Overview

Dataset statistics

Number of variables19
Number of observations299
Missing cells365
Missing cells (%)6.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory49.8 KiB
Average record size in memory170.4 B

Variable types

Text1
Numeric18

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 부동산거래현황 중 주택매매 거래현황의 연도별 행정구역별(면적) 데이터입니다.- (단위 : 천㎡)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068303/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 22 (7.4%) missing valuesMissing
2007 has 27 (9.0%) missing valuesMissing
2008 has 25 (8.4%) missing valuesMissing
2009 has 25 (8.4%) missing valuesMissing
2010 has 19 (6.4%) missing valuesMissing
2011 has 22 (7.4%) missing valuesMissing
2012 has 20 (6.7%) missing valuesMissing
2013 has 20 (6.7%) missing valuesMissing
2014 has 13 (4.3%) missing valuesMissing
2015 has 17 (5.7%) missing valuesMissing
2016 has 17 (5.7%) missing valuesMissing
2017 has 20 (6.7%) missing valuesMissing
2018 has 19 (6.4%) missing valuesMissing
2019 has 20 (6.7%) missing valuesMissing
2020 has 20 (6.7%) missing valuesMissing
2021 has 20 (6.7%) missing valuesMissing
2022 has 20 (6.7%) missing valuesMissing
2023 has 19 (6.4%) missing valuesMissing
지역 has unique valuesUnique

Reproduction

Analysis started2024-04-06 08:41:23.450196
Analysis finished2024-04-06 08:43:12.262241
Duration1 minute and 48.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Text

UNIQUE 

Distinct299
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2024-04-06T17:43:12.930736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length6
Mean length6.3411371
Min length3

Characters and Unicode

Total characters1896
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

Unique299 ?
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%
전북 17
 
2.9%
부산 17
 
2.9%
Other values (265) 331
57.2%
2024-04-06T17:43:14.264148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
299
 
15.8%
141
 
7.4%
122
 
6.4%
109
 
5.7%
93
 
4.9%
89
 
4.7%
72
 
3.8%
56
 
3.0%
50
 
2.6%
48
 
2.5%
Other values (137) 817
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1559
82.2%
Space Separator 299
 
15.8%
Close Punctuation 19
 
1.0%
Open Punctuation 19
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
141
 
9.0%
122
 
7.8%
109
 
7.0%
93
 
6.0%
89
 
5.7%
72
 
4.6%
56
 
3.6%
50
 
3.2%
48
 
3.1%
47
 
3.0%
Other values (134) 732
47.0%
Space Separator
ValueCountFrequency (%)
299
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1559
82.2%
Common 337
 
17.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
141
 
9.0%
122
 
7.8%
109
 
7.0%
93
 
6.0%
89
 
5.7%
72
 
4.6%
56
 
3.6%
50
 
3.2%
48
 
3.1%
47
 
3.0%
Other values (134) 732
47.0%
Common
ValueCountFrequency (%)
299
88.7%
) 19
 
5.6%
( 19
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1559
82.2%
ASCII 337
 
17.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
299
88.7%
) 19
 
5.6%
( 19
 
5.6%
Hangul
ValueCountFrequency (%)
141
 
9.0%
122
 
7.8%
109
 
7.0%
93
 
6.0%
89
 
5.7%
72
 
4.6%
56
 
3.6%
50
 
3.2%
48
 
3.1%
47
 
3.0%
Other values (134) 732
47.0%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct221
Distinct (%)79.8%
Missing22
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean968.07942
Minimum4
Maximum83884
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-06T17:43:14.737659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile13
Q143
median275
Q3718
95-th percentile2075.4
Maximum83884
Range83880
Interquartile range (IQR)675

Descriptive statistics

Standard deviation5433.7612
Coefficient of variation (CV)5.6129292
Kurtosis200.40016
Mean968.07942
Median Absolute Deviation (MAD)247
Skewness13.554031
Sum268158
Variance29525761
MonotonicityNot monotonic
2024-04-06T17:43:15.153360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 7
 
2.3%
34 5
 
1.7%
25 4
 
1.3%
23 4
 
1.3%
32 3
 
1.0%
31 3
 
1.0%
39 3
 
1.0%
51 3
 
1.0%
9 3
 
1.0%
11 3
 
1.0%
Other values (211) 239
79.9%
(Missing) 22
 
7.4%
ValueCountFrequency (%)
4 1
 
0.3%
6 1
 
0.3%
8 1
 
0.3%
9 3
1.0%
10 1
 
0.3%
11 3
1.0%
12 1
 
0.3%
13 7
2.3%
15 2
 
0.7%
16 1
 
0.3%
ValueCountFrequency (%)
83884 1
0.3%
27384 1
0.3%
20353 1
0.3%
5299 1
0.3%
4083 1
0.3%
3475 1
0.3%
3438 1
0.3%
3174 1
0.3%
3114 1
0.3%
2817 1
0.3%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct218
Distinct (%)80.1%
Missing27
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean753.36029
Minimum6
Maximum65137
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-06T17:43:16.245172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile15.55
Q149.5
median241.5
Q3478.25
95-th percentile1827.8
Maximum65137
Range65131
Interquartile range (IQR)428.75

