Overview

Dataset statistics

Number of variables19
Number of observations976
Missing cells5932
Missing cells (%)32.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory162.2 KiB
Average record size in memory170.1 B

Variable types

Text1
Numeric18

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 부동산거래현황 중 순수토지 거래현황의 연도별 토지거래허가처리별(필지수) 데이터입니다.- (단위 : 필지수)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068380/fileData.do

Alerts

2006 is highly overall correlated with 2007 and 10 other fieldsHigh correlation
2007 is highly overall correlated with 2006 and 10 other fieldsHigh correlation
2008 is highly overall correlated with 2006 and 11 other fieldsHigh correlation
2009 is highly overall correlated with 2006 and 10 other fieldsHigh correlation
2010 is highly overall correlated with 2006 and 9 other fieldsHigh correlation
2011 is highly overall correlated with 2006 and 10 other fieldsHigh correlation
2012 is highly overall correlated with 2006 and 8 other fieldsHigh correlation
2013 is highly overall correlated with 2009 and 5 other fieldsHigh correlation
2014 is highly overall correlated with 2006 and 13 other fieldsHigh correlation
2015 is highly overall correlated with 2006 and 10 other fieldsHigh correlation
2016 is highly overall correlated with 2014 and 5 other fieldsHigh correlation
2017 is highly overall correlated with 2014 and 5 other fieldsHigh correlation
2018 is highly overall correlated with 2014 and 8 other fieldsHigh correlation
2019 is highly overall correlated with 2006 and 12 other fieldsHigh correlation
2020 is highly overall correlated with 2007 and 9 other fieldsHigh correlation
2021 is highly overall correlated with 2006 and 11 other fieldsHigh correlation
2022 is highly overall correlated with 2006 and 10 other fieldsHigh correlation
2023 is highly overall correlated with 2018 and 4 other fieldsHigh correlation
2006 has 264 (27.0%) missing valuesMissing
2007 has 216 (22.1%) missing valuesMissing
2008 has 152 (15.6%) missing valuesMissing
2009 has 132 (13.5%) missing valuesMissing
2010 has 104 (10.7%) missing valuesMissing
2011 has 108 (11.1%) missing valuesMissing
2012 has 104 (10.7%) missing valuesMissing
2013 has 108 (11.1%) missing valuesMissing
2014 has 628 (64.3%) missing valuesMissing
2015 has 608 (62.3%) missing valuesMissing
2016 has 592 (60.7%) missing valuesMissing
2017 has 572 (58.6%) missing valuesMissing
2018 has 540 (55.3%) missing valuesMissing
2019 has 476 (48.8%) missing valuesMissing
2020 has 404 (41.4%) missing valuesMissing
2021 has 336 (34.4%) missing valuesMissing
2022 has 300 (30.7%) missing valuesMissing
2023 has 288 (29.5%) missing valuesMissing
2007 is highly skewed (γ1 = 20.02374099)Skewed
2008 is highly skewed (γ1 = 21.18340704)Skewed
2012 is highly skewed (γ1 = 21.25128654)Skewed
2013 is highly skewed (γ1 = 22.00741665)Skewed
지역_토지거래허가처리 has unique valuesUnique
2006 has 136 (13.9%) zerosZeros
2007 has 163 (16.7%) zerosZeros
2008 has 242 (24.8%) zerosZeros
2009 has 328 (33.6%) zerosZeros
2010 has 440 (45.1%) zerosZeros
2011 has 499 (51.1%) zerosZeros
2012 has 593 (60.8%) zerosZeros
2013 has 629 (64.4%) zerosZeros
2014 has 177 (18.1%) zerosZeros
2015 has 248 (25.4%) zerosZeros
2016 has 243 (24.9%) zerosZeros
2017 has 276 (28.3%) zerosZeros
2018 has 334 (34.2%) zerosZeros
2019 has 339 (34.7%) zerosZeros
2020 has 370 (37.9%) zerosZeros
2021 has 367 (37.6%) zerosZeros
2022 has 464 (47.5%) zerosZeros
2023 has 481 (49.3%) zerosZeros

Reproduction

Analysis started2024-03-23 06:04:24.023869
Analysis finished2024-03-23 06:06:14.447133
Duration1 minute and 50.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct976
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
2024-03-23T06:06:14.821552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length24
Mean length12.840164
Min length5

Characters and Unicode

Total characters12532
Distinct characters139
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

Unique976 ?
Unique (%)100.0%

Sample

1st row전국_허가
2nd row전국_불허가
3rd row전국_불허(이용목적부적합)
4th row전국_불허(기타)
5th row서울_허가
ValueCountFrequency (%)
경기 180
 
9.0%
서울 100
 
5.0%
경남 84
 
4.2%
전남 76
 
3.8%
충남 72
 
3.6%
경북 68
 
3.4%
충북 56
 
2.8%
전북 44
 
2.2%
부산 40
 
2.0%
인천 40
 
2.0%
Other values (917) 1248
62.2%
2024-03-23T06:06:15.893601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1032
 
8.2%
_ 976
 
7.8%
976
 
7.8%
732
 
5.8%
) 532
 
4.2%
( 532
 
4.2%
488
 
3.9%
488
 
3.9%
472
 
3.8%
440
 
3.5%
Other values (129) 5864
46.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9460
75.5%
Space Separator 1032
 
