Overview

Dataset statistics

Number of variables12
Number of observations47
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory110.8 B

Variable types

Numeric12

Dataset

Description사립학교교직원연금공단 급여심의회 운영 현황과 관련된 데이터로 연도, 급여심의회 전체 상정건수, 급여심의회 전체 가·부결 건수, 급여심의회 전체 가결률, 직무상요양 가·부결 건수, 유족보상금 가·부결 건수, 장해 가·부결 건수, 유보 건수 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15081271/fileData.do

Alerts

연도 is highly overall correlated with 급여심의회전체상정건수 and 7 other fieldsHigh correlation
급여심의회전체상정건수 is highly overall correlated with 연도 and 7 other fieldsHigh correlation
급여심의회전체 가결건수 is highly overall correlated with 연도 and 7 other fieldsHigh correlation
급여심의회전체 부결건수 is highly overall correlated with 연도 and 7 other fieldsHigh correlation
직무상요양가결건수 is highly overall correlated with 연도 and 7 other fieldsHigh correlation
직무상요양부결건수 is highly overall correlated with 연도 and 7 other fieldsHigh correlation
장해가결건수 is highly overall correlated with 연도 and 7 other fieldsHigh correlation
장해부결건수 is highly overall correlated with 연도 and 7 other fieldsHigh correlation
보류건수 is highly overall correlated with 연도 and 7 other fieldsHigh correlation
연도 has unique valuesUnique
급여심의회전체 가결건수 has unique valuesUnique
직무상요양부결건수 has 3 (6.4%) zerosZeros
장해가결건수 has 9 (19.1%) zerosZeros
장해부결건수 has 16 (34.0%) zerosZeros
보류건수 has 15 (31.9%) zerosZeros

Reproduction

Analysis started2023-12-12 19:09:53.037345
Analysis finished2023-12-12 19:10:10.021265
Duration16.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1998.9787
Minimum1975
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-13T04:10:10.110500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1975
5-th percentile1978.3
Q11987.5
median1999
Q32010.5
95-th percentile2019.7
Maximum2022
Range47
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.748501
Coefficient of variation (CV)0.0068777625
Kurtosis-1.1856959
Mean1998.9787
Median Absolute Deviation (MAD)12
Skewness-0.0096281628
Sum93952
Variance189.02128
MonotonicityStrictly increasing
2023-12-13T04:10:10.287205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1975 1
 
2.1%
1977 1
 
2.1%
2002 1
 
2.1%
2003 1
 
2.1%
2004 1
 
2.1%
2005 1
 
2.1%
2006 1
 
2.1%
2007 1
 
2.1%
2008 1
 
2.1%
2009 1
 
2.1%
Other values (37) 37
78.7%
ValueCountFrequency (%)
1975 1
2.1%
1977 1
2.1%
1978 1
2.1%
1979 1
2.1%
1980 1
2.1%
1981 1
2.1%
1982 1
2.1%
1983 1
2.1%
1984 1
2.1%
1985 1
2.1%
ValueCountFrequency (%)
2022 1
2.1%
2021 1
2.1%
2020 1
2.1%
2019 1
2.1%
2018 1
2.1%
2017 1
2.1%
2016 1
2.1%
2015 1
2.1%
2014 1
2.1%
2013 1
2.1%

급여심의회전체상정건수
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean502.08511
Minimum6
Maximum1286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-13T04:10:10.779532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile17.2
Q1215
median417
Q3780.5
95-th percentile1101.8
Maximum1286
Range1280
Interquartile range (IQR)565.5

Descriptive statistics

Standard deviation363.11124
Coefficient of variation (CV)0.72320656
Kurtosis-0.96658374
Mean502.08511
Median Absolute Deviation (MAD)341
Skewness0.3298027
Sum23598
Variance131849.78
MonotonicityNot monotonic
2023-12-13T04:10:10.952535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
29 2
 
