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 순수토지 거래현황의 연도별 행정구역별(면적) 데이터입니다.- (단위 : 천㎡)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068145/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 05:20:40.385858
Analysis finished2024-03-23 05:22:47.936277
Duration2 minutes and 7.55 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
2024-03-23T05:22:48.635874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length6
Mean length6.4013378
Min length2

Characters and Unicode

Total characters1914
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%
2024-03-23T05:22:50.067594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
317
 
16.6%
141
 
7.4%
122
 
6.4%
109
 
5.7%
93
 
4.9%
89
 
4.6%
72
 
3.8%
56
 
2.9%
50
 
2.6%
48
 
2.5%
Other values (137) 817
42.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1559
81.5%
Space Separator 317
 
16.6%
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 (%)
317
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1559
81.5%
Common 355
 
18.5%

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 (%)
317
89.3%
) 19
 
5.4%
( 19
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1559
81.5%
ASCII 355
 
18.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
317
89.3%
) 19
 
5.4%
( 19
 
5.4%
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 

Distinct275
Distinct (%)99.3%
Missing22
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean32624.094
Minimum38
Maximum2982404
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T05:22:50.541314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38
5-th percentile291.2
Q11762
median10357
Q320022
95-th percentile52988.8
Maximum2982404
Range2982366
Interquartile range (IQR)18260

Descriptive statistics

Standard deviation187197.4
Coefficient of variation (CV)5.7380107
Kurtosis225.61524
Mean32624.094
Median Absolute Deviation (MAD)8807
Skewness14.444617
Sum9036874
Variance3.5042866 × 1010
MonotonicityNot monotonic
2024-03-23T05:22:51.401141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4095 2
 
0.7%
311 2
 
0.7%
4066 1
 
0.3%
15040 1
 
0.3%
15110 1
 
0.3%
18661 1
 
0.3%
15173 1
 
0.3%
19801 1
 
0.3%
8160 1
 
0.3%
21765 1
 
0.3%
Other values (265) 265
88.6%
(Missing) 22
 
7.4%
ValueCountFrequency (%)
38 1
0.3%
86 1
0.3%
132 1
0.3%
133 1
0.3%
135 1
0.3%
158 1
0.3%
172 1
0.3%
175 1
0.3%
195 1
0.3%
212 1
0.3%
ValueCountFrequency (%)
2982404 1
0.3%
495025 1
0.3%
461560 1
0.3%
398343 1
0.3%
357039 1
0.3%
303210 1
0.3%
247889 1
0.3%
209431 1
0.3%
202300 1
0.3%
71767 1
0.3%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct267
Distinct (%)98.2%
Missing27
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean26757.787
Minimum35
Maximum2400381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T05:22:52.013951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile142
Q11290.5
median8672
Q316462.25
95-th percentile36184.9
Maximum2400381
Range2400346
Interquartile range (IQR)15171.75

Descriptive statistics

Standard deviation152293.16
Coefficient of variation (CV)5.6915453
Kurtosis219.98214
Mean26757.787
Median Absolute Deviation (MAD)7461.5
Skewness14.249751
Sum7278118
Variance2.3193205 × 1010
MonotonicityNot monotonic
2024-03-23T05:22:52.540622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142 2
 
0.7%
748 2
 
0.7%
88 2
 
0.7%
101 2
 
0.7%
10326 2
 
0.7%
8604 1
 
0.3%
10492 1
 
0.3%
7161 1
 
0.3%
14100 1
 
0.3%
13307 1
 
0.3%
Other values (257) 257
86.0%
(Missing) 27
 
9.0%
ValueCountFrequency (%)
35 1
0.3%
65 1
0.3%
80 1
0.3%
81 1
0.3%
82 1
0.3%
85 1
0.3%
88 2
0.7%
93 1
0.3%
101 2
0.7%
116 1
0.3%
ValueCountFrequency (%)
2400381 1
0.3%
443193 1
0.3%
343994 1
0.3%
292736 1
0.3%
288615 1
0.3%
275849 1
0.3%
208786 1
0.3%
176575 1
0.3%
171582 1
0.3%
54648 1
0.3%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct269
Distinct (%)98.2%
Missing25
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean24288.92
Minimum18
Maximum2185819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T05:22:53.466193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile126.95
Q1991.25
median8447.5
Q315342.5
95-th percentile33527.6
Maximum2185819
Range2185801
Interquartile range (IQR)14351.25

Descriptive statistics

Standard deviation138204.79
Coefficient of variation (CV)5.6900343
Kurtosis221.42171
Mean24288.92
Median Absolute Deviation (MAD)7307
Skewness14.295482
Sum6655164
Variance1.9100563 × 1010
MonotonicityNot monotonic
2024-03-23T05:22:54.069571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
313 2
 
