Overview

Dataset statistics

Number of variables13
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.7 KiB
Average record size in memory118.7 B

Variable types

Text1
Numeric12

Dataset

Description연령별 공무원연금(퇴직, 유족, 장해 등) 연금수급자 현황에 대한 데이터입니다. 38세 미만부터 시작되며 1세 단위로 구분됩니다.
URLhttps://www.data.go.kr/data/15054097/fileData.do

Alerts

총계 is highly overall correlated with 총계(남) and 10 other fieldsHigh correlation
총계(남) is highly overall correlated with 총계 and 10 other fieldsHigh correlation
총계(여) is highly overall correlated with 총계 and 10 other fieldsHigh correlation
퇴직연금(계) is highly overall correlated with 총계 and 10 other fieldsHigh correlation
퇴직연금(남) is highly overall correlated with 총계 and 10 other fieldsHigh correlation
퇴직연금(여) is highly overall correlated with 총계 and 10 other fieldsHigh correlation
유족연금(계) is highly overall correlated with 총계 and 9 other fieldsHigh correlation
유족연금(남) is highly overall correlated with 총계 and 10 other fieldsHigh correlation
유족연금(여) is highly overall correlated with 총계 and 9 other fieldsHigh correlation
장해연금(계) is highly overall correlated with 총계 and 10 other fieldsHigh correlation
장해연금(남) is highly overall correlated with 총계 and 10 other fieldsHigh correlation
장해연금(여) is highly overall correlated with 총계 and 8 other fieldsHigh correlation
구분 has unique valuesUnique
총계 has unique valuesUnique
총계(남) has unique valuesUnique
총계(여) has unique valuesUnique
퇴직연금(계) has unique valuesUnique
퇴직연금(남) has unique valuesUnique
퇴직연금(여) has unique valuesUnique
유족연금(계) has unique valuesUnique
유족연금(여) has unique valuesUnique
장해연금(계) has 3 (6.1%) zerosZeros
장해연금(남) has 3 (6.1%) zerosZeros
장해연금(여) has 12 (24.5%) zerosZeros

Reproduction

Analysis started2023-12-12 09:48:55.890627
Analysis finished2023-12-12 09:49:10.532921
Duration14.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-12T18:49:10.699413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.122449
Min length3

Characters and Unicode

Total characters153
Distinct characters15
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

Unique49 ?
Unique (%)100.0%

Sample

1st row38세미만
2nd row38세이상
3rd row39세
4th row40세
5th row41세
ValueCountFrequency (%)
38세미만 1
 
2.0%
62세 1
 
2.0%
64세 1
 
2.0%
65세 1
 
2.0%
66세 1
 
2.0%
67세 1
 
2.0%
68세 1
 
2.0%
69세 1
 
2.0%
70세 1
 
2.0%
71세 1
 
2.0%
Other values (39) 39
79.6%
2023-12-12T18:49:11.061460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49
32.0%
4 15
 
9.8%
5 15
 
9.8%
6 14
 
9.2%
7 14
 
9.2%
8 12
 
7.8%
3 8
 
5.2%
9 5
 
3.3%
0 5
 
3.3%
1 5
 
3.3%
Other values (5) 11
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98
64.1%
Other Letter 55
35.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 15
15.3%
5 15
15.3%
6 14
14.3%
7 14
14.3%
8 12
12.2%
3 8
8.2%
9 5
 
5.1%
0 5
 
5.1%
1 5
 
5.1%
2 5
 
5.1%
Other Letter
ValueCountFrequency (%)
49
89.1%
2
 
3.6%
2
 
3.6%
1
 
1.8%
1
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 98
64.1%
Hangul 55
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
4 15
15.3%
5 15
15.3%
6 14
14.3%
7 14
14.3%
8 12
12.2%
3 8
8.2%
9 5
 
