Overview

Dataset statistics

Number of variables16
Number of observations80
Missing cells1
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.3 KiB
Average record size in memory144.6 B

Variable types

Numeric15
Categorical1

Dataset

Description재해보상급여(공무상요양비,공무상요양일시금,유족보상금,유족연금,장해연금,장해유족연금 등) 부조급여 등 정부부담급여 종류별 지급액 데이터입니다.
Author공무원연금공단
URLhttps://www.data.go.kr/data/15052931/fileData.do

Alerts

연도 is highly correlated with 장해연금 and 1 other fieldsHigh correlation
재해보상급여(계) is highly correlated with 요양급여 and 1 other fieldsHigh correlation
요양급여 is highly correlated with 재해보상급여(계)High correlation
순직유족연금 is highly correlated with 위험직무순직유족연금High correlation
장해연금 is highly correlated with 연도 and 1 other fieldsHigh correlation
장해유족연금 is highly correlated with 연도 and 2 other fieldsHigh correlation
위험직무순직유족연금 is highly correlated with 순직유족연금High correlation
부조급여(계) is highly correlated with 사망조위금High correlation
사망조위금 is highly correlated with 부조급여(계)High correlation
위험직무순직유족보상금 has 1 (1.2%) missing values Missing
재해보상급여(계) has unique values Unique
요양급여 has unique values Unique
장해연금 has unique values Unique
부조급여(계) has unique values Unique
공무상요양일시금 has 26 (32.5%) zeros Zeros
순직유족연금 has 60 (75.0%) zeros Zeros
장해유족연금 has 16 (20.0%) zeros Zeros
위험직무순직유족연금 has 48 (60.0%) zeros Zeros
위험직무순직유족보상금 has 47 (58.8%) zeros Zeros
사망조위금 has 6 (7.5%) zeros Zeros
퇴직수당 has 18 (22.5%) zeros Zeros

Reproduction

Analysis started2022-10-29 14:59:12.817170
Analysis finished2022-10-29 14:59:35.707302
Duration22.89 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

연도
Real number (ℝ≥0)

HIGH CORRELATION

Distinct40
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001.5
Minimum1982
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-10-29T23:59:35.757534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1982
5-th percentile1983.95
Q11991.75
median2001.5
Q32011.25
95-th percentile2019.05
Maximum2021
Range39
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation11.6162261
Coefficient of variation (CV)0.005803760231
Kurtosis-1.201199551
Mean2001.5
Median Absolute Deviation (MAD)10
Skewness0
Sum160120
Variance134.9367089
MonotonicityIncreasing
2022-10-29T23:59:35.865799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
19822
 
2.5%
20012
 
2.5%
19842
 
2.5%
19852
 
2.5%
19862
 
2.5%
19872
 
2.5%
19882
 
2.5%
19892
 
2.5%
19902
 
2.5%
19922
 
2.5%
Other values (30)60
75.0%
ValueCountFrequency (%)
19822
2.5%
19832
2.5%
19842
2.5%
19852
2.5%
19862
2.5%
19872
2.5%
19882
2.5%
19892
2.5%
19902
2.5%
19912
2.5%
ValueCountFrequency (%)
20212
2.5%
20202
2.5%
20192
2.5%
20182
2.5%
20172
2.5%
20162
2.5%
20152
2.5%
20142
2.5%
20132
2.5%
20122
2.5%

구분
Categorical

Distinct2
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size768.0 B
건수
40 
금액
40 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row건수
2nd row금액
3rd row건수
4th row금액
5th row건수

Common Values

ValueCountFrequency (%)
건수40
50.0%
금액40
50.0%

Length

2022-10-29T23:59:35.967596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-29T23:59:36.053012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
건수40
50.0%
금액40
50.0%

재해보상급여(계)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57231.175
Minimum1464
Maximum228030
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-10-29T23:59:36.144685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1464
5-th percentile3924.25
Q16357.75
median25728
Q389046.25
95-th percentile184767
Maximum228030
Range226566
Interquartile range (IQR)82688.5

