Overview

Dataset statistics

Number of variables11
Number of observations48
Missing cells187
Missing cells (%)35.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.7 KiB
Average record size in memory100.8 B

Variable types

Text1
Numeric10

Dataset

Description연령별 재직년수별(5년 미만 ~ 40년 이상) 퇴직자 추이에 대한 데이터입니다. 18세 이상부터 연령이 시작되며 5년 단위입니다.
URLhttps://www.data.go.kr/data/15054043/fileData.do

Alerts

계 is highly overall correlated with 10이상15년미만 and 4 other fieldsHigh correlation
5년미만 is highly overall correlated with 5이상10년미만 and 1 other fieldsHigh correlation
5이상10년미만 is highly overall correlated with 5년미만 and 1 other fieldsHigh correlation
10이상15년미만 is highly overall correlated with 계 and 1 other fieldsHigh correlation
15이상20년미만 is highly overall correlated with 5이상10년미만 and 1 other fieldsHigh correlation
20이상25년미만 is highly overall correlated with 40년이상High correlation
25이상30년미만 is highly overall correlated with 계 and 2 other fieldsHigh correlation
30이상33년이하 is highly overall correlated with 계 and 1 other fieldsHigh correlation
33년초과40년미만 is highly overall correlated with 계 and 2 other fieldsHigh correlation
40년이상 is highly overall correlated with 계 and 3 other fieldsHigh correlation
5이상10년미만 has 6 (12.5%) missing valuesMissing
10이상15년미만 has 13 (27.1%) missing valuesMissing
15이상20년미만 has 19 (39.6%) missing valuesMissing
20이상25년미만 has 21 (43.8%) missing valuesMissing
25이상30년미만 has 25 (52.1%) missing valuesMissing
30이상33년이하 has 30 (62.5%) missing valuesMissing
33년초과40년미만 has 33 (68.8%) missing valuesMissing
40년이상 has 40 (83.3%) missing valuesMissing
구분 has unique valuesUnique

Reproduction

Analysis started2023-12-12 07:54:23.867135
Analysis finished2023-12-12 07:54:34.791142
Duration10.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size516.0 B
2023-12-12T16:54:34.973291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.1041667
Min length3

Characters and Unicode

Total characters149
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)100.0%

Sample

1st row18세 이상
2nd row19세
3rd row20세
4th row21세
5th row22세
ValueCountFrequency (%)
18세 1
 
2.0%
42세 1
 
2.0%
53세 1
 
2.0%
44세 1
 
2.0%
45세 1
 
2.0%
46세 1
 
2.0%
47세 1
 
2.0%
48세 1
 
2.0%
49세 1
 
2.0%
50세 1
 
2.0%
Other values (39) 39
79.6%
2023-12-12T16:54:35.340019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 48
32.2%
2 15
 
10.1%
3 15
 
10.1%
4 15
 
10.1%
5 15
 
10.1%
6 10
 
6.7%
1 7
 
4.7%
8 5
 
3.4%
9 5
 
3.4%
0 5
 
3.4%
Other values (4) 9
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 96
64.4%
Other Letter 52
34.9%
Space Separator 1
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 15
15.6%
3 15
15.6%
4 15
15.6%
5 15
15.6%
6 10
10.4%
1 7
7.3%
8 5
 
5.2%
9 5
 
5.2%
0 5
 
5.2%
7 4
 
4.2%
Other Letter
ValueCountFrequency (%)
세 48
92.3%
이 2
 
3.8%
상 2
 
3.8%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 97
65.1%
Hangul 52
34.9%

Most frequent character per script

Common
ValueCountFrequency (%)
2 15
15.5%
3 15
15.5%
4 15
15.5%
5 15
15.5%
6 10
10.3%
1 7
7.2%
8 5
 
5.2%
9 5
 
5.2%
0 5
 
5.2%
7 4
 
4.1%
Hangul
ValueCountFrequency (%)
세 48
92.3%
이 2
 
3.8%
상 2
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97
65.1%
Hangul 52
34.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 48
92.3%
이 2
 