Descriptive statistics

Standard deviation4153.1305
Coefficient of variation (CV)5.5128078
Kurtosis215.91969
Mean753.36029
Median Absolute Deviation (MAD)199.5
Skewness14.146594
Sum204914
Variance17248493
MonotonicityNot monotonic
2024-04-06T17:43:16.685158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 6
 
2.0%
16 5
 
1.7%
21 4
 
1.3%
37 4
 
1.3%
6 3
 
1.0%
10 3
 
1.0%
25 3
 
1.0%
27 3
 
1.0%
26 3
 
1.0%
98 3
 
1.0%
Other values (208) 235
78.6%
(Missing) 27
 
9.0%
ValueCountFrequency (%)
6 3
1.0%
9 1
 
0.3%
10 3
1.0%
11 2
 
0.7%
12 1
 
0.3%
13 1
 
0.3%
14 2
 
0.7%
15 1
 
0.3%
16 5
1.7%
18 2
 
0.7%
ValueCountFrequency (%)
65137 1
0.3%
16907 1
0.3%
11719 1
0.3%
5669 1
0.3%
4249 1
0.3%
3863 1
0.3%
3324 1
0.3%
2869 1
0.3%
2518 1
0.3%
2213 1
0.3%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct217
Distinct (%)79.2%
Missing25
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean771.77372
Minimum7
Maximum67187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-06T17:43:17.147474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile16
Q160.75
median264.5
Q3485.75
95-th percentile2073.55
Maximum67187
Range67180
Interquartile range (IQR)425

Descriptive statistics

Standard deviation4236.6189
Coefficient of variation (CV)5.4894573
Kurtosis223.79761
Mean771.77372
Median Absolute Deviation (MAD)212.5
Skewness14.435477
Sum211466
Variance17948939
MonotonicityNot monotonic
2024-04-06T17:43:17.565587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 5
 
1.7%
22 5
 
1.7%
33 4
 
1.3%
82 3
 
1.0%
658 3
 
1.0%
30 3
 
1.0%
67 3
 
1.0%
24 3
 
1.0%
38 3
 
1.0%
14 3
 
1.0%
Other values (207) 239
79.9%
(Missing) 25
 
8.4%
ValueCountFrequency (%)
7 1
 
0.3%
9 3
1.0%
10 1
 
0.3%
11 2
0.7%
12 1
 
0.3%
13 2
0.7%
14 3
1.0%
16 2
0.7%
18 3
1.0%
20 1
 
0.3%
ValueCountFrequency (%)
67187 1
0.3%
15528 1
0.3%
10999 1
0.3%
5459 1
0.3%
4832 1
0.3%
4731 1
0.3%
3127 1
0.3%
3067 1
0.3%
3024 1
0.3%
2970 1
0.3%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct228
Distinct (%)83.2%
Missing25
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean814.30292
Minimum10
Maximum70338
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-06T17:43:18.051400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile16
Q169.5
median269
Q3549.25
95-th percentile2086.2
Maximum70338
Range70328
Interquartile range (IQR)479.75

Descriptive statistics

Standard deviation4426.8629
Coefficient of variation (CV)5.4363834
Kurtosis225.38077
Mean814.30292
Median Absolute Deviation (MAD)217
Skewness14.495999
Sum223119
Variance19597115
MonotonicityNot monotonic
2024-04-06T17:43:18.579533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 4
 
1.3%
10 4
 
1.3%
128 3
 
1.0%
21 3
 
1.0%
53 3
 
1.0%
31 3
 
1.0%
14 3
 
1.0%
352 2
 
0.7%
427 2
 
0.7%
268 2
 
0.7%
Other values (218) 245
81.9%
(Missing) 25
 
8.4%
ValueCountFrequency (%)
10 4
1.3%
11 1
 
0.3%
12 1
 
0.3%
13 2
0.7%
14 3
1.0%
15 2
0.7%
16 2
0.7%
17 1
 
0.3%
18 2
0.7%
19 1
 
0.3%
ValueCountFrequency (%)
70338 1
0.3%
15991 1
0.3%
10778 1
0.3%
5837 1
0.3%
5053 1
0.3%
3962 1
0.3%
3846 1
0.3%
3608 1
0.3%
3156 1
0.3%
3109 1
0.3%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct223
Distinct (%)79.6%
Missing19
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean748.62857
Minimum5
Maximum66147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-06T17:43:18.999495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile15.95
Q164.75
median233
Q3477
95-th percentile2267.25
Maximum66147
Range66142
Interquartile range (IQR)412.25

