Overview

Dataset statistics

Number of variables11
Number of observations38
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory102.5 B

Variable types

Numeric11

Dataset

Description공무원 사망조위금 지급(공무원, 배우자, 직계존비속, 배우자의 직계존비속) 추이에 대한 데이터입니다. 1985년부터 시작됩니다.
URLhttps://www.data.go.kr/data/15054081/fileData.do

Alerts

구분 is highly overall correlated with 건수계 and 7 other fieldsHigh correlation
건수계 is highly overall correlated with 구분 and 8 other fieldsHigh correlation
금액계 is highly overall correlated with 구분 and 8 other fieldsHigh correlation
공무원건수 is highly overall correlated with 구분 and 6 other fieldsHigh correlation
공무원금액 is highly overall correlated with 건수계 and 3 other fieldsHigh correlation
배우자건수 is highly overall correlated with 구분 and 6 other fieldsHigh correlation
배우자금액 is highly overall correlated with 구분 and 5 other fieldsHigh correlation
직계존비속금액 is highly overall correlated with 구분 and 7 other fieldsHigh correlation
배우자의직계존속건수 is highly overall correlated with 구분 and 8 other fieldsHigh correlation
배우자의직계존속금액 is highly overall correlated with 구분 and 8 other fieldsHigh correlation
구분 has unique valuesUnique
건수계 has unique valuesUnique
금액계 has unique valuesUnique
공무원건수 has unique valuesUnique
공무원금액 has unique valuesUnique
직계존비속건수 has unique valuesUnique
직계존비속금액 has unique valuesUnique
배우자의직계존속건수 has unique valuesUnique
배우자의직계존속금액 has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:25:02.104057
Analysis finished2023-12-12 22:25:15.956310
Duration13.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2003.5
Minimum1985
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T07:25:16.033626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1985
5-th percentile1986.85
Q11994.25
median2003.5
Q32012.75
95-th percentile2020.15
Maximum2022
Range37
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation11.113055
Coefficient of variation (CV)0.0055468208
Kurtosis-1.2
Mean2003.5
Median Absolute Deviation (MAD)9.5
Skewness0
Sum76133
Variance123.5
MonotonicityStrictly increasing
2023-12-13T07:25:16.169403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1985 1
 
2.6%
2014 1
 
2.6%
2007 1
 
2.6%
2008 1
 
2.6%
2009 1
 
2.6%
2010 1
 
2.6%
2011 1
 
2.6%
2012 1
 
2.6%
2013 1
 
2.6%
2015 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
1985 1
2.6%
1986 1
2.6%
1987 1
2.6%
1988 1
2.6%
1989 1
2.6%
1990 1
2.6%
1991 1
2.6%
1992 1
2.6%
1993 1
2.6%
1994 1
2.6%
ValueCountFrequency (%)
2022 1
2.6%
2021 1
2.6%
2020 1
2.6%
2019 1
2.6%
2018 1
2.6%
2017 1
2.6%
2016 1
2.6%
2015 1
2.6%
2014 1
2.6%
2013 1
2.6%

건수계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27051.947
Minimum13519
Maximum39904
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T07:25:16.302819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13519
5-th percentile18930
Q124267.5
median26965.5
Q329474.75
95-th percentile34922.75
Maximum39904
Range26385
Interquartile range (IQR)5207.25

Descriptive statistics

Standard deviation5090.5928
Coefficient of variation (CV)0.18817842
Kurtosis1.1359179
Mean27051.947
Median Absolute Deviation (MAD)2690.5
Skewness-0.15628686
Sum1027974
Variance25914135
MonotonicityNot monotonic
2023-12-13T07:25:16.515426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
13519 1
 
2.6%
29500 1
 
2.6%
30274 1
 
2.6%
30140 1
 
2.6%
29399 1
 
2.6%
33659 1
 
2.6%
32323 1
 
2.6%
30336 1
 
2.6%
30612 1
 
2.6%
28977 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
13519 1
2.6%
17315 1
2.6%
19215 1
2.6%
19578 1
2.6%
21970 1
2.6%
22329 1
2.6%
22810 1
2.6%
23582 1
2.6%
23726 1
2.6%
24119 1
2.6%
ValueCountFrequency (%)
39904 1
2.6%
36406 1
2.6%
34661 1
2.6%
33659 1
2.6%
32323 1
2.6%
30612 1
2.6%
30336 1
2.6%
30274 1
2.6%
30140 1
2.6%
29500 1
2.6%

