Overview

Dataset statistics

Number of variables20
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.7 KiB
Average record size in memory181.7 B

Variable types

Text1
Numeric11
Categorical8

Dataset

Description연령별 사유별(사망, 청산, 유족승계, 재임용후 합산, 퇴직처분 취소 등) 연금수급 종결자 현황에 대한 데이터입니다. 38세 미만부터 시작됩니다.
URLhttps://www.data.go.kr/data/15054096/fileData.do

Alerts

유족연금_청산 has constant value ""Constant
장해연금_청산 has constant value ""Constant
합계 is highly overall correlated with 퇴직연금수급자(계) and 8 other fieldsHigh correlation
퇴직연금수급자(계) is highly overall correlated with 합계 and 8 other fieldsHigh correlation
사망(유족없음) is highly overall correlated with 합계 and 8 other fieldsHigh correlation
퇴직연금_유족승계 is highly overall correlated with 합계 and 8 other fieldsHigh correlation
퇴직연금_재임용후합산신청 is highly overall correlated with 퇴직연금_기타High correlation
퇴직연금_기타 is highly overall correlated with 합계 and 7 other fieldsHigh correlation
유족연금수급자(계) is highly overall correlated with 합계 and 7 other fieldsHigh correlation
유족연금_사망 is highly overall correlated with 합계 and 8 other fieldsHigh correlation
유족연금_기타 is highly overall correlated with 유족연금_직계비속19세도달High correlation
장해연금수급자(계) is highly overall correlated with 합계 and 6 other fieldsHigh correlation
장해연금_유족승계 is highly overall correlated with 합계 and 7 other fieldsHigh correlation
유족연금_직계비속19세도달 is highly overall correlated with 유족연금_기타High correlation
장해연금_사망 is highly overall correlated with 합계 and 6 other fieldsHigh correlation
퇴직연금_청산 is highly imbalanced (69.2%)Imbalance
유족연금_배우자의재혼 is highly imbalanced (59.2%)Imbalance
유족연금_직계비속19세도달 is highly imbalanced (85.6%)Imbalance
장해연금_사망 is highly imbalanced (65.4%)Imbalance
구분 has unique valuesUnique
사망(유족없음) has 18 (36.7%) zerosZeros
퇴직연금_유족승계 has 10 (20.4%) zerosZeros
퇴직연금_재임용후합산신청 has 11 (22.4%) zerosZeros
퇴직연금_기타 has 19 (38.8%) zerosZeros
유족연금_사망 has 16 (32.7%) zerosZeros
유족연금_기타 has 10 (20.4%) zerosZeros
장해연금수급자(계) has 22 (44.9%) zerosZeros
장해연금_유족승계 has 27 (55.1%) zerosZeros

Reproduction

Analysis started2023-12-12 19:35:11.132613
Analysis finished2023-12-12 19:35:27.175734
Duration16.04 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
2023-12-13T04:35:27.366935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.0816327
Min length3

Characters and Unicode

Total characters151
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 row38세
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%
2023-12-13T04:35:27.851267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 49
32.5%
4 15
 
9.9%
5 15
 
9.9%
6 14
 
9.3%
7 14
 
9.3%
8 12
 
7.9%
3 8
 
5.3%
9 5
 
3.3%
0 5
 
3.3%
1 5
 
3.3%
Other values (5) 9
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98
64.9%
Other Letter 53
35.1%

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 (%)
세 49
92.5%
미 1
 
1.9%
만 1
 
1.9%
이 1
 
1.9%
상 1
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 98
64.9%
Hangul 53
35.1%

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 (%)
세 49
92.5%
미 1
 
1.9%
만 1
 
1.9%
이 1
 
1.9%
상 1
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98
64.9%
Hangul 53
35.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 49
92.5%
미 1
 
