Overview

Dataset statistics

Number of variables13
Number of observations48
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 KiB
Average record size in memory118.8 B

Variable types

Text1
Numeric12

Dataset

Description공무원이 퇴직시 선택하는 급여(퇴직일시금, 퇴직연금, 유족연금, 유족일시금 등)종류에 따른 연령별 퇴직자 수 데이터입니다.
URLhttps://www.data.go.kr/data/15053026/fileData.do

Alerts

합 계 is highly overall correlated with 퇴직급여(계) and 3 other fieldsHigh correlation
퇴직급여(계) is highly overall correlated with 합 계 and 3 other fieldsHigh correlation
퇴직일시금 is highly overall correlated with 퇴직연금 and 1 other fieldsHigh correlation
퇴직연금일시금 is highly overall correlated with 조기연금 and 5 other fieldsHigh correlation
퇴직연금 is highly overall correlated with 합 계 and 6 other fieldsHigh correlation
조기연금 is highly overall correlated with 퇴직연금일시금 and 6 other fieldsHigh correlation
수급대기자 is highly overall correlated with 퇴직연금일시금 and 5 other fieldsHigh correlation
퇴직연금공제일시금 is highly overall correlated with 합 계 and 8 other fieldsHigh correlation
유족급여(계) is highly overall correlated with 합 계 and 7 other fieldsHigh correlation
유족일시금 is highly overall correlated with 퇴직일시금 and 1 other fieldsHigh correlation
유족연금일시금 is highly overall correlated with 퇴직연금일시금 and 5 other fieldsHigh correlation
유족연금 is highly overall correlated with 퇴직연금일시금 and 6 other fieldsHigh correlation
구 분 has unique valuesUnique
퇴직연금일시금 has 14 (29.2%) zerosZeros
퇴직연금 has 30 (62.5%) zerosZeros
조기연금 has 25 (52.1%) zerosZeros
수급대기자 has 17 (35.4%) zerosZeros
퇴직연금공제일시금 has 17 (35.4%) zerosZeros
유족급여(계) has 4 (8.3%) zerosZeros
유족일시금 has 13 (27.1%) zerosZeros
유족연금일시금 has 17 (35.4%) zerosZeros
유족연금 has 17 (35.4%) zerosZeros

Reproduction

Analysis started2023-12-12 05:22:00.809440
Analysis finished2023-12-12 05:22:15.493656
Duration14.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구 분
Text

UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size516.0 B
2023-12-12T14:22:15.638806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0625
Min length3

Characters and Unicode

Total characters147
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)100.0%

Sample

1st row18세
2nd row19세
3rd row20세
4th row21세
5th row22세
ValueCountFrequency (%)
18세 1
 
2.0%
43세 1
 
2.0%
44세 1
 
2.0%
45세 1
 
2.0%
46세 1
 
2.0%
47세 1
 
2.0%
48세 1
 
2.0%
49세 1
 
2.0%
50세 1
 
2.0%
51세 1
 
2.0%
Other values (39) 39
79.6%
2023-12-12T14:22:16.253659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48
32.7%
2 15
 
10.2%
3 15
 
10.2%
4 15
 
10.2%
5 15
 
10.2%
6 10
 
6.8%
1 7
 
4.8%
8 5
 
3.4%
9 5
 
3.4%
0 5
 
3.4%
Other values (4) 7
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 96
65.3%
Other Letter 50
34.0%
Space Separator 1
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 15
15.6%
3 15
15.6%
4 15
15.6%
5 15
15.6%
6 10
10.4%
1 7
7.3%
8 5
 
5.2%
9 5
 
5.2%
0 5
 
5.2%
7 4
 
4.2%
Other Letter
ValueCountFrequency (%)
48
96.0%
1
 
2.0%
1
 
2.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 97
66.0%
Hangul 50
34.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 15
15.5%
3 15
15.5%
4 15
15.5%
5 15
15.5%
6 10
10.3%
1 7
7.2%
8 5
 
