Overview

Dataset statistics

Number of variables20
Number of observations41
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.3 KiB
Average record size in memory182.2 B

Variable types

Text1
Numeric11
Categorical8

Dataset

Description공무원연금 수급기간별, 사유별(사망,청산,유족승계, 재임용후 합산,퇴직처분취소 등) 연금수급 종결자 현황 데이터로 1년 미만부터 연 단위로 구분되어 있습니다.
URLhttps://www.data.go.kr/data/15052987/fileData.do

Alerts

청산(유족) has constant value ""Constant
청산(장해) has constant value ""Constant
합계 is highly overall correlated with 퇴직연금수급자(계) and 2 other fieldsHigh correlation
퇴직연금수급자(계) is highly overall correlated with 합계 and 2 other fieldsHigh correlation
사망(퇴직) is highly overall correlated with 재임용후 합산(퇴직) and 4 other fieldsHigh correlation
유족승계(퇴직) is highly overall correlated with 합계 and 3 other fieldsHigh correlation
재임용후 합산(퇴직) is highly overall correlated with 사망(퇴직) and 7 other fieldsHigh correlation
기타(퇴직) is highly overall correlated with 사망(퇴직) and 4 other fieldsHigh correlation
유족연금수급자(계) is highly overall correlated with 사망(퇴직) and 7 other fieldsHigh correlation
사망(유족) is highly overall correlated with 사망(퇴직) and 4 other fieldsHigh correlation
기타(유족) is highly overall correlated with 사망(퇴직) and 7 other fieldsHigh correlation
장해연금수급자(계) is highly overall correlated with 합계 and 2 other fieldsHigh correlation
유족승계(장해) is highly overall correlated with 유족승계(퇴직) and 2 other fieldsHigh correlation
청산(퇴직) is highly overall correlated with 재임용후 합산(퇴직) and 4 other fieldsHigh correlation
퇴직처분취소(퇴직) is highly overall correlated with 재임용후 합산(퇴직) and 5 other fieldsHigh correlation
재혼(유족) is highly overall correlated with 사망(유족)High correlation
직계비속 19세 도달(유족) is highly overall correlated with 유족승계(장해)High correlation
사망(장해) is highly overall correlated with 퇴직연금수급자(계)High correlation
기타(장해) is highly overall correlated with 재임용후 합산(퇴직) and 4 other fieldsHigh correlation
청산(퇴직) is highly imbalanced (64.9%)Imbalance
퇴직처분취소(퇴직) is highly imbalanced (71.7%)Imbalance
재혼(유족) is highly imbalanced (53.9%)Imbalance
직계비속 19세 도달(유족) is highly imbalanced (71.9%)Imbalance
기타(장해) is highly imbalanced (51.2%)Imbalance
구분 has unique valuesUnique
사망(퇴직) has 1 (2.4%) zerosZeros
재임용후 합산(퇴직) has 21 (51.2%) zerosZeros
기타(퇴직) has 36 (87.8%) zerosZeros
유족연금수급자(계) has 2 (4.9%) zerosZeros
사망(유족) has 2 (4.9%) zerosZeros
기타(유족) has 35 (85.4%) zerosZeros
장해연금수급자(계) has 10 (24.4%) zerosZeros
유족승계(장해) has 17 (41.5%) zerosZeros

Reproduction

Analysis started2023-12-12 02:08:35.105046
Analysis finished2023-12-12 02:08:48.946819
Duration13.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-12T11:08:49.141802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length2.9268293
Min length2

Characters and Unicode

Total characters120
Distinct characters16
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

Unique41 ?
Unique (%)100.0%

Sample

1st row1년미만
2nd row1년이상
3rd row2년
4th row3년
5th row4년
ValueCountFrequency (%)
1년미만 1
 
2.4%
31년 1
 
2.4%
23년 1
 
2.4%
24년 1
 
2.4%
25년 1
 
2.4%
26년 1
 
2.4%
27년 1
 
2.4%
28년 1
 
2.4%
29년 1
 
2.4%
30년 1
 
2.4%
Other values (32) 32
76.2%
2023-12-12T11:08:49.659032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
41
34.2%
1 15
 