Descriptive statistics

Standard deviation4084.1697
Coefficient of variation (CV)5.4555355
Kurtosis238.01387
Mean748.62857
Median Absolute Deviation (MAD)193
Skewness14.942055
Sum209616
Variance16680442
MonotonicityNot monotonic
2024-04-06T17:43:19.413737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 4
 
1.3%
38 4
 
1.3%
51 3
 
1.0%
10 3
 
1.0%
29 3
 
1.0%
35 3
 
1.0%
15 3
 
1.0%
31 3
 
1.0%
22 3
 
1.0%
292 3
 
1.0%
Other values (213) 248
82.9%
(Missing) 19
 
6.4%
ValueCountFrequency (%)
5 1
 
0.3%
9 1
 
0.3%
10 3
1.0%
12 2
0.7%
13 2
0.7%
14 2
0.7%
15 3
1.0%
16 2
0.7%
19 1
 
0.3%
20 1
 
0.3%
ValueCountFrequency (%)
66147 1
0.3%
12170 1
0.3%
7078 1
0.3%
7037 1
0.3%
6358 1
0.3%
3914 1
0.3%
3878 1
0.3%
3393 1
0.3%
3198 1
0.3%
3107 1
0.3%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct240
Distinct (%)86.6%
Missing22
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean950.87726
Minimum7
Maximum82390
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-06T17:43:19.864947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile18.8
Q177
median316
Q3594
95-th percentile2926.4
Maximum82390
Range82383
Interquartile range (IQR)517

Descriptive statistics

Standard deviation5125.1858
Coefficient of variation (CV)5.3899551
Kurtosis233.42138
Mean950.87726
Median Absolute Deviation (MAD)250
Skewness14.785954
Sum263393
Variance26267529
MonotonicityNot monotonic
2024-04-06T17:43:20.221983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
282 3
 
1.0%
31 3
 
1.0%
33 3
 
1.0%
14 3
 
1.0%
17 3
 
1.0%
35 3
 
1.0%
42 3
 
1.0%
28 3
 
1.0%
67 2
 
0.7%
413 2
 
0.7%
Other values (230) 249
83.3%
(Missing) 22
 
7.4%
ValueCountFrequency (%)
7 1
 
0.3%
12 1
 
0.3%
13 1
 
0.3%
14 3
1.0%
15 2
0.7%
16 2
0.7%
17 3
1.0%
18 1
 
0.3%
19 1
 
0.3%
21 1
 
0.3%
ValueCountFrequency (%)
82390 1
0.3%
16886 1
0.3%
9149 1
0.3%
6910 1
0.3%
6713 1
0.3%
6272 1
0.3%
5627 1
0.3%
4353 1
0.3%
3829 1
0.3%
3485 1
0.3%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct223
Distinct (%)79.9%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean705.68459
Minimum6
Maximum61818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-06T17:43:20.588845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile19.7
Q165.5
median232
Q3431.5
95-th percentile2122.6
Maximum61818
Range61812
Interquartile range (IQR)366

Descriptive statistics

Standard deviation3825.1558
Coefficient of variation (CV)5.4204894
Kurtosis236.77
Mean705.68459
Median Absolute Deviation (MAD)174
Skewness14.903911
Sum196886
Variance14631817
MonotonicityNot monotonic
2024-04-06T17:43:20.944702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 4
 
1.3%
64 4
 
1.3%
22 4
 
1.3%
40 3
 
1.0%
302 3
 
1.0%
34 3
 
1.0%
30 3
 
1.0%
51 3
 
1.0%
43 3
 
1.0%
39 3
 
1.0%
Other values (213) 246
82.3%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
6 1
 
0.3%
11 2
0.7%
12 1
 
0.3%
13 4
1.3%
14 1
 
0.3%
15 2
0.7%
16 2
0.7%
17 1
 
0.3%
20 2
0.7%
22 4
1.3%
ValueCountFrequency (%)
61818 1
0.3%
12134 1
0.3%
6499 1
0.3%
5274 1
0.3%
4672 1
0.3%
4613 1
0.3%
4557 1
0.3%
3410 1
0.3%
3366 1
0.3%
2774 1
0.3%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct233
Distinct (%)83.5%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean817.47312
Minimum6
Maximum71395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-06T17:43:21.411737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile20
Q168.5
median289
Q3517.5
95-th percentile2196.4
Maximum71395
Range71389
Interquartile range (IQR)449

