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 건축물 거래현황의 연도별 행정구역별(면적) 데이터입니다.- (단위 : 천㎡)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15048975/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 started2024-03-23 07:17:49.458853
Analysis finished2024-03-23 07:19:56.169440
Duration2 minutes and 6.71 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
2024-03-23T07:19:56.861960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length6
Mean length6.3712375
Min length2

Characters and Unicode

Total characters1905
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
 
8.6%
경남 27
 
4.4%
경북 26
 
4.2%
서울 26
 
4.2%
전남 23
 
3.7%
충남 20
 
3.2%
충북 20
 
3.2%
강원 19
 
3.1%
전북 17
 
2.8%
부산 17
 
2.8%
Other values (263) 368
59.7%
2024-03-23T07:19:58.305735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
317
 
16.6%
138
 
7.2%
122
 
6.4%
109
 
5.7%
93
 
4.9%
89
 
4.7%
72
 
3.8%
56
 
2.9%
50
 
2.6%
48
 
2.5%
Other values (137) 811
42.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1556
81.7%
Space Separator 317
 
16.6%
Open Punctuation 16
 
0.8%
Close Punctuation 16
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
138
 
8.9%
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 (%)
317
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1556
81.7%
Common 349
 
18.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
138
 
8.9%
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 (%)
317
90.8%
( 16
 
4.6%
) 16
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1556
81.7%
ASCII 349
 
18.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
317
90.8%
( 16
 
4.6%
) 16
 
4.6%
Hangul
ValueCountFrequency (%)
138
 
8.9%
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 

Distinct248
Distinct (%)89.5%
Missing22
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean1908.6968
Minimum9
Maximum167009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T07:19:58.732473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile47.4
Q1136
median616
Q31264
95-th percentile4678.6
Maximum167009
Range167000
Interquartile range (IQR)1128

Descriptive statistics

Standard deviation10654.889
Coefficient of variation (CV)5.5822847
Kurtosis212.15747
Mean1908.6968
Median Absolute Deviation (MAD)508
Skewness13.996608
Sum528709
Variance1.1352665 × 108
MonotonicityNot monotonic
2024-03-23T07:19:59.262066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
108 3
 
1.0%
283 3
 
1.0%
76 3
 
1.0%
55 3
 
1.0%
75 3
 
1.0%
59 3
 
1.0%
67 3
 
1.0%
147 2
 
0.7%
274 2
 
0.7%
79 2
 
0.7%
Other values (238) 250
83.6%
(Missing) 22
 
7.4%
ValueCountFrequency (%)
9 1
0.3%
18 1
0.3%
24 1
0.3%
26 1
0.3%
28 1
0.3%
34 1
0.3%
36 1
0.3%
37 2
0.7%
38 1
0.3%
40 1
0.3%
ValueCountFrequency (%)
167009 1
0.3%
49091 1
0.3%
33775 1
0.3%
9944 1
0.3%
9690 1
0.3%
9528 1
0.3%
8345 1
0.3%
7648 1
0.3%
5597 1
0.3%
5280 1
0.3%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct249
Distinct (%)91.5%
Missing27
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean1630.7904
Minimum14
Maximum141544
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T07:19:59.785642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile49
Q1151.25
median541.5
Q3936.25
95-th percentile4188.5
Maximum141544
Range141530
Interquartile range (IQR)785

Descriptive statistics

Standard deviation8995.8711
Coefficient of variation (CV)5.5162643
Kurtosis218.6225
Mean1630.7904
Median Absolute Deviation (MAD)392.5
Skewness14.249373
Sum443575
Variance80925696
MonotonicityNot monotonic
2024-03-23T07:20:00.410593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82 4
 
1.3%
87 3
 
1.0%
357 2
 
0.7%
177 2
 
0.7%
696 2
 
0.7%
52 2
 
0.7%
636 2
 
0.7%
814 2
 
0.7%
49 2
 
0.7%
101 2
 
0.7%
Other values (239) 249
83.3%
(Missing) 27
 
9.0%
ValueCountFrequency (%)
14 1
0.3%
16 1
0.3%
21 1
0.3%
31 1
0.3%
32 1
0.3%
34 1
0.3%
37 1
0.3%
38 1
0.3%
39 1
0.3%
40 1
0.3%
ValueCountFrequency (%)
141544 1
0.3%
35338 1
0.3%
24137 1
0.3%
10961 1
0.3%
9789 1
0.3%
8557 1
0.3%
8090 1
0.3%
6906 1
0.3%
6397 1
0.3%
5635 1
0.3%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct255
Distinct (%)93.1%
Missing25
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean1500.3942
Minimum9
Maximum131123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T07:20:00.834322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile46.95
Q1149.25
median511.5
Q3959.25
95-th percentile4042.9
Maximum131123
Range131114
Interquartile range (IQR)810