12.5%
2 14
 
11.7%
3 14
 
11.7%
4 5
 
4.2%
5 4
 
3.3%
6 4
 
3.3%
7 4
 
3.3%
8 4
 
3.3%
9 4
 
3.3%
Other values (6) 11
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72
60.0%
Other Letter 47
39.2%
Space Separator 1
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 15
20.8%
2 14
19.4%
3 14
19.4%
4 5
 
6.9%
5 4
 
5.6%
6 4
 
5.6%
7 4
 
5.6%
8 4
 
5.6%
9 4
 
5.6%
0 4
 
5.6%
Other Letter
ValueCountFrequency (%)
41
87.2%
2
 
4.3%
2
 
4.3%
1
 
2.1%
1
 
2.1%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 73
60.8%
Hangul 47
39.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 15
20.5%
2 14
19.2%
3 14
19.2%
4 5
 
6.8%
5 4
 
5.5%
6 4
 
5.5%
7 4
 
5.5%
8 4
 
5.5%
9 4
 
5.5%
0 4
 
5.5%
Hangul
ValueCountFrequency (%)
41
87.2%
2
 
4.3%
2
 
4.3%
1
 
2.1%
1
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73
60.8%
Hangul 47
39.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
41
87.2%
2
 
4.3%
2
 
4.3%
1
 
2.1%
1
 
2.1%
ASCII
ValueCountFrequency (%)
1 15
20.5%
2 14
19.2%
3 14
19.2%
4 5
 
6.8%
5 4
 
5.5%
6 4
 
5.5%
7 4
 
5.5%
8 4
 
5.5%
9 4
 
5.5%
0 4
 
5.5%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean267.95122
Minimum53
Maximum1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T11:08:49.883302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum53
5-th percentile61
Q1192
median238
Q3310
95-th percentile544
Maximum1012
Range959
Interquartile range (IQR)118

Descriptive statistics

Standard deviation179.05571
Coefficient of variation (CV)0.66823995
Kurtosis7.4004932
Mean267.95122
Median Absolute Deviation (MAD)70
Skewness2.3003972
Sum10986
Variance32060.948
MonotonicityNot monotonic
2023-12-12T11:08:50.029759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
346 2
 
4.9%
192 2
 
4.9%
245 2
 
4.9%
61 2
 
4.9%
390 1
 
2.4%
395 1
 
2.4%
310 1
 
2.4%
354 1
 
2.4%
247 1
 
2.4%
243 1
 
2.4%
Other values (27) 27
65.9%
ValueCountFrequency (%)
53 1
2.4%
55 1
2.4%
61 2
4.9%
93 1
2.4%
133 1
2.4%
134 1
2.4%
135 1
2.4%
156 1
2.4%
168 1
2.4%
192 2
4.9%
ValueCountFrequency (%)
1012 1
2.4%
743 1
2.4%
544 1
2.4%
491 1
2.4%
395 1
2.4%
390 1
2.4%
354 1
2.4%
346 2
4.9%
324 1
2.4%
310 1
2.4%

퇴직연금수급자(계)
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean215.58537
Minimum51
Maximum983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T11:08:50.190038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile57
Q1123
median164
Q3231
95-th percentile450
Maximum983
Range932
Interquartile range (IQR)108

Descriptive statistics

Standard deviation178.01488
Coefficient of variation (CV)0.82572805
Kurtosis8.8806226
Mean215.58537
Median Absolute Deviation (MAD)46
Skewness2.6936882
Sum8839
Variance31689.299
MonotonicityNot monotonic
2023-12-12T11:08:50.407435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
134 2
 
