Overview

Dataset statistics

Number of variables13
Number of observations43
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.0 KiB
Average record size in memory119.1 B

Variable types

Text1
Numeric12

Dataset

Description공무원연금 급여종류별 퇴직자 추이 데이터 현황으로 연 단위(1982년~)로 구분되어 있으며, 퇴직급여, 퇴직연금, 유족급여 등의 데이터가 있습니다.
URLhttps://www.data.go.kr/data/15053008/fileData.do

Alerts

합계 is highly overall correlated with 퇴직급여(계)High correlation
퇴직급여(계) is highly overall correlated with 합계 and 1 other fieldsHigh correlation
퇴직일시금 is highly overall correlated with 퇴직연금일시금 and 6 other fieldsHigh correlation
퇴직연금일시금 is highly overall correlated with 퇴직일시금 and 6 other fieldsHigh correlation
퇴직연금(정상연금) is highly overall correlated with 퇴직급여(계) and 7 other fieldsHigh correlation
퇴직연금(조기연금) is highly overall correlated with 퇴직일시금 and 7 other fieldsHigh correlation
퇴직연금(수급대기자) is highly overall correlated with 퇴직일시금 and 7 other fieldsHigh correlation
퇴직연금공제일시금 is highly overall correlated with 퇴직연금(조기연금) and 3 other fieldsHigh correlation
유족급여(계) is highly overall correlated with 퇴직일시금 and 7 other fieldsHigh correlation
유족일시금 is highly overall correlated with 퇴직일시금 and 7 other fieldsHigh correlation
유족연금일시금 is highly overall correlated with 퇴직일시금 and 6 other fieldsHigh correlation
구분 has unique valuesUnique
합계 has unique valuesUnique
퇴직급여(계) has unique valuesUnique
퇴직일시금 has unique valuesUnique
퇴직연금(정상연금) has unique valuesUnique
퇴직연금공제일시금 has unique valuesUnique
퇴직연금일시금 has 19 (44.2%) zerosZeros
퇴직연금(조기연금) has 20 (46.5%) zerosZeros
퇴직연금(수급대기자) has 19 (44.2%) zerosZeros
유족연금일시금 has 19 (44.2%) zerosZeros

Reproduction

Analysis started2023-12-12 08:40:45.317583
Analysis finished2023-12-12 08:41:02.620671
Duration17.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-12T17:41:02.792148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.8604651
Min length1

Characters and Unicode

Total characters166
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)100.0%

Sample

1st row1982
2nd row1983
3rd row1984
4th row1985
5th row1986
ValueCountFrequency (%)
1982 1
 
2.3%
2004 1
 
2.3%
2006 1
 
2.3%
2007 1
 
2.3%
2008 1
 
2.3%
2009 1
 
2.3%
2010 1
 
2.3%
2011 1
 
2.3%
2012 1
 
2.3%
2013 1
 
2.3%
Other values (33) 33
76.7%
2023-12-12T17:41:03.233484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 37
22.3%
1 32
19.3%
9 32
19.3%
2 31
18.7%
8 12
 
7.2%
3 4
 
2.4%
4 4
 
2.4%
5 4
 
2.4%
6 4
 
2.4%
7 4
 
2.4%
Other values (2) 2
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 164
98.8%
Other Letter 2
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 37
22.6%
1 32
19.5%
9 32
19.5%
2 31
18.9%
8 12
 
7.3%
3 4
 
2.4%
4 4
 
2.4%
5 4
 
2.4%
6 4
 
2.4%
7 4
 
2.4%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 164
98.8%
Hangul 2
 
1.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 37
22.6%
1 32
19.5%
9 32
19.5%
2 31
18.9%
8 12
 
7.3%
3 4
 
2.4%
4 4
 
2.4%
5 4
 
2.4%
6 4
 
2.4%
7 4
 
2.4%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 164
98.8%
Hangul 2
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 37
22.6%
1 32
19.5%
9 32
19.5%
2 31
18.9%
8 12
 