0.7%
13383 2
 
0.7%
212 2
 
0.7%
97 2
 
0.7%
643 2
 
0.7%
6031 1
 
0.3%
13919 1
 
0.3%
7447 1
 
0.3%
13156 1
 
0.3%
11469 1
 
0.3%
Other values (259) 259
86.6%
(Missing) 25
 
8.4%
ValueCountFrequency (%)
18 1
0.3%
36 1
0.3%
37 1
0.3%
39 1
0.3%
52 1
0.3%
69 1
0.3%
73 1
0.3%
79 1
0.3%
90 1
0.3%
97 2
0.7%
ValueCountFrequency (%)
2185819 1
0.3%
418207 1
0.3%
298168 1
0.3%
257578 1
0.3%
253886 1
0.3%
250718 1
0.3%
197795 1
0.3%
182181 1
0.3%
156663 1
0.3%
42522 1
0.3%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct268
Distinct (%)97.8%
Missing25
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean23853.617
Minimum23
Maximum2150780
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T05:22:54.571203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile98.65
Q1945.5
median7601
Q314925
95-th percentile32899.1
Maximum2150780
Range2150757
Interquartile range (IQR)13979.5

Descriptive statistics

Standard deviation135849.26
Coefficient of variation (CV)5.695122
Kurtosis222.28214
Mean23853.617
Median Absolute Deviation (MAD)6878
Skewness14.321151
Sum6535891
Variance1.8455021 × 1010
MonotonicityNot monotonic
2024-03-23T05:22:55.155809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
247 2
 
0.7%
68 2
 
0.7%
14085 2
 
0.7%
170 2
 
0.7%
139 2
 
0.7%
13829 2
 
0.7%
22458 1
 
0.3%
2262 1
 
0.3%
2388 1
 
0.3%
4650 1
 
0.3%
Other values (258) 258
86.3%
(Missing) 25
 
8.4%
ValueCountFrequency (%)
23 1
0.3%
31 1
0.3%
32 1
0.3%
45 1
0.3%
46 1
0.3%
53 1
0.3%
55 1
0.3%
60 1
0.3%
68 2
0.7%
70 1
0.3%
ValueCountFrequency (%)
2150780 1
0.3%
369206 1
0.3%
273723 1
0.3%
255159 1
0.3%
246280 1
0.3%
245040 1
0.3%
241765 1
0.3%
204119 1
0.3%
167587 1
0.3%
54284 1
0.3%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct277
Distinct (%)98.9%
Missing19
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean20286.014
Minimum18
Maximum1863634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T05:22:55.486822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile83.65
Q1742
median7262.5
Q313588.5
95-th percentile26196.8
Maximum1863634
Range1863616
Interquartile range (IQR)12846.5

Descriptive statistics

Standard deviation116386.52
Coefficient of variation (CV)5.7372789
Kurtosis227.75877
Mean20286.014
Median Absolute Deviation (MAD)6504.5
Skewness14.506901
Sum5680084
Variance1.3545822 × 1010
MonotonicityNot monotonic
2024-03-23T05:22:55.890840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152 2
 
0.7%
14826 2
 
0.7%
403 2
 
0.7%
13359 1
 
0.3%
6004 1
 
0.3%
8948 1
 
0.3%
9757 1
 
0.3%
9016 1
 
0.3%
17437 1
 
0.3%
10490 1
 
0.3%
Other values (267) 267
89.3%
(Missing) 19
 
6.4%
ValueCountFrequency (%)
18 1
0.3%
35 1
0.3%
41 1
0.3%
43 1
0.3%
44 1
0.3%
45 1
0.3%
46 1
0.3%
51 1
0.3%
52 1
0.3%
53 1
0.3%
ValueCountFrequency (%)
1863634 1
0.3%
335310 1
0.3%
234331 1
0.3%
215980 1
0.3%
213099 1
0.3%
205679 1
0.3%
199677 1
0.3%
157916 1
0.3%
152918 1
0.3%
39918 1
0.3%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct276
Distinct (%)99.6%
Missing22
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean20268.188
Minimum13
Maximum1844056
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T05:22:56.325805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile92.2
Q1780
median7367
Q312989
95-th percentile23690.6
Maximum1844056
Range1844043
Interquartile range (IQR)12209

