Overview

Dataset statistics

Number of variables11
Number of observations48
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.7 KiB
Average record size in memory100.7 B

Variable types

Categorical1
Numeric10

Dataset

Description공무원의 나이(18세이상~65세이상)별로 근무연수(5년미만~40년이상 등)별 공무원연금 가입자한 수 데이터
Author공무원연금공단
URLhttps://www.data.go.kr/data/15053033/fileData.do

Alerts

구분 has unique values Unique
has unique values Unique
5년미만 has unique values Unique
5년이상10년미만 has 4 (8.3%) zeros Zeros
10년이상15년미만 has 10 (20.8%) zeros Zeros
15년이상20년미만 has 15 (31.2%) zeros Zeros
20년이상25년미만 has 19 (39.6%) zeros Zeros
25년이상30년미만 has 24 (50.0%) zeros Zeros
30년이상33년이하 has 27 (56.2%) zeros Zeros
33년초과40년미만 has 29 (60.4%) zeros Zeros
40년이상 has 36 (75.0%) zeros Zeros

Reproduction

Analysis started2022-11-19 09:50:05.176205
Analysis finished2022-11-19 09:50:19.224011
Duration14.05 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

구분
Categorical

UNIQUE

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size512.0 B
18세이상
 
1
19세
 
1
29세
 
1
20세
 
1
21세
 
1
Other values (43)
43 

Length

Max length5
Median length3
Mean length3.083333333
Min length3

Unique

Unique48 ?
Unique (%)100.0%

Sample

1st row18세이상
2nd row19세
3rd row20세
4th row21세
5th row22세

Common Values

ValueCountFrequency (%)
18세이상1
 
2.1%
19세1
 
2.1%
29세1
 
2.1%
20세1
 
2.1%
21세1
 
2.1%
22세1
 
2.1%
23세1
 
2.1%
24세1
 
2.1%
25세1
 
2.1%
26세1
 
2.1%
Other values (38)38
79.2%

Length

2022-11-19T18:50:19.301909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
18세이상1
 
2.1%
19세1
 
2.1%
53세1
 
2.1%
44세1
 
2.1%
45세1
 
2.1%
46세1
 
2.1%
47세1
 
2.1%
48세1
 
2.1%
49세1
 
2.1%
50세1
 
2.1%
Other values (38)38
79.2%


Real number (ℝ≥0)

UNIQUE

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26279.60417
Minimum33
Maximum43870
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:50:19.484542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile792.1
Q112069.75
median33734
Q337812.25
95-th percentile42701.4
Maximum43870
Range43837
Interquartile range (IQR)25742.5

Descriptive statistics

Standard deviation15192.88691
Coefficient of variation (CV)0.5781246482
Kurtosis-1.020241763
Mean26279.60417
Median Absolute Deviation (MAD)5142.5
Skewness-0.7898024613
Sum1261421
Variance230823812.8
MonotonicityNot monotonic
2022-11-19T18:50:19.674460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
331
 
2.1%
435811
 
2.1%
6921
 
2.1%
9781
 
2.1%
15441
 
2.1%
50371
 
2.1%
104431
 
2.1%
167081
 
2.1%
236941
 
2.1%
300381
 
2.1%
Other values (38)38
79.2%
ValueCountFrequency (%)
331
2.1%
4711
2.1%
6921
2.1%
9781
2.1%
10001
2.1%
10261
2.1%
11041
2.1%
15441
2.1%
22481
2.1%
50371
2.1%
ValueCountFrequency (%)
438701
2.1%
435811
2.1%
427981
2.1%
425221
2.1%
399591
2.1%
393181
2.1%
384351
2.1%
383241
2.1%
381381
2.1%
381311
2.1%

5년미만
Real number (ℝ≥0)

UNIQUE

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5803.166667
Minimum33
Maximum27626
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:50:19.877548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile200.65
Q1586.25
median2006.5
Q36710
95-th percentile24986.9
Maximum27626
Range27593
Interquartile range (IQR)6123.75

Descriptive statistics

Standard deviation7994.157828
Coefficient of variation (CV)1.377550963
Kurtosis1.75196474
Mean5803.166667
Median Absolute Deviation (MAD)1721.5
Skewness1.691797023
Sum278552
Variance63906559.38
MonotonicityNot monotonic
2022-11-19T18:50:20.013390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
331
 
2.1%
37091
 
2.1%
6921
 
2.1%
9781
 
2.1%
15381
 
2.1%
49291
 
2.1%
100041
 
2.1%
162301
 
2.1%
228771
 
2.1%
276261
 
2.1%
Other values (38)38
79.2%
ValueCountFrequency (%)
331
2.1%
1711
2.1%
1941
2.1%
2131
2.1%
2271
2.1%
2661
2.1%
3131
2.1%
3811
2.1%
4661
2.1%
4711
2.1%
ValueCountFrequency (%)
276261
2.1%
274221
2.1%
261231
2.1%
228771
2.1%
227211
2.1%
174051
2.1%
162301
2.1%
142871
2.1%
116971
2.1%
100041
2.1%

