Overview

Dataset statistics

Number of variables6
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory58.1 B

Variable types

Numeric6

Dataset

Description공무원연금공단의 공무원 연도별 부양률에 대한 데이터 입니다. 1991년부터 2022년도까지의 데이터가 있습니다.
URLhttps://www.data.go.kr/data/15110912/fileData.do

Alerts

연도 is highly overall correlated with 가입자수 and 4 other fieldsHigh correlation
가입자수 is highly overall correlated with 연도 and 4 other fieldsHigh correlation
합계 is highly overall correlated with 연도 and 4 other fieldsHigh correlation
퇴직연금 is highly overall correlated with 연도 and 4 other fieldsHigh correlation
유족연금 is highly overall correlated with 연도 and 4 other fieldsHigh correlation
부양률 is highly overall correlated with 연도 and 4 other fieldsHigh correlation
연도 has unique valuesUnique
가입자수 has unique valuesUnique
합계 has unique valuesUnique
퇴직연금 has unique valuesUnique
유족연금 has unique valuesUnique
부양률 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:41:47.865697
Analysis finished2023-12-12 23:41:51.363222
Duration3.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.5
Minimum1991
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T08:41:51.412040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1991
5-th percentile1992.55
Q11998.75
median2006.5
Q32014.25
95-th percentile2020.45
Maximum2022
Range31
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation9.3808315
Coefficient of variation (CV)0.0046752213
Kurtosis-1.2
Mean2006.5
Median Absolute Deviation (MAD)8
Skewness0
Sum64208
Variance88
MonotonicityStrictly increasing
2023-12-13T08:41:51.505340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1991 1
 
3.1%
2008 1
 
3.1%
2022 1
 
3.1%
2021 1
 
3.1%
2020 1
 
3.1%
2019 1
 
3.1%
2018 1
 
3.1%
2017 1
 
3.1%
2016 1
 
3.1%
2015 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
1991 1
3.1%
1992 1
3.1%
1993 1
3.1%
1994 1
3.1%
1995 1
3.1%
1996 1
3.1%
1997 1
3.1%
1998 1
3.1%
1999 1
3.1%
2000 1
3.1%
ValueCountFrequency (%)
2022 1
3.1%
2021 1
3.1%
2020 1
3.1%
2019 1
3.1%
2018 1
3.1%
2017 1
3.1%
2016 1
3.1%
2015 1
3.1%
2014 1
3.1%
2013 1
3.1%

가입자수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1031306.1
Minimum884648
Maximum1280994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T08:41:51.605790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum884648
5-th percentile911375.35
Q1948017.25
median1015458
Q31084119.8
95-th percentile1239366.6
Maximum1280994
Range396346
Interquartile range (IQR)136102.5

Descriptive statistics

Standard deviation106643.24
Coefficient of variation (CV)0.103406
Kurtosis-0.071936042
Mean1031306.1
Median Absolute Deviation (MAD)67574.5
Skewness0.81428949
Sum33001795
Variance1.1372781 × 1010
MonotonicityNot monotonic
2023-12-13T08:41:51.725849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
884648 1
 
3.1%
1030256 1
 
3.1%
1280994 1
 
3.1%
1261421 1
 
3.1%
1221322 1
 
3.1%
1195051 1
 
3.1%
1160586 1
 
3.1%
1120458 1
 
3.1%
1107972 1
 
3.1%
1093038 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
884648 1
3.1%
909155 1
3.1%
913192 1
3.1%
913891 1
3.1%
922098 1
3.1%
930835 1
3.1%
939674 1
3.1%
947616 1
3.1%
948151 1
3.1%
952154 1
3.1%
ValueCountFrequency (%)
1280994 1
3.1%
1261421 1
3.1%
1221322 1
3.1%
1195051 1
3.1%
1160586 1
3.1%
1120458 1
3.1%
1107972 1
3.1%
1093038 1
3.1%
1081147 1
3.1%
1072610 1
3.1%