Descriptive statistics

Standard deviation115911.05
Coefficient of variation (CV)5.7188661
Kurtosis224.28589
Mean20268.188
Median Absolute Deviation (MAD)6256
Skewness14.386869
Sum5614288
Variance1.3435372 × 1010
MonotonicityNot monotonic
2024-03-23T05:22:56.781567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97 2
 
0.7%
13139 1
 
0.3%
12932 1
 
0.3%
12043 1
 
0.3%
7367 1
 
0.3%
2565 1
 
0.3%
2289 1
 
0.3%
4854 1
 
0.3%
159019 1
 
0.3%
15830 1
 
0.3%
Other values (266) 266
89.0%
(Missing) 22
 
7.4%
ValueCountFrequency (%)
13 1
0.3%
20 1
0.3%
43 1
0.3%
47 1
0.3%
51 1
0.3%
52 1
0.3%
54 1
0.3%
62 1
0.3%
66 1
0.3%
74 1
0.3%
ValueCountFrequency (%)
1844056 1
0.3%
332314 1
0.3%
247088 1
0.3%
223573 1
0.3%
217536 1
0.3%
215687 1
0.3%
172073 1
0.3%
159019 1
0.3%
146123 1
0.3%
39544 1
0.3%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct269
Distinct (%)96.4%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean18686.254
Minimum16
Maximum1711624
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T05:22:57.258022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile70.7
Q1966
median6941
Q312025.5
95-th percentile23557.6
Maximum1711624
Range1711608
Interquartile range (IQR)11059.5

Descriptive statistics

Standard deviation107086.33
Coefficient of variation (CV)5.7307543
Kurtosis226.93611
Mean18686.254
Median Absolute Deviation (MAD)5867
Skewness14.484501
Sum5213465
Variance1.1467483 × 1010
MonotonicityNot monotonic
2024-03-23T05:22:57.684169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79 2
 
0.7%
1082 2
 
0.7%
208 2
 
0.7%
966 2
 
0.7%
361 2
 
0.7%
233 2
 
0.7%
4880 2
 
0.7%
770 2
 
0.7%
12963 2
 
0.7%
9963 2
 
0.7%
Other values (259) 259
86.6%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
16 1
0.3%
17 1
0.3%
28 1
0.3%
35 1
0.3%
39 1
0.3%
45 1
0.3%
51 1
0.3%
52 1
0.3%
53 1
0.3%
54 1
0.3%
ValueCountFrequency (%)
1711624 1
0.3%
316714 1
0.3%
228448 1
0.3%
210472 1
0.3%
190681 1
0.3%
167769 1
0.3%
160470 1
0.3%
150978 1
0.3%
139959 1
0.3%
37428 1
0.3%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct275
Distinct (%)98.6%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean18559.502
Minimum12
Maximum1698859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T05:22:58.210733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile98
Q1760.5
median6954
Q312479.5
95-th percentile23711.3
Maximum1698859
Range1698847
Interquartile range (IQR)11719

Descriptive statistics

Standard deviation106236.09
Coefficient of variation (CV)5.7240809
Kurtosis227.37668
Mean18559.502
Median Absolute Deviation (MAD)5826
Skewness14.503671
Sum5178101
Variance1.1286107 × 1010
MonotonicityNot monotonic
2024-03-23T05:22:58.674982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 2
 
0.7%
233 2
 
0.7%
2249 2
 
0.7%
13805 2
 
0.7%
8045 1
 
0.3%
8183 1
 
0.3%
8424 1
 
0.3%
151367 1
 
0.3%
11912 1
 
0.3%
8355 1
 
0.3%
Other values (265) 265
88.6%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
12 1
0.3%
17 2
0.7%
38 1
0.3%
39 1
0.3%
41 1
0.3%
44 1
0.3%
50 1
0.3%
52 1
0.3%
53 1
0.3%
56 1
0.3%
ValueCountFrequency (%)
1698859 1
0.3%
318016 1
0.3%
222750 1
0.3%
199162 1
0.3%
193159 1
0.3%
163150 1
0.3%
156933 1
0.3%
151367 1
0.3%
136886 1
0.3%
50409 1
0.3%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct277
Distinct (%)96.9%
Missing13
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean19507.427
Minimum14
Maximum1827667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T05:22:59.174863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile84.75
Q1926.75
median7282
Q312277
95-th percentile24411
Maximum1827667
Range1827653
Interquartile range (IQR)11350.25

Descriptive statistics

Standard deviation112781.74
Coefficient of variation (CV)5.7814772
Kurtosis234.08039
Mean19507.427
Median Absolute Deviation (MAD)5922
Skewness14.723757
Sum5579124
Variance1.2719721 × 1010
MonotonicityNot monotonic
2024-03-23T05:22:59.710040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10893 2
 
