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(필지수)데이터입니다.- (단위 : 필지수)- 공표시기 : 익월 말일경
Author한국부동산원
URLhttps://www.data.go.kr/data/15068139/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 05:34:23.287382
Analysis finished2024-03-23 05:35:21.259653
Duration57.97 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
2024-03-23T14:35:21.678603image/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%
2024-03-23T14:35:22.517694image/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 

Distinct273
Distinct (%)98.6%
Missing22
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean11190.917
Minimum121
Maximum1015021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T14:35:22.800936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum121
5-th percentile483.2
Q11513
median3481
Q36146
95-th percentile19752.4
Maximum1015021
Range1014900
Interquartile range (IQR)4633

Descriptive statistics

Standard deviation63436.38
Coefficient of variation (CV)5.6685596
Kurtosis229.51637
Mean11190.917
Median Absolute Deviation (MAD)2219
Skewness14.617615
Sum3099884
Variance4.0241743 × 109
MonotonicityNot monotonic
2024-03-23T14:35:23.218334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
914 2
 
0.7%
4643 2
 
0.7%
6146 2
 
0.7%
415 2
 
0.7%
4332 1
 
0.3%
3950 1
 
0.3%
3795 1
 
0.3%
5742 1
 
0.3%
1497 1
 
0.3%
74395 1
 
0.3%
Other values (263) 263
88.0%
(Missing) 22
 
7.4%
ValueCountFrequency (%)
121 1
0.3%
216 1
0.3%
272 1
0.3%
302 1
0.3%
326 1
0.3%
344 1
0.3%
390 1
0.3%
392 1
0.3%
415 2
0.7%
436 1
0.3%
ValueCountFrequency (%)
1015021 1
0.3%
196387 1
0.3%
120988 1
0.3%
113720 1
0.3%
108886 1
0.3%
91171 1
0.3%
89958 1
0.3%
74395 1
0.3%
63003 1
0.3%
26218 1
0.3%

2007
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct270
Distinct (%)99.3%
Missing27
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean10530.518
Minimum93
Maximum937302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T14:35:23.564299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum93
5-th percentile409.2
Q11296.25
median3243
Q36222.25
95-th percentile17293.15
Maximum937302
Range937209
Interquartile range (IQR)4926

Descriptive statistics

Standard deviation59182.075
Coefficient of variation (CV)5.6200534
Kurtosis224.21311
Mean10530.518
Median Absolute Deviation (MAD)2249
Skewness14.436186
Sum2864301
Variance3.5025181 × 109
MonotonicityNot monotonic
2024-03-23T14:35:23.799934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1298 2
 
0.7%
2717 2
 
0.7%
3072 1
 
0.3%
2601 1
 
0.3%
2451 1
 
0.3%
5052 1
 
0.3%
75708 1
 
0.3%
8742 1
 
0.3%
3652 1
 
0.3%
3746 1
 
0.3%
Other values (260) 260
87.0%
(Missing) 27
 
9.0%
ValueCountFrequency (%)
93 1
0.3%
205 1
0.3%
213 1
0.3%
229 1
0.3%
238 1
0.3%
263 1
0.3%
266 1
0.3%
280 1
0.3%
340 1
0.3%
343 1
0.3%
ValueCountFrequency (%)
937302 1
0.3%
179884 1
0.3%
125821 1
0.3%
108795 1
0.3%
98173 1
0.3%
89838 1
0.3%
75708 1
0.3%
67240 1
0.3%
59359 1
0.3%
25395 1
0.3%

2008
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct272
Distinct (%)99.3%
Missing25
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean10824.248
Minimum132
Maximum970528
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T14:35:24.082540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum132
5-th percentile412.95
Q11195
median3383
Q36360.5
95-th percentile17480.2
Maximum970528
Range970396
Interquartile range (IQR)5165.5

Descriptive statistics

Standard deviation61031.66
Coefficient of variation (CV)5.6384203
Kurtosis226.24362
Mean10824.248
Median Absolute Deviation (MAD)2441.5
Skewness14.500693
Sum2965844
Variance3.7248635 × 109
MonotonicityNot monotonic
2024-03-23T14:35:24.432672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7612 2
 
