Overview

Dataset statistics

Number of variables10
Number of observations43
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory92.1 B

Variable types

Text1
Numeric9

Dataset

Description1982년부터 현재까지의 공무원 퇴직사유별 퇴직자 추이(의원면직, 명예퇴직, 정년퇴직, 당연퇴직, 직원면직,사망 등) 데이터입니다.
Author공무원연금공단
URLhttps://www.data.go.kr/data/15053018/fileData.do

Alerts

계 is highly overall correlated with 명예퇴직 and 1 other fieldsHigh correlation
의원면직 is highly overall correlated with 명예퇴직 and 4 other fieldsHigh correlation
명예퇴직 is highly overall correlated with 계 and 4 other fieldsHigh correlation
정년퇴직 is highly overall correlated with 계 and 5 other fieldsHigh correlation
일반퇴직 is highly overall correlated with 의원면직 and 5 other fieldsHigh correlation
당연퇴직 is highly overall correlated with 일반퇴직 and 1 other fieldsHigh correlation
직권면직 is highly overall correlated with 의원면직 and 4 other fieldsHigh correlation
사망 is highly overall correlated with 의원면직 and 4 other fieldsHigh correlation
구분 has unique valuesUnique
계 has unique valuesUnique
정년퇴직 has unique valuesUnique
당연퇴직 has unique valuesUnique
사망 has unique valuesUnique
기타 has unique valuesUnique
의원면직 has 11 (25.6%) zerosZeros
명예퇴직 has 14 (32.6%) zerosZeros
일반퇴직 has 32 (74.4%) zerosZeros
직권면직 has 2 (4.7%) zerosZeros

Reproduction

Analysis started2023-12-12 12:45:22.076874
Analysis finished2023-12-12 12:45:30.186724
Duration8.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-12T21:45:30.579324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.8604651
Min length1

Characters and Unicode

Total characters166
Distinct characters12
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

Unique43 ?
Unique (%)100.0%

Sample

1st row1982
2nd row1983
3rd row1984
4th row1985
5th row1986
ValueCountFrequency (%)
1982 1
 
2.3%
2004 1
 
2.3%
2006 1
 
2.3%
2007 1
 
2.3%
2008 1
 
2.3%
2009 1
 
2.3%
2010 1
 
2.3%
2011 1
 
2.3%
2012 1
 
2.3%
2013 1
 
2.3%
Other values (33) 33
76.7%
2023-12-12T21:45:30.945613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 37
22.3%
1 32
19.3%
9 32
19.3%
2 31
18.7%
8 12
 
7.2%
3 4
 
2.4%
4 4
 
2.4%
5 4
 
2.4%
6 4
 
2.4%
7 4
 
2.4%
Other values (2) 2
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 164
98.8%
Other Letter 2
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 37
22.6%
1 32
19.5%
9 32
19.5%
2 31
18.9%
8 12
 
7.3%
3 4
 
2.4%
4 4
 
2.4%
5 4
 
2.4%
6 4
 
2.4%
7 4
 
2.4%
Other Letter
ValueCountFrequency (%)
남 1
50.0%
여 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 164
98.8%
Hangul 2
 
1.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 37
22.6%
1 32
19.5%
9 32
19.5%
2 31
18.9%
8 12
 
7.3%
3 4
 
2.4%
4 4
 
2.4%
5 4
 
2.4%
6 4
 
2.4%
7 4
 
2.4%
Hangul
ValueCountFrequency (%)
남 1
50.0%
여 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 164
98.8%
Hangul 2
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 37
22.6%
1 32
19.5%
9 32
19.5%
2 31
18.9%
8 12
 
7.3%
3 4
 
2.4%
4 4
 
2.4%
5 4
 
2.4%
6 4
 
2.4%
7 4
 
2.4%
Hangul
ValueCountFrequency (%)
남 1
50.0%
여 1
50.0%

계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36090.233
Minimum19722
Maximum94797
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T21:45:31.120229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19722
5-th percentile24301.6
Q128343
median34762
Q338621
95-th percentile54983.7
Maximum94797
Range75075
Interquartile range (IQR)10278