0.7%
1377 2
 
0.7%
30 2
 
0.7%
194 2
 
0.7%
210 2
 
0.7%
696 2
 
0.7%
255 2
 
0.7%
61 2
 
0.7%
10181 2
 
0.7%
17142 1
 
0.3%
Other values (267) 267
89.3%
(Missing) 13
 
4.3%
ValueCountFrequency (%)
14 1
0.3%
23 1
0.3%
30 2
0.7%
55 1
0.3%
58 1
0.3%
59 1
0.3%
61 2
0.7%
63 1
0.3%
67 1
0.3%
70 1
0.3%
ValueCountFrequency (%)
1827667 1
0.3%
334664 1
0.3%
233829 1
0.3%
215522 1
0.3%
203628 1
0.3%
180071 1
0.3%
168829 1
0.3%
150179 1
0.3%
148288 1
0.3%
69995 1
0.3%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct278
Distinct (%)98.6%
Missing17
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean21837.142
Minimum17
Maximum2013916
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T05:23:00.236003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile132.1
Q11066.25
median7952.5
Q314207.25
95-th percentile26643.7
Maximum2013916
Range2013899
Interquartile range (IQR)13141

Descriptive statistics

Standard deviation125266.16
Coefficient of variation (CV)5.7363807
Kurtosis229.88333
Mean21837.142
Median Absolute Deviation (MAD)6580.5
Skewness14.58626
Sum6158074
Variance1.569161 × 1010
MonotonicityNot monotonic
2024-03-23T05:23:00.767034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
615 2
 
0.7%
75 2
 
0.7%
13624 2
 
0.7%
304 2
 
0.7%
6785 1
 
0.3%
14409 1
 
0.3%
10193 1
 
0.3%
8128 1
 
0.3%
2983 1
 
0.3%
3802 1
 
0.3%
Other values (268) 268
89.6%
(Missing) 17
 
5.7%
ValueCountFrequency (%)
17 1
0.3%
30 1
0.3%
40 1
0.3%
50 1
0.3%
59 1
0.3%
63 1
0.3%
75 2
0.7%
76 1
0.3%
86 1
0.3%
90 1
0.3%
ValueCountFrequency (%)
2013916 1
0.3%
380077 1
0.3%
259408 1
0.3%
252702 1
0.3%
246891 1
0.3%
185466 1
0.3%
172914 1
0.3%
160374 1
0.3%
135034 1
0.3%
88934 1
0.3%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct277
Distinct (%)98.2%
Missing17
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean21843.316
Minimum13
Maximum2006588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T05:23:01.255154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile93.05
Q11154.25
median8063.5
Q313929.25
95-th percentile29330.5
Maximum2006588
Range2006575
Interquartile range (IQR)12775

Descriptive statistics

Standard deviation124800.38
Coefficient of variation (CV)5.7134359
Kurtosis229.88446
Mean21843.316
Median Absolute Deviation (MAD)6765.5
Skewness14.582191
Sum6159815
Variance1.5575136 × 1010
MonotonicityNot monotonic
2024-03-23T05:23:01.902336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
621 2
 
0.7%
165 2
 
0.7%
158 2
 
0.7%
13069 2
 
0.7%
65 2
 
0.7%
7551 1
 
0.3%
26388 1
 
0.3%
2984 1
 
0.3%
29372 1
 
0.3%
171670 1
 
0.3%
Other values (267) 267
89.3%
(Missing) 17
 
5.7%
ValueCountFrequency (%)
13 1
0.3%
19 1
0.3%
32 1
0.3%
40 1
0.3%
45 1
0.3%
47 1
0.3%
58 1
0.3%
65 2
0.7%
74 1
0.3%
76 1
0.3%
ValueCountFrequency (%)
2006588 1
0.3%
354321 1
0.3%
289951 1
0.3%
256971 1
0.3%
224183 1
0.3%
193169 1
0.3%
171670 1
0.3%
168600 1
0.3%
152962 1
0.3%
70698 1
0.3%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct277
Distinct (%)99.3%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean22359.502
Minimum12
Maximum2037895
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T05:23:02.406944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile101
Q11041.5
median8418
Q314161.5
95-th percentile26932.1
Maximum2037895
Range2037883
Interquartile range (IQR)13120

Descriptive statistics

Standard deviation127484.03
Coefficient of variation (CV)5.7015595
Kurtosis227.00045
Mean22359.502
Median Absolute Deviation (MAD)7083
Skewness14.489545
Sum6238301
Variance1.6252178 × 1010
MonotonicityNot monotonic
2024-03-23T05:23:02.969024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
228 2
 