Descriptive statistics

Standard deviation8262.0041
Coefficient of variation (CV)5.5065557
Kurtosis224.51577
Mean1500.3942
Median Absolute Deviation (MAD)376.5
Skewness14.466561
Sum411108
Variance68260711
MonotonicityNot monotonic
2024-03-23T07:20:01.235185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
228 3
 
1.0%
115 2
 
0.7%
1015 2
 
0.7%
773 2
 
0.7%
710 2
 
0.7%
161 2
 
0.7%
70 2
 
0.7%
73 2
 
0.7%
83 2
 
0.7%
69 2
 
0.7%
Other values (245) 253
84.6%
(Missing) 25
 
8.4%
ValueCountFrequency (%)
9 1
0.3%
23 1
0.3%
25 1
0.3%
28 1
0.3%
32 1
0.3%
34 1
0.3%
35 1
0.3%
36 1
0.3%
37 1
0.3%
38 1
0.3%
ValueCountFrequency (%)
131123 1
0.3%
30449 1
0.3%
20674 1
0.3%
10039 1
0.3%
9762 1
0.3%
8817 1
0.3%
7294 1
0.3%
6437 1
0.3%
6393 1
0.3%
5444 1
0.3%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct253
Distinct (%)92.3%
Missing25
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean1516.2956
Minimum13
Maximum131334
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T07:20:01.588169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile54
Q1158.25
median503.5
Q3910.75
95-th percentile4028.45
Maximum131334
Range131321
Interquartile range (IQR)752.5

Descriptive statistics

Standard deviation8294.4557
Coefficient of variation (CV)5.4702101
Kurtosis222.47183
Mean1516.2956
Median Absolute Deviation (MAD)365.5
Skewness14.389905
Sum415465
Variance68797995
MonotonicityNot monotonic
2024-03-23T07:20:02.049673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 3
 
1.0%
124 2
 
0.7%
735 2
 
0.7%
40 2
 
0.7%
79 2
 
0.7%
87 2
 
0.7%
254 2
 
0.7%
779 2
 
0.7%
277 2
 
0.7%
64 2
 
0.7%
Other values (243) 253
84.6%
(Missing) 25
 
8.4%
ValueCountFrequency (%)
13 1
0.3%
23 1
0.3%
27 1
0.3%
34 1
0.3%
35 1
0.3%
37 1
0.3%
40 2
0.7%
41 1
0.3%
43 1
0.3%
44 1
0.3%
ValueCountFrequency (%)
131334 1
0.3%
32302 1
0.3%
20485 1
0.3%
9951 1
0.3%
9334 1
0.3%
7788 1
0.3%
7627 1
0.3%
6191 1
0.3%
6120 1
0.3%
5464 1
0.3%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct245
Distinct (%)87.5%
Missing19
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean1460.0786
Minimum10
Maximum129234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T07:20:02.546669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile47.9
Q1145.75
median461.5
Q3972.5
95-th percentile4300
Maximum129234
Range129224
Interquartile range (IQR)826.75

Descriptive statistics

Standard deviation8037.96
Coefficient of variation (CV)5.5051558
Kurtosis231.4748
Mean1460.0786
Median Absolute Deviation (MAD)348.5
Skewness14.704986
Sum408822
Variance64608801
MonotonicityNot monotonic
2024-03-23T07:20:03.103297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107 6
 
2.0%
745 3
 
1.0%
200 3
 
1.0%
285 2
 
0.7%
62 2
 
0.7%
122 2
 
0.7%
77 2
 
0.7%
248 2
 
0.7%
864 2
 
0.7%
464 2
 
0.7%
Other values (235) 254
84.9%
(Missing) 19
 
6.4%
ValueCountFrequency (%)
10 1
0.3%
28 1
0.3%
30 1
0.3%
32 1
0.3%
34 1
0.3%
35 1
0.3%
36 2
0.7%
37 1
0.3%
38 1
0.3%
40 1
0.3%
ValueCountFrequency (%)
129234 1
0.3%
30364 1
0.3%
15252 1
0.3%
11854 1
0.3%
11041 1
0.3%
7857 1
0.3%
7176 1
0.3%
6520 1
0.3%
6099 1
0.3%
5172 1
0.3%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct254
Distinct (%)91.7%
Missing22
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean1695.7148
Minimum17
Maximum147731
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T07:20:03.609228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile56.8
Q1170
median574
Q31058
95-th percentile4729.8
Maximum147731
Range147714
Interquartile range (IQR)888