Descriptive statistics

Standard deviation4444.1179
Coefficient of variation (CV)5.4364086
Kurtosis231.36619
Mean817.47312
Median Absolute Deviation (MAD)224
Skewness14.701312
Sum228075
Variance19750184
MonotonicityNot monotonic
2024-04-06T17:43:21.861162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 7
 
2.3%
40 4
 
1.3%
37 3
 
1.0%
36 3
 
1.0%
16 3
 
1.0%
59 3
 
1.0%
47 3
 
1.0%
41 3
 
1.0%
33 3
 
1.0%
22 3
 
1.0%
Other values (223) 244
81.6%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
6 1
 
0.3%
10 1
 
0.3%
11 1
 
0.3%
13 1
 
0.3%
14 1
 
0.3%
15 1
 
0.3%
16 3
1.0%
17 1
 
0.3%
18 2
0.7%
19 1
 
0.3%
ValueCountFrequency (%)
71395 1
0.3%
16282 1
0.3%
8785 1
0.3%
5542 1
0.3%
5493 1
0.3%
5333 1
0.3%
4897 1
0.3%
3764 1
0.3%
3568 1
0.3%
2931 1
0.3%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct243
Distinct (%)85.0%
Missing13
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean976.18182
Minimum4
Maximum86983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-06T17:43:22.432542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile21.25
Q183.25
median328.5
Q3633.75
95-th percentile2315.25
Maximum86983
Range86979
Interquartile range (IQR)550.5

Descriptive statistics

Standard deviation5361.4024
Coefficient of variation (CV)5.4922171
Kurtosis234.98866
Mean976.18182
Median Absolute Deviation (MAD)265
Skewness14.802827
Sum279188
Variance28744636
MonotonicityNot monotonic
2024-04-06T17:43:22.758806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 4
 
1.3%
32 3
 
1.0%
33 3
 
1.0%
485 3
 
1.0%
21 3
 
1.0%
182 3
 
1.0%
81 2
 
0.7%
172 2
 
0.7%
28 2
 
0.7%
38 2
 
0.7%
Other values (233) 259
86.6%
(Missing) 13
 
4.3%
ValueCountFrequency (%)
4 1
 
0.3%
10 1
 
0.3%
13 1
 
0.3%
15 1
 
0.3%
16 1
 
0.3%
17 2
0.7%
18 4
1.3%
20 1
 
0.3%
21 3
1.0%
22 1
 
0.3%
ValueCountFrequency (%)
86983 1
0.3%
20498 1
0.3%
12034 1
0.3%
6951 1
0.3%
6238 1
0.3%
5812 1
0.3%
5425 1
0.3%
5003 1
0.3%
4728 1
0.3%
3324 1
0.3%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct241
Distinct (%)85.5%
Missing17
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean1170.1312
Minimum4
Maximum102993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-06T17:43:23.152948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile23.05
Q191.5
median395
Q3814
95-th percentile2669.3
Maximum102993
Range102989
Interquartile range (IQR)722.5

Descriptive statistics

Standard deviation6436.4768
Coefficient of variation (CV)5.5006453
Kurtosis225.6516
Mean1170.1312
Median Absolute Deviation (MAD)328.5
Skewness14.471686
Sum329977
Variance41428233
MonotonicityNot monotonic
2024-04-06T17:43:23.638212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41 4
 
1.3%
23 3
 
1.0%
50 3
 
1.0%
117 3
 
1.0%
73 3
 
1.0%
234 3
 
1.0%
18 3
 
1.0%
37 2
 
0.7%
812 2
 
0.7%
96 2
 
0.7%
Other values (231) 254
84.9%
(Missing) 17
 
5.7%
ValueCountFrequency (%)
4 1
 
0.3%
12 1
 
0.3%
13 1
 
0.3%
16 2
0.7%
18 3
1.0%
19 2
0.7%
21 2
0.7%
23 3
1.0%
24 1
 
0.3%
26 1
 
0.3%
ValueCountFrequency (%)
102993 1
0.3%
25859 1
0.3%
18454 1
0.3%
8499 1
0.3%
6699 1
0.3%
5982 1
0.3%
5897 1
0.3%
5640 1
0.3%
3581 1
0.3%
3561 1
0.3%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct239
Distinct (%)84.8%
Missing17
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean1008.8688
Minimum6
Maximum89266
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-06T17:43:24.095785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile22.05
Q191.25
median341
Q3711.75
95-th percentile2452.8
Maximum89266
Range89260
Interquartile range (IQR)620.5

