Overview

Dataset statistics

Number of variables10
Number of observations91
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.0 KiB
Average record size in memory90.5 B

Variable types

Numeric9
Categorical1

Dataset

Description공무원연금(퇴직연금, 장해연금) 사유별(국회의원, 공무원, 정지대상기관 등)정지자 수 추이에 대한 데이터입니다. 1983년부터 시작됩니다.
URLhttps://www.data.go.kr/data/15054095/fileData.do

Alerts

연도 is highly overall correlated with 정지대상기관(반액) and 1 other fieldsHigh correlation
계 is highly overall correlated with 국회의원등 and 6 other fieldsHigh correlation
국회의원등 is highly overall correlated with 계 and 2 other fieldsHigh correlation
공무원 is highly overall correlated with 계 and 4 other fieldsHigh correlation
정지대상기관(전액) is highly overall correlated with 계 and 1 other fieldsHigh correlation
정지대상기관(반액) is highly overall correlated with 연도 and 1 other fieldsHigh correlation
고의중과실파면등 is highly overall correlated with 계 and 2 other fieldsHigh correlation
기타 is highly overall correlated with 계 and 3 other fieldsHigh correlation
일부정지 is highly overall correlated with 연도 and 3 other fieldsHigh correlation
구분 is highly overall correlated with 계 and 1 other fieldsHigh correlation
국회의원등 has 59 (64.8%) zerosZeros
공무원 has 10 (11.0%) zerosZeros
정지대상기관(전액) has 57 (62.6%) zerosZeros
정지대상기관(반액) has 54 (59.3%) zerosZeros
고의중과실파면등 has 10 (11.0%) zerosZeros
기타 has 43 (47.3%) zerosZeros
일부정지 has 44 (48.4%) zerosZeros

Reproduction

Analysis started2023-12-12 12:18:51.664971
Analysis finished2023-12-12 12:19:02.251739
Duration10.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2004.2527
Minimum1983
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size951.0 B
2023-12-12T21:19:02.346449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1983
5-th percentile1985
Q11994
median2005
Q32015
95-th percentile2021
Maximum2022
Range39
Interquartile range (IQR)21

Descriptive statistics

Standard deviation11.92718
Coefficient of variation (CV)0.0059509363
Kurtosis-1.2530094
Mean2004.2527
Median Absolute Deviation (MAD)10
Skewness-0.19747192
Sum182387
Variance142.25763
MonotonicityIncreasing
2023-12-12T21:19:02.531251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
2014 3
 
3.3%
2022 3
 
3.3%
2021 3
 
3.3%
2020 3
 
3.3%
2019 3
 
3.3%
2018 3
 
3.3%
2017 3
 
3.3%
2016 3
 
3.3%
2015 3
 
3.3%
2013 3
 
3.3%
Other values (30) 61
67.0%
ValueCountFrequency (%)
1983 2
2.2%
1984 2
2.2%
1985 2
2.2%
1986 2
2.2%
1987 2
2.2%
1988 2
2.2%
1989 2
2.2%
1990 2
2.2%
1991 2
2.2%
1992 2
2.2%
ValueCountFrequency (%)
2022 3
3.3%
2021 3
3.3%
2020 3
3.3%
2019 3
3.3%
2018 3
3.3%
2017 3
3.3%
2016 3
3.3%
2015 3
3.3%
2014 3
3.3%
2013 3
3.3%

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size860.0 B
퇴직연금
40 
장해연금
40 
연계연금
11 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row퇴직연금
2nd row장해연금
3rd row퇴직연금
4th row장해연금
5th row퇴직연금

Common Values

ValueCountFrequency (%)
퇴직연금 40
44.0%
장해연금 40
44.0%
연계연금 11
 
12.1%

Length

2023-12-12T21:19:02.705927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:19:02.822579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
퇴직연금 40
44.0%
장해연금 40
44.0%
연계연금 11
 
12.1%

계
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4563.956
Minimum2
Maximum25991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size951.0 B
2023-12-12T21:19:02.968261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile14
Q152
median370
Q33253.5
95-th percentile23007.5
Maximum25991
Range25989
Interquartile range (IQR)3201.5