0.7%
1959 2
 
0.7%
6343 1
 
0.3%
4370 1
 
0.3%
7847 1
 
0.3%
87605 1
 
0.3%
9877 1
 
0.3%
4373 1
 
0.3%
7587 1
 
0.3%
11546 1
 
0.3%
Other values (262) 262
87.6%
(Missing) 25
 
8.4%
ValueCountFrequency (%)
132 1
0.3%
190 1
0.3%
201 1
0.3%
215 1
0.3%
227 1
0.3%
228 1
0.3%
277 1
0.3%
288 1
0.3%
301 1
0.3%
327 1
0.3%
ValueCountFrequency (%)
970528 1
0.3%
178702 1
0.3%
127940 1
0.3%
115826 1
0.3%
102125 1
0.3%
87605 1
0.3%
86345 1
0.3%
78650 1
0.3%
62129 1
0.3%
27626 1
0.3%

2009
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct269
Distinct (%)98.2%
Missing25
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean10873.456
Minimum177
Maximum972315
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T14:35:24.733540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum177
5-th percentile407.7
Q11207.75
median3325.5
Q36458.75
95-th percentile16343.8
Maximum972315
Range972138
Interquartile range (IQR)5251

Descriptive statistics

Standard deviation61208.39
Coefficient of variation (CV)5.6291568
Kurtosis225.29337
Mean10873.456
Median Absolute Deviation (MAD)2473
Skewness14.463972
Sum2979327
Variance3.7464669 × 109
MonotonicityNot monotonic
2024-03-23T14:35:25.013167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
493 2
 
0.7%
3791 2
 
0.7%
6580 2
 
0.7%
1433 2
 
0.7%
1180 2
 
0.7%
3120 1
 
0.3%
6206 1
 
0.3%
4428 1
 
0.3%
5772 1
 
0.3%
7582 1
 
0.3%
Other values (259) 259
86.6%
(Missing) 25
 
8.4%
ValueCountFrequency (%)
177 1
0.3%
196 1
0.3%
218 1
0.3%
250 1
0.3%
261 1
0.3%
262 1
0.3%
275 1
0.3%
277 1
0.3%
289 1
0.3%
318 1
0.3%
ValueCountFrequency (%)
972315 1
0.3%
186921 1
0.3%
125922 1
0.3%
109524 1
0.3%
105515 1
0.3%
104057 1
0.3%
76937 1
0.3%
76625 1
0.3%
58600 1
0.3%
26821 1
0.3%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct275
Distinct (%)98.2%
Missing19
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean9936.4536
Minimum148
Maximum905003
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T14:35:25.406053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum148
5-th percentile334.95
Q11007.25
median3206
Q36507.5
95-th percentile16036.2
Maximum905003
Range904855
Interquartile range (IQR)5500.25

Descriptive statistics

Standard deviation56325.897
Coefficient of variation (CV)5.6686117
Kurtosis230.82536
Mean9936.4536
Median Absolute Deviation (MAD)2389.5
Skewness14.641082
Sum2782207
Variance3.1726066 × 109
MonotonicityNot monotonic
2024-03-23T14:35:25.647672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2874 2
 
0.7%
1323 2
 
0.7%
986 2
 
0.7%
2913 2
 
0.7%
391 2
 
0.7%
4873 1
 
0.3%
6148 1
 
0.3%
11091 1
 
0.3%
76035 1
 
0.3%
5529 1
 
0.3%
Other values (265) 265
88.6%
(Missing) 19
 
6.4%
ValueCountFrequency (%)
148 1
0.3%
160 1
0.3%
168 1
0.3%
180 1
0.3%
189 1
0.3%
222 1
0.3%
227 1
0.3%
237 1
0.3%
248 1
0.3%
253 1
0.3%
ValueCountFrequency (%)
905003 1
0.3%
160484 1
0.3%
123968 1
0.3%
105232 1
0.3%
103438 1
0.3%
91780 1
0.3%
76035 1
0.3%
75053 1
0.3%
53646 1
0.3%
22162 1
0.3%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct275
Distinct (%)99.3%
Missing22
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean10537.942
Minimum183
Maximum948889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T14:35:25.876483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum183
5-th percentile373.2
Q11113
median3406
Q36581
95-th percentile16691.4
Maximum948889
Range948706
Interquartile range (IQR)5468

Descriptive statistics

Standard deviation59359.397
Coefficient of variation (CV)5.6329211
Kurtosis228.51322
Mean10537.942
Median Absolute Deviation (MAD)2471
Skewness14.568117
Sum2919010
Variance3.523538 × 109
MonotonicityNot monotonic
2024-03-23T14:35:26.105459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1326 2
 