4.9%
122 2
 
4.9%
136 2
 
4.9%
164 2
 
4.9%
450 1
 
2.4%
127 1
 
2.4%
286 1
 
2.4%
336 1
 
2.4%
225 1
 
2.4%
229 1
 
2.4%
Other values (27) 27
65.9%
ValueCountFrequency (%)
51 1
2.4%
55 1
2.4%
57 1
2.4%
59 1
2.4%
87 1
2.4%
94 1
2.4%
95 1
2.4%
118 1
2.4%
122 2
4.9%
123 1
2.4%
ValueCountFrequency (%)
983 1
2.4%
713 1
2.4%
450 1
2.4%
440 1
2.4%
369 1
2.4%
364 1
2.4%
336 1
2.4%
294 1
2.4%
286 1
2.4%
258 1
2.4%

사망(퇴직)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.756098
Minimum0
Maximum186
Zeros1
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T11:08:50.589540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q114
median23
Q359
95-th percentile117
Maximum186
Range186
Interquartile range (IQR)45

Descriptive statistics

Standard deviation40.347107
Coefficient of variation (CV)1.0148659
Kurtosis3.2122876
Mean39.756098
Median Absolute Deviation (MAD)17
Skewness1.668902
Sum1630
Variance1627.889
MonotonicityNot monotonic
2023-12-12T11:08:50.786533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
18 3
 
7.3%
1 3
 
7.3%
23 3
 
7.3%
22 2
 
4.9%
0 1
 
2.4%
117 1
 
2.4%
59 1
 
2.4%
81 1
 
2.4%
58 1
 
2.4%
56 1
 
2.4%
Other values (24) 24
58.5%
ValueCountFrequency (%)
0 1
 
2.4%
1 3
7.3%
4 1
 
2.4%
6 1
 
2.4%
7 1
 
2.4%
8 1
 
2.4%
10 1
 
2.4%
11 1
 
2.4%
14 1
 
2.4%
15 1
 
2.4%
ValueCountFrequency (%)
186 1
2.4%
125 1
2.4%
117 1
2.4%
98 1
2.4%
89 1
2.4%
81 1
2.4%
76 1
2.4%
72 1
2.4%
67 1
2.4%
64 1
2.4%

청산(퇴직)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size460.0 B
0
36 
1
 
3
3
 
1
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)4.9%

Sample

1st row1
2nd row3
3rd row0
4th row2
5th row0

Common Values

ValueCountFrequency (%)
0 36
87.8%
1 3
 
7.3%
3 1
 
2.4%
2 1
 
2.4%

Length

2023-12-12T11:08:50.958668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:08:51.078814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 36
87.8%
1 3
 
7.3%
3 1
 
2.4%
2 1
 
2.4%

유족승계(퇴직)
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160.60976
Minimum29
Maximum796
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T11:08:51.234884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile31
Q182
median116
Q3186
95-th percentile368
Maximum796
Range767
Interquartile range (IQR)104

Descriptive statistics

Standard deviation146.10833
Coefficient of variation (CV)0.90971018
Kurtosis9.5517382
Mean160.60976
Median Absolute Deviation (MAD)51
Skewness2.7871531
Sum6585
Variance21347.644
MonotonicityNot monotonic
2023-12-12T11:08:51.403396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
29 2
 
4.9%
114 2
 
4.9%
55 1
 
2.4%
65 1
 
2.4%
588 1
 
2.4%
275 1
 
2.4%
271 1
 
2.4%
210 1
 
2.4%
219 1
 
2.4%
166 1
 
2.4%
Other values (29) 29
70.7%
ValueCountFrequency (%)
29 2
4.9%
31 1
2.4%
34 1
2.4%
42 1
2.4%
55 1
2.4%
64 1
2.4%
65 1
2.4%
76 1
2.4%
81 1
2.4%
82 1
2.4%
ValueCountFrequency (%)
796 1
2.4%
588 1
2.4%
368 1
2.4%
275 1
2.4%
271 1
2.4%
269 1
2.4%
232 1
2.4%
219 1
2.4%
213 1
2.4%
210 1
2.4%

재임용후 합산(퇴직)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2439024
Minimum0
Maximum54
Zeros21
Zeros (%)51.2%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T11:08:51.562271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile20
Maximum54
Range54
Interquartile range (IQR)5