Descriptive statistics

Standard deviation61020.11462
Coefficient of variation (CV)1.066204121
Kurtosis0.426426732
Mean57231.175
Median Absolute Deviation (MAD)22812.5
Skewness1.141983796
Sum4578494
Variance3723454388
MonotonicityNot monotonic
2022-10-29T23:59:36.246753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14641
 
1.2%
208571
 
1.2%
48231
 
1.2%
20781
 
1.2%
47601
 
1.2%
37531
 
1.2%
47861
 
1.2%
49691
 
1.2%
55461
 
1.2%
54141
 
1.2%
Other values (70)70
87.5%
ValueCountFrequency (%)
14641
1.2%
20781
1.2%
37531
1.2%
37771
1.2%
39321
1.2%
46191
1.2%
46461
1.2%
47601
1.2%
47861
1.2%
48231
1.2%
ValueCountFrequency (%)
2280301
1.2%
2222321
1.2%
2002701
1.2%
1983331
1.2%
1840531
1.2%
1757551
1.2%
1714621
1.2%
1575381
1.2%
1553171
1.2%
1274981
1.2%

요양급여
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29944.2625
Minimum712
Maximum176647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-10-29T23:59:36.543359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum712
5-th percentile1457.85
Q13674
median8999.5
Q332649.75
95-th percentile136037
Maximum176647
Range175935
Interquartile range (IQR)28975.75

Descriptive statistics

Standard deviation43891.97159
Coefficient of variation (CV)1.465789033
Kurtosis2.549103568
Mean29944.2625
Median Absolute Deviation (MAD)7129.5
Skewness1.872619078
Sum2395541
Variance1926505170
MonotonicityNot monotonic
2022-10-29T23:59:36.650596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11511
 
1.2%
36921
 
1.2%
43091
 
1.2%
17191
 
1.2%
7461
 
1.2%
34151
 
1.2%
10831
 
1.2%
45681
 
1.2%
14741
 
1.2%
49971
 
1.2%
Other values (70)70
87.5%
ValueCountFrequency (%)
7121
1.2%
7461
1.2%
10831
1.2%
11511
1.2%
14741
1.2%
17141
1.2%
17191
1.2%
17401
1.2%
18391
1.2%
18411
1.2%
ValueCountFrequency (%)
1766471
1.2%
1660941
1.2%
1443131
1.2%
1417561
1.2%
1357361
1.2%
1255401
1.2%
1139881
1.2%
1134281
1.2%
1133871
1.2%
826641
1.2%

공무상요양일시금
Real number (ℝ≥0)

ZEROS

Distinct45
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.6875
Minimum0
Maximum1151
Zeros26
Zeros (%)32.5%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-10-29T23:59:36.759635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median16
Q368.75
95-th percentile290.85
Maximum1151
Range1151
Interquartile range (IQR)68.75

Descriptive statistics

Standard deviation174.2832406
Coefficient of variation (CV)2.302668744
Kurtosis22.46598142
Mean75.6875
Median Absolute Deviation (MAD)16
Skewness4.43305143
Sum6055
Variance30374.64794
MonotonicityNot monotonic
2022-10-29T23:59:36.875372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
026
32.5%
164
 
5.0%
123
 
3.8%
172
 
2.5%
512
 
2.5%
132
 
2.5%
492
 
2.5%
1172
 
2.5%
731
 
1.2%
361
 
1.2%
Other values (35)35
43.8%
ValueCountFrequency (%)
026
32.5%
11
 
1.2%
21
 
1.2%
91
 
1.2%
111
 
1.2%
123
 
3.8%
132
 
2.5%
141
 
1.2%
151
 
1.2%
164
 
5.0%
ValueCountFrequency (%)
11511
1.2%
8411
1.2%
4781
1.2%
4401
1.2%
2831
1.2%
2301
1.2%
2181
1.2%
2111
1.2%
1601
1.2%
1271
1.2%

순직유족보상금
Real number (ℝ≥0)

Distinct78
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3737.7125
Minimum68
Maximum17138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-10-29T23:59:36.987733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum68
5-th percentile77.85
Q1147.75
median1404
Q37100
95-th percentile11376.7
Maximum17138
Range17070
Interquartile range (IQR)6952.25