0.7%
637 2
 
0.7%
11540 1
 
0.3%
9354 1
 
0.3%
8139 1
 
0.3%
2970 1
 
0.3%
3871 1
 
0.3%
6841 1
 
0.3%
79720 1
 
0.3%
5633 1
 
0.3%
Other values (265) 265
88.6%
(Missing) 22
 
7.4%
ValueCountFrequency (%)
183 1
0.3%
192 1
0.3%
196 1
0.3%
211 1
0.3%
213 1
0.3%
225 1
0.3%
234 1
0.3%
256 1
0.3%
280 1
0.3%
302 1
0.3%
ValueCountFrequency (%)
948889 1
0.3%
159969 1
0.3%
139854 1
0.3%
111230 1
0.3%
109927 1
0.3%
95206 1
0.3%
79720 1
0.3%
73640 1
0.3%
58092 1
0.3%
21738 1
0.3%

2012
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct274
Distinct (%)98.2%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean9919.9427
Minimum109
Maximum900969
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T14:35:26.345992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum109
5-th percentile331.3
Q11003.5
median3007
Q36181
95-th percentile13810.1
Maximum900969
Range900860
Interquartile range (IQR)5177.5

Descriptive statistics

Standard deviation56142.171
Coefficient of variation (CV)5.6595258
Kurtosis230.50881
Mean9919.9427
Median Absolute Deviation (MAD)2257
Skewness14.634445
Sum2767664
Variance3.1519434 × 109
MonotonicityNot monotonic
2024-03-23T14:35:26.538679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
903 2
 
0.7%
1999 2
 
0.7%
1312 2
 
0.7%
2842 2
 
0.7%
3745 2
 
0.7%
4329 1
 
0.3%
9191 1
 
0.3%
10174 1
 
0.3%
4654 1
 
0.3%
1972 1
 
0.3%
Other values (264) 264
88.3%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
109 1
0.3%
186 1
0.3%
191 1
0.3%
205 1
0.3%
207 1
0.3%
214 1
0.3%
221 1
0.3%
224 1
0.3%
225 1
0.3%
226 1
0.3%
ValueCountFrequency (%)
900969 1
0.3%
152066 1
0.3%
128637 1
0.3%
104692 1
0.3%
104290 1
0.3%
91911 1
0.3%
78283 1
0.3%
68238 1
0.3%
55761 1
0.3%
23242 1
0.3%

2013
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct275
Distinct (%)98.6%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean9934.6846
Minimum172
Maximum902221
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T14:35:26.814020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum172
5-th percentile334.4
Q1949
median3129
Q36163.5
95-th percentile14252.4
Maximum902221
Range902049
Interquartile range (IQR)5214.5

Descriptive statistics

Standard deviation56174.958
Coefficient of variation (CV)5.6544279
Kurtosis231.24558
Mean9934.6846
Median Absolute Deviation (MAD)2397
Skewness14.664733
Sum2771777
Variance3.1556259 × 109
MonotonicityNot monotonic
2024-03-23T14:35:27.078969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7469 2
 
0.7%
452 2
 
0.7%
3703 2
 
0.7%
487 2
 
0.7%
4945 1
 
0.3%
4954 1
 
0.3%
5373 1
 
0.3%
79154 1
 
0.3%
5321 1
 
0.3%
2268 1
 
0.3%
Other values (265) 265
88.6%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
172 1
0.3%
189 1
0.3%
201 1
0.3%
203 1
0.3%
221 1
0.3%
226 1
0.3%
230 1
0.3%
258 1
0.3%
259 1
0.3%
265 1
0.3%
ValueCountFrequency (%)
902221 1
0.3%
146668 1
0.3%
133144 1
0.3%
106797 1
0.3%
101679 1
0.3%
88289 1
0.3%
79154 1
0.3%
65669 1
0.3%
54602 1
0.3%
27504 1
0.3%

2014
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct280
Distinct (%)97.9%
Missing13
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean10786.469
Minimum199
Maximum1001071
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T14:35:27.290900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum199
5-th percentile371.5
Q11079.5
median3441
Q36642
95-th percentile17272.75
Maximum1001071
Range1000872
Interquartile range (IQR)5562.5

Descriptive statistics

Standard deviation61570.606
Coefficient of variation (CV)5.7081339
Kurtosis237.09469
Mean10786.469
Median Absolute Deviation (MAD)2599
Skewness14.851702
Sum3084930
Variance3.7909396 × 109
MonotonicityNot monotonic
2024-03-23T14:35:28.016232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5700 2
 
