Overview

Dataset statistics

Number of variables19
Number of observations296
Missing cells555
Missing cells (%)9.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory49.3 KiB
Average record size in memory170.4 B

Variable types

Text1
Numeric18

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 부동산거래현황 중 주택 거래현황의 연도별 신탁 신탁해지(동(호)수) 데이터입니다.- (단위 : 동(호)수)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15100936/fileData.do

Alerts

2006 is highly overall correlated with 2007 and 11 other fieldsHigh correlation
2007 is highly overall correlated with 2006 and 15 other fieldsHigh correlation
2008 is highly overall correlated with 2007 and 11 other fieldsHigh correlation
2009 is highly overall correlated with 2007 and 13 other fieldsHigh correlation
2010 is highly overall correlated with 2007 and 15 other fieldsHigh correlation
2011 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2012 is highly overall correlated with 2007 and 15 other fieldsHigh correlation
2013 is highly overall correlated with 2006 and 16 other fieldsHigh correlation
2014 is highly overall correlated with 2006 and 15 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 14 other fieldsHigh correlation
2018 is highly overall correlated with 2007 and 15 other fieldsHigh correlation
2019 is highly overall correlated with 2006 and 15 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 15 other fieldsHigh correlation
2023 is highly overall correlated with 2006 and 14 other fieldsHigh correlation
2006 has 83 (28.0%) missing valuesMissing
2007 has 60 (20.3%) missing valuesMissing
2008 has 51 (17.2%) missing valuesMissing
2009 has 44 (14.9%) missing valuesMissing
2010 has 35 (11.8%) missing valuesMissing
2011 has 33 (11.1%) missing valuesMissing
2012 has 28 (9.5%) missing valuesMissing
2013 has 26 (8.8%) missing valuesMissing
2014 has 35 (11.8%) missing valuesMissing
2015 has 22 (7.4%) missing valuesMissing
2016 has 18 (6.1%) missing valuesMissing
2017 has 19 (6.4%) missing valuesMissing
2018 has 17 (5.7%) missing valuesMissing
2019 has 17 (5.7%) missing valuesMissing
2020 has 17 (5.7%) missing valuesMissing
2021 has 17 (5.7%) missing valuesMissing
2022 has 17 (5.7%) missing valuesMissing
2023 has 16 (5.4%) missing valuesMissing
지역 has unique valuesUnique
2007 has 11 (3.7%) zerosZeros
2008 has 20 (6.8%) zerosZeros
2009 has 31 (10.5%) zerosZeros
2010 has 31 (10.5%) zerosZeros
2011 has 42 (14.2%) zerosZeros
2012 has 32 (10.8%) zerosZeros
2013 has 32 (10.8%) zerosZeros
2015 has 10 (3.4%) zerosZeros
2016 has 10 (3.4%) zerosZeros
2017 has 3 (1.0%) zerosZeros
2018 has 3 (1.0%) zerosZeros
2020 has 4 (1.4%) zerosZeros
2021 has 5 (1.7%) zerosZeros
2022 has 4 (1.4%) zerosZeros
2023 has 7 (2.4%) zerosZeros

Reproduction

Analysis started2024-03-30 07:20:21.204735
Analysis finished2024-03-30 07:22:35.514103
Duration2 minutes and 14.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Text

UNIQUE 

Distinct296
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2024-03-30T07:22:36.112489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length6
Mean length6.2736486
Min length3

Characters and Unicode

Total characters1857
Distinct characters147
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique296 ?
Unique (%)100.0%

Sample

1st row전국
2nd row서울
3rd row서울 종로구
4th row서울 중구
5th row서울 용산구
ValueCountFrequency (%)
경기 53
 
9.2%
경남 27
 
4.7%
경북 26
 
4.5%
서울 26
 
4.5%
전남 23
 
4.0%
충남 20
 
3.5%
충북 20
 
3.5%
강원 19
 
3.3%
부산 17
 
3.0%
전북 17
 
3.0%
Other values (262) 325
56.7%
2024-03-30T07:22:37.381246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
296
 
15.9%
135
 
7.3%
121
 
6.5%
109
 
5.9%
91
 
4.9%
88
 
4.7%
71
 
3.8%
56
 
3.0%
50
 
2.7%
48
 
2.6%
Other values (137) 792
42.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1535
82.7%
Space Separator 296
 
15.9%
Close Punctuation 13
 
0.7%
Open Punctuation 13
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
135
 
8.8%
121
 
7.9%
109
 
7.1%
91
 
5.9%
88
 
5.7%
71
 
4.6%
56
 
3.6%
50
 
3.3%
48
 
3.1%
42
 
2.7%
Other values (134) 724
47.2%
Space Separator
ValueCountFrequency (%)
296
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1535
82.7%
Common 322
 
17.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
135
 
8.8%
121
 
7.9%
109
 
7.1%
91
 
5.9%
88
 
5.7%
71
 
4.6%
56
 
3.6%
50
 
3.3%
48
 
3.1%
42
 
2.7%
Other values (134) 724
47.2%
Common
ValueCountFrequency (%)
296
91.9%
) 13
 
4.0%
( 13
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1535
82.7%
ASCII 322
 