Descriptive statistics

Standard deviation7844.9272
Coefficient of variation (CV)1.7188876
Kurtosis1.0052168
Mean4563.956
Median Absolute Deviation (MAD)355
Skewness1.6238772
Sum415320
Variance61542883
MonotonicityNot monotonic
2023-12-12T21:19:03.180309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 3
 
3.3%
24 2
 
2.2%
31 2
 
2.2%
63 2
 
2.2%
15 2
 
2.2%
60 2
 
2.2%
16 2
 
2.2%
19 2
 
2.2%
319 1
 
1.1%
23 1
 
1.1%
Other values (72) 72
79.1%
ValueCountFrequency (%)
2 1
1.1%
3 1
1.1%
10 1
1.1%
11 1
1.1%
13 1
1.1%
15 2
2.2%
16 2
2.2%
17 1
1.1%
19 2
2.2%
20 1
1.1%
ValueCountFrequency (%)
25991 1
1.1%
25389 1
1.1%
23751 1
1.1%
23490 1
1.1%
23110 1
1.1%
22905 1
1.1%
21602 1
1.1%
19683 1
1.1%
17810 1
1.1%
17788 1
1.1%

국회의원등
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.384615
Minimum0
Maximum178
Zeros59
Zeros (%)64.8%
Negative0
Negative (%)0.0%
Memory size951.0 B
2023-12-12T21:19:03.327051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q317.5
95-th percentile113
Maximum178
Range178
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation36.117945
Coefficient of variation (CV)2.2043816
Kurtosis8.4516959
Mean16.384615
Median Absolute Deviation (MAD)0
Skewness2.8914711
Sum1491
Variance1304.506
MonotonicityNot monotonic
2023-12-12T21:19:03.474367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 59
64.8%
1 4
 
4.4%
27 3
 
3.3%
4 2
 
2.2%
28 2
 
2.2%
40 2
 
2.2%
11 1
 
1.1%
101 1
 
1.1%
178 1
 
1.1%
63 1
 
1.1%
Other values (15) 15
 
16.5%
ValueCountFrequency (%)
0 59
64.8%
1 4
 
4.4%
4 2
 
2.2%
8 1
 
1.1%
10 1
 
1.1%
11 1
 
1.1%
24 1
 
1.1%
25 1
 
1.1%
27 3
 
3.3%
28 2
 
2.2%
ValueCountFrequency (%)
178 1
1.1%
164 1
1.1%
127 1
1.1%
126 1
1.1%
125 1
1.1%
101 1
1.1%
63 1
1.1%
49 1
1.1%
47 1
1.1%
45 1
1.1%

공무원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.461538
Minimum0
Maximum741
Zeros10
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size951.0 B
2023-12-12T21:19:03.647060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median10
Q3128
95-th percentile237
Maximum741
Range741
Interquartile range (IQR)126

Descriptive statistics

Standard deviation111.85907
Coefficient of variation (CV)1.5022396
Kurtosis13.417941
Mean74.461538
Median Absolute Deviation (MAD)10
Skewness2.9050139
Sum6776
Variance12512.451
MonotonicityNot monotonic
2023-12-12T21:19:03.808711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 12
 
13.2%
0 10
 
11.0%
2 6
 
6.6%
3 5
 
5.5%
5 5
 
5.5%
10 4
 
4.4%
67 2
 
2.2%
118 2
 
2.2%
7 2
 
2.2%
143 2
 
2.2%
Other values (41) 41
45.1%
ValueCountFrequency (%)
0 10
11.0%
1 12
13.2%
2 6
6.6%
3 5
5.5%
4 1
 
1.1%
5 5
5.5%
7 2
 
2.2%
8 1
 
1.1%
9 1
 
1.1%
10 4
 
4.4%
ValueCountFrequency (%)
741 1
1.1%
408 1
1.1%
291 1
1.1%
245 1
1.1%
241 1
1.1%
233 1
1.1%
225 1
1.1%
220 1
1.1%
215 1
1.1%
185 1
1.1%