Descriptive statistics

Standard deviation10.589571
Coefficient of variation (CV)2.0194067
Kurtosis11.463861
Mean5.2439024
Median Absolute Deviation (MAD)0
Skewness3.1396989
Sum215
Variance112.13902
MonotonicityNot monotonic
2023-12-12T11:08:51.702927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 21
51.2%
1 4
 
9.8%
5 3
 
7.3%
15 2
 
4.9%
2 2
 
4.9%
54 1
 
2.4%
8 1
 
2.4%
11 1
 
2.4%
33 1
 
2.4%
20 1
 
2.4%
Other values (4) 4
 
9.8%
ValueCountFrequency (%)
0 21
51.2%
1 4
 
9.8%
2 2
 
4.9%
3 1
 
2.4%
4 1
 
2.4%
5 3
 
7.3%
8 1
 
2.4%
10 1
 
2.4%
11 1
 
2.4%
15 2
 
4.9%
ValueCountFrequency (%)
54 1
 
2.4%
33 1
 
2.4%
20 1
 
2.4%
19 1
 
2.4%
15 2
4.9%
11 1
 
2.4%
10 1
 
2.4%
8 1
 
2.4%
5 3
7.3%
4 1
 
2.4%

퇴직처분취소(퇴직)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size460.0 B
0
37 
7
 
1
1
 
1
3
 
1
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique4 ?
Unique (%)9.8%

Sample

1st row7
2nd row1
3rd row3
4th row2
5th row0

Common Values

ValueCountFrequency (%)
0 37
90.2%
7 1
 
2.4%
1 1
 
2.4%
3 1
 
2.4%
2 1
 
2.4%

Length

2023-12-12T11:08:51.841731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:08:51.971585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 37
90.2%
7 1
 
2.4%
1 1
 
2.4%
3 1
 
2.4%
2 1
 
2.4%

기타(퇴직)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4634146
Minimum0
Maximum333
Zeros36
Zeros (%)87.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T11:08:52.094187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile19
Maximum333
Range333
Interquartile range (IQR)0

Descriptive statistics

Standard deviation52.108587
Coefficient of variation (CV)5.5063198
Kurtosis39.933277
Mean9.4634146
Median Absolute Deviation (MAD)0
Skewness6.2871945
Sum388
Variance2715.3049
MonotonicityNot monotonic
2023-12-12T11:08:52.243556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 36
87.8%
333 1
 
2.4%
32 1
 
2.4%
19 1
 
2.4%
3 1
 
2.4%
1 1
 
2.4%
ValueCountFrequency (%)
0 36
87.8%
1 1
 
2.4%
3 1
 
2.4%
19 1
 
2.4%
32 1
 
2.4%
333 1
 
2.4%
ValueCountFrequency (%)
333 1
 
2.4%
32 1
 
2.4%
19 1
 
2.4%
3 1
 
2.4%
1 1
 
2.4%
0 36
87.8%

유족연금수급자(계)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)87.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.439024
Minimum0
Maximum214
Zeros2
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T11:08:52.422666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q115
median46
Q381
95-th percentile109
Maximum214
Range214
Interquartile range (IQR)66

Descriptive statistics

Standard deviation44.812972
Coefficient of variation (CV)0.88845834
Kurtosis2.7455863
Mean50.439024
Median Absolute Deviation (MAD)32
Skewness1.2804704
Sum2068
Variance2008.2024
MonotonicityNot monotonic
2023-12-12T11:08:52.584621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
24 4
 
9.8%
0 2
 
4.9%
6 2
 
4.9%
14 1
 
2.4%
38 1
 
2.4%
47 1
 
2.4%
28 1
 
2.4%
21 1
 
2.4%
17 1
 
2.4%
20 1
 
2.4%
Other values (26) 26
63.4%
ValueCountFrequency (%)
0 2
4.9%
1 1
2.4%
2 1
2.4%
3 1
2.4%
4 1
2.4%
6 2
4.9%
12 1
2.4%
14 1
2.4%
15 1
2.4%
17 1
2.4%
ValueCountFrequency (%)
214 1
2.4%
115 1
2.4%
109 1
2.4%
104 1
2.4%
100 1
2.4%
99 1
2.4%
95 1
2.4%
93 1
2.4%
92 1
2.4%
87 1
2.4%

