Overview

Dataset statistics

Number of variables9
Number of observations42
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory83.1 B

Variable types

Text1
Numeric6
Categorical2

Dataset

Description공무원연금법 시행전 공무원경력(공무원,군인,잡급,전문직 등)을 현재 근무기간에 합산(소급통산)받은 공무원 수 데이터입니다. (단위:명), 주) 소급통산대상 : 공무원연금법 시행전 공무원 등 재직기간 및 가입대상 적용이전 잡급직원 등 재직기간 - 공무원 : "48. 8. 15∼"59. 12. 31. 공무원 경력 - 군인 : "48. 8. 15∼"59. 12. 31 장기하사이하의 군인 경력 - 잡급 : "75. 1. 1∼"80. 6. 30 잡급직원 경력 - 전문직 : "73. 11. 29∼"80. 6. 30 사이의 전문직원 경력
URLhttps://www.data.go.kr/data/15053041/fileData.do

Alerts

합계 is highly overall correlated with 공무원 and 2 other fieldsHigh correlation
시간선택제 is highly overall correlated with 잡급High correlation
공무원 is highly overall correlated with 합계 and 3 other fieldsHigh correlation
군인 is highly overall correlated with 합계 and 3 other fieldsHigh correlation
잡급 is highly overall correlated with 합계 and 4 other fieldsHigh correlation
전문직 is highly overall correlated with 공무원 and 2 other fieldsHigh correlation
기타 is highly overall correlated with 노조해직복직공무원High correlation
노조해직복직공무원 is highly overall correlated with 기타High correlation
기타 is highly imbalanced (72.4%)Imbalance
노조해직복직공무원 is highly imbalanced (72.3%)Imbalance
구분 has unique valuesUnique
시간선택제 has 35 (83.3%) zerosZeros
공무원 has 23 (54.8%) zerosZeros
군인 has 26 (61.9%) zerosZeros
잡급 has 7 (16.7%) zerosZeros
전문직 has 31 (73.8%) zerosZeros

Reproduction

Analysis started2023-12-12 21:29:31.567985
Analysis finished2023-12-12 21:29:35.128207
Duration3.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size468.0 B
2023-12-13T06:29:35.266792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.8571429
Min length1

Characters and Unicode

Total characters162
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

Unique42 ?
Unique (%)100.0%

Sample

1st row1983
2nd row1984
3rd row1985
4th row1986
5th row1987
ValueCountFrequency (%)
1983 1
 
2.4%
2013 1
 
2.4%
남 1
 
2.4%
2006 1
 
2.4%
2007 1
 
2.4%
2008 1
 
2.4%
2009 1
 
2.4%
2010 1
 
2.4%
2011 1
 
2.4%
2012 1
 
2.4%
Other values (32) 32
76.2%
2023-12-13T06:29:35.585508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 37
22.8%
1 31
19.1%
9 31
19.1%
2 30
18.5%
8 11
 
6.8%
3 4
 
2.5%
5 4
 
2.5%
6 4
 
2.5%
7 4
 
2.5%
4 4
 
2.5%
Other values (2) 2
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 160
98.8%
Other Letter 2
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 37
23.1%
1 31
19.4%
9 31
19.4%
2 30
18.8%
8 11
 
6.9%
3 4
 
2.5%
5 4
 
2.5%
6 4
 
2.5%
7 4
 
2.5%
4 4
 
2.5%
Other Letter
ValueCountFrequency (%)
남 1
50.0%
여 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 160
98.8%
Hangul 2
 
1.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 37
23.1%
1 31
19.4%
9 31
19.4%
2 30
18.8%
8 11
 
6.9%
3 4
 
2.5%
5 4
 
2.5%
6 4
 
2.5%
7 4
 
2.5%
4 4
 
2.5%
Hangul
ValueCountFrequency (%)
남 1
50.0%
여 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 160
98.8%
Hangul 2
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 37
23.1%
1 31
19.4%
9 31
19.4%
2 30
18.8%
8 11
 