1.9%
만 1
 
1.9%
이 1
 
1.9%
상 1
 
1.9%
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%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean224.20408
Minimum5
Maximum4062
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T04:35:28.036011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6.4
Q121
median117
Q3235
95-th percentile483.8
Maximum4062
Range4057
Interquartile range (IQR)214

Descriptive statistics

Standard deviation577.12033
Coefficient of variation (CV)2.5740848
Kurtosis42.981537
Mean224.20408
Median Absolute Deviation (MAD)98
Skewness6.3746282
Sum10986
Variance333067.87
MonotonicityNot monotonic
2023-12-13T04:35:28.212860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
70 2
 
4.1%
6 2
 
4.1%
165 2
 
4.1%
50 2
 
4.1%
257 2
 
4.1%
289 1
 
2.0%
186 1
 
2.0%
187 1
 
2.0%
167 1
 
2.0%
235 1
 
2.0%
Other values (34) 34
69.4%
ValueCountFrequency (%)
5 1
2.0%
6 2
4.1%
7 1
2.0%
8 1
2.0%
13 1
2.0%
15 1
2.0%
16 1
2.0%
17 1
2.0%
18 1
2.0%
19 1
2.0%
ValueCountFrequency (%)
4062 1
2.0%
518 1
2.0%
507 1
2.0%
449 1
2.0%
417 1
2.0%
385 1
2.0%
314 1
2.0%
310 1
2.0%
289 1
2.0%
259 1
2.0%

퇴직연금수급자(계)
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)87.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.38776
Minimum3
Maximum2890
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T04:35:28.405097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q116
median111
Q3219
95-th percentile387.6
Maximum2890
Range2887
Interquartile range (IQR)203

Descriptive statistics

Standard deviation412.10045
Coefficient of variation (CV)2.2845257
Kurtosis40.94676
Mean180.38776
Median Absolute Deviation (MAD)96
Skewness6.1563305
Sum8839
Variance169826.78
MonotonicityNot monotonic
2023-12-13T04:35:28.599375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
15 3
 
6.1%
153 2
 
4.1%
5 2
 
4.1%
11 2
 
4.1%
44 2
 
4.1%
237 1
 
2.0%
157 1
 
2.0%
219 1
 
2.0%
154 1
 
2.0%
159 1
 
2.0%
Other values (33) 33
67.3%
ValueCountFrequency (%)
3 1
 
2.0%
4 1
 
2.0%
5 2
4.1%
6 1
 
2.0%
8 1
 
2.0%
11 2
4.1%
12 1
 
2.0%
15 3
6.1%
16 1
 
2.0%
17 1
 
2.0%
ValueCountFrequency (%)
2890 1
2.0%
411 1
2.0%
410 1
2.0%
354 1
2.0%
346 1
2.0%
293 1
2.0%
277 1
2.0%
259 1
2.0%
255 1
2.0%
237 1
2.0%

사망(유족없음)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.265306
Minimum0
Maximum990
Zeros18
Zeros (%)36.7%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T04:35:28.781148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q319
95-th percentile67
Maximum990
Range990
Interquartile range (IQR)19

Descriptive statistics

Standard deviation140.87092
Coefficient of variation (CV)4.2347699
Kurtosis47.033965
Mean33.265306
Median Absolute Deviation (MAD)4
Skewness6.7981886
Sum1630
Variance19844.616
MonotonicityNot monotonic
2023-12-13T04:35:28.931916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 18
36.7%
1 3
 
6.1%
19 3
 
6.1%
2 3
 
6.1%
6 2
 
4.1%
13 2
 
4.1%
17 2
 
4.1%
24 2
 
4.1%
26 2
 
4.1%
49 1
 
2.0%
Other values (11) 11
22.4%
ValueCountFrequency (%)
0 18
36.7%
1 3
 
6.1%
2 3
 
6.1%
4 1
 
2.0%
6 2
 
4.1%
9 1
 
2.0%
11 1
 
2.0%
13 2
 
4.1%
15 1
 
2.0%
17 2
 
4.1%
ValueCountFrequency (%)
990 1
2.0%
84 1
2.0%
79 1
2.0%
49 1
2.0%
48 1
2.0%
42 1
2.0%
32 1
2.0%
29 1
2.0%
26 2
4.1%
24 2
4.1%