5.2%
9 5
 
5.2%
0 5
 
5.2%
7 4
 
4.1%
Hangul
ValueCountFrequency (%)
48
96.0%
1
 
2.0%
1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97
66.0%
Hangul 50
34.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
48
96.0%
1
 
2.0%
1
 
2.0%
ASCII
ValueCountFrequency (%)
2 15
15.5%
3 15
15.5%
4 15
15.5%
5 15
15.5%
6 10
10.3%
1 7
7.2%
8 5
 
5.2%
9 5
 
5.2%
0 5
 
5.2%
7 4
 
4.1%

합 계
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1051.6458
Minimum1
Maximum16421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T14:22:16.409978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.15
Q1405.75
median500
Q3860
95-th percentile2753
Maximum16421
Range16420
Interquartile range (IQR)454.25

Descriptive statistics

Standard deviation2369.2437
Coefficient of variation (CV)2.2528913
Kurtosis39.64819
Mean1051.6458
Median Absolute Deviation (MAD)260.5
Skewness6.0737816
Sum50479
Variance5613315.9
MonotonicityNot monotonic
2023-12-12T14:22:16.602615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
442 2
 
4.2%
7 1
 
2.1%
412 1
 
2.1%
403 1
 
2.1%
468 1
 
2.1%
482 1
 
2.1%
484 1
 
2.1%
598 1
 
2.1%
589 1
 
2.1%
610 1
 
2.1%
Other values (37) 37
77.1%
ValueCountFrequency (%)
1 1
2.1%
7 1
2.1%
8 1
2.1%
17 1
2.1%
36 1
2.1%
68 1
2.1%
75 1
2.1%
164 1
2.1%
309 1
2.1%
387 1
2.1%
ValueCountFrequency (%)
16421 1
2.1%
3237 1
2.1%
2914 1
2.1%
2454 1
2.1%
2028 1
2.1%
1424 1
2.1%
1381 1
2.1%
1265 1
2.1%
1076 1
2.1%
994 1
2.1%

퇴직급여(계)
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1036.0833
Minimum1
Maximum16391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T14:22:16.795160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.15
Q1393.5
median486.5
Q3852
95-th percentile2723.25
Maximum16391
Range16390
Interquartile range (IQR)458.5

Descriptive statistics

Standard deviation2365.7424
Coefficient of variation (CV)2.2833514
Kurtosis39.740181
Mean1036.0833
Median Absolute Deviation (MAD)266
Skewness6.0838579
Sum49732
Variance5596736.9
MonotonicityNot monotonic
2023-12-12T14:22:16.936883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
394 2
 
4.2%
1 1
 
2.1%
392 1
 
2.1%
383 1
 
2.1%
446 1
 
2.1%
456 1
 
2.1%
459 1
 
2.1%
563 1
 
2.1%
558 1
 
2.1%
573 1
 
2.1%
Other values (37) 37
77.1%
ValueCountFrequency (%)
1 1
2.1%
7 1
2.1%
8 1
2.1%
17 1
2.1%
35 1
2.1%
67 1
2.1%
74 1
2.1%
160 1
2.1%
304 1
2.1%
377 1
2.1%
ValueCountFrequency (%)
16391 1
2.1%
3226 1
2.1%
2886 1
2.1%
2421 1
2.1%
1995 1
2.1%
1391 1
2.1%
1377 1
2.1%
1222 1
2.1%
1069 1
2.1%
956 1
2.1%

퇴직일시금
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean263.27083
Minimum1
Maximum947
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T14:22:17.113581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.15
Q191.25
median200.5
Q3323.25
95-th percentile805.15
Maximum947
Range946
Interquartile range (IQR)232

Descriptive statistics

Standard deviation238.65864
Coefficient of variation (CV)0.90651379
Kurtosis1.1741851
Mean263.27083
Median Absolute Deviation (MAD)121.5
Skewness1.3515785
Sum12637
Variance56957.946
MonotonicityNot monotonic
2023-12-12T14:22:17.255671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
35 2
 
