Overview

Dataset statistics

Number of variables12
Number of observations43
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.7 KiB
Average record size in memory111.1 B

Variable types

Numeric12

Dataset

Description경력별(공무원, 군인, 사립학교) 재직기간 합산 승인건수에 대한 자료로 공무원경력, 군경력, 사립학교경력 등에 대한 합산 승인건수 데이터가 포함되어 있습니다.
URLhttps://www.data.go.kr/data/15052953/fileData.do

Alerts

연도 is highly overall correlated with 퇴직연금해당자(공무원) and 5 other fieldsHigh correlation
합계 is highly overall correlated with 공무원경력(계) and 3 other fieldsHigh correlation
공무원경력(계) is highly overall correlated with 합계 and 1 other fieldsHigh correlation
퇴직연금해당자(공무원) is highly overall correlated with 연도 and 4 other fieldsHigh correlation
미수령자(공무원) is highly overall correlated with 연도 and 5 other fieldsHigh correlation
수령자(공무원) is highly overall correlated with 연도 and 4 other fieldsHigh correlation
군경력(계) is highly overall correlated with 합계 and 1 other fieldsHigh correlation
퇴직연금해당자(군) is highly overall correlated with 퇴직연금해당자(사립)High correlation
퇴직일시금해당자(군) is highly overall correlated with 합계 and 1 other fieldsHigh correlation
사립학교경력(계) is highly overall correlated with 연도 and 4 other fieldsHigh correlation
퇴직연금해당자(사립) is highly overall correlated with 연도 and 6 other fieldsHigh correlation
퇴직일시금해당자(사립) is highly overall correlated with 연도 and 4 other fieldsHigh correlation
연도 has unique valuesUnique
합계 has unique valuesUnique
수령자(공무원) has unique valuesUnique
퇴직일시금해당자(군) has unique valuesUnique
퇴직연금해당자(공무원) has 1 (2.3%) zerosZeros
사립학교경력(계) has 3 (7.0%) zerosZeros
퇴직연금해당자(사립) has 21 (48.8%) zerosZeros
퇴직일시금해당자(사립) has 3 (7.0%) zerosZeros

Reproduction

Analysis started2023-12-12 13:23:21.668453
Analysis finished2023-12-12 13:23:36.706084
Duration15.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001
Minimum1980
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T22:23:36.775806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1980
5-th percentile1982.1
Q11990.5
median2001
Q32011.5
95-th percentile2019.9
Maximum2022
Range42
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.556539
Coefficient of variation (CV)0.0062751318
Kurtosis-1.2
Mean2001
Median Absolute Deviation (MAD)11
Skewness0
Sum86043
Variance157.66667
MonotonicityStrictly increasing
2023-12-12T22:23:36.906046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1980 1
 
2.3%
1981 1
 
2.3%
2004 1
 
2.3%
2005 1
 
2.3%
2006 1
 
2.3%
2007 1
 
2.3%
2008 1
 
2.3%
2009 1
 
2.3%
2010 1
 
2.3%
2011 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
1980 1
2.3%
1981 1
2.3%
1982 1
2.3%
1983 1
2.3%
1984 1
2.3%
1985 1
2.3%
1986 1
2.3%
1987 1
2.3%
1988 1
2.3%
1989 1
2.3%
ValueCountFrequency (%)
2022 1
2.3%
2021 1
2.3%
2020 1
2.3%
2019 1
2.3%
2018 1
2.3%
2017 1
2.3%
2016 1
2.3%
2015 1
2.3%
2014 1
2.3%
2013 1
2.3%

합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6656.5349
Minimum2736
Maximum11349
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T22:23:37.032164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2736
5-th percentile4413.1
Q15476.5
median6353
Q37897.5
95-th percentile9555.2
Maximum11349
Range8613
Interquartile range (IQR)2421

Descriptive statistics

Standard deviation1760.8609
Coefficient of variation (CV)0.26453116
Kurtosis0.35848362
Mean6656.5349
Median Absolute Deviation (MAD)1182
Skewness0.36139196
Sum286231
Variance3100631.2
MonotonicityNot monotonic
2023-12-12T22:23:37.149497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
10164 1
 