Descriptive statistics

Standard deviation5594.1476
Coefficient of variation (CV)5.5449704
Kurtosis223.32553
Mean1008.8688
Median Absolute Deviation (MAD)283.5
Skewness14.387106
Sum284501
Variance31294488
MonotonicityNot monotonic
2024-04-06T17:43:24.519802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 4
 
1.3%
41 4
 
1.3%
175 3
 
1.0%
107 3
 
1.0%
40 3
 
1.0%
20 3
 
1.0%
46 3
 
1.0%
130 2
 
0.7%
648 2
 
0.7%
58 2
 
0.7%
Other values (229) 253
84.6%
(Missing) 17
 
5.7%
ValueCountFrequency (%)
6 1
 
0.3%
13 2
0.7%
14 1
 
0.3%
16 1
 
0.3%
17 2
0.7%
18 1
 
0.3%
19 2
0.7%
20 3
1.0%
21 1
 
0.3%
22 1
 
0.3%
ValueCountFrequency (%)
89266 1
0.3%
22856 1
0.3%
17423 1
0.3%
7456 1
0.3%
5571 1
0.3%
5379 1
0.3%
3904 1
0.3%
3293 1
0.3%
3111 1
0.3%
3043 1
0.3%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct232
Distinct (%)83.2%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean903.25806
Minimum5
Maximum79151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-06T17:43:24.918037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile19.9
Q180.5
median310
Q3607.5
95-th percentile2395
Maximum79151
Range79146
Interquartile range (IQR)527

Descriptive statistics

Standard deviation4981.029
Coefficient of variation (CV)5.5145137
Kurtosis221.91226
Mean903.25806
Median Absolute Deviation (MAD)255
Skewness14.350616
Sum252009
Variance24810650
MonotonicityNot monotonic
2024-04-06T17:43:25.380198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 4
 
1.3%
83 3
 
1.0%
33 3
 
1.0%
30 3
 
1.0%
52 3
 
1.0%
27 3
 
1.0%
19 3
 
1.0%
42 3
 
1.0%
18 2
 
0.7%
116 2
 
0.7%
Other values (222) 250
83.6%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
5 1
 
0.3%
10 1
 
0.3%
14 1
 
0.3%
15 1
 
0.3%
16 1
 
0.3%
17 4
1.3%
18 2
0.7%
19 3
1.0%
20 1
 
0.3%
22 2
0.7%
ValueCountFrequency (%)
79151 1
0.3%
20413 1
0.3%
14948 1
0.3%
5404 1
0.3%
4786 1
0.3%
4317 1
0.3%
4307 1
0.3%
3522 1
0.3%
3025 1
0.3%
2761 1
0.3%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct232
Distinct (%)82.9%
Missing19
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean805.73929
Minimum5
Maximum70657
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-06T17:43:25.905546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile19.9
Q171.75
median256.5
Q3542
95-th percentile2135.75
Maximum70657
Range70652
Interquartile range (IQR)470.25

Descriptive statistics

Standard deviation4463.6896
Coefficient of variation (CV)5.5398683
Kurtosis218.02896
Mean805.73929
Median Absolute Deviation (MAD)208
Skewness14.202513
Sum225607
Variance19924525
MonotonicityNot monotonic
2024-04-06T17:43:26.993339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 4
 
1.3%
18 4
 
1.3%
45 4
 
1.3%
17 3
 
1.0%
38 3
 
1.0%
542 3
 
1.0%
197 3
 
1.0%
352 3
 
1.0%
154 2
 
0.7%
257 2
 
0.7%
Other values (222) 249
83.3%
(Missing) 19
 
6.4%
ValueCountFrequency (%)
5 1
 
0.3%
11 1
 
0.3%
12 1
 
0.3%
13 1
 
0.3%
14 1
 
0.3%
15 2
0.7%
17 3
1.0%
18 4
1.3%
20 1
 
0.3%
22 1
 
0.3%
ValueCountFrequency (%)
70657 1
0.3%
19715 1
0.3%
13826 1
0.3%
4402 1
0.3%
4128 1
0.3%
3498 1
0.3%
3184 1
0.3%
3134 1
0.3%
2828 1
0.3%
2677 1
0.3%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct225
Distinct (%)80.6%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean745.18996
Minimum5
Maximum65198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-06T17:43:27.406611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile19
Q168.5
median263
Q3491
95-th percentile1872.7
Maximum65198
Range65193
Interquartile range (IQR)422.5