7.3%
3 4
 
2.4%
4 4
 
2.4%
5 4
 
2.4%
6 4
 
2.4%
7 4
 
2.4%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35490.349
Minimum17631
Maximum94822
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T17:41:03.452309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17631
5-th percentile23872.2
Q128538.5
median33916
Q337643
95-th percentile54468.7
Maximum94822
Range77191
Interquartile range (IQR)9104.5

Descriptive statistics

Standard deviation12703.2
Coefficient of variation (CV)0.35793393
Kurtosis11.048997
Mean35490.349
Median Absolute Deviation (MAD)4764
Skewness2.7792923
Sum1526085
Variance1.6137129 × 108
MonotonicityNot monotonic
2023-12-12T17:41:03.683708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
40851 1
 
2.3%
37594 1
 
2.3%
29455 1
 
2.3%
30604 1
 
2.3%
36586 1
 
2.3%
23822 1
 
2.3%
29152 1
 
2.3%
25437 1
 
2.3%
34173 1
 
2.3%
28029 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
17631 1
2.3%
22647 1
2.3%
23822 1
2.3%
24324 1
2.3%
24665 1
2.3%
25101 1
2.3%
25437 1
2.3%
26485 1
2.3%
27206 1
2.3%
28029 1
2.3%
ValueCountFrequency (%)
94822 1
2.3%
64567 1
2.3%
54912 1
2.3%
50479 1
2.3%
43627 1
2.3%
42992 1
2.3%
41904 1
2.3%
41057 1
2.3%
40851 1
2.3%
38474 1
2.3%

퇴직급여(계)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34193.581
Minimum17411
Maximum92761
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T17:41:03.855596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17411
5-th percentile22963.5
Q127074.5
median32321
Q336554.5
95-th percentile52406.8
Maximum92761
Range75350
Interquartile range (IQR)9480

Descriptive statistics

Standard deviation12521.544
Coefficient of variation (CV)0.36619575
Kurtosis11.050124
Mean34193.581
Median Absolute Deviation (MAD)5154
Skewness2.7815244
Sum1470324
Variance1.5678906 × 108
MonotonicityNot monotonic
2023-12-12T17:41:04.022014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
39448 1
 
2.3%
36070 1
 
2.3%
28599 1
 
2.3%
29765 1
 
2.3%
35710 1
 
2.3%
22959 1
 
2.3%
28295 1
 
2.3%
24512 1
 
2.3%
33301 1
 
2.3%
27167 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
17411 1
2.3%
21671 1
2.3%
22959 1
2.3%
23004 1
2.3%
23238 1
2.3%
23704 1
2.3%
24512 1
2.3%
25381 1
2.3%
25554 1
2.3%
26503 1
2.3%
ValueCountFrequency (%)
92761 1
2.3%
62338 1
2.3%
52704 1
2.3%
49732 1
2.3%
42968 1
2.3%
41125 1
2.3%
40678 1
2.3%
40327 1
2.3%
39448 1
2.3%
37618 1
2.3%

퇴직일시금
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18089.186
Minimum5083
Maximum54447
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T17:41:04.190351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5083
5-th percentile5600.2
Q18468
median11675
Q325972
95-th percentile37735
Maximum54447
Range49364
Interquartile range (IQR)17504

Descriptive statistics

Standard deviation12076.435
Coefficient of variation (CV)0.6676052
Kurtosis0.41283187
Mean18089.186
Median Absolute Deviation (MAD)6122
Skewness0.98449927
Sum777835
Variance1.4584027 × 108
MonotonicityNot monotonic
2023-12-12T17:41:04.353865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
37795 1
 
2.3%
34236 1
 
2.3%
9452 1
 
2.3%
9449 1
 
2.3%
10234 1
 
2.3%
8358 1
 
2.3%
8838 1
 
2.3%
8537 1
 
2.3%
10818 1
 
2.3%
7850 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
5083 1
2.3%
5452 1
2.3%
5553 1
2.3%
6025 1
2.3%
6339 1
2.3%
6458 1
2.3%
7185 1
2.3%
7380 1
2.3%
7850 1
2.3%
8358 1
2.3%
ValueCountFrequency (%)
54447 1
2.3%
42256 1
2.3%
37795 1
2.3%
37195 1
2.3%
34236 1
2.3%
33111 1
2.3%
32099 1
2.3%
28599 1
2.3%
28121 1
2.3%
27510 1
2.3%

