Overview

Dataset statistics

Number of variables10
Number of observations91
Missing cells1
Missing cells (%)0.1%
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 지급정지 해당자(국회의원, 공무원, 일부정지, 중과실파면 등) 수 추이 데이터로 1983년부터 2022년까지 연 단위로 구분하고 있습니다.
URLhttps://www.data.go.kr/data/15052986/fileData.do

Alerts

연도 is highly overall correlated with 정지대상기관(반액) and 1 other fieldsHigh correlation
계 is highly overall correlated with 국회의원 등 and 6 other fieldsHigh correlation
국회의원 등 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
고의_중과실파면등 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
기타 has 1 (1.1%) missing valuesMissing
국회의원 등 has 58 (63.7%) zerosZeros
공무원 has 10 (11.0%) zerosZeros
정지대상기관(전액) has 57 (62.6%) zerosZeros
정지대상기관(반액) has 54 (59.3%) zerosZeros
고의_중과실파면등 has 10 (11.0%) zerosZeros
기타 has 42 (46.2%) zerosZeros
일부정지 has 44 (48.4%) zerosZeros

Reproduction

Analysis started2023-12-12 05:18:14.204089
Analysis finished2023-12-12 05:18:24.085662
Duration9.88 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
2023-12-12T14:18:24.146186image/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
2023-12-12T14:18:24.271679image/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

2023-12-12T14:18:24.385799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:18:24.464838image/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%
Mean4568.4945
Minimum2
Maximum25991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size951.0 B
2023-12-12T14:18:24.563860image/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 deviation7855.0066
Coefficient of variation (CV)1.7193862
Kurtosis1.0066791
Mean4568.4945
Median Absolute Deviation (MAD)355
Skewness1.6244988
Sum415733
Variance61701129
MonotonicityNot monotonic
2023-12-12T14:18:24.692317image/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%
22015 1
1.1%
19683 1
1.1%
17810 1
1.1%
17788 1
1.1%

국회의원 등
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.923077
Minimum0
Maximum413
Zeros58
Zeros (%)63.7%
Negative0
Negative (%)0.0%
Memory size951.0 B
2023-12-12T14:18:24.803696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q324.5
95-th percentile125.5
Maximum413
Range413
Interquartile range (IQR)24.5

Descriptive statistics

Standard deviation55.031957
Coefficient of variation (CV)2.6302038
Kurtosis29.379062
Mean20.923077
Median Absolute Deviation (MAD)0
Skewness4.8169452
Sum1904
Variance3028.5162
MonotonicityNot monotonic
2023-12-12T14:18:24.925562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 58
63.7%
1 4
 
4.4%
27 3
 
3.3%
4 2
 
2.2%
28 2
 
2.2%
40 2
 
2.2%
11 1
 
1.1%
126 1
 
1.1%
178 1
 
1.1%
63 1
 
1.1%
Other values (16) 16
 
17.6%
ValueCountFrequency (%)
0 58
63.7%
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 (%)
413 1
1.1%
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%

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
2023-12-12T14:18:25.045345image/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
2023-12-12T14:18:25.175011image/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
2023-12-12T14:18:25.288793image/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
2023-12-12T14:18:25.401453image/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
2023-12-12T14:18:25.530618image/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
2023-12-12T14:18:25.667038image/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%

고의_중과실파면등
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
2023-12-12T14:18:25.806068image/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
2023-12-12T14:18:26.054691image/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  MISSING  ZEROS 

Distinct42
Distinct (%)46.7%
Missing1
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean66.833333
Minimum0
Maximum1830
Zeros42
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size951.0 B
2023-12-12T14:18:26.232197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q369.5
95-th percentile160.3
Maximum1830
Range1830
Interquartile range (IQR)69.5

Descriptive statistics

Standard deviation221.1459
Coefficient of variation (CV)3.3089163
Kurtosis49.040272
Mean66.833333
Median Absolute Deviation (MAD)1
Skewness6.6535757
Sum6015
Variance48905.511
MonotonicityNot monotonic
2023-12-12T14:18:26.398313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 42
46.2%
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 42
46.2%
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
2023-12-12T14:18:26.549440image/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
2023-12-12T14:18:26.729939image/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

2023-12-12T14:18:22.661378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:14.533452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:15.495853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:16.498765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:17.723898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:18.648499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:19.620853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:20.600366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:21.663834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:22.754147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:14.645467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:15.609322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:16.596809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:17.825973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:18.769681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:19.733637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:20.728336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:21.786496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:22.855201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:14.757535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:15.715134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:16.701370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:17.932453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:18.855340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:19.829798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:20.840091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:21.882844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:22.970530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:14.855753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:15.818955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:16.794239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:18.020339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:18.935026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:19.926958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:20.953066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:21.975546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:23.075940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:14.960454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:15.937814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:16.906984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:18.115967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:19.022192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:20.033558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:21.102058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:22.087843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:23.168015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:15.051223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:16.058721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:16.994021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:18.222834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:19.129797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:20.151125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:21.211496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:22.204985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:23.255610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:15.150304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:16.167038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:17.096122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:18.358118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:19.243116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:20.266062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:21.323447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:22.322455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:23.658021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:15.278034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:16.289156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:17.496464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:18.460116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:19.355933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:20.376491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:21.445611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:22.438501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:23.748603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:15.397085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:16.415769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:17.611807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:18.555094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:19.490333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:20.493942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:21.550407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:22.551983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:18:26.825425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도구분계국회의원 등공무원정지대상기관(전액)정지대상기관(반액)고의_중과실파면등기타일부정지
연도1.0000.1670.4780.2990.4480.4190.5760.7040.0000.471
구분0.1671.0000.6190.4540.8890.4700.4640.5910.0000.634
계0.4780.6191.0000.4700.7590.8170.5080.7900.7350.971
국회의원 등0.2990.4540.4701.0000.8860.0000.5390.6560.6190.668
공무원0.4480.8890.7590.8861.0000.5470.3420.7700.7520.893
정지대상기관(전액)0.4190.4700.8170.0000.5471.0000.2470.9180.3330.849
정지대상기관(반액)0.5760.4640.5080.5390.3420.2471.0000.4420.0000.000
고의_중과실파면등0.7040.5910.7900.6560.7700.9180.4421.0000.0000.819
기타0.0000.0000.7350.6190.7520.3330.0000.0001.0000.710
일부정지0.4710.6340.9710.6680.8930.8490.0000.8190.7101.000
2023-12-12T14:18:26.971415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도계국회의원 등공무원정지대상기관(전액)정지대상기관(반액)고의_중과실파면등기타일부정지구분
연도1.0000.237-0.1090.0530.327-0.747-0.0370.1220.7700.108
계0.2371.0000.5480.8780.5070.0070.7880.7840.5180.502
국회의원 등-0.1090.5481.0000.6190.1070.4620.4000.563-0.1810.205
공무원0.0530.8780.6191.0000.3830.1360.8040.7270.2810.592
정지대상기관(전액)0.3270.5070.1070.3831.000-0.3070.2630.3380.5810.305
정지대상기관(반액)-0.7470.0070.4620.136-0.3071.0000.1060.040-0.7520.323
고의_중과실파면등-0.0370.7880.4000.8040.2630.1061.0000.6650.2870.416
기타0.1220.7840.5630.7270.3380.0400.6651.0000.3220.000
일부정지0.7700.518-0.1810.2810.581-0.7520.2870.3221.0000.322
구분0.1080.5020.2050.5920.3050.3230.4160.0000.3221.000

Missing values

2023-12-12T14:18:23.870070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:18:24.019933image/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

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