정지대상기관(전액)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.824176
Minimum0
Maximum172
Zeros57
Zeros (%)62.6%
Negative0
Negative (%)0.0%
Memory size951.0 B
2023-12-12T21:19:03.948696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile89.5
Maximum172
Range172
Interquartile range (IQR)2

Descriptive statistics

Standard deviation37.93257
Coefficient of variation (CV)2.0150986
Kurtosis3.9035366
Mean18.824176
Median Absolute Deviation (MAD)0
Skewness2.0936038
Sum1713
Variance1438.8799
MonotonicityNot monotonic
2023-12-12T21:19:04.101007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 57
62.6%
2 6
 
6.6%
1 6
 
6.6%
71 3
 
3.3%
70 2
 
2.2%
26 1
 
1.1%
86 1
 
1.1%
84 1
 
1.1%
82 1
 
1.1%
65 1
 
1.1%
Other values (12) 12
 
13.2%
ValueCountFrequency (%)
0 57
62.6%
1 6
 
6.6%
2 6
 
6.6%
3 1
 
1.1%
26 1
 
1.1%
31 1
 
1.1%
45 1
 
1.1%
53 1
 
1.1%
60 1
 
1.1%
65 1
 
1.1%
ValueCountFrequency (%)
172 1
1.1%
148 1
1.1%
128 1
1.1%
117 1
1.1%
93 1
1.1%
86 1
1.1%
84 1
1.1%
82 1
1.1%
75 1
1.1%
74 1
1.1%

정지대상기관(반액)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)29.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean461.2967
Minimum0
Maximum3160
Zeros54
Zeros (%)59.3%
Negative0
Negative (%)0.0%
Memory size951.0 B
2023-12-12T21:19:04.249760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile3024.5
Maximum3160
Range3160
Interquartile range (IQR)4

Descriptive statistics

Standard deviation969.31352
Coefficient of variation (CV)2.10128
Kurtosis2.1522325
Mean461.2967
Median Absolute Deviation (MAD)0
Skewness1.921841
Sum41978
Variance939568.7
MonotonicityNot monotonic
2023-12-12T21:19:04.427514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 54
59.3%
1 5
 
5.5%
3 5
 
5.5%
4 4
 
4.4%
704 1
 
1.1%
2 1
 
1.1%
15 1
 
1.1%
1952 1
 
1.1%
12 1
 
1.1%
1996 1
 
1.1%
Other values (17) 17
 
18.7%
ValueCountFrequency (%)
0 54
59.3%
1 5
 
5.5%
2 1
 
1.1%
3 5
 
5.5%
4 4
 
4.4%
12 1
 
1.1%
15 1
 
1.1%
704 1
 
1.1%
751 1
 
1.1%
784 1
 
1.1%
ValueCountFrequency (%)
3160 1
1.1%
3102 1
1.1%
3048 1
1.1%
3038 1
1.1%
3033 1
1.1%
3016 1
1.1%
2822 1
1.1%
2778 1
1.1%
2592 1
1.1%
2236 1
1.1%

고의중과실파면등
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)70.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179.68132
Minimum0
Maximum1538
Zeros10
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size951.0 B
2023-12-12T21:19:04.622172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median25
Q3198.5
95-th percentile988.5
Maximum1538
Range1538
Interquartile range (IQR)190.5

Descriptive statistics

Standard deviation324.78834
Coefficient of variation (CV)1.8075799
Kurtosis6.2106336
Mean179.68132
Median Absolute Deviation (MAD)25
Skewness2.566165
Sum16351
Variance105487.46
MonotonicityNot monotonic
2023-12-12T21:19:04.813575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10
 