Descriptive statistics

Standard deviation9200.6047
Coefficient of variation (CV)5.4257972
Kurtosis232.43282
Mean1695.7148
Median Absolute Deviation (MAD)430
Skewness14.751787
Sum469713
Variance84651126
MonotonicityNot monotonic
2024-03-23T07:20:04.125880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 3
 
1.0%
1047 2
 
0.7%
98 2
 
0.7%
265 2
 
0.7%
263 2
 
0.7%
885 2
 
0.7%
262 2
 
0.7%
409 2
 
0.7%
50 2
 
0.7%
79 2
 
0.7%
Other values (244) 256
85.6%
(Missing) 22
 
7.4%
ValueCountFrequency (%)
17 1
0.3%
24 1
0.3%
28 1
0.3%
30 1
0.3%
32 1
0.3%
47 1
0.3%
48 1
0.3%
50 2
0.7%
51 1
0.3%
52 1
0.3%
ValueCountFrequency (%)
147731 1
0.3%
31242 1
0.3%
17910 1
0.3%
13177 1
0.3%
11437 1
0.3%
9605 1
0.3%
8971 1
0.3%
8136 1
0.3%
7378 1
0.3%
6996 1
0.3%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct244
Distinct (%)87.5%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2488.362
Minimum15
Maximum223935
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T07:20:04.928870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile54.9
Q1153.5
median490
Q3963
95-th percentile4838.1
Maximum223935
Range223920
Interquartile range (IQR)809.5

Descriptive statistics

Standard deviation15717.701
Coefficient of variation (CV)6.3164848
Kurtosis151.10393
Mean2488.362
Median Absolute Deviation (MAD)368
Skewness11.683372
Sum694253
Variance2.4704612 × 108
MonotonicityNot monotonic
2024-03-23T07:20:05.948132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95 3
 
1.0%
106 3
 
1.0%
86 3
 
1.0%
260 2
 
0.7%
889 2
 
0.7%
490 2
 
0.7%
94 2
 
0.7%
801 2
 
0.7%
1118 2
 
0.7%
39 2
 
0.7%
Other values (234) 256
85.6%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
15 1
0.3%
21 1
0.3%
24 1
0.3%
31 1
0.3%
36 1
0.3%
39 2
0.7%
42 1
0.3%
44 2
0.7%
47 1
0.3%
48 1
0.3%
ValueCountFrequency (%)
223935 1
0.3%
97507 1
0.3%
94807 1
0.3%
27370 1
0.3%
16253 1
0.3%
9995 1
0.3%
9607 1
0.3%
8417 1
0.3%
8393 1
0.3%
8228 1
0.3%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct244
Distinct (%)87.5%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1643.5125
Minimum10
Maximum144181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T07:20:06.435081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile56
Q1151
median567
Q31013
95-th percentile4277.8
Maximum144181
Range144171
Interquartile range (IQR)862

Descriptive statistics

Standard deviation8969.0116
Coefficient of variation (CV)5.4572212
Kurtosis232.0016
Mean1643.5125
Median Absolute Deviation (MAD)422
Skewness14.727902
Sum458540
Variance80443169
MonotonicityNot monotonic
2024-03-23T07:20:06.949233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
855 3
 
1.0%
83 3
 
1.0%
250 3
 
1.0%
95 3
 
1.0%
125 3
 
1.0%
81 3
 
1.0%
638 2
 
0.7%
60 2
 
0.7%
860 2
 
0.7%
89 2
 
0.7%
Other values (234) 253
84.6%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
10 1
0.3%
30 1
0.3%
34 1
0.3%
35 1
0.3%
38 1
0.3%
39 1
0.3%
40 1
0.3%
43 1
0.3%
45 1
0.3%
47 1
0.3%
ValueCountFrequency (%)
144181 1
0.3%
32565 1
0.3%
17636 1
0.3%
11767 1
0.3%
11698 1
0.3%
10304 1
0.3%
9305 1
0.3%
7773 1
0.3%
6348 1
0.3%
6259 1
0.3%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct256
Distinct (%)89.5%
Missing13
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean1898.7622
Minimum8
Maximum170273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T07:20:07.441726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile65.5
Q1188.75
median636
Q31255.25
95-th percentile4842
Maximum170273
Range170265
Interquartile range (IQR)1066.5