사망(유족)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.390244
Minimum0
Maximum116
Zeros2
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T11:08:52.744300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q115
median46
Q377
95-th percentile103
Maximum116
Range116
Interquartile range (IQR)62

Descriptive statistics

Standard deviation36.293855
Coefficient of variation (CV)0.78235965
Kurtosis-1.2346421
Mean46.390244
Median Absolute Deviation (MAD)31
Skewness0.35232939
Sum1902
Variance1317.2439
MonotonicityNot monotonic
2023-12-12T11:08:52.898058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
24 4
 
9.8%
92 2
 
4.9%
0 2
 
4.9%
6 2
 
4.9%
100 2
 
4.9%
3 1
 
2.4%
4 1
 
2.4%
1 1
 
2.4%
2 1
 
2.4%
38 1
 
2.4%
Other values (24) 24
58.5%
ValueCountFrequency (%)
0 2
4.9%
1 1
2.4%
2 1
2.4%
3 1
2.4%
4 1
2.4%
6 2
4.9%
12 1
2.4%
14 1
2.4%
15 1
2.4%
17 1
2.4%
ValueCountFrequency (%)
116 1
2.4%
106 1
2.4%
103 1
2.4%
100 2
4.9%
98 1
2.4%
92 2
4.9%
86 1
2.4%
81 1
2.4%
77 1
2.4%
68 1
2.4%

청산(유족)
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
0
41 

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 41
100.0%

Length

2023-12-12T11:08:53.076600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:08:53.211475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 41
100.0%

재혼(유족)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size460.0 B
0
37 
1

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 37
90.2%
1 4
 
9.8%

Length

2023-12-12T11:08:53.344904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:08:53.474392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 37
90.2%
1 4
 
9.8%

직계비속 19세 도달(유족)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size460.0 B
0
39 
1
 
2

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 39
95.1%
1 2
 
4.9%

Length

2023-12-12T11:08:53.617290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:08:53.742163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 39
95.1%
1 2
 
4.9%

기타(유족)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.902439
Minimum0
Maximum98
Zeros35
Zeros (%)85.4%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T11:08:54.170642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile15
Maximum98
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.449627
Coefficient of variation (CV)4.215217
Kurtosis28.547866
Mean3.902439
Median Absolute Deviation (MAD)0
Skewness5.192925
Sum160
Variance270.59024
MonotonicityNot monotonic
2023-12-12T11:08:54.308712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 35
85.4%
3 2
 
4.9%
40 1
 
2.4%
98 1
 
2.4%
15 1
 
2.4%
1 1
 
2.4%
ValueCountFrequency (%)
0 35
85.4%
1 1
 
2.4%
3 2
 
4.9%
15 1
 
2.4%
40 1
 
2.4%
98 1
 
2.4%
ValueCountFrequency (%)
98 1
 
2.4%
40 1
 
2.4%
15 1
 
2.4%
3 2
 
4.9%
1 1
 
2.4%
0 35
85.4%

장해연금수급자(계)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9268293
Minimum0
Maximum7
Zeros10
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T11:08:54.443437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9286037
Coefficient of variation (CV)1.0009209
Kurtosis0.50578879
Mean1.9268293
Median Absolute Deviation (MAD)1
Skewness1.1419117
Sum79
Variance3.7195122
MonotonicityNot monotonic
2023-12-12T11:08:54.575262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 12
29.3%
0 10
24.4%
2 8
19.5%
4 3
 
7.3%
3 3
 
7.3%
6 3
 
7.3%
5 1
 
2.4%
7 1
 
2.4%
ValueCountFrequency (%)
0 10
24.4%
1 12
29.3%
2 8
19.5%
3 3
 
7.3%
4 3
 
7.3%
5 1
 
2.4%
6 3
 
7.3%
7 1
 
2.4%
ValueCountFrequency (%)
7 1
 
2.4%
6 3
 
7.3%
5 1
 
2.4%
4 3
 
7.3%
3 3
 
7.3%
2 8
19.5%
1 12
29.3%
0 10
24.4%

사망(장해)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size460.0 B
0
35 
1

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 35
85.4%
1 6
 
14.6%

Length

2023-12-12T11:08:54.734701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:08:54.867700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 35
85.4%
1 6
 