0.7%
21787 2
 
0.7%
16820 1
 
0.3%
13718 1
 
0.3%
7477 1
 
0.3%
2774 1
 
0.3%
3698 1
 
0.3%
6472 1
 
0.3%
165890 1
 
0.3%
17445 1
 
0.3%
Other values (267) 267
89.3%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
12 1
0.3%
23 1
0.3%
36 1
0.3%
37 1
0.3%
44 1
0.3%
49 1
0.3%
72 1
0.3%
73 1
0.3%
76 1
0.3%
79 1
0.3%
ValueCountFrequency (%)
2037895 1
0.3%
367580 1
0.3%
286906 1
0.3%
282411 1
0.3%
213294 1
0.3%
201603 1
0.3%
193222 1
0.3%
165890 1
0.3%
134730 1
0.3%
53085 1
0.3%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct277
Distinct (%)98.9%
Missing19
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean20807.332
Minimum9
Maximum1906774
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T05:23:03.577708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile99.65
Q11103
median7653
Q313859.5
95-th percentile25620.75
Maximum1906774
Range1906765
Interquartile range (IQR)12756.5

Descriptive statistics

Standard deviation119177.4
Coefficient of variation (CV)5.7276635
Kurtosis227.01629
Mean20807.332
Median Absolute Deviation (MAD)6417.5
Skewness14.481034
Sum5826053
Variance1.4203252 × 1010
MonotonicityNot monotonic
2024-03-23T05:23:04.089922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15709 2
 
0.7%
230 2
 
0.7%
83 2
 
0.7%
15698 1
 
0.3%
13822 1
 
0.3%
7438 1
 
0.3%
2841 1
 
0.3%
1876 1
 
0.3%
4718 1
 
0.3%
158609 1
 
0.3%
Other values (267) 267
89.3%
(Missing) 19
 
6.4%
ValueCountFrequency (%)
9 1
0.3%
18 1
0.3%
37 1
0.3%
44 1
0.3%
45 1
0.3%
52 1
0.3%
53 1
0.3%
56 1
0.3%
75 1
0.3%
82 1
0.3%
ValueCountFrequency (%)
1906774 1
0.3%
322108 1
0.3%
300457 1
0.3%
264827 1
0.3%
207089 1
0.3%
194348 1
0.3%
178661 1
0.3%
158609 1
0.3%
118679 1
0.3%
44050 1
0.3%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct276
Distinct (%)98.9%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean18885.007
Minimum6
Maximum1717077
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T05:23:04.615462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile89.9
Q11071.5
median7092
Q312234
95-th percentile22133.9
Maximum1717077
Range1717071
Interquartile range (IQR)11162.5

Descriptive statistics

Standard deviation107431.89
Coefficient of variation (CV)5.6887396
Kurtosis226.80084
Mean18885.007
Median Absolute Deviation (MAD)5742
Skewness14.477449
Sum5268917
Variance1.1541611 × 1010
MonotonicityNot monotonic
2024-03-23T05:23:05.208694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138 2
 
0.7%
38 2
 
0.7%
12481 2
 
0.7%
13196 1
 
0.3%
13325 1
 
0.3%
9501 1
 
0.3%
3002 1
 
0.3%
2572 1
 
0.3%
5575 1
 
0.3%
156826 1
 
0.3%
Other values (266) 266
89.0%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
6 1
0.3%
33 1
0.3%
38 2
0.7%
41 1
0.3%
54 1
0.3%
57 1
0.3%
65 1
0.3%
75 1
0.3%
77 1
0.3%
81 1
0.3%
ValueCountFrequency (%)
1717077 1
0.3%
272322 1
0.3%
261308 1
0.3%
256683 1
0.3%
176835 1
0.3%
170638 1
0.3%
165225 1
0.3%
156826 1
0.3%
114805 1
0.3%
38352 1
0.3%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct278
Distinct (%)99.6%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean20480.789
Minimum5
Maximum1864214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T05:23:06.235908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile106.8
Q11124
median6936
Q312995
95-th percentile26741.3
Maximum1864214
Range1864209
Interquartile range (IQR)11871

Descriptive statistics

Standard deviation116760.83
Coefficient of variation (CV)5.7009928
Kurtosis225.86652
Mean20480.789
Median Absolute Deviation (MAD)5908
Skewness14.438962
Sum5714140
Variance1.3633091 × 1010
MonotonicityNot monotonic
2024-03-23T05:23:07.018754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17431 2
 