11.0%
2 4
 
4.4%
8 3
 
3.3%
5 3
 
3.3%
20 3
 
3.3%
12 3
 
3.3%
14 2
 
2.2%
15 2
 
2.2%
243 2
 
2.2%
6 2
 
2.2%
Other values (54) 57
62.6%
ValueCountFrequency (%)
0 10
11.0%
2 4
 
4.4%
3 2
 
2.2%
4 1
 
1.1%
5 3
 
3.3%
6 2
 
2.2%
8 3
 
3.3%
9 2
 
2.2%
10 1
 
1.1%
11 1
 
1.1%
ValueCountFrequency (%)
1538 1
1.1%
1368 1
1.1%
1224 1
1.1%
1112 1
1.1%
1020 1
1.1%
957 1
1.1%
875 1
1.1%
802 1
1.1%
667 1
1.1%
598 1
1.1%

기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)46.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.098901
Minimum0
Maximum1830
Zeros43
Zeros (%)47.3%
Negative0
Negative (%)0.0%
Memory size951.0 B
2023-12-12T21:19:04.964741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q369
95-th percentile160
Maximum1830
Range1830
Interquartile range (IQR)69

Descriptive statistics

Standard deviation220.02546
Coefficient of variation (CV)3.3287309
Kurtosis49.574452
Mean66.098901
Median Absolute Deviation (MAD)1
Skewness6.68893
Sum6015
Variance48411.201
MonotonicityNot monotonic
2023-12-12T21:19:05.135944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 43
47.3%
1 5
 
5.5%
2 3
 
3.3%
7 2
 
2.2%
148 1
 
1.1%
102 1
 
1.1%
34 1
 
1.1%
46 1
 
1.1%
78 1
 
1.1%
129 1
 
1.1%
Other values (32) 32
35.2%
ValueCountFrequency (%)
0 43
47.3%
1 5
 
5.5%
2 3
 
3.3%
3 1
 
1.1%
5 1
 
1.1%
6 1
 
1.1%
7 2
 
2.2%
14 1
 
1.1%
21 1
 
1.1%
30 1
 
1.1%
ValueCountFrequency (%)
1830 1
1.1%
983 1
1.1%
308 1
1.1%
287 1
1.1%
163 1
1.1%
157 1
1.1%
148 1
1.1%
143 1
1.1%
129 1
1.1%
124 1
1.1%

일부정지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)49.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3747.2088
Minimum0
Maximum25064
Zeros44
Zeros (%)48.4%
Negative0
Negative (%)0.0%
Memory size951.0 B
2023-12-12T21:19:05.319088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13
Q3311.5
95-th percentile21865
Maximum25064
Range25064
Interquartile range (IQR)311.5

Descriptive statistics

Standard deviation7591.4874
Coefficient of variation (CV)2.0259046
Kurtosis1.1272592
Mean3747.2088
Median Absolute Deviation (MAD)13
Skewness1.6883096
Sum340996
Variance57630681
MonotonicityNot monotonic
2023-12-12T21:19:05.494501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 44
48.4%
52 2
 
2.2%
194 2
 
2.2%
19 2
 
2.2%
270 1
 
1.1%
22082 1
 
1.1%
209 1
 
1.1%
43 1
 
1.1%
21648 1
 
1.1%
210 1
 
1.1%
Other values (35) 35
38.5%
ValueCountFrequency (%)
0 44
48.4%
8 1
 
1.1%
13 1
 
1.1%
19 2
 
2.2%
43 1
 
1.1%
52 2
 
2.2%
54 1
 
1.1%
69 1
 
1.1%
73 1
 
1.1%
102 1
 
1.1%
ValueCountFrequency (%)
25064 1
1.1%
23040 1
1.1%
22726 1
1.1%
22350 1
1.1%
22082 1
1.1%
21648 1
1.1%
20837 1
1.1%
17619 1
1.1%
17217 1
1.1%
16967 1
1.1%