합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268045.19
Minimum29420
Maximum624508
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T08:41:51.829041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29420
5-th percentile37570.05
Q1118339.75
median243284.5
Q3399674.75
95-th percentile577601.1
Maximum624508
Range595088
Interquartile range (IQR)281335

Descriptive statistics

Standard deviation181691.35
Coefficient of variation (CV)0.6778385
Kurtosis-0.96361885
Mean268045.19
Median Absolute Deviation (MAD)151689
Skewness0.40794199
Sum8577446
Variance3.3011746 × 1010
MonotonicityStrictly increasing
2023-12-13T08:41:51.923583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
29420 1
 
3.1%
276829 1
 
3.1%
624508 1
 
3.1%
594947 1
 
3.1%
563409 1
 
3.1%
531828 1
 
3.1%
502507 1
 
3.1%
476184 1
 
3.1%
449153 1
 
3.1%
422396 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
29420 1
3.1%
34000 1
3.1%
40491 1
3.1%
47622 1
3.1%
55885 1
3.1%
63203 1
3.1%
72355 1
3.1%
88723 1
3.1%
128212 1
3.1%
149673 1
3.1%
ValueCountFrequency (%)
624508 1
3.1%
594947 1
3.1%
563409 1
3.1%
531828 1
3.1%
502507 1
3.1%
476184 1
3.1%
449153 1
3.1%
422396 1
3.1%
392101 1
3.1%
363017 1
3.1%

퇴직연금
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239298.47
Minimum27691
Maximum546010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T08:41:52.015680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27691
5-th percentile35028.8
Q1110726.5
median220858.5
Q3353468
95-th percentile506598.05
Maximum546010
Range518319
Interquartile range (IQR)242741.5

Descriptive statistics

Standard deviation158076.6
Coefficient of variation (CV)0.66058341
Kurtosis-0.97684407
Mean239298.47
Median Absolute Deviation (MAD)132395
Skewness0.36812495
Sum7657551
Variance2.4988211 × 1010
MonotonicityStrictly increasing
2023-12-13T08:41:52.116855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
27691 1
 
3.1%
250476 1
 
3.1%
546010 1
 
3.1%
521486 1
 
3.1%
494417 1
 
3.1%
467143 1
 
3.1%
442241 1
 
3.1%
419968 1
 
3.1%
396743 1
 
3.1%
373529 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
27691 1
3.1%
31797 1
3.1%
37673 1
3.1%
44146 1
3.1%
51713 1
3.1%
58248 1
3.1%
66482 1
3.1%
81991 1
3.1%
120305 1
3.1%
140387 1
3.1%
ValueCountFrequency (%)
546010 1
3.1%
521486 1
3.1%
494417 1
3.1%
467143 1
3.1%
442241 1
3.1%
419968 1
3.1%
396743 1
3.1%
373529 1
3.1%
346781 1
3.1%
321098 1
3.1%

유족연금
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28746.719
Minimum1729
Maximum78498
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T08:41:52.216807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1729
5-th percentile2541.25
Q17613.25
median22426
Q346206.75
95-th percentile71003.05
Maximum78498
Range76769
Interquartile range (IQR)38593.5

Descriptive statistics

Standard deviation23784.89
Coefficient of variation (CV)0.82739495
Kurtosis-0.83976876
Mean28746.719
Median Absolute Deviation (MAD)17012
Skewness0.64045557
Sum919895
Variance5.6572099 × 108
MonotonicityStrictly increasing
2023-12-13T08:41:52.323739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1729 1
 
3.1%
26353 1
 
3.1%
78498 1
 
3.1%
73461 1
 
3.1%
68992 1
 
3.1%
64685 1
 
3.1%
60266 1
 
3.1%
56216 1
 
3.1%
52410 1
 
3.1%
48867 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
1729 1
3.1%
2203 1
3.1%
2818 1
3.1%
3476 1
3.1%
4172 1
3.1%
4955 1
3.1%
5873 1
3.1%
6732 1
3.1%
7907 1
3.1%
9286 1
3.1%
ValueCountFrequency (%)
78498 1
3.1%
73461 1
3.1%
68992 1
3.1%
64685 1
3.1%
60266 1
3.1%
56216 1
3.1%
52410 1
3.1%
48867 1
3.1%
45320 1
3.1%
41919 1
3.1%