5.1%
0 5
 
5.1%
1 5
 
5.1%
2 5
 
5.1%
Hangul
ValueCountFrequency (%)
49
89.1%
2
 
3.6%
2
 
3.6%
1
 
1.8%
1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98
64.1%
Hangul 55
35.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
49
89.1%
2
 
3.6%
2
 
3.6%
1
 
1.8%
1
 
1.8%
ASCII
ValueCountFrequency (%)
4 15
15.3%
5 15
15.3%
6 14
14.3%
7 14
14.3%
8 12
12.2%
3 8
8.2%
9 5
 
5.1%
0 5
 
5.1%
1 5
 
5.1%
2 5
 
5.1%

총계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12821.429
Minimum113
Maximum36608
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T18:49:11.475434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum113
5-th percentile284.6
Q11713
median10706
Q319412
95-th percentile32245.4
Maximum36608
Range36495
Interquartile range (IQR)17699

Descriptive statistics

Standard deviation11562.205
Coefficient of variation (CV)0.9017876
Kurtosis-1.0101078
Mean12821.429
Median Absolute Deviation (MAD)8993
Skewness0.55888946
Sum628250
Variance1.3368459 × 108
MonotonicityNot monotonic
2023-12-12T18:49:11.612830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
271 1
 
2.0%
18902 1
 
2.0%
30442 1
 
2.0%
32009 1
 
2.0%
30202 1
 
2.0%
30809 1
 
2.0%
25626 1
 
2.0%
22710 1
 
2.0%
25025 1
 
2.0%
18392 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
113 1
2.0%
209 1
2.0%
271 1
2.0%
305 1
2.0%
400 1
2.0%
494 1
2.0%
550 1
2.0%
577 1
2.0%
680 1
2.0%
811 1
2.0%
ValueCountFrequency (%)
36608 1
2.0%
33736 1
2.0%
32403 1
2.0%
32009 1
2.0%
31563 1
2.0%
30809 1
2.0%
30442 1
2.0%
30202 1
2.0%
26786 1
2.0%
25626 1
2.0%

총계(남)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8773.2041
Minimum34
Maximum23872
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T18:49:11.750132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile100.6
Q11122
median6408
Q313844
95-th percentile22819.4
Maximum23872
Range23838
Interquartile range (IQR)12722

Descriptive statistics

Standard deviation8261.3491
Coefficient of variation (CV)0.94165701
Kurtosis-1.1348392
Mean8773.2041
Median Absolute Deviation (MAD)6207
Skewness0.56712731
Sum429887
Variance68249890
MonotonicityNot monotonic
2023-12-12T18:49:11.893655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
134 1
 
2.0%
13707 1
 
2.0%
22098 1
 
2.0%
23872 1
 
2.0%
22825 1
 
2.0%
22811 1
 
2.0%
19039 1
 
2.0%
16335 1
 
2.0%
18008 1
 
2.0%
13050 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
34 1
2.0%
54 1
2.0%
85 1
2.0%
124 1
2.0%
134 1
2.0%
172 1
2.0%
198 1
2.0%
201 1
2.0%
292 1
2.0%
422 1
2.0%
ValueCountFrequency (%)
23872 1
2.0%
23005 1
2.0%
22825 1
2.0%
22811 1
2.0%
22406 1
2.0%
22098 1
2.0%
21984 1
2.0%
21462 1
2.0%
19039 1
2.0%
18264 1
2.0%

총계(여)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4048.2245
Minimum79
Maximum15146
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T18:49:12.029119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum79
5-th percentile181
Q1591
median3874
Q35831
95-th percentile9829.8
Maximum15146
Range15067
Interquartile range (IQR)5240

Descriptive statistics

Standard deviation3477.9368
Coefficient of variation (CV)0.85912646
Kurtosis0.65019294
Mean4048.2245
Median Absolute Deviation (MAD)2935
Skewness0.85040547
Sum198363
Variance12096044
MonotonicityNot monotonic
2023-12-12T18:49:12.171542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
137 1
 
