Overview

Dataset statistics

Number of variables10
Number of observations40
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory93.2 B

Variable types

Numeric10

Dataset

Description공무원 퇴직사유별(명예퇴직,정년퇴직 등), 지역별 퇴직자 현황 데이터(서울,부산,대구,인천,광주 등)입니다.
Author공무원연금공단
URLhttps://www.data.go.kr/data/15053021/fileData.do

Alerts

구분 is highly correlated with 의원면직High correlation
의원면직 is highly correlated with 구분High correlation
구분 has unique values Unique
has unique values Unique
정년퇴직 has unique values Unique
당연퇴직 has unique values Unique
사망 has unique values Unique
기타 has unique values Unique
의원면직 has 8 (20.0%) zeros Zeros
명예퇴직 has 14 (35.0%) zeros Zeros
일반퇴직 has 32 (80.0%) zeros Zeros
직권면직 has 1 (2.5%) zeros Zeros

Reproduction

Analysis started2022-11-19 07:42:06.861579
Analysis finished2022-11-19 07:42:19.680789
Duration12.82 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
2022-11-19T16:42:19.729567image/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
2022-11-19T16:42:19.832585image/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
2022-11-19T16:42:19.945302image/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
2022-11-19T16:42:20.083265image/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%

의원면직
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct33
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11393.075
Minimum0
Maximum33498
Zeros8
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T16:42:20.245063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15016
median10345.5
Q317958.75
95-th percentile25343.55
Maximum33498
Range33498
Interquartile range (IQR)12942.75

Descriptive statistics

Standard deviation9083.735726
Coefficient of variation (CV)0.7973032501
Kurtosis-0.582811758
Mean11393.075
Median Absolute Deviation (MAD)6347
Skewness0.4510807635
Sum455723
Variance82514254.74
MonotonicityNot monotonic
2022-11-19T16:42:20.371620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
08
 
20.0%
178891
 
2.5%
151831
 
2.5%
193421
 
2.5%
206811
 
2.5%
219021
 
2.5%
213721
 
2.5%
207041
 
2.5%
153201
 
2.5%
170161
 
2.5%
Other values (23)23
57.5%
ValueCountFrequency (%)
08
20.0%
46771
 
2.5%
49861
 
2.5%
50261
 
2.5%
53301
 
2.5%
54821
 
2.5%
56011
 
2.5%
57101
 
2.5%
57411
 
2.5%
58241
 
2.5%
ValueCountFrequency (%)
334981
2.5%
302371
2.5%
250861
2.5%
219021
2.5%
214811
2.5%
213721
2.5%
207041
2.5%
206811
2.5%
193421
2.5%
181681
2.5%

명예퇴직
Real number (ℝ≥0)

ZEROS

Distinct27
Distinct (%)67.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6385.225
Minimum0
Maximum35409
Zeros14
Zeros (%)35.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T16:42:20.509681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3782.5
Q310347
95-th percentile17517.65
Maximum35409
Range35409
Interquartile range (IQR)10347

Descriptive statistics

Standard deviation7409.44547
Coefficient of variation (CV)1.160404758
Kurtosis4.724020853
Mean6385.225
Median Absolute Deviation (MAD)3782.5
Skewness1.788156224
Sum255409
Variance54899882.18
MonotonicityNot monotonic
2022-11-19T16:42:20.627934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
014
35.0%
64061
 
2.5%
127801
 
2.5%
28751
 
2.5%
148161
 
2.5%
354091
 
2.5%
203421
 
2.5%
53871
 
2.5%
32311
 
2.5%
36211
 
2.5%
Other values (17)17
42.5%
ValueCountFrequency (%)
014
35.0%
27371
 
2.5%
28751
 
2.5%
31221
 
2.5%
32311
 
2.5%
34821
 
2.5%
36211
 
2.5%
39441
 
2.5%
53871
 
2.5%
62261
 
2.5%
ValueCountFrequency (%)
354091
2.5%
203421
2.5%
173691
2.5%
152981
2.5%
148161
2.5%
127801
2.5%
127211
2.5%
123811
2.5%
106931
2.5%
104971
2.5%

정년퇴직
Real number (ℝ≥0)

UNIQUE

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9490.675
Minimum2386
Maximum23870
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T16:42:20.767519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2386
5-th percentile3157.05
Q14972.75
median7865.5
Q313832.25
95-th percentile17475.05
Maximum23870
Range21484
Interquartile range (IQR)8859.5

Descriptive statistics

Standard deviation5356.451746
Coefficient of variation (CV)0.5643910202
Kurtosis-0.2820620921
Mean9490.675
Median Absolute Deviation (MAD)3619
Skewness0.6670892274
Sum379627
Variance28691575.3
MonotonicityNot monotonic
2022-11-19T16:42:21.047715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
23861
 