Descriptive statistics

Standard deviation4240.147619
Coefficient of variation (CV)1.134423158
Kurtosis0.09841070858
Mean3737.7125
Median Absolute Deviation (MAD)1335.5
Skewness0.9203790509
Sum299017
Variance17978851.83
MonotonicityNot monotonic
2022-10-29T23:59:37.085543image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2202
 
2.5%
992
 
2.5%
81921
 
1.2%
36891
 
1.2%
34151
 
1.2%
2341
 
1.2%
30991
 
1.2%
2521
 
1.2%
32151
 
1.2%
2511
 
1.2%
Other values (68)68
85.0%
ValueCountFrequency (%)
681
1.2%
691
1.2%
721
1.2%
751
1.2%
781
1.2%
811
1.2%
821
1.2%
831
1.2%
841
1.2%
861
1.2%
ValueCountFrequency (%)
171381
1.2%
140191
1.2%
137391
1.2%
114851
1.2%
113711
1.2%
107031
1.2%
92931
1.2%
90531
1.2%
89201
1.2%
88141
1.2%

순직유족연금
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1360.8
Minimum0
Maximum18398
Zeros60
Zeros (%)75.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-10-29T23:59:37.182844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q348.5
95-th percentile7001.85
Maximum18398
Range18398
Interquartile range (IQR)48.5

Descriptive statistics

Standard deviation3532.17561
Coefficient of variation (CV)2.595661089
Kurtosis11.48248433
Mean1360.8
Median Absolute Deviation (MAD)0
Skewness3.331891125
Sum108864
Variance12476264.54
MonotonicityNot monotonic
2022-10-29T23:59:37.268036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
060
75.0%
34751
 
1.2%
183981
 
1.2%
3071
 
1.2%
7831
 
1.2%
12551
 
1.2%
12671
 
1.2%
18971
 
1.2%
18301
 
1.2%
28581
 
1.2%
Other values (11)11
 
13.8%
ValueCountFrequency (%)
060
75.0%
1941
 
1.2%
3071
 
1.2%
7831
 
1.2%
12551
 
1.2%
12671
 
1.2%
18301
 
1.2%
18971
 
1.2%
26051
 
1.2%
28581
 
1.2%
ValueCountFrequency (%)
183981
1.2%
160021
1.2%
140061
1.2%
108561
1.2%
67991
1.2%
61361
1.2%
59721
1.2%
51891
1.2%
45891
1.2%
44461
1.2%

장해연금
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17144.45
Minimum46
Maximum51758
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-10-29T23:59:37.424770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile96.8
Q1416.25
median10083
Q333737
95-th percentile44447.95
Maximum51758
Range51712
Interquartile range (IQR)33320.75

Descriptive statistics

Standard deviation17402.85153
Coefficient of variation (CV)1.015072022
Kurtosis-1.474418185
Mean17144.45
Median Absolute Deviation (MAD)9965.5
Skewness0.4070399907
Sum1371556
Variance302859241.3
MonotonicityNot monotonic
2022-10-29T23:59:37.571219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
461
 
1.2%
66291
 
1.2%
2161
 
1.2%
591
 
1.2%
1201
 
1.2%
701
 
1.2%
1441
 
1.2%
971
 
1.2%
2231
 
1.2%
1151
 
1.2%
Other values (70)70
87.5%
ValueCountFrequency (%)
461
1.2%
591
1.2%
701
1.2%
931
1.2%
971
1.2%
1151
1.2%
1201
1.2%
1421
1.2%
1441
1.2%
1611
1.2%
ValueCountFrequency (%)
517581
1.2%
495201
1.2%
469601
1.2%
455871
1.2%
443881
1.2%
432091
1.2%
430141
1.2%
415931
1.2%
402261
1.2%
397421
1.2%

장해유족연금
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct65
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3129.6375
Minimum0
Maximum10830
Zeros16
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-10-29T23:59:37.682890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q187.5
median1669
Q35554.75
95-th percentile9907.6
Maximum10830
Range10830
Interquartile range (IQR)5467.25