부양률
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.65
Minimum3.3
Maximum48.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T08:41:52.440706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.3
5-th percentile4.03
Q112.825
median23.95
Q336.875
95-th percentile46.595
Maximum48.8
Range45.5
Interquartile range (IQR)24.05

Descriptive statistics

Standard deviation14.655264
Coefficient of variation (CV)0.59453405
Kurtosis-1.2400775
Mean24.65
Median Absolute Deviation (MAD)13.5
Skewness0.067932496
Sum788.8
Variance214.77677
MonotonicityStrictly increasing
2023-12-13T08:41:52.544195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
3.3 1
 
3.1%
26.9 1
 
3.1%
48.8 1
 
3.1%
47.2 1
 
3.1%
46.1 1
 
3.1%
44.5 1
 
3.1%
43.3 1
 
3.1%
42.5 1
 
3.1%
40.5 1
 
3.1%
38.6 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
3.3 1
3.1%
3.7 1
3.1%
4.3 1
3.1%
5.0 1
3.1%
5.8 1
3.1%
6.5 1
3.1%
7.4 1
3.1%
9.3 1
3.1%
14.0 1
3.1%
16.5 1
3.1%
ValueCountFrequency (%)
48.8 1
3.1%
47.2 1
3.1%
46.1 1
3.1%
44.5 1
3.1%
43.3 1
3.1%
42.5 1
3.1%
40.5 1
3.1%
38.6 1
3.1%
36.3 1
3.1%
33.8 1
3.1%

Interactions

2023-12-13T08:41:50.579874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:48.055697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:48.611184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:49.146722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:49.696409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:50.130638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:50.868555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:48.145339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:48.699032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:49.231734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:49.783477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:50.210700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:50.943057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:48.245969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:48.789631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:49.318795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:49.877808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:50.295093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:51.024990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:48.326993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:48.872855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:49.396253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:49.946569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:50.380987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:51.100259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:48.434437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:48.955367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:49.486234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:50.005677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:50.447060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:51.180054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:48.534335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:49.059340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:49.604814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:50.073029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:50.518884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:41:52.614135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도가입자수합계퇴직연금유족연금부양률
연도1.0000.8750.9540.9700.9570.979
가입자수0.8751.0000.9240.9170.9380.858
합계0.9540.9241.0000.9930.9830.990
퇴직연금0.9700.9170.9931.0000.9800.996
유족연금0.9570.9380.9830.9801.0000.979
부양률0.9790.8580.9900.9960.9791.000
2023-12-13T08:41:52.701914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도가입자수합계퇴직연금유족연금부양률
연도1.0000.9261.0001.0001.0001.000
가입자수0.9261.0000.9260.9260.9260.926
합계1.0000.9261.0001.0001.0001.000
퇴직연금1.0000.9261.0001.0001.0001.000
유족연금1.0000.9261.0001.0001.0001.000
부양률1.0000.9261.0001.0001.0001.000

Missing values

2023-12-13T08:41:51.257739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:41:51.332508image/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

연도가입자수합계퇴직연금유족연금부양률
01991884648294202769117293.3
11992922098340003179722033.7
21993939674404913767328184.3
31994948151476224414634765.0
41995957882558855171341725.8
51996971303632035824849556.5
61997981759723556648258737.4
71998952154887238199167329.3
81999913891128212120305790714.0
92000909155149673140387928616.5
연도가입자수합계퇴직연금유족연금부양률
22201310726103630173210984191933.8
23201410811473921013467814532036.3
24201510930384223963735294886738.6
25201611079724491533967435241040.5
26201711204584761844199685621642.5
27201811605865025074422416026643.3
28201911950515318284671436468544.5
29202012213225634094944176899246.1
30202112614215949475214867346147.2
31202212809946245085460107849848.8