2.0%
5195 1
 
2.0%
8344 1
 
2.0%
8137 1
 
2.0%
7377 1
 
2.0%
7998 1
 
2.0%
6587 1
 
2.0%
6375 1
 
2.0%
7017 1
 
2.0%
5342 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
79 1
2.0%
137 1
2.0%
155 1
2.0%
220 1
2.0%
276 1
2.0%
322 1
2.0%
349 1
2.0%
379 1
2.0%
388 1
2.0%
389 1
2.0%
ValueCountFrequency (%)
15146 1
2.0%
10731 1
2.0%
9997 1
2.0%
9579 1
2.0%
8522 1
2.0%
8344 1
2.0%
8137 1
2.0%
7998 1
2.0%
7377 1
2.0%
7017 1
2.0%

퇴직연금(계)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11143.061
Minimum104
Maximum32004
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T18:49:12.312618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum104
5-th percentile216.8
Q11522
median8525
Q316919
95-th percentile30099.2
Maximum32004
Range31900
Interquartile range (IQR)15397

Descriptive statistics

Standard deviation10453.178
Coefficient of variation (CV)0.93808854
Kurtosis-0.88973805
Mean11143.061
Median Absolute Deviation (MAD)7656
Skewness0.67710341
Sum546010
Variance1.0926893 × 108
MonotonicityNot monotonic
2023-12-12T18:49:12.452940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
139 1
 
2.0%
15925 1
 
2.0%
28669 1
 
2.0%
30056 1
 
2.0%
28256 1
 
2.0%
28376 1
 
2.0%
23383 1
 
2.0%
20472 1
 
2.0%
22338 1
 
2.0%
16156 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
104 1
2.0%
139 1
2.0%
180 1
2.0%
272 1
2.0%
359 1
2.0%
436 1
2.0%
482 1
2.0%
509 1
2.0%
589 1
2.0%
710 1
2.0%
ValueCountFrequency (%)
32004 1
2.0%
30632 1
2.0%
30128 1
2.0%
30056 1
2.0%
28669 1
2.0%
28376 1
2.0%
28256 1
2.0%
25621 1
2.0%
23383 1
2.0%
22858 1
2.0%

퇴직연금(남)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8632.3469
Minimum32
Maximum23528
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T18:49:12.638423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile67.2
Q11073
median6257
Q313640
95-th percentile22530.8
Maximum23528
Range23496
Interquartile range (IQR)12567

Descriptive statistics

Standard deviation8160.1831
Coefficient of variation (CV)0.94530296
Kurtosis-1.1355711
Mean8632.3469
Median Absolute Deviation (MAD)6073
Skewness0.56725597
Sum422985
Variance66588588
MonotonicityNot monotonic
2023-12-12T18:49:12.785618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
64 1
 
2.0%
13481 1
 
2.0%
21801 1
 
2.0%
23528 1
 
2.0%
22536 1
 
2.0%
22523 1
 
2.0%
18777 1
 
2.0%
16123 1
 
2.0%
17727 1
 
2.0%
12866 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
32 1
2.0%
42 1
2.0%
64 1
2.0%
72 1
2.0%
109 1
2.0%
147 1
2.0%
175 1
2.0%
184 1
2.0%
270 1
2.0%
397 1
2.0%
ValueCountFrequency (%)
23528 1
2.0%
22702 1
2.0%
22536 1
2.0%
22523 1
2.0%
22091 1
2.0%
21801 1
2.0%
21696 1
2.0%
21089 1
2.0%
18777 1
2.0%
18035 1
2.0%

퇴직연금(여)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2510.7143
Minimum72
Maximum9302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T18:49:12.924890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum72
5-th percentile162.8
Q1449
median1207
Q34349
95-th percentile8093.6
Maximum9302
Range9230
Interquartile range (IQR)3900

Descriptive statistics

Standard deviation2620.4898
Coefficient of variation (CV)1.0437228
Kurtosis0.18889698
Mean2510.7143
Median Absolute Deviation (MAD)918
Skewness1.135747
Sum123025
Variance6866966.9
MonotonicityNot monotonic
2023-12-12T18:49:13.070564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
75 1
 