4.3%
804 1
 
2.1%
591 1
 
2.1%
741 1
 
2.1%
744 1
 
2.1%
788 1
 
2.1%
815 1
 
2.1%
782 1
 
2.1%
779 1
 
2.1%
758 1
 
2.1%
Other values (36) 36
76.6%
ValueCountFrequency (%)
6 1
2.1%
9 1
2.1%
13 1
2.1%
27 1
2.1%
29 2
4.3%
57 1
2.1%
72 1
2.1%
108 1
2.1%
159 1
2.1%
164 1
2.1%
ValueCountFrequency (%)
1286 1
2.1%
1198 1
2.1%
1106 1
2.1%
1092 1
2.1%
1047 1
2.1%
977 1
2.1%
845 1
2.1%
815 1
2.1%
809 1
2.1%
804 1
2.1%

급여심의회전체 가결건수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean424.97872
Minimum5
Maximum1107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-13T04:10:11.113448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile13.8
Q1195.5
median356
Q3655.5
95-th percentile942.8
Maximum1107
Range1102
Interquartile range (IQR)460

Descriptive statistics

Standard deviation308.71532
Coefficient of variation (CV)0.72642537
Kurtosis-0.80284521
Mean424.97872
Median Absolute Deviation (MAD)273
Skewness0.38950122
Sum19974
Variance95305.152
MonotonicityNot monotonic
2023-12-13T04:10:11.286568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
18 1
 
2.1%
20 1
 
2.1%
416 1
 
2.1%
473 1
 
2.1%
602 1
 
2.1%
579 1
 
2.1%
659 1
 
2.1%
651 1
 
2.1%
653 1
 
2.1%
657 1
 
2.1%
Other values (37) 37
78.7%
ValueCountFrequency (%)
5 1
2.1%
6 1
2.1%
12 1
2.1%
18 1
2.1%
19 1
2.1%
20 1
2.1%
39 1
2.1%
61 1
2.1%
89 1
2.1%
139 1
2.1%
ValueCountFrequency (%)
1107 1
2.1%
1049 1
2.1%
944 1
2.1%
940 1
2.1%
899 1
2.1%
845 1
2.1%
713 1
2.1%
702 1
2.1%
668 1
2.1%
660 1
2.1%

급여심의회전체 부결건수
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.106383
Minimum1
Maximum179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-13T04:10:11.416792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.5
Q120.5
median63
Q3130.5
95-th percentile163.4
Maximum179
Range178
Interquartile range (IQR)110

Descriptive statistics

Standard deviation57.499805
Coefficient of variation (CV)0.74572043
Kurtosis-1.536012
Mean77.106383
Median Absolute Deviation (MAD)52
Skewness0.18527106
Sum3624
Variance3306.2276
MonotonicityNot monotonic
2023-12-13T04:10:11.545151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
11 2
 
4.3%
20 2
 
4.3%
122 2
 
4.3%
47 2
 
4.3%
21 2
 
4.3%
129 2
 
4.3%
1 2
 
4.3%
18 2
 
4.3%
162 1
 
2.1%
148 1
 
2.1%
Other values (29) 29
61.7%
ValueCountFrequency (%)
1 2
4.3%
3 1
2.1%
8 1
2.1%
9 1
2.1%
11 2
4.3%
18 2
4.3%
19 1
2.1%
20 2
4.3%
21 2
4.3%
24 1
2.1%
ValueCountFrequency (%)
179 1
2.1%
165 1
2.1%
164 1
2.1%
162 1
2.1%
152 1
2.1%
149 1
2.1%
148 1
2.1%
143 1
2.1%
141 1
2.1%
139 1
2.1%
Distinct46
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.433617
Minimum62.07
Maximum92.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-13T04:10:11.692054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum62.07
5-th percentile68.585
Q181.895
median84.72
Q387.46
95-th percentile91.749
Maximum92.71
Range30.64
Interquartile range (IQR)5.565

Descriptive statistics

Standard deviation6.6368918
Coefficient of variation (CV)0.079546974
Kurtosis2.439891
Mean83.433617
Median Absolute Deviation (MAD)2.78
Skewness-1.5119245
Sum3921.38
Variance44.048332
MonotonicityNot monotonic
2023-12-13T04:10:11.862792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
86.08 2
 
