Overview

Dataset statistics

Number of variables20
Number of observations40
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.2 KiB
Average record size in memory183.2 B

Variable types

Numeric20

Dataset

Description공무원 직종별 퇴직자 현황 데이터(정무직,별정직,일반직,경찰소방,기능직,공안직,고용직 등) (단위:명), 주) 1. 2000년까지의 기타에는 정무직, 공안직, 연구직, 지도직, 계약직, 공중보건의 인원이 포함되어 있음 2. 2000년까지의 별정직(국가)에는 국가별정직, 지방별정직, 군무원이 포함되어 있음 3. 2001년 이후 기타에는 공익법무관, 기타직 공무원 등이 포함되어 있음 4. 청원경찰은 2002년까지는 경찰·소방으로 2003년부터는 기타로 분류되어 있음 5. 별정직, 일반직의 경우 2011년 이후 재직공무원의 직종관리체계를 변경함에 따라 지방직을 국가직으로 통합
Author공무원연금공단
URLhttps://www.data.go.kr/data/15053019/fileData.do

Alerts

구분 is highly correlated with 연구직 and 1 other fieldsHigh correlation
공안직 is highly correlated with 군무원 and 2 other fieldsHigh correlation
군무원 is highly correlated with 공안직 and 3 other fieldsHigh correlation
연구직 is highly correlated with 구분 and 4 other fieldsHigh correlation
지도직 is highly correlated with 공안직 and 2 other fieldsHigh correlation
계약직 is highly correlated with 구분 and 1 other fieldsHigh correlation
공중보건의 is highly correlated with 군무원High correlation
구분 has unique values Unique
has unique values Unique
전년대비증가율(퍼센트) has unique values Unique
별정직(국가) has unique values Unique
교육직 has unique values Unique
기능직 has unique values Unique
기타 has unique values Unique
전년대비증가율(퍼센트) has 1 (2.5%) zeros Zeros
정무직 has 19 (47.5%) zeros Zeros
별정직(지방) has 30 (75.0%) zeros Zeros
일반직(지방) has 11 (27.5%) zeros Zeros
고용직 has 15 (37.5%) zeros Zeros
공안직 has 19 (47.5%) zeros Zeros
군무원 has 19 (47.5%) zeros Zeros
연구직 has 19 (47.5%) zeros Zeros
지도직 has 19 (47.5%) zeros Zeros
계약직 has 19 (47.5%) zeros Zeros
공중보건의 has 19 (47.5%) zeros Zeros

Reproduction

Analysis started2022-11-19 09:53:07.766530
Analysis finished2022-11-19 09:53:56.223628
Duration48.46 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

구분
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001.5
Minimum1982
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T18:53:56.295498image/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 deviation11.69045194
Coefficient of variation (CV)0.005840845338
Kurtosis-1.2
Mean2001.5
Median Absolute Deviation (MAD)10
Skewness0
Sum80060
Variance136.6666667
MonotonicityStrictly increasing
2022-11-19T18:53:56.452432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
19821
 
2.5%
20011
 
2.5%
19841
 
2.5%
19851
 
2.5%
19861
 
2.5%
19871
 
2.5%
19881
 
2.5%
19891
 
2.5%
19901
 
2.5%
19921
 
2.5%
Other values (30)30
75.0%
ValueCountFrequency (%)
19821
2.5%
19831
2.5%
19841
2.5%
19851
2.5%
19861
2.5%
19871
2.5%
19881
2.5%
19891
2.5%
19901
2.5%
19911
2.5%
ValueCountFrequency (%)
20211
2.5%
20201
2.5%
20191
2.5%
20181
2.5%
20171
2.5%
20161
2.5%
20151
2.5%
20141
2.5%
20131
2.5%
20121
2.5%


Real number (ℝ≥0)

UNIQUE

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36047.35
Minimum23095
Maximum94797
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T18:53:56.596752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum23095
5-th percentile24485.2
Q128581.5
median34375.5
Q338509.5
95-th percentile55372.25
Maximum94797
Range71702
Interquartile range (IQR)9928

Descriptive statistics

Standard deviation12792.47818
Coefficient of variation (CV)0.3548798506
Kurtosis11.11135722
Mean36047.35
Median Absolute Deviation (MAD)5208.5
Skewness2.852644925
Sum1441894
Variance163647498
MonotonicityNot monotonic
2022-11-19T18:53:56.720818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
388441
 
