Overview

Dataset statistics

Number of variables9
Number of observations41
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory83.2 B

Variable types

Text1
Numeric7
Categorical1

Dataset

Description퇴직사유(정년퇴직, 당연퇴직, 일반퇴직, 사망 등)별 재직년수별(1년 미만 ~ 40년 이상) 퇴직자 추이에 대한 데이터입니다.
URLhttps://www.data.go.kr/data/15054058/fileData.do

Alerts

계 is highly overall correlated with 명예퇴직High correlation
명예퇴직 is highly overall correlated with 계 and 2 other fieldsHigh correlation
정년퇴직 is highly overall correlated with 명예퇴직 and 1 other fieldsHigh correlation
일반퇴직 is highly overall correlated with 명예퇴직 and 2 other fieldsHigh correlation
당연퇴직 is highly overall correlated with 일반퇴직High correlation
구분 has unique valuesUnique
명예퇴직 has 19 (46.3%) zerosZeros
당연퇴직 has 4 (9.8%) zerosZeros
사망 has 1 (2.4%) zerosZeros

Reproduction

Analysis started2023-12-12 20:17:09.191178
Analysis finished2023-12-12 20:17:14.614275
Duration5.42 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
2023-12-13T05:17:14.752148image/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%
2023-12-13T05:17:15.060947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
년 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 (%)
년 41
87.2%
이 2
 
4.3%
상 2
 
4.3%
미 1
 
2.1%
만 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 (%)
년 41
87.2%
이 2
 
4.3%
상 2
 
4.3%
미 1
 
2.1%
만 1
 
2.1%

Most occurring blocks

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

Most frequent character per block

Hangul
ValueCountFrequency (%)
년 41
87.2%
이 2
 
4.3%
상 2
 
4.3%
미 1
 
2.1%
만 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%

계
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1341.2927
Minimum205
Maximum6533
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T05:17:15.205599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum205
5-th percentile283
Q1333
median640
Q32249
95-th percentile3584
Maximum6533
Range6328
Interquartile range (IQR)1916

Descriptive statistics

Standard deviation1399.1382
Coefficient of variation (CV)1.0431267
Kurtosis3.2716655
Mean1341.2927
Median Absolute Deviation (MAD)333
Skewness1.6879379
Sum54993
Variance1957587.8
MonotonicityNot monotonic
2023-12-13T05:17:15.332531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
641 2
 
4.9%
3123 1
 
2.4%
2894 1
 
2.4%
307 1
 
2.4%
381 1
 
2.4%
492 1
 
2.4%
575 1
 
2.4%
720 1
 
2.4%
863 1
 
2.4%
1498 1
 
2.4%
Other values (30) 30
73.2%
ValueCountFrequency (%)
205 1
2.4%
255 1
2.4%
283 1
2.4%
294 1
2.4%
307 1
2.4%
308 1
2.4%
310 1
2.4%
314 1
2.4%
322 1
2.4%
332 1
2.4%
ValueCountFrequency (%)
6533 1
2.4%
3706 1
2.4%
3584 1
2.4%
3538 1
2.4%
3123 1
2.4%
3120 1
2.4%
2894 1
2.4%
2644 1
2.4%
2473 1
2.4%
2305 1
2.4%

명예퇴직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)53.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean338.70732
Minimum0
Maximum3253
Zeros19
Zeros (%)46.3%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T05:17:15.454351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median170
Q3354
95-th percentile1522
Maximum3253
Range3253
Interquartile range (IQR)354

Descriptive statistics

Standard deviation619.79001
Coefficient of variation (CV)1.829869
Kurtosis12.100439
Mean338.70732
Median Absolute Deviation (MAD)170
Skewness3.1738454
Sum13887
Variance384139.66
MonotonicityNot monotonic
2023-12-13T05:17:15.561007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 19
46.3%
189 2
 
4.9%
1237 1
 
2.4%
354 1
 
2.4%
393 1
 
2.4%
486 1
 
2.4%
700 1
 
2.4%
868 1
 
2.4%
1522 1
 
2.4%
3253 1
 
2.4%
Other values (12) 12
29.3%
ValueCountFrequency (%)
0 19
46.3%
2 1
 
2.4%
170 1
 
2.4%
189 2
 
4.9%
202 1
 
2.4%
229 1
 
2.4%
237 1
 
2.4%
275 1
 
2.4%
281 1
 
2.4%
288 1
 
2.4%
ValueCountFrequency (%)
3253 1
2.4%
1632 1
2.4%
1522 1
2.4%
1237 1
2.4%
868 1
2.4%
707 1
2.4%
700 1
2.4%
486 1
2.4%
393 1
2.4%
382 1
2.4%

정년퇴직
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean492.19512
Minimum1
Maximum3290
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T05:17:15.697627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q114
median63
Q3485
95-th percentile1901
Maximum3290
Range3289
Interquartile range (IQR)471