퇴직연금일시금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean675.53488
Minimum0
Maximum2535
Zeros19
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T17:41:04.550909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median963
Q31174.5
95-th percentile1482.6
Maximum2535
Range2535
Interquartile range (IQR)1174.5

Descriptive statistics

Standard deviation663.01804
Coefficient of variation (CV)0.98147121
Kurtosis-0.5908887
Mean675.53488
Median Absolute Deviation (MAD)521
Skewness0.38228413
Sum29048
Variance439592.92
MonotonicityNot monotonic
2023-12-12T17:41:04.713460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 19
44.2%
2535 1
 
2.3%
469 1
 
2.3%
1098 1
 
2.3%
1567 1
 
2.3%
1189 1
 
2.3%
1102 1
 
2.3%
963 1
 
2.3%
1124 1
 
2.3%
1199 1
 
2.3%
Other values (15) 15
34.9%
ValueCountFrequency (%)
0 19
44.2%
469 1
 
2.3%
904 1
 
2.3%
963 1
 
2.3%
1008 1
 
2.3%
1027 1
 
2.3%
1098 1
 
2.3%
1102 1
 
2.3%
1105 1
 
2.3%
1117 1
 
2.3%
ValueCountFrequency (%)
2535 1
2.3%
1567 1
2.3%
1484 1
2.3%
1470 1
2.3%
1454 1
2.3%
1226 1
2.3%
1199 1
2.3%
1189 1
2.3%
1186 1
2.3%
1184 1
2.3%

퇴직연금(정상연금)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14494.14
Minimum1550
Maximum38314
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T17:41:04.874874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1550
5-th percentile1744.7
Q14991
median13271
Q322175
95-th percentile30825.9
Maximum38314
Range36764
Interquartile range (IQR)17184

Descriptive statistics

Standard deviation10270.777
Coefficient of variation (CV)0.70861582
Kurtosis-0.92479136
Mean14494.14
Median Absolute Deviation (MAD)9165
Skewness0.42689874
Sum623248
Variance1.0548885 × 108
MonotonicityNot monotonic
2023-12-12T17:41:05.088165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1653 1
 
2.3%
1834 1
 
2.3%
16792 1
 
2.3%
17984 1
 
2.3%
22607 1
 
2.3%
12159 1
 
2.3%
16705 1
 
2.3%
13271 1
 
2.3%
19374 1
 
2.3%
16390 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
1550 1
2.3%
1653 1
2.3%
1744 1
2.3%
1751 1
2.3%
1834 1
2.3%
2837 1
2.3%
2990 1
2.3%
3761 1
2.3%
3821 1
2.3%
3847 1
2.3%
ValueCountFrequency (%)
38314 1
2.3%
31617 1
2.3%
30848 1
2.3%
30627 1
2.3%
28746 1
2.3%
27519 1
2.3%
26138 1
2.3%
25840 1
2.3%
25633 1
2.3%
25327 1
2.3%

퇴직연금(조기연금)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)55.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.883721
Minimum0
Maximum566
Zeros20
Zeros (%)46.5%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T17:41:05.255342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24
Q3135
95-th percentile346.5
Maximum566
Range566
Interquartile range (IQR)135

Descriptive statistics

Standard deviation132.31334
Coefficient of variation (CV)1.50555
Kurtosis3.4266573
Mean87.883721
Median Absolute Deviation (MAD)24
Skewness1.8682831
Sum3779
Variance17506.819
MonotonicityNot monotonic
2023-12-12T17:41:05.415416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 20
46.5%
167 1
 