2.5%
295091
 
2.5%
347681
 
2.5%
288201
 
2.5%
246511
 
2.5%
255891
 
2.5%
271291
 
2.5%
244961
 
2.5%
278661
 
2.5%
329241
 
2.5%
Other values (30)30
75.0%
ValueCountFrequency (%)
230951
2.5%
242801
2.5%
244961
2.5%
246511
2.5%
248991
2.5%
255891
2.5%
261631
2.5%
271291
2.5%
273841
2.5%
278661
2.5%
ValueCountFrequency (%)
947971
2.5%
643451
2.5%
549001
2.5%
473191
2.5%
446761
2.5%
440101
2.5%
429071
2.5%
403401
2.5%
397811
2.5%
388441
2.5%

전년대비증가율(퍼센트)
Real number (ℝ)

UNIQUE
ZEROS

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.119768964
Minimum-54.13940477
Maximum72.67213115
Zeros1
Zeros (%)2.5%
Negative17
Negative (%)42.5%
Memory size488.0 B
2022-11-19T18:53:56.842909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-54.13940477
5-th percentile-32.23026618
Q1-10.50201752
median2.357293875
Q311.36566302
95-th percentile50.45967314
Maximum72.67213115
Range126.8115359
Interquartile range (IQR)21.86768055

Descriptive statistics

Standard deviation24.22599937
Coefficient of variation (CV)7.765318409
Kurtosis1.721343779
Mean3.119768964
Median Absolute Deviation (MAD)11.73141468
Skewness0.6754555484
Sum124.7907586
Variance586.8990455
MonotonicityNot monotonic
2022-11-19T18:53:56.950494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
01
 
2.5%
-54.139404771
 
2.5%
-5.0158452631
 
2.5%
-17.107685231
 
2.5%
-14.465648851
 
2.5%
3.8051194681
 
2.5%
6.018210951
 
2.5%
-9.7054812191
 
2.5%
13.757348141
 
2.5%
6.8579403461
 
2.5%
Other values (30)30
75.0%
ValueCountFrequency (%)
-54.139404771
2.5%
-34.261114421
2.5%
-32.123379431
2.5%
-21.735741641
2.5%
-21.278579251
2.5%
-17.107685231
2.5%
-17.069588791
2.5%
-14.465648851
2.5%
-13.638455791
2.5%
-12.891626441
2.5%
ValueCountFrequency (%)
72.672131151
2.5%
61.522845631
2.5%
49.87740091
2.5%
35.336161761
2.5%
26.942740291
2.5%
23.702635911
2.5%
19.492704391
2.5%
18.948744381
2.5%
16.35796611
2.5%
13.757348141
2.5%

정무직
Real number (ℝ≥0)

ZEROS

Distinct22
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean209.925
Minimum0
Maximum1329
Zeros19
Zeros (%)47.5%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T18:53:57.077176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median58.5
Q3255.5
95-th percentile803.15
Maximum1329
Range1329
Interquartile range (IQR)255.5

Descriptive statistics

Standard deviation346.1166535
Coefficient of variation (CV)1.648763384
Kurtosis3.203322573
Mean209.925
Median Absolute Deviation (MAD)58.5
Skewness1.911619636
Sum8397
Variance119796.7378
MonotonicityNot monotonic
2022-11-19T18:53:57.210241image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
019
47.5%
6711
 
2.5%
921
 
2.5%
5911
 
2.5%
5651
 
2.5%
12621
 
2.5%
6551
 
2.5%
7791
 
2.5%
5041
 
2.5%
13291
 
2.5%
Other values (12)12
30.0%
ValueCountFrequency (%)
019
47.5%
551
 
2.5%
621
 
2.5%
641
 
2.5%
691
 
2.5%
731
 
2.5%
751
 
2.5%
821
 
2.5%
921
 
2.5%
1091
 
2.5%
ValueCountFrequency (%)
13291
2.5%
12621
2.5%
7791
2.5%
6711
2.5%
6551
2.5%
6031
2.5%
5911
2.5%
5651
2.5%
5041
2.5%
4461
2.5%