0.7%
940 2
 
0.7%
522 2
 
0.7%
6531 2
 
0.7%
1507 2
 
0.7%
1635 2
 
0.7%
2897 1
 
0.3%
4596 1
 
0.3%
4358 1
 
0.3%
7893 1
 
0.3%
Other values (270) 270
90.3%
(Missing) 13
 
4.3%
ValueCountFrequency (%)
199 1
0.3%
212 1
0.3%
216 1
0.3%
236 1
0.3%
249 1
0.3%
279 1
0.3%
294 1
0.3%
299 1
0.3%
301 1
0.3%
306 1
0.3%
ValueCountFrequency (%)
1001071 1
0.3%
172173 1
0.3%
145448 1
0.3%
118524 1
0.3%
107202 1
0.3%
94386 1
0.3%
80133 1
0.3%
69874 1
0.3%
58488 1
0.3%
39817 1
0.3%

2015
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct279
Distinct (%)98.9%
Missing17
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean12314.915
Minimum232
Maximum1124686
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T14:35:28.277909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum232
5-th percentile482.35
Q11242.5
median4036
Q37379
95-th percentile21678.95
Maximum1124686
Range1124454
Interquartile range (IQR)6136.5

Descriptive statistics

Standard deviation69725.061
Coefficient of variation (CV)5.6618386
Kurtosis232.86224
Mean12314.915
Median Absolute Deviation (MAD)2917
Skewness14.715559
Sum3472806
Variance4.8615841 × 109
MonotonicityNot monotonic
2024-03-23T14:35:28.532243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
845 2
 
0.7%
6223 2
 
0.7%
1237 2
 
0.7%
790 1
 
0.3%
7407 1
 
0.3%
8082 1
 
0.3%
7920 1
 
0.3%
3374 1
 
0.3%
3015 1
 
0.3%
6389 1
 
0.3%
Other values (269) 269
90.0%
(Missing) 17
 
5.7%
ValueCountFrequency (%)
232 1
0.3%
301 1
0.3%
312 1
0.3%
330 1
0.3%
338 1
0.3%
373 1
0.3%
380 1
0.3%
400 1
0.3%
405 1
0.3%
413 1
0.3%
ValueCountFrequency (%)
1124686 1
0.3%
212043 1
0.3%
156748 1
0.3%
134801 1
0.3%
117378 1
0.3%
107045 1
0.3%
77725 1
0.3%
74581 1
0.3%
60986 1
0.3%
49396 1
0.3%

2016
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct277
Distinct (%)98.2%
Missing17
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean12168.95
Minimum186
Maximum1111974
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T14:35:28.889560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum186
5-th percentile421.35
Q11331.25
median3734.5
Q37297.75
95-th percentile22128.85
Maximum1111974
Range1111788
Interquartile range (IQR)5966.5

Descriptive statistics

Standard deviation69112.135
Coefficient of variation (CV)5.6793834
Kurtosis230.63117
Mean12168.95
Median Absolute Deviation (MAD)2709.5
Skewness14.634367
Sum3431644
Variance4.7764872 × 109
MonotonicityNot monotonic
2024-03-23T14:35:29.315695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
893 2
 
0.7%
332 2
 
0.7%
7954 2
 
0.7%
1505 2
 
0.7%
1227 2
 
0.7%
3133 1
 
0.3%
7306 1
 
0.3%
7679 1
 
0.3%
6589 1
 
0.3%
3270 1
 
0.3%
Other values (267) 267
89.3%
(Missing) 17
 
5.7%
ValueCountFrequency (%)
186 1
0.3%
194 1
0.3%
200 1
0.3%
248 1
0.3%
255 1
0.3%
264 1
0.3%
265 1
0.3%
327 1
0.3%
332 2
0.7%
364 1
0.3%
ValueCountFrequency (%)
1111974 1
0.3%
238842 1
0.3%
139426 1
0.3%
124221 1
0.3%
115159 1
0.3%
107393 1
0.3%
77930 1
0.3%
75871 1
0.3%
61654 1
0.3%
45331 1
0.3%

2017
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct274
Distinct (%)98.2%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean12853.065
Minimum161
Maximum1160707
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T14:35:29.575405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum161
5-th percentile522.4
Q11357
median3977
Q37644
95-th percentile21776.5
Maximum1160707
Range1160546
Interquartile range (IQR)6287

Descriptive statistics

Standard deviation72579.136
Coefficient of variation (CV)5.6468351
Kurtosis227.48767
Mean12853.065
Median Absolute Deviation (MAD)2793
Skewness14.531984
Sum3586005
Variance5.2677309 × 109
MonotonicityNot monotonic
2024-03-23T14:35:29.809737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
547 2
 