Descriptive statistics

Standard deviation10456.074
Coefficient of variation (CV)5.5067841
Kurtosis238.44713
Mean1898.7622
Median Absolute Deviation (MAD)465.5
Skewness14.93265
Sum543046
Variance1.0932948 × 108
MonotonicityNot monotonic
2024-03-23T07:20:07.997273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125 3
 
1.0%
1502 3
 
1.0%
94 3
 
1.0%
109 2
 
0.7%
148 2
 
0.7%
674 2
 
0.7%
1600 2
 
0.7%
534 2
 
0.7%
438 2
 
0.7%
76 2
 
0.7%
Other values (246) 263
88.0%
(Missing) 13
 
4.3%
ValueCountFrequency (%)
8 1
0.3%
30 1
0.3%
32 1
0.3%
40 1
0.3%
45 2
0.7%
46 1
0.3%
54 1
0.3%
56 1
0.3%
57 1
0.3%
61 1
0.3%
ValueCountFrequency (%)
170273 1
0.3%
37013 1
0.3%
22361 1
0.3%
14482 1
0.3%
13872 1
0.3%
11812 1
0.3%
11298 1
0.3%
9138 1
0.3%
8011 1
0.3%
6803 1
0.3%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct260
Distinct (%)92.2%
Missing17
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean2463.0284
Minimum13
Maximum215496
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T07:20:08.468173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile65.2
Q1214.5
median842.5
Q31553.25
95-th percentile5518.3
Maximum215496
Range215483
Interquartile range (IQR)1338.75

Descriptive statistics

Standard deviation13501.058
Coefficient of variation (CV)5.481487
Kurtosis223.52701
Mean2463.0284
Median Absolute Deviation (MAD)639.5
Skewness14.392662
Sum694574
Variance1.8227856 × 108
MonotonicityNot monotonic
2024-03-23T07:20:09.146248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61 2
 
0.7%
3671 2
 
0.7%
901 2
 
0.7%
170 2
 
0.7%
332 2
 
0.7%
135 2
 
0.7%
76 2
 
0.7%
75 2
 
0.7%
113 2
 
0.7%
429 2
 
0.7%
Other values (250) 262
87.6%
(Missing) 17
 
5.7%
ValueCountFrequency (%)
13 1
0.3%
31 1
0.3%
39 1
0.3%
43 1
0.3%
44 1
0.3%
45 1
0.3%
51 1
0.3%
53 2
0.7%
55 1
0.3%
61 2
0.7%
ValueCountFrequency (%)
215496 1
0.3%
58512 1
0.3%
33722 1
0.3%
16853 1
0.3%
14528 1
0.3%
12755 1
0.3%
12488 1
0.3%
11336 1
0.3%
11046 1
0.3%
9927 1
0.3%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct263
Distinct (%)93.3%
Missing17
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean2236.6738
Minimum13
Maximum197920
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T07:20:10.029145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile68
Q1229.5
median745.5
Q31442
95-th percentile5557.4
Maximum197920
Range197907
Interquartile range (IQR)1212.5

Descriptive statistics

Standard deviation12370.955
Coefficient of variation (CV)5.5309608
Kurtosis225.71045
Mean2236.6738
Median Absolute Deviation (MAD)586
Skewness14.487689
Sum630742
Variance1.5304052 × 108
MonotonicityNot monotonic
2024-03-23T07:20:10.579864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122 3
 
1.0%
125 3
 
1.0%
68 2
 
0.7%
654 2
 
0.7%
100 2
 
0.7%
132 2
 
0.7%
1358 2
 
0.7%
611 2
 
0.7%
3219 2
 
0.7%
112 2
 
0.7%
Other values (253) 260
87.0%
(Missing) 17
 
5.7%
ValueCountFrequency (%)
13 1
0.3%
27 1
0.3%
28 1
0.3%
34 1
0.3%
45 2
0.7%
48 1
0.3%
51 1
0.3%
55 2
0.7%
59 1
0.3%
60 1
0.3%
ValueCountFrequency (%)
197920 1
0.3%
52796 1
0.3%
31950 1
0.3%
15183 1
0.3%
12826 1
0.3%
11084 1
0.3%
9689 1
0.3%
9284 1
0.3%
8282 1
0.3%
7079 1
0.3%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct260
Distinct (%)93.2%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2312.9892
Minimum11
Maximum203208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T07:20:11.088089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile72
Q1214
median743
Q31592.5
95-th percentile5338.3
Maximum203208
Range203197
Interquartile range (IQR)1378.5