별정직(국가)
Real number (ℝ≥0)

UNIQUE

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1303.825
Minimum212
Maximum3214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T18:53:57.358248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum212
5-th percentile290.3
Q1711.5
median1256
Q31512
95-th percentile2840.55
Maximum3214
Range3002
Interquartile range (IQR)800.5

Descriptive statistics

Standard deviation762.3733148
Coefficient of variation (CV)0.5847205835
Kurtosis0.3622440501
Mean1303.825
Median Absolute Deviation (MAD)426
Skewness0.814910858
Sum52153
Variance581213.0712
MonotonicityNot monotonic
2022-11-19T18:53:57.490307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10481
 
2.5%
8251
 
2.5%
13221
 
2.5%
14241
 
2.5%
14981
 
2.5%
14901
 
2.5%
12001
 
2.5%
10711
 
2.5%
12611
 
2.5%
17901
 
2.5%
Other values (30)30
75.0%
ValueCountFrequency (%)
2121
2.5%
2581
2.5%
2921
2.5%
3941
2.5%
4091
2.5%
4231
2.5%
4431
2.5%
5561
2.5%
6531
2.5%
6981
2.5%
ValueCountFrequency (%)
32141
2.5%
29651
2.5%
28341
2.5%
27931
2.5%
24501
2.5%
21351
2.5%
19351
2.5%
17901
2.5%
16771
2.5%
15391
2.5%

별정직(지방)
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.1
Minimum0
Maximum354
Zeros30
Zeros (%)75.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T18:53:57.811289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q332
95-th percentile312.3
Maximum354
Range354
Interquartile range (IQR)32

Descriptive statistics

Standard deviation108.2679013
Coefficient of variation (CV)1.896110356
Kurtosis1.590803138
Mean57.1
Median Absolute Deviation (MAD)0
Skewness1.703783461
Sum2284
Variance11721.93846
MonotonicityNot monotonic
2022-11-19T18:53:57.897158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
030
75.0%
3122
 
5.0%
3541
 
2.5%
1831
 
2.5%
1671
 
2.5%
1351
 
2.5%
3181
 
2.5%
1791
 
2.5%
1961
 
2.5%
1281
 
2.5%
ValueCountFrequency (%)
030
75.0%
1281
 
2.5%
1351
 
2.5%
1671
 
2.5%
1791
 
2.5%
1831
 
2.5%
1961
 
2.5%
3122
 
5.0%
3181
 
2.5%
3541
 
2.5%
ValueCountFrequency (%)
3541
 
2.5%
3181
 
2.5%
3122
 
5.0%
1961
 
2.5%
1831
 
2.5%
1791
 
2.5%
1671
 
2.5%
1351
 
2.5%
1281
 
2.5%
030
75.0%

일반직(국가)
Real number (ℝ≥0)

Distinct39
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6332.9
Minimum1852
Maximum18401
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T18:53:58.003299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1852
5-th percentile2162.95
Q12834.5
median3956.5
Q37897.25
95-th percentile15304.65
Maximum18401
Range16549
Interquartile range (IQR)5062.75

Descriptive statistics

Standard deviation5092.797412
Coefficient of variation (CV)0.80418093
Kurtosis-0.2886114882
Mean6332.9
Median Absolute Deviation (MAD)1570
Skewness1.150384033
Sum253316
Variance25936585.48
MonotonicityNot monotonic
2022-11-19T18:53:58.110463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
28402
 
5.0%
41901
 
2.5%
138761
 
2.5%
35651
 
2.5%
39221
 
2.5%
24731
 
2.5%
27661
 
2.5%
28181
 
2.5%
22511
 
2.5%
29031
 
2.5%
Other values (29)29
72.5%
ValueCountFrequency (%)
18521
2.5%
20671
2.5%
21681
2.5%
22511
2.5%
22601
2.5%
23001
2.5%
24731
2.5%
25421
2.5%
27661
2.5%
28181
2.5%
ValueCountFrequency (%)
184011
2.5%
158111
2.5%
152781
2.5%
152081
2.5%
147611
2.5%
146781
2.5%
141921
2.5%
141721
2.5%
138761
2.5%
98151
2.5%

일반직(지방)
Real number (ℝ≥0)