4.2%
1 1
 
2.1%
126 1
 
2.1%
209 1
 
2.1%
200 1
 
2.1%
208 1
 
2.1%
228 1
 
2.1%
190 1
 
2.1%
205 1
 
2.1%
199 1
 
2.1%
Other values (37) 37
77.1%
ValueCountFrequency (%)
1 1
2.1%
7 1
2.1%
8 1
2.1%
17 1
2.1%
35 2
4.2%
42 1
2.1%
66 1
2.1%
67 1
2.1%
69 1
2.1%
72 1
2.1%
ValueCountFrequency (%)
947 1
2.1%
821 1
2.1%
816 1
2.1%
785 1
2.1%
713 1
2.1%
631 1
2.1%
552 1
2.1%
474 1
2.1%
430 1
2.1%
421 1
2.1%

퇴직연금일시금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.645833
Minimum0
Maximum98
Zeros14
Zeros (%)29.2%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T14:22:17.407146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median34
Q359.5
95-th percentile76.65
Maximum98
Range98
Interquartile range (IQR)59.5

Descriptive statistics

Standard deviation30.654207
Coefficient of variation (CV)0.93899294
Kurtosis-1.4011003
Mean32.645833
Median Absolute Deviation (MAD)33.5
Skewness0.30227891
Sum1567
Variance939.68041
MonotonicityNot monotonic
2023-12-12T14:22:17.579031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 14
29.2%
54 3
 
6.2%
2 2
 
4.2%
34 2
 
4.2%
70 2
 
4.2%
39 1
 
2.1%
50 1
 
2.1%
36 1
 
2.1%
46 1
 
2.1%
98 1
 
2.1%
Other values (20) 20
41.7%
ValueCountFrequency (%)
0 14
29.2%
2 2
 
4.2%
4 1
 
2.1%
8 1
 
2.1%
9 1
 
2.1%
12 1
 
2.1%
15 1
 
2.1%
17 1
 
2.1%
19 1
 
2.1%
34 2
 
4.2%
ValueCountFrequency (%)
98 1
2.1%
79 1
2.1%
77 1
2.1%
76 1
2.1%
74 1
2.1%
72 1
2.1%
70 2
4.2%
69 1
2.1%
68 1
2.1%
67 1
2.1%

퇴직연금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)39.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean658.6875
Minimum0
Maximum15566
Zeros30
Zeros (%)62.5%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T14:22:17.726096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3204.75
95-th percentile2457.45
Maximum15566
Range15566
Interquartile range (IQR)204.75

Descriptive statistics

Standard deviation2313.8745
Coefficient of variation (CV)3.5128562
Kurtosis38.503302
Mean658.6875
Median Absolute Deviation (MAD)0
Skewness5.9690598
Sum31617
Variance5354015.1
MonotonicityNot monotonic
2023-12-12T14:22:17.882990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 30
62.5%
1 1
 
2.1%
791 1
 
2.1%
39 1
 
2.1%
1331 1
 
2.1%
3143 1
 
2.1%
978 1
 
2.1%
15566 1
 
2.1%
2623 1
 
2.1%
2150 1
 
2.1%
Other values (9) 9
 
18.8%
ValueCountFrequency (%)
0 30
62.5%
1 1
 
2.1%
30 1
 
2.1%
39 1
 
2.1%
64 1
 
2.1%
99 1
 
2.1%
158 1
 
2.1%
345 1
 
2.1%
604 1
 
2.1%
791 1
 
2.1%
ValueCountFrequency (%)
15566 1
2.1%
3143 1
2.1%
2623 1
2.1%
2150 1
2.1%
1699 1
2.1%
1331 1
2.1%
1111 1
2.1%
978 1
2.1%
885 1
2.1%
791 1
2.1%

조기연금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.791667
Minimum0
Maximum91
Zeros25
Zeros (%)52.1%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T14:22:18.041934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q314
95-th percentile47.25
Maximum91
Range91
Interquartile range (IQR)14