금액계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60195.737
Minimum4916
Maximum144592
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T07:25:16.664250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4916
5-th percentile8844.35
Q134999.75
median54729.5
Q387947.25
95-th percentile125996.65
Maximum144592
Range139676
Interquartile range (IQR)52947.5

Descriptive statistics

Standard deviation35947.009
Coefficient of variation (CV)0.59716869
Kurtosis-0.44959674
Mean60195.737
Median Absolute Deviation (MAD)30068.5
Skewness0.41301996
Sum2287438
Variance1.2921875 × 109
MonotonicityNot monotonic
2023-12-13T07:25:16.808101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
4916 1
 
2.6%
88117 1
 
2.6%
72177 1
 
2.6%
73314 1
 
2.6%
72190 1
 
2.6%
86008 1
 
2.6%
87438 1
 
2.6%
83740 1
 
2.6%
88206 1
 
2.6%
89896 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
4916 1
2.6%
7373 1
2.6%
9104 1
2.6%
10878 1
2.6%
15784 1
2.6%
19477 1
2.6%
23603 1
2.6%
28031 1
2.6%
32829 1
2.6%
34451 1
2.6%
ValueCountFrequency (%)
144592 1
2.6%
132245 1
2.6%
124894 1
2.6%
101874 1
2.6%
99689 1
2.6%
93049 1
2.6%
91288 1
2.6%
89896 1
2.6%
88206 1
2.6%
88117 1
2.6%

공무원건수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1034.6053
Minimum520
Maximum1824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T07:25:16.937248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile541.75
Q1661.25
median731.5
Q31571
95-th percentile1771.2
Maximum1824
Range1304
Interquartile range (IQR)909.75

Descriptive statistics

Standard deviation476.49642
Coefficient of variation (CV)0.46055867
Kurtosis-1.555577
Mean1034.6053
Median Absolute Deviation (MAD)152
Skewness0.53966012
Sum39315
Variance227048.84
MonotonicityNot monotonic
2023-12-13T07:25:17.072724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1153 1
 
2.6%
621 1
 
2.6%
707 1
 
2.6%
709 1
 
2.6%
660 1
 
2.6%
775 1
 
2.6%
801 1
 
2.6%
687 1
 
2.6%
665 1
 
2.6%
643 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
520 1
2.6%
529 1
2.6%
544 1
2.6%
553 1
2.6%
607 1
2.6%
621 1
2.6%
643 1
2.6%
656 1
2.6%
658 1
2.6%
660 1
2.6%
ValueCountFrequency (%)
1824 1
2.6%
1795 1
2.6%
1767 1
2.6%
1744 1
2.6%
1693 1
2.6%
1683 1
2.6%
1644 1
2.6%
1643 1
2.6%
1590 1
2.6%
1589 1
2.6%

공무원금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5086.8947
Minimum459
Maximum7786
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T07:25:17.240725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum459
5-th percentile726.9
Q14430.5
median5412
Q36273.5
95-th percentile7732.15
Maximum7786
Range7327
Interquartile range (IQR)1843

Descriptive statistics

Standard deviation1893.9115
Coefficient of variation (CV)0.3723119
Kurtosis1.0063095
Mean5086.8947
Median Absolute Deviation (MAD)984
Skewness-1.1081348
Sum193302
Variance3586900.6
MonotonicityNot monotonic
2023-12-13T07:25:17.400212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
459 1
 
2.6%
5685 1
 
2.6%
5259 1
 
2.6%
5399 1
 
2.6%
5044 1
 
2.6%
5805 1
 
2.6%
6480 1
 
2.6%
5908 1
 
2.6%
5873 1
 
2.6%
6029 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
459 1
2.6%
698 1
2.6%
732 1
2.6%
911 1
2.6%
3179 1
2.6%
3498 1
2.6%
3882 1
2.6%
3944 1
2.6%
3966 1
2.6%
4387 1
2.6%
ValueCountFrequency (%)
7786 1
2.6%
7733 1
2.6%
7732 1
2.6%
7075 1
2.6%
7057 1
2.6%
6663 1
2.6%
6657 1
2.6%
6544 1
2.6%
6480 1
2.6%
6355 1
2.6%

배우자건수
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean650.52632
Minimum375
Maximum1284
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T07:25:17.531886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum375
5-th percentile395.7
Q1484.25
median603.5
Q3776.5
95-th percentile1156
Maximum1284
Range909
Interquartile range (IQR)292.25

Descriptive statistics

Standard deviation227.35943
Coefficient of variation (CV)0.34950074
Kurtosis1.431874
Mean650.52632
Median Absolute Deviation (MAD)138.5
Skewness1.2426628
Sum24720
Variance51692.31
MonotonicityNot monotonic
2023-12-13T07:25:17.656761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
538 2
 