0.7%
723 2
 
0.7%
10515 2
 
0.7%
6070 2
 
0.7%
848 2
 
0.7%
327 1
 
0.3%
9577 1
 
0.3%
5347 1
 
0.3%
5316 1
 
0.3%
4385 1
 
0.3%
Other values (264) 264
88.3%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
161 1
0.3%
210 1
0.3%
243 1
0.3%
259 1
0.3%
267 1
0.3%
272 1
0.3%
327 1
0.3%
371 1
0.3%
408 1
0.3%
439 1
0.3%
ValueCountFrequency (%)
1160707 1
0.3%
258209 1
0.3%
135487 1
0.3%
127457 1
0.3%
118708 1
0.3%
116370 1
0.3%
85510 1
0.3%
84543 1
0.3%
64419 1
0.3%
37243 1
0.3%

2018
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct276
Distinct (%)98.6%
Missing19
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean11990.021
Minimum165
Maximum1088670
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T14:35:30.118096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum165
5-th percentile479.65
Q11309.25
median3679
Q36921
95-th percentile18746.55
Maximum1088670
Range1088505
Interquartile range (IQR)5611.75

Descriptive statistics

Standard deviation68080.613
Coefficient of variation (CV)5.678106
Kurtosis226.74228
Mean11990.021
Median Absolute Deviation (MAD)2658.5
Skewness14.502802
Sum3357206
Variance4.6349699 × 109
MonotonicityNot monotonic
2024-03-23T14:35:30.439088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
739 2
 
0.7%
3805 2
 
0.7%
2940 2
 
0.7%
13512 2
 
0.7%
4786 1
 
0.3%
9351 1
 
0.3%
10417 1
 
0.3%
5038 1
 
0.3%
2664 1
 
0.3%
2122 1
 
0.3%
Other values (266) 266
89.0%
(Missing) 19
 
6.4%
ValueCountFrequency (%)
165 1
0.3%
212 1
0.3%
279 1
0.3%
289 1
0.3%
302 1
0.3%
329 1
0.3%
366 1
0.3%
367 1
0.3%
369 1
0.3%
378 1
0.3%
ValueCountFrequency (%)
1088670 1
0.3%
256262 1
0.3%
127128 1
0.3%
117735 1
0.3%
108919 1
0.3%
102909 1
0.3%
82967 1
0.3%
81743 1
0.3%
54501 1
0.3%
33598 1
0.3%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct271
Distinct (%)97.1%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean11357.33
Minimum142
Maximum1023736
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T14:35:30.707729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum142
5-th percentile485
Q11325
median3592
Q36612
95-th percentile19366.9
Maximum1023736
Range1023594
Interquartile range (IQR)5287

Descriptive statistics

Standard deviation64246.552
Coefficient of variation (CV)5.656836
Kurtosis224.39414
Mean11357.33
Median Absolute Deviation (MAD)2515
Skewness14.421658
Sum3168695
Variance4.1276194 × 109
MonotonicityNot monotonic
2024-03-23T14:35:30.962628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11556 2
 
0.7%
870 2
 
0.7%
3592 2
 
0.7%
1079 2
 
0.7%
980 2
 
0.7%
6026 2
 
0.7%
485 2
 
0.7%
1246 2
 
0.7%
8844 1
 
0.3%
2846 1
 
0.3%
Other values (261) 261
87.3%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
142 1
0.3%
152 1
0.3%
199 1
0.3%
212 1
0.3%
291 1
0.3%
303 1
0.3%
307 1
0.3%
335 1
0.3%
356 1
0.3%
360 1
0.3%
ValueCountFrequency (%)
1023736 1
0.3%
251639 1
0.3%
123201 1
0.3%
106883 1
0.3%
106168 1
0.3%
89293 1
0.3%
79101 1
0.3%
73708 1
0.3%
55194 1
0.3%
28671 1
0.3%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct276
Distinct (%)98.9%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean12531.315
Minimum108
Maximum1130569
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T14:35:31.210701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum108
5-th percentile533.9
Q11631
median3881
Q37385.5
95-th percentile20523
Maximum1130569
Range1130461
Interquartile range (IQR)5754.5

Descriptive statistics

Standard deviation70989.765
Coefficient of variation (CV)5.6649891
Kurtosis223.96748
Mean12531.315
Median Absolute Deviation (MAD)2692
Skewness14.408532
Sum3496237
Variance5.0395468 × 109
MonotonicityNot monotonic
2024-03-23T14:35:31.505965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3666 2
 