8.2%
Connector Punctuation 976
 
7.8%
Close Punctuation 532
 
4.2%
Open Punctuation 532
 
4.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
976
 
10.3%
732
 
7.7%
488
 
5.2%
488
 
5.2%
472
 
5.0%
440
 
4.7%
428
 
4.5%
352
 
3.7%
316
 
3.3%
304
 
3.2%
Other values (125) 4464
47.2%
Space Separator
ValueCountFrequency (%)
1032
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 976
100.0%
Close Punctuation
ValueCountFrequency (%)
) 532
100.0%
Open Punctuation
ValueCountFrequency (%)
( 532
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9460
75.5%
Common 3072
 
24.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
976
 
10.3%
732
 
7.7%
488
 
5.2%
488
 
5.2%
472
 
5.0%
440
 
4.7%
428
 
4.5%
352
 
3.7%
316
 
3.3%
304
 
3.2%
Other values (125) 4464
47.2%
Common
ValueCountFrequency (%)
1032
33.6%
_ 976
31.8%
) 532
17.3%
( 532
17.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9460
75.5%
ASCII 3072
 
24.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1032
33.6%
_ 976
31.8%
) 532
17.3%
( 532
17.3%
Hangul
ValueCountFrequency (%)
976
 
10.3%
732
 
7.7%
488
 
5.2%
488
 
5.2%
472
 
5.0%
440
 
4.7%
428
 
4.5%
352
 
3.7%
316
 
3.3%
304
 
3.2%
Other values (125) 4464
47.2%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct282
Distinct (%)39.6%
Missing264
Missing (%)27.0%
Infinite0
Infinite (%)0.0%
Mean670.07584
Minimum0
Maximum129634
Zeros136
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2024-03-23T06:06:16.325663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median17.5
Q3178.25
95-th percentile2194.35
Maximum129634
Range129634
Interquartile range (IQR)177.25

Descriptive statistics

Standard deviation5515.5072
Coefficient of variation (CV)8.2311685
Kurtosis437.50626
Mean670.07584
Median Absolute Deviation (MAD)17.5
Skewness19.779293
Sum477094
Variance30420819
MonotonicityNot monotonic
2024-03-23T06:06:16.851171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 136
 
13.9%
1 46
 
4.7%
2 30
 
3.1%
3 25
 
2.6%
7 21
 
2.2%
4 13
 
1.3%
6 11
 
1.1%
16 9
 
0.9%
13 9
 
0.9%
5 9
 
0.9%
Other values (272) 403
41.3%
(Missing) 264
27.0%
ValueCountFrequency (%)
0 136
13.9%
1 46
 
4.7%
2 30
 
3.1%
3 25
 
2.6%
4 13
 
1.3%
5 9
 
0.9%
6 11
 
1.1%
7 21
 
2.2%
8 7
 
0.7%
9 8
 
0.8%
ValueCountFrequency (%)
129634 1
0.1%
56999 1
0.1%
29411 1
0.1%
12590 1
0.1%
11422 1
0.1%
10704 1
0.1%
7257 1
0.1%
6806 1
0.1%
6305 1
0.1%
6270 1
0.1%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct284
Distinct (%)37.4%
Missing216
Missing (%)22.1%
Infinite0
Infinite (%)0.0%
Mean627.80921
Minimum0
Maximum126683
Zeros163
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2024-03-23T06:06:17.607891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median14.5
Q3156.75
95-th percentile1836.1
Maximum126683
Range126683
Interquartile range (IQR)155.75

Descriptive statistics

Standard deviation5300.3509
Coefficient of variation (CV)8.4426141
Kurtosis446.58977
Mean627.80921
Median Absolute Deviation (MAD)14.5
Skewness20.023741
Sum477135
Variance28093720
MonotonicityNot monotonic
2024-03-23T06:06:18.380554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 163
 
16.7%
1 53
 
5.4%
3 29
 
3.0%
5 18
 
1.8%
6 18
 
1.8%
4 18
 
1.8%
2 17
 
1.7%
10 15
 
1.5%
11 10
 
1.0%
13 9
 
0.9%
Other values (274) 410
42.0%
(Missing) 216
22.1%
ValueCountFrequency (%)
0 163
16.7%
1 53
 
5.4%
2 17
 
1.7%
3 29
 
3.0%
4 18
 
1.8%
5 18
 
1.8%
6 18
 
1.8%
7 9
 
0.9%
8 5
 
0.5%
9 6
 
0.6%
ValueCountFrequency (%)
126683 1
0.1%
62084 1
0.1%
25697 1
0.1%
13626 1
0.1%
11949 1
0.1%
9577 1
0.1%
7903 1
0.1%
7148 1
0.1%
7133 1
0.1%
6247 1
0.1%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct265
Distinct (%)32.2%
Missing152
Missing (%)15.6%
Infinite0
Infinite (%)0.0%
Mean474.15898
Minimum0
Maximum107977
Zeros242
Zeros (%)24.8%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2024-03-23T06:06:18.962826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9
Q3104
95-th percentile1446
Maximum107977
Range107977
Interquartile range (IQR)104

Descriptive statistics

Standard deviation4295.9791
Coefficient of variation (CV)9.0602082
Kurtosis499.53051
Mean474.15898
Median Absolute Deviation (MAD)9
Skewness21.183407
Sum390707
Variance18455436
MonotonicityNot monotonic
2024-03-23T06:06:19.620347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 242
24.8%
1 31
 
