Overview

Dataset statistics

Number of variables7
Number of observations40
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory66.3 B

Variable types

Numeric7

Dataset

Description연금제도 도입이후 축적된 재정추계 데이터(1983년~2022년까지 연도별 구분)로 일반사망, 공무상사망, 사망자수 비율, 사망비율 등을 포함하고 있습니다.
URLhttps://www.data.go.kr/data/15054913/fileData.do

Alerts

연도 is highly overall correlated with 가입자수 and 5 other fieldsHigh correlation
가입자수 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
합계 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
일반사망자수 is highly overall correlated with 연도 and 4 other fieldsHigh correlation
공무상사망자수 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
일반사망자수대비공무상사망자수비율(퍼센트) is highly overall correlated with 연도 and 4 other fieldsHigh correlation
공무상사망률(퍼센트) is highly overall correlated with 연도 and 5 other fieldsHigh correlation
연도 has unique valuesUnique
가입자수 has unique valuesUnique

Reproduction

Analysis started2023-12-12 08:59:59.152987
Analysis finished2023-12-12 09:00:05.075388
Duration5.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2002.5
Minimum1983
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T18:00:05.139594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1983
5-th percentile1984.95
Q11992.75
median2002.5
Q32012.25
95-th percentile2020.05
Maximum2022
Range39
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation11.690452
Coefficient of variation (CV)0.0058379286
Kurtosis-1.2
Mean2002.5
Median Absolute Deviation (MAD)10
Skewness0
Sum80100
Variance136.66667
MonotonicityStrictly increasing
2023-12-12T18:00:05.271734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1983 1
 
2.5%
2004 1
 
2.5%
2006 1
 
2.5%
2007 1
 
2.5%
2008 1
 
2.5%
2009 1
 
2.5%
2010 1
 
2.5%
2011 1
 
2.5%
2012 1
 
2.5%
2013 1
 
2.5%
Other values (30) 30
75.0%
ValueCountFrequency (%)
1983 1
2.5%
1984 1
2.5%
1985 1
2.5%
1986 1
2.5%
1987 1
2.5%
1988 1
2.5%
1989 1
2.5%
1990 1
2.5%
1991 1
2.5%
1992 1
2.5%
ValueCountFrequency (%)
2022 1
2.5%
2021 1
2.5%
2020 1
2.5%
2019 1
2.5%
2018 1
2.5%
2017 1
2.5%
2016 1
2.5%
2015 1
2.5%
2014 1
2.5%
2013 1
2.5%

가입자수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean973138.28
Minimum669733
Maximum1280994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T18:00:05.408059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum669733
5-th percentile696217.5
Q1912182.75
median967948
Q31066506.5
95-th percentile1223326.9
Maximum1280994
Range611261
Interquartile range (IQR)154323.75

Descriptive statistics

Standard deviation153664.28
Coefficient of variation (CV)0.15790591
Kurtosis-0.21164635
Mean973138.28
Median Absolute Deviation (MAD)87234.5
Skewness-0.16574732
Sum38925531
Variance2.3612712 × 1010
MonotonicityNot monotonic
2023-12-12T18:00:05.597216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
669733 1
 
2.5%
964593 1
 
2.5%
1009145 1
 
2.5%
1021771 1
 
2.5%
1030256 1
 
2.5%
1047897 1
 
2.5%
1052407 1
 
2.5%
1057958 1
 
2.5%
1064472 1
 
2.5%
1072610 1
 
2.5%
Other values (30) 30
75.0%
ValueCountFrequency (%)
669733 1
2.5%
682281 1
2.5%
696951 1
2.5%
716629 1
2.5%
737688 1
2.5%
767123 1
2.5%
810069 1
2.5%
843262 1
2.5%
884648 1
2.5%
909155 1
2.5%
ValueCountFrequency (%)
1280994 1
2.5%
1261421 1
2.5%
1221322 1
2.5%
1195051 1
2.5%
1160586 1
2.5%
1120458 1
2.5%
1107972 1
2.5%
1093038 1
2.5%
1081147 1
2.5%
1072610 1
2.5%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1160.375
Minimum696
Maximum1813
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T18:00:05.750565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum696
5-th percentile732.1
Q1857
median954
Q31533.25
95-th percentile1757.4
Maximum1813
Range1117
Interquartile range (IQR)676.25

Descriptive statistics

Standard deviation388.29183
Coefficient of variation (CV)0.33462616
Kurtosis-1.5253448
Mean1160.375
Median Absolute Deviation (MAD)202.5
Skewness0.464264
Sum46415
Variance150770.55
MonotonicityNot monotonic
2023-12-12T18:00:05.915168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
857 2
 