6.9%
3 4
 
2.5%
5 4
 
2.5%
6 4
 
2.5%
7 4
 
2.5%
4 4
 
2.5%
Hangul
ValueCountFrequency (%)
남 1
50.0%
여 1
50.0%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean810.04762
Minimum1
Maximum8553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-13T06:29:35.700043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.05
Q18.25
median76
Q3523
95-th percentile3626.15
Maximum8553
Range8552
Interquartile range (IQR)514.75

Descriptive statistics

Standard deviation1808.811
Coefficient of variation (CV)2.2329687
Kurtosis10.272886
Mean810.04762
Median Absolute Deviation (MAD)72.5
Skewness3.1460552
Sum34022
Variance3271797.1
MonotonicityNot monotonic
2023-12-13T06:29:35.815171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1 2
 
4.8%
6 2
 
4.8%
21 2
 
4.8%
76 2
 
4.8%
4 2
 
4.8%
5 2
 
4.8%
7 1
 
2.4%
12 1
 
2.4%
14 1
 
2.4%
16 1
 
2.4%
Other values (26) 26
61.9%
ValueCountFrequency (%)
1 2
4.8%
2 1
2.4%
3 1
2.4%
4 2
4.8%
5 2
4.8%
6 2
4.8%
7 1
2.4%
12 1
2.4%
14 1
2.4%
15 1
2.4%
ValueCountFrequency (%)
8553 1
2.4%
6923 1
2.4%
3649 1
2.4%
3192 1
2.4%
2960 1
2.4%
1627 1
2.4%
1578 1
2.4%
1091 1
2.4%
861 1
2.4%
647 1
2.4%

시간선택제
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean233.40476
Minimum0
Maximum8553
Zeros35
Zeros (%)83.3%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-13T06:29:35.905507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile214.75
Maximum8553
Range8553
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1319.4232
Coefficient of variation (CV)5.6529403
Kurtosis41.404194
Mean233.40476
Median Absolute Deviation (MAD)0
Skewness6.4160443
Sum9803
Variance1740877.6
MonotonicityNot monotonic
2023-12-13T06:29:35.988493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 35
83.3%
8553 1
 
2.4%
647 1
 
2.4%
218 1
 
2.4%
153 1
 
2.4%
116 1
 
2.4%
42 1
 
2.4%
74 1
 
2.4%
ValueCountFrequency (%)
0 35
83.3%
42 1
 
2.4%
74 1
 
2.4%
116 1
 
2.4%
153 1
 
2.4%
218 1
 
2.4%
647 1
 
2.4%
8553 1
 
2.4%
ValueCountFrequency (%)
8553 1
 
2.4%
647 1
 
2.4%
218 1
 
2.4%
153 1
 
2.4%
116 1
 
2.4%
74 1
 
2.4%
42 1
 
2.4%
0 35
83.3%

공무원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.14286
Minimum0
Maximum1541
Zeros23
Zeros (%)54.8%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-13T06:29:36.074839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3102
95-th percentile639.4
Maximum1541
Range1541
Interquartile range (IQR)102

Descriptive statistics

Standard deviation298.54881
Coefficient of variation (CV)2.145628
Kurtosis11.341658
Mean139.14286
Median Absolute Deviation (MAD)0
Skewness3.0567714
Sum5844
Variance89131.394
MonotonicityNot monotonic
2023-12-13T06:29:36.180365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 23
54.8%
1 2
 
4.8%
1541 1
 
2.4%
513 1
 
2.4%
2 1
 
2.4%
11 1
 
2.4%
9 1
 
2.4%
37 1
 
2.4%
54 1
 
2.4%
75 1
 
2.4%
Other values (9) 9
 
21.4%
ValueCountFrequency (%)
0 23
54.8%
1 2
 
4.8%
2 1
 
2.4%
9 1
 
2.4%
11 1
 
2.4%
37 1
 
2.4%
54 1
 
2.4%
75 1
 
2.4%
111 1
 
2.4%
133 1
 
2.4%
ValueCountFrequency (%)
1541 1
2.4%
678 1
2.4%
644 1
2.4%
552 1
2.4%
513 1
2.4%
504 1
2.4%
466 1
2.4%
320 1
2.4%
192 1
2.4%
133 1
2.4%