퇴직연금_청산
Categorical

IMBALANCE 

Distinct4
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
0
44 
1
 
3
2
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)4.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 44
89.8%
1 3
 
6.1%
2 1
 
2.0%
3 1
 
2.0%

Length

2023-12-13T04:35:29.058077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:35:29.161889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 44
89.8%
1 3
 
6.1%
2 1
 
2.0%
3 1
 
2.0%

퇴직연금_유족승계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.38776
Minimum0
Maximum1900
Zeros10
Zeros (%)20.4%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T04:35:29.275186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median79
Q3193
95-th percentile318
Maximum1900
Range1900
Interquartile range (IQR)190

Descriptive statistics

Standard deviation278.12803
Coefficient of variation (CV)2.0695935
Kurtosis35.189945
Mean134.38776
Median Absolute Deviation (MAD)78
Skewness5.5363558
Sum6585
Variance77355.201
MonotonicityNot monotonic
2023-12-13T04:35:29.413658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 10
20.4%
79 3
 
6.1%
3 3
 
6.1%
4 2
 
4.1%
136 2
 
4.1%
22 2
 
4.1%
233 1
 
2.0%
209 1
 
2.0%
211 1
 
2.0%
248 1
 
2.0%
Other values (23) 23
46.9%
ValueCountFrequency (%)
0 10
20.4%
1 1
 
2.0%
3 3
 
6.1%
4 2
 
4.1%
12 1
 
2.0%
14 1
 
2.0%
15 1
 
2.0%
19 1
 
2.0%
22 2
 
4.1%
31 1
 
2.0%
ValueCountFrequency (%)
1900 1
2.0%
332 1
2.0%
326 1
2.0%
306 1
2.0%
297 1
2.0%
251 1
2.0%
248 1
2.0%
233 1
2.0%
223 1
2.0%
211 1
2.0%

퇴직연금_재임용후합산신청
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3877551
Minimum0
Maximum25
Zeros11
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T04:35:29.549025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile19.6
Maximum25
Range25
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.1907738
Coefficient of variation (CV)1.4109205
Kurtosis3.1030934
Mean4.3877551
Median Absolute Deviation (MAD)2
Skewness1.9652466
Sum215
Variance38.32568
MonotonicityNot monotonic
2023-12-13T04:35:29.661627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 12
24.5%
0 11
22.4%
2 5
10.2%
3 4
 
8.2%
5 3
 
6.1%
4 3
 
6.1%
8 2
 
4.1%
20 2
 
4.1%
6 1
 
2.0%
7 1
 
2.0%
Other values (5) 5
10.2%
ValueCountFrequency (%)
0 11
22.4%
1 12
24.5%
2 5
10.2%
3 4
 
8.2%
4 3
 
6.1%
5 3
 
6.1%
6 1
 
2.0%
7 1
 
2.0%
8 2
 
4.1%
10 1
 
2.0%
ValueCountFrequency (%)
25 1
 
2.0%
20 2
4.1%
19 1
 
2.0%
18 1
 
2.0%
13 1
 
2.0%
10 1
 
2.0%
8 2
4.1%
7 1
 
2.0%
6 1
 
2.0%
5 3
6.1%

퇴직연금_퇴직처분취소
Categorical

Distinct3
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
0
40 
1
5 
2
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 40
81.6%
1 5
 
10.2%
2 4
 
8.2%

Length

2023-12-13T04:35:29.770197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:35:29.847838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 40
81.6%
1 5
 