3.8%
상 2
 
3.8%
ASCII
ValueCountFrequency (%)
2 15
15.5%
3 15
15.5%
4 15
15.5%
5 15
15.5%
6 10
10.3%
1 7
7.2%
8 5
 
5.2%
9 5
 
5.2%
0 5
 
5.2%
7 4
 
4.1%

계
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1145.6875
Minimum1
Maximum16567
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T16:54:35.469340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.15
Q1532.75
median645.5
Q3979.25
95-th percentile2814.55
Maximum16567
Range16566
Interquartile range (IQR)446.5

Descriptive statistics

Standard deviation2376.8061
Coefficient of variation (CV)2.0745675
Kurtosis39.67925
Mean1145.6875
Median Absolute Deviation (MAD)296
Skewness6.0716653
Sum54993
Variance5649207.2
MonotonicityNot monotonic
2023-12-12T16:54:35.993546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
594 2
 
4.2%
532 2
 
4.2%
677 2
 
4.2%
596 1
 
2.1%
538 1
 
2.1%
590 1
 
2.1%
608 1
 
2.1%
710 1
 
2.1%
709 1
 
2.1%
715 1
 
2.1%
Other values (35) 35
72.9%
ValueCountFrequency (%)
1 1
2.1%
7 1
2.1%
8 1
2.1%
17 1
2.1%
39 1
2.1%
70 1
2.1%
84 1
2.1%
180 1
2.1%
344 1
2.1%
510 1
2.1%
ValueCountFrequency (%)
16567 1
2.1%
3266 1
2.1%
2971 1
2.1%
2524 1
2.1%
2104 1
2.1%
1499 1
2.1%
1401 1
2.1%
1349 1
2.1%
1140 1
2.1%
1111 1
2.1%

5년미만
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean277.52083
Minimum1
Maximum1055
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T16:54:36.197407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.15
Q1100
median224.5
Q3332
95-th percentile861.55
Maximum1055
Range1054
Interquartile range (IQR)232

Descriptive statistics

Standard deviation252.57634
Coefficient of variation (CV)0.91011669
Kurtosis1.8265975
Mean277.52083
Median Absolute Deviation (MAD)111.5
Skewness1.4939549
Sum13321
Variance63794.808
MonotonicityNot monotonic
2023-12-12T16:54:36.403065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
39 2
 
4.2%
1 1
 
2.1%
130 1
 
2.1%
240 1
 
2.1%
230 1
 
2.1%
227 1
 
2.1%
244 1
 
2.1%
210 1
 
2.1%
231 1
 
2.1%
222 1
 
2.1%
Other values (37) 37
77.1%
ValueCountFrequency (%)
1 1
2.1%
7 1
2.1%
8 1
2.1%
17 1
2.1%
39 2
4.2%
57 1
2.1%
68 1
2.1%
70 1
2.1%
77 1
2.1%
84 1
2.1%
ValueCountFrequency (%)
1055 1
2.1%
913 1
2.1%
892 1
2.1%
805 1
2.1%
689 1
2.1%
634 1
2.1%
573 1
2.1%
501 1
2.1%
468 1
2.1%
374 1
2.1%

5이상10년미만
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct39
Distinct (%)92.9%
Missing6
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean71.690476
Minimum4
Maximum209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T16:54:36.569822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile7.05
Q131
median56.5
Q397
95-th percentile173.6
Maximum209
Range205
Interquartile range (IQR)66

Descriptive statistics

Standard deviation53.293704
Coefficient of variation (CV)0.74338611
Kurtosis-0.092012341
Mean71.690476
Median Absolute Deviation (MAD)34.5
Skewness0.82090975
Sum3011
Variance2840.2189
MonotonicityNot monotonic
2023-12-12T16:54:36.746238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
31 2
 