ZEROS

Distinct30
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3093.875
Minimum0
Maximum10778
Zeros11
Zeros (%)27.5%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T18:53:58.225030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3245.5
Q34266.5
95-th percentile7620.1
Maximum10778
Range10778
Interquartile range (IQR)4266.5

Descriptive statistics

Standard deviation2493.142186
Coefficient of variation (CV)0.805831582
Kurtosis1.128206944
Mean3093.875
Median Absolute Deviation (MAD)1178.5
Skewness0.6963192159
Sum123755
Variance6215757.958
MonotonicityNot monotonic
2022-11-19T18:53:58.317828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
011
27.5%
39091
 
2.5%
44031
 
2.5%
37681
 
2.5%
30991
 
2.5%
23581
 
2.5%
21781
 
2.5%
22601
 
2.5%
27221
 
2.5%
32801
 
2.5%
Other values (20)20
50.0%
ValueCountFrequency (%)
011
27.5%
21781
 
2.5%
22601
 
2.5%
23581
 
2.5%
27221
 
2.5%
29531
 
2.5%
30991
 
2.5%
31751
 
2.5%
31831
 
2.5%
32111
 
2.5%
ValueCountFrequency (%)
107781
2.5%
80781
2.5%
75961
2.5%
58471
2.5%
54121
2.5%
51471
2.5%
47141
2.5%
45981
2.5%
44451
2.5%
44031
2.5%

경찰소방
Real number (ℝ≥0)

Distinct39
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2947.45
Minimum1250
Maximum7887
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T18:53:58.430441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1250
5-th percentile1521.1
Q12087.25
median2747.5
Q33436
95-th percentile5108.1
Maximum7887
Range6637
Interquartile range (IQR)1348.75

Descriptive statistics

Standard deviation1316.504812
Coefficient of variation (CV)0.4466589127
Kurtosis5.197931838
Mean2947.45
Median Absolute Deviation (MAD)702.5
Skewness1.943306784
Sum117898
Variance1733184.921
MonotonicityNot monotonic
2022-11-19T18:53:58.541224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
30392
 
5.0%
21331
 
2.5%
41401
 
2.5%
19371
 
2.5%
18771
 
2.5%
13901
 
2.5%
15281
 
2.5%
18991
 
2.5%
20161
 
2.5%
27681
 
2.5%
Other values (29)29
72.5%
ValueCountFrequency (%)
12501
2.5%
13901
2.5%
15281
2.5%
17121
2.5%
17181
2.5%
18771
2.5%
18991
2.5%
19241
2.5%
19371
2.5%
20161
2.5%
ValueCountFrequency (%)
78871
2.5%
67061
2.5%
50241
2.5%
41401
2.5%
39151
2.5%
38131
2.5%
37741
2.5%
36601
2.5%
36011
2.5%
34811
2.5%

교육직
Real number (ℝ≥0)

UNIQUE

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8346.2
Minimum4046
Maximum28872
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T18:53:58.661500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4046
5-th percentile4204.9
Q15433.5
median7156.5
Q310368.75
95-th percentile12969.75
Maximum28872
Range24826
Interquartile range (IQR)4935.25

Descriptive statistics

Standard deviation4335.667394
Coefficient of variation (CV)0.5194780133
Kurtosis12.15677018
Mean8346.2
Median Absolute Deviation (MAD)1985
Skewness2.828178164
Sum333848
Variance18798011.75
MonotonicityNot monotonic
2022-11-19T18:53:58.804850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
52631
 
2.5%
53511
 
2.5%
43461
 
2.5%
42131
 
2.5%
40461
 
2.5%
40511
 
2.5%
43721
 
2.5%
51141
 
2.5%
52291
 
2.5%
63221
 
2.5%
Other values (30)30
75.0%
ValueCountFrequency (%)
40461
2.5%
40511
2.5%
42131
2.5%
43461
2.5%
43721
2.5%
50141
2.5%
51141
2.5%
52291
2.5%
52631
2.5%
53511
2.5%
ValueCountFrequency (%)
288721
2.5%
141621
2.5%
129071
2.5%
118051
2.5%
117981
2.5%
115461
2.5%
114331
2.5%
113571
2.5%
111141
2.5%
106231
2.5%

법관검사
Real number (ℝ≥0)