군인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239.09524
Minimum0
Maximum2741
Zeros26
Zeros (%)61.9%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-13T06:29:36.281237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q319.75
95-th percentile1815.9
Maximum2741
Range2741
Interquartile range (IQR)19.75

Descriptive statistics

Standard deviation677.3819
Coefficient of variation (CV)2.8331049
Kurtosis8.3362663
Mean239.09524
Median Absolute Deviation (MAD)0
Skewness3.0521067
Sum10042
Variance458846.23
MonotonicityNot monotonic
2023-12-13T06:29:36.388381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 26
61.9%
3 2
 
4.8%
2691 1
 
2.4%
22 1
 
2.4%
4 1
 
2.4%
2 1
 
2.4%
13 1
 
2.4%
30 1
 
2.4%
75 1
 
2.4%
1700 1
 
2.4%
Other values (6) 6
 
14.3%
ValueCountFrequency (%)
0 26
61.9%
2 1
 
2.4%
3 2
 
4.8%
4 1
 
2.4%
13 1
 
2.4%
22 1
 
2.4%
30 1
 
2.4%
75 1
 
2.4%
135 1
 
2.4%
221 1
 
2.4%
ValueCountFrequency (%)
2741 1
2.4%
2691 1
2.4%
1822 1
2.4%
1700 1
2.4%
349 1
2.4%
231 1
2.4%
221 1
2.4%
135 1
2.4%
75 1
2.4%
30 1
2.4%

잡급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean194.92857
Minimum0
Maximum2663
Zeros7
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-13T06:29:36.499593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.25
median15
Q3216.5
95-th percentile725.85
Maximum2663
Range2663
Interquartile range (IQR)213.25

Descriptive statistics

Standard deviation449.70252
Coefficient of variation (CV)2.3070119
Kurtosis22.616477
Mean194.92857
Median Absolute Deviation (MAD)15
Skewness4.349943
Sum8187
Variance202232.36
MonotonicityNot monotonic
2023-12-13T06:29:37.008541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 7
 
16.7%
4 4
 
9.5%
16 2
 
4.8%
1 2
 
4.8%
14 2
 
4.8%
2663 1
 
2.4%
2 1
 
2.4%
7 1
 
2.4%
3 1
 
2.4%
6 1
 
2.4%
Other values (20) 20
47.6%
ValueCountFrequency (%)
0 7
16.7%
1 2
 
4.8%
2 1
 
2.4%
3 1
 
2.4%
4 4
9.5%
5 1
 
2.4%
6 1
 
2.4%
7 1
 
2.4%
12 1
 
2.4%
14 2
 
4.8%
ValueCountFrequency (%)
2663 1
2.4%
741 1
2.4%
726 1
2.4%
723 1
2.4%
667 1
2.4%
406 1
2.4%
404 1
2.4%
402 1
2.4%
291 1
2.4%
248 1
2.4%

전문직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2619048
Minimum0
Maximum28
Zeros31
Zeros (%)73.8%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-13T06:29:37.115807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.75
95-th percentile4.9
Maximum28
Range28
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation4.4396194
Coefficient of variation (CV)3.518189
Kurtosis33.974304
Mean1.2619048
Median Absolute Deviation (MAD)0
Skewness5.6259522
Sum53
Variance19.710221
MonotonicityNot monotonic
2023-12-13T06:29:37.208019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 31
73.8%
1 4
 
