
0.1%

Interactions

2024-03-23T14:36:00.239645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:02.944612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:05.836315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:09.093379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:12.860937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:16.206798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:19.418852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T14:35:26.503856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T14:35:28.346829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:31.530209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:35.321333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:38.042848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:41.163955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:44.422129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:47.995329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:51.315870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:55.202973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:58.739799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:36:02.389835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:04.934600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:08.022923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:11.738754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:15.028280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:18.526668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:21.682789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:25.577166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:28.502120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:31.799952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:35.439348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:38.192122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:41.407843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:44.623191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:48.138376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:51.551098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:55.456672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:59.312012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:36:02.618739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:05.056536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:08.166895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:11.931741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:15.250614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:18.679808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:21.808552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:25.710397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:28.656540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:31.968569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:35.601085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:38.331568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:41.555612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:44.773744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:48.324532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:51.804863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:55.683643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:59.458554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:36:02.939065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:05.208921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:08.368932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:12.080102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:15.484051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:18.828065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:22.429765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:25.885908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:28.909835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:32.112859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:35.749439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:38.508272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:41.768730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:44.932935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:48.542085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:51.981240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:55.855054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:59.613786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:36:03.124889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:05.370734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:08.586372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:12.265417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:15.697698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:18.976640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:22.572565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:26.053768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:29.150049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:32.292260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:35.878456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:38.699796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:41.983441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:45.114406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:48.749505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:52.161212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:56.049625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:59.765971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:36:03.352001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:05.529048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:08.770193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:12.509750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:15.872777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:19.129634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:22.792185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:26.195344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:29.371494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:32.576975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:35.983454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:38.906478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:42.162575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:45.269618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:48.946714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:52.318556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:56.298946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:59.913673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:36:03.600612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:05.665719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:08.942589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:12.707118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:16.033116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:19.268085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:23.015116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:26.361113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:29.533157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:32.801694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:36.099874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:39.087237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:42.326963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:45.411509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:49.159238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:52.486624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:56.516909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:36:00.046038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T14:36:17.064618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9980.9660.9930.9860.9750.9780.9780.9770.8810.8810.8970.9810.9140.9010.9600.9570.896
20070.9981.0001.0000.9960.9890.9780.9810.9810.9800.8890.8890.9080.9840.9290.9110.9590.9640.901
20080.9661.0001.0000.9820.9600.9490.9530.9530.9510.9870.9870.9910.9080.9800.9910.8970.9050.989
20090.9930.9960.9821.0000.9890.9870.9890.9890.9880.9140.9140.9310.9760.9090.9350.9650.9690.924
20100.9860.9890.9600.9891.0000.9950.9960.9960.9950.9440.9440.9480.9840.9100.9510.9920.9920.942
20110.9750.9780.9490.9870.9951.0001.0001.0001.0000.9860.9790.9770.9910.9320.9790.9900.9900.972
20120.9780.9810.9530.9890.9961.0001.0001.0001.0000.9850.9780.9820.9930.9390.9850.9920.9920.976
20130.9780.9810.9530.9890.9961.0001.0001.0001.0000.9850.9780.9820.9930.9390.9850.9920.9920.976
20140.9770.9800.9510.9880.9951.0001.0001.0001.0000.9890.9820.9790.9920.9350.9820.9910.9910.974
20150.8810.8890.9870.9140.9440.9860.9850.9850.9891.0001.0000.9970.9500.9950.9980.9360.9480.997
20160.8810.8890.9870.9140.9440.9790.9780.9780.9821.0001.0000.9980.9570.9960.9990.9420.9540.997
20170.8970.9080.9910.9310.9480.9770.9820.9820.9790.9970.9981.0001.0000.9961.0000.9620.9891.000
20180.9810.9840.9080.9760.9840.9910.9930.9930.9920.9500.9571.0001.0000.9880.9880.9910.9970.982
20190.9140.9290.9800.9090.9100.9320.9390.9390.9350.9950.9960.9960.9881.0000.9970.9240.9550.995
20200.9010.9110.9910.9350.9510.9790.9850.9850.9820.9980.9991.0000.9880.9971.0000.9580.9841.000
20210.9600.9590.8970.9650.9920.9900.9920.9920.9910.9360.9420.9620.9910.9240.9581.0000.9970.960
20220.9570.9640.9050.9690.9920.9900.9920.9920.9910.9480.9540.9890.9970.9550.9840.9971.0000.986
20230.8960.9010.9890.9240.9420.9720.9760.9760.9740.9970.9971.0000.9820.9951.0000.9600.9861.000
2024-03-23T14:36:17.403497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9680.9570.9430.9410.9470.9420.9420.9440.9460.9400.9360.9340.9260.9270.9300.9250.914
20070.9681.0000.9760.9600.9550.9580.9560.9540.9530.9540.9510.9460.9440.9360.9350.9410.9390.930
20080.9570.9761.0000.9630.9560.9610.9590.9540.9530.9520.9500.9450.9390.9300.9310.9350.9340.924
20090.9430.9600.9631.0000.9730.9690.9660.9620.9580.9600.9560.9540.9530.9440.9440.9470.9470.938
20100.9410.9550.9560.9731.0000.9760.9700.9630.9600.9610.9580.9570.9530.9430.9460.9480.9480.938
20110.9470.9580.9610.9690.9761.0000.9830.9770.9730.9710.9660.9620.9570.9460.9480.9500.9480.943
20120.9420.9560.9590.9660.9700.9831.0000.9820.9760.9740.9700.9650.9580.9490.9490.9520.9520.948
20130.9420.9540.9540.9620.9630.9770.9821.0000.9800.9760.9710.9660.9590.9490.9510.9520.9500.946
20140.9440.9530.9530.9580.9600.9730.9760.9801.0000.9820.9750.9700.9640.9520.9530.9540.9510.945
20150.9460.9540.9520.9600.9610.9710.9740.9760.9821.0000.9840.9770.9720.9610.9610.9600.9570.947
20160.9400.9510.9500.9560.9580.9660.9700.9710.9750.9841.0000.9840.9760.9660.9680.9630.9610.955
20170.9360.9460.9450.9540.9570.9620.9650.9660.9700.9770.9841.0000.9830.9740.9730.9680.9640.961
20180.9340.9440.9390.9530.9530.9570.9580.9590.9640.9720.9760.9831.0000.9790.9750.9670.9640.962
20190.9260.9360.9300.9440.9430.9460.9490.9490.9520.9610.9660.9740.9791.0000.9780.9660.9620.961
20200.9270.9350.9310.9440.9460.9480.9490.9510.9530.9610.9680.9730.9750.9781.0000.9780.9700.964
20210.9300.9410.9350.9470.9480.9500.9520.9520.9540.9600.9630.9680.9670.9660.9781.0000.9800.969
20220.9250.9390.9340.9470.9480.9480.9520.9500.9510.9570.9610.9640.9640.9620.9700.9801.0000.975
20230.9140.9300.9240.9380.9380.9430.9480.9460.9450.9470.9550.9610.9620.9610.9640.9690.9751.000