0.7%
732 2
 
0.7%
16130 2
 
0.7%
10443 1
 
0.3%
6202 1
 
0.3%
2811 1
 
0.3%
2064 1
 
0.3%
4875 1
 
0.3%
87038 1
 
0.3%
8625 1
 
0.3%
Other values (266) 266
89.0%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
108 1
0.3%
135 1
0.3%
219 1
0.3%
289 1
0.3%
292 1
0.3%
329 1
0.3%
331 1
0.3%
383 1
0.3%
400 1
0.3%
438 1
0.3%
ValueCountFrequency (%)
1130569 1
0.3%
283755 1
0.3%
130956 1
0.3%
121596 1
0.3%
115608 1
0.3%
93504 1
0.3%
87038 1
0.3%
77701 1
0.3%
65457 1
0.3%
36536 1
0.3%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct275
Distinct (%)98.6%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean13806.699
Minimum148
Maximum1248084
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T14:35:31.845749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum148
5-th percentile557.1
Q11494.5
median4177
Q38287.5
95-th percentile21278.5
Maximum1248084
Range1247936
Interquartile range (IQR)6793

Descriptive statistics

Standard deviation78446.372
Coefficient of variation (CV)5.6817616
Kurtosis223.1358
Mean13806.699
Median Absolute Deviation (MAD)3042
Skewness14.375236
Sum3852069
Variance6.1538332 × 109
MonotonicityNot monotonic
2024-03-23T14:35:32.073956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2091 2
 
0.7%
771 2
 
0.7%
607 2
 
0.7%
15390 2
 
0.7%
7384 1
 
0.3%
8262 1
 
0.3%
90646 1
 
0.3%
6174 1
 
0.3%
8929 1
 
0.3%
4889 1
 
0.3%
Other values (265) 265
88.6%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
148 1
0.3%
171 1
0.3%
204 1
0.3%
290 1
0.3%
319 1
0.3%
344 1
0.3%
361 1
0.3%
363 1
0.3%
375 1
0.3%
385 1
0.3%
ValueCountFrequency (%)
1248084 1
0.3%
317838 1
0.3%
138999 1
0.3%
135407 1
0.3%
126843 1
0.3%
108654 1
0.3%
90646 1
0.3%
88668 1
0.3%
75644 1
0.3%
45068 1
0.3%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct275
Distinct (%)98.6%
Missing20
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean10787.642
Minimum146
Maximum973807
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T14:35:32.383138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum146
5-th percentile385.1
Q1960.5
median3156
Q36525.5
95-th percentile16335.5
Maximum973807
Range973661
Interquartile range (IQR)5565

Descriptive statistics

Standard deviation61125.88
Coefficient of variation (CV)5.6662877
Kurtosis224.15856
Mean10787.642
Median Absolute Deviation (MAD)2422
Skewness14.404138
Sum3009752
Variance3.7363733 × 109
MonotonicityNot monotonic
2024-03-23T14:35:32.767373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10022 2
 
0.7%
832 2
 
0.7%
3499 2
 
0.7%
388 2
 
0.7%
8831 1
 
0.3%
5627 1
 
0.3%
2126 1
 
0.3%
1537 1
 
0.3%
3663 1
 
0.3%
7757 1
 
0.3%
Other values (265) 265
88.6%
(Missing) 20
 
6.7%
ValueCountFrequency (%)
146 1
0.3%
153 1
0.3%
186 1
0.3%
193 1
0.3%
196 1
0.3%
222 1
0.3%
228 1
0.3%
285 1
0.3%
323 1
0.3%
324 1
0.3%
ValueCountFrequency (%)
973807 1
0.3%
233507 1
0.3%
115811 1
0.3%
109469 1
0.3%
102209 1
0.3%
92665 1
0.3%
71493 1
0.3%
68635 1
0.3%
58878 1
0.3%
31719 1
0.3%

2023
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct274
Distinct (%)97.9%
Missing19
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean7846.0071
Minimum90
Maximum710092
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-03-23T14:35:33.081255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile238.75
Q1797.25
median2502
Q34938
95-th percentile11748.55
Maximum710092
Range710002
Interquartile range (IQR)4140.75

Descriptive statistics

Standard deviation44439.945
Coefficient of variation (CV)5.6640205
Kurtosis225.92732
Mean7846.0071
Median Absolute Deviation (MAD)1895.5
Skewness14.458653
Sum2196882
Variance1.9749087 × 109
MonotonicityNot monotonic
2024-03-23T14:35:33.479046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5412 2
 
0.7%
221 2
 
0.7%
1167 2
 
0.7%
4409 2
 
0.7%
354 2
 
0.7%
813 2
 
0.7%
4534 1
 
0.3%
6599 1
 
0.3%
3463 1
 
0.3%
3617 1
 
0.3%
Other values (264) 264
88.3%
(Missing) 19
 
6.4%
ValueCountFrequency (%)
90 1
0.3%
99 1
0.3%
103 1
0.3%
137 1
0.3%
201 1
0.3%
214 1
0.3%
217 1
0.3%
218 1
0.3%
220 1
0.3%
221 2
0.7%
ValueCountFrequency (%)
710092 1
0.3%
159252 1
0.3%
87183 1
0.3%
85777 1
0.3%
79612 1
0.3%
65808 1
0.3%
55326 1
0.3%
52970 1
0.3%
42301 1
0.3%
21092 1
0.3%