4.3%
62.07 1
 
2.1%
84.28 1
 
2.1%
79.85 1
 
2.1%
80.03 1
 
2.1%
81.24 1
 
2.1%
77.82 1
 
2.1%
83.63 1
 
2.1%
79.88 1
 
2.1%
83.5 1
 
2.1%
Other values (36) 36
76.6%
ValueCountFrequency (%)
62.07 1
2.1%
66.67 1
2.1%
68.42 1
2.1%
68.97 1
2.1%
70.37 1
2.1%
77.82 1
2.1%
79.85 1
2.1%
79.88 1
2.1%
80.03 1
2.1%
80.52 1
2.1%
ValueCountFrequency (%)
92.71 1
2.1%
92.31 1
2.1%
91.89 1
2.1%
91.42 1
2.1%
89.9 1
2.1%
88.97 1
2.1%
88.81 1
2.1%
88.68 1
2.1%
88.6 1
2.1%
88.47 1
2.1%

직무상요양가결건수
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean404.04255
Minimum1
Maximum1071
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-13T04:10:12.017909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.8
Q1176.5
median336
Q3632.5
95-th percentile919.4
Maximum1071
Range1070
Interquartile range (IQR)456

Descriptive statistics

Standard deviation301.88523
Coefficient of variation (CV)0.74716198
Kurtosis-0.819075
Mean404.04255
Median Absolute Deviation (MAD)267
Skewness0.41424408
Sum18990
Variance91134.694
MonotonicityNot monotonic
2023-12-13T04:10:12.193307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
222 2
 
4.3%
628 2
 
4.3%
14 2
 
4.3%
866 1
 
2.1%
923 1
 
2.1%
453 1
 
2.1%
570 1
 
2.1%
565 1
 
2.1%
642 1
 
2.1%
639 1
 
2.1%
Other values (34) 34
72.3%
ValueCountFrequency (%)
1 1
2.1%
3 1
2.1%
7 1
2.1%
13 1
2.1%
14 2
4.3%
34 1
2.1%
51 1
2.1%
74 1
2.1%
124 1
2.1%
126 1
2.1%
ValueCountFrequency (%)
1071 1
2.1%
1012 1
2.1%
923 1
2.1%
911 1
2.1%
866 1
2.1%
815 1
2.1%
680 1
2.1%
679 1
2.1%
645 1
2.1%
642 1
2.1%

직무상요양부결건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)72.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.638298
Minimum0
Maximum158
Zeros3
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-13T04:10:12.349943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.8
Q113
median37
Q3114
95-th percentile141.4
Maximum158
Range158
Interquartile range (IQR)101

Descriptive statistics

Standard deviation53.191788
Coefficient of variation (CV)0.86296653
Kurtosis-1.5862185
Mean61.638298
Median Absolute Deviation (MAD)37
Skewness0.33443142
Sum2897
Variance2829.3663
MonotonicityNot monotonic
2023-12-13T04:10:12.493918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
9 3
 
6.4%
0 3
 
6.4%
132 2
 
4.3%
6 2
 
4.3%
128 2
 
4.3%
25 2
 
4.3%
22 2
 
4.3%
19 2
 
4.3%
15 2
 
4.3%
13 2
 
4.3%
Other values (24) 25
53.2%
ValueCountFrequency (%)
0 3
6.4%
6 2
4.3%
8 1
 
2.1%
9 3
6.4%
10 1
 
2.1%
11 1
 
2.1%
13 2
4.3%
15 2
4.3%
18 1
 
2.1%
19 2
4.3%
ValueCountFrequency (%)
158 1
2.1%
144 1
2.1%
142 1
2.1%
140 1
2.1%
132 2
4.3%
130 1
2.1%
128 2
4.3%
121 1
2.1%
120 1
2.1%
116 1
2.1%
Distinct21
Distinct (%)44.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.191489
Minimum1
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-13T04:10:12.619388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median7
Q313
95-th percentile23
Maximum38
Range37
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.4531531
Coefficient of variation (CV)0.73131147
Kurtosis2.8900571
Mean10.191489
Median Absolute Deviation (MAD)4
Skewness1.4717608
Sum479
Variance55.549491
MonotonicityNot monotonic
2023-12-13T04:10:12.744714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
5 9
19.1%
6 6
12.8%
12 5
10.6%
2 3
 