2.3%
5481 1
 
2.3%
6553 1
 
2.3%
6047 1
 
2.3%
6053 1
 
2.3%
5171 1
 
2.3%
6672 1
 
2.3%
4630 1
 
2.3%
9570 1
 
2.3%
5291 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
2736 1
2.3%
3412 1
2.3%
4389 1
2.3%
4630 1
2.3%
4895 1
2.3%
4953 1
2.3%
4988 1
2.3%
5104 1
2.3%
5171 1
2.3%
5291 1
2.3%
ValueCountFrequency (%)
11349 1
2.3%
10164 1
2.3%
9570 1
2.3%
9422 1
2.3%
8486 1
2.3%
8437 1
2.3%
8331 1
2.3%
8172 1
2.3%
8119 1
2.3%
8097 1
2.3%

공무원경력(계)
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3777.9535
Minimum1051
Maximum9294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T22:23:37.275149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1051
5-th percentile2017.1
Q12952.5
median3320
Q34639.5
95-th percentile5795.8
Maximum9294
Range8243
Interquartile range (IQR)1687

Descriptive statistics

Standard deviation1428.2599
Coefficient of variation (CV)0.37805122
Kurtosis4.0484026
Mean3777.9535
Median Absolute Deviation (MAD)839
Skewness1.3229608
Sum162452
Variance2039926.4
MonotonicityNot monotonic
2023-12-12T22:23:37.404553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
3320 2
 
4.7%
9294 1
 
2.3%
2572 1
 
2.3%
2988 1
 
2.3%
2945 1
 
2.3%
2283 1
 
2.3%
2707 1
 
2.3%
1990 1
 
2.3%
4651 1
 
2.3%
2521 1
 
2.3%
Other values (32) 32
74.4%
ValueCountFrequency (%)
1051 1
2.3%
1761 1
2.3%
1990 1
2.3%
2261 1
2.3%
2283 1
2.3%
2403 1
2.3%
2521 1
2.3%
2572 1
2.3%
2707 1
2.3%
2843 1
2.3%
ValueCountFrequency (%)
9294 1
2.3%
6102 1
2.3%
5839 1
2.3%
5407 1
2.3%
5236 1
2.3%
5217 1
2.3%
5118 1
2.3%
4964 1
2.3%
4766 1
2.3%
4719 1
2.3%

퇴직연금해당자(공무원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.116279
Minimum0
Maximum912
Zeros1
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T22:23:37.533855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.2
Q121.5
median42
Q374
95-th percentile192
Maximum912
Range912
Interquartile range (IQR)52.5

Descriptive statistics

Standard deviation140.92301
Coefficient of variation (CV)1.8040159
Kurtosis30.519507
Mean78.116279
Median Absolute Deviation (MAD)25
Skewness5.186506
Sum3359
Variance19859.296
MonotonicityNot monotonic
2023-12-12T22:23:37.678457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1 2
 
4.7%
21 2
 
4.7%
43 2
 
4.7%
35 2
 
4.7%
67 2
 
4.7%
0 1
 
2.3%
64 1
 
2.3%
41 1
 
2.3%
36 1
 
2.3%
44 1
 
2.3%
Other values (28) 28
65.1%
ValueCountFrequency (%)
0 1
2.3%
1 2
4.7%
3 1
2.3%
7 1
2.3%
12 1
2.3%
14 1
2.3%
16 1
2.3%
17 1
2.3%
21 2
4.7%
22 1
2.3%
ValueCountFrequency (%)
912 1
2.3%
211 1
2.3%
193 1
2.3%
183 1
2.3%
165 1
2.3%
154 1
2.3%
120 1
2.3%
106 1
2.3%
102 1
2.3%
100 1
2.3%

미수령자(공무원)
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean764.93023
Minimum147
Maximum2604
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T22:23:37.813149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum147
5-th percentile208
Q1274.5
median553
Q3890.5
95-th percentile2220.1
Maximum2604
Range2457
Interquartile range (IQR)616

Descriptive statistics

Standard deviation665.48809
Coefficient of variation (CV)0.86999842
Kurtosis1.3662967
Mean764.93023
Median Absolute Deviation (MAD)298
Skewness1.4760486
Sum32892
Variance442874.4
MonotonicityNot monotonic
2023-12-12T22:23:37.944941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
553 2
 