2.5%
76811
 
2.5%
34401
 
2.5%
36251
 
2.5%
33101
 
2.5%
31201
 
2.5%
35761
 
2.5%
43551
 
2.5%
50401
 
2.5%
43361
 
2.5%
Other values (30)30
75.0%
ValueCountFrequency (%)
23861
2.5%
31201
2.5%
31591
2.5%
33101
2.5%
34401
2.5%
35761
2.5%
36251
2.5%
43361
2.5%
43551
2.5%
47711
2.5%
ValueCountFrequency (%)
238701
2.5%
193381
2.5%
173771
2.5%
172611
2.5%
163621
2.5%
158711
2.5%
158231
2.5%
148391
2.5%
144521
2.5%
143491
2.5%

일반퇴직
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2203.95
Minimum0
Maximum15720
Zeros32
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T16:42:21.136238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10622.35
Maximum15720
Range15720
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4585.461439
Coefficient of variation (CV)2.080565094
Kurtosis1.718802801
Mean2203.95
Median Absolute Deviation (MAD)0
Skewness1.785230935
Sum88158
Variance21026456.61
MonotonicityNot monotonic
2022-11-19T16:42:21.213154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
032
80.0%
98921
 
2.5%
86091
 
2.5%
104481
 
2.5%
91671
 
2.5%
100371
 
2.5%
103501
 
2.5%
139351
 
2.5%
157201
 
2.5%
ValueCountFrequency (%)
032
80.0%
86091
 
2.5%
91671
 
2.5%
98921
 
2.5%
100371
 
2.5%
103501
 
2.5%
104481
 
2.5%
139351
 
2.5%
157201
 
2.5%
ValueCountFrequency (%)
157201
 
2.5%
139351
 
2.5%
104481
 
2.5%
103501
 
2.5%
100371
 
2.5%
98921
 
2.5%
91671
 
2.5%
86091
 
2.5%
032
80.0%

당연퇴직
Real number (ℝ≥0)

UNIQUE

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1858.125
Minimum172
Maximum5425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T16:42:21.316011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum172
5-th percentile194.55
Q1557
median1294
Q33388
95-th percentile4444.5
Maximum5425
Range5253
Interquartile range (IQR)2831

Descriptive statistics

Standard deviation1600.537204
Coefficient of variation (CV)0.8613721921
Kurtosis-0.8933186861
Mean1858.125
Median Absolute Deviation (MAD)1076.5
Skewness0.6454549961
Sum74325
Variance2561719.343
MonotonicityNot monotonic
2022-11-19T16:42:21.423553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
5641
 
2.5%
29421
 
2.5%
2451
 
2.5%
5811
 
2.5%
5871
 
2.5%
6411
 
2.5%
14011
 
2.5%
6451
 
2.5%
6541
 
2.5%
17461
 
2.5%
Other values (30)30
75.0%
ValueCountFrequency (%)
1721
2.5%
1861
2.5%
1951
2.5%
2081
2.5%
2161
2.5%
2451
2.5%
2641
2.5%
4081
2.5%
4801
2.5%
5361
2.5%
ValueCountFrequency (%)
54251
2.5%
52331
2.5%
44031
2.5%
41151
2.5%
40491
2.5%
36561
2.5%
35681
2.5%
35391
2.5%
35341
2.5%
34601
2.5%

직권면직
Real number (ℝ≥0)

ZEROS

Distinct38
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1895.8
Minimum0
Maximum16253
Zeros1
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T16:42:21.524873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.9
Q190.5
median496
Q31397
95-th percentile11736.35
Maximum16253
Range16253
Interquartile range (IQR)1306.5

Descriptive statistics

Standard deviation3659.289101
Coefficient of variation (CV)1.930208409
Kurtosis8.121159433
Mean1895.8
Median Absolute Deviation (MAD)491
Skewness2.890048648
Sum75832
Variance13390396.73
MonotonicityNot monotonic
2022-11-19T16:42:21.621835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1122
 
5.0%
52
 
5.0%
41
 
2.5%
39471
 
2.5%
131871
 
2.5%
41451
 
2.5%
14451
 
2.5%
7501
 
2.5%
31231
 
2.5%
38831
 
2.5%
Other values (28)28
70.0%
ValueCountFrequency (%)
01
2.5%
11
2.5%
31
2.5%
41
2.5%
52
5.0%
91
2.5%
281
2.5%
611
2.5%
741
2.5%
961
2.5%
ValueCountFrequency (%)
162531
2.5%
131871
2.5%
116601
2.5%
42061
2.5%
41451
2.5%
39471
2.5%
38831
2.5%
31231
2.5%
18361
2.5%
14451
2.5%

사망
Real number (ℝ≥0)

UNIQUE

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1173.05
Minimum653
Maximum2145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T16:42:21.727030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum653
5-th percentile679.6
Q1835.75
median955.5
Q31529.5
95-th percentile1784.5
Maximum2145
Range1492
Interquartile range (IQR)693.75

Descriptive statistics

Standard deviation423.9397605
Coefficient of variation (CV)0.3613995656
Kurtosis-1.165057131
Mean1173.05
Median Absolute Deviation (MAD)260
Skewness0.5031580914
Sum46922
Variance179724.9205
MonotonicityNot monotonic
2022-11-19T16:42:21.820311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
13841
 