Distinct38
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.25
Minimum19
Maximum330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T18:53:58.919681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile25.8
Q170.5
median135.5
Q3163.75
95-th percentile212.1
Maximum330
Range311
Interquartile range (IQR)93.25

Descriptive statistics

Standard deviation66.9353879
Coefficient of variation (CV)0.5387153956
Kurtosis0.7691680662
Mean124.25
Median Absolute Deviation (MAD)44
Skewness0.4127950418
Sum4970
Variance4480.346154
MonotonicityNot monotonic
2022-11-19T18:53:59.019549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1472
 
5.0%
1492
 
5.0%
1781
 
2.5%
1281
 
2.5%
1701
 
2.5%
221
 
2.5%
371
 
2.5%
381
 
2.5%
351
 
2.5%
301
 
2.5%
Other values (28)28
70.0%
ValueCountFrequency (%)
191
2.5%
221
2.5%
261
2.5%
301
2.5%
351
2.5%
371
2.5%
381
2.5%
481
2.5%
571
2.5%
601
2.5%
ValueCountFrequency (%)
3301
2.5%
2141
2.5%
2121
2.5%
2061
2.5%
1891
2.5%
1881
2.5%
1851
2.5%
1781
2.5%
1701
2.5%
1661
2.5%

기능직
Real number (ℝ≥0)

UNIQUE

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6305.45
Minimum268
Maximum27801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T18:53:59.338489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum268
5-th percentile367.75
Q13023.25
median5046.5
Q38678
95-th percentile15153.9
Maximum27801
Range27533
Interquartile range (IQR)5654.75

Descriptive statistics

Standard deviation5309.686856
Coefficient of variation (CV)0.8420789723
Kurtosis5.762612514
Mean6305.45
Median Absolute Deviation (MAD)3067.5
Skewness1.914745041
Sum252218
Variance28192774.51
MonotonicityNot monotonic
2022-11-19T18:53:59.475061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
64281
 
2.5%
80041
 
2.5%
82241
 
2.5%
30781
 
2.5%
28011
 
2.5%
40791
 
2.5%
48191
 
2.5%
36811
 
2.5%
69711
 
2.5%
99081
 
2.5%
Other values (30)30
75.0%
ValueCountFrequency (%)
2681
2.5%
3631
2.5%
3681
2.5%
5251
2.5%
8911
2.5%
10511
2.5%
15281
2.5%
17521
2.5%
28011
2.5%
28591
2.5%
ValueCountFrequency (%)
278011
2.5%
156081
2.5%
151301
2.5%
131211
2.5%
117331
2.5%
114431
2.5%
99081
2.5%
97251
2.5%
89001
2.5%
88461
2.5%

고용직
Real number (ℝ≥0)

ZEROS

Distinct26
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2425.05
Minimum0
Maximum11346
Zeros15
Zeros (%)37.5%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T18:53:59.622213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median975.5
Q32407.25
95-th percentile10810.1
Maximum11346
Range11346
Interquartile range (IQR)2407.25

Descriptive statistics

Standard deviation3553.973211
Coefficient of variation (CV)1.465525746
Kurtosis1.021301339
Mean2425.05
Median Absolute Deviation (MAD)975.5
Skewness1.539940173
Sum97002
Variance12630725.59
MonotonicityNot monotonic
2022-11-19T18:53:59.747632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
015
37.5%
18671
 
2.5%
17781
 
2.5%
113461
 
2.5%
97261
 
2.5%
81791
 
2.5%
78561
 
2.5%
75311
 
2.5%
57301
 
2.5%
34571
 
2.5%
Other values (16)16
40.0%
ValueCountFrequency (%)
015
37.5%
701
 
2.5%
2511
 
2.5%
3151
 
2.5%
3801
 
2.5%
8661
 
2.5%
10851
 
2.5%
13871
 
2.5%
14181
 
2.5%
14631
 
2.5%
ValueCountFrequency (%)
113461
2.5%
109451
2.5%
108031
2.5%
97261
2.5%
81791
2.5%
78561
2.5%
75311
2.5%
57301
2.5%
34571
2.5%
27441
2.5%

공안직
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean428.575
Minimum0
Maximum1234
Zeros19
Zeros (%)47.5%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T18:53:59.851314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median533
Q3853.5
95-th percentile1019.45
Maximum1234
Range1234
Interquartile range (IQR)853.5

