Overview

Dataset statistics

Number of variables7
Number of observations42
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 KiB
Average record size in memory65.0 B

Variable types

Categorical1
Numeric6

Dataset

Description공무원연금 수급자(퇴직연금, 유족연금, 장해연금) 현황 데이터로 1982년 부터 현재까지 연 단위로 구분되며, 남녀 구분도 포함되어 있습니다.
Author공무원연금공단
URLhttps://www.data.go.kr/data/15052972/fileData.do

Alerts

계 is highly overall correlated with 퇴직연금 and 5 other fieldsHigh correlation
퇴직연금 is highly overall correlated with 계 and 5 other fieldsHigh correlation
유족연금(계) is highly overall correlated with 계 and 5 other fieldsHigh correlation
유족연금(퇴직) is highly overall correlated with 계 and 5 other fieldsHigh correlation
유족연금(장해) is highly overall correlated with 계 and 5 other fieldsHigh correlation
장해연금 is highly overall correlated with 계 and 5 other fieldsHigh correlation
구분 is highly overall correlated with 계 and 5 other fieldsHigh correlation
구분 has unique valuesUnique
계 has unique valuesUnique
퇴직연금 has unique valuesUnique
유족연금(계) has unique valuesUnique
유족연금(퇴직) has unique valuesUnique
장해연금 has unique valuesUnique
유족연금(장해) has 8 (19.0%) zerosZeros

Reproduction

Analysis started2023-06-12 11:42:31.713470
Analysis finished2023-06-12 11:42:43.788252
Duration12.07 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size464.0 B
1982
 
1
1991
 
1
2001
 
1
1984
 
1
1985
 
1
Other values (37)
37 

Length

Max length4
Median length4
Mean length3.8571429
Min length1

Unique

Unique42 ?
Unique (%)100.0%

Sample

1st row1982
2nd row1983
3rd row1984
4th row1985
5th row1986

Common Values

ValueCountFrequency (%)
1982 1
 
2.4%
1991 1
 
2.4%
2001 1
 
2.4%
1984 1
 
2.4%
1985 1
 
2.4%
1986 1
 
2.4%
1987 1
 
2.4%
1988 1
 
2.4%
1989 1
 
2.4%
1990 1
 
2.4%
Other values (32) 32
76.2%

Length

2023-06-12T20:42:43.918832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1982 1
 
2.4%
2012 1
 
2.4%
남 1
 
2.4%
2005 1
 
2.4%
2006 1
 
2.4%
2007 1
 
2.4%
2008 1
 
2.4%
2009 1
 
2.4%
2010 1
 
2.4%
2011 1
 
2.4%
Other values (32) 32
76.2%

계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean208047.43
Minimum3742
Maximum599485
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2023-06-12T20:42:44.215506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3742
5-th percentile7327.15
Q135962
median175820.5
Q3342997
95-th percentile534519.9
Maximum599485
Range595743
Interquartile range (IQR)307035

Descriptive statistics

Standard deviation183043.53
Coefficient of variation (CV)0.87981634
Kurtosis-0.8775989
Mean208047.43
Median Absolute Deviation (MAD)148263
Skewness0.59035955
Sum8737992
Variance3.3504933 × 1010
MonotonicityNot monotonic
2023-06-12T20:42:44.608715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
3742 1
 
2.4%
34333 1
 
2.4%
7235 1
 
2.4%
9078 1
 
2.4%
10926 1
 
2.4%
14832 1
 
2.4%
18084 1
 
2.4%
21204 1
 
2.4%
25396 1
 
2.4%
29719 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
3742 1
2.4%
5618 1
2.4%
7235 1
2.4%
9078 1
2.4%
10926 1
2.4%
14832 1
2.4%
18084 1
2.4%
21204 1
2.4%
25396 1
2.4%
29719 1
2.4%
ValueCountFrequency (%)
599485 1
2.4%
567770 1
2.4%
535992 1
2.4%
506550 1
2.4%
480096 1
2.4%
452942 1
2.4%
426068 1
2.4%
415430 1
2.4%
395630 1
2.4%
366482 1
2.4%

퇴직연금
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184364
Minimum3556
Maximum521486
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2023-06-12T20:42:45.017912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3556
5-th percentile7027.55
Q133266
median152373.5
Q3301931.5
95-th percentile465897.9
Maximum521486
Range517930
Interquartile range (IQR)268665.5

Descriptive statistics

Standard deviation161019.98
Coefficient of variation (CV)0.87338081
Kurtosis-0.95169026
Mean184364
Median Absolute Deviation (MAD)126606
Skewness0.56928293
Sum7743288
Variance2.5927434 × 1010
MonotonicityNot monotonic
2023-06-12T20:42:45.431164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
3556 1
 
2.4%
31797 1
 
2.4%
6940 1
 
2.4%
8691 1
 
2.4%
10435 1
 
2.4%
14196 1
 
2.4%
17186 1
 
2.4%
20023 1
 
2.4%
23844 1
 
2.4%
27691 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
3556 1
2.4%
5390 1
2.4%
6940 1
2.4%
8691 1
2.4%
10435 1
2.4%
14196 1
2.4%
17186 1
2.4%
20023 1
2.4%
23844 1
2.4%
27691 1
2.4%
ValueCountFrequency (%)
521486 1
2.4%
494417 1
2.4%
467143 1
2.4%
442241 1
2.4%
419968 1
2.4%
408976 1
2.4%
396743 1
2.4%
373529 1
2.4%
346781 1
2.4%
321098 1
2.4%