Descriptive statistics

Standard deviation12917.532
Coefficient of variation (CV)0.35792322
Kurtosis9.6146403
Mean36090.233
Median Absolute Deviation (MAD)5398
Skewness2.5812085
Sum1551880
Variance1.6686264 × 108
MonotonicityNot monotonic
2023-12-12T21:45:31.273967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
38844 1
 
2.3%
36604 1
 
2.3%
30021 1
 
2.3%
30909 1
 
2.3%
36934 1
 
2.3%
24280 1
 
2.3%
30035 1
 
2.3%
26163 1
 
2.3%
35408 1
 
2.3%
29364 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
19722 1
2.3%
23095 1
2.3%
24280 1
2.3%
24496 1
2.3%
24651 1
2.3%
24899 1
2.3%
25589 1
2.3%
26163 1
2.3%
27129 1
2.3%
27384 1
2.3%
ValueCountFrequency (%)
94797 1
2.3%
64345 1
2.3%
54993 1
2.3%
54900 1
2.3%
47319 1
2.3%
44676 1
2.3%
44010 1
2.3%
42907 1
2.3%
40340 1
2.3%
39781 1
2.3%

의원면직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10598.209
Minimum0
Maximum33498
Zeros11
Zeros (%)25.6%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T21:45:31.422348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12338.5
median7261
Q317452.5
95-th percentile24767.6
Maximum33498
Range33498
Interquartile range (IQR)15114

Descriptive statistics

Standard deviation9232.8277
Coefficient of variation (CV)0.87116865
Kurtosis-0.59589038
Mean10598.209
Median Absolute Deviation (MAD)7261
Skewness0.52823483
Sum455723
Variance85245108
MonotonicityNot monotonic
2023-12-12T21:45:31.584948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 11
25.6%
33498 1
 
2.3%
5482 1
 
2.3%
11685 1
 
2.3%
9006 1
 
2.3%
7261 1
 
2.3%
6296 1
 
2.3%
5710 1
 
2.3%
5741 1
 
2.3%
5824 1
 
2.3%
Other values (23) 23
53.5%
ValueCountFrequency (%)
0 11
25.6%
4677 1
 
2.3%
4986 1
 
2.3%
5026 1
 
2.3%
5330 1
 
2.3%
5482 1
 
2.3%
5601 1
 
2.3%
5710 1
 
2.3%
5741 1
 
2.3%
5824 1
 
2.3%
ValueCountFrequency (%)
33498 1
2.3%
30237 1
2.3%
25086 1
2.3%
21902 1
2.3%
21481 1
2.3%
21372 1
2.3%
20704 1
2.3%
20681 1
2.3%
19342 1
2.3%
18168 1
2.3%

명예퇴직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6585.6512
Minimum0
Maximum35409
Zeros14
Zeros (%)32.6%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T21:45:31.719732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5387
Q310397
95-th percentile17161.9
Maximum35409
Range35409
Interquartile range (IQR)10397

Descriptive statistics

Standard deviation7232.433
Coefficient of variation (CV)1.0982108
Kurtosis4.6523989
Mean6585.6512
Median Absolute Deviation (MAD)5387
Skewness1.7195719
Sum283183
Variance52308088
MonotonicityNot monotonic
2023-12-12T21:45:31.855475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 14
32.6%
7205 1
 
2.3%
6364 1
 
2.3%
7523 1
 
2.3%
13887 1
 
2.3%
12780 1
 
2.3%
12721 1
 
2.3%
10693 1
 
2.3%
10497 1
 
2.3%
9261 1
 
2.3%
Other values (20) 20
46.5%
ValueCountFrequency (%)
0 14
32.6%
2737 1
 
2.3%
2875 1
 
2.3%
3122 1
 
2.3%
3231 1
 
2.3%
3482 1
 
2.3%
3621 1
 
2.3%
3944 1
 
2.3%
5387 1
 
2.3%
6226 1
 
2.3%
ValueCountFrequency (%)
35409 1
2.3%
20342 1
2.3%
17369 1
2.3%
15298 1
2.3%
14816 1
2.3%
13887 1
2.3%
12780 1
2.3%
12721 1
2.3%
12381 1
2.3%
10693 1
2.3%