4.2%
85 2
 
4.2%
44 2
 
4.2%
30 1
 
2.1%
73 1
 
2.1%
55 1
 
2.1%
56 1
 
2.1%
62 1
 
2.1%
50 1
 
2.1%
36 1
 
2.1%
Other values (29) 29
60.4%
(Missing) 6
 
12.5%
ValueCountFrequency (%)
4 1
2.1%
6 1
2.1%
7 1
2.1%
8 1
2.1%
9 1
2.1%
18 1
2.1%
20 1
2.1%
22 1
2.1%
23 1
2.1%
30 1
2.1%
ValueCountFrequency (%)
209 1
2.1%
179 1
2.1%
174 1
2.1%
166 1
2.1%
144 1
2.1%
141 1
2.1%
138 1
2.1%
133 1
2.1%
114 1
2.1%
109 1
2.1%

10이상15년미만
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct31
Distinct (%)88.6%
Missing13
Missing (%)27.1%
Infinite0
Infinite (%)0.0%
Mean47.142857
Minimum4
Maximum127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T16:54:36.888140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q119
median39
Q374
95-th percentile109.1
Maximum127
Range123
Interquartile range (IQR)55

Descriptive statistics

Standard deviation33.768366
Coefficient of variation (CV)0.71629867
Kurtosis-0.44359723
Mean47.142857
Median Absolute Deviation (MAD)22
Skewness0.71258671
Sum1650
Variance1140.3025
MonotonicityNot monotonic
2023-12-12T16:54:37.049726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
17 2
 
4.2%
37 2
 
4.2%
86 2
 
4.2%
6 2
 
4.2%
18 1
 
2.1%
20 1
 
2.1%
9 1
 
2.1%
43 1
 
2.1%
34 1
 
2.1%
30 1
 
2.1%
Other values (21) 21
43.8%
(Missing) 13
27.1%
ValueCountFrequency (%)
4 1
2.1%
6 2
4.2%
9 1
2.1%
11 1
2.1%
15 1
2.1%
17 2
4.2%
18 1
2.1%
20 1
2.1%
21 1
2.1%
22 1
2.1%
ValueCountFrequency (%)
127 1
2.1%
114 1
2.1%
107 1
2.1%
92 1
2.1%
86 2
4.2%
85 1
2.1%
84 1
2.1%
77 1
2.1%
71 1
2.1%
65 1
2.1%

15이상20년미만
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)89.7%
Missing19
Missing (%)39.6%
Infinite0
Infinite (%)0.0%
Mean49.310345
Minimum3
Maximum192
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T16:54:37.209395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6.4
Q116
median32
Q373
95-th percentile112.4
Maximum192
Range189
Interquartile range (IQR)57

Descriptive statistics

Standard deviation43.639844
Coefficient of variation (CV)0.88500382
Kurtosis2.5789436
Mean49.310345
Median Absolute Deviation (MAD)24
Skewness1.4574147
Sum1430
Variance1904.436
MonotonicityNot monotonic
2023-12-12T16:54:37.381614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
16 2
 
4.2%
31 2
 
4.2%
8 2
 
4.2%
47 1
 
2.1%
35 1
 
2.1%
6 1
 
2.1%
192 1
 
2.1%
7 1
 
2.1%
12 1
 
2.1%
19 1
 
2.1%
Other values (16) 16
33.3%
(Missing) 19
39.6%
ValueCountFrequency (%)
3 1
2.1%
6 1
2.1%
7 1
2.1%
8 2
4.2%
12 1
2.1%
16 2
4.2%
19 1
2.1%
22 1
2.1%
28 1
2.1%
30 1
2.1%
ValueCountFrequency (%)
192 1
2.1%
116 1
2.1%
107 1
2.1%
100 1
2.1%
98 1
2.1%
88 1
2.1%
87 1
2.1%
73 1
2.1%
60 1
2.1%
58 1
2.1%

20이상25년미만
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)88.9%
Missing21
Missing (%)43.8%
Infinite0
Infinite (%)0.0%
Mean74.222222
Minimum2
Maximum342
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T16:54:37.539227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.7
Q129.5
median48
Q3110.5
95-th percentile150.8
Maximum342
Range340
Interquartile range (IQR)81

Descriptive statistics

Standard deviation69.884704
Coefficient of variation (CV)0.94156038
Kurtosis7.4726364
Mean74.222222
Median Absolute Deviation (MAD)30
Skewness2.2799485
Sum2004
Variance4883.8718
MonotonicityNot monotonic
2023-12-12T16:54:37.683647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
28 2
 