Descriptive statistics

Standard deviation432.6473597
Coefficient of variation (CV)1.009502093
Kurtosis-1.710519182
Mean428.575
Median Absolute Deviation (MAD)523.5
Skewness0.1707332225
Sum17143
Variance187183.7378
MonotonicityNot monotonic
2022-11-19T18:53:59.958219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
019
47.5%
8341
 
2.5%
10471
 
2.5%
5931
 
2.5%
7641
 
2.5%
8891
 
2.5%
5691
 
2.5%
8791
 
2.5%
8471
 
2.5%
8731
 
2.5%
Other values (12)12
30.0%
ValueCountFrequency (%)
019
47.5%
4971
 
2.5%
5691
 
2.5%
5931
 
2.5%
6201
 
2.5%
6391
 
2.5%
6651
 
2.5%
7041
 
2.5%
7641
 
2.5%
8001
 
2.5%
ValueCountFrequency (%)
12341
2.5%
10471
2.5%
10181
2.5%
9941
2.5%
9081
2.5%
8891
2.5%
8881
2.5%
8811
2.5%
8791
2.5%
8731
2.5%

군무원
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean473.95
Minimum0
Maximum1217
Zeros19
Zeros (%)47.5%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T18:54:00.066403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median495.5
Q3941.25
95-th percentile1094.1
Maximum1217
Range1217
Interquartile range (IQR)941.25

Descriptive statistics

Standard deviation479.443799
Coefficient of variation (CV)1.011591516
Kurtosis-1.850964901
Mean473.95
Median Absolute Deviation (MAD)495.5
Skewness0.1393024351
Sum18958
Variance229866.3564
MonotonicityNot monotonic
2022-11-19T18:54:00.169258image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
019
47.5%
10001
 
2.5%
10451
 
2.5%
6841
 
2.5%
9571
 
2.5%
9241
 
2.5%
6951
 
2.5%
11721
 
2.5%
10721
 
2.5%
12171
 
2.5%
Other values (12)12
30.0%
ValueCountFrequency (%)
019
47.5%
3761
 
2.5%
6151
 
2.5%
6341
 
2.5%
6841
 
2.5%
6951
 
2.5%
8361
 
2.5%
8661
 
2.5%
8951
 
2.5%
9101
 
2.5%
ValueCountFrequency (%)
12171
2.5%
11721
2.5%
10901
2.5%
10721
2.5%
10451
2.5%
10421
2.5%
10001
2.5%
9991
2.5%
9931
2.5%
9571
2.5%

연구직
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.025
Minimum0
Maximum314
Zeros19
Zeros (%)47.5%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T18:54:00.274312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median120
Q3179.75
95-th percentile264.5
Maximum314
Range314
Interquartile range (IQR)179.75

Descriptive statistics

Standard deviation107.5218493
Coefficient of variation (CV)1.053877474
Kurtosis-1.338747277
Mean102.025
Median Absolute Deviation (MAD)120
Skewness0.4048237643
Sum4081
Variance11560.94808
MonotonicityNot monotonic
2022-11-19T18:54:00.367364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
019
47.5%
1401
 
2.5%
2631
 
2.5%
1321
 
2.5%
1341
 
2.5%
1271
 
2.5%
1131
 
2.5%
1461
 
2.5%
1301
 
2.5%
2481
 
2.5%
Other values (12)12
30.0%
ValueCountFrequency (%)
019
47.5%
1131
 
2.5%
1271
 
2.5%
1301
 
2.5%
1321
 
2.5%
1341
 
2.5%
1401
 
2.5%
1461
 
2.5%
1471
 
2.5%
1691
 
2.5%
ValueCountFrequency (%)
3141
2.5%
2931
2.5%
2631
2.5%
2561
2.5%
2521
2.5%
2481
2.5%
2451
2.5%
2271
2.5%
2041
2.5%
1881
2.5%

지도직
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.95
Minimum0
Maximum273
Zeros19
Zeros (%)47.5%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T18:54:00.468142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median96.5
Q3196.75
95-th percentile253.7
Maximum273
Range273
Interquartile range (IQR)196.75