Descriptive statistics

Standard deviation4080.4329
Coefficient of variation (CV)5.4756949
Kurtosis226.66718
Mean745.18996
Median Absolute Deviation (MAD)206
Skewness14.531972
Sum207908
Variance16649933
MonotonicityNot monotonic
2024-04-06T17:43:27.866048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 4
 
1.3%
15 4
 
1.3%
43 3
 
1.0%
506 3
 
1.0%
34 3
 
1.0%
32 3
 
1.0%
17 3
 
1.0%
76 3
 
1.0%
370 3
 
1.0%
532 3
 
1.0%
Other values (215) 247
82.6%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
5 1
 
0.3%
12 1
 
0.3%
13 1
 
0.3%
15 4
1.3%
16 2
0.7%
17 3
1.0%
19 3
1.0%
20 1
 
0.3%
21 1
 
0.3%
22 1
 
0.3%
ValueCountFrequency (%)
65198 1
0.3%
16343 1
0.3%
10043 1
0.3%
4249 1
0.3%
4079 1
0.3%
4031 1
0.3%
3574 1
0.3%
3258 1
0.3%
3090 1
0.3%
2995 1
0.3%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct238
Distinct (%)85.3%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1175.9928
Minimum5
Maximum102420
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-06T17:43:28.313613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile24
Q194
median402
Q3795.5
95-th percentile3007.6
Maximum102420
Range102415
Interquartile range (IQR)701.5

Descriptive statistics

Standard deviation6450.9835
Coefficient of variation (CV)5.4855636
Kurtosis221.39596
Mean1175.9928
Median Absolute Deviation (MAD)331
Skewness14.347337
Sum328102
Variance41615188
MonotonicityNot monotonic
2024-04-06T17:43:28.793884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 3
 
1.0%
41 3
 
1.0%
94 3
 
1.0%
32 3
 
1.0%
49 3
 
1.0%
60 3
 
1.0%
22 3
 
1.0%
199 3
 
1.0%
43 2
 
0.7%
23 2
 
0.7%
Other values (228) 251
83.9%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
5 1
 
0.3%
13 1
 
0.3%
14 1
 
0.3%
15 1
 
0.3%
17 2
0.7%
20 2
0.7%
22 3
1.0%
23 2
0.7%
24 2
0.7%
25 2
0.7%
ValueCountFrequency (%)
102420 1
0.3%
29538 1
0.3%
13281 1
0.3%
8472 1
0.3%
6563 1
0.3%
6067 1
0.3%
5709 1
0.3%
4662 1
0.3%
4164 1
0.3%
3482 1
0.3%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct229
Distinct (%)82.1%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean899.3405
Minimum5
Maximum78634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-06T17:43:29.209069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile23.8
Q188.5
median315
Q3546
95-th percentile2266
Maximum78634
Range78629
Interquartile range (IQR)457.5

Descriptive statistics

Standard deviation4917.5105
Coefficient of variation (CV)5.4679073
Kurtosis227.46461
Mean899.3405
Median Absolute Deviation (MAD)230
Skewness14.57211
Sum250916
Variance24181910
MonotonicityNot monotonic
2024-04-06T17:43:29.685298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 4
 
1.3%
263 3
 
1.0%
47 3
 
1.0%
270 3
 
1.0%
39 3
 
1.0%
21 3
 
1.0%
545 3
 
1.0%
38 3
 
1.0%
81 3
 
1.0%
62 3
 
1.0%
Other values (219) 248
82.9%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
5 1
 
0.3%
9 1
 
0.3%
15 2
0.7%
16 1
 
0.3%
17 1
 
0.3%
20 4
1.3%
21 3
1.0%
22 1
 
0.3%
24 1
 
0.3%
26 1
 
0.3%
ValueCountFrequency (%)
78634 1
0.3%
20590 1
0.3%
9085 1
0.3%
5908 1
0.3%
5567 1
0.3%
4954 1
0.3%
4562 1
0.3%
4129 1
0.3%
3345 1
0.3%
3328 1
0.3%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct208
Distinct (%)74.6%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean449.18638
Minimum6
Maximum39423
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-06T17:43:30.133169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile20
Q158
median149
Q3259
95-th percentile1179.1
Maximum39423
Range39417
Interquartile range (IQR)201

Descriptive statistics

Standard deviation2442.6379
Coefficient of variation (CV)5.4379163
Kurtosis235.63961
Mean449.18638
Median Absolute Deviation (MAD)95
Skewness14.870273
Sum125323
Variance5966480.1
MonotonicityNot monotonic
2024-04-06T17:43:30.676149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 5
 