4.2%
48 2
 
4.2%
2 2
 
4.2%
65 1
 
2.1%
30 1
 
2.1%
40 1
 
2.1%
64 1
 
2.1%
11 1
 
2.1%
342 1
 
2.1%
29 1
 
2.1%
Other values (14) 14
29.2%
(Missing) 21
43.8%
ValueCountFrequency (%)
2 2
4.2%
11 1
2.1%
12 1
2.1%
28 2
4.2%
29 1
2.1%
30 1
2.1%
34 1
2.1%
39 1
2.1%
40 1
2.1%
44 1
2.1%
ValueCountFrequency (%)
342 1
2.1%
155 1
2.1%
141 1
2.1%
131 1
2.1%
130 1
2.1%
119 1
2.1%
112 1
2.1%
109 1
2.1%
89 1
2.1%
78 1
2.1%

25이상30년미만
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)91.3%
Missing25
Missing (%)52.1%
Infinite0
Infinite (%)0.0%
Mean143.08696
Minimum1
Maximum1351
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T16:54:37.832254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.2
Q138.5
median98
Q3136.5
95-th percentile179.8
Maximum1351
Range1350
Interquartile range (IQR)98

Descriptive statistics

Standard deviation269.95419
Coefficient of variation (CV)1.8866443
Kurtosis20.515025
Mean143.08696
Median Absolute Deviation (MAD)48
Skewness4.4162015
Sum3291
Variance72875.265
MonotonicityNot monotonic
2023-12-12T16:54:37.977711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
64 2
 
4.2%
1 2
 
4.2%
36 1
 
2.1%
130 1
 
2.1%
136 1
 
2.1%
3 1
 
2.1%
137 1
 
2.1%
41 1
 
2.1%
1351 1
 
2.1%
98 1
 
2.1%
Other values (11) 11
22.9%
(Missing) 25
52.1%
ValueCountFrequency (%)
1 2
4.2%
3 1
2.1%
4 1
2.1%
9 1
2.1%
36 1
2.1%
41 1
2.1%
64 2
4.2%
83 1
2.1%
90 1
2.1%
98 1
2.1%
ValueCountFrequency (%)
1351 1
2.1%
180 1
2.1%
178 1
2.1%
157 1
2.1%
146 1
2.1%
137 1
2.1%
136 1
2.1%
134 1
2.1%
133 1
2.1%
130 1
2.1%

30이상33년이하
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)94.4%
Missing30
Missing (%)62.5%
Infinite0
Infinite (%)0.0%
Mean396.16667
Minimum1
Maximum2593
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T16:54:38.118810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.1
Q171.25
median171
Q3573
95-th percentile938.9
Maximum2593
Range2592
Interquartile range (IQR)501.75

Descriptive statistics

Standard deviation598.68535
Coefficient of variation (CV)1.5111957
Kurtosis11.671743
Mean396.16667
Median Absolute Deviation (MAD)158.5
Skewness3.1749052
Sum7131
Variance358424.15
MonotonicityNot monotonic
2023-12-12T16:54:38.287703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
598 2
 
4.2%
458 1
 
2.1%
130 1
 
2.1%
7 1
 
2.1%
71 1
 
2.1%
190 1
 
2.1%
72 1
 
2.1%
2593 1
 
2.1%
1 1
 
2.1%
18 1
 
2.1%
Other values (7) 7
 
14.6%
(Missing) 30
62.5%
ValueCountFrequency (%)
1 1
2.1%
7 1
2.1%
18 1
2.1%
52 1
2.1%
71 1
2.1%
72 1
2.1%
93 1
2.1%
130 1
2.1%
152 1
2.1%
190 1
2.1%
ValueCountFrequency (%)
2593 1
2.1%
647 1
2.1%
636 1
2.1%
598 2
4.2%
498 1
2.1%
458 1
2.1%
317 1
2.1%
190 1
2.1%
152 1
2.1%
130 1
2.1%