5.3%
454 1
 
2.6%
475 1
 
2.6%
514 1
 
2.6%
453 1
 
2.6%
584 1
 
2.6%
544 1
 
2.6%
508 1
 
2.6%
498 1
 
2.6%
394 1
 
2.6%
Other values (27) 27
71.1%
ValueCountFrequency (%)
375 1
2.6%
394 1
2.6%
396 1
2.6%
402 1
2.6%
407 1
2.6%
453 1
2.6%
454 1
2.6%
466 1
2.6%
475 1
2.6%
482 1
2.6%
ValueCountFrequency (%)
1284 1
2.6%
1258 1
2.6%
1138 1
2.6%
923 1
2.6%
842 1
2.6%
841 1
2.6%
819 1
2.6%
810 1
2.6%
801 1
2.6%
783 1
2.6%

배우자금액
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1311.9737
Minimum222
Maximum3232
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T07:25:17.781829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum222
5-th percentile432.7
Q11119.75
median1289
Q31431.25
95-th percentile2869.3
Maximum3232
Range3010
Interquartile range (IQR)311.5

Descriptive statistics

Standard deviation638.38404
Coefficient of variation (CV)0.48658296
Kurtosis3.0822533
Mean1311.9737
Median Absolute Deviation (MAD)158
Skewness1.294415
Sum49855
Variance407534.19
MonotonicityNot monotonic
2023-12-13T07:25:17.919109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1167 2
 
5.3%
1295 1
 
2.6%
1459 1
 
2.6%
1393 1
 
2.6%
1250 1
 
2.6%
1528 1
 
2.6%
1435 1
 
2.6%
1336 1
 
2.6%
1369 1
 
2.6%
222 1
 
2.6%
Other values (27) 27
71.1%
ValueCountFrequency (%)
222 1
2.6%
363 1
2.6%
445 1
2.6%
449 1
2.6%
506 1
2.6%
624 1
2.6%
713 1
2.6%
981 1
2.6%
1061 1
2.6%
1104 1
2.6%
ValueCountFrequency (%)
3232 1
2.6%
3092 1
2.6%
2830 1
2.6%
1709 1
2.6%
1682 1
2.6%
1618 1
2.6%
1528 1
2.6%
1514 1
2.6%
1459 1
2.6%
1435 1
2.6%

직계존비속건수
Real number (ℝ)

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20758.474
Minimum11556
Maximum25592
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T07:25:18.310150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11556
5-th percentile16255.7
Q117962
median21276.5
Q323552
95-th percentile25189.8
Maximum25592
Range14036
Interquartile range (IQR)5590

Descriptive statistics

Standard deviation3396.6885
Coefficient of variation (CV)0.16362901
Kurtosis-0.16504714
Mean20758.474
Median Absolute Deviation (MAD)2790.5
Skewness-0.55793398
Sum788822
Variance11537493
MonotonicityNot monotonic
2023-12-13T07:25:18.432475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
11556 1
 
2.6%
17798 1
 
2.6%
25592 1
 
2.6%
25158 1
 
2.6%
24781 1
 
2.6%
22165 1
 
2.6%
19888 1
 
2.6%
18507 1
 
2.6%
18465 1
 
2.6%
17772 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
11556 1
2.6%
14367 1
2.6%
16589 1
2.6%
16610 1
2.6%
16783 1
2.6%
17469 1
2.6%
17504 1
2.6%
17566 1
2.6%
17772 1
2.6%
17798 1
2.6%
ValueCountFrequency (%)
25592 1
2.6%
25370 1
2.6%
25158 1
2.6%
24876 1
2.6%
24781 1
2.6%
24594 1
2.6%
24375 1
2.6%
23835 1
2.6%
23798 1
2.6%
23589 1
2.6%

직계존비속금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40684.605
Minimum4133
Maximum88527
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T07:25:18.580814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4133
5-th percentile7478.75
Q126846.5
median45113.5
Q353601
95-th percentile75697.6
Maximum88527
Range84394
Interquartile range (IQR)26754.5

Descriptive statistics

Standard deviation20773.04
Coefficient of variation (CV)0.51058723
Kurtosis-0.38011913
Mean40684.605
Median Absolute Deviation (MAD)12592
Skewness0.047825427
Sum1546015
Variance4.3151919 × 108
MonotonicityNot monotonic
2023-12-13T07:25:18.700244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
4133 1
 