5년이상10년미만
Real number (ℝ≥0)

ZEROS

Distinct45
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4384.916667
Minimum0
Maximum20211
Zeros4
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:50:20.188494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1145.25
median1031
Q36836.25
95-th percentile18465.8
Maximum20211
Range20211
Interquartile range (IQR)6691

Descriptive statistics

Standard deviation6273.217427
Coefficient of variation (CV)1.430635495
Kurtosis0.7621261695
Mean4384.916667
Median Absolute Deviation (MAD)1006.5
Skewness1.447358662
Sum210476
Variance39353256.89
MonotonicityNot monotonic
2022-11-19T18:50:20.353566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
04
 
8.3%
202111
 
2.1%
1081
 
2.1%
4391
 
2.1%
4781
 
2.1%
8171
 
2.1%
24121
 
2.1%
67691
 
2.1%
119771
 
2.1%
171481
 
2.1%
Other values (35)35
72.9%
ValueCountFrequency (%)
04
8.3%
61
 
2.1%
431
 
2.1%
561
 
2.1%
601
 
2.1%
791
 
2.1%
1051
 
2.1%
1081
 
2.1%
1371
 
2.1%
1481
 
2.1%
ValueCountFrequency (%)
202111
2.1%
196671
2.1%
187501
2.1%
179381
2.1%
171481
2.1%
154861
2.1%
129611
2.1%
119771
2.1%
103291
2.1%
93781
2.1%

10년이상15년미만
Real number (ℝ≥0)

ZEROS

Distinct39
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3075.1875
Minimum0
Maximum16053
Zeros10
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:50:20.695503image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q128.5
median590.5
Q33620.5
95-th percentile15289.7
Maximum16053
Range16053
Interquartile range (IQR)3592

Descriptive statistics

Standard deviation4991.680234
Coefficient of variation (CV)1.623211669
Kurtosis1.583044863
Mean3075.1875
Median Absolute Deviation (MAD)590.5
Skewness1.703523319
Sum147609
Variance24916871.56
MonotonicityNot monotonic
2022-11-19T18:50:20.847512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
010
 
20.8%
5641
 
2.1%
311
 
2.1%
901
 
2.1%
3401
 
2.1%
11681
 
2.1%
39101
 
2.1%
69781
 
2.1%
103111
 
2.1%
160191
 
2.1%
Other values (29)29
60.4%
ValueCountFrequency (%)
010
20.8%
31
 
2.1%
241
 
2.1%
301
 
2.1%
311
 
2.1%
321
 
2.1%
481
 
2.1%
831
 
2.1%
851
 
2.1%
901
 
2.1%
ValueCountFrequency (%)
160531
2.1%
160191
2.1%
159331
2.1%
140951
2.1%
139111
2.1%
111961
2.1%
103111
2.1%
87871
2.1%
69781
2.1%
57131
2.1%

15년이상20년미만
Real number (ℝ≥0)

ZEROS

Distinct34
Distinct (%)70.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3810.875
Minimum0
Maximum24061
Zeros15
Zeros (%)31.2%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:50:21.011112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median289.5
Q34157
95-th percentile18212.1
Maximum24061
Range24061
Interquartile range (IQR)4157

Descriptive statistics

Standard deviation6588.314719
Coefficient of variation (CV)1.728819423
Kurtosis2.335961059
Mean3810.875
Median Absolute Deviation (MAD)289.5
Skewness1.844935709
Sum182922
Variance43405890.84
MonotonicityNot monotonic
2022-11-19T18:50:21.162080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
015
31.2%
181901
 
2.1%
106641
 
2.1%
135181
 
2.1%
170461
 
2.1%
182241
 
2.1%
240611
 
2.1%
225301
 
2.1%
127571
 
2.1%
66971
 
2.1%
Other values (24)24
50.0%
ValueCountFrequency (%)
015
31.2%
51
 
2.1%
191
 
2.1%
261
 
2.1%
291
 
2.1%
471
 
2.1%
531
 
2.1%
1091
 
2.1%
1131
 
2.1%
2061
 
2.1%
ValueCountFrequency (%)
240611
2.1%
225301
2.1%
182241
2.1%
181901
2.1%
170461
2.1%
135181
2.1%
127571
2.1%
106641
2.1%
90271
2.1%
68611
2.1%

20년이상25년미만
Real number (ℝ≥0)