33년초과40년미만
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)100.0%
Missing33
Missing (%)68.8%
Infinite0
Infinite (%)0.0%
Mean1296.6
Minimum4
Maximum10062
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T16:54:38.418962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile13.1
Q1115.5
median572
Q31384.5
95-th percentile4541.1
Maximum10062
Range10058
Interquartile range (IQR)1269

Descriptive statistics

Standard deviation2519.2514
Coefficient of variation (CV)1.9429673
Kurtosis12.376941
Mean1296.6
Median Absolute Deviation (MAD)554
Skewness3.4114244
Sum19449
Variance6346627.5
MonotonicityNot monotonic
2023-12-12T16:54:38.570043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
4 1
 
2.1%
17 1
 
2.1%
64 1
 
2.1%
167 1
 
2.1%
311 1
 
2.1%
572 1
 
2.1%
1146 1
 
2.1%
1623 1
 
2.1%
2175 1
 
2.1%
10062 1
 
2.1%
Other values (5) 5
 
10.4%
(Missing) 33
68.8%
ValueCountFrequency (%)
4 1
2.1%
17 1
2.1%
18 1
2.1%
64 1
2.1%
167 1
2.1%
311 1
2.1%
348 1
2.1%
572 1
2.1%
601 1
2.1%
686 1
2.1%
ValueCountFrequency (%)
10062 1
2.1%
2175 1
2.1%
1655 1
2.1%
1623 1
2.1%
1146 1
2.1%
686 1
2.1%
601 1
2.1%
572 1
2.1%
348 1
2.1%
311 1
2.1%

40년이상
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing40
Missing (%)83.3%
Infinite0
Infinite (%)0.0%
Mean463.25
Minimum1
Maximum1727
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T16:54:38.683383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.2
Q152.75
median134
Q3686.25
95-th percentile1498.45
Maximum1727
Range1726
Interquartile range (IQR)633.5

Descriptive statistics

Standard deviation629.63589
Coefficient of variation (CV)1.3591708
Kurtosis1.2701334
Mean463.25
Median Absolute Deviation (MAD)127
Skewness1.4633448
Sum3706
Variance396441.36
MonotonicityNot monotonic
2023-12-12T16:54:38.797678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
13 1
 
2.1%
83 1
 
2.1%
1727 1
 
2.1%
185 1
 
2.1%
1074 1
 
2.1%
557 1
 
2.1%
1 1
 
2.1%
66 1
 
2.1%
(Missing) 40
83.3%
ValueCountFrequency (%)
1 1
2.1%
13 1
2.1%
66 1
2.1%
83 1
2.1%
185 1
2.1%
557 1
2.1%
1074 1
2.1%
1727 1
2.1%
ValueCountFrequency (%)
1727 1
2.1%
1074 1
2.1%
557 1
2.1%
185 1
2.1%
83 1
2.1%
66 1
2.1%
13 1
2.1%
1 1
2.1%