Descriptive statistics

Standard deviation20.021221
Coefficient of variation (CV)1.6979127
Kurtosis4.0877842
Mean11.791667
Median Absolute Deviation (MAD)0
Skewness1.9744725
Sum566
Variance400.84929
MonotonicityNot monotonic
2023-12-12T14:22:18.199718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 25
52.1%
2 2
 
4.2%
41 2
 
4.2%
14 2
 
4.2%
1 2
 
4.2%
40 1
 
2.1%
91 1
 
2.1%
37 1
 
2.1%
49 1
 
2.1%
51 1
 
2.1%
Other values (10) 10
 
20.8%
ValueCountFrequency (%)
0 25
52.1%
1 2
 
4.2%
2 2
 
4.2%
3 1
 
2.1%
4 1
 
2.1%
5 1
 
2.1%
8 1
 
2.1%
9 1
 
2.1%
11 1
 
2.1%
14 2
 
4.2%
ValueCountFrequency (%)
91 1
2.1%
51 1
2.1%
49 1
2.1%
44 1
2.1%
41 2
4.2%
40 1
2.1%
38 1
2.1%
37 1
2.1%
31 1
2.1%
29 1
2.1%

수급대기자
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.6875
Minimum0
Maximum518
Zeros17
Zeros (%)35.4%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T14:22:18.365864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median30.5
Q3119
95-th percentile166.3
Maximum518
Range518
Interquartile range (IQR)119

Descriptive statistics

Standard deviation92.278523
Coefficient of variation (CV)1.3241761
Kurtosis10.658402
Mean69.6875
Median Absolute Deviation (MAD)30.5
Skewness2.5429041
Sum3345
Variance8515.3258
MonotonicityNot monotonic
2023-12-12T14:22:18.514972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 17
35.4%
119 3
 
6.2%
96 2
 
4.2%
125 1
 
2.1%
8 1
 
2.1%
518 1
 
2.1%
103 1
 
2.1%
104 1
 
2.1%
138 1
 
2.1%
167 1
 
2.1%
Other values (19) 19
39.6%
ValueCountFrequency (%)
0 17
35.4%
1 1
 
2.1%
2 1
 
2.1%
3 1
 
2.1%
8 1
 
2.1%
10 1
 
2.1%
12 1
 
2.1%
26 1
 
2.1%
35 1
 
2.1%
45 1
 
2.1%
ValueCountFrequency (%)
518 1
2.1%
189 1
2.1%
167 1
2.1%
165 1
2.1%
162 1
2.1%
161 1
2.1%
156 1
2.1%
143 1
2.1%
138 1
2.1%
125 1
2.1%

퇴직연금공제일시금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)39.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.833333
Minimum0
Maximum222
Zeros17
Zeros (%)35.4%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T14:22:18.631425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q313
95-th percentile43.85
Maximum222
Range222
Interquartile range (IQR)13

Descriptive statistics

Standard deviation33.571983
Coefficient of variation (CV)2.4268903
Kurtosis32.686848
Mean13.833333
Median Absolute Deviation (MAD)4
Skewness5.3635004
Sum664
Variance1127.078
MonotonicityNot monotonic
2023-12-12T14:22:18.778550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 17
35.4%
9 4
 
8.3%
4 3
 
6.2%
1 3
 
6.2%
3 2
 
4.2%
6 2
 
4.2%
7 2
 
4.2%
10 2
 
4.2%
13 2
 
4.2%
17 2
 
4.2%
Other values (9) 9
18.8%
ValueCountFrequency (%)
0 17
35.4%
1 3
 
6.2%
3 2
 
4.2%
4 3
 
6.2%
6 2
 
4.2%
7 2
 
4.2%
9 4
 
8.3%
10 2
 
4.2%
13 2
 
4.2%
15 1
 
2.1%
ValueCountFrequency (%)
222 1
2.1%
62 1
2.1%
47 1
2.1%
38 1
2.1%
33 1
2.1%
32 1
2.1%
28 1
2.1%
24 1
2.1%
17 2
4.2%
15 1
2.1%