10.2%
2 4
 
8.2%

퇴직연금_기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9183673
Minimum0
Maximum31
Zeros19
Zeros (%)38.8%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T04:35:29.941086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q314
95-th percentile26.2
Maximum31
Range31
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.5412192
Coefficient of variation (CV)1.2049478
Kurtosis-0.42053821
Mean7.9183673
Median Absolute Deviation (MAD)3
Skewness0.95058434
Sum388
Variance91.034864
MonotonicityNot monotonic
2023-12-13T04:35:30.065167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 19
38.8%
9 3
 
6.1%
3 3
 
6.1%
4 2
 
4.1%
1 2
 
4.1%
2 2
 
4.1%
13 2
 
4.1%
14 2
 
4.1%
17 2
 
4.1%
7 1
 
2.0%
Other values (11) 11
22.4%
ValueCountFrequency (%)
0 19
38.8%
1 2
 
4.1%
2 2
 
4.1%
3 3
 
6.1%
4 2
 
4.1%
7 1
 
2.0%
9 3
 
6.1%
10 1
 
2.0%
12 1
 
2.0%
13 2
 
4.1%
ValueCountFrequency (%)
31 1
2.0%
28 1
2.0%
27 1
2.0%
25 1
2.0%
24 1
2.0%
23 1
2.0%
22 1
2.0%
21 1
2.0%
20 1
2.0%
17 2
4.1%

유족연금수급자(계)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.204082
Minimum1
Maximum1166
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T04:35:30.191496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q321
95-th percentile94
Maximum1166
Range1165
Interquartile range (IQR)17

Descriptive statistics

Standard deviation166.23665
Coefficient of variation (CV)3.9388762
Kurtosis46.11368
Mean42.204082
Median Absolute Deviation (MAD)4
Skewness6.7046367
Sum2068
Variance27634.624
MonotonicityNot monotonic
2023-12-13T04:35:30.387944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
5 7
14.3%
2 5
 
10.2%
4 5
 
10.2%
1 5
 
10.2%
8 3
 
6.1%
9 2
 
4.1%
3 2
 
4.1%
6 2
 
4.1%
29 2
 
4.1%
91 1
 
2.0%
Other values (15) 15
30.6%
ValueCountFrequency (%)
1 5
10.2%
2 5
10.2%
3 2
 
4.1%
4 5
10.2%
5 7
14.3%
6 2
 
4.1%
7 1
 
2.0%
8 3
6.1%
9 2
 
4.1%
11 1
 
2.0%
ValueCountFrequency (%)
1166 1
2.0%
107 1
2.0%
96 1
2.0%
91 1
2.0%
90 1
2.0%
71 1
2.0%
62 1
2.0%
52 1
2.0%
34 1
2.0%
29 2
4.1%

유족연금_사망
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)44.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.816327
Minimum0
Maximum1160
Zeros16
Zeros (%)32.7%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T04:35:30.552814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q320
95-th percentile91.8
Maximum1160
Range1160
Interquartile range (IQR)20

Descriptive statistics

Standard deviation165.77089
Coefficient of variation (CV)4.2706485
Kurtosis46.208142
Mean38.816327
Median Absolute Deviation (MAD)2
Skewness6.7149662
Sum1902
Variance27479.986
MonotonicityNot monotonic
2023-12-13T04:35:30.681684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 16
32.7%
1 5
 
10.2%
2 4
 
8.2%
8 4
 
8.2%
4 2
 
4.1%
23 2
 
4.1%
33 1
 
2.0%
1160 1
 
2.0%
106 1
 
2.0%
93 1
 
2.0%
Other values (12) 12
24.5%
ValueCountFrequency (%)
0 16
32.7%
1 5
 
10.2%
2 4
 
8.2%
4 2
 
4.1%
5 1
 
2.0%
6 1
 
2.0%
8 4
 
8.2%
9 1
 
2.0%
11 1
 
2.0%
15 1
 
2.0%
ValueCountFrequency (%)
1160 1
2.0%
106 1
2.0%
93 1
2.0%
90 1
2.0%
86 1
2.0%
69 1
2.0%
50 1
2.0%
33 1
2.0%
29 1
2.0%
23 2
4.1%