Descriptive statistics

Standard deviation837.14295
Coefficient of variation (CV)1.7008355
Kurtosis3.5841544
Mean492.19512
Median Absolute Deviation (MAD)56
Skewness2.0315396
Sum20180
Variance700808.31
MonotonicityNot monotonic
2023-12-13T05:17:15.827648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
9 2
 
4.9%
1 1
 
2.4%
1139 1
 
2.4%
131 1
 
2.4%
200 1
 
2.4%
261 1
 
2.4%
294 1
 
2.4%
359 1
 
2.4%
485 1
 
2.4%
690 1
 
2.4%
Other values (30) 30
73.2%
ValueCountFrequency (%)
1 1
2.4%
3 1
2.4%
5 1
2.4%
7 1
2.4%
8 1
2.4%
9 2
4.9%
11 1
2.4%
12 1
2.4%
13 1
2.4%
14 1
2.4%
ValueCountFrequency (%)
3290 1
2.4%
3017 1
2.4%
1901 1
2.4%
1773 1
2.4%
1710 1
2.4%
1698 1
2.4%
1527 1
2.4%
1139 1
2.4%
858 1
2.4%
690 1
2.4%

일반퇴직
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean477.92683
Minimum7
Maximum3513
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T05:17:16.011456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile27
Q139
median177
Q3299
95-th percentile3064
Maximum3513
Range3506
Interquartile range (IQR)260

Descriptive statistics

Standard deviation876.36803
Coefficient of variation (CV)1.8336866
Kurtosis5.8622652
Mean477.92683
Median Absolute Deviation (MAD)138
Skewness2.5992051
Sum19595
Variance768020.92
MonotonicityNot monotonic
2023-12-13T05:17:16.165982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
28 2
 
4.9%
52 2
 
4.9%
3064 1
 
2.4%
53 1
 
2.4%
37 1
 
2.4%
32 1
 
2.4%
27 1
 
2.4%
39 1
 
2.4%
33 1
 
2.4%
38 1
 
2.4%
Other values (29) 29
70.7%
ValueCountFrequency (%)
7 1
2.4%
26 1
2.4%
27 1
2.4%
28 2
4.9%
32 1
2.4%
33 1
2.4%
35 1
2.4%
37 1
2.4%
38 1
2.4%
39 1
2.4%
ValueCountFrequency (%)
3513 1
2.4%
3072 1
2.4%
3064 1
2.4%
2205 1
2.4%
1178 1
2.4%
980 1
2.4%
610 1
2.4%
503 1
2.4%
446 1
2.4%
389 1
2.4%

당연퇴직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)31.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6585366
Minimum0
Maximum32
Zeros4
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T05:17:16.283397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q35
95-th percentile17
Maximum32
Range32
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.8932578
Coefficient of variation (CV)1.2650449
Kurtosis11.648536
Mean4.6585366
Median Absolute Deviation (MAD)3
Skewness3.074188
Sum191
Variance34.730488
MonotonicityNot monotonic
2023-12-13T05:17:16.405311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 9
22.0%
4 8
19.5%
2 5
12.2%
0 4
9.8%
5 3
 
7.3%
7 3
 
7.3%
8 2
 
4.9%
3 2
 
4.9%
32 1
 
2.4%
18 1
 
2.4%
Other values (3) 3
 
7.3%
ValueCountFrequency (%)
0 4
9.8%
1 9
22.0%
2 5
12.2%
3 2
 
4.9%
4 8
19.5%
5 3
 
7.3%
6 1
 
2.4%
7 3
 
7.3%
8 2
 
4.9%
9 1
 
2.4%
ValueCountFrequency (%)
32 1
 
2.4%
18 1
 
2.4%
17 1
 
2.4%
9 1
 
2.4%
8 2
 
4.9%
7 3
 
7.3%
6 1
 
2.4%
5 3
 
7.3%
4 8
19.5%
3 2
 
4.9%

직권면직
Categorical

Distinct2
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size460.0 B
0
35 
1
6 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 35
85.4%
1 6
 
14.6%

Length

2023-12-13T05:17:16.544656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:17:16.650812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 35
85.4%
1 6
 
14.6%

사망
Real number (ℝ)

ZEROS 

Distinct22
Distinct (%)53.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.243902
Minimum0
Maximum59
Zeros1
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T05:17:16.749811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q111
median17
Q324
95-th percentile35
Maximum59
Range59
Interquartile range (IQR)13

Descriptive statistics

Standard deviation10.706961
Coefficient of variation (CV)0.58687891
Kurtosis4.0928872
Mean18.243902
Median Absolute Deviation (MAD)7
Skewness1.437401
Sum748
Variance114.63902
MonotonicityNot monotonic
2023-12-13T05:17:16.893101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
10 4
 