Interactions

2024-03-23T14:35:16.056690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:25.065494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:28.035220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:30.755884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:33.664315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:36.917516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:39.700550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:42.661421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:45.658500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:49.169929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:52.158544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:55.247319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:57.777195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:01.383027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:04.022137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:06.757383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:10.120879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:13.099566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-03-23T14:35:14.890276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:18.191611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:26.963586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:29.776797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:32.574116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:35.921438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:38.758363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:41.649913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:44.586233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:48.082627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:51.057337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:54.163433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:56.935250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:00.310932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:03.030230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:05.646392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:08.778231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:12.044516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:15.076336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:18.364506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:27.155747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:29.922745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:32.770576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:36.053991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:38.879790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:41.810172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:44.767468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:48.233242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:51.304916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:54.403801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:57.057147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:00.548714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:03.225442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:05.778542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:09.316201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:12.238470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:15.235699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:18.521690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:27.341562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:30.093176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:32.961015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:36.214317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:39.019354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:41.939957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:44.920640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:48.461759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:51.521547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:54.565990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:57.195061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:00.764761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:03.370521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:05.959657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:09.482397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:12.420039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:15.401917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:18.705972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:27.522581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:30.256949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:33.126981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:36.375466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:39.168323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:42.075729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:45.058936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:48.639450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:51.701908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:54.715416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:57.326340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:00.925162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:03.505595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:06.209252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:09.652818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:12.597526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:15.587382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:18.891792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:27.672781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:30.427497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:33.303976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:36.569203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:39.379754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:42.274057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:45.213690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:48.828693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:51.878614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:54.856822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:57.477618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:01.073886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:03.684705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:06.476073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:09.787702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:12.748580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:15.749048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:19.060864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:27.842494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:30.604650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:33.468466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:36.727115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:39.571237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:42.493657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:45.464631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:48.992821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:52.016211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:55.013079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:34:57.607533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:01.205196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:03.846551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:06.603870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:09.940320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:12.909662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:35:15.895507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T14:35:33.681784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9910.9910.9850.9850.9850.9850.9910.9910.9910.8200.8000.7700.7600.7600.7600.7600.760
20070.9911.0001.0000.9980.9980.9980.9981.0001.0001.0001.0000.8770.8610.8200.8200.8200.8200.820
20080.9911.0001.0000.9980.9980.9980.9981.0001.0001.0001.0000.8770.8610.8200.8200.8200.8200.820
20090.9850.9980.9981.0001.0001.0001.0000.9980.9980.9980.8741.0000.8240.8740.8740.8740.8740.874
20100.9850.9980.9981.0001.0001.0001.0000.9980.9980.9980.8741.0000.8240.8740.8740.8740.8740.874
20110.9850.9980.9981.0001.0001.0001.0000.9980.9980.9980.8741.0000.8240.8740.8740.8740.8740.874
20120.9850.9980.9981.0001.0001.0001.0000.9980.9980.9980.8741.0000.8240.8740.8740.8740.8740.874
20130.9911.0001.0000.9980.9980.9980.9981.0001.0001.0001.0000.8770.8610.8200.8200.8200.8200.820
20140.9911.0001.0000.9980.9980.9980.9981.0001.0001.0001.0000.8770.8610.8200.8200.8200.8200.820
20150.9911.0001.0000.9980.9980.9980.9981.0001.0001.0001.0000.8770.8610.8200.8200.8200.8200.820
20160.8201.0001.0000.8740.8740.8740.8741.0001.0001.0001.0000.9990.9980.9950.9950.9950.9950.995
20170.8000.8770.8771.0001.0001.0001.0000.8770.8770.8770.9991.0000.9960.9990.9990.9990.9990.999
20180.7700.8610.8610.8240.8240.8240.8240.8610.8610.8610.9980.9961.0000.9980.9980.9980.9980.998
20190.7600.8200.8200.8740.8740.8740.8740.8200.8200.8200.9950.9990.9981.0001.0001.0001.0001.000
20200.7600.8200.8200.8740.8740.8740.8740.8200.8200.8200.9950.9990.9981.0001.0001.0001.0001.000
20210.7600.8200.8200.8740.8740.8740.8740.8200.8200.8200.9950.9990.9981.0001.0001.0001.0001.000
20220.7600.8200.8200.8740.8740.8740.8740.8200.8200.8200.9950.9990.9981.0001.0001.0001.0001.000
20230.7600.8200.8200.8740.8740.8740.8740.8200.8200.8200.9950.9990.9981.0001.0001.0001.0001.000
2024-03-23T14:35:34.449236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
200620072008200920102011201220132014201520162017201820192020202120222023
20061.0000.9570.9320.9190.9160.9270.9240.9300.9310.9300.9240.9180.9130.8920.8990.9150.9110.904
20070.9571.0000.9640.9490.9380.9470.9490.9470.9410.9390.9400.9340.9290.9040.9120.9290.9260.924
20080.9320.9641.0000.9520.9440.9480.9520.9450.9370.9290.9310.9240.9110.8860.8980.9060.9050.898
20090.9190.9490.9521.0000.9650.9630.9660.9570.9490.9500.9520.9480.9450.9220.9330.9420.9480.941
20100.9160.9380.9440.9651.0000.9690.9670.9600.9510.9490.9520.9500.9440.9220.9320.9370.9400.931
20110.9270.9470.9480.9630.9691.0000.9840.9770.9670.9620.9560.9510.9390.9140.9240.9350.9340.932
20120.9240.9490.9520.9660.9670.9841.0000.9810.9720.9660.9600.9520.9370.9170.9240.9340.9360.935
20130.9300.9470.9450.9570.9600.9770.9811.0000.9780.9680.9650.9570.9440.9200.9310.9410.9400.936
20140.9310.9410.9370.9490.9510.9670.9720.9781.0000.9820.9730.9610.9480.9270.9310.9380.9360.930
20150.9300.9390.9290.9500.9490.9620.9660.9680.9821.0000.9860.9720.9570.9370.9440.9500.9470.934
20160.9240.9400.9310.9520.9520.9560.9600.9650.9730.9861.0000.9870.9730.9530.9630.9630.9610.951
20170.9180.9340.9240.9480.9500.9510.9520.9570.9610.9720.9871.0000.9850.9670.9720.9680.9630.960
20180.9130.9290.9110.9450.9440.9390.9370.9440.9480.9570.9730.9851.0000.9780.9770.9700.9650.967
20190.8920.9040.8860.9220.9220.9140.9170.9200.9270.9370.9530.9670.9781.0000.9760.9590.9540.962
20200.8990.9120.8980.9330.9320.9240.9240.9310.9310.9440.9630.9720.9770.9761.0000.9810.9710.969
20210.9150.9290.9060.9420.9370.9350.9340.9410.9380.9500.9630.9680.9700.9590.9811.0000.9790.973
20220.9110.9260.9050.9480.9400.9340.9360.9400.9360.9470.9610.9630.9650.9540.9710.9791.0000.976
20230.9040.9240.8980.9410.9310.9320.9350.9360.9300.9340.9510.9600.9670.9620.9690.9730.9761.000