유족연금_청산
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
0
49 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 49
100.0%

Length

2023-12-13T04:35:31.124026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:35:31.241731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

유족연금_배우자의재혼
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
0
45 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 45
91.8%
1 4
 
8.2%

Length

2023-12-13T04:35:31.346759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:35:31.459103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 45
91.8%
1 4
 
8.2%

유족연금_직계비속19세도달
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
0
48 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row2
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 48
98.0%
2 1
 
2.0%

Length

2023-12-13T04:35:31.570705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:35:31.685480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 48
98.0%
2 1
 
2.0%

유족연금_기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2653061
Minimum0
Maximum60
Zeros10
Zeros (%)20.4%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T04:35:31.777629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5.6
Maximum60
Range60
Interquartile range (IQR)2

Descriptive statistics

Standard deviation8.4626028
Coefficient of variation (CV)2.5916721
Kurtosis44.503371
Mean3.2653061
Median Absolute Deviation (MAD)1
Skewness6.5299932
Sum160
Variance71.615646
MonotonicityNot monotonic
2023-12-13T04:35:31.910051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 12
24.5%
0 10
20.4%
2 9
18.4%
3 7
14.3%
4 4
 
8.2%
5 4
 
8.2%
60 1
 
2.0%
6 1
 
2.0%
7 1
 
2.0%
ValueCountFrequency (%)
0 10
20.4%
1 12
24.5%
2 9
18.4%
3 7
14.3%
4 4
 
8.2%
5 4
 
8.2%
6 1
 
2.0%
7 1
 
2.0%
60 1
 
2.0%
ValueCountFrequency (%)
60 1
 
2.0%
7 1
 
2.0%
6 1
 
2.0%
5 4
 
8.2%
4 4
 
8.2%
3 7
14.3%
2 9
18.4%
1 12
24.5%
0 10
20.4%

장해연금수급자(계)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6122449
Minimum0
Maximum7
Zeros22
Zeros (%)44.9%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T04:35:32.037347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5.6
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.039366
Coefficient of variation (CV)1.2649232
Kurtosis0.54051863
Mean1.6122449
Median Absolute Deviation (MAD)1
Skewness1.2293833
Sum79
Variance4.1590136
MonotonicityNot monotonic
2023-12-13T04:35:32.147430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 22
44.9%
1 8
 
16.3%
2 7
 
14.3%
4 4
 
8.2%
5 3
 
6.1%
7 2
 
4.1%
3 2
 
4.1%
6 1
 
2.0%
ValueCountFrequency (%)
0 22
44.9%
1 8
 
16.3%
2 7
 
14.3%
3 2
 
4.1%
4 4
 
8.2%
5 3
 
6.1%
6 1
 
2.0%
7 2
 
4.1%
ValueCountFrequency (%)
7 2
 
4.1%
6 1
 
2.0%
5 3
 
6.1%
4 4
 
8.2%
3 2
 
4.1%
2 7
 
14.3%
1 8
 
16.3%
0 22
44.9%

장해연금_사망
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
0
44 
1
 
4
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 44
89.8%
1 4
 
8.2%
2 1
 
2.0%

Length

2023-12-13T04:35:32.286427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:35:32.397799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 44
89.8%
1 4
 
8.2%
2 1
 
2.0%

장해연금_청산
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
0
49 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 49
100.0%

Length

2023-12-13T04:35:32.519660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:35:32.622316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
100.0%

장해연금_유족승계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2244898
Minimum0
Maximum7
Zeros27
Zeros (%)55.1%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T04:35:32.719982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4.6
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7708909
Coefficient of variation (CV)1.4462275
Kurtosis1.3594845
Mean1.2244898
Median Absolute Deviation (MAD)0
Skewness1.4512257
Sum60
Variance3.1360544
MonotonicityNot monotonic
2023-12-13T04:35:32.832350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 27
55.1%
1 7
 