2.3%
219 1
 
2.3%
347 1
 
2.3%
566 1
 
2.3%
407 1
 
2.3%
342 1
 
2.3%
241 1
 
2.3%
234 1
 
2.3%
214 1
 
2.3%
Other values (14) 14
32.6%
ValueCountFrequency (%)
0 20
46.5%
18 1
 
2.3%
24 1
 
2.3%
25 1
 
2.3%
38 1
 
2.3%
45 1
 
2.3%
51 1
 
2.3%
52 1
 
2.3%
58 1
 
2.3%
81 1
 
2.3%
ValueCountFrequency (%)
566 1
2.3%
407 1
2.3%
347 1
2.3%
342 1
2.3%
241 1
2.3%
234 1
2.3%
219 1
2.3%
214 1
2.3%
178 1
2.3%
167 1
2.3%

퇴직연금(수급대기자)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)55.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean846.83721
Minimum0
Maximum3345
Zeros19
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T17:41:05.545052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median40
Q31651
95-th percentile2655.8
Maximum3345
Range3345
Interquartile range (IQR)1651

Descriptive statistics

Standard deviation1007.3822
Coefficient of variation (CV)1.1895818
Kurtosis-0.75223462
Mean846.83721
Median Absolute Deviation (MAD)40
Skewness0.72815963
Sum36414
Variance1014818.8
MonotonicityNot monotonic
2023-12-12T17:41:05.700595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 19
44.2%
1484 2
 
4.7%
1794 1
 
2.3%
1397 1
 
2.3%
1948 1
 
2.3%
3345 1
 
2.3%
2695 1
 
2.3%
2277 1
 
2.3%
1858 1
 
2.3%
2111 1
 
2.3%
Other values (14) 14
32.6%
ValueCountFrequency (%)
0 19
44.2%
10 1
 
2.3%
23 1
 
2.3%
40 1
 
2.3%
103 1
 
2.3%
694 1
 
2.3%
1097 1
 
2.3%
1200 1
 
2.3%
1242 1
 
2.3%
1315 1
 
2.3%
ValueCountFrequency (%)
3345 1
2.3%
2755 1
2.3%
2695 1
2.3%
2303 1
2.3%
2277 1
2.3%
2111 1
2.3%
1948 1
2.3%
1924 1
2.3%
1858 1
2.3%
1807 1
2.3%

퇴직연금공제일시금
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3144.2558
Minimum222
Maximum24257
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T17:41:05.893735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum222
5-th percentile386.1
Q11080.5
median2297
Q33700
95-th percentile8968.8
Maximum24257
Range24035
Interquartile range (IQR)2619.5

Descriptive statistics

Standard deviation3991.7728
Coefficient of variation (CV)1.2695445
Kurtosis18.978162
Mean3144.2558
Median Absolute Deviation (MAD)1262
Skewness3.9273018
Sum135203
Variance15934250
MonotonicityNot monotonic
2023-12-12T17:41:06.074867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
222 1
 
2.3%
381 1
 
2.3%
2421 1
 
2.3%
2313 1
 
2.3%
2297 1
 
2.3%
1101 1
 
2.3%
1246 1
 
2.3%
1006 1
 
2.3%
1206 1
 
2.3%
1357 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
222 1
2.3%
232 1
2.3%
381 1
2.3%
432 1
2.3%
471 1
2.3%
664 1
2.3%
788 1
2.3%
804 1
2.3%
1006 1
2.3%
1035 1
2.3%
ValueCountFrequency (%)
24257 1
2.3%
11324 1
2.3%
9285 1
2.3%
6123 1
2.3%
5268 1
2.3%
4725 1
2.3%
4115 1
2.3%
3944 1
2.3%
3930 1
2.3%
3926 1
2.3%

유족급여(계)
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1296.7674
Minimum220
Maximum2437
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T17:41:06.231286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum220
5-th percentile653.6
Q1847.5
median949
Q31906
95-th percentile2311.8
Maximum2437
Range2217
Interquartile range (IQR)1058.5

Descriptive statistics

Standard deviation632.1405
Coefficient of variation (CV)0.48747407
Kurtosis-1.2485504
Mean1296.7674
Median Absolute Deviation (MAD)296
Skewness0.46609934
Sum55761
Variance399601.61
MonotonicityNot monotonic
2023-12-12T17:41:06.410244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
856 2
 