Missing values

2024-03-23T14:36:03.913407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T14:36:04.388367image/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.
2024-03-23T14:36:04.868027image/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_전227159215528220372216158203178204621200081201987225272247959239469247483225587208478238249277055214917150642
1전국_답273497257606275401263083242045242146230576238181262244284594270669281742266277248258274057299309225468168395
2전국_대지194558172794177651209679201455232906222990219576242158285212287722291634262718236719272188290629223004158080
3전국_임야235177208370215101207508186023192793172838162554175028200100205117223883222711217614211897214981174485131516
4전국_공장854485457253578862695906563662177656701875527958716172368143970374734852
5전국_기타7608674459747507009966033705176884873706887139980310144510800710421610543112603515640712846096607
6서울_전4624515541177984523376425466564673513483427441553410197
7서울_답4042373513119897468257212312446497290533300303468290204
8서울_대지222831997421336157911287512750998897731162513402141861450613177126401708613151100866991
9서울_임야10641853110714631060805971109010271516133415832388271222351637837671
지역_지목200620072008200920102011201220132014201520162017201820192020202120222023
1784제주 제주시_대지68315191327178120351952204120343429459048615110438635422906387443032863
1785제주 제주시_임야275933603066279525993448316538485581677870485632486239073351384731302362
1786제주 제주시_공장111811223821136353328121091319179
1787제주 제주시_기타188826352208195920872441267229724139474142503162272923882137271623641554
1788제주 서귀포시_전171534232259238324132505316544046150742157914146383628152392277224841502
1789제주 서귀포시_답18934317843513713510515229427830316617611410615911242
1790제주 서귀포시_대지6011069768127314461416135516652175230921513359262115071027163913791516
1791제주 서귀포시_임야177929862307205424022430327534904602810179955824370829722356232218981462
1792제주 서귀포시_공장19102320151015192625721710101647
1793제주 서귀포시_기타139727051883198516432522254029604099542246613200283727832138256123691577