14.3%
2 5
 
10.2%
4 5
 
10.2%
3 2
 
4.1%
5 2
 
4.1%
7 1
 
2.0%
ValueCountFrequency (%)
0 27
55.1%
1 7
 
14.3%
2 5
 
10.2%
3 2
 
4.1%
4 5
 
10.2%
5 2
 
4.1%
7 1
 
2.0%
ValueCountFrequency (%)
7 1
 
2.0%
5 2
 
4.1%
4 5
 
10.2%
3 2
 
4.1%
2 5
 
10.2%
1 7
 
14.3%
0 27
55.1%

장해연금_기타
Categorical

Distinct3
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
0
40 
1
5 
2
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 40
81.6%
1 5
 
10.2%
2 4
 
8.2%

Length

2023-12-13T04:35:32.980625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:35:33.102981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 40
81.6%
1 5
 
10.2%
2 4
 
8.2%

Interactions

2023-12-13T04:35:25.142144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:12.050297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:13.492877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:14.657791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:15.688613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:17.272727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:18.411092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:19.603572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:20.835269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:22.044001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:23.403146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:25.298523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:12.161221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:13.661674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:14.765282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:15.786767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:17.369012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:18.508047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:19.772935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:20.960672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:22.162958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:23.846549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:25.433021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:12.279443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:13.789751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:14.846081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:15.907785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:17.462934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:18.626744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:19.876493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:21.063556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:22.302168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:23.981144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:25.547527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:12.373653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:13.880973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:14.933732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:16.033214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:17.586585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:18.735503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:19.959695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:21.157736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:22.427015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:24.102470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:25.677394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:12.479420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:13.971396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:15.043152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:16.142975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:17.706146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:18.837406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:20.104846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:21.263219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:22.560611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:24.231354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:25.804648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:12.588123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:14.079014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:15.124876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:16.270547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:17.813233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:18.940131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:20.197564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:21.374010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:22.684077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:24.347057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:25.937078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:12.718050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:14.165112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:15.215529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:16.370012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:17.902691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:19.038197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:20.305366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:21.497851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:22.799554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:24.463025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:26.081810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:12.831057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:14.258227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:15.296645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:16.484014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:18.008440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:19.142071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:20.424745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:21.592395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:22.905909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:24.592629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:26.230021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