4.7%
1403 1
 
2.3%
779 1
 
2.3%
839 1
 
2.3%
876 1
 
2.3%
863 1
 
2.3%
857 1
 
2.3%
925 1
 
2.3%
872 1
 
2.3%
862 1
 
2.3%
Other values (32) 32
74.4%
ValueCountFrequency (%)
220 1
2.3%
527 1
2.3%
653 1
2.3%
659 1
2.3%
683 1
2.3%
696 1
2.3%
730 1
2.3%
744 1
2.3%
747 1
2.3%
779 1
2.3%
ValueCountFrequency (%)
2437 1
2.3%
2345 1
2.3%
2314 1
2.3%
2292 1
2.3%
2229 1
2.3%
2208 1
2.3%
2189 1
2.3%
2085 1
2.3%
2061 1
2.3%
2042 1
2.3%

유족일시금
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean791.74419
Minimum47
Maximum1627
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T17:41:06.554132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile95.5
Q1282.5
median447
Q31502.5
95-th percentile1611.1
Maximum1627
Range1580
Interquartile range (IQR)1220

Descriptive statistics

Standard deviation618.34669
Coefficient of variation (CV)0.78099302
Kurtosis-1.7679434
Mean791.74419
Median Absolute Deviation (MAD)352
Skewness0.29618003
Sum34045
Variance382352.62
MonotonicityNot monotonic
2023-12-12T17:41:06.763853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
284 2
 
4.7%
1557 2
 
4.7%
95 2
 
4.7%
1384 1
 
2.3%
368 1
 
2.3%
373 1
 
2.3%
408 1
 
2.3%
362 1
 
2.3%
318 1
 
2.3%
341 1
 
2.3%
Other values (30) 30
69.8%
ValueCountFrequency (%)
47 1
2.3%
95 2
4.7%
100 1
2.3%
120 1
2.3%
125 1
2.3%
127 1
2.3%
142 1
2.3%
160 1
2.3%
240 1
2.3%
281 1
2.3%
ValueCountFrequency (%)
1627 1
2.3%
1614 1
2.3%
1612 1
2.3%
1603 1
2.3%
1591 1
2.3%
1585 1
2.3%
1582 1
2.3%
1572 1
2.3%
1557 2
4.7%
1510 1
2.3%

유족연금일시금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.883721
Minimum0
Maximum218
Zeros19
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T17:41:06.942108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median121
Q3146.5
95-th percentile199.7
Maximum218
Range218
Interquartile range (IQR)146.5

Descriptive statistics

Standard deviation82.24997
Coefficient of variation (CV)0.94666722
Kurtosis-1.7152925
Mean86.883721
Median Absolute Deviation (MAD)79
Skewness0.053921725
Sum3736
Variance6765.0576
MonotonicityNot monotonic
2023-12-12T17:41:07.101857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 19
44.2%
134 2
 
4.7%
135 2
 
4.7%
126 2
 
4.7%
145 1
 
2.3%
166 1
 
2.3%
92 1
 
2.3%
218 1
 
2.3%
187 1
 
2.3%
200 1
 
2.3%
Other values (12) 12
27.9%
ValueCountFrequency (%)
0 19
44.2%
92 1
 
2.3%
119 1
 
2.3%
121 1
 
2.3%
126 2
 
4.7%
131 1
 
2.3%
132 1
 
2.3%
134 2
 
4.7%
135 2
 
4.7%
139 1
 
2.3%
ValueCountFrequency (%)
218 1
2.3%
211 1
2.3%
200 1
2.3%
197 1
2.3%
194 1
2.3%
190 1
2.3%
187 1
2.3%
182 1
2.3%
174 1
2.3%
166 1
2.3%

유족연금
Real number (ℝ)

Distinct42
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean418.13953
Minimum19
Maximum1385
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T17:41:07.294135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile56.9
Q1300
median371
Q3452
95-th percentile920.6
Maximum1385
Range1366
Interquartile range (IQR)152

Descriptive statistics

Standard deviation282.0014
Coefficient of variation (CV)0.67441937
Kurtosis3.1489203
Mean418.13953
Median Absolute Deviation (MAD)81
Skewness1.4818171
Sum17980
Variance79524.79
MonotonicityNot monotonic
2023-12-12T17:41:07.487582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
452 2
 