Descriptive statistics

Standard deviation12793.416
Coefficient of variation (CV)5.5311177
Kurtosis221.77622
Mean2312.9892
Median Absolute Deviation (MAD)565
Skewness14.358493
Sum645324
Variance1.6367149 × 108
MonotonicityNot monotonic
2024-03-23T07:20:11.817514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3163 2
 
0.7%
592 2
 
0.7%
320 2
 
0.7%
101 2
 
0.7%
193 2
 
0.7%
74 2
 
0.7%
1255 2
 
0.7%
72 2
 
0.7%
2503 2
 
0.7%
162 2
 
0.7%
Other values (250) 259
86.6%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
11 1
0.3%
31 1
0.3%
41 1
0.3%
43 1
0.3%
51 1
0.3%
52 1
0.3%
53 2
0.7%
54 1
0.3%
57 1
0.3%
61 1
0.3%
ValueCountFrequency (%)
203208 1
0.3%
56920 1
0.3%
30743 1
0.3%
14046 1
0.3%
13864 1
0.3%
13322 1
0.3%
9994 1
0.3%
9441 1
0.3%
8439 1
0.3%
6597 1
0.3%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct264
Distinct (%)94.3%
Missing19
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean2293.4714
Minimum8
Maximum201525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T07:20:12.543037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile64.95
Q1213.75
median723
Q31452.5
95-th percentile6084.45
Maximum201525
Range201517
Interquartile range (IQR)1238.75

Descriptive statistics

Standard deviation12768.871
Coefficient of variation (CV)5.5674866
Kurtosis216.01013
Mean2293.4714
Median Absolute Deviation (MAD)546
Skewness14.152046
Sum642172
Variance1.6304408 × 108
MonotonicityNot monotonic
2024-03-23T07:20:13.497509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
123 3
 
1.0%
118 2
 
0.7%
1060 2
 
0.7%
3927 2
 
0.7%
96 2
 
0.7%
197 2
 
0.7%
1482 2
 
0.7%
85 2
 
0.7%
166 2
 
0.7%
295 2
 
0.7%
Other values (254) 259
86.6%
(Missing) 19
 
6.4%
ValueCountFrequency (%)
8 1
0.3%
30 1
0.3%
31 1
0.3%
38 1
0.3%
45 1
0.3%
46 1
0.3%
50 2
0.7%
53 1
0.3%
57 1
0.3%
58 1
0.3%
ValueCountFrequency (%)
201525 1
0.3%
63064 1
0.3%
32259 1
0.3%
11639 1
0.3%
11177 1
0.3%
9707 1
0.3%
9056 1
0.3%
8671 1
0.3%
8025 1
0.3%
7418 1
0.3%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct251
Distinct (%)90.0%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2124.2258
Minimum12
Maximum186121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T07:20:14.075723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile70.8
Q1182.5
median663
Q31389
95-th percentile5499.5
Maximum186121
Range186109
Interquartile range (IQR)1206.5

Descriptive statistics

Standard deviation11758.692
Coefficient of variation (CV)5.5355186
Kurtosis219.04356
Mean2124.2258
Median Absolute Deviation (MAD)521
Skewness14.268453
Sum592659
Variance1.3826683 × 108
MonotonicityNot monotonic
2024-03-23T07:20:14.577721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106 2
 
0.7%
305 2
 
0.7%
75 2
 
0.7%
1260 2
 
0.7%
105 2
 
0.7%
84 2
 
0.7%
456 2
 
0.7%
1442 2
 
0.7%
68 2
 
0.7%
136 2
 
0.7%
Other values (241) 259
86.6%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
12 1
0.3%
31 1
0.3%
32 1
0.3%
38 2
0.7%
43 1
0.3%
52 1
0.3%
54 2
0.7%
57 1
0.3%
59 1
0.3%
68 2
0.7%
ValueCountFrequency (%)
186121 1
0.3%
56329 1
0.3%
24541 1
0.3%
13669 1
0.3%
11633 1
0.3%
10315 1
0.3%
9678 1
0.3%
7722 1
0.3%
7530 1
0.3%
6734 1
0.3%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct258
Distinct (%)92.5%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2578.5556
Minimum18
Maximum226175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T07:20:15.109725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile80.8
Q1249.5
median834
Q31623
95-th percentile6307.7
Maximum226175
Range226157
Interquartile range (IQR)1373.5