6.4%
13 3
 
6.4%
4 3
 
6.4%
10 2
 
4.3%
23 2
 
4.3%
18 2
 
4.3%
7 1
 
2.1%
Other values (11) 11
23.4%
ValueCountFrequency (%)
1 1
 
2.1%
2 3
 
6.4%
3 1
 
2.1%
4 3
 
6.4%
5 9
19.1%
6 6
12.8%
7 1
 
2.1%
9 1
 
2.1%
10 2
 
4.3%
11 1
 
2.1%
ValueCountFrequency (%)
38 1
 
2.1%
24 1
 
2.1%
23 2
4.3%
22 1
 
2.1%
19 1
 
2.1%
18 2
4.3%
16 1
 
2.1%
15 1
 
2.1%
14 1
 
2.1%
13 3
6.4%
Distinct24
Distinct (%)51.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.680851
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-13T04:10:12.876977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15.5
median10
Q320.5
95-th percentile29.7
Maximum33
Range32
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.0243785
Coefficient of variation (CV)0.71165401
Kurtosis-0.68237176
Mean12.680851
Median Absolute Deviation (MAD)7
Skewness0.62237183
Sum596
Variance81.439408
MonotonicityNot monotonic
2023-12-13T04:10:12.998341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2 4
 
8.5%
8 4
 
8.5%
11 4
 
8.5%
5 3
 
6.4%
10 3
 
6.4%
3 3
 
6.4%
20 2
 
4.3%
24 2
 
4.3%
22 2
 
4.3%
21 2
 
4.3%
Other values (14) 18
38.3%
ValueCountFrequency (%)
1 2
4.3%
2 4
8.5%
3 3
6.4%
5 3
6.4%
6 2
4.3%
7 1
 
2.1%
8 4
8.5%
9 2
4.3%
10 3
6.4%
11 4
8.5%
ValueCountFrequency (%)
33 1
2.1%
31 1
2.1%
30 1
2.1%
29 1
2.1%
25 1
2.1%
24 2
4.3%
23 1
2.1%
22 2
4.3%
21 2
4.3%
20 2
4.3%

장해가결건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)51.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.723404
Minimum0
Maximum33
Zeros9
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-13T04:10:13.121436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q319
95-th percentile30.4
Maximum33
Range33
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.096711
Coefficient of variation (CV)0.94155836
Kurtosis-0.64444122
Mean10.723404
Median Absolute Deviation (MAD)7
Skewness0.72974148
Sum504
Variance101.94357
MonotonicityNot monotonic
2023-12-13T04:10:13.234864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 9
19.1%
7 3
 
6.4%
8 3
 
6.4%
13 3
 
6.4%
2 3
 
6.4%
21 2
 
4.3%
5 2
 
4.3%
31 2
 
4.3%
24 2
 
4.3%
1 2
 
4.3%
Other values (14) 16
34.0%
ValueCountFrequency (%)
0 9
19.1%
1 2
 
4.3%
2 3
 
6.4%
3 1
 
2.1%
4 2
 
4.3%
5 2
 
4.3%
6 1
 
2.1%
7 3
 
6.4%
8 3
 
6.4%
10 1
 
2.1%
ValueCountFrequency (%)
33 1
2.1%
31 2
4.3%
29 1
2.1%
28 1
2.1%
24 2
4.3%
23 1
2.1%
21 2
4.3%
20 1
2.1%
19 2
4.3%
15 1
2.1%

장해부결건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.787234
Minimum0
Maximum15
Zeros16
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-13T04:10:13.347680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33.5
95-th percentile10
Maximum15
Range15
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation3.6411666
Coefficient of variation (CV)1.3063728
Kurtosis3.2076545
Mean2.787234
Median Absolute Deviation (MAD)1
Skewness1.8153614
Sum131
Variance13.258094
MonotonicityNot monotonic
2023-12-13T04:10:13.459520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 16
34.0%
1 8
17.0%
3 6
 
12.8%
2 5
 
10.6%
6 3
 
6.4%
5 2
 
4.3%
7 2
 
4.3%
10 2
 
4.3%
4 1
 
2.1%
15 1
 
2.1%
ValueCountFrequency (%)
0 16
34.0%
1 8
17.0%
2 5
 
10.6%
3 6
 
12.8%
4 1
 
2.1%
5 2
 
4.3%
6 3
 
6.4%
7 2
 
4.3%
10 2
 
4.3%
14 1
 
2.1%
ValueCountFrequency (%)
15 1
 
2.1%
14 1
 
2.1%
10 2
 
4.3%
7 2
 
4.3%
6 3
 
6.4%
5 2
 
4.3%
4 1
 
2.1%
3 6
12.8%
2 5
10.6%
1 8
17.0%

보류건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)48.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.87234
Minimum0
Maximum44
Zeros15
Zeros (%)31.9%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-13T04:10:13.616103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15
Q323
95-th percentile36.1
Maximum44
Range44
Interquartile range (IQR)23