유족급여(계)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.5625
Minimum0
Maximum43
Zeros4
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T14:22:18.944899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.75
median11
Q326
95-th percentile37.65
Maximum43
Range43
Interquartile range (IQR)20.25

Descriptive statistics

Standard deviation12.727765
Coefficient of variation (CV)0.81784838
Kurtosis-0.86709743
Mean15.5625
Median Absolute Deviation (MAD)9
Skewness0.6072223
Sum747
Variance161.99601
MonotonicityNot monotonic
2023-12-12T14:22:19.094902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 4
 
8.3%
33 3
 
6.2%
1 3
 
6.2%
20 3
 
6.2%
10 3
 
6.2%
11 3
 
6.2%
7 3
 
6.2%
12 2
 
4.2%
26 2
 
4.2%
9 2
 
4.2%
Other values (17) 20
41.7%
ValueCountFrequency (%)
0 4
8.3%
1 3
6.2%
2 1
 
2.1%
4 2
4.2%
5 2
4.2%
6 2
4.2%
7 3
6.2%
9 2
4.2%
10 3
6.2%
11 3
6.2%
ValueCountFrequency (%)
43 1
 
2.1%
41 1
 
2.1%
38 1
 
2.1%
37 1
 
2.1%
35 1
 
2.1%
33 3
6.2%
31 1
 
2.1%
30 1
 
2.1%
28 1
 
2.1%
26 2
4.2%

유족일시금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9583333
Minimum0
Maximum10
Zeros13
Zeros (%)27.1%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T14:22:19.268330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0663467
Coefficient of variation (CV)1.0365116
Kurtosis-0.13759822
Mean2.9583333
Median Absolute Deviation (MAD)2
Skewness0.95582546
Sum142
Variance9.4024823
MonotonicityNot monotonic
2023-12-12T14:22:19.404134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 13
27.1%
1 9
18.8%
3 5
 
10.4%
4 4
 
8.3%
5 4
 
8.3%
2 4
 
8.3%
9 3
 
6.2%
6 2
 
4.2%
10 2
 
4.2%
7 1
 
2.1%
ValueCountFrequency (%)
0 13
27.1%
1 9
18.8%
2 4
 
8.3%
3 5
 
10.4%
4 4
 
8.3%
5 4
 
8.3%
6 2
 
4.2%
7 1
 
2.1%
8 1
 
2.1%
9 3
 
6.2%
ValueCountFrequency (%)
10 2
 
4.2%
9 3
 
6.2%
8 1
 
2.1%
7 1
 
2.1%
6 2
 
4.2%
5 4
8.3%
4 4
8.3%
3 5
10.4%
2 4
8.3%
1 9
18.8%

유족연금일시금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5416667
Minimum0
Maximum17
Zeros17
Zeros (%)35.4%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T14:22:19.541771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q38
95-th percentile14.65
Maximum17
Range17
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.7398641
Coefficient of variation (CV)1.0436398
Kurtosis0.020940508
Mean4.5416667
Median Absolute Deviation (MAD)3
Skewness0.87576901
Sum218
Variance22.466312
MonotonicityNot monotonic
2023-12-12T14:22:19.692119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 17
35.4%
7 5
 
10.4%
3 4
 
8.3%
8 4
 
8.3%
2 3
 
6.2%
9 3
 
6.2%
4 2
 
4.2%
5 2
 
4.2%
10 2
 
4.2%
15 2
 
4.2%
Other values (4) 4
 
8.3%
ValueCountFrequency (%)
0 17
35.4%
1 1
 
2.1%
2 3
 
6.2%
3 4
 
8.3%
4 2
 
4.2%
5 2
 
4.2%
6 1
 
2.1%
7 5
 
10.4%
8 4
 
8.3%
9 3
 
6.2%
ValueCountFrequency (%)
17 1
 
2.1%
15 2
 
4.2%
14 1
 
2.1%
10 2
 
4.2%
9 3
6.2%
8 4
8.3%
7 5
10.4%
6 1
 
2.1%
5 2
 
4.2%
4 2
 
4.2%

유족연금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0625
Minimum0
Maximum28
Zeros17
Zeros (%)35.4%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T14:22:19.840683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q315.25
95-th percentile26
Maximum28
Range28
Interquartile range (IQR)15.25