Descriptive statistics

Standard deviation102.210429
Coefficient of variation (CV)1.043495957
Kurtosis-1.587513042
Mean97.95
Median Absolute Deviation (MAD)96.5
Skewness0.31051906
Sum3918
Variance10446.97179
MonotonicityNot monotonic
2022-11-19T18:54:00.558721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
019
47.5%
1071
 
2.5%
2021
 
2.5%
1271
 
2.5%
1741
 
2.5%
1931
 
2.5%
1551
 
2.5%
2531
 
2.5%
2041
 
2.5%
2261
 
2.5%
Other values (12)12
30.0%
ValueCountFrequency (%)
019
47.5%
921
 
2.5%
1011
 
2.5%
1071
 
2.5%
1271
 
2.5%
1351
 
2.5%
1481
 
2.5%
1551
 
2.5%
1631
 
2.5%
1741
 
2.5%
ValueCountFrequency (%)
2731
2.5%
2671
2.5%
2531
2.5%
2471
2.5%
2421
2.5%
2261
2.5%
2141
2.5%
2041
2.5%
2021
2.5%
1991
2.5%

계약직
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean557.5
Minimum0
Maximum2346
Zeros19
Zeros (%)47.5%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T18:54:00.656629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median252.5
Q31108
95-th percentile1633.15
Maximum2346
Range2346
Interquartile range (IQR)1108

Descriptive statistics

Standard deviation666.3890448
Coefficient of variation (CV)1.195316672
Kurtosis-0.09499520912
Mean557.5
Median Absolute Deviation (MAD)252.5
Skewness0.919214341
Sum22300
Variance444074.359
MonotonicityNot monotonic
2022-11-19T18:54:00.922267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
019
47.5%
11441
 
2.5%
23461
 
2.5%
4271
 
2.5%
5371
 
2.5%
4481
 
2.5%
4761
 
2.5%
6451
 
2.5%
7091
 
2.5%
9911
 
2.5%
Other values (12)12
30.0%
ValueCountFrequency (%)
019
47.5%
781
 
2.5%
4271
 
2.5%
4481
 
2.5%
4761
 
2.5%
5371
 
2.5%
6451
 
2.5%
7091
 
2.5%
8871
 
2.5%
9911
 
2.5%
ValueCountFrequency (%)
23461
2.5%
20541
2.5%
16111
2.5%
14131
2.5%
13811
2.5%
13781
2.5%
13701
2.5%
11441
2.5%
11241
2.5%
11141
2.5%

공중보건의
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean742.45
Minimum0
Maximum1933
Zeros19
Zeros (%)47.5%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T18:54:01.033309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1043.5
Q31362.5
95-th percentile1732.1
Maximum1933
Range1933
Interquartile range (IQR)1362.5

Descriptive statistics

Standard deviation741.8099815
Coefficient of variation (CV)0.9991379642
Kurtosis-1.795566416
Mean742.45
Median Absolute Deviation (MAD)856.5
Skewness0.1162275393
Sum29698
Variance550282.0487
MonotonicityNot monotonic
2022-11-19T18:54:01.137526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
019
47.5%
16101
 
2.5%
10241
 
2.5%
11921
 
2.5%
10991
 
2.5%
13451
 
2.5%
16731
 
2.5%
16601
 
2.5%
18671
 
2.5%
17071
 
2.5%
Other values (12)12
30.0%
ValueCountFrequency (%)
019
47.5%
10241
 
2.5%
10631
 
2.5%
10861
 
2.5%
10991
 
2.5%
11771
 
2.5%
11921
 
2.5%
12291
 
2.5%
12341
 
2.5%
13301
 
2.5%
ValueCountFrequency (%)
19331
2.5%
18671
2.5%
17251
2.5%
17071
2.5%
16731
2.5%
16601
2.5%
16101
2.5%
15311
2.5%
14791
2.5%
13761
2.5%

기타
Real number (ℝ≥0)

UNIQUE

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2498.875
Minimum238
Maximum6705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T18:54:01.257821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum238
5-th percentile653.15
Q11023.75
median2193.5
Q33459.75
95-th percentile5037.55
Maximum6705
Range6467
Interquartile range (IQR)2436