17.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
296
91.9%
) 13
 
4.0%
( 13
 
4.0%
Hangul
ValueCountFrequency (%)
135
 
8.8%
121
 
7.9%
109
 
7.1%
91
 
5.9%
88
 
5.7%
71
 
4.6%
56
 
3.6%
50
 
3.3%
48
 
3.1%
42
 
2.7%
Other values (134) 724
47.2%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct161
Distinct (%)75.6%
Missing83
Missing (%)28.0%
Infinite0
Infinite (%)0.0%
Mean1276.9906
Minimum1
Maximum85726
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T07:22:37.854470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q121
median192
Q3731
95-th percentile3229.8
Maximum85726
Range85725
Interquartile range (IQR)710

Descriptive statistics

Standard deviation6356.3341
Coefficient of variation (CV)4.9775888
Kurtosis150.64344
Mean1276.9906
Median Absolute Deviation (MAD)186
Skewness11.716514
Sum271999
Variance40402983
MonotonicityNot monotonic
2024-03-30T07:22:38.240549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 12
 
4.1%
6 6
 
2.0%
7 6
 
2.0%
2 5
 
1.7%
12 3
 
1.0%
143 3
 
1.0%
19 3
 
1.0%
10 3
 
1.0%
45 3
 
1.0%
3 3
 
1.0%
Other values (151) 166
56.1%
(Missing) 83
28.0%
ValueCountFrequency (%)
1 12
4.1%
2 5
1.7%
3 3
 
1.0%
5 2
 
0.7%
6 6
2.0%
7 6
2.0%
8 2
 
0.7%
9 1
 
0.3%
10 3
 
1.0%
11 1
 
0.3%
ValueCountFrequency (%)
85726 1
0.3%
31261 1
0.3%
14425 1
0.3%
7297 1
0.3%
6672 1
0.3%
6216 1
0.3%
5382 1
0.3%
3935 1
0.3%
3725 1
0.3%
3459 1
0.3%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct178
Distinct (%)75.4%
Missing60
Missing (%)20.3%
Infinite0
Infinite (%)0.0%
Mean1051.1356
Minimum0
Maximum80595
Zeros11
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T07:22:38.694800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q140.75
median184.5
Q3721.25
95-th percentile3130.75
Maximum80595
Range80595
Interquartile range (IQR)680.5

Descriptive statistics

Standard deviation5487.2587
Coefficient of variation (CV)5.2203148
Kurtosis190.11758
Mean1051.1356
Median Absolute Deviation (MAD)182.5
Skewness13.239953
Sum248068
Variance30110008
MonotonicityNot monotonic
2024-03-30T07:22:39.256177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 15
 
5.1%
0 11
 
3.7%
19 3
 
1.0%
16 3
 
1.0%
5 3
 
1.0%
2 3
 
1.0%
123 3
 
1.0%
88 3
 
1.0%
77 3
 
1.0%
4 2
 
0.7%
Other values (168) 187
63.2%
(Missing) 60
 
20.3%
ValueCountFrequency (%)
0 11
3.7%
1 15
5.1%
2 3
 
1.0%
3 1
 
0.3%
4 2
 
0.7%
5 3
 
1.0%
9 2
 
0.7%
10 1
 
0.3%
12 1
 
0.3%
13 1
 
0.3%
ValueCountFrequency (%)
80595 1
0.3%
15532 1
0.3%
14005 1
0.3%
9310 1
0.3%
7838 1
0.3%
7649 1
0.3%
5003 1
0.3%
4183 1
0.3%
4051 1
0.3%
3620 1
0.3%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct181
Distinct (%)73.9%
Missing51
Missing (%)17.2%
Infinite0
Infinite (%)0.0%
Mean990.2
Minimum0
Maximum79423
Zeros20
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T07:22:39.842869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q122
median154
Q3604
95-th percentile3229.4
Maximum79423
Range79423
Interquartile range (IQR)582

Descriptive statistics

Standard deviation5272.0024
Coefficient of variation (CV)5.3241794
Kurtosis202.92546
Mean990.2
Median Absolute Deviation (MAD)149
Skewness13.711887
Sum242599
Variance27794009
MonotonicityNot monotonic
2024-03-30T07:22:40.409757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20
 
6.8%
2 8
 
2.7%
1 7
 
2.4%
5 4
 
1.4%
9 3
 
1.0%
82 3
 
1.0%
7 3
 
1.0%
156 3
 
1.0%
326 2
 
0.7%
928 2
 
0.7%
Other values (171) 190
64.2%
(Missing) 51
 
17.2%
ValueCountFrequency (%)
0 20
6.8%
1 7
 
2.4%
2 8
 
2.7%
3 1
 
0.3%
4 2
 
0.7%
5 4
 
1.4%
7 3
 
1.0%
8 2
 
0.7%
9 3
 
1.0%
10 2
 
0.7%
ValueCountFrequency (%)
79423 1
0.3%
12539 1
0.3%
10857 1
0.3%
8037 1
0.3%
7776 1
0.3%
7602 1
0.3%
6927 1
0.3%
4893 1
0.3%
4346 1
0.3%
4169 1
0.3%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct168
Distinct (%)66.7%
Missing44
Missing (%)14.9%
Infinite0
Infinite (%)0.0%
Mean1035.9286
Minimum0
Maximum83976
Zeros31
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T07:22:41.396785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median93
Q3441.75
95-th percentile3714
Maximum83976
Range83976
Interquartile range (IQR)432.75