Descriptive statistics

Standard deviation12.839506
Coefficient of variation (CV)0.92554721
Kurtosis-0.91931919
Mean13.87234
Median Absolute Deviation (MAD)13
Skewness0.42376697
Sum652
Variance164.85291
MonotonicityNot monotonic
2023-12-13T04:10:13.748219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 15
31.9%
22 3
 
6.4%
10 2
 
4.3%
8 2
 
4.3%
26 2
 
4.3%
15 2
 
4.3%
23 2
 
4.3%
18 2
 
4.3%
2 2
 
4.3%
17 2
 
4.3%
Other values (13) 13
27.7%
ValueCountFrequency (%)
0 15
31.9%
2 2
 
4.3%
6 1
 
2.1%
8 2
 
4.3%
9 1
 
2.1%
10 2
 
4.3%
15 2
 
4.3%
17 2
 
4.3%
18 2
 
4.3%
20 1
 
2.1%
ValueCountFrequency (%)
44 1
2.1%
38 1
2.1%
37 1
2.1%
34 1
2.1%
31 1
2.1%
29 1
2.1%
28 1
2.1%
27 1
2.1%
26 2
4.3%
24 1
2.1%

Interactions

2023-12-13T04:10:08.291467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:53.413559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:54.560900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:55.827981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:57.581215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:58.993118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:00.344217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:01.498027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:02.565398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:04.181446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:05.467169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:06.922845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:08.419884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:53.506201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:54.632464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:55.930510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:57.708939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:59.110507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:00.435030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:01.573879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:02.643166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:04.289911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:05.575023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:07.040233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:08.557973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:53.603567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:54.716108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:56.034802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:57.847169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:59.223488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:00.534212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:01.674670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:02.735170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:04.388344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:05.695524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:07.168787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:08.683452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:53.704538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:54.800345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:56.124826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:57.968665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:59.344249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:00.634101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:01.753560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:02.818399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:04.492930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:05.803479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:07.286232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:08.810230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:53.835601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:54.902363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:56.242303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:58.068427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:59.449146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:00.742777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:01.846167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:02.912932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:04.604388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:05.928606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:07.408396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:08.906275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:53.943350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:54.993976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:56.361396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:58.184177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:59.549525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:00.835320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:01.929992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:03.015649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:04.708243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:06.048507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:07.502126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:08.996601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:54.031712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:55.095870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:56.475085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:58.291987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:59.654039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:00.924733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:02.010125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:03.143016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:04.813605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:06.168402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:07.601063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:09.105106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:54.125646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:55.221891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:56.596524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:58.413493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:59.762300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:01