4.7%
19 1
 
2.3%
420 1
 
2.3%
354 1
 
2.3%
332 1
 
2.3%
347 1
 
2.3%
366 1
 
2.3%
400 1
 
2.3%
449 1
 
2.3%
443 1
 
2.3%
Other values (32) 32
74.4%
ValueCountFrequency (%)
19 1
2.3%
29 1
2.3%
56 1
2.3%
65 1
2.3%
81 1
2.3%
86 1
2.3%
118 1
2.3%
243 1
2.3%
265 1
2.3%
272 1
2.3%
ValueCountFrequency (%)
1385 1
2.3%
1182 1
2.3%
927 1
2.3%
863 1
2.3%
788 1
2.3%
702 1
2.3%
665 1
2.3%
617 1
2.3%
471 1
2.3%
457 1
2.3%

Interactions

2023-12-12T17:41:00.317486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:45.719299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:47.069660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:48.830642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:50.085293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:51.207562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:52.610253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:53.953300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:55.563116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:56.761163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:57.893881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:59.085565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:41:00.480428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:45.840604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:47.197518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:48.946095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:50.197624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:51.332811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:52.717468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:54.078109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:55.691824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:56.865590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:57.999350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:59.173726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:41:00.661230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:45.954495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:47.326330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:49.055822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:50.329175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:51.444213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:52.822354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:54.210939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:55.802103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:56.986846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:58.094362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:59.274580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:41:00.790710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:46.043513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:47.418987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:49.148316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:50.416149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:51.539173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:52.921479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:54.335364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:55.886837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:57.071069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:58.175578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:59.349968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:41:00.929071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:46.140373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:47.529841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:49.234118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:50.499021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:51.631043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:53.019679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:54.426666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:55.983455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:57.174174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:58.258653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:59.438796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:41:01.061162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:46.255218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:47.651415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:49.364313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:50.601561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:51.757806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:53.145326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:54.530730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:56.078592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:57.277996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:58.356574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:59.546412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:41:01.182614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:46.360802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:48.109242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:49.477418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:50.688389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:51.886822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:53.236970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:54.626272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:56.162398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:57.375809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:58.455658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:59.639131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:41:01.312430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:46.497099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:48.215439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:49.586648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:50.777665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:52.012362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:53.431371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:55.034113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:56.267452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:57.466731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:58.564547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:59.724170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:41:01.776600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:46.605085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:48.334878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:49.692680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:50.859991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:52.115801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:53.535355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:55.139899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:56.374817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:57.542305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:58.679487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:59.813223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:41:01.892077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:46.709010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:48.449636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:49.786492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:50.941527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:52.228150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:53.636625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:55.253938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:56.465845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:57.612020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:58.786800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:59.912992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:41:02.030630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:46.813718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:48.573086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:49.882870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:51.040437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:52.354620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:53.751391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:55.377262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:56.589661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:57.704202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:58.893743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:41:00.037340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:41:02.147534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:46.926442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:48.698101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:49.977501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:51.117760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:52.468242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:53.846906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:55.468743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:56.667635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:57.793161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:40:58.985378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:41:00.173832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:41:07.643121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분합계퇴직급여(계)퇴직일시금퇴직연금일시금퇴직연금(정상연금)퇴직연금(조기연금)퇴직연금(수급대기자)퇴직연금공제일시금유족급여(계)유족일시금유족연금일시금유족연금
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
합계1.0001.0000.9980.7870.3300.7890.2350.0000.8380.0000.7850.0000.762
퇴직급여(계)1.0000.9981.0000.7890.3190.7720.3490.0000.8340.0000.7780.0000.760
퇴직일시금1.0000.7870.7891.0000.6480.6740.0000.0000.9620.8970.8610.1210.931
퇴직연금일시금1.0000.3300.3190.6481.0000.7830.7330.7460.0000.5570.7850.9320.370
퇴직연금(정상연금)1.0000.7890.7720.6740.7831.0000.7410.7860.7670.6310.7200.6690.694
퇴직연금(조기연금)1.0000.2350.3490.0000.7330.7411.0000.9360.0000.3100.6160.7320.000
퇴직연금(수급대기자)1.0000.0000.0000.0000.7460.7860.9361.0000.0000.1420.6090.7470.000
퇴직연금공제일시금1.0000.8380.8340.9620.0000.7670.0000.0001.0000.6800.7400.0000.922
유족급여(계)1.0000.0000.0000.8970.5570.6310.3100.1420.6801.0000.8120.4640.840
유족일시금1.0000.7850.7780.8610.7850.7200.6160.6090.7400.8121.0000.7910.705
유족연금일시금1.0000.0000.0000.1210.9320.6690.7320.7470.0000.4640.7911.0000.000
유족연금1.0000.7620.7600.9310.3700.6940.0000.0000.9220.8400.7050.0001.000
2023-12-12T17:41:08.102523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계퇴직급여(계)퇴직일시금퇴직연금일시금퇴직연금(정상연금)퇴직연금(조기연금)퇴직연금(수급대기자)퇴직연금공제일시금유족급여(계)유족일시금유족연금일시금유족연금
합계1.0000.9950.257-0.1630.4510.0990.0950.0350.048-0.080-0.0580.419
퇴직급여(계)0.9951.0000.200-0.1140.5000.1590.155-0.000-0.017-0.149-0.0110.401
퇴직일시금0.2570.2001.000-0.646-0.585-0.859-0.8610.4460.8700.830-0.7190.087
퇴직연금일시금-0.163-0.114-0.6461.0000.5810.6920.769-0.297-0.675-0.6810.918-0.109
퇴직연금(정상연금)0.4510.500-0.5850.5811.0000.7730.783-0.105-0.627-0.7660.6560.389
퇴직연금(조기연금)0.0990.159-0.8590.6920.7731.0000.974-0.596-0.919-0.9280.783-0.014
퇴직연금(수급대기자)0.0950.155-0.8610.7690.7830.9741.000-0.555-0.910-0.9260.8500.019
퇴직연금공제일시금0.035-0.0000.446-0.297-0.105-0.596-0.5551.0000.6830.571-0.3910.482
유족급여(계)0.048-0.0170.870-0.675-0.627-0.919-0.9100.6831.0000.926-0.7650.313
유족일시금-0.080-0.1490.830-0.681-0.766-0.928-0.9260.5710.9261.000-0.7750.066
유족연금일시금-0.058-0.011-0.7190.9180.6560.7830.850-0.391-0.765-0.7751.000-0.084
유족연금0.4190.4010.087-0.1090.389-0.0140.0190.4820.3130.066-0.0841.000