1.7%
81 4
 
1.3%
43 4
 
1.3%
91 4
 
1.3%
19 3
 
1.0%
228 3
 
1.0%
29 3
 
1.0%
22 3
 
1.0%
106 3
 
1.0%
41 2
 
0.7%
Other values (198) 245
81.9%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
6 1
 
0.3%
12 2
0.7%
13 1
 
0.3%
14 1
 
0.3%
15 2
0.7%
16 1
 
0.3%
18 2
0.7%
19 3
1.0%
20 2
0.7%
21 1
 
0.3%
ValueCountFrequency (%)
39423 1
0.3%
8417 1
0.3%
3811 1
0.3%
3421 1
0.3%
3080 1
0.3%
2502 1
0.3%
2274 1
0.3%
2155 1
0.3%
2086 1
0.3%
2073 1
0.3%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct207
Distinct (%)73.9%
Missing19
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean490.74286
Minimum3
Maximum42987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-06T17:43:31.151967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile16
Q156
median164.5
Q3316.5
95-th percentile1387.55
Maximum42987
Range42984
Interquartile range (IQR)260.5

Descriptive statistics

Standard deviation2675.166
Coefficient of variation (CV)5.4512581
Kurtosis231.00602
Mean490.74286
Median Absolute Deviation (MAD)117
Skewness14.700539
Sum137408
Variance7156513
MonotonicityNot monotonic
2024-04-06T17:43:31.562824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 4
 
1.3%
165 3
 
1.0%
25 3
 
1.0%
29 3
 
1.0%
56 3
 
1.0%
26 3
 
1.0%
17 3
 
1.0%
14 3
 
1.0%
28 3
 
1.0%
186 3
 
1.0%
Other values (197) 249
83.3%
(Missing) 19
 
6.4%
ValueCountFrequency (%)
3 1
 
0.3%
4 1
 
0.3%
5 1
 
0.3%
8 1
 
0.3%
10 1
 
0.3%
11 1
 
0.3%
13 2
0.7%
14 3
1.0%
15 2
0.7%
16 2
0.7%
ValueCountFrequency (%)
42987 1
0.3%
10796 1
0.3%
4448 1
0.3%
3068 1
0.3%
2791 1
0.3%
2681 1
0.3%
2502 1
0.3%
2415 1
0.3%
2084 1
0.3%
2078 1
0.3%