3.2%
2 30
 
3.1%
3 25
 
2.6%
6 20
 
2.0%
4 20
 
2.0%
5 18
 
1.8%
7 15
 
1.5%
9 15
 
1.5%
11 12
 
1.2%
Other values (255) 396
40.6%
(Missing) 152
 
15.6%
ValueCountFrequency (%)
0 242
24.8%
1 31
 
3.2%
2 30
 
3.1%
3 25
 
2.6%
4 20
 
2.0%
5 18
 
1.8%
6 20
 
2.0%
7 15
 
1.5%
8 9
 
0.9%
9 15
 
1.5%
ValueCountFrequency (%)
107977 1
0.1%
49854 1
0.1%
24343 1
0.1%
9320 1
0.1%
8435 1
0.1%
6268 1
0.1%
6179 1
0.1%
5289 1
0.1%
4927 1
0.1%
4886 1
0.1%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct192
Distinct (%)22.7%
Missing132
Missing (%)13.5%
Infinite0
Infinite (%)0.0%
Mean221.30332
Minimum0
Maximum50377
Zeros328
Zeros (%)33.6%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2024-03-23T06:06:20.173123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q339
95-th percentile438.7
Maximum50377
Range50377
Interquartile range (IQR)39

Descriptive statistics

Standard deviation2177.276
Coefficient of variation (CV)9.8384244
Kurtosis413.93431
Mean221.30332
Median Absolute Deviation (MAD)3
Skewness19.61413
Sum186780
Variance4740530.6
MonotonicityNot monotonic
2024-03-23T06:06:20.923028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 328
33.6%
1 56
 
5.7%
2 27
 
2.8%
3 22
 
2.3%
4 19
 
1.9%
10 16
 
1.6%
5 16
 
1.6%
13 16
 
1.6%
11 13
 
1.3%
6 11
 
1.1%
Other values (182) 320
32.8%
(Missing) 132
13.5%
ValueCountFrequency (%)
0 328
33.6%
1 56
 
5.7%
2 27
 
2.8%
3 22
 
2.3%
4 19
 
1.9%
5 16
 
1.6%
6 11
 
1.1%
7 7
 
0.7%
8 9
 
0.9%
9 8
 
0.8%
ValueCountFrequency (%)
50377 1
0.1%
35269 1
0.1%
7570 1
0.1%
5575 1
0.1%
5083 1
0.1%
4848 1
0.1%
4694 1
0.1%
3845 1
0.1%
3765 1
0.1%
3575 1
0.1%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct172
Distinct (%)19.7%
Missing104
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean181.56881
Minimum0
Maximum42969
Zeros440
Zeros (%)45.1%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2024-03-23T06:06:21.434611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q319
95-th percentile397.25
Maximum42969
Range42969
Interquartile range (IQR)19

Descriptive statistics

Standard deviation1871.536
Coefficient of variation (CV)10.307585
Kurtosis410.26299
Mean181.56881
Median Absolute Deviation (MAD)0
Skewness19.571073
Sum158328
Variance3502646.9
MonotonicityNot monotonic
2024-03-23T06:06:22.272328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 440
45.1%
1 34
 
3.5%
4 23
 
2.4%
3 23
 
2.4%
2 22
 
2.3%
6 21
 
2.2%
7 19
 
1.9%
10 11
 
1.1%
5 10
 
1.0%
11 9
 
0.9%
Other values (162) 260
26.6%
(Missing) 104
 
10.7%
ValueCountFrequency (%)
0 440
45.1%
1 34
 
3.5%
2 22
 
2.3%
3 23
 
2.4%
4 23
 
2.4%
5 10
 
1.0%
6 21
 
2.2%
7 19
 
1.9%
8 5
 
0.5%
9 4
 
0.4%
ValueCountFrequency (%)
42969 1
0.1%
32035 1
0.1%
7633 1
0.1%
5052 1
0.1%
4134 1
0.1%
3815 1
0.1%
3807 1
0.1%
3743 1
0.1%
3458 1
0.1%
3245 1
0.1%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct150
Distinct (%)17.3%
Missing108
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean114.03802
Minimum0
Maximum27384
Zeros499
Zeros (%)51.1%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2024-03-23T06:06:22.981281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39
95-th percentile246.7
Maximum27384
Range27384
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1172.2122
Coefficient of variation (CV)10.279135
Kurtosis413.18259
Mean114.03802
Median Absolute Deviation (MAD)0
Skewness19.499936
Sum98985
Variance1374081.5
MonotonicityNot monotonic
2024-03-23T06:06:23.732441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 499
51.1%
1 37
 
3.8%
2 25
 
2.6%
3 19
 
1.9%
6 19
 
1.9%
4 14
 
1.4%
5 14
 
1.4%
9 12
 
1.2%
8 8
 
0.8%
13 6
 
0.6%
Other values (140) 215
22.0%
(Missing) 108
 
11.1%
ValueCountFrequency (%)
0 499
51.1%
1 37
 
3.8%
2 25
 
2.6%
3 19
 
1.9%
4 14
 
1.4%
5 14
 
1.4%
6 19
 
1.9%
7 5
 
0.5%
8 8
 
0.8%
9 12
 
1.2%
ValueCountFrequency (%)
27384 1
0.1%
18791 1
0.1%
5972 1
0.1%
5153 1
0.1%
2332 1
0.1%
2193 1
0.1%
1818 1
0.1%
1807 1
0.1%
1753 1
0.1%
1737 1
0.1%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct98
Distinct (%)11.2%
Missing104
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean36.231651
Minimum0
Maximum8676
Zeros593
Zeros (%)60.8%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2024-03-23T06:06:24.610564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile90
Maximum8676
Range8676
Interquartile range (IQR)3