ZEROS

Distinct30
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2554.979167
Minimum0
Maximum17387
Zeros19
Zeros (%)39.6%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:50:21.302505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median72.5
Q32061
95-th percentile14286.8
Maximum17387
Range17387
Interquartile range (IQR)2061

Descriptive statistics

Standard deviation4767.558693
Coefficient of variation (CV)1.865987306
Kurtosis2.764018016
Mean2554.979167
Median Absolute Deviation (MAD)72.5
Skewness1.986124004
Sum122639
Variance22729615.89
MonotonicityNot monotonic
2022-11-19T18:50:21.436771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
019
39.6%
86801
 
2.1%
281
 
2.1%
181
 
2.1%
681
 
2.1%
1881
 
2.1%
5951
 
2.1%
24691
 
2.1%
65191
 
2.1%
105961
 
2.1%
Other values (20)20
41.7%
ValueCountFrequency (%)
019
39.6%
11
 
2.1%
181
 
2.1%
281
 
2.1%
461
 
2.1%
681
 
2.1%
771
 
2.1%
1461
 
2.1%
1881
 
2.1%
2281
 
2.1%
ValueCountFrequency (%)
173871
2.1%
156121
2.1%
145991
2.1%
137071
2.1%
114891
2.1%
105961
2.1%
86801
2.1%
65191
2.1%
56971
2.1%
39231
2.1%

25년이상30년미만
Real number (ℝ≥0)

ZEROS

Distinct25
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2866.125
Minimum0
Maximum20553
Zeros24
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:50:21.553052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.5
Q32244.25
95-th percentile17202.4
Maximum20553
Range20553
Interquartile range (IQR)2244.25

Descriptive statistics

Standard deviation5633.303632
Coefficient of variation (CV)1.965477302
Kurtosis3.465415374
Mean2866.125
Median Absolute Deviation (MAD)4.5
Skewness2.133567366
Sum137574
Variance31734109.81
MonotonicityNot monotonic
2022-11-19T18:50:21.649846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
024
50.0%
86901
 
2.1%
521
 
2.1%
1261
 
2.1%
4501
 
2.1%
9191
 
2.1%
21921
 
2.1%
57651
 
2.1%
115941
 
2.1%
153981
 
2.1%
Other values (15)15
31.2%
ValueCountFrequency (%)
024
50.0%
91
 
2.1%
521
 
2.1%
1261
 
2.1%
1711
 
2.1%
1911
 
2.1%
2971
 
2.1%
4501
 
2.1%
5051
 
2.1%
9191
 
2.1%
ValueCountFrequency (%)
205531
2.1%
195751
2.1%
181741
2.1%
153981
2.1%
135731
2.1%
115941
2.1%
86901
2.1%
62041
2.1%
57651
2.1%
43831
2.1%

30년이상33년이하
Real number (ℝ≥0)

ZEROS

Distinct21
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1851.270833
Minimum0
Maximum14262
Zeros27
Zeros (%)56.2%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:50:21.747537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3729
95-th percentile11372.85
Maximum14262
Range14262
Interquartile range (IQR)729

Descriptive statistics

Standard deviation3876.006712
Coefficient of variation (CV)2.093700523
Kurtosis3.924850573
Mean1851.270833
Median Absolute Deviation (MAD)0
Skewness2.215646275
Sum88861
Variance15023428.03
MonotonicityNot monotonic
2022-11-19T18:50:21.846556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
027
56.2%
12
 
4.2%
431
 
2.1%
121
 
2.1%
5071
 
2.1%
14871
 
2.1%
34761
 
2.1%
56091
 
2.1%
94871
 
2.1%
140761
 
2.1%
Other values (11)11
22.9%
ValueCountFrequency (%)
027
56.2%
12
 
4.2%
121
 
2.1%
431
 
2.1%
1561
 
2.1%
1641
 
2.1%
2201
 
2.1%
4081
 
2.1%
5071
 
2.1%
13951
 
2.1%
ValueCountFrequency (%)
142621
2.1%
140761
2.1%
123491
2.1%
95601
2.1%
94871
2.1%
70051
2.1%
56091
2.1%
48351
2.1%
38081
2.1%
34761
2.1%

33년초과40년미만
Real number (ℝ≥0)

ZEROS

Distinct20
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1847.020833
Minimum0
Maximum15099
Zeros29
Zeros (%)60.4%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:50:21.959910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3276
95-th percentile13954.15
Maximum15099
Range15099
Interquartile range (IQR)276

Descriptive statistics

Standard deviation4231.09826
Coefficient of variation (CV)2.290769105
Kurtosis4.544699649
Mean1847.020833
Median Absolute Deviation (MAD)0
Skewness2.389007503
Sum88657
Variance17902192.49
MonotonicityNot monotonic
2022-11-19T18:50:22.085557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
029
60.4%
79921
 