Descriptive statistics

Standard deviation14289.239
Coefficient of variation (CV)5.5415672
Kurtosis219.03363
Mean2578.5556
Median Absolute Deviation (MAD)620
Skewness14.266721
Sum719417
Variance2.0418235 × 108
MonotonicityNot monotonic
2024-03-23T07:20:15.508312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57 3
 
1.0%
135 3
 
1.0%
1376 2
 
0.7%
84 2
 
0.7%
1437 2
 
0.7%
294 2
 
0.7%
2143 2
 
0.7%
176 2
 
0.7%
1010 2
 
0.7%
1067 2
 
0.7%
Other values (248) 257
86.0%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
18 1
 
0.3%
32 1
 
0.3%
34 1
 
0.3%
47 1
 
0.3%
50 1
 
0.3%
56 1
 
0.3%
57 3
1.0%
65 1
 
0.3%
70 1
 
0.3%
71 1
 
0.3%
ValueCountFrequency (%)
226175 1
0.3%
68253 1
0.3%
29609 1
0.3%
16540 1
0.3%
16516 1
0.3%
12681 1
0.3%
12100 1
0.3%
9896 1
0.3%
9875 1
0.3%
7785 1
0.3%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct261
Distinct (%)93.5%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean2353.2401
Minimum20
Maximum206646
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T07:20:16.192567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile78
Q1258
median756
Q31415
95-th percentile5392.9
Maximum206646
Range206626
Interquartile range (IQR)1157

Descriptive statistics

Standard deviation13022
Coefficient of variation (CV)5.5336471
Kurtosis221.16049
Mean2353.2401
Median Absolute Deviation (MAD)544
Skewness14.346651
Sum656554
Variance1.695725 × 108
MonotonicityNot monotonic
2024-03-23T07:20:16.739254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
790 2
 
0.7%
533 2
 
0.7%
78 2
 
0.7%
1350 2
 
0.7%
870 2
 
0.7%
141 2
 
0.7%
212 2
 
0.7%
1093 2
 
0.7%
1124 2
 
0.7%
590 2
 
0.7%
Other values (251) 259
86.6%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
20 1
0.3%
33 1
0.3%
34 1
0.3%
39 1
0.3%
53 1
0.3%
56 1
0.3%
58 1
0.3%
59 1
0.3%
63 1
0.3%
69 1
0.3%
ValueCountFrequency (%)
206646 1
0.3%
60834 1
0.3%
25145 1
0.3%
15628 1
0.3%
13669 1
0.3%
11889 1
0.3%
11848 1
0.3%
11538 1
0.3%
8868 1
0.3%
7659 1
0.3%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct250
Distinct (%)89.6%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1474.4695
Minimum16
Maximum129962
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T07:20:17.244608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile68
Q1197.5
median436
Q3884
95-th percentile3457.3
Maximum129962
Range129946
Interquartile range (IQR)686.5

Descriptive statistics

Standard deviation8155.5842
Coefficient of variation (CV)5.5311989
Kurtosis224.69057
Mean1474.4695
Median Absolute Deviation (MAD)284
Skewness14.477586
Sum411377
Variance66513554
MonotonicityNot monotonic
2024-03-23T07:20:17.864268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79 4
 
1.3%
53 3
 
1.0%
128 3
 
1.0%
596 3
 
1.0%
68 2
 
0.7%
134 2
 
0.7%
140 2
 
0.7%
413 2
 
0.7%
124 2
 
0.7%
272 2
 
0.7%
Other values (240) 254
84.9%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
16 1
 
0.3%
27 1
 
0.3%
34 1
 
0.3%
35 1
 
0.3%
46 1
 
0.3%
52 1
 
0.3%
53 3
1.0%
56 1
 
0.3%
60 1
 
0.3%
62 1
 
0.3%
ValueCountFrequency (%)
129962 1
0.3%
36548 1
0.3%
13037 1
0.3%
9343 1
0.3%
8699 1
0.3%
8600 1
0.3%
8265 1
0.3%
7175 1
0.3%
5817 1
0.3%
5779 1
0.3%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct243
Distinct (%)86.8%
Missing19
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean1271.3857
Minimum10
Maximum112164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T07:20:18.431022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile57.95
Q1151.75
median363
Q3766.75
95-th percentile3175.9
Maximum112164
Range112154
Interquartile range (IQR)615