2.6%
50834 1
 
2.6%
56930 1
 
2.6%
56906 1
 
2.6%
56673 1
 
2.6%
52992 1
 
2.6%
50860 1
 
2.6%
48621 1
 
2.6%
50806 1
 
2.6%
52638 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
4133 1
2.6%
6021 1
2.6%
7736 1
2.6%
9175 1
2.6%
11639 1
2.6%
14277 1
2.6%
17182 1
2.6%
20357 1
2.6%
24349 1
2.6%
26634 1
2.6%
ValueCountFrequency (%)
88527 1
2.6%
80002 1
2.6%
74938 1
2.6%
59682 1
2.6%
58783 1
2.6%
56930 1
2.6%
56906 1
2.6%
56673 1
2.6%
54519 1
2.6%
53804 1
2.6%

배우자의직계존속건수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4608.3421
Minimum272
Maximum13373
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T07:25:18.815811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum272
5-th percentile552.2
Q1995.25
median1489.5
Q310201.75
95-th percentile12051.05
Maximum13373
Range13101
Interquartile range (IQR)9206.5

Descriptive statistics

Standard deviation4689.0692
Coefficient of variation (CV)1.0175176
Kurtosis-1.4352067
Mean4608.3421
Median Absolute Deviation (MAD)901
Skewness0.68031309
Sum175117
Variance21987370
MonotonicityNot monotonic
2023-12-13T07:25:19.006272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
272 1
 
2.6%
10627 1
 
2.6%
3437 1
 
2.6%
3759 1
 
2.6%
3505 1
 
2.6%
10135 1
 
2.6%
11090 1
 
2.6%
10634 1
 
2.6%
10984 1
 
2.6%
10168 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
272 1
2.6%
508 1
2.6%
560 1
2.6%
617 1
2.6%
718 1
2.6%
776 1
2.6%
809 1
2.6%
832 1
2.6%
866 1
2.6%
984 1
2.6%
ValueCountFrequency (%)
13373 1
2.6%
12278 1
2.6%
12011 1
2.6%
11090 1
2.6%
10984 1
2.6%
10634 1
2.6%
10627 1
2.6%
10310 1
2.6%
10244 1
2.6%
10213 1
2.6%

배우자의직계존속금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13112.263
Minimum102
Maximum46651
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T07:25:19.166936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile262.8
Q11392
median2696.5
Q329712.25
95-th percentile41849
Maximum46651
Range46549
Interquartile range (IQR)28320.25

Descriptive statistics

Standard deviation15501.047
Coefficient of variation (CV)1.1821794
Kurtosis-0.97590477
Mean13112.263
Median Absolute Deviation (MAD)2386.5
Skewness0.82921127
Sum498266
Variance2.4028247 × 108
MonotonicityNot monotonic
2023-12-13T07:25:19.294105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
102 1
 
2.6%
30303 1
 
2.6%
8529 1
 
2.6%
9616 1
 
2.6%
9223 1
 
2.6%
25683 1
 
2.6%
28663 1
 
2.6%
27875 1
 
2.6%
30158 1
 
2.6%
30062 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
102 1
2.6%
205 1
2.6%
273 1
2.6%
347 1
2.6%
460 1
2.6%
632 1
2.6%
702 1
2.6%
868 1
2.6%
1021 1
2.6%
1332 1
2.6%
ValueCountFrequency (%)
46651 1
2.6%
42801 1
2.6%
41681 1
2.6%
34940 1
2.6%
34124 1
2.6%
31961 1
2.6%
30776 1
2.6%
30303 1
2.6%
30158 1
2.6%
30062 1
2.6%