Interactions

2024-04-06T17:43:05.249894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:33.498633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:39.435146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:45.021380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:50.286139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:55.940604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:02.124983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:07.238860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:13.400795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:18.443434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:23.056478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:28.660450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:33.748051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:39.384650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:44.033736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:49.477231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:54.387752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:59.535954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:05.517524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:33.816632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:39.692033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:45.329286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-04-06T17:41:59.980278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:05.591221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:10.872722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:16.737920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:21.418541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:26.145258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:31.967406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:37.483885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:42.510347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:47.843303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:52.684139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:57.777202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:03.474562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:08.965068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:38.018209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:43.380633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:48.837374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:54.384729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:00.365762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:05.900478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:11.838705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:17.036466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:21.729275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:26.498274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:32.233327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:37.791639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:42.756202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:48.084440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:52.944807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:58.077943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:03.765824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:09.265687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:38.315236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:43.735643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:49.107347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:54.682948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:00.739053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:06.155013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:12.224816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:17.322370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:21.973483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:26.798396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:32.507714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:38.087390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:42.988193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:48.340173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:53.246650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:58.351129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:04.001479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:09.544635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:38.581220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:44.055112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:49.376392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:54.969370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:01.137011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:06.383116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:12.463613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:17.611343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:22.206932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:27.050783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:32.907487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:38.372701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:43.230999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:48.630019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:53.549496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:58.629573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:04.314750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:09.854108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:38.876661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:44.362565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:49.723143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:55.304813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:01.499146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:06.733770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:12.800986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:17.884551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:22.515959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:27.341686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:33.212947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:38.667322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:43.500959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:48.937789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:53.849387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:58.968243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:04.618087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:10.104922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:39.153567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:44.676215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:50.055880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:55.652473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:01.861703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:07.012887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:13.169792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:18.202370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:22.816500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:27.711494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:33.491449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:39.053454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:43.803444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:49.223566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:54.122543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:59.290619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:04.937787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:43:31.916661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0001.0001.0001.0000.8411.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20071.0001.0001.0001.0000.8411.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20081.0001.0001.0001.0000.8411.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20091.0001.0001.0001.0000.8411.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20100.8410.8410.8410.8411.0000.8410.9950.8410.8410.8410.8410.8410.8410.8410.8410.8410.9770.841
20111.0001.0001.0001.0000.8411.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20121.0001.0001.0001.0000.9951.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9881.000
20131.0001.0001.0001.0000.8411.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20141.0001.0001.0001.0000.8411.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20151.0001.0001.0001.0000.8411.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20161.0001.0001.0001.0000.8411.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20171.0001.0001.0001.0000.8411.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20181.0001.0001.0001.0000.8411.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20191.0001.0001.0001.0000.8411.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20201.0001.0001.0001.0000.8411.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20211.0001.0001.0001.0000.8411.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20221.0001.0001.0001.0000.9771.0000.9881.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20231.0001.0001.0001.0000.8411.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-04-06T17:43:32.456444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9460.9280.9260.8710.8840.8830.9120.9250.9460.9520.9570.9560.9410.9330.9020.8230.886
20070.9461.0000.9790.9470.9230.9290.9290.9350.9380.9520.9510.9480.9350.9410.9370.9370.8970.919
20080.9280.9791.0000.9620.9380.9380.9330.9420.9410.9540.9530.9510.9360.9460.9410.9430.9080.927
20090.9260.9470.9621.0000.9670.9650.9520.9690.9670.9730.9690.9680.9500.9660.9630.9520.9060.951
20100.8710.9230.9380.9671.0000.9780.9690.9650.9560.9440.9340.9300.9060.9380.9460.9470.9280.948
20110.8840.9290.9380.9650.9781.0000.9830.9760.9660.9490.9290.9290.9100.9440.9530.9480.9330.954
20120.8830.9290.9330.9520.9690.9831.0000.9800.9650.9510.9280.9300.9090.9390.9450.9470.9300.946
20130.9120.9350.9420.9690.9650.9760.9801.0000.9860.9730.9530.9560.9370.9590.9640.9520.9130.953
20140.9250.9380.9410.9670.9560.9660.9650.9861.0000.9810.9600.9620.9450.9620.9630.9470.8960.941
20150.9460.9520.9540.9730.9440.9490.9510.9730.9811.0000.9800.9810.9630.9670.9650.9430.8860.932
20160.9520.9510.9530.9690.9340.9290.9280.9530.9600.9801.0000.9880.9700.9680.9630.9410.8750.927
20170.9570.9480.9510.9680.9300.9290.9300.9560.9620.9810.9881.0000.9860.9810.9710.9420.8770.936
20180.9560.9350.9360.9500.9060.9100.9090.9370.9450.9630.9700.9861.0000.9780.9570.9280.8540.920
20190.9410.9410.9460.9660.9380.9440.9390.9590.9620.9670.9680.9810.9781.0000.9800.9540.8940.954
20200.9330.9370.9410.9630.9460.9530.9450.9640.9630.9650.9630.9710.9570.9801.0000.9620.8990.965
20210.9020.9370.9430.9520.9470.9480.9470.9520.9470.9430.9410.9420.9280.9540.9621.0000.9590.975
20220.8230.8970.9080.9060.9280.9330.9300.9130.8960.8860.8750.8770.8540.8940.8990.9591.0000.954
20230.8860.9190.9270.9510.9480.9540.9460.9530.9410.9320.9270.9360.9200.9540.9650.9750.9541.000

Missing values

2024-04-06T17:43:10.471730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:43:11.082961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-04-06T17:43:11.610351image/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
0전국83884651376718770338661478239061818713958698310299389266791517065765198102420786343942342987
1서울20353117191099910778707891496499878512034184541742314948138261004313281908538114448
2서울 종로구2492162011721351531291521802282161972091472001728181
3서울 중구20414314013685112861131452372171612011321521014671
4서울 용산구692289206270178193151188328435630467500309373354126122
5서울 성동구660276278329173216149249365536511515420363417225106161
6서울 광진구736379372385248316218280433671542511450352398308151137
7서울 동대문구635479428325196282228305460706610569571349452262116161
8서울 중랑구722615484316233370274322555776678562542371519439179135
9서울 성북구981595522469304458334481597933839740804532725438180227
지역200620072008200920102011201220132014201520162017201820192020202120222023
289경남 거창군4946725368676465828411372687682866148
290경남 합천군252325263932374043503346333437363122
291(구)제주183<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
292(구)제주 (구)제주시117<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
293(구)제주 (구)서귀포시29<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
294(구)제주 (구)북제주군20<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
295(구)제주 (구)남제주군16<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
296제주3165235055396768447649071079121311257788176778471032713560
297제주 제주시263415384416541670600710850863786501568509605721486376
298제주 서귀포시53107121122135174165197229350338277249168242312227184