정년퇴직
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9767.1395
Minimum2386
Maximum23870
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T21:45:32.052136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2386
5-th percentile3174.1
Q14905.5
median8050
Q314400.5
95-th percentile19141.9
Maximum23870
Range21484
Interquartile range (IQR)9495

Descriptive statistics

Standard deviation5547.5409
Coefficient of variation (CV)0.5679801
Kurtosis-0.57065015
Mean9767.1395
Median Absolute Deviation (MAD)3714
Skewness0.59361799
Sum419987
Variance30775210
MonotonicityNot monotonic
2023-12-12T21:45:32.196133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
2386 1
 
2.3%
3159 1
 
2.3%
13660 1
 
2.3%
11544 1
 
2.3%
11270 1
 
2.3%
6246 1
 
2.3%
11055 1
 
2.3%
6426 1
 
2.3%
12020 1
 
2.3%
7527 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
2386 1
2.3%
3120 1
2.3%
3159 1
2.3%
3310 1
2.3%
3440 1
2.3%
3576 1
2.3%
3625 1
2.3%
4336 1
2.3%
4355 1
2.3%
4527 1
2.3%
ValueCountFrequency (%)
23870 1
2.3%
20180 1
2.3%
19338 1
2.3%
17377 1
2.3%
17261 1
2.3%
16362 1
2.3%
15871 1
2.3%
15823 1
2.3%
15653 1
2.3%
14839 1
2.3%

일반퇴직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)27.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2961.5814
Minimum0
Maximum19595
Zeros32
Zeros (%)74.4%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T21:45:32.324887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34263
95-th percentile13648.4
Maximum19595
Range19595
Interquartile range (IQR)4263

Descriptive statistics

Standard deviation5381.5284
Coefficient of variation (CV)1.8171131
Kurtosis1.3161303
Mean2961.5814
Median Absolute Deviation (MAD)0
Skewness1.5706675
Sum127348
Variance28960848
MonotonicityNot monotonic
2023-12-12T21:45:32.473488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 32
74.4%
9892 1
 
2.3%
8609 1
 
2.3%
10448 1
 
2.3%
9167 1
 
2.3%
10037 1
 
2.3%
10350 1
 
2.3%
13935 1
 
2.3%
15720 1
 
2.3%
19595 1
 
2.3%
Other values (2) 2
 
4.7%
ValueCountFrequency (%)
0 32
74.4%
8526 1
 
2.3%
8609 1
 
2.3%
9167 1
 
2.3%
9892 1
 
2.3%
10037 1
 
2.3%
10350 1
 
2.3%
10448 1
 
2.3%
11069 1
 
2.3%
13935 1
 
2.3%
ValueCountFrequency (%)
19595 1
2.3%
15720 1
2.3%
13935 1
2.3%
11069 1
2.3%
10448 1
2.3%
10350 1
2.3%
10037 1
2.3%
9892 1
2.3%
9167 1
2.3%
8609 1
2.3%

당연퇴직
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1737.3721
Minimum60
Maximum5425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T21:45:32.630773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile173.4
Q1444
median799
Q33167
95-th percentile4374.2
Maximum5425
Range5365
Interquartile range (IQR)2723

Descriptive statistics

Standard deviation1605.612
Coefficient of variation (CV)0.92416129
Kurtosis-0.77809997
Mean1737.3721
Median Absolute Deviation (MAD)627
Skewness0.73449125
Sum74707
Variance2577990
MonotonicityNot monotonic
2023-12-12T21:45:32.778429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
564 1
 
2.3%
264 1
 
2.3%
5425 1
 
2.3%
4403 1
 
2.3%
1187 1
 
2.3%
1836 1
 
2.3%
1591 1
 
2.3%
613 1
 
2.3%
3534 1
 
2.3%
799 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
60 1
2.3%
131 1
2.3%
172 1
2.3%
186 1
2.3%
191 1
2.3%
195 1
2.3%
208 1
2.3%
216 1
2.3%
245 1
2.3%
264 1
2.3%
ValueCountFrequency (%)
5425 1
2.3%
5233 1
2.3%
4403 1
2.3%
4115 1
2.3%
4049 1
2.3%
3656 1
2.3%
3568 1
2.3%
3539 1
2.3%
3534 1
2.3%
3460 1
2.3%