Descriptive statistics

Standard deviation5541.1943
Coefficient of variation (CV)5.3490119
Kurtosis202.03301
Mean1035.9286
Median Absolute Deviation (MAD)92
Skewness13.599656
Sum261054
Variance30704834
MonotonicityNot monotonic
2024-03-30T07:22:41.952046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31
 
10.5%
1 10
 
3.4%
8 5
 
1.7%
9 5
 
1.7%
35 4
 
1.4%
57 4
 
1.4%
2 4
 
1.4%
6 3
 
1.0%
7 3
 
1.0%
134 3
 
1.0%
Other values (158) 180
60.8%
(Missing) 44
 
14.9%
ValueCountFrequency (%)
0 31
10.5%
1 10
 
3.4%
2 4
 
1.4%
3 1
 
0.3%
4 2
 
0.7%
5 2
 
0.7%
6 3
 
1.0%
7 3
 
1.0%
8 5
 
1.7%
9 5
 
1.7%
ValueCountFrequency (%)
83976 1
0.3%
13362 1
0.3%
12925 1
0.3%
10798 1
0.3%
8493 1
0.3%
7517 1
0.3%
6283 1
0.3%
5781 1
0.3%
5669 1
0.3%
5291 1
0.3%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct192
Distinct (%)73.6%
Missing35
Missing (%)11.8%
Infinite0
Infinite (%)0.0%
Mean984.81992
Minimum0
Maximum80931
Zeros31
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T07:22:42.518414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118
median131
Q3555
95-th percentile3865
Maximum80931
Range80931
Interquartile range (IQR)537

Descriptive statistics

Standard deviation5260.4882
Coefficient of variation (CV)5.3415737
Kurtosis207.6298
Mean984.81992
Median Absolute Deviation (MAD)131
Skewness13.821382
Sum257038
Variance27672736
MonotonicityNot monotonic
2024-03-30T07:22:43.152116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31
 
10.5%
1 10
 
3.4%
2 5
 
1.7%
5 4
 
1.4%
4 3
 
1.0%
28 2
 
0.7%
32 2
 
0.7%
64 2
 
0.7%
38 2
 
0.7%
100 2
 
0.7%
Other values (182) 198
66.9%
(Missing) 35
 
11.8%
ValueCountFrequency (%)
0 31
10.5%
1 10
 
3.4%
2 5
 
1.7%
3 2
 
0.7%
4 3
 
1.0%
5 4
 
1.4%
6 1
 
0.3%
7 1
 
0.3%
8 1
 
0.3%
10 2
 
0.7%
ValueCountFrequency (%)
80931 1
0.3%
19917 1
0.3%
8671 1
0.3%
6930 1
0.3%
6635 1
0.3%
6544 1
0.3%
6390 1
0.3%
4766 1
0.3%
4682 1
0.3%
4469 1
0.3%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct179
Distinct (%)68.1%
Missing33
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean924.42205
Minimum0
Maximum74759
Zeros42
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T07:22:43.820520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median145
Q3503.5
95-th percentile3319.5
Maximum74759
Range74759
Interquartile range (IQR)493.5

Descriptive statistics

Standard deviation4881.0759
Coefficient of variation (CV)5.2801379
Kurtosis202.679
Mean924.42205
Median Absolute Deviation (MAD)145
Skewness13.626413
Sum243123
Variance23824902
MonotonicityNot monotonic
2024-03-30T07:22:44.572011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 42
 
14.2%
1 10
 
3.4%
15 5
 
1.7%
2 4
 
1.4%
40 3
 
1.0%
153 3
 
1.0%
10 3
 
1.0%
66 2
 
0.7%
18 2
 
0.7%
12 2
 
0.7%
Other values (169) 187
63.2%
(Missing) 33
 
11.1%
ValueCountFrequency (%)
0 42
14.2%
1 10
 
3.4%
2 4
 
1.4%
3 1
 
0.3%
4 2
 
0.7%
5 1
 
0.3%
6 2
 
0.7%
7 2
 
0.7%
8 1
 
0.3%
10 3
 
1.0%
ValueCountFrequency (%)
74759 1
0.3%
21400 1
0.3%
8299 1
0.3%
6258 1
0.3%
5446 1
0.3%
4903 1
0.3%
4840 1
0.3%
4661 1
0.3%
4582 1
0.3%
4284 1
0.3%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct168
Distinct (%)62.7%
Missing28
Missing (%)9.5%
Infinite0
Infinite (%)0.0%
Mean777.15672
Minimum0
Maximum67356
Zeros32
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T07:22:45.255858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median83.5
Q3392.75
95-th percentile2395.35
Maximum67356
Range67356
Interquartile range (IQR)388.75

Descriptive statistics

Standard deviation4322.2614
Coefficient of variation (CV)5.5616342
Kurtosis213.06138
Mean777.15672
Median Absolute Deviation (MAD)83.5
Skewness13.973487
Sum208278
Variance18681943
MonotonicityNot monotonic
2024-03-30T07:22:45.938149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32
 