4.7%
217 1
 
2.3%
644 1
 
2.3%
686 1
 
2.3%
661 1
 
2.3%
869 1
 
2.3%
697 1
 
2.3%
652 1
 
2.3%
821 1
 
2.3%
665 1
 
2.3%
Other values (32) 32
74.4%
ValueCountFrequency (%)
147 1
2.3%
171 1
2.3%
207 1
2.3%
217 1
2.3%
228 1
2.3%
237 1
2.3%
238 1
2.3%
243 1
2.3%
245 1
2.3%
255 1
2.3%
ValueCountFrequency (%)
2604 1
2.3%
2570 1
2.3%
2243 1
2.3%
2014 1
2.3%
1843 1
2.3%
1510 1
2.3%
1463 1
2.3%
1443 1
2.3%
1383 1
2.3%
1132 1
2.3%

수령자(공무원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2934.907
Minimum890
Maximum9077
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T22:23:38.068075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum890
5-th percentile1388.3
Q11765
median2477
Q33884
95-th percentile5461.7
Maximum9077
Range8187
Interquartile range (IQR)2119

Descriptive statistics

Standard deviation1621.9032
Coefficient of variation (CV)0.55262509
Kurtosis3.4168389
Mean2934.907
Median Absolute Deviation (MAD)975
Skewness1.5506412
Sum126201
Variance2630570.1
MonotonicityNot monotonic
2023-12-12T22:23:38.198748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
9077 1
 
2.3%
4973 1
 
2.3%
2883 1
 
2.3%
2221 1
 
2.3%
2016 1
 
2.3%
1689 1
 
2.3%
1943 1
 
2.3%
1302 1
 
2.3%
3787 1
 
2.3%
1813 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
890 1
2.3%
1302 1
2.3%
1382 1
2.3%
1445 1
2.3%
1447 1
2.3%
1475 1
2.3%
1482 1
2.3%
1502 1
2.3%
1689 1
2.3%
1736 1
2.3%
ValueCountFrequency (%)
9077 1
2.3%
5753 1
2.3%
5516 1
2.3%
4973 1
2.3%
4920 1
2.3%
4915 1
2.3%
4838 1
2.3%
4452 1
2.3%
4162 1
2.3%
4135 1
2.3%

군경력(계)
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1931
Minimum264
Maximum3938
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T22:23:38.321393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum264
5-th percentile874.4
Q11648.5
median1975
Q32295.5
95-th percentile2964.1
Maximum3938
Range3674
Interquartile range (IQR)647

Descriptive statistics

Standard deviation672.98942
Coefficient of variation (CV)0.3485186
Kurtosis1.4601856
Mean1931
Median Absolute Deviation (MAD)339
Skewness0.06476921
Sum83033
Variance452914.76
MonotonicityNot monotonic
2023-12-12T22:23:38.516602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1760 2
 
4.7%
870 1
 
2.3%
1621 1
 
2.3%
1807 1
 
2.3%
1911 1
 
2.3%
1676 1
 
2.3%
2277 1
 
2.3%
1765 1
 
2.3%
3058 1
 
2.3%
1836 1
 
2.3%
Other values (32) 32
74.4%
ValueCountFrequency (%)
264 1
2.3%
584 1
2.3%
870 1
2.3%
914 1
2.3%
1020 1
2.3%
1093 1
2.3%
1209 1
2.3%
1372 1
2.3%
1451 1
2.3%
1583 1
2.3%
ValueCountFrequency (%)
3938 1
2.3%
3058 1
2.3%
2996 1
2.3%
2677 1
2.3%
2602 1
2.3%
2581 1
2.3%
2379 1
2.3%
2370 1
2.3%
2359 1
2.3%
2355 1
2.3%

퇴직연금해당자(군)
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.674419
Minimum26
Maximum246
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T22:23:38.651031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile35.4
Q154.5
median88
Q3123
95-th percentile173.7
Maximum246
Range220
Interquartile range (IQR)68.5

Descriptive statistics

Standard deviation49.242704
Coefficient of variation (CV)0.52567931
Kurtosis0.74812951
Mean93.674419
Median Absolute Deviation (MAD)35
Skewness0.9104858
Sum4028
Variance2424.8439
MonotonicityNot monotonic
2023-12-12T22:23:38.798041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
140 2
 
4.7%
62 2
 
4.7%
78 2
 
4.7%
136 2
 
4.7%
56 2
 
4.7%
39 2
 
4.7%
50 2
 
4.7%
63 2
 
4.7%
64 1
 
2.3%
101 1
 
2.3%
Other values (25) 25
58.1%
ValueCountFrequency (%)
26 1
2.3%
30 1
2.3%
35 1
2.3%
39 2
4.7%
44 1
2.3%
47 1
2.3%
49 1
2.3%
50 2
4.7%
53 1
2.3%
56 2
4.7%
ValueCountFrequency (%)
246 1
2.3%
194 1
2.3%
175 1
2.3%
162 1
2.3%
160 1
2.3%
146 1
2.3%
140 2
4.7%
136 2
4.7%
124 1
2.3%
122 1
2.3%