2.0%
2444 1
 
2.0%
6868 1
 
2.0%
6528 1
 
2.0%
5720 1
 
2.0%
5853 1
 
2.0%
4606 1
 
2.0%
4349 1
 
2.0%
4611 1
 
2.0%
3290 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
72 1
2.0%
75 1
2.0%
138 1
2.0%
200 1
2.0%
250 1
2.0%
289 1
2.0%
298 1
2.0%
313 1
2.0%
319 1
2.0%
329 1
2.0%
ValueCountFrequency (%)
9302 1
2.0%
8541 1
2.0%
8432 1
2.0%
7586 1
2.0%
6868 1
2.0%
6528 1
2.0%
5853 1
2.0%
5720 1
2.0%
5183 1
2.0%
4846 1
2.0%

유족연금(계)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1602
Minimum9
Maximum13690
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T18:49:13.204131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile34.2
Q1180
median1245
Q32505
95-th percentile3263.6
Maximum13690
Range13681
Interquartile range (IQR)2325

Descriptive statistics

Standard deviation2123.1297
Coefficient of variation (CV)1.3252995
Kurtosis21.787033
Mean1602
Median Absolute Deviation (MAD)1117
Skewness3.9537893
Sum78498
Variance4507679.8
MonotonicityNot monotonic
2023-12-12T18:49:13.373188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
128 1
 
2.0%
2822 1
 
2.0%
1585 1
 
2.0%
1741 1
 
2.0%
1735 1
 
2.0%
2227 1
 
2.0%
2075 1
 
2.0%
2104 1
 
2.0%
2505 1
 
2.0%
2112 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
9 1
2.0%
29 1
2.0%
31 1
2.0%
39 1
2.0%
57 1
2.0%
66 1
2.0%
68 1
2.0%
88 1
2.0%
96 1
2.0%
109 1
2.0%
ValueCountFrequency (%)
13690 1
2.0%
3818 1
2.0%
3310 1
2.0%
3194 1
2.0%
3109 1
2.0%
3103 1
2.0%
2905 1
2.0%
2822 1
2.0%
2777 1
2.0%
2715 1
2.0%

유족연금(남)
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.285714
Minimum2
Maximum317
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T18:49:13.537100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile12.4
Q139
median64
Q385
95-th percentile126.6
Maximum317
Range315
Interquartile range (IQR)46

Descriptive statistics

Standard deviation49.128234
Coefficient of variation (CV)0.71945112
Kurtosis12.902837
Mean68.285714
Median Absolute Deviation (MAD)23
Skewness2.7293979
Sum3346
Variance2413.5833
MonotonicityNot monotonic
2023-12-12T18:49:13.692356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
64 3
 
6.1%
68 2
 
4.1%
20 2
 
4.1%
76 2
 
4.1%
62 2
 
4.1%
123 2
 
4.1%
65 1
 
2.0%
59 1
 
2.0%
139 1
 
2.0%
87 1
 
2.0%
Other values (32) 32
65.3%
ValueCountFrequency (%)
2 1
2.0%
11 1
2.0%
12 1
2.0%
13 1
2.0%
17 1
2.0%
20 2
4.1%
21 1
2.0%
24 1
2.0%
29 1
2.0%
33 1
2.0%
ValueCountFrequency (%)
317 1
2.0%
139 1
2.0%
129 1
2.0%
123 2
4.1%
108 1
2.0%
105 1
2.0%
100 1
2.0%
97 1
2.0%
89 1
2.0%
88 1
2.0%

유족연금(여)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1533.7143
Minimum7
Maximum13373
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T18:49:13.841626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile22.4
Q1141
median1137
Q32400
95-th percentile3205.2
Maximum13373
Range13366
Interquartile range (IQR)2259