Descriptive statistics

Standard deviation7023.2712
Coefficient of variation (CV)5.5241074
Kurtosis225.80464
Mean1271.3857
Median Absolute Deviation (MAD)252
Skewness14.511709
Sum355988
Variance49326339
MonotonicityNot monotonic
2024-03-23T07:20:18.965612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
330 5
 
1.7%
119 4
 
1.3%
621 3
 
1.0%
252 3
 
1.0%
493 3
 
1.0%
334 3
 
1.0%
856 2
 
0.7%
35 2
 
0.7%
118 2
 
0.7%
887 2
 
0.7%
Other values (233) 251
83.9%
(Missing) 19
 
6.4%
ValueCountFrequency (%)
10 1
0.3%
24 1
0.3%
25 2
0.7%
27 1
0.3%
35 2
0.7%
40 1
0.3%
42 1
0.3%
43 1
0.3%
44 1
0.3%
48 1
0.3%
ValueCountFrequency (%)
112164 1
0.3%
31129 1
0.3%
12389 1
0.3%
7544 1
0.3%
7293 1
0.3%
7014 1
0.3%
6620 1
0.3%
6322 1
0.3%
5038 1
0.3%
4759 1
0.3%

Interactions

2024-03-23T07:19:48.252239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:17:52.396272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:17:58.748543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:06.005585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:13.724930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:19.472842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:25.532753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:33.412495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:39.390636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:46.152733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:53.248894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:59.140512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:08.080739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:14.347816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:20.496864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:26.786798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:32.818270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:39.692005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:48.554041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:17:52.929547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:17:59.129048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:06.394396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T07:18:23.382772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:31.076474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:37.278928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:43.997199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:50.963388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:57.300106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:05.469977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:12.613064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:18.323853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:24.701157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:31.108257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:36.945865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:46.204332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:51.965493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:17:57.057583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:03.763547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:12.070797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:18.161355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:23.844126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:31.482371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:37.686888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:44.342421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:51.527660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:57.610381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:05.889544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:12.921834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:18.795859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:25.105256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:31.368469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:37.426805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:46.545697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:52.371899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:17:57.477087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:04.174044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:12.403300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:18.418328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:24.129700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:31.845828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:37.952629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:44.670005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:51.994067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:57.975052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:06.293840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:13.264305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:19.148285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:25.413571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:31.683086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:37.739750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:46.827816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:52.766265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:17:57.825306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:04.531283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:12.737770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:18.671158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:24.407710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:32.290081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:38.281830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:45.022449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:52.249082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:58.228204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:06.723247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:13.515209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:19.402057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:25.719420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:31.993788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:38.136353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:47.144429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:53.047718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:17:58.144823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:04.906598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:12.985948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:18.945222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:24.664684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:32.550657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:38.651048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:45.287704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:52.559438