직권면직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1763.814
Minimum0
Maximum16253
Zeros2
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T21:45:32.936055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.2
Q144.5
median298
Q31339.5
95-th percentile10914.6
Maximum16253
Range16253
Interquartile range (IQR)1295

Descriptive statistics

Standard deviation3559.739
Coefficient of variation (CV)2.0182055
Kurtosis8.9179905
Mean1763.814
Median Absolute Deviation (MAD)297
Skewness3.009922
Sum75844
Variance12671742
MonotonicityNot monotonic
2023-12-12T21:45:33.106242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
112 2
 
4.7%
6 2
 
4.7%
0 2
 
4.7%
5 2
 
4.7%
298 1
 
2.3%
276 1
 
2.3%
11660 1
 
2.3%
74 1
 
2.3%
96 1
 
2.3%
164 1
 
2.3%
Other values (29) 29
67.4%
ValueCountFrequency (%)
0 2
4.7%
1 1
2.3%
3 1
2.3%
4 1
2.3%
5 2
4.7%
6 2
4.7%
9 1
2.3%
28 1
2.3%
61 1
2.3%
74 1
2.3%
ValueCountFrequency (%)
16253 1
2.3%
13187 1
2.3%
11660 1
2.3%
4206 1
2.3%
4145 1
2.3%
3947 1
2.3%
3883 1
2.3%
3123 1
2.3%
1836 1
2.3%
1445 1
2.3%

사망
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1126
Minimum222
Maximum2145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T21:45:33.271956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum222
5-th percentile654.9
Q1828
median926
Q31517.5
95-th percentile1780.3
Maximum2145
Range1923
Interquartile range (IQR)689.5

Descriptive statistics

Standard deviation447.69037
Coefficient of variation (CV)0.39759358
Kurtosis-0.8847372
Mean1126
Median Absolute Deviation (MAD)246
Skewness0.39103858
Sum48418
Variance200426.67
MonotonicityNot monotonic
2023-12-12T21:45:33.404650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1384 1
 
2.3%
1458 1
 
2.3%
844 1
 
2.3%
826 1
 
2.3%
858 1
 
2.3%
835 1
 
2.3%
830 1
 
2.3%
875 1
 
2.3%
837 1
 
2.3%
836 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
222 1
2.3%
526 1
2.3%
653 1
2.3%
672 1
2.3%
680 1
2.3%
711 1
2.3%
729 1
2.3%
735 1
2.3%
748 1
2.3%
763 1
2.3%
ValueCountFrequency (%)
2145 1
2.3%
1813 1
2.3%
1783 1
2.3%
1756 1
2.3%
1745 1
2.3%
1725 1
2.3%
1708 1
2.3%
1704 1
2.3%
1565 1
2.3%
1558 1
2.3%

기타
Real number (ℝ)

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1550.4651
Minimum23
Maximum6511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T21:45:33.583700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile279.2
Q1394
median537
Q31781.5
95-th percentile5679.8
Maximum6511
Range6488
Interquartile range (IQR)1387.5

Descriptive statistics

Standard deviation1846.0576
Coefficient of variation (CV)1.1906476
Kurtosis0.92822547
Mean1550.4651
Median Absolute Deviation (MAD)247
Skewness1.5124179
Sum66670
Variance3407928.8
MonotonicityNot monotonic
2023-12-12T21:45:33.708699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
512 1
 
2.3%
476 1
 
2.3%
333 1
 
2.3%
1221 1
 
2.3%
5318 1
 
2.3%
4116 1
 
2.3%
4611 1
 
2.3%
5720 1
 
2.3%
5760 1
 
2.3%
4948 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
23 1
2.3%
191 1
2.3%
278 1
2.3%
290 1
2.3%
325 1
2.3%
333 1
2.3%
363 1
2.3%
382 1
2.3%
386 1
2.3%
389 1
2.3%
ValueCountFrequency (%)
6511 1
2.3%
5760 1
2.3%
5720 1
2.3%
5318 1
2.3%
4948 1
2.3%
4611 1
2.3%
4116 1
2.3%
3639 1
2.3%
2872 1
2.3%
2642 1
2.3%