2.1%
1451
 
2.1%
21
 
2.1%
61
 
2.1%
221
 
2.1%
2241
 
2.1%
15901
 
2.1%
38431
 
2.1%
53531
 
2.1%
Other values (10)10
 
20.8%
ValueCountFrequency (%)
029
60.4%
11
 
2.1%
21
 
2.1%
61
 
2.1%
221
 
2.1%
1451
 
2.1%
2241
 
2.1%
2671
 
2.1%
3031
 
2.1%
8171
 
2.1%
ValueCountFrequency (%)
150991
2.1%
147401
2.1%
144551
2.1%
130241
2.1%
79921
2.1%
78941
2.1%
53531
2.1%
38431
2.1%
28801
2.1%
15901
2.1%

40년이상
Real number (ℝ≥0)

ZEROS

Distinct13
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.0625
Minimum0
Maximum1229
Zeros36
Zeros (%)75.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:50:22.433089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.25
95-th percentile823.5
Maximum1229
Range1229
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation276.1409913
Coefficient of variation (CV)3.208609921
Kurtosis11.11720065
Mean86.0625
Median Absolute Deviation (MAD)0
Skewness3.473762194
Sum4131
Variance76253.84707
MonotonicityNot monotonic
2022-11-19T18:50:22.568770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
036
75.0%
11
 
2.1%
111
 
2.1%
201
 
2.1%
401
 
2.1%
2221
 
2.1%
10441
 
2.1%
12291
 
2.1%
10551
 
2.1%
4141
 
2.1%
Other values (3)3
 
6.2%
ValueCountFrequency (%)
036
75.0%
11
 
2.1%
111
 
2.1%
201
 
2.1%
211
 
2.1%
311
 
2.1%
401
 
2.1%
431
 
2.1%
2221
 
2.1%
4141
 
2.1%
ValueCountFrequency (%)
12291
2.1%
10551
2.1%
10441
2.1%
4141
2.1%
2221
2.1%
431
2.1%
401
2.1%
311
2.1%
211
2.1%
201
2.1%

Interactions

2022-11-19T18:50:17.544490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:05.431056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:06.887735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:08.293137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:09.656004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:11.250521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:12.388603image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:13.712588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:15.057025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:16.234272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:17.622000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:05.589032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:06.972230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:08.410596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:09.772351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:11.348414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:12.702187image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:13.806076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:15.174950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:16.347223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:17.726257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:05.703360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:07.058292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:08.538811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:09.898645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:11.458361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:12.791608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:13.893233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:15.298913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:16.453287image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:17.859006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:05.860727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:07.156675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:08.682372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:10.038122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:11.587865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:12.893512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:14.015577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:15.433278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:16.770563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:17.964406image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:06.263983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:07.254209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:08.792845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:10.180302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:11.703100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:13.008178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:14.122620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:15.566534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:16.898739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:18.082484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:06.382089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:07.342116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:08.922262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:10.530935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:11.794931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:13.103687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:14.237388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:15.677137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:17.014364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:18.198057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:06.502937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:07.463822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:09.058701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:10.665744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:11.909111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:13.207101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:14.359512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:15.774077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:17.141206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:18.289619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:06.621055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:07.617204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:09.197828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:10.786041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:12.043152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:13.327419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:14.684691image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:15.860591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:17.240450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:18.385473image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:06.708310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:07.775646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:09.374407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:10.921073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:12.144892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:13.464686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:14.825279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:15.985722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:17.339701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:18.660455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:06.797709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:07.934912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:09.528228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:11.084046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:12.272126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:13.608730image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:14.938878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:16.113650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:50:17.448889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-11-19T18:50:22.714136image/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.
2022-11-19T18:50:22.902739image/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.
2022-11-19T18:50:23.117126image/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.
2022-11-19T18:50:23.351740image/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

2022-11-19T18:50:18.888478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-19T18:50:19.127572image/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

구분5년미만5년이상10년미만10년이상15년미만15년이상20년미만20년이상25년미만25년이상30년미만30년이상33년이하33년초과40년미만40년이상
018세이상333300000000
119세47147100000000
220세69269200000000
321세97897800000000
422세1544153860000000
523세503749291080000000
624세10443100044390000000
725세16708162304780000000
826세23694228778170000000
927세300382762624120000000

Last rows

구분5년미만5년이상10년미만10년이상15년미만15년이상20년미만20년이상25년미만25년이상30년미만30년이상33년이하33년초과40년미만40년이상
3856세304675452565648691246438395601302420
3957세276994661943366661103314970051474040
4058세240193811752614768132401483514455222
4159세2386531313716740873121583808150991044
4260세12612227105852064261045139578941229
4361세5582266794811322850540828801055
4462세2248213563253146297220817414
4563세10261944330297719116426731
4664세10001716024264617115630343
4765세이상1104537148834728524314521