Descriptive statistics

Standard deviation337.97776
Coefficient of variation (CV)9.328246
Kurtosis512.58157
Mean36.231651
Median Absolute Deviation (MAD)0
Skewness21.251287
Sum31594
Variance114228.96
MonotonicityNot monotonic
2024-03-23T06:06:25.520766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 593
60.8%
1 37
 
3.8%
4 25
 
2.6%
3 23
 
2.4%
2 18
 
1.8%
7 11
 
1.1%
5 9
 
0.9%
6 8
 
0.8%
26 6
 
0.6%
12 6
 
0.6%
Other values (88) 136
 
13.9%
(Missing) 104
 
10.7%
ValueCountFrequency (%)
0 593
60.8%
1 37
 
3.8%
2 18
 
1.8%
3 23
 
2.4%
4 25
 
2.6%
5 9
 
0.9%
6 8
 
0.8%
7 11
 
1.1%
8 4
 
0.4%
9 4
 
0.4%
ValueCountFrequency (%)
8676 1
0.1%
3977 1
0.1%
1148 1
0.1%
1116 1
0.1%
1091 1
0.1%
990 1
0.1%
816 1
0.1%
778 1
0.1%
739 1
0.1%
692 1
0.1%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct84
Distinct (%)9.7%
Missing108
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean22.535714
Minimum0
Maximum5605
Zeros629
Zeros (%)64.4%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2024-03-23T06:06:26.256838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile59.65
Maximum5605
Range5605
Interquartile range (IQR)1

Descriptive statistics

Standard deviation212.57194
Coefficient of variation (CV)9.4326691
Kurtosis556.22298
Mean22.535714
Median Absolute Deviation (MAD)0
Skewness22.007417
Sum19561
Variance45186.828
MonotonicityNot monotonic
2024-03-23T06:06:26.759984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 629
64.4%
1 30
 
3.1%
2 27
 
2.8%
5 18
 
1.8%
6 12
 
1.2%
3 11
 
1.1%
13 8
 
0.8%
11 7
 
0.7%
4 7
 
0.7%
7 7
 
0.7%
Other values (74) 112
 
11.5%
(Missing) 108
 
11.1%
ValueCountFrequency (%)
0 629
64.4%
1 30
 
3.1%
2 27
 
2.8%
3 11
 
1.1%
4 7
 
0.7%
5 18
 
1.8%
6 12
 
1.2%
7 7
 
0.7%
8 3
 
0.3%
9 3
 
0.3%
ValueCountFrequency (%)
5605 1
0.1%
1600 1
0.1%
1320 1
0.1%
1298 1
0.1%
568 1
0.1%
558 1
0.1%
523 1
0.1%
409 1
0.1%
361 1
0.1%
347 1
0.1%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct54
Distinct (%)15.5%
Missing628
Missing (%)64.3%
Infinite0
Infinite (%)0.0%
Mean28.913793
Minimum0
Maximum3036
Zeros177
Zeros (%)18.1%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2024-03-23T06:06:27.392526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile84.65
Maximum3036
Range3036
Interquartile range (IQR)7

Descriptive statistics

Standard deviation182.88157
Coefficient of variation (CV)6.3250634
Kurtosis214.288
Mean28.913793
Median Absolute Deviation (MAD)0
Skewness13.622332
Sum10062
Variance33445.67
MonotonicityNot monotonic
2024-03-23T06:06:27.982365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 177
 
18.1%
1 30
 
3.1%
2 16
 
1.6%
5 11
 
1.1%
6 10
 
1.0%
4 9
 
0.9%
14 6
 
0.6%
3 6
 
0.6%
7 6
 
0.6%
13 5
 
0.5%
Other values (44) 72
 
7.4%
(Missing) 628
64.3%
ValueCountFrequency (%)
0 177
18.1%
1 30
 
3.1%
2 16
 
1.6%
3 6
 
0.6%
4 9
 
0.9%
5 11
 
1.1%
6 10
 
1.0%
7 6
 
0.6%
8 5
 
0.5%
9 4
 
0.4%
ValueCountFrequency (%)
3036 1
0.1%
815 1
0.1%
811 1
0.1%
781 1
0.1%
473 1
0.1%
371 1
0.1%
306 1
0.1%
239 1
0.1%
212 1
0.1%
162 1
0.1%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct57
Distinct (%)15.5%
Missing608
Missing (%)62.3%
Infinite0
Infinite (%)0.0%
Mean23.130435
Minimum0
Maximum2645
Zeros248
Zeros (%)25.4%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2024-03-23T06:06:28.589140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile72.1
Maximum2645
Range2645
Interquartile range (IQR)3

Descriptive statistics

Standard deviation151.61035
Coefficient of variation (CV)6.554583
Kurtosis246.76077
Mean23.130435
Median Absolute Deviation (MAD)0
Skewness14.715374
Sum8512
Variance22985.7
MonotonicityNot monotonic
2024-03-23T06:06:29.036794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 248
25.4%
3 15
 