9.5%
3 2
 
4.8%
2 2
 
4.8%
28 1
 
2.4%
6 1
 
2.4%
5 1
 
2.4%
ValueCountFrequency (%)
0 31
73.8%
1 4
 
9.5%
2 2
 
4.8%
3 2
 
4.8%
5 1
 
2.4%
6 1
 
2.4%
28 1
 
2.4%
ValueCountFrequency (%)
28 1
 
2.4%
6 1
 
2.4%
5 1
 
2.4%
3 2
 
4.8%
2 2
 
4.8%
1 4
 
9.5%
0 31
73.8%

기타
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size468.0 B
0
39 
4
 
2
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)2.4%

Sample

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

Common Values

ValueCountFrequency (%)
0 39
92.9%
4 2
 
4.8%
1 1
 
2.4%

Length

2023-12-13T06:29:37.316386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:29:37.431311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 39
92.9%
4 2
 
4.8%
1 1
 
2.4%

노조해직복직공무원
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Memory size468.0 B
0
38 
68
 
1
8
 
1
6
 
1
2
 
1

Length

Max length2
Median length1
Mean length1.0238095
Min length1

Unique

Unique4 ?
Unique (%)9.5%

Sample

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

Common Values

ValueCountFrequency (%)
0 38
90.5%
68 1
 
2.4%
8 1
 
2.4%
6 1
 
2.4%
2 1
 
2.4%

Length

2023-12-13T06:29:37.538917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:29:37.650945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 38
90.5%
68 1
 
2.4%
8 1
 
2.4%
6 1
 
2.4%
2 1
 
2.4%

Interactions

2023-12-13T06:29:34.505261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:31.842507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:32.338311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:32.827400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:33.428993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:33.998071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:34.579486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:31.908695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:32.419217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:32.922480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:33.526844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:34.079007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:34.641793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:32.003276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:32.482444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:33.010836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:33.605736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:34.156266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:34.720468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:32.090891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:32.580255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:33.124847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:33.723902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:34.274097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:34.791154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:32.170760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:32.669322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:33.235732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:33.814235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:34.356012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:34.860667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:32.257728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:32.751421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:33.327516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:33.895652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:29:34.430567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:29:37.728365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분합계시간선택제공무원군인잡급전문직기타노조해직복직공무원
구분1.0001.0001.0001.0001.0001.0001.0001.0001.000
합계1.0001.0001.0000.9280.9420.9420.8400.0000.000
시간선택제1.0001.0001.0000.0000.0000.0000.0000.0000.000
공무원1.0000.9280.0001.0000.7350.9770.8490.0000.000
군인1.0000.9420.0000.7351.0000.9230.8940.0000.000
잡급1.0000.9420.0000.9770.9231.0000.9260.0000.000
전문직1.0000.8400.0000.8490.8940.9261.0000.0000.000
기타1.0000.0000.0000.0000.0000.0000.0001.0000.690
노조해직복직공무원1.0000.0000.0000.0000.0000.0000.0000.6901.000
2023-12-13T06:29:37.853759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노조해직복직공무원기타
노조해직복직공무원1.0000.651
기타0.6511.000
2023-12-13T06:29:37.952019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계시간선택제공무원군인잡급전문직기타노조해직복직공무원
합계1.0000.2640.7060.7520.5530.4190.0000.000
시간선택제0.2641.000-0.381-0.336-0.644-0.2610.0000.000
공무원0.706-0.3811.0000.9490.8720.6160.0000.000
군인0.752-0.3360.9491.0000.8690.5750.0000.000
잡급0.553-0.6440.8720.8691.0000.5530.0000.000
전문직0.419-0.2610.6160.5750.5531.0000.0000.000
기타0.0000.0000.0000.0000.0000.0001.0000.651
노조해직복직공무원0.0000.0000.0000.0000.0000.0000.6511.000

Missing values

2023-12-13T06:29:34.959602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:29:35.076596image/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

구분합계시간선택제공무원군인잡급전문직기타노조해직복직공무원
01983692301541269126632800
11984296005131700741600
21985364905042741404000
31986319206441822723300
4198710910466221402200
5198815780678231667200
6198916270552349726000
719908610320135406000
81991563019275291500
91992403013322248000
구분합계시간선택제공무원군인잡급전문직기타노조해직복직공무원
32201540004000
33201610001000
34201720002000
35201885538553000000
362019647647000000
372020218218000000
3820212211530000068
392022128116000048
40남5242000046
41여7674000002