Missing values

2024-03-23T14:35:19.854956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T14:35:20.310235image/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.
2024-03-23T14:35:20.765449image/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
0전국101502193730297052897231590500394888990096990222110010711124686111197411607071088670102373611305691248084973807710092
1서울262182539527626246331768216745135711288715398181361897819102186971816422970189591513811968
2서울 종로구1024105497765581769961451598083778091092573811271170773570
3서울 중구11331407271313281802878806705873818779858778986113111381006695
4서울 용산구117012101100636402514295414541790802652683821226611191465630
5서울 성동구91412091985192690298711486729379771058709739622620563996413
6서울 광진구459205215275189211205172340413332327579303292290228259
7서울 동대문구129016292239989686583660681740612639783824573857805680593
8서울 중랑구4525405771217377431267286330584408644378335400361285217
9서울 성북구267829721697173113049375595138231025132913149209801134912673613
지역200620072008200920102011201220132014201520162017201820192020202120222023
289경남 거창군309440364238437643334890432943314799555156385520515047214895607244913725
290경남 합천군412952375641569350555283536657545925715267546656634256855785719556734687
291(구)제주12810<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
292(구)제주 (구)제주시2293<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
293(구)제주 (구)서귀포시1537<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
294(구)제주 (구)북제주군5192<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
295(구)제주 (구)남제주군3788<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
296제주142832290818256183191883521738232422750439817493964533137243310802447920649251482253015729
297제주 제주시8583123721083810169107791272012787148142247125840244232052717895142781262015679142849623
298제주 서귀포시570010536741881508056901810455126901734623556209081671613185102018029946982466106