10.8%
1 12
 
4.1%
2 10
 
3.4%
4 10
 
3.4%
3 6
 
2.0%
6 5
 
1.7%
12 3
 
1.0%
43 3
 
1.0%
30 3
 
1.0%
23 3
 
1.0%
Other values (158) 181
61.1%
(Missing) 28
 
9.5%
ValueCountFrequency (%)
0 32
10.8%
1 12
 
4.1%
2 10
 
3.4%
3 6
 
2.0%
4 10
 
3.4%
5 1
 
0.3%
6 5
 
1.7%
7 2
 
0.7%
9 1
 
0.3%
10 1
 
0.3%
ValueCountFrequency (%)
67356 1
0.3%
12984 1
0.3%
11567 1
0.3%
7118 1
0.3%
6973 1
0.3%
5894 1
0.3%
5728 1
0.3%
4785 1
0.3%
3233 1
0.3%
3080 1
0.3%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct172
Distinct (%)63.7%
Missing26
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean559.54815
Minimum0
Maximum48001
Zeros32
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T07:22:46.461119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median104
Q3306.25
95-th percentile1570.2
Maximum48001
Range48001
Interquartile range (IQR)298.25

Descriptive statistics

Standard deviation3106.3363
Coefficient of variation (CV)5.5515085
Kurtosis204.91127
Mean559.54815
Median Absolute Deviation (MAD)103
Skewness13.676658
Sum151078
Variance9649325.2
MonotonicityNot monotonic
2024-03-30T07:22:47.128344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32
 
10.8%
1 10
 
3.4%
3 8
 
2.7%
6 6
 
2.0%
2 6
 
2.0%
12 4
 
1.4%
4 4
 
1.4%
11 4
 
1.4%
19 3
 
1.0%
161 3
 
1.0%
Other values (162) 190
64.2%
(Missing) 26
 
8.8%
ValueCountFrequency (%)
0 32
10.8%
1 10
 
3.4%
2 6
 
2.0%
3 8
 
2.7%
4 4
 
1.4%
6 6
 
2.0%
7 1
 
0.3%
8 2
 
0.7%
9 1
 
0.3%
10 1
 
0.3%
ValueCountFrequency (%)
48001 1
0.3%
11515 1
0.3%
11133 1
0.3%
5652 1
0.3%
3864 1
0.3%
3174 1
0.3%
2293 1
0.3%
2119 1
0.3%
2053 1
0.3%
1866 1
0.3%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct210
Distinct (%)80.5%
Missing35
Missing (%)11.8%
Infinite0
Infinite (%)0.0%
Mean971.67816
Minimum1
Maximum79039
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T07:22:47.665726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q152
median206
Q3569
95-th percentile2276
Maximum79039
Range79038
Interquartile range (IQR)517

Descriptive statistics

Standard deviation5174.231
Coefficient of variation (CV)5.3250461
Kurtosis201.88976
Mean971.67816
Median Absolute Deviation (MAD)185
Skewness13.60469
Sum253608
Variance26772666
MonotonicityNot monotonic
2024-03-30T07:22:48.091737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 11
 
3.7%
3 5
 
1.7%
110 4
 
1.4%
50 3
 
1.0%
68 3
 
1.0%
5 3
 
1.0%
93 3
 
1.0%
7 3
 
1.0%
23 3
 
1.0%
36 2
 
0.7%
Other values (200) 221
74.7%
(Missing) 35
 
11.8%
ValueCountFrequency (%)
1 11
3.7%
2 2
 
0.7%
3 5
1.7%
4 1
 
0.3%
5 3
 
1.0%
6 2
 
0.7%
7 3
 
1.0%
8 1
 
0.3%
10 2
 
0.7%
11 2
 
0.7%
ValueCountFrequency (%)
79039 1
0.3%
20019 1
0.3%
14960 1
0.3%
8830 1
0.3%
6552 1
0.3%
5571 1
0.3%
5445 1
0.3%
4200 1
0.3%
3628 1
0.3%
3449 1
0.3%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct213
Distinct (%)77.7%
Missing22
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean1235.6387
Minimum0
Maximum105870
Zeros10
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T07:22:48.763762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q148
median206
Q3707.5
95-th percentile2981.75
Maximum105870
Range105870
Interquartile range (IQR)659.5

Descriptive statistics

Standard deviation6896.3836
Coefficient of variation (CV)5.5812299
Kurtosis197.26279
Mean1235.6387
Median Absolute Deviation (MAD)201.5
Skewness13.365684
Sum338565
Variance47560107
MonotonicityNot monotonic
2024-03-30T07:22:49.212726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 12
 
4.1%
0 10
 
3.4%
4 6
 
2.0%
2 4
 
1.4%
184 3
 
1.0%
117 3
 
1.0%
153 3
 
1.0%
10 3
 
1.0%
5 3
 
1.0%
67 2
 
0.7%
Other values (203) 225
76.0%
(Missing) 22
 
7.4%
ValueCountFrequency (%)
0 10
3.4%
1 12
4.1%
2 4
 
1.4%
3 1
 
0.3%
4 6
2.0%
5 3
 
1.0%
6 2
 
0.7%
8 2
 
0.7%
9 1
 
0.3%
10 3
 
1.0%
ValueCountFrequency (%)
105870 1
0.3%
29560 1
0.3%
27443 1
0.3%
10931 1
0.3%
8375 1
0.3%
7495 1
0.3%
6895 1
0.3%
6262 1
0.3%
5458 1
0.3%
4424 1
0.3%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct220
Distinct (%)79.1%
Missing18
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean1444.3489
Minimum0
Maximum125386
Zeros10
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T07:22:49.753285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q161
median280
Q3963.5
95-th percentile3323.3
Maximum125386
Range125386
Interquartile range (IQR)902.5