Descriptive statistics

Standard deviation9.5969992
Coefficient of variation (CV)1.1903255
Kurtosis-0.72920083
Mean8.0625
Median Absolute Deviation (MAD)3
Skewness0.89628189
Sum387
Variance92.102394
MonotonicityNot monotonic
2023-12-12T14:22:19.979317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 17
35.4%
3 4
 
8.3%
1 3
 
6.2%
5 2
 
4.2%
9 2
 
4.2%
22 2
 
4.2%
16 2
 
4.2%
26 2
 
4.2%
21 2
 
4.2%
24 1
 
2.1%
Other values (11) 11
22.9%
ValueCountFrequency (%)
0 17
35.4%
1 3
 
6.2%
2 1
 
2.1%
3 4
 
8.3%
4 1
 
2.1%
5 2
 
4.2%
6 1
 
2.1%
7 1
 
2.1%
9 2
 
4.2%
10 1
 
2.1%
ValueCountFrequency (%)
28 1
2.1%
27 1
2.1%
26 2
4.2%
25 1
2.1%
24 1
2.1%
22 2
4.2%
21 2
4.2%
16 2
4.2%
15 1
2.1%
14 1
2.1%

Interactions

2023-12-12T14:22:14.417107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:01.173643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:02.390312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:03.759986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:04.543526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:05.557568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:06.913179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:08.071474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:09.709649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:10.804658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:11.920698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:13.243176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:14.486119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:01.271240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:02.489624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:03.830823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:04.613710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:05.683650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:07.020567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:08.182573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:09.791611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:10.911542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:12.011102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:13.345359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:14.554754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:01.368143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:02.901983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:03.897645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:04.699849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:05.802641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:07.124571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:08.316568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:09.881895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:11.004566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:12.113871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:13.461874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:14.627499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:01.461598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:02.984621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:03.953067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:04.766151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:05.880939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:07.214615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:08.406513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:09.966158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:11.098257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:12.228032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:13.560503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:14.708635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:01.563186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:03.078283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:04.014498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:04.872602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:05.985138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:07.304261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:08.496217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:10.055275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:11.176020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:12.351246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:13.678670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:14.788011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:01.715094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:03.204541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:04.082484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:04.955252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:06.086291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:07.423162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:08.641476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:10.154895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:11.263443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:12.472165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:13.809519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:14.857812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:01.819773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:03.284253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:04.140329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:05.028657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:06.183336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:07.504381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:08.755384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:10.244435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:11.342809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:12.564960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:13.905287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:14.928050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:01.928714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:03.356548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:04.202254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:05.103694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