1.5%
1 13
 
1.3%
4 8
 
0.8%
7 6
 
0.6%
2 5
 
0.5%
11 3
 
0.3%
9 3
 
0.3%
8 3
 
0.3%
34 3
 
0.3%
Other values (47) 61
 
6.2%
(Missing) 608
62.3%
ValueCountFrequency (%)
0 248
25.4%
1 13
 
1.3%
2 5
 
0.5%
3 15
 
1.5%
4 8
 
0.8%
5 2
 
0.2%
6 2
 
0.2%
7 6
 
0.6%
8 3
 
0.3%
9 3
 
0.3%
ValueCountFrequency (%)
2645 1
0.1%
637 1
0.1%
632 1
0.1%
588 1
0.1%
300 1
0.1%
227 1
0.1%
209 1
0.1%
197 1
0.1%
192 1
0.1%
191 1
0.1%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct55
Distinct (%)14.3%
Missing592
Missing (%)60.7%
Infinite0
Infinite (%)0.0%
Mean19.140625
Minimum0
Maximum2240
Zeros243
Zeros (%)24.9%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2024-03-23T06:06:29.680809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile62.95
Maximum2240
Range2240
Interquartile range (IQR)2

Descriptive statistics

Standard deviation128.55217
Coefficient of variation (CV)6.7161953
Kurtosis237.01442
Mean19.140625
Median Absolute Deviation (MAD)0
Skewness14.357214
Sum7350
Variance16525.662
MonotonicityNot monotonic
2024-03-23T06:06:30.242025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 243
24.9%
1 34
 
3.5%
3 16
 
1.6%
2 14
 
1.4%
4 8
 
0.8%
9 4
 
0.4%
38 3
 
0.3%
5 2
 
0.2%
14 2
 
0.2%
20 2
 
0.2%
Other values (45) 56
 
5.7%
(Missing) 592
60.7%
ValueCountFrequency (%)
0 243
24.9%
1 34
 
3.5%
2 14
 
1.4%
3 16
 
1.6%
4 8
 
0.8%
5 2
 
0.2%
6 2
 
0.2%
7 1
 
0.1%
9 4
 
0.4%
10 2
 
0.2%
ValueCountFrequency (%)
2240 1
0.1%
661 1
0.1%
660 1
0.1%
438 1
0.1%
386 1
0.1%
215 1
0.1%
152 2
0.2%
150 1
0.1%
137 1
0.1%
104 1
0.1%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct47
Distinct (%)11.6%
Missing572
Missing (%)58.6%
Infinite0
Infinite (%)0.0%
Mean17.769802
Minimum0
Maximum2199
Zeros276
Zeros (%)28.3%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2024-03-23T06:06:30.983317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile72.05
Maximum2199
Range2199
Interquartile range (IQR)2

Descriptive statistics

Standard deviation120.69784
Coefficient of variation (CV)6.7923009
Kurtosis268.48802
Mean17.769802
Median Absolute Deviation (MAD)0
Skewness15.352585
Sum7179
Variance14567.969
MonotonicityNot monotonic
2024-03-23T06:06:31.678178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 276
28.3%
1 26
 
2.7%
3 17
 
1.7%
2 15
 
1.5%
7 7
 
0.7%
6 5
 
0.5%
12 4
 
0.4%
14 3
 
0.3%
4 3
 
0.3%
8 2
 
0.2%
Other values (37) 46
 
4.7%
(Missing) 572
58.6%
ValueCountFrequency (%)
0 276
28.3%
1 26
 
2.7%
2 15
 
1.5%
3 17
 
1.7%
4 3
 
0.3%
6 5
 
0.5%
7 7
 
0.7%
8 2
 
0.2%
9 2
 
0.2%
11 1
 
0.1%
ValueCountFrequency (%)
2199 1
0.1%
602 2
0.2%
245 2
0.2%
185 1
0.1%
184 1
0.1%
168 1
0.1%
156 1
0.1%
154 1
0.1%
144 1
0.1%
115 1
0.1%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct46
Distinct (%)10.6%
Missing540
Missing (%)55.3%
Infinite0
Infinite (%)0.0%
Mean13.988532
Minimum0
Maximum1805
Zeros334
Zeros (%)34.2%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2024-03-23T06:06:32.279141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile47.5
Maximum1805
Range1805
Interquartile range (IQR)0

Descriptive statistics

Standard deviation97.563453
Coefficient of variation (CV)6.9745312
Kurtosis266.10667
Mean13.988532
Median Absolute Deviation (MAD)0
Skewness15.158604
Sum6099
Variance9518.6275
MonotonicityNot monotonic
2024-03-23T06:06:32.814157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 334
34.2%
1 18
 
1.8%
6 7
 
0.7%
2 6
 
0.6%
5 6
 
0.6%
11 5
 
0.5%
10 4
 
0.4%
3 4
 
0.4%
21 4
 
0.4%
23 4
 
0.4%
Other values (36) 44
 
4.5%
(Missing) 540
55.3%
ValueCountFrequency (%)
0 334
34.2%
1 18
 
1.8%
2 6
 
0.6%
3 4
 
0.4%
5 6
 
0.6%
6 7
 
0.7%
7 3
 
0.3%
8 2
 
0.2%
9 1
 
0.1%
10 4
 
0.4%
ValueCountFrequency (%)
1805 1
0.1%
550 2
0.2%
207 2
0.2%
205 1
0.1%
187 1
0.1%
185 1
0.1%
159 1
0.1%
141 1
0.1%
124 1
0.1%
122 1
0.1%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct56
Distinct (%)11.2%
Missing476
Missing (%)48.8%
Infinite0
Infinite (%)0.0%
Mean16.896
Minimum0
Maximum2408
Zeros339
Zeros (%)34.7%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2024-03-23T06:06:33.529590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile59.05
Maximum2408
Range2408
Interquartile range (IQR)2