5.0%
1458 1
 
2.5%
1486 1
 
2.5%
963 1
 
2.5%
893 1
 
2.5%
865 1
 
2.5%
879 1
 
2.5%
858 1
 
2.5%
845 1
 
2.5%
892 1
 
2.5%
Other values (29) 29
72.5%
ValueCountFrequency (%)
696 1
2.5%
715 1
2.5%
733 1
2.5%
748 1
2.5%
755 1
2.5%
762 1
2.5%
801 1
2.5%
845 1
2.5%
847 1
2.5%
857 2
5.0%
ValueCountFrequency (%)
1813 1
2.5%
1784 1
2.5%
1756 1
2.5%
1745 1
2.5%
1725 1
2.5%
1708 1
2.5%
1704 1
2.5%
1602 1
2.5%
1569 1
2.5%
1555 1
2.5%

일반사망자수
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean921.7
Minimum629
Maximum1395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T18:00:06.459531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum629
5-th percentile638.9
Q1692.5
median793.5
Q31189.75
95-th percentile1331.4
Maximum1395
Range766
Interquartile range (IQR)497.25

Descriptive statistics

Standard deviation256.86964
Coefficient of variation (CV)0.27869115
Kurtosis-1.5101701
Mean921.7
Median Absolute Deviation (MAD)142.5
Skewness0.45699481
Sum36868
Variance65982.01
MonotonicityNot monotonic
2023-12-12T18:00:06.645736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
691 2
 
5.0%
779 2
 
5.0%
1165 1
 
2.5%
667 1
 
2.5%
782 1
 
2.5%
756 1
 
2.5%
772 1
 
2.5%
775 1
 
2.5%
802 1
 
2.5%
792 1
 
2.5%
Other values (28) 28
70.0%
ValueCountFrequency (%)
629 1
2.5%
637 1
2.5%
639 1
2.5%
649 1
2.5%
653 1
2.5%
665 1
2.5%
667 1
2.5%
686 1
2.5%
691 2
5.0%
693 1
2.5%
ValueCountFrequency (%)
1395 1
2.5%
1339 1
2.5%
1331 1
2.5%
1313 1
2.5%
1233 1
2.5%
1228 1
2.5%
1217 1
2.5%
1206 1
2.5%
1202 1
2.5%
1201 1
2.5%

공무상사망자수
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean238.675
Minimum58
Maximum556
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T18:00:06.830886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum58
5-th percentile64.85
Q188.5
median273.5
Q3357.5
95-th percentile503.45
Maximum556
Range498
Interquartile range (IQR)269

Descriptive statistics

Standard deviation154.48581
Coefficient of variation (CV)0.64726432
Kurtosis-1.1434386
Mean238.675
Median Absolute Deviation (MAD)163.5
Skewness0.35782642
Sum9547
Variance23865.866
MonotonicityNot monotonic
2023-12-12T18:00:07.001229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
109 2
 
5.0%
90 2
 
5.0%
293 1
 
2.5%
65 1
 
2.5%
165 1
 
2.5%
111 1
 
2.5%
107 1
 
2.5%
83 1
 
2.5%
66 1
 
2.5%
77 1
 
2.5%
Other values (28) 28
70.0%
ValueCountFrequency (%)
58 1
2.5%
62 1
2.5%
65 1
2.5%
66 1
2.5%
67 1
2.5%
68 1
2.5%
69 1
2.5%
77 1
2.5%
83 1
2.5%
84 1
2.5%
ValueCountFrequency (%)
556 1
2.5%
512 1
2.5%
503 1
2.5%
500 1
2.5%
417 1
2.5%
400 1
2.5%
394 1
2.5%
389 1
2.5%
386 1
2.5%
383 1
2.5%

일반사망자수대비공무상사망자수비율(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.5175
Minimum7.3
Maximum48.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T18:00:07.183697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.3
5-th percentile8.485
Q112.475
median24.2
Q333.975
95-th percentile45.465
Maximum48.3
Range41
Interquartile range (IQR)21.5

Descriptive statistics

Standard deviation13.134529
Coefficient of variation (CV)0.53572055
Kurtosis-1.3090876
Mean24.5175
Median Absolute Deviation (MAD)11.55
Skewness0.29199186
Sum980.7
Variance172.51584
MonotonicityNot monotonic
2023-12-12T18:00:07.395985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
10.7 2
 
5.0%
25.2 1
 
2.5%
22.1 1
 
2.5%
20.7 1
 
2.5%
14.2 1
 
2.5%
14.4 1
 
2.5%
13.9 1
 
2.5%
8.5 1
 
2.5%
11.2 1
 
2.5%
8.2 1
 
2.5%
Other values (29) 29
72.5%
ValueCountFrequency (%)
7.3 1
2.5%
8.2 1
2.5%
8.5 1
2.5%
8.7 1
2.5%
9.5 1
2.5%
9.7 1
2.5%
10.0 1
2.5%
10.7 2
5.0%
11.2 1
2.5%
12.9 1
2.5%
ValueCountFrequency (%)
48.3 1
2.5%
46.7 1
2.5%
45.4 1
2.5%
44.4 1
2.5%
41.9 1
2.5%
41.7 1
2.5%
41.5 1
2.5%
40.8 1
2.5%
38.1 1
2.5%
36.0 1
2.5%