Descriptive statistics

Standard deviation8134.388
Coefficient of variation (CV)5.6318719
Kurtosis198.31826
Mean1444.3489
Median Absolute Deviation (MAD)265.5
Skewness13.436383
Sum401529
Variance66168269
MonotonicityNot monotonic
2024-03-30T07:22:50.382218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10
 
3.4%
1 8
 
2.7%
2 6
 
2.0%
6 3
 
1.0%
72 3
 
1.0%
3 3
 
1.0%
20 3
 
1.0%
114 3
 
1.0%
25 2
 
0.7%
50 2
 
0.7%
Other values (210) 235
79.4%
(Missing) 18
 
6.1%
ValueCountFrequency (%)
0 10
3.4%
1 8
2.7%
2 6
2.0%
3 3
 
1.0%
5 2
 
0.7%
6 3
 
1.0%
10 1
 
0.3%
11 2
 
0.7%
12 1
 
0.3%
13 2
 
0.7%
ValueCountFrequency (%)
125386 1
0.3%
39410 1
0.3%
32205 1
0.3%
10158 1
0.3%
8397 1
0.3%
7020 1
0.3%
6048 1
0.3%
5591 1
0.3%
5215 1
0.3%
4251 1
0.3%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct228
Distinct (%)82.3%
Missing19
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean1705.8917
Minimum0
Maximum150333
Zeros3
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T07:22:50.831650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.8
Q182
median378
Q3984
95-th percentile3368
Maximum150333
Range150333
Interquartile range (IQR)902

Descriptive statistics

Standard deviation9740.1981
Coefficient of variation (CV)5.70974
Kurtosis199.85874
Mean1705.8917
Median Absolute Deviation (MAD)346
Skewness13.48692
Sum472532
Variance94871460
MonotonicityNot monotonic
2024-03-30T07:22:51.331152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 11
 
3.7%
2 4
 
1.4%
7 3
 
1.0%
85 3
 
1.0%
0 3
 
1.0%
1080 2
 
0.7%
15 2
 
0.7%
17 2
 
0.7%
5 2
 
0.7%
75 2
 
0.7%
Other values (218) 243
82.1%
(Missing) 19
 
6.4%
ValueCountFrequency (%)
0 3
 
1.0%
1 11
3.7%
2 4
 
1.4%
3 1
 
0.3%
4 2
 
0.7%
5 2
 
0.7%
7 3
 
1.0%
9 2
 
0.7%
10 2
 
0.7%
11 1
 
0.3%
ValueCountFrequency (%)
150333 1
0.3%
45009 1
0.3%
37266 1
0.3%
18594 1
0.3%
10982 1
0.3%
8780 1
0.3%
6389 1
0.3%
5376 1
0.3%
4502 1
0.3%
4193 1
0.3%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct237
Distinct (%)84.9%
Missing17
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean1822.4803
Minimum0
Maximum163284
Zeros3
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T07:22:51.813496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.9
Q189
median403
Q31129.5
95-th percentile4090.7
Maximum163284
Range163284
Interquartile range (IQR)1040.5

Descriptive statistics

Standard deviation10427.873
Coefficient of variation (CV)5.7218029
Kurtosis209.80206
Mean1822.4803
Median Absolute Deviation (MAD)375
Skewness13.861874
Sum508472
Variance1.0874054 × 108
MonotonicityNot monotonic
2024-03-30T07:22:52.476393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4
 
1.4%
0 3
 
1.0%
37 3
 
1.0%
3 3
 
1.0%
818 2
 
0.7%
17 2
 
0.7%
22 2
 
0.7%
1613 2
 
0.7%
710 2
 
0.7%
38 2
 
0.7%
Other values (227) 254
85.8%
(Missing) 17
 
5.7%
ValueCountFrequency (%)
0 3
1.0%
1 4
1.4%
2 2
0.7%
3 3
1.0%
4 2
0.7%
5 2
0.7%
7 1
 
0.3%
8 2
0.7%
11 1
 
0.3%
13 2
0.7%
ValueCountFrequency (%)
163284 1
0.3%
42092 1
0.3%
41164 1
0.3%
12758 1
0.3%
10984 1
0.3%
9765 1
0.3%
8949 1
0.3%
8932 1
0.3%
7202 1
0.3%
6263 1
0.3%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct243
Distinct (%)87.1%
Missing17
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean1823.0466
Minimum0
Maximum162502
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T07:22:53.002445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q1107
median398
Q31134.5
95-th percentile3675.8
Maximum162502
Range162502
Interquartile range (IQR)1027.5

Descriptive statistics

Standard deviation10288.249
Coefficient of variation (CV)5.6434371
Kurtosis216.83815
Mean1823.0466
Median Absolute Deviation (MAD)366
Skewness14.134487
Sum508630
Variance1.0584806 × 108
MonotonicityNot monotonic
2024-03-30T07:22:53.573115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 3
 