:06.295696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:07.584615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:08.863535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:10.360912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:11.441717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:12.672190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:14.024418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:14.992986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:02.004882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:03.424609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:04.263409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:05.186725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:06.412923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:07.675002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:08.965648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:10.453891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:11.515028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:12.763121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:14.105659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:15.065879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:02.092796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:03.494113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:04.324267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:05.274046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:06.502726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:07.758198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:09.061699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:10.540604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:11.603619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:12.899446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:14.183164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:15.132639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:02.181795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:03.574560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:04.394675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:05.353490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:06.685956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:07.856335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:09.506909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:10.620101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:11.729130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:13.028872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:14.267491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:15.200779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:02.296105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:03.680900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:04.469662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:05.452074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:06.807605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:07.975544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:09.622322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:10.725873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:11.843725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:13.158501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:22:14.343487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:22:20.096080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구 분합 계퇴직급여(계)퇴직일시금퇴직연금일시금퇴직연금조기연금수급대기자퇴직연금공제일시금유족급여(계)유족일시금유족연금일시금유족연금
구 분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
합 계1.0001.0001.0000.0000.9641.0000.9850.7180.8380.4450.0000.0000.656
퇴직급여(계)1.0001.0001.0000.0000.9641.0000.9850.7180.8380.4450.0000.0000.656
퇴직일시금1.0000.0000.0001.0000.0000.0000.1670.1920.0000.6050.8940.3010.000
퇴직연금일시금1.0000.9640.9640.0001.0000.8070.8060.8480.7620.6770.0000.8020.875
퇴직연금1.0001.0001.0000.0000.8071.0000.8550.6450.9740.3810.0000.0000.626
조기연금1.0000.9850.9850.1670.8060.8551.0000.7700.8240.8100.0000.6850.807
수급대기자1.0000.7180.7180.1920.8480.6450.7701.0000.6530.9230.4530.6630.726
퇴직연금공제일시금1.0000.8380.8380.0000.7620.9740.8240.6531.0000.5590.0430.2680.656
유족급여(계)1.0000.4450.4450.6050.6770.3810.8100.9230.5591.0000.6950.7530.791
유족일시금1.0000.0000.0000.8940.0000.0000.0000.4530.0430.6951.0000.0000.000
유족연금일시금1.0000.0000.0000.3010.8020.0000.6850.6630.2680.7530.0001.0000.636
유족연금1.0000.6560.6560.0000.8750.6260.8070.7260.6560.7910.0000.6361.000
2023-12-12T14:22:20.270198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합 계퇴직급여(계)퇴직일시금퇴직연금일시금퇴직연금조기연금수급대기자퇴직연금공제일시금유족급여(계)유족일시금유족연금일시금유족연금
합 계1.0000.9990.0950.2670.7130.4440.3000.6520.553-0.0380.3720.476
퇴직급여(계)0.9991.0000.1040.2510.7100.4290.2830.6400.540-0.0320.3570.463
퇴직일시금0.0950.1041.0000.075-0.507-0.2290.033-0.3750.1000.861-0.097-0.212
퇴직연금일시금0.2670.2510.0751.0000.2910.7380.9100.6470.805-0.1040.8930.805
퇴직연금0.7130.710-0.5070.2911.0000.6000.3330.8340.486-0.5110.4670.617
조기연금0.4440.429-0.2290.7380.6001.0000.8580.7300.873-0.3460.8680.900
수급대기자0.3000.2830.0330.9100.3330.8581.0000.6340.888-0.1210.9140.867
퇴직연금공제일시금0.6520.640-0.3750.6470.8340.7300.6341.0000.707-0.4700.7380.843
유족급여(계)0.5530.5400.1000.8050.4860.8730.8880.7071.000-0.0370.8860.918
유족일시금-0.038-0.0320.861-0.104-0.511-0.346-0.121-0.470-0.0371.000-0.278-0.363
유족연금일시금0.3720.357-0.0970.8930.4670.8680.9140.7380.886-0.2781.0000.903
유족연금0.4760.463-0.2120.8050.6170.9000.8670.8430.918-0.3630.9031.000

Missing values

2023-12-12T14:22:15.290811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:22:15.439754image/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

구 분합 계퇴직급여(계)퇴직일시금퇴직연금일시금퇴직연금조기연금수급대기자퇴직연금공제일시금유족급여(계)유족일시금유족연금일시금유족연금
018세111000000000
119세777000000000
220세888000000000
321세171717000000000
422세363535000001100
523세686767000001100
624세164160160000004400
725세309304304000005500
826세436430430000006600
927세557552552000005500
구 분합 계퇴직급여(계)퇴직일시금퇴직연금일시금퇴직연금조기연금수급대기자퇴직연금공제일시금유족급여(계)유족일시금유족연금일시금유족연금
3856세14241391107361111419624330726
3957세20281995954616995110432330825
4058세24542421803921504910333330726
4159세29142886723526233711947281522
4260세1642116391118981556691518222300822
4361세10761069691297828177043
4462세3237322666173143006211137
4563세13811377424133100384031
4664세7574350390011001
4765세 이상947945145979100132200