Missing values

2023-12-12T17:41:02.313879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:41:02.551385image/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

구분합계퇴직급여(계)퇴직일시금퇴직연금일시금퇴직연금(정상연금)퇴직연금(조기연금)퇴직연금(수급대기자)퇴직연금공제일시금유족급여(계)유족일시금유족연금일시금유족연금
01982408513944837795016530022214031384019
11983375943607034236018340038115241495029
21984351913364932099015500047115421486056
319853186330350285990175100150915131448065
419862862526982252380174400184116431557086
5198724324230041924303761003713132012020118
6198827206253812239102990003311182515820243
7198925101232382040102837003170186315910272
8199028452265032268203821003687194916030346
9199130968289262507903847003930204215850457
구분합계퇴직급여(계)퇴직일시금퇴직연금일시금퇴직연금(정상연금)퇴직연금(조기연금)퇴직연금(수급대기자)퇴직연금공제일시금유족급여(계)유족일시금유족연금일시금유족연금
332015384743761863399042874614514842048856284131441
3420163613435451602511662532717827551619683127166390
352017351763443250831199256332142303125974495197452
3620183555834862555311242584023421111060696120190386
37201937692370396458963275192411858788653100182371
3820204362742968839911023084834222771035659125200334
392021410574032798981189261384072695804730160187383
4020225047949732126371567316175663345664747142218387
4132848323217185109821743347194843252795126306
421763117411545246998742191397232220479281