Interactions

2023-12-12T16:54:33.510212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:24.191967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:25.319660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:26.300038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:27.214026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:28.344662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:29.445718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:30.681522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:31.735515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:32.585717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:33.587569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:24.305497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:25.421292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:26.379586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:27.312348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:28.464093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:29.535042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:30.767354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:31.815830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:32.661547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:33.662567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:24.424490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:25.515715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:26.465633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:27.430065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:28.565667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:29.622146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:30.870536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:31.903479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:32.736535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:33.747176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:24.527942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:25.614711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:26.548000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:27.539667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:28.688869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:29.724841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:30.978109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:31.988239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:32.813482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:33.867596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:24.627925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:25.727066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:26.644194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:27.640253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:28.804672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:30.153853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:31.096068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:32.082979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:32.901054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:33.957254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:24.775937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:25.833891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:26.732690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:27.795604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:28.918891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:30.246777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:31.222151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:32.165301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:32.984200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:34.049033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:24.892669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:25.928950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:26.817469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:27.898029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:29.043693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:30.347470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:31.345112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:32.247126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:33.071118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:34.133111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:25.014698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:26.019105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:26.915086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:28.016295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:29.152775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:30.427115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:31.445403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:32.324795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:33.153020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:34.220877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:25.098619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:26.121181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:27.024165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:28.128462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:29.235785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:30.503633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:31.548841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:32.417577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:33.235975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:34.302699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:25.208463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:26.215000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:27.125896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:28.237628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:29.336719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:30.581285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:31.641878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:32.511029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:33.403682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:54:38.905528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분계5년미만5이상10년미만10이상15년미만15이상20년미만20이상25년미만25이상30년미만30이상33년이하33년초과40년미만40년이상
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
계1.0001.0000.0000.0000.2920.7560.9280.9280.6741.0000.672
5년미만1.0000.0001.0000.8610.1240.4860.4630.2330.0000.1990.000
5이상10년미만1.0000.0000.8611.0000.8690.7820.7250.1990.8940.8500.346
10이상15년미만1.0000.2920.1240.8691.0000.8090.6800.6950.8000.8860.247
15이상20년미만1.0000.7560.4860.7820.8091.0000.7350.9130.6690.6350.672
20이상25년미만1.0000.9280.4630.7250.6800.7351.0000.9470.6860.5170.672
25이상30년미만1.0000.9280.2330.1990.6950.9130.9471.0000.6930.6181.000
30이상33년이하1.0000.6740.0000.8940.8000.6690.6860.6931.0000.9070.000
33년초과40년미만1.0001.0000.1990.8500.8860.6350.5170.6180.9071.0000.346
40년이상1.0000.6720.0000.3460.2470.6720.6721.0000.0000.3461.000
2023-12-12T16:54:39.073150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계5년미만5이상10년미만10이상15년미만15이상20년미만20이상25년미만25이상30년미만30이상33년이하33년초과40년미만40년이상
계1.0000.108-0.164-0.541-0.344-0.1450.6030.7670.9460.738
5년미만0.1081.0000.6360.4360.4520.224-0.190-0.168-0.5110.143
5이상10년미만-0.1640.6361.0000.4990.5120.261-0.163-0.144-0.1890.190
10이상15년미만-0.5410.4360.4991.0000.6510.257-0.105-0.020-0.2310.286
15이상20년미만-0.3440.4520.5120.6511.0000.370-0.238-0.263-0.2600.214
20이상25년미만-0.1450.2240.2610.2570.3701.0000.134-0.028-0.1630.548
25이상30년미만0.603-0.190-0.163-0.105-0.2380.1341.0000.515-0.1930.619
30이상33년이하0.767-0.168-0.144-0.020-0.263-0.0280.5151.0000.4400.357
33년초과40년미만0.946-0.511-0.189-0.231-0.260-0.163-0.1930.4401.0000.643
40년이상0.7380.1430.1900.2860.2140.5480.6190.3570.6431.000

Missing values

2023-12-12T16:54:34.418206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:54:34.551309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-12T16:54:34.683886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

구분계5년미만5이상10년미만10이상15년미만15이상20년미만20이상25년미만25이상30년미만30이상33년이하33년초과40년미만40년이상
018세 이상11<NA><NA><NA><NA><NA><NA><NA><NA>
119세77<NA><NA><NA><NA><NA><NA><NA><NA>
220세88<NA><NA><NA><NA><NA><NA><NA><NA>
321세1717<NA><NA><NA><NA><NA><NA><NA><NA>
422세3939<NA><NA><NA><NA><NA><NA><NA><NA>
523세7070<NA><NA><NA><NA><NA><NA><NA><NA>
624세1801764<NA><NA><NA><NA><NA><NA><NA>
725세3443377<NA><NA><NA><NA><NA><NA><NA>
826세5105019<NA><NA><NA><NA><NA><NA><NA>
927세66563431<NA><NA><NA><NA><NA><NA><NA>
구분계5년미만5이상10년미만10이상15년미만15이상20년미만20이상25년미만25이상30년미만30이상33년이하33년초과40년미만40년이상
3856세149912418171639115598572<NA>
3957세2104104222012341306361146<NA>
4058세2524883418164490598162313
4159세297184201772998458217583
4260세16567129858619234213512593100621727
4361세1111683198114172686185
4462세3266772315316413719016551074
4563세1401576116286471601557
4664세843986<NA>237181
4765세이상9721324837354013613034866