공무상사망률(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02664
Minimum0.0049
Maximum0.0572
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T18:00:07.564570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0049
5-th percentile0.00549
Q10.007875
median0.0297
Q30.04325
95-th percentile0.05274
Maximum0.0572
Range0.0523
Interquartile range (IQR)0.035375

Descriptive statistics

Standard deviation0.018229995
Coefficient of variation (CV)0.68430912
Kurtosis-1.6878796
Mean0.02664
Median Absolute Deviation (MAD)0.01915
Skewness0.095940849
Sum1.0656
Variance0.00033233272
MonotonicityNot monotonic
2023-12-12T18:00:07.743955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0.0063 2
 
5.0%
0.0085 2
 
5.0%
0.0437 1
 
2.5%
0.0061 1
 
2.5%
0.0167 1
 
2.5%
0.011 1
 
2.5%
0.0107 1
 
2.5%
0.0104 1
 
2.5%
0.0079 1
 
2.5%
0.0072 1
 
2.5%
Other values (28) 28
70.0%
ValueCountFrequency (%)
0.0049 1
2.5%
0.0053 1
2.5%
0.0055 1
2.5%
0.0058 1
2.5%
0.0061 1
2.5%
0.0063 2
5.0%
0.0072 1
2.5%
0.0076 1
2.5%
0.0078 1
2.5%
0.0079 1
2.5%
ValueCountFrequency (%)
0.0572 1
2.5%
0.0535 1
2.5%
0.0527 1
2.5%
0.0512 1
2.5%
0.0499 1
2.5%
0.0474 1
2.5%
0.0445 1
2.5%
0.0444 1
2.5%
0.0441 1
2.5%
0.0437 1
2.5%

Interactions

2023-12-12T18:00:04.305118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:59.714446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:00.614905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:01.696881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:02.416201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:03.138756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:03.661656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:04.382172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:59.895249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:00.802697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:01.815735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:02.519081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:03.233414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:03.755988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:04.472141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:00.048278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:00.910438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:01.934940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:02.641734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:03.309338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:03.844631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:04.553456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:00.147484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:01.041621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:02.037692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:02.733945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:03.379163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:03.928519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:04.637343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:00.274235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:01.202008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:02.139853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:02.846053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:03.462127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:04.035701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:04.703795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:00.381273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:01.373450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:02.246935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:02.933975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:03.524866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:04.144644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:04.774209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:00.493258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:01.575838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:02.329310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:03.032811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:03.592497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:04.232910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:00:07.858800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도가입자수합계일반사망자수공무상사망자수일반사망자수대비공무상사망자수비율(퍼센트)공무상사망률(퍼센트)
연도1.0000.9570.8370.8010.8490.9040.900
가입자수0.9571.0000.7970.5680.6580.8910.866
합계0.8370.7971.0000.8890.6310.6960.788
일반사망자수0.8010.5680.8891.0000.6200.7250.722
공무상사망자수0.8490.6580.6310.6201.0000.9010.959
일반사망자수대비공무상사망자수비율(퍼센트)0.9040.8910.6960.7250.9011.0000.949
공무상사망률(퍼센트)0.9000.8660.7880.7220.9590.9491.000
2023-12-12T18:00:08.011362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도가입자수합계일반사망자수공무상사망자수일반사망자수대비공무상사망자수비율(퍼센트)공무상사망률(퍼센트)
연도1.0000.962-0.889-0.750-0.793-0.631-0.867
가입자수0.9621.000-0.794-0.586-0.709-0.649-0.776
합계-0.889-0.7941.0000.8310.9200.7260.939
일반사망자수-0.750-0.5860.8311.0000.6290.2430.697
공무상사망자수-0.793-0.7090.9200.6291.0000.8770.977
일반사망자수대비공무상사망자수비율(퍼센트)-0.631-0.6490.7260.2430.8771.0000.824
공무상사망률(퍼센트)-0.867-0.7760.9390.6970.9770.8241.000

Missing values

2023-12-12T18:00:04.882926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:00:05.028828image/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

연도가입자수합계일반사망자수공무상사망자수일반사망자수대비공무상사망자수비율(퍼센트)공무상사망률(퍼센트)
019836697331458116529325.20.0437
119846822811486121726922.10.0394
219856969511448116728124.10.0403
319867166291526122829824.30.0416
419877376881439111432529.20.0441
519887671231569118638332.30.0499
619898100691555120634928.90.0431
719908432621602120240033.30.0474
819918846481725133139429.60.0445
919929220981784139538927.90.0422
연도가입자수합계일반사망자수공무상사망자수일반사망자수대비공무상사망자수비율(퍼센트)공무상사망률(퍼센트)
3020131072610872795779.70.0072
3120141081147847779688.70.0063
3220151093038857799587.30.0053
33201611079727336498412.90.0076
34201711204588017029914.10.0088
35201811605867556659013.50.0078
36201911950517626936910.00.0058
37202012213226966296710.70.0055
3820211261421715653629.50.0049
392022128099474863910917.10.0085