1.0%
30 3
 
1.0%
1 3
 
1.0%
225 3
 
1.0%
27 3
 
1.0%
26 3
 
1.0%
219 3
 
1.0%
342 2
 
0.7%
303 2
 
0.7%
15 2
 
0.7%
Other values (233) 252
85.1%
(Missing) 17
 
5.7%
ValueCountFrequency (%)
0 1
 
0.3%
1 3
1.0%
2 2
0.7%
4 1
 
0.3%
7 1
 
0.3%
8 1
 
0.3%
10 1
 
0.3%
12 2
0.7%
13 1
 
0.3%
14 3
1.0%
ValueCountFrequency (%)
162502 1
0.3%
41947 1
0.3%
33518 1
0.3%
13245 1
0.3%
12896 1
0.3%
10838 1
0.3%
8720 1
0.3%
8292 1
0.3%
5865 1
0.3%
5125 1
0.3%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct248
Distinct (%)88.9%
Missing17
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean1761.9283
Minimum0
Maximum158039
Zeros4
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T07:22:54.134402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q193
median406
Q31033
95-th percentile3836.1
Maximum158039
Range158039
Interquartile range (IQR)940

Descriptive statistics

Standard deviation10115.496
Coefficient of variation (CV)5.7411509
Kurtosis208.49448
Mean1761.9283
Median Absolute Deviation (MAD)365
Skewness13.843896
Sum491578
Variance1.0232327 × 108
MonotonicityNot monotonic
2024-03-30T07:22:54.721842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
1.4%
5 3
 
1.0%
17 3
 
1.0%
4 3
 
1.0%
2 3
 
1.0%
6 2
 
0.7%
28 2
 
0.7%
11 2
 
0.7%
116 2
 
0.7%
365 2
 
0.7%
Other values (238) 253
85.5%
(Missing) 17
 
5.7%
ValueCountFrequency (%)
0 4
1.4%
1 2
0.7%
2 3
1.0%
4 3
1.0%
5 3
1.0%
6 2
0.7%
7 1
 
0.3%
8 1
 
0.3%
9 2
0.7%
11 2
0.7%
ValueCountFrequency (%)
158039 1
0.3%
49717 1
0.3%
31356 1
0.3%
12977 1
0.3%
9734 1
0.3%
8133 1
0.3%
7554 1
0.3%
6585 1
0.3%
6090 1
0.3%
5464 1
0.3%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct236
Distinct (%)84.6%
Missing17
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean1387.8495
Minimum0
Maximum123379
Zeros5
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T07:22:55.216988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q152.5
median326
Q3850.5
95-th percentile3094
Maximum123379
Range123379
Interquartile range (IQR)798

Descriptive statistics

Standard deviation7872.8893
Coefficient of variation (CV)5.6727257
Kurtosis210.36385
Mean1387.8495
Median Absolute Deviation (MAD)303
Skewness13.880088
Sum387210
Variance61982385
MonotonicityNot monotonic
2024-03-30T07:22:56.023853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 6
 
2.0%
1 5
 
1.7%
0 5
 
1.7%
85 4
 
1.4%
38 4
 
1.4%
20 3
 
1.0%
8 3
 
1.0%
3 3
 
1.0%
6 2
 
0.7%
36 2
 
0.7%
Other values (226) 242
81.8%
(Missing) 17
 
5.7%
ValueCountFrequency (%)
0 5
1.7%
1 5
1.7%
2 6
2.0%
3 3
1.0%
4 1
 
0.3%
6 2
 
0.7%
7 2
 
0.7%
8 3
1.0%
9 1
 
0.3%
10 1
 
0.3%
ValueCountFrequency (%)
123379 1
0.3%
31815 1
0.3%
30388 1
0.3%
9771 1
0.3%
8246 1
0.3%
8122 1
0.3%
7847 1
0.3%
7198 1
0.3%
4371 1
0.3%
4302 1
0.3%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct219
Distinct (%)78.5%
Missing17
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean1121.1577
Minimum0
Maximum99244
Zeros4
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T07:22:56.914367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q144
median196
Q3735
95-th percentile2612.6
Maximum99244
Range99244
Interquartile range (IQR)691

Descriptive statistics

Standard deviation6406.3376
Coefficient of variation (CV)5.7140379
Kurtosis201.97421
Mean1121.1577
Median Absolute Deviation (MAD)187
Skewness13.593923
Sum312803
Variance41041162
MonotonicityNot monotonic
2024-03-30T07:22:57.589739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 8
 