Descriptive statistics

Standard deviation2084.0535
Coefficient of variation (CV)1.3588277
Kurtosis21.571504
Mean1533.7143
Median Absolute Deviation (MAD)1061
Skewness3.9346787
Sum75152
Variance4343278.8
MonotonicityNot monotonic
2023-12-12T18:49:14.081485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
60 1
 
2.0%
2746 1
 
2.0%
1462 1
 
2.0%
1602 1
 
2.0%
1648 1
 
2.0%
2139 1
 
2.0%
1975 1
 
2.0%
2020 1
 
2.0%
2400 1
 
2.0%
2048 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
7 1
2.0%
17 1
2.0%
20 1
2.0%
26 1
2.0%
33 1
2.0%
45 1
2.0%
51 1
2.0%
60 1
2.0%
68 1
2.0%
76 1
2.0%
ValueCountFrequency (%)
13373 1
2.0%
3756 1
2.0%
3252 1
2.0%
3135 1
2.0%
3035 1
2.0%
3020 1
2.0%
2840 1
2.0%
2746 1
2.0%
2729 1
2.0%
2664 1
2.0%

장해연금(계)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.367347
Minimum0
Maximum212
Zeros3
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T18:49:14.249478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q119
median46
Q3134
95-th percentile205.6
Maximum212
Range212
Interquartile range (IQR)115

Descriptive statistics

Standard deviation71.590762
Coefficient of variation (CV)0.93745253
Kurtosis-1.021177
Mean76.367347
Median Absolute Deviation (MAD)43
Skewness0.6575362
Sum3742
Variance5125.2372
MonotonicityNot monotonic
2023-12-12T18:49:14.392286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
2 3
 
6.1%
0 3
 
6.1%
60 2
 
4.1%
134 2
 
4.1%
3 2
 
4.1%
19 2
 
4.1%
130 2
 
4.1%
45 2
 
4.1%
4 1
 
2.0%
182 1
 
2.0%
Other values (29) 29
59.2%
ValueCountFrequency (%)
0 3
6.1%
1 1
 
2.0%
2 3
6.1%
3 2
4.1%
4 1
 
2.0%
5 1
 
2.0%
11 1
 
2.0%
19 2
4.1%
20 1
 
2.0%
25 1
 
2.0%
ValueCountFrequency (%)
212 1
2.0%
211 1
2.0%
206 1
2.0%
205 1
2.0%
190 1
2.0%
189 1
2.0%
188 1
2.0%
182 1
2.0%
168 1
2.0%
155 1
2.0%

장해연금(남)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.571429
Minimum0
Maximum205
Zeros3
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T18:49:14.516257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q116
median43
Q3128
95-th percentile196.8
Maximum205
Range205
Interquartile range (IQR)112

Descriptive statistics

Standard deviation68.38494
Coefficient of variation (CV)0.94231216
Kurtosis-1.0237914
Mean72.571429
Median Absolute Deviation (MAD)41
Skewness0.65483676
Sum3556
Variance4676.5
MonotonicityNot monotonic
2023-12-12T18:49:14.650964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
2 5
 
10.2%
0 3
 
6.1%
174 2
 
4.1%
128 2
 
4.1%
56 2
 
4.1%
202 1
 
2.0%
162 1
 
2.0%
176 1
 
2.0%
120 1
 
2.0%
125 1
 
2.0%
Other values (30) 30
61.2%
ValueCountFrequency (%)
0 3
6.1%
1 1
 
2.0%
2 5
10.2%
3 1
 
2.0%
5 1
 
2.0%
10 1
 
2.0%
16 1
 
2.0%
17 1
 
2.0%
19 1
 
2.0%
23 1
 
2.0%
ValueCountFrequency (%)
205 1
2.0%
202 1
2.0%
200 1
2.0%
192 1
2.0%
180 1
2.0%
176 1
2.0%
174 2
4.1%
162 1
2.0%
150 1
2.0%
132 1
2.0%