Descriptive statistics

Standard deviation3471.384261
Coefficient of variation (CV)1.109196915
Kurtosis-0.6896141105
Mean3129.6375
Median Absolute Deviation (MAD)1669
Skewness0.8254262142
Sum250371
Variance12050508.69
MonotonicityNot monotonic
2022-10-29T23:59:37.949762image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016
 
20.0%
8471
 
1.2%
3221
 
1.2%
831
 
1.2%
3861
 
1.2%
801
 
1.2%
4571
 
1.2%
821
 
1.2%
5071
 
1.2%
891
 
1.2%
Other values (55)55
68.8%
ValueCountFrequency (%)
016
20.0%
781
 
1.2%
801
 
1.2%
821
 
1.2%
831
 
1.2%
891
 
1.2%
951
 
1.2%
961
 
1.2%
1091
 
1.2%
1131
 
1.2%
ValueCountFrequency (%)
108301
1.2%
105241
1.2%
103041
1.2%
102231
1.2%
98911
1.2%
94611
1.2%
93371
1.2%
88711
1.2%
88061
1.2%
84441
1.2%

장해일시금
Real number (ℝ≥0)

Distinct70
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean967.0125
Minimum7
Maximum5793
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-10-29T23:59:38.109156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile16.85
Q147.75
median272.5
Q31381.5
95-th percentile3808.1
Maximum5793
Range5786
Interquartile range (IQR)1333.75

Descriptive statistics

Standard deviation1329.493627
Coefficient of variation (CV)1.374846371
Kurtosis3.358406847
Mean967.0125
Median Absolute Deviation (MAD)257
Skewness1.840599855
Sum77361
Variance1767553.304
MonotonicityNot monotonic
2022-10-29T23:59:38.266722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
472
 
2.5%
24242
 
2.5%
202
 
2.5%
132
 
2.5%
182
 
2.5%
482
 
2.5%
602
 
2.5%
342
 
2.5%
222
 
2.5%
542
 
2.5%
Other values (60)60
75.0%
ValueCountFrequency (%)
71
1.2%
132
2.5%
141
1.2%
171
1.2%
182
2.5%
202
2.5%
222
2.5%
261
1.2%
301
1.2%
342
2.5%
ValueCountFrequency (%)
57931
1.2%
55281
1.2%
49661
1.2%
39241
1.2%
38021
1.2%
34201
1.2%
28631
1.2%
26251
1.2%
24511
1.2%
24242
2.5%

위험직무순직유족연금
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct33
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean520.2125
Minimum0
Maximum4397
Zeros48
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-10-29T23:59:38.375362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3652
95-th percentile2381.1
Maximum4397
Range4397
Interquartile range (IQR)652

Descriptive statistics

Standard deviation991.0187533
Coefficient of variation (CV)1.905026798
Kurtosis5.615513935
Mean520.2125
Median Absolute Deviation (MAD)0
Skewness2.383190821
Sum41617
Variance982118.1695
MonotonicityNot monotonic
2022-10-29T23:59:38.468436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
048
60.0%
3421
 
1.2%
9021
 
1.2%
6241
 
1.2%
7361
 
1.2%
5131
 
1.2%
5691
 
1.2%
3571
 
1.2%
2751
 
1.2%
8991
 
1.2%
Other values (23)23
28.7%
ValueCountFrequency (%)
048
60.0%
81
 
1.2%
111
 
1.2%
671
 
1.2%
881
 
1.2%
1591
 
1.2%
2741
 
1.2%
2751
 
1.2%
3421
 
1.2%
3571
 
1.2%
ValueCountFrequency (%)
43971
1.2%
41861
1.2%
37541
1.2%
33901
1.2%
23281
1.2%
19791
1.2%
19081
1.2%
18731
1.2%
18251
1.2%
16331
1.2%

위험직무순직유족보상금
Real number (ℝ≥0)