Descriptive statistics

Standard deviation121.81453
Coefficient of variation (CV)7.2096665
Kurtosis303.56617
Mean16.896
Median Absolute Deviation (MAD)0
Skewness16.198695
Sum8448
Variance14838.779
MonotonicityNot monotonic
2024-03-23T06:06:34.338704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 339
34.7%
1 22
 
2.3%
3 21
 
2.2%
2 15
 
1.5%
4 9
 
0.9%
8 7
 
0.7%
12 7
 
0.7%
5 6
 
0.6%
6 4
 
0.4%
9 4
 
0.4%
Other values (46) 66
 
6.8%
(Missing) 476
48.8%
ValueCountFrequency (%)
0 339
34.7%
1 22
 
2.3%
2 15
 
1.5%
3 21
 
2.2%
4 9
 
0.9%
5 6
 
0.6%
6 4
 
0.4%
7 3
 
0.3%
8 7
 
0.7%
9 4
 
0.4%
ValueCountFrequency (%)
2408 1
0.1%
836 1
0.1%
512 2
0.2%
335 2
0.2%
204 1
0.1%
190 1
0.1%
171 1
0.1%
150 1
0.1%
144 2
0.2%
131 1
0.1%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct69
Distinct (%)12.1%
Missing404
Missing (%)41.4%
Infinite0
Infinite (%)0.0%
Mean29.444056
Minimum0
Maximum4695
Zeros370
Zeros (%)37.9%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2024-03-23T06:06:35.118613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile77
Maximum4695
Range4695
Interquartile range (IQR)2

Descriptive statistics

Standard deviation239.20113
Coefficient of variation (CV)8.123919
Kurtosis279.86566
Mean29.444056
Median Absolute Deviation (MAD)0
Skewness15.71577
Sum16842
Variance57217.179
MonotonicityNot monotonic
2024-03-23T06:06:35.770423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 370
37.9%
1 40
 
4.1%
2 22
 
2.3%
3 17
 
1.7%
4 12
 
1.2%
7 8
 
0.8%
9 7
 
0.7%
8 4
 
0.4%
18 4
 
0.4%
15 4
 
0.4%
Other values (59) 84
 
8.6%
(Missing) 404
41.4%
ValueCountFrequency (%)
0 370
37.9%
1 40
 
4.1%
2 22
 
2.3%
3 17
 
1.7%
4 12
 
1.2%
5 4
 
0.4%
6 3
 
0.3%
7 8
 
0.8%
8 4
 
0.4%
9 7
 
0.7%
ValueCountFrequency (%)
4695 1
0.1%
2642 1
0.1%
1101 1
0.1%
1087 1
0.1%
678 1
0.1%
449 2
0.2%
421 1
0.1%
405 1
0.1%
363 1
0.1%
202 1
0.1%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct95
Distinct (%)14.8%
Missing336
Missing (%)34.4%
Infinite0
Infinite (%)0.0%
Mean63.03125
Minimum0
Maximum11604
Zeros367
Zeros (%)37.6%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2024-03-23T06:06:36.458359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile166.4
Maximum11604
Range11604
Interquartile range (IQR)4

Descriptive statistics

Standard deviation569.93608
Coefficient of variation (CV)9.0421193
Kurtosis320.96818
Mean63.03125
Median Absolute Deviation (MAD)0
Skewness17.286859
Sum40340
Variance324827.14
MonotonicityNot monotonic
2024-03-23T06:06:37.153482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 367
37.6%
1 52
 
5.3%
2 42
 
4.3%
4 14
 
1.4%
7 10
 
1.0%
5 10
 
1.0%
3 9
 
0.9%
10 6
 
0.6%
9 6
 
0.6%
6 5
 
0.5%
Other values (85) 119
 
12.2%
(Missing) 336
34.4%
ValueCountFrequency (%)
0 367
37.6%
1 52
 
5.3%
2 42
 
4.3%
3 9
 
0.9%
4 14
 
1.4%
5 10
 
1.0%
6 5
 
0.5%
7 10
 
1.0%
8 2
 
0.2%
9 6
 
0.6%
ValueCountFrequency (%)
11604 1
0.1%
7960 1
0.1%
1421 1
0.1%
1397 1
0.1%
1213 1
0.1%
802 1
0.1%
775 1
0.1%
717 1
0.1%
688 1
0.1%
686 1
0.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct76
Distinct (%)11.2%
Missing300
Missing (%)30.7%
Infinite0
Infinite (%)0.0%
Mean29.616864
Minimum0
Maximum5807
Zeros464
Zeros (%)47.5%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2024-03-23T06:06:37.788811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile69
Maximum5807
Range5807
Interquartile range (IQR)2

Descriptive statistics

Standard deviation259.78818
Coefficient of variation (CV)8.7716303
Kurtosis387.24195
Mean29.616864
Median Absolute Deviation (MAD)0
Skewness18.631912
Sum20021
Variance67489.899
MonotonicityNot monotonic
2024-03-23T06:06:38.536432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 464
47.5%
1 28
 