Interactions

2023-12-13T07:25:14.611652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:02.424811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:03.592966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:04.715328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:06.243711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:07.405836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:08.503190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:09.657209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:10.822217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:12.003296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:13.418828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:14.720921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:02.564099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:03.712135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:04.806603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:06.348132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:07.505641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:08.610112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:09.781842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:10.918913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:12.116683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:13.532546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:14.835131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:02.674988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:03.824804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:04.900648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:06.444448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:07.589741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:08.717952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:09.866897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:11.024333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:12.204480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:13.644101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:14.943635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:02.771863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:03.934669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:05.011570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:06.561838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:07.680637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:08.833538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:09.964013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:11.129436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:12.587746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:13.747697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:15.033671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:02.866925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:04.029014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:05.117735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:06.664036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:07.778728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:08.942271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:10.058656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:11.238132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:12.692903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:13.853629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:15.123850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:02.966803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:04.120318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:05.243500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:06.780240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:07.889255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:09.040385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:10.168959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:11.363801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:12.818177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:13.973070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:15.194183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:03.051070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:04.215211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:05.377357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:06.889735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:07.977704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:09.134028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:10.262248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:11.448511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:12.919820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:14.067769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:15.276627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:03.159115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:04.325230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:05.499488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:06.995960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:08.062714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:09.231580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:10.373570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:11.554173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:13.007255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:14.185008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:15.374316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:03.284371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:04.439887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:05.946512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:07.101544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:08.176080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:09.349604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:10.494079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:11.684615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:13.117040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:14.310253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:15.449504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:03.375844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:04.535681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:06.045382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:07.186095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:08.281112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:09.455021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:10.598184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:11.800481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:13.204875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:14.404127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:15.543527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:03.486014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:04.639440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:06.141560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:07.303135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:08.410052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:09.563184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:10.732906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:11.901511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:13.327407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:25:14.513878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:25:19.408667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분건수계금액계공무원건수공무원금액배우자건수배우자금액직계존비속건수직계존비속금액배우자의직계존속건수배우자의직계존속금액
구분1.0000.7610.8950.7690.7660.6910.6900.6900.8550.7690.689
건수계0.7611.0000.9100.5930.6160.8730.7410.7940.9240.7390.670
금액계0.8950.9101.0000.7420.7930.8940.7950.6370.9780.9150.868
공무원건수0.7690.5930.7421.0000.5740.7360.7240.6870.5760.4180.000
공무원금액0.7660.6160.7930.5741.0000.6480.5940.7600.7360.5750.362
배우자건수0.6910.8730.8940.7360.6481.0000.8940.6800.7910.5410.000
배우자금액0.6900.7410.7950.7240.5940.8941.0000.5310.7790.0000.000
직계존비속건수0.6900.7940.6370.6870.7600.6800.5311.0000.5750.4720.221
직계존비속금액0.8550.9240.9780.5760.7360.7910.7790.5751.0000.7970.768
배우자의직계존속건수0.7690.7390.9150.4180.5750.5410.0000.4720.7971.0000.900
배우자의직계존속금액0.6890.6700.8680.0000.3620.0000.0000.2210.7680.9001.000
2023-12-13T07:25:19.546132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분건수계금액계공무원건수공무원금액배우자건수배우자금액직계존비속건수직계존비속금액배우자의직계존속건수배우자의직계존속금액
구분1.0000.8400.989-0.7990.489-0.7400.6550.1100.9510.9480.993
건수계0.8401.0000.878-0.5110.701-0.5140.6190.3880.8660.9150.862
금액계0.9890.8781.000-0.7840.552-0.7040.6540.1540.9600.9530.993
공무원건수-0.799-0.511-0.7841.000-0.0440.705-0.4550.138-0.747-0.683-0.788
공무원금액0.4890.7010.552-0.0441.000-0.1760.3320.3780.4870.6100.524
배우자건수-0.740-0.514-0.7040.705-0.1761.000-0.0910.290-0.682-0.707-0.733
배우자금액0.6550.6190.654-0.4550.332-0.0911.0000.5000.6750.6030.645
직계존비속건수0.1100.3880.1540.1380.3780.2900.5001.0000.3130.1690.131
직계존비속금액0.9510.8660.960-0.7470.487-0.6820.6750.3131.0000.9020.953
배우자의직계존속건수0.9480.9150.953-0.6830.610-0.7070.6030.1690.9021.0000.967
배우자의직계존속금액0.9930.8620.993-0.7880.524-0.7330.6450.1310.9530.9671.000

Missing values

2023-12-13T07:25:15.683833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:25:15.890664image/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.

Sample

구분건수계금액계공무원건수공무원금액배우자건수배우자금액직계존비속건수직계존비속금액배우자의직계존속건수배우자의직계존속금액
019851351949161153459538222115564133272102
119861731573731517698923449143676021508205
219871921591041387732658363166107736560273
3198819578108781498911680445167839175617347
419892232915784169331797205061919811639718460
519902281019477158939447576241965514277809632
619912358223603176750067097132033017182776702
719922471328031182458258429812121520357832868
81993256973282917446355841110422246243498661021
919942692436646164466638101167234412748410291332
구분건수계금액계공무원건수공무원금액배우자건수배우자금액직계존비속건수직계존비속금액배우자의직계존속건수배우자의직계존속금액
28201330612882066655873498136918465508061098430158
29201429500881176215685454129517798508341062730303
30201528977898966436029394116717772526381016830062
31201628836930495535304407126517566545191031031961
3220172700791288520542539612831658953804950230776
33201828621996895295526375125617504587831021334124
342019286591018745445882402137017469596821024434940
352020346611248946076657466161821577749381201141681
362021364061322456947733491170922943800021227842801
372022399041445926797732482168225370885271337346651