14.6%

청산(장해)
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
0
41 

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 41
100.0%

Length

2023-12-12T11:08:54.990555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:08:55.101181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 41
100.0%

유족승계(장해)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4634146
Minimum0
Maximum7
Zeros17
Zeros (%)41.5%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T11:08:55.224579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9117735
Coefficient of variation (CV)1.3063786
Kurtosis1.4406031
Mean1.4634146
Median Absolute Deviation (MAD)1
Skewness1.5033307
Sum60
Variance3.654878
MonotonicityNot monotonic
2023-12-12T11:08:55.396579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 17
41.5%
1 11
26.8%
2 5
 
12.2%
4 3
 
7.3%
6 2
 
4.9%
3 1
 
2.4%
5 1
 
2.4%
7 1
 
2.4%
ValueCountFrequency (%)
0 17
41.5%
1 11
26.8%
2 5
 
12.2%
3 1
 
2.4%
4 3
 
7.3%
5 1
 
2.4%
6 2
 
4.9%
7 1
 
2.4%
ValueCountFrequency (%)
7 1
 
2.4%
6 2
 
4.9%
5 1
 
2.4%
4 3
 
7.3%
3 1
 
2.4%
2 5
 
12.2%
1 11
26.8%
0 17
41.5%

기타(장해)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size460.0 B
0
32 
1
4
 
1
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)4.9%

Sample

1st row1
2nd row4
3rd row0
4th row2
5th row1

Common Values

ValueCountFrequency (%)
0 32
78.0%
1 7
 
17.1%
4 1
 
2.4%
2 1
 
2.4%

Length

2023-12-12T11:08:55.547418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:08:55.677711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 32
78.0%
1 7
 