퇴직일시금해당자(군)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1837.3256
Minimum70
Maximum3867
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T22:23:38.917932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile769.2
Q11566
median1921
Q32176.5
95-th percentile2873
Maximum3867
Range3797
Interquartile range (IQR)610.5

Descriptive statistics

Standard deviation675.86733
Coefficient of variation (CV)0.36785387
Kurtosis1.6739346
Mean1837.3256
Median Absolute Deviation (MAD)318
Skewness0.042253174
Sum79005
Variance456796.65
MonotonicityNot monotonic
2023-12-12T22:23:39.099495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
757 1
 
2.3%
70 1
 
2.3%
1479 1
 
2.3%
1647 1
 
2.3%
1736 1
 
2.3%
1575 1
 
2.3%
2031 1
 
2.3%
1603 1
 
2.3%
2965 1
 
2.3%
1773 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
70 1
2.3%
522 1
2.3%
757 1
2.3%
879 1
2.3%
953 1
2.3%
994 1
2.3%
1131 1
2.3%
1327 1
2.3%
1342 1
2.3%
1479 1
2.3%
ValueCountFrequency (%)
3867 1
2.3%
2965 1
2.3%
2901 1
2.3%
2621 1
2.3%
2466 1
2.3%
2435 1
2.3%
2331 1
2.3%
2329 1
2.3%
2253 1
2.3%
2223 1
2.3%

사립학교경력(계)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean947.5814
Minimum0
Maximum1861
Zeros3
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T22:23:39.239987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35.7
Q1680
median934
Q31266.5
95-th percentile1649
Maximum1861
Range1861
Interquartile range (IQR)586.5

Descriptive statistics

Standard deviation453.40984
Coefficient of variation (CV)0.47849171
Kurtosis-0.20313731
Mean947.5814
Median Absolute Deviation (MAD)278
Skewness-0.18595186
Sum40746
Variance205580.49
MonotonicityNot monotonic
2023-12-12T22:23:39.381356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 3
 
7.0%
795 1
 
2.3%
1252 1
 
2.3%
1197 1
 
2.3%
1212 1
 
2.3%
1688 1
 
2.3%
875 1
 
2.3%
1861 1
 
2.3%
934 1
 
2.3%
713 1
 
2.3%
Other values (31) 31
72.1%
ValueCountFrequency (%)
0 3
7.0%
357 1
 
2.3%
414 1
 
2.3%
460 1
 
2.3%
483 1
 
2.3%
536 1
 
2.3%
587 1
 
2.3%
665 1
 
2.3%
671 1
 
2.3%
689 1
 
2.3%
ValueCountFrequency (%)
1861 1
2.3%
1688 1
2.3%
1654 1
2.3%
1604 1
2.3%
1577 1
2.3%
1513 1
2.3%
1481 1
2.3%
1337 1
2.3%
1309 1
2.3%
1301 1
2.3%

퇴직연금해당자(사립)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)39.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.511628
Minimum0
Maximum69
Zeros21
Zeros (%)48.8%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T22:23:39.498393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q38
95-th percentile50
Maximum69
Range69
Interquartile range (IQR)8

Descriptive statistics

Standard deviation17.612728
Coefficient of variation (CV)1.6755472
Kurtosis3.1631833
Mean10.511628
Median Absolute Deviation (MAD)3
Skewness1.9801981
Sum452
Variance310.20819
MonotonicityNot monotonic
2023-12-12T22:23:39.605713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 21
48.8%
8 4
 
9.3%
7 2
 
4.7%
4 2
 
4.7%
3 2
 
4.7%
40 1
 
2.3%
51 1
 
2.3%
37 1
 
2.3%
69 1
 
2.3%
59 1
 
2.3%
Other values (7) 7
 
16.3%
ValueCountFrequency (%)
0 21
48.8%
3 2
 
4.7%
4 2
 
4.7%
5 1
 
2.3%
6 1
 
2.3%
7 2
 
4.7%
8 4
 
9.3%
16 1
 
2.3%
19 1
 
2.3%
23 1
 
2.3%
ValueCountFrequency (%)
69 1
2.3%
59 1
2.3%
51 1
2.3%
41 1
2.3%
40 1
2.3%
37 1
2.3%
26 1
2.3%
23 1
2.3%
19 1
2.3%
16 1
2.3%