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:58.560796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:06.971864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:13.784877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:19.811601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:26.067294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:32.315171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:38.531485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:47.556369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:53.297832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:17:58.467218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:05.653468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:13.322616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:19.219430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:25.093483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:32.906748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:39.007470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:45.834850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:52.951924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:18:58.854380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:07.398384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:14.093585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:20.224095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:26.463374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:32.569672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:39.018628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:19:47.906017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T07:20:19.543035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0001.0001.0001.0001.0001.0000.8011.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20071.0001.0001.0001.0001.0001.0000.9811.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20081.0001.0001.0001.0001.0001.0000.9811.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20091.0001.0001.0001.0001.0001.0000.9811.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20101.0001.0001.0001.0001.0001.0000.9811.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20111.0001.0001.0001.0001.0001.0000.9811.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20120.8010.9810.9810.9810.9810.9811.0000.9810.9810.9810.9810.9810.9810.9810.9810.9810.9810.981
20131.0001.0001.0001.0001.0001.0000.9811.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20141.0001.0001.0001.0001.0001.0000.9811.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20151.0001.0001.0001.0001.0001.0000.9811.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20161.0001.0001.0001.0001.0001.0000.9811.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20171.0001.0001.0001.0001.0001.0000.9811.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20181.0001.0001.0001.0001.0001.0000.9811.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20191.0001.0001.0001.0001.0001.0000.9811.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20201.0001.0001.0001.0001.0001.0000.9811.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20211.0001.0001.0001.0001.0001.0000.9811.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20221.0001.0001.0001.0001.0001.0000.9811.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20231.0001.0001.0001.0001.0001.0000.9811.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-03-23T07:20:19.985415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9530.9370.9430.9190.9220.9140.9150.9190.9400.9310.9410.9390.9240.9420.9140.8620.880
20070.9531.0000.9630.9520.9430.9400.9320.9300.9280.9390.9300.9420.9270.9240.9360.9250.8870.894
20080.9370.9631.0000.9610.9410.9520.9440.9430.9400.9420.9300.9360.9250.9240.9340.9320.8970.902
20090.9430.9520.9611.0000.9590.9600.9490.9560.9510.9560.9400.9490.9450.9350.9480.9360.9030.921
20100.9190.9430.9410.9591.0000.9670.9560.9560.9440.9350.9200.9320.9250.9270.9400.9320.9040.918
20110.9220.9400.9520.9600.9671.0000.9670.9710.9680.9460.9240.9390.9220.9310.9400.9270.9010.911
20120.9140.9320.9440.9490.9560.9671.0000.9680.9630.9480.9250.9370.9230.9290.9400.9330.9060.916
20130.9150.9300.9430.9560.9560.9710.9681.0000.9770.9520.9290.9440.9300.9320.9420.9250.8940.907
20140.9190.9280.9400.9510.9440.9680.9630.9771.0000.9650.9400.9540.9320.9350.9430.9290.8930.905
20150.9400.9390.9420.9560.9350.9460.9480.9520.9651.0000.9540.9600.9480.9440.9520.9260.8920.902
20160.9310.9300.9300.9400.9200.9240.9250.9290.9400.9541.0000.9740.9570.9520.9520.9330.8940.902
20170.9410.9420.9360.9490.9320.9390.9370.9440.9540.9600.9741.0000.9720.9700.9700.9510.9160.930
20180.9390.9270.9250.9450.9250.9220.9230.9300.9320.9480.9570.9721.0000.9750.9700.9480.9160.924
20190.9240.9240.9240.9350.9270.9310.9290.9320.9350.9440.9520.9700.9751.0000.9710.9470.9170.933
20200.9420.9360.9340.9480.9400.9400.9400.9420.9430.9520.9520.9700.9700.9711.0000.9600.9210.938
20210.9140.9250.9320.9360.9320.9270.9330.9250.9290.9260.9330.9510.9480.9470.9601.0000.9570.951
20220.8620.8870.8970.9030.9040.9010.9060.8940.8930.8920.8940.9160.9160.9170.9210.9571.0000.958
20230.8800.8940.9020.9210.9180.9110.9160.9070.9050.9020.9020.9300.9240.9330.9380.9510.9581.000

Missing values

2024-03-23T07:19:53.725998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T07:19:54.721675image/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.
2024-03-23T07:19:55.373689image/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
0전국167009141544131123131334129234147731223935144181170273215496197920203208201525186121226175206646129962112164
1서울337752413720674204851525217910162531763622361337223195030743322592454129609251451303712389
2서울 종로구691601703630401508645629485630529640787530617678413328
3서울 중구978175410726971217818987620845236510417708807861258854596493
4서울 용산구12257514985083884293573895647198689529486391006837433360
5서울 성동구10625514976324274303685067621090111011458657231044759462547
6서울 광진구11098565515673804604234055829101068868779816707553328329
7서울 동대문구1121835778653466591465646899199412501037116511471074795529801
8서울 중랑구101290365747339485451058490710641057918889667963736367279
9서울 성북구15891010863764569678581667794120811631093119511561032734339555
지역200620072008200920102011201220132014201520162017201820192020202120222023
289경남 거창군123104126145107158117145178146229162145137147271225123
290경남 합천군75109869111310911310014113512212411110913914788109
291(구)제주656<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
292(구)제주 (구)제주시436<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
293(구)제주 (구)서귀포시67<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
294(구)제주 (구)북제주군86<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
295(구)제주 (구)남제주군67<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
296제주6161050962103412761514157018252036249925302417222433481672228218491277
297제주 제주시4918247247731034117512611381148915911657167514821159119615771310887
298제주 서귀포시1242262382602423383094445479088747437422189476704539390