2.7%
3 7
 
2.4%
1 6
 
2.0%
9 5
 
1.7%
2 5
 
1.7%
0 4
 
1.4%
62 3
 
1.0%
135 3
 
1.0%
19 3
 
1.0%
10 3
 
1.0%
Other values (209) 232
78.4%
(Missing) 17
 
5.7%
ValueCountFrequency (%)
0 4
1.4%
1 6
2.0%
2 5
1.7%
3 7
2.4%
4 8
2.7%
6 2
 
0.7%
7 1
 
0.3%
9 5
1.7%
10 3
 
1.0%
16 1
 
0.3%
ValueCountFrequency (%)
99244 1
0.3%
33535 1
0.3%
20393 1
0.3%
7649 1
0.3%
6943 1
0.3%
5872 1
0.3%
5355 1
0.3%
4258 1
0.3%
3296 1
0.3%
3162 1
0.3%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct210
Distinct (%)75.0%
Missing16
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean988.58929
Minimum0
Maximum86494
Zeros7
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-30T07:22:58.271510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.95
Q130
median172
Q3534.5
95-th percentile2914.05
Maximum86494
Range86494
Interquartile range (IQR)504.5

Descriptive statistics

Standard deviation5621.3311
Coefficient of variation (CV)5.6862149
Kurtosis196.17344
Mean988.58929
Median Absolute Deviation (MAD)166.5
Skewness13.36099
Sum276805
Variance31599363
MonotonicityNot monotonic
2024-03-30T07:22:58.907279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 9
 
3.0%
1 7
 
2.4%
0 7
 
2.4%
3 6
 
2.0%
5 5
 
1.7%
2 4
 
1.4%
6 4
 
1.4%
7 4
 
1.4%
11 3
 
1.0%
513 3
 
1.0%
Other values (200) 228
77.0%
(Missing) 16
 
5.4%
ValueCountFrequency (%)
0 7
2.4%
1 7
2.4%
2 4
1.4%
3 6
2.0%
4 9
3.0%
5 5
1.7%
6 4
1.4%
7 4
1.4%
8 2
 
0.7%
10 1
 
0.3%
ValueCountFrequency (%)
86494 1
0.3%
30069 1
0.3%
20518 1
0.3%
5966 1
0.3%
5506 1
0.3%
3898 1
0.3%
3771 1
0.3%
3642 1
0.3%
3628 1
0.3%
3454 1
0.3%