퇴직일시금해당자(사립)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean937.06977
Minimum0
Maximum1858
Zeros3
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T22:23:40.051538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35.7
Q1680
median926
Q31261.5
95-th percentile1609.4
Maximum1858
Range1858
Interquartile range (IQR)581.5

Descriptive statistics

Standard deviation444.27862
Coefficient of variation (CV)0.47411478
Kurtosis-0.13690469
Mean937.06977
Median Absolute Deviation (MAD)278
Skewness-0.2070344
Sum40294
Variance197383.5
MonotonicityNot monotonic
2023-12-12T22:23:40.192521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 3
 
7.0%
1058 2
 
4.7%
705 1
 
2.3%
1248 1
 
2.3%
1193 1
 
2.3%
1204 1
 
2.3%
1665 1
 
2.3%
868 1
 
2.3%
1858 1
 
2.3%
926 1
 
2.3%
Other values (30) 30
69.8%
ValueCountFrequency (%)
0 3
7.0%
357 1
 
2.3%
414 1
 
2.3%
460 1
 
2.3%
483 1
 
2.3%
536 1
 
2.3%
587 1
 
2.3%
665 1
 
2.3%
671 1
 
2.3%
689 1
 
2.3%
ValueCountFrequency (%)
1858 1
2.3%
1665 1
2.3%
1613 1
2.3%
1577 1
2.3%
1535 1
2.3%
1454 1
2.3%
1444 1
2.3%
1309 1
2.3%
1296 1
2.3%
1286 1
2.3%

