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

Description공무원 퇴직사유별, 재직년수별 퇴직자 현황 데이터로 연 단위로 구분되어 있으며 퇴직 사유로는 명예퇴직, 일반퇴직, 정년퇴직 등이 있습니다.
URLhttps://www.data.go.kr/data/15053009/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
구 분 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 10:37:46.664074
Analysis finished2023-12-12 10:37:53.115108
Duration6.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구 분
Text

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-12T19:37:53.311783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length2.9756098
Min length2

Characters and Unicode

Total characters122
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 row1년 미만
2nd row1년 이상
3rd row2년
4th row3년
5th row4년
ValueCountFrequency (%)
1년 2
 
4.5%
이상 2
 
4.5%
31년 1
 
2.3%
23년 1
 
2.3%
24년 1
 
2.3%
25년 1
 
2.3%
26년 1
 
2.3%
27년 1
 
2.3%
28년 1
 
2.3%
29년 1
 
2.3%
Other values (32) 32
72.7%
2023-12-12T19:37:54.161895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
년 41
33.6%
1 15
 
12.3%
2 14
 
11.5%
3 14
 
11.5%
4 5
 
4.1%
5 4
 
3.3%
6 4
 
3.3%
7 4
 
3.3%
8 4
 
3.3%
9 4
 
3.3%
Other values (6) 13
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72
59.0%
Other Letter 47
38.5%
Space Separator 3
 
2.5%

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 (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 75
61.5%
Hangul 47
38.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 15
20.0%
2 14
18.7%
3 14
18.7%
4 5
 
6.7%
5 4
 
5.3%
6 4
 
5.3%
7 4
 
5.3%
8 4
 
5.3%
9 4
 
5.3%
0 4
 
5.3%
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 75
61.5%
Hangul 47
38.5%

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
20.0%
2 14
18.7%
3 14
18.7%
4 5
 
6.7%
5 4
 
5.3%
6 4
 
5.3%
7 4
 
5.3%
8 4
 
5.3%
9 4
 
5.3%
0 4
 
5.3%

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
2023-12-12T19:37:54.320252image/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
2023-12-12T19:37:54.485420image/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
2023-12-12T19:37:54.663518image/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
2023-12-12T19:37:54.841367image/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
2023-12-12T19:37:55.036667image/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
2023-12-12T19:37:55.220426image/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
2023-12-12T19:37:55.409974image/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
2023-12-12T19:37:55.580169image/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
2023-12-12T19:37:55.735856image/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
2023-12-12T19:37:55.878241image/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

2023-12-12T19:37:56.041977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:37:56.202293image/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
2023-12-12T19:37:56.356103image/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
2023-12-12T19:37:56.512784image/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
2023-12-12T19:37:56.666286image/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
2023-12-12T19:37:56.836972image/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

2023-12-12T19:37:52.076214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:47.130115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:48.045193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:48.925674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:49.746194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:50.459365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:51.276900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:52.178685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:47.259745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:48.160727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:49.055701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:49.846172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:50.567085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:51.387344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:52.296680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:47.392910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:48.294532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:49.170255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:49.956890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:50.662411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:51.496064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:52.433781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:47.529499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:48.427692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:49.281112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:50.061852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:50.781474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:51.604057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:52.538150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:47.664912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:48.578234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:49.405685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:50.155166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:50.880736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:51.738679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:52.629506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:47.803207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:48.700233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:49.510920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:50.255141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:50.982517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:51.850343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:52.730345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:47.919983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:48.813483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:49.630934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:50.353144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:51.090112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:51.956205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:37:56.976349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구 분계명예퇴직정년퇴직일반퇴직당연퇴직직권면직사망기타
구 분1.0001.0001.0001.0001.0001.0001.0001.0001.000
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
2023-12-12T19:37:57.167321image/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

2023-12-12T19:37:52.881197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:37:53.052918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

구 분계명예퇴직정년퇴직일반퇴직당연퇴직직권면직사망기타
01년 미만31230130643201610
11년 이상31200330721811412
22년2249072205901315
33년358402235131701715
44년12450191178802416
55년1020011980501113
66년6410561040913
77년55002150371108
88년47801344610117
99년3220928821148
구 분계명예퇴직정년퇴직일반퇴직당연퇴직직권면직사망기타
3131년2202123785864402514
3232년28941632113968403813
3333년53738214671001
3434년653332533017186705911
3535년35381522190182202110
3636년264486816986310104
3737년24737001710521064
3838년230548617733500101
3939년19563931527260082
4040년 이상37063543290522071