MISSING
ZEROS

Distinct26
Distinct (%)32.9%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean355.8481013
Minimum0
Maximum3108
Zeros47
Zeros (%)58.8%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-10-29T23:59:38.555581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q314
95-th percentile2764
Maximum3108
Range3108
Interquartile range (IQR)14

Descriptive statistics

Standard deviation833.1453391
Coefficient of variation (CV)2.341294884
Kurtosis4.11958932
Mean355.8481013
Median Absolute Deviation (MAD)0
Skewness2.33371039
Sum28112
Variance694131.1561
MonotonicityNot monotonic
2022-10-29T23:59:38.636641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
047
58.8%
145
 
6.2%
43
 
3.8%
32
 
2.5%
181
 
1.2%
19641
 
1.2%
2611
 
1.2%
5151
 
1.2%
18071
 
1.2%
4421
 
1.2%
Other values (16)16
 
20.0%
ValueCountFrequency (%)
047
58.8%
32
 
2.5%
43
 
3.8%
81
 
1.2%
91
 
1.2%
101
 
1.2%
145
 
6.2%
161
 
1.2%
171
 
1.2%
181
 
1.2%
ValueCountFrequency (%)
31081
1.2%
29861
1.2%
28311
1.2%
28091
1.2%
27591
1.2%
24101
1.2%
19641
1.2%
19621
1.2%
18071
1.2%
16541
1.2%

부조급여(계)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19466.2875
Minimum17
Maximum47807
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-10-29T23:59:38.745102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile122.4
Q111409.75
median15889
Q326470.25
95-th percentile42909.6
Maximum47807
Range47790
Interquartile range (IQR)15060.5

Descriptive statistics

Standard deviation12032.01626
Coefficient of variation (CV)0.6180950661
Kurtosis-0.1324675137
Mean19466.2875
Median Absolute Deviation (MAD)6262
Skewness0.6457118766
Sum1557303
Variance144769415.2
MonotonicityNot monotonic
2022-10-29T23:59:38.857511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
281
 
1.2%
174591
 
1.2%
236921
 
1.2%
1111
 
1.2%
1231
 
1.2%
851
 
1.2%
1281
 
1.2%
135761
 
1.2%
49951
 
1.2%
173801
 
1.2%
Other values (70)70
87.5%
ValueCountFrequency (%)
171
1.2%
281
1.2%
851
1.2%
1111
1.2%
1231
1.2%
1281
1.2%
49951
1.2%
74881
1.2%
95941
1.2%
96601
1.2%
ValueCountFrequency (%)
478071
1.2%
476391
1.2%
470141
1.2%
465691
1.2%
427171
1.2%
396091
1.2%
393931
1.2%
387261
1.2%
370111
1.2%
361231
1.2%

재난부조금
Real number (ℝ≥0)

Distinct65
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.9875
Minimum6
Maximum629
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-10-29T23:59:38.962053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8.95
Q120.5
median82
Q3150
95-th percentile409.5
Maximum629
Range623
Interquartile range (IQR)129.5

Descriptive statistics

Standard deviation126.7506766
Coefficient of variation (CV)1.056365676
Kurtosis3.495624643
Mean119.9875
Median Absolute Deviation (MAD)64
Skewness1.816681886
Sum9599
Variance16065.73402
MonotonicityNot monotonic
2022-10-29T23:59:39.061482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135
 
6.2%
262
 
2.5%
792
 
2.5%
142
 
2.5%
1232
 
2.5%
172
 
2.5%
1262
 
2.5%
62
 
2.5%
782
 
2.5%
182
 
2.5%
Other values (55)57
71.2%
ValueCountFrequency (%)
62
 
2.5%
71
 
1.2%
81
 
1.2%
91
 
1.2%
101
 
1.2%
111
 
1.2%
121
 
1.2%
135
6.2%
142
 
2.5%
172
 
2.5%
ValueCountFrequency (%)
6291
1.2%
4631
1.2%
4491
1.2%
4381
1.2%
4081
1.2%
3881
1.2%
3651
1.2%
3311
1.2%
2661
1.2%
2441
1.2%