Interactions

2023-12-12T22:23:35.419744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:22.012515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:23.160939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:24.245973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:25.488065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:26.716218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:27.864446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:29.169437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:30.342354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:31.387665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:32.671181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:33.961829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:35.509240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:22.111646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:23.233460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:24.381133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:25.596611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:26.810136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:27.945901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:29.265990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:30.414422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:31.487947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:32.773565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:34.053258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:35.593780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:22.228442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:23.305479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:24.456179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:25.669775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:26.890635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:28.022126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:29.350258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:30.484875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:31.583452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:32.871958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:34.138666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:35.665551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:22.302133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:23.378275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:24.554295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:25.785098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:26.988940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:28.090541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:29.435359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:30.560157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:31.692691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:32.952434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:34.223347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:35.739607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:22.386249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:23.456251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:24.637252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:25.887698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:27.083477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:28.190305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:29.532503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:30.643422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:31.796277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:33.067494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:34.312161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:35.820960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:22.482667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:23.543804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:24.724077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:25.974073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:27.177292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:28.291397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:29.623654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:30.720760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:31.904830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:33.184119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:34.392184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:35.902851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:22.565127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:23.639510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:24.811900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:26.066078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:27.281736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:28.372162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:29.728711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:30.793575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:31.987448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:33.280822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:34.488181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:35.994356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:22.655838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:23.755905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:24.917963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:26.177808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:27.404173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:28.466652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:29.821385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:30.886010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:32.119493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:33.431336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:34.597845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:36.085386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:22.758805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:23.841893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:25.020149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:26.272589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:27.492589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:28.537262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:29.919621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:30.971704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:32.229211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:33.527940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:34.685134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:36.205629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:22.865049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:23.933609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:25.133693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:26.391924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:27.577278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:28.913933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:30.015045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:31.077595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:32.334517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:33.637973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:34.771638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:36.287835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:22.959579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:24.029941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:25.243658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:26.502456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:27.666278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:29.006812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:30.147678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:31.192745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:32.449345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:33.753442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:34.871451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:36.383768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:23.054014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:24.131186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:25.345501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:26.615105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:27.761264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:29.085409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:30.238104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:31.282981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:32.567809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:33.849020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:34.966319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:23:40.303042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도합계공무원경력(계)퇴직연금해당자(공무원)미수령자(공무원)수령자(공무원)군경력(계)퇴직연금해당자(군)퇴직일시금해당자(군)사립학교경력(계)퇴직연금해당자(사립)퇴직일시금해당자(사립)
연도1.0000.6570.5910.7890.8400.5880.4230.6590.4680.8740.6200.853
합계0.6571.0000.9100.0000.0000.8040.8550.5780.8280.5740.2920.677
공무원경력(계)0.5910.9101.0000.0000.0000.8210.7470.2310.7420.5340.0000.502
퇴직연금해당자(공무원)0.7890.0000.0001.0000.7170.0000.0000.4480.0000.6500.9240.467
미수령자(공무원)0.8400.0000.0000.7171.0000.0000.0000.0000.0000.7590.9030.541
수령자(공무원)0.5880.8040.8210.0000.0001.0000.5990.4370.6000.4740.0000.479
군경력(계)0.4230.8550.7470.0000.0000.5991.0000.3690.9830.5220.0000.629
퇴직연금해당자(군)0.6590.5780.2310.4480.0000.4370.3691.0000.8190.6600.5880.553
퇴직일시금해당자(군)0.4680.8280.7420.0000.0000.6000.9830.8191.0000.6290.0000.694
사립학교경력(계)0.8740.5740.5340.6500.7590.4740.5220.6600.6291.0000.3740.995
퇴직연금해당자(사립)0.6200.2920.0000.9240.9030.0000.0000.5880.0000.3741.0000.225
퇴직일시금해당자(사립)0.8530.6770.5020.4670.5410.4790.6290.5530.6940.9950.2251.000
2023-12-12T22:23:40.470268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도합계공무원경력(계)퇴직연금해당자(공무원)미수령자(공무원)수령자(공무원)군경력(계)퇴직연금해당자(군)퇴직일시금해당자(군)사립학교경력(계)퇴직연금해당자(사립)퇴직일시금해당자(사립)
연도1.000-0.008-0.3470.8340.839-0.7790.1120.3830.0730.6800.9220.662
합계-0.0081.0000.8270.2060.2550.5200.7000.0520.6930.3080.0180.308
공무원경력(계)-0.3470.8271.000-0.112-0.0110.7680.400-0.0970.416-0.082-0.282-0.081
퇴직연금해당자(공무원)0.8340.206-0.1121.0000.784-0.4950.2460.4410.1940.6790.8270.662
미수령자(공무원)0.8390.255-0.0110.7841.000-0.5120.3410.4080.3000.6360.8440.620
수령자(공무원)-0.7790.5200.768-0.495-0.5121.0000.197-0.2550.223-0.323-0.706-0.309
군경력(계)0.1120.7000.4000.2460.3410.1971.0000.0390.9940.2780.1130.279
퇴직연금해당자(군)0.3830.052-0.0970.4410.408-0.2550.0391.000-0.0300.4170.5900.409
퇴직일시금해당자(군)0.0730.6930.4160.1940.3000.2230.994-0.0301.0000.2270.0650.228
사립학교경력(계)0.6800.308-0.0820.6790.636-0.3230.2780.4170.2271.0000.6480.999
퇴직연금해당자(사립)0.9220.018-0.2820.8270.844-0.7060.1130.5900.0650.6481.0000.628
퇴직일시금해당자(사립)0.6620.308-0.0810.6620.620-0.3090.2790.4090.2280.9990.6281.000

Missing values

2023-12-12T22:23:36.501159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:23:36.647743image/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

연도합계공무원경력(계)퇴직연금해당자(공무원)미수령자(공무원)수령자(공무원)군경력(계)퇴직연금해당자(군)퇴직일시금해당자(군)사립학교경력(계)퇴직연금해당자(사립)퇴직일시금해당자(사립)
0198010164929402179077870113757000
11981548152171243497326419470000
21982489543111333397758462522000
31983113496102734257533938713867130901309
41984547228433238260221698820814600460
519855860332016305299921267820484140414
6198669054067212553791235510222534830483
719875104298712171280417605617043570357
819889422583945278551629969529015870587
919898331511835245483826775626215360536
연도합계공무원경력(계)퇴직연금해당자(공무원)미수령자(공무원)수령자(공무원)군경력(계)퇴직연금해당자(군)퇴직일시금해당자(군)사립학교경력(계)퇴직연금해당자(사립)퇴직일시금해당자(사립)
33201349882572581132138216216415577958787
34201463213261631443175519836219211077191058
35201569063310641510173625811462435101516999
3620166545354081201414452088103198591726891
372017608929601021383147521061191987102340983
3820188172415991214631784235913622231654411613
3920198097398221118431928260213624661513591454
4020208119420118322431775231412221921604691535
4120218437476616525702031219011420761481371444
4220227439429919326041502180314016631337511286