17.1%
4 1
 
2.4%
2 1
 
2.4%

Interactions

2023-12-12T11:08:47.212765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:35.930487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:36.813036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:37.690271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:38.605407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:39.934945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:40.978001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:42.174415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:43.367808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:44.663832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:45.829611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:47.303046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:36.002625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:36.883022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:37.765368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:38.699270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:40.049646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:41.073882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:42.270992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:43.457696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:44.767391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:45.920006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:47.390031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:36.075606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:36.965095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:37.846971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:38.784137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:40.138830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:41.180480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:42.374664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:43.568882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:44.876806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:46.009912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:47.489678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:36.159431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:37.054192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:37.927103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:38.877899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:40.253567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:41.304315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:42.485460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:43.696995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:45.002001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:46.116789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:47.587377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:36.246886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:37.140666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:38.012860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:38.967376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:40.354990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:41.411172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:42.614812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:43.840821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:45.132240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:46.515690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:47.679615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:36.320214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:37.211582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:38.088305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:39.052058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:40.446748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:41.500109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:42.714016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:43.947684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:45.244880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:46.612949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:47.792920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:36.392701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:37.302947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:38.173730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:39.441705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:40.535292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:41.598579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:42.828292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:44.064744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:45.347697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:46.704531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:47.977323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:36.460362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:37.373539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:38.259440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:39.539868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:40.616482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:41.717579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:42.913656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:44.172500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:45.453059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:46.804166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:48.083277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:36.540822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:37.455580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:38.352892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:39.642827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:40.698351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:41.843166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:43.031660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:44.287961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:45.567245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:46.913650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:48.172059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:36.623372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:37.529034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:38.431913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:39.727075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:40.789794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:41.958956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:43.127992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:44.407782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:45.653984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:47.020398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:48.272661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:36.723206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:37.609787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:38.515744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:39.841152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:40.885184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:42.064594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:43.251207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:44.540106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:45.740974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:08:47.119426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:08:55.802361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분합계퇴직연금수급자(계)사망(퇴직)청산(퇴직)유족승계(퇴직)재임용후 합산(퇴직)퇴직처분취소(퇴직)기타(퇴직)유족연금수급자(계)사망(유족)재혼(유족)직계비속 19세 도달(유족)기타(유족)장해연금수급자(계)사망(장해)유족승계(장해)기타(장해)
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
합계1.0001.0000.9120.8610.4630.9190.5550.6081.0000.5370.5630.0000.0000.8580.7220.4250.6570.000
퇴직연금수급자(계)1.0000.9121.0000.7540.0000.9720.0000.0130.5880.4750.5270.2970.0000.2990.4440.6020.5830.000
사망(퇴직)1.0000.8610.7541.0000.0000.7430.0000.0000.0000.4050.4290.0000.0000.0000.0000.7080.0000.000
청산(퇴직)1.0000.4630.0000.0001.0000.0000.8850.8750.7180.7990.7110.0000.0000.9210.0000.0000.0000.979
유족승계(퇴직)1.0000.9190.9720.7430.0001.0000.0000.0000.0000.6150.5420.0000.0000.0000.5480.3580.5580.000
재임용후 합산(퇴직)1.0000.5550.0000.0000.8850.0001.0000.8821.0000.6960.6660.0000.4810.8680.0000.0000.0000.896
퇴직처분취소(퇴직)1.0000.6080.0130.0000.8750.0000.8821.0001.0000.7930.7760.0000.0001.0000.0000.0000.0000.846
기타(퇴직)1.0001.0000.5880.0000.7180.0001.0001.0001.0000.0000.1600.0000.0001.0000.0000.0000.0000.342
유족연금수급자(계)1.0000.5370.4750.4050.7990.6150.6960.7930.0001.0000.8980.2330.0000.7840.4380.2450.2540.858
사망(유족)1.0000.5630.5270.4290.7110.5420.6660.7760.1600.8981.0000.7400.0000.5370.3770.3560.2080.738
재혼(유족)1.0000.0000.2970.0000.0000.0000.0000.0000.0000.2330.7401.0000.0000.0000.0000.0000.1840.169
직계비속 19세 도달(유족)1.0000.0000.0000.0000.0000.0000.4810.0000.0000.0000.0000.0001.0000.0000.0000.0000.8150.000
기타(유족)1.0000.8580.2990.0000.9210.0000.8681.0001.0000.7840.5370.0000.0001.0000.0000.0000.0000.892
장해연금수급자(계)1.0000.7220.4440.0000.0000.5480.0000.0000.0000.4380.3770.0000.0000.0001.0000.2260.9730.328
사망(장해)1.0000.4250.6020.7080.0000.3580.0000.0000.0000.2450.3560.0000.0000.0000.2261.0000.0000.000
유족승계(장해)1.0000.6570.5830.0000.0000.5580.0000.0000.0000.2540.2080.1840.8150.0000.9730.0001.0000.000
기타(장해)1.0000.0000.0000.0000.9790.0000.8960.8460.3420.8580.7380.1690.0000.8920.3280.0000.0001.000
2023-12-12T11:08:56.049103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
직계비속 19세 도달(유족)재혼(유족)퇴직처분취소(퇴직)사망(장해)기타(장해)청산(퇴직)
직계비속 19세 도달(유족)1.0000.0000.0000.0000.0000.000
재혼(유족)0.0001.0000.0000.0000.1000.000
퇴직처분취소(퇴직)0.0000.0001.0000.0000.8110.853
사망(장해)0.0000.0000.0001.0000.0000.000
기타(장해)0.0000.1000.8110.0001.0000.803
청산(퇴직)0.0000.0000.8530.0000.8031.000
2023-12-12T11:08:56.187413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계퇴직연금수급자(계)사망(퇴직)유족승계(퇴직)재임용후 합산(퇴직)기타(퇴직)유족연금수급자(계)사망(유족)기타(유족)장해연금수급자(계)유족승계(장해)청산(퇴직)퇴직처분취소(퇴직)재혼(유족)직계비속 19세 도달(유족)사망(장해)기타(장해)
합계1.0000.8770.1540.8040.2280.1610.4070.3810.1910.5680.3730.1980.4080.0000.0000.2870.000
퇴직연금수급자(계)0.8771.0000.4400.870-0.069-0.1280.0650.038-0.0950.4720.4500.0000.0000.2920.0000.6040.000
사망(퇴직)0.1540.4401.0000.409-0.773-0.563-0.755-0.733-0.587-0.0640.1430.0000.0000.0000.0000.4950.000
유족승계(퇴직)0.8040.8700.4091.000-0.085-0.3000.1490.178-0.2510.5890.6000.0000.0000.0000.0000.3530.000
재임용후 합산(퇴직)0.228-0.069-0.773-0.0851.0000.5110.8720.8500.5980.086-0.1390.7400.8000.0000.3250.0000.755
기타(퇴직)0.161-0.128-0.563-0.3000.5111.0000.5170.4530.7440.172-0.1430.4980.9610.0000.0000.0000.218
유족연금수급자(계)0.4070.065-0.7550.1490.8720.5171.0000.9910.5880.3590.0760.6660.6500.2250.0000.2380.754
사망(유족)0.3810.038-0.7330.1780.8500.4530.9911.0000.5250.3780.1000.4660.3940.5140.0000.2320.494
기타(유족)0.191-0.095-0.587-0.2510.5980.7440.5880.5251.000-0.038-0.2700.6280.9860.0000.0000.0000.570
장해연금수급자(계)0.5680.472-0.0640.5890.0860.1720.3590.378-0.0381.0000.8310.0000.0000.0000.0000.1420.126
유족승계(장해)0.3730.4500.1430.600-0.139-0.1430.0760.100-0.2700.8311.0000.0000.0000.1090.5820.0000.000
청산(퇴직)0.1980.0000.0000.0000.7400.4980.6660.4660.6280.0000.0001.0000.8530.0000.0000.0000.803
퇴직처분취소(퇴직)0.4080.0000.0000.0000.8000.9610.6500.3940.9860.0000.0000.8531.0000.0000.0000.0000.811
재혼(유족)0.0000.2920.0000.0000.0000.0000.2250.5140.0000.0000.1090.0000.0001.0000.0000.0000.100
직계비속 19세 도달(유족)0.0000.0000.0000.0000.3250.0000.0000.0000.0000.0000.5820.0000.0000.0001.0000.0000.000
사망(장해)0.2870.6040.4950.3530.0000.0000.2380.2320.0000.1420.0000.0000.0000.0000.0001.0000.000
기타(장해)0.0000.0000.0000.0000.7550.2180.7540.4940.5700.1260.0000.8030.8110.1000.0000.0001.000