9.8%
25 4
 
9.8%
13 3
 
7.3%
17 3
 
7.3%
18 3
 
7.3%
14 3
 
7.3%
21 3
 
7.3%
24 2
 
4.9%
11 2
 
4.9%
30 2
 
4.9%
Other values (12) 12
29.3%
ValueCountFrequency (%)
0 1
 
2.4%
2 1
 
2.4%
6 1
 
2.4%
7 1
 
2.4%
8 1
 
2.4%
9 1
 
2.4%
10 4
9.8%
11 2
4.9%
13 3
7.3%
14 3
7.3%
ValueCountFrequency (%)
59 1
 
2.4%
38 1
 
2.4%
35 1
 
2.4%
30 2
4.9%
29 1
 
2.4%
25 4
9.8%
24 2
4.9%
21 3
7.3%
20 1
 
2.4%
18 3
7.3%

기타
Real number (ℝ)

Distinct16
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4146341
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T05:17:17.029933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median9
Q313
95-th percentile16
Maximum19
Range18
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.6312828
Coefficient of variation (CV)0.49192382
Kurtosis-0.63784013
Mean9.4146341
Median Absolute Deviation (MAD)4
Skewness-0.15798813
Sum386
Variance21.44878
MonotonicityNot monotonic
2023-12-13T05:17:17.154748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
13 7
17.1%
7 5
12.2%
8 4
9.8%
10 3
7.3%
9 3
7.3%
16 3
7.3%
1 3
7.3%
15 2
 
4.9%
2 2
 
4.9%
4 2
 
4.9%
Other values (6) 7
17.1%
ValueCountFrequency (%)
1 3
7.3%
2 2
 
4.9%
4 2
 
4.9%
5 1
 
2.4%
6 1
 
2.4%
7 5
12.2%
8 4
9.8%
9 3
7.3%
10 3
7.3%
11 2
 
4.9%
ValueCountFrequency (%)
19 1
 
2.4%
16 3
7.3%
15 2
 
4.9%
14 1
 
2.4%
13 7
17.1%
12 1
 
2.4%
11 2
 
4.9%
10 3
7.3%
9 3
7.3%
8 4
9.8%

Interactions

2023-12-13T05:17:13.907181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:09.489148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:10.230676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:11.000333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:11.747391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:12.484014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:13.157985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:13.972436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:09.590837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:10.338332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:11.110585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:11.843595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:12.558613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:13.236208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:14.042327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:09.677807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:10.446424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:11.223702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:11.942950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:12.646951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:13.312399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:14.116349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:09.776558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:10.536360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:11.326884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:12.059690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:12.761415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:13.389386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:14.195523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:09.885556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:10.642107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:11.435008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:12.169578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:12.849504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:13.466384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:14.280801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:10.005129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:10.752601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:11.534686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:12.284943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:12.966203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:13.539844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:14.351617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:10.115319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:10.871225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:11.628078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:12.378669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:13.064810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:17:13.836267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:17:17.255071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분계명예퇴직정년퇴직일반퇴직당연퇴직직권면직사망기타
구분1.0001.0001.0001.0001.0001.0001.0001.0001.000
계1.0001.0000.9080.9060.8280.4660.0000.6430.618
명예퇴직1.0000.9081.0000.9530.0000.0000.0000.7910.705
정년퇴직1.0000.9060.9531.0000.0000.0000.0000.7130.356
일반퇴직1.0000.8280.0000.0001.0000.7090.1800.0000.217
당연퇴직1.0000.4660.0000.0000.7091.0000.0000.0000.000
직권면직1.0000.0000.0000.0000.1800.0001.0000.0000.613
사망1.0000.6430.7910.7130.0000.0000.0001.0000.291
기타1.0000.6180.7050.3560.2170.0000.6130.2911.000
2023-12-13T05:17:17.371284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계명예퇴직정년퇴직일반퇴직당연퇴직사망기타직권면직
계1.0000.5210.3950.0700.279-0.0110.1120.000
명예퇴직0.5211.0000.911-0.689-0.3300.197-0.2070.000
정년퇴직0.3950.9111.000-0.767-0.3770.208-0.2020.000
일반퇴직0.070-0.689-0.7671.0000.569-0.0900.2870.170
당연퇴직0.279-0.330-0.3770.5691.0000.1830.4870.000
사망-0.0110.1970.208-0.0900.1831.0000.4340.000
기타0.112-0.207-0.2020.2870.4870.4341.0000.418
직권면직0.0000.0000.0000.1700.0000.0000.4181.000

Missing values

2023-12-13T05:17:14.455046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:17:14.566293image/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

구분계명예퇴직정년퇴직일반퇴직당연퇴직직권면직사망기타
01년미만31230130643201610
11년이상31200330721811412
22년2249072205901315
33년358402235131701715
44년12450191178802416
55년1020011980501113
66년6410561040913
77년55002150371108
88년47801344610117
99년3220928821148
구분계명예퇴직정년퇴직일반퇴직당연퇴직직권면직사망기타
3131년2202123785864402514
3232년28941632113968403813
3333년53738214671001
3434년653332533017186705911
3535년35381522190182202110
3636년264486816986310104
3737년24737001710521064
3838년230548617733500101
3939년19563931527260082
4040년 이상37063543290522071