2.9%
2 23
 
2.4%
4 10
 
1.0%
3 8
 
0.8%
8 8
 
0.8%
6 8
 
0.8%
12 7
 
0.7%
7 6
 
0.6%
13 6
 
0.6%
Other values (66) 108
 
11.1%
(Missing) 300
30.7%
ValueCountFrequency (%)
0 464
47.5%
1 28
 
2.9%
2 23
 
2.4%
3 8
 
0.8%
4 10
 
1.0%
5 5
 
0.5%
6 8
 
0.8%
7 6
 
0.6%
8 8
 
0.8%
9 4
 
0.4%
ValueCountFrequency (%)
5807 1
0.1%
2958 1
0.1%
1107 1
0.1%
535 1
0.1%
509 1
0.1%
415 1
0.1%
396 1
0.1%
386 2
0.2%
366 2
0.2%
345 1
0.1%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct71
Distinct (%)10.3%
Missing288
Missing (%)29.5%
Infinite0
Infinite (%)0.0%
Mean27.473837
Minimum0
Maximum5650
Zeros481
Zeros (%)49.3%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2024-03-23T06:06:39.429892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile59.65
Maximum5650
Range5650
Interquartile range (IQR)1

Descriptive statistics

Standard deviation257.06705
Coefficient of variation (CV)9.3567946
Kurtosis361.25568
Mean27.473837
Median Absolute Deviation (MAD)0
Skewness17.928686
Sum18902
Variance66083.469
MonotonicityNot monotonic
2024-03-23T06:06:39.940805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 481
49.3%
1 37
 
3.8%
2 34
 
3.5%
4 11
 
1.1%
3 9
 
0.9%
11 6
 
0.6%
5 5
 
0.5%
10 4
 
0.4%
12 4
 
0.4%
15 3
 
0.3%
Other values (61) 94
 
9.6%
(Missing) 288
29.5%
ValueCountFrequency (%)
0 481
49.3%
1 37
 
3.8%
2 34
 
3.5%
3 9
 
0.9%
4 11
 
1.1%
5 5
 
0.5%
6 3
 
0.3%
7 3
 
0.3%
9 3
 
0.3%
10 4
 
0.4%
ValueCountFrequency (%)
5650 1
0.1%
3001 1
0.1%
1519 1
0.1%
868 1
0.1%
700 1
0.1%
505 2
0.2%
376 1
0.1%
369 1
0.1%
354 1
0.1%
293 1
0.1%