Interactions

2023-12-12T21:19:00.566690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:52.080833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:53.090249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:54.092042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:55.518408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:56.548317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:57.513063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:58.489718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:59.514638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:19:00.676429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:52.214164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:53.193886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:54.558495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:55.639551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:56.666992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:57.632770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:58.606515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:59.672005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:19:01.118667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:52.312407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:53.279644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:54.650106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:55.740368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:56.765940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:57.742325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:58.695495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:59.801649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:19:01.241019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:52.425250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:53.373927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:54.748410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:55.854379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:56.878533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:57.832600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:58.815028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:59.914537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:19:01.351745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:52.541861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:53.493133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:54.875493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:55.975245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:56.979974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:57.926413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:58.931467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:19:00.022793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:19:01.459858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:52.651391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:53.614976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:55.000187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:56.088849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:57.083371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:58.034020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:59.052428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:19:00.145488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:19:01.579276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:52.750118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:53.718406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:55.132421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:56.208481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:57.178712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:58.153570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:59.175584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:19:00.238356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:19:01.695693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:52.892483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:53.860447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:55.278302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:56.319696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:57.286695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:58.267968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:59.296468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:19:00.363464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:19:01.821661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:52.999772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:53.982701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:55.396521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:56.419987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:57.391701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:58.400737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:18:59.412915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:19:00.462015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:19:05.636561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도구분계국회의원등공무원정지대상기관(전액)정지대상기관(반액)고의중과실파면등기타일부정지
연도1.0000.1670.4780.5290.4480.4190.5760.7040.0000.471
구분0.1671.0000.6190.5130.8890.4700.4640.5910.0000.634
계0.4780.6191.0000.8120.7590.8170.5080.7900.7350.971
국회의원등0.5290.5130.8121.0000.7220.2210.6780.6560.4570.423
공무원0.4480.8890.7590.7221.0000.5470.3420.7700.7520.893
정지대상기관(전액)0.4190.4700.8170.2210.5471.0000.2470.9180.3380.849
정지대상기관(반액)0.5760.4640.5080.6780.3420.2471.0000.4420.0000.000
고의중과실파면등0.7040.5910.7900.6560.7700.9180.4421.0000.0000.819
기타0.0000.0000.7350.4570.7520.3380.0000.0001.0000.711
일부정지0.4710.6340.9710.4230.8930.8490.0000.8190.7111.000
2023-12-12T21:19:05.828255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도계국회의원등공무원정지대상기관(전액)정지대상기관(반액)고의중과실파면등기타일부정지구분
연도1.0000.237-0.1410.0530.327-0.747-0.0370.1160.7700.108
계0.2371.0000.5130.8780.5070.0070.7880.7800.5180.502
국회의원등-0.1410.5131.0000.6160.0620.4950.3700.524-0.2320.390
공무원0.0530.8780.6161.0000.3830.1360.8040.7240.2810.592
정지대상기관(전액)0.3270.5070.0620.3831.000-0.3070.2630.3270.5810.305
정지대상기관(반액)-0.7470.0070.4950.136-0.3071.0000.1060.047-0.7520.323
고의중과실파면등-0.0370.7880.3700.8040.2630.1061.0000.6570.2870.416
기타0.1160.7800.5240.7240.3270.0470.6571.0000.3120.000
일부정지0.7700.518-0.2320.2810.581-0.7520.2870.3121.0000.322
구분0.1080.5020.3900.5920.3050.3230.4160.0000.3221.000

Missing values

2023-12-12T21:19:01.964861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:19:02.172782image/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

연도구분계국회의원등공무원정지대상기관(전액)정지대상기관(반액)고의중과실파면등기타일부정지
01983퇴직연금7841128267041410
11983장해연금20100010
21984퇴직연금8411030317511450
31984장해연금30100020
41985퇴직연금9292553457841660
51985장해연금100101800
61986퇴직연금9972467538331910
71986장해연금1301011100
81987퇴직연금295227676027782000
91987장해연금1500031200
연도구분계국회의원등공무원정지대상기관(전액)정지대상기관(반액)고의중과실파면등기타일부정지
812019연계연금6005102052
822020퇴직연금234906324182021516322726
832020장해연금203000090194
842020연계연금8115006069
852021퇴직연금23751429184017515723040
862021장해연금190020050183
872021연계연금90013103073
882022퇴직연금2599117840870012814325064
892022장해연금201110032194
902022연계연금1254152020102