장해연금(여)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7959184
Minimum0
Maximum15
Zeros12
Zeros (%)24.5%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T18:49:14.795704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile12.6
Maximum15
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.9103473
Coefficient of variation (CV)1.0301453
Kurtosis1.2165031
Mean3.7959184
Median Absolute Deviation (MAD)3
Skewness1.2625924
Sum186
Variance15.290816
MonotonicityNot monotonic
2023-12-12T18:49:14.914774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 12
24.5%
6 7
14.3%
1 6
12.2%
4 6
12.2%
2 5
10.2%
3 3
 
6.1%
5 3
 
6.1%
12 1
 
2.0%
10 1
 
2.0%
15 1
 
2.0%
Other values (4) 4
 
8.2%
ValueCountFrequency (%)
0 12
24.5%
1 6
12.2%
2 5
10.2%
3 3
 
6.1%
4 6
12.2%
5 3
 
6.1%
6 7
14.3%
7 1
 
2.0%
9 1
 
2.0%
10 1
 
2.0%
ValueCountFrequency (%)
15 1
 
2.0%
14 1
 
2.0%
13 1
 
2.0%
12 1
 
2.0%
10 1
 
2.0%
9 1
 
2.0%
7 1
 
2.0%
6 7
14.3%
5 3
6.1%
4 6
12.2%

Interactions

2023-12-12T18:49:09.159362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:56.262802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:57.397498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:58.483064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:00.101923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:01.173811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:02.197217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:03.246090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:04.511415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:05.987027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:07.163552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:08.119346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:09.244920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:56.348276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:57.487278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:58.580839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:00.205739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:01.265058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:02.295562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:03.343609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:04.619363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:06.091940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:07.250234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:08.197251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:09.328133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:56.448662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:57.567427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:58.658205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:00.288447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:01.345795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:02.379327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:03.457024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:04.696988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:06.178794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:07.331708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:08.286453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:09.413446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:56.547839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:57.649353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:58.751490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:00.399907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:01.451592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:02.468991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:03.572886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:04.787749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:06.284983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:07.414945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:08.379483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:09.495771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:56.664653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:57.747828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:58.858540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:00.493818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:01.535423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:02.566778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:03.735655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:04.872086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:06.394256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:07.502620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:08.459757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:09.590003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:56.742826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:57.840135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:59.327569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:00.574581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:01.614359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:02.642530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:03.832189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:04.951130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:06.493608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:07.587528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:08.531574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:09.670123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:56.818861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:57.921765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:59.433268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:00.649007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:01.692631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:02.713083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:03.923765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:05.023329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:06.593147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:07.666670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:08.609378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:09.765557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:56.912276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:58.010353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:59.543383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:00.739628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:01