Interactions

2024-03-23T06:06:06.333965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:27.059280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:32.786903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:38.843581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:45.860413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:53.101224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:58.245733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:03.042446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:07.942665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:12.821131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:18.067039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:24.593554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:30.650373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T06:05:58.657412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:06:04.137977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:06:09.715734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:30.958750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:36.848434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:43.870375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:50.800326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:56.654474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:01.350255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:06.314765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:11.342922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:16.310229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:22.165683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:28.746241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:34.448664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:40.788696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:45.975948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:52.681779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:58.935965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:06:04.401970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:06:10.058055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:31.238055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:37.141717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:44.213411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:51.224068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:56.965794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:01.610603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:06.563218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:11.603420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:16.582459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:22.532339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:29.071256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:34.850536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:41.048017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:46.361256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:52.977562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:59.293576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:06:04.715081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:06:10.328379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:31.477334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:37.468512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:44.542072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:51.547921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:57.290096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:01.910181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:06.875716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:11.837519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:16.826683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:22.886524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:29.390396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:35.109331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:41.371024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:46.725756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:53.379637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:59.692852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:06:05.195761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:06:10.639843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:31.699039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:37.796193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:44.873132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:51.838587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:57.514669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:02.196872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:07.171005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:12.073608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:17.130047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:23.369187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:29.645263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:35.343657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:41.637898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:47.114161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:53.697953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:06:00.113134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:06:05.442558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:06:10.915031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:32.110854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:38.134795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:45.169791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:52.232113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:57.760147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:02.482387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:07.427295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:12.328344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:17.504469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:23.765928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:29.932868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:35.626903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:41.876896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:47.400583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:54.030480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:06:00.528727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:06:05.787098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:06:11.239778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:32.528054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:38.491606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:45.459121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:52.719630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:04:58.008428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:02.800782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:07.682910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:12.580897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:17.822960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:24.243495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:30.309465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:35.907798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:42.138102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:47.931332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:05:54.357777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:06:00.956137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:06:06.062366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T06:06:40.346021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0001.0001.0000.9820.9820.8410.9820.9360.9350.9810.8920.6730.8920.8400.8400.9820.9820.840
20071.0001.0001.0000.8860.8410.9570.8410.7240.7210.8400.6400.7150.6410.9560.9560.8400.8400.956
20081.0001.0001.0000.9820.9820.8410.9820.9370.9350.9810.8920.6740.8920.8400.8400.9820.9820.841
20090.9820.8860.9821.0000.9880.8860.9820.9370.9350.9810.8920.6740.8930.8400.8400.9820.9820.841
20100.9820.8410.9820.9881.0001.0000.9860.9370.9360.9810.8920.6740.8930.8400.8400.9820.9820.841
20110.8410.9570.8410.8861.0001.0000.8750.7240.7210.8400.6410.7150.6420.9560.9560.8410.8410.957
20120.9820.8410.9820.9820.9860.8751.0000.9640.9680.9920.9510.6740.8930.8400.8400.9820.9820.841
20130.9360.7240.9370.9370.9370.7240.9641.0000.9760.9860.9640.6740.8930.7230.7230.9360.9360.724
20140.9350.7210.9350.9350.9360.7210.9680.9761.0000.9920.9630.6740.8920.9360.9360.9360.9360.721
20150.9810.8400.9810.9810.9810.8400.9920.9860.9921.0000.9640.6740.8930.9810.9810.9810.9810.840
20160.8920.6400.8920.8920.8920.6410.9510.9640.9630.9641.0000.9810.9810.9810.8930.8930.8930.641
20170.6730.7150.6740.6740.6740.7150.6740.6740.6740.6740.9811.0000.9940.9820.8930.8930.8930.642
20180.8920.6410.8920.8930.8930.6420.8930.8930.8920.8930.9810.9941.0000.9820.8930.8930.8930.642
20190.8400.9560.8400.8400.8400.9560.8400.7230.9360.9810.9810.9820.9821.0000.9910.9270.8400.956
20200.8400.9560.8400.8400.8400.9560.8400.7230.9360.9810.8930.8930.8930.9911.0001.0001.0000.991
20210.9820.8400.9820.9820.9820.8410.9820.9360.9360.9810.8930.8930.8930.9271.0001.0000.9920.886
20220.9820.8400.9820.9820.9820.8410.9820.9360.9360.9810.8930.8930.8930.8401.0000.9921.0001.000
20230.8400.9560.8410.8410.8410.9570.8410.7240.7210.8400.6410.6420.6420.9560.9910.8861.0001.000
2024-03-23T06:06:41.045825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9040.8610.7940.6210.5950.5460.4600.6260.5270.4130.3300.3680.5020.4860.5510.5350.408
20070.9041.0000.8740.8030.6070.5770.5060.4170.5780.4980.3730.3000.3710.5150.5010.5530.5380.434
20080.8610.8741.0000.8250.6250.5990.5300.4320.5930.5080.4400.3730.4400.5190.5020.5600.5510.440
20090.7940.8030.8251.0000.7940.7320.6270.5230.5850.4600.3830.3160.3490.5060.4880.5440.5360.423
20100.6210.6070.6250.7941.0000.8620.7160.5940.6270.4870.4110.3390.3570.4890.4820.5510.5020.389
20110.5950.5770.5990.7320.8621.0000.8010.6660.6420.5200.4590.3650.3620.4790.4310.5010.5080.425
20120.5460.5060.5300.6270.7160.8011.0000.7820.7000.5750.5000.3940.3530.4710.4270.4730.4890.382
20130.4600.4170.4320.5230.5940.6660.7821.0000.7540.6090.4880.4400.3500.4560.3810.4060.4330.321
20140.6260.5780.5930.5850.6270.6420.7000.7541.0000.7730.6310.5780.5690.5410.5050.4710.4850.433
20150.5270.4980.5080.4600.4870.5200.5750.6090.7731.0000.6940.6620.6350.5820.5000.4280.4750.413
20160.4130.3730.4400.3830.4110.4590.5000.4880.6310.6941.0000.7330.6730.6130.4980.5190.4740.464
20170.3300.3000.3730.3160.3390.3650.3940.4400.5780.6620.7331.0000.7450.6250.5120.4600.4500.470
20180.3680.3710.4400.3490.3570.3620.3530.3500.5690.6350.6730.7451.0000.7010.5980.5120.5380.507
20190.5020.5150.5190.5060.4890.4790.4710.4560.5410.5820.6130.6250.7011.0000.7200.6570.6830.640
20200.4860.5010.5020.4880.4820.4310.4270.3810.5050.5000.4980.5120.5980.7201.0000.7210.7000.647
20210.5510.5530.5600.5440.5510.5010.4730.4060.4710.4280.5190.4600.5120.6570.7211.0000.7970.627
20220.5350.5380.5510.5360.5020.5080.4890.4330.4850.4750.4740.4500.5380.6830.7000.7971.0000.715
20230.4080.4340.4400.4230.3890.4250.3820.3210.4330.4130.4640.4700.5070.6400.6470.6270.7151.000

Missing values

2024-03-23T06:06:11.833688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T06:06:12.813082image/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.
2024-03-23T06:06:13.678216image/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_토지거래허가처리200620072008200920102011201220132014201520162017201820192020202120222023
0전국_허가1296341266831079775037742969273848676560530362645224021991805240846951160458075650
1전국_불허가125901362693205083413423327393471086382575277124339196184
2전국_불허(이용목적부적합)1886167788538931913947402471613141128973672
3전국_불허(기타)1070411949843546943815219369230784566644386696242160112
4서울_허가26484925488686895767626111985108104108122105678142111073001
5서울_불허가222273224985370311315153214171215473660
6서울_불허(이용목적부적합)1057456422311622302315736
7서울_불허(기타)117199168563059251113123212141110403354
8서울 종로구_허가391901131319200500000001348
9서울 종로구_불허가6290000000000000000
지역_토지거래허가처리200620072008200920102011201220132014201520162017201820192020202120222023
966제주_불허(이용목적부적합)0000000000910134434
967제주_불허(기타)007000000058664913
968제주 제주시_허가<NA><NA><NA>108400<NA><NA>10000007
969제주 제주시_불허가<NA><NA><NA>00000<NA><NA>00000000
970제주 제주시_불허(이용목적부적합)<NA><NA><NA>00000<NA><NA>00000000
971제주 제주시_불허(기타)<NA><NA><NA>00000<NA><NA>00000000
972제주 서귀포시_허가527281191122941660602550512449490366369
973제주 서귀포시_불허가007000000014967713537
974제주 서귀포시_불허(이용목적부적합)0000000000910134434
975제주 서귀포시_불허(기타)007000000058664913