Interactions

2023-12-12T21:45:29.204410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:22.398000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:23.125086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:23.829632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:24.650986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:25.790761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:26.816777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:27.660754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:28.448017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:29.288984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:22.487496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:23.200163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:23.920005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:24.743708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:25.908982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:26.935586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:27.750155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:28.533412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:29.369284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:22.578685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:23.271302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:23.994104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:24.848686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:26.023128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:27.046974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:27.866136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:28.613684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:29.452035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:22.665052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:23.343834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:24.075410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:25.215289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:26.129366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:27.136623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:27.961964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:28.707558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:29.532317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:22.736909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:23.419165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:24.140747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:25.289925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:26.230470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:27.209296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:28.027218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:28.793426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:29.626024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:22.819662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:23.511077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:24.229961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:25.383520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:26.359850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:27.308761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:28.117531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:28.875468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:29.697390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:22.887988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:23.576613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:24.308693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:25.482921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:26.449789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:27.385939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:28.195227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:28.956693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:29.784411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:22.965391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:23.651728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:24.428403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:25.598405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:26.578117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:27.478679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:28.285211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:29.042235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:29.908485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:23.043910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:23.743559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:24.530861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:25.698200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:26.697407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:27.583454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:28.368084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:45:29.121219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:45:33.802218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분계의원면직명예퇴직정년퇴직일반퇴직당연퇴직직권면직사망기타
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
계1.0001.0000.4750.9270.7840.5430.4640.7730.5990.000
의원면직1.0000.4751.0000.4070.4500.2580.6540.3450.7230.387
명예퇴직1.0000.9270.4071.0000.7340.4220.1150.7700.7000.299
정년퇴직1.0000.7840.4500.7341.0000.8640.3450.6640.6360.000
일반퇴직1.0000.5430.2580.4220.8641.0000.0000.0000.3840.000
당연퇴직1.0000.4640.6540.1150.3450.0001.0000.4470.4780.765
직권면직1.0000.7730.3450.7700.6640.0000.4471.0000.7370.339
사망1.0000.5990.7230.7000.6360.3840.4780.7371.0000.386
기타1.0000.0000.3870.2990.0000.0000.7650.3390.3861.000
2023-12-12T21:45:33.931055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계의원면직명예퇴직정년퇴직일반퇴직당연퇴직직권면직사망기타
계1.000-0.1610.5610.5820.466-0.143-0.046-0.2110.120
의원면직-0.1611.000-0.741-0.726-0.7510.3550.8070.8890.105
명예퇴직0.561-0.7411.0000.8020.574-0.141-0.498-0.7180.010
정년퇴직0.582-0.7260.8021.0000.615-0.060-0.505-0.6440.011
일반퇴직0.466-0.7510.5740.6151.000-0.720-0.741-0.728-0.330
당연퇴직-0.1430.355-0.141-0.060-0.7201.0000.5470.4410.318
직권면직-0.0460.807-0.498-0.505-0.7410.5471.0000.8290.212
사망-0.2110.889-0.718-0.644-0.7280.4410.8291.0000.201
기타0.1200.1050.0100.011-0.3300.3180.2120.2011.000

Missing values

2023-12-12T21:45:30.009808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:45:30.135380image/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

구분계의원면직명예퇴직정년퇴직일반퇴직당연퇴직직권면직사망기타
0198238844334980238605645001384512
11983366043023703159026410101458476
21984347682508603440024541451463389
31985288202148103625058114451398290
4198624651181680331005877501558278
51987255891636903120064131232145191
619882712915839035760140138831565865
71989244961532004355064598615201670
81990278661788905040065487515151893
919913081120704047710686129817251627
구분계의원면직명예퇴직정년퇴직일반퇴직당연퇴직직권면직사망기타
332015403400152981434986094809838757
3420163839801029715823104485365680609
35201737059092611726191671950729446
3620183771001049715871100371861711407
3720193978101069317377103501725653531
3820204731901272119338139352083672442
3920214467601278014839157202164735382
4020225499301388720180195951916748386
41남352710752315653110691316526363
42여19722063644527852660022223