0.7%
15603 1
 
0.3%
12906 1
 
0.3%
8603 1
 
0.3%
2656 1
 
0.3%
2846 1
 
0.3%
5502 1
 
0.3%
154236 1
 
0.3%
11187 1
 
0.3%
9917 1
 
0.3%
Other values (268) 268
89.6%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
5 1
0.3%
29 1
0.3%
37 1
0.3%
38 1
0.3%
41 1
0.3%
59 1
0.3%
63 1
0.3%
73 1
0.3%
76 1
0.3%
89 1
0.3%
ValueCountFrequency (%)
1864214 1
0.3%
297463 1
0.3%
292999 1
0.3%
281945 1
0.3%
195124 1
0.3%
190729 1
0.3%
180582 1
0.3%
154236 1
0.3%
124869 1
0.3%
37197 1
0.3%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct276
Distinct (%)98.9%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean22147.534
Minimum11
Maximum2016098
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T05:23:07.836152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile129.4
Q11356
median8212
Q313879
95-th percentile27816.3
Maximum2016098
Range2016087
Interquartile range (IQR)12523

Descriptive statistics

Standard deviation126075.69
Coefficient of variation (CV)5.6925385
Kurtosis227.27813
Mean22147.534
Median Absolute Deviation (MAD)6690
Skewness14.496769
Sum6179162
Variance1.589508 × 1010
MonotonicityNot monotonic
2024-03-23T05:23:08.502852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
505 2
 
0.7%
45 2
 
0.7%
17988 2
 
0.7%
161814 1
 
0.3%
15268 1
 
0.3%
13152 1
 
0.3%
11570 1
 
0.3%
3570 1
 
0.3%
1452 1
 
0.3%
5022 1
 
0.3%
Other values (266) 266
89.0%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
11 1
0.3%
31 1
0.3%
45 2
0.7%
53 1
0.3%
59 1
0.3%
86 1
0.3%
88 1
0.3%
100 1
0.3%
101 1
0.3%
107 1
0.3%
ValueCountFrequency (%)
2016098 1
0.3%
323862 1
0.3%
315538 1
0.3%
272346 1
0.3%
227331 1
0.3%
202265 1
0.3%
184258 1
0.3%
161814 1
0.3%
150681 1
0.3%
37055 1
0.3%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct272
Distinct (%)97.5%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean18422.05
Minimum26
Maximum1675623
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T05:23:09.290557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile88.7
Q1804
median6829
Q311835.5
95-th percentile24376.4
Maximum1675623
Range1675597
Interquartile range (IQR)11031.5

Descriptive statistics

Standard deviation104828.28
Coefficient of variation (CV)5.6903697
Kurtosis226.87719
Mean18422.05
Median Absolute Deviation (MAD)5662
Skewness14.477838
Sum5139752
Variance1.0988968 × 1010
MonotonicityNot monotonic
2024-03-23T05:23:09.870021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11795 2
 
0.7%
120 2
 
0.7%
726 2
 
0.7%
35 2
 
0.7%
247 2
 
0.7%
58 2
 
0.7%
77 2
 
0.7%
7246 1
 
0.3%
15622 1
 
0.3%
8528 1
 
0.3%
Other values (262) 262
87.6%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
26 1
0.3%
35 2
0.7%
44 1
0.3%
45 1
0.3%
56 1
0.3%
58 2
0.7%
59 1
0.3%
60 1
0.3%
65 1
0.3%
68 1
0.3%
ValueCountFrequency (%)
1675623 1
0.3%
267913 1
0.3%
251090 1
0.3%
231854 1
0.3%
196960 1
0.3%
169300 1
0.3%
160022 1
0.3%
133314 1
0.3%
130158 1
0.3%
31547 1
0.3%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct271
Distinct (%)96.8%
Missing19
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean13822.332
Minimum13
Maximum1263789
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T05:23:10.413727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile58.95
Q1564.5
median4562.5
Q38919.75
95-th percentile19798.25
Maximum1263789
Range1263776
Interquartile range (IQR)8355.25

Descriptive statistics

Standard deviation79003.103
Coefficient of variation (CV)5.7156131
Kurtosis226.80268
Mean13822.332
Median Absolute Deviation (MAD)4016.5
Skewness14.465153
Sum3870253
Variance6.2414903 × 109
MonotonicityNot monotonic
2024-03-23T05:23:10.936322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
212 3
 