:12.979482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:14.369545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:15.376689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:16.604698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:18.105678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:19.248627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:20.522077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:21.680349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:23.026116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:24.733845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:26.346733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:13.149006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:14.455853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:15.477506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:17.074898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:18.200098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:19.347530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:20.613922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:21.787771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:23.132802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:24.870837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:26.486639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:13.332054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:14.561363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:15.590914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:17.173791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:18.309230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:19.475367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:20.720790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:21.931982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:23.282795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:35:25.000235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:35:33.194653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분합계퇴직연금수급자(계)사망(유족없음)퇴직연금_청산퇴직연금_유족승계퇴직연금_재임용후합산신청퇴직연금_퇴직처분취소퇴직연금_기타유족연금수급자(계)유족연금_사망유족연금_배우자의재혼유족연금_직계비속19세도달유족연금_기타장해연금수급자(계)장해연금_사망장해연금_유족승계장해연금_기타
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
합계1.0001.0000.9981.0000.0000.9700.0000.0000.0001.0001.0000.2810.0000.0000.7640.9380.0000.000
퇴직연금수급자(계)1.0000.9981.0001.0000.0000.9770.0000.0000.0001.0001.0000.2810.0000.0000.7580.9380.0000.000
사망(유족없음)1.0001.0001.0001.0000.0001.0000.0000.0000.0000.6770.6770.2640.0000.0001.0001.0000.2460.000
퇴직연금_청산1.0000.0000.0000.0001.0000.0000.8740.4320.6490.0000.0000.0000.0000.0000.0000.0000.0000.432
퇴직연금_유족승계1.0000.9700.9771.0000.0001.0000.0000.0000.0001.0001.0000.2780.0000.0000.8370.9370.5120.000
퇴직연금_재임용후합산신청1.0000.0000.0000.0000.8740.0001.0000.8130.6320.0000.0000.3680.0000.0000.0000.0000.0000.889
퇴직연금_퇴직처분취소1.0000.0000.0000.0000.4320.0000.8131.0000.6590.0000.0000.0000.0000.0000.1760.0000.0000.644
퇴직연금_기타1.0000.0000.0000.0000.6490.0000.6320.6591.0000.0000.0000.0000.3250.5170.0000.0000.0000.374
유족연금수급자(계)1.0001.0001.0000.6770.0001.0000.0000.0000.0001.0000.6770.2640.0000.0001.0001.0000.2460.000
유족연금_사망1.0001.0001.0000.6770.0001.0000.0000.0000.0000.6771.0000.2640.0000.0001.0001.0000.2460.000
유족연금_배우자의재혼1.0000.2810.2810.2640.0000.2780.3680.0000.0000.2640.2641.0000.0000.0000.5050.2810.0000.000
유족연금_직계비속19세도달1.0000.0000.0000.0000.0000.0000.0000.0000.3250.0000.0000.0001.0001.0000.0000.0000.0000.000
유족연금_기타1.0000.0000.0000.0000.0000.0000.0000.0000.5170.0000.0000.0001.0001.0000.0000.0000.0000.000
장해연금수급자(계)1.0000.7640.7581.0000.0000.8370.0000.1760.0001.0001.0000.5050.0000.0001.0000.8310.8220.462
장해연금_사망1.0000.9380.9381.0000.0000.9370.0000.0000.0001.0001.0000.2810.0000.0000.8311.0000.3490.000
장해연금_유족승계1.0000.0000.0000.2460.0000.5120.0000.0000.0000.2460.2460.0000.0000.0000.8220.3491.0000.233
장해연금_기타1.0000.0000.0000.0000.4320.0000.8890.6440.3740.0000.0000.0000.0000.0000.4620.0000.2331.000
2023-12-13T04:35:33.366596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
퇴직연금_청산장해연금_사망유족연금_배우자의재혼퇴직연금_퇴직처분취소장해연금_기타유족연금_직계비속19세도달
퇴직연금_청산1.0000.0000.0000.4200.4200.000
장해연금_사망0.0001.0000.4490.0000.0000.000
유족연금_배우자의재혼0.0000.4491.0000.0000.0000.000
퇴직연금_퇴직처분취소0.4200.0000.0001.0000.3050.000
장해연금_기타0.4200.0000.0000.3051.0000.000
유족연금_직계비속19세도달0.0000.0000.0000.0000.0001.000
2023-12-13T04:35:33.511609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계퇴직연금수급자(계)사망(유족없음)퇴직연금_유족승계퇴직연금_재임용후합산신청퇴직연금_기타유족연금수급자(계)유족연금_사망유족연금_기타장해연금수급자(계)장해연금_유족승계퇴직연금_청산퇴직연금_퇴직처분취소유족연금_배우자의재혼유족연금_직계비속19세도달장해연금_사망장해연금_기타
합계1.0000.9820.9570.969-0.314-0.6380.8820.948-0.2230.6110.6460.0000.0000.4490.0000.6950.000
퇴직연금수급자(계)0.9821.0000.9660.988-0.298-0.6260.8170.957-0.2750.6100.6440.0000.0000.4500.0000.6960.000
사망(유족없음)0.9570.9661.0000.965-0.369-0.7050.8060.948-0.3110.5800.6460.0000.0000.1690.0000.9890.000
퇴직연금_유족승계0.9690.9880.9651.000-0.308-0.6630.7950.968-0.3180.6220.6520.0000.0000.4460.0000.6920.000
퇴직연금_재임용후합산신청-0.314-0.298-0.369-0.3081.0000.529-0.442-0.352-0.099-0.028-0.2080.4620.4320.3560.0000.0000.330
퇴직연금_기타-0.638-0.626-0.705-0.6630.5291.000-0.638-0.7000.412-0.382-0.5110.4150.4650.0000.2190.0000.214
유족연금수급자(계)0.8820.8170.8060.795-0.442-0.6381.0000.8290.0680.4180.5070.0000.0000.1690.0000.9890.000
유족연금_사망0.9480.9570.9480.968-0.352-0.7000.8291.000-0.3220.5840.6170.0000.0000.1690.0000.9890.000
유족연금_기타-0.223-0.275-0.311-0.318-0.0990.4120.068-0.3221.000-0.437-0.4460.0000.0000.0000.9890.0000.000
장해연금수급자(계)0.6110.6100.5800.622-0.028-0.3820.4180.584-0.4371.0000.9040.0000.0910.3520.0000.7280.309
장해연금_유족승계0.6460.6440.6460.652-0.208-0.5110.5070.617-0.4460.9041.0000.0000.0000.0000.0000.2350.145
퇴직연금_청산0.0000.0000.0000.0000.4620.4150.0000.0000.0000.0000.0001.0000.4200.0000.0000.0000.420
퇴직연금_퇴직처분취소0.0000.0000.0000.0000.4320.4650.0000.0000.0000.0910.0000.4201.0000.0000.0000.0000.305
유족연금_배우자의재혼0.4490.4500.1690.4460.3560.0000.1690.1690.0000.3520.0000.0000.0001.0000.0000.4490.000
유족연금_직계비속19세도달0.0000.0000.0000.0000.0000.2190.0000.0000.9890.0000.0000.0000.0000.0001.0000.0000.000
장해연금_사망0.6950.6960.9890.6920.0000.0000.9890.9890.0000.7280.2350.0000.0000.4490.0001.0000.000
장해연금_기타0.0000.0000.0000.0000.3300.2140.0000.0000.0000.3090.1450.4200.3050.0000.0000.0001.000