Descriptive statistics

Standard deviation1639.460452
Coefficient of variation (CV)0.6560794165
Kurtosis0.2495311283
Mean2498.875
Median Absolute Deviation (MAD)1258.5
Skewness0.8008482419
Sum99955
Variance2687830.574
MonotonicityNot monotonic
2022-11-19T18:54:01.374837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
34061
 
2.5%
6621
 
2.5%
2381
 
2.5%
14441
 
2.5%
18681
 
2.5%
16061
 
2.5%
22001
 
2.5%
18851
 
2.5%
19371
 
2.5%
28431
 
2.5%
Other values (30)30
75.0%
ValueCountFrequency (%)
2381
2.5%
4851
2.5%
6621
2.5%
6931
2.5%
7881
2.5%
8081
2.5%
8731
2.5%
8871
2.5%
8911
2.5%
9241
2.5%
ValueCountFrequency (%)
67051
2.5%
66821
2.5%
49511
2.5%
49121
2.5%
44251
2.5%
41781
2.5%
39921
2.5%
37271
2.5%
35921
2.5%
35161
2.5%

Interactions

2022-11-19T18:53:53.113345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:08.250449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:10.920139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:12.878535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:15.174768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:17.261057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:19.594204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:21.694537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:24.098707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:25.968953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:28.273172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:30.584353image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-11-19T18:53:37.865618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:40.338905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:42.661886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-11-19T18:53:35.289698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-11-19T18:53:40.434801image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-11-19T18:53:44.791258image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:47.003176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:49.559151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:52.077756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:54.680270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:09.879015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:12.032674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:14.206625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:16.598993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:18.509113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:20.841807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-11-19T18:53:27.229295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-11-19T18:53:35.438914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:38.095295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:40.527249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:42.825599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:44.880475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:47.087348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:49.663288image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:52.207665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:54.776062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:10.013412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:12.280080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:14.325627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:16.681979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:18.603048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:21.079047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:23.377244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:25.398997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:27.311962image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-11-19T18:53:32.741937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-11-19T18:53:30.036582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:32.927492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:35.718761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:38.302072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:40.889347image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:42.992879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:45.058120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-11-19T18:53:50.094106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:53:52.456342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-11-19T18:53:21.355673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-11-19T18:53:30.197372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-11-19T18:53:53.018001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-11-19T18:54:01.501006image/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.
2022-11-19T18:54:01.726471image/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.
2022-11-19T18:54:01.937711image/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.
2022-11-19T18:54:02.155423image/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

2022-11-19T18:53:55.753110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-19T18:53:56.103640image/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.

Sample

First rows

구분전년대비증가율(퍼센트)정무직별정직(국가)별정직(지방)일반직(국가)일반직(지방)경찰소방교육직법관검사기능직고용직공안직군무원연구직지도직계약직공중보건의기타
01982388440.00104804190541221335263196428109450000003406
1198336604-5.766656091204019584723575014486346108030000001258
2198434768-5.015845013220356537681937434622822411346000000238
3198528820-17.107685014240392230991877421337307897260000001444
4198624651-14.465649014980247323581390404638280181790000001868
51987255893.805119014900276621781528405135407978560000001606
61988271296.018211012000281822601899437230481975310000002200
7198924496-9.705481010710225127222016511426368157300000001885
819902786613.757348012610290332802768522960697134570000001937
919913081110.568435015390284036902946577198884627440000002337

Last rows

구분전년대비증가율(퍼센트)정무직별정직(국가)별정직(지방)일반직(국가)일반직(지방)경찰소방교육직법관검사기능직고용직공안직군무원연구직지도직계약직공중보건의기타
3020123540835.33616282150307258024951179814751630665999177148106117252187
31201329364-17.069589119125106349019241028414933950639634147135110615311701
3220144401049.87740164129301527805024115461351528012341042293273137813303592
33201540340-8.33901455698014172037741290712589109941090204199112412292878
34201638398-4.814086965301419203915965813717520881836227196137013583154
35201737059-3.48716110955601476103660901116610510908895245242138112342840
362018377101.756658737160146780342198781495250800866252214161110863441
372019397815.4919126242301520803110111141543680888936256247141311774425
3820204731918.94874475443018401038131135716336301018993314267205413766682
39202144676-5.585494924090158110348111805178268010471045263202234610246705