1.0%
207 2
 
0.7%
46 2
 
0.7%
5971 2
 
0.7%
32 2
 
0.7%
64 2
 
0.7%
402 2
 
0.7%
6733 2
 
0.7%
7541 1
 
0.3%
10733 1
 
0.3%
Other values (261) 261
87.3%
(Missing) 19
 
6.4%
ValueCountFrequency (%)
13 1
0.3%
15 1
0.3%
17 1
0.3%
19 1
0.3%
21 1
0.3%
26 1
0.3%
32 2
0.7%
39 1
0.3%
46 2
0.7%
47 1
0.3%
ValueCountFrequency (%)
1263789 1
0.3%
209356 1
0.3%
191546 1
0.3%
164454 1
0.3%
139326 1
0.3%
134847 1
0.3%
125010 1
0.3%
109551 1
0.3%
100537 1
0.3%
26595 1
0.3%

Interactions

2024-03-23T05:22:38.987325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:20:42.702901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:20:49.588867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:20:56.070422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:02.776890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:11.801527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:19.655625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:27.160919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:33.760997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:40.271828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:47.936387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:54.007331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:59.576023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:05.582264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T05:22:36.628867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:43.174101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:20:47.227361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:20:54.089231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:00.632287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:08.894974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:17.244026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:24.341011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:31.479221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:37.925528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:45.632862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:52.206004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:57.546964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:03.755575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:09.149476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:16.414252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:23.836102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:29.974677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:36.950199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:43.483743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:20:47.629406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:20:54.568614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:01.034407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:09.354609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:17.620997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:25.002439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:31.794445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:38.369271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:46.096595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:52.502881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:57.936474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:04.044773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:09.573517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:16.868882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:24.113219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:30.396489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:37.324166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:43.870475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:20:48.095312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:20:54.908305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:01.309352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:09.878106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:18.050890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:25.508080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:32.227031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:38.813064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:46.411595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:52.836932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:58.308737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:04.377541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:10.032778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:17.341430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:24.389426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:30.681929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:37.605145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:44.109974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:20:48.463705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:20:55.185639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:01.678543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:10.394295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:18.495440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:25.855089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:32.667476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:39.176329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:46.762396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:53.116135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:58.605774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:04.653805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:10.695517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:17.762251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:24.692336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:31.085764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:38.000578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:44.518262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:20:48.837637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:20:55.490621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:02.017473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:10.928461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:18.901952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:26.224008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:33.050752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:39.495510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:47.175945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:53.442467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:58.912514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:04.937334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:11.237191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:18.503956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:25.123877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:31.508312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:38.328340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:44.940364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:20:49.138434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:20:55.787683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:02.293324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:11.386037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:19.363300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:26.644844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:33.471479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:39.830707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:47.533489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:53.723154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:21:59.281734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:05.252791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:11.980154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:18.977095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:25.448534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:31.832293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:22:38.673218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T05:23:11.293115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0001.0001.0000.9990.9991.0000.9980.9980.9980.9980.9980.9981.0000.9980.9940.9940.9940.994
20071.0001.0001.0000.9990.9991.0000.9980.9980.9980.9980.9980.9981.0000.9980.9940.9940.9940.994
20081.0001.0001.0000.9990.9991.0000.9980.9980.9980.9980.9980.9981.0000.9980.9940.9940.9940.994
20090.9990.9990.9991.0001.0000.9990.9950.9950.9950.9950.9950.9950.9990.9950.9990.9990.9990.999
20100.9990.9990.9991.0001.0000.9990.9950.9950.9950.9950.9950.9950.9990.9950.9990.9990.9990.999
20111.0001.0001.0000.9990.9991.0000.9980.9980.9980.9980.9980.9981.0000.9980.9940.9940.9940.994
20120.9980.9980.9980.9950.9950.9981.0001.0001.0001.0001.0001.0000.9980.9910.9980.9850.9850.985
20130.9980.9980.9980.9950.9950.9981.0001.0001.0001.0001.0001.0000.9980.9910.9980.9850.9850.985
20140.9980.9980.9980.9950.9950.9981.0001.0001.0001.0001.0001.0000.9980.9910.9980.9850.9850.985
20150.9980.9980.9980.9950.9950.9981.0001.0001.0001.0001.0001.0000.9980.9910.9980.9850.9850.985
20160.9980.9980.9980.9950.9950.9981.0001.0001.0001.0001.0001.0000.9980.9910.9980.9850.9850.985
20170.9980.9980.9980.9950.9950.9981.0001.0001.0001.0001.0001.0000.9980.9910.9980.9850.9850.985
20181.0001.0001.0000.9990.9991.0000.9980.9980.9980.9980.9980.9981.0000.9980.9940.9940.9940.994
20190.9980.9980.9980.9950.9950.9980.9910.9910.9910.9910.9910.9910.9981.0000.9850.9980.9980.998
20200.9940.9940.9940.9990.9990.9940.9980.9980.9980.9980.9980.9980.9940.9851.0000.9940.9940.994
20210.9940.9940.9940.9990.9990.9940.9850.9850.9850.9850.9850.9850.9940.9980.9941.0001.0001.000
20220.9940.9940.9940.9990.9990.9940.9850.9850.9850.9850.9850.9850.9940.9980.9941.0001.0001.000
20230.9940.9940.9940.9990.9990.9940.9850.9850.9850.9850.9850.9850.9940.9980.9941.0001.0001.000
2024-03-23T05:23:11.951666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9460.9210.9140.9090.9180.9080.9110.9190.9230.9040.9080.9120.9040.8990.9150.9060.901
20070.9461.0000.9640.9360.9360.9410.9420.9400.9430.9460.9250.9330.9350.9240.9190.9320.9240.931
20080.9210.9641.0000.9470.9430.9430.9380.9340.9390.9370.9250.9310.9230.9190.9120.9190.9110.921
20090.9140.9360.9471.0000.9670.9610.9460.9520.9520.9510.9310.9510.9390.9400.9370.9430.9450.950
20100.9090.9360.9430.9671.0000.9580.9470.9530.9430.9400.9230.9470.9420.9330.9320.9420.9410.947
20110.9180.9410.9430.9610.9581.0000.9620.9620.9580.9530.9390.9440.9400.9310.9380.9400.9310.944
20120.9080.9420.9380.9460.9470.9621.0000.9580.9580.9470.9380.9430.9390.9360.9330.9380.9270.941
20130.9110.9400.9340.9520.9530.9620.9581.0000.9680.9600.9500.9550.9540.9430.9420.9440.9380.944
20140.9190.9430.9390.9520.9430.9580.9580.9681.0000.9680.9500.9490.9450.9330.9310.9290.9310.941
20150.9230.9460.9370.9510.9400.9530.9470.9600.9681.0000.9540.9610.9530.9410.9420.9450.9390.947
20160.9040.9250.9250.9310.9230.9390.9380.9500.9500.9541.0000.9540.9530.9390.9380.9370.9330.941
20170.9080.9330.9310.9510.9470.9440.9430.9550.9490.9610.9541.0000.9710.9630.9630.9610.9540.956
20180.9120.9350.9230.9390.9420.9400.9390.9540.9450.9530.9530.9711.0000.9720.9620.9580.9540.960
20190.9040.9240.9190.9400.9330.9310.9360.9430.9330.9410.9390.9630.9721.0000.9700.9610.9500.957
20200.8990.9190.9120.9370.9320.9380.9330.9420.9310.9420.9380.9630.9620.9701.0000.9670.9560.951
20210.9150.9320.9190.9430.9420.9400.9380.9440.9290.9450.9370.9610.9580.9610.9671.0000.9720.962
20220.9060.9240.9110.9450.9410.9310.9270.9380.9310.9390.9330.9540.9540.9500.9560.9721.0000.964
20230.9010.9310.9210.9500.9470.9440.9410.9440.9410.9470.9410.9560.9600.9570.9510.9620.9641.000