.778842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:02.802327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:04.047084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:05.455023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:06.699200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:07.746607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:08.717198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:09.858134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:57.013680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:58.105092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:59.652014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:00.829373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:01.852318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:02.885412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:04.151093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:05.541721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:06.792299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:07.822182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:08.819530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:09.940771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:57.121067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:58.212905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:59.747870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:00.929903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:01.955350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:02.963729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:04.236836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:05.657872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:06.897037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:07.906359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:08.916126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:10.030626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:57.208071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:58.300981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:59.854783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:01.004173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:02.031505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:03.049391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:04.315539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:05.747640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:06.978788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:07.978803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:08.991924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:10.156041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:57.295617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:58.389273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:48:59.968526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:01.085025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:02.106213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:03.147655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:04.396630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:05.863896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:07.067231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:08.045000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:49:09.075466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:49:15.024947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분총계총계(남)총계(여)퇴직연금(계)퇴직연금(남)퇴직연금(여)유족연금(계)유족연금(남)유족연금(여)장해연금(계)장해연금(남)장해연금(여)
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
총계1.0001.0000.9920.9260.9700.9890.9540.8200.7580.8200.8250.8370.722
총계(남)1.0000.9921.0000.9020.9671.0000.9270.9110.8420.9110.8160.8160.608
총계(여)1.0000.9260.9021.0000.8930.8850.8470.8730.8990.8730.8380.8600.716
퇴직연금(계)1.0000.9700.9670.8931.0000.9600.9630.8360.7540.8360.8290.8440.625
퇴직연금(남)1.0000.9891.0000.8850.9601.0000.9240.9110.8400.9110.8020.8060.584
퇴직연금(여)1.0000.9540.9270.8470.9630.9241.0000.5780.6880.5780.8140.7960.787
유족연금(계)1.0000.8200.9110.8730.8360.9110.5781.0000.8051.0000.6910.6880.471
유족연금(남)1.0000.7580.8420.8990.7540.8400.6880.8051.0000.8050.6900.7350.629
유족연금(여)1.0000.8200.9110.8730.8360.9110.5781.0000.8051.0000.6910.6880.471
장해연금(계)1.0000.8250.8160.8380.8290.8020.8140.6910.6900.6911.0000.9990.631
장해연금(남)1.0000.8370.8160.8600.8440.8060.7960.6880.7350.6880.9991.0000.626
장해연금(여)1.0000.7220.6080.7160.6250.5840.7870.4710.6290.4710.6310.6261.000
2023-12-12T18:49:15.200194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총계총계(남)총계(여)퇴직연금(계)퇴직연금(남)퇴직연금(여)유족연금(계)유족연금(남)유족연금(여)장해연금(계)장해연금(남)장해연금(여)
총계1.0000.9930.9810.9930.9930.9040.7370.9020.7370.9510.9510.792
총계(남)0.9931.0000.9670.9901.0000.8990.7390.8970.7400.9620.9610.795
총계(여)0.9810.9671.0000.9840.9670.9390.6880.9150.6900.9310.9330.797
퇴직연금(계)0.9930.9900.9841.0000.9910.9380.6940.9040.6950.9680.9690.818
퇴직연금(남)0.9931.0000.9670.9911.0000.8990.7380.8910.7390.9610.9610.792
퇴직연금(여)0.9040.8990.9390.9380.8991.0000.5170.8860.5180.9380.9400.878
유족연금(계)0.7370.7390.6880.6940.7380.5171.0000.6120.9990.6450.6470.347
유족연금(남)0.9020.8970.9150.9040.8910.8860.6121.0000.6030.8970.8910.834
유족연금(여)0.7370.7400.6900.6950.7390.5180.9990.6031.0000.6430.6450.341
장해연금(계)0.9510.9620.9310.9680.9610.9380.6450.8970.6431.0000.9990.868
장해연금(남)0.9510.9610.9330.9690.9610.9400.6470.8910.6450.9991.0000.857
장해연금(여)0.7920.7950.7970.8180.7920.8780.3470.8340.3410.8680.8571.000

Missing values

2023-12-12T18:49:10.289427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:49:10.458696image/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

구분총계총계(남)총계(여)퇴직연금(계)퇴직연금(남)퇴직연금(여)유족연금(계)유족연금(남)유족연금(여)장해연금(계)장해연금(남)장해연금(여)
038세미만27113413713964751286860422
138세이상11334791043272927000
239세2095415518042138291217000
340세3058522027272200311120220
441세400124276359109250391326220
542세494172322436147289572433110
643세577198379509175334662145220
744세550201349482184298681751000
845세680292388589270319882068321
946세811422389710397313962076550
구분총계총계(남)총계(여)퇴직연금(계)퇴직연금(남)퇴직연금(여)유족연금(계)유족연금(남)유족연금(여)장해연금(계)장해연금(남)장해연금(여)
3976세155701139041801279411201159326617725841151123
4077세12731923035011025491011153241064234667652
4178세12958908438741016289551207271551266481783
4279세1239784623935943883431095290565284054540
4380세14844999748471096298721090381862375664631
4481세118727725414785257631894331058325237361
4582세101126316379669766215761310368303533330
4683세103716620375171426526616319459313535350
4784세88635578328560615507554277748272925232
4885세이상36608214621514622858210891769136903171337360564