Missing values

2023-12-12T11:08:48.477917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:08:48.815506image/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(퇴직)청산(퇴직)유족승계(퇴직)재임용후 합산(퇴직)퇴직처분취소(퇴직)기타(퇴직)유족연금수급자(계)사망(유족)청산(유족)재혼(유족)직계비속 19세 도달(유족)기타(유족)장해연금수급자(계)사망(장해)청산(장해)유족승계(장해)기타(장해)
01년미만544450015554733393530004010001
11년이상346128138381322141160009840004
22년2341181084113191151000001510010
33년24513412933323109106000320002
44년223122160862000100100000010001
55년2321367011415009592000310010
66년2381341011081500104103000100000
77년25416411013419008786010030021
88년2481701501514007777000010001
99년19595408110009998001010001
구분합계퇴직연금수급자(계)사망(퇴직)청산(퇴직)유족승계(퇴직)재임용후 합산(퇴직)퇴직처분취소(퇴직)기타(퇴직)유족연금수급자(계)사망(유족)청산(유족)재혼(유족)직계비속 19세 도달(유족)기타(유족)장해연금수급자(계)사망(장해)청산(장해)유족승계(장해)기타(장해)
3131년135123580650001212000000000
3232년168152560960001515000010010
3333년1331275107600066000000000
3434년93874504200066000000000
3535년1561556409100000000010010
3636년53512003100022000000000
3737년61593002900011000010010
3838년55552602900000000000000
3939년61572303400044000000000
4040년 이상1341316706400033000000000