.019488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:02.101800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:03.377794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:04.936384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:06.297599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:07.725624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:09.220546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:54.229818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:55.338251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:56.742802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:58.526622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:59.882568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:01.115038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:02.203397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:03.494542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:05.044538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:06.419944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:07.839447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:09.325400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:54.316075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:55.467984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:56.870721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:58.650399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:59.992111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:01.214076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:02.307623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:03.579706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:05.139416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:06.539202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:07.953023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:09.440525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:54.405322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:55.571506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:56.999094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:58.755314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:00.119302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:01.330323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:02.398194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:03.662493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:05.253963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:06.664600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:08.059310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:09.551152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:54.490314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:55.701569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:57.476391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:09:58.893616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:00.238362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:01.421360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:02.484123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:04.077721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:05.366779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:06.797628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:10:08.179262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:10:13.844973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도급여심의회전체상정건수급여심의회전체 가결건수급여심의회전체 부결건수급여심의회전체 가결률직무상요양가결건수직무상요양부결건수유족보상금가결건수유족보상금부결건수장해가결건수장해부결건수보류건수
연도1.0000.9390.9400.9230.7420.9350.8030.4760.6890.8520.6060.810
급여심의회전체상정건수0.9391.0000.9970.8730.5200.9950.7930.2710.7080.8940.6020.857
급여심의회전체 가결건수0.9400.9971.0000.8890.4411.0000.7720.2000.6120.9180.6450.862
급여심의회전체 부결건수0.9230.8730.8891.0000.5700.8830.8670.2860.7930.8720.7230.833
급여심의회전체 가결률0.7420.5200.4410.5701.0000.3980.3200.2290.2060.0000.0000.000
직무상요양가결건수0.9350.9951.0000.8830.3981.0000.7380.1590.6290.9220.6440.844
직무상요양부결건수0.8030.7930.7720.8670.3200.7381.0000.0000.6020.7280.7620.708
유족보상금가결건수0.4760.2710.2000.2860.2290.1590.0001.0000.2780.0000.0000.282
유족보상금부결건수0.6890.7080.6120.7930.2060.6290.6020.2781.0000.7080.4080.345
장해가결건수0.8520.8940.9180.8720.0000.9220.7280.0000.7081.0000.6700.881
장해부결건수0.6060.6020.6450.7230.0000.6440.7620.0000.4080.6701.0000.607
보류건수0.8100.8570.8620.8330.0000.8440.7080.2820.3450.8810.6071.000
2023-12-13T04:10:13.998382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도급여심의회전체상정건수급여심의회전체 가결건수급여심의회전체 부결건수급여심의회전체 가결률직무상요양가결건수직무상요양부결건수유족보상금가결건수유족보상금부결건수장해가결건수장해부결건수보류건수
연도1.0000.9770.9790.9350.1060.9780.947-0.3490.2650.9540.7750.816
급여심의회전체상정건수0.9771.0000.9930.9610.0730.9970.964-0.3280.3160.9190.8000.818
급여심의회전체 가결건수0.9790.9931.0000.9380.1160.9970.945-0.3070.2910.9370.7870.821
급여심의회전체 부결건수0.9350.9610.9381.000-0.0770.9490.991-0.3320.4090.8600.8160.806
급여심의회전체 가결률0.1060.0730.116-0.0771.0000.096-0.0450.396-0.1680.129-0.011-0.052
직무상요양가결건수0.9780.9970.9970.9490.0961.0000.952-0.3220.3070.9250.7980.824
직무상요양부결건수0.9470.9640.9450.991-0.0450.9521.000-0.3490.3430.8750.7960.811
유족보상금가결건수-0.349-0.328-0.307-0.3320.396-0.322-0.3491.0000.326-0.305-0.224-0.288
유족보상금부결건수0.2650.3160.2910.409-0.1680.3070.3430.3261.0000.2480.3140.254
장해가결건수0.9540.9190.9370.8600.1290.9250.875-0.3050.2481.0000.6900.763
장해부결건수0.7750.8000.7870.816-0.0110.7980.796-0.2240.3140.6901.0000.701
보류건수0.8160.8180.8210.806-0.0520.8240.811-0.2880.2540.7630.7011.000

Missing values

2023-12-13T04:10:09.704749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:10:09.933711image/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

연도급여심의회전체상정건수급여심의회전체 가결건수급여심의회전체 부결건수급여심의회전체 가결률직무상요양가결건수직무상요양부결건수유족보상금가결건수유족보상금부결건수장해가결건수장해부결건수보류건수
0197529181162.0713952000
119772920968.9714663002
2197857391868.42341058002
319792719870.3714652000
4198065183.333021000
5198196366.671053000
619821312192.317051000
7198372611184.72519102000
81984108891982.417481511000
919851591392087.4212411149100
연도급여심의회전체상정건수급여심의회전체 가결건수급여심의회전체 부결건수급여심의회전체 가결률직무상요양가결건수직무상요양부결건수유족보상금가결건수유족보상금부결건수장해가결건수장해부결건수보류건수
37201380966814182.5764512841019324
38201474262911384.7759110291029122
39201576162613582.2660312121321134
40201684570214383.086791304819531
41201797784513286.498151206524737
422018109294015286.089111405824438
4320191286110717986.081071158511311026
442020110694416285.3592314418201015
452021104789914885.8686612825311522
4620221198104914987.6101213233331444