Interactions

2024-03-30T07:22:27.134216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:20:24.788940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:20:36.041578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:20:47.036574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:20:55.839212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:03.540192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:09.213013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:15.666490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:21.707008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:27.646677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:33.877656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:40.190301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:46.916046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:53.711563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:00.016671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:07.641936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-30T07:20:33.315018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:20:43.251715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:20:52.804088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:01.482645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:07.330962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:13.195803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:19.620631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:25.857569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:31.796150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:38.309826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:44.937956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:51.593782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:57.985270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:05.607257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:12.447542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:18.551870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:25.236781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:31.124273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:20:33.783045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:20:43.686054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:20:53.355879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:01.772161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:07.824592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:13.609154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:19.944337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:26.175169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:32.108268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:38.655982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:45.266879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:52.017274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:58.350836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:05.950707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:12.903594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:18.888128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:25.691603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:31.471406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:20:34.221665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:20:44.256520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:20:53.773101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:02.283538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:08.158287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:14.015608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:20.344906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:26.529138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:32.453802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:39.031569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:45.642991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:52.362075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:58.704720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:06.307482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:13.274956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:19.251405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:26.042835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:31.784335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:20:34.805464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:20:45.351616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:20:54.387368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:02.633442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:08.437463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:14.566960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:20.670321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:26.815042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:33.017677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:39.332352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:45.934903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:52.724709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:59.079995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:06.685871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:13.718856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:19.547520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:26.311110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:32.172941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:20:35.149143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:20:46.132427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:20:54.948243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:02.897020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:08.706150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:14.966313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:21.028363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:27.071238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:33.354554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:39.585322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:46.256469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:53.143024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:59.365745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:07.043596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:14.099163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:19.811534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:26.622892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:32.398526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:20:35.651056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:20:46.650321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:20:55.334350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:03.275607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:08.963597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:15.337644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:21.360682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:27.321745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:33.620351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:39.825014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:46.542520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:53.461862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:21:59.712068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:07.376040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:14.454328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:20.158428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:22:26.922595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-30T07:22:59.403427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.8400.8400.7390.9811.0000.8000.9810.9960.9811.0000.9811.0001.0001.0001.0001.0001.000
20070.8401.0000.9850.9770.7610.8410.9970.8400.8000.8400.8400.8400.9950.9950.8400.9950.8400.840
20080.8400.9851.0000.9770.7610.8410.9770.8410.8000.8400.8400.8410.9950.9950.8410.9950.8410.841
20090.7390.9770.9771.0000.8010.7400.9700.7060.7400.7060.7400.7060.9570.9570.7400.9570.7400.740
20100.9810.7610.7610.8011.0000.9810.7400.9510.9810.9510.9810.9510.8010.8010.9810.8010.9810.981
20111.0000.8410.8410.7400.9811.0000.8010.9810.9960.9811.0000.9811.0001.0001.0001.0001.0001.000
20120.8000.9970.9770.9700.7400.8011.0000.8010.7700.8010.8010.8010.9880.9880.8010.9880.8010.801
20130.9810.8400.8410.7060.9510.9810.8011.0000.9901.0000.9811.0001.0001.0000.9811.0000.9810.981
20140.9960.8000.8000.7400.9810.9960.7700.9901.0000.9900.9960.9900.8610.8610.9960.8610.9960.996
20150.9810.8400.8400.7060.9510.9810.8011.0000.9901.0000.9811.0001.0001.0000.9811.0000.9810.981
20161.0000.8400.8400.7400.9811.0000.8010.9810.9960.9811.0000.9811.0001.0001.0001.0001.0001.000
20170.9810.8400.8410.7060.9510.9810.8011.0000.9901.0000.9811.0001.0001.0000.9811.0000.9810.981
20181.0000.9950.9950.9570.8011.0000.9881.0000.8611.0001.0001.0001.0001.0001.0001.0001.0001.000
20191.0000.9950.9950.9570.8011.0000.9881.0000.8611.0001.0001.0001.0001.0001.0001.0001.0001.000
20201.0000.8400.8410.7400.9811.0000.8010.9810.9960.9811.0000.9811.0001.0001.0001.0001.0001.000
20211.0000.9950.9950.9570.8011.0000.9881.0000.8611.0001.0001.0001.0001.0001.0001.0001.0001.000
20221.0000.8400.8410.7400.9811.0000.8010.9810.9960.9811.0000.9811.0001.0001.0001.0001.0001.000
20231.0000.8400.8410.7400.9811.0000.8010.9810.9960.9811.0000.9811.0001.0001.0001.0001.0001.000
2024-03-30T07:23:00.046597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.6380.4690.4950.4610.5060.4700.5400.5990.6200.5850.5310.4990.5630.6140.6080.5950.563
20070.6381.0000.6970.5630.5200.5820.6040.5840.5050.5490.5350.5200.5160.5340.5830.5650.5140.496
20080.4690.6971.0000.6760.5770.5750.5470.5210.5020.5040.5040.4780.5040.4870.5370.5480.4930.470
20090.4950.5630.6761.0000.7220.6340.5470.5370.4970.5430.5530.4750.5550.5290.5630.5970.5620.510
20100.4610.5200.5770.7221.0000.7790.6500.5840.5040.5680.5960.5590.5900.5700.5830.5850.5560.502
20110.5060.5820.5750.6340.7791.0000.7630.6840.6110.6590.6380.5980.6330.6390.6730.6460.6150.569
20120.4700.6040.5470.5470.6500.7631.0000.8110.6270.6680.6230.6140.6180.6480.6840.6510.6270.594
20130.5400.5840.5210.5370.5840.6840.8111.0000.7480.7210.6680.6830.6550.6680.7030.7110.6870.657
20140.5990.5050.5020.4970.5040.6110.6270.7481.0000.7950.6960.6650.6700.6900.6780.6880.6960.646
20150.6200.5490.5040.5430.5680.6590.6680.7210.7951.0000.7910.7750.7650.7390.7320.7430.7810.702
20160.5850.5350.5040.5530.5960.6380.6230.6680.6960.7911.0000.8480.8030.7790.7550.7660.7660.726
20170.5310.5200.4780.4750.5590.5980.6140.6830.6650.7750.8481.0000.8530.8130.7990.7960.7820.738
20180.4990.5160.5040.5550.5900.6330.6180.6550.6700.7650.8030.8531.0000.8610.7990.7910.7920.698
20190.5630.5340.4870.5290.5700.6390.6480.6680.6900.7390.7790.8130.8611.0000.8650.8060.7860.734
20200.6140.5830.5370.5630.5830.6730.6840.7030.6780.7320.7550.7990.7990.8651.0000.8620.8180.779
20210.6080.5650.5480.5970.5850.6460.6510.7110.6880.7430.7660.7960.7910.8060.8621.0000.8440.807
20220.5950.5140.4930.5620.5560.6150.6270.6870.6960.7810.7660.7820.7920.7860.8180.8441.0000.878
20230.5630.4960.4700.5100.5020.5690.5940.6570.6460.7020.7260.7380.6980.7340.7790.8070.8781.000

Missing values

2024-03-30T07:22:32.828077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T07:22:33.783195image/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.
2024-03-30T07:22:34.726829image/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

지역200620072008200920102011201220132014201520162017201820192020202120222023
0전국8572680595794238397680931747596735648001790391058701253861503331632841625021580391233799924486494
1서울144251400510857628365448299115671113314960295603220537266420923351831356303882039320518
2서울 종로구5618910966621484733507915418716921916831312960
3서울 중구155273231203100917141012781515475316227154550383225
4서울 용산구1552403228246931214234760429048553142349148011404742960
5서울 성동구46181445751531009724019619091834761466187264539
6서울 광진구8866420915895120120270152610133611991196103588614981243744
7서울 동대문구531676122854352192652072121144495763612813394558605
8서울 중랑구204175591114195766251258946102798488211309787215951195
9서울 성북구45578369602064988042164659115192712321328142810931412447
지역200620072008200920102011201220132014201520162017201820192020202120222023
286경남 하동군<NA><NA><NA>16527474722529615924463316252
287경남 산청군<NA><NA>42620001462412211201751
288경남 함양군<NA>1311568861631782271702111151064
289경남 거창군5000381313522753516104190110345
290경남 합천군<NA><NA><NA><NA><NA><NA>1093231130371204174268
291(구)제주1<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
292(구)제주 (구)제주시1<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
293제주21492671362291484367843175782007599358653742311223301431
294제주 제주시21482004235103133501173931115406131262138182714781008
295제주 서귀포시<NA>167941941381234342001858921932273916041285852423