사망조위금
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct75
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19346.3
Minimum0
Maximum47684
Zeros6
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-10-29T23:59:39.317157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111399
median15623
Q326310.5
95-th percentile42819.2
Maximum47684
Range47684
Interquartile range (IQR)14911.5

Descriptive statistics

Standard deviation11990.47302
Coefficient of variation (CV)0.6197811996
Kurtosis-0.1101895172
Mean19346.3
Median Absolute Deviation (MAD)6234
Skewness0.6511143784
Sum1547704
Variance143771443.3
MonotonicityNot monotonic
2022-10-29T23:59:39.410851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06
 
7.5%
108791
 
1.2%
256971
 
1.2%
49161
 
1.2%
173151
 
1.2%
73681
 
1.2%
187361
 
1.2%
91311
 
1.2%
195781
 
1.2%
221991
 
1.2%
Other values (65)65
81.2%
ValueCountFrequency (%)
06
7.5%
49161
 
1.2%
73681
 
1.2%
91311
 
1.2%
96471
 
1.2%
101101
 
1.2%
101801
 
1.2%
103841
 
1.2%
105201
 
1.2%
105771
 
1.2%
ValueCountFrequency (%)
476841
1.2%
474911
1.2%
468881
1.2%
464711
1.2%
426271
1.2%
394591
1.2%
389551
1.2%
386581
1.2%
366461
1.2%
360441
1.2%

퇴직수당
Real number (ℝ≥0)

ZEROS

Distinct63
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean497335.6375
Minimum0
Maximum2566243
Zeros18
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size848.0 B
2022-10-29T23:59:39.504683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120695.25
median35708.5
Q3853831
95-th percentile2249104.55
Maximum2566243
Range2566243
Interquartile range (IQR)833135.75

Descriptive statistics

Standard deviation749146.3253
Coefficient of variation (CV)1.506319413
Kurtosis0.8030681566
Mean497335.6375
Median Absolute Deviation (MAD)35708.5
Skewness1.437419523
Sum39786851
Variance5.612202168 × 1011
MonotonicityNot monotonic
2022-10-29T23:59:39.601847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
018
 
22.5%
412951
 
1.2%
161481
 
1.2%
286391
 
1.2%
2749171
 
1.2%
329951
 
1.2%
4003401
 
1.2%
340061
 
1.2%
4608121
 
1.2%
370291
 
1.2%
Other values (53)53
66.2%
ValueCountFrequency (%)
018
22.5%
30771
 
1.2%
161481
 
1.2%
222111
 
1.2%
237221
 
1.2%
241121
 
1.2%
264581
 
1.2%
264731
 
1.2%
269401
 
1.2%
286391
 
1.2%
ValueCountFrequency (%)
25662431
1.2%
23350121
1.2%
22801491
1.2%
22721051
1.2%
22478941
1.2%
22166371
1.2%
20715301
1.2%
20440821
1.2%
19302351
1.2%
15393961
1.2%