유족연금(계)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22188.905
Minimum140
Maximum74367
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2023-06-12T20:42:45.784939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum140
5-th percentile228.25
Q12437.25
median11966.5
Q338241.5
95-th percentile69634.3
Maximum74367
Range74227
Interquartile range (IQR)35804.25

Descriptive statistics

Standard deviation23800.803
Coefficient of variation (CV)1.0726443
Kurtosis-0.51709186
Mean22188.905
Median Absolute Deviation (MAD)11531.5
Skewness0.9049783
Sum931934
Variance5.664782 × 108
MonotonicityNot monotonic
2023-06-12T20:42:46.154599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
140 1
 
2.4%
2283 1
 
2.4%
225 1
 
2.4%
290 1
 
2.4%
376 1
 
2.4%
494 1
 
2.4%
737 1
 
2.4%
1009 1
 
2.4%
1355 1
 
2.4%
1812 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
140 1
2.4%
169 1
2.4%
225 1
2.4%
290 1
2.4%
376 1
2.4%
494 1
2.4%
737 1
2.4%
1009 1
2.4%
1355 1
2.4%
1812 1
2.4%
ValueCountFrequency (%)
74367 1
2.4%
71363 1
2.4%
69852 1
2.4%
65498 1
2.4%
61019 1
2.4%
56918 1
2.4%
53071 1
2.4%
49496 1
2.4%
45909 1
2.4%
42472 1
2.4%

유족연금(퇴직)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21894.643
Minimum140
Maximum73461
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2023-06-12T20:42:46.951934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum140
5-th percentile228.25
Q12356.75
median11796
Q337746
95-th percentile68776.65
Maximum73461
Range73321
Interquartile range (IQR)35389.25

Descriptive statistics

Standard deviation23511.6
Coefficient of variation (CV)1.0738517
Kurtosis-0.51523674
Mean21894.643
Median Absolute Deviation (MAD)11361
Skewness0.90609285
Sum919575
Variance5.5279533 × 108
MonotonicityNot monotonic
2023-06-12T20:42:47.292689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
140 1
 
2.4%
2203 1
 
2.4%
225 1
 
2.4%
290 1
 
2.4%
376 1
 
2.4%
494 1
 
2.4%
737 1
 
2.4%
1009 1
 
2.4%
1277 1
 
2.4%
1729 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
140 1
2.4%
169 1
2.4%
225 1
2.4%
290 1
2.4%
376 1
2.4%
494 1
2.4%
737 1
2.4%
1009 1
2.4%
1277 1
2.4%
1729 1
2.4%
ValueCountFrequency (%)
73461 1
2.4%
70472 1
2.4%
68992 1
2.4%
64685 1
2.4%
60266 1
2.4%
56216 1
2.4%
52410 1
2.4%
48867 1
2.4%
45320 1
2.4%
41919 1
2.4%

유족연금(장해)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean294.2619
Minimum0
Maximum906
Zeros8
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2023-06-12T20:42:47.638615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q180.5
median170.5
Q3495.5
95-th percentile857.65
Maximum906
Range906
Interquartile range (IQR)415

Descriptive statistics

Standard deviation290.05568
Coefficient of variation (CV)0.98570584
Kurtosis-0.66515711
Mean294.2619
Median Absolute Deviation (MAD)170.5
Skewness0.79762879
Sum12359
Variance84132.296
MonotonicityNot monotonic
2023-06-12T20:42:47.980575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 8
 
19.0%
629 1
 
2.4%
83 1
 
2.4%
80 1
 
2.4%
82 1
 
2.4%
89 1
 
2.4%
95 1
 
2.4%
100 1
 
2.4%
113 1
 
2.4%
272 1
 
2.4%
Other values (25) 25
59.5%
ValueCountFrequency (%)
0 8
19.0%
15 1
 
2.4%
78 1
 
2.4%
80 1
 
2.4%
82 1
 
2.4%
83 1
 
2.4%
89 1
 
2.4%
95 1
 
2.4%
100 1
 
2.4%
109 1
 
2.4%
ValueCountFrequency (%)
906 1
2.4%
891 1
2.4%
860 1
2.4%
813 1
2.4%
753 1
2.4%
702 1
2.4%
661 1
2.4%
629 1
2.4%
589 1
2.4%
553 1
2.4%

장해연금
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1494.5238
Minimum46
Maximum3632
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2023-06-12T20:42:48.540266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile71.35
Q1225.25
median978
Q32824
95-th percentile3445.05
Maximum3632
Range3586
Interquartile range (IQR)2598.75

Descriptive statistics

Standard deviation1326.1342
Coefficient of variation (CV)0.8873289
Kurtosis-1.6943857
Mean1494.5238
Median Absolute Deviation (MAD)894.5
Skewness0.29805391
Sum62770
Variance1758631.8
MonotonicityNot monotonic
2023-06-12T20:42:48.936089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
46 1
 