Missing values

2023-12-13T04:35:26.689311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:35:27.040045image/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

구분합계퇴직연금수급자(계)사망(유족없음)퇴직연금_청산퇴직연금_유족승계퇴직연금_재임용후합산신청퇴직연금_퇴직처분취소퇴직연금_기타유족연금수급자(계)유족연금_사망유족연금_청산유족연금_배우자의재혼유족연금_직계비속19세도달유족연금_기타장해연금수급자(계)장해연금_사망장해연금_청산장해연금_유족승계장해연금_기타
038세미만7080001076200026000000
138세5400010310000100000
239세6500010410000100000
340세6300000330000300000
441세8600030320000200000
542세1511000101040000400000
643세7500030220000200000
744세171200350450000500000
845세2016000301340000400000
946세131100011920000200000
구분합계퇴직연금수급자(계)사망(유족없음)퇴직연금_청산퇴직연금_유족승계퇴직연금_재임용후합산신청퇴직연금_퇴직처분취소퇴직연금_기타유족연금수급자(계)유족연금_사망유족연금_청산유족연금_배우자의재혼유족연금_직계비속19세도달유족연금_기타장해연금수급자(계)장해연금_사망장해연금_청산장해연금_유족승계장해연금_기타
3976세2572291902091002423000140040
4077세2592372602110002020000020020
4178세3142772902480003433000130030
4279세3102553202230005250000231020
4380세4173464902970007169000200000
4481세3852934202510009086000420020
4582세4493544803060009190000140040
4683세5074117903320009693000300000
4784세518410840326000107106000110010
4885세이상406228909900190000011661160010562040