Interactions

2022-10-29T23:59:33.720614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:13.107445image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:14.759757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:16.068217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:17.587887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:18.997481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:20.543866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:21.885463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:23.297486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:24.811190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:26.416037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:27.866985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:29.454929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:30.973418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:32.399687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:33.797479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:13.256318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:14.850226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:16.335292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:17.674108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:19.078075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:20.642282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:21.959770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:23.383611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:24.926296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:26.508638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:27.956096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:29.546297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:31.053895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:32.470888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:33.881273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:13.368236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:14.916863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:16.410133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:17.907722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:19.146579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:20.725953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:22.037381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:23.457750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:25.027788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:26.576930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:28.041890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:29.634233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:31.129658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:32.536812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:33.955711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:13.468180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:14.999911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:16.495511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:17.994568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:19.487073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:20.809931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:22.111265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:23.542666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:25.134323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:26.669761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:28.158245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:29.736490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:31.212739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:32.638219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:34.031117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:13.550330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:15.116138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:16.597664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:18.100574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:19.615618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:21.021192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:22.181081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:23.629708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:25.225769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:26.764575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:28.278225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:29.821201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:31.301626image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:32.734360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:34.107084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:13.653721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:15.198528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:16.699706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:18.224173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:19.727493image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:21.093398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:22.423848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:23.735868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:25.347674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:26.850848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:28.373644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:29.905597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:31.393951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:32.808729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:34.190677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:13.759629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:15.300500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:16.792762image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:18.333631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:19.811295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:21.169282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:22.507300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:24.003022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:25.437772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:26.939614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:28.468931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:29.988447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:31.494030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:32.882528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:34.301094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:13.856753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:15.384979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:16.889404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:18.410725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:19.885140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:21.254785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:22.583516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:24.085665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:25.704439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:27.062522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:28.565419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:30.089552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:31.583211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:32.952926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:34.411472image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:13.952669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:15.474519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:16.985657image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:18.483251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:19.960715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:21.369676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:22.654193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:24.162392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:25.797004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:27.299635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:28.644124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:30.169732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:31.659336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:33.027600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:34.523627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:14.054082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:15.563879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:17.085500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:18.563402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:20.034875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:21.448069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:22.741933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:24.253906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:25.892288image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:27.391465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:28.908895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:30.265042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:31.749077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:33.117600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:34.613212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:14.149998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:15.633732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:17.162818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:18.633846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:20.110170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:21.517140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:22.857577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:24.348928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:26.000239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:27.464967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:29.006433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:30.552280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:31.835400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:33.201997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:34.707188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:14.252722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:15.715965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:17.247877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:18.714396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:20.188874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:21.594159image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:22.965375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:24.428991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:26.080009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:27.537429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:29.096378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:30.664132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:32.101857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:33.281696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:34.796181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:14.350032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:15.791791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:17.325769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:18.782823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:20.271772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:21.667244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:23.034668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:24.498712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:26.165614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:27.624584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:29.199097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:30.740709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:32.187384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:33.507085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:35.074399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:14.432885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:15.892018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:17.407255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:18.854696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:20.364187image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:21.741784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:23.104874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:24.615741image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:26.264815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:27.701254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:29.297385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:30.817644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:32.262698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:33.575798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:35.159391image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:14.681667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:15.981538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:17.497247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:18.923925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:20.452866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:21.813006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:23.207410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:24.702205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:26.343286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:27.777588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:29.377974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:30.902334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:32.331067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-10-29T23:59:33.645304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-10-29T23:59:39.693469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-29T23:59:39.890859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-29T23:59:40.092103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-29T23:59:40.260061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-29T23:59:35.305483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-29T23:59:35.490346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-29T23:59:35.615962image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

연도구분재해보상급여(계)요양급여공무상요양일시금순직유족보상금순직유족연금장해연금장해유족연금장해일시금위험직무순직유족연금위험직무순직유족보상금부조급여(계)재난부조금사망조위금퇴직수당
01982건수14641151022004604700282800
11982금액377771202525093044700171700
21983건수2078171902560590440011111100
31983금액476074603415012004790012312300
41984건수37533415023407003400858500
51984금액4786108303099014404600012812800
61985건수496945680252097052001357657135190
71985금액5546147403215022306340049957949160
81986건수5414499702510115051001738065173150
91986금액63981714036890308068700748812073680

Last rows

연도구분재해보상급여(계)요양급여공무상요양일시금순직유족보상금순직유족연금장해연금장해유족연금장해일시금위험직무순직유족연금위험직무순직유족보상금부조급여(계)재난부조금사망조위금퇴직수당
702017건수22803017664701003475382548216141308161258871258135192
712017금액10379129072090536136443888871957232829864271790426272044082
722018건수1983331443130864446391708806714911413407131339435287
732018금액1061552551308237108564558793374043390283147014126468882071530
742019건수222232166094084518939742946120163391299781298938786
752019금액1207363395408920140064696098911597375416544656998464712335012
762020건수20027014175606859724022610304221908141312591311644488
772020금액126104342170741816002495201022317294186280947807123476842566243
782021건수175755113428081679943014105242218731413054131304140378
792021금액127498306890819218398517581083012704397196447639148474912216637