Missing values

2024-03-23T05:22:45.435193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T05:22:46.346522image/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.
2024-03-23T05:22:47.158337image/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
0전국298240424003812185819215078018636341844056171162416988591827667201391620065882037895190677417170771864214201609816756231263789
1서울1870982565745753458003857753943765305648160766447765884187838959897583098
2서울 종로구2842712629811013919412814215212517720481167250234102
3서울 중구55127310268861115144593076374465111534584
4서울 용산구24314228214512620360171724021754745966791757746
5서울 성동구218181141158905252152847510783151389059133123
6서울 광진구329883681352028176186477917141471456013
7서울 동대문구1728812568101547124587757436314357731074451
8서울 중랑구13215321247375747853581326514075146761005632
9서울 성북구144239518313915083799955901611388287921016564
지역200620072008200920102011201220132014201520162017201820192020202120222023
289경남 거창군158401325611312156481479215929173481247614004161171564619193140311192615966132491245814185
290경남 합천군225212437223817202961699319758181261914018677230781660619792200831361813731153431544611078
291(구)제주54744<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
292(구)제주 (구)제주시4107<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
293(구)제주 (구)서귀포시2847<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
294(구)제주 (구)북제주군31579<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
295(구)제주 (구)남제주군16211<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
296제주459575464842522384343991839544374285040969995889347069853085440503835230741370553154726595
297제주 제주시280343116823818192322415021874209212581039986467834075228544259582105917897234931960614071
298제주 서귀포시179232348018704192021576817670165072459930009421502994624541180921729312844135621194212524