2.5%
9261
 
2.5%
14631
 
2.5%
13981
 
2.5%
15581
 
2.5%
21451
 
2.5%
15651
 
2.5%
15201
 
2.5%
15151
 
2.5%
17831
 
2.5%
Other values (30)30
75.0%
ValueCountFrequency (%)
6531
2.5%
6721
2.5%
6801
2.5%
7111
2.5%
7291
2.5%
7351
2.5%
7631
2.5%
8261
2.5%
8301
2.5%
8351
2.5%
ValueCountFrequency (%)
21451
2.5%
18131
2.5%
17831
2.5%
17561
2.5%
17451
2.5%
17251
2.5%
17081
2.5%
17041
2.5%
15651
2.5%
15581
2.5%

기타
Real number (ℝ≥0)

UNIQUE

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1647.45
Minimum191
Maximum6511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T16:42:21.931947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum191
5-th percentile289.4
Q1408.5
median615
Q32080.25
95-th percentile5722
Maximum6511
Range6320
Interquartile range (IQR)1671.75

Descriptive statistics

Standard deviation1878.744561
Coefficient of variation (CV)1.140395497
Kurtosis0.6073101943
Mean1647.45
Median Absolute Deviation (MAD)266
Skewness1.415470508
Sum65898
Variance3529681.126
MonotonicityNot monotonic
2022-11-19T16:42:22.072258image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
5121
 
2.5%
3961
 
2.5%
3891
 
2.5%
2901
 
2.5%
2781
 
2.5%
1911
 
2.5%
8651
 
2.5%
16701
 
2.5%
18931
 
2.5%
26421
 
2.5%
Other values (30)30
75.0%
ValueCountFrequency (%)
1911
2.5%
2781
2.5%
2901
2.5%
3251
2.5%
3331
2.5%
3821
2.5%
3891
2.5%
3921
2.5%
3961
2.5%
4071
2.5%
ValueCountFrequency (%)
65111
2.5%
57601
2.5%
57201
2.5%
53181
2.5%
49481
2.5%
46111
2.5%
41161
2.5%
36391
2.5%
28721
2.5%
26421
2.5%

Interactions

2022-11-19T16:42:18.113755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:07.066450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:08.550101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:09.492079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:10.911863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:12.334796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:13.378238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:14.705003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:15.848827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:16.736156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:18.208209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:07.215992image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:08.669366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:09.605568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:11.040031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:12.449487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:13.507666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:14.815490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:15.935979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:16.853281image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:18.297733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:07.319124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:08.770437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:09.718957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:11.175743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:12.559572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:13.647510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:14.923398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:16.023924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:16.939175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:18.398607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:07.446302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:08.860887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:09.867972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:11.298387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:12.672072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:13.774858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:15.033270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:16.111205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:17.045819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:18.543581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:07.601945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:08.955431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:10.134620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:11.426698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:12.772092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:13.895405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:15.141579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:16.201419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:17.186388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:18.665047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:07.699835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:09.038595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:10.240691image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:11.532630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:12.860094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:14.179830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:15.232627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:16.276472image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:17.509246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:18.801211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:07.848164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:09.128397image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:10.375063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:11.667825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:12.971407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:14.273513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:15.344942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:16.365923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:17.646487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:18.912117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:07.949785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:09.208913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:10.504850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:11.783851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:13.064427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:14.365064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:15.443941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:16.448126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:17.796894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:19.025345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:08.082990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:09.300564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:10.671520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:11.910403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:13.170206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:14.470440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:15.534635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:16.538982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:17.892413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:19.149410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:08.428144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:09.393986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:10.805658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:12.216824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:13.279788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:14.592055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:15.616073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:16.628952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T16:42:18.001985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-11-19T16:42:22.336715image/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.
2022-11-19T16:42:22.466767image/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.
2022-11-19T16:42:22.610274image/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.
2022-11-19T16:42:22.759942image/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

2022-11-19T16:42:19.477077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-19T16:42:19.631785image/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

구분의원면직명예퇴직정년퇴직일반퇴직당연퇴직직권면직사망기타
0198238844334980238605645001384512
11983366043023703159026410101458476
21984347682508603440024541451463389
31985288202148103625058114451398290
4198624651181680331005877501558278
51987255891636903120064131232145191
619882712915839035760140138831565865
71989244961532004355064598615201670
81990278661788905040065487515151893
919913081120704047710686129817251627

Last rows

구분의원면직명예퇴직정년퇴직일반퇴직당연퇴직직권면직사망기타
302012354084986812512020035341468375760
312013293645330986375270799618364948
32201444010017369144529892408287631098
332015403400152981434986094809838757
3420163839801029715823104485365680609
35201737059092611726191671950729446
3620183771001049715871100371861711407
3720193978101069317377103501725653531
3820204731901272119338139352083672442
3920214467601278014839157202164735382