2.4%
253 1
 
2.4%
70 1
 
2.4%
97 1
 
2.4%
115 1
 
2.4%
142 1
 
2.4%
161 1
 
2.4%
172 1
 
2.4%
197 1
 
2.4%
216 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
46 1
2.4%
59 1
2.4%
70 1
2.4%
97 1
2.4%
115 1
2.4%
142 1
2.4%
161 1
2.4%
172 1
2.4%
182 1
2.4%
197 1
2.4%
ValueCountFrequency (%)
3632 1
2.4%
3501 1
2.4%
3450 1
2.4%
3351 1
2.4%
3290 1
2.4%
3210 1
2.4%
3128 1
2.4%
3043 1
2.4%
2940 1
2.4%
2912 1
2.4%

Interactions

2023-06-12T20:42:41.568872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:32.379269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:34.235817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:36.106376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:38.025265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:40.064042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:41.827564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:32.686582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:34.550551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:36.425087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:38.296519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:40.356215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:42.056851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:33.015826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:34.875668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:36.717798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:38.565769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:40.576961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:42.355914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:33.377506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:35.197158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:37.008118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:38.827357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:40.811040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:42.631207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:33.684194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:35.488853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:37.340941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:39.134640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:41.103869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:42.889325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:33.959795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:35.806267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:37.769154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:39.381765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T20:42:41.329517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-12T20:42:49.223772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
계퇴직연금유족연금(계)유족연금(퇴직)유족연금(장해)장해연금
계1.0000.9980.8870.8870.8810.963
퇴직연금0.9981.0000.8540.8540.8470.971
유족연금(계)0.8870.8541.0001.0000.9970.780
유족연금(퇴직)0.8870.8541.0001.0000.9970.780
유족연금(장해)0.8810.8470.9970.9971.0000.779
장해연금0.9630.9710.7800.7800.7791.000
2023-06-12T20:42:49.603740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
계퇴직연금유족연금(계)유족연금(퇴직)유족연금(장해)장해연금
계1.0000.9970.9330.9330.9130.981
퇴직연금0.9971.0000.9100.9100.8880.992
유족연금(계)0.9330.9101.0001.0000.9940.858
유족연금(퇴직)0.9330.9101.0001.0000.9940.858
유족연금(장해)0.9130.8880.9940.9941.0000.835
장해연금0.9810.9920.8580.8580.8351.000
2023-06-12T20:42:49.921351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
계퇴직연금유족연금(계)유족연금(퇴직)유족연금(장해)장해연금
계1.0000.9840.9090.9090.8770.956
퇴직연금0.9841.0000.8930.8930.8610.972
유족연금(계)0.9090.8931.0001.0000.9690.865
유족연금(퇴직)0.9090.8931.0001.0000.9690.865
유족연금(장해)0.8770.8610.9690.9691.0000.832
장해연금0.9560.9720.8650.8650.8321.000
2023-06-12T20:42:50.237391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
구분계퇴직연금유족연금(계)유족연금(퇴직)유족연금(장해)장해연금
구분1.0001.0001.0001.0001.0001.0001.000
계1.0001.0000.9920.9870.9870.9730.862
퇴직연금1.0000.9921.0000.9640.9640.9640.846
유족연금(계)1.0000.9870.9641.0001.0000.9810.859
유족연금(퇴직)1.0000.9870.9641.0001.0000.9810.859
유족연금(장해)1.0000.9730.9640.9810.9811.0000.907
장해연금1.0000.8620.8460.8590.8590.9071.000
2023-06-12T20:42:50.575912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
계퇴직연금유족연금(계)유족연금(퇴직)유족연금(장해)장해연금구분
계1.0000.9970.9330.9330.9130.9811.000
퇴직연금0.9971.0000.9100.9100.8880.9921.000
유족연금(계)0.9330.9101.0001.0000.9940.8581.000
유족연금(퇴직)0.9330.9101.0001.0000.9940.8581.000
유족연금(장해)0.9130.8880.9940.9941.0000.8351.000
장해연금0.9810.9920.8580.8580.8351.0001.000
구분1.0001.0001.0001.0001.0001.0001.000

Missing values

2023-06-12T20:42:43.316496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-12T20:42:43.653871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

구분계퇴직연금유족연금(계)유족연금(퇴직)유족연금(장해)장해연금
0198237423556140140046
1198356185390169169059
2198472356940225225070
3198590788691290290097
4198610926104353763760115
5198714832141964944940142
6198818084171867377370161
719892120420023100910090172
8199025396238441355127778197
9199129719276911812172983216
구분계퇴직연금유족연금(계)유족연금(퇴직)유족연금(장해)장해연금
32201439563034678145909453205892940
33201542606837352949496488676293043
34201645294239674353071524106613128
35201748009641996856918562167023210
36201850655044224161019602667533290
37201953599246714365498646858133351
38202056777049441769852689928603501
39202159948552148674367734619063632
40남41543040897630042989153450
41여1840551125107136370472891182